On the size and determinants of inter-regional redistribution in European countries over the period 1995–2009

Abstract

The aim of this paper is to analyse cross-country differences in the degree of inter-regional redistribution achieved by means of taxes and expenditures in 21 European countries over the period 1995–2009. We rely on a standard approach based on the observation and comparison of both primary and disposable household income at regional scale. Once the redistributive effect in each country is quantified, we try to explain the drivers of cross-country time-series differences. According to our estimates, cross-national standard deviation is significant and much higher than time variation. Secondly, inter-regional redistribution is strongly and positively related to personal redistribution by means of taxes and social benefits in cash; and is negatively related to both the extent of regional disparities in primary income and to the degree of political and fiscal decentralization.

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Notes

  1. 1.

    Spain is one of the countries where more efforts have been made on this issue. In this case, official estimates are available. On this issue, see again the collective book edited by Bosch et al. (2010). Bosch et al. (2002), Ambrosian et al. (2008), and Hepp and Hagen (2010) perform single-country analyses for Spain, Italy, and Germany, respectively. On the relevance of in-kind redistribution see Currie and Gahvari (2008), Boadway and Marchand (1995), and Bises and Sacchi (2011), Finally, Pfingsten and Wagener (1997) show that a cleverly designed interregional transfer mechanisms makes possible that decentralized intraregional redistribution can yield efficient outcomes.

  2. 2.

    At the end of the day, arguments on unfair regional fiscal menus and unfair federal redistribution are mixed: if the contribution to federal budget drops, household disposable income increases in richer regions, regional taxes can increase and regional public services improve.

  3. 3.

    See Sacchi and Salotti (2014) for an analysis of the effects of fiscal decentralization on household income inequality.

  4. 4.

    Moreover, the extremely low within-variation of variables implies that panel data based on annual data is mostly redundant and tend to inflate t-statistics: while sample size increases, variances of regressors and residuals do not significantly change. For instance, for a bivariate OLS estimate the standard deviation of parameter b is computed as: \( \hat{\sigma }({\text{b}}) = \sqrt {\frac{{\frac{{\text{n}}}{{\text{n}} - 2}\hat{\sigma }^{2} ({\text{e}})}}{{{\text{n}}\hat{\sigma }^{2} ({\text{x}})}}} = \sqrt {\frac{1}{{\text{n}} - 2}\frac{{\hat{\sigma }^{2} ({\text{e}})}}{{\hat{\sigma }^{2} ({\text{x}})}}} \)

  5. 5.

    We checked that this trend is not significantly affected by the lack of data for Denmark in 1995–1999. Excluding observations for this country, the redistribution effect for the entire period was 0.299 (0.296 for 2000–2004, and 0.307 for 2005–2009). While the size of the redistribution effect is substantially higher for Danish regions, they are just five over 277.

  6. 6.

    To avoid inflation of Tables, original estimates are not included in the text. They are available upon request.

  7. 7.

    So we test the correlation between economic growth and inter-regional redistribution efforts made by governments (Kuznets 1955).

  8. 8.

    Disparity in per capita household income can differ depending on the level of aggregation. The number of NUTS2 regions and the average size widely differs between countries.

  9. 9.

    Results were similar when STATA robust option for OLS estimates was used instead of clustering residuals.

  10. 10.

    This is a general test for three kind of specification errors: Omitted variables, incorrect functional form, and correlation between the exogenous variables and the random term.

  11. 11.

    The method begins by fitting the regression, calculating Cook’s D and excluding any observation for which D > 1. Thereafter the method works iteratively: it performs a regression, calculates case weights from absolute residuals, and regresses again using those weights. Iterations stop when the maximum change in weights drops below tolerance. This method was implemented using STATA 12.1. See Li (1985).

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Acknowledgments

This research project received financial support from the Spanish Ministry of Science and Innovation (Grant Codes: ECO2010-15553 and CSO2013-4703-C2-2-R). Authors are grateful for helpful comments from Nicolas Gavoille, Nowook Park, Heikki Pursianen, Jorge Martínez-Vázquez, two anonymous reviewers and participants in the 10th PEARL Conference (Fribourg 2013), the workshop “Endogenizing State and Local Fragmentation” organized by the Condorcet Center for Political Economy (Rennes 2013), and the 69th IIPF Meeting (Taormina 2013). The usual disclaimer applies.

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Lago-Peñas, S., Prada, A. & Vaquero, A. On the size and determinants of inter-regional redistribution in European countries over the period 1995–2009. Empirica 42, 845–864 (2015). https://doi.org/10.1007/s10663-015-9284-4

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Keywords

  • Inter-regional redistribution
  • Regional fiscal imbalance
  • European Union

JEL Classification

  • H11
  • H23
  • H77