The 2016 presidential election provided a unique opportunity to revisit two competing hypotheses for how voters establish their perceptions of electoral integrity. First, mass public opinion is believed to derive from elite messages. In the 2016 presidential campaign, candidate Donald Trump maintained that the election system was “rigged,” while election administration experts and officials received considerable media coverage in their efforts to counter Trump’s claims. Second, literature on voter confidence has established a “winner effect”—voters who cast ballots for winners are more likely than voters on the losing side to believe their vote was counted correctly. Thus, voters were exposed to two theoretically opposite effects. In this paper, we find that the “winner” effect mitigates the effects from strong pre-election cues from elites. We also show the effect of pre-election attention to the rigging issue, find a symmetry of the election outcome effect for winners and losers, and reconsider our explanations of the winner effect. Finally, we go beyond the existing studies of the winner effect to consider the kind of citizens who are most susceptible to that effect.

Political scientists have observed a tendency for citizens on the winning side of an election to be affected in their attitudes about politics differently than citizens on the losing side. This “winner effect” is the positive influence of being on the winning side on citizens’ trust, perceptions of fairness, consent to the outcome, and political efficacy (Anderson and Guillory 1997; Anderson and LoTempio 2002; Clarke and Acock 1989; Ginsberg and Weissberg 1978; Maldonado and Seligson 2014). Because there appear to be negative effects on attitudes about government among citizens on the losing side, these findings cause concern about the effects of elections on system legitimacy (Price and Romantan 2004; Rahn, Brehm, and Carlson 1999), although it is not clear that these attitudes endure.

In the United States, the winner effect has been observed in attitudes about the fairness of elections. The Florida episode of the 2000 presidential election motivated political scientists to begin asking national samples about their confidence in vote counts (Alvarez, Hall, and Llewellyn 2008; Atkeson 2014; Atkeson and Saunders 2007). Survey researchers have asked national samples about their confidence that “your vote was counted as intended” and that “votes across the country were accurately counted,” or similar wording (see Sances and Stewart 2015, Tables A1 and A2 in the online appendix). Nearly without exception, a winner-loser gap was found in the post-election period.

The 2016 presidential election experience provides an opportunity to evaluate the role between elite rhetoric and the winner effect. Would Trump’s claims about vote rigging and responses to those claims affect attitudes about the accuracy of vote counting? Similarly, would the Clinton campaign’s efforts to promote the legitimacy of electoral institutions affect perceptions? The exceptionally strong elite cues in the pre-election period, unique to 2016 among recent presidential election cycles, may have generated partisan differences and altered winner-loser differences after Trump won the election.

Using original panel data, we leverage the dissonance between the winner effect and the winner’s pre-election claims about election rigging. By comparing pre-election panel survey data from September 2014 to October 2016, we demonstrate an effect of elite messaging on beliefs in election rigging. Similarly, by comparing pre-election October 2016 and post-election November 2016 survey data, we show a winner effect. In addition, we examine the kinds of supporters who are most susceptible to these distinct influences. Finally, we investigate the relationship between beliefs about electoral integrity and prominent news stories that may undercut beliefs about legitimacy.

Our findings complement the expanding political science of conspiratorial beliefs (Berinsky 2017a; Nyhan and Reifler 2010). Scholars argue that conspiracy-theorists hold their beliefs as a kind of motivated reasoning (Kunda 1990); that is, conspiracy-theorists rely on their beliefs to maintain their attitudes in the face of attitude-changing information (Lodge and Taber 2013). In this case, a lack of confidence in a voting system reflects disapproval of a nefarious, scheming political opponent—that is, support for the conspiracy theory is driven by a need to align the world with one’s political partisanship (Berinsky 2017b). As our attention is on political competition between two partisans, we focus on partisanship, not ideology, as the principle that drives susceptibility to conspiracy about whether the ballots are counted properly.

