Austerity, Economic Vulnerability, and Populism∗
University of Geneva
May 30, 2022
Governments have repeatedly adjusted ﬁscal policy in recent decades. We examine the
political eﬀects of these adjustments in Europe since the 1990s using both district-level
election outcomes and individual-level voting data. We expect austerity to increase
populist votes, but only among economically vulnerable voters, who are hit the hard-
est by austerity measures. We identify economically vulnerable regions as those with
a high share of low-skilled workers, workers in manufacturing and in jobs with a high
routine-task intensity. The results of a diﬀerence-in-diﬀerences analysis of district-level
election outcomes demonstrate that austerity increases support for populist parties in
economically vulnerable regions, but has little eﬀect in less vulnerable regions. The
individual-level analysis conﬁrms these ﬁndings. Our results suggest that populist
parties’ success hinges on the government’s failure to protect the losers of structural
economic change. The economic origins of populism are thus not purely external; the
populist backlash is triggered by internal factors, notably public policies.
Keywords: ﬁscal policy, globalization, automation, political backlash, elections, West-
Word count: 9,946.
∗Previous versions of this paper were presented at the Annual Conference of the Inter-
national Political Economy Society, the Annual Meeting of the American Political Science
Association, the ETH Public Policy lunch seminar, the Global Research in Political Econ-
omy webinar, the Political Science Guest Speaker Workshop at Stanford University, and
seminars at the European University Institute, Georgetown University, the University of
Oxford, Washington University in Saint Louis, and the WZB Berlin Social Science Cen-
ter. We thank Francesco Amodio, Abel Brodeur, Giorgio Chiovelli, Federico Ferrara, Ken
Scheve, Eric Voeten, and Steve Weymouth for comments on this paper. We thank Giacomo
Lemoli for advice on Stata coding. We thank Akos Mate, Sean Nossek, and Colin Walder for
outstanding research assistance. Leo Baccini acknowledges ﬁnancial support from the Cana-
dian Social Sciences and Humanities Research Council (grant no. 430-2018-1145). Thomas
Sattler acknowledges ﬁnancial support from the Swiss National Science Foundation (grant
no. 165480) and the Swiss Network for International Studies.
Governments have regularly implemented ﬁscal adjustment measures in recent decades.
These policies have strong distributional consequences, especially in contemporary, indus-
trialized democracies. These countries have experienced major economic transformations,
such as globalization or automation, that increase economic insecurity among voters. Pub-
lic safety nets are crucial in this context because they provide insurance against enhanced
economic risk and hence stabilize societies both socially and politically. By contrast, gov-
ernment decisions to cut ﬁscal spending magnify rather than mitigate the adverse eﬀects of
the ongoing economic transformations. Therefore, government decisions are crucial during
periods of structural economic change.
Economic explanations of populism in recent years have paid surprisingly little attention
to governments and their policy choices. Prior studies have signiﬁcantly improved our un-
derstanding of political backlash by highlighting how three economic outcomes in particular
–trade shocks, ﬁnancial crises, and technological innovations – aﬀect voters. The economics
literature has largely overlooked the role of governments. Yet governments have traditionally
been at the center of analyses of globalization politics (e.g., Mosley, 2003) and should play
an important role in how we think about the economic origins of populism and political
backlash. We therefore examine how government policy, particularly ﬁscal austerity, aﬀects
voters’ political behavior during periods of enhanced economic risk.
Our analysis concentrates on the impact of ﬁscal austerity on economically vulnerable
voters. Although ﬁscal cutbacks are generally national-level decisions that apply to the entire
country, exposure to them varies signiﬁcantly across regions and societal groups. Cutbacks
primarily aﬀect economically vulnerable voters, who rely on government support to cope
with increased economic risk. By contrast, voters who have suﬃcient resources to ride out
economic downturns are barely aﬀected by public spending cuts. Austerity policies therefore
cause disenchantment primarily among voters who face social decline and hence are hit the
hardest by ﬁscal cutbacks. As a result, vulnerable voters are increasingly swayed by populist
pledges to rectify their economic situation either by reversing spending cuts or by curtailing
globalization as the original source of economic risk.
To identify economically vulnerable voters, we draw on the international political econ-
omy literature, which has investigated the winners and losers from economic transformations
for decades (e.g., Milner, 1988; Frieden, 2000). This research highlights the extent to which
factor endowment (Scheve and Slaughter, 2001), sectoral competitiveness (Jensen, Quinn
and Weymouth, 2017), and occupational characteristics (Gingrich, 2019; Owen, 2020) aﬀect
income and job security. Following these diﬀerent theoretical logics, low-skilled workers,
workers in manufacturing, and those in routine jobs are particularly vulnerable and suﬀer
the most from austerity. We therefore expect these workers to be more likely to support
populist parties when the government adjusts ﬁscal policy.
Empirically, we examine how austerity has aﬀected voting patterns in Western countries
since the early 1990s using both district-level election outcomes and individual-level voting
data. The results of our diﬀerence-in-diﬀerences (DiD) analysis illustrate that austerity in-
creases support for populist parties in economically vulnerable regions, but has little eﬀect
on voting in less vulnerable regions. Moreover, we ﬁnd that radical right (but not radical
left) parties gain votes in economically vulnerable regions where austerity measures have
been implemented. Our individual-level analyses conﬁrm these results. Overall, our ﬁndings
indicate that ﬁscal cutbacks and the resulting lack of insurance against economic shocks
contribute signiﬁcantly to the rise of populist parties and the backlash against globalization.
We implement two additional tests to strengthen our identiﬁcation strategy. First, we
include lead variables of austerity, which capture anticipatory eﬀects, and show that they
do not aﬀect our outcomes. This test provides evidence that the parallel-trend assumption
holds. Second, exploiting the fact that European countries implement austerity measures
even in good times, we show that our results are not driven by the occurrence of economic
crises. Put diﬀerently, even when macro-economic conditions are normal, economically vul-
nerable areas and individuals support populist parties where austerity measures have been
Our study makes three main contributions. First, it advances the emerging literature on
the backlash against globalization by moving public policy and governments to the center
of the analysis. There is now a large body of evidence that economically vulnerable vot-
ers increasingly turn towards populist parties (Owen and Johnston, 2017; Jensen, Quinn
and Weymouth, 2017; Ballard-Rosa et al., 2021; Colantone and Stanig, 2018; Milner, 2018;
Baccini and Weymouth, 2021; Gingrich, 2019; Broz, Frieden and Weymouth, 2019). In line
with prior single-country studies (Fetzer, 2019; Wiedemann, 2022), our results show that the
success of populist parties across Europe critically hinges on governments’ failure to protect
and help the losers of structural economic change. The economic origins of populism are thus
not purely external and unavoidable; the populist backlash is triggered by internal factors,
notably public policies.
