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Presidential Voting and the Local Variability of Economic Hardship Wendy K. Tam Cho∗

∗ †

James G. Gimpel†

University of Illinois, Urbana-Champaign, [email protected] University of Maryland, College Park, [email protected]

c Copyright "2009 by the authors. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher, bepress, which has been given certain exclusive rights by the author.

Presidential Voting and the Local Variability of Economic Hardship∗ Wendy K. Tam Cho and James G. Gimpel

Abstract We examine variations in the impact of several components of economic hardship on the 2008 presidential vote by county. High gas prices, mounting foreclosures, and rising unemployment all enhance the Democratic vote share in areas critical to winning an Electoral College majority. Using Geographically Weighted Regression (GWR), however, we show the varying impact of these forces, controlling for previous Democratic voting, race, age, and income. Economic problems do not produce anything like a uniform response, and not merely because they are geographically uneven in their intensity. Some populations hit by economic downturn would not have voted for the incumbent’s party under any circumstances, while others supported the in-party in spite of hard times. Even so, the combined weight of rising jobless claims and escalating foreclosures was sufficiently unsettling in key states to make for an early call on Election Night. KEYWORDS: presidential elections, the 2008 presidential election, economic voting, political geography, geographically weighted regression

Wendy K. Tam Cho is associate professor in the Department of Political Science and Department of Statistics and Senior Research Scientist at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign. James G. Gimpel is a professor of government at the University of Maryland, College Park, where he has been on the faculty since 1992. His research interests are wide-ranging, but his recent work has focused on political behavior, political geography, and campaigns and elections. ∗

Cho and Gimpel: The Local Variability of Economic Hardship

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The 2008 presidential election will be a favorite among those who prefer looking at “fundamental” explanations for electoral outcomes (Stigler 1973; Fair 1978; Tufte 1978; Markus 1988; Erikson 1989; Lewis-Beck and Stegmaier 2000). Few contest the idea that widespread economic downturn played a significant role in the 2008 elections. Consistent with expectations, a souring economy worked against the incumbent’s party. Several specific economic travails are said to have played a significant role in shaping the direction and magnitude of the vote against the Republican Party. Here we ask whether some components of the downturn were more influential on the election outcome than others, and to what extent there was geographic variation in their impact. Several indicators of economic hardship were closely monitored by experts and the media throughout 2008. The deflation of the housing bubble led to record levels of mortgage foreclosures. Unemployment reached levels it had not seen in three presidencies. And gas prices soared to historic highs, measured in real terms. It is fair to say that no previous election was preceded by this particular constellation of unfavorable factors. For those who remained personally unaffected by a home foreclosure or a pink slip, the income-eroding peak in gas prices in July of 2008 would not be easily forgotten. None of the economic problems that shadowed the 2008 presidential campaign were invariant across the national landscape. Some places fared poorly, while others were comparatively untouched. Our suggestion in this paper is that because the components of economic downturn were unevenly distributed across the U.S., economic concerns were variably relevant to political evaluations and behavior. Economic evaluations are said to figure in many major elections, but there is a growing recognition of the existence of heterogeneity across populations and across geographic space in the extent to which evaluations of economic performance trigger the predicted anti-incumbency behavior (Gomez and Wilson 2001; Duch, Palmer, and Anderson 2000; Books and Prysby 1999). Part of this variability is the consequence of differences in the propensity to attribute responsibility for economic conditions to specific officeholders (Rudolph 2003). Although technology has diminished the friction of distance in our daily lives, personal experiences of financial hardship are mostly shaped by local conditions. Two workers of similar age, income, and education level may be employed in the same sector of the economy, but one will live in a town where they pass eight foreclosure signs on the way to work, while another will live in a locale with none. Quite aside from individual characteristics, exposure to worsening economic environments will make a difference to the role of the economy as a consideration when voting. For the voter whose economic setting has remained unchanged over long spans of time, either blighted or secure, local economic conditions send less relevant signals for political behavior. In a place

