Job creation in Europe : a firm-level analysis.
Date: 2019-09-11
Institutions: Joint Research Centre
Subjects: Europe, credit, economic cycle, economic growth, entrepreneurship, investment, job creation, labour market, research report, small and medium-sized enterprises

Job Creation in Europe:
A firm-level analysis

Hallak, Issam
Harasztosi, Péter

2019

EUR 29689 EN

 Report EUR xxxxx EN20xx      Forename(s) Surname(s) First subtitle line first line Second subtitle line second Third subtitle line third line First Main Title Line First Line Second Main Title Line Second Line Third Main Title Line Third Line This publication is a Science for Policy report by the Joint Research Centre (JRC), the European Commission's
science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking
process. The scientific output expressed does not imply a policy position of the European Commission. Neither
the European Commission nor any person acting on behalf of the Commission is responsible for the use that
might be made of this publication.

This document is part of the project (cid:16)Micro-Level Analysis of Job Creation and Associated Obstacles(cid:17) (JCOBST)
funded by DG-Employment; Administrative Arrangement No. JRC 347332017.

Contact Information
Name: Issam Hallak, Péter Harasztosi
Address: Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
E-mail: issam.hallak@ec.europa.eu, peter.harasztosi@ec.europa.eu
Tel.: +39 0332 78 9111

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JRC115930

EUR 29689 EN

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ISBN 978-92-76-00775-3

ISSN 1831-9424

doi:10.2760/590043

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Contents

Abstract ....................................................................................................... 1

Acknowledgements ...................................................................................... 2

Executive summary ...................................................................................... 3

Introduction and Overview

1. Job Creation: Prior Knowledge

5

8

1.1
Identifying Job Creating Firms: the Age-Size Debate.......................... 9
1.1.1 The size of the firm................................................................................ 9
1.1.2 The age of the firm ................................................................................ 12
1.1.3 A comparison of the US datasets: Dun & Bradstreet vs. Census Bureau........ 14
1.1.4 Cross-country evidence .......................................................................... 15
1.1.5 Job creation by age and size in business cycles.......................................... 17

Beyond the Age-Size Debate............................................................... 18
1.2
1.2.1 The high-growth firms (cid:21) HGFs ................................................................. 18
1.2.2 The first employee hiring issue ................................................................ 21
1.2.3 A pre-deterministic approach to job creation ............................................. 21
1.2.4 Financing corporate growth..................................................................... 22

1.3
Job Creation and Resource Reallocation in the Economy..................... 23
1.3.1 Reallocation and aggregate productivity: empirical evidence ....................... 24
1.3.2 Job reallocation and productivity in business cycles .................................... 25
1.3.3 Resource misallocation ........................................................................... 27
1.3.4 Falling labour share and reallocation ........................................................ 31
1.3.5 Conclusions .......................................................................................... 32

2. Sampling and Methodological Strategy

34

2.1

Sample construction ........................................................................... 34

2.2

Representativeness and Final Sample................................................. 38

Definitions and Methodology .............................................................. 39
2.3
2.3.1 Conclusions .......................................................................................... 42

3. Empirical Results

43

3.1
Employment ....................................................................................... 44
3.1.1 Employment by size............................................................................... 44
3.1.2 Employment by age ............................................................................... 45
Focus: The Start-up Companies ........................................................................ 47

3.2
The Essentials about Job Creation and Job Destruction ...................... 50
3.2.1 Job creation and destruction by country and sector .................................... 50
3.2.2 Job creation and destruction by age and size............................................. 51
3.2.3 Creation and destruction relative to employment share............................... 53
3.2.4 Entries and exits ................................................................................... 54

i

Focus: Regression Fallacy using EU Aggregate Data. ............................................ 56

3.3
Contribution to the Total Net Job Creation .......................................... 58
3.3.1 Contributions by size.............................................................................. 58
3.3.2 Contributions by age .............................................................................. 58
Focus: Contributions of the High Growth Firms .................................................... 60

3.4
Further Insights and Identification of Obstacles................................. 62
3.4.1 Financial Leverage and Job Creation......................................................... 62
3.4.2 Incidence of corporate independence in job flows....................................... 66
3.4.3 Urban vs. non-urban areas ..................................................................... 67
3.4.4 Productivity .......................................................................................... 68

3.5

Conclusions ........................................................................................ 70

Conclusions and Avenues of Research

Appendix A: Size-Age Country Level Evidence

Appendix B: Representativeness Analysis

Appendix C: Definitions and Formulas

71

82

84

153

ii

Abstract

This note reports an innovative state-of-the-art empirical analysis of Job Creation in the European
Union in the same vein as the classical studies in the United States. It is based on an exceptionally
large sample of firm-level employment data in the period 2004-2015 obtained from Orbis, a databank
published by Moody's Bureau van Dijk. Consistently with our representiveness assessment, the final
sample consists of firms registered in 20 out of the 28 EU Member States.

The study follows the empirical literature and defines three indicators which may be equally plausible
policy objectives. The job creation [destruction] is the total number of jobs created [destroyed]
by growing [shrinking] firms and reflects the job turnover of a market and its capacity to enhance
the labour market dynamism. The net job creation is the difference between job creation and job
destruction and captures the employment growth. The net job creation rate is the employment
growth rate and reflects the capacity of firms of being efficient in creating new jobs.

The main findings are as follows:

(cid:22) The young-SME category is the largest contributor to net job creation (employment growth).
Moreover, its contribution to job creation amounts to 40 percent which is far larger than its
share in employment amounting to 15 percent.

(cid:22) The share of start-up firms in employment varies between 2 and 9 percent across countries
with new Member States typically exhibiting larger shares. Even though to various degrees,
nearly all Member States have experienced a decline of start-ups measured as the share of
employment.

(cid:22) With nearly 60%, high-growth firms (HGF) exhibit the largest incidence on total job creation.
Even though the young HGFs are more numerous, mature HGFs contribute to job creation to a
larger extent.

(cid:22) Firms borrowing for the first time report net job creation rates of about 8 percent higher in the
next three years, on average. Besides, the net job creation rates of firms with bank loans are
less sensitive to economic cycles.

The results yield a number of policy implications. In particular, they provide empirical support to
policies aimed at encouraging young firms and entrepreneurship in Europe. They also show that job
creation of SME may face obstacles in their (cid:16)scaling-up(cid:17) development phase. Last, the impact of
external financing on net job creation rates may vary among Member States (cid:21) especially through the
business cycles. These results deserve further investigation.

By providing a large-scale investigation of job creation in Europe and providing related policy tools,
the report aims to contribute to the European Commission's first priority, (cid:16)to boost jobs, growth and
investment.(cid:17)

1

Acknowledgements

This project benefited to a large extent from the valuable feedback and support from Guy
Lejeune at DG Employment. Guy patiently and kindly followed our work very closely and
provided excellent feedback. We are as thankful to Michela Nardo at the JRC for her
attention to the project and her timely and valuable feedback.

The project also benefited from the feedback of a number of experts in the field. In par-
ticular, we are thankful to (in alphabetical order) Nick Costello (DG EMPL), Aron Kiss (DG
EMPL), Giuseppe Piroli (DG EMPL), and Anneleen Vandeplas (DG ECFIN), who attended in-
termediate meetings and provided valuable feedback and guidelines. We are also thankful
for the valuable reviewing and support from Pierfederico Asdrubali (DG ECFIN), Zsombor
Cseres-Gergely (DG JRC), Xabier Goenaga Beldarrain (DG JRC), Ignacio Gonzalez Vazquez
(DG JRC), Santo Milasi (DG JRC), and Frank Siebern (DG EMPL).

Last, but not least, the report benefited from the financial support from DG Employment,
Unit E1 Job Creation, under the Administrative Arrangement N. JRC 34733(cid:21)2017 (cid:16)Micro-
Level Analysis of Job Creation and Associated Obstacles(cid:17) [JCOBST].

All remaining errors are our own.

2

Executive summary

Creating a (cid:16)new boost for jobs, growth and investment(cid:17) in the European Union is the first
priority of the Juncker Commission and was explicitly recommended in the Five Presidents
Report. In order to calibrate the support to employment, European policy-makers need
a state-of-the-art investigation of job creation in Europe that identifies which firms hold
job creation potentials and whether these firms encounter notable obstacles. The Eurostat
databanks provide aggregate statistics of employment at country, sector, and firm-size,
both in levels and changes. Though, little is known at the firm level. This project aims at
filling the gap.

Hence, we conduct an innovative empirical analysis of Job Creation in the European Union
in the same vein as the classical studies produced in the United States. We use an excep-
tionally large sample of firm-level employment data in the period 2004-2015. The data is
obtained from Orbis, a firm-level database published by Moody's Bureau Van Dijk. Data
limitation in Orbis varies substantially across countries, yet, and consistently with our em-
ployment representativeness analysis, we conduct the study on 20 out of the 28 EU Member
States.

The study follows the empirical literature and defines three indicators which may be used
as policy objectives.

(cid:22) Job creation equals the total number of jobs created by growing firms in a category
(e.g., country, sector, age and size); reciprocally, job destruction is the sum of jobs
destroyed by shrinking firms.

The Job creation and job destruction represent the (cid:16)gross(cid:17) flows in the labour market
and capture the turnover and the dynamism of the latter.

(cid:22) Net job creation equals the difference between job creation and job destruction and

captures the employment growth.

(cid:22) The net job creation rate is equivalent to the employment growth rate and reflects the

capacity of firms of being efficient in creating new jobs.

The empirical analysis yields a number of notable results. First of all, consistently with the
studies in the US, we find that young-small firms exhibit the largest contribution to net
job creation in Europe. Also, even though not the largest contributors, the contribution of
young-small firms to job creation amounts to 40 percent, which is far above their share
in total employment (15 percent). Besides, consistently with previous studies, we too find
that small firms are proportionately larger net job creators at the European level, unless
we control for age and measurement issues.

As we look at employment, the results show substantial disparities among EU Member
States regarding the share of start-up firms, ranging between 2 and 9 percent, new Member
States typically exhibiting larger start-up shares. Further, even though to various degrees
across Member States, we observe an overall decline of start-up firms in employment.

Another striking result is that job creation is concentrated in a small share of high-growth
firms (HGF), defined as firms with an average annual employment growth of at least 20
percent over three years. HGF account for 60 percent of total job creation and the result
is relatively consistent across countries and sectors. The result is not as a tautology as it
may sound; in fact, the HGF category is made of a tiny share of firms and as we break
down the 20+ employment growth rate, the contribution to job creation by sub-category
flattens out. Finally, even though the young HGFs are more numerous in our sample, the
contribution of mature firms to job creation is higher (cid:21) not necessarily in terms of the net
job creation.

3

Another set of results show that firms which access bank loans for the first time report
significantly higher net job creation. Newly-borrowing firms exhibit 8 percent higher net
job creation on average three years after loan contraction. This shows that strong growth
is typically accompanied by banks.

The main empirical findings are summarised as follows:

(cid:22) The young-SME category is the largest contributor to net job creation (employment
growth). Their contribution to job creation amounts to 40%, far higher than their
share in employment.

(cid:22) The share and decline of start-up firms in employment varies across Member States
between 2 and 9 percent; new Member States typically exhibit larger shares. There is
an overall decline of start-ups in the share of employment, with substantial disparities
among Member States.

(cid:22) A few High-growth firms (HGF) have the largest incidence on job creation, 60 per-
cent. Young HGFs are more numerous but mature HGFs are larger contributors to job
creation.

(cid:22) Firms borrowing for the first time experience ca. 8 percent higher net job creation

rates in the subsequent three years.

The empirical analysis provides additional results of interest. We find substantial differences
in the response to economic cycles from firms according to their own characteristics (cid:21) e.g.,
independence, size and age. In particular, we observe that the net job creation rates of
firms with access to finance are less sensitive to economic cycles. Borrowing firms are
likely to benefit from financial cushions in crisis times, but also receive support for growth.
Presumably, there are variations among Member States in the impact of external financing
on net job creation which deserves further investigation.

Apart from access to finance, there seem to be inherent obstacles to labour dynamics. For
instance, in our sample, young SMEs located in urban areas with high population density
take up a share in job creation proportionately higher than their share in employment.
Young SMEs are those most in need of recruiting specific skilled employees; the result thus
suggests than urban areas are more likely to provide such skills than non-urban areas,
where labour dynamics seem more constrained.

The results yield a number of policy implications. By showing the relevance of young and
small firms for job creation and labour market dynamics, they provide empirical support
to policies aimed at encouraging young firms and entrepreneurship in Europe. By showing
the existence of obstacles to growth, they also highlight that job creation of small firms
may face obstacles in their (cid:16)scaling-up(cid:17) development phase and deserves further policy
attention.

4

General Introduction and Overview

My first priority as Commission President will be to strengthen Europe's compet-
itiveness and to stimulate investment for the purpose of job creation. I intend to
present, within the first three months of my mandate and in the context of the
Europe 2020 review, an ambitious Jobs, Growth and Investment Package.

I do not believe that we can build sustainable growth on ever-growing mountains
of debt - this is the lesson learnt in the crisis that we must now heed. I also
know well that it is mainly companies that create jobs, not governments or EU
institutions. However, I do believe that we can make much better use of the
common EU budget and of Union financial instruments [...]. We must make use
of these public funds available at Union level to stimulate private investment in
the real economy. We need smarter investment, more focus, less regulation and
more flexibility when it comes to the use of these public funds.

Jean-Claude Juncker
President of the European Commission
A New Start for Europe: My Agenda for Jobs, Growth, Fairness and Democratic Change.
Strasbourg, 15 October 2014.

When taking over the Presidency of the European Commission, Jean-Claude Juncker set out
the Ten Policy Priorities for his mandate, among which (cid:16)A new boost for jobs, growth and
investment(cid:17) comes first. The Five Presidents Report also emphasises that (cid:16)the employment
and social situations vary widely across the euro area, partly as a result of the crisis but
also because of underlying trends and poor performance pre-dating the crisis. [...] Efficient
labour markets that promote high level of employment and are able to absorb shocks
(J.-C. Juncker, D. Tusk, J.
without generating excessive unemployment are essential.(cid:17)
Dijsselbloem, M. Draghi and M. Schultz, 2015, Completing Europe's Economic and Monetary
Union). The policy tools implemented by the so-called Juncker Plan consist not only in
the coordination of efforts among Member States, but also the implementation of a vast
Investment Plan for Europe that specifically targets job creation and growth. Bearing this
purpose in mind, the plan is aimed to implement smart financial support to corporations,
remove obstacles to their investment and provide visibility and technical assistance.1

Substantial emphasis has been put on the provision of policy tools to encourage job cre-
ation, while as a matter of fact, apart from Criscuolo et al. (2014) who analyse OECD
countries, there hardly exists any wide-scale micro-firm level study depicting the state of
job creation in the European Union. Such knowledge is yet essential to policy making and
this project is aimed to fill the gap. Indeed, we not only provide new evidence about job
creation in the EU that updates and complements Criscuolo et al. (2014), but also we extend
the number of countries and produce innovative results at the EU aggregate level. Remark-
ably, the project proposes techniques to construct an innovative representative dataset of
employment in Europe, which constitutes a policy tool per se since this dataset is likely to
provide scientific support to future policy decisions.

The report is constructed upon three chapters, which are strongly inter-related. Chapter
1 reviews the existing empirical evidence about job creation worldwide, especially in the
United States. We emphasise the age vs. size debate which has been the main driver of

1For a recent review of the Juncker Commission priorities and their implementation, see, e.g, Bassot et al.

(2018) published by the European Parliament Research Service.

5

policy decisions since the post-war era. Historically, the belief that small firms hold the
largest potential to become the main net job creators has dominated the literature and
this motivated a number of major policy programmes. The so-called (cid:16)Think Small First
Principle(cid:17) justifies programmes typically aimed at improving business environments and
resolving SMEs funding issues due to failures in the financial system. The Small Business
Act in the USA voted in 1953 and the European Small Business Act engaged in 2008 are
well representative of these large scale programmes.2 Besides, high-growth firms (HGFs)
are found to play a prominent role in the net job creation growth and typically are young
and small.

General evidence about job creation has been tested using US data. There have been major
disagreements between two influential groups of researchers. On the one hand, the group
led by David Birch in the 1980's and developed by the (cid:16)MIT group(cid:17) insists that small firms
outperform other firms in terms of job creation. Birch praises the dynamism of the business
environment and entrepreneurial spirit in the US that gives chances to entrepreneurs and
young firms. On the other hand, the line of research initiated by Steven Davis and John
Haltiwanger in the 1990s show that size is irrelevant to determine job creation, and the
age of the firms has greater importance.

Chapter 2 details our sampling methodology and empirical strategy, and describes the
final sample. The firm-level information, including employment, is obtained from Orbis,
a databank published by Moody's Bureau van Dijk. Because the project intends to use
innovative firm data at EU level, we are cautious that the data are representative of the
European employment at non-financial corporations. The sampling procedure heavily relies
on our analysis of the representativeness of the Orbis employment data by country and year,
involving the comparison of levels and dynamics (cid:21) the details are reported in the Appendix.
The contribution of the representativeness analysis presented in this report resides, not
only in the dataset, but also in the ability for readers to replicate the process since selection
and imputation techniques are detailed. Based on our analysis, we retain 20 out of 28 EU
countries, namely Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia,
Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Portugal, Romania, Slovakia,
Slovenia, Spain and Sweden. Despite the constrained selection of countries, our sample is
representative of the EU-28 Member States with a good mix of relatively large and small
economies, as well as EU accessing periods.

Chapter 3 reports the empirical analysis. Given that our study involves European data, our
main objective is to identify facts and patterns of job creation in Europe and potentially
differences with the United States, not so much giving a final word in any of the debates
highlighted in the review. Interestingly, already in 1987, David Birch states several reasons
why Europeans and Americans are different (cid:21) e.g., egalitarism and threat of failure (cid:21)
which would explain why Europeans are less entrepreneurial than Americans.3 Even though
Europeans are likely to admit the existence of differences between Europeans and North-
Americans, this view is probably exaggerated.4 Also, Europe has substantially evolved
since the mid-80s, and entrepreneurship spirit has been brought about especially thanks

2See also COSME, the European Commission programme to support the financing of SMEs, and Horizon 2020

Work Programme 2018-2020.

3(cid:16)It is difficult to accumulate wealth in a society wedded to the ideal of social and economic egalitarism. There
are prices to be paid: less private wealth means less private venture capital, which is overwhelmingly the major
source for venture capital in the United States. The shortfall in private venture capital translates into a heavier
reliance on institutional capital (e.g., banks, mutual funds, etc.). Institutional capital is notorious for its aversion
to risk. There are other reasons discouraging entrepreneurship in Europe: a major one is fear of failure. Failure
is catastrophic. To fail there is to acquire a burden of shame and guilt that may be handed down to the next
generation. You are branded as somehow inferior for having tried and failed. Social and economic ostracism is
almost automatic. Having failed, you are very unlikely to ever receive future financial backing.(cid:17) David Birch, (cid:16)Are
We Special?;(cid:17) in David Birch, Job Creation in America, Free Press, 1987.

4The (cid:16)stigmatisation(cid:17) of entrepreneurial failure in Europe is sometimes mentioned in the documents of the
Commission as well, e.g., Press Release of the European Commission Commission gives boost to start-ups in
Europe, 22 November 2016: (cid:16)We want to help start-ups stay and grow in Europe.[...] By making it easier for
them to have a second chance, without being stigmatised if their idea doesn't succeed the first time around.(cid:17)

6

to supportive regulation and new financing tools. Yet, the analysis exposed by David Birch
has the merit of questioning whether Europe and the United-States are different, and more
generally whether there are disparities amongst European countries.

The main findings from the empirical analysis are as follows. First of all, the young and
small firms contribute to job creation proportionately more than their share in employment;
40 and 15 percent, respectively. Also, young-small firms exhibit a disproportionately high
net job creation. Moreover, we observe that the share of start-ups in employment varies
substantially across Member States, with new Member States displaying larger shares. Yet,
there is an overall decline of start-ups measured by their share in employment, but this
decline is uneven across countries. Another set of results shows that job creation and
destruction are highly concentrated in a tiny fraction of firms, the high-growth firms (HGF)
(cid:21) defined as firms growing with an annual net job creation rate of 20 percent over a three
year period. While proportionately very few, HGFs contribute as much as 40 percent of job
creation. The profile of HGFs are typically young and small; yet, mature HGFs contribute
to job creation to larger extents. We also report a set of results looking at obstacles to job
creation and net job creation. Among others, we find that access to finance is associated
with patterns of net job creation rates. Firms accessing bank loans for the first time report
significant higher net job creation rates, especially young firms. Besides, net job creation
rates of leveraged firms are less sensitive to economic cycles. Moreover, young SMEs
located in urban population dense areas disproportionately contribute to job creation and
reflect a more lively business ecosystem, where the labour markets better match skills.
Finally, we observe that the response of firms to economic shocks varies in function of their
characteristics, e.g., independence, size and age.

The findings have a number of policy implications.

(cid:22) By showing the contribution of young and small firms to jobs, the results provide
supporting evidence to the implementation of policies towards entrepreneurship sup-
porting talents. Young and small firms contribute disproportionately to job creation as
well as net job creation and deserve the attention of policy makers.

(cid:22) By showing the existence of potential constraints to growth as well as the decline of
start-ups in employment, the study supports the policy tools being implemented to
assist firms in the start-up and scale-up phases, among others the European Commis-
sion initiative to support start-ups and scale-ups that was launched in 2016. Further
analysis is needed in order to identify the needs for intervention.

(cid:22) The fact that firms vary in their response to economic cycles in terms of employment
also deserves further analysis. Size, age, independence, and access to finance, are
just a few characteristics that we identified as potential determinants of the response
of firms. Besides, each of the Member States probably displays a different structure,
e.g., in terms of corporate ownership, that may induce a different response of the
economy, as a whole. That is likely to have significant policy implications.

7

Chapter 1:

Job Creation: Prior Knowledge

By reviewing the existing policy and academic literature, this chapter portrays the current
knowledge and evidence about job creation of non-financial corporations. We put emphasis
on empirical firm-level evidence describing the economies of the European Union even
though we also report findings to both historical and ongoing debates on job creation
research; those would typically use US data.

This chapter is built around three essential areas of job creation. The fist area aims at
identifying job creation capacities at the individual firm level. Firm level studies identify
the main characteristics of employers and job creators, and whether these characteristics
vary over time, across countries and business environments. This literature has identi-
fied two main results. First, labour is chiefly employed by small and medium enterprises
(SMEs); second, major net job creators are restricted to a subset of relatively young SMEs.
Besides, entry-exit barriers and limited access to finance are non-trivial obstacles to cor-
porate growth and job creation.

The second area in job creation looks beyond age and size. In fact, provided that a vast
majority of young and small firms hardly ever grow, it is relevant to identify those that
do grow. We first review the characteristics of the high-growth firms (HGFs) which are
found to play a prominent role in the job creation dynamics and find they mainly are
young and small. We then question whether founders' decisions taken before and shortly
following entry signal growth ambitions; for instance, the hiring of the first employee,
offering specialised or standardised products, or simply the name of the company. Finally,
we review some aspects of the financial constraints and their impact on corporate growth.

The third area investigates the relative position of the firm within the economy and how
the firm's ability to create jobs may depend on the characteristics of the other firms. The
large heterogeneity in firm productivity even in the most narrowly defined sector suggests
that market frictions and distortions are likely to prevent the flow of resources, including
labour, from less to more efficient firms.5 Also, job creation and destruction (cid:21) job allocation
in general (cid:21) have implications for the aggregate productivity and wealth of the economy.

Although low profits in recessions make the destruction of unproductive units relatively less
costly, there are inherent obstacles that are likely to obstruct the reallocation of resources
towards more efficient units. Obstacles to reallocation comprise regulations, e.g., size
dependent policies, property rights constraints, international trade related distortions, and
inefficient allocation of external funding. We illustrate this last point with the so-called
zombie firm phenomenon; zombies are indebted and financially non-viable firms that are
likely to receive the financial support from their banks. Unproductive zombie firms affect
non-zombies since they retain resources that could be used by other firms otherwise.

Additionally, we address the phenomenon of the falling share of labour in corporate value
added. Such pattern is observed in several countries, especially in the United-States and
is becoming a source of concern for job creation. Although there is no consensus among
economists as of the reasons for the falling labour share, it seems to come along with an
increasing industry concentration, suggesting that competition is diminishing. Also, the fall
in labour shares is concentrated among largest firms.

5An alternative view is that firms in a sector do not compete with each other because they compete in different

markets.

8

1.1 Identifying Job Creating Firms: the Age-Size Debate

1.1.1 The size of the firm

The link between the size of the firm and its ability to create jobs is not straightforward.
Some argue that smaller firms have greater growth capacities because, e.g., individual
market shares are smaller giving them more room for growth. Instead, others may claim
that larger firms hold the extensive know-how and financing capacities that provide them
with competitive advantages over smaller firms to catch new business opportunities.

First empirical findings provided by Gibrat (1931) actually suggest that firm size is irrelev-
ant to explain job creation. The author formulates the Proportionate Job Creation Theory
whereby any firm in an economy is as likely to catch new business opportunities regardless
of its size; as a result, firms grow at the same pace on average. The proposed empirical
methodology is somewhat uncommon for today's standards. In fact, Gibrat (1931) lends
from the mathematics (cid:21) the Law of Laplace (cid:21) and shows that if size group categories are af-
fected by a large number of small changes, independent from each other, then the changes
would generate normally distributed logs of firm size.6

Gibrat (1931) investigates French manufacturing data and finds that the logarithm of the
size of firms is indeed normally distributed. Figure 1 is an illustration of Gibrat (1931)
results in the case of French Manufacturing in 1920 and 1921, and shows a perfect fit with
the prediction.7

Figure 1: Gibrat's Data for French Manufacturing Establishments in 1920 and 1921.

Note: This figure is extracted from Gibrat (1931). It reports the distribution of the logarithm of the number
of employees (X-axis) of firms in French Manufacturing in years 1920 and 1921, and their respective
net employment growth (Y-axis). The author builds on the Law of Laplace and shows theoretically that
should shocks affect firms randomly across size categories, the log-distribution of firm sizes tends towards
linearity.

Subsequent post-war empirical work in the 1950's and 1960's showed mixed evidence of
the phenomenon but could not reject the proportionate job creation theory altogether.8

Nevertheless, the belief grew among policy-makers and scholars that greater growth po-
tentials are more likely to lie among SMEs; this belief came as a justification to official
policies providing small firms a favourable business environments and financial support.
The Small Business Administration programme of subsidies and loan guarantees enacted
in 1953 in the US is representative of SMEs oriented growth policies of the time.

The evidence that small firms were the largest job creators is probably best represented
by the extensive work produced by David Birch in the 1970's and 1980's, especially Birch

6Sutton (1997) proposes a brief proof of the empirical implications of the Proportionate Job Creation Theory.
7Gibrat provided similar results in a number of sectors and geography.
8Geroski (1995) and Sutton (1997) provide remarkable reviews of the early empirical evidence.

9

(1981, 1987). David Birch uses an innovative firm-level dataset (cid:21) the so-called DMI files
published by Dun & Bradstreet (henceforth, D&B) (cid:21) covering years 1969, 1972, 1974, and
1976. In his sample, the author defines job creating [destructing] firms between time (t-1)
and t and within a geographic area, the firms that satisfy one of the following three distinct
conditions: (i) the firm is newly established [closed]; (ii) the firm existed in the previous
period t-1 and expanded [contracted]; (iii) the firm migrates from [to] another geographic
area between time t-1 and t. Aggregate job creation [destruction] in a state equals the sum
of new [lost] jobs created [destroyed] by job creating [destroying] firms; net job creation
is the job creation net of the job destruction.

The main results are as follows. Should one classify states by net job creation rate categor-
ies and compute average destruction and creation rates by category,9 one would observe
that the job destruction rates are stable across categories, not the job creation rates. The
job creation thus seems to drive the net job creation. Moreover, about two-thirds of the
net job creation in the period 1969-1976 are credited to small firms (cid:21) twenty employees
or less (see also Birch (1987)) (cid:21) which proportionately remains greater than their share
in employment.10 David Birch thus demonstrates that firms do not benefit from growth
in a random fashion as suggested by Gibrat's Proportionate Job Creation Theory; instead,
smaller firms are more likely to create jobs than larger firms.
Interestingly, the author
extensively argues in favour of stimulating the business environments of firms rather than
providing mere financial supports: only fitting business environments will allow managers
to express their talents.

The author develops an interesting discussion about the liveliness of an economy like the US
economy that gives space to small firms to grow (cid:21) and replace older firms. In the mind of
the author, the dynamism of smaller firms features the US business ecosystem and its ability
to grow talent internally. We emphasise this interpretation because it certainly constitutes
a leading work in the field that in line with such an interpretation bears a number of policy
implications. Besides, even though the contribution of small firms to net job creation and
the specificity of the US in the field have been both questioned by subsequent studies, the
young and small characteristics of net job creators is rarely questioned.

Indeed, the contribution of small firms to job creation is called into question by Davis and
Haltiwanger (1996). In their influential paper, they point out that the genuine observation
that job creation is negatively correlated with firm size may be due to methodological and
dataset related issues. In particular, size-based job creation analyses need to control for
the following issues:

1. Size Distribution Fallacy: firm-size classification will vary from one period to another

due to reclassification of firms, and alter the firms size distribution.

2. Regression Fallacy: Temporary shocks to the firm employment will technically distort

the size-job creation relationship in favour of smaller firms.

3. Firm size does not necessarily have similar impacts on job creation (created jobs) and

net job creation, and need to be analysed separately.

We further explain the regression fallacy because the issue and associated solutions have
been widely acknowledged among scholars (cid:21) including ourselves; see the analysis in
Chapter 2.3.1. It is a statistical pitfall due to firms' switching size categories for a single
period, typically resulting from a one-shot large command from customers.

Box 1 illustrates the problem and consequences. In the example adapted from Davis et al.
(1996a), Firm 1 benefits from a single period exceptional command from a customer and

9The job destruction rate is the sum of lost jobs in shrinking companies relative to the total employment; the
job creation rate is the sum of new jobs in growing companies relative to the total employment. The net job
creation is the job creation net of destruction.

10Notice that in Birch's sample, (cid:16)virtually none of the employment change in an area is due to firms moving, in

the sense of hiring a moving van and relocating.(cid:17)

10

for this reason recruits ten full time employees on temporary contracts. Firm 1 has 45
employees in Year 1 and is thus classified as a small firm in Year 1.
In Year 2, Firm 1
employs 55 employees so that it becomes a large firm. Finally, in Year 3 it is classified as
a small firm again with 45 employees. The category switching by Firm 1 has a positive
impact on the job creation of small firms category in the period years 1 to 2, and negative
on large firms category in period years 2 to 3. Firm 2 illustrates the opposite case, and
Firm 3 is unchanged in the three periods.

Box 1. The Regression Fallacy

Firm level

Category level

Year 1
Year 2
Year 3

Firm 1
45
55
45

Firm 2
55
45
55

Firm 3
60
60
60

Small firms
45
45
45

Growth, Year 2
Growth, Year 3

0.22
-0.18

-0.18
0.22

0
0

0.22
0.22

Large firms All firms

115
115
115

-0.09
-0.09

1600
1600
1600

0
0

This table illustrates the Regression Fallacy described in Davis et al. (1996b), Chapter 4.
(cid:16)Small Firms(cid:17) are firms with less than 50 employees in the base year; figures are in italics.

Firm 1 is classified as a small firm in Year 1, and its net job creation rate is 22 percent.
Reciprocally the temporary drop in employment of Firm 2 (cid:21) large in Year 1 (cid:21) of 18 percent
classifies the firm as a small firm in Year 2.

Using base year size distorts growth measures against temporary shocks. The solution pro-
posed by Davis et al. (1996a) consists in classifying firms by the average size between the two
periods, and called this measure the (cid:16)current size.(cid:17)

The example provided in Box 1 shows that looking at the base year employment level
results in an upward (downward) bias of aggregate small (large) firms' growth. In order
to resolve the statistical issue Davis et al. (1996a) suggest to compute job creation and
destruction rates from the base period t-1 to period t relative to the average employment
level in these two periods.11 Davis et al. (1996a,b) find that once the above mentioned
criteria are met with the right methodology and necessary longitudinal data requirement,
large plants and firms account for most newly created jobs. Furthermore, these created
jobs are likely to survive. While small firms exhibit high gross job creation rates, due to
the high prevalence of job destruction, the net job creation with small firms is not high.12

Davis et al. (1996a) also argue that the dataset used by Birch (cid:16)and associates(cid:17) is unsuitable
to the analysis for two reasons. First, its representativeness is questionable: the number
of employees exceeds the number of employees in official statistics by about 8 percent.
Second, the birth and death dates are misreported. For instance, firms that were subject
to massive lay-offs and went bankrupt are reported with a change of ownership structure.

Neumark et al. (2011) conduct a thorough and updated analysis using similar data to Birch
(1981) and addresses the critics formulated by Davis et al. (1996b). Overall, the authors

11Davis et al. (1996b) refers to this as the (cid:16)current(cid:17) establishment size measure.
12For a detailed analysis of the regression fallacy, see e.g., Davidsson et al. (1998)

11

In
find results consistent with Birch (1981), namely small firms are net job creators.
fact, they use average employment based measures in order to control for the regression
fallacy. The results provide evidence that small firms are the main net job contributors (cid:21)
even though at a lesser extent than using base year employment. Besides, they use the
new versions firm data provided by D&B and find that the data no longer suffer from the
drawbacks mentioned by Davis et al. (1996b).

One reason why small firms are such a focus of interest relates to the significant share of
the labour force they employ. This pattern is illustrated by Figure 2 obtained from Criscuolo
et al. (2014) for a set of OECD countries, and shows the share of employment by firm size
in the manufacturing sector.

Figure 2: Share of employment by different firm size and by country.

Note: This figure is extracted from Criscuolo et al. (2014). It shows employment shares by firm size
in manufacturing, construction, and non-financial services sectors across OECD countries in the period
2001-2011 (cid:21) with some differences in the period across countries. Countries include Australia (AUT),
Belgium (BEL), Brazil (BRA), Canada (CAN), Finland (FIN), France (FRA), United-Kingdom (GBR), Hun-
gary (HUN), Italy (IT), Japan (JPN), Luxembourg (LUX), Netherlands (NLD), New Zealand (NZL), Norway
(NOR), Portugal (PRT), Spain (ESP), Sweden (SWE), Unites-States (USA).

Figure 2 shows that in general small firms (less than 250 employees) indeed employ the
largest share of the labour force. Nevertheless, there is a wide discrepancy across coun-
tries. Large firms (above 250 employees) employ as little as about 15 percent of the
Japanese labour force, and up to 55 percent in the US. Micro firms (1-9 employees) employ
as much as 40 percent of the labour force in Italy, and as little as 11 percent in the US.
Therefore, the observation that small firms are potentially the main contributors to the net
job creation at the aggregate level is likely to have different impacts national economies.

1.1.2 The age of the firm

Even though the aforementioned studies already cite age as a factor of net job creation,13
scholars specifically shifted their research focus from size to age in years 2000s. In their
empirical study, Lotti et al. (2003) find that Gibrat's Proportionate Job Creation Theory
fails should one consider new entrants, but cannot be rejected in samples of older firms.
In a recent work, Coad et al. (2018) finds very similar results using an autocorrelations
analysis of net job creation rates at firm level: the likelihood of a firm to create new jobs
decreases as it gets older. Firms initially experience higher net job creation rates in the
period shortly after birth; the rates then diminish until they finally become hectic showing

13In fact, Birch (1981) suggests that among small firms, younger small firms are those providing with the largest

share of net job creation.

