TY - JOUR
AU - Marques, Pedro
AB - Abstract Previous studies have posited that elites are willing to advance the redistribution of income and social goods when the negative effects of inequality, such as crime and conflict, threaten their own interests. Although elites acknowledge these negative effects, their support for redistributive policies remains low throughout the Global South. We address this paradox using a multi-method research design. Drawing on 56 in-depth interviews with Brazilian political and economic elites, we document how, when discussing the negative effects of inequality, interviewees consistently characterized the poor as ignorant, irrational and politically incompetent. We use these findings to theorize about the negative impact of such perceptions of the poor on elite support for redistribution. We then test this relationship using survey data gathered from random samples of political and economic elites in Brazil, South Africa and Uruguay (N = 544). We find the relationship to be robust. 1. Introduction Scholars of inequality have good reason to care about elites’ preferences. Elites have the ability to shape the politics of redistribution, owing to the power positions that they occupy (Reis and Moore, 2005; Higley and Burton, 2006; Blofield, 2011a; Khan, 2012; Yamokoski and Dubrow, 2008; López, 2013a,b; Hoffmann-Lange, 2018). Existing scholarship suggests that elites will support more redistribution when they are affected by the negative consequences of inequality. For instance, numerous studies document the connection between inequality and criminal violence (Fajnzylber et al., 2002; Bourguignon et al., 2003; Choe, 2008), the latter of which can directly affect elites’ own safety. Further, inequality poses threats to elites in the form of uprisings and political violence (Boix, 2003; Acemoglu and Robinson, 2005). Consequently, given the right incentives, support for redistribution can be in the interest of elites (de Swaan, 1988; Rueda and Stegmueller, 2016). In the Global South, however, although elites acknowledge the negative consequences of inequality for themselves, they generally have low levels of commitment to redistribution initiatives (de Swaan et al., 2000; Clarke and Sison, 2003; Hossain, 2005; Reis and Moore, 2005). This is true even in contexts of relatively high state capacity, where elites acknowledge that tools for combating inequality either exist or could be developed (Reis and Moore, 2005). This holds even for political elites who, in theory, should be responsive to the redistributive aspirations of their constituencies. What explains elites’ weak support for redistributive policies in contexts where they identify inequality as threatening to themselves? We address this question by focusing primarily on Brazil, a country marked by high inequality and in which the externalities of inequality are extreme, particularly in the form of urban violence. Studies have shown that Brazilian elites recognize inequality as one of the most urgent national problems and identify urban violence as one of its most salient consequences (Reis, 2000; Reis and Moore, 2005; López, 2013b, 2014, 2016). Yet redistributive policies are still far from consensual (Paugam et al., 2017). We begin with an analysis of data collected from in-depth interviews with Brazilian politicians, civil servants and business leaders (N = 56). We find that participants consistently characterized the poor as ignorant, irrational and politically incompetent when explaining their own limited support for redistributive social policies. We use this finding to theorize about the relationship between perceptions of the poor and elite attitudes toward redistribution. We hypothesize that elite support for redistributive policies will be affected by their evaluation of the poor, even when they identify negative externalities of inequality, such as criminal violence. To test this relationship, we analyze data that we collected from a random sample of Brazilian political, civil servant and business elites (N = 180). We find the relationship to be robust. To examine the external validity of our findings, we replicate our models in two other cases of middle-income countries with relatively high state capacity: South Africa (N = 184) and Uruguay (N = 180). In the comparative literature, South Africa is often presented as very similar to Brazil in terms of its high levels of inequality and the negative effects these have generated (Marx, 1998; Lieberman, 2003; Moraes Silva, 2012). We thus expect to see similar patterns in Brazil and South Africa. That said, if the underlying mechanism we identify holds, we should expect to observe it in less extreme cases too. To test this assumption, we replicate our models in the Uruguayan data. Like Brazil and South Africa, Uruguay is a middle-income developing democracy with relatively high state capacity where elites face externalities of inequality. However, it is historically more egalitarian, democratic and socially cohesive than the other two cases and much less unequal. Our findings show that, across all three cases, negative perceptions of the poor result in lowered support for redistributive policies, although the baseline prevalence of such perceptions varies by country and elite sector. By highlighting the impact of perceptions of the poor on elite attitudes toward redistributive policies, we identify cultural processes as the missing link between economic incentives—created by the negative effects of inequality—and subsequent policy outcomes. The idea of culture as a mediator has been promoted by both political economists (Alesina and Giuliano, 2009) and cultural sociologists (Lamont et al., 2014) but rarely mobilized to account for the actions of elites. A number of studies have examined the relationship between perceptions of inequality and attitudes toward redistribution, among both the general population (Oorschot, and Halman, 2000; Oorschot, 2006; McCall and Kenworthy, 2009; Niemelä, 2011; Cavaillé and Trump, 2015) and elites (de Swaan et al., 2000; Hossain, 2005; Reis and Moore, 2005). Yet, to the best of our knowledge, none has estimated the effect of both perceptions of external incentives and perceptions of the poor on the willingness of elites to support redistributive social policies. Bridging political economy and cultural sociology, our study highlights how cultural processes mediate the relationship between material incentives and policy outputs. With its multi-method research design, this article is also an answer to Cousin et al.’s (2018) recent call for greater methodological diversity in elite research. In what follows, we first review the literature on elite attitudes toward redistribution and present our theoretical contribution in light of alternative theories. Moving to our data and methods, we describe our criteria for case-selection and sampling as well as our identification and analysis strategies. We then present our findings in two parts. First, we draw on in-depth interview data to build our argument. Second, we test this argument using the survey data. We conclude by discussing the implications of our findings for the study of elites and redistribution. 2. Explaining elite attitudes toward redistribution Under what conditions will elites support redistributive policies? The bulk of the political economy literature on elites and redistribution tends to assume that, because elites benefit from inequality, they will always try to prevent redistribution. Resource concentration is, after all, a defining characteristic of the elite, who are often responsible for generating inequalities through tax-evasion, labor-market and state-capture practices. Most scholars highlight that elites will only grudgingly agree to redistribution when threatened ‘from below.’ Elite-driven political processes that signal redistribution—such as democratization—are seen to produce limited, and ultimately undesirable, concessions (Boix, 2003; Acemoglu and Robinson, 2005). Studies in this tradition argue that, because elites never actually intend to redistribute but only to signal redistribution in order to pacify the poor, redistributive agendas are often limited, provisory and configured to fail. For instance, Soifer (2013) contends that elites’ support for democratization depends on their anticipation of the state’s failure to effect redistribution. Other studies argue that elites purposefully promote inefficiency by fostering oversized bureaucracies or by embedding institutional roadblocks to redistribution in the constitution (Acemoglu et al., 2011; Albertus and Menaldo, 2014, 2018). Other scholars argue that threats from below have little influence on the decision-making processes of elites and that apparent concessions to the poor occur for reasons completely unrelated to redistributive demands. These include tension between urban economic elites and landed elites (Ansell and Samuels, 2014), constraints posed by international actors (Haggard and Kaufman, 2016), and splits within ruling coalitions (O’Donnell et al., 2013; Haggard and Kaufman, 2016). These approaches view rampant inequality as desirable by—or at least inconsequential to—elites. Redistribution, when it happens, is understood as an unintentional consequence of internal disputes among elites. Largely ignored in this literature is a growing body of empirical work that documents how elites systematically identify poverty and inequality as a problem not only for the poor but also for themselves. Crime and violence, for example, are understood by elites to be undesirable side effects of inequality that negatively impact their own lives (e.g. Reis and Moore, 2005; Rueda and Stegmueller, 2016). Moreover, research has shown that elites view inequality as a threat to democracy and economic stability and as fuel for political practices that they find undesirable, such as clientelism (López, 2014). This is particularly striking in unequal, recently democratized, middle-income countries (e.g. Reis and Moore, 2005), such as the ones we analyze in this article. One way of reconciling elite views of negative externalities with their lack of support for redistributive efforts is by suggesting that elites do not reject redistribution but simply do