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In India, the post-covid-19 unemployment rate reached a 45-year high. However, even before the entrance of Covid-19, the Indian economy was deteriorating. The economists' circle has generally agreed that the growth engine was depleted first by demonetization in November 2016, and subsequently by the installation of GST, which further slowed things down. These broad events are frequently considered as recent causes of weak or negative job growth. However, through the internal dynamics of the organized industrial sector, this research attempts to identify the source of employment rate growth. The fundamental idea is that because the macroeconomic variables affecting them are similar, the unorganized manufacturing sector, primary sector, and services may likewise behave similarly. The rising use of automation in manufacturing, digitalization in the service sector, and mechanization in agriculture all contribute to the use of capital to replace a small amount of labour. Technology, fuel, and energy can also be used to replace labour. According to Acemoglu (1998)[1], a large market size for skill-complementary technology suggests a large share of skilled people in the labour force, which encourages faster upgrading of skilled workers' productivity. As a result, an increase in skill supply initially decreases the skill premium, but later promotes skill-biased technological advancement, which raises the skill premium, possibly even above its initial value. The rapid growth in the share of college graduates in the US labour force in the 1970s, according to the thesis, may have been a causal factor in both the 1970s fall in the college premium and the significant increase in inequality in the 1980s. Since 2008, the share of total salary bill in value added in India's organized manufacturing sector has been slowly increasing. This could be due to the same cause mentioned by Acemoglu. It could be the result of a substantial technological shift in the Indian economy. If this is the case, there is a chance that the technological revolution will extend to other businesses and sectors. As a result, a Cobb-Douglas type input demand function for labour input demand has been estimated in this article.

