Competing for Global Capital or Local Voters?
The Politics of Business Location Incentives
Nathan M. Jensen
George Washington University
Edmund J. Malesky
Duke University
Matthew Walsh
University of California, San Diego
ABSTRACT
The competition for global capital has led to wars between countries, states, and cities as to who can
offer the most attractive incentives to firms. In this study, we examine the domestic politics of this
competition by focusing on incentive use in the United States from 1999–2012. Drawing on data
from municipal incentive programs, we examine how electoral competition shapes the use and
oversight of targeted incentives. Taking advantage of exogeneity in the assignment of city
government institutions and a database of over 2,000 investment incentives from 2010–2012, we
find evidence that cities with elected mayors provide bigger incentives than non-elected city
managers. We also find that elected mayors enjoy more lax oversight of incentive projects than their
appointed counterparts. Our results have important implications for the study of fiscal wars and the
role of electoral pandering in economic policy.
Notes: Thanks to Benjamin Crisman, Lillian Frost, Mi Jeong Shin, and Kathryn Sproule for
excellent research assistance and Brady Baybeck, Adam Bonica, Randy Calvert, Jim Clinger, Bill
Lowry, Gary Miller, and Jessica Trounstine for comments and suggestions. Thanks to Kimberly
Nelson for sharing her data with us. The Weidenbaum Center and Center for New Institutional
Social Sciences at Washington University in St. Louis provided financial support for this project.
1
1. Introduction
The competition for global capital is fierce. Countries, states and local governments offer
lucrative location-based incentives in order to attract job-creating investments to their districts. This
investment competition can literally span the globe. For instance, BMW originally considered 250
different locations in its search for a new plant in 1992, eventually narrowing the search down to
twenty different countries (Greenstone and Moretti 2003). Although many of the factors
determining a firm’s location choice are either outside the control of governments (e.g., size of the
domestic market, geography, etc.) or change very slowly over time (e.g., levels of human capital,
levels of economic development, etc.), one of the few immediate policies that governments can
control are firm-specific location incentives to attract investment.
Although the competition for direct investment is global, our research design exploits the
excellent laboratory that the United States provides for studying the dynamics of fiscal competition.
Most U.S. states now have dedicated agencies for the facilitation of domestic and international
investment, and many U.S. municipalities now offer location-based incentives. Technically defined
as any “deduction, exclusion, or exemption from a tax liability, offered as an enticement to engage in
a specified activity (such as investment) for a certain period,” incentives most often take the form of
tax holidays and abatements.1 In a survey of U.S. city and county managers in 1999, 68% of localities
used at least one form of incentive, though many used several different forms. This number
increased to 95% in 2009. In all, U.S. state and municipal governments now spend approximately
$46.8 billion per year on location incentives to attract foreign and domestic investors and retain
existing investment.2
Unfortunately, oversight of these programs has not kept pace with the growth in their use
(Thomas 2007). Many of the state incentive programs are opaque, providing little information on
1
2
http://www.businessdictionary.com/definition/tax-incentive.html
Thomas (2011).
2
allocation decisions. For incentive programs in U.S municipalities, a survey conducted by the
International City/County Management Association (ICMA) in 2009 found that only 55% of
locations had mandatory performance qualifications for companies receiving incentives, such as the
number of jobs created or amount of capital investment provided, while 28% performed no costbenefit analysis at all.3 Although state investment incentive programs have more oversight, there
remains considerable variation in their reporting and performance requirements (Kansas Inc. 2009).
This lack of information makes it difficult to distinguish if these programs are created for sincere
economic development or exploited by self-interested politicians to harness public capital for their
own political or pecuniary gain.
In this paper, we argue that electoral motivations can drive incentive use and oversight. In
particular, we build off the pandering literature to theorize that municipal executives facing direct
electoral pressure are more generous in their use of incentives and endure less oversight of incentive
programs. To test our theory, we exploit variation in municipality-level political institutions in the
United States. According to Vlaicu and Whalley (2012), over half of U.S. municipalities operate
under a manager-charter system, whereby the position of chief executive (manager) is hired and fired
by an elected council and not by voters;4 we employ the standard term “council-manager” to
describe this principal-agent structure.5 We expect to observe a different pattern of policy selection
from council-manager executives than we observe from directly elected strong mayors in “mayorcouncil systems,” who respond to the voting population as their primary principal. We present
evidence that these different principal-agent structures may account for the observed heterogeneity
in location-based incentive policies.
ICMA (2009).
In the ICMA data, we observe a 75% share of council-manager systems.
5 Many council-manager systems also have directly elected mayors, although they have considerably less
power than mayors in mayor-council systems. To distinguish, we occasionally refer to directly elected mayors
in a mayor-council system as “strong” mayors.
3
4
3
We test the impact of electoral institutions on a dataset of over 2,000 project-level incentives
(icaincentives.com 2013), finding significant support for the role of electoral institutions in shaping
incentive behavior. Accounting for non-random assignment with entropy balancing, mayor-council
systems are no more likely to offer an incentive to an individual firm, but offer 30% more money
per project. We argue that the greater generosity of these strong mayors is facilitated by the fact that
they face less oversight than comparable cities subject to council-manager systems. Tellingly, mayorcouncil systems are 10% less likely to have an oversight program that requires performance criteria,
and 8% less likely to require cost-benefit analyses on the use of incentives.
Our work is in line with previous explorations of U.S. local outcomes which have shown
that municipalities with powerful, directly elected mayors (formally called mayor-council systems)
have some distinctive characteristics. Specifically, they employ more public officials, especially
police officers, and are less likely to privatize public services. Although most analysts agree on these
trends, it remains disputed whether mayors do this because of patronage politics (Enikolopov 2010)
or pandering (Vlaicu and Whalley 2012). In the case of incentives, we find that the pandering
explanation is more salient both theoretically and empirically.
Our paper is organized as follows. Section 2 discusses the economics of firm-specific
location incentives, outlining the debate over these policies. Section 3 presents our theory on the
political use of location incentives. Section 4 explains our research design. Section 5 tests the
allocation of incentives of U.S. municipalities from 2010–2012, specifically examining how the form
of government shapes the frequency and size of incentives offered and how electoral institutions
affect the oversight of location incentives with data on municipal-level incentives in the United
States in 1999, 2004, and 2009 using entropy balancing to address imbalance in observable covariates
(Hainmueller 2012). In Section 6, we attempt to parse out alternative theories by exploring how the
date of the election influences incentive allocations. Section 7 concludes.
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2. The Economics of Location Incentives
A cursory review of major newspapers brims with examples of location-specific incentives.
