Employee flows to improve measures of job creation anddestruction and of firm dynamics. The case of Belgium
Karen Geurts,∗ Monique Ramioul,† Peter Vets‡
June 2009

Abstract
This paper presents a method for improved estimation of measures of firm
dynamics and job creation and destruction. We use employee flow information to re-establish broken links between records of the same firm, and to identify relationships between firms in case of mergers, take-overs, split-offs, andother forms of restructuring. The method is developed on the basis of a linked
employer-employee data set covering all private employment in Belgium.
The employee flow approach results in substantial quality improvement of
the measures discussed. First, it leads to significant reduction of the upward bias
in statistics of firm dynamics and job reallocation. In the period of observation,
we find that 35 to 50 per cent of total entries and exits of firms with at least 10 employees does not coincide with the real opening or closing of a firm. Overall job
creation and destruction levels are revised downwards by 14.5 and 16.6 per centrespectively. Second, correcting for employee flows reduces annual variation inestimated measures, revealing more regular patterns of firm and employment
dynamics, especially at the sectoral level.
Keywords: Firm dynamics, Job creation and destruction, Micro data
JEL-Classification: J23, J21, C81
Paper prepared for the Comparative Analysis of Enterprise Data 2009 Conference, 2-4 October 2009, Hitotsubashi Memorial Hall, Tokyo, Japan∗ HIVA, Katholieke Universiteit Leuven. Email: karen.geurts@hiva.kuleuven.be† HIVA, Katholieke Universiteit Leuven. Email: monique.ramioul@hiva.kuleuven.be‡ Statistics Department of the Belgian Social Security Office, Email: peter.vets@onssrszlss.fgov.be1

1

Introduction1

Measures of net employment growth, usually aggregated at the sectoral or national
level, are still used as predominant indicators to monitor labour market trends, andto compare growth rates in industries and countries. However, these time seriesmask the turbulent process of firm entry and exit, and of job creation and destruction
which takes place continuously in all sectors of the economy. Aggregate employment statistics cannot capture this turbulence, they do not reveal the firm characteristics of success and failure, neither do they allow an analysis of the consequences
for employees of the continuous reallocation of jobs.
Hence, since the late 1970s data on firm dynamics and on job creation and destruction have been studied to enhance understanding of economic turbulence and of job
gain and loss (Birch, 1987; Davis, Haltiwanger & Schuh, 1997; Bartelsman, Scarpetta& Schivardi, 2005; Cahuc & Zylberberg, 2006; Brown, Haltiwanger & Lane, 2006).
These data decompose net employment measures into gross job flows and firm entry
and exit, and therefore facilitate the analysis of the drivers and effects of labour market changes. Empirical studies on job flows have revealed a substantial amount ofchurning underlying net growth rates. Moreover, they have shown that considerable
movements of job creation and destruction often occur simultaneously in the business cycle and within narrowly defined sectors (Davis, Haltiwanger & Schuh, 1997;
Blanchflower & Burgess, 1996; Albaek & Sorensen, 1998; Faggio & Konings, 2001;
Stiglbauer, Stahl, Winter-Ebmer & Zweimüller, 2002; Clayton & Spletzer, 2008). Since
the late 1990s, longitudinal firm level databases have extended research possibilities
in the field and have stimulated academic research on micro-economic drivers andmacro-economic consequences of job creation and destruction, firm dynamics, andtheir impacts on employees (Persson, 1999; Abowd, Corbel & Kramarz, 1999; Foster,Haltiwanger & Krizan, 2001; Baldwin, Beckstead & Girard, 2002; Piekkola & Böckerman, 2002; Davis, Faberman & Haltiwanger, 2005). In policy-relevant research, job
flow data are used as well, for example to study the effect of economic institutions
and policy on employment and productivity growth (Scarpetta, Hemmings, Tresselt
1 This working paper reports results from the research undertaken in the framework of the
KEROSINE-project (Knowledge Economy and Regional Strategies for Organisational and Sustainable Innovation), a project in strategic basic research supported by the Institute for the Promotion of
Innovation by Science and Technology in Flanders. The project is conducted by HIVA and CESO at
KUL and by Vlerick UGent. Promoters are Geert Van Hootegem and Monique Ramioul.

2

& Woo, 2002; Brandt, 2004; Gómez-Salvador, Messina & Vallanti, 2004; Haltiwanger,
Scarpetta & Schweiger, 2006).

Pros and cons of administrative firm level data
Large administrative firm level data sets have become prevalent sources for the estimation of measures of firm dynamics and of job creation and destruction. Crosssectional microdata from business registration, social security, or taxation systems
are used to create longitudinal linked employer-employee data sets both at a national, a European and an OECD level (Albaek & Sorensen, 1998; Baldwin, Dunne &Haltiwanger, 1998; Persson, 1999; Korkeamäki & Kyyrä, 2000; Baldwin et al., 2002;
Eurostat, 2003; Abowd, Haltiwanger & Lane, 2004; Bartelsman et al., 2005). The main
advantages of these data sources are the complete, or nearly complete coverage ofthe target population, access to exact estimates for detailed sub-populations, costeffectiveness, and reduction of the response burden on businesses (Vale, 2003).
Although the advantages of using administrative data are indisputable, these sources
also have some drawbacks, which lead to inaccuracies in the estimations of firm dynamics and hence of job creation and destruction. One of the major problems arises
from failures in the period-to-period linking of individual firms’ records. Individual firms in administrative data sets are usually identified by a unique identification
code, such as the business registration number, which allows the longitudinal integration of firm level information. When, however, the continuity of a firm’s identification code is broken, records are falsely identified as new or closing firms, which
results in an upward bias in firm dynamics and in job flow measures. A second problem is caused by the difficulty to identify changes in firm structure on the basis ofadministrative data, which likewise results in inaccuracies in the estimation of flow
measures.
The first problem, failures in the period-to-period linking of individual firms’ records,
occurs when the identification code of a firm changes. This can be the result of achange in the legal form of the firm, a change of ownership, or simply a changeof accountants (for discussion of the reasons see Vilhuber, 2009 and Baldwin et al.,2002). The disappearance of the previous identification code of a firm from the data
set is recorded as a firm closing involving the destruction of jobs, and the appearanceof a new ID is classified as a new firm creating jobs. Since, however, the new firm3

is economically identical to the previous one, no ’real’ economic demographic event
has taken place (Benedetto, Haltiwanger, Lane & McKinney, 2007; Ahmad, 2008),
neither has the firm’s employment been destroyed or newly created. Such linkage
failures thus falsely define both firm openings and closings and hence yield an upward bias in firm dynamics and in job flow measures (Spletzer, 1998; Brandt, 2004;
Abowd & Vilhuber, 2005).
While the first problem appears in case of broken ’one-to-one’ links of individual
firms, the second problem occurs when multiple firms are involved. Changes infirm structure, such as the fusion of the factors of production of several firms into
one, or the division of a firm into multiple ones, can cause an upward bias in dynamics measures. Consider an example of the many-to-one case, which occurs in
the event of a merger, an acquisition, or more generally a ’consolidation’ (Pinxton &Spletzer, 2002). In this case, one of the previously existing firm IDs disappears fromthe data set, which is then classified as a firm closing involving job destruction. The
successor firm, absorbing the jobs of the previous one(s), is classified as an expanding or a new employer creating jobs. Although the transfer of jobs in case of mergers
and acquisitions is an economically significant event, it is generally accepted that itshould not be included in measures of job creation and destruction (Persson, 1999;
Baldwin et al., 2002; Eurostat, 2003; Benedetto et al., 2007; Ahmad, 2008). Failingto link the records of consolidating firms thus results in an upward bias in job flow
measures. Similarly, it can easily be understood that the opposite ’one-to-many’
event, which occurs in the case of a spin-off, a breakout, or a break-up, also causes
an overestimation of job flow measures.
To address these longitudinal linkage problems in firm level data sets, different
methods have been developed. Commonly applied methods use probabilistic matching based on similarities in partial firm identifiers. More recently, linkage methods
based on employee flows have been developed as a complement or an alternative toprobabilistic matching. This paper contributes to the latter approach by presenting
a method for the establishment of linkages between firm identifiers using employee
flow information. The data that are used stem from a Belgian social security registerwith linked employer-employee data covering 90 per cent of national employment.
The motivation to develop this method has come from the lack of reliable firm level
data on employment and business dynamics in Belgium, which severely hampers
national research on labour market changes. In a recent working paper by the Na4

tional Bank of Belgium on job creation and destruction, for example, the authors
point out that "the calculations are affected by the issue of mergers and acquisitions"(Heuse & Saks, 2009, p. 10). The aim of this paper is to show that a substantial
quality improvement of firm dynamics and job flow measures can be reached by using simple criteria. The presented method is the result of a collaboration between
the statistics department of the Belgian National Social Security Office and the Workand Organization research group of HIVA - Katholieke Universiteit Leuven.
The paper is structured as follows. Section 2 presents a brief overview of existing
methods of linking firm identifiers. Section 3 contains the data and the definitions
that are used. Section 4 describes the procedure that was followed to constructpredecessor-successor links, and the decision rules that were implemented to distinguish between spurious and genuine openings and closings of firms. Section 5
provides an overview of the results for ten types of changes in firm structure or firm
identifier. Section 6 explains the correction formula that is applied to adjust job creation and destruction measures. Section 7 presents the impact of the method on economic estimates: both indicators of firm dynamics and job creation and destruction
measures are discussed. The findings are compared with the results of international
research. Section 8 discusses the weakness of the method presented and proposes
further elaborations. Section 9 concludes the paper.