Published by The Berkeley Electronic Press, 2009

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with rapidly worsening economic circumstances, the signal to voters that a political change may be in order is much clearer (Ebeid and Rodden 2006). The Uneven Terrain of Economic Recession The nation’s housing crisis has not been geographically uniform because the rapid inflation of housing prices was patchy.1 To be sure, housing prices grew much faster than income as reported in national statistical summaries, but the trend was still geographically uneven. Consequently, the incidences of foreclosure that accumulated throughout the year were highly peaked in some locations and virtually non-existent in others. The initial wave of foreclosures hit California and fast-growing Western states the hardest, then spread to Florida and other parts of the country that were experiencing booming growth. Areas of slow growth in the Midwest and Plains never experienced a crash in home values and an accompanying rise in foreclosures, because housing prices had remained stable. Gas prices soared to a peak of over $4.00 per gallon in June and July, but both the price and the practical effect were regionally variable according to state tax rates on gasoline, pollution control regulations that raise the cost of fuel production, distance from suppliers, and local consumption habits (U.S. Department of Energy, EIA 2008). California and the West Coast experienced the highest gas prices, followed by the Northeast, with cheaper prevailing prices along the Gulf Coast and throughout the Midwest. Still, the increasing cost of gasoline at the beginning of the summer was probably the most universal of the economic blows that fell. Finally, economic uncertainty eventually began to take its toll on employment. The national jobless rate hit a 14-year high by Election Day, a 26year high by December. Yet unemployment reached an even higher summit in some locales, most notably Michigan (U.S. Department of Labor, BLS, 2008). By the end of the year, the flagging market for American-made automobiles led to layoffs and the prospect of bankruptcy for one or more of the Big-3 auto manufacturers. Politicians from states containing large auto manufacturing plants, particularly Michigan, Ohio, and Missouri, grew especially concerned about the fate of their constituencies. Before the year was out, the entire Michigan congressional delegation was pleading with the president for a bailout.

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In 2002, in the midst of the boom, housing prices increased in California at a rate of $3,000 per month. Californians had also experienced the greatest deflation in housing prices by November 2008, down some 26% from the same month in the previous year.


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Electoral Change and Presidential Voting Electoral changes observed at the aggregate level across states, counties, and precincts can be the product of several forces. Mobilization and demobilization of voters is one of these, as turnout waxes and wanes across space and over time (Gimpel and Schuknecht 2003). At least some citizens vote in greater numbers during hard economic times, perhaps as a function of blame attribution and outparty mobilizing efforts (Schlozman and Verba 1979; Radcliff 1992; Arceneaux 2003). Swing voting in response to economic crisis, though rare for committed partisans, is another force that shapes and reshapes electoral terrain particularly through the assessments and behavior of less sophisticated voters who readily switch sides from one election to the next. Voters assess the incumbent’s performance in office, and although those evaluations may be tainted by party identification and other subjective influences on judgment, enough reality bleeds through to produce some response (Bartels 1996; Althaus 1998; 2003; Duch, Palmer and Anderson 2000). Better educated and more sophisticated voters have been found to make more precise attributions of economic responsibility than those who are poorly informed (Gomez and Wilson 2001). Nevertheless, weak partisans may also drift from Republicans to Democrats, often according to social influence processes that award momentum and favorable coverage to one candidate, while disadvantaging another. Only a few studies have examined the question of whether heterogeneity in economic voting is a function of objectively heterogeneous conditions (Books and Prysby 1999; Weatherford 1983). There are several good reasons to believe, however, that there is more to economic voting than a consideration of either national economic conditions or personal pocketbook ones. Economic voting may be variable across populations not just because subjective evaluations of impact come into play, but because objective economic conditions do differ from context to context. While sociotropic theories of the influence of economic conditions on voting appear to emphasize national economic conditions (Kinder and Kiewiet 1979, 1981), voters are affected by national economic trends and conditions less than their local economic circumstances. More precisely, national economic indicators can be ominously portrayed in mass media reporting, but these circumstances may seem distant and less real if they are not reflected in local layoffs, inflation-driven income losses, or foreclosed mortgages. The impact of prevailing negative economic information based on nationwide data summaries is potentially multiplied in the presence of local hardship, as a voter is more likely to hear stories of shuttered workplaces and families abandoning their homes to bankers, as well as to experience those conditions themselves. In essence, voters’ sociotropic reflections about economic adversity are filtered through the lens of local economic difficulty (Behr and


