Abstract
This paper attempts to understand the dramatic decline in inflation in India over the past 3 years, and quantify the role that different factors may have played, through the lens of an augmented Phillips curve. Unsurprisingly, we find that the evolution of inflation in India is a complex phenomenon, determined by the state of the business cycle, forward and backward-looking expectations, and institutional mechanisms (agriculture support prices and backward-looking wage indexation) that often amplify the effect of shocks, and create persistence in inflation. Simulations based on our econometric model suggest that lags of inflation which reflect both adaptive inflation expectations and the manner in which wages and support prices are set, a rationalization of Minimum Support Prices (MSPs), and some moderation in forward-looking inflation expectations, likely influenced both by the new monetary regime and the collapse in oil and commodities, explain the bulk of the disinflation between 2013/2014 and 2014/2015. We find the role of global factors to be less significant than is commonly perceived, explained perhaps by the fact that the pass-through of oil prices to retail prices was very limited. Similarly, we find no evidence that the disinflation was achieved at the altar of a large growth sacrifice, as is commonly perceived. Instead, our findings suggest that exogenous shocks to inflation—from lower discretionary component of MSPs, a new monetary regime, and lower global commodity prices—were perpetuated through backward-looking expectations and domestic institutional structures that amplified the influence of the original shocks. Finally, we consistently find that wage growth is more a symptom of previous inflation than a driver of it.
The views represent those of the authors and not of the Reserve Bank of India or any of the institutions to which the authors belong.
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Notes
- 1.
However, core inflation was persistently below headline for most of the period between 2006 and 2010. The purpose of a core inflation index is to get an accurate measure of the current inflation trend. Since core inflation was lower than the headline till 2010, that may have been suggestive of a downward pressure on future headline inflation (see for example Anand et al. 2014 and also Cecchetti 2007 for a critique of using core as a forecast for future headline inflation).
- 2.
Core excludes food, fuel, and transport and communication. The latter is included in the contribution of the fuel category.
- 3.
See the Patel Committee report (2014) for a hybrid model of expectation formation, and Ball et al. (Ball 2015) for estimation of a partial-adjustment model of expectations for India. See also Ball (Ball 2011), and references therein for a review of the voluminous literature on estimating inflation dynamics.
- 4.
See RBI monetary policy statement, January 2014.
- 5.
Note that actual crude prices are already controlled for in the empirical framework, therefore the dummy could only reflect the effect of crude prices on expectations.
- 6.
We use a standard HP filter with a smoothing parameter λ equal to 1600. HP filter is likely to suffer from an end-point bias, i.e., potential output may be affected by actual output at the end-point of the sample; we also use other measures of potential output which may be less subject to this concern (see Table 4 and the robustness section).
- 7.
There is a significant variation in rainfall across geographic regions in India. If rainfall is, for example, below-normal, in regions where crops with high weight in the food basket are grown, that may have a larger impact on food and overall inflation. However, given the absence of a good measure of the spatial distribution of rainfall, it is not included in the empirical analysis. In addition, food inflation is also determined by food management policies of the government, which could be interacted with the monsoon dummy. Again, the lack of good proxies for the latter precludes their inclusion in the empirical specification.
- 8.
The results are unchanged if we use the nominal effective exchange rate (NEER) instead of the Rs./$ rate. We keep the latter in the baseline as most imports are invoiced in US$.
- 9.
We tested for nonstationarity in the three key variables of interest—inflation, MSP growth, and wage growth. Using the methodology in Clemente et al. (Clemente et al. 1998), we rejected the null hypothesis of a unit root in the series for inflation and MSP growth at the 10 % level of significance. We could not, however, reject the null of a unit root in the wage growth series. Therefore, we included the first difference of wage growth in the single-equation model for robustness, but the main findings remained unchanged (see Table 5).
- 10.
Because we use year-on-year inflation rates and a quarterly data, concerns may arise that the serial correlation is by construction. However, even if we use annual data, where there is no correlation by construction, we find lagged CPI to be economically and statistically significant—suggesting it is proxying inflation expectations.
- 11.
See, for example, “India’s food inflation: worrying about the wrong problem,” JP Morgan, July 30, 2015.
- 12.
There could be a legitimate concern that the high R^2 is a sign of overfitting given that we have 60 observations and 24 explanatory variables. However, even if we drop several lags to generate a more parsimonious model, the R^2 is still 91 %, which should allay concerns about overfitting.
- 13.
These findings are consistent with earlier work; see, “India’s food inflation: worrying about the wrong problem,” JP Morgan, July 30, 2015.
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Chinoy, S.Z., Kumar, P., Mishra, P. (2016). What is Responsible for India’s Sharp Disinflation?. In: Ghate, C., Kletzer, K. (eds) Monetary Policy in India. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2840-0_14
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DOI: https://doi.org/10.1007/978-81-322-2840-0_14
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