12

THE DYNAMICS OF EMPLOYMENT GROWTH: NEW EVIDENCE FROM 18 COUNTRIES OECD SCIENCE, TECHNOLOGY AND INDUSTRY POLICY PAPERS   27 Figure 3. Share of employment by different firm size and by country  Note: The period covered is 2001-2011 for Belgium, Canada, Finland,  Hungary, the Netherlands, the United Kingdom and the United States; 2001-2010 for Austria, Brazil, Spain, Italy, Luxembourg, Norway and Sweden; 2001-2009 for Japan and New Zealand; 2001-2007 for France; and 2006-2011 for Portugal. Sectors covered: manufacturing, construction, and non-financial business services. Owing to methodological differences, figures may deviate from officially published national statistics. For Japan data are at the establishment level, for other countries at the firm level. Average across all available years.  Significant differences along the whole firm size distribution and differences in the average size of large businesses help explain the cross-country differences in the weights of micro and small firms. Figure 5 describes this point comparing two polar cases: Italy and the United States. Figure 4. Most firms are small but most employees work for medium and large firms Firm size and employment distribution, average across countries and years  Note: The figure presents the employment and firm size share accounted for firms (or units) of different size classes, averaged over countries and years. The period covered is 2001-2011 for Belgium, Finland,  Hungary, the Netherlands, the United Kingdom and the United States; 2001-2010 for Austria, Brazil, Spain, Italy, Luxembourg, Norway and Sweden; 2001-2009 for Japan and New Zealand; 2001-2007 for France; and 2006-2011 for Portugal. Sectors covered are: manufacturing, construction, and non-financial business services. Owing to methodological differences, figures may deviate from officially published national statistics. For Japan data are at the establishment level, for other countries at the firm level.  negative autocorrelations. Among others, the results are explained by the selection process
that falls over new entrants.14

The relationship between age, size and net job creation is well illustrated by Haltiwanger
et al. (2013) who find a negative relationship between firm's size and net job creation unless
models control for age. Also, the size-job creation relationship is substantially mitigated
whenever the measure of net job creation rate controls for the regression fallacy by using
current size instead of base size.15 Figure 3 is extracted from the article and is illustrative
of the main results.

Figure 3: Relationship between net growth and firm size

Note: This figure is extracted from Haltiwanger et al. (2013) and shows the relationship between size
and net job creation rate. Four approaches are adopted combining two measures: net job creation
rates use either the base size (Base Year Size) or the current size (Current (Avg) Size); and its for age
(with Age Controls) or not. The horizontal axis reports the size category; the vertical axis reports the
difference between the net job creation rate of the size category and that of the largest size category
(500+ employees).

Figure 3 reports the firm size-growth relationships using four approaches of net job creation
rate calculation. On the one hand, net job creation rates are calculated for a number of
size categories using either the base or the current size (cid:21) Base Year Size and Current
(Avg) Size, respectively; then, net job creation rates control for age (with Age Controls)
or not. The results show that the net job creation rates that use standard measure do
decrease as the size of the firm increases, but the decline is mitigated when growth controls
for age. Moreover, the negative slope is far lower using current size measure and turns
positive altogether as it controls for age. Haltiwanger et al. (2013) thus provide empirical
evidence of the existence of regression fallacy issues (cid:21) for the least in their sample of US
manufacturing plants (cid:21) and that age matters in explaining net job creation.16

Further, Haltiwanger et al. (2014) show that the reason why age plays such a major role
relates to extremely high growth rates at firm's entry. Start-ups contribute substantially to
job creation, both in net and gross terms. Since start-ups are usually of small size, they
are mostly responsible for a detected negative relationship between size and growth. Even
though start-ups grow faster than older firms of the same size, they are also more likely
to exit (cid:21) faster growth is thus conditioned on survival. Hence, both large (non-net) job
creation and job destruction are frequent across young firms. Further, mature businesses
of smaller size are more likely to downsize and exit than grow. They exhibit negative net

14See also Lotti et al. (2009) who argue in favour of the long-run validity of the Gibrat's law.
15Current size is the average size between previous and current years sizes; base size is the previous year size.

See previous sub-section Box 1 for a description of the regression fallacy.

16Notice that Lawless (2014) and Anyadike-Danes et al. (2015) document similar patterns for a set of European

countries (cid:21) including Austria, Finland, Germany, Norway, Sweden, UK

13

relationshipbetweennetemploymentgrowthandﬁrmsizewhennotcontrollingforﬁrmage.Controllingforﬁrmage,however,hasadramaticimpactonthesepatterns.Regardlessofthesizeclassiﬁcationmeth-odology,oncewecontrolforﬁrmage,weobservenosys-tematicinverserelationshipbetweennetgrowthandﬁrmsize.Whenweusebase-yearsize,thesmallestsizeclasshasthelargestpositivecoefﬁcient,butthesizeclassesintherangefrom5to499havethemostnegativeeffects.Thisimpliesthatﬁrmsinthe5to499rangehavelowernetgrowthratesonaveragethanthelargestbusinessesoncewecontrolforﬁrmage.Whenweuseaveragesize,weﬁndapositiverelationshipbetweennetgrowthandﬁrmsizeforallthesizeclassesupthrough500workers.Whilethedetailsdiffernontriviallydependingonwhichsizeclassmethodweuse,themainpointisthatoncewecontrolforﬁrmage,thereisnoevidencethatsmallﬁrmssystemati-callyhavehighernetgrowthratesthanlargerbusinesses.Inthelowerpanelofﬁgure2,weshowtheresultswhenwerestricttheanalysistocontinuingﬁrmsonly.Theﬁrstthingtonoteisthatthereisalessdramaticimpactofcon-trollingforﬁrmagesincethereis,byconstruction,noroleforstart-ups.23Exploringthismoredeeply,weﬁndastronginverserelationshipbetweennetgrowthandﬁrmsizeforcontinuingﬁrmswhenweusethebase-sizemethodology.Thisisthecasewhetherornotwecontrolforﬁrmage.However,usingaveragesize,thereisapositiverelationshipbetweennetﬁrmgrowthandﬁrmsizeregardlessofwhetheronecontrolsforﬁrmage.Hence,forcontinuingﬁrms,itisprimarilythesizeclassmethodologythatmatters.Thestarkdifferencesforsmallcontinuingﬁrmsbetweenthebase-sizeandaverage-sizeresultsareconsistentwiththestrongregression-to-the-meaneffectsfortheseﬁrms.Someofthedifferencesbetweenthepatternsacrossthetwopanelsofﬁgure2reﬂecttheroleofﬁrmexits.24Weexplorethisfurtherinﬁgure3,whichshowsthepatternsofjobdestructionfromﬁrmexitbyﬁrmsizewithandwithoutagecontrols.Jobdestructionfromﬁrmexitisdirectlyinter-pretableasanemployment-weightedﬁrmexitrate.Theﬁrmexitratefallsmonotonicallywithﬁrmsizeregardlessofsizeclassmethodologyandwithorwithoutﬁrmagecon-trols.Controllingforﬁrmageyieldssomewhathigherexitratesforsmallbusinesses,butthiseffectisquitemodestwhenusingaverage-sizeclassmethodology.Thus,arobustﬁndingisthatsmallﬁrmsaremorelikelytoexitthanlargerﬁrms,evencontrollingforage.Combiningﬁgure3withthelowerpanelofﬁgure2helpsaccountforthepatternsintheupperpanelofﬁgure2,espe-ciallyfortheresultscontrollingforﬁrmage.Thelowerpanelofﬁgure2showsthatwhencontrollingforﬁrmage,thereisamodestbutincreasingrelationshipbetweennetgrowthandaveragesizeforcontinuingﬁrms.CombiningFIGURE2.—RELATIONSHIPBETWEENNETGROWTHANDFIRMSIZEA.  All FirmsB.  Continuing Firms only-0.2-0.100.10.2Firm Size ClassBase Year SizeCurrent (Avg) SizeBase Year Size with Age ControlsCurrent (Avg) Size with Age Controls-0.2-0.100.10.2Firm Size ClassBase Year SizeCurrent (Avg) SizeBase Year Size with Age ControlsCurrent (Avg) Size with Age ControlsFIGURE3.—FIRMEXITBYFIRMSIZE00.050.10.150.20.25Firm Size ClassBase Year SizeCurrent (Avg) SizeBase Year Size with Age ControlsCurrent (Avg) Size with Age Controls23NWZbrieﬂydiscussasimilarresulttheyobtainedusingtheNETSdatawhentheyexcludestart-ups.24SectionVII.Eoftheonlineappendixshowsthattheoverallneteffectswereportintable2andﬁgure2canbegeneratedbyusingtheesti-matesforthecomponentsofnetgrowth(continuers,jobcreationfromentryandjobdestructionfromexit).Thispropertyholdsforalltheoverallneteffectswereportinthepaper.356THEREVIEWOFECONOMICSANDSTATISTICSjob creation.17 Additional work provided by (Decker et al., 2016) shows that the share
of start-ups (two years and younger firms) in US net job creation has decreased over the
examined period 1976-2011 and the Great Recession gives an extra negative impact.

1.1.3 A comparison of the US datasets: Dun & Bradstreet vs. Census

Bureau

According to leading authors the differences in the results stem from the datasets that
are used for the analysis. On the one hand, the group led by David Birch uses Dun &
Bradstreet's Market Identifier file (henceforth, D&B), and its new version NETS used by
David Neumark; the D&B data based analyses find evidence that small firms contribute
proportionately more to net job creation. On the other hand, the group that includes Steven
Davis and John Haltiwanger uses the Census Bureau Longitudinal Business Database (LBD);
the LBD data based analyses find evidence that size is irrelevant, and if any impact were
to be found, smaller firms would contribute proportionately less.

Looking at the conception of US D&B and LBD databanks is thus relevant for our under-
standing and subsequent empirical analysis. Notice that even though fundamental struc-
tural differences remain, the datasets evolve over time so to address the critics.

The differences between D&B and LBD stem from the source and the way the dataset
is constructed: D&B is firm-level data reporting firm-level employment while LBD is an
employee-level data at the plant level.

Box 2. The US Datasets

US studies rely on two main datasets since the 1980's. Both datasets seem to report discrep-
ancies in the results as to whether small firms are larger net job providers proportionately to
their size. The Census Bureau Longitudinal Business Database (LBD):

The Census Bureau dataset is constructed from Social Security tax on employees, and includes
any plant in the manufacturing sector that has one or more persons employed. The plant is
the unit of observations, and each plant is linked to its firm. The dataset starts as early as
1963, and each year has between 300,000 to 350,000 plants yearly.

The Dun & Bradstreet Databases (D&B):
First called DMI, then a new version was provided called the NETS. Both have firm-, not plant-,
level information. It covers both Manufacturing and Services sectors.

The main arguments given by Neumark et al. (2011) is that the new version of D&B NETS
dataset covers more sectors. Haltiwanger et al. (2013) argues that the LBD is more precise
first because it controls for parents, second, it reports (cid:16)establishment(cid:17) level data rather
than firm level. Establishments are single production location units, while the firm may be
made of several establishments.

Though, the Census Bureau LBD suffers from limitations as well. First, the survey is issued
every five years. The low frequency disables the observation entries and exits in a relatively
long period with non-trivial consequences for the analysis. For instance firms may appear

17Haltiwanger et al. (2014) highlight additional relevant methodological issues: (1) importance of accurate firm
birth and death date information; (2) avoid pitfalls in identifying firm age coming from merger and change in
ownership and opening a new establishment; (3) establishment age shall be given the age of the firm. Growth
by a new affiliate should be counted as growth of an existing firm, not as an entry; (4) size-classification based
on average employment between t and t-1 to avoid regression fallacy; (5) when drawing conclusions be careful
of the interpretation of net and gross job creation results.

14

and disappear between two surveys and will never show up in the sample. Firms in a survey
may exit the following year.

Another major drawback is that age is not actually observed but rather estimated by com-
paring surveys. Authors resolve this issue by using age categories. Moreover, in order to
reduce the measurement errors, some authors opt to reduce the sample to more than five
employees firms. Actually, less five employees plants are differently handled by the Census
Bureau since information is not actually collected by rather imputed from information re-
ported by the Internal Revenue Service (see further discussion about the small firms issues
in Dunne et al. (1989)).

The description of the two datasets and their own limitations allows a better understanding
of the results but also the quality of the data provided by Orbis for our analysis. In many
respects, Orbis is closer to Dun & Bradstreet databases.

1.1.4 Cross-country evidence

The relevance of the dataset (cid:21) but not only (cid:21) justifies the cross-checking of the results
with other countries where dataset are most likely built differently. As a matter of fact, the
emphasis given by Haltiwanger et al. (2013) to control for both size and age is supported by
a wide number of studies using non-US data. We provide a multi-country overview of these
findings in this section and report a review of individual country studies in the Appendix 5.

Figure 4: Young firms contribute disproportionately to job creation

Note: This figure is extracted from Criscuolo et al. (2014), and shows job creation and destruction
contribution by firm size and age in manufacturing, construction, and non-financial services sectors across
OECD countries. These contributions are compared with the relative share of these firm-groups in the
aggregate employment. Job creation [destruction] is the total number of net jobs created [destroyed] by
job creating [destroying] firms. The period is years 2001-2011, with some differences across countries
conditioned on availability.

Chiara Criscuolo proposes two major cross-country analyses of job creation among OECD
countries. For their studies, Criscuolo et al. (2014) and Criscuolo et al. (2017), the au-
thors collect micro-aggregated statistics of labour dynamics from 2001 to 2011 which they
obtained for 18 countries.18

18The compiled dataset's unit of observation is not the (cid:16)firm(cid:17) but is defined by the dimensions of sector, firm-age
and firm-size groups. For proprietary data access limitations, the programming codes were distributed to central
banks and statistical offices.

15

THE DYNAMICS OF EMPLOYMENT GROWTH: NEW EVIDENCE FROM 18 COUNTRIES 38  OECD SCIENCE, TECHNOLOGY AND INDUSTRY POLICY PAPERS Figure 15. Young firms contribute disproportionately to job creation  Employment, job creation and job destruction by firm age and size  Note: The graph reports the contribution to total employment, gross job creation and job destruction by firms in the reported age-size groups in average across all available years and countries (see Type C measures in Table 5). The period covered is 2001-2011 for Belgium, Finland,  Hungary, the Netherlands, the United Kingdom and the United States; 2001-2010 for Austria, Brazil, Spain, Italy, Luxembourg, Norway and Sweden; 2001-2009 for Canada, Japan and New Zealand; 2001-2007 for France; and 2006-2011 for Portugal. Sectors covered are: manufacturing, construction, and non-financial business services. Owing to methodological differences, figures may deviate from officially published national statistics. For Japan data are at the establishment level, for other countries at the firm level.. Data for Canada refer only to organic employment changes and abstract from merger and acquisition activity. The final results reinforce the common finding that, even though net job creators are
essentially small firms, most employees work at medium and large firms and the majority
of employment is in old firms. However, when it comes to job creation, age appears to be
as (cid:21) if not more (cid:21) important as size. They show that young firms aged between one and
five years (excluding firms in their first lifetime year) contribute proportionately more to job
creation than their share in employment. This observation is valid across all 18 observed
OECD countries. Besides, the age structure of small firms varies so much across countries
that the share of non-growing, non-productive old and small firms also varies substantially.

Figure 4 is extracted from Criscuolo et al. (2014) and illustrates the role of firms in em-
ployment and job creation, by age and size categories. It shows that the net contribution
to job creation is chiefly due to young and small firms.

Looking into the aggregate dynamics of the Great Recession shows that during the crisis old
firms shed more jobs while young entrants and incumbents still contribute to job creation
positively, though to a lesser extent than before the crisis. When the firm's weight in the
population is not considered, dynamics reveal that younger firms are affected more by the
crisis in both the areas of the job destruction and creation than old (ages 5 years and more)
are. At the same time their recovery from the crisis was faster irrespective of their size.

Calvino et al. (2015) follow Criscuolo et al. (2014) and specifically look into the contribution
of young firms to job creation and the channels through which jobs are created: start-up
rate, size at entry, survival rate and growth rate of survivor firms. They demonstrate
that, indeed, young firms are significant contributors to net job creation in all countries,
but the channels through which they contribute vary. This suggests that policies aimed at
facilitating job creation of young firms need not be uniform across countries. 19

Ayyagari et al. (2014) conducts a cross-country analysis using the World Bank Enterprise
Surveys (ES) data. The ES samples formally registered firms from over 100 countries,
including emerging markets. They find that small firms have the largest shares of job
creation, and the highest growth rates both in sales and employment. Importantly, the
results are robust to age control, contrasting with Haltiwanger et al. (2013). Interestingly,
small firms have the largest share of job creation in emerging markets, regardless of ages.

Anyadike-Danes et al. (2015) look into the role of firm size in job creation controlling for
firm age. They look at a cohort of firms born in 1988 in six Northern European countries.20
Consistently with other countries, small firms in their sample are many in number, report
lower survival rates, and grow faster. Nevertheless, cross-country comparison shows that
smallest firms in the cohort play a relatively large role in accounting for overall job creation
and explain cross-country differences. Besides, the initial size distribution and the survival
rates hardly explain the discrepancies in the net job creation rates.

Oberhofer and Vincelette (2013) investigate job creation in the new Member States of the
European Union (EU11),21 and find that, construction and manufacturing were essential
to net job creation before the global financial crisis, these industries were more severely
affected by the financial crisis than others. Job creation is thus more volatile in some sectors
than others. At the individual firm level, small and young firms show the highest net job
creation rates, and the quality of the business climate is positively related to growth.

Wit and de Kok (2014) investigate job creation in 27 EU Member States using a dynamic
classification method. The dynamic classification method was introduced by Davidsson
et al. (1998) and consists in attributing the job creation or loss of a firm at the moment
of the creation or loss to the respective categories around category boundaries.22 At the

19On national policies, see Calvino et al. (2016).
20Austria, Finland, Germany, Norway, Sweden and the UK.
21Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia, Lithuania
22For instance, assume a firm grows from 200 to 290 employees during the year, and the size class threshold is
at 250 employees. Then, the 200 to 249 growth is associated with the lower size class and the 250 to 290 growth
to the upper size class.

16

EU level, small firms contribute on a larger scale in job creation than larger ones. Net job
creation rates decrease with each firm-size class. Their results are consistent with those
reported by Neumark et al. (2011) and, similarly to the latter, they do not control for firm
age.

1.1.5 Job creation by age and size in business cycles

In the previous sections, we reported empirical evidence about job creation patterns by
firm size and age. One may yet question whether job creation patterns are constant over
business cycles, i.e., in economic expansions and recessions. While we established that
small and young firms are the major job creators, policy wise it is a relevant question as to
whether and which categories of firms enhance or smooth out business cycles.

In their influential paper, Gertler and Gilchrist (1994) analyse empirically the effects of
the monetary policy on job creation of small and large firms. Monetary policy tightening
(easing) is captured by an increase (decrease) in policy interest rates. Results show that
monetary policy tightening is followed by significantly larger sales reduction among small
firms, which also reduce inventory at a higher pace. Additionally, Sharpe (1994) finds that
in monetary tightening periods, small firms reduce employment significantly more than
large firms. The divergence in employment patterns relates to the divergence in access to
credit: larger firms accumulate credits before shedding inventories. Kudlyak and Sánchez
(2017) finds similar results in the latest 2008 Great Recession. Therefore, small firms' job
creation and destruction follow the business cycles and tend to exacerbate business cycles
(cid:21) they are pro-cyclical.

Results presented by Gertler and Gilchrist (1994) and Sharpe (1994) are questioned by
Chari et al. (2007) who focuses on economic recessions, rather than monetary policy only.
The authors document that small and large firms respond in a fairly even fashion to eco-
nomic recessions. Further, Mehrotra and Crouzet (2017) report that Gertler and Gilchrist
(1994) results stem from the very top 1 percent of the firm size distribution, which exhibit
lower cyclicality and are large enough to drive the aggregate statistics. Interestingly, the
difference in cyclicality is not so much connected to the monetary policy as to the customer
base targeted by large companies. The large companies having more diversified customer
base makes them more resilient to shocks.

Moscarini and Postel-Vinay (2012) generalise the results and look at the response of firms
to deviations from trends in national unemployment levels in Denmark, France and the
United States. They find that large firms destroy more jobs than small firms in bad times,
and create more jobs in good times. In this case, large firms worsen the downturn.

Fort et al. (2013) argues that similarly to size, age may affect job creation diversely across
cycles. They apply a vector-autoregressive approach and present evidence that among all
small firms, younger businesses exhibit different cyclical dynamics compared to those of
older firms. In economic downturns, the latter turn out to be less responsive to unem-
ployment increases than younger businesses do. Further, younger firms are more sensitive
to housing price declines (cid:21) which correlate with the value of collaterals (cid:21) and reduce jobs
even further.

In order to look at employment responses to a fall in aggregate demand, Görg et al. (2017)
look at the role of the full distribution of the firms' size in a sector. Taking into account
the distribution can be important as it provides information about the possible extent of re-
source allocation. They approximate size distribution by estimating industry-specific shape
parameters of the productivity distributions using firm-level data from Germany, Sweden
and the UK. They show that demand shocks have larger effects on employment when the
sector has a lower concentration of high-productivity (large) firms. These results can be
consistent with Moscarini and Postel-Vinay (2012) on the individual firm growth (larger and

17

more productive firms can have larger employment response), however, responses in terms
of exit and entry are larger in industries with lower average productivity.

1.2 Beyond the Age-Size Debate

While young and small firms play an important role in job creation, it is also true that
most of young and small firms will remain small and hardly ever grow in their lifetime.
Therefore, not all small firms actually contribute to job creation to the same extent (Hurst
and Pugsley, 2011; Pugsley et al., 2017) and we are interested in which firms actually
grow. For this purpose, we focus on the High-Growth Firms (henceforth, HGFs). We then
look at the issues faced by small firms such as the hiring of the first employee. We also
report a recent literature that tries to identify pre-determined factors of growth. The last
section addresses the link between job creation and access to finance.

1.2.1 The high-growth firms (cid:21) HGFs

High-Growth Firms (HGFs) are typically defined by the so-called Eurostat-OECD definition
as firms with employment growth of 20 percent per annum over a three-year period, and
at least 10 employees at the beginning of the period.23

Incidence of the HGFs

HGFs are typically few in numbers but bring about significant shares of job creation. For
instance, Birch and Medoff (1994) show that during the 1988-1992 US recession, 4 percent
of the existing firms accounted for ca. 70 percent of the jobs created by pre-existing firms.
The importance of HGFs to job creation is best illustrated by Figures 5 and 6, extracted
from Bravo-Biosca (2016) and Bravo-Biosca et al. (2013), respectively.

Figure 5: Distribution of job creation and destruction: the role of HGFs

Note: This figure is extracted from Bravo-Biosca (2016) and shows the share of job creation and job
destruction by firm growth category; negative scale represents destruction. The columns (cid:16)>20(cid:17) represent
the share of HGFs, years 2002-2005. The values represent averages across Austria, Canada, Denmark,
Finland, Greece, Italy, Netherlands, New Zealand, Norway, Spain, UK, and US.

23Notice that the European Commission uses a similar definition replacing 20 with 10 percent annualised growth

rate.

18

 43 Figure 8: Distribution of job creation/destruction  01020304050<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,20>20<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,20>201+ employees10+ employeesShare of jobs created/destroyed(a) Cross-country average025500255002550<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,20>20<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,20>20<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,20>20<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,20>20AustriaCanadaDenmarkFinlandGreeceItalyNetherlandsNew ZealandNorwaySpainUnited KingdomUnited StatesShare of jobs created/destroyedAverage annual employment growth rate over the period(b) By country (10+ employees)Figure 6: Share of HGFs in job creation

Note: This figure is extracted from Bravo-Biosca et al. (2013) and shows the contribution of HGFs to job
creation across countries. (cid:16)Share of firms(cid:17) is the share of HGFs in the total number of firms; (cid:16)Share of
empl.(cid:17) is the share of employment at HGFs; (cid:16)Share of job creation(cid:17) is the share of jobs created by HGFs.
Shares are taken over surviving firms with 10+ at the beginning of the period. Countries include Austria,
Canada, Denmark, Finland, Greece, Italy, Netherlands, New Zealand, Norway, Spain, UK, and US.

The figures show that the share of job creation is substantially unevenly distributed among
firms when they are classified by growth rates. In Figure 5, the right-most column ((cid:16)>20(cid:17))
shows that firms with the highest growth rates (20 percent) account for 40 to 50 percent
of job creation. Figure 6 also shows that the contribution of HGF's to job creation varies
across countries.

Featuring and Supporting the HGFs

Henrekson and Johansson (2010) observe that even though many of the HGFs are large
and mature firms, HGFs typically are young and small. More importantly, HGFs drive the
empirical finding that young and small firms create more jobs proportionately than their
aggregate share in total employment.24 Some may yet question the importance of HGFs
since high-growth is not persistent and instead seems to be a one-hit wonder, unlikely to
be repeated (Daunfeldt et al., 2014).

Unfortunately, it is nearly impossible to predict whether and when a firm would turn HGF.
Békés and Muraközy (2012) conduct such prediction exercise using financial indicators and
characteristics of the firms (e.g., leverage, wage share, ownership, location, size and age)
and find poor explanatory power of these variables to becoming a HGF.

Moreover, defining the high growth in relative (e.g., firms with growth in the top decile of
the growth distribution) instead of absolute terms (e.g., growth rate above 20 percent) has
consequences on the results. For instance, relative measures result in a lower likelihood of
persistence of being a HGF (Daunfeldt and Halvarsson, 2015; Erhardt, 2017).

The features of HGFs are summarised into seven stylized facts listed by Coad et al. (2014)
who review the existing empirical evidence:

1. The distribution of growth rates among firms is heavy-tailed, so that HGFs are not that rare

after all.

2. A fraction of HGFs contribute to substantially large shares of job creation.
3. HGFs tend to be young but are not necessarily small.
4. Incidence of HGFs in high-tech industries is on a par with other sectors.

24For other similar reviews, see. e.g., Audretsch (2012) and Ács (2015)

19

WHAT DRIVES THE DYNAMICS OF BUSINESS GROWTH? OECD SCIENCE, TECHNOLOGY AND INDUSTRY POLICY PAPERS   17 Figure 5 reports the share of high-growth firms (HGFs), their share in total employment, and their contribution to job creation. In all countries, high-growth firms account for a very small share of the total number of firms and initial employment, but make a disproportionate contribution to job creation.  For instance, in the United Kingdom, United States, and Italy, HGFs account for more than 40% of total job creation by surviving firms with 10+ employees, while they represent on average only 5% of the total number of surviving firms with 10+ employees. Specifically, they account for between 3.2% (Norway) and 6.4% (United Kingdom) of all surviving firms with ten or more employees, yet they account for 40% and 64% of all jobs created by surviving firms with ten or more employees in Norway and United Kingdom, respectively. This justifies the prominence of high-growth firms in the debate about job creation and employment growth, although it leaves between 36% and 74.5% of employment growth to be attributed to other firms. Therefore it is important to look beyond HGFs and explore the full growth distribution. The data also allows exploring some of these empirical regularities at the 2-digit industry level. Figure 6 shows that high-growth firms make a large contribution to job creation across most sectors, although there are some differences across them. Services and construction display more concentrated job creation, with as much as 60% of sectoral employment growth being accounted for by HGFs in service sectors such as computer and related activities (Sector 72) and other business services (Sector 74).  Figure 5. Share of high-growth firms and their contribution to job creation, by country  Note: High growth firms (HGFs) are enterprises with ten or more employees in the beginning of the observation period with average annualised employment growth greater than 20% over a three-year period (2002-2005). Share of firms corresponds to the share of HGFs in the total number of surviving firms in the country with ten or more employees at the beginning of the period; share of employment corresponds to the share of employment of HGFs in the aggregate employment of surviving firms with 10 employees or more at the beginning of the period; share of job creation corresponds to the share of jobs created by high-growth firms relative to all jobs created by surviving firms with ten or more employees during the three-year period 2002-2005.  Source: Nesta-FORA firm growth project. 5. High growth is not time persistent.
6. Predicting HGFs is nearly impossible.
7. Different HGFs measures result in substantially different sets of firms and time persistence.

Bravo-Biosca et al. (2013) find that the likelihood that HGFs emerge in a country is related
to their business environment that can be summarised in five results: (1) higher em-
ployment protection leads to less dynamic firm growth distributions; (2) creditor-friendly
bankruptcy regimes are likely to reduce the overall variance of the growth distribution
because it reduces the firms' willingness to take risks; (3) tight bankruptcy regulation re-
duces the cost of raising external finance, thus supporting corporate growth; (4) providing
finance for under-performing firms can hinder reallocation and growth; (5) R&D fiscal in-
centives correlate with narrower growth distribution because it protects incumbents and
shifts resources away from more innovative new entrants.

Brown et al. (2017) analyses US start-ups and whether the characteristics of the founding
owner have an impact on the likelihood that the firm is a HGF. They find that firms founded
by women, African-American minority and less educated are less likely to become HGFs.
Though, African-American founded firms are less likely to turn HGF shortly after birth, but
the gap with other groups disappears as the firm is older. Instead, prior business ownership
and a larger group of founders strongly and positively increase the probability of becoming
HGF, regardless of firm's age.

Lee (2014) compares high growth SME's (over 20 percent growth over a 2-year period) in
the UK to firms that do not have high growth periods but are observationally equivalent and
firms without observed potential to learn about perceived obstacles of growth. They find
that all groups, actual and potential HGFs and firms with low potentials perceive problems
with obtaining finance, cash flow and management skills as factors that hinder growth.
However, firms with the potential to achieve high growth perceive regulation less of a
problem than those that do not.25 Lee (2014) concludes that the results might suggest
that policies intended to deregulate or to reduce tax are poorly targeted at firms with the
desire and potential to grow.

The Decline of HGFs

In the last decades, the entrepreneurship and business dynamics have slowed down and
since the 2000s the impact of high growth young firms on job creation has also declined.
Decker et al. (2016) compares the dispersion of growth rate distribution of US firms over
time and find that the difference between the 90th and the 50th percentile growth rate
decreased from 16 percent in 1999 to 4 percent in 2011. The lower gap in the growth rate
distribution is due to the decline in the likelihood that new entry firms become high growth
firms.26. Decker et al. (2016) do not identify the underlying causes empirically but suggest
that firms may operate below scale due to imperfect information about their productivity,
demand or credit constraints. Alternatively, for young innovative firms it is more profitable
to be bought by larger and mature companies than to grow as an independent firm. In a
more recent paper, Decker et al. (2017) connect the decline in employment dynamism to
the aggregate productivity slowdown in the US since the early 2000s.27 They show that
a share of the decline in employment dynamics goes together with diminished allocative
efficiency gains and has some effect on declining aggregate productivity growth.

25Regulation problems mentioned by firms include health and safety , tax-related, sector-specific, barriers,

employment and environmental

26To our knowledge, similar evidence has not been conclusively presented yet for the European economies, due

to the limited availability of historical micro-dataset.

27On the productivity slowdown see, e.g., Byrne et al. (2016) or Andrews et al. (2015)

20

1.2.2 The first employee hiring issue

One of the major events in a firm's lifetime and job creation process is the first hiring.
Empirical investigations look into the timing decision of hiring, the consequences on per-
formance and characteristics of the employer.

Coad et al. (2017) using a Danish matched employee-employer dataset show that those
entrepreneurs that hire enjoy faster sales growth in the previous year and enjoy superior
sales outcomes in subsequent years, while the dispersion in profits increases. They also
find that the probability of hiring the first employee increases with the education of the em-
ployer, while first employees are more likely to be less educated and previously unemployed
compared to those hired by incumbent firms.

Fairlie and Miranda (2017) look at patterns of transition from non-employer to employer
firms using various US data sources. They find that if non-employer start-ups hire, then
most of their employment is created within the first years right after birth. Hiring at later
stages in the firm's lifetime is rather rare. Unlike Coad et al. (2017), they do not find that a
higher education level of the entrepreneur would increase hiring probability. However, they
find that female-owned start-ups are less likely to hire, while start-ups owned by Hispanic
are Asian minorities are more likely to hire.

Finally, Henley (2005) use British panel survey data on self-employed to look into job
creation ability. He finds that successful entrepreneurs are likely to have self-employed
parents themselves and be more educated. He also points out the importance of housing
wealth as possible source of collateral.

1.2.3 A pre-deterministic approach to job creation

Skewness in the size and the growth distribution of firms is the symptom of an extraordin-
ary performance of a few firms. Why is the distribution dispersed and skewed even within
narrowly defined industries? Holmes and Stevens (2014) argue that the predetermined
heterogeneity of firms before entry is likely to play a role. To explain firm-size hetero-
geneity within a sector they distinguish between firms by their main products. Firms that
market specialty, often custom-made goods often by face-to-face contact between buyers
and sellers will remain small sized, while firms producing primary, standardized goods using
mass-production techniques will or can grow large. In addition, Holmes and Stevens (2014)
point out that these two types of firms are affected differently by trade and foreign com-
petition.28 These model predictions match well with empirical finding on U.S manufacturing
data.

Abbring and Campbell (2005) use monthly sales histories from 305 new bars in Texas to
estimate a structural model to quantify the importance of pre-entry scale decisions and
future shocks to firm growth. They show that pre-entry decisions account for 40 percent
of the sales variance.

Schoar (2010) argues that entrepreneurs differ in their economic objectives, and differ-
entiates between subsistence and transformational entrepreneurship. The former relates
to the needs of providing income for a living and has little, if any, ambition to grow; in-
stead, transformational entrepreneurship sets goals to undertake major process or product
innovation and eventually scale-up. She points out that distinguishing these two types of
firms is crucial to policy; policy programmes may target subsistence entrepreneurship with
the objective of making them transformational entrepreneurs. Evidence on the subsist-
ence is also documented by Hurst and Pugsley (2011), showing that most businesses have
significant non-pecuniary motivations, for example a choice of life-style ((cid:16)Being your own
boss(cid:17)).

28This pre-entry differentiation approach is used by Pugsley et al. (2017) to explain firm growth where hetero-

geneity and gazelles play an important role in aggregate dynamics.

21

Studies trying to capture ex-ante heterogeneities empirically have been recently published.
For example, Belenzon et al. (2017) highlight that the firms, which are named after their
owners' names (so-called eponomy), outperform other firms. Another example is Guz-
man and Stern (2016), who uses observables at or near the time of business registration,
and constructs indices that predicts future significant growth outcomes (IPO or high value
acquisition). They find that the top 5 percent highest value in their growth predictions
captures about 70 percent of the actual growth events.29

1.2.4 Financing corporate growth

Firms with growth and job creation potential may need external finance in order to pick
up business opportunities. Yet, they may face bankers unwillingness to provide credits
on demand; such firms are called financially constrained. Financially constrained firms are
defined as firms that need or wish to obtain external financing but are faced with or perceive
limitations.30

Growing firms, especially high-growth firms (HGF), typically are (cid:16)cash-starved(cid:17) and ex-
ternal financing in the form of bank loans may turn to be a prerequisite for growth (Ham-
brick and Crozier, 1985; Storey, 1994). Growth typically implies corporate changes in the
short-run, implying some degree of uncertainty and riskiness, which banks may be unwill-
ing to finance and banks may even be an obstacle to growth (Binks and Ennew, 1996;
Hölzl, 2014). Banks may be reluctant to give loans because near future changes make cur-
rent risk assessment hardly reliable, and they have little control over the loaned amounts.
Besides, high-growth firms typically are in relative opaque R&D intensive sectors inducing
further assessment hazard. See e.g., Stam and Wennberg (2009), Mina et al. (2013).

Figure 7: Financial constraints hinder job creation

Note: This figure is extracted from Hallak et al. (2017). It shows the effects of financial constraints on the likelihood that
a firm is a HGF in the next year; HGFs are firms ranking in the top 10 percent in the employment growth distribution. The
authors use three standard measures of financial constraints: Kaplan and Zingales (1995), Whited and Wu (2006) and
Ferrando and Mulier (2015b). The bars report the coefficients obtained from linear probability models. Data is obtained
from Orbis and covers 26 European countries in the period 2008-2013.

To assess how financial constraints affect firm growth, Hallak et al. (2017) use standard
measures of financial constraints in their analysis of high-growth firms in Europe. They
document empirical evidence that the likelihood of being a high growth firm under financial
constraints is lower, but the impact is slightly lower as the firm size is larger. Nevertheless,

29See Roberts et al. (2015) on MIT graduates in start-ups.
30The indices proposed by Whited and Wu (2006) and Kaplan and Zingales (1995) have become two standard
measures of financial constraints. Besides, Annalisa Ferrando formalize an index based on the ability of the firm
to finance investments with debt; e.g., Ferrando and Mulier (2015a) and Ferrando et al. (2015). Finally, Hadlock
and Pierce (2010) show that age and size are the strongest predictors of financial constraints.

