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Farmers' Exit Decisions and Early Retirement Programs in Finland

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  • Pietola, Kyosti
  • Vare, Minna
  • Oude Lansink, Alfons G.J.M.

Abstract

This paper estimates farmer decisions between three discrete occupational choices: exit and close down the farming operation (1), exit and transfer the farm to a new entrant (2), or continue farming and retain the option to exit later on (3). The farmer optimisation problem is formulated as a recursive optimal stopping problem. The unknown parameters are first estimated by a switching-type, reduced form Probit models and, then by the Simulated maximum likelihood (SML) method, controlling for serial correlation in the errors. Serial correlation in the errors is controlled for by the Geweke-Hajivassiliou-Keane (GHK) simulation technique. The results suggest that the timing and the type of farmer exit decisions respond elastically to farmer characteristics, farm characteristics, and economic environment. Early retirement programs and the level of farmer retirement benefits are predicted to play a key role in steering structural development and enhancing family farms in the Nordic agricultural sectors.

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Bibliographic Info

Paper provided by European Association of Agricultural Economists in its series 2002 International Congress, August 28-31, 2002, Zaragoza, Spain with number 24825.

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Date of creation: 2002
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Handle: RePEc:ags:eaae02:24825

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Related research

Keywords: exit; entry; dynamic programming; switching-type Probit; Simulated Maximum Likelihood; Labor and Human Capital;

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  1. Jeffrey H. Dorfman, 1996. "Modeling Multiple Adoption Decisions in a Joint Framework," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(3), pages 547-557.
  2. Zvi Eckstein & Kenneth I. Wolpin, 1989. "The Specification and Estimation of Dynamic Stochastic Discrete Choice Models: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 24(4), pages 562-598.
  3. Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
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  5. Keane, Michael, 1993. "Simulation estimation for panel data models with limited dependent variables," MPRA Paper 53029, University Library of Munich, Germany.
  6. Ariel Pakes, 1986. "Patents as Options: Some Estimates of the Value of Holding European Patent Stocks," NBER Working Papers 1340, National Bureau of Economic Research, Inc.
  7. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-72, November.
  8. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
  9. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
  10. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  11. David Zilberman & Doug Parker, 1996. "Explaining Irrigation Technology Choices: A Microparameter Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(4), pages 1064-1072.
  12. Bill Provencher, 1997. "Structural Versus Reduced-Form Estimation of Optimal Stopping Problems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 357-368.
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