1. 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

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

3. 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

4. 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

5. 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

6. 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?

7. Data and Variables
A- LIS 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

8. 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

9. 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?

10. 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

11. 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

12. 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

13. 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

15. 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

16. 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))

17. 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

18. 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)

19. Results: the role of educational expansion and SBTC

20. Results: the role of educational expansion and SBTC

21. 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

22. 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).

23. Thank You!

24. … 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

25. Logit ranks based education income gap