INFORMATION, SOURCECREDIBILITYAND POLITICALSOPHISTICATION: EXPERIMENTALEVIDENCEONECONOMICVOTING∗

JAMES E.ALT† DAVID D.LASSEN‡ JOHN MARSHALL§

APRIL 2014

How does the source of politically-relevant economic information affect voter beliefs and ultimately political preferences? This paper randomly varies whether voters re- ceive an aggregate unemployment projection from the central bank, government or opposition party using a survey experiment in Denmark with unique access to detailed panel and administrative data. All sources induce voters to update their unemployment expectations. While all voters regard the Danish Central Bank as the most credible source, only sophisticated voters update more after receiving information from a party with political incentives to state otherwise. However, belief updating is no greater when the source is aligned with the voter’s previously expressed political preferences. After decreasing unemployment expectations, we find clear evidence of intended eco- nomic voting, without voters changing their policy preferences: the average respon- dent is 3.5 percentage points more likely to vote for the government. Such economic voting is driven by politically sophisticated rather than swing voters.

∗We wish to thank Alberto Abadie, Charlotte Cavaille, Alex Fouirnaies, Torben Iversen, Horacio Larreguy and Victoria Shineman for valuable advice and comments, as well as participants at the Harvard Political Economy and Comparative Politics workshops, NYU Center for Experimental Social Science Conference 2014, Midwest Political Science Association 2014, and MIT Political Economy Breakfast. Lassen thanks the Danish Council for Independent Research under its Sapere Aude program for financial assistance. †Department of Government, Harvard University, james [email protected]. ‡Department of Economics, University of Copenhagen, [email protected]. §Department of Government, Harvard University. [email protected]. (Corresponding author.)

1 1 Introduction

Possessing and processing politically-relevant information is a central feature of how voters hold governments to account and express their preferences over policies. However, most evidence sug- gests that voters lack basic information about their political or economic contexts (see Anderson 2007). Thus, the provision of credible information has the potential to ensure politicians are more accountable to voters.1 This is particularly true for economic voting, where aggregate economic information updates voter beliefs about a government’s competence in office (Anderson 1995; Ro- goff and Sibert 1988). However, providing voters with credible information is not straight-forward in practice. In- formation is rarely provided by independent sources and without an accompanying slant. Rather, economic and political information is typically communicated by actors with incentives to deceive or persuade recipients (Baron 2006; Besley and Prat 2006; Larcinese, Puglisi and Snyder 2011). Recognizing that much of the information available to voters is biased,2 new information may not affect the beliefs of skeptical voters (Gentzkow and Shapiro 2006). This raises the question of when sources of political information affect voter beliefs and polit- ical preferences. We address this important and unanswered question by examining the conditions under which the source of messages conveying information about future aggregate unemployment— probably the most important indicator of government performance for voters (Anderson 1995)— affect voter beliefs and political preferences using a survey experiment. Our experiment is con- ducted in Denmark, an open economy where macroeconomic concerns have been highly salient in 1For example, information about corruption (Chong et al. 2011; Ferraz and Finan 2008), eco- nomic performance (Bartels 2008; Healy and Lenz 2014) and politician activity (Banerjee et al. 2011), and transparent chains of accountability (Powell Jr. and Whitten 1993) have helped hold governments to account at the polls. 2Goidel and Langley(1995) and Nadeau et al.(1999) document that voters do understand that sources of information may be biased. Similarly, many studies note significant differences in trust across political and media institutions (e.g. Dalton 2008).

2 the aftermath of the financial crisis and where left-right political divisions remain entrenched. The combination of a panel political survey and access to extremely detailed administrative govern- ment data provides a unique opportunity to understand in detail which voters update their beliefs and when such beliefs translate into economic voting. We first examine how the source of unemployment projections affect unemployment expecta- tions. We find that the objective credibility of the information source matters: an unemployment projection from the DCB, which is highly trusted among citizens, causes voters to update their belief more than receiving information from government or opposition political parties. While in- formation from both governing and opposition political parties do still affect voter beliefs, a more sophisticated subset of voters also recognize that a projection from a source with electoral incen- tives to say the opposite is more credible. However, we find no evidence of subjective credibility such that voters update more in response to unemployment projections from the party they favor. Our instrumental variable analysis shows that a percentage point decrease in unemployment expectations increases the probability that the average complier intends to vote for Denmark’s coalition government by 3.5 percentage points. This large effect, which only helped the parties of the Prime Minister and Minister for the Economy and Interior, would have been more than enough to have altered the outcome of Denmark’s recent knife-edge elections. Supporting the economic voting interpretation, we observe a large increase in confidence in the government and no change in support for non-government left-wing parties. Although these results could still reflect unemploy- ment expectations changing voter policy preferences, rather than beliefs regarding the competence of the government, unemployment expectations do not affect attitudes toward redistributive or un- employment insurance policies. Since assigning responsibility for policy outcomes is especially challenging in Denmark’s com- plex political system and very open economy, it is not surprising to find that providing new infor- mation only induces a subset of voters to vote economically. In particular, we find that economic voting is neither concentrated among swing voters nor ideologues. Rather, economic voting is only

3 observed among sophisticated—better informed, more educated and politically-engaged—voters and those who already believe the economy is improving. These results show that politically- relevant information can support democratic accountability, even in political environments where the clarity of responsibility is low, but is not sufficient to induce all voters to reward good per- formance. This finding may explain why parties tend to target target their messages at more politically-engaged voters who appear to be more sensitive to new information (Adams and Ezrow 2009; Gilens 2005). The paper is structured as follows. Section2 distinguishes the objective and subjective cred- ibility of a source of political messages, and considers how economic information might affect political preferences. Section3 details our experiments designed to parse out these effects. Section 4 examines how beliefs change, before Section5 maps these beliefs to vote intention and welfare policy preferences. Section6 concludes.

2 Theoretical motivation

This section first considers how voters may differ in their responses to receiving politically-relevant information from different sources. Focusing on aggregate unemployment expectations, we then consider how such information could affect economic voting.

2.1 Information sources

Despite long-running attention to economic voting and growing interest in political information, it remains unclear what types of new information will change the beliefs and behavior of voters. Many researchers treat information as an unbiased resource helping voters to make the right deci- sion (e.g. Feddersen and Pesendorfer 1996), or assume that voters start from a common prior (e.g. Rogoff and Sibert 1988; Rogoff 1990). In experimental work, information is frequently provided without a source and consequently relies upon the experimenter’s credibility.

4 However, in the real world, most politically-relevant information is conveyed by agents with distinct and often well-understood ideological biases and incentives to distort perceptions of the true state of the world (e.g. Baron 2006; Besley and Prat 2006; Gentzkow and Shapiro 2006; Zaller 1999).3 Empirically, Larcinese, Puglisi and Snyder(2011) have shown that pro-Democrat newspa- pers in the U.S. are more likely to report high unemployment under Republican Presidents, while Durante and Knight(2012) point to significant biases in television coverage in Italy. Accordingly, voters must evaluate the information they receive in terms of the credibility of the source. We distinguish two forms of source credibility that could affect belief updating after receiving new information. Objective credibility reflects beliefs about the source’s credibility that depend upon institutional characteristics of the source that are extrinsic to the voter (see also Ansolabehere, Meredith and Snowberg forthcoming; Zaller 1999). Two important characteristics are institutional expertise and incentives to deceive. Independent central banks are typically relatively credible because they have few political incentives to deceive voters,4 and often successfully establish a reputation for sending accurate messages by virtue of employing highly-trained economists and providing convincing technical data. Conversely, political parties (and certain media channels) have widely-understood biases (e.g. Prior 2013): governments have strong incentives to play up their performance in office, while opposition parties may do the reverse. These features of objective credibility imply the following hypotheses:

H1. (Institutional expertise) Fixing message content, voters change their beliefs more after re- ceiving information from an expert source.

H2. (Institutional incentives) Fixing message content, voters change their beliefs more after re- 3Voters receiving biased information is also a demand side phenomenon as well (see Mul- lainathan and Shleifer 2005). We focus on supply by experimentally varying the sources voters are provided with. 4An influential literature begun in the 1980s persuaded politicians that independent central banks could credibly commit countries to sound monetary policies that politicians would otherwise have incentives to renege on after winning elections (see Barro and Gordon 1983).

5 ceiving information from a source with political incentives to conceal such information.

To be precise, greater belief updating entails larger shifts in the mean of an individual’s probability distribution over the future unemployment rate. Whether an expert source affects beliefs more than receiving information going against the expected bias of a less expert source is an empirical ques- tion that our experiment can answer, but H1 on its own implies that a central bank is regarded as more credible than political parties while H2 implies that positive information from the opposition is more credible. Subjective credibility, on the other hand, depends upon characteristics intrinsic to the receiver of the information. One critical basis for difference among voters in their response to a given source is their political sophistication (see Gomez and Wilson 2001, 2006).5 The most sophis- ticated voters—those that are both politically informed and able to comprehend and assess more technical information—are unlikely to significantly update their beliefs, since their prior is likely to be tighter (Ansolabehere, Meredith and Snowberg forthcoming). Nevertheless, given the best informed voters are generally fairly imperfectly informed (Anderson 2007; Duch and Stevenson 2008), we still expect most voters to respond to specific information. The least sophisticated voters are likely to have the most diffuse priors. Consequently, we expect the least politically sophisti- cated voters to update most:

H3. (Political sophistication) Fixing message content, sophisticated voters change update their beliefs less after receiving new information.

However, while the prior beliefs of the least politically sophisticated are likely to be least accurate, such voters are less likely to discern biases in the source. This points to an important interaction between objective and subjective credibility. 5For example, Duch and Stevenson(2010) and Imai, Hayes and Shelton(2014) find that better educated and more informed voters are better able to disentangle domestic from imported sources of growth.

6 A second dimension of subjective credibility is differences among respondents in their “accu- racy goals” and “directional goals”, where the former types seek to make decisions based on the most accurate information (akin to objective credibility) while the latter only seek information that confirms their prior beliefs (Taber and Lodge 2006). Similarly, Mullainathan and Shleifer(2005) show that with heterogeneity in priors over politically divisive issues, newspapers separate in their reporting of the news and cater to a segmented market where the credibility of information in the eyes of the consumer varies considerably. A large literature in the U.S. has suggested that knowing the position of a political party on an issue strongly conditions a voter’s beliefs and preferences (see Boudreau and MacKenzie 2014; Bullock 2011; Malhotra and Kuo 2008; Healy and Malhotra 2013). Given the U.S. is currently experiencing high political polarization and has only two politi- cal parties, it is not obvious that the partisans in other contexts will respond similarly. Accordingly, we consider:

H4. (Partisanship) Fixing message content, voters change their beliefs more after receiving in- formation from a source the voter is politically close to.

