Validating ESeC: Class of Origin and Educational Inequalities in Contemporary Italy Bled, 29-30 July 2006 Antonio Schizzerotto, Roberta Barone and Laura.

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Validating ESeC: Class of Origin and Educational Inequalities in Contemporary Italy Bled, July 2006 Antonio Schizzerotto, Roberta Barone and Laura Arosio

Research question: Can ESeC classes account for inequalities in access to education and in the attainment of educational credentials?

Data The five waves of the Italian Household Longitudinal Study (ILFI, Indagine Longitudinale sulle Famiglie Italiane), collected in 1997, 1999, 2001, 2003 and 2005: the reference population of the survey is a set of households residing in Italy and registered at municipal registry offices at the end of 1996.

Construction of ESeC ESeC requires to distinguish between self-employed with 10 or more employees and self-employed with less than 10 employees. ILFI database doesn’t allow to make a 10+ distinction: the cut-off benchmarks are set at 0, 1-3, 4-14 and 14+ employees. We have placed self-employed with 4-14 employees in turn in the 10- and 10+ group: the latter solution proved to be more cohoerent with the Italian social structure.

Methods Analysis of educational transitions and educational attainments through conditional logistic regressions (Mare, 1981). Analysis of trends of inequalities in educational opportunities over time through log-linear models (Xie, 1992; Erikson and Goldthorpe, 1992).

Analysis of conditional educational transitions.

EDUCATIONAL TRANSITIONS, BY FATHER’S ESEC CLASS. ILFI Lower secondary school Upper secondary school University Origin: ESeC 1-Large employers, higher professional and higher technical occupations 98,292,372,7 Origin: ESeC 2-Lower professional and lower technical occupations 96,590,659,8 Origin: ESeC 3-Intermediate occupations98,590,953,0 Origin: ESeC 4-Small Employers and self- employed (except agriculture) 86,561,924,6 Origin: ESeC 5-Small employers and self- employed in agriculture 52,625,78,1 Origin: ESeC 6-Lower supervisory and lower technician occupations 91,377,744,6 Origin: ESeC 7-Lower service, sales and clerical occupations 91,072,734,2 Origin: ESeC 8-Lower technical occupations78,247,614,5 Origin: ESeC 9-Routine occupations74,441,111,7 N

Part 1: CONDITIONAL LOGIT MODELS OF EDUCATIONAL TRANSITIONS, ILFI 2005.CONTROL VARIABLES. Lower secondary school Upper Secondary school University CovariatesβSEβ β GENDER (reference category: men) Women -0,67***(0,07)-0,12**(0,06)0,00(0,07) BIRTH COHORT (reference category: ) Birth cohort: ,33***(0,12)0,32*(0,18)0,04(0,27) Birth cohort: ,08***(0,11)0,69***(0,16)0,25(0,24) Birth cohort: ,20***(0,12)1,16***(0,16)0,59***(0,23) Birth cohort: ,89***(0,15)1,33***(0,15)0,33(0,22) Birth cohort: ,04***(0,25)1,53***(0,16)0,51**(0,22) Birth cohort: ,18***(0,72)2,01***(0,18) AREA OF RESIDENCE (reference category: North-West) North-East -0,10(0,10)-0,02(0,09)-0,07(0,11) Centre -0,22**(0,10)0,31***(0,08) 0,20**(0,10) South and Islands -0,61***(0,08)-0,18***(0,07) 0,41***(0,09) PARENTS’ HIGHEST DEGREE IN EDUCATION (reference category: elementary school) Lower secondary and vocational education 1,52***(0,15)1,11***(0,08)0,39***(0,09) Secondary education 2,19***(0,32)1,84***(0,16)0,89***(0,12) Tertiary education 2,24***(0,51)1,97***(0,28)1,69***(0,19) constant 1,40***(0,49) 0,12(0,31)-0,11(0,30) N *** p <0,01;** p <0,05; * p <0,1. Standard errors in brackets.

