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An Empirical Analysis of Pre-Determined Food Demand in Russia

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  • Hovhannisyan, Vardges
  • Shanoyan, Aleksan

Abstract

The Exact Affine Stone Index (EASI) model of Lewbel and Pendakur (2009) offers distinct advantages over its predecessor models, however it does not account for a widely observed phenomenon of pre-committed demand. This may lead to biased elasticity estimates when such pre-commitments are present. This study offers a methodological solution by deriving the generalized EASI (GEASI) model, which incorporates pre-committed quantities into the consumer demand structure. The empirical advantage of the GEASI model is illustrated through its application to the analysis of food demand structure in Russia based on novel provincial-level panel data on household food expenditures over 2007-2014. The results provide strong empirical evidence for the presence of pre-committed demand for key food commodities such as cereals, eggs, and fats/oils. Further comparative analysis highlights the significance of pre-commitment bias in the context of food demand in Russia and illustrates the effectiveness of the GEASI approach in addressing it. The findings extend the empirical literature on food demand in Russia by presenting estimated elasticities that account for potential pre-commitments as well as for unobserved provincial heterogeneity.

Suggested Citation

  • Hovhannisyan, Vardges & Shanoyan, Aleksan, 2018. "An Empirical Analysis of Pre-Determined Food Demand in Russia," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266579, Southern Agricultural Economics Association.
  • Handle: RePEc:ags:saea18:266579
    DOI: 10.22004/ag.econ.266579
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    References listed on IDEAS

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    1. James Banks & Richard Blundell & Arthur Lewbel, 1997. "Quadratic Engel Curves And Consumer Demand," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 527-539, November.
    2. Pollak, Robert A & Wales, Terence J, 1981. "Demographic Variables in Demand Analysis," Econometrica, Econometric Society, vol. 49(6), pages 1533-1551, November.
    3. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521871532.
    4. Joseph G. Altonji & Aloysius Siow, 1987. "Testing the Response of Consumption to Income Changes with (Noisy) Panel Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(2), pages 293-328.
    5. Shiptsova, Rimma & Goodwin, Harold L., Jr. & Holcomb, Rodney B., 2004. "Household Expenditure Patterns for Carbohydrate Sources in Russia," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(2), pages 1-12, August.
    6. Arthur Lewbel & Krishna Pendakur, 2009. "Tricks with Hicks: The EASI Demand System," American Economic Review, American Economic Association, vol. 99(3), pages 827-863, June.
    7. Blundell,Richard & Newey,Whitney & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521871549.
    8. P. A. Samuelson, 1947. "Some Implications of "Linearity."," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 15(2), pages 88-90.
    9. Blundell,Richard & Newey,Whitney & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521692106.
    10. Bollino, Carlo Andrea, 1987. "Gaids: a generalised version of the almost ideal demand system," Economics Letters, Elsevier, vol. 23(2), pages 199-202.
    11. Alston, Julian M. & Chalfant, James A. & Piggott, Nicholas E., 2001. "Incorporating demand shifters in the Almost Ideal demand system," Economics Letters, Elsevier, vol. 70(1), pages 73-78, January.
    12. Martin Browning & Jesus Carro, 2006. "Heterogeneity and Microeconometrics Modelling," CAM Working Papers 2006-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    13. Matthias Staudigel & Rebecca Schröck, 2015. "Food Demand in Russia: Heterogeneous Consumer Segments over Time," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(3), pages 615-639, September.
    14. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
    15. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521692090.
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    Cited by:

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