the gender gap in earning: methods and evidence
DESCRIPTION
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: - PowerPoint PPT PresentationTRANSCRIPT
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The Gender Gap in Earning: Methods and Evidence
Chapter 10
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Regression analysis
Shows relationship between a dependent variable and a set of independent or explanatory variables (or exogenous)
iiiii XXXY ....332211
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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
iiiii XXXY ....332211
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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.
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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
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Regression analysis
Oaxaca Decomposition is:
)(ˆ averagerepresentsbarthewhereXY MMM XY ̂
FFF XY ̂
FMFM
FFMMFM
XX
offormtheinsidehandrightthetozeroaddnext
XXYY
ˆˆ
]*)ˆˆ[()](*ˆ[ FFMFMMFM XXXYY
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A NUMERICAL EXAMPLE OF A OAXACA DECOMPOSITION
%75000,15$)1015(*3000$)(*ˆ orXX FMM
%255000$10*)25003000(*)ˆˆ( orX FFM
WOMEN MEN
Y $25,000 $45,000
X 10 15
2500 3000
EXPLAINED:
UNEXPLAIN:
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EXPLAINING THE GENDER GAP IN EARNINGS, 1976
Table 10.2, p. 372
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 HISTORYYEARS NOT IN THE LABOR FORCEYEARS WITH CURRENT EMPLOYERYEARS OF OTHER WORK EXPERIENCEPROPORTION OF YEARS PART-TIME
.5
8.811.3
9.0%
5.85.88.1
21.0%
4.06.59.3
17.4%
INDICATORS OF LABOR FORCE ATT.HOURS OF WORKED MISSED BECAUSE OF ILLNESSPLACE LIMITS ON JOB HOURS OR LOCATION
40.5
14.5%
55.5
34.2%
83.7
21.6%
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EXPLAINING THE GENDER GAP IN EARNINGS, 1976
Table 10.2, p. 372
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%
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THE IMPACT OF HUMAN CAPITAL AND FAMILY STATUS ON MALE AND FEMALE EARNINGS, 1991
Table 10.3, p. 375
VARIABLE
CONTRIBUTION TO WAGE GAP
EXPLAINED PORTION (%)
UNEXPLAINED PORTION (%)
HUMAN CAPITAL VAR. YEARS OF WORK EXP. EDUCATION
10-6
2313
FAMILY STATUS MARRIED CHILDREN
-5-3
2240
ALL OTHER VAR. TOTAL
-4
-8
10
108
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SOURCES OF CHANGE IN GENDER EARNINGS GAP, 1977-1988, FULL TIME, NONAGRICULTURAL
WORKERS, AGE 18-65Table 10.4, p. 383
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
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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
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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
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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
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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)
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Regression
where Male= 1 if male, 0 if femaleHispanic= 1 if hispanic, 0 otherwiseBlack= 1 if black, 0 otherwiseNorth =1 if individual lives in the N, 0 otherwiseSouth=1 if individual lives in the South, 0 otherwiseNorth =1 if individual lives in the N, 0 otherwise
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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.
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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)
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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)
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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)
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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
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Measuring DiscriminationGender Wage Ratio
0102030405060708090
UnadjustedData
Human Capital
All Adjusments
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RESULT OF BLIND AUDITIONS ON ADVANCEMENT TO NEXT AUDITION ROUND
Table 10.5, p. 389
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 ROUNDWOMEN 38.5% 56.8%
MEN 36.8% 29.5%
DIFFERENCE (% WOMEN ADVANCED - % MEN ADVANCED)
1.7% 27.3%
DIFFERENCE IN DIFFERENCE -25.6%
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RESULT OF BLIND AUDITIONS ON ADVANCEMENT TO NEXT AUDITION
ROUNDTable 10.5, p. 389
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 HIREDWOMEN 2.7% 1.7%
MEN 2.6% 2.7%
DIFFERENCE (% WOMEN ADVANCED - % MEN ADVANCED)
0.1% -1.0%
DIFFERENCE IN DIFFERENCE 1.1%
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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
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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.
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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
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RATIO OF BLACK TO WHITE FEMALE MEDIAN EARNINGS, YEAR-ROUND FULL TIME WORKERS,
1980-2001Figure 10.1, p. 393
105%
100%
95%
90%
85%
80%
75%
1980 1985 1990 1995 2000
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PERCENT FEMALE IN VARIOUS CORPORATE POSITIONS
Table 10.6, p. 396TITLE % 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
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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
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Black Name Index
),Pr(),Pr(
),Pr(,
tWhitenametBlackname
tBlacknameBNI tName
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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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