The dynamism of employment varies significantly across countries. Recent cross-country evidence suggests that these differences exist not only in terms of the entry and exit patterns of firms, but also in the size of firms at entry, and in their post-entry growth performance (Bartelsman, Scarpetta and Schivardi, 2003; Calvino, Criscuolo and Menon, 2015). For example:
The size of entering and exiting firms tends to be smaller in the United States than in Europe.
Successful, young firms tend to expand relatively more quickly in the United States than elsewhere (Bartelsman, Haltiwanger and Scarpetta, 2013).
Europe has a higher share of slow-growing and stagnant firms relative to the United States (Bravo-Biosca, Criscuolo and Menon, 2013).
The gap between the United States and Europe suggests that differences in institutional factors — which shape differences in the cost of reallocating resources — may explain the relative sluggishness of some European countries in capitalising on the information and communications technology revolution, (Conway et al. 2006; Bartelsman, Gautier and de Wind, 2010) and realising the potential for growth embodied in knowledge-based capital.
Accordingly, this paper reviews evidence from recent Organization for Economic Co-operation and Development (OECD) research that examines cross-country differences in business dynamism, especially across sectors and firm types. A number of key findings emerge that carry important implications for public policy.
First, as noted in the U.S. by Decker et al. (2014), a decline in business dynamism has been observed in several, but not all, OECD economies; we document the heterogeneity of the decline across sectors and countries. Second, as suggested by recent U.S. evidence (Haltiwanger, Jarmin and Miranda, 2013), not all small businesses are net job creators. Rather, estimates show that only young businesses — which are predominantly small — are the primary drivers of job creation. While this seems an empirical regularity across countries, we will show significant cross-country differences in the extent to which young businesses contribute to aggregate employment growth.
The paper proceeds as follows:
Section 2 briefly describes the data used in the underlying analysis.
Section 3 documents some stylised facts about the patterns of business dynamism across countries and sectors during the past decade, and shows that the extent of the decline varies significantly across sectors.
Sections 4 and 5 focus on young businesses’ contributions to employment creation and destruction, and their employment growth dynamics respectively.
Section 6 draws policy implications and offers some concluding thoughts.
The analysis underlying the evidence reported in this paper is based mainly on micro-aggregated data from the OECD DynEmp project. We will describe the DynEmp Express and the DynEmp v.2 briefly, in turn. The Data Appendix contains a more detailed description of the DynEmp v.2 database.
The DynEmp data used in this work are the outcome of the ongoing DynEmp project,1 which is led by the OECD Directorate for Science, Technology and Innovation, with the support of national delegates and national experts in member and nonmember economies (see also Criscuolo, Gal and Menon, 2014a; Calvino, Criscuolo and Menon, 2015).
The DynEmp project is based on a distributed data-collection exercise aimed at creating a harmonised, cross-country, micro-aggregated database about employment dynamics from confidential microlevel data. National business registers are the primary sources of firm and establishment data. The project is supported by a network of national experts who run common Stata routines developed centrally by the OECD DynEmp team (see Criscuolo, Gal and Menon, 2014b for details), using the confidential microdata to which they have access. The experts also implement country-specific disclosure procedures to ensure confidentiality is respected.
The purpose of the DynEmp database is to collect cross-country evidence from countries’ business registers to identify the sources of job creation across countries and time. The project aims to quantify the extent to which firms that differ in terms of age, size and sector contribute to job creation and job destruction, and to see how firm entry, growth and exit shape employment dynamics across countries and time. The resulting statistics also provide insights about how business dynamics behaved during the recent global financial crisis.
As emphasized by Criscuolo, Gal and Menon (2014a), the advantages of using harmonised, micro-aggregated data from business registers for the study of business employment dynamics are manifold. First of all, the different channels of employment variation can be identified separately, distinguishing between gross job creation and job destruction, and between the extensive (firm entry and exit) and intensive margin (post-entry growth). Furthermore, the role of firm age and size can be examined. Finally, each of these elements can be compared across countries, sectors, and time.
