Assessing Plant Entry and Exit Dynamics and
Survival –
Does Firms’ Financial Status Matter?*
Laura Vartia**
European University Institute
April 2004
Preliminary draft
Abstract
Despite of the increasing empirical research on entry and exit dynamics, there
are few studies examining plant and firm-specific characteristics of entering and
exiting plants. In particular, little attention has been paid to financial
characteristics of firms owning entering and exiting plants. This paper analyses
these characteristics across industries and over time. The purpose is to compare
the differences between entering, exiting and existing plants and to provide
information on the effects of financial characteristics, such as firm’s
indebtedness, solvency, liquidity and profitability, on plant survival. The results
indicate that firm-level financial distress is likely to decrease the probability of
survival. Recessions and aggregate-level financial distress are also found to
reduce the probability of survival.
Keywords: Entry and Exit Dynamics, Survival, Financial Distress
JEL Classifications: L1, L6, G3, E4
*
This research project is funded by the Foundation of Economic Education and
Kluuvi Foundation, their financial support is gratefully acknowledged. I wish to
thank Professor Omar Licandro and Professor Anindya Banerjee for their advice
and suggestions. I owe special thanks to Mika Maliranta and Satu Nurmi for
their valuable help in the Research Laboratory of the Statistics Finland. I am, of
course, responsible for any remaining omissions and errors.
**
The e-mail of the author: [email protected].
1 Introduction
The creation of new businesses and the market exit of less productive production units are often
considered to have a significant contribution to the business dynamics and economic growth.
Several empirical studies1 have found that a substantial part of aggregate productivity growth can be
explained by the creation and destruction of heterogeneous production units. This process of
creative destruction implies reallocation of resources from low productivity units to high
productivity units. Such dynamics are, in turn, likely to allow the economy to expand the
boundaries of economic activity and to facilitate moving resources away from declining to growing
industries.
As the process of entry and exit is found to play an important role in enhancing productivity growth
and the evolution of industries, it is of interest to examine the factors related to the creation of new
production units and the closing down of existing ones. The empirical literature in this field has
explained entry and exit with different industry-level factors such as industry technology,
innovativeness and R&D expenditure, competition and international trade. Some studies have also
considered policy and institutional structures in explaining entry and exit dynamics. During the
recent years the availability of longitudinal micro data has improved considerably. This has allowed
researchers to consider the characteristics of entering and exiting firms and plants, and triggered a
growing literature in this field. However, little attention has been paid to potentially very important
factors, namely financial firm-specific characteristics and economy-wide periods of financial booms
and distress.
The aim of this study is to assess in detail the entry, exit and survival of plants in Finnish
manufacturing during the period 1989-2000 and the factors related to these dynamics. The focus is
on the potential relationship between plant dynamics and factors reflecting firm-level financial
fragility and aggregate financial distress. To my knowledge there is only one previous study
analysing the relationship between aggregate financial distress and entry and exit dynamics.
Ilmakunnas and Topi (1999) use macroeconomic variables describing the monetary transmission
mechanism to examine entry and exit patterns at the industry level but do not focus on a firm-level
analysis. Furthermore, there are few studies that have explicitly examined firm-specific financial
variables. Fotopoulos and Louri (2000) consider some of these variables in the framework of firm
survival. However, they do not concentrate on entry and exit dynamics and use only non timevarying explanatory variables. Furthermore, the study by Fotopoulos and Louri (2000) is based on
firm-level analysis whereas the present research focuses on plant dynamics. It may be argued that
plant-level data is likely to provide richer and more volatile entry and exit dynamics than a firmlevel data would suggest. Moreover, the effects of financial distress are likely to occur at the plant
level first. As pointed out by Winter (1999), “before an entire firm is closed, it is likely that some of
its plants are closed or sold”.
For the purpose of this study a unique dataset is built by combining data from different sources. The
three main data sources of the study are the LDPM (Longitudinal Database on Plants in Finnish
Manufacturing), the Business Register on plants and the Financial Statements Statistics which are
all provided by the Statistics Finland.2 These databases are combined using plant and firm
1
See, for example, Scarpetta, Hemmings, Tressel and Woo (2002) for OECD country comparison and Disney, Haskel
and Heden (2000), Foster, Haltiwanger and Krizan (1998) and Maliranta (2002) for findings for the UK, the United
States and Finland, respectively.
2
Ilmakunnas, Maliranta and Vainiomäki (2001) provide detailed description on the process of linking various registers
for the analysis of Finnish plants and problems related to linking the registers.
identification codes that are the same for each plant and firm in all the three databases. Furthermore,
changes in ownership and legal status of the plant do not affect the identification codes and thus the
identification of true entry and exit is possible.
In addition to the industry and macro-level variables, the data set used in this study allows us to
examine several plant and firm-specific factors related to entry and exit. In particular, it includes
firm-specific information as provided by firms’ financial statements. To my knowledge such a rich
dataset has not been used to explain plant dynamics before. Nurmi (2003) uses Finnish plant-level
data to analyse plant survival, but the dataset in her study does not include any information
stemming from the financial statements of firms owning the plants. Furthermore, the her study is
based on one primary source of plant-level data, that is the LDPM, whereas the present study uses
both the LDPM and the Business Register. Therefore, it has a wider plant-level coverage than the
LPDM or the Business Register would separately provide.
This study also contributes to the empirical literature on plant dynamics by providing evidence of a
developed country that has experienced fast evolution in its manufacturing sector. Several factors,
such as the collapse of trade with the former Soviet Union, the severe recession of the early 1990’s
and technological change, have led to the change in the structure of the manufacturing sector and
the whole economy. Thus, it is interesting to assess whether the process of creative destruction has
played a role in enhancing the fast adoption to the new economic environment.
In addition, this study provides some information on the effects of recessions on the creation and
closing of plants.3 The period 1989-2000 is particularly interesting from this viewpoint since it
covers the last years of the boom period preceding the recession and the recovery phase following
the recession.4 The recession in the early 1990’s had a significant effect on the plant entry and exit
dynamics in Finnish manufacturing sector. As the real GDP growth declined by 11 percentage
points from 1989 to 1991 exit increased more than 50% and entry decreased more than 40%.
The comparison of firms’ financial characteristics shows that exiting plants are on average owned
by relatively more indebted and insolvent firms than their incumbents. In addition, the owner firms
of such plants are less profitable and more liquidity constrained. Entering plants seem to be also
owned by more financially constrained firms. These constraints are, however, relaxed for the
surviving plants during the years following the entry.
The preliminary results of the survival analysis support the view that financial conditions are
important determinants of plant survival. According to these results firm-level financial fragility is
likely to decrease the conditional probability of survival. Low profits and high degree of
indebtedness are found to increase the risk of failure and thus reduce the likelihood of survival.
Aggregate-level financial distress and recessions are also found to reduce the probability of
survival.
The remainder of this paper is organized as follows. Section 2 provides a review of the empirical
literature on entry and exit dynamics and survival. In Section 3 the description of data and
definitions are provided. Section 4 describes the measures of financial conditions. In Section 5 entry
3
Nurmi (2003) studies the effects of recession and found that exit and survival are sensitive to business fluctuations.
