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Too much of a good thing? On the growth effects of the EU's regional policy

Author

Listed:
  • Becker, Sascha O.

    (University of Warwick)

  • Egger, Peter H.

    (ETH Zurich)

  • Von Ehrlich, Maximilian

    (ETH Zurich)

Abstract

The European Union (EU) provides grants to disadvantaged regions of member states from two pools, the Structural Funds and the Cohesion Fund. The main goal of the associated transfers is to facilitate convergence of poor regions (in terms of per-capita income) to the EU average. We use data at the NUTS3 level from the last two EU budgetary periods (1994-99 and 2000-06) and generalized propensity score estimation to analyze to which extent the goal of fostering growth in the target regions was achieved with the funds provided and whether or not more transfers generated stronger growth effects. We find that, overall, EU transfers enable faster growth in the recipient regions as intended, but we estimate that in 36% of the recipient regions the transfer intensity exceeds the aggregate effciency maximizing level and in 18% of the regions a reduction of transfers would not even reduce their growth. We conclude that some reallocation of the funds across target regions would lead to higher aggregate growth in the EU and could generate even faster convergence than the current scheme does.

Suggested Citation

  • Becker, Sascha O. & Egger, Peter H. & Von Ehrlich, Maximilian, 2012. "Too much of a good thing? On the growth effects of the EU's regional policy," CAGE Online Working Paper Series 70, Competitive Advantage in the Global Economy (CAGE).
    • Handle: RePEc:cge:wacage:70
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    References listed on IDEAS

    as
    1. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    2. Widgrén, Mika, 2008. "The Impact of Council Voting Rules on EU Decision-Making," Discussion Papers 1162, The Research Institute of the Finnish Economy.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    4. Murphy, Kevin M & Shleifer, Andrei & Vishny, Robert W, 1989. "Industrialization and the Big Push," Journal of Political Economy, University of Chicago Press, vol. 97(5), pages 1003-1026, October.
    5. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," IZA Discussion Papers 3255, Institute of Labor Economics (IZA).
    6. Becker, Sascha O. & Egger, Peter H. & von Ehrlich, Maximilian, 2010. "Going NUTS: The effect of EU Structural Funds on regional performance," Journal of Public Economics, Elsevier, vol. 94(9-10), pages 578-590, October.
    7. J. Hirshleifer, 1958. "On the Theory of Optimal Investment Decision," Journal of Political Economy, University of Chicago Press, vol. 66(4), pages 329-329.
    8. Mohl, Philipp & Hagen, Tobias, 2008. "Which is the Right Dose of EU Cohesion Policy for Economic Growth?," ZEW Discussion Papers 08-104, ZEW - Leibniz Centre for European Economic Research.
    9. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    10. Martin, Philippe, 1999. "Public policies, regional inequalities and growth," Journal of Public Economics, Elsevier, vol. 73(1), pages 85-105, July.
    11. Becker Sascha O & Muendler Marc-Andreas, 2008. "The Effect of FDI on Job Security," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(1), pages 1-46, April.
    12. Shapley, L. S. & Shubik, Martin, 1954. "A Method for Evaluating the Distribution of Power in a Committee System," American Political Science Review, Cambridge University Press, vol. 48(3), pages 787-792, September.
    13. Mika Widgrén, 2009. "The Impact of Council Voting Rules on EU Decision-Making -super-1," CESifo Economic Studies, CESifo Group, vol. 55(1), pages 30-56, March.
    14. Author-Name: Jeffrey D. Sachs & John W. McArthur & Guido Schmidt-Traub & Margaret Kruk & Chandrika Bahadur & Michael Faye & Gordon McCord, 2004. "Ending Africa's Poverty Trap," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 35(1), pages 117-240.
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    More about this item

    Keywords

    EU regional policy; Regional growth; Generalized; propensity score estimation; Quasi-randomized experiment;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • H50 - Public Economics - - National Government Expenditures and Related Policies - - - General
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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