2

Methods of linking firm identifiers

2.1

Matching based on partial firm identifiers

The one-to-one linkage problem of firm records in administrative data is well understood and has been tackled by a variety of methods. By connecting previous andsubsequent administrative identifiers of firms, these methods reduce the occurrenceof falsely defined births and deaths of firms, thus allowing more accurate job creation and destruction measures. The most commonly adopted methods are basedon probabilistic matching: probable links between firm records are established on
the basis of a comparison of partial identifiers of firms, such as name, location, andindustry. For example, in a harmonized methodology for business demography, Eurostat and OECD recommend a matching process based on three ’continuity factors’:
5name, location and economic activity (Eurostat/OECD, 2007). The US Bureau ofLabor Statistics adopts a probability-based weighted match process using name, address and phone number to link the records of individual establishments (Robertson,
Huff, Mikkelson, Pivetz & Winkler, 1997; Abowd & Vilhuber, 2005). In some countries, probabilistic matching techniques are preceded by administrative editing andreview based on information from surveys and other administrative data, such as information about the firm’s predecessors and successors (Spletzer, 1998; Bycroft, 2003;
Vartiainen, 2005; Clayton & Spletzer, 2008). Matching processes, implemented bysoftware, are commonly complemented by analyst intervention to validate matchesor, if necessary, to manually link the records of large firms (Eurostat/OECD, 2007, p.
36; Clayton & Spletzer, 2008, p. 6). Vilhuber (2009) provides an overview of current
methods.
The second linkage problem, occurring when multiple firms are involved, is moredifficult to address. Some record linkage methods are similarly adopted to identify relationships between firms that are involved in ’consolidations’ or ’breakouts’(Pinxton & Spletzer, 2002; Mikkelson & Unger, 2006). Most of the above-mentioned
methods, however, are not able to fully capture events such as mergers, split-offs,
takeovers, and other forms of restructuring. Furthermore, compiling accurate job
creation and destruction statistics in the case of these events causes additional difficulties (Pinxton & Spletzer, 2004; Eurostat/OECD, 2007, p. 26; Ahmad, 2008, p. 132).
This is where the need for an alternative approach comes in.

2.2

Methods based on employee flows

The above-mentioned linkage processes make use of information on the continuityof controlling legal unit (name), activity (industry), and/or location (address) to establish links between unmatched records of the same firm. These methods do nottake continuity of employment into account. Data on employee flows between firm
identifiers can be used to fill this gap. If one of the main factors of production, theworkforce, is identical in two administrative records, there is a high probability thatthese records relate to the same firm (Eurostat/OECD, 2007, p. 26; Benedetto et al.,2007, p. 6; Ahmad, 2008, p. 132). Hence, continuity of employment, measured
by flows of clusters of employees between two administrative firm identifiers, is astrong criterion to establish links between unmatched records of the same firm. This6

means that a firm is considered continuous when it uses largely the same factors of
production at two points in time. Furthermore, employee flows can be used to identify restructurings events: in case of a ’consolidation’, employees previously registered in different firms are grouped into one firm record, and in case of a ’breakout’,
the employees of one firm are distributed among two or more records. It is clear thatfor the implementation of employee flow approach, linked employer-employee data
are necessary.
Several countries have started to use employee flows to provide more reliable statistics on business and employment dynamics (Persson, 1999; Korkeamäki & Kyyrä,
2000; Baldwin et al., 2002; Stiglbauer et al., 2002; Bycroft, 2003; Benedetto et al.,2007; Vilhuber, 2009). These methods generally follow the same logic: if a significant number, and/or a significant fraction of employees moves from one firm toanother, then a relationship between a ’predecessor’ and a ’successor’ firm can beestablished. Most studies use the employee flow approach in order to remove spurious firm openings and closings from business dynamics statistics. Some also use thisinformation to adjust job creation and destruction measures, or to identify changes
in firm structure (Korkeamäki & Kyyrä, 2000, Mikkelson & Unger, 2006; Benedetto
et al., 2007). In this paper we do both. First, we use data on employee flows to distinguish between genuine firm openings and closings and other demographic events
such as changes in administrative identifier or in organisational structure. Second,
we propose an algorithm to correct year-to-year employment evolutions of firms involved in such events.

3

Data and definitions

The data in this study were obtained from a linked employer-employee data set
which is maintained by the Belgian National Social Security Office (NSSO). NSSO
collects and manages employer and employee social contributions. All employers
hiring one or more employees have to register with the NSSO. They are required
to submit quarterly declarations with information on wages and working times ofevery employee. Upon registration, the employer is assigned a unique identification
number (NSSO number). Equally, every employee is uniquely identified by a social
security number (INSS number). The quarterly declarations are filled out electronically, which ensures continuity of the employer identification, and which renders the7data ready to be processed. Local and provincial public employers are not includedin the NSSO database. For reasons of confidentiality, NSSO data on unincorporated
enterprises have not been included in the analysis presented in this paper.
The statistical units and indicators used in this paper have been developed according to international definitions and recommendations. This enables future incorporation of Belgian results in comparative research. For the main units of analysis active employers, and employer openings and closings - we followed the joint Eurostat and OECD recommendations on business demography data collection (Eurostat/ OECD, 2007; Ahmad, 2008). For indicators on job creation and destruction, we
adopted the standard definitions proposed by Davis, Haltiwanger & Schuh (1997).
The definitions will only be given briefly below. For detail and motivation, we referto the cited references.
The central unit of analysis is the employer. Employers are firms hiring at least
one employee. The definition of a firm in the NSSO database corresponds with thestatistical unit of the enterprise recommended by Eurostat and OECD for businessdemography data collection:“The enterprise is the smallest combination of legal units that is an organisational unit producing goods or services, which benefits from a certain degree of autonomy in decision-making, especially for the allocationof its current resources. An enterprise carries out one or more activities at
one or more locations. An enterprise may be a sole legal unit.” [Council
Regulation (EEC) No 696/93 of 15 March 1993]
Active employers and employer openings and closings in this paper are hence defined at the firm level. Because the quality of job flow measures depends on the correct identification of opening and closing units, job creation and destruction will also
be estimated at the firm level. In large countries such as the U.S., job flow statistics
are computed using establishment level data. The reason for this is that firm level
data mask the job flows between different establishments of the same firm. For the
study of business demography statistics in smaller countries such as the European
ones, the firm is considered to be the most useful level of analysis (Ahmad, 2008;
Eurostat, 2007). Several European studies, therefore, use firm level data for job flow
measures. In smaller countries, the vast majority of firms (90 per cent in Belgium)
8

