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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 ...
Portugal
Portugal: Selected Issues
The Distributional Impacts of Worker Reallocation: Evidence from Europe
Using individual-level data for 30 European countries between 1983 and 2019, we document the extent and earning consequences of workers’ reallocation across occupations and industries and how these outcomes vary with individual-level characteristics, namely (i) education, (ii) gender, and (iii) age. We find that while young workers are more likely to experience earnings gains with on-the-job sectoral and occupational switches, low-skilled workers’ employment transitions are associated with an earnings loss. These differences in earnings gains and losses also mask a high degree of heterogeneity related to trends in routinization. We find that workers, particularly low-skilled and older workers during recessions, experience a severe earning penalty when switching occupations from non-routine to routine occupations.
The Macroeconomic and Distributional Implications of Fiscal Consolidations in Low-income Countries
We quantitatively investigate the macroeconomic and distributional impacts of fiscal consolidations in low-income countries (LICs) through value added tax (VAT), personal income tax (PIT), and corporate income tax (CIT). We extend the standard heterogeneous agents incomplete markets model by including multiple sectors and rural-urban distinction to capture salient features of LICs. We find that overall, VAT has the least efficiency costs but is highly regressive, while PIT impacts the economy in the opposite way with CIT staying in between. Cash transfers targeting rural households mitigate the negative distributional impacts of VAT most effectively, while public investment leads to little redistribution.
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.
The Routledge Companion to Gender and COVID-19
The Routledge Companion to Gender and COVID-19 is the first comprehensive research guide for researchers and students who seek to study and evaluate the complex relationship between gender and COVID-19. This interdisciplinary collection touches on two major themes: first, how gender played a central role in shaping access to testing, treatment, and vaccines. Second, how the pandemic not only deepened existing gender inequalities, but also those along the lines of race, class, sexuality, disability, and immigration status. Bringing together a diverse range of international scholars across a number of disciplinary perspectives, this intersectional and comparative focus on COVID explores topics including the pandemic’s impact on families, employment, childcare and elder care, human rights, as well as gender and political economy and leadership, public health law, disability rights, and abortion access. The Routledge Companion to Gender and COVID-19 is an essential volume for scholars and students of Law, Gender Studies, Sociology, Health, Economics, and Politics.
Social Versus Individual Work Preferences: Implications for Optimal Income Taxation
The benchmark optimal income taxation model of Mirrlees (1971) finds that the optimal marginal income tax rate (MIT) is always non-negative. A key model assumption is the coincidence between social and individual work preferences. This paper extends the model to allow for differences in social and individual work preferences. The theoretical and simulation analyses show that under this model, when the government places a higher social weight on work than individuals, the optimal MIT schedule is shifted downwards, introducing the possibility for optimal wage subsidies at the bottom of the income distribution. This implies lower revenues, demogrants, and overall progressivity.
How to Achieve Inclusive Growth
This authoritative book explains the sources and scale of current economic challenges and proposes solutions to craft a brighter future by building a sustainable, green, and inclusive society in the years ahead.
