A shot in the arm: Economic support packages and firm performance during COVID-19

Deniz Igan; Ali Mirzaei; Tomoe Moore

doi:10.1016/j.jcorpfin.2022.102340

. 2022 Dec 14;78:102340. doi: 10.1016/j.jcorpfin.2022.102340

A shot in the arm: Economic support packages and firm performance during COVID-19

Deniz Igan ^a,^1,², Ali Mirzaei ^b,^⁎, Tomoe Moore ^c

PMCID: PMC9747690

Abstract

We use firm-level data to provide some early evidence on the effectiveness of COVID-19 economic policy packages. Our empirical strategy relies on the varying degree of vulnerability to the pandemic across industries. We find a robust association of fiscal support with changes in firm performance indicators (as measured by sales-to-assets ratio, profit margin, interest coverage ratio as well as probability of default) in pandemic-prone sectors. We also observe marginal effects of monetary policy on the sales-to-assets ratio and of foreign exchange intervention on the interest coverage ratio in the hardest-hit firms. These results broadly survive a battery of exercises to address endogeneity. Additionally, we show that firms with a better financial position are more likely to take advantage of the support packages to withstand the pandemic shock. Overall, this preliminary evidence suggests that policy interventions have bought time for the hardest-hit industries, by supporting turnover and improving liquidity.

Keywords: Economic support, Pandemic-prone, COVID-19, Policy effectiveness

1. Introduction

COVID-19 prompted authorization and implementation of large economic policy packages around the world, understandably so since a crisis like no other necessitated a response like no other. These packages involved a combination of fiscal, monetary, financial, and capital-account policies. An important question for academics and policymakers alike is how effective these measures have been, especially by helping those sectors most in need.

In this paper, we use firm-level data to provide some answers to this question. Our empirical strategy relies on the varying degree of vulnerability to the pandemic across industries. Firms operating in sectors that rely more on face-to-face interactions when producing goods or providing services are contact-intensive, and thus have a larger portion of jobs that cannot be done at home. As a result, they are more vulnerable to non-pharmaceutical interventions (such as social distancing or lockdown measures) that aim to stop or slow the spread of the virus. By the same token, economic policy support would aim to target these worst-hit industries. Contrary to standard economic crises, stimulating real activity in a crisis like COVID-19 is not only more challenging – given the complex nature of the shock combining supply, demand and uncertainty factors – but could also be undesirable, in particular for the contact-intensive sectors as this would go against the needed public health containment measures. That said, economic policies would try to curb the Keynesian feedback loop triggered by the abrupt and substantial loss of income in firms due to the shock, i.e. to minimize spillovers and dislocation costs associated with business failures as well to ensure that liquidity is sufficient enough to avoid unnecessary bankruptcies. One yardstick of success then is whether policy actions have given more of a lift to these sectors relative to others, especially with respect to supporting firms' liquidity and capital.

To measure how prone different firms are to non-pharmaceutical interventions, we rely on a proxy, namely, “distancing” measures that have been developed in the wake of the pandemic (Kóren and Petö, 2020; Dingel and Neiman, 2020; Hensvik et al., 2020) and utilized also by other researchers (e.g., Pagano et al., 2020; Laeven, 2022). These measures capture the degree to which jobs require customer contact, teamwork, etc. at the sectoral level, as the share of workers in contact-intensive occupations. We first confirm that firms in sectors with higher distancing indices performed worse than the others in the same country, and especially so when the pandemic hit to their country was more severe (as captured by the stringency of the lockdown measures, which is highly correlated with the reported number of COVID-19 cases and deaths).

We then examine whether performance metrics (efficiency, profitability, liquidity, and survival) in firms operating in more pandemic-prone sectors have fared better during the first year of the pandemic (2020), if they were located in countries that deployed more comprehensive economic support packages (covering fiscal, monetary and foreign exchange). In other words, if economic policies during the COVID-19 crisis portray an effective action in response to the pandemic, we would expect this to be reflected in relatively better performance by firms that are more pandemic-prone compared to those that are less so. Our main specification, thus, focuses on the cross-sectional differences in firm performance depending on how sensitive to distancing a sector is, controlling for sector and country fixed effects as well as firm observables such as size, age and cash flow.

We find a robust positive association of fiscal support with efficiency and profitability in pandemic-prone sectors: sales-to-assets ratio (i.e. efficiency) and profit margin in firms that are more sensitive to distancing have grown faster when the fiscal support is larger. Furthermore, we observe positive effects of fiscal packages on firm liquidity and survival: interest coverage ratio (i.e. liquidity) increased while probability of default decreased disproportionately more in pandemic-prone sectors.

Economically, moving from a country at the 10th percentile of the distribution of fiscal support (for example, Sri Lanka) to a country at the 90th percentile (for example, Germany), the change in sales-to-asset ratio of firms in more pandemic-prone sectors is about 2% more than their less pandemic-sensitive counterparts from 2019 to 2020. This is consistent with Laeven and Valencia (2013), who report that fiscal policy disproportionately boosted the growth of firms that were more dependent on external financing in the context of the global financial crisis. Aghion et al. (2014) also find that counter-cyclical fiscal policy supported the growth of manufacturing industries across 17 OECD countries over the period 1980–2005.

Additionally, we find that monetary support is marginally associated with an improvement in the sales-to-assets ratio. Prior to the COVID-19 outbreak, monetary policy stance in major economies was already accommodative, raising questions about central banks' ability to confront the next shock (Gagnon and Collins, 2019). It appears that further easing has proved to be still effective in improving revenues for firms that were hit hardest by the pandemic. In this respect, the monetary policy transmission mechanism seems to have remained functional during the pandemic, as opposed to the case of the global financial crisis when banks were capital constrained and the lending channel was substantially weakened (Laeven and Valencia, 2013).

By contrast, we do not find a robust significant relationship between monetary policy easing and the other firm performance indicators such as liquidity and probability of default. This is in line with the argument that monetary policy may not be particularly well-suited to deal with the implications of COVID-19 because of unsuitability of monetary policy in addressing supply-side shocks and the difficulty to target support to specific sectors that are affected first and foremost by non-pharmaceutical interventions (Chen et al., 2020).

Foreign exchange interventions appear to arrest the decline of interest coverage ratio during the pandemic for the hardest-hit (although this finding is not as robust as those on fiscal and monetary policy measures). One plausible explanation for this finding may be that liquidity in pandemic-prone sectors such as recreation services and tourism are highly responsive to changes in the value of the domestic currency against foreign currencies.

Our findings are robust to a battery of checks, including various strategies to address endogeneity issues and using alternative measures of distancing. We also verify that the results are broadly unbiased to the extent that we remove certain sectors or industries from the sample. Additional analysis suggests that support packages are generally more effective in larger firms and firms entering the crisis with better liquidity, profitability and capital positions. The latter finding provides some comfort that policy interventions in response to this entirely exogenous shock may not have been distortive.

Our paper is linked to two strands of the literature. Firstly, it relates to those studies investigating the effect of a crisis on corporate performance. Many recent additions to this strand examine the real impact of the 2008–09 global financial crisis (see, among others, Duchin et al., 2010; González, 2015; Demirgüç-Kunt et al., 2020), adding to studies that look more broadly at banking crises and sudden stops. Given that the COVID-19 crisis is still unfolding, researchers have so far mostly examined the impact of the pandemic on stock market performance (e.g., Alfaro et al., 2020; Remelli and Wagner 2020; Fahlenbrach et al., 2021). Rather closely related to our analysis, Pagano et al. (2020) find that the impact of COVID-19 on stock performance was more severe for firms that operate in sectors that are more vulnerable to social distancing. Ding et al. (2021) report that the adverse impact of the pandemic on stock returns is more pronounced for those firms that have more anti-takeover devices, lower social and corporate responsibility scores, and that depend more heavily on global supply chains. Papanikolaou and Schmidt (2020) reveal that expected revenue growth of those sectors in which a higher fraction of the workforce is not able to work remotely declined significantly during the COVID-19 pandemic. Glover et al. (2020) document that the impact of the COVID-19 pandemic is more serious among younger generations working in vulnerable sectors. Our analysis adds to these studies by providing early evidence that balance sheet and income indicators also show that the pandemic has taken a heavier toll on firms operating in sectors that are more sensitive to distancing.

Secondly, we contribute to the literature that assesses the effectiveness of government policy measures during a crisis (see, for instance in the context of the global financial crisis, Laeven and Valencia, 2013; Norden et al., 2013). By focusing on the differences across sectors, we also build on studies investigating the channel through which the real effect of a crisis materializes. See, for example, Claessens et al. (2012), Chaney et al. (2012), Chodorow-Reich (2014) and Giroud and Mueller (2017) with regard to the global financial crisis, and Leibovici et al. (2020) and Laeven (2022) with regard to COVID-19. Our study differs from these papers since it focuses on the effectiveness of government economic policies during the COVID-19 pandemic, rather than the transmission of the shock itself, by testing whether firms in pandemic-prone sectors performed disproportionately better, if they are domiciled in countries with more comprehensive or larger economic support packages. Closely related to our analysis, Demirgüç-Kunt et al. (2021) examine the impact of policy measures taken in response to the COVID-19 pandemic but only on the performance of the banking sector. They find that, while policy interventions in the form of liquidity support, borrower assistance and monetary easing, in general, mitigate the adverse impact of the crisis, this is not the case for all banks, nor in all circumstances.

The rest of the paper is organized as follows. Section 2 summarizes the potential channels through which policy support could help firm performance in the hardest-hit sectors. Section 3 lays out the methodology and the data. Section 4 presents the findings. Section 5 concludes.

2. Potential transmission channels

2.1. Fiscal support

Fiscal support packages implemented during the pandemic aimed to support businesses and households at a time economic activity was intentionally curtailed to slow the spread of the virus and allay the burden on public health systems. Specific measures included tax cuts, cash handouts, and social welfare payments on the demand side and tax relief measures and guarantees for access to credit on the supply side (Padhan and Prabheesh, 2021).

There are various ways fiscal measures could help firms. First, corporate tax breaks could lessen the decline of profitability. Tax payment deferral is a common measure, in particular, in less developed countries (OECD, 2020a). Yet, this has limited benefit to the pandemic-prone sectors, as they have hardly generated profits and rather suffered from losses. In this instance, alternative measures such as loss carry-back tax provisions can be more effective (OECD, 2020a; Makin and Layton, 2021). This allows firms to claim the losses against taxable profits in previous years, which potentially reduces the losses incurred during the pandemic. Such provisions have been introduced in some countries for the 2020 tax year.

Second, temporary increases in thresholds for low-value asset write-offs and depreciation allowances could mitigate the decline of investment, since they effectively reduce the tax liability of firms. The benefit should be felt across all sectors. However, if the contact-intensive sectors have to alter their business structure in order to survive the pandemic and if this requires investment, then this support should be more advantageous to these sectors. For instance, restaurants may adapt their services away from in-person dining and toward takeaway and delivery of food, or redesign the layout of the premises to maintain distance among customers. Such changes necessitate new investment and could be supported by investment incentives through temporary changes in the tax code. They would help maintain sales and liquidity.

Third, direct government subsidies such as furlough schemes curb the massive employment loss due to lockdowns. Many countries have helped the hardest-hit sectors retain their workers by providing income support to employees whose working hours have been curtailed or who have been temporarily laid off (OECD, 2020b). The scheme enables firms to maintain the match with their employees and to preserve workers' talent and experience. It also deters a deterioration on the production side, since firms are able to quickly resume operations when the lockdown is eased.

Fourth, many heavily affected businesses have experienced a sharp decline in liquidity. The most common instrument to deal with this decline, especially among developed countries, has been loan guarantee schemes, where the government guarantees all or part of the bank loans granted to eligible businesses (OECD, 2020a). Other measures have included interest-free loans and cash grants. These measures are typically able to target or prioritize those businesses adversely affected by the pandemic, alleviating cash flow difficulties, enabling firms pay suppliers or creditors and, hence, avoid default or bankruptcy.

