Louis-André Vallet (CNRS) Observatoire Sociologique du Changement (UMR CNRS & Sciences Po Paris)

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1 Louis-André allet (CNRS) Observatoire Sociologique du Changement (UMR CNRS & Sciences Po Paris) louisandre.vallet@sciencespo.fr ASSESSING THE PERFORMANCE OF THE THREE ONE-DIGIT ESEG PROTOTYPES WITH THE 2011 EU-SILC MODULE ON THE INTERGENERATIONAL TRANSMISSION OF DISADANTAGES Contribution to the European Statistical System network for the elaboration of European socioeconomic groups (ESEG) This contribution aims at assessing the relative performance of the three one-digit ESEG prototypes taking advantage of the 2011 EU-SILC module on the intergenerational transmission of disadvantages. The rationale it follows is fairly simple. Generally speaking, the ESEG prototypes classify in different groups occupations that differ on the basis of the associated status of employment and the general level of skills that characterize the associated work tasks. As a consequence, occupations that are representative of distinct ESEG groups generally differ with regard to their rank in the occupational hierarchy, the desirability of the working conditions they are associated with, as well as their associated rewards in terms of wage or income and general living conditions. Families are unequally equipped to provide their offspring with the assets that will help them getting access to the most desirable and rewarding occupations. We can then expect that some degree of association will be visible between each one-digit ESEG prototype and the various background variables that can be built on the basis of the 2011 EU-SILC module. Indeed, starting from the view point that a valuable ESEG prototype should be able to describe and reflect the level and pattern of the intergenerational transmission of advantages and disadvantages, we should prefer the ESEG prototype that is most strongly associated with the set of background variables derived from the 2011 EU-SILC module. In the first section, we precisely describe the sample we use, the background variables we build, and the various measures of association we employ. Then the second section presents the results of our statistical analysis conducted on the whole European Union (except Malta). The third section replicates the previous analysis on each of 26 European countries, using a restricted set of background variables and a restricted set of measures of association. Finally, adopting a more descriptive than analytical point of view, the fourth section exhibits and briefly comments the cross-classification of our preferred one-digit ESEG prototype with each of the three major background variables at the level of the European Union. 1. SAMPLE, BACKGROUND ARIABLES, AND MEASURES OF ASSOCIATION In the 2011 EU-SILC PFILE dataset (version 2 of August 2013), we disregard data from Island, Norway and Switzerland, thereby only considering the EU Member States (except Ireland whose data are not currently available). We also eliminate data from Malta as occupations of the respondents are only described in terms of one-digit ISCO-08 so that the ESEG prototypes cannot be properly constructed. After selecting those individuals whose self-defined current economic status is either employee working full time or part time, or self-employed (including family worker) working full time or part time, we build the three one-digit ESEG prototypes and only retain those individuals who belong to ESEG groups 1, 2, 3, 4, 5, 6, or 7 for each prototype. Because of the age limits associated with the intergenerational module, our working sample is composed of individuals aged 25 to 59, i.e., whose birth year is comprised between 1950 and On the basis of the module on the intergenerational transmission of disadvantages, we then build a set of nine background variables defined as follows. 1

