I NFORMATION , SOURCE CREDIBILITY AND POLITICAL SOPHISTICATION : E XPERIMENTAL EVIDENCE ON ECONOMIC VOTING * JAMES E. ALT † DAVID D. L ASSEN ‡ J OHN MARSHALL § APRIL 2014 How does the source of politically-relevant economic information affect voter beliefs and ultimately political preferences? This paper randomly varies whether voters re- ceive an aggregate unemployment projection from the central bank, government or opposition party using a survey experiment in Denmark with unique access to detailed panel and administrative data. All sources induce voters to update their unemployment expectations. While all voters regard the Danish Central Bank as the most credible source, only sophisticated voters update more after receiving information from a party with political incentives to state otherwise. However, belief updating is no greater when the source is aligned with the voter’s previously expressed political preferences. After decreasing unemployment expectations, we find clear evidence of intended eco- nomic voting, without voters changing their policy preferences: the average respon- dent is 3.5 percentage points more likely to vote for the government. Such economic voting is driven by politically sophisticated rather than swing voters. * We wish to thank Alberto Abadie, Charlotte Cavaille, Alex Fouirnaies, Torben Iversen, Horacio Larreguy and Victoria Shineman for valuable advice and comments, as well as participants at the Harvard Political Economy and Comparative Politics workshops, NYU Center for Experimental Social Science Conference 2014, Midwest Political Science Association 2014, and MIT Political Economy Breakfast. Lassen thanks the Danish Council for Independent Research under its Sapere Aude program for financial assistance. † Department of Government, Harvard University, james [email protected]. ‡ Department of Economics, University of Copenhagen, [email protected]. § Department of Government, Harvard University. [email protected]. (Corresponding author.) 1
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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 [email protected].‡Department of Economics, University of Copenhagen, [email protected].§Department of Government, Harvard University. [email protected]. (Corresponding author.)
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).
2
the aftermath of the financial crisis and where left-right political divisions remain entrenched. The
combination of a panel political survey and access to extremely detailed administrative govern-
ment data provides a unique opportunity to understand in detail which voters update their beliefs
and when such beliefs translate into economic voting.
We first examine how the source of unemployment projections affect unemployment expecta-
tions. We find that the objective credibility of the information source matters: an unemployment
projection from the DCB, which is highly trusted among citizens, causes voters to update their
belief more than receiving information from government or opposition political parties. While in-
formation from both governing and opposition political parties do still affect voter beliefs, a more
sophisticated subset of voters also recognize that a projection from a source with electoral incen-
tives to say the opposite is more credible. However, we find no evidence of subjective credibility
such that voters update more in response to unemployment projections from the party they favor.
Our instrumental variable analysis shows that a percentage point decrease in unemployment
expectations increases the probability that the average complier intends to vote for Denmark’s
coalition government by 3.5 percentage points. This large effect, which only helped the parties of
the Prime Minister and Minister for the Economy and Interior, would have been more than enough
to have altered the outcome of Denmark’s recent knife-edge elections. Supporting the economic
voting interpretation, we observe a large increase in confidence in the government and no change in
support for non-government left-wing parties. Although these results could still reflect unemploy-
ment expectations changing voter policy preferences, rather than beliefs regarding the competence
of the government, unemployment expectations do not affect attitudes toward redistributive or un-
employment insurance policies.
Since assigning responsibility for policy outcomes is especially challenging in Denmark’s com-
plex political system and very open economy, it is not surprising to find that providing new infor-
mation only induces a subset of voters to vote economically. In particular, we find that economic
voting is neither concentrated among swing voters nor ideologues. Rather, economic voting is only
3
observed among sophisticated—better informed, more educated and politically-engaged—voters
and those who already believe the economy is improving. These results show that politically-
relevant information can support democratic accountability, even in political environments where
the clarity of responsibility is low, but is not sufficient to induce all voters to reward good per-
formance. This finding may explain why parties tend to target target their messages at more
politically-engaged voters who appear to be more sensitive to new information (Adams and Ezrow
2009; Gilens 2005).
The paper is structured as follows. 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.