Finally, subsidies to consumers for consumption of certain goods and services could also help the suppliers of such goods and services. This can target the hardest-hit sectors, for example, some governments provided subsidies for eating out or domestic travel.

In general, the delivery speed of support should be a key consideration. For instance, countries may find it timelier to provide loan guarantees, business grants, or wage subsidies rather than tax measures. The effect of the latter is only felt at the end of the tax year. In order to achieve prompt delivery, fiscal aid may also be provided broadly across all sectors rather than targeting certain sectors, but then this is subject to taxation of regular profit. This would imply that adversely affected firms are able to keep the full amount of support by documenting the hit to their profits, while the firms whose economic circumstances have been affected the least would return some of the support via the tax system (Mankiw, 2020; Marron, 2020).

There is, however, potentially an unintended side effect of fiscal support. Higher public debt fueled by the pandemic may harm business and household confidence, creating uncertainty about how public debt would be repaid (OECD, 2020a). To the extent that firms perceive higher public debt to imply higher corporate taxes in the future, it would be reflected as a negative repercussion on the firms' performance. Note also that wage subsidy programs implemented in some countries may prove to be an innovative yet extremely costly way of sustaining business activities and employment, accelerating government debt. Besides, this support may simply delay the inevitable re-deployment of labor away from unviable firms and may not bring about a particular benefit to the vulnerable firms.

2.2. Other policy support

With other policy actions such as monetary easing and foreign exchange intervention, unlike fiscal support, the channels of transmission are not as clear. This is partly because it is difficult to target or prioritize specific sectors or firms that have been bruised by the pandemic. Nevertheless, there is some scope for these measures to alleviate the adverse effect of COVID on these sectors.

Expansionary monetary policy may mitigate the effects of COVID-19 on the hardest-hit sectors if firms in these sectors come under pressure from a tightening of credit conditions. For instance, a fall in interest rates may enable vulnerable sectors to ease liquidity concerns and reduce the probability of default.

During the pandemic period, most economies have experienced exchange rate volatility and often intervened in the foreign exchange market. Vulnerable firms engaged in tourism or international trade may disproportionately benefit from such intervention, mitigating a decline of profits and strengthening the ability to meet debt obligations.

3. Methodology and data

3.1. Empirical strategy

Our main empirical strategy is to examine whether firms in industries that are more pandemic-prone perform disproportionately better during the COVID-19 outbreak, if they happen to be located in countries that have larger government support packages. In other words, if policy measures are to help shield firm performance, then one would expect them to have a larger effect on sectors where a significant share of employment is affected by social distancing measures. This inference can be empirically tested by estimating an econometric model in which the effect of government policies on firms is allowed to differ depending on how pandemic-prone is the industry to which the firm belongs.3 Thus, our model specification is given by:

∆ y_{ic, COVID} = ϑ_{j} + ϑ_{c} + \emptyset . {Distancing}_{j} \times {Policy}_{c} + τ . X_{ic, Pre} + ε_{ic, COVID}

(1)

where i stands for firm, j for sector, and c for country. This is a cross-sectional regression where ∆y _{ic, COVID} is the measure of change in performance indicators for firm i in country c between 2020—the latest data available—and 2019—a year prior. Following Claessens et al. (2012), we use the changes in firm-level performance indicators. Given that COVID-19 began to spread in many countries and was declared a pandemic in 2020Q1, the pattern of change in performance indicators is deemed to be due to the pandemic.

We employ four firm performance indicators: (i) change in asset turnover [∆(SaleA)], as measured by the sales-to-total-assets ratio; (ii) change in profit margin [∆(ProfM)], as measured by the net-profit-to-total-revenue ratio; (iii) change in interest coverage [∆(IntrC)], as measured by earnings before interest and tax divided by interest expenses; and (iv) change in probability of default [∆(ProbD)], as measured by the default risk of publicly listed firms by quantitatively analyzing numerous covariates (see Section 3.2 for the details).

Following Claessens et al. (2012), we employ the changes in sales-to-asset ratio and profit margin to investigate the impact of economic support packages on firm efficiency and profitability. In addition, crises have severe effects on firms' financial health in two aspects (Carletti et al., 2020): draining cash generation and liquidity that is necessary for functioning of firms and evaporating capital. Since during the public health crisis firms find it difficult to generate cash and thus could be expected to default on some obligations, we use the interest coverage ratio to determine whether policy measures help a company to pay interest on its outstanding debt. Also, following Gaganis et al. (2020) and Igan et al. (2022), we consider whether a firm will be able to continue operations, i.e. the probability of default. This captures the likelihood of a default over a particular time horizon, reflecting not only the market-based and accounting-based firm-specific attributes but also the macro-financial environment (Duan et al., 2012). Overall, the first three variables intend to gauge whether government policies help firms in maintaining cash flow and, hence, improving liquidity, and the fourth variable aims to capture the impact on firm survival.

Policy_c is a vector of variables that represent the economic support package in country c. We employ three policy variables: (i) cumulative fiscal support expressed in percentage of GDP, (ii) cumulative change in the monetary policy rate expressed in basis points, and (iii) interventions in foreign exchange markets with 0 for no intervention and 1 for intervention. All policy measures are computed over the period January 31st, 2020 (week 1) to December 4th, 2020 (week 43). We investigate the change in the performance of firms over the period 2019–2020 in response to government polices during the period from January to December 2020. We believe that this period represents the most important initial stage of the spread of the crisis, when countries declared the bulk of their policy packages. This is also the period of the collapse of international trade due to non-pharmaceutical public health interventions such as full (or partial) lockdowns and turmoil in financial markets as expectations were quickly revised to take the impact of the pandemic fallout on the global economy into account.

Distancing_j is industry j's degree of sensitivity to a pandemic, computed as the share of industry employment affected by social distancing at the three-digit NAICS level (created by Kóren and Petö, 2020; we describe this proxy further in the following subsection).

X_{ic, Pre} is a vector of firm-level explanatory variables, computed as of 2019. Note that, because of the pure cross-sectional nature of our empirical strategy, we enter all firm-level control variables as pre-determined (as do Laeven and Valencia, 2013). We first consider the following five variables: (i) size (Size), measured as the natural log of total assets; (ii) age (Age), calculated by subtracting the firm's incorporation year from 2020; (iii) cash holdings (CashA), computed as the ratio of cash and cash equivalents to total assets; (iv) investment in R&D (RD_A), measured by the ratio of R&D investment to total assets; and (v) a dummy for private firms (Private). These controls are informed by the literature on the determinants of firm performance. Small firms tend to perform worse than their larger counterparts during a crisis (Gandhi and Lustig, 2015). Younger firms face more constraints (Beck et al., 2006; D'Souza et al., 2017). Firms with larger cash holdings tend to be more resilient during a crisis whilst firms with better growth potential tend to invest more in R&D (Bates et al., 2009). Finally, privately-held firms may be different from their listed counterparts along the dimensions we investigate. For instance, Hall et al. (2014) document that public companies hold less cash given their greater access to capital markets as compared to privately-held firms. In addition to these five variables, we also include lags of the following variables as additional regressors: (vi) SaleA, to control for efficiency in generating revenue for a given level of assets; (vii) ROA, to control for pre-crisis differences in levels of profitability; (viii) IntrC, to control for ability to cover current interest payments with available earnings; and (ix) EqitA, to control for leverage given that more highly leveraged firms may face difficulty raising funds during a crisis (Giroud and Mueller, 2019). Overall, all these nine firm-level control variables are rather common in the literature (e.g., Burns et al., 2017; Barbiero et al., 2020; Demirgüç-Kunt et al., 2020).

The main variable of interest is the interaction term Distancing _j × Policy _c. The coefficient ∅ measures the difference between performance in pandemic-prone sectors in countries with high and low economic support packages. A positive and significant point estimate of ∅ indicates that the vulnerable industries in countries with higher levels of government economic response did not suffer as much from the pandemic. Note, though, that we expect a negative ∅for the probability of default.

ϑ_j refers to a vector of sectoral dummies (at three-digit NAICS level) to control for sector-specific factors that could affect cross-sector performance differentials. ϑ _c are country dummies that account for time-invariant country-specific features that might drive cross-country differences in firm activity, such as the institutional environment. This set of fixed effects absorbs all observable and unobservable time-invariant variations across sectors and countries. Also, they subsume the direct level effects of social distancing and economic policies, namely the Distancing and Policy variables in Eq. (1). By including this set of fixed effects, our identification is obtained by looking at the differential performance of two otherwise identical firms operating in more versus less pandemic-prone sectors.

Eq. (1) is estimated with ordinary least squares (OLS). Residuals from OLS estimations may be correlated across countries, resulting in biased standard errors. Thus, following Demirgüç-Kunt et al. (2020), we cluster standard errors at the country level. An advantage of our empirical strategy is that it incorporates information about heterogeneity across countries in initiating and implementing economic support packages.

One concern is that Eq. (1) is subject to the problem of endogeneity. First, any association between government policies and firm performance may be attributable to omitted variables. Or, it could be that the effect of a particular policy is attributed to another because of their simultaneous implementation. Second, firm performance during a crisis may affect policy responses to the crisis, indicating the possibility of reverse causality.

Our empirical setup provides some leeway in alleviating these two endogeneity issues. By including all policy measures at once, we reduce the issue of simultaneity while our use of sector and country fixed effects mitigates the issue of omitted variable bias. In addition, we control for other potential channels through which policy measures may affect firm performance so as to gain more confidence that distancing remains a relevant channel for policy measures to influence firm performance. Also noteworthy is that endogeneity may even play against the chances to reject the null hypothesis: countries that are affected more by COVID-19 — that is, where pandemic-prone sectors are large and fare particularly bad — may be more likely to deploy large policy packages, giving rise to a negative correlation (which is the opposite of what we find). Nevertheless, we admit that the issue of endogeneity may continue to exist, hence we address this point by conducting several exercises in Section 4.2.4

3.2. Data sources

Applying the empirical strategy laid out in Section 3.1 requires measures of firm performance, sectoral pandemic sensitivity, and economic policy actions. This subsection describes the process of compiling these data.

3.2.1. Firm performance

Firm-level data come from the ORBIS database by Bureau Van Dijk, which provides information on balance sheets and income statements for more than 40 million listed and private companies from more than 100 countries worldwide. As one of the most comprehensive databases of firm-level information, it has been increasingly used in academic research (e.g. Frijns et al., 2016; Baumohl et al., 2019; Demirgüç-Kunt et al., 2020; Barbiero et al., 2020; Cathcart et al., 2020).

We obtain data for 2019 and 2020 (the latest year available at the time of conducting this analysis). This enables us to calculate the change in firm performance during the COVID-19 pandemic. We initially select all firms that belong to the nonfinancial corporate sector, excluding financial firms (identified as firms with NAICS2017 code of 52). We drop firms in sectors with no data on the distancing variable, which are “management of companies and corporations,” “public administration,” and “unclassified establishments” (NAICS2017 codes of 55, 92 and 99, respectively). Also, countries with no data on all of the policy measures of interest are excluded. In addition, we drop offshore financial centers. Following Demirgüç-Kunt et al. (2020), we further restrict our sample to countries with a minimum of 20 firms (with available information for 2020). Last but not least, we drop the United States to avoid any mechanical endogeneity between this variable and firm performance since U.S. data are used to construct the distancing variable.5 Given our interest in evaluating the effectiveness of policy measures in improving firm performance during COVID-19, we focus our baseline analysis on firms that are present in both before and during the crisis. We thus clean the dataset further by excluding all firms with no data available on sales as well as on our main firm-level control variables. This means we focus on the effects of policy measures on the intensive margin only.

As a result, 28,915 firms from 80 countries survive the filtering criteria.6 The number of firms for each country is in Table S1 in the Online Supplementary Appendix (OSA). Note that, after imposing all the criteria, we end up with one country (Mongolia) with less than 20 firms. We confirm the robustness of our results to excluding this country. The number of firms in our dataset varies by country. On average, each country has about 361 firms with available data. We reduce the influence of outliers by winsorizing all dependent variables at the 1st and 99th percentiles.

