The Rich Have a Slight Edge: Evidence from Comparative Data on Income-Based Inequality in Policy Congruence

Abstract

Several recent studies have found unequal policy responsiveness, meaning that the policy preferences of high-income citizens are better reflected in implemented policies than the policy preferences of low-income citizens. This has been found mainly in a few studies from the US and a small number of single-country studies from Western Europe. However, there is a lack of comparative studies that stake out the terrain across a broader group of countries. We analyze survey data on the policy preferences of about 3,000 policy proposals from thirty European countries over nearly forty years, combined with information on whether each policy proposal was implemented or not. The results from the cross-country data confirm the general pattern from previous studies that policies supported by the rich are more likely to be implemented than those supported by the poor. We also test four explanations commonly found in the literature: whether unequal responsiveness is exacerbated by (a) high economic inequality, (b) the absence of campaign finance regulations, (c) low union density, and (d) low voter turnout.

Introduction

Unequal policy responsiveness in the US is one of the most widely reported research findings in the social sciences over the past decade; policies supported by the rich are more often implemented than those supported by the poor (Bartels 2009; Gilens 2005; Gilens 2012; Gilens and Page 2014). While there is now some evidence from single-country studies which show that responsiveness is also biased in favour of high-income citizens in Europe (Elsässer, Hense, and Schäfer 2021; Lupu and Tirado Castro 2022; Mathisen 2022; Mathisen et al. 2021; Persson forthcoming; Schakel 2021), there is a lack of more comprehensive comparative studies. We attempt to fill this gap by analyzing survey data on opinion support for about 3,000 policy proposals from thirty countries over almost forty years, combined with information on whether the policy proposals were implemented in different periods. This makes it the most comprehensive comparative study of policy congruence to date; it includes many countries and many issues as well as measures of support in different income groups.

While other studies have looked at comparative data, such as the recent papers by Lupu and Warner (2022a; 2022b), they look at representation in terms of left-right ideology. Studies of ideological congruence are important but they need to be complemented with other types of analyses. Other studies have looked at responsiveness to specific issues in several countries, such as those by Rasmussen, Reher, and Toshkov (2019), but they do not take inequality between socio-economic groups into account. Studies that have taken inequality between socio-economic groups into account are usually single-country studies (Elsässer, Hense, and Schäfer 2021; Gilens 2012; Schakel 2021). We combine the approach of looking at congruence over many countries, years, and issues by studying inequality between socio-economic groups in a way that has not been done before.

The results from the comparative data confirm the general pattern of previous studies by showing unequal responsiveness: policies supported by the rich are more common than those supported by the poor whereas the middle class receives levels of responsiveness somewhere between the poor and the rich. In addition, we test four explanations commonly found in the literature; i.e. , whether unequal responsiveness is exacerbated by (a) high economic inequality, (b) the absence of campaign finance regulations, (c) low union density, or (d) low voter turnout. We find no systematic evidence that these factors are related to unequal congruence. While the gap is relatively small, it is still substantially important.

State of the research field

Research on political representation has traditionally assumed that a relatively strong relationship exists between citizens' opinions and the opinions and legislative behaviour of their elected representatives (Erikson, MacKuen, and Stimson 2002; Holmberg 1997; Miller and Stokes 1963; Monroe 1979; Monroe 1998; Page and Shapiro 1983; Stimson, MacKuen, and Erikson 1995). In addition, studies have shown that opinions affect policies and that public opinion reacts to policy changes (Soroka and Wlezien 2005; Soroka and Wlezien 2010; Wlezien 1995; Wlezien and Soroka 2007). While much of the literature focuses on the US, several studies from Europe also show that political representatives are (at least) fairly responsive to the public (Binzer Hobolt and Klemmemsen 2005; Binzer Hobolt and Klemmensen 2008; Hakhverdian 2010; Rasmussen, Reher, and Toshkov 2019). These early studies focused on the responsiveness of the public in general, often with a theoretical focus on the median voter. The focus on the middle class does of course make sense from a theoretical point of view – if politicians want to win elections they need to appeal to the median voter but to what extent do they do so? Given the importance of the median voter in winning elections, one could theoretically expect politicians to be even more responsive to middle-income citizens than the high- and low-income citizens.

