# Urban mobility and crime: causal inference using street closures as an instrumental variable

## TL;DR

This study examines whether visitor flows in urban areas cause increases in crime using causal inference methods and New York City data.

## Contribution

The paper introduces a novel instrumental variable approach to estimate the causal effect of visitors on crime.

## Key findings

- Two-way fixed effects models show a significant effect of visitors on various crime forms.
- Instrumental variable estimates find no statistically significant causal impact of visitors on crime rates.
- Large standard errors suggest substantial uncertainty in the relationship between visitors and crime.

## Abstract

The advent of widely available cell phone mobility data in the United States has rapidly expanded the study of everyday mobility patterns in social science research. A wide range of existing literature finds ambient population (e.g., visitors) estimates of an area to be predictive of crime. Much of the past research frames neighborhood visitor flows in predictive terms without necessarily indicating or implying a causal effect. Through the use of two causal inference approaches—conventional two-way fixed effects and a novel instrumental variable approach, this brief research report explicitly formulates the causal effect of visitors in counterfactual terms. This study addresses this gap by explicitly estimating the causal effect of visitor flows on crime rates. Using high-resolution mobility and crime data from New York City for the year 2019, I estimate the additive effect of visitors on the multiple measurements of criminal activity. While two-way fixed effects models show a significant effect of visitors on a wide array of crime forms, instrumental variable estimates indicate no statistically significant causal impact, with large standard errors indicating substantial uncertainty in visitors' effect on crime rates.