Introduction

The distribution and location of industrial activity across regions is a crucial aspect of economic development. The spatial distribution of economic activity is actually the Growth Pole Theory. The former posits that economic development is polarized and occurs through the emergence of growth poles with varying intensities, while the latter highlights the importance of factors such as transportation costs, demand function, and economies of scale in shaping the location of industrial plants. Through an analysis of the West Bengal region in India, this study explores the applicability of this theory and considers additional factors that may influence industrial location, such as access to raw materials, labor, and capital, as well as geographical factors like weather and landscapes. The study finds that the southern part of West Bengal, with its concentration of core industries, linked industries, and growth poles, is a hub of economic activity, while the northern part is dominated by agriculture and has limited industry. Overall, this study provides insights into the complex and multifaceted factors that shape the location of industrial activity and their implications for regional economic development.

Literature review

Previous studies have found significant gaps in income, workforce composition, and industrial development among different states and regions of India. These findings underscore the importance of promoting inclusive growth and addressing the underlying sources of socio-economic disparities in India.

Nayak et al., (2010) conducted a study that examined the Gini coefficient of India between 1980 and 2007. The authors found that the Gini coefficient had increased significantly over time, indicating an increase in the disparity rate of national state domestic product with respect to per capita national income. Sharma and Khosla (2013) investigated inter-state disparities in the Indian industrial sector and identified a significant gap between states that has become more noticeable over time. The authors suggested that the disparity could be attributed to differences in the level of industrialization and infrastructure development among different states.

Gradín (2018) conducted a study that investigated the sources of variability in India and found that the composition of the workforce in each state was strongly associated with inequality gaps. The author suggested that policies aimed at improving educational and employment opportunities for averageized sections of the population could help reduce socio-economic disparities in India. Similarly, S.N. Nandy (2019) found socio-economic disparities among various states, regions, and sectors in India, with the southern and north-western states performing better than the eastern and central parts of India, and the north-eastern states still lagging behind.

To contribute to a better understanding of regional and industrial disparities in India, the present research will focus on six manufacturing industrial states and conduct a comparative disparity study between Output-Capital Ratio, Capital-Labor Ratio, and Output-Labor ratio. The study will also compare the results of different indices to arrive at more accurate findings. Additionally, we will examine the disparity level of the per-capita income rate state-wise to provide a comprehensive analysis of regional and industrial disparities in India.

The literature review discussed various studies that have examined the issue of regional and industrial disparity in India. The studies highlighted the significant gaps in income, workforce composition, and industrial development among different states and regions of India. Additionally, the studies found that the Gini coefficient had increased significantly over time, indicating an increase in the disparity rate of national state domestic product with respect to per capita national income. Differences in the level of industrialization and infrastructure development among different states were also found to contribute to the disparity.

The studies suggested that policies aimed at improving educational and employment opportunities for the marginalized sections of the population could help reduce socio-economic disparities in India. The present research aims to contribute to this understanding by focusing on six manufacturing industrial states and conducting a comparative disparity study between Output-Capital Ratio, Capital-Labor Ratio, and Output-Labor ratio. Additionally, the study will examine the disparity level of the per-capita income rate state-wise to provide a comprehensive analysis of regional and industrial disparities in India.

Overall, the literature review highlights the persistent issue of regional and industrial disparity in India and underscores the need for more detailed analyses of various sectors of the economy and various sections of the population to promote inclusive growth. The findings of the present research can inform policy interventions aimed at promoting more inclusive growth and reducing socio-economic disparities in India.

Study background

The spatial distribution of economic activities and inter-regional intra-industrial disparity have been subjects of interest in economic geography. However, recent research suggests that the distribution of industries is also solely based on these three factors: average efficiency of labor, average efficiency of capital and capital intensity.