Many stories tout the economic benefits of such expenditures, but the sums involved for the
investment incentives used to attract these firms can often be astonishing. For example, Kia
Motor’s decision to invest in Georgia came with a price tag of $258 million in fiscal incentives,
amounting to $195,000 per direct job created (Chapman 2001). Google’s “server farm” in Lenior,
North Carolina will net as much as $260 million in incentives, albeit with little local job creation.6
Only 150 employees work at the facility, and there is little evidence that most of these were local
residents gaining new jobs, as opposed to Google employees being transferred to the facility.7
One explanation for the use of location incentives is as a mechanism to harness capital for
economic development. By targeting specific firms for incentives, governments can price
discriminate between different firms as a way to overcome barriers to investment, such as poor
infrastructure or distance from markets (Morisset and Pirnia 1999). These upfront investments
could generate positive spillovers that help the country, state, or local governments attract future
investment.8 Research by Greenstone and Moretti (2003) finds that a sample of “million dollar
plants” in the United States led to substantial increases in wages and property values with little
evidence of fiscal stress on local government.
These purported benefits of incentives require one to assume that governments have the
capacity and motivation to price discriminate, providing incentives based on an economic, costbenefit calculation. Empirical evidence, however, demonstrates that there are two problems with
this assumption.
Byrnes and Cowan (2007); Langfitt (2009).
Frazier and Henderson (2013).
8 For work on FDI and agglomeration see Head et al. (1995, 2000) and Barrell and Pain (1999).
6
7
5
First, investment incentives may have little impact on the ultimate location decisions of most
firms. Proponents of incentives have difficulty demonstrating that incentives actually changed the
minds of investors, rather than simply compensating them for a decision they would have made
anyway, based on the more mundane factors of human capital, infrastructure, and proximity to
markets. A survey by Grant Thornton in 2013 of 3,400 businesses in 44 different countries found
that 67% would not be tempted to relocate to take advantage of lower taxes (Thornton 2012). More
systematically, Wells et al. (2001) uses the repeal of Indonesia’s tax incentive programs to show that
these incentives had little influence on firm location decisions. Bronzini and de Blasio (2006) find
that the primary impact of Italian tax incentives were on the timing of investments because firms
adjusted their entry decisions to take advantage of subsidies for outlays that they planned to make
anyway. In a study of German manufacturers locating in the United States, Bobonis and Shatz
(2007, 39) find that incentives have “little influence over the location of FDI.” This is not to say
that incentives do not matter at all, but they most likely matter at the margin and are by no means
the primary determinant of investment location choice.
Second, incentives are often excessively large relative to the target investment, leading to
economic inefficiencies (Head et al. 2000; Morisset and Pirnia 1999; Blomstrom and Kokko 2003;
Easson 2004; Fox and Murray 2004; Peters and Fisher 2004; Buettner and Ruf 2007; Keen and
Mansour 2010). These excessive tax incentives have been documented by Moran (1998) and
Thomas and Wishdale (2009). Although this simple accounting of jobs-per-dollar provides prima
facie evidence for this excess, a more systematic study requires the counterfactual of the net impact
of incentives after taking into account their full multiplier effect.9
However, even when governments claim credit for successful job creation, a closer look at
the numbers reveals a great deal of creative accounting. Gabe and Kraybill (2002) study incentives
9
See Glaeser (2001) for a review.
6
in Ohio, finding that they had a marginally negative impact on employment growth. Where they did
seem to have a positive effect, however, was on the potential job growth that was announced at the
onset of the investment. Ohio investment promotion officials pre-announced far larger potential
growth numbers for projects that received incentives, suggesting a pandering element in the
announcements as well. Similarly, in a review of the jobs data reported by the Utah Science
Technology and Research Initiative (USTAR), a large tax incentive program dedicated to improving
the environment for technology-led economic development, the Office of the Legislative Auditor
General pointed out that the jobs reported “included jobs that no longer exist, were based on
projections instead of actuals, and included duplicate counts.” Among the sample set of companies
the audit team examined, 49% of jobs could not be validated (Office of Auditory General, State of
Utah, 2013).
Although there are clearly examples of incentives that can be used to attract investment,
there is little systematic evidence that these policies are effective economic development strategies.
A related literature has examined the use of enterprise zones, which generally provide location-based
incentives to firms (Buss 2001). The conclusions are best summarized in the subtitle of a recent
paper by Greenbaum and Landers (2009) on enterprise zones: “Why Are State Policy Makers Still
Proponents of Enterprise Zones? What Explains Their Actions in the Face of a Preponderance of
Research?”
This debate demonstrates the lack of clear evidence supporting the effectiveness of these
programs, which suggests that many individual incentives may be ineffective or excessively costly.
Thus, considering the controversy over their effectiveness, our primary goal here is to understand
the political factors that might be driving the provision of firm-specific location incentives. Given
the great uncertainty about their utility, why do leaders find location incentives to be such an
attractive policy tool?
7
Jensen et al. (2014) argue that the answer is the electoral benefit of using tax incentives for
political pandering. Building on theories of electoral pandering and an original survey experiment of
U.S. voter evaluations of governors, they find that there are substantial electoral benefits from
providing location incentives. In fact, respondents to their survey changed their level of support for
governors who employed incentives regardless of investment outcomes, allowing incumbents to take
credit when investment occurred and avoid blame when firms chose to locate in another state. This
finding plays an important role in explaining how the use of location incentives may be a dominant
strategy for political leaders, independent of the debate about their effectiveness in attracting
investment.
Showing that voters are receptive to the appeal of incentives, however, only provides half of
the empirical evidence for the political use of location incentives. Therefore, we take up the second
half in this paper by examining whether electoral competition motivates politicians to provide
targeted incentives.
We test the use and oversight of incentive programs by taking advantage of a quasiexperiment generated by U.S. municipalities. The fact that similarly situated municipalities in the
United States are governed by both elected mayors (mayor-council systems) and appointed managers
(council-manager systems) provides variation on the electoral incentives faced by local decisionmakers.
The comparison between council-managers and mayors-council systems has the particular
advantage of stability over time, mitigating threats of endogenous selection into the institutional
setting. A major shift from mayor-council to council-manager systems occurred in the Progressive
Era before 1936 (Knoke 1982, Judd and Swanstrom 2010), well before the use of incentives became
widespread and seventy years before our period of investigation. After that date, switching between
forms was less common. Enikolopov, for instance, documents only 55 adoptions of council8
manager institutions (47 are in the reverse direction) in his dataset of 1,546 cities with populations
over 30,000 between 1987 and 2002 (6.5% of the total sample).
3. Theory
Our paper departs from debates on the economic costs or benefits of location incentives to
explore the political motivations for offering these location-specific incentives. Although
policymakers are clearly interested in the levels of economic growth and direct employment benefits
of firm investment, we argue that different electoral institutions generate alternative motivations for
leaders, which in turn impacts their use of location incentives.
In a world of perfect information and faithful agents, electoral considerations would have a
limited effect on the decision to provide incentives by incumbent politicians. Policymakers and
citizens would have a complete understanding of the effectiveness of incentives, and preferences
over the policy decision would align. Whether or not the politician is directly or indirectly elected
should make little difference in this outcome (Persson, Roland, and Tabellini 1997, Deno and Mehay
1987).
