Decomposing the Rise of the Populist Radical Right∗
Oren Danieli, Noam Gidron, Shinnosuke Kikuchi, Ro’ee Levy
February 1, 2024

Support for populist radical right parties in Europe has dramatically increased in recent
years. We decompose the rise of these parties from 2005 to 2020 into four components: shifts in party positions, changes in voter attributes (opinions and demographics), changes in voter priorities, and a residual. We merge two wide datasets on party
positions and voter attributes and estimate voter priorities using a probabilistic voting
model. We find that shifts in party positions and changes in voter attributes do notplay a major role in the recent success of populist radical right parties. Instead, theprimary driver behind their electoral success lies in voters’ changing priorities. Particularly, voters are less likely to decide which party to support based on parties’ economic
positions. Rather, voters—mainly older, nonunionized, low-educated men—increasingly
prioritize nativist cultural positions. This allows populist radical right parties to tap
into a preexisting reservoir of culturally conservative voters. Using the same datasets,
we provide a set of reduced-form evidence supporting our results. First, while parties’
positions have changed, these changes are not consistent with the main supply-side hypotheses for populist support. Second, on aggregate, voters have not adopted populist
right-wing opinions. Third, voters are more likely to self-identify ideologically based on
their cultural rather than their economic opinions.
JEL codes: D72; P00; H80.
∗
Danieli: Tel Aviv University and CEPR, orendanieli@tauex.tau.ac.il. Gidron: Hebrew University ofJerusalem, noam.gidron@mail.huji.ac.il. Kikuchi: MIT, skikuchi@mit.edu. Levy: Tel Aviv University and
CEPR, roeelevy@tauex.tau.ac.il. We are grateful to Daron Acemoglu, David Autor, Amir Beck, Leonardo
Bursztyn, Nathan J. Canen, Eve Colson-Sihra, Natalia Emmanuel, Benjamin Enke, Makoto Fukumoto, JeffryFrieden, Clément Herman, Alexey Makarin, Yotam Margalit, Vincent Pons, Giacomo A. M. Ponzetto, Moses
Shayo, Carolyn Tsao, Ebonya Washington, and seminar participants at the ASSA meeting, the Tel Aviv
University BMI Conference, Bocconi University, Bologna University, Computational Political Psychology
Network, CEPR Political Economy Programme and CEPR Research and Policy Network on Populism Joint
Symposium, EIEF, Hebrew University, Heinz College CMU, IASEI Annual Conference, the Israel Economic
Association, Japan Applied Econometrics Conference, Japanese Society for Quantitative Political Science
Winter Meeting, the Populism Seminar, NBER Political Economy, the Social Economics Conference, PEPES
Seminar, Princeton, Stockholm Institute of Transition Economics, Swedish Institute for Social Research, Tel
Aviv University, UBC Behavioral/PE Seminar, and UPF for helpful comments and suggestions. We thank
Maor Ankri, Elad Guttman, Gilad Hurvitz, Elior Mihelashvili, Liam Miller, Leanne Morical, Ori Shoham,
Shlomit Shtein, and Eran Yogev for excellent research assistance. Funding for this project was provided by ISF
Grant 2319/21, the Foerder Institute for Economic Research, and the Pinhas Sapir Center for Development
at Tel Aviv University.

The rise of populist radical right parties (PRRP) is one of the most significant politicaldevelopments in recent decades. Once on the fringes of European politics, PRRP now capture
almost 20% of the vote, as shown in Figure 1. PRRP are increasingly present not only inparliaments but also in governments (Akkerman et al., 2016), impacting policy-making on
multiple issues from immigration to welfare policies (Rathgeb and Busemeyer, 2021). In
certain countries, populist governments have resulted in lower GDP and significant erosionof democratic norms and institutions (McCoy and Somer, 2019; Funke et al., 2022).
While a growing literature has identified various factors affecting PRRP support (Rodrik,
2018; Fetzer, 2019; Mudde, 2019; Manacorda et al., 2023), there is still no consensus onwhat is the primary factor for the extensive rise of these parties and which factors are only
idiosyncratic (Guriev and Papaioannou, 2022). Moreover, there is no agreement on the broadmechanisms behind the rise of PRRP, including whether this trend is shaped by supply or
demand (Golder, 2016; Guiso et al., 2017; Dal Bo et al., 2023; Hall et al., 2023). Scholars
focusing on the supply side consider how shifts in party positions, such as the moderation of
PRRP on certain issues, can explain the growing support for these parties (Akkerman, 2015).
On the demand side, an ongoing debate centers on two potential explanations. A common
view argues that voters’ opinions have changed. For example, the support for PRRP may
have increased because public opinion has become more nativist or authoritarian (Hangartner
et al., 2019; Ballard-Rosa et al., 2021). The alternative view argues that voters have changed
their priorities rather than their opinions. For example, many voters may have always held
nativist or authoritarian opinions, and what explains the rise of PRRP is that these opinions
now more strongly shape vote choice (Bartels, 2017; Bonikowski, 2017; Enke et al., 2022).
In this paper, we compare the explanatory power of these three classes of arguments: shifts
in party positions, changes in voter attributes (opinions and demographics), and changes
in voter priorities. We create a new dataset that links data on party positions and voter
attributes and estimate voter priorities using a probabilistic voting model. To directly compare the arguments we develop a novel decomposition method for voting behavior, building
on commonly used methods for wage decomposition (Fortin et al., 2011). This descriptive
method shows that of the three channels, rising support for PRRP is mostly driven by changing priorities. Specifically, we find that over the past two decades, voters have increasingly
prioritized cultural issues over economic issues.

1

We start by outlining a multidimensional probabilistic voting model of how party positions, voter attributes, and voter priorities codetermine which party each voter prefers. In themodel, a voter’s utility from supporting a specific party is a function of that party’s positions(supply) weighted by the voter’s individual “voting weights” (demand).1 Each weight corresponds to a specific party position and can be positive or negative, depending on whether
the voter supports or opposes the position.
The two demand channels—voter attributes and voter priorities—affect voting decisions
through the voting weights. We allow the weights to vary across voters by assuming thatthey are a function of the voter attributes. The mapping between voter attributes and theweights is determined by the voter priorities, which correspond to the parameters of theutility function. We refer to these parameters as priorities since they determine which party
positions voters prioritize given their attributes. This setting of the model allows us toseparately evaluate the two categories of changes in demand discussed in the literature. The
first is a change in attributes, reflecting a shift in voters’ opinions or demographic composition(e.g., a shift toward more nativist attitudes). The second is a change in priorities, reflecting
a shift in the salience of specific topics or the importance voters attach to them (e.g., nativist
opinions become more consequential in shaping vote choice).
We measure party positions and voter attributes by creating a novel dataset that links theComparative Manifesto Project (CMP) and the Integrated Values Survey (IVS). The CMP
provides data on 56 party positions based on the parties’ platforms. The IVS includes a richand consistent set of voter attributes including their opinions and demographics. We analyze
101 attributes over three waves: 2005–2009, 2011–2013, and 2017–2020.2 Our merged dataset
includes approximately 60,000 respondents in 22 European countries. We focus on Europe
since the PRRP fared exceptionally well there in recent years and because the PRRP andtheir voters share similar traits across the continent.
We use our merged dataset to estimate voter priorities (the model parameters), separately
for each survey wave. Since this dataset includes a rich set of voter attributes and party
positions, our parameter space is high-dimensional. Therefore, we leverage machine-learning
1

In Section 5, we show that our results are robust to an alternative bliss-point model.
While the IVS survey data provides us with rich information on voter attributes, it limits our ability
to identify whether respondents would actually vote. Hence, we do not directly study turnout, despite its
importance (Guiso et al., 2017). Reassuringly, as we discuss in Section 2.2, the large rise in PRRP vote sharein our survey data is similar to the actual rise in PRRP support in elections occurring around the same time.
22

techniques to develop a computationally feasible method to estimate the model parameters.
Combining the data on parties and voters with the estimated priorities, we decompose
the rise in support for the PRRP between 2005–2009 and 2017–2020 into four components:
party positions, voter attributes, voter priorities, and a residual. Drawing from descriptive
decomposition methods (DiNardo et al., 1996; Card et al., 2016; Kleven et al., 2019; Danieli,2022), we quantify the contribution of each component to the rise in PRRP support. Specifically, using our model, we calculate partial-equilibrium counterfactual changes in PRRP
support when only one component shifts over time and the other three are held fixed. For
example, to estimate the voter priorities component, we calculate the counterfactual rise insupport for PRRP if party positions, voter attributes, and the residuals were held fixed totheir values in 2017–2020, while only voter priorities changed from their values in 2005–2009
to their values in 2017–2020.
Aggregating results from all countries, changes in voter priorities drive 43.5% of the overallrise of PRRP. By contrast, changes in voter attributes and in party positions explain only
6.1% and 1.0%, respectively. The residual accounts for the remaining 49.4% and is driven
mainly by the large growth of new entrants. These results remain robust across various
specifications, models, estimation methods, decomposition orders, and sub-regions.
To investigate the mechanism driving our results, we perform additional analyses focusing on each of the components. We first show that voters today are more likely to prioritize
parties’ cultural positions rather than their economic positions. We use our model to examine how the changes in the estimated voter priorities affect voting decisions. We observe adeclining tendency among voters to reward or penalize parties based on their economic positions. By contrast, many voters, especially males, individuals without a college degree, older
individuals, and non-union members, increasingly prioritize conservative cultural positions.
We complement these model-based results with independent direct survey evidence onchanging priorities. We use voters’ left-right self-identification as an alternative outcome thatis not included in our main estimation. Consistently, we find that voters increasingly identify
based on their cultural opinions rather than on their economic opinions. Additionally, we finda growing correlation between voters’ opinions on cultural issues and the cultural positionsof the parties they support.
We then inquire into our striking finding that changes in voter attributes contribute only3

a little to the rise of PRRP. We analyze reduced-form trends in various voter opinions andfind that while in some countries voters have adopted views that typically characterize the
PRRP, in others, voters have moved in the opposite direction. In aggregate, we find almost noright-wing shift in public opinion on issues associated with PRRP. For example, in contrast
to multiple media accounts, attitudes have not moved en masse toward greater opposition to
immigration. This supports the hypothesis that a reservoir of PRRP voters existed before
the dramatic rise in their support.
The size of this preexisting reservoir of PRRP voters varies substantially across countries
and can explain some of the geographical variation in PRRP support. We use a spatial
decomposition method to predict the counterfactual support that PRRP would have received
in different countries if voters in all countries were facing the same choice set of parties. We
find that the cross-country variation in voter attributes can partially explain why in somecountries (e.g., Poland) the largest party is a PRRP, while in others (e.g., Germany) the
populist radical right is still relatively small. Our exercise also allows us to estimate the
PRRP reservoir in countries where populist parties struggle to compete due to the first-pastthe-post system. For example, we extend our analysis to the U.S. and show that it has arelatively large PRRP reservoir.
On the supply side, we find that changes in party positions are not consistent with themain supply-side hypotheses explaining PRRP support. For example, the trends we observe
are not consistent with PRRP moderating their cultural positions. While we rule out several
supply-side hypotheses, we note that our decomposition cannot rule out general-equilibrium
effects, in which supply shocks contributed to the rise of PRRP by affecting voter priorities.
Finally, we show that the increase in the residual is driven by new populist radical rightparties entering later than expected. Using the party positions, voter attributes, and voter
priorities from the 2005-2009 wave, our model predicts that PRRP that entered the politicalsystem in the 2010s should have already received support in 2005-2009. Nevertheless, inmost countries, this did not materialize, despite noticeable efforts. We discuss potential
explanations for this belated entry, such as coordination failures.
This paper contributes to the literature on populism (Guriev and Papaioannou, 2022)
by directly comparing contrasting supply and demand theories regarding the rise of PRRP,
which we discussed above. The decomposition method we introduce is not limited to the4study of PRRP and can be used to analyze various other political trends.
This paper also complements the literature on the causal effects of different shocks on
PRRP support, including technological change (Anelli et al., 2019), globalization (Rodrik,2020), financial crises and austerity policies (Fetzer, 2019; Guiso et al., 2022), immigration(Dustmann et al., 2019), trade (Colantone and Stanig, 2018; Autor et al., 2020; Dippel et al.,2020), and new media technology (Guriev et al., 2021; Manacorda et al., 2023). While ourdescriptive analysis is not intended to identify causal effects, it points to the channels throughwhich these shocks might operate. For example, if trade shocks increased PRRP support,
our results suggest these shocks had an effect by changing the priorities of voters rather thantheir opinions. Hence, our descriptive analysis highlights the importance of using priorities
as the outcome variable when studying potential causes for the rise of PRRP.
A smaller literature estimates voters’ priorities and their behavior in different contexts(Johns, 2010; De Vries et al., 2013; Kendall et al., 2015; Sides et al., 2019; Le Pennec andPons, 2023). We contribute to this literature by estimating a comprehensive set of priorities
across Europe. Voter priorities have also been used in recent political economy theories(Bonomi et al., 2021; Enke et al., 2022). Our analysis provides empirical support for recent
papers discussing the prioritization of cultural issues (Kriesi et al., 2008; Enke, 2020; Gethin
et al., 2021; Margalit et al., 2022).
Finally, a related literature shows that PRRP can gain support by activating a reservoir ofculturally conservative voters. For example, Cantoni et al. (2019) shows that the AfD gained
votes by activating right-wing cultural voters and Voigtländer and Voth (2012) find that the
Nazis gained support by activating latent historical reservoirs of anti-Semitic attitudes. The
importance of salience, legitimacy, or priorities in activating conservative cultural attitudes
has also been shown in the US (Bursztyn et al., 2020), Italy (Magistro and Wittstock, 2021),
and the United Kingdom (Sobolewska and Ford, 2020). We contribute to this literature bydirectly comparing voter priorities and voter opinions and showing that changes in priorities
are the main driver behind the recent rise of PRRP across Europe.

5

1

Voting ModelIn this section, we develop a voting model that provides a parsimonious unifying frameworkfor how party positions, voter attributes, and voter priorities codetermine vote choice.
We assume a standard utility maximization framework where voters support the party
that maximizes their utility. Voter i’s utility from voting for party j is a function of theparty’s positions weighted by her individual voting weights. Specifically, we assume
Uij = wi0 zj + ζj + εij .

(1)

The L-dimensional vector zj represents party j’s positions and wi is an L-dimensional
vector of the corresponding voting weights. Each individual weight wil represents the impactof the corresponding party position zjl on voter i’s utility. The sign of the weight is positive
when the voter supports a position (i.e., the voter’s utility increases when she supports aparty with this position) and negative when she opposes it. The weight’s magnitude (inabsolute terms) measures how much the voter cares about this position compared to otherpositions. We use ζj to capture the residual common utility from voting for party j—an
unobserved party property that increases the utility from supporting this party among all
voters. This residual includes both the utility from unobserved party positions and the party’s“valence”—other unobserved party properties that affect voters’ utility from supporting theparty. Valence could capture factors such as the party leader’s charisma or the party’s
historical reputation. Finally, εij represents all unobserved idiosyncratic factors that affectvoting decisions.
We assume that the voting weights are a linear function of voter attributes:
wi = x0i Φ + β.

(2)

The M -dimensional vector xi represents the observed attributes (demographics and opinions)of voter i. The M × L matrix Φ determines how each voter attribute affects the weights
voters place on every party position. Φml > 0 means that a larger value of voter attribute xm
generates larger support for party position z l . For example, if xm measures voter support forredistribution and z l measures party support for larger welfare spending, we would expect
Φml to have a positive value. We standardize the distribution of xi and therefore the Ldimensional intercept vector β represents the average weight of the full population.
6

Combining Equations 1 and 2 implies that the utility is a function of the interaction
between voter attributes and party positions. We define a vector δ that captures the utility
gain from each party j that is common across voters,
δj := β 0 zj + ζj .

(3)

U (xi , zj , εij ) = x0i Φzj + δj + εij .

(4)Hence we can rewrite the utility as

The parameterization of Equation 4 can capture the second-order approximation of any
functional form. It nests the standard bliss-point utility functions where voters vote for theparty closest to them ideologically as discussed in Appendix B. It also allows a more complex
utility function, where voters vote based on multiple dimensions and where demographics,
such as education, can also affect vote choice (Kriesi et al., 2008; Piketty, 2020; Abou-Chadi
and Hix, 2021; Gidron, 2022).
To take this model to the data, we assume that the unobserved idiosyncratic shock εij has
a type-I extreme value distribution (Gumbel). Together with Equation 4, this assumption
allows us to write the conditional probability of voting for a party as
P (zj |xi ) =exp(x0i Φzj + δj ),
Σk∈Jc,t exp(x0i Φzk + δk )(5)where Jc,t is the set of all parties that are on the ballot in country c at period t.

2

Data

In this section, we discuss our data on party positions and voter attributes. We focus on
Europe to analyze PRRP that have long defined themselves in opposition to similar politicaldevelopments, such as European integration, and that have formed transnational networks
of cooperation in supra-national institutions, such as the European Parliament (McDonnell
and Werner, 2020). Analyzing Europe also allows us to focus on a region where PRRP haveparticularly gained strength in recent years (Rodrik, 2018). Finally, most European countries
have proportional representation systems, which enable us to directly quantify the share of
PRRP support in each country.

7

2.1

Party Positions: Comparative Manifesto Project

We characterize party positions on various issues using the Comparative Manifesto Project(CMP) (Volkens et al., 2020). This dataset covers the manifestos (platforms) of parties
running in elections for the lower house. The CMP codes what share of the manifesto
is dedicated to each topic, and for many topics details the share of positive and negative
mentions.3 The dataset covers a large variety of topics, including, but not limited to economic
issues and cultural issues. Our analysis includes all 56 CMP main (three-digit) variables.
Appendix Table B.1 presents the full list of CMP party positions.
When aggregating results for various economic or cultural positions, we rely on two indices of party positions, created by the CMP. The economic index measures the overall party
position on the government intervention-free market scale. It incorporates 19 party positions on issues including the welfare state, economic systems, protectionism, and regulation.
The cultural index (originally called the society index) summarizes cultural positions on a
progressive-conservative scale. It incorporates 11 party positions on issues including traditional morality, national way of life, multiculturalism, and democracy. Both indices areconstructed by adding conservative party positions and subtracting liberal positions suchthat a higher value reflects more support for a free market or more conservative culturalvalues. The party positions included in each index are detailed in Appendix Table B.1. We
focus on preexisting measures of economic and cultural positions to mitigate concerns overcherry-picking. Since this division leaves room for discretion, we verify the robustness of ourresults using multiple alternative indices in Section 6.1.1.
The distinction between economic and cultural issues is imperfect as not all politicalissues neatly fall into one of the two categories (Cramer, 2016). For example, opposition toEuropean integration may lie at the intersection of economic concerns and cultural concerns(Kriesi et al., 2008). Still, distinguishing between economic and cultural issues is a common
feature in electoral politics research (Kitschelt, 1994; L. Hooghe and Marks, 2018; Margalit,
2019; Norris and Inglehart, 2019) and is analytically useful for our purposes.
3

An alternative dataset on party positions is the Chapel Hill Expert Survey (CHES). We use CMP
since the coverage of CHES is more limited than CMP’s and its position measures are more subjective.
Previous work suggests that the CMP measures are strongly correlated with those of CHES (Adams et al.,2019). Furthermore, the CMP has been shown to correlate highly with the survey responses of members ofparliament (Günther, 2023).

