This chapter analyses the impact of the COVID-19 pandemic on business dynamics and firm financial vulnerability. It first outlines the short-term consequences of the crisis on firm entry and exit, examining reductions in the creation of new firms as well as the results of government policy measures to prevent immediate bankruptcies. This part of the chapter also highlights patterns of recovery and new business opportunities arising from the crisis. The chapter then discusses the influence of the pandemic on business dynamism, analysing how aggregate firm characteristics, such as age and size, affect the resilience of the economy, and how the crisis may further decrease business dynamism in the absence of carefully designed policy responses. It outlines how small and medium enterprises are particularly vulnerable to insolvency and increased debt loads, and face difficulty in accessing finance. It concludes with a discussion on policy implications to address concerns surrounding these issues.
Strengthening Economic Resilience Following the COVID-19 Crisis
4. Business dynamics and financial vulnerabilities
Abstract
Key findings
The crisis greatly impacted business dynamics in the short term, but many economies showed greater resilience than feared, which is partly the result of successful support policies. In the initial stage of the crisis, global firm entry declined markedly. However, as the pandemic progressed, there was significant variation in entry across countries, and the crisis also created new business opportunities. Some countries have seen a fast V-shaped recovery, some a U-shaped recovery, and others had not yet rebounded to pre-crisis entry levels by the end of 2020. Firm exit also varied across countries. Contrary to initial fears that the COVID-19-induced shock would trigger a spike in firm closures, the number of bankruptcies in most countries remained below that of the previous year, indicating that government support packages, and the relaxation of legal constraints to firms, were successful policy tools over the short term.
Longer-term risks to business dynamism still remain. Many viable firms will face continued financial hardship in the recovery period, and government support will be necessary to ensure both their survival and ability to invest. There is also a risk of a missing generation of entrants, which may have long-lasting consequences for innovation, employment and productivity growth, and could amplify the effects of the pre-crisis decline in entry rates.
To foster a dynamic recovery, governments should implement policies that target structural challenges. Such policies can not only help make the recovery faster and more efficient, but also more sustainable and inclusive. Policy packages should reduce red tape and barriers to entry, ensure that bank credit or other sources of finance are available for young and small firms, support innovation and experimentation, and facilitate a broad uptake of new technologies and intangible assets. To ensure inclusion, governments should also support workers in upskilling and transitioning to new jobs during the resource reallocation process fostered by these policies.
Introduction
This chapter assesses impacts of the COVID-19 crisis along firm characteristics and examines pre-existing trends in business dynamics, mapping out a number of relevant channels – most importantly, financial vulnerabilities – that vary systematically across different types of firms.
The chapter starts by giving an overview of early impacts of the crisis on new firm entry and bankruptcies based on available data for 2020, and presents recent analytical work on the potential medium-term impacts of the crisis on firm financial health. Financial factors that have important longer-term implications for the recovery period – including on employment, productivity, and industry structure – are also discussed.
Beyond the immediate impact of the crisis, the chapter then discusses vulnerabilities arising from the structure of the business sector, which differ across both industries and countries. It describes pre-crisis characteristics of the business population at the country and industry level that may contribute to shape the aggregate effect of the crisis on business dynamics, and discusses challenges related to a pervasive slowdown in business dynamism over the last two decades. The analysis centres in particular on the prevalence of young and small firms, secular declines in entry rates, and the extent of corporate indebtedness, combining preliminary findings on early impacts with longer-term pre-crisis trends, to map out potential medium- to long-term impacts.
The chapter concludes with a summary of policy measures which may help to support business dynamics in the recovery period, and highlights potential adverse impacts on concentration. These policy suggestions and guidelines aim to help governments design and decide on interventions and measures to support firms through the pandemic and into the recovery period.
Initial impacts of the COVID-19 pandemic on firm entry and bankruptcies
Firm entry
New and young firms are key for job creation, innovation, and economic growth. On average, across OECD countries, they employ around 20% of the total workforce and create almost half of new jobs.1 New firms also drive long-term sectoral transformation and contribute to innovation and long-term productivity growth. Analysing the impacts of the crisis on new firms – including firm entry, growth, and survival – is therefore of particular relevance for an assessment of its longer-term consequences.
Figure 4.1. Percentage change in number of entries per month in 2020 vs. 2019
Note: The figure plots the year-on-year difference in business openings each month (or quarter for Finland, the Netherlands and Norway) in percentage (dark blue bar), comparing the level of business openings to the same month (quarter) of 2019. The light blue line instead plots the difference in percentage of cumulative openings from January to each month considered. When the light blue line goes above the horizontal axis, this means that more firms have been created in 2020 than in 2019, since January of each year. The data usually refer to business registrations, including all businesses (including sole proprietorship) when possible, but similar dynamics are evident when focusing on legal entities only. Data may be preliminary, experimental and subject to revision, and differ from official data.
Source: OECD (2021[1]), “Business dynamism during the COVID-19 pandemic: Which policies for an inclusive recovery?”, https://doi.org/10.1787/f08af011-en.
Monthly and quarterly indicators of firm entry (Figure 4.1) point to substantial initial impacts of the crisis on business formation, with large declines observed between March and May 2020 in all countries. This initially raised concerns about a potential missing generation of new firms, with persistent negative effects on gross domestic product (GDP), aggregate employment and productivity (OECD, 2021[1]; OECD, 2021[2]; Gonzales-Torres, Manaresi and Scoccianti, 2020[3]; Sedláček and Sterk, 2020[4]; Sedláček, 2020[5]; Gourio, Messer and Siemer, 2016[6]). Subsequent developments from May 2020 have proven more heterogeneous across countries, with some displaying a strong recovery in firm entry from June onwards. Indeed some countries (such as Australia, Canada, Norway, the United Kingdom, the United States and Singapore) experienced a V-type recovery, with high levels of business creation starting in June, whereas others (including Italy, Hungary, Portugal and Spain) faced depressed business creation for longer, reinforcing concerns about a missing generation of new firms.
