DETERMINANTS OF TURBULENCE IN ENTRY AND
EXIT FOR HIGH-GROWTH AND DECLINING
INDUSTRIES
Rui Baptista
IN+, Instituto Superior Técnico, Technical University of Lisbon and Max Planck
Institute for Research into Economic Systems, Jena
Murat Karaöz
IN+, Instituto Superior Técnico, Technical University of Lisbon and Süleyman Demirel
University, Isparta
Paper Submitted for Presentation at the Workshop on Firm Exit and Serial
Entrepreneurship – Max Planck Institute for Economics, 13-14 January 2006
Preliminary Draft
Abstract: This paper examines how industry turbulence in entry and exit varies across
industries and why some industries experience higher levels of turbulence than others. The
analysis is conducted using panel data at the six digit industry level, allowing for the
identification of specific emerging and declining product markets within more widely defined
industries. The paper looks at differences in turbulence patterns between high-growth and
declining industries. While controlling for all the main determinants of turbulence, the analysis
focuses mainly on two types of factors influencing turbulence: the ‘vacuum’ effect created by
exit in the previous period, which is partitioned into ‘trial-and-error’ (exit by recent entrants)
and ‘creative destruction’ (exit by older incumbents) processes; and the industry/market growth
rate and its volatility. Estimation results reveal that volatility in growth rates produces positive
effects on industry turbulence in high-growth industries but has no significant effect on
turbulence in declining industries. It is also shown that trial-and-error processes are particularly
significant in high growth industries, while creative destruction is more significant in declining
industries.
Corresponding Address: Rui Baptista, IN+ Centre for Innovation, Technology and
Policy Research, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001 Lisbon,
Portugal. Ph: +351.218.417.379. Fax: +351.218.496.156. E-mail: [email protected].
1. Introduction
The determinants of entry and exit of firms are at the center of the enduring process of
market selection, as industries and products emerge, grow, mature and ultimately fade
from the market in the course of the emergence of novel substitutes. Empirical literature
has revealed that entry and exit rates are strongly correlated and differ significantly
across industries and over time (Geroski, 1991, 1995). Conventional wisdom suggests
that the direction of this relationship should be negative, since an attractive industry
brings in more entrants and also deters more exits. However, a large body of empirical
studies across industries and economies has pictured quite the opposite; in fact, entry
and exit are found to be significantly positively correlated (Geroski, 1995).
Industries that are associated with high levels of entry and exit are deemed “turbulent”
(Beesley and Hamilton, 1984). The theoretical and empirical literature addressing this
issue is considerable and uses a variety of terms to refer to it, such as ‘mobility’,
‘turnover’, ‘turbulence’ (Acs and Audretsch, 1990; Caves, 1998) and market ‘selection
intensity’ (Baldwin and Rafiquzzaman, 1996). Studies of entry, exit and industry
turbulence have shown that the factors underlying these phenomena are very diverse,
being related with industry-specific and firm-specific differences, as well as with
changes in the macroeconomic and political environment. Turbulence is fed by a variety
of events or factors occurring at the firm, market and macroeconomic levels, which can
be either momentary or persistent over time; it restructures industries, relocates their
boundaries and changes the foundations of competition. Factors such as supply and
demand fluctuations, product life cycles, levels of competition and rivalry,
technological innovations and regulatory changes, might generate higher levels of entry
and exit, thereby shaping the structures of industries and economies (Gort and Klepper,
1982; Dunne et al., 1988; Jovanovic and MacDonald, 1994; Siegfried and Evans, 1994;
Caves, 1998).
Industry characteristics such as number and size distribution of firms, market shares,
process technologies and product variety and quality, evolve through trial-and-error and
creative destruction processes through which the market selects successful firms
(Jovanovic 1982; Geroski, 1991; Jovanovic and MacDonald, 1994; Klepper 1996;
Caves, 1998; Baldwin, 1998). Higher levels of entry and exit rates tend to occur in
emerging or growing industries, or in industries under rapid structural change. Large
waves of new entrants – either bringing innovative and more competitive products to
the markets or just trying out their luck – lead to large waves of exits, mainly of those
competitors whose abilities are at the fringe.
The present paper is concerned with how industry turbulence in entry and exit (hereafter
referred to as ‘turbulence’) varies across industries and why some industries experience
higher levels of turbulence than others. This analysis presents the novelty of being
conducted at highest possible level of “disaggregation” – the six-digit industry
classification – using the SISED database, which covers all firms in the Portuguese
industry. A longitudinal data set was built for the period 1986-1993. The six-digit level
of analysis provides unprecedented detail, allowing for the identification of specific
emerging and declining product markets within more widely defined industries. The
paper focuses particularly on differences in turbulence patterns between high-growth
and declining industries. The determinants of turbulence are examined through the use
of panel data estimation methods in order to identify causality effects on turbulence.
1
It is important to note that the eight year time span between 1986 and 1993 covers four
years of macroeconomic expansion followed by four years of recession/slow growth.
Moreover, the analysis starts in 1986, a year when a major external/policy shock
occurred affecting all sectors of the Portuguese industry: entry into the European
Community brought about significant market de-regulation, as well as greater financing
opportunities for new and existing businesses.
While controlling for an encompassing set of determinants of turbulence, the analysis
focuses particularly on two types of factors influencing turbulence. The first type
addresses the ‘vacuum effect’ – whereby incumbent exit at a certain moment in time
creates room for new firm entry and growth in subsequent periods in the current – thus
affecting the levels of turbulence. This effect is partitioned into two sub-determinants,
in order to separate and identify the effects of two different types of exit – ‘trial-anderror’ (exit by recent entrants) and ‘creative destruction’ (exit by older incumbents). The
second type concerns the effects on turbulence of the industry growth rate and its
volatility.
The estimation results reveal that volatility in growth rates produces positive effects on
industry turbulence in high-growth industries but has no significant effect on turbulence
in declining industries. It is also shown that trial-and-error processes are particularly
significant in high growth industries, while creative destruction is more significant in
declining industries.
