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DATE DUE I DATE DUE DATE DUE Aug 52 %a“2waBJ NW N m Gilt-£71 6 @005 0117 0:: 6/01 c:/C|RCJDanDuo.p65-p. 15 THE RELATIONSHIP BETWEEN OCCUPATIONAL GROWTH AND LITERACY PROFICIENCY IN THE UNITED STATES, 1982-1992 BY Suwanna Eamsukkawat A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Counseling, Educational Psychology and Special Education 2000 UMI Number: 3009102 ® UNI] UMI Microform 3009102 Copyright 2001 by Bell & Howell Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. Bell 8. Howell Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, MI 48106-1346 ABSTRACT THE RELATIONSHIP BETWEEN OCCUPATIONAL GROWTH AND LITERACY PROFICIENCY IN THE UNITED STATES, 1982-1992 BY Suwanna Eamsukkawat This study investigates the relationship between occupational growth rate and literacy proficiency and the return to literacy in the United States in 1982—1992. The measure of literacy skills is taken from the National Adult Literacy Survey. The full measure consists of 165 items of three types: prose, document, and quantitative. The measure of occupational growth rates are the proportion of civilian employees by occupation in 1982 and 1992 by the U.S. Department of Labor Bureau of Labor statistics. A two level hierarchical linear model described by Raudenbush and Bryk(l992) was applied to assess the relationship between mean literacy proficiency and occupational growth, and the return to literacy of the workers across 433 occupations between 1982 and 1992 in the U.S. The findings suggest that there was a highly positive association between the occupational growth and mean literacy of an occupation in overall occupations. However, the relationship between occupational growth and mean literacy is largely explained by differences between occupational sectors rather than differences within occupational sectors. The information economy requires workers with higher literacy skills across all occupations between 1982-1992. However, the strength of this relationship between mean literacy and occupational sectors, namely, data occupations and knowledge production occupations after controlling for educational attainment, diminished. Despite the fact that average earnings of workers in the information sector are significantly higher than the average earnings of workers in the non-information sector, no evidence was found regarding the return to literacy skills is greater for workers in the information sector than it is in the non—information sector. No evidence was found indicating that the return to literacy is greater in jobs that are growing rapidly in personal service and knowledge occupations. However, the return to literacy skill of workers in the data handling occupations is greater for jobs that are growing rapidly. Acknowledgment My dissertation research was conducted under a tremendous time constraint due to my commitment to return to my work in my home country. All the way through the completion of this work, beside the enormous research experience I have gained in conducting this study, it has brought me a great opportunity to experience deeply the essence of unselfishness, honesty, sincerity, and kindness that nurture the healthy soul and mind of a respected researcher. Without these genuine ethics, a researcher can only be considered as a person who makes a living doing research but cannot achieve any level of laudable academic success. Such qualities inculcated in human beings govern an individual’s self consciousness in doing things right, without prejudice or bias towards self interest, which creates valuable research output. I am very thankful to my committee members, Professors Kenneth A. Frank, Arron S.Pallas, David N.Plank, James W. Stapleton, and Stephen W.Raudenbush, who are model educators and researchers that I admire. All of these people have demonstrated the characteristics listed above. With their valuable comments on my work, I have had the opportunity to iv improved the quality of my dissertation. I would like to express my deepest thanks to my dissertation director, Professor Stephen W. Raudenbush who provided me indescribable mentoring and support at each phase of my dissertation. In spite of his busy schedule, he spent a great deal of time reading my work and providing me valuable feedback via telephone, Fed-Ex, and e-mail. He and Professor Kenneth A.Frank also assisted me in obtaining financial support in the last period of my program. To Professor James W. Stapleton, I would like to express my deepest gratitude, because he kindly joined my guidance committee in the middle of my dissertation process. Without his coming to rescue me in a critical period, I would have discontinued my dissertation and returned home without completing my study. To Professor Karen L. Klomparens, and Dr. David D. Horner, I would like to express my heart-felt thanks to both of them for assisting and providing me with incredible support and advice in coping with my difficulty in continuing my program of study. I would like to express my special thanks to the International Center and the College of Education for kindly granting me financial support. And my sincere thanks to Margaret B. Arbanas who gave me valuable advice and support regarding international issues. I also would like to express my sincere thanks to Professor John R. Schwille and the College of Education for providing me with facilities while I was working on my dissertation. In addition, I owe gratitude directly and indirectly to the faculty members and many friends here who assisted and provided support in using the facilities. Special thanks go to Professor Richard Houang, Janice M. Benjamin, Dr. Randy P. Fotiu, Kate Baird, Chery A. Moran and Marian L.Stoll. To Marcy Wallace, I give my deepest sincere thanks. She provided me incredible support and took care of my business while I was absent from Michigan State University during my returning to my job in my home country between 1996-1999. In addition, she provided assistance whenever I needed help throughout the completion of my program. I would like to express my deepest sincere thanks to Dr. Talbott W. Huey and Apiradee Veeravithaya Huey, who are my constant friends that have given me incredible moral and other support whenever I needed it. To my dearest friends Annette E. Kelly and Professor Louis M.Sause, I would like to express my deepest gratitude for assisting and providing me with incredible support and taking care of my well being. Besides their moral support vi which made me feel like I was not far away from my home, Annette E. Kelly also personally assisted me with financial support to lessen my financial burden during the time I worked on my dissertation. To Catherine L. Fleck, I would like to give my special thanks to her as a consultant on my dissertation writing, and for providing me helpful information on how to access resources available to me. Finally I would like to thank my family. To my parents and my dearest sisters who have unconditional love and sacrificial support for me. They protected me from discontinuing my study. I would like to thank my father who gave me valuable advice and supported me in any decisions I made for my career, even when he was very sick. Unfortunately, he has not survived to see me complete my dissertation. However, I believe he is still somewhere watching me successfully complete my studies. I would like to express my deepest thanks to my mother who is always kind and is my best friend and my role model. She taught, inspired and encouraged me to be polite, honest and conscious of doing the right things. I would like to give my heart-felt thanks to my four sisters, Sumalee, Suvimol, Sunonglak and Suchada Eamsukkawat who constantly gave me moral support, took care of my business during my study vfi abroad, and took care of our parents. I also would like to extend my thanks to many other friends and faculty members who I have not mentioned here, who have helped me grow as a researcher at Michigan State University. ‘llii TABLE OF CONTENTS LIST OF TABLES ............................................. x CHAPTER 1 INTRODUCTION ............................................... 1 CHAPTER 2 PERVIOUS STUDIES OF SKILL DEMAND AND TECHNOLOGICAL CHANGE ..................................................... 9 Education, Skill, and Productivity ................... 13 Education, Literacy Skill, and Earnings .............. 18 The Conception of Skill .............................. 22 Measurement of Skills ................................ 24 Assumption Underlining Education/Skills, Productivity ......................................... 28 Technological Change, Skill Demand, and Occupational Growth .................................. 28 Research Questions ................................... 31 Hypotheses ........................................... 34 CHAPTER 3 METHODOLOGY ............................................... 36 Samples and Measures ................................. 36 Variables For the Analyses ........................... 37 Models ............................................... 40 CHAPTER 4 RESULTS ................................................... 47 CHAPTER 5 CONCLUSIONS AND DISCUSSION ................................ 80 Summary .............................................. 80 Discussion ........................................... 85 CHAPTER 5 CONCLUSIONS AND DISCUSSION ................................ 80 BIBLIOGRAPHY .............................................. 87 ix 10. ll. 12. 13. 14. 15. LIST OF TABLES Literacy by occupational Sectors ....................... 49 Occupation’s Log-growth Rate by occupational sectors...50 Educational Attainment by Occupational Sectors ........ 51 Regression Coefficient Relating Occupation Log-growth, Sectors and Interaction Terms to Mean Literacy ......... 53 Regression Coefficient Relating Occupation Log-growth, Sectors and Interaction Terms to Mean Literacy .................... 55 Regression Coefficient Relating Occupation Log-growth, Sectors and Mean Educational Attainment to Mean Literacy .............................................. 58 Regression Coefficient Relating Occupation Log-growth and Sectors to Mean Educational Attainment ................. 6O Regression Coefficient Relating Literacy, Occupation Log-growth, Sectors to Educational Attainment .......... 62 Log Wage by Occupational Sectors ....................... 65 Correlation Coefficients Relating to Mean Literacy, Mean Occupation's Log-growth Rate, Means Educational attainment and Mean Log Wage; Among 460 Occupations...66 Regression Coefficients Relating Occupation Log-growth to Mean Earnings and Literacy-earning Relationship .......... 67 Regression Coefficients Relating Occupation Log-growth to Mean Earnings and Literacy-earning Relationship, with and without Information/non-information Sectors .......... ....69 Regression Coefficient Relating Occupational Log-growth to Earnings and Literacy-earning Relationship, with and without Occupational Sectors and Their Interaction Terms ......... 71 Regression Coefficients Relating Occupational Sector to Mean Earnings, Literacy-earning Relationship, and Education- earning Relationship ...................................... 74 Regression Coefficients Relating Occupation Log-growth and Occupational Sector to Mean Earnings, with and without Educational Attainment ................................. 77 Chapter 1 Introduction Literacy is well recognized as a powerful determinant of a nation’s well-being. Recently, literacy has emerged as crucial to the economic performance of industrialized nations (Tuijnman, A. 1995). In the present world economy, the increasing complexity and technical sophistication of economic activities make the use of literate workers more crucial. In this new context of an information-based and knowledge-based economy, higher skills and new skills are needed for the highly automated and high- technology workplace (Blauner 1964; Adler 1983). As the modern U.S. economy is characterized by increasing complexity and technical sophistication, one would expect that jobs would increasingly require high levels of literacy. In recent years, literacy has gained enormous attention from the public and policy makers in the U.S. in its relation to the changing economy. Rapid technological change has a profound impact on the demand of literate workers. The rising use of computers and other technologies are believed to increasingly require higher level of complexity and discretion. Jobs that are growing in the new economy are believed to require workers to have a broad variety of skills and higher literacy skills. However, the notion that literacy is increasingly required is far from unanimous. An opposing view asserts that a large number of the new jobs involving new technologies require no special skills. For example, the advent of the microcomputer has often simplified work tasks and skill requirements (Flynn 1988, Spenner, 1985). 1 Furthermore, it enhances the ability to replace relatively skilled work roles with unskilled ones (Levin, 1987). This view holds that advanced technology make computers much more user-friendly by simplifying previously complicated tasks and making jobs less demanding. For example, warehouse clerks and supermarket checkout clerks who use computer readout devices to read bar codes of the products need not possess any knowledge about computers or a high level of literacy skills in performing those duties. In fact, these jobs require even lower levels of literacy skills than their counterparts in the past had required. In a national study of almost 3,000 small businesses in the U.S., Goldstein and Fraser (1985) found that most workers who use computers in their jobs require very little previous education or training. According to this latter View, technological change has progressively deskilled work. The new technology may thus not enable new, more productive use of human talent. Rather, it may even reduce cognitive skill demands at work, at least on many new, low-paying jobs. The debate about whether the new jobs require higher literacy skills has not yet been resolved. Reviews of past studies on the impact of technologies on skill requirement in the United States have revealed that past technologies have tended to raise the skill requirements of some jobs while lowering the requirements of the others (Rothewell and Zegveld 1979, Spenner, 1988, Flynn, 1988). In the United States in the 1960's, the prospect of computerized production led many people to predict reduced employment and deskilled jobs (Elull, 1967). Recent studies indicated that technological change has not reduced the overall employment (Barryman, 1997; Cert et al., 1987). Rather, the present technological change is biased toward human capital (Bartel, Linchtenberg, 1987; Lillard, Tan, 1986; Mincer, 1989, 1994). Specifically, the industry sector with rapid technological change generates a greater demand for more educated workers within the sector (Berryman, 1997). The explanation for shifts in skill demand in the modern economy is still unclear. A number of researchers have attempted to relate the shifts in labor demand to globalization, sectoral shifts in employment and changes in labor market situations. However, the effects appear to be too small for these explanations (Krugman and Lawrence, 1993). Some researchers have concluded that the phenomenon of skill demand shift in the modern technology economy reflects a “skill-biased technical change,” an increasing change in demand for skilled labor due to rapid transition of technology (Griliches, 1969; Brendt, Morrison and Rosenblum,1992; Bresnahan, 1999; Berman, Bound, and Machine, 1998). The literature on skilled-biased technical change has proposed several reasons for the decline in the demand for unskilled workers. So far, there has been no consensus on this phenomenon. The nature of the skill-biased technical change is still not well understood (Bresnahan, 1999). Others attribute the increasing demand for skilled workers to productivity growth caused by the information technology. Osberg et al.