Contribution and Structure

The contributions of the paper are fourfold and are reflected in the structure of the paper. First, we discuss and theorize factors that should skew analyses either in favor of, or against, finding partisan effects on inequality. Second, we take stock of the inconclusive results from the first systematic review of government effects on inequality, summarizing over three decades of research. Third, our key findings speak directly to the heated debate about the steering capacity of political parties in the politics of inequality. We show that regression analyses that leave out the position of the most affluent, that focus on annual effects rather than medium- and long-term effects, and that include various policies through which partisanship exerts an influence are much less likely to yield positive results. Vice versa, analyses that look at inequality at the top, consider mid- and long-term effects, and avoid policy controls are very likely to show that the composition of governments has a significant effect on inequality.

By contrast, the status of partisanship as an explanatory or control variable plays a less important role. We have included it because we were concerned about publication bias, especially at top journals. Fortunately, this idea was disconfirmed in a variety of tests. Likewise, the expectation—inspired by realignment and welfare research—that party effects are characteristic until the 1970s and then fade away proves to be descriptively correct, but is not robust in the multivariate analyses.

In a fourth step, we discuss the substantive implications for the politics of inequality and summarize what researchers can learn from our results for future studies on the (party) political determinants of inequality. Most importantly—and despite the limitations that we acknowledge—if done right, analyses of party effects show that governments affect inequality.

Theory-Guided Systematic Analysis, 1990–2023

To make sense of competing findings, we build a comprehensive dataset capturing key study characteristics and employ bivariate analysis and meta-regression of over 30 years of research. A meta-regression (Littell, Corcoran, and Pillai Reference Littell, Corcoran and Pillai2008) uses statistical tests (in our case, results from time-series cross-sectional [TSCS] regressions) as units of analysis, study features as explanatory variables, and estimation results as dependent variables. This enables researchers to assess how individual choices in the research process affect the results. It thereby synthesizes empirical evidence, which makes it a key technique for systematic literature reviews that aim for systematic selection, collection, evaluation, and synthesis of research (Dacombe Reference Dacombe2018).

Our point of departure is the inconclusiveness of results found in 43 peer-reviewed articles and 393 results from TSCS models that look at party ideology effects in OECD countries. We describe the three-tier search and filter strategy we used below. What is crucial for now is that only two out of every five results indicate that partisanship has an effect on inequality—and that these studies vary strongly in how they assess this relationship. While the existing literature offers little as to what explains this puzzling variation, we unpack it in two empirical steps—using group comparisons and using multilevel logistic regression. As a robustness test, we complement these analyses with a multilevel meta-analysis based on available (standardized) regression coefficients. Before we conduct any meaningful analyses, two questions are logically prior. First, we need to outline why governments should have an effect. Second, to decipher the inconclusive results in theoretically predictable ways, we theorize how a few key choices affect the findings. Both aspects are addressed hereafter.

Why Should Government Parties Affect Inequality?

The impact of government ideology on economic inequality remains a contested issue. There is ample empirical evidence supporting and rejecting the argument that parties and cabinets can do something about it. Yet before we decipher the evidence further, we need to assess the arguments that speak in favor of, or against, party effects on economic inequality. We find three main perspectives. First, two influential approaches in political science suggest that government partisanship should indeed affect inequality: the power resources approach and partisan theory. Second, the predictions derived from these approaches contrast with three sets of midrange theories that suggest that parties—even left ones—do not strive for equality in unequal contexts. Third, outside political science, a broad literature, discussed in academia and outside it, suggests that governments are either unable (Scheidel Reference Scheidel2017, but cf. Atkinson Reference Atkinson2015) or not committed enough (Piketty Reference Piketty and Goldhammer2020) to fight inequality via legislation.

