Employment Elasticity in India's Organised Manufacturing Sector: Evidence from State-level Panel Data

Employment Elasticity in India's Organised Manufacturing Sector: Evidence from State-level Panel Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Employment Elasticity in India's Organised Manufacturing Sector: Evidence from State-level Panel Data Sadhana Singh, Devdatta Tare This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7064579/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Indian manufacturing sector is considered one of the main drivers of employment creation and has shifted the labour force from agriculture to a more productive manufacturing industry. However, the sector has experienced significant economic reforms that have affected its employment generation capacity. This study aims to quantify the employment elasticity trend in India’s organised manufacturing sector post-economic reform and determine the relative impact of wages, fixed capital and working capital on employment generation across different periods. The study employs panel data analysis using data from twenty-one major Indian states, covering the period from 1998 to 2023. The random effect model is utilised to estimate the employment elasticity coefficient. The analysis incorporates sub-period estimation to capture structural breaks following major economic reforms. The findings reveal that working capital positively impacts employment growth in the sector. However, this effect varies across different time horizons. The study suggests that adequate availability of working capital in the industry could substantially boost employment. JEL Classification - C23, J20, L60 Employment elasticity Manufacturing Sector Working Capital Fixed capital Wage Panel Data Analysis Figures Figure 1 Figure 2 Introduction The effect of the industrial sector growth on job creation is a prominent topic in development economics. It has become more significant for developing countries, where job creation is an important task to reduce poverty and improve the welfare of society. However, the problem of low employment elasticity in the manufacturing sector has been a serious issue in India, especially after globalisation (Majumdar & Sarkar, 2004). The Indian economy is characterised by declining output elasticity of employment generation and increasing informalization and capitalisation of the industrial sector (Basole, 2022). Employment elasticity in the manufacturing sector is defined mainly in terms of value added. However, given the output growth rate, elasticity may also be explained in terms of wages and fixed capital. It examines the responsiveness of employment level to changes in output, wages or other economic variables. The organised manufacturing sector, which includes units registered with the government and operating under legal frameworks, is crucial in creating adequate employment opportunities. However, the sector has faced persistent challenges in creating job opportunities despite experiencing substantial output growth over the past several decades. This phenomenon is often referred to as jobless growth The relationship between real wages and employment growth has been the subject of debate in economic theory. Neoclassical economists have suggested an inverse relationship between the two variables, whereas imperial studies have shown mixed effects (Dube et al., 2012; Khan, 2005; Webster, 2003). Capital and labour are the two major sources of production. It can be assumed that they are interchangeable to some extent. Investing in fixed capital often enhances productivity and efficiency, which can initially lead to higher employment, as a business expands the scale of its operation. The Harrod-Domar model was the first to show that the growth rate of investment is necessary to permit capital to be fully employed. However, increased reliance on fixed capital and automation can lead to a reduction in labour demand. On the contrary, insufficient fixed capital can lead to a reduction in labour demand. Availability of sufficient working capital smooths daily operations, including inventories, accounts receivable, and liquid assets. This plays a crucial role in determining how firms scale their employment levels. Adequate availability of working capital helps firms hire an additional workforce to respond to market opportunities and maintain existing labourers during fluctuations. However, the direct relationship between working capital and employment elasticity has received little attention in the literature. This study bridges this gap and analyses the impact of working capital on employment elasticity growth. Literature Review Low employment elasticity is a major concern worldwide. However, this varies significantly between developed and developing countries. In developing countries, employment elasticity with respect to GDP is relatively low compared to that in developed countries, indicating the possibility of jobless growth in such economies (Haider et al., 2023 ). The demand for labour has become more elastic in economies with a low level of protective legislation (Lichter et al., 2015 ). Interestingly, elasticity differs between countries and demographic groups, suggesting varied circumstances for female employees (Kimmel & Kniesner, 1998). Similar to the global trend, India is also experiencing declining employment absorption capacity for output growth (Papola, 2006; Kannan & Ravindran, 2009). Majumdar and Sarkar (2004) studied employment elasticity in the manufacturing sector for the period 1974–1996, dividing it into three sub-periods. The first period (1974-80) is characterized by high employment-intensive production, the second period (1980-86) exhibits negative employment elasticity, reflecting jobless growth, and the third period (1986-96) is the reform period, during which employment increased but did not reach the level of the first period (Majumdar and Sarkar, 2004). Sectoral studies of employment elasticity demonstrate that the non-agricultural sector has become a key determinant of India's aggregate employment elasticity (Basu and Das, 2016). However, economic development during 2004–2017 can be characterised by a lack of job creation (Pathi et al., 2023). There are substantial variations in employment elasticity across the Indian states. Labour absorption into the industry, construction, and service sectors lagged behind the increase in potential labour supply in most states (Thomas, 2023). Interestingly, states that have implemented more labour reforms show higher elasticity than those with fewer reforms (Dougherty, 2009). In other words, states with greater flexibility in the labour market demonstrate lower employment growth than those with rigid labour markets (Roy et al., 2020). Several studies have examined the relationship between wages and employment elasticity, providing insights into how changes in wages affect employment levels, and vice versa. These studies suggest that an increase in minimum wage has a significant negative effect on employment flow (Dube et al., 2012 ; Dickens et al., 1998 ; Khan, 2005; Webster, 2003 ). Employment elasticity with respect to minimum wage changes is lower, as the effect is primarily seen in employment flow rather than in overall employment levels. In other words, a minimum wage increase affects employment flow but not employment stock (Dube et al., 2012 ). However, even a low share of wages does not appear to support the view that the labour cost has been high and does not restrict firms from adopting labour-saving technologies (Singh and Mitra, 2017). In the case of India, a divergence is found between real wage and labour productivity in the manufacturing sector, suggesting a weakening of the bargaining power of labour (Jain, 2019). Increasing employment elasticity can be reversed by strengthening policies that promote the provisioning of physical, health, and educational infrastructure and encourage the population to acquire better skills and make themselves employable (Mitra, 2023 ). Studies on capital-labour substitution provide crucial insights into how an increase in fixed capital influences employment decisions. Vollrah (2024) examines the elasticity of output with respect to capital and labour and finds an increase in capital elasticity during 1996–2018 in the USA. Cantore et al. ( 2021 ) explore capital labour substitution elasticity using a simulated method of moments approach, which shows that the elasticity of substitution between capital and labour plays an important role in the analysis of economic and policy issues such as factor share. In the context of India, where the workforce is still largely unskilled or semi-skilled, the growth of new-age technology would impact the level of employment. However, new technology could have a smaller impact on the informal sector. Formal or organised sectors could face larger impacts similar to industrialised countries (Dev and Ahmad, 2018). However, the pace of replacement of existing technology in India is slower and more selective in India (Mehta and Avasthi, 2019). The relationship between working capital is not been extensively