Abstract This paper examines how female labor supply in Japan evolved in the medium run following the COVID-19 pandemic. Using a retrospective individual panel survey covering 2014, 2019, and 2024, I analyze employment, working hours, and income five years after the pandemic. On average, labor outcomes follow pre-pandemic trends, but substantial heterogeneity emerges across groups. Mothers of young children increased employment but reduced working hours, indicating adjustments along extensive and intensive margins. Remote workers also worked fewer hours, and the income gap between regular and non-regular workers widened. These findings suggest persistent changes in work arrangements and inequality in the post-pandemic labor market. JEL— J21; J22; J16
We conduct a pre-registered randomized controlled trial to test for income targeting in labor supply decisions among sellers of a Swedish street paper. These workers face liquidity constraints, high income volatility, and discretion over hours. Treated individuals received a 25 percent bonus per copy sold for the duration of an issue, simulating an increase in earnings potential. Treated sellers sold more papers, worked longer hours, and took fewer days off. These findings contrast with studies on intertemporal labor supply that find small substitution effects. Notably, when we apply strategies similar to observational studies, we recover patterns consistent with income targeting.
Covid-19-pandemien forårsaket betydelig turbulens i arbeidsmarkedet knyttet til både midlertidig arbeidsledighet, tyngre arbeidsbelastning i enkelte yrker og økt omstillingsbehov. Ved hjelp av norske registerdata oppdatert til slutten av 2024 studerer vi sysselsettingsmønstre til eldre (60–66 år) før, under og etter pandemien. Siden eldre arbeidstakere står nærmere en mulig varig uttrekning fra arbeidslivet enn yngre, er det av særlig interesse å undersøke hvordan pandemien påvirket denne gruppens arbeidsdeltagelse. Samlet sett finner vi få tegn til at mange eldre forlot arbeidsmarkedet på grunn av pandemien, når vi sammenligner med trendene observert i årene før. Det er imidlertid betydelige forskjeller mellom arbeidstakere som på ulik måte og i ulik grad ble påvirket av pandemien. Vi estimerer negative langsiktige sysselsettingseffekter for eldre arbeidstakere som jobbet enten i yrker med særlig stor arbeidsbelastning gjennom krisen (helsesektoren), i yrker med særlig stort omstillingsbehov (utdanningssektoren) eller i yrker med høy risiko for koronavirussmitte (omsorgssektoren). Vi ser imidlertid ingen langsiktige sysselsettingseffekter for eldre arbeidstakere som jobbet i bransjer med særlig høy risiko for arbeidsledighet. Analysen bygger på en empirisk tilnærming der vi studerer forskjellene i sysselsetting mellom ulike berørte og ikke-berørte yrkesgrupper gjennom pandemiperioden og sammenholder dem med de tilsvarende forskjellene i en førperiode.
Silvio Segundo Salej Higgins, Jorge Alexandre Barbosa Neves, Luciano Mattar
Abstract Following the field of insertion into the labor market, we tested the classic hypothesis of the strength of weak ties in the three largest cities in the Southeast Region of Brazil, with a focus on communities of religious worship. A survey was carried out in 2021 with 900 questionnaires divided equally among the three cities, weighted considering the size of the population and the distribution of religious affiliation. We investigated: (1) whether the strength of ties leverages the labor insertion of adults; (2) whether religious cult communities operate as mechanisms for the circulation of opportunities in the labor market. The data were analyzed with Heckman’s two-stage correction models to mitigate possible censoring biases. Controlling by seniority in the position and participation of three sampled cities in gross national product, inter alia, the results show that strong ties, as predicted by the strength of ties theory, are associated with lower earnings. At the same time, obtaining a paid job through another cult member is negatively associated with earnings. However, when testing our main hypothesis, we found that in the three largest cities in the Southeast Region of Brazil the religious embeddedness, composed of strong ties, high worship attendance and religious motivations coming from maternal ties, operate plausibly in labor market as favorable screen mechanism associated with higher earnings.