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Iyengar 1985). Whether their concerns are magnified or diminished by the degree of correspondence of national conditions with nearby realities, we would expect place-specific changes in support for out-party candidates to vary from one election to the next as economic conditions change. The extent to which economic problems steer presidential voting toward the out-party should not be geographically constant. We consider it important to distinguish between short-term change in economic conditions at each location we study, and the more static measures of long-term conditions at those locales as reflected in the levels of unemployment and gas prices reported in a given month. We fully expect the electorate to be politically responsive to changes in these economic quantities, but perhaps less so to the absolute levels themselves. After all, many citizens become accustomed to living in places with relatively high and enduring levels of unemployment. Over time, they become inured to hardship, and the seeming permanence of economic distress triggers only the weakest desire for political change. New presidential administrations come and go without any material improvements in local conditions. Voters eventually realize that choosing candidates on the basis of economic promises makes little sense given that objective conditions change very little no matter who holds office. Many high-unemployment areas in deindustrialized parts of the Rust Belt (e.g., Pennsylvania, Ohio, and New York), as well as locations in the Midwest vaguely alluded to in Thomas Frank’s (2005) book, What’s the Matter with Kansas, are good examples of locations where voters have become habituated to toilsome difficulty. On the other hand, short-term changes from good to bad (and from bad to worse) may be politicized in ways that longer-term conditions are not, because short-term fluctuations are proximate and direct threats to income and living standards. In short, recessionary job losses across a series of months are more likely to produce anti-incumbency sentiment than having lived in a context of high but steady unemployment for one’s entire life. It is not always clear that hard times cause great stress and difficulty, particularly if the onset of adversity has been gradual or if meager conditions have persisted for long periods of time (Schlozman and Verba 1979). Method of Analysis The foregoing considerations suggest that a single coefficient summarizing the relationship between each explanatory variable and the presidential vote for the entire nation is an oversimplification of the variability in local relationships. If our data exhibit the spatial non-stationarity implied by our theoretical expectations, a technique that might be especially helpful with these data is Geographically Weighted Regression (GWR) (Fotheringham, Brunsdon and Charlton, 2002;


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2001; Fotheringham and Brunsdon 1999). GWR allows us to estimate variable parameters for different locations. We can, for instance, examine how the relationship between economic hardship and support for the Democratic candidate varies in magnitude and statistical significance across the nation’s 3,140 counties. The GWR framework is similar to a weighted least-squares estimator with non-constant weights, but the weights vary according to spatial location. The weights matrix is computed for each location, i, and the weights encompass a measure of proximity of each of the other locations to location i. The observations that are more proximate to i are weighted more heavily in the estimation of the parameter for location i. A spatial kernel, whose size varies depending upon the density of observations over the region of interest, is placed over each unit and determines the weight of each data point in the calibration of the model at location i. Weights decrease as distance from i increases. Instead of producing a single average parameter for a relationship, GWR produces a set of local parameter estimates that can be fruitfully mapped (Fotheringham, Brunsdon and Charlton, 2002, 2001). 2! Data and Measures In our analysis, we focus on spatial heterogeneity in the effect of economic variables, while controlling for other influences on the vote. We utilize countylevel data on voting from the 2008 presidential election, gathered by political geographer David Leip from official reports of state boards of elections and state Secretaries-of-State.3 The county is a particularly convenient level of analysis from the practical standpoint of data availability for a wide range of sociodemographic and economic variables, permitting a more detailed view of the nation’s political geography than could be provided by studying states or other aggregate units of analysis. Our dependent variable is the Democratic percentage of the 2008 presidential vote across counties (shown in Figure 1). The geographic distribution of Republican and Democratic strength bears a strong resemblance to the 2004 and 2000 contests. The Democratic ticket performed best on the two coasts. The !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 2

Notably, GWR offers some advantages that other techniques, such as Hierarchical Linear Models (HLM), do not. Specifically, HLM requires an a priori definition of spatial units at the second level of hierarchy, commonly confining variations in statistical relationships within state boundaries for applications such as ours. This is untenable given that in many parts of the nation, political phenomena, including turnout and voting, are continuous across state boundaries (Cho and Nicley 2008). GWR does not make an assumption about the confines of geographic variation, instead allowing space to be continuous. 3 David Leip provides detailed election returns at low cost at his website: http://www.uselectionatlas.org/, accessed December 30, 2008.