only have one establishment and thus firms and establishments align (Ahmad, 2008,
p. 128). In smaller regions there is also considerable mobility of employees between
establishments of the same firm, which would falsely be considered as job creationand destruction in an establishment approach. Furthermore, the use of firm level
data enhances European comparative research since there is important variation in
the definition of establishments across countries (Messina & Vallanti, 2007).
In this paper, the terms ’employer’, ’firm’, and ’employer firm’ are treated as synonyms. The industry code of an employer is the code of its primary activity, whichis assigned according to the NACE Rev.1 classification.
The opening of an employer firm - an ’employer enterprise birth’ (Eurostat/OECD,
2007, p. 34) - coincides with the birth of a firm that has at least one employee inthe year of birth and of firms that move from zero to one employee. Reactivations of’dormant’ employers having no employees for less than two years are not considered
as openings. However, reactivations after two years or more, are. Thus far, an opening can be identified by using a simple quantitative criterion: each employer with atleast one employee in year t and no employees in the two previous years, coincides
with an opening. An important restriction to this definition is that no other firms
are involved in the event: openings “do not include entries into the population due
to: mergers, break-ups, split-offs or restructuring of a set of enterprises”, neither dothey include entries which are merely the result of a change of name, ownership, legal form, or activity (Eurostat/OECD, 2007, p. 34). In summary, employer openings
involve “the creation of new production factors, in particular new jobs”. To ensurethat all recorded openings meet this definition, spurious openings will be tracked bymeans of clustered employee flows, and removed from the initial population of firm
entries.
The definition of an employer firm closing - ’employer enterprise death’ (Eurostat/OECD, 2007, p. 51) - mirrors that of an opening: the closing of an employer
firm coincides with the “dissolution of a combination of production factors with therestriction that no other enterprises are involved in the event”. They can be identified initially by a mere quantitative criterion, selecting all employers which move
below the threshold of one employee for at least two years. Exits from the population due to changes of identification number, mergers, takeovers, break-ups, or
restructuring of a set of firms are then removed from the initial population by meansof the employee flow method.
9A job is an employment position held by one employee in one firm. ’Employees’
and ’jobs’ are used as synonyms. Employees supplied by temporary employment
agencies are not counted as personnel of the hiring firm, but of the employment
agency. Quarterly employment at the firm level is measured as the total number of
employees on the last day of the quarter. Annual employment at the firm level ismeasured as the number of employees on the last day of the second quarter (June30).
We adopted the standard definitions for measuring job creation and destruction
Davis, Haltiwanger & Schuh (1997) proposed in their research on U.S. manufacturing data. As explained above, however, all measures are computed at the firm level
instead of at the establishment level. Jobs are counted as the number of persons
employed by an individual firm. (Gross) job creation at time t (JCt ) equals employment gains summed over all firms that expand or start-up between t − 1 and t, and(gross) job destruction (JDt ) equals employment losses summed over all firms that
contract or shut down between t − 1 and t. Total job reallocation (JRt ) can then bemeasured as the sum of job creation and destruction, while net employment change(JNt ) is the difference between the two. In this paper we use annual employmentchanges to estimate job flow measures: let Ei,t denote employment of firm i at theend of the second quarter (June 30) of year t, and Ei,t−1 employment of firm i at theend of the second quarter of year t−1, and let Fexpand , Fcontract , Fopen , and Fclose denotethe subsets of expanding, contracting, opening, and closing employers respectively
between t−1 and t, then
Job creation, JCt =∑(Ei,t − Ei,t−1 ) +∑(Ei,t − Ei,t−1 ) +ieFexpandJob destruction, JDt =∑ (Ei,t − 0)

ieFopenieFcontract∑ (0 − Ei,t−1 )ieFclose

To compare levels of job reallocation across countries, industries, or other subsets,normalized rates of job creation and destruction are used. Job creation and destruction rates between t−1 and t in a subset of firms are calculated by dividing total job
creation JCt and destruction JDt in the subset by the average of total employment inyear t−1 and t in the subset. The averaging of the denominator over two periods
results in job flow rates ranging from -2.0 to +2.0 and reflecting creation and destruction symmetrically. Job reallocation and net growth rates are calculated using the10

same denominator. For rates up to about ±20 per cent, results are approximately thesame as when using the more conventional denominator, i.c. employment in year
t−1. However, potential outliers in the data are smoothed.
It is important to note that it is commonly accepted to measure gross job creationand destruction as aggregate net employment changes at the firm level. This implies
that these measures do not reflect the creation and destruction of jobs within firms,
and are hence an underestimation of real job creation and destruction.

4 Using employee flows to link firms and to identify spurious firm
openings and closingsThis section describes the linkage process developed by the NSSO to link acrosstime firms with a different administrative identifier in two consecutive periods but
employing a workforce which is largely or partially the same. The process results
in data sets linking predecessor and successor firms in two consecutive quarters.
Imposing additional criteria on the predecessor-successor links, two subsets are selected: one including spurious firm openings, the other spurious firm closings. In
section 6, this firm-demographic information is used for the estimation of job creation and destruction measures.

4.1

Linking firms by using employee flows

The point of departure of the NSSO linkage process are quarterly microdata sets of
all labour relations. Containing over 3 million observations every quarter, they each
represent an employer-employee link. Both employers and employees are recordedby means of a unique identification number. It is important to note that since thequarterly employer declarations to the NSSO have been collected electronically, theNSSO microdata are unlikely to be contaminated by measurement error: neither
employers nor employees can be assigned a wrong identification number, and an
employee cannot be linked to an employer which is not his/her.
The linkage process consists of two steps. The first step is to compare all employeremployee links of two successive quarters q−1 and q. Depending on whether they11

exist in both quarters, the links are classified into several groups. Table 6 in Annex summarizes the results for the quarters 2003/2 until 2008/1. Of all employeremployee relationships in a certain quarter, on average 92 per cent is continued in
the next quarter, 3.5 per cent consists of employees moving to another employer in
the next quarter, and 4.5 per cent of employees flowing out of the population. When
compared to the previous quarter, 3.3 per cent represents a transition of employees
from another employer, and 4.9 per cent consists of inflow.
In a second step, the paired quarterly employer-employee links are aggregated at
the employer level. For each employer i and for every pair of quarters q−1 and q, adistinction can thus be made between (1) employees staying with employer i in both
quarters, (2) new employees of i in q coming in from outside NSSO employment,(3) new employees of i in q coming in from other employers in q−1, (4) employees
working at i in q−1 moving out of NSSO employment, and (5) employees of i in q−1
moving to other employers in q. For employees changing employer, the IDs of theprevious or the next employer(s) are retained. The subsets (3) and (5), including employees changing employer, are of specific interest to us. When a ’significant’ clusterof employees moves from employer i to employer j, then a relationship between a
so-called predecessor and successor firm can be established. When, however, only asmall cluster of employees is moving from employer i to employer j, this is considered as individual mobility of employees simply changing jobs.
The data sets containing quarterly predecessor-successor relationships are subsequently used to identify openings and closings of employer firms. Before proceeding, however, we briefly discuss the various decisions rules that can be implemented
to identify ’significant’ clusters of employee flows.

4.2

Significant clusters of employee flows

The basic assumption of the employee flow method is that if two firms have a different administrative identifier in two consecutive periods in time, but employ aworkforce which is largely or partially the same, the IDs relate to the same, or toparts of the same firm. Clusters of employees ’moving’ in the business register fromone firm identifier to another thus give a strong indication about changes in firm
structures and firm identifiers. What the minimum ’significant’ size of the cluster
12

of employees should be in order to establish a link between such a predecessor andsuccessor firm is, however, the subject of academic discussion. Depending on theaim of the study, different relative or absolute thresholds are proposed. The drawback of a low cut-off level is that it risks including a considerable amount of mobilityof individual employees, which are simply job changes. A high threshold, on theother hand, risks failing to capture events involving small firms. Relative minimum
levels between 20 per cent and 80 per cent of the firm’s workforce are used, as well
as absolute cut-off levels or a combination of both (see e.g. Albaek & Sorensen, 1998;
Persson, 1999; Korkeamäki & Kyyrä, 2000; Benedetto et al., 2007; Vilhuber, 2009).
When comparing the relative magnitudes of clustered transitions of at least five employees, Benedetto et al. (2007) found that the majority of transitions is either verysmall, accounting for less than 10 or 20 per cent of the predecessor / successor firm’s
workforce, or very large, including at least 80 per cent or more of the predecessor/ successor firm’s employment. It is reasonable to assume that small transitions aremainly the result of individual mobility of employees between firms, whereas largetransitions principally refer to changes in firm structure or identifier.
In the method described in this paper, we take all movements of clusters of at least
10 employees as a point of departure. These are considered ’significant’ employee
flows on the basis of which a link between a predecessor and successor firm can beestablished. Subsequently, additional criteria are imposed in order to identify spurious firm openings and closings. As to movements of less than 10 employees, we believe there is a high probability that the results of a mere quantitative employee flow
method are affected by individual employee mobility. In order to identify changes
in firm structures and identifiers involving small firms, we therefore propose that
other methods are used to complement the employee flow approach (see discussion
in section 8).

