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Major epidemics of the last two decades (SARS, H1N1, MERS, Ebola, and Zika) have been followed by increases in inequality [Furceri et al. (2020), COVID Economics, 12, 138-157]. In this article, we show that the extent of fiscal consolidation in the years following the onset of these pandemics has played an important role in determining the extent of the increase in inequality. Episodes marked by extreme austerity - measured using either the government's fiscal balance, health expenditures, or redistribution - have been associated with an increase in the Gini measure of inequality three times as large as in episodes where fiscal policy has been more supportive. We survey the evidence thus far on the distributional impacts of the COVID-19 pandemic, which suggests that inequality is likely to increase in the absence of strong policy actions. We review the case made by many observers [IMF (2020), Fiscal Monitor; Stiglitz (2020), Finance & Development, Fall 2020; Sandbu (2020b), Financial Times, 26 November 2020)] that fiscal support should not be withdrawn prematurely despite understandable concerns about high public debt-to-GDP ratios.

This chapter provides evidence on the impact of major epidemics from the past two decades on inequality and job prospects. Our results justify the concern that the COVID-19 pandemic could significantly raise inequality; past events of this kind, even though much smaller in scale, have led to increases in the Gini coefficient, lowered the employment-to-population ratio for those with basic education compared to those with higher education, and pushed people into precarious work in the form of self-employment or in the informal sector.

Episodes of account liberalization increase the Gini measure of inequality, based on panel data estimates for 149 countries from 1970 to 2010. These episodes are also associated with a persistent increase in the share of income going to the top. We investigate three channels through which these impacts could occur. First, the impact of liberalization on inequality is stronger where credit markets lack depth and financial inclusion is low; positive impacts of liberalization on poverty rates also vanish when financial inclusion is low. Second, the impact on inequality is also stronger when liberalization is followed by a financial crisis. Third, liberalization seems to alter the relative bargaining power of firms and workers: the labor share of income falls in the aftermath of capital account liberalization.

We present new evidence on the evolution of labor mobility in the United States over the past four decades. Building on the seminal methodology by Blanchard and Katz (1992), combined with multiple sources of regional population and migration data, we show that interstate mobility in response to relative labor demand conditions is not as high as previously established and has been weakening since the early 1990s. In addition, we find that mobility is countercyclical: net migration across regions responds more strongly to spatial disparities in recessions than in normal times. While the declining trend in mobility has been driven by weaker out-migration from states experiencing negative relative shocks, the mobility surge in recessions is mostly accounted for by temporarily stronger in-migration to better-performing states.

We show that an increase in aggregate uncertainty-measured by stock market volatility-reduces productivity growth more in industries that depend heavily on external finance. The mechanism at play is that during periods of high uncertainty, firms that are credit constrained switch the composition of investment by reducing productivity-enhancing investment-such as on ICT capital-which is more subject to liquidity risks (Aghion et al., 2010). The effect is larger during recessions, when financing constraints are more likely to be binding, than during expansions. Our statistical method-a difference-in-difference approach using productivity growth of 25 industries from 18 advanced economies over the period 1985-2010-mitigates concerns with omitted variable bias and reverse causality. The results are robust to the inclusion of other sources of interaction effects, instrumental variable approaches, and different datasets. The results also hold if economic policy uncertainty (Baker et al., 2016) is used instead of stock market volatility as a measure of aggregate uncertainty.