Understanding Food Inflation in India: A Machine Learning Approach

44 Pages Posted: 2 Feb 2017

See all articles by Akash Malhotra

Akash Malhotra

Indian Institute of Technology Bombay

Mayank Maloo

Indian Institute of Technology Bombay

Date Written: January 31, 2017

Abstract

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.

Keywords: Food Inflation, Agricultural Economics, Boosted Regression Trees, Machine Learning, India

JEL Classification: C45, E31, P44, Q11, Q18

Suggested Citation

Malhotra, Akash and Maloo, Mayank, Understanding Food Inflation in India: A Machine Learning Approach (January 31, 2017). Available at SSRN: https://ssrn.com/abstract=2908354 or http://dx.doi.org/10.2139/ssrn.2908354

Akash Malhotra (Contact Author)

Indian Institute of Technology Bombay ( email )

India

Mayank Maloo

Indian Institute of Technology Bombay ( email )

India

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