“Relative income and voting patterns in European countries” Annalisa Cristini, University of Bergamo and Daphne Nicolitsas, University of Crete Presented in session: “5.5.2 Political attitudes and voting” at the 3rd International ESS Conference, 13-­‐15th July 2016, Lausanne, Switzerland Incomplete Draft Abstract Populist and extreme political parties appear to be gaining ground in Europe. This paper uses ESS data to assess this phenomenon. It investigates the evolution of the left/right scale at the two extremes and characterizes the latter in terms of income. Descriptive age profiles by cohort are found to differ for left and right. APC modelling predicts increasing period effects on both extremes but different age and cohort effects. In the recent years an increasing proportion of individuals from the bottom income decile declare to place themselves on the right end of the scale while the reverse does not appear to take place. Keywords Political polarization, Left/Right scale, Income, apc model 1 Section 1: Introduction, research question, hypothesis 1.1 Introduction To be completed 1.2 Research question Populist and extreme political parties appear to be gaining ground in Europe. • How much of this trend is actually observable in the ESS data? • Is a polarization taking place along the left/right scale? • Do high income individuals place themselves systematically on the right and low income individuals systematically on the left of the left/right scale? • Are the long recession and the social tensions going to break down this pattern? • Is the same true for all European countries? Section 2: Data and methods 2.1 Data European Social Survey (ESS) rounds 4-­‐10 for the following countries: BE CH CZ DE DK EE FI FR IE NL NO PL SE SI 2.2 Methods 1. We show the extent of political polarization across years 2008-­‐2014 on the basis of the proportions of people that consider themselves as being left (scoring 0 or 0-­‐1 on the 10-­‐point scale) or right wing (scoring 10 or 9-­‐10). 2. We show how proportions vary against age, by cohort 3. We estimate a age-­‐period-­‐cohort model based on entropy 4. We show how proportions vary with income, focusing on bottom and top income deciles. 5. We investigate the role of income by running the apc model separately for low income and high income individuals. Section 3: Results 3.1. Political polarization Proportion of people on the far ends of the spectrum, both Left and Right has increased since 2008 (Figure 1). In particular, the Right appears to have gained momentum (Figure 1 and Figure 2) 2 Figure 1 .02
.025
.03
.035
.04
Proportions of poeple placing themselves to the left/right
2008
2010
2012
YEAR
2014
left 0
2016
right 10
Note: Proportions are computed over the number of observations per year. Left/Right is defined according to questionB19. “In politics people sometimes talk of "left" and "right". Using this card, where would you place yourself on this scale, where 0 means the left and 10 means the right?” Figure 2 .04
.06
.08
.1
Proportions of poeple placing themselves to the left/right - broader def.
2008
2010
2012
YEAR
2014
left 1-0
right 9-10
Note: See Figure 1 2016
3 In the rest of the paper we focus on the tails of the left/right scale, corresponding to categories 0 (Left) and 10 (Right). 3.2 Age profiles by cohort Figures 3 and 4 depict the Left/Right placement against age, by cohort. Cohorts are here defined across sets of 5 year, according to the year of birth of the respondent. 1 The two age profiles are rather different: the Left-­‐placement age profile is bi-­‐modal with a first clear bump between twenty and mid thirties years of wage, a subsequent sharp dip around the age of forty and a steady increase thereafter, until the age seventy where the second mode is placed; the profile declines afterwards. In contrast, the Right-­‐placement age profile shows a very mild increase until the age of sixty; a steep increase afterwards, especially until the age of seventy, and a milder but again stable increase thereafter. Figure 3 .02
.025
.03
.035
.04
Left placement against age, by cohort
20
40
60
80
100
<1935
1935-39
1940-44
1945-49
1950-54
1955-59
1965-69
1970-74
1975-79
1980-84
1985-89
1990-94
1960-64
Note: the clack line is a polynomial fit 1 Cohort 1: people born before 1934, cohort 2: people born between 1935 and 1939, …. Cohort 13: people born from 1990 onwards. 4 Figure 4 .01
.02
.03
.04
.05
Right placement against age, by cohort
20
40
60
80
100
<1935
1935-39
1940-44
1945-49
1950-54
1955-59
1965-69
1970-74
1975-79
1980-84
1985-89
1990-94
1960-64
