Présentation PowerPoint

Présentation PowerPoint

Louis Chauvel Pr Dr at University of Luxembourg Inequality Across Cohorts CUNY-LIS June 2017 IRSEI Institute for Research on SocioEconomic [email protected] http://www.louischauvel.org 1 4 PARTS 1- Theory of Cohort inequalities 2- Welfare regimes and international comparisons with the LIS 3- Inequalities of education, income, wealth and APC 4- An APC approach to gender inequality in 12 countries 2 Louis Chauvel Pr Dr at University of Luxembourg

INEQUALITY ACROSS BIRTH COHORT PART 1: Theory of Cohort inequalities: France as an extreme case CUNY-LIS June 2017 IRSEI Institute for Research on SocioEconomic [email protected] http://www.louischauvel.org 3 1. From theory to datacrunching: Social generations and cohort analysis Theory of social generations (Karl Mannheim) 1968 gap of generations (Margaret Mead) Demographic metabolism (Norman Ryder) The methodology of APC analysis (Yang Yang) Examples: * suicide in France * consumption in China * political participation * etc. , etc. , etc.

Norman Ryder Margaret Mead 1923-2010 1901-1978 www.louischauvel.org/ryder2090964.pdf Karl Mannheim 1893-1947 Yang Yang 1970?-4 Important references http://www.louischauvel.org/frenchpolcultsoc.pdf www.louischauvel.org/TheMannheim.pdf www.louischauvel.org/TheMead.pdf www.louischauvel.org/TheRyder.pdf http://davidcard.berkeley.edu/papers/vietnam-war-college.pdf www.louischauvel.org/TheYANGASR2008.pdf Margaret Mead 1901-1978 Socialization versus individual and collective history Life course and socialization Primary and secondary socialization The transitionnal socialization

Primary socialization Transitionnal socialization Secondary socialization Until end of compulsory secondary education (?) adulthood 16-18 y.o. 25-30 y.o. Long term impact of the transitionnal socialization : scar effect History and the constitution of a Generationengeist (spirit of generations) and of a Generationenlage (situation of generation) 6 Material-objective or political-cultural generations?... Or all of that Karl Mannheim l The impact of new social contexts on the young: Mental data are of sociological importance not only because of their actual content, but also because they cause the individuals sharing them to form one groupthey have a socializing effect. (dass sie die Einzelnen zur Gruppe verbinden, sozialisierend wirken )

(K. Mannheim, Das Problem der Generationen, 1928) QUESTION 1 From cohort to generations ? How generational cristallization ? QUESTION 2 Does the national/Welfare regime context of entry into adulthood has a durable effect on future life chances of generations ? 7 General question of research on cohort inequalities: Economic crises and the social integration of new cohorts. Scarring effects of youth unemployment (Ellwood 1982 / Gangl 2004). Permanence or resilience of initial trauma and Cumulative advantage/disadvantage (R. Merton 1968, Th. DiPrete 2006) Or compensation, resilience (Luthar & al. 2000, Bonanno 2004) Do states differ in how well they could integrate new cohorts or do we see more pronounced insider-outsider dynamics in some countries? Are some generations sacrificed or do cohorts with a bad start catch up? Goerres and Vanhuysse (2012: 1) developing an integrated body of knowledge to answer the question of which generations get what, when and how. 8 FACTS : Example The French crash test Unemployment rate for the male and female 20 to 24 year old 0.3

0.25 Risks of unemployment 12 months after living school (%) 0.2 0.15 0.1 0.05 0 1950 1960 1970 1980 1990 2000 2010 2020 QUESTION : M F long term consequences of the shock of the 1970? long term consequences of permanent difficulties of entry in the labor market? 9 Young generations as victims of social change France as a crash test Multidimensional generational fractures in France a. b. c. d. e. f.

Relative(?) socio-economic decline Overeducation and educational dclasss Risks of downward mobility Dyssocialisation See Recomposition of risks of suicide Out of the political arena 10 France Lis 1980-2005 Silc 2010 a. Relative(?) socio-economic decline Log level of living (=disposable income per CU) by age group (0= year average) 0.2 0.15 2010 0.1 0.05 1980

0 -0.05 25 30 35 40 50 age 45 55 60 -0.1 -0.15 1980 1985 1990 1995

2000 2005 2010 11 a. Relative(?) socio-economic decline real housing price index (dotted lines) and real household incomes index 200 200 150 100 Housing index 1980 1990 2000 year 2010 2020 1970

