Experiments on the Effects of Opinion Polls and Implication for Laws Banning Preelection. Todd Donovan Western Washington University

Experiments on the Effects of Opinion Polls and Implication for Laws Banning Preelection Polling Todd Donovan Western Washington University Shaun Bowler UC Riverside ABSTRACT Many nations ban the release of pre-election poll results based on the assumption that voters will be adversely influenced by poll information. The AAPOR notes that there is no scientific evidence that voter decisions are influenced by media polls. This study uses survey experiments to assess if respondents might be influenced by a hypothetical candidate s poll standing. It advances our understanding of poll effects by testing which type of people might be most responsive to information about poll standing. Results are consistent with a theory proposing that voters with weaker political preferences (those less politically engaged) may be more likely to support candidates who are leading in media polls. Although the experimental effects are substantial for some of the less politically engaged respondents, these people may be least likely to become aware of media poll information in a real world setting. The effect of poll information on candidate choices is likely to be limited for the electorate overall. Paper prepared for the Montreal Voting Experiments Workshop, sponsored by the Making Electoral Democracy Work Project. 0

Effects of Opinion Polls on Support for Candidates One enduring question in public opinion research is whether information from opinion polls feeds back to affect mass opinion and voting behavior (e.g. Gallup 1940; Simon 1954; Noelle-Neumann 1984). There are both practical and theoretical reasons for being interested in how information from opinion polls might affect a voter s choice for a candidate or party. Observers of elections have long suspected that voter response to polling information can create bandwagon or momentum effects, where voters gravitate toward a candidate found to be leading in media polls (Moy and Rinke 2012; Hardmeier 2008; Mutz 1998; Mutz 1997; Goidel and Shields 1994; McAllister and Studlar 1991; Skalaban 1988; Marsh 1984). In US presidential nomination contests, voters are known to gravitate toward candidates who are successful in the early contests (Abramowitz 1987; Bartels 1988). Strategic voters are also assumed to make use of polling information when deciding how to cast their votes - particularly in multicandidate contests where a voter perceives that her least preferred candidate might win if she does not support the strongest alternative (Cox 1997; Blais et al 2001; Karp et al 2002; Lanoue and Bowler 1992, 1998). Concern about the potential for polling information to influence an individual s vote has led to laws that prohibit the release of pre-election poll results before an election (Bale 2002). Some nations merely prohibit the release of day of election polls until after voting has been completed (e.g. Canada, France) while many (Greece, Italy, Luxemborg, 1

Switzerland and 11 other nations) have bans on releasing poll results within 10 days of voting. One estimate is that over 40 nations having laws that ban pre-election polling as of 2013. 1 The American Association of Public Opinion Research (AAPOR) and the World Association of Public Opinion Research (WAPOR) have taken positions against these laws. AAPOR s statement on the matter rejects two assumptions the laws are based on. The first assumption is that voters can be misled by false poll forecasts. The second assumption is that pre-election poll results influence voters. 2 The AAPOR statement concluded that bandwagon effects where voter decisions are influence by media polls - have not been supported by available scientific evidence. 3 The prohibition on releasing media polls may be based on the assumption that use of information about poll standing causes a voter to make a wrong choice. Normative concerns about the ill effects of voters acting on polling information include the spiral of silence logic. This logic reflects concern that minority perspectives might be muted as people attempt to conform to majority views as expressed in public opinion polls (Noelle- Neumann 1984). An additional normative concern is that voters should make their choices based on information that is qualitatively better than a candidate s poll standing. The latter concern reflects the normative value placed on sincere voting - voting based on information about candidate qualifications and party policy positions, rather than poll standing. Normative issues surrounding voting on the basis of public opinion poll standing may differ by election context. In a single-event contest between rival political 1 ACE: The Electoral Knowledge Network. 2 The French government s ban on reporting poll information was justified, in part, on the assumption that the polls might be wrong. Some of these laws have been overturned. 3 http://pollingmatters.gallup.com/2010/01/role-of-polls-in-massachusetts.html; http://www.aapor.org/the_greek_ban_on_publishing_pre_election_polls_undercuts_h ealthy_democracy.htm 2