not want to pay for it. In this case, elites are not avoiding redistributive social policies per se but rather taxes. Previous studies show, however, that elites have been successful at raising state revenue without paying for it, often relocating fiscal costs to the middle class (Fairfield, 2010; Hacker and Pierson, 2010; Winters, 2011; Bogliaccini and Luna, 2016). If elites identify the negative externalities of poverty and have the capacity to put forward effective social policies without necessarily bearing the fiscal costs themselves, then what prevents them from effectively coordinating around redistribution? Studies in political culture provide an answer by highlighting the role of values, norms and political ideology in determining elite attitudes toward redistribution (e.g. Putnam, 1971; Verba and Orren, 1985; Verba et al., 1987; Pye and Verba, 2015). For instance, in their pioneering cross-national comparison of elite attitudes toward inequalities, Verba et al. (1987) argued that while elites in the USA value political equality and equality of opportunities, elites in Sweden focus much more on equality of outcomes; and this might explain their stronger support for welfare policies. Even if the authors’ conceptualization of political culture as national values is today criticized as essentializing (Somers and Skocpol, 1995), these studies sensitize us to the importance of cultural understandings and interpretations in shaping responses to inequality. Other studies of culture have identified how normative evaluations of the poor—who are seen as either deserving or undeserving—and of social policies—which are seen as either fair or unfair—shape attitudes toward redistributive policies (Alesina and Angeletos, 2005; Katz, 2013). Nevertheless, the lack of redistribution in many countries in the Global South does not seem to operate through perceptions of deservingness or fairness. In Latin America (López, 2016), Africa (Kalati and Manor, 2005; Kalebe-Nyamongo, 2012) and South Asia (Clarke and Sison, 2003), elites generally perceive inequality to be unfair, are aware of its structural causes and view the poor as deserving of relief. There too, however, elites do not fully embrace redistributive social policies. Focusing on the Global South, Reis and Moore’s (2005) influential study suggests that it is elites’ sense of responsibility that accounts for redistributive outcomes. They argue that incentives to redistribute are often present in the Global South but elites relinquish responsibility to the state, do not engage personally in redistributive efforts, and consequently compromise the redistribution that they proclaim to support. Other studies have shown that elites are unlikely to cooperate when they do not think that others will share the costs of redistribution (Feierherd et al., 2017) or when they question the reliability of the institutions charged with the task of overseeing redistribution (Berens and Von Schiller, 2017). Taken together, these studies suggest that the actions of elites are largely determined by their perceptions of what others might do. Our study bridges the economic incentives approach and the cultural processes approach by documenting how the effects of negative externalities on elite support for redistribution are curtailed by their views of the poor. Substantively, we contribute to explanations for why, decades after democratization, so many middle-income countries have remained highly unequal and have witnessed only limited redistribution. Theoretically, our contribution lies in highlighting the cultural mechanisms that mediate the relationship between material (or economic) incentives and policy outputs. Our analysis of in-depth interviews with Brazilian elites confirms that elites are indeed responsive to the negative effects of inequality and believe that redistribution would be in their interests. Nevertheless, even if they see the poor as deserving of help, they perceive them as ignorant, irrational and incapable of making informed economic and political decisions. As a result, they are skeptical about the poor’s ability to lift themselves out of poverty—even with governmental assistance. Views of the poor as ignorant and irrational also make elites suspicious of each other, as they believe that incumbent political elites are capable of easily manipulating the poor for personal gains and will do so before committing to elites’ collective interest. Our analysis of survey data supports this argument about the importance of perceptions of the poor. As hypothesized, we find that perceptions of the poor as irrational substantially reduce elite support for redistributive policies, regardless of their views about negative externalities. In pointing to the role of elite perceptions of the poor, we do not mean to imply that prior studies got things wrong. We acknowledge that there are other important structural and attitudinal variables that impact upon elite attitudes toward redistribution. As documented above, other studies have identified tax avoidance, political culture (e.g. egalitarian vs. liberal ideologies) and perceptions of personal or group responsibility as crucial determinants of elite attitudes. We view these explanations as complementary to our own and included covariates that account for them in our regression models. Our findings are also reconcilable with the large body of literature on political economy that points to the role of distrust in explaining negative attitudes toward social policies (Axelrod, 1984; Tabellini, 2008; Alesina and Giuliano, 2009; Berens and Von Schiller, 2017). In our account, perceptions of the poor as irrational lead elites to be suspicious and distrustful of the actions of others, thus hindering coordination around redistribution. 3. Methods Our study draws on two different types of data—in-depth interview and survey data—from three cases—Brazil, South Africa and Uruguay. Below, we introduce our cases and then detail our data collection and analysis procedures. 3.1 Cases The bulk of our study focuses on Brazil—a country with high levels of inequality and concomitant strong negative consequences, such as crime and political instability. We build on the Brazilian case because it provides a scenario in which we would expect to observe more consistent support for redistributive policies among elites, given the persistent effects of negative externalities in recent decades. We then test the findings that emerge from the Brazilian case in two other developing democracies: South Africa and Uruguay. We view the South African case as most closely resembling Brazil in terms of their historical patterns of inequality and the Uruguayan case as most distinctive from Brazil in this respect. The inclusion of these two cases follows the logic of replication outlined by Yin (2003, p. 47). In the multiple case study design which he outlines, this logic is ‘analogous to that used in multiple experiments’.1 By testing findings emerging from the Brazilian case in the South African and Uruguayan ones, we focus on two cases that represent opposite ends of inequality profiles in the developing world and thus adhere to the type of ‘two-tail design’ described by Yin. Following the same inferential logic, the additional cases can also be considered as shadow cases, in the sense that they are not analyzed in-depth but rather used for external validation (see Gerring and Cojocaru, 2016). Brazil democratized in the late 1980s, opening to competitive politics in the context of extreme fiscal chaos in government, criminal violence in urban areas, land invasions in rural areas and the constant threat of military coup (Hagopian, 2007). Redistribution was perceived to be in elites’ interest, because it would help mitigate dramatic externalities (Reis, 2000). Indeed, Brazilian politics was dominated by an agenda of greater equality and social justice (Weyland, 1996). In the early 2000s, the left-wing Workers’ Party (PT) came into power and, with the assistance of favorable economic times, promoted far-reaching policies of social inclusion and redistribution (Blofield, 2011b; Campello and Zucco, 2016). At first, the business community and other elite sectors endorsed PT’s redistributive agenda and Brazil experienced a decline in income inequality. The expansion of cash transfers and a real increase in the minimum wage played a significant role in this decline (Barros et al., 2010; Azevedo et al., 2013; Ferreira et al., 2014; Lustig et al., 2016), which was facilitated by a scenario of economic growth and high commodity prices (Carvalho, 2018). When economic recession brought the need for a more substantial redistributive agenda, support from elites faded, and in recent years inequality has been on the rise (Lavinas, 2017). Decades after democratization, the country remains one of the most unequal in the globe and both poverty and its externalities remain unsolved. At the time we conducted our survey, Brazil had a Gini coefficient of 0.56,2 reflecting the sharp contrast between a small elite of mostly white Brazilians and a vast majority of mostly black and brown poor (Telles, 2004; Lamont et al., 2016). Brazil thus represents a case of a middle-income country where elites were successful at settling a transition to democracy and where they operate in a context of a fairly capable state bureaucracy; but also one in which strong social boundaries exist between elites and the poor (Moraes Silva and López, 2015; Moraes Silva, 2016). South Africa is often positioned as comparable to Brazil in terms of its high levels of socioeconomic inequality, authoritarian history and race relations (Marx, 1998; Lieberman, 2003; Moraes Silva, 2012). As in the Brazilian case, the South African transition to democracy brought with it a left-leaning government campaigning on a redistributive agenda. Even though the African National Congress (ANC) administration has implemented a variety of social policies aimed at redressing historical inequities, overall inequality remained very high, reaching 0.57, as measured by the Gini coefficient, at the time we collected our data. The nature and scope of redistributive policies remain a key point of contention among South African elites (Seekings and Nattrass, 2015). As in