How Do Voters Retrospectively Evaluate Wasteful Government Spending?
Evidence from Individual-Level Disaster Relief
Jowei Chen and Andrew Healy
Abstract: Why do voters often reward incumbents when they receive government
spending? We develop a model in which distributive spending provides voters not just
with a financial benefit, but also an opportunity to observe and judge the appropriateness
of government decisions. Empirically, we test the model’s predictions using individuallevel data on FEMA disaster relief matched to voter turnout records, precinct-level
election returns, and geographic data on hurricane severity. In accordance with the
model, voters in areas experiencing severe hurricane conditions respond to the receipt of
FEMA disaster aid with significantly higher turnout and electoral support for the
incumbent administration. In contrast, voters show little response to aid in areas that
experienced little damage and that audits identified as having received undeserved FEMA
spending. Politicians thus appear to be constrained in their ability to use distributive
spending to win elections since voters account for the merit of the aid they receive.
Word Count: 9,025
Why do voters often reward incumbents when they receive government spending?
Scholars have repeatedly found that the beneficiaries of distributive programs, such as veterans'
benefits (Mettler and Stonecash 2008) and farm subsidies (Wolfinger and Rosenstone 1980),
exhibit increased voter turnout (e.g., Ansolabehere and Snyder 2006) and stronger electoral
support for incumbent politicians (e.g., Levitt and Snyder 1995). Under a classic view of
distributive politics (e.g., Persson and Tabellini 2000), voters respond positively simply because
they are motivated to maximize their share of government spending. They thus tend to reelect
incumbents who have delivered distributive benefits to them.
But these electoral rewards of distributive spending present an important theoretical
puzzle: If distributive spending is so effective at winning votes, why do incumbents not simply
provide the spending needed to guarantee reelection? Noting previous scholars’ repeated
empirical finding that distributive spending garners votes, Golden and Min (2013) wonder: “It is
perhaps surprising that any politician ever loses elected office given the impressive evidence that
has been amassed showing the politicization of the public purse” (86). Given the existing
literature, then, it seems puzzling why politicians do not engage in even higher amounts of
electorally-motivated distributive spending to win more votes and guarantee reelection.
We help to resolve this puzzle by arguing, both theoretically and empirically, that voters
respond not merely to the quantity of distributive benefits they receive, but also to the
appropriateness of such spending. In other words, voters use their experiences with distributive
programs to evaluate the government’s quality. There are thus clear limits on policymaker
incentives to increase distributive spending in advance of elections, as only spending that
addresses a clear need may win votes.
2
A large literature on retrospective voting (e.g., Key 1966, Fiorina 1981; Markus 1988),
has suggested that voters draw upon their past observations and experiences with government to
retrospectively evaluate politicians' efficacy. As Fiorina (1981) explains, “In order to ascertain
whether the incumbents have performed poorly or well, citizens need only calculate the changes
in their own welfare” (5). By considering accessible metrics such as war casualties, voters can
hold a government accountable for its foreign policy, for example (Fiorina 1981). More
generally, voters can attempt to evaluate policies indirectly by observing their results. The
receipt of distributive benefits actually offers voters an opportunity to directly observe
government policy. Therefore, voters may utilize their receipt of distributive spending as
information about a government’s performance, just as they consider the economy.
Integrating distributive politics with retrospective voting, as we show, provides important
insights into how voters appear to make decisions and the incentives that policymakers face. We
develop and test a new theory in which voters view distributive programs as more than mere
financial transactions. The intuition behind our argument, as illustrated in the paper's formal
model, is as follows: Voters receive utility from distributive spending, but they also prefer highquality politicians who make appropriate policy decisions. Voters use their experiences with
distributive programs to make inferences about the incumbent's intelligence and responsiveness.
Suppose a government attempts to target aid to victims of a natural disaster. A government that
appropriately delivers aid to victims signals both its responsiveness and intelligence. In contrast,
a government that erroneously awards aid to non-victims will be perceived as responsive but
lacking intelligence. Since voters want to re-elect politicians who are both responsive and
intelligent, distributive spending wins more votes and motivates greater turnout when it is
delivered to deserving victims rather than undeserving non-victims.
3
This theoretical framework brings together the literatures on the electoral consequences
of distributive spending and the retrospective evaluation of politicians by voters. Our theoretical
distinction between responding to distributive benefits and rewarding politicians for appropriate
decisions has important implications for democratic accountability. If voters reward incumbents
equally for any distributive spending, a government could simply provide as much spending to
electorally important areas as needed to win the requisite number of votes. On the other hand, if
voters respond primarily to spending when they are deserving recipients, then incumbents are
constrained in their ability to win votes with pre-election spending. Under either theory,
incumbents are incentivized to spend more resources before elections and in electorally
important areas (e.g. Alesina and Cohen 1997; Kriner and Reeves 2013). 1 Only under the latter
theory, however, are incumbents restricted in the kind of spending from which they will benefit.
Our theory thus accords with canonical models in which retrospective voters use their
observations to evaluate government performance. We build upon and extend this literature by
explaining why governments do not spend even more than they do in electorally important areas.
Undeserved spending may have little impact on their electoral fortunes.
To empirically test our theory, we analyze individual-level data on Floridians who
applied for FEMA disaster aid just before the November 2004 elections. In 2004, four separate
hurricanes hit Florida in the three months before Election Day. We exploit this natural
experiment to analyze voters’ responses to individually-targeted disaster assistance payments.
Our data consist of all disaster relief applications made in response to the hurricanes and include
recipients’ addresses, the amounts they received, and the dates of the payments. We link those
1
Much of the research on political business cycles has considered macroeconomic policy (e.g.
Persson and Tabellini 1990; Rogoff 1990; Rogoff and Sibert 1988), although the logic is the
same in terms of incentives to pursue expansionary policy before elections.
4
data to individual-level voter turnout records. Finally, we match each address to the storm
severity that struck that location.
As explained in the following section, victims and non-victims alike received significant
disaster aid. The undeserved aid often occurred due to an excessive emphasis on haste and a lack
of adequate oversight (Department of Inspector General 2005). 2 The results indicate that voter
turnout increased in response to pre-election disaster aid in areas that experienced severe
hurricane conditions. In hard-hit areas, even small amounts of disaster relief (less than $250)
motivated voters to turn out. Amongst these voters, receiving a government check increased
turnout by almost three percentage points. However, receiving a check of a similar size had no
impact on voters’ behavior in areas that did not experience severe hurricane conditions.
This paper proceeds as follows. First, we discuss the FEMA disaster aid process,
explaining how FEMA inappropriately awarded aid to a significant number of non-victims in
Florida. Second, to present our main theory, we develop a formal model of voter behavior,
analyzing how voters respond upon receiving either deserved or undeserved disaster aid. Third,
to test the model, we analyze individual-level data on FEMA applicants and voter turnout. We
consider a variety of robustness checks, including placebo tests using payments received shortly
after the election, that confirm the interpretation of the results. Finally, we also test the formal
model by analyzing precinct-level election returns in November 2004, finding that disaster aid
produced a significantly larger electoral payoff for the incumbent candidate in areas that
experienced severe hurricane conditions.
FEMA Disaster Aid and Government Audits
2
Part of the haste may have been motivated by electoral incentives. Research has found that
disaster aid is higher and disaster declarations happen with greater frequency in election years
(e.g. Downton and Pielke 2001; Garrett and Sobel 2003; Reeves 2011).
5
The FEMA Aid Decision Process: In August and September 2004, Florida was struck by
four severe hurricanes: Charley, Frances, Ivan, and Jeanne. During the three months prior to the
November 2004 election, all 67 counties in Florida were declared eligible by President Bush to
apply for hurricane assistance under FEMA’s Individuals and Households Program (IHP).
