The COVID-19 pandemic has rapidly spawned a literature analysing its impact on macroeconomic aggregates. But there’s also been work that seeks to look at heterogeneity of impacts across industries, households and individuals. This post summarises this literature which seeks to better understand the heterogeneous effects of the pandemic and associated policy responses on income, hours worked and employment status.
Heterogeneity across occupations
The effects of mandatory business closures and social-distancing orders, as well as voluntary change in consumer behaviour to avoid infection varied across sectors and occupations, with non-essential and high face-to-face jobs suffering the most. Beland et al (2020) show that workers in jobs with close proximity to others had the most adverse labour market outcomes in the US, while occupations able to work from home and essential workers were less affected.
A similar study is performed by Leibovici et al (2020) which focuses on the extent to which the job requires working in close proximity to other people (known as “contact intensity”) in the US. In total, high contact-intensity sectors (e.g. barbers, hairstylists, non-essential health service providers) account for one-fifth of total employment and annual labour income. The authors argue that a sizeable fraction of output is unlikely to be fully recovered until social distancing comes to an end. At the other end, low contact-intensity occupations (designers, scientists etc.) account for about one-quarter of total employment and one-third of labour income.
Heterogeneity across workers
Identifying the most vulnerable occupations is only the first step; next comes understanding how being in a vulnerable job correlates with other variables. Mongey et al (2020) finds that, in the US, workers in more vulnerable jobs (low-work-from-home or high-physical-proximity) tend to be less educated, have lower incomes, and are more likely to be renters.
The heterogeneity also occurs with respect to gender and age. Joyce and Xu (2020) estimate that 15% of the UK workforce work in sectors which have largely shut down during the lockdown, for example retail and hospitality. Since women represent a disproportionately large share of the workforce in these sectors, they find that shutdown is likely to have had larger negative impacts on their earnings. Furthermore, the negative effects are largest for under-25s. Nearly a third of all workers in shutdown sectors are under 25 (25% young men, and 36% young women).
The largest disparity of all however, is by earnings. Workers in the bottom income decile of workers are about seven times more likely to work in a shutdown sector than those in the top decile. On top of this current effect, past studies (e.g. Kahn (2010) suggest that cohorts entering the labour market in a weak economy suffer a persistent negative effect on wages in the future.
In the US, there is also evidence that businesses and low-wage earners in affluent areas have suffered disproportionately. Chetty et al (2020) use data from credit card processors and payroll firms to construct statistics on consumer spending and employment rates in the US. They find that high-income households reduced spending more sharply than low income households, leading to a loss of nearly 70% of small businesses’ revenues in wealthy areas. As a result, those businesses resorted to laying off workers, who tended to be in low-wage jobs. Nearly 70% of low-wage workers working in the highest-rent ZIP codes lost their jobs, compared with 30% in the lowest-rent ZIP codes.
Childcare responsibilities and work
For millions of working parents coronavirus and stabilisation policies had additional effects. School and nursery closures meant that many now have to balance work and childcare responsibilities. In Germany, Fuchs-Schündeln et al (2020) estimate that 11% of workers and 8% of working hours will be lost due to school and childcare closures, although they make a strong assumption that one adult per household with a child will have to stop working. Further, these results may not fall equally across gender. Since in 82% of households mothers work less than fathers, they conclude that majority of hours lost will be by females. Additionally, Alon et al (2020) argue that increased childcare is likely to have particularly large negative effects on working mothers, given the evidence that they bear a disproportionately large share of the childcare responsibilities in US households. The effects on their labour market outcomes are likely to be persistent due to high returns to work experience.
A striking example of the heterogeneous effects of lockdown across gender is given by Cui et al (2020). They provide evidence that US-based female researchers’ productivity significantly dropped since lockdown began, in comparison to their male colleagues. Computing the number of papers published by male and female researchers and using a difference-in-differences estimator, they find that during the 10 weeks of lockdown female academics’ productivity dropped by 13.9% compared to that of male academics.
Heterogeneous effects on household balance sheets
Changes in employment status or pay are likely to have significant impacts on consumer behaviour. Cox et al (2020) compare spending and saving behaviour over the initial months of the pandemic to household-specific demographic characteristics in the US. While households across the income distribution all cut spending, that of low-income households rebounded most rapidly. Similar results are seen when comparing effects across sectors. Individuals employed in low-wage industries initially cut spending by less, followed by a rapid recovery. In terms of savings, the authors find that there was a large initial increase in savings with average liquid balances 36% higher in May 2020 compared to the same time last year. Interestingly, lower-income households increased their holdings by more (24% of their initial balances compared to 17% for the highest income quartile). Stimulus programs might have played an important role here, as they would provide a disproportionate increase in income for households towards the bottom of the income distribution. Chetty et al (2020) and Hacioglu et al (2020) also found that high income groups cut spending more sharply in the US and the UK.
Combining income and spending changes for the same households using UK survey data, Brewer and Gardiner (2020) find that 38% of adults in the top income quantile report no income hit alongside a drop in consumption, compared to only 12% in the bottom quantile. In a cross-country study, Zabai (2020) find that, in several countries, households below the median of the wealth distribution hold insufficient liquid balances to withstand a long spell of unemployment without falling behind in debt repayment.
The initial shock of COVID-19 has had large negative impacts on economic aggregates around the world, but these did not fall evenly. The effects were heterogeneous across both occupations and individual characteristics, and for the most part appears to have increased existing disparities by gender, age, income and wealth.
Andrea Šiško works in the Bank’s Research Hub.
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