In the first group of countries, the rebound was sufficiently swift to offset the drop in total entries registered since the beginning of the year within the third quarter of 2020, and sufficiently strong to end the year with a more than 10% increase in entry, relative to 2019. This illustrates that despite high levels of uncertainty and falling demand in many areas, the crisis has also presented new opportunities for firms. While some of these may be short-lived, catering specifically to the crisis period itself, others may persist. The use of new technologies, as well as changes in work and social habits brought about by the pandemic, are likely to continue. For instance, the rapid move towards remote work and e-commerce and the increasing digitalisation of health and education services provide room for start-ups that manage to identify and respond to these new opportunities. Box 4.1 provides an example of how the pandemic, and the resulting need for a rapid upscaling of vaccine production, have introduced new dynamics – but also created new challenges – in the pharmaceuticals industry. Indeed, many successful innovative companies have emerged during periods of crisis and recovery in the past, including a wide range of digital companies such as Dropbox, Uber, Airbnb, WhatsApp, Groupon and Pinterest – which were all founded during or just after the global financial crisis (GFC) – and Alibaba’s Taobao – which was founded during the 2003 SARS outbreak in the People’s Republic of China (hereafter “China”) (OECD, 2020[7]). Such new opportunities could be particularly relevant during the recovery, as they may help firms innovate and adapt to the post-COVID environment, ease the transition to a more digital and green economy, contribute to job creation, and support inclusiveness (Calvino and Scholl, forthcoming[8]). Therefore, ensuring an efficient reallocation of resources and support for the entry of new players will be essential in the post-pandemic phase to help achieve an inclusive, digital and green recovery.
While some countries experienced a quick recovery, others (including Hungary, Italy, Portugal and Spain) seemed to struggle and did not show a clear rebound in entry. In these countries, business registrations rose less significantly after June (and continued to decline in some cases) and, as a result, by September 2020 the total number of entrants in these countries remained significantly below the 2019 level. Other countries for which data are available (Belgium, France, Germany and Iceland) had a U-shaped recovery, in which entry rates slowly increased and drops incurred in the first semester were only offset by the end of 2020.
The impact of the pandemic on firm entry may have significant repercussions on economic outcomes in the long run, most notably on employment (OECD, 2021[1]). Simulations based on the OECD DynEmp3 database (see Box 4.3) indicate that a 25% decline in the number of entering firms in a single year (corresponding to the worst performance recorded across the sampled countries as of September 2020) may lead to a lasting decrease in aggregate employment (Figure 4.3). According to this simulation, such a “missing generation” of firms would depress aggregate employment by around 0.85% three years after the shock. In addition, the negative employment effects of a drop in entries may be exacerbated by below-average growth rates of entering firms, a pattern which has been observed in past recessionary episodes (Sedláček and Sterk, 2017[9]). Conversely, in the event of a strong V-shaped rebound, a 15% growth in entry rates (as registered on average by the best performing countries so far) would raise employment by around 0.5% over the same period, thereby mitigating the economic costs of the pandemic. However, new firms could also still experience lower post-entry growth, reinforcing the need for policies aimed at providing the right conditions for young firms to thrive, as further discussed at the end of the chapter.
Box 4.1. Business innovation dynamics and intellectual property challenges
The COVID-19 pandemic posed unprecedented challenges to the manufacture and distribution of some essential crisis-critical (cc-) products. These products are needed for the prevention, diagnosis and treatment of infections like COVID-19. Recurring waves of the virus, and new mutations, led to repeated demand surges for such products, as well as the rapid development of cc-innovations, such as mRNA-based vaccines (Tietze et al., 2020[10]). Established cc-sector incumbents possessed relevant background intellectual property (B-IP) to produce cc-products, but faced challenges to rapidly scale up production. To solve this problem, firms from other sectors that possessed complementary B-IP – being used in non-cc-sectors – joined incumbents in cc-sectors to deliver the required volumes, and quickly develop further cc-innovations.
Inducing dynamics into the cc-sector innovation ecosystems, these “new entrant” firms followed three entry strategies (Figure 4.2): 1) open innovation (incumbents and new entrants reciprocally cross-license their B-IP), 2) unidirectional licensing (incumbents formally provide authorised access to their B-IP to new entrants), and 3) infringement (new entrants risk injunctions and litigation by using incumbents’ B-IP without seeking prior authorisation).
Figure 4.2. Innovation dynamics of new entrants and incumbents in crisis-critical sectors
The entry dynamics in the cc-sector, combined with the time-critical nature of the production process in the face of the pandemic, create several challenges related to intellectual property rights. Responding quickly to the pandemic might have led some new entrants to infringe on incumbents’ B-IP, but seeking ex-post authorisation to use incumbents’ B-IP will be required for new entrants to stay in the cc-sector beyond the pandemic. Incumbents therefore consider carefully whether to officially grant new entrants – possible future competitors – access to their B-IP.
Rather than granting these rights, incumbents have several options to best leverage their B-IP, through licensing terms and conditions, such as termination and grant/assign-back clauses, in order to minimise the probability of new entrants staying in cc-sectors after the pandemic (Moerchel et al., 2020). An additional challenge is that recombining incumbents’ and new entrants’ B-IP during the pandemic is very likely to result in novel foreground IP (F-IP), for which incumbents and new entrants have to decide on ownership and usage rights. For incumbents, this F-IP might be valuable to strengthen their competitive advantage in a recessionary post-pandemic market setting. For new entrants staying in cc-sectors, ownership and access to this F-IP could prove essential (Moerchel et al., forthcoming[11]).
Policy makers can leverage IP policy instruments to help exit the crisis faster. They need to strike a balance between maximising incentives for investments to develop urgently-needed cc-innovations (e.g. vaccines) during the pandemic, and maximising access to these demanded cc-innovations at a later stage. Existing policy instruments (e.g. compulsory licensing) hardly proved popular among governments (Contreras et al., 2020[13]), and alternative approaches (e.g. IP pledges) for crisis-critical IP innovation policy should be further investigated.
Note: Contributors are (in alphabetical order): Leonidas Aristodemou, Alexander Moerchel, Frank Tietze and Pratheeba Vimalnath.
Sources: Moerchel et al. (forthcoming[11]), “Identifying Crisis-Critical Intellectual Property Challenges during the Covid-19 Pandemic: a visual mapping approach”, https://aom.org/events/annual-meeting/annual-meeting-program/annual-meeting-proceedings; Moerchel et al. (2020[12]), “Identifying Crisis-Critical Intellectual Property Challenges during the Covid-19 Pandemic: A Scenario Analysis and Conceptual Extrapolation of Innovation Ecosystem Dynamics Using a Visual Mapping Approach”, https://doi.org/10.17863/CAM.58372; Tietze et al. (2020[10]), “Crisis-Critical Intellectual Property: Findings From the COVID-19 Pandemic”, https://doi.org/10.1109/tem.2020.2996982.
Figure 4.3. Simulated impact of a shock to firm entry under two scenarios on employment
Note: The figure shows the employment losses or gains associated with a 25% decline (light blue bar) or 15% increase (dark blue bar) in the number of entrants, relative to aggregate employment in the initial year, on average across countries and cohorts of entrants in 1995, 1998, 2001, 2004, 2007, 2010, and 2012. The bands represent low and high values of the effects of the shocks, representing respectively the 25th and 75th percentiles. The simulation is based on the decomposition proposed by Calvino et al. (2016[14]), focusing on A38 industries in manufacturing and non-financial market services. Countries included are Austria, Belgium, Brazil, Canada, Costa Rica, Finland, Hungary, Italy, Japan, Korea, the Netherlands, Norway, Portugal, Spain, Sweden and Turkey.
