The Policy Blame Game: How Polarization Distorts Democratic Accountability across the Local, State, and Federal Level

Abstract

Democratic accountability relies on voters to punish their representatives for policies they dislike. Yet, a separation-of-powers system can make it hard to know who is to blame, and partisan biases further distort voters’ evaluations. During the COVID-19 pandemic, precautionary policies were put into place sometimes by governors, sometimes by mayors, and sometimes by no one at all, allowing us to identify when voters hold out-party versus in-party politicians responsible for policies. With a survey spanning 48 states, we test our theory that attitudes toward policies and parties intersect to determine when selective attribution takes place. We find that as individuals increasingly oppose a policy, they are more likely to blame whichever level of government is led by the out-party. This is most pronounced among partisans with strong in-party biases. We provide important insight into the mechanisms that drive selective attribution and the conditions under which democratic accountability is at risk.

Introduction

Democratic accountability relies on voters’ expectations that their representatives will respond to their preferences and, in equal measure, on politicians’ expectations that voters will punish them for failing to follow through on those preferences (Svolik 2013, 685). The entire premise of “retrospective voting”—one of the most studied topics in American political behavior (Healy, Kuo, and Malhotra 2014)—is that voters look back on what representatives have done and judge them for it (Fearon 1999; Ferejohn 1986). Yet accountability is not so simple. Beyond the cognitive sophistication that is required to assess and allot blame, a separation-of-powers system—like the one that rules the United States—makes blame attribution especially complicated and cognitively demanding (Healy, Kuo, and Malhotra 2014, 145). Adding even more complexity is the tendency for voters to unduly reward politicians from their own party and, conversely, to penalize those from the opposition (Bisgaard 2015). In this study, we identify a unique circumstance that allows us to observationally assess the degree to which voters attribute blame based on partisanship or policy.

In the midst of the coronavirus disease-2019 (COVID-19) pandemic, we carried out a survey of 813 adults respondents across 48 states in America regarding two precautionary policies that were unequally distributed across the country: the mandated usage of masks, and restrictions on businesses. Across the United States, these policies were mandated by Governors, by Mayors, or sometimes by no one at all. Across each state, we asked respondents for their opinion on these policies and for their view on who specifically was responsible for the policy (or lack thereof). Because the partisanship of a mayor does not always match that of a governor, we are able to determine how the partisanship of an elected representative affects the degree to which they face blame from their constituents. We further examine how the intensity of voters’ opposition to a policy determines their willingness to blame the out-party.

Across COVID-related policies regarding masks and businesses, we find strong evidence that, as individuals become increasingly opposed to a policy, they become more likely to blame the leader whose partisanship does not match their own (known as selective attribution). Moreover, we find that this tendency to engage in selective attribution is most pronounced among partisans who report the strongest preference for their own party over the other (i.e., who are affectively polarized). This study affirms what previous work has shown (e.g., Malhotra and Kuo 2008), in terms of the strong influence of partisanship over blame attribution, and we also forge new ground in two important ways: first, by theorizing and demonstrating how selective attribution is exacerbated by policy opposition and, second, by affective polarization. We further build on existing work by examining how selective attribution complicates accountability across different levels of the American federal system.

Our study demonstrates a troubling obstacle to democratic accountability: the intersection of a separation-of-powers, which makes accountability cognitively difficult, with a polarized public who selectively blame the out-party. Our results, however, also point to the limits of partisan-motivated reasoning, as we find that selective attribution only occurs among those who are affectively polarized—but not among roughly a quarter of the population whose in-party preferences are less pronounced. Overall, this study provides important insight into the mechanisms that drive selective attribution and the conditions under which democratic accountability is at risk.

