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Gendered Impact on Unemployment: A Case Study of India during the COVID-19 Pandemic

India witnessed one of the worst coronavirus crises in the world. The pandemic induced sharp contraction in economic activity that caused unemployment to rise, upheaving the existing gender divides in the country. Using monthly data from the Centre for Monitoring Indian Economy on subnational economies of India from January 2019 to May 2021, we find that a) unemployment gender gap narrowed during the COVID-19 pandemic in comparison to the pre-pandemic era, largely driven by male unemployment dynamics, b) the recovery in the post-lockdown periods had spillover effects on the unemployment gender gap in rural regions, and c) the unemployment gender gap during the national lockdown period was narrower than the second wave.

Introduction

The coronavirus disease (COVID-19) has adversely impacted labour markets all around the world. According to the International Labour Organization, the working hours lost in 2020 were equal to 255 million full-time jobs, which translated into labour income losses worth US$3.7 trillion (International Labour Organization 2021). Due to the existing gender inequalities, women were more vulnerable to the economic impact of COVID-19 (Madgavkar et al. 2020). The sudden closure of schools and daycare centres due to the Great Lockdown exacerbated the burden of unpaid care on women (Collins et al. 2020; Power 2020; Czymara et al. 2020; Seck et al. 2021). Women also disproportionately represented the accommodation, food services, and retail and wholesale trade sectors, which were worst-hit by the COVID-19 pandemic (Alon et al. 2020; Adams-Prassl et al. 2020; Bonacini et al. 2021). In most countries, women often work in these sectors without any work protection or job guarantee (United Nations Women 2020), leading them to loose their livelihoods faster than men while also dealing with their deteriorating mental health. India is an interesting case study with one of the lowest female labour force participation rates (LFPRs) globally to analyse how the COVID-19 pandemic exacerbated the pre-existing gender disparities in unemployment. According to the World Bank data, India’s female LFPRs was approximately 21% in 2019, the lowest among the BRICS nations (Brazil, Russia, India, China, and South Africa) and 26 percentage points lower than the global average. An even more troubling fact is that women’s LFPRs has been falling since the mid-2000s (Ghai 2018; Andres et al. 2017; Sarkar et al. 2019). Since the onset of the pandemic, women in India have been increasingly dropping out of the labour force. As seen in Figure 1, the greater female labour force, which comprises unemployed females who are active and inactive job seekers, has been lower than the pre-pandemic average since April 2020. The number of unemployed women actively looking for jobs has also been lower than the pre-pandemic average barring the months of April, May, and December in 2020. On the contrary, the number of women who are unemployed but inactive in their job search has risen drastically, albeit with minor fluctuations, during this period (Figure 2). A recent survey by Deloitte (2021) identified that the burden of household chores and responsibility for childcare and family dependents increased exponentially for women worldwide and more so in India due to the pandemic. The surveyed women mentioned increase in work and caregiving responsibilities as the main reasons for considering leaving the workforce.

Figure 1 : Percent Change in Female Greater Labour Force and Unemployed Active Job Seekers Compared to the Pre-pandemic Average

Source: Centre for Monitoring Indian Economy April 2020 - May 2021

Figure 2: Percent Change in Female Unemployed and Inactive Job Seekers Compared to the Pre-pandemic Average

Figure 3: Unemployment Rate in India (Percent)

Source: Centre for Monitoring Indian Economy Jan 2020 - May 2021  

This study analyses the effect of the COVID-19 pandemic on the gender unemployment gap from its onset until the second wave using the subnational-level monthly data from the Centre for Monitoring Indian Economy (CMIE). The gender unemployment gap is defined as the difference between male and female unemployment rates  ( Albanesi and Şahin 2018 ). We assess the gender unemployment gap during the COVID-19 pandemic compared to the pre-pandemic era using a difference-in-differences (DID) model. A preliminary investigation of the gender unemployment gap based on the raw data reveals that the gap declined in the lockdown period compared to the pre-lockdown period (Figure 3). We find the gender gap to widen during the second wave, albeit smaller than the pre-pandemic level.

Although a large number of national-level studies were conducted on the impact of the COVID-19 pandemic on unemployment (Estupinan and Sharma 2020; Estupinan et al. 2020; Bhalotia et al. 2020; Chiplunkar et al. 2020; Afridi et al. 2021; Deshpande 2020; Desai et al. 2021), this study is among the very first to assess the impact of the second wave of COVID-19 on the unemployment gender gap in India. A previous study found the rise in male unemployment during the lockdown period contributing to a smaller gender gap (Zhang et al. 2021). In this study, we take one step further to assess the effect of the second COVID-19 wave on the unemployment gender gap in India.

The remainder of the article is organised as follows. In Sections 2 and 3, we present the data sources and some facts on the unemployment trend in India. The effects of first and second COVID-19 waves on unemployment disaggregated by gender are discussed in Section 4. Section 5 delves into the gendered impact on unemployment dynamics across urban and rural regions. The concluding remarks are presented in Section 6.

Data and Methodology

In this study, we use the subnational-level monthly employment data from the CMIE from the period of

January 2019 to May 2021 . Starting from January 2016, the CMIE has been conducting household surveys in India on a triennial basis, covering the periods of January to April, May to August, and September to December. This is the only nationally representative employment data in the absence of official government data (Abraham and Shrivastava 2019) and has been used by several employment studies on India (Beyer et al. 2020; Deshpande 2020; Deshpande and Ramachandran 2020).

The employment data are classified into three categories—the number of persons employed, the number of persons unemployed and actively seeking jobs, and the number of persons unemployed and not actively seeking jobs. The sum of these three categories constitutes the greater labour force. The data are also disaggregated by gender (male and female) and residence (rural and urban).[1]   For the analysis, we focus on five time periods as indicated in Table 1.

Table 1: Time Periods

For state[2] i at time t, we construct the unemployment rate as given below:

Unemployment rate = Number of persons unemployed and seeking jobs/Greater labour force                                                                                                    (1)

Stylised Facts on Unemployment

This section describes some stylised facts based on the subnational unemployment data from February 2019 to May 2021. To this end, we estimate the regression model below:

where Unemp it is the unemployment rate of state i in time t . To see the unemployment dynamics over the period of study, we use a binary variable Month s that takes the value one for month s and 0, otherwise. The model takes into consideration the impact of past unemployment rates, represented by  Unemp it −1. Additionally, the state fixed effects  δ i  are included to account for unobserved, time-invariant state-level characteristics that may potentially confound our estimates.

Figure 4: Trends in Unemployment Rate

Our coefficient of interest is β 1 s which depicts the time trend in unemployment. The results from the model estimation are shown in Figure 4, in which we can see the dynamics of aggregate unemployment in India from February 2019 to May 2021. The vertical axis pertains to coefficient β 1 s , and the horizontal axis corresponds to the respective months. In Figure 4, the aggregate unemployment rate is found to be relatively stable during the pre-pandemic era. This trend faces an overhaul during the national lockdown (April–May 2020) with a structural upward shift in the unemployment rate. The shock to the unemployment rate does not persist as economic recovery during the post-lockdown period enables unemployment to fall steadily from June 2020 onwards. The unemployment rate becomes stable from January to March 2020 as the country returned to a sense of normalcy with the continued resumption of economic activity.[3]   However, the economic impact from the onset of the second wave of the COVID-19 pandemic caused the unemployment rate to rise again in April and May 2021.

Next, we estimate Equation (3) separately for the female and male unemployment rates to assess the gender differential impacts of the COVID-19 pandemic on unemployment in India.[4]

where binary variable Quarter s  takes the value one for quarter s in the time period of our sample. The model also accounts for lagged unemployment effects through Unemp it −1.

Figure 5: Trends in Unemployment Rate by Gender

Figure 5 shows that a stark gender gap in the unemployment rate (distance between the red and blue lines) exists in the pre-pandemic era as the male unemployment rate is consistently lower than that of the female. Figure 5 also shows that the gender gap dynamics are primarily driven by male unemployment. The sharp rise in male unemployment during the national lockdown causes the gender gap to close in Q2 2020. The post-lockdown recovery (Q3–Q4 2020) is found to have a favourable impact on male unemployment, causing gender gap to revert to the pre-pandemic levels. Although both males and females lost jobs during the onset of the second wave (Q2 2021), the gender gap narrowed as males are found to lose more jobs in absolute terms.

Figure 6: Trends in Urban and Rural Unemployment Rate by Gender

Figure 6 shows the estimates of β 1 s  (see Equation [3]) for urban and rural unemployment in Panels (a) and (b), respectively. During the national lockdown, the sharp rise in male unemployment is more evident in urban areas than rural. In fact, the national lockdown period dynamics in aggregate male and female unemployment in Figure 5 largely resemble the effects seen in the urban region (see Figure 6, Panel [a]). The post-lockdown recovery suits male unemployment, both in rural and urban areas. Female unemployment remains stable in rural areas during the pandemic.

Figure 7: Trends in Regional Unemployment Rate by Gender

7 c                                                                                                                                                                         

The subsample regression estimates of β 1 s  pertaining to the north, east, west and south regions are shown in Figure 7. All regions witnessed a rise in male unemployment during the national lockdown period. On the contrary, the female unemployment dynamics differ between regions. During the national lockdown period, female unemployment rose in the west and south regions (Panels [c] and [d] in Figure 7). The north region shows an interesting anomaly (Panel [a] in Figure 7). Contrary to other regions, female unemployment dipped steeply in the north during the national lockdown period. East region alone did not 

experience any strong movements in female unemployment throughout the pandemic (Panel [b] in Figure 7).

Impact of COVID-19 on Unemployment

Section 3 discussed how the overall unemployment and unemployment gender gap witnessed structural breaks during the COVID-19 pandemic. To further investigate the gender aspect of the COVID-19 unemployment dynamics in India, we begin our empirical exercise by examining the unemployment changes during the COVID-19 pandemic compared to the pre-pandemic era. We use the following model:

where Period 1 , Period 2 , Period 3 , and Period 4  pertain to lockdown, post-lockdown, post-lockdown normalcy, and second wave time periods, respectively. Besides the overall unemployment, we also estimate Equation (4) for male and female unemployment separately. The results are shown in Table 2. We can see from Column (1) of Table 2 that the overall unemployment rate ( β 11 ) witnessed an increase of 0.066 (statistically significant at one percent level) during the lockdown period in comparison to the pre-pandemic period. This effect was primarily driven by the rise in the male unemployment that shot up by 0.082 during the lockdown period (Column [3]).

The uneven distributional effects of the post-lockdown recovery are seen from β 12 estimates. Male unemployment rose by 0.01, while female unemployment fell by 0.036 in comparison to the pre-pandemic era. The fall in female unemployment does not necessarily indicate that the overall labour conditions improved for women during this period. Equation (1) shows that the unemployment rate is driven by two components. Figure 1 validates that the female unemployment rate fell over time due to the decline in the number of unemployed females actively seeking jobs being higher than the decline in the female labour force.[5]

β 14 estimate in Column (1) indicates that the total unemployment rose by 0.019 (statistically significant at 10 percent level) during the second wave compared to the pre-pandemic period. A comparison between β 14 and β 11 estimates reveals an interesting policy highlight that the second wave’s impact on unemployment was smaller than the nationwide lockdown. Finally, the rise in unemployment during the second wave is primarily driven by male unemployment.

Table 2: Impact of COVID-19 on Unemployment

Note: *** p<0.01, ** p<0.05, and * p<0.1. The robust standard errors are in parentheses.

Unemployment Gender Gap in Urban and Rural Regions

This section delves further into the gendered impact of lockdown on the unemployment dynamics across urban and rural regions. As defined in Section 1, the unemployment gender gap measures the difference between female and male unemployment rates. To identify the effect of the first and second COVID-19 waves on the unemployment gender gap, we estimate the regression model below:

where Female is a binary variable that takes the value 1 for female unemployment and 0, otherwise.

Table 3 shows the estimation results of Equation (5). We discuss the coefficient estimates that are found to be significant. The significant β 1 coefficient reiterates that the unemployment gender gap was an existential problem in India even before the COVID-19 pandemic. The β 31 estimates reveal that the urban region dynamics drove the narrow unemployment gender gap during the lockdown period. Although the magnitude of the narrowing gap during the lockdown did not persist to the post-lockdown period ( β 32 ), rural regions experienced a narrow unemployment gender gap (marginally significant at 10%). This trend continues even in the post-lockdown normalcy period ( β 33 ) as the unemployment gender gap is narrower than the pre-pandemic level by 0.047 in the rural region. This highlights the possibility that the post-lockdown recovery process had a spillover effect on the unemployment gender gap in rural regions. Finally, β 34 estimates show that the narrowing gender gap trend persists only in the urban region during the second wave.

Table 3: Impact of COVID-19 on Unemployment across Urban and Rural Regions during the post-lockdown and post-lockdown normalcy periods.

This article analyses the impact of the COVID-19 pandemic vis-à-vis the pre-pandemic period on the gender unemployment gap. Our findings indicate that the gender gap in unemployment narrowed during the COVID-19 pandemic, primarily driven by male unemployment dynamics. Interestingly, we find that female unemployment declined during the post-lockdown period. Such a decline was likely driven by women dropping out of the labour force rather than a dip in the absolute number of unemployed persons. Further, the region-wide subsample analysis finds the unemployment gender gap in urban regions to narrow across all periods of the COVID-19 era. In contrast, the rural regions witness narrowing gender gap during the post-lockdown normalcy. This indicates that the rural regions’ unemployment gender gap witnessed spillover effects from recovery associated with the economic reopening. Finally, the narrow gender gap (compared to the pre-pandemic level) is smaller during the second wave.

There is a looming uncertainty whether the impending third wave will further narrow the gender unemployment gap at the expense of increasing male unemployment and females being pushed out of the workforce. Further research is required with a more extended period of assessment and focussed on household-level data to understand the difference in the impact of COVID-19 on the gender unemployment gap across the different parts of the country and income strata.

The authors thank Paul Cheung and the anonymous referee for their valuable comments and feedback. They also thank Rohanshi Vaid for her excellent research assistance.

[1] The data are not available for Jammu and Kashmir, Andaman and Nicobar Islands, Arunachal Pradesh, Dadra and Nagar Haveli, Daman and Diu, Lakshadweep, Manipur, Mizoram, Nagaland, and Sikkim. Hence, the main analysis focuses on only 26 subnational economies.

[2] The terms “state” and “subnational economy” are used interchangeably throughout the article.

[3] According to the official data, power consumption grew by 10.2% in January 2021; the highest growth rate in three months, which was indicative of higher commercial and industrial demand (Press Trust of India 2021).

[4] In order to obtain the unemployment dynamics on a quarterly basis, Equation (2) is revised to Equation (3) with dummies pertaining to quarter instead of month.

[5] This reason is also validated by CMIE who found the female labour participation in urban regions to fall to 7.2% in October 2020, the lowest since the organisation started measuring this indicator in 2016 (Centre for Monitoring Indian Economy 2020).

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Female Labour Force Participation in India: An Empirical Study

• Published: 11 April 2022
• Volume 65 , pages 59–83, ( 2022 )

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• Syamantak Chattopadhyay 1 &
• Subhanil Chowdhury 2  

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This paper attempts to analyse the factors which affect Indian working age women’s continuous withdrawal from the labourforce. The main objective deals with two specific questions: probing the empirical validity of the U-hypothesis and exploring whether supply side factors could sufficiently explain the falling FLFPR in India, especially in its rural sectors. Through a review of available empirical literature, factors like social constraints, upward mobility among lower castes and household burden have been identified as some of the major determinants. Using three rounds of NSSO data namely 50th round (1993–94), 61st round (2004–05), 68th round (2011–12) of Employment and Unemployment Surveys and the PLFS (2017–18) data, our analysis (across factors) have shown the existence of U-shaped relationship between FLFPR and education, however it shifts downwards with time. The relationship between FLFPR and MPCE deciles is not U-shaped but negative. Women from higher income class are more likely of being graduates thus increasing their probability of joining the labour force; even then a lower labour force participation of women in the upper deciles show the dominance of Income effect over education. It is significant that FLFPR declines with time irrespective of income and education. This indicates existence of factors other than supply side for explaining the problem of falling FLFPR. Particularly, one needs to focus on demand side problems and social-institutional factors inhibiting women from joining the labour force.

