UBER POLITICS: THE SHARING ECONOMY MEETS AMERICAN FEDERALISM By Erika Rosebrook A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Political Science Doctor of Philosophy 2019 ABSTRACT UBER POLITICS: THE SHARING ECONOMY MEETS AMERICAN FEDERALISM By Erika Rosebrook This dissertation analyzes the policy paths of short - term rentals and ride - sharing to understand how and when state governments intervene in local issues. I use an original dataset of all state sharing economy policy from 2009 - 2018 to detail how the sharing economy emerged into the American policy landscape and was processed by state and local governments and the ways American federalism shaped the outcomes. Expectations from existing research would predict that the processes of short - term rental and ride - sharing policy adoption are similar and driven by partisanship, however through quantitative analysis and detailed case studies I find that instead, the policies proceed differently based on the division of functional responsibilities between state and local governments . Forty - nine states , driven by an alliance between interest groups and the availability of model legi slation, quickly adopted similar ride - sharing regulations grounded in insurance policy that also largely eliminated local policymaking authority. For short - term rentals, the twenty - two states that have adopted state - level policy have moved more deliberatel y, customized the policy to state needs, and left cities with the governing authority to respond to local concerns. The divergent outcomes illustrate how states and cities sort out who governs what by filtering competing arguments and policy preferences th rough their existing functional responsibilities. T his functional fit directs policies toward more receptive venues: if ride - sharing policy is about insurance, states, which typically regulate insurance, are more likely to step in and restrict local policy involvement. Conversely, after states were assured of receiving tax revenue, they left the regulation of short - term rentals to cities, as is typical for other land use - related governance. Ultimately, this research adds to the understanding of how emerging issues enter the policy landscape and when states intrude in local concerns . Copyright by ERIKA ROSEBROOK 2019 v To Charlene Dowdney , whose persistence, heart, and love for education have always guided me vi ACKNOWLEDGEMENTS For a solitary and often self - centered pursuit, finishing a dissertation takes a community, and I am blessed to be part of a supportive and generous community. M y committee is the best of this community, and Josh Sapotichne, Valentina Bali, Sarah Reckhow, Cory Smidt, and Shu Wang made me better and were patient when I did things the long way, and I could not have asked for better people and scholars as mentors. My family, Carol Rosebrook, Jeremy Rosebrook, Michelle Slaggert, Robin Herr, and all of the Dowdneys, have supported me unconditionally, and Tío Jesús was my biggest cheerleader from day one. the first of several women who always seemed to know the right thing to say when I needed to hear it . For at least a year of this process, I tried to be a good political scientist (whatever that is) rk. Sarah Reckhow read some rough initial writing and encouraged me to believe in this work and pursue it, at a time when it was hard for me to see its value. Shu Wang not only shared her data, but she was kind, gave thoughtful feedback, and always had words of encouragement at the ready. I n the first semester of the program, I emailed Valentina Bali my resume and asked if there was anything I could do to he vii find that presumptuous and gave me an opportunity to do work that was meaningful to me while I tried to learn to be an academic and how to do research. In the 7 years since, finish, she read what little I had, and , over coffee and pastries, matter - of - factly explained how she thought I could pull everything together , which made it seem doable. More important than her feedback, which was always thorough and helpful, was her friendship . I admire her curiosity and genuineness, and h er friendship is a gift have. Academia was a culture shock to me in many ways, and there are some parts that will never make sense to me , but one of the best parts of MSU is the abundance of good people and opportunity. I like to think that my talented and supportive cohort - mates Elizabeth Lane, Jamil Scott, and Emma Slonina and I helped establish a positive and welcoming place for Ph.D. students in our department , and I feel fortunate that we, along with Jessica Schoenherr and Lora DiBl asi, got to build a professional and support network of women from the outset. They are amazing, and i t was motivating every day to work alongside them. saac, and Matt Zalewski graciously helped me through the adjustment to academic life , and I appreciated being able to talk real policy with them whenever things got too academic. Matt was a godsend as an officemate , co - author, teaching role model, and frie nd, and his wit and professionalism made viii commiserating fun. When Mike Colaresi gave me the opportunity to work with him as he started Social Science Data Analytics, it was the best crash course I could have ever gotten in data and coding , and nerding out w ith him and Ezra Brooks was a bonus. Cory Smidt brought much - needed West Michigan Dutch energy to my education, and when he and I agree on policy, it is objectively good. The MPP students at MSU have been and continue to be the best. I learn from them ever and my work better , especially Dan Casey - Dunn and Meg Turner. I am especially grateful that my director, co - author, and friend Josh Sapotichne let me do this my way, even when he desperately wished I would just be a normal stude nt . I have learned because he let me jump in, and h e has backed me, advocated for me, challenged rules for me, and given me latitude, which I appreciate immensely. Josh t studen t helped me find my way. From our first ridiculously long and detailed conversation about federalism and policy to equally detailed discussions of Gen X - specific culture to our many, many conversations about this project, he has gifted me his time, support Thank worked years to build, through every meltdown, success, and doubt, Miguel Caba ñ as h as been a true partner listening to me vent, supporting and encouraging my ideas, ix reading every draft while suppressing his natural distrust of political institutions, sacrificing for deadlines, and binging every episode of NCIS: LA with me even though h e is an actual scholar of culture and has standards. This would not have happened without his humor, friendship, and love . x TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ ......................... x i LIST OF FIGURES ................................ ................................ ................................ ...................... x ii KEY TO ABBREVIATIONS ................................ ................................ ................................ ..... x iii CHAPTER 1. American Federalism and the Politics of the Sharing Economy ................... 1 WORKS CITED ................................ ................................ ................................ ............................. 8 CHAPTER 2. The Sharing Economy and State and Local Government ............................ 1 1 Characteristics of the Sharing Economy ................................ ................................ ........... 1 4 Comparing Ride - Sharing and Short - Term Rentals ................................ ......................... 1 8 ................................ ................................ ......................... 2 8 Developing A Framework ................................ ................................ ................................ .. 4 2 WORKS CITED ................................ ................................ ................................ ........................... 4 8 CHAPTER 3. The Sharing Economy in the States ................................ ................................ . 5 8 What Does Sharing Economy Regulation Look Like in the States? ............................. 5 9 Investigating Explanations for State Policy Action ................................ ......................... 6 6 Analysis: State Sharing Economy Policy Adoption ................................ ........................ 7 6 Discussion of Results ................................ ................................ ................................ ........... 80 APPENDIX ................................ ................................ ................................ ................................ .. 90 WORKS CITED ................................ ................................ ................................ ........................... 9 4 CHAPTER 4. How the Sharing Economy Reshaped Regulation in the States ............... 100 Ride - Sharing: A Rapid Road to State Regulation ................................ .......................... 10 1 Framing Ride - Sharing as a State Concern: ALEC Model Legislation ........................ 10 2 Short - Term Rentals: An Adminis trative Path to Regulation ................................ ....... 11 8 Why Do States Govern Ride - Sharing and Cities Short - Term Rentals? ...................... 12 6 WORKS CITED ................................ ................................ ................................ ......................... 13 5 CHAPTER 5. Policy Incorporation and the Sha ring Economy ................................ ......... 1 40 The Importance of Policy Incorporation ................................ ................................ ......... 14 1 WORKS CITED ................................ ................................ ................................ ......................... 14 8 xi LIST OF TABLES Table 2 - 1: Sharing Economy Services and Major Companies ................................ .............. 4 5 Table 2 - 2: Similarities and Differences b etween Ride - Sharing and Short - Term Rentals . 4 6 Table 2 - 3: State and Local Characteristics of Sharing Economy Issues ............................... 4 7 Table 3 - 1: Summary of Hypotheses ................................ ................................ .......................... 8 5 Table 3 - 2: Summary Statistics ................................ ................................ ................................ .... 8 6 Table 3 - 3: State Short - Term Rental Policy Adoption Models ................................ ............... 8 7 Table 3 - 4: State Short - Term Rental Preemption Models ................................ ....................... 8 8 Table 3 - 5: State Ride - Sharing Models ................................ ................................ ...................... 8 9 Table A - 1: State Short - Term Rental Event History Models, Policy Adoption ................... 9 1 Table A - 2: State Short - Term Rental Event History Models, Preemption ........................... 9 2 Table A - 3: State Ride - Sharing Event History Models, Policy Adoption ............................ 9 3 Table 4 - 1: Components of ALEC Draft Transportation Network Company Act ............ 1 30 Table 4 - 2: Comparison of State Ride - Sharing Legislation ................................ ................... 13 1 Table 4 - 3: Comparison of State Short - Term Rental Legislation ................................ ......... 13 3 Table 4 - 4: Functional Fit of Ride - Sharing and Short - Term Rentals ................................ ... 13 4 xii LIST OF FIGURES Figure 1: Year of First Short - Term Rental Policy Adoption ................................ .................. 8 3 Figure 2: Year of First Ride - Sharing Policy Adoption ................................ ........................... 8 4 xiii KEY TO ABBREVIATIONS ACCE American City County Exchange AHLA American Hotel and Lodging Association ALEC American Legislative Exchange Council BLS Bureau of Labor Statistics CEO Chief Executive Officer CTA Chicago Transit Authority FCC Federal Communications Commission MC Marketplace Contractor NAICS North American Industry Classification System NCOIL National Council of Insurance Legislators NIMBY Not in My Backyard NLC National League of Cities NYC New York Cit y PSC Public Service Commission PUC Public Utilities Commission TEL Tax and Expenditure Limitation TNC Transportation Network Company VRBO Vacation Rental by Owner 1 CHAPTER 1. American Federalism and the Politics of the Sharing Economy Atlanta is the quintessential car - dependent American city. Residents of most the city has notoriously inefficient pu blic transportation and is regularly recognized as 2017; Bertaud and Richardson 2019). When Uber arrived in Atlanta in mid - 2012, Mayor Kasim Reed and staff were already a The committee reviewing the ordinance agreed the rules were outdated but was having trouble deciding what the new regulations should be. At a logjam, the City Downtown Improvement District hired a consulta nt on behalf of the committee and the city to equipment and technology, greener cabs, and more accessible vehicles (Rooks and Se 2012). The report was scheduled to be done b y August 2012, and it, along with the every day in local governments across the country: a mayor or elected body elevates a problem to the policy agenda, the city puts a c ommittee together to find politically feasible and locally appropriate solutions, staff and consultants provide data and technical support, and eventually, a report with recommendations is submitted. Then, the city deliberates and takes policy action. 2 For Atlanta, though, a normal delay in the process key players deciding they meant the problem it was trying to address changed entirely. The cautious, deliberate process Mayor Reed took with taxis and cab drivers was not an option with Uber, because it simply started operating in the city. Though Uber was operating at least 6 months before the group made recommendations to the Mayor, and during that time a San Francisco - based national transportation con sultant was preparing a report on the taxi industry, ride - sharing was not part of the discussion. Meanwhile, Uber was building a customer base, and taxi drivers were complaining to the Atlanta Police Department Vehicle for Hire division and preparing a law suit charging that Uber and Lyft were transportation providers, not technology companies (Wenk 2013; Wheatley 2014). Ultimately, the city adopted neither new taxi rules nor ride - sharing regulations before the State of Georgia passed legislation in 2015 tha - sharing companies (Pendered 2014; Wirth 2015). cities in the early 2010s, led by Uber and Lyft with ride - sharing and A irbnb with short - term rentals, cities and states faced competing choices: follow the lead of young, technologically savvy residents and embrace the services as tool for economic development to signal that the city was worthy enough to be included in the ec onomy of the 21st century, or, proceed with caution and assess the effects of the services on 3 existing industries and neighborhoods before allowing them to operate freely. While cities were deliberating these choices, sharing economy companies were also ap proaching states. States, removed from day - to - day issues like increased traffic, loss of taxi service, and neighbor complaints, had different choices: were the services technology companies, or were they taxi and property rental companies that used technol ogy? The sharing economy provides a rare window into a process taken for granted: how do issues get sorted into existing institutions, politics, and regulatory regimes? This dissertation uses this window to: 1) detail how the sharing economy emerged into t he policy landscape and proceeded through state and local governments, and 2) use lessons from the policy paths of short - term rentals and ride - sharing to understand how and when states intervene in local issues. Approach to Research and Roadmap Though new, the sharing economy is not a niche issue. The value of Uber and Airbnb tripled from 2014 to 2016, and the rapid growth was not just Silicon Valley optimism (Yaraghi and Ravi 2017). By 2015, Airbnb hosted more annual guest stays than Hilton and Uber operat ed in over 250 cities across the world (Price Waterhouse Coopers 2015). Today, four years later, the services are everywhere, with a projected annual revenue of $335 billion by 2025. In the last ten years, ride - sharing and short - term rentals have dramatica lly changed how we experience tourism, transportation, and 4 work, yet we know little about how these services have become part of American politics and policy. Capturing the particulars of the policy responses to the sharing economy requires a multi - method approach that borrows concepts from policy process, urban politics, state politics, and economics literatures to develop theory, macro - , and micro - level evidence. I start at the beginning of the sharing economy as we currently know it, primarily the emerge nce of Uber and Lyft and Airbnb. This is a unique case where a new policy issue has emerged and the emergence is publicly documented in real time. State and local legislative and executive action is posted online, founders and users communicate directly vi a social media, companies distribute public talking points, and even in an era of declining local media presence conflict over and embrace of the services attracts attention. The recency, which would be a detriment to understanding many political and polic y phenomena, is an advantage in this case because the process of interest is how new policies are integrated into state or local regulatory authority. As seen in Atlanta, this process is not a neat, orderly trajectory, where problems and issues approach go vernments, ask to be on the agenda, and a reasonable discussion settles both the consensus level of regulation and whether a city or state should provide the regulation. Issues and problems emerge and they invade policy agendas whether governments are prep ared or not, and sharing economy companies counted on the element of surprise to set the terms of the discussion. 5 To document the dynamics surrounding the emergence of the sharing economy and the accompanying state - local policy choices, I begin in Chapter 2 with an overview of the issue. I describe and compare: the macro - level characteristics of the services, the basics of how they work, the nature of the assets at their core, how externalities accrue, partisan positions on the services, and political activ ity of the major companies. This overview provides the foundation for the fundamental question of this dissertation: why, for services that have almost exclusively local spillovers, do states decide to regulate instead of leaving oversight to local governm ents? The final part of this chapter theorizes the answer, that the combination of the functional responsibilities each service crosses and the history and institutional structure surrounding those functions create differential receptivity across venues. F or ride - sharing, this means that once the policy problem was established as a technology company needing insurance regulations, state legislatures were natural venues. For short - term rentals, the strong local authority over land use created a higher barrie r for states to intervene. Chapter 3 relies on extensive state and local data collection to both describe the state sharing economy policy and quantitatively investigate potential explanations for state policy adoption. The models, which cover all ride - sha ring and short - term rental state - level policy activity from 2009 through 2018, test traditional political and institutional explanations for policy adoption, including issue politics, state - local institutional relationships, demographic characteristics, an d preemption. The 6 descriptive statistics in this chapter illustrate the differences between ride - sharing and short - term rental policy activity at the state level. Within 18 months of the first state - level ride - sharing law, over half of states had adopted n early identical laws, and within 36 months, only 8 states had failed to act. Oregon was the only remaining state without a state - level ride - sharing law by the end of 2018. In contrast, states have been slower to act on short - term rentals, with fewer than h alf of states establishing state - level rules between 2011 and 2018. The results of the policy adoption models do not provide strong evidence that state action is motivated by state - level political or institutional factors or preemption, and, in order to in vestigate the fuller picture of the different patterns of state activity around ride - sharing and short - term rentals, Chapter 4 uses detailed case studies of ride - sharing and short - term rental policies. These case studies document and compare the content of ride - sharing and short - term rental legislation and illustrate how the functional responsibilities of each level of government create different levels of receptivity for different types of policy. Model legislation - driven ride - sharing policy that largely p reempts local authority contrasts with short - term rental policy that varies by state and sets few limits on local policymaking, and the final piece of chapter 4 uses lessons from the cases of Maryland and Georgia to develop the concept of functional fit as a mechanism of policy incorporation. Finally, Chapter 5 summarizes the research and discusses the importance of functional fit and policy incorporation and identifies paths for future work. 7 Together, this mix of analysis provides a complete picture of sha ring economy policy in the United States and quantitative and qualitative evidence that connects the policymaking surrounding these emerging issues to broader issues of how federalism intersects with policy and governance. This dissertation addresses the c urrent gap in theory around the process of state and local division of authority by identifying an overlooked mechanism of how federalism structures political and functional paths for policy. Using an issue - led approach in this case ride - sharing and shor t - term rentals highlights how issue and institutional characteristics combine within the structures of federalism to allocate policy governance to a particular level and branch of government. 