Source: OECD (2021[1]), “Business dynamism during the COVID-19 pandemic: Which policies for an inclusive recovery?”, https://doi.org/10.1787/f08af011-en.
Bankruptcies and financial distress
Despite initial fears that the demand shock induced by the pandemic would trigger a wave of firm closures, data for 2020 suggest that government interventions have successfully curbed the potential spike in bankruptcies. Figure 4.4 shows that the number of bankruptcies has dropped substantially, relative to those observed in the same months of 2019, in the 12 OECD countries for which data were available (OECD, 2021[1]). On average, monthly bankruptcies fell by around 32% year-on-year since March 2019. As of November 2020, no significant increase had been observed. This fall in recorded bankruptcies contrasts sharply with estimates of potential financial risk to firms in the absence of policy intervention. It reflects both the financial and other support packages available to firms at this time,2 but also significant changes in court processes and temporary suspensions of firms’ obligations to file for bankruptcy.
Figure 4.4. Change in monthly bankruptcies, 2020 vs. 2019
Note: The figure plots the year-on-year difference in bankruptcies each month (or quarter for Spain) in percentages (dark blue bar), comparing the level of business openings to the same month (quarter) of 2019. The light blue line instead plots the difference in percentage of the cumulative number of bankruptcies from January to each month considered. Data presented, however, may refer to different definitions and bankruptcy laws differ markedly across countries. Data may be preliminary and subject to revisions and may differ from official data.
Source: OECD (2021[1]), “Business dynamism during the COVID-19 pandemic: Which policies for an inclusive recovery?”, https://doi.org/10.1787/f08af011-en.
While policy interventions may have prevented an initial damaging wave of bankruptcies, governments face increasing budgetary pressures as well as policy trade-offs. Limiting bankruptcies may be beneficial for the economy in the short run, as it allows for the support of viable firms that would otherwise exit or shrink, thereby reducing firing and re-hiring costs, and limiting the loss of potential output (OECD, 2020[15]). However, there is a growing concern that if unviable businesses are kept afloat, capital and labour are prevented from being channelled towards new business opportunities and more productive uses. This may negatively affect resource allocation and aggregate productivity growth in the longer run.
Box 4.2. Corporate sector vulnerabilities during the COVID-19 outbreak: Assessment and policy responses
The health crisis caused by the COVID-19 outbreak has led public authorities to take unprecedented measures to contain the propagation of the virus, with negative effects on economies. There is widespread concern that one of these negative effects will be COVID-19 crisis-induced liquidity shortages, which may cause firm bankruptcies on a large scale.
A recent OECD paper (Demmou et al., 2021[16]) examines the financial vulnerability of firms associated with confinement measures, and discusses the immediate steps that governments can take to reduce the risk of widespread bankruptcies. Using a sample of almost one million European firms, and building on existing work (Schivardi and Romano, 2020[17]), the share of firms that would turn illiquid with and without policy interventions are compared.
Figure 4.5 reports the main results for a “downside” scenario, foreseeing a sharp drop in activity lasting two months, a progressive but not complete recovery in the next seven months and a second – relatively smaller – outbreak from the eighth month onwards. Without policy actions (left panel), around 18% of firms in the sample would run out of liquidity after one month, 26% after two months and 30% after three months. The majority of these firms are potentially solvent, but may lack sufficient collateral to bridge a shortfall in liquidity. A decisive public intervention (right panel), in the form of tax deferrals, moratoria on short-term debt, but especially support to wage payments, is found to be crucial. The combination of these policies would decrease the number of firms running out of liquidity after two months by half, from 26% to around 13%, compared to the non-policy scenario.
Figure 4.5. Liquidity shortfalls and the impact of policies
Note: The figure shows the share of firms facing liquidity shortfalls: in panel A, overall (solid line); but still potentially solvent, i.e., if the value of their assets is larger than the value of the liabilities (dashed line); having collateral to pledge to obtain additional bank financing, i.e., if the value of their fixed assets is larger than the value of their non-current liabilities (dotted line). In panel B, the temporary support to wage payments is assumed to be a short-term work scheme, which is conditional on the sectoral size of the shock and modelled through an increase to 0.8 of the elasticity of wage bill to sales. The calculations are based on the downside scenario. The downside scenario foresees a sharp drop in activity lasting two months, a progressive but not complete recovery in the next seven months and a second, relatively smaller, outbreak from the eighth month onwards.
Source: Demmou, Franco, Calligaris and Dlugosch (2021[16]), “Liquidity shortfalls during the COVID-19 outbreak: Assessment and policy responses”, https://doi.org/10.1787/581dba7f-en.
Finally, the current drop in bankruptcies may be swiftly reversed if temporary support and regulatory moratoria were lifted abruptly. This could possibly translate into a significant increase in leverage, and a wave of corporate insolvencies, as poor economic conditions continue to weigh on the corporate sector and firms’ long-term viability. These conflicting considerations highlight the need to implement a balanced strategy to phase out emergency support policies gradually (Demmou et al., 2021[18]). While support to distressed firms is still warranted, interventions should be tailored to avoid the risks associated with debt overhang, relying instead, for instance, on non-debt financing instruments, as well as state-contingent loans. In addition, encouraging timely debt restructuring may help firms continue operations in cases in which support measures do not alleviate financial distress. Finally, policies can help improve the efficiency of liquidation procedures for unviable firms in order to foster resource reallocation.
To guide a balanced policy strategy and gauge the economic risk it must address, OECD work (Demmou et al., 2021[18]) quantitatively evaluates the impact of the pandemic on firms’ long-term viability (see also Box 4.2 for complementary research with these data). The economic shock is modelled as a change in firms’ operating profits, resulting from the sharp reversal in sales and from firms’ limited ability to fully adjust their operating expenses. After calculating the decline in profits and taking job support schemes implemented during the first phase of the crisis into consideration, the model allows the authors to predict: 1) the share of distressed firms (i.e. firms whose net equity is predicted to be negative), which are at high risk of being insolvent, and the share of firms not able to cover interest expenses; and 2) the increase in firms’ leverage ratios caused by the crisis. To proxy the magnitude of the sectoral drop in sales, the analysis relies on the first-round demand and supply shocks computed at a detailed sectoral level (del Rio-Chanona et al., 2020[19]). With respect to the duration of the shock, the model presents two alternative scenarios. An “upside” scenario foresees a sharp drop in activity lasting two months (equivalent to the average duration of the confinement period in Q2 2020), followed by progressive but not complete recovery in the remaining part of 2020. A “downside” scenario initially overlaps with the ‘upside’ scenario, but then models a slower recovery due to more widespread further outbreaks of the virus accompanied by stricter mobility restrictions.
