Policy Research Working Paper 9434

Unmasking the Impact of COVID-19 on Businesses

Firm Level Evidence from Across the World

Marie Christine Apedo-Amah Besart Avdiu Xavier Cirera Marcio CruzElwyn DaviesArti Grover

Leonardo IacovoneUmut Kilinc

Denis MedvedevFranklin Okechukwu Maduko

Stavros PoupakisJesica Torres

Trang Thu Tran

Finance, Competitiveness and Innovation Global Practice October 2020

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Produced by the Research Support Team

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy Research Working Paper 9434

This paper provides a comprehensive assessment of the short-term impact of the COVID-19 pandemic on busi-nesses worldwide with a focus on developing countries. The results are based on a novel data set collected by the World Bank Group and several partner institutions in 51 countries covering more than 100,000 businesses. The paper provides several stylized facts. First, the COVID-19 shock has been severe and widespread across firms, with persistent negative impact on sales. Second, the employment adjustment has operated mostly along the intensive margin (that is leave of absence and reduction in hours), with a small share of

firms laying off workers. Third, smaller firms are dispropor-tionately facing greater financial constraints. Fourth, firms are increasingly relying on digital solutions as a response to the shock. Fifth, there is great uncertainty about the future, especially among firms that have experienced a larger drop in sales, which is associated with job losses. These findings provide a better understanding of the magnitude and distri-bution of the shock, the main channels affecting businesses, and how firms are adjusting. The paper concludes by dis-cussing some avenues for future research.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at Liacovone@worldbank.org.

Unmasking the Impact of COVID-19 on Businesses:Firm Level Evidence from Across the World∗

Marie Christine Apedo-Amah (World Bank)Besart Avdiu (World Bank)Xavier Cirera (World Bank)Marcio Cruz (World Bank)Elwyn Davies (World Bank)Arti Grover (World Bank)

Leonardo Iacovone (World Bank and Hertie School)†Umut Kilinc (World Bank)

Denis Medvedev (World Bank)Franklin Okechukwu Maduko (Central European University and World Bank)

Stavros Poupakis (UCL and World Bank)Jesica Torres (World Bank)

Trang Thu Tran (World Bank)

JEL classification: D22, L20, L25, O10Keywords: COVID-19, Firms, Employment, Sales, Uncertainty

∗The authors gratefully acknowledge comments and feedback on the survey questionnaires as well as on earlier draftsof the paper from Caroline Freund, David Mckenzie, Francisco Campos, Bilal Zia, Nick Bloom, Mark Dutz, Ana PaulaCusolito, Jorge Rodriguez Meza, and Silvia Muzi. We also thank Santiago Reyes Ortega for his outstanding researchsupport. We are grateful to all the colleagues and counterparts institutions that have supported the data collection (acomplete list of all colleagues, collaborators and institutions that have been crucial in providing support during datacollection is provided in the online appendix). We would like to especially thank Tanay Balantrapu, Joao BelivaquaBasto, and Kyungmin Lee for their help during the data collection process. We thank the Enterprise Analysis Unit ofthe Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Surveydata available. Financial support from USAID and World Bank for data collection is gratefully acknowledged. The viewsexpressed in this article are solely those of the authors and do not necessarily reflect those of the World Bank or its Board.†Corresponding author, email: Liacovone@worldbank.org

WB158349Text BoxOriginally published in the Policy Research Working Paper Series on October, 2020. This version is updated on February, 2021.

To obtain the originally published version, please email prwp@worldbank.org.

WB158349Inserted Text

1. IntroductionThe COVID-19 pandemic is resulting in an unprecedented shock to the private sector, threatening

the global progress in poverty reduction and shared prosperity made in recent years . The impacton firms and the destruction of existing productive capacities due to the pandemic could have largeeffects on the growth prospects of developed and developing economies, not only in the short-run butover the longer run; especially if this destruction also affects the more productive firms. Restrictionsto mobility and economic activity, higher transaction costs and potential cross-country trade andcurrency disputes can limit the allocation of resources within countries and across sectors, worseningmisallocation in the economy and lowering aggregate productivity growth.1 Understanding theseverity of the impact of the shock and its distribution, the adjustment mechanisms adopted byfirms, and the uncertainty created, is critical to understand these reallocation channels and designbetter policies that can help smooth the impact of the shock and support recovery.

This paper provides the first comprehensive assessment at the global level of the short-termimpact of the pandemic across the world relying on a novel dataset collected during the monthsof April through August 2020.2 The data collected cover more than 100,000 businesses across 51countries, primarily low- and middle-income countries across the world.3 Data collection aimedat covering almost all sectors of the economy (i.e. manufacturing, services and agriculture) and alarge range of countries with different income levels (i.e. ranging from Afghanistan to Italy) as wellas different levels of exposure to the COVID-19 shock.4 To the best of our knowledge, this is themost comprehensive assessment of the short-term impact of COVID-19 on businesses in terms ofnumber of firms and countries covered.

Our paper contributes to the quickly expanding literature assessing the economic impact ofCOVID-19. Much of the early research attempts to draw parallels with previous crises or usesmacro simulations in combination with some ex-ante measures of vulnerability to the pandemic.Ludvigson et al. (2020), for example, extrapolate from past natural disasters to model COVID-19as a series of large, multiple-period exogenous shocks.5 Bonadio et al. (2020) use a quantitativeframework to simulate a global lockdown as a contraction in labor supply for 64 countries, wherelabor supply is assumed to change with the fraction of work in the sector that can be done fromhome, interacted with the stringency with which countries imposed lockdown measures. They finda major contraction in GDP, with a quarter of the impact attributed to disruptions in global supplychains.

Another strand of the literature uses high-frequency, unconventional data to monitor the pan-

1At the same time, the shock could create pressures on firms to expand their use of digital technologies or increasecompetition which could also lead to higher productivity growth and better resource allocation.

2Data were collected jointly byWorld Bank, national authorities and partner organizations relying on an homogeneousquestionnaire.

3Among richer countries, our dataset covers Cyprus, Greece, Italy, Poland, Romania and Slovenia. Data collectedcover all regions where the World Bank is present: East Asia and Pacific (EAP), Europe and Central Asia (ECA), LatinAmerica and the Caribbean (LAC), Middle East and North-Africa (MNA), South Asia (SAR), and Sub-Saharan Africa(SSA).

4Twenty-eight countries included businesses in agriculture in the sample, and these observations account for around5% of the sample. Most are in the Philippines, Tanzania, Ghana, Kenya, and Tunisia.

5Using a costly disaster index, the authors estimate that under fairly conservative assumptions, the pandemic couldlead to a cumulative loss in industrial production of 20% and in service sector employment of nearly 55 million jobs overthe next 12 months in the United States.

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demic’s impact in real time, including text data from earnings reports (Hassan et al., 2020), unem-ployment claims, and transaction data from credit or debit card purchases (Chetty et al., 2020). Thisdiverse evidence all points towards a severe and immediate impact of the shock on firm revenue andemployment. Existing evidence also suggests that the pandemic affected countries with differentdegrees of lockdown severity alike. Extrapolating estimates from the Republic of Korea, where nolockdown was implemented, Aum et al. (2020) hint at fear of infections rather than lockdown as themain driver of the drop in local employment, and suggests the pandemic has truck high-contactindustries the hardest.