explored in the literature. Fazzari and Petersen (1987) established foundational work on working capital requirements and a firm’s investment decisions. Duchin et al. ( 2010 ) demonstrate how liquidity affects employment during a slowdown and find that firms with higher cash reserves are better at maintaining employment levels during a financial crisis. Dao and Lie (2017) investigate the effect of external financing constraints on employment generation in emerging economies at the firm level and find strong evidence of the role of working capital channelled through external financing on a firm’s employment generation. Despite extensive research on employment elasticity, a gap remains in the understanding of how fixed capital, working capital, and emolument interact as determinants of employment responsiveness. Most existing literature examines these components in isolation, without specifically analysing the mechanism through which working capital availability influences employment elasticity. This study attempts to fill this gap and analyses the impact of output, real wages, fixed capital, and working capital on the growth of employment in the organised manufacturing sector of India. The second section discusses some stylised facts of the data. The third section explains the methodology used in the study. The fourth section presents the findings of the econometric analysis, the fifth section presents the discussion, and the sixth section concludes this study. Data and Stylised Facts Data for the analysis were obtained from the Annual Survey of Industries (ASI) compiled by the Economic and Political Weekly Research Foundation (EPWRS). The ASI provides statistical information on the organised manufacturing sector in India. The variables taken for the purpose of analysis are the number of total employees, emolument (taken as a proxy of wage), fixed capital, working capital, and Gross Value Added (GVA). The fixed capital, working capital, and GAV series are made at constant prices using the wholesale price index, whereas emoluments are made at a constant price with the help of the consumer price index of Industrial Workers. For both indices, 2005 was taken as the base year. The CPI and WPI with different base years were connected using linking factors. The analysis was done for twenty-one major states of India for the period 1998–2023. Some stylized facts about the data are presented in the following subsection. The growth trend of the studied variables is shown in Table 1. Table 1 Growth Trend of Studied Variables Growth Trend (Log Y t = a+ bt) Variable 1998–2008 2009–2014 2015–2023 Fixed Capital 0.0193*** 0.0243*** 0.0035 R 2 = 0.63 R 2 = 0.89 R 2 = 0.096 Employment 0.0137*** 0.0304*** 0.054* R 2 = 0.578 R 2 = 0.966 R 2 = 0.36 Working Capital 0.035*** 0.0048 0.0386*** R 2 = 0.656 R 2 = 0.028 R 2 = 0.792 GVA 0.0378*** 0.015*** 0.015*** R 2 = 0.841 R 2 = 0.870 R 2 = 0.796 Emolument 0.018*** 0.030*** 0.021*** R 2 = 0.809 R 2 = 0.906 R 2 = 0.907 Source- Author’s Calculation (Based on ASI data) Table 1 Share of Emolument and Profit in GAV for the Organised Manufacturing Sector in India Year Share of Emolument in GVA Share of Profit in GVA 1998–1999 24.39 36.25 1999–2000 23.92 32.36 2000–2001 27.69 24.08 2001–2002 26.95 22.11 2002–2003 24.79 32.40 2003–2004 23.05 39.69 2004–2005 20.80 46.70 2005–2006 18.75 48.42 2006–2007 17.96 47.12 2007–2008 17.15 46.16 2008–2009 18.47 38.55 2009–2010 15.66 36.51 2010–2011 16.54 32.99 2011–2012 17.37 26.82 2012–2013 17.44 26.31 2013–2014 18.90 23.23 2014–2015 18.95 21.81 2015–2016 18.23 22.81 2016–2017 18.53 21.52 2017–2018 17.52 21.90 2018–2019 18.97 19.35 2019–2020 20.54 16.62 2020–2021 18.40 19.71 2021–2022 18.60 21.33 Source- Based on Data from ASI Table 2- Descriptive Statistics of the Variables used Variable Obs Mean Std. Dev. Min Max Ln(Working Capital) 480 14.75692 1.338757 10.39398 17.49786 Ln(Fixed Capital) 480 13.48312 1.250725 9.264824 16.39199 Ln(Employment) 480 12.73813 1.04663 10.04603 14.84324 Ln(Emoluments) 480 12.47369 1.151179 9.424538 14.90749 Ln(output) 480 15.75066 1.117889 11.9924 18.15391 Based on ASI data The trend analysis indicates consistent growth in all variables during 1998–2008 (Pre-Crisis Period). GVA experienced the highest growth rate at 3.78 percent annually, followed by working capital at 3.5%, while employment grew by only 1.3 percent annually in this period. The growth rates of fixed capital and emoluments were 1.9 percent and 1.8 percent, respectively. During 2009–2014, employment growth accelerated to 3.04 percent, suggesting a recovery-driven increase in hiring. Fixed capital also rose to 2.43 percent during this period. However, growth in working capital nearly stalled at 0.48 percent, reflecting cautious financial management. The growth rate of emoluments increased to 3 percent over this period. In recent years, from 2015 to 2023, the growth patterns of these variables diverged. Employment surged to 5.4 percent, while emolument growth declined to 2.1 percent, possibly due to an increase in contractual workers. Working capital rebounded strongly to 3.86 percent. Conversely, growth in fixed capital investment dropped sharply to just 0.35 percent and became statistically insignificant. The growth of GVA remained moderate but stable. 3.1 Growth Trend in the Share of Emolument and Profit in GAV of the Manufacturing Sector in India The share of emoluments and profits in GAV for the organised manufacturing sector is shown in Fig. 1. The figure shows significant changes in the share of profits in this sector. However, for emoluments, the variation is not as significant as for profit. From 1998-99 to 2001-02, there was a decline in the percentage share of profit in total GAV, whereas an improvement in the share of emolument was visible in this period. The profit share declined from 36.25 percent in year 1998-99 to 22.11 percent in the year 2001-02 whereas the emolument share increased from 24.39 percent in the year 1998-99 to 26.79 percent in year 2001-02. However, this pattern reversed in the subsequent period. The profit share showed an increasing trend, whereas the emolument showed a declining trend from 2002-03 to 2007-08. Profit share increased by 46.16%, and emolument share shrank to 17.15 percent in year 2007-08. However, this increasing trend in profit share cannot be sustained in the following period. The share remains at 21.33 percent in the year 2022-23. The share of emoluments remained between 17% and 18 percent during this period. This graph shows no trade-off between profit share and emolument during the study period. 3.2 Growth of Labour and Capital The growth rates of labour and capital in the organised manufacturing sector during the study period are shown in Fig. 2. Labour value is the total number of persons employed in the sector, whereas capital value is the sum of fixed capital and working capital. The growth rates of labour and capital show that the growth rate of labour is significantly higher than the growth rate of capital. Capital is taken as the total fixed and working capital. The average annual growth rate of labour during 1998–2022 was 3.15 percent. This growth jumped significantly in 2023 when the growth rate of labour increased by around 200 percent. However, the average annual growth rate of capital for period 1998–2022 was 5.9 percent, which was just 2.98 percent by 2023. As 2023 is an exceptional year, we excluded it from the figure. This figure shows a significant gap between the two growth rates. The growth rate of capital was higher than the growth rate of labour during 2004–2011. However, it started reducing during 2012–2015. Again, during 2014–2021, a widening gap between the two growth rates was visible. However, this trend reverses after 2021, when we notice that the growth rate of labour is higher than that of capital. This reversal can be attributed to the impact of COVID-19 on the manufacturing sector. In 2020, when the lockdown was imposed, there was a sharp decline in labour and capital growth. In 2022, when the situation started to become normal, the growth of labour outperformed the growth of capital. Research Methodology The employment elasticity of the organised manufacturing sector is estimated using the following model- L(Employment) = α + β 1 L(GVA) it + β 2 L(Fixed Capital) + β 3 L(Working Capital) + β 4 L(Emolument)+ε -------------------------(1) All variables were taken in a natural logarithmic form (prefixed with L). The β coefficients are estimated employment elasticities with respect to the independent variables. The independent variables considered for this model are gross value-added (GVA), fixed capital, working capital, and emulation. Fixed capital includes capital in the form of assets such as land, plant, machinery, buildings, vehicles, transportation, and rental capital. The increase in fixed capital can also be referred to as a technological advancement to increase firm productivity. An increase in fixed capital can have both positive and negative effects on employment generation. Working capital is the monetary value of the total working capital used in the manufacturing sector. Working