Hasil untuk "Labor market. Labor supply. Labor demand"

Matthew H. Kilbane

This paper presents a quantitative framework for optimizing human AI workforce allocation in software development, translatable to other labor categories. I formalize baseline and AI-collapsed labor models, derive tipping point equations for safe headcount reduction, and embed them in a multi objective evolutionary optimization setup. NSGAII experiments reveal reproducible, phase specific automation strategies that reduce cost while maintaining quality and stable workloads.

Les auteurs étudient le lien entre traits de personnalité et productivité en télétravail. À partir d’une enquête conduite en Lettonie en 2021, ils mesurent les traits de personnalité du modèle des «cinq grands facteurs de personnalité» chez plus de 1 700 personnes qui ont récemment télétravaillé. Ils constatent un lien positif entre la conscience et la productivité en télétravail. La conscience et l’ouverture sont en outre positivement associées à la volonté de continuer de travailler à distance après la pandémie. Les employeurs favorables au télétravail devraient donc être attirants pour les salariés présentant ces attributs. En revanche, le lien entre extraversion et préférence pour le télétravail est négatif. L’article montre qu’une politique uniforme a peu de chances de maximiser la productivité des entreprises et la satisfaction des salariés.

Aleksandra PARTEKA, Dagmara NIKULIN, Joanna WOLSZCZAK-DERLACZ

Les autrices utilisent un jeu de microdonnées sur les travailleurs de 22 pays européens afin d’évaluer si les technologies influent sur le lien entre les chaînes de valeur mondiales (CVM) et les conditions de travail mesurées par les salaires et par plusieurs dimensions de la qualité de l’emploi. Elles analysent cette influence pour plusieurs types de technologies, comparant les logiciels et robots à l’intelligence artificielle. Globalement, la participation aux CVM a un lien négatif avec les salaires et (légèrement) positif avec certaines dimensions non monétaires de la qualité de l’emploi. L’utilisation des technologies numériques ne modifie pas cette relation de manière économiquement significative.

Fabian Muny

Many programs evaluated in observational studies incorporate a sequential structure, where individuals may be assigned to various programs over time. While this complexity is often simplified by analyzing programs at single points in time, this paper reviews, explains, and applies methods for program evaluation within a sequential framework. It outlines the assumptions required for identification under dynamic confounding and demonstrates how extending sequential estimands to dynamic policies enables the construction of more realistic counterfactuals. Furthermore, the paper explores recently developed methods for estimating effects across multiple treatments and time periods, utilizing Double Machine Learning (DML), a flexible estimator that avoids parametric assumptions while preserving desirable statistical properties. Using Swiss administrative data, the methods are demonstrated through an empirical application assessing the participation of unemployed individuals in active labor market policies, where assignment decisions by caseworkers can be reconsidered between two periods. The analysis identifies a temporary wage subsidy as the most effective intervention, on average, even after adjusting for its extended duration compared to other programs. Overall, DML-based analysis of dynamic policies proves to be a useful approach within the program evaluation toolkit.

Jacob Dominski, Yong Suk Lee

This study investigates the labor market consequences of AI by analyzing near real-time changes in employment status and work hours across occupations in relation to advances in AI capabilities. We construct a dynamic Occupational AI Exposure Score based on a task-level assessment using state-of-the-art AI models, including ChatGPT 4o and Anthropic Claude 3.5 Sonnet. We introduce a five-stage framework that evaluates how AI's capability to perform tasks in occupations changes as technology advances from traditional machine learning to agentic AI. The Occupational AI Exposure Scores are then linked to the US Current Population Survey, allowing for near real-time analysis of employment, unemployment, work hours, and full-time status. We conduct a first-differenced analysis comparing the period from October 2022 to March 2023 with the period from October 2024 to March 2025. Higher exposure to AI is associated with reduced employment, higher unemployment rates, and shorter work hours. We also observe some evidence of increased secondary job holding and a decrease in full-time employment among certain demographics. These associations are more pronounced among older and younger workers, men, and college-educated individuals. College-educated workers tend to experience smaller declines in employment but are more likely to see changes in work intensity and job structure. In addition, occupations that rely heavily on complex reasoning and problem-solving tend to experience larger declines in full-time work and overall employment in association with rising AI exposure. In contrast, those involving manual physical tasks appear less affected. Overall, the results suggest that AI-driven shifts in labor are occurring along both the extensive margin (unemployment) and the intensive margin (work hours), with varying effects across occupational task content and demographics.

Mantas Mazeika, Alice Gatti, Cristina Menghini et al.

AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To measure this, we introduce the Remote Labor Index (RLI), a broadly multi-sector benchmark comprising real-world, economically valuable projects designed to evaluate end-to-end agent performance in practical settings. AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. These results help ground discussions of AI automation in empirical evidence, setting a common basis for tracking AI impacts and enabling stakeholders to proactively navigate AI-driven labor automation.

Wei Meng

In the context of the new mandatory labor compliance in the European Union (EU), which will be implemented in 2027, supply chain enterprises face stringent working hour management requirements and compliance risks. In order to scientifically predict the enterprises' coping behaviors and performance outcomes under the policy impact, this paper constructs a methodological framework that integrates the AI synthetic data generation mechanism and structural path regression modeling to simulate the enterprises' strategic transition paths under the new regulations. In terms of research methodology, this paper adopts high-quality simulation data generated based on Monte Carlo mechanism and NIST synthetic data standards to construct a structural path analysis model that includes multiple linear regression, logistic regression, mediation effect and moderating effect. The variable system covers 14 indicators such as enterprise working hours, compliance investment, response speed, automation level, policy dependence, etc. The variable set with explanatory power is screened out through exploratory data analysis (EDA) and VIF multicollinearity elimination. The findings show that compliance investment has a significant positive impact on firm survival and its effect is transmitted through the mediating path of the level of intelligence; meanwhile, firms' dependence on the EU market significantly moderates the strength of this mediating effect. It is concluded that AI synthetic data combined with structural path modeling provides an effective tool for high-intensity regulatory simulation, which can provide a quantitative basis for corporate strategic response, policy design and AI-assisted decision-making in the pre-prediction stage lacking real scenario data. Keywords: AI synthetic data, structural path regression modeling, compliance response strategy, EU 2027 mandatory labor regulation

Qing Xiao, Rongyi Chen, Jingjia Xiao et al.

This study examines the discursive construction of algorithms and its role in labor management in Chinese live-streaming industry by focusing on how intermediary organizations (Multi-Channel Networks, MCNs) actively construct, stabilize, and deploy particular interpretations of platform algorithms as instruments of labor management. Drawing on a nine-month ethnographic fieldwork and 44 interviews with live-streamers, former live-streamers, and MCN staff, we examine how MCNs produce and circulate structured interpretations of platform algorithms across organizational settings. We show that MCNs articulate two asymmetric yet interconnected forms of algorithmic interpretations. Internally, MCNs managers approach algorithms as volatile and uncertain systems and adopt probabilistic strategies to manage performance and risk. Externally, in interactions with streamers, MCNs circulate simplified and prescriptive algorithmic narratives that frame platform systems as transparent, fair, and responsive to individual effort. These organizationally produced algorithmic interpretations are embedded into training materials, live-streaming performance metrics, and everyday management practices. Through these mechanisms, streamers internalize responsibility for outcomes, intensify self-discipline, and increase investments in equipment, performing skills, and routines to maintain streamer-audience relationship, while accountability for unpredictable outcomes is increasingly shifted away from managers and platforms. This study contributes to CSCW and platform labor research by demonstrating how discursively constructed algorithmic knowledge can function as an intermediary infrastructure of soft control, shaping how platform labor is regulated, moralized, and governed in practice.

Varun Nagaraj Rao, Samantha Dalal, Andrew Schwartz et al.

What happens when a rideshare driver is suddenly locked out of the platform connecting them to riders, wages, and daily work? Deactivation-the abrupt removal of gig workers' platform access-typically occurs through arbitrary AI and algorithmic decisions with little explanation or recourse. This represents one of the most severe forms of algorithmic control and often devastates workers' financial stability. Recent U.S. state policies now mandate appeals processes and recovering compensation during the period of wrongful deactivation based on past earnings. Yet, labor organizers still lack effective tools to support these complex, error-prone workflows. We designed FareShare, a computational tool automating lost wage estimation for deactivated drivers, through a 6 month partnership with the State of Washington's largest rideshare labor union. Over the following 3 months, our field deployment of FareShare registered 178 account signups. We observed that the tool could reduce lost wage calculation time by over 95%, eliminate manual data entry errors, and enable legal teams to generate arbitration-ready reports more efficiently. Beyond these gains, the deployment also surfaced important socio-technical challenges around trust, consent, and tool adoption in high-stakes labor contexts.

