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Competition and Firm Recovery Post-COVID-19

This paper examines the impact of the COVID-19 crisis on the reallocation of economic activity across firms, and whether this reallocation depends on the competition environment. Using the World Bank’s Enterprise Surveys (ES) COVID-19 Follow-Up Surveys for about 8,000 firms in 23 emerging and developing countries in Europe and Central Asia, matched with 2019 ES data, the paper finds that economic activity was reallocated toward firms with higher pre-crisis labor productivity during the COVID-19 crisis. Countries with a strong competition environment experienced more reallocation from less productive to more productive firms than countries with a weak competition environment. The evidence also suggests that reallocation from low to high productivity firms during the COVID-19 crisis was stronger compared to pre-crisis times. Finally, the analysis shows that government support measures implemented in response to the crisis may have adverse effects on competition and productivity growth since they went to less productive and larger firms, regardless of their pre-crisis innovation.

sales and employment than firms with low pre-crisis labor productivity. More productive firms were also more likely to adapt to the crisis by increasing online activity and remote work.
-We find that countries with a strong competition environment experienced more reallocation from less productive to more productive firms than countries with a weak competition environment. This result is robust to using alternative measures of the competition environment. In countries with high competition, firms at the 10th percentile of the pre-crisis labor productivity distribution experienced an 18-percentage point larger drop in sales than firms at the 90th percentile; this difference is only 10 percentage points in countries with low competition. Similarly, in countries with high competition, firms in the 10th percentile of the labor productivity distribution were 15 percentage points more likely to decrease employment and 16 percentage points more likely to anticipate falling into arrears than firms in the 90th percentile. The corresponding differences in countries with low competition were 5 and 8 percentage points, respectively. In countries with high competition, the lowest productivity firms were 19 percentage points less likely to have increased remote work than the highest productivity firms; this difference was only 5 percentage points in countries with low competition. -Investigating how reallocation during the COVID-19 crisis compares to pre-crisis reallocation, we find that the relationship between productivity and employment growth was stronger during COVID-19. More productive firms experienced lower sales growth than less productive firms pre-COVID-19, a pattern which was reversed during the crisis. More productive firms experienced higher employment growth than less productive firms pre-crisis, but to a lesser or equal extent than during the crisis. These findings are subject to the caveat that there are some limits to the comparability of the growth rates of sales and employment. -Examining the determinants of receiving government support, we have three main findings.
First, productive firms were less likely to receive any type of government support, even after controlling for the drop in sales or employment experienced by the firm. Second, larger firms were more likely than smaller firms to receive some types of government support, which could indicate that support was more likely to go to politically connected firms. Third, support measures were given to firms regardless of their pre-crisis level of innovation.
The paper is organized as follows. Section 2 presents the data and summary statistics. Section 3 describes the empirical strategy. Section 4 discusses the main results. Section 5 includes several robustness checks, and section 6 concludes.

Data and summary statistics 2.1 Enterprise Survey data
To measure firm performance and government support received during the COVID-19 crisis, we use data from the ES COVID-19 Follow-Up Surveys. 1 The World Bank has collected one or two rounds of these surveys in 44 countries starting in May 2020. We use the latest available survey round for a country as of June 28, 2021. We conduct our main analysis in a sample of 23 emerging markets and developing countries in Europe and Central Asia (ECA) to focus on a homogenous environment (see table A.1). As a robustness check, we replicate the results in a global sample of 30 countries (see section 5.4).
The sampling frame for the ES COVID-19 Follow-Up Surveys includes all firms that replied to the latest pre-COVID-19 ES, making it possible to link performance during the COVID-19 pandemic back to firm characteristics collected through the ES. The ES cover formal (registered) firms with 5 or more employees and are designed to be nationally representative for the manufacturing and services sectors. For the countries in our sample, the most recent ES was conducted either in 2019 or early 2020.
The ES COVID-19 Follow-Up Surveys ask firms about changes in performance during the crisis, as well as expectations for the next months. Table A.2 provides the definitions of the variables included in our analysis. Table 1 shows summary statistics. The number of observations varies across variables due to nonresponse. Firms reported that their sales dropped by an average of 24 percent during the COVID-19 crisis. The decline in the number of permanent full-time workers (13 percent) was more muted than the drop in sales, a pattern that was also observed by Apedo-Amah and others (2020). Apedo-Amah and others argue that firms reduced their workforces proportionally less than their drop in sales in part because they relied on other mechanisms to adjust output and labor costs, including granting leave, reducing hours, and lowering wages.
Close to 34 percent of firms decreased their number of workers since December 2019. About 26 percent reported that they anticipated falling into arrears on outstanding liabilities in the next six months. To respond to the crisis, 28 percent of firms started or increased online business activity, and 34 percent started or increased remote work arrangements.
The ES COVID-19 Follow-Up Surveys also include questions on government support received during the pandemic. Governments in ECA enacted numerous measures to support firms, many directed to small and medium-size enterprises (SMEs) according to the World Bank's SME-Support Measure Dashboard. 2 The five most common types of measures were new credit (26 percent of measures), fiscal relief (20 percent), wage subsidies (17 percent), payment deferrals (12 percent), and cash grants (7 percent). These five types of measures are also the ones captured in the ES COVID-19 Follow-Up Surveys. These surveys show that the measure reaching by far the largest number of firms was wage subsidies, with 36 percent of firms receiving such subsidies (table 1). The second-most wide-reaching measure were cash transfers, followed by fiscal relief and payment deferrals. Although new credit was the most used measure, according to the World Bank's SME-Support Measure Dashboard, it reached only 5 percent of firms. Altogether, 45 percent of firms reported receiving some type of government support.
From the pre-COVID ES, we obtain our measure of productivity, as well as firm's background characteristics. We use labor productivity, defined as (log of) sales over total number of permanent fulltime employees. 3 Labor productivity is preferred to total factor productivity due to limitations in measuring non-labor inputs in the ES (World Bank 2021a). Panel B in table 1 displays summary statistics for pre-COVID ES data. The average firm in our sample had 31 employees (2.65 in log units) and was 19 years old (2.79 in log units). Only 21 percent of firms had a female top manager. The sample includes few firms with at least 10 percent state or foreign ownership (1 and 8 percent, respectively). Less than one half reported that they innovated a product or process during 2017 to 2019 (40 percent) or had a loan (40 percent), but 65 percent owned a website. Most firms reported that their main market was local (44 percent) or national (44 percent) versus international (12 percent). Slightly over half of the firms are in the services sector (53 percent), followed by manufacturing (26 percent) and retail (21 percent).
To examine how reallocation from less productive to more productive during the COVID-19 crisis compares to pre-crisis reallocation, we use additional data from the latest pre-COVID-19 ES. These surveys collected sales and employment for the year of the survey (typically 2019) and two years prior (typically 2017). We calculate labor productivity for 2017, following the same procedure as for 2019. We extrapolate 2018 sales and employment as the simple average of the 2017 and 2019 values, to then calculate 1-year growth rates, which are most comparable to the sales and employment growth rates collected in the ES COVID-19 Follow-up Surveys. Table 1 shows that the average log of labor productivity in 2017 was only slightly lower than in 2019 (10.47 vs. 10.51, respectively). On average, firms experienced positive sales growth from 2017 to 2018, at a rate of 9.21%. Employment also grew on average, but at a lower rate than sales (4.07%).

