Day 3: Challenges in analyzing vulnerability over time

Measuring trends in group differences: The gender earnings gap

Anne Hartung & Eyal Bar-Haim

University of LuxembourgIRSEI Institute for Research on Scio-Economic Inequality

INGRID Workshop “Vulnerable Groups on the Labour Market”April 01-05, 2019 - ISSR, University of Amsterdam, The Netherlands

Aim of our session

To prepare you to analyze group differences as well as their determinants

To provide you with a tool box to analyze cohort trends in the outcome variable of your interest

Rather than: Giving you a thorough introduction into gender studies broader approach

Explaining the underlying mechanisms and theoretical models leading to labor market inequality

Policy responses

2

Agenda

PART 1

Introduction

Methodology I: Oaxaca-Blinder decomposition of group differences

Methodology II: Age-period-cohort models to estimate trends in gaps

Empirical example: gender earnings gap

PART 2

Your turn! Lab session with Eyal Bar-Haim

3

INTRODUCTIONOn gender inequalities

Gender as a dimension of vulnerability?

Disadvantages among women per se

Accumulation of disadvantages and risks through intersections Family status, e.g. single mothers

Race and ethnicity

…

LGBT

However, here: differences between women and men

6

Where to start

7

Trends in the global gender gap

Gender gap Difference in any area between women and men in terms of their levels of participation, access to

resources, rights, power and influence, remuneration and benefits. (ILO, 2007)

According to the GGGR, all things held equal, with current rates of progress, the overall global gender gap can be closed in: 61 years in Western Europe,

62 years in South Asia,

79 years in Latin America and the Caribbean,

102 years in Sub-Saharan Africa,

128 years in Eastern Europe and Central Asia,

157 years in the Middle East and North Africa, 161 years in East Asia and the Pacific, and

168 years in North America(Global Gender Gap Report GGGR, World Economic Forum WEF 2017)

8

The gender pay gap in Europe

9

Average hourly wages 2017(overall)

Source: Eurostat

10

Puzzling gender trends

Changing gender roles and family formation

The rise of women (DiPrete and Buchman 2013): women caught up and even overtook men in terms of educational attainment (Becker, Hubbard, and Murphy 2010; Breen, Luijkx, Müller and Pollak 2010; Buchmann and DiPrete, 2006; Grant and Behrman 2010)

Narrowing but recently stagnating gender pay gap in many countries (England, Gornick & Shafer 2012; Blau and Kahn 2008, 2016; Cambell and Pearlman 2013; Bernhardt, Morris, and Handcock 1995; Fitzenberger and Wunderlich 2002; Fransen, Plantenga, and Vlasblom 2010)

Despite equal pay, positive action, reconciliation and anti-discrimination policies

How can we make sense of these contradictory trends?

Differences, inequality, discrimination

Gender gap Difference in any area between women and men in terms of their levels of

participation, access to resources, rights, power and influence, remuneration and benefits. (ILO, 2007)

Discrimination Unjust/prejudicial treatment of different groups of people

“Any distinction, exclusion or restriction made on the basis of sex which has the effect or purpose of impairing or nullifying the recognition, enjoyment or exercise by women, irrespective of their marital status, on the basis of equality of men and women, of human rights and fundamental freedoms in the political, economic, social, cultural, civil or any other field” (United Nations, 1979)

Only one possible source of gender gaps

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Perspectives on gender differences

Horizontal dimension Refers to differences in the amount of people of each gender present

across fields of study, occupation, etc. which tend to become “feminine” and “masculine”

STEM fields: Science, Technology, Engineering, Mathematics

Segregation

Vertical dimension* Concerns gender disparities in the social/socio-economic hierarchy, e.g.

level of educational attainment

13

Horizontal dimension:Concentration in fields of study (tertiary education)

0% 20% 40% 60% 80% 100%

General programmesTeacher/Education & H

Social sciences/BusinScience/Math/Comp & E

Agriculture/VeterinarHealth/Welfare

Services

Men Women

Data: PIAAC, weightedSource: Valentova, Hartung and Alieva 201414

Horizontal and vertical inequalities are linkedWomen are concentrated in less-paid occupations and sectorsMen’s penalty entering typically female jobs may be as high

Vertical dimension: Hierarchies

Gender gap higher, the higher the hierarchy

Glass ceiling Set of subtle factors (incl. discrimination) that inhibit their rise in predominantly

male jobs or to higher ranks of hierarchies more generally

Pipeline argument: it takes time to move up through the ranks?