8 WORKS CITED 9 WORKS CITED Regional Commission. http://documents.atlantaregional.com/transportation/TPD2011factb ook_v04.pdf . - Year - Old Cabs, Maybe Renew 2011 Effort to Upgrade Taxis with GPS, Credit Card A 2014. https://saportareport.com/atlanta - to - ok - 10 - year - old - cabs - possibly - renew - 2011 - effort - to - upgrade - taxi - industry/ . Constitution . February 20, 2017. https://www.ajc.com/news/local/atlanta - traffic - among - worst - the - world - study - finds/C6JR110E1z9xZeGGmjJ2HM/ . Intelligence Series. PriceWaterhouseCoopers. https://www.pwc.fr/fr/assets/files/pdf/2015/05/pwc_etude_sharing_economy.pdf . Taxicab Industr https://www.atlantadowntown.com/_files/docs/atlanta - taxi - industry - rfp --- may - 30 - 2012.pdf . Wenk, Amy. 2013. Chronicle. July 12, 2013. https://www.bizjournals.com/atlanta/print - edition/2013/07/1 2/taxi - drivers - sue - claim - monopoly.html . Creative Loafing. January 30, 2014. https://creativeloafing.com/content - 185678 - cover - story - atlanta - s - taxi - industry - declares - war - on - uber . WABE. July 1, 2015. https://www.wabe.org/ga - rolls - out - new - regulations - uber - lyft - and - taxis/ . 10 SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3041207 . 11 CHAPTER 2. The Sharing Economy and State and Local Government - of - its - kind law to officially allow ride - sharing services to operate in the stat e, then - Governor John Hickenlooper proudly proclaimed - growing states, was a prime target for Uber and Lyft, and Hickenlooper, a former entrepreneur, prided his administ ration for leading innovation and creating public - - of - life (Sealover 2018). Hickenlooper embraced the industry - supported ride - sharing - friendly have regulated ride - sharing like taxi services. At the same time, according to the National League of Cities (NLC), states were in the other public writers, attributed this increase in state takeover of local power largely to Republi can control of state governments, calling Republican trifectas a key condition for the success of lobbying by special interests, along with political polarization along urban - rural fault lines. Yet if these partisan trends are so crucial and connected to d ecisions about state and local policymaking power, why would Governor Hickenlooper, a Democrat, so strongly support ride - sharing rules that not only codified 12 regulation at the state level but were drafted with the help of the American Legislative Exchange Council (ALEC), a Republican - affiliated interest group? Governor Hickenlooper and Colorado are not the only Democrats or Democrat - controlled states that supported regulating the sharing economy at the state level rather than allowing local communities to d ecide how services like ride - sharing and short - term rentals fit into their communities. With 49 states regulating ride - sharing as of 2018 1 and 22 regulating short - term rentals, something more than partisanship differences between states and cities is assoc iated with states taking ownership on these issues. In this system of state - (Kincaid 2014, 243), we have two potential explanations for how states and cities decide who governs what. One suggests tha t the mechanisms of this negotiation are political in a partisan way: Republicans, though historically supportive of local control and government at the level closest to the people, now consider disenfranchising Democratic - led cities important enough to ma ke the erosion of local authority a key strategy in their political agenda. The other suggests a more personal political motivation: legislators learn from elected officials at other levels when there is political advantage in doing so, via the process of bottom - up federalism (Shipan and Volden 2006). In the case of preemption, and for the sharing economy in particular, Red States - 1 As of this writing, Oregon is the only state without ride - sharing legislation, though there are bills under consideration during the 2019 regular legislative session. 13 Blue Cities would predict that all Republican - controlled states would proactively adopt regulations that prohibit cities from es tablishing rules governing the services, and bottom - up federalism would predict that states would either adopt policies consistent with those enacted by cities or do nothing, depending on levels of legislative professionalism and interest group activity. W hile both offer partial explanations for how states and cities interact on policy decisions, there are a range of things we know about federalism and policy outcomes that are not accounted for by either. Understanding the state - local relationship is import ant not just for its own sake, but also for adding to what we know about political power, decision - making, and the policy process. Bowman (2017) highlights the need for attention to the state - local relationship, particularly to understand which level of go vernment provides which services. Weissert (2011) calls for both more attention to intergovernmental relations but also greater exchange between comparative and U.S. federalism scholars on the impacts of decentralization and multilevel governance. Weissert and Ice (2014) further note that research on the state - urban politics, and economics/fiscal federalism (72), has over - focused on fiscal relationships and neglected politics and policy - related questions and suffers from a lack of theory. This project addresses these identified needs by developing a theoretical framework about how states and cities negotiate authority over emerging issues. It attacks the assumption that who governs what i s preordained and illuminates how 14 states and cities settle who has the power to make rules, monitor, encourage, and enforce activities within a jurisdiction. Characteristics of the Sharing Economy Ride - sharing, short - opportunities for urban residents to earn money in a more formalized manner from their assets or labor. At its best, the sharing economy engages people in neighborhoods and solves transportation problems, however, it also presents regul atory challenges for cities. Ride - sharing companies operate outside of local taxi licensing and safety regulations and add traffic to airports and other busy corridors, while short - term rentals can be disruptive to neighborhoods and building residents and potentially create complicated tenant rights situations and rental housing shortages (Martin 2017). In response, cities have enacted a variety of policies designed to control the negative externalities of the sharing economy on urban life. For example, in 2015, the Austin, Texas, City Council adopted a ride - sharing ordinance which: banned the pickup of passengers in certain areas; required fingerprint - based background checks for drivers; established corporate reporting requirements; and required vehicle ide ntification and safety checks. Austin voters reaffirmed these requirements in 2016 by rejecting an Uber - and Lyft - funded ballot initiative to weaken the regulations (Hicks and Wear 2016). A d those of 20 other Texas cities) by enacting a state - based regulatory system favored by national companies 15 Uber and Lyft (Lumb 2017; Wear 2017). This dynamic illustrates what Davidson and Infranca (2016) identify as the urban nature of the sharing economy and its accompanying governance challenges. Economic innovation and technology are entering at the local level, which challenges existing systems and regulations and forces cities to make decisions on the frontier of policy, while states face neither the same urgency nor effects with respect to decision - making. Sharing economy services are not the types of market disruptions or economic development cities typically face. Ride - sharing does not appear in the form of a large company selecting a site, seeking zoning approval, infrastructure improvements, or tax incentives, and then promising localized benefits like jobs, investment, and tax revenues. Instead of pushing the boundaries of these traditional regulatory avenues, sharing economy companies often just start operating in a location, an approach Thierer (2016) calls permissionless innovation. As an example of how the corporate partner of individual contractors distances itself from local operations, Airbnb offers the following provide legal advice, but we do want to give you some useful links that may help you better understand laws and re gulations in your town, city, county, or state. This list is not exhaustive, but it should give you a good start in understanding the kinds of laws that may apply to you. If you have questions, contact your local government, or consult 16 a local lawyer or ta the company provides the platform and the property owner is responsible for seeking information, decipher ing what rules apply when, and complying with laws. In essence, the corporation itself has no local presence; it places the risk and thus the liability and consequences for violating local laws on the individual contractors. Companies provide the idea and technology that enables contractors to operate remotely, but they explicitly limit corporate obligations to address any local regulations or impacts of services enabled by their technology. This loosely - networked structure means that the technology may alr eady have a foothold in a community before it appears on the radar of local policymakers. The reactive position of cities in this structure means that local policies tend to reflect how the effects of the services are experienced in a particular city and d iffer across communities (Hirshon et al. 2015). This strategy of exploiting regulatory gaps and changing institutions by market activity is considered evasive entrepreneurship by Elert and Henrekson (2016), who note that sharing economy companies intention ally frame their services ambiguously to obscure which rules might apply. 2 2 Elert and Henrekson consider evasive entrepreneurship as a special category of institu tional entrepreneurship as defined by Li, Feng, and Jiang (2006) because they lead with market activity rather than seeking institutional change first. 17 The relationship between actors and the effects of economic activity in the sharing economy highlights why understanding the state - local relationship is important to understanding u rban governance more generally. Corporations are located remotely (in California) and contract with individuals in cities across the world to provide services to users who are other residents of (or visitors to) those cities. Positive and negative spillove r accrues at the local level, and large economic gains accrue to the corporation states or countries away. On the regulatory side, local governments may or may not have the authority to regulate and monitor services, manage use conflicts, and earn tax reve nue related to the new economic activity. Whether they have that authority or not, however, they incur financial and political expenses to manage the externalities that accompany the activity within their boundaries: increased traffic and congestion, confl icts between neighbors, decreased transit use, rental disputes, etc. States, on the other hand, have no obligation to either regulate services or deal from the direct e ffects of these services creates a receptive venue for the companies behind the services to lobby for their preferred policy regulation, as in states with more rural legislatures there is little political consequence or constituent pressure to protect loca l regulatory authority on urban - centered problems. The state venue is more accessible to well - financed corporate interests than to local tenants, neighborhood organizations, and communities. Sharing economy corporations do not have locations 18 in every city, are not regulated or administered at the local level (no business license, no physical office, etc.), and are backed by extensive capital. Thus, the companies have resources available to move the venue of regulation from the place dealing with the activit y and externalities of the problem (cities) to a place that has authority over cities and has little direct connection to the activity or externalities of the problem (states). As evasive entrepreneurs, corporations seeking state - level regulation use the f ederalist system in a form of regulatory arbitrage. Comparing Ride - Sharing and Short - Term Rentals Sharing economy is used to colloquially to describe a wide range of technology - owd - based - based, maximizes the capacity of assets, decentralized, and blurs the lines between professional and personal and work and leisure. Others distinguish between the sharing economy, colla borative consumption, collaborative economy, peer - to - peer economy, gig economy, with the use and subdivision of each term varying based on discipline (Dredge and Gyimóthy 2015; Cheng 2016). Dredge and Gyimóthy (2015) identified 17 separate terms for the sh aring economy from different academic literatures, each of which emphasizes different aspects based on the disciplinary foundation. Microeconomists focus on the components of the sharing economy related to efficiency, markets, transactions, and assets; bio logists and ecologists on cooperative behavior; sociologists on the cooperative 19 and moral components; and computer scientists on the networked and technological characteristics. Cheng (2016) builds on this work and also finds lack of policy or politics - foc used sharing economy publications. It is unsurprising that political scientists and public policy scholars have been absent from the discussion. Until something enters political discussion or policy is made, there is no real reason to engage the topic, and legal scholars typically conduct prospective analysis of regulation and governance of new technologies (see Kassan and Orsi 2012; Ranchordas 2015; Katz 2015; Miller 2016; Interian 2016). Thelen (2018) and Collier, Dubal, and Carter (2018) each examine pol icy adoption regarding Uber (and to some extent ride - sharing more generally) but as of this writing, there is no other research on either what governments are doing with respect to the sharing economy or the politics surrounding those decisions. Given all of this, the definition of the sharing economy that makes the most sense for this analysis is the common definition: sharing economy services are app - based services that connect users to assets available for sharing (e.g. ride - sharing, short - term rentals, scooter - sharing, etc.) This project focuses on the two parts of the sharing economy that have extended through most of the United States (and the world): ride - sharing and short - term rentals. Table 1 - 1 details major companies that provide each service in th e United States and around the world. 20 As the most well - known parts of the sharing economy, ride - sharing and short - term rentals have several key similarities. 3 Both originated out of Silicon Valley around the recession (Uber in 2009, Airbnb in 2008), at a t ime when smartphone apps were beginning to permeate American households, particularly those of young, educated, and affluent users (Purcell, Entner, and Henderson 2010). Each used technology to connect supply (people with cars or real estate) with demand ( people who need transportation or lodging) at the individual level. The technology behind the smartphone apps allowed ease of payment and implied verification of user identity on each end. Early users appreciated the convenience and efficiency of the apps, and the companies capitalized on word - of - mouth from these early adopters. Both Uber and Airbnb started operating in a city and relied on connected users to build the market. By the time a service would be visible to a local government (or even to its comp etitors), Uber and Airbnb already had a dedicated user base. Once a city decided that the services needed some regulation, Airbnb and Uber used technology to easily mobilize users. The ability to have almost instant coalitions of advocates from across all 3 Uber and Airbnb were the first and remain the dominant corporate forces in each area. They are the com panies that popularized the services, and although their competitors have grown (most prominently Lyft and Home Away) these two companies have driven the expansion of services and the political and policy responses of state and local governments to ride - sh aring and short - term rentals. Unless otherwise noted, these companies are the services cities and states have responded to when making sharing economy regulations. 21 geographic and demographic categories in a city creates an out - sized source of political power predisposed to favor company - endorsed regulations. The other key feature that the services share with respect to policymaking is that there is no clear partisan position on the sharing economy. When the services began, they were not immediately sorted into the neat ideological categories of national party Republican principle, and e want government to encourage the sharing economy and on - demand platforms to compete in an open market, and we believe public policies should encourage the innovation and competition that are essential for an Internet of Things to ty 2016, 6). Yet on the sharing economy specifically, it is difficult to tell the parties apart. Uber and Airbnb have hired Democrat affiliates like David Plouffe (Uber, Obama 2008 Campaign Manager), Eric Holder (Airbnb, Obama Attorney General), Chris Leha ne (Airbnb, Clinton Campaign and White House Counsel), and Valerie Jarrett (Lyft Board Member, Obama Senior Advisor), and Democrats are twice as likely to use ride - sharing services (Smith 2016). During the same convention year that Republicans wrote the sh aring economy into the Party platform, the Republican National Committee rejected a 22 deal to partner with Uber to transport attendees during the convention, a deal the Democratic National Committee accepted for its national meeting in Philadelphia (Primack 2016). That the party made the deal in Philadelphia, a city that was at that time in a difficult fight between taxi drivers, people with disabilities, and Uber over regulation of ride - sharing (Joyce 2016) illustrates the blurred partisan lines on the shari ng economy. Ride - sharing was not permitted in Philadelphia until a few months before the convention, when ride - sharing companies asked the Philadelphia Parking Authority for permission to operate in the city. The Parking Authority, a special district, gove rns on - street and garage parking, parking regulation and enforcement, and impounding and towing, and the Commonwealth granted authorities in first - class cities 4 the power to administer taxi and limousine regulations 5 and red light cameras (Pennsylvania Con solidated Statutes 2001). 6 Philadelphia, as the only first - class city in permission to ride - sharing companies to operate in the Commonwealth, and the Authority initially to ok a combative stance toward the services, operating stings to 4 Over 1 million in population. 5 The statutory change that gave Philadelphia the right to g overn taxis also changed the frequently mentioned by Mayor Nutter when the City, Commonwealth, and Parking Authority were at odds on how to govern ride - sharing (se e Dent 2016 for a more detailed history). 6 In contrast to other special - purpose government agencies that operate in relative obscurity, the Parking Authority was the subject of the A&E reality show Parking Wars from 2008 to 2012 (A&E Network 2019). 23 catch drivers driving for Uber and Lyft illegally (Dent 2016). The Parking Authority ultimately gave Uber and Lyft temporary permission to legally operate in the city during the Democratic Nati onal Convention, justifying its ruling in part due to the national event and transit shortages during that time period (Burdo 2017). When the Philadelphia Taxi Association challenged the ruling in federal court, the district judge sided with the taxi drive rs and issued an injunction to stop the services from operating, - sharing laws (Corso 2016). The budget l anguage allowed ride - sharing in Philadelphia from July 13, 2016, through September 30, 2016, and the Democratic National Convention took place from July 25, 2016, through July 28, 2016. The initial court ruling prohibiting ride - sharing in the city was over turned on appeal, 2 years after the Democratic National Convention (Third Circuit 2018). 7 - sharing had been legally operating in Philadelphia for almost 2 years thanks to state legislative action, with Governo r Tom Wolf, a Democrat, stating at the November 2016 allowing them to become full partners with the cities and communities where they 7 The Uber under the Sherman Antitrust Act was Clinton - appointee Midge Rendell, the former First Lady of Pennsylvania and Philadelphia from her ex - e as Governor and Mayor, respectively. 