It’s retail stores and restaurants, not farms and fisheries, that suffer most from social distancing
Economies worldwide are bracing for the impact of various social distancing and lockdown measures taken by governments to slow the spread of Covid-19. As stock indexes and unemployment claims indicate (Department of Labor 2020, Bundesagentur für Arbeit 2020), the world economy is facing its sharpest contraction since the 2008 Great Recession. It is estimated that the global economy will shrink by between 1.5% and 8.4% (relative to the baseline) in 2020 (Laurence et al. 2020, McKibben and Roshen 2020a), meaning that even a low-end pandemic is expected to reduce global GDP by around $2.4 trillion, while the loss could be up to $9 trillion in 2020 in the case of a more serious outbreak (McKibben and Roshen 2020b).
To mitigate the economic impact of the pandemic, governments are rushing to provide fiscal assistance to businesses hurt by the coronavirus outbreak. Germany introduced a flexible part-time work programme with wage support from the federal government (Look 2020), and in Denmark, Canada and the UK, the government is subsidising the wages of workers who would otherwise be laid off up to 75% (Denmark) or 80% (Canada, UK) (Collington 2020, Department of Finance Canada 2020, HM Treasury 2020). The European Commission has also opened a facility to support businesses, targeting, among others, farms and fisheries (European Commission 2020).
How can scarce budgetary resources be optimally allocated?
To optimally allocate scarce budgetary resources, it is important to target subsidies to businesses that need them the most. There is, to date, little direct evidence on which businesses are most directly hit by the pandemic and the various mitigation measures. In a recent paper (Koren and Pető 2020), we provide systematic estimates of how reliant each industry is on human interaction, which is strictly curtailed by recent social distancing measures.
We use US data from two sources: the O*NET occupation survey and the County Business Patterns of the Census. We identify three groups of occupations at risk from social distancing. The first group, which includes medical occupations and supervisors, are involved in frequent teamwork (i.e. face-to-face interactions among coworkers). The second group, which includes waiting staff, counsellors, social workers and salespersons, are customer facing (i.e. they regularly meet customers face to face). The third group, which includes transit drivers and miners, require physical presence (i.e. close proximity to other people).
We then rank industries by the share of workers in each of three at-risk occupation groups. Healthcare, retail and education have a high share of communication-intensive workers, both for teamwork and for customer contact. Hotels and restaurants are customer facing, but require less teamwork. And transportation is among the industries that require physical presence the most, even if workers there do not communicate much. Overall, combining our estimates with employment statistics from the March 2020 Current Employment Statistics, 49 million US workers work in one of the affected occupations.
We also build a model of human interaction in production, where more frequent interaction facilitates a more efficient division of labour, similarly to Becker and Murphy (1992). In the model, urban areas are subject to a communication externality and, without social distancing, urban businesses are more communication intensive. We find some evidence for this prediction, as customer-facing industries are more frequent in urban areas in the County Business Patterns. Interestingly, teamwork-intensive industries are not necessarily urban; they are evenly spread across US ZIP codes.
We use the model to evaluate the effects of a counterfactual policy that involves an upper limit on the number of human interactions as well as a wage subsidy. This mimics the policy choices some of the governments are currently experimenting with. We compute, in the calibrated model, the percentage wage subsidy that would exactly compensate each business for the disruption of interactions. This is higher for communication-intensive businesses where disruption is more costly, and for urban business where the interaction limit is more binding. When social distancing reduces interactions by half, the average business would require a 12% wage subsidy for each of its workers.
There is, however, considerable heterogeneity across industries in the magnitude of disruption. Figure 1 plots the (seasonally adjusted) employment change between March and February in each industry against the predicted exposure to social distancing, as summarised by the compensating wage subsidy computed in our model.
Retail sectors are at the high end of disruption, requiring a 22% wage subsidy, on average. By contrast, agricultural and manufacturing sectors are less disrupted, requiring only a 4% wage subsidy. Indeed, we find that the industries more exposed to social distancing suffered larger employment losses In March. Retail sectors have contracted as predicted by our analysis; “Food services and drinking places” and “Scenic transportation” contracted even more. “Information services,” “Forestry and logging,” and “Pipeline transportation” are not predicted to be exposed to social distancing, and indeed did not contract as much.
Our results are consistent with parallel research on the potential economic effects of the coronavirus pandemic using O*NET data. Recent research found that about 34% of US jobs can be performed from home (Dingel and Neiman 2020). Our analysis reveals, however, that even among jobs that do not fall into this category, some are more at risk from social distancing than others. Further analysis on how various mitigation measures affect workers and business would help better target fiscal assistance.
We have mainly focused on the production side, but there other corners as well. The first concern is the demand side. While demand for inevitable goods does not decrease, people will postpone their consumption of non-inevitable goods. This postponed consumption will exacerbate the crisis mostly in the manufacturing of durable goods (Baldwin and Tomiura 2020). The second concern is ‘supply chain contagion’ – whenever productivity in any business drops, this shock can propagate to its buyers and suppliers. This will further deepen the crisis mainly also in manufacturing.
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