Brass cities : Innovation policy and local economic transformation

BRASS CITIES:

INNOVATION POLICY AND LOCAL ECONOMIC TRANSFORMATION by

Ben Armstrong

B.A., Northwestern University (2011) Submitted to the Department of Political Science in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Political Science at the

Massachusetts Institute of Technology February 2019

C Ben Armstrong. All Rights Reserved.

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... Department of Political Science

January 15, 2019 Certified by... Accepted by...

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BRASS CITIES:

INNOVATION POLICY AND LOCAL ECONOMIC TRANSFORMATION By

Ben Armstrong

Submitted to the Department of Political Science in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Political Science

ABSTRACT

How have some former industrial cities become hubs for high-wage jobs while others continue to grapple with economic stagnation? This dissertation aims to show how government interventions have shaped U.S. cities' paths to income and employment growth. In the 1980s, nearly every state government in the U.S. began investing in innovation policies aimed at diversifying local economies and stimulating the growth of high-technology industries. Three political obstacles -short-term electoral incentives, industry capture, and barriers to collective action - have made the implementation of these policies difficult. Case studies of U.S. cities illustrate how state

innovation policies have the potential to overcome these obstacles and transform local economies adapting to the decline of manufacturing. Two pairs of cities - Pittsburgh, PA and Cleveland, OH; Albany, NY and Rochester, NY - had similar economic prospects in the early 1980s, but have followed different economic trajectories in the decades since. In Pittsburgh and Albany -national leaders in income and employment growth - the state government played the role of coalition builder, convening local coalitions to identify promising innovation initiatives and

monitoring local coalitions as they implemented the initiatives. In Cleveland and Rochester,

where income and employment growth has been comparatively low, pre-existing local coalitions and powerful incumbent industries crowded out a potential role for the state government. The model of state government intervention that emerges from this research suggests that convening local actors with economic incentives can overcome barriers to collective action and empower new actors - particularly universities - to implement economic development initiatives in the long term. Monitoring can help avoid policy capture by local interests and amplify the initiatives that showed the most potential. Forming local economic coalitions in this model depends on local actors (e.g. universities, firms) identifying regional economic development goals as institutional priorities.

ACKNOWLEDGEMENTS

It has been an extraordinary privilege to work on research that I find both interesting and consequential. I would not have had this privilege without my parents, Kathy and Andy, who have urged me for more than two decades to find work that I think is important and do it.

Mentors and friends at each stage of my life - Bob Cummins, Karen Alter, Dan Galvin, Mark Witte, Nigel Bowles, Samir Mayekar, Ego Obi, Josh To, Will Fitzpatrick, and Corby Kummer -have demonstrated what important work looks like and helped me discover my own interests.

Chris Sell, Zach Ciszon, Dave Litch, Willie Kalema, Sijh Diagne, and Nick Ruge have been terrific friends in setting goals and holding one another accountable in pursuing them.

When I arrived at MIT more than five years ago, I did not realize the extent to which research is teamwork. Suzanne Berger has been at once coach and trainer, inspiring new ideas, refining

existing ones, and asking questions I would have never thought to consider. I frequently entered meetings with Suzanne uncertain about which direction to take the research. I consistently left her office motivated to run down the path we had discussed. I will forever treasure her teaching,

careful reading, and years of encouragement for this project. Andrea Campbell, Kathleen Thelen, Michael Piore, Gina Bateson, and Rick Locke have also been incredibly supportive advisors and teachers whose ideas and feedback have shaped this research from the beginning. Andrea helped build threads between this research and other debates in American politics. Kathy encouraged me to integrate a discussion of power and ideas in political economy around interests and

experimentation. Michael has encouraged me to think about the role of political actors and their discretion in shaping economic and political outcomes. Gina and her course on qualitative methods provided a wonderful toolkit that I used in my field research. Rick has opened my eyes to the potential applications of this research in other cities and encouraged me to pursue them.

Colleagues in MIT's Political Science Department have been essential to the completion of this research, making resources available that I did not know existed and solving problems that I

could not have managed on my own. Thank you to Kate Searle, Helen Ray, Susan Twarog, Maria DiMauro, Paula Kreutzer, and Anita Kafka, among many others, for your patience,

generosity, and savvy. I am also thankful for helpful feedback from participants in various workshops at MIT, Brown University, and the University of Toronto. I am particularly

appreciative of Weihuang Wong and Florian Metzler for the many lunches and discussions where they have engaged with and challenged these ideas.

I was extremely lucky to spend time in several fascinating cities during the course of this project. I am indebted to the insights and generosity of the archivists, librarians, and interviewees who helped sharpen my understanding of each city in this dissertation. I am also grateful to the AirBnB hosts, taxi drivers, and bike paths that enabled me to see these cities from less

conventional angles.

Thank you finally to Gabrielle and Yonah for your patience and love throughout this process. Thank you for discussing these questions ad nauseum at the dinner table, for reading countless drafts late into the night, and for making me laugh each day. I am so grateful to share this work with you.