22

30 We conduct the same analysis and split the sample by size categories and report the results in Table 14. Financially constrained firm indicators have substantially different impacts on the probability of being a high-growth firm.  Whited-Wu finds a positive impact in all instances, while Ferrando-Ruggieri finds result in a negative impact in all instances.  Yet, for each of the financial constraints indicators, the impact is relatively similar across size categories. In order to visualise the economic effect of financial constraints on firms’ growth we report the impacts in percentage points in Table 13 and Table 14 (third row) in Figure 5.  The figure clearly shows the variations across the three indicators of financial constraints and  Figure 5: Growth effects of financial constraints by age  Notes: Bars measure the decline (or rise) in the probability of being a high-growth firm when the firm is financially constrained. The numbers are the impacts in percentage reported in Table 14 and Table 13. We report the Employment-HGF[+1] only. All-in-all, being financial constraints has a negative impact on the likelihood of a firm to become a high-growth firm.  The impact is more striking for start-ups, and to some extent young companies. Nevertheless, even though substantial, the impact is not larger to small companies than to large companies. The impact is sometimes substantially positive on sales based measures of high-growth. The result suggest that some firms actually choose to grow without the support of banks.   financially constrained are less likely to be HGF when they are younger. The results are
reported in Figure 7 which is extracted from Hallak et al. (2017), and show the effects of
financial constraints on the likelihood to be a high-growth firm.

Besides the financial constraints and obstacles to growth, creditors may demand extra
job destruction in bad times. Using plant-level Census Bureau data, Ersahin et al. (2017)
show that financial covenant violations result in the reduction and closure of least profitable
plants and business segments of the firm. Also, Falato and Liang (2016) show that covenant
violations result in sharp and substantial cuts in employment in the firm, and the impact is
sharper as employees have weaker bargaining power.31 Graham et al. (2016) find similar
results following bankruptcy events. Finally, using micro-level data on job and bank loan
contracts in Italy, Berton et al. (2018) show that job destruction is significantly higher
following loan contraction, especially temporary jobs, suggesting that employment is used
as an adjustment to loan contraction.

The results contradict the view that banks have no control rights on managerial decisions
away from bankruptcy and may explain why firms are sometimes reluctant to contract
loans. They may also explain the results documented by Gertler and Gilchrist (1994) who
use US firms data and find that tightening of monetary policy is followed by more severe
contractions in sales and inventories among small firms, even though they are able to
accumulate short-term debts before the contractions. Sharpe (1994) adds evidence that
capital market imperfections are responsible for such decline, as leveraged firms display
larger business cycle sensitivity of employment growth. The so-called (cid:16)labour hoarding(cid:17) is
thus determined by the ability of the firm to access external financing.32

1.3 Job Creation and Resource Reallocation in the Economy

In this section we look beyond individual firm characteristics and ask whether job creation
relates to the (re-)allocation of resources in the economy. The literature on resource alloc-
ation assumes that the availability of inputs (cid:21) like labour (cid:21) is limited and the production
units are heterogeneous in terms of productivity (cid:21) i.e., the capacity of generating value
out of inputs.33 Therefore, the way scarce resources are distributed among heterogen-
eous production units in the economy determines the aggregate wealth, and potentially job
flows.

In fact, firms face continuous change in, e.g., product demand, competition and produc-
tion technologies, that continuously modifies the productivity of their business units and to
which firms respond by re-allocating resources in a timely fashion. Continuous reallocation
of labour across business units within a firm is necessary to optimize wealth and thus gen-
erates job flows across business units within the firm. The same phenomenon is expected
in a general economy where jobs are re-allocated between less and more productive firms
(cid:21) firm entries and exits are most extreme cases. It is worth noting, though, that realloca-

31Financial covenants are financial clauses in the debt contract that restrain managerial decisions, e.g., limita-
tions in dividend pay-outs and leverage, and are meant to prevent managers from taking risk-enhancing decisions
after signature. Covenant violations allow bankers to claim default and demand early repayment. Continuing op-
erations despite covenant violations suggest that managers and bankers have reached an agreement, which is
reflected in subsequent managerial decisions; such events allow to observe bankers' preferences away from bank-
ruptcy. See Nini et al. (2009) and Nini et al. (2012) for early work on creditors rights analyses using covenant
violations.

32Consistent with these results, a recent literature has shown that since higher firm's riskiness result in higher
employment risk, employees will require higher compensations as the firm is riskier and managers decrease risky
leverage as a response. See among others Berk et al. (2010) in a theoretical model, and Agrawal and Matsa
(2013) who use labour insurance benefits shocks as natural experiments.

33Productivity is measured as follows. Using a sample of production units, one estimates the general production
function of the inputs (cid:21) typically fixed assets and employment. The unit-level productivity then equals the gap
between the actual production and the value predicted by the model (in statistics, the residual terms). The lower
the gap (negative values) the less productive is the business unit. It is worth noting that productivity is a (cid:16)relative(cid:17)
not an (cid:16)absolute(cid:17) measure.

23

tion may unrelate with job flows if productivity enhancement occurs at the business units
thanks to, e.g., the adoption of new technologies or work reorganization.

1.3.1 Reallocation and aggregate productivity: empirical evidence

Core results

The analysis of the interaction between productivity and job creation relies on the meas-
urement of the aggregate productivity of the economy and the impact of job flows on the
latter: job flows associated with enhanced aggregate productivity is interpreted as jobs
being re-allocated from less to more productive business units. Productivity is measured
as the total added value produced by combined units of fixed assets and labour (cid:21) Total
Factor Productivity, henceforth, TFP (cid:21) or by full-time employee (cid:21) Labour productivity.34
The aggregate productivity is then equal to the sum of business units productivity meas-
ures adjusted by the respective weights of the business units in the total output. The
main empirical evidence highlighting a link between aggregate productivity and job flows
include Baily et al. (1992), Olley and Pakes (1996), Davis and Haltiwanger (1996), Halti-
wanger (1997), and Bartelsman and Dhrymes (1998) which use the US Census Bureau
Longitudinal Business Database (LBD).

The main observation is that reallocation of resources improves the aggregate productiv-
ity growth, showing that jobs are re-allocated towards more productive business units.
Moreover, even though within, between, and entry-exits contribute in various extents to
growth in the various sectors, reallocation typically takes place within sectors rather than
between sectors, at least in the short-run.35 Actually, the productivity discrepancies among
production units is persistent over time and remain larger within than between sectors.
Finally, the change in aggregate productivity is unpredictable (cid:21) corresponding to a random-
walk process.

More specifically, Baily et al. (1992) find that during economic growth periods, a third of
the aggregate productivity growth is due to resource reallocation; actually, reallocation
maintains positive effects on productivity even in periods experiencing declining aggregate
productivity. More importantly for our study, Davis and Haltiwanger (1996) report that re-
spectively about 20 percent and 15 percent of job creation is due to firms entry-exits.Finally,
looking at productivity at entry and exit firms, Bartelsman and Dhrymes (1998) find that
entry firms are not more productive than old firms; though, conditioned on survival, pro-
ductivity increases over time. Yet, low productivity remains a good predictor of exit (Olley
and Pakes, 1996).36

Cross-country evidence

Bartelsman et al. (2004) provides a rare cross-OECD country analysis of labour reallocation
and aggregate productivity enhancement.37 The authors perform a (cid:16)distributed micro-
data analysis(cid:17) of firm level data in 24 countries and apply the method proposed by Foster
et al. (2001) [FHK] to decompose factors of productivity growth. The main results are
illustrated by Figure 8 which is an extract of Bartelsman et al. (2004). Figure 8 shows that,

34Most commonly used productivity measures rely on revenue information. These not only measure firm effi-
ciency, but also capture market power of the firm through the mark-ups and supply-demand conditions reflected
in the prices of goods sold. For a recent detailed review of the issue see Cusolito and Maloney (2018).

35Nevertheless, there have been long-run and significant structural changes in advanced economies, mainly the

shift from manufacturing to services.

36Notice that magnitudes of the effects vary across studies. Foster et al. (2001) provide a comprehensive review

of the statistical issues that may cause such differences.

37Countries include Argentina, Chile, Colombia, Estonia, Finland, France, Korea, Latvia, Netherlands, Portugal,
Slovenia, Taiwan, UK, USA, West-Germany. Bartelsman et al. (2005) is an interesting companion paper on firm
demographics in some OECD countries.

24

on average, aggregate productivity growth is mainly driven by productivity growth within
firms; the pattern is stronger in European countries. Another important result is that even
though the resource reallocation across existing firms varies significantly across countries,
typically it is positive and small. Therefore, aggregate productivity enhancement typically
occurs through the increase of the market shares of more productive firms, but also drives
the between firms job flows.

Furthermore, Bartelsman et al. (2004) document the significant and positive contribution
of net firms entry (cid:21) the difference between entry and exits (cid:21) to the aggregate productivity
growth in most countries, even though the impact lies in a wide range between 20 and 50
percent. Nevertheless, while the exits below average productive firms in most countries,
the entry contribution is negative in most OECD countries with noteworthy differences;
European entry firms are relatively larger and more productive than in the US. Overall,
market selection and learning effects are larger in the US, surviving entrants generate
faster growth, and exiting firms free resources resulting in a more efficient static allocation
of resources.

Figure 8: Contribution of job flows to aggregate productivity

Note: This figure is extracted from Bartelsman et al. (2005) and shows the Foster et al. (2001) [FHK] decomposition of
productivity growth; the decomposition includes the following terms: (1) Within: the contribution of productivity growth
from individual firms; (2) Between: the contribution from initially (un)productive firms growing (loosing) market share;
(3) Cross: the contribution from productive firms growing a market share and increasing productivity at the same time;
(4) Entry and exit: contribution of firms entries and exits.

1.3.2 Job reallocation and productivity in business cycles

The next question we address is whether the re-allocation of employment described herein-
above is constant over time, especially across economic growth cycles. In fact, job realloc-
ation is found to be more intense in economic downturns (Davis and Haltiwanger, 1992),38
even though economic shocks contribute to job destruction dynamics, less to job creation
(Caballero et al., 1997). To some extent, the results are consistent with the existence of
creative destruction.

Creative destruction is a process that assumes conditions for outdated production units
to be liquidated and substituted with more productive ones; this theory is put forward by
Schumpeter (1939) and is sometimes referred to as the liquidationist theory. According

38Davis and Haltiwanger (1992) define the job reallocation rate as the sum of job creation and destruction rates

25

Figure 8    Firm-level labor productivity decomposition for Total Economy -1.5-1.0-0.50.00.51.0 ChileEstoniaLatviaPortugalWest GermanyChile: 1985-1999. Estonia: 2000-2001. West Germany: 2000-2002.Latvia: 2001-2002. Portugal: 1991-1994.Excluding Brazil and Venezuela.Labor Productivity - Five-Year Differencing, Real Gross OutputFHK Decomposition Shares -  Total EconomyWithinBetweenCrossEntryExitFirm Turnover(i) NB:  Within =  within firm productivity growth between =   productivity growth due to reallocation of labor across existing firms entry =   productivity growth due to entry of new firms exit =   productivity growth due to exit of firms Figure 5.2    Firm-level labor productivity decomposition for Manufacturing -0.50.00.51.01.5 ArgentinaChileColombiaEstoniaFinlandFranceKoreaLatviaNetherlandsPortugalSloveniaTaiwanUKUSAWest GermanyArgentina: 1995-2001. Chile: 1985-1999. Colombia: 1987-1998. Estonia: 2000-2001.Finland: 2000-2002. France: 1990-1995.  West Germany: 2000-2002. Korea: 1988 & 1993.Latvia:  2001-2002. Netherlands: 1992-2001. Portugal: 1991-1994. Slovenia: 1997-2001.Taiwan: 1986, 1991 & 1996. UK: 2000-2001. USA: 1992 & 1997.Excluding Brazil and Venezuela.Labor Productivity - Five-Year Differencing, Real Gross OutputFHK Decomposition Shares -  ManufacturingWithinBetweenCrossEntryExitFirm Turnover(i)   36to this theory, a recession may bring about cleansing effects for the following reasons.
Suppose there are frictions preventing shift of resources from less to more productive
units. By driving down the profitability of production units, recessions make those least
efficient no longer viable, and as a result the latter are liquidated; the owners of those
resources will have an incentive to search for other, more productive arrangements. Hence,
recessions reduce inefficiencies in resource allocations by shifting resources towards more
productive units (Caballero and Hammour, 1994; Cooper and Haltiwanger, 1993; Davis and
Haltiwanger, 1990, 1992).

The liquidationist theory is not unequivocally supported by empirical evidence. For example,
jobs are not significantly reallocated in more productive plants during recessions (Baily
et al., 2001; Griliches and Regev, 1995); recessions are not systematically followed by
larger job creation rates, but rather by abnormally low destruction rate (Caballero and
Hammour, 2005). Also, exit firms have similar productivity and size characteristics across
business cycles (Lee and Mukoyama, 2015), although entry firms are relatively larger and
more productive in bad times than in good times.

On the contrary, some authors emphasise that the characteristics of entry firms may neg-
atively affect the productivity enhancement effects of job reallocation. Indeed, recessions
may screen out potentially outperforming firms because of the negative experience new
entrants go through, so that their beliefs about their growth potentials are downwards
distorted; the so-called (cid:16)scarring effect(cid:17) of recession (Ouyang, 2009).

Frictions in the labour market may also hamper cleansing effects as aggregate shocks affect
the process of matching of workers to vacant jobs.39 Contrasting with the cleansing effect
of recessions, authors thus suggest recessions have sullying effects on employment. The
sullying effect of recessions consists in jobs being reallocated in less productive units. The
hypothesis was formalised theoretically by Barlevy (2002) who argues that a search-and-
match model of the labour market explains the worsening of job quality in recessions if the
model controls for on-the-job search (i.e., job search by employed job seekers); thus, in
such setup job-quality turns pro-cyclical. The intuition is as follows. In recession, fewer
vacancies are created by existing firms, so that fewer jobs may improve working conditions
of employed labour. As a result, in recessions reallocation across workers is slower since
jobs appear with lower match quality and reallocation becomes less productivity-enhancing.
In his model, Barlevy (2002) finds that the sullying effect is larger in recessions than the
cleansing effect. The theory is consistent with previous results that show that jobs created
during recessions are paid less and are more likely to be destroyed than jobs created during
booms (Bowlus (1995); also Mustre-del-Río (2016), and Baydur and Mukoyama (2015);
Fernández (2014); Nagore García and van Soest (2017)).

The Great Recession offers a laboratory for investigating the effects of extreme economic
shocks. The patterns found in this period are consistent with the previous results. The
COMPNET report (Fernández et al., 2017) investigates firms' growth and reallocation in
Europe by looking at job creation and destruction rates over time across European coun-
tries.40 They show that job creation flows moved similarly over time in all countries, apart
from Germany, while job destruction exhibits diverse, heterogeneous patterns. Job de-
struction rates increased especially among new-entrant Members States during the crisis,
and also among countries particularly affected by the crisis, such as Spain, Italy and Slov-
enia. Further, the higher is the firm's productivity, the higher is the job creation rate,
suggesting that jobs were reallocated in productive companies. Nevertheless, the process
of productivity-enhancing reallocation was muted during the Great Recession.

39Search-and-matching employment models by Mortensen and Pissarides (1992, 1994) show that negative
shocks endogenously change the timing of reallocation because they reduce the number of matching vacancies.
40The COMPNET gathers firm-level databases of national statistics agencies across Europe. The sample of the
COMPNET Labour module covers Belgium, Croatia, Estonia, Finland, Italy, Lithuania, Portugal, Romania, Slovenia
and Spain

26

Bartelsman et al. (2017) analyse a sub-group of European countries (cid:21) Belgium, Esto-
nia, Finland, Italy, Slovenia, Spain (cid:21) and finds similar productivity-enhancing realloca-
tion patterns; firms' higher initial productivity is positively correlated with input growth,
in terms of both labour and capital. This effect is stronger for small firms. Yet, the
productivity-enhancement effects of job reallocation fades out with the Great Recession,
showing counter-cyclicality. Only the average amount of productivity enhancing realloca-
tion takes place.41

Finance and labour reallocation

In line with the liquidationist view, the ability to retain employment depends not only on
productivity but also on financial health. Giroud and Mueller (2017) using detailed US data,
argue the importance of leverage in firms' response to recession. Importantly, they show
that employment shedding due to leverage is not driven by underlying low productivity but
to another channel. Firms with more leverage and high financial constraints are unable
to protect their employment by temporary labour hoarding. The reason is that temporary
labour hoarding spares the firm firing costs as well as the costs associated with the hiring
of new employees (including searching and training costs).

Recently, Borio et al. (2016) investigate the empirical link between credit booms, financial
crises and productivity growth on twenty advanced economies, including the core European
countries, and over forty years using the OECD-STAN and the EU-KLEMS databases. They
evaluate the correlation between productivity and share of productive firms over time and
across countries using the Olley and Pakes (1996) decomposition technique. The authors
find that during credit booms labour shifts to less productive industries creating cross-
sectoral misallocation and undermining productivity growth. They also show that in bad
times, the misallocations have a further negative impact on productivity. Both of these
finding suggest a key role of finance in cyclical reallocations.

1.3.3 Resource misallocation

Aggregate productivity clearly varies across countries, and a natural question is whether the
distribution of resources among heterogeneous firms explains these differences. A cross-
country comparison of the extent to which resources are allocated towards more productive
In their influential paper, Hsieh and Klenow (2009) link
firms can provide an answer.
aggregate productivity gains with the allocation of resources. Assuming that the respective
allocations of labour and capital in India and China follow the US resource allocations, the
gain in aggregate productivity would amount to 30-50 percent in China and 40-60 percent in
India. These results show that there are potential productivity gains in India and China, and
production resources are (cid:16)inefficiently(cid:17) allocated; this reflects the so-called misallocation
of resources.

The next question is then, what could explain resources misallocation? Restuccia and
Rogerson (2008) claim that any policy and market frictions that create or maintain hetero-
geneities in prices (of labour, capital or output) faced by individual producers potentially
lead to significant decrease in aggregate output and productivity. The main motivations are
found in market (cid:16)frictions,(cid:17) which Restuccia and Rogerson (2017) classify in four groups:

41More recent studies on the relationship between productivity and reallocation for European countries include
Cantner and Krüger (2006) for Germany, Fontagné and Santoni (2015); Sandoz (2017) for France, Lopez-Garcia
et al. (2007) for Spain, Carreira and Teixeira (2008, 2016) for Portugal, Linarello and Petrella (2016) for Italy,
Dumont (2011); Vanormelingen (2017) for Belgium, Disney et al. (2003); Riley et al. (2014) for the UK, Schneider
(2014) for Austria, De Loecker and Konings (2006) for Slovenia, Brown and Earle (2008) for Romania, Hungary,
and Lithuania, and Harasztosi (2011); Kátay and Wolf (2008) for Hungary.

27

1. Regulation: Regulatory constraints in various markets; for instance, size dependent
firing costs created by policies, housing regulations affecting movement of labour in
space.

2. Property rights: Constraints in rights of owning properties; for instance, limits on farm

size in agriculture.

3. Trade and competition: Limitations in trade, e.g., import quotas and trade tariffs, but

also subsidies.

4. Finance and information: for instance, a financial system that is inefficient at allocating

funding and create undue financial constraints.

To investigate cross-country differences in misallocation, Andrews and Cingano (2014) look
at firm-level data from a wide range of OECD countries. They employ the static decompos-
ition technique proposed by Olley and Pakes (1996), which is composed of an unweighted
firm-level average productivity and a covariance term. The covariance term is a summary
measure of the within-industry cross sectional covariance between size and productivity,
and its low value implies that inputs do not flow to most productive firms. They show
that employment protection, barriers to entry and bankruptcy legislation and restrictions
on foreign direct investment correspond to low values of covariance terms.

The numbers are reported in Figure 9 which is obtained from Andrews and Cingano (2014).
The figure reports the labour allocation effectiveness by country using the covariance term
suggested by Olley and Pakes (1996). The numbers show the substantial variation in the
employment allocation effectiveness across countries. Most effective countries in resource
allocations are Sweden, Finland, and the United States. Least effective countries include
Poland, Greece, and Korea. The European Union as a whole reports about half the allocation
effectiveness of the United States.

Figure 9: Labour misallocation across countries

Note: This figure is extracted from Andrews and Cingano (2014). It shows the cross-country differences in misallocation
of labour in the manufacturing sector in 2005.
It also shows the capacity of respective economies to allocate labour
efficiently into productive firms. The authors used the decomposition of the covariance term suggested by Olley and
Pakes (1996).

As for Europe, Bartelsman et al. (2013) give a picture on misallocation in 7 European
countries using Olley and Pakes (1996) decomposition and compare the results with the
US. They find that between 1993-2001, the US had the highest covariance term, indicating
highest efficiency. Western European countries show considerably lower covariance terms:

28

ofcoursearebasedonasmallersampleofcountriesandfirms.TheTFPestimatesareobtainedusingaSolowresidualtechniqueusing(2-digitNACERev1.1)sec-toral(country-specific)labourcostsharesfromtheOECDSTAN,anddataonvalueadded,employmentandbookcapitalfromORBIS.Thecapitalstockiscal-culatedusingthePerpetualInventoryMethodwhererealinvestmentiscalculatedasthedifferencebetweenthecurrentandlaggedbookvalueoffixedtangibleassetsplusdepreciation,deflatedbycountryandindustryspecificinvestmentdeflators.Sinceweuseindustry-leveldeflatorsintheabsenceoffirmlevelpricedeflators,themeasuresofTFP(aswellasthoseoflabourproductivity)arerevenuebasedmeasures.ItisalsopossibletoobtainsimilarresultsusingTFPestimatesbasedonanOLSproductionfunctionestimationapproach,butwedonotshowtheseresultsforthesakeofbrevity.Weexcludefirmswithoneemployeeaswellasfirmsinthetopandbottom1%ofthelabourproductivitydistributionfromthesample–arelativelycommondatacleaningtechniqueintheliterature.Finally,inORBISitisnotpossibletoaccur-atelydistinguishentryintothemarketfromentryintothesampleandexitfromthemarketfromexitfromthesample.Thispreventsusfromundertakingadynamicdecompositionofindustryproductivitygrowththataccountsforthecon-tributionofentryandexit,andthuswefocusonadecompositionoftheleveloflabourproductivityatasinglepointintime.Bywayofintroductionandforpurelyillustrativepurposes,Figure2showsthedis-tributionofourAEindexacrosscountriesforthemanufacturingsectorin2005.Theestimatessuggestthatsomecountriesaremoresuccessfulatchannellingresourcestohighproductivityfirmsthanothers.Indeed,moreproductivefirmsarelikelytoaccountforamuchlargershareofmanufacturingemploymentintheUnitedStates–0.7–0.5–0.3–0.10.10.30.50.70.9More produc(cid:415)ve ﬁrms have more resources  than if resources were allocated randomly More produc(cid:415)ve ﬁrms have less resources  than if resources were allocated randomly Figure2.OECDcountriesdifferintheirabilitytoallocatelabourtothemostproductivefirms:covarianceacrossfirmsbetweenfirmsizeandlabourproduct-ivity;logpoints;manufacturingsectorinselectedOECDcountriesin2005RESOURCEALLOCATION263Downloaded from https://academic.oup.com/economicpolicy/article-abstract/29/78/253/2918383by EC JRC Ispra site useron 28 February 2018Netherlands and Germany exhibit highest efficiencies among the European countries in the
sample, and Central and Eastern European countries the lowest. Romania and Slovenia
report covariance terms close to zero, which is equivalent to an economy where inputs
were randomly allocated to firm. Studies that focus on a single country can provide a
more detailed approach. Recent examples include Benkovskis (2015) for Latvia, Dias et al.
(2016) for Portugal and García-Santana et al. (2016) for Spain, Calligaris et al. (2017) for
Italy.

While the cross-country differences reflect rankings that one would expect from GDP,
changes in misallocation over time across countries are not straightforward. In fact, labour
misallocation is stable in recent years for most, while capital misallocation is increasing in
some European countries. Gopinath et al. (2017), Gamberoni et al. (2016b) and Franco
(2017) document that while the dispersion of returns to labour across firms is stable after
2000, the dispersion of the return to capital across firms increases somewhat but most
significantly in Spain and Portugal. This increase in dispersion leads to considerable losses
in aggregate productivity. Gopinath et al. (2017) argue that the decline in the real interest
rate, connected to the euro convergence process, facilitated the flow of capital to firms with
high net worth but low productivity.

Just like policy and regulation, corruption may contribute to the resource misallocation.
Gamberoni et al. (2016a) find that corruption plays a significant role in explaining cross-
country differences in input reallocation patterns among Central and Eastern European
countries. The study is based on the Business Environment and Enterprise Performance
Survey (BEEPS), which is a survey of firms investigating the customs and extent of brib-
ing officials to (cid:16)get things done;(cid:17) the BEEPS indicator positively correlates with resource
misallocation.

Zombie firms and misallocation

The zombie firm phenomenon and its impact on other firms' job creation is an illustration
of the adverse effects of resource misallocation between firms. Zombie firms are finan-
cially distressed firms which receive financial support from banks, and would be liquidated
otherwise. The zombie phenomenon was identified in Japan in the 1990's by Hoshi (2006)
among others. Authors identify a pool of firms, which curiously pay interest rates below
the lowest market rates (so-called negative interest rate gap), and show that these firms
are less profitable, more indebted, and more frequent in sectors with lower levels of inter-
national competition. Hence, even though these firms perform poorly, they are subsidised
by banks and kept alive artificially, i.e., they are walking dead.42

42An essential rationale behind zombie phenomenon lies in the banking regulation. In a sample of loans extended
to Japanese listed firms in the 1990's, Peek and Rosengren (2005) find that banks with capital ratios close to
regulatory thresholds are those with the highest likelihood to refinance poorly performing companies at abnormally
low interest rates. This phenomenon is also called bank forbearance.

29

Figure 10: The rise of the zombies

Note: This figure is extracted from McGowan et al. (2017). It shows the prevalence of zombie firms and the share of
employment and capital employed by zombie firms in years 2007, 2010, and 2013. Zombies are defined as ten-years or
older firms and three consecutive years with negative profits after interest and before tax payments.

Even though supporting corporations in distress may be appealing to policy makers, there is
empirical evidence that zombie firms produce negative externalities to healthy non-zombie
firms and deter job creation overall. Unfortunately, by keeping distressed firms artificially
alive, banks not only distort competition in debt markets against well-performing firms,
but also distort competition in the labour and product markets. Higher competition on
non-zombie incumbents and new entrants implies lower economic growth and job creation:
non-zombie incumbents must increase their own productivity which they do by eliminating
or discarding less productive projects (hence, employment) and potential new entrants
must implement projects with higher productivity to enter the market. This is confirmed
by Caballero et al. (2008) who find that as the share of zombies in an industry increases,
non-zombie firms are less likely to grow, and more likely to enhance their own productivity;
also there are fewer entrants in the industry.43

McGowan et al. (2017) highlighted similar patterns since the Great Recession in some
European countries. Figure 10 is extracted from McGowan et al. (2017) and shows the
share of zombies in selected OECD countries in 2007, 2010, and 2013. The ratio of real
assets held by zombies has increased but varies substantially across countries: in 2013 it
ranged from 4 percent in Slovenia to 15 percent in Spain and almost 20 percent in Italy.
Similar results were found by Hallak et al. (2018) in a wider set of European countries, and
using more conservative definitions of zombie firms.44

Interestingly, countries most affected by zombies are well-known for reporting productivity
growth issues. In fact, even though the zombie phenomenon stems from financial sys-
tem failures, it clearly affects the optimal allocation of resources in the economy. In fact,
banking regulation introduces distortions in the input market that are unrelated to pro-
ductivity; without such distortions, resources and jobs held by zombie firms are likely to be
re-allocated into more productive firms.

43The story presented by Caballero et al. (2008) may yet be questioned. They argue that because they are
virtually close to death and have little to lose, zombie firms are extreme risk-lovers and as a result increase
salaries and cut product prices; keeping them alive therefore maintains undue competition pressure on productive
firms. Nevertheless, by providing cheap financing to distressed firms which keep a hope for survival, banks may
actually try and avoid such daredevil attitude. Exogenous reasons for the existence of zombies such as banking
regulation is thus essential to make the story plausible.

44McGowan et al. (2017) and Hallak et al. (2018) present innovative evidence of the zombie phenomenon
among SMEs, not just large and quoted firms. In their study, Acharya et al. (2016) confirm the existence of a
booming zombie phenomenon in Europe using a reduced sample of large firms and more stringent definitions of
zombie firms; they attribute the zombie phenomenon to the unconventional monetary policy implemented by the
European Central Bank.

30

ECO/WKP(2017)4  18 Figure 5. The rise of zombie firms Panel A: The share of zombie firms over time; 9 OECD countries   Panel B: The share of capital sunk in zombie firms in 2013; 13 OECD countries  Note: Firms aged ≥10 years and with an interest coverage ratio<1 over three consecutive years. Capital stock and employment refer to the share of capital and labour sunk in zombie firms. The sample excludes firms that are larger than 100 times the 99th percentile of the size distribution in terms of capital stock or number of employees. Figure A1 shows zombie shares for two additional countries (Greece and Japan), which are not included in the following empirical analysis due to lack of productivity data.  Source: OECD calculations based on ORBIS. 31. Across time, there has been an increase in both the prevalence of zombie firms and the resources sunk in them. In the rest of the paper, we follow Caballero et al. (2008) and utilise the capital sunk in zombie firms as the preferred zombie measure (sometimes referred to as K-share or zombie capital share), which Figure 5, Panel B charts for a broader range of countries in 2013. Looking at this metric, from 2007 to 2010, the prevalence of zombies has increased in Belgium, Spain, Italy, Korea, Finland and Sweden, while it has declined in France, the United Kingdom and Slovenia. Some further divergence is observed from 2010 to 2013. While the capital sunk in zombie firms increased further in Spain, Italy, Korea and 0510152025200720102013200720102013200720102013200720102013200720102013200720102013200720102013200720102013200720102013BELESPFINFRAGBRITAKORSWESVNNumber of firmsEmploymentCapital Stock%0510152025ITAESPBELPRTLUXDEUSWEKORAUTGBRFINFRASVN%1.3.4 Falling labour share and reallocation

There is evidence that the share of the aggregate value added that remunerates labour
has decreased in the last decades. The trend is documented by Elsby et al. (2013) for the
US and by Karabarbounis and Neiman (2014) for a variety of countries. The latter show
that between 1975 and 2012 the decline in labour share is detectable in 42 countries. This
long-term trend is also visible across European countries. Figure 11 is an extract from
Autor et al. (2017b) and shows the time plots of the share of compensation in value added
in France, Germany, Italy, and Spain.45 While the trend itself is well documented, there is
little consensus as of the causes and consequences of the decline, including its implication
for reallocation of jobs and productivity.

There might be a number of reasons as of why the share of labour in value added has been
declining. Elsby et al. (2013) argue that a significant share of the decline in labour share is
due to the sectors affected by import shocks. For example, as a consequence of increased
competition of labour intensive goods from a foreign country, workers in labour-intensive
industries have been relocated to other more capital-intensive sectors. This approach, how-
ever applies to the recent decline only, which was dominated by trade and manufacturing
sectors.

Karabarbounis and Neiman (2014) suggests that the lower share of labour is due to the
decline in the cost of capital relative to labour in relation to the technological advancement
and computerization. Lower relative price of capital induces firms to shift away from labour
and use relatively more real capital. However, they find that the decline in labour share
is attributable to reallocation changes within industries rather than to modifications of the
industry composition of the economy.

Figure 11: Fall of labour share in European countries

This figure is extracted from Autor et al. (2017b).
aggregate compensation over value-added for all industries in a country.

It shows the ratio of

Autor et al. (2017b) challenge the relative price hypothesis, arguing that relative price
changes should affect all firms within an industry evenly. However, empirical evidence
suggests there is no decline in labour share should the non-weighted average of labour

45For a discussion of the falling shares in Germany and a comparison across the EU countries see also Grömling

(2010) and Berger and Wolff (2017).

31

share ratios be considered. The decline in aggregate labour share is actually due to a
composition effect driven by the behaviour of the very large firms.

Barkai (2018) extends Autor et al. (2017b) and highlights that the share of capital in added-
value has been declining together with the labour share. The author shows in a theoretical
model that such trend is possible only in the case of a decline in competition whereby net
profits increase relatively to labour and real capital. In a nutshell, the decline of both capital
and labour shares is due to the increase in the share of profits which is likely due to reduced
competition. The results are consistent with Autor et al. (2017b) who found that leading
large firms explain most of the decline in labour share.

To emphasise the role of the general increase in concentration within industries, Autor et al.
(2017b) develop the so-called superstar firms model. In this model, firms with the highest
productivity in an industry beat all competitors, and generate higher profits. As a result,
the share of labour in their produced value added is lower than other firms. Besides, these
top productivity firms capture and control increasing portions of product markets, and may
grow to such an extent that they drive aggregate labour shares in the economy. In fact,
Autor et al. (2017a) document a negative correlation between market concentration and
the share of labour in the added value. Increase in market concentration and mark-ups
in the US is being widely documented, see e.g., De Loecker and Eeckhout (2017); Grullon
et al. (2017).

Finally, the increasing gap between the top-productive firms with other firms in the in-
dustry is documented by Andrews et al. (2015) in OECD countries. They show that the
productivity gap between firms at the top end and other firms has widened among OECD
countries, both in terms of labour and total-factor productivity. They suggest that the gap
is partially explained by the lack of efficient diffusion of technologies across firms. In fact,
controlling for the national institutional environments, they argue that policies supporting
technology adoption, through, e.g., R&D tax incentives, R&D collaboration between busi-
ness and university, and patent protection, play a significant role in reducing productivity
gaps. Therefore new technologies are likely to be first adapted by the most productive
firms within each country, and presumably only diffuse to other lagging firms once national
top-productive firms have implemented the new technology.

The literature on competition and the remuneration of labour is drawing major attention
lately among policy makers. For instance, the reduced share of labour may also be the
outcome of the increasing bargaining power of a few oligopolistic players in labour markets,
not only in product markets. Equity holders may thus retain a share of the value of labour
away from employees. The decline in competition is yet more of a concern in the US than
in Europe where such trends have not been established. This may be a topic of interest for
further research in Europe.

1.3.5 Conclusions

In this chapter, we review the current knowledge about job creation. The literature has
made the distinction between job creation and job destruction on the one hand, and net
job creation on the other hand. Job creation is the sum of jobs created by growing firms
in a category; reciprocally, job destruction is the sum of jobs lost by shrinking firms. Net
job creation is equal to the difference between job creation and job destruction. Net job
creation is equivalent to employment growth and may be calculated at the firm level or
for a category of firms. job creation and job destruction are always positive while net job
creation may be positive or negative. Firm-level dataset cannot observe the actual number
of jobs created and destroyed which would equal the actual number of employees hired
and lost by the firm, respectively. Employee-level dataset which identify employees, such
as the Census Bureau Longitudinal Business Dataset would. Net job creation rate reflects
the capacity of a category of firms to be efficient in adding employment, i.e. it equals the

32

(cid:16)employment growth rate.(cid:17) The job creation and job destruction rates reflect the turnover
and dynamism of the labour market. The numbers are put in perspective with the aggregate
numbers the respective shares of employment.

We identified three areas where (net) job creation may be affected. Two highlighted the
firm-specific ability to create jobs; one highlighted how this ability depends on other firms.
First of all, a large area has focused on the age-size debate. Authors have provided con-
tradicting evidence: some argue small firms contribute to job creation and net job creation
proportionately more than their employment share, while some underline that young firms
are the main contributors. The debate has involved a discussion as of potential sample and
measurement errors. Yet, there is growing consensus that while small firms may indeed be
disproportionately contributors to job creation and net job creation, among all small firms,
the young are those contributing most, and this is true at all size categories.

Beyond age and size, firms may be affected by a series of factors in their growth. This
includes access to finance, and the capacity to display strong employment growth rates.
Some studies attempt to identify (cid:16)pre-deterministic(cid:17) indicators of growth, such as the name
of the firm. The latter assumes that new entrepreneurs vary in their initial objectives
regarding growth.

Finally, the growth capacity of a firm may be determined by the overall capacity of the
labour market to reallocate resources efficiently. The resource reallocation varies across
economic cycles (recession and expansion); during economic downturns, less productive
firms are more prone to die than in economic expansion periods, which benefit to more
productive firms. Finally, some firms display a declining share of labour in total added
value, and increase in financial capital remuneration, which suggests that more productive
firms may actually benefit from market power.

(cid:22) Small and young firms are major contributors to job creation and net job creation;

(cid:22) Age and growth measurements have an impact on the results.

(cid:22) There may exist pre-deterministic determinants of growth.

(cid:22) In the US, labour reallocation benefits aggregate productivity.

(cid:22) Even though entry-exits improves the aggregate productivity, within firms productivity

gain is the main driver of productivity enhancement.

