Capturing household-level spatial influence in agricultural management using random effects regression
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 ca
(This abstract was borrowed from another version of this item.)
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gerald C. Nelson & GVirginia Harris & Steven W. Stone, 2001. "Deforestation, Land Use, and Property Rights: Empirical Evidence from Darién, Panama," Land Economics, University of Wisconsin Press, vol. 77(2), pages 187-205.
- 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.
- Mark Rosenzweig & Andrew D. Foster, .
"Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture,"
_068, University of Pennsylvania.
- Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
- Nelson, Gerald & Hellerstein, Daniel, 1997.
"Do roads cause deforestation? Using satellite images in econometric analysis of land use,"
25261, University Library of Munich, Germany.
- 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.
- 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.
- Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
- 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.
- 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.
- Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July.
- 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.
When requesting a correction, please mention this item's handle: RePEc:eee:agecon:v:27:y:2002:i:3:p:371-381. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.