Understanding food inflation in India: A Machine Learning approach

TL;DR

This paper uses machine learning, specifically gradient boosted regression trees, to analyze factors influencing food inflation in India, highlighting the importance of MSP and farm wages over international prices.

Contribution

It applies a machine learning approach to identify key determinants of food inflation in India, providing insights for targeted policy reforms.

Findings

MSP and farm wages are significant predictors of food inflation.

International food prices have limited impact on domestic food prices.

All predictor variables significantly influence food price changes.

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.