The “Winner Effect” Studies and the Special Case of 2016 The best study of confidence in vote counts is Sances and Stewart (2015).1 Their meta-analysis shows that a sizable majority, both winners and losers, usually are “very confident” that their own votes are counted accurately. The frequency of “very confident” responses increases somewhat for winners and decreases a little for losers in comparing pre- and post-election polls. In contrast, fewer than half of Americans report being very confident in the vote count accuracy for the country pre-election, with proportion being very confident in post-election surveys dropping below 0.25 for losers and rising to nearly half for winners. Sances and Stewart confirmed the winner effect and personal-country differences in several ways. Like most earlier studies, their aggregate difference-in-differences analysis of pre- and post-election surveys demonstrates different patterns of change in confidence for winners and losers. In addition, individual-level analysis of the effect of vote choice and common demographic characteristics on confidence showed that winners exhibit more confidence than losers. Furthermore, an individual-level difference-in-differences analysis of three pre- and post-election panel surveys shows both the winner effect for both one’s own vote and the country’s vote, with confidence greater for the accuracy of one’s own vote than for the country’s vote. Stewart (2016) reports additional confirmation of the winner effect before the election in 2016, but with a twist. Again, with a pre-election Morning Consult survey (Morning Consult 2016), much greater confidence in one’s own vote being counted accurately was found than for the country’s vote. In the pre-election weeks when Donald Trump was behind Hillary Clinton in the polls and speaking frequently about potential vote rigging, Republican confidence in the national count was low but not lower than it was in pre-election 2012. Stewart was surprised to find the Democrats had much greater confidence in the national count than in 2012 and speculated that Trump’s rhetoric and the media coverage of the safety of the system may have “counter-mobilized” Democrats into support for the system. The studies of the winner effect are impressive, but we are left with questions about the forces at work on individuals. Theory about the individual respondent is given little emphasis in most of these studies. In part, this is a by-product of the focus on aggregate, rather than individual, patterns, and the large effects that are observed. The most common line of argument about individuals is that cognitive dissonance between candidate preference and the election outcome (or likely outcome) influences individuals’ views of the accuracy of vote counting. Other sources of confidence or a lack of confidence in vote counting are given little attention. The 2016 presidential election experience allows us to evaluate the effect of elite cues on vote rigging, both pro and con, on attitudes about the accuracy of vote counting. Trump’s claims were first articulated in August and continued into October when he was behind in the polls by 5 to 10 percent in the polls and were part of a larger narrative, repeated in many speeches and tweets, that polls, the media, and election administration were rigged against him and that (crooked) Hillary Clinton would do whatever was required to steal the election.2 After election experts and officials countered Trumps’ claims, he dropped the “rigged” claims from his campaign speeches but continued to support his statements that illegal immigrants had voted and implied that they would again (House and Dennis 2017; Johnson 2016). The Morning Consult survey that Stewart reports was in the field October 13–15, a period when Clinton led by about 7 percent in the polls. Trump’s claims may have been intended to mobilize his supporters, but they also may have stimulated, reinforced, or enhanced the effect of appearing to be on the losing side in the weeks before the election. The Morning Consult survey was conducted just after the release of the Access Hollywood tape and the second presidential debate but before the release of the FBI director’s letter to Congress about a set of Clinton e-mails. Stewart’s initial finding suggests that Republicans were sour on vote counting in 2012 and not much had changed by October 2016—the “loser” effect was well-established. Trump’s claims about a rigged system were countered by Clinton and others.3 On October 15, Clinton’s campaign manager accused Trump of trying to “undermine” the election.4 The issue came to a head in the third debate, during which Trump said, “I’ll keep you in suspense,” when asked whether he would accept the election outcome. Clinton called Trump’s comment “horrifying,” noting that he was “talking down democracy.”5 Trump’s effort and Clinton’s response are notable for several reasons. First, for a pre-election period, Trump’s claims and Clinton’s response created unusually strong and opposite partisan cues about vote counting accuracy. The combination of cues may have depressed Trump supporters’ confidence in election administration and boosted Clinton supporters’ confidence, which would produce a large difference in the pre-election gap that has not been shown to be significant in studies of previous election cycles. Second, vote counting was part of a larger conspiracy narrative about what Trump’s opponents were doing. Conspiratorial thinking has been a part of past charges of vote stealing, but Trump’s account reached the media, pollsters, and election officials. Third, Trump’s victory created a conflict between his pre-election narrative and that outcome may have had an opposite effect on Clinton supporters. Claims about a “rigged” election besieged the 2016 presidential election. The media also reported on claims about Russian interference. While these stories existed ahead of election, the preponderance of coverage and interest occurred after our study. Thus, we are skeptical that stories regarding the Russian hacking of the Democratic National Convention’s (DNC) e-mails were wholly responsible for movement in vote confidence. Nonetheless, on November 16, officials from the White House disclosed that they “contacted the Russian government directly regarding malicious cyberactivity that was targeting U.S. state election-related systems.”6 According to a search of New York Times following the election, this was the first report of possible interference into voting systems rather than influencing election outcomes by releasing potentially damaging information. Following this report, Clinton supporters and others opposed to a Trump presidency questioned the validity of the outcome and called for recounts in key battleground states.7 If allegations of the Russian government “rigging” ballot counting systems influenced perceptions of vote integrity, we should see a significant shift in confidence following November 15.

