Employment Growth in India: Findings from Organized Manufacturing Industries.

AuthorKumar, Sanjeev


Indian economic growth during the neoliberal period has some peculiar characteristics, which appear to be different from the conventional development models, experienced by most of the countries in the world. The shift of economic growth from agriculture to the service sectors and stagnating manufacturing growth are two typical characteristics of India's structural transformation. However, specific characteristics of the manufacturing sector make it imperative as an 'engine of growth' for the economy and hence make industrialization essential for economic growth. The manufacturing sector, which constitutes just about 12.6 per cent of India's workforce, undoubtedly has the capacity to create more employment than any other sector of the economy. A large number of industrial jobs will be possible only by a rapid and sustained growth of manufacturing sector, which will greatly improve the conditions of India's low/semi skilled labor force through greater productive job creation in the next few years (Kapoor, 2015).

The central problem is that the growth of the manufacturing sector has not been employment intensive for a long period of time. It has been observed that "jobless growth" during the 1980s was followed by an employment boom in early 1990s and retrenchment thereafter. However, during the period 1996-97 to 2003-04 employment growth has declined and this deceleration in employment was because of new liberal economic policy brought a significant change in the nature of product market and domestic competition. Opening up the domestic market for transnational enterprises resulted in high import intensity of domestic manufacturing that in turn reduced the employment in manufacturing (Kannan & Raveendran, 2009). However, during the period 200405 to 2011-12, employment in organized manufacturing has grown significantly and this acceleration in employment has been attributed to the end of jobless growth due to the changes in the labor reforms at the state level (Goldar, 2011). A different line of explanation has been put forward that the recent employment boom in manufacturing sector may not signify the end of jobless industrial growth. Instead, it could be merely a recovery of employment lost over the previous years (Nagraj, 2011).

There has been extensive debate on the employment growth in the organized manufacturing sector in India for different time periods (Nagaraj, 2000; Bhalotra, 1998; Goldar, 2000; Majumder, 2006; Roy, 2016). A few researchers have discussed employment elasticity in the context of Indian organized manufacturing sector. For example, Seth and Seth (1991) estimated labor absorption capacity of 20 major Indian manufacturing industries during 1960-1984 with the help of CES production function. They have concluded that the labor absorption capacity of the manufacturing sector has lagged behind the rate of growth of output.

Furthermore, azumdar and Sarkar (2004) have examined employment elasticity in the manufacturing sector during 1974-1996. They found a clear distinction in employment elasticity in three periods. During the first period 1974 to 1980, they have found higher employment elasticity followed by jobless growth with a negative value of elasticity and finally the reform period 1986-1996 when the employment started to recover with a high rate of output growth.

The analysis of employment elasticity has further been extended by Kannan and Raveendran (2009) up to 2004-05. They claimed that 16 out of 22 industries (2 digit level classification) registered positive employment elasticity during the pre-reform period. These industries belong to the group of industries whose share in total employment is very low. Total employment loss in organized manufacturing during pre-reform period was due to the low performance of two industries i.e. food products and beverages and textiles, which together constitute about 42 percent in total employment of organized manufacturing sector, while in the post-reform period employment growth has witnessed a marginal improvement despite a higher growth rate of output.

The present study attempts to analyze the employment elasticity at a greater disaggregate level in the Indian organized manufacturing sector. This study would make a significant contribution to the prevailing literature at the industries level and the results might be useful to know the pattern of employment intensity across industries in Indian organized manufacturing sector.

Database & Classification of the Industries

The study is based on the Annual Survey of Industries (ASI) data, published by the Central Statistical Organization (CSO) under the Ministry of Statistics and Program Implementation. The time period considered is from 1981 to 2014, which is further divided in to three sub-periods considering major economic events/shocks in Indian economy (19811990, 1991-2007, and 2008-2014). We further classified whole organized manufacturing industries into four subcategories: high-tech, medium high-tech, medium low-tech, and low-tech industries on the basis of relevant technology-intensive industrial classification of Organization for Economic Co-operation and Development (OECD). This study has considered 51 three-digit industries for the analysis.