In many cases, however, politicians and voters operate in asymmetric information
environments, where the policymakers have a better understanding of the optimal policy choices
than his/her constituency. Tullock (2005) famously argued that ignorance regarding highly technical
policies like tax incentives is rational for busy constituents who will never experience the true costs
of the giveaways. As he put it, “The representatives are normally much better informed than the
voters, in fact better informed than the voters could be expected to be” (231). In these situations,
the politician has an incentive to “pander,” choosing the policy that is popular even if it is not in the
direct interests of voters (Maskin and Tirole 2004). Canes-Wrone et al. (2001) take a modeling
approach that begins with voter perceptions of an incumbent’s type. Voters may encourage
9
pandering and ideological rigidity by rewarding incumbents for their ideological position rather than
for making wise decisions based on available data (Tullock 2005: 236). Canes-Wrone et al. (2001)
acknowledge that there are times when politicians may use their policy expertise to persuade voters
rather than pander, but demonstrate that such behavior is constrained by the electoral cycle and
issue complexity. Indeed, Tullock (2005) argues that issue complexity is a critical asset for pandering
because it obscures calculations of the policy choice’s true beneficiaries.
Following Harrington (1993) and Jensen et al. (2014), we argue that voters have an intrinsic
interest in economic outcomes (attracting investment) and have inadequately informed beliefs about
the effectiveness of policy (i.e., investment incentives) in achieving these outcomes. Consistent with
Canes-Wrone et al. (2001), politicians may implement a policy that they suspect is economically
inefficient to signal their own alignment with the voters’ interests. In our policy example, politicians
provide incentives to firms that allow them to take credit for investment that would have come
otherwise, or they use incentives to diffuse blame by offering incentives to firms that are unlikely to
invest in the state. To the degree that this description accurately portrays policy selection, we
present evidence that increased electoral competition for the post of municipal executive creates the
motivation to disregard issue complexity in exchange for a candidate’s more salient objective of
signaling alignment with voters’ interests.
Voters’ beliefs regarding the effectiveness of incentives are an important factor in explaining
this finding. American voters consistently believe that taxes are among the most important factors in
attracting investment and improving economic performance. A 2012 Gallup poll, for instance,
asked respondents an opened-ended question, “In your view, what is the most important thing that
can be done to improve the economy.” The first choice was create more jobs (named by 28%), but
the second choice was “Decrease taxes/improve tax breaks,” listed by 11% of the sample (13%
Republican/10% Democrat) (Newport 2012). Even more tellingly, in 2011 Gallup asked
10
respondents what Obama could do to create jobs, 85% favored “Providing tax cuts for small
businesses, including incentives to hire workers,” and 73% favored “Giving tax breaks to companies
to hire people who have been unemployed for six months.” Surprisingly, there was little difference
between political parties in the responses to these questions (Newport 2011). On the question of
investment attraction, 70% of U.S. respondents believed that tax incentives were a very important
determinant of firm location choice.10
Regardless of whether politicians believe that incentives are warranted for an investment or a
waste, we argue that politicians are motivated to provide these incentives and that an important
determination for the extent of their provision is determined by electoral laws that establish how
chief executives are selected. Previous work has noted that politicians are not punished electorally
for using these expensive policies and even receive additional credit. However, even in this work,
the biggest impact on a politician’s survival is the attraction of investment. Politicians that can
marginally increase the probability of attracting investment find the overuse of tax payer incentives
an enticing strategy. Although political leaders in mayor-council and council-manager systems both
benefit from investment attraction, previous research shows that attraction of investment with
incentives has a direct electoral benefit and essentially no political costs (Jensen et al. 2014). These
electoral benefits provide even more reasons for politicians in mayor-council systems to offer more
incentives. Professional executives in council-manager systems still may provide financial incentives,
but their principals, usually city councils, have better information than the average voter. The ability
of politicians to “overpay” for investment is greater in mayor-council systems than council-manager
systems.
Our first hypothesis focuses on how electoral institutions affect the actual allocation of
incentives. The logic is as follows: Voters do not have the ability to directly observe the factors that
10
Polimetrix (2005).
11
affect the location decisions of firms, but they do have priors on the policy levers that are most
effective in attracting investment. Local politicians can exploit both this information asymmetry and
these priors by using financial incentives to claim credit for new investments in the politician’s
district, or the politicians can reduce blame when firms decide not to come or even leave the district.
More specifically, if the firm locates in the district, politicians can point to the incentives as the main
policy lever used to attract the investment. If the firm does not locate in the district, the politicians
can point to a generous incentive offer as a means of reducing the blame on them for not attracting
the investment. Given that voters believe incentives are an effective policy, politicians can “pander”
to voters by using these policies (i.e., investment incentives) even if politicians truly believe that
these policies are ineffective or too costly.
Following Vlaicu and Whalley (2012), we argue that the electoral incentives in mayor-council
systems are greater than that of council-manager systems. The link between political accountability
and local electoral institutions is well-documented in the literature.11 In fact, the council-manager
form of government, often termed the “reform” choice of government, emerged during the
Progressive Era because of beliefs about the problems posed by electoral institutions, spreading to
over 500 cities by the 1920s (Rice 1977). This form of government was specifically designed as a
means to change the time horizons of leaders and limit the corruption rampant in mayoral systems
(Feiock et al 2003).12
Building on this literature, we argue that strong mayors in mayor-council systems are more
clearly identified with policy choices by voters, and consequently have an interest in pushing for
greater incentive programs. Thus, we theorize that these mayors will offer more generous fiscal
incentive programs.
See Feiock et al. (2003) for a thoughtful discussion of this literature. See Schiesl (1977) for a rich
description of municipal reform from 1880–1920.
12 Rauch (1995) shows that these forms of government do have an impact on economic growth and
infrastructure investment.
11
12
Political leaders, even in council-manger systems, have reasons to pander to the public.
However, the very creation of these systems was partially a response to the use of public policy
around elections. Therefore, rather than portraying council-manager systems as immune from
pandering, we simply note that there is a direct link between mayors and constituencies that
highlights the mayor’s responsibility for economic policy. This leads to our first hypothesis.
Hypothesis 1 (H1): Cities with mayor-council systems will offer more generous firmspecific location incentives than other forms of municipal government.
Although Hypothesis 1 predicts an overall correlation between mayor-council systems, it
does not address how mayors offer more generous incentives than executives. For example, one
interpretation could be that incentives are actually the correct policy choice, attracting investment
through a modest incentive, and that council-manger systems fail to enact these policies due to the
lack of electoral pressures. We do not know of any papers that have pointed out that the lack of
incentives in the U.S. is problematic (the U.S. provides the most and largest incentives), but it is a
theoretical point worth considering. How would we know if politicians were offering incentives to
undeserving projects?
