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Abstract

This article examines the role of economic hardship in contributing to our understanding of the causes of affective polarisation – that is, the animosity between partisan groups. Evidence suggests that a variety of political attitudes and behaviours are influenced by economic hardship, but the relationship between individual economic hardship and affective polarisation has not yet been demonstrated. Thus, the purpose of this study is to investigate whether individuals facing economic insecurities are more affectively polarised. This study leverages two novel surveys, the first conducted in 11 European countries (N = 12,000), the second in Belgium and the Netherlands (N = 2174). Both contain individual-level measurements of economic hardship novel to the field of affective polarisation. Whereas the first allows for making generalisable claims across European multiparty systems, the second contains a more thorough operationalisation of the central concepts. The paper finds little to no evidence to support the existence of a link between economic hardship and affective polarisation. Across countries, objective and subjective measures of economic hardship are not linked to affective polarisation, neither when based on employment or financial status. Future research should now examine whether these cross-sectional findings are supported by longitudinal or experimental evidence.

Keywords

Affective polarisation economic hardship comparative design

Introduction

Since Iyengar et al.’s (2012) seminal article in which they argued that political animosity among citizens is fuelled not by ideology but by affect, we have witnessed an increased interest in affective polarisation in both the scholarly community and the general public. Polarisation is therefore increasingly viewed through an affective lens, next to a manifestation of ideological differences. This interest is largely due to the fact that affective polarisation is associated with highly problematic consequences both within and outside the political sphere. It is said to contribute to the deterioration of inter-group trust and civility (Westwood et al., 2018), the erosion of loser’s consent (Janssen, 2024), and it even seems to drive voters to support their party in undemocratically preventing other parties from gaining power (McCoy et al., 2018).

After more than a decade of research, consensus has been reached on the most important causes of affective polarisation: group attachment (e.g. Harteveld, 2021b; Mason, 2018) and ideology (e.g. Algara and Zur, 2023; Harteveld, 2021a). In contrast, the role played by economic factors is still highly disputed. We know that economic inequality, deprivation, and recessions lead to an array of political outcomes which threaten societal cohesion (e.g. Gallego, 2016; Snower and Bosworth, 2021). Indeed, Gidron et al.’s (2020) finding that income inequality and higher unemployment rates worsen affective polarisation at the country level seems in line with this research. However, a closer inspection of studies investigating the political consequences of economic hardship, that is, the perception/experience of relatively low economic resources (Barrera et al., 2001), reveals that unemployment and financial constrains alienate citizens from engaging with politics (e.g. Emmenegger et al., 2015; Schaub, 2021), which would be expected to result in lower rather than higher affective polarisation. Hence, a contradiction emerges: are voters who are suffering from economic hardship, which itself results in greater disengagement from politics, also more affectively polarised?

To investigate this puzzle, this study examines the relationship between economic hardship and affective polarisation at the individual level by using data from two novel surveys, leveraging the unique strengths of each dataset. In the absence of a strong theoretical framework, an exploratory approach is adopted through the possibility of triangulating different data sources. The first dataset allows for a broader comparative analysis, encompassing 11 European countries (N = 12,000). The second focuses on Belgium and the Netherlands (N = 2174), limiting its scope, but allowing for a more in-depth investigation of economic hardship, as a more diverse set of economic indicators was incorporated.

The analysis finds very limited empirical evidence to support the claim that economic hardship and affective polarisation are in some way connected. Across countries and varying model specifications, neither objective nor subjective measures of economic hardship are linked to affective polarisation. Future research should now examine whether these cross-sectional findings are also supported by longitudinal or experimental evidence.

Conceptual framework

Economic factors such as inequality and economic hardship have been extensively employed to explain political behaviour, especially by authors building on theories of rational choice and economic voting (e.g. Emmenegger et al., 2015; Gallego, 2016). For example, relative economic deprivation increases support for both radical left and right parties, as they often claim to protect group status and mitigate status threat through redistributive economic policies or immigration controls (Snower and Bosworth, 2021), whereas economic recessions are said to exacerbate social divisiveness and polarisation, reinforcing partisan stereotypes and out-party prejudice (Utych et al., 2022).

While Gidron et al. (2020) and Stewart et al. (2020) find that aggregate-level indicators of inequality and economic decline contribute to higher levels of societal affective polarisation, it remains unclear whether individual-level economic factors are also linked to individual affective polarisation. One particular indicator which has been found to significantly shape an individual’s political belief system, is economic hardship, which is the perception or experience of relatively low economic resources (Barrera et al., 2001). Sometimes also referred to as economic insecurity or economic threat, one’s ‘low or precarious socio-economic status’ can refer to income, wealth or employment (Fritsche and Jugert, 2017).