Formally, IHP provides up to $25,000 to compensate households for their disaster-related
“necessary expenses and serious needs” not covered by insurance or other means (44 Code of
Federal Regulations §206.110).
To apply for aid, Florida residents had to contact FEMA by phone, by internet, or at a field
office and request an inspection of their property. Applicants did not request a specific amount of
aid. Instead, a FEMA inspector―generally an independent contractor and not a permanent FEMA
employee―was tasked with identifying and assessing particular types of damage. FEMA then
awarded a predetermined amount of aid for each category of damage using a standard but
unpublished schedule. Inspectors marked damaged property on a checklist using a handheld
computer, but they did not document damage photographically or with written descriptions. Hence,
applicants did not have the opportunity to strategically misrepresent damage, but FEMA inspectors
had the responsibility of making aid determinations with no documentation and no immediate
oversight. In total, FEMA received 2.6 million assistance applications from 1.1 million Florida
households. FEMA awarded a total of $1.2 billion to these households.
Government Audits and Undeserved Aid: The Department of Homeland Security’s
Office of Inspector General (OIG) issued a report in May 2005 that documented widespread
payments to undeserving recipients after the four Florida hurricanes. The report looked at
payments made under IHP and highlighted problems that led to erroneous payments. Some
spending categories stood out as sources of questionable spending. In general, the report found
6
that speed took precedence over accuracy, leading to widespread spending going to undeserving
recipients. It concluded that these issues it found in the one audited county cast “doubt about the
appropriateness of IHP awards made to individuals and households in other counties of the state
as a result of the four hurricanes, particularly those counties that had only marginal damage”
(OIG 2005, 4, emphasis added).
Some of the problems came from FEMA’s initial decision to override President Bush’s
initial disaster declaration that omitted Miami-Dade county and 11 other counties from the group
eligible for expedited assistance after Hurricane Frances. The initial declaration specified that
Miami-Dade could only be eligible for aid after a Preliminary Damage Assessment (PDA),
which FEMA overrode for unclear reasons. The OIG report highlighted that there was little
evidence of substantial damage in Miami-Dade, a finding echoed in Senate hearings:
In fact, the Miami-Dade County of Emergency Management described the
damage from that hurricane as minimal, and the National Weather Service had
no reports of flooding. Yet taxpayers bought Miami-Dade residents thousands
of television sets, air conditioners, and other appliances, from microwave
ovens to sewing machines. The taxpayers also bought rooms full of furniture,
new wardrobes, and paid to repair or replace nearly 800 cars. It provided
rental assistance to people living in undamaged homes. (Senate Committee on
Homeland Security and Governmental Affairs 2005, 2).
The FEMA decision to make Miami-Dade eligible paved the way for spending to flow quickly to
residents, and the resulting reports of improprieties led to the Inspector General’s audits.
According to the audit, the widespread availability of expedited funding combined with
the four hurricanes in a short period of time to stretch FEMA’s resources thin. After Hurricane
Frances, FEMA required its two contractors to increase the number of inspections they
performed each day to around 15,000. To accomplish this task, each contractor hired about 1600
new inspectors, adding to an initial base of roughly 400. According to the report, “the new
inspectors were not familiar with FEMA programs, received only 8 to 12 hours of basic training
7
on the FEMA inspection process, and their work was not closely monitored” (OIG, 29). The
report noted at least the potential for conflicts of interest, as inspectors sometimes worked close
to their own homes. More importantly, despite the speed with which applications were filed, it
was official policy to issue payments with applications being checked only for completeness and
not for errors.
The report identified these errors and the undeserved spending that resulted as occurring
with noticeable frequency in a number of particular spending categories. For these categories,
FEMA provided inspectors with either vague guidance or gave them substantial latitude to
determine eligibility. For example, funeral expenses were reimbursed for deaths with any
hurricane-related connections, including stress-related causes, which could be broadly
interpreted. The Florida Medical Examiners Commission found that more than 200 of the 319
cases of FEMA providing death benefits were for deaths unrelated to the hurricane, including a
series of payments in areas where no deaths were disaster-related (Kestin and O’Matz 2005).
FEMA also inappropriately awarded aid for cars. Vehicles worth as little as $1000 according
their Blue Book value were reimbursed by FEMA for the maximum $6500 (OIG, 38). Some
vehicles were replaced with no documented reason and others for flood damage despite no
flooding occurring according to the National Weather Service.
While funerals and automobile replacement appear to be clear cases of undeserved
spending according to the report, these categories represent only 1-2% of total FEMA relief for
the four hurricanes. Larger amounts of questionable spending occurred in the two categories
FEMA identifies as expedited assistance and rental assistance, which together accounted for
about 28% of awarded aid applications. General expedited assistance could include the kind of
complete room replacement mentioned in the Senate report above. Applicants could receive the
8
replacement cost of an 11-piece bedroom suite if just one item in the room was found to be
damaged (Senate Committee, 14). Likewise, the OIG audit documents found that “4,308
applicants who received rental assistance did not indicate a need for shelter at the time of
registration or the $8.2 million they eventually received.” The audit further investigated a
subsample of this group, finding that inspectors did not document reasons for assistance being
granted, nor was there evidence that recipients resided elsewhere during the two months they
received assistance worth $1452.
Altogether, the evidence from the Inspector General’s audit indicates widespread
undeserved spending after the four Florida hurricanes. Inadequate oversight resulted from the
emphasis on delivering payments quickly. Questionable payments occurred most often for
categories of spending, such as automobile replacement and rental assistance, with vague
standards and limited required documentation. Moreover, the auditors found that FEMA spent
the most on undeserved relief in those areas where residents incurred little damage. We use these
findings to motivate our formal model in the following section, analyzing how voters respond
when government inappropriately delivers disaster aid to undeserving non-victims.
Theoretical Issues
There are two key features of the formal model that drive our analysis.
Incomplete Information: The incumbent's responsiveness and intelligence determine her
likelihood of choosing a policy that benefits voters. As a result, voters prefer to elect a highquality candidate. Voters lack complete information about the incumbent's quality, however, and
must infer her quality by observing and judging her policy choices prior to the election.
9
Hurricane Victimization and Aid: Nature determines whether a voter is victimized by a
hurricane. While the voter knows whether he has been a victim, the government receives an
imperfect signal of victim status, with a more intelligent government receiving a more accurate
signal. Victims are assumed to derive the most benefit from receiving government disaster
assistance, whereas aid directed to non-victims could be better spent in other ways. A responsive
government's challenge is thus to provide disaster aid, but only to victims. Therefore, a voter can
make inferences about the incumbent's quality by observing the government’s actions with
respect to disaster aid.
Formal Model
Players: There is a single voter, V, who may be a victim of a hurricane in Period 1. There
are two politicians. The incumbent, I, holds office in Period 1. The challenger, C, competes
against I in an election held after Period 1. The election winner holds office in Period 2. For
clarity, we use female pronouns for the Incumbent I and male pronouns for the Challenger C.
Figure 1 illustrates the full sequence of play.
[FIGURE 1 ABOUT HERE]
Hurricane Victimization: At the outset of the game, Nature decides, with ½ probability,
whether V is victimized by a hurricane. Let ω ∈ {victim, nonvictim} denote whether or not V was
victimized. The realized value of ω is revealed privately to the Voter V, while each politician
receives only a noisy signal about V's status.
Strategies: In Period 1, the Incumbent I decides whether to deliver disaster aid to the
Voter V by choosing s1 ∈ {0,1}. After observing I's choice, V chooses whether to vote in the
election, and if so, whether to vote for I or the Challenger C. In Period 2, the election winner has
to make a policy decision in an unrelated area in which an intelligent and responsive politician is
10
more likely to choose the correct policy. The politician decides whether to obtain a signal,
θ 2 ∈ {A , B }, that can help her choose an appropriate Period 2 policy. The politician then chooses
policy s 2 ∈ {A, B}.
