RBI recently held its Sixth Annual Stats Conference. This is a really brave annual conference given with huge issues with stats reporting in India. This year’s theme was central banks and data gaps.
RBI Gov. Subbarao gave some great insights in the speech on Indian economy and its stats system. There was some great humor on himself as well. On inflation measurement he says:
By far the most important statistic for the Reserve Bank is inflation which has traditionally suffered from measurement problems. I must admit that even at a personal level, I do not know how to interpret inflation. Twenty years ago, when I had a thick mop of hair, I used to pay `25 for a haircut. Ten years ago, after my hair started thinning, I was paying `50 for a haircut. And now, when I have virtually no hair left, I am paying `150 for a hair cut. I struggle to determine how much of that is inflation and how much is the premium I pay the barber for the privilege of cutting the Governor’s non-existent hair.
He begins looking at how stats system responded post major crises. Great depression led to building National Accounts; Latin American crisis led to SDDS and 2008 crisis to macro-finance stats:
The Great Depression of the 1930s, led to the development of a new statistical framework for economic analysis. As observed by the UN Statistical Commission2, national accounts were born out of the Great Depression and were developed as a consistent and comprehensive measure of economic activity for policymakers. The lessons of the Great Depression also led to the development of statistically estimated macroeconomic time series models by the likes of Tinbergen and Haavelmo, although the approach was criticised by Keynes, who had reservations about the model and the methods (Sims, Nobel Lecture, December 2011).
In the more recent period, following the Latin American financial crisis, the IMF introduced the Data Standards Initiatives to promote transparency of economic and financial statistics comprising the Special Data Dissemination Standard (SDDS) in 1996 and the General Data Dissemination System (GDDS) in 1997. These were aimed at enhancing the availability of timely and comprehensive statistics that would contribute to the pursuit of sound macroeconomic policies. Earlier this year, the IMF put out SDDS plus as an upper tier of its data standards initiative especially targeted at addressing the data gaps identified during the global crisis.
The global crisis that began in 2008, and which is still with us, has triggered a collective initiative under the G-20 umbrella to address data gap issues. The G-20 Data Gaps Initiative aims to bridge data gaps on the build up of risk in the financial sector, cross-border linkages, vulnerabilities of domestic economies to shocks and cross-sectoral interconnectedness and to improve the communication and dissemination of official statistics. Several international bodies including the United Nations Inter Agency Group, IMF, FSB, OECD, BIS and ECB are engaged in this exercise.
What are the data gaps at RBI level:
- Data on Non-Bank Financial Companies
- Data on Cooperative Banks
- Data on Financial Inclusion
- Basel – III and Identifying Inflexions in Economic Cycles
- Data on Corporate Saving
- Data Consistency
On Constraints in Statistical Analysis at RBI:
- (i) Measurement of Inflation
- (ii) Issues Relating to Expenditure Side GDP Estimates
- (iii) Estimation of Potential Output
- (iv) Market Reference Rates
- (v) Data Interpretation: Need to Look Behind the Data
On measurement of inflation, he comments on CPI vs WPI:
In India, we have several measures of inflation – one wholesale price index (WPI) and three legacy consumer price indices (CPIs). The different CPIs capture the heterogeneity of the economic structure and the large differences in the consumption basket across different population segments. In addition, the Government introduced, with effect from January 2011 (with base 2010=100), a new CPI series which has CPI (Rural), CPI (Urban) and CPI (Combined), representing the entire country and a weighting diagram based on the 2004/05 consumer expenditure survey.
25. One the of the problems we have to contend with in assessing inflation trends is the divergence between WPI and the CPIs which is due to differences in coverage and weights. Food has a weight of only 24 per cent in the WPI as against weights in the range of 37-70 per cent in the CPIs. Metals and a few other bulk commodities, whose prices have been volatile in the recent period, have a weight of 10.7 per cent in WPI, but are not directly included in the CPIs. Services, whose prices have been on the rise, have weights in the range of 12-25 per cent in the CPIs but are not reflected in the WPI. The difference in weights and coverage and the divergence in price movements not only create a wedge between the different inflation measures but also sometimes move them in opposite directions.
In core and headline:
Another problem is the measurement and interpretation of core inflation. Core inflation is usually estimated by excluding food and energy prices from the basket of goods and services that represents a household’s typical spending. The rationale for exclusion is that the prices of food and energy tend to fluctuate sharply and such volatility from the supply side, if passed on into the general price index, makes it difficult to interpret the overall trend. The surmise is that core inflation, being less volatile, gives a better sense of future price trends.
Theoretically, the rationale underlying core inflation is valid if variations in the prices of food and energy products are temporary and do not, on average, differ from other prices. If that is not the case, core inflation is neither a good estimate of the underlying inflation nor a reliable predictor of future inflation.
Even as the use and interpretation of core inflation in advanced economies remains contentious, in an economy like India, we have to contend with additional questions. In our economy, where food constitutes nearly 50 per cent of the consumption basket and fuel has a weight of 15 per cent, can a measure of inflation that excludes them be called core? Inflation in fuel and certain protein food items has been persistent over the last three years. Can a persistent component be excluded from the core measure?
On last point on need to look behind data he says:
formation Technology has improved data availability. Paradoxically, this has also increased the likelihood of misinterpretation of data. In his recent book, Thinking, Fast and Slow, Daniel Kahneman explains where we can and where we cannot trust our intuition and how we can tap into the benefits of slow thinking.
Consider an example: in a recent financial daily, an analysis based on last quarter operating results of Indian companies claims that interest costs were 11.1 per cent of total revenues, and infers on that basis that a cut in RBI’s rate can make a significant difference to the prospects of profits of firms.5 Arithmetically, this is correct and will make the reader believe that high interest costs have adversely impacted profitability. The reader would also intuitively attribute the deceleration in real GDP growth to 6.5 per cent in 2011/12 to the increase in the policy rate of the Reserve Bank by 375 bps during March 2010 – October 2011. But a closer look at the same sample suggests that the data used for this analysis includes financial and insurance firms which typically have interest to sales ratio of as high as 65 per cent. In contrast, non-financial firms, which account for more than 85 per cent of business, have interest cost of only 2.7 per cent of sales. Therefore, it is necessary to look behind the data and explore what lies underneath.
He again says interest rates not the only reason for slow growth:
Recently, the Reserve Bank published detailed time series data of weighted average effective lending rates (WALR) of commercial banks, which have been compiled on the basis of contractual interest details of individual loan account data since 1992-93.6 The trend of WALR clearly indicates that the real lending interest rate today is lower than it was in the high growth period of 2003-08, a period when investment boomed. This is the case irrespective of whether we use WPI or the GDP deflator.
47. Some analysts have contested the Reserve Bank’s assessment by using the Benchmark Prime Lending Rate (BPLR) to derive the real lending rate. Use of the BPLR for this analysis is flawed. Flawed because the BPLR of banks failed to be a floor for lending rates: as much as 60-70 per cent of lending by commercial banks used to be done at rates below the BPLR. This was the reason why the Reserve Bank replaced the BPLR by the Base Rate. The BPLR has practically become defunct as a meaningful reference rate and as such real lending rates derived from BPLR would obviously tend to be artificially higher.
48. The Reserve Bank maintains that interest cost is only one of the several factors that have dampened growth, and the increase in policy rate by the Reserve Bank alone cannot explain the investment slow down. I have asked our economic research department to do a detailed study on the time-series relationship between real interest rate and investment activity. We expect to put out that report in the public domain in the next couple of months.
Keenly awaiting the paper…
Superb stuff from the Governor…