There is a long tradition in analysing the impact of recessions on creative destruction and aggregate productivity. This
literature dates back to the times of the “Great Depression”, when discussion on whether recessions are wasteful or not
started. Although most economists today agree that recessions are per se undesirable, they are, nevertheless, often
thought of as periods of cleansing when restructuring takes place at a relatively low cost.
4
2
and exit patterns in Finnish manufacturing sector are analysed. Section 6 presents the results of the
econometric analysis of plant survival including both continuous and discrete time survival models
as well as specifications controlling for unobserved heterogeneity. Finally, conclusions follow in
Section 7.
2 Discussion on the determinants of entry, exit and survival
There has been a long-standing interest in understanding the entry and exit dynamics and their
determinants. The literature in this field has grown considerably during the past decades. The early
studies suffered from problems related to data availability. One limitation in many of these studies
was that they could measure only net entry, that is the change in the total number of firms in an
industry, and not the actual gross flows of entrants and exits.5 Some of the early studies avoided this
measurement problem by using data on specific industries or geographic markets. The studies in
this vein, however, faced the difficulty of drawing general conclusions about entry and exit
dynamics and their determinants.
The problems faced by the early research were well-recognised and substantial progress in
measuring entry and exit has been made since the first studies in the research field. However, only
the recent development of longitudinal databases has allowed researchers to analyse entry and exit
patterns over time and the post-entry performance in different industries.6 These databases provide a
possibility to follow the changes in individual firms and plants over time. Despite this possibility
several studies still aggregate entry and exit dynamics to an industry level.7 This makes it
impossible to identify individual firms’ and plants’ entry and exit decisions and to link them with
firm- and plant-specific characteristics. This limitation is recognised in the recently developed
research on post-entry performance and survival.8
The empirical literature provides several stylised facts about entry and exit9. One of most striking
results emerging from the recent research is that entry and exit are highly correlated. This finding is
difficult to reconcile with the conventional view that entry occurs when super-normal profits are
positive and exit when they are negative since this view implies that the relation between entry and
exit is negative. The positive correlation may be explained by the approach suggesting that entry
and exit form a part of a continuous selection process in which they coexist in a manner that not
only does the entry force exit through displacement but also exit makes room for more entry. This
view has led many researchers to include industry-level exit and entry as explanatory variables
when estimating entry, and exit, respectively.10 It seems that exit is higher in the industries where
there is more entry due to displacement effects and increased competition. There is less evidence on
effects of industry exit on entry dynamics.
An alternative explanation for the positive relation is that the determinants of entry function the
same way as the determinants of exit. This view suggests that entry and exit are not necessarily
5
See, e.g. Orr (1974).
See Dunne, Roberts and Samuelson (1988) and Baldwin and Gorecki (1991) for pioneering work with longitudinal
data sources.
7
Entry and exit are often measured using dependent variables such as entry and exit rates or the number of entrants and
exits in a certain industry.
8
Audretsch and Mahmood (1994 and 1995) are among the first studies to recognise the importance of firm- and plantspecific characteristics in affecting the hazard confronting of the new business. See also Fotopoulos and Louri (2000)
and Nurmi (2003).
9
Geroski (1995) provides a survey on series of stylised facts on entry.
10
See, e.g. Fotopoulos and Spence (1998).
6
3
causally linked, but instead are two related phenomena both caused by the same third factor.11
Consequently, in the empirical literature the regressors used to explain entry are often also used to
explain exit.
The explanatory variables used in explaining entry and exit may be classified to different groups
according to at which level they are measured: (i) Micro-level variables consist of plant and firm
characteristics, and (ii) aggregate variables include industry-specific and macroeconomic
explanatory variables. One of the first micro-level variables studied in the literature was the firm
and plant size. The current size is found to decrease probability of exit and increase the likelihood
of survival.12 The importance of size has also been recognised in the literature analysing entry.13 It
has been argued that factors affecting entry have different impact on entrants depending on their
start-up size. On the one hand, larger firms and plants are often less financially constrained, closer
to the industry’s optimal efficient size and thus they may be in better position to enter. On the other
hand, small firms and plants may be more flexible having the advantage of low overhead costs and
avoiding some factors deterring large-scale entry. Research focusing on the entrants’ size divides
often entrants to small and large-scale entry.14 Nurmi (2003) analyses the factors affecting the sizes
of entrants.
Another explanatory variable, widely used in the analysis of exit, is firm and plant age. The age is
often found to be negatively correlated with the probability of exit and positively correlated with the
conditional probability of survival. However, the effect seems to diminish with the age - the older
the plant or firm the less important is the age for the survival. The finding of a negative (positive)
relation between age and exit (survival) is consistent with the theories describing entry and exit
dynamics as part of learning process. These theories suggest that over time firms learn about their
profitability and decide whether to expand, contract, or exit. Those firms that survive accumulate
experience and assets making them stronger and lowering their probability of failure.
In the empirical literature a lot of attention is devoted on the analysis of the type of entrants.
Entrants have been classified according to whether they are new business starts, i.e. de novo
entrants, or diversifying entrants, i.e. entry by new plants of existing firms.15 It is often argued that
there are systematic differences in the entry and exit patterns of different types of entrants. Several
studies have found that de novo entrants are most affected by the factors deterring entry. Mata
(1993) presents evidence that the determinants of de novo and diversifying entry are different and
there are also significant factors deterring and enhancing diversifying entry.
The question of ownership structure has also been studied in the literature on exit and survival of
plants.16 Several studies have found that the hazard rates are higher for the plants owned by multiplant firms. This result may be explained by the fact that the closing down of a single plant within a
firm with several other plants is less difficult than the closing down of a plant constituting an
independent firm. As Nurmi (2003) points out, it is easier for multi-plant firms to close unprofitable
branches relative to owners of independent plants who may be willing to accept lower rates of
return without closing their plants. However, there are also contradicting studies arguing that the
11
This view is related to the argument suggesting that barriers to entry are also barriers to exit.
See e.g. Mata, Portugal, and Guimarães (1995).
13
Several studies have found that the start-up size of purely new entrants is relatively small.
14
See e.g. Mata (1991).
15
The entrants in the latter group have been further divided into different types of entrants; see e.g. Dunne, Roberts and
Samuelson (1988), Mata (1993), Siegfried and Evans (1994), and Mata, Portugal, and Guimarães (1995).
16
See, e.g. Audretsch and Mahmood (1995), Tveterås and Eide (2000), Nurmi (2003) for analyses with U.S.,
Norwegian, and Finnish data, respectively.
12
4
probability of exit is lower for the plants owned by multi-plant firms. This finding may be explained
by the fact that a new plant owned by an already existing firm may receive financial or other
support from its parent firm whereas a single-plant firm has to survive on its own.
