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How to identify manufacturing companies for GDP estimation

Monday, October 3, 2016 23:41
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(Before It's News)

by Amey Sapre and Pramod Sinha.

In a recent paper, we worked on the problems of gross value added (GVA) estimation for the manufacturing sector. One of the elements in the procedure is the identification of manufacturing companies from the MCA21 data. The GVA formula changes depending on whether a firm is classified as manufacturing or trading, and hence the classification of a firm into manufacturing or services is a critical question. We highlighted four concerns about the present procedures:

  1. The use of reported ITC-HS codes can be misleading as the codes identify a product, and not a business activity.
  2. For the purpose of GDP estimation, identification of companies has to be done every year.  In cases where the ITC-HS codes are unavailable, using the NIC digits in the Company Identification Number (CIN) can also be misleading. The CIN code does not change in time, and does not not track the evolution of the firm over time.
  3. As the top revenue generating products of a company can vary yearly, this will require the statistical authority to identify and re-classify companies on a yearly basis.
  4. In the absence of a feasible solution, wrongly classified companies will show an incorrect GVA contribution. On the aggregate, both manufacturing and services sector will show a distorted picture. These difficulties are compounded by the fact that the appropriate deflator to be used when converting nominal to real differs between the two cases, and has taken substantially different values in recent years.

Presently, the extent of distortion in the GVA estimate is unknown. In the paper, we try to estimate the extent of misclassification by looking for the two cases (i) firms that operate as non-manufacturing entities, but have their NIC codes registered in a manufacturing activity and (ii) firms that are into manufacturing, but have their NIC code registered in any other economic activity. However, we need to go beyond measuring misclassification to algorithms for better classification. In this article, we propose one such solution.

A potential solution
Currently, section II in the Form No. MGT 7, [pursuant to section 92(3) of the Companies Act, 2013 and Rule 12(1) of the Companies (Management and Administration) Rules, 2014] requires companies to furnish up to ten principal business activities . The information deals with disclosures of the main activity group, business activity with respective codes and their share in total turnover. Under this arrangement, the main activity has 21 different codes from A to U, each representing a particular activity. For example, a company reporting code C indicates a manufacturing concern, while code G shows trading. However, when non-reporting takes place, these codes alone will not solve the problem. A scrutiny of product schedules and financial statements is still needed.

Looking at product schedules and financial statements, manually, is expensive. This is particularly when low error rates are demanded. It is desirable to automate this work. We can see the rough contours of algorithmic classification as follows.

For a trading firm: Typically, for a trading company, from the revenue side, the income from trading to total turnover ratio would be higher than income from manufacturing. From the expenditure side, the ratio of purchase of finished goods to total expenses would be higher than the expenses on manufacturing.

For a manufacturing firm: In this case, from the revenue side, the ratio of income from sales to total turnover would be much higher than the ratio of trading income to total turnover. Similarly, from the expenditure side, the ratio of purchase of raw materials to total expenses would be much higher than expenses on trading. Also, for a manufacturing company, excise duty would be form a significant part of the indirect tax payments.

A statistical analysis of the ratios can help identify the characteristics of the manufacturing sector, and be used to classify firms effectively. The ratios can be applied to ascertain the highest revenue contribution on a yearly basis and at the same time allows a cross-check with reported codes and declaration under of Form No. MGT 7. The classification algorithm would need to deal with various categories of observation, including procedures that deal with various possibilities of non-reporting.

Amey Sapre is at the Indian Institute of Technology Kanpur and Pramod Sinha is a researcher at NIPFP.

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