Of course, aspects of both objective and subjective credibility could simultaneously affect vot- ers. By randomly varying sources with differing levels of objective credibility, and comparing responses to a given source across different types of voter, our empirical design separates differ- ences in credibility.

2.2 Political implications for economic voting

The idea that governments may be rewarded or sanctioned by voters on the basis of their economic performance is well-established (see Anderson 2007; Lewis-Beck and Paldam 2000; Lewis-Beck and Stegmaier 2000). The logic underlying this argument is that voters impose sanctions on the basis of economic outcomes to deter re-election seeking politicians from choosing suboptimal policies (Barro 1973; Ferejohn 1986), or looking forward use the available information to select the

7 most competent candidate (Fearon 1999; Rogoff and Sibert 1988; Rogoff 1990).6 Both backward- and forward-looking information can help to evaluate the competence of office-holders. To the extent that economic performance is a key election issue and is deemed to possess the capacity to affect the economy (Duch and Stevenson 2010), information about macroeconomic performance is expected to increase economic voting. The empirical evidence assessing whether economic success translates into higher likelihoods of an incumbent being re-elected has been mixed (Anderson 2007), and has struggled to provide compelling evidence of a causal relationship (Healy and Malhotra 2013). To the extent that voting is economic, most studies conclude that it is macroeconomic “sociotropic” aggregates rather than individual-specific “pocketbook” calculations that drive this relationship (e.g. Kiewiet 1983; Lewis-Beck and Stegmaier 2000). Economic voting models require that voters both obtain and process sufficient information about policy choices—or at least their (expected) outcomes—to attribute responsibility and evalu- ate incumbent performance. These assumptions are now receiving greater scrutiny (see Anderson 2007; Healy and Malhotra 2013). Research has shown that voters often lack even the minimal information required to vote according to economic performance (e.g. Campbell et al. 1960; Delli Carpini and Keeter 1996) or suffer partisan biases in attribution (Fiorina 1981; Rudolph 2003a, 2003b, 2006; Malhotra and Kuo 2008; Tilley and Hobolt 2011), while informed voters have lacked the motivation or cognitive capacity to translate information into responsibility desig- nation (e.g. Bartels 1996; Delli Carpini and Keeter 1996; Krause 1997). These problems are multi- plied in institutional contexts characterized by multiple loci of decision-making power, where even the most willing economic voter may struggle to assign responsibility for economic performance (Anderson 1995; Duch and Stevenson 2008; Nadeau, Niemi and Yoshinaka 2002; Powell Jr. and Whitten 1993). Furthermore, information about performance in office may not persuade extreme or especially partisan voters to act upon it (Ansolabehere and Snyder Jr. 2000). 6The motives underpinning this approach could be either sociotropic or self-interested. As Ansolabehere, Meredith and Snowberg(forthcoming) have shown, parsing out these effects is challenging.

8 Combining these insights, our economic voting hypothesis is stated with significant condition- ality:

H5. (Economic voting) If an individual’s unemployment expectations decrease, the likelihood that they vote for a party in government (in any given institutional context) increases only if the individual has the cognitive capacity and will to assign government responsibility to economic performance.

In this light, economic voting is not the inevitable by-product of providing economic information for all voters.

3 Research design

3.1 Danish political context

Left-right differences over economic policy remain the salient division in Danish politics, with governments oscillating between center-left and center-right coalitions. In 2011, Social Democrat Helle Thorning-Schmidt became Denmark’s first female Prime Minister, having narrowly led the left bloc—containing the Social Democratic, Social Liberal and Socialist People’s parties as coali- tion partners, and supported by the Red-Green Alliance—to victory over a center-right coalition led by the Liberals that had held office since 2001. Dissatisfaction with the government’s economic performance was the major issue in the 2011 election.7 Having sustained very low levels of aggregate unemployment throughout the 2000s, the financial crisis hit Denmark’s trade-dependent economy badly. In early 2008 unemployment hit 7E.g. this Economist article. The Danish Election Study polls, available here, show that the economy was definitively the most importance issue for voters, while nearly 20% specifically cited unemployment. The study also shows that left-wing voters thought the labor market was the biggest issue, while right-wing voters thought the economy in general was the biggest issue. Voters similarly divided over whether a left or right coalition would best fight unemployment.

9 new lows of nearly 3%, but had increased to around 8% by the 2011 election.8 The budget deficit also ballooned, leaving Denmark with hard fiscal choices regarding welfare and pension reform. The center-right’s austerity policies were widely blamed for the failure to produce a stronger eco- nomic recovery.9 Despite this, the left only just achieved a parliamentary majority, as shown by the seat distribution for Denmark’s legislative assembly (the Folketing) in Figure1. In fact, the Social Democrats actually lost one seat relative to the 2007 election, while the Liberals gained one seat. The shift in political power particularly reflected the rise of the Social Liberals at the expense of the Conservative People’s Party. Although the Danish economy has improved since the 2011 election, left-right economic dif- ferences have become more politically salient. In January 2013, gross unemployment had officially fallen to 7.4%.10 Importantly for our study, the DCB expected this rate to fall to just below 7% by January 2014 (which turned out to be exactly right). Nevertheless, the share of Danes regard- ing unemployment as the biggest political problem rose from 18% at the 2011 election to 20% by November 2012, and 36% by late 2013.11 Moreover, within-coalition tensions between the eco- nomically liberal Social Liberals and the socialist Socialist People’s parties increased. The Social Liberals only joined the coalition after agreeing a significant conservative welfare reform with the center-right before the election, and these differences culminated in the Socialist People’s Party leaving the coalition in January 2014 over unpopular plans to privatize the country’s state-owned energy company. Economic policy has been contentious throughout the government’s tenure.12 8Unemployment data from Eurostat here. Although Eurostat computes unemployment using surveys to ensure cross-national comparability, the Danish government uses administrative records to calculate gross unemployment (which is very similar). 9Even though the financial crisis itself was not the fault of Denmark’s government at the time, governments can still be held responsible for exogenous shocks (see Duch and Stevenson 2008), or for failing to respond effectively. 10Gross unemployment is the official unemployment figure used by the government, and is cal- culated using administrative register data. Gross unemployment differs from net unemployment in that participants in active labor market programs are included in the unemployment rate. 11The November 2012 poll was taken from DR Nyheder here, while the December 2013 poll was taken from Jyllands-Posten here. 12Another example is the reduction of the maximum length of unemployment benefits from four

10 Left Right

Danish People's Party Liberal Party Conservative People's Party Liberal Alliance Danish Social Liberal Party Social Democrat Party Socialist People's Party Red-Green Alliance

Figure 1: Folketing seat distribution after 2011 election

Notes: Left bloc shaded in red, right bloc shaded in blue. Intensity of color roughly indicates strength of ideology according to the 2011 Danish Election Study.

3.2 Experimental design

To examine the hypotheses derived above, we embedded a survey experiment in the 2013 wave of the Danish Panel Study of Income and Asset Expectations (Kreiner, Lassen and Leth-Peterson 2013), an annual panel survey of around 6,000 broadly nationally representative Danes conducted to two years, which was subsequently repealed following an agreement to instead reduce benefits in the final two years to 60% of their initial level.

11 every January/February.13 The panel, which has been conducted by telephone since 2010, asks wide-ranging question about the respondent’s financial position as well as their political prefer- ences. Furthermore, the survey data has been linked by Statistics Denmark, using unique per- sonal identifiers from the Danish Central Person Registry, to an extraordinarily rich administrative dataset containing official government register data containing wide-ranging information about all Danes. The final data set made available for research was anonymized. The combination of panel political data and detailed respondent histories permits unprecedented detail in our analysis of differential responses to politically-relevant information. The central goal of our experiment is to evaluate the conditions under which the provision of economic information affects individual beliefs and political preferences. We designed our treat- ments to differentiate the effects of political sources by providing “factual” content in an apolitical manner.

3.2.1 Treatments

We examine source credibility by varying the source of simple unemployment forecasts, as well as the forecast itself. After being asked what they estimate the current unemployment rate is, re- spondents were randomly assigned to one of eight treatment conditions with around 700 members each. The control group received no information, while six treated groups were read the following statement:

“Assume that that the [DCB/government/Liberals] estimates that unemployment in 2013 will be [almost 7%/around 5%].”14 13The first wave randomly chose around 6,000 respondents from the Central Person Registry. Annual attrition is around 20-30%. The sample has been replenished with randomly chosen re- spondents from the Registry. 14Survey treatments and questions are translated from Danish; see Online Appendix for Danish phrasing. It is important to emphasize that in Danish the prime translates as a prospective estimate.

12 Respondents were therefore informed that the DCB, the government or main opposition party project that unemployment over the next year will be “almost 7%” or “around 5%”. The true DCB projection for gross unemployment was almost 7%. However, because only the DCB has publicly stated this, ethical considerations required that our other primes begin with “assume that...”. In order to examine the extent to which such wording weakens the treatment, our final treatment group was truthfully told “The DCB estimates unemployment in 2013 to be almost 7%.” We compare this treatment to the analogous “assume” version, and will show no statistical difference in the distribution of unemployment expectations. These sources vary considerably in their credibility among voters of all political stripes. Unlike some other central banks, the DCB is highly regarded by voters, and is not seen as having a right- wing agenda or being an instrument of government. Asking respondents how much trust they place in each source, 67% of respondents trusted or greatly trusted the DCB while only 17% and 27% trusted or greatly trusted the government and Liberals respectively.15 Eurobarometer data indicates that trust in Denmark’s political parties is very similar to the European Union mean (European Commission 2011).

3.2.2 Outcome variables

We consider two types of outcome variables: unemployment expectations and preferences over political parties. To capture unemployment expectations we asked respondents “What is your best estimate of what unemployment will be in 2013? We would like your best estimate, even if you are not entirely sure.”16 This question was asked immediately after respondents received their treatment, and the 20 respondents who answered that the unemployment rate would exceed 50% 15Only the control group responses were used because this question followed the treatment, and thus including post-treatment responses could bias our estimates. These numbers are in line with mass surveys conducted by Statistics Denmark: in 2011, they found that while 82% trusted the DCB, only 59% trusted Parliament. See report summary here. 16From a Bayesian perspective (see Online Appendix), this response can be thought of as an individual’s posterior unemployment belief (updated after receiving new information).

