The document presents an empirical analysis of gender gaps using microdata from the Survey of Health, Ageing and Retirement in Europe (SHARE). It discusses (1) defining appropriate measures of gender gaps for older populations, (2) examining social mobility patterns from childhood to older ages, and (3) using regression analysis to quantify the effects of various determinants on end-of-life gender gaps across European countries. The analysis finds significant gender gaps in personal income among older Europeans and that these gaps are influenced by factors throughout the life course such as childhood conditions, education, employment, and pensions.

1. 1
PLATON TINIOS & ANTIGONE LYBERAKI
University of Piraeus Panteion University
GREECE GREECE
GENDER GAPS AND LIFETIME INEQUALITY:
AN EMPIRICAL ANALYSIS
OF MICRO-DATA FROM
EUROPE
IFA Conference,
Prague May 2012

2. 2
A new –lifetime - perspective on an old issue
• Attempt to examine empirical implications of a novel and innovative micro
dataset on an old issue of importance for gender balance
• The old issue: ‘the’ Gender gap
▫ Systematic differences in life chances between men & women
▫ Observed in many dimensions, across countries, domains etc.
• The new data: SHARELIFE
▫ SHARE {w1 (2004) w2 (2007)} – European interdisciplinary
panel of people 50+ (comparable to HRS in US, ELSA in UK))
▫ SHARELIFE (2009) – Retrospective lifetime questions for SHARE sample:
 Childhood, education, health, family, work, etc for entire life.
• The new perspective
▫ How are gender gaps generated and how do they solidify in older
ages?
 Cross-country, cross-cohort, various dimensions
 Key (ultimate) motivation: Palliative role of welfare states??
▫ A tour d’ horizon - attempt to quantify effects of different
dimensions
 Clarify issues – See the ‘lie of the land’

3. 3
Outline: Halfway there…
1. Gender gaps and the life time perspective
▫ Conceptual discussion.
2. Derive alternative measures of the gender gap
appropriate to older populations aged 50+
3. Vector of starting deprivation into scalar index
of starting position
4. Examine socioeconomic mobility patterns
5. Attempt to disaggregate gender gap by a means
of a reduced form equation

4. 4
What is the gender gap?
• Gender gap is an achievement gap
▫ women are underpaid, undervalued and overworked.
▫ Gender inequities in own income – economic independence deficit
▫ Why doesn’t wage gap lead to rise in demand?
• In general gender gaps are shrinking over time
• What does it depend on?
▫ Observables (education etc) / discrimination
▫ Oaxaca (1973) method attempts to decompose
 But: occupational segregation complicates
 Bergmann (1974) overcrowding depresses wages
• In career terms – snowball effect of low participation, few hours,
lower wages
▫ Evidence of polarisation among women
▫ Cumulative earning gap (Luxembourg Income Study)
▫ Could solidify further once retire from the labour market
• SHARELIFE should provide data to examine issues as well as a
new perspective:.
▫ E.g. interdisciplinary insights + effect of Welfare State.

5. 5
Conceptual issues
• GENDER GAPS:
▫ Multidimensional. Prominence to remuneration
▫ Here: Cumulative (over life) or end-state.
• Research strategy: DETERMINANTS
▫ Split into different stages of life:
 Initial, education, work/family, pension, health,
 Social protection as a palliative influence through the life course
 ‘Worlds of welfare capitalism’ -- chart the heyday of the Welfare Staete
• 1st approach: Examine total effect on end- result
▫ OLS of variables on end state Gender gap.
 ≅ Reduced form.
 Gives overview of total significance, though not causal (yet)
▫ Could examine each stage in turn as stages cumulate
over the life course, (plus partial attempts at amelioration)
 Approach adopted in FRB (initial and work/family).
 Axel Börsch-Supan et al 2011. Ongoing work.
 E.g. early health⇒ Education⇒ Work chances⇒ End-state

6. 6
How to define an over-50 Gender gap?
• Gender gap usually identified as difference in wages and/or
earnings.
▫ End-state to be examined relatively well-defined
• In a sample of people 50+, situation is more nuanced:
▫ Some have never worked
 Depending on conditions some decades ago.
▫ Earnings exist for those who work.
▫ Pensions cumulate past differences and correct
 depending on welfare state structure and parameters of the
preceding periods
▫ Selection between earnings/pensions endogenous
 Depends on personal preferences and welfare state parameters.
(retirement ages)
• In household level micro-data:
▫ Savings also cumulate – income from property, rents, business
 Categories of income accrue to household. (some social assistance)
 Equivalence scales force gender equality by definition!