TABLE OF CONTENTS

CHAPTER 1: G OOD JOB G ROW TH IN CITIES...6

I. STATE INNOVATION POLICY ... 9

II. PATHS TO GOOD JOB GROWTH IN CITIES... 13

III. EMPIRICAL STRATEGY ... 30

IV . PLAN FOR THE DISSERTATION... 37

CHAPTER 2: INNOVATION POLITICS...43

I. W HO IS THE STATE?... . . . .45

II. THREE CHALLENGES FOR ECONOMIC DEVELOPMENT POLICY ... 65

III. CONTRIBUTIONS TO THE POLITICS OF ECONOMIC DEVELOPMENT POLICY...72

CHAPTER 3: RECONSIDERING THE RUST BELT ... 76

I. A D EINDUSTRIALIZATION PUZZLE ... 77

II. SELECTING M ATCHED PAIRS ... 92

III. PARALLEL HISTORIES SPLIT... 99

CHAPTER 4: PITTSBURGH AND CLEVELAND... 141

I. PITTSBURGH... 143

II. CLEVELAND ... 177

III. W HY PITTSBURGH, NOT CLEVELAND... 196

CHAPTER 5: ALBANY AND ROCHESTER ... 205

I. N EW YORK STATE INNOVATION POLICY ... 207

II. A LBANY...216

III. ROCHESTER...238

IV . W HY A LBANY, N OT ROCHESTER ... 253

CHAPTER 6: THE STATE AS COALITION BUILDER ... 261

I. A M ODEL OF GOVERNMENT INTERVENTION...262

II. M ODEL IN CONTEXT ... 269

III. CONTRIBUTION OF THIS D ISSERTATION ... 277

IV . LIMITATIONS AND OPEN QUESTIONS...279

V . CONCLUDING REMARKS ... 282

REFERENCES...285

TABLE OF FIGURES

Figure 1.1: M odels of G ood Job G row th ... 16

Figure 2.1: Motivations for Economic Development Policy ... 47

Figure 2.2: Innovation Policy Challenges and Their Implications ... 66

Figure 3.1: Summary Statistics for Rust Belt and Non-Rust Belt Cities ... 85

Figure 3.2: Manufacturing Cities in West/South and North/East Compared...88

Figure 3.3: Matching Albany and Pittsburgh with Similar Cities ... 95

Figure 3.4: Income and Employment Growth in Case Study Cities...99

Figure 3.5 New Business Formation in Pittsburgh and Cleveland (1977-2014)...110

Figure 3.6 Income Growth in Pittsburgh and Cleveland (1969-2014) ... 115

Figure 3.7 Job Growth in Pittsburgh and Cleveland (1969-2014)...116

Figure 3.8: Income in Central Pittsburgh and Cleveland Counties (1969-2014)...118

Figure 3.9: Jobs in Central Pittsburgh and Cleveland Counties (1969-2014)...118

Figure 3.10: Income Distribution in Pittsburgh and Cleveland...119

Figure 3.11: Industry Changes in Pittsburgh and Cleveland...120

Figure 3.12: Occupational Wages in Pittsburgh and Cleveland (2014)...121

Figure 3.13: New Business Formation in Albany and Rochester (1977-2014) ... 132

Figure 3.14: Income Growth in Albany and Rochester (1969-2014)...135

Figure 3.15: Employment Growth in Albany and Rochester (1969-2014)...135

Figure 3.16: Job Growth in Albany and Rochester (1969-2014) ... 136

Figure 3.17: Income Distribution in Albany and Rochester ... 137

Figure 3.18: Industry Changes in Albany and Rochester ... 138

Figure 3.19: Occupational Wages in Albany and Rochester...138

Figure 4.1: Tim eline of Key Events in Pittsburgh...144

Figure 4.2: Economic Indicators in Central and Peripheral Pittsburgh Counties...176

Figure 4.3: Tim eline of Key Events in Cleveland ... 179

Figure 4.4: Responses to Political Obstacles in Pittsburgh and Cleveland...197

Figure 5.1: Tim eline of Key Events in A lbany ... 218

Figure 5.2: Economic Indicators in Central and Peripheral Albany Counties ... 236

Figure 5.3 Tim eline of Key Events in Rochester ... 239

Figure 5.4: Responses to Political Obstacles in Albany and Rochester...254

Figure 6.1: The State as Coalition Builder in Context...274

Figure A. 1: Education and Growth Outcomes U.S. Cities...316

Figure A.2: Heavy Industry Legacy and Growth in Rust Belt Cities ... 317

Figure A.3: Manufacturing Legacy and Growth in U.S. Cities...317

CHAPTER 1: GOOD JOB GROWTH IN CITIES

State governments across the United States have professed a common economic objective: more employment with higher pay, or - simply - more "good jobs."I As U.S. manufacturing employment declined sharply in the 1980s, formerly thriving industrial cities faced enormous economic losses. State governments intervened, investing heavily in policies that aimed to promote innovation and create high-wage jobs. Although many former industrial cities appeared to have similarly bleak economic prospects in the early 1980s, some cities experienced remarkable economic turnarounds in the subsequent decades, becoming national leaders in income and employment growth. How have some of these cities become hubs for high-wage jobs while others continue to struggle with economic stagnation? What contribution,

if any, have state government interventions made to good job growth in cities?

Explanations for income and employment growth in U.S. cities2 _{such as Boston, Seattle,}

and San Jose (Silicon Valley) have often focused on entrepreneurs, anchor institutions (universities, hospitals, large corporations), skilled workers, and a cooperative culture - not government intervention. A study of "new Silicon Valleys" suggests that "[t]he right policies" for generating growth in cities "have elements of a 'benign neglect"' (Bresnahan, Gambardella, and Saxenian 2001). A review of entrepreneurship and innovation policies at all levels of government concluded that "it is not obvious that government policy can create

entrepreneurship" (Chatterji, Glaeser, and Kerr 2014). Scholars have emphasized the importance

offederal research and defense spending for local economic development (Markusen et al. 1991;

' Governors of both parties have declared "good jobs" with high wages to be among their goals. For examples, see

(Made in Alabama Staff n.d.; Office of Governor Jerry Brown n.d.)

2 Throughout the dissertation, when I refer to "city," I am referring to the Metropolitan Statistical Area (MSA) that encompasses the central city as well as "adjacent counties having a high degree of social and economic integration

O'Mara 2005), but there has not been consistent evidence that state and local innovation policies support income and employment growth in the U.S. or Europe (Neumark and Simpson 2014; Falck, Heblich, and Kipar 2010; P. Martin, Mayer, and Mayneris 2011).

In this dissertation, I focus on two pairs of cities - Pittsburgh, PA and Cleveland, OH; Albany, NY and Rochester, NY - that seemed to follow parallel economic trajectories until the 1980s, at which point the cities began moving along different economic paths. Pittsburgh and Albany thrived, growing in employment and income, as two nearby cities - Cleveland and Rochester - stagnated by comparison. The economic outcomes that I focus on are income and employment growth over time, which I refer to as "good job growth." The standard theories of good job growth are unable to explain these cases. All four cities have been home to similar levels of startup activity. Each city has formidable research universities, hospitals, and large corporations. And in 1970, the pairs had comparable levels of education and cultures of cooperation. Although state governments intervened in all four city economies, it is unclear whether or how public policy might have made a difference. The state's economic development policies invested similar amounts in similar programs for all four cities.