Sticky floors

Glass escalator (for men) Even within female-dominated occupations, women tend to earn less / have lower

ranks than their male counterparts

15

Measuring vertical labour market inequalities

1. Overall earnings differential irrespective of labor market participation

2. …3. Measures conditional on

employment and other characteristics

Comparison of earnings incl. zero earnings Wider definition Composite measure

… Comparison of gender pay

gap/hourly pay Narrow definition

FTFY condition, “controlled” or detailed decomposition

ignores important dimensions of gender inequality such as access to employment, activity rates, occupational segregation in different sectors 16

Inclusiveness

Reflecting all changes in society

Precision

Comparing the like with the

like

Measuring gaps: Conceptual dilemma

Conceptual dilemma: Precision of measurement vs representativeness Precision of measure: Internal validity

Compare the like with the like

How much of the observed inequalities is due to discrimination vs. other determinants can only be appropriately answered if all relevant variables are included into the model

Unexplained differences – residuals are used as proxy for discrimination

Random experiments are the better alternative

Substantial implications: Inclusiveness and external validity Accumulation of disadvantages: what does it imply for the substantial conclusions to

”explain way the differences” by controlling for differences in education, labour market participation, occupation etc.

what do you want to / are you able to measure?17

Determinants of the trends in the gender wage gap

Human capital: Different qualifications (skills) and work experience Discrimination

Tastes Statistical discrimination Institutional discrimination (not necessarily conscious) – dual labor markets: primary

and secondary jobs

Segregation: Employment in different sectors (occupations and industries) Socialization, preferences, discrimination Overcrowding

Wage structure: Returns to skills and employment in industries/occupations Different effects on women’s and men’s wages If wage structure changes to more highly reward qualifications and sectors where men

are better endowed than women, the gender gap will increase 18

Explaining the decline in the gender wage gap (cf. Blau & Kahn on the US)

Improved skills and qualifications relative to men (education, experience) Shift in women’s occupations

into higher skilled, higher paid professional and managerial jobs, concentration in lower-paying clerical and service jobs fell

Decreasing gap in unionization deunionization affected (traditionally more unionized) men more negatively

Decreasing unexplained portion of the gender wage differential But changes in the wage structure working in the opposite direction (would

have increased the gap) Rise in return to experience (while women have less of it)

Increases in returns in predominantly male occupations and industries 19

The decline in the unexplained portion of the gender pay gap (US and UK, Europe differs)

Decline in labor market discrimination Statistical discrimination (stereotypes based on averages, e.g. labor market

attachment)

Change in attitudes (tastes, prejudices)

Upgrading of women’s unmeasured labor market skills (similarly to their measured skills) E.g. value placed on money and work reflects pay, math scores, STEM and tech

fields, etc.

Increase in the returns to cognitive skills, decline for motor skills (in those jobs men are overrepresented)

20

(Additional) determinants in a wider framework

A wider approach using a composite measure will also reflect (more comprehensively): Access to the labour market & employment

Changing gender roles

Full- vs. part-time employment

Reconciliation of work and family

Effects of occupational segregation

Policy effects

21

Methodology I Oaxaca-Blinder decomposition of the gender earnings gap

Oaxaca Blinder decomposition (Blinder 1973, Oaxaca 1983, Jann 2006)

The Oaxaca Blinder detects the factors that explain the difference between earnings of men and women How much of the gap in y is specific to one of the x’s

Separating factors into: Differences in x’s (explained component)

Difference in beta’s (unexplained component)

It assumes that there is a (linear) earnings structure that connects an individual’s observable variables (e.g. educational levels and work experience) and unobservable variables (e.g. ability) to earnings

Counterfactual approach Dependence on reference group

23

Oaxaca Blinder decomposition

24

Women have lower wages but also less experience: how much of the difference is due to x and how much due to other factors?

Differential returns

The OB decomposition looks at means RIF (recentered influence function)

regression decomposition models look at the difference in the percentiles (quantile reg.)

The steepness of the lines determines the magnitudes of the explained and unexplained gaps

Oaxaca Blinder decomposition

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How much of the gender wage gap is due to differences in education? what women’s wages would be if

women were rewarded the same way as men for their experience

A man with xf years of experience would receive a wage of wf*

Gender wage difference = explained part E plus unexplained part U (differences in coefficients)

E

U

__

Simple example: your turn!