The following is a brief review of related literature. Aghion et al. (1999)[3] analyzed the relationship between inequality and economic growth from two directions. The first part of the survey examined the effect of inequality on growth, showing that when capital markets are imperfect, there is not necessarily a trade-off between equity and efficiency. It therefore provides an explanation for two recent empirical findings, namely, the negative impact of inequality and the positive effect of redistribution upon growth. The second part analyzed several mechanisms whereby growth may increase wage inequality, both across and within education cohorts. Autor et al. (1998)[7] examined the effect of skill-biased technological change as measured by computerization on the recent widening of U. S. educational wage differentials. Their analysis of aggregate changes in the relative supplies and wages of workers by education from 1940 to 1996 indicates strong and persistent growth in relative demand favoring college graduates. Rapid skill upgrading within detailed industries accounts for most of the growth in the relative demand for college workers, particularly since 1970. Also, the analyses of four data sets indicate that the rate of skill upgrading has been greater in more computer-intensive industries. Berman et al. (1994)[8] investigated the shift in demand away from unskilled and toward skilled labor in U. S. manufacturing over the 1980s. Production labor-saving technological change is the chief explanation for this shift. The conclusion is based on three facts: (1) the shift is due mostly to increased use of skilled workers within the 450 industries in U. S. manufacturing rather than to a reallocation of employment between industries, as would be implied by a shift in product demand due to trade or to a defense buildup; (2) trade- and defense-demand are associated with only small employment reallocation effects; (3) increased use of non-production workers is strongly correlated with investment in computers and in R&D. Betts (1997)[9] examined whether technological change has been neutral in Canadian manufacturing industries, using a system of translog-cost share equations for 1962 through 1986. The model used features two classes of labor treated as distinct inputs. The applied tests rejected homotheticity in all industries. Hicks’s neutrality was also rejected in 16 of 18 industries. The most common pattern of non-neutral technical change was a bias away from blue-collar workers. Moreover, formal tests for skill-neutral innovation rejected the hypothesis in ten industries in favor of skill-using technical change. The results suggested that in studies of Canadian manufacturing, aggregation across labor inputs is inappropriate. Black et al. (2001)[10] examined the impact of workplace practices, information technology, and human capital investments on productivity using data from a unique nationally representative sample of businesses and estimated an augmented Cobb-Douglas production function with both cross section and panel data covering the period of 1987-1993, using both within and GMM estimators. It has been observed that it is not whether an employer adopts a particular work practice but rather how that work practice is actually implemented within the establishment that is associated with higher productivity. Unionized establishments that have adopted human resource practices that promote joint decision making coupled with incentive-based compensation have higher productivity than other similar nonunion plants, whereas unionized businesses that maintain more traditional labor management relations have lower productivity. Finally, plant productivity is higher in businesses with more-educated workers or greater computer usage by non- managerial employees. Dhyne (2012)[12] looked at the factors that influence the use of flexible labour contracts as well as the effects of their implementation on labour dynamics. Many countries have eased their legislation since the 1970s by establishing flexible labour contracts or making their use simpler, according to their research. Their research contrasted the administration of temporary contracts in Belgium during the last 20 years to the situation in its neighboring nations. a dynamic Probit was used to model the adoption of fixed-term labour contracts (FTCs), and conventional dynamic labour demand equations were used to examine the influence of labour contracts on labour adjustment at the firm level using a panel of about 8,000 enterprises from 1998 to 2005. From a long-term viewpoint, the findings showed that some companies pursue labour management based on a core and a peripheral component and manage temporary contracts on a "permanent" basis. The Estimates also confirmed a substantially faster adjustment to temporary contracts employment, but the adjustment to ITCs is unaffected by whether or not firms use FTCs. When enterprises manage work organization using both forms of contracts, ITCs short-term employment elasticity with respect to pay suggests that workers' protection against redundancies is increased. FTCs, unlike ITCs, were used to respond to unforeseen demand shocks. Dunne et al. (1996)[13] exploited plant-level data for U.S. manufacturing for the 1970s and 1980s to explore the connections between changes in technology and the structure of employment and wages. They focused on the non-production labor share as the variable of interest. They observed the following (i) aggregate changes in the nonproduction of labor share at annual and longer frequencies are dominated by within plant changes; (ii) the distribution of annual within plant changes exhibits a spike at zero, tremendous heterogeneity and fat left and right tails; (iii) within plant secular changes are concentrated in recessions; and (iv) while observable indicators of changes in technology account for a significant fraction of the secular increase in the average nonproduction labor share, unobservable factors account for most of the secular increase, most of the cyclical variation and most of the cross sectional heterogeneity. Falk (1999)[15] analyzed the link between technological product and processes innovations and expectations about future employment for different types of labor in manufacturing. The empirical model allows for endogeneity of the firms? innovation decision in the labour demand equations. The system of probit equations was estimated using simulated ML based on 800 West German firms. The empirical evidence for different measures of technological innovations indicates that introduction of new market products is more important than any other measure of product innovation in determining the expected employment probabilities for homogeneous labour. Furthermore, as expected, technological innovations have the strongest impact on university graduates. Joint implementation of new products and new processes have a stronger impact on the employment expectations of university graduates than product innovations alone. Labour quality and turnover growth are also important factors of employment growth. Finally, tests of the exogeneity assumption of new market products in the labor demand equations cannot be rejected. Goux et al. (2000)[17] observed that the decline in the unskilled share of French employment is chiefly due to the slackness of domestic demand for those industries with the highest proportion of unskilled workers. The spread of computers has not been particularly conducive to substitution between skilled and unskilled labor. They tested and accepted the hypothesis of technical-progress neutrality within French industries. The mechanisms that generate inequality do not appear to be the same in France and in the United States. The source of inequality isn't so much technical progress per se as its interaction with the institutions that regulate the labor market. Greiner et al. (2001)[18] observed that skill-biased technical change as a major cause for wage inequality. Some modelling and presentation of stylized facts have been undertaken for US time series data. The wage inequality in a model with knowledge as input in an aggregate production function has been presented by Riddell and Romer. They also observed some important forces determining wage inequality and the quantitative impact of each source and differences across countries. They used growth model of the Romer type with innovation-based technical change and two skill groups where the growth of knowledge, the relative supply of the two skill groups, externalities and substitution effects among the two groups are the driving forces for wage inequality. The estimates were undertaken for US time series data and contrast those estimates with results from some European countries. In particular, we compare parameter estimations for US and German time series data. Also, they concluded that there is less wage inequality across skills in Europe in contrast to the US on the macroeconomic level. But, considering disaggregated data it has been observed that some increases in inequality for Germany, too. Although our model reveals important variables for the explanation of wage inequality there may, however, also be other factors, such as trade unions, which have impacted the wage spread. Kim (2016)[21] used industrial panel data to estimate the sources of productivity increase for Japanese manufacturing after controlling for returns to scale, imperfect competition, and capacity utilization. The findings revealed that capital investments had significant embedded technical development and interindustry externalities that boost productivity growth. Furthermore, embodied technical advancement causes R&D capital to have a negligible impact on productivity growth, implying that R&D's benefit is only realized once it is embodied into other capitals. Differences in factors affecting productivity growth between the durable and nondurable manufacturing sectors have been identified based on sector-by-sector calculations. Kim (2021)[22] examined the dynamic production structure of the Japanese manufacturing industry using the adjustment cost approach, showing the industry's distinctive dynamic structure. His research intended to help the Japanese government create and predict industrial strategies in order to stimulate domestic investment. He was able to develop a system of dynamic factor demand and output supply equations by applying the dual technique to the intertemporal value function represented by the Hamilton–Jacobi equation. He used industrial panel data from the Japanese manufacturing industry from 1973 to 2012 to calculate the system of behavioral equations and related elasticities. He investigated the dynamic structure of the Japanese manufacturing industry using hypothesis testing and dynamic elasticities. Labor and capital are quasi-fixed variables that adjust around 0.2 percent annually to the long-run optimum values, according to the estimated results. As is frequently assumed about the Japanese manufacturing business, which employs lifetime employment practices and a delayed decision-making process in investment decisions, estimated adjustment rates are quite slow. The findings also revealed that the elasticities of output supply and factor demand varied substantially depending on the temporal horizon. Factor demand rises in the short run when its own price rises, implying that factor prices have mostly influenced factor adjustment in the past due to sluggish factor adjustment. In the long run, however, as factor adjustment is accomplished, factor demand becomes a regular downward-sloping curve. To take advantage of the benefits of dynamic learning-by-doing, Japanese manufacturing companies hired personnel on a long-term basis and executed investments carefully, taking into account all potential consequences. Adjustment expenses for moving labour and capital stock are minimized under the technique. Machin et al. (1996)[23] examined the evidence that rapid upgrading of the skill structure in recent years was driven by technological change. Four countries were examined who have had different wage inequality and unemployment trends – Denmark, Sweden, the United Kingdom and the United States. The analysis of changes in wage bill shares and employment shares of more skilled workers resulted with the following conclusions: (i) within-industry changes are the driving force of aggregate shifts across all four countries; (ii) there is evidence of skill-biased technical change and capital-skill complementarity in all four countries; (iii) the results are robust to using education instead of occupation as a measure of skill, and computerization instead of R&D as a measure of technology; (iv) in the Anglo-Saxon countries a maximum of one-third of the aggregate change in the skill structure can be accounted for purely by technological factors; (v) the decline of collective bargaining, rather than trade, in the United Kingdom and the United States is an important factor in explaining the changes. Mariacristina et al. (2005)[24] estimated a SUR model for a sample of 400 Italian manufacturing firms, showing that the upskilling is more a function of the re-organizational strategy than a consequence of technological change alone. Moreover, some evidence of super additive effects emerges, consistently with the theoretical hypothesis of a coevolution of technology and organization. Vella (2018)[25] investigated whether labour hoarding can mitigate the impact of a slowdown in output on labour demand. Using data from Malta, a tiny EU country, he used a production function technique in his research. The findings supported the theory, indicating that in the long term, businesses are more willing to hire and fire more workers than in the short run. This conclusion has significant consequences for industrialized countries, including the possibility of labour hoarding in times of economic downturn when shocks are absorbed by internal flexibility. In two respects, the findings of his research add to the current literature. First, his research examined two industries: manufacturing and financial services, in which the former received government assistance to stockpile labour during the financial crisis of 2008. As a result, the dominance of labour hoarding in manufacturing is revealed in comparison to financial services, and the impact of hoarding techniques on labour demand is evaluated. Second, Malta is a fascinating instance since it has one of the world's smallest economies and is highly vulnerable due to the limits imposed by its small size and isolation. As a result, corporations take policy-driven initiatives to reduce the cost of change. Wood (1995)[28] argued that the main cause of the deteriorating economic position of unskilled workers in the United States and other developed countries has been expansion of trade with developing countries. In the framework of a Heckscher-Ohlin model, it outlines the evidence in support of this view, responds to criticisms of this evidence, and challenges the evidence for the alternative view that the problems of unskilled workers are caused mainly by new technology. Zhang & Alstonz (2018)[29] investigated the rate and biases of technical change in the U.S. dairy manufacturing business, focusing on derived demand for farm milk as a processing input. According to their findings, the Marshallian own-price elasticity of farm milk demand is between 0.43 to 1.20. According to the estimates, technical change has resulted in capital using and labour savings. The aforementioned empirical literature has shown that technological change can be considered the main cause of skill bias exhibited by manufacturing employment in developed countries as well as in developing countries.

