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Marriott Philadelphia Downtown, Liberty Ballroom Salon A
Hosted By:
American Economic Association
we estimate the effects of sudden fare changes on market outcomes, focusing
on the supply-side. We explore both the short-run dynamics of
market adjustment, as well as the eventual long-run equilibrium. We
find that the driver hourly earnings rate—essentially the market equilibrium
wage—moves immediately in the same direction as a fare change,
but that these effects are short-lived. The prevailing wage returns to its
pre-change level within about 8 weeks. This return is achieved primarily
through permanent changes in driver “utilization,” or the fraction of
hours-worked that are spent transporting passengers. Our results imply
that the driver supply of labor to ride-sharing markets is highly elastic,
most likely because drivers face no quantity restrictions on how many
hours to supply and new drivers face minimal barriers to entry.
Advances in Big Data Research in Economics
Paper Session
Saturday, Jan. 6, 2018 2:30 PM - 4:30 PM
- Chair: Fatih Guvenen, University of Minnesota
Labor Market Equilibration: Evidence from Uber
Abstract
Using a city-week panel of US ride-sharing markets created by Uber,we estimate the effects of sudden fare changes on market outcomes, focusing
on the supply-side. We explore both the short-run dynamics of
market adjustment, as well as the eventual long-run equilibrium. We
find that the driver hourly earnings rate—essentially the market equilibrium
wage—moves immediately in the same direction as a fare change,
but that these effects are short-lived. The prevailing wage returns to its
pre-change level within about 8 weeks. This return is achieved primarily
through permanent changes in driver “utilization,” or the fraction of
hours-worked that are spent transporting passengers. Our results imply
that the driver supply of labor to ride-sharing markets is highly elastic,
most likely because drivers face no quantity restrictions on how many
hours to supply and new drivers face minimal barriers to entry.
Human Decisions and Machine Predictions
Abstract
We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones.JEL Classifications
- J0 - General
- E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy