Corporate growth, age and ownership structure: empirical evidence in Spanish firms/Kompanijos dydis, amzius ir nuosavybes struktura: empirinis tyrimas Ispanijos imonese.
de Jorge Moreno, Justo ; Castillo, Leopoldo Laborda
1. Introduction
The concept of corporate mobility refers to the process of entry
into and exit from markets of firms and their units. This flow has been
one of the most useful means of explaining the evolution of companies
and their adaptation to their environment. All this has been the object
of much attention from the theoretical perspective, but it has not had
the equivalent empirical attention until recently, perhaps due in large
part to the difficulty of measuring it statistically as Baldwin, Geroski
(1989). In particular, most research on firm turnover and the factors
characterising it focuses on the industrial sector, where authors such
as Dunne et al. (1988), Acs and Audretsch (1990), Baldwin (1995), Sutton
(1997), Caves (1998), Arauzo and Segarra (2005), Rinaldi (2008) find
certain regularities in the dynamics of firms in markets.
Market entry and exit of firms or units is an interesting way of
observing the evolution and adaptation of these productive units to
their environments. In this respect, the literature appears to indicate
that although the theoretical perspective has been object of attention,
the empirical aspect has been somewhat neglected, with analysis of firm
creation and survival concentrating on manufacturing sectors.
In this context, the objective of this work is to analyze corporate
mobility among the different sectors of the Spanish economy (according
to their 1-digit CNAE codes (1), comparing new entrants (ex novo) and
established firms. Within this process of firm mobility analyzed in this
work, we attempt to provide answers to the following questions: i) how
do firms enter markets according to the different annual cohorts and
considering the sector of activity? In this respect, we consider the
size of the new entrants compared to the size of the established firms.
This leads us to ask: ii) can we explain the firms' evolution after
entering the market (post-entry behavior? If yes, do firms of different
sectors, ages or growth rates behave similarly? iii) In terms of
firms' evolution in their markets, do small or medium-sized firms
grow faster than large ones? Is the age of the firm a determinant of its
dynamics? From work such as that of Evans (1987) and Hall (1987) we
observe the existence of a positive relation between a firm's size
and its probability of survival.
There have been relatively few studies tackling these questions in
general terms and in particular for the case of Spain until recent
times. In consequence, we class this work among the group of novel
analyses necessary to understand the corporate spirit in Spain. If there
are not many empirical works studying topics relating to firm creation
and consolidation, there are even fewer analyzing the behavior and
trajectories of firms beyond the initial period of mortality of young
firms.
Analyzing the questions posed in this work about the behavior of
firms entering or exiting markets or established firms, as well as the
factors that characterize them, should help company managers understand
not only how the resources and capabilities in terms of size and
accumulated experience evolve in a sector of activity, but also how they
best adjust under the perspectives of both new entrants and established
firms.
This work is organized as follows: in the second section we present
the theoretical literatures in corporate dynamism. The third section is
concerned with the data. Section 4 describes empirical analysis and
results, beginning first with the characteristics of the new entrants,
comparing new entrants and established firms in function of the sector
of activity. The section closes with the growth of the established
firms, its relation with their age and size, and the firm's
mobility and transition between sectors. Finally in Section 5 we provide
a summary of the most relevant conclusions.
2. Theoretical literatures in corporate dynamism: mobility and
transition
The different theories on corporate mobility provide us with
guidelines in our attempt to answer the previous questions. For example,
the theory of passive learning, with Jovanovic (1982) as its strongest
supporter. The main argument in this approach is that firms do not a
priori know their own cost structures. If this proves to be competitive,
the firms will survive; if in contrast their costs exceed the average of
the established firms, they will end up exiting the market.
From active learning theory (2) (Ericson and Pakes 1990; Hopenhayn
1992), firms can change their characteristics during their time in a
market, consequently varying their chances of survival. The causes of
these changes can be of various types: technological, organizational,
etc.
In contrast with the importance that firm mobility has for
explaining market functioning, there are still many fields to explore.
Dunne et al. (1988) indicate the lack of studies analyzing patterns of
behavior of firms entering or exiting markets and the posi-entry
behavior (performance) of the new firms in terms of analyzing the
characteristics/skills necessary for survivai and growth. In turn,
Schoenecker and Cooper (1998) point out that in spite of the strategic
interest of the issue there has been remarkably little attention paid to
the types of firm that enter markets and when they do so.
According Bentzen et al. (2006), the extensive empirical literature
on the validity of Gibrat's law does not in general verify the law
as it finds that firms' growth rates are negatively correlated with
both firm size and age. However, some studies find that Gibrat's
law holds for sub-samples of firms such as large firms or firms
belonging to special industries. It has been pointed out that these
results are due to the fact that the likelihood of firm survival for
natural reasons is positively related to firm size and age. Whit a
representative sample of Danish firms this study evaluates the validity
of Gibrat's law for different kinds of firms over the period
1990-2003. In contrast to the majority of earlier studies this analysis
corrects for the bias in the estimations by using variables related to
the survival of small firms.
Manjon and Arauzo (2008) find that, in retrospect, the econometric
specifications used in this area have progressively become more
sophisticated, addressing issues such as discrete time, unobserved
heterogeneity and competing risks. These authors identify a number of
firm- and industry-specific covariates that provide largely consistent
results across samples, countries and periods. According Manjon and
Arauzo (2008) the evidence is less clear-cut with regard to ownership
and spatial factors.
Finally, De Jorge et al. (2010) have investigated the determinants
of firm size. Data was collected in face-to-face
structured-questionnaire interviews of 1314 firm founders from 14
counties in Argentina. The results show that the main sets of
explanatory variables related to founder characteristics (age,
experience, education, and vocation) provide a full explanation of firm
size. It has also found evidence that a high degree strategic planning
and a better competitive position are positively related to firm size as
well.
In essence, most recent work on firm mobility takes on one of
following two perspectives according Sutton (1997): (1) Firms'
chances of survivai depending on their age, size and other individual
characteristics; and (2) Firms' growth in function of their age,
size and other individual and sectorial characteristics. For example
Arauzo and Segarra (2005) explore the determinants of firm start-up size
of Spanish manufacturing industries. Their results indicate that the
variables that characterize the structure of the market, the variables
that are related to the behavior of the incumbent firms and the rate of
growth of the industries generate different barriers depending on the
initial size of the entrants. Arauzo and Segarra (2008) conclude that
the industries' barriers to entry affect the ability of potential
entrants to enter the markets and the size range at which they decide to
enter. On the other hand, several studies have analyzed entry in
developed capitalist economies coming to the conclusion that entrants
are usually smaller, less productive and at higher hazard than
incumbents. For example Rinadi (2008) considers if this was the case
also in the rather peculiar situation of those firms which entered
during the period of transition from planned to market economy, in one
of the ex-soviet countries. Additionally Rinadi (2008) considers whether
or not the uncertain environment generated by transition did activate a
process of entry, as situations of uncertainty are generally supposed to
do. The main result of this paper is that despite the fact that
incumbents were firms created and organized to meet the objectives of
the soviet regime, they were not outperformed by subsequently-created
firms which were formed to match the needs of a transitional/quasi
market economy. These results do not support "vintage" and
"liability of obsolescence" models which suggest that new
comers are better fitted to match new conditions.
3. Data
As Velasco (1998) points out, the first certainty when trying to
understand the reality of firm creation in Spain is of moving in a world
of statistical uncertainty. This situation also applies in other
European countries, hindering any international comparison. Spain is not
unaffected by this problem: while some databases allow a partial
analysis of some interesting phenomena such as survival or creation by
means of representative samples, these same databases do not allow study
of the causes or factors behind business success or failure. That is, it
is not possible to use a statistical source to make a clear and direct
analysis of firm creation. One ofthe problems is to determine whether
the firm is a new creation, or whether it is simply a new operational
unit set up by an existing firm.
The sources of data most used by studies on corporate dynamism in
Spain are the industrial survey of the Spanish National Statistics
Institute (INE) for the period 1978-1992 and the Register of Industrial
Establishments (Spanish Ministry of Industry and Energy). There have
been recent attempts to mitigate the lack of databases suited for the
analysis of corporate dynamism, among which we might mention those
developed by the Spanish Chambers of Commerce, Industry and Navigation
and the INCYDE Foundation (Camaras de Comercio 2001), or the research
group from Rovira y Virgili University, based on INE's Central
Directory of Firms (DIRCE).
In spite of the possibilities offered by this database to study the
causes of firms' exit from markets, entry rates, etc., the current
work uses the SABI database for its analysis. This database collects
data on more than 180.000 firms (population) inscribed in the Mercantile
Register (BORME), covering all sectors of business activity in Spain.
One of the competitive advantages of this database is that it allows
researchers to use variables relating to firm management.
The database we use here holds data on the main Spanish firms. It
is highly representative of firms from the 18 Spanish autonomous
"communities" (i.e., regions) that present iheir accounts in
the Mercantile Registers. From the totai population of more than 180,000
firms, we have iaken random samples, as described in each of the
following sections, on the basis of variables chosen in function of the
objectives of the research. The unit of analysis is the newly-created
firm for the case ofthe new entrants in markets.