capital is the most essential component of manufacturing operations. It ensures that the business meets its short-term obligations, such as payment to suppliers, employees, and other expenses, which helps to maintain the smooth functioning of the firm. A smooth availability of work helps to increase employment, whereas constrained working capital financing of a firm affects its job creation (Dao and Tiu, 2017). A positive coefficient of β 3 indicates unconstrained working capital financing and a positive impact of employment generation in the manufacturing sector. A prior expectation of β s β 1 > 0, β 2 0, β 4 < 0 The study is based on panel data taken from 21 major states between 1999 and 2023 from the Annual Survey of Industries. This period is considered for the analysis, as the effect of liberalisation is well noticed after 1999. Findings The objective of this study is to investigate the factors affecting employment elasticity in the organised manufacturing sector, using a panel of twenty-two major states of India. A Lagrange multiplier test was conducted to determine whether the pooled model was appropriate for the analysis. The results showed that the pooled model was not appropriate because the data had significant individual and time effects. The Hausman test was performed to determine if the random-effects model should be used. The test results show that the random effects model is appropriate for the analysis of this dataset. The Levin-Lin Chu test was performed to determine whether the series was non-stationary. Table 2 Robustness Test Results Test Used Test Result Conclusion If the pooled model is appropriate Lagrange Multiplier test p-value = 2.2e-16 The Pooled model is not appropriate If the Random effect model is appropriate Haussman test p-value = 0.1832 The random effect model is appropriate Stationarity of employment series Levin-Lin Chu test p-value = 0.19 The series is not stationary Stationarity of GVA Levin-Lin Chu test p-value = 0.057 The series is stationary Stationarity of Fixed Capital Levin-Lin Chu test p-value = 0.00 The series is stationary Stationarity of Working Capital Levin-Lin Chu test p-value = 0.971 The series is not stationary Stationarity of Emoument Levin-Lin Chu test p-value = 0.00 The series is stationary Source- Based on the Author’s Calculation The employment and working capital series are found to be non-stationary. The different values of these series were taken to avoid the problem of spurious regression. We estimated Eq. 1, which shows the relationship between employment generation and output, fixed capital, working capital, and emoluments. The analysis was done over four time periods. Column 1 shows the result for the period 1998–2023. Column 2 presents the results for the period 1998–2008. Column 3 shows the result for the period 2009–2014, whereas column 4 shows the result for the period 2015–2023. The total period is divided into sub-periods, considering the structural transformation during those periods. The year 2008 was considered a break because of the global slowdown during that period. The year 2014 is considered because during the period 2015–2023, a lot of structural changes occurred in the economy of India. For example, changes in the central government, the implementation of GST, demonetization, and the spread of COVID-19. Table 3 Determinants of the employment elasticity Model 1 Period- 1998–2023 Model 2 Period- 1998–2008 Model 3 Period- 2009–2014 Model 4 Period- 2015–2023 Intercept -0.226** (-2.6300) -0.2832* (-2.1919) 0.0771 (0.5835) 0.1201 (-0.9912) Log(GVA) 0.0348* (2.291) 0.059* (2.3560) 0.3565*** (5.5652) 0.2372** (3.0007) Log(Fixed Capital) 0.0097 (1.323) 0.0267* (2.1814) -0.0034 (-0.1914) 0.0107 (1.1856) Log(Working Capital) 0.1244*** (4.338) 0.1448*** (3.8058) 0.1642** (3.2413) -0.0021 (-0.223) Emolument -0.0346* (-2.401) -0.0776*** (-3.6022) 0.0028 (0.8856) 0.5460*** (4.0726) Adjusted R 2 0.052 0.155 0.313 0.245 p-value of f- Statistics 5.8034e-06 1.1519e-07 1.7899e-10 1.9572e-10 DW test Statistics 2.03 1.90 2.23 2.28 Source- Based on Author’s Estimate Gross Value Added (GVA) has a significant positive impact on employment growth. Overall, Model 1 shows that a one percent increase in GVA increases the employment growth rate by 0.0348 percent. The piecewise regression analysis also showed a positive impact. During 1998–2008 the coefficient is 0.059 that shows a one percent increase in GVA increases employment growth by 0.059 percent. This rate increases to 0.36 percent during 2009–2014. However, it has decreased to 0.24 percent during 2015–2023. Analyses of fixed capital over employment growth do not show any significant impact in any model except for Model 2, which is for the period 1998–2008. For this period, the growth of fixed capital increases employment growth by 0.027 percent. The growth of fixed capital has a negative impact during 2008-15 as expected, but the value is not significant. Working capital has a significantly positive impact on the overall model. With a one percent increase in working capital, employment growth increased by 0.124 percent in the overall model. This is 0.14 percent during 1998–2008 and 0.162 during 2009-14. However, the value becomes insignificant during 2015–2023. This positive value was in accordance with our expectations. An analysis of the impact of emoluments (wages) on employment shows a negative relationship. Overall period, a one percent increase in the emolument decreases employment growth by 0.035 percent. The value was − 0.078 percent during 1998–2008. The effect became insignificant during 2009–2014. The effect became insignificant during 2009-14. However, Model 3, for the period 2015-23, shows a significant positive association between employment growth and emolument. Discussion This study analyses the factors affecting employment elasticity of the organised manufacturing sector in India by taking a panel of twenty-two major Indian states in terms of production. The factors considered for the analysis are real wages in terms of emolument, value added, fixed capital, and working capital. While previous studies have analysed the impact of output and fixed capital on employment generation, this study also considers working capital as an important factor that could affect employment growth. The analysis shows that GVA, fixed capital, working capital, and emoluments influence the growth of employment in the organised manufacturing sector. Like Majumdar and Sarkar (2004) and Das and Basu (2016), this study also finds a positive association between GVA and employment elasticity. The analysis also finds a negative association between employment elasticity and emoluments for the period 1998–2023, which resembles the studies of Khan (2005), Webster ( 2003 ), and Dube et al. ( 2012 ). However, the coefficient is found to be positive for the period 2015–2023, which seems to be the effect of various structural changes during this period. In 2014, the Make in India initiative was launched, which created a positive manufacturing environment and demand for skilled labour, even at higher wages. Apart from that, Post-demonetisation (2016) and GST implementation (2017) pushed more manufacturing units into the organised sector that started paying higher wages but also faced greater compliance requirements and competition. However, the sector was badly impacted due to the outbreak of COVID-19, which made the sector more vulnerable to economic changes. During post-covid period government’s extensive support to the manufacturing sector in the form of the Production Link Incentive (PLI) scheme, tax and regulatory reform, etc., helped to boost the sector. The results for working capital show a significant positive and increasing magnitude in this study. This shows that an adequate supply of working capital could play a significant role in employment growth in the manufacturing sector. In the scope of policy focusing on increasing employment levels, ensuring the availability of sufficient working capital could play a significant role in this direction. Furthermore, the impact of fixed capital on employment generation was not found to be significant. The value is significant only for the period 1998–2008, which shows that capital formation has helped increase employment growth, but for the subsequent period and for the overall period, fixed capital does not show a significant impact. This could be attributed to two reasons, one is the lack of capital formation in the sector and the other is that even if capital formation is happening in the sector is not replacing the labour force, which is similar to Meheta and Awasthi (2019). Further studies could be conducted to analyse capital formation in the sector. Conclusion This study indicates that capital formation in the organised manufacturing sector in India has contributed to employment generation in the short term, but its overall impact remains insignificant over the entire period, possibly due to inadequate capital formation or the inability of capital to replace labour. Additionally, factors such as GVA and working capital positively influence employment elasticity, whereas emolument