Developments in euro area business dynamism Monthly Report – March 2024

Published on 3/19/2024

Developments in euro area business dynamism Monthly Report – March 2024

Measured in terms of firm entry and exit rates, business dynamism in the euro area has weakened markedly over the last twenty years. At the same time, aggregate productivity growth slowed down and allocation efficiency across firms declined. These three trends may be related: firm entries and exits play an important role in the allocation of scarce production factors to productive uses. If allocation efficiency declines as business dynamism slows, this can hamper productivity growth. Therefore, analysing business dynamism can help to explain the observed weak productivity growth.

Data issues complicate the analysis of business dynamism in the euro area. The time series on firm entries and exits in the official statistics contain gaps and structural breaks. This makes it difficult to examine longer periods of time, especially the period before 2008, and therefore to separate cyclical and structural developments. Nevertheless, the evidence suggests that business dynamism in the euro area was already weakening significantly before the onset of the global financial and economic crisis. This indicates that not only cyclical but also structural factors were at work. Analyses for the United States, where the availability of data on business dynamism is far better, can deliver valuable insights into potential drivers of business dynamism in the euro area. 

Besides the general ups and downs of economic activity, one of the cyclical drivers of business dynamism is the level of uncertainty perceived by economic agents. The latter may have had a lasting dampening effect on firm entries in the wake of the global financial and economic crisis and the subsequent European sovereign debt crisis. A decline in business dynamism could also be observed in the context of the severe economic turmoil witnessed during the COVID-19 pandemic. One structural factor is population ageing in the euro area. Regulatory and institutional barriers are also likely to weigh on business dynamism. Insufficient competition represents another possible factor.

A better institutional and regulatory framework could stimulate business dynamism and thereby strengthen productivity growth. Reforms at the European level could play a role here. For key policy areas, however, ownership and responsibility lie at the national level.

Labour productivity growth has been slowing for some time in many advanced economies. 1 One reason for this is the declining growth in total factor productivity (TFP), which is one of the key drivers of labour productivity. TFP growth describes the part of output growth that cannot be explained by changes in the use of production factors such as labour and capital, making it a measure of economic efficiency gains. Growth in TFP declined markedly in many advanced economies, including the euro area. 2

One possible explanation for the slowdown in TFP growth is a decline in allocation efficiency. 3 Data analyses for the euro area show that differences in TFP levels across firms within the same economic sector tended to increase on average from 2003 up to the outbreak of the COVID-19 pandemic. 4 The spread between the firms’ efficiency levels has therefore widened over time. This suggests a decline in allocation efficiency at the firm level, i.e. a less efficient distribution of production factors across firms. 5 An efficient reallocation of production factors from low-productivity firms to highly productive ones is a key driver of aggregate TFP growth, however.

Cumulated change in the standard deviation of TFP across firms within a common economic sector

The reallocation of resources across firms is closely linked to business dynamism. 6 As start-ups enter the market, they strengthen competition and increase the pressure on their competitors to innovate. Less profitable firms succumb to this pressure and exit the market (creative destruction). 7 This frees up resources for more productive firms. Firm entries and exits therefore hold great significance for aggregate productivity growth. 

The analysis of business dynamism in the euro area is hindered by a difficult data situation. The business demography statistics reported by Eurostat do not extend back beyond 1997, with data availability varying widely between countries. 8 9 Furthermore, changes in data collection methods make longer-term comparisons more difficult. In 2008, a comprehensively revised Statistical Classification of Economic Activities in the European Community (NACE) was introduced, which also had an impact on business statistics. 10 In addition, countries individually adjusted their data collection methods over time. These structural breaks had considerable effects in some cases. One example is the time series for Germany which, owing to methodological changes, shows significantly higher business dynamism from 2018 onwards. This suggests that alternative data sources should be used in addition. For an analysis of longer-term developments in business dynamism in Germany on the basis of alternative data sources, see the section on the decline in dynamism in the German corporate sector over the past two decades. 11

Supplementary information

A raft of data sources can be examined to take stock of business dynamism in Germany’s corporate sector.  Business dynamism is determined in large part by firm entries and exits. Their significance for allocation efficiency and productivity growth is outlined in Chapter 1. This section details an examination of long-term firm entry and exit patterns in Germany using data from the Federal Statistical Office’s business registration statistics, which are available over an extended horizon without structural breaks. 1 2 Complementary data on firm exits are taken from the Federal Statistical Office’s insolvency statistics. Another important measure of reallocation dynamics in the corporate sector is the reallocation of jobs and workers between firms. For example, productivity growth will be enhanced if more-productive incumbent firms create more employment. 3 New Federal Employment Agency data deliver valuable insights into worker flows between firms. Furthermore, an analysis of Bundesbank corporate data for the manufacturing sector reveals evidence of the dynamism produced by incumbent firms’ employment decisions.

The last two decades saw a drop in firm exit numbers in Germany. This applies both to business insolvencies, which fell by 55% between 2004 and 2023, and to business closures, which fell by 30% over the same period. 4 The COVID-19 pandemic era saw the downward trend gain pace in spite of the severe economic downturn. This was due to the temporary suspension of the obligation to file for insolvency, alongside comprehensive government support programmes. 5

The last two years have seen a renewed uptick in the number of business insolvencies and business closures. One notable reason for this – alongside the weak state of the economy overall – will probably have been firm exit numbers catching up with pre-pandemic levels. Judging by recent developments, there is no sign as yet that this trend will go into reverse.

Business entries and exits in Germany

Annual firm entry numbers have likewise shrunk over the past two decades. Between 2004 and 2014, the number of annual business recognitions of corporate head offices fell by 30% to around 86,000. They have been fairly stable at a relatively low level for the last ten years or so. Firm entry numbers spiked sharply higher as the wider economy recovered in 2021, but this upturn did not turn out to be sustainable. The most recent figures for firm entries – around 87,000 in 2023 – are broadly consistent with the annual averages observed since 2014. 