The statistics of Table 1 (see Appendix 1) show some relevant data.
The age of the firms considered in this panel is on average 13.6 years,
which implies that the firms are in general relatively young, with some
exceptions (3). The average size, measured by number of employees,
ranges from 25 employees in 1996 to 37 employees in 2001, which
indicates that the sampi e has a significant number of small and
medium-sized firms (SMEs) (4). This implies ihat the sample closely
approximates a real market structure, although logically we have also
considered large firms within the sample. Another of the big databases
refers to the analysis of the new entrants in relation to the sectors of
activity (see Appendix 2, 3).
4. Empirical analysis and results
According Dunne et al. (1988) the importance of firm entry and exit
as determinants of market characteristics is widely recognized. In this
section we carry out an empirical analysis to respond to the questions
posed in the introduction (5). In this respect, this work makes various
contributions to the empirical literature on firm growth.
In the next sections, we examine the relation between growth and
age. This relation is important because some theories of firm growth
predict particular patterns of growth depending on the stage in the
firm's life cycle. We find that firm growth declines with age. This
inverse relation between growth and age is consistent with
Jovanovic's (1982) theory of firm growth, in which firms discover
their true efficiencies over time in a pro cess of Bayesian learning.
Also, we examine the relation between firm growth and age for
various types of firm, considering the characteristics of the sector of
activity We find that growth declines with the size of the firm for
relevant samples. This result is equally important, because some
theories (Simon and Bonini 1958; Lucas 1978) and special cases of
Jovanovic (1982), among others, assume or suggest ihat firm growth is
independent of size, as postulated by Gibrat's law. It is precisely
the variable firm age that has served as a support in our attempt in
this research to explain the effects of growth. In this respect, Evans
(1987) indicates that from the theoretical point of view studies
designed to incorporate age can be expected to make an important
contribution to the literature. This author also recommends caution in
the use of Gibrat's law (6) to explain the distribution of firms by
size i as does Lucas (1978). Authors such as Evans (1989), in the line
of research of empirical work, suggest the importance of age as a
determinant factor of dynamic industri es.
4.1. Characteristics of new entrants
Understanding what happens to firms after entering a market is an
issue of some interest, given that the effects of firm mobility on the
sectorial structure depend not only on the number of firms entering or
exiting the market at any given time, but also on their evolution in the
market where they operate. It is particularly important to understand
the rate at which firms disappear and how they gain market share. The
scarcity of studies to the present day reflects the difficulties new
entrants have surviving in markets, since they are generally small
(Geroski 1991).
What patterns of growth do Spanish firms entering markets display
in the different cohorts? If we can identify a particular pattern, do
all the cohorts of firms behave similarly in their growth?
The theories explaining firm entry into markets are conceived from
a static or dynamic perspective. The first type establishes a direct
relation between the new entrants and sectorial barriers. In this
respect, the entry rate is positively associated with firms'
expectations of potential profits and negatively associated with the
profits sustainable in the long term, which are in turn related with
sectorial characteristics. Geroski (1991) proposes that the entry rate
of firms in a sector is related to the expected profits and the
sectorial variables generating barriers to entry, and that these are
influenced by the speed of response of the firms. On the other hand, the
dynamic approaches explain firm mobility in terms of
innovation-imitation processes, asymmetries in the expectations and the
generation of economies of learning.
As can be seen in Fig. 1, the different cohorts evolve similarly in
their sales7 aggregating the sectors.
[FIGURE 1 OMITTED]
Thus the growth of the new entrants in the sample appears a priori
to indicate interesting expectations for the future. This year-by-year
evolution of the new entrants shows greater rates of evolution. Is the
growth similar for the different cohorts (post-entry behavior)? How are
the starting size and the post-entry evolution related?
Taking as basis Nelson, Winter's (1982) model and Evans (1987)
in relation to the growth rate (dependent variable), which will be
developed in the study of the established firms' growth, we propose
in equation (1) the following explanatory specifications of the model of
new entrants:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1)
where:s represents the net sales turnover in thousands of euros;
[t.sup.*] is the final year of the firms under analysis, corresponding
to 2000; [t.sub.i] is the entry year of the firms, between 1994 and
1999; finally, [t.sup.*] - [t.sub.i] is the difference between the final
year and the initial year for each cohort.
The growth is analyzed for newly-created firms (ex novo). We cannot
distinguish any merger processes that may have occurred, and hence
neither can we determine by which means the growth was achieved, whether
internally or externally (8).
Starting from Equation (1), the following regression models the
growth of the new entrants by cohorts:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (2)
where: [S.sub.ti] measures the sales of the year of entry into the
market in the cohort being considered; [S.sub.ti.sup.2] represents the
term of the quadratic evolution of the sales; and Sec is a dummy sector
variable (10 sectors, according to the 1-digit CNAE code). Although this
was the final model chosen, in Table 1 we compare the results achieved
with different alternative models, considering cumulative growth rates
or not, and including logarithms in the independent variables or not. In
this respect, the models incorporating logarithms are statistically
significant in all the variables considered, and in particular the
quadratic form obtains better goodness of fit coefficients for all
cohorts.
The results of estimation 2 are shown in Table 2. As can be seen,
the new entrants in the cohorts 1996, 1997, 1998 and especially 1999
experience higher growth rates with greater size. This can also be
observed in the descriptive analysis of the distribution of the growth
(see Appendix 3). In terms of elasticities, when there is an increase in
the entry size of firms of 1% the growth evolves particularly from the
cohorts of the year 1996 onwards. This growth is particularly important
for cohorts 1998 and 1999.
The models are shown graphically in Fig. 2. The minimum values of
the curves, as well as the average sizes and the 95% percentiles,
indicate that the relevant area of analysis is to the left of the
curves. This is where the growth rate is inversely related to the size.
Over time, new entrants raise the average size of their units. Audretsch
and Mahmood (1995) argue that this growth in size occurs for two
reasons: i) the exit from the market of firms belonging to the cohorts,
generally small companies; and ii) the growth of the firms remaining in
the market.
In addition to the study of the behavior of the new entrants, we
undertake a descriptive analysis of the firms' productivity and how
this relates with the growth of the economy. The year firms decide to
enter in a market is probably associated with a better economic
situation in Spain. If this is so, firms entering the market in times of
economic expansion benefit, enjoying higher growth rates than firms that
enter the market when the economic situation is not so favorable.
As Segarra et al. (2002) points out for the manufacturing sector,
the net entry rate of firms may be related to the economic cycle, with a
positive correlation between firm creation and expansionary cycles and a
negative correlation during recessions. In turn, Boeri and Bellman
(1995), in a study of the German manufacturing sector, find no evidence
that the economic cycle influences the exit of firms, at the same time
as a weak sensitivity of exits to growth in terms of the number of
employees in established firms. In this sense, when we study growth in
the different cohorts of post-entry behavior the fastest growth is
observed in new entrants from 1996 onwards--the point when the Spanish
economy begins to enter an expansionary cycle. On the other hand, in
Fig. 3 we can see the evolution in productivity (in terms of
sales/number of employees) of the new entrants by cohorts. Although in
general all the new entrants in their respective cohorts show gains in
productivity, it is the cohorts of the years 1996-1999 that follow the
growth in the economy pro-cyclically, above all compared to the cohorts
of 1994 and 1995.
Disaggregating by sector, Fig. 4 presents the evolutions in
productivity of the new entrants of each cohort in the different sectors
of activity. There are generally improvements in productivity in all the
sectors for all the cohorts. In particular, we can see some
characteristic features. The productivity of the new entrants is higher
in the first year in Sector 5 (sales, commerce, etc.) for all the
cohorts, followed, by sectors 1 (food, drinks, etc.) and 6 (transport,
post, etc.) In Sector 5 there are significant gains in average growth
for all the cohorts, and the same is true for sectors 3 (office
machines, electrical material, etc.) and 4 (construction, energy, etc.),
while in sectors 1, 7, 8 and 9 this evolution in growth is most marked
from the year 1996 onwards. Sectors 2 (wood and cork industry,
chemicals, etc.) and 6 present the profiles of least evolution in
productivity.
There are two empirical facts that tend to disconcert economists
when they analyze processes of market entry and corporate turnover. The
first concerns the asymmetric distribution of firms in terms of size,
given that there is a clear predominance of smaller-sized firms. This
could be suggesting a priori that a large number of firms are producing
below a minimum efficient level (Sutton 1997). The second fact is that
the entry of firms is high even in those sectors where the economies of
scale are important, which might suggest that in these sectors this
phenomenon does not discourage the entry of new firms.
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
4.2. Entrants' size compared to established firms' size,
by sector of activity
The statistical data analyzed in the previous section do not
provide information about the diversity of firms entering the market in
relation to the size of the firms already established in the sector. In
Table 3 we report descriptive statistics about the new entrants by
1-digit sector, as well as the relative size of the new entrants as a
proportion of the size of the established firms, under a longitudinal
perspective (1994 to 1999)
Some of the questions that we pose in this section are: are the new
entrants smaller and does this persist over time, or when they enter the
market do they already have a substantial average size at the sector
level? If they do remain small, does this occur in all sectors or does
it differ in function of the structural characteristics of the sector,
or the way the firms compete? If in contrast they enter the market with
a particular size, which then modifies, how long does this adjustment
process take, and is it similar in all the sectors?
In the introduction of this work we discussed the criteria
referring to the new entrants' expectations about the profits they
are likely to obtain and the obstacles or barriers to entry that they
are likely to find. Providing answers to these questions in this section
relates with aspects that the literature has in some cases already
tackled: the new entrants' capacity of adjustment of their cost
structures to the characteristics of the markets. The heterogeneity of
the firms, their learning processes from their entry onwards, which
uncover asymmetries in their efficiency levels, and the differences
between organizations in their development of the skills of imitation
and learning or the incorporation of more efficient capital goods, are
some of the arguments that will prove useful in the analysis.
The findings reported in Table 3 allow us to point out some
stylized facts: (a) As in Dunne, Roberts, Samuelson (1988), the average
relative size of the new entrants as a proportion of the established
firms grows in all sectors and all cohorts. For example, the size of the
new entrants in Sector 5 (mid table) is 13.7% that of the established
firms in the 1994 year of entry. In 1995 this proportion rises to 29.3%
and it continues to grow until it reaches 35.7%, (b) The pattern of
evolution in the growth of the new entrants varies in function of the
sector of activity. While the level of relative size reaches 36.87% in
Sector 5, in other sectors such as Sector 1 it reaches 124.3% for the
1998 cohort. (c) In general, new entrants' processes of adjusting
their size with respect to the established firms take longer than six
years. Consequently, in 1999 with very few exceptions the relative size
of the firms in proportion to the established firms does not exceed 50%.
Geroski (1995) indicates that new entrants are small and that they take
over a decade to achieve sizes comparable to the established firms. (d)
The entry size varies in function of the sector of activity. For
example, in sectors 2, 4, 7 and 9, observing the entrants of each cohort
and comparing them with the sectors 1, 3 and 8. On the other hand, if
survival is related to size this latter may not be acquired immediately.
Some authors Audretsch (1991); Mata and Portugal, (1994); Wagner (1994);
among others provide evidence of the greater variability in survival
rates among different sectors than among new entrants in the same
sector.
4.3. Growth of established firms: size and age
The theories on the relation between age and growth in firms are
closely related with those that link size and growth, due to the
demonstrated relation between age and size. However, the available
evidence does not categorically confirm that new firms--which are
generally smaller than established firms--grow more rapidly. The
theories of corporate growth are basically of two types. On the one
hand, the stochastic theories are based on the marked asymmetry of
distribution of firm sizes that is observed, with less importance been
lent to technological or demand aspects, considering that the evolution
of firm size is influenced by a large number of explanatory factors that
should be treated as random variables.
The determinist theory, on the other hand, is based on the
neo-classical model and holds that growth is closely linked to the idea
of optimal size. According to this approach, firms have the objective of
carrying out a process of adjustment to achieve this more or less
rapidly. The main result from the analysis is that firms wish to reach
their optimal sizes as quickly as possible, but that there are costs of
adjustment that prevent them from achieving this immediately. This
implies that in sectors in which the firms have curves of average
long-term costs that are U-shaped or similar there will be an inverse
relation between size and growth, since large firms tend to have less
need to grow in size than small ones, as the costs derived from having
an inefficient size decline the closer the firm is to its optimal size.
Under this perspective of the corporate growth process--which is
the perspective of this section, as we shall see later--the diversity of
sizes observed in the market is simply a temporary situation caused by
the fact that the firms are all at different stages of the process of
adjustment towards their optimal size.
Fig. 5 suggests that the variability in growth of the sample firms
observed between 1996 and 2001 is related to size and age. Smaller and
younger firms tend to exhibit greater variability in their growth,
considering the rate of cumulative growth (vertical axis). As the firms
age (age groups at the top) and grow in size (Lnsales96), the
variability in their growth declines. Thus, we see that in the early
stages of life smaller firms tend to grow faster, which is in line with
neo-classical growth models.
This finding--that large firms grow more slowly than small
firms--is consistent with work carried out by Kumar (1985), Evans
(1987), Acs and Audrestsch (1990), Dunne and Hughes (1994).
As we mentioned earlier, new entrants are generally smaller than
established firms and consequently try to grow as quickly as possible in
order to compensate for their size disadvantages. However, the existence
of obstacles to investment, which are particularly intense in this type
of firm (perhaps financial investment being the most worrying), means
that many of them cannot achieve this. The existence of greater
asymmetries in their access to investment among small firms than among
large ones appears to be the cause of the greater variability in growth.
[FIGURE 5 OMITTED]
Table 4 reports the same results in a descriptive analysis. As
firms pass from one age range to the next, their rate of growth declines
while their average size increases.
Our analysis demonstrates an inverse causal relation between growth
and age, coinciding with Hart (1962), Mansfield (1962), Hall (1986),
Evans (1987), Dunne and Hughes (1994). This relation is important
because some theories of corporate growth predict particular patterns of
growth in function of the life cycle of the firm.
4.4. Growth of established firms, age, size and sectorial
characteristics
Nelson and Winter (1982) study the circumstances under which firms
experience initial growth in relation to their size and the subsequent
decline in growth. Later and Evans (1987) develops a growth model to
determine the relations between these variables in the manufacturing
sector. We shall apply this model to the database of established firms
between 1994 and 2001. The model is as follows:
[Sales.sup.*.sub.t] = [[G([Age.sub.t], [Sales.sub.t])].sup.d]
([Sales.sub.t]) [[epsilon].sub.t], (3)
where: t represents the period considered, t' > t, d =
t' - t and e is the error term with lognormal distribution and with
possibility of non-constant variance. Equation (3) suggests the
following regression to estimate growth:
([LnSales.sup.*.sub.t] - [LnSales.sub.t]) / d = + LnG([Age.sub.t],
[Sales.sub.t]) + [[epsilon].sub.t], (4)
where: [[mu].sub.t] is a normal distribution with mean zero and
possibility of non-constant variance and independent of age and sales.
According to Evans (1987), taking the second-order expansion of Ln G
(Age, Sales) we obtain:
Ln G = [[beta].sub.0] + [[beta].sub.1] ln [Sale.sub.it] +
[[beta].sub.2]Ln [Sale.sup.2.sub.it] + [[beta].sub.3]Ln [age.sub.it] +
[[beta].sub.4]Ln [age.sub.it.sup.2] + [[beta].sub.5]Ln [Lnage.sup.*] Ln
Sale + [[epsilon].sub.it]. (5)
The sample of firms that has been used for the analysis has
considered the age of firms over three years. The literature on firm
creation and survival holds that if companies survive beyond their first
three years of life--the peak period of organizational mortality - their
chances of survival improve considerably.
Table 5 shows the results obtained. The behavior of the variables
explaining growth--i.e., age and size--presents a quadratic form as in
Evans's (1987) model (in this case with sales rather than
employment to capture the size effect). The signs of the coefficients
and their statistical representativeness show that the relation between
growth and size is U-shaped, independently of the age range considered,
while when we consider the age of the firms in their respective ranges
some differences are observed.
The relation is U-shaped for the youngest and oldest firms, being
inverted U-shaped for the firms of intermediate age (10-20 years). This
behavior may be related to the life cycle of the firm according to a
logistic trajectory. In the stages of birth and development firms grow
when they are young, evolving in size and age. In the maturity stage the
firms continue to grow in size and the age of the firms presents a
convex form, until they reach the stage of full maturity, when the
evolution in the size and age is similar to the initial stage, although
the variation in growth is at approximately 58% of that in the earlier
stages (1.21 compared to 2.08).
The positive coefficient of the variable [age.sup.*]sales indicates
that the effect of the initial sales on growth is stronger the older the
firm, and also that the effect of the age on growth is greater the
higher the initial sales. This might suggest that the initial size of
the firm, or its speed of adjustment, play an important role in its
growth. Firms' greatest risk of failure and hence of abandoning the
sector is associated with the smallest sizes. This implies that firms
deciding to initiate their activity with sizes that are smaller than the
efficient level and that then attempt to achieve the optimal size by
means of the necessary process of adjustment may be at a significant
initial competitive disadvantage. In the case of the oldest range of
firms this effect is inverted.
4.5. The inter-sectorial mobility of firms
In previous sections we analyzed the relations between firm size,
growth and age, depending on the sector of activity. We investigated
whether large firms grow more slowly than small or medium-sized firms,
or whether conversely they grow more quickly. In this section we shall
study the differentiating characteristics of the established firms, by
sector of activity, to subsequently determine the inter-sectorial
mobility of firms.
We define firms to be above the average of their sector when they
are larger than those that are below the average, when we use both sales
and employment level as measure of firm size. Firms that are above the
average are also older--by more than 13 years--being 20 years old on
average.
On the other hand, with the data analyzed, firms with higher than
average values are more productive (sales/number of employees) and
profitable. But their financial profitability is not so favorable,
perhaps as a consequence of the higher debt levels, greater fixed assets
and higher relative labor costs of medium-sized and large firms.
The percentage of firms above the average is consequently small.
Considering the averages of the year 1996, some 12%, of the firms are
above the average, the minimum value is found in sectors 4 and 7
(construction and financial intermediation), with only 6.2% of the firms
above the average, and the maximum in Sector 8, with 27.6%. But it is
true that in this latter case the size of the sample is small (94
firms), hence the average will be sensitive to this.
In some sectors we find differences between the averages of the
initial (1996) and final (2001) years considered, particularly in the
variable sales. We recall that unfortunately we have not been able to
capture processes of mergers and spin-offs that may have occurred at the
sector level.
We carry out the same descriptive analysis with the median as the
frontier or limit, to determine which firms exceed or have possibilities
of exceeding the median. The conclusions drawn from the analysis of the
mean are also valid when the frontier is the median. The differences
between firms exceeding the median and those below it are that the
former are larger in terms of sales, employment, productivity and
economic profitability. Although in this case of course the median
divides the group of firms in two.
In a first approximation Table 6 presents the transitions that have
taken place between quartiles from 1996 to 2001. The values on the table
diagonal indicate the firms remaining in their quartiles. For example,
12.04% of the firms (968) in the first quartile in 1996 remain in this
quartile in 2001. Similarly at the other extreme of the diagonal 22.3%
of the firms that were in the fourth quartile in 1996 remained in that
quartile four years later. Transitions below the diagonal indicate
downward movements from quartiles (demotions), while above the diagonal
they represent promotions to higher quartiles.
There are fewer demotions than promotions, with the norm being
promotions of levels. For example, a total of 8.18%, 3.36% and 1.50% of
the firms belonging to the first quartile of 1996 promote to the second,
third and fourth quartiles, respectively.
Of the firms belonging to the third quartile in 1996, a total of
2.72% were demoted to the second quartile of 2001, and 8.88% promoted to
the fourth quartile of that year. In Table 7 we show the transitions
disaggregated for some sectors of activity.
Sectors 2 and 3 on the left of Table 7 show similar transitions
(their behavior is similar to that of the sectors that have been
omitted: 1, 7 and 9). Sectors 4 and 5 on the right of the table show
more dynamism (this behavior is similar to the omitted sectors 6 and 8).
In particular, Sector 5 exhibits a relatively lower permanence of its
firms in their quartiles (diagonal), which varies with respect to the
rest by some 2% approximately and a greater number of transitions
upwards than the rest of the sectors, particularly promotions to the
highest quartile. On the other hand, the dynamism of this sector is also
observed when we examine the demotions.
Having analyzed the transition of firms in the period of time under
analysis and their dynamism, considering the sector of activity, we
might ask what is the probability that a firm exceeds the frontier in
terms of the quartiles of the sector of activity where it operates? And,
to what extent does growth affect its mobility over time? Finding a
response to the first question could provide some evidence about the
causes of firm survival. The answer to the second meanwhile may be more
related to the question of whether growth really explains survival, and
if so, how it is related with the rate at which the firm achieves an
efficient size to be competitive, or its possibility of catching up if
it does not have adequate growth.
This approach to determine the probability that the firm exceeds
the frontiers (in terms of sales) or not and the temporal effect can be
seen in Fig. 6.
In order to analyze the probability that a firm will promote from
its quartile both in year [t.sub.o] (1996) of the sample and
2001--movements 1 and [1.sup.*] of Fig. 6--as well as to analyze the
probability of improving its quartile to a higher quartile five years
later (position 2), we use probit models. In the first case, we use an
ordered probit, while in the second, where the transitions are linked to
promotion or growth; we use a binary selection model. The starting
equation is as follows:
[y.sup.*] = [beta]'x + [epsilon]. (6)
Prob [[y.sub.1..4]] = [[beta].sub.0] + [[beta].sub.1] Ln age +
[[beta].sub.2] Ln [age.sup.2] + [17.summation over (i=1)][[beta].sub.3]
Communitie + [10.summation over (i=1)] [[beta].sub.4] sector [epsilon],
(7)
where: Prob is a variable taking four values in function of the
quartile in which the firm finds itself; age refers to the age of the
firm and age (square) the quadratic component of the age; communities is
a dummy variable taking 18 values, according to the firm's
autonomous community (i.e., region) of origin; Sector is a dummy
variable taking 10 values in function of the firm's sector,
according to NACE. The results are presented in Table 8.
The results considering the marginal effects are shown in Table 9.
[FIGURE 6 OMITTED]
In 1996 the probability of transitions between lower levels
declines with age, while it increases in the superior levels. For
example, when the age varies by 10% the probability of moving in the low
levels declines by 0.46% and 0.16% in quartiles 1 and 2 and increases by
0.14% and 0.49% in quartiles 3 and 4. This effect is reversed when we
consider the year 2001, when the probability of moving in the lower
quartiles increases and in the higher quartiles declines. These facts
may be related with the growth in the Spanish economy. In 1996 the
expectations were favorable and an expansionary cycle was beginning,
hence the post-entry growth of the firms in their sectors benefited from
this situation. The larger the initial size of the firms, the greater
the effect of the age, and the higher the initial sales (as we have
already said), the smaller the firms growing in the lower quartiles. In
the higher quartiles it is the medium-sized and large firms that
experience growth. On the other hand, in 2001 the effects could be the
reverse, since the expectations of growth diminish, the probability of
transitions is related to an initial minimum size, and the survival of
small firms becomes difficult. Meanwhile, in the higher quartiles it is
the smaller firms that can move, probably to the extent that they have
greater flexibility and can adapt their size to market needs.
In Table 10 we report the results of the analysis of transitions of
firms from their quartiles of 1996 to 2001--Model 1. In Model 2 we
consider the effect of the concentration of shares in the hands of the
main shareholder. In this respect, we consider it relevant to examine
the relation between the governance of the firm--measured by the control
exercised by the majority shareholders--and growth. Models 3 and 4
capture the demotions produced during the same period.
Promotions from any quartile of 1996 to a higher one in 2001 are
positively related with firm age. Moreover, the negative and
statistically significant sign of the dummy variable measuring shares in
the hands of the main shareholder indicate that the probability of
promoting is related to firms with a non-concentrated ownership
structure. Non-concentration of the ownership fosters higher growth than
when the ownership is concentrated. Work such as Zahra (1996) and Zahra
et al. (2000) analyses firms' entry into national and international
markets in relation to the ownership structure. Some of these
authors' findings show that the effects of ownership and governance
can vary from one firm to another depending on their size. Marseguerra
(1998) points to the importance of considering share concentration as a
mechanism of management control.
When there is a certain level of concentration of shares in the
hands of one shareholder, this investor will have sufficient incentive
to break with their rational apathy and control the operation of the
firm. In this sense, two conditions should coincide (Pinillos 2001) to
consider the concentrated ownership as a monitoring mechanism of the
management: i) that there really is a high degree of concentration of
the ownership of the firm, to allow for an active control function to be
exercised; and ii) that the shareholders are guided by performance and
the return on their investments.
The absence of shares in the hands of the managers can cause
opportunistic behavior, with the managers supporting projects that
increase their own personal wealth and favor and ensure their job
security. When the objectives of the managers and shareholders are
closely aligned embarking on new activities both creates value and
pursues the managers' objectives. Berle and Means (1932) point out
that a concentrated ownership of a firm has significant implications for
the development of corporate strategy. Diversification can imply
conflict of interests between managers and shareholders in situations
where the diversification only means maximizing manager wealth.
It is important to consider that agency theory warns of a negative
relation between ownership concentration and strategic diversification.
In this respect, the shareholders' active control will favor the
convergence of the managers' utility functions and the
shareholders' interests. On the other hand, when control and
ownership are separated, and the managers' interests are
consequently directed at promotion, status, etc., expectations of
company growth may improve.
With regards demotions, the probability of transitions to lower
levels/quartiles diminishes with the age of the firm, and in this case
the effect of a firm's ownership concentration is not significant.
Firms that have not promoted from their quartile in the year considered
(to), probably because of not having sufficient age--i.e., not having
achieved sufficient growth rate--do not catch up and the probability of
exceeding the frontier increases with age (comparing 1996 with 2001),
although to a decreasing extent.
5. Conclusions
Although we have not been able to work with data as representative
as those provided by DIRCE or other sources, we have enriched the
analysis by incorporating firm variables at the individual level (sales,
profitability, ownership structure, etc.). This has allowed us to
understand important aspects about the running of the firms, which have
in some cases directly or indirectly suggested important facts regarding
the heterogeneity of the firms.