24 Republican Senator Camera Bartolotta touting the innovation of ride - Tom Wolf 2016). This bipartisan agreement on remo ving the authority of the how the sharing economy straddles traditional party and ideological lines. Though these similarities new technology that creates efficiency for existing activities, introduction without warning, use of technology to mobilize users, and lack of partisan sorting help explain how the sharing economy can be viewed as a single unit, there are key differences that potentially portend different po licy approaches by state and local governments. First, the features of the asset that is being shared change how and where externalities accrue. The nature of the good at the heart of an issue shapes the interdependencies and relationships around the issue and creates different potential political coalitions when governments consider regulations. Short - term rental depends on real estate that is fixed. It exists in a place that is part of a neighborhood, which is part of a system of neighborhoods that make u p a city, and because it is surrounded by real estate that is equally immobile, owned by others with an interest in maintaining its value, the attention to new activity that might affect surrounding property is high. 25 Protests against ride - sharing have come , initially, like in Philadelphia, from taxi drivers, taxi unions, and occasionally riders with disabilities, groups that can be characterized or perceived by policymakers as special interests. One of the main themes of the Philadelphia court challenge is that the taxi association is upset about loss of violations or other legal issues. In other words, for taxi drivers, anti - ride sharing sentiment is personal and partic ularistic. Those who have captured existing regulatory systems are not the most sympathetic political constituencies. Protests against short - term rentals, on the other hand, are driven by dynamics similar to many routine conflicts in local government. In S outh Lake Tahoe, a longtime resident advocating for new rules against short - - of - town investors who buy so - called second homes to turn them into investments as vacation home rentals. Our neighborhoods have fewer full - time residents and (our) sense of Kristin Palmer cited disparate racial impacts an d the harm of rapidly increasing tax bills for long - term residents in her sponsorship of short - term rental regulations, while an Airbnb host argued that short - term rentals are a boon to neighborhood property values, fore short - 26 quotes are indistinguishable from the kind of dialogue that surrounds many other local land use conflicts on a daily basis . The other main difference in the two issue areas is the strategy of the companies toward regulation. Uber intentionally sought favorable regulation at the state level from the beginning and has actively used its technology to mobilize users toward its po litical when clicked, would compare rider wait times under existing rules to those that would - sharing in the city. After the Mayor and City Council. Say NO 25 - minute waits, and was not a customized mod el that would reflect the actual outcome under new regulations (Tepper 2015). New York is a special case in many ways due to other cities, where especially at the local level, it has resisted attempts to regulate it as a transportation service quickly and publicly. Airbnb, on the other hand, was not a strategic political actor until later in its tenure. The company found out about its first big policy battle, in New York , when one 27 - e got to hire market, estimated at $140 million in gross bookings (Zaleski 2018) and $450 milli on (Gallagher 2017) in annual revenue. New York has challenged Airbnb from the state and local level, and the fight has been ongoing since 2010, with millions spent on either side of the issue, however New York is an atypical fight for Airbnb. Though the c ompany did figure out what lobbyists were very quickly, establishing a presence in 19 states by 2018, it has only selectively engaged in visible public battles with state and local legislators. Instead, the company has negotiated voluntary tax disclosure a greements, outside of the legislative process, to collect occupancy taxes in 44 states and many local jurisdictions (beginning with Portland, Oregon, in 2014, through the Airbnb app (Airbnb 2019a; Martineau 2019). For example, in Illinois, a state with loc al - option hotel - motel taxes, Airbnb collects state, county, and city occupancy taxes for 9 different jurisdictions (some overlapping) with different rates and eligibility requirements based on the length of stay (Airbnb 2019b). The company could have fough t at the state level to exclude itself and properties listed on its service from these taxes, but instead it has fought its regulatory battles through bureaucratic agreements and the courts 28 (Martineau 2019). Keeping this negotiation removed from the public eye has created what some characterize as secret lawmaking, with the documents that have been made public showing that the agreements create rules about data disclosure, government knowledge of operators (hosts), audit restrictions, and limit data sharing with other public agencies, characteristics atypical of other voluntary tax disclosure agreements agenda, and there seems to be little desire to revisit the agreements or c odify them legislatively after the initial document. Airbnb is no less aggressive than Uber in protecting its corporate interests, - level legislative protection for itself from ex isting taxi regulations by allying itself with insurance companies and ALEC, and Airbnb reacts to proposed regulation, settling for negotiated rules or court fights out of the public eye when possible. Both companies seek to distribute the impact of regula tion to drivers or hosts, leaving drivers with new insurance requirements and fees for non - compliance and hosts with new tax regulations. The similarities and differences between the services are summarized in Table 1 - 1. Some of the differences in how Uber and Airbnb approach regulation and government action lie in how the services cross different types of rules and regulations 29 at the state and local level. Some issues are more local and some issues are more state, and the abi lity to get the levels of government to agree on those divisions shapes the kind of strategy political actors use to increase the likelihood of achieving desired policy outcomes. There is no single rule or variable that can measure the state - local dividing line. A state that prides itself on a culture of strong local control may retain state level authority over tax options available to cities, so in that state, cities might have wildly different policies on ride - sharing without the state feeling the need t o restrain them or enact its own rules, but if a city tried to enact a local - option sales tax, the state would immediately take measures to stop it. Likewise, a state that requires cities to submit annual budgets for state approval and offers little local flexibility might be fine with a city adopting strict short - term rental regulations if there is a strong legal history of regulating land use at the local level. The intersection of the policy domains an issue crosses makes different venues more likely hom es for regulation. The policy domains and associated state and local responsibilities are summarized in Table 1 - 2. Ride - Sharing Ride - sharing crosses four main state - local policy domains: insurance, licensing, taxi regulation, and traffic management. 8 9 Insu rance may not be the first thing that 8 There are two federal policy domains, outside of the courts, that are not discussed here but could elevate ride - sharing and short - term rental to the federal policy agenda: access for people with 30 comes to mind when thinking of ride - sharing, but it is the most common anchor for state regulation of the industry. Forty - nine states require ride - sharing drivers to carry separate insurance while driving (American Pro perty Casualty Insurance Association 2018), and the insurance requirements are both technical and negotiated. State laws assign which insurance policy covers what based on whether: the driver is driving with the app on but without a passenger or having acc epted a ride; the driver has accepted a ride but has not picked up the passenger; or the driver has a ride - share passenger in the legislation, the insurance coverage requirement s were the key points of contention the Colorado bill were replicated by many states and were included in the model legislation proposed by ALEC, but why was this the linchpin of ride - sharing regulation, disabilities and iss ues with discrimination based on protected class status (see Leong and Belzer (2016 2017) for a detailed examination of public accommodations and the sharing economy.) There have been highly - publicized incidents of racial and gender discrimination at Uber (internally and externally), Lyft, and Airbnb, both in field experiments conducted for academic research and in actual practice (Cui, Li, and Zhang 2016; Dickey 2016; Ge et al. 2016; Kakar et al. 2016; Edelman, Luca, and Svirsky 2017; Cheng and Foley 2018) . Airbnb even hired former Attorney General Eric Holder to draft an anti - discrimination policy (Bhattarai 2016) after a host used racial slurs and cancelled a booking once he learned the guest was Black (Bossip 2016). Uber is currently being sued in federa l court over access for riders with disabilities (Christoph 2018), and research shows that users with disabilities also face difficulties using Airbnb (Ameri et al. 2019), which raises questions about whether and how the Americans with Disabilities Act sho uld extend to the platform economy (Chokshi and Benner 2017). 9 Ride - sharing companies also heavily depend on a National Labor Relations Board Ruling that drivers are independent contractors, not employees (Uber Technologies 2019; Sophir 2019). As of this writing, that question is under review by federal courts and California is considering a state law that would reclassify the drivers as employees. 31 more so than ensuring public safety, mitigating environmental and traffic impacts, integrating ride - sharing into existing taxi regulations, connecting services with public transportation, or leaving decisions to cities? business model depends on insurance companies covering drivers, not Uber itself. In its insurance coverage for Drivers and on other types of insurance for additional risks related to our business. If insurance carriers change the terms of such insurance in a manner not favorable to Drivers or to us, if we are required to purchase additional insurance for other aspects of our busi ness, or if we fail to comply with regulations 2019, 63). With Uber having a strong incentive to protect itself from loss by requiring its drivers to carry insurance to cover l iability, state governments were a natural place to seek policy action. Insurance regulation was historically the province of the states, a role - Ferguson Act, in which insura nce was specifically excluded from the Commerce Act (National Association of Insurance Commissioners 2011; United States Code 1945). Given this strong, entrenched role of the states as insurance regulators, the interest ride - sharing companies have in not h aving to carry insurance to cover the risk of thousands of individual drivers, and the potential development of new insurance products for 32 insurance companies to sell, the state legislature and other state agencies responsible for insurance rules are prefe rred venues for ride - sharing companies. The other state - level policy domain that intersects with ride - sharing is licensing. States typically govern taxis and other transportation carriers under common carrier laws, which require for - profit companies open t o the public that transport people to adhere to certain requirements. 10 Like insurance, this is an area that intersects federal law, with the federal government responsible for interstate carriers and states responsible for intrastate carriers. 11 Essentially , states are broadly responsible for ensuring the equity of public transportation within their boundaries and have the authority to impose rules and sanctions in order to assure equity. The historical and legal precedent means states are the clear home for the regulation of common carriers and public transportation corporate entities, and cities then may have authority in different states to regulate the individual components of the day - to - day operation of the carriers within the city limits (e.g. establish taxicab regulations, caps, etc.). Once again, in its investment prospectus, Uber acknowledges the risks to ride - sharing companies if currently are not required to obtain a com mercial taxi or livery license in their 10 Distinct from common carriers in that they do not serve the general public and can refuse service based on contracts or rules of service, contract carriers and private carriers are subject to different regulations. 11 Common carrier law also covers telecommunications, including internet, and public utilities. 33 respective jurisdictions. However, numerous jurisdictions in which we operate have conducted investigations or taken action to enforce existing licensing rules, including markets within Latin America and the Asia - Pac ific region, and many others, including many countries in Europe, the Middle East, and Africa, have adopted or proposed new laws or regulations that require Drivers to be licensed with local authorities or require us or our subsidiaries to be licensed as a model rests on not being defined as a common carrier, which creates urgency to successfully convince states to exclude them from that definition. 12 Though in the case of ride - sharing states control these two policy domains which are key to the company existing in its current form, cities do have regulatory power that can impact how ride - sharing operates. As noted above, many cities have taxi regulatio ns, which take different forms based on the type of market. Cities with medallion systems (closed markets) regulate the ability of individual cars and drivers to operate, cities with company - based permit systems usually permit companies to operate a specif ic number of cars, cities that open bids for taxi franchises open competitive processes on a regular basis, and cities with open markets allow anyone who meets qualifications (driver - and/or car - based) to operate (Committee for Review of Innovative Urban M obility Services 2016). This wide variation in local regulations, 12 This applies specifically to the American context, because, as noted in the quotation, other countries have not been as receptive to the idea of Uber as a technology company, and the policy outcomes diverge from those in the United States. 34 which tend to be stricter in larger cities, covers everything from passenger safety, availability of services to people with disabilities, meter rates, number of vehicles, pickup rules (stre et hail versus dispatched), to areas of operation. Cities have developed these highly - localized regulations to meet specific community needs and market conditions, but the variation across local contexts does not build into a statewide political movement t o recognize the need for local control of taxis. Many small - to medium - sized local governments lack taxi service altogether, so it is not a primary regulatory function performed consistently among a majority of local governments, making it difficult to mob ilize a political coalition to protect local authority. Further, for ride - sharing, if the companies are not considered transportation companies (common carriers) by a state, it is more difficult for cities to justify governing ride - sharing under existing t axi licensing ordinances. All 49 state laws preempt some portion of local authority to regulate ride - sharing in local ordinances. Cities also typically control traffic and parking regulations that are not otherwise reserved by the state. Usually this power is used for the pieces of traffic control that matter for daily life in a jurisdiction: who can park where and when, rush hour driving rules, traffic signal timing, bike and bus lane placement, etc. An example that illustrates the line between state and l ocal authority over traffic - type functions is the battle over red light cameras, in which cities, beginning with New York City in 1993, started using red - light cameras to enforce traffic laws, automate functions, and generate revenue 35 (Centers for Disease C ontrol and Prevention 2019). States responded, some with bans of local use of the cameras, some setting statewide standards, some with lawsuits, and some embracing the technology, and through the legislative and legal process, states largely were recognize d as having the claim to set general traffic laws. Finally, though it is less connected to directly regulating ride - sharing, cities have a governing interest in the positive externalities of ride - sharing. Cities are responsible for economic development and marketing the city as worthy of investment in an increasingly global and competitive economy. In the words of former Atlanta Mayor technology friendly, you lose national a When Uber and Lyft exist in a city, it is a signal to investors and companies with younger, technologically savvy workforces that the city has arrived, and cities have to balance the competing responsibilities of managing the disruption to existing transportation systems with what Peterson (1998) considers the primary interest of local government maximizing economic prosperity. States are also involved in economic development, through broader incentive progra ms and marketing, but the prioritization and execution of those strategies is devolved to the local level. Thus, in the policy domains that most intersect ride - sharing, the state has a historical and legal foundation which grants it stronger power on issue s that matter to 36 ride - sharing services than that of cities, making it a preferable venue for companies seeking policy environments favorable to their continued operation. Short - Term Rentals Short - term rentals also cross 4 main state - local policy domains: t axes, land use, definitions of property classes, and building safety. Every state except Alaska, California, and Nevada levies either a state lodging tax, sales tax on lodging, or both taxes on accommodations (National Council of State Legislatures 19AD). Some states allow cities to levy additional taxes, but the authority to authorize the tax and set rules and rate ceilings rests with the states, which offer enabling legislation for cities that opt - in to a local tax (which may be in addition to a state tax .) Originally implemented in the 1940s, these targeted sales taxes are designed to capture the economic activity of visitors to a locality, as those visitors rely on public services and goods funded by state and local residents. State tax agencies often co llect the taxes and disburse local receipts to local taxing authorities, as traditional lodging operators may have properties in many cities across the state, and state - level administration helps enforce consistent compliance and administration. Airbnb has taken advantage of this administrative process by offering voluntary tax disclosure agreements to state and local governments in which the company agrees to collect lodging taxes on behalf of property owners via its application and remit the tax payments to the appropriate governing bodies. Through November 2018, Airbnb had agreements with all states except Nebraska, 37 Indiana, West Virginia, Georgia, Hawaii, and Massachusetts. The initial agreements with states for collecting state taxes did not always incl ude local governments, so cities and counties that levy occupancy taxes have had to individually work with Airbnb, a process the company considered cumbersome so it started offering a standard agreement to localities. Some cities are reluctant to enter the agreements because the voluntary agreements do not disclose information about individual properties and property owners, information key to local enforcement and audits (Ebert 2018). Cities like New York have continued to fight the company for more open d ata sharing, but most cities are faced with signing voluntary agreements that guarantee some payment of taxes without individual accountability or attempting to find individual short - term rental properties and pursue tax collection from each individual own er. The anonymity by state treasury departments, which usually include provisions for back tax payments and penalties and interest, and information that supports auditing tax payments. Publicly available state - level agreements with Airbnb have not included any back tax payments, and in further contrast to agreements for other industries and corporations, some agreements require governments to obtain written permission from Airbnb to speak to the media about the agreements and prohibit sharing of data between state 38 taxes, and defining classes of property required to pay occupancy taxes esta blish it as the lead level of government in this policy domain. Land use and building safety have a long history of being regulated at the local level. While current building codes in the United States have been standardized at the state level to some form of the International Building Code, historically, the codes were developed by cities to meet local needs after disasters (e.g. fires in Chicago and Baltimore, earthquakes in San Francisco, etc.) or as they urbanized (Rossberg and Leon 2013). Building code s developed alongside the insurance, building, and engineering industries, and as the industries grew, they developed model codes, which often were adopted by cities and later states (Listokin and Hattis 2005). The three major codes were merged into a sing le code in the late 1990s/early 2000s, and most states now have some version of the International Building Code adopted in statute as the state building code. Seventeen states require local governments to adhere to the state building code and receive state approval before amending it, however local governments in the other 33 another model code (Fisette 1999; C entral United States Earthquake Consortium 2010). Phoenix, Ariz ona, and Pasadena, Texas, for example, have adopted a separate model code developed by the National Fire Protection Association. Even in this mostly standardized policy area, cities retain the ability to customize building standards, which 39 indicates both t relationship between building standards and land use in a community. Managing land use planning and zoning is the most primary function of local government. It is a core responsibility of local government in all 50 states, and courts have repeatedly upheld the rights of local governments to regulate the activities on and manage the uses of property within their boundaries (Briffault 1990; Krane, Rigos, and Hill 2001). Similar to building c odes, local zoning ordinances developed in the early 1900s as the country transitioned into industrial urban life and property uses were separated for health and safety reasons (Silver 2016). As the codes developed, they became more formal, but unlike buil ding codes, they did not have a private industry partner that encouraged the development of universal standards. In zoning, local lawmakers retained the control to shape the community in response to local and hyperlocal needs. With this kind of power able to be so finely attuned to local policy preferences, zoning and land use regulation have been used for both innovation and exclusion. Rules on lot and home sizes systematically exclude groups of people by income and race, and battles over what goes where i n a city are the origin of NIMBY (Not In My Backyard), a term that has entered everyday lexicon to pejoratively describe those 40 prioritizing individual needs over those of the larger community (Dear 1992). 