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Part III - Voters and Demand for Redistribution

Published online by Cambridge University Press:  07 December 2023

Noam Lupu
Affiliation:
Vanderbilt University, Tennessee
Jonas Pontusson
Affiliation:
Université de Genève

Summary

Information

Figure 0

Figure 10.1 What is fair? Who is deserving?

Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 1

Figure 10.2 Fairness reasoning and demand for redistribution: unidimensional approach

Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 2

Figure 10.3 Fairness reasoning and demand for redistribution: two-dimensional approach

Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 3

Table 10.1 Attitude structure in Great Britain: confirmatory factor analysis

Source: British Social Attitudes Survey, 2016.
Figure 4

Table 10.2 Reciprocity beliefs: factor loadings

Sources: ESS 2008 (round 4) and ESS 2016 (round 8).Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 5

Table 10.3 Opposition to redistribution is not predicted by reciprocity beliefs

Sources: ESS 2008 (round 4) and ESS 2016 (round 8).Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 6

Table 10.4 Proportionality beliefs and “redistribution from”: factor loadings

Sources: ISSP 1999 and ISSP 2009.Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 7

Figure 10.4 Changes in fairness beliefsNote: See text for more detail on the measures.

Sources: ESS 2008 (round 4) and ESS 2016 (round 8), ISSP 1999 and ISSP 2009.Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 8

Figure 10.5 Correlation between proportionality and reciprocity beliefsNotes: See text for more detail on the measures. Note that the US score on the X-axis is approximated using Svallfors (2012).

Sources: ESS 2008 (round 4) and ISSP 2009.Reprinted with permission from Cavaillé (2023). Copyright © 2023 by Cambridge University Press.
Figure 9

Figure 11.1 Posttax-and-transfer income share of the top 1 percent of individuals for nineteen advanced democraciesNote: Thick black lines are overtime trends based on pooled OLS regressions.

Source: World Inequality Database (sdiinc992jp99p100).
Figure 10

Figure 11.2 Correlation between the annual rate of GDP growth and annual change in top-1-percent pretax income shares for a broad set of countries, before and after 1980Notes: Quarterly observations. Pre-1980 observations unavailable for certain countries.

Sources: World Inequality Database (sptinc992jp99p100); Kayser and Peress (2021).
Figure 11

Figure 11.3 Correlation between the annual change in the unemployment rate and annual change in top-1-percent pretax income shares for a broad set of countries, before and after 1980Notes: Quarterly observations. Pre-1980 observations unavailable for certain countries.

Sources: World Inequality Database (sptinc992jp99p100); Kayser and Peress (2021).
Figure 12

Figure 11.4 Association between economic news tone and pretax income growth for each income quintile, conditional on income growth for all other quintiles

Sources: World Inequality Database; Kayser and Peress (2021).
Figure 13

Figure 11.5 Association between economic news tone and pretax income growth for top-income groups, controlling for bottom- and middle-income growth

Sources: World Inequality Database; Kayser and Peress (2021).
Figure 14

Figure 11.6 Estimated coefficient ratios from models predicting economic news tone with pretax income growth for different parts of the income distributionNotes: Each row in each panel represents a ratio between the news-tone/income-growth correlation for a top-income group to the news-tone/income-growth correlation for a nonrich group. The diamond represents a normative baseline ratio for each comparison, derived from relative population sizes and the principle of equal per capita weighting. The circle (with 95 percent confidence interval) represents, for each comparison, the actual estimated ratio between the two tone-growth correlations. Confidence intervals not apparent where they are smaller than the radius of the dot representing the point estimate.

Sources: World Inequality Database; Kayser and Peress (2021).
Figure 15

Figure 11.7 Association between economic news tone and disposable income growth for each income quintile, conditional on income growth for all other quintiles

Sources: World Inequality Database; Kayser and Peress (2021).
Figure 16

Figure 11.8 Association between economic news tone and disposable income growth for top-income groups, controlling for bottom- and middle-income growth

Sources: World Inequality Database; Kayser and Peress (2021).
Figure 17

Figure 11.9 Estimated coefficient ratios from models predicting economic news tone with disposable income growth for different parts of the income distributionNotes: Each row in each panel represents a ratio between the news-tone/income-growth correlation for a top-income group to the news-tone/income-growth correlation for a nonrich group. The diamond represents a normative baseline ratio for each comparison, derived from relative population sizes and the principle of equal per capita weighting. The circle (with 95 percent confidence interval) represents, for each comparison, the actual estimated ratio between the two tone-growth correlations. Confidence intervals not apparent where they are smaller than the radius of the dot representing the point estimate.

Sources: World Inequality Database; Kayser and Peress (2021).
Figure 18

Table 11.1 Mechanisms of class-biased economic news

Figure 19

Table 12.1 Characteristics of broadcast communities

Figure 20

Figure 13.1 Social classes’ average predicted perceptions of systemic congruence on social policyNotes: Class as a determinant of perceived systemic congruence. Estimates are based on linear regression models introducing controls for age, sex, trade union membership, and country-FE. The coefficients for all variables are presented in Table 13.A2 in the Appendix.

Figure 21

Table 13.1 Strength of expected class bias in representation of social policy preferences, depending on challenger parties

Figure 22

Figure 13.2 Social class differences in proximity to preferred party and party systemNotes: Class as a determinant of proximity across party systems (coefficients indicate differences to the upper-middle class). Estimates are based on linear regression models introducing controls for age, sex, trade union membership, and country-FE. The coefficients for all variables are presented in Table 13.A3 in the Appendix.

Figure 23

Figure 13.3 Social class differences in subjective proximity to preferred party and party system, by social policy areaNotes: Class as a determinant of proximity across party systems (coefficients indicate differences to the upper-middle class). Estimates are based on linear regression models introducing controls for age, sex, trade union membership, and country-FE. The coefficients for all variables are presented in Table 13.A5 in the Appendix.

Figure 24

Figure 13.4 Social class differences in subjective proximity to preferred party and the party system on social consumption across different party system configurationsNotes: Class as a determinant of proximity across party systems (coefficients indicate differences to the upper-middle class). Estimates are based on linear regression models introducing controls for age, sex, and trade union membership. Average differences for small business owners are not presented because they represent a small group in the sample, with a low number of occurrences when the analyses are disaggregated by party system.

Figure 25

Figure 13.5 Social class differences in subjective proximity to preferred party and the party system on social investment across different party system configurationsNotes: Class as a determinant of proximity across party systems (coefficients indicate differences to the upper-middle class). Estimates are based on linear regression models introducing controls for age, sex, and trade union membership. Average differences for small business owners are not presented because they represent a small group in the sample, with a low number of occurrences when the analyses are disaggregated by party system.

Figure 26

Figure 13.6 Social class differences in subjective proximity to preferred party and the party system on benefits for migrants across different party system configurationsNotes: Class as a determinant of proximity across party systems (coefficients indicate differences to the upper-middle class). Estimates are based on linear regression models introducing controls for age, sex, and trade union membership. Average differences for small business owners are not presented because they represent a small group in the sample, with a low number of occurrences when the analyses are disaggregated by party system.

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