Hasil untuk "Labor policy. Labor and the state"

Maksym Sikalo

Digital transformation has become a defining vector of contemporary development, radically reshaping societal practices and economic processes. The rapid diffusion of digital technologies creates new opportunities to accelerate growth, enhance productivity and quality of life, and modernize public services. At the same time, it brings a range of challenges: widening digital inequality, heightened cybersecurity vulnerabilities, risks to privacy, shifts in the labor market, and more. Under these conditions, adapting systems of public administration to the digital era–above all in the domain of managing economic development–becomes especially important. In a digital economy, traditional governance models require rethinking and supplementation with breakthrough solutions and institutional innovations, enabling governments to implement economic policy more effectively, foster innovation, and ensure sustainable growth. This article analyzes conceptual approaches (“breakthrough ideas”) to state management of economic development based on digitalization, examines new institutional forms and mechanisms introduced in the public sector to support the digital transformation of the economy, and considers practical case studies of their implementation. A review of recent scholarly publications and strategic documents that lay the groundwork for digital development is conducted. It is found that the active formation of a system of public administration for digital development in Ukraine requires a focused state policy and effective work of newly created institutions, in particular the Ministry of Digital Transformation. Examples are given of the implementation of digital projects (superservices and e-systems) in the public administration sphere that have demonstrated practical effects – from increased transparency and budget savings to an improved business climate. It is emphasized that the digital transformation of the public sector has become the basis for its modernization and can significantly stimulate the development of an open information society, democracy, productivity, and an innovation-driven economy. The practical significance of the study lies in identifying promising directions for improving state economic development policy in the digital age and generalizing the experience of implementing institutional innovations, which can be used in developing strategic documents and reforms in the field of public administration.

Oleksandr Babich

The article, through statistical analysis, proved that the labor market in Ukraine under martial law is characterized by significant disparities. Compared to the previous five years, the situation on the labor market has changed to the opposite, since in recent years the number of unemployed and job seekers has exceeded the number of vacancies. Over the past two years, there has been a trend towards a decrease in both vacancies and registered unemployed people, but this indicator does not reflect the real number of job seekers. Along with this, there is a sharp shortage of personnel in certain industries and spheres, such as education, energy, water supply and others, that is, where highly qualified personnel are needed. The article concludes that thanks to the targeted state policy on employment of people with disabilities, the percentage of employed people is increasing. This situation is also influenced by the fact that the number of people with disabilities is increasing as a result of the war, which means that these are people who have both qualifications and work experience, which, with state support, gives them higher chances of employment. However, employment of people with disabilities in conditions of market imbalance is difficult, and due to the sharp absolute increase in the number of such people, the percentage comparison of employed people is not sufficiently accurate. The article proposes to consider the following promising areas of work for public authorities, employers, and the public regarding the employment of persons with disabilities: their legislative support, the development of remote work, professional rehabilitation and training, support for entrepreneurship for persons with disabilities, and activities to popularize the advantages of employers in employing people with disabilities. It is concluded that these areas include the implementation of relevant measures, which should change in the process of their implementation. The article argues that if the trends in state policy on supporting persons with disabilities are maintained, the number of working persons with disabilities may increase sharply.

Jaume AGUSTÍ CULLELL, Jordi AGUSTÍ PANAREDA

Pour les auteurs, la fascination et la peur que suscite l’intelligence artificielle (IA) découlent d’une compréhension erronée de l’intelligence humaine (IH) qui, elle, peine à déployer son potentiel. L’enthousiasme qui entoure l’IA occulte souvent la déshumanisation concomitantede l’IH.  Considérant le travail comme un champ où se dessine le futur de l’intelligence, ils défendent une conception plus étendue de l’IH, rappelant ses différentes dimensions et capacités constitutives et les limites souvent oubliées de l’IA. Ils voient dans les multiples défis de notre époque charnière une chance inédite de cultiver au travail les dimensions intrinsèquement humaines de l’intelligence, indispensables à l’humanisation du travail, mais largement inexploitées.

Yuya Takada, Kiyoshi Izumi

National statistical institutes are beginning to use non-traditional data sources to produce official statistics. These sources, originally collected for non-statistical purposes, include point-of-sales(POS) data and mobile phone global positioning system(GPS) data. Such data have the potential to significantly enhance the usefulness of official statistics. In the era of big data, many private companies are accumulating vast amounts of transaction data. Exploring how to leverage these data for official statistics is increasingly important. However, progress has been slower than expected, mainly because such data are not collected through sample-based survey methods and therefore exhibit substantial selection bias. If this bias can be properly addressed, these data could become a valuable resource for official statistics, substantially expanding their scope and improving the quality of decision-making, including economic policy. This paper demonstrates that even biased transaction data can be useful for producing official statistics for prompt release, by drawing on the concepts of density ratio estimation and supervised learning under covariate shift, both developed in the field of machine learning. As a case study, we show that preliminary statistics can be produced in a timely manner using biased data from a Japanese private employment agency. This approach enables the early release of a key labor market indicator that would otherwise be delayed by up to a year, thereby making it unavailable for timely decision-making.