Hypotheses These theoretical accounts about a mix of factors—strength of partisanship or support for Trump, political sophistication, and conspiracism—predict cross-sectional differences in vote count confidence in the pre-election and post-election periods. They also predict who is likely to change their vote count confidence—the individual-level winner effect. Aggregate Winner Effect. For the post-election period, the winner effect should have made Trump supporters more confident in vote count accuracy than Clinton supporters. Aggregate Elite Cues Effect. For the pre-election period, Trump’s claims lead us to expect that Trump supporters had less confidence that votes would be counted accurately than Clinton supporters. Moreover, the salience of Trump’s pre-election claims and the reaction to them might have suppressed a winner effect. Thus, we want to determine whether the Trump election rigging narrative was strong enough to create a pre-election difference between Clinton and Trump supporters and strong enough to maintain the difference, if one existed in the pre-election period, in the post-election period. Individual-Level Effect of Partisanship. Strength of partisanship (party support, candidate support) should be positively related to acceptance of friendly elite cues and, due to mood effects, the strength of the winner effect. Individual-Level Effect of Sophistication. Theoretical uncertainty about the effect of education on the winner effect lead us to consider three alternative hypotheses: (1) education is negatively related to the strength of the winner effect (independent awareness), (2) education is positively related to the strength of the winner effect (partisan bias), and (3) mid-level education is positively related with the winner effect (curvilinear effect). Individual-Level Effect of Conspiracism. Conspiracism should be negatively related to confidence in the accuracy of vote counting. We have no prediction for the effect of conspiracism on the strength of the winner effect.

Data and Method We use data from The American Panel Survey (TAPS), which allows us to observe change in confidence in the counting of votes among individuals. TAPS is a nationally representative panel survey that conducts an online poll of up to 2,000 adult respondents monthly who are recruited with an address-based sampling frame.8 We ask questions about voter confidence in five waves: September 2014, October 2016, November 2016, December 2016, and January 2017. In each wave, we ask respondents to report their level of confidence that their ballot will be or was counted as they intended. First, we report aggregate levels of confidence in vote counting for Clinton and Trump supporters over five waves covering both pre- and post-election periods. We estimate the winner effect with a difference-in-differences analysis, which provides a result that is consistent with much of the prior literature. We also estimate the priming effects of Trump statements, and further, we establish the absence of measurable change in voter confidence in the post-election waves. Second, we consider the possible determinants of cross-sectional differences in confidence among Clinton and among Trump supporters in the waves of the survey that occur immediately pre- and post-election (October and November). Third, we consider the possible determinants of change in confidence from October to November. Variables included in the following models may be found in Table 1. On a 4-point scale ranging from “not at all confident” to “very confident,” we find that confidence is higher for Clinton voters in the pre-election period, but relatively higher for Trump voters within the post-election period. The average change in confidence reflects this phenomenon.9 Table 1. Descriptive Statistics. View larger version We are also interested in the role strength of partisan and candidate attachment plays in the change of electoral confidence. First, we measure strong partisans as those who identify as “Strong Democrats” among Clinton voters and those who identify as “Strong Republicans” among Trump voters. The summary statistics show that nearly one-half of Clinton supporters identified as Strong Democrats, while the comparable figure for Trump was much lower. To identify if a panelist is an early supporter of a candidate, we create a dichotomous variable identifying if the participant supported either Clinton or Trump before the first primary election in February. Among Clinton voters, nearly two-thirds fell into this category. Conversely, about one-third of Trump voters indicated support before the primaries. We include two variables that capture the attitudes and personalities of general election voters. We employ the same battery as Oliver and Wood (2014) to measure conspiracism, extracting first dimension scores from a factor analysis. We find Trump voters have higher scores on this scale than Clinton voters.10 We are concerned with identifying changes in electoral confidence as a result of the “winner” effect in the 2016 election. Our first task is to establish if significant changes in the perceptions of the election occurred based upon the candidate that a voter supported. To test this claim, we use difference-in-differences estimates. Then, we are interested in determining what drives change among these voters at the individual level. To determine the characteristics and attitude mechanisms by which voters shifted their perceptions of the electoral process, we must first identify what attributes in the mass public influenced pre-election beliefs regarding electoral integrity. By modeling such beliefs, we come to a fuller understanding of citizens’ confidence levels outside of the 2016 result. We use an ordered logistic regression to predict pre- and post-election levels of confidence. Finally, we regress the directional change in confidence on a set of covariates to identify what individual-level attitudes and traits drive perceptions of the election.

