Introduction to KLEMS
The KLEMS methodology is being applied recently, both internationally and in India, as an alternative to the traditional methodology to obtain the productivity measure, where the “value added” is used as a measure of output, with capital (K) and labour (L) as inputs. In KLEMS methodology, the “gross output” is seen as a measure of output with capital (K), labour (L), energy (E), materials (M) and services (S) as inputs. The objective of India KLEMS project is to provide the KLEMS dataset for industries comprising the economy of India. These datasets are envisaged to be used for analysing the sources of growth across sectors in India and also to serve as a supplement to the system of the national accounts. The dataset is prepared for use in the growth accounting methodology for estimating total factor productivity, which allows a decomposition of output growth into the contributions of different inputs and total factor productivity. The industrial classification in India KLEMS with 27 broad industries in this project was built according to the several rounds of NIC (National Industrial Classification) and was made aligned to the EU KLEMS to ensure compatibility with other studies under the World KLEMS initiative. The industrial disaggregation consists of 1 agricultural, 1 mining and quarrying, 13 manufacturing, 1 sector covering electricity, gas and water supply, 1 construction and 9 services industries. In its present form, India KLEMS database provides time series of all inputs under the definition of KLEMS, gross output (GO) and gross value added (GVA), between fiscal years 1980–1981 and 2011–2012. The dataset has been constructed on the basis of National Accounts Statistics (NAS), Annual Survey of Industries (ASI), National Sample Survey Organisation (NSSO) rounds and input–output tables. In the following sub-sections, we provide the definition and some details about the key series that we use in our estimation.
Gross value added and gross output
Gross value added or GVA of an industry is defined as the value of output less the value of its intermediary inputs. The NAS brought out by the CSO, Government of India is the basic source of data for the construction of series on GVA in India KLEMS dataset. NAS provides disaggregated GVA measures in both current and constant (2004–2005) prices at the broad industry level. A higher level of disaggregation in some cases have been obtained using information from ASI and NSSO rounds, for registered and unregistered manufacturing sectors, respectively.
Gross output Estimates of gross output in India KLEMS for sectors agriculture, hunting, forestry and fishing, mining and quarrying, construction and manufacturing sectors are directly obtained from NAS at current and constant prices. For splitting some sectors, as in the case of value added, additional information is used from ASI and NSSO. For other sectors, mainly service sectors, where there was no output information available from NAS, input–output transaction tables, which provides output and value added, are used. The ratio of these two is applied to value added in NAS to obtain consistent estimates of gross output. The benchmark input–output tables for the years 1978, 1983, 1989, 1993, 1998, 2003 and 2007 are used for this purpose, and for the intermediate years, the ratios are linearly interpolated.
India KLEMS database provides separate index of labour input (1980–1918 = 100) for each industry for the period 1980–1981 to 2011–2012. In estimating the index, two dimensions of the labour input are taken into account—labour persons and educational attainment of the workers, to distinguish one type of labour from the other. Employment data in India KLEMS are based on usual principal and subsidiary status (UPSS) concept, and are obtained from the quinquennial rounds of Employment and Unemployment Surveys (EUS) published by National Sample Survey Office (NSSO). The aggregate labour input, used in this study, is obtained as the weighted sum of employment growth rates of workers of various educational levels. The idea is to take into account the embodied human capital in each person, which could be through investment in education, experience, trainings, etc. The contribution from each person to the output comes from this embodied capital and accordingly the wages and earnings vary. Therefore, the project aims at separating out these differences in labour to clearly understand the underlying differences in labour characteristics. Das et al. (2015) defines the aggregate labour input by applying the Törnqvist index of persons worked by individual labour type. In this methodology, the aggregate labour input turns out to be a multiplication of labour employment and labour quality. In the present case, Das et al. (2015) consider only the educational attainment as the aspect of labour quality. For the estimation of labour input across various industries in India, several rounds of the large scale Employment and Unemployment Surveys (EUS) by the NSSO and the estimated population series based on the decennial population census have been used. Available data from these sources enabled the authors to estimate the persons employed in each industry and adjust it for changes in labour skill by calculating the labour education index, and thereby obtaining the education corrected labour input.
Capital services Capital services for the aggregate economy and for industries in India KLEMS are arrived at from industry level investment in three different asset types—construction, transport equipment, and machinery are gathered from NAS for broad sectors of the economy, the Annual Survey of Industries (ASI) covering the formal manufacturing sector and the National Sample Survey Office (NSSO) rounds for unorganised manufacturing. These industry level data are used to construct capital stock using perpetual inventory method.