Second, we contribute to the literature on the political eﬀects of ﬁscal policy by isolating
the impact of ﬁscal cutbacks on diﬀerent groups of voters. The ﬁscal austerity literature has
thus far highlighted the average response of the electorate to ﬁscal adjustments or reforms
(Alesina, Carloni and Lecce, 2011; Giger and Nelson, 2011; Grittersov´a et al., 2016; Arias
and Stasavage, 2019; Bansak, Bechtel and Margalit, 2021). To the extent that voter hetero-
geneity is examined, material explanations are dismissed in favor of ideological ones (Barnes
and Hicks, 2018; H¨ubscher, Sattler and Wagner, 2021). To the best of our knowledge, our
paper is the ﬁrst to demonstrate that voters’ economic vulnerability strongly aﬀects the in-
tensity of their response to ﬁscal austerity, both regionally and individually. The political
disruptions of austerity can therefore be signiﬁcant even if the median voter or the majority
of voters support an austerity package.
Finally, our analysis sheds new light on government accountability in open economies.
It suggests that economic policy continues to inﬂuence popular evaluations even if voters
hold governments less accountable for economic outcomes in open economies (Hellwig and
Samuels, 2007; Kayser and Peress, 2012). While outcomes convey less information about
policymaker competence in such economies, the policy response to these outcomes still signals
the government’s economic priorities to voters. Vulnerable voters infer from ﬁscal cutbacks
that the government’s policy position is incompatible with their needs and interests, and
hold it accountable accordingly.
2 Austerity and the economic origins of populism
2.1 Fiscal adjustments in times of enhanced economic risk
We deﬁne ﬁscal austerity as a government decision to adjust ﬁscal policy to reduce the public
ﬁscal deﬁcit, i.e. the diﬀerence between public expenditures and revenues. These decisions
generally center on reducing government spending, such as by cutting social security enti-
tlements or public investments to reduce public expenditures, but they can also entail tax
increases, such as VAT or income taxes, to increase public revenues.1A prominent exam-
ple is the wave of ﬁscal adjustments in the wake of the European debt crisis (Copelovitch,
Frieden and Walter, 2016). These recent cutbacks, however, are not unique and represent
1This means that we concentrate on policy choices rather than actual changes in public
expenditures and revenues. The former can be directly attributed to the government, while
the latter can also vary for other reasons, such as macro-economic shocks, which are beyond
the government’s direct control.
the peak of a longer-lasting movement towards “permanent austerity” that has been noted
for a long time (Pierson, 2001, ch. 13). As Figure 1 illustrates, most industrialized countries
had implemented signiﬁcant cutbacks long before the start of the global ﬁnancial crisis in
2007.2The ﬁgure also shows that adjustments have been quite common throughout Europe,
including Germany, Austria, and the Scandinavian countries.
Figure 1: Austerity in Industrialized Countries, 1979–2014
Note: Source: Devries et al. (2011); Alesina, Favero and Giavazzi (2019).
Fiscal adjustments can aﬀect a wide range of budgetary areas. We focus on the con-
sistent and strong impact of austerity on policies that are important to vulnerable voters,
such as social transfers and other welfare state policies. As Appendix A shows, government
transfers are central to austerity packages: on average, they are cut more than any other
budgetary category. This is also visible in Figure 2, which illustrates that average social
security transfers in industrialized countries vary considerably over time and declined partic-
ularly strongly during the 1990s and again from 2013 onwards. In line with previous ﬁndings
2We discuss the measurement of austerity used in this ﬁgure in detail in Section 3.
Figure 2: Austerity and Social Security Transfers Over Time
Austerity (% of GDP)
Social Security Transfers (% of GDP)
Note: Annual averages for countries listed in Figure 1. Sources: Devries
et al. (2011); Alesina, Favero and Giavazzi (2019); Armingeon et al. (2019).
(Armingeon, Guthmann and Weisstanner, 2016), our results indicate that these declines in
transfers coincide with the large waves of austerity that governments have implemented in
recent decades. We ﬁnd a similar pattern for public spending on unemployment beneﬁts and
education in Figure C1 in Appendix C. Austerity, therefore, has been strongly associated
with cutbacks in public safety nets and other public schemes that are important for the
welfare of economically vulnerable citizens in open economies.3
Given how ﬁscal adjustments aﬀect core ﬁscal programs, these decisions are generally
intensively debated as part of budgetary debates and in the public discourse. Opposition
3While total social expenditures have gradually increased over time, this is not the case for
the spending items that are central to our argument. The increase in total social expenditures
is due to pensions and health care. It is politically very diﬃcult to make cuts in these areas
related to so-called life-cycle risks (Pierson, 2001). This increases the pressure to concentrate
cuts in other social spending categories.
parties that expect to beneﬁt politically by exploiting discontent with ﬁscal austerity tend
to instigate such debates. This creates a public discourse that informs voters of the govern-
ment’s ﬁscal plans and how they will aﬀect voters. Political saliency also increases with the
size of the ﬁscal adjustment because the impact on voters is more serious and therefore more
contentious. This opposition to large austerity packages often manifests itself in the form
of protests, which raise public awareness of the issue. Our qualitative analysis of newspa-
per reports during major austerity episodes in Europe in the 1990s and 2000s, described in
Appendix B, conﬁrms this. These ﬁndings demonstrate that austerity measures were often
at the center of the political debate, played an important role in the national media and in
electoral campaigns, and that voters were aware of these issues, such as in France in 1997 or
2011/2012, in Italy in 1994/1995, and in Germany in 2004.