8

2.2

Voter Attributes: Integrated Values Survey

We measure voter attributes using the Integrated Values Survey (IVS). The IVS is composedof two large-scale cross-national repeated surveys: the World Values Survey (WVS) and theEuropean Values Survey (EVS). This dataset provides several advantages for our analysis.
First, it includes information on a variety of voter attributes including demographics, religious
beliefs, social values, and opinions on various topics. Second, many of the questions in the
IVS are consistently asked over time.4 Third, the data covers a broad range of countries.
We study the three most recent survey waves: 2005–2009, 2011–2013, and 2017–2020.
We include in our study all 22 European countries that were surveyed in both the 2005–2009
and the 2017–2020 waves, and in which at least 70% of the voters support a party thatcan be matched to the CMP. Figure 2 presents the countries included in our final dataset.
Overall, we analyze 59,610 observations across the three survey waves (see Appendix Table
A.1 for summary by wave). We use sampling weights in all of our analyses. Missing values
are imputed using random forests. Appendix C.1 includes further details on data processing.
The survey documents a large and growing support for PRRP, assuaging concerns regarding the ability of surveys to capture this trend. Discrepancies between survey results andelection outcomes may arise for various reasons such as respondents’ unwillingness to openly
express support for PRRP due to social desirability bias (M. Hooghe and Reeskens, 2007), or
trends in voter turnout not directly captured by the survey.5 However, even though the surveys we analyze are not conducted necessarily around elections, the levels of PRRP support
we document in the surveys align closely with the election outcomes observed in this period.
Looking at recent PRRP support, Figure 2 presents the support that PRRP received in the
2017–2020 survey and in the election closest to the survey year. The correlation between
these values across countries is 0.94. Furthermore, looking at our primary outcome—changes
in PRRP support over time—we find a correlation of 0.57 between the change in PRRP
support in the surveys and the actual change in their support in the closest election.
To estimate changes over time while providing our model with as much flexibility as possible, we include in our analysis nearly all variables that appear in all three survey waves.
4

This feature is critical for our decomposition analysis and does not exist in various similar datasets, suchas the European Social Survey.
5
Most WVS and EVS surveys do not include a question directly measuring turnout. We may be partially
capturing turnout indirectly since respondents who abstain from voting can report not supporting any party.

9

Appendix Table B.2 describes the 101 opinion and demographic variables included in ourdata. Our outcome variable is the respondents’ preferred party, defined as the party participants said they would vote for or the party that appeals to them most. We do not analyze
voter turnout, as it is not properly measured in this data. Appendix C.1 provides moredetails on the data processing and variables analyzed.

2.3

Linking Party and Voter Data

For our main analysis, we create a novel dataset that links data on voter attributes with dataon the positions of the parties they support. We merge the datasets (CMP and IVS) using
Party Facts (Döring and Regel, 2019), a designated platform for linking party datasets. When
Party Facts data is unavailable, we manually link parties using their names. By definition, the
CMP data is measured around elections, while the IVS surveys are not necessarily conducted
close to elections.6 When merging the datasets, we assign to each party the CMP variables
defined for the election closest to when the IVS survey was taken. We include parties in theanalysis if at least five respondents supported them and if we can match them with CMP
data five years before or after the year the survey was conducted. 7 We discuss the process
of merging the data in more detail in Appendix C.2.
Appendix Table A.2 shows that we are able to match 94% of respondents who expressed
support for a party with CMP data. This data can be useful for any future research that
explores the relations between voter attributes and the positions of the parties they support.

2.4

Party Classification

PRRP have at least three common characteristics (Mudde, 2007). First, these parties arenativist; i.e., they consider minorities as a threat to the purity of the “real people”. Second,
these parties are authoritarian; i.e., they hold a “belief in a strictly ordered society, in which
infringements on authority are to be punished severely” (Mudde, 2019, p. 29). Third, theseparties are populist; i.e., their politics is predicated on a moral opposition between corrupt
6

The fact that the IVS is not conducted close to elections may imply that IVS survey respondents are lessinformed about parties. Moreover, the party they support in the survey may be different from the party they
support close to an election. On the other hand, a survey that is not conducted close to an election is lesslikely to be affected by strategic voting, such as voters taking into account polling results (Bursztyn et al.,2023), and thus our sincere voting assumption is more likely to hold in the IVS data.
7
The CMP typically codes parties that gained at least one or two seats in parliament.

10elites and the unified people.
We determine whether a party is a populist radical right party according to PopuList, acomprehensive dataset that is updated periodically (Rooduijn et al., 2019). The dataset classifies European parties since 1989 based on experts’ judgment. We define PRRP as parties
that were classified as radical right parties in the PopuList data since 2005.8 The PopuList
also classifies parties as populist and non-populist. However, we include both populist andnon-populist radical right parties in our PRRP definition for three reasons. First, only ahandful of radical right parties, which represent less than 1% of the overall radical right support share in the IVS, are non-populist. Therefore, our results are substantively unchanged if
non-populist radical right parties are excluded from our definition. Second, populist and nonpopulist radical right voters share similar nativist opinions. Third, excluding non-populist
radical right parties would create spurious fluctuations in measured support for PRRP as
these parties are often close substitutes, and therefore, in some countries (e.g., Greece), voters shifted from populist to non-populist radical right parties. Appendix Table A.3 provides
a full list of the parties we classify as PRRP.
We use CMP data to classify parties into other categories, commonly referred to as party
families. Party families include parties that share broad features such as similar historicalroots, bases of support, ideological worldviews, and shared membership in transnationalorganizations (Mair and Mudde, 1998; De La Cerda and Gunderson, 2023). Specifically, wedefine parties as center-left if the CMP codes them as social democratic; center-right if they
are coded as Christian democratic, or conservative; green if they are coded as ecological; andsocialist if they are coded as such. These party families, along with the PRRP, account on
average for 89% of the vote share of all parties coded in the CMP data.

2.5

Summary Statistics

Panel A of Table 1 presents summary statistics of party positions for PRRP and all otherparties, based on manifestos that correspond to the 2005–2009 and 2017–2020 IVS waves. The
first two rows show the average values for the economic and cultural CMP indices. Positive
index values denote that a larger share of the manifesto is dedicated to right-wing positions.
8

There are three cases where the PopuList classification of a party changes over time. Since such changes
are rare, we define a party as a PRRP if the PopuList defined it as a radical right party at any point.

11

As expected, PRRP are more conservative, particularly on cultural issues. The table alsopresents the average percentage share of the manifestos dedicated to five positions thatmost strongly distinguish between PRRP and non-PRRP and demonstrates clear differences
between them: PRRP dedicate a substantial and increasing amount of their platforms tonationalistic topics (e.g., positive mentions of “national way of life”), which other parties
rarely mention.
Panel B of Table 1 presents summary statistics for the demographics and the five mostdistinctive opinions between PRRP and non-PRRP voters, based on the same two IVS waves.
As expected, PRRP supporters are more likely to be males and live in rural areas, and areless likely to hold a college degree (Gidron and Hall, 2020). Over time, PRRP supporters
hold more culturally conservative opinions.

3

Estimation

In this section, we describe how we estimate the model parameters (voter priorities) using
a two-step procedure. We first estimate the matrix Φ mapping voter attributes to voting
weights and the vector δ of the common utility from each party. These parameters fully
determine the likelihood of voting for each party (Equation 5). We estimate them using apenalized MLE, separately for each IVS wave. We then estimate β, the average voting weights
placed on each position, and ζ, the residuals, based on Equation 3, using the estimands forδ̂ from all three waves.
We assume that the model parameters change over time, but are equal across countries.
This assumption allows us to estimate multiple dimensions of changes in voter priorities overtime by aggregating data across all European countries. In Section 5, we show the main
decomposition results also hold under weaker assumptions. First, we estimate the priority
parameters separately for Western and Eastern European voters and find that the results are
similar for both regions. Second, we estimate the full model excluding one country each timeand show the decomposition results remain similar when analyzing the excluded country.
This means that priorities estimated for other countries predict a similar rise of PRRP in theremaining country (regardless of its voting patterns), suggesting that the relevant priorities
are similar in the remaining country.

12

Moreover, in Appendix D we show that the assumption of equal parameters across countries approximates the data well. We introduce a simple approximation of our model thatpredicts voting behavior for each party separately. The results in Appendix Figure A.12 showthat the parameters predicting support for a specific party in one country can be used to
predict support for a similar party in another country. For example, a model that predicts
support for UKIP in the UK (right column) can predict support for AfD in Germany almost
as well. This is consistent with model parameters (voter priorities) being similar across thecontinent. This result also shows that PRRP supporters are similar across countries, and are
different from supporters of other populist parties.

3.1

First Step: Estimation of Φ and δ

Since we use a wide dataset on voter attributes and party positions, our parameter space ishigh-dimensional and, therefore, we rely on machine-learning techniques to avoid overfitting.
Our dataset is large as we prefer not to make any prior assumptions regarding which combination of variables is important for explaining party support. Instead, we use all available
variables and let the data determine which variables are relevant. As a result, the dimensionof matrix Φ is approximately 5,000 (the number of voter attributes multiplied by the numberof party positions). To avoid overfitting, we restrict the support of Φ such that kΦk < cfor some constant c. We follow recent econometric research (Athey et al., 2021), and use
the nuclear norm as our matrix norm for two reasons. First, the nuclear norm is known togenerate low-rank matrices, which are easier to interpret and imply that the voters decide
which party to support based on relatively few dimensions, as suggested in the literature(Kitschelt, 1994; Poole and Rosenthal, 2001; Kriesi et al., 2008). Second, the nuclear norm
generates a convex optimization problem that is computationally easier to solve.
Specifically, we estimate Φ and δ using a penalized maximum likelihood estimator. We
obtain the following maximization problem using the likelihood derived in Equation 5:X
exp xi Φzj(i) + δj(i)max L (Φ, δ) − λ kΦk = max
log P− λ kΦk .
Φ,δ
Φ,δ
exp
[x
Φz
+
δ
]
i
k
k
k∈J
c(i)
i

(6)

We estimate the parameters separately for each IVS wave. We solve this maximization
problem using proximal gradient descent (Hastie et al., 2019) and choose the value of thepenalization parameter λ using cross-validation.
13

3.2

Second Step: Estimation of β and ζ

In the second step, we use the estimands of δ̂ from the first step to estimate β, the meanvalue for the weight placed on each position, and ζ, each party’s residual. In this step, we
combine information from all three waves. To estimate β and ζ based on Equation 3, wetake the difference between two consecutive survey waves and get the following equation:
t+1
t+1
t+1
∆t+1
t δj = ∆t β z¯j + β̄∆t zj + ∆t ζj ,| {z }| {z }| {z }
Voter Priorities

Party Positions(7)

Residual

where β̄ and z¯j represent the average values over the two periods. This equation decomposes
the overall changes in δ to the contribution of changes in voter priorities (∆β), party positions
t+1(∆z), and the residual (∆ζ).9 The coefficients ∆t+1
t β and β̄ are estimated by regressing ∆t δ̂
t+1
on z¯j and ∆t+1
t zj . Once ∆t β and β̄ are estimated, we calculate the estimates for β̂t , β̂t+1

and plug those in Equation 3 to calculate ζ̂.
Since the number of party positions is relatively large compared to the overall number ofobservations (the total number of parties in each wave) we make two additional assumptions
to avoid overfitting. First, we assume a linear trend in β for countries that appear in the
2011–2013 intermediate wave. This simplifying assumption allows us to use the informationfrom the intermediate wave, to estimate the main trend between our two major waves in 20052009 and 2017-2020 more precisely. This assumption implies that βt+2 − βt+1 = βt+1 − βt ,and therefore, the sum of Equation 7 for ∆t+1
and ∆t+2t
t+1 is
t+2
t+2
t+2
∆t+2
t δj = ∆t β z¯j + β̄∆t zj + ∆t ζj ,(8)

where the averages (β̄, z¯j ) are taken over all three periods.
Second, we assume that the combinations of party positions that generate the most differences in utility among voters are the same factors that determine the average utility acrossall voters.10 We use the singular value decomposition of the matrix Φ estimated in the firststage, Φ = U ΣV T , and calculate the first k columns of matrix V , [V ]k . Intuitively, these are
the k linear combinations of party positions that generate the largest utility differences acrossvoters. We then assume that β is a linear combination of the columns of [V ]k . Appendix E
9Voter attributes do not affect δ since δ is defined as the common utility all voters receive from a party.
This assumption is likely to hold since voters typically support parties based on contested issues. However,
it would be violated if all voters have a homogeneous taste for certain party positions.
10

14

provides more details. The value of k represents the dimension of the average voting weights,
i.e., the number of dimensions of party positions that explain why certain parties are more
popular on average. We use k = 5 for our main analysis to allow for richer explanations foraverage party differences while still avoiding overfitting. Section 5 shows that other k values
yield similar results.

3.3

Estimation Results: 2017–2020 Voting Weights

In this section, we explore the variations in voting weights in the 2017–2020 wave.11 We
calculate the weights each voter places on each party position by combining the observed
voter attributes with the estimated voter priorities (model parameters) using Equation 2.
The weights are measured in units of standard deviation to utility units, defined as how aone standard deviation increase in this position would affect voter utility.12
We also compute aggregated weights for the two CMP indices: the economic and cultural
indices. To do so, we take the average of the weights of all variables included in each index
and flip the sign of the weights for variables that enter the index negatively.13 Appendix
Table A.4 presents the voter attributes with the largest coefficients (in absolute value) in
the two weight indices.14 For each index, we show the values of the ten largest coefficientsin the 2017–2020 wave. The patterns we uncover provide face validity to our analyses. For
example, we find that all else equal, individuals who express confidence in unions tend to
reward parties with left-wing economic positions (i.e., they put a large negative weight on
an index of right-wing economic positions). Moving to the cultural index, individuals whobelieve jobs should prioritize natives reward parties with right-wing cultural positions.
Figure 3 shows that PRRP voters put more weight on conservative cultural issues compared to economic issues. We plot the 25th, 50th, and 75th percentiles of the weight distribution separately for supporters of each party family in the 2017–2020 survey wave. The
top two panels present results for the two CMP indices and the subsequent panels present
the weights on the positions with the largest variation in weights across party families. The
11

In our main decomposition exercise in Section 5, we analyze the changes in these weights over time.
To provide some intuition for these units, with two parties the utility is the logarithm of the odds ratio.
For example, an increase of one utility unit is equivalent to a change from a 50/50 vote share to a 73/27.
13This is equivalent to a single weight on the index (the utility from a change in the index), assuming thata change in the index is driven by all index positions equally.
14
The weight indices are linear functions of the voter attributes, where the coefficients are linear combinations of the columns of matrix Φ that correspond to the positions comprising the CMP indices.
12

15

weights PRRP voters place on the economy index are similar to the weights placed by mainstream right-wing voters. By contrast, PRRP voters care more about conservative cultural
positions (e.g., national way of life) compared to mainstream voters. Reassuringly, Figure
3 also shows that voters of other party families put more weight on positions in the core of
their party’s agenda. For example, green voters place large positive weights on environmental
protection, while socialist voters place large positive weights on support for labor groups.

4

Decomposition

We now turn to the decomposition of the rise of PRRP. The statistic that we decompose isSPt,c , the share of PRRP supporters in country c in period t. This share is defined as the sumof the probabilities that each voter supports a PRRP (marked with the event Π),15Zt,c
SP := P (Π|xi ; θt , Ztc , ζtc )ftc (xi )dxi .

(9)We use θt = (Φt , βt ) to note the voter priorities, Ztc = {zj,t }j∈Jc,t is the matrix of observed
party positions, ζtc = {ζj,t }j∈Jc,t is the vector of residuals for all parties in the option set(Jc,t ), and ftc is the density of voter attributes in country c in period t. Using this notation,
the change in PRRP support between periods t and t + 1 is
Z
Z
 c
t+1 c
c
c
∆t SP = P Π|xi ; θt+1 , Zt+1 , ζt+1 ft+1 (xi ) dxi − P (Π|xi ; θt , Zt , ζtc ) ftc (xi ) dxi .
cThis parameterization allows us to decompose ∆t+1
t SP into the sum of four components:

voter priorities, voter attributes, party positions, and a residual. Formally:
Z
Z
 c
 c
t+1 c
c
c
c
c
ft+1 (xi ) dxi, ζt+1∆t SP =
P Π|xi ; θt+1 , Zt+1 , ζt+1 ft+1 (xi ) dxi − P Π|xi ; θt , Zt+1|{z}
Voter Priorities
Z
Z
 c
 c
c
c
c
c
ft (xi ) dxi+
P Π|xi ; θt , Zt+1
, ζt+1
ft+1 (xi ) dxi − P Π|xi ; θt , Zt+1
, ζt+1|{z}
Voter Attributes
Z
Z
 c
 c
c
c
c
ft (xi ) dxi+
P Π|xi ; θt , Zt+1
, ζt+1ft (xi ) dxi − P Π|xi ; θt , Ztc , ζt+1|
{z}
Party Positions
Z
Z
 c
c
+
P Π|xi ; θt , Ztc , ζt+1
ft (xi ) dxi − P (Π|xi ; θt , Ztc , ζtc ) ftc (xi ) dxi|{z}
Residual(10)
15

Formally, P (Π|xi ; θt , Ztc , ζtc ) = P (arg maxj Uij ∈ P t,c ), where P t,c denotes the relevant set of PRRP.

16

This mathematical identity states that the overall rise in PRRP support is the sum of
the rise driven by each of the four factors. Each component represents a partial-equilibriumcounterfactual increase in PRRP support when only one input changes, while the others areheld fixed (Juhn et al., 1993; DiNardo et al., 1996). We simulate these counterfactuals by
calculating the probability with which each voter is expected to support each party (Equation
5) and aggregating across all voters.
After decomposing the trends in PRRP support in each country separately, we aggregate
the results over all countries. We scale the results in each country by the inverse of the shareof PRRP support in the 2017–2020 wave. This scaling guarantees that the results are notdriven entirely by countries with very high levels of PRRP support (e.g., Hungary).
Quantifying the contribution of party positions, voter attributes, and voter priorities,
relies on our ability to measure them properly in the data. In Section 2.5 we show that the
CMP and IVS include variables on party positions and voter opinions that clearly distinguishboth populist radical right parties and their voters, respectively. In Section 6 we show that
we are also able to capture trends in these variables. Moreover, we show that the opinion
variables explain a large share of the variation in PRRP support across countries. Still, one
limitation we face is that there could always be additional party or voter attributes that areabsent from our data such as the demographics of candidates (Dal Bo et al., 2023) or theircanvassing efforts (Pons, 2018). The decomposition exercise may still capture some changes
in these unobservables if they are correlated with observable variables.
To accommodate parties’ entry and exit, we include all parties that participated in either
period t or t + 1. We then set the residual ζj,t to −∞ if party j does not participate or is too
small to appear in our data in period t. This assures that the predicted voting share for thisparty is set to zero. Therefore, changes in PRRP support that are related to entry and exit
are attributed to the residual component. In Appendix F we further decompose the residualcomponent to calculate the share of the residual driven by entry.
We calculate standard errors using bootstrap. This involves performing 500 bootstrap
iterations, where we repeatedly draw equal-sized samples of voters in each wave. In each
iteration, we repeat our two-stage estimation for the resampled data. We then decompose
the rise of PRRP for the resampled data, using the parameters estimated in each iteration.

