Ayesha Zainudeen (with contributions from Anah Cassim, Anish Fonseka and Chanduni Bandara)[†]
The Asia Pacific region is home to 60% of the world’s population and an estimated 57% of the world’s labor force. However, there is great heterogeneity across the region between the countries and their labor market contexts, differing in their stages of structural and demographic transformation, as well as their socio-economic compositions, level of formality in their economies, as well as the level of policy capacity. While global, regional and local changes and disruptions to social and economic systems pervade, this heterogeneity affects how their impacts manifest –particularly on the most vulnerable and marginalized groups.
Notwithstanding the vast sub-regional differences, on average the region’s largest employers are the agriculture sector (30% of the workforce in 2021), manufacturing (16%), and wholesale/retail trade (15%).[1] The agriculture sector is often characterized by high levels of informality; low, if not zero wage levels; low productivity; poor working conditions; but it is a significant employer of women across the region. The manufacturing sector grapples with the dual challenge of ensuring decent work conditions and mitigating the risks posed by automation, as well as the more recent disruptions to the global order of trade, as well as global supply chain reorganization;[2] gender disparities in the higher growth sectors (which tend to employ more men than women) are a concern. The service sector, also a significant employer across the region, is dominated by the wholesale/retail trade and construction sectors. Heavily hit by the COVID 19 pandemic, the sector has seen varying levels of recovery; high-skilled occupations have recovered much faster than low-skilled occupations. Altogether these sectors which account for over 60% of the region’s employment are characterized by low productivity, low wages, poor working conditions, a lack of job and income security, and little access to social protection. The lack of decent work therefore is a major challenge across the region. Despite substantial economic growth before the COVID-19 crisis, there were shrinking labor income shares and only limited improvements in decent work outcomes. Economic growth in the region has led to the creation of formal jobs and wage employment, but it has not effectively transformed informal employment into formal employment or significantly improved decent work conditions for the majority of workers. [3] As such, informality pervades labor markets across the region (amplified by the platform economy), encompassing 68% of workers as of 2018, with particularly acute instances in South and Southeast Asia. Levels of informality are generally higher among women, the less educated, and rural populations.[4] The COVID-19 pandemic pushed many workers into informal and more vulnerable jobs, leading to the deepening of existing divides.[5] Ensuring greater public investment in labor market institutions is a challenge for countries.
Gender disparities in the workforce also prevail, with female labor force participation considerably low (even declining) in certain countries, compounded by limited access to STEM fields and digital skills training, perpetuating socio-economic disparities. Sectors which are seeing stronger growth in jobs post-COVID are those where men dominate (also high-skilled, high paid), which limits opportunities for women’s participation and economic gain. Women tend to remain in low skill, low pay sectors which are also characterized by high levels of informality; women therefore tend to lack access to social protection. Women also tend to be found in sectors which are more vulnerable to job loss/displacement due to technological change and climate change and transition. These inequities have huge implications for labor market policies, as well as skills policies and systems. These systems need to be responsive to the needs of women, but also able to adapt to the rapidly changing ecosystem.
The region also faces divergent demographic trends, with some areas experiencing rapid aging populations (e.g., China, Sri Lanka, Thailand) juxtaposed with youthful demographics in others (e.g., Pakistan, Philippines). While youthful populations may be more adept to quickly learn and meet the rapidly changing skills requirements, whether skills and education systems can adapt quickly enough to provide those skills is an important question. Furthermore, whether governments can support increasing old age pension and other related social protection expenses with a narrower base of contributions is a concern. The global pandemic led to an overall decline in working conditions for many young people, forcing them into agricultural and other precarious jobs. The post-COVID recovery of jobs and decent work among youth segments has been slower in low-income countries compared to high income countries, deepening global inequalities. These inequalities will have possible implications for labor migration and social cohesion.[6]
Amidst these dynamics, ‘megatrends’ such as technological advancements, climate change and the energy transition, demographic shifts, and globalization are reshaping job markets, ushering in new paradigms while displacing traditional employment patterns, and creating new forms of inequality while exacerbating existing ones. Three such megatrends are explored in the following sections. The combined impacts of these disruptions still being understood, and there is a real danger of these disruptions leading to worsening labor market outcomes, especially for the vulnerable segments of Global South countries which may be ill-prepared to deal with the challenges and make use of the opportunities that simultaneously emerge – in terms of education/skills, social protection, and care sector policy ecosystems. It is clear that already- vulnerable groups are at risk of disproportionate negative effects of these changes, widening existing disparities.