Average experience:Women: 12 years

Men: 14 yearsPay for each additional year of experience:Women: 1 EUR

Men: 2 EUROverall pay difference?Explained component?

Unexplained component?

26

Simple example: Solutions

Overall pay difference=16 EUR=2*14-1*12Using women as reference group:E=4 EUR=2*(14-12)U=12 EUR=(2-1)*12Using women as reference group:E=2 EUR=1*(14-12)U=14 EUR=(2-1)*14= Index problem Use male as ref group Non-discriminatory weighted

average of male and female wage structures

27

Pros and cons of the Oaxaca Blinder decomposition

Pointing towards relevant factors explaining differentials

Policy makers can be guided by results

Analysis of potential discrimination

Easy to implement

Model assumptions may not hold in reality

Results are sensitive to reference group precision of E and U?

No causal interpretation as one cannot change an individual’s group

Results are sensitive to omitted variables

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Source: Firpo 2017

Decomposing the gender wage gap Mean outcome difference can be expressed as the difference in the linear

prediction at the group-specific means of the regressors (Blinder 1973; Oaxaca 1973; Jann 2008)

endowments effect amounts to the expected change of group A’s mean outcome if group A had group B’s predictor levels.

Oaxaca Blinder two-fold decomposition

Explained part E (“endowments effect”)effect of observed characteristics; group differences due to differences in the predictors

Unexplained part: effect of variables not observed in our model; contribution of differences in the coefficients

R overall difference between Group A and BX is a vector containing the predictors β contains the slope parameters and the interceptε is the errorbeta*: nondiscriminatory coefficients vectors

The Stata oaxaca command (Jann 2006)

Estimate group regressions And if specified, a pooled model over both groups

Note that the pooled regression can inappropriately transfer some of the unexplained parts into the explained component and thus overstate the explained part, therefore groupvar is included into the pooled model

Determined the combined variance-covariance matrix of the models Group means of the predictors are estimated (mean) Various decomposition results and their SEs (and covariances) are computed

Attention: threefold decomposition by default, use pooled (or reference) for twofold Option noisily to see the above steps 30

Stata example of a twofold decomposition of the gender earnings gap

3116%84%

Σ 100%

MenWomen

The pooled model I

32Option: noisily

Optional

The pooled model II

33

the pooled regression can inappropriately transfer some of the unexplained parts into the explained component and thus overstate the explained part therefore groupvar is included into the pooled model

Optional

The pooled model III

Omission of d from a pooled regression leads to omitted variables bias in the estimated coefficient on x. the coefficient on x captures both the direct effect of x on y and the effect of d on

y indirectly through the correlation between d and x, it tends to explain “too much” of the gap in outcomes, leading the unexplained gap to be too small

The regression line for the pooled regression (denoted as the dashed line in the figure) must be steeper than either group line due to omitted variables bias

34

Optional

Contribution of each variable

35Option: detail

Negative values for the explained part mean that the gap would increase (not decrease) if the two groups had equal X-levels.

Positive values imply that the gap can be explained away by the Xs

The Blau and Kahn study

36

Methodology II Age-period-cohort models to estimate trends in the gender earnings gap

Why age period cohort models (APC)?

Age, period and cohort all have distinctive influence on individual and groups of individuals (Ryder 1965, Glenn 2005, Glenn 2005, O’Brien 2000, Yang and Land 2013)

APC models aim to separate, for the outcome variable Y, influences associated with the process of aging (e.g. different stages of life), from those associated with the data at which individuals are observed (e.g. events), from those associated with an individual’s date of birth (e.g. generations - successive

birth-cohorts experience different histories, institutions and peer-group socialization incl. gender roles

Identifying cohort replacement mechanisms and predict future trends & social change If younger, more equal cohorts are smaller relative to older ones, an overall slowing

down of the declining gender gap may be observed although the cohort effects points towards a continuation of the declining gender gap as younger cohorts replace older cohorts 38

Structure of data

Lexis table / diagram:

Age a 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 yand controls x

Condition: Large sample with data for each cell (APC) of the Lexis table

39

age cohort10 1 2 3 4 5 6 7

9 2 3 4 5 6 7 88 3 4 5 6 7 8 97 4 5 6 7 8 9 106 5 6 7 8 9 10 115 6 7 8 9 10 11 124 7 8 9 10 11 12 133 8 9 10 11 12 13 142 9 10 11 12 13 14 151 10 11 12 13 14 15 16

