Working Paper: NBER ID: w27287
Authors: Jonathan I. Dingel; Felix Tintelnot
Abstract: We examine the application of quantitative spatial models to the growing body of fine spatial data used to study economic outcomes for regions, cities, and neighborhoods. In “granular” settings where people choose from a large set of potential residence-workplace pairs, idiosyncratic choices affect equilibrium outcomes. Using both Monte Carlo simulations and event studies of neighborhood employment booms, we demonstrate that calibration procedures that equate observed shares and modeled probabilities perform very poorly in such settings. We introduce a general-equilibrium model of a granular spatial economy. Applying this model to Amazon's proposed HQ2 in New York City reveals that the project's predicted consequences for most neighborhoods are small relative to the idiosyncratic component of individual decisions in this setting. We propose a convenient approximation for researchers to quantify the “granular uncertainty” accompanying their counterfactual predictions.
Keywords: quantitative spatial models; urban policies; economic outcomes; granular settings
JEL Codes: C25; F16; R1; R13; R23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| calibrated-shares procedure (G24) | poor performance in granular settings (D29) |
| covariates-based approach (C10) | better prediction of commuting flows (R41) |
| calibrated-shares procedure (G24) | negative slope in predictions (C29) |
| granular model (E10) | distribution of economic outcomes (D30) |
| granular uncertainty (D80) | understanding economic consequences of policies (E64) |
| employment changes (J63) | calibrated-shares procedure performance (C59) |