Following Gaganis et al. (2020) and Igan et al. (2022), the data on probability of default is from the Credit Research Initiative (CRI) of the National University of Singapore. We use a prediction horizon of 1 month. Note that the data on probability of default is not available for all 80 countries and/or 28,915 firms. We have data only for 10,023 firms.

3.2.2. Pandemic sensitivity

Kóren and Petö (2020) estimate each sector's contact intensity, using pre-pandemic data from the Occupational Information Network (O*NET) survey. Specifically, they use information for 809 occupations from the 2010 Standard Occupational Classification System to compute, for each NAICS three-digit code, the share of workers whose job requires a high level of three occupational characteristics: customer contact, teamwork and physical presence.7 They end up reporting two proxies. The first one is a measure of “communication” intensity that incorporates teamwork-intensive and customer-facing activities. The second proxy, “overall” incorporates the physical presence dimension to the first.

In our baseline, we use communication intensity as the metric for Distancing. This arguably captures the nature of non-pharmaceutical interventions put in place in response to COVID-19 due to the fact that shelter-in-place or stay-at-home orders were gradually lifted allowing industries that primarily rely on physical presence (e.g., construction, factories) to get back to work whilst travel restrictions, bans on public gatherings and specific business closures (e.g., gyms and restaurants) remained. We confirm the general robustness of our results to the use of “overall” index. We assume that distancing is an intrinsic characteristic of a sector and, thus, indices derived using U.S. data can be used for the same sector across all countries.

3.2.3. Policy measures

The data source for policy responses is the IMF's Policy Tracker.8 Launched right around the time COVID-19 was declared a pandemic by the WHO on March 11, 2020, this tracker relies on responses by individual country teams to a survey designed by IMF staff. The survey seeks responses on all policy actions taken by the authorities in a country covering fiscal, monetary, external, and financial policies. The survey includes the size of the intervention for fiscal or monetary policy actions. For external and financial policies, information is gathered as a binary variable, with 0 denoting no action and + 1 an intervention.9

In our analysis, we use three policy measures: (i) FIS, cumulative fiscal support expressed in percentage of GDP, (ii) MP_BP, cumulative change in the monetary policy rate expressed in basis points, and (iii) FXI, the interventions in foreign exchange markets. All policy measures are computed over the period January 31, 2020 (week 1) to December 4, 2020 (week 43) to overlap with the firm-level data we have.

The intensity of economic support packages varies considerably (see Figs. S1a and S1b in the OSA for more details). By including countries with no or less strong policy responses as well as those with proactive intervention in our dataset, we reduce concerns that the results may be driven by selection bias.

One shortcoming of this database is the lack of granularity as to the exact measures implemented, in particular, under the fiscal support packages. While a more in-depth analysis would be desirable, it would be better conducted in a set of relatively homogeneous country sample (if not, within a single country). We leave this for future research. That said, we do confirm the robustness of our findings using policy measures from alternative sources (see Section 4.4).

3.2.4. Other variables

Additional data are retrieved from standard databases such as the World Bank's World Development Indicators (WDI). Appendix Table A1 provides the details, in addition to those pertaining to the construction of the main variables that we employ in the analysis.

3.3. Descriptive statistics

Table 1 shows the summary statistics of change in the asset turnover ratio [∆(SaleA)], change in profit margin [∆(ProfM)], change in the interest coverage ratio [∆(IntrC)] and change in the probability of default [∆(ProbD)] over the period from 2019 to 2020. The relation between firm performance and Distancing at the sector level is presented in Panel A. Retail Trade (NAICS 44–45) and Health Care and Social Assistance (NAICS 62) have the highest share of communication-intensive jobs, exceeding 50%. This is followed by Art, Entertainment, and Recreation (NAICS 71) and Accommodation and Food Services (NAICS 72) at around 40 to 44%. These two sectors suffered from the largest decline in sales to asset by 20 to 24% and also in profit margin, dropping by 18 to 24%. We observe the same pattern for the other two indicators of firm performance, change in interest coverage ratio and probability of default, for these two sectors. This heterogeneity across sectors is important to understand the effect of the pandemic and associated policy measures.

Table 1.

Summary statistics.

Panel A: Change in firm performance and distancing by sector
NAICS (2d)	NAICS (3d)	Sub sector			Change in firm performance
NAICS (2d)	NAICS (3d)	Sub sector	Sector	Obs	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	Distancing
11	113–115	3	Agriculture, Forestry, Fishing and Hunting	178	−0.042	−0.020	−3.054	−0.011	0.147
21	211–213	3	Mining, Quarrying, and Oil and Gas Extraction	1012	−0.060	−0.030	−2.146	0.004	0.196
22	221	1	Utilities	1013	−0.044	−0.003	1.548	0.002	0.200
23	236–238	3	Construction	1022	−0.065	−0.021	−1.966	0.002	0.164
31–33	311–339	20	Manufacturing	14,504	−0.073	−0.006	1.188	0.002	0.100
42	423–425	3	Wholesale Trade	2039	−0.108	−0.009	−3.900	0.002	0.154
44–45	441–454	12	Retail Trade	978	−0.152	−0.020	1.016	0.007	0.642
48–49	481–493	9	Transportation and Warehousing	1038	−0.106	−0.050	1.427	0.003	0.134
51	511–519	6	Information	1683	−0.071	−0.022	−3.334	0.002	0.146
53	531–533	3	Real Estate and Rental and Leasing	1293	−0.029	−0.088	−0.952	0.005	0.216
54	541	1	Professional, Scientific, and Technical Services	2016	−0.091	−0.009	−2.038	−0.0001	0.120
56	561–562	2	Administrative and Support and Waste Management …	901	−0.150	−0.042	−6.801	0.008	0.264
61	611	1	Educational Services	126	−0.149	−0.056	−7.885	0.0004	0.190
62	621–624	4	Health Care and Social Assistance	298	−0.084	−0.018	−3.995	0.005	0.596
71	711–713	3	Arts, Entertainment, and Recreation	234	−0.200	−0.179	−14.364	0.003	0.405
72	721–722	2	Accommodation and Food Services	458	−0.239	−0.238	−17.783	0.011	0.440
81	811–813	3	Other Services (except Public Administration)	122	−0.119	−0.064	−11.221	0.005	0.351

Panel B: Summary statistics of main variables
Variable	Obs	Mean	Std	p25	Median	p75
Change in firm performance (∆y_ic,COVID)
∆(SaleA)	28,915	−0.08	0.25	−0.16	−0.05	0.02
sectors more pandemic prone	13,694	−0.10	0.27	−0.17	−0.05	0.01
sectors less pandemic prone	15,221	−0.07	0.22	−0.15	−0.05	0.03

∆(ProfM)	26,993	−0.02	0.17	−0.05	−0.001	0.03
sectors more pandemic prone	12,484	−0.04	0.20	−0.07	−0.01	0.03
sectors less pandemic prone	14,509	−0.01	0.15	−0.04	−0.01	0.04

∆(IntrC)	27,845	−0.73	63.94	−4.55	0.02	4.62
sectors more pandemic prone	13,099	−3.29	61.04	−5.59	−0.33	3.17
sectors less pandemic prone	14,746	1.54	66.33	−3.76	0.39	5.92

∆(ProbD)	10,023	0.002	0.04	−0.002	0.001	0.01
sectors more pandemic prone	4748	0.004	0.03	−0.001	0.001	0.01
sectors less pandemic prone	5275	0.001	0.04	−0.003	0.0003	0.01

Pandemic-prone_j
Distancing	79	0.16	0.13	0.09	0.11	0.16

Policy_c
FIS (% of GDP)	80	11.62	10.29	6.1	6.1	14.6
MP_BP (−1*basis point/100)	80	0.71	1.09	0.15	0.3	1.15
FXI	80	0.24	0.43	0	0	0

Controls (X_ic,Pre)
Size (log)	28,915	11.64	2.25	9.98	11.5	13.13
Age (log)	28,915	3.17	0.77	2.71	3.09	3.66
CashA	28,915	0.13	0.14	0.03	0.09	0.18
RD_A	28,915	0.02	0.04	0	0	0.02
Private (dummy)	28,915	0.05	0.22	0	0	0
SaleA	28,915	0.87	0.85	0.41	0.71	1.09
ROA (%)	28,915	2.70	13.62	0.31	3.84	8.31
IntrC	28,915	38.09	117.42	0.81	4.49	19.89
EqitA	28,915	0.49	0.37	0.35	0.52	0.68

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The summary statistics for the main variables used in the regression analysis are shown in Panel B, Table 1. The sectors are classified as more pandemic-prone (i.e. greater than cross-country median) and less pandemic-prone (i.e. less than median) for the four dependent variables capturing firm performance. Among others, the mean values clearly indicate lower sales, profit margins, and interest coverage and higher probability of default for sectors that are more vulnerable.

4. Empirical findings

Before presenting our baseline results, we first show that the adverse impact of COVID-19 on firm performance is indeed more pronounced for pandemic-prone sectors. This is to validate the hypothesis that pandemic-sensitive sectors suffered more and, hence, needed support more.

Applying a variation of Eq. (1), Table 2 shows the impact of the pandemic. Column 1 displays a negative significant sign on the coefficient of Distancing when the dependent variable is ∆(SaleA). It implies that a decline in sales to be more noticeable for those sectors that are intrinsically more sensitive to social distancing. This result is supportive of a significant channel captured by Distancing, being consistent with Kóren and Petö (2020). For the profit margin,∆(ProM), the coefficient in column 2 is also negative and significant. It is a plausible result in that firms in pandemic-prone sectors are unable to generate profit, whilst experiencing a drop in sales, possibly being forced to cut profit margins in order to survive the pandemic. We observe a negative significant sign for the change in the ratio of earnings to interest expenses, ∆(IntC) in column 3 and a positive significant sign for the probability of default, ∆(ProD) in column 4. The pandemic has not only drained liquidity but has also increased the probability of default in vulnerable firms.

Table 2.

Social distancing and firm performance during COVID-19.

	Without interaction				Interacted with COVID-19 severity
	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Distancing_j	−0.122***	−0.124***	−1.190***	0.014*
	(−6.499)	(−13.098)	(−8.142)	(1.900)
Distancing_j x Covid_Severity_c					−0.002*	−0.002***	−0.015	0.001***
					(−1.771)	(−2.901)	(−1.427)	(3.334)

Controls_ic,pre (Size, Age, CashA, RD_A, Private, SaleA, ROA, IntrC, EqitA)	√	√	√	√	√	√	√	√

Constant	0.019	−0.039***	0.365**	−0.016*	0.011	−0.061	−0.293	−0.025***
	(0.814)	(−3.009)	(2.561)	(−1.871)	(0.470)	(−1.327)	(−0.681)	(−2.744)

Sector FEs	N	N	N	N	Y	Y	Y	Y
Country FEs	Y	Y	Y	Y	Y	Y	Y	Y

# Countries	80	80	80	80	80	80	80	80
# Sectors	79	79	79	79	79	79	79	79
N	28,915	26,993	27,841	10,023	28,860	26,941	27,791	10,023
Adj. R²	0.154	0.096	0.080	0.024	0.175	0.142	0.093	0.035

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This table reports the results estimating ∆y_{ic, COVID} = ϑ_c + ∅ . Distancing_j + τ. X_{ic, Pre} + ε_{ic, COVID} and ∆y_{ic, COVID} = ϑ_j + ϑ_c + ∅ . Distancing_j × Covid_Severity_c + τ. X_{ic, Pre} + ε_{ic, COVID} where i stands for firm, j for sector, and c for country. ∆y_{ic, COVID} is the change in performance ratios for firm i in country c between 2020 and 2019. We use, alternatively, change in asset turnover ratio [∆(SaleA)], change in profit margin [∆(ProfM)], change in interest coverage ratio [∆(IntrC)], and change in probability of default [∆(ProbD)]. Distancing_j is industry j's degree of sensitivity to a pandemic from Kóren and Petö (2020). Covid_Severity_c is a proxy for severity of COVID-19 in country c, using the Oxford stringency index. X_{ic, Pre} is a vector of firm-level explanatory variables, computed as of 2019. We include sector fixed effects (ϑ_j) in Columns 5–8 at the three-digit NAICS level as well as country fixed effects (ϑ_c) in all regressions. See Appendix, Table A1 for detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the country level (associated t-values reported in parentheses). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

In columns 5–8, Distancing _j is interacted with COVID_Severity _c, that is, the country-level severity of the lockdown measures in response to the pandemic. This is a composite measure based on nine response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 with the score 100 being the strictest (Hale et al., 2020). Although the sign on the interaction coefficients is identical, the coefficients are less significant both statistically and in terms of magnitude when compared with those in columns 1–4.10 The implication is that more weight is placed on the vulnerability of the specific sectors, rather than the exposure to the pandemic at the country level, when explaining firm performance. Indeed, this is a reasonable outcome: sectors such as tourism and airlines are severely affected, whereas others such as information technology end up even benefiting from social distancing. This phenomenon is common across countries.11

4.1. Baseline results

Having observed the negative performance of pandemic-prone sectors in Table 2, we explore whether these industries are the ones that benefited more from economic support measures. We specifically examine whether the support measures alleviate the severity of the pandemic's impact on firm performance by interacting the policy measures with the distancing proxy. In Table 3 , all policy measures are simultaneously included.