2 FatherEd - Highest level of education attained by the father (4 categories - constructed using PT110) 0-No - Father could neither read nor write in any language 1-Low - Low level (pre-primary, primary education or lower secondary education) 2-Med - Medium level (upper secondary education and post-secondary non-tertiary education) 3-Hig - High level (first stage of tertiary education and second stage of tertiary education) (all other cases are classified as missing) MotherEd - Highest level of education attained by the mother (4 categories - similarly constructed using PT120) - Highest level of education attained by the parents (4 categories - constructed using FatherEd and MotherEd in the following order) 3-Hig - if at least one of the parents is classified as 3-Hig 2-Med - else, if at least one of the parents is classified as 2-Med 1-Low - else, if at least one of the parents is classified as 1-Low 0-No - else, if at least one of the parents is classified as 0-No (all other cases are classified as missing) FaClass - Occupational class of the father (10 categories - constructed using PT130 (activity status) and PT150 (one-digit ISCO-08 main occupation)) 0 - Independents (self-employed activity status) with ISCO-08 code between 3 and All managers (with employed, self-employed, unemployed, in retirement or don t know activity status) 2 - All professionals (with employed, self-employed, unemployed, in retirement or don t know activity status) 3 - Technicians and associate professionals (with employed, unemployed, in retirement or don t know activity status) and Armed forces occupations (with employed, unemployed, in retirement or don t know activity status) 4 - Clerical support workers (with employed, unemployed, in retirement or don t know activity status) 5 - Service and sales workers (with employed, unemployed, in retirement or don t know activity status) 6 - Skilled agricultural, forestry and fishery workers (with employed, unemployed, in retirement or don t know activity status) 7 - Craft and related trades workers (with employed, unemployed, in retirement or don t know activity status) 8 - Plant and machine operators, and assemblers (with employed, unemployed, in retirement or don t know activity status) 9 - Elementary occupations (with employed, unemployed, in retirement or don t know activity status) (all other cases are classified as missing) MoClass - Occupational class of the mother (10 categories - similarly constructed using PT160 (activity status) and PT180 (one-digit ISCO-08 main occupation)) - Dominant occupational class of the parents (6 categories - constructed using FaClass and MoClass in the following order) a-12 - if at least one of the parents is classified as 1-Managers or 2-Professionals b-0 - else, if at least one of the parents is classified as 0-Independents c-3 - else, if at least one of the parents is classified as 3-Technicians and associate professionals d-45 - else, if at least one of the parents is classified as 4-Clerical support workers or 5-Service and sales workers e else, if at least one of the parents is classified as 6-Skilled agricultural, forestry and fishery workers, 7-Craft and related trades workers or 8-Plant and machine operators, and assemblers 2

3 f-9 - else, if at least one of the parents is classified as 9-Elementary occupations (all other cases are classified as missing) Manag - Managerial position of the parents (2 categories - constructed using PT140 and PT170) 1-Yes - if at least one of the parents has a supervisory position 2-No - if none of the parents has a supervisory position (all other cases are classified as missing) Financ - Financial situation of the household (6 categories - constructed using PT190) 1-Bad++ - ery bad 2-Bad+ - Bad 3-Bad - Moderately bad 4-Good - Moderately good 5-Good+ - Good 6-Good++ - ery good (all other cases are classified as missing) - to make ends meet (6 categories - constructed using PT200) 1-Dif++ - With great difficulty 2-Dif+ - With difficulty 3-Dif - With some difficulty 4-Eas - Fairly easily 5-Eas+ - Easily 6-Eas++ - ery easily (all other cases are classified as missing) Finally, using the personal cross-sectional weight (pb040), we build a series of two-way contingency tables that cross-classify each of the three one-digit ESEG prototypes (in rows) with each of the nine background variables (in columns) in order to compute different measures of association. We therefore stress that, for a given background variable, the association with each of the ESEG prototypes is examined on exactly the same sample. The set of measures of association we compute is composed of seven coefficients that are based on different logics. First, the Phi coefficient and are measures derived from the Pearson chisquare statistic. Second, the categories of our background variables are either totally ordered or at least partially ordered and the three ESEG prototypes also aim at incorporating a partial, but relatively strong, ordinal dimension. We therefore select three measures of association that are appropriate for the cross-classification of two ordinal variables: the coefficient, Kendall s Tau-b and Stuart s Tau-c. Kendall s Tau-b and Stuart s Tau-c are variants of the coefficient that additionally make a correction for ties and, for the latter, an adjustment for table size. Given the order of the ESEG categories and the definition of our background variables, we expect negative values of these coefficients with regard to the FatherEd, MotherEd,, Financ and variables. Third, as a matter of comparison, we also include two coefficients that do not require ordinal variables and are appropriate for nominal variables: the Lambda symmetric coefficient and the symmetric coefficient. We use the symmetric version of both coefficients as there is no reason, in our opinion, to prefer predicting the ESEG category on the basis of the background variable rather than the reverse. 2. ASSOCIATION BETWEEN ESEG PROTOTYPES AND BACKGROUND ARIABLES - ANALYSIS ON THE WHOLE EUROPEAN UNION Table 1 presents the results of this analytical strategy when it is applied to the whole European Union (except Malta). All computed coefficients have the expected sign and reflect a statistical association between the ESEG prototypes and the different background variables. They also clearly suggest that the ESEG prototypes are more strongly linked with the education or occupation of the parents than they are with the financial situation within the family of origin. 3