Politician Types and Information: Nature independently selects two qualities for each
politician: Responsiveness, β I ,C ∈ {0,1}, and Intelligence, δ I ,C ~ U [1 2 ,1]. Responsiveness is a
binary characteristic chosen with equal probabilities: Pr (β I = 1) = Pr (β C = 1) = 1 2 . A Responsive
politician ( β = 1) internalizes the voter’s utility, while an Unresponsive one ( β = 0 ) does not.
Intelligence is a continuous characteristic drawn from the uniform distribution U [1 2 ,1].
A politician's Intelligence, δ , determines the accuracy of the signals she receives about whether
the voter is a victim (Period 1) and about the state of the world (Period 2). During Period 1, the
Incumbent I receives a private signal, θ I ∈ {victim, nonvictim}, such that:
Pr (θ I = victim ω1 = victim ) = Pr (θ I = nonvictim ω1 = nonvictim ) = δ I
Hence, an Incumbent with the highest possible intelligence, δ I = 1, receives a perfect signal
about whether the voter was victimized, whereas an Incumbent with the lowest intelligence,
δ I = 1 2 , receives a completely uninformative signal.
In Period 2, the election winner, p ∈ {I ,C}, chooses whether to obtain a noisy signal
about the state of the world by paying a cost of ε > 0, assumed to be positive but negligibly
small. If p pays this cost, she receives a signal, θ 2 ∈ {A , B }, that accurately represents the state of
the world, ω2 , with a probability that matches her intelligence:
Pr (θ=
A ω=
A=
) Pr (θ=2 B ω=2 B=) δ p .
2
2
Voter Utility: Voter V's payoff during Period 1 is:
11
m + n, if s1 = 1 & ω1 = victim 


U V ,1 =  m, if s1 = 1 & ω1 = nonvictim ,
 0,

otherwise


(1)
where m, n > 0. Thus, receiving aid provides positive utility to a nonvictim voter and an even
greater utility gain when he is a victim.
Voter V's payoff during Period 2 depends on the election winner's policy choice and is:
 K if s 2 = ω 2 
UV ,2 = 
,
− K if s 2 ≠ ω 2 
(2)
where K ∈ (0,1) is the positive utility the voter V enjoys when the policy choice matches the state
of the world. If the politician chooses incorrectly, V incurs a cost of − K .
Finally, Nature determines the cost of voting, γ , which is drawn from the uniform
distribution U [0,1] and revealed to V. The Voter V chooses whether to incur this cost, choosing
either the Incumbent I or the Challenger C if she does. We assume that V resolves indifference in
favor of turning out. Combining the cost of voting with Eqs. 1 and 2, V's utility over the entire
game is:
m + n, if s1 = 1 & ω1 = victim 

 γ if V turns out;  K if s 2 = ω 2 
U V =  m, if s1 = 1 & ω1 = nonvictim  − 
+
,
s
ω
if
≠
K
0
otherwise,
−


2
2


 0,

otherwise


(3)
where the first term represents V's payoff from the Incumbent's Period 1 aid choice, the second
term represents the cost of turning out to vote in the election, and the third term represents V's
payoff from the election winner's Period 2 policy choice.
Politician Utility: The politician serving in office during each period receives a payoff
consisting of two components. First, a Responsive politician (β = 1) internalizes the Voter's
12
utility, while an Unresponsive one (β = 0) does not. Second, the politician incurs a cost if she
chooses to deliver aid to the Voter.
During Period 1, the Incumbent I holds office and enjoys the following utility:
(m + n 2 ) if s1 = 1
U I ,1 = β I ⋅ U V ,1 − 
otherwise,
 0
(4)
where β I ∈ {0,1} denotes the I’s responsiveness, UV ,1 denotes the voter's utility during Period 1,
and the final term represents the politician's cost of delivering aid ( s1 = 1) .
The cost of delivering aid represents both the opportunity cost of spending public funds
on hurricane aid as well as the bureaucratic cost of administering a disaster relief program. We
set the cost of aid delivery as (m + n 2 ) for the following reasons: If the cost of delivering aid
were lower than m, the model would have a trivial pure-strategy equilibrium in which responsive
politicians always deliver aid, regardless of whether the Voter V is a victim. Analogously, if the
cost of aid delivery were higher than (m + n ), then politicians would never deliver aid to either
type of voter, regardless of victimhood. An intermediate cost focuses instead on the more
interesting case in which it is cost-effective to aid victims but not non-victims.
During Period 2, the election-winning politician p ∈ {I , C} holds office and enjoys the
following utility:
ε if p obtains a signal;
U p , 2 = β p ⋅U V , 2 − 
otherwise,
0
(5)
where β p ∈ {0,1} denotes the election-winner's responsiveness, U V , 2 denotes the Voter's utility
during Period 2, and the final term represents the politician's cost of obtaining a signal about the
state of the world. Since this cost is small, a responsive politician will always choose to obtain it
to increase the likelihood of choosing the correct Period 2 action.
13
Equilibrium Results: For conciseness, we present only the subgame perfect Nash
equilibrium results needed to calculate comparative statics for players' equilibrium-path
behavior, omitting formal presentations of off-equilibrium-path strategies. Complete proofs
appear in the Supporting Information (SI). Lemmas A and B describe politicians' strategies, while
Lemmas C and D describe the Voter V's beliefs about the politicians.
Lemma A: A Responsive politician (β p = 1) always obtains a signal in Period 2.
An Unresponsive politician (β p = 0 ) never obtains a signal in Period 2.
Conditional on obtaining a signal, θ 2 , the Period 2 politician always chooses the
policy that matches her signal: s2 = θ 2 .
Lemma A refers to the politician p's decisions in Period 2. First, p is only willing to pay
the cost of obtaining a signal if she is responsive and internalizes the Voter's utility. If p does not
obtain a signal θ 2 , then she has no information about the state of the world and thus randomizes,
producing an expected payoff of zero. If p obtains a signal, she chooses the policy that matches
this signal (s2 = θ 2 ) . Second, intelligent politicians get a stronger signal. As a result, voters are
motivated to reelect a responsive and intelligent incumbent, since she has a greater chance of
choosing the correct policy in Period 2.
Lemma B: In Period 1, an Unresponsive Incumbent (β I = 0 ) will never deliver
aid. A Responsive Incumbent ( β I = 1) will deliver aid if and only if she receives
the signal that the voter is a victim.
Lemma B describes the Incumbent's decision of whether to deliver hurricane assistance
during Period 1. An Unresponsive Incumbent never delivers aid because she does not internalize
the Voter V's payoff from aid. However, a Responsive Incumbent does internalize V's payoff, so
she finds it worthwhile to deliver aid whenever she receives a signal that V was victimized, as
such a signal is accurate with probability greater than ½.
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Lemma C: If V is a victim, then receiving aid increases his estimate of the Incumbent’s
probability of choosing the correct Period 2 policy by 11 45.
Lemma D: If V is a non-victim, then receiving aid increases his estimate of the
Incumbent’s probability of choosing the correct Period 2 policy by 1 21.
Lemmas C and D describes how the delivery of disaster aid enhances the Voter V’s
perception of the Incumbent I. If V is a victim (Lemma C), receiving aid causes him to positively
update his beliefs about both I’s responsiveness and intelligence. Specifically, I’s delivery of
deserved aid causes V to infer not only that she is responsive to the Voter, but also that she is
intelligent enough to correctly identify him as a victim. These positively updated beliefs
significantly increase V’s expectation that the Incumbent I would, if re-elected, select the correct
policy in Period 2.