Table 3.

Social distancing and firm performance during COVID-19: Baseline results.

	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)
	(1)	(2)	(3)	(4)
Distancing_j x FIS_c	0.003**	0.001**	0.030***	−0.001**
	(2.369)	(2.321)	(2.795)	(−2.500)
Distancing_j x MP_BP_c	0.031*	0.023	0.169	−0.004
	(1.922)	(1.649)	(1.186)	(−0.813)
Distancing_j x FXI_c	−0.011	0.026	0.736**	−0.010
	(−0.255)	(0.978)	(2.043)	(−1.518)

Controls_ic,pre (Size, Age, CashA, RD_A, Private, SaleA, ROA, IntrC, EqitA)	√	√	√	√

Constant	0.008	−0.067	−0.443	−0.022**
	(0.303)	(−1.446)	(−0.967)	(−2.493)

Sector FEs	Y	Y	Y	Y
Country FEs	Y	Y	Y	Y

# Countries	80	80	80	80
# Sectors	79	79	79	79
N	28,915	26,993	27,841	10,023
Adj. R²	0.175	0.142	0.093	0.035

Differential in firm performance (%)

Distancing_j x FIS_c	2.03	0.68	20.29	−0.68

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This table reports the results estimating ∆y_{ic, COVID} = ϑ_j + ϑ_c + ∅ . Distancing_j × Policy_c + τ. X_{ic, Pre} + ε_{ic, COVID} where i stands for firm, j for sector, and c for country. ∆y_{ic, COVID} is the change in performance ratios for firm i in country c between 2020 and 2019. We use, alternatively, change in asset turnover ratio [∆(SaleA)], change in profit margin [∆(ProfM)], change in interest coverage ratio [∆(IntrC)], and change in probability of default [∆(ProbD)]. Policy_c is a vector of variables representing government economic support packages in country c. Distancing_j is industry j's degree of sensitivity to a pandemic from Kóren and Petö (2020). X_{ic, Pre} is a vector of firm-level explanatory variables, computed as of 2019. We include sector fixed effects (ϑ_j) at the three-digit NAICS level as well as country fixed effects (ϑ_c) in all regressions. See Appendix, Table A1 for detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the country level (associated t-values reported in parentheses). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Overall, we find evidence of a positive impact of fiscal policy during the pandemic for vulnerable sectors, and indeed, fiscal support appears to be working as intended by policymakers.12 Fiscal policy is statistically significant in all four cases (columns 1–4) at the 5% or 1% level, improving sales, profit margins, and liquidity position and, at the same time, decreasing the probability of insolvency for vulnerable sectors, holding other policy variables constant. This is consistent with the findings reported in studies analyzing the global financial crisis episode (e.g. Aghion et al., 2014; Claessens et al., 2012; Laeven and Valencia, 2013).

Monetary policy easing appears to have been effective in supporting sales revenue, though at the marginal significance level of 10% (column 1). Note that the various robustness tests conducted in the subsequent subsections reveal a clearer effect of monetary policy on firm performance. In this respect, the functioning of monetary policy transmission appears to be, at least, preserved during the pandemic. This is in contrast with the case of the global financial crisis, when bank balance sheet constraints substantially weakened monetary policy transmission (Van den Heuvel, 2009), and attests to the importance of advances in prudential regulation and deleveraging that allowed banks face the pandemic shock in much better shape than they did the subprime mortgage shock.

Foreign exchange intervention seems to have mitigated the decline of interest coverage during the pandemic (column 3). One of the possible explanations of this outcome may be the fact that the earnings of pandemic-prone sectors such as tourism are receptive to changes in the value of the domestic currency against foreign currencies. By limiting excessive volatility in the exchange rate, FXI may have protected earnings and kept interest expenses in check (especially if part of the debt is denominated in foreign currency). In this respect, the positive sign on the coefficient of profit margin (∆(ProfM)) is consistent, though it is insignificant.

Sales and interest coverage appear to be more responsive to policy measures, whereas the profit margin and the probability of default seem to respond only to fiscal measures. This may be explained as follows: during the pandemic, there is little scope for raising margins for those vulnerable firms, culminating in smaller response to other policy support than to specific fiscal instruments such as tax payment deferral or loss carry-back tax provisions that may have a direct impact on the profit margin. Similarly, default is not an unlikely outcome for vulnerable firms, in particular, for those with high levels of debt that were accumulated before the pandemic. Fiscal measures such as loan guarantee schemes, interest-free loans, or cash grants may have effectively mitigated the risk of default.13

A natural question is whether these statistically significant results translate into economic significance. We provide some analysis on this based on the magnitude of coefficients reported in Table 3. For a sector with the average vulnerability to the pandemic (corresponding to a Distancing index value of 0.16), an increase in fiscal support by one standard deviation or 10.29 — nearly the equivalent of increasing fiscal support from the level of Argentina (5.6) to that of the UK (16.1) — would have led to an improvement in the change in sales-to-asset ratio during the pandemic by 0.49%. For retail trade, which carries the greatest vulnerability to the pandemic, the improvement would be 1.98%. Table A2 in the Appendix extends this analysis across the spectrum of pandemic vulnerability and presents the estimated effect of one standard deviation increase in economic support on the change in firm performance for sectors at different percentiles of the distancing index. We find that the policy impact is indeed larger for more pandemic-prone sectors. A one standard-deviation increase in fiscal (monetary) support is associated with an improvement in the change in sales-to-asset ratio of 0.86% (0.95%) for the more pandemic-prone sectors (at the 90th percentile, such as Accommodation and Food), compared to only 0.22% (0.24%) for less pandemic-prone sectors (at the 10th percentile, for example, Manufacturing).

To provide further context, the estimated values for the differential in firm performance between most and least pandemic-prone sectors are shown at the bottom of Table 3 (Differential in firm performance) for fiscal support (the measure that appears to robustly influence).14 Let's focus on the sales-to-assets ratio. The estimation results in column 1 of Table 3 suggest that a firm from an industry at the 90th percentile of distancing would have a change in sales-to-assets that is 2.05 percentage points higher than a firm from a sector that is at the 10th percentile of distancing, if it were located in a country that is at the 90th percentile in fiscal support compared to a country at the 10th percentile. Similarly, the estimation results in column 4 of Table 3 suggest that, relative to less pandemic-prone sectors (at the 10th percentile), the probability of default in more pandemic-sensitive sectors (at the 90th percentile) is around 0.68 percentage points less in a country that launched significant fiscal support (at the 90th percentile) than in a country with a limited fiscal support (at the 10th percentile). Note that both differentials in changes in the sales-to-asset ratio and the probability of default are not negligible, compared to the average rates of change in SaleA and ProbD during the pandemic (which are −8.26% and 0.22%, respectively).

The baseline result is based on the cumulative interventions over the course of 2020 and their impact on outcomes for all of 2020. As an extension, we exploit the differences in timing by decomposing the intervention period after the onset of the pandemic in 2020Q1 into quarterly intervals: Q2, Q3, and Q4. The results are shown in Table 4 . It is noteworthy, though not surprising, that the impact is not monotonic: the initial impact of fiscal support is stronger based on the relatively large magnitudes of coefficients in Q2 as compared with those in Q3 and Q4 (which are pretty close to those of the baseline presented in Table 3). The differences are, however, negligible for monetary policy and foreign exchange intervention.

Table 4.

Social distancing, economic support packages, and firm performance during COVID-19 pandemic: Q2 vs. Q3 vs. Q4.

	Support packages in Q2, 2020				Support packages in Q3, 2020				Support packages in Q4, 2020
	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Distancing_j x FIS_c	0.180***	0.026	1.262***	−0.027*	0.003**	0.001	0.027**	−0.001**	0.003**	0.001**	0.028**	−0.001**
	(5.333)	(0.748)	(2.823)	(−1.999)	(2.306)	(1.508)	(2.419)	(−2.506)	(2.301)	(2.283)	(2.598)	(−2.436)
Distancing_j x MP_BP_c	0.035*	0.021	0.244	−0.003	0.038*	0.024	0.242	−0.004	0.031*	0.024*	0.176	−0.004
	(1.740)	(1.248)	(1.622)	(−0.636)	(1.895)	(1.457)	(1.490)	(−0.802)	(1.921)	(1.701)	(1.210)	(−0.812)
Distancing_j x FXI_c	−0.068*	−0.009	0.222	−0.005	−0.048	−0.005	0.420	−0.010	−0.014	0.014	0.603	−0.009
	(−1.740)	(−0.316)	(0.681)	(−0.602)	(−1.284)	(−0.171)	(1.094)	(−1.489)	(−0.340)	(0.497)	(1.639)	(−1.457)

Controls_ic,pre (Size, Age, CashA, RD_A, Private, SaleA, ROA, IntrC, EqitA)	√	√	√	√	√	√	√	√	√	√	√	√

Constant	0.016	−0.063	−0.361	−0.023**	0.015	−0.062	−0.383	−0.023**	0.009	−0.065	−0.422	−0.022**
	(0.592)	(−1.366)	(−0.800)	(−2.568)	(0.551)	(−1.350)	(−0.837)	(−2.593)	(0.325)	(−1.402)	(−0.920)	(−2.494)

Sector FEs	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
Country FEs	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y

# Countries	80	80	80	80	80	80	80	80	80	80	80	80
# Sectors	79	79	79	79	79	79	79	79	79	79	79	79
N	28,915	26,993	27,841	10,023	28,915	26,993	27,841	10,023	28,915	26,993	27,841	10,023
Adj. R²	0.176	0.142	0.093	0.034	0.176	0.142	0.093	0.035	0.175	0.142	0.093	0.035

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This table reports the results estimating ∆y_{ic, COVID} = ϑ_j + ϑ_c + ∅ . Distancing_j × Policy_c + τ. X_{ic, Pre} + ε_{ic, COVID} where i stands for firm, j for sector, and c for country. ∆y_{ic, COVID} is the change in performance ratios for firm i in country c between 2020 and 2019. We use, alternatively, change in asset turnover ratio [∆(SaleA)], change in profit margin [∆(ProfM)], change in interest coverage ratio [∆(IntrC)], and change in probability of default [∆(ProbD)]. Policy_c is a vector of variables representing government economic support packages in country c, launched either in Q2, Q3 or Q4, 2020. Distancing_j is industry j's degree of sensitivity to a pandemic from Kóren and Petö (2020). X_{ic, Pre} is a vector of firm-level explanatory variables, computed as of 2019. We include sector fixed effects (ϑ_j) at the three-digit NAICS level as well as country fixed effects (ϑ_c) in all regressions. See Appendix, Table A1 for detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the country level (associated t-values reported in parentheses). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

The larger impact of early fiscal intervention on the overall 2020 outcomes is consistent with the notion that the speed of delivery matters (see Section 2.1). Providing a lifeline to the worst-hit firms immediately helps them find ways to adjust to the shock before any deterioration in financial performance sets in. The non-monotonic pattern being visible for fiscal support but not for other policy support also speaks to the feasibility of tailoring measures to those in need: monetary easing and foreign exchange intervention are indirect and less likely to facilitate adjustment by those hit hardest so it makes little difference when they are implemented. That said, the non-monotonic impact finding for fiscal policy could also be an indication that the choice of fiscal measures is consequential: those faster to implement are also the ones that deliver more bang for the buck. We leave the question of whether timing or design of fiscal support packages matters more to future research.

