The Distributive Politics of the Federal Stimulus:
The Geography of the American Recovery and Reinvestment Act of 2009
James G. Gimpel
University of Maryland
[email protected]
Frances E. Lee
University of Maryland
[email protected]
Rebecca U. Thorpe
University of Washington
[email protected]
The geographic distribution of funds allocated under the American Recovery and Reinvestment Act
(ARRA) of 2009 was poorly targeted to economic need. Areas where the recession‘s downturn was most
severe did not receive any more funds on a per capita basis than areas where the recession was least
severe. This paper assesses two theories to determine why funds were not successfully aimed where
economic suffering was greatest. (1) We examine whether the institutional and political factors at the
center of the study of distributive politics can account for the mismatch. (2) We assess the role of ―policy
window‖ effects, as policy advocates steered a variety of loosely related proposals through an open
window of opportunity. We find some support for both theories, but policy window effects were more
important than ―pork barrel politics‖ in explaining why funds were mismatched to need.
Paper prepared for presentation at the 2010 Annual Meeting of the American Political Science
Association, Washington, DC, September 2-5.
1
―Mr. Speaker, 50 years ago a Presidential candidate, John Kennedy, said the following:
The Chinese use two brush strokes to write the word ‗crisis.‘ One brush stroke stands for
danger; the other stands for opportunity. In a crisis, be aware of the danger, but
recognize the opportunity.‖
-Rep. Anna G. Eshoo (D-Calif.)1
In theory, one of the most important responsibilities of a national government in a federal
system is geographic redistribution. The national government has unique capacity to raise
revenue and direct funds to states and localities otherwise unable to provide for their residents‘
needs (Peterson 1995; Callen 2009). These powers are especially important in periods of
economic recession, as only the national government can engage in large-scale deficit spending
to fund government safety net programs and to stimulate demand in the particular areas with the
most troubled economies. In practice, however, there are many reasons to question whether the
federal government will successfully perform these functions.
Scholars of distributive politics have focused on the political and institutional reasons
why the federal government will likely fail to match resources to need. To build legislative
support for federal programs, coalition leaders frequently expand the scope of beneficiaries far
beyond the neediest areas (Arnold 1979). Members of Congress in key institutional positions,
such as leaders or members of the relevant committees of jurisdiction, may extract extra benefits
for their constituents (Evans 1994; Frisch 1998; Lazarus and Steigerwalt 2009; Lee 2003). A
majority party in Congress can use program benefits to enhance the electoral prospects of its
members (Balla, Lawrence, Maltzman, and Sigelman 2002; Bickers and Stein 2000; Lazarus
2009; Lee 2003; Levitt and Snyder 1995; Rundquist and Carsey 2002). Presidents and executive
agencies may direct funds to reward or woo political supporters (Bertelli and Grose 2009; Garrett
and Sobel 2003; Hamman and Cohen 1997; Larcinese, Rizzo, and Testa 2006; Reeves
1
Congressional Record, February 13, 2009, H1560.
2
Forthcoming). Furthermore, the structure of representation itself is a source of bias, in that small
states‘ enhanced representation in the U.S. Senate systematically advantages their residents in the
distribution of federal dollars (Hauk and Wacziarg 2007; Lauderdale 2008; Lee and
Oppenheimer 1999).
A second source of failure in targeting to need lies in the way policymakers exploit
windows of opportunity. As John Kingdon (1995) has famously theorized, policy change often
rests on a confluence of conditions that, only by serendipity, come together at a particular
moment in time. A problem must be widely perceived, often as a result of a crisis or ―focusing
event‖ (Kingdon 1995, 94-100). A policy proposal must be ready to hand when a problem
comes to attention; generally this means that policy ideas are continually being discussed and
vetted among advocacy coalitions, even when immediate prospects for action are poor (Sabatier
1988). Finally, the political environment must be favorable to action, with officeholders in key
elective and appointive positions willing to prioritize the issue. Windows of opportunity for
major legislation are rare. Much of the time, only incremental change is possible, with public
policy characterized by ―punctuated equilibria,‖ in which significant departures occur at
infrequent intervals, followed by periods of relative statis (Baumgartner and Jones 1993).
On occasions when windows of opportunity open, presidents, lawmakers, administration
officials, and policy advocates outside government all seek to capitalize on them to achieve longheld policy goals. Policy windows thus become crowded with proposals: ―[W]hen a window
does open, solutions flock to it‖ (Kingdon 1995, 176). Many of these proposals will be, at best,
only loosely related to the problem on the agenda: ―Solutions become attached to problems,
even though those problems themselves did not necessarily dictate those particular solutions‖
(Kingdon 1995, 177). The difficulty of legislating in an open and decentralized policy process
3
with numerous veto points incentivizes policymakers to make the most of any opportunity for
breakthrough. In short, as Rep. Anna Eshoo (D-Calif.) put it in her floor speech in support of the
Obama administration‘s economic stimulus package, ―in a crisis, be aware of the danger, but
recognize the opportunity.‖
The logic of policy windows is also likely to redirect distributive policy away from any
particular vision of funding need. Under this model, the outcomes of public policy are highly
unpredictable, with unrelated or loosely related solutions ―riding the wave‖ in the wake of a
crisis. Rather than public policy being the product of decisions rationally designed to solve
problems, it is a ―garbage can‖ (Cohen, March, and Olsen 1972) shaped by chance and
contingency, ―depending on which participants are present, which alternatives are available, and
even what catches people‘s eyes‖ (Kingdon 1995, 177).
Despite the wide influence of Kingdon‘s theory, the study of distributive policy has failed
to grapple with the effect of policy windows on the geographic allocation of federal funds. The
central question in the distributive policy literature is one of policy failure: what factors cause
misallocation of federal resources? In answer, there is a vast literature on the ways that
members‘ incentives to claim credit for distributive benefits, in conjunction with power
inequalities inherent in representatives institutions, can misshape federal policy. But policy
windows—in which unrelated policies are bundled together as policy entrepreneurs seek
opportunity in crisis—have not been subject to systematic empirical investigation in the
distributive politics field.
This paper analyzes the American Recovery and Reinvestment Act (ARRA) of 2009 as a
key test of these two theories of policymaking. Passed in the wake of the financial crisis of 2008
and an ensuing grave recession, this sweeping $787 billion legislation was quickly assembled in
4
the first weeks of the Obama presidency and signed into law on February 17, 2009. As will be
shown in the following pages, the geographic distribution of funds allocated under the legislation
was poorly targeted to need. Areas where the recession‘s downturn was most severe did not
receive any more funds on a per capita basis than areas where the recession was mild. Then our
investigation seeks to determine why funds were not successfully aimed where the economic
suffering was greatest. Were the institutional and political factors at the center of the study of
distributive politics a key cause of the mismatch? Or, on the other hand, were funding outcomes
driven by policy window effects, as policy advocates steered a wide variety of loosely related
proposals through an open window of opportunity? Our findings reveal that both causes of
policy failure played a role, but that policy window effects were more important than ―pork
barrel politics‖ in explaining why funds were not targeted to stricken areas.
