INFORMATION, SOURCE CREDIBILITY AND

POLITICAL SOPHISTICATION:EXPERIMENTAL EVIDENCE ON ECONOMIC VOTING∗

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

APRIL 2014

How does the source of politically-relevant economic information affect voter beliefsand ultimately political preferences? This paper randomly varies whether voters re-ceive an aggregate unemployment projection from the central bank, government oropposition party using a survey experiment in Denmark with unique access to detailedpanel and administrative data. All sources induce voters to update their unemploymentexpectations. While all voters regard the Danish Central Bank as the most crediblesource, only sophisticated voters update more after receiving information from a partywith political incentives to state otherwise. However, belief updating is no greaterwhen 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 economicvoting is driven by politically sophisticated rather than swing voters.

∗We wish to thank Alberto Abadie, Charlotte Cavaille, Alex Fouirnaies, Torben Iversen, Horacio Larreguy andVictoria Shineman for valuable advice and comments, as well as participants at the Harvard Political Economy andComparative Politics workshops, NYU Center for Experimental Social Science Conference 2014, Midwest PoliticalScience Association 2014, and MIT Political Economy Breakfast. Lassen thanks the Danish Council for IndependentResearch under its Sapere Aude program for financial assistance.†Department of Government, Harvard University, james alt@harvard.edu.‡Department of Economics, University of Copenhagen, david.dreyer.lassen@econ.ku.dk.§Department of Government, Harvard University. jlmarsh@fas.harvard.edu. (Corresponding author.)

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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 holdgovernments to account at the polls.

2Goidel and Langley (1995) and Nadeau et al. (1999) document that voters do understand thatsources of information may be biased. Similarly, many studies note significant differences in trustacross political and media institutions (e.g. Dalton 2008).

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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

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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. Section 2 distinguishes the objective and subjective cred-

ibility of a source of political messages, and considers how economic information might affect

political preferences. Section 3 details our experiments designed to parse out these effects. Section

4 examines how beliefs change, before Section 5 maps these beliefs to vote intention and welfare

policy preferences. Section 6 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.

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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 areprovided with.

4An influential literature begun in the 1980s persuaded politicians that independent centralbanks could credibly commit countries to sound monetary policies that politicians would otherwisehave incentives to renege on after winning elections (see Barro and Gordon 1983).

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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 bettereducated and more informed voters are better able to disentangle domestic from imported sourcesof growth.

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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

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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. AsAnsolabehere, Meredith and Snowberg (forthcoming) have shown, parsing out these effects ischallenging.

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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 theeconomy was definitively the most importance issue for voters, while nearly 20% specificallycited unemployment. The study also shows that left-wing voters thought the labor market was thebiggest issue, while right-wing voters thought the economy in general was the biggest issue. Voterssimilarly divided over whether a left or right coalition would best fight unemployment.

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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 Figure 1. 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 usingsurveys to ensure cross-national comparability, the Danish government uses administrative recordsto 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 inthat 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 pollwas taken from Jyllands-Posten here.

12Another example is the reduction of the maximum length of unemployment benefits from four

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Left Right

Danish People's Party Liberal PartyConservative People's Party Liberal AllianceDanish Social Liberal Party Social Democrat PartySocialist 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 ideologyaccording 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 benefitsin the final two years to 60% of their initial level.

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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 Danishphrasing. It is important to emphasize that in Danish the prime translates as a prospective estimate.

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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, andthus including post-treatment responses could bias our estimates. These numbers are in line withmass surveys conducted by Statistics Denmark: in 2011, they found that while 82% trusted theDCB, only 59% trusted Parliament. See report summary here.

16From a Bayesian perspective (see Online Appendix), this response can be thought of as anindividual’s posterior unemployment belief (updated after receiving new information).

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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. Tables 8 and 9 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 4omitted 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.