2. Turbulence in Entry and Exit: Assessing the Intensity of Market Selection in
High-Growth and Declining Industries
While there is a significant positive correlation between entry and exit, various studies
of the industry or product life cycle suggest that different phases of the cycle yield
different regularities in entry and exit rates. A series of empirical studies (Gort and
Klepper, 1982; Klepper and Graddy, 1990; Klepper and Miller, 1995; Agarwal and
Gort, 1996, 2002; Agarwal, 1997; Klepper and Simons, 2000, 2005; Agarwal and
Audretsch, 2001) has shown that entry rates are higher than exit rates in the earlier
phases of industry life cycle. As industries age and set standards, or dominant designs
for their products, the focus of innovative activity switches from product to process,
opportunities for scale economies emerge and ‘shakeout’ begins. Exit rates overtake
entry rates and turbulence levels decrease. A significant point thus emerges from these
studies: levels of turbulence are higher in earlier stages of the industry/product life
cycle. Beesley and Hamilton (1984) observe that emerging sectors – those that are not
yet fully developed, or even recognized by the Standard Industrial Classification
indexes – are the ones registering greater levels of turbulence. Klepper and Graddy
(1990) report that the change in the mean number of firms goes from positive to
negative as industries pass from early to late stages of their life cycles. The work by
Agarwal and Gort (1996) on entry, exit and the evolution of markets also suggests that
levels of turbulence (entry plus exit rates) are higher in earlier stages of the life cycle
and decrease as industries mature.
As pointed out above, measures of market turbulence reflect the intensity of market
selection through entry and exit of firms. Since the seminal work of Orr (1974) on the
determinants of entry, a broad range of empirical studies has revealed that the selection
process responds to incentives, barriers or structural characteristics such as profit rates,
2
industry growth rates, market risk, economies of scale, concentration, advertising
intensity, research and development, and “room” for entry (Khemani and Shapiro, 1986;
Shapiro and Khemani, 1987; Audretsch and Acs, 1990; Baldwin and Gorecki, 1991;
Geroski and Schwalbach, 1991; Rosenbaum and Lamort, 1992; Baldwin, 1998;
Fotopoulos and Spence, 1998; Lay, 2003).
The analysis conducted in the present paper aims to examine differences in the shape
and significance of the effects of the determinants of market selection on turbulence
levels for high-growth, declining and other industries. Hence, separate estimations are
carried out for those three different groups of industries. While controlling for an
encompassing set of determinants, the present study of turbulence focuses particularly
on two kinds of factors:
i.
first, the study aims to distinguish between the effects of trial-and-error and
creative destruction processes associated with the ‘vacuum effect that is created
by exit in the previous period.
ii.
second, this study aims to shed light on the relationship between industry growth
rates, and their volatility, and turbulence;
In both cases, the analysis conducted also aims to examine differences in the shape and
significance of the effects of these determinants on turbulence levels for high-growth,
declining and other industries. Hence, separate estimations are carried out for those
three different groups of industries.
2.1 Replacement and Displacement Effects: Trial-And-Error and Creative
Destruction Processes
Exit of incumbent firms creates room for new firms to enter the market and grow
(vacuum effect). Various authors (see, for instance, Baldwin, 1998) have argued that the
exit of recent entrants acts to “create room” and thus attract other, more competitive
firms to enter; this has been dubbed a ‘replacement effect.’ Industries with high growth
rates and high profit margins are expected to attract more of entrants who “try their
luck” hence registering greater replacement effects. Industries with negative growth
rates and low profit margins should encourage less trial-and error entrants. However, a
different kind of market selection process occurs whereby aged incumbents locked into
old technologies exit, creating room for new, innovative firms. This process can be seen
as selection through creative destruction and may be dubbed a ‘displacement effect.’ It
is expected that creative destruction processes of market selection will occur mostly in
declining industries.
It is then possible to formulate the following hypothesis:
H1:
Turbulence in high-growth industries results mostly from high levels of trialand-error/replacement market selection processes, while turbulence in declining
industries results mostly from creative destruction/displacement market
selection processes.
2.2 The Effect on Turbulence of Volatility in Growth Rates
Studies of markets in a variety of disciplines, including industrial organization and
organizational ecology, have shown that demand growth volatility is a significant
3
source of uncertainty and, therefore, of risk (see, for instance, Hannan and Freeman,
1989; Geroski, 1991). Mazzucato (2002) argues that unsystematic risk in an industry
might be higher in early periods of growth since idiosyncratic factors affecting both
supply and demand are stronger. Geroski (1991) suggests that market ‘turbulence’1 in
the forms of price and output shocks, growth and technological change is expected to
increase turnover (entry and exit). Sutton (1997) points out that fluctuations in industry
demand create turbulence in entry and exit, and these fluctuations are of primary
importance in determining market selection. However, such fluctuations may be
difficult to measure or, at least, it may be troublesome or control for their influence
empirically. In industries where the pace of technological, socioeconomic and
regulatory changes is faster, leading to higher volatility in growth rates, it is expected
that turbulence levels in entry and exit will be higher (Owen, 1971; Caves and Porter,
1977; Caves, 1998). It is therefore possible to formulate the following hypothesis:
H2:
Industries experiencing greater volatility in growth rates will register higher
levels of turbulence.
3. Data and Variables
For the present analysis a longitudinal data set on industry entry and exit was built from
the from Portuguese SISED database. This is longitudinal database is the result of
mandatory information submitted by all firms with at least one employee to the
Portuguese Ministry of Employment and Social Security. This information, based on a
sample covering virtually the whole universe of Portuguese firms (sole traders are
excluded), enables the construction of industry-level variables at the six digit level of
industry aggregation, which is the most detailed level of analysis possible, thus allowing
for the recognition of turbulence patterns in markets for specific products within more
widely defined industries. The panel covers a time span of eight years, from 1986-1993.