In addition to the Growth Pole Theory, proposed by Perroux, emphasizes that economic development is not uniform across a region, but rather specific poles or clusters of economic activities exist. Higgins and Savoie (2018) argue that imbalances between industry and geographical areas can hinder economic growth, particularly in regions with varying weather and landscapes. Furthermore, Slusarciuc (2015) notes that development does not appear everywhere at once, but rather occurs at specific points on growth poles with varying intensities.

Analyzing the industrial disparity in India, Myrdal (2021) identified a circular causation process that leads to the rapid development of highly developed regions, while weaker regions tend to remain poor and underdeveloped. The main causes of the backwardness of underdeveloped regions are the strong ‘backwash effect’ and the weak ‘spread effect,’ which determine the rate of growth of lag regions.Footnote 1 Gaile (1980) used the backwash effect to describe the potential negative effects of urban growth on peripheral areas.

The present study focuses on the inter-regional intra-industrial disparity among five major industrial states in India, compared with the state of West Bengal. The issue of regional disparity is a common phenomenon in India, which can be analyzed through inter-regional and intra-industry disparities. The time duration of industrial development in a particular region plays a crucial role in determining regional disparity (Williamson, 1965). Late industrial development in a region leads to an increase in disparity.

The concept of beta convergence suggests that if a poor region grows faster than a rich region, then ultimately, the differences between regions with disparities will diminish (Solow, 1956). The neoclassical growth theory postulates that factors of production, primarily capital, are subject to diminishing returns to scale. This concept of diminishing returns is also suggested by Noorbakhsh (2006) for healthcare and education indicators. Thus, both low and high developed regions will converge over time. It is advantageous for both regions to have a development balance at a high level through the growth pole strategies.

However, in practice, there is a longer period of regional imbalances in the world than a balance at any level of development. Several factors, such as power supply, local skillful labor supply, transportation, supply of raw materials, etc., imply a development balance, which helps mitigate the unequal development of infrastructures and reduces inter-regional disparities. Consequently, industries will not only concentrate in a single region but will be spread all over the nation (O’Hara, 2008).

One of the characteristics of India is the centralization of capital (Chaudhuri et al., 2014). As a result, the concentration of output, capital stock, and employment are near urban areas (Amirapu et al., 2018). Therefore, urbanization is correlated with industrialization.

Study area

In the case of West Bengal, our study shows that the concentration of industries is limited to the southern part of the region, where there is better access to raw materials and high-skilled labor. The emergence of secondary industries and linked industries in the northern part of West Bengal contributes to regional economic diversity.

The regional disparity in the distribution of industrial plants in West Bengal is depicted in Fig. 1 through the use of coordinates. The state is divided into two sub-regions, North Bengal and South Bengal, based on the location relative to the Ganga River which runs through the middle of the state. North Bengal, consisting of the districts of Darjeeling, Jalpaiguri, Coochbehar, North Dinajpur, South Dinajpur, and Malda, is primarily an agro-dominant and rural-based economy with limited infrastructure facilities, hindering the development of industrial activities. The only industries present in North Bengal are primarily focused on tea, timber, plywood, rice, and flour milling, located primarily in the districts of Jalpaiguri and Darjeeling. In contrast, South Bengal is home to the largest industrial belt, the Kolkata-Howrah-Hoogli, and is the center of several industries such as jute, cotton, aluminum, paper, engineering, chemicals, glass, and more. The presence of resources such as the Kolkata port, coal from Ranigunj, and electricity from the Damodar Valley Corporation and Bandel, among others, make the region conducive for industrial development. In South Bengal, several private companies have established their industrial units, despite the presence of the same tax rates as in North Bengal.

Fig. 1
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Source plotted by the author from (ASI, 2017)

Plots of industrial units of West Bengal coordinate wise through spatial data analysis.

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According to data from the Annual Survey of Industry (ASI) from the years 2010 to 2017, it can be observed that some industries in West Bengal experienced growth, excluding industries such as tobacco, wood, pharmaceuticals, chemicals, basic metals (steel), computers, electronics, and motor vehicles and trailers (refer to Table 1 and page no. 17). Our examination of the inter-regional and intra-industry disparities in West Bengal and comparison with five other major industrial states in the country is aimed at providing an explanation for the growth and decline of these industries in the state.

