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Project on Assessment of Key Issues Related to Monetary Policy
[Source: RBI Report on Currency & Finance 2003-04]
Module: 4 Monetary Policy and Inflation
Alternative Indicators of Inflation
The above analysis of inflation trends is based on movement in wholesale price index (WPI). In addition to WPI, two other key measures of inflation in India are consumer price inflation (industrial workers) and GDP deflator. Inflation measured by these alternative indicators over long-periods of time is broadly the same as that measured by WPI. Illustratively, over the period 1970-2004, WPI inflation averaged 8.0 per cent as compared with 8.2 per cent based on CPI inflation and 7.8 per cent based on GDP deflator and, moreover, the indicators show a fairly high correlation. At the same time, year-to-year movements in alternative indicators often diverge from each other - decadal correlations are lower than the full sample period and do not display any consistent pattern. In particular, during the 1990s, correlation between WPI and CPI inflation has been the lowest amongst the three pairs of correlations.
In recent months, gap between inflation rates based on alternative indicators has persisted. While point-to-point WPI inflation increased from 5.1 per cent at end-October 2003 to 7.1 per cent by October 2004, CPI inflation increased from 3.3 per cent to 4.6 per cent over the same period. The CPI inflation has thus been lower than WPI inflation by more than two percentage points. On earlier occasions, there has also been a divergence between the two measures: at times, CPI inflation has been higher than WPI inflation and vice versa. While over the period 1970-2004, CPI inflation has exceeded WPI inflation, decadal pattern is not consistent. WPI inflation exceeded CPI inflation during the 1970s and vice versa during the 1980s and 1990s. In the first four years of the current decade, the pattern has again reversed with CPI inflation trailing WPI inflation. Two key factors - coverage and weights - explain the difference between these alternative indicators. Food group has a larger weight in the CPI while services are excluded from the WPI. In regard to trends during 2003-04, the sub-group "iron and steel" which contributed more than a fifth of overall WPI inflation has almost a negligible weight in the CPI. At the same time, inflation in India in recent months also reflects supply shocks emanating from international markets. The international factors relate primarily to oil but also, to some extent, other commodities and financial markets, including interest rates and exchange rates. Globally, services inflation has consistently exceeded goods inflation in the past four decades (Box V.6)
Box V.6
A stylised fact of the global inflation trends is that services inflation has consistently exceeded that in goods inflation. During the most recent decade, the gap between the two was as high as two percentage points in some of the advanced economies such as the US and the UK. In fact, during the last couple of years, when the world faced a threat of deflation, goods inflation was actually negative and declining even further in a number of the advanced economies. It was the services inflation which held up the headline inflation in countries like the US and the UK and, consequently, the gap between the two series was even higher in 2003.
A number of factors explain the gap. First, services are largely non-tradable and exhibit low productivity. Therefore, the higher services inflation can be attributed to faster productivity growth in manufacturing. Productivity in goods sectors is increasing at a rate of around two per cent per annum - more or less the same rate as the inflation target in most of the advanced economies. If the headline target of two per cent is to be achieved, inflation in goods will have to be close to zero or even declining. Second, as populations age, demand for services gets stronger than that that of goods. With the increasing elderly population in advanced economies, the demand of services has been strong in these economies and this puts pressures on the prices of services. Third, the increased divergence in recent years in the US can also be attributed to sharp exchange rate movements. Although there is evidence that exchange rate pass-through has declined, it appears to have been offset by the large order of appreciation of the US dollar in the 1990s. This appreciation moderated goods inflation while leaving services inflation unaffected. If this hypothesis is correct, the recent depreciation of the US dollar is expected to reduce the gap in the coming years. Difficulties in measurements of prices of services can also contribute to the divergence. For instance, the quality bias in measurement of services exceeds that of goods. |
As noted earlier, over long-periods of time, the inflation rates based on alternative measures tend to converge. This suggests that deviations between the various indicators of inflation appear to be self-correcting and the various inflation measures co-move in the long-run. In other words, inflation indicators are co-integrated. This Section attempts to examine the time series properties of the two main indicators - WPI and CPI - using monthly data from April 1980 to March 2004 in a cointegration framework. Formal econometric evidence confirms that WPI and CPI in India are co-integrated and the results of short-run dynamics indicate that the error correction term is statistically significant in the equation for consumer prices. Thus, these results indicate that shocks that cause short-run divergence between the two indices are corrected through movements in the CPI2. The estimated coefficient of the error correction term at 0.034 suggests that more than three per cent of the divergence from the long-run relationship is corrected every month, i.e., almost one-half of the divergence is corrected within one year .