The study focuses on 320 six-digit sectors which covered about 95 % of the total
industry population employment in 1993. In order to select high-growth and declining
industries, these sectors are ranked in terms of average employment growth rates from
1986 to 1993. Following Birch (1987), industries that are ranked in the highest 10
percent have been defined as ‘high-growth’ industries, while the lowest 10 percent in
terms of average growth have been classified as ‘declining’ industries. This kind of
typology enables us to analyze different forces that could have similar or contrasting
impact on turbulence, depending on the industry growth rate and life cycle. All the
remaining industries form a control group, designated as ‘others.’ A comparative view
of these groups is presented in Table 1.
1
The term “turbulence” used here by Geroski differs in meaning from the term used in this paper, referring primarily
to volatility in costs, prices and demand, and not to market selection intensity or firm turnover, or mobility. As
pointed out before, a diverse set of measures and concepts of turbulence and volatility have been developed by the
economics, organizational ecology, finance, and marketing/strategic management literatures under the broad umbrella
of “environmental turbulence,” In particular, the finance and marketing literatures have used the expression “market
turbulence” to identify short term dynamics of stock market volatility and market demand fluctuations (see, for
instance: Glazer and Weiss 1993; Frömmel and Menkhoff, 2003).
4
3.1. Dependent Variable: Entry, Exit and Turbulence
Following several studies (Caves and Porter, 1976; Beesley and Hamilton 1984;
Audretsch and Acs, 1990; Dunne and Roberts, 1991; Fotopoulos and Spence, 1998) the
dependent variable ‘turbulence’ (TURB) is defined as the sum of entry and exit rates.
Entry rates are calculated as the ratio between the number of entrants in industry i at
time t and the stock of firms in industry i at time t-1. In the same manner, exit rates are
calculated as the ratio between the number of exits in industry i at time t and the stock
of firms in industry i at time t-1.
Comparative descriptive statistics of turbulence rates for the eight-year data set are
presented in Table 2. As expected, high-growth industries experience greater selection
intensity, registering an average turbulence rate of about 26.6%, in comparison to
15.9% for declining industries and 18.1% for other industries. The maximum level of
turbulence registered for high-growth industries during the period under analysis is
about two times larger than the one registered for declining industries.
Table 3 presents the turbulence rates, together with related entry and exit rates for highgrowth, declining and other industries, displaying average turbulence, entry and exit
rates for the time periods 1986-89 (a period of expansion and economic growth) and
1990-93 (a period of recession and economic decline). The evidence suggests that the
business cycle affects turbulence rates in different ways for high growth, declining and
other industries. In particular, while turbulence is clearly lower during recessions for
high-growth and other industries, the difference between turbulence rates in expansion
and recession periods for declining industries is not very high. Entry rates are clearly
lower during recession for all industries, while exit rates remain virtually the same for
high growth and declining industries, and increase more significantly for declining
industries. Regardless of these differences, high-growth industries register persistently
higher levels of turbulence whether under expansion or recession. Evidence in Table 3
also supports the view that entry and exit rates are higher in early stages of business
cycle (Agarwal and Gort, 1996) in comparison to mature and declining stages.
The data set assembled also allows us to examine the notion that entry rates are higher
than exit rates in early stages of the industry/product life cycle, until a ‘shakeout’ or
‘transitional plateau’ begins, as suggested by studies of entry and exit over the product
life cycle such as Gort and Klepper (1982), Agarwal and Gort (1996) and Mazzucato
(2002). It is, however, important to note that product life cycles in Portugal during the
period under analysis are strongly influenced by deregulation, privatization and market
liberalization. Both general and four-year business cycle averages presented in Table 3
support the notion that entry leads exit in earlier periods, while exit rates are higher
entry rates in later periods of industry life cycle. Yet general business conditions are
likely to have important affects on these values. Entry rates lead exit rates in 81 percent
of high-growth industries. This effect is more evident in expansion period of 1986-89.
The opposite happens in declining industries; exit rates lead the entry rates at 62 percent
of declining industries. The effect is more evident in recession period of 1990-93. The
values for other industries’ fall between those for these two groups.
3.2 The Vacuum effect: ‘Trial-and-Error’ and ‘Creative Destruction’ Variables
Number of exits (and exit rates) in each year in an industry can be separated into subgroups considering the age ( k years) of exiting firms. Thus, exits at time t in an industry
5
are divided into sub-groups including exiters that are one years of age or less, and other
sub-group of exiters that are older than one year. The first sub-group of exiting firms
can be seen as infant mortality in each sector. An infant mortality (exit) rate can
therefore be calculated for the exits of firms that are one years of age or less. Similarly,
exit rates can be calculated for various any other set, or sub-group, of exiting firms that
are aged k, which includes the number of exits at time t that entered to the industry k
years before. In this case, omitting the industry and time indices, (total) exit rate at time
t for industry i, XRT, is sum of industry sub-groups’ exit rates.
XRT= ∑(XRT)k (k=1, 2, …,N)
(1)
The above infant mortality rate for exits of one years of age firms, (XRT)k=1, may be
deemed to result from trial-and-error processes, thus representing the part of exit rate
associated with the exit of firms that display less innovative capacities, entrepreneurial
abilities or financial capabilities (see Baptista and Karaöz, 2005 for a review of the
determinants of firm survival in the early years after start-up). In an analogous manner,
a sub-groups of exiters associated with creative destruction processes can also be
obtained. The present study assumes that industry exit rates of firms that are four or
more years of age, ∑(XRT)k≥4, represents the part of the exit rate associated with older
firms that are replaced by new, innovative firms.
Table 4 presents eight-year averages of exit rates for one year old firms – (XRT)k=1 –
and four and more years old firms – ∑(XRT)k≥4 – representing the trial-and-error and
creative destruction processes for declining, high growth and other industries,
respectively. The exit rate of infants suggests that trial-and-error process are
considerably more important in high growth industries than in declining industries. This
suggests that declining industries attract less newcomers that will ‘short-stay’ at the
competitive fringe than growing industries. Creative destruction processes are relatively
more common in declining industries compared to the high growth and other industries,
although differences between groups are less considerable.