Fiscal adjustments are also politically salient because they take place in a context of
increased social risk. Industrialized economies have experienced major transformations in
recent decades, such as a massive increase in trade, oﬀshoring, and the automation of jobs
(Autor, Dorn and Hanson, 2013). In this context, public safety nets are important to stabilize
countries socially and politically because they help voters cope with economic risk in open
economies (Gingrich and Ansell, 2012; Walter, 2010; Kurer and Gallego, 2019). For instance,
these policies increase support for openness (Hays, 2009; Rickard, 2015) and decrease the
risk of political backlash (Rudra, 2005; Burgoon, 2009; Margalit, 2011; Halikiopoulou and
Vlandas, 2016; Richtie and You, 2020; Vlandas and Halikiopoulou, 2022). Yet, austerity has
distributional eﬀects that operate in the same direction as these economic transformations.
It magniﬁes rather than mitigates the negative economic eﬀects of globalization and techno-
logical change and exacerbates the social decline of vulnerable individuals and communities
(Sambanis, Schultz and Nikolova, 2018).
2.2 Vulnerable voters and their political reactions
We draw on theories of comparative and international political economy to identify the eco-
nomically vulnerable voters that are most aﬀected by austerity. In short, economic vulnera-
bility varies according to 1) skill level, 2) economic sector, and 3) routine job intensity. First,
from a factor logic, low-skilled workers tend to be worse oﬀ in open, industrialized economies,
while high-skilled laborers tend to beneﬁt in such countries (Scheve and Slaughter, 2001).
Second, from a sectoral logic, the manufacturing sector faces the greatest competition from
ﬁrms in developing and emerging markets, while high-skilled service industries thrive in open
economies (Jensen, Quinn and Weymouth, 2017). Also, small ﬁrms in a sector ﬁnd it harder
to succeed in open economies, while large, productive ﬁrms are best positioned to exploit the
gains from trade (Baccini, Pinto and Weymouth, 2017). Finally, from an occupational logic,
workers in routine jobs are most likely to lose their jobs due to oﬀshoring or automation
(Gingrich, 2019; Owen, 2020; Gallego, Kurer and Sch¨oll, 2020).
Austerity creates political disenchantment among these vulnerable voters because they
are the most exposed to its material impacts (Wiedemann, 2022). They rely more heavily
on social safety nets and public transfers than more privileged voters, and are directly af-
fected by austerity measures. This particularly applies to large ﬁscal adjustments that limit
governments’ ability to spare vulnerable voters or compensate them with parallel measures.
These measures then raise doubts among vulnerable voters that governments are committed
to make globalization a success for everybody, including the economically vulnerable. They
infer that government parties are more responsive to economically more privileged voters
who are less aﬀected or potentially beneﬁt from austerity than to vulnerable voters when
ﬁscal trade-oﬀs sharpen the divide between them (Bartels, 2008; Hacker and Pierson, 2010).4
4This implies that vulnerable voters respond in similar ways in diﬀerent types of welfare
states because they react to how austerity measures aﬀect their well-being. Our empirical
analysis examines whether this is indeed the case.
Although we focus on voter characteristics (i.e. the demand side), political parties (i.e.
the supply side) play an important role in our argument in two ways. First, as we discuss
in greater detail in Appendix B, populist parties have increasingly positioned themselves
against ﬁscal austerity. In sum, leftist populist parties have opposed austerity on average,
although there was a tendency towards acceptance in the early 2000s. Right-leaning populist
parties were more accepting of austerity in the past, but have become increasingly critical
over the past two decades. This characterization is consistent with our qualitative analysis
of major austerity periods in Europe, and is in line with recent ﬁndings that radical right
parties oppose austerity measures proposed by government parties (Enggist and Pinggera,
2022). This, in turn, is consistent with new ﬁndings that populist parties quickly adapt to
shifting voter opinions and emphasize new issues that allow them to challenge established
parties (De Vries and Hobolt, 2020).5Populist parties, therefore, allow vulnerable voters to
express anti-government sentiment.
Second, our argument requires that many vulnerable voters see a lack of alternatives
among traditional, non-populist parties. A broad consensus among traditional (non-populist)
political parties has supported the recent austerity waves (Blyth, 2013; Hopkin, 2020).6
Again, our qualitative analysis of traditional parties’ positions on major austerity episodes
in European countries conﬁrms this (see Appendix B). These results demonstrate that tra-
5Dissatisﬁed voters can also abstain to express their discontent, and may vote for populist
parties at a later point in time. We empirically examine this possibility to the extent that
our data allows.
6There are multiple possible reasons for this tendency, including ﬁnancial constraints
(Mosley 2000, esp. table 1; Hallerberg and Wolﬀ 2008), international integration (Konstan-
tinidis, Matakos and Hutlu-Eren, 2019), the diﬀusion of pro-austerity ideas (Blyth, 2013)
and institutions that promote ﬁscal restraint (Bodea and Higashijima, 2017).
ditional political parties regularly support austerity measures. If they oppose them, they do
so while in opposition, but support or supported them as government parties in earlier or
later austerity episodes. This gives voters fewer opportunities to sanction governments, for
example by voting for the non-populist opposition, especially when the pro-austerity con-
sensus cuts across political camps. But despite the importance of supply-side politics, our
point is that vulnerable voters are more likely than their better-oﬀ counterparts to support
populist parties when they lack non-populist, anti-austerity alternatives.
More generally, voters’ responses can vary across contexts, countries and time periods,
depending on the existence of populist parties, non-populist parties’ past involvement in
implementing austerity measures, the type of austerity, the nature of welfare states and eco-
nomic conditions. We explore these various moderating factors in the empirical analysis, but
our key goals are to identify which types of voters are aﬀected most strongly and to examine
how their reactions diﬀer from those of non-vulnerable voters. We expect that economically
vulnerable individuals, on average, are more likely than economically safe individuals to vote
for populist parties after an austerity package is implemented. Similarly, populist vote share,
on average, should increase more in economically vulnerable than in economically prosperous
electoral districts after an austerity package is introduced.
3 District-level elections
The ﬁrst part of our analysis examines district-level election results in 12 Western European
countries and (up to) 195 NUTS-2 regions. Our time span covers (up to) 99 elections between
1986 and 2018. We focus on elections for the lower house of the legislature. Each country
appears only in years in which elections are held. The data on party vote shares on the
district level is from the Constituency-Level Elections Archive (CLEA) database (Kollman
et al., 2019).