In the last decade, however, a new strand of research has emerged that focuses on how well the preferences of different subgroups are reflected in either the behaviour of political representatives or in actual policies. Bartels (2009), for example, has shown that the votes of US senators better reflect the views of high-income citizens. Moreover, Gilens (2005; 2012) and Gilens and Page (2014) have shown that policies have a stronger relationship with the preferences of high-income citizens than with the preferences of low- and middle-income citizens. Gilens and Page conclude that ‘ economic elites and organized groups representing business interests have substantial independent impacts on U.S. government policy, while average citizens and mass-based interest groups have little or no independent influence’ (p. 564). Other researchers have offered alternative interpretations, suggesting that group differences in preferences are generally small and that the response gap is not substantial (Bashir 2015; Branham, Soroka, and Wlezien 2017; Enns 2015; Enns and Wlezien 2011; Soroka and Wlezien 2008; Ura and Ellis 2008).¹

In an attempt to settle the debate in the literature, Bowman (2020) evaluates a variety of model specifications and definitions of disagreement and policy change used in these studies and finds that the preferences of the wealthy significantly affect the likelihood of implementation. In contrast, the preferences of the middle class and the poor do not have the same impact. The fact that there is substantial disagreement about the extent of unequal responsiveness in the US means that comparative studies like this can help us to put these findings into perspective.

From a comparative perspective, and especially in comparison with Western European countries, the US is a special case with relatively high levels of income inequality and, presumably, strong ties between the economic and political elites. So, do we find unequal responsiveness elsewhere? Individual country studies from Germany (Elsässer, Hense, and Schäfer 2021); Denmark (Elkjær 2020); the Netherlands (Schakel 2021; Schakel and van der Pas 2020); Norway (Mathisen, 2022); Sweden (Guntermann and Persson 2021; Persson 2021; Persson forthcoming); Spain (Lupu and Tirado Castro, 2022); and EU-level institutions (Lefkofridi and Giger 2020) point to similar patterns of unequal responsiveness. But, as said earlier, this research area lacks cross-country studies that map the terrain in many countries across a range of issues.

There are at least four common but rarely tested hypotheses in the literature which aim to explain the extent of unequal responsiveness: that is, economic inequality, campaign finance regulations, union density, and voter turnout.

First, the literature often points to economic inequality as a possible cause of unequal policy responsiveness. There are several reasons why this might be the case. In economically unequal societies, low-income citizens may be more politically marginalized and lack the means to voice their opinions in politics. Similarly, high-income citizens are likely to be in a better position to influence policy because of their stronger relative position of power in society. On the other hand, however, elites in societies with greater economic equality do not possess the same economic power to exert their influence over low-income citizens. Therefore, the first hypothesis we test is whether higher income inequality is associated with higher levels of unequal responsiveness.

Second, Gilens suggests that unequal responsiveness in the US may be due to high-income citizens making large campaign contributions. This could create a link between the economic and political elites, which compels politicians to grant more influence to high-income citizens. Therefore, regulating campaign contributions could be a way to weaken the undue influence of economic elites. The second hypothesis we test is whether the absence of campaign finance regulations is associated with higher levels of unequal responsiveness.

A third factor that often comes up in the discussion is union density (Becher, Stegmueller, and Käppner 2018). The assumption is that a well-organized labour movement with high union density is advantageous in enforcing the preferences of low-income citizens. At the very least, especially when left-wing parties are in power, high union density might be expected to put pressure on those governments to implement policies that matter to the working class (Kwon and Pontusson 2010). The direct influence of union density on policy under right-wing governments may be less direct but a well-organized labour movement could help to put their preferred issues on the agenda.