There is an urgent need to strengthen labor market institutions and empower workers’ and employers’ groups, especially in key sectors. The region is held back by a lack of decent work, gender disparities, outdated and inadept skills ecosystems, insufficient protections for the most vulnerable workers, high levels of informality and weak labor relations. Strengthening labor market institutions can have wide-reaching impacts on living standards for the region. There is consensus on the need for proactive strategies. Policymakers are called to modernize labor policies and skills and education systems, promote job creation in emerging sectors (digital, green economy), and strengthen institutions for social dialogue so that workers, employers, and governments can jointly navigate the transition. International organizations stress a human-centered approach to the future of work agenda – focusing on decent work, inclusive growth, and social justice as guiding principles .
Technological advancement
Technological changes that are affecting work ecosystems include automation, artificial intelligence (AI), the platformisation of work, inter alia. These changes are leading to both job displacement and creation, as well as the augmentation of jobs while altering and shifting skills demands.[7] These changes are also leading to a change in the mix of formal versus informal jobs, having a knock-on effect on the level of coverage of social protection as well as labor rights and protections, working conditions, wage inequality, potential for career progression, etc. Evidence broadly suggests that the impacts of these changes are uneven, with women, low-skilled workers, young workers, informal workers and other marginalized groups set to face disproportionate negative effects of these changes (although some high-skilled occupations are seen to face negative impacts).[8]
Platformisation of work has been seen to reinforce existing (‘offline’) gender norms that limit women’s participation in the labor market, reinforce the precarity that women and other informal sector workers already face, and lead to women being stuck in low-paying roles, overall leading to poorer labor market outcomes for women. [9] The pandemic accelerated platformisation and more broadly digitization: businesses and governments adopted remote work, e-commerce, and digital services out of necessity, changes that are now becoming permanent features of the work landscape. Indeed, the future of work in Asia is trending toward greater flexibility and digital integration. This means more hybrid work arrangements, gig and freelance work facilitated by online platforms, and cross-border remote collaboration. Such flexibility can improve efficiency and work-life balance but also raises challenges around job security and benefits. Worker well-being has accordingly become a focal point, with employers and policymakers increasingly aware of the need to support mental health, work-life balance, and fair labor practices in the face of rapid change.[10]
Technological advancements leading to the comparative costs of labor versus robots changing, also brings about concerns of reshoring/near-shoring of certain industries such as manufacturing and business process outsourcing; some of these segments are more or less feminized than others, and involve lower levels of skill, therefore potentially leading to uneven effects. While a little more is known about the deployment of AI tools for routine task (including robotics), less is known about the deployment of generative AI in less routine tasks, especially in creative occupations. The ILO’s recent estimation of the impact of generative AI on jobs in high-income countries, suggests that the technology will overall have an augmenting effect, at least in the short run, particularly for knowledge workers, although certain occupations (such as clerical and administrative roles) face higher automation risk.[11] Less is understood about how these impacts are manifesting in lower income countries, where labor markets already face high levels of inequality, costly infrastructure, relatively lower skill and wage levels to adapt, low levels of digital skills, inter alia. If the augmenting effects are similarly applicable to lower income countries, this could potentially exacerbate inequalities between higher socio-economic groups (i.e., knowledge workers), versus lower groups.
With the growing importance of AI workers in the AI and data ‘value chain’, a number of concerns exist. AI data work can involve anything from data labelling or enrichment to writing code to train algorithms on large datasets. Most often, it is outsourced to workers in the Global South. Work arrangements can vary from freelancer platform-based gigs to contract work arrangements at formal BPO-type operations.[12] There are direct parallels with the well documented challenges faced by platform workers broadly: precarity of work, low bargaining power and agency of workers, risks of exploitation–particularly for women and migrant workers, challenges to organizing, and more. Where the work happens across borders,[13] enforcement of labor standards is poor, organizing is hard; origin countries tend to overlook these ‘invisible’ workers. There are other concerns that are more specific to online content moderation , particularly mental health impacts. Some models of social impact operations have emerged in India and the Philippines, however evidence on how the positive elements can be scaled is lacking, as is evidence of success of workers in organizing. There is a case to be made for the displacement of some of these jobs by AI with the addition of synthetic data to train models; there is considerable potential particularly with respect to the traumatic content moderation roles.[14] But this too comes with potential costs and risks; a wider evidence base is needed to understand if AI can play a role in improving outcomes.
