Cem Ertur

Spatial Analysis of Employment and Population Density: The Case of the Agglomeration of Dijon 1999

The aim of this paper is to analyze the intraurban 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. To that purpose, we use a sample of 136 observations at the communal and at the IRIS (infraurban 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 identiÞcation 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.

A Spatial Econometric Analysis of Convergence Across European Regions, 1980–1995

The convergence of European regions has been largely discussed in the macroeconomic and the regional science literature during the past decade. Two observations are often emphasized. First, the convergence rate among European regions appears to be very slow in the extensive samples considered (Barro and Sala-iMartin 1991, 1995; Armstrong 1995a; Sala-i-Martin 1996a, 1996b). Second, as shown in Ertur and Le Gallo (see Chap. 2), the geographical distribution of European per capita GDP is highly clustered.

Spatial Convergence Clubs and the European Regional Growth Process,1980–1995

In international cross-country studies, evidence for club convergence has often been found using different methodologies (Baumol 1986; Durlauf and Johnson 1995; Quah 1996a, 1997). In the case of the European regions, Ertur and Le Gallo (see Chap. 2) and Le Gallo et al. (see Chap. 3) have shown that the convergence rate among European regions is slow and that GDP disparities seem to be persistent despite the European economic integration process and higher growth rates of some poorer regions, as highlighted as well in the European Commission reports (1996, 1999). Moreover, over the 1980–1995 period, Ertur and Le Gallo (see Chap. 2) found that the geographical distribution of European regions exhibits a persistent polarization pattern between rich regions in the north and poor regions in the south.

An Exploratory Spatial Data Analysis of European Regional Disparities, 1980–1995

European integration has stimulated numerous studies of regional economic convergence within the European Union in recent macroeconomic and regional science literature (e.g. Abraham and Von Rompuy 1995; Armstrong 1995a; Neven and Gouyette 1995; Martin 2001). Most of the time, the empirical methods that have been used are identical to the methods employed in international studies. However, spatial effects, particularly spatial autocorrelation and spatial heterogeneity, must be taken into account when analyzing the convergence process at regional scale. There are number of factors — trade between regions, technology, knowledge diffusion and more generally regional spillovers — that lead to geographically dependent regions. Because of spatial interactions between regions, geographical location is important in accounting for the economic performances of regions. Yet for all its importance, the role of spatial effects in convergence processes has only been recently examined using the appropriate spatial statistic and econometric methods (Armstrong 1995b; Fingleton 1999; Lopez-Bazo et al. 1999, for European regions; Rey and Montouri 1999; Rey 2001, for US states).

Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980–1995

Abstract. The aim of this paper is to study the space-time dynamics of European regional per capital GDP. A sample of 138 European regions over the 1980–1995 period provides clear evidence of global and local spatial autocorrelation as well as spatial heterogeneity in the distribution of regional per capita GDP. The detection of spatial clusters of high and low per capita GDP throughout the period is an indication of the persistence of spatial disparities among European regions. The dynamism of European regions is investigated by exploring the spatial pattern of regional growth. Implications for applied econometric work on the convergence of European regions are then suggested.

Regional Growth and Convergence: Heterogenous reaction versus interaction in spatial econometric approaches

This paper presents various approaches dealing with heterogeneous reaction combined with interaction between neighboring units of observation developed in the spatial econometric literature, in the framework of cross-sectional models, and applied to the study of growth and convergence processes. We present the main econometric specifications capturing discrete or continuous spatial heterogeneity: the spatial regimes model and the locally linear, geographically weighted regression (GWR). We then examine how these specifications can be extended to further allow for spatial autocorrelation.

Valuation Effects of Listing on a More Prominent Segment of the Stock Market: Evidence from France

We examine the behaviour of stock prices during the period around the transfer to the Marchea Reglement Mensuel. First, we discuss the financial reasons, which can justify abnormal returns around the transfer. Second, an event study based on a sample of 71 firms is set up to test the existence of the exchange listing effect on the French market. Third, we explore three hypotheses in order to explain the impact on stock returns: the informative content of the transfer, the increase in the relative size of the firm’s investor base, and the reduction of trading costs (immediacy and adverse selection). Cross–sectional regressions show that the increase in the relative size of the firm’s investor base is the only variable, which helps to explain the valuation effect.

Interaction Matrix Selection in Spatial Econometrics with an Application to Growth Theory

The interaction matrix, or spatial weight matrix, is the fundamental tool to model cross-sectional interdependence between observations in spatial econometric models. However, it is most of the time not derived from theory, as it should be ideally, but chosen on an ad hoc basis. In this paper, we propose a modified version of the J test to formally select the interaction matrix. Our methodology is based on the application of the robust against unknown heteroskedasticity GMM estimation method, developed by Lin & Lee (2010). We then implement the testing procedure developed by Hagemann (2012) to overcome the decision problem inherent to non-nested models tests. An application is presented for the Schumpeterian growth model with worldwide interactions (Ertur & Koch 2011) using three different types of interaction matrix: genetic distance, linguistic distance and bilateral trade flows and we find that the interaction matrix based on trade flows is the most adequate. Furthermore, we propose a network based innovative representation of spatial econometric results.