Evaluating India’s GDP: How Reliable Are The Estimates?

ARUN KUMAR

GDP is taken to give an idea of the production of goods and services in an economy. Because of the huge diversity of what is produced in a modern society, aggregation becomes difficult. GDP yields one monetary number to represent it and that is taken to present an aggregate picture of society’s welfare. However, since society is highly segmented and unequal, the benefit of production is unequally divided. There are billionaires living in extreme luxury while large numbers live below or near the poverty line in uncivilized conditions.

The ruling party always wants to project a positive image of itself. One way of doing so is to claim a higher GDP and improved welfare of society. They advertise the growing number of billionaires and claim that large numbers have been raised above the poverty line. India is supposed to have the third largest number of billionaires in the World while in per capita terms India is at the 140th spot. In spite of such numbers, it was officially claimed that India is the fourth most equal society in the world. And, that in 2025, hardly 3% of the Indians are multi-dimensionally poor.

Another showpiece of the government is that India is the fastest growing large economy, growing at an average of 6.5% since 2014-15. So, India which was the 10th largest economy in 2014-15 is projected by the IMF to overtake Japan by 0.014% to become the 4th largest economy by end 2025. Further, it is projected that the economy will soon become the third largest by overtaking Germany. These are impressive claims. Finally, it is claimed that from being a poor country, India will become a developed (viksit) economy soon.

Reality

Can such projections be relied on? That depends on how correct the GDP data is. Doubts arise because the government usually puts a gloss on reality. Since 2014-15, government has rejected adverse data and modified it to suit its purpose.

For instance, the new GDP series with base 2011-12 was rejected since it showed better growth during the UPA period. A new series was then created. Consumption data and unemployment data were also rejected initially, before the 2019 elections. If the GDP data and its distribution across sectioins is incorrect then the official claims mentioned above would turn out to be incorrect.

Even if the GDP data is taken to be correct, one cannot ignore the per capita GDP which represents the average welfare of an individual. India is at the 140th rank in the world. Japan, the nation whose GDP we are supposed to have surpassed has one twelfth our population. So, each Japanese is currently 12 times better off than an Indian. Further, since incomes in Japan are more evenly distributed, the poor in Japan have a far higher standard of living than a poor in India.

When the distribution is highly skewed, as in India’s case, the per capita income does not represent the welfare of the poor. The joke is that if Elon Musk and Jeff Bezos enter a football stadium, the average income of those in the stadium would turn out to be above $1 million.

In brief, India having a slightly higher GDP than Japan does not tell the story of welfare in India. India’s GDP is large because of its huge population and not because of the average person’s higher welfare. Further, the IMF projection may turn out to be incorrect given the huge uncertainty created by actions of President Trump. It has led to the sharp decline of the Rupee compared to the dollar. So, projecting the past growth rates of Indian and Japanese GDP in dollar terms to end 2025 is fraught with difficulties.

GDP Estimation Complexity

The bigger worry is the correctness of Indian GDP. If the GDP has been growing at the officially estimated 6.5%, why has unemployment been a big issue? Even official data from Periodic Labour Force Survey (PLFS) shows a) high unemployment among the educated youth and b) labour force participation rate (LFPR) in India remains much less than in other big economies like, China and Brazil. This is especially true for women. A large number of them in the relevant age group have stopped looking for work. Further, if the situation of the poor has improved dramatically, why has the government been constrained to offer free foodgrains to 800 million people?

To better understand the situation, the official methodology of GDP estimation needs to be analysed.

First, GDP is an estimated number. Second, there are errors in estimation due to methodological deficiencies and insufficiency of data. It is not that enumerators go door to door asking individuals their income and then aggregating them to get the GDP. Third, the Indian economy consists of highly diverse sectors like, agriculture, manufacturing, trade and government services. Since there are vast differences between them, they require different methods and data bases. Each sector and sub-sector is estimated separately and then aggregated to get the GDP. Finally, it is not that individual incomes are added up. Rather, incomes from the sectors and sub-sectors are estimated and added.