4 TABLE 1 - ANALYSIS FOR THE WHOLE EUROPEAN UNION (EXCEPT MALTA) SELECTED MEASURES OF ASSOCIATION IN TWO-WAY TABLES CROSS-CLASSIFYING EACH ESEG PROTOTYPE WITH NINE BACKGROUND ARIABLES European Union (N= ) EU-SILC Intergenerational Module FatherEd Phi (15% missing) Kendall s Tau-b Stuart s Tau-c Lambda Prot.1 0,318 0,183 0,264 0,188 0,177 0,028 0,034 Prot.2 0,304 0,176 0,246 0,176 0,167 0,035 0,031 Prot.3 0,304 0,176 0,250 0,176 0,165 0,051 0,032 MotherEd (13% missing) Prot.1 0,300 0,173 0,259 0,175 0,157 0,026 0,030 Prot.2 0,285 0,164 0,240 0,163 0,148 0,032 0,027 Prot.3 0,285 0,165 0,243 0,164 0,146 0,047 0,028 (10% missing) Prot.1 0,336 0,194 0,274 0,199 0,191 0,036 0,038 Prot.2 0,322 0,186 0,255 0,186 0,180 0,044 0,034 Prot.3 0,321 0,185 0,259 0,187 0,178 0,058 0,035 FaClass (20% missing) Prot.1 0,405 0,165 0,197 0,169 0,168 0,052 0,037 Prot.2 0,400 0,163 0,188 0,162 0,162 0,071 0,036 Prot.3 0,398 0,162 0,195 0,166 0,164 0,069 0,036 MoClass (51% missing) Prot.1 0,436 0,178 0,190 0,163 0,162 0,086 0,042 Prot.2 0,431 0,176 0,184 0,159 0,159 0,085 0,041 Prot.3 0,430 0,175 0,188 0,160 0,159 0,093 0,041 (15% missing) Prot.1 0,408 0,182 0,273 0,226 0,224 0,083 0,042 Prot.2 0,400 0,179 0,260 0,217 0,216 0,098 0,041 Prot.3 0,396 0,177 0,265 0,219 0,215 0,100 0,041 Manag (14% missing) Prot.1 0,197 0,197 0,264 0,157 0,186 0,014 0,016 Prot.2 0,195 0,195 0,250 0,150 0,178 0,029 0,015 Prot.3 0,193 0,193 0,255 0,151 0,177 0,029 0,015 Financ (9% missing) Prot.1 0,183 0,082 0,153 0,119 0,111 0,006 0,010 Prot.2 0,177 0,079 0,144 0,113 0,106 0,020 0,009 Prot.3 0,177 0,079 0,146 0,113 0,104 0,013 0,010 (10% missing) Prot.1 0,203 0,091 0,158 0,126 0,121 0,006 0,012 Prot.2 0,197 0,088 0,150 0,120 0,116 0,023 0,011 Prot.3 0,198 0,089 0,152 0,121 0,115 0,019 0,012 As regards the relative performance of the three ESEG prototypes, the same pattern systematically emerges for all background variables with no exception. In each case, both coefficients based on the chi-square statistic (Phi and ) are slightly larger with ESEG Prototype 1 that adopts the most restricted definition of the lowest group in the occupational hierarchy than with the other two prototypes. The conclusion is exactly the same with the three measures of association for ordinal 4

