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2020 | OriginalPaper | Buchkapitel

Technology, Jobs and Inequality: Evidence from India’s Manufacturing Sector

verfasst von : Radhicka Kapoor

Erschienen in: Accelerators of India's Growth—Industry, Trade and Employment

Verlag: Springer Singapore

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Abstract

Faced with easier access to foreign technology and imported capital goods, firms in India’s organized manufacturing sector adopted advanced techniques of production leading to increasing automation and a rise in the capital intensity of production. This has raised much concern about the ability of the manufacturing sector to create jobs for India’s rapidly rising largely low-skilled and unskilled workforce. However, what has attracted less attention in the literature is the impact of capital-augmenting technological progress on the distribution of income and wage inequality. This paper attempts to fill this gap using enterprise-level data from the Annual Survey of Industries. We find that with growing capital intensity of production, the role of labour vis-à-vis capital has declined. The share of total emoluments paid to labour fell from 28.6 to 17.4% of gross value added (GVA) between 2000–2001 and 2011–2012, while, the share of wages to workers in GVA declined from 22.2 to 14.3%. Importantly, even within the working class, inequalities have increased. The share of skilled labour (supervisory and managerial staff) in the wage pie rose from 26.1 to 35.8%, while, that of unskilled labour (production workers) fell from 57.6 to 48.8% of total wage bill. However, it is not just the growth of capital intensity but another important, though independent change in the labour market (i.e. the rising share of contract workers) that explains rising inequality. Our results also underline the existence of capital-skill complementarity: firms with higher capital intensity employed a higher share of skilled workers and the wage differential between skilled and unskilled workers was higher in these firms.

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Fußnoten
1
The National Sample Survey Organization's survey of unorganized manufacturing enterprises covers firms in the unorganized sector but data on this is available only quinquennially.
 
2
Capital intensity is defined as the ratio of real fixed capital to total persons engaged. Capital is measured by fixed capital as reported in ASI. This represents the depreciated value of fixed assets owned by the factory on the closing day of the accounting year. It is deflated using WPI for machinery and equipment. Total persons engaged include workers (both directly employed and employed through contractors), employees other than workers (supervisory, managerial and other employees) and unpaid family members/proprietor etc.
 
3
In order to classify industries as labour or capital intensive, we calculate the capital intensity for all industries in the organized manufacturing sector for every year from 1999 to 2011. An industry is classified as labour intensive if its capital intensity is below the median value for the manufacturing sector throughout the decade. Similarly, an industry is classified as capital intensive if its capital intensity is above the median value for the manufacturing sector throughout the decade. The remaining industries are classified as ambiguous.
 
4
It is beyond the scope of this study to understand the impact of interest rate policy on these estimates.
 
5
The supervisory and managerial staff reported in the ASI dataset captures the category of skilled workers, while the production workers capture unskilled workers.
 
6
The number of contract workers in the organized manufacturing sector increased from 1.17 million in 2000–2001 to 3.04 million in 2010–2011, while the number of directly employed workers increased from 4.55 to 5.91 million over the same period. The total persons engaged increased from 7.42 to 11.41 million.
 
7
Meschi et al. (2011).
 
8
It may well be the case that this measure is an underestimate of the wage gap since production workers may include some skilled workers.
 
9
Capital is deflated using the WPI created for industry, NIC 29.
 
10
Small firms are defined as those having less than 50 employees, medium firms have 50–199 employees and large firms are defined as those having 200 or more workers.
 
11
The tele-density variable captures the state-wise telephones statistics per 100 population.
 
12
There are also no compelling theoretical reasons to expect technological change always and everywhere to be skill-biased. On the contrary, if replacing skilled workers is more profitable, new technologies may attempt to replace skilled workers, just as interchangeable parts did.
 
13
They examine state-level indexes of labour regulations developed by Besley et al. (2008), and OECD (2007). The Besley and Burgess measure relies on amendments to the IDA as a whole. Bhattacharjea’s measure focuses exclusively on Chapter VB of the IDA—i.e., the section that deals with the requirement for firms to seek government permission for layoffs, retrenchments, and closures. Bhattacharjea considers not only the content of legislative amendments, but also judicial interpretations to Chapter VB in assessing the stance of states vis-à-vis labour regulation. The OECD study is based on a survey of experts and codes progress in introducing changes in recent years to not only regulations dealing with labour issues, but also the relevant administrative processes and enforcement machinery. The regulations covered by the survey go well beyond the IDA and include the Factories Act, the Trade Union Act, and Contract Labour Act among others.
 
14
Andhra Pradesh, Rajasthan, Tamil Nadu, UP and Karnataka are classified as having flexible labour regulations. Maharashtra, Orissa and West Bengal are classified as having inflexible labour regulations. Assam, Bihar, Gujarat, Haryana, Kerala, Madhya Pradesh and Punjab are classified as the neutral states.
 
15
These wages are determined by respective state governments and vary across states and over time—background as to how minimum wages are determined.
 
16
The null hypothesis of each Stock and Yogo’s tests is that the set of instruments is weak. To perform these tests, we must first choose either the largest relative bias of the 2SLS estimator we are willing to tolerate or the largest rejection rate of a nominal 5% Wald test we are willing to tolerate. Since the test statistic exceeds the critical value in each case, we can conclude that our instruments are not weak.
 
17
The Sargan&Basmann’s chi-square test reports a statistically significant test statistic when we include real minimum wages as an instrument, suggesting that we either have an invalid instrument or incorrectly specified structural equation.
 
18
In this equation, we use the log of real minimum wages as an instrument since the Sargan&Basmann’s chi-square test report a statistically insignificant test statistic.
 
19
Here, we cannot use the log of real minimum wages as an excluded exogenous variable as the Sargan & Basmann’s chi-square test report a statistically significant test statistic. It needs to included in the structural equation.
 
20
Firm size is largely driven by the production workers and not non-production workers, as the latter are quite small as a percentage of total persons engaged.
 
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Metadaten
Titel
Technology, Jobs and Inequality: Evidence from India’s Manufacturing Sector
verfasst von
Radhicka Kapoor
Copyright-Jahr
2020
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-32-9397-7_14

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