Democratic Accountability and Selective Attribution

Democratic accountability is inherently connected to the concept of retrospective voting. Evaluating how well a candidate has done in the past is a heuristic that people can use to help make a cognitively difficult electoral choice (Fiorina 1981; Woon 2012). But the act of evaluating politicians’ past performance can be no less difficult. One key factor is the complexity of government bureaucracy and especially a division of powers across national, state, and municipal leaders, making it hard to know who is responsible when things go wrong and who deserves kudos for policy wins (Thompson 1980). Moreover, sometimes politicians intentionally misrepresent their own role in popular policies; for example, after House Republicans unanimously opposed President Biden’s $1.9 trillion stimulus bill in March of 2021 (which passed and was signed into law), several Republicans leaders nevertheless proceeded to tout the popular bill to their constituents via social media, deliberately attempting to take credit for the legislation that they had, in fact, voted against (Lutz 2021).

Accurately identifying who is responsible for policies is important because it can ultimately guide how citizens vote. For example, Arceneaux and Stein (2006) find that voters who held their mayor responsible for the aftermath of Tropical Storm Allison became less likely to vote for them in subsequent elections. Indeed, when we consider the meaning of democracy itself—“a system of governance in which rulers are held accountable for their actions in the public realm by citizens” (Schmitter and Karl 1991)—accountability is the sine qua non.

Complicating the difficult task of correctly attributing credit and blame, voters are known for giving their own party members the benefit of the doubt and unduly placing blame on the out-party. When evaluating a policy or politicians is cognitively burdensome, partisan cues are readily available, and the stakes are not especially high, then individuals are more likely to rely on their partisanship to guide their decisions (Lodge and Taber 2000). In these cases, it is nearly impossible to evaluate politics evenhandedly. Existing work finds that partisan-motivated reasoning can determine how partisans interpret death statistics in the Iraq War (Gaines et al. 2007), identify policy positions of the parties (Johnston, Steenbergen, and Lavine 2012), attribute guilt in the case of sexual misconduct (Klar and McCoy 2021), evaluate the economy (Lewis-Beck, Nadeau, and Elias 2008), and assess the risk and reality of global warming (Joslyn and Haider-Markel 2014).

Partisans can be motivated to evaluate objective facts more even-handedly, for example with incentives like financial rewards (Bullock et al. 2013; Hill 2017; Prior, Sood, and Khanna 2015), accuracy scores (Prior, Sood, and Khanna 2015), or even simply an additional request to justify one’s reasoning (Bolsen, Druckman, and Cook 2014). But even when partisans do agree on the state of the world—for example, whether the economy is doing well or poorly—they nevertheless can disagree on who is responsible. This phenomenon is referred to as “selective attribution” (Malhotra and Kuo 2008; Rudolph and Grant 2002). In fact, being forced to accept unflattering facts about one’s political ingroup can actually provide the cognitive dissonance necessary for selective attribution (Bisgaard 2019).

Tilley and Hobolt (2011) draw an important distinction between policy performance evaluations from responsibility attributions. When confronted with the cognitive dissonance of a poorly performing policy promoted by one’s political ingroup, they suggest that one can resolve that dissonance by either altering one’s evaluation of the facts on the ground (i.e., selective evaluation of the performance of the policy), or one can accept the poor performance of the policy and instead attempt to attribute that poor performance to one’s political outgroup (i.e., selective attribution). Ultimately, they find weak evidence for the former and stronger evidence for the latter. Even non-partisan testimony, as shown in experimental work by Lyons and Jaeger (2014), does not mute partisan-motivated reasoning but is rather weaponized by both sides to defend their own party. Tilley and Hobolt (2011) argue that different factors (e.g., media exposure, political sophistication) can moderate the degree of responsibility assigned but, ultimately, it is partisanship that tends to determine the direction of attribution (i.e., who gets the blame).