(1989), postulated the unbalanced productivity growth model to explain the relationships between occupational growth, skill demand, and earnings distribution of the workers. They argued that industries are differentiated by their efficient use of the new technology. The costs and productivity of educated labor relative to those of other types of labor and capital are central considerations in making production decisions. In the process of increasing productivity, lowering costs, and expanding markets, whether a labor force with more varied skills and either higher or lower skills are needed depends on the sector of production. According to Osberg et al.’s unbalanced growth model(l989), the U.S. economy is increasingly dominated by two different sectors: a traditional, or a non-information sector that involves the production of goods and services and an information-intensive sector that involves the production of knowledge and data manipulation. In the information sector, production shifts from material goods to information processing activities which utilize symbol manipulation in the organization of production in order to enhance productivity. They argued that growth in the two sectors is inherently unbalanced because labor productivity grows at different rates. The unbalanced productivity growth in the two sectors in the present economy is affecting the skill demand and occupational employment in the entire economy. The labor productivity in goods production is substantially progressive, in part due to capital accumulation, and in part from improvements in technology of production. The output of this sector grows increasingly cheap over time as relatively less labor is required for its production. Employment in this sector is expected to decline over time. However, workers in this sector are believed to require higher skill levels to cope with the advanced technology. On the other hand, labor productivity in personal service is described as “stagnant”. The essential aspect of personal service is the service which one individual performs for another. Its output can be increasingly expensive because it involves human activities which cannot be replaced by technical production. Employment in the personal service sector is expected to be rising over time relative to the declining employment rate in the goods processing sector. According to Osberg et al., changes in skill demand in the goods processing sector gradually shifts the occupational structure, from goods processing occupations to personal service occupations. The shift of employment out of the goods processing occupations into personal service is a shift from a sector with relatively high average earnings and low inequality to a sector with low average earnings and high inequality (Osberg et al., 1989). On the other hand, The production in the new economy shifts from material goods to information-processing activities which focus on symbol manipulation in the organization of production and in the enhancement of productivity. The information sector is composed of “knowledge” and “data” workers. The production in this sector involves information processing activities and production of knowledge. Most information workers are engaged in “command, coordination and control” functions of an advanced economic system whose occupations involve the production or manipulation, distribution, and use of symbolic information. An important role of the “knowledge worker' in the information sector involves the creation and transmission of knowledge and technology. Higher productivity is expected to be increasingly generated in this sector. The employment in this sector is expected to increase due to its stagnant labor productivity. The productivity growth of data handling workers is the most asymptotically progressive since parts of their tasks involve the acquisition and transmission of information, which do benefit from technological advancement. Thus, the employment growth rate for data workers is expected to decline over time as well as their wage rate. In the new information-oriented economy, we would expect that workers need to become more flexible and have broader skills than they did previously in order to remain competitive and to best utilize the technology. The effect of information technology on the level of skill required is believed to involve far more than simple automation and labor substitution. AS Rumberger (1987) pointed out, past technologies generally perform physical tasks with productivity increases confined to a single firm or industry. But new technologies can perform mental tasks and raise productivity throughout the economy (Rumberger, 1987). The fast growing jobs are believed to rely much more heavily than before on human cognitive skills. In the traditional economy, we would expected that workers require either lower or higher levels of skill depending on how technological change influences work organization. Technological change and automation may redesign jobs that separate the execution of work from the conception of work (Braverman 1974, Wood 1982, Noble, 1984). In this case, a growing mass of unskilled and semi—skilled labor are expected, particularly in the stagnant sector which cannot benefit from technology to increase productivity. On the other hand, as manufacturing and services may adopt more complex forms of production, more complex skill would be required. Public concern has increased regarding the level and quality of the current U.S. workforce. The worry is that U.S. workers may not be capable of using changing technologies. Cultivating and developing literacy skills of the workers has been considered an essential strategy that makes workers more productive. Life-long learning and on- the-job training are significant means for workers to acquire new competencies and qualifications in coping with the technological change. However, the investment in human capital is slow and results are not self-evident. Empirical results regarding the effect of workers’ literacy skills on job performance are not yet available. In this study I examine the relationship between occupational growth rate and the levels of literacy skills in 460 occupations in the U.S. I categorize the 460 occupations into four occupational groups, namely, goods, personal service, data, and knowledge occupations. I hypothesize that jobs that are growing are associated with higher literacy skills than jobs that are declining. However, this relationship is expected to be stronger in the information sector than in the non-information sector. My research questions are as the following: (1) Is occupational growth rate related to mean literacy of an occupation? (2) Does the relationship between occupational growth rate and mean literacy of literacy depend on sector? Specifically, this relationship is expected to be stronger in the information sector than in the non information sector. (3) Does the return to literacy skill vary across occupations depending on the occupation employment growth rate? (4) Does the return to literacy skill vary across occupations depending on sector? Answers to the question of whether occupational growth is related to the literacy skills of workers will provide evidence either in support of or against the claim that literacy skills are rising in the modern workplace. If the answer is in support, this would also imply that the level of literacy skills of the U.S. workforce tends to rise to keep pace with the progress of the new technology. Otherwise, the demand for literacy skills may not increase in the modern workplace. This may also imply that a skill mismatch exists in the U.S. A skill mismatch occurs when the levels of skill of workers is lower or higher than what his or her job requires (Tsang, 1991). It is important to public policy debates concerning skill gap, and the quality of skills required in the growing jobs. Chapter Two Previous Studies of Skill Demand and Technological Change The effects of technological change during the past tWo decades has drawn much attention from public and policy makers. However, even before this period, the effect of technological change on skill demands and earnings has long been a topic of debate. More specifically, researches have studied effects on workers' skill levels, the number of people be employed, the content of jobs, work organization, and the quality of working life. These debates are interesting and important for educational policy. Different views concerning the effects of technological change have been debated. The common negative view stresses the adverse effects of technology: that new technology might reduce job opportunities, make traditional jobs obsolete, increase alienation, and increase management control (Braverman, 1974; Marglin, 1974; Edwards, 1979; Burwoy, 1979; Burowoy and Skocpol, 1982; Wallace and Kelleberg, 1982). The positive View emphasizes the effect of technology on increasing the aggregate level and ranges of skills, complexity and interrelation of work, efficiency, responsibility, and requiring less close supervision (Pearsons, 1968; Kerr et al.,1960; Jaffe and Froomkin, 1968; Bell, 1973). The third perspective regarding the effect of technology change maintains a mixed effect, depending on the way technology is implemented in the workplace. Studies over skill demands caused by technological change have revealed inconsistent conclusions. Recent studies have found evidence supporting the hypothesis that the demand for skilled workers is increasing, specifically in high-tech manufacturing industries, during the past two decades in the U.S. (Krueger, 1993; Goldin and Katz, 1998; Kahn and Jong-Soo Lim, 1998; Berman, Bound, and Muchin, 1998). The relative wages of skilled workers has been found to increase steadily, while less skilled workers suffer decreasing wages and unemployment (Berman, Bound, and Muchin, 1998). However, some previous studies reveal different results concerning skill demand and wage rate. For example, according to Carnoy (1995), low-skilled jobs in developed countries, including the U.S., remained stable during 19805- 19905. Though the relative wage of low-skilled workers in the U.S. was falling, the absolute real wages of college- educated workers’ are about the same as they were 20 years ago. Manufacturing wages did not rise to reflect manufacturing productivity in 19805 and early 19905. Previous research in this area has several shortcomings that could contribute to inconclusive findings. Firstly, due to extensive revisions in classification in the dictionary of occupational titles and categories, the boundaries between close occupational titles could be blurred due to ambiguous job descriptions. For example, the same occupational functions could refer to craftsman or 10 technician in different revisions of occupational title categories. If so, it is unreliable to make comparisons on skill demand over time (Spenner, 1985). Secondly, case studies that have investigated the impact on skills of specific technologies within a single occupation, firm or industry rarely captured the skill requirement changes due to difficulty in maintaining sufficiently long follow-up studies. As a result, findings are inconclusive about skill demands depending on the stage of technological development at the time of investigation. Finally, the skill effects of technological change are sensitive to the way in which new technologies are implemented in the workplace. Despite these earlier limitations, perhaps the most crucial limitation concerns skill definition and its measurement. There is little agreement on the definition of skills. Also, direct assessments of skills have not typically been available. Even the most recent studies on skill demand in the manufacturing industries have predominantly defined “skill” according to such attributes as non-production/ production (a non-production worker is identified as a skilled worker), education-level (i.e. years of schooling) or wage rate. These indirect measures of skill do not reflect the true skill level of the workers. Moreover, these measures are questionable in the interpretation of the level of skills. For example,“skill” defined by educational level can be misleading since it incorporates skills based on different educational backgrounds. Workers who enter the labor market with similar educational qualifications have not necessarily acquired the 11 same level of occupational skills. To identify non— production workers as skilled workers is debatable, since it does not account for human capital or education level of the workers. Shifts in the demand for skilled labor as measured by education—level or non-production/production work, therefore, could be misleading. For example, “skill” measured by educational level could merely reflect the upgrading of the credentials of the labor supply rather than a shift in the quality of the workers' skill. Hence, using educational attainment as a proxy for workers’ skill can be misleading. Workers with the same educational attainment may have different levels of literacy skills. Many studies have emphasized the distinction between educational quality and educational attainment. For example, McKnight et al.(1987) and Lee et al.(1987) found that even though the U.S. labor force has the most years of educational attainment compared of all OECD countries, the quality of the basic skill training in the U.S. labor force is lower than that provided for labor force entrants of other industrial nations such as Japan. As is mentioned by Bartel et al.(1989), “in 1980, among the developed countries, the U.S. had a labor force with, by far, the most years of education.(The quality of that education is another issue). One in nine members of the civilian labor force had reached what UNESCO calls the third level of education, defined as “the successful completion of education at the post secondary level...” Recent studies have found that the performance of high school graduates in the U.S. is declining over time. The Third International Mathematics and Science Study (1997) revealed unsatisfactory performance in science and mathematics of U.S. students compared to other leading 12 developed countries. In addition, Rosenbaum et al (1997) found that school achievement (grades) have small effects on high school graduates early jobs in the U.S. In sum, research claims regarding skill demand in the U.S. labor force are still questionable due to the questionable validity of skill measures. In addition, the empirical evidence regarding skill upgrading and inconclusive findings on wage rate of the U.S. college graduates during the past two decades do not support the claims that the demand for skills are upgrading. Education, Skill, and Productivity Human capital theory makes three important arguments concerning how education and skill make a person productive. Firstly, a person acquires skills and knowledge from schools. The skills are directly related to the characteristics that the person needs to use other inputs, namely capital and land, more efficiently (Carnoy, 1995). Secondly, a person gains skills and knowledge from school, enabling him to adjust to “disequilibrium”, that is, to enable him to adjust to changes and make better allocative decisions on resources and time, which enhances productivity (Welch, 1970, Schultz, 1975). This implies that a person with additional education is in a position to make better decisions due to enhanced capacities to obtain access to, understanding of, and productive use of information (Schultz, 1975). Highly educated workers can apply the skills and knowledge they acquired in school in order to 13 adjust to changes and adopt new ways of doing things. This implies that the economic return for workers who obtain higher education would be higher when they enter an occupation that allows them to make judgements and decisions in their production process. Thirdly, schooling socializes young people into functioning effectively in modern society (Inkeles and Smith, 1974). School teaches young people to work in response to modern stimuli and to inculcate in them values and norms that are consistent with productive behavior in the workplace. School helps them to respond quickly, willingly, and predictably to demands from supervisors. Workers acquire a stock of capabilities, knowledge and experience that translate into productivity in the workplace and that yield rewards. Skill is portable from one job to another. It is possible to identify the skills in workers to predict how well their performance in a wide variety of job settings (Schmidt and Hunter, 1977). This skill is relevant to studying skills and performance at work. Explanations for why a worker with higher literacy skills is more productive assume that literacy skills and job-related skills are complementary. The contribution of literacy skills to productivity comes about through helping workers learn what they need to in order to do their current and future jobs (Bishop, 1984: COSEPUP Panel on Secondary Education for Changing Workplace, 1984). Literacy skills also make workers productive by making them better at problem solving, showing initiative, and being flexible in learning new skills. l4 The present world economy is becoming more competitive, more global, and increasingly dominated by information and communication technology. Literacy skills and analytical skills become indispensable tools for workers to access information, which broadens their knowledge and helps them to keep pace with the changing world and technology. Literacy skills or basic skills, namely, numeracy, reading and writing, are important in helping a worker to produce materials and goods more efficiently, especially when following directions or making judgements at work is concerned. Literacy skills seems to index how quickly the workers can acquire job skills. The significant role of literacy skills on job performance in the modern workplace is attributed to three explanations. Ginzberg et al.