Business Studies Journal (Print ISSN: 1944-656X; Online ISSN: 1944-6578)

Editorials: 2021 Vol: 13 Issue: 1S

Covid-19 Impact on Business

Pabitra Kumar Patra, Trident College


To explore the hit/effect of coronavirus disease 2019 (COVID-19) on small businesses, we managed and did/done a survey of more than 5,800 small businesses between March 28 and April 4, 2020. (More than two, but not a lot of) themes came out/became visible. First, mass layoffs and closures had already happened just a few weeks into the serious problem. Second, the risk of closure was negatively connected with the expected length of the serious problem. More than that, businesses had widely changing/different beliefs about the likely length of time of COVID-related disruptions. Third, many small businesses are (related to money) delicate and breakable: The middle-point business with more than $10,000 in monthly expenses had only about 2 wk of cash on hand at the time of the survey. Fourth, most businesses planned to look (for) money/giving money (to) through the Coronavirus Aid, Relief, and Money-based Security (CARES) Act. However, many expected/looked ahead to problems with (using/getting to) the program, such as (slow, ineffective government) hassles and (problems, delays, etc.) beginning and building on (ability to be picked/ability to participate). Using experimental difference/different version, we also test/evaluate take-up rates and business toughness effects for loans relative to grants-based programs.


COVID-19, Business, Management, Economic.


The results suggest that the widespread disease had already caused huge dislocation among small businesses just (more than two, but not a lot of) weeks after its beginning and before the availability of government aid through the Coronavirus Aid, Relief, and Money-based Security (CARES) Act. Across the full sample, 43% of businesses had (only for a short time) closed, and nearly all of these closures were due to COVID-19. People who responded that had (only for a short time) closed mostly pointed to reductions in demand and employee health concerns as the reasons for closure, with disruptions in the supply chain being less of a factor. On average, the businesses reported having reduced their active employment by 39% since January. The decline was especially sharp in the Mid-Atlantic area (which includes New York City), where 54% of firms were closed and employment was down by 47%. Hits/effects also varied across businesses, with retail, arts and entertainment, personal services, food services, and hospitality businesses all reporting employment declines going beyond 50%; in contrast, finance, professional services, and real estate-related businesses experienced less disruption, as these businesses were better able to move to remote production.

We received 7,511 responses between March 27 and April 4; 5,843 of these can be traced back to US-based businesses, which is the (clearly connected or related) sample for understanding policy. While the 7,511 responses represent a small fraction (0.017%) of Match up/make evenable's total membership, they represent a much larger share of Match up/make evenable's membership that has engaged with their weekly pulse surveys on COVID-19. Match up/make evenable guesses (of a number) that 50,000 to 70,000 members are taking these pulse surveys weekly, which suggests a 10 to 15% (changing from one form, state, or state of mind to another) rate of these more active people who responded. The survey included a total of 43 questions, with basic information about firm (features/ qualities/ traits) (including firm size and industry), questions about the current response to the COVID-19 serious problem, and beliefs about the future course of the serious problem. Some questions were only displayed based on skip logic, so most people (who were part of a study, etc.) responded to fewer questions.

The survey also includes an experimental module that randomized pictures/situations between people who responded to understand how different federal policies might hit/affect these firms' behavior and survival as the serious problem happens. Specifically, we experimentally varied some of the descriptions of possible policies across the sample to shed light on the possible result of policy attempts (to begin something new) that, at the time, were very uncertain. The survey contains three (a measure of what occurs naturally/sports boundary line) questions which enable us to test/evaluate the representativeness of the sample along (capable of being seen and known) dimensions: number of workers, typical expenses (as of January 31, 2020), and share of expenses that go toward payroll. We are also able to get rough information about geolocation to test/evaluate representativeness by state. The Match up/make evenable network allows users to share customer leads, which could possibly distort our sample toward retail and service businesses that interact directly with people (who use a product or service). Since retail businesses are especially able to be hurt by COVID-19 disruptions, our sample could overstate the group dislocation created by the serious problem. Naturally, businesses ruled by large firms, such as manufacturing, are not fairly represented. However, as we discuss later, our data on the industry mix of responses suggest that the sample represents a wide area of America's smaller businesses.

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