First, starting with the theoretical workhorses and meta-approaches in political science, it is clear that these would suggest a classic partisanship pattern: while the Left is expected to fight inequality, the Right is more indifferent or accepting of material inequalities. Partisan theory (Hibbs Reference Hibbs1977) and power resources theory (PRT) (Esping-Andersen Reference Esping-Andersen1990; Korpi Reference Korpi1978; Reference Korpi1983; Stephens Reference Stephens1979) suggest that the distribution of societal and political power resources and the political coloring of governments are decisive for egalitarian outcomes and income equalization.Footnote ³ Hibbs (Reference Hibbs1977, 1468) claims that “the macro-economic policies pursued by left- and right-governments are broadly in accordance with the objective economic interests and subjective preferences of their class-defined core political constituencies.” Likewise, Korpi (Reference Korpi1983, 107) argues that “the relationship between social position and party preference is generated by the fact that the social structure gives various groups in society partially divergent interests, which the political parties then may attempt to promote and safeguard” in the “democratic class struggle.” With regard to inequality, the rationale is that “[a]ctors relying primarily on economic resources can be expected to favor market distribution, while categories of citizens relatively disadvantaged in terms of economic resources and relying primarily on their labor power are likely to combine in the sphere of politics to modify outcomes of, and conditions for, distributive processes on markets” (Korpi and Palme Reference Korpi and Palme2003, 427). Variations of the argument that left parties strive for economic equality because their constituency demands it can still be found in many of the articles we surveyed. By contrast, the transformation of party systems and new electoral cleavages (Häusermann and Bornschier Reference Häusermann and Bornschier2023) are acknowledged, but do not crowd out the focus on the old class conflict.

Second, there are a number of midrange approaches in political economy that call into question the assumption that left parties fight high inequality. This literature suggests that inequality should be self-reinforcing irrespective of the distribution of political power between left and right parties, either because it is shown that in contexts of higher inequality the (below-median) core constituency of the Left is demobilized and does not vote (Gallego Reference Gallego2015; Solt Reference Solt2008), which renders it less attractive to strive for more equality, or because inequality levels have become part of the status quo perception of voters, who have consequently internalized inequality outcomes as meritocratic (Mijs Reference Mijs2021; Trump Reference Trump2018). Alternatively, it could be that neither parties nor voters are sufficiently cognizant of high inequality (McCall Reference McCall2013; McCall et al. Reference McCall, Burk, Laperrière and Richeson2017).

Third, outside political science, the recent debate about the politics of inequality has been shaped by studies that provide pessimistic answers regarding the impact of parties. The instruments parties have to fight inequality, like reforms and legislation (Atkinson Reference Atkinson2015), do not compensate for the absence of war, pandemics, and state failure—the historical levelers of the economic playing field (Scheidel Reference Scheidel2017). “More benign influences” (2017, 22), such as reforms and legislation, in that view, do not work. There is no way to regulate or vote ourselves to significantly greater equality (9). Or, as Piketty (Reference Piketty and Goldhammer2020) argues, left parties are no longer especially focused on economic equality because their constituencies are increasingly influenced by the highly educated (the “brahmins”), who care less about economic equality than the old left electorate.

Why Studies Underestimate Party Effects

The aforementioned inconclusiveness brings up two key questions: What is it—assuming that the steering capacity of governments is viewed as desirable—that distinguishes optimists from pessimists? And what are the theoretically relevant dimensions that help us to better understand when government partisanship matters? In answering these questions, we rely on a close reading of 43 studies we identified as relevant and of the rationales that their authors provide.

Moreover, we draw on discussions from a field that has successfully agreed on answers to the question of whether and how parties matter. Comparative welfare state research has reduced the uncertainty surrounding the causes of conflicting results regarding the impact of parties. They have identified three factors that help to explain seemingly incompatible results.Footnote ⁴ First, different periods of welfare state evolution have been identified. The effect of partisanship has become less pronounced after a “golden age” of welfare state expansion was surpassed by an era of retrenchment in which competitive and fiscal pressures undermined the autonomy of parties (Green-Pedersen and Haverland Reference Green-Pedersen and Haverland2002; Horn Reference Horn2017; Pierson Reference Pierson1994; Starke Reference Starke2006). Second, a long debate about the dependent variable problem (Clasen and Siegel Reference Clasen, Siegel, Clasen and Siegel2007; Green-Pedersen Reference Green-Pedersen2004) has shown that spending ratios are less susceptible to political control than social rights and welfare benefits. Finally, with regard to government partisanship, a fair test of the impact of parties should complement the use of static party labels with dynamic and issue-specific conceptualizations of ideology (Döring and Schwander Reference Döring and Schwander2015; Horn Reference Horn2017). As a result, it is now common to motivate choices regarding (1) time frame, (2) choice of the dependent variable, and (3) the way party effects (the independent variable) are conceptualized.

In the following, we theorize these three dimensions with regard to inequality effects.

How Did We Select and Summarize Studies?

