Chapter 10 The Gender Gap in Earnings: Methods and Evidence regression analysis evidence regression analysis evidence

Regression analysis two variables: X and Y fit a linear relationship  Y = α + β X + u X is independent variable Y is the dependent variable how does a change in X cause Y to change? two variables: X and Y fit a linear relationship  Y = α + β X + u X is independent variable Y is the dependent variable how does a change in X cause Y to change?

Y = α + βX + u get data on Y, X  multiple observations use regression analysis to estimate α and β Y = α + βX + u get data on Y, X  multiple observations use regression analysis to estimate α and β

multiple regression  many independent variables  X1, X2, X3, X4, … each with their own β multiple regression  many independent variables  X1, X2, X3, X4, … each with their own β

to study the earnings gap dependent variable = earnings independent variables:  years of education  years of work experience  race, ethnicity  urban/rural  region of country  gender dependent variable = earnings independent variables:  years of education  years of work experience  race, ethnicity  urban/rural  region of country  gender

estimating β G coefficient on gender if β G < 0  women paid less than men, all else being equal How has β G changed over time? coefficient on gender if β G < 0  women paid less than men, all else being equal How has β G changed over time?

problemsproblems too many X variables  especially those that may reflect discrimination occupation too few X variables  not capturing human capital differences too many X variables  especially those that may reflect discrimination occupation too few X variables  not capturing human capital differences

analyzing gender differences Oaxaca decomposition  two earnings regressions just the males just the females separate earnings difference  “explained”  “unexplained” Oaxaca decomposition  two earnings regressions just the males just the females separate earnings difference  “explained”  “unexplained”

“explained”  caused by skill differences between men and women  would exist w/out any discrimination “unexplained”  caused by differences in return to skills for men vs. women  evidence of discrimination “explained”  caused by skill differences between men and women  would exist w/out any discrimination “unexplained”  caused by differences in return to skills for men vs. women  evidence of discrimination

datadata Census (decennial) Current Population Survey (annual)  CPS Panel Study of Income Dynamics  PSID National Longitudinal Survey of Youth  NLSY Census (decennial) Current Population Survey (annual)  CPS Panel Study of Income Dynamics  PSID National Longitudinal Survey of Youth  NLSY

EvidenceEvidence cross section time series hiring special groups cross section time series hiring special groups

Cross sectional research Corcoran & Duncan (1979)  1970s data, PSID detailed work histories, big differences bet. men & women  44% of wage gap with White women explained 33% w/ Black women Corcoran & Duncan (1979)  1970s data, PSID detailed work histories, big differences bet. men & women  44% of wage gap with White women explained 33% w/ Black women

Blau & Kahn (1997)  gap in 1979, 1988  about 1/3 of gap explained mostly differences in work experience Blau & Kahn (1997)  gap in 1979, 1988  about 1/3 of gap explained mostly differences in work experience

Impact of family status Waldfogel (1998)  1980, 1991  men and women’s earnings are differently affected by family 22% of gap for marriage 40% of gap for children Waldfogel (1998)  1980, 1991  men and women’s earnings are differently affected by family 22% of gap for marriage 40% of gap for children

family gap is the biggest obstacle to earnings equality  men & women are converging in education experience return to human capital family gap is the biggest obstacle to earnings equality  men & women are converging in education experience return to human capital

Time series explain behavior of earnings ratio over time  flat from (60%)  rising from (75%) explain behavior of earnings ratio over time  flat from (60%)  rising from (75%)

O’Neil (1985) s  working women unrepresentative subset of adult women highly educated attached to LF s  working women unrepresentative subset of adult women highly educated attached to LF

entry of women in  pulled down av. education level  pulled down av. experience entry of women in  pulled down av. education level  pulled down av. experience

women’s average skills FELL BUT return to these skills rose,  altogether, the gap stayed constant  the explained portion of the gap increased women’s average skills FELL BUT return to these skills rose,  altogether, the gap stayed constant  the explained portion of the gap increased

Blau & Kahn (1997) 1979, 1988 in general, rising earnings inequality in U.S.  rise in return to skill 1979, 1988 in general, rising earnings inequality in U.S.  rise in return to skill

women “swimming upstream” less human capital than men  the difference is shrinking BUT greater return to HC  women more penalized for having less HC less human capital than men  the difference is shrinking BUT greater return to HC  women more penalized for having less HC

Hiring discrimination audit study  matched pairs of testers (identical except for sex or race), sent for interviews  may find discrimination in hiring, entry wages, but not in raises or promotion audit study  matched pairs of testers (identical except for sex or race), sent for interviews  may find discrimination in hiring, entry wages, but not in raises or promotion

1994 study, U of Penn waiter/waitress jobs high-priced restaurants  48% of men hired, 9% of women low-priced restaurants  10% of men hired, 38% of women waiter/waitress jobs high-priced restaurants  48% of men hired, 9% of women low-priced restaurants  10% of men hired, 38% of women

Orchestra study impact of “blind” auditions on proportion of women hired explains 25% of increase in proportion of women on 8 major orchestras, impact of “blind” auditions on proportion of women hired explains 25% of increase in proportion of women on 8 major orchestras,

Physical appearance Hamermesh & Biddle (1994)  penalty & premium for appearance actually larger for men  “plain” earn 5-10% less  “beautiful earn 5% premium Hamermesh & Biddle (1994)  penalty & premium for appearance actually larger for men  “plain” earn 5-10% less  “beautiful earn 5% premium

Averett & Korenman (1996)  NLSY & impact of obesity  women have 15% penalty lower penalty for men lower penalty for Black women vs. White women Averett & Korenman (1996)  NLSY & impact of obesity  women have 15% penalty lower penalty for men lower penalty for Black women vs. White women

Black vs. White women earnings ratio 85%, 1988 only about 20% of earnings differences are explained strong evidence of discrimination in occupation choice earnings ratio 85%, 1988 only about 20% of earnings differences are explained strong evidence of discrimination in occupation choice

Executive compensation Bertrand & Hallock (2000) compare male & female top executives  very similar is human capital observable and unobservable  earning ratio 67% 71% of this difference is explained Bertrand & Hallock (2000) compare male & female top executives  very similar is human capital observable and unobservable  earning ratio 67% 71% of this difference is explained