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Are manufacturing jobs growing or declining? Here’s what the data shows

A study based on National Statistical Office (NSO) surveys suggests that manufacturing employment in India has grown in recent years but not to the extent quoted by some experts.

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Key highlights

  • Data does not show much evidence of any major boost in manufacturing employment either in the organised sector or in the unorganised sector
  • Growth in women’s employment has gone hand in hand with the increasing share of unpaid and subsidiary work.

Introduction

There have been claims of rapid growth in Indian manufacturing employment in recent years. The growth is said to be both in the organised and unorganised manufacturing. Also, while the recent labour force surveys have shown improvement in work participation, especially for women, there have been concerns on the quality of the new employment. The data that forms the basis for these contradictory outlooks comes from the well-established surveys of the NSO – the Periodic Labour Force Survey (PLFS), the Annual Survey of Industries (ASI) and the Annual Survey of Unincorporated Sector Enterprises (ASUSE).

The period covered in these surveys is characterised by the impact of Covid-19 on economic activity during 2020-21 and 2021-22, demonetisation of 86 percent of the currency in circulation in November 2016 and the implementation of the Goods and Services Tax (GST) from July 2017. It is in this backdrop that we look at the data from these three sources, although the lack of strict matching reference years in these datasets constrains comparison.

Conceptual disconnect of employment data

These datasets are conceptually very different. The ASI is intended to provide data to understand the changes in the growth, composition and structure of the organised manufacturing sector. The survey is conducted using a list of units registered under different statutes. As is common with all data, it has a certain element of imperfection. While new units are added as and when reported, a closed or non-producing unit does not instantly disappear from the list. The survey is not specifically designed to collect data on manufacturing employment by identifying individual workers. It relies on statutory records for the purpose of obtaining data. 

The ASUSE is aimed at understanding the contribution of the unorganised sector. The data includes both full-time and part-time workers. In this survey, a worker is a person working within the premises of the enterprise and on its payroll as also the working owners and unpaid family workers.

The PLFS collects many more details that allow for a better understanding of the employment situation. Notwithstanding the known limitations of the household surveys, the various rates and ratios estimated from these surveys have been found to be very robust. The fact that the absolute numbers estimated from the surveys are not suitable to be used directly has meant that users apply these ratios on the projected population to obtain national-level estimates of workers and unemployment. However, the dependence on population projections based on assumed rates of fertility, mortality, migration, and rural and urban share is somewhat problematic in the absence of any recent population census. Thus, drawing conclusions of employment numbers in manufacturing that are anchored on population projections is questionable. 

Enterprises in manufacturing

Our understanding of the size and contribution of activities in the unorganised sector is mainly based on nationwide sample surveys of the National Sample Survey Office (NSSO). These surveys are carried out generally once in five years. After the last two five-year surveys in 2010-11 and 2015-16, the NSSO has initiated annual surveys. The coverage area of the survey is non-agricultural unincorporated enterprises belonging to manufacturing, trade and other services excluding construction. Though the ASUSE of 2021-22 and 2022-23 were similar to the earlier surveys, it would be prudent to exclude 2021-22 taking in to account the Covid-19 restrictions.

The overall picture of the unorganised sector shows that during 2015-16 to 2022-23 unorganised manufacturing has not performed well.

The total number of units in unorganised manufacturing declined 9.3 percent during this period. This could be because some traditional manufacturing activities moved to the organised sector or because unorganised sector units failed to compete with registered units. 

In general, the number of factories in the organised sector went up from 2015-16 to 2021-22 for most industries. However, the growth is not very significant and it may not be prudent to credit the increase in the number of factories to any resurgence in manufacturing. 

Employment in manufacturing

We now look at employment growth reported in manufacturing in these three different sources without making any efforts to combine them. While the number of enterprises is available in ASUSE and ASI, the PLFS provides data on workers engaged in manufacturing from the supply side – the households. Here again, we do not have exactly matching periods for making comparisons. But the directions of change in employment in recent years can be analysed.  

The average growth in employment considering all economic activities during 2017-18 and 2022-23 is close to four percent.