(1984) argued that firstly, the most efficient use of information technology leads to a reintegration of tasks that were once parceled among many workers. As intelligent systems take over processing functions and routine repetitive work, workers would focus on higher levels of functioning that require higher levels of cognitive skill, for example on diagnosing, problem- solving, and critical functions. Secondly, operative skills become more predominant in this new context. Oral and communication skills enable workers to direct others in a complex manufacturing operation and in services. And thirdly, workers with good reasoning skills successfully complete tasks without close supervision. Consequently, literacy is essential to perform jobs efficiently, to communicate, and to perform higher levels of functioning for 15 advanced sophisticated technology. Empirical studies have found that literacy skill is positively correlated to skills needed in the workplace. There has been evidence of improvement in job performance and greater general productivity associated with proficiency in literacy (Bishop 1988; Murmane, 1988). Studies have found that more educated workers are better able to deal with technical problems as well as unstable environments created by rapid technological change (Bartel, Lichtenberg, 1987). In addition, workers’ literacy skills significantly affect wages and the opportunities for employment (Raudenbush and Kasim, 1998; Blackburn, Katz and Murphy, 1992 Bloom, and Freeman, 1990;). Critics of human capital theory have long debated the positive direct relationship between cognitive skill and productivity. They have argued that the labor market might well reward higher educational attainment even if school contributed nothing to the individuals' productivity (Arrow, 1973, Spence,1973). Educational attainment might serve as a screening device for employers in selecting prospective employees. Schools employ the screening mechanism through certifying an individuals' ability to employers, who are willing to pay persons who are certified, even if they learned nothing in school which increased their productivity. Individuals with higher education credentials will generally receive higher earnings than those with less education. As a result, increasing the number of educated persons leads to their having a larger piece of the economic pie, at the expense of those with lesser amounts of schooling. Yet higher education does not necessarily lead to 16 high productivity and higher earnings (Berg, 1970; Arrow, 1973; Thurow,1975; Meyer, 1977: and Collins, 1979). Thurow (1975) proposed the "job competition" model in which skills are defined as attributes of jobs. Individuals compete for jobs on the basis of their education and other personal attributes. Higher educational attainment reflects an individual's ability to be "trainable." From this opposite view, skills reside within jobs rather than in persons. This implies that in order to increase productivity, one should create more skilled jobs in the economy. In practice, when an employer is evaluating a prospective employee, educational credentials are viewed as a proxy measure for the employee's productivity, which might serve as a screening or signaling device for employers. Psacharopolous (1984) distinguishes the relationship between education and productivity in screening hypotheses as a weak versus strong relationship. He contends that educational attainment and credentials might have explanatory power for workers entering the labor market. Once these workers are employed, more information is available about the employees' productivity. The assessment of productivity related to credentials and educational attainment becomes less important. So far, the extent to which schools operate as a screening and signaling mechanism has remained an open question. Using educational credentials to determine the workers' potential productivity can be misleading because educational credentials can not be adequate as measures of knowledge or 17 skills. Workers with similar qualification have not necessarily acquired the same level of proficiency in literacy skills or competence at work. Moreover, discrepancies can arise due to the fact that workers do not stop learning upon leaving school. The opportunity to learn varies depending on an individual's personal, situational, and economic factors. The relationship between his initial schooling and literacy skills is not necessarily linear. Education, Literacy Skills, and Earnings The relationship between education and earnings has been widely studied at both the theoretical and empirical levels. In many studies, educational attainment has served as a predictor of earnings. The effect of education on earnings has often been interpreted by human capital theorists as an effect of knowledge or skills associated with school attainment. However, critics of human capital theory have offered different interpretations for the effect of education on earnings. The class reproduction theorists view education as a means for a person's social mobility. The important role of schools is in changing a person’s social background rather than his cognitive skills, because social background is seen as the major determinant of occupation and income. The educational system operates in such a way that it develops different sets of productivity- related to personality traits among children from different social classes. For example, traits produced by schools in those individuals who will fill low-paid jobs will focus on 18 punctuality, obedience, and respect for authority, while traits in those prepared for high-status occupations include self-reliance and ability to make decisions. In this view, workers who have lower social background associated with less education will earn less, regardless of whether their literacy proficiency is low or high. The screening theorists maintain that schooling itself does not affect productivity. But schools act as a selection mechanism to sort out persons those who lack certain kinds of characteristics such as intelligence and motivation which are connected with productivity. In this view, workers who possess a higher education credential will earn more irrespective of their level of literacy proficiency. In the context of labor market theories, based on human capital, wages are determined not only by a person’s educational background, but also by the demand and supply of workers. Differences in earnings by occupation can be observed in all societies. Differences in pay by occupation can be explained by the supply and demand function, consisting of both wage rate and numbers of jobs available for any occupation. The intersection of the supply and demand curve determines the equilibrium wage and the number of jobs for each occupation. In some occupations, the effects of supply and demand are limited for various reasons. Examples of these effects include: entry barriers to working in particular occupations, licensing, compensating wage differentials involving dangerous tasks, personal choices for the degree of risk for low or high wage jobs, imperfect information regarding the labor market, 19 limited access to education due to formal education and training costs, special ability of talent, and regional immobility due to transaction costs for both employer and employee (Tachibanaki, 1995). A large number of empirical studies have found strong positive relations among educational attainment, occupational status, social background, and earnings. Jencks (1985) found the number of years of education is the best single predictor of the occupational status of a worker. Studies have found significant effects of skill acquired from school on job productivity and trainability (Bishop, 1992). In Sweden, Tuijnman (1989) has found a relationship among earnings, education and home background. In addition, he has found that earnings grow with age. Earning differentials as a function of education tend to increase with the level of education. Earnings increases with age at a decreasing rate up to a maximum and then flattens or even declines. The maximum level of earnings tends to be reached at a later age for people with a higher level of education (Carnoy, 1994). Studies have found both educational attainment and occupational achievement have strong relationships with earnings. However, large unexplained residuals were found in the estimation of earnings in the U.S. (Taubman, 1975). Some researchers attribute this unexplained earnings residuals to “luck” (Jencks, 1972). Lazear (1981) has found that competitive labor markets might award higher pay to individuals with more seniority, even if their productivity remains absolutely constant over time. Thus the relationships between earning and skills can be 20 spurious due to this factor. Critics of human capital theory deny that there is a single labor market in which all workers freely compete to sell their capital. Rather, they argue the labor market can be divided into different strata, or segments. A worker's choice of segment is constrained by the nature of social and political power relations (Carnoy, 1995). People with the same level of education do have different earnings depending on their race, gender, ethnicity, ability, and social background. In addition, the relationship between education and productivity/wages differs significantly in part because the labor market is characterized by different technological and organizational conditions of work. In this View, differences in literacy proficiency among groups of workers in the secondary labor markets characterized by low pay, insecurity, and poor working conditions will not lead to increased earnings. The relationship between earnings and literacy proficiency of workers in the secondary labor markets may not the same as in the primary markets. Other critics of human capital theory argue that wage contracts and productivity are matters of negotiation and motivation, both in terms of extracting labor (by the employer) and delivering labor (by the employee)(Mark Blaug, 1970). Bowles and Gintis (1976) argued that the education- wage relationship differs considerably from the human capital concept and that there is considerable evidence of wide variation of wages awarded to employees with similar skills in varied bargaining situations. This implies there is no relationship between literacy proficiency and workers' 21 earnings. The Conception of skill Skill is defined by attributes of individuals, as attributes of their jobs, or in terms of work outcomes that may be influenced by both. There is a long debate over whether it is the workers who are skilled or the jobs that require skill (Rumberger, 1988). In many studies, discussion of the concept of skill has focussed on three aspects: the nature, origin, and dimensionality of skill. First, skills are defined as individual's attributes: innate or learned abilities that enable workers to perform their duties at the workplace. In human capital theory, skill is thus conceived to reside within persons. Workers acquire a stock of capabilities, knowledge and experience that translate into productivity in the workplace and that yield rewards. Skill is portable from one job to another. It is possible to identify the skills in individuals to predict how well their performance in a wide variety of job settings (Schmidt and Hunter, 1977). This skill is relevant to studying skills and performance at work. Critics of the notion of skill as a human attribute argue that the possession of human capital cannot be equated with its use (Spenner, 1983). In addition, the massive upgrading of the schooling levels of workers in industrial societies may not equate with more skilled jobs, and the workplace and that jobs is an imperfect translator of human ability and potential (i.e education) into reality (Berg, 1970. Braverman, 1974; Sullivan, 1978; Clogg, 1979). 22 Second, skill may be defined as the behavioral and ability requirements of jobs. This notion is congruent with the job competition theory that skill resides in jobs (Thurow, 1975). In this perspective, the upgrading or downgrading of skills is implemented through changes in the structure of work rather than changes in an individual's skill. A third notion links skill to work outcomes: skills are the behaviors that individuals actually exhibit in their work or the skills that they actually utilize in doing their jobs (Rumberger, 1988). In this view, skills enable an individual to perform tasks in the real setting. An individual's performance in a real setting is not necessarily reflected by his abstract academic skills (Rogoff and Lave 1984; Lave, Murthaugh, and de La Rocha 1984). This view implies that it does not matter how much skill a person has, but whether or not the skill enables him to perform efficiently in the real work settings. Work settings have important impact on what kinds of skill are needed. Thus, the kind of skill need can be different from setting to setting. Skills may be conceived as generic in nature or interactive in nature (Tsang, 1991). The view that skills are generic quantities means that skills are independent of work context in which they are used and that it is possible to identify these skills in individuals to predict how well they perform in a wide range of jobs settings (Schmidt and Hunter, 1977). In this view, it argued that the notion of skills as individual attributes is the most relevant one in studying skills and performance at work (Tsang, 1991). In the view that skills are interactive with the nature of work 23 argues that it is not meaningful to define skills in the abstraction of the work context. Rather, workers' performance depends on the specificity of work setting that enable workers to perform effectively(Rogoff and Lave 1984). In this study, I define literacy as skills that reside within individuals. Literacy skills refer to knowledge and skills related to verbal and written communication, numeracy, and reasoning. Literacy skills are human capital that workers acquire from formal schooling, adult literacy training, and self-initiated learning. I define literacy as an individual attribute since I assume literacy skills are a kind of human capital that reflects the level of a worker's productivity at work. Literacy skills are viewed as generic skills that are independent of the work context, can be applied to a wide range of jobs setting to determine workers' performance at work. A person with higher literacy skill is believed to be more efficient at work. He can learn new skills, adjust to changes and adopt new ways of doing things faster than less literate person. Measurement of Skills The measurement of skill is closely related to the conceptual basis of skill discussed so far, especially in terms of what skill is measured, how skill is measure, and how the validity of skill measured is ascertained. Based on human capital theory, test scores reflect one important aspect of a worker's skill. Studies of skills can be classified as using direct measurement, indirect measurement, and non-measurement (Spenner, 1990). 