Measuring entrepreneurship and its economic effects in terms of job creation is not an easy task. Appropriate data, taking into account the age — not only the size — of businesses, are necessary. Furthermore, very few databases allow the researcher to follow different cohorts of units of analysis over time, despite the wide recognition that this is crucial when studying business dynamics, especially in the case of entrants (see, for instance, Bartelsman, Haltiwanger and Scarpetta, 2009). Even fewer cross-country databases combine a cohort approach with a detailed industry disaggregation. The DynEmp database provides an ideal framework for this type of investigation.
Mergers and acquisitions could be accounted for in only a few countries.
Differences in the source data might persist, for example, in the minimum threshold above which a business is captured in the relevant country’s business register.
Owing to methodological differences in constructing these indicators, and the efforts made to harmonise the underlying data, DynEmp statistics may deviate from official statistics published by national statistical offices.
3. A General Picture of Business Dynamism Trends
To document the cross-country heterogeneity in the existence and severity of a decline in business dynamism, we examine two measures of economic dynamism. The first is the entry rate of firms, which is calculated as the number of entering units in the sector compared with total number of units in that sector,2 thus indicating how many firms are created during a given year in a given sector. The second measure we consider is sectors’ churning rate as an indicator of their ability to allocate resources, specifically labour.
Figure 1 . Measures of Firm Dynamics for the Period 2002-2010
Figure 1 shows that, during the 2002-2010 period, entry rates consistently fell on average across the countries in our sample (Austria, Belgium, Denmark, Finland, Italy, Hungary, Luxembourg, New Zealand, Sweden).3 The decline seems to have been fairly steady during the period, and exacerbated by the 2007 recession. In parallel, the average size of firms (both incumbents and new entrants) increased only by 6 percent. Thus, if there was a process of concentration, it seems only a partial explanation to the decline in entry rates. Interestingly, a reduced entry rate combined with an increasing size of entrants suggests that barriers to entry may have become more salient with the 2007 recession, allowing only bigger firms — perhaps with more access to resources — to enter the market.
Figure 2. Startup Rates Across Countries
Data collected in the DynEmp Express (see Data Appendix) show that while most economies observed declining startup rates during the 2000s, some exceptions remain. For example, as shown in Figure 2, some countries saw a rise in their entry rates (e.g., the United Kingdom and Finland, until the crisis) or relatively stable entry rates (e.g., Belgium and Portugal until the crisis, and Sweden). Our ongoing research is trying to relate differences in policies to these diverging patterns.
As a second measure of the economy’s dynamism, we consider sectors’ churning rates as an indicator of their ability to allocate resources, more specifically labour. Following Davis and Haltiwanger (1999), we define churning rate of sector j in country i at time t:
L stands for employment, and GJC and GJD stand for gross job creation and gross job destruction, respectively. A higher churning rate indicates that more jobs are reallocated within the sector and reveals the intensity of the Schumpeterian process of creative destruction (see Schumpeter, 1942).
Figure 3 . Measures of Job Reallocation, 2002-2010
Figure 3 depicts the intensity of job reallocation over time, and disaggregates churning into its components: job-creation and job-destruction rates. Over time, the churning rates profile seems rather flat, with a small decline toward the end of the period. However, this apparently stable pattern hides important differences in the periods before and after the crisis. After 2007, overall job destruction rose sharply, while job creation fell across the board. In a well-functioning market, the Schumpetarian process of creative destruction should reallocate jobs from firms destroying jobs to firms creating jobs, so we should observe job destruction and job creation moving together. This is largely the case in our dataset before the crisis. However, after 2007, the crisis created a sharp disconnect between job creation and job destruction: Between 2007 and 2009, the job destruction rate sharply rose by 31 percent while the job creation rate fell by almost the same amount, 30 percent, indicating that the crisis strongly affected the process of job reallocation.