New Federal Employment Agency data on worker mobility between firms suggest that the scale of reallocation has been comparatively stable in recent years. Monthly time series dating back to January 2013 show how the number of new jobs subject to social security contributions has evolved, 6 allowing a job-to-job transition rate (worker flow rate) to be calculated. This rate indicates the percentage of workers who move from one plant to another in a given month. 7 The job-to-job transition rate has increased strongly since 2013 and especially so in the years 2017 and 2018. The rate began to decline at the turn of 2018-19. It fell significantly at the onset of the COVID-19 pandemic. By 2022, however, this had been more than offset relatively quickly as the pent-up need for reallocation that had built up during the acute phase of the pandemic was released. Since then, the rate has been back on the decline (see Chart 2.3). The fact that the rate is persisting well above its 2016 level in spite of the weak state of the real economy probably owes something to the growing labour shortages. Due to the shrinking pool of unemployed people, employers were increasingly poaching workers from each other.

Worker reallocation

The growing labour shortages had an impact on long-term transition rates out of and into unemployment as well. In addition to the job-to-job channel, these rates also have a role to play in the corporate sector reallocation process as new workers can also be sourced from the pool of unemployed workers. On the one hand, transitions out of employment into unemployment relative to the number of employees (the job separation rate) have long been charting a trend decline. 8 This will probably have been due in part to the labour market reforms of the early years of this millennium, alongside demographic factors. 9 These reforms are also likely to have provided a key impetus for the relatively swift uptick in transitions out of unemployment into employment (relative to the number of unemployed persons – the job finding rate) in the second half of the first decade of this millennium. 10 However, this rate has declined significantly of late in the wake of the outbreak of the COVID-19 pandemic and the beginning of Russia’s war of aggression against Ukraine. Overall, these developments reflect the trend decline in the unemployment rate in Germany between 2005 and 2019. 

The intensity of employment reallocation between manufacturing firms saw only marginal change in Germany between 2006 and 2021. If productive companies expand and create jobs, while less efficient companies reduce employment, this has a positive effect on productivity growth. 11 The reallocation of employment between incumbent firms is analysed here using Bundesbank data for the manufacturing sector. 12 The aggregate gross reallocation rate is computed from firms’ job creation and destruction rates. 13 That rate broadly declined between 2006 and 2014, before recovering and then continuing to rise slightly during the COVID-19 pandemic. It has been hovering marginally above its long-term average of late. The rate’s recovery since 2014 is consistent with the rise in the job-to-job transition rate and the job finding rate during this period. The slight increase observed in 2020 is due to very strong job destruction. Job creation, by contrast, declined sharply, mirroring the slump in job-to-job transitions. 14  

All told, dynamism in the German corporate sector has been receding over the past two decades, meaning that it might have been a factor in subdued productivity growth. There was a sharp drop in firm entry and exit numbers in particular. Indicators for the reallocation of workers and employment between firms, by contrast, have remained fairly stable on average over time. This is in line with the mounting tensions in the German labour market over the past ten years, which may have offset the general decline in employment reallocation observed previously. The finding that business dynamism has declined overall in Germany is consistent with developments in the euro area as a whole.

  1. The examination covers start-ups and complete closures of head offices for businesses with greater economic significance. These encompass businesses managed or established by a legal person, a partnership or a natural person. In the case of natural persons, this is predicated on them being (or having been) entered in the commercial register, having (had) an entry in a skilled trades register, or employing (or having employed) at least one person. 
  2. Owing to structural breaks, long-term time series on start-ups and closures of firms in Germany based on business demography statistics are not currently available, which is why information from business registration statistics is used here. Business registration statistics differ from business demography statistics in two important respects. First, results are presented at the legal entity level in business registration statistics, but at the enterprise level in business demography statistics. Second, unlike with the business demography statistics, a business recognition or derecognition in the business registration statistics does not necessarily represent an actual start-up or closure because firm takeovers or transformations and the like are also registered. To get as close as possible to business demography data, the focus here is on start-ups and complete closures of corporate head offices. Another difference is that only businesses with greater economic significance are considered here, whereas the business demography statistics include, for example, small enterprises. 
  3. Furthermore, employment reallocation between firms can enhance productivity if it results in an employee’s expertise and skillset being better suited to the profile of the new job than was the case in their previous job.
  4. In 2022, there were 61,355 business derecognitions of corporate head offices and 14,590 business insolvencies. The vast majority of firm exits, then, did not involve insolvency proceedings. Businesses might also exit the market on account of factors such as worsening earnings prospects.
  5. For further information on how business insolvencies evolved during the COVID-19 pandemic, see also Deutsche Bundesbank (2021b).
  6. A distinction is made here between persons who previously held jobs subject to social security contributions, were otherwise employed, seeking work or were not on file with the Federal Employment Agency (e.g. career starters with no vocational training or immigrants entering the labour market, non-employed persons rejoining the labour market, or self-employed persons/civil servants switching to jobs subject to social security contributions).
  7. This rate is calculated as the ratio of newly employed persons in jobs subject to social security contributions who previously also held jobs subject to social security contributions with a break of no more than one month divided by the total number of jobs subject to social security contributions.
  8. The most recent rate was calculated based on employee numbers from the previous month.
  9. See Dlugosz et al. (2009) and Hartung et al. (2018). 
  10. For further reading, see Krause and Uhlig (2012), Krebs and Scheffel (2013), Launov and Wälde (2016) and Deutsche Bundesbank (2014) as well as the references cited therein. Hence, the pent-up demand for new hires triggered by the reforms was so great that the job finding rate in 2009, when the global financial and economic crisis was at its peak, remained at a higher than average level despite declining slightly.
  11. For more on the productivity effects of employment reallocation during the COVID-19 pandemic, see Deutsche Bundesbank (2022a). 
  12. The JANIS dataset contains annual financial statements of German non-financial corporations which are sent to the Bundesbank for credit assessment purposes, as well as financial statements from public sources; see Becker et al. (2022). This is a non-representative sample of firms in which larger firms feature more strongly. Coverage of individual sectors varies considerably. In addition, information on the number of employees is not necessarily part of the annual financial statements and is therefore sometimes missing. In the manufacturing sector, the information needed for analysis exists for a fairly large number of firms, meaning that the focus here is on these particular firms. The data cover around 45% of workers in the manufacturing sector. 
  13. The calculation follows Davis and Haltiwanger (1992), with firm entries and exits being disregarded due to gaps in the data.
  14. This is consistent with the relatively high level of job destruction in the manufacturing sector during the COVID-19 pandemic. As was the case during the financial crisis, the job destruction rate increased very sharply in 2020 while the job creation rate declined sharply. This left the gross reallocation rate up slightly overall in 2020. An analysis based on Belgian corporate data – which had a broader basis including other sectors – produced similar results for 2020; see Konings et al. (2023). 

