ESSAYS ON IMPACTS OF ARTIFICIAL INTELLIGENCE ON LABOR MARKET OUTCOMES AND EDUCATIONAL CHOICES By Wenjia Cao A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Economics—Doctor of Philosophy 2025 ABSTRACT This dissertation examines impacts of Artificial Intelligence (AI) on labor market outcomes and educational choices. The first chapter focuses on labor supply by exploring the relationship between the growth of AI and college major choices. The second chapter turns to labor demand, studying the impacts of AI job postings on labor market outcomes of heterogeneous skill groups. The last chapter analyzes how AI adoption in firms affects gender wage gaps. The first chapter explores how the rise in AI shapes college major choice. I propose a new method to measure how well a major prepares students to work with AI by matching phrases for AI subfields with college major descriptions. I then define AI skill-related majors as those that provide AI-related skill training. Those majors that are most complementary to AI have systematically high growth rates of bachelor’s degree conferrals from 1990 to 2019. In contrast, I find evidence suggesting that majors that are most exposed to AI-driven substitution grow relatively slowly, especially at elite universities. In the second chapter, I study effects of AI on employment and wages for heterogeneous skill groups in the U.S. by introducing and analyzing a task-based framework. I first categorize labor into four skill groups based on skill specializations: (1) abstract and AI-intensive; (2) abstract-intensive but not yet AI-related; (3) routine-intensive; and (4) manual-intensive. The demand for AI skills is then measured by matching phrases for AI-developing skills to descriptions of online job postings. I document a consistent upward trend in the share of AI postings for the high-skilled AI-complement group during my sampling period, 2012-21. There is a strong growth in both employment and wages for abstract and AI-intensive occupations associated with an increasing demand for AI skills, while abstract but not-yet-AI occupations have much smaller growth. Middle-skilled occupations experience wage declines associated with an increase in the standard deviation of the intensity that AI-developing skills are required for job tasks. Employment and wage gaps between abstract and AI-intensive occupations and other skill groups widen as the labor market favors workers with AI skills, consistent with my theoretical model’s implications. I also discuss whether AI is possibly a general-purpose technology. The last chapter analyzes the link between gender wage gaps and AI adoption. Using a real-time, high-frequency data on AI adoption in business, I construct measures for current, expected, and continuing AI adoption. AI adoption at the state-month level narrows within-occupation gender wage gaps in mean hourly wages, whereas AI adoption at the industry-month level exhibits a non-monotonic pattern in within-industry, between-occupation gender wage gaps across different percentiles of the wage distribution. The gap widens at the 10th percentile and the median, but shrinks at the 90th percentile. However, using data on online job postings that require AI skills, I find that a higher share of AI postings benefits women more than men across the wage distribution. Copyright by WENJIA CAO 2025 This dissertation is dedicated to my parents. Thank you for always believing in me. v ACKNOWLEDGEMENTS I would like to express my deepest gratitude to my advisor Professor Todd Elder for guiding, advising, and supporting me in pursuing my career as an applied economist. He always supported my decisions and believed in me, giving me the confidence to explore every possibility. His patience and guidance played a crucial role in refining my ideas, especially when I found myself lost in the details. He helped me see the logic behind the numbers and equations, constantly encouraging me to look beyond them and grasp the bigger story that my research aimed to tell. His mentorship will continue to guide me throughout my career. I sincerely appreciate Professor Scott Imberman for his guidance and inspiration. He deepened my understanding of the underlying mechanisms my research sought to convey. He also gave me the invaluable opportunity to work alongside him during the early stages of shaping my research agenda. Through co-authoring with him, he not only guided me through every stage of the research progress but also helped me build connections with other researchers, greatly enriching my academic journey. I am deeply grateful to Professor Hanzhe Zhang for his constructive feedback and advice, which significantly enhanced the quality of my research. His insightful comments strengthened my idea, particularly in refining my theoretical models. I also want to thank him for sharing valuable information relevant to my research, which greatly broadened its scope. I extend my heartfelt gratitude to Professors Amanda Chuan and Ben Zou for their thoughtful discussions, guidance, and support. Their fantastic feedback significantly improved my work and deepened my understanding of labor economics and the future of work. I would also like to thank the faculty members in the Department of Economics, including Professors Soren Anderson and Justin Kirkpatrick, as well as Professor Hye Jin Rho from the School of Human Resources and Labor Relations, for their insightful suggestions on my research. I am grateful to the Department of Economics, the Future of Work initiative, and the College of Social Science at Michigan State University for their financial support throughout the past years for conducting my research and presenting my work at conferences. I am especially grateful to Professors Riley Acton, Emily Cook, and Michael Lovenheim for vi the invaluable opportunity to collaborate with them, through which I learned how to think critically and write effectively. Special thanks to Professors Charles Becker and Edward Tower for their generous support since my master’s studies. I deeply appreciate all the comments and suggestions they provided on my research, as well as their encouragement throughout my job market journey. I would like to thank all my friends at Michigan State University for their unwavering support, encouragement, and friendship. The moments we shared were like beacons of light during the darkest nights, helping me navigate through anxious and exhausting days. I would also like to express my deepest thanks to my dearest friends in China, Ruichen Wang and Qizhe Tang, for a friendship that has lasted since high school. They have witnessed my growth over the years, standing by my side through every challenge and milestone. Finally, I am sincerely grateful to my family. My parents have been my greatest support throughout my academic journey. They encouraged me to persevere when I felt discouraged and wanted to give up, and reminded me to stay grounded when I became overly satisfied with minor accomplishments. Most importantly, they have supported every decision I made, doing everything they could to help me along the way. I am forever grateful for their love and support. I would also like to thank my grandfather, whose wisdom laid the foundation for how I approach life and handle challenges. If he were still with us, I know he would be incredibly proud of me. vii INTRODUCTION . . BIBLIOGRAPHY . . . . . TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5 CHAPTER 1 . . . . Introduction . COLLEGE MAJOR CHOICES UNDER THE RAPID GROWTH OF GENERAL-PURPOSE TECHNOLOGY: A STUDY ON AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 7 1.1 1.2 Measuring AI Exposure and Relatedness . . . . . . . . . . . . . . . . . . . . . 11 1.3 Data and Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4 Results . . 28 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 1.5 Conclusion . . . BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 ADDITIONAL FIGURES & TABLES . . . . . . . . . . . . . . 43 APPENDIX 1A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CHAPTER 2 . . . MACHINE VERSUS MUSCLE, BOT VERSUS BRAIN: EFFECTS OF ARTIFICIAL INTELLIGENCE ON HETEROGENEOUS SKILL GROUPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 . Introduction . 2.1 2.2 Theoretical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 2.3 Data and Construction of Skill Groups . . . . . . . . . . . . . . . . . . . . . . 76 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 2.4 Empirical Strategy . 2.5 Results . . 92 2.6 Discussion: AI as a General-Purpose Technology . . . . . . . . . . . . . . . . 110 2.7 Conclusion . . . 114 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 . BIBLIOGRAPHY . PROPOSITIONS AND PROOFS . . . . . . . . . . . . . . . . . 121 APPENDIX 2A ADDITIONAL FIGURES & TABLES . . . . . . . . . . . . . . 129 APPENDIX 2B . 170 4-DIGIT OCCUPATIONS WITHIN A SKILL GROUP . . . . APPENDIX 2C . 183 APPENDIX 2D ML OCCUPATION CLUSTERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CHAPTER 3 . . . . Introduction . AI ADOPTION AND GENDER WAGE GAPS . . . . . . . . . . . . . . 193 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 3.1 3.2 Data and Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 196 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 3.3 Empirical Strategy . . 204 . 3.4 Results . 3.5 Conclusion . . . 216 . BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 ADDITIONAL FIGURES & TABLES . . . . . . . . . . . . . . 220 APPENDIX 3A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii INTRODUCTION The past decades have witnessed rapid technological advances that have profoundly impacted the economy, driving productivity growth, the creation of new tasks, changes in skill requirements, job displacement, and wage inequality. Although the impacts of past technologies, such as computer- ization, automation, and industrial robots, on the labor market have been studied extensively, the influence of Artificial Intelligence (AI), which has grown rapidly over the last decade, remains less discussed but continues to expand. The key difference between previous technologies and AI is the type of tasks they can perform. Past technologies like automation and robots are compatible with routine tasks because these tasks are decomposed into a series of explicitly programmed steps. Existing literature studying these past technologies (e.g., Krusell et al., 2000; Autor et al., 2003; Acemoglu and Autor, 2011; Acemoglu and Restrepo, 2018a,b, 2019, 2020, 2021, 2022; Brussevich et al., 2019; Acemoglu et al., 2020; Deming and Noray, 2020; Moll et al., 2021) finds that middle-skilled or less educated workers are negatively affected in terms of employment and earnings, while assumes high-skilled workers who specialize in abstract tasks that require decision making and problem solving are unaffected. However, AI can "mimic" human reasoning by learning from the big data to predict patterns and make rational decisions (LeCun et al., 2015; Zhang et al., 2022). Thus, AI can not only complement workers and increase their productivity, but also put some high-skilled workers at the threat of being displaced. Therefore, it is important to understand the impact of AI on the labor market, as policymakers should implement measures to reduce inequality and provide guidance to workers on enhancing their comparative advantage when selecting majors and seeking employment. There is a growing literature studying the implications for the labor market of AI, focusing on job displacements, changes in skill requirements, and wage inequality. On the one hand, advances in AI technologies enhance AI’s ability to perform tasks and increase technical capital, thus displacing workers (e.g., Acemoglu et al., 2022; Benmelech et al., 2024; Eloundou et al., 2024). On the other hand, AI can boost the productivity of workers with AI-developing skills (e.g., machine learning, deep learning, natural language processing) and those who utilitize AI-powered 1 tools such as Large Language Models (LLMs) and Generative AI, thus increasing the demand for AI skills (e.g., Hanson, 2021; Autor et al., 2024; Carvajal et al., 2024). However, there are several gaps in existing research on AI, both theoretical and empirical. First, most studies focus on the demand side of the labor market, such as employment and wages, with less attention paid to the impact of AI on labor supply. Second, empirical work primarily examines the substitution effect of AI and its labor market consequences, with limited exploration of the mechanisms through which AI’s complementarity affects workers with different skill sets. Third, while a small but growing body of literature investigates the gender gap in AI adoption (Park and Gelles-Watnick, 2023; Aldasoro et al., 2024; Carvajal et al., 2024; Stöhr et al., 2024; Humlum and Vestergaard, 2025), particularly regarding Generative AI tools like ChatGPT, there is little evidence on how AI adoption differentially impacts wages for women and men. My dissertation attempts to address these gaps. I first explore the influences of AI on the labor supply side by focusing on college major choices under the growth of AI, which are presented in Chapter 1. By matching phrases of AI subfields or applications with college major descriptions, I define AI skill-related majors as those that provide trainings in AI-related skills and prepare students to produce AI, improve the performance of AI, or perform tasks complemented by AI after graduation. The relevance of AI to college majors is then measured by using (1) number of matched AI phrases and (2) changes in academic publications or relative search intensities on AI phrases. In contrast to this major-AI relatedness measure which captures how well a major prepares people to work with AI, I also propose a major-AI exposure measure which captures how easy it is for AI to substitute for the tasks of a major. This major-AI exposure measure is constructed by matching occupations to college majors and using occupational-level AI exposure scores from Felten et al. (2018, 2021) and Webb (2019). Majors that are most closely related to AI have experienced significantly higher growth rates of bachelor’s degrees conferred over the past three decades. I also document a positive relationship between degrees conferred in AI skill-related majors and increases in search intensities or academic publications on rapid-growing AI subfields (e.g., deep learning, machine learning, data mining). In addition, students are less likely to choose 2 majors that are more exposed to AI-driven substitution, especially at elite universities. Chapter 2 turns to analyze how AI impacts the labor demand side. It focuses on AI-developing skills, such as deep learning and machine learning, and measures the effects of online job postings requiring AI skills on labor market outcomes of four skill groups, which are high-skilled AI- complement, high-skilled not-yet-AI, middle-skilled, and low-skilled. I first introduce and analyze a task-based framework extended from Acemoglu and Autor (2011), Acemoglu and Restrepo (2018a), and Autor et al. (2024) to study the economic impacts of AI on these four skill groups regarding job tasks and relative wages, which motivates my empirical analysis. I assume that AI has a higher productivity than automation so that AI can perform more abstract or complex tasks while automation can only perform simpler tasks. My model implies that AI can expand the set of tasks performed by high-skilled labor and widen the wage gap between high-skilled AI-complement group and other skill groups. Leveraging the data on AI job postings at the state-year level, I document a steady increase in the proportion of AI postings for the high-skilled AI-complement group throughout my sampling period, 2012–21. This group experiences significant growth in both employment and wages associated with an increase in the demand for AI-developing skills. More specifically, compared to the low-skilled group, the abstract and AI-intensive occupations have 56 more people employed per 100,000 capita and a 2.5% growth in mean hourly wages associated with a 1 percentage point increase in the AI posting share at the state-year level. The abstract but not-yet-AI occupations also have a significant growth, but much smaller in magnitude compared to high-skilled AI-complement ones. Middle-skilled occupations experience wage declines associated with increased variation in the demand for AI-developing skills across job tasks. Thess findings suggest that gaps in employment and wages between high-skilled AI-intensive occupations and other skill groups expand as the labor market increasingly prioritizes workers with AI skills, aligning with the implications of my theoretical model. Finally, Chapter 3 investigates the link between AI adoption and gender wage gaps. If AI has differential effects on tasks requiring different skill sets, the wage impact of AI is likely to be unevenly distributed between women and men, since these two groups of workers tend to be 3 employed in different types of jobs. By utilizing high-frequency data on businesses’ AI adoption for producing goods or services, I first find that AI adoption at the state-month level reduces the gender gap in mean hourly wages within occupations, suggesting that on average women benefit more than men from AI adoption in firms. To study the distributional effect of AI adoption, I then use the industry-month level AI adoption data to capture industry-specific trends in technological changes and employ the within-industry, between-occupation variation. I document a non-monotonic pattern in the relationship between AI adoption and gender wage gaps at the 10th percentile, median, mean, and 90th percentile. The gap expands at the bottom and middle of the wage distribution, but shrinks at the top. Although this high-frequency AI adoption data could reflect AI’s substitution, complementarity, or both, I use job postings data to more accurately capture AI’s complementarity, as indicated by the anticipated demand for AI skills proxied by job vacancies. Results show that gender wage gaps narrow across the wage distribution associated with a higher share of AI postings at the state-year level, with a stronger correlation at the upper end of the distribution. By studying the impacts of AI on educational choices and inequalities in wages and employ- ment, this dissertation provides insights into the economic consequences of AI. It highlights the importance of upskilling and reskilling to help individuals better adapt to changes in job require- ments driven by AI, as well as the need for training programs and policy interventions to support workers in remaining competitive in the labor market. 4 BIBLIOGRAPHY Acemoglu, D. and Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In Handbook of labor economics, volume 4, pages 1043–1171. Elsevier. Acemoglu, D., Autor, D., Hazell, J., and Restrepo, P. (2022). Artificial intelligence and jobs: evidence from online vacancies. Journal of Labor Economics, 40(S1):S293–S340. Acemoglu, D., Lelarge, C., and Restrepo, P. (2020). Competing with robots: Firm-level evidence from france. In AEA Papers and Proceedings, volume 110, pages 383–88. Acemoglu, D. and Restrepo, P. (2018a). Low-skill and high-skill automation. Journal of Human Capital, 12(2):204–232. Acemoglu, D. and Restrepo, P. (2018b). The race between man and machine: Implications of tech- nology for growth, factor shares, and employment. American Economic Review, 108(6):1488– 1542. Acemoglu, D. and Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2):3–30. Acemoglu, D. and Restrepo, P. (2020). Robots and jobs: Evidence from us labor markets. Journal of Political Economy, 128(6):2188–2244. Acemoglu, D. and Restrepo, P. (2021). Tasks, automation, and the rise in us wage inequality. Technical report, National Bureau of Economic Research. Acemoglu, D. and Restrepo, P. (2022). Demographics and automation. The Review of Economic Studies, 89(1):1–44. Aldasoro, I., Armantier, O., Doerr, S., Gambacorta, L., and Oliviero, T. (2024). The gen ai gender gap. Economics Letters, 241:111814. Autor, D., Chin, C., Salomons, A., and Seegmiller, B. (2024). New frontiers: The origins and content of new work, 1940–2018. The Quarterly Journal of Economics, page qjae008. Autor, D. H., Levy, F., and Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4):1279–1333. Benmelech, E., Fedyk, A., Hodson, J., Mukharlyamov, V., and Papanikolaou, D. (2024). Technology in the banking sector and the importance of modern skills. Brussevich, M., Dabla-Norris, M. E., and Khalid, S. (2019). Is technology widening the gender gap? Automation and the future of female employment. International Monetary Fund. 5 Carvajal, D., Franco, C., and Isaksson, S. (2024). Will artificial intelligence get in the way of achieving gender equality? NHH Dept. of Economics Discussion Paper, (03). Deming, D. J. and Noray, K. (2020). Earnings dynamics, changing job skills, and stem careers. The Quarterly Journal of Economics, 135(4):1965–2005. Eloundou, T., Manning, S., Mishkin, P., and Rock, D. (2024). Gpts are gpts: Labor market impact potential of llms. Science, 384(6702):1306–1308. Felten, E., Raj, M., and Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal, 42(12):2195–2217. Felten, E. W., Raj, M., and Seamans, R. (2018). A method to link advances in artificial intelligence to occupational abilities. In AEA Papers and Proceedings, volume 108, pages 54–57. Hanson, G. (2021). Immigration and regional specialization in ai. In Robots and AI, pages 180–231. Routledge. Humlum, A. and Vestergaard, E. (2025). The unequal adoption of chatgpt exacerbates ex- the National Academy of Sciences, Proceedings of isting inequalities among workers. 122(1):e2414972121. Krusell, P., Ohanian, L. E., Ríos-Rull, J.-V., and Violante, G. L. (2000). Capital-skill complemen- tarity and inequality: A macroeconomic analysis. Econometrica, 68(5):1029–1053. LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444. Moll, B., Rachel, L., and Restrepo, P. (2021). Uneven growth: automation’s impact on income and wealth inequality. Technical report, National Bureau of Economic Research. Park, E. and Gelles-Watnick, R. (2023). Most americans haven’t used chatgpt; few think it will have a major impact on their job. Pew Research Center, 28. Stöhr, C., Ou, A. W., and Malmström, H. (2024). Perceptions and usage of ai chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7:100259. Webb, M. (2019). The impact of artificial intelligence on the labor market. Available at SSRN 3482150. Zhang, D., Maslej, N., Brynjolfsson, E., Etchemendy, J., Lyons, T., Manyika, J., Ngo, H., Niebles, J. C., Sellitto, M., Sakhaee, E., Shoham, Y., Clark, J., and Perrault, R. (2022). The ai index 2022 annual report. AI Index Steering Committee, Stanford Institute for Human-Centered AI, Stanford University. 6 CHAPTER 1 COLLEGE MAJOR CHOICES UNDER THE RAPID GROWTH OF GENERAL-PURPOSE TECHNOLOGY: A STUDY ON AI 1.1 Introduction The growth of emerging technologies profoundly influences society. On the one hand, techno- logical advances improve living standards and productivity, and even create new job opportunities. On the other hand, they potentially increase wage inequality and cause job displacement. Under- standing impacts of technological progress is important to both individuals and policymakers, as technological advances change skill requirements in the labor market as well as the task content of production (Acemoglu and Restrepo, 2019). Individuals need to acquire new skills to make themselves less likely to be replaced by new technologies, while colleges need to adjust curriculum to better align students’ major choices with changes in skill requirements for the workforce shaped by new technologies. Unlike traditional technologies such as computerization and industrial automation, Artificial Intelligence (AI) is compatible with more abstract tasks since AI can analyze big data, predict patterns, and inform decision making (Russell and Norvig, 2021). In this way, AI is more likely to impose threats to the employment prospects of those working in cognitive or abstract fields, while computerization and industrial automation are likely to replace people specialized in routine tasks, especially those in the manufacturing sector (Zhang, 2019; Nedelkoska et al., 2021; Acemoglu and Restrepo, 2022a,b). As newly emerging AI technologies such as deep learning and machine learning have substantially improved AI’s performance (LeCun et al., 2015; Zhang et al., 2022) and AI’s compatibility with task content of production (Acemoglu et al., 2022), workers who perform tasks that can be performed by these technologies are more likely to be replaced by AI, while those who acquire AI-complementary skills may experience employment and earnings gains (Deming and Noray, 2020; Grennan and Michaely, 2020; Alekseeva et al., 2021; Acemoglu et al., 2022). Yet there is little evidence on how people adjust their skill acquisition and educational choices in response to changes in demand for AI skills. 7 This paper investigates the relationship between the rise in AI and students’ college major choices. By matching phrases for AI skills and applications with college major descriptions, I first define AI skill-related majors as those that have concentrations in AI technologies. These majors better prepare students to produce AI, improve the performance of AI, or use AI capabilities to complement their job tasks. Figure 1.1 displays decadal growth rates of bachelor’s degree recipients by major from 1990-2019. Compared to the 1990s, the growth rate in completing AI majors was smaller in the 2000s but became much higher in the 2010s. This growth rate has been consistently higher than all majors, non-AI majors, and non-AI tech majors. To distinguish general trends from responses to technological advances, I further classify AI skill-related majors into three categories, ranging from the most specific to the most general: (1) majors that are most complementary to AI; (2) majors with concentrations in AI-related computer and information processing technologies; and (3) majors associated with general computer skills which are the basic concepts and skills that students need to acquire if they plan to specialize in AI in the future. Next, I construct a measure of AI relatedness (denoted "AI Relevance Score" hereafter) to capture how well a major prepares students to work with AI using two data sources: relative Google search intensities on AI technologies and the number of academic publications in AI subfields. In addition, to capture AI exposure of a major (i.e., how likely students graduating with a major will perform tasks that are highly exposed to AI), I map occupations to college majors and separately aggregate the occupational-level AI exposure measures constructed by Felten et al. (2018, 2021) and Webb (2019) at the college major level. I first document that, on average, majors that are most complementary to AI have experienced a decadal growth rate of 53.3% in bachelor’s degrees conferred over the past three decades. Majors associated with general computer skills also grew fast in the 2010s. These findings are consistent with the trends of degree completion shown in Figure 1.1, as well as the upward trend in undergraduates completing Computer Science (CS) degrees during the 2010s documented by Zhang et al. (2022). I then explore the relationship between AI Relevance Score (i.e., a major’s complementarity 8 Figure 1.1 Decadal Growth Rates of Bachelor’s Degree Recipients Notes: Non-AI tech majors refer to STEM majors that are not AI skill-related. with AI) and college major choices. Over 1990-2019, as fast-growing AI subfields (big data, data mining, deep learning, and machine learning) and AI itself are more intensively discussed by the public or studied by researchers, there is faster growth in completing majors that are most complementary to AI or general computer majors. When students witness the growing popularity of AI, they may view it as a signal for the increasing demand for AI skills. Thus, they may become more likely to choose majors that provide AI skill training to better prepare themselves to work with AI after graduation. Unlike the positive relationship between a major’s complementarity with AI and degree com- pletion, I document a negative relationship between AI exposure and degree completion, especially when restricting to top 100 or top 50 universities in the U.S. This negative relationship indicates that students in top-end universities tend to avoid choosing majors with high AI exposure, thus being less likely to perform tasks that are more substitutable by AI. Following the theoretical work on skill-biased technological change (e.g., Katz and Murphy, 1992; Acemoglu and Autor, 2011) and subsequent studies on how automation (e.g., Autor and Dorn, 2013; Moll et al., 2022) and industrial robots (e.g., Humlum, 2019; Acemoglu and Restrepo, 2020) affect wage inequality and job polarization, there is a growing literature exploring impacts of 9 .6.811.21.4Decadal Growth Rate of Degrees Conferred1990s2000s2010sDecadeAll MajorsAI Skill−Related MajorsNon−AI MajorsNon−AI Tech Majors AI on labor market outcomes. Acemoglu et al. (2022) use online job vacancies data and find that establishments with high AI exposure increase recruitment of workers with AI skills and reduce non-AI hiring, especially after 2014. Grennan and Michaely (2020) show that sell-side analysts with stocks that are more exposed to AI tend to leave the job, while those who stay reallocate their efforts to tasks that need more soft skills. These studies show that AI not only displaces workers with high AI exposure, but also complements those with AI skills. As a complement to these papers that focus on how AI impacts the labor market, I study how students choose their majors in response to the rapid growth of AI. This paper also contributes to the work on college major choices by considering the role of technological change. Previous studies have investigated how college major choices respond to expected earnings (Long et al., 2015), local shocks such as local job losses (Acton, 2021), students’ abilities (Arcidiacono, 2004), gender preferences (Zafar, 2013; Porter and Serra, 2020), and peer effects (De Giorgi et al., 2010; Zölitz and Feld, 2021). Dauth et al. (2021) and Di Giacomo and Lerch (2023) find that higher exposure to automation technologies increases college enrollment in Germany and the U.S., respectively. Zhang et al. (2023) explore the IT-labor relationship and find that IT complements labor with a master’s degree or above. Humlum and Meyer (2022) document a wage premium in Denmark for majors concentrating in firms that produce AI. The most closely related paper to this one is Hemelt et al. (2023), who use online job vacancies data to define majors to be general (e.g., Business and Engineering) and specific (e.g., Nursing) based on how skills associated with each major differ across areas. They find a positive (negative) correlation between earnings and demand for cognitive and financial skills (social and basic computer skills). Unlike Hemelt et al. (2023), I classify majors based on whether they are related to AI to explore whether undergraduates respond to changes in a major’s complementarity with or exposure to AI. Finally, this paper introduces a novel measure of AI complementarity at the college major level. By leveraging Google search intensity and academic publications data on AI subfields, this measure – the AI Relevance Score – captures an objective view of how closely a college major provides up-to-date, popular skills that are related to AI. 10 The rest of the paper proceeds as follows. Section 1.2 introduces how to define AI skill-related majors and proposes measures for a major’s AI exposure and complementarity with AI. Section 1.3 describes the data and presents the empirical strategy. Section 1.4 discusses the main results. Section 1.5 concludes. 1.2 Measuring AI Exposure and Relatedness Section 1.2.1 first presents a methodology of measuring major-AI exposure by mapping oc- cupations to college majors and using occupational-level AI exposure measures constructed by the existing literature. I then define AI skill-related majors in Section 1.2.2 by directly matching phrases for AI skills and applications to college major descriptions. Section 1.2.3 introduces AI Relevance Score which captures the complementarity of AI. Section 1.2.4 provides distributions of these AI measures. 1.2.1 AI Major Exposure I study three different measures of AI Occupational Exposure (AIOE)1 to construct college majors’ exposure to AI by matching occupations to college majors. All of these AIOE measures capture the compatibility of AI and occupational tasks. The higher the AIOE score is, the more likely AI can perform and substitute labor in tasks of an occupation. The first measure is from Webb (2019), who extracts verb-noun phrases from AI-related patents and matches them with verb-noun phrases in occupational descriptions from the Occupational Information Network (O∗NET) database. Occupations matched with more AI patents are classified as more exposed to AI since they have more overlap-ping tasks with AI capabilities. The second measure is from Felten et al. (2018), who use the Electronic Frontier Foundation (EFF) AI Progress Measurement dataset to track the progress on performance across AI applica- tions (e.g., speech recognition, generating images) between 2010 and 2015. They map these AI applications to the 52 occupational abilities listed by O∗NET and use the rate of improvements in AI performance to construct an ability-level AI exposure. Their AIOE is a weighted sum of 52 O∗NET abilities’ AI exposure, where weights are an ability’s prevalence and importance within 1Although the Felten et al. (2018, 2021) and Webb (2019) occupational-level AI exposure measures are named differently, I use AI Occupational Exposure (AIOE) hereafter for convenience. 11 each occupation from O∗NET. The third measure is from Felten et al. (2021). Unlike Felten et al. (2018), the authors use a crowd-sourced dataset to link AI applications (e.g., image recognition, language modeling) chosen from the EFF dataset to the 52 O∗NET occupational abilities. They conduct a survey on "gig workers" from Amazon’s Mechanical Turk (mTurk) web service by asking these respondents whether they think each chosen AI application is related to each of the 52 O∗NET occupational abilities. A matrix of relatedness between AI applications and abilities is then created based on the survey responses. Similar with Felten et al. (2018), the AIOE is calculated as a weighted sum of the ability-level AI exposure. In Appendix Table 1A.1, occupations with the highest (lowest) scores are the most (least) exposed to AI. Tasks of the highest ranking occupations, i.e., occupations that are the most exposed to AI, are more compatible with AI while the least exposed occupations are mostly labor-intensive. Although these three AIOE measures are not highly correlated (with correlations between 0.10 and 0.30), the highest (or lowest) scoring occupations are similar regardless of which measure is used for ranking. Figure 1.2 presents the geographic distribution of AI exposure by commuting zone (CZ) using the Felten et al. (2021) AIOE measure. CZs with a darker color have underwent higher exposure to AI. People who live in CZs with higher AI exposure are more likely to be replaced by AI in the labor market than those who live in CZs with lower AI exposure. The most exposed CZs are largely concentrated in metropolitan cities, e.g., New York City, Chicago, Miami, and Los Angeles. This finding is robust to the Felten et al. (2018) and Webb (2019) AIOE measures with the distribution shown in Appendix Figure 1A.1. It is worth noting that CZs with high AI exposure are different from those that are most exposed to routine employment, trade, or robots. Autor et al. (2013) show that CZs with the highest routine employment shares are human capital-intensive or manufacturing-intensive regions, while the latter ones are also highly exposed to trade. Acemoglu and Restrepo (2020) document that some CZs in the rust belt and Texas have been the most exposed to industrial robots. 12 Figure 1.2 AI Occupational Exposure (AIOE) by Commuting Zone, 2019 Notes: The Felten et al. (2021) AIOE measure is aggregated to the commuting zone level. I then map occupations to college majors and construct AI Major Exposure (AIME) measures using each of the above three AIOE measures separately. AIME captures how likely students graduating with a major will perform tasks with high AI exposure. I use the American Community Survey (ACS) data2 to determine the most common occupation for a major. The AIME score for major 𝑚 in year 𝑡 is constructed as follows: AIME𝑚,𝑡 = 1{𝑜∗ = arg max 𝑜 𝑒𝑚 𝑝𝑜,𝑚,𝑡 } × AIOE𝑜∗, (1.1) where 𝑒𝑚 𝑝𝑜,𝑚,𝑡 is the number of employed workers of occupation 𝑜 in year 𝑡 graduating with major 𝑚. 1{𝑜∗ = arg max𝑜 𝑒𝑚 𝑝𝑜,𝑚,𝑡 } denotes the most common occupation for major 𝑚 in year 𝑡, which is the occupation with the largest number of employed people within the group of students graduating with the same major. AIOE𝑜∗ is one of the three AIOE measures for major 𝑚’s most common occupation 𝑜∗.3 Thus, a total of three AIME measures are constructed. Students graduating with a major with a higher AIME score are more likely to work in occupations that are more exposed to AI. That is, they are more likely to perform tasks with a higher likelihood of being substituted by AI in the labor market.4 2ACS provides employment data by occupations and college majors starting from 2009. The 2018 Standard Occupational Classification (SOC) code is used to represent each occupation. The 4-digit Field of Degree (degfieldd) code classified by the Census Bureau is used to represent each major and is mapped to the 2020 6-digit Classification of Instructional Program (CIP) code in this paper for consistency using the crosswalk between the Field of Degree and CIP code provided by the Census Bureau. 3All of the Felten et al. (2018, 2021) and Webb (2019) AIOE measures are time-invariant. 4Another way to construct the AIME measure could be to weight AIOE using the proportion of people with a 13 0.85 − 4.520.35 − 0.85−0.06 − 0.35−0.49 − −0.06−1.05 − −0.49−7.41 − −1.05 Table 1.1 College Majors with the Highest/Lowest AIME Scores in 2019 Rank Highest Scoring Lowest Scoring 1 2 3 4 5 6 7 8 9 Actuarial Science Accounting Sports, Kinesiology, and Physical Education/Fitness, Other Parks, Recreation, and Leisure Studies Accounting and Related Services, Other Parks, Recreation, Leisure, Fitness, and Kinesiology, Other Accounting and Finance Exercise Science and Kinesiology Business/Managerial Economics Sports, Kinesiology, and Physical Education/Fitness, General Accounting and Business/Management Parks, Recreation, and Leisure Facilities Management, General Accounting Technology/Technician and Bookkeeping Sport and Fitness Administration/Management Auditing Security System Installation, Repair, and Inspection Technology/Technician Investments and Securities Musical Instrument Fabrication and Repair 10 International Finance Vehicle Emissions Inspection and Maintenance Technology/Technician Notes: The AIME scores are constructed by using the Felten et al. (2021) AIOE and equation (1.1). Table 1.1 shows college majors with the highest and lowest AIME scores in 2019 constructed by using the Felten et al. (2021) AIOE measure and equation (1.1). College majors with the highest exposure to AI, i.e., the highest AIME scores, are mostly Accounting and Finance majors. AI and IT are more compatible with accounting or finance tasks (Boukherouaa et al., 2021; Hasan, 2021; Cao, 2022). The least exposed majors align students with labor-intensive occupations that also require social skills. Appendix Table 1A.2 presents that Architecture, Chemical Engineering, and Visual and Per-forming Arts majors are also highly exposed to AI according to the other two AIME measures. Improvements in text-to-image and text-to-video AI such as DALL·E and Sora developed by OpenAI impact the creative industries (Anantrasirichai and Bull, 2022; Cetinic and She, 2022). Venkatasubramanian (2019) shows that AI is used to support chemical engineers and may transform this industry. specific occupation within the group of people graduating with the same college major: AIME𝑚,𝑡 = 𝑒𝑚 𝑝𝑜,𝑚,𝑡 𝑒𝑚 𝑝𝑚,𝑡 ∑︁ 𝑜 × AIOE𝑜, (1.2) where 𝑒𝑚 𝑝𝑚,𝑡 is the number of all employed workers in year 𝑡 graduating with major 𝑚. However, this AIME measure is noiser than that constructed by the most common occupation method using equation (1.1). Since students graduating with the same major may choose different occupations, one major may have multiple weights. About 80% of majors are matched to over 100 occupations. The extreme case is that one major matches to 510 occupations. Thus, this "weighting" version of AIME might be averaged out, resulting in little variation in its distribution which will be discussed further in Section 1.2.4. This multiple weights issue may introduce noise in the AIME measure, making it less precise. By assigning a weight of one to the most common occupation as shown in equation (1.1) might address this issue. 14 1.2.2 Defining AI Skill-Related Majors In contrast to the AIME score, which captures how easy it is for AI to substitute for the tasks of a major, I propose a new methodology to measure how well a major prepares students to work with AI. The biggest difference between this new measure and the AIME score discussed in Section 1.2.1 is that the former captures a major’s complementarity with AI while the latter captures substitutability. Thus, these two measures are polar opposites. To measure a major’s complementarity with AI, I first define AI skill-related majors as those that provide students with AI skill training to better work with AI by mapping AI skills and applications to college major descriptions. The National Center for Education Statistics (NCES) provides a short description for each major represented by a 6-digit Classification of Instructional Program (CIP) code, which briefly describes the main concentrations of and what skills students can learn from a major/program.5 For each 2020 CIP code, the NCES provides information on the program’s title, description, and whether this CIP code, its title, or its definition underwent a notable change compared to the previous version. Next, I extract phrases for AI skills and applications from Zhang et al. (2022) and titles and topics of top journals and conferences in the field of AI (e.g., Institute of Electrical and Electronics Engineers (IEEE) and Association for Computing Machinery (ACM)). If a major’s description includes any of the chosen AI phrases, I consider it as an AI skill-related major. I classify these chosen AI phrases into three categories (from the most specific to the most general) based on Zhang et al. (2022): skills and applications that are the most closely related to AI (category 1), AI- related computer and information processing technologies (category 2), and general computer skills (category 3). Table 1.2 lists all chosen phrases in each category. If a chosen AI phrase is exactly included in a major’s description, it will be considered as "matched" to this major. If the number of a major’s matched AI phrases from category 𝑔 (𝑔 ∈ {1, 2, 3}) is non-zero, then this major will be classified as an AI skill-related major in category 𝑔. Majors in category 1 have concentrations in the most specific AI skills and applications, while those in category 3 are associated with general 5CIP code was originally developed by NCES in 1980. Revisions occurred in 1985, 1990, 2000, 2010, and 2020. 15 Table 1.2 Phrases for AI Skills and Applications Category Phrases Category 1: Skills and applications that are the most closely related to AI artificial intelligence, augmented reality (AR), autonomous driving, big data, computer graphics, computer vision, data mining, deep learning, machine learning, multimedia, natural language processing (NLP), neural network, pattern recognition, robot/robotics, speech recognition, virtual reality (VR), voice recognition, 3D modeling Category 2: AI-related computer and information processing technologies cloud computing, computational intelligence, computational biology, computer-aided design (CAD)/ computer-aided drafting/CAD application, computer network, cybernetics, image processing, internet, internet of things (IoT), symbolic inference Category 3: General computer skills automatic control, automation, cognitive science/cognitive engineering, computer programming, computing theory, geographic information system (GIS), industrial internet, information system, information technology, integrated circuit, intelligent control, microchip/chip design, neuroscience, phenotype, remote sensing, software engineering, statistics, telecommunication, wireless communication computer skills. If a major’s description is matched with phrases in multiple categories, it will be classified into the category with more specific skills (i.e., the category with a smaller index). For example, if a major’s description includes phrases in both categories 1 and 2, it will be considered as a category 1 major. In this way, there is no overlap between different categories. Table 1.3 shows four examples of college major descriptions: one from each category of AI skill-related majors and a non-AI skill-related major. The phrases in red, blue, and orange are the matched AI phrases in categories 1, 2, and 3, respectively, of the corresponding major. The full lists of majors in each category of AI skill-related ones are presented in Appendix Tables 1A.3 to 1A.5. It is worth noting that a bigger number of matched AI phrases does not imply that more advanced AI skills are the concentrations of a major. A smaller number does not indicate that only preliminary AI skills can be learned from choosing this major, either. This number of matched AI phrases only objectively shows how many AI skills or applications students can acquire from the corresponding major as listed in its description provided by NCES. In other words, a greater number of matched AI phrases indicates that more versatile AI skills are the main concentrations of this major, while a smaller number implies that students learn fewer but more specific AI skills from choosing the corresponding major. Although CIP codes have underwent revisions in 2000, 2010, and 2020, none of the college major descriptions changed in 2010 compared to the 2000 version and most of the descriptions 16 Table 1.3 Examples of AI Skill-Related/Non-AI Skill-Related Majors with Descriptions 2020 CIP Code 2020 CIP Title Description 11.0102 Artificial Intelligence A program that focuses on the symbolic inference, representation, and simulation by computers and software of human learning and reasoning processes and capabilities, and the computer modeling of human motor control and motion. Includes instruction in computing theory, cybernetics, human factors, natural language processing, and applicable aspects of engineering, technology, and specific end-use applications. Is It an AI-Skill- Related Major? Yes, category 1. 15.1305 Electrical/Electronics Drafting and Electrical/Electronics CAD/CADD A program that prepares individuals to apply technical knowledge and skills to develop working schematics and representations in support of electrical/electronic engineers, computer engineers, and related professionals. Includes instruction in basic electronics, electrical systems and computer layouts; electrode-mechanical drafting; manufacturing circuitry; computer-aided drafting (cad); and electrical systems specification interpretation. Yes, category 2. 15.1204 Computer Software Technology/Technician A program that prepares individuals to apply basic engineering principles and technical skills to support engineers in developing, implementing, and evaluating computer software and program applications. Includes instruction in computer programming, programming languages, databases, user interfaces, networking and warehousing, encryption and security, software testing and evaluation, and customization. Yes, category 3. 52.0301 Accounting A program that prepares individuals to practice the profession of accounting and to perform related business functions. Includes instruction in accounting principles and theory, financial accounting, managerial accounting, cost accounting, budget control, tax accounting, legal aspects of accounting, auditing, reporting procedures, statement analysis, planning and consulting, business information systems, accounting research methods, professional standards and ethics, and applications to specific for-profit, public, and non-profit organizations. No. Notes: Phrases in red, blue, and orange are the matched AI skills or applications in categories 1, 2, and 3, respectively. did not change in 2020 compared to the 2010 version.6 6% (4 out of 33) of college majors in category 1 and 11% (2 out of 18) in category 2 underwent slight changes in descriptions in 2020 compared to the 2010 version, but none of these changes is related to the chosen AI phrases. Of majors in category 3 that experienced changes in descriptions in 2020 (4%, or 3 out of 73), the phrase "geographic information system (GIS)" was added to one major leading to a change in its number of matched phrases. Since most college major descriptions have not changed over time, the number of matched AI phrases is assumed to be time invariant in this paper. However, due to this time invariance property, the number of matched AI phrases cannot capture the growth in AI. I then propose a new measure to link the growth in AI to college majors in the next section. 6NCES only provides college major descriptions for 2000, 2010, and 2020 CIP codes on its website. For older versions, only CIP codes and the corresponding titles can be found in crosswalks provided by NCES. 17 1.2.3 AI Relevance Score of College Majors To capture the relatedness between the growth in AI and college majors, I construct a new measure denoted as "AI Relevance Score." This measure captures how well a major prepares students to use AI to complement their job tasks, i.e., a major’s complementarity with AI. To measure the growing interest in AI, I use relative Google search activities for each chosen AI phrase from Google Trends data.7 Google Trends data provides an index of relative search volumes by search terms, time ranges, and geographic areas. Although the exact number of search queries on a specific term is not available, Google Trends Index (GTI) is designed to show the relative change in search intensities over a given period and at a given location.8 Appendix Figure 1A.2 shows GTI of search activities on some chosen AI phrases in the U.S. from 2004 to 2020.9 Since users can compare at most five terms per request, I include "Machine Learning" and "Pattern Recognition" in both requests presented in Appendix Figure 1A.2 for comparison. Newly emerging AI technologies, such as machine learning, deep learning, and big data, have been searched more intensively than traditional AI technologies, e.g., pattern recognition and natural language processing. Since GTI represents relative Google search intensities and Google is one of the most popular search engines worldwide, GTI can be used as a proxy for changes in people’s interests in different AI subfields over time. The AI Relevance Score of major 𝑚 in category 𝑔 during decade 𝜏 is then constructed as follows: AI Relevance Score𝑚,𝑔,𝜏 = ∑︁ 𝑖∈AI phrases𝑔 1{𝑖 ∈ Description𝑚} × GTI𝑖,𝜏𝑇 − GTI𝑖,𝜏0 GTI𝑖,𝜏0 , (1.3) where 𝜏, 𝜏0, and 𝜏𝑇 denote a decade, the first year in that decade, and the last year in that decade, respectively. 1{𝑖 ∈ Description𝑚} indicates whether an AI phrase 𝑖 in category 𝑔 (where 7https://trends.google.com/trends/?geo=US. Stephens-Davidowitz and Varian (2014) introduce Google Trends data in details and how it can be used for social science research. Kong and Prinz (2020) use Google Trends data to study the effect of shutdown policies on unemployment during the COVID-19 pandemic. 8GTI ranges between 0 and 100, which is computed based on a term’s proportion of search activities among all search activities on all terms per request. Suppose a user compares term A and B over period 𝜏 in location 𝑔. If term A has a GTI of 100 and term B has a GTI of 50 at time 𝑡, this implies that the number of search activities on term A at time 𝑡 was twice as large as that on term B. GTI is computed separately for each request. Users can compare at most five terms per request. 9Google Trends data starts from Jan. 1st, 2004. 18 𝑔 ∈ {1, 2, 3}) listed in Table 1.2 is matched to major 𝑚’s description. GTI𝑖,𝜏0 and GTI𝑖,𝜏𝑇 are the start-of-decade and end-of-decade indices of Google search queries in the U.S. on an AI phrase 𝑖, respectively. Since Google Trends data starts from 2004, this AI Relevance Score is only available for the 2000s and the 2010s. Due to the same reason, 𝜏0 is set to be 2004 when computing the AI Relevance Score in the 2000s. The underlying assumption is that the differences in relative search intensities from 2004 to 2010 would remain unchanged if the time range is extended back to 2000. When undergraduate students choose their fields of study, it is possible that they search for relevant information to learn more about majors they are interested in on Google. Thus, by using the decadal growth in GTI of chosen AI phrases, I assume that students respond to changes in the attention that an AI subfield has received from the public. A higher AI Relevance Score then implies that AI skills or applications associated with a major have become increasingly popular among the public in the U.S. In addition to GTI, I use growth rates of academic publications in the field of AI to compute an alternative AI Relevance Score. Unlike GTI which captures relative search intensities on AI subfields, the growth rate of academic publications can be viewed as a proxy for course content developments. Through these developments, students can acquire more up-to-date concepts and skills and be better prepared for changes in skill requirements. The growth rate of academic publications whose topic is one of the AI phrases listed in Table 1.2 is computed using the Web of Science (WoS) Core Collection database from Clarivate. WoS Core Collection contains more than 21,100 peer-reviewed journals, books, and proceedings in the field of Science, Social Science, and Arts and Humanities from 1900 to present. Users can search for academic publications with a specific topic published in a specific year on the WoS website.10 Since each academic publication is counted only once by WoS, I use the total number of annual academic publications with each of the chosen AI phrases included in the topic and calculate the decadal growth rate of academic publications for each AI phrase. This alternative AI Relevance 10Appendix Figure 1A.3 presents examples of the search page and results. 19 Score of major 𝑚 in category 𝑔 in decade 𝜏 is constructed as follows: (cid:92)AI Relevance Score𝑚,𝑔,𝜏 = ∑︁ 1{𝑖 ∈ Description𝑚} 𝑖∈AI phrases𝑔 Publications𝑖,𝜏𝑇 − Publications𝑖,𝜏0 Publications𝑖,𝜏0 , × (1.4) where Publications𝑖,𝜏0 and Publications𝑖,𝜏𝑇 are the start-of-decade and end-of-decade numbers of academic publications with an AI phrase 𝑖 in category 𝑔 included in the topic, respectively. By calculating this AI Relevance Score as the sum of growth rates of publications in all AI subfields associated with a major, I equally weight each AI subfield. The underlying assumption for this equal weight is that different concentrations of a major have the same importance. Suppose a major has concentrations in pattern recognition, big data, and machine learning. Although pattern recognition is a mature AI subfield while the rest are newly emerging AI technologies, instructors who teach related courses will not solely focus on pattern recognition or quickly mention the rest, and vice versa. It is equally important for students to learn all of them. Moreover, Appendix Figure 1A.4 shows that more than 80% of AI skill-related majors are matched to only one AI phrase. One potential weight that can be applied to the construction of AI Relevance Score is the total credits of courses in each chosen AI subfield required by a major. Credits can be the proxy for the amount of contents of a specific subject or topic that students need to learn, which in turn implies how in-depth this subject is covered by a major. By using decadal growth rates of academic publications to construct AI Relevance Score, I assume that students are responsive to the trend of AI progress. A consistently high growth rate of an AI subfield indicates that it has consistently and increasingly captured researchers’ attention. In other words, this AI subfield has been a popular research topic that is worth studying. A major with concentrations in this fast-growing AI subfield is consequently assigned a relatively higher AI Relevance Score based on equation (1.4). Thus, a higher AI Relevance Score indicates a more promising future: students who choose a major with a high AI Relevance Score can learn more in-depth and up-to-date AI skills to complement their jobs after graduation. However, a potential threat to this measure is the possibility that a newly emerging technology usually has a high growth 20 rate of academic publications due to its small baseline, while a mature technology that has a large baseline grows slowly. Since the AI Relevance Score computed by equation (1.4) cannot capture changes in the absolute number of academic publications, I then propose a complementary AI Relevance Score using decadal changes in the number of academic publications to address this threat: (cid:94)AI Relevance Score𝑚,𝑔,𝜏 = ∑︁ 1{𝑖 ∈ Description𝑚} × ΔPublications𝑖,𝜏, (1.5) 𝑖∈AI phrases𝑔 where ΔPublications𝑖,𝜏 is the decadal change in the number of academic publications with an AI phrase 𝑖 included in the topic. This alternative AI Relevance Score captures the relationship bewteen the intellectual capital accumulation on AI and college majors. A higher score indicates that AI skills or applications with a larger increase in the intellectual capital are concentrations of a major. Appendix Figure 1A.5 shows decadal changes in and decadal growth rates of academic pub- lications on a few AI phrases that have relative high growth rates over time. Deep learning and machine learning are newly emerging technologies and had a consistently rapid growth during the 2010s, while pattern recognition is a mature AI subfield that was fast-growing back in the 1990s. These trends are consistent with the upward trends in relative Google search intensities on these newly emerging technologies compared to the mature ones shown in Appendix Figure 1A.2. 