Concerning the ownership structure of firms and plants some studies have examined the effects of
foreign ownership on entry and exit patterns. These studies suggest that foreign- and domesticowned firms and plants face different incentives and impediments.17 Thus the foreign ownership
may affect the entry and exit dynamics of firms and plants differently. Foreign owned firms may,
for example differ in the way they open new plants, expand existing plants and close down plants.
In addition, the dominant presence of multinational corporations in an industry may represent an
additional barrier to entry, as has been argued by Khemani and Shapiro (1986).
The literature on entry and exit has hitherto devoted little attention to firm-specific financial
characteristics such as debt and structure of a firm’s assets.18 Fotopoulos and Louri (2000) have
analysed the determinants of firm survival and found that the high values of debt-to-assets ratio
reduce the conditional probability of survival, whereas the ratio of fixed to total assets is found to
increase the likelihood of survival. These findings are consistent with the view that serving
relatively high debt is an obstructive for operation of existing firms leading to potential exit and that
relatively high amount of fixed assets indicates a higher commitment by a firm. Consistent with the
literature, Fotopoulos and Louri (2000) also find that profitability enhances survival.19
The empirical literature proposes a wide range of industry-level factors affecting entry and exit of
plants and firms. Profit opportunities and growth prospects in a certain industry are considered
important determinants of entry and exit. Profitability at an industry level is often measured using
industry price-cost margins. Most studies find that higher price-cost margins induce entry.20
However, there are also some studies finding contradicting results.21 Siegfried and Evans (1994)
explain these results by arguing that “entry may occur in spite of low average profits if the variation
of profits among entrants is high and entrepreneurs are attracted by lottery-type risk”. They also
point out that entry may be deterred even in high profit industries if potential entrants expect a more
aggressive response to entry from incumbents.
The findings on the effects of industry price-cost margins on exit and survival are also ambiguous.
On the one hand, low price-cost margins and low profits may force existing firms and plants to
close down their operations. On the other hand, industry profitability may be positively related to
exit to the extent that industries with low profits attract less entry resulting in less competition and
displacement of existing firms and plants.22
Several studies have found evidence that the expected growth of an industry, measured as a change
in industry sales or employment, is positively correlated with entry.23 This result is consistent with
17
See, e.g. Geroski (1991).
Zingales (1998) has studied the effects of competition and financial constraints on the probability of exit in the
trucking industry in the U.S. He found that firms that are highly leveraged when the competition starts are less likely to
survive.
19
Tveterås and Eide (2000) find also a positive relationship between profitability and survival.
20
See, e.g. Shapiro and Khemani (1987).
21
See, e.g. Mata (1993).
22
See. Dunne and Roberts (1991) and Fotopoulos and Spence (1998).
23
See e.g. Khemani and Shapiro (1986) (measure of growth in terms of industry sales), Dunne and Roberts (1991)
(measure of growth in terms of industry output), and Fotopoulos and Spence (1998) (measure of growth in terms of
industry employment).
18
5
the approach suggesting that expected growth affects expected profits because the higher the growth
rate of an industry, the less an entrant’s new production will depress industry price.24 Overall, it
seems that the likelihood of survival is higher in fast growing industries. This is in line with the
view that exit occurs as incumbent plants and firms sense a permanent deterring in growth
prospects.
Evidence from the literature shows that entry is likely to be impeded in the industries where starting
a new business requires a lot of initial capital. In addition, this may prolong the operation of loss
making firms. In particular, if the capital costs represent sunk costs exit may be discouraged since
such capital does not have valuable alternative uses. Capital-labour ratio is often used to proxy sunk
costs, however, investment rate may also capture sunk costs as industries with higher investment
rates may be thought to have higher sunk costs. Measured at a firm or plant level, high capitallabour ratio may reflect the commitment of a firm or plant to its investment projects and thus reduce
the probability of exit.
The effect of scale economies has widely been studied in the empirical literature on entry and exit
dynamics, and survival. In numerous studies scale economies are captured by Minimum Efficient
Scale (MES)25 determined as the mean size of the largest plants in each industry accounting for one
half of the industry value. This variable measures the extent to which a firm’s or plant’s own size is
smaller than the mean efficient size of the industry. The MES should exert a negative influence on
entry if potential entrants must enter with large output in order to take advantage of large-scale
production. The greater is the difference between the MES size and the actual size of the firm or
plant, the higher (lower) the likelihood of exit (survival) is expected to be. In other words, for any
given firm or plant size, higher levels of the MES are expected to result in a greater cost
disadvantage.26
A high amount of innovated activity in an industry may increase the probability of exit since
although those firms and plants that successfully innovate are less likely to exit, the other firms and
plants not succeeding face a higher probability of exit. Highly innovative environment may also
serve as a barrier to entry due to sunk cost related to research. In addition, highly technological
environment in a certain industry may increase the need for funds used to finance entering and
surviving in the industry.
The theoretical literature on creative destruction often suggests that recessions are times of
“cleansing” when restructuring takes place at a relatively low cost. Based on this theory recessions
should enhance exit but promote entry of new more productive business. However, the empirical
evidence is scarce, as relatively few studies27 examine the effects of business cycles on entry and
exit. Using growth in real GDP to capture business fluctuations Baldwin, et al. (2000) and Nurmi
(2003) finds that booms, i.e. periods of high growth, reduce exit and recessions enhance exit.
Audretsch and Mahmood (1995) and Boeri and Bellman (1995) use unemployment rates as a proxy
for business fluctuation. The evidence from their studies is mixed - the former study is consistent
with the results of Baldwin, et al. (2000) and Nurmi (2003), but the later finds no significant
relation between business cycles and exit. Analysing the effects of business conditions on net entry
Yamawaki (1991) finds a positive relationship between entry and growth in real GNP.
24
See Siegfried and Evans (1994).
Although MES is a crude measure and only a proxy at best, it has proven in several studies at least to reflect the
extent to which scale economies play an important role in an industry. See, e.g. Scherer and Ross (1990).
26
See, e.g. and Audretsch (1995), and Audretsch and Mahmood (1994 and 1995).
27
See also Bartelsman, Scarpetta and Schivardi (2003) and Brandt (2003).
25
6
The studies on business cycles seem to support the view that recessions enhance exit and deter
entry. However, in order to capture the total effects of business fluctuations it is necessary to
examine not only the downturn but also the recovery phase after a recession.28 If firms and plants
are forced to exit during a downturn but entry is promoted during a recovery phase, it may be argue
that recessions enhance the process of creation and destruction.
In sum, several factors seem to affect entry and exit dynamics, and survival. The literature suggests
that both industry-level and aggregate-level business conditions have an impact on firm and plant
entry, exit and survival. Moreover, firm- and plant-specific variables are found to play an important
role in determining the probability of entry and exit. This study follows the previous research in the
choice of most of these determinants. However, the study seeks to contribute to the literature by
focusing on the question how financial fragility of a firm and aggregate financial distress affect
entry and exit dynamics, and survival.