WEF argues that AI can help informal workers by making their skills more visible, improving how they find and secure jobs, and supporting communication in local languages. By building on familiar digital tools (digital identification for example), it argues that AI offers pathways to more stable and dignified work without forcing formal employment structures.[15] The enabling role of digital public infrastructure (DPI) is something of note and interest here, particularly when it comes to identity verification, digital payments, and other elements. The role of DPI in facilitating portability of social benefits has been explored in India – highlighting its potential for vulnerable income groups including migrant workers which make up a large share of the informal economy.[16] While there may be potential to improve the outcomes of informal workers—especially migrant workers—this is an evolving area and needs further exploration, also taking into account the extent of the digital divide in the contexts being studied.
While much research has focused on the disproportionate negative impacts of AI-led automation on women workers, some attention is due to the potential for improving gender equity in previously male-dominated sectors. An example is the AI-led automation of in the ports and logistics industries, where physically demanding outdoor mechanical jobs (e.g., crane operation) are now turned into indoor computer-based jobs, reducing barriers to women’s (and even persons with disabilities) participation, and improving working conditions.[17] Research is required on how such examples can be scaled, and what are the other prerequisites that are also needed to support more diverse participation.
The rapid digitization of economies over the past decade –especially during the Covid-19 pandemic— has also created a constant supply of large amounts of transaction and other data being generated every minute. This gives rise to opportunities for using big data to better understand the labor market in near-real time for labor market policy, though data limitations must be well understood and taken into consideration before conclusions are drawn based on this data. [18]
Climate change and the energy transition
Climate change impacts labor markets across a majority of economic sectors, both in quantity and quality of jobs.[19] Some sectors such as agriculture may be more vulnerable, and therefore, the impact on L-LMIC countries could be more because more people work in agriculture in L-LMIC Asia; these jobs are already more likely to lack decent conditions. On the one hand, the direct impact of climate change which manifests as global warming, ecological disasters, natural disasters, and extinction of species will have consequences on job markets, such as deteriorating working conditions, reduced working hours, reduced labor productivity, displacement of jobs, etc.
On the other hand, the solution to climate change and progress towards sustainability by transitioning to a low carbon economy (or the greening of economies) will have an impact on the job market through the structural changes it entails, with job creation, new skills coming into demand, changes in work practice, and so on.[20] The effects of these changes will in conjunction with those happening in other realms are only beginning to be understood.
For example, AI/technology revolutions could mean not just new jobs and skills but also a change in the number of jobs that can be created. Which sectors will be the net gainers vs the net losers are yet to be understood, moreover knowledge on exactly how jobs and tasks within jobs are changing is even less understood. Difficulties are more pronounced for developing countries which may be majorly driven by carbon intensive sectors which employ large numbers of low skilled workers.[21] Moreover, the effects within Global South job markets may be divergent. For example, workers in certain sectors such as agriculture and construction are more exposed to heat stress; workers in these sectors are low skilled, and often not formal and thus outside of the ambit of social protection; given the high proportion of women employed in agriculture, they are thus also more likely to be impacted by effects of heat stress than men. Evidence suggests that women have a harder time reskilling to fit into these new roles than men. Therefore, there are clear equity challenges to be addressed in this area. With these kinds of inequalities arising, it is critical that policies and institutions are prepared and able to adapt in a way that ensures marginalized groups are not disproportionately affected, and that not excluded from the new opportunities arising through these structural changes. It is crucial to ensure that everyone – women, youth, rural populations, low-skilled workers, persons with disabilities, gender and sexual minorities, ethnic minorities, inter alia — has the ability to adapt and reskill as easily as each other to fit into these new roles.