Finally, it is important to notice that the decline in business dynamism, as captured by firm-entry rate and job-churning rate, has been uneven across sectors. While on average in our dataset, the entry rate fell during the 2002-2010 period, the amount by which it declined differs greatly across sectors. In a number of sectors (e.g. telecommunications) entry rates fell sharply during the period. On the other hand, entry rates have been relatively stable in other sectors (e.g., textiles). This heterogeneity is even more pronounced when comparing how sectors fared after the crisis. During the 2007-2010 period, entry rates experienced annual changes ranging from -17.5 percent in the scientific research and development sector, to a yearly increase of 0.1 percent in the textile sector.4 The heterogeneity observed across rates seems to suggest that entry rates might have been more resilient in sectors in which demand is more inelastic, such as food products, accommodation and textiles.
Figure 4. Three Different Effects of the Crisis on Entry Rates
Source: Blanchenay et al. (forthcoming), based on the OECD DynEmp v.2 preliminary data, June 2015.
Figure 4 shows how entry rates in three different sectors were affected by the crisis. In the textile industry, entry rates only mildly declined during the crisis. The legal and accounting sector experienced a steady decline in entry rates during the period, particularly after 2004, and this downward trend continued during the crisis. In the information-technology services sector, the crisis led to a sharper decrease following the downward trend already evident since 2002.
In line with the preliminary evidence that sectors experienced very different changes during the 2002-2010 period, we further investigate how much of the observed change in the economy-wide firm-entry rate comes from a change of each sector’s entry rate (“within”), or from a change in the relative weights of sectors in the economy (“between sectors”). Following Decker et al. (2014), we implement a shift-share decomposition by decomposing, in a given country, the change in entry rate during the period 2002-2010 as follows:
The first term, “within,” represents the total change of entry rate within sectors holding their shares in the economy constant. The second term, “between,” represents the change in sectors’ weights holding their entry rate constant. The last term, “cross,” is a covariance term, which represents the joint change of weights and entry rate (a positive term means that sectors with increased entry rates also became more important in the economy).
Figure 5. Decomposition of Change in Entry Rates (2002-2010)
Consistent with Decker et al. (2014), the visual representation of each component of the decomposition, given in Figure 5 above, highlights that, in most countries in our dataset, the change in entry rates during the period is driven by within-sector changes, rather than by changes in relative size of the sectors in the economy. The same holds for job-churning rates.5 This seems to suggest, in particular, that changes in the composition of the economy, with a decline in the share of manufacturing and the rise of new technologies of information and communication (NTIC) sectors, is not sufficient to explain the decline of firm-entry rates in all countries in our dataset. As we show below, it is crucial to understand the source of this decline, because young firms account for a disproportionate part of job creation and growth potential in the economy.
4. Firm Age is a Key Characteristic
In addition to a great heterogeneity across countries and across sectors, data from the DynEmp project also can help shed light on firms’ dynamism, depending on their age. Indeed, one of the main contributions of the DynEmp project has been to allow, for the first time, close investigation of the role of age — not just size — in the growth dynamics of firms across eighteen OECD countries covering the period 2001-11. This is particularly important because policies generally have focused on targeting small- and medium-sized enterprises, which typically represent a large proportion of economic activity — firms with fewer than fifty employees represent more than 95 percent of all businesses in an economy (Figure 6) and between 30 and 65 percent of total employment (Figure 7). However, these small firms vary enormously in their age, and the age distribution of small firms varies significantly across countries (Figure 8). The fact that, in some economies (e.g., Italy), most firms are small and old might reflect a lack of dynamism in the economy, as evident also from looking at entry rates and churning rates in the country. Conversely, the large proportion of small firms in Brazil reflects the high rate of new entrants, typical of an emerging economy.
Figure 6. Share of Firms of Different Size by Country
Figure 7. Share of Employment by Different Firm Size and by Country
Figure 8. Age Composition of Small Businesses
Average over time, firms with fewer than fifty employees
4.1. Young Firms are More Dynamic
The cross-country, aggregated microdata also show that, across all countries in the sample, young firms are more dynamic than old firms, both in terms of jobs created and destroyed. However, young firms systematically create more jobs than they destroy, independent of their size (Figure 9). In particular, young firms represent only around 20 percent of total employment, but they account for almost 50 percent of total job creation in the economy; their share in job destruction is around 25 percent. These patterns also hold at the sector level (Figure 10). Small/young firms account for almost 45 percent of job creation in services, and just more than 30 percent in manufacturing. Even during the global financial crisis, the majority of jobs destroyed in most countries reflected the downsizing of mature businesses, while net job growth in young firms (less than five years of age) remained positive. Therefore, the age dimension is particularly important for the design of policy, especially for policies aimed at small firms.