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.

Rishi Bommasani, Scott R. Singer, Ruth E. Appel et al.

The innovations emerging at the frontier of artificial intelligence (AI) are poised to create historic opportunities for humanity but also raise complex policy challenges. Continued progress in frontier AI carries the potential for profound advances in scientific discovery, economic productivity, and broader social well-being. As the epicenter of global AI innovation, California has a unique opportunity to continue supporting developments in frontier AI while addressing substantial risks that could have far reaching consequences for the state and beyond. This report leverages broad evidence, including empirical research, historical analysis, and modeling and simulations, to provide a framework for policymaking on the frontier of AI development. Building on this multidisciplinary approach, this report derives policy principles that can inform how California approaches the use, assessment, and governance of frontier AI: principles rooted in an ethos of trust but verify. This approach takes into account the importance of innovation while establishing appropriate strategies to reduce material risks.

Lei Chen, Chaoyue Gao, Alvin Leung et al.

Generative artificial intelligence (GenAI) like Large Language Model (LLM) is increasingly integrated into digital platforms to enhance information access, deliver personalized experiences, and improve matching efficiency. However, these algorithmic advancements rely heavily on large-scale user data, creating a fundamental tension between information assurance-the protection, integrity, and responsible use of privacy data-and artificial intelligence-the learning capacity and predictive accuracy of models. We examine this assurance-intelligence trade-off in the context of LinkedIn, leveraging a regulatory intervention that suspended the use of user data for model training in Hong Kong. Using large-scale employment and job posting data from Revelio Labs and a Difference-in-Differences design, we show that restricting data use significantly reduced GenAI efficiency, leading to lower matching rates, higher employee turnover, and heightened labor market frictions. These effects were especially pronounced for small and fast-growing firms that rely heavily on AI for talent acquisition. Our findings reveal the unintended efficiency costs of well-intentioned data governance and highlight that information assurance, while essential for trust, can undermine intelligence-driven efficiency when misaligned with AI system design. This study contributes to emerging research on AI governance and digital platform by theorizing data assurance as an institutional complement-and potential constraint-to GenAI efficacy in data-intensive environments.

Kun Chu, Xufeng Zhao, Cornelius Weber et al.

Although there has been rapid progress in endowing robots with the ability to solve complex manipulation tasks, generating control policies for bimanual robots to solve tasks involving two hands is still challenging because of the difficulties in effective temporal and spatial coordination. With emergent abilities in terms of step-by-step reasoning and in-context learning, Large Language Models (LLMs) have demonstrated promising potential in a variety of robotic tasks. However, the nature of language communication via a single sequence of discrete symbols makes LLM-based coordination in continuous space a particular challenge for bimanual tasks. To tackle this challenge, we present LAnguage-model-based Bimanual ORchestration (LABOR), an agent utilizing an LLM to analyze task configurations and devise coordination control policies for addressing long-horizon bimanual tasks. We evaluate our method through simulated experiments involving two classes of long-horizon tasks using the NICOL humanoid robot. Our results demonstrate that our method outperforms the baseline in terms of success rate. Additionally, we thoroughly analyze failure cases, offering insights into LLM-based approaches in bimanual robotic control and revealing future research trends. The project website can be found at http://labor-agent.github.io.

Sally Cripps, Anna Lopatnikova, Hadi Mohasel Afshar et al.

This paper proposes Bayesian Adaptive Trials (BAT) as both an efficient method to conduct trials and a unifying framework for evaluation social policy interventions, addressing limitations inherent in traditional methods such as Randomized Controlled Trials (RCT). Recognizing the crucial need for evidence-based approaches in public policy, the proposal aims to lower barriers to the adoption of evidence-based methods and align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic, ``learning as we go'' approach, enabling the integration of diverse information types and facilitating a continuous, iterative process of policy evaluation. BATs' adaptive nature is particularly advantageous in policy settings, allowing for more timely and context-sensitive decisions. Moreover, BATs' ability to value potential future information sources positions it as an optimal strategy for sequential data acquisition during policy implementation. While acknowledging the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, the paper argues that these are advantageous for decision-makers in social policy, effectively merging the best features of various methodologies.

Susan Athey, Herman Brunborg, Tianyu Du et al.