An important part of the skills discourse is the future of green skills. While there is consensus that there is a need for greater focus on green skills, as well as green technology skills, there is also a reasonable level of understanding of the barriers to these skills systems (outdated programs, lack of adaptability, inability to deal with cross-sectoral needs). There is also some understanding that what is required are context-specific green skill strategies, particularly toward key green sectors (agriculture, energy, waste management) and potential ones (sustainable agriculture, eco-tourism, renewables); further, alignment of education, environment, industrial policies are required. Less understood are the effectiveness of various training approaches in key green sectors for the informal and rural sectors, and the long-term impacts of green skills investments impact employment and social inclusion.[22]
On the positive side, energy transition also presents opportunities for improving labor markets towards a just transition for workers, though how far these will materialize depends on implementation, context, capacity, and other factors. Financing of the just transition for workers is one such opportunity. Various models are evolving, recognizing the instrumental role that the finance sector can play in facilitating access to capital and efficient risk sharing mechanisms for the transition to a low carbon economy, in such a way that decent worker outcomes can be prioritized.[23] This can include various forms of financing, incentives, insurance, impact investing, and so on. But how do we know what works, and under what conditions they work better or worse?[24]
Demographic changes
The three key demographic trends that are affecting various labor markets in the region are ageing populations in some countries (e.g., Sri Lanka, Thailand), more youthful populations in others (e.g., Pakistan, Philippines), and high levels of migration in others (e.g., Afghanistan, India).
Increasing aging populations combined with lower fertility rates place a strain on the ability of middle-income countries to provide old-age pension and provide universal healthcare. This is exacerbated by the size of informal economies as informal workers make minimal contributions to social security provisions.[25] The impacts of increased (unpaid) care burden falls disproportionately on women, and puts women more at risk of falling into poverty in old age.[26] Further, it limits their ability to work and earn in their working years (providing for their retirement/future needs), given the pressure of both childcare and elder care, plus other household duties. The estimated lifetime employment-related costs to women of providing unpaid care have been estimated in the US, taking into account not just the loss of lifetime earnings (due to curtailment of employment, reduced working hours, etc.), but also their subsequent retirement income, amounting to the equivalent of 15% of what women could earn over a lifetime.[27] Similar estimates for the Asia Pacific could help to better prepare countries to prepare for these shifts. Increasing trends towards migration (especially of care workers, seeking better pay and better working conditions abroad) exacerbate the problems. There are therefore significant opportunities in the care economy in these countries, in terms of job creation; however many of these jobs remain unpaid and informal, with poor working conditions unless governments take efforts to reform their care sector and change some of the core perceptions on care work; while some countries are in the process of doing just that, lessons from successful examples and case studies could help in the redesign and rethink of the care sector. Though technological solutions to assist in care work are technically feasible,[28] economic feasibility in L-LMIC settings is a question.
Many nations across Asia are experiencing a demographic dividend where the working age population is greater than dependent population (elderly and child populations). This youth bulge represents an opportunity for economic growth through an increased labor force participation rate but simultaneously creates economic and social challenges related to unemployment if they are not absorbed by the labor force. A key driver of unemployment is the gap between skills and education and the demands of the labor market,[29] pointing towards the need for education and skills policies to be rethought. Given the rapid pace of technological change, and constantly evolving skills demands, governments and businesses are responding with initiatives for continuous learning and reskilling. Lifelong learning systems – including e-learning platforms, vocational training, and on-the-job upskilling – are expanding to help workers adapt to new demands.[30] Nevertheless, ensuring “trainability” – that workers have strong foundational skills and digital literacy to learn new competencies – remains an urgent challenge. Without concerted investment in human capital, the promise of technological innovation could be undermined by structural unemployment or widening inequality.
The Covid-19 pandemic worsened youth unemployment in the Asia Pacific as nearly half of all youth workers in the region (47%) are employed in the four sectors that were hit hardest by the crisis (wholesale/retail trade, manufacturing, business services, and accommodation/food services).[31] It also worsened working conditions for young workers, as they shifted to agriculture in large numbers.[32] This makes them more vulnerable to job losses than older workers. The impact of generative AI on sectors such as the business process outsourcing sector, and other sectors which have previously provided relatively decent work opportunities for young workers, is also an area that requires exploration; on the one hand, such developments could lead to job displacement among some kinds of workers, while on the other hand, certain kinds of jobs could be augmented. How this manifests among vulnerable workers versus less vulnerable workers’ is yet to be seen.