24 A direct measurement of work skill can be an individual's test score or an actual assessment of job requirement. On the other hand, work skill content reflects the skill requirement of jobs. In studies that assuming that work skill content reflects the skill requirement of jobs, an example for direct measurement of job skill requirements is the DOT (Dictionary of Occupational Titles)code. The DOT code provides a direct measure of skill for jobs with indicators that include: (a) level of involvement with data, people and things. (b) General Education Development(GED): Mathematical , language, and reasoning development required in the certain kind of job. (C) tests that measure aptitudes: verbal, numerical, spatial, motor coordination and manual dexterity. Another direct measure of skill requirement of jobs is a direct measurement of skills, in terms of "empirical operations and/or explicit protocols for designation of skill level." According to Spenner (1990), this measure is the most valid strategy among the three strategies. There are two sources of direct measurement: expert ratings of skill levels, and self-report ratings of skill levels. Expert rating systems exist for measuring skill requirements of jobs. The most comprehensive system is the DOT. Expert ratings of skill levels are provided by knowledgeable job analysts and observers outside of the persons or jobs which are the subject under study. Self-report ratings refer to ratings of skill level by informants who are related to or part of the subjects under study. By proper instrument design, bias and error in measurement can be reduced (Tsang, 1991). 25 The indirect measurement of skill include wage rate or educational levels of individuals or occupational groups. In many studies in economics, sociology, and education, indirect measures of work skill includes years of formal schooling, and wages (Spenner, 1983). Formal schooling often serves as a proxy for the skill requirement of jobs. Wages reflect both skills and abilities that individuals bring into their jobs and the characteristics of the job itself (Rumberger, 1988). Non-measurement strategies include employed occupational groupings, such as blue collar, white collar, professional, or clerical, that are assumed to reflect skill level (Braverman, 1974). Non-measurement strategies does not provide detailed information about the skill dimensions. Another important aspect of skill measurement is its dimension. Skill may be measured as uni-dimensional or multidimensional. A uni-dimensional concept of work skills characterizes either jobs or individuals on a continuum varying from low-skill to high-skill. A multi-dimensional concept of work skill takes into account various cognitive, physical and affective dimensions of work skill (Dunnette, 1983; Fleishman and Quanintance, 1984). Earlier studies in economics and sociology tend to rely on a single and indirect measure of skill, such as the level of education. But cognitive and industrial psychology have defined and measured multiple dimensions of skill, in terms of human abilities or job requirements (Dunnette, 1983., Fleishman and Quaintance, 1984, Vroom, 1964). To date, there is no consensus on the domain of dimensionality of skill. For example, researchers differ in the type and number of factors that characterize human abilities (Pellegrino and 26 Varnhagen, 1987). According to Rumberger (1988), a multi- dimensional view of work skill may be useful if technology, work organization, and other forces have different impacts on different dimensions of skills. Some researchers classify human abilities into general skills that are applicable to a wide range of job settings, and specific skills that pertain to particular jobs (Becker, 1964). Some researchers hypothesize a two-dimensional ("substantive complexity" and "autonomy-control" domains with sub-dimensions)domain for skill (Spenner, 1990). Others suggest a three-dimensional (cognitive, physical, and social domains with sub-dimensions) domain of skill (Rumberger, 1988). Since researchers have different notions on skill, there is no consensus on the number of dimensions or subdimensions of skill that form work performance, and it is difficult to distinguish between skill types and skill levels. The literacy skills in this study are test scores from the Adult Literacy Survey Study. The advantage of using literacy skills is that they are a direct measure of an important aspect of the skill and competence of workers. Previous studies of skill demand are limited to indirect measures of skills, such as the number of years of schooling completed, the highest credential, or whether the workers were production or non-production workers. Moreover, literacy proficiency skills are multidimensional measures of knowledge and skills, namely, verbal and written communication, reasoning, and numeracy, and they provide more information regarding workers’ ability compared to using unidimensional measures. Indirect measures such as 27 educational credentials or number of years of education provide very limited information regarding workers’ skill. In addition, educational attainment does not a direct measure of workers’ productivity because labor markets might well award higher educational attainment even if school contributes nothing to students (Berg, 1970; Arrow, 1973; Thurow,1975; Meyer, 1977: and Collins, 1979). .Assumptions Underlining Education/Skills, Productivity and Earnings In this study, I define literacy as an individuals' attributes. Literacy skills refer to knowledge and skills that include verbal and written communication, numeracy, and reasoning. Literacy skills are human capital that workers acquire from formal schooling, adult literacy training, and self-initial learning. I shall assume a fair labor market and that wage offers in a competitive market equal worker's marginal productivity, which is determined by job-related skills and knowledge. Technological Change, Skill Demand, and Occupational Growth Technological change is only one of several factors that influences the demand for skilled labor. Other factors that affect skill demand are: (a) changes in the demand for goods and service, (b)change in the relative input costs to other factors of production such as capital, (c) change in 28 international competition, and (d) changes in the organization of work (Rumberger, 1991). Rumberger (1991) pointed out that the relationship between technological change and skill demand is complex. The impacts of technological changes that influence skill demand and job requirements have no predetermined direction, but rather depend on a host of specific and perhaps conflicting influences that may result in either upgrading or downgrading of work skills. Technological change influences the demand for skills through the demand for goods and service by introducing new products and changing the price of existing products through productivity changes. Osberg et al.(1989) observed the productivity growth in the modern U.S. economy and found that the information technology plays a significant role in affecting unbalanced growth of the modern economy. The productivity in the information intensive sector is more progressive than its counter part due to the benefit of the advanced information technology. In the United States in the 1960's, the prospect of computerized production led many people to predict reduced employment and deskilled jobs (Elull, 1967). Recent studies indicated that technological change has not reduced the overall employment (Barryman, 1997; Cert et al., 1987). Despite the wide spread perception that the information technology is increasing the demand for higher educated labor, there is no sufficient evidence supports the claim that the literacy skills of these workers are rising. While changes in the occupational structure of jobs towards 29 service employment do tend to raise the demand for educated workers, and their literacy skills, changes in the skill demand of individual jobs have not systematically risen (Rumberger, 1991). Rather, available evidence suggested that in some cases technological change has tended to raise skill requirements, while in some cases it has lowered to them. During the past seven decades, studies show employment in the U.S. has been growing in the service sector in which average wages are lower than in manufacturing (Cyert, 1986). At the same time, however, the occupational structure of the U.S. economy has shifted with faster growth in higher-skill, higher wage occupations (Leon, 1982). Lawrence (1984) reveals that high-technology industries have smaller shares of low-paying jobs than manufacturing in general has. Three rapidly growing sectors (financial services, public administration, and professional services)require workers to have more formal education than do the sector of agriculture, mining, wholesale trade, and manufacturing, whose share of national employment has been falling (office of Technology Assessment, 1988; Tan, 1989 Osberg, et al., 1989). Jobs in the high-technology intensive sector are much more knowledge intensive and require literate skills and pay high wages. But some sectors have shown evidence of a growing trend towards less-skilled employment with low wage rates. According to the surveys by the U.S. Bureau of Labor Statistics, occupational employment trends during the 1972- 92 period show steadily increasing shares for managerial, professional, technical, and service occupational groups. Occupational groups with increasing shares of employment have the largest proportions of workers completing at least four years of college and the smallest proportions with less 30 than a high school education. There is an exception for these growing trends: personal service employment has increased over the period, but generally personal service workers do not have post-secondary education (Kutscher, 1993). Research questions: (1) Is occupational growth rate related to mean literacy of an occupation? According to Ginzberg et al. (1986), the computerized and information based technology has significantly changed the present work content at all levels. On one hand, the new technology permits a sharp reduction of paper orientated work. Intelligent systems take over routine, repetitive, processing functions. This makes it possible to broaden the range of responsibility for low-level personnel. In this view, jobs in the modern workplace will be upgraded. Hence, the growing jobs may recruit workers with higher literacy skills. On the other hand, although process automation often increased the level of worker's responsibility, the use of technologically sophisticated equipment may make operator positions less demanding. In addition, technological progress has brought about more powerful software and user- friendly interfaces, which reduces the device-specific skills needed to operate them (Ginzberg et a1. ,1986). The new technology has played a major role in affecting the nature of work that require less literacy skill. The advancement of information technology is seen to assist in increasing the effective supply of low-skilled labors by giving firms much more flexibility to relocate low-skilled 31 production in distance location, or to contract out to small low-wage suppliers. Thus, it assists in expanding low-wage jobs throughout the nation. Thus, jobs that are growing in the modern economy do not necesSarily require higher levels of literacy skills. The new technology may not enable new, more productive use of human talent. Rather, it may reduce the potential ability of an individual at work. However, the payoff for these growing jobs is undoubtly low. The growing economy may tend to move to a part where a small group of people in the society have well-paid professional careers and the vast majority hold low-wage, menial service jobs. (2) Does the relationship between occupational growth rate and mean literacy depend on sector? Specifically, this relationship is expected to be stronger in the information sector than in the non information sector. Recent theory and research suggest that there is a tendency for relationship between occupation rate of growth and the level of workers' literacy skill to be mixed. Literacy skill is either rising or declining depending on the sector of jobs that are growing. Osberg et al.,(1989)'s model of imbalanced productivity growth argues that the modern economy of the U.S. is becoming increasingly dominated by two sectors: a traditional sector that involves the production of goods and personal services and an ‘information economy' which involves the production of knowledge and data manipulation. While the information sector requires a more highly skilled labor force which has high-paying wages compare to the non-information sector, the growing service sector requires a lower skilled labor force which has a low-wage growth. I shall investigate whether the 32 association between literacy levels in each occupation and occupational growth rate depend on sector. (3) Does the return to literacy skill vary across occupations depending on the occupation employment growth rate? In the present information technology economy, one would expect that cognitive skill or literacy should have greater rewards than in the past. However, many would argue that it is expensive to test applicants' literacy skill. Employers would hire workers to jobs based on their educational credentials rather than their cognitive skills. As mentioned before, educational credentials are not direct measures of worker's skill. Workers with the same educational qualification may have different levels of literacy skills. Their wage rates might not reflect their competence and productivity at work. However, firms might recruit workers based on their level of skills because the global competitive economic environment would discourage the wasting of resources. Therefore, firms would carefully select workers according to jobs. Thus, after new workers begin working, their productivity at work and consequently their wage rates should reflect their literacy skills. I shall investigate whether there is a positive relationship between the wage rate and literacy skills of the workers. I expect that higher wages are more associated with higher levels of literacy skill in occupations that are growing than in those are declining. (4)Does the return to literacy skill vary across occupations depend on sector? Based on Osberg et al.