Brazil, South Africans often frame inequality and poverty as the main contributors to social problems that affect the middle class and elites (Teeger, 2014); and also like in the Brazilian case, poverty in South Africa is strongly racialized (Seekings and Nattrass, 2008). Given these similarities, we view South Africa as another case of middle-income country with relatively high state capacity and strong social boundaries between elites and the poor. We expect patterns of elite perceptions there to be similar to those found in Brazil. In contrast, Uruguay is often presented as the Latin American country most dissimilar to Brazil (Moreira, 2000; López, 2013b). Building on a more solid history of partisan politics and democratic rule, the country evidences moderate levels of inequality (Gini of 0.4 at the time of our survey) and is among the safest in the developing world [PNUD (Programa de las Naciones Unidas para el Desarrollo), 2013]. Coordination around redistribution has generally been perceived as effective, particularly in the last administrations of the left-wing coalition Frente Amplio (Blofield, 2011b). Racial inequalities play a minor role in Uruguayan politics, and Uruguayans tend to see themselves as a homogeneous people (Ravecca, 2010), even if there has been growing mobilization for the visibility of Afro-Uruguayans (Andrews, 2010). Nevertheless, in the past decades, Uruguayan elites have also dealt with increasing crime rates and other social problems that arose along with income disparities post-democratization (López, 2013b). The Uruguayan case allows us to address whether the relationship between perceptions of the poor and attitudes toward redistribution holds even in contexts where socioeconomic inequalities have been historically lower, elites seem to be more collectively engaged in redistribution, and social boundaries between elites and the poor are expected to be less relevant. 3.2 Interviews We analyzed 56 in-depth interviews with Brazilian state and market elites. Of those, 42 were conducted in 1998 and 1999 by Reis and colleagues and have been made available to us for analysis (see Reis, 2000, 2005). We supplement this dataset with an additional 14 interviews, which we conducted between 2011 and 2013. The interviews took place with business, political and civil service elites in the cities of São Paulo, Rio de Janeiro, Fortaleza and Salvador. For business elites, participants were CEOs, CFOs, Chairpersons of Boards and leaders in organizations of business representation (e.g. chambers of commerce and industry federations). For political elites, participants were members of Congress from the four largest parties in the country, as well as local legislators and a few members of the state executive branches of Rio de Janeiro, São Paulo, Ceará and Bahia. Civil servants were selected from among those occupying top-tier positions in the federal government or state and municipal secretariats in Rio de Janeiro, São Paulo, Ceará/Fortaleza and Bahia/Salvador. Interviews were semi-structured and designed around issues of social and economic policy. They lasted 50-minutes on average and were recorded, transcribed, inductively coded and then re-coded according to the coding schema described in Table 1. Table 1. Prevalence of codes in percentages by period . 1999 . 2012–2013 . Externality: violence 62 93 Externality: other 62 85.7 The poor as capable 5 7 The poor as good/honest/want to make it 21.4 28.6 The poor as victims/passive/manipulated 66.7 57.1 The poor as irrational/ignorant 64.3 85.7 Elites as rational/more rational than the poor 54.8 57.1 Redistribution is necessary 81 100 N 42 14 . 1999 . 2012–2013 . Externality: violence 62 93 Externality: other 62 85.7 The poor as capable 5 7 The poor as good/honest/want to make it 21.4 28.6 The poor as victims/passive/manipulated 66.7 57.1 The poor as irrational/ignorant 64.3 85.7 Elites as rational/more rational than the poor 54.8 57.1 Redistribution is necessary 81 100 N 42 14 Open in new tab Table 1. Prevalence of codes in percentages by period . 1999 . 2012–2013 . Externality: violence 62 93 Externality: other 62 85.7 The poor as capable 5 7 The poor as good/honest/want to make it 21.4 28.6 The poor as victims/passive/manipulated 66.7 57.1 The poor as irrational/ignorant 64.3 85.7 Elites as rational/more rational than the poor 54.8 57.1 Redistribution is necessary 81 100 N 42 14 . 1999 . 2012–2013 . Externality: violence 62 93 Externality: other 62 85.7 The poor as capable 5 7 The poor as good/honest/want to make it 21.4 28.6 The poor as victims/passive/manipulated 66.7 57.1 The poor as irrational/ignorant 64.3 85.7 Elites as rational/more rational than the poor 54.8 57.1 Redistribution is necessary 81 100 N 42 14 Open in new tab In neither time period were participants primed to evaluate the poor. The finding regarding their perceptions of the poor as ignorant and uninformed emerged inductively when we analyzed the two sets of interviews together. Table 1 presents the frequencies of codes in the two time periods. Although we can see differences in the prevalence of these codes across time, the overall trend remains constant. In both time periods, elites viewed inequality as the source of externalities, redistribution as a desirable outcome and the poor as deserving but ignorant in contrast to maximizing elites. Indeed, these perceptions seem to have solidified and become even more salient by 2013. 3.3 Surveys Between 2013 and 2015, we fielded 544 questionnaires3 in random samples of elites in the same sectors in Brazil, South Africa and Uruguay, with 60 respondents per sector in each country.4 Following a strategy that draws on Hoffmann-Lange’s (2018) positional method, elites were sampled based on their institutional positions. We focused on business leaders, elected officials at the national level and top-tier civil servants. We chose these elites because of their decision-making power and ability to shape public policy. Elected officials have an obvious influence on policy, once it is in their hands to put forward legislation. Externalities are a double concern for them, because they can also face electoral costs if they upset poor voters. Civil servants hold significant power over policy design and agenda-setting, particularly in Latin America (Dargent, 2015). The influence of economic elites on policymaking and agenda-setting processes is well documented (Gilens and Page, 2014). For the Brazilian survey, we stratified our sample into three groups or sectors: (a) elected officials at the federal level from each of Brazil’s four main political parties (PMDB, PT, PSDB and PSD),5 (b) top-tier civil servants within the federal government and (c) business leaders (CEOs, CFOs or Chairpersons of the Boards of the country’s top 300 private firms). Civil servants were sampled from those holding DAS 5 and 6 positions—the highest status for civil servants in the country—in ministérios related to economics, development and social policy in the federal government. The list of the top 300 companies was drawn from a business publication, Revista Exame, which is a Forbes-type publication widely used in the corporate world in Brazil. In South Africa, we sampled elected officials from the two largest parties in the National Assembly: the ruling ANC and the Democratic Alliance (DA), the official opposition party. We sampled in proportion to each party’s representation in the National Assembly, so two-thirds of respondents were from the ANC and one-third from the DA. In constructing our sample of civil servants, we strategically chose 20 government departments charged with a range of fiscal and social policy mandates and selected randomly from top-tier roles (Director Generals, Deputy Director Generals, Chief Operating Officers and Chief Directors). To construct our sample of business leaders, we triangulated a list of the top 300 companies (by market capitalization) from the Johannesburg Stock Exchange with The Africa Report’s6 list of top 500 companies, which had been identified as a widely used list by a senior contact at the Competitions Commission. We randomly sampled from this list respondents holding the following positions: CEO, CFO or Chairperson of the Board. In Uruguay, elected officials were sampled from the country’s three main parties, the ruling Frente Amplio and the opposing Partido Colorado and Partido Nacional. Civil servants were sampled from those holding the position of Director in any of the ministerios of the national government. Business leaders were sampled from a list of the 250 largest private companies, based on tax revenues and number of employees as reported in the national business census. Respondents were CEOs, CFOs or Chairpersons of the Board. The surveys were administered through face-to-face interviews, except for a handful in South Africa that were conducted over the phone. We did not allow potential respondents to nominate others to participate in their place.7 Generating probabilistic samples of elites is challenging due to a high expectation of non-response in an already small population. Our strategy for dealing with refusals to participate was to randomize substitutions and continue to re-sample using the same sampling criteria until we reached a quota of 60 respondents per group. This means that all elites in the sample frame had a known and different-from-zero probability of being selected, but that probability was not necessarily the same, given that new rounds of randomization increased the odds of selection. Still, those greater odds were distributed randomly and should not correlate with any meaningful attribute. The overall response rate was 32.3% for Brazil, 41% for South Africa and 54.4% for Uruguay. Response rates per group can be found in Table 2. Table 2. Response rate per sector . Elected officials, n (%) . Civil servants, n (%) . Business, n (%) . Brazil 60 (30) 60 (51) 60 (25) South Africa 63 (65) 60 (35) 61 (32) Uruguay 60 (86) 60 (67) 60 (30) . Elected officials, n (%) . Civil servants, n (%) . Business, n (%) . Brazil 60 (30) 60 (51) 60 (25) South Africa 63 (65) 60 (35) 61 (32) Uruguay 60 (86) 60 (67) 60 (30) Open in new tab Table 2. Response rate per sector . Elected officials, n (%) . Civil servants, n (%) . Business, n (%) . Brazil 60 (30) 60 (51) 60 (25) South Africa 63 (65) 60 (35) 61 (32) Uruguay 60 (86) 60 (67) 60 (30) . Elected officials, n (%) . Civil servants, n (%) . Business, n (%) . Brazil 60 (30) 60 (51) 60 (25) South Africa 63 (65) 60 (35) 61 (32) Uruguay 60 (86) 60 (67) 60 (30) Open in new tab The probabilistic sampling strategy, face-to-face instrument and relatively high response rates make these datasets unique in the field of studies of elites. To the best of our knowledge, ours is the first elite survey following such standards. In order to estimate the effects of elites’ perceptions of the poor on their support for redistributive social policies, we ran OLS regressions. Below, we provide further information on our variables and model specification. Outcome variable For the outcome variable, we built an index of support for redistributive policies that is a normalized interaction between respondents’ assessments of policies. We asked respondents to evaluate a variety of social policies by telling us whether they thought each was (a) desirable and (b) viable. The policies were: universal healthcare, prioritizing the poor in higher education, free higher education, quotas in higher education, free primary schooling, unemployment insurance, food programs, housing programs, lowering income disparities between occupations and a universal basic-income program. These policies are specific enough to give a sense of what kind of redistributive effects they might advance but general enough that they could not be directly related to the policy positions advanced by recognizable political groups, thus avoiding a response bias caused by partisan preferences. Our index ranges from 0 (no policy is viable regardless of how many are desirable) to 1 (all policies are desirable and viable). In our measure, high scores imply that respondents not only viewed a high number of policies as desirable, but also that they believed that such policies were possible to implement. By the same token, the index punishes ‘dreamers’—respondents who expressed high support for policies but did not believe that they were feasible and thus would not commit time or resources toward implementing them. The distribution of the index by country and elite group can be seen in Figure 1. Figure 1. Open in new tabDownload slide Distribution of policy support. Notes: The horizontal line inside each box indicates the median. The boxes, upper line and bottom line represent quartiles. Index of policy support = (∑ Desirable policies) · (∑ Feasible policies) Explanatory variable The main independent variable, which seeks to capture elite views of poor, is a binary measure representing the respondent’s description of the average citizen as either (0) wise or (1) uninformed and irrational.8 The description was in response to the following question: In your opinion, the majority of Brazilian/South African/Uruguayan voters: vote wisely OR are uninformed and irrational? Note that this question does not mention the poor directly but rather asks about the average voter. In these middle-income and unequal countries, the median voter is understood to be poor. This was confirmed in the interviews, in which participants used these terms interchangeably. The focus on average voters honed in on politics, rather than capturing perceptions of ignorance or irrational behavior from other dimensions of everyday life, such as cultural taste or religious beliefs.9 As will be shown in the results from interviews, elites’ support for redistributive policies is informed specifically by their perception of the poor’s political behavior. Covariates We included three types of control variables in our model: (a) perceptions of externalities, (b) biographical, ideological and socioeconomic characteristics of respondents that could affect their predisposition to endorse social policies and (c) variables that reflect alternative explanatory frameworks identified in the literature. We outline each of these measures below. Elite sectors: The source of elite power is itself an important factor determining the incentives to redistribute (López, 2013a). We expected political and civil service elites to be more favorable to redistributive policies than business elites. We account for this by controlling for elite sector (political, economic and civil service), with civil service elites as the reference category. Perceptions of externalities: Here we measure whether elites view poverty as a source of negative externalities for themselves. Respondents were asked to name the main consequence of poverty in an open-ended question and then choose two other consequences from a list of 10 possible consequences.10 We coded perceptions of externalities as present if respondents named a consequence that could affect their own safety (e.g. violence, social conflict, criminality) in either the open- or closed-ended question. The result is a binary measure in which ‘1’ represents the acknowledgment of externalities. Ideology: Previous studies have pointed to the effect of ideologies about equality on the attitudes of elites (Verba et al., 1987). We thus included a measure for ideology where we asked respondents what they thought was more important: (0) liberty or (1) equality, as asked in the World Values Survey. Perception of responsibility: According to Reis and Moore (2005), perceptions of personal responsibility are a key factor in explaining elite attitudes toward redistribution. We asked respondents to identify who they think is responsible for solving inequality, to which we offered the option People like yourself.11 We added this covariate in the model as a binary measure in which ‘1’ indicates respondents agree that people like them are responsible for solving inequality and ‘0’ indicates that they see others as responsible. Willingness to pay more taxes: Several studies assume that elites oppose redistribution because of the tax implications for themselves (e.g. Boix, 2003; Acemoglu and Robinson, 2005). We asked elites whether they were willing to pay more taxes in order to implement the policies that they identified as desirable and viable. The result is a binary variable where ‘1’ represents respondents’ willingness to pay more taxes. Sex: Respondent sex is coded as 0 = male and 1 = female. Although females are a minority among elites and within our samples, we control for sex in order to mitigate a potential confounding effect. Age: Age could have an effect on political preferences because it implies differences in political socialization. We coded age based on respondent’s year of birth. Race: Given the salience of racial inequality in Brazil and South Africa, it is possible that race could have a confounding effect on perceptions of the poor. We used respondents’ self-attributed racial identity, constructing a binary measure in which respondents who identified as ‘white’ were assigned ‘0’ and ‘1’ was assigned to those who identified as any other racial category. Socioeconomic background: Respondents’ own socioeconomic background may affect their level of engagement with, and perceptions of, the poor. We used two variables to capture socioeconomic background: mother’s education and father’s education. Here, we re-scaled responses into an ordinal measure ranging from 1 to 6, where 6 = college degree or higher, 5 = some college education, 4 = high school diploma, 3 = some high school, 2 = completed primary school and 1 = less than primary school. Table 3 presents the distribution of all referred measures by country. Table 3. Distribution of variables . Brazil . South Africa . Uruguay . Variables . Mean . SD . Min . Max . Mean . SD . Min . Max . Mean . SD . Min . Max . N policies desirable 7.90 1.73 3 10 7.45 1.70 2 10 7.70 1.39 4 10 N policies viable 7.68 1.79 1 10 4.92 2.17 0 10 7.59 1.16 3 10 Index of policy support 0.62 0.22 0.3 1 0.37 0.20 0 1 0.59 0.18 0.15 1 Externalities 0.68 0.46 0 1 0.84 0.36 0 1 0.64 0.48 0 1 Voters are uninformed and irrational 0.77 0.47 0 1 0.59 1.93 0 1 0.33 0.47 0 1 People don’t know how to vote 2.98 1.52 1 5 3.11 1.52 1 5 1.99 1.37 1 5 Freedom over equality 0.65 0.47 0 1 0.46 0.50 0 1 0.64 0.48 0 1 Willingness to pay taxes 0.60 0.49 0 1 0.49 0.50 0 1 0.70 0.45 0 1 Perception of personal responsibility 0.11 0.31 0 1 0.28 0.45 0 1 0.10 0.28 0 1 Age 52.14 10.29 26 82 51.60 9.67 29 80 52.67 10.45 30 79 Race 0.20 0.40 0 1 0.63 0.48 0 1 0.03 0.18 0 1 Female 0.10 0.31 0 1 0.11 0.31 0 1 0.14 0.35 0 1 Father’s education (on a 6-point scale) 3.78 2.17 1 6 3.57 1.69 1 6 3.86 1.61 1 6 Mother’s education (on a 6-point scale) 3.43 1.94 1 6 3.19 1.53 1 6 3.74 1.63 1 6 . Brazil . South Africa . Uruguay . Variables . Mean . SD . Min . Max . Mean . SD . Min . Max . Mean . SD . Min . Max . N policies desirable 7.90 1.73 3 10 7.45 1.70 2 10 7.70 1.39 4 10 N policies viable 7.68 1.79 1 10 4.92 2.17 0 10 7.59 1.16 3 10 Index of policy support 0.62 0.22 0.3 1 0.37 0.20 0 1 0.59 0.18 0.15 1 Externalities 0.68 0.46 0 1 0.84 0.36 0 1 0.64 0.48 0 1 Voters are uninformed and irrational 0.77 0.47 0 1 0.59 1.93 0 1 0.33 0.47 0 1 People don’t know how to vote 2.98 1.52 1 5 3.11 1.52 1 5 1.99 1.37 1 5 Freedom over equality 0.65 0.47 0 1 0.46 0.50 0 1 0.64 0.48 0 1 Willingness to pay taxes 0.60 0.49 0 1 0.49 0.50 0 1 0.70 0.45 0 1 Perception of personal responsibility 0.11 0.31 0 1 0.28 0.45 0 1 0.10 0.28 0 1 Age 52.14 10.29 26 82 51.60 9.67 29 80 52.67 10.45 30 79 Race 0.20 0.40 0 1 0.63 0.48 0 1 0.03 0.18 0 1 Female 0.10 0.31 0 1 0.11 0.31 0 1 0.14 0.35 0 1 Father’s education (on a 6-point scale) 3.78 2.17 1 6 3.57 1.69 1 6 3.86 1.61 1 6 Mother’s education (on a 6-point scale) 3.43 1.94 1 6 3.19 1.53 1 6 3.74 1.63 1 6 Open in new tab Table 3. Distribution of variables . Brazil . South Africa . Uruguay . Variables . Mean . SD . Min . Max . Mean . SD . Min . Max . Mean . SD . Min . Max . N policies desirable 7.90 1.73 3 10 7.45 1.70 2 10 7.70 1.39 4 10 N policies viable 7.68 1.79 1 10 4.92 2.17 0 10 7.59 1.16 3 10 Index of policy support 0.62 0.22 0.3 1 0.37 0.20 0 1 0.59 0.18 0.15 1 Externalities 0.68 0.46 0 1 0.84 0.36 0 1 0.64 0.48 0 1 Voters are uninformed and irrational 0.77 0.47 0 1 0.59 1.93 0 1 0.33 0.47 0 1 People don’t know how to vote 2.98 1.52 1 5 3.11 1.52 1 5 1.99 1.37 1 5 Freedom over equality 0.65 0.47 0 1 