The growth of the new entrants in terms of turnover has similar
patterns of convergence (convex form in the relation of growth and
size). The effects of economic growth are reflected in the evolution of
companies' size. While the entrants among the cohorts of 1994 and
1995 evolve similarly, in the cohorts from 1997 onwards the evolution is
much more intense, with the growth increasing considerably year by year
until 1999. This period corresponds to an expansionary phase in the
economy. When we analyze the productivity--in terms of sales over number
of employees--a similar effect is observed. This fact could suggest that
although all new entrants into a market are affected by the growth in
the Spanish economy (GDP), those belonging to cohorts from 1997-1999
benefit particularly from it.
When we analyze the growth of the new entrants in relation to the
established firms, the following common characteristics are found: (1)
Similarly to the findings of other authors (Dunne et al. 1988), new
entrants' average relative size (in terms of sales) as a proportion
of the established firms increases, in all the different industries and
cohorts. For example, the new entrants in Sector 5 (commerce) have 13.7%
of the size of the established firms. In 1995 this proportion rises to
29.3% and it continues to grow in the following years until it reaches
35.7%. (2) The pattern of evolution of new entrants' growth varies
among the different sectors of activity. While the average size level
reaches 36.87% in Sector 5, in other sectors such as Sector 1 (food,
drink and tobacco), it reaches 124%. (3) In general the processes of
size adjustment of the new entrants with respect to the established
firms take over five years. Geroski (1995) indicates that new entrants
are small in size and that these firms take more than a decade to
achieve sizes comparable to the established firms. (4) The entry size
and hence the level of resources a firm has at its disposal to be in a
position to compete, is a function of its sector of activity. For
example, sectors 2, 4, 7 and 9, observing the new entrants of each
cohort and comparing them with sectors 1, 3 and 8. On the other hand, if
survival is related to size, this latter may not be acquired
immediately. In some works, such as Audretsch (1991), Mata and Portugal
(1994) and Wagner (1994), among others, some evidence is provided of a
greater variability in survival rates between different sectors than
among entrants of the same sector.
When we analyze the characteristics of the new entrants and
established firms, the following characteristics are found: (1) From a
panel of data from 1996 to 2001 we observe that the variance of firm
growth observed is related to the size. The smallest firms have greater
variability of growth than the larger ones. The result obtained in this
work with regards the fact that large firms grow more slowly than small
firms is consistent with other studies carried out by Kumar (1985),
Evans (1987), Acs and Audrestsch (1990), Dunne and Hughes (1994). (2) On
the other hand, we find that growth declines with age, as some authors
have found (Hart 1962; Mansfield 1962; Hall 1986; Evans 1987; Dunne and
Hughes 1994). This causal relation is important, because some theories
of corporate growth predict particular patterns of growth depending on
the stage in the life cycle of the firm. The current analysis confirms
the inverse relation between growth and age. (3) The growth observed in
the established firms--i.e., firms with more than three years of
activity in the market--has a similar behavior to the life cycle of the
firm and confirms the results obtained by Evans (1987). In particular
this analysis has been carried out using three samples of different
ages. For the first sample, where the group of ages ranges from 4 to 9
years, the growth relates to the evolution in sales and the age in a U
shape. For the range of ages between 10 and 20 years the sales continue
to have the same form, but the age changes the trajectory to an inverted
U shape. Finally, for the firms older than 20 years, the behavior of age
and size are the same as for the youngest group, although the
variability of the growth declines (58% of that of the first group). (4)
On the other hand, the effect of age on growth is stronger the higher
the initial sales. This could suggest that the initial size of the firm,
or its rate of adjustment, play an important role in growth. Firms'
greatest risk of failure and hence of abandoning the sector is
associated with smaller size. This implies that firms that decide to
initiate their activity with sizes that are smaller than the efficient
level, and that aim to achieve the optimal size by means of the
necessary learning process, may start out with a substantial competitive
disadvantage.
Finally, when we analyze firms' probability of transition in
function of the quartile (in terms of sales volume) to which they belong
in the years 1996 and 2001, we find that in 1996 the probability of
promotion declines with age among the lower levels (first and second
quartiles), while it increases among the higher levels (third and fourth
quartiles). This effect is inverted when we consider the year 2001, when
the probability of transition increases with age in the lower levels and
decreases in the higher levels. These facts may be related with the
growth in the Spanish economy. In 1996 the expectations were favorable
and an expansionary cycle was beginning, and hence the post-entry growth
of firms in their sectors benefited from this situation. The larger the
initial size of the firms, the stronger the age effect, and the higher
the initial sales, the smaller the firms growing in the lower quartiles.
In the higher quartiles it is the medium-sized and large firms that
experience growth. On the other hand, in 2001 the effects may be
inverted, since the expectations of growth diminish, the probability of
transition is related with a minimum initial size, and the small firms
find it difficult to survive. In the higher quartiles it is now the
smaller firms that can move quartiles, probably to the extent to which
they are more flexible and can adapt their size to market needs.
In the transition of firms from quartiles of 1996 to 2001 we have
considered the promotion of firms to a higher quartile, their demotion
to a lower one and the effect of the concentration of shares in the
hands of the main shareholder. In this respect, we consider it relevant
to examine the relation between the governance of the firm--measured by
the majority shareholders' exercise of control--and growth.
Promotions from any quartile in 1996 to a higher one in 2001 are
positively related with firm age. Moreover, the probability of promotion
is also associated with firms where the ownership structure is not
concentrated.
To conclude this work, we propose some policy recommendations:
First, an efficient corporate governance system may prove as a
significant policy tool for the investment and growth prospective of the
Spanish economy. Second, knowing that regulatory framework of the
Spaniard capital market has been coordinate with the EU standards, the
challenge is now mostly for the firms to adopt the appropriate corporate
governance structures, in order to achieve real convergence, in terms of
productivity and competitiveness, with other developed economies.
doi: 10.3846/16111699.2011.555449
APPENDIX 1
The characteristics of the databases used are as follows
Panel of firms from SABI database between 1996 and 1999:
Established firms
Variable No. observ. Mean Std. dev.
Age (Years) 10142 13.6 8,61
Sales 1994 (000s [euro]) 5066 8050 270
Sales 1995 6380 7604 331
Sales 1996 8044 7213 411
Sales 1997 8889 7794 389
Sales 1998 9617 8212 463
Sales 1999 10034 8973 478
Sales 2000 10100 10096 523
Sales_2001 10672 10827 498
No. empl_94 (No. employees) 2196 25 270
No. empl_95 3693 28 285
No. empl_96 5061 24 311
No. empl_97 5507 26 350
No. empl_98 6382 27 372
No. empl_99 7162 30 391
No. empl_00 7726 36 400
No. empl_01 7829 37 411
Productivity_94 (Sales/no. empl.) 2196 327 1795
Productivity_95 3693 275 1549
Productivity_96 5061 296 1540
Productivity_97 5507 304 1227
Productivity_98 6382 301 1378
Productivity_99 7162 302 1078
Productivity_00 7726 279 801
Productivity_01 7829 289 923
Variable Minimum Maximum
Age (Years) 4 99
Sales 1994 (000s [euro]) 0.1 1.04 [10.sup.6]
Sales 1995 0.5 1.12 [10.sup.6]
Sales 1996 1 1.72 [10.sup.6]
Sales 1997 1.2 1.92 [10.sup.6]
Sales 1998 0.4 2.19 [10.sup.6]
Sales 1999 1 2.52 [10.sup.6]
Sales 2000 1 3.62 [10.sup.6]
Sales_2001 0.6 3.12 [10.sup.6]
No. empl_94 (No. employees) 1 10235
No. empl_95 1 11540
No. empl_96 1 13272
No. empl_97 1 14323
No. empl_98 1 19065
No. empl_99 1 22366
No. empl_00 1 24762
No. empl_01 1 25547
Productivity_94 (Sales/no. empl.) 0.2 19336
Productivity_95 0.6 21314
Productivity_96 0.9 22328
Productivity_97 1.2 122323
Productivity_98 1.7 150328
Productivity_99 1.8 195421
Productivity_00 2.1 40621
Productivity_01 2.0 65248
Source: Author's calculation.
APPENDIX 2
Panel of firms from SABI database between 1994 and 2000: new entrants
Variable No. observ. Mean Std. dev.