The fourth hypothesis we test is whether the level of voter turnout is related to unequal levels of responsiveness. Since research shows that political representatives are more responsive to active citizens rather than inactive citizens (Griffin and Newman 2005; Peters and Ensink 2015), it is reasonable to assume that low political participation by the least affluent citizens could lead to lower consideration of low-income citizens' preferences in the political decision-making process. If low-income citizens participate less than high-income citizens, this is likely to translate into unequal influence in the policymaking process. We also know that economic inequality lowers both voter turnout and non-voter participation, which causes a greater income bias in participation (Solt 2008; Solt 2010). While there are many ways in which citizens might try to influence politicians, we focus here on one particular form of political participation, namely voter turnout. We do so because turnout is central to the distribution of power in society and is of direct importance to policymakers, and also because it is a form of political participation for which it is feasible and possible to use comparative country data. While it is difficult to obtain data on the relative turnout of different socio-economic groups, much research shows that lower turnout is likely to be associated with a more unequal distribution of turnout between the different groups (Persson, Solevid, and Öhrvall 2013; Tingsten 1963). The fourth hypothesis we test is, therefore, whether the level of turnout is related to unequal levels of responsiveness.²

There are, of course, many other factors that might influence the extent of unequal responsiveness but there is no clear consensus in the literature about which factors might facilitate equal representation of citizens' preferences. The role of interest groups remains contested and studies point in different directions about whether they strengthen the link between opinion and policy (Bevan and Rasmussen 2020; Klüver and Pickup 2019). Factors that appear to have a significant ex-ante impact on how well different income groups are represented, such as a proportional voting system, are not effective in empirical studies of ideological congruence (Guntermann 2021). Similarly, Rasmussen, Reher, and Toshkov (2019) show that the effects of electoral systems and power distribution have a minimal impact on political responsiveness and that the number of coalition partners is not associated with lower responsiveness (see also, Toshkov, Mäder, and Rasmussen 2020). Lupu and Warner (2022a; 2022b), in two papers mainly concerned with ideological congruence, find that, while there is clear evidence that the wealthy are more often better represented than the poor, there is no evidence that factors such as political institutions, globalization, or citizens' political behaviour moderate this relationship.

This paper brings two main contributions to the field. First, it moves beyond single-country studies and evaluates responsiveness to specific issues using comparative data. While other studies have also used comparative data, such as the recent papers by Lupu and Warner (2022a; 2022b), they look at representation in terms of left-right ideology. As Broockman (2016) has shown, ideological scores ‘say little about citizens’ views within domains, on issues themselves’ (p. 8). Moreover, when using ideological scores it is not possible to distinguish legislators who are ideologically ‘mixed’ from those who are ‘moderate’. Moreover, several studies have convincingly shown that people have opinions on distinct issues beyond their ideological dispositions (Ansolabehere, Rodden, and Snyder 2008; Tausanovitch and Warshaw 2013).³ This paper takes a country-comparative approach to studying unequal policy responsiveness by using issue-by-issue data. Secondly, it adds an empirical evaluation of the impact of four of the most commonly occurring mechanisms in the field. These mechanisms have been largely ignored in empirical studies.

As the reader may have noticed, there is a lack of conceptual clarity in this research area. Researchers often use terms like ‘representation’, ‘responsiveness’, and ‘congruence’ interchangeably. In general, scholars refer to congruence to study whether there is a match between what kind of policy people want and what kind of policy is in place (note that this is a different approach to the many studies focusing on ideological congruence (see Golder and Stramski 2010)). Responsiveness, on the other hand, is sometimes thought to imply a temporal dimension where some scholars define policy responsiveness as a causal reaction to an opinion, while others define it more loosely as a correlation between opinion and policy: as Wlezien (2017) puts it, ‘when most people want a lot of policy […] they would get a lot of policy’ (p. 562). Others refer to the same relationship as ‘influence’ or simply policy representation (see Bartels (2021) and Wlezien (2017) for conceptual overviews). Using our data we can merely study the correlations between opinions and policy, not whether there is a causal relationship. Still, in our dataset policy, outcomes are measured years after the measurement of opinion, which mitigates the risk of reverse causality. Further methodological considerations are discussed in the next section.