Findings Aggregate Patterns: Difference-in-Differences Analysis TAPS allows us to provide difference-in-differences estimates for the effect of being a Trump or Clinton voter on vote count confidence across five waves of our survey. We compare the average confidence among those who voted for the winning candidate (Trump) with those who voted for the losing candidate (Clinton) before and after the campaign, during the campaign, and after the election. More specifically, we provide estimates comparing the responses of Trump voters with Clinton voters to questions about confidence in vote counts in October 2014, October 2016, post-election November 2016, December 2016, and again in January 2017. We look at trends between November 2016, and we argue that the October and November differences are associated with particular political events and not long-term trends in voter confidence. The changes in confidence in vote counts are far from trivial, heavily weighted in the predicted direction, and remarkably balanced between winners and losers. Although a majority of Clinton supporters did not change their response in November (approximately 61%), nearly one-third (slightly less than 33%) reported lower levels of confidence. Most Clinton supporters with falling confidence shifted one category on the 1–4 scale, but a few (about 10%) shifted by two or more categories. Few Clinton supporters (roughly 6%) increased their confidence. Overall, Trump supporters demonstrated more movement in their level of confidence; only 48 percent of these voters remained at their October 2016 level of confidence. A greater proportion of Trump voters experienced a positive shift in their level of confidence (approximately 39%), while about 13 percent reported a decrease in confidence. The difference-in-differences design assumes “parallel trends”: that voter confidence in the treated category, if untreated, would have followed a similar trend as voter confidence in the control category. While not directly testable, one placebo test that is frequently employed to increase confidence in the causal inference is to evaluate the trends in the two groups in a pre-treatment period. We are not able to provide a placebo test for our estimate of the difference in confidence between winners and losers in the months prior to the 2016 election because Trump voters are more likely to have been exposed to Trump messages about the untrustworthiness of the electoral system in the pre-treatment period, as our estimates show. Indeed, Zaller (1992) argues that citizens will accept political cues from elites who share their political predispositions. We anticipate that Republicans will have less confidence in their electoral system because of claims from Donald Trump. If it has any effect on our inferences, it would be to reduce the probability of discovering a winner effect by suppressing their vote count confidence even after the election. We evaluate difference-in-differences changes for December 2016 and again for January 2017 to determine whether the winner effect lingers after the election. The first quantity of interest is the effect of conflicting messages about election rigging on voter confidence. This effect is calculated as the average difference in voter confidence between Trump voters and Clinton voters that occurs prior to the November 2016 election, minus the average difference in voter confidence between Trump voters and Clinton voters that existed prior to the 2014 general election. We estimate this using a standard difference-in-difference regression given by, Y i t = α s + δ W i t + γ R i t + β W i t × T i t + ε i t , where Y it is a measurement of voter confidence, α s is a state fixed effect to control for any time-invariant unobserved voting system factors, W it is an indicator variable to denote the survey wave, T it is an indicator if the respondent self-reports voting for Trump, and β is the treatment effect. Standard errors are clustered by individual. It is important to emphasize that all reputable survey firms, and most journalism, did not expect a Trump victory. This is important because previous studies lead us to expect any difference—any “winner” effect—to be present in the vote confidence report by Trump supporters in the pre-election (pre-treatment) period. We can think of this as a priming effect; this gives us the effect of Trump’s message regarding the “rigged” election. The second quantity of interest is the winner effect. This is the average difference in voter confidence between Trump voters and Clinton voters that occurs immediately after the November 2016 election, minus the average difference in voter confidence between Trump and Clinton voters that existed immediately prior to the 2016 election. Again, the central issue is whether Trump’s pre-election rhetoric persuaded his supporters that vote rigging was a serious problem and undermined the winner effect for confidence in vote counting. We plot the means of voter confidence for Trump and Clinton voters at each survey wave in Figure 1. We draw an arrow to associate the difference-in-differences effects—and their standard errors in parentheses—to each wave in Figure 1 as well. Effects that are statistically significant using 95 percent confidence intervals are denoted with asterisks.11 Both elite messaging and a winner effect are visible (Figure 1). From the first wave in 2014 to the second wave just before the 2016 election, we observe a somewhat surprising pattern. There is an increase in voter confidence for eventual Clinton voters and a stable level for Trump voters. That is, Trump’s pre-election talk about a rigged election did not change the views of his supporters about vote counting but drove up confidence among Clinton voters, as Stewart reported. This generates a priming effect of −0.32. That is, relative to Clinton voters’ levels, the effect of Trump’s messaging about the “rigged” election appears to have decreased Trump voters’ confidence in the election by about a third of a point on a 4-point scale. The figure shows that, indeed, there is a sizable uptick in Clinton voter confidence in October 2016 while Trump voter confidence remains relatively flat. Download Open in new tab Download in PowerPoint From the pre-election survey in October 2016 to the post-election survey in November 2016, the usual winner effect emerges: confidence levels for Clinton and Trump voters move in opposite directions.12 Confidence declines markedly for the Clinton voters, and this change is matched with an increase in confidence among the Trump voters. The relative effect, then, of the election is to increase the relative confidence of the Trump voters by more than half a category on a scale of 1 to 4.13 Examining the two periods after the election, we observe some small increase in vote count confidence for Trump voters and a small decrease for Clinton voters, but these changes are not statistically significant. That is, the differences in levels of confidence associated with winning and losing persisted into a third month following the election.14 The 2016 pattern confirms the standard winner effect but shows a somewhat surprising “Trump effect.” We had reason to expect that Clinton and Trump supporters would differ in their confidence as a result of Trump’s emphasis on the possibility of a rigged election in the months leading up to the election. We found a difference, unlike in other recent election cycles, but the primary effect of the attention to the issue was to boost Clinton supporters’ confidence. As always, difference-in-differences analyses should be interpreted somewhat cautiously. The primary concern about identification in a difference-in-differences analysis is that of omitted variable bias: to mitigate this threat, we will employ a host of covariates in our individual-level analyses as well. The placebo tests increase our confidence that the observed changes in voter confidence are driven by changes in elite discourse and the winner effect. Individual-Level Patterns: Cross-Sectional Correlates of Vote Count Confidence We are also interested in identifying what drove cross-sectional variation in vote count confidence. To model how covariates relate to the baseline beliefs, we employ an ordered logistic regression with “not at all confident” serving as the outcome variable baseline. The results of this model may be found in Table 2. Table 2. Level Effects Pre- and Post-Election for Individual Vote. View larger version For Clinton voters, we find strong evidence that early support is positively associated with confidence in the vote. This estimated coefficient is not only statistically reliable, its magnitude is quite substantive. For late Clinton supporters, mostly backers of Bernie Sanders, the predicted probability of being “very confident” is approximately 0.63, holding all other covariates at their mean (for continuous variables) and mode (for categorical variables) levels. In contrast, the model predicts the same probability as 0.83 for Clinton’s first voters. The magnitude of this effect weakens after the election when average confidence among early supporters declines. As demonstrated in the difference-in-differences analysis, Trump voters exhibited lower levels of confidence than Clinton voters in the pre-election wave. Within this group, we find strong evidence of heterogeneity with respect to sophistication. The second column demonstrates that among Trump supporters, education is positively associated with confidence. Holding all other covariates at their modes and means, we find that a Trump supporter with a high school diploma as his highest level of education was predicted to be very confident that his ballot was counted correctly with a probability of roughly 0.30 in October; a Trump supporter with a college degree is predicted to have a very high level of confidence, with 0.59 probability. This finding appears to disconfirm the theory that Trump’s most sophisticated supporters would be most receptive to his message about a rigged election. We do not show the results of a test of Zaller’s theory of a curvilinear effect political sophistication on responses to elite cues, but we replicated our approach with models operationalizing years of education as tertile variables. That is, we created a trichotomous indicator in which the least educated were a baseline category and the middle third of educated and the most educated were a second and third category, respectively. We replicated our analysis from Table 2 in Table A6 in the online appendix. In the pre-election period, both medium and highly educated Trump supporters were likely to have higher levels of confidence than the lowest group. The difference between these two higher groups was not significant. Thus, we find no evidence to conclude that at the cross-sectional level reception to elite cues was curvilinear. Rather, if anything, we must conclude that the relationship between sophistication and levels of confidence was relatively linear. With respect to attitudinal predictors, we find asymmetric support for the argument that conspiracism drove early perceptions of vote rigging at the individual level. For Clinton supporters, we find a strong, negative effect of high levels of the susceptibility to agree with conspiracy theories and the confidence in one’s vote being counted correctly. That is, the more likely one was to believe a conspiracy, the more likely they were to question the security of their own vote. Perhaps more accurately, suspicions among Trump voters were not concentrated among the most conspiracy minded in the pre-election period. In the November wave, however, we find a negative, significant effect for both Trump and Clinton voters. Following the election result, those most likely to accept conspiracy theories were the most likely to express skepticism of electoral integrity. Individual-Level Patterns: Change in Vote Count Confidence To explore the loss or gain of confidence among individuals, we first take the difference in the level of confidence from November to October. We then regress November–October difference on a set of time-invariant covariates hypothesized to predict movement among panelists’ whose candidate won or lost the presidential contest. More formally, the model is, Y i N o v − Y i O c t = α + γ X i + ε i , where Y i represents the four-category level of confidence each individual has in her vote being counted as intended in the given month. Higher levels of this variable indicate higher levels of confidence. The difference from the two months results in a 7-point outcome variable in which the highest value, +3, reflects a movement from “not at all confident” in the pre-election period to “very confident” in the post-election period. Likewise, the minimum possible value is a -3, indicating moving from the highest level of confidence to the lowest. X i represents a set of individual specific covariates that are assumed to remain fixed from October to November. We run separate models for both Trump supporters and Clinton supporters. There is only limited support for the hypotheses about change in confidence. Table 3 provides the results for the model regressing the change in confidence on time invariant covariates. For Trump voters, for whom we have seen an increase in confidence from October to November, we find a strong education effect. Less well-educated Trump voters show the greatest positive change in confidence. More precisely, the model predicts that a Trump supporter whose highest level of educational attainment is a high school diploma increased his confidence by roughly 0.59, or one-half category on the 4-point scale. This change in electoral faith is quite large. In contrast, a Trump supporter with a bachelor’s degree as his highest level of educational attainment is predicted to improve his level of confidence by only 0.16. None of the other hypotheses is confirmed among Trump voters. Table 3. Change in Confidence for Individual Vote. View larger version Once again, we tested the curvilinear hypothesis of sophistication. That is, we should expect that mid-level educated Trump supporters would be the most likely to change their confidence. In Table A7 in the online appendix, we demonstrate that this phenomenon is not necessarily so. If anything, we demonstrate that medium-educated Trump voters were significantly less likely to change their level of confidence than the least educated. While the precision of the effect on the most educated Trump supporters relative to the least educated does not reach the 0.05 level, the predicted direction is negative and significant at the 0.10 level. Furthermore, there is no significant difference between the predicted change for the second and third tertiles. For this reason, we must question that in the case of the 2016 election, the effect of elite cues had a curvilinear relationship with voter sophistication. Rather, we find it was the least educated Trump supporters who were most likely to display a winner effect. For Clinton voters, we find support for the predicted effects of strength of partisanship. Strong Democrats were likely to drop their level of confidence by nearly one-fourth of a category relative to their weaker partisan-identifying brethren. Other expectations from various theoretical angles—that the greatest (negative) change in confidence would be exhibited by the most well-educated, earliest supporters, and those high on the conspiracism scale—are not confirmed. To further understand the unique effects we find for the candidates, we also pooled the two-party presidential voters into one model and interacted their effects with vote choice. The interaction term allows us to fully identify the statistical significance of these variables across candidate group. The results of this model may be found in Table A8 in the online appendix. Once again, we find that education and strength of party identification (PID) have distinct effects depending on the panelist’s candidate. That is, we find a negative and significant effect for education among Trump voters and a negative and significant effect for the strength of PID among Clinton voters. Figure 2 provides the difference in predicted effects for both of these interactions across our groups of interest. In the left panel, we find that at the lowest level of figure (a value of 8 represents a panelist whose highest level of education is 12th grade without a diploma), the difference in predicted change across groups in confidence is nearly one whole category. Less sophisticated Trump voters are expected to vastly improve their level, while less educated Clinton voters are expected to slightly decrease their level. The distinction of effects across groups holds through panelists who are at most college graduates (a value of 12). Thus, we clearly find that education’s effect was conditional on voting for the winner or loser. Similarly, we confirm our estimates in Table 3 with the predicted outcomes for strength of partisan identification. Among Trump voters, we see very little change in predicted level of November confidence relative to October based on identifying at the extreme end of the traditional PID scale. Yet, for Democratic Clinton voters, we find the estimated predictions for each group are statistically distinct from each other at the .05 level. Download Open in new tab Download in PowerPoint As for the cross-sectional variation in confidence, there is some systematic variation in change in confidence, but most theorized patterns of change are not observed. The directional change affects many kinds of Clinton and Trump voters, which suggests that a missing variable, such as change in mood, one that would have widespread effects among fellow partisans, accounts for variation in the winner effect among individuals.

