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

Ashley Klein, Edward Raff, Marcia DesJardin

The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.

Darvin Yi, Teng Liu, Mattie Terzolo et al.

As large language model (LLM) agents increasingly undertake digital work, reliable frameworks are needed to evaluate their real-world competence, adaptability, and capacity for human collaboration. Existing benchmarks remain largely static, synthetic, or domain-limited, providing limited insight into how agents perform in dynamic, economically meaningful environments. We introduce UpBench, a dynamically evolving benchmark grounded in real jobs drawn from the global Upwork labor marketplace. Each task corresponds to a verified client transaction, anchoring evaluation in genuine work activity and financial outcomes. UpBench employs a rubric-based evaluation framework, in which expert freelancers decompose each job into detailed, verifiable acceptance criteria and assess AI submissions with per-criterion feedback. This structure enables fine-grained analysis of model strengths, weaknesses, and instruction-following fidelity beyond binary pass/fail metrics. Human expertise is integrated throughout the data pipeline (from job curation and rubric construction to evaluation) ensuring fidelity to real professional standards and supporting research on human-AI collaboration. By regularly refreshing tasks to reflect the evolving nature of online work, UpBench provides a scalable, human-centered foundation for evaluating agentic systems in authentic labor-market contexts, offering a path toward a collaborative framework, where AI amplifies human capability through partnership rather than replacement.

Andrea Cremaschi, Dae-Jin Lee, Manuele Leonelli

As artificial intelligence and robotics increasingly reshape the global labor market, understanding public perceptions of these technologies becomes critical. We examine how these perceptions have evolved across Latin America, using survey data from the 2017, 2018, 2020, and 2023 waves of the Latinobarómetro. Drawing on responses from over 48,000 individuals across 16 countries, we analyze fear of job loss due to artificial intelligence and robotics. Using statistical modeling and latent class analysis, we identify key structural and ideological predictors of concern, with education level and political orientation emerging as the most consistent drivers. Our findings reveal substantial temporal and cross-country variation, with a notable peak in fear during 2018 and distinct attitudinal profiles emerging from latent segmentation. These results offer new insights into the social and structural dimensions of AI anxiety in emerging economies and contribute to a broader understanding of public attitudes toward automation beyond the Global North.

Dana Calacci, Varun Nagaraj Rao, Samantha Dalal et al.

Rideshare workers experience unpredictable working conditions due to gig work platforms' reliance on opaque AI and algorithmic systems. In response to these challenges, we found that labor organizers want data to help them advocate for legislation to increase the transparency and accountability of these platforms. To address this need, we collaborated with a Colorado-based rideshare union to develop FairFare, a tool that crowdsources and analyzes workers' data to estimate the take rate -- the percentage of the rider price retained by the rideshare platform. We deployed FairFare with our partner organization that collaborated with us in collecting data on 76,000+ trips from 45 drivers over 18 months. During evaluation interviews, organizers reported that FairFare helped influence the bill language and passage of Colorado Senate Bill 24-75, calling for greater transparency and data disclosure of platform operations, and create a national narrative. Finally, we reflect on complexities of translating quantitative data into policy outcomes, nature of community based audits, and design implications for future transparency tools.

Stephen Meisenbacher, Svetlozar Nestorov, Peter Norlander

Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demonstrate its reliability and accuracy in out-of-sample and LLM-as-a-Judge testing. We extract more than 10 billion data points from more than 155 million online job ads provided by the National Labor Exchange (NLx) Research Hub, including O*NET tasks, occupation codes, tools, and technologies, as well as wages, skills, industry, and more features. We describe the construction of a dataset of occupation, state, and industry level features aggregated by monthly active jobs from 2015 - 2025. We illustrate the potential for research and future uses in education and workforce development.

Michael Clarke, Michael Joffe

The introduction of generative AI tools such as ChatGPT into creative workplaces has sparked highly visible, but binary worker replacement and augmentation debates. This study reframes this argument by examining how creative professionals re-specify a division of labor with these tools. Through 17 ethnomethodologically informed interviews with international creative agency workers we demonstrate how roles are assigned to generative AI tools, how their contributions are modified and remediated, and how workers practically manage their outputs to reflect assumptions of internal and external stakeholders. This paper makes 3 unique contributions to CSCW: (1) we conceptualize generative AI prompting as a type of workplace situated, reflexive delegation, (2) we demonstrate that workers must continuously configure and repair AI role boundaries to maintain workplace intelligibility and accountability; and (3) we introduce the notion of interpretive templatized trust, where workers devise strategies to adapt automated generative templates for their setting, and reinforce stakeholder trust. This contribution has implications for organizing productive human-AI work in creative and stakeholder centric environments.