3 Data and Definitions
The main data sources of this study are the LDPM (Longitudinal Database on Plants in Finnish
Manufacturing), the Business Register on plants and the Financial Statements Statistics all provided
by the Statistics Finland. These databases are combined using plant and firm identification codes
that are the same for each plant and firm in all the three databases. Furthermore, changes in
ownership and legal status of the plant do not affect the identification codes. The new dataset
attained by linking the three databases is the primary dataset29 used in the study and it covers all
Finnish manufacturing industries.30
The LDPM is available for the period 1974-2001. It covers Finnish manufacturing plants with five
or more employees31 until 1994. From 1995 onwards it, however, includes only plants owned by
firms with at least 20 employees. The Business Register includes, in principal, all the Finnish firms
and their plants which are registered as employers or subject to value added tax. It covers the period
1976-2001, but up to 1988 the data is available only for every second year. The Financial
Statements Statistics covers the years 1986-2001. For the years 1986-1993 this database is compiled
such that it contains all the firms with at least 100 employees and a sample of the smaller firms.
From 1994 onwards it includes 95 to 99 percent of all the firms in the Business Register. Due to the
data limitation in Business Register, this study focuses on the years 1988-2001 and the cut-off limit
for the size of a plant is five employees. A plant is defined as an economic unit that, under single
ownership or control, produces similar goods or services, and usually operates at a single location.
The LDPM contains wide range of data on employment, output, value added, wages and other
plant-level variables. The Business Register includes basic information on plant-specific
characteristics such as labour and net sales. Both of these databases link each plant to the firm
owning it. This enables us to determine whether a plant is owned by a single-plant or multi-plant
firm. Furthermore, this allows us to link plant-level information to firm-level data in the Financial
28
This requires that the period of interest is sufficiently long to cover at least one whole business cycle.
This data set includes 121124 observations for period 1988-2001, for 84789 of these observations it includes
information from the Financial Statements Statistics and for 64853 of these observations it includes information from
the LPDM.
30
See Appendix I for more detailed description of the process of linking the databases.
31
If a plant falls temporarily below the cut-off size of five or twenty, it is not dropped from the survey.
29
7
Statements Statistics. This database contains several variables describing the financial status of
firms.
The linked data set has a wider plant-level coverage than the LPDM or the Business Register would
separately provide. Thus, the identification of the plant entry and exit year is more reliable and
accurate. Although both the LPDM and the Business Register contribute to the linked plant-level
data set, from 1995 onwards the Business Register is the only source of information on entry and
exit of plants that have at least five employees but are owned by firms with less than 20 employees.
A plant is considered as an entry when it first appears in either of the databases. Although the study
period is limited to the years 1988-2001, entry is identified using plant information from the
preceding years of the study period. This allows us to check that a plant has not existed before 1988.
However, due to the cut-off limit for the size these plants may have existed before the first
observation with less than five employees. Therefore, the accurate definition of entry is based on
the time when a plant reaches the size of five employees.
Exit is defined according to the year when a plant last time appears in either of the databases. If a
plant is missing from both of the data set less than three years but then reappears it is considered as
a continuing plant. This allows for temporary disappearances that may be due to other reasons32
than permanent end of operations. However, if a plant is missing for more than two years but
reappears later, the exit year is determined based on whether the ownership of the plant has changed
or whether the number of employees changed considerably. If there the ownership of the plant has
changed or the plant size has increased or decreased by 50%, it is considered that the plant has
exited and that a new different plant has entered the database.33
In addition to the three main data sources, this study uses various other statistics and databases. The
variables capturing the aggregate business fluctuations, i.e. GDP growth and unemployment rates
are obtained from the FinStatistics also provided by the Statistics Finland. Finally, the data on
aggregate level financial development is taken from the International Financial Statistics, IMF and
the statistics of the Bank of Finland.
4 Measures of Financial Fragility and Aggregate Financial Conditions
Hitherto, the empirical literature has provided little evidence on the effects of financial distress on
plant entry and exit dynamics. Fotopoulos and Louri (2000) examine how firm-level financial
structure affects firm survival. However, they do not examine entry and exit dynamics and use only
non time-varying covariates, that is, they take into account only the firm-specific variables in the
first year after the entry of a firm. However, firm characteristics are likely to change over a firm’s
life-time and it is likely that the potential failure of a firm is affected by its current or recent
characteristics and not only by the start-up characteristics.34 This Section presents the explanatory
variables used to proxy firm-specific financial constraints and macro-level financial distress. In the
analysis of plant survival both the aggregate-level ad firm-specific financial variables are varying
over time.
32
There may be data missing due to several reasons such as human errors and change in sampling criteria. See, Nurmi
(2003).
33
If a plant has changed from the service sector to manufacturing or visa versa, the entry and exit years are identified
according to the years the plant entered manufacturing industry and exited from manufacturing industry, respectively.
34
Of course, if the life-span of a firm is very short, the start-up characteristics may be similar to the characteristics when
the firm is closing down.
8
Debt to assets ratio is used as an indicator of a firm’s solvency and it is measured as a firm’s
liabilities divided by the sum of its total assets and liabilities. The greater the ratio the more likely it
is that the firm has a solvency problem. This ratio is assumed to have a positive effect on the
probability of exit and a negative effect on the probability of entry since serving relatively high debt
may be an obstructive for entry and for operation of existing firms leading to potential exit.
A variable frequently used as a measure of financial constraints is interest coverage. It is
determined as a ratio of a firm’s cash flow and interest expenses and it reflects the liquidity position
of the firm. Lower values of interest coverage refer to a possible liquidity problem of the firm. This
ratio is expected to have a negative effect on the probability of exit and positive on the probability
of entry since the lack of liquidity may prevent new firms or plants from starting operation and
force existing firms or plants to exit.
Current ratio is determined as a firm’s current assets divided by current liabilities. The lower the
stock of current assets relative current liabilities the more financially constrained the firm is
assumed to be. A high value of current ratio may be thought of as relaxing the firm’s short-run
financial constraints. Thus, this variable is expected to have a negative effect on the probability of
exit and positive effect on the probability of entry since the less constrained the firm is the easier it
is to enter the market as a new firm or establish new plants and the less likely it is that a firm has to
shut down its plants.
Ratio of fixed assets to total assets describes structure of a firm’s assets. This ratio is may have a
negative or positive effect on the probability of exit since higher values of the ratio are, on the one
hand, expected to restrict a firm’s liquidity but, on the other hand, it is expected to indicate a more
committed firm entity.
Profits are defined as net profits over total assets and they are expected to affect the probability of
entry of a new plant of an existing firm positively and the probability of exit negatively since higher
profits are thought to reflect viability of a firm and are likely to attract investment capital.
Credit to the private sector to GDP and M2 to GDP ratios are included in the study as indicators
of macro-level financial development. These variables are provided by the International Monetary
Fund, International Financial Statistics (lines 32d and 35). High values of these variables reflect
periods of boom and low values periods of financial distress. They are assumed to be positively
related to entry and negatively related to exit, since during periods of financial distress financing
entry is difficult and even existing plants may face difficulties financing their activities and may be
forced to exit.
