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A structural ricardian analysis of climate change impacts and adaptations in African agriculture

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  • Seo, S. Niggol
  • Mendelsohn, Robert

Abstract

This paper develops a Structural Ricardian model to measure climate change impacts that explicitly models the choice of farm type in African agriculture. This two stage model first estimates the type of farm chosen and then the conditional incomes of each farm type after removing selection biases. The results indicate that increases in temperature encourage farmers to adopt mixed farming and avoid specialized farms such as crop-only or livestock-only farms. Increases in precipitation encourage farmers to shift from irrigated to rainfed crops. As temperatures increase, farm incomes from crop-only farms or livestock-only farms fall whereas incomes from mixed farms increase. With precipitation increases, farm incomes from irrigated farms fall whereas incomes from rainfed farms increase. Naturally, the Structural Ricardian model predicts much smaller impacts than a model that holds farm type fixed. With a hot dry climate scenario, the Structural Ricardian model predicts that farm income will fall 50 percent but the fixed farm type model predicts farm incomes will fall 75 percent.

Suggested Citation

  • Seo, S. Niggol & Mendelsohn, Robert, 2008. "A structural ricardian analysis of climate change impacts and adaptations in African agriculture," Policy Research Working Paper Series 4603, The World Bank.
  • Handle: RePEc:wbk:wbrwps:4603
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    References listed on IDEAS

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    1. 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 753-771, September.
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    5. Kurukulasuriya, Pradeep & Mendelsohn, Robert, 2007. "Endogenous irrigation : the impact of climate change on farmers in Africa," Policy Research Working Paper Series 4278, The World Bank.
    6. Brown, James N & Rosen, Harvey S, 1982. "On the Estimation of Structural Hedonic Price Models," Econometrica, Econometric Society, vol. 50(3), pages 765-768, May.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
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    9. Mendelsohn, Robert & Seo, Niggol, 2007. "Changing farm types and irrigation as an adaptation to climate change in Latin American agriculture," Policy Research Working Paper Series 4161, The World Bank.
    10. S. Niggol Seo & Robert Mendelsohn, 2008. "Measuring impacts and adaptations to climate change: a structural Ricardian model of African livestock management-super-1," Agricultural Economics, International Association of Agricultural Economists, vol. 38(2), pages 151-165, March.
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    Cited by:

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    2. Muamba, Francis M. & Ulimwengu, John M., 2010. "Optimal rainfall insurance contracts for maize producers in Ghana’s Northern Region," IFPRI discussion papers 1016, International Food Policy Research Institute (IFPRI).
    3. Asif Sardar & Adiqa K. Kiani & Yasemin Kuslu, 2021. "Does adoption of climate-smart agriculture (CSA) practices improve farmers’ crop income? Assessing the determinants and its impacts in Punjab province, Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10119-10140, July.
    4. Ordaz, Juan Luis & Mora, Jorge & Acosta, Alicia & Serna Hidalgo, Braulio & Ramírez, Diana, 2013. "Belize: effects of climate change on agriculture," Sede Subregional de la CEPAL en México (Estudios e Investigaciones) 26108, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    5. Kibonge Naik, Aziza, 2014. "Agricultural Productivity And Climate Change In Sub-Saharan Africa: Water Scarcity, Precipitation And Temperature," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 172348, Agricultural and Applied Economics Association.

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    Keywords

    Crops&Crop Management Systems; Agriculture&Farming Systems; Livestock&Animal Husbandry; Climate Change; Rural Development Knowledge&Information Systems;
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