The Geography of Need
According to explicit legislative language adopted by Congress, a central goal of the
ARRA was ―to assist those most impacted by the recession.‖2 Although the recession beginning
in December 20073 was unquestionably national in scope, with serious job losses all across the
country, some areas of the country were hit harder than others.
[Figure 1 here]
As a gauge of regional variation in the depth of the recession, Figure 1 displays
histograms of the frequency distribution of average percent unemployment by county for all of
2007 (before the recession was underway) and for the first quarter of 2009 (when the ARRA was
2
American Recovery and Reinvestment Act of 2009, Pub. L. no. 111-5, 123 Stat 115 (2003).
The National Bureau for Economic Research dates the start of the recession in December 2007.
It has not declared an end date for the recession as of this writing.
3
5
considered and adopted). The national character of the recession is evident in the scale of the
downturn. Across all 3,140 counties, average unemployment grew by 54%, from 4.88% to
9.01%; nearly every county suffered job losses. At the same time, regional variability in
unemployment also increased dramatically. The standard deviation around the average grew
from 4.87 to 8.62. This increased variability is graphically evident in the flatter distribution of
unemployment by Q1 of 2009, compared to 2007 (Figure 1). The highly peaked distribution in
2007 reveals low levels of unemployment prevailing across almost the entire country, with only
a relative few pockets of high unemployment. Unemployment in early 2009, however, exhibited
greater regional disparity, with some places still enjoying relatively low unemployment and
others undergoing deep economic difficulty. While both distributions are right-skewed, the
2007 histogram exhibits nothing comparable to the long right tail of the 2009 distribution,
indicating counties where the unemployed share of the population exceeded 15% or even 20%.
Beyond unemployment, another key indicator of worsening economic fortune is the
foreclosure rate. The collapse of a massive housing bubble was one of the precipitating factors
in the recession, with toxic investments in mortgage-backed securities burdening the banking
system and bankrupting major financial institutions, including Bear Stearns, Lehman Brothers,
Washington Mutual, Fannie Mae, and Freddie Mac. For all the national impact of the housing
bubble on the financial system, the problem of foreclosures was highly concentrated in
geographic terms. Table 1 displays the distribution of the cumulative foreclosure rate (per 1,000
households) by county for the first quarter of 2009. As is apparent, foreclosures were
concentrated in a very tiny slice of the country. Fully 70% of counties had a foreclosure rate
lower than the national average of 2.2; 90% of counties had a rate below 6. By contrast, half of
all foreclosures were concentrated in only 35 counties, among which the foreclosure rate was a
6
stunning 21.5 on average. Counties above the top 90th and 95th percentiles of the distribution
are where foreclosures clearly spillover into a serious problem for local governments, businesses,
and neighborhoods extending well beyond the individual affected residents.
[Table 1 here]
Regional inequalities were exacerbated by the fact that some areas severely affected by
foreclosures tended also to have high unemployment rates (Pearson r=.21, p<.001). Figure 2
displays a county map of the United States shaded from light to dark to indicate areas of high
unemployment, with an overlay of green houses graduated in size to denote counties with the
highest rate of foreclosures. The foreclosure crisis was heavily concentrated in Nevada,
California, Florida, Ohio, Georgia, South Carolina, and Michigan, commonly in locales that also
suffered from high unemployment. Areas of very high unemployment also reached beyond the
foreclosure centers, taking in much of Appalachia, the Deep South, and the rest of the industrial
Midwest.
[Figure 2 here]
At the same time, large swaths of the country were afflicted by neither high
unemployment nor foreclosures. A wide interior region extending across Montana and North
Dakota down the Plains through South Dakota, Nebraska, Iowa, Kansas, Oklahoma, and New
Mexico, along with much of Texas and Louisiana had unemployment rates well below the
national average and no significant incidence of foreclosures. The Northeast also fared relatively
well. In short, the pain of the recession was not experienced equally across the country. When
the economy goes sour in a nation as big as the United States, just what is wrong varies by
location (Chinni and Gimpel 2010, 136). A program to assist the locations most severely
afflicted by the recession would need to be crafted with care and aimed with precision.
7
Targeting in the ARRA
The ARRA was omnibus legislation in its most extreme form. It included $267 billion in
direct spending, principally for safety-net programs such as extended unemployment benefits,
food stamps, Temporary Assistance for Needy Families, health insurance subsidies for
unemployed workers, Medicaid, and state fiscal relief. It provided $211 billion in tax cuts,
including bonus depreciation and current loss carry-back for businesses, an increase in the
Alternative Minimum Tax floor, payroll tax breaks, and tax credits for children, home buying,
college, and energy efficiency upgrades. It also funded $308 billion in new discretionary
spending for a host of purposes, including scientific and medical research, clean energy,
transportation and water infrastructure, public housing, education, and modernization of the
electric grid.4 Finally, the Act incorporated transparency provisions requiring that expenditures
for grants, loans, and contracts be released to the public on a specially designed website,
www.Recovery.gov.
Using the information available from Recovery.gov, it is possible to track expenditures
geographically to determine whether funds were directed to locations most affected by the
recession.5 Unlike most scholarship on distributive politics, we examine per capita expenditures
at the county level, rather than the state or congressional district. County-level data afford far
4
Estimated budget impacts of the legislation obtained from the Congressional Budget Office,
―H.R. 1, American Recovery and Reinvestment Act of 2009: Cost Estimate for the Conference
Agreement for H.R. 1,‖ February 13, 2009,
http://www.cbo.gov/doc.cfm?index=9989&zzz=38482.
5
We employed county-level datasets compiled by ProPublica. ProPublica drew its data from the
Recovery.org website, but it then cleaned the data to correct for errors. Most importantly,
ProPublica adjusted the amounts allocated for prime recipients so that they did not include
amounts awarded to sub-recipients. More information on ProPublica‘s methodology is provided
by LaFleur (2009).
8
better granularity for assessing how well funds are targeted to economic need, particularly in a
recession that has had such disparate effects on different sectors of the economy and people at
different education levels.6 We exclude state capital counties from the analysis: because these
counties receive substantial allocations for programs that will subsequently be redistributed
across the state, the per capita funds going to these locations are not appropriately comparable to
other counties. Our data include all funds allocated through the first quarter of 2010. By that
point, 62% of the total funds provided for by the ARRA had been spent (Weise 2010).
