BEE2045 - 2025 - Lecture 4 - Discrimination - Part 2

Lecture Overview: Theories of Discrimination - Part 2

Lecture Content

• Focus on Statistical Discrimination

• Also includes extensions and tests

Lecture Format

• The lecture is being recorded for review purposes.

Statistical Discrimination

Definition and Importance

• Key Sources: Arrow (1972) and Phelps (1972)

• Uncertainty regarding individual productivity leads employers to rely on perceived average productivity based on group characteristics.

• Group characteristics are used as proxies due to limited information on individual applicants.

Examples of Group Characteristics Influencing Perceptions

• Behavioral traits (e.g., aggression, competitiveness)

• Job commitment (e.g., assumptions about pregnant women's reliability)

• Health concerns (e.g., perceptions regarding gay men and HIV)

Case Study Example

Quote from Phelps (1972)

• An employer might discriminate against certain groups if they believe these groups are generally less qualified based solely on limited perceptions—specifically, using race or gender as proxies for productivity.

Relevant Media

• Film Highlight: "Up in the Air" clip used for illustrating statistical discrimination concepts.

Key Points on Statistical Discrimination

• Does not require an employer to hold racist or sexist views.

• Decisions are rational responses to incomplete information.

• With complete information, statistical discrimination does not occur; nonetheless, it remains a form of discrimination and is often illegal.

Second-Order Statistical Discrimination

• Definition: Higher perceived variance in productivity within a less familiar group, potentially leading to discrimination.

• References Klumpp and Su (2013) as a key study discussing phenomena of educational and occupational bias against women due to perceived lower variance in human capital.

Premarket Investments

• The differences in labor market outcomes often stem from the skills and qualifications that individuals bring to the market.

• Investment insights can be influenced by expectations of how different individuals will be treated in job markets.

Efficiency Considerations

• Statistical discrimination can be viewed as a solution to information asymmetry.

• Some economists argue that it could be justified if it maximizes profits by treating individuals of the same expected productivity identically.

Legal Context

• Statistical discrimination is illegal in several jurisdictions when it involves protected categories.

Fairness Issues

• Example: Police racial profiling stems from statistical expectations but leads to unfair consequences for innocent individuals, highlighting the equity-efficiency trade-off.

Audit Studies

• Used to measure discrimination via matched testers from different demographic backgrounds applying for the same roles.

• Focus on initial entry points; limited insights on later stages like promotions.

Case Study: Goldin and Rouse (2000)

• Investigated impact of blind auditions on female musicians in orchestras.

• Found that blind auditions led to increased hiring of women, indicating bias removal.

Key Outcome Discussion

• Difficulty in discerning between taste-based and statistical discrimination due to the nature of blind auditions.

Field Experiment: Bertrand and Mullainathan (2004)

• Resumes sent with racially distinct names to measure callback rates.

• Concluded distinctively Black names received fewer callbacks despite similar qualifications.

Additional Research Insights

• Heckman (1998): Differences in variances among unobservable characteristics could bias results in studies of discrimination.

• List (2004): Examined discrimination in the marketplace; minority sellers often received inferior offers.

Conclusion

• Statistical discrimination remains a critical topic within the discourse on workplace equity and efficiency. Key studies provide evidence of its prevalence and impact across different sectors.