1.2.4 Distribution of AI Measures across College Majors Of the 1,355 college majors (represented by the 2020 6-digit CIP code) in my sample over the past three decades, 2.4% are defined as majors that are most complementary to AI (category 1), 1.3% are with concentrates in AI-related computer and information processing technologies (category 2), 5.4% are associated with general computer skills (category 3), 28.6% are STEM (science, technology, engineering, and mathematics) majors, and 22.9% are non-AI tech majors (i.e., non-AI STEM majors).11 11I use the 2020 STEM Designated Degree Program List provided by the U.S. Department of Homeland Security (DHS) to define STEM majors. 21 Figures 1.3a to 1.3c show the distribution of three AI Relevance Score measures by broad college major categories (represented by the 2020 2-digit CIP code) over 2010-2019 academic years. They highlight that these three AI Relevance Score measures capture different aspects of a major’s complementarity with AI as discussed in Section 1.2.3. The AI Relevance Score constructed using relative search intensities, GTI, is of a similar magnitude for Agriculture, Engineering, Mathematics and Statistics, Physics, and Social Sciences majors. The other two AI Relevance Score measures are especially high for Mathematics and Statistics, Physics, and Social Sciences majors. It is interesting that Computer and Information Sciences majors do not have an extremely high AI Relevance Score. Since the AI Relevance Score is a weighted sum of either GTI or changes in academic publications, it is possible that this score is averaged out as CS majors focus on both traditional (e.g., pattern recognition) and newly emerging (e.g., deep learning) AI technologies. As shown in Appendix Figures 1A.2 and 1A.5, traditional AI technologies usually have lower search intensities and a stagnant growth in academic publications than newly emerging ones. The distribution of AIME measures constructed by assigning a weight of one to the most common occupation following equation (1.1) is presented in Figures 1.3d to 1.3f.12 All three AIME measures have high values for Communications Technology/Technician majors. Liberal Arts and Sciences and Linguistics majors are also high in AIME constructed by using the Felten et al. (2021) AIOE measure. Tasks that students graduating with these majors perform are more substitutable by AI since AI is compatible with processing languages and coverting text to images or videos. 1.3 Data and Empirical Strategy 1.3.1 Data The degree completion data between 1990-91 and 2019-20 academic years are from the Inte- grated Postsecondary Education Data System (IPEDS), which has surveyed all U.S. post-secondary institutions since 1993. Since the CIP codes underwent revisions, I use the crosswalk provided by 12Unlike AIME constructed using the most common occupations, those constructed as a weighted sum of AIOE measure using employment shares following equation (1.2) have less variation across majors as shown in Appendix Figure 1A.6. They are less precise due to the multiple weights issue explained in the footnote of Section 1.2.1. 22 Figure 1.3 AI Measures by Broad College Major Category, 2010-19 (a) AI Relevance Score—by Google Trends Index (b) AI Relevance Score—by Decadal Growth Rates of Aca- demic Publications (c) AI Relevance Score—by Decadal Changes in Academic Publications (d) AIME—by Using the Felten et al. (2021) AIOE (e) AIME—by Using the Felten et al. (2018) AIOE (f) AIME—by Using the Webb (2019) AIOE Notes: Majors with a zero AI Relevance Score in subfigures (a) to (c) are those that do not match with any chosen AI phrase. The AIME measures in subfigures (d) to (f) are constructed by assigning a weight of one to the most common occupation for a major following equation (1.1). The most common occupation is the one with the largest number of employed people within the group of students graduating with the same major. 23 0.2.4.6Average AI Relevance Score Constructed Using Google Trends IndexVisual and Performing ArtsTransportationTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPrecision ProductionPhysicsPhilosophyNatural ResourcesMilitary TechMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHuman SciencesHomeland SecurityHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArea and Cultural StudiesArchitectureAgriculture0.1.2.3.4.5Average AI Relevance Score Constructed Using Decadal Growth Rate of Academic PublicationsVisual and Performing ArtsTransportationTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPrecision ProductionPhysicsPhilosophyNatural ResourcesMilitary TechMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHuman SciencesHomeland SecurityHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArea and Cultural StudiesArchitectureAgriculture0.2.4.6.8Average AI Relevance Score Constructed Using Decadal Changes in #Academic PublicationsVisual and Performing ArtsTransportationTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPrecision ProductionPhysicsPhilosophyNatural ResourcesMilitary TechMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHuman SciencesHomeland SecurityHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArea and Cultural StudiesArchitectureAgriculture0.2.4.6.81Average AIME Constructed by Weighting AIOE from Felten (2021)Visual and Performing ArtsTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPhysicsPhilosophyNatural ResourcesMilitary ScienceMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArchitectureAgriculture0.2.4.6.81Average AIME Constructed by Weighting AIOE from Felten (2018)Visual and Performing ArtsTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPhysicsPhilosophyNatural ResourcesMilitary ScienceMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArchitectureAgriculture0.2.4.6.81Average AIME Constructed by Weighting AIOE from Webb (2019)Visual and Performing ArtsTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPhysicsPhilosophyNatural ResourcesMilitary ScienceMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArchitectureAgriculture the NCES to match all CIP codes from previous versions to the most recent one (2020 CIP codes) for consistency. Due to the geographic variation in AI exposure shown in Figure 1.2 and the fact that the highest ranking or the most popular majors are different across colleges, I use the IPEDS data at the major-by-college-by-decade level. Decadal growth rates of bachelor’s degree recipients for each 6-digit CIP code (i.e., the most detailed college major category) in each college are calculated to explore the relationship between the growth in AI and college major choices over the past decades. I also limit the IPEDS data to 4-year colleges because students enrolled in 4-year colleges usually have a longer period to learn about the field-specific information and their preferences than those who are enrolled in less-than-4-year colleges. Table 1.4 provides summary statistics of average decadal completion rates between 1990-91 and 2019-20 academic years. On average, majors that are most complementary to AI (category 1) had a decadal growth rate of 136.7% for all bachelor’s degree recipients, 69.9% for male, 98.4% for female, 62.9% for Whites, 14.7% for international students, and 140.0% for U.S. citizens over the past three decades. Note that this growth rate is at major-by-college-by-decade level, so they can be either positive or negative for different majors in different colleges in each decade. Thus, the average decadal growth rate for a group of recipients might be smaller if it has more negative rates that are larger in magnitude or fewer positive rates (or both) compared to other groups. Of the overall growth rates in AI majors, 46.6% are negative with an average growth rate of -63.3% while 53.4% are positive with an average of 358.6%.13 Of the growth rates for male (female), 49.7% (47.6%) are negative with an average growth rate of -67.4% (-68.3%) while the average of positive rates is 259.9% (303.4%). Thus, the overall growth rate in AI majors is, on average, higher than that for male or female. Same explanation is applied to the comparison between the overall rate and the rate for other subgroups. Compared with AI majors (category 1), those associated with AI-related computer and infor- 13The average of positive rates is much higher in magnitude than that of negative rates due to observations with a small baseline when calculating decadal growth rates. For negative rates, no matter what the baseline is, the minimum value cannot be smaller than -1. If a major, especially a newly emerging major, has a few completions in the start of a decade but experiences much more completions in the end of a decade, its growth rate will be extremely high. This then substantially increases the average of positive completion rates. 24 Table 1.4 Summary Statistics of Average Decadal Completion Rates, 1990-2019 Average Decadal Growth Rate3 of Bachelor’s Degree Recipients by Major All Recipients Male Female Whites International Students All College Majors1 N = 236,763 0.829 (8.017) 0.479 (3.825) 0.643 (6.595) 0.493 (4.970) 0.181 (2.955) AI Skill-Related Majors2 in Category 1 N = 3,827 1.367 (17.660) Category 2 N = 755 Category 3 N = 8,668 Non-AI Majors N = 223,513 1.225 (4.885) 1.365 (7.043) 0.798 (7.793) Non-AI Tech Majors N = 44,999 0.867 (11.096) 0.699 (3.758) 1.162 (4.598) 0.933 (4.852) 0.453 (3.771) 0.585 (4.101) 0.984 (10.222) 0.336 (3.461) 1.030 (5.674) 0.624 (6.553) 0.704 (9.737) 0.629 (3.171) 0.845 (3.962) 0.912 (6.678) 0.472 (4.918) 0.546 (4.390) 0.147 (2.712) 0.018 (4.138) 0.748 (5.273) 0.154 (2.786) 0.518 (3.773) U.S. Citizens 0.828 (8.643) 1.400 (18.113) 1.167 (4.734) 1.329 (7.079) 0.794 (8.426) 0.893 (12.476) Notes: Standard deviations are shown in parentheses. 1Each observation is a major-college-decade cell. College majors are represented by the 2020 6-digit Classification of Instructional Programs (CIP) code. Observations with missing overall decadal completion rates are not counted. 2Category 1 denotes majors that are most complementary to AI; category 2 includes majors with concentrations in AI-related computer and information processing technologies; category 3 consists of majors associated with general computer skills. 3Growth rates are calculated at the major-college-decade level. mation technologies (category 2) underwent larger growth in completion for male, Whites, and U.S. citizens. General computer majors (category 3) experienced similar decadal growth for all recipients and U.S. citizens with AI majors, but larger growth for other subgroups. Unlike these AI skill-related majors, non-AI tech majors had smaller growth in degrees awarded to all subgroups except international students. Appendix Table 1A.6 further decomposes decadal growth rates presented in Table 1.4 into each decade. On average, AI majors (category 1) experienced the largest overall growth in degree completion during the 2000s, while majors in categories 2 and 3 as well as non-AI tech majors underwent the largest growth during the 2010s. The fastest growth in general computer majors (category 3) for Whites and U.S. citizens occurred in the 1990s. In addition, non-AI majors had stagnant growth over past decades for all subgroups except international students. 25 1.3.2 Empirical Strategy I first document decadal changes in degree completion over the past three decades with the following specification: Δ𝑦𝑚,𝑢,𝜏 = 𝛼 + ∑︁ ∑︁ 𝑘∈{1990𝑠,2000𝑠,2010𝑠} 𝑔∈{1,2,3} 𝛽𝑘,𝑔1{𝑚 ∈ AI Skill-Related Majors𝑔} × 1{𝜏 = 𝑘 } (1.6) + X𝑢,𝜏0𝚽 + 𝛿𝑚2𝑑𝑖𝑔𝑖𝑡 ,𝜏 + 𝜃𝑢,𝜏 + 𝜀𝑚,𝑢,𝜏, where 𝑚, 𝑚2𝑑𝑖𝑔𝑖𝑡, 𝑢, and 𝜏 denote the 2020 6-digit CIP code, the 2020 2-digit CIP code, college, and decade, respectively. 𝑔 represents one of the three categories of AI skill-related majors: majors with concentrations in the most specific AI skills (category 1), majors associated with AI-related computer and information processing technologies (category 2), and majors with specilizations in general computer skills (category 3). Δ𝑦𝑚,𝑢,𝜏 is the decadal growth rate of bachelor’s degree recip- ients in major 𝑚 graduating from college 𝑢 over decade 𝜏. 1{𝑚 ∈ AI Skill-Related Majors𝑔} rep- resents the time-invariant indicator for majors in category 𝑔. 1{𝜏 = 𝑘 }, 𝑘 ∈ {1990𝑠, 2000𝑠, 2010𝑠} are decade dummies. The vectors X𝑢,𝜏0 contain the start-of-decade college controls, including the share of graduates who are male and Whites. 𝛿𝑚2𝑑𝑖𝑔𝑖𝑡 ,𝜏 and 𝜃𝑢,𝜏 are the 2-digit-CIP-by-decade14 and college-decade fixed effects, respectively. These fixed effects capture two different sources of unobserved heterogeneity: changes in preferences for broad major categories (represented by the 2-digit CIP code) across time and differences in unobserved determinants of college major choices across colleges and across time that are correlated with AI. Finally, 𝜀𝑚,𝑢,𝜏 is an idiosyncratic error term. The coefficient of interest is 𝛽𝑘,𝑔, which captures the decadal growth in bachelor’s degrees conferred in AI skill-related majors in category 𝑔. Since the binary indicator for which category a major belongs to, 1{𝑚 ∈ AI Skill-Related Majors𝑔}, is time invariant15, I further interact it with decade dummies to estimate how this growth has changed over decades. 14Instead of the 6-digit-CIP-by-decade fixed effect, the 2-digit-CIP-by-decade fixed effect is used because the binary indicator for AI skill-related majors does not change at the 6-digit CIP level across time. 15As explained in Section 1.2.2, since most of the college major descriptions have not underwent a notable change over time, the binary indicator for AI skill-related majors in category 𝑔 is assumed to be time invariant. 26 Nevertheless, the indicator for AI skill-related majors in equation (1.6) fails to capture differ- ences in the substitutability or complementarity of AI across majors and across time. To explore the relationship between degree completion and major-level AI exposure, I re-estimate equation (1.6) by replacing the interaction term with the AIME measure constructed by equation (1.1). To study the relationship between degree completion and how well a major prepares students to use AI, I first re-estimate equation (1.6) by replacing the interaction term with AI Relevance Score measures to test students’ responsiveness to a major’s complementarity with AI. Second, I additionally include a few fast-growing AI subfields which have substantially improved the performance of AI over one or more decades to explore how students respond to these fast-growing AI technologies. To analyze the relationship between fast-growing AI subfields and degree completion, I estimate the following specification: Δ𝑦𝑚,𝑢,𝜏 = 𝛼 + ∑︁ 𝑔∈{1,2,3} 𝛽𝑔ΔGTI of AI Subfields𝜏 × 1{𝑚 ∈ AI Skill-Related Majors𝑔} ∑︁ + 𝑔∈{1,2,3} 𝛾𝑔AI Relevance Score𝑚,𝑔,𝜏 + X𝑢,𝜏0𝚽 + 𝛿𝑚2𝑑𝑖𝑔𝑖𝑡 ,𝜏 + 𝜃𝑢,𝜏 + 𝜀𝑚,𝑢,𝜏, (1.7) where AI Relevance Score𝑚,𝑔,𝜏 is computed for major 𝑚 in category 𝑔 during decade 𝜏 using relative search intensities data following equation (1.3). ΔGTI of AI Subfields𝜏 represents the decadal change in relative Google search intensities on any of the following phrases: "Artificial Intelligence," "Big Data," "Data Mining," "Deep Learning," and "Machine Learning." I assume that AI itself and these four fast-growing AI subfields jointly, instead of separately, affect students’ college major choices because these subfields not only have largely improved the performance of AI in the 2010s but also have impacted each other over time. There are several reasons why these four newly emerging AI subfields and AI itself are included, rather than other mature AI subfields (e.g., pattern recognition). First, there has been rising interest from both the public and researchers in all of these four AI subfields and AI itself over the past two decades (Zhang et al., 2022; Google Trends data). Second, these four AI subfields are the major contributors of the rapid growth in AI during the 2010s compared to the 1990s and the 2000s (LeCun et al., 2015). By interacting ΔGTI of AI Subfields𝜏 with the AI major indicators, I assume that (1) the rising 27 interest in the aformentioned five AI technologies impact all AI skill-related majors and (2) this impact could vary by the category 𝑔 a major belongs to. Although the general computer majors (category 3) are associated with general computer skills rather than specific AI skills or applications, they could also be affected by these four fast-growing AI subfields and AI itself. First, general computer skills serve as the foundation of AI. Second, students graduating with general computer majors can specialize in AI in the future (e.g., during their graduate studies). Third, students can take courses that cover specific AI skills even if they choose a more general computer major. As explained in Section 1.2.2, changes in relative search activities may not capture the intellectual capital accumulation in AI technologies which can be the proxy for course con- tent developments. Thus, I re-estimate equation (1.7) by (1) changing the variable of interest, ΔGTI of AI Subfields𝜏, to decadal growth rates of academic publications on fast-growing AI tech- nologies and (2) replacing AI Relevance Score with the alternative one generated by the number of academic publications following equation (1.5). 1.4 Results 1.4.1 Trends in College Major Choices over the Past Decades Table 1.5 presents estimates of equation (1.6) by including AI major indicators only (columns 1 to 3) and interacting these indicators with decade dummies (columns 4 to 6). Column 1 shows coefficients estimated from a simple Ordinary Least Squares (OLS) regres- sion with start-of-decade college-level controls. Compared with majors that are unrelated to AI, bachelor’s recipients in majors that are most complementary to AI (category 1) increased by 55.4 percentage points (pp) over the past three decades, while degree completion in general computer majors (category 3) increased by 48.8pp. However, the OLS estimates may be overestimated due to unobserved determinants of students’ preferences across majors, colleges, and time. Column 2 then adds college-decade fixed effects, while column 3 further includes 2-digit-CIP-by-decade fixed effects. After controlling for both fixed effects, the coefficient on AI majors (category 1), 53.3pp, becomes slightly smaller. At the mean decadal growth rate of 82.7%, this percentage-point effect represents an approximate 64.4% increase in decadal growth in AI majors. However, coefficients 28 Table 1.5 Decadal Changes in Bachelor’s Degree Recipients by Major, 1990-2019 Dep. Var.: Decadal Growth Rate of Bachelor’s Degree Recipients by Major (1) (2) All Recipients (4) (3) (5) (6) Majors That Are Most Complementary to AI (Category 1) in Years: 1990 to 2019 1990 to 2000 2000 to 2010 2010 to 2019 0.554∗ (0.302) 0.453 (0.298) 0.533∗ (0.279) 0.454∗∗∗ (0.171) 0.932 (0.601) 0.237 (0.217) 0.372∗∗ (0.182) 0.515 (0.623) 0.433∗ (0.224) 0.492∗∗∗ (0.173) 0.597 (0.615) 0.493∗∗∗ (0.185) Majors with Concentrations in AI-Related Computer and Information Processing Technologies (Category 2) in Years: 1990 to 2019 1990 to 2000 2000 to 2010 2010 to 2019 0.548 (0.674) -0.284 (0.967) -0.511 (0.882) -0.087 (0.220) -0.074 (0.713) 0.952∗ (0.535) -0.406 (0.293) -2.275∗∗ (1.079) 0.969 (0.791) -0.474 (0.376) -2.073∗ (1.078) 0.505 (0.729) Majors Associated with General Computer Skills (Category 3) in Years: 0.488∗ (0.258) 0.372 (0.250) 0.366 (0.231) 1990 to 2019 1990 to 2000 2000 to 2010 2010 to 2019 Observations Outcome Mean Start-of-Decade Controls College-Decade FE 2-Digit-CIP-by-Decade FE 233,519 0.807 ✓ 235,838 0.827 235,838 0.827 ✓ ✓ ✓ 0.349 (0.344) 0.292 (0.361) 0.755∗∗ (0.346) 233,519 0.807 ✓ 0.407 (0.416) -0.067 (0.374) 0.778∗∗ (0.345) 0.612 (0.373) 0.015 (0.423) 0.579∗ (0.302) 235,838 0.827 235,838 0.827 ✓ ✓ ✓ Notes: Each observation is a major-college-decade cell. The coefficients represent the estimate of 𝛽 in equation (1.6). College major-clustered standard errors are shown in parentheses. The estimates in columns 1 to 3 are robust to male, female, and U.S. citizens, while those in columns 4 to 6 are robust to male, female, Whites, U.S. citizens and international students. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. on general computer majors become insignificant with any fixed effect included. I do not find any relationship between degree completion and majors associated with AI-related computer and information processing technologies (category 2), regardless of which specification is used. Estimates shown in columns 5 and 6 indicate that majors associated with the most specific AI skills (category 1) underwent a significant growth in degree completion in both the 1990s and the 29 2010s, after controlling for both fixed effects. However, this growth is not significant in the 2000s. These results are robust to different specifications and different subgroups (i.e., male, female, Whites, U.S. citizens, and international students). These findings can be explained by several reasons. First, neural network and pattern recognition were two of the most popular AI subfields back in the 1990s (Jain et al., 2000) which could lead to a rise in new undergraduate students in these AI majors. During the same decade, the computing system Deep Blue defeated the chess world champion, Garry Kasparov, which caught the public’s attention on AI (Audibert et al., 2022; Shao et al., 2022). Second, AI received increasing attention in the 2010s and the performance of AI was dramatically improved by newly emerging technologies (e.g., deep learning, data mining) in the same period (LeCun et al., 2015; Shao et al., 2022). This, in turn, might attract more students to choose AI-related majors in the 2010s. Third, there was a lack of important advances in AI in the 2000s compared to the 1990s and the 2010s, accompanied with a decline in the share of published books in the U.S. that mention AI (Brooks, 2021; Shao et al., 2022). In contrast to AI majors, those associated with AI-related computer and information processing technologies (category 2) experienced a negative growth in the 2000s. This could be explained by a lack of key advances in AI during this period (Brooks, 2021; Shao et al., 2022). Since category 2 majors are associated with neither the most specific AI skills nor the most general computer skills, fewer students might choose these majors. Bachelor’s degrees awarded in general computer majors (category 3) had a significant and faster growth in the 2010s. The estimate is even larger in magnitude than that for AI majors (category 1). Since general computer majors provide students with computer skill training, advances in AI that also improve methodologies in the field of computer science will have positive impacts on these majors. During the 1990s, pattern recognition was one of the intensively studied AI subfields. Unlike pattern recognition which aims at solving problems of recognizing complex patterns, the newly emerging AI technologies (e.g., big data, deep learning, machine learning) in the 2010s are breakthroughs of fundamental techniques and methodologies in the field of AI (LeCun et al., 2015). This documented increase in completing general computer majors in the 2010s is consistent with 30 the findings of Zhang et al. (2022). They show that the number of new CS undergraduates has largely increased from 2010 to 2020. 1.4.2 The Relationship between AI Complementarity/Exposure and College Major Choices 1.4.2.1 AI Complementarity To further test students’ responsiveness to AI subfields that have been intensively studied and are the main contributors of improvements in AI, I study the relationship between the growth in these AI subfields and college major choices. Table 1.6 presents estimates of 𝛽 and 𝛾 in equation (1.7). Columns 1 to 4 only include AI Relevance Score constructed from equation (1.3), while columns 5 to 8 further add GTI of fast-growing AI subfields (big data, data mining, deep learning, and machine learning) and AI itself. By only including contemporaneous terms, I assume that students are only responsive to the development in AI subfields occurring in the same decade. However, when only including AI Relevance Score in columns 1 and 2, there is no relationship between degree completion and how well a major trains students to learn AI skills over 2000-19.16 Columns 3 and 4 replace the contemporaneous AI Relevance Score with the lagged one. After controlling for both fixed effects, a 1pp increase in the lagged AI Relevance Score of AI majors (category 1) significantly raised the decadal growth in completing these majors by 3.709pp. The estimate on lagged AI Relevance Score for general computer majors (category 3) implies a smaller and less significant effect of 0.691pp. These findings suggest that there is a lag in students learning how well a major prepares them to work with AI when choosing their fields of study without controlling for relative search intensities on fast-growing AI technologies. Columns 5 and 6 further add contemporaneous interaction terms between GTI of fast-growing AI subfields and the binary indicator for majors in category 𝑔. The estimate for the interaction term between GTI and the AI major indicator in column 6 shows that a 1pp increase in GTI of fast-growing AI subfields leads to a 0.12pp increase in the decadal growth of completing these AI majors. The effect of 0.159pp on general computer majors (category 3) is also statistically 16I only explore the relationship between relative Google search intensities and degree completion over 2000-19 because the Google Trends data starts from 2004. More details can be found in Section 1.2.3. 31 Table 1.6 The Relationship between Google Trends Index (GTI) of Fast-Growing AI Subfields and College Major Choices, 2000-19 Dep. Var.: Decadal Growth Rate of Bachelor’s Degree Recipients by Major (1) (2) (3) All Recipients (5) (4) (6) (7) (8) GTI of Fast-Growing AI Subfields × 1{major ∈ Category 1} 1{major ∈ Category 2} 1{major ∈ Category 3} Lagged GTI of Fast-Growing AI Subfields × 1{major ∈ Category 1} 1{major ∈ Category 1} 1{major ∈ Category 1} AI Relevance Score1 of Majors in Category 1 Category 2 Category 3 4.768 (4.019) -2.004 (2.777) 0.329 (0.495) 5.372 (4.004) -2.086 (2.718) 0.294 (0.498) Lagged AI Relevance Score of Majors in Category 1 Category 2 Category 3 Observations Outcome Mean College-Decade FE 2-Digit-CIP-by-Decade FE 171,628 171,628 0.814 0.814 ✓ ✓ ✓ 0.106 (0.070) 0.376∗ (0.202) 0.227∗∗∗ (0.074) 0.120∗ (0.062) 0.231 (0.185) 0.159∗∗ (0.069) 4.746 (4.015) -2.599 (3.036) 0.071 (0.456) 5.392 (3.983) -2.421 (2.840) 0.117 (0.492) 0.179 (0.807) -1.394 (1.433) -0.757 (1.409) 0.920 (0.669) -0.493 (1.315) 0.229 (1.444) 2.797∗∗∗ (1.052) 3.709∗∗∗ (0.977) 7.726 (6.849) 0.901∗∗ (0.430) 79,096 0.726 ✓ 5.082 (4.764) 0.691∗ (0.369) 79,096 0.726 ✓ ✓ 3.408∗ (2.036) 3.209 (2.213) 0.412 (1.121) 79,096 0.726 ✓ 6.644∗∗∗ (1.900) 3.441 (2.383) 0.841 (1.075) 79,096 0.726 ✓ ✓ 171,628 0.814 ✓ 171,628 0.814 ✓ ✓ Notes: Each observation is a major-college-decade cell. The coefficients in each column are estimated by using equation (1.7). Category 1 denotes majors that are most complementary to AI; category 2 includes majors with concentrations in AI-related computer and information processing technologies; category 3 consists of majors associated with general computer skills. College major-clustered standard errors are shown in parentheses. The estimates in columns 1 to 4 are robust to all groups of recipients, while those in columns 5 to 8 are robust to male, female, Whites, and U.S. citizens. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1AI Relevance Score is constructed from equation (1.3) by using Google Trends data. significant and even larger in magnitude compared with AI majors after controlling for both fixed effects in column 6. Students respond to the contemporaneous rising interests in fast-growing AI subfields and AI itself. If fast-growing AI technologies are more intensively searched by the public, students are more likely to choose majors associated with either specific AI skills or general computer skills. 32 Nevertheless, I do not find such significant correlation for category 2 majors (the ones that are neither specific nor general) after including both types of fixed effects. Since only 1.3% of majors are classified into category 2, the estimates might be imprecise due to few observations. Another possible explanation is that students might major in the most specific AI or the most general computer majors with a minor in category 2. Due to the lack of data on minors17, this paper cannot explore this mechanism empirically. In contrast to columns 5 and 6 which include contemporaneous terms, columns 7 and 8 only consider the lagged ones. However, I find no discernible relationship between degree completion and lagged relative search intensities on fast-growing AI subfields and AI itself. Since technology is progressing rapidly, students might be more sensitive to the current technological advances. Findings from Table 1.6 then imply that students respond to contemporaneous increasing attention fast-growing AI subfields (big data, data mining, deep learning, and machine learning) and AI itself have received from the public when choosing majors associated with either the most specific AI skills or the most general computer skills. The contemporaneous popularity of an AI skill-related major’s key concentrations might not be the determinant. I then explore if students are responsive to course content developments proxied by academic publications on fast-growing AI technologies. Similar with Table 1.6, columns 1 to 4 of Appendix Table 1A.7 only include AI Relevance Score measures constructed from equation (1.4), while columns 5 to 8 further add decadal growth rates of academic publications on fast-growing AI subfields and AI itself. In column 6, a 1pp increase in the growth in academic publications on fast-growing AI subfields and AI itself is associated with a 0.052pp increase in the decadal growth rate of degree completion in AI majors (category 1). This finding is consistent with the positive correlation between relative Google search activities on these fast-growing AI technologies and degree completion in AI majors presented in Table 1.6. Similar with relative search intensities, a rise in academic publications on fast-growing AI technologies indicates that they have received increasing attention from researchers. 17IPEDS does not provide degree completion data on students’ minors. 33 Instructors may update syllabi based on theories and methodologies introduced and discussed in academic publications to provide students with the most up-to-date course materials. These course content developments could then affect students’ college major choices. Nonetheless, there is no relationship between degrees conferred in AI skill-related majors and decadal changes in academic publications on these fast-growing AI technologies (estimates are presented in Appendix Table 1A.8). Although some estimates on these decadal changes are statistically significant, they are small in magnitude. For example, in column 8 of Appendix Table 1A.818, a 0.0094pp increase in the growth of AI majors (category 1) is associated with a 1pp increase in lagged decadal changes in academic publications on fast-growing AI technologies. Since undergraduate students are less likely to read journal or conference papers, it is possible that they are not sensitive to the actual changes in the number of academic publications when choosing their fields of study. 1.4.2.2 AI Exposure This section explores the relationship between AI exposure and college major choices. Unlike Section 1.4.2.1 which uses degree completion data over the past three decades, this section specif- ically focuses on the 2010s. This is because ACS has started to collect information on college majors since 2009. Thus, I do not have employment data to map occupations to college majors before 2009. One may argue that the following assumption could be imposed to construct the AIME measure for years before 2009: the mapping between college majors and occupations observed for 2009 to 2019 would also hold for the 1990s and the 2000s. However, this is a strong assumption because occupational choices may have changed over time based on changes in skill requirements. Thus, estimates obtained under this assumption may be biased. Since the top-ranking or the most popular majors vary across colleges, information students’ received prior to college may affect their choices of college or major. Table 1.7 shows results from re-estimating equation (1.6) by (1) replacing the outcome variable, the decadal growth rate of bachelor’s recipients, with the annual growth rate and (2) using the average AIME measure in 18The unit of measurement of the dependent variable in Appendix Table 1A.8 only is a percentage point. In this way, the estimates are scaled differently to avoid presenting numerous estimates of "0.000". 34 Table 1.7 Annual Changes in Bachelor’s Degree Recipients with AI Major Exposure (AIME), 2011-19 Dep. Var.: Annual Growth Rate of Bachelor’s Degree Recipients by Major All Recipients AIME Constructed Using Felten et al. (2021) AIOE Measure AIME Constructed Using Felten et al. (2018) AIOE Measure AIME Constructed Using Webb (2019) AIOE Measure (1) (2) (3) (4) (5) (6) Panel A. Full Sample -0.042 (0.028) 89,377 0.111 -0.023 (0.022) 89,379 0.111 -0.037 (0.027) 89,377 0.111 Panel B. Restricting to Top 50 Universities -0.149∗∗∗ (0.048) -0.142∗∗∗ (0.047) -0.043 (0.041) -0.038∗ (0.021) 89,379 0.111 -0.032 (0.038) 0.015 (0.024) 111,291 0.123 -0.027 (0.048) -0.010 (0.021) 111,289 0.123 -0.107∗∗ (0.042) Avg. AIME in Years Before College1 Observations Outcome Mean Avg. AIME in Years Before College Observations Outcome Mean 4,968 0.058 ✓ 4,958 0.058 ✓ ✓ 4,968 0.058 ✓ 4,958 0.058 ✓ ✓ 6,174 0.087 ✓ 6,159 0.087 ✓ ✓ College-Year FE 2-Digit-CIP-by-Year FE Notes: Each observation is a major-college-year cell. The coefficients in each column are estimated by using equation (1.6) but replacing the interaction term with the AIME measure constructed using equation (1.1). The AIME score is rescaled to have a range between 0 and 1. College major-clustered standard errors are shown in parentheses. In Panel A, the estimates in (1) columns 1 and 2 are robust to female, U.S. citizens, and international students; (2) columns 3 and 4 are robust to female and U.S. citizens; and (3) columns 5 and 6 are robust to male, U.S. citizens and international students. In Panel B, the estimates are robust to U.S. citizens and Whites. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1The average AIME is calculated as the average of AIME measures in students’ sophomore year to senior year of high school. years before college.19 Panel A of Table 1.7 presents results obtained from the full sample. In column 1, when only including college-year fixed effect, a 10pp increase in the average AIME of a major is correlated with a 0.0038pp decrease in its annual growth rate. Note that an increase in the AIME score implies that students graduating with the corresponding major are more likely to perform tasks with high AI exposure. Since IT substitutes college graduates in routine-intensive industries (Zhang et al., 2023), this negative estimate then suggests that students tend to avoid choosing majors with high 19This average AIME is calculated as the average of AIME measures in students’ sophomore year through senior year of high school. Since the AIME measures in different years are highly correlated, including them separately may cause multicollinearity. Specifically, for the AIME measures constructed using the Felten et al. (2021) AIOE measure, the correlation between any two of the AIME measures in sophomore year to senior year of high school is about 0.96. For the AIME measures constructed using the Felten et al. (2018) and Webb (2019) measures, the correlation ranges bewteen 0.96 to 0.98 and 0.96 to 0.97, respectively. 35 AI exposure to be less substituted by AI after graduation. However, none of the estimates on the average AIME are statistically significant after further controlling for 2-digit-CIP-by-year fixed effect (column 2 in Panel A). By including both types of fixed effects, column 2 compares majors within the same broad category (the 2-digit CIP) and among the same college during the same year, while column 1 compares all majors (the 6-digit CIP) offered in the same college during the same year. Thus, the former may lose some variation in AIME as the 6-digit majors within the same 2-digit category may share a high similarity in their AI exposure. This finding suggests that the negative correlation between AI exposure and degree completion stems from the difference in AI exposure across, rather than within, broad major categories. However, there is no discernible relationship between degree completion and the average AIME constructed by using either the Felten et al. (2018) or Webb (2019) AIOE measure (columns 3 to 6).20 The insignificant correlation between the average AIME over years before college and degree completion might be explained by students’ abilities. Students in top-end universities are more likely to have higher abilities and be more sensitive to technological changes. Thus, they may react more quickly to AI exposure by adjusting their human capital investment, e.g., choosing their college majors. Panel B of Table 1.7 presents the relationship between AIME and degree completion by restricting the sample to top 50 universities in the U.S.21 Now estimates (columns 2, 4, and 6 in Panel B) on the average AIME become significantly negative and much larger in magnitude regardless of which AIOE is used to construct AIME. Students enrolled in top-end universities are less likely to choose majors that are highly exposed to AI, compared with students from all 4-year institutions. These results are robust to restricting the sample to top 100 universities as shown in Appendix Table 1A.10, although estimates are smaller in magnitude. 20Appendix Table 1A.9 presents coefficients on the "weighting" version of AIME constructed by equation (1.2) which suffers from the multiple weights issue as explained in the footnote of Section 1.2.1. I do not find any evidence in Panels A and B on the correlation between degree completion and AI exposure. However, in Panel C for which the AIME is the weighted sum of Webb (2019) AIOE measure, the point estimate on the average AIME in column 1 of Panel C is significantly positive. Two possible reasons could explain these inconsistently signed estimates. First, the Webb (2019) measure captures different aspects of AI compared to the Felten et al. (2018, 2021) measures (Acemoglu et al., 2022). Second, this "weighting" version of AIME might be noisy as explained in Section 1.2.1, possibly resulting in imprecise estimates. 21The top 50 universities listed in the Best National University Ranking by U.S. News are used (https://www.usnews. com/best-colleges/rankings/national-universities). 36 Since the above AIME measure captures an aggregate shock, Appendix Tables 1A.11 and 1A.12 present the correlation between geographical variation in a major’s AI exposure across county and state, respectively, and degree completion. Consistent with equation (1.1), the AIME measure for major 𝑚, geographical location 𝑔 (either county or state), and year 𝑡 is constructed as follows: AIME𝑚,𝑔,𝑡 = 1{𝑜∗ = arg max 𝑜 𝑒𝑚 𝑝𝑜,𝑚,𝑔,𝑡 } × AIOE𝑜∗. (1.8) Estimates in Panel B of Appendix Table 1A.11 suggest that majors in counties that are most exposed to AI grow relatively slowly, especially at top-end universities. These estimates are larger in magnitude compared to Table 1.7 but also have larger standard errors. However, I do not find any significant correlation between these geographical variation across state and degree completion as presented in Appendix Table 1A.12.22 Due to the lack of college-level employment data, the underlying assumption of using the ACS employment data to construct 𝐴𝐼 𝑀 𝐸𝑚,𝑔,𝑡 following equation (1.8) is that the distribution of employment by major for people living in a county/state is the same as the distribution for people graduating from a college located in the same county/state. This assumption might be too strong, leading to imprecise estimates in Appendix Tables 1A.11 and 1A.12. 1.5 Conclusion As an intensively studied and growing general-purpose technology over the past decades, AI not only raises human productivity but also leads to job displacement and changes in skill requirements in the labor market. However, the relationship between human capital accumulation and AI has received relatively little attention from researchers. By constructing a new measure which captures how well a college major prepares students to use AI to complement their work after graduation and using the degree completion data, this paper shows that AI skill-related majors have experienced a dramatic growth in bachelor’s degree recipients over the past three decades, 22Appendix Tables 1A.13 and 1A.14 display estimates on geographical variation in the "weighting" version of AIME constructed as follows: AIME𝑚,𝑔,𝑡 = 𝑒𝑚 𝑝𝑜,𝑚,𝑔,𝑡 𝑒𝑚 𝑝𝑚,𝑔,𝑡 ∑︁ 𝑜 × AIOE𝑜. (1.9) Estimates now become much noiser: they are significantly negative using either Felten et al. (2018, 2021) AIOE when restricting to elite universities, but become positive if using Webb (2019) AIOE. 37 especially majors associated with either the most specific AI skills or the most general computer skills. This growth has been statistically significant and similar in magnitude during the 1990s and the 2010s, but not in the 2000s. Moreover, I document a significantly positive relationship between degrees conferred in majors associated with the most specific AI skills and rising interests from both the public and researchers in fast-growing AI subfields (big data, data mining, deep learning, and machine learning) and AI itself. In addition, there is some evidence showing that degree completion is negatively correlated with AI exposure. This negative correlation becomes stronger when restricting the sample to top-end universities. Higher-ability students tend to avoid choosing majors that are more exposed to AI to be less substituted by AI in the labor market. These results suggest that colleges should make adjustments to the curricula of majors that are related to AI to better prepare students to acquire AI-related skills. However, due to the lack of data on college curricula, I am not able to test whether colleges respond quickly to the growth in AI. This is an important area of future research, as it helps colleges take action on advising and providing relevant training for students. Another limitation of this paper is the lack of individual-level data on students’ dynamic decisions on declaring their fields of study. With this individual-level data, researchers would be able to estimate dynamic models of college major choices to explore the role of the growth in AI. Moreover, other determinants of college major choices (e.g., ability and parental influence) can also be taken into account as complements of the impact of AI on major choices by using the individual-level data. Future research can also explore the labor market performance of students who graduate with AI skill-related majors, e.g., whether they perform tasks that are complemented by AI. 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(2018) AIOE Measure (b) Webb (2019) AIOE Measure Notes: Both the Felten et al. (2018) and Webb (2019) AIOE measures are aggregated to the commuting zone level. 43 0.79 − 4.260.27 − 0.790.01 − 0.27−0.33 − 0.01−0.95 − −0.33−6.79 − −0.950.84 − 4.670.31 − 0.84−0.09 − 0.31−0.47 − −0.09−0.96 − −0.47−7.95 − −0.96 Figure 1A.2 Changes in Google Trends Index of Search Activities on Chosen AI Phrases (a) Comparing "Artificial Intelligence," "Deep Learning," "Machine Learning," "Pattern Recognition," and "Computer Vision" (b) Comparing "Big Data," "Natural Language Processing," "Machine Learning," "Pattern Recognition," and "Data Mining" Source: https://trends.google.com/trends/?geo=US. Notes: Google Trends website allows users to compare at most five terms per request. "Machine Learning" and "Pattern Recognition" are included in both subfigures to serve as the comparison group because "Machine Learning" is one of the AI phrases that have received increasing interests recently while "Pattern Recognition" was intensively discussed in the 1990s. 44 Figure 1A.3 An Example of Searching Academic Publications on the Web of Science Website (a) An Example of the Search Page (b) An Example of the Search Result Page Source: Web of Science platform. 45 Figure 1A.4 Share of AI Skill-Related Majors by the Number of Matched AI Phrases (a) Category 1: Majors that are Most Complementary to AI (b) Category 2: Majors with Concentrations in AI-Related Computer and Information Processing Technologies (c) Category 3: Majors Associated with General Computer Skills 46 0.2.4.6.8Proportion of AI Skill−Related Majors123Number of Matched Phrases of AI Skills or Applications0.2.4.6.81Proportion of AI Skill−Related Majors123Number of Matched Phrases of AI Skills or Applications0.2.4.6.8Proportion of AI Skill−Related Majors123Number of Matched Phrases of AI Skills or Applications Figure 1A.5 Decadal Changes in and Growth Rates of Academic Publications on Some AI Subfields (a) Decadal Changes (b) Decadal Growth Rates Source: Web of Science Core Collection database. 47 0200040006000800010000AI1980s1990s2000s2010sDecade050001000015000Big Data1980s1990s2000s2010sDecade0100020003000Data Mining1980s1990s2000s2010sDecade0500010000150002000025000Deep Learning1980s1990s2000s2010sDecade0100002000030000Machine Learning1980s1990s2000s2010sDecade0100002000030000Neural Network1980s1990s2000s2010sDecade0100020003000NLP1980s1990s2000s2010sDecade050010001500Pattern Recognition1980s1990s2000s2010sDecade01002003004003−D Modeling1980s1990s2000s2010sDecadeDecadal Changes in the Number of Academic Publications02468AI1980s1990s2000s2010sDecade0200400600Big Data1980s1990s2000s2010sDecade01234Data Mining1980s1990s2000s2010sDecade0100200300Deep Learning1980s1990s2000s2010sDecade0510Machine Learning1980s1990s2000s2010sDecade020406080100Neural Network1980s1990s2000s2010sDecade012345NLP1980s1990s2000s2010sDecade01234Pattern Recognition1980s1990s2000s2010sDecade246810123−D Modeling1980s1990s2000s2010sDecadeDecadal Growth Rates of Academic Publications Figure 1A.6 "Weighting" Version of AIME Measure by Broad College Major Category, 2010-19 (a) AIME—by Weighting the Felten et al. (2021) AIOE (b) AIME—by Weighting the Felten et al. (2018) AIOE (c) AIME—by Weighting the Webb (2019) AIOE Notes: AIME is constructed as the weighted sum of the AIOE measure following equation (1.2). 48 0.2.4.6.8Average AIME Constructed by Weighting AIOE from Felten (2021)Visual and Performing ArtsTransportationTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPhysicsPhilosophyNatural ResourcesMilitary ScienceMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHuman SciencesHomeland SecurityHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArea and Cultural StudiesArchitectureAgriculture0.2.4.6.8Average AIME Constructed by Weighting AIOE from Felten (2018)Visual and Performing ArtsTransportationTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPhysicsPhilosophyNatural ResourcesMilitary ScienceMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHuman SciencesHomeland SecurityHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArea and Cultural StudiesArchitectureAgriculture0.2.4.6.81Average AIME Constructed by Weighting AIOE from Webb (2019)Visual and Performing ArtsTransportationTech and Religious VocationsSocial SciencesScience TechRecreation, Fitness, and KinesiologyPublic AdministrationPsychologyPhysicsPhilosophyNatural ResourcesMilitary ScienceMechanic TechMathematics and StatisticsLinguisticsLibrary ScienceLiberal Arts and SciencesLegal ProfessionsJournalismInterdisciplinary StudiesHuman SciencesHomeland SecurityHistoryHealth ProfessionsEnglish Language and LiteratureEngineering−Related TechEngineeringEducationCulinary and Entertainment ServicesConstruction TradesComputer and Info SciencesCommunications TechBusiness and ManagementBiologyArea and Cultural StudiesArchitectureAgriculture Table 1A.1 Occupations with the Highest/Lowest AIOE Scores (a) Felten et al. (2021) AIOE Measure 1 2 3 4 5 6 7 8 9 Rank Highest Scoring Genetic Counselors Financial Examiners Actuaries Lowest Scoring Dancers Exercise Trainers and Group Fitness Instructors Helpers—Painters, Paperhangers, Plasterers, and Stucco Masons Budget Analysts Reinforcing Iron and Rebar Workers Judges, Magistrate Judges, and Magistrates Pressers, Textile, Garment, and Related Materials Procurement Clerks Accountants and Auditors Mathematicians Judicial Law Clerks Helpers—Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters Dining Room and Cafeteria Attendants and Bartender Helpers Fence Erectors Helpers—Roofers 10 Education Administrators, Postsecondary Slaughterers and Meat Packers Rank Highest Scoring Lowest Scoring (b) Felten et al. (2018) AIOE Measure 1 2 3 4 5 6 7 8 9 Airline Pilots, Copilots, and Flight Engineers Models Physicists Surgeons Commercial Pilots Air Traffic Controllers Dentists, General Telemarketers Locker Room, Coatroom, and Dressing Room Attendants Graders and Sorters, Agricultural Products Shampooers Maids and Housekeeping Cleaners Biochemists and Biophysicists Cleaners of Vehicles and Equipment Oral and Maxillofacial Surgeons Slaughterers and Meat Packers First-Line Supervisors of Firefighting and Prevention Workers Dining Room and Cafeteria Attendants and Bartender Helpers 10 Microbiologists Food Servers, Nonrestaurant (c) Webb (2019) AIOE Measure Rank Highest Scoring Lowest Scoring 1 2 3 4 5 6 7 8 9 Railroad Brake, Signal, and Switch Operators and Locomotive Firers Cooks, Restaurant Captains, Mates, and Pilots of Water Vessels Agricultural Sciences Teachers, Postsecondary Water and Wastewater Treatment Plant and System Operators Healthcare Support Workers, All Other Political Scientists Social Work Teachers, Postsecondary Civil Engineering Technologists and Technicians English Language and Literature Teachers, Postsecondary Chemical Engineers Criminal Justice and Law Enforcement Teachers, Postsecondary Aerospace Engineering and Operations Technologists and Technicians Credit Authorizers, Checkers, and Clerks Gas Plant Operators Recreation and Fitness Studies Teachers, Postsecondary Administrative Law Judges, Adjudicators, and Hearing Officers Political Science Teachers, Postsecondary 10 Marine Engineers and Naval Architects Morticians, Undertakers, and Funeral Arrangers 49 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Table 1A.2 College Majors with the Highest/Lowest AIME Scores in 2019 (Continued) (a) AIME—by Using the Felten et al. (2018) AIOE Rank Highest Scoring Landscape Architecture Lowest Scoring Entomology Architecture and Related Services, Other Zoology/Animal Biology Interior Architecture Zoology/Animal Biology, Other Architectural Technology/Technician Architectural History and Criticism, General Electrical/Electronics Maintenance and Repair Technologies/ Technicians, Other Parts and Warehousing Operations and Maintenance Technology/ Technician Architecture Alternative Fuel Vehicle Technology/Technician Environmental Design/Architecture Industrial Electronics Technology/Technician City/Urban, Community, and Regional Planning Aircraft Powerplant Technology/Technician Naval Architecture and Marine Engineering Appliance Installation and Repair Technology/Technician 10 Chemical Engineering Communications Systems Installation and Repair Technology/ Technician (b) AIME—by Using the Webb (2019) AIOE Rank Highest Scoring Chemical Engineering Graphic Design Lowest Scoring Entomology Zoology/Animal Biology, Other Commercial Photography Zoology/Animal Biology Illustration Interior Design Industrial and Product Design Christian Studies Philosophy, Other Buddhist Studies Design and Visual Communications, General Religious/Sacred Music Fashion/Apparel Design Pastoral Studies/Counseling Design and Applied Arts, Other Hindu Studies 10 Commercial and Advertising Art Bible/Biblical Studies 50 Table 1A.3 List of AI Skill-Related Majors in Category 1 (Associated with the Most Specific AI Skills) 2020 CIP Code 2020 CIP Title 10.0304 10.0308 11.0102 11.0204 11.0801 11.0803 11.0804 13.0501 14.4201 15.0101 15.0405 15.0406 15.0407 15.1102 16.0102 23.1303 26.1103 30.2501 30.3101 30.3901 30.5202 30.7001 30.7101 30.7102 30.7104 42.2701 50.0402 50.0409 50.0411 50.0913 51.0909 51.2703 52.1301 Animation, Interactive Technology, Video Graphics, and Special Effects Computer Typography and Composition Equipment Operator Artificial Intelligence Computer Game Programming Web Page, Digital/Multimedia and Information Resources Design Computer Graphics Modeling, Virtual Environments and Simulation Educational/Instructional Technology Mechatronics, Robotics, and Automation Engineering Architectural Engineering Technologies/Technicians Robotics Technology/Technician Automation Engineer Technology/Technician Mechatronics, Robotics, and Automation Engineering Technology/Technician Surveying Technology/Surveying Linguistics Professional, Technical, Business, and Scientific Writing Bioinformatics Cognitive Science, General Human Computer Interaction Economics and Computer Science Digital Humanities Data Science, General Data Analytics, General Business Analytics Financial Analytics Cognitive Psychology and Psycholinguistics Commercial and Advertising Art Graphic Design Game and Interactive Media Design Music Technology Surgical Technology/Technologist Medical Illustration/Medical Illustrator Management Science 51 Table 1A.4 List of AI Skill-Related Majors in Category 2 (Associated with AI-Related Computer and Infomation Processing Technologies) 2020 CIP Code 2020 CIP Title 11.0902 11.1003 11.1004 11.1006 14.0999 14.1004 14.4701 15.0305 15.1302 15.1304 15.1305 26.1101 26.1199 27.0303 43.0403 51.2706 52.0208 52.0407 Cloud Computing Computer and Information Systems Security/Auditing/Information Assurance Web/Multimedia Management and Webmaster Computer Support Specialist Computer Engineering, Other Telecommunications Engineering Electrical and Computer Engineering Telecommunications Technology/Technician CAD/CADD Drafting and/or Design Technology/Technician Civil Drafting and Civil Engineering CAD/CADD Electrical/Electronics Drafting and Electrical/Electronics CAD/CADD Biometry/Biometrics Biomathematics, Bioinformatics, and Computational Biology, Other Computational Mathematics Cyber/Computer Forensics and Counterterrorism Medical Informatics E-Commerce/Electronic Commerce Business/Office Automation/Technology/Data Entry 52 Table 1A.5 List of AI Skill-Related Majors in Category 3 (Associated with General Computer Skills) 2020 CIP Code 2020 CIP Title 01.0106 01.8105 01.8110 09.0702 11.0103 11.0104 11.0105 11.0202 11.0205 11.0299 11.0901 11.1001 11.1005 11.1099 13.0603 14.0103 14.0501 14.0902 14.0903 14.1301 14.3701 14.3801 15.0613 15.1204 15.1501 26.0708 26.1102 26.1501 26.1599 27.0304 27.0305 27.0501 27.0502 27.0503 27.0599 27.0601 27.9999 30.2502 30.3801 30.4101 30.4401 40.0403 40.0404 40.0512 40.0601 40.0603 42.2706 42.2813 42.2815 43.0301 43.0407 43.0408 45.0102 45.0202 45.0501 45.0603 45.0701 45.0702 50.0917 51.0706 51.0905 51.2003 51.2007 51.3303 52.0207 52.0209 52.0216 52.1201 52.1206 52.1207 52.1302 52.1304 52.2101 Agricultural Business Technology/Technician Veterinary Anatomy Veterinary Preventive Medicine, Epidemiology, and Public Health Digital Communication and Media/Multimedia Information Technology Informatics Human-Centered Technology Design Computer Programming, Specific Applications Computer Programming, Specific Platforms Computer Programming, Other Computer Systems Networking and Telecommunications Network and System Administration/Administrator Information Technology Project Management Computer/Information Technology Services Administration and Management, Other Educational Statistics and Research Methods Applied Engineering Bioengineering and Biomedical Engineering Computer Hardware Engineering Computer Software Engineering Engineering Science Operations Research Surveying Engineering Manufacturing Engineering Technology/Technician Computer Software Technology/Technician Engineering/Industrial Management Animal Behavior and Ethology Biostatistics Neuroscience Neurobiology and Neurosciences, Other Computational and Applied Mathematics Financial Mathematics Statistics, General Mathematical Statistics and Probability Mathematics and Statistics Statistics, Other Applied Statistics, General Mathematics and Statistics, Other Contemplative Studies/Inquiry Earth Systems Science Environmental Geosciences Geography and Environmental Studies Atmospheric Physics and Dynamics Meteorology Cheminformatics/Chemistry Informatics Geology/Earth Science, General Geophysics and Seismology Behavioral Neuroscience Applied Psychology Performance and Sport Psychology Homeland Security Geospatial Intelligence Law Enforcement Intelligence Analysis Research Methodology and Quantitative Methods Physical and Biological Anthropology Demography and Population Studies Econometrics and Quantitative Economics Geography Geographic Information Science and Cartography Sound Arts Health Information/Medical Records Administration/Administrator Nuclear Medical Technology/Technologist Pharmaceutics and Drug Design Pharmacoeconomics/Pharmaceutical Economics Naturopathic Medicine/Naturopathy Customer Service Management Transportation/Mobility Management Science/Technology Management Management Information Systems, General Information Resources Management Knowledge Management Business Statistics Actuarial Science Telecommunications Management 53 Table 1A.6 Summary Statistics of Average Decadal Completion Rates Decomposed into Each Decade, 1990-2019 Average Decadal Growth Rate3 of Bachelor’s Degree Recipients by Major All Recipients Male Female Whites International Students U.S. Citizens All College Majors1 N = 64,503 0.862 (4.910) AI Skill-Related Majors2 in Category 1 N = 651 1.228 (4.216) Category 2 N = 20 Category 3 N = 1,844 Non-AI Majors N = 61,988 Non-AI Tech Majors N = 12,398 0.717 (2.031) 1.393 (9.249) 0.843 (4.727) 0.769 (3.414) All College Majors N = 92,818 0.892 (10.279) AI Skill-Related Majors in Category 1 N = 1,547 1.784 (26.963) Category 2 N = 281 Category 3 N = 3,343 Non-AI Majors N = 87,647 0.672 (3.633) 1.117 (4.766) 0.869 (9.906) Non-AI Tech Majors N = 17,498 0.804 (17.036) All College Majors N = 79,442 AI Skill-Related Majors in Category 1 N = 1,629 Category 2 N = 454 Category 3 N = 3,481 Non-AI Majors N = 73,878 Non-AI Tech Majors N = 15,103 0.729 (6.967) 1.027 (5.927) 1.589 (5.572) 1.588 (7.503) 0.677 (6.966) 1.020 (4.582) Panel A. 1990-2000 0.476 (3.234) 0.691 (4.159) 1.162 (5.451) 0.878 (2.779) 0.960 (2.321) 1.042 (6.722) 0.453 (3.054) 0.493 (2.667) 0.707 (2.437) 0.560 (1.992) 0.964 (3.357) 0.683 (4.193) 0.636 (2.605) 1.680 (4.285) 1.179 (1.932) 2.671 (15.792) 1.099 (4.689) 1.136 (3.872) Panel B. 2000-2010 0.571 (4.120) 0.646 (8.266) 0.520 (4.924) 0.684 (3.376) 0.719 (3.941) 0.795 (3.218) 0.559 (4.168) 0.543 (5.444) 1.204 (15.042) -0.057 (3.556) 0.537 (3.308) 0.642 (8.227) 0.484 (14.634) 0.651 (3.086) 0.008 (2.083) 0.667 (3.966) 0.514 (4.986) 0.411 (5.319) Panel C. 2010-2019 0.377 (3.888) 0.600 (5.899) 0.381 (4.952) 0.649 (4.356) 1.430 (4.962) 1.010 (5.031) 0.331 (3.795) 0.706 (3.156) 0.875 (4.747) 0.607 (3.449) 1.558 (8.004) 0.555 (5.823) 1.018 (4.719) 0.508 (3.102) 1.232 (4.557) 0.967 (7.145) 0.346 (4.861) 0.641 (3.076) -0.184 (1.869) 0.083 (1.553) 0.000 (0.000) 0.123 (2.069) -0.205 (1.865) -0.133 (1.627) -0.100 (2.083) -0.330 (1.111) -0.625 (0.633) -0.064 (3.455) -0.095 (2.019) -0.062 (1.724) 0.538 (3.747) 0.770 (3.872) 0.622 (5.707) 1.541 (6.542) 0.476 (3.502) 1.208 (5.168) 1.461 (6.717) 1.828 (3.940) 1.001 (2.578) 3.397 (20.432) 1.388 (5.722) 1.557 (5.837) 0.884 (10.145) 1.817 (25.914) 0.645 (3.534) 1.023 (4.313) 0.863 (9.816) 0.784 (16.856) 0.681 (6.742) 0.960 (5.716) 1.492 (5.359) 1.424 (6.680) 0.635 (6.771) 0.951 (4.382) Notes: Standard deviations are shown in parentheses. 