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The Gender Gap in Earning: Methods and Evidence

The Gender Gap in Earning: Methods and Evidence. Chapter 10. Regression analysis. Shows relationship between a dependent variable and a set of independent or explanatory variables (or exogenous). Regression analysis. Where Y=earnings and the Xs explanatory variables so that as an example:

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The Gender Gap in Earning: Methods and Evidence

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  1. The Gender Gap in Earning: Methods and Evidence Chapter 10

  2. Regression analysis • Shows relationship between a dependent variable and a set of independent or explanatory variables (or exogenous)

  3. Regression analysis • Where Y=earnings and the Xs explanatory variables so that as an example: • Earning = α + β1 x Years Education + β2 x Years of Work Experience + β3 x Black + β4 x Hispanic + β5 x Asian + β6 x Gender + β7 x North + β8 x West + μ

  4. Regression analysis • Where years education and years work experience are continuous variables • Black, Hispanic, Asian, Male, North, West are dummy variables. • So that for instance: • Black=1 if individual is Black, 0 otherwise (o.w.) • Hispanic = 1 if individual is Hispanic, 0 o.w. • Male = 1 if individual is Male, 0 o.w.

  5. Regression analysis • There must always be n-1 dummy variables. So in the case of regions if the regions are North, West, and South the: • North =1 if individual leaves in the North, 0 o.w. • West = 1 if individual leaves in the West, 0 o.w. • So the variable left out is South

  6. Regression analysis • Oaxaca Decomposition is:

  7. WOMEN MEN Y $25,000 $45,000 X 10 15 2500 3000 EXPLAINED: UNEXPLAIN: A NUMERICAL EXAMPLE OF A OAXACA DECOMPOSITION

  8. A. AVERAGE WAGE RATE AND SKILLS FOR WHITE MEN, WHITE WOMEN, AND BLACK WOMEN SKILL OR CHARACTERISTIC WHITE MEN WHITE WOMEN BLACK WOMEN HOURLY WAGE $5.60 $3.61 $3.17 YEARS OF EDUCATION 12.9 12.7 11.8 WORK HISTORY YEARS NOT IN THE LABOR FORCE YEARS WITH CURRENT EMPLOYER YEARS OF OTHER WORK EXPERIENCE PROPORTION OF YEARS PART-TIME .5 8.8 11.3 9.0% 5.8 5.8 8.1 21.0% 4.0 6.5 9.3 17.4% INDICATORS OF LABOR FORCE ATT. HOURS OF WORKED MISSED BECAUSE OF ILLNESS PLACE LIMITS ON JOB HOURS OR LOCATION 40.5 14.5% 55.5 34.2% 83.7 21.6% EXPLAINING THE GENDER GAP IN EARNINGS, 1976 Table 10.2, p. 372

  9. B.SOURCES OF THE WAGE GAP BETWEEN WHITE AND BLACK WOMEN AND WHITE MEN EXPLAINED YEARS OF EDUCATION WORK HISTORY LABORFORCE ATTACHMENT TOTAL EXPLAINED - - - - 2% 39% 3% 44% 11% 22% 0% 33% UNEXPLAINED - 56% 67% EXPLAINING THE GENDER GAP IN EARNINGS, 1976 Table 10.2, p. 372

  10. VARIABLE CONTRIBUTION TO WAGE GAP EXPLAINED PORTION (%) UNEXPLAINED PORTION (%) HUMAN CAPITAL VAR. YEARS OF WORK EXP. EDUCATION 10 -6 23 13 FAMILY STATUS MARRIED CHILDREN -5 -3 22 40 ALL OTHER VAR. TOTAL -4 -8 10 108 THE IMPACT OF HUMAN CAPITAL AND FAMILY STATUS ON MALE AND FEMALE EARNINGS, 1991 Table 10.3, p. 375

  11. SOURCE OF CHANGE IN GENDER EARNINGS RATIO CONTRIBUTION TO ABSOLUTE CHANGE IN GENDER EARNINGS RATIO TOTAL CHANGE .102 CHANGE IN SKILLS (“EXPLAINED”) EDUCATION WORK EXPERIENCE OCCUPATION/INDUSTRY/ COLLECTIVE BARGANING .006 .035 .042 TOTAL .083 CHANGE IN REWARDS (“UNEXPLAINED”) EDUCATION WORK EXPERIENCE OCCUPATION/INDUSTRY/ COLLECTIVE BARGANING -.001 -.015 -.049 TOTAL -.065 CHANGE IN WAGE STRUCTURE .084 SOURCES OF CHANGE IN GENDER EARNINGS GAP, 1977-1988, FULL TIME, NONAGRICULTURAL WORKERS, AGE 18-65 Table 10.4, p. 383

  12. Estimating Wage Differentials • As mentioned earlier we have discussed that just looking at the mean wage differences is not a accurate difference measurement • The Oaxaca decomposition measures the difference accounted by some exogenous variables

  13. Estimating Wage Differentials • Now lets turn our attention to the how we can more accurately measure the difference in between two groups • We will use: Male (Female), Hispanic, Black, Asian (White), North, South, West (Mid-West) as the dummy variables

  14. Regression • Earning = α + β1 x Years Education + β2 x Years of Work Experience + β3 x Male - β4 x Hispanic - β5 x Black + β6 x Asian + β7 x North - β8 x South + β9 x West + μ

  15. Regression • Where after estimating the coefficients we obtain the following result: • weekly wage = 100 + 5*(years of education) + 40*(years of experience) + 15*(Male) -75*(Hispanic) - 80*(Black) + 90*(Asian) + 60*(North) - 50*(South) + 40*( West)

  16. Regression • where • Male= 1 if male, 0 if female • Hispanic= 1 if hispanic, 0 otherwise • Black= 1 if black, 0 otherwise • North =1 if individual lives in the N, 0 otherwise • South=1 if individual lives in the South, 0 otherwise • North =1 if individual lives in the N, 0 otherwise

  17. 5 Different Average Individuals • i)a White male, 12 years of education, with 5 years of experience, and living in the North. • ii)a White female, 12 years of education, with 5 years of experience, and living in the South. • iii)a Hispanic male, 12 years of education, with 5 years of experience, and living in the West. • iv)a Black male, 12 years of education, with 5 years of experience, and living in the Mid-West. • v) a Black female, 12 years of education, with 5 years of experience, and living in the South.