1980 2010 2020 200 150 100 1990 2000 year 2010 2020 1970 1980 1990 2000 year 1990 2000 year 2010

2020 2010 2020 year 50 150 50 100 Wages 150 1980 1980 us 100 50 1970

1970 ca 200 au 1990 2000 year 200 1970 50 100 150 uk 50 50 100

200 fr 150 de 2010 2020 1970 1980 1990 2000 year Note: y-axis: Housing index and household incomes adjusted for inflation, indexed to year 2000 (y=100). 12 Source: International House Price Database, Federal Reserve Bank of Dallas. Mack and Martnez-Garca (2011). b. Overeducation and educational dclasss Educational inflation % of GED (bac) (no more no less) holders accessing middle class jobs (service cl h+l) 1970-2012

Age Year French labor force surveys 1970-2012 13 b. Overeducation and educational dclasss Educational inflation % of GED (bac) (no more no less) holders accessing middle class jobs (service cl h+l) 1970-2012 Age Birth cohort French labor force surveys 1970-2012 14 First conclusions: As happy as God in France? (Hypothesis might be true(?) But avoid generalization to the young plz.) Interpreting the French case: l Esping-Andersen Typology of Welfare states: France = corporatist-conservative welfare regime, stabilization of social relations Protection of insiders (protected male workers) against outsiders

l In case of economic brake : Insiderisation of insiders, already in the stable labor force and outsiderisation of new entrants l In France, young people can wait decades Job seeking = Musical chairs game l Increasing poverty rates for young people, stable intracohort inequalities (after taxes and welfare reallocations) Strong problem of social welfare sustainability: Those who pay might experience the collapse of this regime 15 Louis Chauvel Pr Dr at University of Luxembourg INEQUALITY ACROSS BIRTH COHORT PART 2: COMPARING COHORT INEQUALITIE IRSEI Institute for Research on SocioEconomic [email protected] http://www.louischauvel.org

16 Backgrounds A 17 countries comparison of inter-cohort inequalities See also : Chauvel, L. and M. Schrder. 2015. The impact of cohort membership on disposable incomes in West Germany, France, and the United States. European Sociological Review, 31:298-311. 17 mile Durkheim (1897), Le suicide. tude de sociologie, p.1 Interpreting the French case: Emile Durkheims Suicide l Esping-Andersen Typology of Welfare states: France = corporatist-conservative welfare regime, stabilization of social relations Protection of insiders (protected male workers) against outsiders l In case of economic brake : Insiderisation of insiders, already in the stable labor force and outsiderisation of new entrants l In France, young people can wait decades Increasing poverty rates for young people, stable intracohort inequalities (after taxes and welfare reallocations)

18 Theories of Welfare Regimes Decommodification models and welfare regimes De-commodification occurs when a service is rendered as a matter of right, and when a person can maintain a livelihood without reliance on the market (Esping-Anderson, pp. 21-22) Gosta Esping-Andersen (Danish, born 1947) Professor @ Universitat Pompeu Fabra (Barcelona). Central references Pierson Ch. and Castles F.G. (eds) 2006, The Welfare State Reader, 2nd ed, Cambridge: Polity Press. Pierson C., Obinger H., Lewis J., Leibfried S., Castles F.G. (Eds), 2010, The Oxford Handbook of the Welfare State, Oxford ; Ox Univ Pr. 20 Central references Schrder, Martin, 2013: Integrating Varieties of Capitalism and Welfare State Research: A Unified Typology of Capitalisms. New York: Palgrave. 21

Liberal (=Residual) Corporatist (=Conservative) Theoretical equality of opportunity Maintaining social order Social-demo. (=Universalistic) decommodification defamilialistion destartification Degree / Model of decommodification Free Market as the central institution Intermediate level of decommodification Collective social

consumption promoted System of social stratification Protection of the (good) poor, but stigmatization of free riders: Strong economic inequalities but more permeable boundaries between social classes Typical countries US UK Solidarity between Economic, gender, equals: inequality is Intermediate degree minimal and strong of inequality but fluidity (net social boundaries mobility, equality of strongly opportunities &

impermeable outcomes) between classes Germany (France) Sweden 22 Three (+1) modalities Esping-Andersen Typology of Welfare states : Conservative model (Continental Europe) : FRANCE Preservation of (old) social balance, with social insurance excluding unemployed => strong intercohort inequalities and less intracohort inequalities than in the Liberal model Liberal model : (Anglo-saxon world) : US Market as a central institution, residual welfare state against market failures HL0 : more intracohort inequalities HL1 : less intercohort inequality (competition between generations) Social-democrat Model (Nordic Europe) : DENMARK Citizenship and broad participation to discussions and bargaining around social reforms between social groups (gender, generations, etc.) for a long-term development HD0 : less intracohort inequalities HD1 : residual intercohort inequalities (positive compromise between generations) 23