(1989)'s unbalanced growth model, the labor productivity of workers in the two sectors (the 33 information intensive sector and the non-information intensive sector) are growing at different rates in different occupational sectors. Labor productivity in goods and data sectors are substantially progressive while the productivity in service and knowledge sectors are stagnant. The labor demand in the progressive sectors, which can benefit from technology are predicted to require relatively less labor for its production. In contrast, the stagnant sectors, personal service and knowledge workers are predicted to grow at relatively faster rate than the production and data sector. I shall investigate whether the wage rate of workers that are positively associated with the average literacy skills of jobs that are growing depends on the sectors they are in. Hypotheses (l) The mean literacy of an occupation is related to occupation employment growth rate, and (2) The relationship between occupational growth rate and mean literacy depends on whether the occupation is in the information/non-information sector. Specifically, this relationship is expected to be stronger in the information sector than in the non—information sector. (3) The return to literacy skill varies across occupations depending on the occupation employment growth rate, and (4) The return to literacy skill varies across occupations depending on whether the occupation is in the information sector or in the non-information sector. The 34 return to literacy for workers in the information sector is relatively higher for workers in the non-information sector. 35 Chapter 3 Methodology Samples and Measures The NALS sample is composed of 24,944 U.S. adults who come from two sub-samples: a national sample of 13,600 and supplemented samples from eleven states. The supplemented samples are approximately 1,000 respondents from each state. The design of the national sample and each of the eleven states’ supplemented samples involves four stages. In the first stage, primary sampling units (PSU's) were stratified based on region, metropolitan status, percent Black, percent Hispanic, and where possible, per capita incomes. In the second stage, eight to twelve PSU's per state were selected, with probability proportional to population size. There is an exception, for segments with large Black and Hispanic populations were over- sampled. This insures adequate sub-sample sizes of Black and Hispanic sub-groups for the purpose of precise statistical estimation. In the third stage, within each segment, all dwelling units were listed and households were selected at equal opportunity. In the fourth stage, within household, one or two adults (depending on household size) were selected at random from all eligible adults age sixteen to sixty-four in the household. Weighting for the samples were designed in order to ensure unbiased national estimates. Data for the analysis are based on a subsample of the National Adult Literacy Survey which included 11,912 adults 36 between 25 and 59 who were in the labor force in 1992. This includes persons working full time or who wished to work full time at the time of interview. All of the 11,912 adults in this study are persons who do not have zero incomes at the time of interview. Variable for the Analyses My key variables for the analysis are literacy, log wages, occupational growth, sector, Literacy: The measure of literacy skills in this study is the variable referred to as “literacy” in the National Adult Literacy Survey. The full measure consists of 165 items of three types: prose, document, and quantitative. The three types of items are defined as follows: £19§§_lit§;agy “involved the knowledge and skills needed to understand and use information from texts that include editorials, news stories, poems, and fiction; for example, finding a piece of information in newspaper article, interpreting instructions from a warranty, inferring theme from a poem, or contrasting views expressing in editorials.” Dggnment_literagy “concerned the knowledge and skills required to locate and use information contained in materials the include job applications, payroll forms, transportation schedules, maps, tables and graphs; for example, locating a particular intersection on a street map, using a schedule to choose the appropriate bus, or entering information on an application form.” Quantitatixe_literagy “involved the knowledge and skills 37 required to apply arithmetic operations, either alone or sequentially, using numbers embedded in printed materials, for example: balancing a checkbook, figuring out a tip, completing an order form, or determining the amount of interest from a loan advertisement." (All quoted passages are from NCES 1993, p.3.) Items in each type of the sub-scale vary in difficulty as estimated by item response analysis. The test items emphasized tasks that required brief written and/or oral responses and those that required respondents to explain how they would set up and solve a problem. The assessment was administered during the 45 minutes per respondent. Respondents were administered a sub-sample of the 165 items. Five plausible values per sub-scale per person for the proficiency result were computed. The plausible values were drawn randomly from the conditional distribution of the person's true score on the sub-scale, given that person's actual item responses and given all other measure characteristics of the person. The term "occupation" in this study is defined according to the 1980 Census Occupational Classification. Occupation log-growth rate is defined as log of the ratio of total employment in each occupation in the U.S. in year 1992 to the total employment in each occupation in the U.S. in year 1984. Sector is an indicator of whether the person's occupation is identified as being in the information or traditional sector. The information sector includes jobs related to knowledge, knowledge/data, data, information/service/goods, information, and information/service. The traditional sector includes jobs related to goods and personal service. 38 Occupational Classification : In this study, I adopt Osberg et al. '3 (l989)classificatory scheme of occupational sectors: an information sector and a non-information sector. The information sector consists of occupations whose functions involve production, distribution, and use of information. The non- information sector consists of occupations whose functions involve producing goods and performing personal services. As an occupational sector is comprised of various kinds of workers who produce knowledge, data, or services, I emphasized occupational roles as the basis for the classification of the occupational sectors. The information sector consists of two subgroups;(I) knowledge and (ii) data, I shall define “knowledge workers” are those who produce knowledge. Their activities include producing, transforming, interpreting, analyzing, or creating new knowledge. I shall define “data workers” are those who are users of knowledge or information whose activities relate to distributing, processing, transferring, and providing of information. Professional and technical workers have generally been classified as knowledge or data workers, depending on whether they are producers or users of knowledge. In some cases, professional workers have been classified as data/service workers. For example, doctors and nurses who both utilize information and perform personal service in their careers. The non-information sector consists of two subgroups;(I) goods, and (ii)personal service. I shall define a “service worker" as one who does something to people. I shall define a “goods workers” as one who does something to things. 39 Because service workers can be classified into a wide range of occupations based on their output and functions, in this study, I distinguish between information-related services and person-related services. The former are occupations within the information sector, the latter are occupations within the non- information sector. The information service workers are those who provide service in the information sector, for example, the financial service, public administration, and professional services. Personal service workers are those in the service occupations, for example, hair dresser, housekeeper. The measures of the four occupational types are categorized as: (I) knowledge, (ii) data, (iii) personal service, and (iv) goods. An occupation that is a hybrid class including more than one type of functions, for example “data/knowledge”, “data/service” will be classified based on its higher level of skills. For example, a worker whose functions are both producing and using information, or a “knowledge/data” worker, is identified as knowledge worker. A “data/service” worker is identified as a data worker. Models The research questions in this study involve a multilevel or hierarchical modeling problem. I analyze the data by means of ordinary least squares (OLS) and in a hierarchical linear model (HLM) as described by Bryk and Raudenbush (1992) with respondents nested within occupations. The outcome variables, literacy skills and earnings, are measured at the individual level which depend in part on individual work's occupation he is in. The types of occupation and working settings account for differences in 40 literacy skills and earnings between occupations. There are two reasons for using HLM. One is that it takes into account the dependence of the outcome variable of persons within the same occupation. This is important in analyses of occupational variation in the literacy-earnings slope. In analyses of the association between occupational growth and mean literacy, HLM facilitates a weighted least squares analysis, where the weights are the precisions of the sample means for each occupation. My models are: (1) Literacy as dependent variable: yij (Literacy) = boj + rij (1) bOj = gm+gm(Occ Growth)j+uoj (1.1) bOj = goo+g02(sector)+uOj (1.2) PM = gm+gm(0cc Growth)j+g02(Sector)j + gm(Occ Growth)j*(Sector)f+ uoj (1.3) yij is the literacy level of worker I in occupation j is the mean literacy of workers in occupation j rij is the individual randon error g00 the overall average literacy across all occupations 901 is the effect of occupation log-growth rate on mean literacy g02 is the effect of sector on mean literacy 903 is the effect of the interaction term between occupation log-growth and mean literacy bw's are population means literacy for each occupation through out this study wherever literacy used as dependent variable. First, I estimate the relationship between mean literacy and Occupational growth rate without occupational sector (1.1). From 41 model (1.1) I expect positive relationship between occupational growth and mean literacy. Second, I estimate the relationship between mean literacy and sector defined as l = information, 0 = non-information (1.2)and also sector defined as 1 = personal service, 2 = data, and 3 = knowledge. From model (1.2) I expect positive relationship between sector and mean literacy. Third, I estimate the relationship between mean literacy and occupation log-growth rate controlling for sector and interaction terms (1.3). From model (1.3) I expect positive gm, 9%, and gm based on my hypothesis. However, due to the fact that employers hire workers to jobs mostly based on worker’s educational attainment rather than workers' literacy skills, I need to verify whether occupation growth rate relates to literacy after adjusting for education. yU(Literacy) = boj + rij (2) bOj = g00 + g01 (Occ Growth)j + g02(sector)j + gm(Occ Growth)j*(Sector)j + 904(MeanEd) + no,- (2.1) yij is the literacy level of worker I in occupation j bOj is the mean literacy of workers in occupation j is the individual randon error 900 the overall average literacy across all occupations 901 is the effect of occupation log-growth rate on mean literacy g02 is the mean literacy difference between sectors go3 is the effect of the interaction term between occupation log-growth and sector. 42 gm is the effect of educational attainment on mean literacy. Assuming that education has an effect in the relationship in (2.1), the effect of Occ growth, sector, and the interaction term on mean literacy would be wiped out or diminished. I also need to verify the relationship between education and occupational growth, sector, and their interactions: YU(Education) = boj + rij (3) boj = g00 + gm(Occ growth.)j + gm(Sector) go3 (OccGrowth)*(Sector)+ uoj (3.1) I will examine whether either occupational growth rate or sector or both of them are associated with mean education. This will verify the effect of mean education to literacy in model(l). (2) Earnings as Dependent Variable: I hypothesize that a person's wage rate should be high in occupations that are growing, specifically, in the information sector. Also the relationship between literacy and earnings should be strong in occupations that are growing. My key variables in my analyses are earnings (log wage rate), literacy, education, occupational growth, and sector. Other variables relate to earnings, such as age, work experiences, gender, and ethnicity will be added. My analyses models are: yU(earnings) = b, 3 + bu(Literacy)U+ b2j(gender)ij + b”(ethicity)fi+ buj(parent’s education)ij + bmj(work experience)ij+rij (4) boj = g00 + gm(Occ Growth)j+uoj (4.1) b1j = gm+ g11 (occ Growth)f+1hj boj = go0 + g01(Sector)j+ uOj (4.2) blj = gm+ g11 (sector)j+ulj bOj = g00 + gm(Occ Growth)j+-g0,(Sector)j (4.3) + gm(Occ Growth)j*(Sector)j-+ uOj bl' = 910 + 911(OCC GrOWth)j + 912(SeCt0r)j 43 + g13(OccGrowth)j*(Sector)j + ulj yu(earnings) = bm + b1j(Literacy)ij + b%(education)+ r” (5) PM = gm + gm(Occ Growth)j + g02(Sector)j + gm(Occ Growth.)j*(Sector)j + uOj blj = g10 + gn(Occ Growth)j + glz(Sector)j + gla(OccGrowth)j*(Sector)j + ulj b2j = g20 + gn(Occ Growth)j + g22(Sector)j + g23(OccGrowth)j*(Sector)j + u” assume ujDLo(0,I), rm N~40,og Where yij is the logwage rate of worker I in occupation j boj is the intercept for occupation j bIj is the effect of literacy on log wage rate in occupation j gm(4.1) is the effect of occupational growth rate on mean earnings. gm(4.2) is the mean difference on earnings between sectors. gm(4.3) is the effect of occupational growth rate on mean earnings controlling for sectors and their interaction terms. gw(4.3) is the mean difference in earnings between sectors controlling for Occupational Growth rate and their interaction terms. gm(4.3) is the interaction effect of occupational growth rate and sector on earnings. gn(4.3) is the tendency for the strength of the relationship between literacy and earnings to depend on occupational growth controlling for sectors and their interaction terms. g12 is the difference of the strength of the relationship of literacy and earnings between sectors controlling for occupational growth. g13 is the interaction effect controlling for literacy and sector . g21 is the tendency of the strength of the relationship between 44 education and earnings to depend on occupational growth controlling for sectors. 922 is the difference of the strength of the relationship of education on earnings between sector controlling for education. 923 is the interaction effect. First, I estimate the effect of occupation log-growth rate without sectors, controlling for gender, ethnicity, parent education, and work experience (4.1). I expect positive relationship between occupation log-growth and mean of log wage rate. Second, I estimate the effect of sectors without occupation log-growth rate (4.2). I expect positive relationship between information sector and mean log wage rate. Third, I estimate the effect of literacy on log wage rate controlling for sector and occupation log-growth. From model 4.3, I expect gm, and g03 to be positive. This will verify my hypothesis that higher occupational growth rate is associated with higher earnings controlling for sector. Specifically, the association is stronger in the information sector. I expect 902 to be positive which will verify that jobs in the information sector are associated with higher earnings than jobs in the non- information sector. From model (4.3), I expect 911 and g13 to be positive. This will verify my hypothesis that the returns to literacy in jobs that are growing rapidly is higher than in jobs that are growing slower, especially for jobs in the information sector. I expect 912 to positive which will verify that the literacy return in the information sector is higher than the literacy return in the non- information sector controlling for occupational growth rate. Based on the fact that a person gets a job based on his educational attainment, I will examine whether log wage rate is associated with mean education. I shall verify the relationship 45 between literacy and log wage rate controlled for occupational growth rate, sectors, and educational attainment. The overall effects of predictors related to earnings are the interest of this study. No interest in focused on the deviation effects from each occupation. Therefore, all the level-1 predictors except gender and ethnicity are centerd around their grand means. 