The model foresees that, following a sharp reduction in profits, about 7% (9%) of otherwise viable firms would become distressed in the upside (downside) scenario. However, these percentages are heterogeneous across sectors and firm types, as shown in Figure 4.6. Firms in industries that use intangible assets (such as intellectual property, data, or software) intensively are significantly impacted but better positioned to bridge the health crisis in the short term, while the hospitality, entertainment, and transport sectors are the most severely hit.3 In addition, older, more productive and larger companies are relatively better positioned to face the shock compared to their younger, less productive and smaller counterparts, which are likely to have fewer cash reserves and face greater financial constraints.
This fall in equity leads directly to an increase in firms’ leverage ratios, with the median firm predicted to see an increase of 6.7 percentage points in the ratio of total liabilities to total assets in the upside scenario, and of 8.0 percentage points in the downside scenario. In turn, this increase in financial leverage ratios is expected to lead to a decrease in the investment ratio of the median firm by approximately 2.0 percentage points (Figure 4.7). In the post-lockdown period, the preservation of the corporate landscape still warrants high priority. Yet, policy makers need to strike a balance between the risk of phasing out support too early (thereby leading to liquidation of viable firms and thus breaking productive worker-firm matches) and providing across-the-board support for too long (favouring the persistence of unviable firms and slowing down the reallocation process).
Similarly, analysis of the link between financial contractions and employment suggests that maintaining financial liquidity is an essential aspect of economic resilience (Calvino and Verlhac, forthcoming[20]). This research, using micro-aggregated data from the DynEmp project (see Box 4.3), examines the link between firm financial conditions and the employment contraction observed in many countries during the 2008 GFC. At the country level, financial vulnerabilities – reflected in rapid growth in real estate prices, credit-to-GDP gaps, corporate credit growth and increases in banks loans to deposit ratios – are associated with larger contractions in aggregate employment. In addition, the impact falls disproportionately on sectors that are more exposed to financial disruptions due to higher liquidity needs, and which therefore display significantly larger increases in job destruction, resulting primarily from the employment adjustments of incumbent firms.
Figure 4.6. Predicted share of financially distressed firms, by firm characteristics
Note: The figure shows the percentage of distressed firms in the upside (dark blue bars) and downside (light blue bars) scenarios: for the whole economy (panel A); by one-digit NACE Rev.2 sectoral classification (panel B); by sectoral intangible intensity, where intangible intensity is measured following Demmou, Stefanescu and Arquie (2019[21]) as the median ratio (across firms within industries) of intangible over total assets (panel C); by productivity levels, defined according to quartiles within each (two-digit NACE Rev.2) industry of multi-factor productivity computed according to Wooldridge’s (2009[22]) value added based methodology (panel D); by firm age, where age is defined as the difference between 2018 and the year of incorporation of the company and young firms are those with fewer than five years, mature firms those from five to ten years and old those more than ten years (panel E); by firm size, where micro enterprises are those with fewer than ten persons employed, small enterprises those with 10 to 49 employees, medium enterprises those with 50 to 249 employees and large enterprises those with 250 or more persons employed (panel F). Firms are defined as distressed if their book value of equity is predicted to be negative one year after the implementation of confinement measures. Note that the sample is restricted ex-ante to firms having both positive profits and book value of equity in the 2018 reference year. For the sake of exposition, the y-axis scale varies among panels.
Source: Demmou, Calligaris, Franco, Dlugosch, Adalet McGowan and Sakha (2021[18]), “Insolvency and debt overhang following the COVID-19 outbreak: Assessment of risks and policy responses”, https://doi.org/10.1787/747a8226-en.
Figure 4.7. Increase in firms’ leverage and consequent decrease in investment rate
Note: The figure shows: the percentage point increase in the liabilities to total assets ratio for the median firm of the leverage distribution following the COVID-19 outbreak (panel A). Panel B depicts what would be the increase in the investment to fixed assets ratios under the hypothetical increase in the debt over total asset ratios shown in panel A for the median firm.
Source: Demmou, Calligaris, Franco, Dlugosch, Adalet McGowan and Sakha (2021[18]), “Insolvency and debt overhang following the COVID-19 outbreak: Assessment of risks and policy responses”, https://doi.org/10.1787/747a8226-en.
Business dynamics
As established in the previous section, the predicted share of financially distressed firms is higher among small, and notably young, firms. Small and young firms are often more financially constrained (especially in their ability to access external finance), do not yet have strong market positions, and may not be equipped with financial cushions to allow them to survive a prolonged period of reduced activity or revenue (OECD, 2020[23]; Bartik et al., 2020[24]). This suggests that the firms usually contributing substantially to economic dynamism are also more vulnerable to the crisis. Indeed, while under normal circumstances young firms are an important source of innovation, employment and productivity growth (Calvino, Criscuolo and Menon, 2015[25]), they have also been shown to be particularly sensitive to policy settings and economic shocks (Adelino, Ma and Robinson, 2014[26]; Calvino, Criscuolo and Menon, 2016[14]).
As elaborated on in Chapter 5, small firms also tend to have lower uptake of digital technologies that have helped support activity through periods of constrained activity (see Chapter 5, Figure 5.9 and Figure 5.12) and may therefore have been slower or less effective in shifting towards remote work or low-contact delivery of goods and services when COVID-19 restrictions were put in place. Small and young firms are therefore likely to suffer most from the crisis, while they are also overrepresented in the group of low productivity firms (Berlingieri et al., 2020[27]). Young businesses also tend to have lower mark-ups, and hence lower revenue-based productivity, even when their technical efficiency is at or above that of more established firms (Demmou et al., 2021[18]). This reflects their weaker market position, and also further translates into lower financial cushions. Some of these young firms would, under normal circumstances, evolve to contribute to future productivity growth (Berlingieri et al., 2020[27]) and their higher exposure to economic disruptions, combined in some cases with a “lost generation” of young firms, may induce scarring effects on aggregate productivity and employment.
This vulnerability of young and small firms may be a particular concern in countries and sectors where they represent a higher share of the business population, calling for specific attention from policy makers in order to preserve their potential to contribute to economic growth.