1Each observation is a major-college-decade cell. College majors are represented by the 2020 6-digit Classification of Instructional Programs (CIP) code. Observations with missing overall decadal growth rates are not counted. 2Category 1 denotes majors that are most complementary to AI; category 2 includes majors with concentrations in AI-related computer and information processing technologies; category 3 consists of majors associated with general computer skills. 3Growth rates are calculated at the major-college-decade level. 54 Table 1A.7 The Relationship between Decadal Growth Rates of Academic Publications on Fast- Growing AI Subfields on College Major Choices, 1990-2019 Dep. Var.: Decadal Growth Rate of Bachelor’s Degree Recipients by Major (2) ΔPublications1 on Fast-Growing AI Subfields × (1) 1{major ∈ Category 1} 1{major ∈ Category 2} 1{major ∈ Category 3} Lagged ΔPublications on Fast-Growing AI Subfields × 1{major ∈ Category 1} 1{major ∈ Category 2} 1{major ∈ Category 3} AI Relevance Score2 of Majors in Category 1 Category 2 Category 3 3.316 (2.509) -4.208 (3.362) 1.320 (0.829) 3.659 (2.445) -4.042 (3.232) 1.155 (0.763) Lagged AI Relevance Score of Majors in (3) All Recipients (5) (4) (6) (7) (8) 0.044∗ (0.023) 0.079 (0.087) 0.070∗ (0.040) 0.052∗∗∗ (0.018) 0.036 (0.079) 0.050 (0.039) 2.843 (2.338) -5.202 (3.417) -0.039 (1.126) 3.087 (2.278) -4.481 (3.115) 0.215 (1.157) 0.119∗ (0.068) 0.289 (0.231) 0.028 (0.033) 0.132∗∗ (0.064) 0.176 (0.176) 0.055∗ (0.031) Category 1 Category 2 Category 3 -0.425∗ (0.254) -2.263 (1.498) 2.028∗ (1.037) -0.204 (0.236) -2.255 (1.448) 1.488 (0.905) -1.117∗∗ (0.477) -3.638∗∗∗ (1.391) 1.723∗ (0.978) -0.967∗∗ (0.435) -3.063∗∗ (1.308) 0.853 (0.870) 0.827 ✓ ✓ 0.827 ✓ ✓ 235,838 235,838 235,838 235,838 235,838 0.827 0.827 0.827 ✓ ✓ ✓ Observations Outcome Mean College-Decade FE 2-Digit-CIP-by-Decade FE Notes: Each observation is a major-college-decade cell. The coefficients in each column are estimated by using equation (1.7) and replacing terms associated with GTI to decadal growth rates of academic publications. Category 1 denotes majors that are most complementary to AI; category 2 includes majors with concentrations in AI-related computer and information processing technologies; category 3 consists of majors associated with general computer skills. College major-clustered standard errors are shown in parentheses. The estimates in columns 1 to 4 are robust to male, female, and U.S. citizens, while estimates in columns 5 to 8 are robust to all groups except international students. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1ΔPublications denotes decadal growth rates of academic publications. 2AI Relevance Score is constructed from equation (1.4) by using decadal growth rates of academic publications. 235,838 0.827 ✓ ✓ 235,838 0.827 ✓ ✓ 235,838 0.827 ✓ 55 Table 1A.8 The Relationship between Decadal Changes in Academic Publications in Fast-Growing AI Subfields on College Major Choices, 1990-2019 Dep. Var.: Decadal Growth Rate of Bachelor’s Degree Recipients by Major in Percentage Points1 (2) ΔPublications2 on Fast-Growing AI Subfields × (1) 1{major ∈ Category 1} 1{major ∈ Category 2} 1{major ∈ Category 3} Lagged ΔPublications on Fast-Growing AI Subfields × 1{major ∈ Category 1} 1{major ∈ Category 2} 1{major ∈ Category 3} AI Relevance Score3 of Majors in Category 1 Category 2 Category 3 384.4∗ (197.7) -93.9 (161.2) 38.5 (30.0) 376.0∗∗ (191.2) -117.4 (147.4) 56.1∗∗ (23.2) Lagged AI Relevance Score of Majors in All Recipients (3) (4) (5) (6) (7) (8) 0.0002 (0.0003) 0.0029∗∗∗ (0.0011) 0.0011∗∗ (0.0005) 0.0003 (0.0003) 0.0021∗∗ (0.0010) 0.0006 (0.0005) 369.8∗ (206.6) -254.2∗ (141.1) -14.2 (23.6) 356.3∗ (203.5) -226.4 (142.8) 25.8 (22.1) 0.0079∗∗ (0.0039) 0.0458∗∗ (0.0223) 0.0143 (0.0087) 0.0094∗∗∗ (0.0036) 0.0333 (0.0206) 0.0080 (0.0082) Category 1 Category 2 Category 3 76.9 (64.7) -34.5 (126.3) 48.3 (34.6) 78.4 (58.2) -61.7 (115.5) 62.7∗∗ (25.9) 36.9 (59.5) -224.3 (146.5) -19.4 (40.4) 30.6 (56.4) -194.4 (146.6) 24.8 (37.3) 82.7 ✓ 82.7 ✓ ✓ 235,838 235,838 235,838 82.7 ✓ 235,838 82.7 ✓ 235,838 82.7 ✓ 235,838 82.7 ✓ ✓ 235,838 82.7 ✓ ✓ Observations Outcome Mean College-Decade FE 2-Digit-CIP-by-Decade FE Notes: Each observation is a major-college-decade cell. The coefficients in each column are estimated by using equation (1.7) and replacing terms associated with GTI to decadal changes in academic publications. Category 1 denotes majors that are most complementary to AI; category 2 includes majors with concentrations in AI-related computer and information processing technologies; category 3 consists of majors associated with general computer skills. College major-clustered standard errors are shown in parentheses. The estimates in columns 1 to 4 are robust to female and U.S. citizens, while estimates in columns 5 to 8 are robust to all groups. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The dependent variable is shown in percentage points to scale the estimates differently in this table only due to small estimates in columns 5 to 8. Estimates and standard errors are rounded to four decimal places in this table only. 2ΔPublications denotes decadal changes in academic publications. 3AI Relevance Score is constructed from equation (1.5) by using decadal changes in academic publications. 235,838 82.7 ✓ ✓ 56 Table 1A.9 Annual Changes in Bachelor’s Degree Recipients with "Weighting" Ver. AI Major Exposure (AIME), 2011-19 Dep. Var.: Annual Growth Rate of Bachelor’s Degree Recipients by Major All Recipients Panel A. AIME Constructed by Weighting Felten, Raj and Seamans (2021) Measure Panel B. AIME Constructed by Weighting Felten, Raj and Seamans (2018) Measure Panel C. AIME Constructed by Weighting Webb (2019) Measure Avg. AIME in Years Before College1 (1) -0.037 (0.031) (2) -0.057 (0.041) (1) 0.007 (0.037) (2) -0.057 (0.052) (1) 0.150∗∗∗ (0.027) (2) 0.055 (0.037) 355,715 0.112 ✓ 355,715 0.112 ✓ 355,715 0.112 ✓ ✓ Observations Outcome Mean College-Year FE 2-Digit-CIP-by-Year FE Notes: Each observation is a major-college-year cell. The coefficients in each column are estimated by using equation (1.6) but replacing the interaction term with the "weighting" version of AIME measure constructed by (1.2). The AIME score is rescaled to have a range between 0 and 1. College major-clustered standard errors are shown in parentheses. The estimates in (1) both Panels A and B are robust to U.S. citizens and Whites; and (3) Panel C are robust to female, U.S. citizens, and Whites. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1The average AIME is calculated as the average of AIME measures in students’ sophomore year to senior year of high school. 355,715 0.112 ✓ ✓ 355,715 0.112 ✓ ✓ 355,715 0.112 ✓ 57 Table 1A.10 Annual Changes in Bachelor’s Degree Recipients with AI Major Exposure (AIME), Top 100 Universities over 2011-19 Dep. Var.: Annual Growth Rate of Bachelor’s Degree Recipients by Major All Recipients Panel A. AIME Constructed by Using Felten, Raj and Seamans (2021) Measure Panel B. AIME Constructed by Using Felten, Raj and Seamans (2018) Measure Panel C. AIME Constructed by Using Webb (2019) Measure Avg. AIME in Years Before College1 (1) -0.055∗∗ (0.027) (2) -0.094∗∗ (0.045) (1) -0.055∗ (0.029) (2) -0.094∗∗ (0.042) (1) -0.027 (0.041) (2) -0.077∗∗ (0.034) 9,674 0.065 ✓ 9,674 0.065 ✓ 9,673 0.065 ✓ ✓ 9,673 0.065 ✓ ✓ Observations Outcome Mean College-Year FE 2-Digit-CIP-by-Year FE Notes: Each observation is a major-college-year cell. The coefficients in each column are estimated by using equation (1.6) but replacing the interaction term with the AIME measure constructed by equation (1.1). The AIME score is rescaled to have a range between 0 and 1. College major-clustered standard errors are shown in parentheses. The estimates in (1) Panel A are robust to female, U.S. citizens, and international students; (2) Panel B are robust to all groups except Whites; and (3) Panel C are robust to all groups except international students. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1The average AIME is calculated as the average of AIME measures in students’ sophomore year to senior year of high school. 12,005 0.101 ✓ ✓ 12,011 0.101 ✓ 58 Table 1A.11 Annual Changes in Bachelor’s Degree Recipients with Geographical Variation in AI Major Exposure (AIME) across County, 2011-19 Dep. Var.: Annual Growth Rate of Bachelor’s Degree Recipients by Major All Recipients AIME Constructed Using Felten et al. (2021) AIOE Measure AIME Constructed Using Felten et al. (2018) AIOE Measure AIME Constructed Using Webb (2019) AIOE Measure (1) (2) (3) (4) (5) (6) Panel A. Full Sample -0.156 (0.109) 27,968 0.114 0.020 (0.047) 29,381 0.112 -0.060 (0.057) 29,375 0.112 Panel B. Restricting to Top 100 Universities -0.186∗∗ (0.082) -0.238∗∗ (0.102) -0.052 (0.074) 4,374 0.070 4,648 0.067 4,640 0.067 Panel C. Restricting to Top 50 Universities 0.043 (0.095) 0.062 (0.062) 0.027 (0.078) -0.098 (0.103) 27,974 0.114 -0.076 (0.090) 4,382 0.070 0.150∗ (0.077) 0.087∗ (0.045) 40,739 0.120 -0.043 (0.063) 6,742 0.108 0.040 (0.061) 0.037 (0.045) 40,737 0.120 -0.170∗ (0.099) 6,736 0.108 -0.054 (0.071) Avg. AIME in Years Before College1 Observations Outcome Mean Avg. AIME in Years Before College Observations Outcome Mean Avg. AIME in Years Before College Observations Outcome Mean 2,416 0.044 ✓ 2,399 0.046 ✓ ✓ 2,582 0.039 ✓ 2,565 0.040 ✓ ✓ 3,818 0.066 ✓ 3,809 0.067 ✓ ✓ College-Year FE 2-Digit-CIP-by-Year FE Notes: Each observation is a major-college-year cell. The coefficients in each column are estimated by using equation (1.6) but replacing the interaction term with the AIME measure constructed using equation (1.8). The AIME score is rescaled to have a range between 0 and 1. College major-clustered standard errors are shown in parentheses. In Panel A, the estimates are robust to all groups except international students. In Panel B, the estimates are robust to U.S. citizens. In Panel C, (1) the estimates in columns 2, 3, 4, and 6 are robust to all groups; (2) the estimate in column 1 is robust to female, Whites, and international students; (3) the estimate in column 4 is robust to male, U.S. citizens, and Whites. 1The average AIME is calculated as the average of AIME measures in students’ sophomore year to senior year of high school. 59 Table 1A.12 Annual Changes in Bachelor’s Degree Recipients with Geographical Variation in AI Major Exposure (AIME) across State, 2011-19 Dep. Var.: Annual Growth Rate of Bachelor’s Degree Recipients by Major All Recipients AIME Constructed Using Felten et al. (2021) AIOE Measure AIME Constructed Using Felten et al. (2018) AIOE Measure AIME Constructed Using Webb (2019) AIOE Measure (1) (2) (3) (4) (5) (6) Avg. AIME in Years Before College1 Observations Outcome Mean Avg. AIME in Years Before College Observations Outcome Mean -0.015 (0.031) 61,543 0.108 0.001 (0.047) 6,790 0.082 Panel A. Full Sample -0.028 (0.030) 61,540 0.108 -0.006 (0.031) 63,066 0.109 -0.044 (0.030) 63,061 0.109 Panel B. Restricting to Top 100 Universities -0.052 (0.067) 6,782 0.082 -0.020 (0.051) 6,995 0.080 -0.078 (0.073) 6,986 0.080 Panel C. Restricting to Top 50 Universities Avg. AIME in Years Before College 0.028 (0.049) -0.026 (0.070) 0.010 (0.056) -0.046 (0.078) 0.035 (0.032) 86,400 0.119 0.033 (0.059) 9,837 0.099 0.012 (0.046) 0.013 (0.028) 86,398 0.119 -0.002 (0.055) 9,826 0.099 -0.036 (0.055) Observations Outcome Mean 3,489 0.063 ✓ 3,475 0.063 ✓ ✓ 3,594 0.061 ✓ 3,580 0.060 ✓ ✓ 5,140 0.073 ✓ 5,126 0.074 ✓ ✓ College-Year FE 2-Digit-CIP-by-Year FE Notes: Each observation is a major-college-year cell. The coefficients in each column are estimated by using equation (1.6) but replacing the interaction term with the AIME measure constructed using equation (1.8). The AIME score is rescaled to have a range between 0 and 1. College major-clustered standard errors are shown in parentheses. In Panel A, the estimates are robust to male, U.S. citizens, and Whites. In Panels B and C, the estimates are robust to all groups except international students. 1The average AIME is calculated as the average of AIME measures in students’ sophomore year to senior year of high school. 60 Table 1A.13 Annual Changes in Bachelor’s Degree Recipients with Geographical Variation in "Weighting" Ver. AI Major Exposure (AIME) across County, 2011-19 Dep. Var.: Annual Growth Rate of Bachelor’s Degree Recipients by Major All Recipients AIME Constructed Using Felten et al. (2021) AIOE Measure AIME Constructed Using Felten et al. (2018) AIOE Measure AIME Constructed Using Webb (2019) AIOE Measure (1) (2) (3) (4) (5) (6) Avg. AIME in Years Before College1 Observations Outcome Mean Avg. AIME in Years Before College Observations Outcome Mean -0.071 (0.046) 115,992 0.115 -0.015 (0.115) 19,428 0.104 Panel A. Full Sample -0.019 (0.040) 115,991 0.115 0.059 (0.101) 116,826 0.115 0.064 (0.079) 116,825 0.115 Panel B. Restricting to Top 100 Universities 0.100 (0.118) 19,423 0.104 -0.049 (0.127) 19,542 0.105 0.098 (0.173) 19,538 0.105 Panel C. Restricting to Top 50 Universities Avg. AIME in Years Before College -0.134∗∗ (0.060) -0.068 (0.062) -0.197∗∗∗ (0.076) -0.101 (0.078) 0.381∗∗∗ (0.097) 123,325 0.116 0.260∗ (0.134) 20,747 0.110 0.023 (0.119) 0.280∗∗∗ (0.105) 123,322 0.116 0.270 (0.214) 20,744 0.110 0.052 (0.125) Observations Outcome Mean 11,163 0.070 ✓ 11,157 0.070 ✓ ✓ 11,226 0.070 ✓ 11,223 0.070 ✓ ✓ 11,848 0.074 ✓ 11,844 0.074 ✓ ✓ College-Year FE 2-Digit-CIP-by-Year FE Notes: Each observation is a major-college-year cell. The coefficients in each column are estimated by using equation (1.6) but replacing the interaction term with the "weighting" version of AIME measure constructed using equation (1.9). The AIME score is rescaled to have a range between 0 and 1. College major-clustered standard errors are shown in parentheses. In Panel A, the estimates are robust to all groups. In Panel B, the estimates are robust to all groups except international students. In Panel C, the estimates are robust to male, U.S. citizens, and Whites. 1The average AIME is calculated as the average of AIME measures in students’ sophomore year to senior year of high school. 61 Table 1A.14 Annual Changes in Bachelor’s Degree Recipients with Geographical Variation in "Weighting" Ver. AI Major Exposure (AIME) across State, 2011-19 Dep. Var.: Annual Growth Rate of Bachelor’s Degree Recipients by Major All Recipients AIME Constructed Using Felten et al. (2021) AIOE Measure AIME Constructed Using Felten et al. (2018) AIOE Measure AIME Constructed Using Webb (2019) AIOE Measure (1) (2) (3) (4) (5) (6) Avg. AIME in Years Before College1 Observations Outcome Mean Avg. AIME in Years Before College Observations Outcome Mean -0.010 (0.047) 297,729 0.110 -0.149∗ (0.085) 32,372 0.105 Avg. AIME in Years Before College -0.233∗∗∗ (0.067) Observations Outcome Mean Panel A. Full Sample 0.010 (0.040) 297,729 0.110 0.054 (0.065) 298,083 0.110 -0.001 (0.063) 298,083 0.110 Panel B. Restricting to Top 100 Universities -0.231∗∗ (0.096) -0.223∗∗ (0.095) -0.199∗∗ (0.083) 32,369 0.105 32,392 0.105 32,389 0.105 Panel C. Restricting to Top 50 Universities -0.219∗ (0.132) -0.291∗∗∗ (0.085) -0.210∗ (0.123) 0.412∗∗∗ (0.076) 301,197 0.110 0.300∗∗ (0.146) 32,765 0.106 0.092 (0.117) 0.143 (0.090) 301,197 0.110 0.141 (0.147) 32,761 0.106 0.019 (0.138) 17,618 0.082 ✓ 17,606 0.082 ✓ ✓ 17,400 0.082 ✓ 17,389 0.082 ✓ ✓ 17,408 0.082 ✓ 17,397 0.082 ✓ ✓ College-Year FE 2-Digit-CIP-by-Year FE Notes: Each observation is a major-college-year cell. The coefficients in each column are estimated by using equation (1.6) but replacing the interaction term with the "weighting" version of AIME measure constructed using equation (1.9). The AIME score is rescaled to have a range between 0 and 1. College major-clustered standard errors are shown in parentheses. In Panel A, the estimates are robust to all groups except male. In Panel B, the estimates are robust to all groups. In Panel C, the estimates are robust to Whites and U.S. citizens. 1The average AIME is calculated as the average of AIME measures in students’ sophomore year to senior year of high school. 62 CHAPTER 2 MACHINE VERSUS MUSCLE, BOT VERSUS BRAIN: EFFECTS OF ARTIFICIAL INTELLIGENCE ON HETEROGENEOUS SKILL GROUPS 2.1 Introduction The displacement effect of high tech, especially automation and industrial robots, has been intensively studied (e.g., Acemoglu and Autor, 2011; Autor and Dorn, 2013; Acemoglu and Restrepo (2019, 2022a); Dauth et al., 2021; Kogan et al., 2021). Previous literature has largely focused on how low- and middle-skilled workers (those who specialize in manual- and routine- intensive occupations, respectively) are replaced by automation and has assumed that high-skilled workers are unlikely to be negatively affected by automation. However, this assumption may not hold in the case of Artificial Intelligence (AI). AI is an algorithm or a program which aims at recognizing patterns from large datasets and making predictions and rational decisions like humans (Russell and Norvig, 2021). The biggest difference between AI and industrial automation discussed in this paper is that AI is claimed to be a general-purpose technology (GPT) with profound impacts on technological evolution and the economy (e.g., Dafoe, 2018; Brynjolfsson et al., 2019; Cockburn et al., 2019; Crafts, 2021; Hötte et al., 2022; Goldfarb et al., 2023), while industrial automation is not. The latter one specifically substitutes for labor in tasks that follow explicitly defined rules (i.e., routine tasks). Importantly, AI can not only perform more complex and abstract tasks but also increase the productivity of workers who possess AI-developing skills and even create new job opportunities. Yet there is little evidence regarding effects of AI as a GPT on heterogeneous skill groups in the labor market, or how these effects differ from those of traditional high tech that are not considered as GPTs, especially industrial automation. This paper attempts to fill this gap by introducing and analyzing a task-based framework which (1) incorporates both traditional and rapid-growing high tech and (2) categorizes labor into detailed groups based on skill specializations to reflect the complementarity and displacement effects of AI. *I gratefully acknowledge the financial support from the Thompson Endowment Award. 63 This paper focuses on AI-developing skills (e.g., deep learning, machine learning, natural lan- guage processing), which are used to improve the performance of AI technologies, predict patterns, and develop AI-powered tools. To explore the relationship between the demand for AI skills and labor market outcomes of heterogeneous skill groups, I first categorize occupations into four skill groups: (1) high-skilled AI-complement group with a concentration on abstract-intensive tasks that require AI skills (e.g., "Software Developers, Applications and Systems Software" and "Aerospace Engineers"); (2) high-skilled, not-yet-AI-complement group that is abstract-intensive but not yet AI-related (e.g., "Chemists and Materials Scientists" and "Lawyers, and Judges, Magistrates, and other Judicial Workers"); (3) middle-skilled group that is routine-intensive (e.g., "Stock Clerks and Order Fillers" and "Automotive Body and Related Repairers"); and (4) low-skilled group that is manual-intensive (e.g., "Waiters and Waitresses"). Since an occupation comprises a tremendous amount of job postings, I directly match phrases for AI-developing skills to the description of postings using online job postings data to define AI postings, i.e., postings that require AI skills. These postings capture AI’s complementarity; more employers listing AI skills in job postings indicate a higher demand for people specializing in AI-developing activities. Next, I aggregate AI postings to the occupational level to distinguish between AI-complement and not-yet-AI occupa- tions. Abstract, routine, and manual occupations are then defined using the occupational-level task contents measured by Autor and Dorn (2013). Finally, an occupation exclusively falls into one skill group according to the definition of skill groups introduced above. I first document a consistent upward trend in the share of AI postings for the high-skilled AI-complement group during my sampling period, 2012-21. These abstract and AI-intensive oc- cupations experience the largest employment growth and wage gains, associated with an increasing share of AI postings at the state-year level, compared to other skill groups. Specifically, a 1 percent- age point increase in the AI posting share leads to 50 more employed people per 100,000 population, a 3% increase in mean hourly wages, and a 0.078 percentage point increase in the wage income share for high-skilled AI-complement occupations. I also perform a principal component analysis to measure the intensity that AI-developing skills are required for job tasks. I document a signif- 64 icant and positive relationship between this measure and labor market outcomes for high-skilled AI-complement occupations. The second result is that although there is significant growth in employment and wages for high-skilled, not-yet-AI occupations, this growth is much smaller than that for the high-skilled AI- complement group. For example, employment growth for the high-skilled, not-yet-AI group is less than half that of the high-skilled AI-complement group. Findings on the high-skilled occupations suggest that AI has differential effects within the high-skilled group. The employment and wage gaps between abstract, AI-intensive occupations and abstract, not-yet-AI occupations widen when AI becomes more ubiquitous. The third result shows that overall effects of the AI posting share on the employment and wages for middle- and low-skilled occupations are small and negative, but not statistically significant. However, I find that middle-skilled occupations experience a wage decline associated with an increase in the standard deviation of the measure of the intensity with which AI skills are required for job tasks. These findings imply a "J-shaped" curve of changes in employment or wages by skill level, where employment or wages in both the right and left tails are higher than the middle, and the right tail is exceptionally higher than the left tail. The labor market favors people specializing in AI-developing tasks as AI grows, with the employment and wage gaps between abstract and AI-intensive occupations and other skill groups widening over time. My empirical analysis further suggests why AI is possibly a general-purpose technology, akin to the steam engine and electricity. First, AI has a wide range of applications across occupations and sectors. Second, there is an increasing trend in explicitly listing AI skill requirements when employers post new job vacancies, regardless of skill groups or industry sectors. Third, AI tends to impact the whole economy rather than particular occupations or sectors. Although changes in the state-year share of AI postings have strong and differential effects on employment and wages for skill groups, these relationships become insignificant when using the share of AI postings at more 65 granular level, i.e., 2-digit-occupation-by-state-by-year level.1 This implies that the employment and wage gaps between skill groups are driven by between-group variation, not within-group. To provide theoretical explanations for my empirical results, I extend task-based models devel- oped in Acemoglu and Autor (2011), Acemoglu and Restrepo (2018a), and Autor et al. (2024). Tasks can be performed by labor or technology (embodied in capital). Instead of general technology, my model specifically considers AI and industrial automation as factors of production by assuming they have different levels of productivity so that AI can compete against labor in more complex and abstract tasks while industrial automation cannot. Labor is categorized into four skill groups based on skill specializations—high-skilled AI-complement, high-skilled not-yet-AI, middle-skilled, and low-skilled—to better explore the differential effects of AI and industrial automation on labor market outcomes of skill groups. In my model, AI will by assumption displace middle- and high-skilled workers in complex tasks (displacement effect) and in equilibrium expand the set of tasks performed by high-skilled workers (reinstatement effect). In contrast, industrial automation by assumption only has a displacement effect on both low- and middle-skilled workers. The displacement effect driven by AI narrows wage gaps between high-skilled labor and other skill groups, while the displacement effect of industrial automation and the reinstatement effect of AI widen these wage gaps. In addition, this task-based framework explores the differential effects of AI as a labor-augmenting technology by assuming that the growth in AI particularly increases the productivity of high-skilled AI-complement workers. Since these workers specialize in AI-developing activities and can be complemented by AI, the wage gap between high-skilled AI-complement workers and other types of workers widens as AI grows. A substantial amount of literature has developed theoretical models to study the impacts of technology on labor market outcomes (e.g., Katz and Murphy, 1992; Autor et al., 2003; Acemoglu and Autor, 2011; Autor and Dorn, 2013; Acemoglu and Restrepo (2018a,b, 2019); Autor et al., 2024). Although the canonical model (1) explains that changes in factor-augmenting technologies 1The 2-digit occupation is the most broad occupation category in the data used in my empirical analysis. 66 and relative labor supplies are confounding factors of changes in the wage structure and (2) concludes that skill premiums lead to employment and wage inequalities, it fails to provide a reason for why middle-skilled workers experience declines in both wages and employment compared to low- and high-skilled workers (which is referred to as "polarization"). A task-based model has implications on labor market trends and polarization by making a distinction between skills and tasks and allowing labor to have comparative advantages in performing different tasks. The task-based framework introduced in this paper contributes to this body of work by (1) specifically incorporating both industrial automation, which substitutes for low- and middle-skilled workers in simpler and more routine tasks, and AI, which competes against middle- and high-skilled workers in more complex and abstract tasks, and (2) decomposing high-skilled workers into two groups based on the specialization in AI-developing tasks. This paper also contributes to research focusing on the evolution of work, changes in skill demands, and wage gaps. Using a job postings dataset from 1950 to 2000, Atalay et al. (2020) document an upward-sloping (downward-sloping) trend for the frequency of words related to non- routine (routine) tasks in postings. Similarly, Nedelkoska et al. (2021) find that both male and female workers have switched from performing routine and manual tasks to non-routine cognitive tasks since 1970s. Kogan et al. (2021) show that workers who are exposed to technological innova- tions have experienced worse labor market outcomes such as employment and wages, while Autor et al. (2024) state that employment and wages increase in occupations exposed to technological innovations with augmentation effects but decrease in those exposed to innovations with displace- ment effects. Instead of focusing on general technologies or industrial automation that displace labor in routine-intensive tasks, this paper discusses the differential effects of AI, a fast-growing technology that can not only substitute for but also complement higher-skilled labor in performing more abstract tasks, on heterogeneous skill groups. The most closely related to this paper is Acemoglu et al. (2022). They specifically study the effects of AI on hiring and skill requirements using online job vacancies data. They conclude that recruitment of workers with AI skills increases in establishments highly exposed to AI, while 67 non-AI hiring declines in these establishments. However, the measures of AI they used capture the extent an occupation is exposed to AI, i.e., AI’s substitutability. In this paper, I classify job postings into AI and not-yet-AI postings and use AI postings to capture AI’s complementarity. New job vacancies that require AI skills indicate that these jobs need to hire people to perform AI-developing tasks, suggesting the demand for AI skills. I also propose an alternative measure that captures the intensity of AI skills required for job applicants when applying for a job. The rest of this paper proceeds as follows. A task-based framework is introduced in Section 2.2 which motivates my empirical analysis. Section 2.3 describes the data used in my empirical analysis and defines skill groups. My empirical strategy and main results are presented in Sections 2.4 and 2.5, respectively. Section 2.6 discusses why AI can be considered as a general-purpose technology. Section 2.7 concludes. 2.2 Theoretical Model In this section, I follow Acemoglu and Autor (2011), Acemoglu and Restrepo (2018a), and Autor et al. (2024) to introduce a task-based model, which motivates my empirical analysis on exploring the influences of AI on labor market outcomes of heterogeneous skill groups. 2.2.1 Environment I begin with a unique final good 𝑌 produced by combining a unit measure of tasks as follows: 𝑌 = (cid:20)∫ 𝑁 𝑁−1 𝑦(𝑖) 𝜎−1 𝜎 𝑑𝑖 𝜎 𝜎−1 (cid:21) , (2.1) where 𝑦(𝑖) is the output of task 𝑖 ∈ [𝑁 − 1, 𝑁] and 𝜎 ∈ (0, ∞) is the elasticity of substitution between tasks. The index 𝑖 represents the complexity of a task. The higher an index is, the more complex the corresponding task is. Since I assume that 𝑌 is the unique final good, 𝑌 is set to be the numeraire and its price 𝑃 ≡ 1. There are five factors of production, high-skilled AI-complement labor (𝐻 𝐴𝐼), high-skilled not-yet-AI labor (𝐻 𝑁𝑜𝑛), middle-skilled or AI-substitutable labor (𝑀), low-skilled labor (𝐿), and technology which embodied in capital (𝐾). Then the production function for task 𝑖 is: 𝑦(𝑖) = 𝛼𝐻 𝐴𝐼 (𝑖)ℎ 𝐴𝐼 (𝑖) + 𝛼𝐻 𝑁 𝑜𝑛 (𝑖)ℎ𝑁𝑜𝑛 (𝑖) + 𝛼𝑀 (𝑖)𝑚(𝑖) + 𝛼𝐿 (𝑖)𝑙 (𝑖) + 𝛼𝐾 𝑘 (𝑖), (2.2) 68 where 𝛼𝐻 𝐴𝐼 (𝑖), 𝛼𝐻 𝑁 𝑜𝑛 (𝑖), 𝛼𝑀 (𝑖), 𝛼𝐿 (𝑖), and 𝛼𝐾 represent the productivity of the corresponding factor of production; ℎ 𝐴𝐼 (𝑖), ℎ𝑁𝑜𝑛 (𝑖), 𝑚(𝑖), 𝑙 (𝑖), and 𝑘 (𝑖) are the total quantities of the corresponding factor used to perform task 𝑖. I impose the following assumptions on these productivities: Assumption 2.1 𝛼𝐻 𝑗 (𝑖), 𝛼𝑀 (𝑖), 𝛼𝐿 (𝑖), 𝛼𝐻 𝑗 (𝑖) 𝛼𝑀 (𝑖) , and 𝛼𝑀 (𝑖) 𝛼𝐿 (𝑖) , 𝑗 ∈ { 𝐴𝐼, 𝑁𝑜𝑛}, are continuously differ- entiable and strictly increasing. This assumption implies that (1) labor has higher productivity in more complex tasks (i.e., more abstract tasks) which are represented by a higher index; and (2) higher-skilled workers have comparative advantages over lower-skilled workers in performing more complex tasks. Assumption 2.2 ∃𝐼𝐻 ∈ [𝑁 − 1, 𝑁] such that 𝛼𝐻 𝐴𝐼 (𝑖) 𝛼𝐻 𝑁 𝑜𝑛 (𝑖) is continuously differentiable and strictly increasing (decreasing) if 𝑖 > 𝐼𝐻 and requires AI skills (is not yet related to AI). This assumption indicates that within the group of high-skilled workers, those who possess AI skills have comparative advantages in complex tasks that require AI skills. However, not all complex tasks are AI-related. Other soft skills (e.g., cognitive and social skills) may play a pivotal role in these not-yet-AI complex tasks. Assumption 2.2 then implies that high-skilled not-yet-AI workers have comparative advantages in these tasks over high-skilled AI-complement workers. Different from most previous literature that has utilized the supermodular comparative advantage structure across all factors, I follow Acemoglu and Restrepo (2018a) to state that technology can efficiently compete with not only low-skilled labor in simpler tasks but also middle- or high-skilled labor in more complex tasks. I assume that there exists 𝑆 ∈ (𝑁 − 1, 𝑁) such that tasks 𝑖 ∈ (𝑁 − 1, 𝑆) can be automated with productivity 𝛼𝐾 = 1, while tasks 𝑖 ∈ (𝑆, 𝑁) can be performed by AI with productivity 𝛼𝐾 > 1. This assumption indicates that technology can efficiently perform some simpler tasks that low-skilled labor used to specialize in and some more complex tasks that previously utilized middle- or high-skilled labor. Assumption 2.3 ∃𝐼𝐿, 𝐼𝑀 ∈ (𝑁 − 1, 𝑆), where 𝐼𝐿 < 𝐼𝑀, and 𝐼𝐻 ∈ (𝑆, 𝑁) such that 𝑊𝐻 𝑗 𝛼𝐻 𝑗 (𝐼𝐻 ) > 𝑅 𝛼𝐾 , and 𝑊𝑀 𝛼𝑀 (𝐼𝑀 ) > 𝑅 > 𝑊𝐿 𝛼𝐿 (𝐼𝐿) , 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. 69 Figure 2.1 The Equilibrium Task Allocation Notes: 𝐿, 𝑀, 𝐻 𝐴𝐼 , and 𝐻 𝑁 𝑜𝑛 represent low-skilled, middle-skilled, high-skilled AI-complement, and high-skilled not-yet-AI labor, respectively. 𝐼𝐻 = min{𝐼𝐻 𝐴𝐼 , 𝐼𝐻 𝑁 𝑜𝑛 }, 𝑆, 𝐼𝑀 , and 𝐼𝐿 are thresholds used to determine the equilibrium. This assumption ensures that it is strictly cheaper to produce (1) tasks 𝑖 ∈ (𝐼𝐿, 𝐼𝑀] by industrial automation than by low-skilled labor and (2) tasks 𝑖 ∈ (𝑆, 𝐼𝐻] by AI than by high-skilled labor in equilibrium. The equilibrium is then characterized by using the comparative advantage structure in Assumptions 2.1 and 2.2 and the effective cost assumption stated in Assumption 2.3. In particular, there exist some thresholds, 𝐼𝐻, 𝐼𝑀, 𝐼𝐿, and 𝑆, such that low-skilled workers perform tasks 𝑖 ∈ [𝑁 −1, 𝐼𝐿], middle-skilled workers perform tasks 𝑖 ∈ (𝐼𝑀, 𝑆], high-skilled AI-complement workers perform tasks 𝑖 ∈ (𝐼𝐻, 𝑁] with AI skill requirements, and high-skilled not-yet-AI workers perform tasks 𝑖 ∈ (𝐼𝐻, 𝑁] without any AI skill requirements. Tasks 𝑖 ∈ (𝐼𝐿, 𝐼𝑀] are automated and tasks 𝑖 ∈ (𝑆, 𝐼𝐻] are performed by AI. This equilibrium allocation of tasks to factors is depicted in Figure 2.1 and is formally presented as follows: Proposition 2.1 In any equilibrium, ∃𝐼𝐻 𝐴𝐼 , 𝐼𝐻 𝑁 𝑜𝑛, 𝐼𝑀, 𝐼𝐿, and 𝑆 such that 𝑁 − 1 < 𝐼𝐿 < 𝐼𝑀 < 𝑆 < 𝐼𝐻 < 𝑁, where 𝐼𝐻 = min{𝐼𝐻 𝐴𝐼 , 𝐼𝐻 𝑁 𝑜𝑛 }, and (a) for any 𝑖 ∈ (𝐼𝐿, 𝐼𝑀] ∪ (𝑆, 𝐼𝐻], 𝑙 (𝑖) = 𝑚(𝑖) = ℎ 𝐴𝐼 (𝑖) = ℎ𝑁𝑜𝑛 (𝑖) = 0; (b) for any 𝑖 ∈ [𝑁 − 1, 𝐼𝐿], 𝑚(𝑖) = ℎ 𝐴𝐼 (𝑖) = ℎ𝑁𝑜𝑛 (𝑖) = 𝑘 (𝑖) = 0; (c) for any 𝑖 ∈ (𝐼𝑀, 𝑆], 𝑙 (𝑖) = ℎ 𝐴𝐼 (𝑖) = ℎ𝑁𝑜𝑛 (𝑖) = 𝑘 (𝑖) = 0; (d) for any 𝑖 ∈ (𝐼𝐻, 𝑁] and 𝑖 ∈ AI tasks, 𝑙 (𝑖) = 𝑚(𝑖) = ℎ𝑁𝑜𝑛 (𝑖) = 𝑘 (𝑖) = 0; 70 (e) for any 𝑖 ∈ (𝐼𝐻, 𝑁] and 𝑖 ∈ not-yet-AI tasks, 𝑙 (𝑖) = 𝑚(𝑖) = ℎ 𝐴𝐼 (𝑖) = 𝑘 (𝑖) = 0. The intuition behind this proposition is that task allocation is determined by cost minimization and the comparative advantage structure introduced in Assumptions 2.1 and 2.2. 𝐼𝐻 𝐴𝐼 (𝐼𝐻 𝑁 𝑜𝑛) is the threshold where high-skilled AI-complement (high-skilled not-yet-AI) labor and capital can be indifferently used to perform task 𝑖 = 𝐼𝐻 𝐴𝐼 (𝑖 = 𝐼𝐻 𝑁 𝑜𝑛). Since the sets of tasks that high-skilled AI-complement and high-skilled not-yet-AI workers perform in equilibrium are both complex (represented by a higher index) but have different skill requirements (the former ones require AI skills while the latter ones are not yet AI-related), I am not able to determine which type of tasks is more superior. That is, it is insufficient to say all tasks that high-skilled AI-complement workers specialize in are more complex than those performed by high-skilled not-yet-AI workers or vice versa. Therefore, I set 𝐼𝐻 = min{𝐼𝐻 𝐴𝐼 , 𝐼𝐻 𝑁 𝑜𝑛 } to distinguish the set of tasks performed by all high-skilled workers but add conditions of different skill requirements when charaterizing the equilibrium (Propositions 2.1(d) and 2.1(e)). The differences in how technology affects these two types of workers in the labor market will be discussed later. Given the equilibrium allocation of tasks in Proposition 2.1, the equilibrium price of task 𝑖 is shown below: 𝑝(𝑖) = 𝑊𝐿 𝛼𝐿 (𝑖) 𝑅 𝑊𝑀 𝛼𝑀 (𝑖) 𝑅 𝛼𝐾 𝑊 𝑗 𝐻 𝛼𝐻 𝑗 (𝑖)    if if if if if 𝑖 ∈ [𝑁 − 1, 𝐼𝐿], 𝑖 ∈ (𝐼𝐿, 𝐼𝑀], 𝑖 ∈ (𝐼𝑀, 𝑆], 𝑖 ∈ (𝑆, 𝐼𝐻], 𝑖 ∈ (𝐼𝐻, 𝑁] and 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}, (2.3) where 𝑊𝐻 𝐴𝐼 , 𝑊𝐻 𝑁 𝑜𝑛, 𝑊𝑀, and 𝑊𝐿 are the economy-wide wages for high-skilled AI-complement, high-skilled not-yet-AI, middle-skilled or AI-substitutable, and low-skilled labor. 𝑅 is the rental rate of capital. From equation (2.1), the quantity of task 𝑖 can be derived as 𝑦(𝑖) = 𝑌 𝑝(𝑖)−𝜎. (2.4) 71 Combining Proposition 2.1 with equations (2.3) and (2.4), I can obtain the demand for each factor in task 𝑖 as 𝑘 (𝑖) = 𝑌 𝛼−1 𝐾 𝑝(𝑖)−𝜎, if 𝑖 ∈ (𝐼𝐿, 𝐼𝑀] ∪ (𝑆, 𝐼𝐻] 𝑙 (𝑖) = 𝑌 𝛼𝐿 (𝑖)−1 𝑝(𝑖)−𝜎, if 𝑖 ∈ [𝑁 − 1, 𝐼𝐿] 𝑚(𝑖) = 𝑌 𝛼𝑀 (𝑖)−1 𝑝(𝑖)−𝜎, if 𝑖 ∈ (𝐼𝑀, 𝑆] (2.5) ℎ 𝑗 (𝑖) = 𝑌 𝛼𝐻 𝑗 (𝑖)−1 𝑝(𝑖)−𝜎, if 𝑖 ∈ (𝐼𝐻, 𝑁] and 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. Then the factor markets clear in the equilibrium: 𝐿 = 𝑌 A𝐿𝑊 −𝜎 𝐿 , 𝑀 = 𝑌 A𝑀𝑊 −𝜎 𝑀 , 𝐻 𝑗 = 𝑌 A𝐻 𝑗𝑊 −𝜎 𝐻 𝑗 , 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}, (2.6) 𝐾 = 𝑌 A𝐾 𝑅−𝜎, where A𝐾 = (𝐼𝑀 − 𝐼𝐿) + (𝐼𝐻 − 𝑆)𝛼𝜎−1 𝐾 , A𝐿 = ∫ 𝐼𝐿 𝑁−1 𝛼𝐿 (𝑖)𝜎−1𝑑𝑖, A𝑀 = ∫ 𝑆 𝐼𝑀 𝛼𝑀 (𝑖)𝜎−1𝑑𝑖, A𝐻 𝐴𝐼 = ∫ 𝑁 𝐼𝐻 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1𝑑𝑖, A𝐻 𝑁 𝑜𝑛 = ∫ 𝑁 𝐼𝐻 1{𝑖 ∈ not-yet-AI tasks}𝛼𝐻 𝑁 𝑜𝑛 (𝑖)𝜎−1𝑑𝑖, (2.7) can be viewed as the "allocation share" of each factor. Factor prices satisfy the ideal-price condition: A𝐻 𝐴𝐼𝑊 1−𝜎 𝐻 𝐴𝐼 + A𝐻 𝑁 𝑜𝑛𝑊 1−𝜎 𝐻 𝑁 𝑜𝑛 + A𝑀𝑊 1−𝜎 𝑀 + A𝐿𝑊 1−𝜎 𝐿 + A𝐾 𝑅1−𝜎 = 1. (2.8) Proposition 2.2 The equilibrium factor prices and output can be expressed as: 𝑅 = 𝑌 1 𝜎 A 1 𝜎 𝐾 𝐾 − 1 𝜎 , 𝑊𝐿 = 𝑌 1 𝜎 A 1 𝜎 𝐿 𝐿− 1 𝜎 , 𝑊𝑀 = 𝑌 1 𝜎 A 1 𝜎 𝑀 𝑀 − 1 𝜎 , 𝑊𝐻 𝑗 = 𝑌 1 𝜎 A 1 𝜎 𝐻 𝑗 (𝐻 𝑗 )− 1 𝜎 , (2.9) 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}, and (cid:20) 𝑌 = 1 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼) A 𝜎−1 𝜎 + A 1 𝜎 𝐻 𝑁 𝑜𝑛 (𝐻 𝑁𝑜𝑛) 𝜎−1 𝜎 + A 1 𝜎 𝑀 𝑀 𝜎−1 𝜎 + A 1 𝜎 𝐿 𝐿 𝜎−1 𝜎 + A 1 𝜎 𝐾 𝐾 𝜎−1 𝜎 (cid:21) 𝜎 𝜎−1 . (2.10) This proposition provides intuitions for the "allocation share" of each factor defined in equation (2.7). These "allocation shares" can be viewed as the distribution parameters in the equilibrium output in equation (2.10). They indicate how different factors are allocated in producing the final good, 𝑌 . 72 2.2.2 Relationship between Labor Market Outcomes and High Tech In this section, I discuss the relationship between labor market outcomes (e.g., employment, relative wages) of different skill groups and AI or industrial automation. I follow Acemoglu and Restrepo (2019) to define the following effects: (1) the displacement effect means capital substitutes for labor in production; (2) the reinstatement effect means the set of tasks performed by labor is expanded; and (3) the productivity effect means technology increases productivity in production. Proposition 2.3 explores the displacement effect and the reinstatement effect of AI or industrial automation. Propositions 2.4 and 2.5 study effects on relative wages. Proposition 2.6 explores the productivity effect of AI on the income distributed to high-skilled AI-complement labor or capital. Only the inequalities that can be tested in my empirical analysis are presented. Additional inequalities and proofs can be found in Appendix 2A. Proposition 2.3 (Displacement and reinstatement effects of AI or industrial automation) (1) AI can displace workers in some complex tasks, 𝑑A𝐻 𝑗 𝑑𝐼𝐻 < 0, 𝑑A𝐾 𝑑𝐼𝐻 > 0, 𝑑A𝑀 𝑑𝑆 > 0, 𝑑A𝐾 𝑑𝑆 < 0. (2) AI can expand the set of tasks performed by high-skilled workers, 𝑑A𝐻 𝑗 𝑑𝑁 > 0. (3) Industrial automation primarily takes over simpler tasks, 𝑑A𝑀 𝑑𝐼𝑀 < 0, 𝑑A𝐾 𝑑𝐼𝑀 > 0. Note that 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. The growth in AI (represented by an increase in 𝐼𝐻) reduces the share of tasks specialized by high-skilled workers (A𝐻 𝑗 , 𝑗 ∈ { 𝐴𝐼, 𝑁𝑜𝑛}) because AI becomes more efficient in production and can take over some complex tasks previously performed by high-skilled workers. AI also has a displacement effect on middle-skilled workers. A decrease in 𝑆 means that some tasks that previously used middle-skilled workers switch to utilize AI (captured by a decrease in A𝑀 or an increase in A𝐾). An increase in 𝐼𝑀 means improvements in industrial automation, which reduces the share of tasks performed by middle-skilled workers (A𝑀) but increases that of capital (A𝐾). This can be viewed as a direct displacement effect of industrial automation on middle-skilled workers and an 73 indirect displacement effect of AI since improvements in AI may also stimulate developments in industrial automation. Different from industrial automation which mainly brings displacement effects to middle-skilled labor, the growth in AI expands the set of tasks that high-skilled workers can perform by creating new tasks that require high-skilled labor (an increase in 𝑁) or changing task content in favor of high-skilled labor over AI. This is referred to as the reinstatement effect of AI (Acemoglu and Restrepo, 2019). Proposition 2.4 (Relationship between relative wages and AI or industrial automation) (1) The displacement effect of AI narrows wage gaps, 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝐿 𝑑𝐼𝐻 < 0, 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝑀 𝑑𝐼𝐻 < 0. (2) The reinstatement effect of AI widens wage gaps, 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝐿 𝑑𝑁 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝑀 𝑑𝑁 > 0, 𝑑 ( 𝑊𝑀 ) 𝑊𝐿 𝑑𝑁 > 0. > 0, (3) The displacement effect of industrial automation widens wage gaps, 𝑑 ( 𝑊 𝐻 𝑗 𝑊𝑀 𝑑𝐼𝑀 ) > 0, ) 𝑑 ( 𝑊𝑀 𝑊𝐿 𝑑𝐼𝑀 > 0. Note that 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. The main takeaway from this proposition is that the displacement effect of AI (an increase in 𝐼𝐻) narrows wage gaps between high-skilled group and middle- or low-skilled group ( 𝑊𝐻 𝑗 𝑊𝐿 ), while the displacement effect of industrial automation (an increase in 𝐼𝑀) or the reinstatement effect 𝑊𝐻 𝑗 𝑊𝑀 and of AI (an increase in 𝑁) widens these wage gaps. An increase in 𝐼𝐻 leads to a reduction in the share of tasks performed by high-skilled workers, further resulting in lower wagebills for these workers. Since wages for workers from other skill groups are assumed to be not affected under this scenario, the wage gap between high-skilled labor and middle- or low-skilled labor becomes smaller. In contrast, AI can also create tasks that require skills possessed by high-skilled labor or change task contents in favor of high-skilled labor rather than AI. In this way, the wage gap between high- and middle-skilled labor ( 𝑊𝐻 𝑗 𝑊𝑀 ) or between high- and low-skilled labor ( 𝑊𝐻 𝑗 𝑊𝐿 ) widens associated with the growth in AI. Since the reinstatement effect of AI on wage gaps between high-skilled group and other groups is in the opposite direction of the displacement effect of AI, which effect is dominant is indeterminate. 74 However, my empirical findings will shed light on which effect is dominant for different skill groups. Proposition 2.5 (Relationship between relative wages and labor-augmenting AI) The growth in AI widens the wage gap between the high-skilled AI-complement group and other skill groups by increasing the productivity of labor possessing AI skills. 𝑊𝐻 𝐴𝐼 𝑑 ( ) 𝑊𝐿 𝑑𝛼𝐻 𝐴𝐼 (𝑖) > 0, 𝑊𝐻 𝐴𝐼 𝑑 ( ) 𝑊𝑀 𝑑𝛼𝐻 𝐴𝐼 (𝑖) > 0, 𝑊𝐻 𝐴𝐼 𝑑 ( ) 𝑊𝐻 𝑁 𝑜𝑛 𝑑𝛼𝐻 𝐴𝐼 (𝑖) > 0. (2.11) Different from industrial automation which is assumed to be only factor-augmenting in my model and mainly displaces labor, AI not only substitutes for but also complements labor. I view AI as a factor- and labor-augmenting technology since it can increase the productivity of both capital and workers with AI skills. Since high-skilled AI-complement workers possess AI skills and utilize AI to complement their work, AI is assumed to raise the productivity of high-skilled AI-complement workers (𝛼𝐻 𝐴𝐼 (𝑖)) but not other skill groups in this model. As the performance of AI improves, high-skilled AI-complement workers earn more due to an increase in their productivity. As a result, the wage gap between these workers and other skill groups (low-skilled, middle-skilled, or high-skilled not-yet-AI workers) widens. Proposition 2.6 (Income allocated to high-skilled AI-complement labor and AI technologies) (1) An increase in the productivity of high-skilled AI-complement labor widens the gap between the income allocated to this skill group and that allocated to capital, 𝑑 ( 𝐻 𝐴𝐼 𝑊 𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) ) > 0. (2) The relationship between the productivity of AI technologies and this income allocation gap depends on whether the factors are complements or substitutes: if 𝜎 ∈ (0, 1), 𝑑 ( if 𝜎 = 1, 𝑑 ( = 0; if 𝜎 ∈ (1, ∞), 𝑑 ( < 0. 𝐻 𝐴𝐼 𝐻 𝐴𝐼 ) ) 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 ) > 0; (3) If factors are complements, 𝜎 ∈ (0, 1], the productivity effect of high-skilled AI-complement 𝑑 ( 𝐻 𝐴𝐼 𝑊 𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) ) | > | 𝑑 ( 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 ) |; if labor dominates the productivity effect of AI technologies, | 75 factors are substitutes, 𝜎 ∈ (1, ∞), it is indeterminate which effect dominates, | 𝑑 ( | 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 ) |. 𝑑 ( 𝐻 𝐴𝐼 𝑊 𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) ) | ⪌ The takeaway of this proposition is that if factors are complements, an increase in the productivity of either high-skilled AI-complement labor or AI technologies embodied in capital widens the gap between the income allocated to high-skilled AI-complement labor and capital. If factors are substitutes, then an increase in the productivity of high-skilled AI-complement labor (AI technologies) has a positive (negative) effect on this income allocation gap. In this case, it is indeterminate which effect dominates. In summary, my task-based framework implies that AI has a reinstatement effect by creating new tasks that demand high-skilled labor. It also indicates that the growth in AI widens both the wage gap between high-skilled AI-complement group and other skill groups, and the income allocation gap between high-skilled AI-complement labor and capital. In addition, I discuss the relationships between (1) the reinstatement and displacement effects of AI, and (2) the productivity effect of high-skilled AI-complement labor and the productivity effect of AI technologies. 2.3 Data and Construction of Skill Groups Section 2.3.1 describes the datasets I use. Section 2.3.2 introduces how I define skill groups. Section 2.3.3 presents summary statistics of job postings and labor market outcomes by skill group. 2.3.1 Data Online Job Postings. I use the online job postings data from LinkUp. LinkUp has web scraped over 200 million daily online job postings directly from over 60,000 employer websites worldwide since 2007. Postings with missing information on either the posted time, geographic locations, occupational codes, or job descriptions (i.e., the raw text of a posting) are dropped. Since only around 2% of collected postings in the U.S. were posted on and before 2010, my sample comprises postings between 2011 and 2022 in the U.S. These restrictions leave me with a total sample of around 125 million postings. Occupational Descriptions. Since it is difficult for LinkUp, as well as other web-scraping 76 companies, to scrape and collect every single online posting, I use the Occupational Information Network (O∗NET) database as a complement of LinkUp data for this paper. Among all occupational features provided by O∗NET, occupational tasks, technology skills, detailed work activities, and knowledge information are adopted to define skill groups. These features provide descriptions of tasks that are usually performed for an occupation and list skills, software, and knowledge that are commonly required by this occupation. One disadvantage of using the O∗NET database over the job postings data is that the available occupational descriptions provided by O∗NET are time- invariant. Although the O∗NET occupational codes have changed periodically, to the best of my knowledge, the detailed descriptions of occupational features for older versions are not available. Therefore, researchers are not able to track how occupational features have changed within and across occupations over time by only using the O∗NET database. Employment and Wages. The individual-level data on labor market outcomes is from the American Community Survey (ACS) Public Use Microdata Sample (PUMS) data (IPUMS-ACS hereafter) between 2012-21. For my analysis, I restrict to individuals aged 18 to 64 and drop all unemployed individuals with no work experience in the last five years or earlier and individuals who never worked. Individuals whose occupation that is not on the list of my proposed skill groups, which will be introduced in the Section 2.3.2, are also dropped. I then calculate occupational-level employment per 100,000 capita, share of employment, mean hourly wage (all wages are in 2019 U.S. dollars), and share of wage income to explore the relationship between these labor market outcomes and AI.2 2.3.2 Defining Skill Groups This section describes how I define the following skill groups: (1) high-skilled AI-complement occupations that have a concentration of abstract and AI-related tasks; (2) high-skilled, not-yet-AI 2IPUMS-ACS has not collected the exact number of weeks worked during the calendar year before each Census year (the reference period) until 2019 (the "WKSWORK1" variable). However, it provides the interval of weeks worked during the reference period (the "WKSWORK2" variable) for my sampling period, 2012-21. Thus, I treat the midpoint of each interval to be the number of weeks worked to calculate mean hourly wage. In addition, neither the total number of hours (the "HRSWORK1" variable) nor the interval (the "HRSWORK2" variable) that the respondent was at work during the previous week between 2012-21 is provided by IPUMS-ACS. Thus, the "UHRSWORK" variable which represents the number of hours per week that the respondent usually worked, if the person worked during the previous year, is adopted to calculate mean hourly wage. 77 Table 2.1 Phrases for AI Skills and Applications Category Phrases Narrow AI artificial intelligence, augmented reality (AR), autonomous driving, big data, computer graphics, computer vision, data mining, deep learning, machine learning, matlab, multimedia, natural language processing (NLP), neural network, pattern recognition, python, pytorch, robotic, tensorflow, virtual reality (VR), voice recognition, 3D modeling Broad AI All phrases in the Narrow AI category + cloud computing, cognitive science, computational biology, computational intelligence, computer-aided design/drafting (CAD), cybernetics, geographic information system (GIS), image processing, phenotype, remote sensing, symbolic inference Notes: The set of phrases in the narrow AI category is a subset of phrases in the broad AI category. occupations that focus on abstract tasks which are not yet AI-related; (3) middle-skilled occupations that consist of routine tasks; and (4) low-skilled occupations that comprise manual tasks. To categorize occupations into these four groups, I first define AI postings (Section 2.3.2.1) and AI occupations (Section 2.3.2.2) which are those with a specialization in AI-developing activities. Abstract, routine, and manual occupations are then defined based on the occupational-level task contents measured by Autor and Dorn (2013) (Section 2.3.2.3). Occupations are classified into one of these skill groups in Section 2.3.2.4. 2.3.2.1 Defining AI Postings The phrases for AI-developing skills I used to define AI postings are from LeCun et al. (2015), Zhang et al. (2022), and topics of top journals and conferences in the field of AI (e.g., Institute of Electrical and Electronics Engineers (IEEE) and Association for Computing Machinery (ACM)), which are listed in Table 2.1. These phrases are then divided into two categories: (1) the narrow definition of AI or "narrow AI," which refers to AI itself, the major subfields of AI, commonly used programming languages for AI, and AI-powered technologies; and (2) the broad definition of AI or "broad AI," which includes not only all phrases in the "narrow AI" category but also more general computer science (CS) skills and applications that are, to some extent, AI-related. I then directly match the chosen AI phrases to the raw text of online job postings. Including a chosen AI phrase in the job description means that this posting explicitly requires this AI skill when hiring people to fill this position. If a job description includes any chosen AI phrase from the narrow (broad) AI category, then this posting will be defined as a narrow (broad) AI posting. I 78 further define CS postings as those with at least one CS phrase but no narrow AI phrase included in job descriptions.3 Figure 2.2 presents the number and share of narrow AI, broad AI, and CS postings between 2011-22 in the U.S. There was an overall increasing trend for both narrow and broad AI postings, while the share of CS postings remained constant between 2014-22. The number of AI postings dropped from 2019-20 but dramatically increased from 2020-21, which could be due to the COVID-19 pandemic. Although AI and CS postings account for a small share of postings in LinkUp data, this share increased from around 0.15% to 4.58% for narrow AI postings, from 0.28% to 5.70% for broad AI ones, and from 0.13% to 1.11% for CS ones during 2011-22. Note that both the number and share of broad AI postings are higher than either narrow AI or CS ones because phrases that are used to define narrow AI/CS postings also belong to the broad AI category. Appendix Figures 2B.1 and 2B.2 further show trends of AI and CS postings by the Bureau of Labor Statistics (BLS) regions.4 All eight regions have similar trends in the number of AI postings but are different in magnitude. The Western region experienced the largest AI job vacancies while the Mountain-Plains region had the smallest number of AI postings. The share of AI postings was relatively high in the New England, New York/New Jersey, Mid-Atlantic, and Western regions. All eight regions had a relatively small number and share of CS postings with a constant trend. The geographic distribution of the narrow AI posting share from 2011-14, 2015-18, and 2019-22 is presented in Figure 2.3. The darker a state’s color is, the more narrow AI vacancies were posted in that state. During 2011-14, only Washington was in the darkest color with the highest share of narrow AI postings, followed by California and Massachusetts. From 2015-18, both Washington and California were in the darkest red with a few more states in orange and yellow. After 2019, the narrow AI posting share in both the West Coast and the Northeast was the highest in the U.S. Almost all states were in orange or yellow, implying a growth in the narrow AI posting share nationwide over time. Note that the scales also increased over 2011-22. The minimum and maximum share increased from 0.12% to 0.96% and from 5.17% to 8.52%, respectively. These facts indicate spatial 3CS phrases refer to those that belong to the broad AI category but not the narrow AI category as listed in Table 2.1. 4Guam, Puerto Rico, and Virgin Islands are dropped from my sample. 79 Figure 2.2 AI/CS Postings in the U.S. in LinkUp Data, 2011-22 (a) Number of AI/CS Postings (b) Share of AI/CS Postings and temporal patterns of AI postings; the share of narrow AI postings changed differentially across states and consistently increased over time. The share of broad AI postings have similar patterns displayed in Appendix Figure 2B.3. Compared to the online job postings data from Lightcast, formerly known as Burning Glass Technologies, which has been widely used in economic research (e.g., Deming and Noray, 2018, 2020; Bloom et al., 2020; Alekseeva et al., 2021; Acemoglu et al., 2022; Dillender and Forsythe, 2022; Hemelt et al., 2023), LinkUp data has been less utilized. To test the validity of using LinkUp data to examine the relationship between changes in online job postings and labor market outcomes, I compare the annual share of AI postings in the U.S. separately computed using LinkUp and Lightcast data as displayed in Appendix Figure 2B.4. The share from Lightcast data is presented on the x-axis and that from LinkUp data is on the y-axis.5 Each marker represents the annual share of postings in one of the following AI subcategories proposed by Zhang et al. (2022): artificial intelligence, autonomous driving, machine learning, natural language processing, neural networks, robotics, and visual image recognition. Most markers locate on or close to the 45 degree line, implying a high similarity between LinkUp and Lightcast data.6 Specifically, the correlation 5Since Lightcast data is non-public, I use the monthly share of AI postings from 2010-20 in the U.S., made publicly available by Zhang et al. (2022) from the Stanford Institute for Human-Centered Artificial Intelligence (HAI) via https://aiindex.stanford.edu/ai-index-report-2022/, to compute the annual share of AI postings in Lightcast data. 6The share of postings in robotics differs from that of the other subcategories, possibly because the phrases used 80 0500K1M1.5MNumber of Postings20112014201720202022YearNarrow AIBroad AICS0.02.04.06Share of Postings20112014201720202022YearNarrow AIBroad AICS Figure 2.3 Geographic Distribution of the Narrow AI Posting Share in LinkUp Data (a) 2011-14 (b) 2015-18 (c) 2019-22 Notes: Scales are in percentage point. 