Duvvuri Subbarao: Statistics and statistical analysis in Reserve Bank of
India's work
Address by Dr Duvvuri Subbarao, Governor of the Reserve Bank of India, at the Sixth Annual
Statistics Day Conference, Reserve Bank of India, Mumbai, 17 July 2012.
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Statistics day at RBI
The Statistics Day Conference, held to honour the memory of that doyen of statistics, the late
Prof. P. C. Mahalanobis, has become an important event in the Reserve Bank's annual
calendar. Since statistical analysis is a critical input for the Reserve Bank to deliver on its
mandate, we have found this annual conference to be a very valuable learning experience.
2. I want to acknowledge the presence here of several leading experts in statistical analysis
in economics: Prof. R. Radhakrishna, Chairman, National Statistical Commission; Dr. Aurel
Schubert, Director General, European Central Bank; Prof. J R Varma, IIM, Ahmedabad;
Prof. Probal Chaudhuri, ISI, Kolkata; and Prof. Amit Bubna, ISB, Hyderabad. A hearty
welcome once again to all of you.
Inherent uncertainty in real world statistics
We know that uncertainty is inherent in the field of economics where outcomes are shaped
by human behaviour. As I said on another occasion, I cannot change the mass of an electron
by my behaviour, but I can certainly expect to change the price of a derivative by my
behaviour. One of the charges against economists in the context of the crisis has been that
they suffered from "physics envy" which led them to build elegant models, using
sophisticated mathematics and impressive statistical finesse, deluding themselves and the
world at large that their models have more accuracy and predictive power than they actually
did. Forgotten in this misguided quest for precision was the fact that economic outcomes are
a function of human behaviour which, contrary to one of the fundamental assumptions of
economics, is not always rational. Also economic systems are reflexive in the sense that
beliefs about what will happen often influence what does happen.
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In the recent announcement of the discovery of the "God Particle" the Higgs Boson the
European Lab, CERN said that its level of certainty was 4.9 sigma, implying that physicists
"Gold Standard (5 σ)" -
came tantalizingly close to the or one in 2 million chance of the result
being a random fluctuation or noise.
We cannot even dream of such precision in statistical estimates of economic variables where
errors can arise from multiple sources such as errors in reporting, errors in aggregation,
errors in measurement of relationships among the variables and so on. In other words, the
chance of being fooled by randomness is much higher in the assessment of economic
events. That makes deciphering the right signals from economic data and separating the
grain from the chaff, that is distinguishing the trends and cycles from spurious noise, much
more complex. The lesson from all this is that in collecting, analysing and interpreting data
for economic analysis, we should be mindful of its inherent and unavoidable imperfections.
Data gaps and central banks
The theme for this year's conference is "Data Gaps and Central Banks". This is a subject that
is always valid and one that should always be on our check-list. Nevertheless, I believe the
motivation for the theme derives also from the global financial crisis that we have been going
through for the past five years. In the context of the crisis, there are three main critiques
hurled at economists and policy makers: that they helped brew the crisis, they failed to see it
coming and that they have no idea how to fix it.
Let us put these questions in the context of data and statistical analysis. Were data gaps and
shortcomings in statistical measurements behind the crisis? Were the analysis and
interpretation of data so flawed and egregious that signals were missed? Are data gaps and
data analysis hindering crisis management? The short answer to all three questions is that
while data gaps may not have caused the crisis, the crisis did reveal several data gaps.
Equally, shortcomings in the measurement, analysis and interpretation of data inhibited
appropriate and timely response. As Claudio Borio of the BIS said, "The main reason why
crises occur is not lack of statistics, but failure to interpret them correctly and to take remedial
action."
Have we learnt from past crises?
Since the global financial crisis is so much a part of our consciousness, a relevant question
is, have statisticians learnt from past crises? As one of President Obama's advisers said,
"This crisis is too good to be wasted". That is a dictum that economic statisticians have
always been mindful of.
The Great Depression of the 1930s, led to the development of a new statistical framework for
economic analysis. As observed by the UN Statistical Commission 0F1, 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.
Data gaps have been broadly classified into two categories; the first category is gaps for
which a conceptual and statistical framework and a system of collection of data already
exists, and the second is where such frameworks need to be further developed. The data
gaps include strengthening financial stability indicators, data on non-bank financial
institutions, information on exposure of cross border interconnectedness of financial
institutions, sectoral balance sheets, international investments and property price data.
These targets are best achieved through creating a wide range of economic and financial
statistics that are mutually consistent thereby eliminating contradictory signals due to
measurement issues.