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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 towardareas 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 excellentproxy for the prior over future the unemployment rate: among the control group, there is a 0.93correlation between current and future estimates. Our results are almost identical using differencebetween the current and future unemployment estimate as the endogenous variable.

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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 canceledout by the defiers). A sufficient assumption for this to equate to the case with no defiers is that thereare more compliers than defiers for any combination of potential outcomes (Assumption (2.4) andequation (2.2) in de Chaisemartin 2013), while a weaker condition requires only that some subsetof compliers has the same size and marginal distribution over potential outcomes as defiers, or thatthere are more compliers and defiers at each potential outcome (de Chaisemartin 2013).

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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

Figure 2 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 hypothesisof identical sample moments.

24Distributional tests confirm that the variance reduction is statistically significant. Although

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0 5 10 15 20

0.00

0.10

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0.30

Panel A

2013 unemployment expectation (%)

Den

sity

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ControlDCB 7%Assume DCB 7%Assume DCB 5%

0 5 10 15 20

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Panel B

2013 unemployment expectation (%)

Den

sity

0 5 10 15 20

0.00

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ControlAssume govt. 7%Assume govt. 5%Assume opp. 7%Assume opp. 5%

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.

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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.

Table 1 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 hardto see how such explanations could explain the changes in political preferences we documentbelow.

25There is a slight increase in trust of the DCB, but the change cannot explain the large responseto 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 = 0.16Test: Government 7% = Opposition 7% p = 0.65

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

Observations 5,705Outcome mean 7.98Outcome standard deviation 3.55

Notes: Estimated using OLS. Robust standard errors in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. Thecoefficient 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 Table 2 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). Table 2 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 Table 2 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 votingbehavior and our treatments.

27The respondent’s subjective probability of being without a job in the forthcoming year alsohad no interaction effect, but substantially reduced the sample size.

21

Table 2: Heterogeneous effects of information treatments on unemployment expectations (%), byconditional 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 thevariables on the left hand side of the table (see Online Appendix for their definitions). The coefficient for thecontrol 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 Table 3 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 suchthat under-estimators trust the government less.

23

Tabl

e3:

Eff

ects

ofin

form

atio

ntr

eatm

ents

onun

empl

oym

ente

xpec

tatio

ns(%

),by

curr

entu

nem

ploy

men

test

imat

e

Est

imat

e>7.

4%E

stim

ate≤

7.4%

Est

imat

e∈[5

%,7

%]

Est

imat

e∈[5

.4%

,9.4

%]

(1)

(2)

(3)

(4)

Con

trol

11.7

03**

*6.

099*

**6.

505*

**7.

536*

**(0

.282

)(0

.083

)(0

.071

)(0

.080

)D

CB

7%tr

eatm

ent(

com

bine

d)-2

.582

***

0.50

1***

0.29

5***

-0.2

73**

*(0

.303

)(0

.092

)(0

.079

)(0

.087

)D

CB

5%tr

eatm

ent

-2.7

34**

*-0

.361

***

-0.6

23**

*-1

.224

***

(0.3

72)

(0.1

00)

(0.0

91)

(0.1

01)

Gov

ernm

ent7

%tr

eatm

ent

-2.1

83**

*0.

625*

**0.

340*

**-0

.134

(0.3

29)

(0.1

03)

(0.0

90)

(0.0

98)

Gov

ernm

ent5

%tr

eatm

ent

-2.4

29**

*-0

.290

***

-0.4

26**

*-0

.773

***

(0.3

55)

(0.1

06)

(0.0

93)

(0.1

00)

Opp

ositi

on7%

trea

tmen

t-1

.963

***

0.30

5***

0.07

6-0

.311

***

(0.3

46)

(0.1

06)

(0.0

95)

(0.1

02)

Opp

ositi

on5%

trea

tmen

t-2

.145

***

-0.3

96**

*-0

.496

***

-0.9

61**

*(0

.371

)(0

.109

)(0

.098

)(0

.106

)