But if V is a non-victim (Lemma D), receiving aid produces only a small net increase in
his evaluation of the Incumbent. This increase is small because the delivery of undeserved aid
sends mixed signals: I’s delivery of aid demonstrates that she is responsive, but the fact that she
delivered this aid to an undeserving non-victim causes V to question I’s intelligence. Hence,
although V still prefers I because of her demonstrated responsiveness, the undeserved aid
delivery produces only a small net increase in V’s estimate of the probability that the Incumbent I
would select the correct policy in Period 2.
From the Lemmas, we derive the following two propositions, which we test empirically:
Proposition 1: The increase in V's turnout probability from receiving aid during
Period 1 is strictly larger if V is a victim than if he is a non-victim.
Proposition 1, which follows directly from Lemmas C and D, states that the effect of aid
on turnout depends critically on whether the aid was deserved. If V is a victim, then the delivery
of aid sends a positive signal about both I's responsiveness and intelligence, thus unambiguously
enhancing V's incentive to turn out and re-elect I. On the other hand, if V is a non-victim, the
15
delivery of aid sends a mixed signal, suggesting that I is responsive but relatively unintelligent.
Therefore, the turnout effect from aid delivery is larger in magnitude when V is a victim
deserving of assistance.
Proposition 2: The increase in I's expected vote margin from delivering aid
during Period 1 is strictly higher if V is a victim than if he is a non-victim.
Proposition 2, which follows from Proposition 1 and Lemmas C and D, describes I's
relative probabilities of receiving V's vote, according to whether he receives hurricane aid and
whether he is a victim. I's expected vote share is significantly higher when V perceives her to
have high responsiveness and intelligence. When V is victimized, the delivery of aid greatly
enhances V's motivation to turn out and vote for I, thus giving her a significant increase in
expected vote share. But when V is not victimized, aid delivery sends a mixed signal about I's
quality and thus only slightly increases I's expected vote share.
Together, Propositions 1 and 2 present the empirically testable predictions of our formal
model, explaining how voters respond when they receive deserved and undeserved aid. In the
following empirical sections, we test, at the individual level, whether the delivery of FEMA aid
to hurricane victims increases voter turnout by a larger amount than the delivery of undeserved
aid to non-victims (Proposition 1). We then analyze, at the precinct level, whether FEMA aid
enhances the 2004 electoral vote shares of the President George Bush by a larger amount in
victimized precincts than in non-victimized precincts, thus testing Proposition 2.
Data and Summary Statistics
We draw on a series of individual-level and geocoded data sources to test the
propositions. Our data on natural disaster severity includes measures of flooding intensity based
on newly available satellite imagery. We combine the disaster data with voter turnout records
16
and government spending data, matching these records to residential addresses using a new
algorithm. The resulting dataset provides a precise picture of disaster conditions and aid
payments.
FEMA Disaster Aid: The FEMA disaster aid data cover all aid applications made by
Florida residents after the 2004 hurricanes (Chen 2013). These data were made public after
Freedom of Information Act (FOIA) requests from several Florida newspapers. Included in the
data are the date of application, the date of a decision, the decision about whether to award aid,
and the amount awarded. Additionally, the data include the category under which the application
was made (e.g., housing assistance) and the general reason that an application was denied (e.g.,
insufficient damage). Based on the audit evidence described earlier, these variables provide
insights into undeserved aid that we investigate below.
We merge these data to Florida voter registration records, which are compiled by county
election offices and report individual voter turnout in the 2000, 2002, and 2004 general elections.
Most importantly, both the FEMA applicant data and the voter registration files report residential
addresses. We used these residential addresses to merge the 1.1 million FEMA aid applicant
records to the list of 10.3 million registered voters in Florida. We implemented a multi-stage
process where we accounted for small differences in the reporting of address names between the
two data sources. During this process, we merged addresses only where we obtained an exact
match. We were able to merge the large majority of single-family addresses, but we did not
merge the same share of apartment residences, whose addresses are sometimes listed with slight
variations that could not be reconciled. In order to further eliminate cases of ambiguouslymatched addresses, we also restricted our analysis to addresses with no more than six voting
members at the residence.
17
Disaster Intensity Data: We estimate hurricane severity for each registered voter’s
residence in Florida. The National Oceanic and Atmospheric Administration (NOAA) deploys a
large number of remote sensing devices to measure wind vectors in the regions surrounding each
tracked storm. These observed wind vectors, combined with interpolated wind vectors in areas
without a remotely sensed observation, are used to produce estimates of wind speeds across
Florida throughout the duration of each hurricane. We then calculate, for each geocoded
residence, the maximum wind speed observed across the four hurricanes in 2004.
We also include in our measures of hurricane severity whether an address experienced
flooding in the aftermath. To measure flooding, we utilize the satellite data collected and
organized by the Dartmouth Flood Observatory. Those data contain GIS polygons that identify
areas which were covered with surface water and carefully distinguish flood zones from other
image interference such as cloud cover. We code an address as having experienced flooding if it
is within any of the flood zones defined by the series of images for Florida in August and
September of 2004.
Summary Statistics: In Table 1, we report summary statistics for the key variables in the
different datasets. The first set of means, standard deviations, and percentiles refers to voter
turnout and voter demographics. Following that, we describe aid applications and awards.
Finally, we report hurricane intensity and break aid down according to different levels of
intensity.
[TABLE 1 ABOUT HERE]
In November 2004, approximately 70% of the registrants in Florida's voter file turned out
to vote. About a quarter of voters experienced hurricane speeds of over 75 MPH at some point. A
substantial share of registered voters (17.3%) applied for aid, with just over half of applicants
18
receiving some amount. Voters in high wind areas were much more likely to apply for aid. 46%
of voters experiencing winds over 100 MPH applied for aid, while about 9% of those
experiencing 60 MPH or less applied. Aid awards are thus much less frequent in low storm
intensity regions. However, conditional on aid being awarded, the amount distributed is similar
across low and high intensity areas.
Evidence on Undeserving Aid Applicants: The two most common reasons for FEMA's
rejection of an applicant are: 1) the applicant already having insurance are not carrying required
insurance (43.5% of application rejections); and 2) insufficient damage to the applicant's
property (31.2%). While FEMA approved a substantial number of applications in low-wind
areas, FEMA also rejected a significant number of cases in areas that experienced significant
hurricane damage, as Figure 2 illustrates.
The map on the left side of Figure 2 depicts the maximum wind speeds observed across
Florida during Hurricane Charley on August 13-14, 2014. The bright red line across this map
follows the path of the storm's eye, tracing Charley's path as it made landfall on the Gulf Coast
near Port Charlotte, traveled northeast through Orlando, and exited into the Atlantic near
Daytona Beach. The map on the right of Figure 2 depicts the percent of Hurricane Charley
applicants rejected because of insufficient damage, with the greenest areas having no such
rejections and the reddest areas having over 70% of applicants rejected due to insufficient
damage.
[FIGURES 2 AND 3 ABOUT HERE]
Figure 3 illustrates these same patterns at the individual level, with green dots
representing applicants awarded at least $500 in aid and red dots representing applicants rejected
19
because of insufficient damage. In this map, the blue line depicts the path of Charley's eye as the
storm made landfall in Port Charlotte, Florida on August 13, 2004.