81 > 4 (Max: 5.17)3 − 42 − 31 − 20 − 1 (Min: 0.12)> 4 (Max: 6.80)3 − 42 − 31 − 20 − 1 (Min: 0.41)> 4 (Max: 8.52)3 − 42 − 31 − 20 − 1 (Min: 0.96) between the overall share of AI postings in LinkUp and Lightcast data is 0.9490.7 2.3.2.2 Defining AI Occupations Since each occupation comprises a substantial amount of job postings, an AI occupation, 𝐴𝐼𝑜𝑐𝑐 𝑗,𝑐,𝜏, is: 𝐴𝐼𝑜𝑐𝑐 𝑗,𝑐,𝜏 = 1 , 0 ,    if %𝐴𝐼 𝑝𝑜𝑠𝑡 𝑗,𝑐,𝜏 > 1 𝑁 if %𝐴𝐼 𝑝𝑜𝑠𝑡 𝑗,𝑐,𝜏 ≤ 1 𝑁 (cid:205) 𝑗 ∈J %𝐴𝐼 𝑝𝑜𝑠𝑡 𝑗,𝑐,𝜏 (cid:205) 𝑗 ∈J %𝐴𝐼 𝑝𝑜𝑠𝑡 𝑗,𝑐,𝜏, (2.12) where 𝑗, J , and 𝑁 denote an occupation, the set of all occupations, and the number of all occupations. %𝐴𝐼 𝑝𝑜𝑠𝑡 𝑗,𝑐,𝜏 is the share of AI postings in category 𝑐 ∈ {Narrow AI, Broad AI} of occupation 𝑗 in the U.S. during the time period 𝜏 ∈ {2011 − 14, 2015 − 18, 2019 − 22}. If the AI posting share of an occupation is greater than the chosen threshold, the mean of shares across all occupations during a time period, this occupation is treated as an AI occupation. Similar to the occupational classification systems that are updated periodically, my proposed AI occupation indicators are time-variant to capture how AI technologies and the demand for AI skills have changed over time. The time-invariant indicators are also constructed by using the share of AI postings over 2011-22 for a robustness check. To test the validity of my choice of threshold in defining AI occupations, I cross-check AI occupations defined by using LinkUp data with those constructed by using O∗NET data. To make the results comparable across datasets, I match the same set of AI phrases listed in Table 2.1 to tasks, technology skills, detailed work activities, and knowledge information of each occupation provided by O∗NET (Appendix Figure 2B.5 shows an example of these features). If the description of any of the above features is matched to at least one chosen AI phrase, the corresponding occupation will be defined as an AI occupation. Among 901 occupations represented by 2019 O∗NET-SOC code in my sample, (1) 164 are narrow AI occupations defined by using LinkUp data and 212 are to define robotics postings in this paper and in Zhang et al. (2022) are different. While I directly match "Robotic" to descriptions of LinkUp online job postings, Zhang et al. (2022) list phrases such as "Motoman Robot Programming," "Robot Framework," "Robotic Systems," and "Robot Programming" as AI skills in the robotics category. 7This correlation within each of the seven subcategories is: 0.9698 (artificial intelligence), 0.6649 (autonomous driving), 0.9581 (machine learning), 0.8236 (NLP), 0.9660 (neural networks), 0.6459 (robotics), and 0.6797 (visual image recognition). 82 Figure 2.4 Comparison between AI Occupations Defined by Using LinkUp and O∗NET Data (a) Narrow AI (b) Broad AI Notes: The black solid circle in each venn diagram represents the set of AI occupations defined by using LinkUp data with narrow AI (Subfigure 2.4a) or broad AI (Subfigure 2.4b) definition discussed in Section 2.3.2.1, while the red dashed circle represents the set of AI occupations defined by using O∗NET data. The overlapping area represents occupations that are defined as AI occupations in both datasets. The numbers shown in each venn diagram represent the total number of occupations that belong to one of the above sets. defined by using O∗NET data with an overlap of 112 occupations; (2) 182 are broad AI occupations defined by using LinkUp data and 393 are defined by using O∗NET data with an overlap of 170 occupations (shown in Figure 2.4). Due to the advantages and disadvantages of both LinkUp and O∗NET data discussed in Section 2.3.1, I treat the overlapping occupations as the narrow/broad AI occupations in my main analysis. 2.3.2.3 Defining Abstract, Routine, and Manual Occupations The next step is to categorize occupations into high-, middle-, and low-skilled groups, which are respectively proxied by abstract, routine, and manual occupations. I define these three types of occupations based on abstract, routine, and manual task contents measured by Autor and Dorn (2013): 𝑂𝑐𝑐𝑇 𝑦 𝑝𝑒 𝑗 = 𝑥 if 𝑇 𝑥 𝑗,1980 = 𝑚𝑎𝑥 T 𝑗,1980, for 𝑥 ∈ {𝐴𝑏𝑠𝑡𝑟𝑎𝑐𝑡, 𝑅𝑜𝑢𝑡𝑖𝑛𝑒, 𝑀𝑎𝑛𝑢𝑎𝑙}, (2.13) 𝑗,1980 , 𝑇 𝑅𝑜𝑢𝑡𝑖𝑛𝑒 𝑗,1980 where T 𝑗,1980 ≡ {𝑇 𝐴𝑏𝑠𝑡𝑟𝑎𝑐𝑡 the indicator for the type (i.e., abstract, routine, and manual) that occupation 𝑗 belongs to. 𝑇 𝐴𝑏𝑠𝑡𝑟𝑎𝑐𝑡 𝑗,1980 , and 𝑇 𝑀𝑎𝑛𝑢𝑎𝑙 𝑇 𝑅𝑜𝑢𝑡𝑖𝑛𝑒 𝑗,1980 are the abstract, routine, and manual task inputs in each occupation 𝑗 measured } and 𝑗 denotes an occupation.8 𝑂𝑐𝑐𝑇 𝑦 𝑝𝑒 𝑗 represents , 𝑇 𝑀𝑎𝑛𝑢𝑎𝑙 𝑗,1980 𝑗,1980 , 8An occupation in equation (2.13) is represented by occ1990dd occupation classification constructed by Dorn (2009). I map occ1990dd to 2010 Census Occupational Classification using the crosswalk provided by Autor (2015) to merge the occupation indicators with the data on employment and wages. 83 (a) By Autor and Dorn (2013)’s Occupation Group (b) By Abstract, Routine, and Manual Occupation Figure 2.5 Occupational Task Contents Notes: Each marker represents an occupation using the occ1990dd occupation classification constructed by Dorn (2009). According to https://www.ddorn.net/data.htm, "the occ1990dd occupation classification aggregates U.S. Census occupation codes to a balanced panel of occupations for the 1980, 1990, and 2000 Census, as well as the 2005-2008 ACS." There are 330 occupations in Autor and Dorn (2013)’s data. The abstract, routine, and manual task contents have a range between 0 and 10. in 1980 by Autor and Dorn (2013) with a range between 0 and 10. Based on equation (2.13), each occupation falls into only one category.9 Note that since Autor and Dorn (2013) use task inputs in 1980, which is the starting year of their sample, my indicators for abstract, routine, and manual occupations are static over time. Figure 2.5 shows a 3D visualization of each occupation’s task contents. Each marker represents an occupation and the style of the marker distinguishes which group this occupation belongs to. Figure 2.5a displays occupational task contents by Autor and Dorn (2013)’s occupation group, while Figure 2.5b divides occupations into abstract, routine, and manual ones constructed using equation (2.13). Since these figures present three dimensions, they should be viewed as 3D boxes instead of 2D surfaces. The darker the color of and the more solid a marker is, the closer this marker is located to readers (i.e., the closer this marker is located to the space with a high value in routine task contents and a low value in manual task contents, regardless of the abstract task contents which are represented by the vertical axis or the z axis); the lighter the color of and the 9There is no occupation that has the highest value of task inputs with ties in Autor and Dorn (2013)’s data. 84 Routine Task Contents2468Manual Task Contents0246810Abstract Task Contents02468Managers/prof/tech/finance/public safetyProduction/craftTransport/construct/mech/mining/farmMachine operators/assemblersClerical/retail salesService occupationsRoutine Task Contents2468Manual Task Contents0246810Abstract Task Contents02468Abstract occupationsRoutine occupationsManual occupations more transparent a marker is, the further this marker is located to readers (i.e., the closer this marker is located to the space with a low value in routine task contents and a high value in manual task contents). Managers/prof/tech/finance/public safety occupations have a high abstract task intensity, while production/craft and machine operators/assemblers specialize in routine tasks. Manual- intensive occupations are mainly transport/construct/mech/mining/farm and service occupations. Undoubtedly, in Figure 2.5b, the red circles that represent abstract occupations locate in the upper surface of the 3D box with a high value in abstract task inputs but a low value in both routine and manual task inputs. Routine occupations, represented by blue triangles, have the highest concentration in routine tasks, while manual occupations labeled by black squares specialize in manual tasks. 2.3.2.4 Categorizing Occupations into Skill Groups The final step is to categorize occupations into one of the four skill groups as follows: 𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝 𝑗,𝑐,𝜏 = High-skilled AI-complement High-skilled not-yet-AI Middle-skilled Low-skilled , , , ,    if 𝑂𝑐𝑐𝑇 𝑦 𝑝𝑒 𝑗 = 𝐴𝑏𝑠𝑡𝑟𝑎𝑐𝑡 & 𝐴𝐼𝑜𝑐𝑐 𝑗,𝑐,𝜏 = 1 if 𝑂𝑐𝑐𝑇 𝑦 𝑝𝑒 𝑗 = 𝐴𝑏𝑠𝑡𝑟𝑎𝑐𝑡 & 𝐴𝐼𝑜𝑐𝑐 𝑗,𝑐,𝜏 = 0 if 𝑂𝑐𝑐𝑇 𝑦 𝑝𝑒 𝑗 = 𝑅𝑜𝑢𝑡𝑖𝑛𝑒 if 𝑂𝑐𝑐𝑇 𝑦 𝑝𝑒 𝑗 = 𝑀𝑎𝑛𝑢𝑎𝑙, (2.14) where 𝑗, 𝑐 ∈ {Narrow AI, Broad AI}, and 𝜏 denote an occupation, the narrow or broad AI definition, and a time period (2011-14, 2015-18, or 2019-22), respectively. Although the indicators for occupation type, 𝑂𝑐𝑐𝑇 𝑦 𝑝𝑒 𝑗 , are time-invariant, the skill group indicators, 𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝 𝑗,𝑐,𝜏, change across time periods because the indicator for AI occupations, 𝐴𝐼𝑜𝑐𝑐 𝑗,𝑐,𝜏, is time-variant. Note that an occupation is exclusively categorized into one skill group. Table 2.2 lists occupations with the highest and lowest number of narrow AI postings. Most occupations in Panel A with a high number of AI postings are from high-skilled AI-complement group, while most occupations without any AI posting in Panel B are middle-skilled (i.e., routine- intensive). Appendix Table 2B.1 shows a similar pattern by ranking occupations using the narrow AI posting share. Appendix Table 2C.1 provides a full list of all 4-digit occupations by skill group. 85 Table 2.2 Occupations Ranked by the Number of Narrow AI Postings, 2021 OCC2010 Occupation Title Skill Group #Narrow AI Postings Panel A. Occupations with the Top 15 #AI Postings Software Developers, Applications and Systems Software Network and Computer Systems Administrators Computer Scientists and Systems Analysts/Network Systems Analysts/Web Developers Management Analysts Other Business Operations and Management Specialists Managers in Marketing, Advertising, and Public Relations Registered Nurses Engineering Technicians, Except Drafters Industrial Engineers, Including Health and Safety Accountants and Auditors Electrical and Electronics Engineers Licensed Practical and Licensed Vocational Nurses Mathematicians and Statisticians Mechanical Engineers Financial Managers Panel B. Occupations with the Bottom 15 #AI Postings First-Line Supervisors of Correctional Officers Weighers, Measurers, Checkers, and Samplers, Recordkeeping Bailiffs, Correctional Officers, and Jailers Locksmiths and Safe Repairers Carpet, Floor, and Tile Installers and Finishers Elevator Installers and Repairers Insulation Workers First-Line Supervisors of Protective Service Workers, All Other Plasterers and Stucco Masons Fence Erectors Barbers Upholsterers Tour and Travel guides Rail-Track Laying and Maintenance Equipment Operators Reservation and Transportation Ticket Agents and Travel Clerks 1020 1100 1000 710 730 30 3130 1550 1430 800 1410 3500 1240 1460 120 3700 5630 3800 7540 6240 6700 6400 3730 6460 6710 4500 8450 4540 6740 5410 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑀 𝑀 𝐻 𝐴𝐼 𝐻 𝑁𝑜𝑛 𝐻 𝐴𝐼 𝑀 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝑁𝑜𝑛 𝐿 𝑀 𝐿 𝑀 𝑀 𝑀 𝑀 𝐻 𝑁𝑜𝑛 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 596,312 148,016 84,249 44,886 36,653 33,622 27,952 24,981 23,077 22,401 17,778 17,157 13,771 13,115 12,035 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Notes: The number of narrow AI postings in this table is calculated at the 4-digit-occupation-by-year level. There is a tie in the lowest number of narrow AI postings, with 64 occupations having no narrow AI posting. 15 out of 64 occupations are randomly chosen and listed in Panel B. 𝐻 𝐴𝐼 , 𝐻 𝑁 𝑜𝑛, 𝑀, and 𝐿 represent high-skilled AI-complement, high-skilled not-yet-AI, middle-skilled, and low-skilled occupation group, respectively. 2.3.3 Facts about Skill Groups Table 2.3 summarizes the 30 high-skilled AI-complement occupations, 110 high-skilled not- yet-AI ones, 257 middle-skilled ones, and 31 low-skilled ones in my sample.10 Note that the skill group indicators in Table 2.3 are static to better compare statistics over time.11 Panel B of Table 10For the consistency in occupation code, my main analysis use OCC2010 coding system, which is a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification, because my data on employment and wages adopts OCC2010 system. Since LinkUp and O∗NET use 2019 O∗NET-SOC code, I crosswalk 6-digit 2019 O∗NET-SOC to 4-digit OCC2010 as explained in Appendix 2C to construct a skill group indicator for each 4-digit OCC2010. 11Since only 7 out of 428 OCC2010 have their skill group indicator changed across time periods as shown in Appendix Table 2C.1, the statistics are robust to using the time-variant skill group indicators. 86 2.3 shows a large difference in AI and CS postings between the high-skilled AI-complement group and other skill groups. Occupations that are abstract and AI-intensive, on average, have more AI and CS postings than others. On average, 15.5% of job postings for high-skilled AI-complement occupations are narrow AI postings, 24.1% are broad AI postings, and 8.6% are CS postings. Note that the number (share) of CS postings, on average, is the difference between the number (share) of broad and narrow AI postings. This is because CS postings are defined as those whose job descriptions include phrases that belong to broad AI category but not narrow AI category. That is, narrow AI phrases and CS phrases are not only two subsets of broad AI phrases but also mutually exclusive. Panel C presents summary statistics on labor market outcomes for the four skill groups. On average, there are more people employed in high-skilled not-yet-AI occupations (0.34% or 335 per 100,000 capita), while high-skilled AI-complement occupations experience the highest mean hourly wage (44.3 in 2019 U.S. dollars) and the share of wage income (0.41%). By collapsing the occupation-by-state-by-year data to the skill-group-by-year level, Figure 2.6 displays plots of employment per 100,000 capita (Figure 2.6a), the employment share (Figure 2.6b), mean hourly wage (Figure 2.6c), the wage income share (Figure 2.6d), the narrow AI posting share (Figure 2.6e) and CS posting share (Figure 2.6f), where the skill groups are defined using the narrow AI definition. The plots generated using the broad AI definition are presented in Appendix Figure 2B.6. Among all skill groups, middle-skilled group experienced the highest employment, while both high-skilled AI-complement and low-skilled groups employed the smallest number of people between 2012 and 2021. These findings suggest an inverted U-shaped employment distribution by skill level. These trends were relatively constant over time in the U.S. The mean hourly wage for the high-skilled AI-complement group was the highest from 2012-21, more than double that of middle- or low-skilled groups. Thus, the high-skilled AI-complement (low-skilled) group can be considered as the highest (lowest) wage group. In addition, the highest wage income share was allocated to high-skilled not-yet-AI group, followed by middle-skilled group. This could be driven by the large employment in these two skill groups and the relative 87 Table 2.3 Summary Statistics, 2012-21 Skill Group: High-Skilled High-Skilled Middle-Skilled Low-Skilled AI-Complement Group Not-Yet-AI Group Group Group #4-Digit Occ. 30 110 257 31 Panel A. Skill Group Indicators Panel B. Job Postings #Narrow AI Postings #Broad AI Postings #CS Postings %Narrow AI Postings %Broad AI Postings %CS Postings Obs. 101.697 (748.411) 125.916 (812.833) 24.219 (97.897) 0.155 (0.223) 0.241 (0.275) 0.086 (0.188) 23,180 6.936 (28.554) 13.014 (50.407) 6.078 (34.073) 0.034 (0.101) 0.093 (0.192) 0.060 (0.163) 38,472 10.269 (64.013) 14.626 (69.350) 4.358 (19.606) 0.053 (0.144) 0.116 (0.231) 0.064 (0.177) 40,308 1.962 (15.364) 3.197 (20.965) 1.235 (8.812) 0.019 (0.092) 0.037 (0.129) 0.018 (0.091) 4,932 Emp. per 100,000 Capita Panel C. Labor Market Outcomes 335.016 (544.575) 232.128 (324.312) 234.737 (428.403) 289.895 (550.691) %Emp. Mean Hourly Wage %Wage Income Obs. 0.0023 (0.0032) 44.297 (25.188) 0.0041 (0.0056) 13,591 0.0034 (0.0054) 32.477 (31.697) 0.0045 (0.0082) 51,672 0.0024 (0.0043) 21.958 (18.350) 0.0017 (0.0034) 113,244 0.0029 (0.0055) 25.600 (31.603) 0.0022 (0.0046) 13,631 Notes: Standard deviations are shown in parentheses. Occupation is represented by OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. Each observation in Panel B is a 2-digit-occupation-by-state-by-year cell, while each observation in Panel C is at the 4-digit-occupation-by-state- by-year level. This is because the job posting data from LinkUp is collected at the 6-digit 2019 O∗NET-SOC level. Since (1) there is a one-to-one matching between 2-digit Census occupation group and 2-digit O∗NET-SOC and (2) there is neither a direct matching between 4-digit OCC2010 and 6-digit 2019 O∗NET-SOC nor a one-to-one matching between these two occupational classification, the job postings data is collapsed to the 2-digit O∗NET-SOC level first and then merged to IPUMS-ACS labor market outcome data. The skill group indicator in this table is static to make summary statistics comparable over time. The statistics remain consistent when switching to the time-variant skill group indicator, since only 7 (out of 428) 4-digit OCC2010 occupations have their skill group indicator changed across time periods as shown in Appendix Table 2C.1. Only statistics on %employment and %wage income in Panel C are in four decimal places to better compare the magnitudes of statistics across skill groups. 88 Figure 2.6 Plots of Skill-Group-By-Year Employment, Wages, and Postings, 2012-21 (a) Employment per 100,000 Capita (b) Share of Employment (c) Mean Hourly Wage (d) Share of Wage Income (e) Share of Narrow AI Postings (f) Share of CS Postings Notes: Narrow AI definition is used when defining skill groups and computing %AI postings. The skill group indicators in these figures are time-invariant to make statistics comparable across time. The statistics remain consistent when switching to the time-variant skill group indicators, since only 7 (out of 428) 4-digit OCC2010 occupations have their skill group indicators changed across time periods as shown in Appendix Table 2C.1. 89 1000020000300004000050000Employment per 100,000 Capita20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ.1.2.3.4.5Share of Employment20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ2030405060Mean Hourly Wage20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ0.1.2.3.4.5Share of Wage Income20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ0.01.02.03.04Share of Narrow AI Postings20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ0.01.02.03.04Share of CS Postings20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ high mean hourly wage for high-skilled not-yet-AI group. Although there was an upward trend in mean hourly wage for all four skill groups, only high-skilled AI-complement group experienced a growth in the wage income share (from 11% to 15%). Middle-skilled group, in contrast, had a decline in the wage income share (from 38% to 34%). These findings can be viewed as a sign of capital redistribution over time. Undoubtedly, as displayed in Figure 2.6e, high-skilled AI-complement group experienced the highest share of narrow AI postings during 2012-21. More importantly, this share increased dramatically over time. Notably, this share in 2021 was more than three times larger than that in 2012. Shares of narrow AI postings from other three skill groups were smaller than 1% but slightly increased over time, indicating an increasing demand for narrow AI skills in all occupations rather than for a specific skill group. These increasing trends in AI posting shares reflects the reinstatement effect of AI discussed in Proposition 2.3. Nonetheless, the share of CS postings remained consistently small for all skill groups. 2.4 Empirical Strategy To explore the relationship between changes in demand for AI skills and labor market outcomes of heterogeneous skill groups, I adopt the following specification: 𝑦𝑜4,𝑠,𝑡 =𝛼 + 𝛽0%𝐴𝐼 𝑝𝑜𝑠𝑡𝑠,𝑡 + ∑︁ 𝜏𝑘 1{𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝𝑜4 = 𝑘 } 𝑘∈{𝐻 𝐴𝐼 ,𝐻 𝑁 𝑜𝑛,𝑀 } ∑︁ + 𝑘∈{𝐻 𝐴𝐼 ,𝐻 𝑁 𝑜𝑛,𝑀 } 𝛽𝑘 %𝐴𝐼 𝑝𝑜𝑠𝑡𝑠,𝑡 × 1{𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝𝑜4 = 𝑘 } (2.15) + X𝑠,𝑡𝚽 + 𝛿𝑠 + 𝜃𝑡 + 𝜀𝑜4,𝑠,𝑡, where 𝑜4, 𝑠, and 𝑡 denote 4-digit OCC2010 occupation, state, and year, respectively.12 𝑦𝑜4,𝑠,𝑡 is one of the following labor market outcomes: (1) the employment per 100,000 capita; (2) the share of employment; (3) the log mean hourly wage; and (4) the share of wage income, all measured at 4-digit-occupation-by-state-by-year level. %𝐴𝐼 𝑝𝑜𝑠𝑡𝑠,𝑡 is the share of narrow AI postings in state 𝑠 and year 𝑡, which captures changes in demand for AI-developing skills and serves as the proxy for 12The 4-digit code is the most detailed occupation classification in the OCC2010 coding system. 90 growth in AI.13 This share is multiplied by 100; thus the unit of measurement is a percentage point (pp). 1{𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝𝑜4 = 𝑘 } refers to the binary indicator for skill group 𝑘 ∈ {𝐻 𝐴𝐼, 𝐻 𝑁𝑜𝑛, 𝑀 } that an occupation 𝑜4 belongs to, as defined by using narrow AI postings. The chosen excluded group is low-skilled group; thus only three groups are included in the set of skill group indicators 𝑘. By interacting AI posting shares with skill group dummies, equation (2.15) can capture the differential effects on skill groups. X𝑠,𝑡 contains state-year control variables that may affect individuals’ labor market outcomes: the unemployment rate; the sex ratio; the share of population who have a Bachelor’s degree or higher; and the share of population who are White, Black, Asian, or Hispanic. Standard errors, 𝜀𝑜4,𝑠,𝑡, are clustered at the 4-digit-occupation-by-state-by-year level. Equation (2.15) includes skill-group, state, and year fixed effects. The skill-group fixed effect, denoted by 1{𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝𝑜4 = 𝑘 }, accounts for unobserved differences in labor market performance across skill groups. 𝛿𝑠 denotes a state fixed effect which absorbs state-specific time-invariant differences in outcomes. 𝜃𝑡 is a year fixed effect which accounts for general time trends that are constant across states and broad occupation categories. The underlying identification assumption of my approach is that there are no changes in unobserved determinants of labor market outcomes at the skill-group-by-year level that are correlated with changes in AI postings. One threat to this assumption is the possibility of contemporaneous shocks that affect both the AI growth and skill groups’ labor market performances. I estimate specifications that interact the skill-group fixed effects with year fixed effects to account for any unobservable time trends in how a skill group responds or is exposed to AI. Since skill groups are constructed based on 4-digit occupation codes, the set of 2-digit occu- pation groups is not a subset of skill groups, and vice versa. That is, as presented in Appendix Tables 2C.2-2C.4, (1) a skill group consists of 4-digit occupations from different 2-digit occupation groups and (2) 4-digit occupations from the same 2-digit group can be classified to different skill groups. Thus, the identification could be threatened if there are labor market trends at the 2-digit- occupation level. To address this concern, I include a 2-digit-occupation fixed effect which controls 13Results on broad AI postings will be presented in robustness checks. 91 for differences in unobserved determinants of labor market performances across broad occupation categories.14 Taking the above factors into account, my main specification is as follows: 𝑦𝑜4,𝑠,𝑡 =𝛼 + 𝛽0%𝐴𝐼 𝑝𝑜𝑠𝑡𝑠,𝑡 + ∑︁ 𝜏𝑘 1{𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝𝑜4 = 𝑘 } 𝑘∈{𝐻 𝐴𝐼 ,𝐻 𝑁 𝑜𝑛,𝑀 } ∑︁ + 𝑘∈{𝐻 𝐴𝐼 ,𝐻 𝑁 𝑜𝑛,𝑀 } 𝛽𝑘 %𝐴𝐼 𝑝𝑜𝑠𝑡𝑠,𝑡 × 1{𝑆𝑘𝑖𝑙𝑙𝐺𝑟𝑜𝑢 𝑝𝑜4 = 𝑘 } (2.16) + X𝑠,𝑡𝚽 + 𝛿𝑠 + 𝜃𝑡 + 𝛾𝑜2 + 𝜇𝑘,𝑡 + 𝜀𝑜4,𝑠,𝑡, where 𝛾𝑜2 and 𝜇𝑘,𝑡 are 2-digit-occupation and skill-group-by-year fixed effects, respectively. The coefficients of interest are 𝛽0 and 𝛽𝑘 , which capture the relationship between changes in online job postings that require AI-developing skills and labor market outcomes of heterogeneous skill groups. Specifically, 𝛽0 is the change in labor market outcomes of the low-skilled group associated with a 1pp difference in the share of AI postings. 𝛽𝑘 is the gap in labor market outcomes between skill group 𝑘 (high-skilled AI-complement, high-skilled not-yet-AI, or middle-skilled) and the low-skilled group when the share of AI postings changes by 1pp. Thus, 𝛽0 + 𝛽𝑘 is the total change in the outcome variable of skill group 𝑘 associated with a 1pp difference in the share of AI postings at the state-year level. 2.5 Results 2.5.1 Main Results 2.5.1.1 AI and Employment Table 2.4 shows the relationship between narrow AI posting shares and employment. Specif- ically, columns 1-3 focus on employment per 100,000 capita while columns 4-6 look at the em- ployment share. Note that the skill groups are constructed using the narrow AI definition. Column 1 presents estimates from a simple Ordinary Least Squares (OLS) regression on the share of AI postings itself and skill group indicators. It estimates the overall effect of AI postings on all oc- cupations. The coefficient on the share of AI postings, -10.5, indicates a significant decline in the 14Since the 2-digit occupation group is not a subset of skill groups and vice versa, including both skill-group and 2-digit-occupation fixed effects does not lead to collinearity. 92 Table 2.4 Effects of Demand for AI Skills on Employment, 2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Employment per 100,000 Capita Share of Employment1 Dep. Var.: (1) -10.5∗∗∗ (1.5) (2) -10.8 (10.5) 57.9∗∗∗ (15.5) 23.0∗ (12.4) 11.1 (11.6) -187.9 (124.7) -5.2 (131.2) -84.5 (124.5) -77.7 (110.6) 40.4 (112.4) -61.5 (104.3) (3) -6.2 (8.3) 55.8∗∗∗ (14.9) 20.1∗∗ (10.1) 2.9 (9.0) -333.4∗ (187.6) -209.5 (184.7) -192.2 (181.4) (4) -0.010∗∗∗ (0.026) (5) (6) -0.011 (0.002) -0.006 (0.008) 0.058∗∗∗ (0.016) 0.023∗ (0.012) 0.011 (0.012) -0.188 (0.125) -0.005 (0.131) -0.084 (0.124) 0.056∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) -0.333∗ (0.188) -0.209 (0.185) -0.192 (0.181) -0.078 (0.111) 0.040 (0.112) -0.062 (0.104) ✓ 192,008 192,008 192,008 ✓ ✓ ✓ Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ 0.012 0.018 0.018 0.012 ✓ number of employed people associated with a 1pp increase in the AI posting share, regardless of which skill group this occupation belongs to. Column 2 interacts the AI posting share with skill group indicators and includes state, year, and skill-group fixed effects to explore effects of the demand for AI skills on heterogeneous skill groups. The coefficient on the interaction term between the AI posting share and the high-skilled AI-complement group dummy is 57.9, implying that, compared with the low-skilled group, a 1pp 93 increase in state-year AI posting shares leads to roughly 58 more people employed in abstract and AI-intensive occupations per 100,000 capita. This effect is larger than that for the high-skilled not-yet-AI group, indicating an employment gap within high-skilled occupations. Column 3 estimates equation (2.16). By further controlling for 2-digit-occupation and skill- group-by-year fixed effects, the comparison is now among 4-digit occupations in the same 2- digit occupation group and the same skill group across states and over time. Estimates are similar with column 2; now, compared with low-skilled occupations, employment in high-skilled AI-complement and high-skilled not-yet-AI occupations grows by 56 and 20, respectively, per 100,000 capita when AI posting shares increase by 1pp. The overall effects for high-skilled AI-complement and high-skilled not-yet-AI occupations are 50 and 14 more employed people. However, employment for neither middle- nor low-skilled occupations is significantly impacted by changes in the share of AI postings. Estimates in columns 4-6 show a similar relationship between employment shares and AI. These findings support Proposition 2.3 in Section 2.2.2, which implies that the reinstatement effect of AI brings a significant employment growth for high-skilled AI-complement occupations. I also plot estimated coefficients from my main specification for all four skill groups in Figure 2.7a. The red line represents estimates from the regression of employment per capita, while the blue line presents estimates from the regression of the employment share. The right tail of both curves is noticeably higher than the left tail. These results document large employment gaps between occupations that are high in abstract and AI-intensive tasks and other skill groups. It is worth noting that there is also an employment gap within abstract-intensive occupations, depending on whether tasks of an occupation require AI-developing skills. These patterns are consistent with findings of Alekseeva et al. (2021) who document a dramatic increase in hiring people with AI skills. Similarly, Felten et al. (2019) show an employment growth in high wage occupations associated with AI. This is consistent with my finding that high-skilled AI-complement occupations experience an employment growth as the share of AI postings increases. In my paper, high-skilled AI-complement occupations can be considered as high wage occupations because they have the 94 Figure 2.7 Overall Effects of Demand for AI Skills on Labor Market Outcomes, 2012-21 (a) Employment (b) Wages Notes: The coefficient estimates plotted in each subfigure show overall effects of changes in share of narrow AI postings on labor market outcomes. They are obtained by respectively regressing employment per 100,000 capita, share of employment (in percentage point), log mean hourly wages, and share of wage income (in percentage point) on the interaction term between share of narrow AI postings and skill group dummies, using the main specification with a full set of fixed effects (i.e., state, year, skill-group, 2-digit-occupation, and skill-group-by-year fixed effects) included. I also plot the corresponding 95% confidence intervals in each subfigure. highest mean hourly wage as shown in Figure 2.6c. In addition, Felten et al. (2019) do not find a significant relationship between AI and employment growth for low-wage occupations, which is also consistent with my results. 2.5.1.2 AI and Wages Table 2.5 shows relationships between AI and wages for heterogeneous skill groups, with columns 1-3 and columns 4-6 presenting results from regressions of log mean hourly wage and the wage income share. The OLS estimates in column 1 indicate that as AI posting shares increase, the mean hourly wage for all types of occupations significantly increases by 2.7%. After controlling for a full set of fixed effects in column 3, high-skilled AI-complement occupations experience a 2.5% wage growth associated with a 1pp increase in AI posting shares, relative to low-skilled occupations. The overall effect for high-skilled AI-complement occupations is a 3% wage growth. Estimates for other skill groups are much smaller in magnitude and even negative for middle-skilled occupations, but none of them are statistically significant. Coefficients on skill group indicators show an interesting finding: 95 −.020.02.04.06.08Share of Employment−20020406080Employment per 100,000 CapitaLow−Skilled OccMiddle−SkilledOccHigh−SkilledNot−Yet−AI OccHigh−SkilledAI−ComplementOccEmployment per 100,000 CapitaShare of Employment−.030.03.06.09.12Share of Wage Income−.010.01.02.03.04Log Mean Hourly WageLow−Skilled OccMiddle−SkilledOccHigh−SkilledNot−Yet−AI OccHigh−SkilledAI−ComplementOccLog Mean Hourly WageShare of Wage Income Table 2.5 Effects of Demand for AI Skills on Wages, 2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Dep. Var.: Log Mean Hourly Wage Share of Wage Income1 (1) 0.027∗∗∗ (0.001) (2) (3) (4) (5) (6) 0.006 (0.005) 0.005 (0.005) -0.028 (0.020) -0.011 (0.012) -0.011 (0.011) 0.012∗∗ (0.006) 0.004 (0.005) -0.007 (0.005) 0.651∗∗∗ (0.080) 0.284∗∗∗ (0.085) -0.062 (0.078) 0.025∗∗∗ (0.007) 0.007 (0.006) -0.009 (0.005) 0.441∗∗∗ (0.104) 0.126 (0.096) -0.088 (0.092) 0.673∗∗∗ (0.080) 0.289∗∗∗ (0.084) -0.076 (0.078) 0.080∗∗∗ (0.024) 0.020 (0.015) 0.010 (0.013) 0.089∗∗∗ (0.026) 0.026∗ (0.014) 0.005 (0.012) 0.152 (0.111) 0.223∗∗ (0.110) -0.056 (0.083) 0.001 (0.114) 0.182 (0.126) -0.076 (0.105) -0.151 (0.166) -0.029 (0.159) -0.169 (0.141) ✓ 187,960 192,008 187,960 ✓ ✓ ✓ 192,008 ✓ ✓ ✓ Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 0.195 0.205 0.058 0.053 ✓ mean hourly wage for abstract and AI-intensive group is 44.1% higher than the baseline group, the low-skilled group, when the state-year AI posting share is 0. This wage gap widens when AI becomes more ubiquitous. While Proposition 2.4 discusses that the reinstatement (displacement) effect of AI widens (narrows) wage gaps, my empirical findings on wages further argue that the reinstatement effect of AI on high-skilled labor dominates the displacement effect as AI favors high-skilled workers with AI skills. Moreover, the finding on the wage gap between the high- 96 skilled AI-complement group and other skill groups supports Proposition 2.5, which indicates a relative wage gain for workers specializing in abstract and AI-intensive tasks as AI increases their productivity. In addition to mean hourly wage, I explore how AI affects the wage income share which can be viewed as a proxy for the total capital distributed to a skill group. Estimates in column 6 of Table 2.5 show that a 1pp increase in AI posting shares is associated with an overall growth of 0.078pp (0.015pp) in wage income share for high-skilled AI-complement (high-skilled not-yet-AI) occupations. The overall effects for middle- and low-skilled occupations are negative (-0.006pp and -0.011pp) but are not statistically significant. As AI develops, high-skilled AI-complement workers become more productive since they are supposed to use AI-developing skills to complement their work. Then more income will be distributed to this skill group, which is reflected by the relatively larger increase in its wage income share. The finding that the overall effects on the wage income share across all skill groups sum to more than 0 sheds light on Proposition 2.6, suggesting that the effect of an increase in productivity of labor specializing in abstract and AI-intensive tasks outweighs the effect of an increase in capital’s productivity.15 Similar with employment, I document wage gaps between high-skilled AI-intensive occupations and other skill groups. Figure 2.7b shows a "J-shaped" curve of changes in mean hourly wage associated with AI by skill group: (1) both the left and right tails are higher than the middle; and (2) the right tail is extremely higher than the left tail. These findings imply that as AI grows, wages for labor specializing in abstract and AI-intensive tasks increase dramatically compared to labor specializing in other types of tasks. Conversely, middle-skilled occupations experience the largest wage decline among all four skill groups. These findings on wages are consistent with Felten et al. (2019) who conclude that wages for high wage occupations are increased by AI and Alekseeva et al. (2021) who document wage premia for AI skills. In contrast to Webb (2019) who argues that AI is predicted to narrow the wage gap between the 90th and 10th percentile of the wage distribution, 15Suppose the total income that can be allocated to each factor in the production is fixed. Then changes in the share of wage income for labor and capital should sum up to 0. Since my empirical results indicate that the overall effects across all four skill groups are positive, then there should be a negative correlation between the demand for AI skills and the income allocated to capital. 97 I find that the wage gap between high-skilled AI-complement and low-skilled occupations widens as demand for AI skills increases. 2.5.2 Robustness The main results in Section 2.5.1 show employment and wage gaps between the abstract and AI-intensive occupations and other skill groups. The underlying assumption of these results is that, given the controls of my specification, labor outcomes are unrelated to unobserved heterogeneity at the skill-group-by-state-by-year level that are correlated with AI or other technological changes (e.g., more general computer science). This section presents several robustness checks to test this assumption. Since main results on employment per capita and the employment share are comparable, this section focuses on employment per capita and wages, while robustness checks for the employment share are presented in Appendix 2B. First, I test the potential endogeneity issue by adopting a shift-share instrumental variable (SSIV) (Goldsmith-Pinkham et al., 2020). The share of AI postings could be endogenous to the supply of AI skills in the local labor market and the extent to which local firms are developing or adopting AI technologies. The former one is likely to be positively correlated with the AI posting share. If a local labor market has a large supply of workers with AI-developing skills, employers may specify more AI skills when posting job vacancies. The correlation between the latter one and the AI posting share is likely to be unclear. On the one hand, if more firms start to develop AI models or AI-powered tools, the demand for AI skills will increase. On the other hand, it is possible that, as AI grows, AI-substituting technologies have more capabilities in performing tasks that were previously completed by high-skilled labor. The more AI-substituting technologies firms adopt, the less AI hiring is. Due to the lack of firm-level data on what kinds of AI technologies firms develop or adopt which could be used as a possible instrument, I construct a "leave-one-out" SSIV by interacting local employment shares and industry-specific AI posting shares to instrument for the AI posting share. The "leave-one-out" estimator is adopted to address the finite sample bias issue (Angrist et al., 1999; Goldsmith-Pinkham et al., 2020). This "leave-one-out" SSIV for state 98 𝑠 and year 𝑡 is: 𝐴𝐼 𝑝𝑜𝑠𝑡 𝑠ℎ𝑎𝑟𝑒 𝐼𝑉𝑠,𝑡 = 𝐸𝑜2,𝑠,2011 ∑︁ 𝑜2 (cid:205)𝑠′≠𝑠 (cid:205)𝑜4 #𝐴𝐼 𝑝𝑜𝑠𝑡𝑜4,𝑜2,𝑠′,𝑡 (cid:205)𝑠′≠𝑠 (cid:205)𝑜4 #𝑝𝑜𝑠𝑡𝑜4,𝑜2,𝑠′,𝑡 𝑒𝑚 𝑝𝑜2,𝑠,2011 (cid:205)𝑜2′ 𝑒𝑚 𝑝𝑜2′ ,𝑠,2011 , (2.17) where 𝑜2 is the 2-digit 2010 Census OCC code. 𝐸𝑜2,𝑠,2011 = represents the start-of- period share of employment in broad occupation category 𝑜2 in state 𝑠. Columns 1 and 2 of Table 2.6 present the results on employment per capita from my main specification and the "leave-one-out" SSIV, respectively. Although the SSIV estimates in column 2 become much larger in magnitude but less precise compared with OLS estimates in column 1, the relative comparison between skill groups still holds. The effect of the AI posting share for abstract and AI-intensive occupations (112) is almost three times larger than that for abstract, not-yet-AI occupations (47). Another difference between OLS and SSIV estimates is that SSIV estimates show a significant employment decline for low-skilled occupations (-92). These estimates show widened employment gaps between skill groups compared with OLS estimates, especially the gap between abstract, AI-intensive occupations and other skill groups. I also re-conduct SSIV analyses by changing the 2-digit occupation group, 𝑜2, in equation (2.17) to 4-digit occupation, 4- digit North American Industry Classification System (NAICS) code, or an alternative occupational classification constructed by clustering occupations based on skill similarity using a machine learning algorithm.16 Estimates are presented in Appendix Tables 2B.2 and 2B.3, which reassure a consistent pattern in employment gaps between skill groups. Columns 3 and 4 of Table 2.6 focus on log mean hourly wage, while columns 5 and 6 turn to the wage income share. Similar with the comparison between columns 1 and 2, SSIV estimates on the interaction term between the AI posting share and skill group dummies are about double of OLS estimates when focusing on wages. Different from OLS estimates in column 3, SSIV estimates in column 4 indicate a significant mean hourly wage gain for high-skilled not-yet-AI occupations (0.029), although this wage gain is smaller than that for high-skilled AI-complement occupations (0.050). In addition, low-skilled occupations experience a significant decline in the wage income share (-0.101) after adopting a SSIV approach shown in column 6. Estimates from 16Details on how I propose this alternative occupational classification will be explained in Section 2.6.2. 99 Table 2.6 Effects of Demand for AI Skills—Adopting SSIV, 2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Emp. per 100,000 Capita Log Mean Hourly Wage Share of Wage Income1 Dep. Var.: Main Spec. (1) -6.1 (8.3) 55.8∗∗∗ (14.9) 20.1∗∗ (10.1) 2.9 (9.0) -333.4∗ (187.6) -209.4 (184.7) -192.2 (181.4) SSIV (2) -92.3∗∗∗ (19.8) 112.1∗∗∗ (27.1) 47.0∗∗ (19.9) 10.5 (18.0) -361.1∗ (190.3) -220.8 (188.0) -194.6 (184.8) Main Spec. (3) 0.005 (0.005) 0.025∗∗∗ (0.007) 0.007 (0.006) -0.009 (0.005) 0.441∗∗∗ (0.104) 0.126 (0.096) -0.088 (0.092) SSIV (4) 0.010 (0.014) 0.050∗∗∗ (0.010) 0.029∗∗∗ (0.010) 0.005 (0.009) 0.431∗∗∗ (0.104) 0.117 (0.096) -0.094 (0.092) Main Spec. (5) -0.011 (0.011) 0.089∗∗∗ (0.026) 0.026∗ (0.014) 0.005 (0.012) -0.151 (0.166) -0.029 (0.159) -0.169 (0.141) SSIV (6) -0.101∗∗∗ (0.025) 0.158∗∗∗ (0.040) 0.056∗∗ (0.027) 0.014 (0.022) -0.184 (0.168) -0.042 (0.163) -0.172 (0.145) 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 Observations 192,008 ✓ State FE ✓ Year FE ✓ Skill-Group FE ✓ 2-Digit-Occ FE ✓ Skill-Group FE × Year FE R2 0.122 Cragg-Donald Wald F Statistic 2,594.341 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 192,008 ✓ ✓ ✓ ✓ ✓ 0.152 2,594.341 187,960 ✓ ✓ ✓ ✓ ✓ 0.339 2,454.979 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 using SSIVs summing over different occupation groups are presented in Appendix Tables 2B.4 and 2B.5. Regardless of which SSIV being used, the significant wage gaps between high-skilled AI-complement occupations and other skill groups always exist. The second concern is that the main results could be driven by more general CS skills, rather than the AI-developing skills captured in narrow AI postings. To address this concern, I additionally control for the share of CS postings using the same specification as in my baseline model. Table 2.7 shows estimates from regressions of employment per capita and wages. Columns 1, 3, and 5 show 100 Table 2.7 Effects of Demand for AI Skills—Controlling for CS Skills, 2012-21 Emp. per 100,000 Capita Log Mean Hourly Wage Share of Wage Income1 Main Spec. Controlling Main Spec. Controlling Main Spec. Controlling Dep. Var.: %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ %CS Postings3 %CS Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ (1) -6.1 (8.3) 55.8∗∗∗ (14.9) 20.1∗∗ (10.1) 2.9 (9.0) -333.4∗ (187.6) -209.5 (184.7) -192.2 (181.4) for CS (2) -3.3 (7.3) 50.1∗∗∗ (14.0) 17.5∗ (9.1) 1.2 (7.8) -20.6 (16.4) 34.6∗ (19.2) 16.0 (17.9) 10.3 (17.7) -339.6∗ (189.5) -212.1 (186.7) -193.8 (183.4) (3) 0.005 (0.005) 0.025∗∗∗ (0.007) 0.007 (0.006) -0.009 (0.005) 0.441∗∗∗ (0.104) 0.126 (0.096) -0.088 (0.092) for CS (4) 0.004 (0.006) 0.034∗∗∗ (0.008) 0.013∗ (0.007) -0.008 (0.006) 0.006 (0.017) -0.057∗∗ (0.023) -0.033∗ (0.019) -0.004 (0.018) 0.452∗∗∗ (0.104) 0.132 (0.097) -0.087 (0.093) (5) -0.011 (0.011) 0.089∗∗∗ (0.026) 0.026∗ (0.014) 0.005 (0.012) -0.151 (0.166) -0.029 (0.159) -0.169 (0.141) for CS (6) -0.012 (0.010) 0.086∗∗∗ (0.025) 0.030∗∗ (0.014) 0.006 (0.011) 0.000 (0.016) 0.013 (0.027) -0.021 (0.019) -0.006 (0.017) -0.153 (0.167) -0.026 (0.161) -0.168 (0.143) 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 3 Phrases that belong to broad AI category but not narrow AI category are used to compute %CS postings at the state-year level. %CS postings is in percentage point. 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 101 estimates from my main specification, while columns 2, 4, and 6 show estimates obtained from additionally controlling for the CS posting share. Coefficients on the interaction between the share of AI postings and skill group dummies from this robustness check represent effects of the demand for AI skills residualized on the demand for CS skills. They are very similar to estimates from the main specification, implying that my main results are not likely to be driven by the demand for CS skills.17 Appendix Tables 2B.10 and 2B.11 reassure that my main results are not driven by CS skills through controlling for exposure to software and robots using the measures constructed by Webb (2019). These measures capture the capabilities in software and robots for performing an occu- pation’s tasks. After controlling for software and robot exposure, estimates are almost the same with those from my main specification. Appendix Tables 2B.12 and 2B.13 further show adding CS skills to the set of AI phrases does not increase predictability of how AI impacts the labor market. That is, replacing narrow AI phrases with broad AI phrases leads to noisy estimates. In addition, coefficients on broad AI posting shares become smaller compared with coefficients on narrow AI posting shares, indicating that broad AI definition does not capture the true demand for AI-developing skills well. I also conduct robustness checks on the choice of threshold for defining AI occupations. Instead of using the binary AI occupation indicator defined by equation (2.12) to categorize high-skilled occupations into AI-complement and not-yet-AI ones, I decompose high-skilled occupations into five groups using narrow AI posting share quintiles. Thus, there are seven skill groups in total: five groups within high-skilled occupations, middle- and low-skilled groups. Table 2.8 presents estimates from interacting the AI posting share with the new skill group indicators, with the low- skilled group being the baseline group as in my main analysis. These estimates are also plotted in 17I also construct SSIVs for the CS posting share using equation (2.17) by replacing AI postings with CS postings. Appendix Tables 2B.6-2B.9 present estimates from instrumenting both AI posting shares and CS posting shares. Each table focuses on one of the four labor market outcomes. Estimates from adopting any kind of SSIV except the SSIV summing across 2-digit occupation group in column 2 of Appendix Tables 2B.6-2B.9 reassuare that my main results are not driven by CS skills. Regardless of the magnitude and significance level of coefficients on CS posting shares, coefficients on AI posting shares are similar with my main results. Estimates in column 2 are boosted up, especially for regressions of employment, because regressions used in column 2 fit the data poorly implied by the negative R-squared and extremely small F statistic. 102 Figure 2.8 Overall Effects of Demand for AI Skills by AI Posting Share Quintile, 2012-21 (a) Employment (b) Wages Notes: The coefficient estimates plotted in each subfigure show overall effects of changes in share of narrow AI postings on labor market outcomes for each skill group. Instead of the four skill groups in my main specification, the high-skilled occupations are decomposed into five groups using narrow AI posting share quintiles. Thus, there are seven skill groups in total. Estimates are obtained by respectively regressing employment per 100,000 capita, share of employment (in percentage point), log mean hourly wages, and share of wage income (in percentage point) on the interaction term between share of narrow AI postings and skill group dummies, using the main specification with a full set of fixed effects (i.e., state, year, skill-group, 2-digit-occupation, and skill-group-by-year fixed effects) included. I also plot the corresponding 95% confidence intervals in each subfigure. Figure 2.8. There is a monotonic trend in effects of AI postings on employment and wages for high- skilled occupations that fall into the top four AI posting share quintiles. High-skilled occupations in the top quintile always have the highest gain in both employment and wages associated with an increase in the demand for AI skills. Low-skilled occupations (the first row of Table 2.8) have the largest decline in employment and the wage income share (i.e., the estimate is the smallest in magnitude and negative).18 Another test to check the threshold for AI occupations is to measure the variation of AI- developing skills being listed in job postings across occupations (denoted as "AI Skill Prevalence Score" hereafter). To construct this measure, I perform a principal component analysis (PCA) on the matrix of frequencies of a narrow AI phrase being listed in job postings across all occupations and years.19 The AI Skill Prevalence Score indicates the intensity that AI-developing skills are 18These results are robust to using the SSIV approach, with estimates presented in Appendix Tables 2B.14-2B.17. 19Each element in this matrix represents how many times a narrow AI phrase listed in Table 2.1 shows up in all postings of an occupation in a specific year. This matrix uses this frequency for all occupations between 2012 and 2021. Then a static component loading is calculated for each narrow AI skill using PCA, which captures the importance 103 −.04−.020.02.04.06Share of Employment−40−200204060Employment per 100,000 CapitaLow−SkilledOccMiddle−Skilled OccHigh−SkilledOcc x AI Occ(q1)High−SkilledOcc x AI Occ(q2)High−SkilledOcc x AI Occ(q3)High−SkilledOcc x AI Occ(q4)High−SkilledOcc x AI Occ(q5)Employment per 100,000 CapitaShare of Employment−.030.03.06.09.12Share of Wage Income−.04−.020.02.04Log Mean Hourly WageLow−SkilledOccMiddle−Skilled OccHigh−SkilledOcc x AI Occ(q1)High−SkilledOcc x AI Occ(q2)High−SkilledOcc x AI Occ(q3)High−SkilledOcc x AI Occ(q4)High−SkilledOcc x AI Occ(q5)Log Mean Hourly WageShare of Wage Income Table 2.8 Effects of Demand for AI Skills—Using AI Posting Share Quintiles, 2012-21 Emp. per 100,000 Capita Log Mean Hourly Wage Share of Wage Income1 Dep. Var.: %AI Postings2 %AI Postings × High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ Skill Group = High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ (1) -6.2 (8.3) 48.9∗∗∗ (13.0) 38.3∗∗∗ (13.3) 10.1 (10.3) -6.3 (13.5) 11.1 (10.1) 2.9 (9.0) -349.6∗ (187.0) -175.1 (191.1) 70.5 (256.2) 13.3 (212.0) -383.2∗∗ (182.7) -186.6 (181.9) (2) 0.005 (0.005) 0.025∗∗∗ (0.006) 0.013∗∗ (0.006) 0.004 (0.007) 0.006 (0.008) -0.012 (0.015) -0.009 (0.005) 0.334∗∗∗ (0.105) 0.189∗ (0.103) 0.149 (0.121) 0.061 (0.117) 0.045 (0.133) -0.081 (0.093) (3) -0.011 (0.011) 0.076∗∗∗ (0.022) 0.055∗∗ (0.023) 0.008 (0.013) -0.008 (0.017) 0.015 (0.012) 0.005 (0.012) -0.172 (0.164) 0.050 (0.173) 0.364 (0.301) 0.062 (0.213) -0.319∗∗ (0.153) -0.159 (0.141) 192,008 ✓ ✓ ✓ ✓ ✓ 0.143 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 192,008 ✓ ✓ ✓ ✓ ✓ 0.175 187,960 ✓ ✓ ✓ ✓ ✓ 0.339 104 required for performing tasks of an occupation. The higher this score is, the more AI-intensive activities an occupation involves. Appendix Table 2B.18 presents the component loadings of all narrow AI skills used. Of all skills, "python," "machine learning," and "big data" play the most important roles in the AI Skill Prevalence Score. Figure 2.9 plots the average AI Skill Prevalence Score over time.20 The AI Skill Prevalence Score, on average, increased over time, with a big jump between 2012-15 (Figure 2.9a). This big jump is driven by high-skilled AI-complement occupations, which have a much higher AI Skill Prevalence Score on average compared with the other three skill groups (Figure 2.9b). To make trends in this measure by skill group comparable, Figure 2.9c plots the average AI Skill Prevalence Score relative to the baseline year 2012. There was an increasing trend for high-skilled AI-intensive occupations, while this measure dropped for the other skill groups between 2012-14 and gradually went back to their baseline level around 2020. Appendix Table 2B.19 lists occupations with the top and bottom AI Skill Prevalence Score in 2021. "Software Developers, Applications and Systems Software" has the highest score, followed by "Management Analysts" and "Other Business Operations and Management Specialists." All occupations in Panel A with a high score are from the high-skilled AI-complement group. In contrast, most of occupations with a low score are routine-intensive (i.e., from the middle-skilled group). Table 2.9 tests the relationship between AI Skill Prevalence Score and labor market outcomes. Panel A presents estimates from a regression on the 4-digit-occupation-by-year AI Skill Prevalence Score, which is standardized within a year. The source of variation comes from within occupations. A one standard deviation increase in an occupation’s AI Skill Prevalence Score correlates with 34 more employed people per 100,000 capita, a 0.034pp increase in the share of employment, a 0.8% increase in mean hourly wage, and a 0.070pp increase in the wage income share, all of which are statistically significant. However, to make the estimates comparable to my main analysis which captures the between- or weight of a narrow AI phrase in constructing the AI Skill Prevalence Score. Python allows users to choose the number of components to keep. Thus, the multi-dimensional matrix is projected to a one-dimensional space by PCA to construct this single measurement. 20The AI Skill Prevalence Score is standardized within a year. 105 Figure 2.9 Trends in Average AI Skill Prevalence Score, 2012-21 (a) Average AI Skill Prevalence Score across All Occupations (b) Average AI Skill Prevalence Score by Skill Group (c) Average AI Skill Prevalence Score Relative to Baseline Year 2012 Notes: The occupation-year AI Skill Prevalence Score is standardized within a year. In Subfigure 2.9c, the AI Skill Prevalence Score for each skill group in year 2012 is used as the baseline. Each line represents the following ratio, AI Skill Prevalence Score𝑘,𝑡 AI Skill Prevalence Score𝑘,2012 , where 𝑘 represents a skill group and 𝑡 is year. 106 .14.16.18.2Average AI Skill Prevalence Score20122014201620182020Year−.1.1.3.5.7.91.1Average AI Skill Prevalence Score20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ.8.911.11.21.3Relative Average AI Skill Prevalence Score20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ 0.008∗∗ (0.003) 183,018 0.345 0.003 (0.006) 0.024∗∗∗ (0.007) 0.002 (0.007) -0.013∗∗ (0.006) 187,960 0.340 ✓ ✓ ✓ ✓ ✓ (4) 0.070∗∗∗ (0.010) 186,799 0.168 0.012 (0.009) 0.061∗∗∗ (0.023) 0.012 (0.011) 0.002 (0.010) 192,008 0.156 ✓ ✓ ✓ ✓ ✓ Table 2.9 Effects of AI Skill Prevalence on Labor Market Outcomes, 2012-21 Dep. Var.: Emp. per 100,000 Capita %Emp Share1 Log Mean Hourly Wage %Wage Income2 (1) (2) (3) Panel A. Using Occupation-Year AI Skill Prevalence Score AI Skill Prevalence Score3 Observations R2 33.8∗∗∗ (7.1) 186,799 0.132 0.034∗∗∗ (0.007) 186,799 0.132 Panel B. Using State-Year AI Skill Prevalence Score AI Skill Prevalence Score4 AI Skill Prevalence × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Observations R2 16.0∗∗ (6.5) 30.4∗∗∗ (11.0) 6.6 (7.6) 0.2 (7.2) 0.016∗∗ (0.007) 0.030∗∗∗ (0.011) 0.007 (0.008) 0.000 (0.007) 192,008 0.128 ✓ ✓ ✓ ✓ ✓ 192,008 0.128 ✓ ✓ ✓ ✓ ✓ State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in Panel B is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1,2 The unit of the employment share and the share of wage income is a percentage point. 3 The AI Skill Prevalence Score in Panel A is constructed at the 4-digit-occupation-by-year level and standardized within a year. 4 The AI Skill Prevalence Score in Panel B is constructed at the state-year level and standardized within a year. group variation using the state-year AI posting shares, I construct a state-year AI Skill Prevalence Score by performing a PCA on the matrix of AI skill frequencies in job postings across all states and years. This alternative measure captures the prevalence of AI skills being listed in job descriptions (i.e., the intensity of AI-developing activities) at the state-year level. Appendix Table 2B.20 lists states with the top and bottom AI Skill Prevalence Score in 2021. California had the highest score, followed by Texas, New York, Virginia, Massachusetts, and Illinois. Appendix Figure 2B.7a plots this measure by BLS regions. Both New York/New Jersey and the Southwest experienced an increase over time, while other regions had a decline. Although California had the highest 107 score, the Western U.S. had a downward trend due to states with pretty low scores classified into this region (e.g., Alaska, Hawaii, and Nevada). Appendix Figure 2B.7b is the same as Appendix Figure 2B.7a but fixing the range of the y-axis to make these curves more visually comparable. The Western U.S. had a consistently high score over time and New York/New Jersey experienced a consistent growth in this measure. The AI Skill Prevalence Score for both regions was much higher than that for other regions. Panel B of Table 2.9 uses the same specification as my main results but replacing the AI posting share with the state-year AI Skill Prevalence Score. The estimates capture the between- group variation—the difference in the prevalence of AI skills between skill groups within a state. Compared with low-skilled occupations, a one standard deviation increase in this measure is associated with 30 more employed people, 0.030pp increase in the share of employment, a 2.4% mean hourly wage gain, and a 0.061pp increase in the wage income share for high-skilled AI- complement occupations. There is also a 1.3% decline in mean hourly wage for middle-skilled occupations, compared with the baseline group, the low-skilled group. These estimates indicate the existence of employment and wage gaps between abstract and AI-intensive occupations and other skill groups, consistent with my main results in Section 2.5.1. Estimates in Panel B of Table 2.9 also sheds light on Proposition 2.4 in Section 2.2.2 which discusses the relationship between the reinstatement and displacement effect of AI. These estimates indicate a wider wage gap between high- and middle-skilled occupations but a narrower wage gap between low- and middle-skilled occupations, suggesting that the displacement effect of AI on relative wages for middle-skilled labor dominates the reinstatement effect. This can be explained by the following reasons. First, AI has become more productive since AI technologies have been dramatically improved during the late 2010s (LeCun et al., 2015; Russell and Norvig, 2021; Zhang et al., 2022). Thus, AI may take over some tasks that were previously performed by middle-skilled workers. Second, improvements in AI may indirectly improve industrial automation, resulting in more automated tasks and a decline in the share of tasks performed by middle-skilled workers. Although this paper does not empirically explore the relationship between wages for middle-skilled 108 workers and industrial automation, existing literature has demonstrated a negative relationship between labor market outcomes of people exposed to routine tasks and automation (Acemoglu et al., 2020; Acemoglu and Restrepo, 2022a,b; Moll et al., 2022; Autor et al., 2024). Third, the current AI technologies are not able to substitute for tasks heavily relied on social skills, which are low-skilled occupations defined in Section 2.3.2. Although I do not find a significant relationship between mean hourly wages for low-skilled occupations and changes in demand for AI skills, Deming (2017) documents a strong and positive relationship between wages and social-skill-intensive occupations. Finally, I show that my main results are not driven by one specific state or COVID. Appendix Table 2B.21 shows percentiles of the distribution of estimated effects using my main specification with one state left out at a time for all states in my sample. The estimates are consistent with my main specification, implying that my main results are not driven by one specific state (e.g., a state with an extremely high or low AI posting share). My main estimates are also similar with estimates from dropping COVID years presented in Appendix Tables 2B.22 and 2B.23, indicating that my results are not driven by COVID or work-from-home requirements during COVID. 