Entrants in 1994
Sales 1994 (000s [euro]) 901 517 1501
Sales_1995 1986 2941 44133
Sales_1996 3121 2982 42927
Sales_1997 3808 3306 50775
Sales_1998 4619 3484 55929
Sales_1999 5183 3802 61845
Sales_2000 5697 4639 76442
No. empl_94 (no. employees) 439 6 13.7
No. empl_95 1132 20 223.08
No. empl_96 1868 29 304.07
No. empl_97 2354 31 381.2
No. empl_98 3066 33 487.09
No. empl_99 3796 32 498.7
No. empl_00 4501 32 492.6
Productivity_94 (Sales/no. empl) 423 122 265
Productivity_95 1098 189 547
Productivity_96 1841 201 556
Productivity_97 2323 225 655
Productivity_98 3024 193 583
Productivity_99 3751 198 646
Productivity_00 4441 198 683
Entrants in 1995
Sales 1995 (000s [euro]) 1361 688 2940
Sales_1996 3036 883 3463
Sales_1997 4071 1176 4350
Sales_1998 5297 1317 5096
Sales_1999 6092 1519 6848
Sales_2000 6808 1982 2392
No. empl_95 (no. employees) 742 8 22.5
No. empl_96 1798 15 217.8
No. empl_97 2425 15 188.6
No. empl_98 3439 14 157.1
No. empl_99 4456 14 142.8
No. empl_00 5350 12 54.7
Productivity_95 (Sales/no. empl.) 709 129.8 308.0
Productivity_96 1742 139.2 353.8
Productivity_97 2377 176.5 493.9
Productivity_98 3387 187.6 602.6
Productivity_99 4405 179.2 529.7
Productivity_00 715 239.9 1303.6
Entrants in 1996
Sales 1996 (000s [euro]) 4172 787 5796
Sales_1997 7241 1671 11015
Sales_1998 9213 1993 10880
Sales_1999 10454 2238 11358
Sales_2000 8937 2349 13067
No. empl_96 (No. employees) 2357 11 49.90
No. empl_97 4335 14 60.16
No. empl_98 6054 18 80.57
No. empl_99 7480 18 72.06
No. empl_00 6857 18 100.7
Productivity_96 (Sales/no. emp.) 2357 128 486
Productivity_97 4335 223 586
Productivity_98 6054 266 796
Productivity_99 7480 278 1065
Productivity_00 6827 266 736
Entrants in 1997
Sales_1997 4650 977 10587
Sales_1998 8765 2004 23261
Sales_1999 10520 2430 21223
Sales_2000 9257 2662 23324
No. empl_97 2685 16 12995
No. empl_98 5555 25 58124
No. empl_99 7318 26 87333
No. empl_00 6830 26 67095
Productivity_97 2685 128 334
Productivity_98 5555 244 631
Productivity_99 7318 279 823
Productivity_00 6830 304 890
Entrants in 1998
Sales_1998 5157 1024 8473
Sales_1999 9157 3602 103715
Sales_2000 8615 4574 110852
No. empl_98 3227 30 904
No. empl_99 6317 31 700
No. empl_00 6273 34 608
Productivity_98 3227 146 535
Productivity_99 6317 259 1259
Productivity_00 6273 280 795
Entrants in 1999
Sales_1999 2807 1154 13059
Sales_2000 3195 1590 10446
No. empl_99 2077 28 187
No. empl_00 2891 20 279
Productivity_99 2802 875 6307
Productivity_00 3220 1514 9879
Variable Minimum Maximum
Entrants in 1994 20603
Sales 1994 (000s [euro]) 0 135055
Sales_1995 0 159113
Sales_1996 0 236682
Sales_1997 0 289687
Sales_1998 0 331036
Sales_1999 0 584343
Sales_2000 0 228
No. empl_94 (no. employees) 1 6653
No. empl_95 1 7456
No. empl_96 1 12460
No. empl_97 1 19056
No. empl_98 1 22366
No. empl_99 1 24767
No. empl_00 1 3183
Productivity_94 (Sales/no. empl) 0 9931
Productivity_95 0 13135
Productivity_96 0 12498
Productivity_97 0 16284
Productivity_98 0 16982
Productivity_99 0 20177
Productivity_00 0
Entrants in 1995
Sales 1995 (000s [euro]) 0 73582
Sales_1996 0 116903
Sales_1997 0 132742
Sales_1998 0 200879
Sales_1999 0 272906
Sales_2000 0 1848060
No. empl_95 (no. employees) 1 319
No. empl_96 1 9158
No. empl_97 1 9155
No. empl_98 1 9036
No. empl_99 1 8780
No. empl_00 1 3286
Productivity_95 (Sales/no. empl.) 0 5274
Productivity_96 0 8531
Productivity_97 0 11390
Productivity_98 0 13255
Productivity_99 0 12116
Productivity_00 0 16222
Entrants in 1996
Sales 1996 (000s [euro]) 0 288577
Sales_1997 0 661314
Sales_1998 0 613012
Sales_1999 0 655869
Sales_2000 0 745028
No. empl_96 (No. employees) 1 4046
No. empl_97 1 2383
No. empl_98 1 3164
No. empl_99 1 3294
No. empl_00 1 4552
Productivity_96 (Sales/no. emp.) 0 20620
Productivity_97 0 15034
Productivity_98 0 26219
Productivity_99 0 73071
Productivity_00 0 28040
Entrants in 1997
Sales_1997 0 641090
Sales_1998 0 1994720
Sales_1999 0 1873050
Sales_2000 0 1936630
No. empl_97 1 4164
No. empl_98 1 11272
No. empl_99 1 18027
No. empl_00 1 12789
Productivity_97 0 8602
Productivity_98 0 15189
Productivity_99 0 33036
Productivity_00 0 27732
Entrants in 1998
Sales_1998 0 403273
Sales_1999 0 9643620
Sales_2000 7 9592930
No. empl_98 1 51093
No. empl_99 1 51093
No. empl_00 1 45441
Productivity_98 0 20293
Productivity_99 0 84444
Productivity_00 0 25646
Entrants in 1999
Sales_1999 0 538800
Sales_2000 0 355691
No. empl_99 1 6640
No. empl_00 1 11480
Productivity_99 0 229590
Productivity_00 0 352510
Source: Author's calculation.
APPENDIX 3
Distribution of growth of new entrants by cohort
[GRAPHIC OMITTED]
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(1) Spanish equivalent of the European NACE classification of
economic activities.
(2) A third explanatory model concerns adjustment to external
shocks. For this type of model, entries and exits are seen as the
firms' response to external shocks affecting the market. This is an
approach to the phenomenon centring on the explanation of the intense
movements occurring in the population of firms every so often, and not
on the continuous flow of entries and exits commonly observed in
markets, as in the other two types of model.
(3) The age of the SMEs, according to the European Business Study
(Maroto 2001), is based on a sample of firms that are generally more
than 15 years old, among which however the firms of Portugal, Spain,
France and Greece appear to be younger. The statistical sources used
from the SABI database, which holds information about firms from the
BORME (Official Gazette of the Mercantile Register), has introduced
biases into the analysis. For example, the minimum level of turnover of
the firms observed and retained in the database is set at 479.041
[euro]. However, this limitation has certainly turned into an advantage
when analyzing the firms and their evolution over time, as long as they
survive beyond the initial stage of approximately three years of life.
(4) In the above-mentioned European Business Study there appears to
be a relation between on the one hand the variables average firm size
and dominant size class of the firms in each country, and on the other
the competitive position occupied by the countries at the international
level. In the most competitive countries we find Finland, the
Netherlands, Sweden, Ireland and Denmark, with a total of 1,280,000
firms, the average size of firms ranges from 5 to 12 employees per firm
(20-40 companies per 1,000 inhabitants), the predominant structure is
the large firm, and SMEs provide from 60 to 70% of total employment. In
contrast, in less competitive countries such as Portugal, Italy, Greece,
Spain and France, with a total of 10.085.000 firms, the average size is
smaller, between 3 and 7 employees per firm (60-70 firms per 1.000
inhabitants), the predominant structure is the micro firm, and SMEs
generate more than 80% of total employment.
(5) Some work that has served as reference includes: Boeri and
Cramer (1992), Boeri and Bellmann (1995) for firms operating in Germany;
Du Reitz (1984) for Sweden; Mata (1993), Mata and Portugal (2000) for
Portugal; Geroski (1991) for the US; and Baldwin and Geroski (1989,
1999) for Canada.
(6) Gibrat's law or rule permits the construction of models of
the distribution by size of the firms making up a particular market.
This law has been frequently used in empirical work, but the results are
in some cases contradictory.
(7) In general firms' sales volume (in thousands of euros) has
been the most used variable in this current work, both to measure size
and growth. The reason for this lies in the fact that this variable is
the most representative in the SABI database. In particular, it achieves
40% more year-observations than the level of employment. On the other
hand, in much of the literature on entrepreneurs, as Autio et al. (2000)
point out, "and even the growth in sales has allowed us to
distinguish what is and what is not entrepreneurial activity".
(8) However, this problem may not contaminate the final results,
since the average size of the firms, as can be seen in Table 2, does not
exceed 30 employees. An analysis carried out by Hall (1986) in the food
sector finds that mergers and acquisitions make up some 13% of all
disappearances from the database for firms of more than 20 employees.
Justo de Jorge Moreno [1], Leopoldo Laborda Castillo [2]
[1] Department of Economics and Business, University of Alcala,
Plaza de la Victoria, s/n Alcald de Henares, 28802 Madrid, Spain
[2] World Bank, Washington DC, USA
E-mails: [1] justo.dejorge@uah.es (corresponding author); [2]
llabordacastillo@gmail.com
Received 25 May 2010; accepted 10 November 2010
Justo de JORGE MORENO. He is PhD from University of Alcala (Spain),
and professor of Business Sciences Department in Alcala. His work
focuses on problems of efficiency and productivity, entrepreneurship and
growth, analysis of performance in Spanish retailing, and European
railway companies. He is author of articles and monographs on economic
and business about these issues. He has participated in diverse projects
on entrepreneurship for institutions in Spain (i.e. Foundation Rafael
del Pino, City Council of Madrid) and International Organisms (i.e. IDB
Iberamericano of Development Bank).
