The Role of Human Capital and Technological Interdependence in Growth and Convergence Processes: International Evidence

This paper develops a bisectorial growth model with physical and human capital accumulation. Each sector is characterized by a different technology involving different human capital parameters. The model includes human capital externalities together with technological interdependence between economies. It leads to a spatial autoregressive reduced form for the real income per worker at steady state. The structural parameters of the model are recovered and evidence of the insignificance of human capital in explaining per capita growth, that is the human capital puzzle, is reconsidered. In fact, the parameter related to human capital in the consumption good sector is low which is consistent with evidence presented in the growth accounting framework. In contrast it is indeed higher in the education sector. Our model leads to spatial econometric specifications which are estimated on a sample of 89 countries over the period 1960-1995 using maximum likelihood as well as Bayesian estimation methods, which are robust versus outliers and heteroskedasticity. This model yields a spatially augmented convergence equation characterized by parameter heterogeneity. A locally linear spatial autoregressive specification is then estimated providing a different convergence speed estimate for each country of the sample.Conditional convergence, technological interdependence, spatial autocorrelation, parameter heterogeneity, locally linear estimation

Growth, Technological Interdependence and Spatial Externalities - Theory and Evidence

This paper presents a theoretical model, based on the neoclassical growth literature, which explicitly takes into account technological interdependence among economies and examines the impact of location and neighborhood effects in explaining growth. Technological interdependence is supposed working through spatial externalities. The magnitude of the physical capital externalities at steady state, which is usually not identified in the literature, is estimated using a spatial econometric specification explaining the steady state income level. This spatially augmented Solow model yields a conditional convergence equation which is characterized by parameter heterogeneity. A locally linear spatial autoregressive specification is then estimated.

The European Enlargement Process and Regional Convergence Revisited: Spatial Effects Still Matter.

This paper has two main goals. First, it reconsiders regional growth and convergence processes in the context of the enlargement of the European Union to new member states. We show that spatial autocorrelation and heterogeneity still matter in a sample of 237 regions over the period 1993-2002. Spatial convergence clubs are defined using exploratory spatial data analysis and a spatial autoregressive model is estimated. We find strong evidence that the growth rate of per capita GDP for a given region is positively affected by the growth rate of neighbouring regions. The second objective is to test the robustness of the results with respect to non-normality, outliers and heteroskedasticity using two other methods: The quasi maximum Likelihood and the Bayesian estimation methods.

Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model

This paper derives several Lagrange Multiplier statistics and the correspondinglikelihood ratio statistics to test for spatial autocorrelation in a fixed effectspanel data model. These tests allow discriminating between the two main typesof spatial autocorrelation which are relevant in empirical applications, namelyendogenous spatial lag versus spatially autocorrelated errors. In this paper, fivedifferent statistics are suggested. The first one, the joint test, detects the presenceof spatial autocorrelation whatever its type. Hence, it indicates whetherspecific econometric estimation methods should be implemented to account forthe spatial dimension. In case they need to be implemented, the other four testssupport the choice between the different specifications, i.e. endogenous spatiallag, spatially autocorrelated errors or both. The first two are simple hypothesistests as they detect one kind of spatial autocorrelation assuming the otherone is absent. The last two take into account the presence of one type of spatialautocorrelation when testing for the presence of the other one. We use themethodology developed in Lee and Yu (2008) to set up and estimate the generallikelihood function. Monte Carlo experiments show the good performance ofour tests. Finally, as an illustration, they are applied to the Feldstein-Horiokapuzzle. They indicate a misspecification of the investment-saving regressiondue to the omission of spatial autocorrelation. The traditional saving-retentioncoefficient is shown to be upward biased. In contrast our results favor capitalmobility.Testing ; Spatial ; Autocorrelation ; Fixed ; Effects ; Panel Data Model

A Contribution to the Schumpeterian Growth Theory and Empirics

Cet article propose un cadre théorique et méthodologique unifie caractérisé par la priseen compte explicite des intéractions technologiques dans la modélisation des processus decroissance en adoptant une perspective Schumpétérienne. L'interdépendance globale impliquée par les spillovers internationaux de R&D doit être intégrée non seulement dans lamodélisation théorique mais également dans la spécification économétrique qui en découle.L'économétrie spatiale apparaît alors naturellement comme la méthodologie adéquate pourtraiter le problème de l'estimation de telles spécifications. Le modèle économétrique que nousproposons inclut le modèle de croissance néoclassique comme cas particulier. Nous pouvonspar conséquent tester explicitement le rôle joué par les investissements en R&D dans le processusde croissance de long terme contre le modèle de croissance de Solow. Finalement, lespropriétés de notre spécification économétrique spatiale permettent d'évaluer les effets directet indirect des spillovers internationaux de R&D.Contribution; Schumpeterian; Growth; Theory; Empirics