Interest rates and GDP growth may be used as measures aggregate economic development. In
addition, high interest rates may reflect periods of financial distress. During the periods of high
interest rates external finance becomes costly and thus those firms that need external funds to
finance entry are more constrained. In addition, serving existing debts becomes more expensive
which may cause financial problems for already existing firm and thus closing down of plants.
9
5 Entry and Exit Patterns in Finnish Manufacturing
5.1 Analysis of Aggregate and Industry-Level factors
The analysis of plant entry and exit dynamics in Finnish manufacturing shows that there is a huge
variation in entry and exit over time and between industries. Figure 1 presents the aggregate plant
entry and exit patterns for the period 1989-2000.35 The average annual entry rate ranges from 5% in
1999 to 9% in 1989, whereas exit rates vary even more taking the lowest value of 5% in 1995 and
highest value of 13% in 1991. Together on average plant entry and exit account for 15% to 20% of
the total number of plants although during the last years of the study period their share of all plants
has decreased. It is important, however, to keep in mind that these figures may underestimate the
true entry and exit numbers since the analysis excludes the smallest plants, i.e. those plants that
have less than five employees, and entrants, in particular, are often small. In addition, following
Nurmi (2003) plants that exist only for one year are not included since these observations may not
be entirely reliable. If the “one-year plants” are included the entry and exit rates increase. The entry
and exit patterns remain the same.
Figure 2 shows the employment weighted entry and exit rates for the period 1989-2000. These rates
reflect the job creation associated with entering and exiting plants and follow similar patterns as
unweighted entry and exit rates. However, since entering and exiting plants are often smaller than
their incumbents36, their employment share relative to total employment is lower than their share of
the total number of plants. In the late 1990’s this share varied between 2% to 3%, but during the
recession of the early 1990’s the employment weighted exit rate, in particular, was clearly higher. In
1991 it peaked at 7.3%.
The effects of the recession in the early 1990’s are clearly seen from Figure 1. As the real GDP
growth declined from 5 percent in 1989 to –6% in 1991 (See Figure 4) exit almost doubled and
entry decreased almost 50%. As Figure 3 shows this pattern was also reflected to the development
of number of plants in Finnish manufacturing sector. The total number of plants has not researched
the pre-crisis level even during the boom period of the late 1990’s.
35
Entry and exit rates are not defined for the years 1988 and 2001 since entry and exit cannot be accurately identified
for these years.
36
See Sub-section 5.2 for more discussion.
10
FIGURE 1 Entry and exit rates in Finnish manufacturing, 1989-2000
0,14
0,12
0,10
0,08
Entry rate
Exit rate
0,06
0,04
0,02
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
0,00
FIGURE 2 Employment weighted entry and exit rates in Finnish manufacturing, 1989-2000
0,08
0,07
0,06
0,05
Entry
0,04
Exit
0,03
0,02
0,01
11
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
0
FIGURE 3 Total number of plants in Finnish manufacturing, 1988-2001
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
0
The severity of the recession is captured by Figure 4. The growth of real GDP was negative for
three years from 1991 to 1993 and unemployment increased from 3.4% in 1990 reaching a peak of
18% in 1994. Although the recession was probably the most severe economic crisis37 of the past
century in Finland38, the recovery from the crisis was relatively fast in terms of the real GDP
growth. Exit dynamics reflect the recovery phase – from 1994 onwards the exit rate has been
relatively low. However, entry has responded more slowly to the recovery. In addition, entry seems
to have decreased in the late 1990’s. Consequently, the total number of plants has augmented only
slightly.
FIGURE 4 Real GDP growth and interest rate in Finland 1988-2001
20
15
10
Real GDP growth
5
Interest rate
0
-5
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
-10
The ratios of credit to private sector and GDP and M2 to GDP, as well as, interest rates are used to
describe the development in financial environment. Figure 5 presents the two ratios and Figure 4
37
38
Excluding the periods of war.
See Kiander and Vartia (1996) for description of the recession in the 1990’s in Finland.
12
displays the twelve-month money market rate together with the real GDP growth. Both of the ratios
capture the boom and crisis of the financial sector, although the proportional increase and decrease
in private credit are considerably larger. The financial boom started in the mid 1980’s as financial
deregulation was gradually launched. The peak (94% and 48%) was observed in the beginning of
the 1990’s and was followed by the severe crisis. Interest rate reflect the monetary and exchange
rate policy during the pre-crisis and recession periods and thus the general monetary conditions of
the economy.
Although the boom period reflects the general development in economic activity and optimism, it is
also related to the lack of supervision and regulations during the liberalization of the financial
sector. Due to financial liberalization and general optimism borrowing and lending became easier
and many investment projects, even less productive ones, were able to raise external funding mainly
from the banking sector. Thus plant entry may have been encouraged by the availability of funds.
However, during the crisis the potential entry may have been discouraged due to the tightening of
the financial environment. The problems in the financial sector were mainly caused by nonperforming loans and debt losses. These problems led banks and other financial institutions to
liquidate investment projects which, in turn, amplified the recession in the real sector and may have
encouraged closing down plants. The tightening of the financial market and financial institutions
attitudes towards lending resulted in the sale and liquidation of assets by indebted firms and
households, ultimately, leading to deflation in asset prices.
FIGURE 5 Private credit to GDP and M2 to GDP in Finland
1,00
0,90
0,80
0,70
0,60
Private credit to GDP
0,50
M2 to GDP
0,40
0,30
0,20
0,10
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
0,00
The aggregate-level evidence on the recession seems to support the view that recessions enhance
the process of creation and destruction – the recession of the 1990’s has forced plants to exit,
although it has not promoted entry to the same extent. In addition, the recession may be though to
have had a cleansing effect on many industries in the manufacturing sector. The comparison
between different industries shows that during and after the recession some industries declined and
others grew fast. This may be interpreted as evidence on the reallocation of resources from
declining and potentially less productive industries to growing industries that are likely to be more
productive.
Figures A1-A6 in Appendix II display the evidence on changes in the size of different two-digit
level industries over time. The industry size is measured as the number of plants in an industry.
There are some industries with clear changes in their size. The Food & beverages industry and
13
Textile product industries seem to have been declining steadily. In addition to the recession, this
pattern may be explained by the collapse of the trade between Finland and Soviet Union in 1989
and 1990.
The recession resulted in a decline in the number of plants in several industries. After the recession
the number of plants has increased in the Fabricated metal products except machinery and
equipment industry and the production of Radio, television and communication equipment and
Medical precision and optical instruments. In these industries the number of plants grew by 61%,
52% and 41%, respectively. The growth of the two latter industries is related to the ICT boom that
started in Finland in the mid-1990’s. These are also industries that suffered less from the recession
in terms of number of exiting plants. The two industries that suffered the most from the recession
are the production of Other non-metallic mineral products and Motor vehicles, trailers and semitrailers where the number of plants was reduced approximately by 40%.