Methodology I : the base A = P C The Lexis Diagram (1872) Age 80 Isochron: Life line: observation in 1968 C 1918 cohort born in 1948 60 C 1978 40 Age at year of 20 observation: 20 0 1890 1910 1930

1950 1970 1990 2010 2030 Period BUT ! How to distinguish durable scarring effects and fads ??? Hysteresis = stability versus Resilience = resorption of scars 24 Statistical background: Age Period Cohort models Separate the effects of age, period of measurement and cohort. Problematic colinearity: cohort (date of birth) = period (date of measurement) - age (Ryder 1965, Mason et al. 1973, Mason / Fienberg 1985, Mason / Smith 1985, Yang Yang et al. 2006 2008, Smith 2008, Pampel 2012) 25 Remember Whelpton and Frost APC literature: Gospels & Bibles 1970-1990s MASON K. O., MASON W. M., WINSBOROUGH H. H., POOLE K., 1973, Some methodological issues in cohort analysis of archival data, American sociological review, 38, pp. 242-258.

GLENN N. D., 1976, Cohort analysts futile quest : statistical attempts to separate age, period, and cohort effects, American sociological review, 41, pp. 900-905. Adams, J. 1978. Sequential Strategies and the Separation of Age, Cohort, and Time-of-Measurement Contributions to Developmental Data. Psychological Bulletin 85: 1309-16. HASTINGS D. W., BERRY L. G., 1979, Cohort analysis : a collection of interdisciplinary readings, Oxford (Ohio), Scripps Foundation for Research in Population Problems. Rodgers, W.L. 1982. Estimable Functions of Age, Period, and Cohort Effects. American Sociological Review 47:774-87. Holford, T.R. 1983. The Estimation of Age, Period, and Cohort Effects for Vital Rates. Biometrics 39:311-24. Mason W.M. and H.L. Smith. 1985. Age-Period-Cohort Analysis and the Study of Deaths from Pulmonary Tuberculosis. Pp.151-228 in Cohort Analysis in Social Research: Beyond the Identification Problem, edited by W.M. Mason and S.E. Fienberg. New York: Springer-Verlag. MASON W. M., FIENBERG S. E., 1985, Cohort analysis in social research : beyond the identification problem, Berlin, Springer Verlag. Clayton, D. and E. Schifflers. 1987a. Models for Temporal Variation in Cancer Rates I: Age-Period and Age-Cohort Models. Statistics in Medicine 6:449-67. Clayton, D. and E. Schifflers. 1987b. Models for Temporal Variation in Cancer Rates II: Age-PeriodCohort Models. Statistics in Medicine 6:468-81. Hout M. and A.M. Greeley, 1989, The Cohort Doesn't Hold: Comment on Chaves, Journal for the Scientific Study of Religion, n. 29, pp.519-524. WILMOTH J. R., 1990, Variation in vital rates by age, period, and cohort, in C. C. Clogg (ed.), Sociological methodology, Oxford, Basil Blackwell, vol. 20, pp. 295-335. WILMOTH J. R., 2001, Les modles ge-priode-cohorte en dmographie, in G. CASELLI, J. VALLIN, G. WUNSCH (eds.), Dmographie : analyse et synthse. I : La dynamique des populations, Paris, Ined, pp. 379-397. 26 APC literature 2008-2013