4.3

Identifying spurious firm openings and closings

As explained above, we define spurious firm openings and closings as entries or exits to the population which are not the result of the creation or destruction of new orexisting factors of production, but of changes in firm identifiers or of firm restructurings. Our focus on openings and closings implies that only predecessor-successor
relationships involving at least one entry or exit are taken into account. ’Significant’13employee flows between two continuous firms have not been included in our analysis.
To identify spurious firm openings and closings, two subsets of the predecessorsuccessor links are selected. The first subset singles out ’spurious openings’ andtheir predecessors. Spurious openings are employers who (1) move from zero employment in quarter q−1 to employing at least 10 employees in quarter q (’entries’),
and which are (2) involved in at least one ’significant’ employee flow (of at least 10
employees) from a predecessor in q−1. The second subset selects the ’spurious closings’ and their successors. These are employers who move (1) from employing atleast 10 employees in quarter q−1 to zero employment in quarter q (’exits’), (2) involved in at least one ’significant’ transition of employees (at least 10 employees) toat least one successor in q, and (3) of which at least 75 per cent of the workforce has
moved to another employer in q. The latter additional condition for spurious closings is imposed to distinguish bankruptcies, which are true closings entailing real
job destruction, from changes in firm structures (such as mergers and acquisitions)and in firm identifiers. In case of a bankruptcy, an important part of the workforce
may be recruited by local competitors, certainly in industries facing labour market
shortages. Experience of NSSO experts shows that 75 per cent is a natural upper
cut-off value for this kind of employee flows.2 In other words, the exit of a firm,of which less than 75 per cent of the work force has moved to another employer in
the next quarter, is considered as a real closing. This threshold is close to the 80 per
cent cut-off value which is proposed by Benedetto et al. (2007) to identify significant
employee flows from predecessor firms.
The quarterly subsets of spurious firm openings and closings and their respective
predecessors or successors are compiled into annual data sets containing predecessorsuccessor links. They allow the estimation of the intended economic indicators: measures of firm dynamics and of job creation and destruction on an annual basis. In thefour periods of observation, 2003/04 to 2006/07, a stable share of about 0.75 percent of the employer records in the NSSO register coincides with a spurious opening or closing, or with a predecessor or successor linked to one of these. This does
not seem much, but as will be discussed in section 7, these records represent an important share of apparent openings and closings of employers with more than 10
2 One of the reasons that in case of a bankruptcy not more than 75 per cent of the workforce isrecruited by competitors, is that often in Belgium, part of the personnel makes us of the possibility ofearly retirement.

14

employees. Moreover, they are responsible for a disproportionately large share ofapparent job creation and destruction.

5

Ten types of changes in firm structure

The links between predecessors and successor firms employing largely or partiallythe same workforce can be interpreted as different forms of firm restructuring or ofa change of administrative identifier. In Table 1, ten types of possible relationships
are distinguished on the basis of two criteria: (1) the number of predecessors andsuccessors involved in the event, and (2) the predecessors and successor categories(entry, exit, continuation). Table 7 in Annex reports the relative frequencies of theseten categories in the annual predecessor-successor data sets (2003/04 till 2006/07).
Table 2 below summarizes the results.
As shown in table 1, predecessors are divided into exits, which are employers with apositive employment in year t−1 and zero employment in year t, and continuations,which are firms with a positive employment in both years t−1 and t. Similarly, successors are divided into entries, which have a positive employment in year t−1 andzero employment in year t, and continuations. As explained above, the combination of both a continuing predecessor and a continuing successor does not occur inthe data set because only predecessor-successor relationships involving at least one
entry or exit have been taken into account.
The vast majority of employers in the predecessor-successor data sets are involvedin a one-to-one inter firm relationship (Table 7 in Annex). The major part of these
are combinations of one exiting and one entering employer ID. Since both firms
share a significant part of their workforce, there is a strong indication that such apredecessor-successor link represents a mere change of firm identifier.
Table 2 reports that on average 43.7 per cent of the employers in the predecessorsuccessor data set is involved in a change of firm identifier (group 1). The second
largest category comprises employers involved in a merger or takeover (28.5 percent). A takeover is defined as one or more exiting predecessor linked to an existing
successor. On average 21.6 per cent of the employers in the data set is involved in a
one-to-one takeover, and 4.9 per cent in a many-to-one takeover. Similar to this type15

Table 1: Ten types of predecessor-successor relationships
1 predecessor linked to 1 successor
Successor categories:
Predecessor categories: 1 entry
1 exit Change of firm identifier (group1)
1 continuation Split-off of part of an existing
firm (group 3)
n predecessors linked to 1 successor
Successor categories:
Predecessor categories: 1 entry
1 exit Merger of several firms into a
new firm (group 2)
at least 1 of n continues Merger of several (parts of)firms into a new firm (group 4)
1 predecessor linked to n successors
Successor categories:
Predecessor categories: n entries
1 exit Break-up of 1 firm into several
new firms (group 3)

1 continuation
Takeover of a firm by anexisting firm (group 2)

1 continuation
Takeover of several firms by anexisting firm (group 2)

at least 1 of n is a continuation
Break-up of 1 firm and takeoverof at least 1 of the parts by anexisting firm (group 4)

1 continuation

Split-off of several parts of an
existing firm into new firms(group 3)
n predecessors linked to n successors
Split-off/break-up of an existing firm and takeover of atleast 1 of the parts by another firm which also takes over athird firm (group 4)of restructuring are mergers, consisting of two or more exiting employers linked
to one new employer (2.1 per cent). Because the difference between takeovers andmergers on the basis of these quantitative measures represent a technical rather thanan economic distinction, these events are treated as one category (group 2). A third
major group are split-offs and break-ups (group 3; 22.1 per cent). Again, most ofthese (17.6 per cent) are one-to-one relations, i.c. between a continuing predecessor
and an entering successor. Such events are labeled as split-offs. One-to-many splitoffs, consisting of a link between one continuing predecessor and more than one
entering successor, also occur sporadically (2.5 per cent). A third restructuring eventin group 3 is the break-up of one exiting firm into several entering successors (2.0 percent). Again, the distinction between split-offs and break-ups is rather artificial, so
16Table 2: Number and share of employers involved in a predecessor-successor relationship; Belgium, 2003/04 - 2006/07

Group
1.
2.
3.
4.

Group
1.
2.
3.
4.

Change in firm identifier
Merger, take-overBreak-up, split-off
Combination of 2. and 3.
Rest
Total

Change in firm identifier
Merger, take-overBreak-up, split-off
Combination of 2. and 3.
Rest
Total

2003-04

2004-05

2005-06

2006-07

n
524
440
339
47
21
1371
2003-04

n
510
324
233
88
19
1174
2004-05

n
649
385
281
37
22
1374
2005-06

n
649
372
331
30
26
1408
2006-07

%
38.2
32.1
24.7
3.4
1.5
100

%
43.4
27.6
19.8
7.5
1.6
100

%
47.2
28.0
20.5
2.7
1.6
100

%
46.1
26.4
23.5
2.1
1.8
100

4-yearaverage
n
583
380
296
51
22
1332
4-yearaverage
%
43.7
28.5
22.1
3.9
1.6
100

Source: NSSO - HIVA K.U.Leuven

these events have been grouped together. A last and minor group consists of more
complex relations between firms, such as a mergers of parts of several firms into a
new firm, or many-to-many linkages (group 4; 3.9 per cent). A residual categorycomprises cases which are difficult to interpret, such as linkages which are the resultof changes in social security legislation, or employers who are successively successor
and predecessor in two quarters of the same year.
Once spurious openings and closings have been identified, they can easily be removed from the population of entries and exits. This allows the more accurate identification of real openings and closings of employer firms. The impact of these adjustments on measures of firm dynamics is presented in section 7.

