The impact of COVID-19 on small business outcomes and expectations

Survey Design and Details

Our survey was sent out in partnership with Alignable, a network-based platform focused on the small business ecosystem. Alignable enables businesses to share knowledge and interact with one another, and currently has a network of 4.6 million small businesses across North America. Much of the network growth has been organic, with little outside marketing.

Alignable also regularly sends out polls (which they call “pulse surveys”) to users. At the end of a regular pulse poll, participants who took that poll received an email inviting them to participate in a more comprehensive survey being conducted by researchers at Harvard Business School. Participants were shown a disclosure statement and consent protocol. No payments were offered; participation was completely voluntary. The survey was approved by the Harvard University Institutional Review Board.

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 relevant sample for understanding policy. While the 7,511 responses represent a small fraction (0.017%) of Alignable’s total membership, they represent a much larger share of Alignable’s membership that has engaged with their weekly pulse surveys on COVID-19. Alignable estimates that 50,000 to 70,000 members are taking these pulse surveys weekly, which suggests a 10 to 15% conversion rate of these more active respondents.

Our sample, therefore, is selected in three ways: 1) They are firms that have chosen to join Alignable, 2) they are Alignable firms that have chosen to stay actively engaged taking surveys, and 3) they are the set of firms that are active within Alignable that chose to answer our survey. Consequently, there are many reasons to be cautious when extrapolating to the entire universe of America’s small businesses. We will discuss their representativeness based on observable attributes in the next section of this report.

The survey included a total of 43 questions, with basic information about firm characteristics (including firm size and industry), questions about the current response to the COVID-19 crisis, and beliefs about the future course of the crisis. Some questions were only displayed based on skip logic, so most participants responded to fewer questions. The survey also includes an experimental module that randomized scenarios between respondents to understand how different federal policies might impact these firms’ behavior and survival as the crisis unfolds. Specifically, we experimentally varied some of the descriptions of potential policies across the sample to shed light on the potential impact of policy initiatives that, at the time, were very uncertain. We will discuss that module more thoroughly in Anticipated Response to CARES Act Programs. A further experimental module included between-respondent randomization which explored decisions under different hypothetical durations of the crisis.

Firm Characteristics and Representativeness

The survey contains three baseline questions which enable us to assess the representativeness of the sample along observable dimensions: number of employees, 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 assess representativeness by state.

We compare our data with data on businesses from the 2017 Census of US Businesses, using the publicly available statistics published by the US Census Bureau. The underlying data are drawn from the County Business Patterns sampling frame and cover establishments with paid employees, including sole proprietorships if the owner receives a W2. The Census data capture large and small businesses alike, but, for our comparisons, we will look only at businesses with fewer than 500 employees.

The Alignable network allows users to share customer leads, which could potentially skew our sample toward retail and service businesses that interact directly with consumers. Since retail businesses are particularly vulnerable to COVID-19 disruptions, our sample could overstate the aggregate dislocation created by the crisis. Naturally, industries dominated by large firms, such as manufacturing, are underrepresented. However, as we discuss later, our data on the industry mix of responses suggest that the sample represents a wide swath of America’s smaller businesses.

Fig. 1 shows the size distribution of our sample and the size distribution of businesses with fewer than 500 employees in the Economic Census. The match of employment sizes is reassuring. About 64% of the businesses in our sample have fewer than five employees, while about 60% of the firms in the Economic Census are that small. About 18% of businesses in both samples have between five and nine employees. The survey becomes less precisely matched to the Census among the larger employment groupings, and we believe that our survey will capture the experience of larger employers with less accuracy.

 

Fig. 1.

Firm size in the survey and Census. This figure plots the share of firms in each employment category for the 2017 Census of US Businesses and the survey respondents. The sample size for the survey is 4,873 responses, omitting 959 responses with missing employment data.

While our survey does not allow for a direct comparison of payroll expenses with Census data, we constructed a rough comparison by approximating payroll expenses for the Alignable firms from categorical questions about monthly expenses and the share of these expenses going toward payroll. The Census provides annual payroll expenses for W2 employees. To get a sense of the match, we compared our estimated monthly payroll expenses in our sample with one-twelfth of annual expenses in the US Census. To facilitate comparison, we divide by an estimate of total employment.^§ Fig. 2 shows the size distribution of monthly estimated payroll expenses in our sample and a comparable breakdown for the Census using a per capita adjustment. The match is imperfect, especially for larger firms. The discrepancy might reflect the underrepresentation of manufacturing or professional services firms in our sample, which are among the highest paying of all two-digit North American Industry Classification System sectors in the Census data. SI Appendix, Table S1 provides further detail on the industry match to the Census.

