Household Earnings and Income Volatility in the UK, 2009–2017

We study the volatility of sources of individual and household level income in the UK in the years 2009-2017, following the Great Recession and government austerity. We find that the volatility of (pre-tax) earnings and disposable income has fallen for the working-age in this period, largely due to fewer negative and large earnings shocks. For older individuals, we also find a fall in the volatility of private income, mainly due to fewer positive and large income shocks. Taxes and transfers help stabilise incomes, with social security cash benefits and income-dependent refundable tax credits reducing household private income volatility by around a quarter for the working age, and 40 percent for those aged 60 or over. However, over the sample period, taxes and benefits became less well correlated with earnings, reducing their ability to counteract swings in labour income. The findings illustrate the consequences of fiscal retrenchment and the cut-backs to welfare benefits on the stability of incomes.

Absolute Poverty and Sound Public Finance in the Eurozone

The respect of fiscal parameters is supposed to be – according to the official position of the European institutions – the best recipe for granting stability and growth. This optimistic view appears to be in contrast with the recent increase in poverty. The aim of this paper is to individuate the existence of a relation between governments’ decisions about fiscal policy and absolute poverty in 19 Eurozone countries from 2005 to 2017. The attempt is to answer the question as to whether the effect on growth generated by fiscal policy measures can account for the objective of poverty alleviation. The results support the conclusion that absolute poverty increases in the presence of a restrictive fiscal policy, while it decreases in the opposite case. During declining macroeconomic conditions, national governments belonging to the Eurozone appear to be unable to reconcile the objective of sound public finance with that of poverty alleviation.

Distributional effects of macroeconomic shocks in real-time

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.

Income-dependent equivalence scales: A fresh look at German micro-data

Income inequality and poverty risks receive a lot of attention in public debates and current research. To make income comparable across different types of households, applying the “(modified) OECD scale” – an equivalence scale with fixed weights for each household type – has become a quasi-standard in research. Instead, we derive a base-dependent equivalence scale allowing for scale weights that vary with income, building on micro-data from Germany. Our results suggest that appropriate equivalence scales are much steeper at the lower end of the income distribution than they are for higher income levels. We illustrate our findings by applying them to data on family income differentiated by household types. It turns out that using income-dependent equivalence scales matters for applied research on income inequality, especially if one is concerned with the composition, not just the size of the population at poverty risk.

The Income Gradient in Mortality during the Covid-19 Crisis: Evidence from Belgium

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.

Estimating intergenerational income mobility on sub-optimal data: a machine learning approach

Much of the global evidence on intergenerational income mobility is based on sub-optimal data. In particular, two-stage techniques are widely used to impute parental incomes for analyses of lower-income countries and for estimating long-run trends across multiple generations and historical periods. We propose applying machine learning methods to improve the reliability and comparability of such estimates. Supervised learning algorithms minimize the out-of-sample prediction error in the parental income imputation and provide an objective criterion for choosing across different specifications of the first-stage equation. We use our approach on data from the United States and South Africa to show that under common conditions it can limit the bias generally associated to mobility estimates based on imputed parental income.

A note on pessimism in education and its economic consequences

Investigating interaction of the lumpy nature of educational investments and informational frictions on returns to and costs of education, I show that pessimistic beliefs can be self-confirmed in equilibrium. Among some of its consequences, I argue that the commonly pursued research methods may not always identify the true underlying skill distributions.