DO VOTERS BLAME POLITICIANS FOR BAD LUCK?
THE UNEDUCATED ONES DO
Andrew J. Healy*
Loyola Marymount University
June 16, 2008
Abstract
This paper uses the natural experiment induced by tornado occurrence to
show that certain groups of American voters have systematically punished
the incumbent presidential party for events beyond its control. County-level
election returns and individual-level survey data show that an increase in
tornado damage has historically cost the incumbent presidential party a
significant share of the vote. Importantly, the survey data make it possible to
show that tornado damage only affects the decisions made by voters who did
not graduate from high school. The data suggest that this effect of education
on voting does not arise from any differences in the effects that tornadoes
have on the welfare of educated and uneducated voters. Instead, the results
suggest that less educated voters are simply more likely to blame the
incumbent party for negative shocks even when the incumbent party is not
responsible for those events.
Keywords: Voting behavior, economic voting, retrospection, natural experiment
*Contact information: ahealy@lmu.edu
I thank Shawn Cole, Alan Gerber, Tim Groseclose, Laura Stoker, Ebonya Washington, and Chinda Wongngamnit for
helpful comments on earlier drafts of this paper. I also thank Eric Patashnik and participants at the 2008 Midwest
Political Science Association meetings for helpful comments and discussions. All errors are my own.
1
The idea that voters respond to economic conditions accords with casual observation, has
been verified by empirical research, and is almost universally accepted by political scientists.1
This body of research has generally assumed that voters who engage in economic voting are at
least attempting to either ascertain a government’s competence or to hold it accountable for
previous performance. Recent research provides some evidence in favor of this model of
thoughtful economic voting. For example, more well-informed voters may be more likely to
engage in economic voting (Krause 1997; Duch 2001; Gomez and Wilson 2006). Also, voters are
more likely to engage in economic voting in countries where responsibility for economic policies
can be more clearly assigned to a specific individual or party (Powell and Whitten 1993).
Due to the nature of the data that previous research uses, however, it cannot easily identify
causal effects in the relationship between economic conditions and voting behavior. In this paper, I
use the natural experiment created by tornado occurrence to provide new insights into how voters
treat economic gains and losses. I use data on tornado damage to test the hypothesis that voters
respond to income changes for which the incumbent cannot be responsible. If voters engage in
economic voting solely to reward competence and to hold incumbents accountable, then voters
should not punish incumbents for income losses caused by tornadoes in an election year.
I use county-level presidential election returns and individual-level voter survey data to
determine if voters punish politicians for tornado damage.2 Tornadoes provide an ideal natural
1
According to Lewis-Beck and Stegmaier (2007), there have been about 400 published studies on economic voting.
Earlier contributions include Kramer (1971), Fair (1978), Lau and Sears (1981), Lewis-Beck (1983), Alford and
Legge (1984). Research on economic voting has generally distinguished between sociotropic and pocketbook
motivations for economic voting, with some work finding evidence for each of these (Markus 1988, Johnston et al.
2000), with a generally greater emphasis given to sociotropic concerns, particularly in the United States (Kinder and
Kiewiet 1979, 1981). Finally, some recent papers have critiqued the survey-based methods behind earlier results that
suggest the importance of economic conditions in influencing voting decisions (Anderson et al. 2004; Wliezien,
Franklin, and Twiggs 1997), although see Lewis-Beck, Nadeau, and Elias (2008) for a response to those critiques.
2
While I am concerned with the effect of natural events in the year preceding the election, other research has
established that natural events on the day of the election can affect voting behavior. Rain or snow on election day has
historically benefited the Republican presidential candidate (Gomez, Hansford, and Krause 2007).
2
experiment since the damage associated with a tornado is usually confined to a relatively small
area. Areas that are affected can be easily compared to nearby areas that are not affected.
Moreover, the randomness of tornado occurrence ensures that we can be confident that any
significant effects of tornado-caused income changes are, in fact, causal ones.
