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Exposure to Artificial Intelligence and Occupational Mobility: A Cross-Country Analysis
We document historical patterns of workers' transitions across occupations and over the life-cycle for different levels of exposure and complementarity to Artificial Intelligence (AI) in Brazil and the UK. In both countries, college-educated workers frequently move from high-exposure, low-complementarity occupations (those more likely to be negatively affected by AI) to high-exposure, high-complementarity ones (those more likely to be positively affected by AI). This transition is especially common for young college-educated workers and is associated with an increase in average salaries. Young highly educated workers thus represent the demographic group for which AI-driven structural change ...
Gen-AI
Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills
Green Jobs and the Future of Work for Women and Men
The transition to a sustainable and green economy requires workers to move out of carbon-intensive jobs and workers to move into green jobs. The pace and effectiveness of the transition hinge not only on climate policies but also on the skills and adaptability of workers. Evidence suggests that economies with a robust supply of STEM-educated workers and a more equal treatment of women are better placed to transition faster and at a lower cost to a green economy, even after controlling for other country characteristics, because these economies generate more green innovation and face lower bottlenecks in expanding the green workforce. Altogether, climate policies, particularly energy taxes, in...
Labor Market Exposure to AI: Cross-country Differences and Distributional Implications
This paper examines the impact of Artificial Intelligence (AI) on labor markets in both Advanced Economies (AEs) and Emerging Markets (EMs). We propose an extension to a standard measure of AI exposure, accounting for AI's potential as either a complement or a substitute for labor, where complementarity reflects lower risks of job displacement. We analyze worker-level microdata from 2 AEs (US and UK) and 4 EMs (Brazil, Colombia, India, and South Africa), revealing substantial variations in unadjusted AI exposure across countries. AEs face higher exposure than EMs due to a higher employment share in professional and managerial occupations. However, when accounting for potential complementarity, differences in exposure across countries are more muted. Within countries, common patterns emerge in AEs and EMs. Women and highly educated workers face greater occupational exposure to AI, at both high and low complementarity. Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity.
Automation and Welfare: The Role of Bequests and Education
This paper examines the welfare effects of automation in neoclassical growth models with and without intergenerational transfers. In a standard overlapping generations model without such transfers, improvements in automation technologies that would lower welfare can be mitigated by shifts in labor supply related to demographics or pandemics. With perfect intergenerational transfers based on altruism, automation could raise the well-being of all generations. With imperfect altruism, fiscal transfers (universal basic income) and public policies to expand access to education opportunities can alleviate much of the negative effect of automation.
Spillovers from China’s Growth Slowdown to the Singapore Economy
This paper examines the impact of China's economic deceleration on Singapore, highlighting how the deepening trade integration and China's pivotal role in Global Value Chains (GVCs) amplify these spillover effects. Utilizing multi-region input-output tables, empirical estimates, and the IMF's Global Integrated Monetary and Fiscal model, it identifies significant sectoral and aggregate impacts, particularly in electrical and machinery manufacturing, petrochemicals, and financial services. The analysis underscores the vulnerability of Singapore's economy to shifts in Chinese demand and productivity, emphasizing the need for vigilant monitoring and strategic adaptation to mitigate potential risks associated with China's slowdown.
The Green Future: Labor Market Implications for Men and Women
This study examines the green transition's effects on labor markets using a task-based framework to identify jobs with tasks that contribute, or with the potential to contribute, to the green transition. Analyzing data from Brazil, Colombia, South Africa, the United Kingdom, and the United States, we find that the proportion of workers in green jobs is similar across AEs and EMs, albeit with distinct occupational patterns: AE green job holders typically have higher education levels, whereas in EMs, they tend to have lower education levels. Despite these disparities, the distribution of green jobs across genders is similar across countries, with men occupying over two-thirds of these positions. Furthermore, green jobs are characterized by a wage premium and a narrower gender pay gap. Our research further studies the implications of AI for the expansion of green employment opportunities. This research advances our understanding of the interplay between green jobs, gender equity, and AI and provides valuable insights for promoting a more inclusive green transition.
Strengthening Social Protection to Pave the Way for Technological Innovation
This paper investigates the impact of automation on the U.S. labor market from 2000 to 2007, specifically examining whether more generous social protection programs can mitigate negative effects. Following Acemoglu and Restrepo (2020), the study finds that areas with higher robot adoption reduced employment and wages, in particular for workers without collegue degree. Notably, the paper exploits differences in social protection generosity across states and finds that areas with more generous unemployment insurance (UI) alleviated the negative effects on wages, especially for less-skilled workers. The results suggest that UI allowed displaced workers to find better matches The findings emphasize the importance of robust social protection policies in addressing the challenges posed by automation, contributing valuable insights for policymakers.
Harnessing Renewables in Sub-Saharan Africa
Sub-Saharan Africa needs to significantly accelerate its electricity generation. While hydropower is prominent in some countries, solar and wind power generation has lagged other world regions, even though sub-Saharan Africa has some of the most favorable conditions. A mix of domestic and external financing can increase both renewable electricity generation and GDP. In a scenario where about $25 bn in climate finance flows are allocated annually to renewable energy, renewable electricity production could be up to 24 percent higher than in a scenario excluding this financing, and annual GDP growth would be boosted by 0.8 percentage point on average over the next decade, accompanied by stronger labor demand in the electricity sector. Policies can help catalyze climate finance. An ambitious package of governance, business regulations, and external sector reforms is associated with a 20 percent increase in climate finance flows and a 7 percent increase in electricity generation over five years. In addition, implementing climate policies is linked to increases in green foreign direct investment announcements and green electricity production.
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.