46 Chapter 4 Results This study was conducted to investigate the relationship between occupational growth rate, level of worker’s literacy, and returns to literacy in terms of worker’s earnings. Several other factors that might relate to workers' levels of literacy skills and their earnings, such as educational attainment, gender, ethnicity, and years of work experience, were also investigated. By building a two level hierarchical linear model, a test of the relationship of the rate of occupational employment growth to literacy and the rate of return to literacy was estimated. The analyses were of two parts. First, I studied the relationship between occupational growth rate and mean literacy. The level-1 model allowed for variation in workers’ literacy proficiency around an occupation mean. The level—2 model expressed workers’ mean literacy as a function of occupational growth and sector. Second, I studied the relationship between literacy and wage rate. The level-1 model expressed workers’ individual wage rate as a function of individual characteristics, such as gender, number of years of schooling, ethnicity, literacy, and number of years of experience. The level-2 model represented means wage rate and the literacy-earnings slopes as functions of occupational growth, sectors, and interaction terms between occupational growth rate and sector. The analyses were based on 47 11,423 adults between 25 and 59 who were in the labor force in 1992. The sample members represented were classified into 460 <>ccupations by the 1980 three digits dictionary of occupational title census occupations. Occupational sectors were classified into four broad categories, namely, “goods”, “personal service”, “data”,and ”knowledge” occupational sectors based on Osberg's et al.(1989)’s classification scheme. The Association of Literacy and Occupation's Log-growth Rate, Within and Between Occupational Sectors Framing this study is a debate about the skill demands and pay of new jobs in the U.S. economy. Recall from the literature review that some commentators have argued that changes in the U.S. economy require that workers have increasingly high levels of literacy skill. In contrast, other commentators have claimed that new jobs generated by the U.S. economy require lower levels of cognitive skill than do “older” jobs . The National Adult Literacy Survey (NALS) enables us to test these associations between occupational growth rate and mean literacy level, within occupations. If new jobs require greater skill, there ought to be a positive association between occupational growth rate and literacy. On the other hand, if new jobs require less skill, the association ought to be negative. In order to understand the relationship between occupational growth rate and literacy, I shall first examine descriptive 48 statistics and bivariate associations among three key variables: literacy, occupation's log-growth rate, and educational attainment, within and between occupational groups. Table 1 Literacy by Occupational Sectors Literacy N Mean Sd Non-information Sector 4536 256.176 64.370 Goods 2830 256.955 65.640 Personal Service 1706 254.884 62.195 Information Sector 6887 310.206 45.570 Data 4764 306.370 45.636 Knowledge 2123 318.815 44.238 (Total N = 11423) Table 1 displays the means and standard deviations of the key variables of the four occupational groups, namely, ‘goods', ‘service', 'data’, and ‘knowledge' occupation. From table 1, we can see that workers in knowledge occupations have comparatively high levels of literacy compared to workers in other groups, namely, data occupations, goods, and service occupations respectively. 49 Table 2 Occupation's Log-growth Rate by occupational sectors Occ Log-growth N Mean Sd Non-information Sector 4536 0.056 0.398 Goods 2830 0.022 0.365 Personal service 1706 0.114 0.444 Information Sector 6887 0.310 0.546 Data 4764 0.267 0.568 Knowledge 2123 0.424 0.474 (Total N = 11423) Table 2 displays the occupation's log-growth rate. We can see that the occupational growth rate within the information sector is relatively higher than the occupational growth rate within the non-information sector. Within the information sector, occupations that involve knowledge production have higher occupational growth rates than means and standard deviations of do occupations that involve data handling. Within the non— information sector, service occupations have comparatively higher occupational growth rate than occupations that involve goods processing. 50 Table 3 Educational Attainment by Occupational Sectors Educational Attainment N Mean Sd Non-information Sector 4536 11.741 2.585 Goods 2830 11.610 2.652 Personal Service 1706 11.960 2.454 Information Sector 6887 14.462 2.269 Data 4764 14.257 2.238 Knowledge 2123 14.923 2.272 (Total N = 11423) Table 3 displays the means and standard deviation of educational attainment (the number of years of educations) of the workers in the four occupational groups. We can see that in the information sector, workers in the knowledge occupations, which have higher mean literacy, also have comparatively higher mean educational attainment compared to workers those work in the data occupations. However, in the non-information sector, while workers in goods processing occupations have slightly higher mean literacy than do workers in service occupations, workers in goods processing occupation have slightly lower educational attainment compared to workers in service occupations. This evidence suggests that certain types of occupations are growing faster than others. We can see that information occupations are growing faster than non-information occupations. Moreover the information occupations that are growing fastest also have workers with highest average levels of literacy. A deeper analyses was conducted to verify the relationship 51 between occupational growth rate and mean literacy. A two level hierarchical linear model described by Bryk and Raudenbush(1992) was applied to investigate the linear relationship between the two variables, occupational log—growth rate and mean literacy proficiency of an occupation. (1) Does occupational growth rate relate to mean literacy of an occupation? Recall first the question of whether the new information economy requires workers with higher or lower literacy skills. If jobs in the new information technology are growing with a rising demand for literate workers, there should exist a positive relationship between occupational growth rate and mean literacy. First, I assess this association without regard to sector. Second, I shall then adjust for sectors differences to see whether the association between occupational growth rate and mean literacy exists within sectors. 52 Table 4 Regression Coefficient Relating Occupation Log-growth, Sectors and Interaction Terms to Mean Literacy Coefficients a b c without without with Info/non- Occupation Info/Non- Information Log-growth Information Sectors Rate Sectors Occupation’s 17.440*** 9.182 Log-growth Rate (3.356) (4.718) Info/non-information 52.651*** 52.038*** sector (2.845) (2.954) Occupation Log- -5.650 growth*sector . (5.943) percent of Variance 6.04 56.11 56.84 Explained by Predictors in the Model *** p < .001 ** p < .01 * p < .05 From Table 4, the bivariate relationship between literacy and occupation’s log-growth rate indicates a significant positive relationship between occupation’s log— growth rate and mean literacy (b=17.44, p<.001). The proportion of variance explained by occupational growth rate is 6.04 percent. The bivariate relationship between information/non—information sector and mean literacy indicates a positive significant relationship (b=52.651, p<.001). The proportion of variance explained by info/non- information sector is 56.11 percent. Adding information/non- information sector into the model results in the effect of 53 occupational growth disappearing. I therefore conclude that there is a highly statistical significant association between the growth rate of an occupation and the mean literacy of the workers in that occupation. This result is consistent with the claim that economic changes in the U.S. economy create a need for higher level of literacy in the workforce between 1982 and 1992. This relationship is completely explained by sector. That is, once we control for sector, the association between growth rate and mean literacy disappears. 54 Table 5 Regression Coefficient Relating Occupation Log-growth, Sectors and Interaction Terms to Mean Literacy Coefficients a b c d with without with with occupation Occupation Occupation Occupation Log-growth Log-growth Log-growth Log-growth Rate Rate Rate Rate and Sectors Sectors and Interaction Terms Occupational 17.440*** 5.699* 3.408 Log-growth (3.356) (2.521) (5.968) Personal Service 4.785 4.448 2.914 (5.861) (5.752) (5.768) Data 46.711*** 45.315*** 45.249*** (3.260) (3.316) (3.311) Knowledge 67.966*** 66.612*** 68.515*** (3.575) (3.641) (3.822) Occupational 21.787 Log—growth*Service (12.057) Occupational 2.318 Log-growth*Data (6.477) Occupational -5.281 Log- (6.806) growth*Knowledge Percent of 6.04 59.77 60.38 61.96 Variance Explained by Predictors in the Model *** p < .001 ** p < .01 * p<.05 From table 5, we can see that the bivariate relationships between mean literacy and occupational sectors is consistent with the previous results. Data handling and knowledge production occupations which are in the information sector are positively related to mean literacy. Adding occupational sectors and the interaction terms between occupational log-growth rate and occupational sectors into the model, the association between occupations’ 55 log-growth rate on mean literacy disappears. This indicates that the overall association between occupations’ log-growth rate and mean literacy is explained by differences between sectors rather than by differences within sectors. I conclude that there exists a relationship between occupational growth rate and mean literacy but the relationship is explained by differences between sectors rather than differences within sectors. Put simply, the association between occupational growth rate and literacy is largely explained by the fact that occupations in the “information economy" are growing more rapidly and require higher levels of literacy than do occupations in the traditional “goods- services” economy. (2) Does the relationship between the mean level of literacy proficiency and occupational growth depend on sector? From the results in table 5b, we can see that there was no significant effect of the interaction term between occupational growth rate and sectors on mean literacy. I conclude that the relationship between the mean level of literacy proficiency of the workers and occupational growth does not depend on sector. Is occupational growth rate positively related to mean literacy after controlling for mean education? I consider whether the association between occupational growth rate and mean literacy is explained by mean education levels of workers in the 460 occupation. Employees rarely know much about a worker's literacy skills when they make hiring decisions. They may use educational credentials as a proxy for cognitive skill. If so, the association between occupational growth rate and mean literacy will be explained by mean 56 education. With mean literacy as the dependent variable predicted by occupational growth rate and mean education attainment, the results reveal that mean educational attainment is positively related to mean literacy (b=16.81, p <.0001, please see table 6a). 57 Table 6 Regression Coefficient Relating Occupation Log-growth, Sectors and Mean Educational Attainment to Mean Literacy Coefficients a b c without without with Occupational Occupation Occupational Sectors Log-growth Sectors Rate Educational 16.810*** 15.137*** 14.995*** Attainment (0.550) (0.778) (0.778) Occupation Log- 1.144 0.771 0.565 growth Rate (1.832) (1.757) (3.875) Personal Service -4.325 -5.454 (3.697) (3.675) Data 8.814** 9.436*** (2.633) (2.621) Knowledge 8.414 10.225** (3.700) (3.768) Occupation Log- 12.568 growth*service (7.408) Occupation Log- -0.810 growth*Data (4.385) Occ Log- -3.944 growth*knowledge (4.884) Percent of 88.75 89.94 90.40 Variance Explained by T Predictors in the 5 Model ; *** p < .001 i ** p < .01 ~ * p < .05 After controlling for mean educational attainment, the : relationship between occupational growth rate and mean literacy disappears (please see table 5a and 6a). Adding occupational sector into the model, I found a significant positive effect of data handling occupations on mean literacy (please see table 6b). An investigation on the relationship between mean educational attainment and occupational growth rate was conducted 58 in order to verify the relationship between literacy and occupational growth mentioned above. l"? 59 Table 7 Regression Coefficient Relating Occupation Log-growth and Sectors to Mean Educational Attainment Coefficients a b c without without with Occupational Occupational Occupational Sectors Growth Sectors Rate Occupation Log- 0.981*** 0.199 growth Rate (0.164) Personal Service 0.576** 0.510** (0.190) (0.177) Data 2.493*** 2.394*** (0.159) (0.166) Knowledge 4.059*** 4.040*** (0.184) (0.204) Occupation Log- 0.771 growth*Service (0.354) occupation Log- 0.184 growth*Data (0.275) occupation Log— -0.119 growth*Knowledge (0.342) Percent of 9.53 65.32 65.96 Variance Explained by Predictors in the Model *** p < .001 ** p < .01 * p < .05) With mean educational attainment as the dependent variable predicted by occupation's log-growth rate, without controlling for sectors, the result reveals a positive significant relationship between occupational growth rate and mean educational attainment(b=.98l, p < .001, please see table 7a). Thus, I conclude that fast growing jobs required more highly educated workers than did slower growing jobs between 1982 and 1992. 60 ll. Adding occupational sector, and the interaction terms into the model, the result reveals that occupational growth rate disappears. However, there appear significantly differences between occupational sectors, namely, knowledge occupation, data occupation, and service occupations in predicting mean educational attainment. (b=4.04, b=2.394, p < .001; b= 0.510 , p<.01, please see table 7c). 61 Table 8 Regression Coefficient Relating Literacy, Occupation Log- growth, Sectors to Educational Attainment Coefficients a b c without without with Occupational Occupation Occupational Sectors Log-growth Sectors Rate literacy 0.045*** 0.036*** .036*** (0.001) (0.002) (0.002) Occupation’s 109- 0.199** 0.137 growth (0.088) (0.132) personal service 0.351** 0.358** (0.105) (0.112) data 0.755*** 0.719*** (0.105) (0.111) knowledge 1.481*** 1.443*** (0.179) (0.187) occ 109- -0.158 growth*personal (0.239) service occ 109- 0.040 growth*data (0.182) occ log— 0.056 growth*knowledge (0.251) percent of 87.85 89.11 89.08 Variance explained by predictors in the model *** p < .001 ** p < .01 * p < .05 Adding literacy into the model, without controlling for sectors, the relationships between occupational growth and mean educational attainment persists though the strength of the relationship diminishes (b=0.981, p<.001, please see table 7a and b=0.199, p<.01, please see table 7a). A positive significant 62 relationship between mean literacy and mean educational attainment is found (b=.045, p<.001, please see table 8a). The differences in mean educational attainment between sectors diminish (Please see table 8.1 b, b=.510, p<.01, b=2.394, p<.001, b=4.040, p<.001 and table b, b=0.358, p<.01, b=.719 p<.001, b=1.443, p<.001). These results open the question of whether really mean literacy skill is important or whether there is a process of requiring more educational credentials in the rapidly growing occupations. The Association Between Literacy and Earnings In the modern economy, jobs that are growing are believed to require better educated and higher skilled workers. The additional earnings associated with higher education, in particular, grew during the 1980's (OECD, 1993), and these findings have been cited as strong evidence supporting the claim P’ that skill requirement at work are rising (Tuijman, 1998). Osberg et al.