There are several plausible pathways through which economic hardship and affective polarisation might be connected, both direct and indirect. First, the broad assumption linking adverse economic conditions to affective polarisation posits that citizens will experience negative emotions, such as anger and anxiety, as a result of higher exposure to stress and feelings of powerlessness from their economic hardship (Carr and Chung, 2014; Utych et al., 2022). These feelings lower one’s personal status, which can be recuperated through increased emphasis on one’s group identity, such as political or partisan identity (Huddy, 2001; Staub, 2001). In turn, by highlighting political group identity, one can recuperate their personal status, thereby increasing motivated reasoning in political decision-making (Boyer et al., 2020), and hence increasing affective distance and negative bias towards out-groups. Utych et al. (2022) indeed find that economic insecurities increase out-group prejudice ‘when these prejudices align with one’s core ideological values’ (p. 520). According to these pathways, affective polarisation could be viewed as the unintended outcome of the coping mechanism used by those experiencing economic hardship.

There is also evidence that economic hardship leads to ideological polarisation, which is itself related to affective polarisation (Riera and Madariaga, 2023). Under conditions of financial suffering, voters are more likely to turn towards either the radical left (Ramiro and Gomez, 2017) or the radical right (Harteveld et al., 2022). The increase in ideological extremism and ideological polarisation should subsequently heighten affective polarisation (Riera and Madariaga, 2023). These findings show that although economic considerations are instrumental in nature, economic hardship could be plausibly linked to increased levels of affective polarisation. Economic hardship can also contribute to zero-sum thinking, where individuals perceive economic resources as finite and believe that gains for one group necessarily come at the expense of another. This zero-sum mentality can spill over into political attitudes, fostering resentment and hostility toward perceived ‘out-groups’ who are seen as competitors and a threat to scarce resources (Huddy, 2001). During times of economic crisis, individuals may further seek explanations for their predicament and look for scapegoats to blame (Mutz, 2018). Affective polarisation can thus be fuelled by the tendency to attribute economic problems to opposing political ideologies, parties, or social groups. These mechanisms could take different cognitive paths across individuals (and also across contexts). For example, it might be that higher levels of ideological polarisation lead to high affective polarisation, and that economic hardship plays a continued additional effect in affective polarisation, even once voters identify with a given group. Independently of such pathways, the first hypothesis to be tested is the following:

Citizens who experience more economic hardship and insecurity are more affectively polarised.

When considering these mechanisms, it seems necessary to explicitly connect hardship to the main driver of affective polarisation: partisan group attachment. Huddy et al. (2018) identify five characteristics of expressive partisanship, the type of partisanship consistent with affective polarisation: stable partisan identity, motivated reasoning in defence of the party, greater influence of identity than issues and ideology in influencing vote choice and political behaviour, affective polarisation bias in favour of one’s own party, and the existence of strong defensive emotions aroused by partisan threats and reassurances. These traits are especially present in strong partisans (pp. 183–185). It seems reasonable that strong partisans, because of motivated reasoning or underlying ideological convictions, overwrite economic considerations, whilst weak or non-partisans, possibly guided by more instrumental reasoning, do not. The main hypothesis is tested on three different groups: strong partisans, weak partisans, and non-partisans. Overall, it is expected that in strong partisans and (to a lesser degree) in weak partisans, economic hardship has a weaker or no impact.

The association in H1 is weaker for strong partisans than weak and non-partisans.

Data and methods

This study employs two novel data collections. The first survey (N = 12,000) was conducted online via computer-assistant web interviews (CAWI) across 11 countries and 12 political systems. Countries included are Austria, Belgium, Denmark, France, Germany, Greece, Italy, Portugal, Spain, the Netherlands, and the United Kingdom. As Belgium encompasses two separate party systems, data collection there was split for Flanders and Wallonia, resulting in 1.000 respondents per party system. Samples are representative for age, gender, education, and region at NUTS1- or NUTS2-level. Western and Southern European countries were the focus of the data collection due to key institutional factors that they share which could otherwise influence political attitudes that are not the focus of this paper. All sampled countries are established democracies, with numerous parties that have (mixed) proportional electoral systems.¹ The large-scope survey offers a distinct advantage, but available measurements of the central concepts are more limited, which is where the second survey comes in.