33

Chapter 2:

Sampling and Methodological Strategy

This chapter describes the sample and methodology used for our study. We describe the
selection procedure as well as the imputation and data cleaning techniques we applied. The
data quality analysis relies on the representativeness assessment of employment of which
all details are reported in the Appendix. Finally, we describe the final sample.

2.1 Sample construction

Selection of firms and accounts

The study consists of an empirical analysis using micro firm-level data obtained from Or-
bis. Orbis is a databank published by Moody's Bureau van Dijk that collects the financial
statements of any firm worldwide and includes additional firm-level information such as
ownership and employment. Because they rely on bilateral contracts with a wide range of
data providers, Orbis data may vary by country in their quality as well as their represent-
ativeness of official statistics. This report relies on 2017 end-of-year version of Moody's
Bureau van Dijk's so-called (cid:16)historical Orbis(cid:17) product. The historical dataset is a unique file
in text format that contains all employment and financial information available in Orbis at
the file production date. We therefore could consult all reports for all firms and trace back
to the earliest report available in Orbis.

We select all companies in Orbis registered in one of the EU-28 countries in the period 2004-
2015 which report in Orbis (cid:16)industrial format.(cid:17) The BvD identifier is uniquely assigned to
firms by Bureau van Dijk, of which first two letters indicate the country of registration. We
exclude financial services companies with 2-digit industry NACE codes between 64 and 66.

Defining firms by BvD identifiers may yet give rise to some issues. First, for some reasons,
firms may change their identifiers. For example, we know that BvD identifiers build on
national identifiers, e.g., tax numbers, and there might be general changes in national
identifying systems; such event occurred with the Italian tax numbers in 2013. Another
challenge relates to mergers and acquisitions. A firm may be acquired by another firm, and
even though it continues activities regularly, the identifier may be modified. BvD keeps
track of identifiers changes and are available online.46 Yet, we rely on the historical dataset
since BvD claims that it already ensures the continuity of the firms identifiers.47

Among all selected accounts, we retain those with a closing date in years between 2004
and 2015. The coverage period is motivated by two factors. On the one hand, the quality
of the Orbis dataset in terms of representativeness has increased substantially since the
early 2000s with 2004-2005 being a major threshold for a number of countries. The second
reason is that a number of Central European countries joined the European Union in 2004,
and detailed Eurostat data is not available for these countries before this date, hindering
comparison. For some countries, e.g., France and Spain, relatively longer time series is
available.

46http://idchanges.bvdinfo.com/
47We did check such continuity in some instances.

34

Finally, we retain unconsolidated accounts only.48 The reason relates to the comparison
with Eurostat statistics which are constructed at (cid:16)business unit(cid:17) level; accounts are thus
unconsolidated with subsidiaries. Moreover, consolidated accounts by definition consolidate
employment of the firm and its subsidiaries; given that the subsidiaries are likely to be in the
sample too, this may result in double counting of employment. Furthermore, consolidated
accounts include subsidiaries located in other sectors so that one reduces the precision of
the industry classification.

Orbis contains five types of accounting reports: consolidated C1 and C2, unconsolidated U1
and U2, and Limited Financial reports; Box 3 provides all details. We retain unconsolidated
employment exclusively: U1 [only unconsolidated account is available] or U2 [consolidated
account is also available but we retain the unconsolidated account]. Case LF represents
reports with Limited Financial accounts, and cannot be classified as consolidated or uncon-
solidated a priori. Yet, since their average size of LF accounts is small, they are most likely
unconsolidated.

Box 3. Consolidated and unconsolidated accounts in Orbis

Orbis reports five types of reports: unconsolidated U1 and U2, consolidated C1 and C2, and with limited
financial information LF. U's and C's indicate unconsolidated and consolidated reports respectively. U1 (C1)
indicates that no consolidated (unconsolidated) reports are available. U2 (C2) indicates that a consolidated
(unconsolidated) report is available.

U1 - Unconsolidated; no consolidated reports are available.
U2 - Unconsolidated; a consolidated report is available.
C1 - Consolidated; no unconsolidated reports are available.
C2 - Consolidated; an unconsolidated report is available.
LF - Limited Financial.

In our analysis of the data we noticed that (cid:16)Limited Financial(cid:17) format indicates unconsolidated reporting
in most cases. Notice that Limited Financial reports may report employment, not the financial data. We
also check the mix for the same firms of LF, C's and U's accounts across years, and found very few cases.
For our analysis we exclude consolidated accounts.

The selection of firms and accounts may be summarised as follows:

(cid:21) Non-financial firms: exclude NACE 2-digit codes 64-66;
(cid:21) Retain closing date of reports in years 2004-2015;
(cid:21) Unconsolidated accounts.

Provided the potential sensitivity of our sample to excessive employment due to the adding-
up of unconsolidated and consolidated accounts, we screen the effects of consolidated
accounts in our representativeness analysis (cid:21) see the sections hereunder.

Industry classification

We use the core industry NACE classification in Orbis because, in Europe, the national clas-
sification systems closely follow the unified European NACE system. Industry classification
is a (cid:16)static(cid:17) time-invariant item in Orbis, which means that we can know the industry at the
date of download only. This may result in two major issues.

First, the classification process is relatively complex and requires revisions from time to time
so to rearrange activities consistently and/or take into account new and expanding business

48Unconsolidated accounts are accounts that ignore subsidiaries. Companies with no subsidiaries would typically
have unconsolidated accounts only. Firms with subsidiaries may have consolidated, unconsolidated, or both
accounts reported.

35

activities. In 2008, the NACE system was revised into the NACE Rev. 2 system introducing
significant changes. Even though supervisors provide correspondence tables between the
old and new systems, there exist no one-to-one code correspondence tables. Moreover,
so that all European firms in Orbis are classified in the latest European NACE Revision 2
system introduced in 2008, and knowing a firm's sectoral classification in today's Orbis does
not systematically provide the industry code in the prior system.

The second issue relates to the unobserved past industry changes of firms. In fact, firms
may modify their core activities over time and be reclassified over time. This is a relatively
rare event, though, but it may lead to non-trivial problems if some large firm switches
industries. We controlled for this manually as we compare Orbis with Eurostat and identified
a few occurrences that we fixed. Further description is available in Appendix 5.

In some
Last, in rare occurrences, some firms do not report their sector of activities.
countries, the absence of industry classification may be significant, in particular in Denmark,
but also Estonia, United Kingdom, Ireland, the Netherlands, Romania and Sweden. Based
on our experience and knowledge, these firm level limitations are common to most firm-
level databanks.

Construction of a panel dataset

Firm-year level datasets require to identify a single accounting report in each year. As
described in our previous section, for each firm, we select unconsolidated accounts only.
The choice of the consolidation level depends on the economic phenomenon of interest. As
for the employment analysis purpose of this report, we would like to compare Orbis based
computed aggregates to national statistics. We report the amount of information losses
due to the dismissing of consolidated accounts.

Moreover, firms may provide multiple reports at the same consolidation level in the same
year. Thanks to the access to all accounts for each firms, we could identify two main reasons
for multiple reporting. First, Orbis gathers information from multiple data providers. Data
may stem from national sources and/or regional/local sources. Moreover, firms sometimes
submit multiple annual reports within a year when, e.g., they change the (cid:16)end of fiscal
year(cid:17) from the usual 31th December to for example 31th of March. We apply the following
selection rules.

Multiple unconsolidated accounts within the same year: selection rules.

1. Reports with closing date up to June are associated to the previous calendar year.
2. Several reports with closing date after June: select the latest report.

Sometimes we observe that firm-year reports are missing. In this case, we rectangularize
the dataset between the first and the last observed year of the firm and add unfilled extra
firm-years to the dataset.49 Note that this does not mean imputation of values per se. It
only allows to identify firm-years where information or reports should be available.

Imputations

Given the panel construction of the data, we can impute missing value of employment for
firm-year observations with approximations. Here we opt for two alternative strategies.
Step 1 is a less invasive imputation method, while Step 2 assigns an approximate value to
all firms. Step 2 enables to identify the size and the nature of the missing value problem.
Step 2 is aimed at countries with serious missing variable problems.

49An alternative approach could be to fill

in the dataset with extra observation between the firms year of
establishment and 2016 (last year observed in the data). In this dataset we do not take this approach, but this
could be useful in a research with a different focus, e.g., analysing entry and exit.

36

Step 1. (Imputation). This first step imputes missing information for a firm-year when
close past and future data is available. When Orbis data look as follows:

time

employment

t-1

16

t

.

t+1

25

employment for t is imputed by taking the average of the neighbouring values: (16+25)
.
This method lies on the assumption that employment increases linearly from t − 1 to t + 1.
This imputing strategy can be used for multiple consecutive missing values. In this report
we use it for at most three consecutive missing years. Additionally, this imputing strategy
could also allow for using the same linear strategy to missing values not surrounded by
known figures. For example, for the year t + 2 below:

2

time

employment

t-1

16

t

.

t+1

t+2

25

.

for t + 2 we could impute 25 + (25−16)
, which would assume that growth keeps the same
linear path after the last observed value; similar strategy could be followed when there is
no missing data at time t. Nevertheless, since we do not actually know the direction of the
change after the last observed value, we might impute growth when there is a decline in
reality. To avoid this bias, we impute 25 for t + 2; we retain this imputation strategy for
firm-years only where data is available in either the previous or following year.

2

Step 2. (Imputation + average). The dataset still might include firms where reporting
of the variable is concentrated on one or the other end of the time-line of the available
reports. For example:

time

t-2

t-1

employment

15

12

t

.

t+1

t+2

.

.

In these cases we need to be more careful whether imputation does more bad than good.
However in order to assess the quality of Orbis dataset and have an idea about the size and
nature of the missing value problem we assign the average of the available information to
the missing years. Hence, employment for t, t + 1, t + 2 becomes (15+12)
= 13.5, regardless
of the number of consecutive missing firm-year observations.

2

Notice that researchers using Orbis may opt for a more or less conservative imputation
technique, depending on their research question and methodologies.

Detection of outliers

We adopt the following outlier detection technique. We look at particularly high jumps and
drops in firm time series of the variable of interest where the variable returns to values
close to pre-change values. Here is an example:

We detect the outlier at time t as looking for instances when employment changes to 800
(or 1/800) times its previous followed by a similar change of the opposite sign.50 The
outlier is replaced by the average of the neighbouring values.

50The value is designed to capture misreporting of commas by a thousand.

37

time

t-2

t-1

t

t+1

t+2

employment

15

12

1400

20

27

A more sophisticated outlier detection for employment consists in looking at changes in
average wage. This controls for actual large changes in employment, reflected in contem-
poraneous changes in both wages and employment. Should this be the case, the average
wage would remain relatively constant. Notice that this technique cannot be used for the
years around firms birth and death.51 Also, different thresholds to changes are applied
gradually to detect larger and smaller outliers.

Eurostat benchmark statistics

We compare the Orbis aggregate employment with Eurostat aggregate statistics; Eurostat
is the European Union official statistics authority. We use Eurostat National Accounts (code:
nama_10_a64_e) and employment statistics (domestic employee concept) as country and
sector level aggregate benchmarks; for the firm-size level employment information, we
rely on Eurostat Structural Business Statistics (SBS) industry level information, Eurostat
dataset code sbs_na_ind_r2 for sector C (Manufacturing). Services are covered by dataset
sbs_na_dt_r2 for sector G and sbs_na_con_r2 for Construction, dataset sbs_na_1a_se_r2
for sectors H to N. Notice that business statistics are unavailable for sectors classified O to
T. This gives rise to differences between aggregate and firm-size level analysis regarding
sector Services as for the sector coverage.

From the Eurostat databank, we opted for the (cid:16)Employees domestic concept(cid:17) statistics
which cover employees hired by (cid:16)resident production units,(cid:17) excluding self-employees. The
choice is motivated by Orbis providing employment information mainly on incorporated
economic units and hardly any for self-employees and non-incorporated enterprises.

2.2 Representativeness and Final Sample

We conduct an analysis of employment representativeness of Orbis for all EU28 Member
States. Figures and analysis of all other EU-28 Member States are reported in Appendix 5.
The analysis is split into four components. First, we show an overview of the employment
data in Orbis, where we check the consolidation level of the reports and the impact of
the imputation procedure. Second, the representativeness by levels and dynamics. Third,
the representativeness by size category after weighted schemes are applied. Fourth, the
representativeness by levels of sub-industries. From our analysis, we conclude that 20
out of the 28 EU member states report appropriate representativeness of employment.
Based on our representativeness analysis of Orbis employment data, we retained 20 out of
the 28 EU Member States: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark,
Estonia, Finland, France, Germany, Hungary, Italy, Lithuania, Latvia, Portugal, Romania,
Spain, Sweden, Slovenia and Slovakia.

Within the aforementioned countries, we further investigate Manufacturing and Services
as two separate broad sectors. The European system of economic classification NACE
(Revision 2) defines Manufacturing with its Section C, Divisions (two-digit codes) 10 to 33.
Services is defined with Sections F-Construction, G-Wholesale and Retail, H-Transportation,
I-Accommodation and Food, J-Info-communication, L-Real Estate Service, M-Professional

51When firm starts and management is hired first, then workers firm starts with high average wage and low
number of employees before growing. When firm dies, it is likely that there is only a small employment by the
end of the year but firm still pays obligations to its past employees.

38

Services and N-Administrative Services. We omitted economic activities such as Health
care, Education, Energy, Defences and Public Administration where typically states and local
authorities are responsible for a significant share of employment through non-incorporated
public entities.

Eventually, our sample of employment covers the 12-year periods 2004-2015 and include
2.9 million firms in 20 EU countries. On average, our sample includes 70 million employees
yearly, which are comparable to the 75 million employees reported in Eurostat. Table 1
provides a description of the sample by country. It reports the starting and end year in the
sample, the number of firms and the number of employees.

Table 1: Sample Description by Country

Start
Year

2005
2004
2004
2004
2006
2004
2004
2004
2004
2004
2004
2009
2004
2004
2004
2006
2004
2004
2010
2008

End
Year

Number
of firms

Employment

Weighted
Employment

Eurostat
SBS Data

2015
2015
2015
2014
2015
2015
2015
2015
2015
2013
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015

43,074
110,610
154,629
79,619
172,073
25,346
28,937
466,922
54,679
331,655
56,921
172,777
347,298
8,697
56,683
242,382
275,856
166,244
44,034
65,612

1,821,644
1,857,651
1,678,832
2,573,964
12,578,020
870,435
332,472
587,196
6,702,317
1,152,704
867,615
1,830,739
9,069,780
527,902
510,050
2,071,968
3,030,665
1,858,450
406,285
968,681

2,334,623
2,310,107
1,738,736
3,282,646
18,473,000
1,221,897
353,657
1,607,084
9,463,717
1,298,459
997,512
2,188,795
13,468,710
719,470
523,619
2,622,043
3,262,393
2,685,795
490,107
1,208,764

2,410,574
2,344,009
1,570,325
3,093,918
24,056,800
1,485,724
335,356
1,761,638
9,850,826
1,327,349
896,970
2,134,193
12,439,286
737,364
509,760
2,647,881
3,304,165
2,726,629
522,143
1,137,574

Code

AT
BE
BG
CZ
DE
DK
EE
ES
FI
FR
HR
HU
IT
LT
LV
PT
RO
SE
SI
SK

Austria
Belgium
Bulgaria
Czech Republic
Germany
Denmark
Estonia
Spain
Finland
France
Croatia
Hungary
Italy
Lithuania
Latvia
Portugal
Romania
Sweden
Slovenia
Slovakia

Note: This table reports the yearly average number of firms, employment and weighted employment in our sample
by country. For instance, Austria (code AT) is represented between years 2005 and 2015, reporting 43,074 firms
on average. Average weighted number of employees is 1,255,557; Eurostat number of firms is 2,410,574.

2.3 Definitions and Methodology

In this section, we define the main variables that we use in the empirical analysis. We also
address the different options and the reasons why we opt for one or another. As a general
rule, we try and construct the variables as closely as possible to the seminal works in the
field of job creation, especially Davis and Haltiwanger (1996), Haltiwanger et al. (2013) and
Criscuolo et al. (2014). For convenience, we define variables with mathematical formulas
in Appendix 5.

Size of the Firm

The size of the firm is defined in number of employees, with no restrictions in terms of
assets and sales. In Orbis, the number of employees is the number of full-time equivalent
employees at the fiscal year closing date. Given that closing dates fall to a large extent on
December 31, we assume that the employment figures apply to the calendar year.

39

As described by Davis and Haltiwanger (1996), the way size is defined may have con-
sequences on estimates (see Chapter ). Consistently with Davis and Haltiwanger (1996),
we thus define two sizes of the firm, namely base size and current size. The base size is
the number of employees in the year it is reported in Orbis. Current size instead is the
average between the prior and the current years. For instance, the current size of firm f in
2010 is the average number between the number of employees held by f in 2009 and the
number of employees held by f in 2010.

(cid:21) Base Size:

Number of employees at the firm in a year.

(cid:21) Current Size:

Average number of employees at the firm between prior and current years.

We classify firms in four categories according to their size (cid:21) micro, small, medium and large
firms. Our classification follows the official European size classification system, except that
it ignores assets and sales criteria.

Firms' size group categories:

(cid:21) Micro firm: 0 to 9 employees;
(cid:21) Small firm: 10 to 49 employees;
(cid:21) Medium firm: 50 to 249 employees;
(cid:21) Large firm: 250 employees and above.

Age of the Firm

We define the age of the firm the number of years since the firm was incorporated. We
set Age to 0 in the year prior to the incorporation date, and to 1 at the end of the year of
incorporation date. The date of incorporation is provided by Orbis.

(cid:21) Age: Number of years since the date of incorporation.

Though, the date of incorporation may be missing. In this case, Age equals the number of
years since the first appearance in Orbis, including the years prior to our sample period.52

We construct age categories consistently with Criscuolo et al. (2014).

Firms' age categories:

(cid:21) Start-up firm: 0 to 2 years old;
(cid:21) Young firm: 3 to 5 years old;
(cid:21) Mature firm: 6 to 10 years old;
(cid:21) Old firm: more than 10 years old.

In some sections of our study, we simplify the classification into two categories; in this
case, (cid:16)Young(cid:17) firms are 0 to 5 years old and (cid:16)Old(cid:17) firms are 6 years and older.

Job Flows at the Firm's Level

Job flows produced by a firm encompasses any entry and exit of employees in the firm;
entries constitute new hiring, and exits typically are layoffs and resignations. In a year, a

52JRC's subscription to Orbis has no time period limitations and some firms report in years preceding the first year
in our sample. The method is consistent with studies based on Census Bureau's Longitudinal Business Database,
since the latter reports no date of incorporation; see, e.g, Davis et al. (1996a) and Haltiwanger et al. (2013).

40

firm's job destruction is the sum of all exits and its job creation is the sum of all entries. The
net job creation is equal to the difference between the job creation and the job destruction.

Notice that the Orbis dataset reports only the number of full-time equivalent employees
in a year, so that firm-level job destruction and creation are unobserved and the net job
creation is computed as the change in the number of employees between two years.53

Finally, we calculate the net job creation rate as the ratio of the net job creation of the
firm to its size; unless specified otherwise, in our study the job creation rate employs the
current size in the denominator. Notice that by construction the net job creation rate is the
employment growth rate of the firm.

Net job creation at the firm's level:

(cid:21) Net job creation:

Firm's job creation minus firm's job destruction; it equals the change in the number
of employees between two years.

(cid:21) Net job creation rate:

Firm's job creation relative to its current size.

Aggregate Job Flows

While job creation and destruction cannot be calculated at the firm level with Orbis, respect-
ive estimates are computed for group categories of firms. The job creation [destruction] of
a set of firms, say, the small firms, equals the sum of the net job creation of all small firms
with positive [negative] net job creation. The net job creation of a group category of firms
is the sum of the net job creation of all the firms in this group. The job flows at the group
category level are the aggregate job flows.

Aggregate job flows:

(cid:21) Aggregate job creation:

Sum of all firms' net job creation whenever the latter is positive.

(cid:21) Aggregate job destruction:

Sum of all firms' net job creation whenever the latter is negative.

(cid:21) Aggregate net job creation:

Difference between the aggregate job creation and the aggregate job destruction; it
equals the sum of all firms' net job creation.

Contributions to Employment

We are interested in the contribution of a set of firms to the total job flows within, e.g.,
the economy. To assess the magnitude of individual contribution, we define the country-
year total employment as the sum of all employees in our sample in the country and in a
year. Unless stated otherwise, the total employment is the current size of employment of
the economy which equals the average total employment in our sample between prior and
current years.

53It is worth noting that the Orbis dataset is similar to the databanks published by Dun & Bradstreet (D&B).
Instead, the Census Bureau's Longitudinal Business Database (LBD) does provide job destruction and creation
at the firm level. The reason for such a difference is due to the data source: Orbis and D&B's databanks are
constructed upon firm-level reports, while the LBD is built upon individual employees Internal Revenue Service
(IRS) statistics. D&B datasets are used by, e.g., Birch (1987) and Neumark et al. (2011); the LBD is used by,
e.g., Davis et al. (1996b) and Haltiwanger et al. (2013).

41

(cid:21) Job creation contribution:

The job creation of a group category of firms relative to the total employment.

(cid:21) Job destruction contribution:

The job destruction of a group category of firms relative to the total employment.

(cid:21) Net job creation contribution:

The net job creation of a group category of firms relative to the total employment.

2.3.1 Conclusions

In this chapter we detail the sampling and methodological strategy. The firm-level data are
obtained from Orbis, a firm-level databank issued by Moody's Bureau van Dijk. We extract
all non-financial corporations in the sample which report employment in unconsolidated
accounts and are registered in one of the 28 EU Member States. We treat the sample by
imputing missing values and picking outliers. Also, we compare the data with Eurostat
employment aggregate tables in order to assess the representativeness of the dataset. We
apply weighting schemes at country, year, sector, and size-level, so to match the aggregate
employment in Orbis. Consistently with our assessment of the quality of the matching and
the weights we applied, we retain 20 out the 28 Member States.

The final sample contains 2.9 million firms registered in the following countries: Austria,
Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany,
Hungary, Italy, Lithuania, Latvia, Portugal, Romania, Spain, Sweden, Slovenia and Slovakia.
Total employment after weighting schemes are applied amount to ca. 70 million to be
compared with 75 million employees in Eurostat. The sample is relatively well diversified
between country size and EU entry periods.

42

Chapter 3:

Empirical Results

The next sections report our empirical analysis. The study is based on an exceptionally large
micro-level employment data of firms in the European Union described in Chapter 1.3.5.
The data is obtained from Moody's Orbis which has recently gained fame among policy-
makers for its extensive coverage of firms worldwide. Though, given that Orbis database
consists in a patchwork of databases stemming from a wide number of providers, its em-
ployment representativeness may be an issue for some countries. Therefore, we follow our
prior representativeness analysis reported in Chapter 1.3.5, and retain those countries in
our sample whose employment representativeness is adequate. The final sample includes
2.9 million firms located in 20 out of the 28 EU Member States in the period 2004-2015.
The empirical analysis is split into employment, job flows and contributions of firms in
various categories. The study emphasises the traditional debate between age and size
characteristics of job creating firms, but also investigates additional characteristics such
as ownership of firms and their location. Notice that official statistics such Eurostat the
results of employment levels by size are comparable to those of Eurostat, the contribution
of our work is to look at employment and job flows for a wide range of firms characteristics
including age.

The analysis regarding employment by size shows that, even though the incidence of SMEs
in our sample (cid:21) defined as less than 250 employees (cid:21) amounts to nearly 99 percent, their
share in employment is substantially lower at ca. 65 percent. Further, while the incidence
of SMEs hardly varies across countries, their employment share indeed does, suggesting
non-trivial disparities among EU Member States in terms of industrial organization. The
phenomenon is consistent with official statistics. Instead, the incidence of firms by age
category are in line with their respective share of employment. We provide further insights
about start-up companies (0-2 years old) and find that their share varies substantially
across countries between 5 percent and 20 percent (four times as much). There seems
to be a gap chiefly between old and new EU Member States, the latter reporting higher
start-up shares. Overall, we observe a decline in the share of start-ups in EU countries (cid:21)
not in all countries, though.

The analysis of job flows (cid:21) namely job creation, job destruction and net job creation (cid:21) shows
that, even though job creation and destruction rates vary substantially across EU Member
States, the difference between job creation and job destruction within the same country
is relatively thin. The average yearly net job creation rate varies between -3 percent in
Spain to +2 percent in Lithuania. Further, the Manufacturing sector exhibits far lower job
creation and destruction rates than Services in all countries. In our sample, the contribution
of SMEs to job flows is proportionately greater than their share in employment (cid:21) in both
Manufacturing and Services. Taking a closer look, we observe that a large share of the job
creation stems from the young SMEs. Actually, the majority of the job creation in Europe
is concentrated in a few high-growth young SMEs.

Further insights show that ownership plays a role in the job flows; firms that are part of
conglomerates contribute to job flows proportionately more than their share in employment.
Interestingly, firms owned by domestic parents constitute a significant driver of job flow
cycles. Besides, the analysis by location shows that young SMEs in urban areas tend to have
a share in the local job creation larger than their share in employment. This suggests that
beyond finance, matching skills abilities of economies play a role in job flows. Finally, we

43

note that newly established firms report the lowest productivity, exiting and shrinking firms
report similar productivity levels, and net job creating firms are those most productive.
Even though this proves European economies are well-functioning overall, the pronounced
observed disparity across countries deserves further analysis.

The rest of the chapter is organised as follows. The next section describes the data and
the main definitions. Section 1 looks at employment by age and size. Section 2 reports
the results about job flows. Section 3 investigates the contributions of firm categories.
Section 4 provides further insights about job creators. Finally, the last section concludes
and suggests further analysis.

Within these sections, we introduce (cid:16)Focus Sections(cid:17) which are aimed at drawing the at-
tention to specific topics. Focus sections include the analysis of start-ups (Section 1), the
regression fallacy at the EU aggregate level (Section 2) and high-growth firms (Section 3).

3.1 Employment

This section intends to highlight general stylized facts about employment across the European
countries. The distribution by firms size is similar to the distribution in official statistics pro-
duced by Eurostat. We then report the distribution by firms age. Finally, we focus on the
share of start-ups and highlight their decline in the EU.

3.1.1 Employment by size

Figure 12 reports the employment distribution in our sample for each country, by size
category in the number of firms (left-hand panel) and in the share of employment (right-
hand panel). The figures about firms size are comparable to the official statistics provided
by Eurostat. The left-hand panel of Figure 12 shows that more than 70 percent of the
firms are micro firms (cid:21) i.e., employ less than ten employees (cid:21) in every country. In several
countries (cid:21) e.g., in Sweden, Italy, Spain and Portugal (cid:21) the share of micro-firms amounts to
90 percent. The share of small firms is on average 12 percent, while the share of medium
size firms is 2 percent. Large firms represent less than 1 percent for any country in our
sample.

While small firms are numerous, their share of employment in the economy is considerably
smaller than their share the number of firms. As Figure 12 (left-hand panel) reveals that
while micro-firms were more than 70 percent of all firms population in any country, their
average share in employment turns to be less than 30 percent. While the average share
of small firms and medium size firms are 22 percent and 20 percent, respectively.
In
our sample of countries, we find that the share of micro-firms is the highest in aggregate
employment for Italy, Portugal and Estonia. At the same time, large firms employ on
average 30 percent of the workforce. Large firms hold the largest shares of employment
(above 40 percent) in France, Denmark, Finland and Germany.

44

Figure 12: Number of firms and employment shares by firm size

Note: This figure reports the share of each size category in the total number of firms (left-hand figure) and
their respective shares in total employment (right-hand figure) by country. The countries are ranked in a
decreasing order with respect to the largest size category (250+). Country codes are defined in Appendix
5.

3.1.2 Employment by age

Figure 13 reports the distribution of employment by age, and portrays the economy in a
different light. The left-hand panel shows the distribution of firms, and suggests that the
majority of firms are older than five years. However, we find considerable cross-country
variations. While on average 45 percent of all firms are old (10 years or older), the share
of old firms is over 50 percent in Belgium, Finland and Germany, and less than 35 percent
in Romania, Latvia and Bulgaria.54 Interestingly, the share of start-up firms (0-2 years old)
is about 10 percent; the highest start-up rates (above 15 percent) is in Bulgaria, Lithuania
and Latvia, and lowest rates is in Belgium and Spain (half as much, less than 7 percent).

Figure 13, right-hand panel, reports the employment share of the age categories. It shows
that the majority of the employment is held by old firms. While they constitute on average
45 percent of the firms, their average weight in aggregate employment is 70 percent. In
all countries, we observe that old firms' employment share is higher than their share in the
number of firms, and reciprocally for younger firms. The younger the age-category is, the
larger is the relative difference. While the share of mature firms in the number of firms
on average is 23 percent, their employment share is only 18 percent. The corresponding
figure for the youngest age class is 10 percent and 4 percent in employment share.

54Generally speaking, we find that in the new member states, formerly the transition economies, the average

firm is younger.

45

10090807060Share of firms (%)SEPTSIHUESSKITHRLVBEBGROFREECZFIDEATDKLTNumber of firms020406080100Share of employment (%)ITPTSEESSKHUSILVHREEBGCZBERODEATFIFRLTDKEmploymentMicro: 1-9Small: 10-49Medium: 50-249Large: 250+Figure 13: Shares in Number of Firms and Employment by Age Category

Note: This figure reports the share of each age category in the total number of firms (left-hand figure) and
their respective shares in total employment (right-hand figure) by country. The countries are ranked in a
decreasing order with respect to the youngest age category (Start-up: 0-2). Country codes are defined in
Appendix 5.

Figure 14 reports the share of employment by age splitting our sample into the Manufactur-
ing (left-hand panel) and Services (right-hand panel) sectors firms. We observe a similar
relative importance of old firms in employment in both sectors, even though their weight
is significantly higher in the Manufacturing sector. The share of employment of old firms
is on average 80 percent in the Manufacturing sector, and 66 percent in the Services sec-
tor. Services sector is thus more sensitive to young firms' employment, probably showing
higher easiness of entries.

In the next section, we focus on the start-ups since their characteristics convey relevant
information about the easiness to conduct business.

46

020406080100Share of firms (%)BEFIDEESSISEATDKHRITFRCZPTHUEELTSKROLVBGNumber of firms100804060Share of employment (%)BEFRDEDKESSIATHRFISECZPTLTEEHUITSKLVROBGEmploymentStartup: 0-2Young:3-5Mature:6-10Old:10+Figure 14: Shares in Manufacturing and Services Employment by Age

Note: Figure 14 shows the share of age classes in the share of employment by country in the Manufacturing
(left) and in the Services (right) sectors. The countries are ordered in decreasing order with respect to the
oldest age class within each sector. Country codes are defined in Appendix 5.

Focus: The Start-up Companies

On top of creating new jobs, start-up companies constitute an important source of business
renewal in an economy. Therefore, their share indicates the ability of an economy to renew
and encourage entrepreneurship. We dedicate this section to start-ups and analyse whether
countries display similar patterns.

We first look at the average size of start-ups (0-2 years old) and compare it with the size
of old firms (more than 10 years older). The size is likely to reflect the entry costs for new
firms and thus the entrepreneurship potential. For each country and sector in our sample,
Figure 15 exhibits the current size of start-ups (left columns) and old firms (right columns),
in the Manufacturing (left-hand panel) and the Services (right-hand panel) sectors.

Furthermore, substantial variations in the average size of start-ups in Manufacturing and
Services suggest that some countries may suffer from higher entry costs and potential
small entrants encounter disincentives. Interestingly, the average size of start-ups seems
uncorrelated with the average size of large firms, both in the Manufacturing and the Services
sectors.

For a more direct comparison of Manufacturing and Services sectors within a country, Figure
16 provides another perspective by picturing the ratio of the size of old firms to the size
of start-ups in Manufacturing and Services sectors. Each point represents the ratio in
Manufacturing (vertical axis) and Services (horizontal axis) sectors. The closer the point
are to the 45o red line, the closer the ratios are. Points below [above] the red line indicates
that old firms are relative larger than start-ups in the Services [Manufacturing] sectors.
For example, in Latvia (LV), the average old firm is nearly 8 times as large as the average
start-up in Manufacturing, 4.5 times as large in Services; the LV point indeed lies above
the 45o red line.

47

100804060Share of employment (%)BEDKFRSEESDEATSIHUPTCZHRITEELTROFISKLVBGManufacturing100804060Share of employment (%)DKBEFRDEESATSIFIHRPTCZLTSEEEHUROITSKLVBGServicesStartup: 0-2Young:3-5Mature:6-10Old:10+Figure 15: Average firm size grows with age

Note: This figure reports the average size of start-up (0 to 2 years old, left column) and old firms (11
years and older, right columns) firms by sector across countries. The countries are sorted decreasingly by
size of the old firms. Country codes are defined in Appendix 5.

Figure 16: Average size of start-ups compared to old firms

Note: This figure shows the size-ratio of old firms to old start-ups by sector. The point are equivalent to
the within sector size ratios calculated from Figure 15. Country codes are defined in Appendix 5.

All in all, Figure 16 depicts the following stylized facts:

(cid:21) In all countries, the average old firm is larger than the average start-up, both in

Manufacturing and Services sectors.

(cid:21) In most countries, the size gap between the average start-up and the average old firm

is larger in Manufacturing than in Services.

48

0204060Firm sizeDKLTFRATCZBGRODEFIHUBELVSKEEHRSISEITPTESStartups in ManufacturingOld firms in Manufacturing0204060Firm sizeDKLTFRATCZDEFILVBGROHRBEEESKESHUSIITPTSEStartups in ServicesOld firms in ServicesATBEBGCZDEDKEEESFIFRHRHUITLTLVPTROSESISK02468Manufacturing02468Services(cid:21) In each country, the size gaps between start-ups and old groups in Manufacturing and

Services are correlated.

Several studies suggest that in the recent years, especially in the aftermath of the Great
Recession, the economic activity of start-ups have declined; see, e.g., Decker et al. (2016),
Decker et al. (2017), Criscuolo et al. (2014). In order to investigate such pattern in the
EU Member States, Figure 17 compares the employment share of start-ups in the two
periods 2007-2009 and 2012-2014. The left-hand panel reports the share of start-ups
in the total number of firms, and the right-hand panel reports the share start-ups in the
total employment in our sample. We draw a 45o red line that facilitates the comparison
of statistics over time: countries below [above] the 45o line indicates that the share of
start-ups decreased [increased].

Figure 17: The Declining Share of Startups

Note: This figure reports the share of start-up firms in the period 2007-2009 (horizontal axis) and 2012-
2014 (vertical axis). The left-hand panel shows the share of startups in the total number of firms; Right-
hand panel shows the share of startups in total employment. Country codes are defined in Appendix
5.

The results suggest that for a majority of countries the share of start-ups declined or was
unchanged. The largest decline is experienced by Slovakia, Denmark, Romania, Lithuania
and Bulgaria. For instance, Bulgaria reported nearly 10 percent of employment in start-ups
in the period 2007-2009, and 8 percent in the period 2012-2014; this amounts to a 20
percent decline. On the other end, six countries experienced an increase in the share of
start-ups, namely Austria, Croatia, Finland, Italy, Latvia and Sweden. In Italy, the ratio of
start-ups increased from 5 percent of employment ca. 7 percent, amounting to a substantial
40 percent increase.

49

ATBEBGCZDEDKEEESFIFRHRHUITLTLVPTROSESK0510152025share of start-up firms (2012-2014) %0510152025share of start-up firms (2007-2009) %ATBEBGCZDEDKEEESFIFRHRHUITLTLVPTROSESK0246810share employment in start-ups (2012-2014) %0246810share employment in start-ups (2007-2009) %We observe that those countries with the largest old-to-startup firm size ratios such as
Denmark, Lithuania and France in Manufacturing, are among those countries experiencing
the largest decline in start-ups. Reciprocally, countries with the lowest ratios, such as
Spain, Finland, and Italy, have hardly experienced any decline in the share of start-ups (cid:21)
even an increase in the case of Italy.

Therefore, there seems to be a link between the ratio of old-to-startup firm size ratio
and the decline in start-ups. A potential explanation is that larger old incumbents impose
greater obstacles to potential newcomers: the former hold larger economies of scales and
inflict higher competitive pressure on the latter. Should this be true, it implies that there
are features in the business environment such as incumbents' average size that are beyond
the institutional environment and may partially explain the decline in start-ups; this issue
probably deserves deeper analysis.

