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The Rice Market Reaction to El Nino Southern Oscillation Shocks

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  • Ubilava, David
  • Valera, Harold Glenn
  • Pede, Valerien

Abstract

We study the role of El Niño Southern Oscillation (ENSO) in the intermediate-term price dynamics of twenty rice varieties across six key rice-exporting countries over the 2011-2021 period. We apply two inter-related techniques. First, we generate impulse responses for up to 26-week horizons from linear projections to illustrate changes in rice price growth after ENSO shocks. Then, we assess the role of ENSO in predicting rice prices over the considered horizons. We find that ENSO shocks alter the dynamics of rice export prices from Thailand and, to a lesser extent, South America. In these regions, we also find that ENSO-related information facilitates the more accurate intermediate-term price forecasts. Since the price of Thai 5% long-grain white rice is considered the international price or the reference price, the fact that that ENSO shocks alter the price dynamics of rice varieties from Thailand suggests the susceptibility of international rice markets to ENSO shocks. Overall, our findings allude to the heterogeneity of the ENSO effect on rice markets and offer important insights for assessing the repercussions of climate change, which has been hypothesized to amplify ENSO cycles as well as the related weather repercussions in the usually affected regions.

Suggested Citation

  • Ubilava, David & Valera, Harold Glenn & Pede, Valerien, 2022. "The Rice Market Reaction to El Nino Southern Oscillation Shocks," MPRA Paper 123384, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:123384
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    References listed on IDEAS

    as
    1. Shenggen Fan & Connie Chan‐Kang & Keming Qian & K. Krishnaiah, 2005. "National and international agricultural research and rural poverty: the case of rice research in India and China," Agricultural Economics, International Association of Agricultural Economists, vol. 33(s3), pages 369-379, November.
    2. Ruixue Wang & Roderick M. Rejesus & Jesse B. Tack & Joseph V. Balagtas & Andy D. Nelson, 2022. "Quantifying the Yield Sensitivity of Modern Rice Varieties to Warming Temperatures: Evidence from the Philippines," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(1), pages 318-339, January.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    5. Kazushi Takahashi & Christopher B. Barrett, 2014. "The System of Rice Intensification and its Impacts on Household Income and Child Schooling: Evidence from Rural Indonesia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 269-289.
    6. John, Adam, 2013. "Price relations between export and domestic rice markets in Thailand," Food Policy, Elsevier, vol. 42(C), pages 48-57.
    7. David Ubilava, 2018. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(1), pages 239-263.
    8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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