Together, these maps illustrate a striking geographic correspondence between hurricane
wind intensity and applicant rejections. Applicants experiencing extremely high hurricane winds
were virtually never found to have insufficient damage. Meanwhile, applicants who live far from
the hurricane path nonetheless still often applied for FEMA aid and were, not surprisingly,
rejected at relatively high rates because of insufficient damage. Nevertheless, the fact that people
far from any hurricane path frequently applied for aid is consistent with rational behavior, given
that applying for aid was virtually costless and applications were occasionally successful. 3
Analysis of Individual-Level Turnout
Below, we consider a series of regression results that show the clear limits on legislators’
abilities to influence votes with distributive spending. We start by showing this result in Figure
4, which examines registrants who did not vote in 2000 or 2002. In this Figure, the blue points
represent voters who lived in places that were severely hit by the hurricanes, while the red ones
refer to people who lived in areas that were largely spared. 4 The picture thus omits voters who
3
In the supporting materials, we provide more details on the relationship between wind speed
and rejections for insufficient damage. The kernel-weighted local polynomial regression results
show the strong positive relationship between applying for aid and wind speed, along with the
strong inverse relationship between being rejected for insufficient damage and wind speed. In
addition, we collected data on a face-valid set of individuals who might be expected to be more
likely to apply for aid despite not incurring damage: ex-convicts. We collected data on name and
place of residence for 313,760 individuals who have been released from Florida prisons, finding
that ex-convicts were more likely to be rejected for insufficient damage than other applicants,
controlling for census block fixed effects.
4
The former category consists of voters living in locations where the maximum speed exceeded
100 MPH, they were less than 25 miles from a hurricane’s path, and the wind steadiness measure
is less than 0.7. The latter category refers to voters from locations a maximum speed under 45
20
lived in areas of intermediate storm intensity, although they are included in the regression
specifications that follow. In addition to potentially impacting incumbent vote share (Achen and
Bartels 2004), the disaster could make it more difficult for voters to turn out (Gomez, Hansford,
and Krause 2007; Hansford and Gomez 2010). Therefore, for each group, we consider the
relationship between turnout and aid by comparing turnout to the baseline level amongst those
who received no aid.
[FIGURE 4 ABOUT HERE]
Figure 4 illustrates that among disaster victims, aid recipients are substantially more
likely than non-recipients to turn out. By contrast, aid receipt has no such effect for non-victims.
The effect appears at relatively small amounts of aid, consistent with voters responding to their
observation of government performance rather than the money enabling them to vote. The effect
diminishes at higher levels of aid, consistent with increased disruption due to more intense storm
conditions, which we account for in the regressions.
Regression Results: In a series of regressions, we confirm this general result with models
using more general measures of disaster impact. These regressions analyze all voters, covering
the entire range of disaster intensity. In general, we consider regressions of the following form:
Turnoutit = β0 + β1Reliefit + β2DisasterStrengthit + β3Turnoutit-1 + φControlsit + uit
(6)
In equation (6), Reliefit represents the FEMA relief spending that a household receives before
Election Day. Since we observe an apparent effect of aid even at low levels in Figure 4, our main
measure of relief is a dummy variable for the voter receiving positive aid before the election. We
obtain similar results in terms of their interpretation when we control for the amount of spending
that a person receives instead (see SI, Section 2). DisasterStrengthit captures the intensity of
MPH, more than 50 miles from a hurricane’s path, and with a minimum wind steadiness
exceeding 0.7.
21
disaster conditions that a voter experienced. We primarily utilize maximum wind speed at a
voter’s location as the disaster intensity measure, considering alternative measures in the
supporting information. Turnoutit-1 refers to whether the voter turned out in the previous election,
so that the regression identifies the impact of aid conditional on a voter's past turnout. The
control variables include voter characteristics such as Age and Gender, a dummy variable
Applied indicating whether the individual applied for aid, and census block-group demographic
variables. 5 As suggested by Angrist and Pischke (2009), we estimate the regressions as linear
probability models. We obtain almost identical results in terms of interpretation with a logit
specification, as we discuss in Section 2 of the SI. In all of the regressions, we correct the
standard errors for clustering at the zip code level.
Table 2 reports the results obtained by estimating equation (6) for registrants living in
areas with different levels of maximum wind speed. 6 Column 1 includes registrants who
experienced maximum wind speeds over 90 MPH. Columns 2 and 3 include registrants who
experienced top wind speeds between 60 and 90 MPH, and under 60 MPH, respectively. 7
Column 4 includes all registrants.
[TABLE 2 ABOUT HERE]
The results indicate that higher levels of disaster aid lead to increased voter turnout, but
only at high levels of hurricane wind speeds. Receiving aid before the election in an area that
experienced wind speeds of at least 90 MPH predicts that a voter is 2.8 percentage points (p <
5
The regressions we report here include all registered voters. We obtain similar estimates and
standard errors if we restrict our sample only to the applicants instead. Complete regression
results are in the SI.
6
This regression includes controls for a cubic polynomial in wind speed. We obtain similar
results for these coefficients if we include controls for duration of wind speeds and wind
steadiness, as well.
7
We obtain similar results if we interact the aid variable with measures of disaster intensity.
22
.004) more likely to turn out to vote. However, this impact of disaster aid does not extend to the
larger segment of the population that did not experience 90 MPH winds. The results indicate that
aid has a precisely-estimated null effect both for voters living in areas with maximum wind
speeds of 60-90 MPH and those from areas with maximum speeds below 60 MPH. Across all
voters, receiving aid predicts the recipient is 0.7 percentage points more likely to turn out (p =
.015), with this average effect driven entirely by the voters who experienced the most severe
hurricane conditions.
Spending Categories Identified by the Inspector General: As described earlier, the
Department of Homeland Security’s Office of the Inspector General (OIG) identified several
categories of spending that appeared to be particularly wasteful in its audit. OIG argued that the
speed with which expedited payments were made and the lax oversight of various aid categories
resulted in the awarding of significant amounts of undeserved aid. Thus, we consider the
spending allocated under the four categories so identified by the OIG's report: expedited
assistance (11.0% of approved aid applications), rental assistance (17.3%), transportation
replacement (1.2%), and funeral support (0.04%).
Columns 1-4 of Table 3 show regression results for categories of aid that the audit did not
identify as containing questionable spending. Spending in these other categories constitutes the
vast majority of spending in high-wind areas, and we correspondingly find a similar effect for
those areas as we do for overall spending in Table 2. Even in lower wind areas, we find that
these kinds of spending had a small, but positive effect. Altogether, spending outside the
questionable categories was more likely to reflect real needs and accordingly appears to have a
more positive effect on turnout even in places that also received wasteful spending.
[TABLE 3 ABOUT HERE]
23
On the other hand, aid within the categories cited by the OIG's report has no positive
effect on spending anywhere. Even in high wind areas, this kind of spending has a null effect. In
the lower wind areas particularly mentioned in the report, these categories of spending enter with
a negative sign. Hence, FEMA aid delivered to low disaster-damage areas within the four OIGidentified categories is the least likely to cause voters to turn out. In fact, such undeserved
spending actually appears to make voters less likely to turn out.
Robustness Checks and Placebo Tests: To establish the validity and robustness of our
main empirical results, the SI (Section 2) presents a series of extensions and robustness checks.
These additional tests use alternative definitions of hurricane victimization based on flooding
maps, wind speeds, and geographic distance to storm paths. We also re-estimate our empirical
models across voter partisanship and applicant status. Finally, to address possible omitted
variable concerns, we conduct a series of placebo tests to see if there is any correlation between
turnout and FEMA aid delivered just after Election Day. The SI discusses how these various
checks affirm the validity and robustness of the main findings.