shows a negative association over the entire period, but a positive trend in recent years. This can be attributed to the high level of uncertainty in this sector over recent years. Overall, enhancing working capital in the sector and understanding sector-specific capital formation dynamics could play crucial roles in fostering sustainable employment growth in this sector. Moreover, this study did not distinguish between permanent and contractual employment. Further studies could be conducted to determine which of the two has recently been promoted in the sector. This study recommends prioritising the availability of working capital to stimulate employment growth in the manufacturing sector. Additionally, efforts must be made to increase capital formation through targeted investment and financial support. The present study reveals that capital formation within India's organised manufacturing sector has facilitated short-term employment generation. However, its overall impact remains negligible over the entire period, potentially due to insufficient capital formation or the inability of capital to substitute labour effectively. Furthermore, factors such as Gross Value Added (GVA) and working capital exhibit a positive influence on employment elasticity, whereas emoluments demonstrate a negative association throughout the period, albeit with a positive trend in recent years. This trend may be attributed to heightened uncertainty within the sector in recent years. Overall, augmenting working capital and comprehending sector-specific capital formation dynamics could be pivotal in promoting sustainable employment growth within this sector. Additionally, this study does not differentiate between permanent and contractual employment. Future research could explore which type of employment has been more prevalent in recent years. The study recommends prioritising the availability of working capital to stimulate employment growth in the manufacturing sector. Furthermore, efforts should be directed towards enhancing capital formation through targeted investment and financial support. Declarations The authors declare no conflicts of interest regarding the research, authorship, and/or publication of this article. Funding- The authors received no financial support for the research, authorship, or publication of this article. References Alimov, A. (2015b). 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American Economic Journal: Macroeconomics , 16 (4), 470-504. Webster, E. (2003). The Effects of Wages on Aggregate Employment: A Brief Summary of Empirical Studies. Australian Economic Review , 36 (1), 134-142. https://doi.org/10.1111/1467-8462.00274 Zabel, J. E. (2012). Migration, housing market, and labor market responses to employment shocks. Journal of Urban Economics , 72 (2–3), 267–284. https://doi.org/10.1016/j.jue.2012.05.006 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7064579","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512500032,"identity":"e3a6e09f-f3e2-4a5a-9d6b-309092f39aa1","order_by":0,"name":"Sadhana Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYHACMwQzwcAGSDI2HiBey4OKNJCWBuK1MD44cxjMwKtFvv3wtgc/d9gx6LafPSaR2Hbebm37YaAtNTbRuLQYnEkrN+w9k8xgdiYvDajldvK2M4lALcfSchtwaWHIMZPgbWNmMDsAZIC0mB0AamFsOIxTi3z/GzPJv231DGbn34C0nEs2O/8QvxaGGzlm0rxthxnMgAyJhDMH7MxuELDF4MazMmnZtuM8ZjfeGFskVCQnmN0A2pKAxy/y/cnbJN+2VcuZnc8xvPnDwM7e7Hz6wwcfamxwOwwKeGCMRLDKBALKUYA9KYpHwSgYBaNgZAAAbadj6u6GfgEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1420-0495","institution":"Babasaheb Bhimrao Ambedkar University","correspondingAuthor":true,"prefix":"","firstName":"Sadhana","middleName":"","lastName":"Singh","suffix":""},{"id":512500033,"identity":"10efe182-9f26-479e-b371-8c2d24f2d689","order_by":1,"name":"Devdatta Tare","email":"","orcid":"","institution":"Godwana University Gadchiroili, Maharashtra","correspondingAuthor":false,"prefix":"","firstName":"Devdatta","middleName":"","lastName":"Tare","suffix":""}],"badges":[],"createdAt":"2025-07-07 10:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7064579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7064579/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91451714,"identity":"b48e0df4-f72b-4e39-859e-d1b7076368db","added_by":"auto","created_at":"2025-09-16 15:37:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShare of Emolument and Profit in GVA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource- Based on Annual Survey of Industries\u003c/p\u003e","description":"","filename":"drawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7064579/v1/8aa40f99d4970d8727cf9a4a.png"},{"id":91451713,"identity":"7fffa410-13ab-49e0-bd8c-85aaa627500e","added_by":"auto","created_at":"2025-09-16 15:37:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrowth rates of Labour and Capital\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource- Based on data from ASI\u003c/p\u003e","description":"","filename":"drawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7064579/v1/2d0773789865e70663ce0c63.png"},{"id":94036160,"identity":"bbad470d-92c3-4ddd-a019-e041108269b4","added_by":"auto","created_at":"2025-10-21 16:24:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":903763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7064579/v1/de551e18-d445-4937-bb75-f7bd081f1115.pdf"}],"financialInterests":"","formattedTitle":"Employment Elasticity in India's Organised Manufacturing Sector: Evidence from State-level Panel Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe effect of the industrial sector growth on job creation is a prominent topic in development economics. It has become more significant for developing countries, where job creation is an important task to reduce poverty and improve the welfare of society. However, the problem of low employment elasticity in the manufacturing sector has been a serious issue in India, especially after globalisation (Majumdar \u0026amp; Sarkar, 2004). The Indian economy is characterised by declining output elasticity of employment generation and increasing informalization and capitalisation of the industrial sector (Basole, 2022). \u0026nbsp;Employment elasticity in the manufacturing sector is defined mainly in terms of value added. However, given the output growth rate, elasticity may also be explained in terms of wages and fixed capital. It examines the responsiveness of employment level to changes in output, wages or other economic variables. The organised manufacturing sector, which includes units registered with the government and operating under legal frameworks, is crucial in creating adequate employment opportunities. However, the sector has faced persistent challenges in creating job opportunities despite experiencing substantial output growth over the past several decades. This phenomenon is often referred to as jobless growth\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relationship between real wages and employment growth has been the subject of debate in economic theory. Neoclassical economists have suggested an inverse relationship between the two variables, whereas imperial studies have shown mixed effects (Dube et al., 2012; Khan, 2005; Webster, 2003). Capital and labour are the two major sources of production. It can be assumed that they are interchangeable to some extent. \u0026nbsp; Investing in fixed capital often enhances productivity and efficiency, which can initially lead to higher employment, as\u0026nbsp;a business expands the scale of its operation.\u0026nbsp;The\u0026nbsp;Harrod-Domar model was the first to show that the growth rate of investment is necessary to permit capital to be fully employed. However, increased reliance on fixed capital and automation can lead to a reduction in labour demand. On the contrary, insufficient fixed capital can lead to a reduction in labour demand.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of sufficient working capital smooths daily operations, including inventories, accounts receivable, and liquid assets. This plays a crucial role in determining how firms scale their employment levels. Adequate availability of working capital helps firms hire an additional workforce to respond to market opportunities and maintain existing labourers during fluctuations. However, the direct relationship between working capital and employment elasticity has received little attention in the literature. This study bridges this gap and analyses the impact of working capital on employment elasticity growth.