In order to obtain information on developments in euro area business dynamism despite the difficult data situation, a panel approach is used. This way, challenges arising from the difficult data situation in individual countries can be mitigated. Incorporating cross-sectional information – in particular, country-specific sectoral information on firm entry and exit rates – allows us to compare business dynamism over time even if, in some countries, the time series dimension is limited, data gaps exist, and there are shifts in levels due to methodological changes. In this kind of panel approach, country-specific sectoral firm entry and exit rates are regressed on a constant and on indicator variables for countries, years, sectors and structural breaks. 12 13 The average path of annual entry and exit rates across the countries and sectors included in the model can be derived from the estimated coefficients of the time indicators. 

The path of entry and exit rates – summarised in the churn rate – provides clear evidence that business dynamism in the euro area has receded over time. 14 The churn rate is calculated as the sum of firm entry rates and exit rates. Since a major revision of the statistical classification of economic activities was introduced in 2008, we examine two sub-periods. 15 Between 1998 and 2007, business dynamism as measured by the churn rate declined, mainly on account of a diminishing entry rate. This was particularly evident between 1998 and 2003. Business dynamism then recovered somewhat, but the entry rate never returned to the 1998 level. The exit rate fluctuated for some time but did not show a clear trend. When interpreting the results, it should be noted that the dataset for this period is small, varies considerably between countries and lacks information on structural breaks. 16 Results for the period from 2008 onwards are much more reliable. Since then, business dynamism has seen a relatively steady decline. In the wake of the COVID-19 pandemic, the firm entry rate, especially, has decreased of late. 

 Average firm entry and exit rates in the six largest euro area countries

It is particularly noteworthy that firm entry rates did not recover during the favourable economic period between 2013 and 2019. During that spell, euro area GDP rose by an average of 1.6% per year, and the unemployment rate fell from 12.0% to 7.5%. Against this background, one might have expected to see rising entry rates. Such a cyclical pattern can at least be documented for the United States: here, business start-ups tend to be procyclical, business closures countercyclical (see the section on the cyclical development of business dynamism in the United States). 17

Supplementary information

Firm entries and exits are an important characteristic of business cycles. Economic booms or busts can have a substantial impact on the decision to set up or close down businesses. Firm entries and exits, in turn, affect employment and productivity, and thus the business cycle itself. 

For the United States, long time series can be used to analyse how the trend and cyclical components of business dynamism have developed over recent decades. Detailed data from Business Dynamics Statistics (BDS) allow an analysis of firm entry and exit rates over the period from 1979 to 2020. 1 To study the cyclical behaviour, the time series of the entry and exit rates are decomposed into a trend and a cyclical component. 2 According to its trend, business dynamism in the United States, too, has declined significantly in recent decades (see Chart 2.5). 

Trend and cyclical components of firm entry rates and exit rates in the United States between 1979 and 2020

The cyclical component of the firm entry rate in the United States shows a high degree of co-movement with economic activity. The relationship between business dynamism and the business cycle is examined by analysing the correlation between firm entry and exit rates, on the one hand, and the real GDP growth rate, on the other. 3 Here, a statistically significant, strongly positive correlation between the firm entry rate and GDP growth emerges. Hence, in economic upturns, more firms are created (see Table 2.1).

Table 2.1: Correlation between real GDP growth and firm entry and exit rates for selected firm sizes1
Size classFirm entry rateFirm exit rate

Percentage of total number of firms2

All firms






Number of employees:
1 – 4






5 – 9






10 – 19






20 – 99






100 – 2,499






Sources: Bureau of Economic Analysis, U.S. Census Bureau and Bundesbank calculations. 1 Correlation between the firm entry rate (firm exit rate) and real GDP growth over the period from 1979 to 2020 for different size classes of firms in the United States. Statistically significant correlations (at the 10% level) are shown in bold. p-values are shown in brackets. 2 Percentage of firms in each size class out of the total number of firms in the observation year 2020. 

By contrast, the firm exit rate is countercyclical. The firm exit rate is negatively correlated with GDP growth. However, the statistical relationship is less pronounced than it is for the firm entry rate. Nevertheless, the results suggest that firms increasingly exit the market during economic downturns.

Small firms drive the cyclicality of firm entry and exit rates. Firms are usually small when they are created and when they leave the market. An analysis of the interaction between the business cycle and business dynamism for different size classes demonstrates this fact (see Table 2.1). The co-movement of the firm entry rate and GDP growth is strongest for firms with one to nine employees. For enterprises with more than 100 employees, by contrast, there is no statistically significant correlation between the firm entry rate and the business cycle. The firm exit rate is countercyclical in statistically significant terms only for small enterprises with up to four employees. For larger enterprises, no such statistical relationship can be identified. 4

The relationship between business dynamism and GDP growth has weakened over time. Since around 2011, the cyclical component of firm entry rates has shown remarkably little variation. The same applies to the cyclical variation in the firm exit rate, which only picked up again markedly in 2020.

A structural break test suggests that the relationship between firm entry rates and macroeconomic activity dissipated after the global financial and economic crisis subsided. The statistical test provides information on whether the strength of the correlation has changed significantly at an unspecified point in time. 5 Hence, the test does not prescribe any specific break date, but identifies the year with the highest probability of a possible break in a predefined time interval. 6 For the correlation between firm entry rate and GDP growth, the test identifies a significant structural break in 2011. 7 From then on, there is no longer a significant relationship between the firm entry rate and the economic situation (see Table 2.2). The contemporaneous correlation even changes its sign. 8

Table 2.2: Change in the correlation of the firm entry and exit rate with the business cycle over time1
Time spanFirm entry rateFirm exit rate












Sources: Bureau of Economic Analysis, U.S. Census Bureau and Bundesbank calculations. 1 Correlations of the firm entry and exit rate in the United States with real GDP growth from 1979 to 2010 (or 2009), and 2011 (or 2010) to 2020. p-values are shown in brackets. Significant correlations (at the 10% level) are in bold.