7. 7
Two alternative courses of action
I. Define hybrid ‘Personal income’
▫ Personal Income= Personal Income from Pensions +
Personal Income from Employment + Equivalent income
from other household level income sources
 Not so much an ‘earnings gap’ but a ‘disposition of resources
gap’
▫ Wider than gender gap -- cumulative effect over time
▫ In addition to earnings encompasses other disadvantages
▫ Narrower than gender gap --intra-household sharing
 Rich housewives still rich, if household is rich
II. Decompose into (a) participation gap (b)
retirement gap (c) earnings gap for those working
(d) pensions gap for pensioners/ retired.
▫ Richer diagnosis – less easy to interpret.
 Two discrete choices – two alternative earnings
determinations
▫ More complex as a description

8. 8
Overview of empirical work presented
1. Examination of end-state gaps
• Under the two alternative definitions
2. Social Mobility analysis
▫ Derivation of initial state ‘deprivation index’
▫ From initial conditions to end-conditions.
3. Reduced form OLS equation explaining end-gap***
▫ (Equivalent to Oaxaca-type study)
▫ Groups of variables
 ‘Pure gender’ effect – dummy
 Effect of initial conditions
 Education, work/family, pension
▫ Quantify effect of different groups using predicted values for
country groups and cohorts
 For average values
 For the poorest 20%

9. 9
1: Crude gender gaps in 2007/9:
Personal income, 50+ population
Gender Gap based on Mean Personal Income
Gender Gap based on Median Personal Income
48 50
50 46
42 43
39 41
39
45 46
40 33
38 37 39
30 33 33 34 35 27
23
17 18
20
19
16
10 21 13
0
SE DK NL DE BE FR CH AT IT ES GR PL CZ
Gap= 1- (Average for women/ Average for men)
Highest in South; smaller in North/Transition
Median gap – indication significant difference in distribution by gender
Gender gap widest in group 65-80. 80+ falls (influence of widows’
pensions)

10. 10
Personal Income distribution by gender:
Two extremes GR, SE)
Greece Greece
male female
30
Gini
M=0.437
Percent
20
F=0.574
10
0
0 5000 10000 15000 20000 0 5000 10000 15000 20000
personal income
Graphs by male or female
High prevalence of zero incomes -- non-declaration?
Non-working spouse in employee or pensioner household?
Sweden Sweden
male female
15
Gini
10
Percent
M=0.330
5
F=0.342
0
0 10000 20000 30000 40000 0 10000 20000 30000 40000
personal income
Graphs by male or female

11. 11
Detailed participation and separate
earnings and pension gaps
Earnings and pensions gap
Participation gap (for those with +ve values)
Gender Participation Gap (M-W) to Personal Income sources (in Labour income Pension income
percentage points)
Gender Earnings Gap (among those receiving either income source) Pension or labour income
43,1 50
45 40,7
38,8
40 34,8 35,0 34,6 40
32,9
35 30,2
30 27,3
24,2 34,4 30
21,8 21,4 22,5
25
19,4
20 16,4 16,8 20
13,8
15
9,9 9,5
10 6,7 7,8 5,6
10
5 1,2 1,2
0 0
SE DK NL DE BE FR CH AT IT ES GR PL CZ SE DK NL DE BE FR CH AT IT ES GR PL CZ

