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Empirical Approaches to Assessing Discrimination

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Empirical Approaches to Assessing Discrimination While racial wage and income inequality fell drastically in the

middle of the 20th century, convergence slowed way down in the last three decades.

On average, blacks still earn 28 – 37% percent less per hour than similarly aged whites.

Put another way, black workers earn roughly 70 cents on each dollar white workers earn.

This reveals that discrimination in the labor market is exceedingly large, right?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets Suppose we regressed the log of each individual’s wage (in logs) on

an indicator for the individual being black and controls for each individual’s age.

ln(wagei) = α + β1*blacki + β2*agei + εi

What should β1 roughly equal? How should we interpret it? What else should we do if we want to assess the extent of discrimination?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) Suppose we further added in each person’s years of education

ln(wagei) = α + β1*blacki + β2*agei + β3*yrs of schooli+ εi

β1 = -0.22 (s.e. 0.016)

How do you interpret this change in β1 ? What still might be a concern if we want to use this as an estimate of

the extent of discrimination?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) One of the most used datasets in labor economics is the National

Longitudinal Survey of Youth (NLSY) Randomly sampled over 12,000 youth ages 12-19 in 1979. Asked them an extensive battery of questions every year since. Also, almost all of them were given the Armed Forces Qualification

Test (AFQT), an academic skills assessment.

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) Suppose we further added in each person’s AFQT score

ln(wagei) = α + β1*blacki + β2*agei + β3*yrs of schooli + β4*AFQTi+ εi

β1 = -0.10 (s.e. 0.019)

How do you interpret this change in β1 ?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

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-3 -2 -1 0 1 2AFQT Score

Distribution of AFQT Scores - Black

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sity

-3 -2 -1 0 1 2AFQT Score

Distribution of AFQT Scores - Non-Black

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

0.2

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ensi

ty

-3 -2 -1 0 1 2AFQT Score

Distribution of AFQT Scores - Non-Black High School Only

0.2

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ensi

ty

-3 -2 -1 0 1 2AFQT Score

Distribution of AFQT Scores - Black High School Only

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

Table 1: Random Effects GLS Log Wage Regressions(whole labor market)

Conditioning Variable (1) (2) (3) (4)black -0.28*** -0.22*** -0.08*** -0.10***

(0.017) (0.016) (0.018) (0.019)AFQT score - - 0.19*** 0.09***

(0.009) (0.011)AFQT score squared - - 0.01 -0.01*

(0.007) (0.008)age 0.02*** 0.02*** 0.02*** 0.02***

(0.004) (0.003) (0.004) (0.003)12 years or more of educ. - 0.16*** - 0.07***

(0.022) (0.023)14 years or more of educ. - 0.15*** - 0.08***

(0.020) (0.020)

16 years or more of educ. - 0.23*** - 0.17***(0.023) (0.024)

controls for year of observation yes yes yes yescontrols for region of country no yes yes yes

controls for parent's education no no no yesnumber of observations 7,749number of individuals 3,059

Specification

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) Neal and Johnson (1996)

“While we find some evidence of labor market discrimination, we conclude that the disadvantages young black workers now face in the labor market arise mostly from obstacles they faced as children in acquiring productive human capital. Our analysis suggests that public policy should focus on the plight of black children in acquiring skills valued by the labor market.”

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) While racial gap in pre-market academic skills do seem to explain a

large part of the current racial wage gap, a significant unexplained portion still remains. Is it due to discrimination? How could we test this?

Given our theories, in what type of labor markets can discrimination more easily persist?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) What if we divide labor market up into:

White-collar (highly academically skill intensive jobs)

Blue-collar (less academically skill intensive jobs)

Then run similar regressions to before? Why do this? What is hypothesis being tested?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) White-collar

Table 3: Random Effects GLS Log Wage Regressions by Occupation Sector (White Collar Jobs)

Conditioning Variable (1) (2) (3) (4) (5) (6)black -0.14*** 0.01 -0.16** 0.01 -0.17*** -0.01

(0.045) (0.050) (0.067) (0.072) (0.035) (0.038)AFQT score - 0.09** - 0.16*** - 0.11***

(0.037) (0.042) (0.024)AFQT score squared - 0.03 - 0.02 - 0.01

(0.025) (0.033) (0.017)age 0.02** 0.02*** 0.06*** 0.04*** 0.03*** 0.03***

(0.009) (0.008) (0.012) (0.012) (0.007) (0.006)12 years or more of educ. - 0.24* - -0.01 - 0.08

(0.140) (0.138) (0.088)14 years or more of educ. - 0.1 - 0.05 - 0.11***

(0.062) (0.067) (0.041)16 years or more of educ. - 0.20*** - 0.09 - 0.17***

(0.045) (0.070) (0.035)controls for year of observation yes yes yes yes yes yescontrols for region of country no yes no yes no yescontrols for parent's education no yes no yes no yesnumber of observations 1,225 1,225 386 386 2,029 2,029number of individuals 622 622 278 278 1040 1040

Specification

Note: The sample used is a subset of sample used for Table 1 results. Standard errors are in parentheses. One asterisk indicates significance at 10 percent level, two asterisks indicate significance at 5 percent level, three asterisks indicated significance at 1 percent level.