Part 2: CONDITIONAL LOGIT MODELS OF EDUCATIONAL TRANSITIONS, ILFI ESEC CLASSES. Lower secondary school Upper Secondary school University PARENTS’ ESEC CLASS (reference category: ESeC 1-Large employers, higher professional and higher technical occupations) βSEβ β Origin: ESeC 2-Lower professional and lower technical occupations -0,69(0,53) 0,13(0,32)-0,25(0,23) Origin: ESeC 3-Intermediate occupations 0,12(0,59) 0,08(0,32)-0,47**(0,23) Origin: ESeC 4-Small Employers and self- employed (except agriculture) -0,91*(0,49)-0,67**(0,29)-0,74***(0,22) Origin: ESeC 5-Small employers and self- employed in agriculture -2,31***(0,49)-1,26***(0,30)-0,89***(0,27) Origin: ESeC 6-Lower supervisory and lower technician occupations -0,59(0,53)-0,15(0,33)-0,36(0,26) Origin: ESeC 7-Lower service, sales and clerical occupations -0,93*(0,52) -0,52*(0,31)-0,61**(0,25) Origin: ESeC 8-Lower technical occupations -1,83***(0,49) -1,16***(0,30)-1,03***(0,24) Origin: ESeC 9-Routine occupations-2,00***(0,49) -1,34***(0,29)-1,17***(0,23) constant 1,40***(0,49) 0,12(0,31)-0,11(0,30) N *** p <0,01;** p <0,05; * p <0,1. Standard errors in brackets.

The effects of gender, birth cohort and parental education on the odds of entering each educational level decline in intensity and significance as further levels of education are reached (Mare, 1980; Shavit and Blossfeld, 1993).

Nearly all parameters associated to ESeC are significant and synthesise noteworthy differences. In the transition to lower sec. school all significant parameters are negative, showing the privilege of the children of class 1. The most disadvantaged subjects belong to class 5. In the transition to upper sec. school the coefficients drop significantly. We find positive parameters for classes 2, 3 and 6. In the transition to university the parameters drop further, but not as much as in the passage from the 1 st to the 2 nd transition. Once again children of class 1 are the most privileged.

Analysis of educational attainments.

ATTAINMENT OF LOWER SECONDARY, UPPER SECONDARY AND TERTIARY CERTIFICATES AMONG THOSE WHO ATTENDED TO THE CORRESPONDING LEVEL OF EDUCATION BY ESEC CLASSES. ILFI Lower secondary degree Secondary school degree Tertiary school degree Origin: ESeC 1-Large employers, higher professional and higher technical occupations 99,493,357,8 Origin: ESeC 2-Lower professional and lower technical occupations 99,190,149,3 Origin: ESeC 3-Intermediate occupations99,488,151,1 Origin: ESeC 4-Small Employers and self- employed (except agriculture) 95,382,045,6 Origin: ESeC 5-Small employers and self- employed in agriculture 89,677,051,3 Origin: ESeC 6-Lower supervisory and lower technician occupations 96,588,848,3 Origin: ESeC 7-Lower service, sales and clerical occupations 96,084,242,7 Origin: ESeC 8-Lower technical occupations93,175,933,7 Origin: ESeC 9-Routine occupations92,677,840,6 Total94,882,846,9 N

Part 1: CONDITIONAL LOGIT MODEL OF EDUCATIONAL ATTAINMENTS, ILFI CONTROL VARIABLES. Lower secondary schoolUpper secondary school University COVARIATESβSEβ β GENDER (reference category: men) Women-0,13(0,11)0,41***(0,08)0,08(0,10) BIRTH COHORT (reference category: ) Birth cohort: ,21(0,25)-0,51(0,31)-0,42(0,40) Birth cohort: ,13(0,23)-0,01(0,29)-0,47(0,35) Birth cohort: ,77***(0,23)0,24(0,28)-0,75**(0,33) Birth cohort: ,66***(0,25)0,00(0,27)-0,75**(0,33) Birth cohort: ,65***(0,26)-0,12(0,27)-1,25***(0,32) Birth cohort: ,88***(0,37)-1,14***(0,28) AREA OF RESIDENCE (reference category: North-West) North-East-0,08(0,17)-0,31***(0,11)0,13(0,15) Centre-0,17(0,16)0,03(0,11)0,10(0,13) South and Islands-0,40***(0,13)-0,06(0,10)-0,18(0,12) PARENTS’ HIGHEST DEGREE IN EDUCATION (reference category: elementary school) Lower secondary and vocational education1,93***(0,27)0,45***(0,09)0,10(0,13) Secondary education2,12***(0,48)0,93***(0,15)0,12(0,16) Tertiary education1,72**(0,67)1,26***(0,25)0,42**(0,19) constant3,02***(0,82)1,75***(0,38)0,87**(0,39) N *** p <0,01;** p <0,05; * p <0,1. Standard errors in brackets.