Table 1 Growth rate of 2-digit main division industrial units in West Bengal.
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Attributes for measuring disparity

The primary objective of our research is to conduct an analysis of the disparities within six states using three ratios—the Output-Capital Ratio, Capital-Labor Ratio, and Output-Labor Ratio. The analysis aims to identify the level of disparity in capital intensity and the average efficiency of labor and capital for each industry and state.

To determine the average efficiency of labor, we use the Output-Labor Ratio, which is obtained by dividing the output (in terms of net value added) by labor (in terms of labor wages) for each industry and state. Similarly, the average efficiency of capital is determined using the Output-Capital Ratio, which is derived by dividing the output by the capital (in terms of invested capital). The Capital-Labor Ratio measures the capital intensity and indicates the amount of capital invested relative to labor. It is generally observed that firms tend to increase their Capital-Labor Ratio over time as they seek to improve production efficiency through investments in capital and automation.

To compare two industrial regions, for instance, region A and region B, equipped with the same industry groups, we follow specific guidelines with respect to the different ratios. If the output-capital ratio and the growth rate of that ratio in region B are lower than region A, then the average efficiency of capital is lower in region B. If the output-labor ratio of region B is lower than that of region A, it means that the value added per labor will result in the inequality status, and a higher return per labor will encourage additional investment in the sector, reflecting the scope of employment opportunities in region A.

If the capital-labor ratio is higher in region A than region B, the explanation calls for either a technology gap or underutilization of capacity in region B. In this respect, region A is in a better position than region B. If region B has a lower average efficiency of labor and lower capital intensity than region A, then the capacity of region B is underutilized. The concentration of capital, output, and employment will be in region A, leaving region B behind and resulting in the further development of a general crisis.

Comparative units of A will push their product into the market of B by narrowing the market for the units of B. For example, the quality product of region A is much more efficient than the product of region B. By the high quality of the product from region A, they capture the market from region B. The process will run toward non-reciprocity. Unemployment and industrial sickness will be a serious view in region B.

Urbanization in region A will be an expression of the spread effect of industrialization, while urbanization in region B will be the expression of the severity of the agrarian crisis. Therefore, the form of divergence (backwash effect) will be accentuated in terms of rural crisis. ‘Reversed technology transfer’ (that is, immigration of educated people from B to A) will be a common picture between regions.

Review of disparity measures

The analysis of disparities can be accomplished through the use of several indices, including the Gini coefficient, Ricci-Schutz coefficient, Atkinson measure, and Theil's index. This chapter will conduct a comparative study of these indices to determine the rate of disparity in per-capita income between major Indian industrial states. Moreover, the study will quantify the level of disparity in terms of average efficiency of labor and capital, as well as capital intensity, by utilizing the output-labor ratio, output-capital ratio, and capital-labor ratio, respectively. To the best of our knowledge, this chapter is the first to employ this methodology and analyze disparities using a combination of ratios and multiple indices.

The Gini coefficient, a single numerical measure, is commonly used by economists to evaluate inequalities in wealth distributions. The coefficient represents the maximum difference between the Lorenz curve and the 45° line.