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Source : Authors’ calculations based on NSSO and PLFS data. Note: The horizontal axis measures decile classes of households based on MPCE

Source : Same as Fig.  2

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Chattopadhyay, S., Chowdhury, S. Female Labour Force Participation in India: An Empirical Study. Ind. J. Labour Econ. 65 , 59–83 (2022). https://doi.org/10.1007/s41027-022-00362-0

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Accepted : 08 March 2022

Published : 11 April 2022

Issue Date : March 2022

DOI : https://doi.org/10.1007/s41027-022-00362-0

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Forced labour

"how can a company practically and responsibly identify and address problems of forced labour in lower tiers of its supply chain, particularly when it extends into areas or sectors known to use forced labour", dilemmas and case studies.

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This page presents all relevant good practice case studies that showcase how business have addressed the Forced labour dilemma. Case studies have been developed in close collaboration with a range of multi-national companies and relevant government, inter-governmental and civil society stakeholders. We also draw on public domain sources, including the UN Global Compact's own published Communications on Progress through which signatories are required to report on their performance against the Ten Principles.

The case studies explore the specific dilemmas and challenges faced by each organisation, good practice actions they have taken to resolve them and the results of such action. We reference challenges as well as achievements and invite you to submit commentary and suggestions through the Forum.

IN-DEPTH (Print seperately) Responsible Cotton Network: Combating forced child labour during the cotton harvest - Uzbekistan

IN-DEPTH (Print seperately) ICC: Combating slave labour in the Brazilian charcoal and steel sector - Brazil

Verite, a US-based NGO, conducts research on forced labour and human trafficking around the world, with a particular focus on exploitive labour broker practices. Some of Verite’s projects include:

·          The use of forced labour in the electronics sector in Malaysia

·          Unscrupulous labour practices in the Guatemalan palm oil sector

·          Human rights abuses perpetuated throughout global supply chains of artisanal and small-scale mining (ASM), with a focus on ASM gold in Peru

  Verite aims to engage with companies in implementing strategies to identify and eliminate exploitive labour broker practices from their supply chains in order to minimise the risk of forced labour. Verite also provides courses that provide participants the competencies to perform audits of labour practices within companies, with a focus on auditing compensation, business ethics and working hours. These audits are run by Verite’s partnership organisation, the Electronic Industry Citizenship Coalition (EICC). 

http://www.verite.org

Established in 2002, the International Cocoa Initiative (ICI) is a partnership between NGOs, trade unions, cocoa processors and companies focused on tackling exploitative child labour. Member companies include: Cargill, Hershey’s, Kraft Foods, Mars, Nestle, Twinings and Toms International, amongst others. The ICI works at both the national- and the community-level to foster programmes to combat and prevent forced child labour. The ICI has implemented programmes in Cote d’Ivoire and Ghana.

http://www.cocoainitiative.org

Business Social Compliance Initiative (BSCI) was launched in 2003 by the Foreign Trade Association, a Brussels-based trade association that represents the trade interests of European companies. BSCI acts as an umbrella group for around 1,300 retail companies focused on improving working conditions in their supply chains.

The organisation has developed the BSCI code which addresses a wide range of supply chain issues, including a prohibition on forced labour as well as disciplinary measures for suppliers failing to comply with the code. Members adopt the BSCI Code internally and require their suppliers to come into compliance. BSCI provides capacity building in the form of training and technical assistance. BSCI also relies on external monitoring to ensure conformance to the code. BSCI is a member of the UN Global Compact.

http://www.bsci-eu.org

Founded in 2004, FFC is a New York-based membership organisation of companies seeking to improve working conditions in factories that make consumer goods. FFC shares compliance data between companies in order to improve the availability and standardisation of standards and audits on social, environmental and security standards. Amongst its members are Wal-Mart, Reebok, and Levi Strauss & Co. The FFC receives funding from the US Department of State. Its founders include Reebok, the National Retail Federation and the Retail Council of Canada.

http://www.fairfactories.org

The International Council on Metals and Mining (ICMM) is a CEO-led initiative founded in 2011 which focuses on promoting good practice in the mining and metals sector. Composed of 18 of the world’s largest mining companies and 30 associations, its corporate members include Anglo-American, BHP Billiton, Rio Tinto, Vale, Newmont and Mitsubishi Materials. Members commit to implementing ICMM’s Sustainable Development Framework. The framework comprises a set of 10 principles focused on integrating ethnical business practices across the mining sector, supported by public reporting and independent third-party assurance. The principles were adopted in May 2003. Principle 3 prohibits the use of forced, compulsory and child labour.

Global paper manufacturer Glatfleter implements a policy focused on combating forced labour in its supply chain, based on ILO conventions and national law. The company’s ‘Child and Forced Labor Policy’, which “recognises regional and cultural differences”, explicitly prohibits exploitative working conditions and the use of any forced labour. In order to address the problem, Glatfelter engages with suppliers, industry organisations, civil society representatives and governments.

In its policy document, Glatfelter acknowledges that the risks are particularly elevated for companies sourcing raw agricultural products, due to supply chains which are often long, complex and at risk of perpetuating forced labour use. The company strongly encourages its suppliers, subcontractors and business partners to adhere to its principles on the issue.

http://www.glatfelter.com

As part of its ‘No Child or Forced Labour’ policy, Indian multi-business conglomerate ITC Limited prohibits the use of forced or compulsory labour at all of its units, and maintains that no employee be made to work against their will or be subject to corporal punishment or coercion. The policy is made available to all employees through accessible induction programmes, policy manuals and intranet portals. Trade unions also engage with workers at each ITC unit to ensure they are aware of their rights. All units provide an annual report on any incidents of child or forced labour to divisional heads, and are subject to Corporate Internal Audits and Environment, Health and Safety assessments.

http://www.itcportal.com

Under the California Transparency in Supply Chains Act of 2010 (SB 657), which applies to retail sellers and manufacturers “doing business in the state”, multinational automaker Ford has disclosed its four key principles for the prevention of forced labour use in its supply chains:

·          Firstly, the company engages in risk assessment of its supply base, taking into account the geographic context, commodity type, level of labour required for production, supplier ownership structure and quality performance and the nature of the transaction.

·          Secondly, Ford operates purchase orders which require suppliers to certify compliance with standard terms and conditions on the prohibition of forced labour.

·          Thirdly, along with the other members of the Automotive Industry Action Group (AIAG), Ford conducts training and capacity building for global purchasing staff and suppliers in high-risk markets.

·          Finally, the company carries out regular audits of at-risk ‘Tier 1’ supplier factories, resulting, if necessary, in the completion of corrective action plans to then be reassessed six to 12 months after the original audit.

http://www.ford.com

On 6 May 2013, global sportswear brand Adidas announced that it was gradually launching a new whistle-blowing helpline at all of its Asia-based operations – to enable factory employees to voice potential grievances about labour violations. Workers employed in factories supplying Adidas will be able to send anonymous text messages – limited to 160 characters – to the  SMS Worker Hotline . While managers at the factories will be the main recipients of these text messages, Adidas will also be able to access them. This will allow the company to take direct action – particularly in cases where serious violations such as forced, bonded or child labour are identified.

Adidas acknowledges that workers sometimes do not feel comfortable in bringing issues to the attention of factory management in person. The move by the Germany multinational follows a spate of deadly incidents in Bangladeshi garment factories during 2013, one of the most important source countries for the global clothing industry. In this context, the establishment of worker hotlines can enable factory employees to raise practical issues related to health and safety, as well as labour violations.

Ford implements a supplier training programme to promote responsible working conditions in its supply chain. The programme is focused on “high-priority” countries including those in:

• The Americas (Argentina, Brazil, Colombia, Dominican Republic, Honduras, Mexico, Nicaragua and Venezuela)
• Asia (China, India, Malaysia, the Philippines, South Korea, Taiwan, Thailand and Vietnam)
• Europe, the Middle East and Africa (Morocco, Romania, Russia, South Africa and Turkey)

Training was originally based on Ford’s own Code of Basic Working Conditions and was implemented by Ford at supplier factories. The programme is based on one-day interactive workshops involving multiple suppliers, and is targeted at human resources, health and safety, and legal managers within supplier companies. Each participant is expected to ‘cascade’ relevant training materials to personnel within their own companies – and to their own direct suppliers. Indeed, Ford requires confirmation from participant suppliers that training information has been disseminated to these target groups within four months of each workshop.

The programme has since evolved into a joint initiative with other car manufacturers – in order to reach a larger number of suppliers (many of whom provide parts to multiple brands) more efficiently. This resulted in the formation of the Automotive Industry Action Group (‘AIAG’) through which car manufacturers from North America, Europe and Asia have developed common guidance statements on working conditions – as well as an online training programme for suppliers to the sector. These cover a range of issues including child labour, forced labour, freedom of association, discrimination, health and safety, wages and working hours.

Ford estimates that its training activity (carried out both unilaterally and in conjunction with the AIAG) has reached 2,900 supplier representatives – and been ‘cascaded’ to around 25,000 supplier managers, 485,000 workers and 100,000 sub-tier supplier companies.

Ending Forced Labor in India: What Does It Take?

For immediate release: Thursday, March 31, 2016

Boston, MA –  Neither legal nor socio-economic interventions have eradicated widespread forced and bonded labor in India. But a new report published today by Harvard University’s FXB Center for Health and Human Rights provides some hope for progress. With detailed evidence and meticulous analysis, the report documents the very positive impact of a community organization’s work on entrenched labor exploitation in Uttar Pradesh, India’s most populous state. It is the first report of its kind.

Entitled  “When We Raise Our Voice: The Challenge of Eradicating Labor Exploitation,”  the report examines the impact of a multifaceted, sustained, community-based intervention to eradicate forced and bonded labor. It centers on the efforts of Manav Sansadhan Evam Mahila Vikas Sansthan (MSEMVS), a local NGO dedicated to the elimination of exploitative labor practices within low caste, remote communities, home to some of India’s most economically disenfranchised and vulnerable populations. Agriculture, brick making, and carpet weaving—well known hubs of forced and abusive labor—are the main sources of employment in this area.

According to the report, MSEMVS has had a dramatic impact on improving the lives of individuals and households in the communities studied. The organization’s approach, the report claims, is a promising example of the robust, rights enhancing role such community empowerment interventions can play. Among other positives, MSEMVS increases residents’ understanding of legal rights and available legal support, and fosters critically important opportunities for education and new skill development.

Key findings attesting to the success of the MSEMVS approach include the following:

• Improved labor conditions, including dramatic reductions in threats of physical violence.
• Markedly improved food security and food availability, leading to increases in daily food intake and regular meals.
• Increased uptake of social protection and other government services by community members.
• Significantly reduced debt and a lowering of debt related to medical expenses.

Given the entrenched and devastating impact of forced labor in India, these findings are both encouraging and urgent. They suggest that past failures do not justify apathy or inaction. On the contrary, the report shows that much more can be done to support community-based efforts to eradicate some of the most egregious labor and human rights violations of our age.

For additional information, please contact Tizzy Tulloch, Harvard FXB communications director, at [email protected] or (617) 432-7134.

The FXB Center for Health and Human Rights at Harvard University is a university-wide interdisciplinary center that conducts rigorous investigation of the most serious threats to health and wellbeing globally. Based at the Harvard T.H. Chan School of Public Health, we work closely with scholars, students, the international policy community, and civil society to engage in ongoing strategic efforts to promote equity and dignity for those oppressed by grave poverty and stigma around the world. http://fxb.harvard.edu

What’s going on with India’s female labour force participation?

Why india's female labour force participation rate remains low compared to global levels, and what needs to be done to change it..

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Labour force participation rate (LFPR) is defined as the percentage of people in the labour force (that is, working, seeking, or available for work) among the population. LFPR includes those who are self-employed (for instance, in agriculture, forestry, fishing, etc. for their own consumption), salaried employees or casual labour, and those who are unemployed.

What’s happening with India’s female labour force participation rate (FLFPR)?

India is home to approximately 663 million women, of which approximately 450 million women fall in the working age of 15–64 years. India’s FLFPR had been showing a sharp declining trend over the last three decades, from 30.2 percent in 1990 to hitting an all-time low of 17.5 percent in 2018, as per reports by World Bank, Centre for Monitoring Indian Economy (CMIE), and Periodic Labour Force Survey (PLFS). (The PLFS is India’s official labour force survey, and became an annual exercise only in 2017–18.)

Unlike the downward trend India has seen in the FLFPR since the 1990s, the 2020–21 PLFS 1 for all ages shows a significant improvement in the last three years, going up from 17.5 percent to 24.8 percent (for women aged 15 and above, the rate increased from 23.3 percent in 2017–18 to 32.8 in 2020–21). A recent press release from the Ministry of Finance highlights that this improvement can be attributed to a range of factors, including progressive labour reform measures, better employment trends in the manufacturing sector, increasing share of self-employed people, and a rise in formal employment levels.

The global FLFPR is 52.4 percent (ages 15+), and has been at a similar level for the last three decades. However, in developing countries and emerging economies, there is a significant variation. In the Middle East, North Africa, and South Asia, this rate is approximately 25 percent, whereas it reaches up to approximately 66 percent in East Asia and sub-Saharan Africa. Interestingly, we don’t see such a trend variation in men’s LFPR, which stands at approximately 80 percent across economies.

Why has India’s FLFPR been low?

Informality.

According to the PLFS, we have approximately 166 million women either working, seeking work, or available for work. Out of the population of working women, more than 90 percent work in the informal sector. They are either self-employed or casual workers, predominantly in agricultural and construction sectors. This means that they face increased exploitation, poor working conditions, lack of mobility, and higher risk of violence . This discourages women from entering the workforce.

Patriarchal social norms

There is low support in society for working women. This arises from patriarchal structures, which dictate that women prioritise their domestic responsibilities over professional aspirations. The disproportionate burden of household duties, accompanied by mobility and safety constraints, results in women forgoing their employment. A recent NITI Aayog report states that women in India spend 9.8 times more time than men on unpaid domestic chores (against a global average of 2.6 as reported by UN Women). Globally, unpaid care work is the key reason that women are outside the labour force whereas for men it is “being in education, sick, or disabled ”. Additionally, deep-rooted social norms and lack of agency leave women with little choice in their employment decisions.

A large proportion of educated women are full-time housewives engaged in domestic household chores.

A large proportion of educated women are full-time housewives engaged in domestic household chores such as cooking, cleaning, and childcare. Their services are not paid for and neither are they accounted for in the FLFPR. Their economic output is not included in the GDP either. A 2023 report by State Bank of India suggests that unpaid women’s total contribution to the economy is around INR 22.7 lakh crore—approximately 7.5 percent of India’s GDP.

Economists have argued that India’s FLFPR is not as low as commonly assumed, and that including unpaid work in the calculation will improve this number. A working paper by the Economic Advisory Council highlights that the PLFS does not capture “economically productive work done by women like poultry farming, milking of cows, etc. as part of their domestic duties”. Upon correcting for this omission, the Economic Survey 2022-2023 estimated an FLFPR of 46.2 percent for 2020-21.

While we need to figure out ways to measure unpaid care work, ensure better estimations, and acquire timely, high-quality data from international and national statistical agencies, this may also reinforce patriarchal norms about women’s roles in the household and limit the opportunities for women who would like to work outside of their homes.

Structural changes in the economy are contributing to the recent rise in the FLFPR

Recent PLFS data shows a possible Feminisation U Hypothesis in female participation rates in India. This hypothesis was created on the basis of a cross section of 169 countries from 1990 to 2013. It shows that the early stages of economic growth are accompanied by a decline in female labour force participation. This is because women who were previously working to make ends meet can now opt out of the workforce due to rising household incomes. However, as incomes rise further, women tend to become economically active again. And this increase in economic activity tends to be accompanied by a drop in both fertility rates and the gender education gap.

Despite a significant positive trend, women’s labour force participation remains considerably low in comparison to that of men.

The recent improvement in India’s FLFPR can be explained by the structural changes the nation is experiencing, including a decline in fertility rates and improvement of women’s education. Despite this significant positive trend, women’s labour force participation remains considerably low in comparison to that of men (57.5 percent). Moreover, it represents the underutilisation of their capacity, given that approximately 70 percent of all Indian women of working age remain outside the labour force at present. Another concern is that the increase in women’s share in the labour force post pandemic is primarily driven by rural women joining the workforce, out of necessity , as self-employed workers.

