What is discrimination?

Consider the case of a taxi driver who is driving down a street at night in a dangerous part of town and is hailed simultaneously by people on each side of the street. It is equally easy for him to pick up either customer. On the left side of the street is a little old lady. On the right side is a tall African-American teenage boy wearing a hood. The taxi driver unhesitatingly chooses the little old lady over the teenager. Did he discriminate? Is he prejudiced?

“Some cabdrivers and taxi industry representatives assert that cab drivers bypassing blacks is not prejudice, but largely a precautionary response to cab driver concerns about being robbed. But black Americans know from bitter experience that it happens all too often to all sorts of African Americans and Hispanic Americans---males and females, young and old, those dressed casually and those dressed in business and even evening wear---who clearly have nothing more in mind than using a taxi to get where they're going. They know that all too often it's not the cab driver's fear of being robbed, it's the cab driver's prejudice that's responsible.”

—Hugh B. Price, President, National Urban League

• Discrimination: Treating people differently on the basis of their membership in a class– Discrimination need not imply racism, sexism, or prejudice.

• Prejudice: dislike, distaste, or misperception based on innate characteristics such as race or sex– Prejudice can but need not generate discrimination.

Employer decisions that treat identical workers or applicants differently because of characteristics unrelated to their individual productivity.

What is labor market discrimination?

Panera’s Bread: “If you pay attention to the girls waiting on you at the registers, though, you might notice some trends: first, they’re all girls between the ages of 16 and 24. Second, with rare exception, they’re all white.”

—Anna Walsh, “Panera Bread’s racist, sexist practices warrant boycott,” The Tartan, 12/5/2011

Wage discrimination: paying women with identical qualifications less.

Occupational discrimination: shunting women into particular occupations.

“[W]hen I got out of college, they still had ads that were segregated by sex—jobs would say… Jobs Male… Jobs Female… And there was still an incredible amount of sexism in the journalism world when I got out. There were very few women. I was one of the first women in the sort of wave of women who went into journalism in the seventies. And I was part of a sex discrimination suit against the place that I worked at in New Haven because we discovered that the women and the men had pretty much the same education and experience and we were making like a third less.”

Trish Hall, Op-Ed editor of

The New York Times.

Sample from 1980 Census, when Trish would have been roughly 30 years old.

Sample Selection Criteria:

• Editor or Reporter (1980 occupation=195)• Employed by Newspaper (1980 industry=171)

• Worked more than half-time (more than 20 hours per week & 26 weeks per year)

• Wage per hour

Measuring Labor Market Discrimination

Empirical Challenge—how can we separate wage differences due to discrimination from those due to differences in productivity created by different levels of training, education and experience, i.e., human capital?

Male Sample Female Sample

Wage 9.52 6.92lnWage 2.16 1.84Education 15.62 15.31Age 38.71 37.03Sample Size (N) 1658 761

Table 1. Means of Dependent & Explanatory Variables

(𝑊 𝑓

𝑊𝑚)

1980

=6.929.52

=0.727Female journalists earned, on average, 72.7 cents per hour for every dollar earned by male journalists. Their hourly wage was 27.3 % less than male journalists, on average.

( 𝑁 𝑓

𝑁𝑚)

1980

= 7611658

=.315Less than a third of women were journalists

Button from 1970 worn by people protesting pay disparity between women and men

𝑊=9.520 − 2.604𝐹𝑒𝑚𝑎𝑙𝑒(0.181 )

𝑊=𝛽0+𝛽1 𝐹𝑒𝑚𝑎𝑙𝑒+𝜇

𝑙𝑛𝑊=2.155− 0.314𝐹𝑒𝑚𝑎𝑙𝑒(0.019 )

𝑙𝑛𝑊=𝛾0+𝛾 1𝐹𝑒𝑚𝑎𝑙𝑒+𝑣

Female journalists earned, on average, 31.4% less than male journalists, on average.

