Important economic decisions are sta ked on these four numbers. Yet, they can’t be taken at face value.

Source: Outlook Business

Index of Industrial Production (IIP)

What is it?

Released monthly, the IIP captures changes in industrial output—25% of the economy—on a year-on-year (y-o-y) basis. The IIP for, say, May was 2.7%. This means the value of goods produced by India’s factories in May 2009 increased by 2.7% over May 2008. Also, gives industry-wise break-up.

What’s wrong with it?

The list of 543 goods hasn’t changed since 1993-94, but Indian manufacturing has—hugely.

Many factories are defunct, while many new, functional ones remain excluded.

Low initial response rate causes divergence between initial and final estimates.

What’s being done to correct it?

A new series (base year of 2004-05) is likely from calendar 2010, with about 900 products.

DIPP supplies 75% of all data. Now, economic think-tank CMIE will collect data on DIPP’s behalf. This is likely to improve data accuracy.

Gross Domestic Product (GDP)

What is it?

The value of all goods produced and services rendered in India in a particular period. Declared quarterly, GDP growth shows the y-o-y change. Data can be desegregated by industry and usage.

What’s wrong with it?

Final estimate is released two years on, after four revisions.

Primary data sources capture only 25% of GDP.

For households and unorganised sector, where data collection is unfeasible, indirect estimates are made.

State data comes very late. The last available is still for 2007-08.

What’s being done to correct it?

Starting calendar 2010, base year to be upgraded from 1999-2000 to 2004-05.

Survey of unorganised sectors could be incorporated to estimate output from this segment more accurately.

Tap the corporate data filing with the Ministry of Corporate Affairs to double primary coverage in the GDP to 50%.

Wholesale Price Index (WPI)

What is it?

Every week, this commonly tracked inflation index measures y-o-y change in wholesale prices of 435 products.

What’s wrong with it?

Base year for WPI is 1993-94.

No uniformity among collection agencies on a ‘wholesale transaction’.

Initial estimate is based on just 20-25% responses. It’s 70-75% for the final estimate, released two weeks later. This leads to divergence between the initial and final estimates.

Services (55% of GDP) is excluded.

What’s being done to correct it?

A new WPI series (base year of 2004-05, 1,224 items) is in the works.

To improve rural data collection, the Ministry of Statistics has tied up with the postal department.

The Ministry is considering a separate inflation measure for the services sector. Start with financial services and transport-related services, and gradually cover all segments.

Below Poverty Line (BPL) Families

What is it?

A measure of households living below the poverty line computed by the Planning Commission. These are the intended beneficiaries of poverty alleviation schemes. The poverty line is set at 2,400 kilo calories per person, per day, in rural areas and 2,100 kcal in urban areas. Or, Rs 356 and Rs 539.

What’s wrong with it?

Many say the Commission’s figure of 27.5% is an understatement, others say it is an overstatement.

The Commission draws the line and the Rural Development Ministry identifies the BPL families using its own methodology. Often, the Ministry’s BPL estimates exceed the Commission’s.

Very often, the poor are excluded and the non-poor are included.

What’s being done to correct it?

The Ministry has the NC Saxena Committee report on how to streamline the BPL census. The Planning Commission has appointed a panel to define criteria for identifying BPL families.

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Prime Numbers

Macroeconomic numbers are used by or have a bearing on every constituent of the economy.

Government

Uses inflation numbers to manage prices. If it sees, say, sugar prices shooting up, it could reduce import duties to enable more sugar imports.

GDP estimate is the base for Budget projections (tax collections and expenditure).

The direction of social spending comes from poverty numbers.

Reserve Bank Of India

The central bank sets its monetary policy based on the current trends in prices. If, say, it sees the inflation rate rising, it will reduce the amount of money in the system.

Companies

Look at demand, supply and price trends in their sector to decide whether and how much to expand.

Banks

Use desegregated industrial output data to see which sectors are growing and which are not, and decide their lending strategy accordingly.

Set deposit and lending rates.

Second-guess the RBI’s monetary policy thinking.

Investors

Global investors look at GDP growth and IIP to decide in which countries to invest in and how much.

Domestic investors look at sectoral growth to reshuffle their portfolio.

Prices of debt securities take cues from growth and inflation numbers.

Individuals

BPL benefits to the poor.

Deposit rates and lending rates.

