Increasingly polarized? Inequality, prosperity, and perceived socioeconomic conflict in advanced economies (1987-2019)

European Sociological Review, 2025, 00, 1–18 https://doi.org/10.1093/esr/jcaf052 Advance access publication 24 November 2025 Original Article Increasingly polarized? Inequality, prosperity, and perceived socioeconomic conflict in advanced economies (1987–2019) Cristian Márquez Romo * , Simon Bienstman and Markus Gangl Institute for Sociology, Goethe-University Frankfurt, Frankfurt am Main 60323, Germany * Corresponding author. Email: [email protected] Previous studies suggest that in more unequal societies, people perceive stronger antagonistic relations between opposing socioeconomic groups. Given that income inequality and social polarization have both been on the rise in most Western democracies, we expand on this body of work by investigating whether changes in macroeconomic fundamentals have triggered changes in perceived socioeconomic conflict. To assess this proposition, we fit hybrid multilevel models using timeseries cross-sectional data from 26 countries spanning over three decades (1987–2019). Our evidence shows that rising economic prosperity does not reduce the level of perceived conflict once income inequality is accounted for. In contrast, growing inequality is robustly associated with increased salience of perceived socioeconomic conflict. Findings indicate a sociotropic within effect of income inequality, net of changes in economic prosperity and accounting for contextual confounders and individual-level compositional effects. Our results further suggest that income inequality exacerbates classbased polarization in conflict perceptions: it increases perceived conflict across all groups—except the upper-middle class. Alternative model specifications and extensive robustness checks lend additional support to our argument that the distribution of economic resources has a direct impact on the salience of socioeconomic conflict perceptions. Received: December 2024; revised: July 2025; accepted: October 2025 © The Author(s) 2025. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https:// creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact [email protected]. Introduction Early on, Lipset (1959) argued that the level and distribution of economic resources shape both the degree of societal division and its ability to manage conflicts through political means. Similarly, Dahrendorf (1967) identified the institutionalization of class conflict as a key mechanism for containing the potentially disruptive effects of distributive tensions and class antagonism, a process that contributed to the formation of party systems and welfare states during the early 20th century. Against the backdrop of mid-20th-century economic growth and rising prosperity, debates in the 1980s began to question whether class had lost its political relevance (e.g., Pakulski and Waters, 1997; Beck, 2007). Yet, with growing inequality in advanced economies during recent decades (Atkinson, 2015; Zucman, 2019), the salience of distributional conflicts has reemerged (Kerbo, 2012; Hertel and Schöneck, 2019). Existing research emphasizes both persistent and rising levels of perceived conflict as causes for concern, not least because perceived socioeconomic conflict is regarded as a key indicator of social cohesion. High levels of socioeconomic conflict can erode trust, fuel internal disputes such as crime and civil unrest, and exacerbate political polarization (Hsieh and Pugh, 1993; Rothstein and Uslaner, 2005; Delhey and Keck, 2008; Baten and Mumme, 2013). These concerns are increasingly pressing given the rise of anti-liberal movements and parties that challenge established mechanisms for the rulebased resolution of conflicts (Foa and Mounk, 2016; Przeworski, 2019; Fukuyama, Dann and Magaloni, 2025). Ultimately, if democratic support depends on citizens’ belief in the system’s capacity to channel and peacefully resolve social and political issues, increasing perceived socioeconomic conflict may signal the erosion of fundamental democratic consensus (Lipset, 1959). Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 Against this background, we examine whether macroeconomic fundamentals affect perceived socioeconomic conflict by focusing on the relationship between economic prosperity, income inequality, and perceived socioeconomic conflict. Our focus lies on the subjective salience of conflicts between economically opposing social groups (van Drunen, Spruyt and van Droogenbroeck, 2021), i.e., among groups that differ in their socioeconomic status, as opposed to other types of antagonistic relations, such as those pertaining to attributes such as gender, age, or ethnicity (cf. Delhey and Keck, 2008). Previous studies have shown that perceived socioeconomic conflict tends to be higher in more unequal societies, and that inequality between social