Results for Precinct-Level Election Returns
The previous results indicated that disaster aid only motivated turnout when that aid
occurred in the areas hit hardest by the hurricanes. However, the formal model makes predictions
about not only the effect of FEMA aid on voter turnout, but also its effect on the incumbent’s
vote share. Specifically, Proposition 2 explains why the incumbent politician's electoral payoff
from delivering deserved aid to hurricane victims is significantly higher than the electoral payoff
from undeserved aid: The accurate delivery of aid to deserving victims sends a stronger,
24
unambiguously positive signal about the incumbent's quality and hence produces a larger boost
for the incumbent's vote share in the subsequent election.
To test this prediction, we compare George W. Bush’s precinct-level vote shares from the
2000 (pre-hurricane) and 2004 (post-hurricane) presidential elections. We analyze whether changes
in precinct-level Bush vote share can be attributed in part to the relative amount of FEMA aid
awarded in each precinct. We also analyze how this electoral effect of FEMA aid varies with the
hurricane intensity that the precinct experienced.
County election boards are permitted to redraw precinct boundaries between elections.
Consequently, precinct-level vote counts cannot always be directly compared between different
elections. In this section’s data, we include only precincts that satisfy the following three criteria:
1) the precinct's county provided vote counts for the November 2004 and 2000 presidential
elections as well as the November 2002 Florida gubernatorial election; 2) the precinct's county
made available maps of the 2004 precinct boundaries; 3) the precinct's boundaries in the 2000,
2002, and 2004 general elections are geographically comparable; and 4) the precinct had a nonzero population according to 2000 Census counts. Of Florida's 6,616 precincts as of November
2004, 5,073 (77%) satisfy all four of these criteria and are included in our analysis.
Bush’s precinct-level vote shares in the 2000 and 2004 elections have a correlation of 0.92,
suggesting that the 2000 precinct-level vote shares serve as an effective baseline measure of prehurricane partisanship across precincts. A second and more recent measure of precinct partisanship
comes from the results from the 2002 gubernatorial election, in which the President’s brother,
Republican Jeb Bush, defeated Democratic challenger Bill McBride by a margin of 56% to 43%.
Jeb Bush’s 2002 precinct-level vote shares and President Bush’s 2004 vote shares exhibit a
correlation of 0.93.
25
To estimate the electoral effects of FEMA awards, we regress Bush’s 2004 share of the
two-party vote onto the 2000 and 2002 vote shares for George Bush and Jeb Bush, respectively, as
well as two measures of FEMA aid distribution described below. The full model is:
Bush04i = α + β1 ⋅ Reliefi + β 2 ⋅ DisasterStrengthi + β3 ⋅ LagGOPVotei + γ ⋅ Controlsi + εi , (7)
where Reliefi is the measure of FEMA aid distributed to a precinct, which we measure in two ways.
In one set of regressions, we utilize log ( Aidi + 1) , where Aidi is the FEMA aid dollars per capita
directed to precinct i prior to Election Day. In a second set of models, we simply measure Reliefi as
the percent of applicants who were awarded some amount of FEMA aid. As before, we control for
DisasterStrength primarily with the maximum wind speed in a precinct. We control for a precinct’s
pre-hurricane preferences by including George W. Bush’s share of the 2000 presidential vote and
Jeb Bush’s share of the 2002 gubernatorial vote. Finally, Controls represents a vector of the
following variables: the Mean Age of FEMA applicants in the precinct, the Female Proportion
amongst applicants, the Hispanic Proportion for the precinct’s population, the precinct’s AfricanAmerican Proportion, and Median Household Income. All observations in our least-squares
estimates of equation (7) are weighted by precinct population.
[TABLE 4 ABOUT HERE]
Table 4 estimates equation (7) across three subsets of precincts, grouped by their respective
levels of hurricane severity during the 2004 hurricane season: precincts that experienced over 90
MPH wind speeds, precincts that had a maximum speed of 60-90 MPH, and precincts with less
than 60 MPH maximum wind speeds. The results corroborate the theoretical predictions of
Proposition 2. Model 1 estimates that for a highly victimized precinct with over 90 MPH winds, a
10% increase in the approval rate of FEMA applications causes a 1.0% increase in Bush’s 2004
vote share; this estimate has a 95% confidence interval of +0.4% to +1.6%. But in a moderately
26
victimized precinct with 60 to 90 MPH winds, a 10% increase in FEMA’s application approval
rate causes a relatively smaller, 0.3% increase in Bush vote share (Model 2). And in the least
victimized precincts with under 60 MPH winds, the effect is a negligibly small 0.1% increase in
Bush vote share (Model 5). Hence, the results support the theoretical predictions concerning the
electoral effects of disaster aid: FEMA aid boosts the incumbent’s electoral fortunes most strongly
when awarded to hard-hit areas and most weakly when delivered to areas that were less affected.
[FIGURE 5 ABOUT HERE]
Figure 5 illustrates these same findings graphically by plotting the relationship between
FEMA aid and Bush vote share separately across the three groups of precincts. In each plot, the
vertical axis measures the difference within each precinct between George Bush’s (2004) vote
share and Jeb Bush’s (2002) vote share. This measure thus identifies precincts where 2004 posthurricane electoral support for President Bush was high relative to the precinct’s pre-hurricane
partisanship. The horizontal axis in each plot measures the percent of each precinct’s FEMA
applications that were awarded aid. The dashed line in each plot represents the populationweighted least squares fit.
These plots reinforce our main finding that FEMA aid’s positive effect on Bush vote share
is conditional on the level of hurricane victimization. While all three groups of precincts exhibit a
positive electoral responsiveness to FEMA aid, the slope is significantly steeper for the recipients
who experienced the most severe hurricane conditions, consistent with the Proposition 2 prediction
that distributive aid’s electoral impact depends on the deservedness of that spending.
Conclusion
27
Our theory and empirical results have important implications both for electoral incentives
and our understanding of the retrospective voter. First, distributive spending programs are more
than simple financial transactions between government and voters. Voters also take advantage of
their experiences with these programs to learn about the quality of government by judging the
appropriateness of its spending decisions. Government audits found that FEMA often failed to
make accurate assessments about the victimization of aid applicants. But the applicants
themselves, with their personal knowledge of the hurricane conditions and damage at their
respective residences, were well-placed to know whether they deserved any aid they received.
Consistent with the empirical results, they may then make inferences about government efficacy
and update their perceptions about the likelihood that politicians will make appropriate policy
decisions in the future.
Our findings thus build upon a rich, cross-national literature illustrating how voters learn
from distributive programs. 8 For example, Manacorda, Miguel, and Vigorito (2011) found that
Uruguayan recipients of cash transfers used their awards to learn about the policy preferences of
the incumbent government. Thachil (2011) showed that, through receiving transfer payments,
lower-caste voters in India learned about the incumbent government’s policy priorities and
became more likely to support a party generally associated with the upper castes. In addition,
Chong et al. (2010) used an experiment to conclude that simply receiving information about a
government’s spending programs increased turnout and support for the incumbent government. 9
As with those findings, our results indicate that the relationship between distributive programs
8
In addition to voters learning from the personal experiences with receiving and not receiving
distributive spending, they have shown the ability to credit and blame different levels of
government for decisions that impact benefits (Gasper and Reeves 2011).
9
Also, De La O (2013) provides evidence that a Mexican conditional cash transfer program,
Progresa, motivated voters to turn out to support the incumbent government.
28
and election outcomes is driven by more than merely a voter’s financial self-interest. 10
Distributive programs also provide important information about both the priorities and
effectiveness of incumbent politicians.