Two recent working papers employ empirical strategies similar to the one that I use.
Wolfers (2006) looks at whether voters in oil-producing states reward or punish incumbent
governors for shifts in the international price of oil, concluding that votes do depend on the price
of oil. Achen and Bartels (2004a) use state-level presidential election returns to conclude that
weather in an election year influences the share of votes that the incumbent receives. Each of these
papers finds that voters base their decisions on circumstances beyond an incumbent’s control and
therefore concludes that voters are at best partially rational. However, neither of these papers, and
no other research to my knowledge, considers the characteristics of individual voters who respond
to random events beyond any incumbent’s control.
Both the county-level election returns and the individual voter survey data analyzed here
suggest that tornado damage has cost the incumbent party a significant share of the vote across a
series of elections. The results indicate that an increase of $1000 in per-capita tornado damage (at
2008 prices) in the year preceding the election costs the incumbent party around 1.4% of the twoparty vote. Improving on previous research, year effects and county fixed effects are included in
the regressions to ensure that these estimates come from variation in tornado damage across
counties in a given election year.
The evidence from the individual-level data indicates that tornado damage affects different
kinds of voters in different ways. Specifically, tornado damage only affects the decisions made by
voters who did not finish high school. The results indicate that an increase of $1000 in per-capita
3
tornado damage will cost the incumbent party about 5% of the vote among voters who did not
finish high school (from here, uneducated voters) and have no significant effect for voters who did
complete high school (from here, educated voters).
Interestingly, the data show that tornado damage affects the economic welfare of
uneducated and educated voters in the same way. In other words, the data provide no support for
the hypothesis that the reason that tornadoes have a greater effect on the decisions made by
uneducated voters is because tornadoes have a greater effect on the welfare of uneducated voters.
The results instead suggest that voters with less education are simply more likely to blame the
incumbent party for events beyond its control.
Before demonstrating these results, I review in the next section previous empirical and
theoretical research on how voters respond to economic conditions. The third section describes the
data and outlines the empirical strategy used to identify the relationship between tornado damage
and voting decisions. In the fourth section, I present the empirical results for the voting models
and provide evidence about the causes underlying those results. The fifth section concludes.
Economic Voting: Theory and Evidence
Previous research on economic voting suggests that, at the least, there are limits to voters’
abilities to use economic conditions to inform their decisions. For example, voters’ perceptions of
economic conditions can be biased by misleading media reports (Hetherington 1996). Voters also
appear to respond more strongly to negative economic news than to positive news (Nannestad and
Paldam 1997). This evidence is consistent with economists’ and psychologists’ findings that
4
individuals experience greater pain from losses than they experience joy from gains (Kahneman
and Tversky 1979), but inconsistent with a rational accounting of the incumbent’s performance.3
Moreover, there are numerous examples that, while not clear proof of voter irrationality,
certainly suggest that events beyond a politician’s control have affected voting behavior. The
incumbent Republican party suffered electoral losses in Nebraska in 1890 that coincided with a
severe drought and consequently low harvest (Barnhart 1925). Shark attacks that led to a decrease
in tourism in New Jersey appear to have cost Woodrow Wilson votes in coastal New Jersey
counties in the 1916 election (Achen and Bartels 2004a). The unknown Social Credit Party won
provincial elections in Alberta after a drought caused poor harvests (Irving 1959). The New York
Mets’ surprising win in the 1969 World Series may have helped incumbent John Lindsay to a
surprising win in the mayoral election a few weeks later (Cohen 1988). In addition, Hurricane
Betsy appears to have cost the incumbent mayor of New Orleans votes in 1965 (Abney and Hill
1966) and Tropical Storm Allison appears to have cost the mayor of Houston votes in 2001
(Arceneaux and Stein 2006).