The Reserve Bank has been actively engaged in these initiatives pertaining to the financial
sector and has taken important steps towards addressing data gaps, including contributing to
India achieving SDDS compliant status in 2001. For example, the Reserve Bank compiles
and reports financial soundness indicators, international banking statistics, the international
and portfolio investment. India's
investment position as well as data on consolidated direct
Annual Observance Report of the SDDS for 2011, posted on the IMF website, shows that
there are no major deviations from the SDDS undertakings.
Statistics at RBI: some introspection
As a public policy institution, the Reserve Bank is a large producer and consumer of
statistics. Consistent with our mandate, we collect and analyse data both for internal policy
formulation and for external dissemination. Notwithstanding our best efforts, we must admit
that we do not measure and analyse everything that we need to measure and analyse. Let
me draw attention to a few important areas where the Reserve Bank needs to make a
greater effort to fill gaps.
Data on non-bank financial companies
As the central bank, the Reserve Bank has responsibility for compiling monetary and liquidity
aggregates. While our coverage of the banking sector is quite robust, our coverage of
non-bank financial companies (NBFCs) is much less so. This is a gap we must fill as the role
of NBFCs in the financial intermediation process has been growing rapidly. The number of
NBFCs has increased from around 8,500 in 2000 to about 12,500 in 2011. As on March
2011, NBFCs had assets of ₹1.16 trillion and public deposits of ₹120 billion. NBFCs source
their funds largely from borrowings, with nearly half of the borrowings from banks and other
financial institutions. For the Reserve Bank to have a better understanding of the influence of
NBFCs on liquidity and the credit creation process, we need more comprehensive and higher
frequency data on NBFCs.
Data on cooperative banks
The cooperative banking sector plays an important role by providing financial intermediation
services to agriculture and allied activities, small scale industries and self employed workers.
As at end March 2011, there were as many as 97,410 cooperative banks, of which more than
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98 per cent were rural cooperatives. Yet our data on cooperative banks is weak suffering
both from coverage and reporting lags. Considering their systemic importance, geographical
reach and role in financial inclusion, the Reserve Bank, in coordination with NABARD, needs
to improve the statistical data reporting system of cooperative banks.
Data on financial inclusion
Financial inclusion has been among the Reserve Bank's policy priorities. Yet, understanding
how financial inclusion works at the grassroot level remains a challenge for us. There is this
book, Portfolios of the Poor: How the World's Poor Live on $2 a Day (Collins and others,
2009), a book written with great empathy that provides a sobering account on how the poor
manage their everyday finances. The authors used an innovative method of survey data
collection by having a panel of impoverished slum households in Bangladesh, India, and
South Africa maintain financial diaries on an every day basis of their incomes and
expenditures over a period of one year. They then analyzed the transactions, one by one,
penny by penny, to understand how poor households manage their money. The findings
show that most poor households do not live hand to mouth. They fight poverty by employing
surprisingly sophisticated financial management linked to informal networks and family ties.
In the authors' words,
"They push money into savings for reserves, squeeze money out of
creditors whenever possible, run sophisticated savings clubs, and use micro-financing
wherever available."
In order to pursue meaningful financial inclusion, the Reserve Bank needs many studies from
across the country like "Portfolios of the Poor". We need a better understanding of the levels
and patterns of income of the poor, their patterns of expenditure, their marginal propensities
to consume and save, the risks they face and how they cope with them and the transaction
costs they encounter in dealing with formal financial institutions.
Basel - III and identifying inflexions in economic cycles
A critical component of the Basel III package is a countercyclical capital buffer which
mandates banks to build up higher levels of capital in good times that could be run down in
times of economic contraction consistent with safety and soundness considerations. This is
conceptually neat, but operationalising it poses many challenges, as indeed evidenced by
Spain's recent experience. The foremost challenge is identifying the inflexion point in an
economic cycle which would trigger the release of the buffer. This needs to be based on
objective and observable criteria. It also needs long series data on economic cycles. So,
what we need is both a better database and more refined statistical skills in interpreting
economic cycles.
Data on corporate saving
In the compilation of national accounts which is the domain of the CSO, the Reserve Bank is
involved in the estimation of saving, compilation of flow of funds and external accounts. The
standard practice for estimating saving is to estimate household saving in financial assets
and corporate saving, and to determine households' saving in physical assets as
the
residual. The residual method is a valid statistical approach (for that matter, GDP is also
estimated as residual of total output after adjusting for intermediate consumption).
Nevertheless, the method implies that any error in the measurement of corporate saving can
create an error in the estimate of household physical saving, and thereby distort
macroeconomic assessment. The Reserve's Bank data set on the corporate sector is
extensive and rich, but its accuracy has been challenged by analysts. We need to examine
how we can improve this.