To determine the optimal amounts of inputs L and K i.e., to derive the input demand function for labour the following cost function has been minimized with constraint using Lagrange multiplier method: C(w,r,Q,A)= min wL+rK sub. to f (L, K) =Q=ALα+Kβ The derived input demand function for labour is given below: Li = A Qiβ1.ri β2.wi β3.emTi.eui Where, the dependent variables L stands for demand for total employees TE in Regressions (R1, R2 & R3), for wage earning labour W in R4 & R5 and for salaried employees E in R6 & R7. The proxy of level of output Q has been taken with Economic Value Added (EVA). The weighted average cost of capital WACC has been taken as the price of substitute good i.e., capital in the demand function in regressions R1, R3, R4 & R6; whereas the average rate of interest paid r has been used in R2 and R5. The price of the quantity demanded i.e., L in each regression is the average wage given. It is assumed that the annual growth rate of labour demand holding other things constant also exists called m. After taking a natural log, the demand function takes the following shape: Ln Li= Ln A + β1 Ln Qi + β2 Ln ri + β3 Ln wi + mTi + ui ………………... (1) or L*= α + β1Q*+ β2r*+ β3w*+ mTi + ui ……………. (2) it is assumed that the input demand for labour has been growing with CAGR of ‘m’ to capture the effect of secular growth of output. The results have been presented in the next section.