This analysis covers all relevant articles published in English-language peer-reviewed journals between 1990 and 2023. The selection criteria for relevance were that studies (1) conduct a direct test of partisan effects on inequality, (2) focus on OECD countries, (3) include at least 10 countries, and (4) use TSCS analyses with a minimum time-series length of 10 years. These criteria ensure a minimum level of conceptual and methodological comparability across studies, which facilitates systematic analyses (Cooper Reference Cooper, Cooper, Hedges and Valentine2019). The OECD focus has the advantage that the political dynamics and scope conditions for party effects are similar and that functionally equivalent data and measures are available. Part 1 of the supplementary material provides all details on the selection and coding of articles. While a focus on published work could be susceptible to publication bias, empirical evidence suggests that broad criteria, including unpublished work or gray literature, could distort results due to a lack of quality control for such studies (Egger et al. Reference Egger, Peter Jüni, Holenstein and Sterne2003). Moreover, we include studies whose main aim is to assess the impact of parties as well as studies that merely control for partisanship. This design allows us to assess empirically whether studies that primarily test hypotheses of partisan effects on economic inequality are more likely to report that government ideology matters.

To retrieve and select relevant articles, we use three search strategies. While these approaches yield similar results, we deliberately chose this conservative and time-consuming strategy to confirm that we did not miss a study that meets our criteria. First, we ran comprehensive keyword searches in the most important databases covering published peer-reviewed work from disciplines related to the topic: Web of Science and ProQuest. A very broad keyword list generated 2,692 unique hits. We manually inspected all articles and, based on a close reading of titles and abstracts, we excluded irrelevant articles (e.g., single-county studies, non-OECD country samples, and studies on subfields of inequality or welfare) and identified potentially relevant ones. Second, we further checked whether the latter contained results from TSCS regressions with inequality as a dependent and partisanship as an independent or control variable. Third, once this step was completed, we applied two complementary strategies for each relevant article: (1) we checked all articles citing it and (2) inspected its references. We repeated these steps for each new article we discovered and only stopped when we could not retrieve additional ones. Based on our selection criteria, we had to exclude valuable cross-sectoral studies (with a focus on corporatism and the Kuznets curve) and research that did not account for partisanship from the 1990s (Gottschalk and Smeeding Reference Gottschalk and Smeeding1997; Gustafsson and Johansson Reference Gustafsson and Johansson1999; Nielsen and Aldersen Reference Nielsen and Alderson1995). In light of the data constraints scholars faced until the late 1990s, the absence of studies from the 1990s is plausible. Finally, consultation of five authors who have published extensively on the party politics of inequality confirmed that we had not overlooked any relevant studies.

Data and Variables

The dataset contains 43 studies from three disciplines: political science (25), sociology (13), and economics (5). We collected information from 330 regression models presenting 393 coefficients on the relationship between partisanship and inequality in the main text of an article. The different numbers of coefficients stem from studies including variables on left and right partisan shares in the same model. On average, we collected nine coefficients from each article. All studies were published between 1999 and 2023; three-quarters after 2008. The fact that almost one in three studies were published in leading journals, such as World Politics (4), Socio-Economic Review (4), American Political Science Review (3), and Comparative Political Studies (2) attests to the high general relevance of this research field. However, while all leading political science, subfield, and sociology journals are represented as well, the articles appeared in a wide range of journals (e.g., judging based on the journals’ impact factors).

Bivariate Analysis

Time Period

Let us start with the question of whether the prevalence of partisan effects is associated with a certain period. Historians and scholars of the welfare state and inequality often hold that the “golden age” of capitalism (1960s–1970s) coincided with welfare state expansion, followed by an era of retrenchment. During the latter, governments thought they had little choice but to distribute losses rather than gains (Esping-Andersen Reference Esping-Andersen1990; Hobsbawm Reference Hobsbawm1996; Pierson Reference Pierson1994). Studies focusing on the impact of globalization or international trade also tend to see the late 1970s and early 1980s as a watershed moment, after which the political choices of elected governments were increasingly constrained. This was caused by tighter fiscal conditions (after two oil crises) due to lower growth and deeper economic integration, which provided companies and capital with an exit option (Nam Reference Nam2020; Scharpf Reference Scharpf2000). This was perceived as a threat to tax-and-spend policies, predistribution and redistribution (often used to pacify social conflicts), and the postwar compromise between capital and labor (regarding wage bargaining, training, or welfare state schemes) more broadly. As decade or period dummies are not available for all studies, we account for the relative importance of the golden age. On average, this period accounts for 18% of the time series. For graphical presentation, we divide all observations into quartiles and plot the percentage share of models reporting a partisan effect across these groups.

According to figure 1, only one in four results report that parties or cabinets affect economic inequality when the golden age is not covered in the time series. This fraction increases with the growing weight of this time period and peaks at one in two results, suggesting that parties’ capacity to shape economic inequality has continuously declined since the golden age.