Unfortunately, little information is available on most incentive offers to make this sort of
judgment, but we do argue that the very process of how municipalities evaluate incentive allocation
is a telling indication of which cities are more willing to offer excessive incentives.
The canonical literature on principal-agent models provides clear insights into this problem.
These models assume, among other things, that there are different preferences between principals
and agents, along with information asymmetries. In most cases, principals (either voters or
members of the city council) only observe the outcomes of a policy and not its inputs.13 This
question has led to an influential stream of research in political science on the how principals
13
For a review of Principal-Agent models see Miller (2005).
13
monitor and sanction agents. One of the most famous of these works is McCubbins and Schwartz
(1984), which focuses on Congress, shows that rather than continuously monitoring agents (e.g.,
bureaucracies), members of Congress can wait for constituents’ complaints and thus “pull the fire
alarm” to alert members of Congress of bureaucratic shirking.
In our context, we would only observe a “fire alarm” in extremely egregious cases of
corruption or mismanagement. Although interest groups that are for and against incentives may
have reasons to provide additional analysis and information on incentives, multiple competing
principals can actually worsen information asymmetries. As Miller (2005, 211) states: “And in the
context of warring principals, the ability of bureaucratic agents to use information asymmetries to
their own advantage is enhanced.”
We contend that the problem with many incentives is that they are simply rewarding firms
for what they would have done anyway (i.e., investing) and/or providing too generous of incentives
for the economic development impact received. Thus, the monitoring of incentive requires
additional monitoring of inputs (Weingast and Moran 1983). This monitoring requires oversight of
the costs and benefits of incentives along with constraining the use of incentives ex ante.
Therefore, we theorize that the ability of principals to monitor and constrain their agents
varies by institutional form. Mayors have diffuse principals (voters) with limited information on the
effectiveness of incentives. These mayors will be subject to less formal oversight of their incentive
offer, allowing for overly generous incentive allocations.
Conversely, executives in council-manager systems report to a smaller and more informed
set of principals (i.e., members of the city council). These councils can become more informed on
economic development matters and are more motivated to build formal mechanisms of oversight
for these incentive programs, including cost-benefit analyses of the incentives.
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Thus, we expect that leaders will be faced with very different types of oversight based on the
form of government. Mayors are more prone to use incentives to pander to voters, but this is
enabled by discretion in the provision of investment incentives. Although politicians may be
constrained in the formal limits to their power, we argue that the choice of how to oversee incentive
use is a function of institutional type.
Consequently, we hypothesize that elected mayors in mayor-council systems are less likely to
be required to perform a cost-benefit analysis of incentives before they are provided to firms or to
require firms to meet stringent performance requirements in order to obtain incentives. In short,
city councils can use their power to require rigorous oversight of incentive programs, which in turn
mitigates the inefficiencies in these programs.
Interestingly, the information asymmetry between voters and politicians does not solely arise
from the technical nature of offering incentives. Politicians with the most direct links to voters (i.e.,
mayors) will be most likely to limit the flow of information to voters on the effectiveness of
incentives. By contrast, city council members have every motivation to rein in the use of incentives
by city managers, their primary agent, and they have the political ability to pass related legislation
before hiring a manager. This leads to our second hypothesis.
Hypothesis 2 (H2): Mayor-council systems are less likely to have incentive programs with
rigorous oversight; specifically, they are less likely to mandate performance requirements
and cost-benefit analyses.
These two hypotheses highlight how electoral concerns can push for both specific policy
instruments and the minimization of program oversight.
The Open Secret- Anecdotal Evidence for Politicians’ Awareness of Incentive Inefficiencies
15
An important assumption underlying both of our hypotheses is that politicians are aware
that the incentives they offer have a limited ability to attract investment, and they are often
excessively costly even if they do. More directly, we assume politicians disregard the uncertain
effectiveness of incentives because of the political benefits. This assumption differentiates our
pandering story from an alternative theory that local politicians are themselves ignorant of the true
effectiveness of incentives. Although the Ohio (Gabe and Kraybill 2002) and Utah (Office of
Auditory General, State of Utah, 2013) over-reporting of the incentive benefits are illustrative of a
pandering story, they do not demonstrate intentionality. Unfortunately, finding incumbent
politicians willing to go on the record and admit that these policies are deeply flawed is nearly
impossible.
Nevertheless, there is a great deal of revealing circumstantial evidence that can be assembled
regarding the “open secret” among politicians concerning the ineffectiveness of incentives. Perhaps
the best evidence is that a number of current, elected officials were openly skeptical of incentive
politics prior to holding office. For instance, Michigan Governor Rick Snyder criticized tax
incentives during his election campaign, only to continue to push for new incentives (now, relabeled
as grants) once in office (Pluta 2013). According to our data, Michigan remains one of the most
generous states in providing incentives to new businesses. New York Governor Andrew Cuomo,
when pressed on incentives by reporters, stated that “I believe there are instances where you can
find it wasn’t the smartest investment of money.” Cuomo later walked back his statement, clarifying
that he was not talking about a specific incentive deal (Mrozek 2013). Finally, an enlightening burst
of insight occurred in the Texas gubernatorial election, where former Attorney General Greg Abbot
and Republican Chairmen Tom Pauken vied to replace Rick Perry as the Republican nominee for
Governor. Abbot declared his dislike of Texas’ tax incentives—the country’s largest program—
arguing that Texas needs to “get out of the business of picking winners and losers.” His opponent
16
quickly called him out for his hypocrisy, noting that Abbot formally approved nearly all of Perry’s
incentives as Attorney General. He quipped, “It’s nice that suddenly Greg Abbott is completely
reversing himself and agreeing with me on some of these issues, now that he’s a candidate for
governor” (Collier 2013).
A revelatory incident regarding municipal tax incentives also occurred when National Public
Radio’s (NPR) programs This American Life and Planet Money teamed up to explore economic
development strategies in a show called “How to Create a Job” (Glass 2011). The prologue was the
host Ira Glass’ interview with Missouri Governor Jay Nixon, where Nixon admitted that he had
attended an event to celebrate the hiring of a single employee at a Missouri T-Shirt printer that was a
recipient of the governor’s incentive program. In the third segment, titled “Job Fairies,” however,
the reporting took a more controversial turn. Two reporters visited a national convention of
economic development officers in San Diego and chronicled the “boosterism” of municipal officials
(Glass 2011). The tone of this piece mocked elected officials and economic development managers
so sarcastically and derisively—accusing them of “spinning” and “lying”—that NPR was forced to
issue an apology for the segment (Schumacher-Matos 2011). Despite the acerbic tone, the key
message was clear: these economic development agencies were actually creating very small numbers
of jobs by attracting investment from other U.S. locations, and more importantly, they knew it. As
the NPR Ombudsman summarized it, “…even their local impact is negligible” and “their real
interest may be to protect their own jobs” (Schumacher-Matos 2011).
