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‘In Line With International Agencies’: Govt Defends GDP Figures after Ex-CEA Arvind Subramanian Claims Over-estimation

New Delhi: The government on Tuesday defended India’s official growth estimates, strongly arguing that these were backed by a statistically rigorous method that both the International Monetary Fund (IMF) and the World Bank have validated.

“GDP (gross domestic product) growth projections brought out by various national and international agencies are broadly in line with the estimates released by MoSPI (Ministry of Statistics and Programme Implementation). The GDP estimates released by the Ministry are based on accepted procedures, methodologies and available data and objectively measure the contribution of various sectors in the economy,” the MoSPI said in a statement.

The statement came after former chief economic adviser (CEA) Arvind Subramanian in a new research paper suggested that India’s “real” or inflation-adjusted GDP may have grown at an average 4.5% in the years between 2011-12 and 2016-17 instead of about the 7% average as shown by official data.

These six years are evenly spread between two regimes, with three years each falling between the Manmohan Singh-led UPA-2 government (2009-14) and the Narendra Modi-led NDA 2 government (2014-19).
In the paper, Subramanian, who quit as the CEA in June last year, sought to estimate the Indian economy’s growth by posing some basic questions: how often are people buying cars? Are companies and individuals borrowing more or less during a given period?

Household spending give early signals of the onset of an economy-wide revival or a slide. The clearest indications are available in any market or mall.

These, in turn, get reflected in corporate balance sheets, bank loans, tax collections, factory output and other data. If people are buying more cars, ideally, it should imply that banks are lending more, and more vehicles are wheeling out from car factories. The reverse should happen in times of a slowdown.

According to Subramanian, while this positive correlation exists between 2001 and 2011, they diverge after that, suggesting that official statisticians may have overestimated India’s growth by as much as 2.5 percentage points. The Indian economy probably grew at an annual average of 4.5% during 2011-12 to 2016-17, and not sizzled at 7% as the government’s number-crunchers had put out.

“The results in the paper suggest that the heady narrative of a guns-blazing India must cede to a more realistic one of an economy growing solidly but not spectacularly,” he said in the paper.

Hours later, the government rebutted Subramanian’s claims, stating that “the methodology of compilation of macro aggregates has been discussed in detail by the Advisory Committee on National Accounts Statistics (ACNAS) comprising experts from academia, National Statistical Commission, Indian Statistical Institute (ISI), Reserve Bank of India (RBI), Ministries of Finance, Corporate Affairs, Agriculture, NITI Aayog and selected State Governments”.

The decisions taken by these committees are unanimous and collective after taking into consideration the data availability and methodological aspects before recommending the most appropriate approach, it said.

Subramanian’s paper added a fresh round to an ongoing controversy surrounding India’s national income calculations.

A latest report by the National Sample Survey Organisation (NSSO) had raised fresh questions over India’s GDP and national income calculation methodology.

According to Mint, about 38% of companies that the NSSO surveyed from the MCA-21 database of companies used for calculating GDP could not be traced or were wrongly classified.

Gross Domestic Product or GDP, by definition, represents the total value of all the final goods and services that are produced within a country’s borders within a particular time period, typically a year or a quarter. It can be calculated by using three methods — the supply or production method, the income method and the demand or expenditure method and by definition the value of GDP should be identical, irrespective of the method used.

This is because one person’s or entity’s income is another person’s spending on expenditure. For instance, what households spend in buying provisions at a local store is the shop owner’s income. Likewise, an employee’s salary is what his/her company spends.

The base year of the national accounts is chosen to enable inter-year comparisons. It gives an idea about changes in purchasing power and allows calculation of inflation-adjusted growth estimates.

The new series has changed the base to 2011-12 from 2004-05. Every national accounts data set gives GDP calculations for two years: 2011-12 and the current year.

A decision to change the GDP calculation method was taken during the UPA-2 years. The NDA government launched the first set of data, giving out levels of GDP and growth rates from 2011-12.

The key points of dispute, including those raised by Subramanian, have arisen only after the new method was adopted effective from 2011-12.