The relative mean absolute difference of all pairs of items of the population divided by the average income (for example \(\dot{\mathrm{x}}\)) to normalize the scale. Suppose \({\mathrm{x}}_{\mathrm{i}}\) is the income of a person i, and the population is n, then the Gini index looks like:

$$G=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}\left|{x}_{i}-{x}_{j}\right|}{2{x}^{2}\dot{x}}$$
(1)

The Ricci-Schutz index (also referred to as the Hoover index, used in the R package) is a widely used measure of income disparity. It calculates the degree of inequality in a wealth distribution by determining the distance between the Lorenz curve and the 45-degree line. This index has a similar graphical interpretation as the Gini index and is used for Lorenz curve analysis, but it differs in its mathematical model. The mathematical interpretation is as follows:

For the Ricci-Schutz index, suppose \({\mathrm{x}}_{\mathrm{i}}\) is the income of a person i and \(\dot{\mathrm{x}}\) be the mean income. Then the index is looks like:

$$H=\frac{1}{2}\frac{\sum_{i=1}^{n}\left|{x}_{i}-\dot{x}\right|}{\sum_{i=1}^{n}{x}_{i}}$$
(2)

The Atkinson Measure is a well-known index used to quantify income inequality. This measure is based on the observed disparities in income and incorporates a normative approach by introducing a coefficient of income weight, also known as the inequality aversion parameter. The Atkinson index ranges from zero to one, with zero representing complete equality (i.e., when all individuals have the same wealth) and increasing with the level of inequality. In the case of complete inequality, where one individual possesses all the wealth and others have nothing, the Atkinson index approaches one.

The Theil Index, derived from Generalized Entropy (GE), is another widely used measure of disparity. The GE is calculated based on a sensitivity parameter that indicates the extent of distribution variation. In a special case, if the indicators are one and zero, respectively, it is known as Theil's T Index and Theil's L Index. The Theil measure is given by the difference between the maximum possible entropy and the observed entropy of the wealth distribution. Unlike the Gini Coefficient (the most widely used disparity measure in literature), Theil's Index has the property of decomposability. However, in this study, the focus will be solely on the disparity results, and the decomposability property will be disregarded.

Data

The study's data selection process involved utilizing the National Industrial Classification (NIC) system with three-digit codes to identify and collect data from all registered manufacturing industries in India. The Annual Survey of Industry (ASI) (Central Statistical Organization, 2013/2019) conducted by the Central Statistical Organization between 2010 and 2017 was the source of data for this research, including both private and public industries operating in the six major industrial states.

As per Pal (2019), we ranked the states according to their regional specialization coefficients, which ranked the most industrialized state to the least industrialized. This rank allowed us to measure the level of disparity in industries across the six states.

In line with the study's objective, we selected only those industries that were available in all six states and omitted others. We then calculated the capital-labor ratio, output-capital ratio, and output-labor ratio for each industry. To determine the ratios, we utilized value-added data as the output data, invested capital data as capital, and labor wage data as labor.

The results of the ratios' disparity between the six major industrial states were compared using four indices, namely the Gini coefficient, Ricci-Schutz coefficient, Atkinson measure, and Theli's index, to assess their similarity. This approach enabled us to investigate the extent of the disparities in the manufacturing industries' performance across the six states.

Empirical results and interpretations

We conducted an analysis of various economic indicators, including the Gini index, Ricci-Schutz coefficient, Atkinson's measure, and Theil's index, to measure disparities in the capital-labor ratio and output-labor ratio industry group-wise across selected states. Table 2 presents the five highest and lowest levels of disparity in all indices for some common industrial groups. For a comprehensive overview of the results, please refer to page no. 19.

Table 2 Comparative indices values of output-labor ratio and capital-labor ratio (Industry group wise). Source Calculating by author from annual survey of industries (ASI) data-2016–2017. APL—Output-labor ratio and CLR—Capital-labor ratio
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We used Lorenz curve analysis to demonstrate how the curves differ for each industry group at higher and lower levels of disparity. Figures 2 and 3 illustrate the disparity level of capital intensity and average efficiency of labor for two industry groups, namely, Manufacture of consumer electronics (NIC 264) and Manufacture of parts and accessories for motor vehicles (NIC 293). One is identified as having a higher level of disparity, while the other is identified as having a lower level of disparity.

Fig. 2
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Source Calculating by author from annual survey of industries (ASI) data-2016–2017

Lorenz curve analysis for industry group: (NIC 264) with capital-labor ratio and output-labor ratio.