 

Fig. 2.

Average per capita payroll ($1,000s) in the survey and Census. This figure plots per-employee payroll in thousands of dollars by firm size for the 2017 Census of US Businesses aggregates and the survey respondents. The Census data only report annual payroll for W2 workers and the number of firms in an employment size category. To calculate payroll for the survey firms, we take the midpoint of categorical answers for monthly expenses, multiply by the fraction of expenses going toward payroll, and divide by total employees (we cannot distinguish between W2 employees and contractors).

Fig. 3 shows the geographic scope of our sample. The Alignable sample draws particularly from California, the New York region, Florida, and Texas. The sample is sparse in America’s western heartland, which matches the location distribution of smaller businesses in the Economic Census.

 

Fig. 3.

Coverage by state. This figure plots shares of survey responses across different states.

Fig. 4 shows the share of our sample coming from the 10 most populous states. The figure also includes the share of small businesses in the Economic Census that are within each state. For example, California has 14.4% of our Alignable survey sample, 12.5% of small businesses in the Census data, and 11.52% of total US population. Our sample does overrepresent the coasts and underrepresents Illinois. Alignable shared the geographic distribution of their weekly pulse survey takers, and the final set of columns within each grouping allows us to assess selection differences between respondents to the shorter pulse poll and our downstream survey. There are some minor sampling differences across states, but the Alignable pulse poll sample and those taking our broader survey have quite good geographic coverage.

Fig. 4.

Firm locations in the Census, downstream survey, and upstream presurvey Alignable poll. This figure plots the share of firms in each state for the 2017 Census of US Businesses, the survey respondents, and the respondents who took the upstream Alignable poll. Users who took the survey did so after taking the Alignable poll. They were then redirected to the Harvard Business School Qualtrics web link. Note that the upstream poll did not ask questions about firm size or payroll, so prior figures cannot check compositional differences based on firm size or pay.

To shed further light on our sample, we conducted a follow-up phone screen of 400 businesses—a randomly selected set of 200 businesses that responded to our survey and 200 businesses from the broader active Alignable membership (i.e., that filled out their previous pulse poll), but who did not respond to our survey. During the phone screen, we asked each business whether they were still open for business. For businesses that did not answer the phone on a first attempt, we made a second attempt to call. Out of the businesses who responded to our survey, roughly 42% reported being open when we called them. Out of the businesses that are active on Alignable but did not respond to our survey, roughly 56% reported being open.

Overall, while the sample captured by the survey may be an imperfect snapshot for certain pockets of America’s small businesses, it also allows for important insight into the overall small business ecosystem. The sample is large and includes firms from most major industry groups, states, and firm size categories.

Responses to the COVID-19 Pandemic and Lockdown

We now turn to our main results, which we group into three categories. First, we describe the impact of COVID-19 on business operations and employment toward the beginning of the crisis. Second, we report our results on the financial fragility of those businesses, as captured by their cash on hand and ongoing expenses. Third, we turn to their expectations about the duration of the crisis and their own economic survival, as measured at a particularly sensitive point for understanding the impact of future policies.

Financial Fragility.

To measure financial fragility, we asked the respondents “roughly how much cash (e.g. in savings, checking) do you have access to without seeking further loans or money from family or friends to pay for your business?” We then divided this amount by their January 31 monthly expenses to understand how long they could maintain operations without seeking extra credit or outside assistance.^††

Fig. 5 shows a histogram of cash available as a multiple of January 31, 2020 monthly expenses. Approximately one-fourth of firms had cash on hand totaling less than 1 mo of expenses. About one-half of firms had enough cash on hand to cover between 1 mo and 2 mo of expenses.

 

Fig. 5.

Months of cash. This figure plots firms’ months of cash available as a multiple of January 2020 expenses. We compute this measure by taking the midpoint of categorical responses for the amount of cash on hand and dividing by the midpoint of the categorical response for typical monthly expenses prior to the crisis. The sample size is 4,176.

Fig. 6 sorts firms by January 31, 2020 monthly expenses and then tabulates the mean and median cash on hand relative to precrisis expenses. The median firm with under $10,000 in monthly expenses had 1 mo of cash on hand. For all firms with greater than $10,000 in monthly expenses, the median firm typically had less than 15 d of cash on hand, based on their precrisis expense levels. These firms did not have cash on hand to meet their regular expenses.

 

Fig. 6.

Mean and median months of cash split by monthly expenses ($1,000s). This figure plots means and medians of the months of cash available measure across the distribution of typical monthly expenses.