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eLow employment elasticity is a major concern worldwide. However, this varies significantly between developed and developing countries. In developing countries, employment elasticity with respect to GDP is relatively low compared to that in developed countries, indicating the possibility of jobless growth in such economies (Haider et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The demand for labour has become more elastic in economies with a low level of protective legislation (Lichter et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Interestingly, elasticity differs between countries and demographic groups, suggesting varied circumstances for female employees (Kimmel \u0026amp; Kniesner, 1998). Similar to the global trend, India is also experiencing declining employment absorption capacity for output growth (Papola, 2006; Kannan \u0026amp; Ravindran, 2009). Majumdar and Sarkar (2004) studied employment elasticity in the manufacturing sector for the period 1974\u0026ndash;1996, dividing it into three sub-periods. The first period (1974-80) is characterized by high employment-intensive production, the second period (1980-86) exhibits negative employment elasticity, reflecting jobless growth, and the third period (1986-96) is the reform period, during which employment increased but did not reach the level of the first period (Majumdar and Sarkar, 2004). Sectoral studies of employment elasticity demonstrate that the non-agricultural sector has become a key determinant of India's aggregate employment elasticity (Basu and Das, 2016). However, economic development during 2004\u0026ndash;2017 can be characterised by a lack of job creation (Pathi et al., 2023). There are substantial variations in employment elasticity across the Indian states. Labour absorption into the industry, construction, and service sectors lagged behind the increase in potential labour supply in most states (Thomas, 2023). Interestingly, states that have implemented more labour reforms show higher elasticity than those with fewer reforms (Dougherty, 2009). In other words, states with greater flexibility in the labour market demonstrate lower employment growth than those with rigid labour markets (Roy et al., 2020).\u003c/p\u003e\u003cp\u003eSeveral studies have examined the relationship between wages and employment elasticity, providing insights into how changes in wages affect employment levels, and vice versa. These studies suggest that an increase in minimum wage has a significant negative effect on employment flow (Dube et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dickens et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Khan, 2005; Webster, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Employment elasticity with respect to minimum wage changes is lower, as the effect is primarily seen in employment flow rather than in overall employment levels. In other words, a minimum wage increase affects employment flow but not employment stock (Dube et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, even a low share of wages does not appear to support the view that the labour cost has been high and does not restrict firms from adopting labour-saving technologies (Singh and Mitra, 2017). In the case of India, a divergence is found between real wage and labour productivity in the manufacturing sector, suggesting a weakening of the bargaining power of labour (Jain, 2019). Increasing employment elasticity can be reversed by strengthening policies that promote the provisioning of physical, health, and educational infrastructure and encourage the population to acquire better skills and make themselves employable (Mitra, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies on capital-labour substitution provide crucial insights into how an increase in fixed capital influences employment decisions. Vollrah (2024) examines the elasticity of output with respect to capital and labour and finds an increase in capital elasticity during 1996\u0026ndash;2018 in the USA. Cantore et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) explore capital labour substitution elasticity using a simulated method of moments approach, which shows that the elasticity of substitution between capital and labour plays an important role in the analysis of economic and policy issues such as factor share. In the context of India, where the workforce is still largely unskilled or semi-skilled, the growth of new-age technology would impact the level of employment. However, new technology could have a smaller impact on the informal sector. Formal or organised sectors could face larger impacts similar to industrialised countries (Dev and Ahmad, 2018). However, the pace of replacement of existing technology in India is slower and more selective in India (Mehta and Avasthi, 2019). The relationship between working capital is not been extensively explored in the literature. Fazzari and Petersen (1987) established foundational work on working capital requirements and a firm\u0026rsquo;s investment decisions. Duchin et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrate how liquidity affects employment during a slowdown and find that firms with higher cash reserves are better at maintaining employment levels during a financial crisis. Dao and Lie (2017) investigate the effect of external financing constraints on employment generation in emerging economies at the firm level and find strong evidence of the role of working capital channelled through external financing on a firm\u0026rsquo;s employment generation.\u003c/p\u003e\u003cp\u003eDespite extensive research on employment elasticity, a gap remains in the understanding of how fixed capital, working capital, and emolument interact as determinants of employment responsiveness. Most existing literature examines these components in isolation, without specifically analysing the mechanism through which working capital availability influences employment elasticity. This study attempts to fill this gap and analyses the impact of output, real wages, fixed capital, and working capital on the growth of employment in the organised manufacturing sector of India. The second section discusses some stylised facts of the data. The third section explains the methodology used in the study. The fourth section presents the findings of the econometric analysis, the fifth section presents the discussion, and the sixth section concludes this study.\u003c/p\u003e"},{"header":"Data and Stylised Facts","content":"\u003cp\u003eData for the analysis were obtained from the Annual Survey of Industries (ASI) compiled by the Economic and Political Weekly Research Foundation (EPWRS). The ASI provides statistical information on the organised manufacturing sector in India. The variables taken for the purpose of analysis are the number of total employees, emolument (taken as a proxy of wage), fixed capital, working capital, and Gross Value Added (GVA). The fixed capital, working capital, and GAV series are made at constant prices using the wholesale price index, whereas emoluments are made at a constant price with the help of the consumer price index of Industrial Workers. For both indices, 2005 was taken as the base year. The CPI and WPI with different base years were connected using linking factors. The analysis was done for twenty-one major states of India for the period 1998–2023. Some stylized facts about the data are presented in the following subsection. The growth trend of the studied variables is shown in Table 1.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGrowth Trend of Studied Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eGrowth Trend (Log Y\u003csub\u003et\u003c/sub\u003e= a+ bt)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1998–2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2009–2014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015–2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed Capital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0193***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0243***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0137***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0304***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking Capital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0386***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0378***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmolument\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e = 0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eSource- Author’s Calculation (Based on ASI data)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eShare of Emolument and Profit in GAV for the Organised Manufacturing Sector in India\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShare of Emolument in GVA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShare of Profit in GVA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1998–1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1999–2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000–2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2001–2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2002–2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2003–2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2004–2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005–2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2006–2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2007–2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008–2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009–2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010–2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011–2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012–2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2013–2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014–2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015–2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016–2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017–2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018–2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019–2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020–2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021–2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eSource- Based