It should be noted, however, that the project expenditures reported on Recovery.org do
not reflect the direct spending and tax reductions in the ARRA, some of which were aimed at
unemployed workers and struggling families. Recovery.org tracks the grants, loans, and
contracts funded by the stimulus. Unlike the safety net programs funded by direct spending,
these are the programs that were often touted as job creators. In his remarks upon signing the
ARRA into law, for example, President Obama described such projects as follows:
Because of this investment, nearly 400,000 men and women will go to work rebuilding
our crumbling roads and bridges, repairing our faulty dams and levees, bringing critical
broadband connections to businesses and homes in nearly every community in America,
upgrading mass transit and building high-speed rail lines that will improve travel and
commerce throughout the nation.‖7
In the context of the congressional debate over the stimulus package, these expenditures were
justified primarily in terms of their effect on employment. Many are identified around the
country with signs bearing the ARRA emblem to remind Americans, in Obama‘s words, of ―our
commitment . . . to investing your tax dollars wisely to put Americans to work doing the work
6
Analysis at the zip code might be even better, but much of the demographic information that we
find relevant for understanding the distribution of ARRA funds is not available at that unit of
analysis.
7
―Remarks by President Barack Obama at the Signing of the Economic Stimulus Bill,‖ Federal
News Service, February 17, 2009.
9
that needs to be done.‖8 Were these projects well-positioned to put people back to work in areas
of concentrated unemployment?
[Table 2 here]
Table 2 presents frequency distributions for per capita ARRA spending by county, both
for the legislation as a whole, as well as for the transportation and water infrastructure projects
separately.9 The ARRA was far more universal in character than most federal programs (Stein
and Bickers 1994), probably not surprising given its extraordinary scope. Every county received
at least some funding from the legislation; 80% of counties obtained at least minimal support for
local transportation and water projects. Despite the universalism, however, there was significant
targeting to particular counties. The distributions for both total spending and infrastructure
projects are highly right skewed, meaning that a relative few high values pull the mean
substantially higher than the median. For all ARRA spending as of the first quarter of 2010,
counties in the 90th percentile of per capita expenditures received two and a half times as much
funding per resident as the median county. The funds for infrastructure were even more directed.
Counties at the 90th percentile of per capita transportation and water project funding received
nearly six and one half times as much as the median county.
Clearly, the ARRA did target federal resources to particular locations, just the wrong
ones from a need standpoint. Figure 3 displays total per capita spending by county across the
United States as of the first quarter of 2010. Counties in this map are color coded using ground
cover shades typically used to designate areas on a continuum from ―arid‖ to ―extremely fertile.‖
8
―Remarks by President Barack Obama, Transportation Secretary Ray LaHood, and Vice
President Joe Biden; Subject: American Recovery and Reinvestment Act,‖ Federal News
Service, March 3, 2009.
9
Transportation and water infrastructure expenditures were identified as projects managed by the
federal Department of Transportation, the Army Corps of Engineers, or the Bureau of
Reclamation.
10
In this case, the fertile (darker green) colors indicate counties with the highest per capita
allocations under the ARRA; the desert (tan) colors reflect areas with the lowest per capita
allocations. The five different colors shown reflect natural breaks in the data‘s distribution.
Most counties fall into categories shown in the two desert tones, reflecting the fact that most
received less than the mean per capita allocation of $870. Counties coded as lighter and darker
green are scattered throughout the United States, but they are especially prevalent in the Plains
and the Mountain West, in many cases in counties that had very low levels of unemployment and
foreclosures. There is certainly no special concentration of funding for areas hardest hit by the
recession. With the exception of Inyo County, a remote, sparsely populated county in east
central California, all of California‘s counties received per capita allocations in the bottom two
tiles on the map (Figure 3). Similarly, despite its high unemployment and foreclosures, nearly all
counties in Florida fell into the most ―arid‖ category. There were very few concentrated
allocations of money anywhere in the struggling Industrial Midwest.
Multivariate Evaluation of ARRA Distribution
Multiple regression offers a more definitive test of the relationship between economic
need and the geographic distribution of ARRA funds. The dependent variable is the natural log
of per capita ARRA funds by county.10 As is common with geographic data, standard ordinary
least squares approach is inadequate here. OLS makes the assumption that the observations i on
the dependent variable are independent of each other, such that E(eiej)=0. In the case of our data,
however, the observations are spatially dependent, in that the per capita amounts allocated to one
10
The natural log transformation was needed to correct for the non-normal distribution of the
dependent variable, as evident in Table 2. With the natural log transformation, the data are
normally distributed with means and medians close together.
11
county are likely to be related to those allocated to neighboring locations. If unattended, the
resulting correlated errors could result in faulty inferences based on t and F statistics. To address
this statistical problem, the models include a correction for the spatially correlated errors.
Additional detail on the diagnostics we employ and the specific correction method we implement
is provided in Appendix A.
Four indicators of economic need are evaluated in our data analysis. First, two variables
measure the depth of the recession at the time the legislation was drafted by Congress and passed
into law: the cumulative foreclosure rate for the first quarter of 2009 and the change in the
unemployed percent between the 2007 average and the first quarter of 2009. Second, two
variables identify locales with long-standing economic difficulties: median income and the
percent unemployed in 2007.11 Results for the influence of these need indicators on per capita
ARRA allocations are displayed in Table 3.
[Table 3 here]
The findings simply reveal no meaningful targeting to economic need. Areas hardest hit
by the recession, as measured by the scale of their job losses or their rate of foreclosures,
received no additional stimulus funds across all the projects funded by the ARRA. Indeed, the
coefficient for change in unemployed % is negative and statistically significant (p<.001),
meaning that areas with bigger increases in unemployment received slightly less stimulus money
per capita. The model estimates that counties at the 90th percentile of change in unemployed %
received $6 per capita less than counties at the 10th percentile. Low-income areas do not receive
more ARRA project funds; instead, the coefficient, albeit statistically insignificant, is positive.
11
Although there is modest multicollinearity among some of these variables, regression results
do not change if any particular variable is dropped from the model. Percent in poverty was
tested as an alternative for median income, with no change in the results.
12
Only one indicator suggests any project funding being directed to unemployment: Areas that had
high levels of unemployment before the recession began (in 2007) received more stimulus
money (p<.001), but the effect is minimal. According to model estimates, counties at the 90th
percentile of unemployment in 2007 received a minuscule $4 more per capita than counties at the
10th percentile.
The results from the model of expenditures for transportation and water infrastructure
similarly indicate little targeting to economic need. The counties with the most severe job losses
during the recession were granted no additional support compared to counties with fewer freshly
unemployed residents. Furthermore, locations suffering from long-standing economic
difficulties did not receive any additional funding for transportation and water infrastructure.