Yang, Y. and Land, K.C. (2008). Ageperiodcohort analysis of repeated cross-section surveys. Fixed or random effects? Sociological Methods & Research 36(3):297326. Smith, H.L. (2008). Advances in Age-Period-Cohort Analysis. Sociological Methods & Research 36-3:287-96. Yang Y., Schulhofer-Wohl, S., Fu, W. and Land, K. (2008). The Intrinsic Estimator for Age-Period-Cohort Analysis: What It is and How to Use it? American Journal of Sociology, 113:1697-1736. OBrien, R.M. 2011a. Constrained Estimators and Age-Period-Cohort Models. Sociological Methods & Research 40:419-52. Hui Zheng, Yang Yang and Kenneth C. Land, 2011, Variance Function Regression in Hierarchical Age-Period-Cohort Models: Applications to the Study of Self-Reported Health, Am Sociol Rev. 2011 December; 76(6): 955983. Wilson, J.A., Zozula, C. and Gove, W.R. (2011). Age, Period, Cohort and Educational Attainment: The Importance of Considering Gender. Social Science Research 40:136-49. Pampel, F.C. and Hunter, L.M. (2012). Cohort Change, Diffusion, and Support for Environmental Spending in the United States. American journal of sociology 118(2):420448. Campbell Colin, Jessica Pearlman (2013), Period effects, cohort effects, and the narrowing gender wage gap, Social Science Research, Volume 42, Issue 6, p.16931711 Yang Y. and Land, K.C. (2013), Age-period-cohort analysis. New models, methods, and empirical applications. CRC Press, Taylor & Francis Group, Boka Raton, FL Fienberg, S. E. (2013). Cohort analysis unholy quest: A discussion. Demography, 50, 19811984. Luo, L. (2013). Assessing Validity and Application Scope of the Intrinsic Estimator Approach to the Age-Period-Cohort Problem. Demography 50(6):1945-67. Dassonneville, R. (2013). Questioning generational replacement. An age, period and cohort analysis of electoral volatility in the Netherlands, 19712010. Electoral Studies 32(1):37-47 27

APC literature (2014-2015) Grasso, M.T. (2014). Age, Period and Cohort Analysis in a Comparative Context: Political Generations and Political Participation Repertoires in Western Europe. Electoral Studies, 33:6376. Chancel L. (2014). Are Younger Generations Higher Carbon Emitters than their Elders?: Inequalities, Generations and CO2 Emissions in France and in the USA. Ecological Economics, 100:195207. Phillips, J. A. (2014). A changing epidemiology of suicide? The influence of birth cohorts on suicide rates in the United States. Social Science & Medicine, 114, 151-160. Schwadel, P. and Garneau, C. R. H. (2014), An AgePeriodCohort Analysis of Political Tolerance in the United States. The Sociological Quarterly, 55: 421452 Chauvel, L. and Schrder M., (2014). Generational inequalities and welfare regimes. Social forces 92 (4):1259-1283. Chauvel, L. and Smits F.. (2015). The endless baby-boomer generation: Cohort differences in participation in political discussions in nine European countries in the period 1976-2008. In: European Societies. Reither, E. N., Masters, R. K., Yang, Y. C., Powers, D. A., Zheng, H., & Land, K. C. (2015). Should age-period-cohort studies return to the methodologies of the 1970s? Social Science & Medicine. Harper S. Invited commentary: A-P-C . . . Its easy as 1-2-3! Am J Epidemiol. 2015 online publication OBrien RM, 2015, Model Misspecification when Eliminating a Factor in Age-Period-Cohort Models, ASA 2015 Chicago mimeo. 28 APC literature (2015-2017) Chauvel, L. and M. Schrder. 2015. The impact of cohort membership on disposable

incomes in West Germany, France, and the United States. European Sociological Review, 31:298-311. Reither, E. N., Masters, R. K., Yang, Y. C., Powers, D. A., Zheng, H., & Land, K. C. (2015). Should age-period-cohort studies return to the methodologies of the 1970s? Social Science & Medicine. Harper S. Invited commentary: A-P-C . . . Its easy as 1-2-3! Am J Epidemiol. 2015 online publication Lindahl-Jacobsen, R., Rau, R., Jeune, B., Canudas-Romo, V., Lenart, A., Christensen, K., & Vaupel, J. W. (2016). Rise, stagnation, and rise of Danish womens life expectancy. Proceedings of the National Academy of Sciences, 113(15), 4015-4020. Chauvel, L., Leist, A. K., & Smith, H. L. (2016). Cohort factors impinging on suicide rates in the United States, 1990-2010. Annual Meeting of the Population Association of America, March 31 - April 2, 2016, Washington, DC. Full paper available at http://orbilu.uni.lu/handle/10993/25339. Chauvel L, Leist AK, Ponomarenko V (2016) Testing Persistence of Cohort Effects in the Epidemiology of Suicide: an Age-Period-Cohort Hysteresis Model. PLoS ONE 11(7): e0158538. doi:10.1371/journal.pone.0158538 Bell, A. and K. Jones. 2017. The hierarchical ageperiodcohort model: Why does it find the results that it finds? Quality & Quantity: 1-17. 29 Our method A: APCD APCD (detrended): are some cohorts above or below a linear trend of long-run economic growth? Basically, the APCD is a bump detector. y apc a p c 0 rescale (a ) 0 rescale (c) 0 j x j i j