In the previous method, the index of industrial production (IIP) or factory output was the main measure to calculate manufacturing and trading activity.

The limitation was that this only counted volume and did not give an idea about value. For instance, in the old method, the number of motorcycles produced in the plant was counted, as opposed to the motorcycles’ value that the plant rolled out.

In the communication sector, telecom subscriber base was used in the old sector as compared to minutes of usage in the new formula.

Previously, the first GDP estimates were based on IIP data. It was updated every two years, factoring in data from the Annual Survey of Industries (ASI). ASI only gave out goods’ value produced by firms registered under the Factories Act.

Now, the corporate affairs ministry’s MCA 21 records, a wide-ranging compilation of balance sheet data of lakhs of firms, is used.

The use of MCA 21 records for national income calculations have brought to light a segment of organised activity, which was earlier, for the most part, invisible. This is the lower end of the corporate segment. These are companies which are not listed in stock exchanges, and were virtually left out of national income calculations.

The new method adopts a gross value added (GVA)-based approach as compared to a pre-dominantly volume-based calculation previously.

GVA, which is GDP minus taxes, serves as a more realistic proxy to measure changes in the aggregate value of goods and services produced in the economy.

Earlier, the IIP served as the primary metric to gauge manufacturing and trading activity. The problem was, it only counted the number of units produced and did not distinguish, between, say the value of a luxury car and an entry-level hatch-back. It is possible that factory output would have remained stagnant over a period of time, but its value would have multiplied.

One can keep selling the same number of cars, but keep improving the quality so the value goes up. An even better example than cars is computers. A purely output-based method would not be able to capture the innovations and the value additions in such products and industrial activity.

The GVA method also factors in value addition and economic action carried out by activities such as marketing. Such activity can be of a very high value in case of large FMCG companies.

Subramanian’s main question is about the correlation between say growth in bank credit and car sales weakens considerably after 2011-12, raising questions about the statistical robustness of the new model.

Simply put, Subramanian’s argument raises a basic question: How come India is still growing at 7% though bank credit has been growing only at 9% since 2014?

Subramanian’s view about a weak correlation among proxy indicators such as car sales and bank borrowing after 2011-12, however, run contrary to some other experts’ opinion who point out evidence to the opposite.

Such an argument assumes that India’s growth is highly credit dependent, almost one for one.

In an article in Mint, Apoorva Javadekar, an assistant professor of finance at the Indian School of Business (ISB), has contended such an argument was deeply flawed for several reasons.

Javadekar has produced evidence where bank credit and GDP growth diverged during multiple periods even using the older GDP calculation method.

“Between the fourth quarter of 2008 and the corresponding quarter of 2009, bank credit growth tumbled from 26% year-on-year to 11%, but GDP growth rose from around 3% in the first quarter of 2009 to 11% in the same quarter of 2010,” Javadekar pointed out.

Importantly, the correlation between bank credit and corporate and individual finance has been weakening also because of the rise in the shadow banks of non-banking finance companies (NBFCs).

According to Javadekar, more than 50% of new corporate funding is coming from non-bank sources — either equity, non-banking financial companies (NBFCs) or foreign debt. “Hence, one cannot expect a very strong link between bank lending and corporate investment. The bottom line is that bank credit is not a great variable to smell the level of GDP growth,” he said in the article.

The government has responded to Subramanian’s paper by saying that as with any international standard, the data requirements are immense and diverse economies like India take time to evolve the relevant data sources before they can be fully aligned with the System of National Accounts 2008 (SNA) requirements.

SNA is the latest version of the international statistical standard for the national accounts, adopted by the United Nations.

“In absence of data, alternate proxy sources or statistical surveys are used to estimate the contribution of various sectors to the GDP/GVA,” the MoSPI statement said.

The SNA also prescribes that the base year of the estimates may be revised at periodic intervals so that changes in the economic environment, advances in methodological research and the needs of users are appropriately captured.

Base year revisions, MoSPI said, not only use latest data from censuses and surveys, they also incorporate information from administrative data that have become more robust over time.

Source: News18