Just as the conditions are specific when it comes to reducing the effect of partisanship on policy performance evaluation, so too are the conditions specific when it comes to reducing the effect of partisanship on selective attribution. Partisans have been shown to attribute less blame to the out-party when partisans are aware that the policy failure was beyond the purview of the out-party office holder (Malhotra and Kuo 2008). Inversely, partisanship matters more when a disaster is thought to be within the purview of an out-party official (Healy, Kuo, and Malhotra 2014). With an experimental study focusing on the government’s inadequate response to Hurricane Katrina, Malhotra and Kuo (2008) finds that both Republicans and Democrats are more likely to blame officials of the opposing political party than they are to blame their own, though the effects of a party cue are mitigated among respondents who also received information about the specific offices each official held. However, when partisans are not sure who ought to be responsible, they tend to blame whichever governmental official does not share their party affiliation (Brown 2010). We expect that, in the case of COVID-19 policies, we should find support for this earlier work: respondents will be most likely to blame the policies they oppose on officials who do not share their partisanship.

Hypothesis 1: Respondents will blame policies they oppose on officials who do not share their partisanship.

The tendency to follow partisan cues in evaluating decisions is not uniform across the population. Considerable variation can be expected depending on factors including individuals’ preference for their own party over the other. Johnston, Steenbergen, and Lavine (2012) distinguish “ambivalent voters” from “univalent voters.” The former hold a mix of both positive and negative affect toward both parties and subsequently engage in more even-handed evaluation of a given policy matter. The latter (those who are univalent) hold overwhelmingly positive feelings toward their own party and overwhelmingly negative feelings toward the opposing party. These individuals are most likely to engage in biased processing, even supporting policies they might otherwise oppose simply because their preferred party endorses it.

Affective polarization—the tendency to favor one’s own party relative to the other (often measured with feeling thermometer scales)—produces a similar phenomenon. Individuals who are low in affective polarization are, according to Arceneaux and Vander Wielen (2017), most likely to reflect on issues and events. They are—in line with Johnston, Lavine, and Federico (2017)’s work—the least biased in how they process political information. Those high in affective polarization, who are least reflective, engaged in more biased processing.

Empirical work demonstrates how affective polarization can indeed predict partisan biases and, conversely, how those lower on the affective polarization scale appear to evaluate politics with greater objectivity. For example, higher affective polarization predicts behaviors that closely follow elite cues even when it comes to public health (Druckman et al. 2021); those who are affectively polarized are more likely to bias their judgments regarding sexual misconduct in politics (Klar and McCoy 2021); affective polarization similarly predicts support for (or opposition to) democratic norms (Kingzette et al. 2021). Over the past several decades, it appears that Americans overall have grown more affectively polarized (Iyengar et al. 2019). Yet there still remains substantial variation in the degree to which Americans prefer their own party over the opposing one (Krupnikov and Ryan Forthcoming). We thus expect that selective attribution should similarly depend on an individual’s own stated in-party bias.

Hypothesis 2: As in-party bias affective polarization grows, individuals will be more likely to blame the out-party for the policies they oppose.

We next turn to the unique observational setting that allows us to test these hypotheses.

Data and Methods

In response to the outbreak of COVID-19, a variety of preventative measures emerged across some, but not all, 50 states. By late March of 2020, the Center for Disease Control recommended that Americans wear face-masks and maintain distance from one another in order to prevent the spread of COVID. President Trump opted against any type of executive order that might mandate such a policy and instead left it to states to decide whether or not to mandate these precautions. Governors varied in their approaches to these regulations: some mandated masks and restrictions on businesses for their entire state, others left it to local mayors to implement mandates in their cities or towns, and still others signed laws prohibiting such regulations altogether. At the time that we carried out this study (January 2021), 37 states had state-wide mask mandates and the remaining 13 states had left it to local mayors to implement mandates and regulations. Within these 13 states, policies varied widely across towns and cities. This variation across states and municipalities allows us to measure the degree to which respondents attribute policies to their mayors, their governors, or the president himself.