Did Allegations of Russian Hacking Influence Vote Counting Confidence? As stated previously, focus on Russian hacking and interference in the election reached its apex in December, well after the November wave of our data collection was completed. Still, journalists reported that the U.S. government believed the Russian government possessed the potential to attack state election voting systems. The first reporting of this concern, November 16, occurred while our survey was in the field.15 We leverage this timing to find evidence of a potential effect of the allegations of Russian influence on vote confidence. Indeed, the average change in confidence for Clinton voters who completed the survey before November 15 is −0.25, an estimate significant from zero, indicating that the average Clinton voter in the pre-allegation period still lost confidence in her vote. Yet, in the post-allegation period, we find that this average change among Clinton voters increases in magnitude to −0.62, which is not only significant from zero but significantly distinct from the pre-allegation mean. We advise strong caution before drawing a causal relationship to the reporting and an increase in the change in magnitude for loss of confidence. While the shock is exogenous, its assignment is far from random. Indeed, those Clinton voters who answered the survey in the latter part of November were different from those completing it earlier on a host of demographic variables. For example, late finishers were much younger, less educated, paid less attention to politics, and much more likely to believe in conspiracy theories. To get a better sense of the effect of the reporting, we replicated the change model while adding an indicator for time. We report the results of this analysis in Table 4. Although the effect of being a strong partisan holds among Clinton voters, we do find a strong effect based upon when the panelist answered the survey. Holding all else equal, completing the survey after the news of American concerns over voting system hacking broke was associated with a roughly one-third category decrease in confidence. That is, doubts about electoral integrity significantly intensified following this report, even when controlling for a series of covariates. Perhaps most surprising is that Trump voters were not necessarily immune from this effect. In column 2, we find that the estimated effect is also negative and of a relatively substantive magnitude (one-fourth of a category). Although this estimate does not reach traditionally acceptable levels of significance, it is significant at the 0.10 level. Thus, it would appear that allegations of Russian hacking did have an effect on confidence in vote counting across the electorate. Table 4. Change in Confidence for Individual Vote, Controlling for Hacking Story. View larger version