Competition Environment
We use one main measure of the competition environment and report results for additional measures in the section on robustness checks. Our main measure of market organization comes from the 2020 Bertelsmann Stiftung Transformation Index (BTI), which surveys political and economic transformation around the world based on expert opinion for 2019. The measure captures the extent to which the fundamentals of market-based competition have been developed, with a maximum score (10) indicating that "market competition is consistently defined and implemented both macroeconomically and microeconomically. There are state-guaranteed rules for market competition with equal opportunities for all market participants. The informal sector is very small." The minimum score (1) indicates that "market competition is present only in small segments of the economy and its institutional framework is rudimentary. Rules for market participants are unreliable and frequently set arbitrarily. The informal sector is large." We use three additional measures of the competition environment in the section on robustness checks. The first alternative measure is BTI competition policy, which captures the extent to which safeguards exist to protect competition and are enforced, with the maximum score (10) indicating that "comprehensive competition laws to prevent monopolistic structures and conduct exist and are strictly enforced." A score of 1 indicates that "no legal or political measures are taken to prevent monopolistic structures and conduct. As such it is a more narrowly defined measure of the competition environment.
The average score for ECA is 7 for BTI market organization and 8 for competition policy, with significant variation across countries (table 2). A score of 7 corresponds to an assessment that "market competition has a strong institutional framework, but the rules for market competition are not consistent or always uniform for all market participants. The informal sector is small" and "competition laws to prevent monopolistic structures and conduct exist, but are enforced inconsistently." The Czech Republic, Estonia, and Slovenia all achieve the maximum score of 10 for both BTI measures. Croatia, Latvia, Lithuania, and Poland achieve a score of 10 for the competition policy measure. The competitive environment is least developed in Azerbaijan, which scores poorly (4) on both BTI measures. Belarus scores similarly poorly on market organization.
We use BTI market organization data for 2017 when examining reallocation in pre-crisis times. Overall, the scores are similar to the ones in 2019 with an average score of 7 for ECA and a similar pattern of variation across countries. The Czech Republic and Estonia achieve the maximum score of 10, while Azerbaijan and Belarus score poorly (4).
We also use two proxy measures for the size and influence of the state in the economy as alternative competition variables since they may influence the competition environment. Specifically, countries in which the state plays a relatively large and influential role in the economy may have weaker competition environments. One proxy is the share of public sector employees in total employment from Worldwide Bureaucracy Indicators (World Bank 2020b). 4 This share averages 23.6 percent in ECA and ranges from 13.5 percent in Romania to 39.3 percent in Belarus.
The other proxy is the share of public banks in total banking sector assets, available from Anginer, Demirgüç-Kunt, and Mare (2020). A public bank is defined as a commercial bank in which the state has more than 50 percent ownership stake. A large body of literature suggests that public banks may be subject to government influence and that their existence may distort credit allocation, introducing inefficiencies (World Bank 2012). The 12 percent average for the region masks huge variation across countries. The share is as high as 66 percent in Russia and 64 percent in Belarus, but 0 in 10 of the 23 countries in the sample.

Firm Performance, Productivity, and Competition
We examine the relationship between firms' pre-crisis labor productivity and firm performance during the crisis by estimating the following regression specification: where subscript i denotes each firm; j the sector of the firm; and k the country in which the firm is located. Similar specifications have been used by Kozeniauskas, Moreira, and Santos (2020) and Muzi and others (2021) to assess the impact of COVID-19 on firm performance. The dependent variable firm performance is captured by one of six variables: percentage change in sales; percentage change in employment; decreased employment; anticipate falling into arrears; increased online activity; and increased remote work. We estimate all regressions using ordinary least squares.
Our two main variables of interest are Productivity and Productivity x Competition, an interaction term between labor productivity and country-level measures of competition, to examine whether the relationship between labor productivity and firm performance during the crisis varied with market competition. Productivity is proxied by pre-COVID-19 labor productivity measured by sales per worker. Competition is captured by the variable BTI market organization (and additional variables in the robustness section). The country-level measures of competition are de-meaned, so that a value of zero corresponds to the average value of competition across countries. The coefficient on labor productivity in table 3 thus represent the relationship between productivity and firm performance for a country with the average value of competition.
Firm Characteristics is a vector of firm level controls and includes size (number of employees), age, gender of the top manager, innovated a product or process during 2017-19, state ownership, foreign ownership, line of credit or loan, owns a website, and location of main market (local, national, or international). The set of firm characteristics comprise a standard set of variables related to a firm's performance during crisis. 5 Larger and older firms may weather a crisis better because they are more established and have more resources, financial and organizational, to do so. They may also have strong political connections. On the other hand, smaller and younger firms may be nimbler and can thus more easily adjust their operations when faced with a crisis. More innovative firms may be more likely to adapt during a crisis. Firms with state and foreign ownership may be more resilient to crises by having access to more resources, connections, or know-how. At the same time, state ownership may make firms less adaptable in times of crisis. Firms with a line of credit or loan pre-crisis may have easier access to finance during a crisis that allows them to mitigate its impacts. We control for the gender of the top manager as Liu, Wei, and Xu (2021) document that women-led businesses are more likely to close, close longer, and anticipate falling into arrears than men-led business during the COVID-19 pandemic. We also control for owning a website. Wagner (2021) shows that firms with a website are more likely to survive during the pandemic, potentially because having an online presence allows firms to be in contact with potential customers even when in-person contact is restricted.
Finally, we control for location of a firm's main market (local, national, or international) as movement restrictions due to the pandemic might impact firms with different main markets differently. Moreover, export-oriented firms may be able to substitute sales at home with sales abroad during a negative demand shock at home given that, while global in scale, the COVID-19 crisis affected countries differently (Muzi and others 2021). Firms whose main market is national rather than local may similarly substitute sales as the COVID-19 crises often affects regions within a country differently.
S represents sector fixed effects and C country fixed effects. The error term ε is clustered at the countrylevel. The specification is estimated using sampling weights provided in the ES COVID-19 Follow-Up Surveys to correct for unequal probability of selection as well as for ineligibility. We rescale the sampling weights to give equal weight to all countries since the focus of our analysis is on competition which is measured at the country level.
A potential challenge in our analysis of the relationship between firms' pre-crisis labor productivity and firm performance is endogeneity. Firm performance during the crisis might be a predictor of a firm's labor productivity. As noted by Muzi and others (2021), the fact that all our explanatory variables, including labor productivity, come from the matched pre-COVID-19 ES dataset, should alleviate this concern.
Another potential challenge is that a country's competitive environment may be correlated with other country characteristics that could influence the reallocation of economic activity from less to more productive firms. In addition to including alternative measures of competition (see section 2.2), we therefore interact productivity with log GDP per capita instead of our measures of competition as robustness test to check whether we just capture the level of economic development.