Measuring populism. Our main outcome variable measures support for populist parties in
an electoral district in a given election. To compute this variable, we ﬁrst match the CLEA
data with the Global Party Survey’s (Norris, 2019) classiﬁcations of political parties on an 11-
point populism scale.7This allows us to calculate a populism score for each district-election.
This score is the weighted average of the populism scores of all parties in the district-election
(parties are weighted by their vote shares). This variable theoretically ranges from 0 (no
populism, i.e. pluralist parties receive all the votes) to 10 (maximum populism, i.e. populist
parties receive all the votes). This measure varies across electoral districts and over time.8
7In this dataset, parties are classiﬁed according to a range of dimensions based on expert
surveys. The conceptualization and operationalization of populism relies on Norris and In-
glehart (2019), which treats populist rhetoric as antithetical to pluralist rhetoric. Populist
language “typically challenges the legitimacy of established political institutions and em-
phasizes that the will of the people should prevail,” while pluralist language “rejects these
ideas, believing that elected leaders should govern constrained by minority rights, bargaining
and compromise, as well as checks and balances on executive power” (Global Party Survey
codebook, p. 10). Populist rhetoric is measured from 0 (less populist) to 10 (more populist).
Data is available at https://www.globalpartysurvey.org/download-data.
8The Global Party Survey’s classiﬁcation of parties is ﬁxed since it is diﬃcult to judge the
degree of populist rhetoric in the more distant past using current expert surveys. Nonetheless,
populism scores vary over time and across districts when parties’ vote shares in a district
change. Our measure thus captures the demand-side eﬀects that arise when voters switch to
a diﬀerent political party, and rules out supply-side eﬀects that are created when mainstream
parties become more populist. This generates more conservative estimates. We also examine
the vote share of strongly populist parties, which arguably includes those that have been
populist for the entire period. We also rely on diﬀerent (but related) time-varying measures,
such as Colantone and Stanig (2018)’s nationalism score, and ﬁnd similar eﬀects.
We label this variable Populism Score.
We also examine support for radical parties, many of which take a populist position.
Using data from PopuList (Rooduijn et al., 2019), we evaluate the share of votes for radical
left and right parties, which allows us to explore how austerity inﬂuences ideology.
Figures C2 and C3 in Appendix C display the distribution of our outcome variable across
NUTS-2 regions and over time. The ﬁgures illustrate that half of the countries in the sample
had already experienced a surge of votes for populist parties in the 1990s and not only during
the past decade.
Measuring austerity. We measure austerity as the number of deﬁcit-reducing policies
implemented by the government in a given time period (Devries et al., 2011; Alesina, Favero
and Giavazzi, 2019). This indicator is based on government policy documents (e.g. bud-
getary reports) and reports from international organizations, such as International Monetary
Fund country reports, to identify the timing and magnitude of a ﬁscal consolidation package.
It captures policy decisions to reduce public spending or increase taxes as announced by the
government and recorded in these documents, and indicates by how many percentage points
these policies are expected to reduce the deﬁcit (as % of GDP). The indicator captures the
year when the policy change takes eﬀect.9This approach has the advantage of directly cap-
turing the government’s policy decision, unlike actual government expenditures, which are
also inﬂuenced by macro-economic conditions in addition to government policy choices.
9The data distinguishes between the year of announcement and the year of implementa-
tion. In case of multi-year consolidation plans, a large part of the plan is usually implemented
in the year in which it is announced. Some of the announced policy changes, however, only
take eﬀect in later years. Where this is the case, we use the year in which the policy is ef-
fectively implemented Since we measure austerity across electoral periods rather than years,
the announcements and implementation mostly coincide in our dataset.
We use the cumulative number of austerity policies implemented between the two elec-
tions, i.e. the previous election in our dataset and the election for which we examine votes.10
Note that this measure is continuous; 0s indicate countries that do not implement austerity
measures, i.e. our control group. The fact that austerity measures are continuous implies
that treated units receive the treatment with diﬀerent degrees of intensity: some austerity
policies are mild, whereas others are quite severe. We label this variable Austerity.
In additional analyses, we also use a disaggregated version of this indicator that distin-
guishes between the number of deﬁcit-reducing measures that are due to spending cuts vs.
tax increases. Moreover, we build a variable that captures the share of austerity measures
that entail spending cuts over total austerity measures. Similarly, we build a dummy that
takes a value of 1 if austerity measures involving spending cuts are larger than those con-
cerning tax increases. The data comes from Alesina, Favero and Giavazzi (2019). These
last two variables allow us to explore whether cutting welfare expenditure triggers a stronger
demand for populism than increasing taxes.
Figure C4 in Appendix C displays the temporal evolution of our austerity variable by
country. There is evidence that the intensity of this measure varies quite dramatically among
countries and over time. Overall, it illustrates that European governments have implemented
10Originally, the austerity variable was an annual time series for each country: it captures
the number of deﬁcit-reducing measures that a government implements in a particular year.
We sum these annual values for each electoral period, which gives us the total number
of austerity measures (as % of GDP) implemented during an electoral period. This is a
straightforward way to attribute the annual consolidations to an election period in years
without elections. It is trickier for election years; we had to make some judgment calls. We
manually attributed ﬁscal consolidations in election years to one of the two election periods
as accurately as possible.
austerity measures very frequently over the past three decades.
Measuring economic vulnerability. To measure economic vulnerability, we follow
the international political literature on the distributional eﬀects of globalization and au-
tomation. We use the share of unskilled workers and the share of workers in manufacturing.
Low-skilled workers have been negatively aﬀected by both competition with cheap labor from
emerging markets and technological shocks, whereas the manufacturing sector has been par-
ticularly hard hit by trade liberalization over the past 30 years. The data comes from
Colantone and Stanig (2018) and varies by NUTS-2 regions.11 We map each district to its
NUTS-2 region to merge the outcome variable with variables capturing economic vulnera-
We use the share of workers exposed to automation as a further proxy for economic vul-
nerability. To build this variable, we rely on the EU Labour Force Survey, which is a large
household sample survey providing quarterly results on 1) the labor participation of people
aged 15 and over and 2) people outside the labor force. For each labor participant, we have
information on her occupation, which we match to the routine task intensity (RTI) score
developed by Goos et al. (2014). Following Autor (2013), we take the top 33% most routine-
task-intense occupations and count their employment as routine jobs. We then calculate the
share of workers in the most RTI occupations in each NUTS-2 region.