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Industrial Midwestern states remained the critical battlegrounds. The Plains and much of the South voted Republican. Figure 1 also reveals the persistent urbanrural split within states (Gimpel and Karnes 2006). In 2004, approximately 29 percent of the U.S. population resided in 679 counties that could be considered highly competitive—those in which the outcome of the contest was within 10 points (within the 45% to 55% margin) (Gimpel and Lee 2006). In 2008, this was diminished almost imperceptibly to 664 counties containing about 28 percent of the population. At the same time, across the nation’s counties (and including some independent cities), there is marked variation not only in political support but also in local economic performance. Fortuitously, data on gas prices, home foreclosures, and unemployment are available at the county level and on a monthly basis throughout the year.

Figure!1:!Democratic!Percentage!of!the!2008!Presidential!Vote,!by!County

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To evaluate the impact of foreclosure rates, we divided the accumulated total number of foreclosures from January to October of 2008 by the number of total households (in 1000s) in October of 2008.4 The geographic distribution of total foreclosures as a percent of local households by November of 2008 is displayed in Figure 2, by quintiles. That the highest quintile encompasses a vast range of economic conditions, from around 9 foreclosures to as many as 129 foreclosures per thousand households is noteworthy in itself. According to the best data available to gauge the scope of the problem, only seven counties experienced foreclosure rates greater than 100 by November. Of these seven, four were in California, one in Florida, one in Nevada, and one in Virginia. It is in these areas, where the high cost of housing was driven by rapid growth, that the demand for risky mortgage financing triggered the crisis. Throughout the year, the Western states, particularly Nevada, Arizona, California, and Colorado led the nation in foreclosures. Florida also stands out in Figure 2 as a high growth-high foreclosure state, as do more populated areas within other states, including Tennessee, North Carolina, Virginia, Ohio, Indiana, Michigan, and Illinois. Even so, it would be misleading if we did not point out that the problems associated with bad mortgage financing are remarkably concentrated in geographic terms. It is especially worth noting that many low-growth states, as well as mid-sized and small towns within the faster-growing states, appear to have escaped the foreclosure crisis, at least by the time of the Election. Gas prices were obtained on a monthly basis from a source that captures local gasoline prices through an exhaustive network of spotters located throughout the entire United States, reporting average prices on a county basis.5 The geographic distribution of gas prices for the month prior to the election is show in Figure 3, by quintiles. Although the locations with the highest October gas prices might appear to intersect with the locations where high foreclosures also occurred, the correlation is actually quite low (! = 0.11). In fact, there were many locations that experienced very high gas prices without seeing many foreclosures around them, including many rural and remote areas in the Northeast and Upper Midwest. Conversely, there were locations that experienced high foreclosure rates, but where gas prices fell into middling-to-low levels by October; Ohio is an example. The Western states were the least fortunate of all—facing both high foreclosure rates and high gas prices throughout most of the year (see Figure 3). !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 4

The source of the foreclosure information by county is RealtyTrac, a company specializing in data collection on real estate conditions and trends: www.realtytrac.com, accessed January 5, 2009. 5 The source of gasoline price information is GasBuddy. See http://www.gasbuddy.com/ accessed December 30, 2008.


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Figure!2:!Total!Foreclosures!in!Homes!per!1,000,!January!to!October!2008,!by!County

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We measure the change in gas prices as the percentage increase in average county price from July to October price. To be sure, gas prices fell between July and October in all but the most remote parts of Alaska, but they fell further in some places than in others, depending on where they stood at their peak level. Generally smaller reductions in price were reported in the Mountain West (AZ, NM, CO, WY, UT, NV, MT, ID) states (-13% on average), while quite large drops, averaging 20%, were reported throughout the Midwestern agricultural states (MN, IA, MO, ND, SD, NE, KS), in Texas, and Oklahoma.


Figure!3:!October!Gas!Prices!in!Dollars!per!Gallon,!by!County

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Monthly unemployment data are collected by the U.S. Department of Labor, Bureau of Labor Statistics, at the county level and publicly reported two months after they are collected.6 To gauge the change in the level of unemployment, we simply subtract the number of unemployed persons in October from the number unemployed in July, dividing the difference by the July figure to derive the percentage increase/decrease in local jobless claims. These changes were quite variable across the nation’s counties as well, ranging from major increases in unemployment filings in the Pacific Northwest and in Florida, to a negligible rise in unemployment in the most rural Midwestern states. Areas of rising unemployment did not intersect that closely with those experiencing high or rising foreclosures for straightforward reasons. Fragile and weak economies do not attract floods of new residents. Consequently, the correlation between the change in unemployment between July and October and the rate of foreclosure by November is rather modest (!=.24). Of the 124 counties whose jobless claims jumped by 30 percent or greater between July and October, !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 6

