Understanding food inflation in India: A Machine Learning approach
This research addresses food inflation for policymakers in India, but it is incremental as it applies an existing machine learning method to analyze known economic factors.
The study tackled the problem of persistent food inflation in India by analyzing supply-demand mismatch factors using gradient boosted regression trees, finding that MSP and farm wages were the most significant predictors while international food prices had limited relevance.
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.