5 variables (, Tau-b and Tau-c). Only the coefficients that assume we have no more than nominal variables deliver a contradictory and somewhat different message. While the symmetric statistic would also incline us to prefer ESEG Prototype 1, albeit by a very small margin for a few background variables, this is not the case with the Lambda symmetric coefficient that clearly favors either Prototype 2 or Prototype 3. However, on the basis of the strong ordinal dimension that lies behind the ESEG prototypes and our background variables, it is certainly reasonable to put more emphasis on the result delivered by the, Tau-b and Tau-c coefficients and confirmed by the Phi and statistics. In this first analysis based on the whole European Union and the view point of the intergenerational transmission of disadvantages, we conclude that ESEG Prototype 1 should be preferred. It seems that this prototype is more able than the others to describe the specificities of the lowest group within the occupational hierarchy (ESEG group 7). 3. ASSOCIATION BETWEEN ESEG PROTOTYPES AND BACKGROUND ARIABLES - ANALYSIS PER COUNTRY In this section, we replicate the previous analysis separately per country, using a restricted set of background variables and a restricted set of measures of association. Regarding background variables, we select the most synthetic educational and occupational variables that take into account information about both parents ( and ) and the financial variable which is most strongly associated with the ESEG prototypes (). As for measures of association, we only consider, the coefficient and the symmetric coefficient. All detailed country tables are displayed in the Appendix and show that, in some countries - especially Sweden, Finland, Denmark, the Netherlands and Slovenia -, questions about family background are poorly answered and/or the intergenerational module has only been administered to a subsample of respondents so that a large number of missing cases may exist in the corresponding variables 1. On the basis of these detailed tables, Table 2 summarizes the information by highlighting which ESEG prototypes exhibit the strongest associations with each background variable, separately per country. As regards the highest level of education attained by the parents, the three measures of association unambiguously select ESEG Prototype 1 for 18 out of 26 countries. This is also the case for 18 countries with the dominant occupational class of the parents and for 17 with the ability to make ends meet variable. Moreover, in 13 out of 26 countries, this is ESEG Prototype 1 that exhibits the strongest association with all three background variables. To be more precise, this situation characterizes Belgium, Bulgaria, Cyprus, Finland, France, Hungary, Italy, Lithuania, Poland, Portugal, Romania, Slovakia and Spain. In some other countries - Croatia, Germany, the Netherlands, Slovenia, Sweden and the United Kingdom - ESEG Prototype 1 can certainly be chosen although we can also hesitate with another prototype for at least one of the three background variables. This is only for a last group of 7 countries - Austria, the Czech Republic, Denmark, Estonia, Greece, Latvia and Luxembourg - that the criterion of the strongest association would result in selecting different ESEG prototypes for at least two of the three background variables. All in all, the analysis per country rather unambiguously confirms what the analysis based on the whole European Union has already suggested. With regard to the description of the intergenerational transmission of advantages and disadvantages, ESEG Prototype 1 that adopts the most restricted definition of the lowest group within the occupational hierarchy (ESEG group 7) seems to be more efficient than the others. According to our data that cover men and women aged 25 to 59 currently working full time or part time, ESEG group 7 represents 7.5% of the European workforce. This 1 Note that, by using the personal cross-sectional weight (pb040), our data at the level of the European Union likely underestimate the weight of the countries in which the intergenerational module has only been administered to a subsample of respondents. It is however unlikely that this strongly affects the computed measures of association and the comparison between the three ESEG prototypes. 5