17

4.1

Interpretation

The decomposition in Equation 10 sheds light on the drivers behind the rise of the European populist radical right. Decomposition methods inherently follow a partial-equilibrium
approach (Fortin et al., 2011). In our case, each component represents a partial-equilibriumcounterfactual increase in PRRP support when only one input changes. The counterfactuals do not take into account strategic responses between the components (Canen and Song,2023). For example, parties may adjust their positions following changes in mass public
opinion (Adams et al., 2009). Alternatively, changes in voter opinions may be triggered byparties’ strategies (Barber and Pope, 2019). Still, learning which broad mechanisms drive
the increased support for PRRP rules out a set of common explanations for this consequential political development. Moreover, it highlights which factors should be studied further
to explain the rise of PRRP. The rest of this section elaborates on what mechanisms eachcomponent measures, and how they relate to existing theories on the rise of PRRP.
Voter Priorities This demand component captures changes in the parameters Φ and β.
These parameters determine whether voters support or oppose each party position and how
they prioritize the different party positions, given their opinions and demographics.
Changes in priorities may increase PRRP support even in the absence of shifts in voter
attributes or party positions. Specifically, a common argument is that cultural issues increasingly shape political identities and voting behavior (De Vries et al., 2013; Norris andInglehart, 2019; Noury and Roland, 2020). According to Bartels (2023), European PRRP
gained support by activating a preexisting reservoir of voters with culturally conservative
attitudes and not because of a dramatic change in attitudes.
Voter Attributes This demand component captures changes in f , the distribution of
voter attributes xi . This component is associated with the dominant image of the rise of
the populist radical right as a political tsunami: a swift and powerful shift in public opinion
toward the ethnonationalism and authoritarianism of these parties.
Such shifts in public opinion were identified in specific contexts. Hangartner et al. (2019)show that greater exposure to refugees in Greece fueled opposition to immigration.16 Ballard16

Similar findings have been documented in Austria (Rudolph and Wagner, 2021) and Norway (Nordø andIvarsflaten, 2021) although not in some other contexts (Cools et al., 2021; Schaub et al., 2021).

18

Rosa et al. (2021) argue that people who live in regions exposed to trade shocks adopted
more authoritarian values—which, in turn, pushed voters toward PRRP and related causes(e.g., Brexit). In addition, changes in voter attributes include demographic changes, suchas an increase in unemployment, that have been associated with anti-immigration attitudes
and greater PRRP support (Algan et al., 2017; Guiso et al., 2017; Dehdari, 2022).17
Party Positions This component captures changes in the supply of party positions Z.
One set of supply-side hypotheses focuses on changes in the PRRP, which may have moderated their cultural positions in order to appeal to mainstream voters (Akkerman, 2015;
Lancaster, 2020). Alternatively, PRRP could have shifted their economic positions toward
welfare chauvinism, understood as generous welfare benefits that exclude those who are
deemed as unauthentic members of the nation (Schumacher and Van Kersbergen, 2016).
An alternative set of hypotheses focuses on position changes in other parties. Berman(2021) argues that the convergence of center-left and center-right parties on economic issues in
the 1990s opened a space for the PRRP parties. In contrast to this convergence hypothesis,
others claim that the left’s turn toward progressive cultural positions has alienated someworking-class voters, which switched to PRRP (Goodhart, 2017).18
Residual The last component captures changes in the residual ζ of all parties. Changes
in the residual could reflect several channels in which support for PRRP might increase.
First, the increase in the residual component could reflect a change in unobserved party
positions, either by the PRRP or their competitors. Second, it could reflect an increase in
the valence of PRRP (e.g., if these parties had more charismatic leaders in recent years).
Third, the residual component can include an unexplained increase in PRRP due to model
misspecification. Fourth, and most importantly, the residual could capture the entry andexit of parties from the political system, which we model as having ζj,t = −∞.
The order of the four components in the decomposition can affect the results as it determines whether to fix the other components to their level at the start (t) or end (t + 1) of
the period. This order matters when an interaction of several factors also contributes to the17

Other work questions this relationship between economic hardship and support for PRRP (Mutz, 2018).
Former leader of the German Social Democrats, Sigmar Gabriel, argued that “Winning over the hipsters
in California cannot make up for losing the workers of the Rust Bel” Abou-Chadi and Wagner, 2020, p. 247.
18

19

rise of PRRP. For example, a shift toward more nativist opinions would have a larger impact
when nativist voters put a higher priority on nationalist issues.
We set the residual as the last component, such that when calculating the counterfactuals
for party positions, voter attributes, and voter priorities, the residual component is fixed toits value at t + 1. This implies that we quantify the impact of changes in our three main
components as if the new entrants had already participated in the elections in period t. We
choose this order because otherwise the three main components would be mechanically zerofor new entrants, as support cannot grow for a party that has not entered yet (ζj,t = −∞).
We set party positions as the third component. We use manifestos from period t + 1 when
the party did not exist in period t.19 Therefore, the party positions component only captureschanges in the positions of parties that existed in both waves. Setting party positions as thethird component guarantees that when calculating the counterfactuals for voter attributesand voter priorities all party positions are taken from the same time period.
We set voter priorities and voter attributes as the first and second components, respectively. As a result, the change driven by priorities can be interpreted as the counterfactualchange in PRRP support when only priorities change and all other components are fixed totheir value in the same time period (2017–2020). In Section 5, we show that the decomposition results are qualitatively similar when we flip the order of these two components.

5

Decomposition Results

Figure 4 presents our key finding: the aggregated decomposition results. We focus on thechange in PRRP support by fixing the initial value to 0% and the final value to 100%. For
countries that are unavailable in the 2011–2013 wave, we impute their decomposition values
as the average of the 2005–2009 and 2017–2020 waves.
Figure 4 shows that voter priorities explain 43.5% of the overall increase in PRRP support
between 2005–2009 and 2017–2020. Party positions and voter attributes explain only 1.0%
and 6.1%, respectively, and in some periods, their contribution is even negative.20 The
19

There is no need to impute party positions for parties that did not exist in period t + 1 (exits), as in all
counterfactuals in which positions are set to their value at t + 1, residuals are also set to their value at t + 1,
which is −∞. Hence the counterfactual support for exits is zero regardless of their position.
20
A negative value implies that PRRP support would have been expected to decrease during this period
based only on the change in the component with the negative value.

20

remainder of the increase is driven by the residual.
Appendix Table A.5 shows the results by country. Each column presents the counterfactual increase driven by a specific component in raw units. While there is some variation
across countries (e.g., voter attributes matter more in Hungary), in most countries, voter
priorities are a much more important driver of PRRP support than voter attributes andparty positions.
Robustness We conduct a series of analyses to confirm the robustness of our decomposition
results. One possible concern is that despite our usage of penalized MLE, we are overfitting
the model parameters. If this were the case, we would wrongly conclude that changes in
the overfitted voter priorities (model parameters) explain a large share of the rise of PRRP.
An additional concern might be that priorities are in fact different across countries. Hence,using the same parameters for all countries could yield misleading results. To address bothof these concerns, we perform a leave-country-out exercise. For each country, we estimate
all model parameters without data on that country (Φ̂−c , β̂−c ). Hence, the voter priorities
for each country are estimated using data only from other countries such that overfitting is
not possible. We calculate the vectors of residuals (ζ̂−c ) for each party in that country, suchthat the predicted voting shares under these parameters fit the data. We then decompose
the rise of PRRP in that country under these parameters using Equation 10. We aggregate
the results from all countries as before.
Panel A of Appendix Figure A.1 shows that the results of the leave-country-out decomposition resemble those reported in our main analysis. We find that the priorities component
remains similar (42.0% compared to 43.5% in our baseline exercise). Hence, changes in priorities that were estimated in other countries could predict a large share of the increase insupport for PRRP in the remaining country.
Our results also remain similar when we replace our baseline model with a bliss-point
model. Appendix B describes a model where voters have an optimal party position (bliss
point) and vote for the party whose positions are closest to that optimal point. As explainedin the Appendix, this model is similar to the model we describe in Section 1. Panel B ofAppendix Figure A.1 shows that the priorities component now explains 38.7% of the overallrise in support for PRRP, similar to our main results.
The decomposition results are also robust to different choices of the parameter k. This21

parameter sets the number of dimensions we use in the second stage of the estimation (seeAppendix E). In our main results, we chose k = 5. Panels C and D of Appendix Figure A.1
present the results when we repeat our decomposition analysis using k = 3 and k = 7 in ourestimation. In these specifications, the priorities component accounts for 47.9% and 41.6%
for k = 3 and k = 7, respectively, while other components also remain similar.
We next examine the degree to which our results depend on the order of the decomposition. Following the discussion in Section 4, we maintain the order of party positions andresiduals, and alternate the order of voter attributes and priorities. Panel E of Appendix
Figure A.1 shows that while our results remain qualitatively similar, there is an increasein the importance of voter attributes when we switch the order of attributes and priorities.
This result implies that there is some interaction between changes in priorities and changes
in attributes. For example, if nationalist issues do not matter for voting decisions, thenchanges in the opinions of voters on immigration will not matter. However, when votersstart prioritizing nationalist issues at the ballot (as we show in Section 6.1), then changes inopinions on immigration could increase PRRP support. Therefore, if these opinions change,
the results depend on whether we fix the priorities to their values in 2005–2009 or 2017–2020.
Still, regardless of the order of voter attributes and priorities, voter priorities remain a much
more important driver of PRRP support.
The decomposition results are also similar when we use only opinions, and not demographics, as voter attributes. Hypothetically, it is possible that while overall changes inattributes contribute only little to the rise of PRRP, changes in opinions and demographics
operate in opposite directions and cancel each other out. To test whether opinions specifically may be contributing to the rise of PRRP, we estimate the model parameters and run
the decomposition while excluding the demographic variables. Panel F of Appendix Figure
A.1 shows that we find similar results to our baseline estimates.
Lastly, our decomposition results remain consistent when estimating voter priorities separately for Western and Eastern Europe. In the main analysis, we assume that the voter
priorities are the same for all voters, and in Appendix D we show this assumption is a reasonable approximation of the data. To further test the importance of this assumption, we relax
it and allow distinct voter priorities for Western and Eastern European voters. Since this22

decreases the size of our data, we estimate a model with fewer parameters.21 Panels G and
H of Appendix Figure A.1 present the results for Western and Eastern Europe respectively.
While voter priorities matter more in Western Europe, in both regions, they are the main
driver of the rise of PRRP (77.7% in Western Europe and 34.2% in Eastern Europe).

6

Detailed Evidence by Component

To understand what drives the trend we documented in our decomposition exercise we turnto a detailed analysis of the change in each component. We show that voter attributes arestable on aggregate, and party positions are not shifting in ways that are expected to increase
support for PRRP. These findings significantly bolster the hypothesis that the rise of PRRP
support is primarily driven by the “activation” of a preexisting reservoir of potential PRRP
voters (Bartels, 2023) rather than a dramatic change in attitudes. Since the rise of PRRP is
not driven by observed voter attributes or party positions, it could theoretically be driven byeither the voter priorities or the residual. We begin with analyzing voter priorities to shedlight on the systematic changes in voting behavior that lead our decomposition to attributea large portion of the rise of PRRP (43.5%) to this component.

6.1

Changes in Voter Priorities

In this section, we provide evidence that the rise of the voter priorities component reflects
the prioritizing of cultural issues over economic issues. We first analyze in more detail whichpriorities changed and for which voters. Since voter priorities are estimated, and not directly
observed, we then complement our findings with supporting reduced-form evidence.
6.1.1

Which Priorities Changed? Model-Based Evidence

Figure 5 shows that since the early 2000s, voters have come to place more weight on culturalissues relative to economic issues. To isolate changes in voter priorities from changes in voter
attributes, we fix voter attributes to their value in the 2017–2020 survey wave and analyze
21
In the first stage, we choose λ in Equation 6 using cross-validation separately for Western and Eastern
Europe. The smaller data leads to a larger value of λ, which in turn leads to a lower rank matrix Φ̂. In thesecond stage, we use k = 3. Using k = 5 yields similar results for Western Europe and unstable results forEastern Europe, probably since we only have 7 Eastern European countries in our data.

23

the changes in weights that are driven only by changes in voter priorities. We aggregate
weights into two indices based on the CMP party position indices as before. Figure 5 showsthat the distribution of the weights placed on the CMP economy index became substantiallymore concentrated around zero in 2017–2020, compared to weights placed on CMP culturalindex. Ceteris paribus, the economic positions of parties became relatively less decisive inshaping vote choice. This result provides empirical evidence for the claim that cultural issues
became more important than economic issues, which has important ramifications for trade
policy (Grossman and Helpman, 2020), redistribution (Shayo, 2020), support for democratic
norms (Bartels, 2020) and polarization (Gennaioli and Tabellini, 2023).
Appendix Figure A.2 shows that the prioritization of cultural issues, compared to economic issues, does not depend on the choice of particular party position indices. Figures
A.2a and A.2b repeat the exercise in Figure 5 using two alternative party position indicesproposed by Bakker and Hobolt (2013) and Prosser (2014), respectively. The party positions
used in each index can be found in Appendix B.1. The growing importance of cultural issues
is robust to using these alternative indices.
We find that the prioritization of cultural issues at the expense of economic issues issimilar across the different countries. For each voter in the 2017-2020 wave, we calculate theratio between their estimated weights on the CMP cultural index and the CMP economic
index, in absolute values. Appendix Figure A.3 plots the distribution of this ratio for eachcountry individually and on aggregate for all countries. Ratios exceeding one indicate that
voters prioritize cultural issues more than economic issues, and vice-versa. We find thatthe distribution of the ratio of weights shifts to the right, implying that voters increasingly
prioritize cultural issues over economic issues. While there is variation in the magnitude of
the change, the shift toward cultural issues occurs in almost all countries. This homogeneous
trend across countries is especially interesting as we do find substantial variation in voter
attributes across countries, which we discuss in Section 6.2.1.
Looking within countries, we find substantial heterogeneity in how priorities change. Figure 6 plots the 25th, 50th, and 75th percentiles of the weight distribution for various subpopulations in each survey wave. As before, we plot the distributions in 2005–2009 (blue) andin 2017–2020 (yellow), while holding voter attributes fixed to their value in 2017–2020. The
trends in the economic weights are broadly similar across groups, though there are some no-24

ticeable differences (e.g., the priorities college graduates place on economic positions shiftedto the left, in contrast to voters without a college degree).
By contrast, Panel B of Figure 6 shows that cultural weights shift in different directions
across groups. We find a shift to the right for men, voters without a college degree, withouta union membership, and living in rural areas. These voters are increasingly more likely to
reward parties for holding conservative cultural positions (or less likely to penalize them). We
also find a rightward shift in priorities for older voters, consistent with the cultural backlash
theory arguing that many older voters feel disconnected from current cultural values (Norrisand Inglehart, 2019). By contrast, college graduates, women, younger voters, union members,
and urban residents did not experience a similar shift to the right and, in some cases, theirvoting weights shifted to the left. On aggregate, the right panel of Figure 5 shows a moderate
shift to the right in the distribution of the weights placed on the cultural index.
We then directly examine whether prioritizing conservative cultural issues is driven bya reservoir of populist voters. We construct a “PRRP score” that aggregates opinions thatare associated with PRRP support at the individual level. We first run a LASSO regression
and predict support for PRRP in the 2017–2020 survey wave based on voter attributes. We
then predict for each voter in each wave whether they would vote for a PRRP based on their
attributes and define the standardized fitted value as their PRRP score.22 Appendix Figure
A.4 shows that the covariates most correlated with the PRRP score are the belief that jobs
should prioritize natives, low confidence in the EU, and distaste for immigrant neighbors.
The prioritization of conservative cultural issues is driven entirely by potential PRRP
supporters. In the bottom facets of Figure 6 we split the sample in two based on the PRRP
score. We define potential PRRP supporters as voters with an above-median PRRP scoreand find that the shift to the right of the cultural weights is only apparent for these voters.
6.1.2

Reduced-Form Evidence for Changing Priorities

Previous work analyzing survey data has argued that the cultural cleavage, and especiallyimmigration, gained importance over the past two decades (De Vries et al., 2013; Norrisand Inglehart, 2019; Gethin et al., 2021; Schmitt-Beck et al., 2022; Gennaioli and Tabellini,
22

We ensure that all the country indicators are taken into account in the LASSO regression by not allowing
the model to penalize these variables. However, we do not use the country indicators when calculating the
PRRP score in order to allow for cross-country comparisons.