To make clear what we are empirically investigating in this paper, we focus on ‘congruence’. By that term, we refer to the extent to which actual policy changes match public support for those policy proposals. We do this by looking at how large the share of citizens is that hold preferences matching the policies that are already in place. Empirically, we calculate congruence by examining the extent to which political support in different income groups is congruent with policy changes. In the Appendix, we examine responsiveness more traditionally with regression analyses of the influence of political support in different groups on policy changes.

Data

The unit of analysis in the dataset is policy proposals in specific country years. The main independent variables are the level of support in different income groups, while the dependent variables measure whether policy proposals were implemented at different points in time. To collect information on preferences for policy proposals in different income groups, we use data from the following cross-country surveys: The Comparative Study of Electoral Systems (CSES), The Eurobarometer (EB), The European Social Survey (ESS), The European Values Study (EVS), The International Social Survey Programme (ISSP), and The World Values Survey (WVS).

We searched these datasets for questions about specific policy proposals. For a question to be selected, it had to be possible to investigate, in an appropriate way, whether it had been implemented or not. Questions should ask about a preference for particular policy proposals that translate into a single, specific, measurable, and identifiable policy. Thus, we look at questions that ask about citizens' preferences for specific policy proposals such as nuclear energy development, allowing same-sex marriage, tax increases/tax cuts, and public spending in different domains. The questions should be specific enough so that coders know what information to look for to enable them to examine implementation. We did not include questions that asked respondents about priorities, importance, or conditionalities. However, questions may ask about absolute changes (such as joining NATO or closing all nuclear power plants) or relative changes (increasing or decreasing spending in various areas). The questions can also be about new proposals or about policies that have already been implemented. As is common in this field, we changed the direction of the opinion variables for the status quo questions in the empirical analyses so that all opinion variables indicate support for policy changes.

A team of research assistants coded the policy outcomes. For each question in each survey, it was coded whether the policy was implemented at three points in time after the survey (we code the exact month and year of the survey question): that is, (1) by the end of the incumbent government's term, (2) by the end of the successor government's term, and (3) five years after the question was asked. The implementation variables are dichotomous and take the value 1 if a policy is changed and 0 if it is not changed (or if a decision is made in the opposite direction). Thus, the implementation variables are coded 1 if an official government decision was made or an outcome occurred (depending on whether the survey question referred to a decision or an outcome). If no information can be found after exhaustive research, we classify the proposal as not implemented. In cases where the de jure implementation of the policy differs from the de facto implementation, we focus on the official factual data (de jure). We always make a comparison between the three points in time and the time of the survey (for example, if a decision is made three years after the survey but reversed the following year, it is coded as not implemented after five years). For statements about relative change, we look at the differences between the level in the survey year and each of the three time points. If there has been a relative change in the direction of the policy proposal, the implementation variable is coded 1; otherwise, it is coded 0.

The instructions to coders were straightforward: code whether or not the policy proposal was implemented at the specified time points. To this end, coders had to refer to different sources depending on the type of policy. For example, some survey questions asked specifically about policy decisions; for these questions we looked at the minutes of the national parliament. Other questions focus on the implementation of proposals. For these questions, we looked at the relevant sources for the topic in question. For example, these may include budgets, administrative files, documentation on the closure of nuclear power plants, or the construction of an infrastructure project. Also, for some issues that were not implemented, there is simply no source or documentation (as these policy changes were not events).