The explanation for good job growth that emerges from this research focuses on the state government's role as coalition builder convening and monitoring local economic activity. In the cities that ultimately thrived, state and local government interventions forged local economic coalitions among universities, firms, and governments. The state government's role was to convene discussions and fund experiments that enabled local coalition members to identify common interests. These coalitions jointly implemented policy programs around those common interests that amplified the initial investment of the state government, training skilled workers and attracting industries that employed them. As coalitions emerged, the state's role shifted to

"monitor," assuring that coalitions remained focused on projects that aligned with the primary government priorities of local income and employment growth (Sabel 1994). State governments monitored local economic activity in three ways: critiquing local plans that deviated from state economic development priorities, amplifying local projects that aligned with state priorities, and identifying projects that merged state government and local coalition priorities. In the cities that stagnated, the state government was unable to support sustained cooperation, and local actors pursued separate interests. The difference in outcomes between cities originated not with the content of government interventions, but with the political interactions that the interventions helped facilitate as they were implemented.

I refer to the places that have followed a path to good job growth originating with state government intervention and local economic coalitions as Brass Cities. Just as brass is a composite of naturally-occurring elements (zinc and copper), cities such as Pittsburgh and Albany benefited from the state forging partnerships among diverse actors: universities, firms and governments. Brass Cities are defined by a process and an outcome. The process is state-led coalition building and monitoring. The outcome is income and employment growth, or "good job growth." The explanation for growth in Brass Cities is different from theories of good job growth in places like Silicon Valley, which trace the origins of their economic success to

entrepreneurship and excellent local universities. Although Brass Cities and Silicon Valleys might share similar features today - high-technology jobs, innovative anchor institutions and high-growth startups - what separates Brass Cities is the role of government in promoting and guiding local economic cooperation.

This chapter provides an overview of research on good job growth in cities and the role that state and local government interventions can play. The first section defines the government

interventions at the center of this research as "innovation policies" and distinguishes them from other government interventions in the economy. The second section shows that while

governments have frequently intervened to stimulate local economic activity, the literature on good job growth has rarely suggested any significant role for government intervention. And where research has examined place-making, industrial, and innovation policies, particularly in the United States, evidence of the policies' effectiveness has been lacking. The third section introduces the matched pair case study methodology that this research will use to understand the paths to good job growth in cities. The chapter concludes with an outline of the dissertation.

I. STATE INNOVATION POLICY

Since the founding era of the United States, federal and state governments have adopted an array of policies to promote economic development. Economic historians show that American colonies and later states offered inexpensive credit, corporate tax abatements, and targeted infrastructure investments (Norwood 1974; Lively 1955; Eisinger 1988).

"Beginning with the earliest period of settlement in the seventeenth century, the provincial legislatures and administrations had intervened to shape economic institutions and the dynamics of growth.... The colonies' objectives were to maximize immigration, to foster settlement and capital formation in new agricultural areas, to encourage urban investment and growth, and to develop trade in ways that enhanced the various natural advantages that individual colonies enjoyed" (Scheiber 1987, 420).

Today, governments' local economic development policies include a variety of tax incentives, grants, loan guarantees, and even equity investments. This research focuses particularly on a subset of government initiatives that I will refer to as "innovation policies."

In the early 1980s, U.S. states began implementing a series of policies to promote "technological innovation" with an aim toward "creating new jobs and increasing per capita

income" (Atkinson 1991; Office of Technology Assessment 1984). These state programs are what the OECD's Frascati Manual calls innovation activities: "the scientific, technological, organizational, financial and commercial steps, including investments in new knowledge, which actually, or are intended to, lead to the implementation of technologically new or improved products and processes" (OECD 2002). Examples of innovation policies in U.S. states include "support to higher education and research institutes, provision of seed and venture capital, specialized labor training, promotion of high technology complexes, and encouragement of adoption of new manufacturing technology" (Atkinson 1991). Every U.S. state has invested in some combination of "innovation policies" over the last three decades. However, states are not the only level of government to invest in innovation policies. The next sections will distinguish state innovation policies from similar investments at other levels of government.

i. State v. Federal Innovation Policy

The federal government's investments in innovation policy have often focused on

research and development, which the OECD broadly defines as "creative work.. .to increase the

stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications" (OECD 2002, 30). In addition to supporting basic and applied research at universities and in industry through bureaucratic agencies like the National Science Foundation (NSF), the federal government has also assumed the role of "public venture capitalist" (Block 2008, p. 181), investing in promising new technologies through initiatives such as the Small Business Innovation Research (SBIR) program and the Advanced Research Projects Agency (ARPA). Both federal and state innovation policies have focused principally on

However, the motivations behind federal and state innovation policies have often been different. The federal government's agenda was primarily to invest in research teams and areas with proven strengths. Federal innovation programs in the late 1980s such as the Advanced Technology Program were focused on sustaining the U.S. competitive advantage in the face of "surging Japanese competition in high-tech" (Wade 2014). State policies, by contrast,

emphasized local economic recovery. State government leaders "[believed] high-technology industries can be a major force in the revival of distressed regions and cities" (Office of

Technology Assessment 1984, 4). States framed their policy goal as economic "diversification" to reduce their reliance on declining industries (Jones 1986). The stated purpose of state

initiatives has been to invest in new areas of economic activity - not to reinvest in the historical strengths of the local economy.

The difference between federal and state goals for innovation policy has important implications for cities. Federal innovation policy - by investing in businesses and universities in cities with pre-existing strengths in R&D - has the potential to deepen the advantages of places with skilled workforces and high levels of entrepreneurship. State policies, by contrast, aim to invest in developing local strengths that make cities more competitive in attracting federal government investment. States have designed their innovation initiatives to "capitalize" on and "leverage" federal government investment opportunities (Wessner 2013a). The goal of the state is to invest its resources in ways that unlock federal resources for local universities and firms. Thus, if federal innovation expenditures on national laboratories or research centers contribute to high-wage job growth in a city, there are at least two potential explanations. One is that the local universities and industries had pre-existing strengths that attracted the funding. Another is that state interventions - or other support - helped local universities and industries become a magnet

for federal investment.

ii. The Varieties of Economic Development Policy in U.S. States

State economic development policies, according to Eisinger, fit into one of two categories: supply-side and demand-side policies (1988). Supply-side policies, or "business climate policies," are focused on lowering the costs of doing business in a place through tax abatements and subsidies. Nearly every state uses business tax incentives as a core tenet of their economic development policy. A New York Times investigation in 2012 showed that state and local governments spent an estimated $80 Billion in tax relief aimed at attracting business (L. Story 2012). Business attraction policies do not qualify as "innovation activities" (or policies) according to the OECD definition because they are neither contributing to "new knowledge" nor facilitating the creation of "new or improved products and processes" (2002).