Figure 9. Contribution to Employment, Job Destruction and Job Creation, Nonfinancial Businesses
By firm age and size, average across eighteen countries
Figure 10. Contribution to Employment, Gross Job Creation and Gross Job Destruction, Manufacturing and Services
By firm age and size, average across eighteen countries
Differences in the magnitude of this phenomenon across countries points to the importance of national policies and business environments in fostering the birth and growth of new firms. In some countries — for example, Brazil, New Zealand, and Spain — young firms account for more than half of the economy’s total gross job creation. In others — such as Japan and Finland — young firms account for less than 30 percent of jobs created (Figure 11).
Figure 11. Contribution of Young Firms to Employment, Gross Job Creation and Gross Job Destruction
4.2. Deconstructing Job Creation by Entrants
Given the importance of young firms for the firm dynamics, the contribution of startups to the creation of new jobs can be further unpacked into four different elements6 (Calvino, Criscuolo and Menon 2015):
Average size at entry.
Average post-entry growth of survivors.
These four levers help investigate the extent to which entrants contribute to aggregate job creation, with potentially very different policy implications. Indeed, even across economies with similar aggregate startup contributions, these elements interplay in different ways, highlighting the lack of a one-size-fits-all pattern. Figure 12 separately illustrates the four elements described above, focusing on the first three years of activity of surviving entrants. It demonstrates that the startup ratio, average size at entry, and post-entry growth of survivors exhibit substantial cross-country heterogeneity, while the survival rate appears more homogeneous. For instance, the startup rate is very low in Belgium, but the post-entry growth rate of survivors in that country is the highest in the sample. Conversely, in New Zealand and Turkey, the startup rate is higher but the average post-entry growth is significantly lower. Further analysis reveals that, in most countries, startups’ likelihood of exiting is highest at year two and decreases (linearly) beyond that age (Calvino, Criscuolo and Menon, 2015).
This evidence confirms that entrepreneurship is a highly complex phenomenon, and that similar outcomes in terms of net job creation might mask very different startup dynamics across countries. Decomposing net job creation by surviving entrants might help fine-tune policy interventions in the light of the relative weight that each of these four elements has in a specific economy.
Figure 12. Four-Way Decomposition of Job Creation by Surviving Entrants
Panel A. Startup Ratio
Panel B. Survival Rate (after three years)
Panel C. Average Size at Entry
Panel D. Average Post-Entry Growth
4.3 The Dynamics of Micro Startups
The contribution of young firms to aggregate employment growth reflects a process of creative destruction in which success and failure go hand in hand. Young-firm dynamics are characterised by a so-called “up or out” pattern (Haltiwanger, Jarmin and Miranda, 2013). A significant proportion of startups do not survive beyond the first two years, but those that do survive contribute disproportionately to job creation.
Differences in the extent to which young firms grow are first shown in Figure 13. We infer the potential growth of young firms by comparing the average size of startups with the average size of old businesses (more than ten years old). Figure 13 points to some differences in the size of startups across countries, although these are not striking. France, Finland and The Netherlands have the largest new firms.8 The picture is much more heterogeneous when examining the size of older businesses. The average size of old firms in the U.S. is by far the largest — around eighty employees in manufacturing and forty in services. This is even more striking since the average size of startups in the French manufacturing sector is more than double the average size of U.S. startups. This confirms the previous results of Bartelsman, Scarpetta and Schivardi (2003), who found that seven-year-old U.S. firms are, on average, 60 percent larger than their size at entry; in European countries, the figure ranged between 5 and 35 percent. With the current data, The Netherlands shows one of the lowest ratios between the average size of old firms and startups across all countries, with a particularly low ratio of 1.7 for manufacturing firms. In the U.S., this ratio is greater than five for both manufacturing and services firms.
