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Efficient Non-Contractible Investments in a Finite Economy


  • Harold L. Cole
  • George J. Mailath
  • Andrew Postlewaite


Investors making complementary investments typically do not have incentives to invest efficiently when they cannot contract with each other prior to their decisions because of the hold-up problem: when they bargain over the surplus generated by their investments, they will usually not obtain the full fruits of the investment. Intuitively, the hold-up problem should be ameliorated if, in the bargaining stage, each agent has alternatives to the partner he is bargaining with. We characterize the matching and division of surplus in finite economies for any initial investment decisions. We provide conditions on those decisions that guarantee that each agent will capture the change in the aggregate social surplus that results from any investment change he makes. We further show that for any given problem, there exists a bargaining rule by which pairs split their surplus that will support efficient investment choices in equilibrium. We also show, however, that overinvestment or underinvestment can occur for natural bargaining rules.
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  • Harold L. Cole & George J. Mailath & Andrew Postlewaite, "undated". "Efficient Non-Contractible Investments in a Finite Economy," Penn CARESS Working Papers 452f3f87415f37596752b3995, Penn Economics Department.
  • Handle: RePEc:cla:penntw:452f3f87415f37596752b399575585f0

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    1. Moshiri, Saeed & Cameron, Norman E & Scuse, David, 1999. "Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation," Computational Economics, Springer;Society for Computational Economics, vol. 14(3), pages 219-235, December.
    2. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    3. Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
    4. John Cooper, 1999. "Artificial neural networks versus multivariate statistics: An application from economics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 909-921.
    5. Shtub, Avraham & Versano, Ronen, 1999. "Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis," International Journal of Production Economics, Elsevier, vol. 62(3), pages 201-207, September.
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    1. Sergei Severinov & Michael Peters, 2004. "Internet Trading Mechanisms And Rational Expectations," Econometric Society 2004 North American Winter Meetings 551, Econometric Society.

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