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Spatial Approaches to Panel Data in Agricultural Economics: A Climate Change Application

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  • Baylis, Kathy
  • Paulson, Nicholas D.
  • Piras, Gianfranco

Abstract

Panel data are used in almost all subfields of the agricultural economics profession. Furthermore, many research areas have an important spatial dimension. This article discusses some of the recent contributions made in the evolving theoretical and empirical literature on spatial econometric methods for panel data. We then illustrate some of these tools within a climate change application using a hedonic model of farmland values and panel data. Estimates for the model are provided across a range of nonspatial and spatial estimators, including spatial error and spatial lag models with fixed and random effects extensions. Given the importance of location and extensive use of panel data in many subfields of agricultural economics, these recently developed spatial panel methods hold great potential for applied researchers.

Suggested Citation

  • Baylis, Kathy & Paulson, Nicholas D. & Piras, Gianfranco, 2011. "Spatial Approaches to Panel Data in Agricultural Economics: A Climate Change Application," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 43(3), pages 325-338, August.
    • Handle: RePEc:cup:jagaec:v:43:y:2011:i:03:p:325-338_00
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    Cited by:

    1. Azzarri, Carlo & Signorelli, Sara, 2020. "Climate and poverty in Africa South of the Sahara," World Development, Elsevier, vol. 125(C).
    2. Anna Gloria Billé & Marco Rogna, 2022. "The effect of weather conditions on fertilizer applications: A spatial dynamic panel data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 3-36, January.
    3. Tibor András Marton & Anna Kis & Anna Zubor-Nemes & Anikó Kern & Nándor Fodor, 2020. "Human Impact Promotes Sustainable Corn Production in Hungary," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    4. Emediegwu, Lotanna E. & Wossink, Ada & Hall, Alastair, 2022. "The impacts of climate change on agriculture in sub-Saharan Africa: A spatial panel data approach," World Development, Elsevier, vol. 158(C).
    5. AMOUZAY, Hassan & El Ghini, Ahmed, 2024. "A Systematic Review of Key Spatial Econometric Models for Assessing Climate Change Impacts on Agriculture," MPRA Paper 123222, University Library of Munich, Germany, revised 13 Dec 2024.
    6. Yun, Seong Do & Gramig, Benjamin M & Delgado, Michael S. & Florax, Raymond J.G.M., 2015. "Does Spatial Correlation Matter in Econometric Models of Crop Yield Response and Weather?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205465, Agricultural and Applied Economics Association.
    7. Seong Do Yun & Benjamin M. Gramig, 2019. "Agro-Climatic Data by County: A Spatially and Temporally Consistent U.S. Dataset for Agricultural Yields, Weather and Soils," Data, MDPI, vol. 4(2), pages 1-20, May.
    8. Yong Bao & Gucheng Li & Xiaotian Liu, 2024. "A Spatial Sample Selection Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(4), pages 928-950, August.
    9. Wan-Ru Yang & Mike Grieneisen & Huajin Chen & Minghua Zhang, 2015. "Reduction of Crop Diversity Does Not Drive Insecticide Use," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 7(10), pages 1-1, September.
    10. O’Donoghue, Cathal & McKinstry, Alistair & Green, Stuart & Fealy, Reamonn & Heanue, Kevin & Ryan, Mary & Connolly, Kevin & Desplat, J.C. & Horan, Brendan, . "A Blueprint for a Big Data Analytical Solution to Low Farmer Engagement with Financial Management," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(A), pages 1-24.

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