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Digitalization During the COVID-19 Crisis
Digitalization induced by the pandemic was seen both as a possible silver-lining from the crisis that could increase longer-term productivity and a risk for further labor market inequality between digital and non-digital workers. The note shows that the pandemic accelerated digitalization and triggered a partial catch-up by less digitalized entities in advanced economies. Higher digitalization levels shielded substantially productivity and hours worked during the crisis. However, the extent to which the pandemic-induced digitalization led to structural change in the economy is less clear. Less digitalized sectors have rebounded more strongly, albeit after stronger declines, and while workers in digital occupations were more shielded from the crisis, there does not appear to be a structural change in the composition of labor demand. Meanwhile, shifts in labor supply are more likely to be permanent, driven by the increase in working from home.
Labor Market Tightness in Advanced Economies
Two years after the onset of the COVID-19 pandemic, a puzzle has emerged in several advanced economies: unfilled job vacancies have increased sharply even though employment has yet to fully recover. This note sheds light on three contributing factors, namely barriers to returning to work, changing worker preferences away from certain types of jobs, and sectoral and occupational job mismatch. The note also assesses the impact of labor market tightness on wage growth, showing that it has been large for low-pay jobs but milder overall. Bringing disadvantaged groups of workers into the labor force, including by controlling the pandemic itself, would ease labor market pressures while amplifying the recovery and making it more inclusive.
Structural Reforms to Accelerate Growth, Ease Policy Trade-offs, and Support the Green Transition in Emerging Market and Developing Economies
In the aftermath of the COVID-19 pandemic, emerging market and developing economies are grappling with economic scarring, social tension, and reduced policy space. Policy actions are already urgently needed to boost growth in the near term and support the ongoing green transition. At the same time, high public debt and persistently high inflation have constrained policy space, posing difficult policy trade-offs. This Staff Discussion Note focuses on emerging market and developing economies and proposes a framework for prioritization, packaging, and sequencing of macrostructural reforms to accelerate growth, alleviate policy trade-offs, and support the green transition. The note shows that pr...
Market Power and Monetary Policy Transmission
We show that firms’ market power dampens the response of their output to monetary policy shocks, using firm-level data for the United States and a large cross-country firm-level dataset for 14 advanced economies. The estimated impact of a firm’s markup on its response to a monetary policy shock is large enough to materially affect monetary policy transmission. We also find some evidence that the role of markup in monetary policy transmission, while independent from other channels, is greater for firms whose characteristics — notably size and age — are likely to be associated with greater financial constraints. We rationalize these findings through a simple partial equilibrium model in which borrowing constraints amplify disproportionately low-markup firms’ responses to changes in interest rates.
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.
Epidemics, Gender, and Human Capital in Developing Countries
Epidemics have disrupted lives for centuries with deleterious human capital and economic repercussions. In this paper, we investigate how epidemics episodes have impacted school dropouts in developing countries, considering 623 epidemics episodes across countries from 1970 to 2019. Our estimates show that, on average, epidemics reduce completion rates by about 2.6 and 2.1 percentage points in primary and lower secondary education respectively, with girls more severely affected than boys. Using detailed micro data for Senegal, we also estimate the potential loss of lifelong earnings and find that the potential labor earnings loss from dropping out of primary and secondary school is almost double for girls than for boys.
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.
Gender and Employment in the COVID-19 Recession: Evidence on “She-cessions”
Early evidence on the pandemic’s effects pointed to women’s employment falling disproportionately, leading observers to call a “she-cession.” This paper documents the extent and persistence of this phenomenon in a quarterly sample of 38 advanced and emerging market economies. We show that there is a large degree of heterogeneity across countries, with over half to two-thirds exhibiting larger declines in women’s than men’s employment rates. These gender differences in COVID-19’s effects are typically short-lived, lasting only a quarter or two on average. We also show that she-cessions are strongly related to COVID-19’s impacts on gender shares in employment within sectors.
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.