Data on regional and local unemployment are available on-line here: http://www.bls.gov/data/home.htm


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only one (Imperial County) was in California and none were in Nevada. Perhaps the most politically significant state where foreclosures and jobless claims increased side-by-side was Florida. In our data analysis, we also include other variables from U.S. Census Bureau sources such as intracensal estimates for the percentage of college graduates, median household income, percent African American and Latino, population density, the share of the population that is between 18 and 24, and the percentage age 65 or older. A table of means and standard deviations for all variables is included in the appendix. Statistical Results of OLS and GWR Estimation We begin by presenting the results for an ordinary least squares regression analysis of the county level support for the Democratic candidate in 2008 (see Table 1). Given that the units of analysis are not individuals, we do not necessarily expect the same relationships to adhere that we would if we were analyzing a survey of voters (Robinson 1950). To be sure, although aggregate relationships may resemble individual relationships, inferring the direct correspondence of these distinct quantities is unreliable. Nonetheless, the countylevel relationships are also a quantity of interest, and not merely a substitute for individual level analysis.7 Notably, the Democratic vote percentage in 2004 is closely related to the 2008 outcome, a ten-point increase in John Kerry’s vote is associated with a 9.93 percentage point increase in the presidential vote for Barack Obama (see Table 1). Higher income locales, and those with a greater share of college graduates both supported Obama, as did counties with larger black and Latino concentrations. Locations with high concentrations of younger voters supported Obama, as did locations with much older age distributions.

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Ecological variables are commonly thought to be measures of the same construct as the individual variables to which they correspond. But the ecological variable may also be an indicator of a construct that is not measureable at the individual level, such as social and contextual factors.


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Table 1. Explaining the County Level Democratic Percentage of the 2008 Presidential Vote OLS Coefficient

Intercept Population Density Democratic Vote Percentage in 2004 Median Income Percent College Percent Black Percent Latino Accumulated Foreclosures Unemployment Gas Price in October Change in Unemployment Change in Gas Prices Ages 18–24 Ages 65 and up

N R2

-53.57* (3.84) -0.016* (0.005) 0.993* (0.008) 0.131* (0.015) 0.077* (0.018) 0.053* (0.007) 0.078* (0.007) -0.021* (0.009) -0.138* (0.051) 1.165* (0.914) -0.013* (0.006) -0.501* (0.045) 0.136* (0.032) 0.073* (0.027)

Minimum

Mean

Maximum

-194.642

-12.688

218.684

-2.059

0.436

14.131

0.383

0.877

1.458

-0.623

-0.006

0.556

-0.571

0.110

0.738

-2.847

0.067

1.652

-1.018

0.148

1.086

-1.284

0.007

2.401

-1.922

-0.051

1.688

-53.265

4.342

52.661

-0.151

0.004

0.285

-1.917

-0.152

2.055

-0.495

0.050

0.714

-0.874

-0.076

1.306

3141 0.893

Ordinary Least Squares Estimates in first column. Standard Errors in Parentheses. Minimum=minimum value of local regression estimate; Maximum=maximum value of local regression estimate; Mean=mean value of local regression estimate * p < 0.05


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Figure 4 helpfully maps the relationship between the 2004 and 2008 votes based on our geographically weighted regression estimates. For mapping the GWR estimates, the legend categories and shading are determined by Jenks natural breaks that divide the data into subsets based on each subset’s internal homogeneity while maintaining heterogeneity across subsets (Slocum 1999). Accounting for other influences on the county-level vote, we find that the support for John Kerry and Barack Obama was most closely related in the Mountain states, in very Republican areas of the Plains, and throughout most of the West. Correspondence between the two candidates’ vote percentages also ran high in a few other locations where presidential party support exhibits continuity, including the Wisconsin lakeshore, in New England, and in durable Republican pockets in South Carolina and Georgia (see Figure 4). There was also a large territory centered on Arkansas, Louisiana, and Mississippi, shaded in white, where there was less of a relationship between the Kerry and Obama votes (and the Bush and McCain votes). Lighter turnout among ardent Republican populations was most likely responsible for the weak relationship between aggregate presidential preference in 2004 and 2008 in the South and Border States, as well as within some politically conservative pockets of Iowa, Indiana, and Ohio.