Conclusion We have reported two major sets of findings. First, we have reconfirmed the presence of a winner effect and evaluated the influence of the pre-election claims about election rigging. In a five-wave panel study that is unique to the study of this issue, we find significant evidence for the winner effect on vote confidence, while also showing a distinctive pre-election effect of elite cues in 2016. Not only does the winner effect increase Trump voters’ confidence in the electoral process, even having experienced unusually strong cues to the contrary from Trump in the pre-election period, but this effect persists for a matter of months. Moreover, the pre-election difference between Clinton and Trump supporters is consistent with the argument that elite cues boosted Clinton supporters’ confidence and depressed Trump supporters’ confidence in a manner that studies of other election contests did not find. Second, we have tested theoretical accounts that might explain initial and changed attitudes about vote counting. Sophistication and conspiracism play measurable roles in establishing a voters’ baseline level of confidence in the electoral system. More sophisticated individuals report more confidence in the accuracy of vote counts, while individuals with conspiracist attitudes show less confidence. Clinton and Trump voters exhibit different patterns of change. For Clinton voters, strong partisanship is associated with greater confidence in the pre-election period. With respect to longitudinal trends in confidence, Clinton voters change as a consequence of strength in partisanship, while Trump voters change as a consequence of years of sophistication. It is important to note that both effects persist as seen in the difference-in-differences plot. We are unimpressed with the explanations of individual-level change in confidence in vote counts. A few expectations are confirmed, but the overall pattern is that all types of Clinton and Trump supporters exhibit a winner effect. We are left with the working hypothesis that a factor like a change in mood, which is likely to have widespread effects among fellow partisans, underlies the persisting winner effect that we observe. Voter confidence is one metric for trust in elections. Others include beliefs in voter fraud (Atkeson, Alvarez, and Hall 2015; Udani and Kimball 2017), a separate instance where, indeed, partisan divisions expanded on voter fraud concerns in 2016 (Bryant and Gronke 2017). Regardless of the metric, political scientists agree that the legitimacy of elections is a critical component for a successful democratic experiment. We find it encouraging that the winner effect essentially flips the least confident voters into those who have more confidence in the electoral system. Competitive elections, then, will likely produce more winner effects and maintain average high confidence levels as the parties wax and wane in terms of who wins.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes 1.

