Trends in attitudes toward income equalization across European countries. Do changing economic conditions matter? Renzo Carriero and Marianna Filandri Department of Cultures, Politics and Society University of Turin EVS Workshop “European Identity, Solidarity, and Generations” Warsaw April 15 th 2016

Outline Background Aims of the study Data & method Results: descriptive trends and multilevel models Conclusions 2

Background 1 Income inequality rose in many OECD countries over the last 30 years (Cingano 2014) Economic models posit that an increase in income inequality should lead to an increase in demand for redistribution by citizens (e.g. Meltzer and Richard 1981) Few studies were able to connect changes in income inequality to demand for redistribution (or income equalization) Schmidt-Catran (2014) and Jaeger (2013) found a positive relationship between changes in income inequality and demand for redistribution using ESS data ( ) 3

Background 2 However, income inequality varies significantly across countries, but it is relatively stable within countries (e.g. Li, Squire & Zou 1998) Given that income distribution data generally do not move sharply, apparent large changes could play a particularly influential role in the resulting statistical analysis (Atkinson & Brandolini 2009). This is true especially if we consider short time periods between observations 4

Aims of the study To explore long run trends in attitudes toward income equalization across European countries using the longitudinal EVS dataset ( ) To explore the association between income inequality (across countries, between variation) and attitudes toward income equalization at different time points To explore the association between changes in income inequality (within variation) and attitudes toward income equalization 5

Data & method EVS waves 2-4 ( ) Dep. var.: Income should be made more equal (1) vs. There should be greater incentives for individual effort (10). Reverse coded Macro level variables: Gini index (source: WIID 3.0), real GDP (source: PWT 8.0) Sample: countries present in at least 2 waves Model: multilevel linear regression; macro variables included as within and between components (Fairbrother 2014; Fairbrother & Schmidt-Catran 2016) 6

Multi level approach 7 Individuals Wave 2 Wave 3Wave 4 Wave 3Wave 4 Wave 2 Wave 4 Country 32 countries (29*) 80 country-years (73*) N = (97.043*) * Selecting only non-missing cases on macro or micro vars

Trends in average attitudes 8 Avg. difference w4-w3 = 0.13 Avg. difference w3-w2 = 0.97 Avg. difference between waves = 0,53

Trends in income inequality 9 Avg. change in Gini between two consecutive waves = +1.1 (sd=3.7)

Attitudes * Gini, between countries 10

Attitudes * Gini, within countries 11

Multilevel models 12 Note: all models include micro-level control variables (age, sex, marital status, education, occupation) Model 1Model 2Model 3 Coef.Std. Err.Coef.Std. Err.Coef.Std. Err. Constant6,000,896,391,086,161,07 Gini (between)-0,020,03-0,020,03-0,040,03 Gini (within)0,090,040,070,030,020,03 GDP/cap. (between)-0,010,01-0,010,01 GDP/cap. (within)0,070,02 0,03 Wave 30,820,25 Wave 40,850,38 VariancesEstimateStd. Err.EstimateStd. Err.EstimateStd. Err. Country (N=29)0,310,160,370,150,390,15 Country-year (N=73)0,640,140,460,100,370,08 Individual (N=97043)7,150,037,150,037,150,03

Model 4 (countries in w3-4)Model 5 (countries in w2-3-4) Coef.Std. Err.Coef.Std. Err. Constant9,631,627,671,34 Gini (between)-0,100,04-0,100,04 Gini (within)-0,010,04 GDP/cap. (between)-0,030,020,010,02 GDP/cap. (within)0,090,040,020,04 Wave 30,840,29 Wave 4-0,350,271,010,46 Variances Country0,650,230,240,13 Country-year0,150,050,320,08 Individual7,290,047,050,04 N countries1915 N country-years3845 N individuals Multilevel models (cont.) 13 Note: all models include micro-level control variables (age, sex, marital status, education, occupation)

Conclusions On the long run, within-country changes in income inequality are not associated with attitudes to income equalization. Period effects account for all the association between Gini and Y Analyses over longer time span, as compared to shorter time span, can reveal different patterns of association between actual inequality and attitudes Next steps: to investigate about other factors that might explain aggregate changes toward increased demand for income equalization 14