Manufacturing employment grew 5.2 percent in rural areas and 2.3 percent in urban areas, giving an overall growth of 3.5 percent. Employment appears to have recovered from the Covid-19 impact.    

If we consider the six-year period from 2011-12 to 2017-18, we find the annual employment growth is negative for the rural sector and just one percent for the urban sector. The overall growth rate is above four percent during the last three years. However, the rates have not increased uniformly in rural and urban areas. The Covid-19 pandemic during 2020-21 appears to have impacted employment in rural and urban areas differently. The large-scale migration of people from urban centres to their rural homes consequent to the lockdown could have increased rural manufacturing employment to an extent during 2020-21, though there would be a question mark on whether the survey would capture it. Their return to cities would have meant an increase in urban manufacturing employment the following year. The recent growth, however, does not appear to be a continuation of any sustained growth nor in any way an indication of a boost in manufacturing employment. 

A further disaggregation of growth in manufacturing employment in the last three years shows that growth has been much higher for women except during the Covid-19 period.

The increase in women’s employment in manufacturing merits an examination of the nature and type of work they are doing. This is important given that women work for less remuneration and most often in a subsidiary capacity.  The percentage of women in rural areas reporting self-employment in manufacturing was over three-fourth in 2019-20 and is going up. As per the PLFS 2022-23, 83 percent of the rural women are in self-employed capacity. However, it would appear from the survey data that growth in women employment is significantly higher in categories that are considered of poor quality.  

Let us look at growth in manufacturing employment from the three surveys – ASUSE, PLFS and ASI. The ASUSE covers a seven-year period from 2015-16 to 2022-23. For comparison, we cover only the PLFS 2017-18 to 2021-22 as the corresponding data from the ASI is for this period. 

Looking at the average growth in employment reported from these three sources, it is clear that ASUSE employment during 2015-16 and 2022-23 is the lowest. Most industries recorded a decline, probably because this period covers the demonetisation and the Covid-19 pandemic. The 2015-16 survey had shown excellent growth in unorganised manufacturing, but this growth appears to have been brought down by the subsequent economic disruptions and has not yet reached the 2015-16 level. 

Overall, employment in manufacturing in recent years, starting with the post-demonetisation period (except the ASUSE), shows growth of comparable dimensions. The ASUSE has its base in 2015-16 before the demonetisation and hence has a significantly high base to start with. The employment data of the PLFS and the ASI starts after the demonetisation with a lower base. The fact that the ASI employment growth is lower than the PLFS rate would also indicate that incremental employment has been higher outside the registered manufacturing sectors.

Conclusion

The three surveys collect data using different conceptual frameworks. These should be used separately keeping in mind the differences in definitions, reference periods and methodology. 

The PLFS data shows that the employment rates in general went up in 2022-23. This was quite significant for women. The major sectors that clocked significantly high growth during the period 2017-18 to 2022-23 included construction, trade and health. Growth rates in the primary sectors and manufacturing are similar for this period.  

In short, manufacturing employment has grown but not to the extent quoted by some experts. The picture emerging from the three different sources is consistent, but a clear-cut industry pattern is difficult to pinpoint due to major economic disruptions and their differential effects on industries. The sectors contributing to growth in manufacturing employment are mostly the labour-intensive sectors like wearing apparel and food products. The fact that higher growth in women’s employment goes hand in hand with the increasing share of unpaid and subsidiary work and the overwhelming share of self-employment in manufacturing are matters of concern. 


Overall, the available data does not show much evidence of any major boost in manufacturing employment either in the organised sector or in the unorganised sector. A clearer picture will emerge when the new ASUSE and PLFS datasets become available for 2023-24. 

This is a summary version. The full and longer version is available here


To cite this analysis: P C Mohanan (2024), “Full version: Are manufacturing jobs growing or declining? Here’s what the data shows” Centre for Economic Data and Analysis (CEDA), Ashoka University. Published on ceda.ashoka.edu.in

If you wish to republish this article or use an extract or chart, please read CEDA’s republishing guidelines.

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