IDEAS home Printed from
   My bibliography  Save this paper

Understanding food inflation in India: A Machine Learning approach


  • Akash Malhotra
  • Mayank Maloo


Over the past decade, the stellar growth of Indian economy has been challenged by persistently high levels of inflation, particularly in food prices. The primary reason behind this stubborn food inflation is mismatch in supply-demand, as domestic agricultural production has failed to keep up with rising demand owing to a number of proximate factors. The relative significance of these factors in determining the change in food prices have been analysed using gradient boosted regression trees (BRT), a machine learning technique. The results from BRT indicates all predictor variables to be fairly significant in explaining the change in food prices, with MSP and farm wages being relatively more important than others. International food prices were found to have limited relevance in explaining the variation in domestic food prices. The challenge of ensuring food and nutritional security for growing Indian population with rising incomes needs to be addressed through resolute policy reforms.

Suggested Citation

  • Akash Malhotra & Mayank Maloo, 2017. "Understanding food inflation in India: A Machine Learning approach," Papers 1701.08789,
  • Handle: RePEc:arx:papers:1701.08789

    Download full text from publisher

    File URL:
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    1. Rahul Anand & Naresh Kumar & Volodymyr Tulin, 2016. "Understanding India’s Food Inflation; The Role of Demand and Supply Factors," IMF Working Papers 16/2, International Monetary Fund.
    2. Müller, Daniel & Leitão, Pedro J. & Sikor, Thomas, 2013. "Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees," Agricultural Systems, Elsevier, vol. 117(C), pages 66-77.
    3. Rahul Anand & Ding Ding & Volodymyr Tulin, 2014. "Food Inflation in India; The Role for Monetary Policy," IMF Working Papers 14/178, International Monetary Fund.
    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), pages 1-14, January.
    5. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1701.08789. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.