Note that we use economic vulnerability variables at their baseline value at the beginning
11The share of low-skilled workers is the share of employees with a lower secondary ed-
ucation and below in a region, according to Eurostat Regional Statistics. The share of
manufacturing workers is the share of employees in a region who work in the manufacturing
sector, according to Eurostat and national statistical oﬃces. The manufacturing sector is
identiﬁed using NACE two-character alphabetical codes (DA to DN).
of our period of analysis; this value does not change over time. We label these variables Share
of Low-Skilled Workers,Share of Manufacturing Workers, and Share of Workers Exposed to
Automation. Figure C5 in Appendix C displays the geographic distribution of the share of
low-skilled workers across NUTS-2 regions.12
3.2 Empirical strategy
Our analysis at the district level is a standard DiD with a continuous treatment. We estimate
the following baseline model:
ycd,t =α+Xcr (d),baseline ×Austerity0
c,tβ+γct +δr+cd,t ,(1)
where ycd,t is our outcome variable capturing the vote share of populist parties in each district
in each election year. Xcd(r),baseline is a matrix including our measures of economic vulner-
ability at baseline. The function r(d) maps district dto its NUTS-2 region r.Austerityc,t
is a continuous variable scoring strictly positive values in election years in which austerity
measures are implemented. The key coeﬃcient of interest is β, which estimates the inter-
action term between the two main independent variables. It reﬂects how the impact of
national-level austerity measures varies across districts with diﬀerent degrees of economic
We are unable to estimate the coeﬃcient of Xcr(d),baseline alone, because it is absorbed by
(NUTS-2) region ﬁxed eﬀects, i.e. δr. Similarly, we are unable to estimate the coeﬃcient of
Austerityc,t alone, because it is absorbed by country-year ﬁxed eﬀects, i.e. δc,t. These ﬁxed
eﬀects net out time-invariant diﬀerences across districts as well as time-variant diﬀerences
across countries. The term cd,t captures any unaccounted-for variation.
12The ﬁgures for the other two variables are available upon request.
In augmented model speciﬁcations, we enrich our baseline model with potential con-
founders. We include a China shock variable as in Colantone and Stanig (2018). Moreover,
we include foreign direct investment (FDI) inﬂow, FDI outﬂow, and export growth to ac-
count for regional economic conditions. We also include the share of foreign-born people
as a proxy for migration. This set of controls is at the baseline, i.e. they vary only at the
NUTS-2 level. Thus, we interact each of these controls with our austerity variable to esti-
mate their eﬀects. We run ordinary least squares (OLS) regressions with robust standard
errors clustered at the country-election year level.
Identifying the eﬀect of austerity presents at least three challenges. First, to use a DiD
approach to estimate a causal eﬀect, the parallel-trend assumption must be validated. We
therefore include lead variables that fake austerity measures before they are implemented.
If we ﬁnd that these are signiﬁcant, this would be a clear violation of the parallel-trend
assumption because it would indicate that areas with large shares of manufacturing and
low-skilled workers support populism regardless of the presence of austerity measures, which
we do not observe. We also include NUTS-2 speciﬁc trends as a further test to validate
the parallel-trend assumption and ﬁnd no evidence of pre-trend eﬀects, except for Share of
Workers Exposed to Automation. In addition, we show that the results are similar if we
include constituency ﬁxed eﬀects. Appendix D reports the results of all of these tests.
In a second challenge, austerity is a potential outcome of negative economic conditions.
Thus, economic crises may trigger support for populism among vulnerable voters. To ad-
dress this point, we leverage the fact that austerity measures do not perfectly correlate with
negative economic conditions in Western European countries. In other words, while auster-
ity correlates negatively with economic growth and ﬁscal balance, our data indicates that
such measures have also been implemented during periods of economic stability and growth.
Thus, to determine whether periods of economic crisis are driving our estimates, we run our
main models on two sub-samples: 1) observations experiencing sluggish economic growth and
negative ﬁscal balance and 2) observations experiencing average or fast economic growth and
average or positive ﬁscal balance.13
Third, it seems likely that governments strategically implement austerity measures. In
particular, they probably anticipate the negative electoral consequences of austerity mea-
sures and time their implementation to mitigate voters’ responses. For instance, there is a
clear tendency for governments to implement austerity policies early and avoid them later in
the electoral cycle, especially if their legislative majority is at risk (H¨ubscher, 2016; H¨ubscher
and Sattler, 2017). We note that governments’ strategic behavior leads us to underestimate
the eﬀect of austerity on vulnerable voters.
Our goal is to test the general relationship between austerity and populism in a broad
range of countries and periods. Cleaner identiﬁcation strategies are possible for speciﬁc,
well-selected austerity episodes in speciﬁc, well-selected countries (see, for instance, Fetzer
(2019)). The question arises to what extent these results are unique to a particular case,
or whether they apply to a broader range of countries and time periods. This leads to a
well-documented trade-oﬀ between internal and external validity. For a broad, comparative
analysis over time, identiﬁcation is more diﬃcult and requires stronger assumptions. Yet in
exchange, we are able to explore the extent to which austerity contributes to populism in
general, or only in particular and unique circumstances.
Populism. Table 1 reports the results of our main analysis. The coeﬃcient of the interaction
between variables capturing economic vulnerability and Austerity is positive and signiﬁcant,
13We use the value of the lower quartile to split the sample.
as expected, in both the baseline models (Models 1–3) and in models that include controls
(Models 4–6). The share of low-skilled workers, share of manufacturing workers, and share
of workers exposed to automation give similar results; their coeﬃcients remain positive and
signiﬁcant, even when we include both at the same time on the right-hand side of the models
To ease the interpretation of the interaction term, we plot the estimates of the share of
low-skilled workers. Figure 3 displays the linear predictions of Populism Score for diﬀerent
low-skilled workers in the case of austerity measures at their minimum and in the case of an
average value of austerity measures. These ﬁgures reveal three key points. First, support for
populism is always higher with austerity than without, but the diﬀerence is small in areas
that are not economically vulnerable. Second, support for populism does not increase in
regions with high shares of low-skilled workers with minimum levels of austerity, i.e. the
linear prediction is a ﬂat line. Third, Populism Score increases dramatically in regions with
high shares of low-skilled workers with (average) austerity measures.