To test our hypotheses regarding the role of partisanship, policy, and affective polarization in selective attribution, we surveyed 833 adults (aged 18 years or older) across the United States. Our sample was recruited by Dynata, an online public opinion research company that recruits large and diverse samples of adults to participate in opt-in online surveys. Our intention is not to infer the distribution of a variable across the broader population, but rather to test the association between multiple variables within a sample in which the covariates of interest are sufficiently distributed (Druckman and Kam 2011). This convenience sample provides excellent variation on these key variables: partisanship, geographic location, and affective polarization. Hill et al. (2007) argue that small bias found in opt-in Internet samples “is not so great as to vitiate the gains of inexpensive, large, and targeted samples that Internet technology make possible” (3) and all survey work, regardless of mode, should acknowledge any bias that might exist in their sample. Most notably, our sample is more white and more educated than the general population. We have no theoretical bases to expect that these differences should distort our findings. Ultimately, using different data sources is what allows us, collectively as researchers, to explore the external validity of a finding—that is, the extent to which the “causal relationship holds over variations in persons, settings, treatments [and timing], and outcomes” (Shadish, Cook, and Campbell 2002, 83). Our sample is slightly older, more educated, and less racially diverse. An overview of the sample, as well as a comparison with the US census, is provided in our Appendix.

The respondents we reached resided in 724 different towns or cities across the country and in every state, except for Vermont and Wyoming. The geographic distribution of our sample roughly approximates the actual distribution of where the broad American population resides, with more respondents sampled in higher-population states. We display the distribution of where our respondents reside in Figure 1.

Figure 1. Distribution of state residence among respondents.

We asked each respondent for the name of their state and city or town, and for the partisanship of their mayor and their governor. Although we offered a “not sure” option, 94% of respondents were able to tell us if their governor is a Democrat or a Republican. By contrast, only 73% could say whether their mayor is a Democrat or a Republican (a point to which we will return as we contemplate the implications of our results).¹

We then asked each respondent whether there was a mask mandate in the city or town where they lived. We provided the following response options: (1) Definitely; (2) Probably; (3) I’m not sure; (4) Probably not; (5) Definitely not. Only 5% of our respondents were “not sure” while the remaining 95% could tell us whether there was or was not a mask mandate. Next, we asked respondents whether they believe that there should be a mask mandate, with the response options: (1) Definitely; (2) Probably; (3) I’m not sure; (4) Probably not; (5) Definitely not. Finally, we asked them a series of questions about who is responsible for the fact that there is, or is not, a mask mandate in their city or town. For those who told us they have a mandate, we asked: “To what extent is your mayor responsible for the fact that there is a mask mandate in the city or town where you live?” We then asked this same question about their governor and about then-sitting President Trump. For those who told us that they do not have a mask mandate, we asked: “To what extent is your mayor responsible for the fact that there is not a mask mandate in the city or town where you live?” We then asked this same question about their governor and about President Trump.

We then repeated this battery of questions, but instead of mask mandates, we asked about restrictions on businesses. We learned whether business restrictions existed in each respondent’s city or town, whether they themselves support business restrictions, and the degree to which they hold their mayor, governor, and president responsible for the fact that there are, or are not, business restrictions where they live. Finally, we asked a series of demographic questions, including partisan identification, as well as thermometer ratings for both parties (in order to measure their affective polarization score). Each of these questions, in addition to the demographic questions we asked, is listed in our Appendix.

Collectively, these questions provide us with all of the information necessary to test our hypotheses. For testing Hypothesis 1, we used information from the respondent about whether there is a mask mandate and business restrictions in their town and whether the respondent opposes their local policies. We subtracted their support for the policy from their certainty that the policy exists, creating a 5-point variable ranging from 0 to 4, where 0 indicates the most support for their local policy and a 4 indicates the most opposition to their local policy. For example, if a respondent is certain the policy exists in their town and they strongly oppose such a policy, they would score a 4. If a respondent is certain that the policy does not exist in their town and they strongly support such a policy, they would also score a 4. A score of 0 would mean that they are certain the policy exists and they fully support it, or that they are certain that the policy does not exist and they fully oppose such a policy. Midpoints on the scale (i.e., scores of 1–3) reflect variation in the strength of their opposition to the local policy. For both the mask mandates and business restrictions, a slight majority fully supported their local policy (60% for mask mandates and 58% for business restrictions). The remaining 40% (for the mask mandate) and 42% (for business restrictions) expressed some degree of opposition.