0.46 0.50 0 1 0.64 0.48 0 1 Willingness to pay taxes 0.60 0.49 0 1 0.49 0.50 0 1 0.70 0.45 0 1 Perception of personal responsibility 0.11 0.31 0 1 0.28 0.45 0 1 0.10 0.28 0 1 Age 52.14 10.29 26 82 51.60 9.67 29 80 52.67 10.45 30 79 Race 0.20 0.40 0 1 0.63 0.48 0 1 0.03 0.18 0 1 Female 0.10 0.31 0 1 0.11 0.31 0 1 0.14 0.35 0 1 Father’s education (on a 6-point scale) 3.78 2.17 1 6 3.57 1.69 1 6 3.86 1.61 1 6 Mother’s education (on a 6-point scale) 3.43 1.94 1 6 3.19 1.53 1 6 3.74 1.63 1 6 . Brazil . South Africa . Uruguay . Variables . Mean . SD . Min . Max . Mean . SD . Min . Max . Mean . SD . Min . Max . N policies desirable 7.90 1.73 3 10 7.45 1.70 2 10 7.70 1.39 4 10 N policies viable 7.68 1.79 1 10 4.92 2.17 0 10 7.59 1.16 3 10 Index of policy support 0.62 0.22 0.3 1 0.37 0.20 0 1 0.59 0.18 0.15 1 Externalities 0.68 0.46 0 1 0.84 0.36 0 1 0.64 0.48 0 1 Voters are uninformed and irrational 0.77 0.47 0 1 0.59 1.93 0 1 0.33 0.47 0 1 People don’t know how to vote 2.98 1.52 1 5 3.11 1.52 1 5 1.99 1.37 1 5 Freedom over equality 0.65 0.47 0 1 0.46 0.50 0 1 0.64 0.48 0 1 Willingness to pay taxes 0.60 0.49 0 1 0.49 0.50 0 1 0.70 0.45 0 1 Perception of personal responsibility 0.11 0.31 0 1 0.28 0.45 0 1 0.10 0.28 0 1 Age 52.14 10.29 26 82 51.60 9.67 29 80 52.67 10.45 30 79 Race 0.20 0.40 0 1 0.63 0.48 0 1 0.03 0.18 0 1 Female 0.10 0.31 0 1 0.11 0.31 0 1 0.14 0.35 0 1 Father’s education (on a 6-point scale) 3.78 2.17 1 6 3.57 1.69 1 6 3.86 1.61 1 6 Mother’s education (on a 6-point scale) 3.43 1.94 1 6 3.19 1.53 1 6 3.74 1.63 1 6 Open in new tab 3.4 Model specification The model specification is as follows: Y=α+β1PERCEPTION+β2 SEC+β3EXT+β4Xi+µ Where Y= (∑ Desirable policies) · (∑ Feasible policies),12 α represents the intercept, β represents average effects, PERCEPTION stands for respondents’ agreement with the idea that average voters are uninformed and irrational, SEC represents membership of one of the three elite sectors, EXT represents respondents’ perceptions of the negative externalities of inequality, Xi represents all other control covariates and µ represents the error term. We conducted propensity score matching and permutation tests with the full data set (which brings together data from all three countries) to provide an alternative estimation of the size of the effect of perception of the poor as uninformed and irrational. Results can be found in Appendix A. Before presenting results from the survey data, we first outline the findings that emerged from the interviews and show how they inform our explanation of the role of perceptions of the poor in shaping elites’ support for redistributive social policies. 4. In-depth interview findings Prior studies have highlighted the importance of elite perceptions of the negative externalities of poverty and inequality in shaping their attitudes toward redistribution (de Swaan, 1988; Reis and Moore, 2005; Rueda and Stegmueller, 2016). In line with this literature, the Brazilian elites who were interviewed perceived inequality to be a problem affecting not only the poor but also themselves. When discussing the consequences of inequality, they most frequently mentioned violence, crime and safety, although they also pointed to other issues, such as the lack of a qualified workforce. Out of 56 participants, 49 described some kind of negative externality; the most commonly mentioned were violence and criminality, cited in 39 interviews.13 In 2013, for instance, a congressman lamented, ‘There is no fun in having money if you are in a restaurant and there are people hitting the glass window asking for food outside. You don’t enjoy your money as much as you could.’ He then quickly made the connection between poverty and safety: Not to mention public safety! What is the fun in living inside a bulletproof car, in a gated community? All those lights, security staff… I mean, this is no quality of life … What sense does it make to be someone who can only feel safe when vacationing abroad? Similar to this participant, others spoke explicitly of elite concerns about the effect of inequality on their own well-being. In 2013, a civil servant articulated this idea quite succinctly, ‘It is when the rich feel their safety threatened that they say: “Wait a minute, this is affecting my personal safety.”’ Others contrasted their lives in Brazil to their experiences overseas, again highlighting their concerns over safety. For instance, a business leader interviewed in 2012 stated: Look, I have an apartment in New York. Over there, I walk on the street, I feel great, living without worries in New York, but not in São Paulo. I do not walk on the street in São Paulo. Literally, I walk on a treadmill! I don’t walk on a treadmill in New York, I walk on the street. What does that imply in terms of wellbeing? I mean, the saddest thing is that there is no need for that, [Brazil is] a country with an extraordinary economic potential. Given the acknowledgment by Brazilian elites that inequality is a problem affecting not only the poor but also themselves, it should come as no surprise that they have, overall, a positive attitude toward redistribution. Nearly all participants argued that redistribution policies are necessary. More importantly, they explained that less inequality would be in their own interest, because it would allow them to enjoy their wealth and status. By improving the poor’s well-being, they argued, they would be improving their own. In addition, Brazilian participants largely perceived poverty as structural and viewed the poor as deserving of relief, expressing admiration for their hard work and pity for their hardships—attitudes largely associated in the literature with support for redistribution (e.g. Hochschild, 1981; Oorschot and Halman, 2000; Katz, 2013). For example, in 1999, an elected official argued, ‘most of them [poor people] are honest, dignified people,’ while in 2013 a business leader argued that the poor were people who ‘just want to get ahead.’ Participants also proposed that differences in outcomes had to do with differences in opportunities, suggesting that they would support policies aimed at leveling the playing field. In the words of an elected official in 1999, ‘There has never been a commitment to giving the working class its fair share. The reason that these social classes are marginal is not that they don’t want to work. They are marginalized because they are not given opportunities.’ Even though the poor were not perceived as undeserving of help, they were still morally evaluated by the participants. Poor people were recurrently described as uninformed, ignorant, uneducated and even irrational. These types of descriptions appeared spontaneously in 39 of the 56 interviews and were highly salient across elite groups and in both time periods. Such perceptions prompted the elites to question the poor’s ability to act in their own self-interest. For example, in 1999, an elected official explained, ‘Improvements will never come from the excluded masses.’ Conceptualizing the poor as incapable of reason, he argued that little can be expected of them. ‘As I said, we cannot expect anything from them. We cannot expect anything from a person who is not capable of reason, at the most basic level, of [making sense of] what is going on in the world.’ Similarly, in 2013 a business leader invoked an inextricable relationship between poverty and knowledge: The main consequence of poverty is the lack of knowledge. The lack of knowledge. And then health problems, malnutrition, all the educational problems. We don’t have the basic conditions for people to have a minimum amount of knowledge, basic notions so that they can have a better life. Participants described the poor as victims of poverty who are—most of the time, irreversibly—unable to act in their own material interests. They viewed the structural constraints of poverty as imposing strong limitations on rationality. In other words, while the poor were not seen as directly responsible for their own poverty, poverty itself was seen to structure irrational behavior. In 1999, a business leader concisely illustrated this view, ‘Extreme poverty makes one not see anything. It is a black veil over people’s heads. It blinds them.’ In the narratives of participants, the perception of the poor as being uneducated, unwise and incapable of rational decision-making were seen to be particularly consequential for political behavior. Ignorant, poor voters were viewed as a threat due to their susceptibility to manipulation by other elites—a risk explicitly cited in two-thirds of the interviews. Political elites, in particular, were accused of subverting desirable redistributive policies in order to satisfy their immediate clientelist needs, while voters stood by passively. Contrary to their views of the poor, elites saw politicians as rational and selfish maximizers. The relationship between ignorance and political manipulation was invoked in the 1999 and 2013 interviews. In 1999, one business leader distinguished between the poor and the ‘thinking and property-holding classes,’ who—in his view—are ‘not ashamed to keep in place this unjust system.’ Another business leader claimed, ‘The government wastes money in all it does,’ because policies end up serving the ‘individual gains’ of other elites. A business leader in 2013 similarly expressed that there was ‘obviously a political agenda’ behind cash transfer policies, which were a project to ‘garner votes [in order] to remain in power.’ The understanding of poor people as ignorant and irrational led some of the participants to blame the poor for their poverty—not due to their lack of deservingness or laziness but because of their political behavior. A businessperson interviewed in 2013 illustrates this reasoning, ‘The unpreparedness of the population during the electoral process is so serious… it is a process of vote buying, a process of using policies for marketing, and this way the prospect of actual change becomes very limited.’ Politicians and civil servants—accused by business elites of biasing policies to benefit themselves—expressed similar sentiments. When asked about the unintended consequences of social policies, one civil servant, in 2013, said that he viewed ‘it all with great suspicion,’ implying that social policies could be used by political elites to channel benefits to loyalists. As put by a civil servant in 1999, ‘The public officials have the ability to do things right for their community … but sometimes they [public officials] entertain other possibilities because of their clientelist political character.’ When discussing his own policy agenda, an elected official in 1999 similarly argued that politicians were ‘used to making politics out of the basic needs of the population’. In sum, the narratives we found in these interviews with Brazilian elites show that they were aware of the negative effects of inequality, not only for the poor but also for themselves. They expressed an overall positive attitude toward redistribution, which they perceived as fair and desirable, and agreed that the poor were deserving of help. Nevertheless, they remained skeptical of the odds of success of social policies due to the perceived ignorance of the poor. This perceived irrationality was particularly salient when it came to political behavior. The poor were seen by participants as easily manipulated by politicians and civil servants who could use social policies as clientelist tools; as a result, participants remained skeptical regarding the likelihood of success of such policies. The persistence of these perceptions over time is striking. In the period between 1999 and 2013, Brazil shifted from a center-right to a center-left government coalition and from economic crisis to resource abundance. Yet the way in which elites spoke about poor voters showed little change. These narratives point to the importance of distinctions made against the poor in shaping attitudes towards redistributive policies. The elites in these interviews found themselves in a double bind: poverty was a problem, and the poor deserved to be helped, but poverty could not be tackled effectively because of the poor’s lack of political sophistication and strategic reasoning. Our findings suggest that perceptions of the poor as ignorant reduce support for redistributive policies. To test this hypothesis, we turn to the survey data. 5. Survey results In what follows, we test the effect of elite perceptions of the poor on their willingness to support redistributive policies. We begin with the Brazilian case and then replicate the analysis in the two other cases: South Africa and Uruguay. 5.1 Brazil In Brazil, survey data revealed similar patterns to those found in the interview data. Figure 2 presents the distribution of responses for three key variables in both samples in Brazil, highlighting the convergence between them. When asked about the main consequences of poverty and inequality, over 60% of the elites surveyed, and an equal proportion of interview participants pointed to violence or conflict. When asked whether they believed that redistribution was necessary, 95% of respondents answered in the affirmative. This was similar to findings from the interviews, where 86% of respondents spoke generally about redistribution as being necessary. Finally, as did the Brazilian elites who participated in the in-depth interviews, most who took part in the closed-ended survey characterized the poor as ignorant and irrational. When asked about ‘average voters’ (our proxy for elites’ perceptions of the poor as political actors), 77% of respondents described them as ignorant and irrational. This was virtually the same proportion of spontaneous mentions of ignorance in the interviews. If our hypothesis holds, then the presence of this perception should result in a reduction in the willingness of elites to support redistributive social policies, regardless of their perspective of the negative externalities of poverty and inequality. Figure 2. Open in new tabDownload slide Convergence between interview findings and survey results (% response). As discussed in the methods section, we constructed an index to measure elite support for redistributive policies. This allowed us to capture not just an abstract sentiment of global support for redistribution, but a more specific assessment of the views of elites on both the desirability and feasibility of a range of redistributive policies. Table 4 presents the estimated effect of perceptions of the poor. Results show that elite views of the poor as uninformed and irrational reduces their support for redistributive policies. On average, this reduction is between 0.11 and 0.27 points in the index of policy support (where 1 indicates full support). The effect is robust to the inclusion of several covariates, including ideology and tax avoidance. Table 4. Effect of perceptions of the poor on elite support for redistribution in Brazil . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.191*** 0.035 −0.192*** 0.035 −0.129*** 0.034 −0.134*** 0.036 Externalities .018 0.037 −0.002 0.033 0.014 0.034 Business elite −0.152*** 0.037 −0.140*** 0.042 Political elite 0.076* 0.040 0.081* 0.044 Female −0.108 0.079 Father education 0.001 0.012 Mother education −0.003 0.014 Race −0.055 0.042 Age −0.002 0.002 Ideology −0.053 0.036 Perception of self as responsible −0.051 0.048 Willingness to pay more taxes 0.052^ 0.032 Intercept 0.741*** 0.028 0.729*** 0.038 0.732*** 0.043 0.872*** 0.117 R2 0.130 0.170 0.341 0.419 N 147 147 147 135 . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.191*** 0.035 −0.192*** 0.035 −0.129*** 0.034 −0.134*** 0.036 Externalities .018 0.037 −0.002 0.033 0.014 0.034 Business elite −0.152*** 0.037 −0.140*** 0.042 Political elite 0.076* 0.040 0.081* 0.044 Female −0.108 0.079 Father education 0.001 0.012 Mother education −0.003 0.014 Race −0.055 0.042 Age −0.002 0.002 Ideology −0.053 0.036 Perception of self as responsible −0.051 0.048 Willingness to pay more taxes 0.052^ 0.032 Intercept 0.741*** 0.028 0.729*** 0.038 0.732*** 0.043 0.872*** 0.117 R2 0.130 0.170 0.341 0.419 N 147 147 147 135 ^ P < 0.11; * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab Table 4. Effect of perceptions of the poor on elite support for redistribution in Brazil . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.191*** 0.035 −0.192*** 0.035 −0.129*** 0.034 −0.134*** 0.036 Externalities .018 0.037 −0.002 0.033 0.014 0.034 Business elite −0.152*** 0.037 −0.140*** 0.042 Political elite 0.076* 0.040 0.081* 0.044 Female −0.108 0.079 Father education 0.001 0.012 Mother education −0.003 0.014 Race −0.055 0.042 Age −0.002 0.002 Ideology −0.053 0.036 Perception of self as responsible −0.051 0.048 Willingness to pay more taxes 0.052^ 0.032 Intercept 0.741*** 0.028 0.729*** 0.038 0.732*** 0.043 0.872*** 0.117 R2 0.130 0.170 0.341 0.419 N 147 147 147 135 . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.191*** 0.035 −0.192*** 0.035 −0.129*** 0.034 −0.134*** 0.036 Externalities .018 0.037 −0.002 0.033 0.014 0.034 Business elite −0.152*** 0.037 −0.140*** 0.042 Political elite 0.076* 0.040 0.081* 0.044 Female −0.108 0.079 Father education 0.001 0.012 Mother education −0.003 0.014 Race −0.055 0.042 Age −0.002 0.002 Ideology −0.053 0.036 Perception of self as responsible −0.051 0.048 Willingness to pay more taxes 0.052^ 0.032 Intercept 0.741*** 0.028 0.729*** 0.038 0.732*** 0.043 0.872*** 0.117 R2 0.130 0.170 0.341 0.419 N 147 147 147 135 ^ P < 0.11; * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab The impact of perceptions of externalities on elite support for redistributive social policies is, as we predicted, positive, but the effect is rather weak and thus disappointing within the context of the larger story presented here, which follows the political economy literature. Based on confidence intervals and P-values, we cannot dismiss the possibility of a null effect of externalities in the population. On the other hand, it is possible that our binary measure of perceived externalities is generating a ceiling effect because it does not allow respondents to grade their concern, or indicate how much they view themselves to be affected by such externalities. For our purposes, what is important is that the negative effect of perceiving the poor as irrational nullifies any potential positive effect of externalities. It should also be noted that the two variables are not themselves correlated,14 which indicates that the effect of perceptions of the poor is independent from that of perceptions of externalities. Results also show that political elites tend to be more supportive of redistributive measures than other elites in the sample. This is expected given politicians’ electoral connections with poor voters. Still, the effect of perceptions of the poor remains strong even when controlling for elite groups, suggesting that average effects are not induced by partisanship or other electoral incentives. Overall, the regression models support the hypothesis that emerged from the interviews. 5.2 South Africa In South Africa, almost 60% of respondents described the average voter as irrational and uninformed, a similar rate to that found in the Brazilian sample. Table 5 presents the results of OLS regressions run on the South African data. As predicted, the results are very similar to those found in Brazil, with a robust negative effect of perceptions of the poor as uninformed and irrational on elite support for redistributive policies. Table 5. Effect of perceptions of the poor on elite support for redistribution in South Africa . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.152*** 0.034 −0.151*** 0.034 −0.131*** 0.036 −0.100*** 0.036 Externalities −0.017 0.047 −0.008 0.046 0.016 0.045 Business elite 0.057 00.039 0.084* 0.041 Political elite 0.138*** .040 0.129** 0.046 Female −0.041 0.061 Father education 0.042*** 0.015 Mother education −0.051*** 0.016 Race 0.073* 0.043 Age −0.000 0.002 Ideology −0.014 0.035 Perception of self as responsible −0.033 0.036 Willingness to pay more taxes 0.075* 0.033 Intercept 0.469*** 0.027 0.483*** 0.047 0.400*** 0.051 0.320** 0.136 R2 0.125 0.126 0.196 0.357 N 142 142 142 132 . