GENDER AND RACE IN THE LABOR MARKET

GENDER AND RACE IN THE LABOR MARKET

Sponsored by a Grant TÁMOP-4.1.2-08/2/A/KMR-2009-0041 Course Material Developed by Department of Economics,

Faculty of Social Sciences, Eötvös Loránd University Budapest (ELTE) Department of Economics, Eötvös Loránd University Budapest

Institute of Economics, Hungarian Academy of Sciences Balassi Kiadó, Budapest

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Author: Anna Lovász Supervised by Anna Lovász

June 2011

Week 13

Other groups in the labor market, new topics and methods

Topics for the last week

• Discrimination of other groups

• Newest topics/directions in the field

Other groups: immigrants – Hámori (2008)

• Assimilation hypothesis of immigrants (Chiswick 1978):

• Over time, immigrants’ country-specific human capital increases, their labor market disadvantage decreases

goal: to evaluate the assimilation process of immigrants based on their employment chances (EU LFS, 2005)

• International comparison for the EU15 and EU8

• By gender

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• By birthplace (EU or not EU)

• Probit estimation:

• Emp = employment dummy, RES = time spent in the country, demographic controls, C = country dummy

Results – northern EU

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Results – southern EU

Results – western EU and UK

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Results – eastern EU8

Beauty – Hamermesh–Biddle 1994

• Lots of research on discrimination against women, blacks, disabled (USA)

• What about ugly people?

• Why is this interesting?

• Every worker has a characteristic physical attractiveness, it has an important role in success.

• Economists: usually it is difficult to distinguish between discrimination against a group and group-level productivity differences.

In the case of beauty, they can be separated (in many cases).

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Beauty – H&B 1994

• What is beauty?

• Differs by culture.

• Differs within cultures over time.

• But: at a given time, within a given culture there are well-defined expectations regarding the characteristics that determine beauty.

• Goals

• Empirical test: based on regular wage equations, is there a wage differential based on attractiveness?

• Does it differ by gender?

• Examine beauty-based selection in occupations where beauty may be productive.

Model – productivity model

• Vector describing the productive characteristics of worker i: Xi

• Every worker I is either attractive (θi=1) or not (θi=0)

• Wage equation for every occupation j:

• In some occupations, attractive workers are more productive (bj>0).

• May be due to customer discrimination, or more efficient interactions with coworkers.

• Workers choose jobs that offer them the highest wage.

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Productivity model – empirical implications

• Occupational selection:

• Expect some selection based on attractiveness

• Do not expect full segregation

• Wage differential:

• If other characteristics are not correlated with attractiveness, then attractive workers will get higher wages on average, whether we take X-es into account or not.

• Within occupations there will only be wage differentials in cases where attractiveness influences productivity.

Alternative model: employer discrimination

• Becker-style taste discrimination model: employer’s utility is decreased when employing unattractive workers.

• Empirical implications:

• No systematic occupational selection based on attractiveness

• There will be a wage differential, but we have no reason to believe that it will differ by occupation.

Empirical tests

• Regression:

• Where OCCj=1, if attractiveness influences productivity in the given occupation,

8 otherwise =0.

• Models:

• Productivity model: β4>0, β2=β3=0

• Employer discrimination model: β2>0, β3=β4=0

• Occupational selection (crowding): β3>0

Data

• Databases with information on attractiveness:

• 1977 Quality of Employment Survey

• 1971 Quality of American Life Survey

• 1981 Canadian Quality of Life Survey

Makers of the interviews rank attractiveness of respondent on a 5 point scale.

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Results – wage differential

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Results - selection

Conclusions

• Lower-than average attractive workers receive lower wages after controlling for other characteristics.

• Higher-than average attractive workers receive a wage premium, but it is smaller than the wage deficit of unattractive workers.

• The wage disadvantage is higher for men than women.

• Some evidence of selection, but not strong.

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Beauty – Hamermesh–Biddle 1998

• Database that follows men who took part in a given law program for 15 years after graduation

• Based on Yearbook photos, each lawyer was graded on attractiveness Results:

• 1970 cohort: attractive lawyers earn more 5 years after graduation, the difference increases after 15 years.

• In the private sector, lawyers are more attractive on average, selection between sectors based on looks.

• The chance of a promotion is higher for attractive lawyers.

• Importance of selection and customer behavior, but we don’t know if it is customer discrimination, or if attractive lawyers are really better at negotiating.

Results – wage effects

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Results – selection among sectors

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Wage effects of height: Persico et al (2003)

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Wage effects of height, youth height

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Newest research topics and methods

• Experiments analyzing behavior (experiments, field experiments)

• Attitude: stereotypes, discrimination

• Differences in preferences

• Differences in psychological traits

• Group-level differences

• Implication for education

• Labor market effects, occupational characteristics

• Teamwork and diversity

• What are the benefits of a diverse workforce?

”The business case for team diversity” – empirical evidence on diversity and team

productivity

• Competitive advantage – skilled workforce

• Demand for female characteristics

• In new firm structures, diverse skills become important: communication, conflict resolution.

• User-driven innovation

• Women make 80% of consumption decisions, but, for example, 90% of gadgets are developed for men.

• Diverse, multicultural developer teams are usually more successful.

• Firm image, satisfaction of workers

• Decision making

• Better decisions, creativity, problem solving

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Catalyst és McInsey – women in management and financial success

• Still significantly fewer women in management

• Ambiguous results regarding the financial effects of diversity

• Firms that have a higher ratio of female managers perform better

• ROE (return on equity) 35.1% higher

• TRS (total return to shareholders) 34% higher

• Important obstacles:

• ”Anytime anywhere model”: expectations and habits that favor men.

• Endogeneity: women’s effort and investment is affected by their perceived constraints.

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ELTE Faculty of Social Sciences, Department of Economics

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