There are 11 major sectors of the economy and each requires its own method of estimation. Each of these methods pose their own challenge. And, data for each sector has to be obtained from different sources. In effect, there are two possible sources of error in estimation of GDP – data may be incorrect and/or method may not be suitable. These two sources of error are also inter twined. All this makes the GDP estimation a very complex exercise.

With time, each sector changes due to technical change. For example, a car of today with lots of software and motors is vastly different from that of the 1950s. Typing with typewriters or computers is very different. Such changes in the economy create complications when measurement is based on the old method and data base. That is why, the basis of calculation needs to be revised periodically. This has been done almost every decade but now not since 2011. But these changes make comparisons of GDP over time like comparing oranges and apples.

Not only are their major sectors in the economy that are very different from each other, each sector is sub-divided in to the organized and unorganized sectors and public and private sectors. Since the public sector is entirely organized, broadly, each sector has three components that need to be estimated.

So, the eleven major sectors require at least 33 different methods and data bases to estimate their contribution to GDP.

Another problem arises when the economy experiences a shock. It impacts different sectors differently and requires a change in the method of calculation. The Indian economy has suffered four big shocks since 2016 when demonetization was announced. In 2017, the structurally faulty GST was implemented, in 2018 there was the NBFC crisis and in 2020 there was the pandemic induced sudden lockdown. Each of them was different and impacted the economy’s organized and unorganized sectors differently. So, the method of estimation last adopted in 2011-12 became invalid and needed revision but that has not been done, leading to errors in estimation of GDP.

Erroneous Estimation of Unorganized Sector

The unorganized and the organized sectors are vastly different. The former employs 94% of the workforce and produces 45% of the output. It consists of around 11 crore farmers and 6.5 crore small and micro units. Getting data from them is difficult. The organized sector not only consists of large units but it is required to report to government agencies. So, its data is officially available even if it is tainted by the black economy.

The contribution of the unorganized sector to GDP is calculated using the available data as a proxy. Officially it is stated (GoI, 2017):

“Provisional Estimates of Annual GDP and Quarterly Estimates of GDP are compiled using the Benchmark-indicator method i.e., the estimates available for the previous financial year … are extrapolated using the relevant indicators reflecting the performance of sectors.”

The ratio between the organized and the unorganized sector is obtained for a benchmark year in which a survey is carried out to estimate the unorganized sector. This ratio is used in the subsequent years along with some indicators. Since the benchmark remains unchanged in subsequent years, it amounts to saying that the organized sector can act as a proxy for the unorganized sector.

Further, the quote says, previous year’s data is extrapolated. This poses two problems. First, since the unorganized and the organized sector are growing at different rates, the benchmark is changing. Since this change is not taken into account, the method leads to mis-estimation. This is even more true during a shock to the economy that impacts the organized and unorganized sectors differentially. The bench mark changes drastically due to a shock like, demonetization or the pandemic lockdown. Both of them adversely impacted the unorganized sector harder than the organized sector. And the recovery was slower for the former than the latter.

Second, extrapolation from the previous year leads to persistence of previous year’s errors. In fact, projecting from a normal year to a shock year will lead to an over estimate. And projecting from the shock year to the next year compounds the error.

These two issues are pertinent for the Indian economy post demonetization in 2016. As mentioned above, the Indian economy has experienced 4 big shocks since 2016-17. Each of them impacted the unorganized sector much more than the organized sector. Further, since each of the shocks was different, the correctives required were also different but not one corrective has been applied to the method of measurement of GDP.

An example of the error in official statistics is provided by the GDP data for 2016-17, the year of demonetization. The official data showed 8% growth, fastest for the decade of 2010. However, the economy had declined from November 2016 for at least 5 months after that. So, for financial year 2016-17, there was negative growth (Kumar, 2017) [1] and not growth of 8%.

The decline of the unorganized sector due to demonetization was accentuated by the structurally faulty GST implemented in July 2017. It cheapened the output of the organized sector compared to the unorganized sector due to the availability of input credit (ITC) which was not available to the unorganized sector (Kumar, 2019) [2]. So, the cost of inputs for the organized sector declined.