Government Assistance, Productivity, and Firm Characteristics
We examine the relationship between receiving any government assistance or different types of government assistance and firm characteristics by estimating the following regression specification: where subscript i denotes each firm; j the sector of the firm; and k the country in which the firm is located. The dependent variable is Government Assistance received by a firm which is captured by one of six binary variables: received any government support and types of government assistance received (cash transfer; payment deferrals; new credit; fiscal relief; or wage subsidies). We estimate the regression using ordinary least squares.
Here our main variable of interest is Productivity, proxied as in specification (1) by pre-COVID-19 labor productivity measured by sales per worker. Firm Characteristics is the same vector of firm level controls as above as we expect the firm characteristics that are correlated with firm performance during the COVID-19 crisis to be correlated with receiving government assistance to weather the crisis. S represents sector fixed effects, C country fixed effects, and ε the error term, which is clustered at the country-level. The specification is estimated using rescaled sampling weights provided in the ES COVID-19 Follow-Up Surveys to correct for unequal probability of selection as well as for ineligibility.
The challenge in our analysis of the relationship between government assistance, productivity, and firm characteristics is that the data do not include detailed information on when exactly firms received government support, making it hard to assess whether government support may have influenced firm performance. Furthermore, while we know which firms received government assistance; we do not know the criteria which made these firms eligible for such support. As a robustness check, we include additional controls for the percentage change in sales and employment, to control for the possibility that firms that were impacted most were more likely to receive support.

Reallocation from Less Productive to More Productive Firms
The evidence in this section suggests that economic activity in ECA was reallocated toward more productive firms during the crisis, consistent with creative destruction. Table 3 displays the results of estimating equation 1. These coefficients on labor productivity show that firms with high pre-crisis labor productivity experienced statistically significantly smaller drops in sales and employment than firms with low pre-crisis labor productivity. More productive firms were also less likely anticipate falling into arrears and more likely to adapt to the crisis by increasing remote work.
The magnitudes of the coefficients on labor productivity in table 3 reveals that firms in the 10th percentile of the pre-crisis labor productivity distribution experienced a 13-percentage point larger drop in sales and a 9-percentage point larger drop in employment than firms in the 90th percentile. 6 These firms were also 11 percentage points less likely to have increased remote work than firms in the 90th percentile. Firms in the 10th percentile of the pre-crisis labor productivity distribution were 11 percentage points more likely to anticipate falling into arrears than firms in the 90th percentile.
Table 3 further shows that larger firms weathered the crisis better. Firms with female top managers were hit harder by the crisis than firms with male top managers. State-owned firms experienced smaller drops in sales than privately-owned firms, although this is only statistically significant at the ten percent level. A more significant difference appears in state-owned firms' lower ability to adjust to the crisis by increasing online activity and remote work. Firms that were more innovative in the pre-crisis period continued to innovate during the crisis as well, since they were more likely to increase their on-line activity and remote work. Finally, firms that primarily sell to the national market did better than firms that sell to the local market and those with access to international markets were better able to increase remote work and anticipated lower arrears.