In all countries, policy interventions are also needed to address structural challenges, and promote experimentation and dynamism, in order to support a strong recovery that can overcome any long-term macroeconomic weaknesses in the business sector. Indeed, the COVID-19 crisis may further amplify long-term pre-existing trends, such as declining dynamism and increasing industry concentration; a concern reinforced by the heterogeneous effects of the crisis depending on firm age, size and productivity. The pre-crisis period had been characterised by increasing productivity gaps between leaders and laggards, declining entry rates and job reallocation, and increasing industry concentration (Bajgar et al., 2019[28]). Recent OECD work (Berlingieri et al., 2020[27]; Calvino, Criscuolo and Verlhac, 2020[29]) suggests that these structural trends may be grounded to some extent in a lack of capabilities and incentives for younger, smaller and less productive firms to experiment, innovate, and adopt new technologies. The severe economic shock these firms have faced may have further undermined their ability to adapt to new market conditions, experiment and compete with leaders. Therefore, despite the emergence of new business opportunities, it still remains key to address pre-crisis structural weaknesses such as the lack of skills and absorptive capacity, financial constraints or framework conditions favouring incumbents, even in countries experiencing faster rebounds in business registrations.
To offer a longer-term perspective on declining business dynamism, indicators from an OECD-led database (DynEmp3) (see Box 4.3) are exploited to provide additional evidence on the structure of the business population (more specifically, the size and age distribution of firms). These structural indicators (measured in 2014-15, the latest available years) complement the evidence on short-term dynamics related to the crisis presented above. They provide insights on long-term characteristics and trends in business dynamics that shape the resilience of the economy in the medium and long run. This analysis is relevant to inform policies aimed at tackling both pre-existing structural weaknesses, and the legacy of the COVID-19 crisis, to ensure an inclusive recovery and build long-term resilience.
Box 4.3. The DynEmp project: Measuring business dynamics, start-ups and job creation
The DynEmp project is a distributed micro-data project supporting the analysis of business dynamics, start-ups and job creation across a large number of OECD countries and partner economies. It is led by the OECD Directorate for Science, Technology and Innovation (DSTI) with the essential contribution of country delegates and national experts from OECD countries and partner economies.
The distributed micro-data approach adopted in the DynEmp project is based on a common statistical code developed by the OECD DynEmp team. It is run in a decentralised manner by national experts from statistical agencies, academia, ministries or other public institutions, who have access to national micro-level data, which is generally from business registers (Desnoyers-James, Calligaris and Calvino, 2019[30]). The micro-aggregated data generated by the centrally designed but locally executed program codes are then sent back for comparative cross-country analysis to the OECD.
This methodology allows the DynEmp project to provide novel insights on business and employment dynamics based on highly representative and harmonised data suitable for country specific and cross-country analyses. Data presented in this chapter are based on the third wave of data collection, DynEmp3, featuring a more sophisticated statistical routine, and covering 18 OECD countries or partner economies over a period ranging between 1998 and 2015, depending on countries.4
The DynEmp website (OECD, 2021[31]) provides indicators related to business and employment dynamics, as well as additional readings on the topic.
Aggregate firm age and firm size structure
Figure 4.8 and Figure 4.10 provide a cross-country assessment of the importance of young and small firms respectively, based on data from the OECD’s DynEmp3 database. Two countries – Turkey and Brazil – stand out as having a particularly high share of employment in young firms, and also rank high in terms of the share of employment in small firms.5 Several countries that were heavily affected by the initial wave of the pandemic, including Spain and Italy, are also among those with high shares of small and young firms that tend to be more vulnerable to the initial economic downturns, thereby exacerbating the initial disruptions and potentially making the recovery more challenging.
Figure 4.8. Percentage of employment in young firms, by country
Note: Young firms are less than six years old. Numbers refer to 2015 except for Belgium, Japan, New Zealand and Norway (2014). Data for Japan are based on manufacturing only. Data for Austria, Japan and Korea are based on establishments rather than firms. Numbers are calculated as employment-weighted averages of industries (SNA A38 classification).
Source: OECD calculations based on OECD (2021[31]), DynEmp3 Database, http://oe.cd/dynemp.
From an industry perspective, one of the most heavily affected industries, Hotels and food services, stands out as having a particularly high share of young firms, as shown in Figure 4.9. What is more, the industry is among those with the lowest potential to telework (see Chapter 3). Other industries with high shares of young firms, such as IT services, Scientific Research and Development (R&D) and Other business services, are relatively more able to shift to remote work, but may be more financially vulnerable as well as constrained in their ability to finance necessary future investments, due to the low level of tangible assets to offer as collateral, as discussed in Chapter 3.
Besides age, size is another important dimension of a firm’s resilience to the COVID-19 crisis. In some cases, small firms may be more resilient than large firms because they can be more flexible; they may be better able to quickly re-orient production in response to changes in demand or to adapt their production methods to meet requirements for physical distancing of employees. However, on average, small firms tend to have disproportionately smaller liquidity reserves, less access to external finance, and are less likely to have a developed IT infrastructure that would allow them to easily move to telework (see also Chapter 5). Finally, small firms frequently produce in a single location and have a less diversified customer base. Therefore, if they are affected by lockdowns, such firms are more likely to have the entirety of their business operations put on hold (OECD, 2020[23]).
Figure 4.9. Percentage of employment in young firms, by industry
Note: Young firms are less than six years old. Numbers refer to 2015 except for Belgium, Japan, New Zealand and Norway (2014). Countries included are Austria, Belgium, Brazil, Canada, Costa Rica, Finland, Hungary, Italy, Japan, New Zealand, Norway, Spain, Sweden and Turkey. Data for Japan are based on manufacturing only. Data for Austria, Japan and Korea are based on establishments rather than firms.
Source: OECD calculations based on OECD (2021[31]), DynEmp3 Database, http://oe.cd/dynemp.
Employment shares in small firms vary substantially across countries and industries, as shown in Figure 4.10. At the country level, Brazil, Italy, Spain and Hungary have a particularly high share of micro firms (fewer than ten employees). When adding in small firms (up to 50 employees), Turkey and Korea join the group of countries with a relatively high share of small, potentially vulnerable firms.6
Figure 4.10. Employment in small and micro incumbent firms as a percentage of total employment in incumbent firms, by country
Note: Small firms are defined as having 10 to 49 employees, micro firms are those with fewer than ten employees. Numbers refer to 2015 except for Belgium, Japan, New Zealand and Norway (2014). Data for Japan are based on manufacturing only. Data for Austria, Japan and Korea are based on establishments rather than firms. Numbers are calculated as employment-weighted averages of industries (SNA A38 classification).
Source: OECD calculations based on OECD (2021[31]) DynEmp3 Database, http://oe.cd/dynemp.
