The lecture covers:
Discrimination dynamics
Anti-discrimination laws
Insights from theory and evidence
Theory as Basis:
Main reference: Bohren, Imas, and Rosenberg (2019) - "The Dynamics of Discrimination: Theory and Evidence"
Analysis of discrimination as a process rather than a static event.
Most traditional studies focus on static settings, limiting understanding of discrimination sources.
Static settings can obscure the origins of observable patterns, as similar outcomes can arise from different sources.
Statistical Discrimination: Referred to as "belief-based" discrimination.
Taste-Based Discrimination: Referred to as "preference-based" discrimination.
Individuals perform tasks repeatedly, generating outputs and observable histories of evaluations.
Example: Workers on platforms like GitHub increase reputation based on evaluations received.
Initial evaluations may favor one group due to lack of prior performance history.
E.g., female workers may not receive promotions despite similar quality of output compared to male counterparts.
Questions arise about whether initial discrimination mitigates, persist, or reverses over time.
Taste-Based Discrimination: Future evaluations continue to affect women despite similar evaluation sequences to men.
Statistical Discrimination: Involves biases related to perceived group statistics, affecting evaluations despite similar performance.
Prior evaluations signal a worker's ability, which helps reduce ongoing discrimination.
If evaluators initially favor men, women may need to outperform men to gain equal evaluations in future periods.
Awareness of higher standards for women could lead to a reversal of discrimination in subsequent evaluations.
Discrimination based on incorrect beliefs can perpetuate inequality over time.
Correct beliefs can mitigate discrimination but may not reverse it, highlighting the dynamics of biased beliefs in assessments.
Conducted in an online forum (e.g., Stack Exchange) where users earn reputation points based on votes for questions and answers.
Experimental Design: Gender of usernames varied among novice and advanced accounts to test discrimination dynamics without endogeneity issues.
Evaluated quality of answers directly (correctness) versus the more subjective quality of questions.
For Answers: No significant evidence of gender discrimination.
For Questions: Significant initial discrimination observed against female novice accounts.
Statistical Discrimination: Notable difference in male versus female reputation change for questions posted, indicating an unequal advantage.
Notable reversal of discrimination from novice to advanced account evaluations, confirming evidence of biased beliefs underlying initial discrimination.
Relying solely on examples of successful individuals from marginalized groups to argue against discrimination is flawed.
Recognizing earlier discrimination stages may influence favorable treatment in later evaluations (e.g., accomplished women in STEM fields).
Incorrect beliefs about standards can perpetuate inequality despite qualifications.
Policies to correct misconceptions regarding evaluation standards can help close gaps.
UK Equality Act of 2010: Protects against discrimination in multiple dimensions including age, disability, race, and sex.
ECHR: Enshrines rights against discrimination among member states, enforceable by international court.
US laws on discrimination are fragmented, resulting in variable protection across states and groups. Specific protections for sexual and gender minorities may differ significantly from those in the UK.
A tribunal ruled that political views expressed by Fairbanks were not protected philosophical beliefs under the Equality Act, illustrating complexities in discrimination cases.
Next topic: Identity Economics and further implications of discrimination dynamics in economics.