CHAPTER Race and Gender in the Labor Market

CHAPTER 9 9-1 Race and Gender in the Labor Market
9-2 The Discrimination Coefficient 9-3 Employer Discrimination 9-4 Employee Discrimination 9-5 Customer Discrimination 9-6 Statistical Discrimination 9-7 Experimental Evidence on Discrimination 9-8 Measuring Discrimination 9-9 PA: Determinants of the Black-White Wage Ratio 9-10 Discrimination against Other Groups 9-11 Determinants of the Female-Male Wage Ratio Give a brief overview of the presentation. Describe the major focus of the presentation and why it is important. Introduce each of the major topics. To provide a road map for the audience, you can repeat this Overview slide throughout the presentation, highlighting the particular topic you will discuss next.

Introduction Differences in earnings and employment opportunities may be caused by economic factors such as differences in job characteristics and workers’ skills. However, there should not be differences in labor market outcomes among equally skilled workers in the same job Discrimination occurs when the marketplace takes into account such factors as race and sex when making economic exchanges.

Introduction In this chapter, we will cover topics such as:
Empirical evidence on earnings differences across race and gender Economic framework for analyzing discrimination Types of discrimination and how to measure them Impact of public policy on the well-being of discriminated groups

9-1 Race & Gender in the Labor Market
Men earn more than women, and whites usually earn more than non-whites (except Asians). Differences in annual earnings are partially explained by labour supply and educational attainment differentials. (51% gap in Annual earnings vs 40% gap in FT workers)

9-1 Race & Gender in the Labor Market
Considerable wage gap between men and women in most developed countries. Differences not only in the size of the gender wage gap & but also the employment rates of men and women. Gender wage gap is higher in countries where employment gap across gender is smaller (-ve correlation).

9-2 The Discrimination Coefficient
Gary Becker’s PhD Dissertation (1957): The Economics of Discrimination Taste Discrimination: translates the notion of racial prejudice into the language of economics Two types of workers: Whites (W) and Blacks (B) Wage rates: WW and WB respectively (competitive employer) Employer: If prejudiced against blacks, gets disutility from hiring black workers, perceive & act as if the cost of hiring a black worker is: WB(1+d) rather than WB where d is called the “discrimination coefficient”

9-2 The Discrimination Coefficient
Employer: If prejudiced against blacks, perceive & act as if the cost of hiring a black worker is: WB(1+d) where d is discrimination coefficient Example:d=0.5 and WB=$10 If prejudiced, perceive the cost of hiring a black worker as $15 Similarly, if an employer prefer to hire blacks (nepotism), cost of hiring would be perceived as: WB(1 – n) where n is nepotism coefficent 3 types of discrimination: Employer, Employee, Statistical

9-3 Employer Discrimination
Main assumption: Blacks and whites are perfect substitutes q=f(LW+LB) (B and W are equally productive) Hence, same output with either of the combinations below: LW=0 LB=100, LW=50 LB=50 or LW=100 LB=0 Competitive employer: Constant input prices (WB & WW) A color-blind (not prejudiced) employer will hire whichever group is cheaper. Hence, hire only blacks if WB<WW and vice versa. Hire up to the point where WB=VMPL. .

The Employment Decision for a Firm That Does Not Discriminate
VMPE wB LB Employment Dollars If the market-determined black wage is less than the white wage (WB<WW), a firm that does not discriminate will hire only blacks. It hires black workers up to the point where the black wage equals the value of marginal product of labor, L*B.

A prejudiced (against blacks) employer perceives the cost of a black worker as: WB(1+d) where d>0 Hiring decision of a prejudiced employer: Hire only blacks if WB(1+d) < WW Hire only whites if WB(1+d) > WW Hence, assuming that employers have mixed tastes (color-blind and prejudiced): .

Implications of the Becker model If blacks and whites are perfect substitutes, employers have a segregated work force. Level of employment depends on the discrimination coefficient. Larger d, smaller is the number of hires. Prejudiced employers hire the wrong type of worker and/or they hire the wrong number of workers.

Implications of the Becker model 3) Crime does NOT pay: πColor-Blind > πPrejudiced White firms: “High level of prejudice”, high costs (WW) and sub-optimal levels of output (LW < L*) Black firms: Prejudiced firms (low or medium) hire wrong (smaller – suboptimal) number of workers!

Determination of Black/White Wage Ratio in the Labor Market
Black Employment (wB/wW)* N 1 R D S D (wB/wW) If the black-white wage ratio is very high, no firm wants to hire blacks. As the black-white wage ratio falls, more and more firms are compensated for their disutility and the demand for black workers rises. The equilibrium black-white wage ratio is given by the intersection of supply and demand, and equals (wB/wW)*.