p c a (APCD) a p c 0 p c a Slope ( ) Slope ( ) Slope ( ) 0 a a p p c c min( c) c max(c) STATA ssc install apcd => available ado file PLZ see more on www.louischauvel.org/apcdex.htm 30 4. Data Dependent variable We want to explain the living standards of members of different cohorts: Variable dpi (disposable income) from the Luxembourg Income Study. Logged and divided by the square root of household members and adjusted for

inflation: reflects household-equalized real disposable income after taxes and transfers. Independent variables Age, Period of measurement, Cohort-membership of respondent (date of birth). Plus controls for: education (ISCED code), sex, partner in household, # of children, immigrant-status. Main interest How much does the mere date of birth (cohort membership) influence living standards? in terms of deviation from the linear trend 31 clear all capture ssc install apcd set linesize 100 gen d3=. foreach gogo in fr it no us { qui { if "`gogo'"== "fr" local fifi " fr84 fr89 fr94 fr00 fr05 " if "`gogo'"== "it" local fifi " it86 it91 it95 it00 it04 " if "`gogo'"== "no" local fifi "no86 no91 no95 no00 no04 " if "`gogo'"== "us" local fifi "us86 us91 us94 us00 us04 " foreach toto in `fifi' { local perso "$`toto'p" local house "$`toto'h" qui use hid ppopwgt age sex relation educ nchildren immigr educ_c pi deflat partner pmi ptime using `perso' qui joinby hid using `house' keep hid ppopwgt age sex relation educ pi deflat year iso2 hpopwgt dpi /// deflator nchildren immigr educ_c hmi hmx* npers partner pmi ptime

, clear local save "t`toto'" qui save `save' , replace } clear all foreach toto in `fifi' { local save "t`toto'" qui append using `save' } qui recode year (1977/1982=1980) (1983/1987=1985) (1988/1992=1990) (1993/1997=1995) (1998/2002=2000) (2003/2008=2005) qui gen age5=int((age-3)/5)*5+3 qui gen pweight = int(ppop) qui keep if age >= 20 & age < 65 gen page=floor(age/5)*5 keep if (page >= 25 & page <= 64) gen year5=year replace year =int((year-1980)/5) gen educ2=int(educ) } di "`gogo'" gen ldpi=ln(dpi/sqrt(npers)) keep if age5>=25 & age5<60 xi: apcd ldpi [pw= pweight] if year5>=1985 & age5>=25 & age5<60 , age(page) period(year5) }

France : APCD (detrended) cohort coefficient of disposable per uc income cohorts controls for: education (ISCED code), sex, partner in household, # of children, immigrant-status. Luxembourg Income Study microdata 1980s to 2010s 33 APCD (detrended) cohort coefficient of disposable per uc income, w controls controls : education (ISCED code), sex, partner in household, # of children, immigrant-status. de dk es -.1 0 .1 ca

-.2 ca de dk fr il fr il it -.1 0 .1 fi es -.2

fi no uk us -.1 0 .1 nl it -.2 nl 1920 1940 no 1960 1980 1920

1940 uk 1960 1980 1920 1940 1960 us 1980 1920 Luxembourg Income Study microdata coh 1980s to 2010s 1940 1960 1980 34 APCT (trended) cohort coefficient of Gini indexes de

dk es fi fr il it nl no uk us -.1 0 .1 -.1 0

.1 -.1 0 .1 ca 1920 1940 1960 1980 1920 1940 1960 1980 1920 coh Graphs by iso 1940

1960 1980 1920 1940 1960 1980 35 Intercohort inequality (after controls) and intracohort inequality dynamics Intercohort inequality (non flat cohort profile) intracohort inequality dynamics (cohort growth of Gini index) 36 Conclusion France is a very problematic case of young cohort economic slowdown Italy, Spain, share very similar problems => there, the young get worse and the new seniors get relatively better Reason: In conservative welfare state, the protection of insiders (the old) against outsiders (the young) produces strong difficulties in case of eco slow down, and then massive scarring effects

US not so bad? See closer in the details = suicide rates in the US!!! See full paper here : https://paa.confex.com/paa/2016/meetingapp.cgi/Paper/6950 37 Louis Chauvel Pr Dr at University of Luxembourg INEQUALITY ACROSS BIRTH COHORT PART 3: THE INEQUALITIES TO COME IN THE U.S. 3 aspects of inequality and cohorts in the U.S. A- Cohort inequality and rising premium to education C- booming interdecile gaps in wealth IRSEI Institute for Research on SocioEconomic [email protected] http://www.louischauvel.org 38 A- Cohort inequality and rising premium to education U.S. Data Source CPS IPUMS 1975-2015 each 5th year male population Dependent variable