(1989) claimed that post secondary education is highly rewarded in the fast growing sector, the information sector, while in the non-information sector, the same level of education [a is not highly reward. As I mentioned before, educational attainment which has been widely used as the proxy for the measure of workers’ skills can be misleading. Workers with the same educational qualification do not posses the same level of literacy skills. Human capital has been widely believed to be more highly rewarded than it was in the past. Workers’ literacy 63 skills may be the more pertinent measures in reflecting their competency and efficiency at work. In this study, I hypothesize the return to cognitive skills or literacy skill in occupations that are growing is greater than the return in occupations that are growing slower. To test this hypothesis, I expect a more strongly positive relationships between literacy and the wage rate in the rapidly growing occupations. This will support the claim that the return of literacy is higher in occupation that are growing. Prior to the investigation of the relationship between literacy and wage rate, I examine the mean log wage rate in the four occupational sectors, namely, ‘goods’, ‘personal service', ‘data' and ‘knowledge' and their bivariate correlations with literacy, occupational growth rate, and educational attainment has been. PE. 64 Table 9 Log Wage by Occupational Sectors Log Wage N Mean Sd Non-information 4536 5.773 0.675 Goods 2830 5.931 0.607 Personal Service 1706 5.511 0.700 Information 6887 6.222 0.678 Data 4764 6.100 0.655 Knowledge 2123 6.495 0.651 (Total N = 11423) From Table 9, we can see that the mean log wage rate of occupations in the information sector is higher than in the non- information sectors. Among the four occupational groups, knowledge workers earn more compared to data, personal service, and goods occupations. 65 Table 10 Correlation Coefficients Relating to Mean Literacy, Mean Occupation’s Log-growth Rate, Means Educational attainment and Mean Log Wage; Among 460 Occupations Variables Occupation Educational Log Wage Log-growth Rate Attainment Literacy 0.195 0.835 0.611 (<.001) (<.001) (<.001) Occupation’s 0.215 0.115 Log-growth (<.001) 0.017 Educational 0.581 Attainment (<.001) Table 10 displays the bivariate correlations between mean literacy, occupational log-growth, mean years of education, and mean log wage rate. We can see that mean log wage is moderately correlated to occupational log-growth (r=.115) but highly correlated to mean literacy (r=.611) and mean years of education (r=.835). Workers with higher literacy scores or higher number of years of education earn more than do workers with lower literacy scores or less years of education. Moreover, workers in occupations that are growing rapidly earn more than do workers in occupations that are growing slower. (3) Does the return for literacy skill vary across occupations, depending on the occupation growth rate? The purpose of this analysis is to investigate the association between literacy proficiency skills earnings and to determine whether this association depends on occupational growth rate. 66 Table 11 Regression Coefficients Relating Occupation Log-growth to Mean Earnings and Literacy-earning Relationship Coefficients without occupational sectors mean earnings intercept 5.599*** (0.035) occupation’s 0-119*** Log-growth (0-033) female '0.246*** (0.016) black 0.049** (0.019) Hispanic 0.028 (0.019) Asian 0.169*** (0.037) literacy-earning slope 0.2474*** intercept (0.147”) occupation’s ‘8-380: log-growth (- 49 ) parent’s education 0.01o*** (0.002) log experience 0.181*** (0.181) *** p < 001 ** p < 01 * P < 05 Table 11 displays the results of the analysis of the association between worker’s literacy and worker’s wages controlling for gender, ethnicity, parent’s education, and work experience. The result reveals that overall, there is a positive relationship between occupation’s growth rate and earnings (b = 0.119, p<.001, please see table 11). This indicates that occupations that are growing rapidly pay higher wage rate than 67 occupations that are growing slower even after controlling worker literacy. The average rate of return to literacy is .002474 (p<.001, please see table 11). However, no interaction effect between the literacy return and occupational growth exists. No evidence has found to support the hypothesis that worker’s literacy and worker’s wage is greater in more rapidly growing occupations than in less rapidly growing occupations. I conclude that the return to literacy does not depend on occupational growth rate. 68 (4) Does the return for literacy skill depend on sector? Table 12 Regression Coefficients Relating Occupation Log—growth to Mean Earnings and Literacy—earning Relationship, With and Without Information/non- information Sectors Coefficients a d without without without with info/non—info Log- interaction info/non— sectors occupation terms info Growth sectors mean earnings intercept 5.599*** 5.984*** 5.439*** 5.441*** (0.035) (0.025) (0.041) (0.041) Occ Log—growth 0.119*** 0.051 0.022 (0.033) (0.029) (0.054) information 0.331*** O.309*** 0.306*** sector (0.033) (0.034) (0.035) Occ Log— 0.034 growth*info (0.064) female —0.246‘”‘“‘r —0.258*** -O.258*** -O.258*** (0.016) (0.016) (0.016) (0.016) black 0.049** 0.046 0.046* 0.045* (0.019) (0.019) (0.019) (0.019) Hispanic 0.028 0.024 0.024 0.024 (0.019) (0.018) (0.018) (0.018) Asian O.l69*** O.160*** O.16l*** 0.159*** (0.037) (0.037) (0.037) (0.037) literacy— earning slope intercept O.247**** 0.224**** O.225**** 0.2264*** (0.147”) (0.175”) (0.173”) (0.175'H Occ log-growth -0.98'4 -0.133'3 -0.564'3 (0.249'5 (0.258d) (0.328'H information 0.189‘3 0 224'3 O 138'3 sector (0.25”) (O 261“) (0 262”) Occ Log— 0.694‘3 growth*info (0.4344) parent’s 0.010*** 0.009*** 0.009*** 0.009*** education (0.002) (0.002) (0.002) (0.002) log experience 0.181*** 0.187*** 0.187*** 0.187*** (0.011) (0.012) (0.012) (0.012) *** p < 001 ** p < .01 * P < .05 Table 12 displays the result of the analyses of the relationships between earnings and literacy proficiency skills 69 controlling for log of years of work experiences, gender, parent’s education, and ethnicity with and without information/noninformation sector or occupational growth rate. From panel a, we can see that occupational growth rate is significantly related to mean earnings. However, controlling for information sector in the model results in the disappearance of the effect of occupation’s log~growth rate (please see table 12, panel c). Additionally, a significant positive relationship between information sector and earnings is found (b=0.309, p<.001, please see table 12c). This indicates that the association between occupational growth and earnings is explained by differences between sectors (information / non-information sector) rather than the differences within sectors. Workers in the information sector earn more compared to workers in non— information occupations (b=.309, p < .001, please see table 12c). I found the interaction effect between occupational growth and information/non-information sector on literacy—earning slope is nonsignificant. No evidence is found, to indicate that the return of literacy is higher in the information sector than it is in the non-information sector. 70 Table 13 Regression Coefficient Relating Occupation’s Log-growth to Earnings and Literacy-earning Relationship, with and without Occupational Sectors and Their Interaction Terms Coefficients a b c d without without without with Occupational Occupation's Interaction Occupations Sectors Log-growth Terms 1 Sectors Rate and Interaction Terms Mean Earnings Intercept 5.599*** 5.512*** 5.509*** 5.511*** (0.035) (0.042) ((0.042) (0.042) Occupation 0.119*** 0.059* -0.069 Log-growth (0.033) (0.028) (0.053) Service -O.234*** -0.237*** -0.263*** (0.063) (0.062) (0.06) Data 0.183*** 0.169*** 0.153*** (0.037) (0.037) (0.038) Knowledge 0.421*** 0.406*** 0.421*** (0.044) (0.045) (0.049) Service*0ccupation 0.387*** Log-growth rate (0.098) Data*occupation 0.165** Log-growth rate (0.064) Knowledge*occupation 0.043 Log-growth rate (0.086) Female -O.246*** -0.254*** -0.253*** -0.253*** (0.016) (0.016) ((0.016) (0.016) Black 0.049** 0.047* 0.047* 0.046* (0.019) (0.018) (0.018) (0.018) Hispanic 0.028 0.025 0.025 0.026 (0.019) (0.019) (0.018) (0.018) Asian 0.1698“r 0.157*** 0.157*** 0.157*** (0.037) (0.037) (0.037) (0.037) 71 Literacy-earning Slope Intercept 0.247**** 0.243**** 0.241**** 0.2424*** (0.147“) (0.21”) (0.208‘0 (0.205'U Service --0.558'3 -0.558‘3 -0.618“' (0.302”) (0.302”) (0.299”) -0.249’3 —0.232’3 -0.386'3 Data (0.308“) (0.317”) (0.318”) 0.507'3 0.549'3 0.74‘3 Knowledge (0.421”) (0.422”) (0.503”) -0.98“ -0.094'3 -0.717'3 Occupation (0,2494) (0.268”) (0.451”) Log-growth Rate 0.652'3 Service*occupation (0.634) Log-growth Rate 0.113""2 Data*occupation (0,5324) Log-growth Rate -0.004'3 Knowledge*occupation (0,394) Log-growth Rate parent’s education 0.010*** 0.009*** 0.009*** 0.009*** .002 (0.002) (0.002) (0.002) log experience 0.181*** 0.181*** 0.182*** 0.182*** .012 (0.012) (0.012) (0.012) *** p < .001 ** p < .01 * P < .05 Table 13 displays the results of the analyses on the relationships between earnings and occupational growth controlling for occupational sectors and their interaction terms. Including the occupational sectors and their interaction terms into the model result in the effect of occupational growth disappears (please see table 13 panel d). This indicates that the association between occupational growth and earning is largely explained by differences between occupational sectors rather than the differences within occupational sectors. Significant interaction effects between occupational growth and service and between occupational growth and data occupations indicate that the association between earning and occupational growth depends on sectors for data and service occupations. 72 The literacy return for the service occupation is lower compared to goods processing occupation (b=-.000618, p<.05, please see table 13d). Additionally, a significant interaction effect between occupational growth and data handling occupation indicates that the return to literacy depends on sector(b=0.0011, p<.05, please see table 13d). Without controling for educational attainment, the return to litercy in data handling occupation is less than the return to knowledge occupations. Does the return of literacy persist after controlled for education? 73 Table 14 Regression Coefficients Relating Occupational Sector to Mean Earnings, Literacy-earning Relationship, and Education—earning Relationship Coefficients without with Educational Educational Attainment Attainment rate Mean Earnings Intercept 5.512*** 5.49*** (0.042) (0.042) Personal Service -0.234*** -0.260*** (0.063) ((0.05) Data 0.183*** 0.112** (0 037) (0.036) Knowledge 0.421*** 0,307*** (0.044) (0.047) Female -0.254*** -0.242** (0.016) (0.127) Black 0.047* 0.022 (0.018) (0.016) Hispanic 0.025 0.025 (0.019) (0.019) Asian ' o.157*** 0.091* (0.037) (0.041) Literacy-earning Slope Intercept 0.2404*** 0.1914*** (0.21’5) (0.232’2) Personal Service -0.5582' -0.853”* (0.302‘” (0.369TU Data -0.249'3 -0.543’3 (0.308'6) (0.321'3) Knowledge 0.507’6 0.11“3 (0.421‘6) (0.434'3) 74 Education-earning Slope Intercept 0.033*** (0.006) Service 0.017 (0.010) Data 0.023* (0.008) Knowledge 0.019 (0.011) Parent’s Education 0.009*** 0.004 (0.002) (0.002) Log of Years of 0.181*** 0.191*** Experience (0.012) (0.012) *** p < .001 ** p< .01 * P < .05 I now consider the relationship between educational attainment and workers’ coefficients relating occupational sectors, earnings. Table 14 displays the namely, personal service, data, and knowledge occupation to earnings controlling for educational attainment, gender, work experience, and ethnicity. The result reveals that after controlling for educational attainment, the average return of literacy diminishes (b=.191”, p<.001, please see Table 14b). This indicates that educational attainment is also important in predicting the workers’ earnings. However, the negative significant effect of the literacy return in personal service persists (b=-.0009, p< .05). I shall conclude that educational attainment is less in service than in goods. I now consider the return of literacy controlling for educational attainment in the information versus non-information 75 sector. If educational attainment is more important than literacy in the information sector than in the non-information sector, I would find the return of literacy to be diminished after controlling for educational attainment. 76 Table 15 Regression Coefficients Relating Occupation Log-growth and Occupational Sector to Mean Earnings, with and without Educational Attainment Coefficients a b without with Educational Educational Attainment Attainment Mean Earnings Intercept 5.511*** 5.486*** (0.042) (0.041) Occupation Log-growth Rate -0.068 -0.044 (0.053) (0.053) Service -0.263*** -0.290 (0.06) ((0.60) Data 0.153*** 0.089 (0.038) (0.037) Knowledge 0.421*** 0.319 (0.049) ((0.049) Service*occupation 0.387*** 0.358 Log-growth Rate (0.098) ((0.095) Data*occupation 0.165** 0.117 Log-growth Rate (0.064) (0.065) knowledge*occupation 0.043 -0.018 Log-growth Rate (0.086) (0.089) Female -0.253*** —O.24*** (0.016) ((0.015) Black 0.047* 0.021 (0.018) (0.009) Hispanic 0.025 0.026 (0.018) (0.018) Asian 0.157*** 0.093** (0.037) (0.036) 77 Literacy-earning Slope 0.242”*** 0.194”*** Iintercept (0.205”) (0.201”) Occupation Log-growth Rate -0.717” -0.128”* (0.451”) (0.523”) Personal Service -O.618”' -0.906”** (0.299‘U (0.322‘H Data -0.386” -0.628”* (0.318”) (0.32”) Knowledge 0.74” 0.591” (0.503”) (0.54”) Personal Service*occupation Log- 0.652” 0.118'2 growth (0.63”) (0.758”) Data*occupation 0.113*'2 0.145”* Log-growth (0.532”) (0.617”) Knowledge*occupation -0.004” -0.078'3 Log-growth (0.89”) (0.947”) Education-earning Slope Intercept 0.033*** (0.604”) Occupation Log-growth 0.029* (0.0.16) Personal Service 0.016 (0.01) Data 0.021* (0.009) Knowledge 0.010 (0.01) Personal Service*occupation -0.019 Log-growth (0.022) Data*occupation -0.020 Log-growth (0.019) Knowledge*occupation -0.007 Log-growth (0.02) Parent’s Education 0.009*** 0.004* (0.002) (0.189-3) Log of Years of Experience 0.182*** 0.203*** (0.012) (0.011) *** p < 001 ** p < .01 * P < .05 78 Table 15 displays the relationships between occupational growth and literacy return controlling for occupational sectors, with and without educational attainment included in the model. The results reveal that controlling for educational attainment, the average rate of literacy return diminishes (b=0.194”, p<.001). The literacy return for personal service persists(b=- 0.906”, p<.01, please see table 15b). Additionally, there exist a significant effect of data handling occupations and its interaction with occupational growth on literacy(b=0.628”, p < .01, and b=0.145, p < .05). This indicates that the literacy return varies across data handling occupations depend on occupational growth of each occupation. The literacy return for data occupations that are growing faster is greater compared to the literacy return in data handling occupations that are growing slower. Interestingly, the return for educational attainment is positively significant while the return to literacy is negatively significant. This indicates that the return for education attainment is higher in jobs that growing faster than jobs that are growing slower. The returns to one standard deviation of (65.64 points in literacy) involve a 13.59, 7.03, 19.92 and 18.07 percent wage gain for workers within the goods processing, personal service, data, and knowledge occupations respectively, controlling for educational attainment, gender, ethnicity, parent’s education, and work experience. The literacy return for personal service occupations is the lowest among the four occupational sectors and the literacy return for data occupations is the highest among the four occupational sectors. 