Discrimination and Fairness
Tuesday, Jan. 5, 2021 10:00 AM - 12:00 PM (EST)
- Chair: Patrick Kline, University of California-Berkeley
The Effect of Unfair Chances and Gender Discrimination on Labor Supply
AbstractLabor market opportunities and wages may be unfair for various reasons, and how workers respond to different types of unfairness can have major economic consequences. Using an online labor platform, where workers engage in an individual task for a piece-rate wage, we investigate the causal effect of neutral and gender-discriminatory unfair chances on labor supply. We randomize workers into treatments where we control relative pay and chances to receive a low or a high wage. Chances can be fair, unfair based on an unspecified source, or unfair based on gender discrimination. Unequal pay reduces labor supply of low-wage workers, irrespective of whether the low wage is the result of fair or unfair chances. Importantly, the source of unfair chances matters. When a low wage is the result of gender-discriminatory chances, workers matched with a high-wage worker substantially reduce their labor supply compared to the case of equal low wages (-22%). This decrease is twice as large as those induced by low wages due to fair chances or unfair chances coming from an unspecified source. In addition, exploratory analysis suggests that in response to unequal pay, low-wage male workers reduce labor supply irrespective of the source of inequality, whereas low-wage female workers reduce labor supply only if unequal pay is due to gender-discriminatory chances. Our results concerning gender discrimination indicate a new reason for the lower labor supply of women, which is a prominent explanation for the gender gap in earnings.
Who Gets a Second Chance? Effectiveness and Equity in Supervision of Criminal Offenders
Abstract: Most convicted criminals are sentenced to probation and allowed to return home. On probation, however, a technical rule violation such as not paying fees can result in incarceration. Rule violations account for more than 30% of all prison spells in many states and are significantly more common among black offenders. I test whether technical rules are effective tools for identifying likely reoffenders and deterring crime and examine their disparate racial impacts using administrative data from North Carolina. Analysis of a 2011 reform eliminating prison punishments for technical violations reveals that 40% of rule breakers would go on to commit crimes if their violations were ignored. The same reform also closed a 33% black-white gap in incarceration rates without substantially increasing the black-white reoffending gap. These effects combined imply that technical rules target riskier probationers overall, but disproportionately affect low-risk black offenders. To justify black probationers’ higher violation rate on efficiency grounds, their crimes must be roughly twice as socially costly as white probationers’. Exploiting the repeat-spell nature of the North Carolina data, I estimate a semi-parametric competing risks model that allows me to distinguish the effects of particular types of technical rules from unobserved probationer heterogeneity. The estimates reveal that the deterrent effects of harsh punishments for rule breaking are negligible. Rules related to the payment of fees and fines, which are common in many states, are ineffective in tagging likely reoffenders and drive differential impacts by race. These findings illustrate the potentially large influence of facially race-neutral policies on racial disparities in criminal justice outcomes.
The Central Role of the Ask Gap in Gender Pay Inequality
AbstractThe gender ask gap measures the extent to which women ask for lower salaries than comparable men. This paper studies the role of the ask gap in generating wage inequality using novel data from Hired.com, a leading online recruitment platform for full time engineering jobs in the United States. To use the platform, job candidates must post an ask salary, stating how much they want to make in their next job. Firms then apply to candidates by offering a bid salary they are willing to pay the candidate. If the candidate is hired, a final salary is recorded. After adjusting for resume characteristics, the ask gap is 3.3%, the bid gap is 2.4% and the gap in final offers is 1.8%. Remarkably, further controlling for the ask salary explains all of the gender gaps in bid and final salary on the platform. To estimate the market-level effects of an increase in women's ask salary, I exploit a sudden change in how candidates were prompted to provide their ask salary. For a subset of candidates, in mid-2018, the answer box used to solicit the ask salary went from an empty field to a pre-filled entry with the median salary on the platform for a similar candidate. Comparing candidates creating a profile before and after the feature change, I find that this change drove the ask gap and the bid gap to zero. In addition, women received the same number of bids before and after the change, suggesting they face little penalty for demanding wages comparable to men.
- J7 - Labor Discrimination