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Information Asymmetries and an Endogenous Productivity Reversion Mechanism

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  • Nicolás Figueroa
  • Oksana Leukhina

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Abstract

Several empirical studies suggest that the systematic behavior of lending standards, with laxer (tighter) standards applied during expansions (recessions) are responsible for reverting trends in aggregate productivity. We build a dynamic screening model with informational asymmetries in credit markets that rationalizes the observed dependence of lending standards on economic fundamentals and generates reversion of output and productivity trends via the lending standards channel. When the capital stock, which evolves endogenously, is high, liquidity is high for all types of producers, allowing even the unproductive type to meet the early payments on the loan, and thus making signals about entrepreneurs’ type, inferred from such payments, less informative. The early payment required to accomplish screening out the unproductive types thus rises. Because the early payment hurts productive entrepreneurs by restricting their investments, competition among lenders results in the selection of contracts with no screening. Low productivity entrepreneurs enter production along with productive types, the composition effect setting off a recession. The opposite happens for low enough values of capital. JEL Codes: E32, E44, D24.

Suggested Citation

  • Nicolás Figueroa & Oksana Leukhina, 2009. "Information Asymmetries and an Endogenous Productivity Reversion Mechanism," Documentos de Trabajo 264, Centro de Economía Aplicada, Universidad de Chile.
  • Handle: RePEc:edj:ceauch:264
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    References listed on IDEAS

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    1. Lown, Cara & Morgan, Donald P., 2006. "The Credit Cycle and the Business Cycle: New Findings Using the Loan Officer Opinion Survey," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(6), pages 1575-1597, September.
    2. Kiyotaki, Nobuhiro & Moore, John, 1997. "Credit Cycles," Journal of Political Economy, University of Chicago Press, vol. 105(2), pages 211-248, April.
    3. Asea, Patrick K. & Blomberg, Brock, 1998. "Lending cycles," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 89-128.
    4. Carlstrom, Charles T & Fuerst, Timothy S, 1997. "Agency Costs, Net Worth, and Business Fluctuations: A Computable General Equilibrium Analysis," American Economic Review, American Economic Association, vol. 87(5), pages 893-910, December.
    5. Pietro Reichlin & Paolo Siconolfi, 2004. "Optimal debt contracts and moral hazard along the business cycle," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 24(1), pages 75-109, July.
    6. Allen N. Berger, 2003. "The institutional memory hypothesis and the procyclicality on bank lending behavior," Proceedings 845, Federal Reserve Bank of Chicago.
    7. Bernanke, Ben & Gertler, Mark, 1989. "Agency Costs, Net Worth, and Business Fluctuations," American Economic Review, American Economic Association, vol. 79(1), pages 14-31, March.
    8. Giovanni Dell'Ariccia & Robert Marquez, 2006. "Lending Booms and Lending Standards," Journal of Finance, American Finance Association, vol. 61(5), pages 2511-2546, October.
    9. Berger, Allen N. & Udell, Gregory F., 2004. "The institutional memory hypothesis and the procyclicality of bank lending behavior," Journal of Financial Intermediation, Elsevier, vol. 13(4), pages 458-495, October.
    10. Rampini, Adriano A., 2004. "Entrepreneurial activity, risk, and the business cycle," Journal of Monetary Economics, Elsevier, vol. 51(3), pages 555-573, April.
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    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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