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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.

Suggested Citation

  • Swinton, Scott M., 2002. "Capturing Household-Level Spatial Influence In Agricultural Management Using Random Effects Regression," Staff Papers 11516, Michigan State University, Department of Agricultural, Food, and Resource Economics.
  • Handle: RePEc:ags:midasp:11516

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    References listed on IDEAS

    1. 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 509-533, May.
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    1. Ancev, Tihomir & Odeh, Inakwu O.A., 2005. "Use of Spatially Referenced Data in Agricultural Economics Research," 2005 Conference (49th), February 9-11, 2005, Coff's Harbour, Australia 137743, Australian Agricultural and Resource Economics Society.
    2. Bateman, Ian J. & Day, Brett H. & Georgiou, Stavros & Lake, Iain, 2006. "The aggregation of environmental benefit values: Welfare measures, distance decay and total WTP," Ecological Economics, Elsevier, vol. 60(2), pages 450-460, December.
    3. Nelson, Gerald C., 2002. "Introduction to the special issue on spatial analysis for agricultural economists," Agricultural Economics, Blackwell, vol. 27(3), pages 197-200, November.
    4. Kathleen P. Bell & Timothy J. Dalton, 2007. "Spatial Economic Analysis in Data-Rich Environments," Journal of Agricultural Economics, Wiley Blackwell, vol. 58(3), pages 487-501, September.
    5. Swinton, Scott M. & Quiroz, Roberto, 2003. "Is Poverty to Blame for Soil, Pasture and Forest Degradation in Peru's Altiplano?," World Development, Elsevier, vol. 31(11), pages 1903-1919, November.
    6. Lambert, Dayton M. & Griffin, Terry W., 2004. "Analysis Of Government Farm Subsidies On Farmland Cash Rental Rates Using A Fixed Effect Spatial Distributed Lag Model And A Translog Cost Model," 2004 Annual meeting, August 1-4, Denver, CO 19977, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. William Pan & David Carr & Alisson Barbieri & Richard Bilsborrow & Chirayath Suchindran, 2007. "Forest Clearing in the Ecuadorian Amazon: A Study of Patterns Over Space and Time," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(5), pages 635-659, December.
    8. Lewis, David J. & Barham, Bradford L. & Zimmerer, Karl S., 2008. "Spatial Externalities in Agriculture: Empirical Analysis, Statistical Identification, and Policy Implications," World Development, Elsevier, vol. 36(10), pages 1813-1829, October.

    More about this item


    Farm Management;

    JEL classification:

    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods


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