There are also indications of a change in the relationship between firm exit rates and GDP growth after the global financial and economic crisis subsided. The evidence is weaker than for firm entry rates, though. While there are indications of a structural break in 2010, the test statistic is not significant. 9 From this date onwards, however, there is also no significant correlation with the business cycle anymore. If, on the other hand, one examines the behaviour of the exit rate in the period prior to the break, it is considerably more countercyclical and statistically more significant than for the period as a whole.

  1. Data on firm entries and exits are compiled by the U.S. Census Bureau annually in mid-March and consequently reflect developments between mid-March of the preceding year and mid-March of the current year. Owing to this annual structure, the data for 2020 capture the impact of the COVID-19 pandemic only to a limited extent.
  2. The firm entry rate is the ratio of enterprises with an age of zero in the respective year to the total number of enterprises in the previous year. The firm exit rate is the ratio of firm exits in a given year to the total number of enterprises in the previous year; see U.S. Census Bureau (2021). A Hodrick-Prescott filter with a smoothing parameter of 6.25 is used for detrending; see Ravn and Uhlig (2002).
  3. To avoid distorting the estimation of the correlation between firm entry and exit rates and GDP growth, annual data on real GDP are derived from quarterly observations, matching the latter to the reporting period of the BDS data. Hence, the contemporaneous level of annual GDP that is used to calculate the GDP growth rate captures the aggregate economic activity from the second quarter of the previous year to the first quarter of the year in question. For more information, see Tian (2018).
  4. The data on entries and exits of firms with a headcount of more than 2,500 are incomplete in the BDS dataset and are therefore not taken into account here.
  5. See Andrews (1993).
  6. In order to determine the date of a possible break, the test is conducted with a symmetric trimming from both ends of the sample. The trimming is set to 15%, which implies that potential breaks can only be tested for in the period from 1986 to 2014.
  7. This refers to the BDS observation year 2011, i.e. from the second quarter of 2010 to the first quarter of 2011. If a break in 2011 is explicitly tested for, it is highly significant at the 1% level. A test with a known break date has the advantage that no trimming is needed and all available data can be used.
  8. Robustness analyses with panel data on business dynamism in the 50 US states confirm the decrease in the correlation between entry rates and GDP growth after 2011.
  9. When testing for a specific break in the year 2010, the test is statistically significant at the 10% level.

Restricted economic activity during the COVID-19 pandemic was accompanied by significantly reduced business dynamism. The entry rate declined markedly in 2020. However, the exit rate fell slightly as well. This might be connected to the particular nature of the pandemic-induced economic crisis, but it was probably mainly the result of the economic policy response, the aim of which was to keep businesses afloat and preserve jobs. 18

The slowdown in business dynamism was broad across regions. 19 With the exception of Belgium, the churn rate decreased between 2008 and 2020 in all countries under review. The decline was relatively substantial in France and Spain. 20 In Germany and the Netherlands, the rate fell markedly, too. 21 In both countries, diminishing firm entry rates were primarily responsible for the slowdown in business dynamism. In Spain, meanwhile, the declining firm exit rate was mainly to blame. The same was true of the period 2008 to 2012 in France, where the subsequent decline in the churn rate between 2013 and 2020 can, for the most part, be explained by the pandemic-related reduction in the firm entry rate. By comparison, firm entry and exit rates in Italy and Belgium showed less variation, even if the COVID-19 pandemic dampened business dynamism here as well. In Belgium, the churn rate showed no clear trend, whilst in Italy, business dynamism only began to slow markedly in 2015.

Official data on business dynamism in selected euro area countries

In sectoral terms, too, the picture is fairly uniform, with firm entry and exit rates decreasing over time in all the economic activities under consideration. 22 With regard to the firm entry rate, this was, not least, due to the very significant decline in some cases observed in the first year of the COVID-19 pandemic – a development that was especially evident in accommodation and food service activities, for example. However, even before the outbreak of the pandemic, there were already some quite pronounced declines in entry rates in, for instance, the finance and insurance sector, real estate and some services sectors. In manufacturing, by contrast, the change in the entry rate was much less pronounced. Meanwhile, the exit rates saw major movements, particularly in the years following the onset of the global financial and economic crisis and the subsequent European sovereign debt crisis. In construction and the real estate sector the firm exit rate fell markedly during this period, after these industries had experienced in some cases significant turmoil in several countries (including Spain). 23 In the area of administrative and support service activities and professional, scientific and technical activities the exit rates also declined markedly. Sectoral firm entry rates differed more clearly in both their level and their evolution than exit rates. In addition, entry and exit rates in services were significantly higher than in manufacturing, and also fluctuated more. One likely explanation for this is the higher share of small firms in the services sector relative to manufacturing. Studies show that, in aggregate terms, business dynamism is, to a large extent, driven by the market entry and exit of small firms. 24  

Average business dynamism in selected sectors of the six largest euro area countries

Average firm entry and exit rates in manufacturing and selected services sectors in the six largest euro area countries

The decline in business dynamism can have cyclical and structural reasons. Business dynamism depends on various factors. Besides cyclical influences, these include structural obstacles like, for example, excessive bureaucracy, a lack of institutional quality and demographic change. All of these may be reasons, amongst others, for rigidities in product and factor markets. Distinguishing between cyclical and structural influences is very challenging in some cases. Longer-term trends in firm entry and exit rates can be an indication that structural factors are at work. However, based on Eurostat data, robust results can only derived as of 2008 for the reasons mentioned above. Not least due to the occurrence of the global financial and economic crisis and the subsequent European sovereign debt crisis, which fell into this period, it is difficult to identify purely structural factors for the slowdown of business dynamism. 

OECD indicators, which are available over a longer period, provide evidence that cyclical factors are not the sole drivers of the weakening in euro area business dynamism. An analysis for eight euro area countries shows that, in particular, the average firm entry rate was declining even before the global financial and economic crisis. 25 Alongside cyclical factors, structural factors are therefore probably also responsible for the decline in business dynamism in the euro area. The fact that business dynamism likewise experienced a trend decline in a number of large advanced economies outside the euro area – including the United States – is also consistent with this picture (see also the section on the cyclical development of business dynamism in the United States).

Cumulated changes in the firm entry and exit rates of selected euro area countries between 2000 and 2015

The significance of cyclical and structural factors can be investigated more closely using quantitative methods. The difficult data situation in the euro area is one reason why cause-effect analyses for the United States are used here as a reference point. Although the results for the United States cannot be directly applied to the euro area, they still provide valuable evidence of possible drivers in the euro area.