4. Research Design
Although the pandering theory developed elsewhere is compelling, what is left unanswered
in previous work is whether politicians actually behave in the manner predicted—do they actually
17
mobilize incentives to claim credit and deflect blame? Are they more likely to do this in the shadow
of an election?
Thus, rather than showing that there are electoral gains from offering incentives, we focus
on the behavior of elites in the allocation of incentives. We specifically examine how the form of
government shapes both the allocation of incentives and the type of oversight governments provide
to these programs. In doing so, we focus on the United States at the municipal level.
The complete universe of municipalities in the United States consists of thousands of cities,
towns, counties, and other forms of local government. Nevertheless, many of these localities are far
too small to be active in investment attraction. Thus, to keep our analysis grounded, we restrict our
population to cities larger than 10,000 residents. Municipalities of this size constitute as many as
3,839 distinct municipalities in the ICMA database.
Our institutions data are measured at the municipal level, using three waves of economic
development surveys in 1999, 2004, and 2009. ICMA and the National League of Cities (NLC) have
conducted online surveys of local development practitioners. These surveys are large samples of
municipalities, drawn from the ICMA database, providing sample sizes of 406, 378, and 691
localities respectively for a total of 1,475 cities with full data on electoral institutions. Roughly 437
localities are included in two of the three surveys, and 124 localities provided information for all
three surveys. These survey data provide one of the most complete pictures of economic
development activities and electoral institutions at the local level because of the high level of
representation (about one-third of the sample frame).
Relevant for our work, the vast majority of these cities offer some form of investment
incentives. In 1999, 68% of localities offered incentive programs to businesses. This number rose to
72% in 2004 and to 95% in 2009.
18
The Independent Variable: Electoral Incentives
The most useful feature of the ICMA/NLC surveys is that they offer a clear coding of local
political institutions. Our independent variable is the type of political system at the local level,
contrasting elected mayors in mayor-council systems with executives in council-manager systems.
Although there are a number of hybrid systems that could potentially make this simple
comparison difficult (Frederickson et al. 2004), Nelson (2011) argues that these hybrid forms are still
relatively rare. Therefore, we code the variable Elections as 1 for cities with mayor-council systems
and 0 otherwise. In our sample, 370 cities (25% of the 1,475 cities in the ICMA/NLC survey) have
mayor-council institutions.
The Dependent Variable: The Frequency and Size of Incentives
Our dependent variable for H1 is based on the characteristics of incentives offered from
January 2010 through December 2012, as recorded by the ICAIncentives Database, which is a forprofit incentive tracking firm (icaincentives.com 2013). The entire database sample for this time
period consists of 3,894 incentives worth approximately $30 billion. From these incentives, we
removed 43 federal incentives (worth approximately $13 billion) as well as 418 incentives that were
not associated with a specific municipality (worth $1.6 billion).14
The next step was to match incentives to local government institutions, using the ICMA and
NLC data documented above. Of the incentives allocated to municipalities, we were able to match
1,284 of these incentives to the ICMA/NLC data. The remaining incentives either were given for
investment in cities too small to be included in the ICMA data or were in cities that did not respond
to the ICMA survey. Thus, our total sample consists of 1,284 incentives allocated to 1,475
In some cases, it was difficult to separate whether the incentive was provided by the municipality directly or
was provided by the state government after consultation with the municipality. We treat these two situations
as equivalent in the empirical analysis. Fortunately, including state incentives biases against the possibility of
identifying differences between mayors and managers.
14
19
municipalities between January 2010 and December 2012. These data range from providing no
incentives to offering 95 different projects a total of $405 million in incentives.
Balance between the Electoral Treatment and Control Groups
In Online Appendix A1 we present the balance between our treatment (mayor-council) and
control group (council-manager) on key descriptive statistics. The raw difference-in-means of the
dependent variables demonstrates tentative evidence for our core hypotheses. Mayor-council
systems tend to offer more money for incentives and have fewer constraints on their ability to
employ targeted giveaways, such as performance criteria or cost-benefit analysis criteria.
Nevertheless, our treatment is far from an ideal experiment.
Population size is a particularly important example because the statistical difference is large.
Although well-known metropolises (e.g., New York and Chicago) tend to have mayor-council
systems, in general, municipalities with mayor-council systems are much smaller than councilmanager systems. Figure 1 displays the population distribution graphically. Clearly, a
disproportionate share of mayor-council systems (57% versus 48%) is in the 10,000 to 24,000
person category. Since there are reasons to suspect that population size drives incentive decisions,
there is potential for omitted variable bias. We address this problem through entropy balancing.15
<Figure 1 about Here>
Entropy Balancing
In Table 1, we test how the variable Elected Mayors impacts the use of incentives offered by
cities. To assess our theory, we construct two dependent variables: (1) whether an incentive was
offered for a project by a city at all and (2) if offered, the value of the investment in U.S. dollars
measured at the project level, where we take the natural log to ease interpretation. Measuring our
Results are substantively similar, however, if we control for population in a standard regression
specification or dropping very small municipalities.
15
20
dependent variable at the project level poses some empirical challenges that are discussed below, but
it is also essential for sorting out whether mayor-council systems are “overpaying” for particular,
targeted investments. A simple count of incentives in mayor-council systems versus councilmanager systems shows that much of the variance in the dollar amounts between the two types
could be attributed to the number of incentives offered. Municipalities with mayor-council systems
offered on average 1.24 incentives in our two-year sample, while municipalities with councilmanager institutions offered 0.58 incentives on average. If we simply calculated the total dollars
spent on incentives at the city level, we would be unable to distinguish the difference between higher
frequency and greater scale.
Our theoretical discussion, however, was stronger than simply speculating on more frequent
use of the tool. We hypothesized that mayor-council systems would lead to greater generosity
toward firms in their attempt to attract investment, despite the obvious financial costs. This helps
explain some studies finding the inefficient use of incentives where municipalities actually offered
greater financial incentives than they could hope to recover in higher revenue or job creation.
Inefficiencies are generated by offering incentives to firms that would locate in municipalities even
without incentives (partially captured in the difference-in-means in Appendix 1) or by the possibility
that these politicians could be offering incentives for firms that are too large relative to the size of
the investment (or the benefits of the investment to the municipality).
Our data allow for a much more fine-grained test of the generosity of mayor-council versus
council-manager systems in their use of incentive programs. To accomplish this, we transform our
incentive data into a project-level data set of 1,284 incentives to firms that located or expanded
existing operations in our dataset of 1,475 municipalities over the three years of the ICMA survey.
First, we need to figure out which cities, which were at risk of offering incentives, chose not to offer
them. In 353 (26%) of our 1,370 municipal-years, no incentives were offered at all.