Data consistency
The Reserve Bank has a wide mandate. The multiple policy objectives have resulted in
multiple data systems. Each of these data systems is tailored for its specific objective, and
has evolved over time to meet that specific objective. The problem, however, has been that
each data system has evolved independently. We now need to focus on integrating the data
systems to improve consistency across the aggregate database. This is perhaps most
important in the data relating to the priority sector, segments of which have undergone
definitional, conceptual and coverage changes over the years.
Constraints in statistical analysis at RBI
Having indicated areas where the Reserve Bank needs to focus on filling data gaps, let me
now move on to some constraints we face in data measurement and interpretation.
(i) Measurement of inflation
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.
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24. 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.
26. 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
a household's typical spending. The rationale for exclusion is that the prices
that represents
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.
27. Even in advanced economies where food and fuel constitute a relatively small proportion
of the consumption basket, the focus on core inflation for policy formulation is being
challenged. Lorenzo Bini Smaghi, a member of the executive board of the European Central
Bank argues that core inflation is not a good predictor of headline inflation and adds that "it
may have taken a while to realise this but, as the Romans used to say, to err is human, to
persist in erring is diabolical."1F2
There are, of course, counter arguments: economists like Paul
Krugman argue that the efficacy of core inflation in forecasting headline could be period
specific. If one takes a longer series of over three years, there is some evidence that core
inflation does have statistically significant predictive power.
28. 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.
29. 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?
30. Also, structural changes in our economy over the past decade have created an
unprecedented demand for commodities. In the absence of a supply response, this has
resulted in a lasting change in the price level. Therefore, our headline measure of inflation
will necessarily have a larger momentum than core inflation. In the Reserve Bank, we need
to be mindful of these factors in interpreting the movements in headline and core inflation.
31. Against the backdrop of these conceptual and measurement problems, let me now
respond to some criticism against the Reserve Bank with reference to inflation indicators.
32. One criticism has been that analysts are confused about what measure of inflation the
Reserve Bank uses for its policy. I can only reiterate what we said several times in the past,
which is that we study all measures of inflation, both headline and disaggregated, and in
interpreting their levels as well as rates of change, we try to be mindful of their conceptual
and measurement shortcomings.
33. Another criticism has been that the Reserve Bank should be guided by the CPI rather
than the WPI. That we now have the new CPI data is a welcome development. Theoretically,
CPI, which measures changes over time of the general level of prices of goods and services
that households acquire for the purpose of consumption, is considered a better measure of
inflation than WPI. But, the new comprehensive CPI does not have adequate history to
support data analysis and to be used as a sole headline measure of inflation. At the same
time, the Reserve Bank cannot ignore a price index which arguably reflects the most updated
economic structure. So, in our assessment of the inflation situation, we use the new CPI as
also the legacy CPIs, but not them alone,
34. This is not to argue that the WPI is flawless. In its present structure, the WPI does not
capture the price movement of services. Also, it is a hybrid of consumer and producer price
quotes. For example, the index captures the price of important commodities like milk from the
retail markets; not at the producer level. In contrast to CPI, PPI measures price changes from
the perspective of the seller. Sellers' and purchasers' prices differ due to government
subsidies, sales and excise taxes, and distribution costs. For these reasons, it is, therefore,
desirable that we move towards developing a Producer Price Index (PPI) that measures the
average change over time in the sale prices of domestic goods and services.
(ii) Issues relating to expenditure side GDP estimates
35. For the purpose of monetary policy, it is important to assess aggregate demand
conditions by studying the GDP data. Despite their well-known limitations, expenditure-side
GDP data are being used as proxies for components of aggregate demand. However, in
recent quarters, the difference between identified components of GDP data measured by the
output method and the expenditure method has increased. For example, in the expenditure
side data for Q4 of 2011/12, if we add up the year-on-year growth in private final
consumption expenditure, government final consumption expenditure, gross capital formation
and net exports, we end up with a 9 per cent year-on-year GDP growth. But the growth rate
derived from the supply side, by adding indirect taxes and subtracting subsidies yields a
number of 5.6 per cent. The difference between the two numbers as high as 3.4 per cent of
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GDP is the error component. Such a large error component not only makes the data
non-transparent but complicates the assessment of aggregate demand.
