The framework outlined above provides a useful way to quantify the sectoral contributions to aggregate value-added growth and TFP, directly from the industry-level estimates. Research based on the India KLEMS Database has provided a major turnaround in growth and productivity analysis. Moving beyond aggregate and sectoral perspectives it has focused on a disaggregated industry-level approach. As highlighted in Chapter 1, many early studies have already analysed India’s productivity performance at the aggregate and sectoral level104.

One equation is estimated for each benchmark year, and the wage rates estimated for benchmark years have been interpolated, which involves some degree of approximation. The estimated equation takes into account differences in industry affiliation by introducing dummy variables for industry affiliation. This is a second-best approach as estimating separate equations for major sectors of the economy, for external economies accrue due to example, separate estimates for workers engaged in agriculture, industry, and services, might provide a better result. It is needless to say that any econometric issues in the estimated wage rate of self-employed based on the estimated Mincer equation along with approximations being made by using interpolation between benchmark year estimates will affect the estimated wage rate of self-employed income.

external economies accrue due to

For the purpose of analysing sectoral performance, we group the 27 India KLEMS industries into six main sectors, namely—Agriculture, Mining, Manufacturing, Utilities, Construction and Services. Further, three sub-sectors are also explicitly classified within the Manufacturing sector, these include— “Manufacturing of Consumer Goods”, “Manufacturing of Intermediate Goods” and “Manufacturing of Investment Goods”. The Services sector is also broken down into two sub-sectors—“Non-Market Services” and “Market Services”. The list of industries classified under these groups can be found in Table 7A.1 of Annexure 7A at the end of this chapter.

b) External Economies of scale

For instance, Subramanian emphasises that the skilled human capital created in the pre-1980s contributed to faster growth of both industry and services in the 1980s when pro-business policies were implemented. On the other hand, Bhattacharjea argues that the recovery of public investment in the 1980s could have influenced private investment favourably, leading to faster growth. Acharya et al. provides a more demand-side perspective, arguing that strong domestic demand, significant export growth along with liberal supply-side policies led to a high growth in the 1980s. Section 2.2 discusses the approach to measuring productivity in the India KLEMS database, which is the standard neoclassical growth accounting approach. In Section 2.3, the approaches to construct each variable along with the sources of data are discussed in individual sub-sections. It also contains discussion on construction of the series on labour income share in gross value added.

external economies accrue due to

For the overall period, services were large contributors to aggregate growth of both capital and labour input. The relatively lower contribution from manufacturing towards aggregate labour input growth is a matter of concern and reflects the poor employment growth in this sector. Looking at the pre and post Global Financial Crisis growth and productivity dynamics, we find a revival of TFP growth for 12 industries, mainly those belonging to capital-intensive manufacturing and non-market services.

Diseconomies of Scale of Production:

This is done by first deflating wage bill in each industry by the corresponding GVA deflator, then aggregating the industry level real wage bills to the level of the economy. 119 According to National Accounts Statistics Sources and Methods – 2007, Chapter 20, page 172, net operating surplus of Public Administration and Defence is treated as zero, and gross value added is therefore the sum of compensation of employees and consumption of fixed capital. To derive gross value added at constant prices, deflation is done by the consumer price index for industrial workers. Out of the total GVA of Public administration of defence, about 90% is compensation of employees. Thus, in effect, growth in real GVA of Public administration and defence in national accounts is driven by the growth in compensation of employees deflated by the consumer price index for industrial workers.

In other words, the marginal productivity of capital has been higher in Chinese manufacturing. The same was the case with market services, and thereby the overall market economy, although the difference between India and China in the market services sector was less intense than in the manufacturing sector. In contrast, upskilling its workers has a larger productivity impact in India, primarily in its market services, compared to China.

Another point that should be noted here that auxiliary consumption in captive power plants in the manufacturing sector has been taken into account and treated as energy consumption in manufacturing. The regression results presented in Table 9.7 clearly indicate a positive effect of wage-rental ratio on capital intensity , notwithstanding the fact that the results are affected by autocorrelation . There is some indication that a structural shift from agriculture sector to other sectors of the economy has had a positive effect on the capital intensity of the economy (i.e., it tends to raise capital intensity). It needs to be recognised that the econometric results in respect of the variable representing agriculture sector share in aggregate GVA are weak and the inference is based on finding of a negative coefficient.

