Bechtel and Mannino - retrospection-fairness-and-economic-shocks-how-do-voters-judge-policy-responses-to-natural-disasters (1).pdf

Political Science Research and Methods (2022), 10, 260–278 doi:10.1017/psrm.2020.39 ORIGINAL ARTICLE Retrospection, fairness, and economic shocks: how do voters judge policy responses to natural disasters? Michael M. Bechtel1,2* and Massimo Mannino2 1 Department of Political Science, Washington Univ...

Political Science Research and Methods (2022), 10, 260–278 doi:10.1017/psrm.2020.39 ORIGINAL ARTICLE Retrospection, fairness, and economic shocks: how do voters judge policy responses to natural disasters? Michael M. Bechtel1,2* and Massimo Mannino2 1 Department of Political Science, Washington University in St. Louis, St. Louis, MO 63130-4899, USA and 2Swiss Institute for International Economics and Applied Economic Research, University of St. Gallen, Bodanstrasse 8, CH-9000, St. Gallen, Switzerland *Corresponding author. Email: [email protected] (Received 13 September 2019; revised 12 February 2020; accepted 30 April 2020; first published online 6 November 2020) Abstract Which factors explain voters’ evaluations of policy responses to economic shocks? We explore this question in the context of mass preferences over the distribution of disaster relief and evaluate three fairnessbased explanations related to affectedness, need, and political ties. We analyze experimental data from an original survey conducted among American citizens and find that affectedness and need are key drivers of voters’ preferred disaster responses. We then compare these patterns with observed disaster relief distributions (1993–2008). The results suggest that observed relief allocations largely mirror the structure of voter preferences with respect to affectedness and need, but not to political ties. These findings have implications for an ongoing debate over the electoral effects of natural disasters, voters’ retrospective evaluations of incumbent performance, and the extent to which divide-the-dollar politics decisions align with mass preferences. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Keywords: comparative politics; political behavior; political economy; public opinion; voting behavior 1. Introduction Coping with the changing climatic conditions and the resulting increases in natural disasters on this planet poses a major policy challenge (Obradovich et al., 2018). In fact, even recordshattering disasters such as Hurricanes Katrina or Harvey that cause large human and economic losses (CRED, EM-DAT and UNISDR, 2017) no longer constitute rare phenomena.1 The policy responses to extreme weather events are ethically salient, economically significant, and politically consequential. First, compensating individuals for experienced disaster damage, which is the preferred response of elected officials (Healy and Malhotra, 2009), absorbs significant financial resources. Second, these policy responses entail important divide-the-dollar decisions (Kriner and Reeves, 2015) that raise ethical and inequality-related issues as natural disasters place a disproportionate burden on the poor, the elderly, women, and children (Kahn, 2005; Neumayer and Pluemper, 2007; Aldrich and Sawada, 2015). Third, since the policy responses to extreme weather events determine disaster recovery, they affect voter evaluations of incumbent performance (Atkeson and Maestas, 2012) and electoral outcomes. However, empirical findings regarding the impact of natural disasters on elections are contested and seem contradictory at times. Some studies suggest that voters reward incumbents electorally for the provision of disaster relief (Bechtel and Hainmueller, 2011; Gasper and Reeves, 2011). Others conclude that the public engages in blind retrospection (Achen and Bartels, 2017, 2018) by punishing governments for 1 NOAA National Centers for Environmental Information (NCEI). “US Billion-Dollar Weather and Climate Disasters (2017)”, accessed 2 February 2017, https://www.ncdc.noaa.gov/billions/. © The Author(s), 2020. Published by Cambridge University Press on behalf of the European Political Science Association. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Political Science Research and Methods 261 "random events" such as natural disasters that policymakers remain unable to control (Abney and Hill, 1966) even in regions that have received exceptionally high levels of disaster relief (Heersink et al., 2017). These contradictory findings have resulted in an ongoing theoretical and empirical debate over whether citizens’ retrospective evaluations of incumbent performance follow reasonable standards (Healy and Malhotra, 2013; Ashworth and de Mesquita, 2014; Heersink et al., 2017; Achen and Bartels, 2018). The importance of this discussion has been further heightened by the fact that previous scholarship has used extreme weather events to estimate an upper bound on the impact of large-scale economic shocks on electoral behavior and political attitudes (Margalit, 2019; Baccini and Leemann, 2020). In addition, policy responses to major shocks such as natural disasters may also improve or shatter trust in a country’s political system (Damico et al., 2000). We argue that one important element missing from this literature is that retrospective evaluations of incumbent performance depend on both voter preferences over disaster relief allocations and policymakers’ actual allocation decisions. Whether incumbents gain or suffer electorally in the aftermath of extreme weather events hinges on an assessment of the observed policy response benchmarked against voters’ preferred reaction. We offer the first analysis of individual preferences over the provision of disaster relief and present a design that allows us to compare them with the structure of observed relief allocation decisions. Several factors may influence how publics would like policymakers to respond to major economic shocks. For example, preferences over the provision of financial assistance could reflect affectedness and need-based conceptions of fairness. These fairness norms imply that the preferred allocation of disaster relief should mirror the distribution of damage and need. However, distributive preferences may also be influenced by partisan solidarity and electoral considerations. We use data from an original survey conducted on a population-based sample of American citizens (N = 2, 618) to study the structure of disaster relief preferences. The survey includes a novel resource allocation conjoint experiment that asks respondents to distribute relief aid to counties affected by a natural disaster. The attributes specified in this "divide-the-dollar" conjoint experiment capture three theoretically relevant dimensions: affectedness, need, and political ties. This allows us to explore which factors drive citizens’ preferred relief allocations and the relative importance of different conceptions of fairness for our understanding of voter preferences. We analyze 20,000 individual-level allocation decisions and find that respondents allocate significantly more disaster relief to counties that experienced higher levels of economic damage and more fatalities. The results also indicate that individuals prefer relief allocations that provide more relief aid to poorer regions. In contrast, preferences over disaster relief do not mirror electoral or partisan considerations. We then evaluate the plausibility of our theoretical interpretation of the causal effects as reflecting individuals’ conceptions of distributive fairness and subject our results to a large set of additional tests. These include exploring interactions between policy response features and individual-level characteristics such as personal exposure to natural disasters. The findings suggest that the sensitivity to disaster damage and income characteristics is quite stable across levels of personal affectedness as well as partisanship. Finally, we leverage the results from our experimental conjoint to compare the structure of voter preferences over relief aid and the patterns that characterize