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Figure!4:!Spatial!Variation!in!the!Impact!of!the!2004!Kerry!Vote!on!the!2008!Obama! Vote


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The economic variables are not uniform in their general ecological impact, judging from the OLS estimates (see Table 1). Areas of high unemployment by October dropped support for Obama, ceteris paribus. Locations with higher gas prices, on the other hand, strongly supported the Democratic ticket—a one dollar increase in a gallon of gas is associated with a 1.17% jump in the vote for the winner. The accumulation of foreclosures over the course of the year is associated with a slight increase in support for the Republican candidate, net of all other influences in the global ordinary least-squares estimation. Nor did change in economic conditions from the middle of the year to Election Day always work in favor of the challenger. Areas of rising unemployment were marginally more likely to vote for John McCain, on average. The impact of the change in gas prices is a bit more difficult to interpret since they were in decline nearly everywhere beginning in August. Generally, steeper declines in price from their mid-year high increased McCain support, as we would expect. In the general OLS estimate, then, gas prices apparently loomed surprisingly large for the out-party’s November vote share at the county level, even though prices were in decline by Election Day. Apparently the record high prices of just a few months before made a deep and widespread impression. Our goal, however, is not simply to stop with these global estimates of electoral impact, but to examine their variability across the nation. Figure 5 provides a nationwide picture of the results from geographically weighted regression, showing where the change in gas prices was most consequential as a positive force for the Democratic candidate. This map shows that once we control for other influences on the countylevel vote, the locations where the price dropped the least from its July high were the most supportive of the Democratic presidential candidate (some critical territory inside closely contested states, including Florida and parts of Virginia, Missouri, and Pennsylvania). The Democrats’ improved electoral performance also seems to be a function of gas prices in much of Northern California, as well as Minnesota, Wisconsin, and Northern New England. Especially remarkable, we find that where gas prices were more steeply on the decline by October, McCain performed better. These locations are shaded in white on Figure 5, and include some very Republican parts of Ohio, Indiana, and a handful of rural counties in Southern Michigan.


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Figure!5:!Spatial!Variation!in!the!Impact!of!Changing!Gas!Prices!on!the!2008! Democratic Presidential ! Vote

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Figure 6 illustrates the varying impact of the rising unemployment rate from July to October derived from geographically weighted regression. Several noteworthy dark patches in battleground states show locations of Obama strength as a result of the early onset of recession. These include large parts of states such as Pennsylvania and Florida, as well as Indiana, Ohio, Michigan, Wisconsin, Minnesota, and Iowa. Much of Northern New England, again, moves toward Obama as a consequence of rising jobless claims. The conspicuous dark shading through the entire Ohio River Valley contributed to significant victories in Indiana and Ohio, adding between 0.04% and 0.35% to the Democratic vote share for every single percentage point rise in the number of unemployed workers. Yet there were also locations where the rising level of unemployment claims may have marginally boosted the Republican ticket—largely less populated locations in the West and South, shaded in white in Figure 6. According to our estimates, McCain gained between 0.05% and 0.17% of the vote for every one point increase in jobless claims at these locations. To the extent the Republican candidate may have gained in a few places, however, he did not gain by as much as the opposition did elsewhere.


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Figure!6:!Spatial!Variation!in!the!Impact!of!Changing!Unemployment!Rates!on!the! 2008! Democratic Presidential ! Vote

!

Finally, the impact of foreclosures was also very uneven in its political impact (see Figure 7). Contested states in which it significantly enhanced the Democratic Party’s prospects for victory included North Carolina, Indiana, Ohio, Wisconsin, Iowa, and Florida. Within many of these states, a shift of one standard deviation in the number of foreclosures (" = 11.5) is associated with an estimated 1.26% to 9.2% increase in the final Democratic vote share. Accumulated across a large number of counties in each of these states, this margin contributed to a substantial statewide improvement in the Obama vote relative to the Kerry vote in 2004.