Leopoldo LABORDA CASTILLO. He received his PhD. from University of
Alcala (Spain). His work focuses on problems of productivity and growth,
and he is author of articles and monographs on economic and business,
entrepreneurship, and enterprise restructuring in developing countries.
He works as a consultant at the World Bank and as associated researcher
for Institute for Latin American Studies in the University of Alcala.
Table 1. Comparison of models of new entrants
Quadratic Quadratic
without Log. with Log.
(absolute rate) (relative rate)
Year ([S.sub.f] - [S.sub.i])/ (Ln[S.sub.f] - Ln[S.sub.i])/
cohorts [S.sub.i] = [S.sub.i] + [t.sub.f] - [t.sub.i] =
[S.sup.2.sub.i] + d_sect Ln[S.sub.I] + Ln[S.sup.2.sub.i]
+ d_sect
[R.sup.2] [R.sup.2]
1994 non-sig (coef 0.26 **
[S.sup.2.sub.i])
1995 non-sig (coef 0.24 **
[S.sup.2.sub.i])
1996 0.40 ** 0.65 **
1997 non-sig (coef 0.67 **
[S.sup.2.sub.i])
1998 0.11 ** 0.68 **
1999 0.70 ** 0.73 **
Linear Linear
with Log. without Log.
(relative rate) (absolute rate)
Year (Ln[S.sub.f] - Ln[S.sub.i])/ ([S.sub.f] - [S.sub.i])/
cohorts [t.sub.f] - [t.sub.i] = [S.sub.i] =
Ln[S.sub.i] + d_sect [S.sub.i] + d_sect
[R.sup.2] [R.sup.2]
1994 0.23 ** 0.04 **
1995 0.20 ** 0.14 **
1996 0.57 ** non-sig (coef
[S.sup.2.sub.i])
1997 0.58 ** 0.78 **
1998 0.59 ** non-sig (coef
[S.sup.2.sub.i])
1999 0.57 ** non-sig (coef
[S.sup.2.sub.i])
Note: S = sales
Source: Author's calculation.
Table 2. Results of Model 2
1994 1995
Est. Std. Est. Std.
coef. error coef. error
constant 0.95 0.07 ** 1.04 0.06 **
Ln [S.sub.i] -0.20 0.02 ** -0.20 0.01 **
Ln [S.sup.2.sub.i] 0.01 0.00 ** 0.01 0.00 **
Sector (9)
[R.sup.2] 0.26 0.24
No. obs. 875 1310
1996 1997
Est. Std. Est. Std.
coef. error coef. error
constant 1.90 0.04 ** 2.67 0.06 **
Ln [S.sub.i] -0.42 0.01 ** -0.55 0.01 **
Ln [S.sup.2.sub.i] 0.02 0.00 ** 0.03 0.00 **
Sector (9)
[R.sup.2] 0.65 0.67
No. obs. 3444 3916
1998 1999
Est. Std. Est. Std.
coef. error coef. error
constant 3.81 0.07 ** 7.24 0.18 **
Ln [S.sub.i] -0.82 0.02 ** -1.98 0.08 **
Ln [S.sup.2.sub.i] 0.04 0.00 ** 0.13 0.00 **
Sector (9)
[R.sup.2] 0.68 0.73
No. obs. 4320 1642
**, * significant at the 1% and 5% respectively
Source: Author's calculation.
Table 3. Size of new entrants each year as a proportion of size of
established firms
1994 1995 1996 1997 1998 1999
sect_1_94 0.2 6.3 23.3 37.8 36.8 38.4
sect_1_95 4.1 21.7 26.7 30.8 33.6
sect_1_96 60.5 99.7 108.3 94.3
sect_1_97 16.2 29.6 40.0
sect_1_98 68.5 124.3
sect_1_99 39.4
sect_2_94 6.2 28.9 25.1 23.6 24.4 23.7
sect_2_95 7.5 15.5 14.9 15.0 16.3
sect_2_96 8.6 20.1 22.6 21.3
sect_2_97 9.6 24.8 26.0
sect_2_98 6.7 15.3
sect_2_99 9.3
sect_3_94 5.1 40.5 44.6 44.6 37.7 34.2
sect_3_95 21.5 11.2 9.0 10.2 12.2
sect_3_96 21.3 26.8 27.4 28.2
sect_3_97 24.9 42.0 35.9
sect_3_98 26.1 36.7
sect_3_99 12.3
sect_4_94 4.0 11.1 12.7 11.6 12.0 14.3
sect_4_95 6.5 9.3 9.3 10.1 11.2
sect_4_96 8.3 12.8 17.3 20.1
sect_4_97 19.4 36.1 36.9
sect_4_98 8.3 63.2
sect_4_99 27.1
sect_5_94 13.7 29.3 32.9 33.6 30.9 35.7
sect_5_95 16.5 21.4 28.2 26.4 31.6
sect_5_96 14.6 29.6 30.3 36.9
sect_5_97 14.7 26.5 34.3
sect_5_98 14.8 30.1
sect_5_99 15.5
sect_6_94 8.0 27.9 38.7 34.9 35.6 31.9
sect_6_95 11.8 21.1 23.0 22.2 20.0
sect_6_96 15.4 29.3 30.0 30.5
sect_6_97 17.5 39.8 40.8
sect_6_98 11.9 30.0
sect_6_99 15.3
sect_7_94 1.9 29.4 55.6 52.2 49.2 45.4
sect_7_95 8.3 17.5 17.4 17.4 17.8
sect_7_96 9.0 19.0 23.1 25.7
sect_7_97 19.6 23.6 29.6
sect_7_98 15.3 21.5
sect_7_99 33.5
sect_8_94 2.7 28.7 45.1 49.2 51.3 48.1
sect_8_95 13.0 62.6 69.3 46.5 39.3
sect_8_96 20.0 35.1 46.9 55.3
sect_8_97 24.6 48.4 65.6
sect_8_98 12.8 47.5
sect_8_99 48.0
sect_9_94 3.7 5.1 20.6 28.4 19.2 21.6
sect_9_95 4.7 22.7 19.2 13.8 14.0
sect_9_96 9.8 34.8 20.1 17.0
sect_9_97 35.4 38.0 45.9
sect_9_98 25.0 27.4
sect_9_99 14.5
no firm no firm Sectors groups 1
new estab. digit
entrants
sect_1_94 243 1137.0 Mineral extraction,
sect_1_95 345 food and drink,
tobacco, textiles,
sect_1_96 560 leather goods and
shoes
sect_1_97 568
sect_1_98 490
sect_1_99 48
sect_2_94 366 1791 Wood and cork
sect_2_95 567 industries, paper,
chemical
sect_2_96 1185 industry, metallurgy,
machinery
sect_2_97 1130
sect_2_98 1034
sect_2_99 83
sect_3_94 98 521 Manufacture of office
sect_3_95 141 machines, electrical
machinery and
sect_3_96 309 material, optical,
computing equipment,
sect_3_97 289 motor vehicles,
sect_3_98 280 furniture
sect_3_99 116
sect_4_94 624 1669 Construction
sect_4_95 861
sect_4_96 1673
sect_4_97 1676
sect_4_98 1601
sect_4_99 777
sect_5_94 1176 5895 Sale, maintenance and
sect_5_95 1322 reparation of vehicles
sect_5_96 4106 wholesale/retail
sect_5_97 4088 commerce, hostelery
sect_5_98 3475
sect_5_99 157
sect_6_94 127 847 Transport, post and
sect_6_95 135 telegraph, financial
sect_6_96 576 intermediation,
sect_6_97 582 insurance and pension
sect_6_98 560 plnas
sect_6_99 181
sect_7_94 475 2012 Real estate, research
sect_7_95 598 and development,
sect_7_96 1695 public administration
sect_7_97 1920
sect_7_98 1785
sect_7_99 636
sect_8_94 42 251 Education and
sect_8_95 29 healthcare activities
sect_8_96 139
sect_8_97 140
sect_8_98 114
sect_8_99 49
sect_9_94 56 241 Repair of public
sect_9_95 78 installations,
sect_9_96 187 recreational cultural
sect_9_97 218 and sporting activities
sect_9_98 173
sect_9_99 160
Source: Author's calculation.
Table 4. Panel analysis of firms from SABI database 1996-2001
age age
<4 >4 & <10
Grw. Lnsales96 Grw. Lnsales96
Mean 0.25 5.97 0.16 6.48
Std. dev. 0.25 1.46 0.20 1.34
Minimum -0.91 0 -1.11 0
Maximum 2.07 11.59 2.07 14.3
age age
>10 & <20 >20
Grw. Lnsales96 Grw. Lnsales96
Mean 0.09 7.12 0.06 7.64
Std. dev. 0.14 1.20 0.12 1.39
Minimum -0.89 0 -1.42 1.60
Maximum 2.08 13.7 1.21 15.5
Source: Author's calculation.