parties or candidates of rival parties (e.g. a general election), normative democratic theory prefers that election results reflect voter deliberation over substantive differences between the candidates and parties (Berelson et al 1954) rather than voters rallying to whoever was leading in recent polls. However, in multi-party races under FPTP, and in sequential, intra-party nomination contests (e.g. presidential delegate selection caucuses and primaries), a candidates policy-based differences may be so limited that some voters might have a keen interest in relying on polling data to decide how to nominate a viable (electable) candidate (Abramowitz 1989; Kenny and Rice 1994). It is well established that lesser-known candidates gain momentum (increased support) as more voters become familiar with them (Bartels 1988) and more judge them to be viable candidates. There is uncertainty, however, about how voters make assessments that define perceptions of viability. In presidential nomination contests, awareness of candidate victories in earlier states is associated with people in later states switching their support to the candidate who won earlier (Collingwood et al 2012; Abramowitz 1987). But most elections are one-off events where voters have no information from earlier contests. This leaves candidate standing in media polls as a potential source of information. This paper uses several survey experiments to explore how information about candidate standing in opinion polls may affect voter evaluations of candidates. Despite widespread concerns about polls influencing voter choice, we know very little about how many people might be affected by media information about candidate poll standing. Nor do we know much about the potential magnitude of such effects, nor about which voters might be most susceptible to utilizing polling information when voting. Furthermore, 3

there is no individual-level evidence of voters acknowledging that they consider a candidate s standing in opinion polls when they vote. Who, then, are momentum or bandwagon voters? The answer to these questions could help us understand the scope for media polls to affect elections. This, in turn, might also inform us about which types of candidates might be more likely to benefit from momentum associated with strong standing in media polls. A Theory of Voter Use of Opinion Poll Information We begin with the assumption that information about a candidate (or party s) standing in public opinion polls is a lower order source of information for voters, compared to higher order information that voters are known when they evaluate candidates. Higher order information sources include partisanship, candidate issue positions, candidate personality, and candidate traits (Miller, Wattenberg and Mallanchuk 1986). Lower order information can include candidate gender (McDermott 1998), ballot position (Brockington 2003; Kelly and McAllister 1984), and, possibly, standing in opinion polls. We also assume that the propensity to rely on lower order information - and by extension the propensity to respond to poll standing - varies across individuals. In a generic two-candidate contest voters who have strong, fixed, pre-existing preferences for candidates should be less likely to change the candidate they support in response to information about poll standing (Mutz 1997). Strong partisan voters and voters with high levels of education, for example, tend to be more politically interested and informed than others. This being the case, they may be more likely than others to be aware of media 4

polls on candidate standing. Yet by having more firmly established preferences in terms of policies and in terms of preferred characteristics about candidates - the politically engaged have attitudes and preferences that are relatively fixed and unlikely to change (Campbell et al 1960; Converse 1962). 4 Similarly the preferences of voters who are highly educated, highly informed and highly interested in politics are probably not likely to be affected much by an additional piece of information about a candidate. There may be a ceiling effect with such voters when it comes to the marginal return on additional political information. Highly educated and politically engaged voters may have a store of information that trumps the effects of additional (lower order) information. The situation should be different with voters who are relatively indifferent about politics, who have weaker preferences, or who are less informed. These voters may lack fixed preferences, have less of a store of political information or lack the cognitive skills that make it easier to differentiate between rival candidates. With these voters, information about a candidate s lead in media polls may be more consequential particularly if they perceive a lead in polls as validating that many other people approve of the candidate. For voters with weak preferences, opinion poll information might be a cue that signals which candidate other people consider the most credible option. Hypotheses This general theory can be tested with several specific hypotheses. We test how different groups of voters respond in experiments when they are prompted with 4 We might except that intra-party, multi-candidate contests present an exception to this if interested partisans rely on poll data to select the most viable candidate from a crowded field. However, as discussed below, experiments conducted during the 2012 Iowa caucus campaign suggest this was not the case. 5

information about a hypothetical candidate s standing in public opinion polls. Specifically, we expect that younger people, political independents, and those with low levels of political engagement will be most likely to change their candidate preference in response to a prompt informing them that a particular candidate is leading in recent opinion polls. Conversely, we assume that people with higher levels of education, older respondents, partisans, and those who are more politically engaged will have preferences for particular candidate traits that will be more firmly grounded and less influenced by information about candidate poll standing. 5 Experimental Design Much of the existing research of the effects of media polls relies on surveys of voters, or trends in aggregate opinion, over the course of a campaign. Although such studies can track changes in voter intentions, they cannot isolate the effect of polling data on vote intentions. A handful of studies have used experimental designs to demonstrate that voters utilized poll standing when assessing hypothetical candidates (Ansolabehere and Iynegar 1994; Sinclair and Lott 2012) or when offering opinions on policy issues (Nadeau et al 1993), but these were not designed to identify which voters might be more disposed to support candidates due to the candidate s poll standing. But there are some possible difficulties involving measurement issues. If respondents are directly asked if they would support a candidate simply because she is ahead in media polls, it is likely such a question would generate biased responses. Most 5 There is also a potential cognitive effect here: respondents with higher levels of education may be in a stronger position to translate traits of hypothetical candidates into assumptions about the candidate s issue positions, and thus be less sensitive to the prompt about poll standing. 6