policymakers’ actual relief allocations along all three theoretically important dimensions: need, affectedness, and political ties. The results suggest that both preferred and observed allocation choices strongly mirror affectedness and, to some extent, need. However, while voters prefer apolitical distributions of disaster relief, policymakers seem to have engaged in politically motivated allocations of relief payments. This study makes several contributions. First, our results inform ongoing research on retrospective voting and the evaluative standards that voters employ when assessing the policy responses to major economic shocks in the context of life-threatening events such as natural disasters. Second, we present a novel divide-the-dollar conjoint experiment that allows scholarship to gain more detailed insights into mass preferences over public spending decisions and enables 262 Michael M. Bechtel and Massimo Mannino https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press subsequent research to perform more direct comparisons of voters’ preferred relief allocations and policymakers’ actual allocation decisions. Third, by studying the causal drivers of preferences for disaster relief allocations we hope to offer insights that improve the ability of democratic systems to cope with the increasing number of extreme weather events in ways that are widely perceived as fair and therefore more likely to be sustainable politically. 2. Conceptual framework A considerable body of literature has studied the electoral impact of extreme weather events (Abney and Hill, 1966; Bechtel and Hainmueller, 2011; Gasper and Reeves, 2011; Reeves, 2011; Chen, 2012), the role of political communication in the context of natural disasters (Sood et al., 1987; Utz et al., 2013), and how public and private actors assess disaster damage when making decisions about their assistance efforts (Waugh and Streib, 2006; Aldrich, 2016).2 So far, however, we do not know which factors drive individual preferences over government responses to natural disasters although they entail the type of divide-the-dollar decisions that figure prominently in formal models of redistribution (Huber and Ting, 2013). However, since disaster relief aims to help individuals who have become victims of a life-threatening, exogenous event, the prominent distinction between luck and effort that the literature on preferences for redistribution tends to emphasize (Durante et al., 2014; Fisman et al., 2015; Scheve and Stasavage, 2016) may not offer the most promising theoretical starting point for our conceptual framework. As a consequence, our research question requires us to begin exploring largely unknown territory. However, we can build on existing literature on conceptions of distributive justice (Kendrick, 1939; Deutsch, 1975; Lind and Tyler, 1988), equity and the social contract (Rawls, 2009), and recent theoretical contributions (Valentini, 2013) to develop fairness-based explanations of preferences over the distribution of disaster relief. When theorizing about how individuals would like to allocate relief aid, an important distributive fairness norm is the ‘polluter-pays’ or ‘affectedness principle’. Whereas the polluter-pays principle applies to situations that require a decision about how to allocate costs, the affectedness principle is relevant when distributing benefits meant to compensate for experienced losses. This fairness norm specifies that the distribution of benefits should be proportional to the experienced losses.3 In the context of disaster relief programs, financial benefits are explicitly meant to reflect the level of affectedness by compensating for losses due to a natural disaster. According to the Stafford Act, relief aid is intended “to alleviate the suffering and damage which result from such disasters […] by providing federal assistance programs for both public and private losses sustained in disasters” (Robert T. Stafford Disaster Relief and Emergency Assistance Act, I, Sec. 101, b), (6)). If voters subscribe to affectedness-based fairness we would expect that higher levels of economic damage will increase the amount of relief aid that individuals would like to provide. Preferences over relief allocations may also be explained by the ability-to-pay principle. This norm, which is most prominent in the context of tax policy questions and “advocated alike by the scholar in taxation and the common man” (Kendrick, 1939, 92), attempts to determine the amount that constitutes an equal sacrifice when requiring individuals to make a contribution. The ability-to-pay logic can be extended to scenarios that require a distribution of benefits where the allocation would have to account for differences in affluence. When theorizing about how voters judge the provision of disaster relief, this fairness norm suggests that the distribution of relief aid should mirror differences in wealth among disaster victims. Moreover, an important collective goal of relief aid is to help disaster victims recover and this goal may be served best 2 Aldrich (2012) offers a review of the literature on the politics of natural disasters. This proportionality requirement dates back to ancient philosophical treatments of distributive justice that rely on a geometric approach to determining fair allocations of costs and benefits (Judson, 1997). 3 https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Political Science Research and Methods 263 if the allocation of relief aid mirrors the distribution of wealth, as those who are poorer find it more difficult to meet their immediate survival needs and finance economic recovery. Consequently, we expect that individuals prefer to allocate more relief aid to regions that are poorer. Voters could also view the distribution of policy benefits from a political perspective that highlights the role of electoral reciprocity and partisan solidarity. The electoral reciprocity argument builds on the logic of democratic selection and electoral accountability (Downs, 1957). To realize the welfare-enhancing effect of democracy, citizens have to be willing to electorally reward incumbents that have provided them with policy benefits. This incentivizes policymakers to engage in targeted public spending decisions to maximize their re-election chances. While some believe that political leaders tend to pursue universalistic policies, that is, policies that cater to the nation as a whole without favoring particular interests (Cronin and Genovese, 2004), others (Kriner and Reeves, 2015) have argued that political leaders may engage in partisan particularism by advantaging constituencies that have supported them in the past election. Clearly, voters could subscribe to the universalistic ideal which requires incumbents to be responsive to all citizens irrespective of whether they have supported the incumbent or not. This argument implies that citizens should not be responsive to electoral considerations when deciding about how to allocate disaster relief. However, individuals could also follow an electoral reciprocity logic. Allocating more relief aid to regions that have voted for the incumbent in previous elections could be seen as an example of political elites giving back to those who have supported them. Even if this concern may be less pronounced than factors such as affectedness or need, voters could place some value on maintaining this reciprocal relationship between affected citizens and the incumbent. If this argument has empirical merit, we would expect that individuals prefer relief allocations that reflect the extent to which an affected county has voted for the incumbent in the previous election. The partisan solidarity argument highlights that shared partisan orientation could give rise to in-group solidarity. Party identification is not only a stable and powerful predictor of electoral choice and civic engagement (Dalton, 2016), but also offers a shared, loyalty-based identity (Clifford, 2017). This could generate partisan solidarity that gives rise to preferential in-group treatment in which voters who identify with a political party would allocate more relief aid to co-partisans. Our design allows us to test both the electoral reciprocity and the partisan solidarity argument. 