Where Is the Economics in Spatial Econometrics?*

Abstract

Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.

Environmental Regulation, Economic Network and Sustainable Growth of Urban Agglomerations in China

“…Bai et al [50] used the Moran's index to detect the positive spatial autocorrelations across the provinces of China, and applied spatial econometric method to study the determinants of regional economic growth. Corrado and Fingleton [51] stated that different spatial weight matrices can be used to reflect the channels of spillover effect. Therefore, this study will build several spatial weight matrices to describe multidirectional and multi-level relations in the urban agglomeration.…”

Regional labour force participation across the European Union: a time–space recursive modelling approach with endogenous regressors

“…We highlight these interaction effects for the first time in this context. Consistent with the theoretical and empirical rationale outlined in Section 2.3, most of the short-term spillover effects of these labour market determinants are significant; consequently, the coefficient estimate of W × LFP t-1 may pick up these effects when these variables are erroneously omitted from the model (Corrado & Fingleton, 2012).…”

“…Also, as demonstrated by Elhorst (2010), an important limitation of the SAR model is that the ratio between the marginal impacts of changes to explanatory variables in a region on the dependent variable values in other regions (spillover effect) and own region (direct effect) is the same for every explanatory variable, which is unlikely to hold in many applied settings. Also of great significance, as demonstrated by Corrado & Fingleton (2012), is that the empirical evidence in favour of a spatially lagged dependent variable can be misleading, since it may be picking up the effects of interaction effects among explanatory variables erroneously omitted from the model.…”

Raising the bar (1)

“…This might explain why the coefficient of the spatial lag decreases so strongly when spatial and time period fixed effects are controlled for. It also illustrates Corrado & Editorial 3 Fingleton's (2012) point that the coefficient estimate for the spatial lag may be significant because it could be picking up the effects of omitted variables, in this case fixed effects. We therefore recommend our readers study Table A3 of this paper in particular.…”