13 Currently, the use of zoning to reinforce inequity and limit the supply of affordable housing has even traditional proponents of local control and urban - led policy advocating for states to preempt local authority in order to achieve better outcomes and protect the rights of those who do not own property ( Infranca 2019). At the same time, local control has allowed cities to embrace green building practices, smart growth principles (transfer of development rights, urban growth boundaries, etc.), and incentives for other local priorities (affordable housing, architectural look, etc.) that might not appeal to other communities. Yet even with this history of both harmful externalities and innovation that pushes the boundaries of statewide policy preferences, states have for the most part stayed away from preempt ing local land use regulations and it has been up to the federal government and courts to intervene. Cities retain an enormous amount of power over land use, so long as their codes meet the basic standards set in U.S. legal precedents (Bronin 2008). In par t, land use is connected to the primary source of local government revenue, the property tax, so an encroachment by states on how the property is used and slated for development is not only a limit on local authority but on local revenue. That, and the ide a that individuals should have access to the elected leaders making decisions about their property helps maintain local 13 See Wilson (2019) for a history of racism and zoning laws. 41 rarely do challenges of local power ever make it t o court. Instead, the locality of land (Bronin 2008, 240). The state role in land use is primarily to enact statutes that enable zoning (or land use governance) author ity for local governments. All of these state laws Zoning Enabling Act, which defines broad classes of property and components that should be contained in local ordinances b ut specifically delegates the day - to - day governance, details, and implementation to local communities (Advisory Committee on Zoning 1926; Bronin 2008). Short - term rentals sit at the crux of all of these policies, where states define broad codes and categor ies of property for purposes of taxation and safety, and cities have authority over the detailed regulation and enforcement of land use and building safety. With this mix of power, states like Arizona and Indiana that have preempted local regulation of sho rt - term rentals (or states that have proposed such legislation like Iowa), amend the definition of residential property to permit short - term rental as a use by right. Short - term rentals themselves are defined in state law separately as transient lodging or other categories distinct from hotels, which allows them to be taxed under the law (an arrangement Airbnb and other companies have agreed to) but exempts them from the more stringent safety and building requirements that apply under 42 building codes to hote ls and other formal places of lodging. Cities, on the other hand, create policy that balances the competing constituencies and interests that characterize many local conflicts: property owners asserting the right to use their property in whatever way they choose; neighbors complaining about increased noise, parking issues, and safety; residents raising concerns about housing supply, loss of rights and quality of community for permanent residents; and industry representatives seeking new ways to support both economic growth and tax equity between competitors. Developing A Framework The rise of the sharing economy and its interaction with state and local institutions presents an ideal opportunity to investigate how states and cities negotiate policymaking auth ority. The new services are complex and span several technical policy areas, which makes government response and governance difficult and may hide policy implications from interested publics (Teles 2013). The sharing economy is also disruptive, which provi des an opportunity to view how this disruption impacts policy subsystems (May, Sapotichne, and Workman 2009) across the federalist structure and influences state and local action. To address these questions, the newness of the sharing economy is an advanta ge, because its emergence and the response of cities and states can be observed and documented in real time. This means that data, both contextual and quantitative, are the 43 policy process and capture of the surrounding dynamics. It is also an issue area that has yet to be sorted into current partisan political positions. This allows us to observe how issues become part of partisan dialogues and to test hypotheses regardin g preemption, partisanship, and local control. To develop a robust understanding of both the sharing economy and the surrounding policy space, the following chapters will use multiple methods. The information presented in this chapter identifies key charac teristics of ride - sharing and short - term rentals that create incentives and politics for companies, users, interested parties, and governments as the sharing economy enters a community. Building from this understanding of incentives and the nature of the i ssues around ride - sharing and short - term rentals, the chapter then documents the historical policy context of major policy areas surrounding these sharing economy services. This qualitative and historical work develops hypotheses around how issue politics may function around ride - sharing and short - term rentals, and it is used to construct the concept functional fit, as presented in Table 2 - 3. Chapter 3 provides a quantitative description and a test of the political and institutional explanations for policy incorporation, including issue politics, state legal institutions, and state - local policy institutions. The pieces of the process that can be reliably measured are used in this chapter to test conventional and sharing - economy - specific explanations for poli cy adoption. This serves to examine whether the state - level 44 policy activity around ride - sharing and short - term rentals is different from other issue areas, if the activity surrounding both services is similar (i.e. the sharing economy is thought of as a si ngle issue) or if it differs by service (i.e. ride - sharing and short - term rentals are different). The results of this analysis, along with the comparison of functional fit introduced in this chapter, suggest that policymakers do consider the issues differe ntly, so Chapter 4 uses qualitative work to further investigate the mechanisms and underlying processes around the incorporation of ride - sharing and short - term rentals into existing state and local regulatory powers. In Chapter 4, state - level legislation i s compared and coded for content to understand the similarities and differences between state approaches to both regulation of the service and local authority to establish regulation. This is supplemented by case studies that provide detailed information a bout the policy dynamics surrounding policy adoption. Finally, Chapter 5 summarizes the lessons from the analyses and proposes future work. Together, what follows addresses how the divide in functional responsibilities created by federalism creates differe nt policy outcomes for ride - sharing and short - term rentals. 45 Table 2 - 1: Sharing Economy Services and Major Companies Service Definition Companies Ride - Sharing Companies connect drivers with excess vehicle capacity to passengers in need of a ride via smartphone application Uber Lyft Sidecar Carma BlaBlaCar (Europe) Cabify (Spain, Portugal, Latin America) Didi (China) Ola (India) Short - Term Rentals Companies connect property owners with available vacancy to individuals in need of lodging via smartphone application Airbnb HomeAway VRBO FlipKey Wimdu 46 Table 2 - 2: Similarities and Differences between Ride - Sharing and Short - Term Rentals Similarities Differences Both introduced technology that created new access to existing market activities (vacation rentals and hailing a car with driver) The nature of the asset in question (real estate vs. automobile) changes how the externalities accrue: Ride - sharing offers individual convenience to users at locations that are dynamic and contin ually changing, so a city may face citywide or general problems with traffic (double - parking, traffic blocking, more car emission, etc.) or complaints from other transportation operators (taxi drivers, public transit agencies, etc.) but they will not accum ulate in a single space, location, or car to drive for a ride - sharing company does not, in and of itself, diminish the value of cars or property of others nearby. Short - Term Rental depends on a fixed property an d neighborhood characteristics, so issues (transitory population, noise, increase in housing prices, etc.) accumulate in specific places and can affect neighborhood character and the value of nearby properties. Both start operating in a city without asking the government for permission the technology becomes available to users and the services build a user base before they are visible to local governments. Uber is active in state - level lobbying and has from the beginning actively sought favo rable regulation. Airbnb has been more selective about its policy battles. Both have used apps and user data to distribute political messaging and encourage users to become politically active (contact legislators, protest, etc.) Parties do not have clear positions on the issues yet (i.e. there is no Republican or Democratic p osition on ride - sharing or short - term rentals) 47 Table 2 - 3: State and Local Characteristics of Sharing Economy Issues State Characteristics Local Characteristics Ride - Sharing Insurance : regular auto insurance does not cover ride - sharing activities, states regulate insurance Licensing : states often license occupations and transportation carriers, including taxi drivers and taxi companies Taxi Regulation : cities regulate taxis, including company permits, prices, vehicle permits, medallions, proof of competitiveness, and driver background checks Traffic Management : cities have traffic ordinances on stopping, standing, parking, street use, etc. Economic Development : cities competing for jobs and investment seek to attract residents and investors; technology investment and partnerships are a signal that a city is innovative and ready for new investment Short - Term Rentals Taxes : hotel - motel (occupancy) taxes are largely collected at the state level Classes of Property : states broadly define classes of property (lodging, residential, commercial, etc.) through statute for purposes of planning (planning and zoning acts that set general standards for local action) or taxation (types of properties defined as lodging) Building Safety : states often establish statewide standards for property construction, fire safety, etc. that apply to different types of property (e.g. requiring fire suppression systems and marked exits.) Land Use : in every state, cities are granted broad latitude to manage ho w land is used through planning, zoning, and other regulations which govern things like: occupancy, rental regulations, number of units, types of permitted use, impact fees, building requirements, and adjudicating disputes Economic Development : cities can use short - term rentals as a piece of tourism and destination - related economic development strategies 48 WORKS CITED 49 WORKS CITED https://www.govinfo.gov/content/pkg/GOVPUB - C13 - 18b3b6e632119b6d94779f558b9d3873/pdf/GOVPUB - C13 - 18b3b6e63211 9b6d94779f558b9d3873.pdf . http://www.aetv.com/shows/parking - wars . 2018. https://www.airbnb.com/help/article/961/what - regulations - apply - to - my - city . . 2019 Work? - http://www.airbnb.com/help/article/1036 . July 8, 2019. http://www.airbnb.com/help/article/2303 . AT THE INN? 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May 17, 2017. 57 http://www.mystatesman.com/news/transportation/with - passage - ride - hailing - bill - when - will - uber - return - austin/xHxo3SDxieu7u5uPKCcxPI/ . ces: What U.S. The Journal of Politics 73 (4): 965 79. https://doi.org/10.1017/S0022381611000855 . tions Between State and Local The Oxford Handbook of State and Local Government . Economic Education. May 21, 2019. http://fee.org/articles/the - racist - history - of - zoning - laws/ . Bloomberg , July 24, 2018. http://www.bloomberg.com/ news/articles/2018 - 07 - 24/airbnb - s - nyc - bookings - could - be - cut - in - half - by - new - rule . 58 CHAPTER 3. The Sharing Economy i n the States The two major sharing economy services ride - sharing and short - term rentals are in many ways hyper - individual and hyper - local . Individuals use the technology platforms, largely Uber, Lyft and Airbnb, to find other individuals interested in sharing an asset. For the services to reach capacity, they need the density of people and assets in cities in order to match these specific m icro - networks of individual supply and demand and arrange them into a larger comprehensive service. There is little utility in connecting a person in Chicago who needs a ride between the Andersonville and Edgewater neighborhoods to a person in South Shore drive. Technology would offer no advantage over calling or hailing a cab or taking the companies have used their status as remotely - operated technology services, rather than brick - and - mortar business with a local presence, to enter markets without asking local governments for permission (Shahani 2014; Gallagher 2017; Yglesias 2017). Then, as the services reach capacity and the associated benef its and problems begin to aggregate at the local level, cities naturally respond. Some, like Indianapolis, embrace the services and make it easier for city residents to drive or ride with Uber and use their homes to host Airbnb users. Other cities, like Ph iladelphia or San Diego, regulate the services consistent with existing taxi and zoning ordinances so sharing economy uses conform to local regulations and established standards of use and behavior (Hirshon et al. 2015). 59 Given the localness necessary for t he sharing economy to exist and its associated local consequences, why have so many states stepped in and decided to regulate ride - sharing and short - term rentals? This chapter will examine why and under what conditions states choose to regulate sharing eco nomy services and what that says about state policy choices with respect to local authority more generally. What Does Sharing Economy Regulation Look Like in the States? Short - Term Rentals Airbnb famously began in 2007 when roommates in San Francisco adver tised air mattresses in their apartment as a place to stay for conference - goers without a hotel room (Gallagher 2017). The roommates eventually developed electronic infrastructure that allowed the service to expand across the world, with an ethos of home - s haring as an experience that connects travelers in a more personal way to communities. 14 As the service expanded, vacation - destination states were the first to grapple with how to regulate short - term rentals. In 2011, the state of Florida prohibited local g overnments from regulating short - term rentals differently than other residential property and also from banning short - term rentals altogether. The state law grandfathered in local ordinances in place before June 1, 2011, but other cities were subject to th e new state 14 Ther e are other short - term rental facilitators, such as Home Away (which launched its website HomeAway.com pre - Airbnb, in 2006, and was acquired by Expedia in 2015) and VRBO (Vacation Rental By Owner, owned by Home Away), and more listing sites are entering th e market, including sites that aggregate listings from multiple short - term rental sites, however Airbnb is the largest corporate presence and the only company with widespread lobbying presence at the state level. 60 prohibitions. Hawaii was also early to consider legislation related to short - term rentals, 15 passing a law in 2012 that required such rentals to have an on - site local contact whose name was available to property/homeowner associations or other r elevant building management. Florida has since slightly loosened its restrictions on local policymakers, and Hawaii also made minor changes to clarify its law in 2015. The - ter m rentals and also reflect the very different structures of local government: Florida has 412 mostly council - manager cities (Florida League of Cities 2018), and Hawaii has 4 counties and no municipal governments (State of Hawaii 2019). In Florida, cities h Planning Act, which specifically grants municipalities and counties the authority to plan for development and growth, adopt and amend plans to guide that development and growth, adopt regulations on land development in accordance with plans, and establish administrative systems to support authority under the act (State of Florida - related activity under this system of local planning for decades , including developing ordinances with respect to short - term rentals as early as 2003, well before technology existed to facilitate peer - to - peer activity (Hackett 2011). Two forces helped push short - 15 Called transient accommodations in state legislative terminology. 61 state and local policy agenda s around 2010: owners with property they were unable to sell during the recession turned to short - term rental as a way to relieve financial pressure (Alvarez and Van Natta 2011; Hackett 2011) and technology lowered the barrier to entry for homeowners to ac cess short - term rental customers. People had property they needed to monetize, and Airbnb made it so they no longer needed to hire an agency or place multiple ads to attract renters. Discussions in local governments across the state at the time centered ar ound issues like parking and noise, and whether owners were operating a business (Guinta 2010), similar to many other debates about neighborhood land use in any city across the country. Given these routine local zoning interactions, the common nature of va cation rentals in cities, and the state law granting municipalities the right to regulate land use, why did the state suddenly decide to prevent local communities from regulating short - term rentals? At the same time, Hawaii was facing issues similar to tho se in Florida: a state heavily shaped by tourism was dealing with a changing market around vacation accommodations. Hawaii, however, with only 4 sub - state governments, considered a different type of regulation, as a preemption of local regulation of the re ntals would have little practical value. Maui County had a vacation rental ordinance in place that permitted non - owner - occupied short - term or vacation rentals in certain zones and under certain circumstances (Halas 2012), and the state law did not respond specifically to that ordinance. Instead, Hawaii clarified tax payments due from short - term rentals 62 and established a requirement that there be a local contact on the island available for guests, since many property owners live on the mainland (Hawaii Life 2012). The state then debated new legislation every year through 2018, adopting revisions to its 2012 law in 2015 that incorporated short - term rentals into the definition of transient accommodations and added civil penalties for violating the law. The legi slature also passed a bill that would have allowed hosting platforms to act as tax collection agents on behalf of property owners, however it was vetoed by the Governor. In contrast to reased accessibility of short - term rental to property owners into existing systems of taxation and vacation rentals. Even in the early stages of technology - enabled expansion of short - term rentals, Florida and Hawaii honed in on the key issues in policy dis cussion: who has authority to regulate, and how should economic activity be captured (and by whom)? Florida and Hawaii were the first states to tackle short - term rental policy at the state level, however more states have joined their ranks as the years hav e progressed. Figure 1 shows the year of adoption for each of the twenty - one states that adopted a state - level short - term rental statute between 2011 and 2018. 16 Florida revised its policy before many states had even considered the topic at the state level, acting in 2011, then 16 All tables compiled using Hlavac (2018), figures with Wickham (2016) and Chang (2014). 63 revision. States have increasingly deliberated what to do about short - term rentals from 2015, when 14 states considered policy (with California, Hawaii, a nd Montana adopting), through 2018, when half of the 50 states had policy under discussion. 17 State actions have ranged from statutes that preempt local policymaking authority and establish short - term rental use as part of property rights (e.g. Florida, Ari zona, Idaho, Utah, and Indiana) to statutes that expand existing legal definitions of lodging to include short - term rentals for tax purposes (e.g. Oregon, Connecticut, Iowa, Montana, Washington, and New Jersey) to statutes that explicitly permit local gove rnments to regulate short - term rentals (e.g. Nevada, New Hampshire). Model legislation, drafted by the Goldwater Institute to prevent local governments from restricting the right of property owners to use property as a short - term rental (Goldwater Institut e 2016), has been adopted with slight modifications in 2 states: Arizona and Idaho. Preemption legislation was introduced in several states (some based on the Goldwater Institute model, some not), but these types of policies have not been uniformly or imme diately adopted and the bills have rarely gotten out of committee. At the state level, some states with already strong vacation rental markets acted first, other states followed with regulation to include this new economic activity in tax structures and/or placed its 17 The data in this analysis only includes regulations enacted by statute. It does not include the voluntary tax agreements states reached with providers such as Airbnb to collect hotel - motel or other occupancy taxes via administrative negotiation. 64 - local authority, and most states took their time to act. Ride - Sharing 18 In contrast to short - term rental policy, states have been quicker to embrace the role of ride - sharing regulators. From 2014, when Colorado adopted the first state - level ride - sharing law, through 2018, all states but Oregon had adopted some kind of regulation of the services. 19 As detailed in Figure 2, which shows the year each state first established ride - sharing laws, states ac ted quickly. By the end of 2015, one year after Colorado and California acted, 26 states had adopted statewide regulations and another 14 states acted before the end of 2016. How did ride - sharing go from being invisible to states at the beginning of 2014 t o nearly universal regulation by 2018? The strategy of three groups of actors offers potential explanations: Uber actively sought state regulation (Sundararajan 2016), insurance companies got involved, and the American Legislative Exchange Council (ALEC) a nd National Council of Insurance Legislators (NCOIL) helped facilitate and distribute model policy language (American 18 - - sharing as app - based services used to book shuttles. This document uses rid e - sharing to refer to services like Uber and Lyft, as it is the more commonly used term to describe the companies. In statute and policy discussion, the companies are also classified as transportation network companies (TNC). 19 As of this writing, Oregon c ontinues to debate state - level regulations, with one bill in the current session supporting preemption of local ordinances ( HB 3023 ) and another establishing the right of m unicipalities to regulate ( HB 3379 ). 