The second survey was conducted in Belgium and the Netherlands. Two nationally representative samples were collected via CAWI (N = 2174; Belgium: N = 1071; the Netherlands: N = 1103), with quotas for age, gender, and NUTS1. It focuses on people at the working age, only sampling 18- to 69-year-old, and contains a large array of measurements on economic hardship and affective polarisation. The focus on these two countries arises from their contrasting levels of affective polarisation, which is particularly low in the Netherlands due to the relative lack of negative partisanship towards the radical right (Harteveld, 2021a; Wagner, 2021). However, as both countries have strong economies, robust welfare states, and a high median income, they constitute two least-likely cases. This is where the strength of the data triangulation comes in. Although results from the second survey may not travel to more precarious economic contexts, making generalisable claims is nevertheless possible if results between the two surveys align thanks to the larger scope of the first survey.

The most common operationalisation of affective polarisation in multiparty systems is the like-dislike score, where respondents rate out-groups on a scale from strong dislike to like (Wagner, 2021), focussing either on a horizontal (towards fellow citizens) or vertical (toward parties) dimension (Röllicke, 2023). These two dimensions are correlated but not identical, as parties tend to receive more negative scores than voters (Harteveld, 2021a), and scores are more negative and more homogeneous for the out-group (Areal and Harteveld, 2024). The larger survey focuses on the vertical dimension, whereas the Belgian-Dutch dataset encompasses both dimensions.² To measure the horizontal and vertical dimensions, Wagner’s (2021) weighted affective polarisation index is used, which computes a spread of the affect scores for all parties or party supporters, respectively, weighing these scores for party size.³

Another frequently-used horizontal measure of affective polarisation is social distance, which captures how respondents would feel interacting with out-partisans in different social settings, such as their level of comfort if their child were to marry someone from a political out-group (Iyengar et al., 2012). Though like-dislike and social distance are related, social distance seems to better tap into deep-rooted forms of dislike (Druckman and Levendusky, 2019; Vanagt, 2024). Social distance questions were included in the smaller survey and were asked for respondents’ three least-liked parties regarding a close friend and romantic partner. The average of the three party scores is subsequently computed, leading to two distinct social distance scores.

To test a range of possible mechanisms between economic hardship and affective polarisation, the operationalisation of the former encompasses multiple aspect: (1) the components used in the literature to study economy hardship and (2) a subjective versus objective nature. Starting with the first element, studying economic hardship has two main components: income and employment (Fritsche and Jugert, 2017). The latter, commonly referred to as labour market or job insecurity, is often related to the insider-outsider theory of employment and unemployment (Gelepithis and Jeannet, 2018). Assuming rational self-interested behaviour, it pits ‘insiders’, who have full-time contracts, against ‘outsiders’, who only have temporary employment. With regard to the second element, most studies focus on objective rather than subjective/perceptive measures of economic vulnerabilities. However, individuals can experience such hardships very differently depending on personality traits, intra-household relationships, etc. (Marx and Picot, 2020). The feeling of economic hardship is therefore sometimes considered as a better predictor of political outcomes (Hacker et al., 2013; Rooduijn and Burgoon, 2018). As argued by prospect theory, subjective assessments may matter even more in shaping political behaviour than the level of income itself (Bartusevičius and Van Leeuwen, 2022). This leads to four components: actual income, income insecurity, and objective and subjective job insecurity.

Income is measured as household income in 10 deciles for the larger dataset and 11 quantiles for the Belgian-Dutch dataset. Income insecurity is operationalised as (1) the difficulty to pay for usual necessary expenses and (2) the difficulty to buffer a financial shock of approximately one third of the country’s current median income. Objective job insecurity is only measured in the smaller dataset and operationalised according to an expanded version of the insider-outsider theory of employment and unemployment, which considers employment type (employed, unemployed, self-employed), contract duration (temporary vs full-time), number of employees, and job status (Gelepithis and Jeannet, 2018). The secure categories are insiders (employed full-time), self-employed workers with employees, and upscale (high job status such as managers, Class 1 of European Socio-Economic Classification).⁴ Outsiders (unemployed or employed part-time) and self-employed workers without employees constitute the insecure categories. Nonemployed (students, homework, retired, disabled) are a neutral category. Subjective job insecurity is measured by asking respondents how much they worry about losing their job as well as not finding a new job. The latter has the advantage of including unemployed individuals and accounts for the fact that some (more privileged) workers might lose their job but quickly find a new one.