on Data from ASI\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\u003cp\u003e\u003cstrong\u003eTable 2- Descriptive Statistics of the Variables used\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6528%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2435%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0259%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLn(Working Capital)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6528%;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2435%;\"\u003e\n \u003cp\u003e14.75692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0259%;\"\u003e\n \u003cp\u003e1.338757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e10.39398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e17.49786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLn(Fixed Capital)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6528%;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2435%;\"\u003e\n \u003cp\u003e13.48312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0259%;\"\u003e\n \u003cp\u003e1.250725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e9.264824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e16.39199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLn(Employment)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6528%;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2435%;\"\u003e\n \u003cp\u003e12.73813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0259%;\"\u003e\n \u003cp\u003e1.04663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e10.04603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e14.84324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLn(Emoluments)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6528%;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2435%;\"\u003e\n \u003cp\u003e12.47369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0259%;\"\u003e\n \u003cp\u003e1.151179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e9.424538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e14.90749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLn(output)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6528%;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2435%;\"\u003e\n \u003cp\u003e15.75066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0259%;\"\u003e\n \u003cp\u003e1.117889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e11.9924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0898%;\"\u003e\n \u003cp\u003e18.15391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBased on ASI data\u003c/p\u003e\n\n\u003cp\u003eThe trend analysis indicates consistent growth in all variables during 1998–2008 (Pre-Crisis Period). GVA experienced the highest growth rate at 3.78 percent annually, followed by working capital at 3.5%, while employment grew by only 1.3 percent annually in this period. The growth rates of fixed capital and emoluments were 1.9 percent and 1.8 percent, respectively. During 2009–2014, employment growth accelerated to 3.04 percent, suggesting a recovery-driven increase in hiring. Fixed capital also rose to 2.43 percent during this period. However, growth in working capital nearly stalled at 0.48 percent, reflecting cautious financial management. The growth rate of emoluments increased to 3 percent over this period. In recent years, from 2015 to 2023, the growth patterns of these variables diverged. Employment surged to 5.4 percent, while emolument growth declined to 2.1 percent, possibly due to an increase in contractual workers. Working capital rebounded strongly to 3.86 percent. Conversely, growth in fixed capital investment dropped sharply to just 0.35 percent and became statistically insignificant. The growth of GVA remained moderate but stable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Growth Trend in the Share of Emolument and Profit in GAV of the Manufacturing Sector in India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe share of emoluments and profits in GAV for the organised manufacturing sector is shown in Fig. 1. The figure shows significant changes in the share of profits in this sector. However, for emoluments, the variation is not as significant as for profit. From 1998-99 to 2001-02, there was a decline in the percentage share of profit in total GAV, whereas an improvement in the share of emolument was visible in this period. The profit share declined from 36.25 percent in year 1998-99 to 22.11 percent in the year 2001-02 whereas the emolument share increased from 24.39 percent in the year 1998-99 to 26.79 percent in year 2001-02. However, this pattern reversed in the subsequent period. The profit share showed an increasing trend, whereas the emolument showed a declining trend from 2002-03 to 2007-08. Profit share increased by 46.16%, and emolument share shrank to 17.15 percent in year 2007-08.\u003c/p\u003e\n\u003cp\u003eHowever, this increasing trend in profit share cannot be sustained in the following period. The share remains at 21.33 percent in the year 2022-23. The share of emoluments remained between 17% and 18 percent during this period. This graph shows no trade-off between profit share and emolument during the study period.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e3.2 Growth of Labour and Capital\u003c/h2\u003e\n \u003cp\u003eThe growth rates of labour and capital in the organised manufacturing sector during the study period are shown in Fig. 2. Labour value is the total number of persons employed in the sector, whereas capital value is the sum of fixed capital and working capital. The growth rates of labour and capital show that the growth rate of labour is significantly higher than the growth rate of capital. Capital is taken as the total fixed and working capital. The average annual growth rate of labour during 1998–2022 was 3.15 percent. This growth jumped significantly in 2023 when the growth rate of labour increased by around 200 percent. However, the average annual growth rate of capital for period 1998–2022 was 5.9 percent, which was just 2.98 percent by 2023. As 2023 is an exceptional year, we excluded it from the figure.\u003c/p\u003e\n \u003cp\u003eThis figure shows a significant gap between the two growth rates. The growth rate of capital was higher than the growth rate of labour during 2004–2011. However, it started reducing during 2012–2015. Again, during 2014–2021, a widening gap between the two growth rates was visible. However, this trend reverses after 2021, when we notice that the growth rate of labour is higher than that of capital. This reversal can be attributed to the impact of COVID-19 on the manufacturing sector. In 2020, when the lockdown was imposed, there was a sharp decline in labour and capital growth. In 2022, when the situation started to become normal, the growth of labour outperformed the growth of capital.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Research Methodology","content":"\u003cp\u003eThe employment elasticity of the organised manufacturing sector is estimated using the following model-\u003c/p\u003e\u003cp\u003e\u003cem\u003eL(Employment) = α\u0026thinsp;+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eL(GVA)\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+ β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eL(Fixed Capital) + β\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eL(Working Capital) + β\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eL(Emolument)+ε -------------------------(1)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAll variables were taken in a natural logarithmic form (prefixed with L). The β coefficients are estimated employment elasticities with respect to the independent variables. The independent variables considered for this model are gross value-added (GVA), fixed capital, working capital, and emulation. Fixed capital includes capital in the form of assets such as land, plant, machinery, buildings, vehicles, transportation, and rental capital. The increase in fixed capital can also be referred to as a technological advancement to increase firm productivity. An increase in fixed capital can have both positive and negative effects on employment generation.\u003c/p\u003e\u003cp\u003eWorking capital is the monetary value of the total working capital used in the manufacturing sector. Working capital is the most essential component of manufacturing operations. It ensures that the business meets its short-term obligations, such as payment to suppliers, employees, and other expenses, which helps to maintain the smooth functioning of the firm. A smooth availability of work helps to increase employment, whereas constrained working capital financing of a firm affects its job creation (Dao and Tiu, 2017). A positive coefficient of β\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e indicates unconstrained working capital financing and a positive impact of employment generation in the manufacturing sector.