classes is consistently associated with differences in perceived socioeconomic conflict (e.g., Janicka, 2002; Zagórski, 2006; Kerr, 2014; Hertel and Schöneck, 2019; van Drunen, Spruyt and van Droogenbroeck, 2021). Yet, existing research relies almost exclusively on either individual-level drivers or cross-sectional designs. As a result, we still lack the empirical evidence that allows us to draw firm conclusions about whether shifts in income inequality and economic prosperity have triggered changes in perceived socioeconomic conflict. To address this gap, we use a comparative longitudinal design, analyzing repeated cross-sectional data from the International Social Survey Program (ISSP) for 26 countries over three decades (1987–2019). Our study contributes in three ways: First, in line with earlier cross-sectional studies, findings from random effects within-between models show that economic prosperity does not substantively decrease perceived socioeconomic conflict when testing the joint effects of prosperity and income inequality (e.g., Andersen and Curtis, 2012; Evans and Kelley, 2017). Second, we provide robust evidence for a sociotropic effect of overtime changes in inequality on socioeconomic conflict perceptions. That is, as inequality rises within countries, citizens’ conflict perceptions increase, irrespective of their socioeconomic position or other demographic characteristics. Third, we find that inequality intensifies the divergence in conflict perceptions between opposing socioeconomic groups: while those in less advantaged socioeconomic positions are evidently responsive to growing inequality, conflict perceptions among the most advantaged citizens appear largely insulated from shifts in macroeconomic inequality. These results align with a growing body of evidence indicating that the worse-off are comparatively more exposed to the social–psychological effects triggered by rising inequality over the last decades. Extensive robustness checks further reinforce the idea that the uneven distribution of economic resources within a country has a direct impact on the salience, and polarization, of socioeconomic conflict perceptions. In the following, we elaborate on the theoretical arguments linking macroeconomic fundamentals to the salience of perceived conflict, review the existing literature, and detail the data and methodology. We then present our findings and discuss their implications. Theory and previous research Democratic theory has long emphasized the importance of a certain level of economic development for sustaining democracy. Lipset’s (1959) seminal work, as well as several important contributions that followed (see Diamond, 1992; Przeworski and Limongi, 1997; Wucherpfennig and Deutsch, 2009), argued that the provision of minimum material security and living standards renders the question of who holds power less existential, reducing the incentive to seek absolute power and enabling peaceful transitions between governments. Later on, developing a more nuanced perspective on regime change, Boix (2003) emphasized the importance of the distributional consequences of different political regimes, advancing the notion of democracy as an institutional equilibrium designed to contain the potentially disruptive effects of distributive conflict. These perspectives also align with Dahrendorf’s (1967)notion of the institutionalization of class conflicts, suggesting that while the formation of cross-class coalitions is essential for maintaining political and institutional equilibrium, economic changes condition the intensity of social and political conflicts. As these conflicts are often divides regarding the distribution of economic resources, they can either mitigate or exacerbate structural divisions within society. Indeed, periods of economic malaise can have important social and political implications, triggering critical junctures during which the legitimacy of the existing ‘social contract’ can be revisited or called into question (Hassel, 2009; Kriesi, 2016; Liesbet and Marks, 2018). At least since Marx (2010 [1904]), it has been a key premise and thus a recurrent point of debate whether macroeconomic fundamentals influence the form and the intensity of societal conflict. While Lipset and Rokkan (1967) identified class-related conflicts as one fundamental cleavage that was institutionalized in party systems and the concomitant development of welfare states in the early 20th century, scholarly debates in the 1980s revolved around the question of whether class had ceased to be a politically relevant category. The period of rapid and widespread economic growth and increasing prosperity in the mid-twentieth century led post-modern theorists to hypothesize that class would cease to be the dominant basis for social identification and political conflict (Pakulski and Waters, 1997; Beck, 2007). Other scholars have further 2 MÁRQUEZ ROMO, BIENSTMAN AND GANGL Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 highlighted the importance of new, non-traditional distributional conflicts for social cohesion, shifting the salience of socioeconomic conflict toward other non-class-based divisions (e.g., Clark and Lipset, 1991; Beck, 1992; Kikkawa, 2000; Kingston, 2000). The central tenet is that by raising overall standards of living, sustained economic growth in post-industrial Western societies has led to significant social–structural and cultural changes. These changes have diminished hierarchical differentiation and class awareness, increasing the salience of new ‘horizontal’ divides —including ecological concerns and postmaterialist values, as well as cleavages based on ethnicity (immigrants vs. native), gender, and generation (e.g., Hondrich and Caplow, 1994; Inglehart, 1997; Beck, 2007; Delhey and Keck, 2008). The general expectation derived from this body of literature is that rising economic prosperity decreases perceived socioeconomic conflict (H1). However, economic growth is not the only macroeconomic factor shaping socioeconomic conflict. Key in this regard is the trend toward growing inequality in most Western democracies during the last decades (Atkinson, 2015; Zucman, 2019), which has renewed interest in whether macroeconomic factors are reshaping conflicts toward distributional issues. From Iceland’s ‘Pots and Pans Revolution’ in 2008, to Chile’s social upheaval in 2019, massive demonstrations (which often have in their core a critique of the increase in social inequalities, see Kerbo, 2012) suggest conflicts around distributional issues might be reerupting (Bernburg, 2016; Cox, González and Le Foulon, 2023). By now, a growing body of research links income inequality to various negative social outcomes, including (mental) health, democratic orientations, and social trust (e.g., Wilkinson and Pickett, 2009, 2019; Buttrick and Oishi, 2017; Bartram, 2022, 2025). At the heart of this research is the idea that income inequality leads to greater social stratification, triggering awareness of status distinctions and increasing the salience of socioeconomic disparities. Consistent with social psychology research emphasizing that individuals are embedded in a ‘‘socio-ecology’’ that shapes how they perceive themselves and others (e.g., Peters and Jetten, 2023: 524; Jetten and Peters, 2019; see also Manstead, Easterbrook and Kuppens, 2020), this body of work suggests that inequality may intensify socioeconomic group conflicts by enhancing both the valence and salience of material differences. Indeed, some studies suggest that in high-inequality contexts, people are more likely to signal status differences and engage in status-driven consumption (i.e., consuming goods that have symbolic status value, like designer brands, expensive jewelry, or luxury clothing; Walasek and Brown, 2015; Bricker, Krimmel and Ramcharan, 2021; Pybus et al., 2022; Wang et al., 2022). By increasing individuals’ awareness of socioeconomic group disparities, exposure to inequality can also transmit information (or alter views) about these groups, reinforcing class-based stereotypes and distinctions (Durante and Fiske, 2017; Tanjitpiyanond, Jetten and Peters, 2022). For example, Gallego (2016) finds that larger gaps between the rich and the poor increase negative associations toward those located at the opposite end of the income distribution. When material differences between social groups lead to increasingly divergent social conditions, socioeconomic position tends to become a key indicator of where one is located in terms of status or social rank (Walasek, Bhatia and Brown, 2018; Wilkinson and Pickett, 2019: 23). In high-inequality contexts, where class differences are more clearly marked, the relationship between income and class awareness tends to be stronger via a contrast effect (Aronson, 1999; Andersen and Curtis, 2012). As income inequality grows, so does the distance between opposing socioeconomic groups, resulting in greater contrasts, a polarization of the income distribution, and more opportunities for negative comparisons (Hastings, 2019; Sachweh and Sthamer, 2019). By shaping the content of intergroup conflicts and fueling class-based social categorization, these studies suggest that inequality enhances hierarchical differentiation, increasing the salience of antagonistic relations between opposing socioeconomic groups. Based on this framework, we expect rising inequality to increase perceived socioeconomic conflict (H2). The two hypotheses developed so far suggest that changes in income inequality and economic prosperity should have opposite effects on perceived socioeconomic conflict. To the best of our knowledge, the only longitudinal evidence of the relationship between inequality and perceived socioeconomic conflict comes from Kerr (2014). Using a fixed effects approach