Overall, we show that start-ups have declined in Europe on average both in the share of
the number of firms and in the share of employment. Nevertheless, the patterns are very
heterogeneous across countries, and there are exceptions in Italy and Latvia. Obstacles to
newcomers stemming from the industrial organization of a country may partially explain
the decline.

3.2 The Essentials about Job Creation and Job Destruction

In this section we focus on the dynamics of job creation and destruction, as well as on the
resulting net job creation. We first look at general figures at country and sector levels.
Then, we look at age and size in detail. Interestingly, we report innovative evidence of the
regression fallacy at the EU aggregate level which is a look-alike to the evidence in the US
brought forward by Haltiwanger et al. (2013).

3.2.1 Job creation and destruction by country and sector

The average employment growth in our sample over the 2008-2014 period was -0.4 percent
in the Manufacturing and Services sector together. However, in our sample, countries had
varying experience of employment growth from a negative 3.9 percent in Spain to 2.2
percent in Lithuania. These growth rates come as a result as of the sum of job creation and
job destruction processes from the firm level.

Figure 18 decomposes aggregate employment growth rate into its two margins: job cre-
ation and job destruction. The aggregate job creation rate ranges between 4 percent and
14.4 percent, while the aggregate job destruction rate (shown in negatives) also covers
a similar range between 4.3 percent and 13.9 percent. This means that job creation and
destruction rates are significantly higher than the net job creation rates.

50

Figure 18: Job creation and destruction rates by Country and Sector

Note: This figure reports the rates of job creation, job destruction, and net job creation by country. The
columns are calculated as the weighted sum of firm-year values. Country codes are defined in Appendix 5.

Table 2 provides the cross-country correlations averages between job creation, job destruc-
tion, and the net job creation rates. It addresses the question as of which of job creation
and destruction are stronger drivers of the net job creation. With correlation rates equal or
below 0.31, Table 2 suggest that both job creation and destruction rates are poor predictors
of the net job creation rates overall. Nevertheless, job destruction seems to more strongly
drive the job net creation on average. Notice the strong correlation between job creation
and destruction rates (correlation of 0.90) which is a redundant pattern in our results:
whatever is the rate of job creation and destruction, both are very similar so that the net
job creation is far below.

Table 2: Cross-Country Correlation of Growth Rates

Net Job Creation

Job Creation

Job Creation
Job Destruction

0.14
-0.31

0.90

Figure 18 also decomposes job creation and destruction rates into components stemming
from manufacturing employment changes and to those from changes in the services sector.
The high level of destruction and job creation rates compared to employment growth rates
are also present across sectors. Also, sector level analysis shows that Services contribute
to a larger extent to job creation and destruction in all countries. The correlation between
creation and destruction persists within sectors even though there might be a lower cor-
relation which deserves further analysis; it may reflect the capacity of employees to shift
from one sector to another whenever the size of some sectors decreases.

3.2.2 Job creation and destruction by age and size

This section looks at sources of job creation and destruction by decomposing aggregate
job flows by firm characteristics. We look at the intersection of firm age and firm size

51

ESHRDKSKPTFIITLVBEFRCZDEHUBGEEROSISEATLT-15-10-5051015job flow rates (%)Creation rates - ManufacturingCreation rates - ServicesDestruction rates  - ManufacturingDestruction rates - ServicesNet growth rateby creating four categories: (1) young SME's which are start-ups and young firms with
less than 250 employees; (2) old SME's which are mature and old firms with less than
250 employees; (3) young large firms are start-ups and young firms with more than 249
employees (4) old and large firms are mature and old firms with more than 249 employees.

First, we look at the average share of these firm-groups in aggregate job creation and
destruction by country in Figure 19. The left-hand panel shows the job creation for each
of the four categories and points out that the majority of job-creation is done by SME's, on
average 86 percent. The average share of young SME's in job creation is 40 percent, while
that of the old SME's is 46 percent. In contrast, the share of large firms in job creation on
average is 1 percent for the young and 12 percent for the large-old firms.

Figure 19: Share in Job Creation and Job Destruction - Within-Country Averages

Note: This figure shows the share of size-age groups in job creation (left-hand panel) and job destruction
(right-hand panel) by countries. Country codes are defined in Appendix 5.

The share of SME's in job creation is relatively homogeneous across countries. The largest
shares are obtained by Latvia and Austria (above 92 percent); the lowest shares are ob-
tained in France and Slovakia (below 80 percent). Though, there is far more heterogeneity
in the share of young SMEs. In fact, young SMEs share amounts to more than half of job
creation in Portugal, Bulgaria and Latvia, while the share is slightly more than 20 percent
in Belgium, Germany, and France.

SMEs are responsible for most of the job destruction as well, though, to a lesser extent than
job creation: they contribute only to 77 percent of job destruction, which is substantially
less than 86 percent share in job creation. Overall, SMEs are net job creators.

Also, young SME's destroy far less than they create. On average, young SMEs contribution
to job destruction amounts circa 18 percent, while their contribution to job creation amounts
to 40 percent on average. Remarkably, the share of old SMEs is substantially higher: 58
percent of job destruction on average, compared to 46 percent of job creation.

52

020406080100ratio (%)BEDEFRDKSKCZFIEEESSEHUSIITROHRLTATPTBGLVShare in job creation020406080100ratio (%)FICZBEDKESDESKFRATSIEEROSELTHRPTITBGLVHUShare in job destructionYoung SME-sOld SME-sYoung large firmsOld large firmsFindings may be summarized as follows.

(cid:21) SMEs are the main contributors to both job creation and job destruction.

(cid:21) SMEs contributions is relatively even across countries, not the split between young

and old SMEs.

(cid:21) Young SMEs are large net job creation contributors. New member states are those

most benefiting from young SMEs job creation.

3.2.3 Creation and destruction relative to employment share

A simple assessment of job flow contributions consists in making a direct comparison with
employment shares for each of the size-age categories. Figure 20 reports the cross-country
average shares of job creation and destruction together with the average employment
shares for each of the age-size group categories. The figure is designed in such a fashion
that the comparison with Figure [15] reported in Criscuolo et al. (2014) is straightforward.

Figure 20: EU level job creation and destruction - Cross-country averages

Note: This figure reports the average share of firm age-size groups in employment, job destruction and job
creation across countries. For each category, the dark blue bars (left) are the share in employment; the
light blue bars (middle) are the share in job creation; orange bars (right) are the share in job destruction.
Young firms are aged 0 to 5 years old; old firms are above 5 years old.

We find that on average SMEs hold most of the employment (69.2 percent), out of which
old SME's hold 55.5 percent. Large firms hold only about 30 percent of employment and is
overwhelmingly held by old and large firms. This is in line with Figure 12.

Job destruction shows a similar distribution across firms as the stock of employment, with
an exception that young SMEs contribute to job destruction more than their employment
share, and old firms less. At the same time the distribution of job creation across firms
differs more from employment shares.

Young SMEs are responsible for a larger share of job creation than their employment share
in the economy. While they only hold 13.6 percent of the jobs on average, they are re-
sponsible for job creation, 24.5 percent, nearly twice as much. At the same time, both the

53

0204060Young firmsOld firmsYoung firmsOld firmsSME-sLarge firmsshare in employmentshare in destructionshare in creationpercentsgroups of old SME's and large firms take part to job creation to a lesser extent compared
to their importance in the overall employment.

The findings on how job flows shares relate to shares in employment stocks in the case of
SMEs is observable in the great majority of countries, not only on average.

Figure 21: SME's job creation and destruction relative to employment shares

(a) Young SME's

(b) Old SME's

Note: This figure plots the share of SME size-age groups in aggregate job flows against their share in
aggregate employment. Young are 0-5 years old firms, Old are 6 years and older firms. Each point
represents a firm-size group in a country. Red lines represent the 45o line. Country codes are defined in
Appendix 5.

Figure 21 contrasts creation and destruction shares to employment share on 4 scatterplots,
where each point represents a firm-size group in a country. The left-hand panel on young
SMEs shows that their share in job flows is higher than their share in employment, especially
in the case of job creation. In countries where employment share of young SMEs account
for circa 10 percent of employment in our sample, their share in job creation is more than
threefold (cid:21) e.g., Belgium and Germany. In countries where employment share of young
SMEs is above 20 percent, e.g., in Latvia and Bulgaria, their share in job creation is over 55
percent. We find this relationship to be linear, but we do not find that higher employment
share would result in increasingly more job creation.

The right-hand panel of Figure 21 looks at the old SME's. It shows that in this size-age
group employment and job destruction share are related linearly, close to a one-to-one
relationship. This is suggested by the finding that (blue) points are visible closely around
the 45o line. In contrast, in the case of job creation, we find that for all countries flow
shares are below the size-age group's share in employment. The relationship is, however,
non-linear. The job creation share of the old SME's for the countries with the highest
employment share in this group (over 60 percent) like Portugal, Spain and Estonia vary
between 36 percent and 56 percent. For the countries with the lowest shares, e.g., France,
Denmark and Finland the creation share matches employment share.

3.2.4 Entries and exits

We decompose job flows by firm size as well as firms' entry-exits with a view to observing
the incidence of entries and exits in job flows. The (cid:16)entry(cid:17) date equals the incorporation
date, and whenever the latter is missing, we set incorporation date to the closing date
of the earliest report in Orbis (see details about age calculation in Section 1.3.5). The

54

ATBEBGCZDEDKEEESFIFRHRHUITLTLVPTROSESISK0204060share in destruction  (%)010203040share in employment  (%)ATBEBGCZDEDKEEESFIFRHRHUITLTLVPTROSESISK0204060share in creation (%)010203040share in employment  (%)ATBEBGCZDEDKEEESFIFRHRHUITLTLVPTROSESISK3040506070share in destruction  (%)40506070share in employment  (%)ATBEBGCZDEDKEEESFIFRHRHUITLTLVPTROSESISK3040506070share in creation  (%)40506070share in employment  (%)(cid:16)exit(cid:17) date is defined as the last date of report of the firm and we require at least three
consecutive years without reports in Orbis.55 The shares of entry-exits in job flows are
reported in Figure 22. The left- and right-hand panels present, respectively, the share of
entries in job creation and the share of exits in job destruction.

The figure shows that entrants are responsible for an eighth of the job creation on average:
small and large entering firms provide 12 percent and 1.4 percent of the total job creation,
respectively. The share of small entrants is very heterogeneous, though. It ranges from
nearly 6 percent in, e.g., Belgium and Germany, to 20 percent in, e.g., Portugal, Latvia and
Croatia. Notice that small incumbent firms are responsible for 73 percent of job creation,
while large incumbent firms are responsible for 13 percent.

Instead, a larger share of job destruction is due to exits, 20 percent on average: 4.3
percent is due to exiting large firms, and 16.7 percent is due to exiting SMEs. The share
is thus substantially larger than creation both for SMEs and large firms. Yet, large firms
proportionately further contribute to job destruction than job creation.

Similarly to job creation, we also observe significant heterogeneity across countries in
the relative importance of exiting firms. In Estonia, Bulgaria and Romania, the share of
SME exits is less than 6 percent of the job destruction; in Austria, Portugal, Hungary and
Germany, exiting firms contribute to as much as 20 percent of job destruction. Incumbent
firms are responsible for the remaining share of job destruction, SME's in 60 percent and
large firms in 19 percent on average.

Figure 22: Contributions of entries and exits to job creation and destruction

Note: This figure reports the average share of entry and exit by firm size in job destruction and creation
across countries. Country codes are defined in Appendix 5.

Figure 22 provides quite some information about the contribution of entrants and exiters
across the European countries. On the one hand, countries with the largest contribution
of SME's to job destruction not necessarily report the largest share of SME's amongst job
creators (and reciprocally). On the other hand, the overall contribution of incumbents is
substantially heterogeneous across countries: incumbents produce between 60 percent

55It is worth noting that firms in distress and/or in bankruptcy procedure are not labeled as exited as long as

they report employment in Orbis.

55

020406080100%BEDEEECZDKSKFRSISEESROHULTFIITBGATPTLVHRShare in job creationEntry of SMEEntry of LargeIncumbent SMEIncumbents Large020406080100%EEBGROSECZLTSILVFIHRSKESDKFRITBEATHUPTDEShare in job destructionExit of SMEExit of largeIncumbent SMEIncumbents Largeand 70 percent of job destruction in countries like Germany, Hungary, Portugal and Austria,
but amounts to 90-95 percent of job destructions in countries like Estonia Bulgaria and
Romania.

Focus: Regression Fallacy using EU Aggregate Data.

Throughout this study, the net job creation rates are constructed using the current size of
firms instead of the base size.56 The rationale is that using the base size systematically
over-estimates the net job creation of smaller firms. An illustration of such a distortion
is that of a firm which experiences an exceptional one-time shock in the orders from a
customer and needs to employ new staff accordingly on a temporary basis. Because of
the temporary hiring of new staff, the firm switches up to a higher size category in the
sample in the first year, and switches back to the lower size category in the second year.
All in all, even though the firm did not really change its size category, the regression would
over-estimate the net job creation of the lower size category, and under-estimate that of
the upper size category. Taking the average between these two years smoothens out the
employment dip and prevents distortions in regression estimates.

The so-called regression fallacy was first described in the seminal work provided by Davis
et al. (1996b) which motivated the now widely used current size; Haltiwanger et al. (2013)
provides empirical evidence of the regression fallacy using US Manufacturing data as well as
the importance of controlling for firm's age. In this (cid:16)Focus Section,(cid:17) we produce innovative
results illustrating the phenomenon in Europe using aggregate employment data.

Figure 23: Firm size vs. firm growth in the EU economies.

(a) All firms

(b) Continuous firms

Note: This figure displays the relationship between net job creation (y-axis) and firm size (x-axis). The
left-hand Panel (a) reports the values for all firms; right-hand Panel B reports values for continuous firms,
where continuous firms are non-new entrants nor exiters. The Base Year curves use the previous year
employment values of firms. The Current Size firms use the average between prior and current year size.

Figure 23 follows Haltiwanger et al. (2013) and reports the aggregate net job creation rates
by category (see Figure 3 in Chapter 1). We design four specifications. The first and second
specifications use the base size of the firm: firm size class is defined based on n(i,t), the
number of employees in firm i at time t, and firm's net job creation rate is defined by the
(cid:1) to n(i,t). Technically, we regress net job creation rates on size category
ratio (cid:0)n(i,t+1) − n(i,t)
dummies, where the largest size category is dropped, so that the estimated coefficient is
a comparison with the largest size category; regression estimates are weighted by size of
firm. The second specification adds firm age controls to this. Instead, the third and fourth

56As described in Section 3 and in Appendix 5, the base size is the prior year number of employees of the firm;

the current size is the average number of employees between the prior and current years.

56

-10-5051015growth rates (%)1-45-910-1920-4950-99100-249250-499500+Base year sizeCurrent (avg.) sizeBase year size with Age ControlsCurrent (avg.) size with Age Controls-10-5051015growth rates (%)1-45-910-1920-4950-99100-249250-499500+Base year sizeCurrent (avg.) sizeBase year size with Age ControlsCurrent (avg.) size with Age Controlsspecifications use the current size (see Section 3 for the details). The fourth specification
controls for the age of the firm. The left-hand panel uses the sample that includes entering
and exiting firms; the right-hand panel excludes entering and exiting firms. Figure 23 plots
the estimated coefficients of each of the specifications.

The results reported in Figure 23 based on our sample of European firms are very similar
to those reported by Haltiwanger et al. (2013) based on US firms. Should net job creation
rates use the base year, small firms would show larger growth rates; using the current size
corrects the distortion. Moreover, the relationship between firm size and net employment
growth is flattened if we control for the age of the firm.

Figure 23 provides the following results.

(cid:21) Comparing results from base-year regressions with and without age controls shows
that the negative relationship between firm-size and growth is mitigated by controlling
for age. This effect is more visible when we look at continuous firms only in the right-
hand panel of Figure 23.

(cid:21) Changing firm-size definition from base to current size reveals the importance of re-

gression fallacy: the negative relationship between size and growth fades out.

(cid:21) Comparing EU results with Haltiwanger et al. (2013), we find similar patterns with US

figures.

57

3.3 Contribution to the Total Net Job Creation

In this section, we look at whether age and size play a role in the ability of firms to become
a net job creator to the economy. Thus, we look into job creation and destruction of firms
of different size and age, entry and exit, and to what extent these contribute to the total
net job creation.

3.3.1 Contributions by size

This section analyses the contribution of size categories to job flows. For this purpose, we
aggregate contribution rates across the following firm categories: entrant SME's, entrant
large firms, incumbent SME's, incumbent Large firms, Exiting SME's and large firms. We
report all net job creation contributions in Figure 24 at the European Union level, in Man-
ufacturing (left-hand panel) and Services (right-hand panel) broad sectors. Contributions
are above [below] the horizontal axis if the net contribution is positive [negative], i.e., the
group category is a net job creator [destructor] overall.

Figure 24: Contribution by Size to the Total Net Job Creation Rate

(a) EU manufacturing

(b) EU services

Note: This figure decomposes aggregate employment growth rate calculated over the sample European
economies from 2007 to 2013 by sectors: NACE manufacturing sector (C) and service sectors. The
continuous and dashed lines are Eurostat (NAMA) employment growth rates at EU-28 level.

Figure 24 shows the following insights:

1. Incumbent SMEs play a major role in the net job creation of the EU economy. Both
in the Service and Manufacturing sectors the job destruction of incumbent SME's con-
tributed largely to the total employment growth rate.

2. The majority of the negative net job creation is due to SMEs exits.

3.3.2 Contributions by age

This section analyses the contribution of age categories to job flows. For this purpose,
we aggregate contribution rates across the following firm categories: new entrants, young
incumbents, young exiting firms, old incumbents and old exiting firms. We report all net
job creation contributions in Figure 25 at the European Union level (cid:21) notice that the figures

58

-6-4-202growth rates2007200820092010201120122013Entry of SMEEntry of Largeincumbent SMEIncumbent LargeExit of SMEExit of Largesample rateEurostat EU28 rate-6-4-20246growth rates2007200820092010201120122013Entry of SMEEntry of Largeincumbent SMEIncumbent LargeExit of SMEExit of Largesample rateEurostat EU28 rateare not average figures across countries (cid:21) using the 20 countries in our sample, in Manu-
facturing (left-hand panel) and Services (right-hand panel) broad sectors.57 Contributions
are above [below] the horizontal axis if the net contribution is positive [negative], i.e., the
group category is a net job creator [destructor] overall.

Figure 25: Contributions by Age to the Total Net job Creation

(a) EU manufacturing

(b) EU services

Note: This figure decomposes total net job creation rates calculated over the sample European economies
from 2007 to 2013 by sectors: NACE Manufacturing (C) and Services sectors. Eurostat (NAMA) rate
statistics is for EU-28 countries.

As for Manufacturing, we find the following results:

(cid:21) Employment has shrunk in most years.
(cid:21) In all years, new firms entries (0.56 percent) and young incumbent firms (0.9 per-

centage points) support job creation.

(cid:21) With the exception of two years, old firms (cid:21) both continuing and exiting (cid:21) contribute

to the aggregate net job creation negatively (average: -1.5 percent).

(cid:21) During the peak of the crisis in years 2009 and 2010, a large share of the fall in
employment not is due to job destruction among old incumbents, but also the lack of
job creation among young incumbents.

As for Services, we find the following results:

(cid:21) Young firms play a more prominent role in aggregate net job creation in Services than
in Manufacturing. Young incumbent firms contribution to aggregate net job creation
amounts to 0.8 percent, while entrants contribution amounts to 0.9 percent.

(cid:21) Exiting old firms contribute significantly negatively in every year (-1.9 percent). The

contribution of old incumbent firm varies considerably over time.

(cid:21) Old incumbents are strongly in line with business cycles: first, they substantially
contribute to job creation ahead of the crisis, then significantly contribute to job de-
struction at the peak of the crisis, but have finally recovered slowly (cid:21) they have not
reached back the pre-crisis levels, though.

(cid:21) The swing in old incumbents job creation patterns contrasts with young incumbents

which continuously create jobs.

57All the results at country-level in this section are available upon request.

59

-6-4-202growth rates2007200820092010201120122013entry of young young incumbent young exitold incumbentold exitsample rateEurostat EU28 rate-4-2024growth rates2007200820092010201120122013entry of young young incumbent young exitold incumbentold exitsample rateEurostat EU28 rateFocus: Contributions of the High-Growth Firms

There is evidence that a small number of firms contribute to a large share of job creation in
an economy (cid:21) the so-called High-Growth Firms (henceforth, HGFs). Therefore, it is relevant
to dedicate a (cid:16)Focus Section(cid:17) on HGFs contribution to job flows in Europe and identify
their characteristics. Consistently with the so-called Eurostat-OECD definitions (e.g., OECD
(2011)), we define high growth firms, firms with ten employees at the beginning of the
period and an annualised net job creation rate above 20 percent per annum over three
consecutive years.58

In order to illustrate the contribution of HGFs to the job flows in the economy, Figure 26
reports the share of jobs created and destroyed by firms as a function of their own net job
creation rates. We differentiate five categories of firms with positive net job creation rates
and five categories with negative net job creation rates.59 The category at the very right
end on the horizontal axis represents the HGFs category, i.e., firms that report 20 percent
yearly net job creation rate. At the other end (left-hand on the horizontal axis) we report
the firms with the lowest net job creation rate (less than -20 percent). Finally, we split the
sample into Manufacturing (left-hand panel) and Services (right-hand panel).

Figure 26: HGFs Contributions to the Aggregate Job Flows of the Economy

(a) EU manufacturing

(b) EU services

Note: This figure reports the contribution of firms to the total growth rate by net job creation rate.
Horizontal axis indicates the net job creation rate category. The vertical axis indicates the share of the
category in the total jobs created [destroyed] if the net job creation category is positive [negative]. The
left-hand panel (a) reports Manufacturing; the right-hand panel (b) reports Services. Numbers are cross-
country averages. For instance, Manufacturing firms that experience 20-50 percent net job creation rates,
contribute to nearly 55 percent of the total job creation in the economy. The main contributing category is
large and young firms; the second contributing category is small and young firms.

Figure 26 provides the following findings:

(cid:21) HGF's take up more than 50 percent of the job creation (among non-exiting firms) in
the case of EU Manufacturing. In the Services sector, the share is above 60 percent.

(cid:21) The share of job destruction is also concentrated among the firms with the largest
(negative terms) net job creation rates. Such firms, less than -20 percent net job

58Notice that the Commission regulation (EU) No. 439/2014 set the definition of high-growth enterprises as
follows: all enterprises with at least 10 employees in the beginning of their growth and having average annualised
growth in number of employees greater than 10 percent per annum, over a three year period. The main results
are unchanged with this definition.

59We use the same categories as Bravo-Biosca (2016) replicated Figure 4 in the Literature review to facilitate

an easier comparison.

60

010203040506070Share of jobs created/destroyed<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,2020+Large and OldLarge and YoungSmall and OldSmall and Young010203040506070Share of jobs created/destroyed<-20-20,-15-15,-10-10,-5-5,-1-1,11,55,1010,1515,2020+Large and OldLarge and YoungSmall and OldSmall and Youngcreation rates contribute to 50 percent of total job destruction in the case of Manufac-
turing, and more than 60 percent of job destruction in Services.

(cid:21) High growth firms are not only young and small.

In Manufacturing, while young-
small HGFs contribute to 17 percent of the total job creation in our sample, small-old
HGFs contribution amounts to 22 percent. In Services, figures are 24 percent and 27
percent, respectively. This prominence of small and old firms compared to previous
results is due to the exit of many young and small firms within the first three year.

(cid:21) Once we condition on the three year survival, as in Figure 26 the share of jobs cre-
ated by the small-and-young and the large-and-old firms are similar in the case of
Manufacturing, 22 and 24 percent respectively. In the case of the Services, small-
and-young are responsible for a larger share of job creation of 30 percent, while the
share of large-and-old firms in job creation is only 20 percent. In both sectors, the
largest share of job creation is carried out by the small-and old, nearly 50 percent.

Figure 27: Prevalence of High Growth Firms

Panel A: Manufacturing

(a) Share of HGF

(b) Share in job creation

Panel B: Services

(c) Share of HGF

(d) Share in job creation

Note: HGF firms are defined by over 20 percent average employment growth over three years. The sample
period covers years 2008 to 2015.

Figure 27 takes a closer look at HGFs and, besides depicting their shares in job creation
by age and sector, it provides information as of the number of firms. Panel A and B report

61

01020304050Share of HGF in category (%)Micro: 1-9Small: 10-49Medium: 50-249Large: 250+Startup: 0-2Young:3-5Mature:6-10Old:10+0246810Share in total job creation (%)Micro: 1-9Small: 10-49Medium: 50-249Large: 250+Startup: 0-2Young:3-5Mature:6-10Old:10+01020304050Share of HGF in category (%)Micro: 1-9Small: 10-49Medium: 50-249Large: 250+Startup: 0-2Young:3-5Mature:6-10Old:10+0246810Share in total job creation (%)Micro: 1-9Small: 10-49Medium: 50-249Large: 250+Startup: 0-2Young:3-5Mature:6-10Old:10+Manufacturing and Services figures, respectively. For each of the two panels, we provide
two sub-figures. The left-hand figure shows the share of HGF firms within a size and age
category (averaged across sample period and countries). For example, the first column
in Panel A left-hand figure shows that, on average, 23 percent of the micro-size start-ups
that continue to exist for three years are defined as HGFs. Right-hand figures show the
share in net job creation of HGF's by size and age category. For example, the first column
in Panel A, right-hand figure shows that, on average, micro-size start-ups that continue to
exist for three years are responsible for 2 percent of the net job creation. In the case of
Manufacturing, the sum of all the values of the columns is equal to 54 percent. This value is
the average share of HGFs in the net job creation by continuous firms over three years and
is in line with the value of the rightmost column in Figure 26, Panel (a). The corresponding
value in the case of service sectors is 66 percent.

Figure 27 provides the following findings.

(cid:21) Most HGFs are young, very few are old in all size categories.

(cid:21) Old HGFs contribute far more to job creation than younger firms.

(cid:21) The old-young gap in job creation is thinner in Services.

3.4 Further Insights and Identification of Obstacles

In this section, we explore the job creation beyond the age and size debate and investig-
ate specific characteristics of firms that may affect the job creation and destruction. We
first look at job creation and destruction of leveraged and non-leveraged firms. We then
investigate the contribution of domestically and foreign owned firms to job creation and
destruction. Then we look at urban and non-urban areas; job supply and demand in non-
urban areas being less intense may result in lower job creation and destruction. Finally, we
look at the link between job productivity levels and the net job creation by country.

3.4.1 Financial Leverage and Job Creation

There has been substantial policy efforts to promote access to external funding in order to
promote job creation. SMEs typically access external funding in the form of bank loans.
Some authors yet claim that banks have a stronger negative impact on employment in
firms in distress. Generally speaking, banks receive an interested are little interested in
growth prospects of a firm which add on risk to the loans. Therefore, we are interested in
looking at the impact of access to finance on job creation and destruction. In this section,
we define leverage the share of financial debt in total assets.60

Figure 28 reports the average job destruction and creation in our sample in function of
the leverage levels of the firm. It turns out that firms without leverage ((cid:16)no leverage(cid:17))
absorb the largest share of employment but their contribution to job destruction is close
to three times as much, and creation only twice as much. Firm with low leverage (below
median in our sample) report job destruction and creation far lower proportionately to
their employment shares. The leveraged firms seem to produce lower job flows than non-
leveraged firms.

60Financial debt is the sum of Orbis items LOAN and LTDB. Notice that data availability implies smaller sample.

62

Figure 28: Small Firms: Financial Leverage, Employment, Job Creation, and Job Destruction.

Note: This figure reports the employment (left-dark blue), job creation
(mid-orange) and job destruction (right-light blue) averages of young-
small firms.

In order to investigate the impact of the access to loans over business cycles we look at
net job creation at non-leveraged firms and leveraged firms by quartiles. The results are
shown in Figure 29.
In line with the previous figure, we observe that the no leverage
firms display a larger volatility than below median leverage firms. Therefore, the access to
finance seems to work as a buffer against economic shocks.

Figure 29: Net Job Creation by Financial Leverage

Note: Average net job creation by financial leverage quartiles. Zero: no debt in prior year; below p50:
below median of all indebted firms; p50-p75: between 50% and 75% quantiles; above p75: top quartile
(75% percentile); very high: financial leverage is above 75% of the total assets of the firm.

Finally, we explore whether accessing bank loans have some effect on employment growth.
We construct a sample of firms which access loans for the first time: first-time borrowing
firms in a year are firms reporting no loans in their balance sheet for three consecutive
years prior to reporting positive loan account. We compare the employment growth rates
of first-time borrowing firms with those of other firms by running an difference-in-difference

63

0.02.04.06.08percentsNo leverageBelow medianp50-p75Above p75share in employmentshare in creationshare in destruction-10-505average growth (percentage)200820092010201120122013Firm growth by financial leveragezerobelow median3rd quartiletop quartileOLS regression. The model controls for the size and age of the firm, its sector of activity,
the year and country of registration. We call the last year with zero leverage - year 0. The
year in which the loans are contracted - year +1. We look at the coefficients obtained from
the OLS estimates expressing differences across firm in employment growth from year 0
to year (-2), to year (-1), and from year 0 to (+1), to (+2) and (+3). Notice that, in order
to avoid special cases, we discard firms whose new debt-to-assets ratio is above median
(at sector-country-year level).61 The results are reported in Table 3 and the corresponding
Figure 30.

Table 3: Firm employment changes when firms access loans for the first time

(-2)

(-1)

(+1)

(+2)

(+3)

New Borrowing Firm -1.539*
[0.795]

-0.683
[0.553]

4.253***
[0.427]

6.111***
[0.613]

8.134***
[0.828]

Note: This table reports the coefficients obtained from the OLS estimates of the change in employment
(%) at the firm between year 0, the year prior to first time borrowing, and the previous and subsequent
years. Each coefficient is obtained from a separate regression and represent percentage point differences.
A firm is defined as first time borrower if the (cid:16)LOAN(cid:17) item reports zero for three consecutive years before
reporting positive values. The sample includes firms without loan and first-time borrower firms. The model
controls for firm size, age, sector, year and country. Standard errors are clustered by firm as shown in
brackets.

Figure 30: Net job creation around date: new borrowing firm.

Note: Employment growth of first time borrower compared to non leveraged firms. Growth is calculated
with respect to date 0 the year before the firm contracts loans for the first time. First time borrowers
are firms which did not contract any loan (leverage equals zero) in the three years prior to borrowings
appearing on its balance sheet resulting in lower than p50 of sectoral leverage.

The results provide evidence that newly-borrowing firms display higher employment growth
rates following the first time a loan is contracted. This difference in growth is statistic-
ally significant at the 1% level. At the year when the loan is contracted, the new-loan-

61In unreported, results we also look at new borrowing firms reporting leverage up to 75% quantile; results are

unchanged.

64

-50510Employment growth (%)-2-10123First time borrowerscontracting firms display growth rates about 4 percentage point higher than non-indebted
firms. The size difference between indebted and non-indebted firms remains even 2 years
after the loan is taken and increases over time. Bank loans thus support net job creation.62

Figure 31 provides a more detailed insight into the job creation of first time borrowers.
On the one hand, results analogous to those in Table 3 for below median leverage borrow-
ers are shown for Manufacturing and Service sectors separately. On the other hand, we
differentiate the growth responses to first-time loans by firm age.

Figure 31: Net job creation around date: new borrowing firm by age

Note: Employment growth of first time borrower compared to non leveraged firms, examined by age and
sector. Growth is calculated with respect to date 0 the year before the firm contracts loans for the first
time. First time borrowers are firms which did not contract any loan (leverage equals zero) in the three
years prior to borrowings appearing on its balance sheet resulting in lower than p50 of sectoral leverage.
We split the sample between Manufacturing and Services. Young are firms 5 years old or younger; Old
firms are above 5 years old. Results are obtained from regressions using all firms within each industry,
age specific results are obtained by interaction terms.

We find that in both sectors firms grow faster after borrowing than non-leveraged firms.
Two years after taking out the loan the Manufacturing firm is about 5 percentage points,
while a Service industry firm is more than 10 percentage points bigger in employment than
otherwise similar firms with zero leverage. Both in the case of Manufacturing and Service
sectors loan contracting results in higher employment growth in percentage points for young
than for old firms. We also find that in the case of young service sector firms future
borrowers exhibit higher (though not significant) employment growth than non leveraged
firms. In contrast, young future borrowers in the Manufacturing seem to show a decline in
growth just before they seek outside finance.

Overview

(cid:21) Net Job Creation of below-median leveraged firms is less volatile than non-leveraged.

Both follow business cycles.

(cid:21) Leveraged and non-leveraged old-small firms display similar job flows; far different

for young-small firms.

(cid:21) Unleveraged young-small firms seems to display larger gaps between job creation and

destruction rates. Needs further analysis.

62It is important to point out that comparing growth with respect to time (t) means that a negative growth

differences in time t-1 means higher growth - equivalent to an employment growth from a lower base.

65

-5051015growth %-1012a) Manufacturing-5051015growth %-1012b) Services (F-N)All firmsOld firmsYoung firms(cid:21) Access to loans for the first time is preceded by growth. Low leverage firms continue

growth: +8% after two years; not those highly leveraged.

3.4.2 Incidence of corporate independence in job flows

This section investigates the incidence of corporate independence in job creation and de-
struction, where corporate independence distinguishes between independent firms and sub-
sidiaries. In the literature, there is evidence that large firms tend to reallocate jobs more
easily among subsidiaries so that they may play a larger role in job creation and destruction
than independent firms proportionately to their employment shares. The independence of
a firm is identified in our sample thanks to the ownership information provided by the Orbis
dataset.63 Also, in order to identify the incidence of foreign ownership, we split the group
of subsidiaries between those owned by domestic GUOs (domestic subsidiary) and those
owned by foreign GUOs (foreign owned).

Figure 32: Incidence of Ownership in Job Flows

Note: This figure reports the job creation and destruction, by age, size, and dependence characteristics. In
order to define the dependence characteristics, we use the information about the Global Ultimate Owners
(GUO) provided by Orbis: the GUO of a firm is its last corporate owner in the chain of ownership, that holds
more than 50 percent of ownership in the firm. Independent firms are firms with without GUOs; Domestic
subsidiaries are firms with a domestic GUO; Foreign owned firms are firms with a foreign GUO.

Figure 32 reports the composition of employment stock, job creation and job destruction
by corporate independence category (cid:21) namely (cid:16)independent,(cid:17) (cid:16)domestic subsidiary(cid:17) and
(cid:16)foreign owned.(cid:17) To make the figure comparable to Figure 20, we create four Age-Size
firm categories that combine Young (0-5 years old) and Old (6 years and older) on the one
hand, and SME's (less than 250 employees) and large firms (250 or more employees) on
the other hand.

It turns out from Figure 32 that foreign owned small-young firms contribute to job creation
[destruction] proportionately more [less] than their employment share. Besides small-old

63Orbis reports the so-called Global Ultimate Ownership (GUO), which is the final owner in the chain of ownership,
and whose share of ownership (cid:21) whether directly in the firm or via other subsidiaries (cid:21) is above 50 percent. We
retained corporate ownership only, thus excluding individuals and funds.

66

01020304050employmentdestructioncreationemploymentdestructioncreationemploymentdestructioncreationemploymentdestructioncreationsmall and youngsmall and oldlarge and younglarge and oldindependent firmsdomestic-ownedforeign-owneddomestic subsidiaries are large net job creators, while small-old independent firms are net
destructors of jobs. Small-old independent firms contribute to job destruction far more
than their employment share, and far less in job creation; instead, small-old domestic and
foreign owned firms seem to break even. Finally, among large-old firms, all three categories
are negative net job creators.

One may yet wonder whether the results reported in Figure 32 are constant over time.
In fact, during the crisis, parent companies may have opted for reducing employment of
low-performing subsidiaries and favouring the high-performing ones; independent firms do
not have such options and thus are more likely to maintain employment despite the low
performance. For the same reasons, foreign parent companies have more options across
countries so that they may have destroyed more jobs in a country than domestic parents.
We thus apply the same classification to employment growth as in Figure 25 and report the
figures across years 2007-2013 in Figure 33.