12. 12
Comparison of more familiar concepts:
earnings and pension gaps
• Define for two groups Gender Income Gap
Labour income: Persons 50-64
relatively close in age. by income source among
those receiving each source
▫ Earnings 50-64 Pension income: Persons 65-80
▫ Pensions 65-80 Pension and labour income: Persons 50+
• Could also be seen as ‘a 60
look into the future of the
pension gap’. 50
• No clear pattern
▫ Earnings>Pensions 40
 DK, BE,CH,AT,CZ, ES
30
▫ Pensions> Earnings
 FR,IT,GR, PL 20
▫ Almost the same
 SE,DE 10
• Need to understand
differences 0
SE DK NL DE BE FR CH AT IT ES GR PL CZ

13. 13
2. Mobility analysis
• Childhood deprivation index
▫ Described in Lyberaki, Tinios, Georgiadis 2011
 Related to end-state persistent poverty + soc. protection
 Index of relative deprivation by country at age 10.
 Weight more deprivation of those qualities more widely enjoyed
 Constructed by 11 indicators (housing, family indicators)
• Gives an idea of starting point
▫ As chiefly family deprivation - gender balanced.
▫ Cohort sensitive; though
▫ However, the two sexes have different chances to alter
personal position
• Chart personal changes in rank
▫ Change of quintile in distribution
▫ Spearman rank order coefficients

14. 14
Upward mobility Remained stable Downward mobility Mobility – Change in
100 quintile ranks
25,0 21,2 22,6 Considerable change over time
36,0 31,3 36,9
42,1 43,2
75
From starting point of gender
equality (household status
50 same for brother & sister)
To end point
25 51,3 53,5 53,1 46,3 41,4 Males move up
38,6 32,6 32,3 Females down
0
M F M F M F M F Biggest difference in South
Nordics Continental Southern Eastern
Spearman rank-order
coefficient (on percentiles)
Biggest difference in Continent
Smallest in North

15. 15
3. Description of Reduced form OLS
equation
• Dependent: Log (Personal income in 2007/9)
• ‘Pure gender effect’ = Gender dummy
• ‘Indirect gender effect’ = through gender differences in other
determinants of income
▫ (Assuming as 1st approx. coefficients same for men and women).
1. Initial effects – Cohort, Index, + GNP pc at 1960.
2. Education – years of schooling, higher dummy
3. Family status – Never married, widowed, divorced, married,
Number of children
4. Work – years, years squared
5. Health – bad health during life, at end
6. Pensions - pensioner, years since pension
7. Country group dummies. Log of GDP pc in 1970
Approach similar to Oaxaca decomposition - proceed step by step

16. Y=Log(Personal income) Pooled 16
Variables Coef. Std. Err.
Age: 50-64 years 0,4631** 0,0430
Age: 65-79 years 
Age: 80 years 0,1807** 0,0374
Female -0,4049** 0,0355
Pooled equation
Childhood non deprivation index:
0 to 1 (no deprivation) 0,6276** 0,1243
Years in education 0,0432** 0,0048
Dummy: Higher Education 0,4067** 0,0431
Pooled equation – presumes
Single 0,0743
Married 
0,0652
independent variables
Divorced 0,0444 0,0639
operate in the same way
Widowed 0,5488** 0,0375
for men and women.
Number of Children 0,0207* 0,0109 Use as first approximation
Years in employment 0,0485** 0,0041
Reasonable fit
Squared term: Years in employment -0,0005** 0,0001 Intuitive results
Dummy: Pensioner 1,5025** 0,0497
“pure gender effect” –
Years in retirement -0,0090**
Ever had physical injury to
0,0013
implies a gap of 33% if all
disability -0,0850* 0,0454
else is equal.
Less than good health -0,1630** 0,0347 Employment crucial.
Country-specific
Nordic 
All stages appear to have to
Continental -0,0936** 0,0343
‘expected’ effect
Southern -0,2374** 0,0610 Country dummies included in
Transition -0,1134 0,1496 lieu of social protection. Effect
Log of GDP per capita 1970 0,8777** 0,0729
not easy to interpret.
Constant term -1,6337* 0,7340
# Observations 23113
R2 0.323