Figure 1. Time Periods and Partisan Effects on Inequality

Notes: Number of observations on top of bars. Whiskers indicate 90% confidence intervals. Y-axis shows the share of models that indicate direct and significant party effects (0.8 = 80%). TS: time series.

Type of Inequality

Measures of inequality vary in terms of focus and granularity (Atkinson Reference Atkinson2015; Jensen and van Kersbergen Reference Jensen and van Kersbergen2017). Though a few studies try to convey that they study economic inequality as a whole despite a narrow focus on one specific kind, many of the 43 studies acknowledge that different political incentives and logics apply to the measures at hand. Consider the Occupy Wall Street slogan “we are the 99 percent,” directed against the increasing share of income held by the 1 percent; discussions about the backsliding of the middle class vis-à-vis the economic elites; and concerns about growing overall inequalities according to Gini coefficients. Top-share inequality, ratios between income groups, and measures that summarize the inequality of income distribution have different political implications. In terms of political steering capacity, we expect effects for inequalities that tap into the opposition of “rich” or “well-off” versus “the rest” rather than those that tap into a less observable Gini coefficient or the mean-to-median ratios.

To assess whether a dependent variable pattern applies, we summarize inequality measures into three broader categories reflecting their operationalization and data sources:Footnote ⁷ (1) Gini coefficients, (2) income ratios, and (3) top income shares. As panel (a) of figure 2 shows, Ginis are the most commonly used indicator, yet they are also the least likely to yield partisan effects (35%) compared to income ratios (42%) or top income shares (38%).

Figure 2. Measure of Inequality and Partisan Effects on Inequality

Notes: Number of observations on top of bars. Whiskers indicate 90% confidence intervals. Y-axis shows the share of models that indicate direct and significant party effects (0.8 = 80%).

As the three categories are quite heterogeneous, we further explore variation within and across indicators. First, we compare (1) pre-tax, (2) pre- versus post-tax, and (3) post-tax Ginis. The former only picks up income inequality based on wages and is less susceptible to direct political influence than post-tax measures, which account for parties’ and cabinets’ capacity to redistribute wealth based on taxes or spending. As shown in panel (b) of figure 2, party effects were up to four times more likely when Ginis account for redistribution (55%) compared to pre-tax and transfers Ginis (18%). As we argue that top incomes (including those of the more reliably measured well-off) might be more susceptible to partisan effects than broader indicators, we consolidate our data into measures pertaining to top incomes versus the rest (panel [c]). The former contains top income shares (top 1%, top 10%) and ratios including this group (90th–10th or 80th–20th percentile). The latter summarizes the Gini coefficients and income ratios that contrast middle- and lower-income groups (50th–10th percentile). The figure’s right panel confirms that partisan effects are more likely for top income groups (44%) than for the residual category (32%). Hence, the influence of parties appears to be greater for measures relating to high- and top income groups that are channeled through redistributive policies.

Partisanship

Research on partisan effects has argued that the measurement of partisanship will affect the ability of researchers to detect such effects (Döring and Schwander Reference Döring and Schwander2015; Horn Reference Horn2017). Typically, these studies make a strong case that fine-grained (e.g., dynamic and issue-specific) measures are more likely to capture meaningful variation than simpler approaches relying on party labels (e.g., left versus right shares measured via historic expert judgments or party family affiliation). Yet thus far, research on partisan effects on inequality has almost exclusively used party labels (e.g., ordinal measures based on expert judgments). Only two (Neal Reference Neal2013; Rueda Reference Rueda2008) out of 43 studies incorporate variation across parties using a more specific measure derived from the quantitative content analysis of election programs (Volkens et al. Reference Volkens, Burst, Krause, Lehmann, Theres, Regel, Weßels and Zehnter2021). Yet even these studies rely on rather broad left–right scores, which encompass many categories that are unrelated to a party’s stance on inequality. This neglect of dynamic and issue-specific preferences is in itself a striking finding, but the lack of variation in the measurement of party profiles does not allow us to include this aspect in our analysis.

First, we look at short-term versus long-term effects of government partisanship. Studies that assume immediate or short-term effects operate at the time of observation t0 or at t − 1 (Kwon Reference Kwon2018; Sjöberg Reference Sjöberg2009; Wallerstein Reference Wallerstein1999). By contrast, studies that assume that the political effects on inequality unfold over the long term use a cumulative measure that sums up the share of left (or right) parties in cabinets over a long time (Beramendi and Cusack Reference Beramendi and Cusack2009; Bradley et al. Reference Bradley, Huber, Moller, Nielsen and Stephens2003; Huber and Stephens Reference Huber and Stephens2014). As shown in panel (a) of figure 3, studies using a cumulative measure were twice as likely to report partisan effects (50%) than those looking at short-term effects (25%).

Figure 3. Conceptualization of Partisan Effects on Inequality

Notes: Number of observations on top of bars. Whiskers indicate 90% confidence intervals. Y-axis shows the share of models that indicate direct and significant party effects (0.8 = 80%). IV: independent variable.

Likewise, articles with an explicit focus on partisan effects are more likely to report effects. We compare whether partisanship was presented as an independent or a control variable. The former group of studies formulate explicit hypotheses regarding partisan effects and reflect on the importance of this variable in the title, abstract, introduction, or conclusion; the latter merely treat it as a control. Panel (b) of figure 3 shows that half of the studies hypothesizing about party effects on inequality report such effects (47%), a figure that drops below a third for studies with another research focus. These differences could suggest publication bias or point to the importance of including other variables that absorb or condition party effects. While supplementary analyses indeed suggest that high-impact journals are less likely to publish nonfindings, this effect vanishes once we account for the inclusion of policy channels (see section S-B in part 2 of the supplementary material), which alleviates our concerns about publication bias.

Finally, we turn to the role of direct and indirect policy channels through which parties may not exert an impact on inequality. Though not all studies consider policies, and the ones that do look at widely different policies, we group these variables into four groups: (1) policies (e.g., public spending, taxes, and decommodification); (2) corporatism (e.g., unions and wage bargaining); (3) postindustrial transformation (e.g., changes in the labor market or the education sector); and (4) globalization (e.g., financial and economic openness).Footnote ⁸

We also sum these variables up to account for analyses incorporating variables such as wage inequality, welfare expenditures, tertiarization of the labor market, and trade openness in the same models. Some of the channels are theoretically related to cabinet ideology (policies), reflect a country’s political power relations (corporatism), or represent more general scope conditions for party action (financial openness). As outlined above, these variables can moderate or absorb party effects (Pontusson, Rueda, and Way Reference Pontusson, Rueda and Way2002; Rueda Reference Rueda2008). On average, studies accounting for a multitude of policy channels should be less likely to detect partisan effects. The right panel of figure 3 presents evidence based on a median split of the number of policy channels (evidence for separate policy channels is available in figure S-B1 in the supplementary material). Including a moderate/low number of such variables means that studies are more likely to report partisan effects compared to studies where parties and cabinets had to “compete” with up to eight policy channels. Thus, controlling for policy channels can reduce direct effects of parties and cabinets. As these channels include policy interventions (e.g., social policies, taxes) or reflect power relations (corporatism), this finding is not surprising.

In sum, there is strong bivariate evidence that the explanations that we identified determine partisan effects on inequality. To be sure, some of these variables are correlated, and our observations are not independent of each other (we have multiple results from the same studies). To conduct an authoritative test, we proceed with multilevel logistic regressions.

Multivariate Analysis

To examine whether the strong bivariate patterns hold in multivariate analyses, we specify logistic regression models. Our dependent variable indicates whether a result reports a significant partisan effect on inequality (1) or not (0). As multiple results from the same article are not independent of each other, we add random effects at the level of articles. The number of clusters (43) is rather high for the total number of observations (393), and some clusters have few observations, which can be challenging for multilevel models as estimates may be less precise (Raudenbush Reference Raudenbush, de Leeuw, Meijer and Goldstein2008). Yet the dependency of observations and model diagnostics suggests that the heterogeneity between studies requires multilevel modeling (the random effects compose approximately 70% of the total residual variance). As a consequence, some standard errors are somewhat large, yet results are robust across alternative specifications (such as ordinary least squares [OLS] multilevel models and logit models with article-clustered/robust standard error) or if we rerun our estimation with observations from articles with at least five or 10 results per cluster.

The models jointly include the explanatory factors outlined above. As some of the variables are correlated, we present separate models for each of them in the supplementary material. We further add the variables outlined in the previous section (journal impact, logged number of observations) as controls. The supplementary material provides additional robustness checks, including different model specifications (OLS multilevel models and logit models with article-clustered/robust standard errors) accounting for different data sources of inequality, and presents results for samples of observations from articles with at least five or 10 results per cluster. As discussed in greater detail in the robustness section, we also present results based on pooling an overall effect for each study and measure of inequality. We examine whether party effects differ for studies on inequality and redistribution, or if market inequality or disposable income inequality (after taxes and transfers) yield different results. We also use a more conservative aggregation rule for top-level inequality and control for studies using country-fixed effects. We also reproduce the core results based on standardized regression coefficients. All of the additional analyses corroborate the results reported below.