Log wage-income gap between BA holders and the others (many variants processed). (log wages medianized by year) Independent variables Age, Period of measurement, Cohort-membership of respondent (date of birth). Method apctlag [included in the STATA ssc install apcgo ] of the gap between diploma and non diploma holders Bootstrapped 20 times Main interest How much does the mere date of birth (cohort membership) influence the diploma premium? 39 .9 A- Cohort inequality and rising premium to education Diploma premium : gap in log wage income of BA/noBA U.S. .5 .6 .7 .8 75% premium 55%

premium .4 45% premium 30% premium 1920 1940 coh 1960 1980 40 A- Cohort inequality and rising premium to education Diploma premium : gap in log wage income of BA/noBA Tuition and fees & other costs constant 2015$ 4-year "Table 330.10. Average undergraduate tuition and fees and room and board rates charged for full-time students in degree-granting postsecondary institutions, by level and control of institution: 1963-64 through 2015-16" 41

C- booming interdecile gaps in wealth Data Source Survey of consumer finance 1989-2013 Dependent variable Log wealth ( medianized by year) Independent variables Age, Period of measurement, Cohort-membership Contrast beteween [ top decile versus median ] . Method apctlag [included in the STATA ssc install apcgo ] of the gap between top decile and the median Bootstrapped 20 times Main interest How much does the mere date of birth (cohort membership) influence the wealth gap? 42 1.5 2 2.5 3 3.5 Difference of Log-wealth top decile D10 to the median M U.S. SCF

1920 1940 coh 1960 See in annex My problem with Gini 1980 43 Louis Chauvel Pr Dr at University of Luxembourg INEQUALITY ACROSS BIRTH COHORT PART 4: APCGO AND A GENDER GAP APC The gender wag gap across cohorts: the role of education in 12 countries IRSEI Institute for Research on SocioEconomic

[email protected] http://www.louischauvel.org 44 The gender wag gap across cohorts: the role of education in 12 countries Louis Chauvel, Anne Hartung, Eyal Bar Haim University of Luxembourg, PEARL Institute for Research on Socio-Economic Inequality (IRSEI) 45 Gender trends 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, Mller and Pollak 2010; Buchmann and DiPrete, 2006; Grant and Behrman 2010) Narrowing but recently stagnating gender 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) Education is seen as the most important predictor of

wages (Mincer 1958) and the gender wage gap (Polachek 1993) Since education is at first a cohort phenomenon, cohort analysis is required Campbell, C. and J. Pearlman. 2013. Period effects, cohort effects, and the narrowing gender wage gap. Social Science Research, 42(6): 1693-1711. 46 46 Our specific contribution Analysis of the gap by cohort to understand timing / socialization Role of education versus labor participation of women Wage distribution when a large, declining share of the pop has wage = 0 Compare intensity of the gender gap in each educational level 47 Reversed education gender gap and maintained wage gender gap in the U.S. Male to female wage income ratio BA (or +) owners Birth cohort

Source : IPUMS-CPS 1985-2010 Birth cohort Two relevant processes See Paper Online (1) Educational expansion l Educational expansion equipped women with better degrees and should eradicate the legitimate reason for the gender gap l 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 gap is and has been limited. (2) Labour market transformation l Disappearance of relatively well-paid, typically male occupied jobs in manufacturing strongest equalization among lowest educated in the US l 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 wage gap differ between low and highly educated. Gender gaps across space and time See Paper Online Countries differ considerably in the gender wage gap (Harkness 2010; England, Gornick & Shafer 2012; Mandel 2012; Christofides et al. 2013) l Not consistent with existing welfare state typologies (Mandel 2012)

Prevalence of cohort effect while most studies do not distinguish period and cohort effects l Cohort effects (changes among young cohorts leaving education or entering the labour force) in education and labour market rather than period effects (effecting all age groups similarly) l Clear example: Educational attainment changes across cohorts but is relatively stable across age l Campbell and Pearlman (2013) showing that US exhibits strong cohort effects in the gender wage gap l 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 Data and variables Luxembourg Income Study (LIS) l 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 l Cross-sectional survey approx. each 5th year between 1985 and 2010 l Sample: aged 25-59 years so that we can observe graduation from tertiary education and exclude elderly Variables l 5-year birth cohorts between 1935 and 1980 l Highest level of education: non- tertiary vs tertiary education l Wages: comprise paid employment income including basic wages, wage supplements, director wages and casually paid employment income but not selfemployment income l Standardised with logit-rank transformation as proposed by Chauvel (2016)

