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This paper investigates the relationship between health and labour market participation considering the potential role played by the presence of children and elderly persons (with/without disabilities) in Italian households.

The authors use longitudinal data from the European Union Statistics on Income and Living Conditions and full-information maximum likelihood to estimate a two-equation model (one equation for labour force participation and one for health status) with instruments to address the endogeneity of the labour force participation choice. The model is estimated separately by gender.

The authors find that while the presence of children, elderly persons or both is positively associated with the health status of both genders, the presence of disabled elderly persons exerts a negative role. As for participation, interesting differences emerge. The presence of children discourages women's participation but is positively associated with men's labour force participation. Interestingly, a caring role for elderly persons without disability emerges for both genders when the presence of children is combined with that of elderly people. Gender differences are also at work for the role of childcare services and elderly and/or disabled home care/assistance.

The findings indicate a possible caring role for elderly persons without disabilities, neutralizing the effect of the presence of children on the labour force participation of both genders. The results also suggest that greater coverage of care services should increase the active participation of women in the labour market.

This paper aims to assess how cocoa supply chain companies disclose sustainable development goals (SDGs) information in their sustainability reports. This assessment highlights strategic aspects of sustainable supply chain management and reveals leveraging sustainability points in the cocoa industry.

The two-step qualitative approach relies on text-mining company reports and subsequent content analysis that identifies the topics disclosed and relates them to SDG targets.

This study distinguishes 18 SDG targets connected to cocoa traders and 30 SDG targets to chocolate manufacturers. The following topics represent the main nexuses of connections: decent labour promotion and gender equity (social), empowering local communities and supply chain monitoring (economic) and agroforestry and climate action (environmental).

By highlighting the interconnections between the SDGs targeted by companies in the cocoa supply chain, this paper sheds light on the strategic SDGs for this industry and their relationships, which can help to improve sustainability disclosure and transparency. One interesting input for companies is the improvement of climate crisis prevention, focusing on non-renewable sources minimisation, carbon footprint and clear indicators of ecologic materiality.

This study contributes to policymakers to enhance governance and accountability of global supply chains that are submitted to different regulation regimes.

To the best of the authors’ knowledge, no previous study has framed the cocoa industry from a broader SDG perspective. The interconnections identified reveal the key goals of the cocoa supply chain and point to strategic sustainability choices for companies in an important global industry.

This paper examines the changes suggested by maritime stakeholders to achieve gender equality in seafaring, a male-dominated profession.

Adopting a four-stage career cycle framework, this paper analyzes changes proposed by 423 industry stakeholders to promote gender equality in seafaring. These proposed changes were posted on the Day of Seafarers 2019 virtual wall set up by the International Maritime Organization, which served as a forum for industry stakeholders from all over the world to voice their opinions and suggestions.

The data analysis shows that the suggested changes reflect many challenges and barriers women seafarers face. While stakeholders from Organisation for Economic Co-operation and Development (OECD) countries are more likely to call for changes to remove barriers in the retention and development stage, gender equality in seafaring in non-OECD countries is still seriously hindered by barriers in the recruitment stage. The paper also reveals that comparatively male stakeholders are less likely to appreciate the problems women seafarers face.

This paper takes a comparative approach, comparing the changes proposed by seafarers and other industry stakeholders from different parts of the world. This approach provides a nuanced understanding of issues related to gender equality in seafaring by showing that stakeholders from different backgrounds have different priorities.

While the economic literature mostly tackled discrimination looking at labour costs, this work focuses on its relation to labour productivity, arguing that discrimination may worsen the performance of female employees. In this view, it represents a source of allocative inefficiency, which contributes to reducing output.

Female discrimination is both a social and an economic problem. In social terms, consolidated gender stereotypes impose constraints on women’s behaviour, worsening their overall well-being. In economic terms, women face generally worse labour market conditions. Using long-run Italian data spanning from 1861 to 2009, the authors propose a novel measure of female discrimination based on the observed frequency of discriminating epithets. Following social capital theory, the authors distinguish between structural and voluntary discrimination, and use Data Envelopment Analysis for time series data to assess the extent of inefficiency that each component of discrimination induces in the production process.

The results draw the trajectory of female discrimination in Italy and provide evidence in favour of the idea that female discrimination reduces productive efficiency. In particular, the structural component of female discrimination, although less sizeable than the voluntary component, plays a major role, especially in recent years, where more stringent beauty standards fuel looks-based discrimination.

The contribution of this work is twofold. First, based on contributions from social sciences different from economics, it proposes a novel theoretical framework that explores the effect of discriminatory language on labour productivity. Second, it introduces a novel and direct measure of female discrimination at the country level, based on the bidirectional link between language and culture. The indicator is easily understood by policymakers and may be used to evaluate the effectiveness of anti-discrimination policies.

The purpose of this study is to analyse the gender wage gap (GWG) in Spain adopting a labour market segmentation approach. Once we obtain the different labour segments (or idiosyncratic labour markets), we are able to decompose the GWG into its observed and unobserved heterogeneity components.

We use the data from the Continuous Sample of Working Lives for the year 2021 (matched employer–employee [EE] data). Contingency tables and clustering techniques are applied to employment data to identify idiosyncratic labour markets where men and/or women of different ages tend to match/associate with different sectors of activity and occupation groups. Once this “heatmap” of labour associations is known, we can analyse its hottest areas (the idiosyncratic labour markets) from the perspective of wage discrimination by gender (Oaxaca-Blinder model).