to study redistribution demands and the acceptance of wage differentials, this study finds that changes in income inequality are positively associated with the item ‘conflict between the rich and poor’. In the current study, we assess whether both rising economic prosperity and income inequality shape the salience of perceived conflict over a longer time span, relying on a latent and more comprehensive dimension of socioeconomic conflict. Moreover, acknowledging the importance of where one falls in the income distribution, we further assess whether growing inequality has triggered polarization in perceived socioeconomic conflict. Extant scholarship offers different expectations about how inequality should affect different socioeconomic groups. First, a key line of research on the consequences of inequality argues that, overall, INCREASINGLY POLARIZED? 3 Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 equality is ‘better for everyone’ (Wilkinson and Pickett, 2009, 2019; Buttrick and Oishi, 2017). Given that ‘larger income differences across a society immerse everyone more deeply in issues of status competition and insecurity’ (Wilkinson and Pickett, 2019: xxi), inequality is expected to affect more and less advantaged individuals alike. Due to its harmful consequences that increase social dysfunctions affecting important social indicators such as health, happiness or crime, some studies offer empirical evidence showing that income inequality can also affect individuals with higher socioeconomic positions (e.g., Subramanian and Kawachi, 2006; Dimick, Rueda and Stegmueller, 2016; Rueda and Stegmueller, 2016; Sachweh and Sthamer, 2019; Romero-Vidal, 2021). Therefore, the expectation derived from this line of argument is that rising inequality increases perceived conflict among all socioeconomic groups (H3a). By contrast, an alternative body of work emphasizes that individuals with lower economic positions are comparatively more sensitive to changes in relative socioeconomic status (e.g., Gallego, 2016). If people’s awareness of their position in the economic hierarchy is formed by comparing themselves to other groups from their social environment, for the worse off, the cohabitation and interaction with those who are better off can become a constant reminder of their own position of relative economic deprivation (e.g., Runciman, 1966; Hastings, 2019). Income inequality can increase the salience of economic comparisons among individuals, polarizing public beliefs across income-based lines (e.g., Andersen and Curtis, 2012; Newman, Johnston and Lown, 2015). As relative socioeconomic status becomes increasingly important in shaping people’s differential experiences in everyday interactions, individuals with higher socioeconomic positions will tend to have the better end of the majority of interactions they are exposed to. And when resource differentials grow, larger socioeconomic resource differences will only tilt the balance further towards the better-off. While individuals with lower socioeconomic positions can experience material difficulties and simultaneously witness how others are systematically out-competing them—experiencing the social–psychological effects of relative disadvantage—better-off groups can be more psychologically and economically insulated from changes in income inequality. Therefore, the social– psychological effects of relative disadvantage and the feeling of conflict are expected to grow among the worse-off and have little to no effect among the betteroff. Based on these considerations, we expect conflict perceptions to become increasingly divergent with growing income inequality, and that these changes affect individuals from lower socioeconomic positions relatively more strongly (H3b). Data and methods Data Our focus on the contextual effect of economic prosperity and income inequality and its moderating impact on individual-level relationships requires a cross-national comparative research design. We draw on microdata from five rounds of the International Social Survey Program’s (ISSP) (2024) Social Inequality module (1987, 1992, 1999, 2009, and 2019) in combination with country-level indicators from different sources (see Supplementary Table A1, Supplementary materials). Given our focus on the longitudinal association between macro-level variables and conflict perceptions, we restrict our sample to respondents above the age of 18, retaining only countries that participated at least twice and have non-zero variation in inequality. 