\u003c/p\u003e\u003cp\u003eA prior expectation of \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026gt;\u0026thinsp;0, β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0, β\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026gt;\u0026thinsp;0, β\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0\u003c/p\u003e\u003cp\u003eThe study is based on panel data taken from 21 major states between 1999 and 2023 from the Annual Survey of Industries. This period is considered for the analysis, as the effect of liberalisation is well noticed after 1999.\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003eThe objective of this study is to investigate the factors affecting employment elasticity in the organised manufacturing sector, using a panel of twenty-two major states of India. A Lagrange multiplier test was conducted to determine whether the pooled model was appropriate for the analysis. The results showed that the pooled model was not appropriate because the data had significant individual and time effects. The Hausman test was performed to determine if the random-effects model should be used. The test results show that the random effects model is appropriate for the analysis of this dataset. The Levin-Lin Chu test was performed to determine whether the series was non-stationary.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTest Used\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTest Result\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIf the pooled model is appropriate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLagrange Multiplier test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;2.2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe Pooled model is not appropriate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIf the Random effect model is appropriate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHaussman test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.1832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe random effect model is appropriate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStationarity of employment series\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevin-Lin Chu test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe series is not stationary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStationarity of GVA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevin-Lin Chu test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe series is stationary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStationarity of Fixed Capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevin-Lin Chu test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe series is stationary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStationarity of Working Capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevin-Lin Chu test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe series is not stationary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStationarity of Emoument\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevin-Lin Chu test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u0026thinsp;=\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe series is stationary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource- Based on the Author\u0026rsquo;s Calculation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe employment and working capital series are found to be non-stationary. The different values of these series were taken to avoid the problem of spurious regression.\u003c/p\u003e\u003cp\u003eWe estimated Eq.\u0026nbsp;1, which shows the relationship between employment generation and output, fixed capital, working capital, and emoluments. The analysis was done over four time periods. Column 1 shows the result for the period 1998\u0026ndash;2023. Column 2 presents the results for the period 1998\u0026ndash;2008. Column 3 shows the result for the period 2009\u0026ndash;2014, whereas column 4 shows the result for the period 2015\u0026ndash;2023. The total period is divided into sub-periods, considering the structural transformation during those periods. The year 2008 was considered a break because of the global slowdown during that period. The year 2014 is considered because during the period 2015\u0026ndash;2023, a lot of structural changes occurred in the economy of India. For example, changes in the central government, the implementation of GST, demonetization, and the spread of COVID-19.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDeterminants of the employment elasticity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003cp\u003ePeriod- 1998\u0026ndash;2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003cp\u003ePeriod- 1998\u0026ndash;2008\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003cp\u003ePeriod- 2009\u0026ndash;2014\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003cp\u003ePeriod- 2015\u0026ndash;2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.226**\u003c/p\u003e\u003cp\u003e(-2.6300)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2832*\u003c/p\u003e\u003cp\u003e(-2.1919)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0771\u003c/p\u003e\u003cp\u003e(0.5835)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1201\u003c/p\u003e\u003cp\u003e(-0.9912)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog(GVA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0348*\u003c/p\u003e\u003cp\u003e(2.291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.059*\u003c/p\u003e\u003cp\u003e(2.3560)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3565***\u003c/p\u003e\u003cp\u003e(5.5652)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2372**\u003c/p\u003e\u003cp\u003e(3.0007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog(Fixed Capital)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0097\u003c/p\u003e\u003cp\u003e(1.323)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0267*\u003c/p\u003e\u003cp\u003e(2.1814)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0034\u003c/p\u003e\u003cp\u003e(-0.1914)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0107\u003c/p\u003e\u003cp\u003e(1.1856)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog(Working Capital)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1244***\u003c/p\u003e\u003cp\u003e(4.338)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1448***\u003c/p\u003e\u003cp\u003e(3.8058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1642**\u003c/p\u003e\u003cp\u003e(3.2413)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0021\u003c/p\u003e\u003cp\u003e(-0.223)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmolument\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0346*\u003c/p\u003e\u003cp\u003e(-2.401)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0776***\u003c/p\u003e\u003cp\u003e(-3.6022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0028\u003c/p\u003e\u003cp\u003e(0.8856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5460***\u003c/p\u003e\u003cp\u003e(4.0726)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value of f- Statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.8034e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1519e-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.7899e-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.9572e-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW test Statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource- Based on Author\u0026rsquo;s Estimate\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGross Value Added (GVA) has a significant positive impact on employment growth. Overall, Model 1 shows that a one percent increase in GVA increases the employment growth rate by 0.0348 percent. The piecewise regression analysis also showed a positive impact. During 1998\u0026ndash;2008 the coefficient is 0.059 that shows a one percent increase in GVA increases employment growth by 0.059 percent. This rate increases to 0.36 percent during 2009\u0026ndash;2014. However, it has decreased to 0.24 percent during 2015\u0026ndash;2023. Analyses of fixed capital over employment growth do not show any significant impact in any model except for Model 2, which is for the period 1998\u0026ndash;2008. For this period, the growth of fixed capital increases employment growth by 0.027 percent. The growth of fixed capital has a negative impact during 2008-15 as expected, but the value is not significant.