There are some important differences between these examples. In the last two cases, voters
punished a politician who was deemed to have performed poorly in preparing for the disaster that
cost him votes (Abney and Hill 1966, Arceneaux and Stein 2006). The other examples, while not
conclusive, suggest that voters may make decisions based on their personal successes, failures,
and emotions, and not a simple accounting of the circumstances for which the incumbent is
responsible. Barnhart (1925, 540) proposes a plausible psychological explanation for how such
behavior may have arisen in the case of Nebraska in 1890. He writes of the drought:
3
Adding to the research on how economic conditions influence voting behavior is a literature in economics that shows
how economic voting can distort a government’s incentives. A government has incentive to inflate the economy
before an election at the expense of longer term economic health (Rogoff and Sibert 1988; Rogoff 1990).
5
The situation of many farmers forced them to think about the things that
had brought about that situation. This contemplation resulted in a
determination to remedy such matters as lay within their power. They could
not make it rain, but they thought they could lower railroad rates…The
farmers were already incensed at the treatment they were getting from the
railroads. The drouth (sic) not merely made the economic position of the
farmer temporarily worse, but it put him in a receptive frame of mind for
the arguments of the Independent Party leaders.
In other words, bad times may cause voters to focus on what they dislike about the incumbent
party, including conditions that it may actually be able to control, like the railroad rates in the
above passage. Voters who behave in this way are not able to achieve a completely rational
accounting of the incumbent’s performance. Instead, emotions interfere and their decisions are
influenced by events beyond any politician’s control.
More recent research has advocated the position that economic voting often reflects little
evidence that voters are actually evaluating the incumbent’s performance at all. Achen and Bartels
(2004b) argue that economic voting boils down to voters simply looking at income growth in the
months before an election. They also conclude that, since governments that are reelected do not
achieve higher growth after reelection, to the extent that competence leads to higher growth, voters
do not appear to select more competent governments. Consistent with this theory of voter
behavior, I provide evidence that economic voting is perhaps less about accountability and
competence than has been previously presumed. Voters seem to penalize income losses even when
they are caused by entirely random events.
Data and Empirical Strategy
In this paper, I utilize voting data from a series of US presidential elections. Using the data
collected by Clubb, Flanigan, and Zingale (2006), I look first at aggregate voting behavior across
counties to test the hypothesis that votes for the incumbent party depend on tornado damage in the
6
year of the election. Then individual-level voting data from the American National Election
Studies (ANES) are used both to confirm the county-level results and to determine the kinds of
voters who punish the incumbent party for tornado damage. To determine tornado damage, I use
the county-level records collected by the National Climatic Data Center (2008) from 1950-1988.
This database consists of all tornado occurrences recognized by the National Weather Service.
Finally, for county population (used to create the estimates of per-capita disaster damage), I use
the Census estimates of county-level population and interpolate those amounts to create estimates
for the intercensal years.
Following Lewis-Beck, Nadeau, and Elias (2008), I consider the following general model
of voting behavior, where I will later add government action to the set of variables that can
influence the vote:
Vote = f (Party Identification, Economic Conditions, Demographic Variables)
As many other researchers have noted, estimating regressions based on this model using survey
data requires a great deal of care. Even if each is measured correctly, there are obvious
endogeneity problems with both perceived economic conditions and partisan identification
(Fiorina 1981). To deal with these issues, Lewis-Beck, Nadeau, and Elias (2008) use panel data to
identify the relationship between economic conditions, partisan identification, and voting. For my
empirical specification, identification of the desired causal effects arises naturally from the
randomness of tornado damage. If any correlation is found between tornado damage and voting
decisions, we can be confident that the relationship runs from tornado damage to voting and not
the other way around. Controlling for time and location fixed effects, randomness also ensures that
there is no correlation between partisan identification and tornado-caused income losses. It is thus
possible to get unbiased estimates of the effect of tornado damage on voting, even without
7
controlling for partisan identification. I nevertheless consider specifications with and without
partisan identification to test the robustness of the empirical results.