1 After listwise deletion of missing values, our main analytical sample contains 87658 respondents in 90 country-years and 26 countries. Table 1 shows the number of observations by country and year included in the analysis. Dependent variable Our dependent variable is measured using a question that asks respondents to rate the level of conflict they perceive between different socioeconomic groups: ‘poor people and rich people’, ‘working class and middle class’, and ‘management and workers’. This item has been validated in previous studies that measure perceived socioeconomic conflict (PSC) (e.g., Delhey and Keck, 2008; Schöneck, 2017; Hertel and Schöneck, 2019). Answers to the three questions were recorded on a four-point scale (1 ‘Very strong conflicts’, 2 ‘Strong conflicts’, 3 ‘Not very strong conflicts’, 4 ‘There are no conflicts’). To facilitate substantive interpretation, we reversed the items so that higher values indicate greater PSC and rescaled them to range from 0 to 3. Subsequently, by taking the mean of all valid responses, we combined the three items and constructed an index probing respondents’ perception of socioeconomic conflict (α=0.753). Descriptive statistics of our dependent variable can be seen in the Supplementary Appendix (Supplementary Table A1, Supplementary materials). Independent variables The main focus of our research lies on the relationship between macroeconomic changes and people’s perception of socioeconomic conflict. Hence, our independent variables are income inequality and economic prosperity. To measure income inequality, we rely on the Gini coefficient of disposable income from the Standardized World Inequality Database (Solt, 2020). For economic prosperity, we use the gross domestic product (GDP) 4 MÁRQUEZ ROMO, BIENSTMAN AND GANGL Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 per capita measure provided by the Maddison Project Database (Bolt and van Zanden, 2025), expressed as a logarithm with base 2. 2 In additional analyses, we also substitute GDP for median net household equivalized income, obtained from the Luxembourg Income Study (LIS) and the Organization for Economic Co-operation and Development (OECD), as well as for median household equivalized income estimates from the ISSP. All country-level variables are measured one year prior to the survey round. Our primary indicator for individual socioeconomic position is Oesch’s (2006) five-class schema. This measure distinguishes between unskilled workers, skilled workers, small business owners, lower-grade salariat, and higher-grade salariat. 3 The Oesch classes are constructed to differentiate occupational classes vertically – in terms of their relative advantage in employment relations – and horizontally – in terms of their work logic (Oesch, 2006). We opt for the five-class schema instead of a more differentiated version mainly for technical reasons, as only this reduced schema can be constructed for some of the countries in the earliest ISSP rounds (see Oesch and Vigna, 2022). The five-class schema nevertheless serves our purpose since it differs from more finegrained versions mostly in the degree of horizontal differentiation. Control variables At the individual level, we employ controls for age, gender, and labor force status (paid work, unemployed, and others not in the labor force), as prior research suggests these characteristics are often related to individual differences in PSC (e.g., Janicka, 2002; Zagórski, 2006; van Drunen, Spruyt and van Droogenbroeck, 2021). Table 1 Number of respondents by country and year Country 1987 1992 1999 2009 2019 Total Australia 1,302 1,247 752 1,173 632 5,106 Austria 755 746 366 632 1,014 3,513 Bulgaria — — 798 489 981 2,268 Canada — 618 719 — — 1,337 Chile — — 826 1,050 679 2,555 Czech Republic — 561 1,376 767 1,146 3,850 France — — 1,359 2,271 1,311 4,941 Germany 717 2,245 667 1,053 1,035 5,717 Hungary 2,343 1,110 834 736 — 5,023 Israel — — — 774 925 1,699 Italy 606 605 — 665 507 2,383 Japan — — 812 614 907 2,333 Latvia — — 741 684 — 1,425 New Zealand — 742 717 620 906 2,985 Norway — 1,077 911 1,186 1,083 4,257 Philippines — — 853 1,056 3,872 5,781 Poland — 1,459 545 965 — 2,969 Portugal — — 968 520 — 1,488 Russia — 1,251 645 1,151 1,377 4,424 Slovakia — 313 824 842 — 1,979 Slovenia — 574 692 536 787 2,589 Spain — — 789 532 — 1,321 Sweden — — 794 892 1,349 3,035 Switzerland 725 — — 788 2,300 3,813 United Kingdom 1,006 878 681 796 1,269 4,630 United States 1,353 1,053 977 1,365 1,489 6,237 Total (Subjects) 8,807 14,479 18,646 22,157 23,569 87,658 Total (Countries) 8 15 23 25 19 26 INCREASINGLY POLARIZED? 5 Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 Additionally, a country’s level of income inequality and economic prosperity is likely to be influenced by its sociodemographic composition. Although this concludes the list of variables included in our main specification, we also present models controlling for years of education 4 and household income. With respect to income, the ISSP cumulative dataset includes only a relative measure that differentiates between low, middle, and high household incomes based on the distributions of the original underlying variables. We instead opt for a newly harmonized measure of equivalized household income, expressed in 2010 purchasing power parities and adjusted for inflation using the consumer price index. 