econometrics   2035

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More COPS, Less Crime
I exploit a unique natural experiment to estimate the causal effect of police on crime. The American Recovery and Reinvestment Act increased funding for the COPS hiring grant program from $20million in 2008 to $1 billion in 2009 and over $150 million annually in 2010-2013. During this period, grant applications were scored and funding was allocated according to a fuzzy cutoff rule. I leverage quasi-random variation in grant receipt by comparing the change over time in police and crimes for cities above and below the score threshold. Relative to low-scoring applicants, cities above the cutoff experience increases in police levels of about 3.6% and decreases in violent and property crimes of about 4.8% and 3%, respectively. The effects are driven by large and statistically significant effects of police on robbery, larceny, and auto theft. I also find evidence that police reduce murders, with the point estimate implying that one life can be saved by hiring eleven officers. Arrest rates do not increase with police force expansions, suggesting a deterrence mechanism underlying the crime reductions. The program passes a cost-benefit test under some assumptions but not others. The results highlight that police hiring grants may offer higher benefit-cost ratios than other stimulus spending.
crime  policing  causal_inference  econometrics  natural_experiment  dmce  teaching  via:nyhan 
10 days ago by rvenkat
[1607.00699] The State of Applied Econometrics - Causality and Policy Evaluation
In this paper we discuss recent developments in econometrics that we view as important for empirical researchers working on policy evaluation questions. We focus on three main areas, where in each case we highlight recommendations for applied work. First, we discuss new research on identification strategies in program evaluation, with particular focus on synthetic control methods, regression discontinuity, external validity, and the causal interpretation of regression methods. Second, we discuss various forms of supplementary analyses to make the identification strategies more credible. These include placebo analyses as well as sensitivity and robustness analyses. Third, we discuss recent advances in machine learning methods for causal effects. These advances include methods to adjust for differences between treated and control units in high-dimensional settings, and methods for identifying and estimating heterogeneous treatment effects.
causal_inference  intervention  econometrics  review  susan.athey 
12 days ago by rvenkat
The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers
The growth of the "gig" economy generates worker flexibility that, some have speculated, will favor women. We explore one facet of the gig economy by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. Perhaps most surprisingly, we find that there is a roughly 7% gender earnings gap amongst drivers. The uniqueness of our data—knowing exactly the production and compensation functions—permits us to completely unpack the underlying determinants of the gender earnings gap. We find that the entire gender gap is caused by three factors: experience on the platform (learning-by-doing), preferences over where/when to work, and preferences for driving speed. This suggests that, as the gig economy grows and brings more flexibility in employment, women’s relatively high opportunity cost of non-paid-work time and gender-based preference differences can perpetuate a gender earnings gap even in the absence of discrimination
income  economics  gender  platform_economics  labor  econometrics 
15 days ago by rvenkat

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