Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies

TL;DR

This paper develops a framework to analyze how spatial inequality influences the effectiveness of AI-based resource allocation policies, revealing that targeted approaches can outperform neighborhood strategies even in highly segregated areas.

Contribution

It introduces the RENT metric and a stylized Mallows model to evaluate the impact of spatial inequality on targeting policies, reconciling conflicting prior findings.

Findings

Targeted policies significantly increase high-risk household outreach.

Spatial concentration of risk affects the relative efficiency of targeting.

Results are calibrated with eviction court data from a medium-sized US city.

Abstract

AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial distribution of inequality affects the effectiveness of door-to-door outreach policies. We introduce the RENT (Relative Efficiency of Non-Targeting) metric, which we use to assess the effectiveness of targeting approaches compared with neighborhood-based approaches in preventing tenant eviction when high-risk households are more versus less spatially concentrated. We then calibrate the model parameters to eviction court records collected in a medium-sized city in the USA. Results demonstrate considerable gains in the number of high-risk households canvassed through individually targeted policies, even in a highly segregated metro area with concentrated risks of eviction. We conclude that apparent discrepancies in the prior literature can be reconciled by considering 1) the source of deployment costs and 2) the observed versus modeled concentrations of risk. Our results inform the deployment of AI-based solutions in social service provision that account for particular applications and geographies.