It is expected that low-skilled labor migration will prevail in Asia in the coming decade. Major source countries include Bangladesh, India, Indonesia, Nepal, Myanmar, Philippines, Sri Lanka, Cambodia, Vietnam. These countries have relatively lower per capita incomes, abundant labor supply, and lower human development indicators. Major destinations include Thailand, Malaysia, Singapore, Japan, South Korea, Hong Kong. These countries have higher per capita incomes, face labor shortages, and have higher human development.[33] Migration is driven by income considerations, but also the lack of quality jobs and high unemployment, and increasingly climate-induced factors from origin countries. Increasing demands for care workers due to aging populations in the destination countries, as well as economic transformation of middle-income countries creating infrastructure jobs relying on migrants.[34] While automation is unlikely to change migration trends of low-skilled workers within the region, they are at the risk of job loss unless provided access to skills development opportunities.[35] Intra-country migration –driven by economic needs as well as increasing frequency and intensity of climate shocks– also presents major challenges. Often this migration is from rural to urban causing cities to expand (Asian cities are growing at alarming rates), and impacts the demographic mix of labor in both the origin and destination. At the same time, the transition to greener economies could create new job opportunities (e.g., in renewable energy, energy efficiency, sustainable construction), for those with the right skills.[36]It is thus important that migrant workers are not excluded from skills initiatives (especially green skills) and social protection schemes which can both increase their resilience in the face of the disruptions and risks that they are vulnerable to. The rise of digitally-enabled risks and harms that migrant workers often are affected by is of concern. This includes online recruitment scams, unregulated remittance and finances apps, identity theft, forced labor, and other forms of exploitation. [37] Ensuring that appropriate policies, domestic as well as cross-border, are in place to a balance in terms of minimizing the harm –both due to the risk as well as the policy response—is key; how effective these are in practice needs to be understood.
Women being employed more often in vulnerable sectors (e.g., those where they face disproportionate risk of automation, or climate-induced job loss) and informal jobs lacking labor rights and protections, as well as income disparities within origin countries pushes women to migrate in search of higher pay. The rising care sector in ageing economies presents opportunities for employment for women migrant workers. However, as this sector mostly falls under the informal economy, women migrant workers lack protection from existing labor laws and protections.
Given the disruptions caused by these mega changes (as well as the recent disruptions in the global order of trade), and the opportunities and challenges that are manifesting, how do we ensure that the most vulnerable workers in developing Asia are taken care of? That they are sufficiently equipped with access to the right skills and education, to labor rights and protections, and to affordable, quality care services, as well as other facilitators of work to ensure decent work outcomes and greater resiliency for the workforce.
There is a need for high quality South-centric knowledge that takes a holistic view of these challenges (as opposed to a siloed approach), and is able to provide solutions to ensure a sustainable and inclusive future of work for the Global South.
There is a key need to move beyond describing the challenges that are already known, to focus on what is and can be done to address these challenges, in terms of policy and solutions, particularly in the realms of education and skills policy, labor rights and protections, and the care economy. These three areas are key foundations posts upon which inclusive and decent work outcomes depend on. There is an urgent need to understand what works, what doesn’t, what factors contribute to this, and how can best/good practices be adapted to other contexts.
[†] This write-up is based on a scoping exercise that was conducted to understand the evolving challenges faced by workers in the Asian region, particularly those more vulnerable such as women, informal workers, etc. This was done as a precursor to issuing a region-wide call for proposals for policy-relevant research toward an inclusive and sustainable future of work for the region, to identify potential research gaps that can be addressed. The first call for proposals was issued in 2024 by LIRNEasia under the IDRC-funded FutureWORKS Asia initiative. The review was updated in 2025 prior to a second such call for proposals was issued, and is contained herein.
[1] https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@dcomm/@publ/documents/publication/wcms_862410.pdf
[2] https://www.theguardian.com/business/2025/apr/06/trumps-tariffs-may-be-perilous-for-small-heavily-indebted-countries-in-global-south
[3] ILO. (2022). Asia–Pacific Employment and Social Outlook 2022: Rethinking sectoral strategies for a human-centred future of work International Labor Office – Geneva: ILO, 2022.
