MODELING THE ECONOMIC IMPACT OF EL NINO ON FOOD INFLATION IN INDIA: AN ECONOMETRIC ANALYSIS WITH HYPERRAG-BASED EVIDENCE RETRIEVAL

Abstract

This study examines the relationship between El Nino-Southern Oscillation (ENSO) and monthly food price inflation in India and proposes a mixed-methods framework that integrates quantitative econometric modelling with a hypergraph-based retrieval-augmented generation (HyperRAG) system for qualitative corroboration. Using 162 monthly observations of the Oceanic Nino Index (ONI), Brent crude oil prices, the USD/INR exchange rate, fuel prices, and a domestic food price index, a series of incremental ordinary least squares (OLS) regressions on differenced series following stationarity testing, correlation analysis, and lag-structure exploration is estimated. Results indicate that fuel price changes are the dominant short-run predictor of food price changes (p < .001), while ONI shows no statistically significant direct effect once other macroeconomic controls are included, despite a significant unconditional Spearman correlation ( = -0.323, p<.001). To interpret this apparent tension, a hypergraph-structured knowledge base was developed from 29 Reserve Bank of India (RBI) and India Meteorological Department (IMD) annual reports (2011-2023), retrieving text passages that describe qualitative transmission channels such as rainfall deficits, reservoir levels, and input-cost pass-through, which are not captured by a linear monthly regression. The integration suggests ENSO’s influence on food prices operates through delayed, nonlinear, and season-specific channels rather than a stable monthly linear relationship, depicting the value of combining economic precision with retrieval-augmented qualitative evidence in climate-economy research. Keywords: El Nino-Southern Oscillation (ENSO), Food Price Inflation, India, Econometric Analysis, Hypergraph Retrieval-Augmented Generation (HyperRAG), Oceanic Nino Index (ONI), Climate-Economy Nexus, Knowledge Retrieval.