The basic regression equation for an individual i’s vote in year y is:
Voteiy     c   y  1 Damagecy   2 Educiy  3 Educiy * Damagecy   4 PartyIDiy   iy
In the above equation,  c is a county fixed effect and  y is a year fixed effect. The dependent
variable is one if the voter chose the incumbent party and zero if he chose a different party. The
model is the same for the county-level analysis with two exceptions: 1) The individual voter
characteristics are not included and 2) the incumbent party’s previous vote share in the county is
used in place of an individual voter’s partisan identification. Including the incumbent party’s
previous vote share amounts to controlling for the underlying preferences for the incumbent party
in the county, meaning that the coefficient for disaster damage captures the effect of tornadoes on
the change in those preferences.
Empirical Results
Before considering the interaction between individual characteristics and tornado damage,
I first estimate the effect of tornado damage on voting behavior at the county level. Table 1 reports
these regression results. In the first column, the independent variables are per-capita tornado
damage, county dummies, and year dummies. The coefficient estimate for tornado damage
suggests that an additional $1000 in per-capita tornado damage costs the incumbent party 2.1
percentage points of the vote. In the second column, the incumbent party’s share of the vote in the
previous election is included as a control, and the estimated coefficient for tornado damage is
reduced. The point estimate suggests that an additional $1000 in per-capita damage leads to a 1.4
percentage point decrease in the incumbent party’s vote share. The estimate is highly significant in
8
each column. The standard error is significantly smaller in column 2 than in column 1, since the
incumbent party’s previous vote share captures much of the variation in voting behavior.
To put this estimate into perspective, consider the implied cost of tornado damage in terms
of the number of votes that the incumbent party loses. The regression results suggest that, on
average, $1000 of tornado damage will cause a loss of .014 votes, so that a loss of $100,000 will
lead to a loss of 1.4 votes, and a loss of $100 million is predicted to cost the incumbent party about
1400 votes. Since state-level tornado damage in the year before the election exceeded $100
million in only 1.7% of the election year observations, the effect of tornado damage is only likely
to be decisive in determining outcomes in extremely close elections such as in Florida in 2000.4
To the extent that other natural events such as hurricanes and fires influence voting decisions in
the same way that tornadoes do, however, the cumulative effect of all natural events on voting
decisions will have a greater influence on election outcomes.
The primary focus of this paper is, however, not the effect that disasters have on election
outcomes, but the effect that these events beyond any government’s control have on individual
voter behavior. The county-level election returns show evidence of such an effect; individual-level
data from the ANES both confirm the existence of this effect and provide insights into the types of
voters whose decisions are influenced by natural disasters.
The first column in Table 2 reports the results obtained by regressing an individual’s vote
on the amount of tornado damage, along with county and year fixed effects. Following other
research, I use a probit to estimate the model (Fiorina 1981, Markus 1988, Lewis-Beck, Nadeau,
and Elias 2008). Since some authors, particularly in economics, advise using linear regression
4
Tornado damage ($6.7 million) and hurricane damage ($5.1 million) were actually relatively small in Florida in
2000, so that these disasters played little role in the election. Still, Achen and Bartels (2004a) and an earlier draft of
this paper argue that a different natural event, insufficient rainfall, may have cost Gore more than the 537 votes that he
needed to win the state.
9
results even with binary dependent variables, I include the linear regression results in an appendix.
Those results are nearly the same as those in Table 2 that were obtained using the probit. For ease
of interpretation, I report the coefficients in Table 2 as marginal effects. For example, the effect in
the first column indicates that a $1000 increase in per-capita tornado damage makes a voter 1.5%
less likely to support the incumbent party. This effect is almost identical in magnitude to the effect
seen in Table 1 that was obtained using the incumbent party’s vote share in the county as the
dependent variable. The effect is right on the edge of significance at the 5% level (p = 0.052). As
in Table 1, all regressions in Table 2 include county and year fixed effects.