3. Data and research design To empirically evaluate our theoretical predictions, we develop a conjoint experiment (Bechtel and Scheve, 2013; Hainmueller et al., 2014) that asked respondents to make divide-the-dollar decisions (see below). The conjoint experiment was embedded in an online survey fielded to a population-based quota sample of American citizens (N = 2, 618) in December, 2016. The sample was provided by Respondi and based on their online panel. This panel is constructed by drawing a random sample from the population which is then converted into panels and sub-panels on the basis of known population margins (Respondi, 2015). Our survey contains a standard set of sociodemographic questions and several novel question items needed to empirically test the theoretical mechanisms. The full survey instrument is included in the replication archive. The Supplementary Appendix offers detailed information about these questions (Appendix Table A.1) and reports descriptive statistics in Appendix Table A.2. In addition, Appendix Table A.3 shows that the distribution of sociodemographic characteristics in our sample closely matches the population margins. Unless stated otherwise, we report results that employ weights, although the findings remain unchanged when estimated on the unweighted sample. 264 Michael M. Bechtel and Massimo Mannino 3.1. Experimental design: relief allocation conjoint and divide-the-dollar decisions To explore which factors explain individual preferences over the distribution of disaster relief, the conjoint presents respondents with theoretically relevant characteristics of two counties that were affected by a natural disaster. In reality, policymakers (governors and the president) issue countyspecific disaster declarations which establish the eligibility of individuals residing in these counties for applying for disaster relief.4 This turns the distribution of disaster relief into a “divide-thedollar politics” (Kriner and Reeves, 2015) scenario. We mirror this decision by developing a novel outcome measure that asks respondents to split a fixed total amount of relief aid between the affected counties. This modification of the standard conjoint design in combination with countylevel scenarios offers two advantages. First, it reflects that budgets are always constrained as this design places a limit on what a respondent can spend in total. Even if disaster relief is often funded by supplemental appropriations, these resources remain ultimately finite. Second, it allows us to perform a more direct comparison between voter preferences and policymakers’ actual relief allocations which are recorded at the county level. Figure 1 shows the instructions of the conjoint experiment along with the divide-the-dollar outcome question. Our conjoint experiment asks each respondent to distribute a total of $10 million in relief aid between two counties and this task was repeated four times. In total, respondents made 20,944 county-level relief spending decisions. The conjoint contained six dimensions tied to the theoretical expectations: economic damage and the number of fatalities caused by the disaster, average household income, unemployment rate, presidential partisanship, and a county’s vote in the 2012 presidential election. For each dimension we used between two and five different values. We detail and justify these dimensions and values below. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press 3.2. Selection of attribute values in the divide-the-dollar conjoint Table I presents the attribute values for each dimension. We choose the attribute levels to approximate the historically observed county characteristics as closely as possible. To examine the role of affectedness considerations, we include two attributes that capture the most prominent ways of measuring affectedness: economic damage ($100,000, $1 million, $4 million, $24 million) and the number of fatalities (0, 5). While it is possible for respondents to fully compensate the economic damage whenever disaster losses are $4 million or less, we intentionally include one higher damage level ($24 million) that exceeds the available budget. The purpose of this design is to actually observe individual allocation decisions under a more difficult, but also potentially more realistic scenario in which the available resources are not sufficient to compensate the experienced damage in full. The robustness section reports results suggesting that the structure of mass preferences over disaster relief are quite stable even if the available resources only allow for compensating a fraction of the financial losses. We also include two attributes that measure wealth and need-based characteristics: the average annual household income in a county ($10,000, $40,000, $70,000, $100,000) and the level of joblessness (3, 5, 7, 9 percent). Based on data from 1993 to 2008, 25 percent of all counties experienced unemployment rates between 0 and 4 percent. The corresponding value in the conjoint experiment is the 12.5th percentile, which in this case amounts to 3 percent. Due to inflation and income changes we decided to accept slight deviations from the historically observed values to more closely mirror the more recent distribution of those variables. Finally, to estimate the causal effects of electoral and partisan characteristics, we include presidential partisanship (Democratic, Republican) and five two-party vote share levels that distinguish swing (50 percent/50 percent), core (60 percent/40 percent), core stronghold (70 percent/30 percent), opposition (40 percent/60 percent), and opposition stronghold counties (30 percent/70 percent). 4 See, e.g., https://www.eda.gov/programs/disaster-recovery/. Political Science Research and Methods 265 https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Figure 1. Instructions for the divide-the-dollar conjoint experiment (screenshot). Note: This figure shows the instructions for the conjoint experiment which asked respondents to allocate relief aid between two affected counties. In the conjoint tasks, the values of all attributes were randomized except for presidential partisanship which was randomized across binary comparisons but held constant within each comparison to avoid internally inconsistent hypothetical scenarios. To remove any order effects, the order of the dimensions was randomized across individuals. The conjoint fully randomizes all values with the exception of those related to presidential partisanship as it would be illogical if this characteristic differed between counties in a contemporaneous setting. Our design avoids such combinations by keeping presidential partisanship constant within each conjoint comparison while randomizing it across conjoint tasks. This means that whenever a respondent considers two affected counties in a divide-the-dollar conjoint task, presidential partisanship is the same for both of these regions. All other attribute values are fully randomized since none of the remaining potential value combinations result in logically inconsistent scenarios. In addition, we randomize the order of the dimensions between respondents to avoid order effects. However, we keep the order of the dimensions constant within respondents to reduce the complexity of the task (Bechtel and Scheve, 2013). 