13 were removed.17 Political preferences are primarily measured by voting intentions and evaluations of the govern- ment, although we also consider various placebo tests. We code indicator variables for intending to vote for Denmark’s main political parties, as well as groups for the governing coalition (Social Democrats, Social Liberals and Socialist People’s parties) and right-wing parties. Vote intention was elicited 18 questions after the treatment was administered. Because turnout in Denmark regu- larly exceeds 85%,18 and 72% of respondents ultimately reported voting for the party they intended to vote for eight months prior to the 2011 election, vote intention represents a good approximation for what would happen if an election was held immediately. To assess voter perceptions of gov- ernment competence, we asked respondents how much confidence they have in the government. Respondents were provided a five-point scale ranging from little great mistrust (1) to great trust (5) in the government.19

3.3 Identification and estimation

Treatment status is well balanced across pre-treatment covariates. Tables8 and9 in the Online Appendix confirm balance across 16 political and socioeconomic variables frequently included in observational studies regressing political preferences on a set of covariates. Given random assign- ment, our empirical analysis can straight-forwardly identify the causal effects of the treatments. To estimate the average treatment effect on the treated (ATT) for each information treatment on unemployment expectations U expecti, we estimate the following equation using OLS:

U expecti = Ziα + εi, (1) 17These individuals were very evenly spread across treatment conditions, with between 2 and 4 omitted respondents in each group. Removing these observations does not affect the results. 18See Institute for Democracy and Electoral Assistance. 19This question was asked 11 questions after the treatment was administered.

14 where Zi is the vector of treatment assignments. Interaction terms are added to allow for hetero- geneous responses to treatments, and thus aid characterization of which types of individual the treatments affect. Robust standard errors are reported throughout. To identify our ultimate quantity of interest—the causal effects of unemployment expectations on political preferences—we use our information treatments as instruments for unemployment expectations. Instrumenting overcomes the obvious concern that economic expectations may be correlated with omitted variables that also affect political preferences. Taking equation (1) as the first stage, we estimate the local average causal response (LACR) (Angrist and Imbens 1995), averaging the causal effects for compliers—individuals for whom our randomly-assigned informa- tion treatments induced respondents to change their unemployment expectations—across different unemployment expectation levels.20 Accordingly, we estimate the following structural equation using 2SLS:

Yi = τU expecti + δU nowi + ξi, (2)

where Yi is vote intention, confidence in the government, or a policy preference placebo test. The respondent’s estimate of the current unemployment rate (U nowi), a good approximation for an individual’s prior unemployment expectation, is included to enhance efficiency.21 Consistent estimation of the LACR requires two assumptions beyond the randomization of our instruments: monotonicity and an exclusion restriction (Angrist, Imbens and Rubin 1996; Imbens and Angrist 1994). Monotonicity entails that each individual would update their unemployment expectations in the same direction upon receipt of the treatment. Although it is hard to imagine 20The LACR here is the linearized causal effect of unemployment expectations, weighted toward areas where the density function of complier responses is greatest. 21The (average) prior belief is most accurately estimated using the control group’s unemploy- ment expectation. However, the estimate of the current unemployment rate is also an excellent proxy for the prior over future the unemployment rate: among the control group, there is a 0.93 correlation between current and future estimates. Our results are almost identical using difference between the current and future unemployment estimate as the endogenous variable.

15 when prominent public sources would induce voters to update their beliefs against the information provided, respondents with low prior unemployment expectations may increase their unemploy- ment expectations, especially after the 7% treatments. Fortunately, the monotonicity assumption can be weakened in ways consistent with our data. In general, 2SLS estimation recovers a very similar quantity of interest to the LACR when “few subjects are defiers, or if defiers and compliers have reasonably similar distributions of potential outcomes” (de Chaisemartin 2013: 7)—and is identical under constant causal effects (see Angrist, Imbens and Rubin 1996).22 In this application, 27% of respondents upwardly update their unem- ployment expectations relative to their current estimate. Since upward and downward effects may be very similar, and given that compliers significantly outnumber defiers, the presence of defiers is relatively unproblematic. Nevertheless, our results are very similar when we restrict the sample to the 5% treatments with almost no defiers. Our analysis also considers a variety of other subgroup analyses, based on respondents’ current estimates of unemployment, where monotonicity almost certainly holds.

The exclusion restriction, which requires that the instrument only affects Yi through U expecti, is usually more problematic in empirical studies. Although such violations are unlikely in this application, perhaps the most plausible violation arises where information treatments prime re- spondents to think more carefully about government performance and policies (beyond the effect of changing beliefs about unemployment expectations), inducing bias if such thinking systemati- cally affects support for the government. We assess this possibility by looking at whether belief in the importance of political information for either private economic decisions or as part of the 22Rather than recover the local average treatment effect, the Wald estimator (with no covariates) recovers the average treatment effect for a smaller group of compliers (precisely those not canceled out by the defiers). A sufficient assumption for this to equate to the case with no defiers is that there are more compliers than defiers for any combination of potential outcomes (Assumption (2.4) and equation (2.2) in de Chaisemartin 2013), while a weaker condition requires only that some subset of compliers has the same size and marginal distribution over potential outcomes as defiers, or that there are more compliers and defiers at each potential outcome (de Chaisemartin 2013).

16 respondent’s job differs across treatments groups (or comparing the control to all treated respon- dents), and find no difference.

4 Effects of information source on economic expectations

We first show that the information treatments substantially change unemployment expectations. While we find evidence for both forms of objective credibility, and differential responses by prior knowledge of the current unemployment rate, there is no evidence that political preferences cause voters to update differentially. We first examine the distribution of the data, before proceeding to regressions identifying average effects and then heterogeneity in voter responses.

4.1 Results

Figure2 plots the distribution of unemployment expectation responses by treatment condition. Before turning to our main results, it is clear from Panel A that the “assume” wording does not affect the distribution of the DCB 7% projection responses.23 This suggests that the statement wording is not biasing the results. Henceforth we pool the DCB 7% treatment groups. Although this similarity may not necessarily extend to other treatments, it suggests that any differences are likely to be small, while if anything our treatment effects are lower bounds. The leftward shift in density associated with all treatments indicates that all information sources reduce average unemployment expectations. This reduction reflects systematic pessimism in a population where the average member of the control group expected an unemployment rate of 9.0%. Despite its optimism relative to the true DCB claim, the 5% treatments dragged expectations below those receiving the 7% treatments. In all cases, the information treatments reduced the variance of the distributions, providing further evidence that the treatments affected voters.24 We 23Tests comparing the mean and variance of the distributions cannot reject the null hypothesis of identical sample moments. 24Distributional tests confirm that the variance reduction is statistically significant. Although

17 Panel A

0.30 Control DCB 7% Assume DCB 7% 0.20 Assume DCB 5% Density 0.10 0.00 0 5 10 15 20

2013 unemployment expectation (%)

Panel B

0.30 Control Assume govt. 7% Assume govt. 5% 0.20 Assume opp. 7% Assume opp. 5% Density 0.10 0.00 0 5 10 15 20

2013 unemployment expectation (%)

Figure 2: Unemployment expectations by DCB treatments

Notes: For graphical exposition, the x-axis is truncated so that the 1% of the sample with expectations above 20% are not visible.

18 now turn to our source credibility hypotheses. Consistent with differences in expertise (H1), receiving information from political parties caused the average voter to update their beliefs less than receiving information from the DCB. The DCB treatments also induced more similar responses from voters (i.e. a smaller standard deviation in re- sponses), especially compared to the opposition treatments. Although it could have been the case that simply being primed by a source increased confidence in the source, the Online Appendix shows that receiving a treatment does not affect trust in either political party.25 Partisan sources also reduced unemployment expectations. Panel B clearly shows a downward shift in modal unemployment expectations for both the government and opposition treatments. Surprisingly, given that the opposition has a political incentive to criticize government economic performance, the Liberal projections did not cause voters to differentially update their beliefs rela- tive to the predictably optimistic government message. We therefore find little support for H2, on average. Table1 confirms our graphical analysis by estimating equation (1). Receiving a 7% treatment reduces unemployment expectations by around 1 percentage point, while a 5% treatment subtracts a further 0.5 percentage points. For both levels, the DCB has a larger effect on unemployment expectations. Supporting the importance of differences in institutional expertise (H1), the p-values associated with F-tests comparing the DCB source coefficients to the party source coefficients generally show a credibility difference for both the 7% and 5% treatments. Contrary to H2, there is no discernible difference between the government and opposition 7% or 5% treatments in the full sample. these belief shifts could in part reflect anchoring biases (Tversky and Kahneman 1974), it is hard to see how such explanations could explain the changes in political preferences we document below. 25There is a slight increase in trust of the DCB, but the change cannot explain the large response to the DCB treatments.

19 Table 1: Effect of information treatments on unemployment expectations (%)

Unemployment expectations (%)

Control 9.012*** (0.185) DCB 7% treatment (combined) -1.123*** (0.197) DCB 5% treatment -1.663*** (0.230) Government 7% treatment -0.848*** (0.213) Government 5% treatment -1.218*** (0.233) Opposition 7% treatment -0.923*** (0.223) Opposition 5% treatment -1.335*** (0.236)

Test: DCB 7% = Government 7% p = 0.03** Test: DCB 7% = Opposition 7% p = 0.16 Test: Government 7% = Opposition 7% p = 0.65

Test: DCB 5% = Government 5% p = 0.02** Test: DCB 5% = Opposition 5% p = 0.10 Test: Government 5% = Opposition 5% p = 0.57

Observations 5,705 Outcome mean 7.98 Outcome standard deviation 3.55

Notes: Estimated using OLS. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. The coefficient tests at the foot of the table report the p value from a two-sided F test of coefficient equality.

20 4.2 Heterogeneous effects: when and how do voters update their beliefs?