Results

What determines partisan effects on inequality? Table 1 and figure 4 confirm most of the bivariate patterns presented above except for the share of the “golden age.” While the time-period effect has the expected positive effect in the separate model (see table S-C1, model 1, in the supplementary material), the insignificant coefficient even turns negative with a large standard error once we add the remaining explanatory variables. Thus, the strong bivariate pattern of the golden age and partisan effects is explained away by more substantive factors.

Table 1. Explaining Partisan Effects on Inequality

Notes: Robust standard errors in parentheses.

* p < 0.10

** p < 0.05

*** p < 0.01

Figure 4. Predicted Probabilities Plot

Notes: Y-axis shows predicted probabilities along with 90% confidence intervals. Dashed line indicates average effect. IV: independent variable. Results are based on table 1. All remaining variables are held constant at their observed values.

Turning to the role of different types of inequality, models 1–3 and figure 4 corroborate and nuance our conclusions from the discussion above. For model 1, positive coefficients of income ratios and top income shares confirm that the choice of dependent variable matters. Results from these indicators are more likely to report partisan effects (p-value below 0.05 for top income shares). To provide a more intuitive interpretation of the effects, we plot the predicted probabilities based on model 1. Figure 4 shows that studies using top income shares as a dependent variable were 16 percentage points more likely to report partisan effects than studies relying on the Gini coefficient. Compared to Gini indicators, drawing on income ratios increased the rate of positive findings by 7 percentage points. Thus, researchers using data on top income shares (top 1%, top 10%) or ratios comparing the better-off to other groups (90:10, 80:20) were considerably more likely to find that partisanship has an effect on economic inequality.

Regression coefficients in model 2 and predicted probabilities in figure 4 confirm strong variation for research relying on different Gini indicators. While standard errors are somewhat larger due to the smaller sample size, predicted probabilities in the central panel of figure 4 show differences in reporting partisan effects when comparing pre-tax Ginis (22%) and post-tax Ginis (34%) to pre- versus post-tax Ginis (55%).

Looking into differences in partisan effects for studies focusing on top income groups or other segments of the population, model 3 reports a positive effect (coefficient of 2.34, p-value below 0.05). This suggests that the political leaning of cabinets is more relevant when studies examine high- and very high-income groups (via top shares or vis-à-vis middle and/or lower income groups). Predicted probabilities in figure 4 attest to a substantial difference: on average, partisan effects were 20 percentage points more likely for measures including high and top incomes compared to the residual category.

Turning to the conceptualization of partisan effects, the temporal distinction of short-term versus cumulative effects shows positive coefficients in models 1 and 3 (p-values below 0.01). Studies arguing that ideology operates over the long term were thus more likely to capture partisan effects on inequality than those focusing on more immediate effects. Predicted probabilities in figure 4 show a sizable effect: studies relying on a long-term measure of partisanship were 19% more likely to report that partisanship matters for economic inequality than those using a short-term indicator (based on model 3, p < 0.01).

For the status of partisanship in a study, both models 1 and 3 show a negative coefficient, suggesting that studies merely controlling for partisanship were less likely to report that parties and cabinets matter for inequality. Figure 4 shows that studies using partisanship as a control variable were roughly 18 percentage points less likely to report effects than those treating it as an independent variable (predicted probabilities based on model 3). While the effect is substantive, it is not entirely robust: adjusting for other variables of interest reduces the effect and turns it insignificant (see also table S-C1, model 5, in the supplementary material). Thus, despite sizable differences resulting from the focus of a study on partisanship (or not), other explanatory variables account for much of the variation that stems from it.

Finally, the inclusion of (too) many direct or indirect policy channels, such as social spending, reduces the ability of parties to shape inequality. In figure 4, predicted probabilities for partisan effects drop by 27% across the empirical range (from 59% for models adjusting for no policy channels to 32% for models including eight such variables). To be sure, there is a sensitive balance between omitting important variables and the overdetermination of models that goes beyond the scope of a secondary analysis. However, including a multitude of policy channelsFootnote ¹⁰ obviously decreases the rate of partisan effects.Footnote ¹¹

Joint Effects of Predictors: When Do We Find Party Effects on Inequality?