Demand-side policies - which include state innovation policies - are investments to generate new technologies and businesses (Eisinger 1988). In the 1980s, there was a shift in state economic development policy from "business climate" policies - which intensified in the 1960s and 1970s - to innovation policies (Eisinger 1988, 10). Atkinson estimates that the number of states with innovation programs went from nine in 1980 to forty-five approximately a decade later (1991). Eisinger counts thirty-seven states that wrote strategic economic development plans between 1981 and 1988 (Eisinger 1990). The state innovation policies that spread from state to state fell primarily into three categories: supporting new ventures, building technology centers, and investing in R&D. These demand-side policies are the primary focus of this research.

Based on the distinction between supply- and demand-side policies, there appear to be two potential roles for states in economic development. First, states can "bid for business" to

invest locally (Wassmer and Anderson 2001). When states seek to attract firms with supply-side policies, they compete with other areas to make the best offer. Second, states can act as "venture capitalists," making equity investments in young firms or offering grants and infrastructure to research teams with promising technologies (Lerner 1999). The literature in the next section, Section II suggests that governments have not been particularly effective at either role.

II. PATHS TO GOOD JOB GROWTH IN CITIES

Cities have been exalted in recent years as humans' "greatest invention" (Glaeser 2011). Cities and their surrounding metropolitan areas can be "engines of prosperity," more likely to produce new ideas and generate new economic activity than more sparsely populated areas (Katz and Bradley 2013). Yet not every city has a thriving economy replete with high-wage jobs. Porter shows that during the 1990s cities experienced sharp differences in their levels of income and employment growth (Porter 2003). Moretti labels a select group of economically advanced cities "brain hubs" for their concentrations of skilled labor and high-wages (Moretti 2012). At the other extreme are places "once dominated by traditional manufacturing, which are declining rapidly, losing jobs and residents" (ibid.). Understanding how some places follow a path to become "brain hubs," whereas others follow a path to decline, is a generations-old area of inquiry.

When firms and people concentrate in one place, they can become more productive than if they had dispersed across many places - a phenomenon often referred to as "agglomeration economies" (Glaeser and Gottlieb 2009). The places where firms and skilled people concentrate and thrive have acquired a variety of labels: "brain hubs," "industry clusters," and "industrial districts" are only a few. More than a century ago, Alfred Marshall studied agglomeration

economies in the United Kingdom, identifying three mechanisms that enable firms and people to become more productive when they work near one another. First, firms and people can benefit from their proximity to new ideas surrounding them. As Marshall writes, "if one man starts a new idea, it is taken up by others and combined with suggestions of their own" (1961). This process of "knowledge spillover" between people and firms can enable innovations (Audretsch and Feldman 2004). Economists have recognized that knowledge accumulation and

technological progress in an economy are principal determinants of its long-term productivity growth (Arrow 1962; Romer 1986). Productivity, which is often associated with standard of living, is defined as "the quantity of goods and services produced from each unit of labor output" (Mankiw 2016). Second, the concentration of industry in one place generates economies of scale for local firms. If related businesses demand a higher volume of a given input, they can acquire that input for a lower price. As Marshall notes, high demand is also beneficial for local firms that might provide the good or service. Since they know they will have high local demand, they are willing to invest in "expensive machinery" because they are able to make "constant use" of it (Marshall 1961). Third, firms that cluster in one city or region benefit from what Marshall calls "a constant market for skill" (Marshall 1961). Skilled workers are attracted to businesses that demand their skill, and vice versa, Marshall argues. More recent scholarship has sought to understand why skilled labor clusters in some cities and not in others (Berry and Glaeser 2005).

Although economists and others have widely accepted that concentrated economic activity leads to higher output per worker, there has been debate over what type of concentrated economic activity yields the most benefits. Glaeser and co-authors have argued that

agglomeration in related industries (as Marshall suggested) is less likely than agglomeration in diverse industries -- as Jacobs argued (J. Jacobs 1970) -- to lead to employment growth in cities

(Glaeser et al. 1992). Porter and others continue to advocate for the economic benefits of clusters of related and "interconnected" firms and other local institutions (Porter 2000).

"Agglomeration economies" in this research refers to industry concentration that results in higher productivity through knowledge spillovers, economies of scale, and a common talent pool. It encompasses the economic benefits that might result from diverse or related industries locating near one another. I focus particularly on the concentration of what the NSF calls "knowledge and technology-intensive" industries, which include knowledge-intensive services (e.g. software, higher education, hospitals) and high-technology manufacturing (e.g.

semiconductors, pharmaceuticals) (National Science Board 2016). These sectors - which I will abbreviate as "knowledge industries" - can be a source of good job growth. They pay high wages and have positive spillover effects on the wages and growth of other local employers (D. Hill 2014; Moretti 2012). In this section, I identify from the social science and urban studies literatures four paths that cities have followed to agglomeration economies and - eventually -income and employment growth. Each of these paths explores how high-wage jobs come to be concentrated in a particular place. None of the paths originates with public policy interventions. I also discuss research that investigates the impact of public policy interventions on economic outcomes in cities. This research identifies a role for specific policy interventions to have a limited impact, but it does not typically study the politics of achieving these interventions or their potential to stimulate broader economic transformation.

Figure 1.1: Models of Good Job Growth

Origin Intermediary steps U.S. Examples

Entrepreneurship Industries concentrate around high- Seattle (Moretti); Silicon Valley

growth startups. (Storper et al.)

Knowledge spillovers and technology Seattle (Gray et al.); Anchor institutions _{transfer produce spin-off companies. Durham, NC (Link);}

(firms, universities) Common talent pool attracts skill- Atlanta (Youtie and Shapira) intensive industries.

Highly skilled individuals are more

Skilled labor likely to start companies in growth Boston (Glaeser) industries.