65 adopting the first state ride - sharing regulations offe red an example for those that followed. Lyft and Uber entered the Colorado market in September 2013, and by June - sharing (Vuong 2014). Then - Governor Hickenlooper promoted the law, which was largely supported b y Uber and Lyft, as the state leading by encouraging innovation and removing unnecessary barriers to doing supporting language that created insurance requirements for dif ferent phases of ride - sharing operation. Further, in spite of being championed by a Democratic governor and passed by a Democrat - reflects the language of a model bill for ride - sharing regulatio n distributed by ALEC, 20 With a few exceptions this process repeated itself in the other states: over the objections of taxi companies and some cities, states adopted similar regulations that: a) established states as exclusive regulators of ride - sharing (transportation network) companies; b) defined ride - sharing services as separate from 20 Text comparison of the Colorado law ( SB 125 of 2014 ) and reveals that other than mino 66 other transportation providers; and c) created tiered insurance requirements for drivers and a minimal liability insurance requirement for the ride - sharing company. 21 Investigating Explanations for State Policy Action Short - term rentals and ride - sharing took different paths through the state willingness to se t sharing economy rules. The empirical analysis presented in this section serves three purposes. First, to understand the state - local relationship, it is critical to understand the state and local policy processes separately. These separate analyses identi fy potential mechanisms and key variables at each level of government that influence the relationship as a whole. Second, a state - level analysis serves as a check on the assumption that this is a state - local process. Red States - Blue Cities, for example, as sumes that state policy action exists as a response to the threat of cities taking opposing action. In this case, we know that states controlled by both political parties have acted to restrict local authority, so it is possible that this has little to do with cities, but instead states, as sovereign governments, are just defining their authority over new issues as they arise. Finally, comparing the state responses to ride - sharing and short - term rentals can offer evidence for the state vs. local policy doma in theories presented in Chapter 1. 21 More detailed case studies and information on state legi slation are presented in the next chapter. 67 Political Dynamics: Partisanship and Interested Parties The most popular current perspective on the relationship between states and cities is that Republican - dominated Red States are using state - level power to prevent De mocrat - dominated Blue Cities from enacting progressive policies at the local level. This conceptualization of the partisan divide between states and cities appeared after the reelection of George W. Bush, in Living Blue in Red States (Starkey 2007) and tho ugh it was only used intermittently (usually around elections) until 2014, 22 since then it has developed more formally into a shorthand for sub - national political polarization. The phrase and its current use imply that the traditional partisan positions hav e switched: Republicans are no longer the party of local control. There is some anecdotal evidence for this, both broadly, and in state - level sharing economy policy. ALEC, the American Legislative Exchange Council, is organized around the principle of retu rning power to states and has aligned corporate and Republican interests, in part by drafting and circulating model legislation (Hertel - - level is embedded even in its network of loc al officials (American City County Exchange), 22 - 2019 68 ty Exchange 2019). Although this Republican - aligned organization is moving away from the idea of local control to promote state - level regulation, the national Republican Party platform still emphasizes the importance of local control, specifically with res pect to education and zoning decisions (Republican Party 2016). On sharing economy - specific legislation, the conservative - leaning Goldwater Institute has distributed a model home - sharing bill that preempts local regulatory authority over short - term rentals (Goldwater Institute 2016), which, after its - term rentals (American Legislative Exchange Council 2016). As the i dea of preemption as a political strategy has emerged in the public sp here, empirical work is beginning to follow. Fowler and Witt (2019) and Flavin and Shufeldt (2019) take a multi - issue approach to examining state preemption, and sharing economy policies (ride - sharing and short - term rentals) are included as part of the iss ue groups in both analyses. Fowler and Witt (2019) find that states with higher average percentages of Republican legislators are more likely to preempt local authority on a greater number of issues. Flavin and Shufeldt (2019) find evidence that Republican - controlled states are more likely to preempt, and they include a measure of ALEC overall preemption or sharing economy preemption specifically. 69 Given this, at the sta te level, if the positions of the parties on local control have flipped, regardless of competition between states and cities, it is expected that states with Republican - controlled governments would be more likely to adopt state - level regulations than those with Democrat - controlled governments, irrespective of the policy issue. For both the short - term rental and ride - sharing models, Partisan Control is measured using the Ranney Index (Ranney 1976), as updated by Klarner (2013) through 2010 and then extended through 2018 using data from the National Council of State Legislatures (National Council of State Legislatures 2019). 23 partisan control in a given year on a range from Republican Unity (0) to Democrat Unity (1). Ride - sharing and short - term rentals also have private - sector political constituencies invested in different state policy outcomes. For each model, Lobbying indicates whether a company (Uber and Lyft for ride - sharing, Airbnb for short - term rentals) was registered to lobby or had an agent registered to lobby in a state in a given year (1) or was not registered (0). Data were collected from a review of each individual 24 States where a corporation is registered to lobby 23 Data for Partisan Control , Number of Municipalities , and Legislative Professionalism were drawn from Jordan and Grossmann (2017), verified from the ori ginal sources, and extended through the time period of analysis either by using the original methodology or by carrying forward variables from previous years, as noted in the description of each individual variable. 24 The review found only Uber, Airbnb, an d Lyft as active participants in lobbying state legislatures during the time period of analysis. 70 the state legislatu re are expected to be more likely to adopt policy regulating the service. In addition to corporate - specific interests, there are other groups with an economic interest in sharing economy policy. For the short - term rental model, the percentage of the workin g Population Employed in Real Estate represents potential constituents with interest in developing and managing properties for use as short - term rentals (i.e. potential political allies for Airbnb.) States with a larger percentage of the population employe d in real estate are expected to be more likely to adopt short - term rental policy. The cases of Hawaii and Florida suggest that Tourism States may be more likely to adopt short - term rental policy, so a variable was constructed from data collected by the US Travel Association (2019) on the impact of tourism to state economies. California, New York, Florida, Texas, Illinois, Georgia, Nevada, and Hawaii are coded high tourism states (1), based on having annual tourism spending over $25 billion and annual state tourism - generated tax receipts greater than $4 billion. The remaining states are coded as zero (0). 25 Finally, states that have acted to preempt rent control have some history of preempting local authority on housing and land use, so the variable Rent Control Preemption , which takes a value of 1 in states that have preempted local rent control ordinances and 0 in states that have no law in place, is 25 Only the 2017 data are available to non - data, but this is not a measure that is expected to change much over the time of this analysis. 71 short - term rent al policy. 26 In the ride - sharing model, the percent of working Population Employed in Transportation represents potential opposition to the state implementing ride - sharing regulations, so states with stronger existing transportation industries are expected to be less likely to adopt ride - sharing policy. The Percentage of Workers Unionized in a state is also included in the ride - sharing model, as unionized workers represent another group of potential political opposition to ride - sharing regulations and taxi a ssociations and other unions have opposed ride - sharing laws that weaken worker protections (Joyce 2016). The model legislation proposed by NCOIL and ALEC and all of the state - enacted laws establish mandatory insurance requirements for ride - sharing drivers, so the percent of working Population Employed in Finance and Insurance is included in the model to represent a source of political support for state - level regulations. All annual employment numbers were drawn from the American Community Survey using Recht (2019), and unionization rates were drawn from the Bureau of Labor Statistics using Eberwein (2019). 26 A categorical variable that combined states that preempt rent control, states that do not preempt but do not have cities with local rent control ordinances, and states that have cities with local ordinances in place was a lso tested, and the results of all models remain the same. 72 State Institutions: State vs. Local Autonomy fundamental opinion that cities are creatures of the state, and regardless of the differences in how states construe that ruling, every state enacts rules that set the bounds of local power. Frug and Barron (2008) call the set of state policies that construct the local decision - o monitor and hold cities accountable. States that grant municipalities more policymaking authority are expected to be less likely to adopt state - level regulations on ride - sharing or short - term rentals, while states that retain more control over local deci sion - making are expected to be more likely to act. For this analysis, the key measures of the overall local autonomy in a state are Home Rule , which represents the general level of authority granted to municipalities, and Tax and Expenditure Limitation (TE L) which represents the permission granted to local governments to raise revenue to support local policy priorities. Home Rule is measured using the Wolman et al. (2008) index, which is a relative measure of the autonomy of local governments in a state. Th e index contains factors of equally - weighted dimensions representing a broad range of local autonomy: the importance of local government in a state, discretion available to local government, and the diversity of revenue sources available to municipalities. As these characteristics are 73 largely stable, the index does not change over the time period under analysis. 27 Scores potentially range from - 1 to 1, with higher values indicating more state control and lower values more local control. While the Wolman et a l. (2008) index contains local fiscal autonomy as a component, Tax and Expenditure Limitations are perhaps the most popular form of state control or preemption of local authority, so they are included as a separate olicymaking. Some forms of limitation on local tax and spending existed before, but the tax revolt of the 1970s produced a wave of state - level, often voter - led, limitations on the ability of cities to raise revenue (Skidmore 1999). These limitations can be strict, even in states that grant cities a wide range of the policy choices of local governments and are more punitive to urban core areas (Mullins 2004). TEL is measur ed using the Amiel, Deller, and Stallmann (2009) index, which was extended through 2018 using data from Wang (2018). The index categorizes the nature of the TEL (statutory or constitutional), how it was approved, whether growth is restricted, how it can be overridden, the type of limitation, and whether there are exemptions. Together, Home Rule and TEL represent two fundamental but distinct components of the city structures in a state ( = - .18). 27 Several categorical versions of this measure and its components were tested in the analysis, and the results were consistent across all models. 74 State Policy as a Response to Local Action Cities, as the lo cal nodes of sharing economy, adopt policies to address local concerns. That may mean loosening rules to encourage ride - sharing or short - term rental, or it may mean cities cracking down on the services. Either way, cities taking action may suggest to the s tate that it is time for it to do something about the services, either to codify local policies statewide or to establish its authority (Shipan and Volden 2006). Further, since sharing economy enters in large cities first, if Republican states are reacting to restrain out - of - control Democratic cities by enacting state policy, it is expected that the largest city acting to regulate the sharing economy would make states more likely to act. Thus, Largest City Policy and an interactive term Largest City x Parti san Control are included in ride - sharing and short - term rental models, with the expectation that if states are reacting to local policy alone, they will be more likely to adopt state regulations, and if they are reacting conditional on partisanship, states with unified Republican control will be more likely to adopt policy once the largest city in the state acts. 28 The Largest City variable is coded 0 in each year that the city has not 28 The author has collected data on all local ride - s December 2018. Following Shipan and Volden (2006), several variables summarizing local policy activity were created and tested in ride - sharing models, including number and proportion of cities with a policy (ann ual adoption and cumulative), cumulative percentage of cities with a policy, and percentage of cities in the state with Uber service. None of the variables performed better than others in the model, and some created proportional hazards issues, so for cons istency between the ride - sharing and short - term rental models, policy in the largest city was used in each analysis. 75 adopted a policy and 1 from the first year the city adopted a policy thro ugh the end of the series. State Characteristics and Demographics willingness to adopt sharing economy policy regulation: Legislative Professionalism , Population , and Number of Mu nicipalities. More professionalized legislatures are less likely to adopt model legislation (Hertel - Fernandez 2014; Jansa, Hansen, and Gray 2019), and thus, since influential groups active at the state level have developed model legislation, less professio nalized states are expected to be more likely to adopt sharing economy regulations. More populous states have more urbanized areas and more constituents who are users of or potentially benefit from the introduction of sharing economy services. The services , which are concentrated in urban areas, enter more populous states first, and thus more populated states are expected to be more likely to adopt state - level regulation. Legislative professionalism is measured using the updated Squire Index (Squire 2017), and population estimates are taken from the Annual Community Survey. 29 States with more municipalities do not necessarily allow those municipalities more authority to conduct their own affairs, but more municipalities in a state presents 29 Data compiled for analysis using Recht (2019). 76 two important condi tions for this analysis: more individual communities potentially affected by a statewide regulation, and more places the sharing economy is likely to enter the market. This could affect regulation in either direction, with communities (and their larger num ber of elected officials) better able to convince state lawmakers that they should retain the right to govern sharing economy services locally, or, state officials may find convincing the industry argument that a single statewide set of rules is better for economic outcomes. Values for each state are U.S. Census estimates, compiled and updated by Sorens, Muedini, and Ruger (2008) and carried forward through 2018. Table 3 - 1 summarizes the hypotheses and expected direction of each variable, and summary statis tics are in Table 3 - 2. Analysis: State Sharing Economy Policy Adoption Short - Term Rentals The dependent variable in the state - level short - term rental policy adoption models is whether a state adopted a short - term rental statute in a given year from 2011 to 2018. Data were collected via Legiscan search of all legislation in the 50 states from - T he search returned 1,309 results, which were read and if relevant to short - term rentals, coded for legislative outcome. In the following analyses, legislation signed into law by the governor is coded as policy adoption (1), and all other outcomes (no bill considered, 77 bill introduced, or bill vetoed) are coded as no policy adoption (0). Twenty - one states adopted short - term rental regulations via statute through December 2018, beginning with Florida in 2011. Figure 1 shows policy adoption by state and year. T hree logit models with year fixed effects were estimated: the base model with state - level independent variables (model 1), the base model plus Largest City (model 2), and the base model plus the interactive term Largest City x Partisan Control . P - values ar e estimated using state cluster bootstrapped t - statistics, following Esarey and Menger (2019). For the policy adoption models, results show no statistically significant predictors of state policy adoption at = 0.05. At = 0.1, states with a larger per centage of population employed in real estate are more likely to adopt state - level short term rental policy. In Models 2 and 3, which more directly test the red state - blue city hypothesis, the results do not change substantially between the models, and the re is no evidence, in this case, to support the red state - blue city hypothesis. Full results for the policy adoption models are presented in Table 3 - 3. 30 The policy adoption models in Table 3 - 3 combine all types of state action together, so, to investigate whether there is a difference between states that took some 30 Models with the same dependent and independent variables were also estimated using event history analysis, as is common for studies of policy adoption. Due to the heavily tied nature of the data event history analysis is a more limited model for this part icular data and issue area, but the models are included as appendices. 78 sort of action (e.g. to incorporate short - term rentals into tax code) and those that limit local authority, the same models are estimated with a different dependent variable. The Preemption depend ent variable takes the value of 1 if a state has acted to limit local authority over short - term rentals. In this case, preemption generally does not ban local governments from regulating short - term rentals altogether. Preemption of short - term rentals consi sts of three potential state actions: a ban on local bans of short - term rentals, a requirement that short - term rentals be allowed by right in residential zones, or a ban on local regulation. This leaves 9 states which took preemptive regulatory action on s hort - term rentals between 2011 and 2018. 31 Rent Control Preemption is excluded from the preemption models, as all 9 preempting states are among the 33 states that preempt local rent control. In addition, there are insufficient observations across the intera ction ( Policy in Largest City x Partisan Control ) to provide reliable estimates for the third model, as each state that preempted local authority and had a policy in its largest city was also Republican - controlled. The results of the two preemption models are presented in Table 3 - 4. Similar to the policy adoption models, none of the variables have authority, with the exception of legislative professionalism. States wi th higher levels of 31 To define preemption in the broadest terms possible, Wisconsin and New Hampshire are coded as preempting states, though their restrictions on local action are not as broad as the othe r states. The results of the model do not change substantially if they are not coded as preempting states. 79 legislative professionalism are substantially less likely to preempt local authority to regulate short - term rentals than less professionalized states. Ride - Sharing The dependent variable in all models of state - level ride - sharing policy adoption is whether a state adopted a ride - sharing statute in a given year from 2014 to 2018. Goodin and Moran (2017) data on state ride - sharing policies through 2017 were verified and extended through 2018 via Legiscan search of all legislation in the 50 states that - following analyses, legislation signed into law by the governor is coded as policy adoption (1), and all other outcomes (no bill considered, bill introduced, or bill vetoed) are coded as no policy adoption (0). All states except Oregon had adopted a ride - sharing policy via statute through December 2018, beginning with Colorado and California in 2014. As in the short - term rental analyses, three logit models with year f ixed effects were estimated: the base model with state - level independent variables (Model 1), the base model plus Largest City (Model 2), and the base model plus the interactive term Largest City x Partisan Control (Model 3). 32 Results of all models are pre sented in Table 3 - 32 As with short - term rentals event history models were also estimated and are included as an appendix. 