We argue that identification problems bedevil most applied spatial research Spatial econometrics solves these problems by deriving estimators assuming that functional forms are known and by using model comparison techniques to let the data choose between competing specifications We argue that in most situations of interest this, at best, achieves only very weak identification Worse, in most cases, such an approach will simply be uninformative about the economic processes at work rendering much applied spatial econometric research ‘pointless’, unless the main aim is simply description of the data We advocate an alternative approach based on the ‘experimental paradigm’ which puts issues of identification and causality at centre stage

Mostly pointless spatial econometrics

Spatial econometrics has become a mainstay for regional scientists aiming to estimate geographic spillovers in regional outcomes. Yet, many remain skeptical, especially urban economists who prefer natural experimental approaches. Their concerns revolve around identification and a general lack of a theoretical foundation in the estimation of spatial econometric models. This theme issue includes three papers from leading regional scientists to appraise the status of spatial econometrics. The outcome is sweeping proposals from (1) abandoning standard spatial econometrics because it cannot identify causality, (2) using nonparametric approaches, and (3) implementing more nuanced changes revolving around better theoretical and empirical modeling.

Editorial: Introduction: Whither spatial econometrics?

This paper seeks to understand how the determinants of economic growth in Brazil may manifest themselves differently on various spatial scales between 1991 and 2000. Analysing this issue sheds light on the modifiable areal unit problem (a measurement issue). In addition, it also suggests potential explanations for the origin of this variability. This latter issue relates to the scale-dependent determinants of economic growth (a structural issue). The analysis reveals that the results change as the scale level changes and suggests a general framework for dealing with multiple spatial scales and model uncertainty. Moreover, the extent of the effects of spatial externalities and the issue of spatial heterogeneity are investigated.



Resumen

El objetivo de este articulo es entender como los factores determinantes del crecimiento economico de Brasil pueden mostrarse de manera diferente segun la escala espacial entre 1991 y 2000. El analisis de este tema ilustra el problema de la unidad de superficie modificable (problema de medicion). Ademas, sugiere tambien posibles explicaciones para el origen de esta variabilidad. Este ultimo asunto esta relacionado con los factores dependientes de la escala que determinan el crecimiento economico (un problema estructural). El analisis revela que los resultados cambian a medida que lo hace la escala y sugiere un marco general para tratar con escalas espaciales multiples y modelizacion de incertidumbre. Ademas, se investiga el alcance de los efectos de las externalidades espaciales y el problema de la heterogeneidad espacial.

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Multiple dimensions of regional economic growth: the Brazilian case, 1991−2000

Lending technologies and access to finance for SMEs in the hospitality industry.

The New Economic Geography (NEG) has moved from being almost purely theoretical to now becoming a basis for empirical research. The question is, given its origins in international economics, can the NEG model explain the economic geography of small regions within countries? This article reviews some of the literature relating to this question, concluding that we should consider simpler, easier to estimate rival models rather than assume that NEG will be successful at a low level of spatial aggregation.

The empirical performance of the NEG with reference to small areas

The goal of this paper is to evaluate the results of regional economic growth model estimations at multiple spatial scales using spatial panel data models. The spatial scales examined are minimum comparable areas, microregions, mesoregions and states between 1970 and 2000. Alternative spatial panel data models with fixed effects were systematically estimated across those spatial scales to demonstrate that the estimated coefficients change with the scale level. The results show that the conclusions obtained from growth regressions depend on the choice of spatial scale. First, the values of spatial spillover coefficients vary according to the spatial scale under analysis. In general, such coefficients are statistically significant at the MCA, microregional and mesoregional levels, however, at state level those coefficients are no longer statistically significant, suggesting that spatial spillovers are bounded in space. Moreover, the positive average-years-of-schooling direct effect coefficient increases as more aggregate spatial scales are used. Population density coefficients show that higher populated areas are harmful to economic growth, indicating that congestion effects are operating in all spatial scales, but their magnitudes vary across geographic scales. Finally, the club convergence hypothesis cannot be rejected suggesting that there are differences in the convergence processes between the north and south in Brazil. Furthermore, the paper discusses the potential theoretical reasons for different results found across estimations at different spatial scales.

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Patrícia Alessandra Morita Sakowski

Papers

Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models