Growth and Spatial Dependence - The Mankiw, Romer and Weil model revisited

The aim of this paper is to analyze the theoretical and econometric implications of omitting spatial dependence in the Mankiw, Romer, and Weil model. Indeed, the international distribution of income levels and growth rates suggests the existence of large international disparities, and therefore the important role of location on economic performance. However, taking spatial dependence into account requires resorting to the methods of Spatial Econometrics, not only for a valid statistical inference, but also for revaluating the impact of the variables generally considered as crucial in the growth phenomenon and finding the processes underlying growth rates and income levels.

"Dual' gravity: Using spatial econometrics to control for multilateral resistance"

We propose a quantity-based 'dual' version of the gravity equation that yields an estimating equation with both cross-sectional interdependence and spatially lagged error terms. Such an equation can be concisely estimated using spatial econometric techniques. We illustrate this methodology by applying it to the Canada-U.S. data set used previously, among others, by Anderson and van Wincoop (2003) and Feenstra (2002, 2004). Our key result is to show that controlling directly for spatial interdependence across trade flows, as suggested by theory, significantly reduces border effects because it captures 'multilateral resistance'. Using a spatial autoregressive moving average specification, we find that border effects between the U.S. and Canada are smaller than in previous studies: about 8 for Canadian provinces and about 1.3 for U.S. states. Yet, heterogeneous coefficient estimations reveal that there is much variation across provinces and states.

‘Dual’ gravity: using spatial econometrics to control for multilateral resistance

We propose a quantity-based `dual' version of the gravity equation that yields an estimating equation with both cross-sectional interdependence and spatially lagged error terms. Such an equation can be concisely estimated using spatial econometric techniques. We illustrate this methodology by applying it to the Canada-U.S. data set used previously, among others, by Anderson and van Wincoop (2003) and Feenstra (2002, 2004). Our key result is to show that controlling directly for spatial interdependence across trade flows, as suggested by theory, significantly reduces border effects because it captures `multilateral resistance'. Using a spatial autoregressive moving average specification, we find that border effects between the U.S. and Canada are smaller than in previous studies: about 8 for Canadian provinces and about 1.3 for U.S. states. Yet, heterogeneous coefficient estimations reveal that there is much variation across provinces and states.gravity equations, multi-region general equilibrium trade models; spatial econometrics, border effects

The European Regional Convergence Process, 1980-1995: Do Spatial Regimes and Spatial Dependence Matter?

We show in this paper that spatial dependence and spatial heterogeneity matter in the estimation of the b-convergence process among 138 European regions over the 1980-1995 period. Using spatial econometrics tools, we detect both spatial dependence and spatial heterogeneity in the form of structural instability across spatial convergence clubs. The estimation of the appropriate spatial regimes spatial error model shows that the convergence process is different across regimes. We also estimate a strongly significant spatial spillover effect: the average growth rate of per capita GDP of a given region is positively affected by the average growth rate of neighboring regions.convergence, club convergence, spatial econometrics, European regions, spatial regimes, spatial autocorrelation

SPATIAL ANALYSIS OF EMPLOYMENT AND POPULATION DENSITY: THE CASE OF THE AGGLOMERATION OF DIJON, 1999

The aim of this paper is to analyze the intra-urban spatial distributions of population and employment in the agglomeration of Dijon (regional capital of Burgundy, France). We study whether this agglomeration has followed the general tendency of job decentralization observed in most urban areas or whether it is still characterized by a monocentric pattern. In that purpose, we use a sample of 136 observations at the communal and at the IRIS (infra-urban statistical area) levels with 1999 census data and the employment database SIRENE (INSEE). First, we study the spatial pattern of total employment and employment density using exploratory spatial data analysis. Apart from the CBD, few IRIS are found to be statistically significant, a result contrasting with those found using standard methods of subcenter identification with employment cut-offs. Next, in order to examine the spatial distribution of residential population density, we estimate and compare different specifications: exponential negative, spline- exponential and multicentric density functions. Moreover, spatial autocorrelation, spatial heterogeneity and outliers are controlled for by using the appropriate maximum likelihood, generalized method of moments and Bayesian spatial econometric techniques. Our results highlight again the monocentric character of the agglomeration of Dijon.Bayesian spatial econometrics, exploratory spatial data analysis, outliers, population density, spatial autocorrelation, spatial heterogeneity, employment subcenters