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Capturing Household-Level Spatial Influence In Agricultural Management Using Random Effects Regression

  • Swinton, Scott M.

Data on agricultural and natural resource management typically have spatial patterns related to the landscapes from which they came. Consequently, econometric models designed to explain the determinants of humans' natural resource management practices or their outcomes often have spatial structure that can bring bias or inefficiency to parameter estimates. Although econometric tools are available to correct for spatial structure, such tools are largely lacking for use with discrete dependent variable models. While one obvious solution would be to develop the necessary tools, an alternative is to identify conditions under which spatial dependency can be managed effectively without formal spatial autoregressive models. This study examines conditions under which spatial structure corresponds closely to defined agro-ecological zones, making it possible to model spatial effects by random effects regression. Using household survey data sampled along agro-ecological zone strata, this article develops two models of links between farmer assets and agricultural natural resource degradation in southern Peru. The first stage model looks at determinants of crop yield loss over time (an index of soil productivity), while the second stage model looks at determinants of the extent of fallow cycles in crop rotation, a key agricultural practice reducing crop yield loss. Diagnostic statistics for spatial dependency reveal spatial structure, particularly in the fallow model. This spatial dependency is eliminated in the ordinary least squares (OLS) models by inclusion of the agro-ecological zone random effects. In the spatially dependent fallow model, comparison of coefficient estimates between OLS and the spatial autoregressive maximum likelihood models showed OLS with random effects to give virtually identical results to the spatial autoregressive models, making the latter unnecessary. These results show that spatial structure in natural resource management models can sometimes be captured by zonal variables. When this occurs, random effects regression can largely eliminate spatial dependency. A necessary precondition for this approach with household survey data is prior sample stratification according to landscape characteristics. Where random effects models can effectively capture spatial structure, they may also offer analysts greater flexibility in analyzing models with limited dependent variables.

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File URL: http://purl.umn.edu/11516
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Paper provided by Michigan State University, Department of Agricultural, Food, and Resource Economics in its series Staff Papers with number 11516.

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Date of creation: 2002
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Handle: RePEc:ags:midasp:11516
Contact details of provider: Postal: Justin S. Morrill Hall of Agriculture, 446 West Circle Dr., Rm 202, East Lansing, MI 48824-1039
Phone: (517) 355-4563
Fax: (517) 432-1800
Web page: http://www.aec.msu.edu/agecon/Email:


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  1. Nancy E. Bockstael, 1996. "Modeling Economics and Ecology: The Importance of a Spatial Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(5), pages 1168-1180.
  2. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
  3. Kathleen P. Bell & Nancy E. Bockstael, 2000. "Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 72-82, February.
  4. Gerald C. Nelson & GVirginia Harris & Steven W. Stone, 2001. "Deforestation, Land Use, and Property Rights: Empirical Evidence from DariƩn, Panama," Land Economics, University of Wisconsin Press, vol. 77(2), pages 187-205.
  5. Gerald C. Nelson & Daniel Hellerstein, 1997. "Do Roads Cause Deforestation? Using Satellite Images in Econometric Analysis of Land Use," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 80-88.
  6. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July.
  7. Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
  8. Mark Rosenzweig & Andrew D. Foster, . "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Home Pages _068, University of Pennsylvania.
  9. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  10. Swinton, Scott M. & Jones, Kezelee Q., 1998. "From Data To Information: The Value Of Sampling Vs. Sensing Soil Data," Staff Papers 11674, Michigan State University, Department of Agricultural, Food, and Resource Economics.
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