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)

iiiii XXXY ....332211

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


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.

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

Regression analysis

Oaxaca Decomposition is:

)(ˆ averagerepresentsbarthewhereXY MMM XY ̂

FFF XY ̂

FMFM

FFMMFM

XX

offormtheinsidehandrightthetozeroaddnext

XXYY

ˆˆ

]*)ˆˆ[()](*ˆ[ FFMFMMFM XXXYY

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:

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%

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%

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

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

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

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

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


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)

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

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.

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)


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)


Individual 5: 230

230 = 100 + 5*(12) + 40*(5)

+ 15*(0) -75*(0) - 80*(1) + 90*(0) + 60*(0) - 50*(1) + 40*(0)

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



Measuring DiscriminationGender Wage Ratio

0102030405060708090

UnadjustedData

Human Capital

All Adjusments

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%


1.7% 27.3%

DIFFERENCE IN DIFFERENCE -25.6%

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%


23.5% -4.6%


PERCENT HIREDWOMEN 2.7% 1.7%

MEN 2.6% 2.7%


0.1% -1.0%


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


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.


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

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

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

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

Black Name Index

),Pr(),Pr(

),Pr(,

tWhitenametBlackname

tBlacknameBNI tName

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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The Gender Gap in Earning: Methods and Evidence