1 2 3 4 5 6 7 period

c = p –a + A

The fundamental identification problem

a, p and c are perfect linear combinations of each other identification problem in linear apc models Impossible to observe independent variation in these variables

Linear regression techniques cannot separate these effects Dropping one of the effects results in over-identification (e.g. no age effect)

Better are weaker assumptions (two adjacent ages are equal)

Different solutions have been proposed: Impose parameter restrictions (Smith 2008, Chauvel & Schroder 2015, Yang et al

2004, 2007, 2008, Fitzenberger et al 2004) Problem: a priori information for reasonable constraints is scarce

Non-linear models / estimate differences (Chauvel 2013, Kuang, Nielsen & Nielsen 2008, Zheng et al 2011, Freedman 2016 40

APCD detrended model

“bump detector” in variable y (Chauvel & Schröder 2014, Chauvel & Smits 2015, Chauvel et al. 2016, Kuang Nielsen & Nielsen): The APCD is able to identify specific accelerations/decelerations on age, period, cohort on specific outcomes

The vectors reflect exclusively the non-linear apc effects; each vector therefore sums up to zero if the dependent variable is linear on the respective time variable

The variables a0Rescale(a) and g0Rescale(c) absorb the linear trend (hyperplane); they are transformations to standardize the coefficients a0 and g0, since Rescale is a linear operator that transforms age from the initial code amin to amax to -1 to +1

For a unique decomposition, appropriate constraints are necessary: each of the vectors aa, pp and gc add up to zero.

the slope of each vector equals zero.

we suppress the eldest and youngest cohorts, so that each one is measured at least twice over period time.

41

(Chauvel 2013)

b0 constant bj control coefficients aa age effect vectorpp period vector gc cohort vector

APCD detrended model

Stata: ssc install apcd

GLM based

But cannot measure the cohort gaps between several subgroups However, you can calculate the trends for each group separately

Therefore we developed the APC-GO based on the APCD

42

Fig.: Cohort deviations from the overall income trend

APC-GO “Gap Oaxaca”

• APC-GO is a specific APC model able to measure cohort changes in gaps in outcomes between 2 groups after controlling for relevant explanatory variables Bivariate or continuous variable

1. Oaxaca Blinder in each cell of the initial Lexis table aggregated Oaxaca Lexis table of measures of gaps (un)explained by controls Twofold decomposition (see methodology part i)

2. APCT-lag of the Oaxaca Lexis table deliver notably γc coefficients

3. Bootstrapping to obtain confidence intervals43

APCT-lag model

Constraint: the estimated linear component of the age effect a equates the observed average age shift of cohorts in the observed Lexis table (= the average difference between u(a+1, p+1, c) and its cohort lag uapc across the table)

The cohort coefficients show the average gap and the fluctuations show possible non-linear accelerations or deceleration in the cohort trend

44 gap in theintensity specific themeasure cohorts of aging ofeffect average theequals ndlinear tre their and zero sum are where

zero; trendand zero sum are where)(

c

a

p

cpaapc lagAPCTgap

γα

π

εγπα −+++=

APCT-lag model

s

45

age cohort10 1 2 3 4 5 6 7

9 2 3 4 5 6 7 88 3 4 5 6 7 8 97 4 5 6 7 8 9 106 5 6 7 8 9 10 115 6 7 8 9 10 11 124 7 8 9 10 11 12 133 8 9 10 11 12 13 142 9 10 11 12 13 14 151 10 11 12 13 14 15 16

1 2 3 4 5 6 7 period

α

Operator Trend for age coefficients:

Alpha is the average longitudinal age effect along cohorts & represents the average shift for a cohort c when it becomes one age group older in thenext period across the window of observation of a age groups and p periods

• APC-lag delivers a unique estimate of vector γc a cohort indexed measure of gaps

• Average γc is the general intensity of the gap• Trend of γc measures increases/decreases of

the gap in the window of observation• Values of γc show possible non linearity• γc can be compared between countries

APC-GO in Stata

ssc install apcgo

See also: Bar-Haim, Chauvel, Gornick & Hartung, 2018. LIS Working papers 737, https://ideas.repec.org/p/lis/liswps/737.html

What do you need: sufficiently long time series data

46

Group variable, e.g. female

# of bootstrap repetitions (try with 3, then increase)

https://ideas.repec.org/p/lis/liswps/737.html

OUR EMPIRICAL EXAMPLE"The Persistence of the Gender Earnings Gap:

Cohort Trends and the Role of Education in Twelve Countries"

Bar-Haim, Chauvel, Gornick & Hartung, 2018.