79 Chapter 5 Conclusions and Dicussion Summary This study investigated the relationship between occupational growth rate and literacy proficiency and the return to literacy in the United States in 1992. Literacy skills are defined as verbal and numerical skills that are needed in order to read and understand text and apply arithmetic operations. The measure of literacy skills is taken from the National Adult Literacy Survey. The full measure consists of 165 items of three types: prose, document, and quantitative. The measure of occupational growth rates are the proportion of civilian employees by occupation by the U.S. Department of Labor Bureau of Labor statistics in 1982 and 1992. A two level hierarchical linear model described by Raudenbush and Bryk(l992) was applied to assess the relationship between mean literacy proficiency and occupational growth and the return to literacy of the workers across 460 occupations between 1982 and 1992 in the U.S. The study draws on Osberg et al.'s (1989) theoretical framework on the relations between unbalanced productivity 80 growth and occupational employment rate in the modern U.S. economy. They argued that the present U.S. economy is becoming increasingly dominated by two sectors: (1)a modern information sector which is characterized by jobs that involve ‘data handling’ and ‘knowledge production’, and (2)a traditional sector which is characterized by jobs that involve personal service and goods processing. The steadily increasing productivity growth in ‘goods processing’ and ‘data handling’ occupations leads to the expectation of declining occupational employment rates while the stagnant productivity growth in the personal service and knowledge production occupation is expected to raise the employment rate. To the other hand, the skill demand in the information sector is hypothesized to be rising relative to the skill demand in the non-information sector. The most recent view in economic circles claims that the new information technology has increased the demand for highly skill workers relative to low skilled workers because of the more complex requirements of information systems and flexible organization of production. In addition, workers at a lower level in the new economy are required to access to information that can give them more discretion and decision- making ability. Thus, higher levels of workers’ skill are demanded in the growing jobs. This view is challenged by those who have argued that technological change progressively deskills tasks in order to lower wage rates and the bargaining power of workers in the wage contracts. My findings on the relationship between occupational 81 growth rate and wokers’literacy proficiency do not confirm the view of skill degrading. Overall, across all occupations, I found a highly positive statistical association between the occupational growth and mean literacy of an occupation. However, the relationship between occupational growth and mean literacy is largely explained by differences between occupational sectors rather than differences within occupational sectors. The findings suggest that the information economy increasingly required workers with higher literacy skills across all occupations between 1982-1992. Because the information economy was growing faster than the traditional economy, and because the information economy requires higher skill, growing occupations required higher literacy skills than other occupations. However, the strength of the relationship between mean literacy and occupational sectors, after controlling for educational attainment, diminished. Thus, I cannot preclude that literacy is really important in jobs that are growing rapidly. This would leave open the question of whether literacy is important or there is a process of requiring more educational credentials in the rapidly growing occupations. I found workers in the personal service sector to have higher mean educational attainment relative to workers in goods processing sector. However, no evidence was found that mean literacy of workers in personal service occupations is higher compared to the mean literacy of workers in the goods processing sector. This result can be 82 explained by the reasoning that in the personal service sector, credentials may play an important role rather than literacy skills in recruiting workers to jobs. According to Osberg et al (1989), the wage rate of the workers in the four occupational sectors are determined by the sector to which the workers belong. They argued that post—secondary education rewards workers in the information sector more than it does workers with the same educational level in the non-information sector. My findings on the return of literacy do not confirm Osberg et al’s claims. Despite the fact that average earnings of workers in the information sector are significantly higher than the average earnings of workers in the non-information sector, I found no evidence that the return of literacy skills is greater for workers in the information sector than it is in the non- information sector. I found no evidence that the return to literacy is greater in jobs that are growing rapidly in personal service and knowledge occupations. However, I found that the return to literacy skill of workers within the data handing occupations is greater for jobs that are growing rapidly than jobs that are growing slower. I found l)that across all occupations, jobs that are growing rapidly have higher literacy skills than jobs that are growing slower; 2) that within occupational sectors, no evidence has found that jobs that are growing rapidly have higher mean levels of literacy proficiency, 3) that over all, jobs that are growing rapidly have higher mean wage rates, and 4) that within occupational sectors, no evidence 83 found that the literacy return is greater in jobs that are growing rapidly than jobs that are growing slower. Discussion The purpose of this study is to investigate the relationship between occupational growth rate and literacy proficiency of the workers in the U.S. Previous studies had revealed inconsistent results on the skill demand due to technological change. In the United States, the prospect of computerized production led many people to predict reduced employment and deskilled jobs. However, recent studies indicated that technological change has not reduced overall employment. Rather, despite the widespread perception that the information technology is increasing the demand for higher educated labor, there is no sufficient evidence supports the claim that the literacy skills of these workers are rising. The result from my study reveals a significant positive relationship between occupational growth and literacy. It supports the claims that the skill demand was rising overall between 1982 and 1992. However, the relationship between mean literacy and occupational growth rate is largely explained by differences between sectors. No relationship was found within occupational sectors. This confirms the claim that literacy levels are linked to occupational sectors. Higher mean literacy levels are found in the knowledge and data occupations and low means literacy are 84 found in the personal service sectors. I found workers in the information sector have significantly higher mean literacy than workers in the non- information sector. This supports the claim that the wide spread of computerization in the information sector is increasing demand for higher level of cognitive skill workers. Occupational growth was found to be highly associated with educational attainment after controlling for literacy skills. The connection between educational attainment and occupational growth could be interpreted in many ways; educational attainment may indicate qualification, self- selection or screening in the labor market, or a proxy of knowledge or skill. From the screening perspective, educational attainment serves as a signal for pre-existing ability or the qualification to get jobs. If educational attainment serves as a screening devise, employers recruit workers based on their credentials without knowing the workers’ levels of skill. Though educational attainment is a poor substitute for direct assessment of workers’ skill, such as literacy skills (Tuijman,l997), in this study I found educational attainment is strongly correlated to literacy. Thus, educational attainment may serve as a proxy for the measure of skill rather than just an admission ticket for certain professionals. Despite the fact that the literacy skills are high in jobs that are growing in the economy. I found no evidence for a higher return of literacy for knowledge workers. The 85 return to literacy skill is significant in a specific occupational group, the data occupation. Empirical studies have found a positive relationship between literacy and wages. However, educational attainment is the more important determinant on pre-tax income(OECD, 1997). The significant return to literacy in this study confirms these previous findings. A significant return to educational attainment was found in occupations in the information sector espeCially, in data handling occupations. Educational attainment is a more important determinant on earnings than are literacy skills. As pointed out earlier, educational attainment is not a measure of knowledge or skill, as screening theorists maintained that the labor market might well reward higher educational attainment even if schooling contributed nothing to individual’ productivity (Arrow, 1973, Spence, 1973). The extent to which schools operate as a screening mechanism has remained an open question. However, educational attainment might serve not only to indicate qualifications, but also to indicate the trainability of workers in the modern workplace. As we can see, the return to literacy of workers in data occupations is higher than the return of literacy in traditional occupations. 86 BIBLOGRAPHY Adler, P. (1983). Rethinking the skill requirements of new technologies. Working paper HBS84-27. Cambridge, MA: Graduate School of Business Administration, University. Harvard Bell, Daniel. (1973). The Coming of Post—industrial Society. New York: Basic. Braverman, H. (1974). Labor and Monopoly Capital: The Degradation of Work in the Twentieth Century. New York: Monthly Review Press. Bresnahan, F. Thimothy, Brynjolfsson, (1999) Information Technology, Brick, and Hitt, Lorin M. the Demand forSkill Labor: Workplace Organization, and Firm-Level Evidence. National Bureau of Economic Research. Working paper 7136. Bright, J. R. (1958). Does automation raise skill requirement? Harvard Business Review. 36:84-98. (1966). The relationship of increasing automation and skill requirements. (cited from Spenner K. Sociological Review. (1983). American 48:824-837. Burawoy, M. (1979). Manufacturing Consent. Chicago: University of Chicago Press. Burawoy, Michael and Theda Skocpol. Studies of Labor, Class, Chicago Press. (1982). Marxist Inquirers: and States. Chicago: University of Berman E., Bound J., and Griliches Z. (1994). Changes in the demand for skilled labor within U.S. Manufacturing: Evidence from the Annual survey of manufactures. Quarterly Journal of Economics. May 1994. p 367-397. Blauner, R. (1964). Alienation and freedom: The factory worker and his industry. Chicago: University of Chicago Press. Cain, P. S., and Treiman D.J. (1981). The dictionary of occupational titles as a source of occupational data. Cappelli, P. (1992). Are skill requirement rising? Evidence from production and clerical jobs. National Center on the Educational Quality of the Workforce, Document. Philadelphia, PA. ERIC 87 Carnoy, M. (1995). Education and Technological Change. In International Encyclopedia of Economic of Education. Second Edition. p205-211. Cyert, R.M., (1987). Technology and Employment. Innovation and Growth in the U.S. Economy, National Academy Press, Washington.D.C. Edwards, Richard. (1979). Contested Terraian: The Transformation of the Workplace in the Twentieth Century. New York: Basic. Flynn, P.M. (1988). Facillating Technological Change: The Human Resource Challenge. Ballinger, Cambridge, Massachusetts. Goldstein, H., and Bryna S. Fraser. (1985). “Training for Work in the Computer Age: How Workers Who Use Computer Get Their Training” (Res. Rep. No. 85-09). National Commission for Employment Policy. Washington, D.C. Ginzberg, E, Noelle, T.J.,Stanback, T.M.,(1986). Technology and Employment: Concepts and Clarifications. Westview Press, Boulder and London. Hall, R.H. (1975). Occupations and the social structure. Second edition. Englewood Cliffs: Prentice—Hall. Jaffe, A. J. and Joseph Froomklin. (1968). Technology and Jobs, Automation in Perspective. New York: Praeger. Jaikumar, R. (1986). " Postindustrial Manufacturing" Business Review. Harvard (November-December): 69-76. Howel, D.R.,Wolff E.N. (1991). Trends in the growth and distribution of skills in the U.S. Workplace, 1960—19885. Industrial and Labor Relations Review 44:486-502. Kutcher R.E. (1991). The American workforce: 1992-2005. Kerr, Clark. John T. Dunlop. Frederick Harbison and Charles A. Myers. (1960). Industrialism and Industrial Man. Oxford University Press. -.- 1:. 'e_'_' I!” New York: Maglin, Stephen A. (1974). " What do bosses do?" The origin of hierarchy in capitalist production." Review of Radical Political Economics, 4:60-112. Moor, P. G. (1990). The skills challenge of the nineties. Royal Statistic society. 153, 265—285. Murnane, R.J., Willet, J.B., Levy, F. (1995). The growing importance of skills In wage determination. The Review 0 Economic and Statistics. pp. 251-266. 88 Osberg, L., Wolff, E.W., & Baumol, W.J. (1989). The information economy: the implications of unbalanced growth. Halifax, Nova Scotia: Institute for Social Research on Public Policy Parsons, Talbot. (1968). Sills (ed.), Sciences. "Professions". pp. 536—47 in David L. International Encyclopedia of the Social 12. New York: Macmillan. Rogoff, B., and Lave, J.(1984). Everyday cognition: It’s development in social context. Cambridge, MA: Harvard University Press. Rothwell, R., Zegveld, W. (1979). Technological Change and Employment. St Martin’s Press, New York. Rumberger, R. W. (1981). The changing skill requirements of jobs in the U.S. economy. Industrial and Labor Relations Review. 34:578-91. Spenner, K. (1983). Deciphering prometheus: temporal change in the skill level of works. American Sociological Review. 48(December: 824—837). (1988).Technological Change, skill requirements and education: The case for uncertainty. In: Cyret R. M. Mowery D.C.(eds.) 1988 The Impact of Technological Change on Employment and Economic Growth. Ballinger, Cambridge, Massachusetts. Silvestri, G. T. (1990). 56-135. The American Workforcezl992-2005. pp. Thurow, Lester C. (1975). Generating Inequality: Mechanisms of Distribution in the U.S. Economy. New York: Basic. Tachibanaki, T. (1991). Education, Occupation, and Earnings. In International Encyclopedia of Economic of Education. Second Edition. p149-154. Tuijnman, Albert, (1995). Literacy, Economy and Society. Ontario: Statistics Canada. Wallace, Michael and Erne, L. Kalleberg. (1982). " Industrial transformation and the decline of craft: the decomposition of skill in the printing industry. 1931-1978." Sociological Review, 47:307-24. American 89 ii(1111111111