Our results point to a number of individual factors that can shape partisan effects on inequality. Yet the choice of the dependent variable and the conceptualization of partisan effects reflect distinct choices by researchers. To further consolidate our understanding of partisan effects on inequality, we need to understand the joint effect of various combinations of the factors outlined above. Therefore, we estimate predicted probabilities based on the co-occurrence of the key variables identified in our previous analyses (regression results are based on table 1, model 3). Figure 5 presents the results based on all empirical combinations from the studies. It provides evidence that the conceptualization of inequality and partisan effects massively codetermine the likelihood that a study reports that partisanship matters.

Figure 5. Combined Predicted Probabilities for Partisan Effects on Inequality

Notes: Y-axis shows predicted probabilities with 90% confidence intervals. Results based on table 1 (model 3). All remaining variables are held constant. Bars show the number of observations, dashed line indicates the average predicted probabilities. In case of multiple combinations, values represent average effects.

The x-axis of figure 5 simply indicates how many variables are at values that were associated with partisan effects in our previous analyses, with values ranging from zero to four predictors. On the left-hand side of the x-axis, a value of zero represents results that (1) do not focus on top income groups, and (2) use a short-term measure of partisan effects (3) as control variable while (4) adjusting for six to eight policy channels (above median). For such studies/models, the rate of party effects is a mere 11 percentage points. This very low value septuples to 78% of the results for studies (1) with a focus on top incomes, (2) with a cumulative measure of partisan effects, (3) with partisanship as an independent variable, and (4) with few (zero to five) policy channel controls.

In sum, the choices individual researchers make with respect to their dependent, independent, and control variables massively codetermine the likelihood of reporting partisan effects on inequality—and they do so in theoretically predictable ways. Our systematic analysis (cf. Haselmayer and Horn Reference Haselmayer and Horn2024 for replication files) provides strong evidence that we can consolidate our understanding of the capacity of parties and cabinets to shape economic inequality if we consider the explanatory factors we have identified.

Additional Considerations and Robustness Tests

Beyond the robustness tests discussed above, we present further considerations and tests to make sure that the results presented above are robust. The results can be found in table S-C11 to table S-C17 in the supplementary material. We (1) examine whether party effects differ for inequality and redistribution, (2) show that our results hold for studies that focus on market inequality, (3) examine whether our results on top-level inequality are driven by how we aggregate and merge data into the top-versus-rest, (4) reproduce the main results based on the standardized regression coefficients, and (5) examine the impact of original studies using country-fixed effects, as well as the impact of differences in the number of cross-sectional units and the length of time series (numbers of countries divided by the number of years).Footnote ¹² These are outlined in more detail below.

1. 1. We run our original models and include a dummy indicator to capture whether the result is driven by inequality or redistribution. We then rerun the models for studies of inequality only, and run a model for the small number of models with redistribution as a dependent variable. If anything, these additional results show that the effects reported in the main analyses appear to be somewhat stronger for inequality alone. However, due to the small number of models with a redistribution dependent variable, we do not emphasize this result.

2. 2. In principle, the arguments we discussed for left–right gradients apply to predistribution and redistribution. Yet, to tease out potential differences in partisan effects between inequality before and after taxes and transfers, a dummy variable is employed to capture whether inequality before or after taxes and transfers is used. This has no effect on our results. Likewise, rerunning the analyses with market inequality effects alone reproduces our findings.

3. 3. In our analyses of inequality measures that pitch top earners versus the rest, we pool top shares with ratios that involve groups that may also be regarded as upper middle class (e.g., the 80th percentile). While this approach seems justified in light of the modest case numbers and extant research on yardsticks of inequality (Kevins et al. Reference Kevins, Horn, Jensen and van Kersbergen2018), it is still a fair question to ask if a narrower focus on the rich makes a difference. Thus, we further disaggregate and reaggregate our measure of “top versus the rest” inequality. As the aim is to work with a narrower measure of top inequality, we only use the shares or ratios that include the top 1% to top 10% (compared to other segments of income distribution). Additional regression models include a “full model” with all results, as well as a reduced model where we only include studies that use income shares or ratios as the dependent variable. For both models, the results are basically the same.

4. 4. We reproduce the core results based on standardized regression coefficients. As we pointed out above, this strategy has a number of drawbacks, to which we now add a few feasibility concerns. Most studies neither report standardized coefficients nor provide replication material or descriptive information needed to calculate them (i.e., standard deviations of inequality and partisanship). Due to nonresponses from original study authors, often for understandable reasons (some are dead, some have left academia), we could only gather results for four out of 10 regression results. As a result, the sample is not “representative” as it mostly includes newer studies, and there is a particularly low number of results from studies using the Gini coefficient as a dependent variable (eight papers). We discuss these constraints and quantify the biases in section S-C15b of the supplementary material. For all of these reasons, we are extremely cautious when it comes to the substantive relevance of the results in table S-C15a. Despite these serious reservations due to the loss of roughly half of our previously analyzed papers and several biases, the multilevel meta-regressions largely confirm the initial results. All effects point in the expected direction and (with one exception: control variables) are statistically significant too.