The results suggest that old independent firms have been the largest net contributors to
job destruction in all years while young domestic subsidiaries have been the largest net
contributors to job creation in all years. These results apply both to the Manufacturing and
Services sectors. Besides, (cid:16)young independent(cid:17) are net job creators in all years, except in
2009 in the Manufacturing sector (cid:21) and fairly positive in the Services sector. Foreign owned
firms are small net contributors except in Manufacturing 2009 where old-foreign firms had
an impressive increase in the share of net job destruction.

Figure 33: Contribution to Job Creation and Destruction (cid:21) Ownership and Age

(a) EU manufacturing

(b) EU services

Note: This figure reports the net job creation of firms, by age, size, and ownership. Left-hand Panel (a)
reports the Manufacturing sector; right-hand Panel (b) reports the Services sector.

3.4.3 Urban vs. non-urban areas

Some authors argue that the magnitude of job flows relates to the matching skills capacities
of an economy in general and/or in specific business cycle phases (e.g., Kerr et al. (2015)).
In order to investigate this hypothesis, we focus on the job creation of SMEs across NUTS3
geographic areas within countries. We are interested in a comparison between urban and
non-urban areas where the labour force basis is more extensive and the business ecosystem
is more dense. We suspect that, thanks to the access to a broader labour force basis, urban
firms recruitment is easier so that they can substitute employees with equivalent or better
skills than in non-urban areas. The same is true for urban employees: the dense urban
business ecosystem gives them access to a wider range of employment options than non-
urban employees, which allows to switch to more suitable jobs more frequently.

67

-6-4-2022007200820092010201120122013young independent young domestic groupyoung foreign groupold independentold domestic groupold foreign groupsample rateEurostat EU28 rate-4-202462007200820092010201120122013young independent young domestic groupyoung foreign groupold independentold domestic groupold foreign groupsample rateEurostat EU28 rateThus, we compare the share of job creation of young SMEs with their share of total em-
ployment in our sample at NUTS-3 level.64 We look at young SMEs only because SMEs job
flows are likely to be more sensitive on the location than large firms. Moreover, an SME
is less likely to actually be a headquarter which may distort the results. In Figure 34, we
report the results of four selected countries (cid:21) Spain, Italy, France and Portugal (cid:21) which are
representative of our findings.

Figure 34: Job creation and employment shares of young SME's by location.

Note: This figure exhibits the share of young SME's in job creation (vertical axis) against their share in
employment (horizontal axis) across NUTS3 locations in four countries: Spain, Italy, France and Portugal.
The green line is the 45o line. The red line is the quadratic fitting line.

Results provide evidence that young SMEs hold proportionately higher shares in job creation
than their respective shares in employment in all locations. Moreover, the contribution of
young SMEs is larger in urban areas, e.g., Madrid and Barcelona in Spain, and Milan and
Rome in Italy. Therefore, the demographic density is a potential obstacle to job flows.

3.4.4 Productivity

In this section, we report additional results about the net job creation rates by productivity
level. Theory predicts that the higher the productivity of the firm is, the more likely it
creates job. We are interested not only in highlighting such a phenomenon but also to what
extent it may vary across countries.

Figure 35 reports the average productivity levels of four categories of firms, namely newly
established firms (Entrants), positive net job creators (Growing), negative net job creators
(Shrinking), and exiting firms (Exiters). The results are obtained from regression estimates
that control for years, sectors and current size of firms. The figure reports individual country
estimates (circles) as well as Eurostat EU20 averages (red triangles).

64The (cid:16)Nomenclature of Territorial Units for Statistics(cid:17) (NUTS) is a classification system of geographic areas.
NUTS-3 level is the smallest geographic administrative level before the municipalities. For instance, in Britain
NUTS-3 areas are the (cid:16)counties.(cid:17) Orbis reports the town of location for most of the firms in the database.

68

MadridBarcelonaAlicanteValencia012345creation share (%)0.511.52Share in employment (%)SpainTorinoMilanNapoliRoma01234creation share (%)0.511.52Share in employment (%)ItalyParisHauts-de-SeineSeine-Saint-DenisBouches-du-Rhône0.511.522.5creation share (%)0.2.4.6.8Share in employment (%)FranceAveGrande PortoTâmegaGrande Lisboa02468creation share (%)01234Share in employment (%)PortugalFigure 35: Net Job Creation and Productivity

Note: This figure compares labour productivity differences across firm-types within European economies in
the period 2007-2013. The productivity of firms is compared with the productivity of firms reporting no net
job creation (stable firms) represented by the red horizontal unity line. Shrinking firms are firms reporting
negative net job creation. Growing firms are firms reporting positive net job creation. Entrants are newly
established firms. Exiters are exiting firms. Circles are individual country averages. Red triangles are EU20
averages.

The figure suggests the following findings:

(cid:21) On average, entrants are less productive than other firms. Their productivity lies
between 33 percent and 88 percent of benchmark firms with no net job creation
(Stable firms).

(cid:21) The productivity of net job creation firms (Growing) is the highest, and is ca. 15
percent higher than the productivity of the benchmarking stable firms. In some coun-
tries, e.g., Slovakia, Czech Republic, growing firms are 30 percent more productive
than stable firms.

(cid:21) Firms with negative net job creation (Shrinking) are less productive than firms with
positive net job creation (Growing): productivity is about ca. 85 percent of the stable
firms.

(cid:21) Similarly to shrinking firms, exiting firms (exiters) are also less productive than stable
firms. The relative low productivity of shrinking and exiting firms can be considered a
suggestive evidence for productivity enhancing reallocation.65

65Notice that the computation of the effective aggregate gain in productivity would require a representativeness
analysis of the added value in Orbis similar to the one we conducted on employment. Such an assessment goes
beyond the scope of this study, but could be the subject of a subsequent study.

69

0.511.52entrantsgrowingshrinkingexiterscountry levelEU averageProductivity difference (wrt. stable firms)3.5 Conclusions

The empirical analysis of job creation reported in this chapter is based on an exceptionally
large micro-level employment data of firms in the European Union including 2.9 million
firms located in 20 out of the 28 EU Member States in the period 2004-2015. The data are
obtained from Moody's Bureau Van Dijk Orbis, and 20 countries were selected based on the
Orbis representativeness assessment in Chapter 1.3.5.

The results regarding employment by size and age show that nearly all firms in our sample
are SMEs (cid:21) less than 250 employees (cid:21) and their share in employment amounts to ca. 65
percent on average. The share varies substantially across countries, though, suggesting
non-trivial disparities in industrial organization among EU Member States. Besides, the
shares in net job creation of respective age categories are more aligned with the respective
incidence in employment. We provide further insights about start-up companies (0-2 years
old) and find that their share varies substantially across countries ranging between 5 and
20 percent. There seems to be a gap chiefly between old and new EU Member States: new
members tend to report higher start-up shares. Yet, we observe a decline in the share of
start-ups in Europe (cid:21) not in all countries, though.

The analysis of job flows (cid:21) i.e., job creation, job destruction and net job creation (cid:21) shows
that, even though job creation and destruction rates vary substantially across EU Member
States, the difference between job creation and job destruction within the same country
is relatively thin. As a result, the average yearly net job creation rate varies between -3
percent in Spain to +2 percent in Lithuania. Further, the Manufacturing sector exhibits
far lower job creation and destruction rates than Services in all countries. In our sample,
the SMEs contribution to job flows is proportionately greater than the share of workers
they employ, in both Manufacturing and Services. Taking a closer look, we observe that
a large share of SMEs job creation stems from the young SMEs (cid:21) up to five years old.
Finally, the majority of the job creation in Europe is concentrated in a few high-growth
young SMEs. This result supports the implementation of policies supporting entrepreneur-
ship among young and growth firms. Further analysis aimed to identify those firms with
growth potential and whether the latter indeed need support would have important policy
implications.

Further insights show that ownership play a role in the job flows. Firms that are part of
conglomerates contribute to job flows proportionately more than their shares in employ-
ment. Interestingly, firms owned by domestic parents are the main drivers of the job flows
cycles. Finally, we note that newly established firms are those reporting the lowest pro-
ductivity; exiting and shrinking firms have similar productivity; net job creating existing
firms are more productive than other firms. Nevertheless, there is substantial disparity
across countries which may require additional analysis.

70

Conclusions and Avenues of Research

This report contributes to the first objective of the Juncker Commission that aims to promote
job creation in the European Union. To our knowledge, we are the first to conduct the
analysis of job creation at the European level using firm-level data of employment. Our
sample is constructed using Moody's Bureau Van Dijk Orbis. Although Orbis utilisation has
substantially gained in popularity among scolars in the last decade, its data coverage and
quality remains uneven across countries. Thus, we gather employment information in the
period 2004-2015 of all non-financial companies registered in the EU-28 Member States,
and conduct our own assessment of the data representativeness and reliability by country.
Eventually, our sample gathers 2.9 million non-financial corporations registered in 20 out
of 28 EU Member States: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark,
Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Portugal, Romania,
Slovakia, Slovenia, Spain, and Sweden.

Our empirical analysis shows the following results and policy implications.

1. Small firms are the largest employer, and contribute to job creation proportionately
more than their employment share, unless we control for age. In fact, among small
firms, the young ones are those creating proportionately more jobs than their share
in employment. Official policies oriented towards entrepreneurship (cid:21) young and small
firms (cid:21) are thus consistent with empirics.

2. Start-up companies contribute to employment and job creation among countries, with
remarkable discrepancies. The observed decline of start-ups in most but not all Mem-
ber States may reflect decline in entrepreneurship liveliness yielding a renewed need
for policy interventions.

3. Among all firms, high-growth firms (HGFs) contribute to a large extent to the total job
creation (cid:21) HGFs are firms whose average net job creation rates are above 20 percent
per annum on a three-year period; in our sample, 60 percent of job creation comes
from high-growth firms. In this category of firms, many are young and small, but
small- and medium-sized mature firms contribute to job creation proportionately more.
Therefore, policy-makers need to address the financing obstacles that experienced
firms with strong growth potential are faced with by addressing the scale-up problems.
Financial markets participants are reluctant to finance risky projects requiring large
funding amounts.

4. Financial support plays a role: firms accessing bank loans for the first time continue
growing in the years following access to finance. Moreover, indebted firms display
less volatile net job creation rates in our sample period. There may yet be differences
among Member States.

5. Generally speaking, in nearly each of the country-level analyses, we find common fea-
tures among countries, nonetheless with major disparities. For instance, results are
uneven regarding the decline of start-up companies; the relative productivity of grow-
ing and shrinking firms vary substantially across countries too; the regional analysis
of job creation shows that young and small firms located in urban areas create more
jobs than those located in non-urban areas. All these results justify the conduct of
European level studies of job creation so to better calibrate the European policy tools.

The analysis suggests further policy-oriented avenues of research.

71

1. The Decline of the Start-ups

The ability of an economy to renew its eco-system is essential. The analysis of the
decline of start-ups has provided some insights and hints at potential policy interven-
tions. For instance, new entrants and small firms are likely to further benefit from
the EU support in economies and/or sectors where large firms dominate. Our ana-
lysis also shows substantial differences among Member States which require further
investigation.

2. The Role of Mature High-Growth Firms

Although most high-growth firms (HGFs) are young and small, a large share of em-
ployment created by HGFs stem from mature small- and medium-sized firms. The
gap between incidence and contribution may reflect the existence of obstacles at the
scale-up development phase of firms; small and medium firms with valuable exper-
ience miss growing opportunites because the financial market fails in financing this
market segment where both funding amounts and riskiness are high. This problem
has major implications for the European Union and deserves further investigation.

3. Access to Finance

We observed that job flows vary substantially across countries, not so much net job
flows. There is growing evidence that facilitated access to finance is a determinant
of firms' growth and job creation, but also job destruction in economic downturns.
Limited access to external finance may thus explain the discrepancies in job flows
rates and deserves further investigation.

4. Job Creation Monitoring Tool

The study is aimed at picturing job creation and related obstacles in Europe. Gener-
ally speaking, our results are consistent with those provided by official statistics offices
such as Eurostat and some of the key results in the literature. Further, they show sub-
stantial variations in corporate job flows in function of the characteristics of the firms
and the business cycles; the patterns would hardly be captured without micro firm
level data. Therefore, beyond the stylised facts highlighted by this study, job creation
requires reliable measurement tools aimed at monitoring refined policy interventions.
The reliability and comprehensiveness of our data would likely help constructing such
monitoring tools, to the extent that additional data becomes available so that we can
recalibrate the sample.

72

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List of Figures

Figure 1. Gibrat's Data for French Manufacturing Establishments in 1920 and 1921.
Figure 2. Share of employment by different firm size and by country. . . . . . . . . .
Figure 3. Relationship between net growth and firm size . . . . . . . . . . . . . . . .
Figure 4. Young firms contribute disproportionately to job creation . . . . . . . . . .
Figure 5. Distribution of job creation and destruction: the role of HGFs . . . . . . . .
Figure 6. Share of HGFs in job creation . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 7. Financial constraints hinder job creation . . . . . . . . . . . . . . . . . . . .
Figure 8. Contribution of job flows to aggregate productivity . . . . . . . . . . . . . .
Figure 9. Labour misallocation across countries . . . . . . . . . . . . . . . . . . . . .
Figure 10. The rise of the zombies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 11. Fall of labour share in European countries . . . . . . . . . . . . . . . . . . .
Figure 12. Number of firms and employment shares by firm size . . . . . . . . . . . .
Figure 13. Shares in Number of Firms and Employment by Age Category . . . . . . .
Figure 14. Shares in Manufacturing and Services Employment by Age . . . . . . . . .
Figure 15. Average firm size grows with age . . . . . . . . . . . . . . . . . . . . . . . .
Figure 16. Average size of start-ups compared to old firms
. . . . . . . . . . . . . . .
Figure 17. The Declining Share of Startups . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 18. Job creation and destruction rates by Country and Sector . . . . . . . . . .
Figure 19. Share in Job Creation and Job Destruction - Within-Country Averages . . .
Figure 20. EU level job creation and destruction - Cross-country averages . . . . . . .
Figure 21. SME's job creation and destruction relative to employment shares . . . . .
Figure 22. Contributions of entries and exits to job creation and destruction . . . . .
Figure 23. Firm size vs. firm growth in the EU economies. . . . . . . . . . . . . . . . .
Figure 24. Contribution by Size to the Total Net Job Creation Rate . . . . . . . . . . .
Figure 25. Contributions by Age to the Total Net job Creation . . . . . . . . . . . . . .
Figure 26. HGFs Contributions to the Aggregate Job Flows of the Economy . . . . . .
Figure 27. Prevalence of High Growth Firms . . . . . . . . . . . . . . . . . . . . . . . .
Figure 28. Small Firms: Financial Leverage, Employment, Job Creation, and Job De-
struction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 29. Net Job Creation by Financial Leverage . . . . . . . . . . . . . . . . . . . . .
Figure 30. Net job creation around date: new borrowing firm. . . . . . . . . . . . . . .
Figure 31. Net job creation around date: new borrowing firm by age . . . . . . . . . .
Figure 32. Incidence of Ownership in Job Flows . . . . . . . . . . . . . . . . . . . . . .
Figure 33. Contribution to Job Creation and Destruction (cid:21) Ownership and Age . . . .
Figure 34. Job creation and employment shares of young SME's by location.
. . . . .
Figure 35. Net Job Creation and Productivity . . . . . . . . . . . . . . . . . . . . . . . .
Figure 36. Employment in Orbis (cid:21) Austria . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 37. Representativeness by Levels and Dynamics (cid:21) Austria . . . . . . . . . . . .
Figure 38. Representativeness by Size (cid:21) Austria . . . . . . . . . . . . . . . . . . . . . .
Figure 39. Representativeness in Sectors (cid:21) Austria . . . . . . . . . . . . . . . . . . . .

9
12
13
15
18
19
22
25
28
30
31
45
46
47
48
48
49
51
52
53
54
55
56
58
59
60
61

63
63
64
65
66
67
68
69
85
85
87
89

80

List of Tables

39
Table 1. Sample Description by Country . . . . . . . . . . . . . . . . . . . . . . . . . .
51
Table 2. Cross-Country Correlation of Growth Rates . . . . . . . . . . . . . . . . . . .
64
Table 3. Firm employment changes when firms access loans for the first time . . . .
Table 4. Employment weights: Austria, NACE code 10, year 2015.
88
. . . . . . . . . .
Table 5. Representativeness Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Table 6. Weighting Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Table 7. List of Countries and Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

81

Appendix A:

Size-Age Country Level Evidence

This appendix summarises the main empirical evidence at single country level about the
link between age, size and job creation in EU-28.

Austria
Huber et al. (2017) find that young firms provide the largest contribution to net job creation.
Even though small firms job creation rates (cid:21) conditioned on survival (cid:21) are similar to those
of large firms, higher exit rates result in total negative net job creation. Moreover, the
Great Recession reduced job creation rates and enhanced the relative importance of small
firms.

Finland
Hohti (2000) shows that small establishments create and destroy relatively more jobs;
yet, the net job creation of small and large firms are similar. Fornaro and Luomaranta
(2015) document that small businesses have experienced higher growth rates on average.
Moreover, large firms are more pro-cyclical compared to small ones (cid:21) i.e., further reduce
(increase) jobs in bad (good) times; see also Deschryvere (2008) and Ilmakunnas and
Maliranta (2003).

Germany
Fackler et al. (2013a) and Fackler et al. (2013b) investigate the mortality (exit) of firms
between 1975 and 2006 by size and age, and find that mortality risk falls with establishment
size. Results show that determinants of exit differ substantially between young and mature,
a (cid:16)shadow of death(cid:17) turns up well before exit: the workforce becomes more skilled, more
female gender, and older. Earlier studies about Germany, see, e.g., Acs and Audretsch
(1989),Wagner (1995), Brüderl and Preisendörfer (2000), and Fuchs and Weyh (2010).

Greece
Voulgaris et al. (2015) investigate job flows during the crisis and compare the results with
those obtained in pre-crisis data (Voulgaris et al., 2005). They find that, in the period
1995(cid:21)99, the contribution of SMEs to net employment creation was important, with a
higher profile in the high-tech and capital-intensive manufacturing sectors; also, age is
negatively related to job creation. In contrast, in the recent period, although small firms
continue to substantially contribute to job creation, today a majority of the creation stems
from older firms.

Hungary
Earle and Telegdy (2011) analyse Hungarian data to assess a relationship between firm
characteristics and net job creation. They confirm the importance of handling regression
Interestingly,
fallacy and controlling for firm age when looking at effects of firm size.
controlling for firm age reverses the net job creation firm-size relationship, that is, large
firms create more jobs. They show that in the Great Recession job creation rates remain
unchanged, dynamics are driven by job destruction. Net growth decreased for exporters
and foreign owned firm, but not for state-owned companies.

Ireland
Although smaller firms make an important contribution to new job creation, the contribution
is driven by age rather than size (Lawless, 2014); this is consistent with Haltiwanger et al.
(2013). In addition, the documented negative relationship between firm size and growth is
valid for young firms only and fades out as firms are more mature.

82

Italy
There is a negative relationship between growth and size, and the relationship holds even
after controlling for firm's age. Also, there exists a positive relationship between growth
and export status, which fades away with age (Grazzi and Moschella, 2016).

The Netherlands:
Broersma and Gautier (1997) look at the manufacturing firms in the period 1978-1991,
and find higher job creation and destruction rates among small firms. Also, large firms
restructure labour force during economic downturns, unlike small firms, which restructure
independently of business cycles. Jobs created by small firms during downturns are more
likely to survive than those created by large firms.

Norway
Klette and Mathiassen (1996) look at the Norvegian manufacturing firms in the period
1976-1986. They find that small firms are net creators of jobs in the period and highlight
the long-run importance of entry-exits.

Portugal
Damas de Matos and Parent (2016) show that in Portugal job construction and destruction
rates are similar to those in the US (Haltiwanger et al. (2013)). Authors highlight the
relevance of the type of contracts: young firms, start-ups typically create jobs using fixed-
term contracts. At the same time, job destruction may be due to small but mature firms
shedding jobs with open-ended contract.

Sweden
Heyman et al. (2018) find that, like in the US, young firms are the most important for net
job creation in Sweden. Interestingly, the authors innovate by applying to productivity the
approach suggested by Davis et al. (1996a) for job creation.

United-Kingdom
Hijzen et al. (2010) provides evidence on job creation and destruction in all sectors between
1997 and 2008. Small firms account for a disproportionately large fraction of job flows
relative to their share of employment. Nevertheless, unlike most studies, jobs created by
small firms do not have lower survival rates. Notice that there is a wide range of studies
about job creation in the United Kingdom (e.g., Gallagher et al. (1991), and Barnes and
Haskel (2002)), but most of them focus on Manufacturing.

83

Appendix B:

Representativeness Analysis

In this Appendix section, we describe our analysis of employment representativeness of
Orbis. We describe the Austrian case in detail as an illustration of the interpretation. Figures
and analysis of all other EU-28 Member States are reported in subsequent sections of the
Appendix.66 The analysis is split into four components. First, we show an overview of
the employment data in Orbis, where we check the consolidation level of the reports and
the impact of the imputation procedure. Second, the representativeness by levels and
dynamics. Third, the representativeness by size category after weighted schemes are
applied. Fourth, the representativeness by levels of sub-industries.

The analysis is constructed around four components and associated figures to each of them.
Figures are reported in Appendix 5 and take the same number for each country, just like in
the case of Austria in the section here above. The first component of our representativeness
analysis is based on the Figure 1 and investigates whether or not limited financial accounts
constitute large shares of our sample and the impact of the application of imputation and
averaging techniques described in sections here above. The second component is based
on Figure 2 and looks at whether there is a structural break in 2009-2010 due to the
change of the sector classification system (cid:21) from NACE Rev. 1.2 to NACE Rev. 2. Figure
2 supports the third component of the analysis that looks at the levels and dynamics of
net job creation. We associate to this analysis macro-sectors Manufacturing and Services
(Figure 3) and there sub-sectors (Figure 4).

The Austrian case

The first step of the representativeness analysis consists in looking at the sample in general,
as well as at the effects of imputations and averaging. A set of four sub-figures are gathered
into Figure 36 for Austria in the period 2004-2015. The first sub-figure on the left shows the
distribution of employment in Orbis that Orbis indicate as (cid:16)estimated(cid:17) employment.67 The
second sub-figure reports the distribution of employment for which industry classification
is not indicated. The third sub-figure shows the distribution of employment reported into
consolidated, unconsolidated, and limited financial accounts. Finally, the fourth sub-figure
shows the sample following imputations and averaging.

In the case of Austria, we observe that unconsolidated accounts provide a vast majority of
the stock of employment in Orbis and that imputation has slight effects in the later years
while it has in the early years. This indicates a structural change in Orbis: this break
stems from substantial unavailability of employment before 2010. The feature is apparent
from the last right-hand subfigure 36, which reveals that firms are actually observed in the
sample in the pre-2010 years, only employment information is missing.

66In some exceptions, whenever investigations reveal generally poor quality of data, we show the main graphs

only. All graphs are available upon request.

67Orbis estimates are either directly provided by the data providers or computed by Orbis. For instance, some-
times firms are reported within a size category, without the actual number of employees; the firm then receives the
average number of employees in this category reported in other firms of this category. Incidence and construction
methods of estimates depends on data providers.

84

Figure 36: Employment in Orbis (cid:21) Austria

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

We then turn to a comparison of our sample with Eurostat. Figure 37 looks at macro
sectors Manufacturing and Services: Panel (a) compares official sector level employment
aggregates from Eurostat to aggregated obtained from Orbis (cid:21) red continuous line is Orbis
aggregate using imputation, and blue dashed line is Orbis using imputation and averaging.
In the case of Austria, we find that Orbis dataset can capture 60-80 percent of the official
employment statistic.

Figure 37: Representativeness by Levels and Dynamics (cid:21) Austria

(a) Levels

(b) Dynamics

The right-hand Panel (b) of Figure 37 provides valuable insights regarding the employment
dynamics of the macro-sectors Manufacturing and Services. It plots three pieces of inform-
ation in the graph: the aggregate net job creation rates obtained from Eurostat (dark blue
bars) and the summary of three aggregate net job creation rates that we compute from our
Orbis sample (red line and light blue band). We construct the aggregate net job creation
rate by summing individual firms' weighted net job creation rates.

Weighted Net Job Creation Ratei,t of firm i in year t is defined as follows:

Weighted Net Job Creation Rate(i,t) =

Change in Employment(i,t)
Size(i,t)

× Weight(i,t)

(1)

where the change in employment of firm i is the change in the number employees working
at firm i between t − 1 and t, size is firm i's Current Size (cid:21) i.e., the average size (cid:21) in number
of employees between t − 1 and t;68 Weighti,t is the average share of firm i's employment
in the total employment between year t − 1 and t.69

68Notice that Size is corrected using imputation techniques, without averaging.
69Put in formula, assuming n(i,t) is the number of employees hired by firm i at time t:

85

0.511.52millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employement0.511.52millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidetifiedStatistics for AT0123millions200420052006200720082009201020112012201320142015Data0123millions200420052006200720082009201020112012201320142015Imputed0123millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6millions200520102015Manufacturing (C)0.511.522.5millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.050.05.1growth200520102015Manufacturing (C)-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)Nevertheless, exit and entry firms may create some issues: by definition, the prior year
size of an entry firm is zero, and similarly the end of year size of an exiting firm is zero. We
thus compute three separate aggregate net job creation rates: (1) retains continuing firms,
and excludes entry and exit firms; (2) retains continuing and exit firms, and excludes entry
firms; (3) retains continuing and entry firms, excludes exit firms.

In Figure 37 Panel (b) the red continuous line represents the net job creation rates retaining
continuing firms only. The light blue coloured band reports the highest and lowest values
of the three net job creations rates. The purpose of the graph is to assess whether firm-
level net job creation rates represent the aggregate employment dynamics in Eurostat
at satisfactory levels. In the case of Austria, dynamics are well replicated; however, the
structural break in 2009-2010 limits the comparison, and entries and exits play a significant
role in explaining growth variance.

The third set of pictures reported in Figure 38 compares employment levels by firm size. We
split the sample between macro sectors Manufacturing and Services. We restrain Services
to sectors F-N only in the case of Services due to Eurostat business statistics limitations.
Also, in Manufacturing, we omit Tobacco Production (Nace 12) and Coke production (Nace
19) because the sectors are highly concentrated in most countries and for this reason,
official statistics often are unreported in Eurostat SBS (cid:21) because of confidentiality.

Manufacturing

Food, Drink, Tobacco: 10, 11, 12
Textile, Clothes, Leather: 13, 14, 15
Wood, Paper, Printing: 16, 17, 18
Coke, Chemicals, Drugs, Rubber: 19, 20, 21, 22

Ceramics, Metals: 23, 24, 25
Optics, Electronics, Machines: 26, 27, 28
Motors, Vehicles: 29, 30
Furniture and others: 31, 32, 33

Services

F - Construction
G - Wholesale and retail
H - Transportation
I - Accommodation and food
J - Info-communication
L - Real estate service

M - Professional services
N - Administrative services
P - Education
Q - Health and social work
R - Arts, entertainment
S - Other services

The first column of the Figure from the left depicts the Eurostat employment values, Manu-
facturing (ex. 12 and ex. 19) in the upper and Service sectors (F-N) in the lower panel. The
second column presents the corresponding statistics calculated from Orbis dataset. We use
the imputed employment plus imputed average statistics for this exercise to better account
for missing employment information in Orbis.

In addition, we provide figures for the employment level comparison at a more disaggreg-
ated level in Figures 39. For the manufacturing sectors we combine NACE rev. 2. 2-digit
sectors in accordance with Eurostat data availability:

Change in employment(i,t) = n(i,t) − n(i,t−1); Sizei,t =
(cid:19)

(cid:18)

n(i,t−1)
Total Employment(t−1)

+

n(i,t)
Total Employment(t)

Weighti,t = 1
2

n(i,t−1)+n(i,t)
2

;

.

86

Figure 38: Representativeness by Size (cid:21) Austria

(a) Manufacturing

(b) Services (F-N)

Analogously to the left panel of Figure 37, we provide three statistics: official employ-
ment statistics of the sector, aggregate firm level employment (imputed) and aggregate
firm employment calculated from Orbis, when all still missing employment information is
substituted for firm's average employment size. Figure 39 shows results for manufacturing
sectors70 and Figure 39 for Service sector branches at the letter aggregation level.

For Austria, we find that the share of employment we capture with Orbis data varies greatly
across sectors. The closest match is in the textile-clothes sector in Manufacturing, while
the poorest one is in Real estate services. In fact, in Real estate services, the Orbis dataset
provides higher total employment than Eurostat aggregates. This might be the result of
sector misclassification in the data.

The imputation of averages reveals that in the early-period there is a large share of missing
employment in all sectors. Nevertheless, imputing averages gives a fair approximation of
the share of missing employment, and may even account for some level changes (see, e.g.,
sectors such as Textile and Clothes, Motors and Vehicles).

70For brevity, we combine 2-digit NACE rev. 2. industries together

87

0.2.4.6Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6Millions20082009201020112012201320142015Orbis0.2.4.6Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.52Millions20082009201020112012201320142015Eurostat sbs.0.511.52Millions20082009201020112012201320142015Orbis0.511.52Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Table 4: Employment weights: Austria, NACE code 10, year 2015.

Firm Size

0-9
10-19
20-49
50-249
250 +

(1)
Eurostat SBS
employment

(2)
ORBIS
employment

(3)
SBS employment
ORBIS employment

(4)
Weights

8,635
8,758
13,374
21,404
22,657

1,515
2,796
7,545
18,501
16,813

5.70
3.13
1.77
1.16
1.35

5.70
3.13
1.77
1.00
1.00

Note: This table reports the weighting scheme of Austria, NACE Code 10, Year 2015, that maximises represent-
ativeness of Orbis data by firm size. Firm size categories follow Eurostat Structural Business Statistics (SBS).
Column (1) reports the size category in number of employees. Column (2) reports EUROSTAT SBS total number
of employees working at firms in the size category. Column (2) reports ORBIS total number of employees working
at firms in the size category. Column (3)) reports the ratio SBS Employment
ORBIS Employment , i.e., Column (2) to Column (3).
Column (4) reports the weight applied to the size category; by construction, the large and medium-sized firms
are systematically assigned weight=1.

88

Figure 39: Representativeness in Sectors (cid:21) Austria

(a) Manufacturing

(b) Services (F-N)

Regarding Services, Figure 39 suggests that representativeness is somewhat more of a
problem. A significant share of the Eurostat statistic is replicated by Orbis in Construction,
Professional Services and Info-Communication sectors. Comparing columns (3) and (4)
shows that both in the case of Manufacturing and Service sectors the small companies
are under-represented for Austria. A possible remedy for this is to calculate aggregate
employment statistics from Orbis using weights. In order to do this, we follow Gal (2013)

89

0.02.04.06.08millions200520072009201120132015Food, Drink, Tobacco0.01.02.03millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08millions200520072009201120132015Wood, Paper, Printing0.02.04.06millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.05.1.15millions200520072009201120132015Optics, Electronics,  Machines0.01.02.03.04millions200520072009201120132015Motors, Vehicles0.02.04.06.08millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2.25millions200520072009201120132015Construction0.2.4.6millions200520072009201120132015Wholesale and retail0.05.1.15.2millions200520072009201120132015Transportation0.05.1.15.2.25millions200520072009201120132015Accommodation and food0.02.04.06.08.1millions200520072009201120132015Info-communication0.02.04.06millions200520072009201120132015Real estate service0.05.1.15.2millions200520072009201120132015Professional services0.05.1.15.2.25millions200520072009201120132015Administrative services0.1.2.3millions200520072009201120132015 Education0.1.2.3.4millions200520072009201120132015Health and social work0.01.02.03.04.05millions200520072009201120132015Arts, entertainment0.02.04.06.08.1millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.and calculate weights at the two-digit sector and firm-size category level for each year. As
Gal (2013) we apply unity weights when SBS employment statistics is smaller than Orbis
counterpart. In addition, we also apply unity weights for firm-sizes above 50 employees to
avoid over-weighting very large firms. Table 4 displays the weighting scheme we obtained
for the Austrian Manufacturing in year 2015 as an illustration.

The results obtained by using weighting-scheme as those depicted in the third column of
Table 4. The results suggest that weighting can successfully replicate aggregate dynamics
and firm-size structure for Austria. Notice, however, that to achieve this result we used
imputed employment data in the pre-2010 years.

Short analysis of other EU28 countries

Belgium

The data contains ca. 2 million employees. We detect no structural break in the aggregate
data. Imputation and adding firm level averages insignificantly alter the data. Nearly all
employment data stem from unconsolidated accounts. The representativeness of employ-
ment is generally good in Belgium even at sub-sector levels. We find that the effect of
NACE code changes and possible misclassification affect Furniture and Real estate services
sub-sectors. As expected, Education and Health services sectors are insufficiently repres-
ented because of the employment is in public sector entities. Employment dynamics in the
aggregate Manufacturing and Services sectors are satisfactory. The firm size distribution is
well represented in both macro sectors. Weights are largest in Services for firms with less
than 10 employees.

Bulgaria

The data contains ca. 2.5-3 million employees. Consolidated accounts play little role, nor
do data imputation and averaging. The increasing statistics, especially its staggered nature
with jumps in 2008 and 2013 raises some suspicions. The 2008 change in the data can be
attributed to two factors. On the one hand as 2005-2008 employment is lower than official
statistics in most sectors, the discrepancy between Orbis and Eurostat number might come
from sample selection due to data collection by BvD. On the other hand, in the case of
some sector NACE rev 1 to NACE rev 2 change caused misclassification can also play a role.
We could not find any explanations to the 2013 increase in Orbis employment which does
not appear in Eurostat. The increase cannot be explained by the usual suspects: there is
no apparent firm population entering the dataset (excluding firms by size, legal status or
incorporation type does not change this finding). There was no reporting change in the
methodology how firms report employment71 nor apparent changes in the Orbis sample
by reporting type, legal entity or unexpected increase in the number of firms. As for the
comparison of employment levels by firm-size, in Bulgaria the employment distribution by
firm size is quite good in both investigated sectors.

Croatia

Orbis total employment stemming from unconsolidated amounts to nearly 0.9 million em-
ployees. The simple imputation affects the data slightly, the additional imputation of em-
ployment average is not necessary in the case of Croatia.
In the case of Croatia, the
Eurostat data at NACE rev. 2 classification is only available from 2008 on. As the pan-
els show, Eurostat data for the years 2005-2007 is missing, thus comparison cannot be

71We are grateful to Todor Davidkov from the Bulgarian Statistical Office for the help.

90

done. However, we find that the representativeness of Orbis for Croatia is very good for
the 2008-2015 period. Both for levels and dynamics of employment.

Figures show the diversity of representativeness across sub-sectors of Manufacturing and
Services. While representativeness is generally good, exceptions include Real Estate sectors
and service sectors from P to S. As for the comparison of employment levels by firm-size. In
Croatia the employment distribution by firm size is quite good in both investigated sectors.
There is some employment not captured in the size-class 1-9, however this can be attended
to by using weighting-schemes.

Cyprus

The data allow for identifying a corporate employment aggregate of about 80 thousand
at the maximum when including all consolidation levels available. This falls short official
statistics, since the employment in manufacturing and services should be above 0.23 million
at least for the end of the examined period. In general we find the data for Cyprus not
sufficiently good for national representativeness and therefore omit the presentation the
detailed sector level analysis. All figures are available upon request.

Therefore, we do not retain Cyprus in the final sample.

Czech Republic

The data identify total corporate employment amounting to ca. 3 million stemming from
unconsolidated accounts and 1 million from limited financial accounts. We observe sharp
increase in 2010 that suggests a change in sampling method / availability of data in Orbis.

Regarding the Czech data the employment statistic of the firm must be handled with cau-
tion. Both eye-balling the data and confirmation from BvD reveals that statistics are im-
puted by the original data source. This is that there are preferred firm size values in the
data representing mid-points of usual categories. For example, firms between 0-5 em-
ployees are given the value 3 and firms between sizes of 5-10 are given the value 7, and
15 for between 10-20. These are the most common values in the dataset, present for 77
percent of the non LF population. Other popular values include 38, 75, 150, 375, 750 and
so on. This implies that most likely researchers are able to assess the size of the firm with
acceptable accuracy but attempts to analyse firm employment dynamics most be carried
out with great caution, if at all. Note, that this is not equivalent to the share of estimated
employment discussed in the Appendix. In that case the estimation is performed by BvD
and not by the original data source.