As the pandemic unfolds over time, more direct evidence, particularly using surveys, is emergingabout its impact on jobs and business activities at different stages of the pandemic. In the shortterm, severe impacts have been documented across countries in terms of revenue loss, businessclosures, mass layoffs, and liquidity (see Dai et al. (2020b) for evidence in China and Bartik et al.(2020); Humphries et al. (2020a); Adams-Prassl et al. (2020); Fairlie (2020a,b) for evidence in theUnited Kingdom, United States and Germany). Firms are hit by multiple channels, with depresseddemand being the most frequently reported concern in recent firm surveys and becoming moreprominent over time (Dai et al., 2020a; Balleer et al., 2020). Acharya and Steffen (2020) find a sharpincrease in US companies’ cash holdings in March, as COVID-19 fears started to materialize. Theyattributed this increase in cash to a concern among managers that their firm’s credit rating would bedowngraded on account of pandemic-related business disruption, and that it would become moreexpensive or even impossible to raise cash after that. With falling revenue, most SMEs are now inmajor financial distress (Bartik et al., 2020; Zhang, 2020).

While there is much uncertainty about the longer term impact of the pandemic, evidence frompast crises points to potential scarring effects in the long run. Reallocation effects of the COVID-19shock are likely to persist long after the pandemic recedes (Barrero et al., 2020). All firms arevulnerable to persistently low demand, but in large systemic crises good and bad firms may beweeded out alike (Hallward-Driemeier and Rijkers, 2013; Foster et al., 2016). More recent estimatesfrom Bosio et al. (2020) using Enterprise Survey data suggest that in multiple shock scenarios, firmssuffer liquidity shortages regardless of age, size and productivity levels. Uncertainty can furtherdeter economic activities. Balla-Elliott et al. (2020) find that post-lockdown delays in businessreopening in the United States can be explained by low levels of expected demand rather thanby health concerns. During past crises, firms were less likely to undertake radical innovation anddisproportionately cut back on intangible investment Granja and Moreira (2019); Duval et al. (2020).There has been an unprecedented level of uncertainty documented during this pandemic andfirms have been found to respond by significantly cutting expenditures on innovation and generalmanagement improvements, which will likely affect future productivity growth (Baker et al., 2020).

Despite this quickly expanding literature, there has been no systematic cross-country evidenceabout the impact of the pandemic on firms. Some cross-country evidence exists but only focuses onsmall and relatively selected samples of firms. For example, using survey responses from early Aprilacross nearly 500 listed firms in 10 emerging markets, Beck et al. (2020) find that the vast majorityof firms have been negatively affected by COVID-19 and reacted by reducing investment ratherthan payrolls. Surveys of businesses on e-commerce platforms have similarly found widespread

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negative impacts on sales and employment among SMEs (Facebook et al., 2020). The limitationsof these studies is that they rely on narrow samples of firms, and are less likely to provide anaccurate assessment of overall impact at the country level. Besides filling this evidence gap on theimpact of the pandemic, our paper is also the first to document systematic evidence on policiesto support firms in response to the pandemic. While there have been multiple efforts to takestock of current government policies on COVID-19 (for example, the IMF Policy Tracker and theOxford COVID-19 Government Response Tracker), not much is known about firm-level accessto government support globally. Evidence from past crises suggests that temporary support tofirms can be effective. Mexican wage subsidies after the global financial crisis helped speed upemployment recovery, especially for smaller firms (Bruhn, 2020). In Sri Lanka, cash grants helpedmicro-firms survive the crisis and accelerated their recovery following the 2004 Tsunami (De Melet al., 2012). Nevertheless, as the crisis continues to deepen and the need for support intensifies,it is critical to better understand how accessible and effective current support policies are. In theUnited States, the Paycheck Protection Program has been found to be associated with increasedemployment and business survival probability (Bartlett and Morse, 2020; Humphries et al., 2020b).At the same time, its first-come first-serve design skewed its resources towards larger firms andmay have reduced its effectiveness (Humphries et al., 2020b). In China, support in the form ofpayment deferrals and exemptions appear to improve SMEs’ cash flows. Yet loans do not appear tobe effective in alleviating SMEs’ cash constraints or encouraging the reopening of small businesses,potentially due to difficulties in accessing policy-oriented loans and misallocation of credit (Chenet al., 2020). More systematic and timely evidence is needed to inform policy debates on how tobest support firms during the COVID-19 pandemic, especially when countries are running out ofthe fiscal policy space to sustain stimulus packages.

We show several stylized facts that allow us to understand the magnitude of the shock, the mainchannels affecting businesses, how firms are adjusting, and a potential role for policy interventions.First, while most businesses are likely to be already open 6 weeks after the peak of the shock, thenegative impact on their sales has been large and persistent, with significant heterogeneity acrossfirms. Second, the short-term adjustment on employment has been mostly on the intensive margin,with firms in the short-term more likely to grant leave, reduce hours of work, or reduce wages,compared to lay-offs. Third, we find a large degree of heterogeneity in liquidity constraints acrosscountries, but smaller firms are disproportionately affected by financial constraints. Fourth, firms’main response consisted in increasing the use of digital technologies, although this varies greatlyacross countries and is significantly lower among smaller firms. Fifth, uncertainty is very highespecially for businesses that were hit harder. Finally, we conclude by pointing to various avenuesfor future research.

The remaining part of the paper is structured as follows. Section 2 briefly describes the data.Section 3 describes the impact of the crisis with a focus on operations, sales and employment. Section4 focuses on liquidity and survival of firms, while section 5 discusses firms’ responses. Section 6addresses the issue of uncertainty relying on a novel measure which builds on Altig et al. (2020a)and Barrero et al. (2020). Finally, section 7 concludes discussing avenues for future research.

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2. Description of the survey and the datasetThe World Bank Group (WBG) has developed a brief firm survey instrument to collect data

measuring the impact of the COVID-19 pandemic on the private sector. The questionnaire checks thepulse of businesses measuring the impact on some critical dimensions: operations of the business,sales, liquidity and insolvency, labor adjustments, firms responses, expectations and uncertaintyabout the future, and preferred mechanisms of public support. The appendix shows the standardversion of the questionnaire.