5.2 Analysis of Firm and Plant-Level Characteristics
This Sub-Section analyses the differences in firm and plant characteristics between entering, exiting
existing plants. There seems to be clear differences between such plants. One of the most distinctive
characteristics of plants is their size. Tables 1 and 2 show the average and median plant size over
years and across industries. Overall, the mean size of a plant is 54 employees. However, entering
and exiting plants are considerably smaller than those existing in the industry. Their average size is
less than half of the size of their incumbents, i.e. those plants that do not enter or exit the current
period.
Over time the mean size of incumbents is relatively stable around 60 employees. The mean size of
entering and exiting plants, instead, varies more during the period 1989-2000. However, this
variation may reflect the entry and exit of one or two large plants in some specific years. Thus it
may be more informative to focus on the median size of plants. This measure is also more stable for
entry and exit. However, the median size of exiting plants tends to decrease towards the end of the
study period.
Table 2 shows that the average size of a plant depends on the industry in which it is operating.
Overall, the plant size is highest in the industries of Paper and paper products, Petroleum and
nuclear fuel, Basic metals, and Radio, television and communication. These are industries with
large investment rates and sunk costs. The industries with on average smallest plants are the
production of Fabricated metal products, Printing and publishing, and Instruments. The differences
in the average plant size of entering and exiting plants between industries are similar to those of
incumbent plants. However, the median size of exiting plants seems to be slightly higher than that
of entering plants. In particular, exiting plants seem to be larger in the production of Paper and
paper products and Office equipment.
14
TABLE 1 Mean and Median Plant Size over Years
Total
Incumbents
Year
Mean Median
Mean
Median
1989
57
18
62
20
1990
56
18
61
20
1991
54
17
59
19
1992
52
17
58
19
1993
50
15
56
18
1994
52
16
57
18
1995
54
16
58
18
1996
51
14
56
17
1997
52
15
58
17
1998
54
16
58
18
1999
55
16
59
18
2000
55
16
60
19
TABLE 2 Mean and Median Plant Size across Industries
Total
Incumbents
Industry
Mean Median
Mean
Median
Food&beverage
54
21
58
23
Textiles
36
16
39
18
Textile products
43
18
49
21
Footwear&leather
39
18
42
20
Wood&cork
42
15
47
19
products
Paper
191
72
203
81
Printing&publishing
39
13
42
14
Petroleum&nuclear
164
36
180
45
fuel
Chemicals
85
32
92
35
Rubber&plastic
46
18
50
21
Other non-metallic
39
16
42
18
mineral products
Basic Metals
135
35
149
42
Fabricated metal
26
12
28
13
products
Machinery
56
18
62
21
Office
93
24
99
25
Electrical apparatus
67
19
73
22
Radio, television &
152
32
170
40
communication
Instruments
45
12
51
13
Motor vehicles
57
19
64
21
Other transport
118
20
134
24
equipment
Entry
Mean Median
30
9
39
9
31
9
25
10
15
7
24
7
21
7
19
7
16
7
21
8
23
8
20
8
Mean
37
24
30
25
24
23
39
20
15
20
18
21
Exit
Entry
Mean Median
31
10
17
8
16
8
33
7
14
7
Mean
26
16
21
23
21
Median
10
8
11
9
8
Median
9
9
10
10
9
9
10
8
7
7
7
7
Exit
94
17
85
20
7
17
121
16
39
43
7
13
38
18
26
10
7
10
33
13
21
12
7
9
33
12
9
7
24
14
9
7
27
49
40
60
8
12
9
11
26
94
24
74
10
25
10
14
16
18
28
7
8
10
11
15
71
7
8
10
The type of a plant, i.e. whether it is owned by a multi-plant or single-plant firm, is argued to have
an effect on plant entry and exit. Table 3 displays the fraction of entry and exit by plants
constituting an independent enterprise and their median size compared to the size of plants owned
by multi-plant firms. The majority of entering and exiting plants are themselves independent
enterprises. The fraction of such plants varies between 60% and 80% during the period 1989-2000.
However, entering and exiting plants owned by multi-plant firms are at least twice larger. Thus their
labor share of all entrants and exiting plants is much higher ranging between 50% and 80%. The
median size of entering and exiting plants owned by single-plant firms is relatively stable over the
period, although the size of exiting plants decreases slightly in the late 1990’s. It seems also that the
labor share of entering de novo plants has increased during the second half of the period 1989-2000.
15
TABLE 3 Entry and exit by type
Fraction of single-plants
Entry
Exit
Year
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
0.696
0.590
0.611
0.652
0.775
0.784
0.720
0.800
0.758
0.704
0.741
0.748
0.670
0.751
0.672
0.604
0.644
0.584
0.640
0.684
0.777
0.732
0.738
0.723
Median size
Entry
Singleplant
7
7
7
8
7
7
6
6
7
7
7
7
Multiplant
22
26
20
20
15
17
22
19
13
16
18
17
Exit
Singleplant
8
8
8
8
8
7
8
7
6
6
6
7
Labor share of singleplants
Entry
Exit
Multiplant
15
20
26
19
16
16
27
22
17
17
15
19
0.323
0.175
0.262
0.320
0.465
0.336
0.302
0.447
0.463
0.435
0.423
0.519
0.257
0.401
0.306
0.317
0.368
0.295
0.265
0.346
0.483
0.415
0.391
0.367
The analysis of plant characteristics39 shows that there are clear differences between entering,
exiting and incumbent plants. (See Table 4.) Overall, entrants are less capital intensive than exiting
and existing plants in the manufacturing sector. Instead, entrants have higher investment rate in the
entry year than the other type of plants. This is plausible since establishing a plant is likely to
require large investments. However, during the three years following the plant entry the investment
rate decreases clearly. Due to the fact that entering plants have not been able to accumulated capital
previous to their entry their capital intensity is low relative to existing and exiting plants. The
investment rate of exiting plants is lower than their incumbents reflecting the exit decision. Entering
and exiting plants have also lower export intensities than their incumbent plants.
Overall, entrants seem to have the highest net sales growth rate. However, their labor productivity
growth is lower than their incumbents in the year after the entry.40 This is consistent with the
literature suggesting that in the beginning plants are less productive but those plants that survive the
first years grow faster and after some years from the entry their contribution to the aggregate
productivity growth is considerable. According to the Table 4 entrants’ productivity growth is
increased after three years from the entry. Table 4 shows also that the two measures used to proxy
plant growth are both negative for exiting plants. In addition, the comparison of plants’
characteristics during three years preceding exit, not presented in this paper, suggests that plant
growth rates are not only low in the year of exit but also during the years previous to the exit
decision. This finding supports the view that low growth rates predict exit.
39
See Appendix II, Table A1, for detailed description of the plant-level variables.
The growth rates of entering plants cannot be determined for the entry year, that is at t, since there is no data from the
years previous to entry.