The potential impact of different information sources depends on which voters update their beliefs. We explore this issue using heterogeneous effects and sub-samples as a means of identifying how new information affects voter beliefs. We first test for partisan subjective credibility (H4). The bottom row of Table2 shows that there is no evidence for differential updating: respondents who voted for a government (right) party at the 2011 election did not differentially update their beliefs when provided with information from the government (opposition).26 Given these results are surprising from the perspective of previous findings in the U.S., we examined various alternative definitions of political disposition. We similarly found no difference when defining left and right-wing supporters as respondents who intended to vote for the same left or right party in the 2011 and 2012 surveys. Looking for differences within education groupings and alternatives measures of political ideology all yielded no differential response. The results therefore strongly suggest that the political beliefs of Danes do not affect their views on politically-relevant information. While prior political dispositions do not cause voters to respond differentially, there are system- atic differences by voter political sophistication (H3). Table2 shows that men and respondents with greater education, higher wage income, and faith that the Danish economy will improve relative to the previous year update less in response to unemployment information.27 However, a respondent’s current unemployment estimate effectively serves as a “sufficient statistic” for these characteristics representing political sophistication: the Online Appendix shows that once a respondent’s prior is included as an interaction with the treatments, the interaction coefficients in Table2 dramati- cally decline in magnitude and leave only the interaction with perceptions of national economic prospects as statistically significant. 26There is similarly no difference if we examine only the interaction between previous voting behavior and our treatments. 27The respondent’s subjective probability of being without a job in the forthcoming year also had no interaction effect, but substantially reduced the sample size.

21 Table 2: Heterogeneous effects of information treatments on unemployment expectations (%), by conditional marginal effect

DCB 7% DCB 5% Govt. 7% Govt. 5% Opp. 7% Opp. 5%

Linear effect -5.006*** -5.14*** -4.203*** -4.778*** -4.263*** -4.364*** (1.239) (1.492) (1.379) (1.519) (1.386) (1.489) × News every day 0.798* 0.501 0.016 0.605 0.342 0.306 (0.442) (0.538) (0.509) (0.526) (0.514) (0.53) × Denmark economic prospects 0.615** 0.611* 0.764** 0.47 0.637** 0.388 (0.279) (0.325) (0.299) (0.389) (0.308) (0.337) × Wage income (log) 0.105** 0.049 0.099** 0.161*** 0.072 0.127** (0.044) (0.053) (0.045) (0.047) (0.051) (0.05) × Medium education 1.448*** 1.638** 1.019 0.9 1.443** 1.183* (0.549) (0.642) (0.624) (0.645) (0.61) (0.645) × High education 1.748*** 1.534** 1.02 0.703 1.93*** 1.593* (0.631) (0.722) (0.692) (0.731) (0.689) (0.844) × Woman -1.6*** -1.133** -1.39*** -1.038** -1.092** -1.492*** (0.384) (0.445) (0.416) (0.463) (0.438) (0.469) × Voted left at last election 0.084 0.095 -0.134 -0.083 -0.34 0.193 (0.386) (0.451) (0.42) (0.465) (0.437) (0.467)

Notes: All coefficients are estimated from a single OLS equation interacting all treatments conditions with the variables on the left hand side of the table (see Online Appendix for their definitions). The coefficient for the control group is 15,429***(1.173). The sample size is 5,446. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

22 Simply linearly interacting a respondent’s current unemployment estimate with the informa- tion shocks suggests that better informed voters are not affected by our treatments. However, this approach could miss important variation in responses by political sophistication, or ignore voters with low current unemployment estimates who may revise up their posterior beliefs. To provide a clearer picture, we first split the sample between under- and over-estimators according to whether a respondent’s current unemployment estimate exceeds the true 7.4% rate, before restrict- ing attention to respondents with initial estimates between the treatment rate bounds and within 2 percentage points of the truth. The Online Appendix confirms that the latter three samples include more sophisticated voters with more education, who discuss politics more often and watch the news more often. The distribution of political preferences, however, is very similar across these samples. The results in columns (1) and (2) of Table3 reveal that under- and over-estimators respond quite differently. Unsurprisingly, over-estimators experience the largest declines in unemploy- ment expectations. Coefficient comparison tests show that the DCB reduces expectations further than political parties, but again identifies no difference between political parties. However, under- estimators do exhibit an understanding of differential political incentives: supporting H2, under- estimators increase their expectations after receiving the government 7% projection relatively more than after receiving the opposition 7% projection. The greater institutional credibility of the DCB cannot be distinguished from the credibility arising from the government providing information that goes against their political incentives. Although the relative magnitudes support similar con- clusions for the 5% projections, we cannot reject coefficient similarity.28 Turning to columns (3) and (4), a similar picture of sophisticated voting emerges among re- spondents for whom the treatments imply belief updating in opposing directions and respondents whose current unemployment estimate was within 2 percentage point of the truth. Column (3) 28Controlling for current unemployment guess, there is a statistically significant difference such that under-estimators trust the government less.

23 Table 3: Effects of information treatments on unemployment expectations (%), by current unemployment estimate

Estimate>7.4% Estimate≤7.4% Estimate∈[5%,7%] Estimate∈[5.4%,9.4%] (1) (2) (3) (4)

Control 11.703*** 6.099*** 6.505*** 7.536*** (0.282) (0.083) (0.071) (0.080) DCB 7% treatment (combined) -2.582*** 0.501*** 0.295*** -0.273*** (0.303) (0.092) (0.079) (0.087) DCB 5% treatment -2.734*** -0.361*** -0.623*** -1.224*** (0.372) (0.100) (0.091) (0.101) Government 7% treatment -2.183*** 0.625*** 0.340*** -0.134 (0.329) (0.103) (0.090) (0.098) Government 5% treatment -2.429*** -0.290*** -0.426*** -0.773*** (0.355) (0.106) (0.093) (0.100) 24 Opposition 7% treatment -1.963*** 0.305*** 0.076 -0.311*** (0.346) (0.106) (0.095) (0.102) Opposition 5% treatment -2.145*** -0.396*** -0.496*** -0.961*** (0.371) (0.109) (0.098) (0.106)

Test: DCB 7% = Government 7% p = 0.05** p = 0.09* p = 0.50 p = 0.04** Test: DCB 7% = Opposition 7% p = 0.01*** p = 0.01*** p = 0.00*** p = 0.60 Test: Government 7% = Opposition 7% p = 0.41 p = 0.00*** p = 0.00*** p = 0.04**

Test: DCB 5% = Government 5% p = 0.35 p = 0.41 p = 0.02** p = 0.00*** Test: DCB 5% = Opposition 5% p = 0.08* p = 0.70 p = 0.15 p = 0.00*** Test: Government 5% = Opposition 5% p = 0.38 p = 0.27 p = 0.44 p = 0.04**

Observations 2,955 2,750 2,317 3,132

Notes: See Table1. further confirms that voters respond to our treatments, increasing their expectations following the 7% treatment and decreasing expectations following the 5% treatment. The coefficient tests again show that the 7% projection from the DCB and government are equally credible, and both signifi- cantly exceed the opposition treatment. For the 5% treatment, only institutional credibility appears to matter, although the reduction in expectations is again larger for the opposition than the govern- ment source. Column (4), which considers the voters with the most accurate current unemployment estimates, demonstrates that DCB and opposition information reduce unemployment expectations more than claims by the government—for both the 7% and 5% treatments. It again shows that new information affects even the voters with the most accurate prior assessments, demonstrating that all types of respondent can be considered compliers for our instrumental variable analysis. We thus find significant support for H2, but only among a subset of more politically sophisticated voters.

5 Effects on political preferences

The preceding analysis has shown that information about aggregate unemployment projections affects voter beliefs about the economy’s prospects. However, does this matter for political pref- erences? This section shows that exogenously changing expectations causes informed and cogni- tively able voters to change their vote intentions in accordance with economic voting motivations, but does not affect their policy opinions. By showing that lowering unemployment expectations increases confidence in the government without affecting policy preferences, these results imply that aggregate unemployment expectations are principally used to evaluate the competence of the government.

5.1 Results

Table4 reports estimates of equation (2), identifying the LACR of a percentage point increase in unemployment expectations on political preferences for individuals affected by the instruments.

25 The outcomes in columns (1)-(6) are indicators for supporting a particular party or group of parties. The large F statistic unsurprisingly indicates a very strong first stage.29 The results are highly consistent with a significant proportion of citizens engaging intending to engage in economic voting. The decrease in unemployment expectations induced by the informa- tion treatments causes compliers to increase their support for the parties of government on average by 3.5 percentage points for each percentage point decrease in aggregate unemployment expecta- tions.30 Increased government support is almost exactly mirrored by the decrease in support for right-wing parties in column (5), with the majority of votes coming from the main right-wing Lib- eral party shown in column (6). In the context of coalition politics, and especially the extremely close recent Danish elections, information about aggregate unemployment could easily have al- tered the composition of government. Even by the standards of countries with greater clarity of responsibility, the effect is very substantial—in spite of vote intention being asked 18 questions after the treatment. While the allocation of credit and blame for the economy’s progress is usually relatively clear when there is a single-party government, voter sanctioning is not obvious among coalition partners (Anderson 1995; Duch and Stevenson 2008). Columns (2)-(4) disaggregate the government vote share by the three parties in the governing coalition. The results clearly indicate that the two largest coalition partners—the Social Democrats and the Social Liberal Party, who had 44 and 17 seats and 10 and 6 cabinet positions respectively—are the sole beneficiaries, both gaining 1.6 percent- age point increases in the probability of a respondent voting for them for each percentage point decrease in unemployment expectations. This represents a relatively larger gain for the smaller Social Liberal party. In line with the findings of Anderson(1995) and Duch, Przepiorka and Stevenson(forthcoming), responsibility is assigned to the parties with greatest control over eco- 29The Online Appendix provides the first stages estimated, which are very similar to Table1. 30The reduced form estimates show similar results in the Online Appendix. Examining the DCB, government and opposition treatments as separate groups, the LACR magnitudes are consistent across information sources rather than being driven by particular sources.