Eduardo Sá Barreto

O artigo examina o descompasso atual entre a formação científica de economistas e a acelerada transformação de seu objeto, tanto em sua dimensão propriamente socioeconômica quanto em sua dimensão ambiental. Orientada para a temática ecológica, a discussão apresenta as principais tradições de pensamento econômico a ela dedicadas, apontando como as limitações e possibilidades de cada uma refletem os imperativos e impossibilidades do próprio objeto de investigação ao qual se dedicam. Isso fornece uma chave de leitura para entendermos alguns déficits de formação decisivos que povoam a formação corrente de economistas Palavra-chave: Ensino de economia; Economia ambiental; Economia ecológica; Ecologia marxista.

Alla Tkachenko , Oleksii Demchenko

The current state of Ukraine’s economy requires solving a number of fundamental problems. Among the many issues facing the economic mechanism of Ukraine, the issue of e-commerce development is the first to be addressed. Today, there are serious prerequisites for the development of e-commerce in Ukraine. This statement is based on the fact that we traditionally have a high level of education and, in addition, the supply and demand in the Ukrainian IT sector is constantly increasing. In recent years, this sector has become a significant market factor with sales growth rates of 15-25% per year. The development of e-commerce improves market information: buyers and sellers receive almost instantaneous information on prices, quality, and delivery terms of goods offered by different competitors. E-commerce can have a positive impact on the structure and functioning of the Ukrainian labor market by expanding it and utilizing skilled workers. The need for customer service created by e-commerce is another common area of job creation in Ukraine. In addition, the Internet economy will create related small businesses in Ukraine, driven by the need for computer technology and administrative services, including security, accounting, and transportation services for customers. E-commerce can play a crucial role in marketing and selling the products of domestic enterprises, while increasing exports of goods and services. Exports of goods and services can increase manifold if legal restrictions do not impede this. As the domestic industry strengthens its position and the quality of its products improves, Ukrainian companies can sell their products in other countries. The main obstacles for small businesses in this area of activity are imperfect customs legislation and the high cost of cargo transportation. The development of e-commerce can increase the profitability of companies and allow them to reduce costs, in particular, virtual stores and online contact addresses allow goods to be stored closer to the place of their direct production, which speeds up the distribution of goods and related costs.

Beata Mäihäniemi

This article oscillates around the intersection of sustainability and digital platforms, which is an increasingly important and complex area of study in which digital platforms can have both a positive and a negative influence. The interaction between sustainability and digital platforms is crucial at a time when the EU is promoting the twin transition in its economy, a process that entails focusing on both environmental sustainability and digitalization. Online users should have a chance to buy sustainable products and services through digital platforms. However, environmental issues are becoming more and more pressing, and users are more willing to, among others, compensate for their flight emissions, to buy organic food and to ensure the protection of endangered species. The following question arises: who is responsible for ensuring the sustainability of the products and services that are offered online? Is it consumers, who often feel that they want to be environmentally friendly but choose cheaper products over sustainable ones out of habit? Or is it the government and the EU, which are promoting the twin transition in the first place? I argue that these responsibilities could be allocated through competition law. In particular, the article focuses on the possible manipulation of consumers into overconsumption or the purchase of unsustainable products and services, which could be classified as exploitative abuse of dominance. Consumer welfare (i.e., the well-being of consumers) would be enhanced if consumers could buy more sustainable goods and services or if they were not manipulated into overconsumption. Such a development would cohere with the recent attempts to broaden competition law into non-pricerelated goals that respond to societal needs for transformation (here, the sustainability transition in the face of the environmental crisis). This article is intended to answer the following question: how can different kinds of manipulation into the purchase of unsustainable products and services or overspending be classified as decreasing consumer welfare and, consequently, as exploitative abuses of dominance under EU competition law? For example, digital platforms often nudge consumers into specific behaviours that may not be in their best interests.