In Spain, in general, men are paid more than women, and this is not always justified by their respective attributes. Among our results, the fact stands out that women tend to move to those idiosyncratic markets (biclusters) where the GWG (in favour of men) is smaller.

It has not been possible to obtain remuneration data by job-placement, but an annual EE relationship is used. Future research should attempt to analyse the GWG across the wage distribution in the different idiosyncratic markets.

Our combination of methodologies can be adapted to other economies and variables and provides detailed information on the labour-matching process and gender wage discrimination in segmented labour markets.

Our contribution is very important for labour market policies, trying to reduce unfair inequalities.

The study of the GWG from a novel labour segmentation perspective can be interesting for other researchers, institutions and policy makers.

In recent years, both academics and institutions have acknowledged the crucial role multinational enterprises (MNEs) can play in addressing the sustainability challenges, as formalized by the sustainable development goals (SDGs). Nevertheless, because of their extensiveness and their design as country-level targets, SDGs have proven challenging to operationalize at a firm level. This problem opens new and relevant avenues for research in international business (IB). This chapter attempts to frame the topic of extended value chain sustainability in the IB literature. In particular, it addresses a specific topic, that is, how sustainability and resilience-building practices interact in global value chains (GVCs). To do so, the present study develops the case of STMicroelectronics (ST), one of the biggest semiconductor companies worldwide.

This study conducts a comparative analysis of the impact of educational mismatch on Spanish wages. This paper aims to focus on the industrial, construction and service sectors at three levels of disaggregation: sector, occupation and gender.

The over-education, required education and under-education (ORU model), was applied to data from the 2018 Spanish Wages Structure Survey conducted by the Spanish National Statistics Institute.

The industrial sector is the one that best manages over-education by offering the highest returns to each year of over-education. It is also the sector that most values the education of women, particularly those in highly qualified positions.

This study compares the wage effects of educational mismatch in the service, industry and construction sectors. Previous literature has ignored the latter sectors in this field of study, but the results of the present study show that the industrial sectors significantly value and remunerates worker education. Therefore, it may be worthy to focus certain economic and social policies on this sector, to contribute to reducing gender wage gaps and gender employment discrimination in the economy.

This study examines the relevance of information and communication technologies in the effect of gender economic inclusion on environmental sustainability.

The focus is on a panel of 42 sub-Saharan African countries over the period 2005–2020. The empirical evidence is based on generalized method of moments. The environmental sustainability indicator used is CO2 emissions per capita. Three indicators of women’s economic inclusion are considered: female labour force participation, female employment and female unemployment. The chosen ICT indicators are mobile phone penetration, Internet penetration and fixed broadband subscriptions.

The results show that: (1) fixed broadband subscriptions represent the most relevant ICT moderator of gender economic inclusion for an effect on CO2 emissions; (2) negative net effects are apparent for the most part with fixed broadband subscriptions (3) both positive ICT thresholds (i.e., critical levels for complementary policies) and negative ICT thresholds (i.e., minimum ICT levels for negative net effects) are provided; (4) ICT synergy effects are apparent for female unemployment, but not for female employment. In general, the joint effect of ICTs or their synergies and economic inclusion should be a concern for policymakers in order to better ensure sustainable development. Moreover, the relevant ICT policy thresholds and mobile phone threshold for complementary policy are essential in promoting a green economy.

The study complements the extant literature by assessing linkages between information technology, gender economic inclusion and environmental sustainability.

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

An analysis of existing literature serves as the foundation for understanding the impact, while the supply and demand model helps assess the effects of ChatGPT. A text-mining approach is utilized to analyze the International Standard Occupation Classification, identifying occupations most susceptible to disruption by ChatGPT.

The study reveals that 32.8% of occupations could be fully impacted by ChatGPT, while 36.5% might experience a partial impact and 30.7% are likely to remain unaffected.

While this study offers insights into the potential influence of ChatGPT and other generative AI services on the labor market, it is essential to note that these findings represent potential implications rather than realized labor market effects. Further research is needed to track actual changes in employment patterns and job market dynamics where these AI services are widely adopted.

This paper contributes to the field by systematically categorizing the level of impact on different occupations, providing a nuanced perspective on the short- and long-term implications of ChatGPT and similar generative AI services on the labor market.

In Brazil, over 4.7 million women enrolled in university in the year 2017. However, Brazilian women have been consistently overrepresented in humanities and care majors and underrepresented in science, technology, engineering and mathematics (STEM). Given that observed gender differences in math-intensive fields have lasting effects on gender inequality in the labor market, and that observed gender variations do not necessarily associate with differences in innate ability, in this paper we explore the paths of societal gender bias and gender differences in a Brazilian university.

We conduct a social experiment at a University in Southeastern Brazil, applying the gender-STEM Implicit Association Test.

We found that women in STEM are less likely to show gender-STEM implicit stereotypes, compared to women in humanities. The results indicate a negative correlation between implicit gender stereotyping and the choice of math-intensive majors by women.

The stereotype-congruent results are indicative of the gender bias in Brazilian society, and suggest that stereotypes created at early stages in life are directly related to future outcomes that reinforce gender disparities in Brazil, which can be observed in career choices.