Extended Abstract
Background: The inadequacy of the supply of agricultural inputs with the demand for various products of this sector reveals the need for the optimal use of resources and increasing productivity. In this regard, addressing the issue of productivity in rice production is very important due to its essential role in feeding different sections of society, providing food security, reducing dependence on imports and foreign exchange, strengthening trade interactions with other countries, generating income, creating employment, creating balance in the business and capital market, and many other issues. In 2022, Mazandaran Province produced 1.6 million tons of paddy as a strategic product, responsible for 44.47% of Iran's paddy production, and in this sense, it has been ranked first in the country. This province has long been known as the hub of rice production, and this user product, having about 76% of Mazandaran's irrigated crop area, has always made an important contribution to the province's employment. For this purpose, the present study aimed to identify factors affecting the prosperity of the rice production business in Mazandaran Province, focusing on measuring the inefficiency of various production inputs, especially the labor force.
Methods: Three institutional, managerial, and policy-market criteria effective in the prosperity of rice production business were extracted in this study. The input criterion includes all production factors affecting the productivity of this product, which includes eight subcriteria as water, labor, land, fertilizer, poison, machinery, capital, and seed. The management criterion is all management actions by relevant organizations and bodies (Jahad Keshavarzi, Regional Water, Room of Commerce), which includes six regulatory, executive, organizational, service, and innovation options. The political-market criterion also covered the macro-government policies that can affect the productivity of rice, and there are six financial, economic, structural, commercial, marketing, and strategic development options. Thus, 19 effective options in the productivity of rice production were considered in this study. In this study, factors affecting the productivity of this product were exracted and prioritized using the Analytical Hierarchy Process (AHP) method, measuring the production efficiency of important cultivars of this product (high-quality rice and high-yielding rice) using the data envelopment analysis method (DEA), and then examining productivity changes over time using the Malmquist Index (MI). The data needed for identifying and prioritizing factors in this research were collected by designing a questionnaire, which was completed based on the opinions of 18 experts, including those from the Agricultural Jihad Organization of Mazandaran Province and Sari City, as well as the academic community. The statistics and information of the Agricultural Jahad Organization of the province were used to complete the data in measuring the productivity of production and efficiency of inputs.
Results: The results indicate that among the eight production factors, water, mechanization, and land are the most important input factors in rice production with weights of 0.36, 0.2, and 0.14, respectively. Among the five management factors, benefiting from the opinions of agricultural experts, implementing the optimal cultivation pattern of crops according to the climatic conditions and the status of water resources in the province, and using new technologies in agricultural operations with weights of 0.40, 0.25, and 0.14, respectively, were known as three important and superior factors for the management of rice production business. In addition, the financial, economic options, and improvement of the structure of the rice product marketing system were determined with the weights of 0.30, 0.22, and 0.19, respectively, as three policy-market subcriteria affecting the rice productivity of this province. Based on the findings in the agricultural year of 2017-2018 in the east of this province, Qaemshahr City, the land, machinary, poison, and fertilizer inputs were inefficient at 52.68%, 48.26%, 34.37%, and 33.16%, respectively. In 2018, the inefficiency rates in the use of land, labor, and poison inputs were 71.36%, 15.09%, and 4.46%, respectively. In the production of high-yielding rice in the east of the province, there has been inefficiency in the use of land, machinary, seed, water, and fertilizer inputs. Accordingly, Behshahr City acted inefficiently in consuming the mentioned inputs by 68.29, 52.60, 16.65, 12.63, and 7.55%, respectively. In 1998, the cities of Behshahr and Neka acted inefficiently in the consumption of all the investigated inputs, except for machinery. The percentages of inefficiency in the labor input are 16.14 and 42.07%, respectively. In addition, the productivity growth index values of Malmquist in the production of high-quality rice and high-yielding rice are 1.155 and 1.094, respectively. Hence, it can be concluded that the production productivity of this product has increased in this province.
Conclusion: The results indicate that the productivity of different rice varieties has increased during the studied period. In the case of high-yielding rice, however, the technical efficiency of producers in newer technology is lower than in older technology. Therefore, it is necessary for trustee organizations and knowledge-based companies to invest in the research, innovation, and promotion of new technology in training to use this technology. In this study, "water" has been determined as the most important input affecting the productivity of this product; therefore, it is recommended to take necessary measures to promote water storage and reduce its consumption. It is also suggested to provide financial support to rice farmers and the development of knowledge-based companies to provide new irrigation systems. Referring to the results of this study, the use of "machinery" is considered the second most effective factor in increasing productivity. In addition to reducing the cost of manpower and saving time, the uniformity and accuracy of the work are increased with mechanized cultivation, and seedlings are exposed to less damage. However, this issue does not mean to ignore the role and importance of the workforce in the production and elimination of job opportunities. Rather, it is recommended to train skilled and specialized human resources to benefit from mechanization for the long-term stability of the rice production business and stable rural employment.