Further, if the organized sector purchases inputs from the unorganized sector it has to pay ‘reverse charge’ which increases its working capital requirement. For both these reasons, the organized sector has reduced its purchases from the unorganized sector. Finally, the unorganized sector even when it has come on board the GST platform finds it costly and that reduces its viability.

In 2018, the unorganized sector was hit by the shock of NBFC crisis and in 2020 by the sudden lockdown. So, not only has the unorganized sector not had time to recover from the shock of demonetization in 2016-17, it has continued to decline since then.

In brief, for several reasons, demand has shifted from the unorganized sector to the organized sector so that the former has been declining while the latter has been growing at its expense.

Unfortunately this decline is not captured in the official GDP data, as argued above, since the unorganized sector is not independently measured. This introduces an upward bias in the GDP estimation. In fact, the more rapid the growth of the organized sector due to the greater decline in the unorganized sector – the more the over estimation of GDP.

The decline of the unorganized sector which employs 94% of the workforce leads to higher unemployment of various kinds – unemployment, under employment, disguised unemployment and low labour force participation. If the unorganized sector was growing at anywhere close to the official average growth rate of 6.5% (over the last decade), plenty of work would have been created and unemployment would have declined.

Evidence of shift in demand from the unorganized sector to the organized sector has come from various industries. In the case of trade, the second largest employer after agriculture, demand has been shifting from neighbourhood stores to the much more automated e-commerce. This shift accelerated during the pandemic. Such a shift in demand is also reported from leather goods, pressure cooker, luggage, FMCG, etc., industries.

The growth of organized sector at the expense of the unorganized sector results in increase in inequality. This impacts demand and leads to slowing down of the growth rate of even the organized sector. This slowing down was visible in the pre pandemic period when the official growth rate declined from 8% in Q4 of 2017-18 to 3.1% in Q4 of 2019-20 (just before the pandemic hit the country).

So, what is the correct GDP now? If the GDP is corrected for the decline in the unorganized sector, the rate of growth of the economy turns out to be around 2% since demonetization. This implies that since 2016-17, GDP is over estimated by an average of 4.5% per annum or cumulatively over estimated by 48.34% in 8 years. So, instead of being around $4 trillion, it would be $2.83 trillion. Thus, India would be the 7th largest economy of the world, ahead of Italy, if only the corrected white economy data is taken into account.

IMF Assessment

IMF (2025) [3] in Annex VII on Data Issues (p.66) has given a ‘C’ to quality of data used for National Accounts in India. What does a ‘C’ imply? “The data provided (by India) to the Fund have some shortcomings that somewhat hamper surveillance”. Surveillance is done to assess the reliability of the official data for policy purposes.

Even though reliability is questioned, the IMF is mandated to accept government data only. Further, the Report gives a way out to the government by stating that the data base is being reformulated and the new series for GDP and CPI will be announced by February 2026.

But, the fact remains that a C grade implies that Indian official data has not been up to the mark in recent years. And, IMF could not reliably assess India’s GDP. There are other indicators too that put doubt on India having a high growth rate. For instance, there are reports of investment projects being withdrawn and/or curtailed and of net FDI becoming negative. These are not the signs of a robust economy growing rapidly.

IMF Pointers

For questioning the reliability of Indian GDP data, the IMF points to:

a) Use of an outdated base year (2011/12),

b) Use of wholesale price indices as deflators rather than the producer prices,

c) Excessive use of single deflation,

d) Sizable discrepancies in the GDP data, possibly due to the inadequacy of informal sector data,

e) Weak statistical techniques used in the quarterly national accounts compilation, and

f) Lack of consolidated data on States and local bodies after 2019.

None of these points are new since they have been raised by several analysts since demonetization in November 2016. But, the IMF flagging them now carries more weight than the critics had.

GDP New Series Questioned

The IMF does not flag an even more important lacuna in the estimates of GDP in the new series with base year 2011-12 (Kumar, 2025d) [4]. Namely, it was manipulated to show higher growth during the NDA period than during the UPA period.