5 Finally, we also present models including net migration and unemployment rates as additional country-year control variables. Both indicators are drawn from the World Development Indicators (WDI, 2024). Net migration rates are transformed to measure the total number of immigrants minus emigrants per 10.000 inhabitants (population size obtained from Coppedge et al., 2025). 6 Unemployment is measured as the percent of the total labor force. 7 Descriptive statistics for all variables included in the analyses are shown in Supplementary Table A1 in the Supplementary Appendix. Analytical strategy The cumulation of ISSP data can be characterized as comparative longitudinal survey data (CLSD, see Fairbrother, 2014). CLSD include newly surveyed individuals nested in survey waves, which are in turn nested in countries. Besides the need to account for this clustering in order to obtain more appropriate inferences, using repeated observations of countries over a period of over thirty years offers a unique opportunity to assess country-level effects by employing methods that leverage the longitudinal features of the ISSP. To analyze CLSD, the Random Effects Within and Between (REWB) specification for multilevel models has become a valued method because it enables the simultaneous estimation of effects between countries (i.e., cross-sectional) and within countries over time (i.e., longitudinal). Employing the REWB model in our analyses allows us to move beyond the crosssectional relationships that most previous studies have relied on in order to assess the within-country association between economic prosperity, income inequality, and perceived socioeconomic conflict. In the REWB specification, contextual effects are decomposed into their between and within components by demeaning all country-year level variables. The resulting between effect will thus capture the relationship between the outcome variable and overall levels in the predictor, whereas the within effect captures the relationship between changes in the predictor and changes in the outcome. By demeaning the explanatory variables, the within estimates of the REWB model are equivalent to those obtained from a standard Fixed Effects model and thus equally benefit from weaker assumptions regarding unobserved covariates (see Firebaugh, Warner and Massoglia, 2013). Specifically, the within estimates obtained from either REWB or Fixed Effects regression models are free from the influence of any time-constant country characteristics, minimizing the risk of omitted variable bias. With these preliminaries, the main threat to giving our estimates a causal interpretation stems from between-country differences that change over time, an issue we address with a series of sensitivity analyses to bolster the credibility of our results. Of course, one important implication of using CLSD is that within estimates are not available at the individual level, and the potential to include additional covariates is limited by their availability in the ISSP. For that reason, we are more cautious in extending a causal interpretation to the impact of social class on perceived socioeconomic conflict and the moderating role of income inequality. In the subsequent analysis, we fit a series of REWB models with country and country-year random intercepts to simultaneously model the between and within relationship between economic contexts and perceived socioeconomic conflict—restricting causal interpretations to the within term for the reasons described above. In order to examine the polarizing effect of income inequality, we fit a series of cross-level interaction models to examine the moderating impact of income inequality on the relationship between social class and PSC. Here, it is to be noted that, while the within estimates of contextual effects are net of unobserved heterogeneity between countries, the same is not true for cross-level interaction effects (see Giesselmann and Schmidt-Catran, 2019). Therefore, to properly account for between-country unobserved heterogeneity in the interaction effects, we employ the country Fixed Effects and Slopes specification (cFES). This amounts to a regression including not only the covariates and interaction terms of interest (as well as country and period fixed effects and clustered standard errors by country and country-years), but also additional interactions between country dummies and all terms included in the interaction. In other words, on top of the interaction between social class and the demeaned Gini coefficient, the cFES also includes interactions between the country dummies and both the social class and inequality terms. Finally, we note that we will follow conventional statistical practice and report standard errors and statistical significance levels for all parameter estimates from our regression