25

2023). However, scholars also recognize the limitations of using direct survey questions toestimate priorities. Sides et al. (2022) show that survey questions about the most important
problems facing the country do not necessarily predict vote choice. Moreover, people may notdirectly connect what they think is important with how they vote. For example, Kuziemko
et al. (2015) find that information about inequality increases the chances that voters reportinequality as an important problem, yet does not affect policy preferences. Finally, thesurvey questions that do ask respondents what issues they prioritized at the ballot are notasked consistently over time and across many countries.
For these reasons, we propose an alternative method to measure changes in priorities using
respondents’ left-right self-identification. We analyze responses to an IVS question that asks
participants where they place themselves on a left-right political scale ranging from 1 (left)
to 10 (right). This question forces voters to reduce their opinions on a variety of topics to asingle dimension of ideology, which likely relates to their voting decision.
To link left-right self-identification to opinions, we construct indices of cultural and economic opinions based on IVS questions. We classify IVS variables as cultural or economic
variables if they resemble one of the variables included in the cultural and economic CMP
indices, respectively (the variables are specified in Appendix Table B.2). We then define theloading of each variable using principal component analysis in order to capture the common
variation between the variables included in the index.
Using these opinion indices, we analyze the relationship between economic and cultural
attitudes and left-right self-identification over time (De Vries et al., 2013). We first nonparametrically regress left-right self-identification on the economic and cultural opinion indices,
separately for the 2005–2009 and 2017–2020 survey waves. Formally, writing y as the leftright self-identification and xC , xE as the cultural and economic opinion indices, we estimate
E[y|xC , xE ] = fˆt (xC , xE ).
The function ft is estimated separately for each survey wave with nonparametric regressions. Using the estimation results, we predict the reported ideology for each combination ofquantiles of the economic and cultural opinion indices. Finally, for each combination of thexC , xE quantiles, we subtract the 2005-2009 predictions from the 2017–2020 predictions to26

examine how left-right self-identification changed, conditional on opinions∆fˆ(xC , xE ) = fˆt+1 (xC , xE ) − fˆt (xC , xE ).
The results in Figure 7 show that between the 2005–2009 and the 2017–2020 waves, thecultural dimension became more dominant in shaping left-right self-identification. Darker
colors mean that individuals in specific quantiles of the economic and cultural indices in
2017–2020 are expected to identify as more right-leaning compared to individuals in similar
quantiles in 2005-2009. We find that left-right self-identification changes are primarily basedon voters’ cultural opinions, with darker colors appearing toward the top and lighter colors
at the bottom. This means that voters holding more conservative cultural opinions (the topof the figure) now define themselves as more right-leaning and voters holding more progressive cultural opinions now define themselves as more left-leaning (the bottom of the figure).
By contrast, the economic index is very weakly associated with changes in left-right ideology. This result provides suggestive evidence supporting the argument that voters’ identities
shifted from class to culture (Gennaioli and Tabellini, 2023).
The increased importance of cultural issues is most salient for voters who have conflicting
views on culture and economics (the top-right and bottom-left corners of Figure 7). Overtime, these individuals’ left-right self-identification became more associated with their cultural views rather than their economic views. Considering that the dependent variable, leftright self-identification, is not used in our model estimation, this figure provides independent
evidence that voters are increasingly prioritizing cultural issues over economic issues.
As an alternative reduced-form method to show the increased importance of culturalissues in shaping voting decisions, we use vote choice directly. Appendix Figure A.5 shows
the correlation between voter opinions and the positions of their preferred parties, separately
for economic and cultural issues. We use the same voter opinions indices for economic andcultural issues as in the previous figure, and the same CMP indices for party positions as
before. All indices are standardized at the country-wave level. The correlation between thecultural opinions of voters and the cultural positions of parties has increased over time. By
contrast, the correlation between the economic opinions of voters and the economic positionsof parties remained stable. This figure provides intuition for the kind of variation our model27

uses when estimating Φ.23

6.2

Changes in Voter Attributes

Our finding that both changes in opinions and changes in demographics explain a relatively
small share of the increases in PRRP support challenges common narratives, which we presented in Section 4. In this section, we explore this finding in more detail using reduced-form
analyses of the IVS data.
Figure 8 shows that on average, voters did not move closer to the positions of PRRP
since 2005. The top left panel shows the changes in the PRRP score defined in Section 6.1.1,
which aggregates opinions that are associated with PRRP support at the individual level.
The thick blue line shows the average value of the PRRP scores across all countries, whereas
the thin gray lines show the trends in each country. While in specific countries there aresome significant idiosyncratic changes in public opinion, there has not been a substantial
shift in voter attributes toward opinions associated with PRRP on aggregate. The average
PRRP score difference between the 2005–2009 and 2017–2020 waves is only 0.054 standard
deviations, which is 7% of the difference between the average PRRP score of PRRP supporters
and other voters.
Examining trends in specific voter opinions yields similar results. The remaining eight
panels of Figure 8 plot the change in the voter opinions that are most correlated with the
PRRP score, such as lack of confidence in the EU and not wanting immigrants as neighbors,in each country and on aggregate.24 Once again, we find that on average, voter opinions
on these cultural issues are relatively stable. For some cultural issues (homosexuality is notjustifiable), opinions have even moved away from the populist radical right.
Moreover, the rise in PRRP support has been documented not only in countries where
opinions are shifting toward the populist radical right (e.g., Hungary) but also in countries
where they are shifting away from it (e.g., Germany). Indeed, Appendix Figure A.6 showsthat with the exception of Hungary, there is virtually no correlation between the change in
the PRRP score and the change in PRRP support between 2005 and 2017. This is why our23

The estimated parameters solve the first-order conditions of Equation 6, which imply that the observed
correlations between the attributes of voters and the positions of the parties they support (as reported inAppendix Figure A.5) are close to the correlations predicted by the model.
24
The correlations are shown in Appendix Figure A.4.

28

decomposition analysis finds that voter attributes explain only a small share of the rise of
PRRP. This finding demonstrates the importance of broad comparative perspectives in the
study of electoral developments such as the rise of PRRP.
Looking within countries, Appendix Figure A.7 shows that the shifts in voter opinions aremostly homogeneous. Hypothetically, even if voter opinions are stable on average, support
for PRRP could still increase if there is polarization where voter cultural opinions are shifting
to the right among potential PRRP voters and shifting to the left among other voters. To
test this, we present the distribution of PRRP scores for each country in both the 2005–2009
and 2017–2020 waves. Similar to Figure 8, there are differences across countries. However,within countries, the trends are similar across the distribution. The PRRP score increases
homogeneously in some countries (e.g., Hungary and the Czech Republic) and decreases
homogeneously in others (e.g., Germany and the UK).
Which voter opinions do change? In Appendix Figure A.8 we take into account all IVS
opinion variables and present the opinions that changed the most between the 2005–2009
and 2017–2020 survey waves. Interestingly, some of the most important changes move inthe opposite direction of PRRP positions. For example, voters developed less conservative
opinions on traditional morality issues, such as abortion and divorce. This result is consistent
with our decomposition finding that the rise of PRRP is not driven by a shift in public opinion.
6.2.1

The Reservoir of Potential PRRP Voters across Countries

The stability of public opinion on issues associated with PRRP suggests that even before
the electoral success of PRRP, there was a preexisting reservoir of potential PRRP voters.
The size of this reservoir varies across countries, which could explain why these parties are
more successful in some countries than in others. However, measuring the number of potential
PRRP voters across countries is challenging because voters in different countries face different
options of parties they can vote for, nested in different political systems.
In this section, we use a similar decomposition exercise, across space instead of over time,
to measure the size of the PRRP reservoir across countries. We focus on the 2017–2020 survey
wave. For voters in each country, we simulate their counterfactual support for the National
Front, had they faced the same choice set of parties they could vote for as voters in France.
In other words, we fix the parties, their positions, and their residuals to the values in France29and allow only the voter attributes to differ across countries. Fixing all other components
allows us to consistently compare the size of the reservoir of potential PRRP voters acrosscountries. Formally, we calculate the following counterfactual for every country (c) in the
2017–2020 IVS wave (t) based on Equation 9:Zc,tS̃P = P (Π|xi ; θt , ZtF rance , ζtF rance )ftc (xi )dxi .

(11)

We find substantial cross-national variations in the size of the reservoir of potential PRRP
voters. Figure 9 presents the counterfactual support for the National Front and finds geographical patterns that are consistent with the actual support for PRRP shown in Figure 2.
Potential support for PRRP is smallest in the Nordic countries and largest in Eastern Europe. While differences in voter attributes do not explain the increase in support for PRRP
over time, they do explain some of the variation in support across countries.
In some countries, the electoral system limits the number of parties, and therefore, thefull strength of PRRP may not be observed directly. Figure 9 demonstrates that our method
can calculate the size of the reservoir of potential PRRP supporters even in such countries.
For example, we predict a much larger share of counterfactual support for PRRP in the U.K.
than what is observed in practice. This is likely due to the first-past-the-post system thatputs higher barriers for entry to parliament (Norris, 2005; Fujiwara et al., 2011).
Finally, our method allows us to calculate the reservoir of potential PRRP voters in nonEuropean countries, such as the United States.25 Even though the two-party system in the
U.S. impedes PRRP from gaining votes, we can estimate their potential latent support. We
find that the PRRP reservoir is relatively large but smaller than in Eastern Europe. These
results suggest that the U.K. and U.S. both have a large reservoir of potential PRRP voterswithin the voter base of other parties.
Since voter attributes generate variation in PRRP support across countries, our findingthat voter attributes do not drive the rise of PRRP support over time is unlikely to stemfrom measurement problems or omitted variables. As a robustness test, Appendix Figure A.9
conducts a similar exercise using German parties and presents the counterfactual support for
the AfD. The results are similar, demonstrating that our finding is not unique to France.
25

For the U.S., we impute one variable related to the EU to its average level in our sample.

30

6.3

Changes in Party Positions

Our decomposition estimates reveal that changes in party positions contribute rather little
to the rise in PRRP support since 2005, in contrast to several theories discussed in Section
4.26 In this section, we further investigate changes in party positions over time. To guidethe discussion, Figure 10 presents average changes in the two CMP indices for our five main
party families: PRRP, center-left, center-right, Green, and socialists. Within each party
family and year, we calculate the weighted average of the two indices. We weight each party
by its vote share within its country and then weight all countries equally. Since elections are
typically held every few years, we present five-year moving averages.
We find no evidence for convergence in the economic positions of center-left and centerright parties. Previous work has found that in the 1990s, the economic positions of mainstream parties converged, which could have pushed some dissatisfied voters toward PRRP
(Berman, 2021). The top panel of Figure 10 shows that in the time period we study, theeconomic positions of the center-left and center-right parties have in fact slightly diverged.
There is also no evidence that PRRP gained votes by moderating their positions. On
economic issues, PRRP positions remained stable over time. On cultural issues, which particularly distinguish PRRP from the other party families, PRRP became even more extreme,
as shown in the bottom panel of Figure 10. To better understand the shift of PRRP to thecultural right, Appendix Figure A.10 shows the trends for the six party positions with thelargest distinction between PRRP and non-PRRP. The most substantial change occurred inpositive mentions of a national way of life. The PRRP today dedicate almost 10% of their
manifestos to this issue, while the other parties dedicate approximately 1%–3%.
We find some support for the claim that left-wing parties shifted even further to theleft on cultural issues, which may in turn have alienated working-class voters. However, thesubstantial shift to the left occurred among socialist parties and we do not see a similar shift
among centrist parties. Therefore, it is unlikely that this change would dramatically increase
support for PRRP.
One concern with Figure 10 is that the trend we present could reflect changes in demand.
The weighted average of each party position could also change as a result of a change in the26

Relatedly, Vasilopoulou and Zur (2022) argue that PRRP have little to gain from changes in positions
and more to gain from changes in the salience of cultural topics.

31

vote share, which we use as weights within countries. To isolate changes in supply, Appendix
Figure A.11 repeats the same exercise, allowing only the party positions to change over time.
For every five years, we estimate the average change in each party’s positions, weighting
parties within countries by their initial support in that five-year period (while keeping equal
weights for each country). We then plot the cumulative change for each party family. The
results are similar to those reported in Figure 10, suggesting that the trends we find in party
positions occurred mainly within existing parties.
To conclude, the reduced-form results rule out the major supply-side hypotheses. This
provides intuition for why our decomposition suggests that on aggregate, changes in party
positions are not a significant driver of the increased support for PRRP.

6.4

Changes in Residuals

The second substantial component in our decomposition is the residual, which accounts for
49.4% of the overall increase in support for PRRP. As discussed in Section 4, this component
captures factors such as changes in unobserved party positions or valence, model misspecification, and new entrants. In Appendix F, we show that most of the increase in this component
is driven by new entrants. However, we do not interpret the entry of parties as a simple
supply shock. In most countries with new entrants, there were already previous unsuccessfulattempts of PRRP to gain support. Hence, the option to vote for a PRRP already existed.
Based on the other three components, our model predicts that PRRP should have received
support if they entered earlier. Therefore, the large increase in support for new entrants
more likely reflects parties overcoming strategic considerations or coordination failures thatprevented them from receiving substantial support in the past. For example, voters may coalesce around a party only when they anticipate that the party will have substantial support
and pass the threshold needed to enter parliament (Fredén, 2014).
We note that the residual component could capture other forces, including more nuanced
supply-side theories that are not captured in our data or model. PRRP could have improved
their image by recruiting more professional candidates and providing them with stronger
training (De Lange and Art, 2011), while maintaining similar positions. In addition, theresidual could capture delayed responses to changes in party positions. For example, voters’ choices in the 2010s could respond to the convergence in party positions in the 1990s.
32Appendix F further discusses these interpretations.

7

Conclusions

There is no lack of explanations for the rise of the populist radical right. Our goal in thismanuscript is not to introduce another factor that may have contributed to these parties’
electoral success but rather to provide a framework for organizing existing factors into distinct
categories—changes in party positions, voter attributes, and voter priorities—and assessing
their explanatory power.
Using decomposition methods to analyze a dataset that links voter attributes and party
positions, we find that growing priorities attached to the issues owned by the PRRP moststrongly explain their growing electoral appeal. We provide comprehensive empirical evidence
for Bartels’s (2023) memorable argument that PRRP are not surfing into power on a wave of
growing nativism and authoritarianism in public opinion; instead, these parties have proved
apt at mobilizing preexisting reservoirs of potential support. This implies that significant
electoral changes can occur not only when people change their minds but also when certain
issues become more consequential in shaping vote choice.
Lastly, we find that the priorities voters place on cultural issues grew in comparison toeconomic issues. This result raises an open question, which is beyond the scope of this paper:
why did cultural issues become more important? The answer is most likely a multicausal
process (Gidron and Hall, 2017) that combines various potential factors: rising incomes
may have resulted in voters focusing more on moral goods (Inglehart, 1981; Enke et al.,2022); smartphones and social media may have allowed political entrepreneurs to affect the
public agenda and expose individuals to views that promote tribalism (Manacorda et al.,2023; Melnikov, 2023); skill-biased technical change and trade shocks may have amplified
conflict between cultural groups (Bonomi et al., 2021); the decreasing power of national
representatives to set economic policy compared to supranational organizations and nongovernment actors may have led voters to focus less on economic issues (Mounk, 2018).
Whatever the reason, voter priorities dramatically shifted in ways that have reshaped thepolitical map in Europe. Understanding the sources of this shift is a promising path for
future research.

33

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38Tables and Figures
Table 1: CMP and IVS Descriptive Statistics
2005–2009PRRP

2017–2020

Other
PRRP
Parties

Other
Parties

Panel A: Comparative Manifesto Project
CMP Position Indices (Net Share in %)Economic Index-5.90
Cultural Index
13.50

-8.70
-7.80

-6.50
20.20

-16.00-8.00

Most Distinctive Positions (Share in %)European Community/Union: Negative
Internationalism: Negative
Multiculturalism: Negative
National Way of Life: Positive
Traditional Morality: Positive

0.30
0.10
0.60
1.50
1.20

3.60
1.20
2.80
11.10
3.10

0.60
0.10
0.90
2.40
0.70

3.00
1.40
3.40
5.30
2.80

Panel B: Integrated Value Survey
Demographics
Age
Male
Urban
College education
Full time employment

45.56
0.54
0.21
0.15
0.50

47.95
0.49
0.26
0.27
0.51

48.44
0.55
0.20
0.17
0.53

50.00
0.48
0.27
0.33
0.51

Most Distinctive Opinions (SD from Mean)
Confidence in EU-0.16
Jobs should prioritize natives
0.38
Confidence in UN-0.13
Do not want immigrant neighbors
0.08
Confidence in the press
-0.10

0.04
-0.11
0.07
-0.13
0.05

-0.55
0.48
-0.41
0.46
-0.35

0.01
-0.21
0.04
-0.10
0.04

Notes: This table provides descriptive statistics on party positions in the CMP data (Panel A) and voter
attributes in the IVS data (Panel B). The first two columns present the average of each variable in the
2005–2009 wave and the last two columns present their average in the 2017–2020 wave. In Panel A, the firsttwo rows show the averages of the CMP economic and cultural indices. The indices measure net shares, i.e.,the difference between the share of platforms dedicated to right-wing positions and left-wing positions. See
Appendix Table B.1 for further details on each position, as well as a list of variables included in the two
indices. The next five rows show the positions with the largest difference between PRRP and non-PRRP
across both waves. Each variable represents the share of the platform mentioning that position in percentile
terms. In Panel B, the first five rows show key demographic variables. We define urban as living in a citywith more than 100,000 people. The next five rows focus on the opinions with the largest difference between
PRRP and non-PRRP supporters. Opinions are measured in units of standard deviations from the mean. In
both panels, averages are calculated with equal weight for each country. In Panel A, within countries, parties
are weighted based on their vote share. In Panel B, within countries, we use the IVS sampling weights.

39

Figure 1: Support for Populist Radical Right Parties over Time
20%

1990

1995

2000

2005

2010

2017−2020
survey wave

5%

2011−2013
survey wave

10%2005−2009
survey wave

PRRP Vote Share

15%

2015

2020

Year

Notes: This figure shows the average vote share of PRRP in the 22 European countries in our data. Withineach country, the vote share every year is calculated as the average PRRP vote share among all parties
appearing in the CMP dataset in all parliamentary elections in the five-year window centered around thatyear. We then calculate the average share across all 22 countries. The gray bars mark when the surveys ofthe three IVS waves used throughout the paper were held.

40

Figure 2: Support for Populist Radical Right Parties by Country, 2017–2020 IVS Wave

NOR
10.4%
(15.5%)

ISL
0%

FIN
11.2%

(0%)

(18.1%)

SWE
8.2%(17.8%)

Support
for
RRP

EST
16.6%
(19.5%)

80

DNK
14.2%

LTU
0%

(11.6%)

GBR
4.1%

60

(0%)

(0%)

NLD
14.7%

40

POL
67.9%

DEU
6.4%

(15.1%)

(44%)

CZE
8.6%

(13.3%)(11.4%)

20
FRA
12.8%

0

(14.6%)

CHE
23.2%

AUT: 20.5%(16.7%)

SVN: 27%(27.2%)

SVK: 25.5%(25.4%)
HUN
79.2%(73.2%)(33.1%)

ITA
28.3%(22.7%)

PRT
0%

ESP
0%

GRC
7.3%

(13.5%)(1.4%)(7%)

Notes: This figure shows the average PRRP support in the 2017–2020 IVS wave for the 22 countries coveredin our analysis. The actual vote share in the closest election appears in gray in parentheses. Note that theclosest elections can occur several years before or after the survey, which can generate large gaps between thetwo numbers in some countries (e.g., Spain). The PRRP support and vote share are calculated as a share of
all parties supported or voted for that appear in the data. IVS sampling weights are used.

41

Figure 3: Voting Weights Distribution by Supported Party Family, 2017–2020
Economic Index

Cultural Index

PRRP

PRRP

Center−Right

Center−Right

Center−Left

Center−Left

Socialist

Socialist

Green

Green
−0.4

0.0

0.4

0.8

−0.4

National Way of Life: Positive
PRRP

PRRP
Center−Right

Center−Left

Center−Left

Socialist

Socialist

Green

Green
0.0

0.4

0.4

0.8

Labour Groups: Positive

Center−Right

−0.4

0.0

0.8

−0.4

Equality: Positive

0.0

0.4

0.8

Free Market Economy

PRRP

PRRP

Center−Right

Center−Right

Center−Left

Center−Left

Socialist

Socialist

Green

Green
−0.4

0.0

0.4

0.8

−0.4

Economic Goals
PRRP

Center−Right

Center−Right

Center−Left

Center−Left

Socialist

Socialist

Green

Green
0.0

0.4

0.8

−0.4

Environmental Protection
PRRP

PRRP
Center−Right

Center−Left

Center−Left

Socialist

Socialist

Green

Green
0.0

0.8

0.4

0.0

0.4

0.8

Civic Mindedness: Positive

Center−Right

−0.4

0.4

Multiculturalism: Negative

PRRP

−0.4

0.0

0.8

−0.4

0.0

0.4

0.8

Voting Weights (p25, median, p75)

Notes: This figure shows the distribution of weights voters place on the economic and cultural positionindices and eight individual manifesto positions in the most recent survey wave (2017–2020) separately forsupporters of different party families. For each combination of party family and index/position, we present
the 25th, 50th, and 75th percentiles of the corresponding weight distribution. We estimate the model on
the 2017–2020 survey wave and for each voter calculate the weights based on her attributes using Equation
2. Weights are measured in units of standard deviation to utility units, i.e., the increase in utility for an
increase of one standard deviation in the position. We aggregate individual weights into indices based on
the CMP economic and cultural indices (Section 2.1, Appendix Table B.1). Specifically, we take a simple
average of the weights on all positions used in the CMP index, multiplied by -1 for left-wing positions. The
eight individual positions presented are the ones with the largest variance in weight between supporters ofthe different party families. We use IVS sampling weights within countries and equally weight each country.
The party families are described in Section 2.4.