\u003c/p\u003e\u003cp\u003eWorking capital has a significantly positive impact on the overall model. With a one percent increase in working capital, employment growth increased by 0.124 percent in the overall model. This is 0.14 percent during 1998\u0026ndash;2008 and 0.162 during 2009-14. However, the value becomes insignificant during 2015\u0026ndash;2023. This positive value was in accordance with our expectations. An analysis of the impact of emoluments (wages) on employment shows a negative relationship. Overall period, a one percent increase in the emolument decreases employment growth by 0.035 percent. The value was \u0026minus;\u0026thinsp;0.078 percent during 1998\u0026ndash;2008. The effect became insignificant during 2009\u0026ndash;2014. The effect became insignificant during 2009-14. However, Model 3, for the period 2015-23, shows a significant positive association between employment growth and emolument.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyses the factors affecting employment elasticity of the organised manufacturing sector in India by taking a panel of twenty-two major Indian states in terms of production. The factors considered for the analysis are real wages in terms of emolument, value added, fixed capital, and working capital. While previous studies have analysed the impact of output and fixed capital on employment generation, this study also considers working capital as an important factor that could affect employment growth. The analysis shows that GVA, fixed capital, working capital, and emoluments influence the growth of employment in the organised manufacturing sector. Like Majumdar and Sarkar (2004) and Das and Basu (2016), this study also finds a positive association between GVA and employment elasticity. The analysis also finds a negative association between employment elasticity and emoluments for the period 1998\u0026ndash;2023, which resembles the studies of Khan (2005), Webster (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and Dube et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the coefficient is found to be positive for the period 2015\u0026ndash;2023, which seems to be the effect of various structural changes during this period. In 2014, the Make in India initiative was launched, which created a positive manufacturing environment and demand for skilled labour, even at higher wages. Apart from that, Post-demonetisation (2016) and GST implementation (2017) pushed more manufacturing units into the organised sector that started paying higher wages but also faced greater compliance requirements and competition. However, the sector was badly impacted due to the outbreak of COVID-19, which made the sector more vulnerable to economic changes. During post-covid period government\u0026rsquo;s extensive support to the manufacturing sector in the form of the Production Link Incentive (PLI) scheme, tax and regulatory reform, etc., helped to boost the sector.\u003c/p\u003e\u003cp\u003eThe results for working capital show a significant positive and increasing magnitude in this study. This shows that an adequate supply of working capital could play a significant role in employment growth in the manufacturing sector. In the scope of policy focusing on increasing employment levels, ensuring the availability of sufficient working capital could play a significant role in this direction. Furthermore, the impact of fixed capital on employment generation was not found to be significant. The value is significant only for the period 1998\u0026ndash;2008, which shows that capital formation has helped increase employment growth, but for the subsequent period and for the overall period, fixed capital does not show a significant impact. This could be attributed to two reasons, one is the lack of capital formation in the sector and the other is that even if capital formation is happening in the sector is not replacing the labour force, which is similar to Meheta and Awasthi (2019). Further studies could be conducted to analyse capital formation in the sector.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study indicates that capital formation in the organised manufacturing sector in India has contributed to employment generation in the short term, but its overall impact remains insignificant over the entire period, possibly due to inadequate capital formation or the inability of capital to replace labour. Additionally, factors such as GVA and working capital positively influence employment elasticity, whereas emolument shows a negative association over the entire period, but a positive trend in recent years. This can be attributed to the high level of uncertainty in this sector over recent years. Overall, enhancing working capital in the sector and understanding sector-specific capital formation dynamics could play crucial roles in fostering sustainable employment growth in this sector. Moreover, this study did not distinguish between permanent and contractual employment. Further studies could be conducted to determine which of the two has recently been promoted in the sector. This study recommends prioritising the availability of working capital to stimulate employment growth in the manufacturing sector. Additionally, efforts must be made to increase capital formation through targeted investment and financial support.\u003c/p\u003e\u003cp\u003eThe present study reveals that capital formation within India's organised manufacturing sector has facilitated short-term employment generation. However, its overall impact remains negligible over the entire period, potentially due to insufficient capital formation or the inability of capital to substitute labour effectively. Furthermore, factors such as Gross Value Added (GVA) and working capital exhibit a positive influence on employment elasticity, whereas emoluments demonstrate a negative association throughout the period, albeit with a positive trend in recent years. This trend may be attributed to heightened uncertainty within the sector in recent years. Overall, augmenting working capital and comprehending sector-specific capital formation dynamics could be pivotal in promoting sustainable employment growth within this sector. Additionally, this study does not differentiate between permanent and contractual employment. Future research could explore which type of employment has been more prevalent in recent years. The study recommends prioritising the availability of working capital to stimulate employment growth in the manufacturing sector. Furthermore, efforts should be directed towards enhancing capital formation through targeted investment and financial support.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003eThe authors declare no conflicts of interest regarding the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding-\u003c/strong\u003e The authors received no financial support for the research, authorship, or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlimov, A. (2015b). 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Migration, housing market, and labor market responses to employment shocks. \u003cem\u003eJournal of Urban Economics\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(2\u0026ndash;3), 267\u0026ndash;284. https://doi.org/10.1016/j.jue.2012.05.006\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Employment elasticity, Manufacturing Sector, Working Capital, Fixed capital, Wage, Panel Data Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7064579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7064579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Indian manufacturing sector is considered one of the main drivers of employment creation and has shifted the labour force from agriculture to a more productive manufacturing industry. However, the sector has experienced significant economic reforms that have affected its employment generation capacity. This study aims to quantify the employment elasticity trend in India’s organised manufacturing sector post-economic reform and determine the relative impact of wages, fixed capital and working capital on employment generation across different periods. The study employs panel data analysis using data from twenty-one major Indian states, covering the period from 1998 to 2023. The random effect model is utilised to estimate the employment elasticity coefficient. The analysis incorporates sub-period estimation to capture structural breaks following major economic reforms. The findings reveal that working capital positively impacts employment growth in the sector. However, this effect varies across different time horizons. The study suggests that adequate availability of working capital in the industry could substantially boost employment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e- C23, J20, L60\u003c/p\u003e","manuscriptTitle":"Employment Elasticity in India's Organised Manufacturing Sector: Evidence from State-level Panel Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 15:37:41","doi":"10.21203/rs.3.rs-7064579/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f9041ef-1f47-4668-ac28-06794cd33abb","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T16:15:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 15:37:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7064579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7064579","identity":"rs-7064579","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}