13. Average Size of Startup and Old Firms across Industries and across Countries
These findings are consistent with anecdotal and survey evidence that, in some countries, entrants might be able to start off at a smaller size because it is less costly to experiment. Moreover, they can exit more easily if they are not successful. This, in turn, might contribute to stronger growth prospects for very productive and successful businesses by freeing up scarce resources, such as skilled labour. It also suggests that barriers to growth (e.g., access to markets, burdensome regulation on starting businesses, and lack of competition) might hinder the growth potential of young businesses in some countries.
To better understand the growth dynamics of startups, the OECD DynEmp database follows cohorts of entrants (firms aged zero to two at the beginning of the period) with fewer than ten employees for three, five and seven years.
5. Post-Entry Growth
Previous work already has highlighted that the average growth rate of startups — although generally positive overall — entails a substantial degree of heterogeneity within cohorts of otherwise similar entrants, with the large majority of small startups growing very slowly, and a tiny proportion of them experiencing very fast growth (Criscuolo, Gal, and Menon, 2014c; Anyadike-Danes et al., 2014). It is, therefore, important to further explore this firm-level heterogeneity in the growth performance of startups in order to draw useful policy implications.
Figure 14 analyses the post-entry dynamics of micro entrants (entrants with zero to nine employees) in the nonfinancial business sector, classifying them according to their size class five years later, or in the exit group if they did not survive. From Panel A, which presents the figures in terms of number of units, it is evident that most micro startups either remain stable (i.e., at the end of the period they are in the same size class as at the beginning of the period) or exit the market. In every country, the number of micro startups moving to a higher size class at the end of the period is extremely small — on average around 3 percent and never more than 8 percent. The graph also shows that, in all economies but Austria, Brazil, and Turkey, the number of surviving micro startups is higher than the number of exiting ones.
Figure 14. Focus on Micro Entrants: Stable vs. Growing vs. Exiting
Panel A. Size class of micro entrants (zero to nine employees) five years after entry
Panel B. Contribution to total net job creation
The pattern is, however, completely different if, rather than looking at firms, one looks at jobs. Panel B in Figure 14 illustrates post-entry dynamics of micro entrants in terms of their net job creation (the difference between employment at the beginning and at the end of the five-year period, respectively). The very few micro-entrants with more than twenty employees at the end of the horizon are responsible for most job creation of micro startups in all countries — on average, 37 percent with a maximum of 52 percent in Sweden. Furthermore, in most countries, gross job creation by surviving micro startups more than compensates for gross job destruction by exiting ones.9
Three main features are worth noting:
Very few micro startups – between 2 and 9 percent – grow to include more than ten employees, but their contributions to employment change ranges from 19 to 54 percent.
- Most firms remain within the same size class (i.e., they still employ fewer than ten employees after three years), and while they still create a reasonable number of jobs, their contribution is less than proportional to their weight in terms of number of firms. For some cohorts and countries, these small startups actually contribute negatively to net job creation.
- The extent to which micro startups survive is very different across countries.
Understanding the role that differences in policies play in explaining these features is crucial for evidence-based policymaking, as discussed in the next section.
6. The Role of Public Policy: Discussion and Future Research
The paper has shown some empirical regularities about the important role of startups for net job creation and about the up-or-out dynamics that characterise their growth profiles. Nevertheless, the new DynEmp database also points to some important cross-country differences in the magnitude of young firms’ contributions to net job creation, and in the extent to which they can upscale. Both the empirical regularities and the cross-country differences convey important evidence for policymakers.
