2. A field experiment

3. A field experiment
They conduct a large-scale field experiment to measure labour market discrimination in Australia, one quarter of whose population was born overseas.
They use distinctively Anglo-Saxon, Indigenous, Italian, Chinese and Middle Eastern names.
In all cases we applied for entry-level jobs and submitted a CV indicating that the candidate attended high school in Australia.
We find significant differences in callback rates: ethnic minority candidates would need to apply for more jobs in order to receive the same number of interviews. These differences vary systematically across ethnic groups.

4. A field experiment

5. Introduction
Discrimination occurs when the marketplace takes into account such factors as race and sex when making economic exchanges.
However, it is difficult to distinguish statistical discrimination, which refers to racial or gender inequality based on stereotypes, from discrimination based on preferences.

6. Race and Gender in the Labor Market
Men earn more than women, and whites usually earn more than nonwhites.
Differences in educational attainment between whites and nonwhites account for a portion of the wage differential.

7. The Discrimination Coefficient
Taste discrimination translates the notion of racial prejudice.
Racial prejudice causes employers to blindly perceive the costs of hiring blacks as being higher than the true cost.
Even though it costs wb dollars to hire one person-hour of black labor, the employer acts as if it costs wb(1+d) dollars, where d, d>0, is the discrimination coefficient.

8. Employer Discrimination
Implication of the Becker model
If blacks and whites are perfect substitutes, employers have a segregated work force.
Even non-discriminating employers have a segregated work force, as they employ all black workers
Discrimination does not pay.
Employers hire the wrong type of worker and/or they hire the wrong number of workers.

9. The Employment Decision for a Firm That Does Not Discriminate
VMPE wB EB
Employment
Dollars
If the market-determined black wage is less than the white wage, 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, E*B.

10. The Employment Decision for a Prejudiced Firm
VMPE EW wW
Employment
Dollars EB
wB(1+d1)
wB(1+d0) wB
EB*
EB1
(a) White Firm
(b) Black Firm
Firms that discriminate can be either white firms (if the discrimination coefficient is very high) or black firms (if the discrimination coefficient is relatively low). A white firm hires white workers up to the point where the white wage equals the value of marginal product. A black firm hires black workers up to the point where the utility-adjusted black wage equals the value of marginal product. Firms that discriminate hire fewer workers than firms that do not discriminate.

11. Profits and Discrimination
Dollars
Discrimination Coefficient dW
MAX W
White Firms
Black Firms
Discrimination reduces profits in potentially two ways. A discriminatory firm that hires only white workers will hire too few workers at a very high wage. Even a discriminatory firm that only hires black workers is harmed by its actions as it hires too few workers.

12. 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.

13. Determination of Black/White Wage Ratio in the Labor Market
If the black-white wage ratio is very high, no firm in the labor market will want 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)*. 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.
Black-White Wage Ratio
Black Employment
(wB/wW)* N 1 R D S D
(wB/wW)

14. Employee Discrimination
Employee discrimination does not generate a wage differential between equally skilled black and white workers.
Employee discrimination does not affect the profitability of firms.
Work places will be segregated

15. Customer Discrimination
If customers discriminate, their perceived price of a good is utility-adjusted with a discrimination coefficient.
When a firm cannot hide black workers, customer discrimination can have an adverse effect on black wages.

16. 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.

17. 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. (a) 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. (b) 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.

18. Competition widens the profit gap between discriminating and non-discriminating firms
If market clearing wages for female lower than that for males (even slightly), firms that hire more women will make more profit
Earlier models use this result to suggest that this discrimination cannot occur in the long-run because the more profitable firms will buy out the discriminating firms
This does not have to be the case, however. Firms that value hiring males will accept lower profits.

19. What have we ignored?
We’ve assumed that these two types of workers are perfect substitutes
This does not have to be the case
Culture, language differences, and customer preferences may lead to worker segregation without lowering profits

20. Statistical discrimination: 2 important characteristics
There really are differences in productivity between groups, on average (like signaling model)
Employers cannot observe true productivity, but can observe certain demographic characteristics (e.g. gender) (like signaling model)

21. An example: even if a small fraction in group have low productivity, can lead to statistical discrimination for entire group

22. Example, continued Suppose 2 types of female workers:
First type with same family commitments and likelihood of leaving firm as males
Second type with more commitments and more likelihood of leaving
Even if few of second type, if employers cannot distinguish at time of hiring, reluctant to hire any female (or would prefer paying them a lower wage)
Note, both of these acts illegal in Canada

23. Example, continued
With statistical discrimination in labour market, individuals in group above mean productivity benefit from full information
Individuals below mean would not benefit from full information
If employers could observe commitment to firm, the first type of females would see market wages rise to those for males, but see market wages fall for the second type

24. Temp. Conclusions
More revealed information helps deal with the statistical discrimination.
From another angle, it is controversial to regard the statistical discrimination as “discrimination”.

25. Measuring Discrimination
One possible measure of discrimination is the difference in mean wages.
A better measure would compare the wages of equally skilled workers.
Oaxaca decomposition: a technique that decomposes the raw wage differential into a portion related to a difference in skills and a portion attributable to labor market discrimination.

26. Oaxaca decomposition
The first 2 terms combined captures the differential due to discrimination, while the last term is the component of differential that can be explained by skills.

