Does innovation stimulate employment? Evidence from China

Zhu, Chen; Qiu, Zhiyi; Liu, Fengjun

doi:10.1016/j.econmod.2020.02.041

Cited by 37 publications

(16 citation statements)

References 30 publications

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“…With an aim of achieving increasing returns to scale, these technologies started replacing labors, which has created a slack in the labor demand from the developing and low developed economies. Rising demand for highly skilled labors in the newly emerging job opportunities gradually started crowding out the labors with low or backdated skillset, and as a result, overall labor market started shrinking, without having an impact on the output generation (Zhu et al, 2020). As a result, in mathematical terms, as per the definition provided by ILO ( 2019), the labor productivity will demonstrate a rise, without having any impact on the output generation.…”

Section: Place For Figurementioning

confidence: 99%

Analyzing global inequality in access to energy: Developing policy framework by inequality decomposition

Sinha¹,

Balsalobre‐Lorente

Zafar

et al. 2022

Journal of Environmental Management

100

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Energy poverty is a critical policymaking problem in the world, while the outlined solutions in academic and policy literature talks about the solutions, without addressing the possible cause of the problem. The interaction between labor and energy market might pave a way to address the issue. Within the context of energy poverty, this interaction might turn out to be a major roadblock in the way to attain the objectives of Sustainable Development Goals (SDGs). From this perspective, this study aims at analyzing the constituents of inequality in access to energy, and in that pursuit, it has employed Kaya-Theil Decomposition method. The study is carried out at the global level over the period of 1990-2019. The study outcomes demonstrate all the inequality components to be rising during the study period. Presence of a possible feedback loop in the association might create the Vicious Circle of Energy Poverty around the globe. This study contributes to the literature by addressing the demand-side dimension of the energy poverty issue, while using the Kaya-Theil Decomposition method as an estimator of demandside factors. Based on the study outcomes, a policy framework has been recommended, and it is aimed at helping the nations to achieve the objectives of SDG 7, SDG 8, and SDG 10.

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Section: Place For Figurementioning

confidence: 99%

Analyzing global inequality in access to energy: Developing policy framework by inequality decomposition

Sinha¹,

Balsalobre‐Lorente

Zafar

et al. 2022

Journal of Environmental Management

100

View full text Add to dashboard Cite

show abstract

“…Similarly, Gagliardi (2019) investigates the effect of foreign technological innovation on domestic employment through the industry mix of the UK over 2000-2007 and finds the reduction in employment by around 5%. Recently, Zhu et al (2021) present a dual effect of technological innovation in case of Chinese firms, i.e. process and product innovation shows the positive and negative impact, respectively, on employment opportunities.…”

Section: Impact Of Technological Innovation On Employmentmentioning

confidence: 99%

Innovational duality and sustainable development: finding optima amidst socio-ecological policy trade-off in post-COVID-19 era

Sinha¹,

Adhikari

Jha

2022

JEIM

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PurposeThis study aims to analyze the socio-ecological policy trade-off caused by technological innovations in the post-COVID-19 era. The study outcomes are utilized to design a comprehensive policy framework for attaining sustainable development goals (SDGs).Design/methodology/approachStudy is done for 100 countries over 1991–2019. Second-generation estimation method is used. Innovation is measured by total factor productivity, environmental quality is measured by carbon dioxide (CO2) emissions and social dimension is captured by unemployment.FindingsInnovation–CO2 emissions association is found to be inverted U-shaped and innovation–unemployment association is found to be U-shaped.Research limitations/implicationsThe study outcomes show the conflicting impact of technological innovation leading to policy trade-off. This dual impact of innovation is considered during policy recommendation.Practical implicationsThe policy framework recommended in the study shows a way to address the objectives of SDG 8, 9 and 13 during post-COVID-19 period.Social implicationsPolicy recommendations in the study show a way to internalize the negative social externality exerted by innovation.Originality/valueThis study contributes to the literature by considering the policy trade-off caused by innovation and recommending an SDG-oriented policy framework for the post-COVID-19 era.

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“…For example, factories start using more technology makes workers redundant (Choudhry and van Ark, 2010). Labor displacement occurs with an increase in TFP through various mechanisms like automation (Acemoglu and Restrepo, 2019), use of artificial intelligence (Gruetzemacher et al, 2020), robotization (Jung and Lim, 2020), innovation (Woltjer et al, 2021;Zhu et al, 2021), implementation of Industry 4.0 production technologies (Fromhold-Eisebith et al, 2021) and digitalization (Balsmeier and Woerter, 2019). However, although an increase in TFP is labor displacing, various "compensating mechanisms" might be labor augmenting (International Labor Organization, 2005;Calvino and Virgillito, 2018;Dosi et al, 2021).…”

Section: Theoretical Priors and Literature Backgroundmentioning

confidence: 99%

Productivity and job reallocation: evidence from the Indian manufacturing

Goswami

2022

IJM

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PurposeThis study aims to examine the association between productivity growth and job reallocation in terms of job creation and job destruction. It also finds this productivity employment relationship for heterogeneous labor market institutions of the Indian states.Design/methodology/approachThe paper uses longitudinal data of the Annual Survey of Industries of Indian manufacturing from 2009–2010 to 2015–2016 and employs fixed-effect multinomial logistic regression.FindingsThe paper finds a mixed result of total factor productivity (TFP) increase on employment. An increase in TFP is positively associated with employment by reducing the probability of destroying jobs, while adversely associated with employment by reducing the probability of creating jobs. The negative association of the increase in TFP on employment is more in the Indian states with strict labor regulations.Research limitations/implicationsThe relationship between TFP and employment can be endogenous. TFP is calculated as output changes that are not explained by inputs, capital and labor. So, job creation/destruction may affect TFP by changing the composition of employment and/or returns on labor/capital. In addition, this study is only restricted to Indian organized manufacturing.Practical implicationsThe results from this study help deeply understand the Indian labor market. In particular, it provides valuable insights into the “jobless growth” in Indian manufacturing and the recent changes in labor laws.Social implicationsThe findings from this study provide useful information to enhance the robust growth of productive jobs and efficient reallocation of labor.Originality/valueUnlike most papers, which analyzed the relationship between productivity growth and net employment changes, the present paper finds the relationship between improvement in productivity and job reallocation in terms of simultaneous creation of new jobs and destruction of existing jobs. In addition, the paper links this relationship with the existing heterogeneous labor laws of the Indian states.

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Does innovation stimulate employment? Evidence from China

Cited by 37 publications

References 30 publications

Analyzing global inequality in access to energy: Developing policy framework by inequality decomposition

Analyzing global inequality in access to energy: Developing policy framework by inequality decomposition

Innovational duality and sustainable development: finding optima amidst socio-ecological policy trade-off in post-COVID-19 era

Productivity and job reallocation: evidence from the Indian manufacturing

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