In particular, the “empirical facts” that young businesses are net job creators across the board, while they also are characterised by an up-or-out dynamic, suggest that often-heard statements such as “SMEs are net job creators” should be qualified as “young SMEs are net job creators.” At the same time, the facts speak against using “picking winners” policies for this group of firms, given the high turbulence characterising startups. Rather, both of these facts characterising startups indicate that an environment that favours experimentation, and allows for a healthy process of creative destruction with high entry and exit rates, and the realisation of high growth potential, are key for both employment and productivity growth. The existence of cross-country differences in the growth contribution from startups, and in the possibility for them to upscale points to the importance of national structural features and institutions. In particular, it highlights the importance of designing public policies such that entrepreneurs can enter and operate in an experimentation-friendly and growth-supportive environment. This entails policies that can reduce the costs of entry and post-entry growth (regulations affecting product, labour and financial markets), uncertainty (contract enforcement and judicial efficiency) and exit (employment-protection legislation and bankruptcy laws).
To that end, ongoing research using the DynEmp v.2 database has looked into policies that relate to entry and exit rates, size at entry, and post-entry growth performance. Preliminary results of this research point to the importance of competition in product markets with tight product-market regulations and barriers to foreign competition being associated with reduced firm entry. Slower growth of firms, in line with existing evidence that links the lack of competition with a less efficient allocation of resources, is associated with slower growth of innovative firms and lower exit rates of inefficient firms.
Secondly, regulation of the labour market is also significantly related to the growth performance of young businesses. This is in line with previous OECD research (Bravo-Biosca, Criscuolo and Menon, 2013, and Andrews, Criscuolo and Menon, 2014) showing that less stringent employment-protection legislation is associated with higher increases in the ability of innovative firms to attract resources that are required to implement and commercialize new ideas, especially for young businesses. It is consistent with strict employment-protection legislation slowing down the reallocation process, and making it less attractive for firms to experiment with highly uncertain technologies by raising the costs of exit in case of business failures
Third, well-developed financial and judicial systems are also an important policy lever that can explain differences in growth performance of entrants. In particular, availability of seed and early-stage venture capital, clear enforcement of contracts and efficient judicial systems are key.
Finally, given the high risk of failing when starting a business, bankruptcy legislation that does not excessively penalise failure is associated with both higher entry rates and faster post-entry growth. By contrast, if the time it takes to wind down a business is particularly lengthy, risky entrepreneurial ventures might not be brought to the market to avoid incurring high exit costs in case of failure.
From a methodological perspective, the evidence presented in this paper and the ongoing research agenda at the OECD point to the importance of accessing, harmonizing and using microdata for constructing the evidence base needed for policymaking. The DynEmp project has been an important step in bringing this to the attention of policymakers in the area of employment dynamics.
Using the same methodology as the DynEmp project, we also are working on providing new cross-country evidence about the microdrivers of productivity, thanks to a new project called MultiProd. The MultiProd project aims at building a collection of statistics at the detailed sectoral level for different firm characteristics, such as wages, profits, investment and, in particular, for various measures of labour and multifactor productivity. The objective is to better understand the heterogeneity in the Schumpeterian process of creative destruction across countries and sectors. In particular, it will focus on four issues:
First, MultiProd gauges whether resources are efficiently allocated through the analysis of the entire firm-level productivity distribution, with further refinements by size, age, and ownership categories.
Second, the project identifies firms at the “frontier”—the best performers— and understands how they differ across countries, what drives their performance, and how much they contribute to aggregate productivity growth.
Third, it investigates the cross-country differences in firm-level productivity performance and in allocative efficiency before, during and after the financial crisis. Finally, it looks at the effect of productivity dispersion on wage inequality.
Thanks to this new collection of data, the project will be able to examine the effectiveness of various policy frameworks aimed at shaping firm productivity and enhancing resource allocation to more productive firms. The MultiProd project also will complement the evidence gathered by the DynEmp project because it will be useful to understand whether young firms, which disproportionately contribute to job creation in the economy, also contribute to the growth of aggregate productivity. The project will allow comparing the characteristics of young firms to old ones, as well as comparing the age of firms in the top and bottom of the distribution of productivity growth.
About the Authors
Patrick Blanchenay, Flavio Calvino and Chiara Criscuolo are with the Directorate for Science, Technology and Innovation, of the Organization for Economic Co-operation and Development (OECD).
The views expressed in this paper do not reflect the view of the OECD or of its member countries. Errors remain our own. We are grateful to Isabelle Desnoyers-James for statistical assistance.