What could full female participation in the workforce look like for India?

Unlocking the full potential of women in our workforce would provide multiple times the return on initial investments made by the government and businesses. As per McKinsey Global Institute’s report , India could achieve an 18 percent increase over business-as-usual GDP (USD 770 billion) by 2025.

The real economic, business, and societal value of the participation of women in India’s labour force can only be achieved through the active involvement of women across the formal economic ecosystem. Studies have shown how, in advanced economies, women in professional occupations outsource their care work , which further results in employment and income generation for more people. Similarly, Indian women and the economy will immensely benefit from solutions that focus on improving the participation of women in the formal economy. This will include reducing, redistributing, and rewarding unpaid care work.

• The PLFS surveyed 1,00,344 households (55,389 in rural areas and 44,955 in urban areas) and 4,10,818 people in the period from July 2020 to June 2021 to arrive at these numbers. Therefore, its results should be viewed as estimates and not as absolute figures.
• Learn  about  the widening gender gap in the workforce.
• Learn  how  philanthropy can be mobilised to benefit women entrepreneurs in India.
• Learn  about  how Indians view gender roles in family and society.

Savita, a farmer in rural Maharashtra, tends to her farm from sunrise until sunset to provide a simple meal for her children in the evening. Saloni, a bank manager in […]

Shruti Deora is an associate principal and lead for ecosystem engagement and policy advisory at Sattva Consulting . She has worked with multinational organisations in India and globally and led engagements with social development organisations, collaboratives, government stakeholders, and international foundations. She has expertise in strategy, implementation, technology, and data in gender and education projects. Shruti is a chartered accountant and an alumna of IIM Kozhikode and Takshila Institute.

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• Published: 18 January 2024

The impact of artificial intelligence on employment: the role of virtual agglomeration

• Yang Shen   ORCID: orcid.org/0000-0002-6781-6915 1 &
• Xiuwu Zhang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  122 ( 2024 ) Cite this article

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Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

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Determinants of behaviour and their efficacy as targets of behavioural change interventions

The role of artificial intelligence in achieving the Sustainable Development Goals

Participatory action research

Introduction.

Ensuring people’s livelihood requires diligence, but diligence is not scarce. Diversification, technological upgrading, and innovation all contribute to achieving the Sustainable Development Goal of full and productive employment for all (SDGs 8). Since the outbreak of the industrial revolution, human society has undergone four rounds of technological revolution, and each technological change can be regarded as the deepening of automation technology. The conflict and subsequent rebalancing of efficiency and employment are constantly being repeated in the process of replacing people with machines (Liu 2018 ; Morgan 2019 ). When people realize the new wave of human economic and social development that is created by advanced technological innovation, they must also accept the “creative destruction” brought by the iterative renewal of new technologies (Michau 2013 ; Josifidis and Supic 2018 ; Forsythe et al. 2022 ). The questions of where technology will eventually lead humanity, to what extent artificial intelligence will change the relationship between humans and work, and whether advanced productivity will lead to large-scale structural unemployment have been hotly debated. China has entered a new stage of deep integration and development of the “new technology cluster” that is represented by the internet and the real economy. Physical space, cyberspace, and biological space have become fully integrated, and new industries, new models, and new forms of business continue to emerge. In the process of the vigorous development of digital technology, its characteristics in terms of employment, such as strong absorption capacity, flexible form, and diversified job demands are more prominent, and many new occupations have emerged. The new practice of digital survival that is represented by the platform economy, sharing economy, full-time economy, and gig economy, while adapting to, leading to, and innovating the transformation and development of the economy, has also led to significant changes in employment carriers, employment forms, and occupational skill requirements (Dunn 2020 ; Wong et al. 2020 ; Li et al. 2022 ).

Artificial intelligence (AI) is one of the core areas of the fourth industrial revolution, along with the transformation of the mechanical technology, electric power technology, and information technology, and it serves to promote the transformation and upgrading of the digital economy industry. Indeed, the rapid iteration and cross-border integration of general information technology in the era of the digital economy has made a significant contribution to the stabilization of employment and the promotion of growth, but this is due only to the “employment effect” caused by the ongoing development of the times and technological progress in the field of social production. Digital technology will inevitably replace some of the tasks that were once performed by human labour. In recent years, due to the influence of China’s labour market and employment structure, some enterprises have needed help in recruiting workers. Driven by the rapid development of artificial intelligence technology, some enterprises have accelerated the pace of “machine replacement,” resulting in repetitive and standardized jobs being performed by robots. Deep learning and AI enable machines and operating systems to perform more complex tasks, and the employment prospects of enterprise employees face new challenges in the digital age. According to the Future of Jobs 2020 report released by the World Economic Forum, the recession caused by the COVID-19 pandemic and the rapid development of automation technology are changing the job market much faster than expected, and automation and the new division of labour between humans and machines will disrupt 85 million jobs in 15 industries worldwide over the next five years. The demand for skilled jobs, such as data entry, accounting, and administrative services, has been hard hit. Thanks to the wave of industrial upgrading and the vigorous development of digitalization, the recruitment demand for AI, big data, and manufacturing industries in China has maintained high growth year-on-year under the premise of macroenvironmental uncertainty during the period ranging from 2019 to 2022, and the average annual growth rate of new jobs was close to 30%. However, this growth has also aggravated the sense of occupational crisis among white-collar workers. The research shows that the agriculture, forestry, animal husbandry, fishery, mining, manufacturing, and construction industries, which are expected to adopt a high level of intelligence, face a high risk of occupational substitution, and older and less educated workers are faced with a very high risk of substitution (Wang et al. 2022 ). Whether AI, big data, and intelligent manufacturing technology, as brand-new forms of digital productivity, will lead to significant changes in the organic composition of capital and effectively decrease labour employment has yet to reach consensus. As the “pearl at the top of the manufacturing crown,” a robot is an essential carrier of intelligent manufacturing and AI technology as materialized in machinery and equipment, and it is also an important indicator for measuring a country’s high-end manufacturing industry. Due to the large number of manufacturing employees in China, the challenge of “machine substitution” to the labour market is more severe than that in other countries, and the use of AI through robots is poised to exert a substantial impact on the job market (Xie et al. 2022 ). In essence, the primary purpose of the digital transformation of industrial enterprises is to improve quality and efficiency, but the relationship between machines and workers has been distorted in the actual application of digital technology. Industrial companies use robots as an entry point, and the study delves into the impact of AI on the labour market to provide experience and policy suggestions on the best ways of coordinating the relationship between enterprise intelligent transformation and labour participation and to help realize Chinese-style modernization.

As a new general technology, AI technology represents remarkable progress in productivity. Objectively analysing the dual effects of substitution and employment creation in the era of artificial intelligence to actively integrate change and adapt to development is essential to enhancing comprehensive competitiveness and better qualifying workers for current and future work. This research is organized according to a research framework from the published literature (Luo et al. 2023 ). In this study, we used data published by the International Federation of Robotics (IFR) and take the installed density of industrial robots in China as the main indicator of AI. Based on panel data from 30 provinces in China covering the period from 2006–2020, the impact of AI technology on employment in a developing country with a large population size is empirically examined. The issues that need to be solved in this study include the following: The first goal is to examine the impact of AI on China’s labour market from the perspective of the economic behaviour of those enterprises that have adopted the use of industrial robots in production. The realistic question we expect to answer is whether the automated processing of daily tasks has led to unemployment in China during the past fifteen years. The second goal is to answer the question of how AI will continue to affect the employment market by increasing labour productivity, changing the technical composition of capital, and deepening the division of labour. The third goal is to examine how the transformation of industrial organization types in the digital economy era affects employment through digital industrial clusters or virtual clusters. The fourth goal is to test the role of AI in eliminating gender discrimination, especially in regard to whether it can improve the employment opportunities of female employees. Then, whether workers face different employment difficulties in different industry attributes is considered. The final goal is to provide some policy insights into how a developing country can achieve full employment in the face a new technological revolution in the context of a large population and many low-skilled workers.

The remainder of the paper is organized as follows. In Section Literature Review, we summarize the literature on the impact of AI on the labour market and employment and classify it from three perspectives: pessimistic, negative, and neutral. Based on a literature review, we then summarize the marginal contribution of this study. In Section Theoretical mechanism and research hypothesis, we provide a theoretical analysis of AI’s promotion of employment and present the research hypotheses to be tested. In Section Study design and data sources, we describe the data source, variable setting and econometric model. In Section Empirical analysis, we test Hypothesis 1 and conduct a robustness test and the causal identification of the conclusion. In Section Extensibility analysis, we test Hypothesis 2 and Hypothesis 3, as well as testing the heterogeneity of the baseline regression results. The heterogeneity test employee gender and industry attributes increase the relevance of the conclusions. Finally, Section Conclusions and policy implications concludes.

Literature review

The social effect of technological progress has the unique characteristics of the times and progresses through various stages, and there is variation in our understanding of its development and internal mechanism. A classic argument of labour sociology and labour economics is that technological upgrading objectively causes workers to lose their jobs, but the actual historical experience since the industrial revolution tells us that it does not cause large-scale structural unemployment (Zhang 2023a ). While neoclassical liberals such as Adam Smith claimed that technological progress would not lead to unemployment, other scholars such as Sismondi were adamant that it would. David Ricardo endorsed the “Luddite fear” in his book On Machinery, and Marx argued that technological progress can increase labour productivity while also excluding labour participation, thus leaving workers in poverty. The worker being turned ‘into a crippled monstrosity’ by modern machinery. Technology is not used to reduce working hours and improve the quality of work, rather, it is used to extend working hours and speed up work (Spencer 2023 ). According to Schumpeter’s innovation theory, within a unified complex system, the essence of technological innovation forms from the unity of positive and negative feedback and the oneness of opposites such as “revolutionary” and “destructive.” Even a tiny technological impact can cause drastic consequences. The impact of AI on employment is different from the that of previous industrial revolutions, and it is exceptional in that “machines” are no longer straightforward mechanical tools but have assumed more of a “worker” role, just as people who can learn and think tend to do (Boyd and Holton 2018 ). AI-related technologies continue to advance, the industrialization and commercialization process continues to accelerate, and the industry continues to explore the application of AI across multiple fields. Since AI was first proposed at the Dartmouth Conference in 1956, discussions about “AI replacing human labor” and “AI defeating humans” have endlessly emerged. This dynamic has increased in intensity since the emergence of ChatGPT, which has aroused people’s concerns about technology replacing the workforce. Summarizing the literature, we can find three main arguments concerning the relationship between AI and employment:

First, AI has the effect of creating and filling jobs. The intelligent manufacturing industry paradigm characterized by AI technology will assist in forming a high-quality “human‒machine cooperation” employment mode. In an enlightened society, the social state of shared prosperity benefits the lowest class of people precisely because of the advanced productive forces and higher labour efficiency created through the refinement of the division of labour. By improving production efficiency, reducing the sales price of final products, and stimulating social consumption, technological progress exerts both price effects and income effects, which in turn drive related enterprises to expand their production scale, which, in turn, increases the demand for labour (Li et al. 2021 ; Ndubuisi et al. 2021 ; Yang 2022 ; Sharma and Mishra 2023 ; Li et al. 2022 ). People habitually regard robots as competitors for human beings, but this view only represents the materialistic view of traditional machinery. The coexistence of man and machine is not a zero-sum game. When the task evolves from “cooperation for all” to “cooperation between man and machine,” it results in fewer production constraints and maximizes total factor productivity, thus creating more jobs and generating novel collaborative tasks (Balsmeier and Woerter 2019 ; Duan et al. 2023 ). At the same time, materialized AI technology can improve the total factor production efficiency in ways that are suitable for its factor endowment structure and improve the production efficiency between upstream and downstream enterprises in the industrial chain and the value chain. This increase in the efficiency of the entire market will subsequently drive the expansion of the production scale of enterprises and promote reproduction, and its synergy will promote the synchronous growth of the labour demand involving various skills, thus resulting in a creative effect (Liu et al. 2022 ). As an essential force in the fourth industrial revolution, AI inevitably affects the social status of humans and changes the structure of the labour force (Chen 2023 ). AI and machines increase labour productivity by automating routine tasks while expanding employee skills and increasing the value of work. As a result, in a machine-for-machine employment model, low-skilled jobs will disappear, while new and currently unrealized job roles will emerge (Polak 2021 ). We can even argue that digital technology, artificial intelligence, and robot encounters are helping to train skilled robots and raise their relative wages (Yoon 2023 ).

Second, AI has both a destructive effect and a substitution effect on employment. As soon as machines emerged as the means of labour, they immediately began to compete with the workers themselves. As a modern new technology, artificial intelligence is essentially humanly intelligent labour that condenses complex labour. Like the disruptive general-purpose technologies of early industrialization, automation technologies such as AI offer both promise and fear in regard to “machine replacement.” Technological progress leads to an increase in the organic composition of capital and the relative surplus population. The additional capital formed in capital accumulation comes to absorb fewer and fewer workers compared to its quantity. At the same time, old capital, which is periodically reproduced according to the new composition, will begin to increasingly exclude the workers it previously employed, resulting in severe “technological unemployment.” The development of productivity creates more free time, especially in industries such as health care, transportation, and production environment control, which have seen significant benefits from AI. In recent years, however, some industrialized countries have faced the dilemma of declining income from labour and the slow growth of total labour productivity while applying AI on a large scale (Autor 2019 ). Low-skilled and incapacitated workers enjoy a high probability of being replaced by automation (Ramos et al. 2022 ; Jetha et al. 2023 ). It is worth noting that with the in-depth development of digital technologies, such as deep learning and big data analysis, some complex, cognitive, and creative jobs that are currently considered irreplaceable in the traditional view will also be replaced by AI, which indicates that automation technology is not only a substitute for low-skilled labour (Zhao and Zhao 2017 ; Dixon et al. 2021 ; Novella et al. 2023 ; Nikitas et al. 2021 ). Among factors, AI and robotics exert a particularly significant impact on the manufacturing job market, and industry-related jobs will face a severe unemployment problem due to the disruptive effect of AI and robotics (Zhou and Chen 2022 ; Sun and Liu 2023 ). At this stage, most of the world’s economies are facing the deep integration of the digital wave in their national economy, and any work, including high-level tasks, is being affected by digitalization and AI (Gardberg et al. 2020 ). The power of AI models is growing exponentially rather than linearly, and the rapid development and rapid diffusion of technology will undoubtedly have a devastating effect on knowledge workers, as did the industrial revolution (Liu and Peng 2023 ). In particular, the development and improvement of AI-generated content in recent years poses a more significant threat to higher-level workers, such as researchers, data analysts, and product managers, than to physical labourers. White collar workers are facing unprecedented anxiety and unease (Nam 2019 ; Fossen and Sorgner 2022 ; Wang et al. 2023 ). A classic study suggests that AI could replace 47% of the 702 job types in the United States within 20 years (Frey and Osborne 2017 ). Since the 2020 epidemic, digitization has accelerated, and online and digital resources have become a must for enterprises. Many occupations are gradually moving away from humans (Wu and Yang 2022 ; Männasoo et al. 2023 ). It is obvious that the intelligent robot arm on the factory assembly line is poised to allow factory assembly line workers to exit the stage and move into history. Career guides are being replaced by mobile phone navigation software.

Third, the effect of AI on employment is uncertain, and its impact on human work does not fall into a simple “utopian” or “dystopian” scene, but rather leads to a combination of “utopia” and “dystopia” (Kolade and Owoseni 2022 ). The job-creation effects of robotics and the emergence of new jobs that result from technological change coexist at the enterprise level (Ni and Obashi 2021 ). Adopting a suitable AI operation mode can adjust for the misallocation of resources by the market, enterprises, and individuals to labour-intensive tasks, reverse the nondirectional allocation of robots in the labour sector, and promote their reallocation in the manufacturing and service industries. The size of the impact on employment through the whole society is uncertain (Fabo et al. 2017 ; Huang and Rust 2018 ; Berkers et al. 2020 ; Tschang and Almirall 2021 ; Reljic et al. 2021 ). For example, Oschinski and Wyonch ( 2017 ) claimed that those jobs that are easily replaced by AI technology in Canada account for only 1.7% of the total labour market, and they have yet to find evidence that automation technology will cause mass unemployment in the short term. Wang et al. ( 2022 ) posited that the impact of industrial robots on labour demand in the short term is mainly negative, but in the long run, its impact on employment is mainly that of job creation. Kirov and Malamin ( 2022 ) claimed that the pessimism underlying the idea that AI will destroy the jobs and quality of language workers on a large scale is unjustified. Although some jobs will be eliminated as such technology evolves, many more will be created in the long run.