Determination of Black/White Wage Ratio in the Labor Market
Black Employment (wB/wW)* N 1 R D S D (wB/wW) If some firms prefer to hire blacks, they would be willing to hire blacks even if the black-white wage ratio exceeds 1, shifting the demand curve up to D. If the supply of blacks is sufficiently small, it is then possible for the black-white wage ratio to exceed 1.

The Black-White Wage Ratio in the Labor Market
Employer discrimination generates a wage gap between equally skilled black and white workers. The quantity demanded for black labor increases as the black-while wage ratio falls.

9-4 Employee Discrimination
Suppose that the source of discrimination is the employees (rather than the employer) Moreover, assume that blacks are indifferent to working with whites, however, white workers dislike working with black workers. Hence, if a firm adopts a mixed racial composition in its workforce, white workers perceive their income to be lower than it actually is: WW(1-d) where d is the discrimination coefficient

9-4 Employee Discrimination
Hence, a color-blind firm would find a multi-racial workforce suboptimal, since it would have to pay a wage premium to white workers, which would lower its profits. Therefore, a color-blind firm would hire black or white workers only! (whichever is cheaper). Employee discrimination implies a completely segregated workforce! There will be no wage gap. Some hire only Blacks and others hire only Whites, but wages will be equal in the equilibrium as long as skills are identical (same VMP): WW=WB Consequently, employer discrimination does not affect profitability.

9-5 Customer Discrimination
Customers may have a taste for discrimination. In particular, customers may derive disutility from purchasing goods and services sold by minorities. Hence, they may perceive the prices of these goods and services to be higher than they actually are: p(1+d) where d>0 Profit maximizing firms will react to this via allocating black workers to positions that require little/no customer contact.

In effect, the employer segregates the workforce so that white workers fill sensitive positions such as sales and black workers fill non-sensitive positions such as production. Equally skilled black and white workers would receive the same wage. Catering to customer tastes doesn’t reduce the firm’s profits. However, if a (competitive) firm cannot hide black workers, then it would have to lower the price, which would lower the profits. Therefore, customer discrimination can have an adverse effect on black wages.

Looking at contact firms, firms with predominantly black customers hire significantly more black workers than firms with predominantly white customers. However, to assess the true difference, we need to compare the difference in the hiring behavior of firms with “no contact” firms. Difference-in-Differences estimate of the impact of customer discrimination is much smaller (14.6%).

9-6 Statistical Discrimination
Statistical discrimination is based on treating an individual on the basis of membership in a group and knowledge of that group’s history. Example: 2 candidates for a job (Software Development) Identical resumes Same interview performance (flying colors) Same education (college graduates with same field) Enrollment in same courses with similar class rankings Both perceived to be bright, motivated and knowledgeable Both assert that they intend to stay at the firm for the next years Only difference: One man and the other a woman

To make an informed decision, the employer will evaluate the employment histories of similarly situated men and women that this firm hired at the past. Suppose that statistical evidence from the employment records show that many women leave the firm when they reach their late twenties. The employer has no way of knowing whether the female candidate intends to leave the firms in a similar manner. Nevertheless, the employer infers from the statistical data that the woman has a higher probability of quitting her job prior to the completion of the software program. The profit-maximizing employer offers the job to the man.

Statistical discrimination arises because of the underlying uncertainty. The employer is encouraged to use statistics about the average performance of the group to predict a particular applicant’s productivity. Statistical discrimination arises not only in the labour market but in many other markets as well. Insurance companies: Premiums and Life expectancy (Males vs Females) Premiums and Auto Insurance (Teenagers vs Adults)

The Impact of Statistical Discrimination on Wages
Dollars White Black Test Score T* T (a) Whites have higher average score (b) Test is better predictor for white workers Suppose all the information gathered for a candidate is summarized with a test score (T). Uncertainty of the test score’s accuracy to predict personal productivity Employers may link productivity to the individual’s test score (T) and the average group score (Tave): W = α T + (1- α) Tave

The Impact of Statistical Discrimination on Wages
Dollars White Black Test Score T* T (a) Whites have higher average score (b) Test is better predictor for white workers The worker’s wage depends not only on his own test score, but also on the mean test score of workers in his racial group. If black workers, on average, score lower than white workers, a white worker who gets a score of T* earns more than a black worker with the same score. If the test is a better predictor of productivity for white workers, high-scoring whites earn more than high-scoring blacks, and low-scoring whites earn less than low-scoring blacks.