Note that the focus of the analysis is on units with positive employment.
Note that the trends illustrated in Figure 1 are weighted country averages. Larger countries, therefore, count more in driving the dynamics. A certain degree of cross-country heterogeneity remains present, as also shown in Figure 2.
Note that changes in entry rate are expressed in compound annual changes, so small differences in percentages can amount to large discrepancies when compounding during the 2002-2010 period.
See Blanchenay et al. (forthcoming).
See Annex A in Calvino, Criscuolo and Menon (2015) for the exact formula
Measured as number of startups across total employment in the economy
These differences might be partly influenced by the intensity of merger and acquisition (M&A) activity across countries, and whether the new businesses that result from M&A deals appear as “entrants” in a country’s business register.
A caveat is, however, necessary for this analysis. Due to confidentiality requirements and the relatively small size of their economy, the partial blanking of the result datasets for Denmark, Finland, New Zealand, and Netherlands may lead to underestimating the share of micro startups moving to a higher size class, as well as their contributions to net job creation.
More information: https://www.oecd.org
- Data relative to 2010-2011 in Norway have been excluded from the analysis due to ongoing checks on unusual dynamics in the underlying data; data relative to 2006 in The Netherlands have been also excluded due to the redesign of the Business Register in that year.
M. Anyadike-Danes, C.M. Bjuggren, S. Gottschalk, W. Hölzl, D. Johansson, M. Maliranta, A.G. Myrann, “Accounting for job growth: disentangling size and age effects in an international cohort comparison,” IFN Working paper (2014)
D. Andrews, C. Criscuolo, C. Menon, “Do Resources Flow to Patenting Firms? Cross-country Evidence from Firm Level Data,” OECD Economics Department Working Papers No 1127 (2014)
E. Bartelsman, J. Haltiwanger, S. Scarpetta, “Measuring and Analysing Cross-Country Differences in Firm Dynamics,” Producer Dynamics: New Evidence from Micro Data (University of Chicago Press) pp. 15-76 (2009)
E. Bartelsman, J. Haltiwanger, S. Scarpetta, “Cross-Country Differences in Productivity: The Role of Allocation and Selection,” The American Economic Review 103 (1): 305–34 (2013)
E. Bartelsman, S. Scarpetta, F. Schivardi, “Comparative Analysis of Firm Demographics and Survival: Micro-Level Evidence for the OECD Countries,” OECD Economics Department Working Papers, No. 348 (OECD, Paris) 2003
E. Bartelsman, P.A. Gautier, J. De Wind, “Employment Protection, Technology Choice, and Worker Allocation,” IZA Discussion Paper No. 4895 (2010)
A. Bravo-Biosca, C. Criscuolo, C. Menon, “What Drives the Dynamics of Business Growth?” OECD Science, Technology and Industry Policy Papers, No 1, OECD Publishing (2013)
P. Blanchenay, F. Calvino, C. Criscuolo, (forthcoming), “Cross-Country Evidence on the Decline of Business Dynamism”
F. Calvino, C. Criscuolo, C. Menon, “Cross-country Evidence on Start-up Dynamics,” OECD Science, Technology and Industry Working Papers, 2015/06 (OECD Publishing, Paris) 2015
P. Conway, D. de Rosa, G. Nicoletti, F. Steiner, “Product Market Regulation and Productivity Convergence,” OECD Economic Studies, Vol. 2006(2), pp. 39-76, (OECD Publishing, Paris) 2006
C. Criscuolo, P. Gal, C. Menon, “The Dynamics of Employment Growth: New Evidence from 18 Countries,” OECD Science, Technology and Industry Policy Papers, No. 14, OECD Publishing, (2014a)
C. Criscuolo, P. Gal, C. Menon, “DynEmp: A Stata® Routine for Distributed Micro-data Analysis of Business Dynamics,” OECD Science, Technology and Industry Working Papers, 2014/2, OECD Publishing (2014b)
C. Criscuolo, P. Gal, C. Menon, “Do Micro Startups Fuel Job Creation? Cross-country Evidence from the DynEmp Express Database,” OECD Science, Technology and Industry Policy Papers, forthcoming
S.J. Davis, J. Haltiwanger, “Gross job flows,” Handbook of Labor Economics, 3, 2711-2805 (1999)
R. A. Decker, J. Haltiwanger, R.S. Jarmin, J. Miranda, “The Secular Decline in Business Dynamism in the U.S.” 2014
J. Haltiwanger, J.R. Jarmin, J. Miranda, “Who Creates Jobs? Small vs. Large vs. Young,” Review of Economics and Statistics, 95(2), 347-361 (2013)