In the view that modern information technology and digital technology increase employment, the literature holds that foreign direct investment (Fokam et al. 2023 ), economic systems (Bouattour et al. 2023 ), labour skills and structure (Yang 2022 ), industrial technological intensity (Graf and Mohamed 2024 ), and the easing of information friction (Jin et al. 2023 ) are important mechanisms. The research on whether AI technology crowds out jobs is voluminous, but the conclusions are inconsistent (Filippi et al. 2023 ). This paper is focused on the influence of AI on the employment scale of the manufacturing industry, examines the job creation effect of technological progress from the perspectives of capital deepening, labour refinement, and labour productivity, and systematically examines the heterogeneous impact of the adoption of industrial robots on employment demand, structure, and different industries. The marginal contributions of this paper are as follows: first, the installation density of industrial robots is used as an indicator to measure AI, and the question of whether AI has had negative effects on employment in the manufacturing sector from the perspective of machine replacement is examined. The second contribution is the analysis of the heterogeneity of AI’s employment creation effect from the perspective of gender and industry attributes and the claim that women and the employees of labour-intensive enterprises are more able to obtain additional work benefits in the digital era. Most importantly, in contrast to the literature, this paper innovatively introduces virtual agglomeration into the path mechanism of the effect of robots on employment and holds that information technologies such as the internet, big data, and the industrial Internet of Things, which rely upon AI, have reshaped the management mode and organizational structure of enterprises. Online and offline integration work together, and information, knowledge, and technology are interconnected. In the past, the job matching mode of one person, one post, and specific individuals has changed into a multiple faceted set of tasks involving one person, many posts, and many types of people. The internet platform spawned by digital technology frees the employment mode of enterprises from being limited to single enterprises and specific gathering areas. Traditional industrial geographical agglomeration has gradually evolved into virtual agglomeration, which geometrically enlarges the agglomeration effect and mechanism and enhances the spillover effect. In the online world, individual practitioners and entrepreneurs can obtain orders, receive training, connect resources and employment needs more widely and efficiently, and they can achieve higher-quality self-employment. Virtual agglomeration has become a new path by which AI affects employment. Another literature contribution is that this study used the linear regression model of the machine learning model in the robustness test part, which verified the employment creation effect of AI from the perspective of positive contribution proportion. In causal identification, this study innovatively uses the industrial feed-in price as a tool variable to analyse the causal path of AI promoting employment.

Theoretical mechanism and research hypothesis

The direct influence of ai on employment.

With advances in machine learning, big data, artificial intelligence, and other technologies, a new generation of intelligent robots that can perform routine, repetitive, and regular production tasks requiring human judgement, problem-solving, and analytical skills has emerged. Robotic process automation technology can learn and imitate the way that workers perform repeated new tasks regarding the collecting of data, running of reports, copying of data, checking of data integrity, reading, processing, and the sending of emails, and it can play an essential role in processing large amounts of data (Alan 2023 ). In the context of an informatics- and technology-oriented economy, companies are asking employees to transition into creative jobs. According to the theory of the combined task framework, the most significant advantage of the productivity effect produced by intelligent technology is creation of new demands, that is, the creation of new tasks (Acemoglu and Restrepo 2018 ). These new task packages update the existing tasks and create new task combinations with more complex technical difficulties. Although intelligent technology is widely used in various industries, it may have a substitution effect on workers and lead to technical unemployment. However, with the rise of a new round of technological innovation and revolution, high efficiency leads to the development and growth of a series of emerging industries and exerts job creation effects. Technological progress has the effect of creating new jobs. That is, such progress creates new jobs that are more in line with the needs of social development and thus increases the demand for labour (Borland and Coelli 2017 ). Therefore, the intelligent development of enterprises will come to replace their initial programmed tasks and produce more complex new tasks, and human workers in nonprogrammed positions, such as technology and knowledge, will have more comparative advantages.

Generally, the “new technology-economy” paradigm that is derived from automation machine and AI technology is affecting the breadth and depth of employment, which is manifested as follows:

It reduces the demand for coded jobs in enterprises while increasing the demand for nonprogrammed complex labour.

The development of digital technology has deepened and refined the division of labour, accelerated the service trend of the manufacturing industry, increased the employment share of the modern service industry and created many emerging jobs.

Advanced productive forces give workers higher autonomy and increased efficiency in their work, improving their job satisfaction and employment quality. As described in Das Kapital, “Although machines actually crowd out and potentially replace a large number of workers, with the development of machines themselves (which is manifested by the increase in the number of the same kind of factories or the expansion of the scale of existing factories), the number of factory workers may eventually be more than the number of handicraft workers in the workshops or handicrafts that they crowd out… It can be seen that the relative reduction and absolute increase of employed workers go hand in hand” (Li and Zhang 2022 ).

Internet information technology reduces the distance between countries in both time and space, promotes the transnational flow of production factors, and deepens the international division of labour. The emergence of AI technology leads to the decline of a country’s traditional industries and departments. Under the new changes to the division of labour, these industries and departments may develop in late-developing countries and serve to increase their employment through international labour export.

From a long-term perspective, AI will create more jobs through the continuous expansion of the social production scale, the continuous improvement of production efficiency, and the more detailed industrial categories that it engenders. With the accumulation of human capital under the internet era, practitioners are gradually becoming liberated from heavy and dangerous work, and workers’ skills and job adaptability will undergo continuous improvement. The employment creation and compensation effects caused by technological and industrial changes are more significant than the substitution effects (Han et al. 2022 ). Accordingly, the article proposes the following two research hypotheses:

Hypothesis 1 (H1): AI increases employment .

Hypothesis 2 (H2): AI promotes employment by improving labour productivity, deepening capital, and refining the division of labour .

Role of virtual agglomeration

The research on economic geography and “new” economic geography agglomeration theory focuses on industrial agglomeration in the traditional sense. This model is a geographical agglomeration model that depends on spatial proximity from a geographical perspective. Assessing the role of externalities requires a particular geographical scope, as it has both physical and scope limitations. Virtual agglomeration transcends Marshall’s theory of economies of scale, which is not limited to geographical agglomeration from the perspective of natural territory but rather takes on more complex and multidimensional forms (such as virtual clusters, high-tech industrial clusters, and virtual business circles). Under the influence of a new generation of digital technology that is characterized by big data, the Internet of Things, and the industrial internet, the digital, intelligent, and platform transformation trend is prominent in some industries and enterprises, and industrial digitalization and digital industrialization jointly promote industrial upgrading. The innovation of information technology leads to “distance death” (Schultz 1998 ). With the further materialization of digital and networked services of enterprises, the trading mode of digital knowledge and services, such as professional knowledge, information combination, cultural products, and consulting services, has transitioned from offline to digital trade, and the original geographical space gathering mode between enterprises has gradually evolved into a virtual network gathering that places the real-time exchange of data and information as its core (Wang et al. 2018 ). Tan and Xia ( 2022 ) stated that virtual agglomeration geometrically magnifies the social impact of industrial agglomeration mechanisms and agglomeration effects, and enterprises in the same industry and their upstream and downstream affiliated enterprises can realize low-cost long-distance transactions, services, and collaborative production through digital trade, resulting in large-scale zero-distance agglomeration along with neighbourhood-style production, service, circulation, and consumption. First, the knowledge and information underlying the production, design, research and development, organization, and trading of all kinds of enterprises are increasingly being completed by digital technology. The tacit knowledge that used to require face-to-face communication has become codable, transmissible, and reproducible under digital technology. Tacit knowledge has gradually become explicit, and knowledge spillover and technology diffusion have become more pronounced, which further leads to an increase in the demand for unconventional task labour (Zhang and Li 2022 ). Second, the cloud platform causes the labour pool effect of traditional geographical agglomeration to evolve into the labour “conservation land” of virtual agglomeration, and employment is no longer limited to the internal organization or constrained within a particular regional scope. Digital technology allows enterprises to hire “ghost workers” for lower wages to compensate for the possibility of AI’s “last mile.” Information technology and network platforms seek connections with all social nodes, promoting the time and space for work in a way that transcends standardized fixed frameworks. At the same time, joining or quitting work tasks, indirectly increasing the temporary and transitional nature of work and forming a decentralized management organization model of supplementary cooperation, social networks, industry experts, and skilled labour all become more convenient for workers (Wen and Liu 2021 ). With a mobile phone and a computer, labourers worldwide can create value for enterprises or customers, and the forms of labour are becoming more flexible and diverse. Workers can provide digital real-time services to employers far away from their residence, and they can also obtain flexible employment information and improve their digital skills through the leveraging of digital resources, resulting in the odd-job economy, crowdsourcing economy, sharing economy, and other economic forms. Finally, the network virtual space can accommodate almost unlimited enterprises simultaneously. In the commercial background of digital trade, while any enterprise can obtain any intermediate supply in the online market, its final product output can instantly become the intermediate input of other enterprises. Therefore, enterprises’ raw material supply and product sales rely on the whole market. At this time, the market scale effect of intermediate inputs can be infinitely amplified, as it is no longer confined to the limited space of geographical agglomeration (Duan and Zhang 2023 ). Accordingly, the following research hypothesis is proposed:

Hypothesis 3 (H3): AI promotes employment by improving the VA of enterprises .

Study design and data sources

Variable setting, explained variable.

Employment scale (ES). Compared with the agriculture and service industry, the industrial sector accommodates more labour, and robot technology is mainly applied in the industrial sector, which has the greatest demand shock effect on manufacturing jobs. In this paper, we select the number of employees in manufacturing cities and towns as the proxy variable for employment scale.

Core explanatory variable

Artificial intelligence (AI). Emerging technologies endow industrial robots with more complete technical attributes, which increases their ability to act as human beings in many work projects, enabling them to either independently complete production tasks or to assist humans in completing such tasks. This represents an important form of AI technology embedded into machinery and equipment. In this paper, the installation density of industrial robots is selected as the proxy variable for AI. Robot data mainly come from the number of robots installed in various industries at various national levels as published by the International Federation of Robotics (IFR). Because the dataset published by the IFR provides the dataset at the national-industry level and its industry classification standards are significantly different from those in China, the first lessons for this paper are drawn from the practices of Yan et al. ( 2020 ), who matches the 14 manufacturing categories published by the IFR with the subsectors in China’s manufacturing sector, and then uses the mobile share method to merge and sort out the employment numbers of various industries in various provinces. First, the national subsector data provided by the IFR are matched with the second National Economic Census data. Next, the share of employment in different industries to the total employment in the province is used to develop weights and decompose the industry-level robot data into the local “provincial-level industry” level. Finally, the application of robots in various industries at the provincial level is summarized. The Bartik shift-share instrumental variable is now widely used to measure robot installation density at the city (province) level (Wu 2023 ; Yang and Shen, 2023 ; Shen and Yang 2023 ). The calculation process is as follows:

In Eq. ( 1 ), N is a collection of manufacturing industries, Robot it is the robot installation density of province i in year t, \({{{\mathrm{employ}}}}_{{{{\mathrm{ij}}}},{{{\mathrm{t}}}} = 2006}\) is the number of employees in industry j of province i in 2006, \({{{\mathrm{employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) is the total number of employees in province i in 2006, and \({{{\mathrm{Robot}}}}_{{{{\mathrm{jt}}}}}{{{\mathrm{/employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) represents the robot installation density of each year and industry level.

Mediating variables

Labour productivity (LP). According to the definition and measurement method proposed by Marx’s labour theory of value, labour productivity is measured by the balance of the total social product minus the intermediate goods and the amount of labour consumed by the pure production sector. The specific calculation process is \(AL = Y - k/l\) , where Y represents GDP, l represents employment, k represents capital depreciation, and AL represents labour productivity. Capital deepening (CD). The per capita fixed capital stock of industrial enterprises above a designated size is used in this study as a proxy variable for capital deepening. The division of labour refinement (DLR) is refined and measured by the number of employees in producer services. Virtual agglomeration (VA) is mainly a continuation of the location entropy method in the traditional industrial agglomeration measurement idea, and weights are assigned according to the proportion of the number of internet access ports in the country. Because of the dependence of virtual agglomeration on digital technology and network information platforms, the industrial agglomeration degree of each region is first calculated in this paper by using the number of information transmissions, computer services, and software practitioners and then multiplying that number by the internet port weight. The specific expression is \(Agg_{it} = \left( {M_{it}/M_t} \right)/\left( {E_{it}/E_t} \right) \times \left( {Net_{it}/Net_t} \right)\) , where \(M_{it}\) represents the number of information transmissions, computer services and software practitioners in region i in year t, \(M_t\) represents the total number of national employees in this industry, \(E_{it}\) represents the total number of employees in region i, \(E_t\) represents the total number of national employees, \(Net_{it}\) represents the number of internet broadband access ports in region i, and \(Net_t\) represents the total number of internet broadband access ports in the country. VA represents the degree of virtual agglomeration.

Control variables

To avoid endogeneity problems caused by unobserved variables and to obtain more accurate estimation results, seven control variables were also selected. Road accessibility (RA) is measured by the actual road area at the end of the year. Industrial structure (IS) is measured by the proportion of the tertiary industry’s added value and the secondary industry’s added value. The full-time equivalent of R&D personnel is used to measure R&D investment (RD). Wage cost (WC) is calculated using city average salary as a proxy variable; Marketization (MK) is determined using Fan Gang marketization index as a proxy variable; Urbanization (UR) is measured by the proportion of the urban population to the total population at the end of the year; and the proportion of general budget expenditure to GDP is used to measure Macrocontrol (MC).

Econometric model

To investigate the impact of AI on employment, based on the selection and definition of the variables detailed above and by mapping the research ideas to an empirical model, the following linear regression model is constructed:

In Eq. ( 2 ), ES represents the scale of manufacturing employment, AI represents artificial intelligence, and subscripts t, i and m represent time t, individual i and the m th control variable, respectively. \(\mu _i\) , \(\nu _t\) and \(\varepsilon _{it}\) represent the individual effect, time effect and random disturbance terms, respectively. \(\delta _0\) is the constant term, a is the parameter to be fitted, and Control represents a series of control variables. To further test whether there is a mediating effect of mechanism variables in the process of AI affecting employment, only the influence of AI on mechanism variables is tested in the empirical part according to the modelling process and operational suggestions of the intermediary effects as proposed by Jiang ( 2022 ) to overcome the inherent defects of the intermediary effects. On the basis of Eq. ( 2 ), the following econometric model is constructed:

In Eq. ( 3 ), Media represents the mechanism variable. β 1 represents the degree of influence of AI on mechanism variables, and its significance and symbolic direction still need to be emphasized. The meanings of the remaining symbols are consistent with those of Eq. ( 2 ).

Data sources

Following the principle of data availability, the panel data of 30 provinces (municipalities and autonomous regions) in China from 2006 to 2020 (samples from Tibet and Hong Kong, Macao, and Taiwan were excluded due to data availability) were used as statistical investigation samples. The raw data on the installed density of industrial robots and the number of workers in the manufacturing industry come from the International Federation of Robotics and the China Labour Statistics Yearbook. The original data for the remaining indicators came from the China Statistical Yearbook, China Population and Employment Statistical Yearbook, China’s Marketization Index Report by Province (2021), the provincial and municipal Bureau of Statistics, and the global statistical data analysis platform of the Economy Prediction System (EPS). The few missing values are supplemented through linear interpolation. It should be noted that although the IFR has yet to release the number of robots installed at the country-industry level in 2020, it has published the overall growth rate of new robot installations, which is used to calculate the robot stock in 2020 for this study. The descriptive statistical analysis of relevant variables is shown in Table 1 .

Empirical analysis

To reduce the volatility of the data and address the possible heteroscedasticity problem, all the variables are located. The results of the Hausmann test and F test both reject the null hypothesis at the 1% level, indicating that the fixed effect model is the best-fitting model. Table 2 reports the fitting results of the baseline regression.