40
16
TABLE 4 Medians of Plant-level Characteristics
Variable
Total Incumbents
Entry
Exit
Capital intensity at t
23.424
24.379
4.049
13.920
Investment rate at t
0.008
0.009
0.034
0.000
Export intensity at t
0.003
0.006
0.000
0.000
Real labor productivity growth at t
0.019
0.021
-0.025
Net sales growth at t
0.048
0.055
-0.066
Real labor productivity growth at t+1
0.019
0.020
0.002
Net sales growth at t+1
0.041
0.036
0.170
Capital intensity at t+3
27.889
28.797
6.166
Investment rate at t+3
0.007
0.007
0.007
Export intensity at t+3
0.024
0.026
0.000
Real labor productivity growth at t+3
0.025
0.026
0.012
Net sales growth at t+3
0.048
0.046
0.087
Notes: From 1995 onwards the analysis does not include the plants owned by firms with less than 20 employees due to
the data limitations.
The main interest of this study is to analyze how the financial status of a firm is related to
establishing and closing down of plants. Table 5 presents the differences in the financial status of
firms owning entering, existing and exiting plants. The analysis of the different proxies of a firm’s
financial status shows that firms establishing new plants are relatively more indebted and liquidity
constrained compared to their incumbents. This result is expected and may be explained by the fact
that firms investing in new plants often require external funds in order to finance the opening of a
new plant.41 This, in turn, increases their debt burden and interest expenses leading to lower interest
coverage. However, firms owning entering plants seem to be the most profitable of the firms.
Consistent with the previous findings on capital intensity, they also hold less fixed assets in the year
of the entry than their incumbents in the same year.
Although firms establishing new plants may be financially constrained in the year of the plant entry,
they become relatively more solvent during the following years as shown by the figures measured
three years after the entry. The debt to asset ratio decreases and approaches the level of the
incumbents. Similarly, interest coverage and current ratio increase. Furthermore, the difference in
profitability between these firms and their incumbents increases in three years. However, it should
be kept in mind that the comparison after three years of entry includes only those firms whose new
plants have survived and that the incumbents may have established new plants during this period.
Firms closing down their plants seem to be relatively more indebted and liquidity constrained. Their
debt to assets ratio is higher than that of their incumbents. In addition, their interest coverage and
current ratio are lower than those of firms owning existing plants. Furthermore, exiting plants are
owned by clearly less profitable firms than entering and existing plants. Their median profits are
only 36% of that of their incumbents. The comparison of firms’ financial status during three years
preceding exit, not presented in this paper, shows that these patterns do not reflect only the year of
exit but also the years previous to the exit.
41
In particular, it is often thought that small firms starting a new business are most likely to need external financing.
17
TABLE 5 Medians of Firm-level Financial Characteristics
Variable
Total Incumbents
Entry
Exit
Debt to assets at t
0.403
0.399
0.431
0.426
Coverage at t
5.429
5.613
5.508
3.277
Current ratio at t
1.491
1.503
1.374
1.429
Fixed to total assets at t
0.447
0.451
0.402
0.423
Profits at t
0.051
0.053
0.056
0.019
Debt to assets at t+3
0.380
0.377
0.401
Coverage at t+3
7.205
7.175
7.529
Current ratio at t+3
1.464
1.473
1.360
Fixed to total assets at t+3
0.449
0.452
0.405
Profits at t+3
0.059
0.058
0.066
Notes: The comparison is limited to the period 1994-2000 since from 1994 onwards the Financial Statements Statistics
has a full coverage of firms with different size.
6 Plant Survival in Finnish Manufacturing
6.1 Cox Proportional Hazard Model
Instead of analysing plant entry and exit dynamics at time t, an alternative approach is to examine
the survival of new plants. Such analysis is often conducted using duration models and conditional
hazard functions. This implies studying the probability that a plant exits during time t+∆
conditional on that it has survived up to time t. In the framework of the duration models the hazard
function is the rate at which the life-spans of plants are contracted after duration t given that they
extend at least until t.
The probability of survival is studied in the context of the semi-parametric proportional hazards
model introduced by Cox (1972). The advantages of this model are that it enables the use of several
explanatory variables (covariates) and that the basic model is easily extended to allow for timevarying explanatory variables. In addition, Cox’s model allows the analysis of more than one cohort
of plants. Cox’s model is parametric in the sense that it specifies a regression model with a specific
functional form but it is non-parametric in the sense that it does not specify the exact probability
distribution over duration time, i.e. transition from entry to exit varies in some unknown way over
time. However, this probability distribution over duration time is assumed to be the same for all
plants.
In the Cox proportional hazards model the hazard rate is given by
h(t ) = h0 (t ) ∗ exp( X t ∗ β) .
(1)
The first term, h0 (t ) , on the right-hand side is the unspecified baseline hazard function at time t. X t
and β indicate the matrix of time-varying covariates and the vector of associated coefficients. The
estimators of the coefficients are derived by maximising the partial log-likelihood and they have the
similar properties as the usual maximum likelihood estimators. A positive (negative) coefficient can
be interpreted as increasing (decreasing) the hazard rate and thus indicates a negative (positive)
relationship with survival.
In order to identify factors affecting the survival of plants, several industry-specific, firm-specific
and plant-specific variables are examined in the study. Many of these variables and their expected
effects on survival are discussed in Section 2. The precise definitions of all the explanatory
18
variables are presented in Appendix, Table A1. The analysis covers the period 1989-2000. Only
those plants that entered after 1988 are included in the survival analysis. The plants that were alive
at the end of the study period, that is they survived up to the year 2000, are censored. Exit is not
defined for the year 2001 since exit cannot be accurately identified for this year. The number of
plants varies in different model specifications because there are some missing values in the
explanatory variables, particularly, in the firm-specific financial variables.
Table 6 shows the estimation results of different specifications of the proportional hazard model. In
all the specifications the null hypothesis that all parameters equal zero is rejected using overall chisquare tests. The first column focuses on the macro-level and industry-level variables. In the second
column the plant-specific characteristics are included. The following two columns analyse the firmspecific financial variables. Finally, the last columns address the problem of multicollinearity.
As discussed in Section 4 macro-level financial conditions may be measured using several different
variables. The ratios of credit to private sector and GDP, M2 and GDP, as well as, interest rates are
often used to describe macroeconomic financial environment.42 However, these variables may be
correlated with each other and the GDP growth, thus, introducing multicollinearity to the estimated
model. As Greene (2003, p.57) points out the estimated coefficients may have high standard errors
and low significance levels, and may have the “wrong” sign or implausible magnitudes if regressors
are highly, although not perfectly, correlated. The ratio of credit to private sector and GDP is highly
correlated with GDP growth and the other measures of aggregate financial conditions. Therefore,
the first specifications include interest rates, M2 to GDP ratio and real GDP growth as macro-level
explanatory variables. Specifications 5-7 examine further the question of multicollinearity and
introduce other combinations of macro-level variables.
The real GDP growth seems to have a negative and highly significant effect on the probability of
plant hazard in all the specifications. This result is consistent with the finding of Nurmi (2003) and
the creative destruction view suggesting that recessions have a cleansing effect on the economy.