For H2, partisans are distinguished from non-partisans based on whether they identify with a particular political party or feel closer to one party than all others. To compare strong vs weak partisans, a 7-point Likert is used which asks partisans to what extent they identify with their in-party. For all the analyses, the following controls are included: (1) ideological extremism, derived from the self-reported left-right ideological spectrum and recoded with extreme left and right as the highest values, (2) political interest, (3) positive partisanship as one’s in-party attachment, and (4) negative partisanship as the extent to which one is repulsed by their out-party, measured as the mean index of the extent to which one could never vote for this party due to their worldviews, and the importance that they are not considered a voter of this party (Mayer and Russo, 2024). All analyses also control for age, gender and education. Linear regression models are computed with country and party-affiliation fixed-effects.⁵ Appendix A shows that correlations between the dependent and independent/control variables are generally (very) low, except for positive partisanship and vertical affective polarisation in the large dataset (r = 0.38).

Results

This section first presents the findings of the larger dataset with weighted like-dislike scores towards parties as the dependent variable. Descriptives, which already suggest that a link between economic hardship and affective polarisation is absent, are included in Appendix B. Indeed, as shown in Figure 1, none of the regression models return any significant results (p > .05), despite the large number of observations (N = 5687–9775) (see Appendix C for full regression results). Political predictors are consistent with previous research: higher degrees of positive and negative partisanship, political interest, and ideological extremism are significantly associated with higher levels of affective polarisation (p < .05). Additionally, cross-country differences are tested by splitting the data per sample. Results are also highly robust across different country contexts (see Appendix D for country-specific results).

Figure 1.

Like-dislike towards parties (large dataset). Note: Unstandardised coefficients are shown. For full regression results, see Tables A1–A2.

Results for the Belgian-Dutch data are shown in Figure 2. The results are consistent with the findings of the larger dataset. For objective job insecurity, some results are significant, but indicate that citizens with more secure jobs might be slightly more polarised. This finding is however very inconsistent. Worry over losing one’s job slightly increases the like-dislike score towards voters, but only barely (.01 < p < .05). Replicating the results for the two social distance measures does not produce any evidence to support the hypothesis (see Figure A8 in Appendix E). Once again, results for the political control variables are in line with previous research. Full regression tables are presented in Appendix F.

Figure 2.

Like-dislike towards parties and voters (small dataset). Note: Unstandardised coefficients are shown. For full regression results, see Tables A13–A16.

Models of both datasets provided no robust evidence in support of H1. To test the interactions of H2, only the large dataset is utilised for reasons of statistical power. Two sets of models are computed, one separating partisans from non-partisans, the other excluding non-partisans but separating weak from strong partisans. Results are presented in Appendix G. Once again, evidence is inconclusive with all but two insignificant results. Lower household income is significantly associated with higher affective polarisation for weak partisans, whereas it is not for strong partisans (in line with H2). However, this effect is negligibly small and only just significant (β = 0.003; .01 < p < .05), and partisans as a whole do not differ from non-partisans, going against H2 (p > .05). Second, non-partisans who experience less income insecurity are more polarised compared to partisans, defying previously formulated expectations. Considering the overwhelming insignificant results, the null hypothesis of H2 cannot be rejected.

Additional robustness checks provide no further support to reject the null hypotheses. When using data from the Comparative Study for Electoral Systems (N = 181,523), no evidence is found that links household income (in quintiles) and employment status to vertical affective polarisation (see Appendix H). Models excluding control variables which could introduce multicollinearity (ideological extremism and political interest) or are conceptually similar to the dependent variable (positive and negative partisanship), show that the results are robust, regardless of model specification (see Appendix I). Results are also robust when testing for a potential interaction with ideological camp affiliation, operationalised through voting intention (see Appendix J). When controlling for a non-linear relationship between economic factors and affective polarisation by recoding the economic variables as the distance from the midpoint, the analysis reveals mixed evidence (see Appendix K). Only some models return significant results, in which low and high levels of economic hardship are associated with more affective polarisation, and effect sizes are very small (β = 0.032–0.076). Finally, another series of models considers in-group and out-group affect ratings as the dependent variables.⁶ Whereas results are highly mixed for in-group affect (see Appendix L), economic hardship seems somewhat more consistently associated with a significant decrease in out-group negative affect, defying the study’s expectations (see Appendix M). Although effect sizes are generally small (β = 0.025–0.163, one outlier of 0.227) and evidence is not robust across all our data and models, these results at least suggest that economic hardship may decrease out-group dislike in some cases.

Discussion and conclusion

In the past years, scholarly focus on the drivers of affective polarisation has greatly advanced our knowledge on this matter. However, the role played by economic factors remained unclear, despite driving many political phenomena (e.g. Emmenegger et al., 2015; Gallego, 2016; Snower and Bosworth, 2021). While Gidron et al. (2020) found that income inequality and higher unemployment rates worsen societal affective polarisation, the effect at the individual level had yet to be investigated. Hence, this study set out to empirically examine the link between economic hardship and affective polarisation at the individual level by using multiple operationalisations of both concepts and by triangulating two novel surveys to strengthen the robustness of the findings.

