The Effects of Mandatory Speed Limits on Crash Frequency - A Causal Machine Learning Approach

This study analyzes the effects of binding speed limits on crash frequency on German motorways. Various geo-spatial data sources are merged to a new data set providing rich information on roadway characteristics for 500-meter segments of large parts of the German motorway network. The empirical analysis uses a causal forest, which allows to estimate the effects of speed limits on crash frequency under fairly weak assumptions about the underlying data generating process and provides insights into treatment effect heterogeneity. The paper is the first to explicitly discuss possible pitfalls and potential solutions when applying causal forests to geo-spatial data. Substantial negative effects of three levels of speed limits on accident rates are found, being largest for severe, and especially fatal crash rates, while effects on light crash rates are rather moderate. The heterogeneity analysis suggest that the effects are larger for less congested roads, as well as for roads with entrance and exit ramps, while heterogeneity regarding shares of heavy traffic is inconclusive.