Test

:DC

B7%

=G

over

nmen

t7%

p=

0.05

**p=

0.09

*p=

0.50

p=

0.04

**Te

st:D

CB

7%=

Opp

ositi

on7%

p=

0.01

***

p=

0.01

***

p=

0.00

***

p=

0.60

Test

:Gov

ernm

ent7

%=

Opp

ositi

on7%

p=

0.41

p=

0.00

***

p=

0.00

***

p=

0.04

**

Test

:DC

B5%

=G

over

nmen

t5%

p=

0.35

p=

0.41

p=

0.02

**p=

0.00

***

Test

:DC

B5%

=O

ppos

ition

5%p=

0.08

*p=

0.70

p=

0.15

p=

0.00

***

Test

:Gov

ernm

ent5

%=

Opp

ositi

on5%

p=

0.38

p=

0.27

p=

0.44

p=

0.04

**

Obs

erva

tions

2,95

52,

750

2,31

73,

132

Not

es:S

eeTa

ble

1.

24

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

Table 4 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 Table 1.30The reduced form estimates show similar results in the Online Appendix. Examining the DCB,

government and opposition treatments as separate groups, the LACR magnitudes are consistentacross 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 32.64Observations 5,705 5,705 5,705 5,705 5,705 5,705Outcome mean 0.32 0.17 0.09 0.06 0.41 0.28Outcome 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. Robuststandard 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 Table 5 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 32.64Observations 5,688 5,705 5,614 5,705Outcome mean 2.69 3.20 2.23 0.06Outcome standard deviation 1.00 1.02 0.61 0.25

Notes: All specifications estimated using 2SLS, and control for current unemployment expectations. Robuststandard 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 Table 5 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. Figure 3 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, Figure 3 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 influencedresponses. We find similar effect for discussion of politics, aggregating the indicators for dis-cussing politics with friends, family, neighbors, workers and others.

30

Non-swing voter

Swing voter

Non-extreme voter

Extreme voter

News less than every day

News every day

High school only

Beyond high school education

Non-improving economic prospects

Improving economic prospects

-.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 agovernment 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 unemploymentestimate

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 66.38Observations 2,955 2,750 2,317 3,132Outcome mean 0.30 0.34 0.34 0.34Outcome standard deviation 0.46 0.47 0.48 0.47

Notes: See Table 4.

the Danish economy would improve in 2013 relative to 2012.

Figure 3 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 Table 6 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.

35

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

probability pi(zi) ∈ (0,1) their income is yLi , and with probability 1− pi(zi) their income is yH

i ,

where yHi > yL

i 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,

define i’s expected income as Yi(zi) ≡ pi(zi)yLi +(1− pi(zi))yH

i .

Voters are also uncertain about the aggregate distribution of income. In particular, with voter

i assigns probability q(zi) ∈ (0,1) to economy-wide average income being yL, and probability

1− q(zi) to economy-wide average income being yH , where yH > yL. Assume q is differentiable

and monotonic in zi. Define i’s expected average income in the economy as Y (zi)≡ q(zi)yL+(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

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 ′(τ) >

0,φ ′′(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: u′(·) > 0,u′′(·) < 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:

τ∗i = max

{(φ ′)−1

(1− Yi(zi)

Y (zi)

),0}

. (5)

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 τ∗i = 0.

In the space where τ∗i > 0, or for voters with expected incomes exceeding the average expected

income, the comparative static with respect to new information is:

dτ∗idzi

=p′i(zi)(yH

i − yLi )Y (zi)−q′(zi)(yH− yL)Yi(zi)

φ ′′(τ)[Y (zi)]2. (6)

Given yHi − yL

i > 0 and yH − yL > 0, and expected individual and aggregate income is positive, it

is clear that we have opposing effects when sgn(p′i(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

q′(zi) = 0 and p′i(zi) = 0 respectively.