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Fig. 3
figure 3

Source Calculating by author from annual survey of industries (ASI) data-2016–2017

Lorenz curve analysis for industry group: (NIC 293) with capital-labor ratio and output-labor ratio.

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Figures 4 and 5 compare the capital intensity and average efficiency of labor state-wise within the same industry group. For industry group NIC 264, the state-wise levels of capital intensity and average efficiency labor are not equal, indicating as per the attribute underutilization of capacity in West Bengal. Conversely, for industry group NIC 293, the figures indicate that the levels of capital intensity and average efficiency of labor are almost equal state-wise.

Fig. 4
figure 4

Source Calculating by author from annual survey of industries (ASI) data-2016–2017

State wise comparative study of capital-labor ratio and output-labor ratio for industry group: (NIC 264).

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Fig. 5
figure 5

Source Calculating by author from annual survey of industries (ASI) data-2016–2017

State wise comparative study of capital-labor ratio and output-labor ratio for industry group:(NIC 293).

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In Table 3 the second part of our interpretation, we classified the same five industry groups into two sections based on higher and lower levels of disparity and observed that almost all industries within the same group ranked consistently higher or lower in terms of disparity levels across all four types of indices, including the average efficiency of capital. The full results can be found on page no. 21. We further examined two industry groups, NIC 264 and NIC 293, and depicted the disparity levels of their average efficiency of capital using the Lorenz curve in Fig. 6.

Table 3 Comparative indices values of output-capital ratio (Industry group wise). Source Calculating by author from annual survey of industries (ASI) data-2016–2017. OCR—Output-capital ratio
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Fig. 6
figure 6

Source Calculating by author from annual survey of industries (ASI) data-2016–2017

Comparative Lorenz curve analysis for industry group: (NIC 264) and (NIC 293) with output-capital ratio.

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We calculated the growth rate of average efficiency of capital industry group-wise for every state from 2012 to 2017. Figure 7 illustrates that for industry group NIC 264, the growth rate of average efficiency of capital is lower in Gujarat, Tamil Nadu, and West Bengal compared to the other three states. Conversely, for industry group NIC 293, the growth rate of average efficiency of capital in West Bengal is higher than in the other states.

Fig. 7
figure 7

Source Calculating by author from Annual Survey of Industries (ASI) data-2016–2017. We are unable to take 2010–11 as a base year because Haryana had no data for industry group: 264

State wise comparative growth rate of output-capital ratio for industry group: Manufacture of consumer electronics (NIC 264) and (NIC 293).

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Moving on to the third part of our interpretation, we examined the rate of disparity in per-capita income between West Bengal and the other five selected states. Table 3 depicts the differences in per capita income between West Bengal and the other five states from 2010 to 2017. Using this small set of time series data, we plotted five trend lines on scattered plots. Table 4, 6 shows the equation for each trend line, organized by state. The rates of per capita income disparity are as follows: 0.47 for Maharashtra–West Bengal, 0.37 for Karnataka–West Bengal, 0.53 for Haryana–West Bengal, 0.72 for Gujarat–West Bengal, and 0.15 for Tamil Nadu–West Bengal. The R-squared values indicate a good fit with the actual data, and all the linear trend lines show a continuous rise in per capita income disparity between West Bengal and the other five states from 2010 to 2017.

Table 4 Yearly per capita income differences between West Bengal and five other states.
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Table 5 provides an overview of the yearly state-wise rank of average efficiency of labor, capital intensity, and average efficiency of capital. It is apparent that West Bengal consistently ranks lowest in output-labor ratio and output-capital ratio compared to other states since 2010. While West Bengal has the third-highest level of capital-labor ratio in 2017, indicating an improvement in capital intensity across sectors, underutilization still persists.