Moreover, consistent with Key (1966), voters appear to be doing much more than
applying a heuristic of rewarding governments that provide them with benefits. Our results
suggest that voters do not treat all kinds of spending equally. While such different treatment can
result from voters not wanting the same things from government (e.g., Lazarus and Reilly 2010),
our results suggest that different effects can also arise from variation in how voters learn from
those spending decisions. In this sense, our theory builds upon and extends a significant literature
on the policy feedback effects of social welfare programs. Soss (1999; 2002) argues that many
income-based welfare programs, because of their paternalistic administration, may stigmatize
clients and discourage their participation in politics. Meanwhile, universal, non-income-based
programs, such as Medicare, Social Security, and veterans benefits (Campbell 2002; Mettler
2005; Mettler and Stonecash 2008), may encourage participation by giving recipients an
enhanced stake in politics.
Indeed, a voter’s interactions with a distributive spending program can do more than just
change how voters feel about their own place in politics. These interactions offer a small,
personal window through which a voter can judge the efficacy of government. Our results
suggest that FEMA applicants retrospectively evaluate the government’s quality based on their
respective individual interactions with the government. The results illustrate that an electionminded politician cannot expect to sway voters simply by gratuitously distributing financial
10
In addition to wanting to keep future benefits flowing, voters may also respond to distributive
spending due to reciprocity. For example, Finan and Schechter (2012) find that politicians target
spending to voters who surveys identify as likely to reciprocate. They also find that spending
engenders a feeling of obligation in the recipient.
29
benefits to them. Rather, she must distribute benefits to voters in a manner that simultaneously
reflects well upon her decision-making abilities, as voters evaluate not merely the distributive
generosity of politicians but also the merit of their spending decisions.
30
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34
Figure 1: Sequence of Play
Nature determines the politicians' types: β I , β C ∈ {0,1} and δ I , δ C ~ U ( 1 2 ,1).
Nature determines whether Voter V is victimized, ω1 ∈ {victim, nonvictim}
I receives a signal about whether V is a victim: θ1 ∈ {victim, nonvictim}
I decides whether to aid the voter, choosing: s1 ∈ {0,1}
Nature determines V’s cost of turnout, γ ~ U [0, 1 2 ]
V chooses whether to turn out.
Turnout
No Turnout
V elects either
I or C
I
Nature determines
the election winner
C
I
C
Nature determines
the state of the
world, µ ∈ {A, B}
Nature determines
the state of the
world, µ ∈ {A, B}
I chooses whether
to obtain signal
C chooses whether
to obtain signal
I chooses policy:
s 2 ∈ {A, B}
C chooses policy:
s 2 ∈ {A, B}
35
Figure 2: Hurricane Charley: Maximum Sustained Wind Speeds and the Percentage of
FEMA Aid Applicants Rejected due to Insufficient Damage
Highest Max. Wind Speeds (134 M.P.H.)
73% of Applicants Rejected for Insufficient Damage
Lowest Max. Wind Speeds (10 M.P.H.)
0% of Applicants Rejected for Insufficient Damage
Note: The left map depicts maximum sustained wind speeds measured throughout Florida during
Hurricane Charley (August 13-14, 2004), with lower wind speeds shaded in blue and higher wind speeds
shaded in red. The right map depicts the rate at which FEMA rejected aid applications due to insufficient
damage. Areas with lower rejection rates are shaded in green, while areas with higher rejection rates (up
to 73% rejected because of insufficient damage) are shaded in red.
36
Figure 3: FEMA Aid Applicants Rejected due to Insufficient Damage
(Port Charlotte, Florida: Hurricane Charley Landfall on August 13, 2004)
Hurricane Charley applicants awarded at least $500 in FEMA Aid
Hurricane Charley applicants rejected because of insufficient damage
Path of Hurricane Charley’s center eye during landfall (Port Charlotte, Florida, August 13, 2004)
Note: The red line represents the center of Hurricane Charley's path during its August 2014
landfall. Green dots represent registered voters who applied for FEMA aid and were awarded at
least $500. Red dots depict registrants who applied for FEMA aid but were rejected on the
grounds of having insufficient damage.
37
Figure 4: Impact of Aid on Turnout for Victims vs. Non-Victims
0.1
2004 Turnout (relative to baseline)
0.08
0.06
0.04
0.02
Victim
0
Non-victim
-0.02
-0.04
-0.06
-0.08
-0.1
Aid = $0
ΨϬфŝĚчΨϭ<
Ψϭ<фŝĚчΨϮ<
38
ΨϮ<фŝĚчΨϯ<
ŝĚхΨϯ<
Figure 5: George W. Bush Precinct-Level Vote Share and FEMA Aid Awards
20%
40%
60%
80% 100%
0.2
0.1
−0.1
−0.2
−0.1
−0.2
0%
Percent of Applicants Awarded Aid
Non−Victimized Precincts
(Under 60 M.P.H. Winds)
0.0
0.1
0.2
Moderately−Victimized Precincts
(60−90 M.P.H. Winds)
0.0
0.1
0.0
−0.1
−0.2
George W. Bush 2004 minus Jeb Bush 2002 vote share
0.2
Heavily Victimized Precincts
(Over 90 M.P.H. Winds)
0%
20%
40%
60%
80%
100%
Percent of Applicants Awarded Aid
0%
20%
40%
60%
80%
100%
Percent of Applicants Awarded Aid
Note: Each point in this figure represents a single precinct. The vertical axes measure the difference between George W. Bush's
November 2004 vote share and Jeb Bush's November 2002 gubernatorial election vote share. The dashed line in each plot depicts the
least-squares fit. Observations are weighted by each precinct’s voting-age population.
39
Table 1: Summary Statistics
Mean
Standard
deviation
(25th percentile,
75th percentile)
0.703
0.564
0.587
52.45
0.681
0.544
0.457
0.496
0.492
17.882
0.466
0.498
(0,1)
(0,1)
(0,1)
(38.781,65.951)
(0,1)
(0,1)
0.173
0.091
1759.818
1957.242
0.378
0.288
3065.773
3628.182
(0,0)
(0,0)
(835.97,1364.49)
(835.97,1537.49)
60.561
0.016
56.608
18.624
0.125
38.535
(46.5,75.5)
(0,0)
(31.157,73.701)
Aid in low intensity areas (max speed < 50 MPH)
Did voter apply?
Did voter receive aid?
Pre-election aid received (recipients)
0.073
0.039
2006.428
0.26
0.194
3878.299
(0,0)
(0,0)
(835.97,1874.74)
Aid in high intensity areas (max speed > 100 MPH)
Did voter apply?
Did voter receive aid?
Pre-election aid received (recipients)
0.459
0.25
2056.031
0.498
0.433
3509.123
(0,1)
(0,0)
(852.87,1275)
Voters
Voted in 2004
Voted in 2002
Voted in 2000
Age
White
Female
Aid
Did voter apply?
Did voter receive aid?
Pre-election aid received (recipients)
Total aid received (recipients)
Hurricane intensity
Maximum speed encountered
Flood zone
Distance from hurricane path
40
Table 2: Effect of Deserved vs. Undeserved Aid on Voter Turnout
Dependent variable: Turnout in 2004
Maximum wind speed
Over 90 MPH
60-90 MPH Under 60 MPH
(1)
(2)
(3)
Pre-election aid > 0
Maximum speed
Applied for aid
Age
Age2
Female
Local income
Local share black
Local share white
Local mean age
Voted in 2002
Constant
Observations
R-squared
All voters
(4)
0.0281***
(0.00958)
-0.00132
(0.00141)
0.0737***
(0.0122)
0.00451**
(0.00181)
0.000252
(0.00257)
-0.00227***
(0.000774)
0.0328***
(0.00607)
0.00992***
(0.000816)
0.00166
(0.00257)
-0.000422
(0.000383)
0.00750***
(0.00241)
0.0114***
(0.000212)
0.00729**
(0.00299)
-0.00190***
(0.000333)
0.0291***
(0.00512)
0.0103***
(0.000431)
-4.53e-05***
(1.51e-05)
0.0171***
(0.00293)
-0.00105
(0.0118)
0.129**
(0.0533)
0.0360
(0.0642)
0.000761
(0.000863)
0.633***
(0.0412)
0.168
(0.162)
-9.49e-05***
(6.62e-06)
0.0254***
(0.00113)
0.00945***
(0.00201)
0.0546
(0.0404)
0.0848**
(0.0417)
-0.000116
(0.000328)
0.491***
(0.0162)
0.223***
(0.0787)
-0.000102***
(1.77e-06)
0.0290***
(0.00105)
0.0165***
(0.00120)
2.94e-05
(0.00789)
-0.0111
(0.00843)
0.000561***
(0.000163)
0.391***
(0.00451)
0.139***
(0.0200)
-9.49e-05***
(3.51e-06)
0.0266***
(0.000787)
0.0133***
(0.00132)
0.00778
(0.0158)
0.00404
(0.0169)
0.000328
(0.000211)
0.447***
(0.00901)
0.224***
(0.0318)
228,314
0.426
1,757,059
0.303
2,419,047
0.225
4,404,420
0.272
Notes: Standard errors, clustered at the zip-code level, are in parentheses. * p< 0.10, ** p< 0.05, and *** p< 0.01.