21
Two potential issues pose inferential threats. First, mayor-council and council-manager
systems differ on a number of observable characteristics. In a study like ours, there is always a fear
that observable and unobservable features drive both the selection of city institutions and incentive
activities. In addition to population, we are also concerned about the primary competitors for
investment. Although the majority of managers indicated cities within their own state or cities in
neighboring states as the primary competitors, the response “foreign locations” increased
dramatically after 1999. In 1999, 12% of managers indicated foreign locations were the primary
competitors; this jumped to just over 20% in both 2004 and 2009. We include the variable Foreign,
coded as 1 for cities identifying foreign locations as the main competitors. Regional patterns are also
important. Previous research has identified regional patterns of local government form (Montjoy
and Watson 1993), as well as variation based on whether a municipality is in a metropolitan area
(Metro) or suburbs of metropolitan area (Suburb). Finally, two additional confounders affect a
municipality’s likelihood of offering incentives, including the local unemployment rate (Unemployment
Rate) and a categorical variable, from 1–5, on the existence of other tax policies. For example, cities
that have a tax on personal property may offer an exemption to firms on this tax. Cities that do not
have this tax cannot offer a selective exemption.
Although far from the experimental ideal, matching techniques have been proposed as one
possible remedy to this problem. In this section, we employ a variant of matching, called entropy
balancing (ebalance), which is suggested by Hainmueller (2012). Ebalance reweights the observations
to statistically generate a region of common support where mayor-council and council-manager
systems are comparable on structural covariates. Ebalance does this by directly incorporating
covariate balance into the weight function that is applied to the sample units.
To apply the technique, we impose a set of balance constraints, which imply that the
covariate distributions of the treatment and control group in the preprocessed data match exactly on
22
all pre-specified moments, taking care to only use pre-treatment variables in the balancing equation.
The entropy balancing algorithm then searches for the set of weights that satisfies the balance
constraints but remains as close as possible to a set of uniform base weights to retain information.
This recalibration technique assures maximum balance between treatment and control groups
(Hainmueller 2012). After re-weighting, mayoral and council-manager systems are now directly
matched in terms of average value, variation, and skew (See Table 1).
<Table 1 about Here>
Taking advantage of this statistically generated region of common support, we estimate the
following sets of analyses. First, we examine the probability of a city offering an incentive, and
second the size of incentives in Models 1 and 2. These are presented in the top panel of Table 1.
The results are very similar to the naïve specifications.16 Taking survey effects into account, the
results show that the mayors offer 30.7% more in incentives per project.
Although the results are compelling, our theory also offers an additional observable
implication that mayor-council systems would offer less rigorous oversight of their incentives. In
this section, we test the second hypothesis by focusing on three questions in these surveys to
address the political use of incentives directly. First, do these incentive programs have written
performance criteria, requiring specific elements (such as job creation) to be eligible for the incentive
program? Second, do municipalities perform a cost-benefit analysis in the offering of location
incentives? Third, if performance criteria are used, how many different items is the respective
project assessed against in order to measure effectiveness. The ICMA survey presents respondents
with a list of six items, asking them to check off all that they currently use. The list includes: jobs
created, capital invested in construction and labor, capital invested in land, existing company sales,
number of new businesses attracted, and other. These policies directly limit the discretionary use of
We replicate all results using OLS in Table A5 of the Appendix and include a number of additional
robustness tests.
16
23
location incentives for political gain. We expect to find greater use of them in municipalities
governed by council-manager systems, rather than by mayor-council systems which would desire
more political wiggle room.
To maximize our explanatory power, we combine the data from the 1999, 2004, and 2009
surveys.17 We present our results in Models 3-5. We find that mayor-council systems are 9.9% and
7.6% less subject to performance and cost-benefit requirements (respectively), and require 0.37%
fewer performance criteria per project. Note that our standard errors are considerably larger for
these models than our OLS results we present in Online Appendix III. This leads our cost-benefit
analysis dependent variables to no longer achieve conventional statistical significance levels.
Nevertheless, these results are consistent with the previous results, showing that even after
we address the non-random assignment of elected mayors, we still find that electoral motivations
have perverse effects on the use of dubious incentives.
Matching techniques, even advanced ones like entropy balancing, however, have difficulty
addressing unobserved heterogeneity. This is also a concern in our analysis. Although city
institutions tend to be regionally clustered and few cities change their institutions, there is the
possibility that cities (or citizens) select their city institutions in order to mitigate abuse of incentives.
In fact, council-manager systems were labeled “reform” institutions during the Progressive Era
specifically because cities attempting to root out corruption changed their form of government. If
this is the case, we might be attributing causality when what we are observing is merely a correlation
between two variables that are both capturing concerns for governmental malfeasance.
Fortunately we have a theoretically informed identification strategy. Many U.S. states have
formal laws on the “default” institution of cities (Nelson 2011). Although the laws across states
vary, for these states, there is a status quo bias in selection of mayor-council institutions for cities.
Coefficient size remains similar when models are run separately by survey year, but are less efficiently
estimated because of the reduced statistical power.
17
24
Nelson (2011) documents that 21 U.S. states have laws requiring cities to have mayoral-council
institutions as the default form of government for newly chartered cities.18 In our data of 1,475
cities, 526 (36%) of these cities are in states that have this requirement.
Default clauses provide an exogenous obstacle to switching forms of government. As Figure
2 shows, the status quo bias has an important influence on the share of cities in a state with mayorcouncil systems. Although the sample distribution changes slightly by year, mayor-council systems
govern about 37% of the cities in states with default clauses, but only 18% of the cities without
them.
<Figure 2 about Here>
To test this, we rerun the entropy balance analysis, balancing this time on whether the state
has a default clause (Panel 2 of Table 1), thereby treating mayors as a post-treatment variable.19 In
other words, how much of the variance in incentive behavior can we explain simply by studying the
impact of the state-level conditioning regulations before municipalities even have the opportunity to
alter their exogenously imposed institutions?20 The results show that even after addressing the
threat of endogenous selection, the results are very similar. Cities in states with default mayor
requirements face significantly fewer constraints on the usage of incentives, and therefore offer
significantly greater incentives to new projects.
The sizes of the effects are much greater in the bottom half of Table 1, which we attribute to
a Local Average Treatment Effect (LATE) because our identification strategy essentially eliminates
the mitigating influence of mayor-council systems in states without default clauses.
Nelson (2011) creates an index of institutions that shape municipal government. We focus on this single
instrument as outlined in Appendix II. We thank her for sharing this data.
19 See online Appendix Table A4 for entropy balancing descriptives for this variables.
20 Note in Appendix Table A2 that states with and without default clauses already differ very little on a range
of reasonable covariates, including demographics, wealth, economic structure, government spending, and
political leanings.
18
25
5. The Electoral Mechanisms
Thus far we have shown that municipalities with council-manager systems provide less
generous incentives, coupled with more extensive oversight of these programs. We show that the
large size of this effect is consistent across different empirical specifications and of control variables.
We also addressed the non-random assignment of municipal institutions by using state laws
governing local institutions.