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.152*** 0.034 −0.151*** 0.034 −0.131*** 0.036 −0.100*** 0.036 Externalities −0.017 0.047 −0.008 0.046 0.016 0.045 Business elite 0.057 00.039 0.084* 0.041 Political elite 0.138*** .040 0.129** 0.046 Female −0.041 0.061 Father education 0.042*** 0.015 Mother education −0.051*** 0.016 Race 0.073* 0.043 Age −0.000 0.002 Ideology −0.014 0.035 Perception of self as responsible −0.033 0.036 Willingness to pay more taxes 0.075* 0.033 Intercept 0.469*** 0.027 0.483*** 0.047 0.400*** 0.051 0.320** 0.136 R2 0.125 0.126 0.196 0.357 N 142 142 142 132 * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab Table 5. Effect of perceptions of the poor on elite support for redistribution in South Africa . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.152*** 0.034 −0.151*** 0.034 −0.131*** 0.036 −0.100*** 0.036 Externalities −0.017 0.047 −0.008 0.046 0.016 0.045 Business elite 0.057 00.039 0.084* 0.041 Political elite 0.138*** .040 0.129** 0.046 Female −0.041 0.061 Father education 0.042*** 0.015 Mother education −0.051*** 0.016 Race 0.073* 0.043 Age −0.000 0.002 Ideology −0.014 0.035 Perception of self as responsible −0.033 0.036 Willingness to pay more taxes 0.075* 0.033 Intercept 0.469*** 0.027 0.483*** 0.047 0.400*** 0.051 0.320** 0.136 R2 0.125 0.126 0.196 0.357 N 142 142 142 132 . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.152*** 0.034 −0.151*** 0.034 −0.131*** 0.036 −0.100*** 0.036 Externalities −0.017 0.047 −0.008 0.046 0.016 0.045 Business elite 0.057 00.039 0.084* 0.041 Political elite 0.138*** .040 0.129** 0.046 Female −0.041 0.061 Father education 0.042*** 0.015 Mother education −0.051*** 0.016 Race 0.073* 0.043 Age −0.000 0.002 Ideology −0.014 0.035 Perception of self as responsible −0.033 0.036 Willingness to pay more taxes 0.075* 0.033 Intercept 0.469*** 0.027 0.483*** 0.047 0.400*** 0.051 0.320** 0.136 R2 0.125 0.126 0.196 0.357 N 142 142 142 132 * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab As in Brazil, results in South Africa are robust to the inclusion of several covariates. The South African findings support the hypothesis that perceptions of the poor as ignorant, uninformed and irrational results in reduced support for redistributive policies. Again, differences between elite groups are most notable in politicians’ higher levels of support for redistributive policies. 5.3 Uruguay In Uruguay, only 33% of respondents described the average voter as irrational. Among politicians and civil servants, this was less than 15%. Among business elites, however, around 70% characterized the poor as irrational. This suggests that differences between elite sectors are even more relevant in Uruguay than in the other two cases. Table 6 presents results for the Uruguayan data. Estimations are limited, however, by the very strong correlation between belonging to the business sector and perceiving the average voter as irrational. Nevertheless, as in Brazil and South Africa, here too we see that elite perceptions of the average voter as irrational result in a reduction in policy support. Table 6. Effect of perceptions of the poor on elite support for redistribution in Uruguay . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.117*** 0.029 −0.120*** 0.029 −0.057^ 0.034 −0.033 0.037 Externalities 0.028 0.030 0.037 0.029 0.019 0.034 Business elite −0.105** 0.038 −0.096** 0.045 Political elite 0.015 0.034 −0.001 0.040 Female −0.040 0.044 Father education −0.014 0.012 Mother education 0.018 0.012 Race 0.146 0.122 Age −0.002 0.001 Ideology −0.015 0.031 Perception of self as responsible −0.035 0.048 Willingness to pay more taxes 0.064* 0.037 Intercept 0.633*** 0.017 0.615*** 0.026 0.620*** 0.032 0.716*** 0.113 R2 0.097 0.103 0.165 0.237 N 151 151 151 126 . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.117*** 0.029 −0.120*** 0.029 −0.057^ 0.034 −0.033 0.037 Externalities 0.028 0.030 0.037 0.029 0.019 0.034 Business elite −0.105** 0.038 −0.096** 0.045 Political elite 0.015 0.034 −0.001 0.040 Female −0.040 0.044 Father education −0.014 0.012 Mother education 0.018 0.012 Race 0.146 0.122 Age −0.002 0.001 Ideology −0.015 0.031 Perception of self as responsible −0.035 0.048 Willingness to pay more taxes 0.064* 0.037 Intercept 0.633*** 0.017 0.615*** 0.026 0.620*** 0.032 0.716*** 0.113 R2 0.097 0.103 0.165 0.237 N 151 151 151 126 ^ P < 0.11; * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab Table 6. Effect of perceptions of the poor on elite support for redistribution in Uruguay . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.117*** 0.029 −0.120*** 0.029 −0.057^ 0.034 −0.033 0.037 Externalities 0.028 0.030 0.037 0.029 0.019 0.034 Business elite −0.105** 0.038 −0.096** 0.045 Political elite 0.015 0.034 −0.001 0.040 Female −0.040 0.044 Father education −0.014 0.012 Mother education 0.018 0.012 Race 0.146 0.122 Age −0.002 0.001 Ideology −0.015 0.031 Perception of self as responsible −0.035 0.048 Willingness to pay more taxes 0.064* 0.037 Intercept 0.633*** 0.017 0.615*** 0.026 0.620*** 0.032 0.716*** 0.113 R2 0.097 0.103 0.165 0.237 N 151 151 151 126 . Model 1 . Model 2 . Model 3 . Model 4 . . Coef . SE . Coef . SE . Coef . SE . Coef . SE . Poor are irrational −0.117*** 0.029 −0.120*** 0.029 −0.057^ 0.034 −0.033 0.037 Externalities 0.028 0.030 0.037 0.029 0.019 0.034 Business elite −0.105** 0.038 −0.096** 0.045 Political elite 0.015 0.034 −0.001 0.040 Female −0.040 0.044 Father education −0.014 0.012 Mother education 0.018 0.012 Race 0.146 0.122 Age −0.002 0.001 Ideology −0.015 0.031 Perception of self as responsible −0.035 0.048 Willingness to pay more taxes 0.064* 0.037 Intercept 0.633*** 0.017 0.615*** 0.026 0.620*** 0.032 0.716*** 0.113 R2 0.097 0.103 0.165 0.237 N 151 151 151 126 ^ P < 0.11; * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab 6. Discussion and conclusions Our findings show that elites’ acknowledgment of the negative externalities of inequality could indeed foster positive attitudes toward redistribution, but also that the way they see the poor impacts upon their support for redistributive social policies. Results suggest that elites’ limited support for redistribution is not the result of a belief that poverty and inequality pose minimal threats (Haggard and Kaufman, 2012, 2016; Ansell and Samuels, 2014) or that existing institutions shield them from such threats (Albertus and Menaldo, 2014). Even if elites rely on predatory state practices, low-paid labor and other institutions that generate vicious cycles of impoverishment and inequality, our results show that elites worry about the consequences of such inequality for their own safety and well-being, as indeed predicted by existing literature (see de Swaan, 1988; Reis and Moore, 2005; Rueda and Stegmueller, 2016). All things considered, elites envision a scenario in which some redistribution would solve many of their problems at a reasonable cost—a cost that they claim to be willing to pay if they have some assurance that such policies will work. This belief, however, does not automatically translate into more support for redistributive policies. Instead, elite views of the poor curtail any such commitment. Our findings point to the role of elite perceptions of the poor in hindering their willingness to pursue such policy goals. Their views of the poor as ignorant, irrational and susceptible to manipulation by other elites ultimately lead them to reject social policies that they would otherwise find in their own interest. Our interpretation of results is as follows: because elites view the poor as ignorant and irrational, they do not trust them to make informed decisions and instead view them as susceptible to manipulation by other, utility-maximizing, elites. Elite perceptions of the poor, in other words, disrupt the effect of their acknowledgment of the negative externalities of inequality on their commitment to redistributive social policies. In documenting these trends, we have argued that cultural variables—in the form of perceptions of others—are crucial to explaining why redistributive policies have garnered only limited elite support. The theoretical contribution of our study, therefore, is to highlight how culture mediates the effect of material interests on policy preferences. Is it possible, however, that our data reflect nothing but the discursive justification of a crude rational calculus? Although we do not necessarily take everything said in the interviews at face value, we do not believe that the participants were deliberately deceiving themselves or the interviewers. Why would elites not prefer a scenario in which they could still hold power and wealth but not suffer the negative externalities of poverty? After all, political and economic elites have the tools to allow some redistribution while allocating costs elsewhere (e.g. to the middle class). Our findings suggest that perceptions of the poor play a key role in explaining why elites do not see such solutions as viable. We also do not think that the survey data on policy support reflect respondents’ justifications of their pre-existing policy preferences. Our outcome variable is composed of the interaction between two series of questions. It would be highly unlikely that respondents could anticipate their score. Nevertheless, one might argue that elite perceptions of the poor are caused by their observation of the tendency for redistributive policies to fail. Such a causal relationship is indeed possible but not incompatible with our argument. It suggests that past experiences of failed redistribution may lead elites to construct explanations that blame the poor, thus hindering future support for redistributive policies. Although our main findings point to perceptions of the poor as a key predictor of support for redistributive policies at the individual level, our results also suggest important cross-national differences in the prevalence of such perceptions across cases, with Brazilian and South African elites describing the average voter as ignorant and irrational much more frequently than their Uruguayan counterparts. Blofield (2011a) argues that overall levels of inequality foster a degree of social distance between elites and average citizens. This may explain the baseline differences we observe in negative perceptions of the poor between our cases. In Brazil and South Africa, our two cases with extreme and enduring inequalities, elites were much more likely to describe the poor as ignorant and uninformed than they were in Uruguay, with its less marked history of inequality. While our data are limited in their ability to unpack how perceptions