people assume polls influence how people vote, and when asked directly are convinced that polls have such effects but [they] emphatically deny any influence on themselves. In other words, polls are assumed to affect opinions of others but not one s own (Land and Lang 1984:133). As a result, we expect that social desirability effects would bias the use of survey questions that asked voters, would you be more likely to support a candidate if you knew she was leading in recent opinion polls? Although the potential for measurement contamination here may not be as severe as when measuring racial prejudice (Kuklinski et al 1997) or support for a female president (Streb et al 2007), it could be consequential. Civic education places great value on deliberation in politics, and on citizens having a duty to cast an informed vote. It is likely, then, that many people will be unwilling to reveal that they use lower-order information such as poll standing. A 2005 UK study asking voters "which of these items, if any, have influenced the way you intend to vote" listed 11 items, including debates, posters, newspapers and opinion polls. Only 3% of respondents reported being influence by polls (Baines, Worcester and Mortimore 2007). Without addressing potential for biased responses, we cannot know if this (low) reported use of polling data reflects reality. Survey experiments placed on the 2010 Cooperative Congressional Election Study 6 were designed to assess how (or if) information about poll standing affects 6 CCES is an opt-in, Internet platform survey administered by YouGov Polimetrix. A common content portion is administered to over 30,000 subjects. The data used here are from a stratified national sub-sample of 1,000 registered and unregistered adults. The data may not be particularly well suited for making inferences about the actual voting population, but the platform is ideal for conducting random assignment experiments. 7

candidate evaluations. 7 One experiment in particular was designed to minimize social desirability effects. A random assignment list experiment with a prompt about candidate poll standing was placed on the CCES, as well as a standard question wording experiment. The list experiment was also replicated on a state-wide poll conducted in Iowa during the 2012 presidential nomination contest. List experiments are particularly useful here, as they are designed to account for problems with social desirability bias in survey response (Kuklinski, et al 1997). Rather than ask, are you more likely to vote for someone if they were leading in the most recent poll, we added information about poll standing to a list of three candidate attributes that included the candidate s education, the candidate s business experience, and the candidate s family background. Half the sample received a list of the latter three items, and half received the list of all four. A comparison of responses across groups provides a test for the effect of information about poll standing. The list experiment was presented as: Please read the following three things that people might want to know about a candidate before voting. Please tell me how many of them are things that make you more likely to support a candidate. I don t want to know which ones, just how many. Just enter a number from 0 to 3. The baseline (control) list of candidate traits included: 1) The candidate graduated from a prestigious college 7 The questions were included with the [deleted] CCES Module. The author thanks [deleted] and the [deleted] Department of Political Science for the opportunity to piggyback on to their section of the CCES. 8

2) The candidate ran a business 3) The candidate s family background Half of the experimental subjects were randomly assigned this question, with these three items, the other subjects were randomly assigned the same question, but with a fourth item added to the list that said, The candidate is leading in recent opinion polls. The idea here is that respondents can express their propensity to make use of a candidate s standing in polls without having to explicitly say they do. With this list experiment, we can estimate the percent of respondents (or the percent of some subset of respondents) who might use of opinion poll data when evaluating candidates by subtracting the mean number of items in the baseline condition from the mean number in the treatment condition and multiply by 100 (Kuklinski et al 1997:406). Given randomization, and with the (reasonable) assumptions that 1) the treatment item does not affect answers to the control items, and 2) people are not lying, a difference in means test will be an unbiased estimator here (Blair and Imai 2011). A second experiment provides a variant of this, one that also allows for multivariate analysis of the potential effect of the experimental treatment. In this second experiment, respondents were randomly assigned to one of two versions of a question, with the baseline (control) condition being: Consider the following hypothetical candidates running for office. Candidate A ran a successful business. Candidate B runs a major charity organization. Knowing this, if you had to choose between Candidate A and Candidate B, which one would you vote for? 1) Candidate A 9

