Policy

As part of a joint research project on “Understanding Price Dynamics of Major Agricultural Commodities and Identifying Ways to Improve Value-chains” by the RBI and ICRIER, this paper delves into the price dynamics of tomato, onion and potato (TOP) in India. We assess the value chains associated with these commodities and estimate the farmers’ share in the final consumer price using secondary and primary sources of information. Developing a novel framework of monthly balance sheets to capture supply-demand dynamics, we find a significant and negative relationship between monthly availability/availability to usage ratio and prices while controlling for other factors like input costs, rainfall and wages. Further, we show that SARIMAX models incorporating these balance sheet variables perform better compared to other models in forecasting TOP inflation over different forecast horizons. 

The extent of price stickiness or the degree of responsiveness of retail prices to changing market conditions has a considerable bearing on persistence observed in the inflation process and the impact of monetary policy actions on macroeconomic variables. The box analyses the price stickiness in CPI basket (i.e. at item level) for the period 2014 to 2024 using the indirect frequency approach. The analysis shows that core prices tend to be stickier while food prices are vulnerable to exogenous shocks. Given the overlapping nature of shocks to food sector in recent times, the evolving food price trajectory also needs to be carefully monitored for its implications on headline inflation persistence and anchoring of inflation expectations.

Against the backdrop of large supply-side shocks to the inflation process since 2020 brought about by COVID-19, the war in Ukraine and adverse climatic events, this article analyses the desirable statistical properties of various Consumer price Index (CPI) core inflation measures for their suitability in capturing underlying inflation movements. We find that various core inflation measures – exclusion based, trimmed means, reweighted CPI, and the trend CPI – held up vis-à-vis the pre-COVID period. Since early 2020, multiple supply side shocks, particularly in food and energy, have led to some degree of persistence in headline inflation. This led to spillovers from non-core to core inflation weakening some properties of core inflation, although in the long-run, non-core inflation still converges to core inflation.

Elevated inflation and its persistence reflect a series of multiple supply side shocks. It has been observed that as the direct impact of the shocks wane, second-round effects can take hold, causing generalisation of price pressures. Moreover, some sectors are systemically more important than others in this propagation mechanism. Direct and indirect effect of domestic cost-push shocks to various sectors of the economy is analysed using input-output tables (IOTs). The IOTs are mapped to CPI items/sub-groups to understand the total impact in the CPI basket from a shock to each of the sectors. The impact of the sectoral shocks on headline inflation is estimated by using the Leontief Inverse Matrix. 

Elevated input price pressures resulting from higher global energy prices, commodity prices and supply chain disruptions could inflate output prices depending on the stage of the business cycle and the pricing power of firms. Understanding the dynamics of pass-through of input prices to retail prices in varying market conditions is critical for an appropriate monetary policy response. Empirical analyses in the article suggest modest pass- through from input prices to output prices in India, with the sensitivity of output prices to input prices found to be non-linear, rising more when incidence of input prices is high.

Sustained high inflation can lead to higher uncertainty and allocative inefficiency (Friedman, 1977; Ball, 1992). The impact of inflation variability can, however, be ambiguous, depending on whether the central bank tolerates higher inflation (Cukierman and Meltzer, 1986) or it is committed to the inflation target and adopts a contractionary policy (Holland, 1995).

Using CPI data (month-on-month percentage changes of seasonally adjusted data) for the period April 2005 to July 2022, time-varying inflation uncertainty (or inflation volatility) is estimated by employing a generalized autoregressive conditional heteroskedasticity (GARCH) model. Three specifications are estimated to address the various hypotheses using GARCH (2,1) estimates. The results suggest that inflation volatility exhibits high degree of persistence but is mean reverting.