develop and take hold, the cross-national differences in our study point to important avenues for future research that account for the role played by historical and contemporary social and economic cleavages between elites and the poor. At the same time, country-level variables cannot be assumed to be the sole, or even the main, explanation for aggregated levels of elite distrust toward the poor. Business elites in the three cases show similar extreme views of the poor and concomitant lower support for redistributive policies. This is particularly striking in the case of Uruguay, where business elites display patterns much more similar to their Brazilian and South African counterparts than to local political and civil servant elites. This pattern suggests that elite sectors likely mediate any relationship between macrosocial factors and elites’ individual attitudes. It further points to the importance of considering variation in elites’ economic capital as well as their social distance from and dependence on voters in shaping their perceptions of the poor. Finally, it is important to consider the role that racial boundaries might play across our cases. In Brazil and South Africa, ‘average voters’ are likely to be perceived as black or brown, whereas in Uruguay they are likely to be perceived as white. This racialized understanding of poverty may factor into elites’ views of the poor. Although we cannot get at this type of association between race and poverty in our survey data, we might have expected it to emerge as significant in the in-depth interviews conducted with Brazilian elites. It did not. This of course does not mean that such images and boundaries do not exist. It means that they were not mobilized by participants during interviews—a silence that plausibly reflects a type of self-censorship built into a history of color-blind racism in Brazil (Moraes Silva, 2016). Despite these cross-national differences, our findings about the processes that connect economic incentives, culture and attitudes toward redistribution hold in the three cases and are likely generalizable to other developing democracies. The comparative trends offer important avenues for future research. In addition to the effects of overall levels of inequality, researchers may wish to examine the role of other macro-level variables in explaining micro-level perceptions and preferences. These might include the legacies of authoritarian rule, the type of welfare regime and—as discussed above—the salience of racial boundaries, to name but a few. Such research would continue to advance our knowledge of both the cultural and economic determinants of redistribution. Supplementary material Supplementary material is available at Socio-Economic Review Journal online. Funding This project received funds from CONICYT (Chile), Wenner-Gren Foundations (Sweden), the Institut de Hautes Etudes Internationales et du Developpement (Switzerland), the LSE International Inequalities Institute (UK), and the PRONEX program (Brazil). Footnotes 1 Yin goes on to say that ‘upon uncovering a significant finding from a single experiment, the immediate research goal would be to replicate this finding by conducting a second, third, and even more experiments…only with such replication would the original finding be considered robust’. The same logic of inference applies to multiple-case study designs 2 The Gini coefficient ranges from 0 (perfect equality) to 1 (perfect inequality). All measures of the Gini coefficient mentioned in this paper can be found at https://data.worldbank.org 3 The questionnaire can be found in the Supplementary Material. 4 In South Africa, we had 63 politicians and 61 businesspeople because the contracted survey company conducted interviews beyond the agreed-upon quota. Since all of the interviewees were selected randomly, we decided to maintain the extra four cases in the dataset. 5 By ‘main parties’ we refer to those with the largest number of seats in parliament in 2013. 6 See https://www.theafricareport.com. 7 Except in one South African case, in which we allowed the CEO of Strategic Projects to participate. 8 Descriptives for each elite sector in each case can be found in the Supplementary Material. 9 We also ran alternative specifications with each component of the output variable and with an ordinal measure for the perceived ignorance of the poor and. The ordinal measure indicates on a 5-point scale participants’ agreement with the statement: People do not know how to vote properly. Results support those found using the binary measure and are available in the Supplementary Material. 10 Listed consequences of poverty were: scarcity of skilled labor, violence and criminality, political patronage/populism, epidemics, international embarrassment, racial conflict, conflict among social classes, lack of a solid domestic consumer market, decline in moral standards. 11 The other options were: NGOs, wealthy individuals, government, businessmen, organized civil society, social movements. 12 For effects on each particular policy we ran logistic regressions. Results can be found in the Supplementary Material. 13 As discussed earlier, these frequencies were similar in both periods when interviews were conducted. The exact frequency of all codes mentioned in this section are summarized in Table 1 in the ‘Methods’ section. 14 See correlation matrix in the Supplementary Material. Acknowledgements For helpful feedback and conversations, we thank Frances Hagopian, Juan Pablo Luna, Kevin Lewis, Michele Lamont, Lorenza Fontana, Antoinnete Hetzler, Markus Holdo, Fernando Bizarro, Magnus Axelson, Agustín Goenaga, Jan Teorell, Jake Bowers, Kammila Naidoo, Lucia Selios, and the SER editor and anonymous reviewers. We are particularly indebted to Elisa Reis for sharing her data with us and in so many ways inspiring and encouraging this project. 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( 2003 ) Case Study Research: Design and Methods , 3 rd edn, Thousand Oaks, CA , Sage . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Appendix A A.1 Results with non-parametric methods As an alternative estimation of the average magnitude of the effect of perceptions, we used propensity score matching and permutation tests in which we ran 1,000 linear models in subsamples of 100 randomly selected observations. Results show effect sizes that are similar to those observed in the previous linear models. Table A1. Matching results with the full sample . Result 1 . Result 2 . . Coef . SE . Coef . SE . Poor are irrational −0.195*** 0.023 −0.120*** 0.025 Intercept 0.598*** 0.025 0.633*** 0.023 Elite sector fixed effect No Yes Country fixed effect No Yes R2 0.178 0.380 N 340 340 . Result 1 . Result 2 . . Coef . SE . Coef . SE . Poor are irrational −0.195*** 0.023 −0.120*** 0.025 Intercept 0.598*** 0.025 0.633*** 0.023 Elite sector fixed effect No Yes Country fixed effect No Yes R2 0.178 0.380 N 340 340 Notes: Observations matched on propensity score, using ‘nearest’ method. Covariates included are: race, age, female, father education, mother education, elite sector, externalities, ideology, willingness to pay taxes and country. Balance was achieved in all covariates, except elite sector and country, which are accounted for in Result 2. * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab Table A1. Matching results with the full sample . Result 1 . Result 2 . . Coef . SE . Coef . SE . Poor are irrational −0.195*** 0.023 −0.120*** 0.025 Intercept 0.598*** 0.025 0.633*** 0.023 Elite sector fixed effect No Yes Country fixed effect No Yes R2 0.178 0.380 N 340 340 . Result 1 . Result 2 . . Coef . SE . Coef . SE . Poor are irrational −0.195*** 0.023 −0.120*** 0.025 Intercept 0.598*** 0.025 0.633*** 0.023 Elite sector fixed effect No Yes Country fixed effect No Yes R2 0.178 0.380 N 340 340 Notes: Observations matched on propensity score, using ‘nearest’ method. Covariates included are: race, age, female, father education, mother education, elite sector, externalities, ideology, willingness to pay taxes and country. Balance was achieved in all covariates, except elite sector and country, which are accounted for in Result 2. * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab Table A2. Permutation test with the full sample . Permutation 1 . Permutation 2 . . Estimate . SE . Estimate . SE . Poor are irrational −0.164*** 0.045 −0.110*** 0.021 Externalities 0.024 0.020 Intercept 0.619*** 0.032 0.719*** 0.072 Elite sector fixed effect No Yes Country fixed effect No Yes Controls No Yes . Permutation 1 . Permutation 2 . . Estimate . SE . Estimate . SE . Poor are irrational −0.164*** 0.045 −0.110*** 0.021 Externalities 0.024 0.020 Intercept 0.619*** 0.032 0.719*** 0.072 Elite sector fixed effect No Yes Country fixed effect No Yes Controls No Yes Note: 1000 samples of n = 100 with no replacement. * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab Table A2. Permutation test with the full sample . Permutation 1 . Permutation 2 . . Estimate . SE . Estimate . SE . Poor are irrational −0.164*** 0.045 −0.110*** 0.021 Externalities 0.024 0.020 Intercept 0.619*** 0.032 0.719*** 0.072 Elite sector fixed effect No Yes Country fixed effect No Yes Controls No Yes . Permutation 1 . Permutation 2 . . Estimate . SE . Estimate . SE . Poor are irrational −0.164*** 0.045 −0.110*** 0.021 Externalities 0.024 0.020 Intercept 0.619*** 0.032 0.719*** 0.072 Elite sector fixed effect No Yes Country fixed effect No Yes Controls No Yes Note: 1000 samples of n = 100 with no replacement. * P < 0.10; ** P < 0.05; *** P < 0.01. Open in new tab © The Author(s) 2020. Published by Oxford University Press and the Society for the Advancement of Socio-Economics. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial 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 journals.permissions@oup.com © The Author(s) 2020. Published by Oxford University Press and the Society for the Advancement of Socio-Economics.
TI - Economic and cultural determinants of elite attitudes toward redistribution
JF - Socio-Economic Review
DO - 10.1093/ser/mwaa015
DA - 2022-07-08
UR - https://www.deepdyve.com/lp/oxford-university-press/economic-and-cultural-determinants-of-elite-attitudes-toward-0rSpZK04mV
SP - 489
EP - 514
VL - 20
IS - 2
DP - DeepDyve
ER -