As noted earlier, over long-periods of time, the inflation rates based on alternative measures tend to converge. This suggests that deviations between the various indicators of inflation appear to be self-correcting and the various inflation measures co-move in the long-run. In other words, inflation indicators are co-integrated. This Section attempts to examine the time series properties of the two main indicators - WPI and CPI - using monthly data from April 1980 to March 2004 in a cointegration framework. Formal econometric evidence confirms that WPI and CPI in India are co-integrated and the results of short-run dynamics indicate that the error correction term is statistically significant in the equation for consumer prices. Thus, these results indicate that shocks that cause short-run divergence between the two indices are corrected through movements in the CPI2. The estimated coefficient of the error correction term at 0.034 suggests that more than three per cent of the divergence from the long-run relationship is corrected every month, i.e., almost one-half of the divergence is corrected within one year . Divergence between various indicators of headline inflation complicates the conduct of monetary policy as it becomes difficult to make a correct assessment of the potential inflationary pressures based on the available data for the current period. While there are uncertainties, it is perhaps useful to look at the recent inflation history for an assessment of inflationary expectations. Measures of underlying inflation - core inflation - can also be useful (Box V.7). From the viewpoint of formulation of monetary policy, it is the underlying inflation or core inflation that is important and analytical work on defining appropriate "core inflation" for India may be worth exploring.
Box V.7
Core Inflation
Headline inflation reflects not only the effect of demand pressures but also supply shocks which impart transitory noise and bias to the headline. Thus, a supply shock arising from crop failures will have the effect of raising the headline inflation. On the other hand, a positive supply shock - say, from a good harvest - may reduce the headline inflation for some time even if underlying inflationary pressures are building up. In the event of such supply disturbances, policy actions to counter the impact on the aggregate price level will tend to accentuate the output effects of the disturbances, generating a short-run conflict between the central bank's inflation and output objectives. According to Woodford (2003), it is the stickiness in prices that leads to deviation of actual output from its natural (potential) level of output. As all goods prices are not sticky, central banks should target a measure of core inflation that places greater weight on those prices which are stickier.
The term core inflation was propounded by Eckstein (1982) who defined it as 'the trend increase of the cost of factors of production' that 'originates in the long-term expectations of inflation in the minds of households and businesses, in the contractual arrangements which sustain the wage-price momentum, and in the tax system'. A number of approaches are used to compute core inflation. The most common approach is the 'exclusion' approach which excludes specific components of the headline inflation that are regarded as subject to extreme price variations due to temporary factors. A key feature of this method is its simplicity. A major criticism of this approach is that temporary disturbances are not necessarily limited to specific sub-components. Moreover, completely removing the volatile items has the potential risk of a permanent loss of significant information. These weaknesses are overcome to some extent by the limited influence estimator approach of Bryan and Cecchetti (1993). The basic premise of this approach is that in the face of relative price shocks, the empirical distribution of disaggregated price change is not normal and hence the sample mean looses its robustness. Under these circumstances, a robust measure of core inflation can be devised through statistical measures of trimmed mean or weighted median. Under the trimmed mean approach, the sample points are rearranged from the lowest to the highest values, and a fixed percentage of the lowest and highest sample points is ignored in computing the mean value. However, by excluding components experiencing a very large relative price change, the trimmed mean method may miss price changes that provide useful information on trend inflation. If prices of some goods adjust faster than others, trimming the large price changes will exclude such quick-to-rise components that signal a shift in aggregate demand and underlying inflation trend.
A third approach estimates core inflation through a structural Vector Auto Regression (VAR) method. The authors define core inflation as that component of measured inflation that has no long-run impact on real output - a notion consistent with the vertical long-run Phillips Curve interpretation of movement in inflation and output. This definition excludes the impact of supply shocks that may have a permanent impact on the price level, but no lasting impact on the rate of inflation. The underlying proposition of this approach that inflation is neutral in its effects on the real economy is debatable; and, if it is assumed that the proposition is correct, then it raises the issue as to why should a central bank be concerned about price stability. Morana (2004) has recently attempted to compute core inflation, based on recent theoretical developments in the estimation of fractionally co-integrated process. Howsoever, they are measured, for core measures to be useful for monetary policy formulation, they should be computable in real time and have some predictive power for future inflation.
Two alternatives, exclusion-based and limited influence estimators (trimmed mean), have been examined for India. However, the loss of information content in the construction of core inflation and the relatively greater public acceptability of the headline inflation make the core measures useful only as indicators of the underlying inflationary process rather than as policy targets. Furthermore, in developing countries, a measure of core inflation excluding food items - which can account for more than half of the weight in the index -may not be very meaningful. |
A related issue is the relevance of inflation targeting for a country like India. As discussed in Section I, during the 1990s, both IT and non-IT countries have succeeded in reducing inflation and there is no clear evidence that IT countries perform better than non-IT countries. As the empirical evidence showed, the record of EMEs that have adopted IT is relatively weaker than that of advanced economies for a variety of reasons discussed earlier. In addition, there are several constraints such as recurrent supply shocks, persistence of fiscal dominance and absence of fully integrated financial markets in pursuing an IT framework in India (see Module: 2).
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