4. Experimental results We estimate the causal effects of the conjoint attributes by regressing the amount of relief aid a respondent allocated to a county on treatment variables that indicate the presence or absence of the randomly assigned attribute values for each of the six attributes. Figure 2 reports the causal effects along with 95 percent confidence intervals computed from standard errors clustered by conjoint comparison. Dots without error bars represent the reference categories. Since presidential partisanship was kept constant at the conjoint-task level, we report the results separately by 266 Michael M. Bechtel and Massimo Mannino Table 1. Distributions of explanatory variables (historical data) and attribute levels for the relief allocation conjoint experiment Variable/attribute Economic damage Fatalities Average income Unemployment rate Historical data $0–456,000 $456,000–2,277,000 $2,277,000–11,800,000 $11,800,000–max 0 More than 0 $40,000–max $34,000–40,000 $29,000–34,000 $0–29,000 0–4% 4–5% 5–7% 7%–max Presidential partisanship Presidential vote in 2012 Swing: Dv [ ( − 5, 5) Core: Dv [[5,25) Core stronghold: Dv [[25,100] Opposition: Dv [ [ − 5, − 25) Opposition stronghold: Dv [ [ − 25, − 100] Conjoint attribute levels $100,000 $1 million $4 million $24 million 0 5 $100,000 $70,000 $40,000 $10,000 3% 5% 7% 9% Democratic Republican 50% Democrat, 60% Democrat, 70% Democrat, 40% Democrat, 30% Democrat, 50% 40% 30% 60% 70% Republican Republican Republican Republican Republican Note: This table shows the dimensions and values used in the conjoint experiment. Ranges for economic damage, fatalities, income, and unemployment rate are based on the 25, 50, and 75 percentile of the empirical distributions of these variables in the historical data. All values are randomly assigned except for presidential partisanship which varies randomly between conjoints, but is constant for a given conjoint. Dv denotes the two-party vote share difference in percentage points. government partisanship. Supplementary Appendix Figure A.1 reports the pooled results using the weighted and unweighted sample. In what follows, we first focus on the causal effects of the conjoint attributes. We then perform various subgroup estimations to evaluate the plausibility of the theoretical mechanisms. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press 4.1. The causal drivers of preferences over disaster relief We find that all treatment indicators for the economic damage dimension have positive and significant effects on individuals’ relief spending preferences. An increase in damage from $100,000 to $1 million causes an increase in preferred relief aid by about $500,000, which equals 50 percent of the damage. Larger damages trigger higher amounts of relief aid. For example, experiencing $4 million in damage increases relief aid allocated to a county by $750,000 while large damages of $24 million leads relief aid to increase by $1.2 million. This pattern holds across Republican and Democratic partisanship and is consistent with the affectedness argument. When we conceptualize affectedness as having experienced fatalities, we find that counties that experienced casualties receive about $100,000 more disaster relief than counties without any fatalities. Overall, these sensitivities are consistent with the argument that affectedness-based fairness norms help explain disaster relief preferences. How important are need-related characteristics for understanding voter evaluations of policy responses to natural disasters? The results in Figure 2 suggest that respondents allocate about $4 million more in relief to a county in which households make about $40,000 per year compared to a wealthier county in which the average household income is $100,000. This sensitivity seems to be rooted in the desire to provide support as a function of the level of wealth and not as a function of regional employment conditions since voters systematically change their preferred relief allocation in response to higher joblessness. When examining the impact of a region’s past voting record, we find that almost all effects are statistically indistinguishable from zero. Overall, individuals tend to prefer relief aid allocations https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Political Science Research and Methods 267 Figure 2. The causal effects of county characteristics on relief spending preferences by presidential partisanship. This plot shows the causal effects of randomly assigned attribute values on the amount of relief aid allocated to an affected county. Estimates are based on a linear regression of Relief Amount on indicator variables with standard errors clustered by respondent. Horizontal lines indicate 95 percent robust confidence intervals. Points without confidence intervals indicate the reference category for a given attribute. Baseline levels of relief aid: Republican president = $4.1 million, Democratic president = $4.1 million. N (county-level relief spending decisions) = 20,944. that reflect affectedness and need, while the political orientation of a county does not seem to play an important role in individuals’ allocation choices. In the next section we explore which theoretical mechanisms may underlie these treatment effects. 4.2. Theoretical mechanisms We now explore whether variables related to the underlying theoretical mechanisms systematically moderate the treatment effects in ways consistent with the theory. For example, if affectedness-based norms of distributive justice drive allocation preferences, we would expect the causal effect of economic damage to be stronger among individuals subscribing to an affectedness-based conception of justice. To implement an empirical test of each of the theoretical mechanisms we collect information about whether individuals subscribe to fairness ideas related to affectedness, need, and political ties. We use this data to reestimate the causal effects of county characteristics by theoretically meaningful subgroups. To avoid priming a specific sensitivity https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press 268 Michael M. Bechtel and Massimo Mannino Figure 3. The causal effects of economic damage and fatalities on relief spending preferences by affectedness-based fairness. This plot shows the causal effects of randomly assigned attribute values on the amount of relief aid allocated to an affected county. Estimates are based on a linear regression of Relief Amount on indicator variables with standard errors clustered by respondent. D reports the difference in the treatment effects between a given attribute level and the reference group. Horizontal lines indicate 95 percent robust confidence intervals. Points without confidence intervals indicate the reference category for a given attribute. Baseline levels of relief aid: Affectedness-based fairness: High = $3.7 million, Affectedness-based fairness: Low = $4.3 million. N(county-level relief spending decisions) = 20,944. N(respondents) per group = 1,309. The measure of support for affectedness-based fairness is based on the question: “People sometimes experience negative events and go through difficult times. To what extent do you agree or disagree with the following statement: ‘Those who are more strongly affected should receive more support than those who are less affected’.” Respondents’ answers on a 1–10 scale were converted into a binary indicator based on the median. The indicator variable is 1 for those whose level of agreement with the statement was greater than the median and is zero otherwise. Supplementary Appendix Table A.4 reports the full estimation results for all attributes. among respondents to the attributes included in our relief aid conjoint, these measures of individuals’ fairness norms came after the conjoint experiment. In the robustness section we report additional results that rely on recoded reference categories for these subgroup estimations. Our findings remain unchanged. 