[4] https://theaseanmagazine.asean.org/article/informal-workers-why-their-inclusion-and-protection-are-crucial-to-the-future-of-work/
[5] ILO. (2022). Asia–Pacific Employment and Social Outlook 2022: Rethinking sectoral strategies for a human-centred future of work International Labor Office – Geneva: ILO, 2022.
[6] ILO. (2023). Has youth employment recovered? ILO Brief. https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@ed_emp/documents/publication/wcms_885192.pdf
[7] ILO. (2019) Preparing for the future of work: National policy responses in ASEAN +6 https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@asia/@ro-bangkok/@sro-bangkok/documents/publication/wcms_717736.pdf ; AfDB, ADB, EBRD, IDB (African Development Bank, Asian Development Bank, European Bank for Reconstruction and Development, Inter-American Development Bank). 2018. The Future of Work: Regional Perspectives. Washington, DC. ; ADB. (2019). The Digital Revolution in Asia and Its Macroeconomic Effects – https://www.adb.org/sites/default/files/publication/535846/adbi-wp1029.pdf
3 AfDB, ADB, EBRD, IDB (African Development Bank, Asian Development Bank, European Bank for Reconstruction and Development, Inter-American Development Bank). 2018. The Future of Work: Regional Perspectives. Washington, DC; JJN. (2022). Technology and the Future of Work: Artificial Intelligence. https://justjobsnetwork.org/files/technology-and-the-future-of-work_oct_2022.pdf ; Hegewisch, Ariane & Bendick, Marc & Gault, Barbara & Hartmann, Heidi. (2016). Pathways to Equity: Narrowing the Wage Gap by Improving Women’s Access to Good Middle-Skill Jobs. ; Hegewisch, Ariane & Childers, Chandra & Hartmann, Heidi. (2019). Women, Automation, and the Future of Work; ) Brussevich, M., Dabla-Norris, E., and Khalid, S. (2019). Is Technology Widening the Gender Gap? Automation and the Future of Female Employment. IMF Working Paper; Gmyrek, P., Berg, J., Bescond, D. Generative AI and jobs: A global analysis of potential effects on job quantity and quality. ILO Working Paper 96. Geneva: International Labor Office, 2023
[9] JustJobs Network. (2023). Empowerment or Exploitation: Global Perspectives on Women’s Work in the Platform Economy; Ecosystems of Engagement: Digital Platforms and Women’s Work in Sri Lanka and India https://justjobsnetwork.org/files/empowerment-or-exploitation-global-perspectives-on-womens-work-in-the-platform-economy_may-2023.pdf
[10] International Labour Organization (ILO). (2022). Working time and work-life balance around the world. ILO. https://www.ilo.org/publications/working-time-and-work-life-balance-around-world
[11] Gmyrek, P., Berg, J., Bescond, D. Generative AI and jobs: A global analysis of potential effects on job quantity and quality. ILO Working Paper 96. Geneva: International Labor Office, 2023.