Overall White CollarMangerialProfessional

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.) Blue-collar

Table 4: Random Effects GLS Log Wage Regressions by Occupation Sector (Blue Collar Jobs)

Conditioning Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)black -0.26*** -0.12** -0.24*** -0.09** -0.17*** -0.08** -0.18*** -0.09** -0.24*** -0.12***

(0.042) (0.052) (0.03) (0.034) (0.028) (0.034) (0.035) (0.043) (0.018) (0.021)AFQT score - 0.11*** - 0.06*** - 0.01 - 0.02 - 0.07***

(0.030) (0.019) (0.021) (0.031) (0.012)AFQT score squared - -0.05** - -0.03* - -0.03** - -0.04** - -0.03***

(0.024) (0.015) (0.015) (0.020) (0.009)age 0.03*** 0.02* 0.02*** 0.02*** 0.02*** 0.02*** 0.02*** 0.02*** 0.02*** 0.01***

(0.009) (0.009) (0.006) (0.006) (0.006) (0.006) (0.008) (0.008) (0.004) (0.004)12 years or more of educ. - 0.1 - 0.10*** - 0.10*** - 0.07 - 0.07***

(0.089) (0.037) (0.036) (0.044) (0.023)14 years or more of educ. - 0.05 - 0.04 - 0.04 - 0.07 - 0.07***

(0.048) (0.034) (0.04) (0.056) (0.022)16 years or more of educ. - 0.04 - 0.07 - 0.01 - -0.06 - 0.04

(0.056) (0.067) (0.082) (0.102) (0.034)controls for year of observation yes yes yes yes yes yes yes yes yes yescontrols for region of country no yes no yes no yes no yes no yescontrols for parent's education no yes no yes no yes no yes no yesnumber of observations 622 622 1,667 1,667 1,475 1,475 911 911 5,720 5,720number of individuals 410 410 967 967 875 875 640 640 2,431 2,431

Note: The sample used is a subset of sample used for Table 1 results. Standard errors are in parentheses. One asterisk indicates significance at 10 percent level, two asterisks indicate significance at 5 percent level, three asterisks indicated significance at 1 percent level.

SpecificationClerical Craftsmen Overall Blue CollarOperators Laborers

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

If there is indeed less discrimination in the white-collar sector, what would that imply about behavior of workers in career selection?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

Table 8: Random Effects Probit Analysis of Probability of Working in White-Collar Occupation

Conditioning Variable [1] [2] [3] [4]black -1.35*** -0.51*** 0.43*** -0.03

(0.010) (0.114) (0.120) (0.111)AFQT score - - 1.29*** 0.61***

(0.060) (0.060)AFQT score squared - - 0.25*** 0.07

(0.046) (0.044)age 0.05*** 0.05*** 0.04*** 0.03***

(0.012) (0.013) (0.012) (0.012)lived with both parents at 14 - 0.12 0.08 0.04

(0.133) (0.130) (0.119)mother high school grad - 0.77*** 0.41*** 0.27**

(0.121) (0.119) (0.109)mother college grad - 0.68*** 0.37** 0.08

(0.179) (0.172) (0.156)mother professional - 0.48*** 0.30** 0.19

(0.149) (0.144) (0.130)father high school grad - 0.74*** 0.43*** 0.29***

(0.122) (0.120) (0.110)father college grad - 0.77*** 0.47*** 0.13

(0.153) (0.148) (0.134)father professional - 0.95*** 0.61*** 0.38***

(0.129) (0.125) (0.114)controls for year of observation yes yes yes yescontrols for region of country no yes yes yescontrols for own years of educ. no no no yeslog likelihood -4,740 -4,435 -4,162 -3,942

Dependent variable equals 1 if workerworks in Professional or Managerial Occupation

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

If statistical discrimination is key factor regarding the greater conditional wage gap in the blue-collar sector relative to the white-collar sector, what must be true?

So what else should we see?

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

If statistical discrimination is key factor regarding the greater conditional wage gap in the blue-collar sector, what else should we see? Table 9: OLS Regressions of Log of Hours Spent Assessing Skill

Per Hired Worker

Conditioning Variable [1] [2] [3]White Collar Job 0.50*** 0.50*** 0.48***

(0.077) (0.078) (0.080)Sales Job 0.07 0.13 0.18*

(0.096) (0.098) (0.101)Service Job -0.34*** -0.32*** -0.29***

(0.073) (0.072) (0.079)Number of Employees - 0.11*** 0.11*** (in hundreds) (0.017) (0.017)Number of Employees Squared - -0.001*** -0.001***

(0.0002) (0.0003)Age > 50 - - -0.16

(0.123)Age 30 - 50 - - 0.07

(0.061)female - - 0.13*

(0.056)controls for industry no no yesR-squared 0.04 0.06 0.08number of observations 1,951

Specification

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Empirical Approaches to Assessing DiscriminationAssessing discrimination using large datasets (cont.)

In general, this work suggests that the fundamental nature of racial wage inequality might differ across job sectors. Very little evidence of direct discrimination in white-collar sector.

However, evidence consistent with substantial discrimination in blue-collar sector.

Suggests that any labor market interventions (affirmative action, legal actions, targeted recruiting, etc.) may have bigger impact on blue-collar sector.

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination

Audit Studies What is an “Audit” study?

What are general concerns regarding Audit studies?

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Bertrand and Mullianathan “Are Emily and Greg More Employable than Lakisha and Jamal?” What is their research design?

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Bertrand and Mullianathan: Other Findings Neighborhoods Matter

Those with addresses in more white, or richer neighborhoods more likely to get callbacks.

Neighborhood doesn’t have different impact by race.

Worker characteristics matter, but effect does not differ by race.

“Equal Opportunity Employers” nor Federal Contractors appear to treat African-Americans more favorably than “other” employers.

Differential treatment does not appear to differ by employer size.

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Bertrand and Mullianathan: Interpretation Do these results suggest racial discrimination?

What else might these “black” names indicate?

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand” What are they studying?

How do they study this?

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand”

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand”

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand”

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand”

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand”

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand”

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Empirical Approaches to Assessing DiscriminationOther Approaches to Assessing discrimination (cont.)

Doleac and Stein “The Visible Hand”