42Figure 4: Decomposition of the Rise in Populist Radical Right Support
Residual
100%

Party
positionsVoter
attributes

Voter
priorities

Total

+28%

+25%
75%

−1%
−3%

+16%
50%

+10%
+25%

25%

+2%

0%

2005−20092011−20132017−2020

Notes: This figure presents the results of our main decomposition exercise. The black bars mark the overall
increase in PRRP support between 2005–2009 and 2017–2020, which we set to 100%. We aggregate acrossall countries with PRRP support in our data by using a weighted average of each country’s decomposition
results (see Appendix Table A.5). Weights are the inverse of the share of PRRP support in the 2017–2020
wave. We use IVS sampling weights within countries.

43

Figure 5: Changes in the Distribution of Voting Weights, Holding Attributes Fixed
Economic Index Weights

Cultural Index Weights

1.00

Density

0.75

0.50

0.25

0.00
−2

−1

0
Indifferent

1

2

−2

−1

0
Indifferent

12

Weight2005−20092017−2020

Notes: This figure shows the distribution of the weights voters place on the economic and cultural indicesof party positions for different waves, holding voter attributes fixed at their level in 2017–2020. We use the
CMP economic and cultural indices that are described in Section 2.1 and their manifesto components aredescribed in Appendix Table B.1. Weights are calculated based on Equation 2 using the estimated voter
priorities (utility parameters) for the 2005–2009 wave (solid blue lines) and 2017–2020 wave (dashed yellow
lines). The voter attributes are fixed to their distribution in 2017–2020 so that weights only change due toa change in priorities. The weight placed on an index is the average weight corresponding to each party
position included in the CMP index, where weights for positions that enter the index with a negative sign
are multiplied by -1. Weights are measured in units of standard deviation to utility units, i.e., the increase inutility for an increase of one standard deviation in the index. We use IVS sampling weights within countries
and equally weight each country.

44

Figure 6: Voting Weights by Subpopulations, Holding Attributes Fixed
Panel A: Economic Index

Panel B: Cultural Index

College
No College

Female
Male

16−35
36−65
66+

Union Member
Not aUnion Member

Urban
Rural

Other Voters
Potential PRRP Supporters(Above−Median PRRP Score)
−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

Voting Weights (p25, median, p75)
2005−20092017−2020

Notes: This figure shows the subpopulation distribution of the weights voters place on the economic andcultural indices of party positions for different waves. We use the CMP economic and cultural indices thatare described in Section 2.1. Their manifesto components are described in Appendix Table B.1. Weights arecalculated based on Equation 2, using the estimated voter priorities (utility parameters) for the 2005–2009
wave (top blue bars) and 2017–2020 wave (bottom yellow bars). The voter attributes are fixed to theirdistribution in 2017–2020 so that weights only change due to a change in priorities. Weights are measured
in units of standard deviation to utility units, i.e., the increase in utility for an increase of one standard
deviation in the index. The weight placed on an index is the average weight corresponding to each party
position included in the index, where weights for positions that enter the index with a negative sign are
multiplied by -1. Potential PRRP supporters (other voters) are voters with a PRRP score above (below)
the median. See Section 6.1 for further details on the PRRP score. We use IVS sampling weights withincountries and equally weight each country.

45

Figure 7: Change in Predicted Ideological Self-Identification
1.0

0.9

Voter Culture Index Quantile

0.8

0.7

Change
0.6

0.3
0.2

0.5

0.1

Ideology shifted rightward

0.0

0.4

−0.1

0.3

Ideology remained similar

0.2

0.1

Ideology shifted leftward
0.0
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Voter Economic Index Quantile

Notes: The figure shows the change in the predicted ideological self-identification, on a 1-10 scale, between
the 2005–2009 survey wave and the 2017–2020 survey wave as a function of voters’ economic and culturalopinions. Economic and cultural opinions are aggregated into two indices using the first principal component.
The list of IVS variables included in each index appears in Appendix Table B.2. We use a nonparametric
regression model to predict ideological self-identification (higher values are associated with more right-wing
positions), based on the voter’s economic and cultural opinion indices, for each survey wave separately. We
then subtract the prediction of the earlier wave from the latter. The two contour lines mark the values of-0.1 and 0.1. We use IVS sampling weights within countries and equally weight each country.

46

Figure 8: Evolution of Voter Opinions
PRRP score

Jobs should prioritize
natives

Lack of confidence in EU

Don't want immigrantneighbors

Lack of trust in otherpeopleHomosexuality notjustifiable

Lack of confidence inthe UN

Lack of confidence injustice system

Country needs strong
leader0.5
0.0Standard Deviations

−0.5

0.5
0.0
−0.5

0.5
0.0

Aggregate

2017−2020

2011−20132005−20092017−2020

2011−20132005−20092017−2020

2011−20132005−2009−0.5

Individual Countries

Notes: This figure presents the average of voters’ opinions by survey wave on eight cultural issues that are
most strongly associated with PRRP support, and the PRRP score. The thin gray lines show the trends ineach country, while the thick blue line is the average across all 22 countries. We use IVS sampling weights.
The PRRP score is calculated by running a LASSO regression predicting PRRP support. The regression
is run on the most recent survey and includes all IVS variables in our final dataset, along with country
fixed effects, with no penalty on the country coefficient. We then standardize the fitted value based on the
regression coefficients (excluding country fixed effects). In the eight opinion variables, we standardize thevariables within each country using standard deviations from the 2005–2009 IVS wave. We omit Italy fromthe analysis regarding the justifiability of homosexuality since this survey question was not asked in the
country in 2005–2009. In all panels, we set the mean value in the 2005–2009 wave to zero in all countries.
For more details on each variable see Appendix Table B.2.

47

Figure 9: Counterfactual Support for the National Front by Voter Attributes

NOR
10.3%

ISL
8%

Predicted
Vote
Share

FIN
13.6%
SWE
7.9%

EST
18.6%

25
20

DNK
10.2%

15

LTU
16.6%

GBR
16.5%

10

NLD
13.5%

5
0
FRA
14.1%

POL
21.6%

DEU
9.7%

CHE
13.9%

CZE
25%

SVK: 22.2%
HUN
25%
SVN: 16.3%

AUT: 17.1%

ITA
20.6%
USA
16.8%

PRT
17.9%

ESP
12.2%

GRC
21.1%

Notes: This figure calculates the counterfactual support for the National Front in the 2017–2020 wave if
French voters had the attributes of voters in other countries. We calculate the counterfactual support for
the National Front separately for each country based on the formula in Equation 11. In all countries, we use
the party positions of French parties in the 2017–2020 wave (ZtF rance ) along with the estimated residuals for
French parties (ζtF rance ) and the voter priorities that were estimated for this wave (Φ̂t , β̂t ). For each country,
we predict the share of National Front supporters according to the voter attributes in that country (ftc (xi )).
For the U.S. sample, we impute the responses to one IVS question about the European Union based on thesample average. For all countries, we use IVS sampling weights.

48

Figure 10: Changes in Party Positions over TimeCMP Economic Index−10

Net Percent of Manifesto

−20

PRRP

−30
Center−Right
2005

2010

2015

2018
Center−Left

CMP Cultural Index
10

Socialist

0
Green
−10
−20
2005

2010

2015

2018

Year

Notes: This figure shows the changes in the CMP economic and cultural indices for five party families–PRRP,
center-right, center-left, socialist, and green– since 2005. Indices measure the net share of the manifesto
dedicated to right-wing positions. Each index is constructed by adding the manifesto shares of conservative
positions and subtracting the shares of liberal positions such that positive values reflect more support fora free market or more conservative cultural values. The figure presents five-year moving averages for eachindex and each party family. Each country is weighted equally, and parties within each country are weighted
by their voting share. The indices are discussed in Section 2.1, their manifesto components are described inAppendix Table B.1, and the party classification is described in Section 2.4

49

Appendix
A

Additional Tables and Figures
Table A.1: IVS Data
IVS Wave

Countries

Parties

PRRP

Observations

PRRP
Support Share

2005-20092010-20142017-2021

22
7
22

147
51
170

19
6
28

26,1406,373
27,097

0.11
0.12
0.18

Notes: This table provides descriptive statistics on the final dataset analyzed. Each row represents an
Integrated Values Survey wave. The observations include only respondents who were successfully matchedwith the Comparative Manifesto Project data. PRRP support share is the average support for PRRP acrossthe 22 countries. We use IVS sampling weights within countries and equally weight each country.

50

Table A.2: IVS Data Matched with CMP

1) All data
2) Respondents supporting a party
3) Respondents matched with CMP

UniqueParties

Unique
PRRP

Observations

PRRP
Supporters

.
353
205

.
.
32

91,42563,18759,610.
.
7,934

Notes: This table provides descriptive statistics on the Integrated Values Survey data. The first row shows
the total number of respondents in the country waves we analyzed. The second row presents descriptive
statistics on the subset of respondents supporting a specific party. The third row presents statistics on
respondents who supported a party that could be matched with the CMP and that was supported by at least
five participants.

51

Table A.3: List of Populist Radical Right Parties
Country

Party

Austria

Freedom Party of Austria (FPO), Alliance for the Future of Austria(BZO)
Czech Republic Freedom and Direct Democracy (SPD), Dawn of Direct Democracy
Denmark
Danish People’s Party
Estonia
Estonian Conservative People’s Party
FinlandFinns Party
FranceNational Front
Germany
Alternative for Germany (AfD)
GreecePopular Orthodox Rally (LAOS), Golden Dawn
Hungary
Fidesz Hungarian Civic Alliance, Movement for a Better Hungary(Jobbik)
Italy
League, Brothers of Italy
Netherlands
Party for Freedom (PVV), Forum for Democracy
NorwayProgress Party
PolandLaw and Justice (PiS), League of Polish Families (LPR), Kukiz 15
Slovakia
Slovak National Party, We Are Family, People’s Party Our Slovakia
Slovenia
Slovenian National Party, Slovenian Democratic Party
SwedenSweden Democrats
Switzerland
Swiss People’s Party, Swiss Democrats, Ticino League, Movement of
the Citizens of French-speaking Switzerland
UK
UK Independence Party (UKIP)
Notes: This table provides a list of parties classified as PRRP in our data. Section 2.4 provides details on
how we classify parties.

52

Table A.4: Voter Attributes With the Largest Coefficients in the Weight Indicies
Economic Index

Cultural Index

Variable

Coefficient

Variable

Coefficient

Confidence in unions-0.172

Jobs should prioritize
natives

0.145

Government ownership ofbusiness should be
increased-0.137

Pride in nationality

0.139

Oppose redistribution

0.094

Confidence inenvironmentalorganizations-0.137

Willing to demonstrate-0.084

Confidence in armed forces

0.120

Confidence inenvironmentalorganizations-0.083

Confidence in EU-0.116

Confidence in churches

0.079

Oppose redistribution

0.088

Confidence in major
companies

0.077

Willing to demonstrate-0.083

Willing to join strike-0.076

Confidence in major
companies

0.076

Income decile

0.074

Confidence in police

0.073

Personal over govt.
responsibility

0.073

Confidence in the press-0.073

Notes: This table presents the largest coefficients (in absolute terms) on the IVS variables generating the
economic and cultural weight indices. We calculate weights on individual party positions using Equation
2. We aggregate individual weights into indices based on the economic and cultural party position indicessuggested by the CMP (Section 2.1). Specifically, we take a simple average of the weights on all positions
used in the CMP index, multiplied by -1 for left-wing positions. Taken together, this procedure generates
two linear functions from the voter attributes to their weights on both indices. For each index, we present
the ten largest coefficients in absolute values.

53Table A.5: Decomposition of the Rise in PRRP Support by Country
Voter Priorities

Voter Attributes

Party Positions

Residuals

Agg.

Austria

0.084(0.006)-0.0217(0.005)-0.0014(0.004)-0.0737(0.009)-0.0128

Czech Republic

0.0354(0.004)0.0077(0.002)0.0006(0.002)0.0505(0.005)0.0942

Denmark

0.0181(0.008)0.0215(0.006)-0.0052(0.005)0.0161(0.009)0.0505

Estonia-0.0247(0.018)0.0096(0.01)-0.0099(0.004)0.2169(0.024)0.1919Finland0.0222(0.004)-0.0018(0.002)-0.0132(0.003)-0.0128(0.004)-0.0056

France0.0063(0.006)0.0283(0.007)0.0224(0.005)0.031(0.002)

0.088

Germany

0.0382(0.002)-0.0205(0.003)0.0142(0.003)0.0279(0.003)0.0598Greece0.0461(0.006)-0.023(0.005)0.0001(0.003)0.007(0.005)0.0302

Hungary0.1118(0.013)0.0471(0.013)0(0.007)0.0031(0.012)

0.162

Italy

0.0488(0.008)0.0536(0.007)-0.0914(0.011)0.1889(0.007)0.1999Netherlands

0.0408(0.006)0.0327(0.005)0.0054(0.006)

0.0413(0.004)0.1202

Norway-0.0466(0.006)0.0178(0.007)0.053(0.004)-0.126(0.004)-0.1018

Poland0.2282(0.012)-0.0664(0.01)-0.1738(0.012)0.2884(0.01)

0.2764

Slovakia-0.0449(0.015)0.0364(0.011)0.0593(0.008)0.1081(0.011)0.1589Slovenia-0.0075(0.008)0.0204(0.01)

0.0038(0.006)-0.0508(0.005)-0.0341

Sweden-0.0058(0.004)0.0109(0.003)0.003(0.003)0.0471(0.003)0.0552

Switzerland

0.1006(0.007)0.0173(0.005)-0.0498(0.006)-0.0571(0.008)0.011

UK0.0215(0.002)-0.0114(0.002)-0.0066(0.002)0.0477(0.004)0.0512

Total (0-1 scale)

0.4349(0.039)0.0611(0.022)0.0105(0.017)0.4936(0.037)1.0001

Notes: This table presents decomposition results for the 18 countries in our data that had any PRRP support
in one of our survey waves (2005–2009, 2011–2013, 2017–2020). Columns (1)-(4) present the counterfactual
increase in the share of support for the PRRP in that country between the 2005–2009 and the 2017–2020
waves, based on Equation 10. Column (5) sums Columns (1)-(4) to show the overall increase in the share of
support for PRRP in that country in our data. The last row presents aggregated results from all countries,
weighting each country by the inverse share of support for PRRP in the last wave, as in Figure 4. Standard
errors are calculated using 500 bootstrap simulations. We use the IVS sampling weights within countries.

Figure A.1: Decomposition Robustness
Panel A: Leave Country Out
100%
75%

Panel B: Bliss−Point Model
+42%

+39%

+75%
+2%

50%

+56%

+1%

+4%

−19%

25%
0%
Panel C: K = 3

Panel D: K = 7
+48%

100%

+42%

75%
+53%

+5%

+52%

+1%

+4%

50%
−5%

25%
0%
Panel E: Reverse
100%
75%
50%

Panel F: No Demographics
+32%

+41%

+18%
+49%

+8%

+51%

+1%

0%

25%
0%
Panel G: Western Europe

Panel H: Eastern Europe
+78%

100%

+34%

75%

+14%
+56%

50%

−4%

+36%

25%
−13%

0%

Party
positionsVoter
attributes

0%
Residual

Voter
priorities

ResidualParty
positionsVoter
attributes

Voter
priorities

Notes: These figures plot the decomposition results for changes in PRRP support between the 2005–2009
and 2017–2020 survey waves under different specifications. Panel A shows results from a leave-country-out
exercise, where for each country, we estimate the model parameters using data from all other countries. Panel
B shows the decomposition results when we use a bliss-point model, as described in Appendix B. Panels C andD estimate the βt parameters (Equation 7) using a higher (k = 7) and lower (k = 3) number of dimensionsfor βt , compared to our original specification (k = 5). Panel E reverses the order of the decomposition in
Equation 10 between voter attributes and voter priorities. Panel F repeats the entire exercise, excluding
demographic variables from the voter attributes. Panels G and H estimate voter priorities and run thedecomposition separately for Eastern and Western European countries. The Eastern European countries are
the Czech Republic, Estonia, Hungary, Lithuania, Poland, Slovakia, and Slovenia. The Western European
countries are Austria, Denmark, Finland, France, Germany, Greece, Iceland, Italy, Netherlands, Norway,
Portugal, Spain, Sweden, Switzerland, and the UK. All figures aggregate results across countries, weighting
each country by the inverse share of PRRP support in the 2017–2020 wave. We use the IVS sampling weights
within countries. See further details in Section 5.

55

Figure A.2: Changes in the Distribution of Voting Weights, Holding Attributes Fixed:
Alternative Economic and Cultural Indices(a) Bakker and Hobolt (2013)
Libertarian−Authoritarian
Index Weights

Economic Index Weights
12

Density

9
6
3
0
−0.2

−0.1

0.0

0.1

0.2

0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

Weight2005−20092017−2020(b) Prosser (2014)
Social Liberal−ConservativeIndex Weights

Economic Index Weights

Density

15

10

5

0
−0.1

0.0

0.1

0.2

−0.1

0.0

0.1

0.2

Weight2005−20092017−2020

Notes: This figure shows the distributions of the weights voters place on two alternative economic andcultural party position indices proposed by (a) Bakker and Hobolt (2013) and (b) Prosser (2014). Weights
are calculated based on Equation 2 using the estimated voter priorities (utility parameters) for the 2005–2009 wave (solid blue lines) and 2017–2020 wave (dashed yellow lines). The voter attributes are fixed to theirdistribution in 2017–2020 so that weights only change due to a change in priorities. The weight placed on
an index is the average weight corresponding to each party position included in the index, where weights
for positions that enter the index with a negative sign are multiplied by -1. Appendix Table B.1 describes
the variables included in each index. Weights are measured in units of standard deviation to utility, i.e., theincrease in utility for an increase of one standard deviation in the index. We use IVS sampling weights withincountries and equally weight each country.

56

Figure A.3: Ratio of Weights on Cultural and Economic Issues Over Time and by Country
Aggregate

Austria

Czech Republic

Denmark

0.6
0.4

Prioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
Culture

0.2
0.0
0.1

1.0

10.0

0.1

1.0

Estonia

10.0

0.1

1.0

Finland10.0

0.1

France1.0

10.0

Germany

0.6
0.4

Prioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
Culture

0.2
0.0
0.1

1.0

10.0

0.1

Greece

1.0

10.0

0.1

1.0

Hungary

10.0

0.1

Iceland

1.0

10.0

Italy

0.6
0.4

Prioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
Culture

0.2
0.0
0.1

1.0

10.0

Lithuania

0.1

1.0

10.0

0.1

1.0

Netherlands

10.0

0.1

Norway1.0

10.0

Poland0.6
0.4

Prioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
Culture

0.2
0.0
0.1

1.0

10.0

0.1

Portugal

1.0

10.0

0.1

Slovakia

1.0

10.0

0.1

Slovenia

1.0

10.0

Spain

0.6
0.4

Prioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
Culture

0.2
0.0
0.1

1.0

10.0

Sweden

0.1

1.0

10.0

0.1

Switzerland

1.0

10.0

0.1

1.0

10.0

UK

0.6
0.4

Prioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
CulturePrioritize
Economics

Prioritize
Culture

0.2
0.0
0.1

1.0

10.0

0.1

1.0

10.0

0.1

1.0

10.0

Weights on culture in absolute value / weights on economics in absolute value
2005−20092017−2020

Notes: This figure shows the ratio of the absolute value of weights voters placed on the cultural position index
over the absolute value of weights voters placed on the economic position index, by country. Weights arecalculated based on Equation 2 using the estimated voter priorities (utility parameters) for the 2005–2009
wave (solid blue lines) and 2017–2020 wave (dashed yellow lines). The voter attributes are fixed to theirdistribution in 2017–2020 so that weights only change due to a change in priorities. The weight placed on
an index is the average weight corresponding to each party position included in the index, where weights
for positions that enter the index with a negative sign are multiplied by -1. Appendix Table B.1 describes
the variables included in each index. A shift of the distribution to the right indicates that voters prioritized
cultural issues more over time, compared to economic issues.