The trial-and-error (XRT1) and creative destruction (XRT4) variables (exit rates) are
included in the analysis of turbulence determinants in order to capture their effects in
generating turbulence in subsequent periods. It is expected, according to hypothesis H1,
that XRT1 will have a more significant effect in generating turbulence in high-growth
industries, while XRT4 should be more significant in generating turbulence in declining
industries.
3.3 Volatility in Industry Growth Rates
Higher levels of growth rates are usually positively correlated with higher levels of
volatility. Thus the question that arises from this relationship is: what are the individual
effects of both differences in growth and in its volatility on industrial turbulence. In
order to obtain reliable results, one needs to separate the magnitude and volatility of
growth rates and mitigate the effects of linear correlation that could cause spurious
results.
Volatility or fluctuations have been measured in a variety of ways (see Camerun et al.
1987 for a brief review). Orr (1974) used the standard deviation of industry profit rates
while Khemani and Shapiro (1986) use the variance of the same variable to measure
industry-specific risk. A more commonly used indicator seems to be the variance of
growth rates across time. Yet Camerun et al. (1987) points out that variance has a major
6
drawback: it cannot separate the effect of volatility from magnitudes and fluctuation
trends. For instance, the two progressions of ‘1 2 3 4 5’ and ‘4 3 1 5 2’ have the same
variance of 2.5, yet the second progression shows significant fluctuations while the first
one is a monotonic progression. Thus, magnitudes and fluctuations need to be separated.
Camerun et al. (1987) proceeds by regressing the related data against time. Those
authors utilize Pearson correlation coefficient values and create a “coefficient of
alienation”, (1-r2), to measure the volatility through badness of fit. Yet this measure has
also a drawback: it does not differentiate positive from negative trends. Schwartz and
Altman (1973) provides an alternative by simply using the residuals of the same
regression, instead of the correlation coefficients.
Using the industry growth rates (EMPGRO) for the 1986-93 period the present study
generates two average volatility indexes for each four year period (1986-89 and 199093). In order to calculate each of the four-year average volatility indexes, we divided the
sum of absolute values of residuals by the number of years (in the present case, four) in
the regression. In order to mitigate the relatively high level of correlation between these
two variables we use the natural logarithm of volatility (LNVOL) in the regression
analysis. Calculating an eight-year averaged volatility index or two four-year period
volatility indexes is essentially a matter of choice. For the present study, it was decided
to use two separate indexes for two reasons. First, business cycles are major events that
alter the trajectory of the economy, and thus are likely to affect the way volatility
impacts on turbulence; second, having more than one value for volatility during the
eight year span of the analysis enables the use of this variable in fixed-effect model
estimation. Taking the natural logarithm of volatility substantially mitigated the
problem of linear correlation that was present between growth and its volatility, so that
it enabled us to use them together.
An important drawback of our growth and volatility measure is that we use employment
data instead of sales, due to the unreliability of sales data in the SISED database.
However, estimations with volatility indexes based on corrected sales data, which are
not reported here, yielded similar results. The growth and volatility measures are
included in the analysis of the determinants of turbulence. According to hypothesis H2,
it is expected that higher volatility will have a positive impact on turbulence.
3.4 Other Variables
Variables related with barriers to entry and structural characteristics have been
constructed with the purpose of controlling for other significant effects on turbulence
across sectors. Variables include the share of micro-firms – with five or less employees
in the sector (SMAL5); the Herfindahl concentration index (HERF), and the change in
the Herfindahl index across time (CHERF); the share of employment in the sector in
total employment in the economy (ECOSHR); the logarithm of the average number of
establishments per firm (LNAVNEST) in the sector; the log of the average value of
equity per firm (LNAVSCAP), the coefficient of variation in sector employment
(COVPEMP); and the lagged entry rate (ENTLAG). Also, a period dummy (YEARDM)
variable has been added to capture the effects of business cycles (expansion vs.
recession) on turbulence levels. A detailed description of the variables is provided in
Table 5.
Davies and Geroski (1997) examined the determinants of inter-industry differences in
within industry variance of market shares for the five leading firms (a different measure
7
of turbulence than the one used in the present paper) in UK industries between the years
of 1979 and 1986, finding that this particular measure of turbulence (arguably more a
measure of intensity in competition and rivalry between large firms than of intensity in
market selection) was positively affected by industry R&D efforts more innovative
industries experienced more turbulence), while higher levels of advertising intensity
among leading firms act like a barrier to mobility and suggested less turbulence. Both
R&D and advertising are not used as explanatory variables in the present study, since it
is suggested that such variables are more likely to influence variations in the relative
positions of incumbents than market selection per se. Even tough it is recognized that
both higher R&D efforts and advertising by incumbents do generate barriers to entry
and exit, other measures of such barriers are already included in the study which are
believed to provide an appropriate control for this particular determinant of turbulence.
The symmetry hypothesis postulates that barriers to entry (such as capital requirements)
also deter firms from exit (Shapiro and Khemani 1987). Various measures are
considered to account for these determinants. Following Yamawaki (1991); Mayer and
Chappell (1992); and Baldwin (1998), the present study measures entry and exit barriers
using the logarithm of average firm size (as measured by equity) (LNAVSCAP) and the
logarithm of average number of establishments per firm (LGNEST) variables. Average
employment per firm was also considered but not included in the model due to its high
correlation with the average number of establishments (0.89).
Turbulence is expected to be negatively related with LGNEST and LVAVSCAP since,
as the capital or scale requirements increase, both entry and exit are likely to be
deterred. However, these variables are expected to be less significant in declining
industries. Industries in which demand is declining are likely to register excess capacity
leading to the exit of large firms (Ghemawat and Nalebuff, 1985), or to downsizing
(Horowitz and Horowitz, 1965; Harrigan, 1980, 1982; Ghemawat and Nalebuff, 1990;
Lieberman, 1990). Moreover, declining industries attract lower levels of entry due to
low profit margins and growth. Entry barriers are also likely to be insignificant in highgrowth industries which are at their early stages of development and in which the
minimum efficient scale is not yet settled, and where a dominant design has not yet
been generated. These sectors are likely to register abundant trial-and-error processes
over time.
