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Policy Innovation in Local Jurisdictions: Testing the Neighborhood Influence Against the Free-Riding Hypothesis

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  • Johannes Rincke

    (Zentrum für Europäische Wirtschaftsforschung)

Abstract

Before making di±cult decisions, individuals tend to collect information on decision makers in reference groups. With respect to policy innovations in a decentralized public sector, this may give rise to positive neighborhood influence on adoption decisions. On the other hand, due to learning externalities, an incentive exists to free-ride on policy experiments of others. In this paper, U.S. data on school district policies are used to show that with respect to policy experiments, decision makers indeed are heavily affected by decision makers in reference groups. The results suggest that if a given district's neighbors' expected benefits from adopting a new policy increase, this substantially increases the original district's probability of adoption. The paper thus rejects the free-riding hypothesis and supports the view that in federal systems the discusion of policy innovations is stimulated by horizontal interactions between jurisdictions.

Suggested Citation

  • Johannes Rincke, 2005. "Policy Innovation in Local Jurisdictions: Testing the Neighborhood Influence Against the Free-Riding Hypothesis," Public Economics 0511017, EconWPA.
  • Handle: RePEc:wpa:wuwppe:0511017
    Note: Type of Document - pdf; pages: 23. ZEW Discussion Paper No. 05-08
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    File URL: http://econwpa.repec.org/eps/pe/papers/0511/0511017.pdf
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    References listed on IDEAS

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    1. Besley, Timothy & Case, Anne, 1995. "Incumbent Behavior: Vote-Seeking, Tax-Setting, and Yardstick Competition," American Economic Review, American Economic Association, vol. 85(1), pages 25-45, March.
    2. William A. Brock & Steven N. Durlauf, 2001. "Discrete Choice with Social Interactions," Review of Economic Studies, Oxford University Press, vol. 68(2), pages 235-260.
    3. Katz, Michael L & Shapiro, Carl, 1986. "Technology Adoption in the Presence of Network Externalities," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 822-841, August.
    4. Scharfstein, David S & Stein, Jeremy C, 1990. "Herd Behavior and Investment," American Economic Review, American Economic Association, vol. 80(3), pages 465-479, June.
    5. Krasker, William S. & Kuh, Edwin & Welsch, Roy E., 1983. "Estimation for dirty data and flawed models," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 11, pages 651-698 Elsevier.
    6. Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
    7. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
    8. David Hirshleifer & Siew Hong Teoh, 2003. "Herd Behaviour and Cascading in Capital Markets: a Review and Synthesis," European Financial Management, European Financial Management Association, vol. 9(1), pages 25-66.
    9. Hautsch, Nikolaus & Klotz, Stefan, 2003. "Estimating the neighborhood influence on decision makers: theory and an application on the analysis of innovation decisions," Journal of Economic Behavior & Organization, Elsevier, vol. 52(1), pages 97-113, September.
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    Cited by:

    1. Johannes Rincke, 2005. "Neighborhood Influence and Political Change: Evidence from US School Districts," Public Economics 0511011, EconWPA.
    2. Rincke, Johannes, 2005. "Neighborhood Influence and Political Change: Evidence from US School Districts," ZEW Discussion Papers 05-16, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.

    More about this item

    JEL classification:

    • D6 - Microeconomics - - Welfare Economics
    • D7 - Microeconomics - - Analysis of Collective Decision-Making
    • H - Public Economics

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