The results in the second column include terms for a voter’s education and the interaction
of the education variable with tornado damage. The education variable is a dummy that is one if
the voter did not complete high school and is zero otherwise. The results show that tornado
damage has essentially no effect on voters who finished high school, while it has a significant
effect on those voters who did not. The coefficient for the tornado damage variable is reduced to
-.009, indicating no significant effect on the voters who completed high school. The coefficient for
the interaction term between low education and tornado damage is significant, with the point
estimate indicating that an additional $1000 in per-capita damage makes an uneducated voter 4.3%
less likely to support the incumbent party, relative to an educated voter. For these voters, tornado
damage has a large and significant effect (p < .001) on the decisions that they make.
The results in the third column include variables for a voter’s religiosity. These variables
are included since we might expect voters who have stronger religious faiths to act differently in
terms of how they reward or punish politicians for “acts of God” such as tornadoes. To capture
religiosity, I classify a voter as a religious service attendee if he either said that he attended
religious services regularly (pre-1970) or if he said that he attended church services at least once a
10
week (1970 and after). This classification appears to do a good, but not perfect, job of defining the
religion variable in the same way across all years. According to this variable, 76.7% of voters are
classified as religious service attendees in 1966, 72.7% in 1968, 75.2% in 1970, and 70.8% in
1972. The slight increase from 1968 to 1970 is likely due to the change in the wording of the
survey question as opposed to a genuine change in religiosity.
The regression results provide no evidence that tornado damage affects religiously
observant voters any differently than it affects other kinds of voters. The coefficient on the
interaction term between religiosity and tornado damage is insignificant and the point estimate is
close to zero. The estimate for the effect of tornado damage on voters who have not attended
college remains large and significant, with the estimate here being that tornado damage makes a
voter who did not finish high school 4.7% less likely to support the incumbent party. As in the
second column, this effect is highly significant (p < .001).
To make the model of voting behavior complete, the results in the fourth and fifth columns
expand the earlier results to include a control for a voter’s partisan identification. As in the earlier
models, I use a probit to estimate the model that includes partisan identification. Some research
that has been concerned with the effects of partisanship on voting has employed a two-stage
procedure to deal with the endogeneity of partisan identification (Fiorina 1981; Lewis-Beck,
Nadeau, and Elias, 2008). As this research has generally acknowledged, two-step procedures rely
heavily on the validity of the instrumental variables used to estimate the models. Here no such
endogeneity concerns arise, since we are interested not in the coefficient attached to the
endogenous variable, but in the coefficients for variables that are exogenous by definition, namely
the tornado damage variable and the interaction of tornado damage with a voter’s personal
characteristics. As a result, the thorny issue of the endogeneity of partisan identification that has
11
been a cause of concern for other voting research does not arise here, due to the fact that the
natural experiment provided by tornado damage ensures that the estimates of the coefficients of
interest will be unbiased.
The results in the fourth and fifth columns indicate that there is a large and significant
effect of tornado damage on the decisions made by voters who have not finished high school, even
when controls for partisan identification are included in the model. The coefficient on the
interaction term between education and tornado damage goes up slightly and remains significant at
the 1% level in each case. If partisan identification is included without the controls for religiosity,
the estimated effect is that $1000 of damage costs the incumbent 5.2% of the uneducated vote. If
the religiosity variables are also included, the estimate indicate that $1000 of damage costs the
incumbent party 5.7% of the uneducated vote. As before, there is no significant effect of tornado
damage on the decisions made by voters who have at least finished high school. In contrast,
tornado damage has a large effect on the decisions that less educated voters make.
While these results suggest that less educated voters blame the incumbent party for
circumstances beyond its control, the fact that tornado damage has a greater effect on the decisions
made by uneducated voters could be caused by the fact that the uneducated are more likely to be
affected by tornadoes. Alternatively, voters with different levels of education could be impacted
by tornadoes in similar ways, but those impacts have distinct effects on the voters’ decisions.
