International Labour Organization

We explore the relationship between the internationalization of production through Global Value Chains and child labour at the sector level, using data from 26 low‐ and middle‐income countries. We find that sectors with stronger participation in foreign markets by exporting inputs to firms that will process them and export them to third countries (forward linkages), exhibit less child labour. However, other forms of participation in foreign markets through gross exports or exports of goods that embed foreign value‐added (backward linkages) are not associated with lower levels of child labour.

Objectives Burden of disease estimation commonly requires estimates of the population exposed to a risk factor over a time window (yeart to yeart+n). We present a microsimulation modelling approach for producing such estimates and apply it to calculate the population exposed to long working hours for one country (Italy).Methods We developed a three-model approach: Model 1, a multilevel model, estimates exposure to the risk factor at the first year of the time window (yeart). Model 2, a regression model, estimates transition probabilities between exposure categories during the time window (yeart to yeart+n). Model 3, a microsimulation model, estimates the exposed population over the time window, using the Monte Carlo method. The microsimulation is carried out in three steps: (a) a representative synthetic population is initiated in the first year of the time window using prevalence estimates from Model 1, (b) the exposed population is simulated over the time window using the transition probabilities from Model 2; and (c) the population is censored for deaths during the time window.ResultsWe estimated the population exposed to long working hours (i.e. 41-48, 49-54 and ≥55 hours/week) over a 10-year time window (2002-11) in Italy. We populated all three models with official data from Labour Force Surveys, United Nations population estimates and World Health Organization life tables. Estimates were produced of populations exposed over the time window, disaggregated by sex and 5-year age group.Conclusions Our modelling approach for estimating the population exposed to a risk factor over a time window is simple, versatile, and flexible. It however requires longitudinal exposure data and Model 3 (the microsimulation model) is stochastic. The approach can improve accuracy and transparency in exposure and burden of disease estimations. To improve the approach, a logical next step is changing Model 3 to a deterministic microsimulation method, such as modelling of microflows.

This chapter explores the significance of geographical processes in interindividual inequalities by covering all dimensions of access to employment. It deals with digital space to explore whether some of the changes brought about or facilitated by digital technologies might erase the geographical dimension of access to employment, and their consequences in terms of unequal access to employment. The economic literature pays particular attention to the role of interregional labor migration as a mechanism for balancing the performance of regional labor markets. When jobs are far away from the people seeking them, there is a spatial mismatch. The space in which the household establishes its main residence is one of the most structurally significant, since it is one of the determinants of its members' labor markets. Free entry into the labor market is marked by information asymmetries for both employers and workers.

Background The relationship between health and labour has received considerable attention. There have been several studies exploring the link between health shocks and labour supply. However, there are only few systematic reviews and meta-analyses in this area. The current work aims to fill the gap by undertaking a systematic review and meta-analysis on the effects of health shocks and labour supply. Purpose The purpose of this work is to conduct a systematic review and meta-analysis in order to produce pooled estimates of the effects of health shocks on labour supply. This provides two main contributions to the literature. First, it offers a comprehensive systematic review on the relationship between health and labour supply, an area of research where systematic reviews are uncommon. Second, it goes beyond a standard qualitative synthesis by performing a meta-analysis to quantify the combined effects of health shocks on labour supply. This might offer policy makers more accurate and credible evidence as pooled effects have the advantage of being based on larger sample sizes. Methods We conduct a thorough search using the databases EconLit and Medline together with grey literature to identify relevant papers for the analysis. We check results of these papers and extract the necessary information following an extraction tool. We calculate partial correlations to determine effect sizes and estimate the overall effect sizes by using the random effects model captioned in forest plots. Sub-group analyses are conducted based on geography, publication year and model type to assess the sources of heterogeneity. We also employ multivariate and univariate meta regressions to further examine the sources of heterogeneity. Moreover, we test for publication bias by using a funnel plot, the Egger’s test, the Begg’s test and the trim and fill methodology. Results We find a negative and statistically significant pooled estimate of the effect of health shocks on labour supply. The studies exhibited substantial heterogeneity with the I² test showing 96.6 percent. Sub-group analysis and univariate meta regressions showed that sample size, geography, model type and publication year were significant sources of heterogeneity. The funnel plot and the Egger’s test showed some level of publication bias, but this was contrasted by both the Begg’s test and the trim and fill methodology. Conclusion We undertook a systematic review and meta-analysis on the effects of health shocks on labour supply. We searched the Econ Lit and Medline databases together with grey literature. Using partial correlations, we estimated the overall effect size by employing a random effects model and found a negative pooled effect of health shocks on labour supply. Sub-group analyses along with meta regressions were undertaken to deal with the observed high heterogeneity among studies and we established that geography, sample size, model type and publication year are significant sources of heterogeneity. Our results are novel in that this is the first meta-analysis on the topic directly filling the gap regarding understanding of pooled effects of health shocks on labour supply. The study is relevant for the understanding of policies regarding social protection, disability allowance and other relevant policies emanating from the health -labour relationship.