The Herfindahl index (HERF) and the change in Herfindahl index (CHERF) across time
are used to measure the effect of market concentration on turbulence. As industries
evolve toward later stages of their life cycle, an equilibrium level (Carree and Thurik,
2000) or steady state (Rosenbaum, 1993) industry concentration level is expected to
emerge. Effects of industry concentration (HERF) on turbulence are expected to
produce different results depending on the effects of growth rate, profit and mobility
barriers on concentration. Small scale entry requires lower capital commitment (Mata,
1991) and also makes exit easier due to lower sunk costs. In contrast, large entry
requires large capital commitment, deterring entry and exit and thus reducing turbulence
(Beesley and Hamilton, 1984; Audretsch and Acs, 1990; Rosenbaum and Lamort, 1992;
Fotopoulos and Spence, 1998). Industries with more small firms are expected to be
more turbulent, so the share of micro-firms in the sector (SMAL5) should influence
turbulence positively. However, in high growth markets sunk costs are not expected to
be as important as in the rest of the industries, since it is expected that it would be easier
for any firm to find a buyer to liquidate its assets in the short term.
8
The level of turbulence in relation to industry size is also investigated. Various studies
of entry and exit suggest that larger industries are subject to more entry and exit
activities (Orr, 1974; Khemani and Shapiro, 1986), hence the inclusion of the share of
industry employment in total employment in the economy (ECOSHR). We expect
larger industries to be more turbulent. It should also be pointed out that greater size
heterogeneity within industries is expected to be linked with greater turbulence,
although heterogeneity in sizes may imply specialization of large and small firms that
coexist in the market with little turbulence (Baldwin, 1998). As in Lieberman (1990),
the coefficient of variation in employment (COVPEMP) is used in order to capture the
effects of heterogeneity in sizes of firms. Finally, the simultaneity hypothesis
(Fotopoulos and Spence, 1998) postulates that, after new firms enter, competitive
pressures lead to the exit of those incumbents at the fringe; this is the ‘displacement
effect’. The lagged entry rates (ENTLAG) are expected to capture this effect.
4. Empirical Results and Conclusions
Random-effect, fixed-effect and first-difference robust estimation (Baltagi, 2005;
Cameron and Trivedi, 2005) techniques were performed for declining, high-growth and
other industries separately in order to identify differences in the patterns of the effects of
the explanatory variables on turbulence across different stages of product life cycles.
The estimation results are presented in Tables 7, 8 and 9. The results suggest that there
are in fact different market selection processes determining turbulence in high-growth
and declining industries.
Employment growth (EMPGRO) variable produced strong positive relationship in both
high growth and other industries. Inclusion of the squared growth rate in the regression
reveals that this relationship is mixed for declining industries, with an early negative
downward movement. The volatility in growth rates (LNVOL) variable produced
positive significant results in high-growth and other industries, except in the fixed-effect
estimation. Results show that higher positive growth rates are associated with higher
levels of growth rate volatility, and both factors contribute for increasing turbulence in
entry and exit. Volatility in growth rates does not significantly influence turbulence in
declining industries. It seems therefore that uncertainty and risk, in the form of volatility
in growth rates, does not affect market selection in industries associated with (mostly)
negative growth.
The lagged values of infant exit rates (exit rates of one year old firms – XRT1) produce
the expected effects on turbulence, as defined in hypothesis H1. Trial-and-error
processes of market selection have a significant effect on turbulence in high-growth and
other industries, but not in declining industries. Trial-and-error processes occur mostly
in those industries that display high positive levels of growth across the whole period
under analysis – high growth industries display average yearly growth levels of over
18%. Declining industries (average yearly growth of -1.28%) register little trial-anderror activity.
The lagged values of the exit rate of firms that are four years of age or older has a
significant and positive effect on turbulence levels in declining industries and also in
other industries, but has no significant effect on turbulence in high-growth industries.
This again confirms hypothesis H1, suggesting that creative destruction processes do
not play a significant role in generating turbulence in high-growth markets. However,
9
creative destruction becomes significant in generating turbulence before industries reach
decline in their life cycles, since the coefficient for variable XRT4 is significant and
positive for both declining and other industries.
Regardless of the estimation method or industry group, the dummy variable
representing the business cycle (YEARDM) always shows a significant positive effect
on turbulence – i.e. turbulence is higher during expansion (and, in the present case, deregulation) than during recession – although its significance is slightly lower for
declining.
The lagged entry variable (ENTLAG) produces similar significant positive results in
seven out of nine estimations. The variable became insignificant for declining and other
industries in fixed-effect estimations only. These results suggest that displacement
effects are a persistent source of turbulence in all industries.
The industry’s share in total employment in the economy (ECOSHR) has insignificant
effects on turbulence in both high-growth and declining industries, while it positively
affects turbulence in all other industries. A possible reason for these is that industries in
the early stages of their life cycle will register high levels of turbulence even though
their overall share of the economy might still be small, while declining industries
register decreasing demand, profits and employment regardless of turbulence.
The share of micro-firms (SMAL5) has a positive significant effect on turbulence for all
groups of industries, except in the fixed-effect estimations for high-growth and
declining industries. The share of micro-firms in a sector seems therefore to play a
particular important role in generating turbulence in industries that are register medium
or low levels of growth, suggesting that the ability of micro-firms to co-exist with large
firms after the early stages of the industry life cycle contributes for entry and exit rates
to remain relatively high. Estimation results for the level of heterogeneity in industries,
as measured by the coefficient of variation in employment (COVPEMP) confirm this
suggestion, since heterogeneity has positive significant effects on turbulence for other
industries, while remaining insignificant for both high-growth and declining industries.
