Identification of Spatial Spillovers: Do's and Don'ts

ABSTRACT The notion of spatial spillovers has been widely used in applied spatial econometrics. In this paper, we consider how they can be identified in both structural and causal reduced‐form models. First, discussing the various threats to identification in structural models, we point out that the typical estimation framework proposed in the applied spatial econometric literature boils down to considering spatial spillovers as a side‐effect of a data‐driven chosen specification. We also discuss the limits of blindly relying on interaction matrices purely based on geography to identify the source and content of spillovers. Then, we present reduced forms impact evaluation models for spatial data and show that the current spatial versions of usual impact evaluation models are not fully satisfactory when considering the identification issue. Finally, we propose a set of recommendations for applied articles aimed at identifying spatial spillovers.

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Publication Details

Journal
Journal of Economic Surveys
Published
2025-03-11
DOI
https://doi.org/10.1111/joes.12692
Citations
5
Primary Topic
Spatial and Panel Data Analysis
Type
article
Field-Weighted Citation Impact
15.66
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article

Identification of Spatial Spillovers: Do's and Don'ts

Julie Le Gallo, Nicolas Debarsy
5 citations
Journal of Economic Surveys
Spatial and Panel Data Analysis
15.66
article

Identification of Spatial Spillovers: Do's and Don'ts

Julie Le Gallo, Nicolas Debarsy
article en
5 citations

Abstract

ABSTRACT The notion of spatial spillovers has been widely used in applied spatial econometrics. In this paper, we consider how they can be identified in both structural and causal reduced‐form models. First, discussing the various threats to identification in structural models, we point out that the typical estimation framework proposed in the applied spatial econometric literature boils down to considering spatial spillovers as a side‐effect of a data‐driven chosen specification. We also discuss the limits of blindly relying on interaction matrices purely based on geography to identify the source and content of spillovers. Then, we present reduced forms impact evaluation models for spatial data and show that the current spatial versions of usual impact evaluation models are not fully satisfactory when considering the identification issue. Finally, we propose a set of recommendations for applied articles aimed at identifying spatial spillovers.

Journal of Economic SurveysVol. 39(5)
Centre National de la Recherche Scientifique (FR), Université de Lille (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Centre d'économie et de sociologie appliquées à l'agriculture et aux espaces ruraux (FR), Institut d'Economie Scientifique Et de Gestion (FR), Lille Économie Management (FR)
Openalex Percentile: Top 2%
Spatial and Panel Data Analysis
15.66
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Identification of Spatial Spillovers: Do's and Don'ts — Julie Le Gallo, Nicolas Debarsy · Journal of Economic Surveys (2025) | TGRS Research Map | TGRS