2) Candidate B 3) Don t know The other half of respondents were randomly assigned to a similar question. As a treatment, they received the same wording as presented above, with the only difference being Candidate B runs a major charity organization and has a large lead in the most recent public opinion poll. It is important to note some key aspects of these experiments, and how they differ. The list experiment is not designed to differentiate the candidates in terms of any particular traits that might cause respondents to make inferences about the candidates partisanship or issue positions. In contrast, the question wording experiment does give respondents cues that differentiate the candidates cues that are designed to distinguish the candidates, implicitly, in terms of traits that should have very different appeals to Democrats and Republicans. (Republicans are expected to value business experience much more than others, while Democrats are expected to value work in the non-profit sector more than others). 8 The first experiment provides some leverage on estimating the substantive magnitude of the treatment effect (among CCES respondents). The second does this, while also illustrating how these effects vary when people are faced with making a decision between two (hypothetical) candidates who are defined quite distinctly. 9 8 We could simply defined Candidate A as a Republican, and Candidate B as a Democrat, but this could produce a ceiling effect (at least among partisans) such that any prompt about additional traits might not affect respondent perceptions. 9 In this second experiment, the treatment (the prompt about poll standing) could have been applied to either candidate. Likewise, additional survey items and randomization could have expanded the design to allow tests of how poll information affected support for the two types of candidates. I have no reason, a priori, to expect that the treatment 10

Analysis / Results Results of the CCES list experiment conducted during 2010 US congressional elections are displayed in Table 1. When the results are considered across the entire sample, the overall effect of poll standing on voting appears minimal. The (insignificant) difference between the control and treatment group estimates that only 3.2% of respondents would consider poll standing as something that would make them more likely to support a hypothetical candidate. There are substantial differences, however, among some subgroups of voters that are consistent with the theory proposed above. Table 1 about here Substantively, the most striking effect here is with younger respondents (people born after 1970). The list experiment suggests nearly 41% of younger people would be more likely to support a candidate who was ahead in recent polls (p <.01). Fourteen percent of respondents with no more than a high school education (p =.17) and about 12% of respondents who report they are not politically active (p=. 10) are estimated to consider poll standing when evaluating candidates. Statistical significance is constrained here by the relatively small sizes of these sub-samples, but the substantive magnitude of these estimated effects are not trivial. As expected, self-identified partisans, and strong partisans, do appear less interested in candidate s poll standing than independents, but the differences were not statistically significant. Table 1 also illustrates that, independent of the effect of the prompt about poll standing, some respondents valued certain candidate traits in the baseline (control would have a greater effect when applied to a candidate described as running a major charity (vs. one described as running a successful business). 11

condition) list more than others. For example, Democrats on average listed 1.17 of these traits (prestigious college, running a business, family background) as making them more likely to support a candidate, while Republicans on average listed 1.62 of these traits. The main factor here is a partisan difference in the value placed on business experience as a desirable candidate trait. Although the 2010 CCES contained many items asking respondents about congressional candidates, it is not possible to gauge what sort of real-world electoral context (if any) voters might have been affected by when they responded to the poll standing list experiment. Many CCES respondents likely resided in places where there was no meaningful campaign activity. This could increase the possibility that results displayed in Table 1 are an artifact of the experiment being conducted in a vacuum where there was no contested election. That is, when voters are in the heat of a competitive campaign more of them could be exposed to information prompting them to consider numerous other factors that could render a prompt about poll standing as irrelevant. Conversely, if the experiment were conducted during a heated election with media polls readily available to voters, such an electoral context could prime the importance of poll standing for respondents. Given this, the list experiment was repeated with a sample of registered voters in Iowa during the Republican presidential nomination campaign in Iowa. 10 In the month leading to when the survey was conducted in early December 2011 (the caucus January 3, 2012), 14 media polls reported on candidate standing in Iowa an 10 The experiment was placed on a University of Iowa Hawkeye Poll conducted between November 30 and December 7, 2011. The sample was 982 registered voters, including 277 respondents who indicated they were "somewhat likely" or "very likely" to attend the 2012 Republican caucuses. The author is not affiliated with the University of Iowa and accepts sole responsibility for any errors in evaluating these data. 12