The measures of scale economies – log of average social capital (LNAVSCAP) and log
average number of establishments (LNAVNEST) – also display no significant effects
on turbulence in high-growth and declining industries, while having both negative and
significant effects on turbulence in other industries. It seems therefore that barriers to
entry and exit only significantly preclude market selection in industries that are in
medium/low growth and maturity stages of their life cycles. While it is no surprise that
scale economies and barriers to entry play no significant role in reducing turbulence in
high-growth markets and industries in the early stages of their life cycle, where
dominant designs and minimum efficient scales have not yet been fully determines, the
result is a bit more unexpected for declining industries.
There could be three possible, interrelated reasons why scale economies and barriers to
entry are not significant for declining industries. The first reason could be that declining
industries attract lower levels of entry due to lower profit margins, declining demand
and negative growth; hence entry barriers would not create any more pressure to
preclude entry anyway. A second reason is that declining demand creates excess
capacity in firms, leading to more intense price competition and thus deterring new
entry regardless of scale economies and the number of establishments in the market.
The third reason is that in large industries incumbents may decide to reduce capacity by
downsizing, instead of exiting (Horowitz and Horowitz, 1965; Harrigan, 1980, 1982;
10
Ghemawat and Nalebuff, 1985, 1990; Lieberman, 1990), so the number of
establishments per firm and the levels of scale economies are reduced sharply.
This last phenomenon is visible in Table 1. Within the eight-year time span, the average
number of establishments in declining industries is reduced from 2.75 to 1.76. Also, the
average employment per firm in declining industries is reduced from 266.6 to 121.4.
This corresponds to a reduction of about 55 % from initial levels in 1986, which is the
highest change among three groups. Concurrently, high-growth industries have
considerably lower numbers of employees per firm, and register no growth in this figure
from 1986 (about 24.2) to 1993 (about 20). An important reason for this is that roughly
78% of high-growth industries are in the service sector, as opposed to 33% of declining
industries and 54% of other industries. The services sector, particularly financial,
insurance consultancy and real estate, experienced the highest levels of de-regulation
and growth post 1986 in Portugal, which helps to explain these shares.
With the exception of declining industries, the change in the Herfindahl index (CHERF)
variable has a negative effect on turbulence, i.e. reductions in concentration increase
turbulence by reducing the entry barriers. The lack of significant effects of
concentration changes on turbulence for declining industries suggests that reductions in
the shares of the larger incumbents in a declining industry do not bring about greater
opportunities for entry, possibly due to low profit expectations.
The absolute level of concentration (HERF) variable produces somewhat mixed effects
on turbulence, with no discernible pattern. The lack of a stable pattern for the effects of
concentration on turbulence may be explained the existence of interactions between
concentration and factors such as industry growth, profit, cost structures and mobility
barriers that affect the concentration in industries. Klepper (1996) argues that the stage
of the product/industry life cycle plays an important role in determining both
concentration and turbulence. Davis and Geroski (1997) found that more concentrated
industries (as measured by the five-firm concentration ratio) exhibit more turbulence (in
the case of their study, turbulence was measured by variance in market shares of the five
largest firms in each industry), an effect for which there seemed to be no easy
explanation. Sawyer (1971) suggests that higher levels of growth can bring about
increases in concentration levels through the decline of small entrants and fast growth of
large firms. The results on high-growth industries seem to support this view.
Some general, preliminary conclusions can be derived from this exploratory analysis of
detailed, “disaggregated” industry/market level data. Long term positive industry
growth rates generate higher levels of turbulence, but declining industries, where
growth rates are negative or, at least, close to zero, display no significant correlation
between growth and turbulence. Higher levels of volatility in growth are strongly
correlated with higher levels of positive growth rates and both factors generate
significant turbulence. Volatility in growth rates associated with declining industries
does not cause turbulence.
Trial-and-error entrant activity is particularly associated with long term positive growth
rates and contributes significantly to generate turbulence through market selection of
infant industries. Declining industries do not experience significant levels of trial-anderror entry. Creative destruction processes are strongly associated with both declining
and other (medium and low growth) industries. In these cases, displacement of
relatively older firms by new, innovative firms seems to play a major role in generating
turbulence.
11
The persistence of micro-firms in industries across all stages of the product/industry life
cycle significantly contributes to turbulence in all industries. The ability of small firms
to coexist with large firms in a market seems to be an important factor in creating
turbulence for all industries. Reductions in concentration seem to contribute positively
to turbulence, but the absolute level of concentration does not have a clear effect on
turbulence, suggesting that interactions between this variable and other factors
influencing turbulence should deserve be further explored.
