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Sharing Demographic Risk - Who is Afraid of the Baby Bust?

  • Ludwig, Alexander

    ()

    (Mannheim Research Institute for the Economics of Aging (MEA) and Sonderforschungsbereich 504)

  • Reiter, Michael

    (IHS, Wien)

We model the optimal reaction of a public PAYG pension system to demographic shocks. We compare the ex-ante first best and second best solution of a Ramsey planner with full commitment to the outcome under simple third best rules that mimic the pension systems observed in the real world. The model, in particular the pension system, is calibrated to the German economy. The objective of the social planner is calibrated such that the size of the German pension system was optimal under the economic and demographic conditions of the 1960s. We find that the German system comes relatively close to the second-best solution. Furthermore, the German system and a constant contribution rate lead to a lower variability of lifetime utility than does the second best policy. The recent baby-boom/baby-bust cycle leads to welfare losses of about 5% of lifetime consumption for some cohorts. We argue that it is crucial for these results to model correctly the labor market distortions arising from the pension system.

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Paper provided by Sonderforschungsbereich 504, Universität Mannheim & Sonderforschungsbereich 504, University of Mannheim in its series Sonderforschungsbereich 504 Publications with number 08-47.

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Length: 45 pages
Date of creation: 01 Aug 2008
Date of revision:
Handle: RePEc:xrs:sfbmaa:08-47
Note: We thank Alan Auerbach, Axel Boersch-Supan, Juan Carlos Conesa, Peter Diamond, Martin Gervais, Ashok Kaul, Dirk Krueger, Alice Schoonbroodt and seminar participants at SCE 2006 in Cyprus, Netspar 2007 Workshop in Tilburg, MIT, Institute of Advanced Studies in Vienna, Universitat Pompeu Fabra, U.C. Berkeley, U.C. Santa Barbara, University of Cologne, University of Frankfurt, University of Mannheim, LMU Munich, University of Oslo and University of Wuerzburg for helpful comments and discussions. Alex Ludwig gratefully acknowledges financial support from the German National Research Foundation (DFG) through SFB 504, the State of Baden-Wuerttemberg and the German Insurers Association (GDV).
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