This paper estimates the effect of Hurricane Harvey on wages and employment in the construction labor industry across impacted counties in Texas. Based on data from the Quarterly Census of Employment and Wages (QCEW) for the period 2016-2019, I adopted a difference-in-differences event study approach by comparing results in 41 FEMA-designated disaster counties with a set of unaffected southern control counties. I find that Hurricane Harvey had a large and long-lasting impact on labor market outcomes in the construction industry. More precisely, average log wages in treated counties rose by around 7.2 percent compared to control counties two quarters after the hurricane and remained high for the next two years. Employment effects were more gradual, showing a statistically significant increase only after six quarters, in line with the lagged nature of large-scale reconstruction activities. These results imply that natural disasters can generate persistent labor demand shocks to local construction markets, with policy implications for disaster recovery planning and workforce mobilization.
We study how increases in remote work opportunities for men affect their spouses' labor supply. Exploiting variation in the change in work-from-home (WFH) exposure across occupations before and after the COVID-19 pandemic, we find that increases in men's WFH exposure led to sizable improvements in their wives' labor-market outcomes: annual employment rose by roughly 2.5 percentage points (from a 69% pre-treatment mean), earnings increased by about 5%, weekly hours worked rose by roughly half an hour, weeks worked increased by about 1.3%, and the likelihood of part-time work declined by approximately 9%. Evidence from time-use diaries and childcare questionnaires suggests these effects are driven by intra-household reallocation of child-caring time: women are less likely to engage in primary childcare activities, while men working at home partially compensate by covering more for their spouse. These results highlight the role of households in shaping the labor market consequences of remote work.
The rapid advancement of Large Language Models (LLMs) has generated considerable speculation regarding their transformative potential for labor markets. However, existing approaches to measuring AI exposure in the workforce predominantly rely on concurrent market conditions, offering limited predictive capacity for anticipating future disruptions. This paper presents a predictive study examining whether online discussions about LLMs can function as early indicators of labor market shifts. We employ four distinct analytical approaches to identify the domains and timeframes in which public discourse serves as a leading signal for employment changes, thereby demonstrating its predictive validity for labor market dynamics. Drawing on a comprehensive dataset that integrates the REALM corpus of LLM discussions, LinkedIn job postings, Indeed employment indices, and over 4 million LinkedIn user profiles, we analyze the relationship between discussion intensity across news media and Reddit forums and subsequent variations in job posting volumes, occupational net change ratios, job tenure patterns, unemployment duration, and transitions to GenAI-related roles across thirteen occupational categories. Our findings reveal that discussion intensity predicts employment changes 1-7 months in advance across multiple indicators, including job postings, net hiring rates, tenure patterns, and unemployment duration. These findings suggest that monitoring online discourse can provide actionable intelligence for workers making reskilling decisions and organizations anticipating skill requirements, offering a real-time complement to traditional labor statistics in navigating technological disruption.
Large language models (LLMs) like ChatGPT have significantly lowered the cost of producing written content. This paper studies how LLMs, through lowering writing costs, disrupt markets that traditionally relied on writing as a costly signal of quality (e.g., job applications, college essays). Using data from Freelancer.com, a major digital labor platform, we explore the effects of LLMs' disruption of labor market signaling on equilibrium market outcomes. We develop a novel LLM-based measure to quantify the extent to which an application is tailored to a given job posting. Taking the measure to the data, we find that employers have a high willingness to pay for workers with more customized applications in the period before LLMs are introduced, but not after. To isolate and quantify the effect of LLMs' disruption of signaling on equilibrium outcomes, we develop and estimate a structural model of labor market signaling, in which workers invest costly effort to produce noisy signals that predict their ability in equilibrium. We use the estimated model to simulate a counterfactual equilibrium in which LLMs render written applications useless in signaling workers' ability. Without costly signaling, employers are less able to identify high-ability workers, causing the market to become significantly less meritocratic: compared to the pre-LLM equilibrium, workers in the top quintile of the ability distribution are hired 19% less often, workers in the bottom quintile are hired 14% more often.