In this paper we describe the results using harmonized data based on the implementation of thefirst wave of the business pulse survey in 51 countries over 6 regions.6 In most countries interviewswere conducted over the phone, but in a few countries such as Colombia or Turkey, the questionnairewas administered online. In 31 of these countries, a fresh sample of businesses was collected and thesurvey was implemented in collaboration with private sector associations, statistical agencies, andother government agencies (mainly Ministries of Finance and Economy). Data for the remaining 20countries were collected as a follow-up of the World Bank Enterprise Survey, using a questionnairethat excluded some questions from the standard version.7

The data include micro, small, medium, and large businesses, across all main sectors (i.e. agricul-ture, manufacturing, retail, and other services, including construction). Micro-businesses accountfor 51% of observations in the cross-country data; only around 8% of observations report morethan 100 employees before the COVID-19 shock (January 2020; Figure A2). Most businesses inthe data are in wholesale and retail (28%), manufacturing (17%), and food preparation services(15%; Figure A3). The sampling frame in some countries excluded micro-firms and/or businessesin agriculture (see Table A2 and Table A3) and in some countries, when micro-businesses wereincluded, the survey instrument offered simplified versions of some questions in order to facilitatedata collection.8

The sampling frame in most countries where the pulse survey was not a follow-up of theEnterprise Survey was based on censuses from Statistics Agencies, Ministries of Finance or Economy,or business listings from Business Associations, and typically only included registered businesses.In the case of the Enterprise Survey, by design the implementation covers only formal firms. OnlyCambodia, Gabon, Ghana, Pakistan, the Philippines, Senegal, South Africa, Sudan, and Tunisiainclude informal firms in their sample.

Given some of the heterogeneity related to the differences in country samples, implementationstrategy, and timing of the surveys, we introduce different controls in the analysis. To control fordifferences in the composition of the sample, we include in the analysis dummies for size andsub-sector (10 groups), in addition to country fixed-effects. The timing of implementation of the

6In Brazil, the standard pulse survey was implemented not at the national level but on two representative states; Ceara,one of the poorest states located in the North-East, and São Paulo, the largest and richest of the country, concentratingalmost one third of Brazil’s GDP.

7Source: Enterprise Surveys, The World Bank, http://www.enterprisesurveys.org. The two instruments, the standardpulse survey and the Enterprise Survey follow-up, were implemented in Togo. In Bangladesh, the standard pulse surveywas implemented on different samples and at different times of the shock. The survey instrument differed across countriesbut in most cases the Enterprise Survey COVID-19 follow-up excludes some questions on the adjustment to employmentand the channels affecting the operations of the business, the module on expectations, and most questions on the adoptionof technology as a response to the crisis.

8As a result, in some areas when there is no equivalent question, micro firms are excluded from the analysis.

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http://www.enterprisesurveys.org

first wave of the pulse survey differed across countries–collection time averaged around 4 weeks percountry and spanned from mid-April through the end of August. The survey captures businessesat different stages of the COVID-19 shock in each country (see Figure A1) and to correct for thesedifferences in the timing of the survey, we also include dummies for the number of weeks beforeor after the peak of the COVID-19 shock, which we proxy using Google mobility data aroundtransit stations (Google, 2020). For each country, we identify the date when mobility reached thetrough and then count the number of weeks between the date of the interview and the trough. Forcountries where Google mobility data are not available, we predict mobility using the stringency ofthe lockdown restrictions provided in Hale et al. (2020). We describe this exercise and detail on theharmonization and trimming of variables in the appendix. Finally, to control for differences in thenumber of observations in each sample, we weight our results using the inverse of the number ofobservations in each country, that is, each country has the same weight in our summary statistics.9

3. Impact of the crisisThe COVID-19 outbreak led many firms to close or reduce operations, with large drop in sales,

and significant adjustments in employment, but with large heterogeneity in effects across firms.This section assesses the impact of the outbreak across these three dimensions: operation status,sales, and employment. While operation status (e.g. open or temporary closed) refers to the statusof the business at the moment of the interview, the results on sales and employment refer to changesin these variables 30 days before the interview compared to the same period of 2019. Because thesurvey was implemented in different countries at different time, and those countries were facingdiverse stages of the COVID-19 outbreak, the data provide a comprehensive picture of the waybusinesses have been performing and adjusting under the current crisis.

3.1 Operations of the business

The likelihood of a business being open at the peak of COVID-19 crisis and up to 4 weeks afterthe peak is under 30%, but it significantly increases to almost 75% or more 6 weeks after the peak.Figure 1 presents the results based on the predicted probability of a firm to be open or partiallyopen, controlling for country, size category, sector, and timing fixed effects.

Yet, tourism and small businesses in general are more likely to be closed. 6 weeks after the peakof the COVID-19 outbreak the predicted probability of being open is still under or around 75% fortourism related businesses, such as accommodation (58%) and food and beverage services (73%)(Figure 2). These are the businesses with lowest probability of being open. There are also differencesin terms of size after week 6, with large firms being more likely to be open (89%) compared to micro(79%), small (82%), and medium sized (86%) firms.10

9In some countries, sampling weights are available in order to produce nationally representative results at the countrylevel, but for comparison purposes, we do not include these weights in the analysis and only weight observations by theinverse of the number of businesses in each sample.

10The differences between size groups are statistically significant.

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3.2 Impact on sales

The negative impact on sales has been large and widespread across firms. About 84% of firms onaverage, across countries, have reported a reduction in sales in the last 30 days before the interview,compared to the same period in the previous year. The cross-country average suggests a reductionin sales of about 49% compared to the same period in the last year, with a standard deviation of 0.25.

While the biggest impact of the COVID shock is around the peak of the crisis, the drop in sales ispersistently large even 10 weeks later. Figure 3 shows that the average drop in sales in the first 4weeks following the peak of the shock is between 60% and 75%. In the following months, the dropin sales reduces to 47% in week 8, 47% in week 12, and 43% after week 16. The results show thatdespite almost 90% of business are open after 10 weeks from the peak of the outbreak, the negativeimpact on sales is still looming large.

There are significant differences across countries in the impact of the shock on sales, even whencontrolling for the sector and size composition of the sample. Figure 4 shows that in some countries,such as South Africa, Bangladesh, Sri Lanka, Tunisia, and Nepal, the average drop in sales inour sample is beyond 60%, with significant dispersion between the 10th and the 90th percentiles.However, because the interview was conducted in different periods across countries with differentsectoral and size distribution of firms, it is important to control for these characteristics.11 Once wecontrol for the size, sector, timing of survey, and country fixed effects, we observe some importantchanges on the expected value of changes in sales. South Africa is still the country with the largestexpected drop in sales, followed by Bangladesh, Nepal, Honduras, India, and Jordan, all withaverage estimates larger than 60% for the drop in sales. Senegal, Sri Lanka, and Côte d’Ivoire exhibita significant difference between the unconditional and the predicted mean once we control for thecharacteristics previously mentioned. Figure 4 shows significant dispersion in the change in saleseven after we control for differences in the composition of the sample. In other words, both thebetween-country and the within-country variation in the drop in sales is significant.

There are also important differences across sectors with tourism related activities being the mostnegatively affected. These results are in line with fact that these activities usually require moreface-to-face interactions and are more likely to be closed even after six weeks following the peakof the outbreak. Moreover, these activities have been widely facing more constraints for operationrelated to containment measures.

Despite differences across countries, sectors, and firm size, there is a large heterogeneity effectacross firms within these groups, which implies that the shock is affecting similar firms differently.Figure 5 compares the distribution of the variation of changes in sales between and within country-size-sector groups. The results show that there is significant variation in the percentage change insales that is not explained by the interaction of country, size, and sector effects. Indeed, only about19% of the variation in sales relative to the same period in the previous year is explained by country,sector, size group, number of weeks before and after the COVID-19 outbreak peak, and severityof the crisis. Among these variables, country effects are the most significant variable, explainingabout 14% of the variation observed in sales. In other words, almost 80% of the variation in sales

11Part of these differences stem from survey implementation as well as by real differences across countries.