80 with ride - sharing policy adoption across all 3 models. A state with 6.8% of its workforce unionized (1st quartile) had a predicted probability of adopt ing a policy of 0.06 in 2014, probability jumped again to 0.93. In contrast, a state with 1 in 5 of its workers unionized (between the 3rd quartile and the maximum) had a pred icted probability of adopting ride - sharing policy of next to zero (0.006) in 2014, 0.20 in 2014, and 0.57 in 2015. Although all states except Oregon eventually adopted ride - sharing legislation, states with higher rates of unionization moved slower than sta tes without unionized workforces. Discussion of Results Individually, the models offer limited support for hypotheses that existing levels of local autonomy and issue - specific political activity are predictors of states taking on an issue rather than leavi ng it to cities. The balance of how those factors align in an issue area is dynamic: for ride - - level policy change, in an area not as clearly defined as a space of local authority, led to nearly universal state po licy within 4 years, and no systemic pattern of empirical evidence emerges regarding which states were more likely to adopt ride - sharing policies first. For short - term rentals, a space more traditionally defined as locally controlled (zoning and property u se), states with a larger share of working population employed in real estate industries were more likely to adopt state - level regulations, and when they did, 81 they took more time to do so, allowing more space for negotiation and local interests to be repre sented in the process. In a closer look at states that preempted local authority, the states with less professional legislatures were more likely to adopt policy, supporting the hypothesis that professional legislatures are less susceptible to model legisl ation. In spite of current popular conversation and the prominence of the idea that red states use power to constrain blue cities from enacting locally - preferred policies, in these two policy areas, there is no evidence that state - level policy action is mo tivated solely by party or in response to local action. In some ways, this is an obvious point not all states with Republican - controlled governments have identical policies in any areas. But even taking the idea of Red States - Blue Cities more broadly tha n literally, this analysis suggests that it is too general to explain why states choose to limit local authority. Partisanship alone is not an explanation for state decisions about local power. Partisanship may manifest itself through state structures (see ALEC facilitating discussions between insurance companies and Uber, alignment of industry groups and lf, it is not enough to explain state policy choices. All of the models are limited by data: there are only 50 states and each state essentially adopted one policy in each area. Boehmke (2009) outlines alternative 82 approaches for more nuanced modeling for policy adoption, including modeling adoption of differe nt policy components. That approach is limited in the case of ride - sharing because there is little difference between the policies. Separating the components of short - term rental is a bit easier, since state legislatures have adopted more varied policies, however with only 9 states having adopted preemption policies, some hypotheses cannot be tested due to data issues. Yet the lack of strong empirical relationships does not mean that the dynamics surrounding sharing economy policy adoption cannot be identif ied. Complex policy processes are difficult to measure with precision, and there is no way to add data to a U.S. - based analysis, but those limitations on quantitative analysis present opportunity to supplement the empirical work with qualitative analysis. Thus, the following chapter uses case studies to illustrate and compare the details of sharing economy policy adoption in the states and provide more context to these results. 83 84 85 Table 3 - 1: Summary of Hypotheses Variable Expected Direction Summary of Hypothesis Party Control - Unified Republican states more likely to adopt Legislative Professionalism - More professionalized states less likely to adopt Home Rule + Lobbying activity in state, more likely to adopt TEL + Higher limits, more likely to adopt Corporate Lobbying + Lobbying activity in state, more likely to adopt Strength, Allied Industries + Larger presence of economic partners, more likely to adopt Strength, Opposition - Stronger presence of economic opposition, less likely to adopt Number of Municipalities +/ - Either creates need for state policy or lobbying strength Population +/ - Either direction Largest City Has Policy + Largest city has policy, state more likely to adopt Largest City x Partisan Control + Largest city has policy, unified Republican states more likely to adopt 86 Table 3 - 2: Summary Statistics Statistic N Mean St. Dev. Min Max Number of Municipalities 400 390.29 322.77 1 1,299 Legislative Professionalism 400 0.21 0.11 0.03 0.63 Partisan Control 392 0.38 0.39 0.00 1.00 Tax and Expenditure Limitations 400 16.66 10.30 0 38 Home Rule Index 400 0.0000 0.42 - 0.84 0.98 State Population 400 6,376,749 7,099,657 567,224 39,557,045 % Working Population Unionized 400 11.51 5.19 2.60 26.10 % Working Population Employed, Transportation 400 4.11 0.80 2.20 7.20 % Working Population Employed, Finance and Insurance 400 4.51 1.23 1.70 8.50 % Working Population Employed, Real Estate 400 1.74 0.45 0.90 3.50 State Tourism Level 400 0.16 0.37 0 1 Ride - Sharing Lobbying 400 0.56 0.50 0 1 Short - Term Rental Lobbying 400 0.15 0.36 0 1 87 Table 3 - 3: State Short - Term Rental Policy Adoption Models Dependent variable: Policy Adoption (1) (2) (3) Number of Municipalities - 0.002 - 0.002 - 0.002 (0.001) (0.001) (0.002) Legislative Professionalism - 3.23 - 3.46 - 1.83 (5.16) (5.17) (5.20) Split Partisan Control 0.53 0.49 0.40 (0.64) (0.64) (0.87) Unified Democratic Control 1.05 1.02 0.32 (1.12) (1.11) (1.23) Tax and Expenditure Limitation 0.003 0.0003 - 0.01 (0.04) (0.04) (0.03) Home Rule 1.52 1.59 1.59 (1.08) (1.08) (1.10) Rent Control Preemption 0.43 0.38 0.30 (0.99) (1.01) (0.87) % Working Population Employed, Real Estate 1.87 1.80 1.72 (1.07) (1.03) (1.02) High Tourism State 1.34 1.43 1.49 (1.85) (1.85) (1.63) Short - Term Rental Lobbying 0.82 0.80 0.65 (0.91) (0.93) (0.95) Population (log) 0.50 0.53 0.49 (0.91) (0.68) (0.74) Policy in Largest City 0.35 - 0.09 (0.72) (1.50) Policy in Largest City x Split Partisan Control 0.005 (2.66) Policy in Largest City x Unified Democratic Control 2.94 (14.98) Constant - 15.60 - 15.88 - 14.79 (8.66) (8.79) (9.39) AIC 234.7 236.2 235.1 Observations 392 392 392 Log Likelihood - 98.34 - 98.09 - 95.56 Note: **p < 0.5; ***p < 0.01 88 Table 3 - 4: State Short - Term Rental Preemption Models Dependent variable: Preemption (1) (2) Number of Municipalities - 0.01 - 0.01 (0.004) (0.005) Legislative Professionalism - 37.71** - 36.56 (16.24) (19.06) Split Partisan Control - 1.84 - 2.17 (4.91) (3.69) Unified Democratic Control - 1.28 - 1.25 (2.54) (2.47) Tax and Expenditure Limitation - 0.02 0.02 (0.04) (0.04) Home Rule 2.66 1.97 (2.28) (2.19) % Working Population Employed, Real Estate 4.74 4.62 (4.32) (3.64) High Tourism State - 2.45 - 3.06 (8.19) (5.76) Short - Term Rental Lobbying - 0.65 - 0.44 (1.44) (1.52) Population (log) 4.10 4.32 (2.06) (2.07) Policy in Largest City - 2.16 (1.46) Constant - 66.72** - 69.87** (28.41) (30.17) AIC 113.7 111.2 Observations 392 392 Log Likelihood - 38.87 - 36.61 Note: **p < 0.5; ***p < 0.01 89 Table 3 - 5: State Ride - Sharing Models Dependent variable: Policy Adoption (1) (2) (3) Number of Municipalities 0.0003 0.0003 0.0004 (0.001) (0.001) (0.001) Legislative Professionalism 2.45 2.45 1.66 (7.45) (7.54) (7.82) Split Partisan Control - 0.24 - 0.24 0.26 (0.78) (0.93) (1.03) Unified Democratic Control - 0.75 - 0.75 1.16 (2.87) (3.13) (2.08) Tax and Expenditure Limitation 0.04 0.04 0.05 (0.05) (0.06) (0.05) Home Rule 0.68 0.68 0.35 (1.05) (1.11) (1.22) % Working Population Unionized - 0.18 ** - 0.18 ** - 0.18 ** (0.08) (0.08) (0.09) % Working Population Employed, Transportation 0.15 0.15 0.02 (0.49) (0.54) (0.50) % Working Population Employed, Finance and Insurance - 0.02 - 0.02 - 0.20 (0.35) (0.33) (0.22) TNC Lobbying 0.28 0.28 0.06 (1.08) (1.17) (1.21) Population (log) 0.33 0.33 0.48 (0.77) (0.78) (0.68) Policy in Largest City 0.01 1.10 (1.00) (2.31) Split Partisan Control x Largest City Policy - 1.16 (4.9) Democratic Partisan Control x Largest City Policy - 3.84 (9.72) Constant - 8.20 - 8.19 - 9.89 (12.30) (12.67) (13.05) AIC 162 164 160.9 Observations 245 245 245 Log Likelihood - 65.01 - 65.01 - 61.45 Note: **p < 0.5; ***p < 0.01 90 APPENDIX 91 Table A - 1: State Short - Term Rental Event History Models, Policy Adoption Dependent variable: Time to Policy Adoption (1) (2) (3) Number of Municipalities - 0.002 ** - 0.002 ** - 0.002 ** (0.001) (0.001) (0.001) Legislative Professionalism - 2.38 - 2.42 - 2.32 (1.93) (1.94) (1.93) Split Partisan Control 0.39 0.37 0.31 (0.38) (0.38) (0.48) Unified Democratic Control 0.80 0.79 0.58 (0.49) (0.50) (0.58) Tax and Expenditure Limitation 0.004 0.003 0.002 (0.01) (0.01) (0.01) Home Rule 0.77 0.79 0.81 (0.52) (0.52) (0.53) Rent Control Preemption 0.29 0.28 0.24 (0.39) (0.39) (0.39) % Working Population Employed, Real Estate 0.82 ** 0.80 ** 0.78 (0.40) (0.41) (0.42) High Tourism State 0.75 0.76 0.74 (0.48) (0.49) (0.51) Short - Term Rental Lobbying 0.55 0.56 0.57 (0.33) (0.33) (0.33) Population (log) 0.53 0.54 0.55 (0.30) (0.31) (0.31) Policy in Largest City 0.10 - 0.11 (0.35) (0.57) Policy in Largest City x Split Partisan Control 0.13 (0.76) Policy in Largest City x Unified Democratic Control 0.49 (0.71) AIC 431.7 433.7 437.1 Observations 392 392 392 Log Likelihood - 204.87 - 204.83 - 204.54 Note: **p < 0.5; ***p < 0.01 92 Table A - 2: State Short - Term Rental Event History Models, Preemption Dependent variable: Time to Policy Preemption (1) (2) Number of Municipalities - 0.01 - 0.01 ** (0.003) (0.003) Legislative Professionalism - 25.89 *** - 24.65 *** (8.93) (9.13) Split Partisan Control - 1.13 - 1.42 ** (0.66) (0.70) Unified Democratic Control - 0.77 - 0.96 (1.01) (1.04) Tax and Expenditure Limitation - 0.004 0.02 (0.03) (0.04) Home Rule 1.42 0.95 (1.01) (1.06) % Working Population Employed, Real Estate 2.58 2.38 (1.50) (1.55) High Tourism State - 1.83 - 2.17 (1.19) (1.23) Short - Term Rental Lobbying - 0.23 - 0.07 (0.64) (0.64) Population (log) 2.99 *** 3.10 *** (0.94) (0.97) Policy in Largest City - 1.25 (0.71) AIC 131 129.6 Observations 392 392 Log Likelihood - 55.48 - 53.79 Note: **p < 0.5; ***p < 0.01 93 Table A - 3: State Ride - Sharing Event History Models, Policy Adoption Dependent variable: Time to Policy Adoption (1) (2) (3) Number of Municipalities 0.0001 0.0001 0.0001 (0.0003) (0.0003) (0.0003) Legislative Professionalism 0.70 0.73 0.51 (1.27) (1.27) (1.32) Split Partisan Control - 0.04 - 0.04 0.10 (0.20) (0.20) (0.26) Unified Democratic Control - 0.35 - 0.34 0.13 (0.31) (0.31) (0.43) Tax and Expenditure Limitation 0.01 0.01 0.01 (0.01) (0.01) (0.01) Home Rule 0.13 0.14 0.08 (0.27) (0.27) (0.27) % Working Population Unionized - 0.04 - 0.04 - 0.04 (0.02) (0.02) (0.03) % Working Population Employed, Transportation 0.04 0.04 0.02 (0.11) (0.11) (0.11) % Working Population Employed, Finance and Insurance 0.02 0.02 - 0.02 (0.07) (0.08) (0.08) TNC Lobbying 0.20 0.20 0.18 (0.29) (0.29) (0.29) Population (log) 0.01 0.01 0.04 (0.13) (0.14) (0.14) Policy in Largest City - 0.04 0.14 (0.19) (0.24) Split Partisan Control x Largest City Policy - 0.28 (0.37) Democratic Partisan Control x Largest City Policy - 0.83 (0.52) AIC 1062.3 1064.2 1065.5 Observations 245 245 245 Log Likelihood - 520.15 - 520.12 - 518.75 Note: **p < 0.5; 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George Was hington Institute for Public Policy, Washington, Working Paper 35. http://www.jstor.org.proxy1.cl.msu.edu/stable/prancotamamnta.101.377 . Vox. March 21, 2017. http://www.vox.com/new - money/2017/3/21/14980502/uber - toxic - culture - rule - breakin g - explained . 100 CHAPTER 4. How the Sharing Economy Reshaped Regulation in the States States acted quickly to establish authority over ride - sharing policy in very similar ways, especially where lobbied by ride - sharing companies and where their largest cities were already regulating the service. With short - term rentals, states have been much slower to act and more likely to leave governing authority to cities. Why were the processes so different for two technology - driven policies that entered markets and polic political dynamics around state legislative action on each issue to illustrate the mechanism by which the policy preferences of ride - sharing and short - term rental companies were deba ted and integrated into policy. First, I describe the case of state - level ride - sharing policy by examining the key legislative priorities of Uber, the development of model legislation by ALEC and the National Council of Insurance Legislators, and how Uber learned how to match its policy goals and venue though its experience in Maryland and Baltimore. Next, for the case of short - term rentals, I provide an overview of the components of state - level short - term rental laws and then use the case of the Georgia to illustrate how Airbnb also aligned its policy goals with venue to achieve its desired outcomes. Together, the cases highlight the importance of functional fit the combination of problem definition, functional responsibility, and the power associated wit h a policy venue to both policy outcomes and which level of government ultimately has authority over an issue. Functional fit helps explain why 101 ride - sharing is governed almost exclusively by states and short - term rentals are more likely to face local law s. Ride - Sharing: A Rapid Road to State Regulation machinery and fabric th at a lot of people owe him favors. - Travis Kalanick, Uber 33 - founder and former CEO Travis Kalanick rarely holds back his n inveterate flouter of both workplace rkets and starts operating, regardless of local regulations. Yet for its highly curated public image as a maverick startup that breaks all the rules, Uber has pursued a conventional corporate political strategy that allied the company with traditional powe rs to achieve favorable regulation. Its top three regulatory priorities to discharge insurance risk to drivers, avoid classification as a transportation company, and maintain drivers as independent 33 At Code Conference 2014, as quoted in Nuzzi (2014). 102 contractors are mainly governed at the state level, an d so, Uber partnered with two nationally powerful state - level interest groups to ensure that governance of its services remained at the state level and would increase the likelihood of the company achieving its preferred regulatory outcomes. This section o characteristics of the state policy venue supported a rapid adoption of state - level ride - sharing regulations and limited the opportunities for c ities to intervene and create local policy. Framing Ride - Sharing as a State Concern: ALEC Model Legislation The American Legislative Exchange Council (ALEC) exists to facilitate the distribution of model policy through a network of state legislators (ALEC 2019). Originally founded in 1973, the organization has become more prominent in the 2000s as it has built its network of corporate and state legislative partners, which it claimed in 2016 was 20% of Congress, 8 governors, and 1,800 local elected officials (American conservative and Republican priorities, though there are occasions it works with Dem ocrats, most publicly on criminal justice reform and asset forfeiture. Growing from its original founding as a forum for state legislators, ALEC continues to prioritize codifying its policy agenda in the states. This approach was no different with the 103 shar ing economy, where ALEC and ride - sharing companies Uber and Lyft developed model legislation for state - level policy to regulate the services. The origins and details of the partnership to develop the legislation are somewhat unclear, but from the available state law. To understand the influence of model legislation on state ride - sharing policy, it is useful to identify the main components of the model bill in order to see the extent to which states adopted the model without change, adopted pieces of it, or adopted something else entirely, and whether it matters from a policy standpoint. Components of Ride - Sharing Model Legislation 34 w as distributed to its Communications and Technology Task Force in October 2014, not long after Colorado had adopted the first state ride - sharing statute in June 2014. The model De other model policies, the Transportation Network Company Act is not archived on its website, nor was it promoted. Archived agendas do not indicate whether the act was passed b y either task force or the ALEC Board of Directors (the required steps to become ALEC model policy), but regardless of its official status or the status of Uber 34 Throughout this section TNC and transportation network company refer to ride - sharing, which is the term used to describe ride - sharing companies under statute. 104 and Lyft within the organization, the components of the model legislation became law in many st ates. 35 The draft legislation contains 12 sections, 4 of which, including the title, are companies to carry liability insurance, conduct background checks on transportation net work company drivers, inspect transportation network company vehicles, and Council 2014, 1). This summary describes the sections of the act, however it elides the details of the content and how it shapes the dynamics of transportation and insurance policy in a state. establish companies like Uber and Lyft as technology companies, 2) exclude the co mpanies from definitions of common carriers (transportation companies), and 3) statutorily establish definitions regarding individual users, drivers, and vehicles for insurance purposes. The first two definitions are critical to the existence of ride - shari ng companies in the United States in their current form. In some ways, they are versions of the same thing Uber and Lyft are technology companies, not transportation 35 Uber and Lyft, like many technology companies, are not official members of ALEC, but they present to annual meetings and engage with the group. There was some controversy about the ride - Uber began its state legislative push (Riestenberg 2014). 105 companies but the double definition provides statutory protection in two ways. First, that provides a digital network to connect drivers and riders. That definition is - 2) whic h defines those terms, typical under state common carrier statutes, to exclude transportation network companies. Together, these establish a new category of company and distinguish it from taxi companies specifically (exempts from local and state taxi regu lations, including employment and control over vehicles and liability) and common carriers generally (exempts from larger state rules regarding public transportation providers.) To make these exemptions clearer, the first section of the act after the defin itions reserves governance of TNCs exclusively to the Act, reinforcing that transportation network companies are different from transportation companies. That section also assigns TNC supervision to the body in the state that governs public utilities, plac ing TNC regulation in the rule - making process rather than under a cabinet agency or the legislature (2 - 3). transportation network company and its drivers. This section incorporat es the key definitions that detach responsibility and risk from the company and place it on individual drivers. The company or the driver are required have $1,000,000 in primary liability insurance that covers ride - share drivers any time the driver is tran sporting a 106 passenger in a personal vehicle, which the Act defines as property of the driver, not the company. 36 In this language, the state is agnostic as to whether the driver or the company has the coverage, so long as there is sufficient insurance covera ge while a driver is has a passenger in the car. For the TNC, if it does not provide primary insurance, each individual driver is obligated to do so. Insurance requirements are then surance holds coverage when the driver is using the car but not logged into a TNC app, and when a driver is logged into an app but has not accepted a ride, he or she has to provide minimum coverage for accidents and property damage. 37 In practice, this mean s that TNC drivers typically buy a rider to their regular auto insurance policy which covers the two mandatory coverage periods: the time a driver is logged into the app, and the time from acceptance of a ride to the end of a ride. Along with the definitio ns that classify TNCs as technology companies, these driver - level insurance requirements and lack of TNC ownership over vehicles are the key pieces that protect the viability of companies like Uber and Lyft. Together, they distribute cost and risk to drive rs and protect the status of transportation network companies to operate outside of transportation regulations. 36 The $1,000,000 in coverage applies to each accident, not a total liability amount. 37 Again, with per incident limits. 107 The next pieces of the Act apply to operations: safety; requirements for receipts and fare disclosure; driver age and work hours; substance abus e policy; driving and criminal history; and vehicle characteristics and safety. For each of these requirements, the state has no role in verifying the data or compiling it. The transportation network company is charged with verifying driving history, insur ance coverage, criminal history, and vehicle inspections, and with retaining records. There are no protections in the Act for drivers, other than the mandate that TNCs notify drivers that driving for a ride - sharing company may invalidate agreements with th eir insurance company and/or lien holder. Rider - focused provisions of the Act are: receipt requirements, conditions under which service may be refused, 38 assurance of the same fees for persons with disabilities, and the right to be accompanied by a service animal. If a driver refuses to actions unless the violation was reported to the company in writing and it failed to address it. Additional pieces of the model Act provide: a mechanism for existing taxi or shuttle companies to convert to a transportation network company; a procedure for an annual company permit issued by the state utility commission, and rules for the distribution of funds. The first two pieces creat e barriers to entry for competing 38 endangering behavior, inability to care for oneself without a companion. 108 companies, while the third creates a designated fund in the state budget, reserves the interest earned to be retained in the fund, and prohibits transfers from the designated fund to the general fund. There are two key reg ulatory components that the ALEC model legislation does not address: preemption and the status of drivers as contractors. The definitions state should govern TNCs, ho wever it does not specifically preempt local governments from regulating the services. The main components of the legislation are summarized in Table 4 - 1. How Similar is State Ride - Sharing Policy? Beginning in 2014, Uber advocated for state - level legislati on that contained all of the components and language of the model legislation ALEC distributed to its Task - lev el lobbying followed suit. 39 Table 4 - 2 shows a comparison of state ride - sharing legisla tion for the 49 39 There are more automated methods of text comparison of model legislation that allow comparison of multiple bills over multiple years and can help detect patterns of influence of different interest groups. For this case, a manual search was sufficient, but Kroeger (2016) and Burgess et al. (2016) examine the challenges of automated analysis and offer examples of the power automation can bring to comparative analysis of legislative influence, and several scholars are investigating the broader 109 states that adopted policy through 2018. The table contains the year the state first adopted policy, 40 and whether the legislation includes four components: 1) requirements for driver and/or company - level insurance, 2) exemption from common carrier laws, 3) a preemption of local government authority over ride - sharing, and 4) declaration on the status of drivers as employees. 