Figure A.4: Covariates Most Strongly Correlated with the PRRP Score
Jobs should prioritize natives (+)
Confidence in EU (−)
Don't want immigrant neighbors (+)
Homosexuality justifiable (−)
Confidence in UN (−)
Trust other people (−)
Confidence in courts (−)
Country needs strong leader (+)
Like idea of army rule (+)
Confidence in environmental organizations (−)
Confidence in the press (−)
Abortion justifiable (−)
Don't want different race neighbors (+)
Confidence in civil services (−)
Divorce justifiable (−)
Don't want homosexual neighbors (+)
Like idea of democracy (−)
Confidence in unions (−)
Jobs should prioritize men (+)
Importance of children respecting other people (−)
0.3

0.4

0.5

0.6

Correlation with PRRP Score

Notes: This figure presents the voter attributes most strongly correlated with the PRRP score. The scoreis calculated by running a LASSO regression predicting PRRP support. The regression is run on the 2017–2020 survey wave and includes all IVS variables in our final dataset, along with country fixed effects, withno penalty on the country coefficient. To calculate the PRRP score we standardize the fitted value based on
the regression coefficients (excluding country fixed effects) with each country weighted equally. We use IVS
sampling weights within countries and equally weight each country.

58

Figure A.5: Correlations of Voter Opinions and the Positions of their Preferred Party
Economy Index

Culture Index

0.50

Party Positions

0.25

0.00

−0.25

−2

−1

0

1

2

−2

−1

0

1

2

Voter Opinions
2005−20092017−2020

Notes: This figure displays a binned scatter plot of the correlation between the opinions of voters andthe positions of the parties they supported in the 2005–2009 and 2017–2020 survey waves. We use IVS
sampling weights within countries and equally weight each country. The positions of parties are based on the
Comparative Manifesto Project economic and cultural indices. The components of these indices are shownin Appendix Table B.1. The voter opinion indices are created using a principal component analysis of IVS
variables related to economics and culture. The variables included in the principal component analysis appear
in Appendix Table B.2.

59

Figure A.6: Changes in PRRP Score and PRRP Support, 2005–2017

0.3

Hungary

Changes in PRRP Voting Share

0.2

SwedenItaly
Finland
PolandGermany
Estonia

Slovakia
Netherlands

0.1

Czech Republic

FranceAustriaUK

Spain

GreeceDenmark

0.0

Portugal

IcelandLithuania

Switzerland

NorwaySlovenia
−0.25

0.00

0.25

0.50

Changes in PRRP Score, Standard Deviations

Notes: This figure plots the changes in the average voters’ PRRP score and the changes in the PRRP voting
share by country. Changes in PRRP score are between the 2005–2009 survey wave and the 2017–2020 survey
wave. Changes in PRRP voting share are between 2005 and 2017. For both years (2005 and 2017) we takethe average of all elections held in a five-year window centered around that year. The PRRP score aggregates
opinions that are associated with PRRP support at the individual level and is described in Section 6.1.1.
The change in PRRP score is normalized by the standard deviation of the score in the 2005–2009 survey foreach country. IVS sampling weights are used. PRRP vote shares are calculated as the vote share among all
parties appearing in the CMP dataset in all the parliamentary elections in the five-year window.

60

Figure A.7: Voters’ PRRP Score by Country
Austria

Czech Republic

Denmark

Estonia

FinlandFranceGermany

GreeceHungary

Iceland

Italy

Lithuania

Netherlands

NorwayPolandPortugal

Slovakia

Slovenia

Spain

SwedenSwitzerland

UK

0.6
0.4
0.2
0.0
0.6
0.4
0.2

Density

0.0
0.6
0.4
0.2
0.0
0.6
0.4
0.2
0.0

−5

0

5

−5

0

5

−5

0

5

0.6
0.4
0.2
0.0

−5

0

5

−5

0

5

PRRP Score, Standard Deviations
2005−20092017−2020

Notes: This figure presents the distribution of voters’ PRRP score by country and survey wave. The PRRP
score is calculated by running a LASSO regression predicting PRRP support. The regression is run on themost recent survey and includes all IVS variables in our final dataset, along with country fixed effects, withno penalty on the country coefficient. To calculate the PRRP score we standardize the fitted value basedon the regression coefficients (excluding country fixed effects) with each country wave weighted equally. The
dashed lines are the average values (normalized to 0) and the dotted lines are the minimum and maximum
possible values. IVS sampling weights are used.

61

Figure A.8: Opinions that Changed the Most, 2005–2017

Divorce justifiable (+)
Abortion justifiable (+)
Child suffers when mom works (−)
Homosexuality justifiable (+)
Like idea of democracy (+)
Confidence in police (+)
Confidence in armed forces (+)
Cheating on taxes justifiable (−)
Don't want immigrant neighbors (+)
Have freedom of choice (+)
Importance of children's obedience (−)
Belief in God (−)
Suicide justifiable (+)
Importance of children's religious faith (−)
Like idea of army rule (+)
Importance of leisure time (+)
Confidence in civil services (+)
Pride in nationality (+)
Don't want drug−addicted neighbors (+)
Importance of politics (+)
0.00

0.01

0.02

0.03

Average Yearly Changes in Opinions (in SD units)

Notes: This figure shows the opinions that changed the most between the 2005–2009 and 2017–2020 survey
waves. We run a separate regression of every opinion variable on the survey year and country fixed effects.
Opinions are measured in standard deviation units. Each dot represents the time coefficient in this regression.
95% confidence intervals are reported. We use IVS sampling weights within countries and equally weight
each country.

62

Figure A.9: Counterfactual Support for the AfD by Voter Attributes
NOR
5.8%

Predicted
Vote
Share

ISL
4.4%

FIN
9.8%

SWE
4.5%

25

EST
15.9%

20

DNK
5.8%

15

GBR
13.5%

10

NLD
9.5%

FRA
11.3%

POL
23.8%
CZE
24.8%
SVK: 22.5%
AUT: 14.1% HUN
24.1%
SVN: 14.6%

DEU
7.5%

5
0

LTU
13.5%

CHE
12%

ITA
21.1%
USA
17.9%

PRT
12.1%

ESP
10.2%

GRC
32.4%

Notes: This figure calculates the counterfactual support for the AfD in the 2017–2020 wave if German voters
had the attributes of voters in other countries. We calculate the counterfactual support for the AfD separately
for each country based on a formula similar to Equation 11. We use IVS sampling weights. For more details
see Figure 9.

63Figure A.10: Changes in Most Distinctive Party Positions over Time
EuropeanCommunity/Union:
Negative

Internationalism:
Negative

9

9

6

6

3

3

0

0
2005

2010

2015

2020

2005

Percent of Manifesto

Multiculturalism:
Negative

2010

2015

2020

National Way of
Life: Positive

9

9

6

6

PRRP

Center−Right

Center−Left
3

3

0

0
2005

2010

2015

2020

Socialist
2005

Traditional
Morality: Positive

9

6

6

3

3

0

0
2010

2015

2015

2020
Green

Military: Positive

9

2005

2010

2020

2005

2010

2015

2020

Year

Notes: This figure shows the average position by party family for the six positions with the largest difference
between PRRP and the other parties. Each position is measured as the average percentage of the platformthat is dedicated to the position by parties in that family. The manifesto variables are described in Appendix
Table B.1. The figure presents five-year moving averages for each position and each party family. Eachcountry is weighted equally, and parties within each country are weighted by their voting shares.

64Figure A.11: Within-Party Position Changes

Cumulative change in net percent of manifesto

CMP Economic Index

0

−5
PRRP
2005−20092010−2014

2015−2019

Center−Right
Center−Left

CMP Cultural Index

Socialist
Green

5

0

2005−20092010−2014

2015−2019

Notes: This figure shows the cumulative within-party changes of the CMP economic and cultural indicesby party family. The manifesto components included in each index are described in Appendix Table B.1.
The figure presents cumulative average changes within each party since 2005. In particular, for each five-year
period, we first compute position changes at the party level. We then aggregate the change across parties andcountries. Each country is weighted equally, and parties within each country are weighted by their averagevoting share in the initial period. Thus, changes in the vote share do not affect the change in positions
between consecutive periods. We present the cumulative change aggregating all changes since 2005. Notethat the set of parties is not identical across periods.

65

Figure A.12: Similarities between Party Voters
Other
Pop.

PRRP

0.21

−0.15

−0.05

−0.11

−0.04

0.03

0.05

0.09

0.01

−0.04

0.05

−0.03

−0.1

−0.05

0.07

0.06

0.11

0.17

0.22

0.25

Green

Socialist Center−Left

PRRP

Center−Right

Other
Pop.

Socialist Center−Left

Center−Right

Green

0.38

1.0

0.5

0.0

−0.5

UKIP, UK
SwedeDems, SWE
SlovenianNational, SVN
SlovenianDemocratic, SVN
National, SVK
Kotleba, SVK
Family, SVK
Law, POL
Kukiz, POL
Progress, NOR
Freedom, NLD
DemocracyForum, NLD
League, ITA
BrothersOfItaly, ITA
Jobbik, HUN
Fidesz, HUN
GoldenDawn, GRC
AfD, GER
National, Front, FRA
Finns, FIN
ConservativePeoples, EST
DanishPeoples, DNK
FreedomDirectDemocracy, CZE
DawnDirectDemocracy, CZE
TicinoLeague, CHE
People, CHE
MovementFrenchSpeaking, CHE
Freedom, AUS
UnitedLeft, SVN
OrdinaryIndependent, SVK
DirectionSocialDemocracy, SVK
WeCan, SPA
Socialist, NLD
OrderJustice, LTU
Labour, LTU
GoItalyParty, ITAFiveStar, ITA
Independent, GRC
Unsub, FRA
ANO, CZE
FederalDemocraticUnion, CHE
LibDem, UK
Conserv, UK
Moderate, SWE
Liberals, SWE
ChristianDems, SWE
NewSlovenianChristian, SVN
ModernCentre, SVN
AlenkaBratušek, SVN
Network, SVK
FreedomSolidarity, SVK
ChristianDemocratic, SVK
Popular, SPA
Citizens, SPA
Modern, POL
Civic, POL
Liberal, NOR
Conservative, NOR
ChristianDemocratic, NOR
FreedomDemocracy, NLD
Democrats66, NLD
ChristianUnion, NLD
ChristianDemocraticAppeal, NLD
LiberalRepublic, LTU
HomelandUnionChristianDemocrat, LTU
MoreEU, ITA
Independence, ISL
Bright, ISL
UnionCentrists, GRC
NewDemocracy, GRC
FDP, GER
CSU, GER
UnionDI, FRA
Repub, Onward, FRA
Repub, FRA
Dem, FRA
NationalCoalition, FIN
ChristianDem, FIN
Reform, EST
ProPatriaResPublica, EST
Free, EST
Center, ESTSocialLiberal, DNK
LiberalAlliance, DNK
Liberal, DNK
ConservativePeoples, DNK
TOP09, CZEMayorsIndependents, CZE
CivicParty, CZE
ChristianDemUnion, CZE
RadicalDemocratic, CHE
Protestant, CHE
ConservativeDemocrat, CHE
ChristianDemocratic, CHE
Peoples, AUS
NewAustria, AUS
Labour, UK
SocialDem, SWE
SocialDemocrats, SVN
Socialist, SPA
Labour, NOR
Labour, NLD
DENK, NLD
SocialDemocratic, LTU
Democratic, ITA
SocialDemAlliance, ISL
Socialist, HUN
River, GRC
SPD, GER
Socialist, FRA
SocialDem, FIN
SocialDem, DNKSocialDems, CZESocialDemocrat, CHE
SocialDems, AUS
Left, SWE
UnitedLeft, SPA
SocialistLeft, NOR
Dialogue, HUN
DemCoalition, HUN
PopularUnity, GRC
Communist, GRC
Left, GER
Communist, FRA
LeftAlliance, FIN
SocialistPeoples, DNK
RedGreen, DNK
CommunistBohemia, CZELabour, CHE
Green, UK
Green, SWE
Green, NOR
GreenLeft, NLD
LeftGreen, ISL
PoliticsDif, HUN
Green, GER
Green, FRA
GreenLeague, FIN
Green, CZEGreenLiberal, CHE
Green, CHE
PilzList, AUS
Green, AUS−1.0

Correlation

Notes: This figure presents the similarities between voters of different parties across countries. For eachparty, we use a linear regression to predict support for that party based on voter attributes, using data fromthe 2017–2020 IVS wave in the relevant country. Then for every two parties in two different countries, we
calculate the correlation between the fitted values among all voters in both of the parties’ countries. For
example, to find the correlation between AfD and the National Front, we estimate the correlation between
the fitted probability that voters in both Germany and France would support AfD and the fitted probability
that those voters would support the National Front, where both fitted probabilities are a linear prediction
based only on their attributes. The labels show the average correlation between all parties in each family. We
determine whether a party is PRRP based on the PopuList dataset classification of radical-right parties. We
use the PopuList definition of populism for the “other populist parties” category. We classify the remaining
parties into party families based on the CMP data as explained in Section 2.4. The figure is based on allparties in our data that received support from over 50 respondents. IVS sampling weights are used in all
regressions. More details appear in Appendix D.

1.2

1.0

2000

2005

2010

2017−2020
survey wave

0.6

2011−2013
survey wave

0.8

2005−2009
survey wave

Number of Populist Radical Right Parties

Figure A.13: Average Number of PRRP by Country

2015

2020

Year

Notes: This figure shows the average number of PRRP that received at least 1% of the vote share by country.
Within each country, the number of PRRP in each year is calculated as the average number of PRRP receiving
at least 1% of the vote in all parliamentary elections in the five-year window centered around that year, basedon the CMP data. We then calculate the average number across all 22 countries, with all countries weighted
equally.

67

Figure A.14: Decomposition with New Entrants
Existing
parties
residual

100%

Party
entry
residualParty
positionsVoter
attributes

Voter
priorities

Total

+28%

+44%
75%

−1%
−3%

+16%
50%

+10%
+25%

25%

−20%

+2%

0%

−1%
2005−20092011−20132017−2020

Notes: This figure presents the results of our decomposition exercise, where the residual component is further
decomposed into the residual of new PRRP entrants and the remaining residuals (Equation 14). We calculate
the residual component in Equation 10 separately for PRRP that appeared in our data in the first wave (2005–2009) and those that did not. The black bars present the share of the increase in PRRP support between
2005–2009 and 2017–2020, which we set to 100% overall. We aggregate across all countries with PRRP
support by using a weighted average of their decomposition results. The value for each bar is a weighted
average of the decomposition results in each country with the weights defined as the inverse of the share ofradical right support in the 2017–2020 wave. We use the IVS sampling weights within countries.

68

B

Bliss-Point ModelIn this section, we discuss the similarities between our model and a simple bliss-point modeland explain how we estimate the parameters of the bliss-point model for our robustness
analysis in Section 5.
Assume that voters have a bliss point party positions vector that is an affine transformation of their observables, Axi + b. Voters support parties that are closest to their bliss point.
Formally, define the distance between two vectors of party positions as
dist(u, v)2 = (u − v)0 D(u − v),
where D is a diagonal matrix with a weakly positive diagonal representing the relative importance of different party positions. Using this distance function, we can define the bliss-point
utility function as
Uij = dist(zj , Axi + b)2 + ζj + εij .
We define Φ = −2AT D, and define δj = (zj − 2b)0 Dzj + ζj . We can then write the utility
function as
Uij = xi Φzj0 + δj ,which is exactly the utility function we estimate in the first stage (Equation 4). Therefore,
our estimation of the first stage will be identical to the case of a bliss-point model.
This equivalence between the two models implies that any counterfactual change to voter
attributes would be invariant to which of the two models we use. Any counterfactual changes
in the distribution of the attributes depend only on the first-stage parameters Φ, δ. Since
these parameters would be the same in both models, the counterfactual support of any party
would be the same in both models.27
By contrast, we need to adjust our estimation of the second stage to accommodate thequadratic terms. Specifically, we rewrite δ as2
δj,t = γt zj,t
+ βt zj,t + ζj,t ,
2
1 2
L 2
= ((zj,t) , ..., (zj,t) ) is the vector of party positions squared. Similar to Section 3.2,
where zj,twe take the difference between two consecutive survey waves and get the following equationthat extends Equation 7 to the quadratic case:
t+1
t+1 2
t+1
t+1 ¯2
t+1∆t+1
t δj = ∆t γ zj + ∆t β z¯j + γ̄∆t zj + β̄∆t zj + ∆t ζj .
{z
} | {z }|{z} |Party Positions

Voter Priorities

Residual

We follow a similar procedure to reduce the dimensionality of the party position vectors,
as described in Section 3.2 and Appendix E. We use the same dimension reduction technique2
for zj,t and zj,t: we first reduce the dimension of zj,t to k as before, and then take the squaresof the reduced dimension vector. We choose k = 3, such that, in total, ∆t+1
t δj is a linear
function of 2k = 6 variables, similar to the five-dimensional z vectors we had in the linear27

Our results for voter attributes in Panel B of Appendix Figure A.1 are close but not identical to theresults in Figure 4 because of different randomization seeds.

69case.
In Section 5, we present decomposition results using the bliss-point model described inthis appendix. We estimate the parameters θt = (Φt , βt , γt ) as explained here and thendecompose the rise of PRRP using Equation 10 as in our main decomposition. The only
difference from our original decomposition is that the priority component now includes theparameter vector γ as well.

