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Estimating the Neighborhood Influence on Decision Makers: Theory and an Application on the Analysis of Innovation Decisions

When making decisions, agents tend to make use of decisions others have made in similar situations. Ignoring this behavior in empirical models can be interpreted as a problem of omitted variables and may seriously bias parameter estimates and harm inference. We suggest a possibility of integrating such outside in uences into models of discrete choice decisions by defining an abstract space in which agents with similar characteristics are neighbors who possibly in uence each other. In order to correct for correlations between the characteristics, the design of this space allows for nonorthogonality of its dimensions. Several Monte Carlo simulations show the small sample properties of spatial models with binary choice. When applying the estimator to innovation decisions data of German firms, we find evidence for the existence of neighborhood effects.

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Paper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 01-04.

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Length: 23 Pages
Date of creation: Jun 2001
Date of revision:
Handle: RePEc:knz:cofedp:0104
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  1. George A. Akerlof, 1997. "Social Distance and Social Decisions," Econometrica, Econometric Society, vol. 65(5), pages 1005-1028, September.
  2. Bernheim, B Douglas, 1994. "A Theory of Conformity," Journal of Political Economy, University of Chicago Press, vol. 102(5), pages 841-77, October.
  3. Brock,W.A. & Durlauf,S.N., 2000. "Discrete choice with social interactions," Working papers 7, Wisconsin Madison - Social Systems.
  4. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
  5. Akerlof, George A & Yellen, Janet L, 1990. "The Fair Wage-Effort Hypothesis and Unemployment," The Quarterly Journal of Economics, MIT Press, vol. 105(2), pages 255-83, May.
  6. 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.
  7. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," NBER Working Papers 7580, National Bureau of Economic Research, Inc.
  8. Manski, Charles F, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Wiley Blackwell, vol. 60(3), pages 531-42, July.
  9. Licht, Georg & Harhoff, Dietmar, 1993. "Das Mannheimer Innovationspanel," ZEW Discussion Papers 93-21, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  10. Scharfstein, David S & Stein, Jeremy C, 1990. "Herd Behavior and Investment," American Economic Review, American Economic Association, vol. 80(3), pages 465-79, June.
  11. Poirier, Dale J & Ruud, Paul A, 1988. "Probit with Dependent Observations," Review of Economic Studies, Wiley Blackwell, vol. 55(4), pages 593-614, October.
  12. Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
  13. Case, A.C. & Katz, L.F., 1991. "The Company You Keep: The Effects Of Family And Neighborhood On Disadvantaged Younths," Harvard Institute of Economic Research Working Papers 1555, Harvard - Institute of Economic Research.
  14. Topa, Giorgio, 2001. "Social Interactions, Local Spillovers and Unemployment," Review of Economic Studies, Wiley Blackwell, vol. 68(2), pages 261-95, April.
  15. Baptista, Rui, 2000. "Do innovations diffuse faster within geographical clusters?," International Journal of Industrial Organization, Elsevier, vol. 18(3), pages 515-535, April.
  16. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-40, June.
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