additional dozen polls reported on candidate standing nationally. Table 2 about here Result from the list experiment conducted in Iowa are displayed in Table 2. These suggest that a poll-rich campaign environment might actually encourage people to support frontrunners - particularly those with less education and less interest in politics. Overall, 15% of Iowans are estimated to consider frontrunner status as something that would make them more likely to support a candidate. As with the national sample, the effect is pronounced among those less engaged politically - 43% of those with a high school education or less (p <.01) and 25% of those who reported low interest in politics (p <.01) list poll standing as a favorable trait. The Iowa list experiment results also support more of a bandwagon rather than strategic voter portrait of how voters might use information about a candidate s poll standing. Despite numerous media polls being focused on the crowded 2012 Iowa Republican presidential field, and contrary to expectations that strategic voters gravitate toward viable candidates in nomination contests, the list experiment treatment had little effect on Republican identifiers in Iowa (strong or otherwise), nor on Iowans who reported they would attend a Republican caucus. Another experiment on the 2010 CCES was designed examine how the addition of information about a candidate s frontrunner status affected voter choice between two hypothetical candidates described as being distinct from each other (without being described in explicit partisan terms). Independent of any treatment effect, the traits used to distinguish the candidates from each other should tap real preferences respondents had for candidates. Offering respondents a choice between candidates defined with different 13

characteristics provides a conservative test of the added effect of information on poll standing. It is evident in Table 3 that the descriptions of hypothetical candidates tapped into pre-existing voter preferences. Candidate A (the business person) had much more appeal overall (44.8% support in the control/baseline condition) than Candidate B (the director of a non-profit). More to the point, Candidate A had strong support from Republicans while Candidate B had strongest support among self identified Democrats. Table 3 about here As with the list experiment conducted with the national CCES sample, there was no significant effect of the prompt about Candidate B being the frontrunner across the entire sample. Support for Candidate A is 3.5% lower, with minor increases in support for Candidate B and don t know among people prompted that Candidate B has a large lead in the most recent public opinion poll. However, when we examine sub-sets of respondents we see results that are consistent with the theory, and with those from list experiment in the national sample and in Iowa. Support for Candidate B ran 11.3% higher among younger respondents who were told that Candidate B had a large lead in a poll (p=.15). The prompt about frontrunner status also moved younger respondents from undecided to support for Candidate B. Respondents with a high school education or less were 13% less likely to support Candidate A and 9% more likely to support Candidate B when informed that Candidate B was the frontrunner. Support for Candidate A ran 10% lower among respondents in the treatment group with low political interest, while support ran 9% higher for Candidate B in the group prompted about B s lead in the poll (p=.09). Candidate B also ran 9% stronger (p =.09) among respondents who were less politically active when those respondents were informed that Candidate B was ahead in the polls. 14

As with results reported above, self-identified partisans and strong partisans were unaffected by information about poll standing. Table 4 about here Table 4 provides a multi-nomial logistic regression estimation of results from the candidate choice question wording experiment shown in Table 3. The model is estimated with a three category dependent variable (support Candidate A, support Candidate B, and don t know). The multi-variate estimates allow us to isolate the effect of the treatment (being told that Candidate B had a large lead in opinion polls) while controlling for differences in voter preferences for candidates across sub-sets of the respondents. Coefficients in Table 4 demonstrate that independent of information about poll standing, Democrats, women, and younger voters, respectively, preferred Candidate B to Candidate A. Republicans, Tea Party supporters and older voters preferred Candidate A to B. Results in Table 3 also illustrate that when age, education, partisanship, political engagement and other demographic controls are accounted for - the prompt about poll standing has a statistically significant, independent effect on candidate choice. When respondent demographics (which correspond with differences in preferences for various candidate traits) are accounted for, information that a candidate leads in opinion polls increased the likelihood that a voter would support the candidate who was ahead in the polls. Information about the strong poll standing of Candidate B, all else equal, also increased the likelihood of a voter being undecided (versus supporting the candidate who was not leading in the polls). Although the effect of the prompt about polling information is statistically significant in the multi-variate estimates, the substantive magnitude of any unique effect of poll information (across the sample when 15