The Role of Competition
The importance of competition has been evident during the COVID-19 crisis. The coefficients on the interaction terms in table 3 shows how the relationship between labor productivity and firm performance depends on market competition. For example, the positive coefficient on "Log(labor productivity)*BTI market organization" in the first column of table 3 indicates that the relationship between labor productivity and the drop in sales was stronger in countries with greater competition.
Overall, the coefficients on the interaction terms in table 3 suggest that in ECA countries with more competitive markets, there was more reallocation of economic activity from less productive to more productive firms. Figure 1 illustrates the findings from table 3. In countries with high competition, the relationship between labor productivity and firm performance during the crisis is steeper than in countries with low competition. In countries with high competition, firms at the 10th percentile of the pre-crisis labor productivity distribution experienced an 18-percentage point larger drop in sales than firms at the 90th percentile; this difference is only 10 percentage points in countries with low competition. Similarly, in countries with high competition, firms in the 10th percentile of the labor productivity distribution were 15 percentage points more likely to decrease employment and 16 percentage points more likely to anticipate falling into arrears than firms in the 90th percentile. The corresponding differences in countries with low competition were 5 and 8 percentage points, respectively. In countries with high competition, the lowest productivity firms were 19 percentage points less likely to have increased remote work than the highest productivity firms; this difference was only 5 percentage points in countries with low competition.

Reallocation Pre-COVID-19
We now examine how reallocation during the COVID-19 crisis compares to pre-crisis reallocation. The results in panel A of table 4 show that more productive firms experienced lower sales growth pre-COVID-19, but they experienced higher employment growth. Comparing panels A and B, we find that the relationship between productivity and employment growth was weaker pre-COVID-19 than during COVID-19. This finding is subject to the caveat that the growth rates of employment do not always cover the same period, since the growth rate during the pandemic is relative to employment in December 2019 and the ES COVID-19 Follow-up Surveys were conducted in different months of 2020 and 2021 (see table A.1). Nevertheless, unlike studies that have compared reallocation during previous crises to noncrisis times in the US and Indonesia (Foster, Grim, and Haltiwanger 2016; Hallward-Driemeier and Rjikers 2013), we find no evidence that reallocation of sales and employment from less to more productive firms decreased during COVID-19. On the contrary, this reallocation in ECA appears to have increased during COVID-19.
Other notable results in table 4 are that younger and smaller firms in ECA grew faster pre-COVID-19 (consistent with Ayyagari, Demirguc-Kunt, and Maksimovic 2020 and Haltiwanger, Jarmin and Miranda 2013), while larger firms performed better than small firms and age was not correlated with growth during the pandemic. Countries with more competitive markets experienced more reallocation from less productive to more productive firms during COVID-19, but not before COVID-19. Table 5 shows the results from estimating equation 2 to examine the correlates of receiving government assistance. Three main findings emerge from this analysis.

Government Assistance and Firm Characteristics
First, more productive firms were less likely to receive any type of government support. Firms in the 10th percentile of the pre-crisis labor productivity distribution were 8 percentage points more likely to receive government support than firms in the 90th percentile. This result is consistent with Kozeniauskas, Moreira, and Santos (2020), who find that higher productivity firms in Portugal were less likely to receive government support, controlling for age, total income and an indicator for whether a firm is located in the Lisbon region.
Second, larger firms were more likely than smaller firms to receive payment deferrals and fiscal relief. This result is in line with that of Cirera and others (2021), who find that, across 60 countries, support was more limited for smaller firms than larger firms. This finding could indicate that support was more likely to go to politically connected firms, which tend to be much larger than firms that are not politically connected. Before COVID-19, politically connected firms were more likely to receive subsidies (Francis, Hussain, and Schiffbauer 2018).
Third, pre-crisis innovation is not correlated with receiving any type of government support. Table 5 further shows that firms with a female top manager, firms that had a line of credit or loan, and firms that own a website were more likely to receive government support.
Taken together, we find that the broad support governments provided disproportionately went to less productive and larger firms, irrespective of their pre-crisis innovativeness. Less productive firms that stay in the market with the help of government support can stifle innovation and productivity growth in the recovery phase and beyond. The provision of more support to large firms can also increase industry concentration and market power.