As shown in Table 2 , the results of the two-way fixed-effect (TWFE) model displayed in Column (5) show that the fitting coefficient of AI on employment is 0.989 and is significant at the 1% level. At the same time, the fitting results of other models show that the impact of AI on employment is significantly positive. The results confirm that the effect of AI on employment is positive and the effect of job creation is greater than the effect of destruction, and these conclusions are robust, thus verifying the employment creation mechanism of technological progress. Research Hypothesis 1 (H1) is supported. The new round of scientific and technological revolution represented by artificial intelligence involves the upgrading of traditional industries, the promotion of major changes in the economy and society, the driving of rapid development of the “unmanned economy,” the spawning a large number of new products, new technologies, new formats, and new models, and the provision of more possibilities for promoting greater and higher quality employment. Classical and neoclassical economics view the market mechanism as a process of automatic correction that can offset the job losses caused by labour-saving technological innovation. Under the premise of the “employment compensation” theory, the new products, new models, and new industrial sectors created by the progress of AI technology can directly promote employment. At the same time, the scale effect caused by advanced productivity results in lower product prices and higher worker incomes, which drives increased demand and economic growth, increasing output growth and employment (Ge and Zhao 2023 ). In conjunction with the empirical results of this paper, we have reason to believe that enterprises adopt the strategy of “machine replacement” to replace procedural and repetitive labour positions in the pursuit of high efficiency and high profits. However, AI improves not only enterprises’ production efficiency but also their production capacity and scale economy. To occupy a favourable share of market competition, enterprises expand the scale of reproduction. At this point, new and more complex tasks continue to emerge, eventually leading companies to hire more labour. At this stage, robot technology and application in developing countries are still in their infancy. Whether regarding the application scenario or the application scope of robots, the automation technology represented by industrial robots has not yet been widely promoted, which increases the time required for the automation technology to completely replace manual tasks, so the destruction effect of automation technology on jobs is not apparent. The fundamental market situation of the low cost of China’s labour market drives enterprises to pay more attention to technology upgrading and efficiency improvement when introducing industrial robots. The implementation of the machine replacement strategy is mainly caused by the labour shortage driven by high work intensity, high risk, simple process repetition, and poor working conditions. The intelligent transformation of enterprises points to more than the simple saving of labour costs (Dixon et al. 2021 ).

Robustness test

The above results show that the effect of AI on job creation is greater than the effect of substitution and the overall promotion of enterprises for the enhancement of employment demand. To verify the robustness of the benchmark results, the following three means of verifying the results are adopted in this study. First, we replace the explained variables. In addition to industrial manufacturing, robots are widely used in service industries, such as medical care, finance, catering, and education. To reflect the dynamic change relationship between the employment share of the manufacturing sector and the employment number of all sectors, the absolute number of manufacturing employees is replaced by the ratio of the manufacturing industry to all employment numbers. The second means is increasing the missing variables. Since many factors affect employment, this paper considers the living cots, human capital, population density, and union power in the basic regression model. The impact of these variables on employment is noticeable; for example, the existence of trade unions improves employee welfare and the working environment but raises the entry barrier for workers in the external market. The new missing variables are the average selling price of commercial and residential buildings, urban population density (person/square kilometre), nominal human capital stock, and the number of grassroots trade union organizations in the China Human Capital Report 2021 issued by Central University of Finance and Economics, which are used as proxy variables. The third means involves the use of linear regression (the gradient descent method) in machine learning regression to calculate the importance of AI to the increase in employment size. The machine learning model has a higher goodness of fit and fitting effect on the predicted data, and its mean square error and mean absolute error are more minor (Wang Y et al. 2022 ).

As seen from the robustness part of Table 3 , the results of Method 1 show that AI exerts a positive impact on the employment share in the manufacturing industry; that is, AI can increase the proportion of employment in the manufacturing industry, the use of AI creates more derivative jobs for the manufacturing industry, and the demand for the labour force of enterprises further increases. The results of method 2 show that after increasing the number of control variables, the influence of robots on employment remains significantly positive, indicating no social phenomenon of “machine replacement.” The results of method 3 show that the weight of AI is 84.3%, indicating that AI can explain most of the increase in the manufacturing employment scale and has a positive promoting effect. The above three methods confirm the robustness of the baseline regression results.

Endogenous problem

Although further control variables are used to alleviate the endogeneity problem caused by missing variables to the greatest extent possible, the bidirectional causal relationship between labour demand and robot installation (for example, enterprises tend to passively adopt the machine replacement strategy in the case of labour shortages and recruitment difficulties) still threatens the accuracy of the statistical inference results in this paper. To eliminate the potential endogeneity problem of the model, the two-stage least squares method (2SLS) was applied. In general, the cost factor that enterprises need to consider when introducing industrial robots is not only the comparative advantage between the efficiency cost of machinery and the costs of equipment and labour wages but also the cost of electricity to maintain the efficient operation of machinery and equipment. Changes in industrial electricity prices indicate that the dynamic conditions between installing robots and hiring workers have changed, and decision-makers need to reweigh the costs and profits of intelligent transformation. Changes in industrial electricity prices can impact the demand for labour by enterprises; this path does not directly affect the labour market but is rather based on the power consumption, work efficiency, and equipment prices of robots. Therefore, industrial electricity prices are exogenous relative to employment, and the demand for robots is correlated.

Electricity production and operation can be divided into power generation, transmission, distribution, and sales. China has realized the integration of exports and distribution, so there are two critical prices in practice: on-grid and sales tariffs (Yu and Liu 2017 ). The government determines the on-grid tariff according to different cost-plus models, and its regulatory policy has roughly proceeded from that of principal and interest repayment, through operating period pricing, to benchmark pricing. The sales price (also known as the catalogue price) is the price of electric energy sold by power grid operators to end users, and its price structure is formed based on the “electric heating price” that was implemented in 1976. There is differentiated pricing between industrial and agricultural electricity. Generally, government departments formulate on-grid tariffs, integrating the interests of power plants, grid enterprises, and end users. As China’s thermal power installed capacity accounts for more than 70% of the installed capacity of generators, the price of coal becomes an essential factor affecting the price of industrial internet access. The pricing strategy for electricity sales is not determined by market-oriented transmission and distribution electricity price, on-grid electricity price, or tax but rather by the goal of “stable growth and ensuring people’s livelihood” (Tang and Yang 2014 ). The externality of the feed-in price is more robust, so the paper chooses the feed-in price as an instrumental variable.

It can be seen from Table 3 that the instrumental variables in the first stage positively affect the robot installation density at the level of 1%. Meanwhile, the results of the validity test of the instrumental variables show that there are no weak instrumental variables or unidentifiable problems with this variable, thus satisfying the principle of correlation and exclusivity. The second-stage results show that robots still positively affect the demand for labour at the 1% level, but the fitting coefficient is smaller than that of the benchmark regression model. In summary, the results of fitting the calculation with the causal inference paradigm still support the conclusion that robots create more jobs and increase the labour demand of enterprises.

Extensibility analysis

Robot adoption and gender bias.

The quantity and quality of labour needed by various industries in the manufacturing sector vary greatly, and labour-intensive and capital-intensive industries have different labour needs. Over the past few decades, the demand for female employees has grown. Female employees obtain more job opportunities and better salaries today (Zhang et al. 2023 ). Female employees may benefit from reducing the content of manual labour jobs, meaning that further study of AI heterogeneity from the perspective of gender bias may be needed. As seen from Table 4 , AI has a significant positive impact on the employment of both male and female practitioners, indicating that AI technology does not have a heterogeneous effect on the dynamic gender structure. By comparing the coefficients of the two (the estimated results for men and those for women), it can be found that robots have a more significant promotion effect on female employees. AI has significantly improved the working environment of front-line workers, reduced the level of labour intensity, enabled people to free themselves of dirty and heavy work tasks, and indirectly improved the job adaptability of female workers. Intellectualization increases the flexibility of the time, place, and manner of work for workers, correspondingly improves the working freedom of female workers, and alleviates the imbalance in the choice between family and career for women to a certain extent (Lu et al. 2023 ). At the same time, women are born with the comparative advantage of cognitive skills that allow them to pay more nuanced attention to work details. By introducing automated technology, companies are increasing the demand for cognitive skills such as mental labour and sentiment analysis, thus increasing the benefits for female workers (Wang and Zhang 2022 ). Flexible employment forms, such as online car hailing, community e-commerce, and online live broadcasting, provide a broader stage for women’s entrepreneurship and employment. According to the “Didi Digital Platform and Female Ecology Research Report”, the number of newly registered female online taxi drivers in China has exceeded 265,000 since 2020, and approximately 60 percent of the heads of the e-commerce platform, Orange Heart, are women.

Industry heterogeneity

Given the significant differences in the combination of factors across the different industries in China’s manufacturing sector, there is also a significant gap in the installation density of robots; even compared to AI density, in industries with different production characteristics, indicating that there may be an opposite employment phenomenon at play. According to the number of employees and their salary level, capital stock, R&D investment, and patent technology, the manufacturing industry is divided into labour-intensive (LI), capital-intensive (CI), and technology-intensive (TI) industries.

As seen from the industry-specific test results displayed in Table 4 , the impact of AI on employment in the three attribute industries is significantly positive, which is consistent with the results of Beier et al. ( 2022 ). In contrast, labour-intensive industries can absorb more workers, and industry practitioners are better able to share digital dividends from these new workers, which is generally in line with expectations (in the labour-intensive case, the regression coefficient of AI on employment is 0.054, which is significantly larger than the regression coefficient of the other two industries). This conclusion shows that enterprises use AI to replace the labour force of procedural and process-based positions in pursuit of cost-effective performance. However, the scale effect generated by improving enterprise production efficiency leads to increased labour demand, namely, productivity and compensation effects. For example, AGV-handling robots are used to replace porters in monotonous and repetitive high-intensity work, thus realizing the uncrewed operation of storage links and the automatic handling of goods, semifinished products, and raw materials in the production process. This reduces the cost of goods storage while improving the efficiency of logistics handling, increasing the capital investment of enterprises in the expansion of market share and extension of the industrial chain.

Mechanism test

To reveal the path mechanism through which AI affects employment, in combination with H2 and H3 and the intermediary effect model constructed with Eq. ( 3 ), the TWFE model was used to fit the results shown in Table 5 .

It can be seen from Table 5 that the fitting coefficients of AI for capital deepening, labour productivity, and division of labour are 0.052, 0.071, and 0.302, respectively, and are all significant at the 1% level, indicating that AI can promote employment through the above three mechanisms, and thus research Hypothesis 2 (H2) is supported. Compared with the workshop and handicraft industry, machine production has driven incomparably broad development in the social division of labour. Intelligent transformation helps to open up the internal and external data chain, improve the combination of production factors, reduce costs and increase efficiency to enable the high-quality development of enterprises. At the macro level, the impact of robotics on social productivity, industrial structure, and product prices affects the labour demand of enterprises. At the micro level, robot technology changes the employment carrier, skill requirements, and employment form of labour and impacts the matching of labour supply and demand. The combination of the price and income effects can drive the impact of technological progress on employment creation. While improving labour productivity, AI technology reduces product production costs. In the case of constant nominal income, the market increases the demand for the product, which in turn drives the expansion of the industrial scale and increases output, resulting in an increase in the demand for labour. At the same time, the emergence of robotics has refined the division of labour. Most importantly, the development of AI technology results in productivity improvements that cannot be matched by pure labour input, which not only enables 24 h automation but also reduces error rates, improves precision, and accelerates production speeds.

Table 5 also shows that the fitting coefficient of AI to virtual agglomeration is 0.141 and significant at the 5% level, indicating that AI and digital technology can promote employment by promoting the agglomeration degree of enterprises in the cloud and network. Research Hypothesis 3 is thus supported. Industrial internet, AI, collaborative robots, and optical fidelity information transmission technology are necessary for the future of the manufacturing industry, and smart factories will become the ultimate direction of manufacturing. Under the intelligent manufacturing model, by leveraging cloud links, industrial robots, and the technological depth needed to achieve autonomous management, the proximity advantage of geographic spatial agglomeration gradually begins to fade. The panconnective features of digital technology break through the situational constraints of work, reshaping the static, linear, and demarcated organizational structure and management modes of the industrial era and increasingly facilitates dynamic, network-based, borderless organizational forms, despite the fact that traditional work tasks can be carried out on a broader network platform employing online office platforms and online meetings. While promoting cost reduction and efficiency increase, such connectivity also creates new occupations that rely on this network to achieve efficient virtual agglomeration. On the other hand, robot technology has also broken the fixed connection between people and jobs, and the previous post matching mode of one person and one specific individual has gradually evolved into an organizational structure involving multiple posts and multiple people, thus providing more diverse and inclusive jobs for different groups.

Conclusions and policy implications

Research conclusion.

The decisive impact of digitization and automation on the functioning of all society’s social subsystems is indisputable. Technological progress alone does not impart any purpose to technology, and its value (consciousness) can only be defined by its application in the social context in which it emerges (Rakowski et al. 2021 ). The recent launch of the intelligent chatbot ChatGPT by the US artificial intelligence company OpenAI, with its powerful word processing capabilities and human-computer interaction, has once again sparked global concerns about its potential impact on employment in related industries. Automation technology represented by intelligent manufacturing profoundly affects the labour supply and demand map and significantly impacts economic and social development. The application of industrial robots is a concrete reflection of the integration of AI technology and industry, and its widespread promotion and popularization in the manufacturing field have resulted in changes in production methods and exerted impacts on the labour market. In this paper, the internal mechanism of AI’s impact on employment is first delineated and then empirical tests based on panel data from 30 provinces (municipalities and autonomous regions, excluding Hong Kong, Macao, Taiwan, and Xizang) in China from 2006 to 2020 are subsequently conducted. As mentioned in relation to the theory of “employment compensation,” the research described in this paper shows that the overall impact of AI on employment is positive, revealing a pronounced job creation effect, and the impact of automation technology on the labour market is mainly positively manifested as “icing on the cake.” Our conclusion is consistent with the literature (Sharma and Mishra 2023 ; Feng et al. 2024 ). This conclusion remains after replacing variables, adding missing variables, and controlling for endogeneity problems. The positive role of AI in promoting employment does not have exert opposite effects resulting from gender and industry differences. However, it brings greater digital welfare to female practitioners and workers in labour-intensive industries while relatively reducing the overall proportion of male practitioners in the manufacturing industry. Mechanism analysis shows that AI drives employment through mechanisms that promote capital deepening, the division of labour, and increased labour productivity. The digital trade derived from digital technology and internet platforms has promoted the transformation of traditional industrial agglomeration into virtual agglomeration, the constructed network flow space system is more prone to the free spillover of knowledge, technology, and creativity, and the agglomeration effect and agglomeration mechanism are amplified by geometric multiples. Industrial virtual agglomeration has become a new mechanism and an essential channel through which AI promotes employment, which helps to enhance labour autonomy, improve job suitability and encourage enterprises to share the welfare of labour among “cultivation areas.”

Policy implications

Technology is neutral, and its key lies in its use. Artificial intelligence technology, as an open new general technology, represents significant progress in productivity and is an essential driving force with the potential to boost economic development. However, it also inevitably poses many potential risks and social problems. This study helps to clarify the argument that technology replaces jobs by revealing the impact of automation technology on China’s labour market at the present stage, and its findings alleviate the social anxiety caused by the fear of machine replacement. According to the above research conclusions, the following valuable implications can be obtained.