The calculated aggregate from Orbis, keeping in mind the influx of LF companies to the
sample, captures the official employment mass in the case of Manufacturing between 2009-
2014. In the pre-2009 years data is less satisfactory due to selection into sample or sectoral
misclassification or both. As for 2015, the Orbis statistic falls short of the official statistics
due to missing latest reports. Similar problems are detected in Service sectors, however
the representativeness is much lower.

As for the comparison of employment levels by firm-size, employment distribution by firm
size is quite good in both investigated sectors, however we find that smaller firms are
have smaller share in aggregate employment as suggested by Eurostat SBS data. This is
especially so in the case of Service sectors.

Denmark

The number of employees obtained from unconsolidated accounts amounts to 0.9-1.1 mil-

91

lion; there is a single Limited Financial account reported in 2006. Imputation has slight
effects, while using averaging techniques augments employment for the later period of the
data. This suggests that there may be some attrition in employment reporting over time.
Orbis captures about 75-90 percent of Eurostat statistics in Manufacturing and less than 50
percent in Services. The general dynamics obtained from Orbis data captures the features
of Eurostat in both sectors.

The representativeness of Orbis increases over the period. A potential reason is the pre-
2008 misclassification of sectors, especially in the case of Motors, Vehicles and Textiles
sectors. Orbis data are unevenly representative of Eurostat across Services sub-sectors:
Construction, Info-Communication, Administrative Services, Retail and Wholesale sectors
are as representative as Manufacturing; Education, Health and Arts and Other Services are
poorly representative. Regarding the comparison of employment levels by firm-size, Euro-
stat SBS data contains a structural break in the employment statistics in the Manufacturing
data between 2010 and 2011 which hinders the comparison with our data. Second, our
weighting-scheme that unweights larger firms is insufficient to represent total employment
(cid:21) see especially Manufacturing in 2011 and later. Still, for consistency purposes, we opt
not to weight large firms 250+ employees.

Estonia

Nearly all employment is obtained from unconsolidated accounts and amounts to ca. 0.35-
0.40 million. The Orbis sample matches Eurostat both total employment and dynamics
relatively well, except at beginning and end of period. Discrepancies at start of period stem
from companies with sector classification problems in pre-NACE Rev. 2 years 2005-2008
and these firms hold a significant share of employment. Despite these shortcomings above,
the general employment dynamics calculated from Orbis, seems to be able to replicate the
aggregate employment dynamics of Services well and middle-period of the Manufacturing
sectors as the right panel. The employment distribution by firm size is fine in both macro
sectors Manufacturing and Services.

Finland

Employment obtained from unconsolidated accounts amounts to 1 million employees; Lim-
ited Financial accounts provide an additional 0.5 million employees. Orbis employment
represents about 90-95 percent of Eurostat Services (F-N) and Manufacturing employment.
Employment dynamics replicates rather well the official aggregate statistics. Disaggregated
employment statistics also paint a satisfactory picture. In Manufacturing, we find discrep-
ancies in the early years in Textile and Chemical and Motor vehicle sub-sectors, probably
due to NACE codes re-classification in 2008. At the same time, we find that Orbis counts
for more employment than necessary for professional services; we assume this is due to
NACE sector misclassification. In addition, in the case of Real estate services sub-sector
averaging employment for missing employment probably explains the overshooting.
In
fact, missing employment in these cases should possibly be treated as nil. Again, the rep-
resentativeness of Services sub-sectors P-S is low. Finally, employment distribution by firm
size is quite satisfactory both in Manufacturing and Services.

France

Employment obtained from unconsolidated accounts amounts to 6-7 million employees.
However, the captured employment depend on data cleaning procedures. Applying the
simple imputation technique can raise employment level by millions. This might call for a

92

careful treatment of the panel structure of firm level data for France. Employment level
calculated from Orbis captures Eurostat employment levels in Manufacturing well, especially
in the 2010-2014 period. Yet, micro-aggregated employment dynamics for Manufacturing
is not able to fully replicate the ever shrinking size dynamics of the French manufacturing.
Orbis data suggest positive growth for French Manufacturing for several years. However,
the inclusion of entries and exits give a nice replication of Eurostat. Despite employment
in Services obtained from Orbis is less than half of the Eurostat value, the employment
dynamics can be replicated sufficiently well.

Another important note regarding French Orbis data is that towards the end of the sample
period, in 2014 and in 2015, the level of calculated employment declines. This decline
is higher than the one observable in Eurostat figures in the case of Manufacturing, while
in the case of Services, Eurostat figures show an increase in employment rather than a
decline. The right panel of figure suggests that the decline is due to missing firms. The
decline in the Orbis data for 2014 and 2014 is also visible in sub-sectors in Manufacturing
and Services; e.g., Real Estate services.

Employment levels for separate service sectors, where we find large across sector het-
erogeneity in representativeness. Sectors P-S show very low representativeness for Orbis
data, while Transportation, Real Estate services data for Info-Communication is much bet-
ter. Employment distribution by firm-size is good in both Manufacturing and Services for
the mid-sized and larger firms; however a share of employment (30-50 percent of the
size-class) is missing from micro-firms (1-9 employees).

There are two issues here that are important to mention. First, for many sectors SBS
statistics are not available before 2010, for these years we applied the weighting scheme
of the first available year. Second, we apply the weighting scheme estimated in year 2014
to year 2015, so that the drop in employment level in 2015 is not corrected by the weights.

Germany

The data allow for identifying aggregate corporate employment of ca. 10-20 million em-
ployees. While most of the employment comes from unconsolidated accounts, there is a
considerable share from limited financial accounts as well (5-10 million), and the share var-
ies across years. Data imputation has little effect, however, employment reporting quality
considerably increases after 2010. Once, missing statistics are imputed with average we
can account for a stable mass of unconsolidated employment between 2007-2014.

As we compare Orbis with Eurostat, we see that Orbis captures about half the employ-
ment both in Manufacturing and Services. Despite the relatively low share of represented
employment levels, the aggregate dynamics are well reflected in Orbis, after weighting
schemes are applied, both in the case of manufacturing and service sectors. Actually, Man-
ufacturing is reasonably well represented over time. But we must keep in mind that within
each sub-sectors Orbis represents about 50-60 percent of Eurostat and the structural break
in the employment statistic availability in Orbis is detectable in each sector. The picture
is similar in Services, even though several Services sub-sectors report relatively low rep-
resentativeness. These are the usual sub-sectors (cid:21) i.e., Education and Health Sectors (cid:21)
but also a few additional sub-sectors (cid:21) e.g., Administrative Services and Professional Ser-
vices. In some sub-sectors, imputation results in non-matching dynamics compared with
Eurostat.

Regarding the firm-size representativeness, the employment distribution favours the larger
firms. While in the case of Manufacturing there is a substantial employment missing from

93

the group of large firms (250+), we capture less than a third of the employment with firms
hiring less than 20 employees. This issue is more visible when we look at sectors F and G
in Services, where the majority of missing employment is attributable to micro and small
firms. As a consequence in the case of Germany, only re-weighting even the largest of
firms could replicate the Eurostat employment data.

Greece

Employment obtained from unconsolidated accounts amounts to 0.80-0.85 million employ-
ees; no employment is obtained from Limited Financial accounts.
Imputation has little
effect on the sample. Imputing average augments aggregate employment statistic in the
pre-2009 years relatively more, suggesting enhanced reporting quality over time in Orbis.
Orbis sample nearly fully replicate Eurostat employment levels in Manufacturing, while in
Services Orbis captures 50 percent. Dynamics of our sample are close to those of Eurostat,
suggesting that appropriate weighting result in good representation.

Representativeness is satisfactory in Manufacturing except for Optics and Electronics. In
the latter cases, Orbis employment is larger which is likely due to sector mis-classification
of several firms; we could not identify these firms. The figures suggest good represent-
ativeness for many sectors within Services, especially in F to N sub-sectors. We do not
capture neither level nor dynamics in the case of Construction and Real Estate sectors.
Also we detect, possible mis-classification for the Info-communication sector.

As for the comparison of employment levels by firm-size, the employment distribution by
firm size is good in both investigated sectors for firms above 50 employees. However,
a significant share of employment is not captured by the Orbis data in the case of firms
with employee less than 20. This issue is there in both investigated sectors, implying that
weight used for these firms would be high: for firms between 1-9 employees the weight is
on average 26 across sectors, while for firms between 10-19 the average weight is 2.

Hungary

Unconsolidated employment data amount to about 2 million people. However, the size of
the sample varies over time. There seems to be sampling issues and missing employment
reports before 2008. While part of the imputation can help improve employment levels, the
effects of imputation vary across years and sectors; see the difference between Manufac-
turing and Services. A detailed look at the sub-sectors of the Manufacturing and Services
also indicate that data quality of Orbis for Hungary increases over time. For many sector,
e.g., Info-communication, Real Estate services, Administrative services the discrepancy
between statistics calculated from Orbis and Eurostat figures are minimal. Like in many
other countries, in the case of Hungary, we also detect the low representativeness of Orbis
in the sectors S to P. As for the comparison of employment levels by firm-size, Hungary the
employment distribution by firm size is quite good in both investigated sectors. Especially,
closer to the end of the examined period, where levels are more reliable.

Ireland

Employment data before 2006 is overwhelmingly coming from consolidated reports. The
Orbis data improve over time: in years 2006 to 2009, the data add an extra 0.7-0.8 million
employees as unconsolidated accounts increase in numbers, and after 2010, some firms
start reporting both unconsolidated and consolidated employment (cid:21) we retain unconsolid-
ated employment only. As a result, before 2010 statistics calculated from Orbis are only less
than half of the corresponding Eurostat figures, while the gap is close to zero after 2010.

94

Moreover, there seems to be some sampling change in 2006 and a structural change in the
reporting in 2010. Also, firm-size representativeness analysis is unsatisfactory. Because
of the underlying structural and sampling issues in the data. Applying weighting schemes
might bring misleading results. Weighting schemes increase the employment level attrib-
uted to micro and small firms and can influence aggregate dynamics accordingly.

All in all, the assessment of representativeness is nearly impossible and poor, so that we
exclude Irish firms from the final sample.

Italy

Unconsolidated employment data amount to 5-9 million employees with an increase between
2005 and 2015. Additionally, the data include reports on firms with limited financial ac-
counts in the last three years. Employment data imputation has some but decreasing effects
on the results. Averaging increases aggregate employment relatively more in the pre-2009
period, which suggests an increase in the quality of Orbis employment data. Represent-
ativeness in Manufacturing is satisfactory, both in levels and dynamics, even sub-sectors.
In many cases the statistics calculated from Orbis matches Eurostat data almost perfectly;
e.g., Optics and Electronics, Motors and Vehicles or Ceramics. In Services the quality of the
representativeness is heterogeneous reflecting the presence of public entities: Education,
Health and Social Work. However, in other sub-sectors, Orbis data matches Eurostat figures
rather well: Info and Communication, Transportation, Wholesale and Retail. Real Estate
services sector is likely to be affected by mis-classification of firms. Possibly between Real
estate services and construction.

As for the comparison of employment levels by firm-size, in Italy the employment distri-
bution by firm size is quite good in both investigated sectors above ten employees. In the
lowest size class about 20-40 percent of the size class's employment is not captured by
Orbis. Relatively this issue is more important in the case of service sectors and less so in
the case of manufacturing. However, since there are not major data problems, applying
weights can reproduce Eurostat indicated aggregate levels and dynamics of employment in
both sectors for the SME sectors.

Latvia

The unconsolidated employment data account for about 0.6 million employees in years
2005-2015. Additionally, the data includes reports on firms with limited financial accounts
from 2007 to 2011. The sector level analysis reveals that Orbis reflects the Eurostat stat-
istics very well. Possibly, the differences between the Orbis and Eurostat are due to the
mis-classification of sectors of some firms. Employment distribution by firm size is excel-
lent.

Lithuania

Unconsolidated employment accounts increases from 0.25 million employees in 2005 to
0.5 million in 2015. Additionally, the data includes reports on firms with limited financial
accounts from 2007 on. This latter source is suggestive of employment of a similar or even
bigger size than accounted for by the unconsolidated reports. Comparing unconsolidated
information to Eurostat data reveals a structural break in the sampling between 2006 and
2007. However, the level and dynamics of employment calculated from Orbis data are
satisfactory from 2007 to 2015. The representativeness in Manufacturing and Services
sectors are satisfactory for the period 2007-2015. For many sub-sectors Orbis statistics
match Eurostat figures well: Ceramics, Optics, Furniture, Transportation and Administrative

95

Services. We also detect possible misclassification of firms in the Real Estate services. As
for the comparison of employment levels by firm-size, the employment distribution is quite
good in both Manufacturing and Services sectors with a small share of employment missing
from all size classes. Once weighting scheme is employed, the levels and dynamics of
employment statistics calculated from Orbis is satisfactory.

Luxembourg

Unconsolidated employment data in Orbis stem overwhelmingly from consolidated ac-
counts. Separate investigation of Manufacturing and Services reveals changes of sampling
representativeness over time, increasing continuously from 2005 to 2011. Data is reliable
between 2011 and 2015 for Manufacturing. As for the Services, representativeness seems
equally low along the full period, even though dynamics suggest potential reliability of the
data between 2011 and 2015. Unfortunately, Eurostat data for Luxembourg is unavailable
for many sectors, which constrains the comparison.

Therefore, we do not retain Luxembourg firms in the final sample.

Malta

The data suffers from sampling bias in the beginning of the period and attrition towards
the end of the period. This finding is illustrated in the Figures 2 and 3 that compares Orbis
data with Eurostat. In addition, sampling issues are particularly severe in Manufacturing.
Notice that employment is unavailable in Eurostat for a large number of sectors, which
makes further comparison impossible.

Therefore, we do not retain Malta firms in our final sample.

Netherlands

Dutch Orbis data excessively rely on consolidated accounts. About half of the employment
is reported in consolidated accounts, amounting to 6-7 million employees. About two-thirds
of the remaining employment comes from unconsolidated accounts, while the rest comes
from limited financial reports. The level and dynamics of employment is not affected by data
imputation, which suggests no sampling or structural change in the data. Orbis employment
captures less than half Eurostat employment, and the dynamics in Orbis matches Eurostat's.
Also, both in the Manufacturing and Services, unconsolidated employment in Orbis capture
about or less than half of the employment, but the dynamics follow the Eurostat figures. The
problem with the representativeness for Netherlands is better understood when we look at
the data by firm-size. In the Netherlands, the largest share of employment not accounted
for comes from the largest firms because the latter report chiefly at consolidated levels,
and we opted to retain unconsolidated accounts only. Nevertheless, the employment levels
and dynamics of the small and medium sized companies is well representative.

Therefore, despite satisfactory representativeness of SMEs, we do not retain the Nether-
lands in the final sample.

Poland

Employment data are obtained from unconsolidated and limited financial accounts. The
representativeness of data deteriorates towards the end of the period. The missing inform-
ation in reports can only partially be compensated by imputation, which suggests a drop in
Orbis firms coverage. The figures show that employment levels by firm-size of Orbis hardly
match those of Eurostat. The employment distribution by firm size is quite good in both

96

investigated sectors above the firms with employment of 20. For the small and micro firms,
Orbis does not capture the upward drift of employment among these firms in Eurostat.

Therefore, we exclude the Polish firms from the final sample.

Portugal

Unconsolidated employment data amount to 2.5 million people in years 2005(cid:21)2015. Im-
putation helps improve levels and dynamics representativeness to some extent; yet, Orbis
shows satisfying levels of representativeness from 2007. The same is true at Manufac-
turing and Services sectors level. Sub-sectors analysis shows that most sub-sectors are
representative both in levels and dynamics. There is again a potential misclassification in
sub-sector Real Estate Service. As for the comparison of employment levels by firm-size,
Portugal employment distribution by firm size is quite satisfactory.

Romania

Unconsolidated employment data amount to 4 million employees on average in years 2005(cid:21)
2015. Unconsolidated accounts constitute nearly all of employment data. Orbis captures
about 60 percent of Eurostat employment and replicate dynamics in both Manufacturing and
Services. These findings also apply for most of the individual sub-sectors. The employ-
ment distribution by firm size shows that Orbis is representative of all firm sizes; applying
weighting schemes has only a small effect on aggregate levels and employment dynamics
by firm size.

Slovakia

Employment obtained from unconsolidated accounts amounts to 0.6-1.1 million employees,
and increases between 2005 and 2015; an additional million is provided by Limited Financial
accounts in the 2012-2014 period.

Orbis data for Slovakia is good in terms of representativeness. Manufacturing data from
Orbis captures about 70-90 percent of employment data from Eurostat, for the service
sectors the similar value is about half. However, like in the case of many countries, the low
representativeness of the service sectors in Orbis is coming from sectors P to S.

The comparison of Orbis and Eurostat SBS employment levels by firm-size in Slovakia the
employment distribution by firm size shows that Orbis is representative of all firm sizes,
applying weighting schemes has only a small effect on aggregate levels and employment
dynamics by firm size. Note, however, that there is a structural break in the Eurostat data.

Slovenia

Unconsolidated employment amounts to 3.5-4.5 million employees on average between
2005 and 2015; there is a structural break between 2009 and 2010. Representativeness
is satisfactory especially since 2009. The employment distribution by firm size shows the
same structural break in 2010 likely due to the missing firm reports from firms with less
than 10 employees.

Spain

Unconsolidated employment amounts to 7-8 million employees; there is no employment in
Limited Financial accounts. In Manufacturing, Orbis captures up to 80 percent of Eurostat
employment, depending on years; in Services, it captures about half of it, and discrepancies
come from sectors P to S only. At the end of the time period, Services sectors F-N in Orbis
accounts for more than 5 out of the about 6.5 million employees in Eurostat. The levels

97

and dynamics are similarly satisfactory for Manufacturing and Services (F-N). Sub-sectors
analysis shows that some are nearly perfectly representative all over the time period 2004-
2015, e.g., Motor vehicles, Real Estate, Construction and Info-communication Services.
Finally, the distribution by firm size is satisfactory in both Manufacturing and Services even
though there is a share of employment missing from the smallest size. Using weighting
scheme enables us to both capture the level of employment in Manufacturing and Services
and capture employment dynamics of the various firm-size categories.

Overall, the data is probably the one providing with the best representativeness amongst
the five largest EU economies.

Sweden

Unconsolidated employment data amount to 2-2.5 million employees in years 2005-2015;
Limited Financial accounts add an additional million employees starting in 2009. Sector
level representativeness of levels and dynamics are satisfactory: Orbis captures 70 to
90 percent of Eurostat employment in Manufacturing and about 50 percent in Services.
Though, as usual, low representativeness of Services in Orbis is due to sectors P to S
where public entities are more common. Firm-size distribution of employment in Orbis
is representative of all firm sizes, so that weighting schemes have limited effects on the
aggregate levels and employment dynamics by firm size.

United-Kingdom

Unconsolidated employment data amount to 10-11 million employees. Consolidated ac-
counts report a larger share of employment too, 11-12 million employees. Imputation has
little effect on employment levels and dynamics. Despite apparent low employment share,
unconsolidated employment in Orbis captures a significant share of Eurostat employment
levels and dynamics, both in Manufacturing and in Services (F-N). Interestingly, in sub-
sectors where Orbis captures only half of Eurostat employment, the dynamics still match
Eurostat. Firm-size analysis shows satisfactory representativeness for firms 50+ employ-
ees, in both Manufacturing and Services. However, a significant share of employment is
not captured by Orbis among firms with less than 20 employees. In fact, eventually the
weights applied to micro and small firms turn to be high: an average weight of 20 is applied
on 1-9 employees firms, and 11 on 10-19 firms.

Therefore, despite satisfactory representativeness, we exclude the United-Kingdom be-
cause of the large average weights applied on micro and small firms.

98

All countries overview

We proceed with a similar analysis as the one presented in the case of Austria for all
countries. This section reports a summary of the analysis; the detailed analysis and pictures
by country are reported in Appendix 5, together with the pictures. Table 5 pictures the
analysis summary.

Group 1: Adequate Representativeness

We identify a group of twenty countries for which we find sufficient data reporting as well as
representativeness. Countries include Austria, Belgium, Bulgaria, Croatia, Czech Republic,
Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Portugal,
Romania, Slovakia, Slovenia, Spain, and Sweden. In these countries, the unconsolidated
employment accounted for a large share of employment even though in some countries,
such as Slovakia, firms report quite some (cid:16)Limited Financial(cid:17) accounts (cid:21) unconsolidated
accounts alone yet provide fair representation of Eurostat employment.

In these countries, we find that unconsolidated accounts match Eurostat data relatively
well, both in levels and dynamics. Apart from the levels and dynamics we do care about
the size-distribution of firms, and that average weights by firm category remains below 10.
The highest weight is 9.7 (cid:21) micro firms in Lithuania Services.

Spanish employment data match Eurostat remarkably well and is probably the best among
the five largest EU countries. In Manufacturing, Orbis captures up to 80 percent of Eurostat
employment. Dynamics are also matching at country and sub-sectors levels. Some major
sub-sectors such as Motor Vehicle and Real Estate are nearly perfectly matching.

Group 2: Adequate but Unreliable Representativeness

A second group of countries show good employment reporting, nevertheless the repres-
entativeness is insufficient or would require excessive weights. Countries in Group 2 are
Greece, Ireland, the Netherlands, Poland, and the United-Kingdom. For instance, in the
Netherlands largest firms report consolidated accounts almost exclusively so that even
though smaller firms are well represented, it is almost impossible to obtain total employ-
ment. Large firms typically are multinational firms so that consolidated accounts are likely
to include employment abroad.

The United-Kingdom is another example. Even though more than half of employment
is reported into consolidated accounts, 50+ employees firms are well representative; and,
actually, imputation has little effect on aggregate employment showing a fairly good report-
ing quality. Nevertheless, small and micro firms are poorly represented. Using weights, we
could match Eurostat figures; we found weights excessive, though (cid:21) micro-firms average
weight equals 20.

Therefore, we decided not to use the countries in Group 2, even though some may opt
otherwise.

Group 3: Insufficient data and Poor Representativeness

The third group is constituted of countries for which the data reported is very poor in the
first place and as a result we did not conduct the thorough representativeness analysis by
dynamics and firm size. The lack of data may be due to a few missing multinational firms
which report only consolidated accounts in Orbis. This group includes Cyprus, Luxembourg

99

and Malta. For instance, Cyprus reports about 80 thousands firms taking all consolidation
accounts, which is far below 230 thousands firms reported in Eurostat on average. Sector
level analysis falls at even lower levels of representativeness. Another example is Lux-
embourg for which accounts are chiefly consolidated. Data would actually be reliable in
Manufacturing after 2011. Here, Eurostat is short of sector level reports so that we were
unable to compare the data. Provided such features, Luxembourg could not be included.

Table 5: Representativeness Summary

Country

Sufficient

Country

Unconsolidated Aggregates

Sectors Reasonable
& Size

Weights

Included in
Final Sample

Austria
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United-Kingdom

+
+
+
+
-
+
+
+
+
+
+
+
+
-
+
+
+
-
-
-
-
+
+
+
+
+
+
-

+
+
+
+

+
+
+
+
+
+
+
+
+
+
+
+

+
+
+
+
+
+
+
+
+

+
+
+
+

+
+
+
+
+
+
+
+
+
+
+
+

+
+
+
+
+
+
+
+
+

+
+
+
+

+
+
+
+
+
+
-
+
+
+
+
+

-
-
+
+
+
+
+
+
-

Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No

Note: This table summarizes the representativeness assessment. Sufficient Unconsolidated indicates that uncon-
solidated employment is sufficient; Country Aggregates: the sample is representative of the total employment;
Sectors & Size: the sample is representative of employment by broad sector and firm-size category. (cid:16)+(cid:17) ((cid:16)-(cid:17))
indicates (un)satisfactory assessment. The last column indicates whether we retain the country in our sample.

We compare our sample of employment with Eurostat Structural Business Statistics (SBS)
and design weighting schemes by firm size category in order to improve its (cid:16)representat-
iveness.(cid:17) The procedure we adopted closely follows the procedure proposed by Gal (2013).
The weights are calibrated at the 2-digit NACE sector and firm-size category level for each
year and country.

100

Table 6 exhibits the averages of the country level weights of Manufacturing and Services
for the three firm size categories, namely micro (less than 10 employees), small (10-19
employees), and small (20-49 employees).72

Table 6: Weighting Schemes

Country

Code Micro
(1-9)

Small
(10-19)

Small
(20-49)

Micro Small
(1-9)

(10-19)

Small
(20-49)

Manufacturing

Services

Austria
AT
Belgium
BE
BG
Bulgaria
Czech Republic CZ
DE
Germany
DK
Denmark
EE
Estonia
ES
Spain
FI
Finland
FR
France
HR
Croatia
HU
Hungary
IT
Italy
LT
Lithuania
LV
Latvia
PT
Portugal
RO
Romania
SE
Sweden
SI
Slovenia
SK
Slovakia

2.1
1.5
1.3
4.4
4.3
2.5
1.2
1.9
1.5
2.2
1.8
1.4
2.8
5.8
1.0
1.6
1.3
1.5
1.4
2.3

1.3
1.1
1.1
1.5
2.2
2.1
1.2
1.2
1.2
1.4
1.1
1.1
1.6
2.3
1.1
1.1
1.1
1.0
1.1
1.2

1.1
1.1
1.0
1.2
1.0
1.9
1.2
1.2
1.1
1.3
1.0
1.0
1.1
1.3
1.1
1.1
1.1
1.0
1.0
1.1

3.1
1.9
1.1
3.7
5.0
3.3
1.1
2.6
1.3
2.5
1.7
1.8
3.8
9.7
1.1
1.8
1.4
2.3
1.6
2.1

1.5
1.2
1.0
1.3
2.1
2.0
1.2
1.4
1.2
1.5
1.1
1.1
1.5
2.7
1.0
1.1
1.2
1.1
1.1
1.1

1.3
1.1
1.0
1.2
1.6
2.0
1.1
1.2
1.1
1.4
1.1
1.0
1.1
1.6
1.0
1.1
1.1
1.1
1.1
1.0

Note: This table reports the country level weighting schemes by country and size; weights are 2-digits NACE
averages.

All in all, we observe substantial discrepancy in weights across countries and size categories
which reflects the heterogeneity of the representativeness of Orbis employment data. It is
no surprise that micro firms systematically receive weights above one, since micro firms
tend to be under-represented in Orbis. The largest weighting numbers are given to micro
firms in Lithuania in both Services (weight=9.7) and Manufacturing (weight=5.8), and
Latvian data in Orbis is the most balanced data as weights are all close to one.

72We systematically apply the unity weight to firms with 50 employees and above so to avoid excessive repres-

entation of large firms.

101

Figures

Figure 1: Levels of Employment in Orbis.

(cid:21) Subfigure 1: Reported and estimated employment.

Bars split the sample between reported and estimated employment. Estimated employment is
employment for which Orbis indicates the figure is an estimate.

(cid:21) Subfigure 2: Industry classification reporting.

Bars are split into employment for which industry code is reported and those which have no
industry codes.

(cid:21) Subfigure 3: Employment by consolidation.

Bars are split into employment obtained from C1, U1, U2, and LF accounting reports in Orbis.
C1: consolidated; U1 and U2: unconsolidated; LF: Limited financial report.

(cid:21) Subfigure 4: Employment by consolidation, after imputation and averaging techniques are

applied.

Figure 2: Representativeness by Levels and Dynamics.

(cid:21) Panel A: Eurostat and Orbis data.

The figure compares Orbis employment data before and after imputations and averaging (red
and blue lines), with Eurostat (bars). The Panel is split between Manufacturing (left) and Ser-
vices (right).

(cid:21) Panel B: Dynamics.

The figure compares Orbis employment dymanics data after imputations (red line), with Euro-
stat (bars). The band indicates the 90% confidence level. The Panel is split between Manufac-
turing (left) and Services (right).

Figure 3: Representativeness by Size Category.
The figure reports the total employment in Orbis, where bars are split in size categories of firms:
1-9, 10-19, 20-49, 50-249, and 250+ employees. Panel A reports aggregate Manufacturing sector;
Panel B reports aggregate Services sector. In each panel, left-hand figure reports the Eurostat SBS
numbers, and right-hand figure reports the Orbis data after weights are applied.

Figure 4: Representativeness in Subsectors.
The figure reports the total employment in Orbis by subsectors. For each subsector, we report the
total Eurostat employment and Orbis employment (before and after averaging).

102

Austria

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

103

0.511.52millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employement0.511.52millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidetifiedStatistics for AT0123millions200420052006200720082009201020112012201320142015Data0123millions200420052006200720082009201020112012201320142015Imputed0123millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6millions200520102015Manufacturing (C)0.511.522.5millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.050.05.1growth200520102015Manufacturing (C)-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.2.4.6Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6Millions20082009201020112012201320142015Orbis0.2.4.6Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.52Millions20082009201020112012201320142015Eurostat sbs.0.511.52Millions20082009201020112012201320142015Orbis0.511.52Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Austria - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

104

0.02.04.06.08millions200520072009201120132015Food, Drink, Tobacco0.01.02.03millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08millions200520072009201120132015Wood, Paper, Printing0.02.04.06millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.05.1.15millions200520072009201120132015Optics, Electronics,  Machines0.01.02.03.04millions200520072009201120132015Motors, Vehicles0.02.04.06.08millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2.25millions200520072009201120132015Construction0.2.4.6millions200520072009201120132015Wholesale and retail0.05.1.15.2millions200520072009201120132015Transportation0.05.1.15.2.25millions200520072009201120132015Accommodation and food0.02.04.06.08.1millions200520072009201120132015Info-communication0.02.04.06millions200520072009201120132015Real estate service0.05.1.15.2millions200520072009201120132015Professional services0.05.1.15.2.25millions200520072009201120132015Administrative services0.1.2.3millions200520072009201120132015 Education0.1.2.3.4millions200520072009201120132015Health and social work0.01.02.03.04.05millions200520072009201120132015Arts, entertainment0.02.04.06.08.1millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Belgium

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

105

0.511.522.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.522.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for BE0.511.522.5millions200420052006200720082009201020112012201320142015Data0.511.522.5millions200420052006200720082009201020112012201320142015Imputed0.511.522.5millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6millions200520102015Manufacturing (C)0123millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.050.05.1growth200520102015Manufacturing (C)-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.2.4.6Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6Millions20082009201020112012201320142015Orbis0.2.4.6Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.52Millions20082009201020112012201320142015Eurostat sbs.0.511.52Millions20082009201020112012201320142015Orbis0.511.52Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Belgium - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

106

0.02.04.06.08.1millions200520072009201120132015Food, Drink, Tobacco0.01.02.03.04millions200520072009201120132015Textile, Clothes, Leather0.01.02.03.04.05millions200520072009201120132015Wood, Paper, Printing0.02.04.06.08.1millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.02.04.06.08millions200520072009201120132015Optics, Electronics,  Machines0.02.04.06millions200520072009201120132015Motors, Vehicles0.01.02.03.04millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2.25millions200520072009201120132015Construction0.1.2.3.4.5millions200520072009201120132015Wholesale and retail0.05.1.15.2.25millions200520072009201120132015Transportation0.05.1.15millions200520072009201120132015Accommodation and food0.02.04.06.08.1millions200520072009201120132015Info-communication0.01.02.03millions200520072009201120132015Real estate service0.05.1.15millions200520072009201120132015Professional services0.1.2.3.4millions200520072009201120132015Administrative services0.1.2.3.4millions200520072009201120132015 Education0.1.2.3.4.5millions200520072009201120132015Health and social work0.01.02.03millions200520072009201120132015Arts, entertainment0.02.04.06.08millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Bulgaria

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

107

0.511.522.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.522.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for BG01234millions200420052006200720082009201020112012201320142015Data01234millions200420052006200720082009201020112012201320142015Imputed01234millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6.8millions200520102015Manufacturing (C)0.511.52millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.2.4.6Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6Millions20082009201020112012201320142015Orbis0.2.4.6Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.5Millions20082009201020112012201320142015Eurostat sbs.0.511.5Millions20082009201020112012201320142015Orbis0.511.5Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Bulgaria - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

108

0.05.1.15millions200520072009201120132015Food, Drink, Tobacco0.05.1.15.2.25millions200520072009201120132015Textile, Clothes, Leather0.01.02.03.04millions200520072009201120132015Wood, Paper, Printing0.02.04.06millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.02.04.06.08millions200520072009201120132015Optics, Electronics,  Machines0.005.01.015.02.025millions200520072009201120132015Motors, Vehicles0.02.04.06millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.1.2.3millions200520072009201120132015Construction0.2.4.6millions200520072009201120132015Wholesale and retail0.05.1.15.2millions200520072009201120132015Transportation0.05.1.15.2millions200520072009201120132015Accommodation and food0.02.04.06.08.1millions200520072009201120132015Info-communication0.02.04.06.08.1millions200520072009201120132015Real estate service0.05.1.15millions200520072009201120132015Professional services0.05.1.15millions200520072009201120132015Administrative services0.05.1.15.2millions200520072009201120132015 Education0.05.1.15millions200520072009201120132015Health and social work0.01.02.03.04millions200520072009201120132015Arts, entertainment0.01.02.03.04millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Czech Republic

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

109

0123millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0123millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for CZ012345millions200420052006200720082009201020112012201320142015Data012345millions200420052006200720082009201020112012201320142015Imputed012345millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.511.5millions200520102015Manufacturing (C)0.511.522.5millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.2-.10.1growth200520102015Manufacturing (C)-.2-.10.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.511.5Millions20082009201020112012201320142015Eurostat sbs.0.511.5Millions20082009201020112012201320142015Orbis0.511.5Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.52Millions20082009201020112012201320142015Eurostat sbs.0.511.52Millions20082009201020112012201320142015Orbis0.511.52Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Czech Republic - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

110

0.05.1.15millions200520072009201120132015Food, Drink, Tobacco0.02.04.06.08.1millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08.1millions200520072009201120132015Wood, Paper, Printing0.05.1.15millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.1.2.3millions200520072009201120132015Ceramics, Metals 0.1.2.3millions200520072009201120132015Optics, Electronics,  Machines0.05.1.15.2millions200520072009201120132015Motors, Vehicles0.05.1.15millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.1.2.3millions200520072009201120132015Construction0.2.4.6millions200520072009201120132015Wholesale and retail0.1.2.3millions200520072009201120132015Transportation0.05.1.15millions200520072009201120132015Accommodation and food0.05.1.15millions200520072009201120132015Info-communication0.02.04.06.08.1millions200520072009201120132015Real estate service0.05.1.15.2millions200520072009201120132015Professional services0.05.1.15.2.25millions200520072009201120132015Administrative services0.1.2.3millions200520072009201120132015 Education0.1.2.3millions200520072009201120132015Health and social work0.02.04.06millions200520072009201120132015Arts, entertainment0.02.04.06millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Denmark

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

111

0.511.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for DK0.511.5millions200420052006200720082009201020112012201320142015Data0.511.5millions200420052006200720082009201020112012201320142015Imputed0.511.5millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.1.2.3.4millions200520102015Manufacturing (C)0.511.52millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.1.2.3.4Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3.4Millions20082009201020112012201320142015Orbis0.1.2.3.4Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.5Millions20082009201020112012201320142015Eurostat sbs.0.511.5Millions20082009201020112012201320142015Orbis0.511.5Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Denmark - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

112

0.02.04.06millions200520072009201120132015Food, Drink, Tobacco0.002.004.006.008millions200520072009201120132015Textile, Clothes, Leather0.01.02.03millions200520072009201120132015Wood, Paper, Printing0.01.02.03.04.05millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.02.04.06millions200520072009201120132015Ceramics, Metals 0.02.04.06.08.1millions200520072009201120132015Optics, Electronics,  Machines0.005.01millions200520072009201120132015Motors, Vehicles0.01.02.03.04millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2millions200520072009201120132015Construction0.1.2.3.4.5millions200520072009201120132015Wholesale and retail0.05.1.15millions200520072009201120132015Transportation0.05.1.15millions200520072009201120132015Accommodation and food0.02.04.06.08.1millions200520072009201120132015Info-communication0.01.02.03.04millions200520072009201120132015Real estate service0.05.1.15millions200520072009201120132015Professional services0.05.1.15millions200520072009201120132015Administrative services0.05.1.15.2.25millions200520072009201120132015 Education0.1.2.3.4.5millions200520072009201120132015Health and social work0.02.04.06millions200520072009201120132015Arts, entertainment0.02.04.06millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Germany

