Using data from the Current Population Survey, we investigate the distributional consequences of the Covid-19 pandemic and the associated public policy response on labor earnings and unemployment benefits in the United States up until February 2021. We find that year-on-year changes in labor earnings for employed individuals were not atypical during the pandemic months, regardless of their initial position in the earnings distribution. The incidence of job loss, however, was substantially higher among low earners, leading to a dramatic increase in labor income inequality among the set of individuals who were employed prior to the onset of the pandemic. By providing very high replacement rates for individuals displaced from low-paying jobs, the initial public policy response was successful in reversing the regressive nature of the pandemic's impacts. We estimate, however, that recipiency rates for displaced low earners were lower than for higher earners. Moreover, from September 2020 onwards, when policy changes led to a decline in benefit levels, earnings changes became less progressive.

We measure the distributional impact of the COVID-19 pandemic using newly released population register data in Sweden. Monthly earnings inequality increased during the pandemic, and the key driver is income losses among low-paid individuals while middle- and high-income earners were almost unaffected. In terms of employment, as measured by having positive monthly earnings, the pandemic had a larger negative impact on private-sector workers and on women. In terms of earnings conditional on being employed, the effect was still more negative for women, but less negative for private-sector workers compared to publicly employed. Using data on individual take-up of government COVID-19 support, we show that policy significantly dampened the inequality increase, but did not fully offset it. Annual total market income inequality, which also includes capital income and taxable transfers, shows similar patterns of increasing inequality during the pandemic.

This paper combines data on family, school, neighborhood, and city contexts with survey data from the Year 9 ( = 2,193) and Year 15 ( = 2, 236) Fragile Families and Child Wellbeing Study to study children in America's inner-cities who are "beating the odds". We identify children as beating the odds if they were born to families of low socio-economic status but scored above the state average in reading, vocabulary and math at age 9, and were academically on-track by age 15. We also examine if the influences of these contexts are developmentally nuanced. We find that living in two parent households where harsh parenting methods are absent (family context) and living in neighborhoods where two parent families predominate (neighborhood context) are protective factors that help children beat the odds. We also find that city-wide contexts of higher levels of religiosity and fewer single parent households contribute to children beating the odds, however, these macro predictors are weaker when compared with family/neighborhood contexts. We find that these contextual effects are indeed developmentally nuanced. We conclude with a discussion of some interventions and policies that could help increase the number of at-risk children who beat the odds.

Using new data from a three-wave panel survey administered in Germany between May 2020 and May 2021, this paper studies the impact of a negative shock affecting all strata of the population, such as the development of COVID-19, on preferences for redistribution. Exploiting the plausibly exogenous change in the severity of the infection rate at the county level, we show that, contrary to some theoretical expectations, the worse the crisis, the less our respondents expressed support for redistribution. We provide further evidence that this is not driven by a decrease in inequality aversion but might be driven by the individuals' level of trust.

We implement a dynamic bivariate probit model to explore the possible relation between at-risk-of-poverty and NEET (Not in Employment, Education or Training) in 21 European countries using 2016-2019 European Union Statistics on Income and Living Conditions panel data. We identify genuine state dependence and account for possible feedback effects from past poverty to the NEET status. We also consider two alternative definitions of NEET, i.e. unemployed and inactive NEET and inactive NEET only. We find that both poverty and NEET are characterized by significant genuine state dependence. We also observe a vicious circle between the phenomena, especially when adopting the definition that includes unemployed and inactive NEETs. This suggests a leading role of unemployment in the detrimental effect of being NEET on poverty. We offer supplementary analyses and further insights on country heterogeneity by looking at the role of social protection expenditure. Finally, we stress that for young NEETS living outside of the family of origin, the NEET condition is not detrimental for poverty, conditional on the provision of adequate youth support.

[This corrects the article DOI: 10.1007/s10888-021-09499-2.].