9-7 Experimental Evidence on Discrimination
Difficult to measure a particular employer’s discrimination coefficient since it is illegal to discriminate. A number of studies conducted labor market experiments to bypass this measurement problem. Employers are contacted at random. 5,000 fake resumes sent to 1,300 real job ads at Boston and Chicago newspapers. Resumes did not specify the applicant’s race but included hints (i.e. name: Lakisha Washington vs Jamal Jones). Holding the skills constant, the applicants with black-sounding names got 1 callback for every 15 resumes sent. A black applicant needed 8 more years of work experience to even out the gap.

Experimental approach extended beyond fake resumes to experimental human beings sent to actual job interviews. Summer 1989, hiring audit conducted in Chicago and San Diego areas and 360 firms were audited. Average job applicant was… a neatly dressed 22 year old man, with a high school diploma, did not have a criminal record, had some college credits, some work experience as a stockperson or waiter Only difference is racial and visual indicators (Hispanic looking with an accent vs White)

Systematic differences were found in the way that employers responded. White job applicants was 33 percent more likely to be interviewed, And 52 percent more likely to receive a job offer.

9-8 Measuring Discrimination
One possible measure of discrimination is the difference in mean wages: The definition is unappealing because it is comparing apples and oranges. Many factors, other than discrimination, generate wage differentials between men and women. A better measure would compare the wages of equally skilled workers.

Suppose that only one variable, schooling affects earnings Male earnings function: wM = αM + βM SM Female earnings function: wF = αF + βF SF “β” measures how much a wage increases if an individual gets one more year of schooling If employers value the education acquired by both gender equally, βM = βF Raw wage differential can be written as follow:

Oaxaca decomposition (Ronald Oaxaca): A technique that decomposes the raw wage differential into two components: a portion related to a difference in skills. and a portion attributable to labor market discrimination. Consider the raw wage differential: Lets add & subtract the term to the right hand side:

Oaxaca decomposition (Ronald Oaxaca): A technique that decomposes the raw wage differential into two components: a portion related to a difference in skills. and a portion attributable to labor market discrimination.

Measuring the Impact of Gender Discrimination on the Wage
The average woman has years of schooling and earns The average man has years of schooling and earns Part of the wage differential arises because men have more schooling than women. If the average woman was paid as if she were a man, she would earn w*F. A measure of discrimination is then given by Differential attributable to skill differential Should be Actual

Does the Oaxaca Decomposition Really Measure Discrimination?
Validity of this decomposition depends on whether all the dimensions in which the skills of the groups differ have been controlled. If there are some skills characteristics (ability, effort, motivation) that affect earnings but are left out, there will be an incorrect measure of labor market discrimination. For example, if men & women attend systematically attend institutions that vary in quality, the Oaxaca decomposition generates a biased measure of discrimination. If blacks attend lower-quality schooling than whites, it would be incorrect to label wage differences between workers with same level of schooling as discrimination.

9-9 PA: Determinants of the White-Black Wage Ratio
The Oaxaca Decomposition of the Black-White Wage Differential, 1995 Controls for Differences in Education, Age, Sex, and Region of Residence Controls for Differences in Education, Age, Sex, Region, and Occupation and Industry Raw log wage differential -0.211 Due to differences in skills -0.082 -0.144 Due to discrimination -0.134 -0.098 Magnitude of the differentials attributed to discrimination (13.4% vs 9.8%) depends on the the list of controls used. On the other hand, part of the differences may be due to employment barriers that prevent blacks into moving certain type of jobs?

Overall, long-run trends are clear: The relative wages of black men and women are substantially higher today than they were in the late 1960s. In 1967: Ratio of 0.65 In 1980: Ratio of 0.71 In 2009: Ratio of 0.77

There is a number of hypotheses proposed to explain the improving economic status of African Americans: Increases in the quality and quantity of black schooling. Impact of Affirmative Action The Decline in the LFPR of Black Unobserved Skill Differences

Increases in the quality and quantity of black schooling Quantity of schooling: Years of schooling obtained by a typical 30 yr old man In 1940: white man 9.9 yrs vs black man  6.6 yrs In 1980: white man 13.6 yrs vs black man  12.2 yrs Quality of schooling: Rate of return to school for a worker who entered the labor market In 1940: white man 9.8% vs black man  4.7% In 1970: white man 8.5% vs black man  9.6%

Impact of Affirmative Action Enactment of 1964 Civil Rights Act: Prohibits employment discrimination on the basis of race and sex. The federal civil rights program prohibited discrimination (by race and sex) among government contractors. Executive orders brought by this program compel federal contractors to not discriminate and take affirmative action to ensure they don’t. Response: Detailed affirmative action plans with employment goals (such as quotas) for affected groups and timetables for meeting these goals.