6 percentage ranges from 3.6% in Sweden, 4.3% in the Netherlands, 4.4% in Finland, 4.7% in the Czech Republic and 4.8% in Romania to 10.4% in Spain, 10.8% in Bulgaria, 10.9% in France, 11.6% in Portugal, 12.4% in Latvia, 12.6% in Luxembourg and 14.3% in Cyprus. TABLE 2 - ESEG PROTOTYPES THAT EXHIBIT THE STRONGEST ASSOCIATIONS WITH EACH OF THREE BACKGROUND ARIABLES, SEPARATELY PER COUNTRY The detailed country tables are presented in the appendix. European Union (N= ) EU-SILC Intergenerational Module France Prot.1 Prot.1 Prot.1 Germany Prot.1 or 3 Prot.1 Prot.1 United Kingdom Prot.1 or 3 Prot.1 Prot.1 or 3 Sweden Prot.1 or 2 Prot.1 or 2 Prot.1, 2 or 3 Austria Prot.1 Prot.3 Prot.3 Belgium Prot.1 Prot.1 Prot.1 Bulgaria Prot.1 Prot.1 Prot.1 Cyprus Prot.1 Prot.1 Prot.1 Czech Republic Prot.3 Prot.1 or 3 Prot.2 Denmark Prot.1 Prot.1 or 3 Prot.3 Estonia Prot.3 Prot.1 or 3 Prot.1 Spain Prot.1 Prot.1 Prot.1 Finland Prot.1 Prot.1 Prot.1 Greece Prot.1 Prot.3 Prot.1 Croatia Prot.1 Prot.1 Prot.1 or 3 Hungary Prot.1 Prot.1 Prot.1 Italy Prot.1 Prot.1 Prot.1 Lithuania Prot.1 Prot.1 Prot.1 Luxembourg Prot.2 or 3 Prot.1 or 3 Prot.1 Latvia Prot.1 or 3 Prot.1 or 2 Prot.3 Netherlands Prot.1 Prot.1 Prot.1, 2 or 3 Poland Prot.1 Prot.1 Prot.1 Portugal Prot.1 Prot.1 Prot.1 Romania Prot.1 Prot.1 Prot.1 Slovenia Prot.1 or 3 Prot.1 Prot.1 or 3 Slovakia Prot.1 Prot.1 Prot.1 4. CROSS-CLASSIFICATION OF THE PREFERRED ESEG PROTOTYPE WITH THE MAJOR BACKGROUND ARIABLES To conclude this contribution with a more descriptive than analytical point of view, Table 3 presents the cross-classification of our preferred one-digit ESEG prototype (Prototype 1) with each of the three major background variables at the level of the European Union. Row percents are displayed in order to present the composition of each ESEG group in terms of the highest level of education attained by the parents (first table), the dominant occupational class of the parents (second table), and the ability for the family of origin to make ends meet (third table). 6

7 TABLE 3 - CROSS-CLASSIFICATION OF ESEG PROTOTYPE 1 WITH THE THREE MAJOR BACKGROUND ARIABLES European Union (N= ) EU-SILC Intergenerational Module Prototype 1 * Highest level of education attained by the parents proto_1 Row Pct 0-No 1-Low 2-Med 3-Hig Total ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1-Man ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 2-Pro ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 3-Ind ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 4-P I ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 5-WCS ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 6-BCS ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 7-LES ƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total Prototype 1 * Dominant occupational class of the parents proto_1 Row Pct a-12 b-0 c-3 d-45 e-678 f-9 Total 1-Man Pro Ind P I WCS BCS LES Total Prototype 1 * to make ends meet proto_1 Row Pct 1-Dif++ 2-Dif+ 3-Dif 4-Eas 5-Eas+ 6-Eas++ Total 1-Man Pro Ind P I WCS BCS LES Total These tables effectively demonstrate a clear association of ESEG Prototype 1 with the background variables. For instance, with regard to the education variable, the percentage of those whose at least one of the parents got tertiary education declines from 35.1% in the professional group to 5.7% in the group composed of the most elementary occupations while the percentage of those whose neither of the parents were able to read and write grows from 0.4% in the former group to 5.0% in the latter. As for the class variable, the percentage of those whose parents belonged to the same broadly defined class culminates in the self-employed group (42.2%) and the skilled working class (40.0%). It is also 7

8 notable that about one third of the professionals and the managers have parents in the highest class position. Finally, even if the ESEG groups less sharply differentiates the financial situation in the family of origin, the percentage of those whose parents made ends meet with some difficulty, with difficulty or with great difficulty grows from 30.4% in the professional group to 56.2% in the group composed of the most elementary occupations. 8