This paper examines labor market polarization through a comparative analysis of skill-based employment and wage distributions in India and the United States during 2018-2023, with particular attention to differential automation risks and AI preparedness. Using detailed occupation-level data, automation risk metrics, and a series of statistical tests including wage premium analysis, employment share tests, and wage-employment regressions, we document significant structural differences in labor markets between developing and developed economies. Our analysis yields four key findings. First, we find statistically significant differences in employment distribution patterns, with India showing disproportionate concentration in low-skill employment compared to the US, particularly in occupations with high automation risk. Second, regression analysis reveals that wage premiums differ systematically between the two countries, with significantly larger skill-based wage gaps in India. Third, we find robust evidence of a negative relationship between employment size and wages, suggesting stronger labor supply effects in developing economies. Fourth, analysis of occupation-specific automation risk reveals that developing economies face a "double vulnerability" - concentration of employment in both low-skill occupations and jobs with higher automation potential, complicated by lower AI preparedness scores. These findings provide novel empirical evidence on how development stages influence labor market polarization patterns and carry important implications for skill development and technology adoption policies in developing economies. Our results suggest that traditional approaches to labor market development may need significant modification to account for the differential impacts of AI across development stages.
How labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan's spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2\% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.
Supply chain networks are complex systems that are challenging to analyze; this problem is exacerbated when there are illicit activities involved in the supply chain, such as counterfeit parts, forced labor, or human trafficking. While machine learning (ML) can find patterns in complex systems like supply chains, traditional ML techniques require large training data sets. However, illicit supply chains are characterized by very sparse data, and the data that is available is often (purposely) corrupted or unreliable in order to hide the nature of the activities. We need to be able to automatically detect new patterns that correlate with such illegal activity over complex, even temporal data, without requiring large training data sets. We explore neurosymbolic methods for identifying instances of illicit activity in supply chains and compare the effectiveness of manual and automated feature extraction from news articles accurately describing illicit activities uncovered by authorities. We propose a question tree approach for querying a large language model (LLM) to identify and quantify the relevance of articles. This enables a systematic evaluation of the differences between human and machine classification of news articles related to forced labor in supply chains.
Simpson Zhang, Tennison Liu, Mihaela van der Schaar
Current labor markets are strongly affected by the economic forces of adverse selection, moral hazard, and reputation, each of which arises due to $\textit{incomplete information}$. These economic forces will still be influential after AI agents are introduced, and thus, agents must use metacognitive and strategic reasoning to perform effectively. Metacognition is a form of $\textit{internal reasoning}$ that includes the capabilities for self-assessment, task understanding, and evaluation of strategies. Strategic reasoning is $\textit{external reasoning}$ that covers holding beliefs about other participants in the labor market (e.g., competitors, colleagues), making strategic decisions, and learning about others over time. Both types of reasoning are required by agents as they decide among the many $\textit{actions}$ they can take in labor markets, both within and outside their jobs. We discuss current research into metacognitive and strategic reasoning and the areas requiring further development.
Jens-Joris Decorte, Jeroen Van Hautte, Chris Develder
et al.
Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skill extraction achieve strong performance, they depend on large language models (LLMs), which are computationally expensive and slow. In this paper, we propose \textbf{ConTeXT-match}, a novel contrastive learning approach with token-level attention that is well-suited for the extreme multi-label classification task of skill classification. \textbf{ConTeXT-match} significantly improves skill extraction efficiency and performance, achieving state-of-the-art results with a lightweight bi-encoder model. To support robust evaluation, we introduce \textbf{Skill-XL}, a new benchmark with exhaustive, sentence-level skill annotations that explicitly address the redundancy in the large label space. Finally, we present \textbf{JobBERT V2}, an improved job title normalization model that leverages extracted skills to produce high-quality job title representations. Experiments demonstrate that our models are efficient, accurate, and scalable, making them ideal for large-scale, real-time labor market analysis.