4.2. Endogeneity issues

The key challenges to our identification strategy are the two usual endogeneity problems: omitted variable bias and reverse causality. We conduct several exercises to address them.

4.2.1. Omitted variable bias

The established positive correlation between economic support and firm performance is in line with our hypothesis, which suggests that government policies during the pandemic have provided life support for hardest-hit firms. Yet, the support packages may automatically pick up the effects of some country-level and/or sectoral-level latent variables that could also affect firm performance (Hyytinen and Toivanen, 2005). For instance, countries more open to trade and cross-border capital flows may launch more economic support given their larger exposure to the negative shocks and the broader lift to the economy cushions firms' performance, and this may, in turn, lead to a spurious positive association between support packages and firm outcomes. To address the omitted variable bias, we evaluate the significance of such variables by controlling for observable characteristics – especially at the country/industry level – that may affect firms' performance.

Following the existing literature, we consider two sets of characteristics. The first set includes those that are related to firm activities through country characteristics (Other country characteristics). These include five variables classified into four groups of a) pandemic resilience, b) channels of transmission, c) bank stability, and d) macroeconomic stability (see, for example, Claessens et al., 2010; Martin and Nagler, 2020; Igan et al., 2022). These country-specific features are interacted with Distancing.

a)
Pandemic resilience: We consider the overall vulnerability of a country to the pandemic by utilizing the variable of private health expenditure per capita (HealE). Higher spending can reasonably be interpreted as greater resilience, to the extent that it captures ease of access to health services and a widespread healthcare infrastructure. Data are collected from the World Health Organisation as reported by the World Bank.
b)
Channels of transmission: Previous research has highlighted the role of real and financial channels through which a crisis can spread across countries. While arguably less applicable to the case of COVID-19 given the different nature of the shock, these channels may still matter in the transmission of the economic effects. For instance, given restrictions imposed on movement across and within borders, supply can be disrupted and countries that are more connected to global value chains may feel the effects more profoundly. We consider two variables: (i) foreign direct investment (FDI), as a proxy for financial interconnectedness, and (ii) total exports and imports in % of GDP (Trade), as a proxy for a country's economic integration with the rest of the world. Data are retrieved from the World Bank.
c)
Bank stability: The health of bank balance sheets could be an amplifier of the economic shocks. In order to capture bank health, we include the ratio of non-performing loans to total loans (NPL). Data come from the World Bank.
d)
Macroeconomic stability: We capture the general macroeconomic stability of a country by including inflation (Inflation). Data are collected from the WDI.15

Other channels of propagation emphasized in the literature involve liquidity constraints and sensitivity to consumer demand in non-financial firms.16 Hence, as a second set of characteristics, we consider the effect of these two sectoral characteristics, which may interact particularly with financial policy measures. It follows that we control for sectoral characteristics by interacting the variables of external financial dependence (FinDep) and demand sensitivity (DemSen) of individual sectors, respectively, with the policy variables. We use the Rajan and Zingales (1998) index for external finance dependence and an index of sensitivity to demand shocks based on the stock price response to the September 11 shock, as computed by Tong and Wei (2008). We examine the extent to which policy measures affect firm performance through these two potential channels.

In Table 5 , we present the model by controlling for interactions of both country and sector characteristics simultaneously. The main results are close to those in the baseline reported in Table 3 in terms of the sign, magnitude, and significance of the coefficients (or even better with more significant coefficients). This highlights that pandemic-prone firms disproportionately benefit from fiscal support, in general.

Table 5.

Addressing omitted variable bias: Controlling for other country and sector characteristics.

	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)
	(1)	(2)	(3)	(4)
Distancing_j x FIS_c	0.004***	0.002**	0.023**	−0.001**
	(3.116)	(2.144)	(2.026)	(−2.485)
Distancing_j x MP_BP_c	0.035**	0.020	0.246*	−0.013***
	(2.403)	(1.288)	(1.670)	(−3.078)
Distancing_j x FXI_c	0.001	0.042	0.894**	−0.013
	(0.015)	(1.337)	(2.157)	(−1.317)

Other country characteristics
Distancing_j x HealE_c	−0.000	−0.000**	−0.001	−0.000
	(−0.445)	(−2.582)	(−1.523)	(−0.881)
Distancing_j x FDI_c	0.002	−0.001	−0.001	−0.000
	(0.901)	(−0.982)	(−0.098)	(−0.816)
Distancing_j x Trade_c	0.002	0.001	0.032	−0.000
	(0.658)	(0.320)	(1.074)	(−0.132)
Distancing_j x NPL_c	−0.001	0.002	−0.023*	0.000
	(−0.784)	(1.576)	(−1.850)	(0.703)
Distancing_j x Inflation_c	−0.014	0.008	−0.166	0.002
	(−0.619)	(0.353)	(−0.834)	(0.306)
Other sector characteristics	−0.098**	−0.028	−0.138	0.004
FinDepj x FIS_c	(−2.613)	(−0.686)	(−0.291)	(0.369)

FinDep_j x MP_BP_c	0.022	0.014	0.435	−0.009
	(0.668)	(0.511)	(1.641)	(−1.660)
FinDepj x FXI_c	−0.004***	−0.004	−0.042	−0.000
	(−2.844)	(−1.151)	(−1.494)	(−0.577)
DemSen_j x FIS_c	0.001*	0.000	0.003	−0.000
	(1.815)	(0.982)	(1.228)	(−0.420)
DemSen_j x MP_BP_c	0.004	−0.002	−0.008	−0.003
	(1.500)	(−0.644)	(−0.408)	(−1.474)
DemSen_j x FXI_c	0.001	0.003	0.027	0.008***
	(0.291)	(0.822)	(0.322)	(3.045)

Controls_ic,pre (Size, Age, CashA, RD_A, Private, SaleA, ROA, IntrC, EqitA)	√	√	√	√

Constant	−0.145**	−0.199***	−2.160***	0.028
	(−2.541)	(−4.552)	(−4.890)	(1.482)

Sector FEs (4-digit level)	Y	Y	Y	Y
Country FEs	Y	Y	Y	Y

# Countries	80	80	80	80
# Sectors	79	79	79	79
N	25,581	23,884	24,648	9039
Adj. R²	0.174	0.155	0.098	0.033

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This table reports the results estimating ∆y_{ic, COVID} = ϑ_j + ϑ_c + ∅ . Distancing_j × Policy_c + τ. X_{ic, Pre} + ∇ . Z_{ijc, Pre} + ε_{ic, COVID} where i stands for firm, j for sector, and c for country. ∆y_{ic, COVID} is the change in performance ratios for firm i in country c between 2020 and 2019. We use, alternatively, change in asset turnover ratio [∆(SaleA)], change in profit margin [∆(ProfM)], change in interest coverage ratio [∆(IntrC)], and change in probability of default [∆(ProbD)]. Policy_c is a vector of variables representing government economic support packages in country c. Distancing_j is industry j's degree of sensitivity to a pandemic from Kóren and Petö (2020). X_{ic, Pre} is a vector of firm-level explanatory variables, computed as of 2019. Z_{ijc, Pre} is a vector of country-specific or sector-specific (interacted with Distancing or Policy variables) new control variables. We include sector fixed effects (ϑ_j) at the three-digit NAICS level as well as country fixed effects (ϑ_c) in all regressions. See Appendix, Table A1 for detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the country level (associated t-values reported in parentheses). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Contrary to previous studies (e.g. Aghion et al., 2014; Laeven and Valencia, 2013), we do not find much of a significant effect of fiscal support on firms that are more dependent on external finance and firms that are more demand sensitive. A plausible interpretation of this result is that, in the context of the COVID-19 shock's impact on firm performance and survival, the main channel through which fiscal support helped is the alleviation of and facilitation of adjustment to the impact of non-pharmaceutical interventions rather than an easing of financial constraints or protection against a general drop in aggregate demand. This result manifests itself that the fiscal support is rather successful in targeting the vulnerable sectors caused by the pandemic.

Expansionary monetary policy continues to put a floor on sales and mitigates the risk of default. The latter result is new relative to the baseline in Table 3 and a plausible outcome as monetary policy easing can relieve debt service pressures. The impact of FXI on ∆(IntrC) is still attributable to differences across sectors in terms of how vulnerable they are to distancing rather than other sectoral characteristics (column 3). As before, the liquidity position in vulnerable sectors improves when there is intervention in the foreign exchange market.

While these control variables account for a reasonable amount of country- and sector-specific information, they may not entirely capture all relevant factors. Then, our results may still be biased due to unobservable variables that may be correlated with support packages and subsequently with firm performance. We make selection on these observable factors to determine the likelihood that our estimates are being driven by unobserved heterogeneity across countries and sectors. The results also remain intact in this exercise (See Table S5 together with the related discussion in the Online Supplementary Appendix (OSA)).

4.2.2. Reverse causality

Even if the COVID-19 shock is exogenous, the reaction of policymakers may not be random (Demirgüç-Kunt et al., 2021). For instance, companies, especially large ones, that were adversely affected by the pandemic and associated lockdown measures may be more likely to be supported by the government.

To deal with this endogeneity concern, we apply three different strategies. First, we drop the top 3 pandemic-prone industries in each country from our sample. The underlying idea is that the most vulnerable sectors in a country may be the ones to influence government policies. Second, we exclude large firms (firms with revenue greater than USD 5 billion) from the dataset. If activities of firms determine the degree of government intervention, then this would be more likely to be the case with large influential firms. By contrast, one may expect that smaller firms are more vulnerable to COVID-19 and, thus, may benefit more from government policies, rather than the other way round. Finally, we remove countries with a high share of pandemic-prone sectors to the GDP. This is because the reverse causality effect should be in tandem with the size of vulnerable industries relative to the overall size of the economy (Levintal, 2013). In other words, one would expect a larger reverse causality bias in countries where the pandemic-prone sectors constitute a significant portion of GDP. We measure this share as ${Share}_{c} = \sum_{j = 1}^{n} {Distancing}_{j} \times {ValuA}_{j} / {GDP}_{c}$ where ValuA is value added of sector j computed as the sum of earnings before taxes, depreciation and labor expense (Laeven and Valencia, 2013). ValAdd _j/GDP _c is measured for year 2019. We then remove countries in the 75th percentile of Share.

The results are in Table 6 and resonate with those in the baseline reported in Table 3. Removing the top 3 pandemic-prone sectors (columns 1–4), largest firms (columns 5–8) or countries with a high share of pandemic-prone sectors (columns 9–12) do not alter the findings and, actually, in some cases deliver larger and more statistically significant coefficients.

Table 6.

Addressing reverse causality.