51 APC-GO (Gap/Oaxaca) model Now on Stata: ssc install apcgo APC-GO is a APC model to provide a cohort analysis in gaps in outcomes between 2 groups after controlling for relevant explanatory variables l e.g. (gender) gaps in income net of education effects or (racial) gaps in education net of State/county effects Ingredients: 1. Computation of Oaxaca decomposition in unexplained/explained gaps by A x P cell 2. Estimate of APC-lag gaps with a focus on cohort 3. Bootstrapping to obtain confidence intervals Structure of data See Paper Online Lexis table / diagram: Condition: Large sample with data for each cell (APC) of the Lexis table

cohort 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 4 5 6 7 8 9 10 11 12 13 14 15 6 7 8 9 10

11 12 13 14 15 16 7 period c=pa+A 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 y and controls x age 10 1 2 3 9 2 3 4 8 3 4 5 7 4 5 6 6 5 6 7 5 6 7 8 4 7 8 9 3 8 9 10 2 9 10 11 1 10 11 12

1 2 3 53 Part II: APC-lag of the u apc See Paper Online APC-Detrended as an identifiable solution of age, period and cohort non-linear effects (Chauvel, 2013, Chauvel and Schrder. 2014, Chauvel et al., 2016) u apc a p c 0 rescale(a) 0 rescale(c) 0 ( APCD) where a , p , c are sum zero and trend zero; 0and 0 absorb age and cohort trend 0 is the constant 0 rescale (a) 0 rescaleis(ca) two-dimensional linear (=hyperplane) trend a , p , c are 3 vectors of age, period and cohort fluctuations To solve the identification problem (a=p-c ), a meaningful constraint is needed: trend in a = the average of the longitudinal shift observed in u apc 55 Part II: APC-lag of the u apc

See Paper Online The APC-lag solution age 10 1 2 3 9 2 3 4 8 3 4 5 7 4 5 6 6 5 6 7 5 6 7 8 4 7 8 9 3 8 9 10 2 9 10 11 1 10 11 12 1 2 3 cohort 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13

13 14 4 5 6 7 8 9 10 11 12 13 14 15 6 = [S (u(a+1, p+1, c) - uapc)] / [(A-1) (P-1)] is the average longitudinal age effect along cohorts (= the average difference between ( APCL) uu (a+1, p+1, c) where S( ) 0 and S( ) 0 ; Trend( ) 0; Trend( ) and its cohort lag uapc across the table) Operator Trend for age coefficients: 7 8 apc

a p c 9 a p p a 10 11 Trend( a ) 12[S a (2i A 1)] / [(A - 1)A(A 1)] 12 13 APC-lag delivers a unique estimate of 14 vector c a cohort indexed measure of gaps 15 Average c is the general intensity of the gap 16 Trend of c measures increases/decreases of 7 period the gap in the window of observation Values of c show possible non linearity The c can be compared between countries Summary APC-GO combines the different steps 1. Oaxaca of the cells of the initial Lexis table data generates an aggregated Oaxaca Lexis table of measures of gaps

unexplained by controls 2. APC-lag of the Oaxaca Lexis table deliver notably c coefficients 3. Bootstrapping to obtain confidence intervals See Stata ado file, ssc install apcgo 57 THE GENDER GAP IN EDUCATION & WAGES IN 12 COUNTRIES 58 Educational expansion by gender Figure 1: Attainment of tertiary education among men (blue) and women (red), over birth cohort DK ES FI FR IL IT LU NL

NO UK US 0 .2 .4 .6 .8 0 .2 .4 .6 .8 0

.2 .4 .6 .8 DE 1940 1960 1980 1940 1960 1980 1940 Birth cohort Graphs by cnt 1960

1980 1940 1960 1980 59 Source: LIS Reversal of gender gap in education Figure 2: Difference in attainment of tertiary education between men and women, over birth cohort ES FI FR IL IT LU NL NO

UK US .2 DK .2 0 -.2 .2 0 -.2 Female advantage -.2 0 Male advantage DE 1940 1960

1980 1940 1960 1980 1940 Birth cohort Graphs by country 1960 1980 1940 1960 60 1980 Narrowing of gender wage gap Figure 3: Gender gap in logit-rank of wages, over birth cohort