9 APPENDIX - ANALYSIS PER COUNTRY SELECTED MEASURES OF ASSOCIATION IN TWO-WAY TABLES CROSS-CLASSIFYING EACH ESEG PROTOTYPE WITH THREE BACKGROUND ARIABLES In country tables, we only show the percentage of missing values when it is at least equal to 10%. France (N=9 252) Germany (N=10 680) Prot.1 0,203 0,415 0,045 Prot.2 0,199 0,390 0,043 Prot.3 0,194 0,396 0,041 Prot.1 0,162 0,271 0,035 Prot.2 0,158 0,255 0,033 Prot.3 0,155 0,260 0,033 Prot.1 0,097 0,192 0,014 Prot.2 0,096 0,182 0,013 Prot.3 0,096 0,187 0,014 Prot.1 0,168 0,319 0,030 Prot.2 0,162 0,301 0,028 Prot.3 0,163 0,321 0,030 (11% missing) Prot.1 0,150 0,267 0,031 Prot.2 0,146 0,248 0,029 Prot.3 0,145 0,265 0,030 Prot.1 0,086 0,166 0,011 Prot.2 0,084 0,158 0,010 Prot.3 0,083 0,162 0,011 United Kingdom (N=6 107) (15% missing) Prot.1 0,157 0,259 0,025 Prot.2 0,156 0,257 0,024 Prot.3 0,157 0,260 0,025 (17% missing) Prot.1 0,130 0,228 0,024 Prot.2 0,129 0,225 0,024 Prot.3 0,128 0,224 0,023 (14% missing) Prot.1 0,070 0,123 0,008 Prot.2 0,070 0,119 0,007 Prot.3 0,072 0,121 0,008 9

10 Sweden (N=6 075) (52% missing) Prot.1 0,225 0,317 0,037 Prot.2 0,226 0,303 0,037 Prot.3 0,217 0,303 0,034 (89% missing) Prot.1 0,185 0,304 0,054 Prot.2 0,187 0,291 0,053 Prot.3 0,184 0,293 0,052 (50% missing) Prot.1 0,073 0,071 0,008 Prot.2 0,074 0,072 0,008 Prot.3 0,070 0,074 0,007 In the variable, the 0-No category does not exist. Austria (N=5 207) Belgium (N=4 711) Prot.1 0,188 0,309 0,036 Prot.2 0,179 0,282 0,033 Prot.3 0,178 0,290 0,033 Prot.1 0,195 0,274 0,050 Prot.2 0,195 0,267 0,049 Prot.3 0,196 0,275 0,051 Prot.1 0,084 0,141 0,010 Prot.2 0,087 0,139 0,011 Prot.3 0,087 0,147 0,011 Prot.1 0,209 0,362 0,046 Prot.2 0,200 0,344 0,042 Prot.3 0,202 0,356 0,044 (11% missing) Prot.1 0,185 0,311 0,047 Prot.2 0,183 0,296 0,045 Prot.3 0,181 0,304 0,045 Prot.1 0,104 0,183 0,016 Prot.2 0,098 0,171 0,014 Prot.3 0,098 0,177 0,014 10

11 Bulgaria (N=5 718) Cyprus (N=4 072) Prot.1 0,265 0,422 0,069 Prot.2 0,234 0,363 0,056 Prot.3 0,230 0,360 0,055 (12% missing) Prot.1 0,201 0,365 0,058 Prot.2 0,189 0,331 0,051 Prot.3 0,185 0,337 0,050 Prot.1 0,100 0,190 0,015 Prot.2 0,091 0,179 0,013 Prot.3 0,088 0,176 0,012 Prot.1 0,225 0,375 0,052 Prot.2 0,216 0,343 0,048 Prot.3 0,215 0,350 0,050 Prot.1 0,155 0,207 0,034 Prot.2 0,151 0,195 0,032 Prot.3 0,150 0,198 0,032 Prot.1 0,133 0,223 0,026 Prot.2 0,123 0,208 0,022 Prot.3 0,123 0,215 0,023 Czech Republic (N=7 533) (28% missing) Prot.1 0,279 0,396 0,054 Prot.2 0,285 0,385 0,055 Prot.3 0,286 0,399 0,057 (29% missing) Prot.1 0,179 0,339 0,046 Prot.2 0,179 0,325 0,046 Prot.3 0,179 0,332 0,047 (35% missing) Prot.1 0,059 0,093 0,005 Prot.2 0,062 0,102 0,006 Prot.3 0,060 0,099 0,005 In the variable, the 0-No category does not exist. 11