Instead, the coefficient for median income is positive (p<.05), indicating that high income areas
received more assistance than low income locales, a difference of about $25 per capita between
the 10th and 90th percentile. Unemployment levels in 2007 had no statistically significant effect,
and the coefficient is negative. Although the results in Table 3 do suggest that areas with high
foreclosures received a boost (p<.05), this finding does not hold up in the full model of
infrastructure spending presented later in the paper, so we do not place much confidence in it.
Taken together, the regression estimates confirm systematically what was evident
visually in the map in Figure 3. Although ARRA funds were targeted to some counties, the
beneficiaries were not those most adversely affected by this economic downturn.
Policy Windows vs. the Pork Barrel
We have taken a glimpse at some key data showing that the ARRA's performance has not
met its promise. Now the task is to account for the gap between aspirations and outcomes in
13
legislation intended to assist those hit hardest by the recession. Traditional scholarship on pork
barrel politics would look for explanations for the ways institutional power shapes access to
political benefits. An alternative theory, one that has received no attention in the literature on
distributive politics, is that the geographic distribution of stimulus funds may have been diverted
away from need as a result of policy window effects. The reason that the ARRA did not direct
scarce resources toward the areas hardest hit by the recession is because the legislation became a
vehicle for a multiplicity of unrelated policy goals as political entrepreneurs took advantage of a
gaping window of opportunity.
Testing the Theories
One of the difficulties of testing policy windows theory is its ―indeterminacy‖
(Mucciaroni 1992, 482). In the abstract, of course, it is not possible to identify the universe of
alternative policy goals that might potentially be ―coupled‖ (Kingdon 1995, 173) with the
complex problem of an economic downturn. Indeed, any government spending program or tax
reduction could be framed as having a stimulative effect on the macroeconomy. In particular
instances, however, it is possible to identify the range of policy proposals on the national agenda
at the time a piece of legislation is crafted and to employ systematic indicators to measure how
those policies would likely drive the geographic distribution of funds.
Use of these indicators can then afford a falsifiable, empirical test of the policy windows
model. If such indicators can better explain patterns in distribution than economic need, it stands
to reason that these alternative policy purposes were driving geographic allocation. From such a
finding, one might reasonably infer that the economic downturn opened a policy window for
political entrepreneurs to pass legislation for purposes that go well beyond putting unemployed
14
people back to work, or putting a floor under a sinking housing market. Below we identify and
explain the indicators we employed to identify the alternative policy goals likely to steer the
distribution of federal funds in the ARRA.12
At the time the ARRA was written, the Obama administration had a number of other
policy ambitions in addition to responding to the nation‘s economic difficulties. One goal,
widely touted during the 2008 campaign, was to promote research and development of clean
energy sources. To the extent that this goal was incorporated into the ARRA, one would expect
to find centers of science and technology around the country receiving more funds than other
areas. Residents of these areas would be in a better position to apply for the research grants and
to develop development project proposals capable of obtaining funding support. A second top
administration priority was to foster transformations in health care technology, in particular the
transition to electronic medical records. If the stimulus presented a window of opportunity to
pursue these goals, one would expect funds to flow to counties in which higher proportions of
the workforce are employed in computing and science or to counties with large percentages
employed in health and social services. Also, if a share of research grants went to university
scientists, the percent enrolled in college and universities would proxy for the presence of large
higher education facilities.
Another policy goal—a priority not just for the administration but for many members of
Congress—was to improve the nation‘s infrastructure. Although enhancing the nation‘s
infrastructure was commonly presented as a means of addressing unemployment, it also has the
potential to drive the distribution of funds as an end in itself. In other words, if the policy goal is
upgrading and repairing infrastructure—rather than using infrastructure projects as a tool to
12
Appendix A identifies all variables and sources used in the quantitative analysis.
15
alleviate unemployment—one would expect funding to stream toward existing public
infrastructure all around the country, rather than toward infrastructure projects in areas with
relatively high unemployment. Our models include five measures to capture the presence of
infrastructure at the county level: interstate highway mileage, U.S. highway mileage, national
parks, water area (to measure need for bridges and other water projects), and medium and small
airports.13
Two other policy goals were very prominent during the development of the ARRA. A
number of senators saw the stimulus as an opportunity to increase support for medical and
scientific research. In the end, the legislation included fully $10 billion in new resources for the
National Institutes of Health and $3 billion for the National Science Foundation. To take
account of this goal, the model includes a dummy variable for the locations of the National
Institutes of Health (in Maryland and North Carolina). In addition, percent enrolled in colleges
and universities measures the medical and scientific research funds flowing to educational
institutions, just as it gauges allocations for energy research. Finally, given budgetary shortfalls
among governments at all levels, support for state and local programs, especially education, was
a high priority in Congress. To measure the extent to which stimulus funds subsidized
preexisting state and local government activities around the country, regardless of how seriously
these areas were affected by the recession, we included the percent employed in state and local
government.
13
Medium and small airports have greater needs for federal subsidies (for security and other
purposes) than large hub airports, because they cannot capitalize on economies of scale. Our
expectation was that the presence of these airports might attract more federal per capita stimulus
dollars than large airports. However, we also experimented with including a variable for large
airports in the model, as well, finding that it had no effect.
16
Given the incredible scope of the legislation, further investigation into the policy goals of
key administration and congressional actors as they developed the stimulus would undoubtedly
reveal many additional policy ―solutions‖ that became ―coupled‖ with the economic recession.
Such an investigation would identify other relevant variables (though there are some data
availability challenges in studying county-level demography). However, the measures we have
included take account of a number of the most prominent and costly alternative policy goals that
may account for the geographic distribution of ARRA funds.
To test for a traditional pork barrel account of the distribution of stimulus funds, a series
of institutional and political variables are employed. First, representation on either of the two
House committees with jurisdiction over the grants, loans and projects funded by the ARRA
might affect the amount a county receives: dummy variables reflect whether a member
representing the county served on House Appropriations or House Energy and Commerce.14
Membership on the House Transportation and Infrastructure Committee may also have allowed
members to influence ARRA infrastructure spending, even though the legislation was not
referred to this committee. The ARRA did not receive formal consideration by Senate
committees, but membership on key committees, Senate Appropriations or Environment and
Public Works, might have permitted some senators informal sway over the legislation.
Three variables are designed to measure whether the majority party reserved special
benefits for itself in the legislation: % of the House delegation Democratic, % of the Senate
delegation Democratic, and the county‘s Democratic share of the presidential vote ’08. Finally,
two variables measure how the structure of representation might advantage or disadvantage
14
House Ways and Means and Senate Finance also considered the legislation, but had
jurisdiction over the direct spending and tax programs, not the grants, contracts, and loans
tracked geographically in the Recovery.org data.
17
counties. First, the greater representational power of small states in the Senate is measured by
the reciprocal of state population (1/state population).15 Second, more populous counties, with
more House representation, might gain greater benefits.