C

Data Appendix

C.1

IVS Data Processing

We clean categorical variables in the IVS data to keep the number of potential categories
reasonable and merge similar variables when possible. For example, we aggregate the answers
to the question asking the respondent about her religion to the following variables: Protestant,Catholic, other Christian, Muslim, Jew, Hindu, Buddhist, and other.
For all variables, we impute missing values using random forests for each country-wave
combination separately. When a value is missing for an entire country-wave combination,
we typically exclude the variable from our final dataset. In rare cases where the variable
is available for almost all other countries in all survey waves, we impute the values for the
specific missing country-wave combination using the nearest survey waves for that country.
When the variable is available in both a preceding and a succeeding wave, we impute thevariable as a linear interpolation of the mean values in each of these waves, according to theyear when each survey was taken. When the variable is available in only a preceding or a
succeeding wave, we impute the missing data as the mean value of the available wave. For
the imputation process, we also use survey waves conducted before 2005.
Since our analysis is data-driven we do not choose a specific set of variables and instead
use virtually all variables that are available for the vast majority of country waves. The fulllist of IVS variables along with the way they were coded is described in Appendix Table
B.2. We exclude five survey questions from our main analysis and keep the remaining 101.
Specifically, we exclude a question on general political identity in which respondents are asked
to position themselves on a left-right scale. Instead of using it in our main analysis, we use
this question as an alternative outcome in Section 6.1.
We also exclude from our data variables that are affected by a party’s coalition status:
confidence in parliament, confidence in political parties, and confidence in government. While
our estimation relies on the assumption that voters choose a party based on their opinions,in some cases the reverse could be true and the support for a party might shape some of thevoters’ opinions (Barber and Pope, 2019). This is especially common when a new government
is elected and, as a result of this change in power, voters immediately change their opinions
on the government (Hetherington and Rudolph, 2015). Therefore, we exclude the opinions
that tend to change once parties join the governing coalition. To do so, for each opinion
in our data, we run a linear regression where the dependent variable is the opinion and70the independent variable is whether the party supported by the respondent is part of thegoverning coalition, as determined by the ParlGov dataset (Döring and Manow, 2020). We
include party fixed effects and country-wave fixed effects. We exclude from our data the threevariables most strongly affected by a party’s coalition status.
Finally, we exclude one variable that asks directly about priorities. Instead, we estimate
priorities separately. The survey question we exclude asks about only four specific priorities,
which do not seem to be relevant to the priorities we estimated in the data: a stable economy,
progress toward a less impersonal and more humane society, progress toward a society inwhich ideas count more than money, and the fight against crime.
Our outcome variable is the respondents’ preferred party, defined as the party participants
said they would vote for or the party that appeals to them most. Most surveys asked
participants who they would vote for and if participants said they did not know, they wereasked which party they support. In the last EVS wave, participants were only asked whichparty appeals to them most. We use both questions to define the outcome for as many
respondents as possible.
We use the survey weights in all of our analyses with minor modifications. We multiply
the weights by a normalizing constant for each country, such that every country would haveequal weight on aggregate. In countries that had two surveys in the same wave (i.e., both
EVS and WVS) we multiply the weights in all observations by a different constant for eachsurvey, such that both surveys will have equal weight on aggregate for this country-wave
combination.

C.2

Merging Datasets

We merge party data across the various datasets using Party Facts (Döring and Regel, 2019)
when possible and manually in other cases. To assign party positions to parties in the IVS
data, we first match each party with a party in the CMP data and then in each survey wave
assign the party positions from the closest election. The closest election is determined basedon the distance between the mean date when a survey was conducted in a specific country
and wave and the date when the election was held. We exclude IVS observations if no CMPdata is available five years before or after the survey.28 We also exclude 25 respondents who
support parties supported by fewer than five respondents in the IVS. We use this thresholdto allow for the cross-validation exercise described in Section 3.1.
Overall, we are able to match 94% of the IVS respondents who supported a party with amanifesto in the CMP data within five years of the survey date. Moreover, 92% are matchedwith a manifesto in the closest election to the survey date. We do not match all parties for
28

Although an IVS wave may be composed of both an EVS wave and a WVS wave that were not necessarily
conducted in the same year, we assign to each IVS wave and country combination a single date when merging
with party positions. We do so in order to assign a single manifesto to each party in each survey wave.
However, calculating the mean date separately for the EVS and WVS waves would have only changed theassigned manifesto of a party in a handful of cases and would not have changed the set of observations weare able to match to CMP data within five years.

71the following reasons: a party may not publish a manifesto, the manifesto of the party may
not be coded in the CMP data,29 a party may run in an alliance, and a party may haveexisted when the survey was conducted but not during the election.
When parties change names or run in various coalitions, it is often not clear if a new
party was established or whether the same party ran under a different name or as part of analliance. We follow the CMP to deal with this issue and define unique parties according totheir CMP ID. The CMP also indicates when one party is a successor of another. However,there are very few pairs of parties where both the predecessor and successor parties appear
in our data and therefore we do not merge predecessor and successor parties.

D

Cross-Country Similarities in Voting Decision

D.1

Empirical Analysis

This appendix discusses the assumption that voter priorities (model parameters) are equal
across countries. This assumption implies that voters with similar attributes would tend tosupport similar parties across the continent. By contrast, if priorities were different acrosscountries, voters with similar opinions could prioritize different topics. For example, in somecountries, voters could choose parties mainly based on economic issues, while in other countries voters could choose parties mainly based on cultural issues. Moreover, the support fora set of positions, such as populist radical right positions could be motivated by different
factors. In some countries, voters could support PRRP because of their opposition to immigration, while in others voters could support them because of religious values, implying thatdifferent voters would support these parties across countries
To test this assumption, we regress the support for each party on voter attributes using
IVS data. Specifically, for each party j, we estimate the linear probability modelIi,j = x0i ηj + εij ,where Ii,j is a dummy variable for voter i supporting party j, using only respondents fromthe country of that party. In Section D.2, we show that based on the model in Section
1, the coefficient ηj depends on three things: the voter priorities in the country of partyj, the party positions zj , and the positions of the competing parties. Therefore, if voter
priorities are similar across countries, then the coefficient vector ηj will be similar for any
two parties with similar positions, assuming the positions of competing parties are similar
as well. However, if voter priorities are different across countries, this coefficient could besubstantially different even for parties with very similar positions.
We then test whether parameters that were estimated for a specific party in a specificcountry can be used to predict voting for a different party in another country. We predict the29

CMP codes manifestos for parties receiving at least 1 seat in the elections for the lower house in Western
Europe and 2 seats in elections in Central and Eastern Europe. In some cases, the platforms of parties that
met these conditions in the past are also coded.

72

0fitted value for each voter and party using the estimated parameters for this party, Ic
bj .
i,j = xi ηWe then calculate ρIdd , the correlation between the fitted values for every two parties
i,j0 ,Ii,j1
with over fifty supporters, j0 , j1 , using all voters in both of the parties’ countries.
If voter priorities are similar across countries, we expect the correlations within a family
to be substantially higher than across families. This is because parties in the same party
family tend to hold similar positions (De La Cerda and Gunderson, 2023). However, wenote that this exercise is not expected to yield perfect correlations within party families,
for at least three reasons. First, there is still heterogeneity in party positions even within
party families (AfD does not have exactly the same positions as UKIP even though both are
PRRP). Second, the probability of voting for a party also depends on the competing parties,
which could vary substantially across countries. Third, different countries have different
electoral systems, which also affect voting decisions.
Appendix Figure A.12 shows the estimated correlation for each party pair. Red squares
indicate similarities between the attributes of the voters of the two parties (positive correlation), while purple indicates dissimilarities. For example, UKIP voters (right column) areparticularly similar to AfD voters, such that a model that predicts support for UKIP in the
UK can predict support for AfD in Germany.
We classify parties into families, as explained in Section 2.4, and report the averagecorrelations between and within each family. Our classification generates cohesive categories,
with positive correlations between parties in the same family. Despite the limitations of using
models estimated on completely different countries, we find a positive correlation between
different PRRP. By contrast, the correlations between PRRP and parties from other families
are negative on average. We also find that PRRP are not correlated on average with other(non-right) populist parties, supporting our decision to focus only on right-wing populism(Rooduijn, 2018). These results are fairly consistent with our assumption that voter priorities
are equal across countries. They also support the cross-country consistency of our party
families.

D.2

Link to Main Model

To clarify the underlying assumptions of this empirical exercise, we now link it to the model
we outline in Section 1. Using Equation 4, the utility of voter i from voting for party j is alinear function of her attributes.
Ui,j = x0i η̃j + δj + εij ,
c(j)

c(j)

(12)

where η˜j = Φt zj , and the matrix Φt is the priority parameters of the voters in the countryc(j) of party j, at period t. Therefore, if two parties hold the same positions (zj0 = zj1 )and the voters in the countries where these parties are located have the same priorities
c(j )
c(j )
(Φt 0 = Φt 1 ), then the coefficients would be equal η̃j0 = η̃j1 .
The probability of supporting a party depends also on the positions and residuals ofc(j) c(j)all competing parties in the country (Zt , ζt ). Using Equation 5 we can write the log
73

probability of voter with attributes xi to support party j as
c(j)

c(j)

log Pi,j = x0i η̃j + δj + C(xi , θt , Ztc(j, ζt ).

The constant C is the log of the denominator in Equation 5 and guarantees that the probabilities would sum to one. It is common for all parties in a country and depends on the voter
priorities, as well as the positions and residuals of all parties in that country. The coefficientη estimated in Equation 12 is a linear approximation of this expression. It will be equal fortwo parties with the same positions and same opponents if voter priorities are equal acrosscountries.
In practice, the party positions are not identical across countries. Even for parties in thesame party family, positions are not identical. Moreover, the positions of competing parties
could be fundamentally different. The electoral systems also differ across counties, which
generates differences in the residuals. Therefore, the coefficients in Equation 12 are expected
to be more similar within the same family, yet still substantially different.

E

Estimation Appendix

In this section, we discuss the dimension reduction in our second-step estimation which
estimates the β parameter. We assume that combinations of party positions that generate
differences in utility among voters are the same factors that determine the average utility
across all voters. Formally, the voting weights for every voter are given by Equation 2. UsingΦ = U ΣV T to describe the singular value decomposition of matrix Φ, this can be written
as w(xi ) = xi U ΣV T + β. Defining β̃ = βV we can write w(x) = (xi U Σ + β̃)V T . Since werestrict the nuclear norm of Φ in the first stage, the last components of xi U Σ would be close
to zero (assuming the diagonal of Σ is ordered). Similarly, we restrict β̃ such that only the
first k components are different from zero. Therefore, β has to be a linear combination of
the first k components in matrix V , such that β ∈ span{[V ]k }.30
We estimate β with a constrained least squares estimator based on Equation 8:β̂ = argmin(βt ,βt+2) ∈span{[V ]k }

t+2
t+2
2
(∆t+2
t δ̂j − ∆t β z¯j − β̄∆t zj ) .

(13)

To solve this equation we first estimate the following equation using ordinary least squares:
t+2∆t+2
t δj = κ([V ]k z¯j ) + ρ([V ]k ∆t zj ) + .

The dependent variable is the estimated change in δ (∆t+2
t δ̂j ). The independent variables
are the k linear combinations of the average position ([V ]k z¯j ) and the change in positions([V ]k ∆t+2
t zj ). We calculate V based on our estimation of Φ̂ in the 2017-2020 wave. We
calculate the average party position (z¯j ) using all survey waves available for this party, andthe change in party positions (∆t+2
t zj ) is the difference between the 2017–2020 and 2005–2009
30

Using the first k components in an SVD of a matrix yields the best approximation for the matrix for the
Frobenius norm based on the Eckart–Young–Mirsky Theorem.

74

survey waves. We weight each observation by the aggregate vote share of that party in all
survey waves. The estimated parameters κ̂, ρ̂ are two k-dimensional vectors. We can thenretrieve β with a linear transformation.
"
# "
#
β̂t
2κ̂ − ρ̂=[V ]k .
β̂t+22κ̂ + ρ̂
These values are the solution for Equation 13. We choose k = 5 for our main specification.
In Section 5, we show that other values yield similar results.

F

Results Appendix: Residual Component

In this section, we focus on new PRRP entrants and how they contribute to the increase in
the residual component. Appendix Figure A.13 shows that the average number of PRRP
doubled during the period of our study. To create this figure, we use CMP data to countthe average number of PRRP that received at least 1% of the vote across all elections in thepast five years and then average the result over all the countries in our sample.
We decompose the residual component to measure how much of its rise is explained by new
entrants. We calculate the same counterfactual as before, where only the residual component
changes over time, and other components are held fixed at their initial level in 2005–2009.
We then separate the overall support in PRRP (Π) into support for new PRRP entrants(Πnew ) and PRRP that already existed in our data in the 2005–2009 wave (Πexist ). Formally,
we decompose the residual component into the following two components:
Z
Z
 c
c c
P Πnew |xi ; θt , Zt , ζt+1 ft (xi ) dxi − P (Πnew |xi ; θt , Ztc , ζtc ) ftc (xi ) dxi{z}|
New Entrants
Z
Z
.
(14)
 c
c
+ P Πexist |xi ; θt , Ztc , ζt+1
ft (xi ) dxi − P (Πexist |xi ; θt , Ztc , ζtc ) ftc (xi ) dxi{z}|Remaining Residual

New entrants are defined as PRRP that do not appear in our 2005-2009 wave data. Parties
typically appear in the CMP data if they enter parliament (or receive at least two seats inCentral and Eastern Europe).
In contrast to the decomposition in Equation 10, which represents four different counterfactuals, this additional decomposition calculates the impact of the same counterfactual(change in the residuals) on two different sets of parties. We calculate the counterfactual riseof new and existing PRRP in each country and aggregate the results as before. The “New
Entrants” component measures the counterfactual support for new entrants in 2005–2009, if
they had the same residual as the later waves, instead of the residual of −∞ that we assigned.
Appendix Figure A.14 shows that the rise of the residual component is more than fully
driven by new entrants. We find that on aggregate 70% of the overall rise of PRRP is driven
by the residual growth of new parties. This large residual for new PRRP implies that theactual growth in support for new PRRP entrants is substantially larger than what we would75

expect based only on party positions, voter attributes, and voter priorities. By contrast, forexisting PRRP, the residual is negative. Hence, based on party positions, voter attributes,
and voter priorities we would have expected the growth of these parties to be lower. This
could reflect the fact that due to idiosyncratic reasons, in some countries (e.g., Norway and
Switzerland) PRRP gained popularity earlier and therefore might not have had as muchpotential for additional growth (Mudde, 2007).
The residual rise of new entrants is unlikely to be the result of simple supply or demand
factors. On the demand side, our counterfactual analysis suggests that demand for suchparties already existed in 2005–2009. Yet, these parties still failed to enter. The unexplained
rise of new entrants is also less likely to be a simple supply shock where PRRP were createdfor idiosyncratic reasons as voters already had the option of supporting PRRP. There are fivecountries in our data where the share of support for PRRP was virtually zero in 2005–2009
and rose to a positive value by 2017–2020: Sweden, the UK, Germany, the Czech Republic,
and Estonia. In both Sweden and the UK, the Sweden Democrats and UKIP, respectively,
participated in elections in the past and failed to garner substantive support. They "entered"
the political system in the sense of receiving at least 1% of the vote, but voters could havesupported them in earlier elections. In Germany, the Czech Republic, and Estonia, while thecurrent PRRP were formed more recently, other radical-right parties attempted to enter theparliament and failed (Mudde, 2007).
We cannot conclusively rule out alternative supply-side developments that are not captured in the CMP data. For example, several PRRP have made concerted efforts to detoxify
their electoral brand. The French National Front was renamed the party Rassemblement
National in 2018 “in an attempt to rebrand it as a more acceptable mainstream party with awider social agenda” (Moffitt, 2022, p. 393). Another supply-side strategy involves recruiting
better candidates for office or providing candidates with stronger political training (De Langeand Art, 2011). This strategy does not operate via party positions but rather via the ability
to convey these positions more persuasively to the public. Closely related, PRRP have alsosignificantly increased the share of women among their candidates for office in order to attract more women voters (Weeks et al., 2023). If such party changes are not correlated withthe shifts in party positions, they will be manifested in the residual component.
Furthermore, we cannot entirely dismiss supply-side theories that are outside the scope ofour model. One such hypothesis posits a significant delay between changes in party positionsand voter responses. Scholars of electoral politics have argued that the ideological convergenceof mainstream parties during the 1990s has opened a space for PRRP to emerge (Berman
and Snegovaya, 2019). Since this shift in positions occurred before the period covered in our
analysis, it is not captured in the party positions component of the decomposition analysis.
Instead, if ideological convergence in the 1990s affected voting decisions in the 2010s, theeffect would be attributed to the residual component.
Our preferred explanation for the residual increase of new entrants is strategic considerations and coordination failures. It has been shown that many voters coalesce around aparty only when they anticipate that the party will have substantial support (Fredén, 2014).
76

Therefore, one plausible hypothesis is that some voters did not support PRRP in their earlystages since these parties lacked sufficient support. The prioritization of cultural issues has
potentially generated a critical mass of support for PRRP. This solved the coordination problem, and therefore, increased the support for new PRRP entrants, even beyond the direct
expected increase from the change in priorities. Such excessive support would be attributed
to the residual component. In contrast to the previous explanations, this explanation predicts an increase in the residual component specifically for new entrants. Therefore, we view
it as most consistent with our findings.

77

G

Supplementary Data Tables
Table B.1: CMP Economic and Cultural Indices
CMP

Variable

Description (from the CMP codebook)Foreign Special Relationships:
Positive (per101)

Favourable mentions of particular countries with which the manifesto country
has a special relationship; the need for co-operation with and/or aid to suchcountries
Negative mentions of particular countries with which the manifesto country hasa special relationship
Negative references to imperial behaviour and/or negative references to one
state exerting strong influence over other states
The importance of external security and defence
Negative references to the military or use of military power to solve conflicts

Foreign Special Relationships:
Negative (per102)
Anti-Imperialism (per103)
Military: Positive (per104)
Military: Negative (per105)
Peace (per106)

78

Internationalism: Positive(per107)
European Community/Union:
Positive (per108)
Internationalism: Negative(per109)
European Community/Union:
Negative (per110)

Econ.

Soc.

Bakker and Hobolt (2013)

Prosser (2014)

Econ.

Econ.

Lib.-Auth.

Soc.

-

Any declaration of belief in peace and peaceful means of solving crises absent
reference to the military
Need for international co-operation, including co-operation with specificcountries other than those coded in Foreign Special Relationships
Favourable mentions of European Community/Union in general-

Negative references to international co-operation+-

Negative references to the European Community/Union

Freedom and Human Rights(per201)
Democracy (per202)
Constitutionalism: Positive(per203)
Constitutionalism: Negative(per204)
Decentralization (per301)

Favourable mentions of importance of personal freedom and civil rights in the
manifesto and other countries
Favourable mentions of democracy as the only game in town
Support for maintaining the status quo of the constitution

Centralisation (per302)
Governmental and Administrative
Efficiency (per303)

General opposition to political decision-making at lower political levels
Need for efficiency and economy in government and administration and/or thegeneral appeal to make the process of government and administration cheaperand more efficient

Opposition to the entirety or specific aspects of the manifesto country’s
constitution
Support for federalism or decentralisation of political and/or economic power

-

-

-

-

-

-

+

Table B.1: CMP Economic and Cultural Indices (continued)
CMP
Variable

Description (from the CMP codebook)

Political Corruption (per304)

Need to eliminate political corruption and associated abuses of political and/or
bureaucratic power
References to the manifesto partys competence to govern and/or other partys
lack of such competence
Favourable mentions of the free market and free market capitalism as an
economic model

Political Authority (per305)
Free Market Economy (per401)

Econ.

Soc.

Bakker and Hobolt (2013)

Prosser (2014)

Econ.

Econ.