models. We see these as indicators of the inherent uncertainty of our coefficient estimates, but at the same time acknowledge the scholarly debate around prioritizing 6 MÁRQUEZ ROMO, BIENSTMAN AND GANGL Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 substantive effect sizes over statistical significance (see Bernardi, Chakhaia and Leopold, 2017), particularly in cross-nationally comparative research, where neither countries nor country-years represent a random sample from a superpopulation of societies (see Lucas, 2014, for a more detailed discussion). In our subsequent presentation of results, we will primarily emphasize effect sizes when describing our results, while providing full transparency in reporting our estimates to allow readers to judge the evidence that we present against the scientific criteria that she or he may best see fit. 8 Results Exploratory analysis We begin our analysis with an exploration of the bivariate relationship between the level and distribution of economic resources and the level of PSC. While panel A in Figure 1 plots the country means (across all available Figure 1 Bivariate between-correlations INCREASINGLY POLARIZED? 7 Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 waves) of logged GDP per capita and PSC, panel B shows the same for the Gini coefficient. Focusing on differences across countries, there is a clear negative association between economic prosperity and the average levels of PSC. By contrast, countries with higher inequality exhibit higher levels of PSC. Shifting towards a longitudinal perspective, the two panels in Figure 2 plot the country-year deviations from the overall country mean on economic prosperity (A), income inequality (B), and PSC. While these figures are in line with the general expectations regarding how changes in macroeconomic characteristics should affect changes in PSC, the relationships over time differ notably from those across countries. While both types of variation suggest a negative association between economic prosperity and PSC, and a positive association between income inequality and PSC, the longitudinal associations appear much weaker. Therefore, to assess whether these relationships are robust to the inclusion of individual and contextual-level controls, the next section presents the multivariate results. Figure 2 Bivariate within-correlations 8 MÁRQUEZ ROMO, BIENSTMAN AND GANGL Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 Table 2 The effect of economic prosperity and income inequality on PSC M1 M2 M3 M4 Lower-grade service class 0.049*** 0.027*** 0.027*** (0.007) (0.007) (0.007) Small business owners 0.066*** 0.013 0.013 (0.008) (0.009) (0.009) Skilled workers 0.101*** 0.051*** 0.053*** (0.006) (0.007) (0.007) Unskilled workers 0.138*** 0.072*** 0.071*** (0.007) (0.008) (0.008) HH equiv. income −0.049*** −0.049*** (0.002) (0.002) Years of education −0.006*** −0.006*** (0.001) (0.001) Gini index (BE) 0.017*0.017*0.017*0.017* (0.007) (0.007) (0.007) (0.008) Gini index (WE) 0.023** 0.023** 0.022** 0.017 a (0.009) (0.009) (0.009) (0.009) Log GDP/capita (BE) −0.074 −0.061 −0.062 −0.075 (0.053) (0.052) (0.052) (0.077) Log GDP/capita (WE) −0.004 −0.001 0.050 0.111 (0.074) (0.073) (0.072) (0.082) Unemployment rate (BE) −0.006 (0.015) Unemployment rate (WE) 0.010 (0.009) Net migration (BE) 0.000 (0.002) Net migration (WE) 0.001 a (0.000) Intercept 1.909*1.748*1.890*2.160 a (0.855) (0.851) (0.840) (1.227) Period FE Yes Yes Yes Yes Individual controls No Yes Yes Yes Akaike information criterion (AIC) 161074.459 160099.460 159546.941 143626.297 Bayesian information criterion (BIC) 161187.033 160287.084 159753.327 143858.179 Log likelihood −80525.229 −80029.730 −79751.471 −71788.148 Var (country-year) 0.012 0.012 0.011 0.011 Var (country) 0.033 0.033 0.032 0.038 Var (individual) 0.366 0.362 0.359 0.359 N countries 26 26 26 26 N country-years 90 90 90 82 N respondents 87,658 87,658 87,658 78,851 Notes: Models control for gender, age, and labor force status. a p<0.10. *P<0.05. **P<0.01. ***P<0.001. INCREASINGLY POLARIZED? 9 Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaf052/8341093 by guest on 25 November 2025 Sociology, the XVII Spanish Congress of Political Science and Administration, and the 2024 ECSR General Conference. We thank all participants, Claudia Traini, Sven Ehmes, Ildefonso Marqués-Perales, Macarena Ares, as well as the editors and three anonymous reviewers for their helpful comments. We also thank Sara Hueber, Emir Zecovic, and Stelios Nakos for valuable research assistance in the project. Supplementary data Supplementary data are available at ESR online. Funding This research is part of the POLAR project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No 833196). Open access funding agency statement Open access publication is supported by Goethe University Frankfurt am Main. 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