2.5.3 Heterogeneity Since the main results presented in Section 2.5.1 remain static over the whole sampling period, I examine heterogeneity over time in this section. Appendix Figure 2B.8 plots how estimates for each skill group change over time when interacting the AI posting share in the main specification with year dummies. Effects on employment and the wage income share remain pretty constant over time, while effects on mean hourly wage show an increasing trend, especially for high-skilled AI- complement occupations before COVID. A possible explanation is that AI has been dramatically improved and received increasing attention from the public since the late 2010s (LeCun et al., 2015; Zhang et al., 2022), but there was a stagnation in economic growth during COVID years. It is also worth noting that there were large employment and wage gaps between high-skilled AI- complement occupations and other skill groups over the whole sampling period. Specifically, the wage gap widened prior to COVID and slightly narrowed during COVID. Appendix Figure 2B.9 further shows estimates by interacting state-year AI Skill Prevalence 109 Score with year dummies. Different from Appendix Figure 2B.8, there is now an upward trend for high-skilled AI-complement occupations in terms of all four outcomes. The employment gap between abstract and AI-intensive group and other skill groups was the largest from 2019-20 while the gap in mean hourly wage was the largest between 2018-19. But on the whole, the time-varying effects of AI Skill Prevalence Score are consistent with those of the AI posting share. Abstract occupations that are AI-intensive experienced the largest growth in both employment and wages over time. 2.6 Discussion: AI as a General-Purpose Technology This section discusses that AI is possibly one of the general-purpose technologies (GPT) which have profound impacts on the whole economy. Section 2.6.1 presents results from re-estimating equation (2.16) but using the share of AI postings at more granular level and argues that AI tends to affect the whole economy rather than specific occupation categories. Section 2.6.2 introduces an alternative occupation classification system based on the similarity in skill requirements of an occupation using machine learning. I then discuss which occupation clusters have a surge in AI hiring and the differential effects of the demand for AI skills on these occupation clusters. 2.6.1 AI Postings at More Granular Level and Labor Marker Outcomes In my main specification, equation (2.16), the share of AI postings used as the proxy for AI growth is computed at the state-year level. The underlying assumption is that people respond to all kinds of contemporaneous job postings intended to hire workers specializing in AI-developing activities posted in the state where they live. This assumption could be threatened if AI only affects some occupations instead of the whole economy, i.e., only people from certain occupations are responding to AI postings from those specific occupations. Therefore, I re-estimate equation (2.16) but use the share of narrow AI postings at the 2-digit-occupation-by-state-by-year level. Now 𝛽0 and 𝛽𝑘 in equation (2.16) capture how changes in the AI posting share from a specific 2-digit occupation category affect labor market outcomes. Appendix Table 2B.24 focuses on employment. Different from my main results, Table 2.4 in Section 2.5.1, the share of AI postings used in Appendix Table 2B.24 is computed at more granular 110 level—the 2-digit-occupation-by-state-by-year level. The source of variation is now from within 2-digit occupation groups, rather than between groups. After controlling for a full set of fixed effects following my main specification, equation (2.16), none of the coefficients on AI postings are statistically significant. This finding also holds in terms of wages, with estimates presented in Appendix Table 2B.25. Similarly, when interacting the occupation-year AI Skill Prevalence Score with skill group dummies as presented in Appendix Table 2B.26, estimates become much noisier compared with using this measure at more aggregated level in Panel B of Table 2.9. This is due to the different source of variation in the prevalence of AI skills listed in job postings: the former one is within- group variation, while the latter one is between-group variation. These findings accompanied with my main results indicate that the employment and wage gaps between high-skilled AI-complement occupations and other skill groups can be due to the variation in the demand for AI skills between groups, rather than within groups. Thus, AI may have impacts on the whole economy by widening the employment and wage gaps between workers with a specialization in AI-developing tasks and others who do not possess such skills, instead of only impacting people within specific sectors. These results suggest that AI is a general-purpose technology, which is consistent with Cockburn et al. (2019), Acemoglu (2021), Crafts (2021), and Hötte et al. (2022). 2.6.2 Alternative Classification of Occupations This section introduces an alternative occupation classification system to replace the 2-digit occupation group in my main specification. The broad occupation groups classified by Census or BLS are based on general work performed, but may not reflect specific skill requirements of an occupation. For example, both "Advertising and Promotions Managers" and "Architectural and Engineering Managers" are classified into "Management Occupations" (a 2-digit occupation group). The description of the former one is to "plan, direct, or coordinate advertising policies and programs," while the latter one is to "plan, direct, or coordinate activities in such fields as 111 architecture and engineering."21 Both occupations have the same general work performed (i.e., plan, direct, or coordinate activities), but require different specific skill sets (the former one needs knowledge of advertising and marketing while the latter one requires knowledge of architecture and engineering). However, they are classified into the same 2-digit occupation group. Using these broad occupation groups classified based on general work performed might lead to measurement errors. Thus, I propose an alternative occupation classification system (denoted as "ML occupation clusters" hereafter) based on skill similarity across occupations. I cluster occupations by skill similarity using the skill requirements reflected in job postings and a machine learning clustering algorithm.22 Occupations with high similarity in skills are classified into the same cluster (Fogel and Modenesi, 2023). The detailed definition of this alternative occupation classification system and the procedure of developing this system are provided in Appendix 2D. Appendix Tables 2D.1-2D.3 present the relationship between the ML occupation clusters and the four skill groups introduced in Section 2.3.2, while Appendix Table 2D.4 provides a list of the composition of each ML occupation cluster (i.e., the 4-digit occupations that are classified into each cluster). Figure 2.10 shows that Engineering, Environment, Finance, IT, Media, and Life Sciences occu- pations had higher and increasing demand for AI-developing skills. However, the trends in narrow AI posting shares by 2-digit Census occupation group in Appendix Figure 2B.10 are pretty flat, ex- cept "Computer and Mathematical Occupations" and "Architecture and Engineering Occupations." Appendix Figures 2B.11a and 2B.11b plot the mean hourly wage by ML occupation cluster and by 2-digit Census occupation group, respectively, from 2012-21. There is relatively larger variation in wages across ML occupation clusters. People who work in Engineering, Environment, Finance, IT, Public Safety, Policy, and Social Science occupations experienced higher wages with an upward 21The descriptions of these two occupations are from BLS (https://www.bls.gov/soc/2010/2010_major_groups. htm). Although the mapping between 4-digit 2010 Census Occupational Classification and the 6-digit 2010 Standard Occupational Classification (SOC) is not always one-to-one (in a few cases this mapping is one-to-many), there is a one-to-one mapping between the 2-digit Census occupation groups and the 2-digit SOC groups provided by BLS (https://www.bls.gov/cps/cenocc2010.htm). Since I do not find a detailed description of each 4-digit 2010 Census occupation, I use the 6-digit 2010 SOC code as examples. Note that the 4-digit (6-digit) code is the most detailed occupational classification in the Census (BLS) system. 22I use over 1,800 general and specific skills (e.g., "audit software," "clerical support," "equipment repair," "javascript") to cluster occupations. I set the total number of occupation clusters to be the same as the total number of 2-digit Census Occupational Classification, which is 23. 112 Figure 2.10 Plots of %AI Postings by ML Occupation Cluster, 2012-21 trend in mean hourly wage.23 These differences could stem from how an occupation system is developed. Since the Census classification of occupations is constructed based on general work performed rather than skill specification, it is possible that both high- and low-skilled occupations are classified into the same category which averages out the outcomes (e.g., mean hourly wage) for this category. In addition, I plot the share of narrow AI postings by ML occupation cluster relative to the baseline year, 2012, in Appendix Figure 2B.15. Almost all clusters experienced an overall increasing trend, indicating that the demand for AI skills has been increased in almost every sector of the economy. To examine the relationship between labor market outcomes for ML occupation clusters and the 23There is larger variation in the magnitude of the wage income share across ML occupation clusters (Appendix Figure 2B.12a) than across Census 2-digit occupational classifications (Appendix Figure 2B.12b). The plots of employment are noisier though (Appendix Figures 2B.13 and 2B.14). 113 0.05.1.15.2%Narrow AI Postings20122014201620182020YearPostsecondary Educators0.05.1.15.2%Narrow AI Postings20122014201620182020YearService and Retail Workers0.05.1.15.2%Narrow AI Postings20122014201620182020YearSpecialized Service Professionals0.05.1.15.2%Narrow AI Postings20122014201620182020YearConstruction and Craft Workers0.05.1.15.2%Narrow AI Postings20122014201620182020YearFinancial Management Professionals0.05.1.15.2%Narrow AI Postings20122014201620182020YearPre−Secondary Educators0.05.1.15.2%Narrow AI Postings20122014201620182020YearBuilding Improvement Technicians0.05.1.15.2%Narrow AI Postings20122014201620182020YearPublic Safety, Policy, and Social Science0.05.1.15.2%Narrow AI Postings20122014201620182020YearLife Sciences and Quality Assurance0.05.1.15.2%Narrow AI Postings20122014201620182020YearEngineering Technicians and Technologists0.05.1.15.2%Narrow AI Postings20122014201620182020YearHealthcare Professionals and Practitioners0.05.1.15.2%Narrow AI Postings20122014201620182020YearTechnical Maintenance Workers0.05.1.15.2%Narrow AI Postings20122014201620182020YearWorkplace Safety and Training Specialists0.05.1.15.2%Narrow AI Postings20122014201620182020YearIT and Data Management Specialists0.05.1.15.2%Narrow AI Postings20122014201620182020YearSales and Marketing Professionals0.05.1.15.2%Narrow AI Postings20122014201620182020YearMedia Production and Broadcasting0.05.1.15.2%Narrow AI Postings20122014201620182020YearRegulatory Compliance Specialists0.05.1.15.2%Narrow AI Postings20122014201620182020YearManual Workers and Machine Operators0.05.1.15.2%Narrow AI Postings20122014201620182020YearService and Administrative Professionals0.05.1.15.2%Narrow AI Postings20122014201620182020YearInfrastructure Architecture and Engineering0.05.1.15.2%Narrow AI Postings20122014201620182020YearCreative and Communication Support Workers0.05.1.15.2%Narrow AI Postings20122014201620182020YearTechnical and Service Support Personnel0.05.1.15.2%Narrow AI Postings20122014201620182020YearEnvironmental and Earth ScientistsShare of Narrow AI Postings demand for AI skills, I re-estimate my main specification but interacting the AI posting share with ML occupation cluster dummies instead. To make the estimates comparable to my main results, I choose the "service and retail workers" cluster to be the baseline group. Estimates are presented in Table 2.10, which complements my main results by further showing which occupation clusters within a skill group experience growth or decline in labor market outcomes. Abstract and AI- intensive clusters experience significant growth in both employment and wages, e.g., "engineering technicians," "IT and data management," and "media production and broadcasting." In contrast, clusters with a high concentration in middle-skilled jobs, such as "technical maintenance workers" and "manual workers and machine operators," face significant declines in wages. Different from my main results that document significant correlations between labor market outcomes and the high-skilled group only, coefficients from Table 2.10 show that almost all occupation clusters are significantly impacted by the demand for AI skills. 2.7 Conclusion AI has been receiving increasing attention from academia, the industry, and the public. How- ever, researchers have not reached a consensus on the consequences of AI to skill changes, task reallocation, inequalities, and changes in employment and wages. This paper explores how the demand for AI-developing skills influences employment and wages for heterogeneous skill groups in the U.S. I first categorize labor into four skill groups based on skill specializations: (1) a high-skilled AI-complement group that specializes in abstract tasks and possesses AI skills; (2) a high-skilled, not-yet-AI group with a concentration on abstract tasks that are not yet AI-related; (3) a middle-skilled group that is routine-intensive; and (4) a low-skilled group that is manual-intensive. I then measure changes in the demand for AI skills proxied by changes in the share of job postings that explicitly require AI skills using online job postings data. A task-based model is proposed to provide explanations for my main findings: 1. High-skilled AI-complement occupations have experienced the largest growth in employment and wages among all four skill groups associated with an increasing demand for AI skills. This growth is more than double that of high-skilled not-yet-AI occupations. 114 Table 2.10 Effects of Demand for AI Skills by ML Occupation Cluster, 2012-21 Dep. Var.: Emp. per 100,000 Capita %Emp Share1 Log Mean Hourly Wage %Wage Income2 %AI Postings3 %AI Postings × Postsecondary Educators Specialized Service Professionals Construction & Craft Workers Finance Professionals Pre-Secondary Educators Building Improvement Technicians Public Safety, Policy, & Social Science Life Sciences & Quality Assurance Engineering Technicians Healthcare Professionals & Practitioners Technical Maintenance Workers Workplace Safety & Training Specialists IT & Data Management Sales & Marketing Professionals Media Production & Broadcasting Regulatory Compliance Specialists Manual Workers & Machine Operators Service & Administrative Professionals Infrastructure Architecture & Engineering Creative & Communication Support Technical & Service Support Personnel Environmental & Earth Scientists (1) -18.6∗∗ (8.9) 32.3∗∗∗ (10.0) 24.0∗∗ (9.7) 15.2 (12.0) 29.2∗ (15.4) 13.7 (28.7) 24.5∗∗∗ (9.4) 34.2∗∗ (13.5) 34.6∗∗∗ (11.2) 33.0∗∗∗ (11.2) 15.3 (11.1) 13.7 (9.8) 25.7∗∗ (10.2) 64.9∗∗∗ (19.2) 44.3∗∗∗ (15.4) 36.0∗∗∗ (11.0) 122.9 (76.9) 14.3 (10.3) 23.8∗∗ (10.9) 18.8 (14.2) -75.8 (60.5) 24.7∗∗ (9.8) 48.0∗∗∗ (17.2) (2) -0.019∗∗ (0.009) 0.032∗∗∗ (0.010) 0.024∗∗ (0.010) 0.015 (0.012) 0.029∗ (0.015) 0.014 (0.029) 0.024∗∗∗ (0.009) 0.034∗∗ (0.014) 0.035∗∗∗ (0.011) 0.033∗∗∗ (0.011) 0.015 (0.011) 0.014 (0.010) 0.026∗∗ (0.010) 0.065∗∗∗ (0.019) 0.044∗∗∗ (0.015) 0.036∗∗∗ (0.011) 0.123 (0.077) 0.014 (0.010) 0.024∗∗ (0.011) 0.019 (0.014) -0.076 (0.061) 0.025∗∗ (0.010) 0.048∗∗∗ (0.017) (3) 0.005 (0.004) 0.001 (0.004) -0.012 (0.013) -0.003 (0.012) 0.022∗ (0.013) 0.009 (0.006) 0.011 (0.011) 0.007 (0.010) 0.009 (0.011) 0.013 (0.008) -0.009∗ (0.005) -0.019∗∗∗ (0.004) 0.026∗∗∗ (0.004) 0.015∗∗∗ (0.005) 0.028∗∗∗ (0.008) 0.051∗∗∗ (0.012) 0.002 (0.008) -0.022∗∗∗ (0.005) 0.007 (0.005) -0.002 (0.007) -0.016 (0.011) 0.005 (0.005) 0.014 (0.021) (4) -0.018∗ (0.010) 0.001 (0.012) 0.022∗∗ (0.010) 0.017 (0.011) 0.040∗ (0.024) -0.008 (0.035) 0.024∗∗ (0.010) 0.040∗ (0.023) 0.040∗∗∗ (0.014) 0.041∗∗∗ (0.014) 0.003 (0.014) 0.007 (0.010) 0.024∗∗ (0.012) 0.091∗∗∗ (0.034) 0.050∗∗ (0.021) 0.038∗∗∗ (0.012) 0.178 (0.122) 0.012 (0.011) 0.027∗∗ (0.013) 0.009 (0.021) -0.097 (0.079) 0.024∗∗ (0.010) 0.054∗∗ (0.025) Observations State FE Year FE Skill-Group FE ML-Clustering-Group FE Skill-Group FE × Year FE R2 190,712 ✓ ✓ ✓ ✓ ✓ 0.128 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the "service and retail workers" cluster. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1,2 The unit of the employment share and the share of wage income is a percentage point. 3 Narrow AI definition is used when computing %AI postings at the state-year level. %AI postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.158 186,742 ✓ ✓ ✓ ✓ ✓ 0.304 190,712 ✓ ✓ ✓ ✓ ✓ 0.128 115 2. There is no significant relationship between changes in demand for AI skills and employment for middle- or low-skilled occupations. However, I document a significant and negative correlation between the intensity of AI-developing skills required in job tasks and the mean hourly wage for middle-skilled occupations. 3. The above findings suggest employment and wage gaps between abstract and AI-intensive occupations and other skill groups. These results reflect (1) a "J-shaped" curve of changes in employment associated with AI by skill group and an employment gap between high- skilled AI-complement occupations and other skill groups, and (2) wage polarization, where middle-skilled workers experience the largest decline in wages compared with other types of workers. My main results are limited by my measures of AI and skill group classifications. Although existing literature is used as references when choosing AI phrases, there are possibly omitted phrases that can also be counted toward a "narrow AI" or "broad AI" phrase. Future research could improve the completeness of my chosen AI phrases adopted to distinguish between AI and not-yet-AI postings/occupations. Another future research direction is to explore the impacts of Generative AI (GenAI) tools, also known as Large Language Models (LLMs). My empirical analysis mainly focuses on the complementarity of AI-developing skills, but does not discuss how GenAI tools like ChatGPT may affect the economy. This can be explained by several reasons. First, although GenAI can both complement (e.g., people may use ChatGPT to help with job tasks or problems they encounter during work such as writing emails and doing simple math) and substitute (e.g., Eloundou et al. (2023) argue that most occupations are, to some extent, exposed to LLMs) labor, employers may not list the use of these GenAI tools as one of the requirements in job postings. The access to GenAI tools like ChatGPT is simple and does not require any specialized knowledge or skill. However, people who possess AI-developing skills are essential to the improvements in GenAI. Thus, this paper focuses on workers specializing in AI-developing skills by tracking changes in the demand 116 for these skills rather than workers using the easily accessible GenAI tools. 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Propositions 3′ and 4′ add additional inequalities to the ones presented in Propositions 2.3 and 2.4 in Section 2.2.2, while both Propositions 2A.7 and 2A.8 are only included in the Appendix. Proposition 2A.7 discusses the productivity effect of AI and industrial automation on the final output. Proposition 2A.8 sheds light on the relationship between relative wages and labor supplies. Appendix 2A.2 shows the proofs of all propositions. 2A.1 Additional Propositions Proposition 3′ (Displacement and reinstatement effects of AI or industrial automation) Inequalities presented in Proposition 2.3 in Section 2.2.2: (1) AI can displace workers in some complex tasks, 𝑑A𝐻 𝑗 𝑑𝐼𝐻 < 0, 𝑑A𝐾 𝑑𝐼𝐻 > 0, 𝑑A𝑀 𝑑𝑆 > 0, 𝑑A𝐾 𝑑𝑆 < 0. (2) AI can expand the set of tasks performed by high-skilled workers, 𝑑A𝐻 𝑗 𝑑𝑁 > 0. (3) Industrial automation primarily takes over simpler tasks, 𝑑A𝑀 𝑑𝐼𝑀 < 0, 𝑑A𝐾 𝑑𝐼𝑀 > 0. Additional inequalities: (4) While the set of tasks performed by high-skilled workers is expanded by AI, the simplest tasks that were performed by low-skilled workers may disappear, 𝑑A𝐿 𝑑𝑁 < 0. (5) Labor has a learning effect, 𝑑A𝐿 𝑑𝐼𝐿 > 0, 𝑑A𝐾 𝑑𝐼𝐿 < 0. Note that 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. Since there is a unit measure of tasks as shown in equation (2.1), if new tasks favoring high-skilled labor are created (an increase in 𝑁), then the simplest tasks will disappear. Proposition 3′ implies the learning effect of labor. An increase in 𝐼𝐿 can represent a higher productivity of low-skilled workers in completing slightly more complex tasks. Their productivity can be increased by having more education, participating in on-the-job trainings, etc. Thus, 121 low-skilled workers are able to perform some tasks that were previously automated due to the higher productivity of low-skilled workers and less costs of using low-skilled labor, resulting in an increase in the share of tasks performed by low-skilled workers (A𝐿) and a decrease in the share of automated tasks (A𝐾). Similar explanation can be applied to the learning effect of middle-skilled labor (represented by an increase in 𝑆). Proposition 4′ (Relationship between relative wages and AI or industrial automation) Inequalities presented in Proposition 2.4 in Section 2.2.2: (1) The displacement effect of AI narrows wage gaps, 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝐿 𝑑𝐼𝐻 < 0, 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝑀 𝑑𝐼𝐻 < 0. (2) The reinstatement effect of AI widens wage gaps, 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝐿 𝑑𝑁 𝑑 ( ) 𝑊 𝐻 𝑗 𝑊𝑀 𝑑𝑁 > 0, 𝑑 ( 𝑊𝑀 ) 𝑊𝐿 𝑑𝑁 > 0. > 0, (3) The displacement effect of industrial automation widens wage gaps, 𝑑 ( 𝑊 𝐻 𝑗 𝑊𝑀 𝑑𝐼𝑀 ) > 0, ) 𝑑 ( 𝑊𝑀 𝑊𝐿 𝑑𝐼𝑀 > 0. Additional inequalities: (4) The displacement effect of AI represented by a decrease in 𝑆 widens the wage gap between the high- and middle-skilled groups, but narrows the wage gap between the middle- and low-skilled groups, 𝑑 ( 𝑊 𝐻 𝑗 𝑊𝑀 𝑑𝑆 ) < 0, 𝑑 ( 𝑊𝑀 ) 𝑊𝐿 𝑑𝑆 > 0. (5) The displacement effect of industrial automation represented by a decrease in 𝐼𝐿 widens wage 𝑑 ( gaps, 𝑊 𝐻 𝑗 𝑊𝐿 𝑑𝐼𝐿 ) < 0, ) 𝑑 ( 𝑊𝑀 𝑊𝐿 𝑑𝐼𝐿 < 0. Note that 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. The displacement effect of AI on high-skilled labor (an increase in 𝐼𝐻) narrows wage gaps between high-skilled group and middle- or low-skilled group ( 𝑊𝐻 𝑗 𝑊𝐿 ), while this displacement effect on middle-skilled labor (a decrease in 𝑆) narrows wage gaps between the middle- and low- 𝑊𝐻 𝑗 𝑊𝑀 and skilled groups ( 𝑊𝐻 𝑗 𝑊𝑀 ). The displacement effect of industrial automation (an increase in 𝐼𝑀 or a decrease in 𝐼𝐿) and the 𝑊𝑀 𝑊𝐿 ) but widens the wage gap between the high- and middle-skilled groups ( reinstatement effect of AI (an increase in 𝑁) widen these wage gaps. 122 Proposition 2A.7 (Productivity effect of AI or industrial automation) 𝑑𝑌 𝑑𝐼𝑀 𝑑𝑌 𝑑𝐼𝐻 = = (cid:20) (cid:20) 𝜎 𝜎 − 1 𝜎 𝜎 − 1 𝑌 𝑌 > 0. 𝑅1−𝜎 − ( 𝑊𝑀 𝛼𝑀 (𝐼𝑀) (cid:21) )1−𝜎 > 0, ( 𝑅 𝛼𝐾 )1−𝜎 − 1{𝑖 ∈ AI tasks}( 𝑊𝐻 𝐴𝐼 𝛼𝐻 𝐴𝐼 (𝐼𝐻) )1−𝜎 − 1{𝑖 ∈ not-yet-AI tasks}( 𝑊𝐻 𝑁 𝑜𝑛 𝛼𝐻 𝑁 𝑜𝑛 (𝐼𝐻) (cid:21) )1−𝜎 (2A.1) Improvements in AI (represented by an increase in 𝐼𝐻) or industrial automation (represented by an increase in 𝐼𝑀) both have a positive productivity effect on the final output, 𝑌 . This can be easily explained by the fact that technological improvements raise the productivity of 𝑊𝐻 𝐴𝐼 𝛼𝐻 𝐴𝐼 (𝐼𝐻 ) +1{𝑖 ∈ 𝛼𝐾 is, the less costly it is to replace more expensive labor with cheaper technologies in production. The larger the gap 𝑊𝐻 𝑁 𝑜𝑛 𝛼𝐻 𝑁 𝑜𝑛 (𝐼𝐻 ) − 𝑅 𝑊𝑀 𝛼𝑀 (𝐼𝑀 ) − 𝑅 or 1{𝑖 ∈ AI tasks} not-yet-AI tasks} capital and the greater productivity gains are (these gaps are positive due to Assumption 2.3). Proposition 2A.8 (Relationship between relative wages and labor supplies) 𝑊𝐻 𝑗 𝑑 ln( 𝑊𝐿 𝑑 ln 𝐻 𝑗 𝑑 ln( 𝑊𝑀 𝑊𝐿 𝑑 ln 𝑀 ) ) < 0, < 0, 𝑊𝐻 𝑗 𝑑 ln( 𝑊𝑀 𝑑 ln 𝐻 𝑗 𝑊𝐻 𝑗 𝑑 ln( 𝑊𝐿 𝑑 ln 𝐿 ) ) < 0, > 0, 𝑑 ln( 𝑊𝐻 𝑗 𝑊𝑀 𝑑 ln 𝑀 𝑑 ln( 𝑊𝑀 𝑊𝐿 𝑑 ln 𝐿 ) ) > 0, > 0, 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. (2A.2) When the task allocation among different skill groups remains unchanged, an increase in the labor supply of a specific skill group will put a downward pressure on wages for that group because there are more workers competing in the same set of tasks. In particular, an increase in the supply of high-skilled workers (𝐻 𝑗 , 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}) will reduce their wages and consequently have a negative impact on relative wages 𝑊𝐻 𝑗 𝑊𝐿 and 𝑊𝐻 𝑗 𝑊𝑀 . An increase in the supply of middle-skilled workers (𝑀) widens the wage gap between high- and middle-skilled workers ( 𝑊𝐻 𝑗 𝑊𝑀 ) but reduces the wage gap between middle- and low-skilled workers ( 𝑊𝑀 𝑊𝐿 ) because middle-skilled workers earn less. Similarly, an increase in the supply of low-skilled workers (𝐿) increases the wage gap between low- and middle-skilled workers ( 𝑊𝑀 𝑊𝐿 ) or between low- and high-skilled workers ( 𝑊𝐻 𝑗 𝑊𝐿 ). 123 2A.2 Proofs 2A.2.1 Proof of Proposition 2.1 The proof of this proposition is similar to the proof of Lemma 1 in Acemoglu and Autor (2011). Intuitively, given factor prices of labor and capital, task 𝑖 = 𝐼𝑀 can be performed by either industrial automation or middle-skilled labor because the cost of producing this task using either type of factors is the same. That is, 𝑅 = 𝑊𝑀 𝛼𝑀 (𝐼𝑀 ) .1 Since Assumption 2.3 assumes that ∃𝐼𝑀 ∈ (𝑁 − 1, 𝑆) such that > 𝑅 and Assumption 2.1 assumes that 𝛼𝑀 (𝑖) is strictly increasing in 𝑖, then (1) the cost 𝑊𝑀 𝛼𝑀 (𝐼𝑀 ) of automating any tasks 𝑖 < 𝐼𝑀 is lower than using middle-skilled labor and (2) it is less costly to produce tasks 𝑖 > 𝐼𝑀 using middle-skilled labor than industrial automation. The same argument applies to comparisons of other factors. 2A.2.2 Proof of Proposition 2.2 I first show the proof of the ideal-price condition presented in equation (2.8). Given the CES production function expressed in equation (2.1), the marginal cost of producing the final good 𝑌 is: 𝑃 = (cid:20)∫ 𝑁 𝑁−1 𝑝(𝑖)1−𝜎𝑑𝑖 (cid:21) 1 1− 𝜎 . (2A.3) Equation (2.8) can then be derived by combining equations (2.3) and (2A.3): ∫ 𝐼𝑀 )1−𝜎𝑑𝑖 + 𝑅1−𝜎𝑑𝑖 + 1 ≡ 𝑃 = (cid:20)∫ 𝐼𝐿 𝑁−1 ∫ 𝑁 𝐼𝐻 + ( ( 𝑊𝐿 𝛼𝐿 (𝑖) 𝑊𝐻 𝐴𝐼 𝛼𝐻 𝐴𝐼 (𝑖) 𝐼𝐿 ∫ 𝑁 )1−𝜎𝑑𝑖 + ( 𝐼𝐻 ∫ 𝑆 ( 𝐼𝑀 𝑊𝑀 𝛼𝑀 (𝑖) (cid:21) 1 1− 𝜎 )1−𝜎𝑑𝑖 𝑊𝐻 𝑁 𝑜𝑛 𝛼𝐻 𝑁 𝑜𝑛 (𝑖) ∫ 𝐼𝐿 ⇒ 1 = 𝑅1−𝜎 (cid:2)𝐼𝑀 − 𝐼𝐿 + (𝐼𝐻 − 𝑆)𝛼𝜎−1 𝐾 (cid:3) + 𝑊 1−𝜎 𝐿 )1−𝜎𝑑𝑖 + ∫ 𝐼𝐻 𝑆 ( 𝑅 𝛼𝐾 )1−𝜎𝑑𝑖 𝛼𝐿 (𝑖)𝜎−1𝑑𝑖 + 𝑊 1−𝜎 𝑀 ∫ 𝑆 𝐼𝑀 𝛼𝑀 (𝑖)𝜎−1𝑑𝑖 𝑁−1 + 𝑊 1−𝜎 𝐻 𝐴𝐼 ∫ 𝑁 𝐼𝐻 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1𝑑𝑖 + 𝑊 1−𝜎 𝐻 𝑁 𝑜𝑛 1{𝑖 ∈ not-yet-AI tasks}𝛼𝐻 𝑁 𝑜𝑛 (𝑖)𝜎−1𝑑𝑖 ∫ 𝑁 𝐼𝐻 + A𝐾 𝑅1−𝜎. = A𝐻 𝐴𝐼𝑊 1−𝜎 𝐻 𝐴𝐼 + A𝐻 𝑁 𝑜𝑛𝑊 1−𝜎 𝐻 𝑁 𝑜𝑛 + A𝑀𝑊 1−𝜎 𝑀 + A𝐿𝑊 1−𝜎 𝐿 The equilibrium factor prices expressed in equation (2.9) can be easily obtained by re-arranging terms of equation (2.6). Replacing factor prices of the ideal-price condition, equation (2.8), with 1The productivity of industrial automation is set to be 1 introduced in Section 2.2.1. (2A.4) 124 expressions for these factor prices presented in equation (2.9), I can obtain the equilibrium output shown in equation (2.10): 1 = A𝐻 𝐴𝐼𝑊 1−𝜎 𝐻 𝐴𝐼 + A𝐻 𝑁 𝑜𝑛𝑊 1−𝜎 𝐻 𝑁 𝑜𝑛 + A𝑀𝑊 1−𝜎 𝑀 + A𝐿𝑊 1−𝜎 𝐿 + A𝐾 𝑅1−𝜎 = A𝐻 𝐴𝐼 (cid:20) 𝑌 1 𝜎 A 1 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼)− 1 𝜎 (cid:21) 1−𝜎 + A𝐻 𝑁 𝑜𝑛 (cid:20) 𝑌 1 𝜎 A (cid:21) 1−𝜎 (cid:20) 𝑌 1 𝜎 A + A𝑀 1 𝜎 𝑀 𝑀 − 1 𝜎 (cid:21) 1−𝜎 1 𝜎 𝜎 𝐻 𝑁 𝑜𝑛 (𝐻 𝑁𝑜𝑛)− 1 (cid:21) 1−𝜎 + A𝐿𝑊𝐿 (cid:20) 𝑌 1 𝜎 A 1 𝜎 𝐿 𝐿− 1 𝜎 (cid:21) 1−𝜎 (cid:20) 𝑌 1 𝜎 A + A𝐾 1 𝜎 𝐾 𝐾 − 1 𝜎 (cid:20) = 𝑌 1− 𝜎 𝜎 1 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼) A 𝜎−1 𝜎 + A 1 𝜎 𝐻 𝑁 𝑜𝑛 (𝐻 𝑁𝑜𝑛) 𝜎−1 𝜎 + A 1 𝜎 𝑀 𝑀 𝜎−1 𝜎 + A 1 𝜎 𝐿 𝐿 𝜎−1 𝜎 + A 1 𝜎 𝐾 𝐾 𝜎−1 𝜎 (cid:21) . (2A.5) 2A.2.3 Proof of Propositions 2.3 and 3′ I present the proof for 𝑑A𝐻 𝐴𝐼 𝑑𝐼𝐻 > 0 (that is, 𝑑A𝐻 𝑗 𝑑𝐼𝐻 > 0, 𝑗 ∈ { 𝐴𝐼, 𝑁𝑜𝑛}, when 𝑗 = 𝐴𝐼) in Proposition 2.3. The proof for other inequalities in Propositions 2.3 and 3′ is analogous. Given equation (2.7), 𝑑 ∫ 𝑁 𝐼𝐻 𝑑A𝐻 𝐴𝐼 𝑑𝐼𝐻 = 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1𝑑𝑖 𝑑𝐼𝐿 = 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑁)𝜎−1 𝑑 (𝑁) 𝑑𝑖 = 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝐼𝐻)𝜎−1 > 0. − 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝐼𝐻)𝜎−1 (2A.6) 2A.2.4 Proof of Propositions 2.4 and 4′ Given the equilibrium factor prices presented in equation (2.9) and Proposition 2.3, < 0, 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. (2A.7) ) 𝑑 ( 𝑊𝐻 𝑗 𝑊𝐿 𝑑𝐼𝐻 = 𝑑 (cid:21) (cid:20) A1/𝜎 𝐻 𝑗 (𝐻 𝑗 ) −1/𝜎 A1/𝜎 𝐿 𝐿 −1/𝜎 𝑑𝐼𝐻 𝑑A𝐻 𝑗 𝑑𝐼𝐻 = 1− 𝜎 𝜎 1 𝜎 A A1/𝜎 𝐻 𝑗 (𝐻 𝑗 )−1/𝜎 𝐿 𝐿−1/𝜎 Similar for the other inequalities in Propositions 2.4 and 4′. 125 2A.2.5 Proof of Proposition 2.5 Given the equilibrium factor prices presented in equation (2.9) and Assumption 2.1, 𝑊𝐻 𝐴𝐼 𝑑 ( ) 𝑊𝐿 𝑑𝛼𝐻 𝐴𝐼 (𝑖) = = = 1 𝜎 A 𝑑A𝐻 𝐴𝐼 𝑑𝛼𝐻 𝐴𝐼 (𝑖) 1− 𝜎 𝜎 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼)− 1 𝐿 𝐿− 1 A 1 𝜎 𝜎 𝑑 (cid:104)∫ 𝑁 𝐼𝐻 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1𝑑𝑖 (cid:105) 1 𝜎 A 1− 𝜎 𝜎 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼)− 1 𝐿 𝐿− 1 1 𝜎 𝜎 𝑑𝛼𝐻 𝐴𝐼 (𝑖) 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1 𝐻 𝐴𝐼 (𝑖) 𝛼′ 1 𝜎 A 1− 𝜎 𝜎 A 𝐻 𝐴𝐼 (𝐻 𝐴𝐼)− 1 𝐿 𝐿− 1 A 1 𝜎 𝜎 𝜎 > 0. Similar for the other inequalities in Proposition 2.5. 2A.2.6 Proof of Proposition 2.6 Given the equilibrium factor prices presented in equation (2.9) and Assumption 2.1, 𝑑 ( 𝐻 𝐴𝐼𝑊𝐻 𝐴𝐼 𝑑 ( 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) ) = 𝐻 𝐴𝐼𝑌 1/𝜎A1/𝜎 𝐾𝑌 1/𝜎A1/𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼 ) −1/𝜎 𝐾 𝐾 −1/𝜎 ) = 𝑑A1/𝜎 𝐻 𝐴𝐼 𝑑𝛼𝐻 𝐴𝐼 (𝑖) (𝐻 𝐴𝐼) A1/𝜎 𝐾 𝐾 𝜎−1 (cid:105) 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1𝑑𝑖 𝜎−1 𝜎 𝜎 𝑑𝛼𝐻 𝐴𝐼 (𝑖) (cid:104)∫ 𝑁 𝐼𝐻 𝑑 = 1 𝜎 A 1− 𝜎 𝜎 𝐻 𝐴𝐼 𝑑𝛼𝐻 𝐴𝐼 (𝑖) 𝜎−1 𝜎 (𝐻 𝐴𝐼) A1/𝜎 𝐾 𝐾 𝜎−1 𝜎 (2A.8) (2A.9) = 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1 𝐻 𝐴𝐼 (𝑖) 𝛼′ 1 𝜎 A 1− 𝜎 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼) 𝐾 𝐾 𝜎−1 A 1 𝜎 𝜎 𝜎−1 𝜎 > 0. Similarly, 𝑑 ( 𝐻 𝐴𝐼𝑊𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐾 ) 1 𝜎 A = − 1 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼) 𝐾 𝐾 𝜎−1 1+𝜎 𝜎 𝜎 A 𝜎−1 𝜎 (𝐼𝐻 − 𝑆) (𝜎 − 1)𝛼𝜎−2 𝐾 > 0 if 𝜎 ∈ (0, 1), = 0 if 𝜎 = 1, (2A.10) < 0 if 𝜎 ∈ (1, ∞).    126 Thus, when 𝜎 ∈ (0, 1], both we want to show 𝑑 ( 𝐻 𝐴𝐼 𝑊 𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) ) 𝑑 ( 𝐻 𝐴𝐼 𝑊 𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 𝑑 ( > | 𝐻 𝐴𝐼 ) > 0 and 𝑑 ( 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 ) > 0. However, when 𝜎 ∈ (1, ∞), if ) |, then we need to prove the following is true: 𝐻 𝐴𝐼𝑊𝐻 𝐴𝐼 𝑑 ( 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) ) 𝑑 ( > | 𝐻 𝐴𝐼𝑊𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐾 ) | ⇐ ⇐ ⇐ 1 𝜎 A 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1 𝐻 𝐴𝐼 (𝑖) 𝛼′ 1{𝑖 ∈ AI tasks}𝛼𝐻 𝐴𝐼 (𝑖)𝜎−1 𝐻 𝐴𝐼 (𝑖) 𝛼′ 1{𝑖 ∈ AI tasks} 𝐻 𝐴𝐼 (𝑖) 𝛼′ A𝐾 A𝐻 𝐴𝐼 A𝐾 A𝐻 𝐴𝐼 (cid:20) 𝛼𝐻 𝐴𝐼 (𝑖) 𝛼𝐾 (cid:21) 𝜎−1 1− 𝜎 𝜎 1 𝜎 𝜎−1 𝜎 1 𝜎 > 1 𝜎 A 𝐻 𝐴𝐼 (𝐻 𝐴𝐼) 𝐾 𝐾 𝜎−1 > (𝐼𝐻 − 𝑆) (𝜎 − 1)𝛼𝜎−2 𝐻 𝐴𝐼 (𝐻 𝐴𝐼) 𝐾 𝐾 𝜎−1 1+𝜎 𝜎 A A 𝜎 𝜎 𝐾 𝜎−1 𝜎 (𝐼𝐻 − 𝑆) (𝜎 − 1)𝛼𝜎−2 𝐾 > (𝐼𝐻 − 𝑆) (𝜎 − 1)𝛼−1 𝐾 . (2A.11) However, we cannot determine whether the last inequality is true or not without knowing the range of parameters. Thus, 𝑑 ( 𝐻 𝐴𝐼 𝑊 𝐻 𝐴𝐼 𝐾 𝑅 𝑑𝛼𝐻 𝐴𝐼 (𝑖) 𝑑 ( ) ⪌ | 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑊 𝐾 𝑅 𝑑𝛼𝐾 ) |. 2A.2.7 Proof of Proposition 2A.7 Rewrite equation (2.10), the expression for the equilibrium output, as 𝑌 𝜎−1 𝜎 = A 1 𝜎 𝐻 𝐴𝐼 (𝐻 𝐴𝐼) 𝜎−1 𝜎 + A 1 𝜎 𝐻 𝑁 𝑜𝑛 (𝐻 𝑁𝑜𝑛) 𝜎−1 𝜎 + A and differentiate both sides with respect to 𝐼𝐿: 1 𝜎 𝑀 𝑀 𝜎−1 𝜎 + A 1 𝜎 𝐿 𝐿 𝜎−1 𝜎 + A 1 𝜎 𝐾 𝐾 𝜎−1 𝜎 , (2A.12) 𝜎 − 1 𝜎 𝑌 − 1 𝜎 𝑑𝑌 𝑑𝐼𝑀 𝑑𝑌 𝑑𝐼𝑀 𝑑𝑌 𝑑𝐼𝑀 𝑑𝑌 𝑑𝐼𝑀 𝑑𝑌 𝑑𝐼𝑀 = A 𝜎 𝜎 (cid:20) 1− 𝜎 𝜎 𝑌 1 𝑀 𝑀 𝜎−1 (cid:20) 𝜎 𝜎 − 1 𝜎 𝜎 − 1 𝜎 𝜎 − 1 𝜎 𝜎 − 1 𝑌 𝑌 (cid:20) = = = = + A 𝑑A𝑀 𝑑𝐼𝑀 1− 𝜎 𝑀 𝑀 𝜎−1 𝜎 𝜎 A 𝜎 1− 𝜎 𝜎 𝐾 𝐾 𝜎−1 𝑑A𝑀 𝑑𝐼𝑀 𝑑A𝐾 𝑑𝐼𝑀 1− 𝜎 𝐾 𝐾 𝜎−1 𝜎 𝜎 + A (𝑌 A𝑀) + (𝑌 A𝐾) 𝜎 𝑀 𝜎−1 1− 𝜎 𝜎 𝑑A𝑀 𝑑𝐼𝑀 𝑀 𝛼𝑀 (𝐼𝑀)𝜎−1(cid:3) 𝑊𝑀 𝛼𝑀 (𝐼𝑀) )1−𝜎 (cid:21) > 0. 𝑌 (cid:2)𝑅1−𝜎 − 𝑊 1−𝜎 𝑅1−𝜎 − ( (cid:21) 𝑑A𝐾 𝑑𝐼𝑀 1− 𝜎 𝜎 𝐾 𝜎−1 𝜎 (cid:21) 𝑑A𝐾 𝑑𝐼𝑀 (2A.13) According to Assumption 2.3, ∃𝐼𝑀 ∈ (𝑁 − 1, 𝑆) such that ( 𝑊𝑀 𝛼𝑀 (𝐼𝑀 ) )1−𝜎 > 𝑅1−𝜎 and 𝜎 𝜎−1 > 0. Similar for 𝑑𝑌 In both cases, 𝑑𝑌 𝑑𝐼𝐻 𝑑𝐼𝑀 𝑊𝑀 𝛼𝑀 (𝐼𝑀 ) < 0. Otherwise if 𝜎 ∈ (1, ∞), ( 𝑊𝑀 𝛼𝑀 (𝐼𝑀 ) )1−𝜎 < 𝑅1−𝜎 and 𝜎 𝜎−1 > 0. > 𝑅. Then if 𝜎 ∈ (0, 1), > 0. 127 2A.2.8 Proof of Proposition 2A.8 Given the equilibrium factor prices presented in equation (2.9), 𝑊𝐻 𝑗 𝑑 ln( 𝑊𝐿 𝑑 ln 𝐻 𝑗 = ) = (cid:21) (cid:20) A1/𝜎 𝐻 𝑗 (𝐻 𝑗 ) −1/𝜎 A1/𝜎 𝐿 𝐿 −1/𝜎 𝑑 ln 𝐻 𝑗 𝜎 ln A𝐻 𝑗 − 1 𝑑 ln 𝑑 ( 1 𝜎 ln 𝐻 𝑗 − 1 𝑑 ln 𝐻 𝑗 𝑗 ∈ {𝐴𝐼, 𝑁𝑜𝑛}. = − 1 𝜎 < 0, 𝜎 ln A𝐿 + 1 𝜎 ln 𝐿) (2A.14) Similar for the other inequalities in Proposition 2A.8. 128 APPENDIX 2B ADDITIONAL FIGURES & TABLES Figure 2B.1 Number of AI/CS Postings by BLS Region in LinkUp Data, 2011-22 129 050K100K150K200K250K300K350KNumber of Narrow AI Postings2011201520192022YearNarrow AI050K100K150K200K250K300K350KNumber of Broad AI Postings2011201520192022YearBroad AI050K100K150K200K250K300K350KNumber of CS Postings2011201520192022YearCSNew EnglandNew York/New JerseyMid−AtlanticSoutheastMidwestSouthwestMountain−PlainsWestern Figure 2B.2 Share of AI/CS Postings by BLS Region in LinkUp Data, 2011-22 130 0.01.02.03.04.05.06.07.08Share of Narrow AI Postings2011201520192022YearNarrow AI0.01.02.03.04.05.06.07.08Share of Broad AI Postings2011201520192022YearBroad AI0.01.02.03.04.05.06.07.08Share of CS Postings2011201520192022YearCSNew EnglandNew York/New JerseyMid−AtlanticSoutheastMidwestSouthwestMountain−PlainsWestern Figure 2B.3 Geographic Distribution of the Share of Broad AI Postings in LinkUp Data (a) 2011-14 (b) 2015-18 (c) 2019-22 Notes: Scales are in percentage point. 131 > 5 (Max: 6.86)4 − 53 − 42 − 30 − 2 (Min: 0.51)> 5 (Max: 8.73)4 − 53 − 42 − 30 − 2 (Min: 0.90)> 5 (Max: 10.28)4 − 53 − 42 − 30 − 2 (Min: 1.61) Figure 2B.4 Comparison between LinkUp and Lightcase Data, 2010-20 Notes: Shares of AI postings in Lightcast online job postings data is from the Stanford Institute for Human-Centered Artificial Intelligence (HAI) who purchased Lightcast data. Since Lightcast is a non-public data, HAI only shares (1) the monthly share of AI postings in each of the seven AI subcategories (artificial intelligence, autonomous driving, machine learning, natural language processing (NLP), neural networks, robotics, and visual image recognition) in the U.S. between 2010 and 2020 and (2) the state-year share of AI postings between 2019 and 2021. These data are publicly available at https://aiindex.stanford.edu/ai-index-report-2022/, provided by Zhang et al. (2022). Since the total number of AI postings in Lightcast data is not available, I compute the average monthly share of AI postings each year from (1) in Lightcast data and treat it as the annual share to compare with the annual share in LinkUp data. 132 0.002.004.006.008.01Share of AI Postings from LinkUp0.00000.00200.00400.00600.00800.0100Share of AI Postings from LightcastArtificial IntelligenceAutonomous DrivingNatural Language Processing (NLP)Neural NetworksMachine LearningRoboticsVisual Image Recognition Figure 2B.5 Occupation-Specific Information from O∗NET: Using Actuaries (2019 O∗NET-SOC Code: 15-2011.00) as an Example (a) Tasks (b) Technology Skills (c) Detailed Work Activities (d) Knowledge Source: https://www.onetonline.org/link/summary/15-2011.00 133 Figure 2B.6 Plots of Skill-Group-By-Year Employment, Wages, and Share of AI Postings (Using Broad AI Definition), 2012-21 (a) Employment per 100,000 Capita (b) Share of Employment (c) Mean Hourly Wage (d) Share of Wage Income (e) Share of Broad AI Postings (f) Share of CS Postings 134 1000020000300004000050000Employment per 100,000 Capita20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ.1.2.3.4.5Share of Employment20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ20304050Mean Hourly Wage20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ0.1.2.3.4.5Share of Wage Income20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ0.01.02.03.04Share of Broad AI Postings20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ0.01.02.03.04Share of CS Postings20122014201620182020YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ Figure 2B.7 Average AI Skill Prevalence Score by BLS Region, 2012-21 (a) Not Fixing the Range of the Y-Axis (b) Fixing the Range of the Y-Axis Notes: The AI Skill Prevalence Score is constructed at the state-year level and standardized within a year. 135 −.25−.2−.15Avg. AI Skill Prevalence Score20122014201620182020YearNew England.4.6.81Avg. AI Skill Prevalence Score20122014201620182020YearNew York/New Jersey−.10.1.2Avg. AI Skill Prevalence Score20122014201620182020YearMid−Atlantic−.25−.2−.15−.1Avg. AI Skill Prevalence Score20122014201620182020YearSoutheast−.25−.2−.15Avg. AI Skill Prevalence Score20122014201620182020YearMidwest−.2−.10.1Avg. AI Skill Prevalence Score20122014201620182020YearSouthwest−.35−.3−.25−.2Avg. AI Skill Prevalence Score20122014201620182020YearMountain−Plains.6.7.8Avg. AI Skill Prevalence Score20122014201620182020YearWesternAverage AI Skill Prevalence Score−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearNew England−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearNew York/New Jersey−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearMid−Atlantic−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearSoutheast−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearMidwest−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearSouthwest−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearMountain−Plains−.32.12.561Avg. AI Skill Prevalence Score20122014201620182020YearWesternAverage AI Skill Prevalence Score Figure 2B.8 Time-Varying Effects of Demand for AI Skills on Labor Market Outcomes, 2012-21 (a) Employment per 100,000 Capita (b) Share of Employment (c) Log Mean Hourly Wage (d) Share of Wage Income Notes: The coefficient estimates plotted in each subfigure show overall time-varying effects of changes in share of narrow AI postings on labor market outcomes. They are obtained by respectively regressing employment per 100,000 capita, share of employment (in percentage point), log mean hourly wages, and share of wage income (in percentage point) on the triple interaction term between share of narrow AI postings, skill group dummies, and year dummies, using the main specification with a full set of fixed effects (i.e., state, year, skill-group, 2-digit-occupation, and skill-group-by-year fixed effects) included. I also plot the corresponding 95% confidence intervals in each subfigure. 136 −20020406080100Employment per 100,000 Capita201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ−.020.02.04.06.08.1Share of Employment201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ−.020.02.04.06Log Mean Hourly Wage201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ−.030.03.06.09.12.15Share of Wage Income201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ Figure 2B.9 Time-Varying Effects of AI Skill Prevalence on Labor Market Outcomes, 2012-21 (a) Employment per 100,000 Capita (b) Share of Employment (c) Log Mean Hourly Wage (d) Share of Wage Income Notes: The coefficient estimates plotted in each subfigure show overall time-varying effects of AI Skill Prevalence Score on labor market outcomes. They are obtained by respectively regressing employment per 100,000 capita, share of employment (in percentage point), log mean hourly wages, and share of wage income (in percentage point) on the triple interaction term between state-year AI Skill Prevalence Score, skill group dummies, and year dummies, controlling for the full set of fixed effects (i.e., state, year, skill-group, 2-digit-occupation, and skill-group-by-year fixed effects) as my main specification. I also plot the corresponding 95% confidence intervals in each subfigure. 137 −20020406080100Employment per 100,000 Capita201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ−.020.02.04.06.08.1Share of Employment201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ−.020.02.04.06.08Log Mean Hourly Wage201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ−.030.03.06.09.12.15Share of Wage Income201320142015201620172018201920202021YearHigh−Skilled AI−Complement OccHigh−Skilled Not−Yet−AI OccMiddle−Skilled OccLow−Skilled Occ Figure 2B.10 Plots of %AI Postings by 2-Digit Occupational Classification, 2012-21 Notes: The total number of 2-digit Census Occupational Classification is 23, which is the same as the total number of my proposed ML occupation clusters. However, the "Military Specific Occupations" group is excluded from my main sample due to the absence of O∗NET occupational descriptions, which are necessary for constructing the AI occupation indicators and, consequently, the skill group indicators, as explained in Section 2.3.2.2. Thus, only 22 Census 2-digit groups are included in my sample for plotting the share of narrow AI postings. 138 0.1.2.3%Narrow AI Postings20122014201620182020YearManagement Occupations0.1.2.3%Narrow AI Postings20122014201620182020YearBusiness and Financial Operations0.1.2.3%Narrow AI Postings20122014201620182020YearComputer and Mathematical Occupations0.1.2.3%Narrow AI Postings20122014201620182020YearArchitecture and Engineering0.1.2.3%Narrow AI Postings20122014201620182020YearLife, Physical, and Social Science0.1.2.3%Narrow AI Postings20122014201620182020YearCommunity and Social Service0.1.2.3%Narrow AI Postings20122014201620182020YearLegal Occupations0.1.2.3%Narrow AI Postings20122014201620182020YearEducational Instruction and Library0.1.2.3%Narrow AI Postings20122014201620182020YearArts, Design, Sports, and Media0.1.2.3%Narrow AI Postings20122014201620182020YearHealthcare Practitioners and Technical Occ0.1.2.3%Narrow AI Postings20122014201620182020YearHealthcare Support Occupations0.1.2.3%Narrow AI Postings20122014201620182020YearProtective Service Occupations0.1.2.3%Narrow AI Postings20122014201620182020YearFood Preparation and Serving0.1.2.3%Narrow AI Postings20122014201620182020YearBuilding and Maintenance0.1.2.3%Narrow AI Postings20122014201620182020YearPersonal Care and Service0.1.2.3%Narrow AI Postings20122014201620182020YearSales and Related Occupations0.1.2.3%Narrow AI Postings20122014201620182020YearOffice and Administrative Support0.1.2.3%Narrow AI Postings20122014201620182020YearFarming, Fishing, and Forestry0.1.2.3%Narrow AI Postings20122014201620182020YearConstruction and Extraction0.1.2.3%Narrow AI Postings20122014201620182020YearInstallation, Maintenance, and Repair0.1.2.3%Narrow AI Postings20122014201620182020YearProduction Occupations0.1.2.3%Narrow AI Postings20122014201620182020YearTransportation and Material MovingShare of Narrow AI Postings Figure 2B.11 Plots of Mean Hourly Wage by Different Occupation System, 2012-21 (a) By ML Occupation Cluster (b) By 2-Digit Census Occupational Classification Notes: The total number of 2-digit Census Occupational Classification is 23, which is the same as the total number of my proposed ML occupation clusters. However, the "Military Specific Occupations" group is excluded from my main sample due to the absence of O∗NET occupational descriptions, which are necessary for constructing the AI occupation indicators and, consequently, the skill group indicators, as explained in Section 2.3.2.2. Thus, only 22 Census 2-digit groups are included in my sample for plotting the mean hourly wage. 139 15304560Mean Hourly Wage20122014201620182020YearPostsecondary Educators15304560Mean Hourly Wage20122014201620182020YearService and Retail Workers15304560Mean Hourly Wage20122014201620182020YearSpecialized Service Professionals15304560Mean Hourly Wage20122014201620182020YearConstruction and Craft Workers15304560Mean Hourly Wage20122014201620182020YearFinancial Management Professionals15304560Mean Hourly Wage20122014201620182020YearPre−Secondary Educators15304560Mean Hourly Wage20122014201620182020YearBuilding Improvement Technicians15304560Mean Hourly Wage20122014201620182020YearPublic Safety, Policy, and Social Science15304560Mean Hourly Wage20122014201620182020YearLife Sciences and Quality Assurance15304560Mean Hourly Wage20122014201620182020YearEngineering Technicians and Technologists15304560Mean Hourly Wage20122014201620182020YearHealthcare Professionals and Practitioners15304560Mean Hourly Wage20122014201620182020YearTechnical Maintenance Workers15304560Mean Hourly Wage20122014201620182020YearWorkplace Safety and Training Specialists15304560Mean Hourly Wage20122014201620182020YearIT and Data Management Specialists15304560Mean Hourly Wage20122014201620182020YearSales and Marketing Professionals15304560Mean Hourly Wage20122014201620182020YearMedia Production and Broadcasting15304560Mean Hourly Wage20122014201620182020YearRegulatory Compliance Specialists15304560Mean Hourly Wage20122014201620182020YearManual Workers and Machine Operators15304560Mean Hourly Wage20122014201620182020YearService and Administrative Professionals15304560Mean Hourly Wage20122014201620182020YearInfrastructure Architecture and Engineering15304560Mean Hourly Wage20122014201620182020YearCreative and Communication Support Workers15304560Mean Hourly Wage20122014201620182020YearTechnical and Service Support Personnel15304560Mean Hourly Wage20122014201620182020YearEnvironmental and Earth ScientistsMean Hourly Wage15304560Mean Hourly Wage20122014201620182020YearManagement Occupations15304560Mean Hourly Wage20122014201620182020YearBusiness and Financial Operations15304560Mean Hourly Wage20122014201620182020YearComputer and Mathematical Occupations15304560Mean Hourly Wage20122014201620182020YearArchitecture and Engineering15304560Mean Hourly Wage20122014201620182020YearLife, Physical, and Social Science15304560Mean Hourly Wage20122014201620182020YearCommunity and Social Service15304560Mean Hourly Wage20122014201620182020YearLegal Occupations15304560Mean Hourly Wage20122014201620182020YearEducational Instruction and Library15304560Mean Hourly Wage20122014201620182020YearArts, Design, Sports, and Media15304560Mean Hourly Wage20122014201620182020YearHealthcare Practitioners and Technical Occ15304560Mean Hourly Wage20122014201620182020YearHealthcare Support Occupations15304560Mean Hourly Wage20122014201620182020YearProtective Service Occupations15304560Mean Hourly Wage20122014201620182020YearFood Preparation and Serving15304560Mean Hourly Wage20122014201620182020YearBuilding and Maintenance15304560Mean Hourly Wage20122014201620182020YearPersonal Care and Service15304560Mean Hourly Wage20122014201620182020YearSales and Related Occupations15304560Mean Hourly Wage20122014201620182020YearOffice and Administrative Support15304560Mean Hourly Wage20122014201620182020YearFarming, Fishing, and Forestry15304560Mean Hourly Wage20122014201620182020YearConstruction and Extraction15304560Mean Hourly Wage20122014201620182020YearInstallation, Maintenance, and Repair15304560Mean Hourly Wage20122014201620182020YearProduction Occupations15304560Mean Hourly Wage20122014201620182020YearTransportation and Material MovingMean Hourly Wage Figure 2B.12 Plots of %Wage Income by Different Occupation System, 2012-21 (a) By ML Occupation Cluster (b) By 2-Digit Census Occupational Classification Notes: The total number of 2-digit Census Occupational Classification is 23, which is the same as the total number of my proposed ML occupation clusters. However, the "Military Specific Occupations" group is excluded from my main sample due to the absence of O∗NET occupational descriptions, which are necessary for constructing the AI occupation indicators and, consequently, the skill group indicators, as explained in Section 2.3.2.2. Thus, only 22 Census 2-digit groups are included in my sample for plotting the share of wage income. 140 0.05.1.15.2Share of Wage Income20122014201620182020YearPostsecondary Educators0.05.1.15.2Share of Wage Income20122014201620182020YearService and Retail Workers0.05.1.15.2Share of Wage Income20122014201620182020YearSpecialized Service Professionals0.05.1.15.2Share of Wage Income20122014201620182020YearConstruction and Craft Workers0.05.1.15.2Share of Wage Income20122014201620182020YearFinancial Management Professionals0.05.1.15.2Share of Wage Income20122014201620182020YearPre−Secondary Educators0.05.1.15.2Share of Wage Income20122014201620182020YearBuilding Improvement Technicians0.05.1.15.2Share of Wage Income20122014201620182020YearPublic Safety, Policy, and Social Science0.05.1.15.2Share of Wage Income20122014201620182020YearLife Sciences and Quality Assurance0.05.1.15.2Share of Wage Income20122014201620182020YearEngineering Technicians and Technologists0.05.1.15.2Share of Wage Income20122014201620182020YearHealthcare Professionals and Practitioners0.05.1.15.2Share of Wage Income20122014201620182020YearTechnical Maintenance Workers0.05.1.15.2Share of Wage Income20122014201620182020YearWorkplace Safety and Training Specialists0.05.1.15.2Share of Wage Income20122014201620182020YearIT and Data Management Specialists0.05.1.15.2Share of Wage Income20122014201620182020YearSales and Marketing Professionals0.05.1.15.2Share of Wage Income20122014201620182020YearMedia Production and Broadcasting0.05.1.15.2Share of Wage Income20122014201620182020YearRegulatory Compliance Specialists0.05.1.15.2Share of Wage Income20122014201620182020YearManual Workers and Machine Operators0.05.1.15.2Share of Wage Income20122014201620182020YearService and Administrative Professionals0.05.1.15.2Share of Wage Income20122014201620182020YearInfrastructure Architecture and Engineering0.05.1.15.2Share of Wage Income20122014201620182020YearCreative and Communication Support Workers0.05.1.15.2Share of Wage Income20122014201620182020YearTechnical and Service Support Personnel0.05.1.15.2Share of Wage Income20122014201620182020YearEnvironmental and Earth ScientistsShare of Wage Income0.05.1.15.2Share of Wage Income20122014201620182020YearManagement Occupations0.05.1.15.2Share of Wage Income20122014201620182020YearBusiness and Financial Operations0.05.1.15.2Share of Wage Income20122014201620182020YearComputer and Mathematical Occupations0.05.1.15.2Share of Wage Income20122014201620182020YearArchitecture and Engineering0.05.1.15.2Share of Wage Income20122014201620182020YearLife, Physical, and Social Science0.05.1.15.2Share of Wage Income20122014201620182020YearCommunity and Social Service0.05.1.15.2Share of Wage Income20122014201620182020YearLegal Occupations0.05.1.15.2Share of Wage Income20122014201620182020YearEducational Instruction and Library0.05.1.15.2Share of Wage Income20122014201620182020YearArts, Design, Sports, and Media0.05.1.15.2Share of Wage Income20122014201620182020YearHealthcare Practitioners and Technical Occ0.05.1.15.2Share of Wage Income20122014201620182020YearHealthcare Support Occupations0.05.1.15.2Share of Wage Income20122014201620182020YearProtective Service Occupations0.05.1.15.2Share of Wage Income20122014201620182020YearFood Preparation and Serving0.05.1.15.2Share of Wage Income20122014201620182020YearBuilding and Maintenance0.05.1.15.2Share of Wage Income20122014201620182020YearPersonal Care and Service0.05.1.15.2Share of Wage Income20122014201620182020YearSales and Related Occupations0.05.1.15.2Share of Wage Income20122014201620182020YearOffice and Administrative Support0.05.1.15.2Share of Wage Income20122014201620182020YearFarming, Fishing, and Forestry0.05.1.15.2Share of Wage Income20122014201620182020YearConstruction and Extraction0.05.1.15.2Share of Wage Income20122014201620182020YearInstallation, Maintenance, and Repair0.05.1.15.2Share of Wage Income20122014201620182020YearProduction Occupations0.05.1.15.2Share of Wage Income20122014201620182020YearTransportation and Material MovingShare of Wage Income Figure 2B.13 Plots of Emp. per Capita by Different Occupation System, 2012-21 (a) By ML Occupation Cluster (b) By 2-Digit Census Occupational Classification Notes: The range of the y-axis is not fixed for each occupation group within an occupation system. The total number of 2-digit Census Occupational Classification is 23, which is the same as the total number of my proposed ML occupation clusters. However, the "Military Specific Occupations" group is excluded from my main sample due to the absence of O∗NET occupational descriptions, which are necessary for constructing the AI occupation indicators and, consequently, the skill group indicators, as explained in Section 2.3.2.2. Thus, only 22 Census 2-digit groups are included in my sample for plotting the employment per capita. 141 8509009501000Emp. per Capita20122014201620182020YearPostsecondary Educators1400015000160001700018000Emp. per Capita20122014201620182020YearService and Retail Workers050100150Emp. per Capita20122014201620182020YearSpecialized Service Professionals5006007008009001000Emp. per Capita20122014201620182020YearConstruction and Craft Workers270028002900300031003200Emp. per Capita20122014201620182020YearFinancial Management Professionals38004000420044004600Emp. per Capita20122014201620182020YearPre−Secondary Educators50100150200Emp. per Capita20122014201620182020YearBuilding Improvement Technicians500100015002000Emp. per Capita20122014201620182020YearPublic Safety, Policy, and Social Science200300400500600Emp. per Capita20122014201620182020YearLife Sciences and Quality Assurance9001000110012001300Emp. per Capita20122014201620182020YearEngineering Technicians and Technologists1020010400106001080011000Emp. per Capita20122014201620182020YearHealthcare Professionals and Practitioners9500100001050011000Emp. per Capita20122014201620182020YearTechnical Maintenance Workers240260280300320340Emp. per Capita20122014201620182020YearWorkplace Safety and Training Specialists5000550060006500Emp. per Capita20122014201620182020YearIT and Data Management Specialists34003500360037003800Emp. per Capita20122014201620182020YearSales and Marketing Professionals300400500600700Emp. per Capita20122014201620182020YearMedia Production and Broadcasting01000200030004000Emp. per Capita20122014201620182020YearRegulatory Compliance Specialists8000850090009500Emp. per Capita20122014201620182020YearManual Workers and Machine Operators160001650017000175001800018500Emp. per Capita20122014201620182020YearService and Administrative Professionals8009001000110012001300Emp. per Capita20122014201620182020YearInfrastructure Architecture and Engineering235024002450250025502600Emp. per Capita20122014201620182020YearCreative and Communication Support Workers8000850090009500Emp. per Capita20122014201620182020YearTechnical and Service Support Personnel0100200300400Emp. per Capita20122014201620182020YearEnvironmental and Earth ScientistsEmployment per 100,000 Capita1000010500110001150012000Emp. per Capita20122014201620182020YearManagement Occupations48005000520054005600Emp. per Capita20122014201620182020YearBusiness and Financial Operations2500300035004000Emp. per Capita20122014201620182020YearComputer and Mathematical Occupations16001800200022002400Emp. per Capita20122014201620182020YearArchitecture and Engineering40060080010001200Emp. per Capita20122014201620182020YearLife, Physical, and Social Science16001650170017501800Emp. per Capita20122014201620182020YearCommunity and Social Service40060080010001200Emp. per Capita20122014201620182020YearLegal Occupations600061006200630064006500Emp. per Capita20122014201620182020YearEducational Instruction and Library17001800190020002100Emp. per Capita20122014201620182020YearArts, Design, Sports, and Media560058006000620064006600Emp. per Capita20122014201620182020YearHealthcare Practitioners and Technical Occ24002500260027002800Emp. per Capita20122014201620182020YearHealthcare Support Occupations1600180020002200Emp. per Capita20122014201620182020YearProtective Service Occupations4500500055006000Emp. per Capita20122014201620182020YearFood Preparation and Serving32003400360038004000Emp. per Capita20122014201620182020YearBuilding and Maintenance3200340036003800Emp. per Capita20122014201620182020YearPersonal Care and Service80009000100001100012000Emp. per Capita20122014201620182020YearSales and Related Occupations1100012000130001400015000Emp. per Capita20122014201620182020YearOffice and Administrative Support0200400600800Emp. per Capita20122014201620182020YearFarming, Fishing, and Forestry35004000450050005500Emp. per Capita20122014201620182020YearConstruction and Extraction320033003400350036003700Emp. per Capita20122014201620182020YearInstallation, Maintenance, and Repair52005400560058006000Emp. per Capita20122014201620182020YearProduction Occupations62006300640065006600Emp. per Capita20122014201620182020YearTransportation and Material MovingEmployment per 100,000 Capita Figure 2B.14 Plots of %Employment by Different Occupation System, 2012-21 (a) By ML Occupation Cluster (b) By 2-Digit Census Occupational Classification Notes: The total number of 2-digit Census Occupational Classification is 23, which is the same as the total number of my proposed ML occupation clusters. However, the "Military Specific Occupations" group is excluded from my main sample due to the absence of O∗NET occupational descriptions, which are necessary for constructing the AI occupation indicators and, consequently, the skill group indicators, as explained in Section 2.3.2.2. Thus, only 22 Census 2-digit groups are included in my sample for plotting the share of employment. 