26 Table 4: Effect of unemployment expectations on political preferences

(1) (2) (3) (4) (5) (6) Govt. Soc. Dem. Soc. Lib. Soc. Peop. Right Liberals

Unemployment expectations (%) -0.035** -0.016 -0.016* -0.003 0.034** 0.024* (0.014) (0.011) (0.009) (0.007) (0.015) (0.014)

First stage F statistic 32.64 32.64 32.64 32.64 32.64 32.64 Observations 5,705 5,705 5,705 5,705 5,705 5,705 Outcome mean 0.32 0.17 0.09 0.06 0.41 0.28 Outcome standard deviation 0.47 0.37 0.29 0.24 0.49 0.45

Notes: All specifications estimated using 2SLS, and control for current unemployment expectations. Robust standard errors in parentheses. nomic policy: while the Social Democrats led the coalition and held the Premiership, the leader of the Social Liberals—who campaigned on their centrist economic agenda—became Minister for the Economy and Interior. The intended vote share of the more extreme left-wing Socialist People’s Party, which held 16 seats and 6 cabinet positions, is essentially unaffected. After observing macroeconomic performance, the key theoretical claim underpinning eco- nomic voting is that unemployment expectations affect vote choice through voter perceptions of government competence. Strongly supporting this mechanism, column (1) in Table5 shows that lower unemployment expectations significantly increase confidence in the government. Nevertheless, a potentially confounding explanation of our results is that evaluations of govern- ment competence are not changing, but rather that lower unemployment expectations have shifted policy preferences toward those associated with left-wing parties (e.g. Meltzer and Richard 1981; Moene and Wallerstein 2001). Self-interested voters maximizing their expected income should decrease their support for redistribution and unemployment insurance to the extent that higher ag- gregate unemployment expectations are taken as a signal of economy-wide, rather than individual- specific, economic prospects. If aggregate unemployment expectations instead primarily update a

27 Table 5: Mechanism and placebo tests

(1) (2) (3) (4) Conf. govt. Redist. U. insurance Red-Green

Unemployment expectations (%) -0.100*** 0.032 -0.011 0.004 (0.029) (0.030) (0.018) (0.008)

First stage F statistic 28.65 32.64 33.51 32.64 Observations 5,688 5,705 5,614 5,705 Outcome mean 2.69 3.20 2.23 0.06 Outcome standard deviation 1.00 1.02 0.61 0.25

Notes: All specifications estimated using 2SLS, and control for current unemployment expectations. Robust standard errors in parentheses. voter’s subjective probability of being unemployed, support for redistribution and unemployment insurance should increase. We show these predictions formally in the Online Appendix. However, changes in policy preferences cannot account for the results observed here. First, we examine five- and three-point scales that respectively increase with general support for redis- tribution and specific support for unemployment benefits. The precisely estimated null effects in columns (2) and (3) of Table5 show no support for either claim, despite the question about un- employment insurance being asked one question after the treatment was administered.31 Second, the existence of left-wing parties outside the government provide a further placebo test for our economic voting interpretation. The Red-Green Alliance—the most left-wing party represented in the Danish Parliament—might expect to pick up votes if the information treatments were inducing a change in preferences. Column (4) shows that changes in unemployment expectations do not affect the probability of voting for the Red-Green Alliance. Together, this evidence reinforces the conclusion that economic voting is the principal political manifestation of changes in aggregate unemployment expectations. 31Unreported results show that the effect does not differ by income level.

28 As noted above, the monotonicity assumption is violated for the 7% information treatments. We confirm that defiers are not biasing the results by restricting the sample to cases where mono- tonicity almost certainly holds. Focusing only on the 5% treatments where the cumulative distri- bution of unemployment expectations lies almost everywhere to the left of the control group, the Online Appendix shows very similar LACR estimates.

5.2 Heterogeneous effects: who are the economic voters?

To better understand how economic voting works, we investigate which types of voters act po- litically on their unemployment expectations. Our detailed data provides significant leverage to examine the heterogeneous effects implied by existing theories. Political economy models typically regard swing voters as the most likely to transfer their votes to a party on the basis of competence, while the vote choices of partisans are unaffected (e.g. Ansolabehere and Snyder Jr. 2000; Persson and Tabellini 2000). However, in practice it is hard to empirically differentiate such swing voters from capricious disengaged voters. Furthermore, swing voters may lack the cognitive capacity or political engagement required to link unemployment expectations to government accountability for economic policy (Campbell et al. 1960). We test for whether swing voters are driving the changes in vote intention by exploiting the panel structure of the dataset. We define an indicator for the 43% of respondents who reported voting for different parties at the 2007 and 2011 elections. Figure3 demonstrates that such swing voters are not driving changes in government support. Rather, the effect of unemployment expec- tations among swing voters is indistinguishable from zero. Given the first stage for swing voters is especially strong, this result does not reflect swing voters failing to update their unemployment expectations. To ensure our definition of swing voters is not picking up shifts to parties offering similar platforms, we also calculated measures for left and right party groupings and examined swings to the left and swing to the right and in each case found similar results. The results are sim- ilarly robust to defining swing voters as individuals whose 2011 and 2012 survey vote intentions

29 differed. That economic voting is concentrated among respondents who have expressed consistent re- cent political preferences may at first seem surprising. However, assigning responsibility over economic policy to different parties is complicated in Denmark, where coalition governments are the inevitable outcome of a PR electoral system with many parties and unstable alliances in the po- litical center (Anderson 1995; Powell Jr. and Whitten 1993). This is particularly challenging if, as in the U.S., swing voters are less politically engaged (Campbell et al. 1960) and less likely to link their voting decisions to government actions or retrospective economic assessments (Delli Carpini and Keeter 1996). We similarly find that swing voters in Denmark are characterized by low polit- ical sophistication: swing voters discuss politics less with friends, family and neighbors, are less educated and have lower math test scores, and follow economics and politics in the news less regu- larly. Given this lack of political engagement and cognitive capacity, our results suggest that swing voters are unable or unwilling to link economic performance to evaluations of the government. Although the respondents whose vote intention was affected were not swing voters, they are not ideological extremists. Coding the 17% of the sample who provided the most extreme responses (from either end) to the redistribution question in the 2012 survey, Figure3 shows that the response of such voters is statistically insignificant and significantly below non-extreme voters. Our data permit more detailed tests of the claim that political sophistication is essential for economic voting (H5). We measure political engagement by defining an indicator for the 72% of respondents who read or watch economics or politics on the news every day.32 To capture cognitive capacity, we define an indicator for the 77% of respondents with education beyond high school. Finally, an individual’s initial view of the Danish economy’s prospects could induce subjective experience biases. We measure this with an indicator for the 34% of the sample who expected that 32Although this question was asked after the treatment was administered, regressing this vari- able on all information treatments provided no evidence to suggest that the treatments influenced responses. We find similar effect for discussion of politics, aggregating the indicators for dis- cussing politics with friends, family, neighbors, workers and others.

30 Improving economic prospects

Non-improving economic prospects

Beyond high school education

High school only

News every day

News less than every day

Extreme voter

Non-extreme voter

Swing voter

Non-swing voter -.15 -.1 -.05 0 .05

Marginal effect of unemployment expectations on voting for government party

Figure 3: Heterogeneous effect of unemployment expectations on intending to vote for a government party (95% confidence intervals)

Notes: Estimates are from separate 2SLS regressions instrumenting for unemployment expectations and its inter- action. Regression coefficients are provided in the Online Appendix.

31 Table 6: Effect of unemployment expectations on political preferences, by current unemployment estimate

Estimate>7.4% Estimate≤7.4% Estimate∈[5%,7%] Estimate∈[5.4%,9.4%] (1) (2) (3) (4)

Unemployment expectations (%) -0.014 -0.050** -0.065** -0.047** (0.011) (0.023) (0.028) (0.022)

First stage F statistic 33.74 74.00 64.34 66.38 Observations 2,955 2,750 2,317 3,132 Outcome mean 0.30 0.34 0.34 0.34 Outcome standard deviation 0.46 0.47 0.48 0.47

Notes: See Table4. the Danish economy would improve in 2013 relative to 2012. Figure3 shows the conditional economic voting estimates. While differences between types of voters are not always quite statistically significant, the effect of unemployment expectations accords with economic voting only among voters who regularly watch the news, completed higher levels of education, and expect to experience improved aggregate economic performance. As noted above, the current unemployment estimate is a good proxy for political sophistication. The results in Table6 reinforce our preceding findings: economic voting is only detected in the more sophisticated subsamples where respondents processed the political incentives of different sources and disproportionately comprise better educated and more politically engaged voters. An alternative explanation for swing and less sophisticated voters not engaging in economic voting is that economic competence is not a salient issue among these voters. Rising immigration in Denmark has become a second political cleavage in recent years, so it is possible that such voters are instead principally concerned with this issue. However, voter opinions and contextual data find no support for this possibility. Results in the Online Appendix show that if anything economic voting is more prevalent among those supporting the reinstatement of separate and lower state

32 benefits for immigrants,33 and in parishes (or municipalities) with higher shares of immigrants. An apparent tension underlying our results is that those with the least accurate beliefs about current unemployment update their beliefs most, but economic voting is concentrated among better educated, more politically engaged and non-extreme voters. However, these results are consistent with H5 and a large psychological literature pointing to the importance of cognitive awareness and subjective biases (see Healy and Malhotra 2013). Furthermore, Lassen and Serritzlew(2011) similarly find that the merging of Danish municipalities only decreased the political efficacy of well-educated and politically informed voters. We argue that our results highlight an important limit on the provision of political information: only a subset of those who update their beliefs translate them into actions, and those who update the most are not necessarily most likely to act on the updates.

6 Conclusion

Given that politically-relevant information cannot be transmitted to voters in a vacuum, a key question for democratic accountability is when different sources cause voters to update and act politically on their beliefs. We find that providing voters with unemployment forecasts causes all types of voters—regardless of prior partisan affiliations—to update their unemployment expecta- tions, particularly when faced with an expert source like the DCB. However, only a subset of more sophisticated voters, which excludes voters that have recently switched their votes, understand that political parties differ in their incentives to portray the state of the economy. Ultimately, although all voters update their beliefs, only among non-ideologically extreme sophisticated voters does this affect political preferences. We find clear evidence of a large economic voting response, which re- flects changes in evaluations of government competence rather than changes in policy preferences. At least in the case of Denmark, we conclude that it is primarily the objective credibility of a source 33We use 2012 survey responses here because the 2013 question is post-treatment.