These limited levels of cash on hand help to shed light on why layoffs and shutdowns were so prevalent. Absent these actions, it is hard to understand how these firms could have met payroll.

Predicting the Path of the Crisis.

Finally, we ask the firms to predict how long the COVID-19 crisis will last and whether they believe they will be open again at the end of 2020. To predict the end of the crisis, we asked the survey respondents “the most likely date” when the crisis would be over. We also asked them their confidence about this belief on a 1 to 10 scale.

Fig. 7 shows the distribution of expected end dates. The figure shows that roughly 20% of respondents believed that the crisis would be over by the end of May. Thirty percent of respondents believed that the crisis would end between the end of May and the start of July. Just over one half of the firms answered that they thought that the crisis would still be going at the start of July.

 

Fig. 7.

Cumulative distribution function of expected COVID end date. This figure plots the distribution function across respondents for the expected end date of COVID-related disruptions. The y axis represents the share of respondents who believe that COVID disruptions will end on or before the date given on the x axis.

However, the firms were not particularly confident about their answers. Fifty percent of respondents reported their confidence level as 5 or less on the 1 to 10 scale. Sixteen percent gave their confidence a 2 or less. Their uncertainty highlights the broader uncertainty that was present throughout the world at the time.

Fig. 8 shows the histogram of responses about whether firms will be open on December 31, 2020. Overall, more than 90% thought it is at least somewhat likely that they would be open. More than 63% reported that it is very or extremely likely that they would be open—which we later use as a measure of the probability of being open. A growing literature has found entrepreneurs to be overoptimistic about their prospects (see, for example, ref. 13). This suggests that true survival rates may be even lower than predicted by businesses.

 

Fig. 8.

Likelihood of remaining open or reopening by December. This figure displays the frequency of answers to a question about the likelihood of being open in December 2020. Responses are plotted based on whether the firm has more than the median number of months of cash on hand given their pre-COVID expenses.

The firms with more cash on hand were more confident about their future, as evidenced by the split based on whether the firm had more or less cash on hand (relative to usual monthly expenses) than the median in our sample. Fifty percent of those firms with more than the median cash on hand thought it was extremely likely that they would be open at the end of the year. Thirty-one percent of firms with less cash on hand, relative to the median, thought that they would be open at the end of the year. One interpretation of these findings is that liquidity generated confidence in the ability to survive this crisis. Among firms with at least 20 employees, 71% expressed that they were either very likely or extremely likely to survive, which may indicate greater access to outside resources despite having a higher expense base.

Fig. 9 shows that the share of firms that think that they are “very likely” or “extremely likely” to be open varies based on their belief about the duration of the crisis. The firms that thought that the crisis will be short also believed that they are more likely to survive. Those who believed in a longer crisis were more pessimistic.

 

Fig. 9.

Likelihood of remaining open or reopening by December 2020 as a function of beliefs about COVID end date. This figure plots the likelihood of being open in December, 2020 as a function of respondents’ expected COVID end date. Averages are plotted, and the shaded region is the CI. The opening likelihood is computed as the share of respondents who answered “Extremely likely” or “Very likely.”

Anticipated Responses to CARES Act Programs

In this section, we discuss the survey’s questions about take-up of the CARES Act PPP loans and their expected impact on employment. One important aspect of the CARES program is that “loans will be fully forgiven when used for payroll costs, interest on mortgages, rent, and utilities,” as long as 75% of the forgiven amount is spent on payroll and the employer either maintains or quickly rehires workers and maintains salary levels. Consequently, a significant portion of the “loans” can be seen as a grant rather than traditional debt.

The high level of loan forgiveness means that this represents a large potential transfer to small businesses. We assess the importance of the grant component of the CARES loans relative to a pure (and far less expensive) loan program. One-third of the survey respondents were randomly asked about their interest in a CARES-like program, which was describe as a loan program which “will be forgiven by the amount spent on payroll, lease, rent, mortgage, and utility payments in the 8 weeks after origination.” One-third of the respondents were randomly asked about their interest in a loan program that was otherwise identical, but without prompting any possibility of forgiveness.^‡‡ As part of the display, the amount of liquidity was varied, with the caveat to respondents that these policies may not be the actual policies currently available to them. This was designed to measure how program generosity affects take-up and perceived business resilience.^§§

Fig. 10 shows the expected take-up of the two programs (the exact details of the question wording is contained in the figure legend). Seventy-two percent of respondents who were told about the loans with forgiveness said that they would like to take them up. Fifty-nine percent of respondents were interested in taking up the loan program without forgiveness. While there was substantial interest in a pure-loan program, there was significantly more interest in the loan program with forgiveness.

 

Fig. 10.