From an industry perspective, Hotels and food services have the highest share of small firms across countries, with Other business services, Legal and accounting, and Wholesale and retail also comprised of relatively smaller firms compared to other industries. While business services industries have the advantage of a relatively high telework potential, Hotels and food services and many parts of Wholesale and retail trade have been subjected to lengthy or repeated closures, and suffer from a combination of vulnerabilities (as discussed in more detail in Chapter 6) including low telework potential, high dependence on face-to-face contact and, in the case of Hotels and food services, a heavy reliance on travel and tourism.
Among the groups of countries with high shares of small and young firms (Figure 4.8 and Figure 4.10), tourism accounts for a major part of employment in Spain, and also plays an important role in Italy, Hungary and Turkey (see Chapter 6). These countries therefore stand out as particularly vulnerable, in terms of their firm and industry structure. Italy and Spain were also most heavily affected in the early stages of the pandemic after mandating particularly strict and long lockdowns, exacerbating their unfavourable position. Korea provides a contrasting example. While it also has a high share of small and young establishments across sectors, it has managed to keep the virus at bay with much less severe restrictions on economic activity. The intensive use of digital technologies to effectively implement a test-trace-treat policy to contain the pandemic has been key to this positive outcome (Pak, 2021[32]).
Figure 4.11. Employment in small incumbent firms as a percentage of employment in incumbent firms, by industry
Note: Small firms with fewer than 50 employees. This includes micro firms. Numbers refer to 2015 except for Belgium, Japan, New Zealand and Norway (2014). Countries included are Austria, Belgium, Brazil, Canada, Costa Rica, Finland, Hungary, Italy, Japan (manufacturing only), Korea, New Zealand, Norway, Spain, Sweden and Turkey. Data for Austria, Japan and Korea are based on establishments rather than firms.
Source: OECD calculations based on OECD (2021[31]) DynEmp3 Database, http://oe.cd/dynemp.
From a static perspective, a higher share of young firms may be associated with a firm population that is more vulnerable to the short-term effects of the crisis, which should also be taken into account during the recovery, when firm support policies and restrictions on bankruptcies phase out. However, a high share of young firms may also be indicative of an overall more dynamic business environment, with possibly greater potential for experimentation and for developing new business models. Such an environment is crucial for economic recovery, including for employment as displaced workers may look to move into new firms and activities. Policies should continue to ensure and improve the underlying conditions which support business dynamism, notably the entry and growth of new firms.
COVID-19 and long-term declines in business dynamism
Besides the implied short-term vulnerabilities, the disproportionate adverse effect of the crisis on young firms and small and medium enterprises (SMEs), as well as on new business creation, may also be a concern for long-term business dynamism, as well as broader economic outcomes, including employment and productivity growth. The crisis may have impaired the ability of more vulnerable firms to experiment, innovate, compete and ultimately contribute to the process of creative destruction, while reinforcing the advantage of leaders who are better equipped to face shocks. In addition, the COVID-19 crisis occurs in a context of long-term declines in business dynamism, which reflects the existence of barriers already faced by firms prior to the crisis, that are key to future economic growth.
Figure 4.12. Average declines in entry rates across countries and sectors
Note: This figure reports, for each country (panel A) and industry (panel B), average yearly within-country-sector changes in entry rates, based on the trend coefficient of regressions within country-sector, for available years over the period 2000-15.
Source: Calvino, Criscuolo and Verlhac (2020[29]), “Declining business dynamism: Structural and policy determinants”, https://doi.org/10.1787/77b92072-en based on OECD (2021[31]) DynEmp3 Database, http://oe.cd/dynemp.
As documented in recent OECD work (Calvino, Criscuolo and Verlhac, 2020[29]), entry rates and job reallocation rates declined prior to the pandemic, by 3 and 5 percentage points, respectively, over the 2000-15 period. By contrast, exit rates remained rather stable over the same period, implying declines also in net entry rates. Panel A of Figure 4.12 shows that declines have been pervasive, and that all countries and industries displayed some signs of weakening business dynamism prior to the crisis – evidenced in declining entry rates. Further results show that declines in job reallocation rates have also been pervasive across countries and industries. However, countries differ in the magnitude of the decline, as illustrated in panel A of Figure 4.12, with Costa Rica, Turkey and Hungary displaying the largest drops of more than 0.4 percentage points every year – accumulating to substantial long-term declines over time –, and Finland, Japan and Sweden the lowest, with less than 0.1 percentage points yearly declines (Calvino, Criscuolo and Verlhac, 2020[29]).
Panel B of Figure 4.12 further uncovers relevant differences across sectors, with Telecommunications, IT services and Scientific R&D clearly showing the strongest declines, while Pharmaceuticals, Food products, and Textiles and apparel experiencing the mildest.
Exploiting cross-country and cross-sector differences in the magnitude of declines, Calvino, Criscuolo and Verlhac (2020[29]) provide evidence that drops in entry rates and job reallocation rates are related to structural factors. Declines may, to some extent, reflect a process of consolidation related to the industry life cycle of initially more dynamic industries. However, industry concentration and productivity dispersion between leaders and laggards also play a significant role in accelerating the speed of decline in business dynamism, even after accounting for sector maturity. Winner-takes-most dynamics and barriers to technology diffusion are important drivers of the declines across countries, and the underlying mechanisms are reinforced by the digital transformation and the corresponding rising importance of intangible assets.
The digital transformation has indeed contributed to increasing divergences and gaps between firms (Andrews, Criscuolo and Gal, 2016[33]; Gal et al., 2019[34]; Berlingieri et al., 2020[27]; Corrado et al., forthcoming[35]). Some firms – typically those that are larger and more productive – may benefit more from digitalisation, as they are able to innovate, adopt new technologies and exploit complementarities with intangible assets. By contrast, other firms – young firms and SMEs in particular – may face barriers to the adoption of technology and to the accumulation of intangibles, as well as significant challenges when competing with leaders. Hurdles include a lack of absorptive capacity and incentives (for instance, related to financial constraints), a lack of skills, or regulations favouring more established firms.
Overall, this may prevent potential entrants or laggard firms from taking advantage of existing knowledge or learning from the best, and could discourage business formation, thereby reducing reallocation. Higher concentration of sectors may also be associated with discouragement effects, barriers to entry and more stable job flows possibly linked to lower levels of creative destruction and competition. Larger gaps in productivity and in share of sales between frontier firms and followers in an industry may indeed reduce the chances of laggards to catch up with leaders, potentially reducing incentives for experimentation and innovation.