33 and the sophistication of voters, not prior partisanship, that matters most for explaining when new information will affect political behavior. The democratic implications of these results are somewhat mixed. While economic voting is generally regarded as a positive for democratic accountability (Anderson 2007), our results show that information about aggregate unemployment is insufficient to induce non-sophisticated voters to link their unemployment expectations to government performance. Nevertheless, finding any effect in Denmark’s complex institutional environment and open economy is an important result because it may represent a lower bound cross-nationally. Our results also illuminate the behavior of political parties. That the least politically engaged voters do not translate their information into political action could explain why political parties in developed polities target their platforms toward prominent and well-informed voters (Adams and Ezrow 2009; Gilens 2005). Furthermore, our results suggest that parties can benefit electorally from providing specific macroeconomic information, and this is of course prevalent among suc- cessful governments. However, given incorrect information also affects voter beliefs, our results question why parties do not distort the facts more often. While this may entail losing credibility in some instances (see Druckman 2001), the line between proclaiming truths and falsehoods is often unclear if multiple numbers are available. Further research should explore these issues in greater detail. While this paper provides a first step toward understanding how voter beliefs are formed and map to political preferences, there are important further steps to take. First, outside experimen- tal intervention, it is critical to understand how individuals can be induced to acquire politically relevant information. Second, the media represent a crucial mediator and communicator of in- formation, and future work should also enlighten the black box of how voters respond to media exposure. Third, the belief updating process itself deserves further attention as political scientists know little about how political information is processed or what a distribution of beliefs looks like at either a particular point in time or over multiple horizons. Finally, although electoral politics is

34 often based on short-term responses to stimuli, an important next step in this research agenda is to assess the durability of our results over time and in the face of repeated interventions.

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42 7 Appendix

7.1 Bayesian interpretation of information updating

Our approach can be clearly shown in a Bayesian updating framework. Specifically, we write individual i’s conditional posterior belief about future unemployment level, U, as:

P(Zi|U = u,Xi) P(U = u|Xi,Zi) = P(U = u|Xi) , P(Zi|Xi)

where Zi is an information shock received by i, and Xi captures i’s characteristics (e.g. ideology and sophistication). The location and specificity of i’s prior belief, P(U = u|Xi), depends upon

Xi. The likelihood P(Zi|U = u,Xi) represents i’s interpretation of the informativeness of the signal they received: P(Zi|U = u,Xi)/P(Zi|Xi) = 1 or P(Zi|U = u,Xi) = P(Zi|Xi) captures i not believing that receiving signal Zi is related to the likelihood that the state of the world is U = u. H1 and H2 hypothesize that P(Zi|U = u,Xi) (or more simply P(Zi|U = u) because individual characteristics play a weak role in objective credibility) is large where Zi comes from an expert or surprising source. H4 instead hypothesizes that updating depends upon the interaction of the source of Zi and Xi. H3 allows for P(U = u|Xi) to be large, but also implies that Zi does not add much new information.

7.2 Formal model of information and policy preferences

We extend the Romer(1975) and Meltzer and Richard(1981) framework to include uncertainty over income in a simple way. Take a continuum of voters of unit mass, differentiated by their income prospects. Voter i’s

+ realized income is yi ∈ Y ⊆ R . We build in uncertainty in a simple fashion. Individual i’s uncertainty at the time of determining their policy preferences is operationalized as follows: with

43 L H probability pi(zi) ∈ (0,1) their income is yi , and with probability 1 − pi(zi) their income is yi , H L where yi > yi and zi denotes the amount of information i possess about the economy (increases in zi represent more information). Assume pi(zi) is differentiable and monotonic in zi. To save space, L H define i’s expected income as Yi(zi) ≡ pi(zi)yi + (1 − pi(zi))yi . Voters are also uncertain about the aggregate distribution of income. In particular, with voter

L i assigns probability q(zi) ∈ (0,1) to economy-wide average income being y¯ , and probability H H L 1 − q(zi) to economy-wide average income being y¯ , where y¯ > y¯ . Assume q is differentiable L and monotonic in zi. Define i’s expected average income in the economy as Y¯ (zi) ≡ q(zi)y¯ + (1− q(zi))y¯ The government must choose a tax and benefit policy pair (τ,T ) to be implemented after income is realized, where τ ∈ [0,1] is a proportional tax rate levied on y and T ≥ 0 is a lump-sum R transfer made to all citizens. There is a cost φ(τ)y¯ to increasing τ, where y∈Y ydF(y) = y¯ is the realized mean income and φ : [0,1] 7→ R+ is a convex-increasing function such that φ 0(τ) > 0,φ 00(τ) > 0 and φ(0) = 0. This cost could be labor supply disincentives, capital misallocation or the inefficiency of revenue collection. We assume that the tax rate cannot depend upon the realized state of the world. We now derive individual i’s preferences over policies before income is realized. From the perspective of voter i, the ex ante government budget constraint is:

[τ − φ(τ)]Y¯ (zi) ≤ T. (3)

Since the budget constraint will bind in equilibrium, the problem is reduced to a single dimensional problem in τ. A voter with income y has the following policy utility function, receiving utility from post-tax

44 income and the lump-sum transfer:

  u (1 − τ)Yi(zi) + [τ − φ(τ)]Y¯ (zi) + (1 − τ)Y¯ (zi) + [τ − φ(τ)]Y¯ (zi)   = u (1 − τ)Yi(zi) + [τ − φ(τ)]Y¯ (zi) , (4) where u : R 7→ R is a concave-increasing function: u0(·) > 0,u00(·) < 0 and u(0) = 0. Tax rates have two effects on voter utility: redistribution of income and a (disincentive) cost to increasing taxation.

Given preferences are strictly concave in τ, they are single-peaked. We can identify the ideal policy of voter i as:

    ∗ 0 −1 Yi(zi) τi = max (φ ) 1 − ,0 . (5) Y¯ (zi)

This reiterates the Romer-Meltzer-Richard logic that i’s preference for taxation is increasing as

their expected income relative to the expected average income falls. Note that all Yi(zi) > Y¯ (zi) ∗ prefer τi = 0. ∗ In the space where τi > 0, or for voters with expected incomes exceeding the average expected income, the comparative static with respect to new information is:

∗ 0 H L 0 H L dτi pi(zi)(yi − yi )Y¯ (zi) − q (zi)(y¯ − y¯ )Yi(zi) = 00 2 . (6) dzi φ (τ)[Y¯ (zi)]

H L H L Given yi − yi > 0 and y¯ − y¯ > 0, and expected individual and aggregate income is positive, it 0 0 is clear that we have opposing effects when sgn(pi(zi)) = sgn(q (zi))—this is the obvious case for this paper as it is very unlikely that aggregate information would cause voters to differentially update. Intuitively, the first term in the numerator captures how information affects i’s taxation preferences associated with their own expected income, while the second term captures how infor-

45 mation affects taxation preferences in the rest of the economy. These are easiest to see by setting

0 0 q (zi) = 0 and pi(zi) = 0 respectively. Without loss of generality (the results will just be the opposite), let us focus on the case where

0 0 sgn(pi(zi)) = sgn(q (zi)) ≤ 0; this turns out to be the most appropriate case for our analysis because our information treatments cause voters to become more positive about the economy. It is now clear that new information causing voters to reduce their belief of being unemployed and reduce their belief of others being unemployed has the expected effects: the fall in the likelihood of i being unemployed reduces their preference for taxation (first term in (6)), while the fall in the likelihood of others being unemployed increases their preference for taxation (second term in (6)). These effects clearly conflict, with the individual income incentive overpowering the general economy incentive when:

0 H L pi(zi) (y¯ − y¯ )Yi(zi) 0 > H L , (7) q (zi) (yi − yi )Y¯ (zi) or when the change in pi(zi) is sufficiently large relative to the change in q(zi). These insights can be easily extended to voter in Denmark, where politics is primarily based on a left-right axis. Simplifying the analysis such that voters choose between left and right parties, it is clear that any increase in preferred tax rate should correspond to increased support for the left-wing government.

7.3 Variable definitions and summary statistics

Information source treatments. Respondents were randomly assigned to a control group receiving no information, one of six groups receiving the prime “Assume that that the [DCB/government/Liberals] estimates unemployment in 2013 will be [almost 7%/around 5%]” or “The DCB estimates unem- ployment in 2013 to be almost 7%.” Respectively, these statements translate as: “Antag at [Na- tionalbanken/Regeringen/Venstre] vurderer at arbejdsløsheden i 2013 vil være [knap 7%/ca. 5%]”

46 and This translates from: “Nationalbanken vurderer at arbejdsløsheden i 2013 vil være knap 7%.” In the main text, treatments are denoted, for example, by “DCB 7% treatment”. Unemployment expectations. The percentage (not restricted to integers) reported by the re- spondent in response to the question “What is your best estimate of what unemployment will be in 2013? We would like your best estimate, even if you are not entirely sure.” This translates from: “Hvad er dit bedste bud pa˚ hvad arbejdsløsheden vil blive i 2013? Vi vil gerne have dit bedste bud, ogsa˚ selvom du ikke er helt sikker.” This question immediately followed the treatment. Current unemployment estimate. The percentage (not restricted to integers) reported by the respondent in response to the question “Unemployment in Denmark is typically measured by the unemployment rate, that is, the share of people who want to work but don’t have a job. Over the last 25 years, the unemployment rate has been between 1.5 and 12 %. What is your estimate of the current unemployment rate in Denmark? We would like your best estimate, even if you are not entirely sure.” This question immediately preceded the treatment. Vote intention. Respondent stated the party that would vote for in response to the question “How would you vote tomorrow?” Respondents choose one of the following: Social Democrat Party, Social Liberal Party, Conservative People’s Party, Socialist People’s Party, Danish People’s Party, Liberal Party, Liberal Alliance, or Red-Green Alliance. Answers not stating a party were: blank, no answer, other, would not vote, and don’t know. We counted the Conservative People’s Party, Danish People’s Party, Liberal Party, and Liberal Alliance as right-wing parties. We counted the Social Democratic Party, Social Liberal Party and Socialist People’s Party as parties in govern- ment. Confidence in the government. Responded were asked, on a scale from great mistrust (1) to great trust (5), how much they trust the government. This question was asked 11 questions after the treatment was administered. Redistribution. This variable measures support for redistribution on a five-point scale ranging from “every man for himself” (1) to the government “should help the poor a lot” (5). This was

47 in response to the prime “Some think the Government should do all it can to raise the standard of living for poor Danes: that is 1 on the scale. Others think it is not the responsibility of government, each should take care of themselves: that is 5.” This question was 19 questions after the treatment. Unemployment insurance. Three-point “less-same-more” response to the question “The eco- nomic crisis has meant that many people have lost their job. Do you think that the government should support the unemployed?” this question was asked immediately after the question eliciting the respondent’s unemployment expectations. Lower immigrant benefits. Indicator coded 1 for respondents who responded that separate and lower benefits for immigrants should be reinstated in 2012. Denmark economic prospects. Five point scale, from “worsen considerably” to “improve con- siderably, response to the question of how the Danish economy overall will do in 2013. Improving economic prospects. Indicator coded 1 for individuals responding that the Danish economy for 2013 will be “much better” or “better” than 2012. This variable is re-coded from Denmark economic prospects. Discuss politics. The sum of the set of indicators coded 1 for respondents who answered that they talk to family, friends, neighbors, work colleagues or others about politics. News every day. Indicator coded 1 for respondents who state that they watch or read about politics and economics in the news “every day”. Wage income (log). Total wage income before taxation; we then added 1 and took the natural logarithm. Own job risk. The probability assigned by the respondent to the possibility that they will experience a period of unemployment in the forthcoming year. Education. Three-point scale indicating the level of education achieved by the respondent: 1 is less than university education; 2 is some or complete undergraduate university; 3 is further academic study. Woman. Indicator coded 1 for women.