Differences in policy take-up across loans versus CARES Act PPP split by hypothetical limits on borrowing amount. This figure displays policy take-up rates for loans versus the stylized PPP policy using a between-subjects design. The borrowing base was also randomized between subjects as a multiple of typical monthly expenses prior to the crisis. The text displayed for the PPP program was, “Imagine a policy where the government allows you to borrow up to [borrowing base] times your typical monthly expenses without posting any collateral. You could use this money to cover any of your business expenses. The loan will be forgiven by the amount spent on payroll, lease, rent, mortgage, and utility payments in the 8 weeks after origination (you can consider this amount to be a grant). The remainder of the loan (that is not spent on these items) will have deferred payments for 1 year. After that, the loan would have an annual interest rate of 4% (deferred for 1 year) and you would have up to 10 years to repay the loan. For example, if you borrow $50,000 and you have no qualifying expenses to offset the loan, the required monthly payment starting 1 year from today would be $506 per month for 10 years. If you borrow $50,000 and spend $40,000 to pay your employees during the first 8 weeks, you will have 10 years to pay the remaining $10,000 with monthly payments of $102.” Subjects in the loan condition saw the text, “Imagine the government offers a loan allowing you to borrow up to [borrowing base] times your typical monthly expenses without posting any collateral. You could use this money to cover any of your business expenses. The loan would have an annual interest rate equivalent of 4% and principal and interest payments would be deferred for 1 year. You would have up to 10 years to repay the loan. For example, if you borrow $50,000, the required monthly payment starting 1 year from today would be $506 per month for 10 years.” Pooled means for the loan and CARES Act responses are 0.59 and 0.72, respectively. The sample size is 2,610, and the pooled t-statistic on the difference between policies is 6.97.

A primary reason to forgive loans is that such a subsidy might do more to maintain employment and keep businesses open in the long term.^¶¶ We therefore reasked businesses to project their likelihood of being open and their expectations about employment after we told them about the loan programs. Figs. 11 and 12 show the expected probability of being open and the expected employment (relative to January 2020 employment) for the two groups of respondents.

 

Fig. 11.

Differences in policy effects on the propensity to remain open in December of 2020, split by hypothetical limits on borrowing amount. This figure plots differences in the propensity to remain open under different policies. The measure is computed using a follow-up question after policy information displayed, using the fraction that chose “Very likely” or “Extremely likely” to be open in December of 2020. See Fig. 10 legend for additional detail about the policy display. Pooled means for the loan and CARES Act are 0.805 and 0.848, respectively. The sample size is 2,550, and the pooled t-statistic on the difference between policies is 2.76.

 

Fig. 12.

Differences in policy effects on relative employment between December and January. This figure plots differences in the ratio of relative employment between December 2020 and January 2020 under different policies. The December 2020 employment measure is computed using a follow-up question after policy information displayed. See Fig. 10 legend for additional detail about the policy display. Pooled means for the loan and CARES act responses are 0.86 and 0.94, respectively. The sample size is 2,341, and the pooled t-statistic on the difference between policies is 2.42.

Before they were told about the policies, both groups had experienced similar employment declines since January, and both groups expected their employment in December 2020 to be about 40% less relative to January 2020 (that is, assignment was balanced). After the respondents were told about the CARES-like loans, they projected their employment would decline by only 6% by December 2020. The respondents who were told about loans without forgiveness predicted their employment levels would fall by 14%. (Because we randomize the policy and the generosity, this analysis is equally weighted across firms.) We are unable to distinguish precisely whether it is the conditional nature of the PPP program or the more favorable credit terms that drive these differences.

When asked about their expectation of remaining in business in December 2020, businesses responded similarly. Before being told about the loans, the businesses thought that they had a 62 to 63% chance of being open in December 2020. The probability rose to 81% among those who were told about the standard loans. The projected chance of survival increased to 85% for the businesses who were informed about the PPP loans that came with forgiveness. Again, the flow of credit seems important, but forgiveness did have a statistically significant impact on the expectation of staying in business.

Why would businesses not take the aid that comes with such generous forgiveness terms? Fig. 13 asks the 28% of firms that said that they would not take a CARES-like loan why they would turn down such a generous offer. The most common response, given by 35% of refusers, was that they did not need the cash, which suggests that one-tenth of our sample truly feels confident with their financial security.***

Fig. 13.

Reasons for not using the resources in the CARES Act. This figure contains the frequency of responses for reasons that respondents would not take up aid under the CARES Act policy condition; 383 respondents indicated they would not use the policy, and 382 answered this question. Respondents could select more than one option, so percentages need not sum to 100. Fifty percent of respondents selected an additional reason not displayed or filled in the free text entry for other.