17

6

Adjusting measures of job creation and destruction

Since significant employee flows between firms represent the movement rather thanthe creation and destruction of factors of production, job creation and destruction
measures not taking into account this information are biased. The upward bias ofboth measures is clear: in the case of a predecessor, the bare comparison of employment in year t−1 and t results in an overstatement of job destruction. For successors,
it results in an overstatement of job creation. For example, when firm A, with 50
employees in year t−1, is taken over by firm B, with 100 employees in year t−1 and150 in year t, unadjusted job flow measures will report the destruction of 50 jobs (atthe level of predecessor A) and the creation of 50 jobs (by successor B).
The identification of spurious openings and closings of firms and their respective
predecessors and successors allows the adjustment of annual job flow measures. Below, we propose the formula’s for the adjustment of job creation and destruction at
firm level for the employers involved in a clustered worker flow. The adjustment
is achieved for every pair of years t−1 and t, by correcting the employment levels
in t−1 of the employers involved in a predecessor-successor relationship. The employment levels in t are left unaffected; they are considered as the correct point of
departure for the computation of job flow measures in t. The correction formulawhich is applied imposes a continuation of the workforce at the level of the successor between t−1 and t. In other words, we proceed as if the employees who havemade a transition from the predecessor to the successor between t−1 and t, werealready part the workforce of the successor in t−1. In our example, the employment
levels in t−1 of firm A and B after adjustment will be 0 and 150 respectively, yielding
job creation and destruction measures of zero.
Let Si,t and Si,t−1 denote the registered number of jobs of a successor i in year t andyear t−1, and Pj,t and Pj,t−1 the registered number of jobs of a predecessor i in year t
and year t−1, and let ∑in=1 Si denote the sum of the jobs of the n successors linked tothe same predecessor, and ∑m
j=1 Pj the sum of the jobs of the m predecessors linked
to the same successor, then the adjusted number of jobs of a successor i in year t−1 ,adjSi,t−1 , is given by"adj
Si,t−1 = Si,t−1 +m|Si,t − Si,t−1 |× ∑ Pj,t−1n∑m
j=1 Pj,t + ∑i=1 (|Si,t −Si,t−1 |)
j=1

18

#

adj

and the adjusted number of jobs of a predecessor j in year t−1 , Pj,t−1 , by"Pj,t−1adj
Pj,t−1 = Pj,t−1 −×Zm∑j=1 Pj,t−1
with

n

#

Z = ∑ Si,t−1
adj

i =1

Table 8 in Annex reports the specific versions of the correction formula for the different categories of predecessor-successor links. In most cases, the formula is easy
to interpret. For example, in case of a predecessor exit, the total number of jobs of
the predecessor is assigned to the successor. If an exiting predecessor is linked tomultiple successors, the jobs of the predecessor are distributed over the successors
according to the relative size of the successors in year t. In case of a predecessor continuation, expression of the correction formula is more complicated: only a certain
proportion of the jobs of the predecessor(s) is assigned to the successor: this proportion corresponds to the relative share of the jobs of the successor in the sum of thejobs of the predecessor and the successor in year t. As a last example, in case of multiple successors, the relative shares of the jobs of the predecessor are assigned to thesuccessors according to the successor’s relative sizes in year t. For the specificationof the correction formula in other situations we refer to Table 8.
When applied to the four years of observation, adjusted annual figures of job creation appear to be on average 14 per cent lower than registered job creation, andjob destruction figures are reduced by 17 per cent after adjustment. The next section
presents the main impacts of these corrections on job flow measures.

7 Impacts on measures of firm dynamics and of job creation anddestruction
In this section we compare unadjusted statistics on employer openings and closingsand on job creation and destruction with adjusted measurements based on the employee flow method. The impacts on economic indicators are documented and theresults are compared with international measures.
19

The period the data in this paper relate to is 2003/04 until 2006/07. These were four
years of stable and moderate employment growth without any important aggregate
fluctuations. Unadjusted data, however, show strong annual variation, both in thenumber of firm openings and closings, and in job creation and destruction levels. After adjustment, annual variation is considerably reduced, hence resulting in a more
realistic picture of firm and employment dynamics in the years under consideration.
In order to present the impact of the method on economic estimates, we limited thescope of the data to NACE sections C to K. Thus activities relating to production,
construction, trade, and private services are covered, but agriculture, public administration, and public services such as education and health services are not. The reason for this limited coverage is that it allows us to compare the results for Belgium
with those published by Eurostat for other European countries.
We start by documenting the impact of the employee flow method on firm dynamics
statistics. We then turn to measures of job creation and destruction.

7.1

Employer openings and closings

Table 3 reports unadjusted figures of firm entry and exit and adjusted estimates after
suppression of spurious openings and closings. Because our data set does not contain information on firm employment in 2002, openings can only be defined from2004/05 on and closings only until 2005/06 (see section 3 for definitions). On average, 2.9 per cent of apparent firm openings are spurious ones and 3.5 per cent of all
firms apparently closing down can be identified as spurious closings.
The shares of spurious openings and closings in total firm entry and exit are broken
down by size in Table 9 in Annex. Because our method only identifies spurious
openings and closings of firms with at least 10 employees, the size classes in table
9 are above this threshold. In the period of observation, we find that 35 to 50 per
cent of total entries and exits of employer firms with at least 10 employees does notcorrespond to the real opening or closing of a firm. Not surprisingly, as the size ofthe entering/exiting employer increases, it becomes more probable that it is not areal opening or closing. Of all entering firms with 10 to 19 employees in the yearof entry, almost one in three is the result of clustered employee flow. Once above
the threshold of 100 employees, it becomes very unlikely that a new firm entering
20

Table 3: Unadjusted and adjusted estimates of employer enterprise openings andclosings, and birth and death rates; Belgium, NACE Rev. 1.1 Sections C to K, 2003/04- 2006/07
Openings (n)Unadjusted Adjusted Percentage
difference
2003/04
2004/05
2005/06
2006/07

2003/04
2004/05
2005/06
2006/073-year avg.

13630
13291
13826
13415
14489
14023
Birth rate (%)
Unadjusted Adjusted
10.0
10.0
10.4
10.1

2.5
3.0
3.2

Closings (n)Unadjusted Adjusted Percentage
difference
11363
10966
3.5
11570
11225
3.0
11494
11045
3.9
Death rate (%)
Unadjusted Adjusted
8.7
8.4
8.6
8.4
8.5
8.1

9.8
9.7
10.0
9.8

8.6

8.3

Source: NSSO - HIVA K.U.Leuven

the data register coincides with the real opening of an employer. The correction of
employer closings shows a similar pattern. Of all employers disappearing from thedata register, and employing 10 to 19 employees in the year of exit, 25 to 36 percent does not correspond to the real closing of an employer firm. Among employer
closings with more than 100 employees, 50 to 100 per cent of the exits is not a real
closing.
What is the impact of these corrections on common indicators of firm dynamics? We
first discuss birth and death rates of employer firms. Birth rates in the period t−1 to t
are calculated as the number of employer openings in t−1 to t divided by the numberof active employers in t. Death rates in the period t−1 to t are calculated as thenumber of employer closings in t−1 to t divided by the number of active employers
t−1. Since spurious openings and closings represent a relatively small share of total
firm entry and exit, birth and death rates of employer firms after correction are only
slightly lower than unadjusted rates (Table 3). In the three years under consideration,birth rates drop on average from 10.1 to 9.8 per cent, and death rates from 8.6 to 8.3
per cent.
Birth and death rates allow the comparison of the levels of entrepreneurship acrosscountries and across industries. As an example, in Figure 1 in Annex the unadjusted
21

and adjusted birth rates for Belgium are compared with the available estimates for
the European member states published by Eurostat (Schrör, 2008; http://epp.Eurostat.ec.europa.eu).
The estimates for the member states are produced according to the definitions discussed in section 3. They cover the same sectors as the Belgian results (NACE C to K).
Having relatively low employer firm birth rates, Belgium’s position in the rankingof available estimates for the other member states does not change after correction
for spurious openings.
Although spurious openings make up only a small share of total firm entries, they
represent a disproportionally large share of total employment created by apparently
new employers. This is where the importance of the correction method becomes
clear. Table 4 shows that annual job creation by new employers on the basis of unadjusted measures ranges from 54 000 to 68 000 jobs in the years under consideration.
Between 35.6 per cent and 48.5 per cent of this apparent job creation is the result ofclustered employee flows moving from other employers. After correction, annual
job creation by new employers is reduced to a range of 35 000 to 36 500 jobs. Not
only is this a considerable decrease, the impact of the correction is also that the hugeannual differences in job creation by new employers are flattened to smooth fluctuations, which certainly is a more realistic picture of the contribution of firm openings
to employment in the period considered. The effect of correcting for spurious closings on job destruction by exiting employers is similar. Total job destruction due toemployer closings is reduced by more than one third, and annual leaps are flattened
considerably. Persson (1998) and Korkeamäki & Kyyrä (2000), studying the impactof employee flow corrections on the basis of Swedish and Finnish data respectively,
report equally high impacts of spurious openings and closings on estimates of job
creation and destruction associated with firm births and deaths.
To allow a cross-country or cross-industry comparison of the (direct) contributionof births and deaths to employment, normalized rates of job creation and destruction are used. These rates are calculated by dividing total employment in opening
or closing employers by the average of total employment in all active employers in
the period concerned. The impact of correcting for spurious openings and closingson these indicators is shown in Table 4. After correction, job creation rates by employer openings are reduced from 2.8 to 1.7 per cent on average, and job destruction
rates associated with employer closings are reduced from 2.9 to 1.8 per cent. As isevident from Figure 2 in Annex, this adjustment considerably changes the position
22

Table 4: Unadjusted and adjusted estimates of job creation and destruction by opening and closing employer enterprises; Belgium, NACE Rev. 1.1 Sections C to K ,2003/04 - 2006/07
Job creationby employer openings (n)Unadjusted Adjusted Percentage
difference
2003/04
2004/05
2005/06
2006/073-year avg.