41 The ALEC model legislation is mostly silent on whether drivers are employees (in its definitions it says that a driver as noted earlier, this condition is key to the current structure of ride - sharing companies, so it is included in the comparison. Very few states adopted the model legislation word - for - word, with Colorado, Alaska, and Illinois having the closest resemblance to the document. Yet although the states did not follow the template exactly, the laws governing ride - sharing are strikingly sim ilar across the country. All 49 states have an insurance requirement, established in language very similar to that in the ALEC bill, down to the dollar amounts of coverage implications of the spread of model legislation (e.g. Jansa (2015) a nd Hirsch and Shotts (2018), and Hertel - Fernandez (2019), focused specifically on ALEC activity in the states across issue areas). 40 In a few cases, a state amended legislation either later in the session or in a following year. If the amendment changed th e categorization of a statute (i.e. the new law added preemption or clarified legislatively - adopted policies are included in the table. 41 All state statutes were reviewed and coded by the author, and after coding, were compared with Goodin and Moran (2017) and Racabi (2018), who coded insurance and employment laws, respectively. 110 for different components of the insurance policy for the company and drivers; the co verage periods; and the provisions that give companies the option to require that drivers carry coverage rather than the company providing it. Though the language existed in the 2014 ALEC bill, publicly it was presented as developed and agreed to by Uber, Lyft, the National Council of Insurance Legislators (NCOIL), and the Property Casualty Insurers Association of America, who then worked together to assure its passage at the state level (VanHulle 2016; Ducassi 2017). 42 This partnership between ride - sharin g companies who cannot afford to assume the risk for every driver and rider that uses an app, and insurers who had a new market to serve naturally sought out state legislatures. States already govern insurance, the insurance industry has a relationship w ith state legislatures, and in the words of Florida State Representative across states to justify adoption was that this agreement between the companies and insurers addressed the concerns of insurers, and states that adopted the language would be consistent with other states if they supported the agreed - upon language. This 42 The National Association of Insurance Commissioners, a group of the top insuran ce regulators in each state, was not a partner to the agreement, but it released a white paper in 2015 offering guidance for legislators and supporting the division of insurance coverage into the three coverage periods outlined in the model legislation, wh ich were already enshrined in Colorado and California statute (National Association of Insurance Commissions 2015). The Association did not endorse the model bill, but it did recommend most of its provisions (National Association of Insurance Commissions 2 015, 19). 111 framing then chan neled the bill into committees dealing with insurance (not those dealing with local government) and centered debate over technical insurance details, such as which policies cover drivers when, and the limits of personal insurance to cover commercial activi ty. Baltimore and Maryland: The Fight that Hones a Strategy If ALEC had predisposed state legislatures to believe they are the appropriate home for regulation across a variety of areas and introduced the foundation of model ride - sharing legislation, the ag reement with insurers gave ride - sharing companies a vehicle to advance its other regulatory priorities. Forty - two states either specifically exclude transportation network companies from common carrier regulations or establish a separate category of common carrier for TNCs that is then exempted from regulations imposed on other categories of carrier. This exemption is explicitly stated twice in both the ALEC and NCOIL legislation (National Conference of Insurance Legislators 2015), and it, along with the la nguage from the NCOIL legislation that states in statutory law two concepts critical to the TNC business model, both of which are have shaped this strategy. 112 Uber launched in Baltimore in January 2013, one of its early expansions in the United States. Uber c hose Baltimore in part because of its success in nearby population is dispersed into neighborhoods without frequent taxi service, it has major sporting events that overwhel m existing transportation options, and it has demographics receptive to technology and cashless transactions (Lynch 2013). The media coverage of the launch was typical of the time: Uber, the innovator, was bringing modern technology and luxury to a core ci co - founder and then - drivers from a local tech incubator and hosti ng media and locals at a launch party (Lynch 2013; Zaleski 2013), taxi companies were preparing a challenge. The operators of major cab companies came together to file a lawsuit against Uber and a complaint with the Maryland Public Service Commission, the regulatory body that makes rules for electricity, telecommunications, gas, water, and transportation in the state. In April 2014, an administrative law judge upheld the complaint, ruling that Uber was a common carrier and giving the company 60 days to appl y for a permit to operate (Lazo 2014). The Public Service Commission affirmed the - hire carrier and directed its staff to draft new rules to incorporate ride - his 113 fellow Democrat Governor Hickenlooper in Colorado, Maryland Governor Martin regulations to incl Maryland as the #1 state in the nation for innovation and entrepreneurship for three years in a row. As new innovations change the transportation services landscape, we must ensure that our laws a nd regulations evolve as well we try to limit a and the Maryland General Assembly to ensure that our laws and regulations accommodate and foster new innovations to ensure that Marylanders have choices, this time, Uber offered different tiers of service: UberBlack, UberSUV, and UberX, and and UberSUV services because they were partnerships with existing limousine and transportation companies, and the vehicles were owned by those companies, whereas UberX (the service people typically te vehicles. 43 to the ruling previewed its political strategy and the components of state legislation to come. 43 114 a fancy name for a ny characterized the ruling and common carrier regulations as outdated and not designed for technology. In part, this is true common carrier laws in the United States were initially designed for railroads and have covered everything from communications a nd vehicle transport to internet service providers. This argument is also not unique to Uber: the classification of the debate over net neutrality and FCC rulemaking rega rding internet service (Brodkin point - of - view, is to ensure that systems and companies that provide goods and service for the benefit of the public do not exclude segments here, like that of broadband providers, is that it provides technology that links customers to drivers but has nothing to do with the actual transportation. In one of its - finding inquiries, leads for other industries (Uber Technologies 2013), and in its public writing on the issue it equates regulating Uber as transportation to reg ulating the website Orbitz, a travel booking site, as an airline because it books flights (Shwetha 2014). company 014). 115 Though the cars for UberBlack and UberSUV are owned by other limousine or car service companies, and the drivers are employed or otherwise contracted by those companies, Uber says it has no control over the vehicles or drivers. Its technology helped the company framed the classification of Uber as a transportation company as a move that would destroy the businesses of entrepreneurs who partner with Uber and use gy to build their own independent companies. Though at the time of this dispute Uber only operated in 45 cities, the company was already focused on eliminating regulatory barriers to its business model and had arranged a disciplined communications strategy around its priorities (Holt 2013). The communications strategy surrounding these regulatory priorities sets Uber up as the bold innovator, stifled by a stodgy government that does not understand technology and entrenched political interests that use the e xisting system to enrich themselves. It is not subtext, as noted explicitly by co - founder and then - CEO Travis Kalanick in the quotation at the beginning of the section. A less vulgar version of his transportation alternative something Marylanders value and provides partner drivers with the option to expand their economic opportunities. Over all, Uber promotes consumer choice and competition both of which have been recognized by the General 116 proposed order will protect special interests, not promote public s 2014). Positioning itself as an innovator challenging the status quo is not an uncommon message for a corporation or industry seeking favorable public opinion and treatment from policymakers, and Uber used this strategy to position itself as a true disruptor that existing rules were incapable of managing, rather than taxis with an app. Few public officials want to oppose innovation, which creates a receptive audience for the - innovation and anti - est ablishment language oppose legislation in 2014, with the first lines of the e innovation, would take the lead in killing it. Governor Brown is committed to leading California into the future, but some in the legisla ture are anonymously doing the From the initial Public Service Commission staff report in May 2013, thro ugh the and launched public relations campaigns in a process that Uber would streamline and deploy in cities and states across the country. The state of Maryland passed legislation 117 authorizing transportation network companies to operate in the state on the last day of legislative process was ongoing throughout the administrative negotia tions. Though it had some state - specific requirements and language grounded in the details of the ongoing Public Service Commission case, like the states that adopted closer versions of established TNCs as separate from taxis, limousines, and car services; specified insurance requirements; created background check requirements for drivers and minimum vehicle standards; and assigned governance to the Public Service Commission and directed it to develop regulations and procedures for implementation. The act does not refer specifically to the employment status of drivers, though the Public Servic independent contractors in its order (State of Maryland Public Service Commission vehicles for UberBlack and Ube rSUV services, which would establish it as a common carrier. Throughout all of this one key player is absent: the City of Baltimore. Though dialogue and conflict is between taxi companies, Uber, state government, and state 118 elected officials. The Public Service Commission already governed taxis in the City of Baltimore, so there was no place for the City to assert itself as the authority that should govern ride - sharin g, even if it had desired to do so. Short - Term Rentals: An Administrative Path to Regulation responsibility for what happens on your platform. We changed our point - of - v - Brian Chesky, Airbnb Co - Founder and CEO 44 ALEC also released model state legislation for short - term rentals, and unlike the model bill for ride - sharing, it was approved by the ALEC Board as an official model policy (American Legislative Exchange Cou ncil 2016). In another key difference from - sharing policy, the short - term rental legislation has not gained traction in the states. The model bill which terms hosting platforms like Airbnb and HomeAway proposes regulation and taxation at the state level (preemption of most local authority) and privacy protections for individuals who list properties for short - term rental (reserves the right of hosting platforms to submit consolidated data and withhold individual information from the government.) The American Hotel and Lodging Association (AHLA) also proposed model legislation to govern the industry, which, as one might expect, took the opposite approach. The 44 As quoted in an interview with Reuters (2018). 119 AHLA bill proposed: individual fees of $2,500 to registe r short - term rental units; hosting platform fees of $10,000 plus $60 per unit listed; a database with property address and owner contact information for every unit; mandatory health and safety inspections; mandatory data - sharing with cities and neighbors; restriction on the types of property that could be listed (owner - occupied, in non - rent - controlled buildings); and age restrictions on renters (Carson 2016). Both pieces of legislation were circulated in fall 2016, a time when states were just starting to c onsider and adopt short - term rental policy. ALEC also circulated a model policy through its local arm, the American City County Exchange (ACCE). In January 2016, ACCE adopted principles of short - term housing rental that are complementary to the state legis lation that ALEC approved 9 months later. The principles suggest that local officials should: govern short - term rentals as any other residential property (no new laws); place tax obligations on property owners, not hosting platforms; and limit registration requirements and fees to avoid harming the industry (American City County Exchange 2016). In spite of these model policies being available, advanced by powerful interests, and a demonstrated willingness to embrace similar legislation to govern other issue areas, states have taken their time in acting on short - term rentals. When states do act, most preserve the authority of cities. 120 Comparing State Short - Term Rental Laws Preemption, as defined by urbanists and proponents of Red States - Blue Cities, presumes that when given the opportunity to restrict local authority, states do so broadly and with few exceptions. This is mostly true for state ride - sharing laws, but in the case of short - term rentals, preemption is not so simple. In almost all of the 38 states that explicitly preempt local governments from regulating ride - sharing, the statutory language establishes ride - sharing as a state concern, designates the state as having authority over its regulation, and prohibits any other government entity in the state from creating laws regarding its operation. 45 For short - term rentals, preemption is enacted as only a ban on banning short - term rentals. That is, local governments can regulate short - term rentals, but they have to allow them to exist within the municipalit y. This is similar to other types of state - level regulations concerning land use authority, such as restricting local governments from zoning out mobile homes and mobile home parks (e.g. Michigan Mobile Home Commission Act of 1987). As an example of the mo re nuanced approach to local authority in state short - term rental laws, in 2017, Virginia banned local governments from banning short - term rentals, but the same legislation enabled local governments to create registration requirements for 45 There are occasionally exemptions for local taxes or fees under certain circumstances, but the terms are narrow. The four states that delegate a uthority to public utility commissions do not have the same preemption language, although by virtue of the public utility commission having authority over regulation, municipalities cannot or do not regulate. 121 short - term rental properties in their jurisdictions. Of the 24 states that addressed short - term rentals legislatively, only 7 banned local bans on rentals, with another 2 states having a partial ban on bans. 46 Instead of a preoccupation with establishing sole authority or f alling in line with what corporate interests recommend, states have focused their legislative attention on taxation. As noted earlier, many states have negotiated administrative agreements with Airbnb to collect taxes via its application on behalf of indiv idual taxpayers. Fifteen states have codified the taxation of short - term rentals, incorporating them into short - term rentals from occupancy taxes. Within these laws, s tates that allow municipalities to levy occupancy taxes authorize municipalities to extend those taxes to short - term rentals. In some cases, the law was enacted to force non - Airbnb listing agencies (e.g. VRBO and HomeAway) to comply with tax laws in the sa me way Airbnb already was via administrative agreement (Sanders 2018). Outside of preemption and taxation, state laws on short - term rentals have few consistent themes. Five states established study committees as part of their laws, and Georgia, which estab lished a study committee via House resolution in 2015, decided not 46 w, which allows cities to prohibit short - term rentals of less than 7 consecutive days but restricts cities from prohibiting rentals of 7 - 29 consecutive days. 122 to regulate short - 47 These types of study committees indicate a more measured response by states to short - term rentals than to ride - shar ing, and of the publicly available reports, all acknowledge the right of cities to regulate land use and the need for locally - appropriate regulation. Table 4 - 3 contains a summary of state short - term rental laws. Georgia: A Decision Not to Regulate In April 2015, Georgia House Resolution 810 created a study committee to, expanding market for short - term rentals in the state ( Resolution Creating the House Study Committee on Short - Term Rental Providers 2015, 1). As part of its meetings, the Committee heard testimony from hosting platforms, the hotel and lodging industry, city tourism officials, state tax and consumer protection officials, the Georgia Municipal Association and Association of County Commissioners, apartment industry representatives, short - term rental hosts, and the Georgia Association of Realtors. From this testimony, the Committee identified several conflicting issues: short - term rentals do not pay taxes and do not have to meet health, safety, and insurance regulations; most short - term rentals are not full - time lodging operators; short - term rental and hotel customers are different; 47 Other states have established work groups, study committees, or task forces in a less formal man ner than legislation (See VanHulle 2019), as have many cities. 123 short - term rentals fill important gaps in lodging needs for areas with limited lodging supply that host annual events; hosting platform companies have different capabilities to collect payment and remit taxes; local communities have specific needs (e.g. must be considered in ordinances; private groups also regulate property use (e.g. property owners associations, condo associations, homeowner associations, apartment rules, etc.); short - term rental operators are unaware of their tax obligations; and tax revenue due to the industry is diffi cult to determine because short - term rentals do not have a separate NAICS code. 48 Recognizing the complexity surrounding the issue and the fluid nature of whether properties that serve as short - term rentals are personal or business assets, the Committee recommended that: local governments should regulate short - term rental operations; th e municipal government associations should work together to develop - local - term rentals; short - term rentals should pay state tourism fees; and the state Department of Revenue and local taxation agencies should enter agreements with hosting platforms and continue trainings for operators about tax obligations. Further, the Committee determined that 48 North American Industry Classification System (NAICS) codes are used by federal agencies for business data classification. Short - term rentals are grouped with cabins, cottages, guest houses, hostels tourist courts, tourist homes, and youth hostels in one code 721199: All Other Traveler Accommodation (NAICS Association 2017). 124 no legislative action was necessary to implement its recommendations or to address the issue of short - term rentals in the state (Study Committee on Short - Term Rental - level legislation was enacted t o govern short - term rentals. - term rental providers have continued to significantly increase in number in the ensuing to revisit the issue ( Resolution Creating the House Study Committee on Short - Term Rental Providers 2018, 1). The committee heard testimony from essentially the same groups. The Georgia Association of Realtors discussed property rights, Airbnb hosts stated they fill a niche in the market, the municipal associations argued that leaving regulation to locals had been successful, hotel operators stressed the need for a level playing field, and the hosting platforms asked for consistency in regulation. As part o process, Airbnb reported that Georgia had 12,000 Airbnb hosts, with 85% of those only - term vehicle rentals, and the discussion was similar to that surrounding short - term property rentals. Car rental companies argued that car - sharing platforms should be regulated as car rental companies and testified on the need to even the playing field, because they pay taxes and meet safety standards and vehicle - sh aring companies do not. Turo, a car - sharing company, claimed that vehicle owners pay tax and that they provide sufficient 125 insurance coverage. Representatives of local government spoke on the challenges of regulating shared vehicles and scooters. The 2018 C - arching regulations regarding short - term property rentals to ensure uniform guidance while also allowing local go vernments the flexibility to craft ordinances based on the needs acknowledged that the state needed more enforceable policies to ensure state and local tax compliance. Further, on t axation, the Committee stated that it did not have sufficient information to recommend policy for short - term vehicle rental but did recommend updating the tax code to include those services. So over the span of 4 years and 2 legislative committees, Georgia , a high - tourism state 49 with around average levels of home rule, might be expected to adopt state - level regulations on short - term rentals, however the study process produced no consensus on what the regulations should be, other than the state should ensure taxes are collected and locals should enact ordinances to address local priorities. 