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Using analytical examples, 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 these high\u2010dimensional settings. Parsimonious specifications of spatial linkages deliver better counterfactual predictions. To quantify the uncertainty about counterfactual outcomes induced by the idiosyncratic component of individuals' decisions, we introduce a quantitative spatial model with a finite number of individuals. Applying this model to Amazon's proposed second headquarters in New York City reveals that its predicted consequences for most neighborhoods vary substantially across realizations of the individual idiosyncrasies.<\/jats:p>","DOI":"10.3982\/ecta19350","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:11:56Z","timestamp":1774591916000},"page":"407-464","source":"Crossref","is-referenced-by-count":4,"title":["Spatial Economics for Granular Settings"],"prefix":"10.3982","volume":"94","author":[{"given":"Jonathan I.","family":"Dingel","sequence":"first","affiliation":[{"name":"Department of Economics, Columbia University"},{"name":"NBER"},{"name":"CEPR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Tintelnot","sequence":"additional","affiliation":[{"name":"Department of Economics, Duke 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