As with the opinion data, the original response options were coded in different ways. Therefore, following the standard practice in the field (Elsässer, Hense, and Schäfer 2021; Gilens 2005; Gilens 2012; Schakel 2021), we standardized each survey item so that it captured the percentage of respondents that indicated support for policy change, from 0 to 100. Because income was measured in different ways in the different surveys, we also harmonized the income scales by assigning the respondents to income values that corresponded to the middle percentile of their respective income categories, taking into account the distribution of income groups in each survey. To estimate support for change across income groups, we calculated the average support for each proposal in each country among respondents with a percentile rank in the bottom 20 per cent, the middle 60 per cent, and the top 20 per cent of the income distribution. This is our measure of support for the policy proposals among the poor, the middle class, and the wealthy.

In total, we use data on the policy preferences of about two million individuals. Overall, we cover thirty countries and thirty-eight years from 1978 to 2017 (516 country years in total). The countries included all EU countries except Malta) plus Iceland, Norway, Switzerland, and the UK. For a full list of countries and years see the Appendix.⁴

The policy proposals cover a wide range of matters. For example, economic issues that mainly concern welfare spending, redistribution and state-market regulations; fiscal policies; and non-economic issues that cover foreign policy, immigration/multiculturalism, liberties and rights, environmental policies, and law and order. See the Appendix for further information on the number of issues in the different areas.

To test the impact of income inequality, we used data from ‘The Standardized World Income Inequality Database’ (SWIID) (Solt 2020). We used SWIID because it is the database on inequality that covers most countries and years. The Luxembourg Income Study (LIS) is considered the standard measure of inequality in comparative research and the SWIID ‘estimates the relationships between Gini indices based on the LIS and all of the other Ginis available for the same country years and uses these relationships to estimate what the LIS Gini would be in country-years not included in the LIS but available from other sources’ (p. 1,183).

As for campaign regulations, we used data from the V-Dem Institute (Coppedge et al. 2021). These data are based on expert interviews with country experts that provide information on individual years and countries. We used the question, ‘Are there disclosure requirements for donations to national election campaigns?’ with responses on the following scale: 0 (No), 1 (Not really), 2 (Ambiguous), 3 (Mostly), 4 (Yes).

We used the information on turnout in the last general election from the Comparative Political Data Set, 1960–2016 (Armingeon, Engler, and Leeman 2020). From the same dataset, we used data on union density, defined as the ‘net union membership as a proportion of wage and salary earners in employment’.⁵

Results

We begin by examining how well the opinions of different groups of citizens are reflected in policy changes. To do this, we use a congruence measure recently proposed by Bartels (2021): ‘If a policy change was adopted, the extent of congruence for any given subgroup is measured by the proportion of that subgroup that favored the policy change, regardless of whether it is more or less than half; if policy remained unchanged, the extent of congruence is simply the proportion that opposed the policy change.’⁶ This measure has the advantage of directly illustrating the extent to which groups of citizens agree with the policy changes that have been made. The measure clearly illustrates to what extent public policies are congruent with support for the same issues.

An alternative would be to look at the proportion of policies supported by a majority across groups and study the ‘win rates’ of different groups. But estimating reliable point estimates of majorities is difficult because the estimated levels of support depend, for example, on the wording of the questions, the treatment of missing values, and the ‘do not know’ responses. When testing a large number of model specifications, Bowman (2020) shows that ‘win rates are highly inconsistent across definitions of disagreement’ and that different definitions generate contradictory findings (p. 1,019).⁷ The congruence measure used here can use more information from the data, does not rely on arbitrary cut-off points, and captures the extent to which different groups are satisfied with policy changes. The public support variables are coded to theoretically range from 0 (no support) to 100 (100 per cent support), while the policy change variable is dichotomous. Thus, the resulting measure reflects the percentage in each group whose preferences are congruent with a policy change (or a policy remaining unchanged).

We use the five-year window between the time of the survey and the policy change for our main specifications presented in this paper. Thus, we first computed the congruence levels issue-by-issue and then calculated the levels for each income group.