Results of Estimation
The complete regression analyses of total ARRA expenditures and of infrastructure
projects are detailed in Appendix B.16 Our estimates provide confidence that the findings are
robust to alternative specification, even though we explain only a modest amount of the total
variation in the dependent variable. Coefficient estimates from the complete models (which
include all the variables tracking pork barrel and policy windows effects, along with economic
need) are by-and-large consistent with those from the partial models testing the pork barrel and
policy windows theories separately. Because the dependent variable is log transformed,
however, the regression coefficients are somewhat difficult to interpret in substantive terms. For
a better sense of model findings, we present Table 4 to show how each of the statistically
significant coefficients is predicted to affect a county‘s per capita receipts (unlogged), given the
range of the variables.
[Table 4 here]
Clearly, the traditional pork barrel account of bias in distributive outcomes fares rather
poorly. In most cases, counties represented by members on the committees with jurisdictional
claim to the ARRA did not obtain any extra benefits. Counties represented by House and Senate
Appropriators gained no appreciable boost in funding. Representation on Senate Environment
15
Prior work has shown this to be the most appropriate functional form for estimating the small
state advantage in distributive policy (Lauderdale 2008).
16
Because the models in Appendix B are for infrastructure projects, only the policy window
variables relevant to infrastructure are entered.
18
and Public Works had a small positive effect, estimated at $1.08 for all stimulus programs and at
$1.60 for infrastructure projects (p<.01). Representation on the House Energy and Commerce
Committee generated an additional $1.08 per capita (p<.001) in the model for all ARRA
programs.
Small or nonexistent benefits for committee members are consistent with the perception
that committee autonomy in the contemporary Congress has been weakened (Sinclair 2000). In
addition, counties represented by Democrats in the House and Senate did not obtain any
statistically significant funding increase. In fact, in the model of infrastructure spending, %
Senate delegation Democratic exhibits a negative coefficient, albeit statistically insignificant.
These across-the-board negative results for congressional representation also make sense in that
the legislation included no earmarked projects, diminishing Congress‘s ability to direct funds
toward particular states and districts.
Notably, however, there is a partisan tilt toward counties that were stronger for the
Democratic party in 2008. All else equal, counties at the 90th percentile of support for candidate
Obama received between $35 and $36 more per capita in both overall funding and infrastructure
projects than counties at the 10th percentile (p<.001). Although much of the early work on
distributive politics found no consistent party effects, this finding corresponds with recent
scholarly analysis of the more partisan contemporary period. The new work documenting
partisanship in administration is especially relevant because most of the grants, loans, and
contracts funded by the ARRA are in discretionary programs overseen by administrative
agencies (Bertelli and Grose 2009; Garrett and Sobel 2003; Larcinese, Rizzo, and Testa 2006;
Reeves Forthcoming).
19
The results also testify to the continuing relevance of the Senate‘s representational
scheme for understanding distributive outcomes. Counties in small-population states received
larger per capita allocations than counties in large-population states for both all ARRA funds, as
well as for infrastructure spending. Between the 10th and 90th percentiles on the small state
advantage measure (1/state population), the difference is just under $6 per capita. However, the
10th-90th percentile range of small state advantage understates the effect. Because there are
relatively few counties in the smallest population states, the 90th percentile does not include any
counties in the nation‘s 12 least populous states. If one compares counties across the full range
of state population, the estimated difference between per capita allocations in California counties
as compared to Wyoming counties is around $19 for both models. Consistent with previous
work (Lee 1998), the states with the smallest populations (those with only one House district)
gain the biggest advantages from Senate representation. Of course, even a $1 per capita
reduction across a state with the population of California or New York involves a significant
sum. Counties with more House representatives do not obtain any increases in per capita
funding.
As an account of the geographic distribution of ARRA funds, the ―policy windows‖
thesis gains considerable support from the models. Unquestionably, funds were far better aimed
at existing infrastructure than unemployed persons. Counties at the 90th percentile of interstate
highway mileage received $50 more per capita than counties at the 10th percentile. Counties at
the 90th percentile of U.S. highway mileage gained fully $86 more per capita than counties at the
10th percentile. Extensive amounts of water area attracted additional infrastructure funds. The
presence of a national park also boosted receipts by between $1 and $2 per capita in each of the
models. These findings reveal that ARRA funds were better allocated for the policy purpose of
20
improving infrastructure throughout the nation than for redressing recessionary effects. The
geographic targeting of funds was more toward standing investments in public infrastructure than
to pockets of high unemployment or collapsing housing values.
Other policy goals beyond infrastructure maintenance also exerted an effect on the
geographic distribution of ARRA funds. Consistent with the expectation that the
administration‘s initiatives promoting research and development in clean energy and medical
technology would have an effect, the results indicate that funds were targeted to centers of
science and technology. Areas with large institutions of higher education received substantially
more resources. Counties at the 90th percentile of college enrollment received $17.46 more per
capita than counties at the 10th percentile (p<.001). In addition, counties at the 90th percentile
of employed in computing and science received $2.71 more per capita than counties at the 10th
percentile (p<.001). Counties housing National Institutes of Health facilities received an
additional $2 per capita. Finally, it is clear that ARRA funds were broadly distributed to sustain
state and local government services in all areas, regardless of their economic distress. Counties
with a larger share of residents employed in state and local government received more money,
with counties at the 90th percentile receiving $11.45 more per capita than counties at the 10th
percentile.
Taken together, our findings support the conclusion that that both pork barrel and policy
window effects drove the distribution of ARRA funds to a greater extent than the economic
needs present from the recession. Remarkably, even after controlling for the political,
institutional and policy factors affecting distribution, areas where the recession dug deepest did
not receive additional funds. Only one indicator of need had the expected effect and in only one
of the two complete models. Counties at the 90th percentile of unemployed % 2007 received
21
$4.23 per capita more ARRA funds than counties at the 10th percentile (almost precisely the
same effect documented in the simple need model reported in Table 3). The areas of the country
that were hardest hit by the recession in terms of job losses after 2007 or by home foreclosures in
the initial months of 2009 received no additional support from the ARRA.
Of the two models of distribution, then, we conclude that the policy windows thesis fares
better than the distributive politics account. Congress members advantaged by committee
jurisdiction or majority party membership did not procure more ARRA funds for their
constituencies. To be sure, there is non-trivial evidence that allocations went more toward
Democratic areas of the country, as measured by their presidential vote in 2008. And Senate
representation had its characteristic effect of advantaging less populous states, in particular those
with the very smallest populations. Generally speaking, though, the policy window variables
have greater effects on the geographic distribution of benefits than the variables in the pork
barrel model.
