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Understanding India’s Food Inflation: The Role of Demand and Supply Factors

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  • Rahul Anand
  • Naresh Kumar
  • Mr. Volodymyr Tulin

Abstract

Over the past decade, India has seen a prolonged period of high inflation, to a large extent driven by persistently-high food inflation. This paper investigates the demand and supply factors behind the contribution of relative food inflation to headline CPI inflation. It concludes that in the absence of a stronger food supply growth response, food inflation may exceed non-food inflation by 2½–3 percentage points per year. The sustainability of a long-term inflation target of 4 percent under India’s recently-adopted flexible inflation targeting framework will depend on enhancing food supply, agricultural market-based pricing, and reducing price distortions. A well-designed cereal buffer stock liquidation policy could also help mitigate food inflation volatility.

Suggested Citation

  • Rahul Anand & Naresh Kumar & Mr. Volodymyr Tulin, 2016. "Understanding India’s Food Inflation: The Role of Demand and Supply Factors," IMF Working Papers 2016/002, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:2016/002
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    References listed on IDEAS

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    1. Kozicka, Marta & Kalkuhl, Matthias & Saini, Shweta & Brockhaus, Jan, 2014. "Modeling Indian Wheat and Rice Sector Policies," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169808, Agricultural and Applied Economics Association.
    2. Ganesh-Kumar, A. & Mehta, Rajesh & Pullabhotla, Hemant & Prasad, Sanjay K. & Ganguly, Kavery & Gulati, Ashok, 2012. "Demand and supply of cereals in India: 2010-2025:," IFPRI discussion papers 1158, International Food Policy Research Institute (IFPRI).
    3. Brian P. Poi, 2002. "From the help desk: Demand system estimation," Stata Journal, StataCorp LP, vol. 2(4), pages 403-410, November.
    4. Kumar, Praduman & Shinoj, P. & Raju, S.S. & Kumar, Anjani & Rich, Karl M. & Msangi, Siwa, 2010. "Factor Demand, Output Supply Elasticities and Supply Projections for Major Crops of India," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 23(1), January.
    5. Thangzason Sonna & Himanshu Joshi & Alice Sebastin & Upasana Sharma, 2014. "Analytics of Food Inflation in India," Working Papers id:6174, eSocialSciences.
    6. Timothy K.M. Beatty & Erling Røed Larsen, 2005. "Using Engel curves to estimate bias in the Canadian CPI as a cost of living index," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 38(2), pages 482-499, May.
    7. Surabhi Mittal, 2010. "Application of the Quaids Model to the Food Sector in India," Journal of Quantitative Economics, The Indian Econometric Society, vol. 8(1), pages 42-54, January.
    8. Sen Gupta, Abhijit & Bhattacharya, Rudrani & Rao, Narhari, 2014. "Understanding Food Inflation in India," MPRA Paper 58319, University Library of Munich, Germany.
    9. Richard Blundell & Alan Duncan & Krishna Pendakur, 1998. "Semiparametric estimation and consumer demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 435-461.
    10. Yatchew,Adonis, 2003. "Semiparametric Regression for the Applied Econometrician," Cambridge Books, Cambridge University Press, number 9780521812832.
    11. Rahul Anand & Ding Ding & Mr. Volodymyr Tulin, 2014. "Food Inflation in India: The Role for Monetary Policy," IMF Working Papers 2014/178, International Monetary Fund.
    12. Deepankar Basu & Debarshi Das, 2014. "Social Hierarchies and Public Distribution of Food in Rural India," UMASS Amherst Economics Working Papers 2014-05, University of Massachusetts Amherst, Department of Economics.
    13. Blanciforti, Laura & Green, Richard, 1983. "An Almost Ideal Demand System Incorporating Habits: An Analysis of Expenditures on Food and Aggregate Commodity Groups," The Review of Economics and Statistics, MIT Press, vol. 65(3), pages 511-515, August.
    14. Blundell, Richard & Pashardes, Panos & Weber, Guglielmo, 1993. "What Do We Learn About Consumer Demand Patterns from Micro Data?," American Economic Review, American Economic Association, vol. 83(3), pages 570-597, June.
    15. Shweta Saini & Marta Kozicka, 2014. "Evolution and Critique of Buffer Stocking Policy of India," Working Papers id:6153, eSocialSciences.
    16. Brian P. Poi, 2008. "Demand-system estimation: Update," Stata Journal, StataCorp LP, vol. 8(4), pages 554-556, December.
    17. Mr. James P Walsh, 2011. "Reconsidering the Role of Food Prices in Inflation," IMF Working Papers 2011/071, International Monetary Fund.
    18. Rakesh Mohan & Muneesh Kapur, 2015. "Pressing the Indian Growth Accelerator: Policy Imperatives," IMF Working Papers 2015/053, International Monetary Fund.
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    Cited by:

    1. Sajjid Chinoy & Pankaj Kumar & Ms. Prachi Mishra, 2016. "What is Responsible for India’s Sharp Disinflation?," IMF Working Papers 2016/166, International Monetary Fund.
    2. Ginn, William & Pourroy, Marc, 2022. "The contribution of food subsidy policy to monetary policy in India," Economic Modelling, Elsevier, vol. 113(C).
    3. Chetan Ghate & Sargam Gupta & Debdulal Mallick, 2018. "Terms of Trade Shocks and Monetary Policy in India," Computational Economics, Springer;Society for Computational Economics, vol. 51(1), pages 75-121, January.
    4. Chandana Maitra & Sriram Shankar & D.S. Prasada Rao, 2016. "Income Poor or Calorie Poor? Who should get the Subsidy?," Discussion Papers Series 564, School of Economics, University of Queensland, Australia.
    5. Holtemöller, Oliver & Mallick, Sushanta, 2016. "Global food prices and monetary policy in an emerging market economy: The case of India," Journal of Asian Economics, Elsevier, vol. 46(C), pages 56-70.
    6. Akash Malhotra & Mayank Maloo, 2017. "Understanding food inflation in India: A Machine Learning approach," Papers 1701.08789, arXiv.org.

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