Economies of Scale

Our measure of labour input incorporates qualitative aspects of the labour force in terms of education while defining a new, refined measure of labour input as the product of labour person and quality index. The capital input defined in terms of capital service takes the asset heterogeneity into account as against a measure of capital stock which is devoid of it. In the following subsections, we discuss the estimates and sources of labour productivity and output growth using the value added and gross output version of the production function, respectively. The onset of information and communication technologies has changed the way the production process is organised and coordinated. Consequently, the use of ICT in the production process has significant productivity spillover. An earlier study by Erumban and Das made an attempt to estimate ICT investments at the aggregate economy level, and further extension of their work in Erumban and Das provides industry-level estimates for the organised manufacturing.

In the 1990s, China’s positive productivity sectors shrank to 65 per cent of the GDP, and also their TFP summed to a smaller value of 2.9 per cent, 0.3 points lesser than the previous period. However, that was confined to industries that produce less than half the output (see the second panel from the top of Figure 11.7). In other words, while industries that constitute 65 per cent of GDP in China had registered positive productivity growth, only those sectors that constitute 48 per cent of the aggregate economy could do so in India in the 1990s. Indeed, India had a much larger share of industrial production that came less efficiently, with negative TFPG. Finally, due to lack of information, especially for India, we use the number of employed persons as a measure of labour input rather than hours worked. This means that we implicitly assume that an average worker’s hour intensity is constant over time, across industries, and between the two economies.

  • In terms of the most interesting variable—TFP, non-market services performed relatively better, followed by financial services—both registered a 2 per cent annual TFP growth rate.
  • The departments can be divided in term of broad areas, like production, sales, finance, accounting, material, research etc.
  • Therefore, intermediate inputs play a crucial role in explaining economic growth at the industry level, which is why, we have argued for the use of gross output instead of value-added in productivity analysis.
  • Benchmarking developing Asia’s manufacturing sector, International Journal of Development Issues, Emerald Group Publishing, vol.

Figure 5.4 shows the growth rate of total investment across all assets, and in Figures 5.5 and 5.6, we divide the total investment into equipment and non-equipment assets, respectively. However, the investment to GDP ratio measured in real terms was the highest in the utility industry, which is not unexpected, given the high capital intensity in the energy sector (Figure 5.3). Among the other sectors, the highest investment to GDP ratio was in the non-market services, which includes the government sector. However, investment intensity increased in all the sectors in the 2000s compared with the 1990s, except manufacturing.

Since the initiation of pro-market reforms in India in 1991 and the revival of Chinese reforms and opening-up programme after Deng Xiaoping’s southern China tour in 1992 following the 1989 Tiananmen Square protests, the global presence and impact of these two giant economies have increased. Finally, it is also very important to understand the drivers of growth in services in two emerging markets—China and India. As a part of this future research agenda, we intend to follow up on the present study with a comparative analysis of service industries in India and China using the KLEMS dataset for each country.

A. Economies of scale prevails at small levels of output

In terms of GVA shares of 27 industries in 2017, although agriculture and allied activities share declined by 19.1 percentage points, it was still the largest sector with 17.2 per cent share in 2017. Further, seven of the top 10 industries in terms of GVA share were from the service sector. The weaknesses of the estimates of gross output of services industries and the estimated break up of intermediate inputs, noted above, can be eased to a large extent by constructing a timeseries of IO tables using the available IO tables and applying RAS technique. Such tables have been made and are available for India in the World Input-Output database . The number of sectors into which the economy is divided in the WIOD tables is much smaller than the number of sectors available in the IO tables, which have been used for constructing India KLEMS database. Constructing detailed IO tables for India for each year would involve a great deal of effort, but this is needed to improve estimates of gross output of services industries and estimates of energy, materials, and services inputs.

Write a short note on Economies and dis-economies of scale.

In the current version of India KLEMS, we use an external rate of return based on the real interest rate. The literature has debated the use of external versus internal rate of return in the calculation of user cost of capital (Berndt and Fuss, 1986; Harper, Berndt and Wood 1989; Hulten 1990; Diewert 2001; Erumban, 2008; Oulton, 2007). Since firms make their investment plans taking the expected rate of return into account, which are often evaluated using the prevailing interest rate in the economy, it is argued that an external rate of return is appropriate for capital service growth rate. However, using an external rate of return compromises internal consistency within the national income identity. Also, it is argued that the exante return does not reflect the realised marginal products, which are more theoretically consistent for productivity analysis .

In what follows, we discuss the construction of variables relevant to this chapter in the China KLEMS. Table 3.6 shows the annual average growth of gross output, intermediate inputs and three categories of intermediate inputs—material, energy, and service—for all 27 industries. There was wide variation across industries with the growth rate of input ranging from 15.7 per cent in business services to 2.1 per cent in agriculture and allied sector.

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