This paper builds an empirical model that predicts a worker's next occupation as a function of the worker's occupational history. Because histories are sequences of occupations, the covariate space is high-dimensional, and further, the outcome (the next occupation) is a discrete choice that can take on many values. To estimate the parameters of the model, we leverage an approach from generative artificial intelligence. Estimation begins from a ``foundation model'' trained on non-representative data and then ``fine-tunes'' the estimation using data about careers from a representative survey. We convert tabular data from the survey into text files that resemble resumes and fine-tune the parameters of the foundation model, a large language model (LLM), using these text files with the objective of predicting the next token (word). The resulting fine-tuned LLM is used to calculate estimates of worker transition probabilities. Its predictive performance surpasses all prior models, both for the task of granularly predicting the next occupation as well as for specific tasks such as predicting whether the worker changes occupations or stays in the labor force. We quantify the value of fine-tuning and further show that by adding more career data from a different population, fine-tuning smaller LLMs (fewer parameters) surpasses the performance of fine-tuning larger models. When we omit the English language occupational title and replace it with a unique code, predictive performance declines.

Vedant Das Swain, Qiuyue "Joy" Zhong, Jash Rajesh Parekh et al.

Client-Service Representatives (CSRs) are vital to organizations. Frequent interactions with disgruntled clients, however, disrupt their mental well-being. To help CSRs regulate their emotions while interacting with uncivil clients, we designed Care-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use. Our comparative analyses between 665 human and Care-Pilot-generated support messages highlight Care-Pilot's ability to adapt to and demonstrate empathy in various incivility incidents. Additionally, 143 CSRs assessed Care-Pilot's empathy as more sincere and actionable than human messages. Finally, we interviewed 20 CSRs who interacted with Care-Pilot in a simulation exercise. They reported that Care-Pilot helped them avoid negative thinking, recenter thoughts, and humanize clients; showing potential for bridging gaps in coworker support. Yet, they also noted deployment challenges and emphasized the indispensability of shared experiences. We discuss future designs and societal implications of AI-mediated emotional labor, underscoring empathy as a critical function for AI assistants for worker mental health.

S. Chernyh, Svetlana Timofeeva

Ensuring labor protection and safety in the workplace is the responsibility of the employer. It is necessary to systematically carry out procedures aimed at improving working conditions and increasing the level of preparedness for actions in the conditions of localizing and eliminating the consequences of realized professional risks. One of the directions of state policy in the field of labor protection is the creation of conditions for the development of a healthy lifestyle. Note that the lives of many people are characterized by a lack of time, chronic fatigue and stressful situations. Permanent stress at work can become a source of professional burnout. This article examines teaching staff whose work is characterized by high responsibility and emotional intensity. The effectiveness of teachers’ work activities depends not only on the correct organization of educational and educational work, but also on their well-being. The professional health of teachers has a direct relationship with the level of training and education of the younger generation. Therefore, it is especially important to study the psychosocial factors that influence the professional health of teaching staff, to solve problems related to eliminating production stress and optimizing working conditions. The purpose of the work was to study the main symptoms of professional burnout and their impact on professional performance.

Jonas Emanuel Pinto Magalhães

Essa tese tem como tema a noção de competências socioemocionais e como objeto de estudo a sua incorporação nas políticas curriculares da educação nacional. De modo mais específico, busca identificar e analisar os fundamentos econômicos, políticos, ideológicos e epistemológicos que orientam as propostas de formação de competências socioemocionais na atualidade. Além disso, e tendo como referencial teórico-metodológico o materialismo histórico e dialético, analisamos, no contexto da crise do capital e do neoliberalismo, a construção e implementação dessa nova agenda educacional por organizações como a Organização para a Cooperação e Desenvolvimento Econômico (OCDE) e o Instituto Ayrton Senna (IAS). A progressiva projeção alcançada pela noção de competências socioemocionais no Brasil, combinadas com a apropriação apressada e, via de regra, pouco crítica desse novo slogan pedagógico justifica a investigação de suas bases epistemológicas, políticas e sócio-históricas, bem como suas implicações ideológicas e pedagógicas.

Aleksandra Matykiewicz

THE INFLUENCE OF USING FORMS OF DIRECT DEMOCRACY AND THE PRINCIPLE OF FEDERALISM ON THE SHAPE OF SWISS IMMIGRATION AND INTEGRATION POLICY Switzerland’s migration policy today is the result of international commitments, the needs of the labor market and an aging society, as well as the fears of some Swiss people about the influx of foreigners. Despite the restrictive immigration policy, 39.5% of permanent residents of Switzerland are immigrants or descendants of immigrants in the first generation. Such a state requires measures to integrate newcomers with the host society. The aim of the article is to show to what extent Switzerland’s immigration and integration policy is dependent on the political solutions characteristic of this country – often used forms of direct democracy, such as referendum or popular legislative initiative, and the strong position of the cantons.