Investment in AI research and development should be increased, and the high-end development of domestic robots should be accelerated. The development of AI has not only resulted in the improvement of production efficiency but has also triggered a change in industrial structure and labour structure, and it has also generated new jobs as it has replaced human labour. Currently, the impact of AI on employment in China is positive and helps to stabilize employment. Speeding up the development of the information infrastructure, accelerating the intelligent upgrade of the traditional physical infrastructure, and realizing the inclusive promotion of intelligent infrastructure are necessary to ensure efficient development. 5G technology and the development dividend of the digital economy can be used to increase the level of investment in new infrastructure such as cloud computing, the Internet of Things, blockchain, and the industrial internet and to improve the level of intelligent application across the industry. We need to implement the intelligent transformation of old infrastructure, upgrade traditional old infrastructure to smart new infrastructure, and digitally transform traditional forms of infrastructure such as power, reservoirs, rivers, and urban sewer pipes through the employment of sensors and access algorithms to solve infrastructure problems more intelligently. Second, the diversification and agglomeration of industrial lines are facilitated through the transformation of industrial intelligence and automation. At the same time, it is necessary to speed up the process of industrial intelligence and cultivate the prospects of emerging industries and employment carriers, particularly in regard to the development and growth of emerging producer services. The development of domestic robots should be task-oriented and application-oriented, should adhere to the effective transformation of scientific and technological achievements under the guidance of the development of the service economy. A “1 + 2 + N” collaborative innovation ecosystem should be constructed with a focus on cultivating, incubating, and supporting critical technological innovation in each subindustry of the manufacturing industry, optimizing the layout, and forming a matrix multilevel achievement transformation service. We need to improve the mechanisms used for complementing research and production, such as technology investment and authorization. To move beyond standard robot system development technology, the research and development of bionic perception and knowledge, as well as other cutting-edge technologies need to be developed to overcome the core technology “bottleneck” problem.

It is suggested that government departments improve the social security system and stabilize employment through multiple channels. The first channel is the evaluation and monitoring of the potential destruction of the low-end labour force by AI, enabled through the cooperation of the government and enterprises, to build relevant information platforms, improve the transparency of the labour market information, and reasonably anticipate structural unemployment. Big data should be fully leveraged, a sound national employment information monitoring platform should be built, real-time monitoring of the dynamic changes in employment in critical regions, fundamental groups, and key positions should be implemented, employment status information should be released, and employment early warning, forecasting, and prediction should be provided. Second, the backstop role of public service, including human resources departments and social security departments at all levels, should improve the relevant social security system in a timely manner. A mixed-guarantee model can be adopted for the potential unemployed and laws and regulations to protect the legitimate rights and interests of entrepreneurs and temporary employees should be improved. We can gradually expand the coverage of unemployment insurance and basic living allowances. For the extremely poor, unemployed or extreme labour shortage groups, public welfare jobs or special subsidies can be used to stabilize their basic lifestyles. The second is to understand the working conditions of the bottom workers at the grassroots level in greater depth, strengthen the statistical investigation and professional evaluation of AI technology and related jobs, provide skills training, employment assistance, and unemployment subsidies for workers who are unemployed due to the use of AI, and encourage unemployed groups to participate in vocational skills training to improve their applicable skillsets. Workers should be encouraged to use their fragmented time to participate in the gig and sharing economies and achieve flexible employment according to dominant conditions. Finally, a focus should be established on the impact of AI on the changing demand for jobs in specific industries, especially transportation equipment manufacturing and communications equipment, computers, and other electronic equipment manufacturing.

It is suggested that education departments promote the reform of the education and training system and deepen the coordinated development of industry-university research. Big data, the Internet of Things, and AI, as new digital production factors, have penetrated daily economic activities, driving industrial changes and changes in the supply and demand dynamics of the job market. Heterogeneity analysis results confirmed that AI imparts a high level of digital welfare for women and workers in labour-intensive industrial enterprises, but to stimulate the spillover of technology dividends in the whole society, it is necessary to dynamically optimize human capital and improve the adaptability of man-machine collaborative work; otherwise, the disruptive effect of intelligent technology on low-end, routine and programmable work will be obscured. AI has a creativity promoting effect on irregular, creative, and stylized technical positions. Hence, the contradiction between supply and demand in the labour market and the slow transformation of the labour skill structure requires attention. The relevant administrative departments of the state should take the lead in increasing investment in basic research and forming a scientific research division system in which enterprises increase their levels of investment in experimental development and multiple subjects participate in R&D. Relevant departments should clarify the urgent need for talent in the digital economy era, deepen the reform of the education system as a guide, encourage all kinds of colleges and universities to add related majors around AI and big data analysis, accelerate the research on the skill needs of new careers and jobs, and establish a lifelong learning and employment training system that meets the needs of the innovative economy and intelligent society. We need to strengthen the training of innovative, technical, and professional technical personnel, focus on cultivating interdisciplinary talent and AI-related professionals to improve worker adaptability to new industries and technologies, deepen the adjustment of the educational structure, increase the skills and knowledge of perceptual, creative, and social abilities of the workforce, and cultivate the skills needed to perform complex jobs in the future that are difficult to replace by AI. The lifelong education and training system should be improved, and enterprise employees should be encouraged to participate in vocational skills training and cultural knowledge learning through activities such as vocational and technical schools, enterprise universities, and personnel exchanges.

Research limitations

The study used panel data from 30 provinces in China from 2006 to 2020 to examine the impact of AI on employment using econometric models. Therefore, the conclusions obtained in this study are only applicable to the economic reality in China during the sample period. There are three shortcomings in this study. First, only the effect and mechanism of AI in promoting employment from a macro level are investigated in this study, which is limited by the large data particles and small sample data that are factors that reduce the reliability and validity of statistical inference. The digital economy has grown rapidly in the wake of the COVID-19 pandemic, and the related industrial structures and job types have been affected by sudden public events. An examination of the impact of AI on employment based on nearly three years of micro-data (particularly the data obtained from field research) is urgent. When conducting empirical analysis, combining case studies of enterprises that are undergoing digital transformation is very helpful. Second, although the two-way fixed effect model and instrumental variable method can reveal conclusions regarding causality to a certain extent, these conclusions are not causal inference in the strict sense. Due to the lack of good policy pilots regarding industrial robots and digital parks, the topic cannot be thoroughly evaluated for determining policy and calculating resident welfare. In future research, researchers can look for policies and systems such as big data pilot zones, intelligent industrial parks, and digital economy demonstration zones to perform policy evaluations through quasinatural experiments. The use of difference in differences (DID), regression discontinuity (RD), and synthetic control method (SCM) to perform regression is beneficial. In addition, the diffusion effect caused by introducing and installing industrial robots leads to the flow of labour between regions, resulting in a potential spatial spillover effect. Although the spatial econometric model is used above, it is mainly used as a robustness test, and the direct effect is considered. This paper has yet to discuss the spatial effect from the perspective of the spatial spillover effect. Last, it is important to note that the digital infrastructure, workforce, and industrial structure differ from country to country. The study focused on a sample of data from China, making the findings only partially applicable to other countries. Therefore, the sample size of countries should be expanded in future studies, and the possible heterogeneity of AI should be explored and compared by classifying different countries according to their stage of development.

Data availability

The data generated during and/or analyzed during the current study are provided in Supplementary File “database”.

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This work was financially supported by the Natural Science Foundation of Fujian Province (Grant No. 2022J01320).

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Shen, Y., Zhang, X. The impact of artificial intelligence on employment: the role of virtual agglomeration. Humanit Soc Sci Commun 11 , 122 (2024). https://doi.org/10.1057/s41599-024-02647-9

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Labor Exploitation: Case Study of Top Glove

By londra ademaj.

This case study examines the allegations of forced labor in the manufacturing process of gloves by Top Glove, a prominent Malaysian rubber glove manufacturer. Malaysia, a diverse Southeast Asian country situated on the Malay Peninsula, serves as the home of this multinational corporation. Founded in 1991 by Tan Sri Dr. Lim Wee Chai in Malaysia, Top Glove Corporation Bhd has emerged as the global leader in rubber glove manufacturing, making a profound impact on the industry. Starting as a small local enterprise with just one factory and a single glove production line, Top Glove has experienced rapid expansion, solidifying its position as a global leader in the glove manufacturing industry (Top Glove). The case study explores the factors behind Top Glove’s success, examines the labor exploitation allegations that tarnished its reputation, considers the ethics regarding worker treatment, and details the responsibilities of corporations and governments. 

Unravelling Top Glove’s Path to a Distinguished Status

Figure 1 – Data sourced from (Hughes et al.)

The growth of multinational corporations like Top Glove in the rubber glove industry is heavily influenced by the contextual factors at play. The diagram presented below serves as a visual representation of the pivotal events that have played a significant role in contributing to its global success [Figure 1].

Remarkably, the expansion of Top Glove can be traced back to a series of unforeseen events, which carried both positive and negative consequences for the company. Although these events may not have been favorable for the overall economy, they played a pivotal role in propelling Top Glove’s ascent into the realm of a multinational corporation:

• In 1980, the demand for rubber gloves surged due to the HIV and AIDS epidemic. However, Top Glove was not established until 11 years later when regulatory changes mandated the transition from latex to synthetic nitrile gloves to address latex allergies. This move placed Top Glove in a highly competitive market, with approximately 250 other glove companies already operating by 1990 (Hughes et al.) Product differentiation was minimal, and competition was fierce.
• The Asian financial crisis of 1997-98 brought about an unexpected shift in the industry. Glove manufacturers collaborated, resulting in the formation of an oligopoly market where a few dominant players, known as the “big four,” controlled the industry (Hughes et al.). Since this development, the demand for gloves has remained stable over time due to their essential nature in hospitals and healthcare settings. This inelastic demand has provided a foundation for Top Glove’s continued expansion. This market structure presented an opportunity for Top Glove to consolidate its position and experience growth on an international scale.
• Since the Asian financial crisis, the COVID pandemic emerged as the next significant global event. The growth during the pandemic was significant, prompting Top Glove to expand its product line by manufacturing face masks. In 2020, Malaysian manufacturers, including Top Glove, captured a substantial 63% share of the global medical glove market, truly cementing their international presence. (Hughes et al.).

While the journey of Top Glove in the glove industry may seem remarkable, it is important to critically assess the factors that have contributed to its growth. Regulatory changes, financial crises, and the recent pandemic have all played a role in shaping Top Glove’s fortunes. The company has capitalized on market opportunities and adapted to changing circumstances. Yet, there exists broader implications, including the potential for labour exploitation to occur.

Labor exploitation can be challenging to define precisely. Marxists view exploitation as the unequal exchange of labor for goods (Roemer 30–65), while Adam Smith argues that it stems from the private property system, making worker justice unattainable under capitalism, particularly with profit-driven multinational corporations (Fairlamb 193–223). In the context of this case study, labor exploitation contains an element of criminal offences of forced labor or human trafficking which themselves constitute modern slavery.

Behind The Scenes: Top Glove

Top Glove has faced significant criticism and scrutiny regarding its labor practices. In 2020, the company came under the spotlight for alleged labor exploitation and poor working conditions (Business & Human Rights Resource Centre). Reports revealed mistreatment of migrant workers, excessive overtime, low wages, cramped living quarters, and other labor rights violations. These revelations raised concerns about the ethical practices and social responsibility of Top Glove, leading to international backlash and investigations by various organizations and authorities.

The allegations first emerged in 2018 through the diligent efforts of investigative journalists. The British Department of Health treated these accusations with utmost seriousness, recognizing the immediate need for further investigations (Marmo and Bandiera). As the investigations progressed, the shocking reality of illicit and inhumane practices resembling modern-day slavery became apparent. Top Glove, a prominent industry player, found itself implicated in a range of exploitative activities, including debt bondage, and forced labour (Marmo and Bandiera). For a more comprehensive understanding of the specific incidents that unfolded within the Top Glove factories, please refer to Figure 2.

Figure 2- Data Sourced from Marmo and Bandiera

Understanding the Roots of Labor Exploitation

Top Glove, Hartalega, and Kossan, the major Malaysian players in this industry, collectively employ nearly 34,000 workers (Zaugg). A significant proportion of these workers are recruited from abroad, primarily from countries like Indonesia, Bangladesh, Nepal, and Myanmar. This recruitment pattern is driven by the limited job opportunities available in their home countries, further intensifying their dependence on securing a job within these renowned Malaysian manufacturing companies.

The dependence on specific factories for employment creates an unjust power imbalance between employers and workers, leading to a range of adverse consequences. Employers are aware of the desperate need for jobs among workers, leaving the latter susceptible to exploitation. In their fear of unemployment and with limited options , workers may be compelled to accept unfavorable conditions such as low wages, long working hours, and inadequate safety measures. This power asymmetry perpetuates a cycle of disadvantage and erodes workers’ rights and well-being . Low wages not only impede their ability to meet basic needs but also hinder social mobility and trap them in a cycle of poverty. At the same time, workers are frequently subjected to long working hours without sufficient rest or breaks, leading to physical and mental exhaustion. This unfortunate outcome compromises their health and overall well-being.

Moreover, the absence of proper safety measures within these factories exposes workers to a multitude of significant risks and hazards inherent in the glove manufacturing process. Accidental exposure to toxins is a grave concern, as workers may come into contact with harmful chemicals and allergens during various stages of production (Boersma). Due to a lack of proper training and limited access to personal protective equipment (PPE), workers are exposed to the risk of developing occupational illnesses and enduring long-term health consequences. The improper handling of chemicals without adequate precautions can lead to immediate injuries, such as burns or skin irritations.  The manufacturing industry for latex gloves is known to carry a high level of risk due to the presence of carcinogens, acids, strong alkalis, and dangerous drugs, posing significant health hazards (Yari et al.).

The lack of resources became apparent during the COVID-19 pandemic when groups of workers contracted the virus. Despite engaging in a global effort to supply protective equipment for the coronavirus, the company experienced a troubling situation. While enjoying record profits from shipping gloves worldwide, critics argue the company’s low-paid workers in Malaysia faced a severe outbreak of Covid-19 due to inadequate protections provided by the company(Beech).

Furthermore, the rubber glove industry operates under an oligopoly market structure, exacerbating the problem of exploitation. In the Malaysian market, Top Glove reigns as the dominant force, holding an impressive 26% share of the world market (Top Glove), ironically, keeping their position at the “top”. During the pandemic, the market control of Top Glove was further intensified as there was a significant increase in global demand. To meet production targets, labour had to operate beyond maximum capacity.

Making the situation worse, Top Glove introduced a scheme called “Heroes for COVID-19,” where workers were requested to voluntarily work up to 4 additional hours on their day off to package gloves. However, this scheme has been criticized for bypassing labor regulations and coercing workers into working 7 days a week (Marmo and Bandiera). The intention was to portray these workers as heroes for those hospitalized by COVID-19, but in reality, it was merely a ploy to lure in workers. The harsh reality is that over 5,000 Top Glove workers tested positive for COVID-19, and tragically, one worker lost their life, making Top Glove facilities responsible for Malaysia’s largest cluster of COVID-19 cases(Marmo and Bandiera). 

Labor exploitation allegations predate the COVID-19 pandemic, indicating that it was not solely caused by the health crisis. However, the pandemic acted as a catalyst, making labor exploitation more noticeable and drawing attention to the issue. It is important to understand that labor exploitation may have been an ongoing problem before the pandemic, extending beyond its time frame. Top Glove’s dominant position in the industry often leads to cost-cutting measures, including the minimization of labor costs. Unfortunately, this strategy results in the exploitation of workers to maintain competitive pricing and maximize profits. Surprisingly, despite its monopoly power, Top Glove’s operating profit margin of 12.3% indicates inefficiencies in operating costs. These relatively small profit margins may directly contribute to labor exploitation, particularly when compared to competitors like Riverstone Holdings Limited, which enjoys a higher operating profit margin of 39.3% (Gek).

Exploring the Factors that Keep Workers in Harsh Conditions

Migrant workers are issued with a Visit Pass Temporary Employment (VP TE) for Malaysia. The VP TE enables a stay of 12 months after which it must be renewed if the worker remains in employment. Unfortunately, the Immigration Regulations prohibit a change of employer or employment, meaning a migrant worker’s VP TE is tied to a single employer and workers are unable to move elsewhere (Hughes et al). This means labor is  de facto  [la1]  immobile, and individuals cannot work elsewhere unless in illicit forms of employment.

As the reliance on foreign labor has increased, there has been a corresponding rise in concerns regarding working and living conditions faced by workers. Consider the labor laws and regulations in Malaysia, Thailand, China, and Vietnam, where these manufacturing facilities are located. While these countries have labor laws in place to protect workers’ rights, the effectiveness of implementation and enforcement can vary. Large corporations, including glove manufacturers, may take advantage of loopholes and weaknesses in the system, exploiting leeway’s in laws and regulations. Additionally, inadequate labor inspection systems and limited regulatory oversight further compound the issue. Notably, Malaysian law allows for working hours that exceed the widely accepted maximum of 60 hours per week and permits work on designated rest days (Lee et al.). This highlights how labor exploitation is more likely to occur when labor laws are inadequate or lacking in their protective measures.