4.2.1. Affectedness Figure 3 shows the causal effects separately for the group of respondents that are relatively more supportive of affectedness-based fairness and the group of respondents that are less supportive of this conception of justice. This split is based on the question: “People sometimes experience negative events and go through difficult times. To what extent do you agree or disagree (1–10) with the following statement: ‘Those who are more strongly affected should receive more help than those who are less affected’.” This direct question approach seems preferable since—to the best of our knowledge—established and validated indirect measures of the types of fairness norms in which we are interested do not yet exist. Therefore, if we had designed a novel indirect question https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Political Science Research and Methods 269 the results would not have been very informative.5 We also note that the question wording does not make any reference to the attribute names used in the conjoint (e.g., economic damage, fatalities, etc.). To avoid priming respondents in a specific direction, this question was asked later in the survey. Moreover, the conjoint and the questions regarding support for different types of fairness principles were separated by several question items on other issues. As Supplementary Appendix Table A.2 reports, the median response on this question is 8 which indicates that half of our respondents are quite supportive of an affected-based conception of fairness given that the underlying response scale ranges from 1 (completely disagree) to 10 (completely disagree). We convert respondents’ answers into a binary indicator based on the median that is 1 for those whose level of agreement with the statement was greater than the median, and is zero otherwise. Supplementary Appendix Table A.4 reports the full estimation results for all attributes. The results suggest that respondents who are more supportive of a general affectedness-based fairness norm are much more sensitive to the level of economic losses when allocating relief aid. For example, damage in the order of $4 million increases relief aid by $500,000 among individuals who are relatively less supportive of affectedness-based justice. In contrast, the same amount of economic damage increases relief spending allocated to a county by close to $1 million. Thus, the treatment effect doubles for respondents who are more supportive of affectedness-based justice. We formally test the hypothesis that the treatment effects for the two groups are statistically different from each other by computing effect differences along with the corresponding 95 percent confidence intervals based on the corresponding variance–covariance matrix. Figure 3 reports these results. We find that respondents who are generally more supportive of affectedness-based fairness provide counties that have experienced $1 million in damage with about $300,000 more relief aid than individuals who are less supportive of this fairness norm. We find similar patterns when examining the effects of higher levels of economic damage. In fact, the difference between the treatment effects becomes larger as disaster damage increases. This heterogeneity in the treatment effects is consistent with the argument that an affectedness-based norm of distributive justice may explain individuals’ strong sensitivity to economic damage when allocating relief aid. Figure 3 also shows that we estimate a significant and positive difference when comparing the causal effect of five fatalities on relief preferences among Affectedness-based fairness: High respondents and Affectedness-based fairness: Low respondents. This suggests that individuals who advocate affectedness-based justice in the abstract tend to provide more relief aid to counties that experienced casualties than respondents who are less supportive of this notion of justice. 4.2.2. Need Our main results indicate that voter preferences over disaster relief are sensitive to income differences between affected counties. To explore whether this pattern can be explained by a needbased norm of distributive justice, we again use direct question wording that seeks to measure general agreement with this norm while at the same time avoiding any of the terms used in the conjoint attributes: “People sometimes experience negative events and go through difficult times. To what extent do you agree or disagree with the following statement: ‘Those who are richer should receive less support than those who are poorer even if they are equally affected.’” We again split the sample at the median and reestimate the causal effects for both subgroups.6 5 For example, in case of a null finding it would have been unclear whether this is due to the survey item not measuring the relevant construct or whether the null result is valid. Similarly, in the case of a significant finding one may question whether the item is really measuring the relevant underlying trait. Employing a direct measure avoids these complications. 6 We note that the median response to this question is 6 which is quite close to the natural midpoint of 5.5 on the 1 (completely disagree) to 10 (completely agree) answer scale (see Supplementary Appendix Table A.2). https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press 270 Michael M. Bechtel and Massimo Mannino Figure 4. The causal effects of household income and unemployment on relief spending preferences by need-based fairness. This plot shows the causal effects of randomly assigned attribute values on the amount of relief aid allocated to an affected county. Estimates are based on a linear regression of Relief Amount on indicator variables with standard errors clustered by respondent. D reports the difference in the treatment effects between a given attribute level and the reference group. Horizontal lines indicate 95 percent robust confidence intervals. Points without confidence intervals indicate the reference category for a given attribute. Baseline levels of relief aid: Need-based fairness: High = $4 million, Need-based fairness: Low = $4.2 million. N(county-level relief spending decisions) = 20,944. N(respondents) per group = 1,309. The measure of support for affectedness-based fairness is based on the question: “People sometimes experience negative events and go through difficult times. To what extent do you agree or disagree with the following statement: ‘Those who are richer should receive less support than those who are poorer even if they are equally affected.’” Respondents’ answers on a 1–10 scale were converted into a binary indicator based on the median. The indicator variable is 1 for those whose level of agreement with the statement was greater than the median and is zero otherwise. Supplementary Appendix Table A.4 reports the full estimation results for all attributes. Figure 4 shows the causal effects of need-related factors on the amount of relief aid allocated to a county by our respondents separately for the two groups. We find that the effects of a county’s average household income are much stronger among individuals who generally support a need-related notion of distributive justice. For example, individuals subscribing to need-based justice allocate $300,000 more in relief aid to counties with an average household income of $40,000 than individuals who are less supportive of this type of fairness. The heterogeneity in the treatment effects is even larger for very poor counties ($10,000 average household income). Are these effects significantly different from each other when comparing the two groups? Our estimate of the difference in the effects, D, which we report with the corresponding 95 percent confidence interval, suggests that this is the case, as the confidence interval excludes zero. These results support the argument that the sensitivity to a county’s level of wealth is driven by a need-based notion of distributive justice. When examining the effects of a county’s economic standing in terms of joblessness, we find that individuals respond very similarly unless the unemployment rate reaches 9 percent. Given Political Science Research and Methods 271 high levels of unemployment, respondents that favor need as a criterion for distributive fairness allocate about $150,000 more in relief payments than individuals less supportive of need-based justice. The difference between those two effects fails to reach significance at the 5 percent level, although it is significant at the 10 percent level. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press 4.2.3. Political ties: electoral reciprocity and partisan solidarity A third explanation for voter preferences over relief aid allocations focuses on the role of political ties. If the electoral reciprocity argument is valid, we would expect voters who are more sympathetic toward this fairness norm to allocate more relief aid to counties that have supported the president’s party in the previous election. We offer an evaluation of this prediction by first classifying respondents based on their agreement with the statement: “Governments should prioritize the needs of those who voted for them.” The question was asked after the divide-the-dollar conjoint and was separated by a set of items that elicited individuals’ views on other topics. Respondents’ answers on a 1–10 scale were converted into an indicator variable based on the median response.7 We then further partition our data according to whether the conjoint specified a scenario in which a Democratic or a Republican president was in power. Figures 5A and B report the causal effects along with the differences in the treatment effects (D). The estimates reported in Figure 5A indicate that high electoral reciprocators do not systematically allocate more relief aid to Democratic strongholds under a Democratic administration. If anything, such respondents tend to allocate somewhat less relief aid to counties that provided more support for either candidate in the past election, which is inconsistent with the electoral reciprocity argument. We find a similar pattern when exploring respondents’ relief allocations in conjoint scenarios that featured a Republican president. An alternate version of the political ties argument assumes that respondents’ hold in-group views based on partisanship which could give rise to solidarity among co-partisans. Individuals who identify with a specific party would then allocate more relief aid to regions that are core supporters of their preferred party. To evaluate this argument we break down the results by respondents’ partisan identification and government partisanship. The causal effects of a county’s presidential vote reported in Figure 6 indicate that Democratic respondents have a slight preference for favoring Democratic strongholds under a Democratic administration. Republicans do not seem to systematically favor Republican strongholds during times of Republican government partisanship. Overall, the results indicate that even when focusing on political scenarios that should give rise to allocation behavior consistent with partisan solidarity, affectedness and need seem to remain the main concerns. 5. Robustness We subject our results to a large set of robustness tests which Supplementary Appendix I reports in detail. First, we explore whether the disaster relief preferences of likely voters differ from those of the rest of the adult population. The results in Supplementary Appendix Figure A.2 indicate that this is not the case. Second, we test if voters’ sensitivities depend on whether the available resources are sufficient to fully compensate for experienced disaster losses. We re-estimate the results for the subset of allocation decisions in which the combined damage in both divide-the-dollar conjoint scenarios was equal to or less than $10 million. As the results in Supplementary Appendix Table A.6 show, the structure of mass preferences over disaster relief remains quite comparable to the main results reported above. Preferences may be shaped by personal exposure to extreme weather events. Supplementary Appendix Figure A.3 suggests, however, that the causal effects do not depend on whether a respondent has been personally affected by a natural disaster in the past ten years or not. We find a similar level of homogeneity 7 The median response to this question is 5, see Supplementary Appendix Table A.2. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press 272 Michael M. Bechtel and Massimo Mannino Figure 5. The causal effects of previous presidential vote on relief spending choices by electoral reciprocity and government partisanship. (a) Democratic President. (b) Republican President. This plot shows the causal effects of randomly assigned attribute values on the amount of relief aid allocated to an affected county. Estimates are based on a linear regression of Relief Amount on indicator variables with standard errors clustered by respondent. Points without confidence intervals indicate the reference category for a given attribute. D reports the difference in the treatment effects between a given attribute level and the reference group. Horizontal lines indicate 95 percent robust confidence intervals. Baseline levels of relief aid: electoral reciprocity low and Republican president = $3.9 million, electoral reciprocity high and Republican president = $4.3 million, electoral reciprocity low and Democratic president = $3.9 million, electoral reciprocity high and Democratic president = $4.4 million. N(county-level relief spending decisions) = 20,944. N(respondents) per group = 1,309. Political Science Research and Methods 273 https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Figure 6. The causal effects of county characteristics on relief spending preferences by respondents’ partisan identification and government (presidential) partisanship. This plot shows the causal effects of randomly assigned attribute values on the amount of relief aid allocated to an affected county. Estimates are based on a linear regression of Relief Amount on indicator variables with standard errors clustered by respondent. Horizontal lines indicate 95 percent robust confidence intervals. Points without confidence intervals indicate the reference category for a given attribute. Baseline levels of relief aid: Republican with a Republican president = $4.1 million, Democrat with a Democratic president = $4 million. N(county-level relief spending decisions) = 20,944. N(respondents) = 2,618. across levels of attentiveness (Supplementary Appendix Figure A.4) and time spent on the survey (Appendix Figure A.5). We also explore the existence of racial differences in how voters judge the policy responses to natural disasters. The results in Supplementary Appendix Figure A.6 suggest that relief allocations of white and non-white respondents are equally sensitive to economic damage and household income. Moreover, the structure of voter preferences over disaster policy responses are quite stable across different regions (see Supplementary Appendix Figure A.7). We also find that the results are not sensitive to which attribute level is used as the reference category (see Supplementary Appendix Table A.7). 6. Comparing voter preferences over relief spending with observed relief allocations An important question in research on democratic representation asks whether redistributive spending choices made by public officials correspond to citizens’ preferences. Our data allow us to perform a novel and unusually direct juxtaposition of the structure of voter preferences and policymakers’ actual spending decisions. Clearly, the coefficients from our experimental results and those based on the historical data should not be compared as precisely as two models estimated on the same data. Yet, they offer useful information about whether the empirical https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press 274 Michael M. Bechtel and Massimo Mannino patterns are broadly consistent with each other. For this comparison we collect county-level data from the Consolidated Federal Funds Report (CFFR) from 1993 to 2008 and identify those spending items that are related to natural disasters. Supplementary Appendix Tables A.8 and A.9 provide a detailed list of these programs. Our outcome of interest is the amount of relief spending received by a county during a presidential term.8 This type of temporal aggregation is necessary to evaluate explanations related to government partisanship. We combine these data with information about the occurrence, damage, and associated fatalities of natural disasters from the Spatial Hazard Events and Losses Database for the United States.9 As others have argued (Kriner and Reeves, 2015), there exist many opportunities for elected officials to affect the allocation of relief aid. These include appointing bureaucrats