41
Table 3: Effect of Spending Outside and Within the Categories Identified by the Inspector General
Dependent variable: Turnout in 2004
Pre-election aid > 0
Maximum speed
Applied for aid
Age
Age2
Female
Local income
Local share black
Local share white
Local mean age
Voted in 2002
Constant
Observations
R-squared
Categories not identified as questionable
Maximum wind speed
All voters
Over 90 MPH
60-90 MPH Under 60 MPH
(1)
(2)
(3)
(4)
Categories that were identified as questionable
Maximum wind speed
All voters
Over 90 MPH
60-90 MPH Under 60 MPH
(5)
(6)
(7)
(8)
0.0296***
(0.00764)
-0.00138
(0.00141)
0.0760***
(0.0135)
0.00446**
(0.00181)
-4.49e-05***
(1.51e-05)
0.0172***
(0.00295)
-0.00102
(0.0118)
0.131**
(0.0535)
0.0363
(0.0642)
0.000769
(0.000865)
0.633***
(0.0413)
0.174
(0.162)
0.00570**
(0.00252)
-0.00227***
(0.000762)
0.0306***
(0.00644)
0.00994***
(0.000810)
-9.50e-05***
(6.57e-06)
0.0254***
(0.00113)
0.00945***
(0.00200)
0.0534
(0.0393)
0.0833**
(0.0405)
-0.000114
(0.000327)
0.491***
(0.0161)
0.223***
(0.0765)
0.00904***
(0.00260)
-0.000419
(0.000387)
0.00540**
(0.00239)
0.0114***
(0.000212)
-0.000102***
(1.77e-06)
0.0291***
(0.00106)
0.0165***
(0.00121)
-0.000279
(0.00792)
-0.0116
(0.00842)
0.000566***
(0.000164)
0.391***
(0.00452)
0.140***
(0.0200)
0.0126***
(0.00260)
-0.00190***
(0.000332)
0.0278***
(0.00558)
0.0102***
(0.000431)
-9.49e-05***
(3.50e-06)
0.0267***
(0.000789)
0.0133***
(0.00132)
0.00774
(0.0158)
0.00383
(0.0168)
0.000330
(0.000211)
0.447***
(0.00901)
0.224***
(0.0318)
-0.0164
(0.0141)
-0.00133
(0.00141)
0.0898***
(0.0135)
0.00448**
(0.00178)
-4.55e-05***
(1.49e-05)
0.0169***
(0.00297)
-0.00123
(0.0119)
0.133**
(0.0537)
0.0387
(0.0646)
0.000741
(0.000864)
0.633***
(0.0416)
0.171
(0.161)
-0.0380***
(0.00495)
-0.00225***
(0.000764)
0.0363***
(0.00611)
0.00992***
(0.000808)
-9.48e-05***
(6.56e-06)
0.0255***
(0.00113)
0.00936***
(0.00200)
0.0556
(0.0394)
0.0848**
(0.0406)
-0.000116
(0.000327)
0.490***
(0.0162)
0.222***
(0.0767)
-0.0531***
(0.00499)
-0.000413
(0.000387)
0.0126***
(0.00271)
0.0114***
(0.000212)
-0.000102***
(1.77e-06)
0.0291***
(0.00106)
0.0165***
(0.00120)
0.000135
(0.00793)
-0.0114
(0.00842)
0.000564***
(0.000164)
0.390***
(0.00452)
0.140***
(0.0200)
-0.0388***
(0.00495)
-0.00189***
(0.000333)
0.0359***
(0.00559)
0.0102***
(0.000429)
-9.49e-05***
(3.48e-06)
0.0267***
(0.000793)
0.0132***
(0.00132)
0.00874
(0.0159)
0.00468
(0.0169)
0.000325
(0.000211)
0.447***
(0.00903)
0.224***
(0.0318)
228,314
0.426
1,770,790
0.303
2,405,316
0.225
4,404,420
0.272
228,314
0.425
1,770,790
0.303
2,405,316
0.225
4,404,420
0.272
Notes: Standard errors, clustered at the zip-code level, are in parentheses. * p< 0.10, ** p< 0.05, and *** p< 0.01.
42
Table 4: Effect of FEMA Aid Awards on Electoral Support for George W. Bush
Dependent variable: G.W. Bush Nov. 2004 vote share in the precinct
Over 90 MPH Precincts
(1)
(2)
FEMA Aid Dollars Per Capita (logged)
0.016***
(0.004)
Percent of Applicants Awarded Aid
Mean Applicant Age (100's of years)
Female Proportion of Applicants
Median Household Income ($100,000s)
Hispanic Proportion of Population
Black Proportion of Population
G.W. Bush Nov. 2000 Vote Share
Jeb Bush Nov. 2002 Vote Share
Constant
Observations
R-squared
Maximum wind speed
60-90 MPH Precincts
(3)
(4)
0.006***
(0.001)
-0.082
(0.074)
-0.377**
(0.142)
-0.021
(0.034)
0.029
(0.040)
0.001
(0.050)
0.461***
(0.057)
0.474***
(0.062)
0.171
(0.092)
0.102**
(0.032)
-0.058
(0.079)
-0.454**
(0.146)
-0.025
(0.035)
0.026
(0.042)
0.012
(0.051)
0.437***
(0.059)
0.481***
(0.064)
0.240**
(0.092)
0.847
245
0.84
245
Notes: Standard errors are in parentheses. * p< 0.10, ** p< 0.05, and *** p< 0.01.
43
Under 60 MPH Precincts
(5)
(6)
0.003***
(0.001)
-0.114***
(0.023)
0.107*
(0.043)
0.024**
(0.008)
0.123***
(0.013)
0.081***
(0.014)
0.201***
(0.017)
0.702***
(0.018)
-0.101***
(0.026)
0.032***
(0.008)
-0.118***
(0.024)
0.118**
(0.044)
0.021**
(0.008)
0.118***
(0.013)
0.086***
(0.015)
0.212***
(0.017)
0.699***
(0.018)
-0.096***
(0.026)
-0.167***
(0.017)
0.009
(0.023)
0.006
(0.006)
0.094***
(0.005)
0.050***
(0.008)
0.379***
(0.013)
0.561***
(0.015)
0.025
(0.016)
0.013***
(0.004)
-0.168***
(0.017)
0.009
(0.023)
0.002
(0.006)
0.100***
(0.005)
0.060***
(0.007)
0.379***
(0.013)
0.565***
(0.015)
0.022
(0.016)
0.908
1681
0.906
1681
0.948
3147
0.947
3147