Table 5 Linear trend line equations from differences between West Bengal and five other states.
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Furthermore, Fig. 8 illustrates the annual per-capita profit share within the six major industrial states, indicating that West Bengal has consistently had the smallest profit share since 2010 compared to the other five states. (Table 6)

Fig. 8
figure 8

Source Calculating by author from annual survey of industries (ASI) data-2010/2017

Per-capita profit share within six different states 2010–2017 (Rs. in millions). . 1 lakh = 0.1 million

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Table 6 Comparative efficiency ratios of manufacturing industries: 2010–11 to 2016–17.
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Conclusion

Based on our findings, it appears that the manufacture of consumer electronics (NIC 264) in West Bengal has lower capital intensity and lower average labor efficiency compared to other states in India. This suggests that the region is not utilizing its capacity efficiently in this industry, resulting in an inter-regional intra-industry disparity in India, particularly in West Bengal. The poor performance of industrial activity in the region creates a barrier against new firms with advanced technologies, thereby keeping the region less industrialized.

To identify the primary causes of excess capacity industry-wise, we will investigate the responses of capital, labor, and capital intensity. Myrdal's circular and cumulative causation theory consists of three main Kaldor's laws, which discuss the effects of increasing returns in the manufacturing sector on macroeconomic dynamics. According to the first law, the growth rate of manufacturing production is positively related to GDP, and increasing returns-to-scale prevails in the manufacturing sector. These have dynamic and macroeconomic effects, including "learning by doing" and technological innovation.

The unequal distribution of high-skilled labor supply is due to the unequal distribution of per capita income and the unequal distribution of upgraded machines. If workers are not upgraded by using advanced technology, they are not offered high-paying jobs (Gradín, 2018). Advanced technology always encourages producing more efficient output, leading to more profits, revenue, and higher wages for labor. One of the reasons behind the migration of workers to other states is job uncertainty, a reduction in livelihood opportunities, and low growth rates in agro-dominated rural areas (Mahapatro, 2013).

Policies and suggestion

Various policies and schemes have been introduced in India to reduce the differences among the six major industrial states, with the central and state governments implementing various measures. One such scheme is the Pradhan Mantri Kaushal Vikas Yojna (PMKVY), a grant-based initiative launched on 2 October 2016 with the aim of providing free training and certification for skill development in more than 252 job roles, enhancing employability among school and college dropouts and unemployed youth, and improving the quality of training infrastructure. The PMKVY also seeks to encourage standardization in the certification process and create a registry of skills.

In addition, the Credit Linked Capital Subsidy Scheme (CLCSS) has been introduced by the Development Commissioner, MSME (Ministry of Micro, Small and Medium Enterprises), to support manufacturing industries. The CLCSS aims to provide an upfront capital subsidy to upgrade plants and machinery for existing and new manufacturing units that adopt eligible and proven technology approved under the scheme guidelines. The objective of the CLCSS is to promote the adoption of upgraded technologies and provide import subsidies, promote zero-defect and zero-effect practices in manufacturing processes, and encourage the adoption of new global standard technologies (Ministry of Micro, Small and Medium Enterprises, 2006).

However, these policies and schemes are not evenly promoted throughout the country, and many regions remain unaware of their benefits. To address this issue, policymakers should focus on promoting and redesigning labor training schemes. To promote schemes regionally and industry-wise, media attention such as television, banners, posters, newspapers, etc., should be used. This redevelopment process can encourage existing entrepreneurs to expand their units to different regions, while new entrepreneurs in backward regions can be motivated to establish new units, leading to job creation. This expansion and job creation will increase the demand for training camps. The training schemes should be redesigned to include not only short-term but also long-term courses and refurbished with advanced and quality training infrastructure. The successful reimplementation of these schemes requires greater involvement from state governments and the central government.

In conclusion, these policies and schemes have been introduced to address the inter-regional intra-industry disparity in India. However, more efforts are needed to promote them equally across the country. The suggestion is to focus on the use of promotion and redesign of labor training schemes and to involve all state governments with the central government in the process to achieve successful reimplementation.