  18. Estimated Wages Are: • Individual 1: 435 • 435 = 100 + 5*(12) + 40*(5) + 15*(1) -75*(0) - 80*(0) + 90*(0) + 60*(1) - 50*(0) + 40*(0) • Individual 2: 310 • 310 = 100 + 5*(12) + 40*(5) + 15*(0) -75*(0) - 80*(0) + 90*(0) + 60*(0) - 50*(1) + 40*(0)

  19. Estimated Wages Are: • Individual 3: 340 • 340 = 100 + 5*(12) + 40*(5) + 15*(1) -75*(1) - 80*(0) + 90*(0) + 60*(0) - 50*(0) + 40*(1) • Individual 4: 295 • 295 = 100 + 5*(12) + 40*(5) + 15*(1) -75*(0) - 80*(1) + 90*(0) + 60*(0) - 50*(0) + 40*(0)

  20. Estimated Wages Are: • Individual 5: 230 • 230 = 100 + 5*(12) + 40*(5) + 15*(0) -75*(0) - 80*(1) + 90*(0) + 60*(0) - 50*(1) + 40*(0)

  21. Compare Wages Holding Other Factors Constant • If We use Individual 1 as the comparison group, then: • Individual 2 earns 71 cents to $1 of individual 1 (I.e. 310/435) • Individual 3 earns 78 cents to $1of individual 1 • Individual 4 earns 68 cents to $1of individual 1 • Individual 5 earns 53 cents to $1of individual 1

  22. Measuring DiscriminationGender Wage Ratio

  23. PERCENT ADVANCED-PRELIMINARY ROUND BLIND NOT BLIND WOMEN 28.6% 19.3% MEN 20.2% 22.5% DIFFERENCE (% WOMEN ADVANCED - % MEN ADVANCED) 8.4% -3.2% DIFFERENCE IN DIFFERENCE 11.6% PERCENT ADVANCED-SEMIFINAL ROUND WOMEN 38.5% 56.8% MEN 36.8% 29.5% DIFFERENCE (% WOMEN ADVANCED - % MEN ADVANCED) 1.7% 27.3% DIFFERENCE IN DIFFERENCE -25.6% RESULT OF BLIND AUDITIONS ON ADVANCEMENT TO NEXT AUDITION ROUND Table 10.5, p. 389

  24. PERCENT ADVANCED-FINAL ROUND BLIND NOT BLIND WOMEN 23.5% 8.7% MEN 0% 13.3% DIFFERENCE (% WOMEN ADVANCED - % MEN ADVANCED) 23.5% -4.6% DIFFERENCE IN DIFFERENCE 28.1% PERCENT HIRED WOMEN 2.7% 1.7% MEN 2.6% 2.7% DIFFERENCE (% WOMEN ADVANCED - % MEN ADVANCED) 0.1% -1.0% DIFFERENCE IN DIFFERENCE 1.1% RESULT OF BLIND AUDITIONS ON ADVANCEMENT TO NEXT AUDITION ROUND Table 10.5, p. 389

  25. Discrimination on The basis of Beauty • Hamermesh and Biddle (1994) suggest that there is a selection criteria that seems to set “more attractive” people into job occupations where their “beauty” makes them more productive. For instance, jobs that interact with the public more

  26. Discrimination on The basis of Beauty • Averett and Korenman (1996) suggest that individuals with higher body mass index than the recommended range had lower wage than those with the recommend ranges. It is interesting that women had 15% lower wage and men about half that.

  27. Discrimination on The basis of Beauty • Averett and Korenman (1996) (cont.) • Also, while men under the recommend range experienced earning penalties the women did not. • Finally, obesity penalties were larger for White women than for Black women

  28. 105% 100% 95% 90% 85% 80% 75% 1980 1985 1990 1995 2000 RATIO OF BLACK TO WHITE FEMALE MEDIAN EARNINGS, YEAR-ROUND FULL TIME WORKERS, 1980-2001 Figure 10.1, p. 393

  29. TITLE % FEMALE CEO/CHAIR .52 VICE CHAIR .85 PRESIDENT 1.71 CFO 6.44 COO 1.836 EXEC. VP 1.58 OTHER CHIEF OFFICER 2.66 SENIOR VICE PRESIDENT 3.45 GROUP VICE PRESIDENT .81 VICE PRESIDENT 4.27 OTHER OCCUPATIONS 2.88 PERCENT FEMALE IN VARIOUS CORPORATE POSITIONS Table 10.6, p. 396

  30. Is there Discrimination in a Name • The Causes and Consequences of Distinctively Black Names • By • Roland G. Fryer and Steven D. Levitt • NBER Working paper # 9938 • 2003

  31. Black Name Index

  32. Black Name Index • Such that • BNI = 0 if only White Kids receive this name • BNI = 100 if only Black Kids receive this name

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