5 tinios gender ifa-tinios-2012
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5 tinios gender ifa-tinios-2012 Presentation Transcript

  • 1. 1 PLATON TINIOS & ANTIGONE LYBERAKI University of Piraeus Panteion University GREECE GREECEGENDER GAPS AND LIFETIME INEQUALITY:AN EMPIRICAL ANALYSISOF MICRO-DATA FROMEUROPE IFA Conference, Prague May 2012
  • 2. 2A 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. 3Outline: 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. 4What 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. 5Conceptual 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. 6How 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. 7Two alternative courses of actionI. 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 richII. 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. 8Overview of empirical work presented1. Examination of end-state gaps • Under the two alternative definitions2. 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. 91: 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 CZGap= 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=0.437Percent 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 5045 40,7 38,840 34,8 35,0 34,6 40 32,935 30,230 27,3 24,2 34,4 30 21,8 21,4 22,525 19,420 16,4 16,8 20 13,815 9,9 9,510 6,7 7,8 5,6 105 1,2 1,20 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. 132. 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 in100 quintile ranks 25,0 21,2 22,6 Considerable change over time 36,0 31,3 36,9 42,1 43,275 From starting point of gender equality (household status50 same for brother & sister) To end point25 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. 153. Description of Reduced form OLSequation• 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 dummy3. Family status – Never married, widowed, divorced, married, Number of children4. Work – years, years squared5. Health – bad health during life, at end6. Pensions - pensioner, years since pension7. Country group dummies. Log of GDP pc in 1970Approach 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** 0,0430Age: 65-79 years Age: 80 years 0,1807** 0,0374Female -0,4049** 0,0355 Pooled equationChildhood non deprivation index:0 to 1 (no deprivation) 0,6276** 0,1243Years in education 0,0432** 0,0048Dummy: Higher Education 0,4067** 0,0431 Pooled equation – presumesSingle 0,0743Married  0,0652 independent variablesDivorced 0,0444 0,0639 operate in the same wayWidowed 0,5488** 0,0375 for men and women.Number of Children 0,0207* 0,0109 Use as first approximationYears in employment 0,0485** 0,0041 Reasonable fitSquared term: Years in employment -0,0005** 0,0001 Intuitive resultsDummy: 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 alldisability -0,0850* 0,0454 else is equal.Less than good health -0,1630** 0,0347 Employment crucial.Country-specificNordic  All stages appear to have toContinental -0,0936** 0,0343 ‘expected’ effectSouthern -0,2374** 0,0610 Country dummies included inTransition -0,1134 0,1496 lieu of social protection. EffectLog of GDP per capita 1970 0,8777** 0,0729 not easy to interpret.Constant term -1,6337* 0,7340# Observations 23113R2 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** 0,0630Age: 80 years 0,0898 0,0513 0,2089** 0,0491Childhood deprivationindex: 0 to 1 0,9018** 0,1647 0,3925** 0,1772 OLS equation byYears in education 0,0327** 0,0067 0,0513** 0,0065 genderDummy: Higher Education 0,3104** 0,0565 0,4787** 0,0627Single -0,2142** 0,0915 0,2643** 0,0931 By gender– presumes variablesMarried   operate differently forDivorced -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 0,0147Years in employment 0,0204** 0,0081 0,0452** 0,0052 Higher explanatory power for women.Squared employment -0,0001 0,0001 -0,0004** 0,0001Dummy: Pensioner 1,0146** 0,0707 1,8432** 0,0691 Initial conditions lessYears in retirement -0,0159** 0,0024 -0,0066** 0,0015 importantEver had physical injury to Education moredisability -0,1039* 0,0554 -0,0498 0,0711Less 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 oppositeSouthern -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 dampeningLog of GDP per capita 1970 0,6701** 0,0973 1,0112** 0,1031 by group – women benefitConstant term 1,3380** 0,9899 -3,4974** 1,0391 more from initial high GDP.# Observations 10404 12709R2 0.245 0.339
  • 18. 18First 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. 19Separate equations for work andpensions: 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??