Tianwei Chang

Existing studies have shown that the monetization of silver in the Ming Dynasty effectively promoted the prosperity of trade in the Ming Dynasty, while the prices of labor, handicraft products and grain were long suppressed by the deformed economic structure. With the expansion of silver application, the fluctuation of silver supply and demand exacerbated the above contradictions. Capital accumulation that should have been obtained through the marketization of labor was easily plundered by the landlord gentry class through silver. This article re-discusses the issue from the perspective of supply and demand. Compared with the increase and then decrease of silver supply, the evolution of silver demand is more complicated: at the tax level, the widespread use of silver leads to a huge difference in the elasticity of production and trade taxes. When government spending surges, the increase in tax burden will be mainly borne by agriculture and handicrafts. At the production level, the high liquidity of silver makes the concentration of social wealth more convenient, while the reduction in silver supply and the expansion of demand have rapidly expanded deflation, further exacerbating the gap between the rich and the poor. Such combined effect of supply and demand factors has caused the monetization of silver to become an accelerator of the economic collapse of the Ming Dynasty.

Tyler M. Paine, Anastasia Bizyaeva, Michael R. Benjamin

This paper addresses the problem of adaptively controlling the bias parameter in nonlinear opinion dynamics (NOD) to allocate agents into groups of arbitrary sizes for the purpose of maximizing collective rewards. In previous work, an algorithm based on the coupling of NOD with an multi-objective behavior optimization was successfully deployed as part of a multi-robot system in an autonomous task allocation field experiment. Motivated by the field results, in this paper we propose and analyze a new task allocation model that synthesizes NOD with an evolutionary game framework. We prove sufficient conditions under which it is possible to control the opinion state in the group to a desired allocation of agents between two tasks through an adaptive bias using decentralized feedback. We then verify the theoretical results with a simulation study of a collaborative evolutionary division of labor game.

Esrat Farhana Dulia, Syed A. M. Shihab

Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence mass eVTOL production with our model, enabling them to make optimal decisions and profits even under potential disruptions.

Rena Li, Sara Kingsley, Chelsea Fan et al.

Recent years have witnessed an interesting phenomenon in which users come together to interrogate potentially harmful algorithmic behaviors they encounter in their everyday lives. Researchers have started to develop theoretical and empirical understandings of these user driven audits, with a hope to harness the power of users in detecting harmful machine behaviors. However, little is known about user participation and their division of labor in these audits, which are essential to support these collective efforts in the future. Through collecting and analyzing 17,984 tweets from four recent cases of user driven audits, we shed light on patterns of user participation and engagement, especially with the top contributors in each case. We also identified the various roles user generated content played in these audits, including hypothesizing, data collection, amplification, contextualization, and escalation. We discuss implications for designing tools to support user driven audits and users who labor to raise awareness of algorithm bias.

Dandan Qiao, Huaxia Rui, Qian Xiong

This study investigates how artificial intelligence (AI) influences various online labor markets (OLMs) over time. Employing the Difference-in-Differences method, we discovered two distinct scenarios following ChatGPT's launch: displacement effects featuring reduced work volume and earnings, exemplified by translation & localization OLM; productivity effects featuring increased work volume and earnings, exemplified by web development OLM. To understand these opposite effects in a unified framework, we developed a Cournot competition model to identify an inflection point for each market. Before this point, human workers benefit from AI enhancements; beyond this point, human workers would be replaced. Further analyzing the progression from ChatGPT 3.5 to 4.0, we found three effect scenarios, reinforcing our inflection point conjecture. Heterogeneous analyses reveal that U.S. web developers tend to benefit more from ChatGPT's launch compared to their counterparts in other regions. Experienced translators seem more likely to exit the market than less experienced translators.