Concept of 'Employment Elasticity'

Employment elasticity explains the sensitivity of the labor market with a change in the macroeconomic conditions of the economy. It is estimated by dividing percentage change in employment by percentage change in output growth (Kapsos, 2005). A high value of employment elasticity explains that growth in output leads to substantial job creation, whereas lower value indicates a weak association between output and employment growth (Misra & Suresh, 2014). In the present study, we used log-linear regression model to calculate employment elasticity:

InL = [alpha] + [beta] In Y (1)

Where L represents employment, Y stands for output, and In indicates the natural logarithm of the variable. b coefficient stands for the employment elasticity. It shows that for one percentage point increase in gross value added, employment will be increased by b percentage point.

The contributions of both labor input and labor productivity are important in economic growth. However, a clear interpretation of both employment and productivity are significant while studying the employment elasticity. Studying the world trends of employment elasticity, Kapsos (2005) provided a theoretical relationship between employment and productivity growth through an arithmetic identity given in equation 2.

[Y.sub.i] + [E.sub.i] x [P.sub.i] (2)

Where K stands for output, Et stands for employment and stands for productivity. Equation 2 shows that for a given change in output the following will hold:

[DELTA][Y.sub.i] = [DELTA][E.sub.i] + [DELTA][P.sub.i] (3)

Dividing the equation we will get the following:

[Please download the PDF to view the mathematical expression] (4)

Equation 4 shows the relationship between employment and labor productivity with a given output growth rate.

Simple Exponential Growth Rate

Consider the following linear form:

ln([Q.sub.t]) = a + bt + [u.sub.t] (5)

Where, [Q.sub.t] = output, t = time, b = coefficient of time, and a = con-stant. The coefficient of time, b, is the continuous rate of growth. It closely approximates the annual compound growth rates. Therefore, the estimates of b are presented as growth rates. This technique is used to estimate the overall growth rate (1981-2014).

Kinked Exponential Growth Rate

The study also computed kinked-exponential growth rates for the sub-periods 1981-1990, 1991-2007 and 2008-2014, in which the trend lines of the three sub-periods are forced to meet at the midpoint that divides the sub-periods. Considering a time series for the period t = 1, ...., n is broken at two points' [k.sub.1] and [k.sub.2]. Discontinuous growth rate estimates for the three resulting sub-periods could be derived by estimating them separately or, equivalently, by fitting the unrestricted (discontinuous) single equation:

[Please download the PDF to view the mathematical expression] (6)

The estimated growth rates from (1), [[??].sub.1], [[??].sub.2] and [[??].sub.3], are the same as if exponential trends were fitted separately to the data for each sub-period. The two-kinked exponential model is derived by imposing linear restrictions such that the sub-period trend lines meet at [k.sub.1] and [k.sub.2]:

[[alpha].sub.1] + [[beta].sub.1][k.sub.1] = [[alpha].sub.2] + [[beta].sub.2] [k.sub.1] (7a)

[[alpha].sub.2] + [[beta].sub.2][k.sub.2] = [[alpha].sub.3] + [[beta].sub.3][k.sub.2] (7b)

Substituting for [[alpha].sub.2] and [[alpha].sub.3], we obtain the two-kink exponential model:

[Please download the PDF to view the mathematical expression] (8)

The growth rates in the three sub-periods are now given by the OLS estimates of the coefficients of the resulting composite variables. The kinked exponential growth model reduces discontinuity bias, provides a better basis for growth rate comparison, reduces instability or cyclical fluctuations, and uses a full set of available information to estimate the growth rates for each sub-period in a single step (Boyce, 1986).

Trends in Employment Elasticity

This section examines the trends in the employment intensity of the output of the organized manufacturing sector at the aggregate level. The results of employment elasticity for the three time periods: 1981-1990,1991-2007 and 20082014 along with overall period 1981-2014 are presented in Table 1. It has been observed that the employment elasticity for the period 1981-2014 of the total organized manufacturing industry is 0.11. The employment elasticity during the period from 1981-1990 has been very low and statistically...

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