48 Voted left at last election. Indicator coded 1 for respondents who voted for one of the Social Democrats, Social Liberals, Socialist People’s, or Red-Green Alliance parties in the 2011 election. Swing voter (previous votes). Indicator coded 1 for respondents who provided different re- sponses to the question asked respondents to recall who they voted for in the 2007 and 2011 elections. Swing voter (previous intentions). Indicator coded 1 for respondents who provided different responses to the vote intention questions asked in 2011 and 2012. Extreme voter. Indicator coded 1 for respondents who answered either “should help the poor a lot” or “every man for himself” to the redistribution question in 2012. Municipal immigration share. The share of immigrants in the municipality that the respondent resides in. Denmark contains 98 municipalities. Parish immigration share. The share of immigrants in the parish that the respondent resides in. The average parish contains around 2,500 residents. Parishes are the lowest administrative units in Denmark.

7.4 Support for the identification assumptions

Tables8 and9 look at balance over pre-treatment covariates from both the Register and the survey. F tests of all treatment coefficients being equal are rejected with regularity consistent with chance: only in one of 16 tests was the joint test statistically different from zero at the 5% level (and also once at the 10% level). Even in those cases, the differences between treatment conditions are small. Accordingly, we do not include controls, although the results are robust to including such variables. Figures4 and5 show the cumulative density functions plotting the proportion of individuals for each instrument expecting unemployment below a certain level. The key point to note is that while the 7% treatments lie almost entirely to the left of the control group, the 5% treatments do not. This implies that the monotonicity assumption underpinning the instrumental variable does

49 Table 7: Summary statistics

Obs. Mean Std. dev. Min. Max.

Dependent variables Social Democrat Party 5,705 0.17 0.37 0 1 Social Liberal Party 5,705 0.09 0.29 0 1 Socialist People’s Party 5,705 0.06 0.24 0 1 Liberal Party 5,705 0.28 0.45 0 1 Red-Green Alliance 5,705 0.06 0.25 0 1 Confidence in government 5,688 2.69 1.00 1 5 Redistribution 5,705 3.20 1.02 1 5 Unemployment insurance 5,614 2.23 0.61 1 3

Dependent/endogenous variable Unemployment expectations 5,705 7.98 3.55 0 45

Treatment variables Control 5,705 0.13 0.33 0 1 DCB 7% treatment (combined) 5,705 0.25 0.44 0 1 DCB 5% treatment 5,705 0.13 0.33 0 1 Government 7% treatment 5,705 0.12 0.33 0 1 Government 5% treatment 5,705 0.12 0.33 0 1 Opposition 7% treatment 5,705 0.12 0.33 0 1 Opposition 5% treatment 5,705 0.12 0.33 0 1

Covariates Current unemployment estimate 5,705 8.58 4.31 0 45 Swing voter (previous votes) 3,827 0.43 0.50 0 1 Swing voter (previous intentions) 4,566 0.23 0.42 0 1 News every day 5,705 0.72 0.45 0 1 Improving economic prospects 5,675 0.34 0.47 0 1 Municipal immigrant share 5,704 9.74 5.53 3.67 32.75 Parish immigrant share 5,704 8.76 7.09 0 69.72 Medium education 5,642 0.67 0.47 0 1 High education 5,642 0.11 0.31 0 1 Woman 5,705 0.49 0.50 0 1 Discuss politics 5,705 2.30 1.14 1 4 Voted left at last election 5,705 0.50 0.50 0 1 Extreme voter 4,566 0.17 0.38 0 1 Wage income (log) 5,532 10.19 5.15 0 19.83 Lower immigrant benefits 5,705 0.25 0.43 0 1

50 Table 8: Balance tests 1

(1) (2) (3) (4) (5) (6) (7) (8) Current U Est. Woman Age Basic Edu. Med. Edu Long Edu. Voted Govt. Voted Left

Control 8.704*** 0.493*** 1961.541*** 0.243*** 0.660*** 0.097*** 0.432*** 0.492*** (0.177) (0.019) (0.434) (0.016) (0.018) (0.011) (0.019) (0.019) DCB 7% treatment -0.264 -0.002 0.869 0.003 -0.016 0.013 0.021 0.015 (0.230) (0.026) (0.608) (0.023) (0.025) (0.016) (0.026) (0.026) DCB 7% treatment (true) -0.364 -0.009 0.171 0.002 0.012 -0.014 0.012 -0.000 (0.226) (0.027) (0.617) (0.023) (0.025) (0.015) (0.027) (0.027) DCB 5% treatment -0.249 0.025 0.374 -0.047** 0.026 0.021 0.049* 0.042

51 (0.229) (0.026) (0.601) (0.022) (0.025) (0.016) (0.026) (0.026) Government 7% treatment -0.086 0.009 0.234 -0.033 0.010 0.024 0.004 -0.002 (0.240) (0.027) (0.601) (0.022) (0.025) (0.017) (0.026) (0.027) Government 5% treatment 0.224 -0.004 -0.190 -0.030 0.012 0.017 0.031 0.017 (0.252) (0.027) (0.626) (0.022) (0.025) (0.016) (0.026) (0.027) Opposition 7% treatment -0.265 -0.024 0.443 -0.009 0.013 -0.004 -0.000 -0.007 (0.239) (0.027) (0.607) (0.023) (0.025) (0.016) (0.026) (0.027) Opposition 5% treatment 0.011 -0.012 0.529 -0.021 -0.010 0.031* 0.050* 0.036 (0.249) (0.027) (0.602) (0.023) (0.025) (0.017) (0.026) (0.027)

Observations 5,705 5,705 5,692 5,642 5,642 5,642 5,705 5,705 F test of equality over treatments p = 0.19 p = 0.75 p = 0.78 p = 0.18 p = 0.74 p = 0.07 p = 0.29 p = 0.47

Notes: All specifications estimated using OLS. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. Table 9: Balance tests 2

(1) (2) (3) (4) (5) (6) (7) (8) Voted right Wages (log) Exp. Income (log) Homeowner Econ. prospects Tenured Job Risk Risk averse

Control 8.704*** 0.493*** 1961.541*** 0.243*** 0.660*** 0.097*** 0.432*** 0.492*** (0.177) (0.019) (0.434) (0.016) (0.018) (0.011) (0.019) (0.019) DCB 7% treatment -0.264 -0.002 0.869 0.003 -0.016 0.013 0.021 0.015 (0.230) (0.026) (0.608) (0.023) (0.025) (0.016) (0.026) (0.026) DCB 7% treatment (true) -0.364 -0.009 0.171 0.002 0.012 -0.014 0.012 -0.000 (0.226) (0.027) (0.617) (0.023) (0.025) (0.015) (0.027) (0.027) DCB 5% treatment -0.249 0.025 0.374 -0.047** 0.026 0.021 0.049* 0.042

52 (0.229) (0.026) (0.601) (0.022) (0.025) (0.016) (0.026) (0.026) Government 7% treatment -0.086 0.009 0.234 -0.033 0.010 0.024 0.004 -0.002 (0.240) (0.027) (0.601) (0.022) (0.025) (0.017) (0.026) (0.027) Government 5% treatment 0.224 -0.004 -0.190 -0.030 0.012 0.017 0.031 0.017 (0.252) (0.027) (0.626) (0.022) (0.025) (0.016) (0.026) (0.027) Opposition 7% treatment -0.265 -0.024 0.443 -0.009 0.013 -0.004 -0.000 -0.007 (0.239) (0.027) (0.607) (0.023) (0.025) (0.016) (0.026) (0.027) Opposition 5% treatment 0.011 -0.012 0.529 -0.021 -0.010 0.031* 0.050* 0.036 (0.249) (0.027) (0.602) (0.023) (0.025) (0.017) (0.026) (0.027)

Observation 5,705 5,532 5,554 5,705 5,675 4,540 4,540 5,580 F test of equality over treatments p = 0.20 p = 0.95 p = 0.75 p = 0.44 p = 0.38 p = 0.68 p = 0.26 p = 0.03

Notes: See Table8. iue4 uuaiedniyposo nmlyetepcain yifraintetet1 treatment information by expectations unemployment of plots density Cumulative 4: Figure Cumulative density 0 20 40 60 80 100 0 DCB 5%treatment Control 10 Unemployment expectations(%) 20 53 30 DCB 7%treatment(true) DCB 7%treatment 40 50 iue5 uuaiedniyposo nmlyetepcain yifraintetet2 treatment information by expectations unemployment of plots density Cumulative 5: Figure Cumulative density 0 20 40 60 80 100 0 Opposition 5%treatment Government 5%treatment Control 10 Unemployment expectations(%) 54 20 Opposition 7%treatment Government 7%treatment 30 40 Table 10: Effects of treatments on belief that political information is important

(1) (2) Info. important Info. important

Control 0.742*** 0.742*** (0.022) (0.022) DCB 7% treatment (combined) -0.004 (0.021) DCB 5% treatment -0.005 (0.025) Government 7% treatment 0.018 (0.025) Government 5% treatment 0.022 (0.025) Opposition 7% treatment -0.009 (0.025) Opposition 5% treatment -0.014 (0.025) Any treatment -0.006 (0.019)

Observations 5,803 5,803

Notes: Dependent variable is a dummy for whether the respondent believes political information is important for either private economic decisions or as part of the respondent’s job. Both models control for current unemploy- ment estimate. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. not hold in such cases. The implications of this are discussed in the main text. As noted in the main text, Table 10 shows no treatment affects the respondent’s belief that political information is important. This serves as an important robustness check for the exclusion restriction concern that simply receiving the treatment inducing respondents to think about politics differentially without being affected by the particular unemployment information provided. Finally, Table 11 provides our first stage estimates for vote intention regressions. The results are very similar to the coefficients provided in Table 1 of the main paper, but gain precision due to

55 Table 11: Effect of information treatments on unemployment expectations (%)—controlling for current unemployment estimate (first stage)

Unemployment expectations (%)

Control 3.523*** (0.216) DCB 7% treatment (combined) -0.927*** (0.104) DCB 5% treatment -1.501*** (0.127) Government 7% treatment -0.792*** (0.122) Government 5% treatment -1.360*** (0.126) Opposition 7% treatment -0.756*** (0.120) Opposition 5% treatment -1.342*** (0.137) Current unemployment estimate 0.631*** (0.025)

Observations 5,705 First stage F statistic 32.64

Notes: Estimated using OLS. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. The coefficient tests at the foot of the table report the p value from a two-sided F test of coefficient equality. the inclusion of the current unemployment estimate.

