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Examine the Agriculture, Poverty, and Climate Change Nexus in Vietnam

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  • Yu, Bingxin
  • Zhu, Tingju
  • Breisinger, Clemens
  • Manh Hai, Nguyen

Abstract

Vietnam is likely to be among the hardest hit countries by climate change, which may threaten the recent progress it has made in accelerating agricultural growth and poverty reduction. To examine how agriculture and the rural poor may be affected by a changing climate, this paper measures Vietnamese farmers’ adaptation behavior in terms of adjustments to the production portfolio and input usage. Specifically, the paper estimates a rice yield function based on household-level crop production, long-term climate measurements and recent weather shocks. The results suggest that rice production will suffer from climate change. However, Vietnamese farmers are likely to respond to changes in rainfall and temperature by adjusting input usage. While this will help maintaining productivity levels, expanding irrigation and agricultural intensification will be key components of climate change adaptation strategies at farm and national level. Localized policy packages aiming at increasing yield by focusing on vulnerable groups (ethnic-minority and/or the poor) can help achieve multiple development goals of poverty reduction, food security and climate change adaptation.

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Bibliographic Info

Paper provided by International Association of Agricultural Economists in its series 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil with number 126876.

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Date of creation: 2012
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Handle: RePEc:ags:iaae12:126876

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Keywords: Climate change; poverty; Vietnam; rice; control function; weak instruments; multiple endogenous variables; Heckman; Agricultural and Food Policy; Environmental Economics and Policy; Food Security and Poverty;

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  1. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
  2. Jesus Felipe & Rana Hasan & J. S. L. McCombie, 2008. "Correcting for biases when estimating production functions: an illusion of the laws of algebra?," Cambridge Journal of Economics, Oxford University Press, vol. 32(3), pages 441-459, May.
  3. John Shea, 1997. "Instrument Relevance in Multivariate Linear Models: A Simple Measure," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 348-352, May.
  4. Glewwe, Paul & Kremer, Michael & Moulin, Sylvie & Zitzewitz, Eric, 2004. "Retrospective vs. prospective analyses of school inputs: the case of flip charts in Kenya," Journal of Development Economics, Elsevier, vol. 74(1), pages 251-268, June.
  5. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
  6. Dasgupta, Susmita & Laplante, Benoit & Meisner, Craig & Wheeler, David & Jianping Yan, 2007. "The impact of sea level rise on developing countries : a comparative analysis," Policy Research Working Paper Series 4136, The World Bank.
  7. Fan Zhai & Juzhong Zhuang, 2009. "Agricultural Impact of Climate Change: A General Equilibrium Analysis with Special Reference to Southeast Asia," Working Papers id:1944, eSocialSciences.
  8. Minot, Nicholas & Baulch, Bob & Epperecht, Michael, 2006. "Poverty and inequality in Vietnam: spatial patterns and geographic determinants," Research reports 148, International Food Policy Research Institute (IFPRI).
  9. Alejandro Nin-Pratt & Bingxin Yu & Shenggen Fan, 2010. "Comparisons of agricultural productivity growth in China and India," Journal of Productivity Analysis, Springer, vol. 33(3), pages 209-223, June.
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