[12] Muldoon, J., Cant, C., Wu, B., & Graham, M. (2024). A typology of artificial intelligence data work. Big Data & Society. https://doi.org/10.1177/20539517241232632
[13] CEPR. (2019). Working conditions on digital labor platforms: Opportunities, challenges, and the quest for decent work. https://cepr.org/voxeu/columns/working-conditions-digital-labor-platforms-opportunities-challenges-and-quest-decent
[14] https://www.turing.ac.uk/sites/default/files/2024-01/exploring_responsible_applications_report_november_2023_-_final_report.pdf
Wullach, T., Adler, A., & Minkov, E. (2021). Towards Hate Speech Detection at Large via Deep Generative Modeling. IEEE Internet Computing, 25(2), 48–57. https://doi.org/10.1109/MIC.2020.3033161
[15] World Economic Forum. (2025, May 22). How AI is reshaping the future of informal work in the Global South. https://www.weforum.org/stories/2025/05/ai-reshaping-informal-work-global-south/
[16] Vedavalli, P., Kwatra, N., Srinivasan, S., & Sinha, V. (2024). Leveraging digital public infrastructure for building inclusive social protection systems (Version 2.1). Artha Global. https://artha.global/wp-content/uploads/2024/04/Leveraging-Digital-Public-Infrastructure-V2-1.pdf
[17] https://www.dpworld.com/insights/how-technology-is-reshaping-the-workforce-in-our-ports-and-terminals; https://archives1.dailynews.lk/2019/06/18/finance/188644/%E2%80%98port-automation-will-attract-more-women%E2%80%99
[18] https://lirneasia.net/2022/02/exploring-the-use-of-online-job-portals-for-labor-market-analysis/
[19] Harris, J. M., & Roach, B. (2017). Environmental and natural resource economics: A contemporary approach. Routledge. http://students.aiu.edu/submissions/profiles/resources/onlineBook/H9K3x5_Environmental%20and%20Natural%20Resource%20Economics%202017.pdf
[20] Bowen, A., & Kuralbayeva, K. (2015). Looking for green jobs: the impact of green growth on employment. Grantham Research Institute Working Policy Report. London: London School of Economics and Political Science, 1-28. http://portal.gms-meoc.org/uploads/resources/3382/attachment/Looking_for_green_jobs_the_impact_of_green_growth_on_employment_GGGI_Grantham_Research_Institute_on_Climate_Change_on_the_Environment_0.pdf
[21] Van der Ree, K. (2019). Promoting green jobs: Decent work in the transition to low-carbon, green economies. In The ILO@ 100 (pp. 248-272). Brill Nijhoff. https://library.oapen.org/bitstream/handle/20.500.12657/37968/9789004399013_webready_content_text.pdf#page=269
[22] International Labour Organization. (2019). Skills for a greener future: A global view based on 32 country studies. https://www.ilo.org/wcmsp5/groups/public/—ed_emp/—ifp_skills/documents/publication/wcms_709121.pdf
[23] International Labor Organization. (2021). Climate change and financing a just transition. https://www.ilo.org/resource/other/climate-change-and-financing-just-transition
[24] Ibid.
[25] International Labor Organization. (2019) Preparing for the future of work: National policy responses in ASEAN +6 https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@asia/@ro-bangkok/@sro-bangkok/documents/publication/wcms_717736.pdf
[26] International Labor Organization. (2018). Game Changers: Women and the Future of Work in Asia and the Pacific. https://www.ilo.org/media/414776/download
[27] Johnson, R.W., Smith, K.E., and Butrica, B.A. (2023). Lifetime Employment-Related Costs to Women of Providing Family Care. https://www.dol.gov/sites/dolgov/files/WB/Mothers-Families-Work/Lifetime-caregiving-costs_508.pdf
[28] ILO. (2019) Preparing for the future of work: National policy responses in ASEAN +6 https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@asia/@ro-bangkok/@sro-bangkok/documents/publication/wcms_717736.pdf
[29] JustJobs Network. (2022). Reimagining Employability for the 21st-century: 10 Million Apprentices in 10 Years. https://justjobsnetwork.org/files/reimagining-employability-for-the-21st-century-10-million-apprentices-in-10-years_aug_2022.pdf
[30] Asian Development Bank. (2024). The future of work, artificial intelligence, and digital government: Policy perspectives from Asia. ADB. https://www.adb.org/sites/default/files/publication/995866/adbi-future-work-artificial-intelligence-and-digital-government-policy-perspectives-asia.pdf
[31] International Labor Organization, Bangkok (Thailand), and Asian Development Bank, Manila (Philippines). (2020). https://www.ilo.org/wcmsp5/groups/public/—asia/—ro-bangkok/documents/publication/wcms_753369.pdf
[32] International Labor Organization. (2023). ILO Brief: Has youth employment recovered? https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@ed_emp/documents/publication/wcms_885192.pdf
[33] ILO. (2023). Labor Migration in Asia: What Does the Future Hold? https://roasiapacific.iom.int/sites/g/files/tmzbdl671/files/documents/2023-07/iom_labor-migration-in-asia_what-does-the-future-hold.pdf
[34] Ibid.
[35] Ibid.
[36] ADBI-OECD-ILO. (2024). Labor Migration in Asia: Trends, Skills Certification and Seasonal Work.
[37] Ibid.