142 0.05.1.15.2Share of Employment20122014201620182020YearPostsecondary Educators0.05.1.15.2Share of Employment20122014201620182020YearService and Retail Workers0.05.1.15.2Share of Employment20122014201620182020YearSpecialized Service Professionals0.05.1.15.2Share of Employment20122014201620182020YearConstruction and Craft Workers0.05.1.15.2Share of Employment20122014201620182020YearFinancial Management Professionals0.05.1.15.2Share of Employment20122014201620182020YearPre−Secondary Educators0.05.1.15.2Share of Employment20122014201620182020YearBuilding Improvement Technicians0.05.1.15.2Share of Employment20122014201620182020YearPublic Safety, Policy, and Social Science0.05.1.15.2Share of Employment20122014201620182020YearLife Sciences and Quality Assurance0.05.1.15.2Share of Employment20122014201620182020YearEngineering Technicians and Technologists0.05.1.15.2Share of Employment20122014201620182020YearHealthcare Professionals and Practitioners0.05.1.15.2Share of Employment20122014201620182020YearTechnical Maintenance Workers0.05.1.15.2Share of Employment20122014201620182020YearWorkplace Safety and Training Specialists0.05.1.15.2Share of Employment20122014201620182020YearIT and Data Management Specialists0.05.1.15.2Share of Employment20122014201620182020YearSales and Marketing Professionals0.05.1.15.2Share of Employment20122014201620182020YearMedia Production and Broadcasting0.05.1.15.2Share of Employment20122014201620182020YearRegulatory Compliance Specialists0.05.1.15.2Share of Employment20122014201620182020YearManual Workers and Machine Operators0.05.1.15.2Share of Employment20122014201620182020YearService and Administrative Professionals0.05.1.15.2Share of Employment20122014201620182020YearInfrastructure Architecture and Engineering0.05.1.15.2Share of Employment20122014201620182020YearCreative and Communication Support Workers0.05.1.15.2Share of Employment20122014201620182020YearTechnical and Service Support Personnel0.05.1.15.2Share of Employment20122014201620182020YearEnvironmental and Earth ScientistsShare of Employment0.05.1.15Share of Employment20122014201620182020YearManagement Occupations0.05.1.15Share of Employment20122014201620182020YearBusiness and Financial Operations0.05.1.15Share of Employment20122014201620182020YearComputer and Mathematical Occupations0.05.1.15Share of Employment20122014201620182020YearArchitecture and Engineering0.05.1.15Share of Employment20122014201620182020YearLife, Physical, and Social Science0.05.1.15Share of Employment20122014201620182020YearCommunity and Social Service0.05.1.15Share of Employment20122014201620182020YearLegal Occupations0.05.1.15Share of Employment20122014201620182020YearEducational Instruction and Library0.05.1.15Share of Employment20122014201620182020YearArts, Design, Sports, and Media0.05.1.15Share of Employment20122014201620182020YearHealthcare Practitioners and Technical Occ0.05.1.15Share of Employment20122014201620182020YearHealthcare Support Occupations0.05.1.15Share of Employment20122014201620182020YearProtective Service Occupations0.05.1.15Share of Employment20122014201620182020YearFood Preparation and Serving0.05.1.15Share of Employment20122014201620182020YearBuilding and Maintenance0.05.1.15Share of Employment20122014201620182020YearPersonal Care and Service0.05.1.15Share of Employment20122014201620182020YearSales and Related Occupations0.05.1.15Share of Employment20122014201620182020YearOffice and Administrative Support0.05.1.15Share of Employment20122014201620182020YearFarming, Fishing, and Forestry0.05.1.15Share of Employment20122014201620182020YearConstruction and Extraction0.05.1.15Share of Employment20122014201620182020YearInstallation, Maintenance, and Repair0.05.1.15Share of Employment20122014201620182020YearProduction Occupations0.05.1.15Share of Employment20122014201620182020YearTransportation and Material MovingShare of Employment Figure 2B.15 %AI Postings by ML Occupation Cluster Relative to Baseline Year, 2012-21 Notes: The baseline year for each ML occupation cluster is the first year when there were narrow AI postings of this %Narrow AI postings𝑖,𝑡 , where 𝑖 represents a ML occupation cluster, 𝑡 group. Each line represents the following ratio, %Narrow AI postings𝑖,𝑡𝑏𝑎𝑠𝑒 is year, and 𝑡𝑏𝑎𝑠𝑒 is the baseline year. 143 11.522.533.5Relative %AI Postings20122014201620182020YearPostsecondary Educators010203040Relative %AI Postings20122014201620182020YearService and Retail Workers0246Relative %AI Postings20122014201620182020YearSpecialized Service Professionals0.2.4.6.81Relative %AI Postings20122014201620182020YearConstruction and Craft Workers051015Relative %AI Postings20122014201620182020YearFinancial Management Professionals.511.522.5Relative %AI Postings20122014201620182020YearPre−Secondary Educators0.2.4.6.81Relative %AI Postings20122014201620182020YearBuilding Improvement Technicians.511.522.5Relative %AI Postings20122014201620182020YearPublic Safety, Policy, and Social Science.4.6.811.2Relative %AI Postings20122014201620182020YearLife Sciences and Quality Assurance11.21.41.61.8Relative %AI Postings20122014201620182020YearEngineering Technicians and Technologists123456Relative %AI Postings20122014201620182020YearHealthcare Professionals and Practitioners.6.811.21.41.6Relative %AI Postings20122014201620182020YearTechnical Maintenance Workers.6.811.21.4Relative %AI Postings20122014201620182020YearWorkplace Safety and Training Specialists.811.21.41.61.8Relative %AI Postings20122014201620182020YearIT and Data Management Specialists010203040Relative %AI Postings20122014201620182020YearSales and Marketing Professionals11.52Relative %AI Postings20122014201620182020YearMedia Production and Broadcasting1234Relative %AI Postings20122014201620182020YearRegulatory Compliance Specialists01234Relative %AI Postings20122014201620182020YearManual Workers and Machine Operators0510152025Relative %AI Postings20122014201620182020YearService and Administrative Professionals.6.811.2Relative %AI Postings20122014201620182020YearInfrastructure Architecture and Engineering051015Relative %AI Postings20122014201620182020YearCreative and Communication Support Workers0246810Relative %AI Postings20122014201620182020YearTechnical and Service Support Personnel11.522.5Relative %AI Postings20122014201620182020YearEnvironmental and Earth ScientistsRelative %Narrow AI Postings Table 2B.1 Occupations Ranked by Share of Narrow AI Postings, 2021 OCC2010 Occupation Title Skill Group %Narrow AI Postings Panel A. Occupations with the Top 15 %AI Postings Statistical Assistants Physical Scientists, All Other Mathematicians and Statisticians Computer Hardware Engineers Software Developers, Applications and Systems Software Astronomers and Physicists Woodworkers Including Model Makers and Patternmakers, All Other Computer, Automated Teller, and Office Machine Repairers Economists and Market Researchers Computer Programmers Electrical and Electronics Engineers Mechanical Engineers Actuaries Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers Surveying and Mapping Technicians Panel B. Occupations with the Bottom 15 %AI Postings Chemical Processing Machine Setters, Operators, and Tenders Barbers First-Line Supervisors of Correctional Officers Bailiffs, Correctional Officers, and Jailers Plasterers and Stucco Masons Entertainers and Performers, Sports and Related Workers, All Other Reservation and Transportation Ticket Agents and Travel Clerks Ushers, Lobby Attendants, and Ticket Takers Weighers, Measurers, Checkers, and Samplers, Recordkeeping Logging Workers Pumping Station Operators Travel Agents Postal Service Clerks Directors, Religious Activities and Education Automotive Glass Installers and Repairers 5920 1760 1240 1400 1020 1700 8550 7010 1800 1010 1410 1460 1200 1000 1560 8640 4500 3700 3800 6460 2760 5410 4420 5630 6130 9650 4830 5540 2050 7160 𝑀 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑀 𝑀 𝑀 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝑁𝑜𝑛 𝑀 𝑀 𝐿 𝐿 𝑀 𝐻 𝑁𝑜𝑛 𝑀 𝑀 𝑀 𝐿 𝑀 𝑀 𝑀 𝐻 𝑁𝑜𝑛 𝑀 0.5134408 0.5022625 0.4595848 0.4479544 0.4286178 0.2866242 0.25 0.2305805 0.2304875 0.2256069 0.2237831 0.2184486 0.2033132 0.2027434 0.2007183 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Notes: The share of narrow AI postings in this table is calculated at the 4-digit-occupation-by-year level. There is a tie in the lowest share of narrow AI postings, with 64 occupations having no narrow AI posting. 15 out of 64 occupations are randomly chosen and listed in Panel B. 𝐻 𝐴𝐼 , 𝐻 𝑁 𝑜𝑛, 𝑀, and 𝐿 represent high-skilled AI-complement, high-skilled not-yet-AI, middle-skilled, and low-skilled occupation group, respectively. 144 Table 2B.2 Effects of Demand for AI Skills on Employment per Capita—Adopting Different SSIVs, 2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Dep. Var.: Employment per 100,000 Capita "Leave-One-Out" SSIV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster1 (2) -92.339∗∗∗ (19.785) 112.148∗∗∗ (27.069) 46.987∗∗ (19.926) 10.546 (18.021) -361.106∗ (190.281) -220.774 (187.992) -194.626 (184.771) (3) -23.431 (15.930) 104.546∗∗∗ (25.826) 43.239∗∗ (19.693) 10.996 (18.083) -282.738 (228.930) -127.816 (236.239) -114.134 (230.363) (4) -17.202 (14.465) 107.041∗∗∗ (25.458) 44.447∗∗ (18.122) 10.270 (15.803) -356.421∗ (189.121) -219.972 (186.713) -195.785 (183.521) (5) -71.854∗∗∗ (16.847) 115.332∗∗∗ (27.218) 51.323∗∗∗ (19.648) 14.428 (17.382) -204.808 (135.222) -138.956 (132.753) -161.660 (118.682) OLS (1) -6.146 (8.282) 55.756∗∗∗ (14.940) 20.065∗∗ (10.099) 2.868 (8.991) -333.383∗ (187.602) -209.460 (184.689) -192.177 (181.375) 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.122 2594.341 202,796 ✓ ✓ ✓ ✓ ✓ 0.129 7772.793 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.121 2722.659 192,008 ✓ ✓ ✓ ✓ ✓ 0.128 3414.392 145 Table 2B.3 Effects of Demand for AI Skills on Share of Employment—Adopting Different SSIVs, 2012-21 %AI Postings3 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ OLS (1) -0.006 (0.008) 0.056∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) -0.333∗ (0.188) -0.209 (0.185) -0.192 (0.181) Dep. Var.: Share of Employment1 "Leave-One-Out" SSIV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) -0.092∗∗∗ (0.020) 0.112∗∗∗ (0.027) 0.047∗∗ (0.020) 0.011 (0.018) -0.361∗ (0.190) -0.221 (0.188) -0.195 (0.185) (3) -0.023 (0.016) 0.105∗∗∗ (0.026) 0.043∗∗ (0.020) 0.011 (0.018) -0.283 (0.229) -0.128 (0.236) -0.114 (0.230) (4) -0.017 (0.014) 0.107∗∗∗ (0.025) 0.044∗∗ (0.018) 0.010 (0.016) -0.356∗ (0.189) -0.220 (0.187) -0.196 (0.184) (5) -0.072∗∗∗ (0.017) 0.115∗∗∗ (0.027) 0.051∗∗∗ (0.020) 0.014 (0.017) -0.205 (0.135) -0.139 (0.133) -0.162 (0.119) 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.122 2594.341 192,008 ✓ ✓ ✓ ✓ ✓ 0.128 3414.392 202,796 ✓ ✓ ✓ ✓ ✓ 0.129 7772.793 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 3 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.121 2722.659 146 Table 2B.4 Effects of Demand for AI Skills on Mean Hourly Wage—Adopting Different SSIVs, 2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ OLS (1) 0.005 (0.005) 0.025∗∗∗ (0.007) 0.007 (0.006) -0.009 (0.005) 0.441∗∗∗ (0.104) 0.126 (0.096) -0.088 (0.092) Dep. Var.: Log Mean Hourly Wage Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster1 (2) 0.010 (0.014) 0.050∗∗∗ (0.010) 0.029∗∗∗ (0.010) 0.005 (0.009) 0.431∗∗∗ (0.104) 0.117 (0.096) -0.094 (0.092) (3) -0.002 (0.010) 0.048∗∗∗ (0.010) 0.025∗∗∗ (0.009) 0.003 (0.008) 0.431∗∗∗ (0.098) 0.127 (0.094) -0.101 (0.090) (4) -0.005 (0.013) 0.055∗∗∗ (0.011) 0.029∗∗∗ (0.010) 0.002 (0.010) 0.428∗∗∗ (0.103) 0.117 (0.095) -0.092 (0.091) (5) 0.012 (0.013) 0.044∗∗∗ (0.010) 0.026∗∗∗ (0.009) -0.002 (0.009) 0.381∗∗∗ (0.115) 0.171 (0.109) -0.098 (0.095) 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 198,588 ✓ ✓ ✓ ✓ ✓ 0.341 7515.481 187,960 ✓ ✓ ✓ ✓ ✓ 0.339 2454.979 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 186,742 ✓ ✓ ✓ ✓ ✓ 0.302 2595.872 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 3252.866 147 Table 2B.5 Effects of Demand for AI Skills on Wage Income Share—Adopting Different SSIVs, 2012-21 %AI Postings3 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ OLS (1) -0.011 (0.011) 0.089∗∗∗ (0.026) 0.026∗ (0.014) 0.005 (0.012) -0.151 (0.166) -0.029 (0.159) -0.169 (0.141) Dep. Var.: Share of Wage Income1 Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) -0.101∗∗∗ (0.025) 0.158∗∗∗ (0.040) 0.056∗∗ (0.027) 0.014 (0.022) -0.184 (0.168) -0.042 (0.163) -0.172 (0.145) (3) -0.026 (0.020) 0.148∗∗∗ (0.038) 0.051∗ (0.027) 0.015 (0.022) -0.090 (0.221) 0.072 (0.231) -0.165 (0.206) (4) -0.025 (0.018) 0.148∗∗∗ (0.036) 0.056∗∗ (0.025) 0.014 (0.020) -0.177 (0.167) -0.042 (0.162) -0.174 (0.144) (5) -0.082∗∗∗ (0.021) 0.159∗∗∗ (0.040) 0.059∗∗ (0.027) 0.015 (0.022) 0.037 (0.131) 0.101 (0.115) -0.127∗ (0.076) 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 192,008 ✓ ✓ ✓ ✓ ✓ 0.152 2594.341 202,796 ✓ ✓ ✓ ✓ ✓ 0.154 7772.793 192,008 ✓ ✓ ✓ ✓ ✓ 0.156 3414.392 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 3 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.150 2722.659 148 Table 2B.6 Effects of Demand for AI Skills on Employment per Capita—Controlling for CS Skills with SSIV Approach,2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ %CS Postings3 %CS Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ OLS (1) -3.319 (7.305) 50.117∗∗∗ (13.951) 17.475∗ (9.068) 1.214 (7.837) -20.644 (16.402) 34.595∗ (19.244) 15.984 (17.927) 10.290 (17.749) -339.642∗ (189.454) -212.117 (186.650) -193.763 (183.421) Dep. Var.: Employment per 100,000 Capita Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster1 (2) 230.823∗∗∗ (69.433) 74.264 (47.849) 48.567 (33.341) (3) -21.448∗ (11.001) 80.628∗∗∗ (27.198) 31.679∗∗ (15.230) 15.045 (31.375) 10,597.27∗∗∗ (2659.423) -13.824 (10.474) -258.415∗∗∗ (84.086) 440.349∗ (230.409) 203.431 (185.603) 203.121 (175.500) -522.176∗∗ (217.974) -345.447∗ (205.772) -416.912∗∗ (203.557) 118.009 (100.192) 57.044 (85.209) 135.208∗ (80.918) -372.870∗ (200.537) -226.329 (197.692) -215.101 (194.491) (4) -11.059 (8.898) 76.245∗∗∗ (20.555) 32.214∗∗∗ (10.759) -0.380 (9.124) -160.357∗∗ (71.667) 212.925∗∗∗ (75.302) 83.368 (71.702) 71.801 (64.526) -396.763∗∗ (197.764) -233.944 (196.731) -206.787 (193.099) (5) -81.387∗∗∗ (23.373) 148.067∗∗∗ (38.270) 65.866∗∗ (27.776) 18.935 (25.563) -287.229∗∗∗ (93.169) -189.905∗∗ (94.463) -97.905 (79.981) -52.257 (75.213) -267.485 (166.744) -188.700 (162.947) -147.224 (156.835) 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.126 83.690 192,008 ✓ ✓ ✓ ✓ ✓ -18.991 1.396 192,008 ✓ ✓ ✓ ✓ ✓ 0.123 234.610 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 3 Phrases that belong to broad AI category but not narrow AI category are used to compute %CS postings at the state-year level. %CS postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.097 292.027 149 Table 2B.7 Effects of Demand for AI Skills on Share of Employment—Controlling for CS Skills with SSIV Approach,2012-21 %AI Postings3 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ %CS Postings4 %CS Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ OLS (1) -0.003 (0.007) 0.050∗∗∗ (0.014) 0.017∗ (0.009) 0.001 (0.008) -0.021 (0.016) 0.035∗ (0.019) 0.016 (0.018) 0.010 (0.018) -0.340∗ (0.189) -0.212 (0.187) -0.194 (0.183) Dep. Var.: Share of Employment1 Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) 0.231∗∗∗ (0.069) 0.074 (0.048) 0.049 (0.033) 0.015 (0.031) 10.597∗∗∗ (2.659) 0.440∗ (0.230) 0.203 (0.186) 0.203 (0.176) -0.522∗∗ (0.218) -0.345∗ (0.206) -0.417∗∗ (0.204) (3) -0.021∗ (0.011) 0.081∗∗∗ (0.027) 0.032∗∗ (0.015) -0.014 (0.010) -0.258∗∗∗ (0.084) 0.118 (0.100) 0.057 (0.085) 0.135∗ (0.081) -0.373∗ (0.201) -0.226 (0.198) -0.215 (0.194) (4) -0.011 (0.009) 0.076∗∗∗ (0.021) 0.032∗∗∗ (0.011) -0.000 (0.009) -0.160∗∗ (0.072) 0.213∗∗∗ (0.075) 0.083 (0.072) 0.072 (0.065) -0.397∗∗ (0.198) -0.234 (0.197) -0.207 (0.193) (5) -0.081∗∗∗ (0.023) 0.148∗∗∗ (0.038) 0.066∗∗ (0.028) 0.019 (0.026) -0.287∗∗∗ (0.093) -0.190∗∗ (0.094) -0.098 (0.080) -0.052 (0.075) -0.267 (0.167) -0.189 (0.163) -0.147 (0.157) 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.123 234.610 192,008 ✓ ✓ ✓ ✓ ✓ 0.126 83.690 192,008 ✓ ✓ ✓ ✓ ✓ -18.991 1.396 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 3 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 4 Phrases that belong to broad AI category but not narrow AI category are used to compute %CS postings at the state-year level. %CS postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.097 292.027 150 Table 2B.8 Effects of Demand for AI Skills on Mean Hourly Wage—Controlling for CS Skills with SSIV Approach,2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ %CS Postings3 %CS Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ OLS (1) 0.004 (0.006) 0.034∗∗∗ (0.008) 0.013∗ (0.007) -0.008 (0.006) 0.006 (0.017) -0.057∗∗ (0.023) -0.033∗ (0.019) -0.004 (0.018) 0.452∗∗∗ (0.104) 0.132 (0.097) -0.087 (0.093) Dep. Var.: Log Mean Hourly Wage Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) -0.006 (0.020) 0.032 (0.021) 0.033∗∗ (0.016) -0.006 (0.015) -0.807 (0.663) 0.090 (0.085) -0.034 (0.061) 0.045 (0.058) 0.419∗∗∗ (0.106) 0.129 (0.098) -0.087 (0.094) (3) 0.004 (0.014) 0.037∗∗ (0.015) 0.030∗∗ (0.012) 0.001 (0.012) 0.018 (0.065) 0.058 (0.063) -0.026 (0.051) 0.011 (0.049) 0.421∗∗∗ (0.105) 0.123 (0.098) -0.095 (0.094) (4) 0.010 (0.018) 0.060∗∗∗ (0.019) 0.021 (0.016) -0.011 (0.016) 0.025 (0.111) -0.037 (0.081) 0.049 (0.069) 0.085 (0.068) 0.436∗∗∗ (0.107) 0.108 (0.099) -0.109 (0.096) (5) 0.008 (0.019) 0.073∗∗∗ (0.024) 0.048∗∗∗ (0.018) -0.002 (0.018) 0.102 (0.080) -0.143 (0.095) -0.115∗ (0.069) 0.005 (0.067) 0.555∗∗∗ (0.105) 0.236∗∗ (0.098) -0.002 (0.095) 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 225.433 187,960 ✓ ✓ ✓ ✓ ✓ 0.275 1.819 187,960 ✓ ✓ ✓ ✓ ✓ 0.338 77.326 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 3 Phrases that belong to broad AI category but not narrow AI category are used to compute %CS postings at the state-year level. %CS postings is in percentage point. 186,742 ✓ ✓ ✓ ✓ ✓ 0.341 278.088 151 Table 2B.9 Effects of Demand for AI Skills on Wage Income Share—Controlling for CS Skills with SSIV Approach,2012-021 %AI Postings3 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ %CS Postings4 %CS Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ OLS (1) -0.012 (0.010) 0.086∗∗∗ (0.025) 0.030∗∗ (0.014) 0.006 (0.011) 0.000 (0.016) 0.013 (0.027) -0.021 (0.019) -0.006 (0.017) -0.153 (0.167) -0.026 (0.161) -0.168 (0.143) Dep. Var.: Share of Wage Income1 Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) 0.204∗∗∗ (0.067) 0.113∗ (0.059) 0.093∗∗ (0.040) 0.029 (0.031) 10.574∗∗∗ (2.637) 0.481∗ (0.254) 0.002 (0.181) 0.138 (0.162) -0.355∗ (0.210) -0.132 (0.190) -0.382∗∗ (0.178) (3) -0.041∗∗∗ (0.014) 0.124∗∗∗ (0.039) 0.061∗∗ (0.024) 0.000 (0.012) -0.189∗∗ (0.076) 0.115 (0.122) -0.066 (0.086) 0.072 (0.069) -0.196 (0.179) -0.027 (0.172) -0.183 (0.153) (4) -0.022∗ (0.012) 0.116∗∗∗ (0.033) 0.048∗∗∗ (0.016) 0.005 (0.012) -0.159∗ (0.082) 0.224∗∗ (0.100) 0.051 (0.085) 0.061 (0.070) -0.220 (0.176) -0.050 (0.173) -0.183 (0.155) (5) -0.111∗∗∗ (0.030) 0.215∗∗∗ (0.055) 0.106∗∗∗ (0.040) 0.031 (0.032) -0.251∗∗ (0.101) -0.296∗∗ (0.137) -0.271∗∗∗ (0.104) -0.109 (0.082) -0.041 (0.152) 0.019 (0.148) -0.117 (0.116) 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 192,008 ✓ ✓ ✓ ✓ ✓ 0.153 234.610 192,008 ✓ ✓ ✓ ✓ ✓ 0.155 83.690 192,008 ✓ ✓ ✓ ✓ ✓ -13.519 1.396 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 3 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 4 Phrases that belong to broad AI category but not narrow AI category are used to compute %CS postings at the state-year level. %CS postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.123 292.027 152 Table 2B.10 Effects of Demand for AI Skills on Employment—Controlling for Software/Robot Exposure, 2012-21 Dep. Var.: Emp. per 100,000 Capita Share of Emp.1 Main Spec. Controlling for Exposure Main Spec. Controlling for Exposure (1) -6.146 (8.282) 55.756∗∗∗ (14.940) 20.065∗∗ (10.099) 2.868 (8.991) -333.383∗ (187.602) -209.460 (184.689) -192.177 (181.375) to Software/Robot (2) -6.068 (8.286) 55.665∗∗∗ (14.935) 20.157∗∗ (10.126) 2.754 (9.011) -29.705 (84.576) 41.908 (59.383) -328.487∗ (189.279) -202.830 (186.656) -196.507 (179.849) (3) -0.006 (0.008) 0.056∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) -0.333∗ (0.188) -0.209 (0.185) -0.192 (0.181) to Software/Robot (4) -0.006 (0.008) 0.056∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) -0.030 (0.085) 0.042 (0.059) -0.328∗ (0.189) -0.203 (0.187) -0.197 (0.180) %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Software Exposure3 Robot Exposure4 Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 190,859 ✓ ✓ ✓ ✓ ✓ 0.129 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 3,4 The software and robot exposure scores are constructed by Webb (2019), which measure the capabilities in software and robots for performing an occupation’s tasks. 190,859 ✓ ✓ ✓ ✓ ✓ 0.129 153 Table 2B.11 Effects of Demand for AI Skills on Wages—Controlling for Software/Robot Exposure, 2012-21 Dep. Var.: Log Mean Hourly Wages Share of Wage Income1 Main Spec. Controlling for Exposure Main Spec. Controlling for Exposure (1) 0.005 (0.005) 0.025∗∗∗ (0.007) 0.007 (0.006) -0.009 (0.005) 0.441∗∗∗ (0.104) 0.126 (0.096) -0.088 (0.092) to Software/Robot (2) 0.005 (0.005) 0.025∗∗∗ (0.007) 0.008 (0.006) -0.008 (0.005) 0.144∗ (0.081) -0.092∗∗ (0.042) 0.440∗∗∗ (0.103) 0.128 (0.095) -0.089 (0.089) (3) -0.011 (0.011) 0.089∗∗∗ (0.026) 0.026∗ (0.014) 0.005 (0.012) -0.151 (0.166) -0.029 (0.159) -0.169 (0.141) to Software/Robot (4) -0.011 (0.011) 0.089∗∗∗ (0.026) 0.027∗ (0.015) 0.005 (0.012) 0.000 (0.064) -0.010 (0.041) -0.144 (0.167) -0.020 (0.161) -0.165 (0.140) %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Software Exposure3 Robot Exposure4 Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 186,911 ✓ ✓ ✓ ✓ ✓ 0.345 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 3,4 The software and robot exposure scores are constructed by Webb (2019), which measure the capabilities in software and robots for performing an occupation’s tasks. 190,859 ✓ ✓ ✓ ✓ ✓ 0.157 154 Table 2B.12 Effects of Demand for AI Skills on Employment—Comparing Narrow vs. Broad AI Definition, 2012-21 Emp. per 100,000 Capita Share of Emp.1 Dep. Var.: Main Spec.: Narrow AI Def. (1) -6.146 (8.282) 55.756∗∗∗ (14.940) 20.065∗∗ (10.099) 2.868 (8.991) -333.383∗ (187.602) -209.460 (184.689) -192.177 (181.375) Broad AI Def. (2) -6.426 (7.180) 40.379∗∗∗ (11.235) 17.276∗ (8.877) 2.654 (7.791) -359.568∗ (187.916) -207.855 (186.267) -193.676 (182.588) Main Spec.: Narrow AI Def. (3) -0.006 (0.008) 0.056∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) -0.333∗ (0.188) -0.209 (0.185) -0.192 (0.181) Broad AI Def. (4) -0.006 (0.007) 0.040∗∗∗ (0.011) 0.017∗ (0.009) 0.003 (0.008) -0.360∗ (0.188) -0.208 (0.186) -0.194 (0.183) %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 In columns 1 and 3 (2 and 4), narrow (broad) AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 192,008 ✓ ✓ ✓ ✓ ✓ 0.130 192,008 ✓ ✓ ✓ ✓ ✓ 0.130 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 155 Table 2B.13 Effects of Demand for AI Skills on Wages—Comparing Narrow vs. Broad AI Definition, 2012-21 Log Mean Hourly Wages Share of Wage Income1 Dep. Var.: Main Spec.: Narrow AI Def. (1) 0.005 (0.005) 0.025∗∗∗ (0.007) 0.007 (0.006) -0.009 (0.005) 0.441∗∗∗ (0.104) 0.126 (0.096) -0.088 (0.092) Broad AI Def. (2) 0.004 (0.004) 0.017∗∗∗ (0.005) 0.005 (0.005) -0.007 (0.004) 0.353∗∗∗ (0.108) 0.126 (0.096) -0.086 (0.092) Main Spec.: Narrow AI Def. (3) -0.011 (0.011) 0.089∗∗∗ (0.026) 0.026∗ (0.014) 0.005 (0.012) -0.151 (0.166) -0.029 (0.159) -0.169 (0.141) Broad AI Def. (4) -0.010 (0.009) 0.061∗∗∗ (0.019) 0.022∗ (0.013) 0.004 (0.010) -0.213 (0.166) -0.026 (0.161) -0.172 (0.143) %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 In columns 1 and 3 (2 and 4), narrow (broad) AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 187,960 ✓ ✓ ✓ ✓ ✓ 0.335 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 156 Table 2B.14 Effects of Demand for AI Skills on Employment per Capita—Using AI Posting Share Quintiles, 2012-21 %AI Postings3 %AI Postings × High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ Skill Group = High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ OLS (1) -6.216 (8.286) 48.853∗∗∗ (12.974) 38.268∗∗∗ (13.342) 10.131 (10.305) -6.320 (13.476) 11.129 (10.147) 2.876 (8.991) -349.610∗ (187.009) -175.111 (191.082) 70.477 (256.242) 13.270 (212.031) -383.211∗∗ (182.666) -186.631 (181.869) Dep. Var.: Employment per 100,000 Capita Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) -85.276∗∗∗ (19.489) 99.140∗∗∗ (24.210) 79.798∗∗∗ (25.707) 31.974 (19.617) 11.232 (23.393) 12.545 (23.463) 10.715 (18.012) -373.521∗∗ (189.883) -191.474 (193.761) 62.172 (258.880) -46.608 (241.724) -385.143∗∗ (186.549) -189.173 (185.228) (3) -36.123∗∗ (15.230) 90.803∗∗∗ (21.893) 73.485∗∗∗ (23.289) 26.344 (18.004) 2.387 (22.058) 19.884 (20.025) 10.850 (16.224) -368.446∗ (189.206) -189.621 (193.115) 63.610 (258.203) -16.595 (238.168) -387.530∗∗ (185.660) -190.062 (184.440) (4) -13.202 (14.441) 96.294∗∗∗ (22.795) 80.581∗∗∗ (25.579) 27.874 (18.882) 0.804 (20.595) 23.376 (17.422) 10.264 (15.799) -370.143∗∗ (188.629) -192.923 (192.522) 62.475 (257.453) -11.378 (233.475) -388.782∗∗ (184.996) -190.342 (183.981) (5) -56.901∗∗∗ (15.184) 101.648∗∗∗ (23.490) 83.682∗∗∗ (25.421) 31.521∗ (18.635) 6.653 (22.658) 13.795 (23.596) 10.607 (16.758) -324.360∗∗ (164.379) -218.962 (168.215) 81.577 (233.694) -23.859 (239.980) -358.280∗∗ (161.318) -153.562 (157.866) 192,008 ✓ ✓ ✓ ✓ ✓ 0.143 192,008 ✓ ✓ ✓ ✓ ✓ 0.137 1,417.981 192,008 ✓ ✓ ✓ ✓ ✓ 0.142 2,625.620 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 190,712 ✓ ✓ ✓ ✓ ✓ 0.137 1,495.119 192,008 ✓ ✓ ✓ ✓ ✓ 0.141 1,932.669 157 Table 2B.15 Effects of Demand for AI Skills on Share of Employment—Using AI Posting Share Quintiles, 2012-21 %AI Postings3 %AI Postings × High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ Skill Group = High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ OLS (1) -0.006 (0.008) 0.049∗∗∗ (0.013) 0.038∗∗∗ (0.013) 0.010 (0.010) -0.006 (0.013) 0.011 (0.010) 0.003 (0.009) -0.350∗ (0.187) -0.175 (0.191) 0.070 (0.256) 0.013 (0.212) -0.383∗∗ (0.183) -0.187 (0.182) Dep. Var.: Share of Employment1 Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) -0.085∗∗∗ (0.019) 0.099∗∗∗ (0.024) 0.080∗∗∗ (0.026) 0.032 (0.020) 0.011 (0.023) 0.013 (0.023) 0.011 (0.018) -0.374∗∗ (0.190) -0.191 (0.194) 0.062 (0.259) -0.047 (0.242) -0.385∗∗ (0.187) -0.189 (0.185) (3) -0.036∗∗ (0.015) 0.091∗∗∗ (0.022) 0.073∗∗∗ (0.023) 0.026 (0.018) 0.002 (0.022) 0.020 (0.020) 0.011 (0.016) -0.368∗ (0.189) -0.190 (0.193) 0.064 (0.258) -0.017 (0.238) -0.388∗∗ (0.186) -0.190 (0.184) (4) -0.013 (0.014) 0.096∗∗∗ (0.023) 0.081∗∗∗ (0.026) 0.028 (0.019) 0.001 (0.021) 0.023 (0.017) 0.010 (0.016) -0.370∗∗ (0.189) -0.193 (0.193) 0.062 (0.257) -0.011 (0.233) -0.389∗∗ (0.185) -0.190 (0.184) (5) -0.057∗∗∗ (0.015) 0.102∗∗∗ (0.023) 0.084∗∗∗ (0.025) 0.032∗ (0.019) 0.007 (0.023) 0.014 (0.024) 0.011 (0.017) -0.324∗∗ (0.164) -0.219 (0.168) 0.082 (0.234) -0.024 (0.240) -0.358∗∗ (0.161) -0.154 (0.158) 192,008 ✓ ✓ ✓ ✓ ✓ 0.143 192,008 ✓ ✓ ✓ ✓ ✓ 0.142 2,625.620 192,008 ✓ ✓ ✓ ✓ ✓ 0.137 1,417.981 192,008 ✓ ✓ ✓ ✓ ✓ 0.141 1,932.669 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic 190,712 ✓ ✓ ✓ ✓ ✓ 0.137 1,495.119 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 3 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 158 Table 2B.16 Effects of Demand for AI Skills on Mean Hourly Wage—Using AI Posting Share Quintiles, 2012-21 %AI Postings2 %AI Postings × High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ Skill Group = High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ OLS (1) 0.005 (0.005) 0.025∗∗∗ (0.006) 0.013∗∗ (0.006) 0.004 (0.007) 0.006 (0.008) -0.012 (0.015) -0.009 (0.005) 0.334∗∗∗ (0.105) 0.189∗ (0.103) 0.149 (0.121) 0.061 (0.117) 0.045 (0.133) -0.081 (0.093) Dep. Var.: Log Mean Hourly Wage Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster1 (2) 0.012 (0.014) 0.051∗∗∗ (0.010) 0.040∗∗∗ (0.010) 0.023∗ (0.012) 0.022 (0.014) 0.001 (0.026) 0.005 (0.009) 0.324∗∗∗ (0.105) 0.178∗ (0.103) 0.140 (0.121) 0.007 (0.125) 0.040 (0.133) -0.088 (0.093) (3) 0.004 (0.011) 0.049∗∗∗ (0.009) 0.035∗∗∗ (0.009) 0.018∗ (0.011) 0.021∗ (0.013) -0.001 (0.023) 0.003 (0.008) 0.324∗∗∗ (0.105) 0.180∗ (0.103) 0.142 (0.121) 0.009 (0.125) 0.040 (0.133) -0.087 (0.093) (4) -0.005 (0.013) 0.056∗∗∗ (0.011) 0.037∗∗∗ (0.011) 0.022∗ (0.012) 0.029∗∗ (0.014) -0.002 (0.029) 0.002 (0.010) 0.321∗∗∗ (0.105) 0.179∗ (0.102) 0.141 (0.120) -0.015 (0.125) 0.040 (0.133) -0.086 (0.092) (5) 0.018 (0.013) 0.047∗∗∗ (0.009) 0.036∗∗∗ (0.010) 0.018 (0.012) 0.020 (0.013) 0.019 (0.028) -0.001 (0.009) 0.424∗∗∗ (0.104) 0.258∗∗ (0.101) 0.242∗∗ (0.119) 0.020 (0.122) 0.095 (0.135) -0.000 (0.092) 187,960 ✓ ✓ ✓ ✓ ✓ 0.339 187,960 ✓ ✓ ✓ ✓ ✓ 0.338 1,338.611 187,960 ✓ ✓ ✓ ✓ ✓ 0.338 2,515.017 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic 186,742 ✓ ✓ ✓ ✓ ✓ 0.340 1,424.106 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 187,960 ✓ ✓ ✓ ✓ ✓ 0.338 1,839.275 159 Table 2B.17 Effects of Demand for AI Skills on Wage Income Share—Using AI Posting Share Quintiles, 2012-21 %AI Postings3 %AI Postings × High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ Skill Group = High-Skilled Occ × AI Occ (q5) High-Skilled Occ × AI Occ (q4) High-Skilled Occ × AI Occ (q3) High-Skilled Occ × AI Occ (q2) High-Skilled Occ × AI Occ (q1) Middle-Skilled Occ OLS (1) -0.011 (0.011) 0.076∗∗∗ (0.022) 0.055∗∗ (0.023) 0.008 (0.013) -0.008 (0.017) 0.015 (0.012) 0.005 (0.012) -0.172 (0.164) 0.050 (0.173) 0.364 (0.301) 0.062 (0.213) -0.319∗∗ (0.153) -0.159 (0.141) Dep. Var.: Share of Wage Income1 Leave-One-Out IV by Summing across 2-Digit OCC 4-Digit OCC 4-Digit NAICS ML Occupation Cluster2 (2) -0.088∗∗∗ (0.024) 0.139∗∗∗ (0.034) 0.106∗∗ (0.042) 0.025 (0.025) 0.005 (0.030) 0.023 (0.024) 0.014 (0.022) -0.201 (0.167) 0.030 (0.175) 0.357 (0.304) 0.018 (0.253) -0.323∗∗ (0.157) -0.162 (0.145) (3) -0.040∗∗ (0.019) 0.127∗∗∗ (0.032) 0.098∗∗ (0.039) 0.020 (0.023) -0.004 (0.028) 0.028 (0.021) 0.014 (0.020) -0.194 (0.167) 0.032 (0.174) 0.358 (0.303) 0.049 (0.248) -0.324∗∗ (0.156) -0.162 (0.144) (4) -0.019 (0.018) 0.133∗∗∗ (0.032) 0.111∗∗ (0.044) 0.027 (0.023) -0.004 (0.026) 0.029 (0.021) 0.014 (0.020) -0.196 (0.166) 0.027 (0.173) 0.355 (0.302) 0.048 (0.243) -0.325∗∗ (0.156) -0.163 (0.144) (5) -0.064∗∗∗ (0.019) 0.142∗∗∗ (0.034) 0.112∗∗∗ (0.043) 0.024 (0.024) -0.000 (0.029) 0.024 (0.023) 0.013 (0.021) -0.132 (0.146) -0.031 (0.155) 0.415 (0.298) 0.041 (0.252) -0.311∗∗ (0.136) -0.130 (0.120) 192,008 ✓ ✓ ✓ ✓ ✓ 0.175 192,008 ✓ ✓ ✓ ✓ ✓ 0.173 2,625.620 192,008 ✓ ✓ ✓ ✓ ✓ 0.173 1,932.669 192,008 ✓ ✓ ✓ ✓ ✓ 0.170 1,417.981 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Cragg-Donald Wald F Statistic 190,712 ✓ ✓ ✓ ✓ ✓ 0.166 1,495.119 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 The ML occupation cluster is an alternative occupation classification constructed by clustering occupations based on skill requirements using machine learning. Details are presented in Section 2.6.2. 3 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 160 Table 2B.18 Component Loadings for the AI Skill Prevalence Score (Ranked from the Highest to the Lowest) AI Skill Principal Component 1 Python Machine learning Big data Artificial intelligence Robotic Matlab Natural language processing (NLP) Deep learning Data mining TensorFlow Computer vision Autonomous driving PyTorch Augmented reality (AR) Virtual reality (VR) Neural network 3-D modeling Computer graphics Voice recognition Multimedia Pattern recognition 0.89732448 0.30575326 0.2575757 0.11524112 0.07181856 0.06659661 0.0557427 0.04594767 0.04416461 0.03561318 0.03542881 0.02640705 0.02231685 0.01904324 0.01846953 0.01384596 0.01179794 0.00799434 0.0071089 0.00704629 0.00428575 Notes: Then component loadings are static across time, which capture the importance or weight of a narrow AI phrase in constructing the AI Skill Prevalence Score. Python allows users to choose the number of components to keep. Thus, the multi-dimensional skill set is projected to a one-dimensional space by principal component analysis (PCA) to construct this single measurement. 161 Table 2B.19 Occupations Ranked by AI Skill Prevalence Score, 2021 OCC2010 Occupation Title AI Skill Prevalence Score Skill Group Panel A. Occupations with the Top 15 AI Skill Prevalence Score 1020 710 730 1100 1000 840 1240 950 110 30 1650 1220 1410 4930 1400 7160 8420 7850 940 8940 8920 8450 6700 6240 8720 8540 8640 6010 8730 3260 Software Developers, Applications and Systems Software Management Analysts Other Business Operations and Management Specialists Network and Computer Systems Administrators Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers Financial Analysts Mathematical Science Occupations, All Other Financial Specialists, All Other Computer and Information Systems Managers Managers in Marketing, Advertising, and Public Relations Medical Scientists, and Life Scientists, All Other Operations Research Analysts Electrical and Electronics Engineers Sales Engineers Computer Hardware Engineers 19.75286 2.638285 2.111124 1.96713 1.562139 0.5148678 0.4935438 0.4659868 0.4455855 0.4433329 0.4405924 0.3378968 0.3365522 0.3293847 0.3147703 Panel B. Occupations with the Bottom 15 AI Skill Prevalence Score Automotive Glass Installers and Repairers Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders Food Cooking Machine Operators and Tenders Tax Preparers Tire Builders Molders, Shapers, and Casters, Except Metal and Plastic Upholsterers Elevator Installers and Repairers Carpet, Floor, and Tile Installers and Finishers Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders Woodworking Machine Setters, Operators, and Tenders, Except Sawing Chemical Processing Machine Setters, Operators, and Tenders Agricultural Inspectors Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders Health Diagnosing and Treating Practitioners, All Other -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 -0.0926943 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝑀 𝑀 𝑀 𝐻 𝑁𝑜𝑛 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝐻 𝑁𝑜𝑛 Notes: The occupation-year AI Skill Prevalence Score is standardized within a year. There is a tie in the lowest AI Skill Prevalence Score, with 66 occupations having the lowest score, -0.0926943. 15 out of 66 occupations are randomly chosen and listed in Panel B. 𝐻 𝐴𝐼 , 𝐻 𝑁 𝑜𝑛, 𝑀, and 𝐿 represent high-skilled AI-complement, high-skilled not-yet-AI, middle-skilled, and low-skilled occupation group, respectively. 162 Table 2B.20 States Ranked by AI Skill Prevalence Score, 2021 Panel A. States with the Top 15 AI Skill Prevalence Score Panel B. States with the Bottom 15 AI Skill Prevalence Score State AI Skill Prevalence Score State AI Skill Prevalence Score California Texas New York Washington Virginia Massachusetts Illinois Florida Georgia North Carolina Pennsylvania Colorado New Jersey Maryland Ohio Notes: The state-year AI Skill Prevalence Score is standardized within a year. Wyoming Alaska South Dakota Hawaii Vermont West Virginia Montana Mississippi North Dakota Maine Rhode Island Nebraska Oklahoma New Hampshire Nevada 5.356526 2.384235 1.545264 1.441433 0.9939204 0.9668954 0.6540916 0.5013081 0.4332158 0.3860596 0.2700901 0.2696101 0.2091825 0.1105196 0.0961018 -0.6126739 -0.6096864 -0.6029171 -0.6021812 -0.5961387 -0.5951093 -0.5942092 -0.5881292 -0.5876687 -0.5788439 -0.5525354 -0.5342934 -0.5319125 -0.5318903 -0.5297292 163 Table 2B.21 Distribution of Effects with One State Left Out, 2012-21 Max p75 p50 p25 Min Panel A. Outcome: Employment per 100,000 Capita %AI Postings1 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ -3.289 (7.474) -5.937 (8.271) -6.179 (8.282) -6.433 (8.220) -8.388 (10.615) 68.137∗∗∗ (17.595) 26.533∗∗ (13.017) 56.032∗∗∗ (14.999) 20.313∗ (10.581) 55.775∗∗∗ (14.965) 20.102∗∗ (10.117) 55.306∗∗∗ (15.138) 19.879∗∗ (10.085) 46.573∗∗∗ (13.923) 14.571∗ (8.759) 3.369 (9.004) 3.015 (9.017) 2.893 (9.010) 2.756 (8.675) 1.998 (9.433) Panel B. Outcome: Share of Employment2 %AI Postings %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ -0.003 (0.007) -0.006 (0.008) -0.008 (0.008) -0.006 (0.008) -0.008 (0.011) 0.068∗∗∗ (0.018) 0.027∗∗ (0.013) 0.003 (0.009) 0.056∗∗∗ (0.015) 0.020∗ (0.011) 0.003 (0.009) 0.056∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) 0.055∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) 0.047∗∗∗ (0.014) 0.015∗ (0.009) 0.002 (0.009) %AI Postings %AI Postings × Panel C. Outcome: Log Mean Hourly Wage 0.005 (0.005) 0.008 (0.005) 0.006 (0.005) 0.005 (0.005) 0.003 (0.005) High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ 0.034∗∗∗ (0.009) 0.015∗∗ (0.007) -0.007 (0.005) 0.025∗∗∗ (0.007) 0.025∗∗∗ (0.007) 0.025∗∗∗ (0.007) 0.023∗∗∗ (0.007) 0.008 (0.006) -0.008 (0.005) 0.007 (0.006) -0.009 (0.005) 0.007 (0.006) -0.009∗ (0.005) 0.004 (0.006) -0.010∗ (0.006) Panel D. Outcome: Share of Wage Income3 %AI Postings %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ -0.008 (0.010) -0.011 (0.010) -0.011 (0.011) -0.0011 (0.011) -0.016 (0.014) 0.103∗∗∗ (0.000) 0.038∗∗ (0.019) 0.007 (0.015) 0.089∗∗∗ (0.001) 0.027∗ (0.015) 0.005 (0.012) 0.089∗∗∗ (0.001) 0.026∗ (0.014) 0.005 (0.012) 0.089∗∗∗ (0.001) 0.026∗ (0.014) 0.005 (0.012) 0.082∗∗∗ (0.003) 0.019 (0.012) 0.004 (0.011) Notes: The table shows percentiles from the distribution of estimated effects using my main specification, equation (2.16), but leaving out one state from the analysis at a time. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 2,3 The unit of the share of employment and the share of wage income is a percentage point. 164 Table 2B.22 Effects of Demand for AI Skills on Employment—Dropping COVID Years, 2012-19 Dep. Var.: Emp. per 100,000 Capita Share of Emp.1 Main Spec. (1) -6.146 (8.282) 55.756∗∗∗ (14.940) 20.065∗∗ (10.099) 2.868 (8.991) -333.383∗ (187.602) -209.460 (184.689) -192.177 (181.375) Dropping COVID Years (2) -4.594 (9.038) 55.237∗∗∗ (15.319) 20.410∗ (10.900) 2.822 (9.797) -331.370∗ (188.507) -209.577 (185.677) -197.897 (182.244) Main Spec. (3) -0.006 (0.008) 0.056∗∗∗ (0.015) 0.020∗∗ (0.010) 0.003 (0.009) -0.333∗ (0.188) -0.209 (0.185) -0.192 (0.181) Dropping COVID Years (4) -0.005 (0.009) 0.055∗∗∗ (0.015) 0.020∗ (0.011) 0.003 (0.010) -0.331∗ (0.189) -0.210 (0.186) -0.198 (0.182) %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 153,221 ✓ ✓ ✓ ✓ ✓ 0.130 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 153,221 ✓ ✓ ✓ ✓ ✓ 0.130 165 Table 2B.23 Effects of Demand for AI Skills on Wages—Dropping COVID Years, 2012-19 Dep. Var.: Log Mean Hourly Wages Share of Wage Income1 Main Spec. (1) 0.005 (0.005) 0.025∗∗∗ (0.007) 0.007 (0.006) -0.009 (0.005) 0.441∗∗∗ (0.104) 0.126 (0.096) -0.088 (0.092) Dropping COVID Years (2) 0.001 (0.006) 0.029∗∗∗ (0.007) 0.009 (0.006) -0.006 (0.006) 0.438∗∗∗ (0.106) 0.126 (0.097) -0.086 (0.093) Main Spec. (3) -0.011 (0.011) 0.089∗∗∗ (0.026) 0.026∗ (0.014) 0.005 (0.012) -0.151 (0.166) -0.029 (0.159) -0.169 (0.141) Dropping COVID Years (4) -0.009 (0.012) 0.088∗∗∗ (0.026) 0.027∗ (0.016) 0.005 (0.013) -0.145 (0.166) -0.027 (0.160) -0.173 (0.142) %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-year level. %AI postings is in percentage point. 150,071 ✓ ✓ ✓ ✓ ✓ 0.347 192,008 ✓ ✓ ✓ ✓ ✓ 0.158 153,221 ✓ ✓ ✓ ✓ ✓ 0.156 166 Table 2B.24 Effects of Demand for AI Skills on Employment—Using AI Posting Shares at More Granular Level, 2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Dep. Var.: Employment per 100,000 Capita Share of Employment1 (1) (2) (3) (4) (5) (6) -4.497 (4.136) -64.619 (40.120) -33.822 (30.439) -0.004 (0.004) -0.065 (0.040) -0.034 (0.030) 73.963∗ (40.642) 51.016 (40.994) 49.184 (40.533) 44.004 (30.912) 35.362 (31.099) 33.548 (30.824) -45.713 (107.731) -165.488 (116.754) -321.744∗ (190.593) 47.720 (113.162) 44.293 (124.792) -205.102 (185.128) -58.853 (104.351) -68.505 (114.289) -193.837 (181.114) -0.046 (0.108) 0.048 (0.113) -0.059 (0.104) 0.074∗ (0.041) 0.051 (0.041) 0.049 (0.041) -0.165 (0.117) 0.044 (0.125) -0.069 (0.114) 0.044 (0.031) 0.035 (0.031) 0.034 (0.031) -0.322∗ (0.191) -0.205 (0.185) -0.194 (0.181) 192,008 192,008 192,008 ✓ ✓ ✓ Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns 2, 3, 5, and 6 is the low-skilled group. Occupation- clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of employment is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-by-year-by-2-digit- occupation level. %AI postings is in percentage point. 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ ✓ ✓ 0.129 192,008 ✓ ✓ ✓ 0.022 0.012 0.022 0.012 167 Table 2B.25 Effects of Demand for AI Skills on Wages—Using AI Posting Shares at More Granular Level, 2012-21 %AI Postings2 %AI Postings × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Skill Group = High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Dep. Var.: Log Mean Hourly Wage Share of Wage Income1 (1) 0.016∗∗∗ (0.003) (2) (3) (4) (5) (6) 0.012 (0.033) 0.004 (0.028) 0.001 (0.006) -0.042 (0.031) -0.020 (0.023) -0.011 (0.033) 0.008 (0.034) 0.020 (0.033) -0.002 (0.028) -0.002 (0.028) 0.003 (0.028) 0.557∗∗∗ (0.084) 0.263∗∗∗ (0.085) -0.085 (0.078) 0.669∗∗∗ (0.090) 0.258∗∗∗ (0.092) -0.097 (0.083) 0.468∗∗∗ (0.108) 0.135 (0.098) -0.092 (0.094) 0.147 (0.101) 0.222∗∗ (0.112) -0.056 (0.083) 0.058∗ (0.033) 0.027 (0.033) 0.039 (0.032) 0.027 (0.111) 0.235∗ (0.122) -0.066 (0.090) 0.038 (0.025) 0.018 (0.025) 0.023 (0.023) -0.138 (0.169) -0.018 (0.158) -0.170 (0.139) 187,960 187,960 ✓ ✓ ✓ 192,008 192,008 192,008 ✓ ✓ ✓ Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in columns 2, 3, 5, and 6 is the low-skilled group. Occupation- clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The unit of the share of wage income is a percentage point. 2 Narrow AI definition is used when defining skill groups and computing %AI postings at the state-by-year-by-2-digit- occupation level. %AI postings is in percentage point. 187,960 ✓ ✓ ✓ ✓ ✓ 0.340 ✓ ✓ ✓ ✓ ✓ 0.157 0.052 0.060 0.198 0.213 168 Table 2B.26 Effects of Occupation-Year AI Skill Prevalence Interacting with Skill Group Dummies, 2012-21 AI Skill Prevalence Score3 AI Skill Prevalence × High-Skilled AI-Complement Occ High-Skilled Not-Yet-AI Occ Middle-Skilled Occ Dep. Var.: Emp. per 100,000 Capita %Emp Share1 Log Mean Hourly Wage %Wage Income2 (1) 34,346.52∗∗∗ (8,689.240) -34,313.02∗∗∗ (8,689.252) -33,103.8∗∗∗ (8,707.614) -28,173.44∗∗∗ (8,916.063) (2) 34.347∗∗∗ (8.689) -34.313∗∗∗ (8.689) -33.104∗∗∗ (8.708) -28.173∗∗∗ (8.916) (3) 7.875 (8.106) -7.866 (8.106) -7.107 (8.113) -6.950 (8.130) (4) 40.574∗∗∗ (4.538) -40.504∗∗∗ (4.538) -38.406∗∗∗ (4.636) -32.674∗∗∗ (5.242) 186,799 ✓ ✓ ✓ ✓ ✓ 0.177 Observations State FE Year FE Skill-Group FE 2-Digit-Occ FE Skill-Group FE × Year FE R2 Notes: Each observation is an occupation-state-year cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS-ACS based on the 2010 Census Occupational Classification. All columns include a set of state-year controls. The baseline group in Panel B is the low-skilled group. Occupation-clustered standard errors are shown in parentheses. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1,2 The unit of the employment share and the share of wage income is a percentage point. 3 The AI Skill Prevalence Score is constructed at the 4-digit-occupation-by-year level and standardized within a year. 186,799 ✓ ✓ ✓ ✓ ✓ 0.225 183,018 ✓ ✓ ✓ ✓ ✓ 0.347 186,799 ✓ ✓ ✓ ✓ ✓ 0.177 169 APPENDIX 2C 4-DIGIT OCCUPATIONS WITHIN A SKILL GROUP Since the LinkUp job postings data used to construct the skill groups introduced in Section 2.3.2 is collected at the 2019 O∗NET-SOC level and the labor market outcome data from IPUMS-ACS uses a harmonized occupation system, OCC20101, constructed by IPUMS-ACS based on the 2010 Census Occupational Classification, a crosswalk is needed to map these two occupational classification systems. Due to the lack of available crosswalk to directly map 2019 O∗NET-SOC to OCC2010, I first construct skill group indicators at 6-digit 2019 O∗NET-SOC level, and then map 2019 O∗NET-SOC to 2010 O∗NET-SOC to 2010 SOC to OCC2010 to obtain skill group indicators for each 4-digit OCC2010. For the majority of occupations, there is a one-to-one mapping between different occupational coding systems. In the case of one-to-many mapping, if an occupation is mapped to multiple occupations with different skill group indicators, I keep the skill group indicator with a higher number of total postings. In my final sample, there are 428 OCC2010 between 2012 and 2021. Note that the IPUMS-ACS OCC2010 coding scheme has 493 occupations in total. The ones that are not included in my sample are due to two reasons. First, some of these occupations do not have a detailed description in O∗NET since my AI occupation indicator is constructed as an intersection between AI occupations defined by using LinkUp data and those defined by using O∗NET data (as explained in Section 2.3.2.2).2 Second, some occupations did not show up in IPUMS-ACS data between 2012 and 2021, such as "Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic" (7960) and "Shoe Machine Operators and Tenders" (8340). Appendix Table 2C.1 lists the time-variant skill group indicator for all 4-digit occupations in my final sample. Since I also construct a static skill group indicator, each panel of Appendix Table 2C.1 shows occupations that are classified into the 1https://usa.ipums.org/usa-action/variables/OCC2010#description_section. The OCC2010 coding systems from Census and IPUMS-ACS are not exactly the same. According to IPUMS, "In the interest of harmonization, however, the scheme has been modified to achieve the most consistent categories across time. That is, some categories that provide more detail in the 2010 scheme were grouped together because earlier categories are inseparable when more than one occupation is coded together." These two systems can be easily mapped based on occupation titles. In my analysis, I use IPUMS-ACS OCC2010 system because my labor market outcome data is from IPUMS-ACS. 2My empirical results are robust to using AI occupation indicator defined by using only LinkUp data instead of taking the intersection. 170 corresponding skill group using this time-invariant skill group system.3 3My empirical results are robust to using the time-invariant skill group indicators. 171 Table 2C.1 Time-Variant Skill Group Indicators for 4-Digit Occupations OCC2010 Occupation Title Skill Group Indicator During Ever 2011-14 2015-18 2019-22 Changed Panel A. High-Skilled AI-Complement Group Using Time-Invariant Skill Group Indicator ✓ ✓ 0030 0110 0300 0710 0730 0840 0950 1000 1010 1020 1060 1100 1200 1220 1240 1320 1350 1400 1410 1430 1440 1450 1460 1530 1650 1710 1760 1800 2840 4930 0010 0020 0100 0120 0130 0140 0150 0205 0220 0230 0310 0330 0350 0360 0410 0420 0430 0500 0510 0520 0530 Managers in Marketing, Advertising, and Public Relations Computer and Information Systems Managers Architectural and Engineering Managers Management Analysts Other Business Operations and Management Specialists Financial Analysts Financial Specialists, All Other Computer Scientists and Systems Analysts/Network systems Analysts/Web Developers Computer Programmers Software Developers, Applications and Systems Software Database Administrators Network and Computer Systems Administrators Actuaries Operations Research Analysts Mathematical Science Occupations, All Other Aerospace Engineers Chemical Engineers Computer Hardware Engineers Electrical and Electronics Engineers Industrial Engineers, including Health and Safety Marine Engineers and Naval Architects Materials Engineers Mechanical Engineers Engineers, All Other Medical Scientists, and Life Scientists, All Other Atmospheric and Space Scientists Physical Scientists, All Other Economists and Market Researchers Technical Writers Sales Engineers Chief Executives and Legislators/Public Administration General and Operations Managers Administrative Services Managers Financial Managers Human Resources Managers Industrial Production Managers Purchasing Managers Farmers, Ranchers, and Other Agricultural Managers Constructions Managers Education Administrators Food Service and Lodging Managers Gaming Managers Medical and Health Services Managers Natural Science Managers Property, Real Estate, and Community Association Managers Social and Community Service Managers Managers, All Other (Including Postmasters) Agents and Business Managers of Artists, Performers, and Athletes Buyers and Purchasing Agents, Farm Products Wholesale and Retail Buyers, Except Farm Products Purchasing Agents, Except Wholesale, Retail, and Farm Products 172 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 / 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 Panel B. High-Skilled Not-Yet-AI Group Using Time-Invariant Skill Group Indicator OCC2010 Occupation Title Skill Group Indicator During Ever Table 2C.1 (cont’d) 0540 0600 0620 0700 0720 0800 0810 0820 0830 0850 0860 0910 0930 0940 1050 1300 1310 1360 1420 1520 1560 1600 1610 1640 1720 1820 1840 2000 2010 2020 2040 2050 2100 2200 2300 2310 2320 2330 2340 2400 2430 2540 2550 2700 2760 2810 2825 2850 2860 2920 3000 3010 3030 3040 3060 Claims Adjusters, Appraisers, Examiners, and Investigators Cost Estimators Human Resources, Training, and Labor Relations Specialists Logisticians Meeting and Convention Planners Accountants and Auditors Appraisers and Assessors of Real Estate Budget Analysts Credit Analysts Personal Financial Advisors Insurance Underwriters Credit Counselors and Loan Officers Tax Examiners and Collectors, and Revenue Agents Tax Preparers Computer Support Specialists Architects, Except Naval Surveyors, Cartographers, and Photogrammetrists Civil Engineers Environmental Engineers Petroleum, Mining and Geological Engineers, Including Mining Safety Engineers Surveying and Mapping Technicians Agricultural and Food Scientists Biological Scientists Conservation Scientists and Foresters Chemists and Materials Scientists Psychologists Social Scientists, All Other Counselors Social Workers Community and Social Service Specialists, All Other Clergy Directors, Religious Activities and Education Lawyers, and Judges, Magistrates, and other Judicial Workers Postsecondary Teachers Preschool and Kindergarten Teachers Elementary and Middle School Teachers Secondary School Teachers Special Education Teachers Other Teachers and Instructors Archivists, Curators, and Museum Technicians Librarians Teacher Assistants Education, Training, and Library Workers, All Other Actors, Producers, and Directors Entertainers and Performers, Sports and Related Workers, All Other Editors, News Analysts, Reporters, and Correspondents Public Relations Specialists Writers and Authors Media and Communication Workers, All Other Television, Video, and Motion Picture Camera Operators and Editors Chiropractors Dentists Dieticians and Nutritionists Optometrists Physicians and Surgeons 173 2011-14 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 𝐻 𝐴𝐼 𝐻 𝑁 𝑜𝑛 2015-18 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 2019-22 Changed 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 ✓ ✓ ✓ ✓ 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝐴𝐼 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 OCC2010 Occupation Title Skill Group Indicator During Ever Table 2C.1 (cont’d) 2011-14 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 2015-18 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 2019-22 Changed 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 3110 3120 3140 3150 3160 3200 3210 3220 3230 3260 3730 4210 4320 4460 4600 4620 4640 4650 4700 4800 4820 4850 4920 4940 4950 5000 5100 5520 5840 6200 7700 9000 9240 9260 0160 0560 0900 1540 1550 1700 1740 1830 1900 1910 1920 1930 1960 2140 2150 2440 2600 2630 2800 2900 Physician Assistants Podiatrists Audiologists Occupational Therapists Physical Therapists Radiation Therapists Recreational Therapists Respiratory Therapists Speech Language Pathologists Health Diagnosing and Treating Practitioners, All Other Supervisors, Protective Service Workers, All Other First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers First-Line Supervisors of Personal Service Workers Funeral Service Workers and Embalmers Childcare Workers Recreation and Fitness Workers Residential Advisors Personal Care and Service Workers, All Other First-Line Supervisors of Sales Workers Advertising Sales Agents Securities, Commodities, and Financial Services Sales Agents Sales Representatives, Wholesale and Manufacturing Real Estate Brokers and Sales Agents Telemarketers Door-to-Door Sales Workers, News and Street Vendors, and Related Workers First-Line Supervisors of Office and Administrative Support Workers Bill and Account Collectors Dispatchers Insurance Claims and Policy Processing Clerks First-Line Supervisors of Construction Trades and Extraction Workers First-Line Supervisors of Production and Operating Workers Supervisors of Transportation and Material Moving Workers Railroad Conductors and Yardmasters Subway, Streetcar, and Other Rail Transportation Workers 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 / 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 Panel C. Middle-Skilled Group Using Time-Invariant Skill Group Indicator 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 Transportation, Storage, and Distribution Managers Compliance Officers, Except Agriculture Financial Examiners Drafters Engineering Technicians, Except Drafters Astronomers and Physicists Environmental Scientists and Geoscientists Urban and Regional Planners Agricultural and Food Science Technicians Biological Technicians Chemical Technicians Geological and Petroleum Technicians, and Nuclear Technicians Life, Physical, and Social Science Technicians, All Other Paralegals and Legal Assistants Legal Support Workers, All Other Library Technicians Artists and Related Workers Designers Announcers Broadcast and Sound Engineering Technicians and Radio Operators, and Media and Communication Equipment Workers, All Other 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 174 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝐻 𝑁 𝑜𝑛 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 Table 2C.1 (cont’d) OCC2010 Occupation Title Skill Group Indicator During Ever 2011-14 2015-18 2019-22 Changed 2910 3050 3130 3250 3300 3310 3320 3400 3410 3500 3510 3520 3530 3540 3600 3610 3620 3630 3640 3650 3900 3910 3930 3950 4000 4010 4030 4040 4050 4060 4120 4130 4140 4150 4200 4220 4240 4250 4300 4340 4350 4400 4420 4430 4500 4510 4520 4540 4610 4720 4740 4750 4760 4810 4830 4840 4900 5010 5020 5110 5120 Photographers Pharmacists Registered Nurses Veterinarians Clinical Laboratory Technologists and Technicians Dental Hygienists Diagnostic Related Technologists and Technicians Emergency Medical Technicians and Paramedics Health Diagnosing and Treating Practitioner Support Technicians Licensed Practical and Licensed Vocational Nurses Medical Records and Health Information Technicians Opticians, Dispensing Health Technologists and Technicians, All Other Healthcare Practitioners and Technical Occupations, All Other Nursing, Psychiatric, and Home Health Aides Occupational Therapy Assistants and Aides Physical Therapist Assistants and Aides Massage Therapists Dental Assistants Medical Assistants and Other Healthcare Support Occupations, All Other Animal Control Private Detectives and Investigators Security Guards and Gaming Surveillance Officers Law Enforcement Workers, All Other Chefs and Cooks First-Line Supervisors of Food Preparation and Serving Workers Food Preparation Workers Bartenders Combined Food Preparation and Serving Workers, Including Fast Food Counter Attendant, Cafeteria, Food Concession, and Coffee Shop Food Servers, Nonrestaurant Food Preparation and Serving Related Workers, All Other Dishwashers Host and Hostesses, Restaurant, Lounge, and Coffee Shop First-Line Supervisors of Housekeeping and Janitorial Workers Janitors and Building Cleaners Pest Control Workers Grounds Maintenance Workers First-Line Supervisors of Gaming Workers Animal Trainers Nonfarm Animal Caretakers Gaming Services Workers Ushers, Lobby Attendants, and Ticket Takers Entertainment Attendants and Related Workers, All Other Barbers Hairdressers, Hairstylists, and Cosmetologists Personal Appearance Workers, All Other Tour and Travel Guides Personal Care Aides Cashiers Counter and Rental Clerks Parts Salespersons Retail Salespersons Insurance Sales Agents Travel Agents Sales Representatives, Services, All Other Models, Demonstrators, and Product Promoters Switchboard Operators, Including Answering Service Telephone Operators Billing and Posting Clerks Bookkeeping, Accounting, and Auditing Clerks 175 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 Table 2C.1 (cont’d) OCC2010 Occupation Title Skill Group Indicator During Ever 2011-14 2015-18 2019-22 Changed 5130 5140 5150 5160 5200 5220 5230 5240 5250 5260 5300 5310 5320 5330 5340 5350 5360 5400 5410 5500 5510 5530 5540 5550 5560 5600 5610 5620 5630 5700 5810 5820 5850 5860 5900 5910 5920 5940 6010 6040 6050 6210 6220 6230 6240 6250 6260 6300 6320 6330 6355 6360 6400 6420 6440 6460 6515 Gaming Cage Workers Payroll and Timekeeping Clerks Procurement Clerks Bank Tellers Brokerage Clerks Court, Municipal, and License Clerks Credit Authorizers, Checkers, and Clerks Customer Service Representatives Eligibility Interviewers, Government Programs File Clerks Hotel, Motel, and Resort Desk Clerks Interviewers, Except Eligibility and Loan Library Assistants, Clerical Loan Interviewers and Clerks New Account Clerks Correspondent Clerks and Order CLerks