LIS Working papers 737, https://ideas.repec.org/p/lis/liswps/737.html

https://ideas.repec.org/p/lis/liswps/737.html

Aims

1. Decompose the gender wage gap and investigate the role of education

2. Link educational expansion and the gender wage gap at the macro level and offer explanations why countries differ considerably in the gender wage gap

3. Provide a comparative APC analysis of long-term trends based on the Luxembourg Income Study (LIS) to identify cohort societal change Priority: longest trends possible, balancing the cost of precision of measurement

48

Why a cohort study?

Prevalence of cohort effect while most studies do not distinguish period and cohort effects Cohort effects (changes among young cohorts leaving education or entering the

labour force) rather than period effects (effecting all age groups similarly) E.g. educational attainment – changes across cohorts but is relatively stable across age

Cohort studies can help understanding why and when women, based on their educational attainment relative to men, caught up in terms of wages in some countries, but not in others – and when

Campbell and Pearlman (2013) show that US exhibits strong cohort effects in the gender wage gap

No comparative cohort study on gender earnings gap existing

49

Research questions

Does the narrowing of the gender earnings gap slow down?

Are younger cohorts more equal than older ones?

Are there differences in the gender wage gap across educational levels?

What is the role of education and other factors in the gender wage gap?

What is the role of educational differences between women and men in predicting the gender wage gap? Does the increase in tertiary education among women translate into commensurate female wages?

50

Two relevant processes

1. Educational expansion Educational expansion equipped women with better degrees and should eradicate the

“legitimate” reason for the gender gap

Occupation, work experience and industry are more relevant than education to explain the US gender wage gap (Blau and Kahn 2016)

H1: The role of education in explaining the gender earnings gap is limited.

2. Labour market transformation & wage structure Disappearance of relatively well-paid, typically male occupied jobs in manufacturing

strongest equalization among lowest educated in the US

US wage gap is wider at the top (Blau and Kahn 2016); female glass ceiling (Christofides et al 2013)

H2: The trends in the gender earnings gap differ between low and highly educated.

51

Data and variables

Luxembourg Income Study (LIS) LIS is part of the INGRID network Germany (DE), Denmark (DK), Spain (ES), Finland (FI), France (FR), Israel (IL), Italy (IT), Luxembourg

(LU), the Netherlands (NL), Norway (NO), the UK and the US

Cross-sectional survey – approx. each 5th year between 1985 and 2010

Sample: aged 25-59 years so that we can observe graduation from tertiary education

Variables 5-year birth cohorts between 1935 and 1980

Earnings: comprise paid employment income including basic wages, wage supplements, director wages and casually paid employment income; self-employment income

Earnings positions: Standardised by logit-rank transformation (Chauvel 2016)

Highest level of education: non- tertiary vs tertiary education

Control variables: employment, occupation, partner in hh, number of children (except IT, NO)

52Let p∈[0;1] be the percentile rank of individual i in the income distribution, so that the logged odds of the percentile measures the relative social power of individual i (“Logitrank” (Copas, 1999))

Educational expansion by gender

53

0.2

.4.6

.80

.2.4

.6.8

0.2

.4.6

.8

1940 1960 1980 1940 1960 1980 1940 1960 1980 1940 1960 1980

DE DK ES FI

FR IL IT LU

NL NO UK US

Birth cohortGraphs by cnt Source: LIS

Figure: Attainment of tertiary education among men (blue) and women (red), over birth cohort

Reversal of gender gap in education

54

Figure: Difference in attainment of tertiary education between men and women, over birth cohort

Male advantage

Female advantage

Narrowing of gender wage gap

55

Figure 3: Gender gap in logit-rank of wages, over birth cohort

Source: LIS

Male advantage

Gender parity

Summarizing both trends

56

Gender gaps in education and wages, by cohort

57

Figure 6: Cohort estimates of the gender gap in education and wages in 12 countries (Cohorts 1940-1975)