Table 3 reports regression results aimed at distinguishing between these hypotheses. I
estimate the effect that tornado damage has had on a voter’s economic welfare, allowing the effect
to vary according to a voter’s education level. To conduct this analysis, I use the ANES variable
on whether a voter’s financial situation has improved in the last year. From this variable, I define a
new variable that is zero if a voter became worse off and one if a voter’s financial situation either
12
stayed the same or improved. We would expect tornado damage to generally cause voters to
become worse off, so that the effect of tornado damage on voter welfare should be negative. As
with the estimates of the voting models, I report probit model results in Table 3 and linear
regression results in an appendix.
In the first column of Table 3, I report the results obtained by estimating a probit where the
dependent variable is a voter’s welfare change and the independent variables are per-capita
tornado damage, year fixed effects, and county fixed effects. As expected, tornado damage has a
significantly negative effect on a voter’s welfare. An additional $1000 of per-capita tornado
damage in the county makes a voter 1.5% more likely to declare himself worse off.
The second column expands the specification in the first column to include the education
variable, and the interaction between education and tornado damage. The results in the second
column suggest that the effect of tornado damage on welfare is not significantly different for
uneducated and educated voters. In fact, the coefficient on the interaction between education and
tornado damage is nearly zero, suggesting that there is almost no difference between the effect that
tornadoes have on voters who did not finish high school compared to other voters who have.
Taken together, the results in Tables 2 and 3 suggest that voters with different levels of education
are affected in similar ways by tornado damage, but that these similar welfare changes have
different effects on the decisions that these voters make.
Discussion and Conclusion
The results in this paper demonstrate a need to revisit some of the implicit assumptions
underlying previous research on economic voting, since some voters show a willingness to base
their voting decisions on economic circumstances beyond any incumbent’s control. Specifically,
13
tornado damage has a large and significant effect on the voting decisions made by voters who have
not attended high school. This behavior is not easily reconcilable with the idea that voters engage
in economic voting to either select competent officeholders or to hold officeholders accountable
for their performance.5 Even though, controlling for county and year effects, tornadoes have
similar welfare effects on voters with different education levels, voters with different education
levels respond in different ways to tornado damage. The data thus suggest that less educated voters
are simply more likely to blame the incumbent party for negative events beyond its control.
In addition to increasing our understanding of the mechanism underlying economic voting,
the results speak to the importance of allowing for heterogeneity in models of voter behavior.
Other recent research also indicates the importance of recognizing the fact that voters with
different education levels may treat similar circumstances in very different ways (Gomez and
Wilson, 2006). An important step for future research is to further identify the cognitive differences
that individuals with different personal characteristics bring into the voting booth.
5
One other possibility is that governments respond better to tornadoes and other disasters in areas that have higher
shares of educated people. A comprehensive analysis of all government disaster relief spending since 1984 finds no
evidence of any such effect for the United States (Healy 2008).
14
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17
Appendix
Table A1 reports the results obtained by using a linear regression to estimate the individual
voting model. It is analogous to Table 2 in the main text, which used a probit to estimate the
model. Table A2 reports the results obtained by using a linear regression to estimate the equation
in which the voter welfare measure was the dependent variable. It is analogous to Table 3 in the
main text.
18
Table 1: The Effect of Tornado Damage on Presidential Voting, 1952-1988
(Ordinary Least Squares Estimates)
Dependent variable: Incumbent party's share of the two-party vote in the county
Per-capita tornado damage year before
the election (in thousands of $)
(1)
(2)
-2.168*
(.657)
-1.406*
(.382)
Incumbent party's previous vote
share in the county
.623*
(.007)
County dummies?
YES
YES
Year dummies?
YES
YES
Previous vote share included?
NO
YES
R2
.266
.585
30854
30594
Number of observations
Notes: (a) Robust standard errors are in parentheses.
(b) * indicates significance at the 5% level.
19
Table 2: Voter Characteristics and Responses to Tornado Damage
(Probit Estimates)
Dependent variable: Did the voter choose the incumbent party?
