Highlights Individuals in the lowest quintiles of income pre-pandemic, and those from minority ethnic groups have experienced the worst labour market shocks Mitigation of shocks through borrowing and transfers from family and friends are most prevalent among those most in need Data derived from probability samples are crucial to capturing accurately the differential effects of the aggregate shock

Using new data from the first two waves of the Understanding Society COVID-19 Study collected in April and in May 2020 in the UK, we study the labour market shocks that individuals experienced in the first wave of the pandemic, and the steps they and their households took to cope with those shocks. Understanding Society is based on probability samples and the Covid-19 Study is constructed carefully to support valid population inferences. The Covid-19 Study collected novel data on the mitigation strategies that individuals and households employ. Further, prior observation of respondents in the panel allows us to characterize regressivity with respect to pre-pandemic economic positions. Our key findings are that those with precarious employment, aged under 30 and from minority ethnic groups faced the biggest labour market shocks. Almost 50% of individuals have experienced declines in household earnings of at least 10%, but declines are most severe in the bottom pre-pandemic income quintiles. Methods of mitigation vary substantially across groups: borrowing and transfers from family and friends are most prevalent among those most in need.

Using new data from the Understanding Society: COVID 19 survey collected in April 2020, we show how the aggregate shock caused by the pandemic affects individuals across the distribution. The survey collects data from existing members of the Understanding Society panel survey who have been followed for up to 10 years. Understanding society is based on probability samples and the Understanding Society Covid19 Survey is carefully constructed to support valid population inferences. Further the panel allows comparisons with a pre-pandemic baseline. We document how the shock of the pandemic translates into different economic shocks for different types of worker: those with less education and precarious employment face the biggest economic shocks. Some of those affected are able to mitigate the impact of the economic shocks: universal credit protects those in the bottom quintile, for example. We estimate the prevalence of the different measures individuals and households take to mitigate the shocks. We show that the opportunities for mitigation are most limited for those most in need.

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Abstract:We consider a difference-in-differences setting with a continuous outcome. The standard practice is to take its logarithm and then interpret the results as an approximation of the multiplicative treatment effect on the original outcome. We argue that a researcher should rather focus on the non-transformed outcome when discussing causal inference. The first step should be to decide whether the time trend is more likely to hold in multiplicative or level form. If the former, it is preferable to estimate an exponential model by Poisson Pseudo Maximum Likelihood, which does not require statistical independence of the error term. Running OLS on the log-linearised model might instead lead to confounding distributional and mean changes. We illustrate the argument with a simulation exercise.