12 Denmark (N=5 208) (44% missing) Prot.1 0,191 0,263 0,026 Prot.2 0,190 0,244 0,025 Prot.3 0,186 0,248 0,024 (47% missing) Prot.1 0,163 0,292 0,039 Prot.2 0,160 0,285 0,037 Prot.3 0,164 0,291 0,039 (44% missing) Prot.1 0,073 0,114 0,008 Prot.2 0,074 0,110 0,008 Prot.3 0,075 0,121 0,008 In the variable, the 0-No category does not exist. Estonia (N=4 284) Prot.1 0,231 0,319 0,037 Prot.2 0,232 0,322 0,037 Prot.3 0,241 0,332 0,040 Prot.1 0,141 0,285 0,030 Prot.2 0,138 0,281 0,029 Prot.3 0,140 0,289 0,030 Prot.1 0,074 0,136 0,008 Prot.2 0,072 0,126 0,008 Prot.3 0,071 0,136 0,008 In the variable, the 0-No category does not exist. Spain (N=10 764) Prot.1 0,176 0,323 0,035 Prot.2 0,174 0,300 0,033 Prot.3 0,173 0,312 0,034 Prot.1 0,175 0,272 0,040 Prot.2 0,169 0,251 0,038 Prot.3 0,165 0,260 0,037 Prot.1 0,092 0,177 0,013 Prot.2 0,092 0,175 0,013 Prot.3 0,091 0,173 0,013 12

13 Finland (N=8 440) Greece (N=4 082) Croatia (N=4 311) (45% missing) Prot.1 0,191 0,272 0,038 Prot.2 0,190 0,248 0,036 Prot.3 0,189 0,251 0,036 (67% missing) Prot.1 0,164 0,204 0,036 Prot.2 0,157 0,187 0,033 Prot.3 0,160 0,190 0,034 (47% missing) Prot.1 0,076 0,119 0,009 Prot.2 0,072 0,110 0,008 Prot.3 0,070 0,114 0,007 Prot.1 0,223 0,225 0,050 Prot.2 0,219 0,201 0,047 Prot.3 0,217 0,206 0,047 Prot.1 0,184 0,199 0,050 Prot.2 0,183 0,195 0,049 Prot.3 0,185 0,202 0,050 Prot.1 0,127 0,150 0,023 Prot.2 0,124 0,139 0,022 Prot.3 0,124 0,140 0,023 (13% missing) Prot.1 0,223 0,363 0,055 Prot.2 0,214 0,305 0,049 Prot.3 0,210 0,317 0,049 (19% missing) Prot.1 0,186 0,257 0,045 Prot.2 0,182 0,230 0,042 Prot.3 0,176 0,241 0,040 (12% missing) Prot.1 0,096 0,177 0,014 Prot.2 0,097 0,150 0,013 Prot.3 0,097 0,167 0,014 13

14 Hungary (N=9 920) Italy (N=16 073) Lithuania (N=4 217) Prot.1 0,242 0,401 0,058 Prot.2 0,238 0,376 0,055 Prot.3 0,238 0,389 0,056 Prot.1 0,192 0,349 0,050 Prot.2 0,186 0,317 0,047 Prot.3 0,185 0,332 0,047 Prot.1 0,084 0,167 0,011 Prot.2 0,077 0,154 0,009 Prot.3 0,077 0,152 0,009 Prot.1 0,186 0,313 0,036 Prot.2 0,180 0,294 0,033 Prot.3 0,179 0,301 0,033 (10% missing) Prot.1 0,178 0,271 0,041 Prot.2 0,174 0,258 0,039 Prot.3 0,171 0,264 0,039 Prot.1 0,102 0,204 0,015 Prot.2 0,097 0,193 0,014 Prot.3 0,099 0,199 0,015 Prot.1 0,227 0,379 0,056 Prot.2 0,219 0,344 0,052 Prot.3 0,216 0,360 0,051 Prot.1 0,159 0,299 0,038 Prot.2 0,155 0,276 0,036 Prot.3 0,154 0,278 0,036 Prot.1 0,111 0,202 0,019 Prot.2 0,104 0,191 0,017 Prot.3 0,103 0,185 0,016 14