Table 1 provides the main results for congruence across all countries. The average congruence among low-income citizens is 53.7 per cent, for middle-income citizens it is 55.1 per cent, and that of high-income citizens is 57.1 per cent. Thus, there is an income-based congruence difference of more than 3 percentage points – policy changes are more congruent with the preferences of high-income citizens than with the preferences of low-income citizens. The table also shows the support for policy change. Low-income citizens are somewhat more positive to policy change than high-income citizens. This implies that high-income citizens likely benefit from status quo bias (Persson forthcoming).

Table 1. Support for change and congruence in different income groups

Means and standard errors are in parentheses.

For both variables, the estimates for the middle class fall in between the poor and the rich. In the Appendix, we provide regression analyses where the preferences in the income groups are regressed on policy change, which is the standard procedure in the field used by, for example, Gilens (2012); Schakel (2021); and Elsässer, Hense, and Schäfer (2021). The results in the Appendix confirm the general pattern shown in the results provided below.

One could object that a 3 per cent difference might not appear to be that large and while it is not the case that high-income citizens always get what they want and low-income citizens never do, the difference is large enough to matter. In particular, the finding is consistent in different countries and different years; hence, it is not the size of the gap but the consistency with which it occurs that is the most striking finding.

In Fig. 1 we plot the congruence level of low-income and high-income citizens for (a) all observations, (b) averaged across countries (we restrict this plot to countries with at least fifty observations), (c) averaged across years, and (d) across topics. To distinguish between issues on which high-income citizens are more likely to be congruent than low-income citizens, and vice versa, the issues on which high-income citizens are more likely to be congruent with policy than low-income citizens are above the line (red) and the issues on which low-income citizens are more likely to be congruent are below the line (blue). While we can see that there is a high degree of correlation between congruence in income groups, the red dots dominate the graphs – it is more common for high-income citizens to be more congruent with policy change than low-income citizens. For 64 per cent of the observations, the preferences of high-income citizens are more congruent-are more congruent with policy changes than the preference of the low-income citizens.

Figure 1. Issue congruence for low- and high-income citizens.

Note: For all four graphs the y-axis illustrates the congruence levels for the high-income citizens, while the x-axis illustrates the congruence levels for the low-income citizens. The upper left bar shows the estimates for all issues, while the other four show averages for countries, years, and issues. Red dots are estimates where the high-income citizens have higher congruence than low-income citizens and blue dots are estimates where the low-income citizens have higher congruence than high-income citizens.

In the upper right part of the same figure we look at countries. In almost all countries, there is a larger share of issues on which the preferences of high-income citizens are more congruent with policy than the preferences of low-income citizens. However, we also find large differences between countries, from Switzerland with a mean of just over 50 per cent for low-income citizens to countries such as Denmark and Portugal where the mean is over 60 per cent for high-income citizens and about 58 per cent for low-income citizens. Thus, the differences are more considerable between countries than the income-related differences within countries. In most countries, the difference in congruence between low-income citizens and high-income citizens is between 2 and 5 percentage points. Thus, while countries differ in their absolute levels of congruence, the difference between low- and high-income citizens is relatively similar in many countries. The Appendix provides further illustrations of differences in congruence across countries. In the bottom two graphs, we look at the average of years (high-income citizens are more congruent in 92 per cent of years) and issues (66 per cent). Whether we look at the observations or the average across years, countries, and issues, high-income citizens are more likely to agree with policy changes than low-income citizens.

We will now look at the four hypotheses on the factors that potentially drive unequal responsiveness by using a regression approach. For each explanation, we estimate models where we use the difference in congruence between P90 and P10 as the dependent variable. This dependent variable directly captures whether the independent variable is associated with greater inequality in congruence. Table 2 shows the results from the models.

Table 2. The relationship between congruence and income inequality, campaign finance regulations, and voter turnout

Note: ∗denote statistical significance at the 5 per cent level. The models include fixed effects at the country and year levels. Entries are for OLS regressions with heteroscedastic-consistent standard errors in parentheses.