The magnitude of these eﬀects is quite large. In countries implementing (average) aus-
terity measures, the share of votes for populist parties increases by 6.7%, moving Share of
Low-Skilled Workers from one standard deviation below the mean to one standard deviation
The role of crises. To assess whether crises are a potential confounder, we re-run our
main models, splitting the sample according to macro-economic conditions, i.e. low ﬁscal
14We are unable to include the share of workers exposed to automation with the other two
measures of economic vulnerability due to their very high collinearity, i.e. ρ > 0.9.
15The result is similar for Share of Manufacturing Workers and Share of Workers Exposed
to Automation (see Appendix D)
Table 1: Austerity and Populism: Main Results
(1) (2) (3) (4) (5) (6) (7)
Share of Low Skilled Workers*Austerity
Share of Manufacturing Workers*Austerity
Share of Workers Exposed to Automation*Austerity
NUTS-2 fixed effects
Country-election year fixed effects
*** p<0.01, ** p<0.05, * p<0.1
Note: OLS regressions with robust standard errors clustered by county-election year in parentheses. The unit of observation
is NUTS-2-election year. The outcome variable is populism score. The key independent variable is the share of low-skilled
workers, the share of manufacturing workers or the share of workers exposed to automation interacted with austerity measures.
District-level controls are interacted with austerity.
Figure 3: Austerity and Populism: Share of Low-Skilled Workers
4 4.5 5 5.5
Linear Prediction of Populism Score
0 .8.1 .2 .3 .4 .5 .6 .7
Share of Low Skilled Workers
Austerity (min) Austerity (mean)
0 5 10 15
Share of Low Skilled Workers (%)
Note: Linear predictions from Model 2 in Table 1.
balance and high economic growth and high ﬁscal balance (Table 2). As expected, the
eﬀects are larger when ﬁscal balance is negative than when it is positive. However, our main
ﬁndings remain unchanged even if there are no economic crises, i.e. when macro-economic
conditions are sound. Our results are similar even when we interact ﬁscal balance with
the share of low-skilled workers (Model 3). In short, our results are not a by-product of
the correlation between austerity and economic crisis: austerity independently sways voters
In Appendix E, we show that our results are similar if we use (i) other proxies of economic
vulnerability and (ii) economic growth rather than ﬁscal balance.
Types of austerity. In Table 3, we distinguish between diﬀerent types of austerity. We
show that our results are similar if we rely on spending cuts rather consolidation (Models
1 and 2). Moreover, Models 3 and 4 indicate that measures of austerity involving spending
cuts trigger more support for populism than measures of austerity concerning tax increases.
Table 2: Austerity and Populism: The Role of Fiscal Balance
(1) (2) (3)
Low Fiscal Balance
High Fiscal Balance
Share of Low Skilled Workers*Austerity
Share of Low Skilled Workers*Fiscal Balance
NUTS-2 fixed effects
Country-election year fixed effects
*** p<0.01, ** p<0.05, * p<0.1
Note: OLS regressions with robust standard errors clustered by county-election year in
parentheses. The unit of observation is NUTS-2-election year. The outcome variable is
the share of votes for populist parties. The key independent variable is the share of low-
skilled workers interacted with austerity measures. District-level controls are interacted with
The higher the share of spending cuts is, the stronger the support for populism (Model 2).
Similarly, when spending cuts are larger than tax increases, support for populism increases
more than vice versa (Model 3).16 Moreover, Models 5 and 6 show that the eﬀect is similar
for countries with low and high levels of welfare spending.17
In addition, we run our main model speciﬁcation, replacing Austerity with two dummies
to indicate high and low austerity measures. We use the average value of (strictly positive)
Austerity to create these two dummies. Models 7 and 8 report the results, which show that
severe austerity measures are driving our results. This ﬁnding has three implications. First,
it seems to indicate that voters are more likely to be aware of and observe the economic eﬀects
of large consolidation measures, and therefore to electorally sanction the mainstream parties
that implement them. Second, to the extent that large consolidation measures are typically
implemented by cross-party agreement (Blyth, 2013; Hopkin, 2020), there is evidence that
the lack of alternatives to the dominant narrative in favor of austerity fuels populism because
dissatisﬁed voters have no other means of expressing their discontent. Yet, we note that this
is only the case for vulnerable voters, as our theory suggests. Third, severe austerity measures
make it particularly diﬃcult for governments to spare, with targeted policies, economically
vulnerable workers, which may explain why they turn their vote to populist parties.
In Appendix E, we show that our results are similar when we use other proxies for eco-
Austerity and ideology. Table 4 reports the results of support for radical left and
right parties. There are two take-away ﬁndings. First, only radical right parties gain from
austerity; we ﬁnd no eﬀect for radical left parties (Models 1 and 2).18 Second, whether left
16Government transfers, which are relevant for job security, and government consumption,
which disproportionately hits lower-income voters, drive these results.
17We use data on baseline social expenditure to distinguish between countries with low
(i.e. below average) and high (above average) levels of welfare spending.
