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NSSO Data and MCA21

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  • Published
    17th May, 2019

A National Sample Survey Office (NSSO) report has highlighted deficiencies in key government database of Indian companies.



A National Sample Survey Office (NSSO) report has highlighted deficiencies in key government database of Indian companies.


A technical study conducted by the NSSO between June 2016 to June 2017, examined parts of the corporate affairs ministry’s database, found that 37% of the companies could either not be traced, had shut down or were wrongly classified in terms of what sector they belonged to.

The quality of ministry’s company database, often referred to as ‘MCA-21’, has been in the spotlight over the last four years, after the Central Statistics Office made it a key component of how it calculated the new GDP series.

The survey office observations on the MCA-21 database, however, came while it was collecting a wide range of economic and operational data on service sector enterprises in India, a broad effort that the organization says was conceived as “a prelude to a proposed Annual Survey on Services Sector”.

NSSO: The National Sample Survey Office (NSSO) headed by a Director General is responsible for conduct of large scale sample surveys in diverse fields on All India basis.

Primarily data are collected through nation-wide household surveys on various socio-economic subjects, Annual Survey of Industries (ASI), etc.

National Sample Survey Office (NSSO), Ministry of Statistics and Programme Implementation (MOSPI), Government of India, has been conducting nationwide integrated large scale sample surveys, employing scientific sampling methods, to generate data and statistical indicators on diverse socio-economic aspects.

MCA21 is an e-Governance initiative of Ministry of Company Affairs (MCA), Government of India that enables an easy and secure access of the MCA services to the corporate entities, professionals and citizens of India.



NSS 74th ROUND: Technical Report on Services Sector Enterprises in India

Though the services sector in India has the largest share in the Gross Domestic Product of the country, there is no comprehensive database to monitor the growth and other related aspects of this sector in India.

Since there is a strong demand from different quarters to have a comprehensive source of data on a regular basis of the services sector, the Ministry of Statistics & Programme Implementation (MOSPI) envisaged an Annual Survey of Services Sector (ASSSE) in similar lines as the Annual Survey of Industries (ASI) conducted every year by MOSPI.

Scope and Coverage

Geographical coverage: The survey covered the whole of the Indian Union.

Subject Coverage: NSS 74th round (July 2016 – June 2017) was a survey on services sector enterprises.

Coverage of services sector: From the coverage of the services sector, activities of the following enterprises/sectors were excluded:

Government Enterprises /PSU; Air Transport; Financial and insurance sector; Private Money Lenders; Self Help Groups (SHG)

Under the coverage of the services sector, the following types of enterprises were included:

Proprietary, partnership, limited liability companies, Non-Government companies; Co-operative Societies; Trusts / Non-Profit Institutions

Summary observations:

  • Non-response rate as a result of limitations of frame was about one-third of the sample enterprises.
  • In the MCA frame, there was no condition on workers since the information was not available in the frame, neither was it updated. In the sample of operational enterprises, about 20% of the enterprises were found to have less than 10 workers.
  • The frame, therefore, might be a highly truncated frame and aggregates estimates are likely to be highly underestimated and difficult to interpret.
  • There was different proportion of non-response units which might have affected the estimates of each frame differently. The numbers of units in the frames were also different.
  • It is not possible to conclude if one set of result is better than the other in absence of any benchmark data on service sector enterprises.


The MCA21 data reliability problem:

While the government has maintained that using the MCA-21 database allowed for a more granular approach, as it drills down to the level of balance sheet data, some economists have argued that it is untested and may have unknowingly boosted India Inc’s contribution to the country’s growth figures.

The GDP series issues

  • GDP, or gross value added (GVA), is a measure of goods and services produced in an economy in a year, net of intermediate inputs. Broadly-speaking, it is a statistical construct based on innumerable estimations of value addition taking place in an economy.
  • GDP is estimated following the UN System of National Accounts (UNSNA) – a global template, revised periodically to account for evolving economic activities. Both models have many accomplishments and impairments.
  • GDP is re-based regularly to account for changing production structure, relative prices and better recording of economic activities. Crucially, the re-basing also allows for introducing newer methodologies and improved databases.
  • The last revision was somewhat peculiar as it resulted in a 2.3 percentage point shrinkage of the absolute GDP (GVA) size in the base year (2011-12), thus raising India’s growth rates in the following years. For instance, in 2013-14, the growth rate of the manufacturing sector swung from (-) 0.7% in the old series to (+) 5.3% in the new series.
  • The revision also altered the institutional composition of India’s GDP: in particular, the private corporate sector’s (PCS) size was enlarged while the unorganized/informal/household sector got contracted (with public sector’s share remaining the same) due to the introduction of questionable methodological changes.

What were these changes?

One example was how data from the Annual Survey of Industries (ASI) was replaced with Ministry of Corporate Sector’s (MCA’s) company financial data (MCA-21) for estimating manufacturing sector growth. It was claimed that ASI did not capture value addition taking place outside of the factory premises, but within the enterprise (or a firm).

It is this MCA21 database which has been questioned in the current report.

The use of MCA database in particular could have misleadingly enlarged the private corporate sector’s share in the Indian economy and its growth rate.

The NSSO’s findings:

  • The company database was used as a survey sampling frame along with two other sources of data – a list of establishments as per the Economic Census (EC) and list of establishments as per the Business Register (BR).
  • According to the NSSO, there were 3, 49,500 enterprises in the frame of the MCA, out of which 35,456 were selected for survey.
  • Over 20% of the firms were found to be “out of coverage”, which means that they likely no longer operate in the services sector, even though that is how they are registered and thus captured in India’s national accounts.
  • Nearly 15% of the firms were either untraceable or had closed down.
  • To top it off, almost 7% of the companies had been correctly identified but simply chose not to respond to survey questions or had not maintained accounts in a proper format.
  • The NSSO notes the issue of non-responses was far worse in the MCA frame when compared to the “EC/BR frame”.
  • The problem of non-response was severe in case of units chosen from MCA frame. About 45% of MCA units were found to be out-of-survey/casualty while EC/BR frame had about 18% of such cases, the report notes.

What should be done?

The CSO should have done some kind of critical scrutiny and validation before using the MCA-21 database in the new GDP series, either through quick surveys or by comparing with other databases, or consultations with accountants familiar with company filings.

Learning Aid

Practice Question:

GDP measures the monetary value of goods and services produced by a country, mostly for sale in markets. This analysis rests on purity of primary data sources such as that of MCA21 and related NSSO findings. However, recent findings questions the validity of raw data itself. Critically examine the methodology of GDP evaluations.


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