Per-capita tornado damage
(in thousands of $)
(1)
(2)
(3)
(4)
(5)
-.015
(.008)
-.009
(.008)
-.015
(.009)
.006
(.009)
.0002
(.010)
.422*
(.009)
.423*
(.009)
Partisan identification
Dummy for not graduating
from high school
.009
(.018)
.008
(.018)
.007
(.020)
.007
(.020)
Non-high school graduate*
per-capita tornado damage
-.043*
(.014)
-.047*
(.006)
-.052*
(.015)
-.057*
(.015)
Dummy for regularly attending
religious services
.050*
(.012)
.054*
(.013)
Religious service attendee*
per-capita tornado damage
.012
(.009)
.012
(.010)
County dummies?
YES
YES
YES
YES
YES
Year dummies?
YES
YES
YES
YES
YES
Pseudo-R 2
.062
.063
.066
.311
.313
Number of observations
8418
8374
8293
8345
8264
Notes: (a) Robust standard errors are in parentheses.
(b) Partisan identification is defined to be 1 if the voter identifies strongly with the challenger party, 2 if the
voter identifies strongly with neither party, and 3 if the voter identifies strongly with the incumbent party.
(c) Coefficients are reported as marginal effects.
(d) * indicates significance at the 5% level.
20
Table 3: Voter Welfare and Tornado Damage
(Probit Estimates)
Dependent variable: Is a voter better off?
(1 = better off or the same, 0 = worse off)
Per-capita tornado damage
(in thousands of $)
(1)
(2)
-.015*
(.007)
-.015*
(.007)
Dummy for not graduating
from high school
-.049*
(.023)
Non-high school graduate*
tornado damage measure
-.002
(.011)
County dummies?
YES
YES
Year dummies?
YES
YES
Pseudo-R 2
.064
.065
Number of observations
7396
7356
Notes: (a) Robust standard errors are in parentheses.
(b) Coefficients are reported as marginal effects.
(c) * indicates significance at the 5% level.
21
Table A1: Voter Characteristics and Responses to Tornado Damage
(Ordinary Least Squares Estimates)
Dependent variable: Did the voter choose the incumbent party?
Per-capita tornado damage
(in thousands of $)
(1)
(2)
(3)
(4)
(5)
-1.37
(.74)
-.773
(.761)
.391
(.63)
-1.403
(.857)
-.037
(.709)
Partisan identification
32.875*
(.495)
32.837*
(.498)
Dummy for not graduating
from high school
.813
(1.681)
.267
(1.385)
.747
(1.691)
.229
(1.394)
Non-high school graduate*
per-capita tornado damage
-3.99*
(1.285)
-3.336*
(1.037)
-4.405*
(1.294)
-3.673*
(1.042)
Dummy for regularly attending
religious services
4.587*
(1.109)
3.78*
(.916)
Religious service attendee*
per-capita tornado damage
1.189
(.841)
.896
(.687)
County dummies?
YES
YES
YES
YES
YES
Year dummies?
YES
YES
YES
YES
YES
.086
.088
.369
.091
.372
8448
8408
8379
8326
8297
R
2
Number of observations
Notes: (a) Robust standard errors are in parentheses.
(b) Partisan identification is defined to be 1 if the voter identifies strongly with the challenger party, 2 if the
voter identifies strongly with neither party, and 3 if the voter identifies strongly with the incumbent party.
(c) * indicates significance at the 5% level.
22
Table A2: Voter Welfare and Tornado Damage
(Ordinary Least Squares Estimates)
Dependent variable: Is a voter better off?
(1 = better off or the same, 0 = worse off)
Per-capita tornado damage
(in thousands of $)
(1)
(2)
-.016*
(.007)
-.016*
(.007)
Dummy for not graduating
from high school
-.043*
(.021)
Non-high school graduate*
tornado damage measure
-.004
(.011)
County dummies?
YES
YES
Year dummies?
YES
YES
R2
.064
.076
Number of observations
7396
7410
Notes: (a) Robust standard errors are in parentheses.
(b) Coefficients are reported as marginal effects.
(c) * indicates significance at the 5% level.