This paper studies the effect of structural change on the historical path of aggregate labor productivity growth for a large sample of European countries, and it builds a quantitative multi-sector growth model to analyze the potential impact that structural change may have on future productivity growth. We document that the observed reallocation of economic activity since the 1970s towards the service sector has exerted a strongly negative effect on aggregate productivity growth in most European countries. Moreover, we perform a quantitative analysis to show that the expected path of structural change might continue to have a sizable dent on future productivity growth in Europe. By contrast, the impact in the U.S. is expected to rapidly diminish. We show that this differential result can be explained by the large expansion, in Europe, of certain service sub-sectors characterized by stagnant productivity.

Digitalization and artificial intelligence increasingly affect the world of work. Rising risk of massive job losses have sparked technological fears. Limited income and productivity gains concentrated among a few tech companies are fueling inequalities. In addition, the increasing ecological footprint of digital technologies has become the focus of much discussion. This creates a trilemma of rising inequality, low productivity growth and high ecological costs brought by technological progress. How can this trilemma be resolved? Which digital applications should be promoted specifically? And what should policymakers do to address this trilemma? This contribution shows that policymakers should create suitable conditions to fully exploit the potential in the area of network applications (transport, information exchange, supply, provisioning) in order to reap maximum societal benefits that can be widely shared. This requires shifting incentives away from current uses toward those that can, at least partially, address the trilemma. The contribution analyses the scope and limits of current policy instruments in this regard and discusses alternative approaches that are more aligned with the properties of the emerging technological paradigm underlying the digital economy. In particular, it discusses the possibility of institutional innovations required to address the socio-economic challenges resulting from the technological innovations brought about by artificial intelligence.

The COVID-19 pandemic has generated an unprecedented economic, health, labour and social crisis in the world. Latin America is one of the regions that has been most strongly affected. The main aim of this paper is to assess the dynamics of family income inequality and its components since the onset of the pandemic in six Latin American countries -Argentina, Brazil, Colombia, Costa Rica, Peru and Uruguay-. The unequalizing impact of the worsening of the labour market during the contraction phase was mainly associated with the significant loss of informal, low-paid, jobs. This effect was offset, at least partially, by the equalizing role of cash transfers policies put in place in the outbreak of the pandemic. An opposite impact of these income sources appears during the recovery phase, as most countries gradually reduced or stopped those transfers as employment and, therefore, labour incomes partially recovered. Nearly two years into the COVID-19 pandemic inequality is higher than 2019 in almost all countries studied exacerbating existing high-income gaps in one of the world's most unequal regions.