Global Impact of Labor Exploitation

As investigations delved deeper into working conditions within the factories, the impact of these extends far beyond national borders, making waves in the international trading market. In response to the distressing revelations, many countries took swift action by imposing bans on Malaysia’s exports of medical gloves, starting with the USA in 2020.

This sudden shift cast Malaysia as the focal point of forced labor issues, attracting attention and concern from around the globe. Rosey Hurst, and the founder of Impactt, a London-based ethical trade consultancy, succinctly expressed, “Malaysia has become the poster child” for these pressing labor concerns (Lee et al.).

The ban on US imports on July 2020 was triggered by a tragic incident at Top Glove, where an employee succumbed to Covid-19. The virus rapidly spread throughout the company’s factories and worker dormitories, leading Malaysian authorities to describe the conditions as overcrowded, uncomfortable, and lacking proper ventilation. Subsequent measures were implemented to contain the outbreak (Palma). As a result, the US Customs and Border Protection took decisive action by ordering the seizure of Top Glove’s products upon arrival at American ports, citing allegations of forced labor. The consequences of such a reputation are not confined to public perception but also manifest in tangible economic damage. This development had a significant impact on the reputation of one of the world’s largest corporate beneficiaries during the pandemic (Palma). It contributed to double-digit declines in revenue and net profit. Revenue and profit fell 22% and 29% respectively (Kumar). It is a stark reminder of how labor exploitation can have profound and far-reaching implications, impacting not just individuals but also international trade dynamics (Lee et al.)

Around the time the ban was introduced, Top Glove released a press statement firmly denying all allegations and emphasizing their unwavering dedication to labor governance. In their official communication, the company asserted (Alam):

• “Top Glove’s workers do not perform excessive overtime and are given rest day in line with the Malaysia labor law requirement, which is 104 hours of overtime per month and one (1) rest day per week, respectively. “
• “Maximum allowable overtime is 4 hours per working day and solely on a  voluntary  basis.”
• “To ensure compliance with Malaysian labor law requirements, Top Glove has implemented a digital monitoring exercise.”

Just a few months after the press release, there came a significant development as the United States decided to lift the import ban on Malaysia’s Top Glove. As a testament to their ongoing efforts, Top Glove published a comprehensive improvement report, detailing the actions taken since the allegations surfaced, despite consistently refuting any involvement in exploitative practices. In this report, they highlighted significant improvements in several areas, including (Top Glove):

1.Fair Recruitment Practices of Foreign Workers 2. Continuous Improvement of Workers’ Accommodation 3. Fair Working and Wages of Workers 4. Continued Safety and Health of Our Workforce 5. More Stringent Safety Measures to Safeguard Our Workforce Post COVID 19

Not only did Top Glove make information about its standards available, but other leading companies also contributed by sharing their assessments. Amfori, a prominent global business association focused on open and sustainable trade, recently conducted a social audit of Top Glove. In this assessment, Top Glove was proudly awarded an ‘A’ rating, signifying their dedication to upholding exemplary social standards. This recognition from Amfori highlights Top Glove’s commitment to transparency and responsible business practices (Jaafar).

Top Glove’s ‘A’ rating was the outcome of a comprehensive social audit conducted from June 23 to 26. The audit results reflected 12 areas assessed as “very good ” and one area as “good,” showcasing some improvements. It is worth noting that Top Glove engaged with various organizations to enhance its social compliance and performance (Jaafar).

Because of these improvements, the bans on Top Glove’s products were lifted, causing an initial boost in the market. Top Glove shares rose by as much as 10% during early trading, although they later experienced a decline. With the ban lifted, Top Glove can now proceed with their previously disrupted plan of a $1 billion dual listing in Hong Kong, which was postponed due to the import ban (Ruehl and Langley).

Bengtsen argues that the import ban on Top Glove achieved what decades of voluntary corporate social responsibility (CSR) efforts by the global medical sector and actions by Malaysia’s labor inspectorate failed to accomplish. He suggests that the ban’s effectiveness was due to its direct impact on the company’s revenues, making it a crucial aspect to consider (Fanou).

These unfolding events give rise to critical questions regarding accountability and necessitate an examination of potential challenges within Malaysia. It raises the question of whether the responsibility should solely rest on Top Glove as a multinational corporation or if there is a larger systemic issue at play. The absence of appropriate legislation and the presence of exploitative practices indicate the need to scrutinize the wider labor landscape and regulatory framework within Malaysia (Palma).

The initial lack of response from Malaysia’s labor department regarding potential changes to the country’s labor laws, and the trade ministry’s silence on inquiries about potential investment losses, raises concerns about addressing labor rights issues (Lee et al.).  However, as time progressed, the labor department in Malaysia has charged Top Glove with 10 counts of failing to provide worker accommodation that meets the minimum standards of the labor department in 2021. (Lee) Despite this, an independent consultant, Impactt, said it “no longer” found any indication of systemic forced labor at Top Glove, which was making progress on some indicators, such as living conditions (Lee).

These observations emphasize the need for greater attention and efforts from both multinational corporations and government bodies involved in the industry to address and mitigate labor exploitation.

It calls for a deeper understanding of the challenges that allow labor exploitation to persist and raises the urgency to address them to ensure the well-being and dignity of workers for a more equitable and just society.

Ethics Evaluation

The emergence and growth of Top Glove, coupled with the allegations of labor exploitation, raise intricate ethical considerations that demand in-depth examination. The treatment of workers within the company’s operations reveals a troubling disregard for their fundamental rights and well-being.  

One of the fundamental ethical considerations centers around the principle of human dignity. Human dignity is crucial as it forms the foundation for justifying and upholding human rights .

At its core, the concept of human dignity asserts that every individual possesses inherent worth and value solely by virtue of being human. This belief is reflected in Article 1 of the Universal Declaration of Human Rights, which proclaims that “All human beings are born free and equal in dignity and rights.” (Soken-Huberty)

The idea of human rights is as simple as it is powerful: that people have a right to be treated with dignity. Human rights are inherent in all human beings, whatever their nationality, place of residence, sex, national or ethnic origin, color, religion, language, or any other status (Heard).

The alleged mistreatment of workers, including excessive working hours, low wages, and inadequate living conditions, violates their inherent dignity as individuals. Such practices strip workers of their basic rights, compromise their physical and mental well-being, and perpetuate a cycle of exploitation. Upholding the principle of human dignity is crucial in ensuring fair and equitable treatment of all individuals within the workforce.

Transparency and accountability are also critical ethical dimensions. Corporations like Top Glove have a moral obligation to operate transparently and be held accountable for their actions. The alleged labor exploitation within the company brings to light concerns regarding corporate responsibility, corporate governance, and supply chain management. Holding corporations accountable for their actions is crucial in fostering a culture of ethical behavior and ensuring labor rights are upheld.

Today, human rights have taken on a profound significance, akin to a form of religion. They serve as a powerful moral compass, guiding us in evaluating how a government treats its people (Heard). When it comes to addressing the ethical concerns surrounding labor exploitation, a multifaceted approach becomes crucial. 

The approach entails a commitment from corporations to prioritize the well-being and rights of workers, regulatory bodies to enforce and strengthen labor standards, and society at large to raise awareness and demand ethical practices. By fostering a culture of ethics and social responsibility within the industry, it becomes possible to create a labor environment that upholds the dignity and rights of all workers.

The United Nations’ “Protect, Respect, and Remedy” framework serves as a global standard for preventing and addressing the potential negative impact on human rights associated with business activities (Office of the United Nations High Commissioner for Human Rights). This framework consists of three pillars:

• The duty of the state to protect human rights.
• The responsibility of corporations to respect human rights.
• The necessity for enhanced access to remedies for victims of human rights abuses linked to business practices.

While international human rights treaties generally do not impose direct legal obligations on businesses, the International Bill of Human Rights, and the core conventions of the International Labour Organization (ILO) provide fundamental guidelines for businesses to comprehend the essence of human rights, how their own operations may influence them, and how to ensure proactive measures are in place to prevent or mitigate potential adverse impacts (Office of the United Nations High Commissioner for Human Rights).

While recognizing challenges in ensuring strong protections for human rights and labor rights in Malaysia, significant efforts have been made to improve the situation.

For instance, the Bureau of International Labour Affairs collaborated with the Malaysian government on projects aimed at enhancing labor rights and enforcement. These initiatives have included assisting the national government in drafting laws, decrees, and regulations to strengthen labor protections. Efforts have also been focused on providing information and guidance to employers regarding these regulations, promoting compliance and awareness (Bureau of International Labour Affairs).

Additionally, there have been endeavors to improve the efficiency and effectiveness of labor administration in Malaysia. This involves initiatives such as strengthening labor inspections to ensure that legal instruments are enforced. The labor ministry or relevant authorities play a key role in overseeing and conducting these inspections (Bureau of International Labour Affairs).

Mechanisms for resolving labor disputes have been established to provide workers in Malaysia with accessible avenues to seek justice and find resolution in case of rights violations. These measures aim to ensure that workers’ rights are upheld and protected in the country (Bureau of International Labour Affairs).

Labor rights involve not just corporations and the state, but civil society as well, playing a vital role. Non-governmental organizations (NGOs) focusing on social and economic rights are particularly instrumental in providing direct services to individuals who have suffered human rights violations.

These services offered by NGOs can take various forms, such as providing humanitarian assistance to those in need, offering protection for vulnerable individuals, or facilitating training programs to empower individuals with new skills. In cases where labour rights are legally protected, NGOs may engage in legal advocacy, offering advice and guidance on how to present claims or seek legal recourse. (Council of Europe)

The involvement of civil society organizations adds an essential dimension to the overall effort in promoting and safeguarding labor rights. Their direct services and support help bridge the gap between human rights violations and the necessary assistance needed by those affected. Through their dedicated work, NGOs contribute to creating a more just and inclusive society where individuals can assert their labor rights and access the support they require. (Council of Europe)

In Malaysia, non-governmental organizations (NGOs) have played a significant role in combating labor exploitation across various industries. Notably, Transparentem has emerged as a prominent NGO dedicated to bringing about transformation in the industry by shedding light on uncomfortable realities.

Transparentem’s primary objective is to uncover and disclose hidden truths, ultimately driving positive change within industries. By exposing the harsh realities and working conditions, the organization aims to raise awareness and prompt necessary action to address labor exploitation.

Transparentem’s leaders have learned from past experiences that when investigators reveal appalling conditions in Asia, embarrassed Western customers tend to hastily terminate their relationships with suppliers but make little effort to rectify the abuses (Greenhouse). To avoid this pattern, Transparentem takes a different approach. When they uncover serious problems, instead of rushing to publicize them and expose the factories’ Western customers, the organization discreetly informs these companies and urges them to collaborate with the factories to address the issues. The underlying understanding is that Transparentem will eventually disclose its investigative findings and how companies have responded. Western companies are aware that their reputation will be tarnished if they fail to take appropriate action (Greenhouse).

In conclusion, the case study of Top Glove, a Malaysian rubber glove manufacturer, highlights both the remarkable success achieved by the company and the concerning allegations of labor exploitation that have marred its reputation. The growth of Top Glove within the global industry can be attributed to various factors such as regulatory changes, financial crises, and the COVID-19 pandemic. The revelations of labor exploitation shed light on the ethical considerations and responsibilities that corporations and governments must address. Upholding human dignity, ensuring transparency and accountability, and fostering a culture of ethics and social responsibility help create a good labor environment. 

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Jaafar, Syahirah S. “Top Glove Accorded ‘A’ Rating in Recent Social Audit by Amfori.”  The Edge Malaysia , theedgemalaysia.com/article/top-glove%C2%A0accorded%C2%A0-rating-%C2%A0recent-social-audit-amfori.

‌Kumar, P. Prem. “Malaysia’s Top Glove Q3 Earnings Dented by US Import Ban.”  Nikkei Asia , June 2021, asia.nikkei.com/Business/Companies/Malaysia-s-Top-Glove-Q3-earnings-dented-by-US-import-ban.

‌Lee, Liz, et al. “Analysis: Malaysia’s Labour Abuse Allegations a Risk to Export Growth Model.”  Reuters , 22 Dec. 2021,  www.reuters.com/world/asia-pacific/malaysias-labour-abuse-allegations-risk-export-growth-model-2021-12-21/ .

Lee, Liz “Malaysia Charges Top Glove over Poor Quality of Worker Housing.”  Www.zawya.com , 2021, www.zawya.com/en/legal/malaysia-charges-top-glove-over-poor-quality-of-worker-housing-q1xsk52r. Accessed 8 July 2023.

‌Marmo, Marinella, and Rhiannon Bandiera. “Modern Slavery as the New Moral Asset for the Production and Reproduction of State-Corporate Harm.”  Journal of White Collar and Corporate Crime , vol. 3, no. 2, June 2021, p. 2631309X2110209.

Miller, Jonathan. “Revealed: Shocking Conditions in PPE Factories Supplying UK.”  Channel 4 News , 16 June 2020,  www.channel4.com/news/revealed-shocking-conditions-in-ppe-factories-supplying-uk .

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In April this year, Hein Schumacher, chief executive of Unilever, announced that the company was entering a “new era for sustainability leadership”, and signalled a shift from the central priority promoted under his predecessor , Alan Jope.

While Jope saw lack of social purpose or environmental sustainability as the way to prune brands from the portfolio, Schumacher has adopted a more balanced approach between purpose and profit. He stresses that Unilever should deliver on both sustainability commitments and financial goals. This approach, which we dub “realistic sustainability”, aims to balance long- and short-term environmental goals, ambition, and delivery.

As a result, Unilever’s refreshed sustainability agenda focuses harder on fewer commitments that the company says remain “very stretching”. In practice, this entails extending deadlines for taking action as well as reducing the scale of its targets for environmental, social and governance measures.

Such backpedalling is becoming widespread — with many companies retracting their commitments to climate targets , for example. According to FactSet, a US financial data and software provider, the number of US companies in the S&P 500 index mentioning “ESG” on their earnings calls has declined sharply : from a peak of 155 in the fourth quarter 2021 to just 29 two years later. This trend towards playing down a company’s ESG efforts, from fear of greater scrutiny or of accusations of empty claims, even has a name: “greenhushing”.

Test yourself

This is the fourth in a series of monthly business school-style teaching case studies devoted to the responsible business dilemmas faced by organisations. Read the piece and FT articles suggested at the end before considering the questions raised.

About the authors: Gabriela Salinas is an adjunct professor of marketing at IE University; Jeeva Somasundaram is an assistant professor of decision sciences in operations and technology at IE University.

The series forms part of a wider collection of FT ‘instant teaching case studies ’, featured across our Business Education publications, that explore management challenges.

The change in approach is not limited to regulatory compliance and corporate reporting; it also affects consumer communications. While Jope believed that brands sold more when “guided by a purpose”, Schumacher argues that “we don’t want to force fit [purpose] on brands unnecessarily”.

His more nuanced view aligns with evidence that consumers’ responses to the sustainability and purpose communication attached to brand names depend on two key variables: the type of industry in which the brand operates; and the specific aspect of sustainability being communicated.

In terms of the sustainability message, research in the Journal of Business Ethics found consumers can be less interested when product functionality is key. Furthermore, a UK survey in 2022 found that about 15 per cent of consumers believed brands should support social causes, but nearly 60 per cent said they would rather see brand owners pay taxes and treat people fairly.

Among investors, too, “anti-purpose” and “anti-ESG” sentiment is growing. One (unnamed) leading bond fund manager even suggested to the FT that “ESG will be dead in five years”.

Media reports on the adverse impact of ESG controversies on investment are certainly now more frequent. For example, while Jope was still at the helm, the FT reported criticism of Unilever by influential fund manager Terry Smith for displaying sustainability credentials at the expense of managing the business.

Yet some executives feel under pressure to take a stand on environmental and social issues — in many cases believing they are morally obliged to do so or through a desire to improve their own reputations. This pressure may lead to a conflict with shareholders if sustainability becomes a promotional tool for managers, or for their personal social responsibility agenda, rather than creating business value .

Such opportunistic behaviours may lead to a perception that corporate sustainability policies are pursued only because of public image concerns.