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drops are to be explained by unobservable factors, such as firm capabilities related to managementpractices and technologies, among others. As an example, there are 6 retail firms with initially 10full-time employees in Senegal for which data were collected in the same week. Their sales dropped73% on average, with a standard deviation of 43.7. These findings suggest that well establishedknowledge regarding large heterogeneity in the performance of firms within country, sector, and sizevariation is also evident during the COVID-19 outbreak. These results have relevant implications forpolicy decisions because they suggest that in a crisis of this magnitude, targeting variables that arerelatively easy to observe might be a poor proxy to identify the firms that need most.

3.3 Impact on employment

The employment response to the shock has been dominated by adjustments along the intensivemargin, withmany firms providing granted leave or reducing hours of workers or wages, and a smallshare of firms laying-off workers. This is in line with previous findings in the literature suggestingthat an adverse shock has little effects on established employment relationships, without bulge ofjob loss on the onset of a recession (Hall, 2005). Figure 6 shows that around 57% of businessesadjusted their employment levels either on the intensive (44%), the extensive and intensive margin(12%), or only the extensive margin (7%).

Firms that experienced larger sales drops also experienced a larger reduction in employment.Figure 7 shows a positive association between percentage change in sales in the last month, comparedto the same period in the previous year, and the average percentage change in employment, as afraction of the number of workers in January 2020. The estimated elasticity is 0.077, suggestingthat for every 1 percentage point in sales reduction, there is a 0.077 percentage point reduction inemployment.12 This number reflects only the adjustment along the extensive margin, which is notthe main channel of adjustment in the short-term, and already suggests a significant negative effectin the short term given the average drop in sales experienced by firms in the first 4-6 weeks aroundthe peak of the shock.

Larger firms are significantly more likely to lay off workers and grant leave. Figure 8 shows thatthe predicted probability of larger firms to reduce workers is about 26%, and this is statisticallysignificantly larger than micro (12%), small (19%), and medium sized (22%) firms. When it comesto granting leave, large firms have a probability of 53%, against 27% for micro firms, 45% for smallfirms, and 50% for medium sized firms. These differences in terms of size are not as marked whenconsidering the likelihood of reducing wages or hours of working, which are on average about30%-40% for firms independently of their size (except relative to micro firms).

Tourism related activities, specially accommodation, are also comparatively more likely to layoff workers and grant leave. On average, firms in the accommodation sector exhibit the highestprobability of granting leave (52%), and cutting wages (32%). For reduction in hours worked, retailand wholesale exhibit the highest predicted probability with 38%.

An important finding is that firms that are adjusting labor, are doing so through multiplechannels at the same time. Businesses that adjust on the intensive margin are also more likely to lay

12As an example, a firm with initially 100 employees that faced an average reduction in sales of about 53%, comparedto the same period in the last year, has reduced its size to about 96 employees.

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off workers (Figure 10). These findings, combined with the strong association between changes inemployment and changes in sales, suggest that firms that were most negatively affected by the shockare relying on multiple mechanism of adjustments of their workforce and driving the adjustmentson employment.

4. Liquidity and survivalSharp increases in corporate and personal insolvency typically follow a crisis period. Firms in

developing economies with shallower financial markets are most vulnerable to liquidity problemsand insolvency risks (WBG, 2020). The slowdown of economic activity caused by the COVID-19outbreak and related lock-down measures implemented to tackle the health crisis have led to severedifficulties for companies to meet their financial obligations. Fixed operating costs for firms, suchas rents and interest payments, still need to be paid while the cash flow and sales that meet theseobligations has contracted substantially (OECD, 2020). Not surprisingly, many otherwise soundcompanies are facing acute liquidity constraints that eventually might become solvency problems.Our data allows us to assess liquidity constraints and the extent to which COVID-19 has worsenedthe firm liquidity and risks pushing businesses into bankruptcy.13

Consistent with the growing literature on the impact of COVID-19 on bankruptcy and liquiditychallenges among firms (e.g. Bircan et al. (2020)), we find a significant degree of heterogeneityin liquidity constraints across countries. Nearly 88% of firms in South Africa have fallen or expectto fall into debt, while only 13% report so in Indonesia (Figure 11). In general, it appears that theproblem is more acute among countries where financial development is lower (e.g. Nepal andBangladesh). We observe instead less cross-country variation when assessing the liquidity of firmsusing the cash at hand measure. Interestingly, while firms in upper-middle income or high incomecountries (e.g. the Russian Federation and Greece) are less likely to fall into arrears, they do nothave large cash at hand to cover their costs (Figure 12). It should be noted that cash availabilityamong firms captures their liquidity position but does not imply insolvency because firms with nocash may still be able to survive by selling out their non-liquid assets when in need. It is possiblethat firms in upper-middle income countries with developed financial systems usually do not needto maintain a lot of cash at hand to handle their operations and instead rely on financial markettransactions, given their efficiency during regular times.

Within country, differences between firms facing liquidity problems are larger than those betweencountries. For instance, the bottom 10 percentile of firms in Côte d’Ivoire can expect to cover only14 days relative to the top 10 percentile, which can cover as much as 112 days of costs. Likewise,in Kenya, Senegal and Tanzania the bottom 10 percentile of firms can cover none, while the top 10percentile of the firms can cover about a year of costs (Table 1). This finding is similar to Bircanet al. (2020) who use data on 16 emerging economies and three Western European countries tosuggest similar within- country variation. Their work suggests that, for instance, about half of allsmall businesses in Ukraine have cash reserves of less than one month’s wages, while around 20% of

13The surveys measure liquidity constraints among firms through two questions: (i) Is it expected that this establish-ment will fall in arrears in any of its outstanding liabilities in the next 6 months? (ii) As of today, for how many dayscould this establishment continue paying all costs and payments with the cash available? See appendix for details.

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them have the liquidity to pay more than six months’ wages. Within- country heterogeneity pointsto the huge inequality among firms in terms of their access to finance and therefore their likelihoodof managing to survive through the crisis.

Smaller firms tend to face more severe financial constraints during COVID-19 even in advancedcountries. For example, in the United States 50% of small businesses have fewer than 15 days inbuffer cash and even healthy SMEs have less that two months of cash reserves (NYFED, 2020). TheOECD also finds that there is a risk of otherwise solvent SMEs going bankrupt while containmentmeasures are in force (OECD, 2020). Consistent with this fact, our results suggest that larger firmshave a lower probability of falling into arrears and can cover their costs with cash at hand for a longerperiod (Figure 13). Larger firms can cover up to 65 days as compared to 59 days in medium-sizedfirms, 53 and 50 days in small and micro firms respectively. While 36% of large firms and 45% ofmedium sized firms expect to fall into arrears, a significantly lower probability than other firms,the differences between micro and small firms are not statistically significant, as the probability offalling into arrears for these firms is respectively 53% and 50%.