Human Resources Assistants, Except Payroll and Timekeeping Receptionists and Information Clerks Reservation and Transportation Ticket Agents and Travel Clerks Cargo and Freight Agents Couriers and Messengers Meter Readers, Utilities Postal Service Clerks Postal Service Mail Carriers Postal Service Mail Sorters, Processors, and Processing Machine Operators Production, Planning, and Expediting Clerks Shipping, Receiving, and Traffic Clerks Stock Clerks and Order Fillers Weighers, Measurers, Checkers, and Samplers, Recordkeeping Secretaries and Administrative Assistants Data Entry Keyers Word Processors and Typists Mail Clerks and Mail Machine Operators, Except Postal Service Office Clerks, General Office Machine Operators, Except Computer Proofreaders and Copy Markers Statistical Assistants Office and Administrative Support Workers, All Other Agricultural Inspectors Graders and Sorters, Agricultural Products Agricultural Sorkers, All Other Boilermakers Brickmasons, Blockmasons, and Stonemasons Carpenters Carpet, Floor, and Tile Installers and Finishers Cement Masons, Concrete Finishers, and Terrazzo Workers Construction Laborers Paving, Surfacing, and Tamping Equipment Operators Construction Equipment Operators Except Paving, Surfacing, and Tamping Equipment Operators Drywall Installers, Ceiling Tile Installers, and Tapers Electricians Glaziers Insulation Workers Painters, Construction and Maintenance Pipelayers, Plumbers, Pipefitters, and Steamfitters Plasterers and Stucco Masons Roofers 176 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 Table 2C.1 (cont’d) OCC2010 Occupation Title Skill Group Indicator During Ever 2011-14 2015-18 2019-22 Changed 6520 6530 6600 6660 6700 6710 6720 6730 6740 6765 6800 6820 6830 6840 6940 7000 7010 7020 7030 7040 7100 7110 7120 7130 7140 7150 7160 7200 7210 7220 7240 7260 7300 7315 7320 7330 7340 7350 7360 7410 7420 7430 7510 7540 7560 7610 7630 7710 7720 7730 7740 Sheet Metal Workers, Metal-Working Structural Iron and Steel Workers Helpers, Construction Trades Construction and Building Inspectors Elevator Installers and Repairers Fence Erectors Hazardous Materials Removal Workers Highway Maintenance Workers Rail-Track Laying and Maintenance Equipment Operators Construction workers, All Other Derrick, Rotary Drill, and Service Unit Operators, and Roustabouts, Oil, Gas, and Mining Earth Drillers, Except Oil and Gas Explosives Workers, Ordnance Handling Experts, and Blasters Mining Machine Operators Extraction workers, All Other First-Line Supervisors of Mechanics, Installers, and Repairers Computer, Automated Teller, and Office Machine Repairers Radio and Telecommunications Equipment Installers and Repairers Avionics Technicians Electric Motor, Power Tool, and Related Repairers Electrical and Electronics Repairers, Transportation Equipment, and Industrial and Utility Electronic Equipment Installers and Repairers, Motor Vehicles Electronic Home Entertainment Equipment Installers and Repairers Security and Fire Alarm Systems Installers Aircraft Mechanics and Service Technicians Automotive Body and Related Repairers Automotive Glass Installers and Repairers Automotive Service Technicians and Mechanics Bus and Truck Mechanics and Diesel Engine Specialists Heavy Vehicle and Mobile Equipment Service Technicians and Mechanics Small Engine Mechanics Vehicle and Mobile Equipment Mechanics, Installers, and Repairers, All Other Control and Valve Installers and Repairers Heating, Air Conditioning, and Refrigeration Mechanics and Installers Home Appliance Repairers Industrial and Refractory Machinery Mechanics Maintenance and Repair Workers, General Maintenance Workers, Machinery Millwrights Electrical Power-Line Installers and Repairers Telecommunications Line Installers and Repairers Precision Instrument and Equipment Repairers Coin, Vending, and Amusement Machine Servicers and Repairers Locksmiths and Safe Repairers Riggers Helpers–Installation, Maintenance, and Repair Workers Other Installation, Maintenance, and Repair Workers Including Wind Turbine Service Technicians, and Commercial Divers, and Signal and Track Switch Repairers Aircraft Structure, Surfaces, Rigging, and Systems Assemblers Electrical, Electronics, and Electromechanical Assemblers Engine and Other Machine Assemblers Structural Metal Fabricators and Fitters 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 177 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 Table 2C.1 (cont’d) OCC2010 Occupation Title Skill Group Indicator During Ever 2011-14 2015-18 2019-22 Changed 7750 7800 7810 7830 7840 7850 7900 7920 7930 7940 7950 Assemblers and Fabricators, All Other Bakers Butchers and Other Meat, Poultry, and Fish Processing Workers Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders Food Batchmakers Food Cooking Machine Operators and Tenders Computer Control Programmers and Operators Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic Forging Machine Setters, Operators, and Tenders, Metal and Plastic Rolling Machine Setters, Operators, and Tenders, metal and Plastic Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic 8000 Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic 8010 Lathe and Turning Machine Tool Setters, Operators, and Tenders, 8030 8040 8100 8130 8140 8220 8250 8300 8310 8320 8330 8350 8400 Metal and Plastic Machinists Metal Furnace Operators, Tenders, Pourers, and Casters Molders and Molding Machine Setters, Operators, and Tenders, Metal and Plastic Tool and Die Makers Welding, Soldering, and Brazing Workers Metal workers and plastic workers, All Other Prepress Technicians and Workers Laundry and Dry-Cleaning Workers Pressers, Textile, Garment, and Related Materials Sewing Machine Operators Shoe and Leather Workers and Repairers Tailors, Dressmakers, and Sewers Textile Bleaching and Dyeing, and Cutting Machine Setters, Operators, and Tenders 8410 Textile Knitting and Weaving Machine Setters, Operators, and Tenders 8420 Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders 8450 8460 8500 8510 8530 8540 Upholsterers Textile, Apparel, and Furnishings workers, All Other Cabinetmakers and Bench Carpenters Furniture Finishers Sawing Machine Setters, Operators, and Tenders, Wood Woodworking Machine Setters, Operators, and Tenders, Except Sawing 8550 Woodworkers Including Model Makers and Patternmakers, All 8600 8610 8620 8630 8640 8650 8710 8720 8730 8740 8750 8760 Other Power Plant Operators, Distributors, and Dispatchers Stationary Engineers and Boiler Operators Water Wastewater Treatment Plant and System Operators Plant and System Operators, All Other Chemical Processing Machine Setters, Operators, and Tenders Crushing, Grinding, Polishing, Mixing, and Blending Workers Cutting Workers Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders Inspectors, Testers, Sorters, Samplers, and Weighers Jewelers and Precious Stone and Metal Workers Medical, Dental, and Ophthalmic Laboratory Technicians 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 178 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 Table 2C.1 (cont’d) OCC2010 Occupation Title Skill Group Indicator During Ever 2011-14 2015-18 2019-22 Changed 8800 8810 8830 8850 8910 8920 8930 8940 8950 8965 9040 9360 9510 9520 9560 9610 9620 9630 9640 9650 9750 2720 2740 2750 3700 3710 3720 3740 3750 3800 3820 3940 4110 4230 4530 6005 6100 6120 6130 9030 9050 9100 9130 9140 9200 9300 9310 9350 9410 9420 9600 9720 Packaging and Filling Machine Operators and Tenders Painting Workers and Dyers Photographic Process Workers and Processing Machine Operators Adhesive Bonding Machine Operators and Tenders Etchers, Engravers, and Lithographers Molders, Shapers, and Casters, Except Metal and Plastic Paper Goods Machine Setters, Operators, and Tenders Tire Builders Helpers–Production Workers Other Production Workers Including Semiconductor Processors and Cooling and Freezing Equipment Operators Air Traffic Controllers and Airfield Operations Specialists Automotive and Watercraft Service Attendants Crane and Tower Operators Dredge, Excavating, and Loading Machine Operators Conveyor Operators and Tenders, and Hoist and Winch Operators Cleaners of Vehicles and Equipment Laborers and Freight, Stock, and Material Movers, Hand Machine Feeders and Offbearers Packers and Packagers, Hand Pumping Station Operators Material Moving Workers, All Other 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 Panel D. Low-Skilled Group Using Time-Invariant Skill Group Indicator Athletes, Coaches, Umpires, and Related Workers Dancers and Choreographers Musicians, Singers, and Related Workers First-Line Supervisors of Correctional Officers First-Line Supervisors of Police and Detectives First-Line Supervisors of Fire Fighting and Prevention Workers Firefighters Fire Inspectors Sheriffs, Bailiffs, Correctional Officers, and Jailers Police Officers and Detectives Crossing Guards Waiters and Waitresses Maids and Housekeeping Cleaners Baggage Porters, Bellhops, and Concierges First-Line Supervisors of Farming, Fishing, and Forestry Workers Fishing and Hunting Workers Forest and Conservation Workers Logging Workers Aircraft Pilots and Flight Engineers Flight Attendants and Transportation Workers and Attendants Bus and Ambulance Drivers and Attendants Driver/Sales Workers and Truck Drivers Taxi Drivers and Chauffeurs Locomotive Engineers and Operators Sailors and marine oilers, and ship engineers Ship and Boat Captains and Operators Parking Lot Attendants Transportation Inspectors Transportation Workers, All Other Industrial Truck and Tractor Operators Refuse and Recyclable Material Collectors 179 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐻 𝑁 𝑜𝑛 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐻 𝑁 𝑜𝑛 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 / 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝑀 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 𝐿 ✓ Table 2C.2 The Overlap between 2-Digit OCC2010 and Time-Variant Skill Groups, 2012-14 2-Digit Occ. Title OCC2010 AI-Complement Group Not-Yet-AI Group Group Group 4-digit High-Skilled High-Skilled Middle-Skilled Low-Skilled Total #Occ. Number of 4-Digit Occupations in Skill Group: Management Occ. Business and Financial Operations Occ. Computer and Mathematical Occ. Architecture and Engineering Occ. Life, Physical, and Social Science Occ. Community and Social Service Occ. Legal Occ. Education, Training, and Library Occ. Arts, Design, Entertainment, Sports, and Media Occ. 0010-0430 0500-0950 1000-1240 1300-1560 1600-1980 2000-2060 2100-2150 2200-2550 2600-2920 Healthcare Practitioners and Technical Occ. Healthcare Support Occ. Protective Service Occ. Food Preparation and Serving Related Occ. Building and Grounds Cleaning and 3000-3540 3600-3650 3700-3950 4000-4150 4200-4250 Maintenance Occ. Personal Care and Service Occ. Sales and Related Occ. Office and Administrative Support Occ. Farming, Fishing, and Forestry Occ. Construction and Extraction Occ. Installation, Maintenance, and Repair Occ. Production Occ. Transportation and Material Moving Occ. 4300-4650 4700-4965 5000-5940 6005-6130 6200-6940 7000-7630 7700-8965 9000-9750 2 4 8 10 5 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 18 18 1 5 5 5 1 10 7 15 0 1 0 1 6 7 4 0 0 0 1 4 1 2 0 2 8 0 2 1 5 13 6 4 10 4 11 8 42 3 31 32 59 11 0 0 0 0 0 0 0 0 3 0 0 8 1 1 1 0 0 4 0 0 0 12 21 24 9 17 18 5 3 11 16 28 6 13 11 6 18 16 46 7 31 32 60 27 31 255 109 Total Notes: The counted occupations are from my final sample used for my main analysis. The 2-digit occupation classification used is from 2010 Census Occupational Classification. The 2-digit IPUMS-ACS OCC2010 code is mostly the same with 2010 Census Occupational Classification but further divides the following three 2-digit groups into more detailed ones: (1) "Business Operations Specialists" and "Financial Specialists" instead of "Business and Financial Operations Occ.;" (2) "Architecture and Engineering" and "Technicians" instead of "Architecture and Engineering Occ.;" (3) "Construction" and "Extraction" instead of "Construction and Extraction Occ." Since there is a one-to-one mapping between the 2-digit 2010 Census Occupational Classification and 2-digit O∗NET-SOC code, I use the 2-digit 2010 Census Occupational Classification rather than the 2-digit IPUMS-ACS OCC2010 to better merge the job postings data to labor market outcome data. The column of 4-digit OCC2010 shows the range of 4-digit OCC2010 code classified into each 2-digit group. The skill group indicator in this table is time-variant, which is consistent within years between 2011-14, 2015-18, and 2019-22. 425 30 180 Table 2C.3 The Overlap between 2-Digit OCC2010 and Time-Variant Skill Groups, 2015-18 2-Digit Occ. Title OCC2010 AI-Complement Group Not-Yet-AI Group Group Group 4-digit High-Skilled High-Skilled Middle-Skilled Low-Skilled Total #Occ. Number of 4-Digit Occupations in Skill Group: Management Occ. Business and Financial Operations Occ. Computer and Mathematical Occ. Architecture and Engineering Occ. Life, Physical, and Social Science Occ. Community and Social Service Occ. Legal Occ. Education, Training, and Library Occ. Arts, Design, Entertainment, Sports, and Media Occ. 0010-0430 0500-0950 1000-1240 1300-1560 1600-1980 2000-2060 2100-2150 2200-2550 2600-2920 Healthcare Practitioners and Technical Occ. Healthcare Support Occ. Protective Service Occ. Food Preparation and Serving Related Occ. Building and Grounds Cleaning and 3000-3540 3600-3650 3700-3950 4000-4150 4200-4250 Maintenance Occ. Personal Care and Service Occ. Sales and Related Occ. Office and Administrative Support Occ. Farming, Fishing, and Forestry Occ. Construction and Extraction Occ. Installation, Maintenance, and Repair Occ. Production Occ. Transportation and Material Moving Occ. 4300-4650 4700-4965 5000-5940 6005-6130 6200-6940 7000-7630 7700-8965 9000-9750 2 4 8 10 4 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 18 18 1 5 6 5 1 10 7 15 0 1 0 1 6 7 4 0 1 0 1 4 1 2 0 2 8 0 2 1 5 13 6 4 10 4 11 8 42 3 31 32 61 11 0 0 0 0 0 0 0 0 3 0 0 8 1 1 1 0 0 4 0 0 0 12 21 24 9 17 18 5 3 11 16 28 6 13 11 6 18 16 46 7 32 32 62 27 30 257 111 Total Notes: The counted occupations are from my final sample used for my main analysis. The 2-digit occupation classification used is from 2010 Census Occupational Classification. The 2-digit IPUMS-ACS OCC2010 code is mostly the same with 2010 Census Occupational Classification but further divides the following three 2-digit groups into more detailed ones: (1) "Business Operations Specialists" and "Financial Specialists" instead of "Business and Financial Operations Occ.;" (2) "Architecture and Engineering" and "Technicians" instead of "Architecture and Engineering Occ.;" (3) "Construction" and "Extraction" instead of "Construction and Extraction Occ." Since there is a one-to-one mapping between the 2-digit 2010 Census Occupational Classification and 2-digit O∗NET-SOC code, I use the 2-digit 2010 Census Occupational Classification rather than the 2-digit IPUMS-ACS OCC2010 to better merge the job postings data to labor market outcome data. The column of 4-digit OCC2010 shows the range of 4-digit OCC2010 code classified into each 2-digit group. The skill group indicator in this table is time-variant, which is consistent within years between 2011-14, 2015-18, and 2019-22. 428 30 181 Table 2C.4 The Overlap between 2-Digit OCC2010 and Time-Variant Skill Groups, 2019-21 2-Digit Occ. Title OCC2010 AI-Complement Group Not-Yet-AI Group Group Group 4-digit High-Skilled High-Skilled Middle-Skilled Low-Skilled Total #Occ. Number of 4-Digit Occupations in Skill Group: Management Occ. Business and Financial Operations Occ. Computer and Mathematical Occ. Architecture and Engineering Occ. Life, Physical, and Social Science Occ. Community and Social Service Occ. Legal Occ. Education, Training, and Library Occ. Arts, Design, Entertainment, Sports, and Media Occ. 0010-0430 0500-0950 1000-1240 1300-1560 1600-1980 2000-2060 2100-2150 2200-2550 2600-2920 Healthcare Practitioners and Technical Occ. Healthcare Support Occ. Protective Service Occ. Food Preparation and Serving Related Occ. Building and Grounds Cleaning and 3000-3540 3600-3650 3700-3950 4000-4150 4200-4250 Maintenance Occ. Personal Care and Service Occ. Sales and Related Occ. Office and Administrative Support Occ. Farming, Fishing, and Forestry Occ. Construction and Extraction Occ. Installation, Maintenance, and Repair Occ. Production Occ. Transportation and Material Moving Occ. 4300-4650 4700-4965 5000-5940 6005-6130 6200-6940 7000-7630 7700-8965 9000-9750 3 4 8 10 4 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 16 18 1 5 6 5 1 10 7 15 0 1 0 1 6 7 4 0 1 0 1 3 1 2 0 2 7 0 2 1 5 13 6 4 9 4 10 8 40 3 30 31 56 10 0 0 0 0 0 0 0 0 3 0 0 8 1 1 1 0 0 4 0 0 0 13 20 24 9 17 17 5 3 11 16 28 6 13 10 6 17 16 44 7 31 31 57 26 31 244 108 Total Notes: The counted occupations are from my final sample used for my main analysis. The 2-digit occupation classification used is from 2010 Census Occupational Classification. The 2-digit IPUMS-ACS OCC2010 code is mostly the same with 2010 Census Occupational Classification but further divides the following three 2-digit groups into more detailed ones: (1) "Business Operations Specialists" and "Financial Specialists" instead of "Business and Financial Operations Occ.;" (2) "Architecture and Engineering" and "Technicians" instead of "Architecture and Engineering Occ.;" (3) "Construction" and "Extraction" instead of "Construction and Extraction Occ." Since there is a one-to-one mapping between the 2-digit 2010 Census Occupational Classification and 2-digit O∗NET-SOC code, I use the 2-digit 2010 Census Occupational Classification rather than the 2-digit IPUMS-ACS OCC2010 to better merge the job postings data to labor market outcome data. The column of 4-digit OCC2010 shows the range of 4-digit OCC2010 code classified into each 2-digit group. The skill group indicator in this table is time-variant, which is consistent within years between 2011-14, 2015-18, and 2019-22. 414 31 182 APPENDIX 2D ML OCCUPATION CLUSTERS This section introduces how I construct the ML occupation clusters using job postings data and machine learning algorithms. I first extract over 1,800 skills from (1) the skill dictionary provided by Lightcast, formerly known as Burning Glass Technologies; (2) basic skills, technology skills, knowledge, and hot technologies from O∗NET; and (3) my chosen AI phrases listed in Table 2.1. Some examples of these skills are "algorithm development," "audit software," "bioinformatics," "clerical support," "direct marketing," "equipment repair," and "javascript." Next, I match skills to the raw text of over 200 million job postings collected by LinkUp and calculate the frequency of each skill appeared in a posting. I then collapse the posting-skill matrix to an occupation-skill matrix as an occupation consists of numerous postings. Finally, I use machine learning clustering algorithms to cluster occupations based on the similarity in skills. Occupations with higher similarity in skills are supposed to fall into the same cluster. To compare my proposed ML occupation clusters and Census/BLS 2-digit groups, I create a visualization of multi-dimensional skills by different occupation system in Appendix Figure 2D.1. Each marker in the figure represents an occupation. Different colors and symbols are used to distinguish clusters. Since there are over 1,800 skills (i.e., over 1,800 dimensions), I use a dimensionality reduction algorithm to reduce the high dimensions to only two dimensions. Thus, the x- and y-axes in Appendix Figure 2D.1 have no empirical meaning. They represent the projection of a high-dimensional data. The most important takeaway of this figure is the relative distance between occupations within the same cluster. The closer two markers are, the higher similarity in over 1,800 skills they share. Occupations within the same Census/BLS 2-digit group scatter everywhere (Appendix Figure 2D.1a),1 while most occupations within the same ML group proposed by me cluster together (Appendix Figure 2D.1b). This finding further supports that the Census/BLS occupation system does not capture specific skill requirements of an occupation or skill similarity between occupations. Appendix Tables 2D.1-2D.3 show the overlap between ML 1There is a one-to-one mapping between the 2-digit Census Occupational Classification and the 2-digit BLS SOC groups. Thus, in Appendix Figure 2D.1a, the 2-digit SOC code is used to represent the broad occupation group. 183 occupation clusters and the time-variant skill groups during different time periods, while Appendix Table 2D.4 lists the ML occupation cluster for all 4-digit occupations. The titles of each cluster are named based on 4-digit occupation titles within the cluster. 184 Figure 2D.1 2-D Visualization of Multi-Dimensional Skills (a) By 2-Digit Census Occupational Classification (b) By ML Occupation Cluster Notes: The AI Skill Prevalence Score is constructed at the state-year level and standardized within a year. 185 −30−20−10010203040−30−20−10010203011.013.015.017.019.021.023.025.027.029.031.033.035.037.039.041.043.045.047.049.051.053.055.0−30−20−10010203040−30−20−1001020301.02.03.04.06.07.09.010.011.013.016.017.018.019.020.021.022.023.024.025.026.027.028.0 Table 2D.1 The Overlap between ML Occupation Clusters and Time-Variant Skill Groups, 2012-14 ML Occupation Cluster: #1 Postsecondary educators #2 Service & retail workers #3 Specialized service professionals #4 Construction & craft workers #6 Finance professionals #7 Pre-Secondary educators #9 Building improvement technicians #10 Public safety, policy, & social science #11 Life sciences & quality assurance #13 Engineering technicians #16 Healthcare professionals & practitioners #17 Technical maintenance workers #18 Workplace safety & training specialists #19 IT & data management #20 Sales & marketing professionals #21 Media production & broadcasting #22 Regulatory compliance specialists #23 Manual workers & machine operators #24 Service & administrative professionals #25 Infrastructure architecture & engineering #26 Creative & communication support workers #27 Technical & service support personnel #28 Environmental & earth scientists Number of 4-Digit Occupations in Skill Group: High-Skilled High-Skilled Middle-Skilled Low-Skilled Total #Occ. AI-Complement Group Not-Yet-AI Group Group Group 0 0 0 0 2 0 0 1 3 7 0 0 0 11 2 1 0 0 0 3 0 0 1 1 6 1 0 3 5 0 2 3 1 18 1 1 7 7 3 1 3 32 4 0 8 1 0 23 3 5 1 0 3 1 1 1 17 58 0 1 2 3 4 47 36 2 2 44 1 0 1 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 4 6 0 2 13 0 1 30 4 5 6 5 3 6 7 9 35 61 1 19 112 7 5 54 74 9 4 65 3 Total Notes: The counted occupations are from my final sample used for my main analysis. The index for ML occupation clusters is a randomly chosen number. There is no meaning for this index. The skill group indicator in this table is time-variant, which is consistent within years between 2011-14, 2015-18, and 2019-22. 424 108 255 31 30 186 Table 2D.2 The Overlap between ML Occupation Clusters and Time-Variant Skill Groups, 2015-18 ML Occupation Cluster: #1 Postsecondary educators #2 Service & retail workers #3 Specialized service professionals #4 Construction & craft workers #6 Finance professionals #7 Pre-Secondary educators #9 Building improvement technicians #10 Public safety, policy, & social science #11 Life sciences & quality assurance #13 Engineering technicians #16 Healthcare professionals & practitioners #17 Technical maintenance workers #18 Workplace safety & training specialists #19 IT & data management #20 Sales & marketing professionals #21 Media production & broadcasting #22 Regulatory compliance specialists #23 Manual workers & machine operators #24 Service & administrative professionals #25 Infrastructure architecture & engineering #26 Creative & communication support workers #27 Technical & service support personnel #28 Environmental & earth scientists Number of 4-Digit Occupations in Skill Group: High-Skilled High-Skilled Middle-Skilled Low-Skilled Total #Occ. AI-Complement Group Not-Yet-AI Group Group Group 0 0 0 0 2 0 0 1 3 7 0 0 0 11 2 1 0 0 0 2 0 0 1 1 6 1 0 3 5 0 2 3 1 18 1 1 7 7 3 1 3 32 5 0 8 1 0 23 3 5 1 0 3 1 1 1 17 60 0 1 2 3 4 47 36 2 2 44 1 0 1 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 4 6 0 2 13 0 1 30 4 5 6 5 3 6 7 9 35 63 1 19 11 7 5 54 74 9 4 65 3 Total Notes: The counted occupations are from my final sample used for my main analysis. The index for ML occupation clusters is a randomly chosen number. There is no meaning for this index. The skill group indicator in this table is time-variant, which is consistent within years between 2011-14, 2015-18, and 2019-22. 257 426 109 30 30 187 Table 2D.3 The Overlap between ML Occupation Clusters and Time-Variant Skill Groups, 2019-22 ML Occupation Cluster: #1 Postsecondary educators #2 Service & retail workers #3 Specialized service professionals #4 Construction & craft workers #6 Finance professionals #7 Pre-Secondary educators #9 Building improvement technicians #10 Public safety, policy, & social science #11 Life sciences & quality assurance #13 Engineering technicians #16 Healthcare professionals & practitioners #17 Technical maintenance workers #18 Workplace safety & training specialists #19 IT & data management #20 Sales & marketing professionals #21 Media production & broadcasting #22 Regulatory compliance specialists #23 Manual workers & machine operators #24 Service & administrative professionals #25 Infrastructure architecture & engineering #26 Creative & communication support workers #27 Technical & service support personnel #28 Environmental & earth scientists Number of 4-Digit Occupations in Skill Group: High-Skilled High-Skilled Middle-Skilled Low-Skilled Total #Occ. AI-Complement Group Not-Yet-AI Group Group Group 0 0 0 0 2 0 0 1 3 7 0 0 0 11 3 1 0 0 0 2 0 0 1 1 6 1 0 3 5 0 2 3 1 18 1 1 7 6 3 1 3 31 5 0 7 1 0 23 3 5 1 0 3 1 1 1 17 56 0 1 2 3 4 44 33 2 2 41 1 0 1 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 4 6 0 2 14 0 1 30 4 5 6 5 3 6 7 9 35 59 1 19 11 7 5 1 70 9 4 62 3 Total Notes: The counted occupations are from my final sample used for my main analysis. The index for ML occupation clusters is a randomly chosen number. There is no meaning for this index. The skill group indicator in this table is time-variant, which is consistent within years between 2011-14, 2015-18, and 2019-22. 244 412 106 31 31 188 Table 2D.4 4-Digit Occupations by ML Occupation Cluster OCC2010 Occupation Title OCC2010 Occupation Title 2200 Postsecondary Teachers ML Occupation Cluster #1: Postsecondary Educators ML Occupation Cluster #2: Service and Retail Workers 20 310 510 520 2630 3520 4000 4010 4030 4120 4140 4200 4610 4700 4720 4740 3910 4460 6210 6230 6330 120 800 820 2310 2320 2330 6240 6400 10 1640 1800 360 1240 1350 1610 1320 1400 1410 1430 1450 350 1820 2000 2010 2020 2040 3030 3050 3060 3110 3120 3130 3140 3150 3160 3200 3210 3220 General and Operations Managers Food Service and Lodging Managers Buyers and Purchasing Agents, Farm Products Wholesale and Retail Buyers, Except Farm Products Designers Opticians, Dispensing Chefs and Cooks First-Line Supervisors of Food Preparation and Serving Workers Food Preparation Workers Food Servers, Nonrestaurant Dishwashers First-Line Supervisors of Housekeeping and Janitorial Workers Personal Care Aides First-Line Supervisors of Sales Workers Cashiers Counter and Rental Clerks 4750 4760 4900 4950 5300 5620 6010 7800 7810 7840 8300 8810 9050 9640 Parts Salespersons Retail Salespersons Models, Demonstrators, and Product Promoters Door-to-Door Sales Workers, News and Street Vendors, and Related Workers Hotel, Motel, and Resort Desk Clerks Stock Clerks and Order Fillers Agricultural Inspectors Bakers Butchers and Other Meat, Poultry, and Fish Processing Workers Food Batchmakers Laundry and Dry-Cleaning Workers Painting Workers and Dyers Flight Attendants and Transportation Workers and Attendants Packers and Packagers, Hand Private Detectives and Investigators Funeral Service Workers and Embalmers 6360 8450 Glaziers Upholsterers ML Occupation Cluster #3: Specialized Service Professionals ML Occupation Cluster #4: Construction and Craft Workers Boilermakers Carpenters Drywall Installers, Ceiling Tile Installers, and Tapers 8500 8540 Cabinetmakers and Bench Carpenters Woodworking Machine Setters, Operators, and Tenders Except Sawing Financial Managers Accountants and Auditors Budget Analysts ML Occupation Cluster #6: Financial Management Professionals Financial Analysts Financial Specialists, All Other Bookkeeping, Accounting, and Auditing Clerks 840 950 5120 Elementary and Middle School Teachers Secondary School Teachers Special Education Teachers 2340 2540 Other Teachers and Instructors Teacher Assistants ML Occupation Cluster #7: Pre-Secondary Educators Carpet, Floor, and Tile Installers and Finishers Insulation Workers 6765 Construction Workers, All Other ML Occupation Cluster #9: Building Improvement Technicians ML Occupation Cluster #10: Public Safety, Policy, and Social Science Chief Executives and Legislators/Public Administration Conservation Scientists and Foresters Economists and Market Researchers 1830 3720 3820 Urban and Regional Planners First-Line Supervisors of Fire Fighting and Prevention Workers Police Officers and Detectives ML Occupation Cluster #11: Life Sciences and Quality Assurance Natural Science Managers Mathematical Science Occupations, All Other Chemical Engineers Biological Scientists 1650 1720 1910 Medical Scientists, and Life Scientists, All Other Chemists and Materials Scientists Biological Technicians Aerospace Engineers Computer Hardware Engineers Electrical and Electronics Engineers Industrial Engineers, including Health and Safety Materials Engineers ML Occupation Cluster #13: Engineering Technicians and Technologists Mechanical Engineers Engineers, All Other Agricultural and Food Scientists Astronomers and Physicists 1460 1530 1600 1700 ML Occupation Cluster #16: Healthcare Professionals and Practitioners Medical and Health Services Managers Psychologists Counselors Social Workers Community and Social Service Specialists, All Other Clergy Dieticians and Nutritionists Pharmacists Physicians and Surgeons Physician Assistants Podiatrists Registered Nurses Audiologists Occupational Therapists Physical Therapists Radiation Therapists Recreational Therapists Respiratory Therapists 3230 3260 3300 3310 3320 3400 3410 3500 3510 3530 3540 3600 3610 3620 3640 3650 Speech Language Pathologists Health Diagnosing and Treating Practitioners, All Other Clinical Laboratory Technologists and Technicians Dental Hygienists Diagnostic Related Technologists and Technicians Emergency Medical Technicians and Paramedics Health Diagnosing and Treating Practitioner Support Technicians Licensed Practical and Licensed Vocational Nurses Medical Records and Health Information Technicians Health Technologists and Technicians, All Other Healthcare Practitioners and Technical Occupations, All Other Nursing, Psychiatric, and Home Health Aides Occupational Therapy Assistants and Aides Physical Therapist Assistants and Aides Dental Assistants Medical Assistants and Other Healthcare Support Occupations, All Other 5310 Interviewers, Except Eligibility and Loan 189 OCC2010 Occupation Title OCC2010 Occupation Title ML Occupation Cluster #17: Technical Maintenance Workers Table 2D.4 (cont’d) 1550 4250 6260 6300 6320 6355 6440 6500 6520 6530 6600 6700 6730 6740 6800 6820 7000 7010 7020 7030 7040 7100 7130 7140 7150 7200 7210 7220 7240 7300 7315 7330 7340 7350 7360 7410 Engineering Technicians, Except Drafters Grounds Maintenance Workers Construction Laborers Paving, Surfacing, and Tamping Equipment Operators Construction Equipment Operators Except Paving, Surfacing, and Tamping Equipment Operators Electricians Pipelayers, Plumbers, Pipefitters, and Steamfitters Reinforcing Iron and Rebar Workers Sheet Metal Workers, Metal-Working Structural Iron and Steel Workers Helpers, Construction Trades Elevator Installers and Repairers Highway Maintenance Workers Rail-Track Laying and Maintenance Equipment Operators Derrick, Rotary Drill, and Service Unit Operators, and Roustabouts, Oil, Gas, and Mining Earth Drillers, Except Oil and Gas First-Line Supervisors of Mechanics, Installers, and Repairers Computer, Automated Teller, and Office Machine Repairers Radio and Telecommunications Equipment Installers and Repairers Avionics Technicians Electric Motor, Power Tool, and Related Repairers Electrical and Electronics Repairers, Transportation Equipment, and Industrial and Utility Security and Fire Alarm Systems Installers Aircraft Mechanics and Service Technicians Automotive Body and Related Repairers Automotive Service Technicians and Mechanics Bus and Truck Mechanics and Diesel Engine Specialists Heavy Vehicle and Mobile Equipment Service Technicians and Mechanics Small Engine Mechanics Control and Valve Installers and Repairers Heating, Air Conditioning, and Refrigeration Mechanics and Installers Industrial and Refractory Machinery Mechanics Maintenance and Repair Workers, General Maintenance Workers, Machinery Millwrights Electrical Power-Line Installers and Repairers 7420 7430 7510 7540 7710 7720 7900 7930 Telecommunications Line Installers and Repairers Precision Instrument and Equipment Repairers Coin, Vending, and Amusement Machine Servicers and Repairers Locksmiths and Safe Repairers Aircraft Structure, Surfaces, Rigging, and Systems Assemblers Electrical, Electronics, and Electromechanical Assemblers Computer Control Programmers and Operators Forging Machine Setters, Operators, and Tenders, Metal and Plastic 7950 Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic 7960 Drilling and Boring Machine Tool Setters, Operators, and 8000 8010 8030 8130 8140 8150 8210 8220 8600 8610 8620 8630 8740 8965 9240 9410 9420 9510 9520 9650 9750 Tenders, Metal and Plastic Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic Machinists Tool and Die Makers Welding, Soldering, and Brazing Workers Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic Tool Grinders, Filers, and Sharpeners Metal Workers and Plastic Workers, All Other Power Plant Operators, Distributors, and Dispatchers Stationary Engineers and Boiler Operators Water Wastewater Treatment Plant and System Operators Plant and System Operators, All Other Inspectors, Testers, Sorters, Samplers, and Weighers Other Production Workers Including Semiconductor Proces- sors and Cooling and Freezing Equipment Operators Railroad Conductors and Yardmasters Transportation Inspectors Transportation Workers, All Other Crane and Tower Operators Dredge, Excavating, and Loading Machine Operators Pumping Station Operators Material moving workers, All Other 130 Human Resources Managers ML Occupation Cluster #18: Workplace Safety and Training Specialists 100 110 140 150 220 300 530 700 710 1000 30 730 2825 2850 4800 4810 1710 2600 2700 2800 2810 430 560 900 ML Occupation Cluster #19: IT and Data Management Specialists Administrative Services Managers Computer and Information Systems Managers Industrial Production Managers Purchasing Managers Constructions Managers Architectural and Engineering Managers Purchasing Agents, Except Wholesale, Retail, and Farm Products Logisticians Management Analysts Computer Scientists and Systems Analysts/Network Systems 1010 1020 1050 1060 1100 1200 1220 2840 5920 Analysts/Web Developers Computer Programmers Software Developers, Applications and Systems Software Computer Support Specialists Database Administrators Network and Computer Systems Administrators Actuaries Operations Research Analysts Technical Writers Statistical Assistants ML Occupation Cluster #20: Sales and Marketing Professionals Managers in Marketing, Advertising, and Public Relations Other Business Operations and Management Specialists Public Relations Specialists Writers and Authors Advertising Sales Agents Insurance Sales Agents 4820 4840 4850 4930 4940 Securities, Commodities, and Financial Services Sales Agents Sales Representatives, Services, All Other Sales Representatives, Wholesale and Manufacturing Sales Engineers Telemarketers ML Occupation Cluster #21: Media Production and Broadcasting Atmospheric and Space Scientists Artists and Related Workers Actors, Producers, and Directors Announcers Editors, News Analysts, Reporters, and Correspondents 2900 Broadcast and Sound Engineering Technicians and Radio Operators, and Media and Communication Equipment Workers, All Other 2920 Television, Video, and Motion Picture Camera Operators and Editors ML Occupation Cluster #22: Regulatory Compliance Specialists Managers, All Other (Including Postmasters) Compliance Officers, Except Agriculture Financial Examiners 7120 Electronic Home Entertainment Equipment Installers and Repairers 7320 Home Appliance Repairers 190 Table 2D.4 (cont’d) OCC2010 Occupation Title OCC2010 Occupation Title ML Occupation Cluster #23: Manual Workers and Machine Operators 4220 4230 5540 5550 5560 5610 5850 5900 6050 6220 6250 6420 6940 7160 7260 7560 7610 7700 7730 7740 7750 7830 7850 7920 7940 8040 8100 Janitors and Building Cleaners Maids and Housekeeping Cleaners Postal Service Clerks Postal Service Mail Carriers Postal Service Mail Sorters, Processors, and Processing Machine Operators Shipping, Receiving, and Traffic Clerks Mail Clerks and Mail Machine Operators, Except Postal Service Office Machine Operators, Except Computer Agricultural Workers, All Other Brickmasons, Blockmasons, and Stonemasons Cement Masons, Concrete Finishers, and Terrazzo Workers Painters, Construction and Maintenance Extraction Workers, All Other Automotive Glass Installers and Repairers Vehicle and Mobile Equipment Mechanics, Installers, and Repairers, All Other Riggers Helpers–Installation, Maintenance, and Repair Workers First-Line Supervisors of Production and Operating Workers Engine and Other Machine Assemblers Structural Metal Fabricators and Fitters Assemblers and Fabricators, All Other Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders Food Cooking Machine Operators and Tenders Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic Rolling Machine Setters, Operators, and Tenders, Metal and Plastic Metal Furnace Operators, Tenders, Pourers, and Casters Molders and Molding Machine Setters, Operators, and Tenders, Metal and Plastic 8200 Plating and Coating Machine Setters, Operators, and Tenders, Metal and Plastic 8310 8320 8340 8400 Pressers, Textile, Garment, and Related Materials Sewing Machine Operators Shoe Machine Operators and Tenders Textile Bleaching and Dyeing, and Cutting Machine Setters, Operators, and Tenders 8410 Textile Knitting and Weaving Machine Setters, Operators, and Tenders 8420 Textile Winding, Twisting, and Drawing Out Machine Setters, 8510 8530 8640 8650 8710 8720 8730 8760 8800 8850 8860 8920 8930 8940 8950 9000 9260 9300 9560 9600 9620 9630 9720 Operators, and Tenders Furniture Finishers Sawing Machine Setters, Operators, and Tenders, Wood Chemical Processing Machine Setters, Operators, and Tenders Crushing, Grinding, Polishing, Mixing, and Blending Workers Cutting Workers Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders Medical, Dental, and Ophthalmic Laboratory Technicians Packaging and Filling Machine Operators and Tenders Adhesive Bonding Machine Operators and Tenders Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders Molders, Shapers, and Casters, Except Metal and Plastic Paper Goods Machine Setters, Operators, and Tenders Tire Builders Helpers–Production Workers Supervisors of Transportation and Material Moving Workers Subway, Streetcar, and Other Rail Transportation Workers Sailors and Marine Oilers, and Ship Engineers Conveyor Operators and Tenders, and Hoist and Winch Operators Industrial Truck and Tractor Operators Laborers and Freight, Stock, and Material Movers, Hand Machine Feeders and Offbearers Refuse and Recyclable Material Collectors ML Occupation Cluster #24: Service and Administrative Professionals 160 205 230 330 410 420 500 540 620 720 810 830 850 860 910 930 1310 1560 1900 1920 1930 1960 2050 2100 2140 2150 2400 2430 2440 2550 2860 3700 3710 3730 3750 3800 3900 3930 Transportation, Storage, and Distribution Managers Farmers, Ranchers, and Other Agricultural Managers Education Administrators Gaming Managers Property, Real Estate, and Community Association Managers Social and Community Service Managers Agents and Business Managers of Artists, Performers, and Athletes Claims Adjusters, Appraisers, Examiners, and Investigators Human Resources, Training, and Labor Relations Specialists Meeting and Convention Planners Appraisers and Assessors of Real Estate Credit Analysts Personal Financial Advisors Insurance Underwriters Credit Counselors and Loan Officers Tax Examiners and Collectors, and Revenue Agents Surveyors, Cartographers, and Photogrammetrists Surveying and Mapping Technicians Agricultural and Food Science Technicians Chemical Technicians Geological and Petroleum Technicians, and Nuclear Technicians Life, Physical, and Social Science Technicians, All Other Directors, Religious Activities and Education Lawyers, and Judges, Magistrates, and Other Judicial Workers Paralegals and Legal Assistants Legal Support Workers, All Other Archivists, Curators, and Museum Technicians Librarians Library Technicians Education, Training, and Library Workers, All Other Media and Communication Workers, All Other First-Line Supervisors of Correctional Officers First-Line Supervisors of Police and Detectives Supervisors, Protective Service Workers, All Other Fire Inspectors Sheriffs, Bailiffs, Correctional Officers, and Jailers Animal Control Security Guards and Gaming Surveillance Officers 4300 4320 4530 4540 4620 4640 4830 4920 5000 5100 5110 5140 5150 5160 5200 5220 5240 5250 5320 5330 5340 5350 5360 5400 5500 5520 5600 5700 5810 5840 5860 5910 5940 6005 6840 9040 First-Line Supervisors of Gaming Workers First-Line Supervisors of Personal Service Workers Baggage Porters, Bellhops, and Concierges Tour and Travel Guides Recreation and Fitness Workers Residential Advisors Travel Agents Real Estate Brokers and Sales Agents First-Line Supervisors of Office and Administrative Support Workers Bill and Account Collectors Billing and Posting Clerks Payroll and Timekeeping Clerks Procurement Clerks Bank Tellers Brokerage Clerks Court, Municipal, and License Clerks Customer Service Representatives Eligibility Interviewers, Government Programs Library Assistants, Clerical Loan Interviewers and Clerks New Account Clerks Correspondent clerks and order clerks Human Resources Assistants, Except Payroll and Timekeeping Receptionists and Information Clerks Cargo and Freight Agents Dispatchers Production, Planning, and Expediting Clerks Secretaries and Administrative Assistants Data Entry Keyers Insurance Claims and Policy Processing Clerks Office Clerks, General Proofreaders and Copy Markers Office and Administrative Support Workers, All Other First-Line Supervisors of Farming, Fishing, and Forestry Workers Mining Machine Operators Air Traffic Controllers and Airfield Operations Specialists 191 OCC2010 Occupation Title OCC2010 Occupation Title Table 2D.4 (cont’d) 600 1300 1360 1420 1440 1520 2740 5020 940 2300 2720 2750 2910 3000 3010 3040 3250 3630 3740 3940 3950 4040 4050 4060 4110 4130 4150 4210 4240 4340 4350 4400 4420 4430 4500 4510 4520 4600 5010 5130 5230 5260 5410 360 1740 ML Occupation Cluster #25: Infrastructure Architecture and Engineering Cost Estimators Architects, Except Naval Civil Engineers Environmental Engineers Marine Engineers and Naval Architects Petroleum, Mining and Geological Engineers, Including Mining Safety Engineers 1540 6200 Drafters First-Line Supervisors of Construction Trades and Extraction Workers 6660 Construction and Building Inspectors Dancers and Choreographers Telephone Operators 5820 9130 Word Processors and Typists Driver/Sales Workers and Truck Drivers ML Occupation Cluster #26: Creative and Communication Support Workers ML Occupation Cluster #27: Technical and Service Support Personnel Tax Preparers Preschool and Kindergarten Teachers Athletes, Coaches, Umpires, and Related Workers Musicians, Singers, and Related Workers Photographers Chiropractors Dentists Optometrists Veterinarians Massage Therapists Firefighters Crossing Guards Law Enforcement Workers, All Other Bartenders Combined Food Preparation and Serving Workers, Including Fast Food Counter Attendant, Cafeteria, Food Concession, and Coffee Shop Waiters and Waitresses Food Preparation and Serving Related Workers, All Other Host and Hostesses, Restaurant, Lounge, and Coffee Shop First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers Pest Control Workers Animal Trainers Nonfarm Animal Caretakers Gaming Services Workers Ushers, Lobby Attendants, and Ticket Takers Entertainment Attendants and Related Workers, All Other Barbers Hairdressers, Hairstylists, and Cosmetologists Personal Appearance Workers, All Other Childcare Workers Switchboard Operators, Including Answering Service Gaming Cage Workers Credit Authorizers, Checkers, and Clerks File Clerks Reservation and Transportation Ticket Agents and Travel Clerks 5510 5530 5630 6040 6100 6120 6130 6460 6515 6710 6720 6830 7110 7550 7630 8060 8250 8330 8350 8460 8550 8750 8830 8910 9030 9100 9140 9200 9230 9310 9350 9360 9610 Couriers and Messengers Meter Readers, Utilities Weighers, Measurers, Checkers, and Samplers, Recordkeeping Graders and Sorters, Agricultural Products Fishing and Hunting Workers Forest and Conservation Workers Logging Workers Plasterers and Stucco Masons Roofers Fence Erectors Hazardous Materials Removal Workers Explosives Workers, Ordnance Handling Experts, and Blasters Electronic Equipment Installers and Repairers, Motor Vehicles Manufactured Building and Mobile Home Installers Other Installation, Maintenance, and Repair Workers Including Wind Turbine Service Technicians, and Commercial Divers, and Signal and Track Switch Repairers Model Makers and Patternmakers, Metal and Plastic Prepress Technicians and Workers Shoe and Leather Workers and Repairers Tailors, Dressmakers, and Sewers Textile, Apparel, and Furnishings Workers, All Other Woodworkers Including Model Makers and Patternmakers, All Other Jewelers and Precious Stone and Metal Workers Photographic Process Workers and Processing Machine Operators Etchers, Engravers, and Lithographers Aircraft Pilots and Flight Engineers Bus and Ambulance Drivers and Attendants Taxi Drivers and Chauffeurs Locomotive Engineers and Operators Railroad Brake, Signal, and Switch Operators Ship and Boat Captains and Operators Parking Lot Attendants Automotive and Watercraft Service Attendants Cleaners of Vehicles and Equipment Natural Science Managers Environmental Scientists and Geoscientists 1760 1840 Physical Scientists, All Other Social Scientists, All Other ML Occupation Cluster #28: Environmental and Earth Scientists Notes: There are 10 occupations that do not have any observations in 2012-2021 IPUMS-ACS data. These occupations are: Reinforcing Iron and Rebar Workers (6500), Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic (7960), Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic (8150), Tool Grinders, Filers, and Sharpeners (8210) from ML Occupation Cluster #17 Technical Maintenance Workers; Plating and Coating Machine Setters, Operators, and Tenders, Metal and Plastic (8200), Shoe Machine Operators and Tenders (8340), Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders (8860) from ML Occupation Cluster #23 Manual Workers and Machine Operators; Manufactured Building and Mobile Home Installers (7550), Model Makers and Patternmakers, Metal and Plastic (8060), Railroad Brake, Signal, and Switch Operators (9230) from ML Occupation Cluster #27 Technical and Service Support Personnel. They are not included in my main analysis as my sampling period is between 2012 and 2021. 192 CHAPTER 3 AI ADOPTION AND GENDER WAGE GAPS 3.1 Introduction Throughout the past decade, there have been substantial advancements in Artificial Intelligence (AI) capabilities. Progress in AI subfields, such as machine learning, deep learning, computer vision, robotics, and natural language processing, has not only enhanced AI’s ability to automate both routine-cognitive and routine-manual tasks (e.g., Webb, 2019; Hatzius et al., 2023; Kogan et al., 2023; Pizzinelli et al., 2023) but also improved worker productivity in cognitive, non-routine, and AI-complementary tasks (e.g., Acemoglu and Restrepo, 2018; Acemoglu and Restrepo, 2020; Brynjolfsson et al., 2023; Pizzinelli et al., 2023; Georgieff, 2024). AI’s displacement effect leads to job losses and wage declines, while its augmentation effect increases labor demand and drives wage growth (Acemoglu and Restrepo, 2018). However, these effects may vary by gender. Given differences in task composition across female- and male-dominated roles, AI could widen or narrow existing gender wage gaps. This paper examines the impact of AI adoption on gender wage gaps in the U.S. labor market. Leveraging real-time, high-frequency data from the Census Business Trends and Outlook Survey (BTOS), which has been collecting data since September 2023, I measure firms’ actual AI imple- mentation using the proportion of businesses who current use or expect to use AI in producing goods or services. To quantify the persistence of AI adoption among firms, I measure continuing AI adoption as the unconditional proportion of businesses reporting both current and expected AI use.1 The proportion of businesses reporting AI adoption has grown rapidly since September 2023, particularly among those with continuing AI adoption. I provide three key findings. First, I document that AI adoption at the state-year-month level is associated with a narrowing of within-occupation gender wage gaps, as it increases the mean hourly wage for women more than for men. More specifically, a 1 percentage point (pp) increase in 1Since the BTOS data is publicly available only at aggregated levels, such as state, sector, or firm size, but not at more granular levels like the firm level, I am unable to compute the conditional proportion of continuing AI adoption. 193 the state-year-month share of businesses reporting current, expected, or continuing AI usage leads to a 0.5%, 0.4%, and 3.2% increase, respectively, in women’s mean hourly wage relative to men. I additionally include lagged and lead AI adoption variables to distinguish between short-term and long-term effects. The results show a significant relationship between the lagged AI adoption and the mean hourly wage but a insignificant relationship for the current AI adoption, suggesting the long-term effect narrows the gender wage gap at the mean. This finding may be due to the high correlation between the current and lagged AI adoption variables, resulting in multicollinearity issue, or the stronger power of the lagged effect in explaining the variation. To gain a deeper understanding on the distributional effect of AI adoption on gender wage gaps, I use the within-industry, between-occupation variation along with the industry-month AI adoption to better capture industry-specific patterns. I find a non-monotonic pattern in the relationship between AI adoption and gender wage gaps across the wage distribution, where AI adoption widens gender wage gaps at the bottom and middle of the wage distribution (e.g., the 10th percentile and median) but narrows gaps at the top (e.g., the 90th percentile). Low- and middle-wage women primarily specialize in routine-intensive tasks, such as clerical and administrative jobs, making them more vulnerable to AI-driven substitution. In contrast, their male counterparts are more concentrated in manual, non-routine occupations which are less susceptible to either substitution or complementarity by current AI technologies. Thus, women at the bottom and middle of the wage distribution are disadvantaged by AI relative to men. At the top of the distribution, women can be complemented by AI, rather than being displaced, to boost their productivity, leading to greater wage gains compared to men. Finally, I employ the state-year level data on job postings demanding AI skills to provide a clearer depiction of AI’s complementarity because it is not easy to distinguish between the substitution and complementarity effect of AI using the data on AI adoption in firms. Different from results on the relationship between AI adoption and gender wage gaps, I document a monotonic trend for the impact of the AI job posting share. An increase in this share, reflecting a higher demand for AI skills, narrows gender wage gaps at the 10th percentile, median, mean, and 90th percentile, with 194 stronger effects at the top of the distribution. This could be explained by the fact that the AI job posting share and AI adoption capture different aspects of AI, where the former one measures the expected demand for AI vacancies while the latter one captures the actual implementation of AI in producing goods or services in business. The existence of gender wage gaps in the U.S. labor market has been extensively studied and well documented. Previous literature discusses how changes in gender wage gaps can be explained by human capital differences (Mincer and Polachek, 1974; Altonji and Blank, 1999; Blau and Kahn, 2017), occupational segregation (Goldin, 1990; Cortes and Pan, 2018), discrimination (Neumark et al., 1996; Bertrand and Mullainathan, 2004), workplace flexibility and work preferences (Bertrand et al., 2010; Goldin, 2014), bargaining and negotiation (Babcock and Laschever, 2003; Card et al., 2016), heterogeneous unobserved skills (Bacolod and Blum, 2010), and technology like computer, robots, and automation (Ge and Zhou, 2020; Domini et al., 2020). My paper contributes to this large body of work by examining how AI, a rapidly evolving technology with profound impacts, affects gender wage gaps through the mechanisms of complementarity and substitutability. Research focusing on the link between AI and gender wage gaps is less common, with more studying its impact on the wage inequality in general. Skare et al. (2024) leverage a dataset on AI capital stock in the U.S., the EU, and Japan from 1995 to 2020 and show that AI capital stock accumulation is positively correlated with wealth disparity. Similarly, Felten et al. (2019) document a positive correlation between the exposure to AI and income inequality. Chapter 2 of my dissertation finds that workers specializing in abstract-intensive, AI-complement tasks experience the largest wage gains due to the complementarity of AI, widening wage gaps between this skill group and the rest. However, Acemoglu et al. (2022) find no significant wage effects for occupations or industries that are most exposed to AI substitution. A few studies futher examines AI’s impacts on gender wage gaps. Georgieff (2024) studies the relationship between AI exposure and wage inequality in 19 OECD countries from 2014 to 2018 and finds AI does not affect gender wage gaps within occupations. Domini et al. (2020) reach to a similar conclusion by employing an event study methodology to examine changes in gender wage gaps within French firms from 2002 to 2017 in 195 response to a surge in firm investments in automation or AI. However, the time periods studied in these two papers are prior to the period when AI gained significant public attention. The study most closely related to this paper is Huang (2025), which uses AI adoption data from 2021 and employs a long-differencing approach to investigate the impacts of AI adoption on employment, under the assumption that AI adoption was absent in 2010. My paper differs from these studies by examining the distributional effects of AI adoption in the U.S. during the 2020s on gender wage gaps. The rest of this paper is organized as follows. Section 3.2 describes the data on AI adoption and wages. My empirical strategy is presented in Section 3.3. My main results are discussed in Section 3.4. Section 3.5 concludes. 3.2 Data and Descriptive Statistics In this section, I will first introduce the data sources to measure AI adoption and construct gender wage gaps, and then describe patterns of current AI adoption, expected future AI use, continuing AI usage, and gender wage gaps in the U.S. 3.2.1 AI Adoption The AI adoption data is from the Census Business Trends and Outlook Survey (BTOS), which is a high-frequency survey collecting data from representative U.S. employer businesses since September 2023. The survey asks respondents whether their business used AI technologies cur- rently (Question 7)2 and whether they expect their business to use AI during the next six months (Question 26)3. For each of these two questions, respondents can select one from three options: "Yes," "No," or "Do not know." The BTOS data consists of approximately 1.2 million businesses, divided into six representative panels. Each panel participates in the survey once every 12 weeks for a year. Data are released every two weeks and are available at the national, 2017 North American Industry Classification 2According to the BTOS questionnaire, Question 7 is framed as follows: "Between MMM DD – MMM DD, did this business use Artificial Intelligence (AI) in producing goods or services? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.)." A definition of AI was added on October 23, 2023, stating: "AI Definition: Computer systems and software that are able to perform tasks normally requiring human intelligence, such as decision-making, visual perception, speech recognition, and language processing." 3Question 26 is framed as follows: "During the next six months, do you think this business will be using Artificial Intelligence (AI) in producing goods or services? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.)." 