other factors are held constant) is minimal. Potential effects are better understood among subgroups of voters, as seen with the results in Table 3. 11 Results in Table 3, like those from the list experiments, demonstrate that the likelihood of voters making use of polling data when evaluating candidates is not uniform across the population. People assumed to have weaker or unfixed preferences for candidates (the young, the less educated, and the less politically engaged) appear much more likely than others to consider a lead in public opinion polls as a desired candidate trait. Additional Experiment To assess the robustness of these results, we conducted an additional experiment where the electoral context was altered. Experiments discussed thus far were designed replicated the list experiment, but changed the electoral context to a US congressional election. Although party was not specified, we expected that this contest might cue partisanship more than in the experiments discussed above, and thus mute some of the effects we have reported. Respondents were asked about a "list of four things people might know about a candidate for Congress before voting," with "the candidate is leading in recent opinion polls" being the sensitive list item. For this experiment, 600 respondents were recruited via Amazon.com's Mechanical Turk in February, 2014 (see Berinsky et al 2011 for a discussion of sample characteristics). The congressional election list experiment conducted on mechanical Turk 11 Results from Clarify simulations derived from estimates of the model reported in Table 4 show the unique effect of information about candidate poll standing (with all other variables set to modal values) reduces the probability of supporting for Candidate A from.51 (se =.18) to.43 (se =.17), while increasing the probability of supporting for Candidate B from.24 (se.15) to.29 (se.15). However, note the large standard errors for these estimates. 16

estimated that across all respondents, 25% (t=3.05; p. <.00) found a congressional candidate's poll standing (having a lead) as something which would make them more likely to vote for the candidate. This is far larger than the (null and low) effects for all respondents that was estimated from the experiments conducted on the CCES and Iowa platforms. This likely reflects something about the Mechanical Turk respondents (who are younger, with a median age of 32), rather than our specifying that the electoral context was a US congressional election. The size of the effect was similar across age categories, but there was limited variance in respondent s age within this Mechanical Turk sample. Among respondents reporting a higher level of political interest, an insignificant 11% (prob. t =.26) of respondents were estimated to use poll standing when selecting hypothetical congressional candidates. Among those in all other categories of (lower) political interest, the estimate is 30.2% (t=3.18, p. <.00). Once again, we find reported use of poll information concentrated among those least interested in politics. Discussion Results of these experiments have important implications for our understanding of the larger role that public opinion polls play in society, and for our understanding of how voters make decisions on candidates and parties. These experiments demonstrate that, in an artificial setting, some people view frontrunner status as a desirable candidate trait. Above and beyond individual-level variation in preferences for distinct candidate traits, information about a candidate s lead in opinion polls was associated with increased support for that candidate. The effect does not extend to most respondents - these 17

experiments demonstrate that voters who are assumed to have weaker or ill-formed preferences about politics were the ones to acknowledge that they preferred voting for a candidate who was leading in public opinion polls. Overall, an insignificant 3% of respondents in the 2010 US midterm election sample are estimated to have found frontrunner status a desirable candidate trait. However when the experiment was conducted during the 2012 presidential caucus campaign in Iowa 15% of respondents are estimated to have viewed frontrunner stats as something that would make them more likely to support a hypothetical candidate. The primary normative question that motivated this research asked if regular opinion polling, as reported in popular media, has damaging consequences for democracy. Laws banning the release of pre-election poll data, for example, assume this information is damaging to the health of a democratic society. If that assumption is accepted, some may view the results of this study as lending support to the prohibition of media release of pre-election opinion data. The prospect of unengaged voters supporting a candidate because she is a frontrunner conflicts with the normative assumption that elections should be decided by engaged citizens making decisions based on substantive differences in candidate and party policy positions. However, there is nothing in this study that establishes that the use of polling information leads voters to make a wrong decision. Regardless of whether we identify this as a bandwagon effect, the theory and results presented here are consistent with less engaged voters engaging in satisficing or even strategic behavior, rather than irrational behavior. These are conclusions from experimental data and, as always, there are limits to how much we can generalize beyond the experimental effects. The actual impact of 18

information from pre-election media polls in a real world election may very likely be lower than the limited effects that are demonstrated here. Partisan differences between candidates were largely muted in these experiments. In an actual inter-partisan setting, voter choice may be much more anchored and less sensitive to information about candidate poll standing. Thus, it is not plausible to conclude from Table 2 that 43% of less educated voters will support a candidate in an actual election setting simply because she is ahead in the polls. Furthermore, it is important to stress that even these experimental results suggest that use of poll information to evaluate candidates is rather rare in the general population. Those found here to consider poll data when evaluating candidates are people who are probably least likely to be exposed to information about media polls, and who are also among those least likely to turnout in an election. Given this, prohibitions on the release of polling information likely do little, if anything, to improve the quality of democratic elections. Indeed, prohibitions on reporting preelection poll results that exist in several nations may do actual harm if voters use poll standing strategically to assess party or candidate viability. 19