49 In addition to tourism centers like Savannah and the coast, Georgia also frequently hosts large sporting events like The Masters, the Super Bowl, NCAA tournament games, etc. and annual tourism spending in 2017 was over $3.3 billion. 126 Why Do States Govern Ride - Sharing and Cities Short - Term Rentals? Though sharing economy services similarly use technology to connect individuals with an asset to others in terested in using that asset, the policy outcomes surrounding the services is very different. With ride - sharing, states quickly accepted the corporate and interest group framing that the most important thing to regulate was insurance, and, because states h ave responsibility for insurance regulation, they were the natural home for regulation. Uber, after learning from early run - ins with regulators, partnered with existing networks and interest groups to develop model insurance regulations (with key Uber prio rities nested inside), and states adopted the regulations with few exceptions. With short - term rentals, there is a sense from policymakers that issues surrounding the service are localized, and that even if personal property rights should allow owners to l ist properties as rentals if they would like, cities should have the right to manage the operation of and conflicts from that use, similar to how they manage other property in the jurisdiction. Leaving land use regulation to the cities, states focused on c orrectly applying and collecting taxes, and Airbnb has pursued administrative agreements to comply with state rules. Ride - sharing and short - term rentals emerged both as services and as parts of the policy agenda quickly and publicly. This emergence allows us to see the elements of policy incorporation come together in real time. The previous chapter modeled the 127 measurable pieces of the institutional policies and issue politics surrounding the sharing economy, and the results of the models showed that there are key pieces of the process that still need to be defined. Bringing together the models and evidence from the cases above, through the highly - visible emergence of the sharing economy we can see the mechanism that links institutional and political charact eristics to policy outcomes functional fit. The components of functional fit are illustrated with examples from ride - sharing and short - term rentals in Table 4 - 4. The first column identifies the key priorities of the industries seeking particular regulati on as observed in the case studies in the previous section. The industries share two priorities: maintaining the ability to operate in all markets and to be regulated as technology companies. Neither of these priorities is specifically attached to a functi onal responsibility of state or local government. Between Airbnb is more flexible in the policy conditions it can accept. Certainly, there are other groups with politica l interests surrounding the sharing economy process, but those groups are reacting to the emergence of Uber and Airbnb rather than advancing policy on the sharing economy proactively, so the company preferences are considered the primary policy goals in th is case. In order for those preferences to be enacted, there needs to be a match between how a level of government defines the issue and whether 128 the level of government has both authority and an existing regulatory structure into which the policy can be in tegrated. In the case of ride - service as a technology company, created state - level political allies to support its policy objectives, and most importantly, gave cities little room in either issue definition or venue fit to stake a claim to regulating the service at the local level. Although Airbnb goals could have been enacted at the state level in a similar way to ride - sharing laws, they were not. States defined the issue as taxation, and occasionally as a property right, but they did not extend regulation much past those state functional responsibilities. Instead, they recognized local authority to regulate the ta x - generating activity, much as they do other property use within their jurisdictions. The different combinations of issue definition and venue fit across the two sharing economy services highlight the conditional nature of their intersection: it is not eno ugh to win a framing war to define an issue in a particular way. The definition has to align with the functional responsibility associated with a venue. If Uber needed to discharge insurance risk but cities had a history of regulating insurance, it would h ave had a more difficult path to state - level regulation. Conversely, if Airbnb wanted to avoid local regulation of short - term rentals, it would have had to convince states to define it as entirely outside of other types of lodging and supersede decades of local authority over community - level 129 land use. Functional fit describes this relationship between the definition of the issue and the authority available within a policy venue. The policy areas into which sharing economy services are slotted help determine how the authority to govern them will be distributed. Cities, and especially urban areas, feel the externalities of ride - sharing most acutely, but they do not have a clearly defined rance regulation. Once the definition of the issue was solidified as insurance regulation, states became the natural regulatory home, so while cities may make policies around the margins, they have lost the power to make substantive decisions about ride - sh aring in their jurisdictions. Short - term rentals, however, remain within local purview, as once states were assured of receiving tax revenues, they largely left locals to exercise their regular authority over land use. 130 Table 4 - 1: Components of ALEC Draft Transportation Network Company Act Act Provision Summary of Contents Definition of Transportation Network Company (TNC) Establishes TNCs as technology companies, separate from taxi companies Exclusion from Common Carrier Definition Prohibits TNCs from being considered common carriers Reserves Governance of TNC Defines statutory control of TNCs as exclusive to this Act Creates Insurance Obligation and Categories Requires levels of insurance coverage and sets categories of driver o peration for insurance Sets Driver and Vehicle Requirements Creates standards for driver age, criminal and driving history, and work hours. Sets vehicle inspection requirements. Grants Service Rights to Riders Provides right to service and documentation of ride via receipt Path to Conversion for Taxi Companies Allows taxi companies to convert to TNC State Permits Assigns public utility commission to monitor company permit to operate and set rules for TNCs State TNC Fund Designates a sp ecial fund associated with TNC regulation and prevents funds from being transferred to the general fund 131 Table 4 - 2: Comparison of State Ride - Sharing Legislation State Year of First Policy Insurance Requirements Common Carrier Exemption Local Preemption Designates Drivers Non - Employees Alaska 2017 x x x x Alabama 2016 x x x x Arkansas 2015 x x x x Arizona 2015 x x x MC California 2014 x PUC Colorado 2014 x x PUC x Connecticut 2017 x x x Delaware 2016 x x x x Florida 2017 x x x x Georgia 2015 x x x x Hawaii 2016 x x Iowa 2016 x x x x Idaho 2015 x x x x Illinois 2015 x x Indiana 2015 x x x x Kansas 2015 x x x Kentucky 2015 x x MC Louisiana 2015 x Massachusetts 2016 x x x Maryland 2015 x x PUC Maine 2015 x x x Michigan 2016 x x x x Minnesota 2015 x Missouri 2016 x x x x Mississippi 2016 x x x x Montana 2015 x x x x North Carolina 2015 x x x North Dakota 2015 x x x Nebraska 2015 x x PUC New Hampshire 2016 x x x x New Jersey 2017 x x x New Mexico 2016 x x x x 132 Table 4 - 2 State Year of First Policy Insurance Requirements Common Carrier Exemption Local Preemption Designates Drivers Non - Employees Nevada 2015 x x x X New York 2017 x x x Ohio 2015 x x x x Oklahoma 2015 x x x x Pennsylvania 2016 x x x Rhode Island 2016 x x x x South Carolina 2015 x x x x South Dakota 2016 x x x x Tennessee 2015 x x x x Texas 2015 x x x x Utah 2015 x x x Virginia 2015 x x x Vermont 2018 x x Washington 2015 x x x Wisconsin 2015 x x x x West Virginia 2016 x x x x Wyoming 2017 x x x x PUC: Delegated to Public Utilities Commission; MC: Separate Marketplace Contractor Law 133 Table 4 - 3: Comparison of State Short - Term Rental Legislation State Year of First Policy Taxation Ban on Local Bans Study Committee Other Arizona 2016 x x California 2015 x Connecticut 2017 x Florida 2011 x Hawaii 2012 x Iowa 2018 x Idaho 2017 x x Indiana 2018 x Louisiana 2017 x Massachusetts 2018 x Montana 2015 x New Hampshire 2016 x Partial x New Jersey 2018 x Nevada 2017 x New York 2016 x Oregon 2013 x x Pennsylvania 2018 x Tennessee 2018 x x Utah 2017 x Virginia 2016 x x x Vermont 2016 x x Washington 2018 x Wisconsin 2017 x Partial West Virginia 2016 x Georgia 2015 Georgia did not adopt a policy on Short - Term Rentals, but it did establish a study committee by resolution. 134 Table 4 - 4: Functional Fit of Ride - Sharing and Short - Term Rentals Service Company Policy Objectives State Issue Definition + Venue Fit Local Issue Definition + Venue Fit Ride - Sharing Discharge Insurance Risk Technology company, not common carrier/taxi Drivers are not employees Ability to operate in all markets Issue : need for consistent insurance regulations Venue Fit : State responsibility for insurance and common carrier regulation; state ability to limit additional local regulations Issue : disruption of local taxi rules/markets, traffic, uncertainty Venue Fit : Local authority over taxis, street use Short - Term Rentals Ability to operate in all markets Minimal regulations for hosts Minimal data disclosure Technology company, not hotel operator Issue : Tax parity with other transient accommodations Venue Fit : State responsibility for occupancy sales tax collection and administration; state authority t o enable local land use; more generally, tourism promotion Issue : new uses in existing properties/neighborhoods, positive and negative spillover (new visitors, effects on nearby property owners and neighborhoods) Venue Fit : Local authority over land use, zoning, and property regulations 135 WORKS CITED 136 WORKS CITED http://www.alec.org/about/ . 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Ube http://www.crainsdetroit.com/article/20161106/BLOG020/1611 09904/proposed - legislation - clarifies - insurance - rules - for - uber - lyft . - Term Rental Advocates, Foes Work Toward a Deal on Laws http://www.bridgemi.com/michigan - government/short - term - rental - advocates - foes - work - toward - deal - laws - michigan . - Demand Car Service Officially Launches, Will Be Available All February 1, 2013. http://technical.ly/baltimore/2013/02/01/uber - baltimore - on - demand - car - service - officially - launches - will - be - available - all - day - super - bowl - sunday/ . 140 CHAPTER 5. Policy Incorporation and the Sharing Economy The preceding chapters describe the policy space of th e sharing economy and reveal that, though the services may be considered by users as similar, as both use smartphone applications to quickly connect users with transportation or lodging, important differences create different policy outcomes. Federalism di stributes functional responsibility across the levels of government, and ride - sharing and short - term rentals cross functional responsibilities that are allocated to different levels of government. The overview of the sharing economy in Chapter 2 lays out t he characteristics of short - term rentals and ride - sharing to demonstrate both their disproportionate locality and the issue politics that surround the services. The delineation of the service characteristics and externalities illustrates how they cross sta te and local responsibilities: ride - sharing intersects with state - level regulatory power, while short - term rentals cross responsibilities of local governments. Chapter 3 tests political and institutional explanations for state - level policy adoption using u nique state and local datasets and finds that, as expected, traditional models of policy adoption do not explain either state sharing economy policy adoption overall or the different patterns of policy adoption between ride - sharing and short - term rentals. The overviews of all state policies and action on ride - sharing and short - term rentals presented in Chapter 4, along with case studies of Maryland and Georgia, illustrate the concept of functional fit the functional responsibility of a level of government and the power 141 available to that level of government to assert its authority. Collectively, this adds two main contributions to the literature. First, it suggests that existing academic and popular ideas of when states get involved in local issues need to move beyond partisan explanations (Flavin and Shufeldt 2019; Fowler and Witt 2019). The concept of preemption needs to be unpacked and considered within the larger context of state and local autonomy. Second, it proposes that the relationship between state s and cities is more than home rule. Rather than decisions being made exclusively based on long - standing rules of local autonomy, on an ad hoc basis, or through the lens of state - local power, states and cities sort out who governs what by filtering competi ng arguments and policy preferences through their existing functional responsibility. There are multiple ways that policy alternatives can be framed and constructed, and identifying how those alternatives correspond to functional fit provides insight into how issues are slotted into state or local policy domains. The Importance of Policy Incorporation Policy is not static, and how a policy is incorporated into state or local regulation does not set its place forever, but that initial policy decision shapes what comes afterward these choices about how governance is allocated between states and cities have implications for future policy and for larger societal outcomes. For example, ride - sharing being regulated at the state level as insurance policy is likel y not its final regulatory resting place. The space Uber in particular carved out for ride - sharing 142 through its model legislation is already being eroded. California, the home of Silicon Valley, passed AB 5 in September 2019, which places the burden on comp anies to demonstrate that workers are not employees. Under the new law, workers are business, b) is not directed or controlled by the employer, and c) is part of a work separately established independent business or trade (Myers, Bhuiyan, and Roosevelt n.d.). This legislation, which codified a California Supreme Court ruling, closes one of the regulatory gaps on which many sharing economy companies rely Uber and Ly now be classified as employees rather than independent contractors using technology to at set ride - sharing insurance requirements without comment (Office of Governor Edmund G symbo most importantly, what true public policy should be a collective process for all Newsom signed middle - class has been 40 years in the making, and the need to create lasting economic 143 security for our workforce de mands action. Assembly Bill 5 is an important step. A next step is creating pathways for more workers to form a union, collectively bargain to earn more, and have a stronger voice at work all while preserving flexibility and . In just 5 years, the regulatory landscape for ride - sharing is already shifting. But the starting point is important, because it sets initial regulatory conditions and allocation of externalities, and future regulation proceeds from that starting point. I t can be difficult to assess counterfactuals in policymaking, but AB 5 and recent developments in the City of Chicago offer clues to how different paths of policy incorporation can change outcomes. If the debate about worker classification surrounding AB 5 had happened when ride - sharing initially appeared on the scene, regulation likely still would have occurred at the state level (perhaps at the federal level) because cities largely do not establish employment tests, but, the debate would have centered on issues surrounding workers and the construction of sharing economy companies as employers rather than technology providers. Since Uber and Lyft entered the market and its status as a technology company was recognized by states, ride - sharing drivers have la bored without protection, often for less than minimum wage. Cities and states are now acting on behalf of workers, with New Jersey fining Uber $649 million for misclassifying employees as independent contractors (Haag and McGeehan 2019) and New York establ ishing and Los Angeles considering minimum wages for 144 drivers (Campbell 2018; Bilbao 2019). This is the normal business of governments addressing problems and harm within their jurisdictions, but in the five years between ride - rket and this new wave of more restrictive policy action, taxi drivers and companies who complied with local rules and regulatory systems were devastated financially as taxi medallions became worthless compared to the loans that financed their purchase (Ro senthal 2019; Said 2019). In San Francisco, the earliest ride - sharing market, only 17% of taxi medallion holders earn a sustainable income, and the San Francisco Federal Credit Union, which finances a large portion of loans used to purchase taxi medallions in the city, has filed suit against the San Francisco Municipal Transportation Agency for failing to regulate ride - sharing and allowing the taxi industry to collapse (Said 2019). Even in Chicago, where the city has retained the ability to regulate ride - sh aring, the taxi industry has suffered, with the city losing almost half of its licensed cabs by 2018 as it gained 66,000 active ride - sharing drivers (Channick 2018). The regulatory correction to account for employment protection for ride - share drivers addr esses an important question sharing economy companies made more urgent, but the changes to the taxi industry and individuals harmed by the ride - sharing disruption have yet to be addressed by governments in the U.S. context. Chicago is one of a limited numb er of cities in the United States that are allowed to govern ride - sharing without restriction, as Illinois did not preempt local authority. 145 Illinois still created insurance requirements, established that ride - sharing companies were not transportation compa nies, and provided other general state - level regulation, but within that framework, the City of Chicago established its own rules for ride - sharing companies. One of the most critical was that the City required ride - sharing companies to share data on trips and services, and when the first data was released from that requirement this year, it showed that the 102.5 million ride - sharing trips covering 603.4 million miles in the city in 2018, created several problems for the community and municipal services (Fre und 2019; Lightfoot 2019). The data allowed to the City from ride - sharing, and the results are stark. Congestion has increased in areas and during times where conge stion was already bad, increasing pollution from emissions (246,563 metric tons of greenhouse gas emissions from ride - sharing in 2018), slowing bus transportation, and placing more wear and tear on already strained roads and infrastructure. In addition to slowing buses to a pace only slightly faster than walking, ride - sharing is substituting for public transit use, with the Chicago Transit Authority (CTA) losing 48 million trips annually since the introduction of ride - sharing and 48% of CTA customers report ing that they would take CTA if ride - sharing was not available and 31% of riders reporting a reduction of CTA trips since the introduction of ride - sharing (Lightfoot 2019). In response, Mayor Lightfoot is proposing new ride - sharing fees to address the spec ific types of trips driving many of the negative 146 spillovers: $3 per solo trip downtown, plus a reduction in fees for shared trips (Wisniewski 2019). This evidence - based policy response has not been embraced by ride - sharing companies, and as of this writing , Uber and Lyft were vigorously opposing the plan, and Uber was organizing a group of 35 Black ministers to rally support on the c meetings (Greenfield 2019). Cities like Boston and Seattle are considering similar proposals, though there are hundreds of cities across the country that have no option to either collect data or fees from ride - sharing companies. The lessons from Chicago and the data collection and case studies conducted for this project highlight that local policy decisions are the next, important step in advancing this project, in order to more fully understand the sharing economy policy space and policy incorporation fr om a local perspective. California and Chicago illustrate the importance of studying both when states intervene in local issues and the impacts of those initial governance choices. How a gime sets up initial winners and losers, or, more formally, who reaps the initial positive and negative externalities by establishing institutional structures that privilege certain interests over others. Even if the regulations are rolled back or amended substantially later, these initial choices matter. While policymakers are figuring things out, industries are disrupted and established, air quality is degraded, and affordable housing is lost. The 147 choice of a state to intervene both sets up these initial structures and potentially extends the consequences of those initial choices because state laws are less responsive to locally - identified issues and take more time to amend to account for learning at the local level. California and Chicago also suggest fut ure directions for specific sharing economy research. This analysis suggests that traditional models of understanding state policy choices are not sufficient to explain when states are viable venues for local policy issues, and, through its examination of the hyper - locality of the sharing economy, proposes an alternative lens for viewing policy choices: functional fit. Going forward, the sharing economy will continue to challenge policymakers with services like scooters, ghost kitchens, payday loans, and se rvices that have yet to be created, and the framework proposed here can help predict how those services will be governed. 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