CA40

CA45

CA50

CA55

CA60

CA65

CA70CA75

DE40

DE45

DE50

DE55

DE60

DE65

DE70DE75

DK40

DK45

DK50

DK55DK60

DK65

DK70DK75

ES40

ES45ES50

ES55

ES60ES65

ES70

ES75

FI40

FI45FI50

FI55FI60

FI65

FI70FI75

FR40

FR45

FR50

FR55

FR60FR65FR70

FR75

IL40

IL45IL50

IL55

IL60 IL65

IL70IL75

IT40

IT45

IT50

IT55

IT60

IT65

IT70

IT75

LU40

LU45

LU50

LU55

LU60 LU65

LU70

LU75

NL40

NL45

NL50

NL55

NL60NL65

NL70

NL75

NO40NO45

NO50

NO55

NO60NO65NO70

UK40

UK45

UK50

UK55

UK60

UK65

UK70

UK75

US40

US45

US50

US55US60

US65US70

US75

.51

1.5

2G

ende

r wag

e ga

p

-.1 -.05 0 .05 .1Gender gap in education

R2(red)=0.210 R2(green)=0.446

Increasing female education has led to lower wage gap to the point where women and men reach parity in education

Gender wage gap explained by education

58

Source: LIS

Contribution of different factors to explaining the gender earnings gap across cohorts

59

Explained and unexplained differencesFigure: Cohort trends in the total (cumulative line), unexplained and explained gender earnings gap

Note: Blinder-Oaxaca decomposition of the gender earnings gap into a part explained by education, household characteristics (living with partner, number of children in the household), employment status and occupation as well as an unexplained part. Note that for Italy and Norway consistent information on occupation was not available and is therefore omitted from the model in these two countries. Source: LIS.

60

Returns to education for men and women

61

Source: LIS

Figure. Cohort trends in earning returns to tertiary education men (solid) and women (dashed)

Conclusions

Gender earnings gap decreased over cohorts in all the countries

Small decreases in countries with already low gender gap: FI and US

Large but sharply declining gender gap largely due to declining observed differences

At the micro level, the role of education is limited in explaining the gender wage gap

At the macro level, educational expansion and gender wage gap are linked up to the point where the educational gap reverses

A persistent unexplained part of the gap over cohorts in most countries

Conclusion: Future stagnation of gender wage gap

Slowing down in the most recent cohorts in NL, FI, FR, IL, NL, US

Far from the level of gender equality (no signs of reversal as observed for education)

Egalitarian access to education is not sufficient

62

Questions & lab session

= standardisation of income (Chauvel 2016)

Income ranks (percentiles): probability of observing income that is less or equal to yt in that society at time t in the cumulative distribution function cdf

logitrank is equivalent to the log of the medianized income times Gini (cf. Chauvel 2016)

Advantage: not affected by changes in inequality over space or time

Mirrors social hierarchy

Allows us to include zero wages avoids the common limitation of ignoring selection into employment (and thus underestimation of true level of gender gap)

Logitrank of income

64

�Day 3: Challenges in analyzing vulnerability over time��Measuring trends in group differences: �The gender earnings gap Aim of our sessionAgendaINTRODUCTIONGender as a dimension of vulnerability? Where to startTrends in the global gender gapThe gender pay gap in EuropeSlide Number 10Differences, inequality, discriminationPerspectives on gender differencesHorizontal dimension:�Concentration in fields of study (tertiary education)Vertical dimension: HierarchiesMeasuring vertical labour market inequalitiesMeasuring gaps: Conceptual dilemmaDeterminants of the trends in the gender wage gapExplaining the decline in the gender wage gap (cf. Blau & Kahn on the US)The decline in the unexplained portion of the gender pay gap (US and UK, Europe differs)(Additional) determinants in a wider frameworkMethodology I Oaxaca Blinder decomposition (Blinder 1973, Oaxaca 1983, Jann 2006)Oaxaca Blinder decompositionOaxaca Blinder decompositionSimple example: your turn!Simple example: SolutionsPros and cons of the Oaxaca Blinder decompositionDecomposing the gender wage gapThe Stata oaxaca command (Jann 2006)Stata example of a twofold decomposition of the gender earnings gapThe pooled model IThe pooled model IIThe pooled model IIIContribution of each variableThe Blau and Kahn studyMethodology II Why age period cohort models (APC)?Structure of dataThe fundamental identification problemAPCD �detrended modelAPCD detrended modelAPC-GO “Gap Oaxaca”APCT-lag modelAPCT-lag modelAPC-GO in StataOUR EMPIRICAL EXAMPLEAimsWhy a cohort study?Research questionsTwo relevant processesData and variablesEducational expansion by genderReversal of gender gap in educationNarrowing of gender wage gapSummarizing both trendsGender gaps in education and wages, by cohortGender wage gap explained by educationSlide Number 59Explained and unexplained differencesReturns to education for men and womenConclusionsQuestions & lab sessionLogitrank of income