In this paper ASI data of organized manufacturing sector has been used from 1981-82 to 2017-18 at all India aggregate level on Value Added, total persons engaged, number of labors, total wage bill/ emoluments, interest charges, loan and invested capital.  

The present agricultural crisis reflected in the long peasants’ movements against three farm bills is the crisis of whole of the economy. The inability of the manufacturing and service sector to subsidize the food for poor, electricity and fertilizer for the farmers and inefficiency of the state storage and PDS is stopping the government to nod a ‘Yes’ to MSP. The manufacturing economy might be generating enough surplus value if, return on capital is more than the cost of raising it. The figure 1 shows the return on capital, Interest cost and WACC in the selected period. One will be surprised that the organized manufacturing sector as a whole has been surviving despite of low return on capital as compare its cost. This could be explained only in terms of huge concessions given to the industry in the form of reduced interest rates, soft loans and tax holidays with creeping liberalization in the initial period and drastic reforms after 1990s. The Indian Manufacturing sector has come off age and by 2002, it started earning big profits. The market corrections started taking place after international financial crisis in 2008. We postulate that the industry uses every crisis as an opportunity to negotiate with employees for lower wages. The less skilled and experienced or otherwise low productive employees are retrenched after every crisis. However, the industry has to hire the skilled labor at higher cost. The industry might have become more rational but it is once again struggling to maintain the required return. The share of profit in value added became more than the workers and employees in post liberalization period and has been more than double post international financial crisis (see figure 2). The surging inequalities are the result of big difference in return on capital and GDP growth rate (see in figure 3). After describing the interplay of these variables, it will be interesting to observe the regression results of Input demand function for labor. 