Table 1: Effect of Opinion Poll Standing on Support for Candidate: List experiment conducted during the 2010 US congressional election. Experimental condition Estimated % more likely to Baseline Ahead in Polls support candidate ahead in polls All subjects 1.36 (.04) a 1.39 (.05) 3.2% 406 b 431 Age groups Younger 1.04 (.10) 1.46 (.12) 40.9% (p <.01) c 85 92 Older 1.48 (.07) 1.38 (.08) -9.8% 169 163 Education High School 1.33 (.09) 1.47 (.12) 14.1% 99 97 BA or more 1.39 (.08) 1.40 (.07) 1.6% 143 191 Partisanship Independent 1.43 (.08) 1.48 (.09) 4.4% 117 127 Democrat 1.17 (.08) 1.19 (.09) 1.8% 152 153 Republican 1.62 (.08) 1.62 (.08) 0.0% 99 123 Strong D /R 1.36 (.07) 1.34 (.07) 2.4% 177 199 Engagement Not pol. active 1.30 (.06) 1.42 (.07) 12.3% (p=.10) 200 203 Note: Mean number of items listed as things that make you more likely to support a candidate. a Standard error of the estimate. b Number of cases. c P-value based on t-test of difference between mean of treatment and control group. P values listed if p=.15 or less. 20

Table 2: Effect of Opinion Poll Standing on Support for Candidate: List experiment conducted during the 2012 Iowa caucus campaign. Experimental condition Estimated % more likely to Baseline Ahead in Polls support candidate ahead in polls All subjects 1.57 (.04) a 1.73 (.05) 15.2% (p <.01) c 452 b 443 Education High School or less 1.50 (.06) 1.93 (.10) 43.3% (p <.01) 128 121 BA or more 1.59 (.05) 1.65 (.06) 5.7% 292 305 Partisanship Independent 1.52 (.07) 1.67 (.08) 14.3% (p =.11) 137 139 Democrat 1.54 (.09) 1.72 (.12) 17.8% (p=.12) 93 95 Republican 1.66 (.06) 1.73 (.07) 6.4% 186 182 Strong D /R 1.62 (.06) 1.74 (.08) 11.8% (p =.12) 175 178 Strong R 1.67 (.07) 1.74 (.09) 7.1% Attend Republican 1.58 (.07) 1.67 (.07) 9.2% caucus 126 137 Engagement Low interest 1.59 (.06) 1.84 (.08) 25.1% (p <.01) 202 194 Note: Mean number of items listed as things that make you more likely to support a candidate. a Standard error of the estimate. b Number of cases. c P-value based on t-test of difference between mean of treatment and control group. P values listed if p=.15 or less. 21

Table 3: Effect of Knowing Candidate B is Ahead in Opinion Polls, on Support for Candidate B ; Question wording experiment. Support Support Candidate A Candidate B Don't know total n / p All subjects Baseline 44.8% 24.6% 30.5% 841 Cand. B is ahead 41.3 26.2% 31.5% -3.5% +1.6% +1.0% Age Groups Younger (born after 1970) Baseline 15.4% 34.1% 50.5% 177 Cand. B is ahead 18.6 45.4 36.5 p=.15 +3.2% +11.3% -14.0% Older (born 1950 or earlier) Baseline 56.8% 20.7% 22.5 332 Cand. B is ahead 55.8 15.8 28.5-1.0% +4.9% +6.0% Education High School Baseline 47.0% 17.0% 36.0% 197 Cand. B is ahead 34.0% 25.6% 40.2% p=.13-13.0% +8.6 +4.2 BA or more Baseline 39.5% 29.1% 31.5 333 Cand. B is ahead 39.3% 23.7% 37.0-0.2% -5.4% +5.5% Note: Probability based on Chi-square test. P values listed if p=.15 or less. 22

Table 3 (continued): Effect of Knowing Candidate B is Ahead in Opinion Polls, on Support for Candidate B ; Question Wording Experiment Support Support Candidate A Candidate B Don't know total n / p Partisanship Independent Baseline 50.9% 18.6% 30.4% 246 Cand. B is ahead 43.7% 18.7% 37.5% -7.2% -0.1% +7.1% Democrat Baseline 15.3% 42.0% 42.7% 307 Cand. B is ahead 13.3% 46.0% 40.1% -2.0% +4.0% -2.6% Republican Baseline 80.0% 7.8% 12.2% 224 Cand. B is ahead 76.1% 8.3% 15.6% -3.9% +0.5% +3.4% Strong D / R Baseline 42.8% 28.3% 28.9% 353 Cand. B is ahead 40.5% 30.1% 29.5% -2.3% +1.8% +0.6% Engagement Low interest Baseline 30.7% 28.6% 40.7% 285 Cand. B ahead 20.7 37.9 41.4 p=.09-10.0% +9.3% +0.7% Not politically active Baseline 40.8% 20.9% 38.3% 404 Cand. B ahead 37.0% 30.3% 32.7% p=.09-3.8% +9.4-5.6% Note: Probability based on Chi-square test. P values listed if p=.15 or less. 23