17. Y=Log(Personal income) Males Females 17
Std.
Variables Coef. Err. Coef. Std. Err.
Age: 50-64 years 0,2686** 0,0513 0,4965** 0,0630
Age: 80 years 0,0898 0,0513 0,2089** 0,0491
Childhood deprivation
index: 0 to 1 0,9018** 0,1647 0,3925** 0,1772
OLS equation by
Years in education 0,0327** 0,0067 0,0513** 0,0065
gender
Dummy: Higher Education 0,3104** 0,0565 0,4787** 0,0627
Single -0,2142** 0,0915 0,2643** 0,0931 By gender– presumes variables
Married   operate differently for
Divorced -0,4089** 0,1078 0,3102** 0,0762 men and women.
Widowed 0,1414** 0,0548 0,5698** 0,0450
Obviously so.
Number of Children 0,0182 0,0148 0,0210 0,0147
Years in employment 0,0204** 0,0081 0,0452** 0,0052 Higher explanatory power for
women.
Squared employment -0,0001 0,0001 -0,0004** 0,0001
Dummy: Pensioner 1,0146** 0,0707 1,8432** 0,0691 Initial conditions less
Years in retirement -0,0159** 0,0024 -0,0066** 0,0015 important
Ever had physical injury to Education more
disability -0,1039* 0,0554 -0,0498 0,0711
Less than good health -0,1510** 0,0439 -0,1713** 0,0496
Employment crucial (non-
Country-specific linear for W)
Continental -0,0315 0,0452 -0,1238** 0,0486 Some variables have opposite
Southern -0,2944** 0,0868 -0,1453 0,0828 sign M/F. (family variables)
Transition -0,5211** 0,1986 -0,1327 0,2132
Some evidence of dampening
Log of GDP per capita 1970 0,6701** 0,0973 1,0112** 0,1031 by group – women benefit
Constant term 1,3380** 0,9899 -3,4974** 1,0391
more from initial high GDP.
# Observations 10404 12709
R2 0.245 0.339

18. 18
First results at decomposing differences
% explained by differences in employment by differences in education
60
53,6
50
44,3 44,4
38,9
40 36,0 35,5
24,5
30,5 30,3
30 24,9
21,9 48
20
12,7 33 36 35
27 31
10 25 23 24
18 16
12
0
50- 65- 80+ 50- 65- 80+ 50- 65- 80+ 50- 65- 80+
64 80 64 80 64 80 64 80
Nordics Continental Southern Transition
• Use common Loading factors (pooled equation) i.e. no separate effect, (as
in Neumark (1988) – non discrimination). How different are explanatory
variables vs ‘pure’ gender effect (gender dummy)
• Look at effect of different ‘endowments’ in four country groups.
• Leave different loading factors for future (> complex interpretation) as in
usual Oaxaca models.
• Results considerably different by country group and cohort.
▫ Most equalising effect in Nordics

19. 19
Separate equations for work and
pensions: preliminary observations
In the earnings equation:
• Family variables are very significant but of opposite sign by gender.
• Favourable initial conditions affect men more than women
• Wider differences M/W in effect of initial conditions
• Years in employment has no apparent influence. Education has a very strong
influence.
• Being in a rich country affects men more than women
• Collecting a pension depresses income from earnings
In the pensions equation:
• Overall gender effects are comparable to earnings.
• Women have systematically lower pensions than men in the Continental countries
(social insurance systems?). In Transition countries (ceteris paribus) women are
better off.
• Education is far more important for women (due to participation effects?).
• Number of children exerts a strong negative effect on women – presumably
accounting for dropping out of the labour market and other constraints on working.
• Early deprivation has smaller effect, confined to men. It appears that the social
protection system to some extent corrects for initial disadvantage.
• Years of employment have a non-linear effect which diminishes with years
• Early retirement is associated with lower pensions.

20. 20
“There’s gold in them thar hills…”
(Virginia gold rush – 1840s’)
• First results are encouraging
• Long way in a short space. Attempt to be
parsimonious with numbers!!
• There is much to be explained + much still left out.
▫ Easy to lose the wood from the trees.
• The wood:
• The exercise using retrospective data of the
European 50+ population may chart the
success or failure of social protection system.
• How are today’s pension gaps related to past
events? What are the prospects for the
future??