Gianfranco Piras

Convergence in Per-capita GDP across European Regions using Panel Data Models Extended to Spatial Autocorrelation Effects

Most of the empirical works in regional convergence are based on either cross-sectional or “a-spatial” panel data models. In this paper, we propose the use of panel data econometrics models that incorporate an explicit consideration of spatial dependence effects (Anselin, 1988; Elhorst, 2001; 2003). This allows us to extend the traditional convergence models to include a rigorous treatment of regional spillovers and to obtain more reliable estimates of the parameters. We consider two models respectively based on the introduction of a spatial lag among the explanatory variables (the “spatial lag model”) and imposing a spatial autoregressive structure to the stochastic component (the “spatial error model”). We apply such a modelling framework to the long-run convergence of per-capita GDP of 125 EU-NUTS2 regions observed yearly in the period 1977-2002. A comparison of the results obtained using the two spatial panel data specifications with the main evidence available in the literature is also provided.

The European Regional Growth Process Revisited

Abstract With the 2004 enlargement to the East, the EU regional growth process can no longer be seen in the frame of the traditional core–periphery pattern. This is why this article proposes an innovative methodology to endogenously detect convergence clubs while accounting for spatial autocorrelation across regions. Our model is estimated on 244 EU25 regions over 1991–2003. Our results indicate that four distinctive clubs are present in our sample. In addition, the model we use does not rely on the traditional neoclassical model but on Verdoorns law, which allows us to account for the presence of increasing returns to scale. Our conclusions give new insights for policy makers interested in convergence and regional policies developed to promote it.

Using Spatial Panel Data in Modelling Regional Growth and Convergence

In this paper we use spatial dependence panel data models to analyse regional growth behaviour in Italy. Controlling for fixed-effects allows us to disentangle the effect of spatial dependence (or spatial interaction) from that of spatial heterogeneity and of omitted variables and, thus, to properly investigate the regional convergence process within the country.

Does Evidence on Regional Economic Convergence Depend on the Estimation Strategy? Outcomes from Analysis of a Set of NUTS2 EU Regions

Abstract The regional economic convergence/divergence issue has been discussed extensively recently, but results obtained are not always interpretable unequivocally as a consequence of the different estimation strategies used. As it is widely recognized, the most common theoretical framework applied to measure the speed of economic convergence among countries or regions remains the β-convergence approach, linked to the neoclassical Solow model. There have been many attempts to consider variations of the basic cross-sectional specification ranging from panel data models to Bayesian spatial econometric techniques. The application of spatial econometric methodologies is an essential tool for proper statistical inference on regional data. In this context, the aim of this paper is to connect the different results obtained in the literature. More specifically, we address whether or not evidence on convergence depends upon the estimation strategy, by taking the same set of data and systematically comparing the results obtained from different estimation strategies. The results from a set of NUTS2 EU regions conclude that both the model implied by the cross-sectional analysis and the one referring to the space-time dynamics incorporated in the panel specification point to convergence. The concept of convergence implied is, however, quite different, as demonstrated throughout the paper.

The Journey to Safety: Conflict-Driven Migration Flows in Colombia

While there is a growing econometrics literature on the modeling of conflict and the interactions with trade, there has been relatively little evidence modeling the interregional migration behavior of individuals internally displaced by conflicts. The current article models the flows of households forced to leave their residence because of violent conflicts in Colombia. Results shed light on the main determinants of what we call journey to safety. Violence appears to be one of the most relevant pushing effects together with the absence of institutions and the dissatisfaction with the provision of basic needs. Furthermore, for regions with extreme violence levels, individuals appear to be willing to relocate to more distant locations. On the destination side, most populated regions are more attractive as well as areas with a sufficient level of fulfillment of basic needs.

Spatial J-test: some Monte Carlo evidence

Researchers using spatial econometric methods generally assume a known structure for the process being modeled embedded in a spatial weights matrix. The present paper evaluates the performance of the J-test in selecting the most appropriate spatial structure in the context of a Monte Carlo study. Results suggest that the J-test performs well when used to select between different weights matrices. Increases in power are associated with the use of the full set of instruments.

The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates

This article compares results from non-spatial and new spatial methods to examine the reliability of welfare estimates (direct and multiplier effects) for locational housing attributes in Seattle, WA. In particular, we assess if OLS with spatial fixed effects is able to account for the spatial structure in a way that represents a viable alternative to spatial econometric methods. We find that while OLS with spatial fixed effects accounts for more of the spatial structure than simple OLS, it does not account for all of the spatial structure. It thus does not present a viable alternative to the spatial methods. Similar to existing comparisons between results from non-spatial and established spatial methods, we also find that OLS generates higher coefficient and direct effect estimates for both structural and locational housing characteristics than spatial methods do. OLS with spatial fixed effects is closer to the spatial estimates than OLS without fixed effects but remains higher. Finally, a comparison of the direct effects with locally weighted regression results highlights spatial threshold effects that are missed in the global models. Differences between spatial estimators are almost negligible in this study.

A new class of spatial concentration measures

In this paper we propose a new class of spatial concentration measures to verify hypotheses on the spatial concentration of empirical phenomena. Our proposed measures incorporate the ideas of both a-spatial concentration and spatial autocorrelation. We discuss many issues related to the approximate sampling theory and we suggest a Monte Carlo test for the presence of significant spatial concentration. A simulation experiment studying the relationship of the new measure to different spatial patterns and to different levels of a-spatial variability is also reported. A discussion related to irregular space is included.

Service industry and cumulative growth in the regions of Europe

European regions have experienced a greater presence of service producers in their economy over the last few decades. Indeed, the manufacturing sector increasingly contracts out many activities to intermediate producer services. This is mostly because they are located close to each other and because services experience increasing returns to scale which reduce their marginal costs. In this paper, we propose to measure the extent to which productivity in services has converged across European regions. The model we use, originally developed by Verdoorn (1949), takes the increasing returns to scale explicitly into account. We apply spatial econometric techniques and control for border effects by introducing two different spatial weights matrices under the assumption that economic interactions decrease very substantially when a national border is passed. Furthermore, we take proper care of the presence of both types (spatial and non-spatial) of endogeneity by using spatial two stages least squares (Kelejian and Prucha 1998). Our conclusions bring new insights in the identification of regional productivity differentials.

a spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tract level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran’s I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran’s I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran’s I=0.452, P=0.001), and in the OLS regression residuals (Global Moran’s I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=−0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=−0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.