We also find that firms in sectors that faced larger reductions in demand tend to have biggerfinancial woes. About 62% and 56% of firms in the accommodation and food preparation servicessector expect to fall into arrears as compared to 35% and 43% of firms in financial services andinformation and communication technology (Figure 14). This sectoral result is in line with thefinding that businesses experiencing a larger drop in sales are more likely to fall into arrears duringthe next 6 months and can cover their costs for fewer days (Figure 15).14

5. Firms responsesOverall around 49% of firms made greater use of technology, changed the product mix, or

both.15. The most common firm response to the pandemic shock has been to expand the use ofdigital platforms; although this response differs across countries, sectors and firms. A criticalchallenge of the pandemic has been the fact that lockdown measures and fear about contagion haverestricted mobility and social interactions leading both to a large shock to demand, while restrictingthe ability of firms to perform key business activities such as onsite (i.e. in person) sales. Figure 16shows that around 34% of firms have increased (around 22%) or started (around 8%) to use theinternet, social media and digital platforms; and 17% of firms have invested in new equipment,software or digital solutions in response to the pandemic. The shock has clearly accelerated digitaladoption and this could lead to productivity gains in the future.

The survey also measures two types of adjustment related to product innovation (i.e. changesin the product and services mix of the firm). Around 5% of firms sought the occasion to diversifytowards some health related products or services. More importantly, around 21% of firms opted forchanging some of their products or services, or by adding new ones. This suggests that one in four

14This is consistent with Schivardi and Romano (2020) who determine the set of Italian firms that will be liquidityconstrained using changes in sales as one of the three main ingredients determining a firm’s financial position.

15In most countries the surveys ask about some key firm’s responses to the crisis, the increase in use of digital platforms,investment in digital technologies, the introduction of changes in the product/services mix. Some of the countries wherethe questions were not included are India, Côte d’Ivoire, Mali and Colombia. We also exclude the Philippines due tothe very high non-response rate. In some countries, micro firms were not asked these questions and, therefore, we alsoexclude them from the analysis.

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firms performed some kind of product innovation, either by introducing a new product or serviceor by changing some of the products or services attributes.

This increase in the use of digital platforms is quite heterogeneous across countries and correlatedwith firm size. The use of digital solutions is much lower among smaller firms. Figure 17 shows thepredicted probabilities of each firm size group for different responses to the shock. The probabilityof using and increasing use of digital technologies and also the probability of investing in digitalsolutions is increasing in firm size. On the other hand, the probabilities of diversifying towards healthproducts and changing the product bundle are similar across firm sizes. In addition, responsesdiffer largely across countries. For example, the fraction of businesses that increased their use ofonline platforms ranges from 11% in Ghana to 81% in Indonesia.16

One interesting element that emerges from the analysis is the evidence of potential complemen-tarities across the different responses as we observe evidence of bundling of responses. For example,firms that expand the use of digital technologies, diversify into health and health related products,or change the product or services bundle are also more likely to invest in digital solutions (Fig-ure 18). In the case of increased use of and investments in digital technologies, the complementaritywith investments in digital equipment is evident, since firms may need to upgrade their digitalinfrastructure in order to expand its use. More surprising is the bundling with other responses suchas changes in the product mix, which suggests that some firms are responding more actively inchanging key elements of their business model.

6. UncertaintyAs discussed in recent work by Altig et al. (2020a); Barrero et al. (2020), COVID-19 represents a

fundamental shock to the economy through the uncertainty that it injected among economic actors.17

This uncertainty depends primarily on health related aspects (i.e. mortality rate associated with thevirus and expectation regarding time to find a successful vaccine) but also to policy responses (i.e.type and length of government support), as well as behavioral responses of economic actors (i.e.consumption and travel patterns).

To quantify the uncertainty generated by the COVID-19 shock, we rely on a measure that buildson Altig et al. (2020b). This measure collects subjective probability distributions over own-firmfuture anticipated outcomes and allows us to measure subjective micro-level uncertainty, whichis likely to drive firms’ decisions in the future. To build this measure, we asked firms to providetheir expected changes in sales for the next six months, compared to the same period last year, withthe probability that they believe this would happen (regular scenario). We then follow up withquestions on two additional scenarios, one more optimistic and one more pessimistic, with theirrespective subjective probabilities. We use the average of these three scenarios, weighted by theirrespective subjective probability, to measure expectation of sales growth. We then calculate the

16Similarly, the fraction of firms that increased their investment in digital solutions is 3% in Bangladesh, 10% inRomania, and 25% in Colombia.

17Policy makers have emphasized the importance of this uncertainty channel as highlighted by the speech of FEDChairman Jerome Powell: “We are now experiencing a whole new level of uncertainty, as questions only the virus cananswer complicate the outlook. Indeed, there is massive uncertainty about almost every aspect of the COVID-19 crisis”(May 21 2020).

11

standard deviation of this prediction as a measure of uncertainty.Our measure differs from the original one developed by Altig et al. (2020b) in two ways. First, it

elicits only three scenarios with subjective probability distributions over the establishment’s futuresales growth instead of five scenarios. Second, it focuses on a shorter horizon of 6 months instead of12 months.18

Previous research showed that this measure of uncertainty is predictive of future growth andsignificantly correlated with recent news about the firm’s growth expectations. We report here fivemain findings based on our results.

First, we find that on average businesses expectations are three times more pessimistic than thosein the United States (US) with an average expected sales growth of about -9% in the cross-countrydataset and -3% in the United States. Moreover, businesses are significantly more uncertain thanbusinesses in the United States as the average growth rate of uncertainty is close to 21% which isabout 4 times larger than uncertainty among US businesses.19

Second, a striking finding is that uncertainty is broad-based and cuts across all firms irrespectiveof their size, and strikingly similar across sectors too, as shown in Figure 19 and Figure 20, with theexception of the hospitality industry (food preparation services and accommodation) where thesituation is significantly worse.

Third, expectations and uncertainty clearly reflect the firm-level experience at the micro level asfirms that experienced larger sales drop in the last 30 days are both more pessimistic (i.e. expectlower sales growth in the future) and have higher uncertainty (Figure 21). Similarly, at the macrolevel, the size of the shock experienced at the country level is correlated with both expectations anduncertainty as businesses in those countries hit by a larger crisis (as measured by the magnitude ofthe mobility drop) are significantly more pessimistic and have higher uncertainty (Figure 22).20

Fourth, while country characteristics, proximity to the peak of the crisis, and sectoral character-istics all matter to explain expectations, these are much less important in explaining uncertaintywhich is largely explained by firm-level unobserved characteristics (Figure 23 and Figure 24). Inother words, as shown by Figure 24, the variation in the uncertainty between country-size-subsectorgroups is smaller than the variation between firms within these groups.

Finally, expectations and uncertainty have an immediate connection with jobs and welfare. Infact, we find that those businesses that are pessimistic or have higher uncertainty are significantlymore likely to have fired more workers (Figure 25).

7. ConclusionsThe results from the business pulse surveys draw a sobering picture on the impact of the shock.

Firms have been severely affected by the COVID-19 pandemic, often through multiple shocks atthe same time. Not only do firms report a significant drop in sales, but the financial situation is

18The reasons for these differences are due mainly to the fact that we implemented our survey over the phone andwith many entrepreneurs that had varying levels of literacy, and we had more stringent needs in terms of simplificationand time.

