Quality and Innovation in the Labor Markets
Paper Session
Saturday, Jan. 4, 2025 8:00 AM - 10:00 AM (PST)
- Chair: Polona Domadenik Muren, University of Ljubljana
Ranking Forty-nine Countries by the Quality of Their Labor Market Policies
Abstract
This paper presents the Sustainable, Shared-prosperity Policy Index (SSPI) , which scores and ranks forty-nine countries on their economic policies that provide wellbeing for their residents. The SSPI compares and ranks countries by their national policies across a broad range of government functions that affect national wellbeing. National policies directly affect production and distribution; hence policy choices determine the well-being of a country, now and in the future.The SSPI divides national policies into three pillars—Sustainability, Market Structure, and Public Goods—that represent the government functions of protecting the environment, structuring markets, and delivering products and services. The three pillars are further divided into sixteen categories, which together contain over sixty policy indicators. The indicators are normalized to range from 0 to 100 with higher scores indicating better policies. The SSPI provides data across countries that compares policies in 2018. The policy observed point out where a country is relatively strong or relatively weak, and help lawmakers explore how countries can improve specific policies.
This paper focuses on policies that affect workers, employers, and the labor market, including those that regulate wages and hours; occupational health; collective bargaining; paid time off; retirement income; and income distribution. We compare countries that have strong labor market policies to the United States, and point out how specific policies can be improved. Overall, nations vary widely in their national policies, and the SSPI provides a road map of policies that create the socio-economic system that supports universal well-being.
Navigating Workforce Transformation: AI Augmentation and Automation Risks Across Four Occupation Zones and Various Similarity Measures
Abstract
The integration of artificial intelligence (AI) into contemporary workplaces has raised significant concerns about potential AI-human substitution and the growing issue of job skill obsolescence. This paper emphasizes the critical need for a comprehensive exploration of AI's multifaceted impact on the workforce, highlighting the necessity for an in-depth analysis of AI's capabilities and the evolving skill demands it entails. AI integration introduces both challenges and opportunities, reshaping job roles, boosting productivity, and necessitating a shift towards continuous learning and adaptability. This transformative process is driven by AI's automation of routine tasks and augmentation of human capabilities, leading to potential job displacements while also creating new roles that necessitate collaboration between humans and AI systems. Adapting to the requisite skills for an AI-driven era is essential. This study focuses on identifying transferable skills crucial for success in an AI-driven economy. The acquired knowledge informs the development of standards for evaluating technology products and emphasizes the pivotal role of career navigators in adult education programs. Moreover, AI integration not only revolutionizes workforce development but also ushers in a new era of educational innovation, ensuring the competitiveness and readiness of the entire workforce, including marginalized segments, to tackle future challenges effectively. Specifically, this paper delves into occupational skill transferability, examining the transformative impact of AI-related jobs on various occupations within our AI-driven economy. It constructs two AI indices based on task automation and human capability augmentation, respectively, meticulously analyzing how each index influences job skill transferability across diverse occupations among US workers post-Great Recession. Employing robust methodologies such as text analytics, network analysis, multilevel multivariate modeling, and leveraging Current Population Survey data, the study concludes with insightful policy implications to guide future workforce strategies and labor relations frameworks.AI on the Workplace: The Role of Workers' Participation in Decision Making
Abstract
Over the past decade, digital technologies like robotics and automation have gradually impacted specific employee segments and tasks. However, artificial intelligence (AI) technologies are set to revolutionize the workforce. AI extends automation beyond routine non-cognitive tasks and permeates all aspects of work and daily life (Minevich, 2023). At the macroeconomic level, digital technologies have not only automated tasks but also led to employment and wage polarization, affecting middle-skill workers the most (Acemoglu and Restrepo, 2022). This polarization is linked to "Polanyi's paradox," where some tasks require tacit knowledge and are hard to automate, while routine tasks, common among middle-skill workers, are more susceptible to automation. From a theoretical perspective, AI's impact on labor demand is complex (Charles et al., 2022). AI can displace human labor (displacement effect), boost demand in AI-unaffected jobs due to increased productivity (productivity effect), and create entirely new jobs (reinstatement effect). The net effect on aggregate labor demand is uncertain and might depends on the role of other stakeholders (especially workers) in decision-making process (Autor, 2023). Our study's primary objective is to analyze the initial response to AI technologies in manufacturing and finance across nine OECD countries. Using dataset of approximately 5,500 respondents, we closely examine how AI influences productivity, employment, wages, and working conditions, including job satisfaction, physical health, mental well-being, and fairness in management. We explore whether AI integration leads to labor reinstatement effects or reshapes employment and assess the influence of labor market institutions and different types of workers participation (direct vs indirect participation) on technological change. Our paper contributes empirical insights into the immediate impact of AI technologies on workers. It sheds light on productivity, employment, and wages, as well as broader working conditions. These insights enhance our understanding of the complex dynamics at the intersection of AI and the labor market.Discussant(s)
Tingting Zhang
,
University of Illinois-Urbana-Champaign
JEL Classifications
- J1 - Demographic Economics