Without loss of generality (the results will just be the opposite), let us focus on the case where

sgn(p′i(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:

p′i(zi)

q′(zi)>

(yH− yL)Yi(zi)

(yHi − yL

i )Y (zi), (7)

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

Tables 8 and 9 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.

Figures 4 and 5 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 variablesSocial Democrat Party 5,705 0.17 0.37 0 1Social Liberal Party 5,705 0.09 0.29 0 1Socialist People’s Party 5,705 0.06 0.24 0 1Liberal Party 5,705 0.28 0.45 0 1Red-Green Alliance 5,705 0.06 0.25 0 1Confidence in government 5,688 2.69 1.00 1 5Redistribution 5,705 3.20 1.02 1 5Unemployment insurance 5,614 2.23 0.61 1 3

Dependent/endogenous variableUnemployment expectations 5,705 7.98 3.55 0 45

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

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

50

Tabl

e8:

Bal

ance

test

s1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Cur

rent

UE

st.

Wom

anA

geB

asic

Edu

.M

ed.E

duL

ong

Edu

.Vo

ted

Gov

t.Vo

ted

Lef

t

Con

trol

8.70

4***

0.49

3***

1961

.541

***

0.24

3***

0.66

0***

0.09

7***

0.43

2***

0.49

2***

(0.1

77)

(0.0

19)

(0.4

34)

(0.0

16)

(0.0

18)

(0.0

11)

(0.0

19)

(0.0

19)

DC

B7%

trea

tmen

t-0

.264

-0.0

020.

869

0.00

3-0

.016

0.01

30.

021

0.01

5(0

.230

)(0

.026

)(0

.608

)(0

.023

)(0

.025

)(0

.016

)(0

.026

)(0

.026

)D

CB

7%tr

eatm

ent(

true

)-0

.364

-0.0

090.

171

0.00

20.

012

-0.0

140.

012

-0.0

00(0

.226

)(0

.027

)(0

.617

)(0

.023

)(0

.025

)(0

.015

)(0

.027

)(0

.027

)D

CB

5%tr

eatm

ent

-0.2

490.

025

0.37

4-0

.047

**0.

026

0.02

10.

049*

0.04

2(0

.229

)(0

.026

)(0

.601

)(0

.022

)(0

.025

)(0

.016

)(0

.026

)(0

.026

)G

over

nmen

t7%

trea

tmen

t-0

.086

0.00

90.

234

-0.0

330.

010

0.02

40.

004

-0.0

02(0

.240

)(0

.027

)(0

.601

)(0

.022

)(0

.025

)(0

.017

)(0

.026

)(0

.027

)G

over

nmen

t5%

trea

tmen

t0.

224

-0.0

04-0

.190

-0.0

300.

012

0.01

70.

031

0.01

7(0

.252

)(0

.027

)(0

.626

)(0

.022

)(0

.025

)(0

.016

)(0

.026

)(0

.027

)O

ppos

ition

7%tr

eatm

ent

-0.2

65-0

.024

0.44

3-0

.009

0.01

3-0

.004

-0.0

00-0

.007

(0.2

39)

(0.0

27)

(0.6

07)

(0.0

23)

(0.0

25)

(0.0

16)

(0.0

26)

(0.0

27)

Opp

ositi

on5%

trea

tmen

t0.

011

-0.0

120.

529

-0.0

21-0

.010

0.03

1*0.

050*

0.03

6(0

.249

)(0

.027

)(0

.602

)(0

.023

)(0

.025

)(0

.017

)(0

.026

)(0

.027

)

Obs

erva

tions

5,70

55,

705

5,69

25,

642

5,64

25,

642

5,70

55,

705

Fte

stof

equa

lity

over

trea

tmen

tsp=

0.19

p=

0.75

p=

0.78

p=

0.18

p=

0.74

p=

0.07

p=

0.29

p=

0.47

Not

es:A

llsp

ecifi

catio

nses

timat

edus

ing

OL

S.R

obus

tsta

ndar

der

rors

inpa

rent

hese

s.∗ p

<0.