19The comparison with the United States is relative to expected sales growth ranging between -3% and 0% as reportedby Atlanta FED Uncertainty Survey available here.

20To measure severity, countries in the dataset are sorted into quartiles based on the drop in mobility at the peak of thecrisis.

12

https://www.frbatlanta.org/research/surveys/business-uncertainty?panel=1

worsening by reduced access to finance, and firms face significant uncertainties about the future. Asubstantial group of firms indicate that they are on the brink of insolvency, potentially leading tomore employment losses, and uncertainty is exceptionally high which also is likely to lead to lowerinvestments and more job losses. Against this backdrop, public policies play a key role to mitigatethese negative impacts, help firms weather the initial storm, and support recovery.

In response to the shock countries have introduced a wide variety of support programs for firmsand workers, varying from covering wage payments for workers (e.g., Brazil’sMP936 scheme orSouth Africa’s Temporary Employer-Employee Relief Scheme), offering subsidized loans (e.g., Ghana’sAdom and Anidaso loans scheme) to allowing deferrals of tax payments (e.g., deferral of corporatetaxation in Brazil and Mexico).21 Many of these early stage policies are aimed to help firms in“keeping the lights on” by improving cash flow, often with limited targeting.

Cirera et al. (2020) show that despite these efforts, access to support programs is very limited,especially in poorer countries and among smaller firms. Their results also highlight a criticalchallenge for policy makers: it is difficult to identify which firms should be targeted. We show thatin a crisis of this magnitude, targeting variables that are relatively easy to observe, such as size andsector, might be a poor proxy to identify the firms most in need of support. This is because mostof the variations in sales drops are not explained by the variables we can usually observe in a dataset. Given how widespread the shock is across the economy, similar cautions regarding targetingcriteria for policy interventions supporting businesses pre-COVID19 are needed, by taking intoaccount the large heterogeneity between firms belonging to the same sector and of similar size. Infuture research, it is very important to assess the distribution of policy support programs and howthese interventions can support the recovery, or even further spurring technological adoption andproductivity growth.

With COVID-19 cases still increasing globally, countries and their firms will continue to beaffected by health and economic shocks in the foreseeable future. As this survey was conductedduring the initial stages of the pandemic, the ability of firms to adapt to the new reality will becomea more important factor determining performance and survival. For this reason, continued andhigh-frequency data collection efforts have a crucial role to play in helping us to better understandlonger-term effects of the crisis.

Finally, a concern for many countries is the extent to which the crisis could have scarring effectsand worsen growth prospects. We think productivity growth could be an especially importantchannel to analyze as the crisis could effectively impair productivity growth through differentmechanisms, by reducing incentives or resources for investment in innovation aswell as byworseningmisallocation of resources between firms and sectors (Di Mauro and Syverson, 2020).

21As shown by World Bank (2020) more than 140 countries had announced some sort of policy to support SMEs byend of April 2020.

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https://www.worldbank.org/en/data/interactive/2020/04/14/map-of-sme-support-measures-in-response-to-covid-19

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https://www.slideshare.net/ifpri/the-impact-of-covid19-on-smes-in-chinahttps://www.slideshare.net/ifpri/the-impact-of-covid19-on-smes-in-china

APPENDIXA. Figures

Figure 1: Average predicted probability of business open or partially open.

0.22 0.260.28

0.400.48

0.650.74 0.77

0.82 0.83 0.84 0.84 0.850.88 0.88 0.91 0.89

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16+Weeks before or after peak of the mobility shock

Note: Average predicted probability of being open/partially open across number of weeks before or after the peak in theshock (proxied using mobility data) from a Probit that controls also for country, size, and sector fixed effects.

Figure 2: Average predicted probability of business open or partially open after 6 weeks from thepeak of the COVID-19 shock.

0.87 0.85 0.830.88

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Note: Average predicted probability of being open/partially open across number of weeks before or after the peak in theshock (proxied using mobility data) from a Probit that controls for country, size, and sector fixed effects.

17

Figure 3: Average changes in sales after the peak of the COVID-19 shock.

-75 -75-65 -63 -64

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Note: Average predicted mean conditioning on weeks from the peak from a linear regression controlling for country, size,sector, and weeks before and after peak of the shock.

Figure 4: Average changes in sales across countries.

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ZAF BGD NPL HND IND JOR TUN KEN XKX PHL LKA MDG SLV GIN BRA ZWE PAK GTM DZA NER MDA ALB KHM VNM SDN ZMB GAB IDN TGO GEO LBR MNG ITA TCD TUR BGR CYP TZA GRC ROU NIC RUS CIV POL SEN SVN

Unconditional mean

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-60 -60 -60 -60 -59 -59 -59 -57 -57-54 -54 -54 -54 -53 -53 -51 -50

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ZAF BGD NPL HND IND JOR TUN KEN XKX PHL LKA MDG SLV GIN BRA ZWE PAK GTM DZA NER MDA ALB KHM VNM SDN ZMB GAB IDN TGO GEO LBR MNG ITA TCD TUR BGR CYP TZA GRC ROU NIC RUS CIV POL SEN SVN

Predicted mean

Mean 10th and 90th percentile

Note: The predicted change conditions on country comes from a linear regression controlling also for size, sector, andweeks before and after the peak of the shock.

18

Figure 5: Distribution of the percentage change in sales. Between firms in same country-size-sectorvs. between country-size-sectors.

0

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-100 -50 0 50 100 150Percentage change in sales relative to same period of 2019

Variation betweencountry-size-subsectoraverages

Variation withincountry-size-subsectorgroups

Note: The between variation shows the distribution of the average change in sales in each country-size-sector group. Thewithin variation shows the distribution in change in sales after removing the country-size-sector mean.

19

Figure 6: Fraction of businesses in each margin of adjustment in employment.

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Note: Unconditional mean. Businesses can adjust on one or more margins at the same time. Leave of absence includesboth paid and unpaid.

Figure 7: Average percentage change in employment and change in sales.

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-100 (-100,-90] (-90,-80] (-80,-70] (-70,-60] (-60,-50] (-50,-40] (-40,-30] (-30,0] 0+

Percentage change in sales

Predicted mean

Note: The left panel groups the percentage change in sales into bins and computes the average percentage change inemployment for each bin. The right panel shows the average predicted change conditioning on sales bin from a linearregression that controls also for country, size, sector, and weeks before/after peak of the crisis.

20

Figure 8: Average predicted probability for each adjustment margin across size of the firm.

0.120.19 0.22

0.26 0.27

0.45 0.500.53

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Micro Small Medium Large Micro Small Medium Large

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Note: Average predicted probability for size from a Probit that controls for change in sales, dummies forcountry, size, sector, and weeks before and after the peak of the mobility shock.

Figure 9: Average predicted probability for each adjustment margin across sectors.

0.15 0.16 0.18 0.18 0.18 0.19 0.19 0.200.20 0.20

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Reduced hours

Note: Average predicted probability for sector from a Probit that controls for change in sales and dummies for country,size, sector, and weeks before and after the peak of the mobility shock.

