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Reconstructing Disaggregate Production Functions

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  • Howitt, Richard E.
  • Msangi, Siwa

Abstract

This paper demonstrates a method for reconstructing flexible form production functions using minimal disaggregated data sets. The policy focus of our approach puts emphasis on the ability of the model to reproduce the existing production system and predict the disaggregate outcomes of policy changes. We combine Positive Mathematical Programming (PMP) with Generalized Maximum Entropy (GME) estimation to capture the individual heterogeneity of the local production environment, and allow the reconstructed production function to precisely replicate the input usage and outputs produced in the base year. Since we can generate demand, supply and substitution elasticities from the reconstructed model we can represent a wide range of policy responses. The empirical application used in this paper is a production model of California's irrigated crop sector that was constructed to measure the economic effect of environmental policy changes to irrigation water supplies, as part of a joint State and Federal program termed CalFed. We demonstrate that the disaggregate regional models give greater predictive precision, when compared with the model reconstructed on the aggregate data, and that they show a significant variation in the calculated regional elasticities of input demand and output response. From this, we conclude that any gains from aggregation - namely the reduction of small sample bias of the parameter estimates - would be swamped by the distortion of production response to policy changes, given the heterogeneity of the regions and the resultant bias.

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Bibliographic Info

Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2002 Annual meeting, July 28-31, Long Beach, CA with number 19585.

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Date of creation: 2002
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Handle: RePEc:ags:aaea02:19585

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Keywords: Production Economics;

References

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  1. Sergio H. Lence & Douglas J. Miller, 1998. "Recovering Output-Specific Inputs from Aggregate Input Data: A Generalized Cross-Entropy Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 80(4), pages 852-867.
  2. John M. Antle & Susan M. Capalbo, 2001. "Econometric-Process Models for Integrated Assessment of Agricultural Production Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 83(2), pages 389-401.
  3. Alig, Ralph J. & Adams, Darius M. & McCarl, Bruce A., 1998. "Impacts Of Incorporating Land Exchanges Between Forestry And Agriculture In Sector Models," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, Southern Agricultural Economics Association, vol. 30(02), December.
  4. Golan, Amos & Judge, George G. & Perloff, Jeffrey M., 1995. "Estimating the size distribution of firms using government summary statistics," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series, Department of Agricultural & Resource Economics, UC Berkeley qt14b416tk, Department of Agricultural & Resource Economics, UC Berkeley.
  5. Coelli, T. J., 1995. "Recent Developments in Frontier Modelling and Efficiency Measurement," 1995 Conference (39th), February 14-16, 1995, Perth, Australia, Australian Agricultural and Resource Economics Society 148798, Australian Agricultural and Resource Economics Society.
  6. Just, Richard E & Antle, John M, 1990. "Interactions between Agricultural and Environmental Policies: A Conceptual Framework," American Economic Review, American Economic Association, vol. 80(2), pages 197-202, May.
  7. Lence, Sergio H & Miller, Douglas J, 1998. "Estimation of Multi-output Production Functions with Incomplete Data: A Generalised Maximum Entropy Approach," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 25(2), pages 188-209.
  8. Golan, Amos & Judge, George & Robinson, Sherman, 1994. "Recovering Information from Incomplete or Partial Multisectoral Economic Data," The Review of Economics and Statistics, MIT Press, vol. 76(3), pages 541-49, August.
  9. H. Alan Love, 1999. "Conflicts between Theory and Practice in Production Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 81(3), pages 696-702.
  10. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  11. Paris, Quirino & Caputo, Michael R., 2001. "Sensitivity Of The Gme Estimates To Support Bounds," Working Papers, University of California, Davis, Department of Agricultural and Resource Economics 11966, University of California, Davis, Department of Agricultural and Resource Economics.
  12. Mendelsohn, Robert & Nordhaus, William D & Shaw, Daigee, 1994. "The Impact of Global Warming on Agriculture: A Ricardian Analysis," American Economic Review, American Economic Association, vol. 84(4), pages 753-71, September.
  13. Quirino Paris & Richard E. Howitt, 1998. "An Analysis of Ill-Posed Production Problems Using Maximum Entropy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 80(1), pages 124-138.
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Cited by:
  1. Cai, Ximing & Ringler, Claudia & You, Jiing-Yun, 2008. "Substitution between water and other agricultural inputs: Implications for water conservation in a River Basin context," Ecological Economics, Elsevier, vol. 66(1), pages 38-50, May.

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