1,∗∗

p<

0.05

,∗∗∗

p<

0.01

.

51

Tabl

e9:

Bal

ance

test

s2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Vote

dri

ght

Wag

es(l

og)

Exp

.Inc

ome

(log

)H

omeo

wne

rE

con.

pros

pect

sTe

nure

dJo

bR

isk

Ris

kav

erse

Con

trol

8.70

4***

0.49

3***

1961

.541

***

0.24

3***

0.66

0***

0.09

7***

0.43

2***

0.49

2***

(0.1

77)

(0.0

19)

(0.4

34)

(0.0

16)

(0.0

18)

(0.0

11)

(0.0

19)

(0.0

19)

DC

B7%

trea

tmen

t-0

.264

-0.0

020.

869

0.00

3-0

.016

0.01

30.

021

0.01

5(0

.230

)(0

.026

)(0

.608

)(0

.023

)(0

.025

)(0

.016

)(0

.026

)(0

.026

)D

CB

7%tr

eatm

ent(

true

)-0

.364

-0.0

090.

171

0.00

20.

012

-0.0

140.

012

-0.0

00(0

.226

)(0

.027

)(0

.617

)(0

.023

)(0

.025

)(0

.015

)(0

.027

)(0

.027

)D

CB

5%tr

eatm

ent

-0.2

490.

025

0.37

4-0

.047

**0.

026

0.02

10.

049*

0.04

2(0

.229

)(0

.026

)(0

.601

)(0

.022

)(0

.025

)(0

.016

)(0

.026

)(0

.026

)G

over

nmen

t7%

trea

tmen

t-0

.086

0.00

90.

234

-0.0

330.

010

0.02

40.

004

-0.0

02(0

.240

)(0

.027

)(0

.601

)(0

.022

)(0

.025

)(0

.017

)(0

.026

)(0

.027

)G

over

nmen

t5%

trea

tmen

t0.

224

-0.0

04-0

.190

-0.0

300.

012

0.01

70.

031

0.01

7(0

.252

)(0

.027

)(0

.626

)(0

.022

)(0

.025

)(0

.016

)(0

.026

)(0

.027

)O

ppos

ition

7%tr

eatm

ent

-0.2

65-0

.024

0.44

3-0

.009

0.01

3-0

.004

-0.0

00-0

.007

(0.2

39)

(0.0

27)

(0.6

07)

(0.0

23)

(0.0

25)

(0.0

16)

(0.0

26)

(0.0

27)

Opp

ositi

on5%

trea

tmen

t0.

011

-0.0

120.

529

-0.0

21-0

.010

0.03

1*0.

050*

0.03

6(0

.249

)(0

.027

)(0

.602

)(0

.023

)(0

.025

)(0

.017

)(0

.026

)(0

.027

)

Obs

erva

tion

5,70

55,

532

5,55

45,

705

5,67

54,

540

4,54

05,

580

Fte

stof

equa

lity

over

trea

tmen

tsp=

0.20

p=

0.95

p=

0.75

p=

0.44

p=

0.38

p=

0.68

p=

0.26

p=

0.03

Not

es:S

eeTa

ble

8.

52

020

4060

8010

0

Cum

ulat

ive

dens

ity

0 10 20 30 40 50

Unemployment expectations (%)

Control DCB 7% treatmentDCB 5% treatment DCB 7% treatment (true)

Figure 4: Cumulative density plots of unemployment expectations by information treatment 1

53

020

4060

8010

0

Cum

ulat

ive

dens

ity

0 10 20 30 40

Unemployment expectations (%)

Control Government 7% treatmentGovernment 5% treatment Opposition 7% treatmentOpposition 5% treatment

Figure 5: Cumulative density plots of unemployment expectations by information treatment 2

54

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 foreither 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 forcurrent 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 5,705First stage F statistic 32.64

Notes: Estimated using OLS. Robust standard errors in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01. Thecoefficient 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 thevariables on the left hand side of the table (see Online Appendix for their definitions). The coefficient for thecontrol 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

Tabl

e14

:Com

pari

son

ofsu

b-sa

mpl

ech

arac

teri

stic

s

Est

imat

e¿7.