21

Figure 10: Average predicted probability of laying off workers across predicted probabilities ofadjusting on the intensive margin.

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reducing hours of employees

Note: Predicted probabilities from Probit models that control for change in sales, and dummies for country, size, sector,and weeks before and after the peak of the mobility shock. The analysis first conditions on the probability on the x-axisand computes the average probability of businesses in that category in the y-axis.

22

Figure 11: Fraction of businesses in arrears or expected to fall in arrears.

612 13

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Unconditional mean

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ho b

elie

ve w

ill fa

ll in

6 m

onth

s)

TCD RUS IDN CYP GRC GEO BGR POL SVN ALB TUR ITA ZMB ROU ZWE GTM SLV XKX TGO NIC SDN GIN TZA PAK JOR MDA HND IND VNM NER GAB LKA SEN KEN MNG NPL BGD ZAF

Predicted mean

Note: Predicted probability from a Probit model that controls for country, size, sector, and weeks before and after thepeak of the mobility shock.

23

Figure 12: Available liquidity - Number of days during which establishment can cover its costs withcash on hand.

8 12 927 37

4125

47 45 4429

5232

49 38 40 36 42 4452

27

6546 54

59 59 61 61 6636

6883

48

87 82 6899 99

59

101

0

50

100

150

200

250

300

350

Day

s th

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tabl

ishm

ent c

an c

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ith c

ash

avai

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XKX GAB TUR SDN RUS GRC ZAF HND GTM TGO ROU MNG SLV ZWE TUN TCD GEO IDN NPL BRA MDA SVN NER ALB PAK ZMB ITA CYP GIN NIC BGR MDG LKA VNM POL BGD KEN TZA CIV SEN

Unconditional mean

4 7 821 27 28 29 30 31 33 33 33 34

35 37 38 38 39 40 40 41 44 4549 50 51 51 52 53 55

63 70 73 7577 84 85

100

168192

0

50

100

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350

Day

s th

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tabl

ishm

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an c

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ith c

ash

avai

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XKX GAB TUR SDN RUS GRC ZAF HND GTM TGO ROU MNG SLV ZWE TUN TCD GEO IDN NPL BRA MDA SVN NER ALB PAK ZMB ITA CYP GIN NIC BGR MDG LKA VNM POL BGD KEN TZA CIV SEN

Predicted mean

Mean 10th and 90th percentile

Note: The predicted mean comes from a linear regression that controls for country, size, sector, and weeks before andafter the peak of the mobility shock.

24

Figure 13: Probability of falling in arrears and available liquidity: Differences across businesses withdifferent sizes.

5350

45

36

0

10

20

30

40

50

60

Frac

tion

of b

usin

esse

s in

arre

ars

(now

or w

ho b

elie

ve w

ill fa

ll in

6 m

onth

s)

Micro (0-4) Small (5-19) Medium (20-99) Large (100+)

Fraction in arrears

5053

59

66

0

10

20

30

40

50

60

70

Day

s th

e es

tabl

ishm

ent c

an c

over

cos

ts

Micro (0-4) Small (5-19) Medium (20-99) Large (100+)

Available liquidity

Note: The left panel shows the predicted probability from a Probit that controls for country, size, sector, andweeks before and after the peak of the mobility shock. The right panel shows the predicted mean from alinear regression on the same controls.

25

Figure 14: Probability of falling in arrears and available liquidity: Differences across sectors.

3535

434546474849

5662

0

10

20

30

40

50

60

70

Frac

tion

of b

usin

esse

s in

arre

ars

(now

or w

ho b

elie

ve w

ill fa

ll in

6 m

onth

s)

Accomm Food prepserv

Const andutilities

Manuf Transp andstorage

Other serv Retail andwholesale

Info andcomm

Agro andmining

Fin serv

Fraction in arrears

48 5252 53

56 5659 60

6675

0

10

20

30

40

50

60

70

80

90

Day

s th

e es

tabl

ishm

ent c

an c

over

cos

ts

Food prepserv

Const andutilities

Manuf Transp andstorage

Other serv Accomm Retail andwholesale

Agro andmining

Info andcomm

Fin serv

Available liquidity

Note: The top panel shows the predicted probability from a Probit that controls for country, size, sector, and weeks beforeand after the peak of the mobility shock. The bottom panel shows the predicted mean from a linear regression on thesame controls.

26

Table 1: 10th and 90th percentile of the distribution of available liquidity in each country.

10th per-centile

90th per-centile

EAP IDN 0 84EAP MNG 7 112EAP VNM 0 195ECA ALB 7 98ECA ARM 0 70ECA BGR 0 196ECA CYP 7 168ECA GEO 0 84ECA GRC 14 84ECA ITA 14 154ECA MDA 0 56ECA POL 0 182ECA ROU 0 90ECA RUS 7 84ECA SVN 7 168ECA TUR 0 24ECA XKX 0 30LAC BRA 0 180LAC COL 7 280LAC GTM 7 84LAC HND 7 84LAC NIC 7 84LAC SLV 7 56

10th per-centile

90th per-centile

MNA TUN 0 90SAR BGD 0 180SAR LKA 0 140SAR NPL 0 90SAR PAK 0 180SSA CIV 14 112SSA GAB 3 20SSA GHA 0 90SSA GIN 28 112SSA KEN 0 365SSA MDG 20 180SSA NER 7 105SSA SDN 0 60SSA SEN 0 365SSA TCD 7 70SSA TGO 14 84SSA TZA 0 360SSA ZAF 0 60SSA ZMB 7 140SSA ZWE 14 84

27

Figure 15: Change in sales and available liquidity: number of days to cover costs and averagepredicted probability of falling in arrears.

0

50

100

150

Aver

age

num

ber o

f day

s th

at th

e bu

sine

ssca

n co

ver c

osts

with

cas

h av

aila

ble

-100 -50 0 50Percentage change in sales

.3

.4

.5

.6

.7

Aver

age

pred

icte

d pr

obab

ility

of fa

lling

into

arre

ars

or e

xpec

ted

to fa

ll in

the

com

ing

6 m

onth

s

-100 -50 0 50Percentage change in sales

Note: Predicted probability from Probit models that control for change in sales, and dummies for country, size, sector,and weeks before and after the peak of the mobility shock. The analysis first conditions on the variable on the x-axis andcomputes the average over businesses in that category in the y-axis.

Figure 16: Fraction of businesses across responses to the shock.

34

17

5

21

0

5

10

15

20

25

30

35

Increased useof digitalplatforms

Investment indigital

solutions

Innovationinto healthproducts

Other productsinnovation

Note: Unconditional mean.

28

Figure 17: Average predicted probability of implementing change.

0.18

0.29

0.36

0.41

0.13

0.17

0.26

0.09 0.09 0.10

0.22 0.23 0.23

0.00

0.10

0.20

0.30

0.40

0.50

Micro Small Medium Large Micro Small Medium Large Micro Small Medium Large Micro Small Medium Large

Increased useof digitalplatforms

Investment indigital solutions

Innovationinto healthproducts

Other productsinnovation

Note: Average predicted probability for size from a Probit analysis that controls for country, size, subsector,and weeks before and after the peak of the mobility shock.