Taken together, the findings reveal that there were systematic policy reasons for some
areas receiving more ARRA funds per capita than others. However, those reasons were not that
some areas were experiencing greater economic hardship. Instead, particular counties received
more federal support because such a distribution better advanced policy goals in promoting clean
energy, fostering medical and scientific research, repairing existing infrastructure, and
subsidizing state and local government services throughout the country.
Policy Windows and Distributive Politics
―I believe White House Chief of Staff Rahm Emanuel characterized this Democrat
spending bill best when he said, ‗You never want a serious crisis to go to waste. And
what I mean by that is an opportunity to do things you think you could not do before.‖
22
--Rep. Cliff Stearns (R-Fla.)17
The results of this study of the ARRA of 2009 point toward a fruitful synthesis of two
divergent theoretical perspectives on policymaking. The study of distributive politics has
centered on the way basic institutional facts—committee influence, party control,
representational power—can misdirect federal policy. Scholars of distributive politics, however,
have not drawn upon insights from the literature on the policy process. But just as federal
distributive policy is vulnerable to pork barrel politics, it can also fail to accomplish stated policy
goals because of the crowding of policy windows, as various political entrepreneurs successfully
exploit available opportunities to push through legislation advancing disparate policy objectives.
Work on distributive policy needs to be informed by scholarship on the public policy
process, broadly speaking. In this case, the ARRA failed in terms of focusing resources on the
areas of greatest economic suffering, becoming instead a vehicle for an amalgam of different
policy purposes. Our findings, of course, do not speak to the substantive merit of the various
public policies funded by the stimulus. They may well be worthy investments of government
resources. It is also undoubtedly true that the legislation had macroeconomic effects of
stimulating employment and demand, another central goal of the legislation. But analyzed in
terms of distributive outcomes, the legislation did not do nearly as much as it could have to
alleviate economic hardship where it was most severe.
A long history of research in distributive politics has established that there are significant
institutional obstacles to geographic redistribution focused on need in the U.S. system. Pork
barrel politics may have played a role in the ARRA, especially with respect to the Senate‘s small
state advantage and the favorable tilt toward Democratic counties. But our analysis of the
17
Congressional Record, February 13, 2009, H1580.
23
ARRA highlights how a legislative process, particularly one increasingly dominated by omnibus
bills (Krutz 2001), may be especially vulnerable to the kind of unpredictable policy outcomes
that concerned Kingdon (1995). Efforts to distribute federal resources to needy areas can be
undermined not only by pork barrel politics, but also by the efforts of political entrepreneurs to
hitch their bills to any legislative train leaving the station.
At the same time, this study suggests that scholars of the policy process could benefit
from more attention to distributive policy. Scholars focusing on the policy process have not
taken much interest in policy decisions relating to geographic distribution, instead paying more
attention to other policy types, especially regulation (e.g., Kingdon 1995, Sabatier 1988).
Distributive policy, however, offers a good testing-ground for theories of the policy process.
One of the advantages of this policy type is the presence of outcomes that can be compared on a
metric (dollars), easing the task of systematic analysis. In this study, we found that it was
possible to obtain empirical indicators that allowed us to test for the ways different policy goals
drove the geographic distribution of ARRA funds. In so doing, our results yielded support for a
longstanding theory of the policy process that has been difficult to empirically evaluate.
Reflecting on the difficulty of empirically verifying theories of the policy process, Sabatier
(1991, 153) wrote, ―The paramount task facing policy scholars during the 1990s will be to apply
these theories in a variety of empirical settings.‖ A review of the subsequent literature, however,
does not find many new efforts to test for the effects of policy windows, even though ―nothing
has changed the direction of thinking‖ about their importance to understanding policymaking
(John 2003, 482). In the case of the ARRA of 2009, policy windows theory provided more
guidance in accounting for the distribution of federal dollars than the traditional factors at the
heart of the distributive politics literature.
24
Finally, we hope that our findings will inform contemporary political debate over the
legislation. It is ironic that the dominant criticisms of the ARRA that have characterized
American political discourse since the passage of the legislation have centered on its purported
politicized funding distribution. As early as February 8, 2009, the New York Times reported that
the term ―porkulus‖ had appeared in nearly 70 instances in the Lexis-Nexis database since Rush
Limbaugh had first used the moniker.18 The results of this analysis do not entirely discredit
charges of pork barreling, but they provide considerably more support for the less influential
criticism raised by Rep. Cliff Stearns (R-Fla.), quoted above. Rather than a uniquely egregious
exercise in pork barreling, the ARRA brightly illuminates the politics of making the most of
crisis.
18
―Porkulus,‖ Idea of the Day: Must Reads from the Week in Review Staff, Weblog, New York
Times, February 8, 2009, http://ideas.blogs.nytimes.com/2009/02/08/porkulus/.
25
Table 1: County Foreclosure Rate, per 1000 Households
Mean
Median
Minimum
Maximum
Percentiles
10
20
30
40
50
60
70
80
90
95
97
98
99
2.2
0.5
0.0
54.8
0.0
0.0
0.1
0.2
0.5
1.0
2.0
3.3
5.9
9.4
12.8
15.3
21.0
26
Table 2: Distribution of Economic Stimulus Funds Across Counties
Transportation and
Water Funds Per
Capita through Q1,
2010
3083*
$200
$62
$675
13
269
$0
$19,485
$0
N
Mean
Median
Std. Deviation
Skewness
Kurtosis
Minimum
Maximum
Percentiles
10
Total Per Capita
Funds through
Q1, 2010
3083*
$870
$570
$1,248
8
85
$15
$21,339
$271
20
$354
$0
30
$425
$19
40
$496
$40
50
$570
$62
60
$675
$90
70
$807
$126
80
$1,025
$201
90
$1,485
$401
*Counties encompassing state capitals are excluded because they receive large allocations for
state programs that are then redistributed across the state.
27
Table 3: Targeting to Need in the ARRA
Total Per Capita
Spending
Transportation and
Water Funds Per Capita
Coeff. (Std Err)
Coeff. (Std Err)
Depth of recession
Foreclosure rate, Q1 '09
Change in unemployed %, '07-Q1 '09
0.004
-0.037**
(0.004)
(0.007)
0.019*
0.019
(0.011)
(0.022)
Long term economic difficulty
Median Income
Unemployed % '07
0.0004
0.045**
(0.0001)
(0.011)
0.001*
-0.004
(0.0005)
(0.032)
Constant
Lambda
R-squared
6.347**
0.273**
0.065
(0.100)
(0.026)
3.002**
0.297**
0.068
(0.297)
(0.026)
*p<0.05; **p-<0.01
Note: Dependent variable is total logged per capita ARRA allocations per county through the
first quarter of 2010. N=3,084. State capital counties are excluded.