Figure 1: Return on Capital, Interest Cost & WACC

 

 Figure 2: Returns on Capital and GDP Growth Rate in India (1981-82 TO 2017-18)

Figure 3:  Profits, Wages & Salaries in Value Added 

*Figures are based on ASI Data taken from http://mospi.nic.in/ website prepared by author

Observe in the Table given below with regression 1 we can say that secular growth rate of total employees is decreasing significantly but has been largely increasing with growth of output without conclusive evidence. The increase in weighted average cost of capital causes significant decline in the demand for total employees. If we use interest rate as price of capital, then the regression 2 gives expected results of a normal demand function where price of the commodity (labor) is inversely related and the price of substitute (capital) is directly related. The regression 3 (regression from origin) shows that the increase in rate of interest would lead to significant decrease in demand for total employees and vice a versa whereas the increase in wage rate would lead to increase in the demand for total employees. This regression has been found highly significant (F-Value=8135, d.f.:2,35). The claims may be exaggerated and the coefficients biased due to presence of negative autocorrelation (DW=0.275).  With a pinch of salt, we can say that in Indian Economy Keynesian promise of increasing employment with higher wages still holds true. The regression 4 further strengthen the Keynesian proposition as it is observed that the demand for wage earning labor which we can called unskilled labor in this paper for our purpose definitely increases with higher wages. The regression 4 is having F-Value i.e., 66.026 (d.f.: 4, 32) and no serial autocorrelation. The regression 5 the coefficient of output has been found significant. This is also evident from that the demand for permanent employees which is proxy of the skilled labor deceases significantly with increase in emoluments but increases with increase in cost of capital.

Table 1: Regression Results of Input Demand Function

Reg.	D.V.	α	β1	β2	β3	m	R	R-Square	Adj. R-Square	F-Value	DW
R1	LNTE	114.345 (2.266)	0.121 (0.811)	-0.902 -(2.759)	0.542 (1.348)	-0.054 -(2.006)	0.915	0.838	0.818	41.422 (4,32)	0.499
R2	LNTE	-35.775 -(0.842)	0.466 (3.794)	0.189 (1.227)	-0.744 -(2.456)	0.026 (1.159)	0. 899	0.809	0.785	33.8 (4,32)	0.321
R3	LNTE			-3.395 -(7.850)	0.817 (10.151)		0. 999	0. .998	0.998	8135.538 (2,35)	0.275
R4	LNW	119.734 (4.624)	-0.045 -(0.43)	-1.273 -(6.200)	0.907 (4.142)	-0.058 -(4.143)	0.944	0.892	0.878	66.026 (4,32)	1.156
R5	LNW	39.871 (1.074)	0.272 (2.031)	-0.139 -(0.818)	-0.018 -(0.062)	-0.014 (0.716)	0.876	0.767	0.738	26.335 (4,32)	0.155
R6	LNE	-53.751 -(1.057)	0.579 (5.417)	0.243 (1.104)	-0.859 -(2.921)	0.035 (1.284)	0.917	0.841	0.822	42.432 (4,32)	0.405
R7	LNE	-73.635 -(2.010)	0.549 (6.357)	0.291 (3.142)	-0.954 -(4.812)	0.046 (2.335)	0.935	0.874	0.858	55.572 (4,32)	0.752

*Figures in parentheses are t-ratios, ** Figures in parentheses under F- value are d.f. 

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