We evaluate the COVID-19 resilience of a Continental welfare regime by nowcasting the implications of the shock and its associated policy responses on the distribution of household incomes over the whole of 2020. Our approach relies on a dynamic microsimulation modelling that combines a household income generation model estimated on the latest EU-SILC wave with novel nowcasting techniques to calibrate the simulations using external macro controls which reflect the macroeconomic climate during the crisis. We focus on Luxembourg, a country that introduced minor tweaks to the existing tax-benefit system, which has a strong social insurance focus that gave certainty during the crisis. We find the system was well-equipped ahead of the crisis to cushion household incomes against job losses. The income-support policy changes were effective in cushioning household incomes and mitigating an increase in income inequality, allowing average household disposable income and inequality levels to bounce back to pre-crisis levels in the last quarter of 2020. The share of labour incomes dropped, but was compensated by an increase in benefits, reflecting the cushioning effect of the transfer system. Overall market incomes dropped and became more unequal. Their disequalizing evolution was matched by an increase in redistribution, driven by an increase in the generosity of benefits and larger access to benefits. The nowcasting model is a "near" real-time analysis and decision support tool to monitor the recovery, scalable to other countries with high applicability for policymakers.

The goals of ending extreme poverty by 2030 and working towards a more equal distribution of incomes are part of the United Nations' Sustainable Development Goals. Using data from 166 countries comprising 97.5% of the world's population, we simulate scenarios for global poverty from 2019 to 2030 under various assumptions about growth and inequality. We use different assumptions about growth incidence curves to model changes in inequality, and rely on a machine-learning algorithm called model-based recursive partitioning to model how growth in GDP is passed through to growth as observed in household surveys. When holding within-country inequality unchanged and letting GDP per capita grow according to World Bank forecasts and historically observed growth rates, our simulations suggest that the number of extreme poor (living on less than $1.90/day) will remain above 600 million in 2030, resulting in a global extreme poverty rate of 7.4%. If the Gini index in each country decreases by 1% per year, the global poverty rate could reduce to around 6.3% in 2030, equivalent to 89 million fewer people living in extreme poverty. Reducing each country's Gini index by 1% per year has a larger impact on global poverty than increasing each country's annual growth 1 percentage point above forecasts. We also study the impact of COVID-19 on poverty and find that the pandemic may have driven around 60 million people into extreme poverty in 2020. If the pandemic increased the Gini index by 2% in all countries, then more than 90 million may have been driven into extreme poverty in 2020.

We investigate the short-term effects of COVID-19 on labor market flows and how they are mediated by labor market policy. Using Italian administrative data on a sample of active contracts between 2009 and the second quarter of 2020, we show that, before the pandemic, a higher share of female compared to male, young compared to old and low educated compared to high educated workers is employed in non-essential activities. When we look at the change in hirings and separations, from the 9th week of 2020 - the time when first cases and deaths due to COVID-19 were recorded -, we find a pronounced drop in hirings and endings of fixed-term contracts. Layoffs and quits increase after the 9th week, and then decline significantly, reflecting the effects of government intervention. The lifting of the lockdown triggers a slow recovery of labor market flows. Young workers, those on temporary contracts, low-educated workers, those employed in the South and those with no opportunities of working from home experience a greater decline in separation probability, indicating that government policy partly protected them from the labor market impact of the recession. The decline in the separation probability for women is lower than that for men.

We here use panel data from the COME-HERE survey to track income inequality during COVID-19 in France, Germany, Italy, and Spain. Relative inequality in equivalent household disposable income among individuals changed in a hump-shaped way between January 2020 and January 2021, with an initial rise from January to May 2020 being more than reversed by September 2020. Absolute inequality also fell over this period. Due to the pandemic some households lost more than others, and government compensation schemes were targeted towards the poorest, implying that on average income differences decreased. Generalized Lorenz domination reveals that these distributive changes reduced welfare in Italy.