Culture of Innovative networks of firms create Silicon Valley (Saxenian); cooperation agglomeration economies and Allentown, PA (Safford)

knowledge spillovers.

i. Entrepreneurship

Entrepreneurs who create high-growth startup companies in a city can attract a pool of skilled workers and related companies that seek to locate near the growing firm. Scholars have long seen innovative activity like entrepreneurship as central to economic growth: "the

fundamental impulse that sets and keeps the capitalist engine in motion comes from... new methods of production.. .[and] the new forms of industrial organization that capitalist enterprise creates" (Schumpeter 2010). Models of economic growth have recognized that innovation originates with "forward-looking, profit-maximizing agents" - entrepreneurs (Romer 1986).

There is also empirical evidence of the economic benefits of entrepreneurship. Haltiwanger and colleagues identify that young firms - more than small firms - are substantial contributors to net

The entrepreneurial path to good job growth begins with risk-taking entrepreneurs starting firms that bring a new industry to a city (Bresnahan, Gambardella, and Saxenian 2001). These firms can be important for generating agglomeration economies in related industries, some scholars have argued, because a cluster of related firms would serve the entrepreneur's business interests: if related firms locate near the entrepreneur, their startup is more likely to succeed (Feldman, Francis, and Bercovitz 2005). However, new startup firms have a history of locating where related firms are already concentrated (Delgado, Porter, and Stem 2010). The location of entrepreneurship is not random; instead, some industries and areas are more conducive to entrepreneurship than others (Chinitz 1961). These findings suggest that entrepreneurship may be a lagging indicator - not a catalyst - of agglomeration economies.

Government has invested in policies to promote entrepreneurship, including public venture capital funding, but there has not been evidence that these policies alone have been effective at stimulating entrepreneurship (Chatterji, Glaeser, and Kerr 2014). For example, companies with purely government venture capital investment have a worse record of exits than companies with mixed public-private or purely private investment (Brander, Du, and Hellmann 2014). Although government venture capital funds have had lackluster results, Lerner suggests that there might be strategies (e.g. "invest in building relationships" and "appreciate the need for flexibility") for publicly-funded investment operations to become more effective (Lerner 2013).

There have been individual cases where it appears entrepreneurs have played a large role in building new local industries. Bill Gates and Paul Allen moved Microsoft to from

Albuquerque, NM to Seattle, WA, which has since developed a large software industry. William Shockley moved from Bell Labs to what would become Silicon Valley, recruiting talent to his semiconductor startup that would later found an array of other semiconductor companies in the

region, including Intel (Morris 2014). In these instances, it appears that entrepreneurs chose their new cities for personal reasons. Gates and Allen were originally from Washington state (Moretti 2012). Shockley reportedly moved to California be closer to his mother (Storper et al. 2015). In these cases, government did not influence the resources for entrepreneurship or play much of a direct role in the entrepreneurs' ultimate success.

ii. Anchor Institutions

Alternative histories of high-wage job growth in Seattle and Silicon Valley have focused on the importance of Boeing and Stanford University: large "anchor institutions" with a sizeable impact on the growth of knowledge industries in the local economy. Anchor institutions - large corporations, universities, and hospitals - can generate knowledge spillovers through spin-off companies, attract related firms to the region through industry linkages and R&D, and produce a skilled workforce.

a. Universities

Universities have created knowledge spillovers by spinning out start-up companies, licensing technologies to local firms, and helping launch technology parks that attract research-oriented firms. Etzkowitz claims that universities have become "entrepreneurial" and "research groups in universities have become 'quasi-firms'." He argues that universities are motivated by the economic potential of generating startup companies and commercializing research

(Etzkowitz 1983, 2003). Yet Feller has argued that it is unclear whether universities are wise to focus on commercialization given that only a few university ventures are likely to prove

to local economic expansion - MIT in Boston and Stanford in Silicon Valley - inspired researchers and policymakers to explore the university's direct contributions to the economy more closely (Bercovitz and Feldman 2006).

Universities can have an impact on the local economy through the technology that they commercialize (Breznitz 2014). Early research in this area found that university research was associated with local private sector patenting (Jaffe 1989; R. Henderson, Jaffe, and Trajtenberg 1998). In biotechnology, for example, although university researchers contribute to private sector innovations; however, the link between university knowledge spillovers and new startup

companies and other local economic activity is less clear. Audretsch and Stephan find that "approximately 70 percent of the links between biotechnology companies and the university-based scientists are nonlocal" (Audretsch and Stephan 1996). Bania and co-authors found a link between university research and new start-up companies, but only in one industry area: Electrical and Electronic Equipment (Bania, Eberts, and Fogarty 1993). The literature suggests that

knowledge spillovers from universities could generate startups and local economic activity, but not in every industry or every place.

The 1980 Bayh-Dole Act - "which gave U.S. universities property rights to federally funded inventions" - was the federal government's effort to promote the patenting and commercial licensing of university research (Shane 2004). Although it increased university patenting in areas where the invented technology could be licensed, the average quality of each university patent - measured by citations - diminished after Bayh-Dole was passed (Shane 2004; R. Henderson, Jaffe, and Trajtenberg 1998). Moreover, university technology transfer offices have only on rare occasions generated substantial licensing revenue that rivals their R&D investments (Nelsen 2011; Valdivia 2014). The licensing revenue is "[d]ominated by a few very

large royalties from fewer than 1% of total patents," Lita Nelsen (Former Director of MIT's Technology Licensing Office) cautioned in a presentation on technology transfer from

universities (2011). Bayh-Dole changed the way universities approach technology transfer, but commercial licensing from universities does not seem to have had a substantial impact on local income and employment growth.

Public funding has also been allocated to support startup incubators and research parks, which are often associated with universities (Chatterji, Glaeser, and Kerr 2014; Luger 1991). The theory is that incubators and research parks might support the growth of local companies and the commercialization of innovations from local universities. Although there is evidence that firms in research parks tend to perform better than matched firms not in research parks (Link et al. 2015; LUfsten and Lindelhf 2002; Link and Scott 2006), it is unclear whether research parks or technology incubators can effectively bring innovation and agglomeration to a declining region. There is wide recognition that some research parks - such as Research Triangle Park in the Durham, NC region - have been effective magnets for regional economic activity (Chatterji, Glaeser, and Kerr 2014; Link 1995; Link and Scott 2003). However, many planned parks have flopped (Goldstein and Luger 1990; Luger 1991). Research Triangle Park (RTP) has been an anomaly: the example that helped spark a national interest in government-supported research parks, but that other places were unable to replicate.