4
However, in the real world, most politically-relevant information is conveyed by agents with
distinct and often well-understood ideological biases and incentives to distort perceptions of the
true state of the world (e.g. Baron 2006; Besley and Prat 2006; Gentzkow and Shapiro 2006; Zaller
1999).3 Empirically, Larcinese, Puglisi and Snyder (2011) have shown that pro-Democrat newspa-
pers in the U.S. are more likely to report high unemployment under Republican Presidents, while
Durante and Knight (2012) point to significant biases in television coverage in Italy. Accordingly,
voters must evaluate the information they receive in terms of the credibility of the source.
We distinguish two forms of source credibility that could affect belief updating after receiving
new information. Objective credibility reflects beliefs about the source’s credibility that depend
upon institutional characteristics of the source that are extrinsic to the voter (see also Ansolabehere,
Meredith and Snowberg forthcoming; Zaller 1999). Two important characteristics are institutional
expertise and incentives to deceive. Independent central banks are typically relatively credible
because they have few political incentives to deceive voters,4 and often successfully establish a
reputation for sending accurate messages by virtue of employing highly-trained economists and
providing convincing technical data. Conversely, political parties (and certain media channels)
have widely-understood biases (e.g. Prior 2013): governments have strong incentives to play up
their performance in office, while opposition parties may do the reverse. These features of objective
credibility imply the following hypotheses:
H1. (Institutional expertise) Fixing message content, voters change their beliefs more after re-
ceiving information from an expert source.
H2. (Institutional incentives) Fixing message content, voters change their beliefs more after re-
3Voters receiving biased information is also a demand side phenomenon as well (see Mul-lainathan and Shleifer 2005). We focus on supply by experimentally varying the sources voters 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).
5
ceiving information from a source with political incentives to conceal such information.
To be precise, greater belief updating entails larger shifts in the mean of an individual’s probability
distribution over the future unemployment rate. Whether an expert source affects beliefs more than
receiving information going against the expected bias of a less expert source is an empirical ques-
tion that our experiment can answer, but H1 on its own implies that a central bank is regarded as
more credible than political parties while H2 implies that positive information from the opposition
is more credible.
Subjective credibility, on the other hand, depends upon characteristics intrinsic to the receiver
of the information. One critical basis for difference among voters in their response to a given
source is their political sophistication (see Gomez and Wilson 2001, 2006).5 The most sophis-
ticated voters—those that are both politically informed and able to comprehend and assess more
technical information—are unlikely to significantly update their beliefs, since their prior is likely
to be tighter (Ansolabehere, Meredith and Snowberg forthcoming). Nevertheless, given the best
informed voters are generally fairly imperfectly informed (Anderson 2007; Duch and Stevenson
2008), we still expect most voters to respond to specific information. The least sophisticated voters
are likely to have the most diffuse priors. Consequently, we expect the least politically sophisti-
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.
6
A second dimension of subjective credibility is differences among respondents in their “accu-
racy goals” and “directional goals”, where the former types seek to make decisions based on the
most accurate information (akin to objective credibility) while the latter only seek information that
confirms their prior beliefs (Taber and Lodge 2006). Similarly, Mullainathan and Shleifer (2005)
show that with heterogeneity in priors over politically divisive issues, newspapers separate in their
reporting of the news and cater to a segmented market where the credibility of information in the
eyes of the consumer varies considerably. A large literature in the U.S. has suggested that knowing
the position of a political party on an issue strongly conditions a voter’s beliefs and preferences
(see Boudreau and MacKenzie 2014; Bullock 2011; Malhotra and Kuo 2008; Healy and Malhotra
2013). Given the U.S. is currently experiencing high political polarization and has only two politi-
cal parties, it is not obvious that the partisans in other contexts will respond similarly. Accordingly,
we consider:
H4. (Partisanship) Fixing message content, voters change their beliefs more after receiving in-
formation from a source the voter is politically close to.
Of course, aspects of both objective and subjective credibility could simultaneously affect vot-
ers. By randomly varying sources with differing levels of objective credibility, and comparing
responses to a given source across different types of voter, our empirical design separates differ-
ences in credibility.