We use population-wide data from linked administrative registers to study the distributional pattern of mortality before and during the first wave of the Covid-19 pandemic in Belgium. Over the March-May 2020 study period, excess mortality is only found among those aged 65 and over. For this group, we find a significant negative income gradient in excess mortality, with excess deaths in the bottom income decile more than twice as high as in the top income decile for both men and women. However, given the high inequality in mortality in normal times, the income gradient in all-cause mortality is only marginally steeper during the peak of the health crisis when expressed in relative terms. Leveraging our individual-level data, we gauge the robustness of our results for other socioeconomic factors and decompose the role of individual vs. local effects. We provide direct evidence that geographic location effects on individual mortality are particularly strong during the first wave of the Covid-19 pandemic, channeling through the local number of Covid infections. This makes inference about the income gradient in excess mortality based on geographic variation misguided.

This paper provides evidence on the impact of major epidemics from the past two decades on income distribution. The pandemics in our sample, even though much smaller in scale than COVID-19, have led to increases in the Gini coefficient, raised the income share of higher-income deciles, and lowered the employment-to-population ratio for those with basic education compared to those with higher education. We provide some evidence that the distributional consequences from the current pandemic may be larger than those flowing from the historical pandemics in our sample, and larger than those following typical recessions and financial crises.

This paper provides novel evidence on the importance of the phenomenon of poverty and its heterogeneity across European countries. We analyze the determinants of poverty in Europe and their evolution over time by disentangling the role of genuine state dependence and heterogeneity. We apply alternative dynamic probit models accounting for endogenous initial conditions and correlated random effects to the pre-Great Recession period of 2005-2008 and the post- Great Recession period of 2015-2018 using EU-SILC longitudinal datasets for a sample of European countries in order to estimate genuine state dependence and uncover the role of observable and unobservable factors in determining the risk of poverty. Our findings suggest that the degree of genuine state dependence is relevant in Europe and that it increased slightly from pre- to post-Great Recession. This suggests that measures aimed at lifting individuals out of poverty, including cash transfers, have become even more important during the Europe 2020 decade. Our analysis also reveals that Europe is characterized by an increasing scarring effect of poverty, the trend of which has been exacerbated in the post-recession period. The analysis at the country level clarifies why the evolution of genuine state dependence was heterogeneous. While a clear pattern within macro-regions does not emerge, we find an association between country-level variation in genuine state dependence and some macroeconomic indicators. Finally, our results suggest that the protective role of higher education has diminished over time, while the role of employment stability and of childcare provision during early childhood has become even more important in the post-recession period.

This study examines disaggregated impacts of participation in off-farm employment on household vulnerability to food poverty in Ghana. We use household-level data collected from smallholder farmers in Ghana. This study employs the multinomial endogenous switching regression model to account for selection bias due to both observed and unobserved heterogeneity. Our results indicate that participation in off-farm employment activities, such as petty trading, significantly decreases household vulnerability to food poverty. Our findings further show that households that do participate in arts and crafts as an off-farm activity are more vulnerable to food poverty had they not participated. This paper provides useful policy insights to enable smallholders involved in off-farm work activities to improve food consumption expenditure and reduce their risk of food poverty.

[This corrects the article DOI: 10.1007/s10888-021-09506-6.].

The highly dynamic nature of the COVID-19 crisis poses an unprecedented challenge to policy makers around the world to take appropriate income-stabilizing countermeasures. To properly design such policy measures, it is important to quantify their effects in real-time. However, data on the relevant outcomes at the micro level is usually only available with considerable time lags. In this paper, we propose a novel method to assess the distributional consequences of macroeconomic shocks and policy responses in real-time and provide the first application to Germany in the context of the COVID-19 pandemic. Specifically, our approach combines different economic models estimated on firm- and household-level data: a VAR-model for output expectations, a structural labor demand model, and a tax-benefit microsimulation model. Our findings show that as of September 2020 the COVID-19 shock translates into a noticeable reduction in gross labor income across the entire income distribution. However, the tax benefit system and discretionary policy responses to the crisis act as important income stabilizers, since the effect on the distribution of disposable household incomes turns progressive: the bottom two deciles actually gain income, the middle deciles are hardly affected, and only the upper deciles lose income.