First, much of the previous scholarship on this matter suggests that economic inequality drives the relationship. According to this argument, the larger the economic inequality is, the harder it is for low-income citizens to make their voices heard and the stronger the bonds between the economic and political elites are. We estimate regressions using the data from the SWIID where the Gini measure is imputed from fifty variables, thereby taking each observation's uncertainty into account. We find no significant relationship between the difference in congruence between the income groups and economic inequality. Second, we look at a similar model, with campaign finance regulations as the independent variable. Are countries with more strict campaign finance regulations showing more similar patterns of congruence? The results from Table 2 do not show any evidence that that is the case; again, the estimates are not significantly different from zero. Third, does the level of voter turnout affect congruence? Are policy changes better aligned with citizens' preferences when they make their voices heard in elections? Again, we do not find any significant relationship between the independent variable and the difference in congruence. The same goes for the fourth explanation, union density, which also does not have any significant relationship with congruence. The Appendix supplies additional graphical illustrations of the relationship and analyses of whether the variables affect the absolute levels of congruence for the low- and high-income citizens respectively.

In summary, income-related differences in congruence and responsiveness were present in most of the countries we studied. However, these differences are not easily explained by the factors most commonly cited in the literature, such as levels of economic inequality, campaign finance regulations, union density, or voter turnout.

Conclusions

We provide cross-country results showing that the preferences of high-income citizens are somewhat better reflected in policy changes than the preferences of low-income citizens. The gap is relatively small but is still substantial important. It should be noted, of course, that the gap is ‘only’ a few percentage points. Indeed it is not the case that high-income citizens always get what they want while low-income citizens never do. Nevertheless, since the preferences of low- and high-income citizens are highly correlated in our data (and in similar studies), such an outcome would not possible to observe. This is an important finding in itself.

Since about half of both high-income and low-income respondents tend to see their preferred policy realized, one might get the impression that policy outcomes resemble a coin toss – essentially random. But there remains an imbalance: for some reason, the coin tends to fall more often on the side of the affluent. Moreover, this pattern is observed again and again across issues, countries, and years to a degree that defies random probability. The rich have a slight edge. If these relatively small changes in congruence repeatedly appear they can add up to having a strong impact on the development of policies over time.

Moreover, we find no evidence that the differences in unequal congruence are correlated witheconomic inequality, campaign finance regulations, voter turnout, or union density. As they have been extensively discussed in the literature, but never tested in a comparative setting such as this before, the null findings are striking.

As research on unequal responsiveness began with studies of the United States, the proposed explanations have naturally focused on factors that separate the US from other advanced democracies. Our research, together with findings from single-country studies, convincingly show that the US is hardly unique in terms of outcomes. This, in turn, suggests that the explanations for unequal responsiveness are more general than initially thought and that the responsiveness literature has searched for explanations in the wrong places. Given that we find unequal congruence in quite different contexts, it should be emphasized that explanatory factors of unequal congruence may be more likely to be found at the issue level or the individual level rather than at the system level.

Future studies should focus more on the dynamics between voters and political representatives that lead to unequal congruence and responsiveness. One possible explanation, pointed out by Rosset and Kurella, is that party supply and the ability of citizens to choose representatives who match their preferences drive unequal responsiveness (Rosset and Kurella 2021). Others have pointed to the descriptive over-representation of economically advantaged candidates in parliaments as a driver of this type of inequality (Carnes 2012; Persson 2021).

Finally, our results are important for assessing how democratic systems work. If democracies deviate from the ideal of giving everyone equal influence, the social contract on which democracies are based could be called into question. Indeed, research has shown that how well citizens are represented matters for how satisfied they are with democracy (Ferland 2021). At a time when democracies around the world face both external threats from authoritarian regimes and internal threats from populist demagogues, it is of vital importance that representatives strive to deliver on the promise of a government of, for, and by, all people.