Unfortunately, there are alternative hypotheses to explain the correlation between local
institutions and incentive allocations. For example, council-manager systems may have more veto
players involved in policy making, limiting the ability of governments to allocate incentives in the
first place.21 A similar argument could be made on how municipal institutions affect the timehorizons of leaders. Council-manager systems, by design, are not only indirectly elected but they
often have longer time horizons than mayors serving two- or four-year terms (Clingermayer and
Feinock 2001), and this has been linked to greater risk aversion in economic policy (see Feinok,
Steinacker, and Park 2009).
Our final test is designed to differentiate the electoral mechanism from these other plausible
mechanisms by taking advantage of the variation in electoral timing across municipalities. Using
data on election timing from the U.S. Conference of Mayors as well original data collection, we
created a database of 439 municipal election dates. Using this data, we coded a dummy variable
representing municipalities that were expected to have local elections in 2012.22 Our expectation is
that although municipalities will not alter oversight programs from year to year, executives in mayor-
However, we find no evidence of this in our empirical analysis.
Given the lack of comprehensive, historical municipal election data, we coded all municipalities with
elections in 2014 as also having expected elections in 2010. Although this coding could introduce some
measurement error, it is unlikely to bias our results.
21
22
26
council systems are more likely to offer larger incentives than council-manager systems, and this
effect will be amplified during elections years.
<Table 2 about Here>
In Table 2, we present OLS results that replicate our fully specified model of the size of
incentives with an additional interaction between our dummy variable for a mayor-council system
and for an election in 2012.23 The interaction term is insignificant, which we ascribe to the limited
power of this model—only 17 mayoral systems had elections in 2012. Nevertheless, the signs on all
three coefficients are in the right direction and the coefficients are sizable, despite being imprecisely
estimated. Thus, for illustrative purposes, we explore the predicted effects from the estimation in
Table 3.
<Table 3 about Here>
In the first panel, we show the predicted effects for the four main groups generated by the
interaction. Although none of the cells are significantly different from one another, the substantive
differences in the average size of the incentives are quite sizable. To illustrate this, we undo the log
transformation in the second panel. Here, we can see clearly that an election year does not appear to
generate additional incentive allocations in council-manager systems. In fact, cities with councilmanagers facing an election actually marginally reduced their incentive usage from $400,000 to
$366,000 per project. On the other hand, elected mayors demonstrated increased usage of
incentives in an election year, increasing allocations from $670,000 to $717,000 per project.
These final results are suggestive, but not a smoking gun. Our sample size is less than half
of our fully specified model and measurement error in the timing of previous elections works against
finding clear results. In future research we hope to more clearly address the electoral mechanism by
23
See Model 7, Table A5
27
taking advantage of more comprehensive municipal election data.24 However, we believe that these
results, coupled with the previous evidence on incentives, provide a relatively comprehensive picture
of how electoral institutions shape the use of fiscal incentives.
7. Conclusion
In this paper, we directly examine the use of fiscal incentives to attract investment to U.S.
municipalities using observational data. We argue that politicians can exploit their information
advantage over citizens to offer generous incentives for political gain—both providing too many and
too generous incentives for firms. Although all types of municipalities offer incentives to firms, we
argue that the form of government shapes these economic development policies.
Firms considering investments in municipalities with mayor-council systems are offered
generous fiscal incentives. In addition, mayor-council municipal governments are less likely to
impose conditions on firms to qualify for these incentives and often fail to require a simple costbenefit analysis of incentives for investment attraction. Further, the impact of mayor-council
institutions on incentives is heightened during reelection years.
These findings have broad implications for our understanding of tax competition and
domestic politics. In this instance, globalization has increased the policy levers available to
politicians, allowing them to take credit or avoid blame for economic outcomes. Economic
competition, therefore, provides a critical domestic political benefit to incumbent officials: an
increased ability to pander for votes.
Our research design is to use data on future elections to predict future incentive allocations by designing
our observational data analysis and pre-registering it at the Experiments in Governance and Politics (EGAP)
registry.
24
28
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31
Table 1: Entropy Balancing
(Treatment = City has a Mayor-Council System)
A. Before Entropy Balancing
Before:
Jobs created (ln)
Capital value (ln, US)
Population (1 to 7)
Unemployment Rate (%)
Other Taxes (1 to 5)
Foreign Competition=1
Brand New Investment=1
Location
Northeast=1
Northcentral=1
South=1
Metro Area=1
Surburb=1
Sector
Automotive =1
Basic Materials=1
Consumer Goods=1
Creative Industries=1
Electronics=1
Food and Drinks=1
Industrial Goods=1
Information Tech.=1
Tourism=1
Life Sciences=1
Non-Renewable Energy=1
Energy=1
Services=1
Survey Year
2004 =1
2009 =1
Treatment=Elected Mayor
Mean
3.75
10.62
3.68
6.11
1.98
0.19
0.37
Variance
2.30
53.50