For other studies that address confidence in vote counts but do not examine the winner effect, see Atkeson and Saunders (2007). That study shows that voting method, interaction with poll workers, and other features of voting experience can affect confidence in vote counts. 2.

For a review of the episode, see Darren Samuelson (2016). 3.

http://www.politico.com/story/2016/10/donald-trump-rigged-election-guide-230302; http://thehill.com/blogs/ballot-box/presidential-races/301398-election-officials-scoff-at-trumps-claim-of-rigged-vote. 4.

http://www.nj.com/politics/index.ssf/2016/10/how_the_clinton_campaign_reacted_to_trump.html. 5.

https://www.nytimes.com/2016/10/20/us/politics/presidential-debate.html?_r=0; https://www.washingtonpost.com/politics/both-campaigns-grapple-with-day-after-scenarios-amid-trump-defiance-over-election-integrity/2016/10/20/1b8c973c-96b5-11e6-bc79-af1cd3d2984b_story.html?utm_term=.d6779029d2c2. 6.

Sanger (2016). 7.

Trip and Sanger (2016). 8.

More technical information about the survey is available at http://taps.wustl.edu. 9.

In October and November 2016, both sets of voters reported lower levels of confidence in the state vote being counted as intended. While differences exist, the correlations between state and individual confidence for each month were particularly high: 0.75 for October 2016, 0.72 for November 2016, and 0.79 for December 2016. Our results are robust to the particular question wording used. 10.

We include a variety of variables measuring identity of the panelists. Gender is operationalized using a dummy variable in which Female is indicated with a value of 1. Age is measured using a continuous variable. We identify rural voters as those who do not live in a Metropolitan Statistical Area as designated by the Office of Budget and Management. Income is measured using a sixteen-category variable ranging from those earning under $10,000 dollars per year to those reporting incomes of more than $300,000 annually. Racial identity is measured using three binary indicators for participants indicating they are white, black, or Hispanic. Within multivariate models, the baseline category represents those who do not fall into these three categories. 11.

Estimated coefficients from all models are available in Table A5 in the online appendix. 12.

It is also of interest to note that this outcome is eerily similar to the initial position for both Republican and Democratic voters as reported in Figure 1 by Sances and Stewart (2015). 13.

This effect is quite large. We also conducted the difference-in-differences analysis using a dichotomous indicator for reporting “very confident.” The “winner effect” in this specification was 0.33. For comparison, averaging over four elections, Sances and Stewart (2015) estimate the similar effect for voting for the winner to be only 0.22. 14.

Interestingly, the average level of confidence we observe for both Trump and Clinton voters in 2014 is statistically indistinguishable with the average level of confidence we observe for both Trump and Clinton voters in January 2017. What has changed is the relative ordering: now Clinton voters have less confidence and Trump voters have more confidence. These results are robust to the inclusion of state fixed effects, to clustering or not at the respondent level, and to the particular question: we observe a similar pattern of results if instead the respondent addressed confidence in her state or county. 15.

Of the 1,542 panelists who completed the survey in November, 1,054 did so before November 15, the day the Obama administration’s warning was disclosed.

Supplemental Materials

Supplemental materials for this article are available with the manuscript on the Political Research Quarterly (PRQ) website.