27. Measuring the Impact of Gender Discrimination on the Wage
Dollars
Men’s Earnings Function
Women’s Earnings Function
Schooling
sF
sM
wM wF M
wF F
The average woman has sF years of schooling and earns w–F. The average man has sM years of schooling and earns w–M. 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 w*F–w–F.

28. Policy Application: Determinants of the White-Black Wage Ratio
There has been an upward trend in the wages of blacks in recent years.
This has been attributed to increases in the quality and quantity of black schooling.
Government programs have positively affected black wages.

29. 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
Source: Joseph G. Altonji and Rebecca M. Blank, “Race and Gender in the Labor Market,” in Orley Ashenfelter and David Card, editors, Handbook of Labor Economics, vol. 3C, Amsterdam: Elsevier, 1999, Table 5. The log wage differential between any two groups can be interpreted as being approximately equal to the percentage wage differential between the groups.

30. The Trend in the Black-White Earnings Ratio, 1967-2009

31. Male Labor Force Participation Rates, by Race, 1955-2009

32. The Decline in the Labor Force Participation of Blacks and the Average Black Wage
Frequency
Wage rate
w~1
w~2
w1
w2

33. Discrimination Against Other Groups
Differences in wages can be linked to varying educational attainment.
Less skilled workers earn less, just as human capital theory proposes.
Asians tend to earn more than white, mainly due to schooling.

34. The Trend in the Earnings Ratio of Hispanics and Asians, 1974-2009

35. Policy Application: Determinants of the Male-Female Wage Ratio
Occupational crowding has segregated women into particular occupations where the return to education is lower.
Human capital is more profitable the longer the payoff period.
Women are better off if they enter occupations in which their skills do not deteriorate during the years they spend in the household sector.

36. Trend in the Female-Male Earnings Ratio, 1960-2009

37. Discrimination across beauty
Hamermesh and Biddle collected survey data with earnings and photographs
Hired undergraduates to rate people’s looks ranging from strikingly beautiful or handsome to homely.

38. Effect of above or below average looks on log earnings

39. What could explain beauty premium: is it discrimination?
Beautiful workers may be more productive in job
Beautiful workers may be more confident and better communicators (during job interview)
Customers may prefer firm has beautiful employees (consumer discrimination)
Employers may erroneously believe beautiful workers are more productive
Employers may prefer to hire beautiful workers (taste discrimination)

40. To answer this, Mobius and Rosenblat conduct experiment
Simulate labour market: 10 undergrads arrive to ‘lab’
5 assigned as ‘workers’, 5 as employers and moved to separate rooms
Workers fill out resume and take practice test of completing mazes
At the end of test, tries to predict the average number of mazes they can complete every 10 minutes (they lose money if their guess is off)

41. To answer this, Mobius and Rosenblat conduct experiment
Each worker interviews with all 5 employers separately
Workers assigned fixed amount of money, and must decide how much to pay employees to complete mazes
Emp. 1 only sees resume of worker
Emp. 2 sees resume and picture
Emp. 3 sees resume and conducts phone interview
Emp. 4 sees resume, photo, and conducts phone interview
Emp. 5 sees resume, photo and conducts fact to face interview

42. To answer this, Mobius and Rosenblat conduct experiment
Experiment designed so that employers must try to correctly guess worker productivity
Employers do not have contact with employees after interview
33 experiments conducted
Other undergrads judged level of beauty
Completing mazes shown to be uncorrelated with beauty

43. Find 12% wage premium for beauty
Estimate 20% of this due to beautiful workers are more confident with their performance (more likely to over estimate their actual performance)
30% of this due to employers acting as if beautiful workers are more productive (when they are not)
50% of this due to performance of beautiful worker in interview (communication, persuasion skills)

44. New Perspectives on Gender
The two main factors being discussed as sources of the gender gap in earnings before the 2000s were
differences in human capital accumulation and
Discrimination (taste-based or statistical)
Starting the 2000s, a major development of new classes of explanations:
psychological attributes and preferences between men and women
social norms regarding gender identity

45. Psychological attributes
Gender differences in psychological attributes and preferences may make some occupations more attractive to women and others more attractive to men.
Psychological factors:
Risk preferences
Attitudes toward competition
Attitudes toward negotiation
Social preferences

46. Games and experiments
One ways to measure one’s attributes is lab experiments
For example, the competition game and the risk attitude game:

47. Where do such gender differences come from?
Biological differences
physical strength
Brain structure
Exposure to sex hormones (eg, testosterone)
Environmental factors
Education, eg. Booth and Nolen (2009a) show that gender differences in risk attitudes in a sample of English 15-year-olds depend on whether the girls have attended a single-sex school or mixed-gender school.
Hoffmann and Oreopoulos (2009): Assignment to a same- gender teacher improves performance for both girls and boys

48. Gender identity One’s identity of self and one’s identity of others
Fortin (2009) shows that the evolution of women’s own role attitudes over time appears to map very well with the evolution of female labor force participation over time.
Booth and van Ours (2009) show that parttime women are more satisfied with working hours than full-time women, and that women’s life satisfaction is increased if their partners work full-time but decreased if they themselves work full-time. Male partners’ life satisfaction is unaffected by their partners’market hours but is greater if they themselves are working full-time.