We analyze the association between spouses' earnings taking account of non-linearities along both spouses' distribution of earnings. We also document the non-linearity of the relationships between earnings and labor force participation, earnings and couple formation, and earnings and number of children. Using simulations, we then analyze how changes in spouses' rank-dependence structure, labor force participation and couple formation contribute to the upsurge in inequality in the U.S between 1967 and 2018. We find that an increased tendency towards positive sorting contributed substantially to the rise in inequality only among dual-earner couples, while it contributed little to overall inequality across households. Temporal and distributional heterogeneity are important, as earnings association had a more substantial role in the bottom of the earnings distribution and in recent years. The decline in couple formation contributed substantially to the rise in inequality, while the increase in female labor force participation and the fertility decline had equalizing effects.

This paper examines employment patterns by wage group over the course of the coronavirus pandemic in the United States using microdata from two well-known data sources from the U.S. Bureau of Labor Statistics: the Current Employment Statistics and the Current Population Survey. We find establishments paying the lowest average wages and the lowest wage workers had the steepest decline in employment and experienced the most persistent losses. We disentangle the extent to which the effect observed for low wage workers is due to these workers being concentrated within a few low wage sectors of the economy versus the pandemic affecting low wage workers in a number of sectors across the economy. Our results indicate that the experience of low wage workers is not entirely due to these workers being concentrated in low wage sectors - for many sectors, the lowest wage quintiles in that sector also has had the worst employment outcomes. From April 2020 to May 2021, between 23% and 46% of the decline in employment among the lowest wage establishments was due to within-industry changes. Another important finding is that even for those who remain employed during the pandemic, the probability of becoming part-time for economic reasons increased, especially for low-wage workers.

UK official statistics on income distribution have incorporated top-income adjustments to household survey data since 1992. This article reviews the work undertaken by the Department for Work and Pensions and the Office for National Statistics, and the academic research that influenced them, and reflects on the lessons to learn from the UK experience.

We analyse the UK policy response to Covid-19 and its impact on household incomes in the UK in April and May 2020, using microsimulation methods. We estimate that households lost a substantial share of their net income of 6.9% on average. But policies protected household incomes to a substantial degree: compared to the drop in net income, GDP per capita fell by 18.9% between the first and second quarter of 2020. Earnings subsidies (the Coronavirus Job Retention Scheme) protected household finances and provided the main insurance mechanism during the crisis. Besides subsidies, Covid-related increases to state benefits, as well as the automatic stabilisers in the tax and benefit system, played an important role in mitigating the income losses. However, analysing the impact of a near-decade of austerity on the UK safety net, we find that, compared to 2011 policies, the 2020 tax-benefit policies would have been less effective in insuring incomes against the shocks. We also assess the potential distributional impact of introducing a Universal Basic Income (UBI) instead of the Covid emergency measures and find that a UBI would have supported the incomes of different vulnerable groups but would have provided less protection to those hit hardest by the labour market shocks.

This article explains how the compilation of distributional results in line with national accounts' totals may assist in overcoming some of the challenges faced by micro data statistics in measuring inequality, including capturing the top tail. As national accounts rely on a harmonised system of concepts and definitions in which multiple data sources are brought together in order to arrive at comprehensive, coherent and consistent results, they may capture elements that may be missing from underlying statistics and may provide more reliable estimates for items that may be more prone to quality issues in underlying statistics. This implies that aligning micro data to national accounts totals may improve the overall quality of distributional results, mainly depending on the way in which any gaps between the micro and macro data are allocated to underlying households. This article provides an overview of possible underlying reasons for the micro-macro gaps, including the issue of the missing rich, and provides guidance on how to deal with them in order to arrive at the best possible distributional results.