7.5 Additional results

7.5.1 Effects of information source on unemployment expectations

Table 12 replicates Table 3 in the main paper with the exception that a respondent’s current unem- ployment estimate is included as an additional interactive control. The results clearly show that the measures of political sophistication cease to be significant predictors of updating once the current

56 Table 12: Heterogeneous effects of information treatments on unemployment expectations (%), by conditional marginal effect—controlling for current unemployment estimate

DCB 7% DCB 5% Govt. 7% Govt. 5% Opp. 7% Opp. 5%

Linear effect 1.928*** -0.891 2.525*** 0.693 1.771* 0.31 (0.747) (0.934) (0.735) (0.869) (0.911) (1.026) × Current unemployment estimate -0.498*** -0.265*** -0.517*** -0.348*** -0.384*** -0.367*** (0.058) (0.081) (0.076) (0.07) (0.08) (0.083) × News every day 0.081 0.016 -0.368 -0.004 -0.267 0.072 (0.201) (0.274) (0.26) (0.275) (0.249) (0.298) × Denmark economic prospects 0.398*** 0.505*** 0.538*** 0.242 0.359** 0.317* (0.117) (0.161) (0.143) (0.172) (0.152) (0.177) × Wage income (log) -0.003 -0.025 -0.022 0.022 -0.005 0.022 (0.017) (0.022) (0.02) (0.02) (0.022) (0.026) × Medium education 0.097 0.328 0.103 0.106 -0.072 0.378 (0.208) (0.293) (0.275) (0.297) (0.269) (0.36) × High education 0.072 0.408 -0.315 0.007 0.017 0.617 (0.257) (0.332) (0.313) (0.341) (0.303) (0.457) × Woman -0.006 0.042 -0.031 -0.156 0.017 -0.233 (0.191) (0.257) (0.212) (0.241) (0.228) (0.259) × Voted left at last election 0.228 0.128 -0.038 0.058 -0.024 0.14 (0.172) (0.224) (0.208) (0.231) (0.204) (0.249)

Notes: All coefficients are estimated from a single OLS equation interacting all treatments conditions with the variables on the left hand side of the table (see Online Appendix for their definitions). The coefficient for the control group is 3.204***(0.450). The sample size is 5,446. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. unemployment estimate is included. As noted in the main text, this suggests that the current un- employment estimate—which is a highly statistically significant interaction for each treatment—is almost a sufficient statistic for political sophistication in this context. Table 13 shows that of the treatment sources, only the DCB treatment significantly increases trust in the source of the information. Trust is a dummy variable for trusting or greatly trusting the institution. This test was designed to ameliorate the concern that simply hearing the source’s name, independently of the information, is driving the results. Although this is not quite possible

57 Table 13: Effect of information treatments on confidence in sources

(1) (2) (3) Trust DCB Trust government Trust opposition

Control 0.662*** 0.166*** 0.263*** (0.018) (0.014) (0.016) DCB 7% treatment (combined) 0.070*** (0.021) DCB 5% treatment 0.072*** (0.024) Government 7% treatment 0.008 (0.020) Government 5% treatment 0.032 (0.020) Opposition 7% treatment 0.015 (0.023) Opposition 5% treatment (0.015) (0.023)

Observations 2,980 2,177 2,180

Notes: Estimated using OLS. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. for the DCB, it large effects combined with the high level of initial trust, suggest that this should not be a problem. Table 14 shows the summary statistics in terms of political disposition for the four subsamples that we analyze in the main paper.

7.5.2 Effects of information source on political preferences

Table 15 shows the reduced form estimates. Panel A, which fully separates treatments, generally shows that the more powerful treatment has a larger effect on support for a political party. That is to say the treatment effects look to be fairly monotonic given the fact that most individuals over-estimated the future unemployment rate relative to the true projection. Although most rela-

58 Table 14: Comparison of sub-sample characteristics

Estimate¿7.4% Estimate≤7.4% Estimate∈[5%,7%] Estimate∈[5.4%,9.4%] Mean St. dev. Mean St. dev. Mean St. dev. Mean St. dev.

59 Medium education 0.666 0.472 0.662 0.473 0.660 0.474 0.667 0.471 High education 0.087 0.282 0.134 0.341 0.135 0.342 0.125 0.330 Discuss politics 2.211 1.138 2.383 1.135 2.385 1.137 2.370 1.138 Voted left at last election 0.509 0.500 0.499 0.500 0.502 0.500 0.515 0.500 Voted right at last election 0.403 0.491 0.428 0.495 0.426 0.495 0.411 0.492 News every day 0.668 0.471 0.768 0.422 0.777 0.417 0.752 0.432 Woman 0.568 0.495 0.410 0.492 0.407 0.491 0.430 0.495 tionships are not statistically significant, this is due to three reasons. First, as noted in the text, the 7% treatments cause updating from both directions and thus average over countervailing effects. Second, the reduced form averages give greater weight to those with a large first stage, which are generally the individuals who seem to be those least capable of mapping information to political preferences. And finally, we use many treatments and thus relatively small sample sizes for each separate treatment, whereas the 2SLS estimates pool information across treatments. In fact, our 2SLS estimates are highly consistent with these reduced form estimates—it is easy to see this by noting the monontonic relationship between the treatments. This is particularly the case once we group together treatment levels: Panel B shows that grouping together the 5% and 7% treatments across source produces clearly statistically significant results. Table 16 shows the heterogeneous effect estimates underlying the results shown in Figure 3 in the main paper, as well as comparable results for intending to vote for a right party. Table 17 show how the effect of unemployment expectations varies by local immigration ex- periences and respondent views on immigration policy. The results clearly show that there is no significant difference in economic voting by either measure of immigration.

60 Table 15: Reduced form effect of unemployment expectations on political preferences

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Panel A: all treatments Govt. Soc. Dem. Soc. Lib. Soc. Peop. Conf. govt. Red-Green Right Liberals Redist. U. insurance

DCB 7% treatment 0.020 0.031* -0.007 -0.004 0.091** -0.015 -0.018 -0.017 -0.091** -0.019 (0.021) (0.017) (0.013) (0.011) (0.045) (0.022) (0.021) (0.011) (0.045) (0.028) DCB 5% treatment 0.049** 0.018 0.024 0.007 0.108** -0.050* -0.043* -0.012 -0.075 0.009 (0.025) (0.019) (0.016) (0.013) (0.052) (0.026) (0.024) (0.013) (0.052) (0.031) Government 7% treatment 0.007 0.013 -0.012 0.006 0.050 0.005 0.002 -0.007 -0.084 -0.008 (0.024) (0.019) (0.015) (0.013) (0.051) (0.026) (0.024) (0.014) (0.053) (0.032) Government 5% treatment 0.027 0.009 0.019 -0.001 0.076 -0.032 -0.018 -0.007 -0.035 0.025 (0.025) (0.019) (0.016) (0.012) (0.052) (0.026) (0.024) (0.014) (0.053) (0.032) Opposition 7% treatment -0.011 -0.002 -0.007 -0.003 -0.028 0.014 -0.015 -0.010 -0.069 0.009 (0.024) (0.019) (0.015) (0.012) (0.051) (0.026) (0.024) (0.013) (0.053) (0.033) Opposition 5% treatment 0.041* 0.040** 0.000 0.001 0.202*** -0.027 -0.030 -0.008 -0.056 0.001 61 (0.025) (0.020) (0.015) (0.012) (0.053) (0.026) (0.024) (0.013) (0.053) (0.033)

Observations 5,803 5,803 5,803 5,803 5,786 5,803 5,803 5,803 5,803 5,709

(11) (12) (13) (14) (15) (16) (17) (18) (19) (20) Panel B: combined treatment levels Govt. Soc. Dem. Soc. Lib. Soc. Peop. Conf. govt. Red-Green Right Liberals Redist. U. insurance

All 7% treatment 0.009 0.019 -0.008 -0.001 0.051 -0.003 -0.012 -0.013 -0.084** -0.009 (0.019) (0.015) (0.012) (0.010) (0.041) (0.021) (0.019) (0.011) (0.041) (0.025) All 5% treatment 0.039** 0.022 0.015 0.002 0.129*** -0.036* -0.030 -0.009 -0.055 0.012 (0.020) (0.016) (0.013) (0.010) (0.043) (0.021) (0.020) (0.011) (0.043) (0.026)

Observations 5,803 5,803 5,803 5,803 5,786 5,803 5,803 5,803 5,803 5,709

Notes: All specifications estimated using OLS, and control for current unemployment expectations. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. Table 16: Heterogeneous effects—2SLS estimates

(1) (2) (3) (4) (5) (6) Govt. Govt. Govt. Govt. Govt. Govt.

Unemployment expectations (%) -0.059** -0.055*** -0.053*** -0.014 -0.020 -0.014 (0.023) (0.019) (0.019) (0.020) (0.016) (0.015) × swing voter (previous votes) 0.033 (0.030) × swing voter (previous intentions) 0.046 (0.029)

62 × ideologically extreme voter 0.058* (0.034) × news every day -0.033 (0.026) × beyond high school education -0.023 (0.022) × improving economic prospects -0.066** (0.029)

Observations 3,827 4,566 5,705 5,642 5,675

Notes: All specifications estimated using 2SLS, and control for current unemployment expectations and linear term for each interaction. Robust standard errors in parentheses. ∗ p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. Table 17: Heterogeneous effect of unemployment expectations by immigration exposure and preferences

(1) (2) (3) Govt. Govt. Govt.

Unemployment expectations (%) 0.046 0.004 -0.027* (0.103) (0.096) × parish immigrant share -0.008 (0.010) × municipality immigrant share -0.004 (0.009) × lower immigrant benefits -0.010

Observations 5,704 5,704 5,705

Notes: See Table 16.

63