18These results are robust to the use of other proxies for economic vulnerability and dif-
Table 3: Austerity and Populism: Types of Austerity
(1) (2) (3) (4) (5) (6) (7) (8)
Full Full Full Full Low Welfare High Welfare Full Full
Share of Low Skilled Workers*Austerity (cuts) 1.090**
Share of Low Skilled Workers*Share of Spending cuts 1.781**
Share of Low Skilled Workers*Predominantly Spending cuts 1.013**
Share of Low Skilled Workers*Austerity (consolidation) 0.923* 1.119**
Share of Low Skilled Workers*Austerity (low) -0.371 -0.480
Share of Low Skilled Workers*Austerity (high)
(0.114) (0.119) (0.065) (0.041) (0.120) (0.162) (0.133) (0.102)
Observations 14,109 11,606 12,438 12,438 5,333 8,776 14,109 11,606
R-squared 0.867 0.847 0.871 0.870 0.856 0.809 0.868 0.848
Controls No Yes Yes Yes No No No Yes
NUTS-2 fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
Country-election year fixed effects
*** p<0.01, ** p<0.05, * p<0.1
Note: OLS regressions with robust standard errors clustered by county-election year in parentheses. The unit of observation
is NUTS-2-election year. The outcome variable is the share of votes for populist parties. The key independent variable is the
share of low-skilled workers interacted with types of austerity measures. District-level controls are interacted with austerity.
or right governments implement austerity measures does not generally aﬀect explanations
of support for radical right/left parties (Models 3–6).19 This ﬁnding is conﬁrmed when we
use the share of manufacturing workers to proxy for economic vulnerability. For the share of
workers exposed to automation, we ﬁnd some evidence that radical right (left) parties gain
electoral consensus when mainstream left (right) parties implement austerity (see Appendix
Furthermore, we illustrate how austerity aﬀects a set of socio-economic policies (see
Table F1 in Appendix F). We replace our main outcome, Populism Score, with outcomes
that capture support for parties that are against international trade, the EU, migration, and
minorities, and that support conservative values. Our results indicate that where austerity
measures are implemented, economically vulnerable areas experience a surge of support
for parties advocating autarky or conservative values, as well as Eurosceptic parties and
anti-migration parties. Radical right parties typically hold such positions, which implies
that austerity causes economically vulnerable voters to move rightwards on socio-economic
Robustness checks. We perform four additional robustness checks and report the re-
sults in Appendix G. First, our results are similar if we use other measures of support for
populism. Second, our results are similar if we use the raw value of austerity rather than
its logged value. Third, we ﬁnd no evidence that austerity aﬀects turnout. Fourth, we show
that our results are not driven by the post-2010 period. Finally, we show that our results
hold if we exclude one country at a time; thus they do not depend on the inclusion of any
speciﬁc country in our sample.
ferent measures of support for radical left and right parties (see Appendix F).
19Left/right incumbency measures the ideology of the cabinet before the election using the
average left–right position of all parties in government. The data comes from the Compara-
tive Manifestos Project.
Table 4: Austerity, Economic Vulnerability, and Radical Parties
(1) (2) (3) (4) (5) (6)
Share of Low Skilled Workers*Austerity
NUTS-2 fixed effects
Country-election year fixed effects
Share of Votes for Radical Left Parties Share of Votes for Radical Right Parties
*** p<0.01, ** p<0.05, * p<0.1
Note: OLS regressions with robust standard errors clustered by county-election year in parentheses. The unit of observation
is NUTS-2-election year. The outcome variables are the share of votes for radical left and right parties. The key independent
variable is the share of low-skilled workers interacted with austerity measures. District-level controls are interacted with austerity.
4 Individual-level voting
Our individual-level analysis includes 12 Western European countries and (up to) 46,918
respondents for whom we have data. Our time span covers (up to) eight waves of the
European Social Survey (ESS) administered between 2002 and 2016. Below, we describe the
data and the empirical strategy and report our main results. In line with the district-level
analysis, we exploit variation in individual exposure to national austerity policies depending
on our measures of economic vulnerability. We expect economic vulnerability to moderate
the extent to which national austerity measures aﬀect individuals’ tendency to vote populist.
Our main outcome variable measures ESS respondents’ support for populism. We use each
party’s populism score as described in the previous section and match it to the party the
respondent voted for in the most recent election before the survey.20
To measure austerity, we rely on the variable described in the previous section. We cap-
ture the austerity packages that were implemented during the electoral period that is leading
up to the election recorded in a particular ESS wave.21 In our main model speciﬁcation, we
use a dummy that takes a value of 1 if any austerity measure was introduced during the
electoral period before the election that we analyze.22 To capture economic vulnerability, we
20For instance, ESS wave 6 from 2012 captures the vote of Irish respondents in the 2011
21In the example of the 2011 Irish election recorded in ESS wave 6, the austerity variable
reﬂects the ﬁscal consolidation that the Irish government implemented between the preceding
election in 2007 and the 2011 election.
22In additional analyses (available upon request), we show that our results are virtually
the same for education if we use a continuous measure of austerity. They are weaker for
manufacturing and RTI, though the sign of the main coeﬃcient remains the same.
use the number of years of education of each respondent, which identiﬁes low-skilled workers
in line with the district-level analysis. Years of education is homogenous across countries
that have diﬀerent education systems. We split this variable into three dummies: Lower
Secondary (less than 10 years of education), 2) Upper Secondary (10–15 years of education),
and 3) Tertiary (more than 15 years of education). Tertiary is the baseline category in the
analysis; i.e. it is the excluded variable. Furthermore, we use a dummy coded as 1 if respon-
dents work in manufacturing. This variable is built on the NACE trade category reported
in the ESS.
We also include a variable measuring exposure to automation at the individual level. Fol-
lowing Goos, Manning and Salomons (2014), we convert varying occupational measures into
a 2-digit ISCO-88 code and link it to an aggregated RTI index.23 Then, following Gingrich
(2019), we aggregate the RTI measure into ﬁve quintiles, rescaled to 0 (least aﬀected) to 1
(most aﬀected), which allows us to identify broad categories of exposure.24
4.2 Empirical strategy
In line with the district-level analysis, our analysis at the individual level is a standard DiD.
We estimate the following baseline model:
yic,w =α+Xic,wζ0+Xic,w ×Austerity0
c(i),wη+γcw +ic,w ,(2)
where yic,w is our outcome variable, which captures respondent’s isupport for populism in
wave w.Xic,w is a matrix that includes our measures of economic vulnerability: education,
23The RTI index categorizes occupations based on the skills most aﬀected by automation
in the 1980s and 1990s. This measure is missing for three major occupational groups (ISCO
23, 33, and 61), which are excluded from the analysis.