36. This is possibly an appropriate context for me to cite a few concerns about some official
statistics. Economic measures such as income, debt, deficit and balance of payments are
primary off-shoots of good book-keeping cum accounting systems. Many of the variables in
these data have to be estimated using statistical methods and simplifying assumptions. This
is in part because it is costly and time consuming to keep or process every record. This is
also because some of these variables cannot be measured directly: think about the huge
unorganised informal segment of the Indian economy.
37. How do we cope with this? A part of the data is supplemented by regular surveys. But
surveys suffer from sampling and non-sampling errors. Also economic cycles, particularly
economic slow-down, pose significant challenges to the accuracy of economic indicators,
including those that are used as source data for the early GDP estimates. During this period,
some firms go out of business, some may complete surveys only partially, and some may
choose not to respond at all to voluntary surveys. Therefore, devising reliable and accurate
methodologies for what goes into which account, even with today's technology, is no trivial
task, especially for a modern, complex economy.
(iii) Estimation of potential output
38. An accurate estimate of potential output is critically important for central banks to assess
demand conditions and the output gap. Reliability and timeliness are critical. For example, an
output gap measurement mistake led the Federal Reserve to pursue policies that eventually
led to the Great Inflation of the 1970s.2F3
39. Potential GDP cannot be directly observed, but has to be estimated using statistical
techniques. Such an estimate is vulnerable to errors for three main reasons. First and most
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obviously, real GDP data is often revised sometimes substantially after the initial
estimates are published. Second, there is considerable uncertainty in the level of productivity
growth at any point in time. Finally, there do not exist widely agreed assumptions or a unique
methodology for estimating potential GDP. These reasons get accentuated in the case of
India because of lack of comprehensive and consistent data on employment.
40. The uncertainty surrounding economic activity has heightened in the post-crisis period.
India is no exception. Assessing India's potential growth rate, consistent with our objective of
low and stable inflation, remains a challenge. In its annual report for 2009/10, the Reserve
Bank had reported that the potential output of the Indian economy may have dropped from
8.5 per cent pre-crisis to 8.0 per cent post-crisis. Latest assessment following the standard
filtering technique suggests that potential output growth may have further fallen to around
7.5 per cent.
(iv) Market reference rates
41. The recent LIBOR controversy has drawn the world's attention to how a few large global
financial institutions allegedly manipulated one of the most commonly used market rates.
42. Can we have problem similar to LIBOR here in India? The counterpart of LIBOR in India
is the less used MIBOR which is set by FIMMDA-NSE. The Reserve Bank, on its part,
compiles and publishes every day the reference exchange rates for spot USD-INR and spot
EUR-INR following a transparent statistical method. Earlier on, we used to set the rate by
averaging the mean of the bid/offer rates polled from a few select banks as at 12 noon on
every working day. A few months ago we changed the method. Now we poll a select list of
contributing banks at a randomly chosen five minute window between 11.45 am and
12.15 pm and put out the reference rate at 12.30 pm. The contributing banks are selected on
the basis of their market-share in the domestic foreign exchange market and representative
character. The Reserve Bank periodically reviews the procedure for selecting the banks and
the methodology of polling so as to ensure that the reference rate remains a true reflection of
market activity.
(v) Data interpretation: need to look behind the data
43. Information 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.
44. 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
rate can make a significant difference to
revenues, and infers on that basis that a cut in RBI's
the prospects of profits of firms.3F4 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
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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.
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45. Consider another important variable the real interest rate. Research in the Reserve
Bank shows that real interest rate and investment activity are inversely related. A crucial
question is, whether high real interest rate is the only factor behind the investment slow
down?
46. 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 1992-93.4F5 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.
Summing up
49. Let me now conclude. I have spoken about statistical analysis in the context of the crisis,
and about how every major economic and financial crisis has led to improvements in data
standards and in the tool kits for statistical analysis. I referred to the international response to
the data gaps identified in the ongoing global financial crisis. I then looked inwards at some
of the more important data gaps within the Reserve Bank and highlighted why it is important
to rectify them. Following that, I moved on to some constraints in data measurement and
interpretation, and where necessary and possible, indicated how we address those
constraints.
50. In conclusion, I want to reiterate that the Reserve Bank is deeply conscious of positioning
itself as a knowledge institution. We place great store by the timeliness and accuracy of data
and in the skill sets for analysing and interpreting that data. Oftentimes, the complex
problems of the real world test the adequacy and the integrity of our data sets as also our
ability to analyse and interpret them. We make best efforts to improve our data collection and
analysis and to be mindful of the conceptual and measurement errors. We are not the best
practice, but will spare no effort to reach the best practice, if not set the best practice itself.
51. Finally, a hearty welcome once again to our external experts, and my best wishes for the
success of this Annual Statistics Conference.
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