An Information-Based Sample-Selection Estimation Model of Agricultural Workers' Choice between Piece-Rate and Hourly Work
AbstractThis paper presents a new generalized maximum entropy (GME) approach to estimation of sample-selection models with small data sets, such as are found in many empirical agricultural economic analysis. For small samples, the GME approach produces more stable estimates and has smaller mean square error measures than other well-known estimators such as ordinary least squares, Heckman's two-step method, full-information maximum likelihood, and Ahn and Powell's method. The technique is used to analyze whether hired agricultural workers will work in piece-rate or time-rate jobs and to compare female-male wage differentials for both types of jobs.
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Bibliographic InfoArticle provided by Agricultural and Applied Economics Association in its journal American Journal of Agricultural Economics.
Volume (Year): 81 (1999)
Issue (Month): 3 ()
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Other versions of this item:
- Golan, Amos & Morttie, Enrico & Perloff, Jeffrey M, 1998. "An information based sample-selection estimation model of agricultural workers' choice between piece-rate and hourly work," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt1bz9m60s, Department of Agricultural & Resource Economics, UC Berkeley.
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- Kostov, Philip & Patton, Myles & Moss, Joan & McErlean, Seamus, 2006.
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- Peeters, Ludo M. K., 2004. "Estimating a random-coefficients sample-selection model using generalized maximum entropy," Economics Letters, Elsevier, vol. 84(1), pages 87-92, July.
- Qiuqiong Huang & Richard Howitt & Scott Rozelle, 2012. "Estimating production technology for policy analysis: trading off precision and heterogeneity," Journal of Productivity Analysis, Springer, vol. 38(2), pages 219-233, October.
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