2.2 Political implications for economic voting
The idea that governments may be rewarded or sanctioned by voters on the basis of their economic
performance is well-established (see Anderson 2007; Lewis-Beck and Paldam 2000; Lewis-Beck
and Stegmaier 2000). The logic underlying this argument is that voters impose sanctions on the
basis of economic outcomes to deter re-election seeking politicians from choosing suboptimal
policies (Barro 1973; Ferejohn 1986), or looking forward use the available information to select the
7
most competent candidate (Fearon 1999; Rogoff and Sibert 1988; Rogoff 1990).6 Both backward-
and forward-looking information can help to evaluate the competence of office-holders.
To the extent that economic performance is a key election issue and is deemed to possess the
capacity to affect the economy (Duch and Stevenson 2010), information about macroeconomic
performance is expected to increase economic voting. The empirical evidence assessing whether
economic success translates into higher likelihoods of an incumbent being re-elected has been
mixed (Anderson 2007), and has struggled to provide compelling evidence of a causal relationship
(Healy and Malhotra 2013). To the extent that voting is economic, most studies conclude that it is
macroeconomic “sociotropic” aggregates rather than individual-specific “pocketbook” calculations
that drive this relationship (e.g. Kiewiet 1983; Lewis-Beck and Stegmaier 2000).
Economic voting models require that voters both obtain and process sufficient information
about policy choices—or at least their (expected) outcomes—to attribute responsibility and evalu-
ate incumbent performance. These assumptions are now receiving greater scrutiny (see Anderson
2007; Healy and Malhotra 2013). Research has shown that voters often lack even the minimal
information required to vote according to economic performance (e.g. Campbell et al. 1960;
Delli Carpini and Keeter 1996) or suffer partisan biases in attribution (Fiorina 1981; Rudolph
2003a, 2003b, 2006; Malhotra and Kuo 2008; Tilley and Hobolt 2011), while informed voters
have lacked the motivation or cognitive capacity to translate information into responsibility desig-
nation (e.g. Bartels 1996; Delli Carpini and Keeter 1996; Krause 1997). These problems are multi-
plied in institutional contexts characterized by multiple loci of decision-making power, where even
the most willing economic voter may struggle to assign responsibility for economic performance
(Anderson 1995; Duch and Stevenson 2008; Nadeau, Niemi and Yoshinaka 2002; Powell Jr. and
Whitten 1993). Furthermore, information about performance in office may not persuade extreme
or especially partisan voters to act upon it (Ansolabehere and Snyder Jr. 2000).
6The motives underpinning this approach could be either sociotropic or self-interested. AsAnsolabehere, Meredith and Snowberg (forthcoming) have shown, parsing out these effects ischallenging.
8
Combining these insights, our economic voting hypothesis is stated with significant condition-
ality:
H5. (Economic voting) If an individual’s unemployment expectations decrease, the likelihood
that they vote for a party in government (in any given institutional context) increases only
if the individual has the cognitive capacity and will to assign government responsibility to
economic performance.
In this light, economic voting is not the inevitable by-product of providing economic information
for all voters.
3 Research design
3.1 Danish political context
Left-right differences over economic policy remain the salient division in Danish politics, with
governments oscillating between center-left and center-right coalitions. In 2011, Social Democrat
Helle Thorning-Schmidt became Denmark’s first female Prime Minister, having narrowly led the
left bloc—containing the Social Democratic, Social Liberal and Socialist People’s parties as coali-
tion partners, and supported by the Red-Green Alliance—to victory over a center-right coalition
led by the Liberals that had held office since 2001.
Dissatisfaction with the government’s economic performance was the major issue in the 2011
election.7 Having sustained very low levels of aggregate unemployment throughout the 2000s, the
financial crisis hit Denmark’s trade-dependent economy badly. In early 2008 unemployment hit
7E.g. this Economist article. The Danish Election Study polls, available here, show that 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.
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
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.
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.
12
Respondents were therefore informed that the DCB, the government or main opposition party
project that unemployment over the next year will be “almost 7%” or “around 5%”. The true DCB
projection for gross unemployment was almost 7%. However, because only the DCB has publicly
stated this, ethical considerations required that our other primes begin with “assume that...”. In
order to examine the extent to which such wording weakens the treatment, our final treatment
group was truthfully told “The DCB estimates unemployment in 2013 to be almost 7%.” We
compare this treatment to the analogous “assume” version, and will show no statistical difference
in the distribution of unemployment expectations.