Over the past 100 years, life expectancy has increased dramatically in nearly all nations. Yet, these extra years of life gained have not all been healthy, particularly for older people aged 60 years and over. In 2020, the World Health Organisation (WHO) and United Nations (UN) member states embraced a sweeping 10-year global plan of action to ensure all older people can live long and healthy lives, formally known as the UN Decade of Healthy Ageing (2021–2030). With the adoption of the UN Decade of Healthy Ageing resolution, countries are committed to implementing collaborative actions to improve the lives of older people, their families and the communities in which they reside. The Decade addresses four interconnected areas of action. Adopting the UN's resolution on the Decade of Healthy Ageing has caused excitement, but a question that has weighed on everyone's mind is how governments will be held accountable? Besides, there have been no goals or targets set for the UN Decade of Healthy Ageing from a programmatic perspective for the action areas, and guidance on measures, data collection, analysis and reporting are urgently needed to support global, regional and national monitoring of the national strategies, programmes and policies. To this end, WHO in collaboration with UN agencies and international agencies established a Technical Advisory Group for Measurement of Healthy Ageing (TAG4MHA) to provide advice on the measurement, monitoring and evaluation of the UN Decade of Healthy Ageing at the global, regional and national levels.

This paper analyzes whether taxation can be successfully used to reduce the incidence of labor informality and achieve higher equality in a globalized economy. To this purpose, it develops a two-area model: a developed country and an emerging country. The two areas differ according to the size of the informal sector, which is characterized by a more flexible labor market and lower productivity. To illustrate the potential role of taxation in achieving a more fair income distribution, the paper introduces a trade shock to simulate the effects of trade liberalization. Trade expansion has often been blamed for leading to an expansion of the informal sector and a widening of wage income disparities. In this context, the paper analyzes whether a budget-neutral tax reform – switching the tax burden from payroll taxes paid by firms operating in the formal sector to a consumption tax – can mitigate possible adverse effects of trade liberalization and support labor formalization. The effects of taxation are seen in the context of the trade-offs between growth, labor formality and equity. The analysis suggests that small improvements in formalization, resulting from the tax reform, come at the cost of widening income inequality. To reduce the incidence of low-quality jobs, tax policy interventions should go hand in hand with more effective social protection systems and labor laws.

The paper studies the influence of linguistic proximity on the labour market outcomes of the asylum population. Asylum seekers are randomly assigned to a location upon arrival in Switzerland. Switzerland is divided by the dominant language spoken in each municipality, either German or Romance (French and Italian) languages. Using an administrative dataset, I compare the outcomes of asylum seekers from different countries from 2010 to 2014. I find that linguistic proximity increases employment, especially among the earlier arrival cohorts. I find neither discernible effect of proximity to English on economic integration nor differences in outcomes across language regions. These findings provide descriptive evidence in support of acquiring local languages.

In the last decades, the world economy is facing a massive rise in automation, robotics and Artificial Intelligence (AI) which, according to some analysts, could lead to significant job losses or job polarization and hence widen income and wealth disparities. This scenario may impede the achievement of the Sustainable Development Goal 8 (SDG 8). In this context, the role of government and regulation becomes crucial in order to prevent an undesirable scenario, where technological change, namely automation and AI, comes at the cost of mass unemployment and growing inequality. This paper focuses on the role of taxation as a possible tool for sharing the gains from automation and AI. Nowadays, advances in technology may have a direct impact on tax systems, which should be re-adapted to take into account new forms of jobs and new business models. The paper discusses pros and cons of several possible solutions and then compares progresses achieved in different countries. Concerning robot tax and digital taxes there are already some concrete steps undertaken both at national and international level, while other proposals remain still nebulous. Of course, taxation per se, and any single policy in general, is not sufficient to achieve a more inclusive and equal growth. It is instead crucial to create synergies across policies and a strong link between employment creation strategies, redistributive policies, skill development and social protection systems.

