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Spatial Economics for Granular Settings

Jonathan Dingel () and Felix Tintelnot ()
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Felix Tintelnot: University of Chicago - Department of Economics; NBER; CEPR

No 2020-71, Working Papers from Becker Friedman Institute for Research In Economics

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: Commuting; granularity; gravity equation; quantitative spatial economics (search for similar items in EconPapers)
JEL-codes: C25 F16 R1 R13 R23 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2020
New Economics Papers: this item is included in nep-geo and nep-ure
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https://repec.bfi.uchicago.edu/RePEc/pdfs/BFI_WP_202071.pdf (application/pdf)

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Working Paper: Spatial Economics for Granular Settings (2020) Downloads
Working Paper: Spatial Economics for Granular Settings (2020) Downloads
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