We document the extent of measurement errors in the basic data set on police used in the literature on the effect of police on crime. Analyzing medium to large U.S. cities over 1960 to 2010, we obtain measurement error-corrected estimates of the police elasticity. The magnitudes of our estimates are similar to those obtained in the quasi-experimental literature, but our approach yields much greater parameter certainty for the most costly crimes, the key parameters for welfare analysis. Our analysis suggests that U.S. cities are substantially underpoliced.

Frölich (2004) compares the finite sample properties of reweighting and matching estimators of average treatment effects and concludes that reweighting performs far worse than even the simplest matching estimator. We argue that this conclusion is unjustified. Neither approach dominates the other uniformly across data-generating processes (DGPs). Expanding on Frölich's analysis, this paper analyzes empirical as well as hypothetical DGPs and also examines the effect of misspecification. We conclude that reweighting is competitive with the most effective matching estimators when overlap is good, but that matching may be more effective when overlap is sufficiently poor.

Forward-looking behavior on the part of the monetary authority makes it difficult to estimate the effect of monetary policy interventions on output. We present instrumental variables estimates of the impact of interest rates on quarterly real output for several European countries, using German interest rates as the instrument. These estimates confirm a strong forward-looking bias in least squares estimates that persists even conditional on standard controls for the history of the system. Due to the potential for correlation of output shocks across countries, we interpret our estimates as lower bounds for the effect of monetary policy on real output.