DA of government employees. Consumer Price Index for Industrial Workers

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For economists like Sumita Kale, who works with Delhi-based research firm Indicus Analytics, forecasting has become a professional hazard. It’s not the global crisis that is making her life difficult, but government macroeconomic data that simply can’t be taken at face value. “It’s sort of a delusion,” says Kale. “Either we are proven wrong when the government releases its initial numbers only to be proven right when the final numbers come out, or vice versa.”

The list of 543 goods in the Index of Industrial Production (IIP) hasn’t changed since 1993-94, but Indian manufacturing has—hugely.

A few months back, Kale predicted that the Index of Industrial Production (IIP), a measure of India’s industrial output, would increase by 2.9% in November 2008. However, in its ‘initial estimate’, the Central Statistical Organisation (CSO), the government entity that collates economic data, said the IIP grew 1.7% that month. Kale was way off the mark. The CSO released its ‘final estimate’ two months later, as is the norm, and revised the figure to 2.5%—closer to Kale’s estimate.

The revision was too big and it came too late. Indicus had circulated the 2.9% IIP figure to its clients and had also used it internally to make other economic projections. Its clients—who could be banks, corporates, brokerages, governments, high net worth individuals and research outfits, among others—would have used that IIP figure to make further projections like gross domestic product (GDP) growth as well as economic decisions that had thousands of crores riding on them.

Effectively, economic forecasters and their clients were first being told that they had got it significantly wrong. So, they realigned their numbers to what the government was saying was the true picture, only to be told some days later that they actually got it right the first time around. They might all as well shut shop and go home.

Such is the disorder and frustration one inaccurate macroeconomic number can cause. Such variations in macroeconomic numbers are becoming more frequent. A marginal variation is normal, but the magnitude of such variations is becoming alarming. “Direction is not a problem, magnitude is,” says Shubhada Rao, an economist with Yes Bank. “Either it is over-estimated or under-estimated.”

Such deviation is just one symptom of the problem, which is more fundamental. There’s a big question mark over whether these numbers accurately represent what they set out to represent. It’s a matter of concern, as it has implications, sometimes major, on the lives of every Indian.

More Than Numbers

Macroeconomic numbers are summations of a time and state of the economy or a part of it. But they are not merely academic exercises that live and die on spreadsheets. These numbers live in the real world, they touch real lives.

Either we are proven wrong when the government releases initial numbers only to be proven right with the final numbers, or vice versa.—Sumita Kale, Economist, Indicus Analytics

Like that of the villager who can’t put together a half-decent meal for his family. Or the elderly couple who have entrusted their life savings to a mutual fund. Or the class-III government employee whose annual increment, in the absence of periodic Pay Commissions, is the dearness allowance (DA) declared by the government from time to time.

The fate of each of these lives is inextricably woven to these esoteric numbers. So, where the government draws the poverty line—and there are big issues with that—will decide whether the villager gets rice and wheat at subsidised prices or not. The investment decisions the mutual fund makes after reading the IIP and GDP trends will have a bearing on the returns of the elderly couple. And how much DA the government decides to give to the class-III employee is based on the rate of inflation.

Economists like Kale stake their reputation on those numbers. “Reliability of data is critical for the right prognosis,” said DK Joshi, an economist with Crisil, a ratings and advisory firm. “When the indicator is not fully representative and is frequently revised, one is forced to look at other supporting evidence while analysing trends in the economy.”

Given the sweep of lives they touch, it is important India’s macroeconomic numbers capture what they are supposed to capture. The problem is four key numbers fail on that count. In the way they are constructed, collated and computed, they are plagued with serious shortcomings and flaws.

Outdated Construction

It starts at the first step: construction. Some of these indices were constructed at a time when the Indian economy looked very different, and they haven’t been updated since. For instance, the IIP, when it was launched, intended to represent the industrial sector the way it looked in 1993-94 (or, what is called the ‘base year’). Problem is, the IIP still captures industry in 1993-94.

When an indicator is not fully representative and is frequently revised, one has to look at other supporting evidence to analyse trends.—DK Joshi, Economist, Crisil

The list of 543 goods in the IIP hasn’t changed since 1993-94, but Indian manufacturing has—hugely. So, for instance, the list includes obsolete items like typewriters and black & white television sets. Simultaneously, it doesn’t recognise products that have emerged since, like mobile phones and LCD TVs, to name just two. Also, a number of establishments that have shut down or have become defunct are still on the data-collection list, whereas new, functional ones haven’t been added.