5. 5. Including a control for whether the models include country-fixed effects or the number of countries divided by the length of the time series does not affect our initial results.

Implications for Future Research and the Inequality Discourse

We first discuss three substantive implications and lessons for future inequality research. On this basis, we argue that our results can be seen as good news for the heated inequality debate.

First, our results suggest that scholars should question one-size-fits-all inequality measures for economic inequality, discuss different measures (as in Huber and Stephens Reference Huber and Stephens2014; Huber, Huo, and Stephens Reference Huber, Huo and Stephens2019; Huber, Gunderson, and Stephens Reference Huber, Gunderson and Stephens2020), and vary the measure to be explained accordingly (or at least carefully defend their choices, cf. Rueda Reference Rueda2008). If inequality debates focus increasingly on income concentration at the top, the price of top inequality, and the role of elite affluence and influence, this should be reflected in the use of measures that try to capture this income concentration. A relevant test of the impact of parties on inequality should, thus, also include either top shares or ratios that involve top income groups.

Second, for most instruments parties have at their disposal, robust short-term effects are unlikely. As argued already by Bradley and colleagues (Reference Bradley, Huber, Moller, Nielsen and Stephens2003), given how “sluggishly” inequality measures behave, a fair test of partisan effects must take into account medium- and long-term effects. In general, such effects are more plausible than short-term party effects. While more cumulative approaches are not new in the “politics matter” literature (e.g., Huber and Stephens [Reference Huber and Stephens2001], in a book mostly concerned with welfare policies), it is the exception that this constraint is acknowledged in inequality studies with negative findings. Such temporally nuanced approaches could also help us to better understand the time horizons of different policy approaches. It is well documented that left and right governments hold different views and pursue different policies regarding the welfare state, the role of unions, and corporatism (Jensen Reference Jensen2014)—aspects that in the longer term have pro-rich or pro-poor consequences—because they concern automatic equality stabilizers, the reservation wage, or wage bargaining.

Right parties rarely attack the welfare state or equal-wage policies with risky frontal assaults. Anticipating a backlash against significant cutbacks, they instead limit further redistribution, slowly defund old programs, and complement them with private alternatives (marketization). In parallel, they try to undermine the institutional power base of unions and welfare states more broadly (erode and attack, as exemplified in the reform of the Danish Ghent system).

Third, to warrant a fair test of the impact of government partisanship on inequality, the role of policy channels and policies should, by default, be considered carefully to avoid the (frequent) underestimation of party effects. Ideally, the effects of policies and their links or interactions with partisanship should be discussed a priori—otherwise, policy effects absorb party effects. Policy contexts or channels should, if possible, be further distinguished along the lines of more direct and more indirect links to parties. For instance, we know that welfare and wage policy follow a left–right gradient. The inclusion of fiscal measures, social spending, minimum wages, welfare generosity, or taxation are likely to absorb short-term party effects. By contrast, the effects that left and right parties have on corporatism and unions have less direct effects. Still, corporatist bargaining structures and unions are likely to exert an effect on predistribution (Korpi Reference Korpi1983; Rathgeb and Klitgaard Reference Rathgeb and Klitgaard2022) and, as discussed above, are decisive for whether parties take the risk to fight for more or less redistribution and welfare (Jensen Reference Jensen2014).

In sum, our results help to consolidate research on party effects in theoretically predictable ways: If income gains at the top are considered, if we avoid including policies that are a product of left and right cabinet parties, and if we move beyond short-term effects, we find that parties have an effect (a predicted probability of 78%). By contrast, if these aspects are not considered, we obtain a nonfinding (11% predicted probability). The ramifications for the heated equality debate are clear: we provide a rare dose of good news, backed by broad empirical evidence. Contrary to skeptical voices (Milanovic Reference Milanovic2016; Scheidel Reference Scheidel2017), political conflict still influences pro-rich/pro-poor outcomes in accordance with theories about party effects. Thus, we remain critical of attempts to endogenize political conflict as a result of globalization or skill-biased technological change, or to frame welfare reforms and cutbacks as necessary reactions.