Danny R. Daneri

Despite a half-century of decline in membership and political influence, unions still play a prominent role in lobbying for congressional policy. For much of post-war history, policies around labor and employment have divided along partisan lines, seemingly confining labor’s influence to politicians in the Democratic Party. In this paper, we construct a dataset of labor and employment legislation from 1970 to 2020 to examine the influence of state-level union density on Republican Senators’ votes over the past half-century. We use a two-stage ordinary least squares model with a novel instrument to provide causal estimates of the effect of unionization on Senator voting behavior. The results suggest that the percentage of employed workers who are in unions in a state is strongly predictive of whether Republicans will defect in favor of labor policies supported primarily by Democrats.

Mohsin Yousufi, Charlotte Alexander, Nassim Parvin

This paper brings attention to epistemic injustice, an issue that has not received much attention in the design of technology and policy. Epistemic injustices occur when individuals are treated unfairly or harmed specifically in relation to their role as knowers or possessors of knowledge. Drawing on the case of making heat complaints in New York City, this paper illustrates how both technological and policy interventions that address epistemic injustice can fail or even exacerbate the situations for certain social groups, and individuals within them. In bringing this case to the workshop, this paper hopes to provide another generative and critical dimension that can be utilised to create better technologies and policies, especially when they deal with diverse and broad range of social groups

Haiming Liu, Y. Tang, Wasim Iqbal et al.

Settling a “green recovery” at the center of all economic recuperation procedures is progressively seen as the finest and as the only way nations could restore their economies. Therefore, this study assesses the role of energy finance, green policies, and investment towards green economic recovery in the USA by using a linear econometric approach and nonlinear (DSGE) model. Considering the fiscal tax-lowering rate, for instance, the study evaluates the effects of fiscal measures on local fiscal pressures in the USA regarding the pandemic. The regression analysis shows that both energy finance and green energy policies have positive and statistically significant impacts on green investment. The results from the linear econometric approach indicate that every additional state green energy policy tool adopted is associated with 1% more green investment in the USA. In addition, the findings show that green policies in human resources and R&D of green energy technologies prompt a sustainable green economy through labor and technology-oriented production activities. Implications for scholars, investors, technology managers, and policymakers are derived and discussed.

Regis Eduardo Coelho Argüelles

Integrando a seção Memória e Documentos da revista Trabalho Necessário, o objetivo desse texto é analisar criticamente a lei do salário-educação, proposta em 1946 e instituída a partir de 1964. Pretende-se, a partir da discussão do contexto histórico de proposição e execução da lei, colocar em perspectiva os limites e as possibilidades da garantida do direito à educação no capitalismo. O texto está dividido em 3 seções. Na primeira, empreenderemos um esforço de compreensão teórica do Estado e de suas políticas públicas, considerando as relações políticas determinadas em uma sociedade de classes. Esse debate servirá de suporte à apreensão histórica do salário-educação na seção a seguir, possibilitando a discussão histórica do empresariamento da educação. Por último, reproduziremos, para fins de ilustração, o texto da lei do salário-educação e demais regulamentações ligadas e ela.

Yusuf Alper

The Turkish social insurance system has practically turned into Lego blocks in terms of legislation regulating social insurance rights and obligations with frequent interventions. In the past, Türkiye’s Social Insurance Act No. 506 and Pension Fund Act No. 5434 are single-roof laws that aim to consolidate the disparate social insurance system. Türkiye’s Social Insurance and General Health Insurance Act No. 5510 is also a single-roof law that was prepared and put into effect for the same reasons. However, in the past 16 years since the enactment of Act No. 5510, the need for a comprehensive rearrangement has emerged through the frequent interventions similar to previous periods. In short, the Turkish social insurance system is again on its way to the work-break-rework dilemma.

Amaç Herdağdelen, Lada Adamic, Bogdan State

We use exploratory factor analysis to investigate the online persistence of known community-level patterns of social capital variance in the U.S. context. Our analysis focuses on Facebook groups, specifically those that tend to connect users in the same local area. We investigate the relationship between established, localized measures of social capital at the county level and patterns of participation in Facebook groups in the same areas. We identify four main factors that distinguish Facebook group engagement by county. The first captures small, private groups, dense with friendship connections. The second captures very local and small groups. The third captures non-local, large, public groups, with more age mixing. The fourth captures partially local groups of medium to large size. The first and third factor correlate with community level social capital measures, while the second and fourth do not. Together and individually, the factors are predictive of offline social capital measures, even controlling for various demographic attributes of the counties. Our analysis reveals striking patterns of correlation between established measures of social capital and patterns of online interaction in local Facebook groups. To our knowledge this is the first systematic test of the association between offline regional social capital and patterns of online community engagement in the same regions.