Table 5 Analysis of growth of established firms 1996-2001
Lnsales01-Lnsales96 age age
5 >4 & <10 >10 & <20
Variables Coef. (St. Error) Coef. (St. Error)
Lnsales96 -0.334 (0.010) ** -0.347 (0.013) **
[(Lnsales96).sup.2] +0.016 (0.0006) ** +0.018 (0.0006) **
Lnage -0.791 (0.240) ** +0.867 (0.402) **
[(Lnage).sup.2] +0.125 (0.068) ** -0.211 (0.081) **
Lnage *Lnsales96 +0.003 (0.0012) ** +0.002 (0.0006) **
Sector dummies
(9) ([dagger])
Adjusted [R.sup.2] 0.54 0.30
No. observations 3069 3588
Lnsales01-Lnsales96 age
5 >20 (and 15)
Variables Coef. (St. Error)
Lnsales96 -0.153 (0.010) **
[(Lnsales96).sup.2] +0.009 (0.0007) **
Lnage -0.584 (0.1769) **
[(Lnage).sup.2] +0.107 (0.031) **
Lnage *Lnsales96 -0.0006 (0.0001) **
Sector dummies
(9) ([dagger])
Adjusted [R.sup.2] 0.13
No. observations 2667
**, * Significant at different at 1% and 5% respectively
([dagger]) There are statistically significant differences between the
10 sectors of activity F (9.7691) = 7.76
Source: Author's calculation.
Table 6. Transition of firms from 1996 to 2001
1996 1 2 3 4 Fila
Total
1 968 658 270 121 2017
12,04% 8,18% 3,36% 1,50% 25,08%
2 349 826 719 110 2004
4,34% 10,27% 8,94% 1,37% 24,92%
3 64 219 1014 714 2011
0,80% 2,72% 12,61% 8,88% 25,01%
4 27 23 159 1801 2010
0,34% 0,29% 1,98% 22,39% 24,99%
Columna 1408 1726 2162 2746 8042
Total 17,51% 21,46% 26,88% 34,15% 100,00%
Source: Author's calculation.
Table 7. Transition of firms from 1996 to 2001 by sector
Sector 2
1996 2001
Fila
1 2 3 4 Total
1 153 83 25 4 2 65
14,49% 7,86% 2,37% 0,38% 25,09%
2 46 134 73 10 263
4,36% 12,69% 6,91% 0,95% 24,91%
3 6 30 175 53 264
0,57% 2,84% 16,57% 5,02% 25,00%
4 0 1 22 241 264
0,00% 0,09% 2,08% 22,82% 25,00%
Columna 205 248 295 308 1056
Total 19,41% 23,48% 27,94% 29,17% 100,00%
Sector 3
1996 2001
Fila
1 2 3 4 Total
1 35 24 10 1 70
12,37% 8,48% 3,53% 0,35% 24,73%
2 13 32 24 2 71
4,59% 11,31% 8,48% 0,71% 25,09%
3 4 6 44 17 71
1,41% 2,12% 15,55% 6,01% 25,09%
4 114 65 71
0,35% 0,35% 1,41% 22,97% 25,09%
Columna 53 63 82 85 283
Total 18,73% 22,26% 28,98% 30,04% 100,00%
Sector 4
1996 2001
Fila
1 2 3 4 Total
1 104 67 40 15 226
11,65% 7,50% 4,48% 1,68% 25,31%
2 55 99 62 6 222
6,16% 11,09% 6,94% 0,67% 24,86%
3 16 41 10 60 223
1,79% 4,59% 11,87% 6,72% 24,97%
4 6 5 33 178 222
0,67% 0,56% 3,70% 19,93% 24,86%
Columna 181 212 241 259 893
Total 20,27% 23,74% 26,99% 29,00% 100,00%
Sector 5
1996 2001
Fila
1 2 3 4 Total
1 369 298 112 60 839
10,99% 8,87% 3,34% 1,79% 24,99%
2 83 288 408 61 840
2,47% 8,58% 12,15% 1,82% 25,01%
3 13 32 358 437 840
0,39% 0,95% 10,66% 13,01% 25,01%
4 7 7 26 799 839
0,21% 0,21% 0,77% 23,79% 24,99%
Columna 472 625 904 1357 3358
Total 14,06% 18,61% 26,92% 40,41% 100,00%
Source: Author's calculation.
Table 8. Ordered probit: Movements 1 and 1 *
Probability in 1996 Probability in 2001
Est. Coef. Std. error Est. Coef. Std. error
Lnage 0.159 0.045 ** -0.190 0.054 **
[Lnage.sup.2] 0.081 0.012 ** 0.134 0.012 **
Comunidad (17)
Sector (9)
Quartile_1 -.557 .313 -1.14 .254
Quartile_2 .196 .313 -.456 .254
Quartile_3 .948 .313 .262 .254
No. obs. 7714 12121
LR chi2 1137.59 1158.62
Prob > chi2 0.0000 0.0000
Pseudo [R.sup.2] 0.0532 0.0346
Source: Author's calculation.
Table 9. Marginal effects: Movements 1 and 1 *
Probability in 1996
dy/dx dy/dx dy/dx dy/dx
Quar- Quar- Quar- Quar-
tile = 1 tile = 2 tile = 3 tile = 4
Lnage -0.046 ** -0.016 ** 0.014 ** 0.049 **
[Lnage.sup.2] (0.013) (0.00) (0.013) (0.014)
Communities (17) -0.02 ** -0.00 ** 0.00 ** 0.025 **
Sector (9) (0.00) (0.00) (0.00) (0.00)
Probability in 2001
dy/dx dy/dx dy/dx dy/dx
Quar- Quar- Quar- Quar-
tile = 1 tile = 2 tile = 3 tile = 4
Lnage 0.054 ** 0.020 ** -0.011 ** -0.063 **
[Lnage.sup.2] (0.015) (0.006) (0.003) (0.018)
Communities (17) -0.03 ** -0.01 ** 0.025 ** 0.045 **
Sector (9) (0.00) (0.00) (0.00) (0.00)
Source: Author's calculation.
Table 10. Promotion and demotion from quartiles of 1996 to 2001
Promotion
Transition Transition
(Promotion) Marginal (Promotion) Marginal
1996-2001 effect 1996-2001 effect
Model 1 (dy/dx) Model 2 (dy/dx)
Constant -5.60 ** -4.66 **
Lnage (0.383) 0.90 ** (0.507) 0.84 **
[Lnage.sup.2] 3.64 ** (0.033) 3.38 ** (0.053)
Acc. Share (0.160) -0.18 ** (0.261) -0.17 **
(1 = >50%; -0.74 ** (0.007) -0.71 ** (0.011)
0 = <50%) (0.033) (0.054) -0.02 *
Communities (17) -0.08 * (0.01)
Sectors (9) (0.07)
No. obs. 12256 3904
LR chi2 1175.70 366.51
Prob > chi2 0.0000 0.0000
Pseudo [R.sup.2] 0.0925 0.0909
Log likelihood -5766.7 -1832.6
Demotion
Transition Transition
(Demotion) Marginal (Demotion) Marginal
1996-2001 effect 1996-2001 effect
Model 3 (dy/dx) Model 4 (dy/dx)
Constant 4.08 ** 2.79 **
Lnage (0.317) -1.16 ** (0.508) -0.88 **
[Lnage.sup.2] -2.97 ** (0.040) -2.50 ** (0.056)
Acc. Share (0.100) 0.17 ** (0.152) 0.12 **
(1 = >50%; 0.45 ** (0.008) 0.35 ** (0.012)
0 = <50%) (0.021) (0.033) 0.02
Communities (17) 0.07 (0.017)
Sectors (9) (0.048)
No. obs. 12256 3904
LR chi2 4039.44 1304.6
Prob > chi2 0.0000 0.0000
Pseudo [R.sup.2] 0.2443 0.2652
Log likelihood -6247.6 -1807.4
Prob [[y.sub.1/0]] = [[beta].sub.0] + [[beta].sub.1]Lnage +
[[beta].sub.2][Lnage.sup.2] + [17.summation over (i=1)] Communitie +
[10.summation over (i=1)][[beta].sub.4]sector + [epsilon]
Prob [[y.sub.1/0]] = [[beta].sub.0] + [[beta].sub.1]Lnage +
[[beta].sub.2][Lnage.sup.2] + [17.summation over (i=1)] Communitie +
[10.summation over (i=1)][[beta].sub.4]sector + [[beta].sub.4]Share +
[epsilon]
Source: Author's calculation.
Fig. 2. Relation between entry growth of cohorts and size
Min Mean 95% percentile
Cohort 1994 8.33 5.2 7.4
Cohort 1995 9.09 5.3 7.6
Cohort 1996 8.75 5.3 6.9
Cohort 1997 8.87 5.4 6.7
Cohort 1998 8.91 5.5 7.0
Cohort 1999 7.33 4.4 6.5
