000			02049nab a2200193 4500
003			OSt
005			20230731115017.0
007			cr aa aaaaa
008			230731b |||||||| |||| 00| 0 eng d
100			_aKolak, Marynia _956533
245			_aA Spatial Perspective on the Econometrics of Program Evaluation/
260			_bSage, _c2020.
300			_aVol 43, Issue 1-2, 2020( 128–153 p.)
520			_aEmpirical 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.
700			_aAnselin, Luc _956534
773	0		_011129 _917016 _dSage, 2019. _tInternational regional science review
856			_uhttps://doi.org/10.1177/0160017619869781
942			_2ddc _cEJR
999			_c14103 _d14103