Part 2: CONDITIONAL LOGIT MODEL OF EDUCATIONAL ATTAINMENTS, ILFI ESEC CLASSES. Lower secondary school Upper Secondary school University PARENTS’ ESEC CLASS (reference category: ESeC 1-Large employers, higher professional and higher technical occupations) βSEβ β Origin: ESeC 2-Lower professional and lower technical occupations -0,50(0,87)-0,14(0,30)-0,19(0,20) Origin: ESeC 3-Intermediate occupations-0,04(0,94)-0,28(0,30)-0,06(0,22) Origin: ESeC 4-Small Employers and self- employed (except agriculture) -0,93(0,81)-0,37(0,29)-0,24(0,22) Origin: ESeC 5-Small employers and self- employed in agriculture -1,36*(0,81)-0,68**(0,32)-0,06(0,32) Origin: ESeC 6-Lower supervisory and lower technician occupations -1,00(0,85)-0,06(0,34)-0,23(0,26) Origin: ESeC 7-Lower service, sales and clerical occupations -1,14(0,83)-0,28(0,32)-0,30(0,26) Origin: ESeC 8-Lower technical occupations-1,43*(0,81)-0,63**(0,30)-0,61**(0,27) Origin: ESeC 9-Routine occupations-1,45*(0,81)-0,56*(0,30)-0,41*(0,24) constant3,02***(0,82)1,75***(0,38)0,87**(0,39) N *** p <0,01;** p <0,05; * p <0,1. Standard errors in brackets.

Parents who have any degree higher than elementary school enhance their children’s chances of attaining both a lower secondary and upper secondary degree, but not a laurea degree, the attainment of which is more likely only if parents have completed tertiary education themselves. The significant parameters associated to birth cohorts show a negative sign, particularly so with respect to the attainment of a laurea degree. This result is due to the fact the university population has become less and less selected across cohorts.

ESeC classes display a low degree of significance, but we can notice three patterns : 1: all the parameters show a negative sign  steady privilege carried along by the children of class 1 in each one of the three models; 2: classes 8 and 9 are consistently disadvantaged in the conditional attainment of each one of the degrees, while class 5 loses its relative disadvantage for what concerns the attainment of a laurea degree. 3: if we move from the first column to the third one, we detect the same reduction in the parameters as pointed out in the models of educational transitions.

Log-linear models.

GOODNESS-OF-FIT STATISTICS OF FOUR DIFFERENT LOG-LINEAR MODELS APPLIED TO ESEC BY EDUCATIONAL ATTAINMENT BY BIRTH COHORT CROSS-TABULATION. ILFI ModelNdfX2pG2pReduction in G2 BICDI Conditional independence ,70,001977,30,000,0729,917,5 Constant association ,90,00158,20,0092,0-870,63,7 Unidiff ,10,00155,30,0092,1-828,03,6 Constant association + blocked cells (largemp+smallagr+lowsale+lo wtech) ,040,00124,300,1593,7-868,13,4

The Unidiff model shows only marginal improvement if compared to the constant association model. The reduction of misclassified cases (DI), as well as the reduction in G2 and X2, are negligible when compared to the loss of 5 degrees of freedom; also the BIC statistic favours the simpler model; in the light of these results, we view the constant association model as our best choice. By blocking a selection of the cells that displayed an absolute value of the residuals greater than 1,96, we obtain the results shown in the 4 th line of the previous table: losing 4 degrees of freedom, we obtain an improvement of the goodness-of-fit statistics and also an acceptable fit to the data.

Comparison between ESeC, two versions of Cobalti-Schizzerotto class scheme and EGP. GOODNESS-OF-FIT STATISTICS OF FOUR DIFFERENT LOG-LINEAR MODELS APPLIED TO ESEC, TWO VERSIONS OF AN ITALIAN CLASS SCHEME AND EGP. ILFI Constant association modelNBICDI ESeC ,63,7 Cobalti-Schizzerotto (eight-fold schema) ,43,6 Cobalti-Schizzerotto (six-fold schema) ,93,5 EGP ,64,1

Cobalti-Schizzerotto schemes are better with respect to ESeC in that they misplace a lower percentage of observations, but the difference is negligible (3,6 % and 3,5% compared to ESeC’s 3,7%); ESeC works better if we look at the BIC; EGP retains less descriptive power in addition to a worse BIC when compared to ESeC.

It is reassuring to see how, even when compared to well-established classifications, ESeC not only maintains its informative power, but can even be preferable and thus an useful tool in social research.