Table 4: Effect of Knowing Candidate B is Ahead in Polls on Support for Candidate B : Multinomial Logit Estimate. Support Don t Know Cand. B (vs. Cand. A) (vs. support Cand. A) Treatment (prompt) Candidate B ahead in poll.42 (.21)**.32 (.19)+ Age Younger -1.3 (.31)*** -1.1 (.29)*** Older.51 (.25)**.34 (.22) Education High School or less -.16 (.29).52 (.26)** BA degree or more.19 (.19).58 (.23)*** Partisanship Democrat 1.8 (.29)*** 1.4 (.28)*** Republican -1.6 (.43)*** -1.5 (.33)*** Supports Tea Party -2.1 (.35)*** -1.3 (.25)*** Political engagement Low interest in politics.33 (.27).34 (.24) Not politically active -.47 (22)** -.20 (.22) Controls Female.47 (.22)**.32 (.20)+ Nonwhite -.05 (.28) -.16 (.26) Constant -.72 (1.0).03 (.94) Number of cases 833 Pseudo R2.22 Note: Dependent variable has three categories, support Candidate A, support Candidate B, and don t know. * p <.10 (two tail) ** p <.05 (two tail) *** p <.01 (two tail) + p =.10 (two tail) 24

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Appendix, Survey questions: List Experiment, VERSION A Please read the following three things that people might want to know about a candidate before voting. Please tell me how many of them are things that make you more likely to support a candidate. I don't want to know which ones, just how many. Just enter a number from 0 to 3. [RANDOMIZE ORDER OF STATEMENTS] 1) The candidate graduated from a prestigious college. 2) The candidate ran a business. 3) The candidate's family background. List Experiment, VERSION B Please read the following four things that people might want to know about a candidate before voting. Please tell me how many of them are things that make you more likely to support a candidate. I don't want to know which ones, just how many. Just enter a number from 0 to 4. [RANDOMIZE ORDER OF STATEMENTS] 1) The candidate graduated from a prestigious college. 2) The candidate ran a business. 3) The candidate's family background. 4) The candidate is leading in recent opinion polls. Question wording experiment, VERSION A Consider the following hypothetical candidates running for office. Candidate A runs a successful business. Candidate B runs a major charity organization. Knowing this, if you had to choose between Candidate A or Candidate B, which one would you vote for? Candidate A Candidate B don't know Question wording experiment, VERSION B Consider the following hypothetical candidates running for office. Candidate A runs a successful business. Candidate B runs a major charity organization and has a large lead in the most recent public opinion poll. Knowing this, if you had to choose between Candidate A or Candidate B, which one would you vote for? Candidate A Candidate B don't know 29

Independent variables and categories of respondents from 2010 CCES Younger: V215, year born. "Younger" are those born after 1970 Older: V215, year born. "Older are those born before 1950. High school, V213, education. High school = No high school degree and high school graduate. BA degree, V213, education. BA degree are those with a 4 year degree or higher. Democrat, V212a, 3-point party ID. 1 if respondent replied "Democrat," otherwise 0. Republican, V212a, 3-point party ID. 1 if respondent replied "Republican," otherwise 0. Independent, V212a, 3-point party ID, 1 if respondent replied "independent" otherwise 0. Supports Tea Party, CC424, tea party favorability rating. 1if "very positive" favorability rating of Tea Party, 0 if otherwise. Strong Party ID, V212d, Strong Democrat or Strong Republican Low interest, V24, interest in news and public affairs. 1 if respondent replied "some of the time," "only now and then," "hardly at all" and "don't know. 0 if replied "most of the time" Don't read paper, CC301_3, media use. 1 if respondent said "no" when asked if read newspaper, 0 if yes. Not politically active, CC417a_6, political activity. 1 if coded "yes" the item representing that the respondent did none of listed political activities (attend meetings, have political sign, work for campaign, donate money). 30