Trade and spatial economic interdependence

Abstract. Regional scientists have had a long fascination with spatial economic interdependence, initially stimulated by the development of a suite of models in Isard (1960). In recent years there have been two major developments; first, a movement towards greater integration in model development not only with large-scale models, but also with careful attempts to make more components of the system endogenous. The second development has been the pursuit of greater integration than in earlier studies between theory and applied regional economic modelling, most notably in the area of trade and economic development. We explore some of the recent developments of this latter approach and provide an assessment of the current state-of-the art of some future opportunities.     

Intraregional trade shares for goods-producing industries: RPC estimates using EU data

The lack of subnational trade data has dampened the development of reliable regional and multiregional models for regional policy development. So, most researchers and vendors of regional and interregional economic models continue to rely on location quotients, supply–demand pool techniques, or minor modifications of them, despite knowing that they under-estimate interregional trade. In this piece, we analyse the relative viability of estimates of intraregional trade—so called “regional purchase coefficients” (RPCs). We do so for manufacturing sectors in 28 EU countries using the World Input–Output Database. We introduce an RPC-estimating technique using a quasi-binomial regression approach for goods-producing industries; we apply standard supply/demand ratios as RPCs for service-based industries. We then apply the estimates to an aggregate EU input–output (I-O) table and measure how closely the results approximate the I-O tables (direct requirements matrices) for each of the 28 EU nations. We compare these findings to those obtained by other conventional approaches. We also evaluate their ability to replicate the country Leontief inverses and output multipliers. We find quasi-binomial regression approaches superior across the board.     

Crime and regional economic growth in Mexico: A spatial perspective

The aim of this paper is to examine the effects of crime on regional economic growth in Mexico. We estimate a growth model using a spatial panel data model and test for the presence and type of spatial interaction, as proposed by Elhorst. Our results suggest that a two‐way spatial Durbin model best fits the data, crime exerts a negative total effect on economic growth across Mexican states, particularly homicides and robbery, and significant spillover effects seem to reinforce the negative impact on regional growth.     

Trade openness and regional income spillovers in Brazil: A spatial econometric approach*

This paper employs a spatial Durbin growth model to estimate the impact of trade openness on regional per capita income in Brazil using a data set of 469 Brazilian micro‐regions over the period 2004–2007. We calculate the direct, indirect and cumulative impact on per capita income of trade openness and human capital in these micro‐regions. Results indicate that greater trade openness in a region promotes economic development locally, while exerting negative influence on per capita income of the neighbouring regions. Our findings also show that human capital has a positive – direct and indirect – impact on the economic development of Brazilian micro‐regions.