We next constructed variables to indicate whether a respondent shares the same party identification as their mayor, governor, and president (who, for all respondents, was Trump). We classify leaners as partisans and create a binary variable in each case: 0 if the candidate is from the out-party and 1 if they share the same partisanship.

To test Hypothesis 2, we constructed the standard measure of affective polarization by subtracting respondents’ out-party ratings from their in-party ratings. The thermometer rating ranges from 1 to 10, and so a score of 9 would mean that a respondent expressed the greatest warmth for their own party (10) and the lowest for the out-party (1). The distribution of affective polarization scores is shown in Figure 2. The negative values indicate that just over 1% of respondents reported to favor the out-party over the in-party. We keep these outliers in the dataset and include them in our analyses, but all of the results we present in this paper are identical when we exclude these outliers and instead scale the measure from 0 to 1.

Figure 2. Distribution of in-party bias (also known as affective polarization) among respondents.

We test both hypotheses with one single model, which contains the variables and interactions we require for all of our tests. In this manuscript, we plot all of our results. In our Appendix, we include all of the output from the regression tables. We next turn to our analyses and results. We examine two distinct policies in this study: mask mandates and business closures. We present results for both of these policies as we move sequentially through our hypotheses.

Results

Hypothesis 1: Policy Opposition & Selective Attribution

Our first hypothesis states that respondents will blame policies they oppose on officials who do not share their partisanship. Here we use two key variables: the degree to which respondents oppose their local policy and the (mis)match between their own partisanship and that of their elected representative. We also control for several demographics that might account for selection bias in our observational data (including age [coded categorically from youngest to oldest], racial minority status [coded as 1 for white respondents and 0 for non-white respondents], gender [coded as 1 for women and 0 for men], and education [coded categorically from least amount of educational attainment to most]; see the Appendix for complete details).

We begin at the local level and examine the degree to which opposition to local COVID policies determines the blame that respondents place on their local mayor. In Figure 3 (left panel), the x-axis illustrates increasing opposition to the respondent’s local mask policy. The dark solid line shows the marginal effect of opposition to the policy on the degree to which respondents blame a mayor who shares their partisanship. The dotted line shows the same relationship, but for those whose mayor is from the opposing party. At the left-most side of the x-axis, where respondents express support for the policy, there is no statistically significant difference in whether out-party versus in-party mayors are deemed responsible for the popular policy. But, moving right-ward along the x-axis, we see that as opposition to the policy grows, so does polarization of blame. As respondents become more opposed to their local mask policy, they become significantly more likely to blame an out-party mayor and significantly less likely to blame an in-party mayor.

Figure 3. Marginal effect of opposition to mask mandates (left panel) and business restrictions (right panel) on blaming in-party (solid line) versus out-party (dashed line) mayors.

Note. Ninety-five percent confidence intervals are highlighted.

We find identical results with respect to the business closures (see Figure 3, right panel). Again, as opposition to the policy grows, respondents become more likely to blame mayors who align with the opposing party and less likely to blame in-party mayors. Among those who support the policy, the partisanship of the mayor has no impact on the blame they receive. Substantively, the greatest shifts occur with respect to in-partisans: when it comes to local business closures, as partisans move from most supportive to most opposed to the policy, they shift by one full point on a 5-point scale toward saying that an in-party mayor is not responsible for the policy. Opposing the policies significantly increases the tendency to blame out-partisans but the substantive effect is largest when it comes to shielding in-party mayors from blame.