In this context, the economic crisis associated with COVID-19 is an additional threat to business dynamism in the long term. In some countries, the shock to business creation and the risk of a missing generation of new firms may amplify the effects of the long term decline in entry rates, which has been pervasive across countries and sectors prior to the crisis. The crisis may also have strengthened underlying mechanisms that have been identified as possible drivers of the decline in business dynamism. These concerns relate in particular to possible effects of the crisis on industry concentration and barriers to technology and knowledge diffusion. Long-term effects of the crisis on market structure – notably market shares and margins – are still uncertain. In some cases, the entry of new firms may spur competition and energise markets through innovation. However, established leader firms may have weathered the crisis better, and strengthened their market power. In addition, despite the support provided through emergency policy measures, widespread financial vulnerability and a possible debt overhang could reduce investment, especially in assets that are key for firm performance but more difficult to finance, such as intangible assets.
Therefore, while the crisis may have spurred the adoption of new technologies and the uptake of new work practises such as teleworking (as discussed next in Chapter 5), it may also undermine investment in complementary assets (training, databases, management) that allow firms to reap the full benefits from technology adoption and organisational changes. These risks are not evenly distributed across firms, and the disproportionate effect of the crisis on young firms, SMEs, and those that are less digital- and intangible-intensive, implies that firms struggling to keep up with the frontier before the crisis also are more likely to suffer the most from long-term consequences of the economic disruptions related to the COVID‑19 outbreak.
Emergency measures may have helped prevent a damaging wave of firm failures. However, the possible interaction between the impacts of the COVID-19 crisis and pre-crisis structural trends warrants policy interventions that address both simultaneously. Addressing structural drivers of long-term declines in business dynamism may also allow for better exploitation of business opportunities that emerge during or after the pandemic. In addition to the phasing out of emergency measures discussed in the previous section, appropriate structural policies are presented at the end of this chapter, following a discussion of additional challenges related to pre-crisis corporate financial vulnerability, in the next section.
Corporate financial vulnerability
Firms differ in their vulnerability to the financial shocks associated with the crisis along a number of dimensions, including industry, size and age. There are also substantial important differences across countries in terms of the level of pre-crisis aggregate corporate debt, and the extent to which governments and financial sectors may be prepared to bridge the funding gaps experienced through the crisis.
Looking across countries, Figure 4.13 and Figure 4.14 provide an initial indication of level of debt held by non-financial firms. Concerns about high levels of corporate debt, particularly in the form of corporate bonds, had emerged in the pre-COVID-19 period (OECD, 2017[36]; Çelik, Demirtaş and Isaksson, 2019[37]). This increase in debt amplified financial pressures during the COVID-19 outbreak (Aramonte and Avalos, 2020[38]), with highly indebted firms predicted to see stronger impacts on leverage ratios and future investment (Demmou et al., 2021[18]). High corporate debt prior to the crisis could therefore be considered as an aggravating factor for the risk of debt overhang. Credit to private non-financial corporations provides an aggregate measure of firms’ indebtedness and their reliance on credit. High levels of credit-to-GDP at the onset of the COVID-19 crisis may indicate a higher aggregate risk of insolvency in the face of decreasing revenues that can spill over to exposed creditors.7
Figure 4.13 shows that corporate credit-to-GDP ratios have increased significantly in some countries since the 2008 financial crisis, leading to high levels of debt-to-GDP (above 150% in Belgium, China, France, Ireland, Luxembourg, the Netherlands and Sweden). As well as affecting the survival and performance of the indebted firms themselves (also discussed below with respect to the cost of debt servicing), this risk can spill over to exposed creditors and reduce willingness to lend, even to financially sound firms.
The debt service ratio provides further insight into firms’ ability to pay their debt with revenues. It is defined as the ratio of interest payments plus amortisations to income, and indicates the debt burden of the corporate sector. The debt service ratio reflects the aggregate vulnerability of the corporate sector in the event of a drop in revenues, and is also considered an early warning indicator for systemic banking crisis (see for example Drehmann and Juselius (2014[39])).
Countries with high debt service ratios in non-financial corporate sectors have generally experienced significant increases in this ratio since 2007, and consequently entered the COVID-19 crisis with higher risks (Figure 4.14). In Belgium, Italy, the United States and Sweden, firms’ debt service took up almost 50% of their income in 2019. In Spain and Denmark, the debt service ratio is even higher, at 55% and 60% respectively. Conversely, other countries have experienced significant improvements in the aggregate debt service ratio, including Norway, which displays one of the lowest values in the sample, down from the highest value in 2007.
Figure 4.13. Total credit to private non-financial corporations as a percentage of GDP (2019 vs. 2007)
Note: Total credit to private non-financial corporations as percentage of GDP, for the years 2007 and 2019.
Source: Bank for International Settlements (2020[40]), BIS total credit statistics (database), https://www.bis.org/statistics/totcredit.htm (accessed in July 2020).
Figure 4.14. Debt service ratios of non-financial corporations (2019 vs. 2007)
Note: The figure displays the debt service ratios (DSR) of the non-financial corporations sector for 2007 and 2019. The DSR measures the share of income used to service debt.
Source: Bank for International Settlements (2020[41]), BIS debt service ratios statistics (database), https://www.bis.org/statistics/dsr.htm (accessed in July 2020).
When considering appropriate policy responses that strike a balance between health and economic priorities and the extent of support to offer to firms and households that have been affected by the pandemic, governments face a number of constraints. Countries that went into the crisis with healthy economic conditions and low debt levels are in a better position to support firms and workers through prolonged periods of restrictions on activity, and against the short-term shocks induced by the crisis.8
Levels of unemployment before the crisis are an important indicator of the state of the labour market in a country, and of business cycle conditions. Differences across countries also reflect structural characteristics of the labour market. A well-functioning labour market is likely to also adapt better during the crisis, and allow a more efficient recovery. Importantly, it also implies a lower burden on state budgets before the crisis, as fewer workers are dependent on benefits. With some exceptions, government debt levels are strongly correlated with pre-crisis unemployment rates, implying challenging trade-offs for governments in meeting current financing and support needs.
Several of the countries that have been through strict lockdowns and suffered substantial reductions in economic activity, including France, Italy and Spain (Figure 2.3 to Figure 2.5), also had relatively high levels of unemployment and government debt prior to the crisis, suggesting structural factors that might also hinder the reallocation of resources during the recovery phase. Specific policies enabling the formation of the necessary skills for a green and digital recovery may also be particularly effective in supporting a more inclusive recovery by aiding the labour market inclusion of disadvantaged workers, such as young labour market entrants.
Conclusions and policy implications
The COVID-19 crisis has had a huge impact on firms, and has brought with it substantial challenges, especially for new and small businesses. The effects of the crisis on business dynamics may be long-lasting, particularly for countries that tend to have a more vulnerable business structure. Going forward, government policies will be a decisive factor in the strength and success of recovery.