58241
54451
68044

36522
35054
35076

37.3
35.6
48.5
40.5

Job creation rateby openings (%)
Unadjusted Adjusted
2003/04
2004/05
2005/06
2006/073-year avg.

2.8
2.6
3.2
2.8

Job destruction
by employer closings (n)Unadjusted Adjusted Percentage
difference
62722
37383
40.4
60288
39823
33.9
56336
35339
37.3
37.2
Job destruction rateby closings (%)
Unadjusted Adjusted
3.0
1.8
2.9
1.9
2.7
1.7

1.7
1.7
1.6
1.7

2.9

1.8

Source: NSSO - HIVA K.U.Leuvenof Belgium in an international comparison of employment creation by newly bornfirms.

7.2

Job creation and destruction

Finally, we turn to measures of job creation and destruction. Table 5 compares unadjusted and adjusted estimates. In the four years under consideration, on average
14.5 per cent of apparent total job creation and 16.6 per cent of apparent total job destruction is the result of clustered employee flows. A large part of this is accounted
for by spurious openings and closings of firms, as illustrated in the previous paragraphs. The other part is explained by existing firms taking over (parts of) otherfirms, in case of job creation; and by split-offs or mergers of parts of existing firms,
in case of job destruction. Real job creation and destruction is not only lower than it
appears from unadjusted data, annual fluctuations are also leveled off. It should be
mentioned that the slight differences between net employment evolutions before andafter adjustment are caused by employee flows between predecessors and successors
23which do not belong both to the population of selected sectors (NACE C to K). We
Table 5: Unadjusted and adjusted estimates of job creation and destruction; Belgium,
NACE Rev. 1.1 Sections C to K, 2003/04 - 2006/07

2003/04
2004/05
2005/06
2006/074-year avg.

2003/04
2004/05
2005/06
2006/074-year avg.

Job creation (n)Unadjusted Adjusted Percentage
difference
185336159023
14.2
187178160076
14.5
19096816820411.9
22502618629817.2
14.5
Job creation rate (%)
Unadjusted Adjusted
Ppt
difference
9.0
7.7
1.3
9.0
7.7
1.3
9.0
7.9
1.1
10.4
8.6
1.8
9.3
8.0
1.4

Job destruction (n)Unadjusted Adjusted Percentage
difference
170775
14433215.5
15767513377415.2
160231137801
14.0
17579413734021.9
16.6
Job destruction rate (%)
Unadjusted Adjusted
Ppt
difference
8.3
7.0
1.3
7.6
6.4
1.1
7.6
6.5
1.1
8.1
6.4
1.8
7.9
6.6
1.3

Source: NSSO - HIVA K.U.Leuven

conclude with rates of job creation and destruction, which allow the comparison of
results across industries and countries. The impact of the use of the employee flow
approach on overall rates of job creation and destruction is considerable. The impact
on industry-specific job creation and destruction rates is even more pronounced. Table 5 shows that unadjusted job creation rates are reduced by 1.1 to 1.8 percentage
points after correcting for employee flows. This results in real job creation rates ofbetween 7.7 and 8.6 per cent in the four years considered, which are markedly lower
rates than those of other countries. The adjustments of job destruction rates are of
the same size and result in job destruction rates of between 6.4 and 7.0 per cent.
The differences between unadjusted and adjusted measures broken down by industry are presented in Table 10 in Annex. A first conclusion is that job creation anddestruction rates are considerably reduced after correcting for employee flows, although not to the same extent in all years and industries. In NACE DI (Manufactureof other non-metallic mineral products), DJ (Manufacture of basic metals and fabricated metal products) and DL (Manufacture of electrical and optical equipment), forexample, certain annual rates are reduced by 4 percentage points or more, whereas
in other sectors and years, unadjusted and adjusted rates do not differ. Second, and24

in line with the general trends discussed above, in most sectors, adjusted measures
show less annual fluctuation in job creation and destruction. Where unadjusted measures report puzzling annual leaps in industry-specific job creation or destruction
rates, these are mostly substantially reduced after correcting for employee flows.
A third and last conclusion is that ’traditional’ differences in job reallocation levels
across industries remain unaffected by the correction method: industries with highjob reallocation rates, such as construction (NACE F), hotel and restaurant sector(NACE H) and business services (NACE K), remain the ones with the highest levels
after adjustment, and the gap between relatively low job creation and destruction
rates in manufacturing and relatively high rates in services exists both before andafter correction. In summary, we can conclude that the employee flow approach results in overall lower levels of job creation and destruction mainly by eliminating
unreal annual leaps in specific sectors.
It is interesting to note how these adjustments affect the position of Belgium in an international comparison of job reallocation measures. Unlike statistics related to employer openings and closings, an internationally harmonized framework for job creation and destruction statistics does not exist. Although the calculation of the rates
proposed by Davis & Haltiwanger is generally adopted, definitions of the building
components (firm/establishment, employer, job) vary across countries. Moreover,the coverage of the population varies strongly as well, with regard to sectoral activity
as well as to other aspects of the data. Nevertheless, when compared to other recent
studies which adopt similar definitions and coverage, the job flow rates we foundusing the proposed method, are markedly lower. Studies covering both manufacturing and services, usually report annual job creation and destruction rates above 10
per cent, or in the U.S even above 15 per cent (see e.g. Persson, 1998; Piekkola andBöckerman, 2002; Pinkston and Spletzer, 2004). Even for a continental, ’rigid’ economy as the one in Belgium, and in an economically favorable period, job creationand destruction rates of 8.0 and 6.6 per cent respectively are extremely low.
In a recent working paper on job flows in Belgium, based on another data source, theNational Bank of Belgium (NBB) reports job creation and destruction rates in the four
years considered which are, on average, equally low (Heuse & Saks, 2009). Since nocorrection for changes in firm structure was made, one would expect these rates to behigher than the adjusted measures found in this paper. The NBB results, however,exclude temporary work agencies (NACE 74.5) and do not reach full coverage in25

small firms. Exactly these two firm categories are characterized by typically high job
reallocation, hence excluding them lowers average rates. Despite similar averages,
however, the variation in annual job creation and destruction rates reported in thispaper is much smaller than the annual variation reported by the NBB. This is in linewith our conclusion that correcting for employee flows reduces annual fluctuations.
Four explanations can account for the low job creation and destruction rates for Belgium reported in this paper. First, the Belgian economy has a rather rigid labour
market where employment protection legislation prevents employers from causing
strong cyclical adjustments in the workforce (Heuse & Saks, 2009). Second, the period the data in this paper relate to was a short and economically stable one. Third,
the firm level was used for analysis, and not the establishment level, as is the casein most other studies on job flows. This implies that our data do not account forjob reallocation taking place across establishments of the same firm. And finally, by
adopting the employee flow approach, 15 per cent of apparent job flows could betraced as not corresponding to the real creation or destruction of jobs. Hence, annual
job creation and destrcution rates had to be revised downwards by 1.4 percentage
points on average.

8

Discussion

The presented method for the adjustment of measures of firm dynamics and of job
creation and destruction does not take into account changes in firm structure and in
firm identifiers involving employee flows of less than 10 employees. This means thatit does not provide a criterion to distinguish between genuine and spurious openings and closings, and real and false job creation and destruction in small firms. The
reason why a lower cut-off level for the tracking of clustered employee flows is not
used, is because we believe that flows of less than 10 employees possibly include
numerous cases of individual mobility of employees simply changing jobs. Therefore, in the case of small firms, the merely quantitative criteria of the employee flow
approach should be complemented by a more qualitative approach based on additional information from other data sets. A possible way of proceeding could be to
replicate the proposed method for clustered employee flows of less than 10 employees, and then randomly check the predecessor-successor relationships on the basis
26

of other administrative registers. The results of this sample can then be extrapolated
to the total population.