+
+

+

79

Incentives: Positive (per402)
Market Regulation (per403)
Economic Planning (per404)
Corporatism/Mixed Economy(per405)
Protectionism: Positive (per406)

Favourable mentions of supply side oriented economic policies
Support for policies designed to create a fair and open economic market
Favourable mentions of long-standing economic planning by the government
Favourable mentions of cooperation of government, employers, and trade unions
simultaneously
Favourable mentions of extending or maintaining the protection of internalmarkets

+
-

+
-

-

-

Protectionism: Negative (per407)
Economic Goals (per408)

Support for the concept of free trade and open markets
Broad and general economic goals that are not mentioned in relation to any
other category
Favourable mentions of demand side oriented economic policies

+

+

-

-

The paradigm of economic growth++

Keynesian Demand Management(per409)
Economic Growth: Positive(per410)
Technology and Infrastructure:
Positive (per411)
Controlled Economy (per412)
Nationalisation (per413)

Economic Orthodoxy (per414)
Marxist Analysis (per415)
Anti-Growth Economy: Positive(per416)
Environmental Protection(per501)

Lib.-Auth.

General policies in favour of protecting the environment, fighting climatechange, and other green policies

+
+

-

+

Importance of modernisation of industry and updated methods of transportand communication
Support for direct government control of economy
Favourable mentions of government ownership of industries, either partial orcomplete; calls for keeping nationalised industries in state hand or nationalising
currently private industries
Need for economically healthy government policy making
Positive references to Marxist-Leninist ideology and specific use of
Marxist-Leninist terminology by the manifesto party
Favourable mentions of anti-growth politics

Soc.

-

-

-

-

+
-

+
-

+

-

-

-

-

Table B.1: CMP Economic and Cultural Indices (continued)
CMP
Variable

Description (from the CMP codebook)

Culture: Positive (per502)
Equality: Positive (per503)
Welfare State Expansion (per504)

Need for state funding of cultural and leisure facilities including arts and sport
Concept of social justice and the need for fair treatment of all people
Favourable mentions of need to introduce, maintain or expand any public social
service or social security scheme
Limiting state expenditures on social services or social security

Welfare State Limitation (per505)

80

Education Expansion (per506)
Education Limitation (per507)
National Way of Life: Positive(per601)
National Way of Life: Negative(per602)
Traditional Morality: Positive(per603)

Need to expand and/or improve educational provision at all levels
Limiting state expenditure on education
Favourable mentions of the manifesto country’s nation, history, and generalappeals
Unfavourable mentions of the manifesto country’s nation and history

Traditional Morality: Negative(per604)
Law and Order: Positive (per605)
Civic Mindedness: Positive(per606)
Multiculturalism: Positive(per607)
Multiculturalism: Negative(per608)
Labour Groups: Positive (per701)
Labour Groups: Negative(per702)
Agriculture and Farmers: Positive(per703)
Middle Class and Professional
Groups (per704)
Underprivileged Minority Groups(per705)

Bakker and Hobolt (2013)

Prosser (2014)

Soc.

Econ.

Econ.

-

-

-

+

+

+

+

+

+

Econ.

Lib.-Auth.
-

Soc.
-

+

+

+

-

-

-

Favourable mentions of traditional and/or religious moral values++

Opposition to traditional and/or religious moral values--

Favourable mentions of strict law enforcement, and tougher actions against
domestic crime
Appeals for national solidarity and the need for society to see itself as united++

Favourable mentions of cultural diversity and cultural plurality within domestic
societies
The enforcement or encouragement of cultural integration

-

-

-

+

+

+

Favourable references to all labour groups, the working class, and unemployedworkers in general
Negative references to labour groups and trade unions

+

-

-

-

+

+

+

Specific policies in favour of agriculture and farmers
General favourable references to the middle class
Very general favourable references to underprivileged minorities who are definedneither in economic nor in demographic terms

+

-

-

Table B.1: CMP Economic and Cultural Indices (continued)
CMP
Variable

Description (from the CMP codebook)Non-economic Demographic
Groups (per706)

General favourable mentions of demographically defined special interest groupsof all kinds

Econ.

Soc.

Bakker and Hobolt (2013)

Prosser (2014)

Econ.

Econ.

Lib.-Auth.
-

Soc.
-

Notes: This table presents the descriptions of all major (three-digit) CMP positions. All of these positions were included in the decomposition exercise.
The third and fourth columns show which positions are part of the CMP economic and cultural indices (the culture index is termed "Society" in the CMP).
These are the primary party position indices used in the paper. The next four columns refer to alternative constructions of economic and social indices using
CMP positions. These indices are only used in Figure A.2.

81

Table B.2: IVS Variables
Variable
Demographics
Religious

Description

Coding and notes"Independently of whether you go to church or not,would you say you are..." A religious person, Not a
religious person, A convinced atheist

1 = A religious person, 0 = {All otheroptions}AtheistMale
Age
Married or living
together

Respondent’s sex"This means you are _______ years old (write in
age in two digits).""Are you currently...": Married, Living together as
married, Divorced, Separated, Widowed, Single

Divorced, separated,or widowed
SingleNumber of children"How many children have you ever had?", "Howmany children do you have, deceased children notincluded?" (EVS 2008-2010)

Employment status"Are you employed now or not? If yes, about how
many hours a week? If more than one job: only forthe main job." Scale: Yes, paid employment = {Fulltime employee (30 hours a week or more); Part-time
employee (less than 30 hours a week);
Self-employed}. No, no paid employment ={Retired/pensioned; Housewife not otherwise
employed; Student; Unemployed}

Self-employed
RetiredHousewife
Student
Unemployed
Other employment
Income decile

Protestant"On this card is an income scale on which 1 indicatesthe lowest income group and 10 the highest income
group in your country. We would like to know inwhat group your household is. Please specify theappropriate number, counting all wages, salaries,
pensions and other incomes that come in."
"Do you belong to a religion or religious
denomination? If yes, which one?"

Catholic
MuslimOther type of
Christian82

1 = A convinced atheist, 0 = {Allother options}
1 = Male, 0 = Female
Open numeric response
1 = {Married; Living together as
married; Living apart but steadyrelation (married,cohabitation)}, 0 ={All other options}
1 = {Divorced; Separated; Widowed;
Divorced, Separated or Widowed}, 0= {All other options}
1 = {Single/Never married}, 0 = {Allother options}
Open numeric response. For the US,
the 2005-2009 wave is imputed basedon the 1999-2004 and 2011-2013
waves.
2 = {Full time; Self-employed}, 1 =Part time, 0 = {Retired; Housewife;
Students; Unemployed}

1 = Self-employed, 0 = {All otheroptions}
1 = Retired, 0 = {All other options}
1 = Housewife, 0 = {All otheroptions}
1 = Student, 0 = {All other options}
1 = Unemployed, 0 = {All otheroptions}
1 = Other, 0 = {All other options}
1 = Lowest income group, 2,...,9, 10 =Highest income group. For Portugal,
the 2017-2020 wave is imputed basedon the 2005-2009 wave.

1 = Protestant, 0 = {All otheroptions}
1 = Roman Catholic, 0 = {All otheroptions}
1 = Muslim, 0 = {All other options}
1 = {Other Christian(Evangelical/Pentecostal/Free
church/etc.); Orthodox(Russian/Greek/etc.)}, 0 = {All otheroptions}

Index

Table B.2: IVS Variables (continued)
Variable

Description

Coding and notes

Jew
Other religion
No religion/atheist
Education level"What is the highest educational level that you haveattained?"

Education years"At what age did you (or will you) complete yourfull-time education, either at school or at an
institution of higher education?"83

1 = Jew, 0 = {All other options}
1 = {Other; Buddhist; Hindu}, 0 ={All other options}
1 = Do not belong to a denomination,
0 = {All other options}
The possible answers to this question
depend on the survey wave. We codededucation into six levels: 0 ={Inadequately completed elementary
education; Not applicable/No formal
education; ISCED 0/ no education;Less than primary}, 1 = {Completed(compulsory) elementary education;
ISCED 1; Primary}, 2 = {Incomplete
secondary school:
technical/vocational type; Incomplete
secondary: university-preparatory
type/Secondary; ISCED 2; Lower
secondary}, 3 = {Complete secondary
school: technical/vocational
type/secondary; Complete secondary:university-preparatory type/fullsecondary; ISCED 3; Upper
secondary}, 4 = {Some university
without degree/higher education lower-level tertiary; ISCED 4; ISCED
5; Postsecondary nontertiary;
Short-cycle tertiary}, 5 = {University
with degree/higher education upper-level tertiary; ISCED 6, ISCED
7; ISCED 8; Bachelor or equivalent;
Master or equivalent; Doctoral orequivalent}.
Open numeric response. Winsorized
at 70. For Greece, the 2017-2020 wave
is imputed from the 2005-2009 wave.
For the US, the 2017-2020 wave isimputed from the 2011-2013 wave.

Index

Table B.2: IVS Variables (continued)
Variable

Description

Coding and notes

Town size

Size of town where the interview was conducted

Frequency of attending
religious services"Apart from weddings, funerals and christenings,
about how often do you attend religious services
these days?"

Member ofenvironmentalorganization"Now I am going to read out a list of voluntaryorganizations; for each one, could you tell me
whether you are a member, an active member, an
inactive member or not a member of that type oforganization?"Labor union
Church or religious organization

The possible answers depended on theexact survey: {2,000 and less; under
5,000; 2,000-5,000; 5,000-10,000;
10,000-20,000; 5,000-20,000;
20,000-50,000; 50,000-100,000;
20,000-100,000; 100,000-500,000;
500,000 and more}. For every rangeof town size we use the log of theaverage of the two bounds. For the
top category, for which we have no
upper bound, we calculated the log ofthe minimum value multiplied by 8.35
(Rosen and Resnick, 1980). For
Germany, the 2011-2013 wave isimputed based on the 2005-2009 and2017-2020 waves. For Iceland, the2005-2009 wave is imputed based on
the 1999-2004 and 2017-2020 waves.
For the Netherlands, the 2017-2020
wave is imputed based on the
2011-2013 wave. For the UK, the2017-2020 wave is imputed based on
the 2005-2009 wave. For the US, the2011-2013 wave is imputed based on
the 2005-2009 and 2017-2020 waves.
0 = Never practically never, 1 = Less
often, 2 = Once a year, 3 = Other
specific holy days, 4 = Only on
special holy days/Christmas/Easterdays, 5 = Once a month, 6 = Once aweek, 7 = More than once a week.
1 = {Active member}, 0 = {Not amember; Inactive member}

Member of labor union
Member of religiousorganization
Member of sports
organization
Member of artisticorganization
Member of political
party
Member of
professionalorganization
Member of otherorganization
State of healthSport or recreational organization, football, baseball,rugby teamArt, music or educational organization
Political party
Professional association

Other organization"All in all, how would you describe your state ofhealth these days? Would you say it is..."Opinions

84

For Germany, the 2011-2013 wave isimputed based on the 2005-2009 and2017-2020 waves.
0 = Very poor, 1 = Poor, 2 = Fair, 3= Good, 4 = Very good

Index

Table B.2: IVS Variables (continued)
Variable

Description

Coding and notes

Willing to sign
petition"Now I’d like you to look at this card. I’m going toread out some different forms of political action thatpeople can take, and I’d like you to tell me, for eachone, whether you have actually done any of thesethings, whether you might do it or would never,under any circumstances, do it." Signing a petition
Joining in boycotts
Attending peaceful demonstrations

0 = Would never do, 1 = Might do, 2= Have done

Joining strikes

For the US, the 2005-2009 wave isimputed based on the 1999-2004 and2011-2013 waves.
0 = Bad thing, 1 = Don’t mind, 2 =Good thing

Willing to join boycott
Willing todemonstrate
Willing to join strike

Respect for authority

Jobs should prioritize
natives

Jobs should prioritizemen
Government ownershipof business should be
increased

Personal over govt.
responsibility"Here is a list of various changes in our way of lifethat might take place in the near future. Please tellme for each one, if it were to happen whether you
think it would be a good thing, a bad thing, or don’t
you mind?:" Greater respect for authority"Do you agree, disagree or neither agree nor disagreewith the following statements?" When jobs are
scarce, employers should give priority to people ofthis country over immigrants
When jobs are scarce, men should have more right toa job than women"Now I’d like you to tell me your views on various
issues. How would you place your views on thisscale? 1 means you agree completely with thestatement on the left; 10 means you agree completely
with the statement on the right; and if your views fall
somewhere in between, you can choose any number
in between." Private vs state ownership of businessGovernment responsibility

Competition inmarkets is good
Oppose redistribution

Competition good or harmful

Country needs strong
leader"I’m going to describe various types of politicalsystems and ask what you think about each as a wayof governing this country. For each one, would you
say it is a very good, fairly good, fairly bad or very
bad way of governing this country?" Having a strong
leader who does not have to bother with parliamentand elections
Having experts, not government, make decisions
according to what they think is best for the country
Having the army rule
Having a democratic political system"How proud are you to be of nationality of thiscountry?"

Expert should makedecisions, not govt.
Like idea of army rule
Like idea of democracy
Pride in nationality

Income equality

85

Index

0 = Disagree, 1 = Neither, 2 = AgreeCult.

0 = Disagree, 1 = Neither, 2 = Agree.
For Greece the 2017-2020 wave isimputed based on the 2005-2009 wave.
1 = Private ownership of businessshould be increased, 2,...,9, 10 =Government ownership of businessshould be increased

Cult.

1 = The government should take more
responsibility, 2,...,9, 10 = People
should take more responsibility
1 = Competition is harmful, 2,...,9, 10= Competition is good
1 = Incomes should be made moreequal, 2,...,9, 10 = We need larger
income differences as incentive.
0 = Very bad, 1 = Fairly bad, 2 =Fairly good, 3 = Very goodEcon.

0 = Not at all proud, 1 = Not very
proud, 2 = Quite proud, 3 = Veryproud, missing = Not applicable/
Foreigner

Econ.

Econ.
Econ.

Cult.

Cult.
Cult.
Cult.

Table B.2: IVS Variables (continued)
Variable

Description

Coding and notes

Happiness"Taking all things together, would you say you are:"

Trust other people"Generally speaking, would you say that most people
can be trusted or that you need to be very careful indealing with people?""All things considered, how satisfied are you withyour life as a whole these days?""Some people feel they have completely free choiceand control over their lives, while other people feelthat what they do has no real effect on what happens
to them. Please use this scale where 1 means ’none
at all’ and 10 means ’a great deal’ to indicate howmuch freedom of choice and control you feel you haveover the way your life turns out.""For each of the following aspects, indicate how
important it is in your life. Would you say it is very
important, rather important, not very important or
not important at all?" Family
Friends
Leisure time

0 = Not at all happy, 1 = Not very
happy, 2 = Quite happy, 3 = Veryhappy
0 = Can’t be too careful, 1 = Most
people can be trusted

Life satisfaction
Have freedom of choice

Importance of family

Importance of friends
Importance of leisure
time
Importance of politics
Importance of work
Importance of religion
Importance ofchildren’s hard work

Importance ofchildren’s
independence
Importance ofchildren’s
responsibility
Importance ofchildren’s imagination
Importance of children
respecting otherpeopleImportance ofchildren’s thrift
Importance ofchildren’s
determination
Importance ofchildren’s religious
faith
Importance ofchildren’s unselfishness
Importance ofchildren’s obedience
Don’t wantdrug-addictedneighbors

Politics
Work
Religion"Here is a list of qualities that children can be
encouraged to learn at home. Which, if any, do you
consider to be especially important? Please choose
up to five." Hard work
Independence

Index

0 = Dissatisfied, 1,...,8, 9 = Satisfied
1 = None at all, 2,...,9, 10 = A greatdeal

0 = Not at all important, 1 = Notvery important, 2 = Rather
important, 3 = Very important

0 = Not mentioned, 1 = Important.

Same as above.

Feeling of responsibility

Imagination
Tolerance and respect for other people

Thrift saving money and things
Determination, perseverance

Religious faith

Cult.

Not being selfish (unselfishness)
Obedience"On this list are various groups of people. Could you
please mention any that you would not like to haveas neighbors?" Drug addicts

86

0 = Not mentioned, 1 = Mentioned.

Cult.

Table B.2: IVS Variables (continued)
Variable

Description

Coding and notes

Don’t want different
race neighbors
Don’t want immigrantneighbors
Don’t wanthomosexual neighbors
Don’t want
heavy-drinking
neighborsChild suffers when
mom works

People of a different race

Cult.

Immigrants/foreign workers

Cult.

Homosexuals

Cult.

Heavy drinkers

Cult.

"When a mother works for pay, the children suffer"

Level of politicalinterest"How interested would you say you are in politics?"

Confidence in thepress"I am going to name a number of organizations. For
each one, could you tell me how much confidence you
have in them: is it a great deal of confidence, quite a
lot of confidence, not very much confidence or noneat all?" The press
Labor unions
The police
The courts
The United Nations
The churches (mosque, temple, etc.)
The civil services

Confidence in unions
Confidence in police
Confidence in courts
Confidence in UN
Confidence in churches
Confidence in civil
services
Confidence in major
companies
Confidence inenvironmentalorganizations
Confidence in EU

Cult.

Econ.

Cult.

Environmental organizations

The European Union
The armed forces

Belief in hell
Importance of God in
life

Hell
"How important is God in your life?" Please use thisscale to indicate. 10 means ’very important’ and 1means ’not at all important’""Please tell me for each of the following statements
whether you think it can always be justified, never bejustified, or something in between." Avoiding a fareon public transport
Abortion
Divorce
Someone accepting a bribe in the course of theirduties
SuicideAbortion justifiable
Divorce justifiable
Accepting a bribejustifiable
Suicide justifiable

Cult.

Major companies

Confidence in armed
forcesBelief in God

Avoiding public transit
fare justifiable

0 = Strongly disagree, 1 = Disagree, 2= Agree, 3 = Agree strongly. For theUS, the 2005-2009 wave is imputedfrom the wave 2011-2013 wave.
0 = Not at all interested, 1 = Notvery interested, 2 = Somewhat
interested, 3 = Very interested
0 = None at all, 1 = Not very much,
2 = Quite a lot, 3 = A great deal

Index

"In which of the following things do you believe, if
you believe in any?" God87

For the U.S. sample, values are
impute based on the sample average
For Iceland, the 2017-2020 wave isimputed based on the 2005-2009 wave
0 = No, 1 = Yes. For the US, the2005-2009 wave is imputed based on
the 1999-2004 and 2011-2013 waves

Cult.

1 = Not at all important, 2,...,9, 10 =Very important
1 = Never justifiable, 2,...,9, 10 =Always justifiable

Cult.
Cult.

Cult.

Table B.2: IVS Variables (continued)
Variable

Description

Coding and notes

Cheating on taxes
justifiable

Cheating on taxes if you have a chance

Homosexuality
justifiable

Homosexuality

Prostitution justifiable

Prostitution

For Germany, the 2011-2013 wave was
imputed based on the 2005-2009 and2017-2020 waves
For Italy, the 2005-2009 wave was
imputed based on the 1999-2000 and2017-2020 waves
For Spain, the 2011-2013 wave was
imputed based on the 2005-2009 and2017-2020 waves. For the US the
2011-2013 wave is imputed based on
the 2005-2009 and 2017-2020 waves

Index

Cult.

Cult.

Notes: This table presents all IVS variables included in our data. The third column provides information on
how variables were processed. The fourth columns show which variables are part of the economic and culture
opinions indices used in Figure 7 and Appendix Figure A.5.

88

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