“It is quite irritating,” says an economist with a large business conglomerate who didn’t want to be named. “The base year is very old and the composition of indices is also dated. The data gives an incomplete picture of what’s happening in the economy.” This makes decision-making difficult. Companies, for instance, planning a capacity expansion look at broad macroeconomic numbers and narrow sector numbers to determine whether their investment will get them the required returns or not. If the data is not representative or is wrong, it will impact their decision too.

The other notable mis-representation is of the services sector. Post-liberalisation, the Indian economy has changed from being manufacturing- and agriculture-led to being services-led. The services sector, today, accounts for 55% of the GDP, but it is completely excluded in the WPI. So, the cost of services like a phone call and water services—essentials today—is not measured at all. Thus, there could be divergence between what the inflation numbers capture and what is actually consumed.

Erratic Collation

Even in what is captured, there are shortcomings. The response rate—or, the percentage of data collected—is low. In the case of the WPI, it’s just 20-25%. That means, of the 100 establishments that have to submit their price data to the government every week, only 20-25 do so in time. In the case of WPI, the initial estimate comes within two weeks. So, for instance, for the week ended July 11, the government will release its initial estimate on July 25. This number is based only on 20-25% responses. For the remaining data points, the previous week numbers are extrapolated.

Even after the initial estimate has been released, price data keeps trickling in. Six weeks later, the government comes out with a final estimate, which is, typically, based on a response rate of about 70%. That’s better, but still not good—the higher response rate is one reason why the initial estimate is revised sharply almost every time. And it comes way too late to allow for quick informed decision-making. “The initial estimate sets the mood and expectations,” says Kale. “The revised numbers are not given importance.”

Sometimes, the numbers make for baffling reading. For instance, the IIP data showed a contraction in consumer non-durables—items of everyday use—in the January-March period. This was at a time when fast-moving consumer goods (FMCG) companies were showing a healthy growth in output.

Some of the blame lies on firms themselves, as they don’t take their responsibility of providing complete and timely data seriously. Some of them file late intentionally as they are wary of data theft. “With liberalisation, the motivation of industry to furnish regular and timely data to the government has reduced,” says Joshi of Crisil. Companies don’t need the government as much as they did pre-liberalisation. And, adds Joshi, “this has adverse implications on the quality and timeliness of data, particularly industrial production data.”

The Ministry of Statistics is planning to use the recently passed Collection of Statistics Act to get companies to fall in line and improve data collection. Penalties for non-filing and delays have been increased. Under the old Act, which was passed in 1953, the penalty was Rs 500 for the first default and Rs 200 per day thereafter. Firms didn’t mind paying up. In the new dispensation, companies will have to pay a fine of Rs 1,000 and they will be given a 14-day notice period to comply. If the information is still not provided, the penalty increases to Rs 5,000 per day.

This Act also empowers data-collection officials to forcibly collect data. It even empowers the government to press for jail sentences for officials who refuse to comply. Industry lobby groups have called these provisions draconian as they fear officials will misuse it. The government, however, has promised adequate safeguards against possible misuse.

Data at the sub-national level—state and district—is also an issue. It’s been seven months since the last financial year (2008-09) closed, and there’s still no estimate of GDP for any of India’s 28 states. Further, only a few states publish GDP data at the district level. “The need for sub-national income data is being increasingly felt by businesses to facilitate their decision-making,” says Joshi. “However, state-level GDP data is not synchronised with national GDP,” he adds.

Faulty Computation

All these imperfections shape the final figures. Having a very old base year and less responses results in large sampling errors—deviation from the real figure. When the products tracked in the IIP and WPI are based on industrial production patterns that are over 15 years old, sampling error increases, say experts.

The government has woken up to this. It is working on a new series for three key macroeconomic numbers—GDP, IIP and WPI—with a base year of 2004-05. These are expected to kick off in calendar 2010, and improve reliability. Even on the data collection front, the government is taking steps. For instance, for GDP, it plans to tap the corporate data filing with the Ministry of Corporate Affairs to double primary data coverage to 50% (the rest is from secondary sources or extrapolated).

Even as experts grapple with deficiencies in the existing data series, they want more structured data on key facets of the economy to make their decision-making more efficient. These include employment, retail sales and housing. “Globally, bond market participants use a lot of data to decide on their trading strategy. In India, the use of data is limited due to the quality of data,” says Golaka C Nath, an economist with Clearing Corporation of India, which maintains data relating to government and corporate bond markets. “More importantly, someone has to certify that the data is correct,” sums up Nath.