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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
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.
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.
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.
World Economic Outlook, April 2024
The latest World Economic Outlook reports economic activity was surprisingly resilient through the global disinflation of 2022–23, despite significant central bank interest rate hikes to restore price stability. Risks to the global outlook are now broadly balanced compared with last year. Monetary policy should ensure that inflation touches down smoothly, while a renewed focus on fiscal consolidation is needed to rebuild room for budgetary maneuver and to ensure debt sustainability. Structural reforms are crucial to revive medium-term growth prospects amid constrained policy space.
Peru
Peru: Selected Issues
The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions
We review the literature on the effects of Artificial Intelligence (AI) adoption and the ongoing regulatory efforts concerning this technology. Economic research encompasses growth, employment, productivity, and income inequality effects, while regulation covers market competition, data privacy, copyright, national security, ethics concerns, and financial stability. We find that: (i) theoretical research agrees that AI will affect most occupations and transform growth, but empirical findings are inconclusive on employment and productivity effects; (ii) regulation has focused primarily on topics not explored by the academic literature; (iii) across countries, regulations differ widely in scope and approaches and face difficult trade-offs.
Algorithmic Aspects in Information and Management
This volume constitutes the proceedings of the 11th International Conference on Algorithmic Aspects in Information and Management, AAIM 2016, held in Bergamo, Italy, in July 2016. The 18 revised full papers presented were carefully reviewed and selected from 41 submissions. The papers deal with current trends of research on algorithms, data structures, operation research, combinatorial optimization and their applications.