196 System (NAICS) sector (2-digit NAICS), subsector (3-digit NAICS), employment size, sector by employment size, state, and the 25 most populous Metropolitan Statistical Areas (MSAs) level. To measure the current (expected) AI adoption in firms, I use the proportion of businesses that answered "Yes" to Question 7 (26) in the BTOS. I aggregate the bi-weekly BTOS data at the monthly level by averaging the shares to integrate it with the monthly wage data. Figure 3.1 presents the trends in AI adoption in the U.S. from September 2023 to February 2025. Although the proportion of businesses currently using or expecting to use AI in producing goods or services in the U.S. remained low (Figures 3.1a and 3.1c), it grew rapidly compared to the baseline period, September 2023, as shown in Figures 3.1b and 3.1d. Meanwhile, the proportion of businesses that neither currently use nor expect to use AI slightly declined. Figure 3.1e plots the trend in continuing AI adoption in firms, which is an unconditional share of businesses currently using and expecting to use AI computed by multiplying the proportions of businesses that answered "Yes" to both Questions 7 and 26.4 This unconditional share of continuing AI adoption has been rising sharply over time, especially since May 2024, indicating an accelerating trend of businesses consistently adopting AI in producing goods or services. Figure 3.2 presents the geographic distribution of the proportion of businesses currently adopting AI by state. The darker a state’s color is, the more businesses adopted AI in producing goods or services in that state.5 The current AI adoption greatly increased over time for almost all states in the U.S., especially for the West Coast and the East Coast. Almost all states were in yellow during September 2023 to February 2024, but turned into orange and red during September 2024 to February 2025. The minimum and maximum proportion increased from 3.22% to 4.36% and from 11.50% to 16.64%, respectively. Appendix Figures 3A.1 and 3A.2 display similar trends in expected and continuing AI adoption: AI adoption varied by state but consistently increased over time. I additionally plot the current, expected, and continuing AI adoption by 2-digit NAICS code 4Since more granular BTOS data, such as firm-level data, is not publicly available, I am unable to compute the conditional share of businesses currently using or expecting to use AI. 5States with no data means that, according to BTOS, their estimate "does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality." 197 Figure 3.1 Trends in AI Adoption, Sep. 2023 - Feb. 2025 (a) Current AI Adoption, Raw Numbers (in pp) (b) Current AI Adoption, Relative to Sep. 2023 (c) Expected AI Adoption, Raw Numbers (in pp) (d) Expected AI Adoption, Relative to Sep. 2023 (e) Continuing AI Adoption Data: Business Trends and Outlook Survey (BTOS) Notes: In Subfigure 3.1e, the unconditional share of businesses that used and will use AI in producing goods or services is computed by multiplying the proportions of businesses that answered "Yes" to both Question 7 ("Did this business use AI in producing goods or services?") and Question 26 ("During the next six months, will this business use AI in producing goods or services?") in the BTOS. 198 020406080100Proportion of Business (in pp)2023m102024m12024m42024m72024m102025m1Year−MonthYesNoDo Not Know.811.21.41.61.8Relative Business Proportion (Sep 2023 = 1)2023m102024m12024m42024m72024m102025m1Year−MonthYesNoDo Not Know020406080Proportion of Business (in pp)2023m102024m12024m42024m72024m102025m1Year−MonthYesNoDo Not Know.811.21.41.6Relative Business Proportion (Sep 2023 = 1)2023m102024m12024m42024m72024m102025m1Year−MonthYesNoDo Not Know11.522.53Relative Business Proportion (Sep 2023 = 1).3.4.5.6.7.8Proportion of Business (in pp)2023m102024m12024m42024m72024m102025m1Year−MonthRaw Number (in pp)Relative to Sep 2023 Figure 3.2 Geographic Distribution of Current AI Adoption by State (a) Sep. 2023 - Feb. 2024 (b) Mar. 2024 - Aug. 2024 (c) Sep. 2024 - Feb. 2025 Data: Business Trends and Outlook Survey (BTOS) Notes: Scales are in percentage point. These figures show the proportion of businesses that answered "Yes" to Question 7 ("Did this business use AI in producing goods or services?") in the BTOS. States with no data indicate that, according to BTOS, their estimate "does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality." 199 > 7.26.2 − 7.25.2 − 6.24.2 − 5.23.2 − 4.2< 3.2No data> 7.26.2 − 7.25.2 − 6.24.2 − 5.23.2 − 4.2< 3.2No data> 7.26.2 − 7.25.2 − 6.24.2 − 5.23.2 − 4.2< 3.2No data in Appendix Figures 3A.3 to 3A.5. The information industry experienced the highest level of AI adoption, showing an upward trend in the proportion of businesses answering "Yes" and a downward trend in the proportion answering "No" to both the current and expected AI adoption questions. The finance, real estate, professional and scientific services, management, education, and healthcare industries also show a trend of narrowing the gap between the proportion of businesses answering "No" and "Yes" to AI adoption questions, particularly for the expected adoption question. 3.2.2 Gender Wage Gaps The data source to construct the hourly wage from September 2023 to December 2024 is from the Current Population Survey (CPS) data sourced from Integrated Public Use Microdata Series (IPUMS). My sample includes individuals aged 18 to 64 and excludes all individuals who are unemployed or never worked.6 Since the CPS does not directly provide the hourly wage for each individual, I compute it using their usual hours worked per week and rounded weekly earnings7 in the CPS data. To ensure consistency in usual hours worked per week, I take several steps using the CPS data. First, I drop individuals with missing values or those reporting "hours vary." Second, I exclude individuals who report working 168 hours or more per week, as this is the theoretical maximum of hours per week. Finally, I restrict the sample to full-time workers by excluding individuals who report working fewer than 35 hours per week.8 I apply the following restrictions to create a consistent wage series in the CPS data. First, I drop individuals with "Not in Universe (NIU)" values in weekly earnings. Second, I adjust weekly earnings to 2019 U.S. dollars using the Consumer Price Index for All Urban Consumers (CPI-U) provided by the Bureau of Labor Statistics. Finally, I apply a Winsorization approach to cap earnings at the 99th percentile instead of relying on the CPS topcoding system. This is due to changes in the Census Bureau’s topcoding system during my sampling period. From April 2023 6Individuals who are unemployed or never worked are coded as "Not in Universe (NIU)" in the CPS data. 7Beginning in April 2023, the Census Bureau began rounding weekly earnings as a privacy protection measure. 8Without this restriction, the mean hourly wage may be overestimated or the wage distribution may be skewed due to observations with extremely high weekly earnings but very low reported weekly hours worked. For example, some CPS observations show weekly earnings exceeding $2,000 with only 0 or 1 hour worked per week. 200 Figure 3.3 Distribution of Hourly Wage by Gender, 2019-24 (a) Female (b) Male Data: the Current Population Survey (CPS) to March 2024, weekly earnings were topcoded at $2,884.61 (nominal). Starting from April 2024, the Census Bureau used the weighted average of the reported earnings of the top 3% of earners as the "dynamic" topcode. Winsorized mean hourly wage had a consistent trend over time (Appendix Figure 3A.6a), while uncapped one disproportionately increased after April 2024 (Appendix Figure 3A.6b), especially for high-skilled groups (Appendix Figure 3A.7). Figure 3.3 presents the distribution of hourly wages for female (Figure 3.3a) and male (Figure 3.3b) workers from 2019 to 2024. Both distributions exhibit a right-skewed shape, indicating that most workers earn lower hourly wages, while a smaller proportion earns substantially higher wages. However, the wage distribution for male shows a slightly wider right tail and is less right-skewed than the female wage distribution, suggesting that men are more likely than women to earn higher wages and have more access to high-paying jobs. The male wage distribution also has a lower peak and a relatively wider spread, suggesting that men have a more even distribution of wages compared to women. The kernel density estimates (red lines) reinforce these patterns by smoothing out the histogram. To visualize gender wage gaps across the wage distribution, Figure 3.4a displays hourly wages by gender at the 10th percentile, median, and 90th percentile over time. From 2019 to 2024, the hourly wages for male were consistently higher than female. The gender wage gap is much wider 201 0.02.04.06Density020406080100Hourly WageHistogram (Density)Kernel Density0.02.04.06Density020406080100Hourly WageHistogram (Density)Kernel Density Figure 3.4 Hourly Wage across Percentiles, 2019-24 (a) Hourly Wage by Gender (b) Female-to-Male Wage Ratio Data: the Current Population Survey (CPS) at the top of the wage distribution compared to the bottom, but it shrinks at both ends. Figure 3.4b shows the female-to-male wage ratio at the 10th percentile, median, mean, and 90th percentile, highlighting a non-uniform gender wage gap across the distribution. The gap was the narrowest and showed a tendency to close at the bottom of the distribution, where women’s hourly wages increased from 88% to 92% of men’s hourly wages. The female-to-male wage ratio is lower at the top of the wage distribution, indicating that women tend to be underrepresented in high-paying jobs. The trend where the ratio at the mean is higher than at the median is consistent with Figure 3.3, suggesting that the wage distribution for female is more right-skewed. 3.3 Empirical Strategy I study the relationship between AI adoption and gender wage gaps using the following speci- fication: ln(𝑊 𝑎𝑔𝑒𝑜4,𝑠,𝑡,𝑔) = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒𝑔 + 𝜏 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡 + 𝛾(𝐹𝑒𝑚𝑎𝑙𝑒𝑔 × 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡) (3.1) + X𝑠,𝑡𝚽 + 𝜇𝑜4 + 𝛿𝑠 + 𝜃𝑡 + 𝜀𝑠,𝑡, where 𝑜4, 𝑠, 𝑡, and 𝑔 denote 4-digit OCC2010 occupation, state, time period (year-month), and gender, respectively. The time period used in my sample is from September 2023 to December 2024. 𝑊 𝑎𝑔𝑒𝑜4,𝑠,𝑡,𝑔 is the mean hourly wage (in 2019 U.S. dollars) measured at the occupation- 202 1522.53037.54552.560Hourly Wage (p10)201920212023YearAt 10th Percentile1522.53037.54552.560Hourly Wage (Median)201920212023YearAt Median1522.53037.54552.560Hourly Wage (p90)201920212023YearAt 90th PercentileFemaleMale.84.86.88.9.92.94Female−to−Male Wage Ratio201920202021202220232024YearAt p10At MedianAt MeanAt p90 by-state-by-year-month-by-gender level. 𝐹𝑒𝑚𝑎𝑙𝑒𝑔 equals one if 𝑔 is female and zero otherwise. 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡 measures the state-year-month level current, expected, or continuing AI adoption by firms. It represents one of the following: (1) the proportion of businesses in state 𝑠 using AI to produce goods or services during the current time period 𝑡; (2) the proportion of businesses in state 𝑠 at time 𝑡 expecting to use AI in producing goods or services within the next six months; or (3) the proportion of businesses in state 𝑠 at time 𝑡 that reported both currently using and expecting to use AI.9 These shares are multiplied by 100; thus the unit of measurement is a percentage point (pp). X𝑠,𝑡 contains state-year-month control variables that may affect individuals’ hourly wages: the share of female employment; the share of Black population; the share of Hispanic population; and the share of population who earned a Bachelor’s degree or above. Standard errors, 𝜀𝑠,𝑡, are clustered at the state-year-month level to account for the fact that the AI adoption variable is an aggregated measure. The coefficient of interest, 𝛾, captures how the relationship between wages and AI adoption in firms differs for females compared to males. By including occupation, state, and year-month fixed effects, coefficients are identified using within-occupation variation, while accounting for state-specific time-invariant differences in wages and general time trends. To test this relationship in both the short term and long term, I include the lagged and lead AI adoption variables in the following specification: ln(𝑊 𝑎𝑔𝑒𝑜4,𝑠,𝑡,𝑔) = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒𝑔 + ∑︁ 𝜏𝑘 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑘 𝑘∈{𝑡−3,𝑡,𝑡+3} ∑︁ + 𝑘 ∈{𝑡−3,𝑡,𝑡+3} 𝛾𝑘 (𝐹𝑒𝑚𝑎𝑙𝑒𝑔 × 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑘 ) + X𝑠,𝑡𝚽 + 𝜇𝑜4 + 𝛿𝑠 + 𝜃𝑡 + 𝜀𝑠,𝑡, (3.2) where 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡−3 and 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡+3 represent the AI adoption three months prior and three months ahead, respectively. I only include the 𝑡 − 3, 𝑡, and 𝑡 + 3 terms for 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑘 to mitigate potential multicollinearity. 9Due to the lack of the firm-level data, the measurement of continuing AI adoption is an unconditional share computed by multiplying the proportions of businesses that answered "Yes" to both Question 7 ("Did this business use AI in producing goods or services?") and Question 26 ("During the next six months, will this business use AI in producing goods or services?") in the BTOS. 203 To better capture the distributional effects of AI adoption on gender wage gaps, I construct wages at the industry-by-state-by-year-month-by-gender level for different percentiles of the wage distribution and include industry, state, and year-month fixed effects: ln(𝑊 𝑎𝑔𝑒 𝑝 𝑖𝑛𝑑,𝑠,𝑡,𝑔) = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒𝑔 + 𝜏 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑖𝑛𝑑,𝑡 + 𝛾(𝐹𝑒𝑚𝑎𝑙𝑒𝑔 × 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑖𝑛𝑑,𝑡) + X𝑠,𝑡𝚽 + 𝜇𝑖𝑛𝑑 + 𝛿𝑠 + 𝜃𝑡 + 𝜀𝑖𝑛𝑑,𝑡, (3.3) where 𝑖𝑛𝑑 denotes 2-digit NAICS code and 𝑝 represents the 𝑝th percentile. This approach allows me to analyze how AI adoption influences wage dispersion within industries while maintaining between-occupation variation. Compared to the previous specification, equation (3.3) employs industry-year-month AI adoption to better reflect sector-specific technological adoption patterns. In this way, this specification better captures how AI impacts gender wage gaps at different percentiles of the wage distribution within industries, rather than relying on state-level measures which may absorb industry-level heterogeneity. Standard errors are clustered at the industry-year-month level to align with the industry-year-month level AI adoption variable. 3.4 Results 3.4.1 AI Adoption and within-Occupation Gender Wage Gaps I first look at the relationship between within-occupation gender wage gaps and AI adoption varying across states and over time. Columns 1-3 of Table 3.1 estimate equation (3.1) using current AI adoption. The Ordinary Least Squares (OLS) estimates in column 1 show that women earn, on average, 13.5% lower hourly wages than men in the absence of AI adoption in businesses, but there is no significant relationship between AI adoption and wages. Column 2 adds state and year-month fixed effects, while column 3 further controls for occupation fixed effect; thus, column 3 captures within-occupation effects. The coefficient on the interaction term, 𝐹𝑒𝑚𝑎𝑙𝑒𝑔 × 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡, is now significant and positive, implying that women may experience slightly more positive wage changes from current AI adoption compared to men. Specifically, a 1pp increase in the share of businesses currently adopting AI at the state-year-month level is associated with a 0.5% higher mean hourly wage for women relative to men. This result remains consistent regardless of the 204 Table 3.1 Effects of Current AI Adoption by State on Gender Wage Gaps (1) -0.135∗∗∗ (0.017) -0.006∗∗∗ (0.002) 0.005 (0.003) Female %Businesses Using AI1 in Current Month (t) Female × %Businesses Using AI in Current Month (t) %Businesses Using AI 3 Months Ago (t-3) Female × %Businesses Using AI 3 Months Ago (t-3) %Businesses Using AI 3 Months later (t+3) Female × %Businesses Using AI 3 Months later (t+3) Dep. Var.: Log Mean Hourly Wage (2) -0.137∗∗∗ (0.017) (3) -0.147∗∗∗ (0.015) (4) -0.186∗∗∗ (0.022) (5) -0.210∗∗∗ (0.023) -0.003 (0.004) 0.005∗ (0.003) -0.004 (0.003) 0.005∗∗ (0.003) -0.003 (0.004) 0.001 (0.005) -0.002 (0.005) 0.011∗ (0.006) -0.002 (0.005) 0.004 (0.007) -0.003 (0.006) 0.016∗∗ (0.006) -0.002 (0.005) -0.002 (0.006) 62,480 62,480 ✓ ✓ Observations State FE Year-Month FE Occupation FE Outcome Mean R2 Notes: Each observation is an occupation-state-year-month-gender cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS based on the 2010 Census Occupational Classifi- cation. All columns include a set of state-year-month controls. Standard errors shown in parentheses are clustered at the state-year-month level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of businesses currently using AI is measured as the average monthly share of businesses at the state level that answered "Yes" to Question 7 in the Business Trends and Outlook Survey (BTOS), which asked "Between MMM DD – MMM DD, did this business use Artificial Intelligence (AI) in producing goods or services? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.)." The unit is a percentage point. 62,479 ✓ ✓ ✓ 3.162 0.310 47,845 ✓ ✓ ✓ 3.161 0.312 42,800 ✓ ✓ ✓ 3.160 0.316 3.162 0.038 3.162 0.033 inclusion of occupation fixed effect. Note that the OLS estimates underestimate gender wage gaps without AI adoption. After controlling for state, year-month, and occupation fixed effects, on average, women earn 14.7% less in hourly wages than men. Column 4 estimates equation (3.2) by including the lagged term of AI adoption, which refers to the reported AI adoption from three months ago, to capture the short-term and long-term effects. The coefficient on the interaction between female and current AI adoption (𝐹𝑒𝑚𝑎𝑙𝑒𝑔 × 205 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡) is now statistically insignificant (0.001), while the coefficient on the lagged interaction term (𝐹𝑒𝑚𝑎𝑙𝑒𝑔 × 𝐴𝐼 𝐴𝑑𝑜 𝑝𝑡𝑖𝑜𝑛𝑠,𝑡−3) is significantly positive (0.011), indicating that AI adoption by business could have a delayed effect on female wages. This finding suggests a long-term effect of AI adoption on narrowing gender wage gaps, which is in contrast to the short-term effect shown in column 3. Several reasons could explain this contradiction. First, the current and lagged AI adoption variables might be highly correlated, leading to multicollinearity issue. Second, the lagged period’s effect might explain more of the variation, leading to a weaker effect of the current period. Column 5 further includes the lead term of AI adoption, the reported AI adoption from three months later. The relationship between lagged AI adoption and female wages is stronger: a 1pp increase in the state-year-month share of businesses using AI three months ago is associated with a 1.6% higher mean hourly wage for women compared to men at the current stage. This result strengthens the idea that there might be a delayed response in the labor market to the actual implementation of AI in businesses. Table 3.2 estimates equation (3.2) separately for each of the following four skill groups proposed by Chapter 2 of my dissertation: high-skilled AI-complement, high-skilled not-yet-AI, middle- skilled, and low-skilled groups. Panel A only considers current and lagged AI adoption. Column 1 of Panel A is the same as column 4 of Table 3.1, which uses the full sample of occupations. Coefficients in Panel A show a significant relationship between lagged AI adoption and mean hourly wages for female from the middle-skilled group. Compared to middle-skilled men, mean hourly wages for middle-skilled women increase by 1.2% if the share of businesses reported using AI three months ago at the state-year-month level increases by 1pp. This could be explained by the substitutability effect of AI, where middle-skilled occupations, being routine-intensive, are particularly vulnerable to AI-driven displacement (Acemoglu and Restrepo, 2018, 2019; Huang, 2025). Since these middle-skilled, routine-intensive occupations tend to be male-dominated, their mean hourly wages are more negatively affected by AI adoption than females. In addition, women in routine-intensive roles were more likely to shift to high-skilled, high-wage occupations compared 206 to men (Cortés et al., 2024), leading to a narrower gender wage gap within the middle-skilled occupations. In Panel B of Table 3.2, I use the share of businesses reporting expected AI adoption during the next six months at time period 𝑡, which reflects businesses’ future plans and strategies, instead of the share of businesses reporting using AI currently (𝑡) and three months ago (𝑡 − 3). In column 1 of Panel B, the expected AI adoption benefits female workers slightly more than male workers, reflecting the effect of forward-looking expectation on narrowing gender wage gaps in general. Same as Panel A, when decomposing occupations into the four skill groups, Panel B only shows a significantly positive correlation between gender wage gaps within middle-skilled occupations and expected AI adoption. Since BTOS asks businesses whether they expect to use AI in producing goods or services, it is possible that businesses plan to adopt AI for routine-intensive tasks to replace labor but have not yet implemented it. Panel C of Table 3.2 uses the continuing AI adoption at the state-year-month level, which is an unconditional share computed by multiplying shares of businesses reporting both current and expected AI adoption. Since the AI adoption is likely to be an ongoing event, this continuing AI adoption measure captures how pervasive and sustained AI adoption is expected to be. Continuing AI adoption narrows gender wage gaps more than current or expected AI adoption. The coefficient on the interaction term in column 1 of Panel C indicates that a 1pp increase in the unconditional share of businesses reporting both current and expected AI adoption leads to a 3.2% increase in mean hourly wages for women compared to their male counterparts. Since the unconditional continuing AI adoption share reflects both current and expected AI usage, these findings suggest that businesses anticipating greater AI adoption are shifting their wage structures to favor women, thus narrowing gender wage gaps. 3.4.2 AI Adoption and within-Industry, between-Occupation Gender Wage Gaps While Section 3.4.1 focuses on the mean hourly wage using within-occupation variations, Section 3.4.2 looks at the distributional effects of AI adoption. Table 3.3 presents estimates of equation (3.3), utilizing industry-year-month specific AI adoption and industry-by-state-by-year- 207 Table 3.2 Effects of AI Adoption by State on Gender Wage Gaps by Skill Group Dep. Var.: Log Mean Hourly Wage (1) All Occ. (2) High-Skilled AI-Complement Occ. Not-Yet-AI Occ. (3) (4) High-Skilled Middle-Skilled Low-Skilled Occ. Occ. (5) Female %Businesses Using AI in Current Month (t) Female × %Businesses Using AI in Current Month (t) %Businesses Using AI 3 Months Ago (t-3) Female × %Businesses Using AI 3 Months Ago (t-3) Observations R2 Female %Businesses Reporting Expected AI Adoption Female × %Businesses Reporting Expected AI Adoption Observations R2 Female %Businesses Continuing AI Adoption1 Female × %Businesses Continuing AI Adoption Observations R2 Panel A. Current AI Adoption -0.154∗∗ (0.060) -0.186∗∗∗ (0.022) -0.003 (0.004) 0.001 (0.005) -0.002 (0.005) 0.011∗ (0.006) 47,845 0.312 -0.010 (0.013) 0.001 (0.011) 0.001 (0.014) -0.003 (0.015) 5,030 0.111 Panel B. Expected AI Adoption -0.143∗∗∗ (0.038) -0.147∗∗∗ (0.014) -0.004∗ (0.002) 0.004∗∗ (0.002) 69,417 0.312 0.001 (0.006) -0.002 (0.005) 7,258 0.106 -0.148∗∗∗ (0.037) -0.013∗ (0.007) -0.003 (0.007) -0.004 (0.009) 0.012 (0.010) 16,694 0.212 -0.112∗∗∗ (0.021) -0.005 (0.004) 0.002 (0.003) 24,293 0.208 Panel C. Continuing AI Adoption -0.145∗∗∗ (0.021) -0.132∗∗∗ (0.008) -0.103∗∗∗ (0.013) -0.016 (0.022) 0.032∗∗ (0.015) 0.044 (0.052) -0.025 (0.032) -0.046 (0.039) 0.015 (0.023) 62,479 0.310 ✓ ✓ ✓ 6,580 0.104 ✓ ✓ ✓ 21,825 0.207 ✓ ✓ ✓ -0.239∗∗∗ (0.028) 0.001 (0.005) 0.009 (0.006) -0.000 (0.006) 0.012∗ (0.007) 22,714 0.236 -0.181∗∗∗ (0.019) -0.005∗ (0.003) 0.008∗∗∗ (0.002) 32,943 0.231 -0.158∗∗∗ (0.012) -0.012 (0.027) 0.078∗∗∗ (0.023) -0.210∗∗ (0.094) 0.021 (0.018) -0.004 (0.025) 0.003 (0.016) 0.018 (0.025) 2,735 0.236 -0.194∗∗∗ (0.062) -0.001 (0.008) 0.009 (0.008) 3,998 0.239 -0.173∗∗∗ (0.036) -0.001 (0.077) 0.104 (0.066) 29,618 0.231 ✓ ✓ ✓ 3,609 0.240 ✓ ✓ ✓ State FE Year-Month FE Occupation FE Notes: Each observation is an occupation-state-year-month-gender cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS based on the 2010 Census Occupational Classi- fication. The skill group indicators are constructed by Chapter 2 of my dissertation. All columns include a set of state-year-month controls. Standard errors shown in parentheses are clustered at the state-year-month level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of businesses continuing AI adoption is an unconditional measure, computed by multiplying the proportions of businesses that responded "Yes" to both Question 7 (currently using AI) and Question 26 (expecting to use AI) in the Business Trends and Outlook Survey (BTOS). 208 month-by-gender hourly wages at the 10th percentile, median, mean, and 90th percentile of the wage distribution. Coefficients on the binary indicator for women, 𝐹𝑒𝑚𝑎𝑙𝑒𝑔, align with the trends in gender wage gaps shown in Figure 3.4. The gap is wider at the top of the wage distribution but narrower at the bottom. Panel A of Table 3.3 focuses on the current AI adoption. The coefficients on the interaction term show a non-monotonic pattern in the relationship between current AI adoption and gender wage gaps across the wage distribution. At the 10th percentile, the coefficient on the interaction term is significant and negative (-0.010), implying that a 1pp increase in the industry-year-month share of businesses currently adopting AI leads to a 1% decline in hourly wages for women at the 10th percentile of the distribution compared to men. This result suggests that current AI adoption is associated with a wider gender wage gap at the bottom of the wage distribution. This negative effect persists but slightly diminishes at the median, where the estimate is smaller in magnitude (-0.005) but still statistically significant, indicating a weaker effect of current AI adoption on the gender wage gap at the median than at the bottom of the distribution. In contrast, the coefficient on the interaction term turns positive (0.009) and significant at the 90th percentile, suggesting that high-wage women benefit more from current AI adoption relative to men in similar high-wage roles. This positive relationship reduces the gender wage gap at the top of the wage distribution. However, at the mean, the interaction term is insignificant (-0.001), indicating a lack of clear relationship between industry-specific AI adoption and the average gender wage gap within industries but across occupations. It is possible that the negative effects at the bottom of the distribution and the positive effects at the top appear to offset each other, resulting in an insignificant net effect at the mean. These results remain robust across different combinations of state, year-month, and industry fixed effects, as shown in Appendix Table 3A.1. However, they become insignificant after including lagged AI adoption terms, potentially due to multicollinearity. These findings indicate that current AI adoption exacerbates gender wage gaps at the bottom of the wage distribution but reduces gaps at the top. This non-monotonic pattern occurs because AI adoption by business disproportionately disadvantages women in low- and middle-wage jobs 209 Table 3.3 Effects of AI Adoption by Industry on Gender Wage Gaps across the Wage Distribution Dep. Var.: Log Hourly Wage (1) At p10 (2) (3) At Median At Mean (4) At p90 Female Panel A. Current AI Adoption -0.068∗∗∗ (0.020) -0.143∗∗∗ (0.012) %Businesses Using AI in Current Month (t) Female × %Businesses Using AI in Current Month (t) Panel B. Expected AI Adoption -0.056∗∗∗ (0.021) -0.131∗∗∗ (0.012) 0.005 (0.007) -0.010∗∗∗ (0.003) 13,478 0.121 0.002 (0.004) -0.005∗∗ (0.002) 13,478 0.331 0.003 (0.006) -0.008∗∗∗ (0.002) 13,478 0.121 0.003 (0.003) -0.005∗∗∗ (0.002) 13,478 0.332 -0.129∗∗∗ (0.012) -0.230∗∗∗ (0.016) 0.000 (0.004) -0.001 (0.002) 13,478 0.346 -0.004 (0.004) 0.009∗∗∗ (0.003) 13,478 0.331 -0.125∗∗∗ (0.012) -0.236∗∗∗ (0.017) 0.002 (0.003) -0.001 (0.001) 13,478 0.348 -0.001 (0.003) 0.007∗∗∗ (0.002) 13,478 0.331 Panel C. Continuing AI Adoption -0.159∗∗∗ -0.104∗∗∗ (0.009) (0.016) -0.133∗∗∗ (0.009) -0.197∗∗∗ (0.012) Observations R2 Female Observations R2 Female %Businesses Reporting Expected AI Adoption Female × %Businesses Reporting Expected AI Adoption %Businesses Continuing AI Adoption1 Female × %Businesses Continuing AI Adoption Observations R2 State FE Year-Month FE Occupation FE 0.022 (0.026) -0.028∗ (0.015) 13,478 0.120 ✓ ✓ ✓ 0.014 (0.015) -0.017∗ (0.030) 13,478 0.331 ✓ ✓ ✓ 0.006 (0.013) -0.005 (0.008) 13,478 0.346 ✓ ✓ ✓ -0.003 (0.012) 0.030∗∗ (0.011) 13,478 0.330 ✓ ✓ ✓ Notes: Each observation is an industry-state-year-month-gender cell. Industry is represented by 2-digit NAICS code. All columns include a set of state-year-month controls. Standard errors shown in parentheses are clustered at the industry-state-year-month level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of businesses continuing AI adoption is an unconditional measure, computed by multiplying the proportions of businesses that responded "Yes" to both Question 7 (currently using AI) and Question 26 (expecting to use AI) in the Business Trends and Outlook Survey (BTOS). 210 while benefiting women in high-wage jobs. AI adoption tends to replace routine tasks, which are primarily concentrated at the bottom or middle of the wage distribution (Acemoglu and Restrepo, 2018, 2022). On the one hand, women in low- and middle-wage jobs are overrepresented in routine- intensive roles like clerical and administrative occupations, which are highly likely to be replaced by AI-powered automation (Brussevich et al., 2019; Cazzaniga et al., 2024). This displacement effect of AI adoption may lead to stagnating or declining wages for women. On the other hand, men at the bottom or middle of the wage distribution tend to specialize in manual, non-routine tasks which are more AI-resilient than routine-intensive tasks. Thus, they might be less affected by AI adoption in businesses because these manual, non-routine tasks are not easily performed by current AI capabilities. At the upper end of the wage distribution, the complementarity or augmentation effect of AI dominates its substitution effect (Chapter 2 of my dissertation). Women in high-wage jobs may use AI to enhance their productivity, leading to greater wage gains or more promotion opportunities for high-paying women relative to men. This is consistent with Carvajal et al. (2024), which find that women with top grades can significantly enhance their job prospects by acquiring AI skills, and Cazzaniga et al. (2024), which suggest that women are more likely to benefit from the complementarity of AI. Panels B and C of Table 3.3 show a similar non-monotonic pattern: both expected and continuing AI adoption widen gender wage gaps at the lower and middle parts of the wage distribution but narrow the gap at the top. Notably, the non-monotonic effect of continuing AI adoption is much larger in magnitude than the effect of current or expected AI adoption. Since continuing AI adoption is the unconditional share of businesses reporting both current and expected AI usage, it reflects the persistent AI use by business. This long-term adoption is likely to have larger effects on the labor market compared to one-time adoption or future plans. I plot the coefficients on the interaction term between female and AI adoption estimated from equation (3.3) in Figure 3.5 to illustrate the heterogeneous impacts of AI adoption on gender wage gaps across the wage distribution. In addition to the estimates presented in Table 3.3, I also run regressions at the 5th and 95th percentiles to provide a more comprehensive overview of how AI 211 Figure 3.5 Effects of AI Adoption on Women Relative to Men in the Hourly Wage Notes: The coefficient estimates plotted are the estimates of 𝛾 from equation (3.3). They represent the difference in the effect of current, expected, and continuing AI adoption, respectively, between women and men. The corresponding 95% confidence intervals are also shown. adoption affects gender wage gaps at both the lower and upper ends of the wage distribution. The coefficient plot visualizes the non-monotonic trend in how the impact of AI adoption on women’s hourly wages differs from men’s across the wage distribution. It reveals that the lower an individual is in the wage distribution, the stronger the widening effect of AI adoption on the gender wage gap is. In contrast, at the upper end of the distribution, AI adoption is associated with a narrowing of the gap. 3.4.3 AI Postings and Gender Wage Gaps Since the framing of the AI-related question in the BTOS does not clearly differentiate between measuring the substitutability or complementarity effect of AI adoption, I use the share of job postings requiring AI skills to better capture the complementarity effect of AI by adopting the following specification: 212 −.075−.05−.0250.025.05.075Coefficient on Interaction Termp5p10MedianMeanp90p95PercentileFemale * %Current AI AdoptionFemale * %Expected AI AdoptionFemale * %Continuing AI Adoption Table 3.4 Effects of AI Postings on Gender Wage Gaps by Skill Groups, 2019-24 Dep. Var.: Log Mean Hourly Wage (1) All Occ. (2) High-Skilled AI-Complement Occ. Not-Yet-AI Occ. Female %AI Postings1 Female × %AI Postings -0.154∗∗∗ (0.005) -0.032∗∗∗ (0.008) 0.033∗∗∗ (0.005) -0.183∗∗∗ (0.016) -0.026 (0.020) 0.048∗∗∗ (0.013) (5) (3) (4) High-Skilled Middle-Skilled Low-Skilled Occ. -0.165∗∗∗ (0.007) -0.028∗∗∗ (0.010) 0.031∗∗∗ (0.006) -0.124∗∗∗ (0.011) -0.029∗∗ (0.013) 0.029∗∗∗ (0.009) Occ. -0.139∗∗∗ (0.024) 0.002 (0.021) -0.043 (0.031) 8,847 ✓ ✓ ✓ 3.580 0.168 100,090 ✓ ✓ ✓ 3.136 0.408 Observations State FE Year FE Occupation FE Outcome Mean R2 Notes: Each observation is an occupation-state-year-gender cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS based on the 2010 Census Occupational Classification. The skill group indicators are constructed by Chapter 2 of my dissertation. All columns include a set of state-year controls. Standard errors shown in parentheses are clustered at the state-year level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of AI postings is measured at the state-year level. The unit is a percentage point. The data is from the AI Index Report by Stanford Institute for Human-Centered AI, who provides the Lightcast data on AI posting shares at the state-year level for the public. 51,333 ✓ ✓ ✓ 3.006 0.330 31,520 ✓ ✓ ✓ 3.260 0.364 6,273 ✓ ✓ ✓ 2.986 0.326 ln(𝑊 𝑎𝑔𝑒𝑜4,𝑠,𝑦𝑟,𝑔) = 𝛼 + 𝛽𝐹𝑒𝑚𝑎𝑙𝑒𝑔 + 𝜏 𝐴𝐼 𝑃𝑜𝑠𝑡𝑖𝑛𝑔𝑠𝑠,𝑦𝑟 + 𝛾(𝐹𝑒𝑚𝑎𝑙𝑒𝑔 × 𝐴𝐼 𝑃𝑜𝑠𝑡𝑖𝑛𝑔𝑠𝑠,𝑦𝑟) (3.4) + X𝑠,𝑦𝑟𝚽 + 𝜇𝑜4 + 𝛿𝑠 + 𝜃 𝑦𝑟 + 𝜀𝑠,𝑦𝑟, where 𝑦𝑟 represents year (from 2019 to 2024) and 𝐴𝐼 𝑃𝑜𝑠𝑡𝑖𝑛𝑔𝑠𝑠,𝑦𝑟 is the state-year level share of job postings requiring AI skills (in percentage points), which is provided by Zhang et al. (2022), Maslej et al. (2023), and Maslej et al. (2024) from Stanford Institute for Human-Centered AI (HAI).10 The coefficient of interest is still 𝛾, which captures the changes in the mean hourly wage for women relative to men associated with a 1pp increase in the state-year level share of AI job postings. Table 3.4 presents results estimated from equation (3.4) for all occupations and each skill group separately. The coefficient on the interaction term in column 1 indicates that, compared 10Stanford HAI aggregates online job postings data from Lightcast at the state-year level and provides free public access. However, more granular data is not publicly available. 213 to men, a 1pp increase in the share of AI postings at the state-year level leads to a 3.3% mean hourly wage growth for women. When restricting the sample to each one of the skill groups, women in high-skilled AI-complement jobs have the largest mean hourly wage growth relative to men, suggesting a narrower gender wage gap within high-skilled AI-complement occupations driven by the complementarity effect of AI. Note that for low-skilled group in column 5, the coefficient on the interaction term (0.001) is insignificant, implying that a higher demand for AI skills does not disproportionately benefit or disadvantage women in low-skilled jobs compared to men. Furthermore, the coefficient on the share of AI postings (-0.043) is not significant. These findings could be due to the fact that low-skilled occupations are less likely to require AI skills, as discussed in Chapter 2 of my dissertation, and therefore, the AI posting share does not significantly affect wages for low-skilled workers. Appendix Table 3A.2 tests the short- and long-term effects of the AI posting share by including its lagged term, which represents the share from the previous year. The coefficient on the interaction term between female and AI postings in the current year is significantly positive, particularly for high-skilled AI-complement occupations. In contrast, the coefficient on the interaction between female and the lagged term is insignificant, except for low-skilled occupations. Since job postings requiring AI skills signal expectations for and anticipated changes in AI skills in the future, the share of AI job postings may have a more immediate impact on wages compared to AI adoption, which reflects the actual implementation of AI in firms. Table 3.5 re-estimates equation (3.4) but replacing the outcome variable with the industry-by- state-by-year-by-gender hourly wage at the 10th percentile, median, mean, and 90th percentile of the wage distribution. Different from Table 3.3, coefficients on the interaction term in Table 3.5 show a monotonic trend in the relationship between the share of AI postings and gender wage gaps across the wage distribution, with coefficients plotted in Appendix Figure 3A.8. A higher demand for AI skills narrows gender wage gaps across the distribution, while the AI adoption by business widens the gap at the bottom of the distribution but narrows the gap at the top. This difference may arise because AI job postings and AI adoption reflect distinct aspects of AI. AI job postings 214 Table 3.5 Effects of AI Postings on Gender Wage Gaps across Wage Distribution, 2019-24 Dep. Var.: Log Hourly Wage (1) At p10 -0.132∗∗∗ (0.016) -0.033∗ (0.018) 0.024∗ (0.013) 9,144 ✓ ✓ ✓ 2.534 0.302 (2) At Median -0.182∗∗∗ (0.016) -0.019 (0.017) 0.028∗∗ (0.012) 9,144 ✓ ✓ ✓ 3.114 0.626 (3) At Mean -0.162∗∗∗ (0.012) -0.027 (0.017) 0.044∗∗∗ (0.010) 9,144 ✓ ✓ ✓ 3.087 0.685 (4) At p90 -0.228∗∗∗ (0.013) -0.018 (0.018) 0.056∗∗∗ (0.010) 9,144 ✓ ✓ ✓ 3.777 0.489 Female %AI Postings1 Female × %AI Postings Observations State FE Year FE Industry FE Outcome Mean R2 Notes: Each observation is an industry-state-year-gender cell. Industry is represented by 2-digit NAICS code. All columns include a set of state-year controls. Standard errors shown in parentheses are clustered at the industry-year level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of AI postings is measured at the state-year level. The unit is a percentage point. The data is from the AI Index Report by Stanford Institute for Human-Centered AI, who provides the Lightcast data on AI posting shares at the state-year level for the public. capture expected demand for AI-related vacancies, which may benefit low-wage women more than men. It is possible that some female-dominated clerical or administrative jobs may require workers to use AI-powered tools rather than being fully automated, thus providing upskilling or reskilling opportunities that benefit women more than men. AI adoption indicates the implementation of AI in producing goods or services in business, leading to job displacement which disproportionately disadvantages low-wage women. However, low-wage men usually specialize in manual, non-routine jobs, which are less likely to be complemented by AI or be substituted by AI at the present stage. Estimates in column 4 of Table 3.5 further supports the finding that, at the top of the wage distribution, AI narrows the gender wage gap by benefiting women more than men. These high- wage jobs are more likely to involve problem-solving, decision-making, and cognitive tasks that can be complemented by AI rather than being replaced. Women in high-paying jobs can utilize AI tools or acquire AI skills to enhance their productivity, leading to wage gains. The increasing demand for AI skills in these jobs may also provide women with greater opportunities for employment, 215 upskilling, potential promotions, or transitions into higher-wage roles. After including the lagged share of AI postings variable in Appendix Table 3A.3, the results align with those on within-occupation mean hourly wages in Appendix Table 3A.2. The AI posting share reflects labor market expectations, leading to quicker wage adjustments, particularly at the upper end of the wage distribution. 3.5 Conclusion The rapid advancement of AI raises questions about its impact on labor market outcomes. While most of the existing literature focuses on how AI affects employment and wages from the perspective of the exposure to AI, my study explores the relationship between AI adoption in firms and gender wage gaps in the U.S. during September 2023 to December 2024. I first find that an increase in the share of businesses reporting current, expected, or continuing AI adoption in producing goods or services narrows the within-occupation gender gaps in mean hourly wage. Using the AI adoption data by industry to capture industry-specific patterns in technological changes, I document a non- monotonic pattern in the distributional effect of AI adoption on gender wage gaps: AI adoption widens gaps at the lower end and middle of the distribution but narrows the top. I further test the correlation between the complementarity of AI and gender wage gaps using the data on online job postings requiring AI skills. Results suggest that the higher demand for AI skills narrows gender wage gaps across the wage distribution, with more pronounced effects at the top of the distribution. The real-time, high-frequency data on AI adoption at the state or industry level allows me to examine the differential effects of dynamic changes in AI adoption on wages for females versus males. 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AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, March 2024. 219 APPENDIX 3A ADDITIONAL FIGURES & TABLES Figure 3A.1 Geographic Distribution of Expected AI Adoption by State (a) Sep. 2023 - Feb. 2024 (b) Mar. 2024 - Aug. 2024 (c) Sep. 2024 - Feb. 2025 Data: Business Trends and Outlook Survey (BTOS) Notes: Scales are in percentage point. These figures show the proportion of businesses that answered "Yes" to Question 26 ("During the next six months, will this business use AI in producing goods or services?") in the BTOS. States with no data indicate that, according to BTOS, their estimate "does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality." 220 > 98 − 97 − 86 − 75 − 6< 5No data> 87 − 86 − 75 − 64 − 5< 4No data> 87 − 86 − 75 − 64 − 5< 4No data Figure 3A.2 Geographic Distribution of Continuing AI Adoption by State (a) Sep. 2023 - Feb. 2024 (b) Mar. 2024 - Aug. 2024 (c) Sep. 2024 - Feb. 2025 Data: Business Trends and Outlook Survey (BTOS) Notes: Scales are in percentage point. These figures show the unconditional share of businesses currently using and expecting to use AI in producing goods or services, computed by multiplying the proportions of businesses that answered "Yes" to both Question 7 ("Did this business use AI in producing goods or services?") and Question 26 ("During the next six months, will this business use AI in producing goods or services?") in the BTOS. States with no data indicate that, according to BTOS, their estimate "does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality." 221 > 0.60.5 − 0.60.4 − 0.50.3 − 0.40.2 − 0.3< 0.2No data> 0.60.5 − 0.60.4 − 0.50.3 − 0.40.2 − 0.3< 0.2No data> 0.60.5 − 0.60.4 − 0.50.3 − 0.40.2 − 0.3< 0.2No data Figure 3A.3 Current AI Adoption by Industry (in pp), Sep. 2023 - Feb. 2025 Data: Business Trends and Outlook Survey (BTOS) Notes: Scales are in percentage point. Industries are represented by the 2-digit NAICS code. Industries with missing data points indicate that, according to BTOS, their estimate "does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality." 222 020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAgriculture, Forestry, Fishing and Hunting020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthMining, Quarrying, and Oil and Gas Extraction020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthUtilities020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthConstruction020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthWholesale Trade020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthRetail Trade020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthTransportation and Warehousing020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthInformation020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthFinance and Insurance020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthReal Estate and Rental and Leasing020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthProfessional, Scientific, and Tech. Services020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthManagement of Companies and Enterprises020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAdministrative and Support020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthEducational Services020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthHealth Care and Social Assistance020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthArts, Entertainment, and Recreation020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAccommodation and Food Services020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthOther Services (except Public Administration)YesNoDo Not Know Figure 3A.4 Expected AI Adoption by Industry (in pp), Sep. 2023 - Feb. 2025 Data: Business Trends and Outlook Survey (BTOS) Notes: Scales are in percentage point. Industries are represented by the 2-digit NAICS code. Industries with missing data points indicate that, according to BTOS, their estimate "does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality." 223 020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAgriculture, Forestry, Fishing and Hunting020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthMining, Quarrying, and Oil and Gas Extraction020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthUtilities020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthConstruction020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthWholesale Trade020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthRetail Trade020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthTransportation and Warehousing020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthInformation020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthFinance and Insurance020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthReal Estate and Rental and Leasing020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthProfessional, Scientific, and Tech. Services020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthManagement of Companies and Enterprises020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAdministrative and Support020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthEducational Services020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthHealth Care and Social Assistance020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthArts, Entertainment, and Recreation020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAccommodation and Food Services020406080100Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthOther Services (except Public Administration)YesNoDo Not Know Figure 3A.5 Continuing AI Adoption by Industry (in pp), Sep. 2023 - Feb. 2025 Data: Business Trends and Outlook Survey (BTOS) Notes: Scales are in percentage point. Industries are represented by the 2-digit NAICS code. These figures show the unconditional share of businesses currently using and expecting to use AI in producing goods or services, computed by multiplying the proportions of businesses that answered "Yes" to both Question 7 ("Did this business use AI in producing goods or services?") and Question 26 ("During the next six months, will this business use AI in producing goods or services?") in the BTOS. Industries with missing data points indicate that, according to BTOS, their estimate "does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality." 224 0.05.1Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAgriculture, Forestry, Fishing and Hunting.07.08.09.1.11.12Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthMining, Quarrying, and Oil and Gas Extraction0123Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthUtilities0.05.1.15Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthConstruction0.1.2.3.4Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthWholesale Trade.1.15.2.25.3Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthRetail Trade.02.03.04.05.06.07Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthTransportation and Warehousing34567Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthInformation.511.5Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthFinance and Insurance.6.811.21.4Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthReal Estate and Rental and Leasing1.522.533.54Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthProfessional, Scientific, and Tech. Services22.533.54Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthManagement of Companies and Enterprises.2.3.4.5.6Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAdministrative and Support.511.522.53Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthEducational Services.2.4.6.8Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthHealth Care and Social Assistance.2.4.6.8Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthArts, Entertainment, and Recreation.02.04.06.08.1Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthAccommodation and Food Services.05.1.15.2Proportion of Business (in pp)2023m92024m32024m92025m2Year−MonthOther Services (except Public Administration)Unconditional Share (in pp) Figure 3A.6 Mean Hourly Wage in the U.S. (in 2019 U.S. Dollars), 2019-24 (a) Earnings Capped at the 99th Percentile (b) Uncapped Earnings Notes: In Subfigure 3A.6a, earnings are winsorized at the 99th percentile to mitigate the influence of outliers. In Subfigure 3A.6b, earnings follows the Census Bureau’s topcoding system: from April 2023 to March 2024, weekly earnings were topcoded at $2,884.61 (nominal); beginning in April 2024, the maximum value of weekly earnings is the weighted average of the reported earnings of the top 3% of earners during the reported month. 225 15253545Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthFemaleMale15253545Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthFemaleMale Figure 3A.7 Mean Hourly Wage in the U.S. by Skill Group (in 2019 U.S. Dollars), 2019-24 (a) Earnings Capped at the 99th Percentile (b) Uncapped Earnings Notes: The skill group indicators are constructed by Chapter 2 of my dissertation. In Subfigure 3A.7a, earnings are winsorized at the 99th percentile to mitigate the influence of outliers. In Subfigure 3A.7b, earnings follows the Census Bureau’s topcoding system: from April 2023 to March 2024, weekly earnings were topcoded at $2,884.61 (nominal); beginning in April 2024, the maximum value of weekly earnings is the weighted average of the reported earnings of the top 3% of earners during the reported month. 226 15253545Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthHigh−Skilled AI−Complement Occ15253545Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthHigh−Skilled Not−Yet−AI Occ15253545Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthMiddle−Skilled Occ15253545Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthLow−Skilled OccFemaleMale15304560Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthHigh−Skilled AI−Complement Occ15304560Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthHigh−Skilled Not−Yet−AI Occ15304560Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthMiddle−Skilled Occ15304560Mean Hourly Wage2019m12020m12021m12022m12023m12024m1Year−MonthLow−Skilled OccFemaleMale Figure 3A.8 Effects of AI Postings on Women Relative to Men in the Hourly Wage Notes: The coefficient estimates plotted are the estimates of 𝛾 from equation (3.4), but with one modification: replacing the outcome variable with the industry-by-state-by-year-by-gender hourly wage at the 10th percentile, median, mean, and 90th percentile of the wage distribution. The corresponding 95% confidence intervals are also shown. 227 −.04−.020.02.04.06.08Coefficient on Interaction Termp5p10MedianMeanp90p95PercentileFemale * %AI Postings Table 3A.1 Effects of Current AI Adoption by Industry on Gender Wage Gaps (1) Panel A. At 10th Percentile Dep. Var.: Log Hourly Wage (2) (3) (4) Female %Businesses Using AI1 in Current Month (t) Female × %Businesses Using AI in Current Month (t) %Businesses Using AI 3 Months Ago (t-3) Female × %Businesses Using AI 3 Months Ago (t-3) -0.068∗∗∗ (0.018) 0.036∗∗∗ (0.002) -0.010∗∗∗ (0.003) -0.068∗∗∗ (0.020) 0.037∗∗∗ (0.003) -0.010∗∗∗ (0.003) -0.068∗∗∗ (0.020) 0.005 (0.007) -0.010∗∗∗ (0.003) -0.064∗∗∗ (0.024) 0.016∗∗ (0.008) -0.006 (0.013) -0.021∗ (0.013) -0.004 (0.015) 0.066 0.081 0.121 0.114 Panel B. At Median 0.220 0.236 0.331 0.324 Panel C. At Mean %Businesses Using AI in Current Month (t) Female × %Businesses Using AI in Current Month (t) %Businesses Using AI 3 Months Ago (t-3) Female × %Businesses Using AI 3 Months Ago (t-3) %Businesses Using AI in Current Month (t) Female × %Businesses Using AI in Current Month (t) %Businesses Using AI 3 Months Ago (t-3) Female × %Businesses Using AI 3 Months Ago (t-3) R2 Female R2 Female R2 Female %Businesses Using AI in Current Month (t) Female × %Businesses Using AI in Current Month (t) %Businesses Using AI 3 Months Ago (t-3) Female × %Businesses Using AI 3 Months Ago (t-3) R2 Observations State FE Year-Month FE Industry FE -0.143∗∗∗ (0.012) 0.044∗∗∗ (0.002) -0.005∗∗∗ (0.002) -0.143∗∗∗ (0.012) 0.046∗∗∗ (0.003) -0.005∗∗ (0.002) -0.143∗∗∗ (0.012) 0.002 (0.004) -0.005∗∗ (0.002) -0.142∗∗∗ (0.014) 0.004 (0.008) 0.002 (0.011) -0.008 (0.009) -0.008 (0.013) -0.129∗∗∗ (0.012) 0.038∗∗∗ (0.001) -0.001 (0.002) -0.129∗∗∗ (0.012) 0.040∗∗∗ (0.003) -0.001 (0.002) -0.129∗∗∗ (0.012) 0.000 (0.004) -0.001 (0.002) -0.135∗∗∗ (0.013) 0.001 (0.007) -0.002 (0.008) -0.009 (0.008) 0.001 (0.010) -0.230∗∗∗ (0.014) 0.033∗∗∗ (0.001) 0.009∗∗∗ (0.002) -0.230∗∗∗ (0.016) 0.035∗∗∗ (0.004) 0.009∗∗∗ (0.003) -0.230∗∗∗ (0.016) -0.004 (0.004) 0.009∗∗∗ (0.003) -0.244∗∗∗ (0.018) -0.003 (0.008) 0.002 (0.011) -0.008 (0.009) 0.010 (0.014) 0.171 0.199 0.331 0.334 13,478 13,478 ✓ ✓ 13,478 ✓ ✓ ✓ 10,914 ✓ ✓ ✓ 0.192 Panel D. At 90th Percentile 0.217 0.346 0.349 Notes: Each observation is an industry-state-year-month-gender cell. Industry is represented by 2-digit NAICS code. All columns include a set of state-year-month controls. Standard errors shown in parentheses are clustered at the industry-state-month level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of businesses currently using AI is measured as the average monthly share of businesses at the industry level that answered "Yes" to Question 7 in the Business Trends and Outlook Survey (BTOS), which asked "Between MMM DD – MMM DD, did this business use Artificial Intelligence (AI) in producing goods or services? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.)." The unit is a percentage point. 228 Table 3A.2 Short-Term Versus Long-Term Effects of AI Postings on Gender Wage Gaps by Skill Groups, 2019-24 Dep. Var.: Log Mean Hourly Wage (1) All Occ. (2) High-Skilled AI-Complement Occ. Not-Yet-AI Occ. (5) (3) (4) High-Skilled Middle-Skilled Low-Skilled Occ. -0.162∗∗∗ (0.009) -0.120∗∗∗ (0.012) Female %AI Postings1 in Year 𝑡 Female × %AI Postings in Year 𝑡 %AI Postings in Year 𝑡 − 1 Female × %AI Postings in Year 𝑡 − 1 -0.150∗∗∗ (0.006) -0.027∗∗∗ (0.010) 0.029∗∗∗ (0.007) -0.020∗ (0.011) 0.001 (0.007) -0.179∗∗∗ (0.018) -0.012 (0.025) 0.047∗∗ (0.021) -0.044∗ (0.026) 0.001 (0.024) Occ. -0.124∗∗∗ (0.031) -0.094∗∗∗ (0.035) 0.032 (0.029) 0.011 (0.039) -0.052∗ (0.030) -0.021 (0.017) 0.020 (0.016) -0.008 (0.016) 0.008 (0.014) -0.021 (0.014) 0.028∗∗∗ (0.009) -0.029∗ (0.017) -0.001 (0.009) 7,039 ✓ ✓ ✓ 3.587 0.187 78,956 ✓ ✓ ✓ 3.146 0.412 Observations State FE Year FE Occupation FE Outcome Mean R2 Notes: Each observation is an occupation-state-year-gender cell. Occupation is represented by 4-digit OCC2010, a harmonized occupation system constructed by IPUMS based on the 2010 Census Occupational Classification. The skill group indicators are constructed by Chapter 2 of my dissertation. All columns include a set of state-year controls. Standard errors shown in parentheses are clustered at the state-year level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of AI postings is measured at the state-year level. The unit is a percentage point. The data is from the AI Index Report by Stanford Institute for Human-Centered AI, who provides the Lightcast data on AI posting shares at the state-year level for the public. 40,294 ✓ ✓ ✓ 3.017 0.331 25,036 ✓ ✓ ✓ 3.268 0.367 4,922 ✓ ✓ ✓ 2.996 0.331 229 Table 3A.3 Short-Term Versus Long-Term Effects of AI Postings on Gender Wage Gaps across Wage Distribution, 2019-24 Dep. Var.: Log Hourly Wage (2) At Median -0.176∗∗∗ (0.018) (3) At Mean -0.156∗∗∗ (0.015) (4) At p90 -0.225∗∗∗ (0.016) -0.008 (0.025) 0.021 (0.020) -0.017 (0.027) 0.003 (0.020) -0.033 (0.022) 0.036∗∗ (0.018) -0.021 (0.023) 0.006 (0.018) 0.005 (0.022) 0.040∗∗ (0.017) -0.048∗∗ (0.022) 0.015 (0.018) (1) At p10 -0.118∗∗∗ (0.019) -0.055∗∗ (0.022) 0.033 (0.021) 0.026 (0.034) -0.024 (0.019) Female %AI Postings1 in Year 𝑡 Female × %AI Postings in Year 𝑡 %AI Postings1 in Year 𝑡 − 1 Female × %AI Postings in Year 𝑡 − 1 Observations State FE Year FE Industry FE Outcome Mean R2 7,286 ✓ ✓ ✓ 2.549 0.302 Notes: Each observation is an industry-state-year-gender cell. Industry is represented by 2-digit NAICS code. All columns include a set of state-year controls. Standard errors shown in parentheses are clustered at the industry-year level. ∗∗∗ 𝑝 < 0.01, ∗∗ 𝑝 < 0.05, ∗ 𝑝 < 0.1. 1 The share of AI postings is measured at the state-year level. The unit is a percentage point. The data is from the AI Index Report by Stanford Institute for Human-Centered AI, who provides the Lightcast data on AI posting shares at the state-year level for the public. 7,286 ✓ ✓ ✓ 3.083 0.693 7,286 ✓ ✓ ✓ 3.785 0.488 7,286 ✓ ✓ ✓ 3.125 0.638 230