Aggregate capital services growth rate is derived as a weighted growth rate of individual capital assets, the weights being the compensation shares of each asset type. Das et al. (2015) use the Törnqvist approximation to the continuous Divisia index under the assumption of instantaneous adjustability of capital. In the following steps, Das et al. (2015) derive capital stock estimates for detailed asset types and the shares of each of these assets in total capital remuneration, which is used for calculating capital services growth.
As the first step towards obtaining the industry-level estimates for capital services, Das et al. (2015) define total investment by asset category for each industry. The primary source of data in this regard is the NAS, which provides information on aggregate capital formation under nine broad sectoral heads. However, to bring larger disaggregation (as there are 27 industries in India KLEMS), detailed data on industry and assets were collected from CSO, ASI and NSSO. Broadly, Das et al. (2015) defined the following asset types: construction, transport equipment, and machinery and equipment,20 for both private sector and public sector, separately. Total investment in each asset category is then calculated as the sum of private and public sector investments. Second, the estimates of capital service required time series data on asset-wise capital stock, which has been constructed using the perpetual inventory method (PIM), where capital stock (S) is defined as a weighted sum of past investments with weights given by the relative efficiencies of capital goods at different ages. This aggregation required data on current investment, investment prices and depreciation rate by asset type. For the implementation of PIM, Das et al. (2015) take the NAS estimate of real net capital stock in 1950 as the benchmark capital stock for non-manufacturing sectors whilst the same for the year 1964 is taken as the benchmark capital stock for manufacturing sector. The current period investment is already estimated in the previous step. The investment price deflator in each case has been derived using the investment data in current and constant prices by industry and asset type, as provided by CSO to the India KLEMS team. The depreciation rates for the non-ICT assets have been derived using the detailed information on assumed life by asset type, provided by NAS. Lastly, the final aggregation to arrive at the capital services requires estimates of rental prices. The rental price of capital stock is equal to the investment price in current period times the rate of nominal return, adjusted for the depreciation rate, to reduce the changes in investment price of the asset type from previous period. The compensation to each asset type is calculated by multiplying the capital stock with the rental price. Having obtained (1) the capital stock in step 2 and (2) the rental prices/compensation in the last step, Das et al. (2015) express the index of capital services (1980–1981 = 100) using the Törnqvist index for each industry.
Factor income shares
The factor income share is defined as the ratio of total remuneration to a factor of production, to GVA. Under the assumption of constant returns to scale with two factors of production, i.e. labour and capital, the sum of labour income share and capital income share is one. India KLEMS dataset provides detailed estimates of labour income share for all 27 industries over 1980–1981 to 2011–2012. The capital income share is obtained as one minus the labour income share.
Das et al. (2015) provide following details about the estimation of labour income share. There are no published data on factor income shares in Indian economy at a detailed disaggregate level. National Accounts Statistics (NAS) publishes the Net Domestic Product (NDP) series comprising compensation of employees (CE), operating surplus (OS) and mixed income (MI) for the NAS industries. The income of the self-employed persons, i.e. MI is not separated into the labour component and capital component of the income. Therefore, to compute the labour income share out of value added, one has to take the sum of the compensation of employees and that part of the MI which are wages for labour. The computation of labour income share for the 27 study industries involves two steps. First, estimates of CE, OS and MI have to be obtained for each of the 27 study industries from the NAS data which are available only for the NAS sectors. Second, the estimate of MI has to be split into labour income and capital income for each industry for each year.
It is already pointed out that NAS classifies the aggregate GVA into nine broad sectors. Therefore, in the present exercise, a number of these sectors were further disaggregated to obtain 27 industries in total, using the methodology already discussed in sub-section I. In these cases, the CE, OS and MI for a particular sector in NAS have been distributed amongst the newly classified sectors in KLEMS, according to the gross value added by these smaller industries. In the following step, MI has been split into labour income and capital income, by assuming that labour income in an industry is a constant (i.e. not varying over time) proportion of the MI. The estimation of this proportion has been done with the help of NSS survey-based estimates of employment of different categories of workers (number of persons and days of work) and the wage rates. Finally, the total compensation to labour is obtained as a sum of CE and the portion of MI classified as labour income.
Total factor productivity
Following the estimations of gross value added, indices of labour input, capital services and shares of labour and capital into GVA, Das et al. (2015) obtain estimation for the total factor productivity for the 27 industries over the sample period. For an individual or industry, productivity measure can be based on a value added concept. In this concept, GVA is considered as the industry’s output which is generated and shared by only the primary inputs such as labour and capital. The productivity measures obtained using the GVA can be valid complements to gross output-based measures.