A significant number of those who said that they wouldn’t take the CARES assistance cited other concerns. Thirty percent of these respondents said that they didn’t think that they would qualify. Nearly 20% said that they didn’t trust the government to forgive the debt. Over one-tenth thought that it would be too much of a hassle. These results suggest that clarity about the program and a streamlined process are important policy considerations to ensure a high take-up rate.

We also randomly informed survey recipients about the changes in unemployment insurance under the CARES act. We found that informing employers about the increased generosity of unemployment insurance was associated with lower employment projection in December 2020, among those businesses that were told about the CARES-like loans. Information about unemployment insurance had no impact on the expected probability of remaining open. More work is needed to understand how interactions between programs may influence economic outcomes.

Industry Differences in Response to Crisis Duration

COVID-19 disruptions do not affect all businesses equally. Some are deemed essential and remained open, while others were required to close. Some businesses could shift employees to remote work, while others were ill equipped for the transition. In this section, we explore the cross-industry variation in its effects.

Our results suggest that disparities will be larger if the pandemic ends up lasting for several months. Specifically, we asked businesses the following: “We want to understand how the duration of the COVID-19 disruptions might change your answers. Suppose that most COVID-19 disruptions continue for X months, what is the likelihood of your business remaining operational by Dec. 31, 2020? Please provide your best guess.” We randomize the duration (X) to be 1 mo, 4 mo, or 6 mo, and offer respondents a five-point scale ranging from extremely unlikely to extremely likely. As before, we transform this answer into a binary outcome of likely or unlikely to remain open for ease of exposition.

Table 5 displays the responses to this question by industry. When firms are told to expect a 1-mo crisis, the expectation of remaining open by the end of the 2020 hovers above 68% across all industries, with the exception of arts and entertainment, personal services, and tourism and lodging. In those industries, the expectation of remaining open drops to 66%, 57%, and 63%, respectively. When firms are told to expect a 6-mo crisis, the average expectation of remaining open falls to 39%, and there is significant heterogeneity between sectors. The expected survival probability for firms in Arts and Entertainment drops precipitously to 45% under a 4-mo crisis, and 35% if the crisis lasts 6 mo. The expected probability of being open for Personal Services firms falls to 19% if the crisis lasts 6 mo.

The restaurant industry also seems particularly vulnerable to a long crisis. Restaurateurs believed that they had a 74% chance of survival if the crisis lasts 1 mo, but if the crisis lasts 4 mo, they gave themselves a 29% chance of survival. Under a 6-mo crisis, they expected to survive with only a 19% probability. Likewise, the chance of survival for firms in tourism and lodging drops to 25% by the 6-mo mark. Meanwhile, banking and finance, real estate, and professional services reported they will be able to weather extended disruptions far better than these more exposed sectors.

In Table 6, using the results in Table 5 around closure probabilities as a function of crisis duration, we examine how employment separations might evolve due to firm closure. Building on our estimates of the impact of crisis duration on job loss, Table 6 estimates the impact of COVID-19 on aggregate job loss from small business closures and how businesses expected this to vary with crisis duration. Specifically, we begin with the number of workers who are projected to lose their jobs from small firm closures. We then multiply the initial employment level (based on the 2017 Economic Census), at the employment size level, by the survey-based estimate of the share of firms that will be closed in December depending on the length of the crisis. The first row shows that there were 5.9 million workers in firms with fewer than five employees in 2017. In our survey, 43% of those smaller firms expected to be closed in December even if the crisis lasted for only 1 mo. Next, we multiplied 0.43 times 5.9 million workers to project 1.6 million separations due to firm closings (in the absence of additional aid beyond what was expected at the time of the survey). These smaller firms are extremely fragile, but, since they represent a relatively small share of employment, their closures add only modestly to overall job losses. Firms with over 50 employees are more optimistic about their survival, even if the crisis lasts for several months. Yet, even among these firms, 54% expected to be closed in December if the crisis lasts at least 4 mo. Those closures would create 14.6 million separations. This figure may be an overestimate, because this firm size category is large, and the closure rates may be lower for larger firms.

Taken altogether, the closures are projected to create 32.7 million job losses if the crisis lasts for 4 mo and 35.1 million job losses if the crisis lasts for 6 mo. Moreover, these job losses look only at business closures and do not account for the reduction in the number of workers by firms that remain open or job losses among workers who are employed by larger firms.

These results suggest that the damage to our economy and its network of small businesses will be far larger if the crisis lasts for many months. This suggests large potential economic benefits for policies that can safely lead to reopening the economy quickly.