Table 4: Determinants of ARRA Funding Receipts
Hypothesis
Pork barrel
Policy window
Variable
Change in x
(from, to)
Change in expected
per capita receipts
for all ARRA programs
Change in expected
per capita receipts
for infrastructure only
House Transportation & Infrastructure
0, 1
n.s.
n.s.
House Appropriations
0, 1
n.s.
n.s.
House Energy & Commerce
0, 1
$1.08
n.s.
Senate Environment & Public Works
0, 1
$1.10
$1.60
Senate Appropriations
0, 1
n.s.
n.s.
% House delegation Democratic
0, 100
n.s.
n.s.
% Senate delegation Democratic
0, 100
n.s.
n.s.
Democratic share presidential vote ‗08
24.11, 59.36
Small state advantage (1/state pop)
.411, 6.56
Total # of House members
$35.40
$35.96
$5.84
$5.80
1, 4
-$2.86
n.s.
National park
0, 1
$1.22
$1.78
Medium & small airports
0, 1
n.s.
n.s.
Interstate mileage
0, 50.15
$50.22
$50.40
U.S. highway mileage
0, 86.07
$86.21
$86.60
Water area (logged %)
2.33, 6.91
National Institutes of Health
0, 1
College enrollment (%)
8.34, 25.58
Employed in health & social services (%)
7.85, 14.45
Employed in computing and science (%)
.48, 3.09
n.s.
$4.89
$2.00
--
$17.46
--
n.s.
--
$2.71
--
1
Need
Employed in state and local government (%)
8.97, 20.20
Change in unemployed %, ‘07-Q1 ‘09
1.36, 7.23
Foreclosure rate (per 1000), Q1 ‗09
$11.45
--
-5.78
n.s.
0, 5.79
n.s.
n.s.
Unemployed (%), 2007
2.96, 7.04
4.23
n.s.
Median income
32,019, 57,602
n.s.
n.s.
Note: The entries in the second column reflect the values at the 10th and 90th percentiles of the continuous variables and 0 and 1 for dichotomous
variables. The entries in the third column employ the regression coefficients reported in the full model of Table 4 to predict the change in total per
capita receipts for all ARRA programs associated with the changes shown in the second column. The entries in the fourth column employ the
regression coefficients reported for the full model in Table 5 to predict the change in per capita receipts for infrastructure associated with the
changes in the second column.
Figure 1: Frequency Distribution of County Unemployment Rate, 2007 and 2009
Figure 2: Unemployment rate and Foreclosures, by County, First Quarter of 2009
1
Figure 3: Total ARRA Funds through Q1 2010 Per Capita, by County
Appendix A: Spatial Error Estimation
Proper model specification in ordinary least squares can often account for spatial
dependency in error terms, but not always. When unobserved factors exert influence on the
dependent variable, the inability to account for these latent forces will yield spatially correlated
errors (LeSage and Pace 2009; Anselin 1988).
The procedure to test for spatial dependency is to start by estimating the regression model
with ordinary least squares and examine the resulting residuals using the test statistic, Moran's I
(Anselin 1988, 16-19). A positive and significant Moran’s I indicates clustering in space of
similar values. Such tests assume that the researchers have first explored the data and specified a
particular neighbor structure to gauge spatial dependency. In our tests, we utilize a binary
contiguity between adjacent units, expressed as 1, 0 values. The contiguity is first order so that if
two spatial units have a common border, they are assigned a value of 1, and if non-contiguous, a
value of 0.19 This makes sense for county-level data analysis because boundaries between
counties are arbitrarily drawn, and the stimulus funds distributed to adjacent counties are likely
to be related. Commonly capital projects, for instance, serve more than just a single jurisdiction,
and their construction may draw upon local human and capital resources from several
neighboring jurisdictions.20
Starting from the basic linear regression model:
19
By convention, the diagonal elements of the spatial weights matrix are set to zero, and to
facilitate interpretation, the elements of each row are standardized such that they sum to one.
20
Choice of this spatial structure was dependent upon exploratory data analysis, and higher order
contiguity matrices in which more distant neighbors are considered linked, were also considered,
but ultimately found to be unnecessary to correct the least squares estimates. An additional merit
of the first order autoregressive structure is its simplicity.
1
the spatially autoregressive error can be written as:
where λ is the spatially autoregressive coefficient, W is the binary contiguity weights matrix, and
µ is an error term that satisfies the requirements of independent and identical distribution (i.i.d.)
and homoscedastic variance (Anselin 1988, p. 100-101).
Tests of our OLS residuals for each of our models showed modest but statistically
significant positive spatial dependency. In other words, the per capita amount allocated to
nearby locations could be used for prediction, meaning that the data generation process is
spatially autoregressive (Anselin and Bera 1998). Related diagnostics indicated that the
appropriate course of action was to estimate with maximum likelihood the following linear
regression model with a spatially autoregressive error:
Where λ is the coefficient for the spatial error lag. The value of the dependent variable for each
location is therefore influenced by the errors at neighboring locations through the term (I - λW)1.
The resulting coefficients will have appropriate standard errors.
2
Appendix A Table 1. Variable Definitions, Means and Standard Deviations
Variable Name
Mean
Std. Deviation
Natural Log of Total Cumulative Spending Per Capita through Q1, 2010
6.4
0.8
Natural Log of Total Infrastructure Spending Per Capita through Q1, 2010
includes Highway Projects through U.S. DOT and Water Projects through U.S.
Department of Interior, and U.S. Army Corps of Engineers.
County Represented by a House Transportation Member
3.6
2.2
0.4
0.6
County Represented by a House Approps Member
0.4
0.6
County Represented by a House Energy and Commerce Member
0.3
0.5
County Represented by a Senate Environment and Pub Works Member
0.3
0.5
County Represented by a Senate Approps Member
0.6
0.5
Total Members of House Serving County
2.4
1.3
% Democratic House Members Serving County
47.9
37.7
% Democratic Senators Serving County
49.3
42.2
% Democratic Presidential Vote 2008
41.3
13.7
% Unemployment 2007
4.9
1.8
Foreclosure Rate, Quarter 1, 2009
2.1
4.3
Change in Unemployment Rate 2007 to Q1 of 2009
4.2
2.4
43,985
11,436
NIH Facilties in County
0.0
0.0
Major National Parks in County
0.0
0.1
Medium and Small Hub Airports in County
0.0
0.2
Mileage of Interstate Highway in County
18.7
31.7
Mileage of Major US Highway in County
43.4
37.3
Natural Log of % Water Area in County
4.5
1.8
% College+Grad School Enrollment 2000
16.0
9.6
% Health & Social Services Employment
11.1
2.7
1.6
1.4
% State & Local Gov't. Employment
14.0
5.0
% Unemployment Quarter 1, 2010
10.4
3.8
% Unemployment Quarter 4, 2009
9.1
3.4
% Unemployment Quarter 1, 2009
9.0
3.5
63,761,534
249,376,860
Median Household Income
% Computer & Scientific Employment
Cumulative Total Amount Awarded for All Programs through Q1, 2010
3
Cumulative Total Amount Awarded Per Capita through Q1, 2010
Infrastructure Total Amount Awarded through Q1, 2010
870.0
1,248.1
10,180,998
44,350,934
200.4
675.4
Infrastructure Total Amount Per Capita through Q1, 2010
Total N =3084
Data Sources
For award amounts: ProPublica. http://www.propublica.org/
Unemployment statistics: Bureau of Labor Statistics. Local Area Unemployment Statistics (LAUS).