The new series was announced in 2015 soon after the NDA came to power. However, work on it had started during UPA II. The new series when announced was not accompanied by a back series which is needed to compare the new series with the earlier one.

In response to the criticism, a Committee was asked to take care of this lacuna. But their work was rejected since it showed higher growth for the UPA period than the NDA period. Next, NITI Ayog was asked to produce the GDP series, even though it is not competent to do so. It produced a series that was accepted since it showed higher growth for NDA than the UPA period – a clear case of manipulation.

Soon after this, the former CEA showed that GDP was being over stated by 2.5% or more. Next, 3 lakh of the18 lakh companies in the MCA21 data base were removed as they were said to be shell companies. This should have impacted the GDP estimate. After all, shell companies are used for under and over invoicing so as to divert incomes from the legitimate companies to the shell companies. So, removal of the shell companies should have impacted GDP estimates but that did not happen.

Further it was found that 35% of the companies were missing from their declared address, etc.. These were possibly fake companies and giving fake data. This put a further question mark on GDP data. Finally, the black economy has implications for estimating GDP and this has multiple implications as shown below.

In other words, GDP data with base 2011-12 was seriously flawed because of manipulations. So, the IMF flagged flaws in GDP estimation are a sub-set of the serious flaws in GDP data. In effect, the reliability of Indian GDP data is even more suspect than what IMF has flagged.

Discrepancies

Kumar (2025a) [5] pointed to the sizeable discrepancies between the two measures of GDP –using the production or the expenditure approaches. This has now also been flagged by the IMF Report. By definition these two approaches should give the same estimate since they are like the two sides of the same coin. But, differences creep in because these estimates are arrived at by different methods and this is called ‘Discrepancy’ between these two estimates.

Actually, both estimates have errors because of lack of independent data for the unorganized sector. The missing data impacts the two estimates differently and results in the discrepancy which changes from quarter to quarter and year to year. Prior to demonetization in 2016, discrepancy was a small per cent but since then, it has not only become large but has swung wildly from positive to negative and back. This points to lacunae in estimation of GDP post demonetization.

Kumar (2023) [6] points out that for quarterly estimates of GDP, most current data are not available thereby necessitating the use of proxies. But, as pointed out above, the use of the growing organized sector as a proxy for the declining unorganized sector leads to over estimation of GDP.

Since the unorganized sector produces most of the consumption goods and services, it’s over estimation leads to over estimation of consumption in the economy. Correspondingly, investment gets under estimated. All these factors lead to discrepancies in GDP.

GDP Errors due to Black Economy

Further complications arise in estimating GDP due to the existence of a huge black economy in India. Its basis is non-declaration of taxable incomes so that value added is under estimated. It impacts all the sectors of the economy. If it was small and/or restricted to a few sectors of the economy, it could perhaps be ignored. But since the mid-1970s when its size became more than 10% of GDP it cannot be ignored. Kumar (2016) [7] estimated it to have reached 62% of GDP in 2012 and that certainly cannot be ignored.

Black incomes are generated by falsifying data via under-invoicing of revenue and over invoicing of costs so as to report lower profits. This leads to errors in both the method of estimation and the data of each of the sectors. Consequently, the official GDP which may be called the white economy data, under-estimates the true output and incomes.

Some people argue that multiplying the white economy data by a fixed per cent should enable one to get the true output of the economy. This would be inappropriate since the black economy impacts different sectors differently. Thus, using the same ratio would be erroneous.

For instance, since there is no income tax on agricultural incomes, they are not suppressed and, therefore, there is no generation of black incomes. Incomes maybe diverted from other sectors to agriculture to lower tax liability. But, since the black income is generated in another sector, it should be attributed to that sector. Similarly, construction sector is estimated using the input-output approach and its true output gets captured even if construction activity evades taxes. So, the actual incomes of these two sectors are captured in GDP and multiplication by the per cent of black economy will over state their output.

