Toward Sustainable Economic Growth: A Spatial Panel Data Analysis of Regional Income Convergence in US BEA Economic Areas

Abstract

Abstract:A question fundamental to sustainable economic growth is whether a poor region tends to grow faster than a rich one, such that the poor region catches up with the rich region in terms of the level of per capita income. In this article, we apply the spatial panel data approach to the analysis of regional income convergence across 177 economic areas in the contiguous US states over the period from 1969 to 2009. Using data on per capita incomes in the functionally defined economic areas, we find that the absolute value of the estimated coefficient of the initial per capita income decreases in the spatial and time-period fixed effects spatial lag model and increases in the spatial and time-period fixed effects spatial error model. This result implies that the growth rate in a specific economic area will be not only directly affected by an exogenous shock introduced into that economic area but also be impacted more by both the indirect effects of the first-order neighboring economic areas and the induced effects of the higher-order neighboring economic areas. This gives helpful hints on the issue of spatial interaction and regional policy coordination to start a virtuous circle of sustainable economic growth.

Convergence of food consumption across Ukrainian regions: approach using spatial panel data models

“…That is, the convergence rate is lower in spatial autoregressive models. Such results are quite expected and are supported by a number of studies devoted to modeling convergence using spatial panel data models (Abreu et al, 2005;Arbia et al, 2008;Lim & Kim, 2015) and can be interpreted as follows. The β parameter in the spatial autoregressive model represents only the direct marginal effect of increasing the initial consumption level, while the β parameter in the spatial error model accumulates the influence of both the direct marginal effect and indirect effects.…”

“…For example, Giuseppe Armani and Gianfranco Piras (Aria & Piras, 2004) showed that taking into account spatial interaction in the convergence analysis significantly improves the estimated values of the convergence rate between European regions. A thorough analysis of convergence between European regions using spatial econometric models was carried out in the studies (Armstrong, 1995;López-Bazo et al, 2004;Fingleton & Lopez-Bazo, 2006;Piras & Arbia, 2007;Rey & Le Gallo, 2009), between regions of the USAin the studies (Rey & Montouri, 1999;Lim & Kim, 2015). Also, the concept of convergence using spatial econometric models has been discussed not only in the literature on economic growth.…”

“…The diagonal elements of matrix equal a zero. There are several approaches to constructing a weight matrix, more detailed information about which can be found in the works (Anselin, 1988;Haining, 2003;Cliff & Ord, 1973;Linderhof et al, 2011). In this work we used two of the most common weight matrix types such as contiguity matrix and inverse distance matrix.…”