Foreclosures: RealtyTrac. http://www.realtytrac.com/
Committee Memberships: Almanac of American Politics.
Highway Mileage: U.S. DOT. Bureau of Transportation Statistics. U.S. National Transportation Atlas.
http://www.bts.gov/publications/north_american_transportation_atlas_data/
Airports: U.S. DOT, Federal Aviation Administration. Air Carrier Activity Information System; and the
U.S. Geological Survey and National Atlas of the United States at NationalAtlas.gov, provided by
the U.S Department of the Interior, U.S. Geological Survey.
National Parks: U.S. Department of the Interior, U.S. Geological Survey.
Water Area: U.S. Department of Agriculture. Economic Research Service.
National Institutes of Health: U.S. Department of Health and Human Services, National Institutes of
Health.
Congressional District Data: Clerk of the U.S. House of Representatives. For 111th Congressional
District political boundaries. U.S. Department of Commerce, Bureau of the Census. Geography
Division.
Employment and Income Data: U.S. Department of Commerce, Bureau of the Census.
For 2008 Presidential Vote: David Leip's Atlas of U.S. Presidential Elections.
http://www.uselectionatlas.org/
For GIS boundary files and coverages: Environmental Systems Research Institute, Inc. ESRI Data &
Maps DVD licensed for use with ESRI® software.
4
Appendix B, Table 1: Predicting Total ARRA Receipts, Policy Windows and Pork Barrel Models
National Institutes of Health
National park
Medium and small airports
Interstate mileage
US highway mileage
Water area (logged %)
College enrollment (%)
Employed in computing & science (%)
Employed in health & social svc (%)
Employed in state and local gov’t (%)
Lambda
Constant
House Transportation & Infrastructure
House Appropriations
House Energy and Commerce
Senate Environment & Public Works
Senate Appropriations
% House delegation Democratic
% Senate delegation Democratic
Democratic share presidential vote ‘08
Small state advantage (1/state pop)
Total # of House members
Lambda
Constant
Lambda
Policy Windows
Pork Barrel
Full Model
Coeff. (Std Err)
Coeff. (Std Err)
Coeff. (Std Err)
0.791*
0.225*
-0.042
0.001*
0.001***
0.003
0.006***
0.0268*
0.011
0.025***
0.264***
5.70**
(0.363)
(0.103)
(0.085)
(0.000)
(0.000)
(0.008)
(0.002)
(0.012)
(0.005)
(0.003)
(0.026)
(0.080)
0.016
0.035
0.069*
0.115*
-0.050
0.00002
0.0001
0.010***
0.582***
-0.036**
0.163***
5.885***
(0.025)
(0.026)
(0.029)
(0.032)
(0.117)
(0.0004)
(0.0004)
(0.001)
(0.045)
(0.013)
(0.028)
(0.057)
0.695*
0.196*
-0.067
0.001**
0.002**
0.006
0.007**
0.039**
-0.002
0.0186**
(0.350)
(0.100)
(0.083)
(0.001)
(0.000)
(0.008)
(0.002)
(0.014)
(0.005)
(0.003)
0.024
0.014
0.074**
0.098**
-0.034
0.0001
0.001
0.004**
0.567**
-0.048**
(0.331)
(0.025)
(0.028)
(0.031)
(0.032)
(0.0004)
(0.0004)
(0.001)
(0.044)
(0.025)
0.123**
(0.028)
5
Constant
5.602**
Unemployed % ‘07
Foreclosure rate, Q1 ‘09
Change in unemployed %, ’07-Q1 ‘09
Median income
N
R-squared
3,084
0.148
3,084
0.122
(0.133)
0.035**
(0.001)
-0.003
(0.004)
-0.014*
(0.007)
-0.003
(0.002)
3,084
0.169
*p<0.05; **p<0.01; ***p<.001
Note: Dependent variable is total logged per capita ARRA allocations per county through the first quarter of 2010. N=3,084. State
capital counties are excluded.
Appendix B, Table 2: Predicting Expenditures on Infrastructure Projects, Policy Windows and
Pork Barrel Models
National parks
Medium and small airports
Interstate mileage
U.S. highway mileage
Water area (logged %)
Lambda
Constant
Policy Window
Pork Barrel
Full Model
Coef (Std Err)
Coef (Std Err)
Coef (Std Err)
0.772*
0.165
0.007***
0.006***
0.092***
0.304***
2.733***
0.574
0.048
0.005**
0.006**
0.066**
(0.304)
(0.250)
(0.002)
(0.001)
(0.025)
0.053
0.027
-0.046
0.467***
0.005
0.012
0.000
-0.002
0.020***
0.590***
(0.518)
(0.084)
(0.095)
(0.107)
(0.109)
(0.047)
(0.001)
(0.001)
(0.004)
(0.154)
Unemployed (%), 2007
Foreclosure rate (per 1000), Q1 ‘09
Change in unemployed %, ’07-Q1 ‘09
Median income
-0.036
-0.001
0.03
0.005
(0.033)
(0.939)
(0.022)
(0.005)
Lambda
Constant
0.268***
(0.026)
1.629***
(0.033)
3,084
0.111
Transportation and Infrastructure
House Appropriations
House Energy & Commerce
Senate Environment & Public Works
Senate Appropriations
Total # of House members
% House delegation Democratic
% Senate delegation Democratic
Democratic share presidential vote ‘08
Small state advantage (1/state pop)
Lambda
Constant
N
R-squared
*p<0.05; **p<0.01; ***p<.001
(0.304)
(0.249)
(0.001)
(0.001)
(0.024)
(0.026)
(0.130)
0.014
-0.007
-0.075
0.533***
-0.125
0.132**
-1.256
-0.002
0.023***
0.671***
0.265***
2.118***
3,084
0.095
(0.083)
(0.083)
(0.095)
(0.107)
(0.105)
(0.043)
(0.001)
(0.001)
(0.004)
(0.150)
(0.026)
(0.026)
3,084
0.092
Note: Dependent variable is total logged per capita allocations for transportation and water
projects per county through the first quarter of 2010. N=3,084. State capital counties are
excluded.
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