	Removing top 3 pandemic-prone sectors				Removing large firms				Removing countries with a high share of pandemic-prone sectors to GDP
	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Distancing_j x FIS_c	0.003*	0.002**	0.027***	−0.001**	0.003**	0.001**	0.031***	−0.001**	0.004**	0.001**	0.035***	−0.001***
	(1.794)	(2.561)	(2.692)	(−2.517)	(2.400)	(2.318)	(2.978)	(−2.513)	(2.571)	(2.025)	(4.473)	(−5.276)
Distancing_j x MP_BP_c	0.033*	0.028*	0.193	−0.001	0.032*	0.023	0.171	−0.004	0.035*	0.029**	0.122	−0.007
	(1.761)	(1.981)	(1.391)	(−0.279)	(1.986)	(1.655)	(1.200)	(−0.826)	(1.810)	(2.064)	(0.864)	(−1.098)
Distancing_j x FXI_c	−0.025	0.028	0.794**	−0.016**	−0.010	0.027	0.749**	−0.010	−0.001	0.019	1.302***	−0.015*
	(−0.572)	(1.000)	(2.010)	(−2.100)	(−0.243)	(0.995)	(2.076)	(−1.549)	(−0.014)	(0.636)	(4.523)	(−1.786)

Controls_ic,pre (Size, Age, CashA, RD_A, Private, SaleA, ROA, IntrC, EqitA)	√	√	√	√	√	√	√	√	√	√	√	√

Constant	0.011	−0.066	−0.451	−0.022**	0.006	−0.067	−0.434	−0.022**	0.021	−0.067	−0.629	−0.004
	(0.391)	(−1.415)	(−0.989)	(−2.570)	(0.214)	(−1.442)	(−0.950)	(−2.572)	(0.719)	(−1.428)	(−1.398)	(−0.445)

Sector FEs	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
Country FEs	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y

# Countries	80	80	80	80	80	80	80	80	63	63	63	63
# Sectors	76	76	76	76	79	79	79	79	79	79	79	79
N	28,775	26,858	27,709	9950	28,825	26,904	27,751	9980	24,246	22,867	23,308	7632
Adj. R²	0.173	0.142	0.093	0.035	0.175	0.142	0.093	0.035	0.169	0.135	0.088	0.039

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To address any remaining endogeneity issue, we utilize the shock components for each policy as instruments. We also use a proxy for the quality of the institutional environment to further orthogonalize the policy response. The details of the instrumental-variable (IV) strategy are in the Online Supplementary Appendix (OSA) together with the result in Table S6 in OSA. We find that fiscal support relates positively and statistically significantly to the performance of pandemic-prone sectors, with a magnitude which is even larger than that reported in Table 3 for the OLS case. Overall, the IV estimator supports our baseline results.

To sum up, in this subsection, we have conducted a number of exercises to verify that the association we unveiled in Table 3 between economic support packages and firm performance during COVID-19 can reasonably be considered to not suffer from omitted variable bias and reverse causality.17

4.3. Additional analyses

Next, we investigate whether pre-COVID firm characteristics influence the link between policies and performance during COVID-19. This is of interest to shed some more light on how different policies could be having an impact on different firms.

We focus on four firm characteristics commonly-studied in the literature (Giroud and Mueller, 2017). The first is size, and the other three relate to liquidity constraints and leverage. On the one hand, because of the adverse impact of COVID-19 on revenues and free cash flow, one may expect that smaller firms and firms with less cash, more leverage and less profitability to be more vulnerable and, thus, benefit more from economic support policies. On the other hand, larger firms and those with stronger financial positions may be better situated to utilize the lifelines provided by the policy measures, including by spreading out the fixed costs and taking advantage of economies of scale.

In related research, Ding et al. (2021) find that stock prices of firms entering the COVID-19 crisis with a better position in terms of cash holdings, leverage, and profitability performed relatively better during the crisis. Fahlenbrach et al. (2021) report that financial flexibility (proxied, for example, by cash holdings) is one of the factors explaining why some firms performed better during COVID-19. Laeven (2022) finds that large firms and firms with cash buffers were better able to absorb the pandemic shock. Carletti et al. (2020) also report that distress in terms of book value of equity is more frequent for small and medium-sized enterprises and for firms with high pre-COVID leverage.18

Table 7 shows the results obtained by re-estimating the baseline specifications of Table 3 with respect to pre-crisis firm size (Panel A), cash holdings (Panel B), profitability (Panel C), and leverage (Panel D). Columns 1–4 (5–8) in all four panels include only the firms that are below (above) the sample median value with respect to the firm characteristic in question.

Table 7.

Heterogeneity in firms' size and financial positions entering the pandemic.

	(<Mdn.)				(>Mdn.)
	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Panel A: Size
Distancing_j x FIS_c	0.002	0.002	0.026	−0.001	0.004***	0.001	0.031***	−0.001***
	(1.020)	(1.446)	(1.507)	(−0.847)	(3.070)	(1.391)	(3.190)	(−3.379)
Distancing_j x MP_BP_c	0.026	0.028***	0.108	−0.005	0.037**	0.023	0.253	−0.003
	(1.031)	(2.726)	(0.642)	(−0.688)	(2.127)	(1.024)	(1.412)	(−0.705)
Distancing_j x FXI_c	−0.010	0.046	0.940**	−0.021**	−0.015	−0.002	0.418	0.003
	(−0.174)	(1.043)	(1.996)	(−2.040)	(−0.346)	(−0.037)	(1.271)	(0.321)
N	14,440	13,237	13,630	2361	14,475	13,756	14,211	7662
Adj. R²	0.155	0.122	0.096	0.080	0.231	0.183	0.091	0.045

Panel B: Cash
Distancing_j x FIS_c	0.003	0.001	0.021*	−0.001**	0.004***	0.001	0.044***	−0.001***
	(1.016)	(1.362)	(1.937)	(−2.165)	(3.622)	(1.514)	(3.028)	(−2.793)
Distancing_j x MP_BP_c	0.021	0.038**	0.178	−0.003	0.058**	−0.016	0.035	−0.005
	(1.077)	(2.535)	(1.053)	(−0.523)	(2.471)	(−0.763)	(0.136)	(−1.056)
Distancing_j x FXI_c	0.009	−0.001	0.546	−0.018	−0.036	0.073*	0.892*	−0.001
	(0.217)	(−0.035)	(1.422)	(−1.533)	(−0.499)	(1.966)	(1.915)	(−0.128)
N	14,446	13,162	14,065	3903	14,469	13,831	13,776	6120
Adj. R²	0.168	0.158	0.088	0.059	0.181	0.134	0.100	0.012

Panel C: Profitability (ROA)
Distancing_j x FIS_c	0.002	0.002	0.011	−0.001**	0.005***	0.001	0.046***	−0.0002
	(1.452)	(1.582)	(0.845)	(−2.703)	(2.666)	(1.216)	(3.536)	(−1.399)
Distancing_j x MP_BP_c	0.031	0.027	0.010	−0.007	0.035	0.016	0.433**	−0.001
	(1.414)	(1.462)	(0.080)	(−0.721)	(1.198)	(1.223)	(2.171)	(−0.673)
Distancing_j x FXI_c	0.010	0.014	0.661**	−0.019	−0.032	0.046	0.700	−0.003
	(0.240)	(0.342)	(2.389)	(−1.475)	(−0.526)	(1.498)	(1.248)	(−0.611)
N	14,471	12,929	13,904	4798	14,444	14,064	13,937	5225
Adj. R²	0.162	0.158	0.095	0.052	0.190	0.140	0.097	0.040

Panel D: Leverage (EqitA)
Distancing_j x FIS_c	0.002	0.000	0.003	−0.001**	0.007***	0.003***	0.090***	−0.001***
	(0.939)	(0.313)	(0.297)	(−2.128)	(8.007)	(2.933)	(4.885)	(−3.037)
Distancing_j x MP_BP_c	0.028	0.018	0.020	0.003	0.054**	0.039**	0.526**	−0.009**
	(1.368)	(1.333)	(0.159)	(0.718)	(2.463)	(2.040)	(2.347)	(−2.086)
Distancing_j x FXI_c	−0.030	0.003	0.758**	−0.016	0.012	0.031	0.431	−0.007*
	(−0.676)	(0.084)	(2.521)	(−1.214)	(0.200)	(1.061)	(0.685)	(−1.975)
N	14,708	13,739	14,467	4913	14,207	13,254	13,374	5110
Adj. R²	0.179	0.166	0.095	0.052	0.181	0.128	0.100	0.021
All regressions
Controls_ic,pre (Size, Age, CashA, RD_A, Private, SaleA, ROA, IntrC, EqitA)	√	√	√	√	√	√	√	√
Sector FEs	Y	Y	Y	Y	Y	Y	Y	Y
Country FEs	Y	Y	Y	Y	Y	Y	Y	Y
# Countries	80	80	80	80	80	80	80	80
# Sectors	79	79	79	79	79	79	79	79

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This table reports the results estimating ∆y_{ic, COVID} = ϑ_j + ϑ_c + ∅ . Distancing_j × Policy_c + τ. X_{ic, Pre} + ε_{ic, COVID} where i stands for firm, j for sector, and c for country. ∆y_{ic, COVID} is the change in performance ratios for firm i in country c between 2020 and 2019. We use, alternatively, change in asset turnover ratio [∆(SaleA)], change in profit margin [∆(ProfM)], change in interest coverage ratio [∆(IntrC)], and change in probability of default [∆(ProbD)]. Each panel displays the results obtained by running the regression in a subsample determined by the median value of various pre-crisis financial variables. Policy_c is a vector of variables representing government economic support packages in country c. Distancing_j is industry j's degree of sensitivity to a pandemic from Kóren and Petö (2020). X_{ic, Pre} is a vector of firm-level explanatory variables, computed as of 2019. We include sector fixed effects (ϑ_j) at the three-digit NAICS level as well as country fixed effects (ϑ_c) in all regressions. See Appendix, Table A1 for detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the country level (associated t-values reported in parentheses). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Larger firms benefit more from fiscal support (see the significant coefficient on the interaction for FIS in column 5, 7, and 8 of Panel A). Yet, smaller firms benefit more from foreign exchange intervention (see the significant coefficient on the interaction for FXI in columns 3 and 4). Monetary policy easing seems to exert a favorable effect on smaller firms by raising profits (column 2) and on larger firms by increasing sales (column 5).

Firms with a low pre-COVID level of cash holdings appear to have improved ability to meet debt obligations and lowered risk of insolvency due to fiscal support, given the significant coefficient on the interaction for FIS in columns 3 and 4 of Panel B in Table 7. For firms with more cash holdings, the results echo those for larger firms in Panel A: both fiscal and monetary support help, with significant coefficients on the interaction terms for FIS (column 5, 7, and 8) and MP_BP (column 5).

Panel C suggests that firms in the higher profitability group seem to benefit from fiscal and, to a lesser degree, from monetary support more than their lower-profitability counterparts do (see column 5 and 7, where the interaction terms are highly significant). Similar to the case for smaller firms in Panel A, however, foreign exchange intervention seems to help firms with lower profitability service their debt (see column 3).

Looking at Panel D, there is a clear distinction between more versus less leveraged firms. Predominantly, economic policy packages favor the latter, in particular both fiscal and monetary policy measures help improve firm performance. By contrast, less leveraged firms appear to only benefit from foreign exchange intervention through a better interest coverage ratio (column 3) and from fiscal support through a lower probability of default (column 4).

While a more thorough examination is left for future research, taken together, these results seem to point to a need for firms to have a certain level of financial health to be able to seize the lifeline provided by fiscal and, perhaps to a somewhat lesser extent, monetary policy measures. FXI, by contrast, appears to help smaller, less healthy firms stay afloat.19 These differences could speak to the feasibility of targeting fiscal support, albeit with some leakage, to firms that have a better chance of surviving the crisis.20 It might also be an inevitable outcome due to, for instance, structural changes brought by the pandemic, favoring digitization and economies of scale.

4.4. Other robustness tests

We conduct several further robustness tests in order to ascertain the baseline results in Table 3. The details of the tests, results, and related discussions are in the OSA, where alternative data sources for policy response to COVID-19 are examined in Table S7A, an alternative proxy for pandemic sensitivity is explored in Table S7B, the baseline model is re-estimated with the weighted least square method and by excluding critical sectors in Table S7C, and the specification is expanded with additional firm-specific variables in Table S7D. The main findings remain broadly the same.

5. Conclusion

In this paper, we use firm-level data to provide some early evidence on the effectiveness of COVID-19 economic policy packages. Our empirical strategy relies on the varying degree of vulnerability to the pandemic across industries. If policy actions have worked as intended, they would give a lift to pandemic-prone sectors.

After confirming that firms in sectors with higher distancing indices performed worse than the others in the same country, we find a robust positive association of fiscal support with growth in the sales-to-assets ratio, profit margin, interest coverage ratio and probability of default in pandemic-prone sectors: firms that are more sensitive to distancing have performed better when the fiscal support is larger. There is also some evidence that monetary easing has been associated with improved sales and foreign exchange intervention with increased interest coverage ratio for the hardest-hit firms. The evidence also indicates that fiscal support packages are more effective than other policies during the COVID-19 pandemic.

