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Chapter 10 The Gender Gap in Earnings: Methods and Evidence

Chapter 10 The Gender Gap in Earnings: Methods and 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?.

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Chapter 10 The Gender Gap in Earnings: Methods and Evidence

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

  2. 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?

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

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

  5. 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

  6. estimating βG • coefficient on gender • if βG < 0 • women paid less than men, all else being equal • How has βG changed over time?

  7. problems • too many X variables • especially those that may reflect discrimination • occupation • too few X variables • not capturing human capital differences

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

  9. “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

  10. data • Census (decennial) • Current Population Survey (annual) • CPS • Panel Study of Income Dynamics • PSID • National Longitudinal Survey of Youth • NLSY

  11. Evidence • cross section • time series • hiring • special groups

  12. 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

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

  14. 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

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

  16. Time series • explain behavior of earnings ratio over time • flat from 1960-80 (60%) • rising from 1980-95 (75%)

  17. O’Neil (1985) • 1955-82 • 1950s • working women unrepresentative subset of adult women • highly educated • attached to LF

  18. entry of women in 1960-80 • pulled down av. education level • pulled down av. experience

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

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

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

  22. 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

  23. 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

  24. Orchestra study • impact of “blind” auditions on proportion of women hired • explains 25% of increase in proportion of women on 8 major orchestras, 1970-96

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

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

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

  28. 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

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