Corporate sustainability due diligence (CSDD) is needed in order to achieve a transition towards a green economy and deliver the UN Sustainable Development Goals. Therefore, the Commission proposed the Corporate Sustainability Due Diligence Directive (CSDDD) in 2019. In March 2024, the Council and the European Parliament finally reached an agreement on the CSDDD. This Directive obliges companies, if need be, to “collaborate” within the supply chain in order to reach the goals of the act, as long as such collaboration is in compliance with “Union law, including competition law”. This begs the question how much room these companies will have under EU competition law to collaborate with other companies in the value chain. In this article, a discussion will take place on the different aspects at stake. Companies should collaborate with subsidiaries and business partners. These companies will, from a competition law perspective, sometimes be seen as part of one undertaking. In instances where we have different undertakings, Article 101 will play a role. This article will then further delve into the question of how much room undertakings will have in light of the cartel prohibition to create agreements with the aim of achieving the CSDDD goals. Therefore, the article will also discuss whether such agreements do infringe the cartel prohibition.
Diana Da Silva Ribeiro, Arminda Rachel Botelho Mourão
O artigo em questão tem como objetivo discutir a Pedagogia da Alternância como uma proposta de educação para além do capital e compreender a sua contribuição para a educação atual no Brasil. Desta forma, as discussões trazem a história do movimento da Pedagogia da Alternância e a temática da educação a partir de Mészáros (2008). Os resultados mostram como é possível, mesmo dentro de uma estrutura capitalista, vivenciar algumas experiências pedagógicas que se caracterizam como educação contra hegemônica, materializando-se a partir das lutas da sociedade civil organizada.
Palavra-chave: Pedagogia da Alternância; Educação contra hegemônica; Educação para além do capital.
Special aspects of education, Labor market. Labor supply. Labor demand
Ana Paula Dalmás Rodrigues, Sandro Benedito Sguarezi, Douglas Alexandre de Campos Castrillon Junior
O artigo apresenta o aplicativo que está sendo construído junto às Organizações de Catadoras/es de Materiais Recicláveis (OCMR) do Alto Pantanal Mato-Grossense. O objetivo é analisar dados de campo, identificando os desafios pelo método da pesquisa-ação suportada pela técnica bibliográfica, descritiva, diagnóstico socioeconômico e entrevistas junto aos sujeitos da pesquisa. Espera-se que o aplicativo aprimore processos de comercialização direta entre as OCMR e as indústrias que adquirem os materiais recicláveis fortalecendo o poder de barganha das OCMR.
Palavras-chave: Aplicativo; Associação; Cooperativa; Resíduos Sólidos.
Special aspects of education, Labor market. Labor supply. Labor demand
Med en aldrende befolkning vil det bli et økende press på de eldste arbeidstakerne om å yte omsorg til foreldre og samtidig jobbe mer. Ved hjelp av kvalitative intervjuer, spørreskjemadata og registerdata belyser vi denne tidsklemmen. Kvalitative data og spørreskjemadata viser at voksne barn yter flere typer praktisk hjelp til sine foreldre, særlig digital hjelp. Få pleier foreldre. Vi finner lite tegn til at omsorgsgivning påvirker yrkesdeltakelsen, men fravær, spesielt sykefraværet, øker, og mange opplever det som belastende å kombinere jobb og omsorg. Sykelønnsordningen ser ut til å redusere belastningene. Informanter som har deltatt i undersøkelsene, oppgir at en betalt korttidspermisjonsordning for arbeidstakere med omsorgstrengende foreldre tilsvarende ordningen som finnes for småbarnsforeldre, vil kunne gjøre det lettere for dem å kombinere omsorgssituasjonen med jobb.
In this project, we examine how freelancers changed their strategic positioning on an online work platform following the launch of ChatGPT in November 2022 - a major advance in AI technologies. We document that post-ChatGPT, freelancers bid on fewer jobs and reposition themselves by differentiating their distribution of bids (i.e., job applications) relative to their prior behavior. We disentangle heterogeneity in strategic responses by exploring how exposure to changes in demand or supply shape incumbent repositioning. We find that the launch of ChatGPT was associated with a short-term decrease in labor demand and an increase in labor supply, though these changes vary across work domains. In response to decreases in labor demand, workers changed their horizontal positioning and withdrew from the platform. In response to increases in labor supply, workers were less likely to decrease bidding or reposition horizontally but shifted their vertical position by targeting lower-value jobs. We further show that repositioning is less likely for high-skill freelancers who face greater adjustment costs. This research contributes to our understanding of how and why workers respond to technological change in the context of recent advances in AI technologies.