A class of spatial econometric methods in the empirical analysis of clusters of firms in the space

In this paper we aim at identifying stylized facts in order to suggest adequate models of spatial co–agglomeration of industries. We describe a class of spatial statistical methods to be used in the empirical analysis of spatial clusters. Compared to previous contributions using point pattern methods, the main innovation of the present paper is to consider clustering for bivariate (rather than univariate) distributions, which allows uncovering co–agglomeration and repulsion phenomena between the different industrial sectors. Furthermore we present the results of an empirical application of such methods to a set of European Patent Office (EPO) data and we produce a series of empirical evidences referred to the the pair–wise intra–sectoral spatial distribution of patents in Italy in the nineties. In this analysis we are able to identify some distinctive joint patterns of location between patents of different sectors and to propose some possible economic interpretations

A class of spatial econometric methods in the empirical analysis of clusters of firms in the space

In this paper we aim at identifying stylized facts in order to suggest adequate models of spatial co–agglomeration of industries. We describe a class of spatial statistical methods to be used in the empirical analysis of spatial clusters. Compared to previous contributions using point pattern methods, the main innovation of the present paper is to consider clustering for bivariate (rather than univariate) distributions, which allows uncovering co–agglomeration and repulsion phenomena between the different industrial sectors. Furthermore we present the results of an empirical application of such methods to a set of European Patent Office (EPO) data and we produce a series of empirical evidences referred to the the pair–wise intra–sectoral spatial distribution of patents in Italy in the nineties. In this analysis we are able to identify some distinctive joint patterns of location between patents of different sectors and to propose some possible economic interpretations.Agglomeration, Bivariate K–functions, co–agglomeration, Non parametric concentration measures, Spatial clusters, Spatial econometrics

Weighting Ripley’s K-function to account for the firm dimension in the analysis of spatial concentration

The spatial concentration of firms has long been a central issue in economics both under the theoretical and the applied point of view due mainly to the important policy implications. A popular approach to its measurement, which does not suffer from the problem of the arbitrariness of the regional boundaries, makes use of micro data and looks at the firms as if they were dimensionless points distributed in the economic space. However in practical circumstances the points (firms) observed in the economic space are far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital and so on. In the literature, the works that originally introduce such an approach (e.g. Arbia and Espa, 1996; Marcon and Puech, 2003) disregard the aspect of the different firm dimension and ignore the fact that a high degree of spatial concentration may result from both the case of many small points clustering in definite portions of space and from only few large points clustering together (e.g. few large firms). We refer to this phenomena as to clustering of firms and clustering of economic activities. The present paper aims at tackling this problem by adapting the popular Kfunction (Ripley, 1977) to account for the point dimension using the framework of marked point process theory (Penttinen, 2006)Agglomeration, Marked point processes, Spatial clusters, Spatial econometrics

Measuring industrial agglomeration with inhomogeneous K-function: the case of ICT firms in Milan (Italy)

Why do industrial clusters occur in space? Is it because industries need to stay close together to interact or, conversely, because they concentrate in certain portions of space to exploit favourable conditions like public incentives, proximity to communication networks, to big population concentrations or to reduce transport costs? This is a fundamental question and the attempt to answer to it using empirical data is a challenging statistical task. In economic geography scientists refer to this dichotomy using the two categories of spatial interaction and spatial reaction to common factors. In economics we can refer to a distinction between exogenous causes and endogenous effects. In spatial econometrics and statistics we use the terms of spatial dependence and spatial heterogeneity. A series of recent papers introduced explorative methods to analyses the spatial patterns of firms using micro data and characterizing each firm by its spatial coordinates. In such a setting a spatial distribution of firms is seen as a point pattern and an industrial cluster as the phenomenon of extra-concentration of one industry with respect to the concentration of a benchmarking spatial distribution. Often the benchmarking distribution is that of the whole economy on the ground that exogenous factors affect in the same way all branches. Using such an approach a positive (or negative) spatial dependence between firms is detected when the pattern of a specific sector is more aggregated (or more dispersed) than the one of the whole economy. In this paper we suggest a parametric approach to the analysis of spatial heterogeneity, based on the socalled inhomogeneous K-function (Baddeley et al., 2000). We present an empirical application of the method to the spatial distribution of high-tech industries in Milan (Italy) in 2001. We consider the economic space to be non homogenous, we estimate the pattern of inhomogeneity and we use it to separate spatial heterogeneity from spatial dependence.

Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects.

This paper studies the convergence of per capita GDP across European regions over a fairly long period. Most of the works are based on either cross-sectional or fixed-effects estimates. We propose the estimation of convergence in per capita GDP across European regions by making use of panel-data models extended to include spatial error autocorrelation and spatially lagged dependent variable (Anselin,1988;Elhorst,2002). This will allow us to extend the traditional ß convergence model to include a rigorous treatment of the spatial correlation among the intercept terms. A spatial analysis of such intercept terms will also be performed in order to shed light on the concept spatially conditional convergence.

Clusters of firms in space and time

The use of the K-functions (Ripley, 1977) has become recently popular in the analysis of the spatial pattern of firms. It was first introduced in the economic literature by Arbia and Espa (1996) and then popularized by Marcon and Puech (2003), Quah and Simpson (2003), Duranton and Overman (2005) and Arbia et al. (2008). In particular in Arbia et al. (2008) we used Ripley’s K-functions as instruments to study the inter-sectoral co-agglomeration pattern of firms in a single moment of time. All this researches have followed a static approach, disregarding the time dimension. Temporal dynamics, on the other hand, play a crucial role in understanding the economic and social phenomena, particularly when referring to the analysis of the individual choices leading to the observed clusters of economic activities. With respect to the contributions previously appeared in the literature, this paper uncovers the process of firm demography by studying the dynamics of localization through space-time K-functions. The empirical part of the paper will focus on the study of the long run localization of firms in the area of Rome (Italy), by concentrating on the ICT sector data collected by the Italian Industrial Union in the period 1920- 2005.Agglomeration, Non-parametric measures; Space-time K-functions, Spatial clusters, Spatial econometrics.

Analyzing Intra-Distribution Dynamics: A Reappraisal

In this paper we suggest an alternative estimator and an alternative graphical analysis, both developed by Hyndman et al. (1996), to describe the law of motion of cross-sectional distributions of per-capita income and its components in Europe. This estimator has better properties than the kernel density estimator generally used in the literature on intra-distribution dynamics (cf. Quah, 1997). By using the new estimator, we obtain evidence of a very strong persistent behavior of the regions considered in the study, that is poor regions tend to remain poorer and rich regions tend to remain richer. These results are also in line with the most recent literature available on the distribution dynamic approach to regional convergence (Pittau and Zelli, 2006).

Institutions and geography: Empirical test of spatial growth models for European regions

This article provides an empirical assessment of the growth experiences of European regions, during the period 1991-2004, by taking into account the spatial effects due to both institutions and geography. These effects have been modeled by means of specific controls and by using a non-conventional spatial weight matrix. Results favour a model dealing with substantive spatial externalities. Within this framework, the country-specific institutions are strongly and positively related to the regional productivity’s growth rate. In addition, the geo-institutional proximity increases the spatial dependence of the regional output per worker and raises the speed of convergence. By contrast, the pure geographical metrics is underperforming, while underestimating the convergence dynamics.Regional growth, income convergence, institutions, geography, spatial effects.