Severe macroeconomic turmoil constitutes one possible explanation for the lacklustre business dynamism in the euro area. Fluctuations in firm entries and exits are a key feature of business cycles. In cyclical upturns, more firms are typically established, while firms tend to be closed more frequently during downturns. Analyses for the United States show that the cyclical pattern of firm entry rates is significantly more pronounced than that of firm exit rates (see also the section on the cyclical development of business dynamism in the United States). Sharp economic downturns – as in the course of the global financial and economic crisis – can significantly depress the rate at which firms enter the market and thus weaken business dynamism overall. 26

A crisis-induced rise in uncertainty can dampen the firm entry rate on a lasting basis. Uncertainty can affect the entry rate through various channels. Altered investment behaviour plays an important role in this regard. 27 Investment – such as establishing a firm – often entails considerable sunk costs. As a consequence, it is possible that plans to set up a firm might rather be postponed when uncertainty is elevated in order to allow the decision to be taken later, when better information is available. 28 Financial market responses to uncertainty – such as rising risk premia and limited lending – can also stand in the way of business start-ups. 29  

Estimates for the United States show a sustained decline in firm entries following an unexpected rise in uncertainty. In order to assess in more detail the potential impact of uncertainty on the firm entry rate, a structural vector autoregressive (SVAR) model is estimated. 30 In addition to a number of macroeconomic variables, the model includes an indicator of financial market uncertainty 31 as well as data on firm entries. 32 The impulse-response function derived from the model shows that after an unexpected increase in financial market uncertainty, firm entries decrease significantly and for a noticeably long period.

Impact of an uncertainty shock on firm entries in the United States

Heightened uncertainty in the euro area is also a possible explanation for weak firm entry rates in the post-crisis period. Indicators for the euro area show that financial market uncertainty was exceptionally high on numerous occasions during the global financial and economic crisis and the subsequent European sovereign debt crisis. Looking at the estimation results for the United States, this provides a potential explanation for the strikingly weak developments in the euro area firm entry rate, which extend well beyond the crisis period.

Financial uncertainty in the euro area and in the United States

Policy responses to macroeconomic developments can likewise have an impact on business dynamism. These include, first, fiscal policy support for the economy. Analyses based on a structural macroeconomic model show, for example, that the German government's programme to stabilise the economy during the COVID-19 pandemic reduced the probability of firms exiting the market, in some cases significantly beyond the immediate crisis phase. 33 Second, monetary policy can likewise have an impact on developments in firm entries and exits, for example by contributing to economic stabilisation. The consequences for business dynamism thereby also depend on the design of monetary policy. There are, for instance, indications that the extended measures taken in response to the global financial and economic crisis and the subsequent sovereign debt crisis helped unprofitable firms to remain in the market and thus contributed to the strikingly weak euro area business dynamism in the economic upturn that followed (see also the section on the possible side effects of a prolonged expansionary monetary policy stance on business dynamism in the euro area).

Supplementary information

In response to the global financial and economic crisis and the subsequent European sovereign debt crisis, key interest rates in the euro area were kept at low, sometimes negative, levels for a long period. In addition, there were non-standard monetary policy measures, such as the asset purchase programme and announcements that key interest rates would not be raised in the foreseeable future (forward guidance). 1 This helped to cushion the decline in economic output and to stabilise the inflation rate in line with the monetary policy mandate. 

Economic research has focused intensively on the macroeconomic effects of a prolonged expansionary monetary policy stance, including potentially undesirable side effects. 2 One concern is that a persistent expansionary monetary policy stance could hamper business dynamism, leading to a productivity-dampening misallocation of production resources. There are a number of plausible transmission channels. On the one hand, the funding costs for firms, including less productive ones, decrease in a low interest rate environment. This can prevent firms from exiting the market. 3 As a result, profit margins would decrease due to increased competition for sales markets and production factors, hindering the market entry of potential candidates. 4 A prolonged expansionary monetary policy stance can also render credit institutions less profitable. 5 In order to avoid writing off claims, realising losses or setting aside provisions, banks may be tempted to provide additional funds to uncompetitive firms. 6

Various empirical studies suggest that the prolonged expansionary monetary policy stance in the euro area may have weighed on business dynamism. Microdata for 12 EU countries, including Germany, France, Italy and Spain, suggest that the share of unprofitable firms with access to favourable financing conditions increased due to the monetary policy stance following the global financial and economic crisis. 7 Moreover, according to panel estimates, this was associated with lower firm entry and exit rates. 8 This finding could explain why, despite the broad-based cyclical upturn, business dynamism in the euro area remained weak during the low interest rate period (see also the fourth paragraph of chapter 2).

Bundesbank simulations suggest that the degree of monetary policy accommodation may have influenced business dynamism in the subsequent normalisation phase as well. The analysis is based on a stylised New Keynesian dynamic stochastic general equilibrium (DSGE) model with endogenous firm entry and exit. 9 Households can invest their income not only in bonds and physical capital, but can also use it to promote business start-ups. Monetary policy is represented by a Taylor rule, with the short-term nominal interest rate being constrained by a zero lower bound. To outline the impact of interest rates that are low for longer, it is assumed that the model economy is hit by a strong demand shock. 10 Two scenarios, which differ in terms of their degree of monetary policy accommodation, are then compared. In both scenarios, the demand shock causes a significant decline in consumption and investment at first. Due to weaker demand, firms reduce their production and lower their prices, resulting in a rise in firm exits. Monetary policymakers lower the interest rate until it reaches the zero lower bound. The two scenarios differ in terms of how long the short-term interest rate is held at the zero lower bound.