with similar political orientations as well as strategically crafting budget proposals that facilitate electorally profitable spending decisions. We regress Relief Aidc,t received by county c in term t in logs per capita on covariates that capture need-based, affectedness-based, and electoral considerations.10 Since the outcome variable is log-transformed, the coefficients measure changes in percent (not percentage points). Our predictor variables closely mirror the attributes used in our conjoint experiment. We quantify affectedness in terms of both economic damage and human losses. Damage measures the value of economic damage a county has experienced due to natural disasters in millions of dollars. Fatalities counts the number of individuals who were killed by a disaster. To account for need-based considerations, Household Income measures the average household income in the county and Unemployment Rate is the county-level share of unemployed individuals. To explore the importance of electoral motivations we classify counties into core, core stronghold, swing, opposition, and opposition stronghold counties based on the two-party vote share difference in the previous presidential election.11 All regressions include county and period fixed effects, and a set of time-varying sociodemographic and political covariates. Figure 7 shows the results. We find that disaster damage significantly and positively correlates with the amount of federal relief aid. Compared to the reference group (Economic Damage,$401,000), counties that experienced losses between $2 and $11 million receive about 25 percent more relief payments. The results also suggest that the allocation of relief aid mirrors the number of casualties. Counties that suffered human losses receive 20 percent more financial support than counties without fatalities. This patterns seem broadly consistent with the structure of voter preferences over the distribution of relief aid. The estimates reported in Figure 7 also suggest that poorer regions receive more relief aid which is consistent with a need-based fairness norm that also seems to help explain voter preferences over the allocation of disaster assistance. In contrast, we find strongly divergent patterns when examining the correlation between a county’s previous voting record and the amount of relief aid it receives. For example, core stronghold counties receive 40 percent more relief payments than observably similar swing states while opposition strongholds on average receive 40 percent less relief aid. The former finding is consistent with the core voter model (Cox, 2010) which argues that rewarding loyalists offers additional advantages over an allocation that targets 8 For example, relief aid for county c in 2000 is the sum of relief aid spent in 1997, 1998, 1999, and 2000. Overall, we have four presidential terms (1996, 2000, 2004, 2008). 9 These data are accessible at http://hvri.geog.sc.edu/SHELDUS. If a disaster affects multiple counties, the total damage and associated fatalities are distributed equally across these counties. 10 For counties that did not receive any payments we add one to the per capita spending measure, divide it by population size and take the natural logarithm. 11 We first compute the two-party vote share difference by subtracting the vote share of the opposition party from the vote share of the presidential party. Counties with an electoral margin between ( − )5 and ( − )25 percentage points are coded as core (opposition) counties. Counties with an electoral margin greater (less) than (minus) 25 percentage points are coded as core stronghold (opposition stronghold) counties. Counties with a vote share difference of −5 to 5 percentage points are coded as swing counties. Political Science Research and Methods 275 https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Figure 7. Observed and preferred disaster relief allocations. Note: This plot reports linear regression coefficients. Horizontal lines indicate 95 percent robust confidence intervals. For the observed relief allocation results, the dependent variable is the natural logarithm of relief aid received. This model uses county-fixed effects and standard errors clustered at the county level. Controls include preparedness spending as well as socio-economic and political characteristics. Points without confidence intervals indicate the reference categories. The preferred allocation results are based on the main conjoint estimates. N = 12,424. swing voters because of its mobilizing and coordination-enhancing effect. The result is also in line with previous empirical work on the relationship between federal grant aid and a county’s historical voting record (Kriner and Reeves, 2015). 7. Conclusion Despite unprecedented increases in wealth and major technological advances, humans remain vulnerable to existential threats such as natural disasters. Although most would agree that resources should be made available to provide assistance to affected individuals and compensate at least some of the experienced losses, political conflict may arise over how these finite resources should be distributed. Moreover, since policymakers seek re-election, one may view policy responses to natural disasters as providing yet another example of “electoral politics as a redistributive game” (Cox and McCubbins, 1986) in which incumbents engage in targeted spending decisions to cater to their voter base. At the same time, there may exist a broad consensus among voters as to which principles should guide public spending decisions in response to major shocks. Perhaps even more importantly, affected and unaffected individuals may disapprove of redistribution decisions that reflect electoral considerations. 276 Michael M. Bechtel and Massimo Mannino We devise a disaster relief conjoint that we embed in a population-based sample of American citizens and find that individuals allocate significantly more relief aid to counties that experienced more damage, had more fatalities, are poorer, and have higher unemployment rates. Moreover, our findings show that mass preferences over disaster relief do not or at most weakly reflect the political characteristics of a county. These sensitivities are robust across a large set of subgroups including whether an individual has been personally affected by a natural disaster in the past. When contrasting these results with the observed distribution of federal relief aid, we find a notable degree of congruence. For example, and in line with voter preferences, more affected counties receive significantly more relief aid. The alignment between voter preferences and observed relief distributions may seem surprising since respondents in our survey and policymakers may operate under different incentive structures. Yet, since policymakers seek re-election, there still exists a strong incentive to engage in spending decisions that mirror voter preferences. At the same time, we find that core strongholds receive significantly higher payments, a pattern that conflicts with the preferences of voters who favor relief allocations that are not affected by electoral considerations. There exist several avenues for future research. First, subsequent work may use our approach to generate information about the extent to which governments’ actual policy decisions deviate from voters’ preferred policy responses in order to more accurately model electoral choice. If the lack of congruence between public policy and mass preferences explains variation in voting behavior, scholars could use information about the structure of voter preferences to improve forecasts of electoral outcomes in the aftermath of extreme weather events. One could also explore how other factors such as preparedness, private as opposed to public losses, or growth and recovery potential influence individual preferences over disaster responses using the divide-the-dollar conjoint design. Finally, subsequent work could study whether the structure of spending preferences over the policy responses to economic shocks induced by extreme weather events generalize to other causes such as financial crises, violent conflict, and pandemics. This seems particularly pressing given the unprecedented levels of financial assistance that governments are disbursing in response to COVID-19 since knowledge about which allocation decisions the public deems desirable can help policymakers to design stimulus programs that minimize distributive conflict during times of widespread financial distress. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/psrm.2020.39. Acknowledgements. We thank Karen Jusko, Jens Hainmueller, Kenneth F. Scheve, and audiences at the 2017 Annual Meeting of the American Political Science Association for helpful comments on previous versions of this manuscript. We also thank Ellery Salluck, John Schmitt, and Jintong Yu for excellent research assistance. We gratefully acknowledge financial support from the Swiss National Science Foundation (grant no. 100017_146170/1). This study was approved by the Internal Review Board at Washington University in St. Louis (IRB ID 201607129). References Abney FG and Hill LB (1966) Natural disasters as a political variable: the effect of a hurricane on an urban election. American Political Science Review 60(4), 974–981. Achen CH and Bartels LM (2017) Democracy for Realists Why Elections Do Not Produce Responsive Government. Princeton: Princeton University Press. Achen CH and Bartels LM (2018) Statistics as if politics mattered: a reply to Fowler and Hall. Journal of Politics 80(4). Aldrich DP (2012) The politics of natural disasters. Oxford Bibliographies in Political Science (June). Aldrich DP (2016) It’s who you know: factors driving recovery from Japan’s 11 March 2011 disaster. Public Administration 94(2), 399–413. Aldrich DP and Sawada Y (2015) The physical and social determinants of mortality in the 3.11 tsunami. Social Science and Medicine 124, 66–75. Ashworth S, de Mesquita EB (2014) Is voter competence good for voters? Information, rationality, and democratic performance. American Political Science Review 108(3), 565–587. https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Political Science Research and Methods 277 Atkeson LR and Maestas CD (2012) Catastrophic Politics: How Extraordinary Events Redefine Perceptions of Government. Cambridge, MA: Cambridge University Press. Baccini L and Leemann L (2020) Do natural disasters help the environment? How voters respond and what that means. Political Science Research and Methods, DOI: https://doi.org/10.1017/psrm.2020.25. Bechtel MM and Hainmueller J (2011) How lasting is vvoter gratitude? An analysis of the short- and long-term electoral returns to beneficial policy. American Journal of Political Science 55(4), 851–867. Bechtel MM and Scheve KF (2013) Mass support for global climate agreements depends on institutional design. Proceedings of the National Academy of Sciences 110(32), 13763–13768. Chen J (2012) Voter partisanship and the effect of distributive spending on political participation. American Journal of Political Science 57(1), 200–217. Clifford S (2017) Individual differences in group loyalty predict partisan strength. Political Behavior 39(3), 531–552. Cox GW (2010) Swing voters, core voters and distributive politics. In I Shapiro, SC Stokes, EJ Wood and AS Kirshner (eds), Political Representation. Cambridge: Cambridge University Press, pp. 342–357. Cox GW and McCubbins MD (1986) Electoral politics as a redistributive game. Journal of Politics 48(2), 370–389. CRED, EM-DAT and UNISDR (2017) The human cost of weather-related disasters 1995–2016. Technical report CRED, EM-DAT, and UNISDR. Cronin TE and Genovese MA (2004) Paradoxes of American Democracy. New York: Oxford University Press. Dalton RJ. (2016) Party identification and its implications. Oxford Research Encyclopedia of Politics. Thompson WR (ed.) Oxford: Oxford University Press. Damico AJ, Conway MM and Damico SB (2000) Patterns of political trust and mistrust: three moments in the lives of democratic citizens. Polity 32(3), 377–400. Deutsch M (1975) Equity, equality, and need: what determines which value will be used as a basis of distributive justice? Journal of Social Issues 31, 137–149. Downs A (1957) An Economic Theory of Democracy. New York: Harper & Row. Durante R, Putterman L, van der Weele J (2014) Preferences for redistribution and perceptions of fairness: an experimental study. Journal of the European Economic Association 12(4), 1059–1086. Fisman R, Jakiela P, Kariv S and Markovits D (2015) The distributional preferences of an elite. Science 349(6254), aab0096. Gasper JT and Reeves A (2011) Make it rain? Retrospection and the attentive electorate in the context of natural disasters. American Journal of Political Science 55(2), 340–355. Hainmueller J, Hopkins D and Yamamoto T (2014) Causal inference in conjoint analysis: understanding multidimensional choices via stated preference experiments. Political Analysis 22, 1–30. Healy A and Malhotra N (2009) Myopic voters and natural disaster policy. American Political Science Review 103(3), 357–406. Healy A and Malhotra N (2013) Retrospective voting reconsidered. Annual Review of Political Science 16, 285–306. Heersink B, Peterson BD and Jenkins JA (2017) Disasters and elections: estimating the net effect of damage and relief in historical perspective. Political Analysis 25(2), 260–268. Huber JD and Ting MM (2013) Redistribution, pork, and elections. Journal of the European Economic Association 11(6), 1382–1403. Judson L (1997) Aristotle on fair exchange. Oxford Studies in Ancient Philosophy XV, 147–175. Kahn ME (2005) The death toll from natural disasters: the role of income, geography, and institutions. Review of Economics and Statistics 87(2), 271–284. Kendrick MS (1939) The ability-to-pay theory of taxation. American Economic Review 29(1), 92–101. Kriner DL and Reeves A (2015) Presidential particularism and divide-the-dollar politics. American Political Science Review 109(1), 155–171. Lind EA and Tyler TR (1988) The Social Psychology of Procedural Justice. New York and London: Plenum Press. Margalit Y (2019) Political responses to economic shocks. Annual Review of Political Science (forthcoming). Neumayer E and Pluemper T (2007) The gendered nature of natural disasters: the impact of catastrophic events on the gender gap in life expectancy, 1981–2002. Annals of the Association of American Geographers 97(3), 551–566. Obradovicha N, Tingley D and Rahwana I (2018) Effects of environmental stressors on daily governance. Proceedings of the National Academy of Sciences 115(35), 8710–8715. Rawls J (2009) A Theory of Justice. Cambridge (Mass.): Harvard University Press. Reeves A (2011) Political disaster: unilateral powers, electoral incentives, and presidential disaster declarations. Journal of Politics 73(4), 1142–1151. Respondi (2015) Our Answers to the 28 Esomar Questions for Determining the Quality of Online Samples and Online Panels. Technical report Respondi. Scheve KF and Stasavage D (2016) Taxing the Rich: A History of Fiscal Fairness in the United States and Europe. Princeton: Princeton University Press. Sood R, Stockdale G and Rogers EM (1987) How the news media operate in natural disasters. Journal of Communication 37(1), 27–41. 278 Michael M. Bechtel and Massimo Mannino https://doi.org/10.1017/psrm.2020.39 Published online by Cambridge University Press Utz S, Schultz F and Glocka S (2013) Crisis communication online: how medium, crisis type and emotions affected public reactions in the Fukushima Daiichi nuclear disaster. Public Relations Review 39(1), 40–46. Valentini L (2013) Justice, charity, and disaster relief: what, if anything, is owed to Haiti, Japan, and New Zealand?. American Journal of Political Science 57(2), 491–503. Waugh Jr WL and Streib G (2006) Collaboration and leadership for effective emergency management. Public Administration Review 66(s1), 131–140. Cite this article: Bechtel MM, Mannino M (2022). Retrospection, fairness, and economic shocks: how do voters judge policy responses to natural disasters? Political Science Research and Methods 10, 260–278. https://doi.org/10.1017/psrm.2020.39