Runa Brandal Myklebust, Erika Braanen Sterri, Mari Teigen

Ellis Scharfenaker, Bruno Theodosio, Duncan K. Foley

Adam Smith's inquiry into the emergence and stability of the self-organization of the division of labor in commodity production and exchange is considered using statistical equilibrium methods from statistical physics. We develop a statistical equilibrium model of the distribution of independent direct producers in a hub-and-spoke framework that predicts both the center of gravity of producers across lines of production as well as the endogenous fluctuations between lines of production that arise from Smith's concept of "perfect liberty". The ergodic distribution of producers implies a long-run balancing of "advantages to disadvantages" across lines of employment and gravitation of market prices around Smith's natural prices.

Andrew Ireland, David Johnston, Rachel Knott

We use daily longitudinal data and a within-worker identification approach to examine the impacts of heat on labor force dynamics in Australia. High temperatures during 2001-2019 significantly reduced work attendance and hours worked, which were not compensated for in subsequent days and weeks. The largest reductions occurred in cooler regions and recent years, and were not solely concentrated amongst outdoor-based workers. Financial and Insurance Services was the most strongly affected industry, with temperatures above 38°C (100°F) increasing absenteeism by 15 percent. Adverse heat effects during the work commute and during outdoor work hours are shown to be key mechanisms.

Kamil Bader El Dine, Noujoud Nader, Mohamad Khalil et al.

Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. To address this problem, this paper will provide a new approach by analyzing the EHG signals, which are recorded on the abdomen of the mother during labor and pregnancy. The EHG signal reflects the electrical activity that induces the mechanical contraction of the myometrium. Because EHGs are known to be non-stationary signals, and because we anticipate connectivity to alter during contraction, we applied the windowing approach on real signals to help us identify the best windows and the best nodes with the most significant data to be used for classification. The suggested pipeline includes i) divide the 16 EHG signals that are recorded from the abdomen of pregnant women in N windows; ii) apply the connectivity matrices on each window; iii) apply the Graph theory-based measures on the connectivity matrices on each window; iv) apply the consensus Matrix on each window in order to retrieve the best windows and the best nodes. Following that, several neural network and machine learning methods are applied to the best windows and best nodes to categorize pregnancy and labor contractions, based on the different input parameters (connectivity method alone, connectivity method plus graph parameters, best nodes, all nodes, best windows, all windows). Results showed that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are 2, 4, and 5. The classification results obtained by using only these best nodes are better than when using the whole nodes. The results are always better when using the full burst, whatever the chosen nodes. Thus, the windowing approach proved to be an innovative technique that can improve the differentiation between labor and pregnancy EHG signals.

Liyue Chen, Jielan Ding, Donghuan Song et al.

Scientific contributions are a direct reflection of a research paper's value, illustrating its impact on existing theories or practices. Existing measurement methods assess contributions based on the authors' perceived or self-identified contributions, while the actual contributions made by the papers are rarely investigated. This study measures the actual contributions of papers published in Nature and Science using 1.53 million citation contexts from citing literature and explores the impact pattern of division of labor (DOL) inputs on the actual contributions of papers from an input-output perspective. Results show that experimental contributions are predominant, contrasting with the theoretical and methodological contributions self-identified by authors. This highlights a notable discrepancy between actual contributions and authors' self-perceptions, indicating an 'ideal bias'. There is no significant correlation between the overall labor input pattern and the actual contribution pattern of papers, but a positive correlation appears between input and output for specific types of scientific contributions, reflecting a 'more effort, more gain' effect. Different types of DOL input in papers exhibit a notable co-occurrence trend. However, once the paper reaches the dissemination stage, the co-occurrence of different types of actual contributions becomes weaker, indicating that a paper's contributions are often focused on a single type.

Victor Gagno Grillo, Maria Amélia Dalvi

O artigo apresenta o tema da uberização do trabalho, a partir do filme Você não estava aqui, de Ken Loach. A análise, inspirada no materialismo histórico, permite compreender a uberização como uma nova estratégia para exploração humana e acumulação de capital, na sociedade marcada pela divisão social em classes antagônicas. O estudo permite concluir que a precarização do trabalho, para além do âmbito econômico, prejudica a estrutura afetiva e relacional dos sujeitos; o cinema, a despeito de suas contradições, pode atuar no desvelamento crítico do processo. Palavra-chave: Uberização; Cinema; Formação Humana. Palavras-chave: Trabalho; Uberização; Acumulação de capital; Cinema.

Alexandre Marinho Pimenta

Diante do contexto atual de militarização de escolas públicas no Brasil, o artigo revisa a teoria dos Aparelhos Ideológicos de Estado de Louis Althusser e a teoria do poder disciplinar de Michel Foucault. Indica que a utilização heurística e articulada de tais autores possibilita construir os fundamentos de uma dimensão repressivo-disciplinar da educação no capitalismo. Diante de tais diretrizes analíticas, espera-se contribuir ao estudo das práticas repressivas na/da educação e das dinâmicas de dominação política, reforçadas e rearticuladas em escolas que adotam o modelo militarizado. Palavra-chave: Educação Básica; Militarização das Escolas Públicas; Aparelhos Ideológicos de Estado; Poder Disciplinar.

