TY - JOUR
T1 - A Spatial Perspective on the Econometrics of Program Evaluation
AU - Kolak, Marynia
AU - Anselin, Luc
N1 - Funding Information:
Comments by the participants in the sessions are gratefully acknowledged, especially from the discussants Jan Rouwendal and Sandy Dall Erba. Thanks also to Julia Koschinsky, Paul Elhorst, and James LeSage for useful suggestions. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded in part by Award 1R01HS021752-01A1 from the Agency for Healthcare Research and Quality (AHRQ), “Advancing spatial evaluation methods to improve healthcare efficiency and quality.”
Publisher Copyright:
© The Author(s) 2019.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Empirical work in regional science has seen a growing interest in causal inference, leveraging insights from econometrics, statistics, and related fields. This has resulted in several conceptual as well as empirical papers. However, the role of spatial effects, such as spatial dependence (SD) and spatial heterogeneity (SH), is less well understood in this context. Such spatial effects violate the so-called stable unit treatment value assumption advanced by Rubin as part of the foundational framework for empirical treatment effect analysis. In this article, we consider the role of spatial effects more closely. We provide a brief overview of a number of attempts to extend existing econometric treatment effect evaluation methods with an accounting for spatial aspects and outline and illustrate an alternative approach. Specifically, we propose a spatially explicit counterfactual framework that leverages spatial panel econometrics to account for both SD and SH in treatment choice, treatment variation, and treatment effects. We illustrate this framework with a replication of a well-known treatment effect analysis, that is, the evaluation effect of minimum legal drinking age laws on mortality for US states during the period 1970–1984, a classic textbook example of applied causal inference. We replicate the results available in the literature and compare these to a range of alternative specifications that incorporate spatial effects.
AB - Empirical work in regional science has seen a growing interest in causal inference, leveraging insights from econometrics, statistics, and related fields. This has resulted in several conceptual as well as empirical papers. However, the role of spatial effects, such as spatial dependence (SD) and spatial heterogeneity (SH), is less well understood in this context. Such spatial effects violate the so-called stable unit treatment value assumption advanced by Rubin as part of the foundational framework for empirical treatment effect analysis. In this article, we consider the role of spatial effects more closely. We provide a brief overview of a number of attempts to extend existing econometric treatment effect evaluation methods with an accounting for spatial aspects and outline and illustrate an alternative approach. Specifically, we propose a spatially explicit counterfactual framework that leverages spatial panel econometrics to account for both SD and SH in treatment choice, treatment variation, and treatment effects. We illustrate this framework with a replication of a well-known treatment effect analysis, that is, the evaluation effect of minimum legal drinking age laws on mortality for US states during the period 1970–1984, a classic textbook example of applied causal inference. We replicate the results available in the literature and compare these to a range of alternative specifications that incorporate spatial effects.
KW - spatial dependence
KW - spatial econometrics
KW - spatial heterogeneity
KW - treatment effects
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U2 - 10.1177/0160017619869781
DO - 10.1177/0160017619869781
M3 - Article
AN - SCOPUS:85071652290
SN - 0160-0176
VL - 43
SP - 128
EP - 153
JO - International Regional Science Review
JF - International Regional Science Review
IS - 1-2
ER -