More Necessary and Less Sufficient: Age-Period-Cohort Approach to Overeducation in a Comparative Perspective Eyal Bar-Haim, Anne Hartung and Louis Chauvel University of Luxembourg, PEARL Institute for Research on Socio-Economic Inequality (IRSEI), Luxembourg

Our aims Overeducation Theories and Definitions Datasets & variables Methodology: APCGO and apctlag Results: between SBTC and educational inflation

Overeducation Returns to education might change due to structural changes: High skilled labor supply Skill biased technological change (SBTC) Overeducation: a decrease in the returns to education due to a structural change Might be correlated

Definitions of overeducation Relative to previous cohorts (at the same age) Relative to the less educated If Edu. expansion > Eco. Growth Overeducation = lower wages for higher education (but “undereducation” could happen as well!...) The gap in resources of educated juniors relative to less educated changes over time

SBTC/educational expansion More growth (Skill biased) Education is more necessary (the gap between the have and have nots increases) SBTC/educational expansion Education is Less Sufficient (the returns to education decline over cohorts) Educational (credential) Inflation

Research questions Did the income gap between the tertiary and the non-tertiary educated increase? Did the returns to tertiary education decrease over cohorts? Did the educational expansion affect the returns to education (relative to the less educated and relative to previous cohorts)? Did the SBTC affect the returns to education?

Data and Variables A- LIS 1985-2010 each 5 years, 12 countries => “long” term Variables: Income: Household disposable income per consumption unit (PPP adjusted) Income rank: logit rank of DPI Education: Binary education variable (tertiary/less than tertiary) based on ISCED Skill biased occupations: Category 2 and 3 in ISCO88 Gender as control variable AT CA DE DK ES FI FR IL IT LU NL NO UK US

The Data structure Combined LIS datasets (both household and individual) Structure (U.S. example) AU85 AU89 AU95 AU01 AU03 AU10 CA87 CA91 CA94 CA98 CA04 CA10 DE84 DE89 DE94 DE00 DE04 DE10 DK87 DK92 DK95 DK00 DK04 DK10 ES80 ES90 ES95 ES00 ES04 ES10 FI87 FI91 FI95 FI00 FI04 FI10 FR84 FR89 FR94 FR00 FR05 FR10 IL86 IL92 IL97 IL01 IL05 IL10 IT86 IT91 IT95 IT00 IT04 IT10 LU85 LU91 LU94 LU00 LU04 LU10 NL83 NL90 NL93 NL99 NL04 NL10 NO86 NO91 NO95 NO00 NO04 NO10 UK86 UK91 UK94 UK99 UK04 UK10 US86 US91 US94 US00 US04 US10 Age/ Period 1985 1990 1995 2000 2005 2010 25 13,603 12,227 10,572 13,126 12,512 12,941 30 13,432 13,381 12,387 15,916 14,393 13,874 35 12,059 12,859 12,356 18,278 15,717 13,928 40 9,932 11,495 11,267 18,988 17,333 15,032 45 7,806 9,306 9,930 16,119 16,207 15,114 50 6,961 7,416 7,830 12,768 13,450 14,038 55 7,024 6,379 6,294 8,737 10,470 11,870

Methods APCGO and apctlag (ssc install apcgo) Age-Period-Cohort methods designed to capture cohort trends APCtlag - trends in dependent variable over cohorts APCGO - trends in the gaps between two groups over cohorts Why cohorts?

Structure of data Lexis table / diagram Age indexed by a from 1 to A Period by p from 1 to P Cohort by c = p – a + A from 1 to C Cross-sectional surveys including one outcome y and controls x Large sample: each cell (apc) of the Lexis table has data c = p – a + A

apctlag 40 years of APC debate and the last 10 years improvements APC-Detrended as an identifiable solution of age, period and cohort non linear effects (Chauvel, 2013, Chauvel and Schröder. 2014, Chauvel et al., 2016) b0 is the constant is a two-dimensional linear (=hyperplane) trend are 3 vectors of age, period and cohort fluctuations Detrended APC works well. Trended APC & the “identification problem” (a=p-c ) Solution  implement a relevant constraint The meaningful constraint is trend in aa equate the average of the longitudinal shift observed in uapc

apctlag The apctlag solution s = [S (u(a+1, p+1, c) - uapc)] / [(A-1) (P-1)] is the average longitudinal age effect along cohorts (= the average difference between u(a+1, p+1, c) and its cohort lag uapc across the table) Operator Trend for age coefficients is a APCtlag delivers a unique estimate of vector gc a cohort indexed measure of gaps Average gc is the general intensity of the gap Trend of gc measures increases/decreases of the gap in the window of observation Values of gc show possible non linearity The gc can be compared between countries

First step: decompose the gap in each cell of the Lexis table APCGO First step: decompose the gap in each cell of the Lexis table For each cell of the Lexis Table one Blinder Oaxaca decomposition is proceeded So we transform the yapc in each cell in an aggregate: the Oaxaca-blinder unexplained part uapc Second step: we use apctlag on the unexplained gap To obtain CIs, we bootstrap the two steps together Stata: ssc install APCGO

Results – has education expanded? Figure 1: Cohort Change in the Proportion Tertiary Education Holders APCtlag of the proportion of tertiary educated (controlled for gender) See: ssc install apcgo Proportion

Results – has the gap widened (relative returns increased)? Figure 2: Economic Returns of Tertiary Relative to Non-Tertiary Educated Over Birth Cohorts ln(income) gap unexplained by gender (Oaxaca decomposition) See: ssc install apcgo Economic returns: household disposable income (PPP) adjusted per standard adult (DPI) Unexplained gap (ln(income))

Results – have absolute returns to tertiary education declined? Figure 3: Returns to Tertiary Education (Logit rank DPI) APCtlag of the (income(controlled for gender) See: ssc install apcgo Income ranks

Cohort-countries (N=100 due to data limitation) OLS analysis Cases: Cohort-countries (N=100 due to data limitation) Cohort coefficients obtained from apctlag and apcgo Dependent variables: Returns to education in terms of income (relative and absolute) Independent variables: Educational expansion (apctlag for tertiary education) SBTC (apctlag for in high-skilled occupations) ISCO-88 Categories 3 (professionals) and 3 (technical occupations)

Results: the role of educational expansion and SBTC

Results: the role of educational expansion and SBTC

Conclusions Less common (but the opposite is also rare) Education is More growth (Skill biased) Education is more necessary (the gap between the have and have nots increases) SBTC/educational expansion Education is Less Sufficient (the returns to education decline over cohorts) Educational (credential) Inflation Very common

Conclusions Educational expansion increased overeducation (relative or absolute) Unless strong SBTC has occurred But, SBTC for itself has no effect on overeducation In the context of educational expansion and strong SBTC change absolute as well as relative return for tertiary educated increase Tertiary education is sometimes more necessary (where relative returns increased) but usually less sufficient (as absolute returns decreased).

Thank You!

… Logit-Rank & Applications Logit-rank transformation is a convenient tool to transform ordinal variables in ]–infinite ; + infinite[ standardized distribution In the context of distributional analysis, it provides a “net of distributional change” relative reference position of individuals and of groups It is more convenient than percentiles levels [between 0 and 1] that present border issues Useful in income volatility analysis and in contexts where “positional” aspects are central To nutshellize Logit-Rank & Applications 2 is close to top decile 4 is close to top 2% 0 is median … 1 is close to top quartile 3 is close to top vingtile

Logit ranks based education income gap