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Agroecosystem Productivity and the Dynamic Response to Shocks

In: The Economics of Poverty Traps

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  • Jean-Paul Chavas

Abstract

This paper investigates the nonlinear dynamic response to shocks, relying on a threshold quantile autoregression (TQAR) model as a flexible representation of stochastic dynamics. The TQAR model can identify zones of stability/instability and characterize resilience and traps. Resilience means high odds of escaping from undesirable zones of instability toward zones that are more desirable and stable. Traps mean low odds of escaping from zones that are both undesirable and stable. The approach is illustrated in an application to the dynamics of productivity applied to historical data on wheat yield in Kansas over the period 1885-2012. The dynamics of this agroecosystem and its response to shocks are of interest as Kansas agriculture faced major droughts, including the catastrophic Dust Bowl of the 1930’s. The analysis identifies a zone of instability in the presence of successive adverse shocks. It also finds evidence of resilience. We associate the resilience with induced innovations in management and policy in response to adverse shocks.
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  • Jean-Paul Chavas, 2017. "Agroecosystem Productivity and the Dynamic Response to Shocks," NBER Chapters, in: The Economics of Poverty Traps, pages 291-314, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:13836
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    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. Richard Hornbeck, 2012. "The Enduring Impact of the American Dust Bowl: Short- and Long-Run Adjustments to Environmental Catastrophe," American Economic Review, American Economic Association, vol. 102(4), pages 1477-1507, June.
    3. Common, Mick & Perrings, Charles, 1992. "Towards an ecological economics of sustainability," Ecological Economics, Elsevier, vol. 6(1), pages 7-34, July.
    4. Sandra Derissen & Martin Quaas & Stefan Baumgärtner, 2009. "The relationship between resilience and sustainable development of ecological-economic systems," Working Paper Series in Economics 146, University of Lüneburg, Institute of Economics.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    6. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Christopher B. Barrett & Michael R. Carter, 2013. "The Economics of Poverty Traps and Persistent Poverty: Empirical and Policy Implications," Journal of Development Studies, Taylor & Francis Journals, vol. 49(7), pages 976-990, July.
    9. Jesse Tack & Ardian Harri & Keith Coble, 2012. "More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1037-1054.
    10. Dorian Burnette & David Stahle, 2013. "Historical perspective on the dust bowl drought in the central United States," Climatic Change, Springer, vol. 116(3), pages 479-494, February.
    11. Salvatore Di Falco & Jean-Paul Chavas, 2008. "Rainfall Shocks, Resilience, and the Effects of Crop Biodiversity on Agroecosystem Productivity," Land Economics, University of Wisconsin Press, vol. 84(1), pages 83-96.
    12. Charles Perrings, 1998. "Resilience in the Dynamics of Economy-Environment Systems," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 11(3), pages 503-520, April.
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    Cited by:

    1. Knippenberg, Erwin & Jensen, Nathaniel D. & Constas, Mark A., 2017. "Measuring Resilience in Malawi," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258229, Agricultural and Applied Economics Association.
    2. Knippenberg, Erwin & Jensen, Nathaniel & Constas, Mark, 2019. "Quantifying household resilience with high frequency data: Temporal dynamics and methodological options," World Development, Elsevier, vol. 121(C), pages 1-15.

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    More about this item

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture

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