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TFP Convergence Across European Regions: A Comparative Spatial Dynamics Analysis

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Geography, Institutions and Regional Economic Performance

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

This paper proposes a fixed-effect panel methodology to estimate Total Factor Productivity and investigate the spatial dimension of regional EU TFP from both a static and a dynamic perspective. The sample includes 199 regions in EU15 (plus Norway and Switzerland) between 1985 and 2006. First of all, we find the absence of an overall process of convergence, since TFP dispersion is virtually constant along time. Furthermore, exploratory spatial data techniques show that there are interesting interregional dynamic patterns. We find that polarization patterns in Europe have significantly changed along time. Overall, results suggest that only few TFP leaders are emerging and they are distancing themselves from the rest, while the cluster of low TFP regions is widening.

JEL code: C23, O47, O33, R11

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Notes

  1. 1.

    On this see Tabellini (2010).

  2. 2.

    A large array of methodologies is currently available to estimate TFP and none has emerged as a recognized standard. See Del Gatto et al. (2011) for a recent survey.

  3. 3.

    EU27 TFP dynamics is analysed in Marrocu et al. (2012) whilst regions of the EU10 group are analysed in this book by Monastiriotis 2011.

  4. 4.

    See Del Gatto et al. (2011).

  5. 5.

    See also Caselli et al. (1996) and Islam (2003) among others.

  6. 6.

    See Jorgenson (2005).

  7. 7.

    As is standard in this literature, (g + δ) is assumed equal to 0.05.

  8. 8.

    Therefore, our sample includes the following years: 1985, 1988, 1991, 1994, 1997, 2000, 2003 and 2006.

  9. 9.

    See Di Liberto et al. (2011) for a full discussion of all technical econometric issues.

  10. 10.

    This estimation may be performed under very different assumptions about the endogeneity of the included regressors. In this study we adopt three different hypotheses (column V, VI and VII in table 1) on the additional regressors x’s.

  11. 11.

    The lack of empirical support for human capital in convergence regressions based on large international datasets is a well known problem. A number of possible explanations have been put forward. See Pritchett (1997), Temple (1999), and Krueger and Lindahl (2001).

  12. 12.

    According to Rey and Montouri (1999) and López-Bazo et al. (2004) among others, the study of convergence across states and regions should take into account the possibility for spatial spill-overs across territorial units which may lead to spatial dependence. Such a possibility has been tested and the suggested model, the so called spatial error model (or SEM, see Anselin 1988), has been estimated by means of Maximum Likelihood and results are reported in column 3.

  13. 13.

    See Kiviet (1995); Judson and Owen (1999); Everaert and Pozzi (2007).

  14. 14.

    The whole set of estimated regional TFP values and the variation of the rankings in the two sub-periods can be found in a previous version of this study. See Di Liberto and Usai (2010).

  15. 15.

    See also Di Liberto and Usai (2010) where for each region in Table A1 in the appendix we include both the first and second sub-period ranking position in terms of relative TFP levels and in the last two columns we include the change of rank in relative TFP levels and that observed in per capita VA levels between the initial and the final observation.

  16. 16.

    To model spatial dependence, a connectivity grid, that is a spatial weight matrix, has to be specified. A spatial weight matrix W specifies exogenously the connection among regions and it can refer to either contiguity or to distance. In this paper we refer to contiguity, which implies that the wij element of the W matrix is set to unity when regions are contiguous and zero otherwise.

  17. 17.

    It is interesting to note that quite a similar result is obtained by Ertur and Koch (2006) in their analysis of income per capita across EU15 and EU27 regions: in 2000 they find 75 % of positive spatial dependence.

  18. 18.

    Similar result have been found in Di Liberto et al. (2011) at the country level.

  19. 19.

    In Table 2A in Di Liberto and Usai (2010) more detailed information is given about the Local Indicator of Spatial Association which provides the significance of the relationship for each region and its neighbours (Anselin 1995).

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Acknowledgments

The research leading to these results has received funding from the ESPON project KIT, Knowledge, Innovation, Territory. We thank Claudio Deiana for his excellent research assistance.

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Authors and Affiliations

  1. Università di Cagliari and Crenos, via S. Ignazio 78, 09123, Cagliari, Italy

    Adriana Di Liberto & Stefano Usai

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  1. Adriana Di Liberto
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  2. Stefano Usai
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Correspondence to Adriana Di Liberto .

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Editors and Affiliations

  1. , Dept. of Geography and Environment, London School of Economics, Houghton Street, London, WC2A 2AE, United Kingdom

    Riccardo Crescenzi

  2. , Institutional Analysis and, Università Bocconi, via Rontgen 1, Milano, 20121, Italy

    Marco Percoco

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Di Liberto, A., Usai, S. (2013). TFP Convergence Across European Regions: A Comparative Spatial Dynamics Analysis. In: Crescenzi, R., Percoco, M. (eds) Geography, Institutions and Regional Economic Performance. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33395-8_3

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