Impact of Affirmative Action Likelihood of employment at federal contractors In 1966: Black men were 10% less likely to work in federal contractors In 1980: Black men were 25% more likely to work in federal contractors Fraction of black employment at federal programs: Textile industry in South Carolina – main manufacturing employer Between 1910&1964: The fraction of black employment 4-5% By 1970: Nearly 20% of the workers in the industry were black

The Decline in Black LFP In 1950: 85% of the black and white participated in the LF By 2009: Gap between LFPR of white and black workers were over 7%.

The Decline in Black LFP
Suppose that the decline in the LFPR is due to an increase in the reservation wage (due to large scale public assistance programs in 1960s) If the portion of blacks that drop out of the LF are relatively low skill, this would to a higher average wage. The increase in the relative wage of black men may be an illusion created by sample selection bias (i.e. not an improvement in employment opportunities but due to low income blacks no longer participating in the Labor Force.)

Unobserved Skill Differences & the B-W Wage Differential There may be other unobserved skill differences between two groups that may account for the wage differential labeled as discrimination. Some studies use a particular measure of skills: Armed Forces Qualification Test (AFQT) Standard test given to all recruits in the U.S. military Substantial racial differences in the AFQT scores Blacks tend to have lower scores than whites Racial differences in AFQT scores practically account for the entire wage differential between black and white

Unobserved Skill Differences & the B-W Wage Differential Given the same AFQT score, a black worker only earns 5% less than a white worker What exactly does the AFQT measure? Convincing evidence that it is not a straightforward measure of innate ability Persons who have more schooling or go to better schools have higher scores Partly measures skills acquired prior to labor market entry Hence, these studies indicate that much of the discrimination is attributable to skills differentials between these groups (observed and unobserved).

9-10 Discrimination Against Other Groups
Astounding growth of the Hispanic population in the U.S.: U.S. Population (1980) Hispanics 6.4% vs Blacks 11.7% U.S. Population (2002) Hispanics 13.4% vs Blacks 12.7% Declining earnings ratio for Hispanic men and women.

9-10 Discrimination Against Other Groups
Differences in wages can be linked to varying educational attainment: Less skilled workers earn less Hispanics have a higher % of HS dropouts and lower % of college graduates relative to white workers Therefore, adverse changes in earnings ratio of Hispanics are due to observed skills differences

9-11 PA: Determinants of the Male-Female Wage Ratio
The F-M Wage Gap and Labor Market Experience Decompositions conducted above ignore a key determinant of female earnings: Differences in labor market histories Women drop out of the labor market during child-raising years. By late 1980s, % of potential years worked: Typical man 93% vs Typical woman 71% Argument: The value of woman’s human capital is reduced by her intermittent labor market attachment.

Hypothesis: Discontinuity in female LS over the life cycle generates a gender wage gap for two reasons: Relatively, males acquire more human capital The child-raising years increase the wage gap because woman’s skills tend to depreciate during that period. Evidence supports the hypothesis but disagreement of the magnitude explained by labor market histories: U of Michigan law school classes of 1973 and 1975. Annual earnings of attorneys 15 years after graduation: Males: $141,000 vs Females: $86,000 2/3 of the gap can be explained by labor market history Females worked PT for 3 years during child-raising years Earnings permanently reduced by 17%!!!

Occupational crowding
Occupational crowding hypothesis: Women are intentionally segregated into particular occupations.

Occupational crowding
Crowding may simply the result of a social climate in which young women are taught that the some occupations are “not for girls” and channeled into “appropriate” jobs. The crowding of women into a relatively smaller number of occupations inevitably reduces the wage of so-called female jobs and generate a gender wage gap. Studies typically find that “female jobs pay lower wages” even after holding constant the worker’s human capital and other factors: Typical man/woman in a “female job” earn 14% less than a typical man/woman in a “male dominant” job It is the femaleness of the job that leads to lower wages rather than the gender of the person in that job.

Trend in Female –Male Earnings Ratio
F-M wage ratio hovered around 60% between s. F-M wage ratio increased rapidly in the beginning of 1980s. By 2009, it stood at 77%.

Trend in Female–Male Earnings Ratio
Constant F-M wage ratio between period masks the improvement in the economic status of women due to comparison of different samples of working women. Cohort effects: Samples toward 1980s had a disproportionate number of newer labor market entrants with lower wages. In fact, growth of female wages were 20% higher than the growth of male wages prior to 1980. Also explains the negative correlation between the magnitude of the gender wage gap and the differences in the employment rates observed in Figure 9-1 (Smallest employment differences paired with largest wage gap.) Finally, 50% of the increase in the F-M wage ratio after 1980s can be attributed to the increasing work attachment of women.

CHAPTER Race and Gender in the Labor Market

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