Background: Globally, 13% of the youth are not in education, employment or training (NEET). Moreover, this persistent problem has been exacerbated by the shock of Covid-19 pandemic. More youth from disadvantaged backgrounds are likely unemployed than those from better off backgrounds. Thus, the need for increased use of evidence in the design and implementation of youth employment interventions to increase effectiveness and sustainability of interventions and outcomes. Evidence and gap maps (EGMs) can promote evidence-based decision making by guiding policy makers, development partners and researchers to areas with good bodies of evidence and those with little or no evidence. The scope of the Youth Employment EGM is global. The map covers all youth aged 15-35 years. The three broad intervention categories included in the EGM are: strengthening training and education systems, enhancing labour market and, transforming financial sector markets. There are five outcome categories: education and skills; entrepreneurship; employment; welfare and economic outcomes. The EGM contains impact evaluations of interventions implemented to increase youth employment and systematic reviews of such single studies, published or made available between 2000 and 2019. Objectives: The primary objective was to catalogue impact evaluations and systematic reviews on youth employment interventions to improve discoverability of evidence by decision makers, development patterners and researchers, so as to promote evidence-based decision making in programming and implementation of youth employment initiatives. Search methods: Twenty databases and websites were searched using a validated search strategy. Additional searches included searching within 21 systematic reviews, snowballing 20 most recent studies and citation tracking of 10 most recent studies included in the EGM. Selection criteria: The study selection criteria followed the PICOS approach of population, intervention, relevant comparison groups, outcomes and study design. Additional criterion is; study publication or availability period of between 2000 and 2021. Only impact evaluations and systematic reviews that included impact evaluations were selected. Data collection and analysis: A total of 14,511 studies were uploaded in EPPI Reviewer 4 software, upon which 399 were selected using the criteria provided above. Coding of data took place in EPPI Reviewer basing on predefined codes. The unit of analysis for the report is individual studies where every entry represents a combination of interventions and outcomes. Main results: Overall, 399 studies (21 systematic reviews and 378 impact evaluations) are included in the EGM. Impact evaluations (n = 378) are much more than the systematic reviews (n = 21). Most impact evaluations are experimental studies (n = 177), followed by non-experimental matching (n = 167) and other regression designs (n = 35). Experimental studies were mostly conducted in both Lower-income countries and Lower Middle Income countries while non-experimental study designs are the most common in both High Income and Upper Middle Income countries. Most evidence is from low quality impact evaluations (71.2%) while majority of systematic reviews (71.4% of 21) are of medium and high quality rating. The area saturated with most evidence is the intervention category of 'training', while the underrepresented are three main intervention sub-categories: information services; decent work policies and; entrepreneurship promotion and financing. Older youth, youth in fragility, conflict and violence contexts, or humanitarian settings, or ethnic minorities or those with criminal backgrounds are least studied. Conclusions: The Youth Employment EGM identifies trends in evidence notably the following: Most evidence is from high-income countries, an indication of the relationship between a country's income status and research productivity.The most common study designs are experimental.Most of the evidence is of low quality. This finding serves to alert researchers, practitioners and policy makers that more rigorous work is needed to inform youth employment interventions. Blending of interventions is practiced. While this could be an indication that blended intervention could be offering better outcomes, this remains an area with a research gap.

We exploit a unique historical setting to investigate how refugee-specific government aid affects the medium-term outcomes of refugees who migrate as children and young adults. Among German Democratic Republic (GDR) refugees who escaped to West Germany between 1946 and 1961, only the subgroup acknowledged as being “political refugees” were eligible for refugee-targeted aid, and only after 1953. We combine several approaches to address identification issues resulting from the fact that refugees eligible for aid were both self-selected and screened by local authorities. We find positive effects of aid eligibility on educational attainment and income among male and female refugees who migrated as young adults (aged 15 to 24). Among male refugees who migrated as children (aged 1 to 14), we find that aid eligibility at arrival leads to an increased likelihood of enrollment in the academic track of secondary school, but we see no such effect on female refugees who arrived as children.