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Figure!7:!Spatial!Variation!in!the!Impact!of!Cumulative!Foreclosures!per!1000! Households!on!the!2008! Democratic !Presidential Vote

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To be sure, there were again places where evident foreclosures were less consequential to the ultimate election outcome, including the Dakotas and, ironically, much of the foreclosure-hit West, where basic partisanship and voter demographics ensure more lopsided majorities in favor of one party or the other. There were even a few places where foreclosure rates seem to be associated with support for the GOP, particularly rural locales where one or two foreclosures in a small community, though noticeable, did not incline citizens to favor a change in party control of the presidency. Discussion The economy was a force in the 2008 election, but not all of the elements of the economic downturn can be easily separated from the entrenched political preferences of the most affected populations. For example, a large number of people hit by foreclosures were middle- and lower- income minorities living in the West. These voters would not have supported the Republican ticket even in good times. The recessionary travails that were evident by mid-year did not cause


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these voters to support the Obama-Biden ticket any more than they would have in any other election. California is a very safe Democratic state in good times and in bad. High gas prices in the Golden State simply made a blue state even more securely Democratic. Likewise, it is not entirely clear that Michigan would have voted Republican even if the local economy were not in such dire straits. Economic unease was an important force in this election, but it surely did not lead to a wholesale disregard for customary voting cues. The election, after all, was close, concluding at 52.9% to 45.6%, in spite of dreary economic reports. Our evidence is consistent with the notion that intense campaigning in battleground states augmented the impact of economic variables on the vote in those locations. Interestingly, the collected counties within these states did not necessarily experience the worst of the economic downturn measured in purely objective terms. Communities within Colorado, Virginia, Iowa, Pennsylvania, and Ohio experienced unpleasant economic conditions, to be sure, but other places were worse off (e.g., California). Even so, the response in more populous communities within the closely watched states directly benefitted the Democrats, even after we account for other important influences. Apparently, the campaign had the effect of heightening the concern about the economy in voters’ minds in these traditional battleground areas, whereas in other places that were measurably worse off, there was far less campaign-related hype. As a countervailing economic force, falling gas prices helped the McCainPalin ticket in many of these same places – those exhibiting positive regression coefficients in Figure 5, including Florida, Virginia, and Pennsylvania. But prices did not fall far enough by Election Day for McCain to win any of those states, and easing fuel costs could not overcome the growing electoral consternation over unemployment and housing finance. Not surprisingly, our results reveal that rising joblessness between July and October exerted heavy downward pressure on the Republican vote share. Even so, the impact of unemployment was not uniform because in many locations other economic and non-economic variables played more consequential roles. There are long-term components to unemployment and poverty that are unrelated to political preference in any given election. Residents of perennially depressed locations seem to recognize that economic circumstances in many places are beyond any politician’s control, deciding quite rationally to make up their minds on other grounds. Finally, if the rising unemployment rates in the Midwest were not enough to push several of the 2004 Bush states into the Obama column, there were the accounts of accumulating foreclosures in more populous areas of Florida, North Carolina, Ohio, Indiana, and Wisconsin that also helped to move them. This study has contributed to the growing body of evidence suggesting that response to economic downturn is mixed across the electorate, partly because


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the economic conditions themselves are geographically variable. Interestingly, though, even when the economic problems are of similar scope and magnitude from one place to another, the local response may still vary. In either case, these results demonstrate strong evidence of a spatially variable effect of sufficient magnitude so as to render a global estimate devoid of insight, and perhaps misleading. We cannot simply assume that because a particular location has been hardhit by economic calamity, this will produce a massive political shift toward the out-party. Moreover, only a modest downturn at other locales could yield a substantial change in political support from one election to the next. This variability in response is complex and should account for the variable intensity of campaigning across the landscape, the strength of local partisan commitments, as well as related characteristics such as race and socioeconomic status that have long shaped political allegiances. Going forward, studies of electioneering would benefit from a greater than average amount of local knowledge as we reach for a better grasp of the decision-making aspects of human behavior. Tools and methods that permit the exploration and display of variable estimates of impact are indispensible to this end. Appendix Table 1. Means and Standard Deviations for Variables included in Table 1. Standard Variable Name Mean Deviation Democratic Presidential Vote 2008 Democratic Presidential Vote 2004 Population Density (100s) Percent with 4 Yr College Degrees Median HH Income (1000s) Percent Black Percent Hispanic Percent Age 65 and Older Percent Age 18-24 Change in Unemployment Change in Gas Prices Cumulative Foreclosure Rate Percent Unemployment October Gas Price October Valid N=3,140

41.55 38.76 252.49 16.55 36.94 9.21 7.45 15.41 9.85 0.58 -27.28 5.87 5.83 2.94

13.86 12.49 1721.44 7.82 9.54 14.50 13.14 3.83 3.24 15.97 5.47 11.54 2.29 0.27


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