4%E

stim

ate≤

7.4%

Est

imat

e∈[5

%,7

%]

Est

imat

e∈[5

.4%

,9.4

%]

Mea

nSt

.dev

.M

ean

St.d

ev.

Mea

nSt

.dev

.M

ean

St.d

ev.

Med

ium

educ

atio

n0.

666

0.47

20.

662

0.47

30.

660

0.47

40.

667

0.47

1H

igh

educ

atio

n0.

087

0.28

20.

134

0.34

10.

135

0.34

20.

125

0.33

0D

iscu

sspo

litic

s2.

211

1.13

82.

383

1.13

52.

385

1.13

72.

370

1.13

8Vo

ted

left

atla

stel

ectio

n0.

509

0.50

00.

499

0.50

00.

502

0.50

00.

515

0.50

0Vo

ted

righ

tatl

aste

lect

ion

0.40

30.

491

0.42

80.

495

0.42

60.

495

0.41

10.

492

New

sev

ery

day

0.66

80.

471

0.76

80.

422

0.77

70.

417

0.75

20.

432

Wom

an0.

568

0.49

50.

410

0.49

20.

407

0.49

10.

430

0.49

5

59

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

Tabl

e15

:Red

uced

form

effe

ctof

unem

ploy

men

texp

ecta

tions

onpo

litic

alpr

efer

ence

s

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Pane

lA:a

lltr

eatm

ents

Gov

t.So

c.D

em.

Soc.

Lib

.So

c.Pe

op.

Con

f.go

vt.

Red

-Gre

enR

ight

Lib

eral

sR

edis

t.U

.ins

uran

ce

DC

B7%

trea

tmen

t0.

020

0.03

1*-0

.007

-0.0

040.

091*

*-0

.015

-0.0

18-0

.017

-0.0

91**

-0.0

19(0

.021

)(0

.017

)(0

.013

)(0

.011

)(0

.045

)(0

.022

)(0

.021

)(0

.011

)(0

.045

)(0

.028

)D

CB

5%tr

eatm

ent

0.04

9**

0.01

80.

024

0.00

70.

108*

*-0

.050

*-0

.043

*-0

.012

-0.0

750.

009

(0.0

25)

(0.0

19)

(0.0

16)

(0.0

13)

(0.0

52)

(0.0

26)

(0.0

24)

(0.0

13)

(0.0

52)

(0.0

31)

Gov

ernm

ent7

%tr

eatm

ent

0.00

70.

013

-0.0

120.

006

0.05

00.

005

0.00

2-0

.007

-0.0

84-0

.008

(0.0

24)

(0.0

19)

(0.0

15)

(0.0

13)

(0.0

51)

(0.0

26)

(0.0

24)

(0.0

14)

(0.0

53)

(0.0

32)

Gov

ernm

ent5

%tr

eatm

ent

0.02

70.

009

0.01

9-0

.001

0.07

6-0

.032

-0.0

18-0

.007

-0.0

350.

025

(0.0

25)

(0.0

19)

(0.0

16)

(0.0

12)

(0.0

52)

(0.0

26)

(0.0

24)

(0.0

14)

(0.0

53)

(0.0

32)

Opp

ositi

on7%

trea

tmen

t-0

.011

-0.0

02-0

.007

-0.0

03-0

.028

0.01

4-0

.015

-0.0

10-0

.069

0.00

9(0

.024

)(0

.019

)(0

.015

)(0

.012

)(0

.051

)(0

.026

)(0

.024

)(0

.013

)(0

.053

)(0

.033

)O

ppos

ition

5%tr

eatm

ent

0.04

1*0.