Impact of a demand shock at varying degrees of monetary policy accomodation

According to model simulations, a more expansionary monetary policy stance can stabilise the overall economy faster, but it dampens business dynamism during the subsequent normalisation. The simulation results show that the economy as a whole recovers more quickly in the scenario with a more expansionary monetary policy stance. The firm entry rate likewise reaches its pre-crisis level sooner. The firm exit rate even falls below that level for a time. However, a more accommodative monetary policy stance also entails lower entry and exit rates in the subsequent monetary policy normalisation phase, during which the real interest rate rises. Consequently, returns on capital and business investment must likewise increase in order for households to invest in these assets. 11 To this end, in addition to the physical capital stock, the number of firms in the market, which had previously been increased by the more expansionary monetary policy stance, must also decrease. The higher the previous degree of monetary policy expansion, the more the firm entry rate in particular decreases during monetary policy normalisation. This, in turn, contributes to weaker overall business dynamism. 12

  1. See, inter alia, Deutsche Bundesbank (2022b).
  2. See, inter alia, Dell’Ariccia et al. (2018), Acharya et al. (2019) and Acharya et al. (2022). 
  3. See, inter alia, Adalet McGowan et al. (2018), Banerjee and Hofmann (2018), Laeven et al. (2020) and Acharya et al. (2024).
  4. See also Caballero et al. (2008), Acemoglu et al. (2018) and Aghion et al. (2019).
  5. A core business area of banks is to transform short-term liabilities, such as savings deposits, into longer-term assets such as mortgages, asset-backed securities and business loans. If interest rates on medium-term and long-term loans are lowered by monetary policy measures, the spread over short-term interest rates narrows and the profitability of this business model decreases. See, inter alia, Borio et al. (2017) and Claessens et al. (2018).
  6. See also Deutsche Bundesbank (2017b), Banerjee and Hofmann (2018) and Blattner et al. (2023).
  7. See also Acharya et al. (2024). For further evidence, see Acharya et al. (2019) and Banerjee and Hofmann (2020).
  8. See Acharya et al. (2024). It should be noted that this is an aggregate analysis of the countries considered. Developments in the individual countries and sectors can be quite heterogeneous. Further information can be found in the empirical work of Andrews and Petroulakis (2019) for 11 European countries (including Germany, France and Spain) and of Schmidt et al. (2023) for Spain.
  9. In addition to a representative household, the model also covers goods-producing firms as well as a central bank. The monetary policy rule is modelled in line with Gust et al. (2017) and Gutiérrez et al. (2021), inter alia. The modelling of firm entries and exits follows the approach taken by Jaimovich and Floetotto (2008) and Cavallari (2015). The DSGE model contains typical New Keynesian elements such as wage and price rigidities and investment adjustment costs.
  10. This is modelled as an unexpected abrupt increase in the risk premium, i.e. the spread between the risk-free interest rate and the return on risky assets. This approach is consistent with numerous model-based analyses of the impact of the global financial and economic crisis in 2008. See, inter alia, Gust et al. (2017).
  11. It is assumed that the model excludes arbitrage opportunities between different investment options. 
  12. The firm exit rate is also somewhat lower under the more expansionary monetary policy stance. Monetary policy had previously significantly increased firm values. In the more accommodative monetary policy scenario, firm profits were significantly higher during the macroeconomic recovery period. As firm values decline only slowly during the normalisation phase, the probability of a firm exit is temporarily lower, in line with the assumptions made.

Demographic change is another possible cause of the decline in business dynamism. This could conceivably take place through various channels. As a population ages, labour supply tends to decline. 34 Moreover, innovation and the willingness to take risks could diminish as people get older. 35 Yet these factors play a key role in setting up businesses. In addition, declining labour force growth and increasing life expectancy could stimulate saving. This tends to depress the real interest rate, which makes it easier for incumbent firms to refinance fixed assets. This may lead to fewer firms closing. The result can be a competitive disadvantage for potential market entry candidates, which is reflected in a lower firm entry rate in the long term. 36

Empirical evidence for the United States shows that population ageing has a significant impact on business dynamism. The analyses are based on a vector autoregressive model that was estimated for a panel of 50 US states over the period from 1980 to 2018. 37 The results show that the old-age dependency ratio had a marked negative impact on US business dynamism. According to the U.S. Census Bureau's population projections, demographic pressures are expected to increase in the coming years. Model simulations illustrate that population ageing is likely to significantly slow down US business dynamism in the future as well. 38

Simulated effects of population ageing on business dynamism in the United States

The results for the United States suggest that population ageing is also dampening business dynamism in the euro area. In the four largest euro area countries, the old-age dependency ratio is, in some instances, even significantly higher than in the United States, mainly because the population has a higher life expectancy. 39 In addition, the old-age dependency ratio has risen significantly earlier and more strongly in recent decades. Thus, it seems likely that demographic change has contributed to an even greater extent to the decline in business dynamism in the euro area.

Old-age dependency ratio in selected advanced economies

The quality of the regulatory and institutional environment is another structural factor with a potential impact on business dynamism. A functioning legal system and efficient administration are an important framework for business activity. Studies show that corruption or insufficient protection of intellectual property, for example, have a negative impact on business start-ups. 40 Difficult access to finance or an inefficient insolvency regime likewise place constraints on business dynamism. 41 In addition, the scale and nature of regulation have an impact on business dynamism. Impediments in product markets like a high administrative burden, privileges for individual professions or complex regulatory processes can make it more difficult to set up firms. 42 There is also evidence that rigidities in labour markets – such as very strict employment protection – dampen firm entries and exits. 43

Structural reforms are one possible way to increase business dynamism. Policy measures that improve the institutional and regulatory framework for macroeconomic processes can help to sustainably increase business dynamism. 44 Simulation analyses based on a structural macroeconomic model suggest that lower regulatory market entry costs could provide a sustainable boost to business dynamism in the euro area. 45

Impact of a reduction in firm entry costs on business dynamism

In the euro area, there is potential for reform but only moderate reform momentum. Clear evidence that regulatory and institutional barriers exist can be found, amongst other things, in the country-specific recommendations issued by the European Commission in the context of the European Semester. 46 These include, for example, reducing the administrative burden on enterprises or increasing the efficiency of the public administration and the judicial system. 47 The results of surveys amongst EU enterprises fit this pattern, with firms complaining of complex administrative procedures and regulatory costs as significant obstacles, to name but a few. 48 Despite clear evidence that reforms are needed, data on the status of implementation of the European Commission’s recommendations indicate that the pace of reforms in the euro area in the areas labour market, product market and business environment has tended to ease off in recent years. 49