9

Conclusion

Overestimation in measures of firm dynamics and hence job creation and destruction
based on large administrative firm level data sets is well understood. The failure toproperly link the identifiers of one and the same employer, and of predecessor andsuccessor firms involved in a restructuring, results in an upward bias in statistics offirm dynamics and job reallocation. Commonly applied methods to address these
problems use probabilistic matching based on similarities in partial firm identifiers.
More recently, alternative linkage methods based on employee flows have been developed in the Scandinavian countries and the U.S. In keeping with this novel approach, this paper presents a method for the establishment of linkages between firm
identifiers by using employee flow information. The lack of reliable firm level dataon employment and business dynamics in Belgium has urged the development of
a new approach. Taking advantage of the possibilities of a rich employer-employee
data set covering all private employment in Belgium, the method was developed on
the basis of a limited number of years (2003-2007). After evaluation, it is the intention to extend the analysis to a longer period, starting at the end of the 1990s, and to
implement it as a standardized method, allowing annual updates of the results. Thisshould lead to the establishment of a longitudinal linked employer-employee data
set in Belgium, containing accurate information on firm and employment dynamics,
and opening a range of possibilities in national and comparative research into labour
market changes.
The method presented in this paper takes into account all movements of clusters ofat least 10 employees between two firm identifiers to establish links between predecessor and successor firms. These links are interpreted as different forms of firm
restructuring, of which ten types are distinguished. Additional criteria are then imposed in order to identify spurious firm openings and closings, such as thresholds
for the employment level in the opening or closing year. This information is further
used to adjust measures of firm dynamics and job creation and destruction.
The first conclusion is that restoring missing links between firms by making use of27

clustered employee flows leads to a significant reduction of the upward bias in statistics of firm dynamics and of job reallocation. In the period of observation, we findthat 35 to 50 per cent of total entries and exits of employer firms with at least 10
employees does not coincide with the real opening or closing of a firm. Not surprisingly, as the size of the entering of exiting firm increases, it becomes more likely thatan entering or exiting does not correspond to a real opening or closing. After correcting for these ’spurious’ events, the real contribution of firm births and deaths toannual job creation and destruction turns out to be considerably lower than is generally concluded on the basis of unadjusted data. Overall job creation and destructionlevels are revised downwards as well after adjustment (by 14.5 and 16.6 per cent).
This results in markedly lower annual job creation rates of around 8.0 per cent in thefour years considered, and job destruction rates of around 6.6 per cent.
Secondly, the applied method reveals that annual variation in firm and employment
dynamics measures is substantially lower than it appears from unadjusted data. Unadjusted data often report strong annual fluctuations, both in the number of firm
openings and closings and in job creation and destruction levels. After adjustment,these fluctuations are considerably flattened, revealing more regular annual patterns
in firm and employment dynamics. This is particularly true at the sectoral level. Puzzling annual leaps in industry-specific job creation or destruction rates are mostly
substantially reduced after correcting for employee flows. This certainly yields a
more realistic picture of employment changes in the considered period.
A third and last conclusion is that ’traditional’ average differences in job reallocationlevels across industries remain unaffected by the correction method. For example,
the gap between typically high job creation and destruction rates in services compared to low rates in manufacturing, is not narrowed.
In summary, we can conclude that the employee flow approach results in overalllower levels of firm births and deaths and job creation and destruction, mainly by
eliminating unreal annual leaps in specific sectors. The difference in results illustrates the importance of correcting for spurious openings and closings when using firm level data for macro- and micro-economic research. It shows that unadjusted data may have a considerable distorting impact on findings, especially whenindustry-specific patterns are considered.

28

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30

[26] Haltiwanger J.C., Scarpetta S. & Schweiger H. (2006), Assessing job flows across countries: the role of industry, firm size, and regulations, World Bank Policy Research Working Paper 4070.
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University of Chicago Press.

A

Appendix

Figure 1: Comparison of employer enterprise birth rates, business economy (NACE
Rev. 1.1 Sections C to K excluding 74.15); 2004/05
18

%

16
14
12
10
8
6
4
2

Ita
ly
Au
st
ria

La
tv

ia
Fi
nl
an
N
>
et
d
Be
he
lg
rla
iu
nd
m
s
un
>
ad
Be
j
us
lg
te
iu
m
d
ad
ju
st
ed

R

om
an
ia
Sl
ov
ak
ia
Es
to
ni
a
Li
th
ua
ni
a
Sp
ai
n
D
en
C
m
ze
ar
ch
k
R
ep
u
bl
Lu
ic
xe
m
bo
ur
g
H
un
ga
ry
Bu
lg
ar
ia

0

Source: Eurostat (SBS, Business demography); for Belgium NSSO - HIVA K.U.Leuven

32

Figure 2: Share of employment in newly born enterprises, business economy (NACE
Rev. 1.1 Sections C to K excluding 74.15); 2005
7

%

6
5
4
3
2
1

Sp
ai
n
D
en
m
ar
k
R
o
C
m
ze
an
ch
ia
R
ep
ub
li c
H
un
ga
ry
Fi
nl
an
d
La
tv
ia
Li
th
ua
ni
a
Bu
lg
ar
ia
Es
to
ni
>
a
Be
lg
iu
Ita
m
ly
un
ad
ju
st
ed
Au
st
Lu
ria
xe
m
bo
ur
N
g
e
t
>
he
Be
rl a
lg
nd
iu
s
m
ad
ju
st
ed

Sl
ov
ak
ia

0

Source: Eurostat (SBS, Business demography); for Belgium NSSO - HIVA K.U.Leuven

33Quarter q-1
2003/q2
2003/q3
2003/q4
2004/q1
2004/q2
2004/q3
2004/q4
2005/q1
2005/q2
2005/q3
2005/q4
2006/q1
2006/q2
2006/q3
2006/q4
2007/q1
2007/q2
2007/q3
2007/q4
2008/q1
Average

Total
employeremployee
links in q-1
31979483184043
31806873207132
3229828
3225853
3229666
32553453274820326977732528623293002
3311646332706633145273357427
336587133929083394801
3427770

Links
continued
in q292849329348112928932
29907882960200
2979982
2899443
303055530024963018441
300514030593623031915305577330414923097957
308685431273853124728
3179879

Employeetransition
to other
employer
97720
93129
11307793082
10669692273
19755795848
10654394458
120485108741
120865112613131691120164120813108212135303114205
Employeeoutflowfrom thepopulation
171735156103138678123262
162932
153598132666128942165781
156878127237124899158866
158680141344139306
158204157311134770133686

Links not continued in q
Employeeinflow intothepopulation
154406154785
162635144742
163482162270161937150537165291145215170024
146835178798150619177609150293
188925163319170610

Employeetransition
from other
employer
93048
88023
10961490122
10187886886
19100893088
10170388997
117081104207116083106908128154117021116433103457131785

New link in q
Total
employeremployee
links in q-1
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Links
continued
in q
91.6%
92.2%
92.1%
93.3%
91.7%
92.4%
89.8%
93.1%
91.7%
92.3%
92.4%
92.9%
91.6%
91.8%
91.8%
92.3%
91.7%
92.2%
92.0%
92.8%
92.1%

Employeetransition
to other
employer
3.1%
2.9%
3.6%
2.9%
3.3%
2.9%
6.1%
2.9%
3.3%
2.9%
3.7%
3.3%
3.6%
3.4%
4.0%
3.6%
3.6%
3.2%
4.0%
3.3%
3.5%
Employeeoutflowfrom thepopulation
5.4%
4.9%
4.4%
3.8%
5.0%
4.8%
4.1%
4.0%
5.1%
4.8%
3.9%
3.8%
4.8%
4.8%
4.3%
4.1%
4.7%
4.6%
4.0%
3.9%
4.5%

Links not continued in q

2.9%
2.8%
3.4%
2.8%
3.2%
2.7%
5.9%
2.8%
3.1%
2.7%
3.6%
3.1%
3.5%
3.2%
3.8%
3.5%
3.4%
3.0%
3.8%
3.3%

Employeetransition
from other
employer
4.8%
4.9%
5.1%
4.5%
5.1%
5.0%
5.0%
4.6%
5.1%
4.5%
5.2%
4.4%
5.4%
4.5%
5.3%
4.5%
5.6%
4.8%
5.0%
4.9%

Employeeinflow intothepopulation

New link in q

Table 6: Categories of employer-employee links in two consecutive quarters. Belgium, quarters 2003/2 - 2008/1

34

Source: NSSO

Table 7: Ten types of predecessor-successor relationships: relative frequencies in theannual predecessor-successor data sets; Belgium, 2003/04 - 2006/07
Successor
categories
1 entry

1 to 1
1 exit1 continuation

2003/04
2004/05
2005/06
2006/07
Average
2003/04
2004/05
2005/06
2006/07
Averagen to 1

Predecessor categories

n exits

min 1 continues

1 continuationn to nUnclear

Total

23.9
22.3
20.7
19.5
21.6

1 entry

1 contin.

