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Forecasting Profitability


  • Mark Rosenzweig
  • Christopher R. Udry


We use newly-available Indian panel data to estimate how the returns to planting-stage investments vary by rainfall realizations. We show that the forecasts significantly affect farmer investment decisions and that these responses account for a substantial fraction of the inter-annual variability in planting-stage investments, that the skill of the forecasts varies across areas of India, and that farmers respond more strongly to the forecast where there is more forecast skill and not at all when there is no skill. We show, using an IV strategy in which the Indian government forecast of monsoon rainfall serves as the main instrument, that the return to agricultural investment depends substantially on the conditions under which it is estimated. Using the full rainfall distribution and our profit function estimates, we find that Indian farmers on average under-invest, by a factor of three, when we compare actual levels of investments to the optimal investment level that maximizes expected profits. Farmers who use skilled forecasts have increased average profit levels but also have more variable profits compared with farmers without access to forecasts. Even modest improvements in forecast skill would substantially increase average profits.

Suggested Citation

  • Mark Rosenzweig & Christopher R. Udry, 2013. "Forecasting Profitability," NBER Working Papers 19334, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:19334
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    References listed on IDEAS

    1. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
    2. Dean Karlan & Robert Osei & Isaac Osei-Akoto & Christopher Udry, 2014. "Agricultural Decisions after Relaxing Credit and Risk Constraints," The Quarterly Journal of Economics, Oxford University Press, vol. 129(2), pages 597-652.
    3. Marcel Fafchamps & David McKenzie & Simon Quinn & Christopher Woodruff, 2011. "When is capital enough to get female microenterprises growing? Evidence from a randomized experiment in Ghana," CSAE Working Paper Series 2011-11, Centre for the Study of African Economies, University of Oxford.
    4. Maurizio Mazzocco & Shiv Saini, 2012. "Testing Efficient Risk Sharing with Heterogeneous Risk Preferences," American Economic Review, American Economic Association, vol. 102(1), pages 428-468, February.
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    Cited by:

    1. Shrader, Jeffrey, 2014. "Forecasts and Adaptation," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170626, Agricultural and Applied Economics Association.

    More about this item

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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