Growth and Structure of Workforce in India

Motkuri, Venkatanarayana; Naik, Suresh

doi:10.1177/0019466216652753

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…Apart from the different reasons identified in the existing literature, the factor intensity condition and work preference of women might also explain the falling FLFP in India. According to Census data, in 2011, 49.8 per cent of males and 65.1 per cent of females were engaged in agriculture (as cultivators and agricultural labourers) while 50.1 per cent of males and 34.9 per cent of females were employed in non-farm sector and household industry (Motkuri and Naik, 2016), indicating that agriculture is more female labour intensive in India. Second, there are evidences to confirm the conservative attitude in the Indian society regarding women's outside work.…”

Section: Discussionmentioning

confidence: 99%

Trade liberalization and gender inequality: role of social norms

Mukhopadhyay¹

2018

IGDR

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Purpose The purpose of this paper is to examine the effects of trade liberalization on gender earning differentials and female labour force participation by considering the interaction between changes in relative wages, intra-household bargaining power and social norms. Design/methodology/approach A three-sector general equilibrium model is developed where female labour supply is determined as a collective household decision and depends on male and female wages and intra-household power distribution. On the other hand, the effect of power distribution on female labour supply depends on social norms. Findings Comparative static analysis shows that a tariff cut may reduce female labour force participation and widen gender earning inequality if (i) the agricultural sector is more male labour-intensive than the informal sector, and the marginal utility of the woman from household work is higher than that of the man or (ii) the agricultural sector is more female labour-intensive than the informal sector, and the marginal utility of the woman’s household work is higher to the man than the woman. Policies to raise the empowerment of women might lead to favourable labour market outcomes for women if the marginal utility of the woman’s household work is higher to the man than the woman irrespective of the factor intensity condition. Research limitations/implications The results signify that the effect of trade liberalization hinges on both factor intensity conditions and the relative work preferences of women vis-à-vis men, which in turn is shaped by social norms. Originality/value The paper contributes to the scant theoretical literature on labour market consequences of trade liberalization by considering the gender equality implications of trade liberalization from a supply side perspective. The results of the model are used to explain the recent gendered labour market consequences in India in the aftermath of trade liberalization.

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Section: Discussionmentioning

confidence: 99%

Trade liberalization and gender inequality: role of social norms

Mukhopadhyay¹

2018

IGDR

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“…There are many research achievements in economic structure and economic growth ( [23][24][25][26][27][28], creating employment opportunities, promoting economic growth and industrial evolution are important reading materials for policymakers and relevant researcher (Audretsch, D. and Thurik, R., 1999) [29]. Population structure is an important part of economic growth (Motkuri, V. and Veslawatha, S. N., 2013; Soni, S. and Subrahmanya, M., 2020) [30,31]. If the population structure is differentiated by age, the population structure can affect economic growth through various channels (Wongboonsin, K., and Phiromswad, P., 2017) [32].…”

Section: Structural Deviation and Economic Growthmentioning

confidence: 99%

Industrial Structure, Employment Structure and Economic Growth—Evidence from China

2023

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This paper takes China’s economic data from 1978 to 2020 as a sample, combined with indicators such as a correlation coefficient, degree of deviation, and employment elasticity, to carry out a correlation analysis on employment structure, industrial structure, and economic growth. On this basis, a regression model is established to characterize the impact of economic structure deviation on economic growth, and the linkage relationship between employment structure, industrial structure, and economic growth is further explored through a vector self-regression model. The research results show that: the degree of deviation of China’s economic structure is weakening, and the economic structure is continuously optimized; the improvement of the degree of deviation of the economic structure has a significant contribution to China’s economic growth; the impact of the industrial structure on the employment structure is first strong and then slow; the impact of economic growth on the industrial structure is lagging and long-term; the impact of the optimization of the employment structure on economic growth is most obvious in the early stage; the impact of economic growth on the employment structure is more direct; the adjustment of the industrial structure shows a certain stickiness; and that economic transformation and high-quality development require continuous advancement. This paper describes the impact of structural deviation on economic growth and reveals the interactive impact of industrial structure, employment structure, and economic growth, which can provide decision-making reference for policy makers. Specifically, it includes formulating policies to realize skills spillover to improve the employment structure; formulating policies to promote industrial integration to optimize industrial structure; formulating policies to encourage innovation so as to promote the improvement of total factor productivity and then drive the adjustment and optimization of industrial structures and employment structures; and finally, encouraging policy makers to ensure the persistence and consistency of relevant policies so that the effects of relevant policies can be truly realized.

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“…In contrast, there is a clear trend of a negative working effect getting more prominent in the earlier age groups, which is supported by an increasing prevalence of higher education and government intervention. In the context of increasing demand for education, a considerable proportion of the younger population is either withdrawing from the workforce or postponing their entry into the workforce to attend educational institutions and pursue higher studies (Motkuri & Naik, 2016). The positive contribution of working effect at higher ages (age above 55), especially for females, is encouraging from a population with increasing longevity.…”

Section: Decomposition Of Change In Working Life Years Decomposition ...mentioning

confidence: 99%

Change in the Span of Working Life in India: An analysis by Decomposition

Saikia

Saikia²

2023

Indian Journal of Human Development

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Insights on economically active populations form a strong foundation for accurate budgeting, projections and policymaking decisions for a nation. We analysed workforce participation and decomposition of the change in working life years in India. An extension of Arriaga’s method of decomposition, as used by Nusselder and Loonman (2004) on healthy life expectancy, is used on working life years. The study highlights the decrease in the number of person-years lived in the working state for both genders. The positive contribution from decline in mortality is offset by negative contributions from workforce participation. The dynamics of working life years vary vastly by gender and states in India. In India, the increase in longevity has not translated fully into economically active years. The study highlights the contributing factors towards change in working life years in India, which are important for policy-making decisions.

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Growth and Structure of Workforce in India

Abstract: Census 2011 brings new dimension to ongoing debate on the decline in the growth

Cited by 14 publications

References 3 publications

Trade liberalization and gender inequality: role of social norms

Trade liberalization and gender inequality: role of social norms

Industrial Structure, Employment Structure and Economic Growth—Evidence from China

Change in the Span of Working Life in India: An analysis by Decomposition

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