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The Distribution of Gains from Globalization
We study economic globalization as a multidimensional process and investigate its effect on incomes. In a panel of 147 countries during 1970-2014, we apply a new instrumental variable, exploiting globalization’s geographically diffusive character, and find differential gains from globalization both across and within countries: Income gains are substantial for countries at early and medium stages of the globalization process, but the marginal returns diminish as globalization rises, eventually becoming insignificant. Within countries, these gains are concentrated at the top of national income distributions, resulting in rising inequality. We find that domestic policies can mitigate the adverse distributional effects of globalization.
Women in the Labor Force
Despite the increase in female labor force participation over the past three decades, women still do not have the same opportunities as men to participate in economic activities in most countries. The average female labor force participation rate across countries is still 20 percentage points lower than the male rate, and gender gaps in wages and access to education persist. As shown by earlier work, including by the IMF, greater gender equality boosts economic growth and leads to better development and social outcomes. Gender equality is also one of the 17 United Nations Sustainable Development Goals that 193 countries committed to achieve by 2030.
Macro-Structural Policies and Income Inequality in Low-Income Developing Countries
Despite sustained economic growth and rapid poverty reductions, income inequality remains stubbornly high in many low-income developing countries. This pattern is a concern as high levels of inequality can impair the sustainability of growth and macroeconomic stability, thereby also limiting countries’ ability to reach the Sustainable Development Goals. This underscores the importance of understanding how policies aimed at boosting economic growth affect income inequality. Using empirical and modeling techniques, the note confirms that macro-structural policies aimed at raising growth payoffs in low-income developing countries can have important distributional consequences, with the impact dependent on both the design of reforms and on country-specific economic characteristics. While there is no one-size-fits-all recipe, the note explores how governments can address adverse distributional consequences of reforms by designing reform packages to make pro-growth policies also more inclusive.
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.
Rising Corporate Market Power
Corporate market power has risen in recent decades, and new estimates in this note suggest that the likely wave of small and medium-sized enterprise bankruptcies from the ongoing pandemic will further strengthen market concentration. Whether and how policymakers should address this issue is hotly debated. This note provides new evidence on the policy relevance of rising market power and highlights possible implications for the design of competition policy frameworks and macroeconomic policies.
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
A Quantitative Analysis of Female Employment in Senegal
Female-to-male employment in Senegal increased by 14 percentage points between 2006 and 2011. During the same period years of education of the working age population increased 27 percent for females and 13 percent for males, reducing gender gaps in education. In this paper, we quantitatively investigate the impact of this increase in education on female employment in Senegal. To that end, we build an overlapping generations model that captures barriers that women face over their life-cycle. Our main findings are: (i) the improvement in years of education can explain up to 44 percent of the observed increased in female-to-male employment ratio and (ii) the rest can be explained by a decline in the discrimination against women in the labor market.
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.
COVID-19 She-Cession: The Employment Penalty of Taking Care of Young Children
The COVID-19 outbreak and the measures to contain the virus have caused severe disruptions to labor supply and demand worldwide. Understanding who is bearing the burden of the crisis and what drives it is crucial for designing policies going forward. Using the U.S. monthly Current Population Survey data, this paper analyzes differences in employment responses between men and women. The main finding is that less educated women with young children were the most adversely affected during the first nine months of the crisis.The loss of employment of women with young children due to the burden of additional childcare is estimated to account for 45 percent of the increase in the employment gender gap, and to reduce total output by 0.36 percent between April and November 2020.
Informality and Gender Gaps Going Hand in Hand
In sub-Saharan Africa women work relatively more in the informal sector than men. Many factors could explain this difference, including women’s lower education levels, legal barriers, social norms and demographic characteristics. Cross-country comparisons indicate strong associations between gender gaps and higher female informality. This paper uses microdata from Senegal to assess the probability of a worker being informal, and our main findings are: (i) in urban areas, being a woman increases this probability by 8.5 percent; (ii) education is usually more relevant for women; (iii) having kids reduces men’s probability of being informal but increases women’s.