The results are highly similar across the datasets, model specifications, and various operationalisations. By and large, the bivariate and multivariate analyses found little to no evidence to support the claim that economic hardship and affective polarisation are linked, and both datasets show that there is no common pattern across countries. Neither is any robust evidence found that points into the direction of the mechanism operating through partisanship, where economic hardship would have diverging effects on non-partisans, weak or strong partisans. It therefore seems unlikely that economic hardship plays any role in shaping affective polarisation in Europe.

Although Gidron et al. (2020) and Stewart et al. (2020) found evidence of a relationship between affective polarisation and well-known economic indicators at the aggregate level, this study shows that this finding does not extend to one’s individual economic status. We propose five arguments for why the relationship between economic hardship and affective polarisation may not hold at the individual level. First, Schaub’s (2021) findings that economic hardship leads citizens to disengage from politics could explain why they do not become more affectively polarised. Indeed, citizens disengaging from politics as a result of them experiencing economic hardship may explain why this study found that economic hardship – in some cases – was associated with less negative affect towards one’s out-party, even if effect sizes were fairly small. Second, as affective polarisation stems from a social and expressive partisan identity (Huddy et al., 2018), people may align themselves with particular social or political groups based on shared values, beliefs, or identities, rather than on economic interests. As a result, economic hardship may not directly influence one’s group identity and animosity toward opposing groups. Third, as affective polarisation is influenced by several psychological and cognitive processes, such as social cognition, motivated reasoning, and cognitive biases (Boyer et al., 2020; Martherus et al., 2019), people may engage in selective exposure, interpretation, and recall of information that reinforces their pre-existing beliefs and attitudes, regardless of economic considerations. Fourth, voters might assess the economy through a sociotropic lens, as posited by Mansfield and Mutz (2009), rather than through personal financial circumstances. This perspective suggests that voters’ evaluations are influenced by broader economic conditions affecting society at large, rather than their individual economic situations. Thus, as for other political phenomena, one could theorise that there is indeed a relationship present at the country level, but that it does not operate at the individual level (see Welzel and Inglehart, 2007). Finally, it could be that economic shocks, which cause fluctuations in job security or employment status, play a significant role in driving polarisation. Such shocks could lead to increased economic insecurity and consequently more polarised political views. However, such dynamic is better captured through panel data or methods that can account for exogenous changes in the economic environment, highlighting the importance of considering temporal changes in economic security rather than static levels of insecurity. Thus, it could be that we do not capture any association because of the cross-sectional nature of this study’s data.

Apart from exploring whether using longitudinal data would lead to different results, multiple theoretical pathways remain unexplored. The role of cultural factors as a potential link between economic hardship and affective polarisation were not considered, even though culture has been found to be more pervasive in shaping affective polarisation than economy (Harteveld, 2021a). Furthermore, threat perceptions are more diverse than group status threats alone. Instead, individuals may experience particular demographic threats or threats to democracy as drivers of affective polarisation (Renström et al., 2021). Future analysis could therefore explore the role of culture and diverse threat perceptions to determine whether these factors potentially connect economic hardship to affective polarisation, as well as examining whether these cross-sectional findings are supported by longitudinal or experimental evidence.

Supplemental Material

Supplemental Material - The economic divide that isn’t: A comparative study on economic hardship and affective polarisation

Supplemental Material for The economic divide that isn’t: A comparative study on economic hardship and affective polarisation by Jochem Vanagt and Luana Russo in Journal of Research & Politics.

Footnotes

Acknowledgements

We want to thank our colleagues at the political science departments of the universities of Antwerp and Leuven, participants of the workshop on affective polarisation at Politicologenetmaal (2022) and the panel on ‘Polarization over social and economic divides’ at the ECPR General Conference (2023), as well as the three anonymous reviewers. Their constructive feedback was invaluable in this paper’s transformation from a master thesis to a published article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement no. 759736) and from the Department of Politics at the Faculty of Arts and Social Sciences of Maastricht University, and was supported by the Belgian FNRS-FWO EOS project NotLikeUs (EOS project no. 40007494; FWO no. G0H0322N; FNRS no. RG3139). This publication reflects the authors’ views. The agencies are not responsible for any use that may be made of the information it contains.

Ethical statement

ORCID iD

Jochem Vanagt

Supplemental Material

Supplemental material for this article is available online.

Data replication files are available at:

Notes

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