Implementation of country-specific recommendations in the euro area

A number of studies point to the possible relationship between declining competition intensity and weak business dynamism. Competition between firms is an important mechanism for the efficient distribution of production resources. The intensity of competition is influenced by various factors. These include, for example, barriers to market entry, openness to foreign competition and anti-trust and competition regulations. 50 However, enterprises can also gain competitive advantages through innovations. 51 The macroeconomic consequences of weakening intensity of competition have long been the subject of intense debate. 52 Recent analyses point to a possible dampening impact of increasing pricing power on business dynamism. 53 For example, it is conceivable that incumbent firms exploit their market position to prevent potential competitors from entering the market. 54 A dominant market position as a result of technological progress or the growing importance of information-based goods, which are characterised by comparatively high fixed costs and low marginal costs, can also explain a decline in business dynamism. 55

However, there is insufficient evidence of a causal relationship between competition intensity and business dynamism. This also applies to the euro area. In addition to the difficult data situation with regard to business dynamism, this is likely due, in particular, to the fact that previous studies on developments in market concentration and price mark-ups in euro area countries paint a mixed picture. While there is evidence of a significant increase in market power, there are also estimates that suggest no, or at most a moderate, decline in the intensity of competition. 56  

According to firm entry and exit rates, business dynamism in the large euro area countries has tended to weaken over the past few decades. This development may have caused misallocation in the corporate sector and is one possible explanation for the declining productivity growth in the euro area.

Cyclical factors are likely to be one element behind the slowdown in business dynamism in the euro area. Uncertainty among market participants caused by the global financial and economic crisis and the subsequent sovereign debt crisis is one explanation for the weak momentum in the firm entry rate in the post-crisis period. Developments in connection with the COVID-19 pandemic were another factor that weighed on business dynamism. Firm entry and exit rates may have been influenced by policy measures taken in the context of macroeconomic developments.

Indications that firm entry and exit rates were already declining before the global financial and economic crisis suggest that structural factors may have been at play as well. These include, amongst others, demographic change. Excessive regulation and a lack of institutional quality are also likely to have a negative impact on business dynamism in the euro area.

Tasks such as the digital and the ecological transformation of the European economy will increase the relevance of a dynamic business landscape for growth and prosperity. To capitalise on the potential offered by digitalisation, access to sales markets and production resources must be facilitated for innovative start-ups. For this to happen, unproductive firms must exit the market. The transition to a low-emission economy also calls for a dynamic market environment. The success of the ecological transformation depends, amongst other things, on how sensitive firms and sectors are to climate policy signals.

Structural reforms represent a key element of the scope of government action. Given the potential for reform, improving institutional and regulatory frameworks is an important option for strengthening business dynamism. Besides reforms at the EU level, national governments must also act. These can rely on a range of analyses and policy proposals, not least from international organisations and the European Commission. A fundamental improvement in framework conditions is needed to stimulate business dynamism in the long term.

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Tomasi, C., F. Pieri and V. Cecco (2023), Red tape and industry dynamics: a cross-country analysis, Journal of Industrial and Business Economics, Vol. 50, pp. 283-320.

U.S. Census Bureau (2021), BDS Codebook and Glossary (2021 release).

von Eschwege, K. (2021), Neuerungen im statistischen Unternehmensregister: Auswertungskonzept, Relevanzschwellen und weitere Quellen, WISTA – Wirtschaft und Statistik, Issue 5/2021.