Another complication in estimating contribution of the black economy to actual GDP is that a large part of the black economy is like ‘digging holes and filling them’. This constitutes ‘social waste’. This ought not to be counted in GDP. Another part of the black economy is based on illegal activities. Like smuggling, narcotic drug trafficking, etc. These constitute ‘social bads’ that reduce social welfare and are not counted in GDP. If these are around 10% of GDP then legal activity GDP would be 52% and if 25% is social waste (Kumar, 2006) [8], then GDP contribution of black economy can be taken as 39% over the white economy.

The large black economy in India is hugely problematic. It lowers investment productivity, leads to substantial social waste, causes policy failure, complicates doing business and makes living conditions uncivilized. Therefore, it prevents the economy from reaching its potential rate of growth and employment. So, in spite of higher GDP, unemployment persists. Also, since black incomes are concentrated in the hands of the top 3% of the income earners inequality is more than what is estimated using only the white economy. This leads to a slowing down of the economy. Effectively, the black economy is a ‘social bad’ and a higher GDP due to its existence is not comparable to GDP of other nations.

Conclusion

In brief, GDP is under estimated by 39% due to black economy and over-estimated by 48% due to the incorrect methodology used to account for the unorganized sector. Combining the two, over estimation becomes 9%. So, in 2024-25 GDP would be $3.5 trillion. This is not comparable with other economies since they too have a black economy and their GDP also needs to be corrected.

Officials argue that if unorganized sector data is not available, then some assumptions have to be made to estimate it. True, but then the estimates are conditionally correct. And, when the economy experiences shocks like, demonetization, these assumptions become invalid and so do the estimates based on them. The assumptions need to be changed but that has not been done. Consequently, the gap between reality and the official data widens. Finally, the current estimates are not comparable with the data for earlier years.

It is argued here, the higher growth rate of the organized sector is due to the shift of demand from the unorganized sector and its consequent decline. If the growth of the organized sectors is partly based on the decline of unorganized sector, the error in estimation of GDP becomes larger. Higher the growth of the economy, faster the decline of the unorganized sector and lower the actual GDP growth compared to the official figures. Finally, reliability of data is not just a numbers game. It is required for sound policy making. This is the reason, IMF has been forced to acknowledge unreliability of Indian GDP data.

References:-

[1] Kumar, A. (2017). Demonetization and the Black Economy. Gurgaon: Penguin Random House.

[2] Kumar, A. (2019). Ground Scorching Tax. Gurgaon: Penguin Random House

[3] Kumar, A. (2025c). Why the IMF doesn’t buy India’s GDP data? National Herald, 2025 December 6

[4] Kumar, A. (2025d). Estimating GDP in India: Challenges Due to Black Economy and Unorganized Sector. Manorama Yearbook 2026, pp. 579-616.

[5] Kumar, A. (2025a). Questions on GDP estimates: The role of discrepancies in recent data needs to be examined. The Hindu Businessline, May 6, 2025. At, https://www.thehindubusinessline.com/opinion/gdp-growth-estimation-and-the-role-of-discrepancies/article69544603.ece

[6] Kumar, A. (2023).GDP Growth: The Gap between Reality and Rhetoric. The Leaflet, October 6, 2023. https://theleaflet.in/gdp-growth-the-gap-between-reality-and-rhetoric/

[7] Kumar, A. (2016). Estimation of the Size of the Black Economy in India, 1996–2012. Economic & Political Weekly, 26 November, Vol. LI, No 48, pp. 36–42.

[8] Kumar, A. (2006). The Flawed Macro Statistics: Overestimated Growth and Underestimated Inflation. Chapter in the Alternative Economic Survey Group (Ed.) Alternative Economic Survey, India 2005-06: Disempowering Masses. 2006. Pp. 29-44. N Delhi: Daanish Books.

About the Author

Arun Kumar is a retired professor of economics at JNU and was the Malcolm S. Adiseshiah chair professor at the Institute of Social Sciences, New Delhi. He is the author author of Demonetization and the Black Economy, Penguin (India).

The article was first published in Mainstream Weekly as Estimating GDP in India: How Erroneous is it? on 24th January 2026.

Disclaimer: All views expressed in the article belong solely to the author and not necessarily to the organization.