Abstract
The unexpected outburst of the COVID-19 pandemic in the USA in March 2020 hit small businesses across the country, triggering mass job losses and closures. Beyond the severity of the pandemic itself, policy responses adopted by state governments produced yet another set of changes in small business operating environments. Using data from the Small Business Pulse Survey and the Current Population Survey, this paper provides evidence of how small businesses experienced these policy changes during the first few months of the pandemic in terms of perceptions of the pandemic, adjustments in employment levels, and employee schedule, as well as changes in overall self-employment activity. Policy variables include the Paycheck Protection Program (PPP) and a State Orders database. We find that the PPP per firm on the state level has a strong positive impact on lessening firms’ negative perceptions, alleviating the need to downsize, and recovering self-employment activities. The lifting of shelter-in-place, non-essential business closures, and restaurant dine-in services restrictions all helped, though their impact was more modest than PPP’s. The magnitudes of both effects vary by industry and owner groups.
Plain English Summary
Policy responses enacted at the state level during the COVID-19 pandemic significantly impact small businesses’ recovery. This study explores how state reopening policies and the PPP impacted small businesses’ reactions to the pandemic, including their perceptions, workforce reductions, and self-employment activity. We find that the PPP amount per firm by state is significant in alleviating the negative impact. State-level policy responses vary in their scope and timeline, and we find that these policy environments (shelter-in-place, non-essential business closures, and restaurant dine-in restrictions) shaped business recovery activities. These effects vary by industry and owner groups. More research is needed to identify the access barriers of different groups to business support, and to evaluate the longer-term effect of these policies.
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Data availability
All data used in this research is publicly available. Replication files can be obtained from the authors.
Notes
We also note that individuals with self-employment income were eligible for PPP, provided that their activity was in operation by February 15, 2020, they filed a Form 1040 Schedule C for 2019 or 2020, and their principal place of residence was the USA.
Pulse data is released as aggregated tabulations of each possible answer to each question, such that it is not possible to observe individual firms. For interpretation, a decrease in the percent of values that reduced employment suggest an increase in the share of businesses that either increase employment or did not change it.
The database compiled by Goolsbee et al. (2020) also includes information on local level policies. However, we are not able to leverage these as the datasets (Pulse or CPS) do not identify areas smaller than states.
Combining the Pulse weekly data into months would make it more comparable to CPS but at the cost of significant information loss in weekly variation. Therefore, we opted to keep each data source at its original periodicity. We use all weeks available on Pulse phase 1 (weeks between April 26 until June 27) and restrict CPS to the months of April until August, when the number of businesses increased to pre-pandemic levels (Fig. 3), and the number of new cases in the USA started decreasing.
Using the cumulative number of COVID-19 cases until the current week or month rather than new cases does not change the conclusions regarding the policies’ impacts significantly. However, in understanding that policy decisions, business perceptions, and decision-making (especially during the first phase of the pandemic) were guided by the current severity of the pandemic, we adopted new cases as our preferred specification.
The CPS analysis excluded the following industries: public administration, agriculture, forestry and fisheries, mining, and wholesale trade. Public administration had no active self-employment, and mining and wholesale trade had self-employed workers in only 8 and 31 states, respectively. Finally, agriculture, forestry, and fisheries were excluded for consistency with Pulse, which focuses on nonfarm businesses. For a detailed overview of the relevance of self-employment as a share of total employment across industries, see Table A1 in the Supplementary Information file.
The weekly Pulse data does not include information for all industries consistently across states and time, and the reasons for the omission are unclear. In our analysis, we kept only industries that were present in at least 70% of the state-time periods in Pulse phase 1, namely those that appear in the data at least 321 times out of 459 (9 weeks × 51 states). Based on this criterion, the Pulse analysis features the following eight industries: professional, scientific, and technical services (434 observations); health care and social assistance (424); retail trade (413); wholesale trade (393); manufacturing (385); construction (356); other services (except public administration) (338); and accommodation and food services (329). Conversely, the following nine industries were excluded: finance and insurance (301); administrative and support and waste management and remediation services (301); transportation and warehousing (245); real state and rental and leasing (224); arts, entertainment, and recreation (202); information (150), educational services (120), mining, quarrying, and oil and gas extraction (25); and utilities (7).
Models featuring reopening of non-essential businesses and restaurants dine-in services are available in the Supplementary Information file.
As multiple policies were enacted at similar times, one relevant question is whether the multiplicity of policy interventions affected businesses’ perceptions and attitudes. To explore this question, we replicated our models using the Oxford COVID-19 Government Response Tracker’s Stringency Index (Hale et al., 2021). The stringency index varies from 0 to 100 and captures the stringency of closure policies affecting each state based on the following policies: school closures, workplace closures, cancellation of public events, restrictions on gathering size, closures of public transportation, stay-at-home requirements, restrictions on internal movements, and restrictions on internal travels. The results show that closure policies were associated with worse perceptions, reductions in employment in total and hourly, and lower self-employment recovery rates—therefore, confirming our conclusions. Results are available from the authors upon request.
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Liu, C.Y., Nazareno, L. State responses during the COVID-19 pandemic and their impacts on small businesses. Small Bus Econ (2024). https://doi.org/10.1007/s11187-024-00923-1
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DOI: https://doi.org/10.1007/s11187-024-00923-1