24The results are similar if we use the continuous version of the RTI developed by Goos,
Manning and Salomons (2014).
manufacturing, and exposure to automation.25 Austerityc,w is a dummy scored as 1 if coun-
try cimplements austerity measures in ESS wave w. The function c(i) maps respondent i
to its country c. In this analysis, the key coeﬃcient of interest is η, which estimates the
interaction term between the two main independent variables. We are unable to estimate
the coeﬃcient of Austerityc,w alone, because it is absorbed by country-year ﬁxed eﬀects, i.e.
γc,t. The term ic,w captures the residuals.
In the augmented model speciﬁcations, we enrich our baseline model with a host of
individual-level characteristics including gender and age, which absorb an important vari-
ation of our outcome. We also add dummies for retired respondent, student, unemployed
respondent, self-employed respondent, and respondent working in services. We interact each
of these controls with Austerity to estimate their eﬀects. We run OLS regressions with robust
standard errors clustered at the country-wave level.26
We report the results in Table 5. In all models, the interaction terms between economically
vulnerable individuals and austerity measures are positive and statistically signiﬁcant, con-
ﬁrming the results of the district-level analysis. Importantly, the coeﬃcient of the interaction
term remains the same when we include individual-level controls interacted with austerity.
To ease the interpretation of the results, we plot the estimates of the interaction term
between education and austerity in Figure 4. The ﬁgure displays the marginal eﬀect of Lower
Secondary Education on Populism Score with and without austerity. Less-educated individ-
uals are signiﬁcantly more likely than highly educated individuals to support populist parties
25We are unable to use the baseline values of our measures of economic vulnerability, since
the ESS is a repeated cross-section rather than a panel: diﬀerent respondents take part in
26All estimates include post-stratiﬁcation weights, including design weights.
Table 5: Austerity and Populism: Individual-level Analysis
(1) (2) (3) (4) (5) (6)
Lower Secondary Education 0.054 0.047
Upper Secondary Education 0.096** 0.090**
Manufacturing -0.011 -0.016
RTI -0.026 0.011
Lower Secondary Education*Austerity (dummy)
Upper Secondary Education*Austerity (dummy)
Manufacturing*Austerity (dummy) 0.093** 0.085**
RTI*Austerity (dummy) 0.092* 0.111**
(0.040) (0.162) (0.005) (0.146) (0.017) (0.168)
Observations 86,944 86,722 82,322 82,133 72,617 72,453
R-squared 0.301 0.303 0.299 0.301 0.299 0.301
Controls No Yes No Yes No Yes
NUTS-2 fixed effects Yes Yes Yes Yes Yes Yes
Country-wave fixed effects Yes Yes Yes Yes Yes Yes
*** p<0.01, ** p<0.05, * p<0.1
Note: OLS regressions with robust standard errors clustered at the country-wave level in
parentheses. The unit of observation is individual-survey wave. The outcome variable is
populism score. The key independent variables are economic vulnerability variables
interacted with austerity measures. Individual-level controls are interacted with austerity.
Figure 4: Austerity and Populism: Lower Secondary Education
-.1 0 .1 .2 .3 .4
Marginal Effect of Lower Secondary Education on Populism Score
No Austerity Austerity
0 20 40 60
Note: Marginal eﬀects from Model 2 in Table 5.
if austerity measures have been implemented. Without austerity measures, less-educated in-
dividuals are not more likely to support populist parties than highly educated individuals.27
The eﬀect is sizable: Populism Score is six times higher with austerity than without. We also
ﬁnd that individuals with an upper secondary education are more likely to support populist
parties where austerity policies have been introduced, though the magnitude of the eﬀect is
signiﬁcantly smaller than that found for individuals with a lower secondary education.28
Finally, we perform a large number of robustness checks in line with the district-level
analysis (see Appendix H). All these tests leave our results unchanged. All in all, the
individual-level analysis conﬁrms our district-level ﬁndings: austerity increases support for
27Recall that we obtain these marginal eﬀects controlling for student status, unemployed
status, and age. Thus, years in school does not proxy for (youth) unemployment.
28The result is similar for manufacturing workers and for workers exposed to automation
(see Appendix H).
populism more among the losers than among the winners from globalization and automation.
This paper examines the political eﬀects of ﬁscal austerity in open economies. It shows that
economically vulnerable voters – i.e. low-skilled workers, workers in the manufacturing in-
dustry, and workers in routine jobs – increasingly turn to populist parties when governments
implement ﬁscal cutbacks. We ﬁnd this eﬀect for both district-level election and individual-
level voting data in Western European countries since the 1990s. Austerity has distributional
eﬀects that magnify, rather than mitigate, the negative economic eﬀects of globalization and
technological change for many workers. These voters therefore begin to question government
promises to make globalization a success for everyone.
These results imply that economic policy and government decisions play a crucial role in
the mechanism that led to the backlash against globalization. Governments have a variety
of ways to moderate the adverse eﬀects of globalization and technological change. But if
they fail to use these means to compensate voters for the increased social risk they face
in open economies, populist parties will be able to exploit the growing anti-globalization
sentiment among dissatisﬁed voters. The economic origins of populism, therefore, are not
purely external or unavoidable. Public policies – especially austerity policies – are cru-
cial because they undermine the “embedded liberalism” compromise of the postwar period
that protected vulnerable workers from the enhanced social risks inherent in open economies.
These ﬁndings also have important implications for government policy after the Covid-19
crisis. Governments have spent large amounts to reduce the pandemic’s economic impact.
A crucial, long-term question is how to deal with the public debts this has generated. Our
results demonstrate that a return to austerity policies after the crisis would be very politi-
cally contentious. While government spending has helped prevent large-scale economic and
political destabilization, the pandemic has had very unequal eﬀects across societal groups
(Bambra, Lynch and Smith, Forthcoming). If vulnerable groups are left to repay the bulk of
the cost of these government interventions, this will likely fuel further support for populist
rhetoric and populist parties.
Our study examines the overall eﬀect of austerity on votes in diﬀerent political and eco-
nomic contexts. We evaluate how reactions to austerity vary across these contexts, but
our research design cannot provide deﬁnitive answers to this question. We leverage a large
amount of regional and individual variation for our key moderating factor, economic vulner-
ability, but there is less variation in our data on national contextual factors. Future research
should further explore how the political–economic context matters, in addition to the voter
characteristics that we study.
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