Figure 18: Correlation between average predicted probability of implementing each solution.

0

.1

.2

.3

.4

Aver

age

of th

e pr

edic

ted

prob

abilit

y of

inve

stin

g in

dig

ital s

olut

ions

0 .2 .4 .6 .8 1Predicted probability of increasing

use of digital technology

0

.1

.2

.3

.4

Aver

age

of th

e pr

edic

ted

prob

abilit

y of

inve

stin

g in

dig

ital s

olut

ions

0 .05 .1 .15 .2 .25Predicted probability of introducing

non-health products innovations

0

.1

.2

.3

.4

Aver

age

of th

e pr

edic

ted

prob

abilit

y of

inve

stin

g in

dig

ital s

olut

ions

0 .1 .2 .3 .4Predicted probability of introducing

health products innovations

Note: Average predicted probabilities from Probits that control for country, size, sector, and weeks before and after thepeak of the mobility shock. The analysis first conditions on the variable on the x-axis and computes the average overbusinesses in that category in the y-axis.

29

Figure 19: Average expected sales growth for the coming 6 months and average uncertainty of theprediction. Differences across sizes.

-10

0

10

20

Micro (0-4) Small (5-19) Medium (20-99) Large (100+)

Average expectation Average std. dev.

Note: Predicted expectation and predicted standard deviation for size from a linear regression controlling forcountry, size, sector, and weeks before and after the peak of the mobility shock.

Figure 20: Average expected sales growth for the coming 6 months and average uncertainty. Differ-ences across sectors.

-20

-10

0

10

20

Food prepserv

Accomm Transp andstorage

Other serv Retail andwholesale

Const andutilities

Manuf Agro andmining

Info andcomm

Fin serv

Average expectation Average std. dev.

Note: Predicted expectation and predicted standard deviation for sector from a linear regression controlling for country,size, sector, and weeks before and after the peak of the mobility shock.

30

Figure 21: Average expectations and uncertainty about the future and change in sales.

-80

-60

-40

-20

0

20

Aver

age

expe

cted

gro

wth

in s

ales

for t

he n

ext 6

mon

ths

-100 -50 0 50Percentage change in sales the 30 days prior to the interview

0

10

20

30

40

Aver

age

stan

dard

dev

iatio

n of

the

pred

ictio

n

-100 -50 0 50Percentage change in sales the 30 days prior to the interview

Note: The analysis first conditions on the variable on the x-axis and computes the average over businesses in that categoryin the y-axis.

Figure 22: Average expected sales growth for the coming 6 months and average standard deviationof the prediction. Differences across severity of the shock.

-40

-20

0

20

40

Quartile 1in mobility drop

Quartile 2in mobility drop

Quartile 3in mobility drop

Quartile 4in mobility drop

Average expectation about sales growth

-40

-20

0

20

40

Quartile 1in mobility drop

Quartile 2in mobility drop

Quartile 3in mobility drop

Quartile 4in mobility drop

Average standard deviation

Note: Average predicted expectation and predicted standard deviation for severity of the shockfrom a linear regression controlling for severity, size, sector, and weeks before and after the peakof the mobility shock. To measure severity, countries in the sample are divided into quartilesbased on the magnitude of the drop in mobility at the peak of the crisis.

31

Figure 23: Fraction of the variation in expectations and uncertainty explained by variation in eachobservable.

38.85

26.39

18.94

3.35

0.74

0

10

20

30

40

Countryeffects

Weeksbefore/after

the peak

Severity ofthe crisis

Subsectoreffects

Sizeeffects

Expectations on sales growth

20.42

5.85

0.441.48 0.87

0

10

20

30

40

Countryeffects

Weeksbefore/after

the peak

Severity ofthe crisis

Subsectoreffects

Sizeeffects

Uncertainty of the prediction

Note: R-squared from separate linear regressions of the dependent variable (expectations oruncertainty) to each observable.

Figure 24: Distribution of the expectation for sales growth and the uncertainty of the prediction.

0

.005

.01

.015

.02

Den

sity

-100 -50 0 50 100Expectations on sales growth for the coming 6 months

Variation betweencountry-size-subsectoraverages

Variation withincountry-size-subsectorgroups

Expectations on sales growth

0

.01

.02

.03

.04

.05

Den

sity

-40 -20 0 20 40 60Standard deviation of the prediction

Variation betweencountry-size-subsectoraverages

Variation withincountry-size-subsectorgroups

Uncertainty of the prediction

Note: The between variation shows the distribution of the dependent variable in each country-size-sector group. Thewithin variation shows the distribution in the dependent variable after removing the country-size-sector mean. In theleft panel the dependent variable is the expectation for sales growth for the coming 6 months; in the right panel thedependent variable is the standard deviation of the prediction.

32

Figure 25: Average percentage change in employment and expectations about the future.

-20

-15

-10

-5

0

Perc

enta

ge c

hang

e in

em

ploy

men

t

-100 -50 0 50 100Average expected growth in sales for the next 6 months

-20

-15

-10

-5

0

Perc

enta

ge c

hang

e in

em

ploy

men

t

0 20 40 60Average standard deviation of the prediction

Note: The analysis first conditions on the variable on the x-axis and computes the average over businesses in that categoryin the y-axis.

33

B. Description of the data

B.1 Countries covered in the analysis

Table A1: Countries covered in the analysis

Number of firms

EAP IDN BPS 865EAP KHM BPS 501EAP PHL BPS 63,193EAP VNM BPS 497EAP MNG WBES 330ECA BGR BPS 940ECA POL BPS 1,335ECA ROU BPS 937ECA TUR BPS 1,424ECA XKX BPS 2,083ECA ALB WBES 374ECA ARM WBES 116ECA CYP WBES 236ECA GEO WBES 684ECA GRC WBES 598ECA ITA WBES 726ECA MDA WBES 357ECA RUS WBES 1,277ECA SVN WBES 409LAC BRA BPS 1,981LAC COL BPS 3,684LAC GTM WBES 341LAC HND WBES 326LAC NIC WBES 327LAC SLV WBES 705

Number of firms

MNA DZA BPS 427MNA TUN BPS 3,659MNA JOR WBES 535SAR AFG BPS 386SAR BGD BPS 900SAR IND BPS 715SAR LKA BPS 500SAR NPL BPS 504SAR PAK BPS 1,293SSA CIV BPS 529SSA GAB BPS 865SSA GHA BPS 3,928SSA KEN BPS 1,752SSA LBR BPS 474SSA MDG BPS 865SSA MLI BPS 292SSA SDN BPS 1,376SSA SEN BPS 487SSA TGO BPS 157SSA TZA BPS 978SSA ZAF BPS 2,022SSA GIN WBES 149SSA NER WBES 147SSA TCD WBES 147SSA TGO WBES 145SSA ZMB WBES 571SSA ZWE WBES 935

34

B.2 Survey period in context

Figure A1: Survey period in context.

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TCD

Period of interviewStringency oflockdownrestrictions

Mobility aroundtransit stations

Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions.The mobility trends are smoothed using 7-day moving averages.

35

Figure A1: Survey period in context–contd.

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