12
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15
Table 1: Comparative Summary of High-Growth, Declining and Other Industries
HighOther
Growth
Industries
Industries
51
64
205
301274
124865 1330849
227906
229130 1657152
Declining
Industries
Variables
Total number of sectors in the sample
Total Employment in 86
Total Employment in 93
Group Averages Calculated from Industry Values:
Employment growth 86
Employment growth 93
Average Employment 86
Average Employment 93
Herfindahl index 86
Herfindahl index 93
Average number of establishments 86
Average number of establishments 93
Share of number of firms with 5 or less employees 86
Share of number of firms with 5 or less employees 93
Average per firm industry wage (inflation adjusted) 86
Average per firm industry wage (inflation adjusted) 93
Average per firm share of employees with college degrees 86
Average per firm share of employees with college degrees 93
-4.54
-10.89
266.63
121.37
2875
2824
2.75
1.76
24.07
29.61
29793
77209
3.61
5.89
6.91
5.85
24.22
20.00
1736
1162
1.36
1.28
48.33
50.11
28889
78905
6.28
9.19
1.33
-1.10
224.38
165.19
1256
1078
3.56
3.49
32.86
36.84
29778
75953
3.38
5.56
Table 2: Descriptive Statistics of Turbulence Rates
Descriptive Statistics
Mean
Standard Deviation
Skewness
Range
Minimum
Maximum
Number of Industries in the sample
DECLINING
15.93
7.93
0.49
33.14
2.17
35.30
51
HIGH-GROWTH
26.59
12.20
0.90
67.43
3.13
70.55
64
OTHER
18.12
8.01
1.48
60.76
3.13
63.89
205
Table 3: Some Descriptive Statistics of Entry and Exit Rates
Descriptive Statistics
DECLINING
HIGH-GROWTH
OTHER
AVERAGE TURBULENCE RATE (1986-93)
15.93
26.59
18.12
Average Turbulence rate (86-89)
16.87
31.97
21.45
Average Turbulence rate (90-93)
AVERAGE ENTRY RATE (1986-93)
14.99
7.19
21.21
16.10
14.8
9.99
Average entry rate (86-89)
9.30
21.25
13.29
Average entry rate (90-93)
5.08
10.95
6.70
AVERAGE EXIT RATE (1986-93)
8.74
10.49
8.13
Average exit rate (86-89)
7.57
10.72
8.16
Average exit rate (90-93)
9.91
10.26
8.10
16
Table 4: Trial-and-Error vs. Creative destruction (eight-year averages: 1986-93)
Average Exit Rates
Type of Process
DECLINING
HIGHGROWTH
OTHER
Trial-and-error
1.76
4.01
2.07
process
Creative
4.12
3.69
3.70
∑(XRT)k≥4
destruction process
(XRT)k=1: average exit rate of infants in an industry, age=1 (average industry exit rate of firms
with one years of age)
∑(XRT)k≥4: average exit rate of old firms, age>=4 (average industry exit rate of firms with four
or more years of age)
(XRT)k=1
Table 5: Variable Descriptions and Summary Statistics
Variable
Description
# Obs
Mean
Std.
Dev.
Min
Turbulence Rate= Entry rate +
2560
19.47
16.35
0
Exit rate
LNAVSCAP
Log of Average social capital
2531
16.29
2.22
5.73
Coefficient of variation of
COVPEMP
2528 188.81
97.73
20
employment *100
Log of Average per firm
LNAVNEST
2559
0.30
0.62
-0.69
number of establishments
Share of small firms with 5 or
SMAL5
2559
36.24
22.8
0
less employees in industry (%)
Share of industry in economy
ECOSHR
2559
0.29
0.606 0.00014
(%)
Change in Herfindahl index
CHERF
2565
0.79
41.87
-94.5
(%)
HERF
Herfindahl index
2566 1474.96
2213
3
EMPGRO
Employment Growth (%)
2558
6.38
44.23
-93.41
Employment Growth Squared
EMPGRO2
2558 1995.77
33569
0
(%)
Log of four years averages of
LNVOL
2552
2.02
1.06
-1.48
volatility
ENTLAG
Lag values of Entry rate
2560
11.06
13.73
0
Lag values of one year old
XRT1
2559
2.41
3.91
0
firms' exit rate (%)
Lag values of one year old
XRT12
2559
21.10 214.18
0
firms' exit rate squared (%)
Lag values of four or more
XRT4
2239
3.96
4.64
0
years old firms' exit rate (%)
YEARDM
1986 to 1989 =1 else =0
5656
0.50
0.50
0
*29 cases out of 2558 observations display growth rates above 100
TURB
Max
200
22.94
635.34
5.41
100
6.61
814.4
10000
1080*
1165725
5.93
200
100
10000
100
1
17
1
0.076
0.125
0.105
-0.006
-0.015
0.056
-0.122
0.031
1
0.863
0.238
0.073
0.069
0.067
-0.014
0.065
1
0.175
0.009
0.026
0.017
-0.020
0.019
1
0.107
0.041
0.066
0.033
0.337
1
0.305
0.198
0.018
0.099
1
0.741
0.006
-0.049
XRT4
1
0.937
0.095
0.126
0.235
-0.019
-0.034
0.070
-0.135
0.037
XRT12
EMPGRO2
1
0.105
0.084
0.363
0.373
0.110
-0.036
-0.029
-0.028
0.025
-0.052
XRT1
EMPGRO
1
-0.031
-0.142
-0.064
-0.029
-0.020
-0.274
0.037
0.009
-0.025
-0.003
-0.001
ENTLAG
HERF2
1
-0.045
0.031
-0.238
-0.149
0.043
-0.007
-0.045
0.251
0.216
0.025
0.132
-0.039
LNVOL
HERF
1
-0.255
0.152
-0.002
0.409
0.399
0.000
0.018
-0.095
-0.065
-0.108
-0.013
-0.122
-0.024
CHERF
1
0.081
0.063
0.317
0.187
0.050
0.048
0.037
0.042
-0.162
0.041
-0.001
-0.060
0.009
-0.022
ECOSHR
1
0.128
0.372
-0.573
0.050
0.001
0.320
0.241
-0.037
0.008
-0.029
-0.151
-0.159
-0.051
-0.140
-0.162
SMAL5
LNAVNEST
1
-0.252
0.032
-0.137
0.336
0.030
-0.050
-0.055
-0.026
0.201
0.104
0.208
0.398
0.273
0.104
0.164
0.240
COVPEMP
TURB
LNAVSCAP
COVPEMP
LNAVNEST
SMAL5
ECOSHR
CHERF
HERF
HERF2
EMPGRO
EMPGRO2
LNVOL
ENTLAG
XRT1
XRT12
XRT4
YEARDM
LNAVSCAP
TURB
Table 6: Correlation Coefficients
1
-0.033
1
0.020 0.071
18
Table 7: Estimation Results – Declining Industries
Random-Effect
Obs
Groups
R2
overall
Hausman
LNAVSCAP
COVPEMP
LNAVNEST
SMAL5
ECOSHR
CHERF
HERF
HERF2
EMPGRO
EMPGRO2
LNVOL
ENTLAG
XRT1
XRT4
YEARDM
CONSTANT
337
50
Wald Chi(2)
Prof>chi(2)
126.3
0.0
First-Dif. Robust
337
50
337
50
F
Prob>F
0.282
5.45
0.0
0.039
182.69
Coef. z value
0.3779
1.17
0.0030
0.42
1.2904
0.96
0.1635
3.58
0.6112
0.39
0.0086
0.57
-0.0020
-2.15
0.0000
2.05
-0.0650
-1.92
0.0003
1.90
0.7960
0.98
0.2925
4.65
0.2765
1.40
0.6314
5.24
3.6790
2.38
-3.9819
-0.66
Fixed-Effect
Prof>chi(2)
F
Prob>F
75.10
0.0
0.649
0.0
p value
Coef.