  1. There has been a slowdown in labour productivity growth across the board, both in terms of output per hour worked and output per person employed. See also Deutsche Bundesbank (2021a).
  2. See European Central Bank (2021) and Deutsche Bundesbank (2021a, 2023).
  3. For a detailed analysis of the slowdown in productivity growth and possible explanatory factors, particularly within the euro area, see Deutsche Bundesbank (2021a).
  4. See also Deutsche Bundesbank (2021a). The calculation of TFP at the firm level is based on a Cobb-Douglas production function with capital, labour and materials serving as production factors. Production elasticities are calculated at the sectoral level with an OLS estimation; see CompNet (2022).
  5. For more information, see Hsieh and Klenow (2016).
  6. See, inter alia, Foster et al. (2001, 2006, 2008), Lentz and Mortensen (2008) and Decker et al. (2016, 2017).
  7. See, inter alia, Schumpeter (1934), Nelson (1981), Aghion and Howitt (1992), Caballero and Hammour (1996) and Aghion et al. (2014).
  8. Business demography statistics are mainly sourced from national business registers, which are primarily based on administrative and statistical sources. Administrative sources generally comprise i) tax registers; ii) obligatory registers, e.g. for limited liability companies or companies listed on stock exchanges; iii) social security statistics; and iv) other public or private data collections. Statistical sources generally include responses from various surveys. For more information, see Overview – Business demography – Eurostat ( and Eurostat (2007).
  9. On top of differences with regard to when records began, Eurostat’s country-specific data on business demography are incomplete in some cases. The dataset used extends to 2020. Owing to comprehensive methodological changes, data on business dynamism are reported separately by Eurostat from 2021 onwards.
  10. The acronym NACE (Nomenclature générale des activités économiques dans les Communautés européennes) is the name given to the European standard classification of productive economic activities. NACE presents the universe of economic activities partitioned in such a way that a NACE code can be associated with a statistical unit carrying them out. Between 2000 and 2007, the NACE classification was fundamentally overhauled. The updated version (NACE Rev. 2) has been applicable for statistics referring to economic activities performed from January 2008 onwards; see Eurostat (2008).
  11. For more detailed information on the methodological changes, see von Eschwege (2021) and Rink and Seiwert (2021). 
  12. The entry rate (exit rate) is defined as the ratio of the number of business start-ups (business closures) to the total number of active firms in the respective year. The analysis only considers firms with at least one employee so as to ensure better comparability across countries. Sole proprietorships (including those established with the help of one-person business start-up grants known as “Ich-AGs”, which were widespread in Germany for a time) are therefore excluded. See Eurostat (2007).
  13. Our analysis included economic activities from NACE sections B, D and E, C, F, G, I, J and H, K (excluding NACE group 642) as well as L to N.
  14. The average churn rate in the euro area is approximated using data for Belgium, France, Germany, Italy, the Netherlands and Spain.
  15. See Eurostat (2008).
  16. Eurostat documents structural breaks for NACE Rev. 2 data only.
  17. See also Koellinger and Thurik (2012), Lee and Mukoyama (2015) and Tian (2018).
  18. See also Hinterlang et al. (2023). Note here that the results for the past few years exclude developments in Germany due to the methodological problems mentioned above. That said, alternative indicators do suggest that business dynamism, too, remained listless there in the years after 2017 and did not diverge significantly from developments in the other large euro area countries (see also the section on declining dynamism in the German corporate sector over the last two decades).
  19. Here again, caution is warranted when interpreting the results owing to the methodological changes, particularly those relating to the revision of the statistical classification of economic activities (changeover from NACE Rev. 1.1 to NACE Rev. 2). The structural breaks reported by Eurostat denote significant methodological changes. These are particularly striking in Germany in 2018 and in France in 2013, leading to significant shifts in the time series in some instances.
  20. In France, there was a structural break in 2013 owing to methodological changes. This caused the firm entry rate to rise significantly, by around 2 percentage points, between 2012 and 2013. There were no such anomalies in the firm exit rate. The churn rate, however, fell markedly between 2008 and 2012 and again between 2013 and 2020.
  21. Owing to the methodological problems mentioned above, developments for Germany are described only up to and including 2017.
  22. Owing to the conceptual revision of the statistical classification of economic activities in the European Community (NACE), the sectoral analysis does not begin until 2008.
  23. The decline in the exit rate may have been partly attributable to the onset of the low interest rate environment and the subsequent upswing in the real estate market, which benefited from it.
  24. For more information, see the section on the cyclical development of business dynamism in the United States as well as Tian (2018).
  25. This group of countries comprises Austria, Belgium, Finland, France, Italy, the Netherlands, Portugal and Spain. Firm entry and exit rates are based on confidential microdata, which were prepared and evaluated as part of the OECD DynEmp project. See also OECD, Criscuolo et al. (2014) and Desnoyers-James et al. (2019).
  26. For more information, see Bergin et al. (2018) and Ayres and Raveendranathan (2023).
  27. See, inter alia, Bernanke (1983) and Pindyck (1991). See also Deutsche Bundesbank (2018).
  28. See, inter alia, Dixit (1989), Folta et al. (2006) and Fajgelbaum et al. (2017).
  29. See, inter alia, Brand et al. (2019).
  30. The impulse responses are derived from a Bayesian SVAR model. The estimation period ranges from the first quarter of 1961 to the first quarter of 2023. The structural shocks are obtained by recursive identification. Uncertainty shocks are assumed to have a direct impact on the other variables. The endogenous variables enter the model with a maximum lag of four periods. The model equations also contain a constant as deterministic components. See also Kilian and Lütkepohl (2017).
  31. The indicator of financial market uncertainty is based on the volatility of estimation errors resulting from the forecasting of a broad selection of business cycle-relevant financial market data. The fluctuation intensity of forecast errors determines the degree of uncertainty. For a detailed description of the methodology, see Ludvigson et al. (2021).
  32. In addition to the indicator of financial market uncertainty, the model also contains a short-term shadow interest rate as a measure of the monetary policy stance, see Krippner (2013), as well as the log consumer price index, log real corporate earnings, log market entries and log real GDP. The firm entry time series combines data from the Survey of Current Business produced by the Bureau of Economic Analysis (1996) for the period from 1960 to 1993 with data provided by the Bureau of Labor Statistics for the years 1994 to 2023; see also Brand et al. (2019) and Cieślik and Turgut (2023).
  33. See Hinterlang et al. (2023).
  34. See, inter alia, Karahan et al. (2019) and Hopenhayn et al. (2022).
  35. See, inter alia, Ouimet and Zarutskie (2014), Engbom (2019) and Liang et al. (2018).
  36. See Röhe and Stähler (2020) and Deutsche Bundesbank (2021a).
  37. The endogenous model variables include macroeconomic variables such as the growth rate of per capita GDP, the unemployment rate and the (short-term) real interest rate as well as information on annual firm entry and exit rates. The old-age dependency ratio and the young-age dependency ratio are included as exogenous variables. The former measures the ratio of the population over the age of 64 to the working-age population (15 to 64 year olds). The latter reflects the ratio of the under-15s to the working-age population. The endogenous variables are included in the model with a delay of one period. Time and state-specific fixed effects are taken into account in the estimation.
  38. The procedure follows the approach used by Aksoy et al. (2019).
  39. According to the OECD, life expectancy in 2021 was around 81 years in Germany, 82 years in France, 83 years in Italy and 83 years in Spain. In the United States, life expectancy was around 76 years.
  40. See, inter alia, Desai et al. (2003) and Aidis et al. (2012).
  41. See, inter alia, Aghion et al. (2007), Kerr and Nanda (2009) and Calvino et al. (2016, 2020).
  42. See, inter alia, Klapper et al. (2006), Dreher and Gassebner (2013), Chambers and Munemo (2019), Gutiérrez and Philippon (2019), Calvino et al. (2020) and Tomasi et al. (2023).
  43. See, inter alia, Autor et al. (2007), Kugler and Pica (2008), Bottasso et al. (2017) and Cooper et al. (2024).
  44. See also European Central Bank (2018).
  45. The analytical framework is a simple New Keynesian dynamic stochastic general equilibrium (DSGE) model without physical capital, with imperfect competition and quadratic price adjustment costs, as well as endogenous firm entry and exit (see, inter alia, Cavallari (2015)).
  46. The OECD likewise provides evidence suggesting the need for reform; see OECD (2023).
  47. See European Commission for more information. 
  48. For more information, see Altomonte and Aquilante (2012) and Eurochambres (2019, 2024).
  49. Labour market reforms include, amongst other things, changes in active labour market policy, unemployment benefits, education and training, employment protection and wage setting. Product market reforms constitute, for example, changes to the competition and regulatory framework or to the rules regulating state-owned enterprises and state aid. Reforms of the business environment involve, amongst other things, changes to insolvency law, the quality of the judicial system, access to financing for enterprises or the digitalisation of enterprises and public administrations.
  50. See also Deutsche Bundesbank (2017a).
  51. See, inter alia, Lashkari et al. (2020).
  52. See, inter alia, Hall (2018), Basu (2019) and De Loecker et al. (2020).
  53. See, inter alia, Decker et al. (2020) and De Loecker et al. (2020, 2021).
  54. See, inter alia, Cunningham et al. (2021) and Ederer and Pellegrino (2023).
  55. See, inter alia, Akcigit and Ates (2021) and De Ridder (2024).
  56. See Deutsche Bundesbank (2017a), De Loecker and Eeckhout (2018), Cavalleri et al. (2019) and Kouvavas et al. (2021).