Over the initial months of the crisis, governments around the world have stepped in with strong measures to support firms and workers through periods of economic lockdown and reduced activity.9 As confirmed by early data, these measures have thus far prevented a wave of corporate bankruptcies (Figure 4.4) and reduced impacts on employment (OECD, 2020[42]). As countries move beyond lockdowns and broad emergency support measures, more specific policies to support recovery and to encourage a productivity-enhancing reallocation of resources will be required – not least because state budgets are limited and some countries are already facing discussions on the sustainability of new debt, and the ability to provide support through further rounds of lockdowns.
Maintaining the current stock of firms should not be taken as the sole goal of policy support. In the post-pandemic period, the balance of support provided to firms becomes more complex. Policy makers must strike a balance between phasing out support too soon, thereby risking a wave of failures of otherwise viable firms, and maintaining support for too long, creating incentives to keep firms afloat that will not be viable in the post-COVID-19 economy (and preventing the reallocation of resources to more productive opportunities).
The progressive withdrawal of direct financial support should be accompanied by broad-based policies to increase resilience and improve the business models of existing firms while promoting active reallocation of resources across firms. A sustainable and inclusive recovery relies on both existing and new firms being able to recognise and take advantage of new opportunities. To this end, policies to support healthy business dynamics remain a key aspect of the recovery effort. In particular, promoting experimentation, for instance through lowering barriers to entrepreneurship and to firm growth, is key for a vibrant business environment, as economies enter into the recovery period and need to tackle long-term challenges.
Recent OECD research (Calvino, Criscuolo and Verlhac, 2020[29]) suggests key policy areas that may help to support firm entry, entrepreneurship and creative destruction. These include reducing barriers to entry and red tape through simplifying administrative processes and reducing the cost and complexity of product market regulations, and ensuring that bank credit or other sources of finance are available on reasonable terms for young and small firms rather than just for larger incumbents.
Similarly, governments can play a role in enabling innovation in smaller and younger firms through more direct government financing of R&D, rather than solely through tax credits, which, depending on their design and their carry-forward provisions, might not be able to support innovation spending in cash-stripped or profit-losing firms. This implies a need to look at alternative methods of financial support, such as equity and quasi-equity (especially for SMEs) injections, and allowance for corporate equity and debt-equity swaps, which may play a longer-term role in recapitalising firms while minimising the negative impacts of debt overhang (Demmou et al., 2021[18]). Measures to support debt restructuring, such as granting priority over unsecured existing creditors for new financing, and promoting pre-insolvency frameworks, may also help to reduce default and enable distressed firms to invest during the recovery.
Ensuring efficient bankruptcy procedures and contract enforcement will help free up resources and speed up the process of reallocation. Labour market policies that enable experimentation and job mobility – for example, by ensuring access to benefits and health insurance for individuals with atypical career paths – can also help to enable entrepreneurship while limiting hardship associated with job loss during the crisis (OECD, 2020[43]). These factors have been shown to be important for managing the pervasive decline in business dynamism observed over recent decades, and have become even more critical in the wake of the COVID-19 crisis.
As elaborated on in more detail in the next chapter, the COVID-19 pandemic and its associated restrictions on mobility and interaction have led to a rapid increase in the uptake of digital and remote work technologies. While these technologies have the potential to improve productivity and reduce entry costs for firms, developments in recent decades have shown that digital-, skill-, and intangibles-intensive industries have also experienced a more rapid increase in concentration and productivity dispersion, and a more substantial decrease in business dynamism (Berlingieri et al., 2020[27]; Bajgar et al., 2019[28]; Calvino, Criscuolo and Verlhac, 2020[29]; Corrado et al., forthcoming[35]). The ability of firms to access and benefit from new technology developments may therefore become even more critical post-COVID-19. The next chapter considers the adoption of key remote and digital technologies, focusing on long-term investments such as infrastructure and upskilling. Complementing these, short-term actions to support technology uptake – for example, through targeted financial supports – may also help firms to adjust to the changes required by COVID-19.
Both the crisis itself and the dynamics of recovery have the potential for adverse effects on market structure and concentration. Concentration can have implications for various economic phenomena, such as product market competition, but also for the potential of monopsonies in inputs and labour markets on the contractual terms for suppliers and workers (OECD, 2008[44]). This in turn can affect innovation and productivity growth, and wage inequality across workers, firms and regions. Recent OECD work has highlighted the importance of policies that ensure a sufficient level of competition to avoid negative consequences, such as those challenging exploitative pricing behaviours, reviewing merger activity, and evaluating the potential to decrease entry costs (OECD, 2020[45]). These need to be complemented by a careful analysis of the potentially anti-competitive effects of support policies, keeping in mind the post-crisis concerns about concentration and competition.
In recent years, empirical evidence has pointed towards a trend of increased industry concentration both in the United States and in Europe, as discussed in Bajgar et al. (2019[28]). The COVID-19 crisis may further accelerate this trend through asymmetric impacts on firms of different size, age and productivity. These asymmetric impacts are potentially further exacerbated by differing propensities to digitalise, as discussed in more detail in Chapter 5. It is therefore crucial to ensure equal access to public support funds and measures, to avoid further reinforcing pre-existing divides.
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Notes
← 1. Data are from OECD (2021[31]). New and young firms also display positive net job creation rates: their yearly net job creation is around 2.5% of total employment (Criscuolo, Gal and Menon, 2014[46]). Older firms usually display negative net job creation rates.
← 2. Annex D summarises early policy measures put in place to support firms and workers in the early stages of the crisis, particularly over the period of initial lockdowns.
← 3. In the short term, intangible assets may be a factor of resilience (e.g. lower reliance on physical capital during lockdowns, better management and skills, stronger customer base, higher ability to telework). However, intangibles are difficult to finance. Therefore, in the long term, firms in sectors that rely more on intangibles may face higher financial constraints.
← 4. At the time of writing, a new wave of data collection is ongoing for the OECD’s DynEmp project to extend country coverage and update data to cover a more recent period.
← 5. While shown in the graph, Korean results are based on establishment (plant) level data, rather than enterprise (firm) level, so are not fully comparable – nevertheless, results indicate that Korea has more young establishments than Japan and Austria, for which data also relies on plant-level data.
← 6. In Korea this may partly reflect the use of establishment level, rather than enterprise level data, if multi-establishment firms are a significant part of the economy.
← 7. Alongside bank credit, trade credit is a further source of vulnerability for firms. Trade debts may be an even stronger concern for policy makers as they have the potential to spread the negative liquidity shock throughout the economy and the policy toolbox for addressing this type of financial shock is more limited than for bank debts.
← 8. Brazil, for example, is facing fiscal constraints on the continuation of its social support programmes for mitigating the short-term impacts of the crisis on the poor (The Economist, 2020[47]).
← 9. Annex D provides a summary of labour market and tax policies which have been enacted to support firms over this period.