But, perhaps, it is because male journalists have more experience or education than female journalists

𝑙𝑛𝑊=1.163+0.032𝐸𝑑+0.0126 𝐴𝑔𝑒−0.283 𝐹𝑒𝑚𝑎𝑙𝑒(0.0042 ) (0.0008 ) (0.0182 )

𝑙𝑛𝑊=𝛼0+𝛼1𝐸𝑑+𝛼2 𝐴𝑔𝑒+𝛼3 𝐹𝑒𝑚𝑎𝑙𝑒+𝜀

Interpretation: The wages of female journalists in 1980 were 28.3% lower than their male colleagues, on average, holding age and education constant.

Method is called “Decomposition of wage differentials”

What portion of the wage gap is due to human capital factors, i.e., to differences in productivity?

The portion of the wage gap not explained by these differences is attributed to discrimination.

Limitations of the wage gap of 28.3% as evidence of discrimination.

1. Not directly linked to discriminatory behavior—it is an unexplained difference between males and females. It may be biased due to omitted variables.

Wood, Corcoran and Courant (1993)

Sample of Michigan Law School Grads of 1972-75.

Advantages

Disadvantage:

𝑙𝑛𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠=⋯+𝛽 𝑓 𝑓𝑒𝑚𝑎𝑙𝑒⋯

Three measures of academic performance

Grade Point Average (GPA)Dummy variable for whether they participated in moot court.Dummy variable for whether they were are the staff of the law review.

Sample is not representative of the general population

Female Male

Hours 2280 2510

Wage (1989$) 37.14 55.21

Months—Part-time for kids 10.07 0.12

Gov. Lawyer .136 .047

Big Firm Lawyer .123 .273

Sample Means Fifteen Years After Graduation

𝑙𝑛𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠=⋯+𝛽 𝑓 𝑓𝑒𝑚𝑎𝑙𝑒⋯

Explanatory Variables

None – 0.485

Hours, exp, ed – 0.170

Hours, exp, ed, job-type – 0.124

Women

Litigation

Trusts

25%

100%

240

180

8 16

Earnings(thou. of $ per year)

Unplanned Absences (days per year)

𝝅𝑺

𝑼𝑴

𝑼𝑪

C—CharlesM—Michelle

240

180

8 16

Earnings(thou. of $ per year)

Unplanned Absences (days per year)

𝝅𝑺

𝑼𝑴

𝑼𝑪

𝑼𝑴2 1

A

ΔEM

ΔEC

ΔA = –1

Earnings(thou. of $ per year)

Unplanned Absences (days per year)

𝑼𝑴

𝑼𝑪

A

240

180

8 16

Earnings(thou. of $ per year)

Unplanned Absences (days per year)

𝝅𝑺

𝑼𝑴

𝑼𝑴2

1

Limitations of the wage gap of 28.3% as evidence of discrimination.

2. Assumes that Education and Experience are not affected by discrimination. (Story about female funeral directors struggles to find apprenticeships.)

“The observed productive characteristic that contributes most of the wage gap between women and men in the same occupation is labor market experience” (Ehrenberg & Smith, p. 404)

3. Assumes that the return to education and experience is the same for both groups. Story about the black cotton-picker.

𝑙𝑛𝑊=0.714+0.0600𝐸𝑑+0.0057 𝐴𝑔𝑒Women

Men 𝑙𝑛𝑊=1.258+0.0178𝐸𝑑+0.0160 𝐴𝑔𝑒

(0.0077 ) (0.0015 )

(0.0049 ) (0.0009 )

Question: why were women journalists getting a lower return from experience and a higher one from education?

Lets Ignore Ed temporarily

𝑙𝑛𝑊𝑚=𝛽0𝑚+𝛽1𝑚 𝐴𝑚

𝑙𝑛𝑊 𝑓=𝛽0 𝑓+𝛽1 𝑓 𝐴𝑓

It must be true that:

𝑙𝑛𝑊𝑚=𝛽0𝑚+𝛽1𝑚 𝐴𝑚

𝑙𝑛𝑊 𝑓=𝛽0 𝑓+𝛽1 𝑓 𝐴𝑓

𝑙𝑛𝑊𝑚− 𝑙𝑛𝑊 𝑓= (𝛽0𝑚− 𝛽0 𝑓 )+𝛽1𝑚𝐴𝑚− 𝛽1 𝑓 𝐴 𝑓

Add and subtract from the RHS

Taking the diff:

𝑙𝑛𝑊𝑚− 𝑙𝑛𝑊 𝑓= (𝛽0𝑚− 𝛽0 𝑓 )+𝛽1𝑚𝐴𝑚− 𝛽1𝑚 𝐴𝑓 −𝛽1 𝑓 𝐴 𝑓+𝛽1𝑚 𝐴 𝑓

¿ ( 𝛽0𝑚− 𝛽0 𝑓 )+𝛽1𝑚 (𝐴𝑚−𝐴 𝑓 )+𝐴 𝑓 ( 𝛽1𝑚− 𝛽1 𝑓 )

𝑙𝑛𝑊𝑚− 𝑙𝑛𝑊 𝑓= (𝛽0𝑚− 𝛽0 𝑓 )+𝛽1𝑚( 𝐴𝑚− 𝐴𝑓 )+𝐴 𝑓 (𝛽1𝑚− 𝛽1 𝑓 )

where

= the difference in the average levels of experience—human capital—between men and women

= the differences in which the average levels of human capital are rewarded in the male and female wage equations.

Often called… “unjustified discrimination”

𝑙𝑛𝑊=0.714+0.0600𝐸𝑑+0.0057 𝐴𝑔𝑒Women

Men 𝑙𝑛𝑊=1.258+0.0178𝐸𝑑+0.0160 𝐴𝑔𝑒

(0.0077 ) (0.0015 )

(0.0049 ) (0.0009 )

3. Assumes that the return to education and experience is the same for both groups. Story about the black cotton-picker.

Question: why were women journalists getting a lower return from experience and a higher one from education?

Focus on the relationship between wages and experience:

𝑙𝑛𝑊= (𝛽0+𝛽2 𝐸𝑑 )+𝛽1 𝐴𝑔𝑒

𝑙𝑛𝑊=1.633+0.0057 𝐴𝑔𝑒Women

Men 𝑙𝑛𝑊=1.536+0.0160 𝐴𝑔𝑒

0.714+(0.0600 ∙ 15.31 )

1.258+ (0.0178 ∙15.62 )

25 45 651.7

2.5Male wage equation

Female wage

equation

Slope =

Slope =

𝐴 𝑓𝐴𝑚

𝑙𝑛𝑊= �̂�0 𝑓+ �̂�1 𝑓 𝐴 𝑓

A

B

C

𝑙𝑛𝑊= �̂�0𝑚+ �̂�1𝑚 𝐴 𝑓

𝑙𝑛𝑊= �̂�0𝑚+ �̂�1𝑚 𝐴𝑚

Distance

AB ( �̂�0𝑚+ �̂�1𝑚𝐴𝑚)− ( �̂�0𝑚+ �̂�1𝑚 𝐴 𝑓 )=�̂�0𝑚 (𝐴𝑚− 𝐴𝑓 )

( �̂�0𝑚+ �̂�1𝑚𝐴 𝑓 )− ( �̂�0 𝑓 + �̂�1 𝑓 𝐴 𝑓 )=( �̂�0𝑚− �̂�0 𝑓 )+ 𝐴𝑓 ( �̂�1𝑚− �̂�1 𝑓 )BC

Part of wage gap due to differences in human capital

Part of wage gap due to “unjustified discrimination”

Distance

AB ( �̂�0𝑚+ �̂�1𝑚𝐴𝑚)− ( �̂�0𝑚+ �̂�1𝑚 𝐴 𝑓 )=�̂�1𝑚 (𝐴𝑚−𝐴 𝑓 )

( �̂�0𝑚+ �̂�1𝑚𝐴 𝑓 )− ( �̂�0 𝑓 + �̂�1 𝑓 𝐴 𝑓 )=( �̂�0𝑚− �̂�0 𝑓 )+ 𝐴𝑓 ( �̂�1𝑚− �̂�1 𝑓 )BC

Part of wage gap due to differences in human capital

Part of wage gap due to “unjustified discrimination”

AB

BC

𝑙𝑛𝑊=1.633+0.0057 𝐴𝑔𝑒Women

Men 𝑙𝑛𝑊=1.536+0.0160 𝐴𝑔𝑒

25 45 651.7

2.5Male wage equation

Female wage

equation

𝐴 𝑓𝐴𝑚

A

B

C

�̂� 𝑓 𝑚

�̂�𝑚𝑚

0.280

0.027

�̂� 𝑓𝑓

Interpretation of the Graph

• Male wage function is steeper and thus shows a greater payoff for additional investments in experience

• and represent average levels of experience for males and females respectively

• is the expected wage of males, i.e., it is the predicted conditional mean.

