Studies based on KLEMS data are widely cited to refute claims about poor job creation in the country. This database has been developed as part of an international project and has a very reputable provenance, fed by scholars at the Delhi School of Economics and ICRIER since 2009 and hosted by the Reserve Bank of India since 2022. It is therefore necessary for us to examine the methodology of generating the data, looking at the sectoral details and the original sources.
The KLEMS database, containing data on capital (K), labour (L), energy (E), materials (M) and services (S), currently available for the period 1980 to 2024, is intended to provide a “measurement tool for monitoring and assessing productivity growth at the industry level as well as for the economy as a whole”. It uses data from different rounds of the Employment and Unemployment Surveys (EUS), Periodic Labour Force Surveys (PLFS), National Accounts Statistics and the Annual Survey of Industries. In the absence of annual data from the National Statistical Office, available data are used as reference points and interpolated for other years.
According to the methodology, EUS and PLFS data are used to determine the sectoral distribution of workers according to the usual principal and subsidiary status (UPSS) for four groups: rural men, rural women, urban men and urban women. Since the surveys do not provide the absolute number of workers, the estimated worker-population ratios for the four survey groups are multiplied by the total population. The population for the survey years can be interpolated using census figures or taken from the population projections of the National Population Commission of the Ministry of Health and Family Welfare (MoHFW).
In the methodology segment of the RBI report, it is observed that for 2017-18, 2018-19 and 2019-20, the all-India figures for employed persons are taken from the Economic Survey 2021-22. For 2020-21 onwards, the MoHFW population projections are used. But these projections are available only for males and females and accordingly, uniform growth is applied to project populations in rural and urban segments. The numbers of workers are then distributed across industrial groups, considered in KLEMS, according to their employment shares as in PLFS.
It is important to note that the population figures projected by the Ministry of Health, Welfare and Social Welfare are higher due to a sharp decline in the fertility rate over the period 2010 to 2020. This implies that the total labour force and the labour force, obtained by multiplying the projected population by the fertility rate, would be overestimated. The estimated rural population would also be higher because it is assumed to grow at the same rate as the urban population, whereas empirically, the rate in rural areas is much lower. Since the fertility rate in rural areas is higher than in urban areas, the total employment generated in the 1920s would turn out to be higher than the actual ones.
The above explanation clearly shows that the RBI does not produce employment figures independently. The UPSS-based WPR is used on a projected population to arrive at the figures. There is a significant fall in the WPR, as per UPSS, from 2011-12 to 2017-18, as we move from EUS to PLFS and the KLEMS assumes no time comparability issue. The WPR, however, has increased significantly for rural women with some increases for the other segments of the population as well, in the subsequent years. These WPR values applied to somewhat higher population estimates, as mentioned above, would produce inflated employment figures.
In the KLEMS database, employment in agriculture increased from 20 crore before 2018-19 to 25 crore in 2022-23. Accordingly, employment in the services sector increased from 17.2 crore to 20.2 crore. Employment in manufacturing grew from 5.5 crore to 6.3 crore.
The number of workers would systematically increase due to the increase in population and the projection methodology, even when the per capita employment rate remains the same. Similarly, employment in manufacturing is increasing, although the share of manufacturing workers to total workers is decreasing, according to the budget employment survey. It is important to note that the employment data includes those in secondary employment, which implies the inclusion of people who have a tenuous connection to work. A large majority of them are employed as unpaid family workers. Therefore, using the budget employment survey/budget data along with the projected population to claim that employment has been generated would be misleading, without any reference to the nature and quality of work.
A study by SBI economists compares the projected total employment based on the Annual Survey of Unincorporated Sector Enterprises (ASUSE) data with the figures available in the RBI’s KLEMS database. The ASUSE survey covers a subset of all unorganised enterprises and excludes those in construction, the corporate sector and the government, besides those registered as factories and cooperatives. The survey estimated the number of people employed in such enterprises to be only 10.96 crore. This figure is being inflated to claim that total employment in 2022-23 is 56.8 crore, close to the KLEMS data. This needs to be investigated.
Employment in enterprise surveys indicates a position in enterprises. It is not easy to relate it to information on individuals, collected in household employment surveys, which are considered superior for employment data. Independent estimates from these two sources do not agree for well-known reasons. Similarly, data on registration of MSME units on the Udyam portal usually does not imply creation of new jobs, nor do monthly changes in EPFO subscription mean generation of additional employment.
Given the methodological limitations of these data, it is surprising that rapid growth in employment, and also in decent jobs, is reported.
Kundu is an Emeritus Professor at LJ University, Ahmedabad and Mohanan is a former member of the National Statistical Commission.
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