Contrary to popular perception, then, we do not find food price inflation to be higher, more volatile or persistent than core inflation. There is evidence, however, the changes in food inflation lead to subsequent changes in core inflation, but not the other way around. This reinforces a finding of Mishra and Roy (2012).<sup>21</sup> We confirm that this relationship has not changed post inflation targeting.

Other authors (e.g. Chhiber 2020) have suggested that the RBI should “look through” (i.e. disregard) movements in food price inflation on the grounds that food prices are volatile, and that focusing on them distorts the conduct of policy. In responding to food price inflation, the central bank will be focusing on transitory inflation threat and neglecting other more important objectives of policy. We find, to the contrary, that food-price inflation feeds through to core inflation as producers mark up the prices of other products. Central banks in advanced economies have been able to look through fluctuations in food and fuel price inflation without consequences for core inflation and therefore without jeopardizing their inflation targets. In India, where food is a much more important component of consumption baskets, this may not be the case. This is not an argument that the central bank should react to each and every movement in headline and food inflation. But it does suggest that neglecting food price inflation that diverges from target for an extended period of time can have negative consequences.

# **4. Reaction functions**

We now ask whether monetary policy decisions are influenced more by the output gap or inflation, whether the reaction function has changed with the adoption of inflation targeting, whether output gap and inflation carry different weights in the reaction function at high and low values, and whether the reaction function is different for headline and core inflation.<sup>22</sup> All estimated reaction functions include the output gap and inflation, but in an augmented version we also include the lagged policy rate, the percentage change in exchange rate, and the budget deficit or government borrowing.

A number of earlier papers have estimated reaction functions for the RBI (see Appendix A4). These studies find that the output gap is important (Hutchison et al 2013, Mohanty and Klau 2005) but that the exchange rate also matters, especially from the late 1990s when it became more flexible. Inflation in general has a much smaller coefficient, both absolutely and relative to the Taylor rule benchmark of 1.5.

These analyses typically use quarterly data, since quarterly GDP growth is available from 1997; we follow this standard practice.<sup>23</sup> For inflation, we use the WPI inflation until 2013 and the CPI inflation thereafter, since these are the inflation rates monitored by the RBI.

21 This is consistent with Ball, Chari and Mishra (2016) who show that changes in headline inflation feed into expected inflation and future core inflation. Raj et al (2020) evaluate several possible measures of core inflation and similarly find that headline inflation does not converge to core inflation.

> 22 Rangarajan (2020) suggests that inflation targeting in India is flexible in the sense that “what inflation targeting demands is that when inflation goes beyond the comfort zone, the exclusive concern of monetary policy must be to bring it back to the target level. When inflation is within the comfort zone, authorities can look to other objectives.”

> 23 Papers estimating reaction functions at a monthly frequency use the industrial production index as a proxy for output, which we regard as unreliable.

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