There is also evidence that research universities have stimulated R&D investments for firms in related fields. When universities spend on R&D, their investment has been associated with more local R&D investment in similar fields, particularly for specialties like

pharmaceuticals research (Jaffe 1989; Anselin, Varga, and Acs 1997; Abramovsky, Harrison, and Simpson 2007). However, university investments are associated more with small business

R&D than larger corporate R&D operations (Acs, Audretsch, and Feldman 1994). And the role universities play in the local economy can depend on which industries are already concentrated in a region. There is skepticism that universities can spark agglomeration in a specific industry on their own (Varga 2000).

b. Firms

State and local governments have both for decades engaged in what has been called "smokestack chasing." Government officials offer tax and other incentives to entice large companies to invest locally - either create jobs at a branch office, build a plant, or relocate their headquarters. The objective is sometimes for these companies to become "anchor firms" and play a role in attracting related firms to their city (Agrawal and Cockburn 2003). There is

evidence that large firms like Boeing have helped support a local supply chain of smaller companies in related sectors and ultimately a cluster of high-technology activity in the region (M. Gray, Golob, and Markusen 1996). Large biotechnology companies have played a similar role in attracting smaller firms in related industries to locate nearby (Feldman 2003).

There is an ongoing debate among researchers and policymakers over whether tax incentives to attract businesses play a significant role in stimulating local job creation. Reviews of the literature suggest that the evidence is mixed, and studies often suffer from poor data availability (Buss 2001; Bartik 1991; Wasylenko 1999). Advocates for using tax incentives to attract businesses recognize that taxes alone cannot generate agglomeration economies; however, they argue that by reducing the costs of doing business, tax incentives can stimulate local job creation (Bartik 1991). When states succeed in attracting large companies to invest in a local

plant or relocate their headquarters, there is evidence of spillover benefits for the productivity of the local industry (Greenstone, Hornbeck, and Moretti 2008; Garcia-Mila and McGuire 2002).

However, there is only a tenuous association between tax incentives and business relocation decisions. Firms consider many other factors beyond taxes in relocating (Lee 2008), and states with more "business-friendly" corporate tax structures do not necessarily perform better economically (Prillaman and Meier 2014). Although firms that receive tax incentives might report higher employment growth publicly, it is unclear whether they have actually realized that growth (Gabe and Kraybill 2002). Studies of individual states' tax incentives programs suggest that they promote a "race to the bottom" in business tax incentives among localities (Cassell and Turner 2010; Grady 1987). Reporting from The New York Times

highlights the problems of the race to the bottom: companies can benefit from the tax incentives that local economies offer, then leave in search of an even more generous tax deal (L. Story 2012).

Despite the mixed evidence, there are high-profile cases of government business incentives that seem to have been effective at sparking new economic activity. For example, Austin, TX attracted the headquarters of the Microelectronics and Computer Technology

Corporation (MCC) in competition with places like Silicon Valley and the Research Triangle in North Carolina. The relocation of the MCC - before the founding of Dell, after the relocation of

IBM offices to Austin - is associated with the founding of the computer industry in Austin (Ladendorf 2013). Similarly, BMW's investment in a South Carolina plant in the early 1990s helped develop a new industry in the region (Collins 2012; Nash 2011). However, these cases of high-impact relocations were not the result of tax incentives alone. In Austin, the government offered the use of UT Austin facilities and the use of Ross Perot's private plane (Gibson and

Rogers 1994). In South Carolina, the state's community college system was cited as important for meeting the skill requirements of German auto manufacturers and their supply chains (Nash 2011). These examples suggest that while tax incentives alone might not stimulate the local economy, governments have used tax incentives in tandem with other targeted resources to attract local anchor institutions.

Scholars have also explored the local economic benefits of the federal government's large research and defense investments in local areas. What O'Mara calls "cities of knowledge," she

argues, "are products of Cold War spending patterns" (O'Mara 2005). The federal military-industrial complex, Markusen and her co-authors argue, lifted the local economies that became hubs for defense industries. "Entire urban economies depend on the aerospace complex, which,

in turn, relies on continued government spending" (Markusen et al. 1991, 8). Where the federal government makes investments in research and defense is not random. It is a function of a political appropriations process and the pre-existing industries and military infrastructure available in each place.

But, as O'Mara notes, large commitments of federal spending are not guaranteed to result in local economic expansion. O'Mara suggests that successful "cities of knowledge" can emerge around a university with "power," which she suggests that government interventions can bolster

(2005). Atlanta, for example, received large defense appropriations, but its flagship research

institution - the Georgia Institution of Technology was not "politically empowered and

economically engaged" (O'Mara 2005). The result, she argues, was less economic development for the Atlanta metro area than there was for Silicon Valley, which benefited from Stanford University's "entrepreneurial administrators" and research park (O'Mara 2005). O'Mara's recommendation for "the next Silicon Valley" is that it will need "a lot of money" invested and

"a powerful university" (O'Mara 2005, 226-27). However, she does not explore how universities derive their power in the local economy or the role of the state government as a constructive force in that process.

Large research-oriented firms and universities have also helped develop and maintain a common pool of talent in cities. Before Microsoft moved to Seattle, Boeing had attracted a "regional pool of computer specialists, some of whom went on to form their own companies" (M. Gray, Golob, and Markusen 1996). The presence of a university in a city is associated with higher levels of education in the city population and higher wages in its workforce (Moretti 2012). Richard Florida has argued that universities are most important as an institution for training and maintaining a skilled workforce rather than as an "engine" for economic activity through innovation (Florida 1999). MIT's Associate Provost Richard Lester captures the concept well: "It is often said that the best form of technology transfer is the moving van that transports the PhD from his or her university laboratory to a new job in industry" (Lester 2005). Of course, economists are quick to caution that the talent universities produce is highly mobile (Moretti 2012). The van might travel beyond the city where the university is located.

iii. Skilled workforce

Cities with a more skilled or educated workforce - a high proportion of college graduates - have been associated with higher population growth and higher wages (Glaeser and Saiz 2003; Glaeser and Resseger 2010). These skilled cities are better equipped to "reinvent" themselves with changes in the predominant industries in the economy (Glaeser and Saiz 2003). Glaeser's example is Boston, which has been able to shift from an industrial economy to a knowledge economy - he argues - with help from its highly-educated population (Glaeser 2005).