Kwonhyung Lee, Yejin Lim, Sunghyun Cho

Migrant laborers subject to ROK's Employment Permit System(EPS) must strike a balance between host country's high wage and 'Depreciation of skill-relevance entailed by immigration', whilst taking account of the 'migration costs'. This study modelizes the optimization mechanism of migrant workers and the firms hiring them -- then induces the solution of the very model, namely, 'Subgame Perfect Nash Equilibrium(SPNE)', by utilizing game theory's 'backward induction' method. Analyzing the dynamics between variables at SPNE state, the attained stylized facts are what as follows; [1]Host nation's skill-relevance and wage differential have positive correlation. [2]Emigrating nation's skill-relevance and wage differential have negative correlation. Both stylized facts -- [1,2] -- are operationalized into 'Host nation skill-relevance hypothesis(H1)' and 'Emigrating nation skill-relevance hypothesis(H2)', respectively; of which are thoroughly tested by OLS linear regression analysis. In all sex/gender parameters(Total/Men/Women), test results support both hypotheses with statistical significance, thereby inductively substantiating the constructed model. This paper contributes to existing labor immigration literature in three following aspects: (1)Stimulate the economic approach to migrant labor analysis, and by such means, break away from the overflow of sociology, anthropology, political science, and jurisprudence in prior studies; (2)Shed a light on the EPS's microeconomic interaction process, of which was left undisclosed as a 'black box'; (3)Seek a complementary synthesis of two grand strands of research methodology -- that is, deductive modeling and inductive statistics.

Monique Nunes Fiuza Dias

Neste artigo apresento as atribuições do Agente Comunitário de Saúde (ACS) em seu trabalho real e prescrito sob a ótica do próprio profissional, e o valor que lhes atribui. Optou-se por um estudo qualitativo que analisou dados primários. Constatou-se que o trabalho real destoa do prescrito. Entre as atribuições, aquelas que envolvem a presença no território e participação em grupos de educação em saúde, são valorizadas pelo ACS. Mesmo após constantes reformulações direcionadas ao cargo, o ACS valoriza as suas raízes, enquanto promotores de saúde. Palavra-chave: Atenção Primária à Saúde; Agente Comunitário de Saúde; Trabalho em saúde.

Marisa Oliveira Santos

  O filósofo brasileiro, José Arthur Gianotti (1966), ao inferir atenção do homem para realidade e as ideias do seu tempo, enfatiza que os fenômenos sociais despertam naquele que o observa a simpatia ou aversão e, por esse motivo, exige dele a compreensão de seus motivos e seus fins, até que, num dado instante, esse percebe sua condição de sujeito e objeto da análise.  

Mahalia Gomes de Carvalho Aquino

Para essa reportagem fotográfica apresentada a Revista Trabalho Necessário, o lócus da viagem de campo consiste na Bacia do Rio Doce, região diretamente atingida pelo crime ambiental que repercutiu no rompimento de uma das barragens de rejeitos minerários do Complexo de Germano: Fundão (Mariana – MG), em novembro de 2015. A barragem é de responsabilidade da mineradora Samarco e suas acionistas, as multinacionais extrativistas Vale e BHP Billiton. Busca-se com esta reportagem fotográfica evidenciar o meio ambiente da Bacia do Rio Doce após o crime do rompimento da barragem de Fundão e de como esse ambiente não favorece, ou não propicia mais, as condições necessárias para a realização do trabalho com a pesca e com a terra. 

Keyon Vafa, Emil Palikot, Tianyu Du et al.

Labor economists regularly analyze employment data by fitting predictive models to small, carefully constructed longitudinal survey datasets. Although machine learning methods offer promise for such problems, these survey datasets are too small to take advantage of them. In recent years large datasets of online resumes have also become available, providing data about the career trajectories of millions of individuals. However, standard econometric models cannot take advantage of their scale or incorporate them into the analysis of survey data. To this end we develop CAREER, a foundation model for job sequences. CAREER is first fit to large, passively-collected resume data and then fine-tuned to smaller, better-curated datasets for economic inferences. We fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it on small longitudinal survey datasets. We find that CAREER forms accurate predictions of job sequences, outperforming econometric baselines on three widely-used economics datasets. We further find that CAREER can be used to form good predictions of other downstream variables. For example, incorporating CAREER into a wage model provides better predictions than the econometric models currently in use.

Or Honovich, Thomas Scialom, Omer Levy et al.

Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions. In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor. We collect 64,000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth. This set is then expanded by prompting the model to rephrase each instruction, creating a total of approximately 240,000 examples of instructions, inputs, and outputs. Experiments show that despite containing a fair amount of noise, training on Unnatural Instructions rivals the effectiveness of training on open-source manually-curated datasets, surpassing the performance of models such as T0++ and Tk-Instruct across various benchmarks. These results demonstrate the potential of model-generated data as a cost-effective alternative to crowdsourcing for dataset expansion and diversification.