Measurement of Competition
A challenge when estimating equation 1 is that competition is likely correlated with other country characteristics that could also influence reallocation from less to more productive firms. That is, the interaction term in equation 1 may not specifically capture the role of competition, but the role of other characteristics, such as the level of economic development.
We conduct two robustness checks to address this concern. First, we show that our findings are similar when using three alternative measures of the competitive environment. Table A.3 shows the results of estimating equation 1 when using the BTI competition policy measure. The coefficients on the interaction terms are similar to using the BTI market organization but only statistically significant for decreased employment and anticipating falling into arrears.
Public sector employment and ownership of banks can also affect competition. Results for these two measures are reported in tables A.4 and A.5. Labor markets may be less competitive and dynamic when the share of public sector employment is high. Using the share of public employment as a proxy for competition reveals that a larger share of employment in the public sector was associated with reduced reallocation from lower productivity to higher productivity firms during the COVID-19 crisis. Public ownership of banks can limit dynamism in the economy by leading to less efficient allocation of credit. The coefficients on the interaction terms suggest that reallocation from lower productivity to higher productivity firms during the COVID-19 crisis was lower in countries in which a larger share of banking sector assets was government-owned.
Second, we use log GDP per capita instead of the BTI in the interaction term with labor productivity to test whether the BTI measures of competition just capture the level of economic development. The results in table A.6 provide little evidence that reallocation from less to more productive firms was greater in countries with higher GDP per capita. The interaction term is only statistically significant at the ten percent level for one outcome variable: decreased employment. Higher productivity firms were less likely to decrease employment in countries with higher GDP per capita. However, GDP per capita does not mediate the correlation between productivity and change in sales, anticipation of falling into arrears, or probability of increased remote work.
The robustness checks in tables A.3 to A.6 thus suggest that competition instead of economic development more broadly played a role in increasing reallocation from less to more productive firms during the COVID-19 crisis.

Controlling for Severity of COVID-19 Restrictions
Another identification concern is that measures of competition may be correlated with a country's response to the crisis. For example, movement restrictions may have been more stringent in countries with a more competitive environment. We thus run a robustness test where we add an interaction term of labor productivity and a COVID-19 restrictions index to the specification in table 3. Here, COVID-19 restrictions are measured by the number of days the Oxford COVID-19 Government Response Tracker's Stringency Index-which captures the strictness of 'lockdown style' policies that primarily restrict people's behavior along nine dimensions ranging from 0 to 100, with higher values indicating more restrictions 8 -is above 70 preceding the field work start day of the ES COVID-19 Follow-Up Surveys module in each country. , with the exception that the interaction of labor productivity and the BTI market organization is not statistically significantly correlated with the probability of increasing remote work. Overall, the relationship between competition and reallocation from less to more productive firms during the crisis is thus not driven by differences in COVID restrictions across countries.

Endogeneity of Government Assistance and Productivity
A potential source of endogeneity when estimating the relationship between government assistance and productivity in table 5 is that more productive firms may have been less likely to receive government support than less productive firms since the former experienced smaller drops in sales and employment. The ES data does not include detailed information on when firms received government support, making it hard to assess whether a drop in sales/employment or government support came first. This timing issue is the reason why we do not control for drop in sales or employment in equation 2 (which we estimate in table 5).
However, we conduct robustness checks where we add change in sales or in employment to equation 2. The results are in tables A.8 and A.9. They show that firms with larger drops in sales were more likely to receive government support, but for a given change in sales or employment, firms with lower labor productivity were still more likely to receive support.

Global Sample
To examine whether our findings are specific to ECA countries, we replicate tables 3 and 5 in the global sample of 30 countries for which all data we use is available, thus adding seven countries outside ECA (listed in panel B of table A.1). This sample includes all countries for which pre-COVID ES data is available in 2018 to 2020 (with fieldwork completion dates in 2019 to 2020). While ES data is available for five additional countries (Cyprus, Greece, Italy, Malta, and Portugal), these countries are not covered by the competition measure from the Bertelsmann Stiftung Transformation Index (BTI).