FI FR IL IT LU NL NO UK US 0 2 0 2 ES 2

Gender parity DK 0 Male advantage DE 1940 1960 1980 1940 1960 1980 1940 Birth cohort Graphs by country 1960

1980 1940 1960 61 1980 Source: LIS Gender wage gap by education Figure 4: Gender wage gap for non-tertiary educated (red) and tertiary educated (blue), over birth cohort Male advantage Gender parity 62 Source: LIS The unexplained gender wage gap Figure 6: Blinder-Oaxaca decomposition including education, family and employment status : Total wage difference (blue) and unexplained difference (green)

63 Source: LIS Convergence of the gender composition of the top Quartile U.S. 1 Figure 7: percentage male population in the top quartile group Q4 .6 .7 .8 .9 Male advantage 50% Gender parity 1920 1940 coh 1960

1980 Convergence of the gender composition of the top Decile U.S. 1 Figure 8: percentage male population in the top decile group D10 .6 .7 .8 .9 Male advantage 50% Gender parity 1920 1940 coh 1960

1980 Convergence of the gender composition of the top Decile U.S. Male advantage .6 .7 .8 .9 1 Figure 8: percentage male population in the top ventile group V20 (top 5%) 50% Gender parity 1920 1940 coh 1960

1980 Conclusions Gender wage gap decreased over cohorts in all the countries l Small decreases in countries with already low gender gap: FI and US l Large but sharply declining gender gap over cohorts in DE, ES, IT, NL and LU l Largely due to declining in explained differences The intrinsic role of education is limited in explaining the gender wage gap l = large and continuing wage gaps among higher educated A persistent unexplained part of the gap over cohorts except for UK Slowing down in the most recent cohorts in NL, FI, FR, IL, NL, US Future studies l Compare the role of the different equalizing factors (educ. / labor participation / etc.) l Gender composition of top incomes l Compare the role of welfare regimes in the reducing gender gap 67 Thanks! 68 Annex: My problem with Gini 2 completely different distributions can give the same Gini Index

1.5 2 distributions, same mean, with the same Gini of .30 D1: GB2(5.13;1;1;.5) Poverty = .041 Richness = .097 We can show that A lower Gini can go with higher relative poverty rates 0 .5 Density 1 D2: GB2(5.13;1;1;1.5) Poverty = .115 Richness = .0986 0 1 2 3

Annex: My problem with Gini 2 completely different distributions can give the same Gini Index .35 Solution: the Isograph [ STATA ssc install isograph ] Y = ISO D2 = GB2(2.81;1;1;1.5) Poverty = .115 Richness = .0986 .25 .3 (intensity of inequality) D1 = GB2(5.13;1;1;.5) Poverty = .041 Richness = .097 X = Logitrank .2 (fractile on the distribution) -5 0

A1 Bottom 1% 5 Median Top 5% Top 1% ISOGRAPH Chauvel, L. (2016). The intensity and shape of inequality: the ABG method of distributional analysis. Review of Income and Wealth, 62(1), 5268. X is the logit of the rank of socioeconomic order (income quantiles, education, etc.) Y is the log medianized income (divided by the median income) ISO=Y/X is a measure of Level-specific inequalities If ISO= (constant) Champernowne-Fisk (double Pareto) distribution with = Gini (Dagum, 1977) reading the ISOGRAPH (see examples in next slides) l Each point represent ISO at the X (specific-level inequality) l Differences in inequality between levels indicate variation in inequality levels

ISOGRAPH We speak sometimes of meta-Gini for ISO since it is a local measure of the Gini index at a specific level of the distribution The higher ISO, the higher the inequality at this specific level (=stronger stretch of the distribution) X = Logit rank of disposable Income [aggregated in 9 categories ci={-3 to 3}] We compare 3 shapes of distributions: l ISO3 pertains to equivalized disposable income = level of living Y3 = (Log medianized disposable income) ISO3 = Y3 / X l ISO1 pertains to income before taxes and transfers (initial income = labor + capital) ISO1 = (Log medianized initial income by ci group)) / X ; l ISO2 pertains to the difference (effort) ISO2 = ISO1 ISO3 72 dk1987 dk2010 de1978 de2010 fr1978 fr2010 uk1979 uk2010

us1979 us2010 il1979 il2010 .2 .3 .4 .5 .6 .2 .3 .4 .5 .6 ISO3 pertains to equivalized disposable income = level of living -4

-2 0 2 4 -4 -2 0 2 4 -4 -2 0 2 4

Graphs by col 73

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