Thus, this early evidence seems to suggest that policy interventions have bought time for the hardest-hit industries, by supporting sales and improving liquidity, and especially for firms that entered the crisis with healthier financial positions. This is cautiously encouraging news for policymakers: giving a helping hand in response to exogenous shocks may suffer less from concerns about misallocation of resources and moral hazard. As for corporate managers, there is perhaps some lessons to be learned in terms of building resilience and financial soundness in good times, since these qualities may be crucial not only to be able to survive a crisis itself but also to be able to benefit from policy support, assuming any would be provided.

Editor: Dr K Hankins

This approach is an augmentation of the literature that examines the relationship between government intervention and firm performance during a financial crisis (see, for example, Norden et al., 2013 and Laeven and Valencia, 2013).

⁴

Arguably, our use of firm-level data, with distancing measured at the sectoral level, also introduces some degree of separation. While it is plausible that policies are more likely to be enacted where pandemic-prone sectors make up a larger portion of the economy, it is unlikely that government policy responds only to the performance of a particular firm in a pandemic-prone sector. Indeed, the correlation between policies and average distancing of firms in a given country is at most 7% (between distancing proxy and fiscal policy variable; for other policies, the correlation is less than 3%).

⁵

Kóren and Petö (2020) use U.S. firm data to establish the benchmark of an industry's pandemic sensitivity. One may argue that this proxy could be endogenous to the performance of U.S. firms. Therefore, following other studies that apply the Rajan and Zingales (1998) approach such as Igan and Mirzaei (2020), we drop U.S. firms from all regressions. Yet, for completeness, we check the robustness of the findings to also including U.S. firms. See Section 4.2.

⁶

We acknowledge that the sample of firms we study is biased toward larger firms as almost all firms (about 95%) reporting 2020 data are listed firms. Thus, we are conservative when interpreting our results, as we cannot analyze the overall effect of policy measures on the performance of small and medium-sized enterprises during COVID-19.

⁷

Some industries will have high scores in all three dimensions while others may have high scores only in one. For instance, most manufacturing requires physical presence but not necessarily face-to-face customer contact.

⁸

Available at https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19.

⁹

The survey gathers, in addition to foreign exchange market intervention, information on capital flow management and broader financial policy actions such as loan forbearance and debt moratoria. We do not include these in our analysis given that there is not enough variation across countries to tease out any differential effects.

¹⁰

Note that, in the specifications in columns 5–8, we include sector fixed effects. Hence, the coefficient on distancing itself is absorbed.

¹¹

As a separate exercise, we run a regression of policy measures without interacting them with Distancing and then with the interactions so that we can see the direct impact of policy measures on firm performance. The results are in Table S2 together with the relevant discussion in the Online Supplementary Appendix (OSA).

¹²

We also run a regression with two indicators of real activity as dependent variables: change in ‘the number of employees’ and change in ‘value added’. The results are consistent with those using financial performance indicators as dependent variables and are shown in Table S3 in the OSA.

¹³

Due to differences in number of observations across our four dependent variables, it is worth confirming the validity of the findings in the restricted sample (that is, only the firms with non-missing observations for all variables). Table S4 columns 1–4 in the OSA indicate that our main findings remain broadly unchanged, except for the case where profit margin is the dependent variable.

¹⁴

These are calculated as ∅[(Distancing^90th − Distancing^10th)(Policy^90th − Policy^10th)]. See Eq. (1) for further elaboration on the variables and the coefficients.

¹⁶

See, for instance, Tong and Wei (2008), who examine how the subprime crisis spilled over to the real economy and find that these two channels indeed explain the differential negative impact on stock prices during the global financial crisis.

¹⁷

Recall that we exclude the United States due to potential reverse causality. The results obtained when we include the US data are in Table S4, columns 5–8 in the OSA and are almost identical to those in Table 3.

¹⁸

Although the nature of the shock is very different, these findings are in line with those reported in studies looking at the 2008 financial crisis (e.g., Duchin et al., 2010 find that the decline in investment is greatest for firms that have low cash reserves and are financially more constrained).

¹⁹

One of the possible reasons that FXI helps smaller firms in pandemic-prone sectors more than it does larger ones could be that smaller firms do not have in place mechanisms and resources that could protect them from exchange rate fluctuations, e.g. hedging instruments or diversification of operations, that larger firms might have.

²⁰

The finding that larger firms benefit more could also be due to their stronger influence on the government. Igan et al. (2021), for instance, show in the context of the Payroll Protection Program enacted in the United States in response to COVID-19 that larger firms were more likely to lobby the government and at the same time obtained larger loans under the program. To the extent that lobbying conveys information about the specific needs of the firms allowing fiscal support to be more tailored, the effects of fiscal support could be more significant.

^{Appendix B}

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jcorpfin.2022.102340.

¹⁵

One of the important country-level variables that may affect firm performance is the sovereign debt-to-GDP ratio. The ability of a country in diminishing the adverse impact of a crisis on corporate activity and risk may be related to its level of debt (Martin and Nagler, 2020). However, Benmelech and Tzur-Ilan (2020) show that debt-to-GDP is positively related to fiscal spending during COVID-19. Indeed, we face high correlation (more than 0.8) between the sovereign debt ratio and the size of the fiscal response to the crisis. The correlation further increases to 0.9 when interacted with distancing. There are other likely control variables one could consider, such as the share of population aged 65+ and the number of hospital beds. However, due to the problem of multicollinearity as indicated by a large variance inflation factor (VIF), we have to be selective and exclude all these potential control variables from the baseline model.

Appendix A. Appendix

Table A1.

Variable definitions and sources.

Variable	Definition	Source
Change in firm performance (∆y_ic,COVID)
∆(SaleA)	The change in a bank sale to asset ratio (SaleA) between 2019 and 2020, calculated as ∆(SaleA) = (SaleA20-SaleA19). SaleA is an asset turnover ratio, which measures the efficiency of a company's assets to generate revenue or sales.	Bureau van Dijk, ORBIS, and own calculation.
∆(ProfM)	The change in a bank profit margin (ProfM) between 2019 and 2020, calculated as ∆(ProfM) = (ProfM20-ProfM19). Profit margin is a measure of profitability where it is calculated as the net profit as a share of the revenue.	“
∆(IntrC)	The change in a bank interest coverage ratio (IntrC) between 2019 and 2020, calculated as ∆(IntrC) = (IntrC20-IntrC19). Interest coverage ratio is a company's ability to meet its debt obligations. The interest coverage ratio is calculated by dividing a company's earnings before interest and taxes by its interest expense.	“
∆(ProbD)	The change in indicator of probability of default (ProbD) between Dec. 2019 and Dec. 2020. ProbD reflects the default risk of publicly listed firms by quantitatively analyzing numerous covariates that cover market-based and accounting-based firm-specific attributes, as well as macro-financial factors. We use a prediction for horizon of 1 month. Higher figures denote higher risk.	Credit Research Initiative – CRI, National University of Singapore.
Pandemic-prone_j
Distancing	Kóren and Petö' (2020) sectoral pandemic-prone proxy, using data from O*NETZ. It represents share of worker whose job requires a high level of teamwork and customer contact. Kóren and Petö document that US industries are different when it comes to reliance on teamwork and customer contact in their operations. This proxy suggests that firms in economic sectors with a high degree of such pandemic-prone proxy are particularly vulnerable to social distancing.	Kóren and Petö (2020)
Policy_c
FIS	Fiscal policy: Cumulative fiscal support package (% of GDP) from January to December 2020 (week 1 to week 43).	IMF, and own calculation.
MP_BP	Monetary policy: cumulative change in basis points from January to December 2020 times (−1) divided by 100.	“
FXI	Foreign exchange intervention (0 = No, 1 = Yes): cumulative from January to December 2020.	“
Controls_ic,pre
Size	Natural logarithm of a firm total assets in 2019.	Bureau van Dijk, ORBIS, and own calculation.
Age	Firm age measured by logarithm of subtracting the firm's year of incorporation from year 2020.	“
CashA	Firm cash assets to total assets ratio in 2019.	“
RD_A	Research and development expenditure divided by total assets in 2019.	“
Private (dummy)	A dummy variable that takes value 1 if the firm is a private firm, and 0 otherwise.	“
SaleA	Firm sales to total assets ratio in 2019.	“
ROA	Return on assets, which is defined as profit before tax as a percentage of average assets of a bank, in 2019.	“
IntrC	Interest coverage ratio is earnings before interest and taxes (EBIT) to interest expenses ratio in 2019. It determines how easily a company can pay interest on its outstanding debt.	“
EqitA	The ratio of shareholder fund (equity) to total assets of a firm in 2019.	“
Other variables
Covid_Severity	The country-level severity of the lockdown measures in response to the pandemic. This is a composite measure of the scale of school closures, workplace closures and travel bans based on the data on the 31st December 2020. The indicator is normalised to be from 0 to 100 with the score100 being the strictest.	Hale et al. (2020).
HealE	Current private expenditures on health per capita expressed in international dollars at purchasing power parity in year 2019.	World Bank - WDI.
FDI	Foreign direct investment, which refers to direct investment equity flows in the reporting economy, as % of GDP in year 2019.	“
Trade	Total exports and imports as % of GDP in year 2019.	“
NPL	The ratio of a country bank nonperforming loans to total gross loans in year 2019.	“
Inflation	Inflation, measured by consumer price index, which is defined as the yearly change in the prices of a basket of goods and services in year 2019.	“
FinDep	External financial dependence of U.S. firms by 3-digit SIC codes. This is an industry-level median of the ratio of capital expenditures minus cash flow over capital expenditures. Cash flow is defined as the sum of funds from operations, decreases in inventories, decreases in receivables, and increases in payables. Capital expenditures include net acquisitions of fixed assets. Source: Rajan and Zingales (1998).	Tong and Wei (2008).
DemSen	Demand sensitivity is a sector-level index on the sensitivity to demand shocks, based on stocks' response to the 9/11/2001 shock.	“

OverA	The ratio of a company's overheads cost (other operating expenses) to its total assets in year 2019.	Bureau van Dijk, ORBIS, and own calculation.
CashFlowA	The ratio of a company's cash flow to its total assets in year 2019.	“
Tobin's Q	Total market value of common equity divided by total book value of assets in year 2019.	“

Open in a new tab

Table A2.

Magnitude of estimates

		Fiscal policy (FIS)				Monetary policy (MP_BP)	Foreign exchange policy (FXI)
Percentile	Distancing	∆(SaleA)	∆(ProfM)	∆(IntrC)	∆(ProbD)	∆(SaleA)	∆(IntrC)
Percentile	Distancing	(1)	(2)	(3)	(4)	(5)	(6)
10th	0.07	0.22	0.07	2.16	−0.07	0.24	2.22
25th	0.09	0.28	0.09	2.78	−0.09	0.30	2.85
50th	0.11	0.34	0.11	3.40	−0.11	0.37	3.48
75th	0.16	0.49	0.16	4.94	−0.16	0.54	5.06
90th	0.28	0.86	0.29	8.64	−0.29	0.95	8.86

Open in a new tab

Marginal effects (in %) of economic supporting packages on firm performance at different levels of distancing. Calculations are based on estimated coefficients from Table 3 and at the 10th, 25th, 50th, 75th, and 90th of distancing.

Appendix B. Supplementary data

Supplementary material

mmc1.docx^{(1.1MB, docx)}

Data availability

Data will be made available on request.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

mmc1.docx^{(1.1MB, docx)}

Data Availability Statement

Data will be made available on request.