We next turn to the blame assigned to one’s governor regarding the mask policy (Figure 4, left panel) and the business restrictions (Figure 4, right panel). Here we find strong support for our first hypothesis. As opposition to the mask policy and to business restrictions increase, the gap between blaming in-partisan and out-partisan governors grows significantly. The movement occurs primarily with respect to in-party governors, who become more immune to criticism as opposition grows, while out-party governors become just slightly more likely to receive blame. Again, the shift is substantively greatest when it comes to attitudes toward in-party governors. While partisans are significantly more likely to blame out-party governors as their opposition to the policy grows, the substantively largest shifts occur in their growing tendency to deny that an in-party mayor could be responsible for a policy they dislike.

Figure 4. Marginal effect of opposition to mask mandates (left panel) and business restrictions (right panel) on blaming in-party (solid line) versus out-party (dashed line) governors.

Note. Ninety-five percent confidence intervals are highlighted.

Finally, we turn to the President himself. At the time that we administered this study, Republican President Trump led the nation. Therefore, in this case, all of our Democratic respondents are out-partisans and all of our Republican respondents are in-partisans. Again, we present the blame attributed to Trump for the mask policy (Figure 5, left panel) and for the business restrictions (Figure 5, right panel).

Figure 5. Marginal effect of opposition to mask mandates (left panel) and business restrictions (right panel) on blaming in-party (solid line) versus out-party (dashed line) president.

Note. Ninety-five percent confidence intervals are highlighted.

As before, selective attribution significantly grows as opposition to the policy increases. We find that as opposition grows, Trump receives dramatically more blame from out-party members (i.e., Democrats). In-party members (i.e., Republicans) remain unlikely to hold the president responsible for their local policy, regardless of their opposition to it. Over both mask mandates and policy closures, and across all three levels of government (mayor, governor, and president), we find that individuals become significantly more likely to attribute a policy they oppose on a level of government that aligns with the opposing party. This is substantively largest among Democrats who become much more likely to blame Trump as their opposition to the policy grows: they shift by three points on the 5-point blame scale when it comes to masks, and by more than one and a half full points when it comes to business closures.

Hypothesis 2: The Exacerbating Role of Affective Polarization

Individuals who are affectively polarized—that is, who hold a strong personal preference for their own party over the other—are uniquely prone to ascribe blame based on partisanship. This leads us to our second hypothesis: selective attribution for disliked policies will be most pronounced among those who are affectively polarized. We test this by including a measure of each respondent’s in-party bias (also known as affective polarization), calculated based on their thermometer ratings. As before, we will begin at the local level (analyzing the blame that is attributed to one’s mayor) and will work our way up toward the presidency.

Figure 6 (left panel) illustrates the marginal effect of opposing one’s local mask policy on blame (y-axis) as in-party bias increases (x-axis). The solid line shows the effect on blame among in-partisans and the dashed line displays the effect on blaming among out-partisans. If our hypothesis were supported, we would see a significant interaction between the two: blame attributed to in-party mayors should decrease and out-party mayors should increase as affective polarization grows. Our hypothesis regarding the role of in-party bias is thus not supported when it comes to mayors. We find no role of affective polarization in determining selective attribution. This runs counter to our expectation and, after we turn to the results for governor and the presidency, we will discuss this particular result further.

Figure 6. Marginal effect of in-party bias on blaming in-party (solid line) versus out-party (dashed line) mayors as opposition to mask policy increases (left panel) and opposition to business restrictions increases (right panel).

Note. Zero line is darkened and 95% confidence intervals are highlighted.

When it comes to the blame that is attributed to the governor, however, the data strongly support our hypothesis. The blame attributed to in-party governors is displayed with the solid line in Figure 7, and out-party governors is displayed with the dashed line. We display the results for local mask policies in the left panel and business closures in the right panel. Here we see that, among those lowest in in-party biases, there is no significant difference in how opposition to a policy drives in-party versus out-party members’ attribution of blame. As in-party bias increases, again, we see stark selective attribution in the case of mask mandates (left panel) and business closures (right panel). With respect to mask mandates, as in-party bias grows, there is a substantial shift both in the tendency for partisans to blame the out-party governor as well as to defend those from the in-party. When it comes to business restrictions, the shift is greatest in partisans’ growing reluctance to blame governors from their own party.