The Sources of Economic Growth in OECD Countries (OECD Publishing, Paris, 2003)
J. Schumpeter, Capitalism, Socialism, and Democracy (New York, Harper & Bros., 1942)
Data Appendix: The DynEmp v.2 database
This paper uses data collected by the OECD for a database called DynEmp. A first phase of the project (DynEmp Express) has collected nonconfidential, comparable statistics about employment, and gross job creation and destruction by firm age, size and macrosectors for eighteen countries: Austria, Belgium, Brazil, Canada, Finland, France, Hungary, Italy, Japan, Luxembourg, The Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, the United Kingdom, and the U.S. The second phase of the project is ongoing. It aims to collect detailed data at the two-digit, industry-code level, and follow cohorts of businesses through three, five and seven years.
The data used in this report are the intermediate outcome of the ongoing second round of data collection within the DynEmp project, which is led by the OECD Directorate for Science, Technology and Innovation, with the support of national delegates and national experts in member and nonmember economies.
The DynEmp project is based on a distributed data-collection exercise aimed at creating a harmonised cross-country micro-aggregated database about employment dynamics from confidential microlevel data for which the primary sources of firm and establishment data are national business registers. The main building blocks of the data produced by the DynEmp v.2 routine can be summarized as: i) “flow datasets,” ii) “transition matrices,” and iii) “distributed regressions”.
The flow datasets contain annual statistics about gross job flows (such as gross job creation and gross job destruction, defined as the total job variation of growing and shrinking units, respectively) and about several statistical indicators of unit-level employment growth (mean, median, and standard deviation).
The transition matrices summarize the growth trajectories of cohorts of units from years 2001, 2004, and 2007, observed after three-, five- and seven-year periods. The matrices contain a number of statistics for different combinations of age classes and size classes at time t and t + j, plus a focus on the dynamics of high-growth units.
The DynEmp v.2 network also is collecting three sets of distributed regression outputs. The first set of OLS regressions focuses on growth-rate dynamics. The second set of regressions investigates (by means of a Linear Probability Model) the units’ exit probabilities. These regressions control for two-digit industries specificities and for different age-size effects, including a focus on pre- vs. post-crisis dynamics. The third set of regressions is aimed at describing the firm or establishment growth distribution, and at identifying potentially significant discontinuities in these distributions, possibly due to the institutional and regulatory environment.
At the time of writing, sixteen countries have been successfully included in the DynEmp v.2 database (namely, Austria, Belgium, Brazil, Costa Rica, Denmark, Finland, Hungary, Italy, Luxembourg, The Netherlands, Norway, New Zealand, Portugal, Spain, Sweden, and Turkey). Data at firm level are available for all the above-mentioned countries. For most countries, the time period between 2002 and 2011 is covered. For Costa Rica and Portugal, only fewer years are available, while the time horizon for Austria, Denmark, Norway and Sweden is longer. For Costa Rica, no transition matrix is available due to the limited-time extension of the source data. Details about temporal coverage by country are summarized in Table 1.11
Table 1. Temporal Coverage DynEmp v.2 Through Time by Country
Note: Temporal coverage by country of the database used for the analysis. Years for which annual flow data are available are coloured. For Costa Rica, no transition matrix is available due to the limited-time extension of the source data. Texture boxes correspond to years that have been excluded from the analysis due to data issues.
For section 3 of this work, we focus on data ranging from 2002 to 2010 for Austria, Belgium, Denmark, Finland, Italy, Hungary, Luxembourg, New Zealand, and Sweden.