• is the expected wage of females, assuming that their experience was rewarded like men.

• is the expected wage of females.

Allowing the returns to experience and education to vary across the genders, women journalists in 1980 earned 30.7% less than men, on average, holding education constant. Part of the reason that the gap is so big is that women appear to have substituted education for experience. 8.8% of the gap ((.027/.307)*100) is due to the difference in the mean amount of experience and the rest 91.2% is due to their different returns to education—what some economists call unjustified discrimination.

Difference Rate of Return to Human Capital

Wage Gap ()

Unconditional Means 72.7%

Conditional Means across genders 71.7%

Conditional Means across genders 72.0%

Hence, we have produced three estimates of the wage gap between male and female journalists in 1980: (1) difference in sample means, i.e., unconditional means; (2) difference in conditional means, assuming that the rate of return to education and experience is the same for men and women; and (3) difference in conditional means allowing the rates of returns to differ across men and women.

Differences in labor market preferences across genders?

• Tastes for parenting• Physical attributes• Societal and parental expectations—How are these formed?– Past discrimination – Anticipated discrimination (Statistical discrimination

becomes a self-fulfilling prophecy.)

Is the wage gap changing over time?

Sample from 2000 Census, when Trish would have been roughly 50 years old.

Sample Selection Criteria:

• Editor (2000 code=283) or Reporter (281)• Employed by Newspaper (2000 industry=647)

• Worked more than half-time (more than 20 hours per week & 26 weeks per year)

• Wage per hour

Male Sample Female Sample

Wage 21.43 17.54lnWage 2.91 2.71Education 15.66 15.56Age 41.92 40.63Sample Size (N) 1740 1330

Table 1. Means of Dependent & Explanatory Variables

𝑙𝑛𝑊=1.177+0.073𝐸𝑑+0.0141 𝐴𝑔𝑒−0.174 𝐹𝑒𝑚𝑎𝑙𝑒(0.0052 ) (0.0009 ) (0.0181 )

𝑙𝑛𝑊=𝛼0+𝛼1𝐸𝑑+𝛼2 𝐴𝑔𝑒+𝛼3 𝐹𝑒𝑚𝑎𝑙𝑒+𝜀

𝑙𝑛𝑊=0.880+0.0965𝐸𝑑+0.0081 𝐴𝑔𝑒Women

Men 𝑙𝑛𝑊=1.363+0.0461𝐸𝑑+0.0197 𝐴𝑔𝑒

(0.0076 ) (0.0014 )

(0.0071 ) (0.0012 )

Difference Rate of Return to Human Capital

Wage Gap ()

1980 2000

Unconditional Means

72.7%

Conditional Means

across genders 71.7%

Conditional Means

across genders 72.0%

1950 1960 1970 1980 1990 2000 2010 20200

10

20

30

40

50

60

Source: U.S. Census Bureau, Historical Income Tables, Table P-38. Full-Time, Year-Round Workers by Median Earnings and Sex

Median Annual Earnings of Full-Time Workers by Gender, 1960-2010

Earnings(thou. of 2010$)

Male

Female

1950 1960 1970 1980 1990 2000 2010 20200

50

100

Gender Earnings Gap for Full-Time, Full-Year Workers, 1960-2010

𝑬 𝒇𝒆𝒎𝒂𝒍𝒆

𝑬𝒎𝒂𝒍𝒆

∙𝟏𝟎𝟎

60.7

77.4

All Measures of the Gender Gap are Narrowing

Why is the gender earnings gap closing?

• Increases in labor market experience of women• Increases in the education of women.

• Decreases in unionization• Increases in the demand for intellectual skills relative

to physical strength. • Shifts of women into higher paying occupations• Decreases in discrimination?