Figure 7. Marginal effect of in-party bias on blaming in-party (solid line) versus out-party (dashed line) governors as opposition to mask policy increases (left panel) and opposition to business restrictions increases (right panel).

Note. Zero line is darkened and 95% confidence intervals are highlighted.

Figure 7 (left panel) illustrates the marginal effect of opposing one’s local mask policy (left panel) and business restrictions (right panel) on blaming Trump (y-axis) as in-party bias increases (x-axis). The dashed line shows the effect on blame among out-partisans (i.e., Democrats evaluating Republican President Donald Trump) and the solid line displays the effect on blaming among in-partisans (i.e., Republicans evaluating Republican President Donald Trump). Beginning with out-partisans (dashed line), we can see that as in-party bias increases, opposing a policy makes individuals more likely to blame the out-party president. Turning to in-partisans (the solid line), we see that, as affective polarization increases, opposing a policy makes people less likely to blame their in-party president. At the lowest levels of in-party bias, Republicans are actually slightly more likely to attribute the policy to their in-party president than are Democrats. But as in-party bias grows, opponents of the policy become resolute in blaming the president only if he is aligned with the opposing party.

We find this exact same pattern with respect to attitudes toward local business closures (Figure 8, right panel). Among those low in in-party bias, opposing a policy leads to no significant difference in how Democrats versus Republicans blame President Trump. But, again, as in-party bias grows, the divergence is stark. At the highest levels of in-party bias, opposition to a policy leads Democrats to blame the Republican party and leads Republicans to shield their in-party president from blame. Substantively, we find very similar patterns with the president as we did for governors: when it comes to mask policies, there are substantive shifts both in the tendency for Democrats to increasingly blame Trump and for Republicans to increasingly deny Trump’s responsibility; but, for business restrictions, the largest substantive shift is out-partisans’ tendency to blame the president for his role as their in-party bias grows.

Figure 8. Marginal effect of in-party bias on blaming in-party (solid line) versus out-party (dashed line) president as opposition to mask policy increases (left panel) and opposition to business restrictions increases (right panel).

Note. Zero line is darkened and 95% confidence intervals are highlighted.

Overall, we find consistent support for Hypothesis 1 across all three levels of government (local, state, and federal). We find support for Hypothesis 2 only at the state level and federal level, but not among mayors. We next devote some more attention to the possible sources for these mixed findings.

What Makes Mayors Different?

We find that, as partisans increasingly oppose a policy, their tendency to blame out-partisan mayors grows. Yet, contrary to our second hypothesis, this is not exacerbated by affective polarization. In other words, personal feelings of dislike toward the out-party exacerbate selective attribution only against governors and the president, but not against the mayor. In this brief section, we consider why this might be. Druckman et al. (2021) show that affective polarization increases partisan-motivated reasoning only when the target is sufficiently politicized. It seems, with the benefit of hindsight, that mayors are not sufficiently politicized. This is evident in our own data, as well as from existing research. Nearly 27% of our respondents could not identify the partisanship of their mayor. This conforms with a dataset of mayoral elections compiled by de Benedictis-Kessner and Warshaw (2016); only 66% of the mayoral elections they examine occurred between two candidates who can be identified as a Democrat or a Republican. Recently, Das et al. (2022) examined the political rhetoric used on Twitter by different levels of government. They find that political rhetoric from governors tends to align with national politics and parties, whereas mayors are largely focused on local issues and remain relatively distant from national politics. Das and coauthors conclude that, “unlike elected officials at other levels of government, the average American mayor currently appears to be unaffected by the nationalization of American politics” (28). Given the lesson from Druckman et al. (2021) that politicization is a perquisite for affective polarization to impact policy attitudes, we can speculate that mayors are seemingly insufficiently politicized.

