Measuring India’s manufacturing sector remains a data challenge

Source: The post is based on the article “Measuring India’s manufacturing sector remains a data challenge” published in Live Mint on 16^th May 2023.

Syllabus: GS 3 – Growth & Development, Infrastructure

Relevance: concerns associated with the calculations of MCA21

News: The article discusses the advantages and limitations of the Ministry of Corporate Affairs (MCA) 21 data-set.

About MCA21 data-set

The MCA21 data-set was introduced in place of the Annual Survey of Industries (ASI) to expand coverage of the organized sector.

This data set along with manufacturing also includes service-sector companies in its calculations unlike the ASI.

How is MCA21 data set different from ASI?

The MCA21 represents an administrative dataset, fundamentally distinct in structure from the ASI.

The implementation of such datasets for national accounting purposes is a difficult venture because it requires substantial collaboration among ministries to understand data gathering procedures, definitions, aims, etc.

These data sets are not collected or generated via any statistical design, schedule or questionnaire intended for statistical purposes.

As a result, the statistical agency struggles with data structures derived from regulatory practices, accounting standards, and the administrative processes of the ministry in charge of data creation, over which it has no authority.

Thus, the shift from ASI to MCA included replacing survey data with administrative data, which introduced many new challenges.

What are the advantages of the MCA21 data set?

The MCA21 data offers a broad aggregate picture of our corporate sector. It offers wider coverage data, offers broader scope to estimate value addition, and offers a faster way to prepare annual estimates.

What are the limitations of the MCA21 dataset?

Improper Classification: The MCA21 dataset has both manufacturing and service-sector companies unlike the ASI. However, MCA21 lacks clear identifiers of economic activity within the registered entities for correctly classifying companies into respective sectors.

MCA21 provides information on the product-level revenue of enterprises. However, the problem comes in the case of diversified enterprises that have multiple products and services and operate at several locations.

Therefore, in such scenarios, sector-wise estimates are distorted by misclassification, and data available are inconsistent with other metrics of industrial activity.

Lack of Geographical Indicators: The MCA21 data also lacks geographical indicators, making it problematic for computing state-level aggregates.

Lacks Quality Data: The MCA adds companies on a monthly basis and de-registers companies as per the norm of de-registration.

GDP data from 2012-17 showed that on average, about 60% of active companies file their financial statements and are thus available for estimation. These companies are considerably different from the universe captured by ASI.

Therefore, a year-on-year mapping of ASI and MCA is required to get the clear data from the shift.

Lack of Data for Unorganized Manufacturing Sector: There are limitations to find data for the unorganized manufacturing sector because there has been transformation in the enterprise landscape, particularly after GST implementation and due to the effect Covid.

Measurement errors: GVA values are extended by using MCA growth rates for the organised sector, which might result in considerable measurement errors.

Therefore, in such situations, it is difficult to examine India’s manufacturing industry because, despite changes in data and methodology, it has introduced new complexity.