These sources vary considerably in their credibility among voters of all political stripes. Unlike
some other central banks, the DCB is highly regarded by voters, and is not seen as having a right-
wing agenda or being an instrument of government. Asking respondents how much trust they place
in each source, 67% of respondents trusted or greatly trusted the DCB while only 17% and 27%
trusted or greatly trusted the government and Liberals respectively.15 Eurobarometer data indicates
that trust in Denmark’s political parties is very similar to the European Union mean (European
Commission 2011).
3.2.2 Outcome variables
We consider two types of outcome variables: unemployment expectations and preferences over
political parties. To capture unemployment expectations we asked respondents “What is your best
estimate of what unemployment will be in 2013? We would like your best estimate, even if you
are not entirely sure.”16 This question was asked immediately after respondents received their
treatment, and the 20 respondents who answered that the unemployment rate would exceed 50%
15Only the control group responses were used because this question followed the treatment, 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).
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.
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.
15
when prominent public sources would induce voters to update their beliefs against the information
provided, respondents with low prior unemployment expectations may increase their unemploy-
ment expectations, especially after the 7% treatments.
Fortunately, the monotonicity assumption can be weakened in ways consistent with our data.
In general, 2SLS estimation recovers a very similar quantity of interest to the LACR when “few
subjects are defiers, or if defiers and compliers have reasonably similar distributions of potential
outcomes” (de Chaisemartin 2013: 7)—and is identical under constant causal effects (see Angrist,
Imbens and Rubin 1996).22 In this application, 27% of respondents upwardly update their unem-
ployment expectations relative to their current estimate. Since upward and downward effects may
be very similar, and given that compliers significantly outnumber defiers, the presence of defiers is
relatively unproblematic. Nevertheless, our results are very similar when we restrict the sample to
the 5% treatments with almost no defiers. Our analysis also considers a variety of other subgroup
analyses, based on respondents’ current estimates of unemployment, where monotonicity almost
certainly holds.
The exclusion restriction, which requires that the instrument only affects Yi through U expecti,
is usually more problematic in empirical studies. Although such violations are unlikely in this
application, perhaps the most plausible violation arises where information treatments prime re-
spondents to think more carefully about government performance and policies (beyond the effect
of changing beliefs about unemployment expectations), inducing bias if such thinking systemati-
cally affects support for the government. We assess this possibility by looking at whether belief
in the importance of political information for either private economic decisions or as part of the
22Rather than recover the local average treatment effect, the Wald estimator (with no covariates)recovers the average treatment effect for a smaller group of compliers (precisely those not 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).
16
respondent’s job differs across treatments groups (or comparing the control to all treated respon-
dents), and find no difference.
4 Effects of information source on economic expectations
We first show that the information treatments substantially change unemployment expectations.
While we find evidence for both forms of objective credibility, and differential responses by prior
knowledge of the current unemployment rate, there is no evidence that political preferences cause
voters to update differentially. We first examine the distribution of the data, before proceeding to
regressions identifying average effects and then heterogeneity in voter responses.
4.1 Results
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
Figure 2: Unemployment expectations by DCB treatments
Notes: For graphical exposition, the x-axis is truncated so that the 1% of the sample with expectations above 20%are not visible.
18
now turn to our source credibility hypotheses.
Consistent with differences in expertise (H1), receiving information from political parties caused
the average voter to update their beliefs less than receiving information from the DCB. The DCB
treatments also induced more similar responses from voters (i.e. a smaller standard deviation in re-
sponses), especially compared to the opposition treatments. Although it could have been the case
that simply being primed by a source increased confidence in the source, the Online Appendix
shows that receiving a treatment does not affect trust in either political party.25
Partisan sources also reduced unemployment expectations. Panel B clearly shows a downward
shift in modal unemployment expectations for both the government and opposition treatments.
Surprisingly, given that the opposition has a political incentive to criticize government economic
performance, the Liberal projections did not cause voters to differentially update their beliefs rela-
tive to the predictably optimistic government message. We therefore find little support for H2, on
average.
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
× 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
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
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
× 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