2003/04
2004/05
2005/06
2006/07
Average
2003/04
2004/05
2005/06
2006/07
Average

2.4
1.8
1.4
2.7
2.1
1.6
1.3
1.1
1.7
1.4
n entries

5.8
3.5
5.9
4.3
4.9

2003/04
2004/05
2005/06
2006/07
Average
2003/04
2004/05
2005/06
2006/07
Average

4.2
1.5
1.6
0.9
2.0
4.7
1.3
1.0
2.9
2.5

1 to n1 exit1 contin.

38.2
43.4
47.2
46.1
43.7
15.9
17.0
17.8
19.7
17.6

min 1 cont.
1.1
5.2
0.9
0.4
1.9

2003/04

0.7

2004/05
2005/06
2006/07

1.0
0.7
0.0

Average

0.6

2003/04
2004/05
2005/06
2006/07

1.5
1.6
1.6
1.8

Average

1.6

2003/04
2004/05
2005/06
2006/07

100.0
100.0
100.0
100.0

Average

100.0

35
Source: NSSO - HIVA K.U.LeuvenTable 8: Correction formulas for the adjustment of job creation and destruction measuresȱȱȱȱȱȱȱȱȱȱȱȱȱȱȱ¢ȱȬŗȱ¢ȱȬŗȱ¢ȱȱ

¢ȱȱ

¢ȱȱ¢ȱȱ

ȱ

ȱ
ȱ

Ȭŗȱ

Ȭŗȱ

ŗȱȱȱȱŗȱ

1 to 1

Pt −1

0

0

St

0

Pt −1

Pt −1

0

S t −1St0

Pt −1 + S t −1

Pt −1

Pt

0

St

Pt −1 − [Z 1 ] St∗ Pt −1  = Z 1 Pt + S tȱȱȱȱŗȱȱ∑Pm

Pj ,t −10

Pj ,t −1

0

0

St

0

St

0

St




 P
j ,t −1∗ Z2 
Pj ,t −1 −  m
Pj ,t −1 j =1j =1∑Pm

S t −1

m to 1

Pj ,t −1

Pj ,t

0

j ,t −1

j =1∑

j ,t −1+ S t −1





m
St


∗
P
j ,t −1 = Z 2
m


j =1
Pj ,t + S t j =1∑∑ŗȱȱȱȱȱȱS i ,t

Pt −1

0

0

∗ Pt −10∑ S i ,tS i ,t0 S −Si ,ti ,t −1
S i ,t −1 +  nS i ,t − S i ,t −1 i =1

S i ,tPt −1 − ∑ [Z 3 ]S i ,tn

i =1

1 to nPt −1

0

S i ,t −1

n

Pt −1

Pt

0

i =1

36

∑



 ∗ Pt −1








S i ,t = Z3
∗
P
t
−
1
n


P
+
S
i ,t
 t

i =1



∑

Table 9: Unadjusted and adjusted job creation and destruction rate by sector; Belgium, NACE Rev. 1.1 Sections C to K, 2003/04 - 2006/07
Job creation rate
NACE Section
C
Unadjusted
C
Adjusted
DA
Unadjusted
DA
Adjusted
Unadjusted
DB-DC
DB-DC
AdjustedDD
Unadjusted
DD
Adjusted
DE
Unadjusted
DE
Adjusted
Unadjusted
DF-DG
DF-DG
AdjustedDH
Unadjusted
DH
AdjustedDI
Unadjusted
DI
AdjustedDJ
Unadjusted
DJ
Adjusted
DK
Unadjusted
DK
AdjustedDL
Unadjusted
DL
AdjustedDM
Unadjusted
DM
Adjusted
DN
Unadjusted
DN
Adjusted
E
Unadjusted
E
AdjustedF
Unadjusted
F
AdjustedG
Unadjusted
G
Adjusted
H
Unadjusted
H
Adjusted
I
Unadjusted
I
AdjustedJ
Unadjusted
J
Adjusted
K*
Unadjusted
K*
Adjusted
74.502
Unadjusted
74.502Adjusted
74.15
Unadjusted
74.15
Adjusted
Total
Unadjusted
Total
Adjusted

2003/04
%
6.5
3.4
6.8
5.8
2.9
2.2
7.9
6.9
4.8
4.2
5.9
2.2
7.2
3.7
4.4
3.8
5.1
4.4
5.7
4.9
11.2
1.9
6.0
5.8
6.9
5.8
1.7
1.7
10.8
10.0
10.0
8.7
21.0
18.4
6.0
5.6
3.7
3.3
12.5
10.9
12.2
12.2
18.2
15.8
9.0
7.7

2004/05
%
3.7
3.7
7.2
6.3
3.5
2.9
10.2
6.3
4.5
4.1
5.2
2.9
4.7
3.2
8.3
4.3
9.7
7.9
5.8
4.6
4.6
3.5
3.7
3.6
5.8
5.1
1.8
1.8
11.8
10.9
8.8
8.1
21.4
18.5
5.7
5.0
3.4
2.8
14.4
12.5
10.9
7.4
11.4
11.0
9.0
7.7

2005/06
%
5.0
2.9
6.3
5.1
4.6
2.7
6.1
5.8
6.1
4.7
4.4
3.2
6.2
3.4
5.0
4.7
8.2
6.8
5.4
4.9
3.2
2.6
3.8
3.4
7.4
5.3
5.5
5.4
13.7
12.8
8.9
8.1
15.6
14.7
5.6
4.9
3.9
2.9
16.2
13.9
8.8
8.5
17.0
15.4
9.0
7.9

Job destruction rate
2006/07
%
3.3
3.3
5.8
5.5
5.5
3.6
6.1
5.8
5.6
4.3
7.2
6.0
5.5
4.8
7.1
3.8
10.5
5.8
7.7
6.9
8.5
5.5
4.0
3.2
8.7
6.2
10.4
7.4
12.8
12.1
10.6
8.9
16.4
15.4
7.5
6.0
5.6
4.2
16.9
13.3
12.7
11.7
10.6
9.5
10.4
8.6

Source: NSSO - HIVA K.U.Leuven37

2003/04
%
-7.9
-4.8
-6.8
-5.8
-8.7
-8.0
-8.1
-6.5
-6.8
-5.9
-7.8
-4.1
-10.5
-5.7
-6.8
-6.4
-8.6
-7.4
-6.6
-5.8
-14.8
-8.8
-9.4
-9.0
-9.9
-9.2
-7.2
-7.2
-10.8
-9.5
-8.3
-7.0
-17.6
-14.9
-5.5
-5.0
-5.8
-5.4
-9.2
-7.8
-1.6
-1.6
-14.6
-13.2
-8.3
-7.0

2004/05
%
-4.7
-4.7
-7.3
-6.4
-10.2
-9.5
-9.5
-5.5
-6.2
-6.0
-5.6
-3.5
-5.3
-4.5
-9.6
-5.5
-6.7
-5.1
-6.0
-4.1
-9.2
-8.2
-3.9
-3.9
-10.0
-9.3
-2.8
-2.8
-10.4
-9.4
-7.2
-6.4
-16.2
-13.2
-6.4
-5.8
-4.6
-3.4
-9.1
-7.9
-3.6
-2.1
-8.5
-6.4
-7.6
-6.4

2005/06
%
-5.0
-3.5
-6.2
-5.2
-10.8
-9.0
-5.9
-5.8
-7.6
-6.2
-3.5
-2.9
-8.4
-4.7
-5.9
-5.6
-9.2
-7.8
-3.9
-3.3
-5.8
-5.6
-4.3
-3.9
-10.6
-7.7
-0.4
-0.3
-9.2
-8.3
-7.7
-6.9
-16.4
-15.5
-5.2
-4.6
-4.1
-3.1
-9.4
-7.3
-6.7
-6.4
-13.2
-6.9
-7.6
-6.5

2006/07
%
-4.1
-3.4
-5.6
-5.0
-10.1
-8.3
-6.4
-5.6
-7.7
-6.4
-6.6
-5.3
-3.9
-3.5
-5.1
-3.9
-9.8
-5.0
-3.0
-2.4
-11.9
-6.7
-9.8
-10.0
-12.9
-7.6
-5.7
-2.7
-9.4
-8.7
-7.6
-6.2
-15.1
-14.1
-7.0
-5.4
-4.9
-3.2
-10.1
-6.7
-4.7
-3.6
-17.7
-13.6
-8.1
-6.4