15 Luxembourg (N=5 552) Latvia (N=4 876) Netherlands (N=10 219) Prot.1 0,305 0,537 0,099 Prot.2 0,312 0,534 0,103 Prot.3 0,311 0,547 0,106 Prot.1 0,228 0,357 0,071 Prot.2 0,227 0,356 0,071 Prot.3 0,226 0,368 0,073 Prot.1 0,144 0,262 0,030 Prot.2 0,139 0,251 0,028 Prot.3 0,140 0,251 0,029 Prot.1 0,199 0,314 0,041 Prot.2 0,196 0,301 0,040 Prot.3 0,197 0,315 0,041 Prot.1 0,148 0,286 0,033 Prot.2 0,149 0,276 0,033 Prot.3 0,147 0,283 0,033 Prot.1 0,053 0,092 0,004 Prot.2 0,055 0,091 0,005 Prot.3 0,057 0,093 0,005 (50% missing) Prot.1 0,197 0,310 0,039 Prot.2 0,185 0,291 0,036 Prot.3 0,184 0,298 0,035 (51% missing) Prot.1 0,157 0,246 0,034 Prot.2 0,153 0,233 0,033 Prot.3 0,150 0,235 0,032 (48% missing) Prot.1 0,072 0,069 0,008 Prot.2 0,073 0,059 0,008 Prot.3 0,073 0,057 0,008 15

16 Poland (N=11 471) Portugal (N=4 646) Romania (N=6 146) Prot.1 0,234 0,296 0,057 Prot.2 0,225 0,265 0,052 Prot.3 0,226 0,264 0,053 Prot.1 0,230 0,271 0,070 Prot.2 0,228 0,263 0,069 Prot.3 0,227 0,263 0,069 Prot.1 0,083 0,160 0,010 Prot.2 0,081 0,148 0,010 Prot.3 0,081 0,146 0,010 Prot.1 0,235 0,425 0,061 Prot.2 0,231 0,403 0,057 Prot.3 0,231 0,406 0,059 Prot.1 0,199 0,259 0,054 Prot.2 0,197 0,249 0,053 Prot.3 0,197 0,255 0,054 Prot.1 0,155 0,282 0,036 Prot.2 0,154 0,265 0,035 Prot.3 0,151 0,264 0,035 Prot.1 0,283 0,380 0,091 Prot.2 0,280 0,334 0,087 Prot.3 0,282 0,341 0,088 (23% missing) Prot.1 0,270 0,267 0,102 Prot.2 0,267 0,257 0,098 Prot.3 0,268 0,252 0,099 Prot.1 0,114 0,098 0,020 Prot.2 0,112 0,093 0,019 Prot.3 0,113 0,093 0,019 16

17 Slovenia (N=10 385) (56% missing) Prot.1 0,220 0,422 0,053 Prot.2 0,218 0,401 0,053 Prot.3 0,217 0,417 0,054 (57% missing) Prot.1 0,192 0,285 0,050 Prot.2 0,186 0,268 0,047 Prot.3 0,184 0,276 0,047 (55% missing) Prot.1 0,090 0,140 0,011 Prot.2 0,087 0,129 0,011 Prot.3 0,085 0,142 0,011 Slovakia (N=6 094) Prot.1 0,215 0,320 0,033 Prot.2 0,208 0,301 0,031 Prot.3 0,206 0,305 0,031 Prot.1 0,154 0,285 0,035 Prot.2 0,152 0,268 0,033 Prot.3 0,151 0,271 0,033 Prot.1 0,081 0,162 0,010 Prot.2 0,076 0,147 0,009 Prot.3 0,076 0,143 0,009 In the variable, the 0-No category does not exist. 17

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