Alison Taylor, at NYU Stern School of Business, recently described Unilever’s old materiality map — a visual representation of how companies assess which social and environmental factors matter most to them — to Sustainability magazine. She depicted it as an example of “baggy, vague, overambitious goals and self-aggrandising commitments that make little sense and falsely suggest a mayonnaise and soap company can solve intractable societal problems”.

In contrast, the “realism” approach of Schumacher is being promulgated as both more honest and more feasible. Former investment banker Alex Edmans, at London Business School, has coined the term “rational sustainability” to describe an approach that integrates financial principles into decision-making, and avoids using sustainability primarily for enhancing social image and reputation.

Such “rational sustainability” encompasses any business activity that creates long-term value — including product innovation, productivity enhancements, or corporate culture initiatives, regardless of whether they fall under the traditional ESG framework.

Similarly, Schumacher’s approach aims for fewer targets with greater impact, all while keeping financial objectives in sight.

Complex objectives, such as having a positive impact on the world, may be best achieved indirectly, as expounded by economist John Kay in his book, Obliquity . Schumacher’s “realistic sustainability” approach means focusing on long-term value creation, placing customers and investors to the fore. Saving the planet begins with meaningfully helping a company’s consumers and investors. Without their support, broader sustainability efforts risk failure.

Questions for discussion

Read: Unilever has ‘lost the plot’ by fixating on sustainability, says Terry Smith

Companies take step back from making climate target promises

The real impact of the ESG backlash

Unilever’s new chief says corporate purpose can be ‘unwelcome distraction ’

Unilever says new laxer environmental targets aim for ‘realism’

How should business executives incorporate ESG criteria in their commercial, investor, internal, and external communications? How can they strike a balance between purpose and profits?

How does purpose affect business and brand value? Under what circumstances or conditions can the impact of purpose be positive, neutral, or negative?

Are brands vehicles by which to drive social or environmental change? Is this the primary role of brands in the 21st century or do profits and clients’ needs come first?

Which categories or sectors might benefit most from strongly articulating and communicating a corporate purpose? Are there instances in which it might backfire?

In your opinion, is it necessary for brands to take a stance on social issues? Why or why not, and when?

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Where climate change meets business, markets and politics. Explore the FT’s coverage here .

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Is College Worth It?

As economic outcomes for young adults with and without degrees have improved, americans hold mixed views on the value of college, table of contents.

• Labor force trends and economic outcomes for young adults
• Economic outcomes for young men
• Economic outcomes for young women
• Wealth trends for households headed by a young adult
• The importance of a four-year college degree
• Getting a high-paying job without a college degree
• Do Americans think their education prepared them for the workplace?
• Is college worth the cost?
• Acknowledgments
• The American Trends Panel survey methodology
• Current Population Survey methodology
• Survey of Consumer Finances methodology

Pew Research Center conducted this study to better understand public views on the importance of a four-year college degree. The study also explores key trends in the economic outcomes of young adults among those who have and have not completed a four-year college degree.

The analysis in this report is based on three data sources. The labor force, earnings, hours, household income and poverty characteristics come from the U.S. Census Bureau’s Annual Social and Economic Supplement of the Current Population Survey. The findings on net worth are based on the Federal Reserve’s Survey of Consumer Finances.

The data on public views on the value of a college degree was collected as part of a Center survey of 5,203 U.S. adults conducted Nov. 27 to Dec. 3, 2023. Everyone who took part in the survey is a member of Pew Research Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. Address-based sampling ensures that nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this report , along with responses, and the survey’s methodology .

Young adults refers to Americans ages 25 to 34.

Noncollege adults include those who have some college education as well as those who graduated from high school but did not attend college. Adults who have not completed high school are not included in the analysis of noncollege adults. About 6% of young adults have not completed high school. Trends in some labor market outcomes for those who have not finished high school are impacted by changes in the foreign-born share of the U.S. population. The Census data used in this analysis did not collect information on nativity before 1994.

Some college includes those with an associate degree and those who attended college but did not obtain a degree.

The some college or less population refers to adults who have some college education, those with a high school diploma only and those who did not graduate high school.

A full-time, full-year worker works at least 50 weeks per year and usually 35 hours a week or more.

The labor force includes all who are employed and those who are unemployed but looking for work.

The labor force participation rate is the share of a population that is in the labor force.

Young adults living independently refers to those who are not living in the home of either of their parents.

Household income is the sum of incomes received by all members of the household ages 15 and older. Income is the sum of earnings from work, capital income such as interest and dividends, rental income, retirement income, and transfer income (such as government assistance) before payments for such things as personal income taxes, Social Security and Medicare taxes, union dues, etc. Non-cash transfers such as food stamps, health benefits, subsidized housing and energy assistance are not included. As household income is pretax, it does not include stimulus payments or tax credits for earned income and children/dependent care.

Net worth, or wealth, is the difference between the value of what a household owns (assets) and what it owes (debts).

All references to party affiliation include those who lean toward that party. Republicans include those who identify as Republicans and those who say they lean toward the Republican Party. Democrats include those who identify as Democrats and those who say they lean toward the Democratic Party.

At a time when many Americans are questioning the value of a four-year college degree, economic outcomes for young adults without a degree are improving.

After decades of falling wages, young U.S. workers (ages 25 to 34) without a bachelor’s degree have seen their earnings increase over the past 10 years. Their overall wealth has gone up too, and fewer are living in poverty today.

Things have also improved for young college graduates over this period. As a result, the gap in earnings between young adults with and without a college degree has not narrowed.

The public has mixed views on the importance of having a college degree, and many have doubts about whether the cost is worth it, according to a new Pew Research Center survey.

• Only one-in-four U.S. adults say it’s extremely or very important to have a four-year college degree in order to get a well-paying job in today’s economy. About a third (35%) say a college degree is somewhat important, while 40% say it’s not too or not at all important.
• Roughly half (49%) say it’s less important to have a four-year college degree today in order to get a well-paying job than it was 20 years ago; 32% say it’s more important, and 17% say it’s about as important as it was 20 years ago.
• Only 22% say the cost of getting a four-year college degree today is worth it even if someone has to take out loans. Some 47% say the cost is worth it only if someone doesn’t have to take out loans. And 29% say the cost is not worth it.

These findings come amid rising tuition costs and mounting student debt . Views on the cost of college differ by Americans’ level of education. But even among four-year college graduates, only about a third (32%) say college is worth the cost even if someone has to take out loans – though they are more likely than those without a degree to say this.

Four-year college graduates (58%) are much more likely than those without a college degree (26%) to say their education was extremely or very useful in giving them the skills and knowledge they needed to get a well-paying job. (This finding excludes the 9% of respondents who said this question did not apply to them.)

Views on the importance of college differ widely by partisanship. Republicans and Republican-leaning independents are more likely than Democrats and Democratic leaners to say:

• It’s not too or not at all important to have a four-year college degree in order to get a well-paying job (50% of Republicans vs. 30% of Democrats)
• A college degree is less important now than it was 20 years ago (57% vs. 43%)
• It’s extremely or very likely someone without a four-year college degree can get a well-paying job (42% vs. 26%)

At the same time that the public is expressing doubts about the value of college, a new Center analysis of government data finds young adults without a college degree are doing better on some key measures than they have in recent years.

A narrow majority of workers ages 25 to 34 do not have a four-year college degree (54% in 2023). Earnings for these young workers mostly trended downward from the mid-1970s until roughly a decade ago.

Outcomes have been especially poor for young men without a college degree. Other research has shown that this group saw falling labor force participation and sagging earnings starting in the early 1970s , but the last decade has marked a turning point.

This analysis looks at young men and young women separately because of their different experiences in the labor force.

Trends for young men

• Labor force participation: The share of young men without a college degree who were working or looking for work dropped steadily from 1970 until about 2014. Our new analysis suggests things have stabilized somewhat for this group over the past decade. Meanwhile, labor force participation among young men with a four-year degree has remained mostly flat.
• Full-time, full-year employment: The share of employed young men without a college degree who are working full time and year-round has varied somewhat over the years – trending downward during recessions. It’s risen significantly since the Great Recession of 2007-09, with the exception of a sharp dip in 2021 due to the COVID-19 pandemic. For employed young men with a college degree, the share working full time, full year has remained more stable over the years.

• Median annual earnings: Since 2014, earnings have risen for young men with some college education and for those whose highest attainment is a high school diploma. Even so, earnings for these groups remain below where they were in the early 1970s. Earnings for young men with a bachelor’s degree have also trended up, for the most part, over the past 10 years.
• Poverty: Among young men without a college degree who are living independently from their parents, the share in poverty has fallen significantly over the last decade. For example, 12% of young men with a high school diploma were living in poverty in 2023, down from a peak of 17% in 2011. The share of young men with a four-year college degree who are in poverty has also fallen and remains below that of noncollege young men.

Trends for young women

• Labor force participation: The shares of young women with and without a college degree in the labor force grew steadily from 1970 to about 1990. Among those without a college degree, the share fell after 2000, and the drop-off was especially sharp for young women with a high school diploma. Since 2014, labor force participation for both groups of young women has increased.
• Full-time, full-year employment: The shares of employed young women working full time and year-round, regardless of their educational attainment, have steadily increased over the decades. There was a decline during and after the Great Recession and again (briefly) in 2021 due to the pandemic. Today, the shares of women working full time, full year are the highest they’ve ever been across education levels.

• Median annual earnings: Median earnings for young women without a college degree were relatively flat from 1970 until about a decade ago. These women did not experience the steady decline in earnings that noncollege young men did over this period. By contrast, earnings have grown over the decades for young women with a college degree. In the past 10 years, earnings for women both with and without a college degree have risen.
• Poverty: As is the case for young men without a college degree, the share of noncollege young women living in poverty has fallen substantially over the past decade. In 2014, 31% of women with a high school diploma who lived independently from their parents were in poverty. By 2023, that share had fallen to 21%. Young women with a college degree remain much less likely to be in poverty than their counterparts with less education.

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From businesses and banks to colleges and churches: americans’ views of u.s. institutions, fewer young men are in college, especially at 4-year schools, key facts about u.s. latinos with graduate degrees, private, selective colleges are most likely to use race, ethnicity as a factor in admissions decisions, most popular, report materials.

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New lawsuit alleges Agape Boarding School violated former student's Constitutional rights

A new federal lawsuit alleges that a now-closed Stockton boarding school violated a former student's Constitutional rights.

Rebecca Randles, of Randles, Mata LLC, is representing former student P.H. The case is filed against not only Agape Boarding School, but also former dean Brent Jackson, former medical director Scott Dumar and former staff members Julio Sandoval, John Wilke and Robert Graves. The case also lists Cedar County Sheriff James McCrary for his failure to take action despite "reports of abuse occurring at (Agape)."

The complaint, filed May 16, alleges that while P.H. was at Agape, he did not receive education and was instead performing forced labor including excavating and clearing space for a new building. It also says that on one occasion, Sandoval and Jackson "beat P.H. senseless."

More: Victim advocates call on Missouri officials to hold religious boarding schools accountable

The lawsuit asks for eight counts to be levied against the defendants, including violation of a U.S. code that prohibits human trafficking, slavery, indentured servitude and peonage .

Agape and its staff not only transported students across state lines, but they engaged in threats of force and physical restraint as well as the use of force to obtain labor from students there, including P.H., according to court records. The lawsuit identifies this as peonage, where someone is worked by lawless methods against their will "for the purpose of compelling him to discharge real or alleged obligations."

The lawsuit also alleges that in order to keep students on campus, "staff often wrote kids up for things that were untrue in order to scare parents into keeping them at Agape."

Randles is asking for a jury trial in this case as well as damages.

Request for comment for Agape Baptist Church's legal representation has not yet been answered.

Despite closure, Agape Boarding School continues to gather lawsuits

Investigators began looking into the Stockton-based Christian reform school in 2021 due to abuse allegations. On Jan. 20, 2023 it closed "solely due to the lack of financial resources to continue caring for the boys," according to a statement by former director of Agape Boarding School Bryan Clemensen in a press release.

There are currently 26 ongoing cases against Agape Boarding School filed in federal court by former students, with complaints detailing graphic accounts of the alleged abuse . According to reporting by the Kansas City Star , 16 civil lawsuits in state court were settled for undisclosed amounts as of March 7, 2023, and dismissed with prejudice, meaning they cannot be tried again.

In June 2023, the  school lost its accreditation  from the National Council of Private School Accreditation and the Association of Christian Teachers and Schools.

More: ‘A huge slap in the face’: Former Agape Boarding School students worry about prosecution of abuse allegations

In September 2021,  Cedar County Prosecutor Ty Gaither filed 13 low-level "Class E" felony assault charges  against five people linked to Agape Boarding School. As of December 2022, the majority of the charges were dismissed or reduced to misdemeanors .

In August 2022, Sandoval was  accused of transporting a teen against his will  from California to Stockton. The case is still ongoing.

Susan Szuch reports on health and food for the Springfield News-Leader. Follow her on X, formerly known as Twitter, at @szuchsm. Story idea? Email her at [email protected].

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Critical analysis of property tax assessment as revenue for government: a case study of port harcourt.

ESV Ekine, Augustine Nordi Prince,

Property tax assessment is a vital source of revenue for governments worldwide, and Nigeria is no exception. In Port Harcourt, the Rivers State government relies heavily on property tax assessment as a means of generating revenue to fund public goods and services. However, the effectiveness of property tax assessment as a revenue stream for government has been a subject of debate among scholars and policymakers. This analysis will critically examine the property tax assessment system in Port Harcourt, highlighting its strengths, weaknesses, and potential for improvement. Strengths:

• Stable Revenue Source: Property tax assessment provides a stable source of revenue for the government, as property owners are required to pay taxes annually.
• Wide Coverage: The tax net is cast wide, covering both residential and commercial properties, ensuring that a significant portion of the population contributes to the revenue pool.
• Easy to Administer: Property tax assessment is relatively easy to administer, as the tax base is easily identifiable, and the tax rate is fixed. Weaknesses:
• Inequitable Distribution: The tax burden is not distributed equitably, as some property owners pay more than others, despite having similar properties.
• High Tax Rate: The tax rate is high, which can lead to tax evasion and a reduction in compliance.
• Inefficient Administration: The tax administration process is often inefficient, leading to delays and loss of revenue. Case Study: Port Harcourt

Port Harcourt, the capital city of Rivers State, has a large and growing population, with a significant number of properties, both residential and commercial. The city generates a substantial amount of revenue from property tax assessment, which is used to fund public goods and services. However, the property tax assessment system in Port Harcourt faces several challenges, including:

• Inadequate Tax Base: The tax base is narrow, with many properties exempt from taxation, reducing the revenue potential.
• Low Compliance: Tax compliance is low, with many property owners failing to pay their taxes, resulting in a significant loss of revenue.
• Inefficient Tax Administration: The tax administration process is often inefficient, leading to delays and loss of revenue. Conclusion: Property tax assessment is a vital source of revenue for governments, but its effectiveness is dependent on several factors, including the tax base, tax rate, and tax administration. In Port Harcourt, the property tax assessment system faces several challenges, including an inadequate tax base, low compliance, and inefficient tax administration. To improve the effectiveness of property tax assessment as a revenue stream for government, the following recommendations are made:
• Broaden the Tax Base: The tax base should be broadened to include all properties, both residential and commercial, to increase the revenue potential.
• Reduce the Tax Rate: The tax rate should be reduced to make it more affordable for property owners, increasing compliance and reducing tax evasion.
• Improve Tax Administration: The tax administration process should be improved, with the use of technology and efficient administrative processes, to reduce delays and increase revenue collection.

References:

• Adeoye, B. (2017). Property Taxation in Nigeria: A Review of the Legal Framework. Journal of Law and Policy, 12(1), 1-15.
• Aluko, O. (2019). Property Tax Assessment in Nigeria: A Critical Analysis. Journal of Taxation, 10(2), 1-12.
• Federal Inland Revenue Service (FIRS). (2020). Tax Reform: A Guide to the New Tax Law. FIRS Publication.
• International Association of Assessing Officers (IAAO). (2019). Property Tax Assessment: A Guide to Best Practices. IAAO Publication.
• Rivers State Government. (2020). Port Harcourt Property Tax Assessment Law. Rivers State Government Publication. Note: The references provided are a selection of examples, and there are many more studies and resources available that support the critical analysis of property tax assessment as revenue for government, with a case study of Port Harcourt.

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