3.05
5.34
2.06
0.15
0.23
Skewness
-0.56
-0.68
-0.31
1.12
0.25
1.62
0.55
B. After Entropy Balancing
Control=Council
Mean
3.96
10.65
2.84
6.98
2.49
0.26
0.42
Variance
2.41
56.21
2.47
42.51
4.98
0.20
0.24
Treatment=Elected Mayor
Skewness
-0.59
-0.63
0.60
10.58
1.82
1.07
0.34
Mean
3.75
10.62
3.68
6.11
1.98
0.19
0.37
Variance
2.30
53.50
3.05
5.34
2.06
0.15
0.23
Skewness
-0.56
-0.68
-0.31
1.12
0.25
1.62
0.55
Control=Council
Mean
3.75
10.62
3.68
6.11
1.98
0.19
0.37
Variance
2.69
54.63
2.74
6.84
2.89
0.15
0.23
Skewness
-0.55
-0.65
0.10
0.83
1.24
1.62
0.54
0.11
0.40
0.44
0.71
0.17
0.10
0.24
0.25
0.21
0.14
2.49
0.40
0.23
-0.92
1.72
0.09
0.37
0.43
0.53
0.33
0.09
0.23
0.25
0.25
0.22
2.78
0.52
0.27
-0.10
0.74
0.11
0.40
0.44
0.71
0.17
0.10
0.24
0.25
0.21
0.14
2.49
0.40
0.23
-0.92
1.72
0.11
0.40
0.44
0.71
0.17
0.10
0.24
0.25
0.21
0.14
2.49
0.40
0.23
-0.92
1.72
0.08
0.08
0.11
0.03
0.03
0.10
0.16
0.06
0.04
0.10
0.02
0.00
0.16
0.07
0.07
0.09
0.03
0.03
0.09
0.13
0.06
0.04
0.09
0.02
0.00
0.14
3.16
3.22
2.57
5.97
5.29
2.72
1.88
3.69
4.93
2.68
6.59
15.13
1.82
0.08
0.10
0.11
0.01
0.03
0.09
0.14
0.09
0.02
0.10
0.02
0.03
0.14
0.08
0.09
0.10
0.01
0.03
0.08
0.12
0.08
0.02
0.09
0.02
0.03
0.12
3.03
2.73
2.50
8.63
5.18
2.91
2.10
2.94
7.23
2.65
7.23
5.98
2.10
0.08
0.08
0.11
0.03
0.03
0.10
0.16
0.06
0.04
0.10
0.02
0.00
0.16
0.07
0.07
0.09
0.03
0.03
0.09
0.13
0.06
0.04
0.09
0.02
0.00
0.14
3.16
3.22
2.57
5.97
5.29
2.72
1.88
3.69
4.93
2.68
6.59
15.13
1.82
0.08
0.08
0.11
0.03
0.03
0.10
0.16
0.06
0.04
0.10
0.02
0.00
0.16
0.07
0.07
0.09
0.03
0.03
0.09
0.13
0.06
0.04
0.09
0.02
0.00
0.14
3.16
3.22
2.57
5.98
5.29
2.72
1.88
3.69
4.93
2.68
6.58
15.10
1.82
0.23
0.48
0.18
0.25
1.25
0.08
0.24
0.50
0.18
0.25
1.25
-0.02
0.23
0.48
0.18
0.25
1.25
0.08
0.23
0.48
0.18
0.25
1.25
0.08
32
Table 2: Main Regression Results after Entropy Balancing
1) Results after Entropy Balancing on Whether City has a Mayor-Council System
Dependent Variable
Elected Mayors
Constant/Pbar
Observations
(Pseudo) R-squared
Chi-Squared
Log Likelihood
Offered Incentive=1
(1)
-0.019
(0.033)
0.750
1,475
0.000414
0.312
-829.3
Value of Incentive in Performance Criteria Cost Benefit Analysis Performance Criteria
Millions USD (ln)
(=1)
(=1)
(0 to 6)
(2)
(3)
(4)
(5)
0.307**
-0.099**
-0.076
-0.379***
(0.142)
(0.047)
(0.046)
(0.123)
13.188***
0.557
0.678
1.467***
(0.181)
(0.108)
1,143
1,106
1,106
1,106
0.008
0.00724
0.00529
0.014
4.290
2.596
-2201
-753.8
-691.2
-2087
2) Results after Entropy Balancing on Whether State has Default Mayor Policy
Value of Incentive in Performance Criteria Cost Benefit Analysis Performance Criteria
Millions USD (ln)
(=1)
(=1)
(0 to 6)
(1)
(2)
(3)
(4)
(5)
Default Mayor Clause
-0.065**
0.378*
-0.116***
-0.025
-0.473***
(0.025)
(0.217)
(0.038)
(0.038)
(0.157)
Constant/Pbar
6.300
12.916***
8.808
0.425
-707.8
(0.193)
-718.8
-663.9
-2164
Observations
1,475
1,143
1,106
1,106
1,106
(Pseudo) R-squared
0.00719
0.012
0.0110
0.000622
0.019
Chi-Squared
4.235
6.896
2.117
Log Likelihood
-1532
-2229
-355.5
-307.4
-1092
(*** p<0.01, ** p<0.05, * p<0.1). All models implement STATA's ebalance procedure (Hainmueller 2012) to address observed differences between 1)
mayoral and council-manager systems and 2) states with default clauses and those without on pretreatment covariates. Robust standard errors are
clustered at the state level.
Dependent Variable
Offered Incentive=1
33
Table 3: Interaction between Mayor-Council System and Election in 2012
Value of Incentive in Millions USD (ln)
OLS
OLS
OLS
(1)
(2)
(3)
Elected Mayors
0.333*
0.534**
0.495*
(0.192)
(0.234)
(0.280)
Election in 2012
-0.054
-0.103
(0.199)
(0.234)
Mayor * Election
0.176
(0.438)
Population
0.068
0.070
0.068
(0.056)
(0.053)
(0.052)
Foreign Competition
0.214
0.138
0.132
(0.180)
(0.197)
(0.199)
Metro Area
0.334
0.305
0.310
(0.257)
(0.281)
(0.279)
Suburb
0.539**
0.354
0.360
(0.230)
(0.242)
(0.241)
Jobs created (ln)
0.555***
0.547***
0.548***
(0.069)
(0.069)
(0.069)
Capital value (ln, US)
0.034***
0.037***
0.038***
(0.011)
(0.010)
(0.010)
Brand New Investment
0.129
-0.057
-0.057
(0.175)
(0.182)
(0.182)
Unemployment Rate (%)
-0.010
-0.006
-0.006
(0.007)
(0.006)
(0.006)
Other Taxes (1 to 5)
0.005
0.010
0.011
(0.034)
(0.031)
(0.032)
Constant
10.714***
10.798***
10.778***
(0.729)
(0.728)
(0.753)
Survey Year FE
Yes
Yes
Yes
State FE
Yes
Yes
Yes
Sector FE
Yes
Yes
Yes
Observations
534
439
439
City
260
215
215
States
38
38
38
R-squared
0.467
0.468
0.468
RMSE
1.351
1.296
1.297
(*** p<0.01, ** p<0.05, * p<0.1). For Models 1-3, the unit of analysis is
the individual project. And the dependent variable is the natural log (ln)
of size of the incentive in millions of USD. Data on incentives is from
2011 and 2012, but basic city information was captured in different
ICMA/NLC surveys. Fixed effects address confounding based on survey
year.
Independent/Dependent
Variables
34
Table 4: Predicted Effects of City Government & Elections in 2012
1. Local Government System
No
Yes
Scheduled Election in 2012
Council-Manager
12.92
( 12.8
13.1 )
Mayor-Council
13.41
( 13.0
13.8 )
n=640
n=74
12.81
13.48
( 12.4
13.2 )
n=117
( 13.1
13.9 )
2. Predicted Incentive Size in USD
Council-Manager
Mayor-Council
$406,801
$667,310
$366,873
$717,315
n=17
Panel 1 shows predicted effects of Table 3 (Model 3). Natural Log of
incentives in millions of USD. Parentheses display 90% Confidence
Interval.
Panel 2 presents the same predictions in USD.
35
Figure 1: Population Statistics by Type of City Government
Mayor-Council
38.22
34.69
25
30
35
40
45
50
55
60
Council-Manager
23.24
19.38
20
19.27
15
16.43
10.39
10
9.551
5
4.723
10.53
9.41
3.589
0
.5668
10-24
25-49
50-99
100-249 250-499 500-999
1000+
10-24
25-49
50-99
100-249 250-499 500-999
1000+
Population Size (Thousands)
Percentage
Graphs by Municipal Government System
36
Figure 2: Default Mayor Clauses and the Share of Mayors
Range bars are 95% Confidence Intervals
No
Yes
.5
.4
.3
.2
.1
.1
.2
.3
.4
Share of Cities w/Mayor-Council System
.4
.3
.2
.1
Share of Cities w/Mayor-Council System
2009
.5
2004
.5
1999
No
Yes
No
Yes
State has Default Mayor Rule
37