Entrepreneurship is the mechanism that allows cities with human capital to adapt to

technological change and develop a competitive advantage in new industries over time. Skilled individuals are more likely to engage in entrepreneurship, and entrepreneurial ventures are more likely to demand other skilled labor (Berry and Glaeser 2005; Glaeser, Ponzetto, and Tobio 2014). The importance of human capital reflects the features of agglomeration economies: firms are attracted to common pools of talent, and knowledge spillovers within skilled populations are likely to generate successful entrepreneurial ventures.

A city's level of human capital appears to be path-dependent: the cities that have

historically had the highest levels of human capital have only become more educated compared to cities with a legacy of less education. Human capital in cities appears to diverge rather than converge (Berry and Glaeser 2005; Simon and Nardinelli 2002). The pattern of "brain hubs" becoming brainier reflects the theory behind agglomeration economies: a talented workforce attracts firms in knowledge industries, which attract more talented workers.

Researchers have emphasized two types of human capital as particularly important for positive economic outcomes: "star scientists" and "creatives." Individual researchers with many patents - "star scientists" - can have large effects on the number of local startups in their areas of research (Zucker and Darby 2007). Government subsidies to attract star scientists seem to have been effective. There is evidence that local tax incentives for biotechnology industries have

attracted star scientists in biotechnology - the top 5% of patenters in their field - to new places. However, research has not identified whether the subsidies to attract talent have generated more

economic activity than they cost (Moretti and Wilson 2014).

What Richard Florida calls the "creative class" includes scientists, engineers, and artists. Individuals in these groups, he argues, are attracted to a city's tolerance, diversity, and amenities

- not just its job opportunities. His measures for these features include what he calls a Gay Index and a Bohemian Index for cities (Florida 2003). Florida's ideas have been popular among

politicians, but there is not clear evidence of their effectiveness (Peck 2005). Glaeser shows that after controlling for other features of a city, such as the share of its population with a college education, Florida's creative class measures do not appear to predict city growth (Glaeser 2005).

iv. Culture of Cooperation

A group of scholars studying diverse regional economies have argued that places with high "social capital" - "norms and networks that enable people to act collectively" - are more likely to experience innovation, agglomeration, and other economic benefits (Woolcock and Narayan 2000). Saxenian claims that the "rich networks of social, professional and commercial relationships" in Silicon Valley - at once cooperative and competitive - is what differentiated its high-growth technology industry (Saxenian 1990). Putnam and colleagues argue that northern regions in Italy have grown faster because the dense social networks between organizations allowed their regional governments to invest in economic development more substantially than others (Helliwell and Putnam 1995; Putnam, Leonardi, and Nanetti 1994). Safford agrees that social networks enable regions to take collective economic action, but argues that diverse -rather than dense - "civic infrastructure" is important (Safford 2009).

Social capital theories draw on a long tradition in sociology that emphasizes the importance of social norms and relationships - not just market incentives - in determining economic behavior (Granovetter 1985; Portes 1998). They align with Ostrom's work illustrating that social norms can lead to collective action even when the actors have the competitive

factor in the innovation process (Teece 1992). Powell and co-authors highlight the importance of cooperative networks of firms to innovating in biotechnology (Powell, Koput, and Smith-Doerr 1996). Porter's cluster theory and Markusen's review of industrial districts both argue that the regional economy is not only defined by market competition, but also by cooperation among local economic actors (Porter 2000; Markusen 1996).

Cooperation is important for innovation and economic development because "states, firms, and communities alone do not possess the resources needed to promote broad-based, sustainable development; complementarities and partnerships forged both within and across these different sectors are required" (Woolcock and Narayan 2000). The challenge is that it is

often unclear how a region forges cooperation or builds social capital. Scholars often appear to assume that local culture and social capital are inherited from deep historical processes. Boix and Posner suggest that the available explanations for the origins of social capital - actors'

expectation of repeated interactions, agreements to produce private rather than public goods, or state enforcement of inter-group cooperation - do not seem to be supported empirically. They propose that social cooperation might emerge spontaneously, and the real challenge is how to sustain it (Boix and Posner 1998). Evans also questions whether an economy can "construct" social cooperation on its own. He argues - in the context of developing countries - that political competitiveness and a well-functioning state might be the best conditions to promote economic cooperation (P. Evans 1996).

The government interventions that have sought to promote cooperation and build social capital among local firms, universities, and governments have been called "cluster policies." Their aim has been to support the growth of a set of related industries that have a local legacy. They draw on the research of Michael Porter, who argues that industry clusters make cities more

competitive in high-wage, tradable industries. Although Porter argues that clusters form "spontaneously based on market forces," he suggests that federal governments can provide incentives to provide club goods and "reinforce" cluster formation (Porter 1990; Porter 2007).

In practice, cluster policies have been widely criticized as unduly complex, and the concept of clusters itself has been critiqued as "deliberately vague" (Martin and Sunley 2003; Nathan and Overman 2013; Duranton 2011). Empirical studies that have tried to evaluate the

effects of cluster policies report mixed results. A review of French cluster policies reports that public investments did not seem to have an effect on the employment or the productivity of the recipient firms (Martin, Mayer, and Mayneris 2011). In Bavaria, however, a series of cluster policies called the 'High Tech Offensive' are associated with more local "innovative activity," but less R&D expenditures for firms in the "target industries" (Falck, Heblich, and Kipar 2010). The Bavaria program, despite some positive results, cost more than 1 Billion Euros to implement (Falck, Heblich, and Kipar 2010; Nathan and Overman 2013). Large government investments in a local economy - what some economists call "big push" policies - can generate long-run

economic benefits. Moretti and Kline show that infrastructure investments through the Tennessee Valley Authority (TVA) have yielded long-term benefits for manufacturing employment in the region (Kline and Moretti 2014). It is unclear whether the positive results of the 'High Tech

Offensive' were merely a stimulus from increased local spending, or if they were the result of increased economic cooperation among universities and firms in Bavaria.