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

113

051015millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment051015millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for DE0102030millions200420052006200720082009201020112012201320142015Data0102030millions200420052006200720082009201020112012201320142015Imputed0102030millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF02468millions200520102015Manufacturing (C)0510152025millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)02468Millions20082009201020112012201320142015Eurostat sbs.02468Millions20082009201020112012201320142015Orbis02468Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)05101520Millions20082009201020112012201320142015Eurostat sbs.05101520Millions20082009201020112012201320142015Orbis05101520Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Germany - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

114

0.2.4.6.8millions200520072009201120132015Food, Drink, Tobacco0.05.1.15.2millions200520072009201120132015Textile, Clothes, Leather0.1.2.3.4.5millions200520072009201120132015Wood, Paper, Printing0.2.4.6.81millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.511.5millions200520072009201120132015Ceramics, Metals 0.511.52millions200520072009201120132015Optics, Electronics,  Machines0.2.4.6.81millions200520072009201120132015Motors, Vehicles0.2.4.6millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.511.52millions200520072009201120132015Construction012345millions200520072009201120132015Wholesale and retail0.511.52millions200520072009201120132015Transportation0.511.5millions200520072009201120132015Accommodation and food0.2.4.6.81millions200520072009201120132015Info-communication0.1.2.3.4millions200520072009201120132015Real estate service0.511.52millions200520072009201120132015Professional services0123millions200520072009201120132015Administrative services0.511.52millions200520072009201120132015 Education012345millions200520072009201120132015Health and social work0.1.2.3.4.5millions200520072009201120132015Arts, entertainment0.511.5millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Estonia

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

115

0.1.2.3.4.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.1.2.3.4.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for EE0.2.4.6millions200420052006200720082009201020112012201320142015Data0.2.4.6millions200420052006200720082009201020112012201320142015Imputed0.2.4.6millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.05.1.15millions200520102015Manufacturing (C)0.1.2.3.4millions200520102015Services (F-S)EurostatOrbisOrbis+avg-.2-.10.1.2growth200520102015Manufacturing (C)-.2-.10.1.2growth200520102015Services (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.05.1.15Millions20082009201020112012201320142015Eurostat sbs.0.05.1.15Millions20082009201020112012201320142015Orbis0.05.1.15Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.1.2.3Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3Millions20082009201020112012201320142015Orbis0.1.2.3Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Estonia - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

116

0.005.01.015.02millions200520072009201120132015Food, Drink, Tobacco0.005.01.015.02.025millions200520072009201120132015Textile, Clothes, Leather0.005.01.015.02.025millions200520072009201120132015Wood, Paper, Printing0.002.004.006.008.01millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.005.01.015.02millions200520072009201120132015Ceramics, Metals 0.005.01.015.02millions200520072009201120132015Optics, Electronics,  Machines0.002.004.006millions200520072009201120132015Motors, Vehicles0.005.01.015.02millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.02.04.06millions200520072009201120132015Construction0.02.04.06.08millions200520072009201120132015Wholesale and retail0.01.02.03.04.05millions200520072009201120132015Transportation0.005.01.015.02.025millions200520072009201120132015Accommodation and food0.005.01.015.02.025millions200520072009201120132015Info-communication0.005.01.015.02.025millions200520072009201120132015Real estate service0.01.02.03millions200520072009201120132015Professional services0.01.02.03millions200520072009201120132015Administrative services0.02.04.06millions200520072009201120132015 Education0.01.02.03.04millions200520072009201120132015Health and social work0.005.01.015.02millions200520072009201120132015Arts, entertainment0.005.01millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Spain

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

117

0246810millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0246810millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for ES0246810millions200420052006200720082009201020112012201320142015Data0246810millions200420052006200720082009201020112012201320142015Imputed0246810millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0123millions200520102015Manufacturing (C)051015millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.511.522.5Millions20082009201020112012201320142015Eurostat sbs.0.511.522.5Millions20082009201020112012201320142015Orbis0.511.522.5Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0246810Millions20082009201020112012201320142015Eurostat sbs.0246810Millions20082009201020112012201320142015Orbis0246810Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Spain - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

118

0.1.2.3.4.5millions200520072009201120132015Food, Drink, Tobacco0.05.1.15.2.25millions200520072009201120132015Textile, Clothes, Leather0.05.1.15.2.25millions200520072009201120132015Wood, Paper, Printing0.1.2.3millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.2.4.6.8millions200520072009201120132015Ceramics, Metals 0.1.2.3millions200520072009201120132015Optics, Electronics,  Machines0.05.1.15.2.25millions200520072009201120132015Motors, Vehicles0.1.2.3millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.511.522.5millions200520072009201120132015Construction0123millions200520072009201120132015Wholesale and retail0.2.4.6.8millions200520072009201120132015Transportation0.511.5millions200520072009201120132015Accommodation and food0.1.2.3.4.5millions200520072009201120132015Info-communication0.05.1.15.2millions200520072009201120132015Real estate service0.2.4.6.8millions200520072009201120132015Professional services0.511.5millions200520072009201120132015Administrative services0.511.5millions200520072009201120132015 Education0.511.5millions200520072009201120132015Health and social work0.1.2.3.4millions200520072009201120132015Arts, entertainment0.1.2.3.4millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Finland

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

119

0.511.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for FI0.511.52millions200420052006200720082009201020112012201320142015Data0.511.52millions200420052006200720082009201020112012201320142015Imputed0.511.52millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.1.2.3.4millions200520102015Manufacturing (C)0.511.5millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.1.2.3.4Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3.4Millions20082009201020112012201320142015Orbis0.1.2.3.4Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.2.4.6.81Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6.81Millions20082009201020112012201320142015Orbis0.2.4.6.81Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Finland - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

120

0.01.02.03.04millions200520072009201120132015Food, Drink, Tobacco0.005.01.015millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08millions200520072009201120132015Wood, Paper, Printing0.01.02.03.04millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.02.04.06.08millions200520072009201120132015Ceramics, Metals 0.05.1.15millions200520072009201120132015Optics, Electronics,  Machines0.005.01.015.02millions200520072009201120132015Motors, Vehicles0.01.02.03.04millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2millions200520072009201120132015Construction0.1.2.3millions200520072009201120132015Wholesale and retail0.05.1.15millions200520072009201120132015Transportation0.02.04.06.08millions200520072009201120132015Accommodation and food0.02.04.06.08.1millions200520072009201120132015Info-communication0.01.02.03millions200520072009201120132015Real estate service0.05.1.15millions200520072009201120132015Professional services0.05.1.15millions200520072009201120132015Administrative services0.05.1.15.2millions200520072009201120132015 Education0.1.2.3.4millions200520072009201120132015Health and social work0.01.02.03.04millions200520072009201120132015Arts, entertainment0.02.04.06millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.France

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

121

02468millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment02468millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for FR051015millions200420052006200720082009201020112012201320142015Data051015millions200420052006200720082009201020112012201320142015Imputed051015millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0123millions200520102015Manufacturing (C)05101520millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0123Millions20082009201020112012201320142015Eurostat sbs.0123Millions20082009201020112012201320142015Orbis0123Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0246810Millions20082009201020112012201320142015Eurostat sbs.0246810Millions20082009201020112012201320142015Orbis0246810Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+France - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

122

0.2.4.6millions200520072009201120132015Food, Drink, Tobacco0.05.1.15millions200520072009201120132015Textile, Clothes, Leather0.05.1.15.2.25millions200520072009201120132015Wood, Paper, Printing0.1.2.3.4millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.2.4.6millions200520072009201120132015Ceramics, Metals 0.1.2.3.4millions200520072009201120132015Optics, Electronics,  Machines0.1.2.3.4.5millions200520072009201120132015Motors, Vehicles0.1.2.3.4millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.511.5millions200520072009201120132015Construction0123millions200520072009201120132015Wholesale and retail0.511.5millions200520072009201120132015Transportation0.2.4.6.81millions200520072009201120132015Accommodation and food0.2.4.6.8millions200520072009201120132015Info-communication0.1.2.3.4millions200520072009201120132015Real estate service0.511.52millions200520072009201120132015Professional services0.511.52millions200520072009201120132015Administrative services0.511.52millions200520072009201120132015 Education01234millions200520072009201120132015Health and social work0.2.4.6millions200520072009201120132015Arts, entertainment0.2.4.6millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.United Kingdom

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

123

051015millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment051015millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for GB0102030millions200420052006200720082009201020112012201320142015Data0102030millions200420052006200720082009201020112012201320142015Imputed0102030millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0123millions200520102015Manufacturing (C)05101520millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0123Millions20082009201020112012201320142015Eurostat sbs.0123Millions20082009201020112012201320142015Orbis0123Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)051015Millions20082009201020112012201320142015Eurostat sbs.051015Millions20082009201020112012201320142015Orbis051015Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+United Kingdom - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

124

0.1.2.3.4millions200520072009201120132015Food, Drink, Tobacco0.05.1.15millions200520072009201120132015Textile, Clothes, Leather0.1.2.3millions200520072009201120132015Wood, Paper, Printing0.1.2.3.4millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.1.2.3.4.5millions200520072009201120132015Ceramics, Metals 0.1.2.3.4.5millions200520072009201120132015Optics, Electronics,  Machines0.1.2.3millions200520072009201120132015Motors, Vehicles0.1.2.3millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.511.5millions200520072009201120132015Construction01234millions200520072009201120132015Wholesale and retail0.511.5millions200520072009201120132015Transportation0.511.52millions200520072009201120132015Accommodation and food0.51millions200520072009201120132015Info-communication0.1.2.3.4millions200520072009201120132015Real estate service0.511.52millions200520072009201120132015Professional services0.511.522.5millions200520072009201120132015Administrative services0.511.522.5millions200520072009201120132015 Education01234millions200520072009201120132015Health and social work0.2.4.6millions200520072009201120132015Arts, entertainment0.2.4.6millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Greece

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

125

0.2.4.6.81millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.2.4.6.81millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for GR0.2.4.6.81millions200420052006200720082009201020112012201320142015Data0.2.4.6.81millions200420052006200720082009201020112012201320142015Imputed0.2.4.6.81millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.1.2.3.4millions200520102015Manufacturing (C)0.511.52millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.1.2.3.4Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3.4Millions20082009201020112012201320142015Orbis0.1.2.3.4Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.52Millions20082009201020112012201320142015Eurostat sbs.0.511.52Millions20082009201020112012201320142015Orbis0.511.52Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Greece - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

126

0.02.04.06.08.1millions200520072009201120132015Food, Drink, Tobacco0.02.04.06millions200520072009201120132015Textile, Clothes, Leather0.01.02.03.04millions200520072009201120132015Wood, Paper, Printing0.01.02.03.04.05millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.02.04.06.08millions200520072009201120132015Ceramics, Metals 0.01.02.03.04millions200520072009201120132015Optics, Electronics,  Machines0.005.01.015millions200520072009201120132015Motors, Vehicles0.01.02.03.04millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2.25millions200520072009201120132015Construction0.2.4.6millions200520072009201120132015Wholesale and retail0.05.1.15millions200520072009201120132015Transportation0.05.1.15.2millions200520072009201120132015Accommodation and food0.02.04.06.08.1millions200520072009201120132015Info-communication0.005.01.015millions200520072009201120132015Real estate service0.02.04.06.08.1millions200520072009201120132015Professional services0.02.04.06.08.1millions200520072009201120132015Administrative services0.1.2.3millions200520072009201120132015 Education0.05.1.15.2millions200520072009201120132015Health and social work0.01.02.03.04millions200520072009201120132015Arts, entertainment0.05.1.15millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Croatia

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

127

0.2.4.6.81millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.2.4.6.81millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for HR0.51millions200420052006200720082009201020112012201320142015Data0.51millions200420052006200720082009201020112012201320142015Imputed0.51millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.1.2.3millions200520102015Manufacturing (C)0.2.4.6.8millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.1.2.3Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3Millions20082009201020112012201320142015Orbis0.1.2.3Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.2.4.6.8Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6.8Millions20082009201020112012201320142015Orbis0.2.4.6.8Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Greece - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

128

0.02.04.06.08millions200520072009201120132015Food, Drink, Tobacco0.01.02.03.04.05millions200520072009201120132015Textile, Clothes, Leather0.01.02.03millions200520072009201120132015Wood, Paper, Printing0.01.02.03millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.02.04.06millions200520072009201120132015Ceramics, Metals 0.01.02.03.04millions200520072009201120132015Optics, Electronics,  Machines0.005.01.015.02.025millions200520072009201120132015Motors, Vehicles0.01.02.03millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15millions200520072009201120132015Construction0.05.1.15.2.25millions200520072009201120132015Wholesale and retail0.02.04.06.08.1millions200520072009201120132015Transportation0.02.04.06.08.1millions200520072009201120132015Accommodation and food0.01.02.03.04millions200520072009201120132015Info-communication0.005.01.015.02.025millions200520072009201120132015Real estate service0.02.04.06millions200520072009201120132015Professional services0.01.02.03.04.05millions200520072009201120132015Administrative services0.05.1.15millions200520072009201120132015 Education0.02.04.06.08.1millions200520072009201120132015Health and social work0.01.02.03millions200520072009201120132015Arts, entertainment0.005.01.015.02.025millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Hungary

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

129

0.511.522.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.522.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for HU0.511.522.5millions200420052006200720082009201020112012201320142015Data0.511.522.5millions200420052006200720082009201020112012201320142015Imputed0.511.522.5millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6.8millions200520102015Manufacturing (C)0.511.522.5millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.2.4.6.8Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6.8Millions20082009201020112012201320142015Orbis0.2.4.6.8Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.52Millions20082009201020112012201320142015Eurostat sbs.0.511.52Millions20082009201020112012201320142015Orbis0.511.52Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Hungary - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

130

0.05.1.15millions200520072009201120132015Food, Drink, Tobacco0.02.04.06.08.1millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08millions200520072009201120132015Wood, Paper, Printing0.02.04.06.08.1millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.05.1.15.2.25millions200520072009201120132015Optics, Electronics,  Machines0.05.1millions200520072009201120132015Motors, Vehicles0.02.04.06.08millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2.25millions200520072009201120132015Construction0.2.4.6millions200520072009201120132015Wholesale and retail0.05.1.15.2.25millions200520072009201120132015Transportation0.05.1.15millions200520072009201120132015Accommodation and food0.05.1millions200520072009201120132015Info-communication0.02.04.06.08millions200520072009201120132015Real estate service0.05.1.15.2millions200520072009201120132015Professional services0.05.1.15.2millions200520072009201120132015Administrative services0.1.2.3millions200520072009201120132015 Education0.1.2.3millions200520072009201120132015Health and social work0.02.04.06.08millions200520072009201120132015Arts, entertainment0.02.04.06.08millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Ireland

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

131

0.511.522.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.522.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for IE0123millions200420052006200720082009201020112012201320142015Data0123millions200420052006200720082009201020112012201320142015Imputed0123millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.05.1.15.2.25millions200520102015Manufacturing (C)0.511.5millions200520102015Services (F-S)EurostatOrbisOrbis+avg-.10.1.2growth200520102015Manufacturing (C)-.10.1.2growth200520102015Services (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.05.1.15.2Millions20082009201020112012201320142015Eurostat sbs.0.05.1.15.2Millions20082009201020112012201320142015Orbis0.05.1.15.2Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.5Millions20082009201020112012201320142015Eurostat sbs.0.511.5Millions20082009201020112012201320142015Orbis0.511.5Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Ireland - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

132

0.02.04.06millions200520072009201120132015Food, Drink, Tobacco0.005.01.015millions200520072009201120132015Textile, Clothes, Leather0.005.01.015.02millions200520072009201120132015Wood, Paper, Printing0.01.02.03.04millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.01.02.03.04millions200520072009201120132015Ceramics, Metals 0.02.04.06millions200520072009201120132015Optics, Electronics,  Machines0.002.004.006millions200520072009201120132015Motors, Vehicles0.01.02.03.04millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15.2millions200520072009201120132015Construction0.1.2.3millions200520072009201120132015Wholesale and retail0.02.04.06.08millions200520072009201120132015Transportation0.05.1.15millions200520072009201120132015Accommodation and food0.02.04.06.08millions200520072009201120132015Info-communication0.01.02.03millions200520072009201120132015Real estate service0.05.1.15.2millions200520072009201120132015Professional services0.05.1.15millions200520072009201120132015Administrative services0.05.1.15millions200520072009201120132015 Education0.05.1.15.2.25millions200520072009201120132015Health and social work0.02.04.06.08.1millions200520072009201120132015Arts, entertainment0.01.02.03.04millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Italy

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

133

0246810millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0246810millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for IT051015millions200420052006200720082009201020112012201320142015Data051015millions200420052006200720082009201020112012201320142015Imputed051015millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF01234millions200520102015Manufacturing (C)0246810millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)012345Millions20082009201020112012201320142015Eurostat sbs.012345Millions20082009201020112012201320142015Orbis012345Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0246810Millions20082009201020112012201320142015Eurostat sbs.0246810Millions20082009201020112012201320142015Orbis0246810Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Italy - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

134

0.1.2.3.4millions200520072009201120132015Food, Drink, Tobacco0.2.4.6millions200520072009201120132015Textile, Clothes, Leather0.1.2.3millions200520072009201120132015Wood, Paper, Printing0.1.2.3.4millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.2.4.6.81millions200520072009201120132015Ceramics, Metals 0.2.4.6.8millions200520072009201120132015Optics, Electronics,  Machines0.1.2.3millions200520072009201120132015Motors, Vehicles0.1.2.3.4millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.511.5millions200520072009201120132015Construction0.511.52millions200520072009201120132015Wholesale and retail0.2.4.6.81millions200520072009201120132015Transportation0.2.4.6.81millions200520072009201120132015Accommodation and food0.1.2.3.4.5millions200520072009201120132015Info-communication0.05.1.15.2.25millions200520072009201120132015Real estate service0.2.4.6millions200520072009201120132015Professional services0.51millions200520072009201120132015Administrative services0.511.5millions200520072009201120132015 Education0.511.5millions200520072009201120132015Health and social work0.05.1.15.2millions200520072009201120132015Arts, entertainment0.1.2.3.4millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Lithuania

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

135

0.2.4.6.8millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.2.4.6.8millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for LT0.511.5millions200420052006200720082009201020112012201320142015Data0.511.5millions200420052006200720082009201020112012201320142015Imputed0.511.5millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.05.1.15.2.25millions200520102015Manufacturing (C)0.2.4.6.8millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.05.1.15.2.25Millions20082009201020112012201320142015Eurostat sbs.0.05.1.15.2.25Millions20082009201020112012201320142015Orbis0.05.1.15.2.25Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.2.4.6Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6Millions20082009201020112012201320142015Orbis0.2.4.6Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Lithuania - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

136

0.01.02.03.04.05millions200520072009201120132015Food, Drink, Tobacco0.01.02.03.04millions200520072009201120132015Textile, Clothes, Leather0.01.02.03.04millions200520072009201120132015Wood, Paper, Printing0.005.01.015.02millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.01.02.03millions200520072009201120132015Ceramics, Metals 0.005.01.015.02millions200520072009201120132015Optics, Electronics,  Machines0.002.004.006millions200520072009201120132015Motors, Vehicles0.01.02.03.04millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.05.1.15millions200520072009201120132015Construction0.05.1.15.2millions200520072009201120132015Wholesale and retail0.02.04.06.08.1millions200520072009201120132015Transportation0.01.02.03.04millions200520072009201120132015Accommodation and food0.01.02.03millions200520072009201120132015Info-communication0.005.01.015millions200520072009201120132015Real estate service0.01.02.03.04millions200520072009201120132015Professional services0.01.02.03.04.05millions200520072009201120132015Administrative services0.05.1.15millions200520072009201120132015 Education0.02.04.06.08.1millions200520072009201120132015Health and social work0.01.02.03millions200520072009201120132015Arts, entertainment0.005.01.015.02millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Latvia

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

137

0.2.4.6.8millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.2.4.6.8millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for LV0.2.4.6.8millions200420052006200720082009201020112012201320142015Data0.2.4.6.8millions200420052006200720082009201020112012201320142015Imputed0.2.4.6.8millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.05.1.15millions200520102015Manufacturing (C)0.2.4.6.8millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.2-.10.1growth200520102015Manufacturing (C)-.2-.10.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.05.1.15Millions20082009201020112012201320142015Eurostat sbs.0.05.1.15Millions20082009201020112012201320142015Orbis0.05.1.15Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.1.2.3.4.5Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3.4.5Millions20082009201020112012201320142015Orbis0.1.2.3.4.5Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Latvia - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

138

0.01.02.03.04millions200520072009201120132015Food, Drink, Tobacco0.005.01.015.02millions200520072009201120132015Textile, Clothes, Leather0.01.02.03.04millions200520072009201120132015Wood, Paper, Printing0.002.004.006.008.01millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.005.01.015.02millions200520072009201120132015Ceramics, Metals 0.005.01millions200520072009201120132015Optics, Electronics,  Machines0.001.002.003.004.005millions200520072009201120132015Motors, Vehicles0.005.01.015.02millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.02.04.06.08.1millions200520072009201120132015Construction0.05.1.15.2millions200520072009201120132015Wholesale and retail0.02.04.06.08millions200520072009201120132015Transportation0.01.02.03millions200520072009201120132015Accommodation and food0.01.02.03millions200520072009201120132015Info-communication0.01.02.03.04millions200520072009201120132015Real estate service0.01.02.03.04millions200520072009201120132015Professional services0.01.02.03.04millions200520072009201120132015Administrative services0.02.04.06.08millions200520072009201120132015 Education0.01.02.03.04.05millions200520072009201120132015Health and social work0.005.01.015.02.025millions200520072009201120132015Arts, entertainment0.005.01.015millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.the Netherlands

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

139

0123millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0123millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for NL0246810millions200420052006200720082009201020112012201320142015Data0246810millions200420052006200720082009201020112012201320142015Imputed0246810millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6.8millions200520102015Manufacturing (C)0246millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.2.4.6.8Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6.8Millions20082009201020112012201320142015Orbis0.2.4.6.8Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)01234Millions20082009201020112012201320142015Eurostat sbs.01234Millions20082009201020112012201320142015Orbis01234Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+the Netherlands - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

140

0.05.1.15millions200520072009201120132015Food, Drink, Tobacco0.005.01.015.02millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08millions200520072009201120132015Wood, Paper, Printing0.02.04.06.08.1millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.05.1.15millions200520072009201120132015Optics, Electronics,  Machines0.01.02.03.04millions200520072009201120132015Motors, Vehicles0.05.1.15.2millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.1.2.3.4millions200520072009201120132015Construction0.511.5millions200520072009201120132015Wholesale and retail0.1.2.3.4millions200520072009201120132015Transportation0.1.2.3.4millions200520072009201120132015Accommodation and food0.05.1.15.2.25millions200520072009201120132015Info-communication0.02.04.06.08millions200520072009201120132015Real estate service0.1.2.3.4.5millions200520072009201120132015Professional services0.2.4.6.8millions200520072009201120132015Administrative services0.1.2.3.4.5millions200520072009201120132015 Education0.511.5millions200520072009201120132015Health and social work0.02.04.06.08.1millions200520072009201120132015Arts, entertainment0.05.1.15millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Poland

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

141

0246millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0246millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for PL051015millions200420052006200720082009201020112012201320142015Data051015millions200420052006200720082009201020112012201320142015Imputed051015millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0123millions200520102015Manufacturing (C)02468millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.511.522.5Millions20082009201020112012201320142015Eurostat sbs.0.511.522.5Millions20082009201020112012201320142015Orbis0.511.522.5Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)012345Millions20082009201020112012201320142015Eurostat sbs.012345Millions20082009201020112012201320142015Orbis012345Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Poland - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

142

0.1.2.3.4.5millions200520072009201120132015Food, Drink, Tobacco0.1.2.3.4millions200520072009201120132015Textile, Clothes, Leather0.1.2.3millions200520072009201120132015Wood, Paper, Printing0.1.2.3.4millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.2.4.6millions200520072009201120132015Ceramics, Metals 0.1.2.3.4millions200520072009201120132015Optics, Electronics,  Machines0.1.2.3.4millions200520072009201120132015Motors, Vehicles0.1.2.3.4millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.2.4.6.81millions200520072009201120132015Construction0.511.52millions200520072009201120132015Wholesale and retail0.2.4.6.8millions200520072009201120132015Transportation0.1.2.3millions200520072009201120132015Accommodation and food0.1.2.3millions200520072009201120132015Info-communication0.05.1.15.2.25millions200520072009201120132015Real estate service0.1.2.3.4millions200520072009201120132015Professional services0.1.2.3.4millions200520072009201120132015Administrative services0.511.5millions200520072009201120132015 Education0.2.4.6.8millions200520072009201120132015Health and social work0.05.1.15.2millions200520072009201120132015Arts, entertainment0.05.1.15.2millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Portugal

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

143

0123millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0123millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for PT0123millions200420052006200720082009201020112012201320142015Data0123millions200420052006200720082009201020112012201320142015Imputed0123millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6.8millions200520102015Manufacturing (C)0123millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.2.4.6.8Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6.8Millions20082009201020112012201320142015Orbis0.2.4.6.8Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.52Millions20082009201020112012201320142015Eurostat sbs.0.511.52Millions20082009201020112012201320142015Orbis0.511.52Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Portugal - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

144

0.05.1millions200520072009201120132015Food, Drink, Tobacco0.05.1.15.2.25millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08millions200520072009201120132015Wood, Paper, Printing0.01.02.03.04.05millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.02.04.06millions200520072009201120132015Optics, Electronics,  Machines0.01.02.03.04.05millions200520072009201120132015Motors, Vehicles0.02.04.06.08millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.1.2.3.4.5millions200520072009201120132015Construction0.2.4.6.8millions200520072009201120132015Wholesale and retail0.05.1.15.2millions200520072009201120132015Transportation0.05.1.15.2.25millions200520072009201120132015Accommodation and food0.02.04.06.08millions200520072009201120132015Info-communication0.01.02.03.04millions200520072009201120132015Real estate service0.05.1.15millions200520072009201120132015Professional services0.1.2.3millions200520072009201120132015Administrative services0.1.2.3millions200520072009201120132015 Education0.1.2.3.4millions200520072009201120132015Health and social work0.01.02.03.04millions200520072009201120132015Arts, entertainment0.02.04.06.08millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Romania

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

145

012345millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment012345millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for RO012345millions200420052006200720082009201020112012201320142015Data012345millions200420052006200720082009201020112012201320142015Imputed012345millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.511.52millions200520102015Manufacturing (C)01234millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.511.5Millions20082009201020112012201320142015Eurostat sbs.0.511.5Millions20082009201020112012201320142015Orbis0.511.5Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.522.5Millions20082009201020112012201320142015Eurostat sbs.0.511.522.5Millions20082009201020112012201320142015Orbis0.511.522.5Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Romania - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

146

0.1.2.3.4millions200520072009201120132015Food, Drink, Tobacco0.2.4.6millions200520072009201120132015Textile, Clothes, Leather0.05.1.15millions200520072009201120132015Wood, Paper, Printing0.05.1.15millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.1.2.3millions200520072009201120132015Ceramics, Metals 0.1.2.3millions200520072009201120132015Optics, Electronics,  Machines0.05.1.15.2.25millions200520072009201120132015Motors, Vehicles0.05.1.15.2millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.2.4.6millions200520072009201120132015Construction0.2.4.6.81millions200520072009201120132015Wholesale and retail0.1.2.3.4.5millions200520072009201120132015Transportation0.05.1.15.2millions200520072009201120132015Accommodation and food0.05.1.15millions200520072009201120132015Info-communication0.02.04.06.08millions200520072009201120132015Real estate service0.05.1.15.2millions200520072009201120132015Professional services0.1.2.3millions200520072009201120132015Administrative services0.1.2.3.4millions200520072009201120132015 Education0.1.2.3.4millions200520072009201120132015Health and social work0.02.04.06.08millions200520072009201120132015Arts, entertainment0.05.1.15millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Sweden

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

147

0.511.522.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.522.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for SE01234millions200420052006200720082009201020112012201320142015Data01234millions200420052006200720082009201020112012201320142015Imputed01234millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.2.4.6.8millions200520102015Manufacturing (C)01234millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.2.4.6.8Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6.8Millions20082009201020112012201320142015Orbis0.2.4.6.8Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.511.522.5Millions20082009201020112012201320142015Eurostat sbs.0.511.522.5Millions20082009201020112012201320142015Orbis0.511.522.5Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Sweden - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

148

0.02.04.06millions200520072009201120132015Food, Drink, Tobacco0.002.004.006.008.01millions200520072009201120132015Textile, Clothes, Leather0.02.04.06.08.1millions200520072009201120132015Wood, Paper, Printing0.01.02.03.04.05millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.05.1.15millions200520072009201120132015Ceramics, Metals 0.05.1.15.2millions200520072009201120132015Optics, Electronics,  Machines0.02.04.06.08.1millions200520072009201120132015Motors, Vehicles0.01.02.03.04.05millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.1.2.3millions200520072009201120132015Construction0.2.4.6millions200520072009201120132015Wholesale and retail0.05.1.15.2.25millions200520072009201120132015Transportation0.05.1.15.2millions200520072009201120132015Accommodation and food0.05.1.15.2millions200520072009201120132015Info-communication0.02.04.06.08millions200520072009201120132015Real estate service0.1.2.3millions200520072009201120132015Professional services0.05.1.15.2.25millions200520072009201120132015Administrative services0.1.2.3.4.5millions200520072009201120132015 Education0.2.4.6.8millions200520072009201120132015Health and social work0.02.04.06.08.1millions200520072009201120132015Arts, entertainment0.02.04.06.08.1millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Slovenia

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

149

0.1.2.3.4.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.1.2.3.4.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for SI0.2.4.6millions200420052006200720082009201020112012201320142015Data0.2.4.6millions200420052006200720082009201020112012201320142015Imputed0.2.4.6millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.05.1.15.2.25millions200520102015Manufacturing (C)0.1.2.3.4.5millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.1-.050.05.1growth200520102015Manufacturing (C)-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.05.1.15.2.25Millions20082009201020112012201320142015Eurostat sbs.0.05.1.15.2.25Millions20082009201020112012201320142015Orbis0.05.1.15.2.25Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.1.2.3.4Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3.4Millions20082009201020112012201320142015Orbis0.1.2.3.4Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Slovenia - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

150

0.005.01.015.02millions200520072009201120132015Food, Drink, Tobacco0.01.02.03millions200520072009201120132015Textile, Clothes, Leather0.005.01.015.02.025millions200520072009201120132015Wood, Paper, Printing0.01.02.03millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.01.02.03.04.05millions200520072009201120132015Ceramics, Metals 0.01.02.03.04.05millions200520072009201120132015Optics, Electronics,  Machines0.005.01.015millions200520072009201120132015Motors, Vehicles0.005.01.015.02millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.02.04.06.08millions200520072009201120132015Construction0.05.1millions200520072009201120132015Wholesale and retail0.01.02.03.04.05millions200520072009201120132015Transportation0.01.02.03millions200520072009201120132015Accommodation and food0.005.01.015.02.025millions200520072009201120132015Info-communication0.002.004.006.008millions200520072009201120132015Real estate service0.01.02.03.04.05millions200520072009201120132015Professional services0.01.02.03.04.05millions200520072009201120132015Administrative services0.02.04.06.08millions200520072009201120132015 Education0.02.04.06millions200520072009201120132015Health and social work0.005.01.015millions200520072009201120132015Arts, entertainment0.002.004.006.008.01millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Slovakia

Figure 1: Employment in Orbis

(a) Estimated and unclassified by sector

(b) Consolidation Level, Imputation and Averaging

Figure 2: Representativeness by Levels and Dynamics

(a) Levels

(b) Dynamics

Figure 3: Representativeness by Size Category

(a) Manufacturing

(b) Services (F-N)

151

0.511.5millions20052006200720082009201020112012201320142015Estimated in Orbisdataestimated employment0.511.5millions20052006200720082009201020112012201320142015Sector classificationsector identifiedunidentifiedStatistics for SK0.511.52millions200420052006200720082009201020112012201320142015Data0.511.52millions200420052006200720082009201020112012201320142015Imputed0.511.52millions200420052006200720082009201020112012201320142015Imputed + AverageC1U1U2LF0.1.2.3.4.5millions200520102015Manufacturing (C)0.511.5millions200520102015Sevices (F-S)EurostatOrbisOrbis+avg-.15-.1-.050.05.1growth200520102015Manufacturing (C)-.15-.1-.050.05.1growth200520102015Sevices (F-S)Orbis growth bandEurostat (aggr)Orbis growth (cont)0.1.2.3.4.5Millions20082009201020112012201320142015Eurostat sbs.0.1.2.3.4.5Millions20082009201020112012201320142015Orbis0.1.2.3.4.5Millions20082009201020112012201320142015Orbis, weightedManufacturing (C)0.2.4.6.8Millions20082009201020112012201320142015Eurostat sbs.0.2.4.6.8Millions20082009201020112012201320142015Orbis0.2.4.6.8Millions20082009201020112012201320142015Orbis, weighted Services (F-N)1-9 employees10-19 20-4950-249250+Slovakia - continued

Figure 4: Representativeness by Levels

(a) Manufacturing

(b) Services (F-N)

152

0.01.02.03.04.05millions200520072009201120132015Food, Drink, Tobacco0.02.04.06.08millions200520072009201120132015Textile, Clothes, Leather0.01.02.03.04millions200520072009201120132015Wood, Paper, Printing0.01.02.03.04.05millions200520072009201120132015Coke, Chemicals, Drugs, Rubber 0.02.04.06.08.1millions200520072009201120132015Ceramics, Metals 0.02.04.06.08.1millions200520072009201120132015Optics, Electronics,  Machines0.02.04.06.08millions200520072009201120132015Motors, Vehicles0.01.02.03.04millions200520072009201120132015Furniture and othersEurostatOrbisOrbis+avg.0.02.04.06.08.1millions200520072009201120132015Construction0.1.2.3millions200520072009201120132015Wholesale and retail0.05.1.15millions200520072009201120132015Transportation0.02.04.06.08millions200520072009201120132015Accommodation and food0.01.02.03.04.05millions200520072009201120132015Info-communication0.01.02.03.04millions200520072009201120132015Real estate service0.02.04.06.08.1millions200520072009201120132015Professional services0.02.04.06.08.1millions200520072009201120132015Administrative services0.05.1.15.2millions200520072009201120132015 Education0.05.1.15millions200520072009201120132015Health and social work0.01.02.03millions200520072009201120132015Arts, entertainment0.005.01.015.02millions200520072009201120132015Other servicesEurostatOrbisOrbis+avg.Appendix C:

Definitions and Formulas

Country Codes

Table 7: List of Countries and Codes

Included in the final sample

Excluded from the final sample

Country

Code Country

Code

Cyprus
Greece
Ireland
Malta
Luxembourg
Netherlands
Poland
United-Kingdom

CY
GR
IE
MT
LU
NL
PL
UK

Austria
Belgium
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Finland
France
Germany
Hungary
Italy
Latvia
Lithuania
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden

AT
BE
BG
HR
CZ
DK
EE
FI
FR
DE
HU
IT
LV
LT
PT
RO
SK
SI
ES
SE

153

Definitions using Formulas

In this section we formalise the definitions presented in Section 3 using mathematics and notations.

Notations

Firm
Time
Total Employment
Universe of firms in an economy
Universe of firms in group category γ

Firm: Size and Net Job Flows

Base Size:

Current Size:

i
t
Et
Φ
Φγ

Sb
i,t

Sc

i,t =

i,t−Sb
(Sb
i,t+Sb
Sb

i,t−1)

i,t−1

Net Job Creation:

Ci,t = (Si,t − Si,t−1)

Net Job Creation Rate:

Aggregate Job Flows

Aggregate Job Creation:

Aggregate Job Destruction:

Aggregate Net Job Creation:

Ji,t = (Si,t−Si,t−1)

Sc

i,t

(cid:80)t

i∈Φ M ax(0, Ci,t)

(cid:80)t

i∈Φ M in(0, Ci,t)

(cid:80)t

i∈Φ Ci,t

Group Category Contribution

Job creation contribution:

Job destruction construction:

2 × (cid:80)t

i∈Φγ M ax(0, Ci,t)/(Et + Et+1)

2 × (cid:80)t

i∈Φγ M in(0, Ci,t)/(Et + Et+1)

Net job creation contribution:

2 × (cid:80)t

i∈Φγ Ci,t/(Et + Et+1)

154

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doi:10.2760/590043  
ISBN 978-92-76-00775-3