Did parental involvement laws grow teeth? The effects of state restrictions on minors’ access to abortions. 2020, Journal of Health Economics. With Caitlin Myers
We compile data on the locations of abortion providers and enforcement of parental involvement laws to document dramatic increases in the distances minors must travel if they wish to obtain an abortion without involving a parent or judge: from 58 miles in 1992 to 454 in 2016. Using both double and triple-difference estimation strategies, we estimate the effects of parental involvement laws, allowing them to vary with the distances minors might travel to avoid them. Our results confirm previous findings that parental involvement laws did not increase teen births in the 1980s, and provide new evidence that in more recent decades they have increased teen birth by an average of 3 percent. The estimated effects are increasing in avoidance distance to the point that a confidential abortion is more than a day's drive away, and also are substantially larger in the poorest quartile of counties.
Works in Progress
Presented at 2020 Urban Economics Association Meeting and 2021 ASSA-ARUEA Meeting
This paper shows that households have lower levels of housing investment when they live in areas with labor markets that are more correlated with their industry of employment. In other words, if a household lives in an area where many other households work in the same or similar industries, then housing may be a riskier asset as it is more correlated with labor market income. Thus households decrease their investment in housing. Using US microdata from 2007-2017 a one-standard deviation increase in a household's correlated labor market risk is associated with a decline in housing investment by around $6,750. This decline is driven by concentrations and riskiness of other correlated industries, suggesting agglomeration in one industry can have negative spillovers to workers of other related industries.
With Ian Burn, Daniel Firoozi and David Neumark
We explore whether ageist stereotypes in job ads are detectable using machine learning methods measuring the linguistic similarity of job-ad language to ageist stereotypes identified by industrial psychologists. We then conduct an experiment to evaluate whether this language is perceived as biased against older workers. We find that language classified by the machine learning algorithm as closely related to ageist stereotypes is perceived as ageist by experimental subjects. The scores assigned to the language related to ageist stereotypes are larger when responses are incentivized by rewarding participants for guessing how other respondents rated the language. These methods could potentially help enforce anti-discrimination laws by using job ads to predict or identify employers more likely to be engaging in age discrimination.
Loyalty rewards and redemption behavior: Stylized facts for the U.S. airline industry. With Alexander Luttmann
Presented at 2020 International Transportation Economics Association Meeting