Scholars have debated the extent to which mayors’ partisanship impacts even their own policy-making. Gerber and Hopkins (2011) found that mayoral partisanship affected the budget they allocate to public safety, but had no impact on tax policy, social policy, or other areas with overlapping authority. Work by de Benedictis-Kessner and Warshaw (2016), though, demonstrates a greater impact of a mayor’s partisanship: Democrats, they show, increase overall city expenditures in a variety of areas mostly through increasing city debt rather than by increasing taxes. This might be a trend that is shifting, though; Hopkins argues that mayors (and other local officials) are becoming more closely tied to national politicians, and mayoral votes are increasingly cast “with an eye toward national politics” (19). If this is indeed the case, then one might expect that affective polarization becomes relevant for mayoral politics in the future. This is certainly a question for future researchers to revisit.

Discussion

Mask mandates and business regulations were evidently a highly salient issue for most Americans: barely more than 5% of our sample did not know whether their city had a mask mandate at the time of our survey, and less than 4% did not know whether regulations were being imposed on businesses. These policies were front-and-center in people’s lives as the country coped with a deadly pandemic. Without any nation-level executive orders, governors and mayors were left to chart their own course in response to the COVID-19 pandemic. This provides a unique opportunity to assess the degree to which voters rely on partisanship as they attribute blame for these highly salient policies.

Our results both affirm preexisting work and also contribute new findings and implications to how we understand democratic accountability, particularly in a polarized era. We provide real-world evidence that partisanship plays a key role in how voters attribute blame or credit. We take this a step further by demonstrating that the degree to which voters oppose a policy exacerbates their likelihood of engaging in selective attribution. Moreover, we introduce the role of in-party bias (or affective polarization) and show that the tendency to selectively blame out-party officials is more pronounced among Americans who are affectively polarized. For those with low in-party biases, there is ostensibly less motivation to blame governmental officials who align with the other party. Selective attribution is this conditional on both attitudes toward the policy and feelings toward the parties.

For this study, we rely on a non-probability-based opt-in panel, which are often used when researchers are interested in studying the relationship between particular variables that are sufficiently distributed across a convenience sample (for e.g., Malhotra and Kuo 2008). These types of data sources tend not to create specific “panel effects” which would bias survey responses, nor does panel attrition lead to selection bias (Dennis et al. 2005; Dennis 2001). Importantly, the intention of our study is not to infer the distribution of a variable across the broader population, but rather to test the association between multiple variables. We thus simply require a sample in which the covariates of interest are sufficiently distributed (Druckman and Kam 2011). Our convenience sample provides an excellent variation on key variables: partisanship, geographic location, and affective polarization. There are also variables for which our sample does not match that of the population. Most notably, our sample is more white and more educated than a national population. We have no theoretical bases to expect that race or education should distort our findings but, ultimately, using different data sources is what allows us to, collectively as researchers, explore the external validity of a finding (Shadish, Cook, and Campbell 2002, 83).

This study also leads to several other important questions for future research. The political context in which our study takes place is arguably unique: a pandemic gripped the nation and the parties stood notably divided regarding their approaches to it. While existing work tends to argue that highly-salient issues are least susceptible to partisan-motivated reasoning (e.g., Mummolo 2016), our work shows that this very highly-salient issue is subject to evident partisan-driven evaluations—but only among those who are most polarized. Furthermore, COVID policies were particularly subject to competing elite cues, making them especially ripe for polarization (Druckman et al. 2021). Given the enormous implications of selective attribution on maintaining a representative democracy, the specific conditions under which individuals are likely to engage in selective attribution are worth continued inquiry. Overall, our study suggests that when blame for policies is not easily identifiable—thanks, in part, to the separation of powers that allows for various levels of government to pass the buck—partisanship plays an especially important role in determining who gets blamed, rightly or wrongly. As the aftermath of these COVID-specific policies unfolds, the retrospective evaluations that guide electoral decision-making will no doubt be distorted by the prism of partisanship.