Federal government interventions in the U.S. - including tax breaks for firms funding university research and NSF funding for universities tied to industry partnerships - have been

associated with an increase in partnerships between universities and industry (Cohen et al. 1998; Florida 1999). However, these partnerships are often riven with conflict over Intellectual

Property (IP). Whereas the corporation is focused on maximizing shareholder value (Lazonick and O'Sullivan 2000), a university seeks to "increase its eminence" through generating new discoveries and recruiting "the most revered academics" (Florida 1999). Industries "prefer less disclosure of research to increase the appropriability of the profits of any...innovations that may grow out of the research," but restrictions on disclosure "compromise the norm of open science valued by researchers as an end in itself' (Cohen et al. 1998, 186; 191). Corporations have expressed frustration working with universities due to contentious IP discussions. Florida quotes one R&D executive as lamenting: "The university takes this money, then guts the relationship" (Florida 1999, 69). The assumption in the study of university-industry interactions has often been that the actors' interests are fixed, which makes cooperation difficult. Universities pursue open knowledge for eminence, and firms pursue private knowledge for profit. Federal government policy appears to have encouraged interactions between universities and firms without actively facilitating cooperation that could prove beneficial for good jobs in the local economy.

There is ample evidence that certain industrial, institutional, demographic, and cultural features of cities are associated with positive long-term economic outcomes. In general, places with more skilled labor or innovative research findings tend to become hubs for industries with high-wage job opportunities. And specifically, case studies of cities with successful track records of high-wage job growth attribute their economic success to the growth of entrepreneurial

ventures, the contributions of large local organizations, and cultures of cooperative exchange among businesses, non-profits, and civic organizations. Indeed, these ingredients all seem important for a city's growth. Yet it is unclear how a city without these features might create

them. Research on clusters, for example, suggests that they emerge "spontaneously," and the formation of new Silicon Valleys is due in significant part to "luck" (M. Porter 2007; Bresnahan, Gambardella, and Saxenian 2001). What can government interventions do to change the

economic trajectory of cities? The conclusion from the literature appears to be not much. Rare cases of "big push" infrastructure policies seem to make a difference, as do targeted

interventions such as investments in "star scientists" (Moretti and Wilson 2014; Kline and Moretti 2014), but it is unclear how cities might amass the political support for a "big push" or how attracting "star scientists" might translate into broader economic development. In this dissertation, I study a group of government interventions targeted at cities that lacked some or all of the local ingredients associated with high-wage job growth. By comparing the economic trajectories of similar cities and their governments' implementation of these interventions, my objective is to investigate how - if at all - public policies can influence a place's path to growth.

III. EMPIRICAL STRATEGY

My research focuses on state governments' innovation policies targeted at large U.S. cities in response to the decline of manufacturing. These government interventions have unfolded over the course of more than three decades - roughly 1980-2014. Beginning in the early 1980s, U.S. cities with a manufacturing legacy faced similar economic pressures from industrial decline, and state governments across the country responded with a similar set of innovation policies. These policies were part of a strategy to stimulate long-term investment in "knowledge and technology-intensive industries" with long-term growth prospects that offered high-wage jobs. In some cities that received targeted investment, the local economy - the

decades consistent with the objectives of state policy. In others, the local economy continued to decline. I study these policies during this period for three primary reasons. First, beginning with cases that share an historical context invites comparative research to hone in on the mechanisms that could have enabled some government interventions to be influential, but not others. Second, studying government interventions in response to deindustrialization over many years includes the implementation of government policy as well as its design. The study of public policy often compares places that implemented a particular policy to places that did not implement the policy (Storper et al. 2015, 112). It often fails to explore how similar policies -- implemented differently across city or state contexts - might lead to different outcomes. The study of policies'

heterogeneous effects is particularly relevant subnationally, where policy diffusion might lead many jurisdictions to adopt a similar set of policy ideas (Shipan and Volden 2012). Third, the long time period - more than thirty years - is necessary for the study of industrial change in U.S. cities. Although a set of government interventions might show progress in the first years after they are enacted, the stated goals of state innovation policies have been long-term economic transformation. If the period for this research had ended in 1985 or even 2000, this research would have selected different case studies and come to different conclusions. As I recognize in subsequent chapters, the time period in this research might still be too narrow. In another ten or twenty years, new data might suggest that some policies have been far more - or far less -influential than they appeared in this research.

The methodological approach that I take in this research is primarily qualitative.

Qualitative methods comprise a set of "overlapping approaches such as the case-study method, small-N analysis, the comparative method, the comparative-historical method," and others. "Mainstream" quantitative methods, by contrast, are "based on the use of regression analysis and

related techniques for causal inference" (Brady, Collier, and Seawright 2010, 15-16).

Quantitative research has typically focused on "effects-of-causes" research questions, which aim to identify the "average effects" of a particular treatment on an outcome of interest (e.g. the effect of tax policy on employment growth). Qualitative research is more suited to answer "causes-of-effects" questions for individual cases, aiming to understand the potential explanations for an outcome of interest (Goertz and Mahoney 2012, 41-47). The research

questions in this dissertation are focused on "causes-of-effects," or explanations how some cities experienced more good job growth than others. The questions call for an exploration of multiple potential causal paths to income and employment growth.

There are three advantages that qualitative research tools have for this research. The first is that qualitative methods can generate new hypotheses or potential explanations for social science puzzles. George and Bennett highlight how qualitative case studies, or "instance[s] of a class of events" can be used for inductive theory development (A. George and Bennett 2005, 17). Intensive studies of "deviant cases," or "observations with outcomes that do not conform to theoretical predictions," can help generate new theories to explain the outcome of interest (James Mahoney 2007, 125; A. George and Bennett 2005, 111). Mahoney points out that new theories can also emerge from historical case comparisons, which "juxtapose multiple features of cases with one another" and examine "the unfolding of events over time" (James Mahoney 2007, 125-26). Since quantitative methods are designed to test hypotheses with available data, they are ill-equipped for generating new theories (James Mahoney 2007). This dissertation is focused on generating a theory of state involvement in cities' economic growth based on a study of cases that deviated from theoretical expectations: cities that were predicted to experience economic stagnation, but actually grew.