Conclusion
We use ES data to show that, during the COVID-19 crisis, economic activity was reallocated toward more productive firms. More productive firms were also more likely to adapt to the crisis by increasing remote work. These findings suggest that the COVID-19 crisis has resulted in "creative destruction." However, as of June 2021, even highly productivity firms reported losses in sales and employees (though these were smaller in magnitude than in low productivity firms). The crisis will thus only have a silver lining if the release of resources by low productivity firms is followed by increased growth in more productive firms during the recovery (Caballero and Hammour 2005).
We find that reallocation from low to high productivity firms was greater in countries with more competitive markets. This result is consistent with the previous literature showing that competitive markets are better at allocating resources towards more productive firms (Arnold, Nicoletti, and Scarpetta 2011; Brown and Earle 2001;Caballero 2008).
We also find that reallocation of sales and employment from low to high productivity firms during the COVID-19 crisis was stronger compared to pre-crisis times. This finding is subject to the caveat that there are limits to the comparability of the growth rates of employment for the two periods. Nevertheless, our result contrasts with previous studies that find that reallocation from less to more productive firms during recent (non-COVID-19) crises is weaker than during non-crisis times (Foster, Grim, and Haltiwanger 2016;Hallward-Driemeier and Rjikers 2013).
Many governments implemented broad policy support schemes to promptly address the economic fallout from the COVID-19 crisis and provide immediate relief to protect firms and workers from the worst effects. These government support measures and some exemptions to competition law and policies may be appropriate, if they are temporary and designed to minimize the disruptive effects they can have on competition (Akcigit and others 2021; Pop and Amador 2020). Our analysis suggests that the government support measures implemented in ECA may have adverse effects on competition and productivity growth since they went to less productive and larger firms, regardless of their pre-crisis innovation.
Government policy measures aimed at supporting firms through the economic fall-out of the pandemic have also reignited concerns about propping up so-called "zombie firms"-firms that are unprofitable but remain in operation only because of cheap credit and debt forbearance (ECB 2021). Zombie firms may reduce economy-wide productivity by crowding out resources for new, more productive firms. In the wake of the global financial crisis, low interest rates and weak insolvency frameworks contributed to a significant increase in the share of zombie firms in several European countries (Acharya and others 2020). Government support measures enacted in response to the pandemic may have further increased that share.
As economies enter the economic recovery phase, it will be important for policy makers to phase out policy support measures as soon as appropriate and focus on fostering a competitive business environment while continuing to protect vulnerable households. Such an environment is key to a strong recovery; resilience to future crises; and sustainable, long-term economic growth.
---. 2021b. Supporting Firms in Restructuring and Recovery. EFI Insight: Finance, Competitiveness and Innovation, Washington, DC. is, the average the y-value plotted against the average productivity in a bin/percentile range of productivity. The plots control for number of employees; firm age; gender of the top manager; innovation; state ownership, foreign ownership, access to credit or loan, ownership of website, location of the main market (local, national, or international); sector; and country fixed effects-that is, both the yand xvariables are residuals (with the mean added back in). The lines are estimated using the underlying data, not binned data, equivalent to estimating equation 1, but with high/low competition dummies (two interaction terms with productivity) instead of a continuous variable interaction term.

Started or increased online business activity
Equals 100 if firm started or increased online business activity and 0 otherwise.

Started or increased remote work arrangements for workforce
Equals 100 if firm started or increased remote work arrangements for workforce and 0 otherwise.

Received government support
Equals 100 if firm received any national or local government assistance provided in response to the COVID-19 outbreak and 0 otherwise.

Cash transfer
Equals 100 if the government support received by firm was a cash transfer and 0 otherwise.

Payment deferrals
Equals 100 if the government support received by firm was a payment deferral and 0 otherwise.

New Credit
Equals 100 if the government support received by firm was new credit and 0 otherwise.

Fiscal relief
Equals 100 if the government support received by firm was fiscal relief and 0 otherwise.

Wage subsidies
Equals 100 if the government support received by firm was wage subsidies and 0 otherwise.  Source: Most recent ES COVID-19 Follow-up Surveys and Enterprise Surveys for 23 countries in ECA and Transformation Index of the Bertelsmann Stiftung (2020). Note: All regressions are OLS and include sector and country fixed effects. BTI competition policy is based on the question "To what extent do safeguards exist to protect competition, and to what extent are they enforced?" Change in employment is not available for the Russian Federation. BTI competition policy is centered on its mean to facilitate interpretation of the coefficients. Standard errors are clustered at the country level. *** p < 0.01, ** p < 0.05, * p < 0.1.       Note: All regressions are OLS and include sector and country fixed effects. BTI competition policy is based on the question "To what extent do safeguards exist to protect competition, and to what extent are they enforced?" Change in employment is not available for the Russian Federation. BTI competition policy is centered on its mean to facilitate interpretation of the coefficients. Standard errors are clustered at the country level. *** p < 0.01, ** p < 0.05, * p < 0.1. Note: Regressions include sector and country fixed effects. Standard errors are clustered at the country level. *** p < 0.01, ** p < 0.05, * p < 0.1.