t value p value Coef. t value p value
0.24
1.1792
1.42
0.16 0.1543
1.37
0.18
0.68 -0.0022
-0.12
0.90 -0.0010
-0.13
0.89
0.34 -13.4102
-2.60
0.01 0.4615
0.32
0.75
0.00
0.0848
0.97
0.33 0.1447
2.58
0.01
0.70 -6.6963
-0.78
0.43 1.2590
1.15
0.26
0.57
0.0103
0.70
0.49 -0.0010
-0.12
0.91
0.03
0.0024
1.90
0.06 -0.0001
-0.11
0.91
0.04
0.06 -0.1062
-2.91
0.00
0.06
0.0004
2.53
0.01 0.0001
1.88
0.07
0.33
0.7494
0.57
0.57 0.7085
1.21
0.23
0.00
0.0689
1.06
0.29 0.2805
4.00
0.00
0.16 -0.3675
-1.76
0.08 0.2940
1.19
0.24
0.00
0.6273
5.01
0.00 0.6436
3.10
0.00
0.02
3.9220
2.35
0.02 3.2579
2.25
0.03
0.51 -13.2671
-0.89
0.38
19
Table 8: Estimation Results – High-growth Industries
Random-Effect
Fixed-Effect
First-Dif. Robust
Obs
440
440
440
Groups
64
64
64
Wald Chi(2)
221.0
F
11.07
F
150.84
Prof>chi(2)
0.0
Prob>F
0.0
Prob>F
0.0
2
R
overall
Hausman
0.363
397.71
0.313
Prob>chi(2) 0.0
z
p value
value
0.1220
0.26
0.80
0.0174
1.50
0.13
-1.7722
-0.50
0.62
0.1380
2.84
0.00
-0.2834
-0.08
0.94
-0.1006
-5.58
0.00
-0.0020
-2.55
0.01
Coef.
LNAVSCAP
COVPEMP
LNAVNEST
SMAL5
ECOSHR
CHERF
HERF
HERF2
EMPGRO
LNVOL
ENTLAG
XRT1
XRT12
XRT4
YEARDM
CONSTANT
0.756
Coef.
t value
p value
Coef.
t value
p value
-1.6641
0.0228
-0.5060
0.0858
-5.2714
-0.0851
-1.59
0.97
-0.07
0.76
-0.30
-4.35
0.11 0.0323
0.33 0.0145
0.94 -1.6231
0.45 0.1211
0.76 -0.3345
0.00 -0.1053
0.15
1.31
-0.41
2.23
-0.15
-2.16
0.88
0.19
0.69
0.03
0.88
0.04
-2.04
7.38
1.34
2.85
0.91
-0.15
2.78
1.86
0.04 -0.0016
0.00 0.0985
0.18 2.0919
0.01 0.3237
0.36 0.8254
-0.0098
0.88 0.1697
0.01 8.4261
0.06
-1.19
1.92
2.63
5.09
1.92
-2.28
1.31
5.21
0.24
0.06
0.01
0.00
0.06
0.03
0.20
0.00
0.0967
2.4376
0.2916
0.2163
8.13
2.58
5.75
1.68
0.00
0.01
0.00
0.09
0.0000
0.0920
2.2540
0.1464
0.1248
0.1138
7.9132
-0.1391
0.59
4.44
-0.02
0.55
0.00
0.99
-0.0303
6.8677
33.1627
20
Table 9: Estimation Results – Other Industries
Random-Effect
Obs
Groups
1421
205
Wald Chi(2)
794.86
Prof>chi(2)
0.0
R2
overall
Hausman
First-Dif. Robust
1421
205
F
Prob>F
0.361
27.85
0.0
F
Prob>F
0.206
z value
Prob>chi(2) 0.0
p
Coef.
t value
value
0.02
-2.0913 -4.25
0.08
0.0188
2.28
0.01
-4.8057 -2.05
0.00
0.2853
5.87
0.00
-2.6042 -0.56
1421
205
395.01
0.0
0.787
601.82
Coef.
LNAVSCAP
COVPEMP
LNAVNEST
SMAL5
ECOSHR
ECOSHR2
CHERF
HERF
EMPGRO
EMPGRO2
LNVOL
ENTLAG
XRT1
XRT4
YEARDM
CONSTANT
Fixed-Effect
p
value
0.00
0.02
0.04
0.00
0.57
-0.4844
0.0058
-1.5355
0.1244
1.6150
-2.28
1.73
-2.66
6.78
3.57
-0.0281
0.0008
0.0258
-3.43
4.17
2.39
0.00
0.00
0.02
-0.0314
0.0023
0.0299
-3.61
3.17
2.89
0.00
0.00
0.00
1.5222
0.2348
1.0861
0.3801
5.2558
8.1446
4.42
8.76
9.13
4.47
8.19
2.09
0.00
0.00
0.00
0.00
0.00
0.04
0.5727
-0.0282
0.1707
0.2662
5.5855
33.9327
0.86
-1.01
1.35
3.04
7.17
3.80
0.39
0.31
0.18
0.00
0.00
0.00
Coef.
t value p value
-0.0597
0.0072
-1.2680
0.1465
-0.75
1.90
-1.63
4.91
0.45
0.06
0.10
0.00
0.2313
-0.0333
0.0007
0.1094
-0.0002
1.5289
0.2237
1.0977
0.4502
5.4486
1.77
-4.01
1.72
3.11
-2.84
2.83
3.21
3.30
3.30
8.50
0.08
0.00
0.09
0.00
0.01
0.01
0.00
0.00
0.00
0.00
21