[bb0005] Aghion P., Hémous D., Kharroubi E. Cyclical fiscal policy, credit constraints, and industry growth. J. Monet. Econ. 2014;62:41–58. [Google Scholar]

[bb0010] Alfaro L., Chari A., Greenland A.N., Schott P.K. 2020. Aggregate and Firm-Level Stock Returns during Pandemics, in Real Time, NBER Working Paper No. 26950. [Google Scholar]

[bb0015] Barbiero F., Popov A., Wolski M. Debt overhang, global growth opportunities, and investment. J. Bank. Financ. 2020;120 [Google Scholar]

[bb0020] Bates T.W., Kahle K.M., Stulz R.M. Why do U.S. firms hold so much more cash than they used to? J. Financ. 2009;64(5):1985–2021. [Google Scholar]

[bb0025] Baumohl E., Iwasaki I., Kočenda E. Institutions and determinants of firm survival in European emerging markets. J. Corp. Finan. 2019;60:431–453. [Google Scholar]

[bb0030] Beck T., Demirgüç-Kunt A., Laeven L., Maksimovic V. The determinants of financing obstacles. J. Int. Money Financ. 2006;25(6):932–952. [Google Scholar]

[bb0035] Benmelech E., Tzur-Ilan N. 2020. The Determinants of Fiscal and Monetary Policies during the Covid-19 Crisis, NBER Working Papers 27461. [Google Scholar]

[bib291] Burns N., Jindra J., Minnick K. Sales of private firms and the role of CEO compensation. Journal of Corporate Finance. 2017;43:444–463. [Google Scholar]

[bb0045] Carletti E., Oliviero T., Pagano M., Pelizzon L., Subrahmanyam M.G. The COVID-19 shock and equity shortfall: firm-level evidence from Italy. Rev. Corp. Financ. Stud. 2020;9:534–568. [Google Scholar]

[bb0050] Cathcart L., Dufour A., Rossi L., Varotto S. The differential impact of leverage on the default risk of small and large firms. J. Corp. Finan. 2020;60 [Google Scholar]

[bb0055] Chaney T., Sraer D., Thesmar D. The collateral channel: how real estate shocks affect corporate investment. Am. Econ. Rev. 2012;102(6):2381–2409. [Google Scholar]

[bb0060] Chen S., Igan D., Pierri N., Presbitero A.F. Tracking the economic impact of COVID-19 and mitigation policies in Europe and the United States. CEPR Covid Econ. 2020;36 [Google Scholar]

[bb0065] Chodorow-Reich G. The employment effects of credit market disruptions: firm- level evidence from the 2008–09 financial crisis. Q. J. Econ. 2014;129(1):1–59. [Google Scholar]

[bb0070] Claessens S., Dell’Ariccia G., Igan D., Laeven L. Cross-country experiences and policy implications from the global financial crisis. Econ. Policy. 2010;25(62):267–293. [Google Scholar]

[bb0075] Claessens S., Tong H., Wei S.J. From the financial crisis to the real economy: using firm-level data to identify transmission channels. J. Int. Econ. 2012;88(2):375–387. [Google Scholar]

[bb0085] Demirgüç-Kunt A., Peria M.S.M., Tressel T. The global financial crisis and the capital structure of firms: was the impact more severe among SMEs and nonlisted firms? J. Corp. Finan. 2020;60 [Google Scholar]

[bb0090] Demirgüç-Kunt A., Pedraza A., Ruiz-Ortega C. Banking sector performance during the COVID-19 crisis. J. Bank. Financ. 2021;133 doi: 10.1016/j.jbankfin.2021.106305. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bb0095] Ding W., Levine R., Lin C., Xie W. Corporate immunity to the COVID-19 pandemic. J. Financ. Econ. 2021;141(2):802–830. doi: 10.1016/j.jfineco.2021.03.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bb0100] Dingel J.I., Neiman B. How many jobs can be done at home? J. Public Econ. 2020;189:1–8. doi: 10.1016/j.jpubeco.2020.104235. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bb0105] D’Souza J., Megginson W.L., Ullah B., Wei Z. Growth and growth obstacles in transition economies: privatized versus de novo private firms. J. Corp. Finan. 2017;42:422–438. [Google Scholar]

[bb0110] Duan J.C., Sun J., Wang T. Multiperiod corporate default prediction –a forward intensity approach. J. Econ. 2012;170(1):191–209. [Google Scholar]

[bb0115] Duchin R., Ozbas O., Sensoy B. Costly external finance, corporate investment, and the subprime mortgage credit crisis. J. Financ. Econ. 2010;97:418–435. [Google Scholar]

[bb0120] Fahlenbrach R., Rageth K., Stulz R.M. How valuable is financial flexibility when revenue stops? Evidence from the COVID-19 crisis. Rev. Financ. Stud. 2021;34(11):5474–5521. [Google Scholar]

[bb0125] Frijns B., Dodd O., Cimerova H. The impact of cultural diversity in corporate boards on firm performance. J. Corp. Finan. 2016;41:521–541. [Google Scholar]

[bb0130] Gaganis C., Lozano-Vivas A., Paradimitri P., Pasiouras F. Macroprudential policies, corporate governance and bank risk: cross-country evidence. J. Econ. Behav. Organ. 2020;169:126–142. [Google Scholar]

[bb0135] Gagnon J.E., Collins C.G. Are central banks out of ammunition to fight a recession? Not Quite, Peterson Institute for International Economics Policy Brief, November, 2019 19–18. [Google Scholar]

[bb0140] Gandhi P., Lustig H. Size anomalies in U.S. bank stock returns. J. Financ. 2015;70:733–768. [Google Scholar]

[bb0145] Giroud X., Mueller H.M. Firm leverage, consumer demand, and employment losses during the great recession. Q. J. Econ. 2017;132:271–316. [Google Scholar]

[bb0150] Giroud X., Mueller H.M. Firms’ internal networks and local economic shocks. Am. Econ. Rev. 2019;109(10):3617–3649. [Google Scholar]

[bb0155] Glover A., Heathcote J., Krueger D., Ríos-Rull J.V. 2020. Health Versus Wealth: On the Distributional Effects of Controlling a Pandemic, CEPR Discussion Paper 14606. [Google Scholar]

[bb0160] González V.M. The financial crisis and corporate debt maturity: the role of banking structure. J. Corp. Finan. 2015;35:310–328. [Google Scholar]

[bb0165] Hale T., Webster S., Petherick A., Phillips T., Kira B. Blavatnik School of Government; 2020. Oxford Covid-19 Government Response Tracker. [DOI] [PubMed] [Google Scholar]

[bb0170] Hall T., Mateus C., Mateus I.B. What determines cash holdings at privately held and publicly traded firms? Evidence from 20 emerging markets. Int. Rev. Financ. Anal. 2014;33(3):104–116. [Google Scholar]

[bb0175] Hensvik L., Le Barbanchon T., Rathelot R. Evidence from the American Time Use Survey, CEPR Discussion Paper No. 14611. 2020. Which jobs are done from home? [Google Scholar]

[bb0180] Hyytinen A., Toivanen O. Do financial constraints hold back innovation and growth? Evidence on the role of public policy. Res. Policy. 2005;34(9):1385–1403. [Google Scholar]

[bb0185] Igan D., Mirzaei A. Does going tough on banks make the going get tough? Bank liquidity regulations, capital requirements, and sectoral activity. J. Econ. Behav. Organ. 2020;177:688–726. [Google Scholar]

[bb0190] Igan D., Lambert T., Mishra P. 2021. The Politics of the Paycheck Protection Program, CEPR Discussion Paper No. 16842. [Google Scholar]

[bb0195] Igan D., Mirzaei A., Moore T. Does macroprudential policy alleviate the adverse impact of COVID-19 on the resilience of banks? J. Bank. Financ. 2022 doi: 10.1016/j.jbankfin.2022.106419. (in press) [DOI] [PMC free article] [PubMed] [Google Scholar]

[bb0200] Kóren M., Petö R. Business disruptions from social distancing. PLoS One. 2020;15(9) doi: 10.1371/journal.pone.0239113. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bb0205] Laeven L. Pandemics, intermediate goods, and corporate valuation. J. Int. Money Financ. 2022;120 doi: 10.1016/j.jimonfin.2021.102505. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bb0210] Laeven R., Valencia F. The real effects of financial sector interventions during crises. J. Money Credit Bank. 2013;45(1):147–177. [Google Scholar]

[bb0215] Leibovici F., Santacreu A.M., Famiglietti M. 2020. How the Impact of Social Distancing Ripples through the Economy, St. Louis Fed On the Economy Blog, April 7. [Google Scholar]

[bb0220] Levintal O. The real effects of banking shocks: evidence from OECD countries. J. Int. Money Financ. 2013;32:556–578. [Google Scholar]

[bb0225] Makin A.J., Layton A. The global fiscal response to COVID-19: risks and repercussions. Econ. Anal. Policy. 2021;69:340–349. [Google Scholar]

[bb0230] Mankiw G. A Proposal for Social Insurance During the Pandemic. 2020. http://gregmankiw.blogspot.com/2020/03/a-proposal-for-social-insurance-during.html

[bb0235] Marron D. If We Give Everybody Cash To Boost The Coronavirus Economy, Let’s Tax It | Tax Policy Center. 2020. https://www.taxpolicycenter.org/taxvox/if-we-give-everybody-cash-boostcoronavirus-economy-lets-tax-it

[bb0240] Martin T., Nagler F. Sovereign Debt and Equity Returns in the Face of Disaster (September 15, 2020) 2020. https://ssrn.com/abstract=3572839 Available at SSRN: or. [DOI]

[bb0245] Norden L., Roosenboom P., Wang T. The impact of government intervention in banks on corporate borrowers’ stock returns. J. Financ. Quant. Anal. 2013;48(5):1635–1662. [Google Scholar]

[bb0250] OECD Tax and Fiscal Policy in Response to the Coronavirus Crisis: Strengthening Confidence and Resilience. 2020. https://www.oecd.org/coronavirus/policy-responses/tax-and-fiscal-policy-in-response-to-the-coronavirus-crisis-strengthening-confidence-and-resilience-60f640a8/

[bb0255] OECD Supporting People and Companies to Deal with the COVID-19 Virus: Options for an Immediate Employment and Social-Policy Response. 2020. https://read.oecdilibrary.org/view/?ref=119_119686-962r78x4do&title=Supporting_people_and_companies_to_deal_with_the_Covid-19_virus

[bb0260] Padhan R., Prabheesh K.P. The economics of COVID-19 pandemic: a survey. Econ. Anal. Policy. 2021;70:220–237. doi: 10.1016/j.eap.2021.02.012. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bb0265] Pagano M., Wagner C., Zechner J. 2020. Disaster Resilience and Asset Prices, CEPR Discussion Paper No. 14773. [Google Scholar]

[bb0270] Papanikolaou D., Schmidt L.D.W. 2020. Working Remotely and the Supply-side Impact of COVID-19, NBER Working Paper No. 27330. [Google Scholar]

[bb0275] Rajan R., Zingales L. Financial dependence and growth. Am. Econ. Rev. 1998;88(3):559–586. [Google Scholar]

[bb0285] Tong H., Wei S.J. 2008. Real Effects of the Subprime Mortgage Crisis: is it a Demand or a Finance Shock? IMF Working Paper No. 08/186. [Google Scholar]

[bb0290] Van den Heuvel S. Society for Economic Dynamics; 2009. The Bank Capital Channel of Monetary Policy, 2006 Meeting Papers 512. [Google Scholar]

PERMALINK

A shot in the arm: Economic support packages and firm performance during COVID-19

Deniz Igan

Ali Mirzaei

Tomoe Moore

Abstract

1. Introduction

2. Potential transmission channels

2.1. Fiscal support

2.2. Other policy support

3. Methodology and data

3.1. Empirical strategy

3.2. Data sources

3.2.1. Firm performance

3.2.2. Pandemic sensitivity

3.2.3. Policy measures

3.2.4. Other variables

3.3. Descriptive statistics

Table 1.

4. Empirical findings

Table 2.

4.1. Baseline results

Table 3.

Table 4.

4.2. Endogeneity issues

4.2.1. Omitted variable bias

Table 5.

4.2.2. Reverse causality

Table 6.

4.3. Additional analyses

Table 7.

4.4. Other robustness tests

5. Conclusion

Appendix A. Appendix

Table A1.

Table A2.

Appendix B. Supplementary data

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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