Contributed equally to this work with: Miklós Koren Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing * E-mail: korenm@ceu.edu Current address: Central European University, Budapest, Hungary Affiliations Central European University, Budapest, Hungary, Centre for Economic and Regional Studies, Budapest, Hungary, CEPR, London, United Kingdom
Roles Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing Affiliation Centre for Economic and Regional Studies, Budapest, Hungary ⨯
Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence about the role of human interactions across different lines of business and about which will be the most limited by social distancing. Here we provide theory-based measures of the reliance of U.S. businesses on human interaction, detailed by industry and geographic location. We find that, before the pandemic hit, 43 million workers worked in occupations that rely heavily on face-to-face communication or require close physical proximity to other workers. Many of these workers lost their jobs since. Consistently with our model, employment losses have been largest in sectors that rely heavily on customer contact and where these contacts dropped the most: retail, hotels and restaurants, arts and entertainment and schools. Our results can help quantify the economic costs of social distancing.
Citation: Koren M, Pető R (2020) Business disruptions from social distancing. PLoS ONE 15(9): e0239113. https://doi.org/10.1371/journal.pone.0239113
Editor: Federica Angeli, University of York, UNITED KINGDOM
Received: March 28, 2020; Accepted: August 31, 2020; Published: September 18, 2020
Copyright: © 2020 Koren, Pető. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The mobility data used in this paper [21] is proprietary, but may be obtained free of charge for COVID-19-related research from the COVID-19 Consortium. The authors are not affiliated with this consortium. Researchers interested in access to the data can apply at https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com). After signing a Data Agreement, access is granted within a few days. The Consortium does not require coauthorship and does not review or approve research results before publication. Datafiles used: /monthly-patterns /patterns backfill/2020/05/07/12/2020/02/ patterns-part [1–4].csv.gz (Monthly Places Patterns for February 2020, released May 7, 2020), /monthly-patterns/patterns/2020/06/05/06/patterns-part [1–4].csv.gz (Monthly Places Patterns for February 2020, released June 5, 2020) and /core/2020 /06/Core-USA-June2020-Release-CORE POI-2020 05-2020-06-06.zip (Core Places for June 2020, released June 6, 2020). The COVID-19 Consortium will keep these datafiles accessible for researchers. The authors will assist with any reasonable replication attempts for two years following publication. The code and all other data underlying our analysis are licensed for public use and are available on Zenodo at http://doi.org/10.5281/zenodo.4016325.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Social distancing measures are effective non-pharmaceutical interventions against the rapid spread of epidemics [1–4]. Many countries have implemented measures such as school closures, prohibition of large gatherings and restrictions on non-essential stores and transportation to slow down the spread of the 2019–20 coronavirus pandemic [5–8]. What are the economic effects of such social distancing interventions? Which businesses are most affected by the restrictions?
Past research has analyzed the efficacy of social distancing interventions on reducing the spread of epidemics using the 1918 Spanish Flu in the U.S. [1–3] and seasonal viral infections in France [9]. Our knowledge of economic impacts, however, is limited [10]. For this question, past data may be less relevant, as the importance of face-to-face communication has increased steadily in the last 100 years through urbanization [11, 12] and specialization increased in business services as well [13, 14]. Even if advances in information and communication technology made it increasingly possible to communicate with co-workers and customers without the need for physical face-to-face interactions, personal contacts are still inevitable in some industries [15, 16].
The starting point of this paper is the observation that many sectors rely heavily on face-to-face communication in the production process [17, 18]. We build a model of communication to understand how limiting face-to-face interaction increases production costs. Without social distancing, workers specialize in a narrow range of tasks and interact with other workers completing other tasks. This division of labor reduces production costs but requires frequent contact between workers. In the model, the number of contacts per worker is the most frequent in businesses where the division of labor is important. When face-to-face interaction is limited, these are exactly the businesses that suffer the most.
To measure business disruptions from social distancing, we turn to recent data on the task descriptions of each occupation [19], the precise geographic location of non-farm businesses in the U.S. [20], and customer mobility patterns [21]. We construct three groups of occupations. First, some occupations require face-to-face communication several times a week with other workers. Examples of these teamwork-intensive occupations include maintenance, personal care related occupations and health care professionals. Other occupations require frequent face-to-face contact with customers. Retail salespersons, social workers and waiters and waitresses are examples of such customer-facing occupations. The third group of workers may need to be in physical proximity of one another even if they do not communicate, for example, to operate machinery or access key resources. Examples of such occupations requiring physical presence are drivers and machine operators, especially in mining and water transport, where crammed working environments are common. With our occupation level measures, we aim to capture the jobs that can be performed less efficiently from home. We validate our indexes by using the American Time Use Survey (ATUS) [22], which directly asks about the possibility of working from home.
To study how the patterns of interaction have changed in the U.S. during the Covid-19 pandemic, we use customer mobility data from SafeGraph [21]. This dataset measures the number of visits to a business in a given month, as captured from several cell phone apps and made available to researchers in an anonymized form. We study how the reduced number of customer visits is correlated with changes in sectoral employment.
When workers communicate with others, they can divide labor more effectively. Production involves sequentially completing tasks indexed by z ∈ [0, 1]. A single worker can do a range of tasks, but there is a benefit to specialization and division of labor [23, 24]. The labor cost of a worker completing Z < 1 measure of tasks is Z 1+ γ /γ, where γ > 0 captures the benefits to the division of labor. As we show below, the higher the γ, the more specialized each worker will be in a narrower set of tasks. Without loss of generality, we normalize the wage rate of workers to one so that all costs are expressed relative to worker wages.
Once the range of tasks Z is completed, the worker passes the unfinished product on to another worker. This has a cost of τ, which can capture the cost of communicating and interacting across workers. After all the tasks are completed, another step of communication with cost τ is needed to deliver the product to the customer. This cost leads to the Marshallian externality that firms want to be close to their customers and customers want to be close to their suppliers [25, 26].
The firm will optimally decide how to share tasks between workers. The key trade-off is economizing on the cost of communication while exploiting the division of labor [24]. Let n denote the number of workers involved in the production process. Because workers are symmetric, each works on Z = 1/n range of tasks before passing the work to the next worker. Production involves n − 1 “contacts” (instances of communication) and there is an additional contact with the customer.
Fig 1 illustrates the division of labor between workers. Horizontal movement represents production along a range of tasks (Z = 1/n), vertical movement represents interaction (τ). We note three potential interpretations of our model. First, when workers work in teams, they can efficiently divide labor among themselves (panel A). The benefit of a larger team is better specialization. Law firms, management teams, and IT service firms are prime examples of businesses where intensive communication leads to narrow specialization [27]. Second, communication may involve the customer (panel B). The benefit of more frequent interaction with the customer is a product or service that is better suited to their needs. Restaurants, beauty salons, personal and social services require such frequent interaction exactly because their service is so customized. Third, workers may need access to a key physical resource (panel C). In this case, even if they do not communicate, they may be subject to social distancing measures. For example, operators of machines, vehicle drivers or workers on an oil rig are all very much tied to a key resource to do their job. The key assumption behind all three interpretations is that frequent interaction increases productivity, whether happening between workers, between workers and customers, or between workers and machines.