040*

*0.

000

0.00

10.

202*

**-0

.027

-0.0

30-0

.008

-0.0

560.

001

(0.0

25)

(0.0

20)

(0.0

15)

(0.0

12)

(0.0

53)

(0.0

26)

(0.0

24)

(0.0

13)

(0.0

53)

(0.0

33)

Obs

erva

tions

5,80

35,

803

5,80

35,

803

5,78

65,

803

5,80

35,

803

5,80

35,

709

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

Pane

lB:c

ombi

ned

trea

tmen

tlev

els

Gov

t.So

c.D

em.

Soc.

Lib

.So

c.Pe

op.

Con

f.go

vt.

Red

-Gre

enR

ight

Lib

eral

sR

edis

t.U

.ins

uran

ce

All

7%tr

eatm

ent

0.00

90.

019

-0.0

08-0

.001

0.05

1-0

.003

-0.0

12-0

.013

-0.0

84**

-0.0

09(0

.019

)(0

.015

)(0

.012

)(0

.010

)(0

.041

)(0

.021

)(0

.019

)(0

.011

)(0

.041

)(0

.025

)A

ll5%

trea

tmen

t0.

039*

*0.

022

0.01

50.

002

0.12

9***

-0.0

36*

-0.0

30-0

.009

-0.0

550.

012

(0.0

20)

(0.0

16)

(0.0

13)

(0.0

10)

(0.0

43)

(0.0

21)

(0.0

20)

(0.0

11)

(0.0

43)

(0.0

26)

Obs

erva

tions

5,80

35,

803

5,80

35,

803

5,78

65,

803

5,80

35,

803

5,80

35,

709

Not

es:

All

spec

ifica

tions

estim

ated

usin

gO

LS,

and

cont

rol

for

curr

ent

unem

ploy

men

tex

pect

atio

ns.

Rob

ust

stan

dard

erro

rsin

pare

nthe

ses.∗ p

<

0.1,∗∗

p<

0.05

,∗∗∗

p<

0.01

.

61

Tabl

e16

:Het

erog

eneo

usef

fect

s—2S

LS

estim

ates

(1)

(2)

(3)

(4)

(5)

(6)

Gov

t.G

ovt.

Gov

t.G

ovt.

Gov

t.G

ovt.

Une

mpl

oym

ente

xpec

tatio

ns(%

)-0

.059

**-0

.055

***

-0.0

53**

*-0

.014

-0.0

20-0

.014

(0.0

23)

(0.0

19)

(0.0

19)

(0.0

20)

(0.0

16)

(0.0

15)

×sw

ing

vote

r(pr

evio

usvo

tes)

0.03

3(0

.030

)×

swin

gvo

ter(

prev

ious

inte

ntio

ns)

0.04

6(0

.029

)×

ideo

logi

cally

extr

eme

vote

r0.

058*

(0.0

34)

×ne

ws

ever

yda

y-0

.033

(0.0

26)

×be

yond

high

scho

oled

ucat

ion

-0.0

23(0

.022

)×

impr

ovin

gec

onom

icpr

ospe

cts

-0.0

66**

(0.0

29)

Obs

erva

tions

3,82

74,

566

5,70

55,

642

5,67

5

Not

es:A

llsp

ecifi

catio

nses

timat

edus

ing

2SL

S,an

dco

ntro

lfor

curr

entu

nem

ploy

men

texp

ecta

tions

and

linea

rter

mfo

reac

hin

tera

ctio

n.R

obus

tsta

ndar

der

rors

inpa

rent

hese

s.∗ p

<0.

1,∗∗

p<

0.05

,∗∗∗

p<

0.01

.

62

Table 17: Heterogeneous effect of unemployment expectations by immigration exposure andpreferences

(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