Hanlin Li, Nicholas Vincent, Stevie Chancellor et al.

Many recent technological advances (e.g. ChatGPT and search engines) are possible only because of massive amounts of user-generated data produced through user interactions with computing systems or scraped from the web (e.g. behavior logs, user-generated content, and artwork). However, data producers have little say in what data is captured, how it is used, or who it benefits. Organizations with the ability to access and process this data, e.g. OpenAI and Google, possess immense power in shaping the technology landscape. By synthesizing related literature that reconceptualizes the production of data for computing as ``data labor'', we outline opportunities for researchers, policymakers, and activists to empower data producers in their relationship with tech companies, e.g advocating for transparency about data reuse, creating feedback channels between data producers and companies, and potentially developing mechanisms to share data's revenue more broadly. In doing so, we characterize data labor with six important dimensions - legibility, end-use awareness, collaboration requirement, openness, replaceability, and livelihood overlap - based on the parallels between data labor and various other types of labor in the computing literature.

José F. Fontanari, Viviane M. de Oliveira, Paulo R. A. Campos

The response threshold model explains the emergence of division of labor (i.e., task specialization) in an unstructured population by assuming that the individuals have different propensities to work on different tasks. The incentive to attend to a particular task increases when the task is left unattended and decreases when individuals work on it. Here we derive mean-field equations for the stimulus dynamics and show that they exhibit complex attractors through period-doubling bifurcation cascades when the noise disrupting the thresholds is small. In addition, we show how the fixed threshold can be set to ensure specialization in both the transient and equilibrium regimes of the stimulus dynamics. However, a complete explanation of the emergence of division of labor requires that we address the question of where the threshold variation comes from, starting from a homogeneous population. We then study a structured population scenario, where the population is divided into a large number of independent groups of equal size, and the fitness of a group is proportional to the weighted mean work performed on the tasks during a fixed period of time. Using a winner-take-all strategy to model group competition and assuming an initial homogeneous metapopulation, we find that a substantial fraction of workers specialize in each task, without the need to penalize task switching.

Germán Reyes

Cognitive endurance -- the ability to sustain performance on a cognitively-demanding task over time -- is thought to be a crucial productivity determinant. However, a lack of data on this variable has limited researchers' ability to understand its role for success in college and the labor market. This paper uses college-admission-exam records from 15 million Brazilian high school students to measure cognitive endurance based on changes in performance throughout the exam. By exploiting exogenous variation in the order of exam questions, I show that students are 7.1 percentage points more likely to correctly answer a given question when it appears at the beginning of the day versus the end (relative to a sample mean of 34.3%). I develop a method to decompose test scores into fatigue-adjusted ability and cognitive endurance. I then merge these measures into a higher-education census and the earnings records of the universe of Brazilian formal-sector workers to quantify the association between endurance and long-run outcomes. I find that cognitive endurance has a statistically and economically significant wage return. Controlling for fatigue-adjusted ability and other student characteristics, a one-standard-deviation higher endurance predicts a 5.4% wage increase. This wage return to endurance is sizable, equivalent to a third of the wage return to ability. I also document positive associations between endurance and college attendance, college quality, college graduation, firm quality, and other outcomes. Finally, I show how systematic differences in endurance across students interact with the exam design to determine the sorting of students to colleges. I discuss the implications of these findings for the use of cognitive assessments for talent selection and investments in interventions that build cognitive endurance.

Lia Tiriba, José Luiz Antunes, Jacqueline Botelho et al.

.O número 42 da Revista Trabalho Necessário é publicado em uma conjuntura de grande gravidade política e social no país, que expressa o avanço da estratégia ultraliberal e ultraconservadora frente às políticas sociais e a organização classista da extrema direita, via fascistização da sociedade. O empresariamento da educação, para os setores dominantes, além de representar a ação organizada de disputa do fundo público e incentivo à privatização no campo educacional pelos setores rentistas, também configura a destruição do projeto de escola pública forjado nas lutas sociais. Destacamos, por sua contribuição histórica a esse projeto de escola pública, a figura militante do educador socialista Florestan Fernandes, cujo aporte à leitura da particularidade do capitalismo dependente na formação social brasileira é irrefutável para a compreensão das contrarreformas também como marca do caráter não-clássico da revolução burguesa no Brasil.