Note: the clack line is a polynomial fit 3.3 Age, Period, Cohort model Given the way the depicted profiles have been constructed, it is immediately clear that they are the results of influences associated with the process of aging, of course, but also with experiences that the subject has undergone because of her specific cohort (date of birth) as well as with those influences due to the events of the period at which the interview has been carried out. These interacting influences give rise to the well-­‐known problem, long present in the literature, of identifying the three effects: age, period and year of birth are in fact linearly related, hence independent variations in them cannot be observed.2 In the context of the political placement we are investigating, age effects may include life-­‐cycle experiences related, for example, to the time of education, of starting a family and raising children, of retirement. Period effects may include business cycle influences, like, specifically in our case, the effects of the great recession as well as the ensuing policy changes. Cohort effects surely include the students’ and workers’ movements of the sixties and mid seventies, migratory movements, changes in working conditions and in compulsory education and general changes in social norms. As a first attempt to identify the role of the three effects, we use the method proposed by Browning et al. (2010), based on the maximum entropy principle. Since this method can 2 For a concise and clear introduction to the issue, see for example, Browning, Crawford and Knoef (2010) and references therein. 5 be applied to variables that have bounded support, it is feasible for our case as the support of our dependent variable, i.e. the proportion of people the place themselves Left/Right on the left-­‐right scale, is 0-­‐1.3 Figures 5 and 6 report the estimated coefficients and relative confidence intervals of the three variables: age (agea), period (YEAR) and cohort (yrbrn) in the two cases: placement Left and placement Right. Figure 5 Coefficients and CIs
-.01 -.005 0 .005 .01 .015
Coefficients and CIs
-.001 0 .001 .002 .003
Left: Age, Period, Cohort Coefficients
40
1920
1940
60
agea
80
100
1960
yrbrn
1980
2000
2008
2010
2012
YEAR
2014
2016
Coefficients and CIs
-.01 -.005
0
.005
20
The results confirm some of the earlier descriptive results. The period effect is mostly positive, rising in time and similar in the two cases. The age, when in early twenties and after forty, has a positive effect on the left wing placement. Rather differently, the age below thirty years has a slightly negative effect on the right-­‐wing placement and a positive effect afterwards, following a profile similar to the one seen in the earlier figures. The coefficient of ‘age’ declines for ages beyond sixty, more sharply for the left, and precisely estimated. The estimated cohort (yrbrn) coefficient is also rather different in the two cases; it is precisely estimated for the old cohorts and especially so for the left wing placement. The cohort effect is negative and declining until the cohorts of the early sixties; it is rising in the younger cohorts of the seventies but never positive. On the contrary, the effect of the cohort on the left placement rises from the 1940 cohorts and declines thereafter quite steeply, with the exception of a spike in the cohort of the late seventies. 3 The Stata code (apc.ado) is written by Cormac O’Dea and we have it downloaded from http://www.ifs.org.uk/publications/5998 6 Figure 6 40
60
agea
80
100
1920
1940
1960
yrbrn
1980
2000
2008
2010
2012
YEAR
2014
2016
Coefficients and CIs
-.02 -.015 -.01 -.005 0 .005
20
Coefficients and CIs
-.0005 0 .0005.001.0015.002
-.01
Coefficients and CIs
0
.01 .02 .03
Right: Age, Period, Cohort Coefficients
3.4 Political polarization and income Figure 7 shows the proportions of people that place themselves to right/left of the scale by income decile. Specifically, we consider people in the first decile and in the top decile only and compare the proportions of those declaring to be left or right in both cases. The figure suggests a positive trend, since 2009, of low income people placing themselves on the Right , a proportion that by 2014 has reached that of low income people declaring to be left wing. On the contrary, the proportion of high income individuals declaring to be Right/Left has remained relatively more stable in the years of the sample. In order to have some intuition of the basic driving forces, we next repeat the apc analysis separately for the bottom and top decile individuals. 7 Figure 7 0
.05
pct_lrYinc
.1
.15
.2
Proportions of low/high income people placing themselves to the left/right
2008
2010
2012
YEAR
2014
left low income
right high income
right low income
left high income
2016
3.4.1 Income and political polarization; apc analysis To be completed Section 4 (Conclusion/Discussion) To be completed References (to be completed) Browning, M., Crawford, I. and Knoef, M. (2010). The Age-­‐Period-­‐Cohort Problem; Set identification and point identification. Working Paper. http://www.economics.ox.ac.uk/members/ian.crawford/papers/apc.pdf Dettrey B. J. & Campbell J.E. (2013). Has Growing Income Inequality Polarized the American Electorate? Class, Party, and Ideological Polarization. Social Science Quarterly, 94: 4, 1062-­‐1083 Gelman, A., L. Kenworthy & Y. Su (2010). Income inequality and partisan voting in the United States. Social Science Quarterly, 91:5, 1203-­‐19. O’Dea, Cormac. (2012). APC: Stata module to estimate age, period and cohort 8 effects. Available at: www.ifs.org.uk/publications/5998 9