The coefficients of both the M2 to GDP ratio and the interest rate are significant in most of
specifications where these variables are considered together with GDP growth. It seems that the
higher the M2 to GDP ratio the lower is the probability of hazard. This may be interpreted such that
the higher the supply of money the easier it is to finance plant survival. High interest rates, on the
other hand, reduce the probability of survival and thus increase the probability of hazard. This may
be explained by the fact that aggregate-level interest rates reflect at least to certain extent the level
of the costs of external financing, although each firm and plant may face different individual
costs.43 However, it is reminded that the problem of multicollinearity may also concern these
specifications and thus these results should be interpreted with caution.
The industry-specific explanatory variables are measured at three-digit industry level using SIC
(Standard Industrial Classification) adopted in 1995. An industry’s investment rate and the level of
hourly wages have a negative and significant effect on hazard and thus enhance survival. One
explanation for this result is given by Audretsch and Mahmood (1995) and Nurmi (2003). They
suggest that a high level of investment and wages may indicate a commitment to some labor and
capital related sunk costs. A stronger commitment, in turn, increases the likelihood of survival. On
42
See literature on financial crises, as well as, research on finance and growth.
This result differs from that of Audretsch and Mahmood (1995) who find that high interest rates reduce hazard rates.
They explain their result by arguing that most new businesses in the U.S. are not dependent on external capital. If this is
the case, the differences in the results concerning the effect of interest rates may be due to differences in capital
structure of firms and financial markets in the two countries.
43
19
the other hand, industry-level capital intensity seems to reduce the probability of plant survival.
This finding is consistent with Audretsch and Mahmood (1995) and may be explained by arguing
that new plants are often confronted with a size disadvantage in industries with high capital
intensity. This argument holds if entrants are less capital intensive than their incumbents. As shown
in Section 5 entrants have on average lower capital intensities than their incumbents. The risk of
failure seems to be lower in high export intensity industries than in low intensity industries although
the effect of this variable is not statistically significant in some of the specification.
Consistent with previous studies, an industry’s Minimum Efficient Scale (MES) level of output has
a positive, although relatively small, effect on the probability of hazard. This result may be
interpreted following Audretsch and Mahmood (1995): “For any business of a given size higher
levels of the MES will result in a greater cost disadvantage”. Several recent studies have examined
the relationship between entry and exit. It is often argued that due to displacement effects and
increased competition the risk of failure is greater in industries where there is more entry. The
estimation results support this view, as the coefficient of entry rate is significant and positive in all
of the estimations.
An industry’s growth opportunities are measured by the average growth of net sales. The results
show that plants operating in industries with higher growth rates have lower probability of hazard.
However, the effect of the growth rate on survival is not statistically significant in Specifications 34 and 6. The results are similar if the average growth of real value added is used to proxy industry
growth, instead of the growth of net sales.
Specification 2 adds plant-specific characteristics into the analysis. The current size of a plant is
defined in terms of the logarithm of the number of employees and has a negative statistically
significant effect on the hazard rate of plants. The sign of the effect is as expected since larger
plants are assumed to suffer less from the size disadvantage, i.e. from being smaller than the
industry’s efficient plant size. The three last new covariates in Specification 2 are variables
describing a plant’s foreign ownership structure, and the plant type, i.e. whether the plant is a
production unit, and whether it is owned by a multi-plant firm, respectively. Foreign ownership
seems to decrease the hazard rate of plants. Plants functioning as production units are more likely to
face hazard than other types of plants. However, it is worth noting that the large majority of plants
in the manufacturing sector are production units. Consistent with Nurmi (2003) plants belonging to
a firm with group of plants have higher hazard rates than independent plants. As Nurmi (2003)
points out, it is easier for multi-plant firms to close unprofitable branches relative to owners of
independent plants who may be willing to accept lower rates of return without closing their plants.
Specification 3 and 4 introduce the firm-level financial variables discussed in Section 4. The results
show clearly that the profitability and the degree of indebtedness of the owner firm are important
financial factors determining plant survival. High profits and low degree of debt tend to reduce
likelihood of failure and thus enhance survival. These findings are consistent with the firm survival
analysis of Fotopoulos and Louri (2000). They find also that the structure of a firm’s assets,
determined by fixed to total asset ratio, has a negative significant effect on hazard. The estimation
results of Specifications 3 and 4 support this result as high ratio of fixed to total assets is found to
decrease the probability of hazard.
20
The interest coverage (the ratio of cash flow and interest expenses)44 and current ratio (current
assets divided by current liabilities) of a firm may be thought to reflect the possible liquidity
constraints of firms. Low values of these variables restrict a firm’s liquidity and thus impede
survival. According to the results, a high ratio of a firm’s cash flow and interest expenses tends to
increase the risk of hazard. On the other hand, current ratio has a significant, but unexpected impact
on hazard. High values of this ratio seem to increase the hazard rates of plants. This somewhat
counter-intuitive result may be due to fact that the current ratio measures the firm’s liquidity of an
entire accounting year. It may well be that the firm liquidates a plant before the end of this period
and thus obtains suddenly liquid assets during the period. In this case, the relationship between a
firm’s liquidity and plant failure may be positive. It is, however, the liquidation of the plant that
causes the improvement in the firm’s current ratio and not vice versa. Therefore, it may be of
interest to consider the values of this ratio in the years previous to the plant failure.
Specification 4 is similar to Specification 3, but instead of the current value of a firm’s current ratio
it captures the lagged value of the ratio two years preceding the plant failure. In this specification
the current ratio seems to have a negative effect on hazard. However, this effect is statistically
insignificant.
The correlation between the covariates may be reduced by examining the changes in the
explanatory variables. This decreases to some extent the correlation between the private credit to
GDP ratio and GDP growth, as well as, between the M2 to GDP ratio and GDP growth. The relative
changes in these financial ratios are, however, correlated with each other. In addition, interest rates
are correlated with the GDP growth. In order to assess the question of multicollinearity and its
possible effects Specifications 5-6 present the financial ratios together with the GDP growth and
Specification 7 displays interest rates separately as explanatory variables in the estimated model.
The relative change in private credit to GDP and M2 to GDP ratios has a negative effect on hazard.
This implies that the growth of credit to private sector and M2 enhances survival. However, the
effect of the change in M2 to GDP ratio is not statistically significant. In addition, both of these
ratios are insignificant if the model specification does not include firm-specific financial variables.
Interest rates, on the other hand, seem to have obvious effects on survival. A rise in interest rates
clearly increases the probability of hazard as indicated by Specification 7.
Adding the variables describing firms’ financial status in Specifications 3-7 reduces the number of
plants included in the survival analysis. This is due to the fact that there is no information on the
financial status of some firms. The plants owned by such firms are not included in the analysis. It is
worth noting that due to the difference in the sample size the estimations are not perfectly
comparable. It addition, it should also be kept in mind that in the period 1989-1993 small firms are
more likely to have missing information on their financial status since during this period only
sample of small firms was included in the Financial Statements Statistics. From 1994 onwards this
database includes 95-99% of all the firms in the Business Register.
In order to check whether the results of the survival analysis are affected by the missing data in the
Financial Statements Statistics in the first years of the study period, a similar analysis is conducted
using the period 1994-2000.45 The results concerning the impact of firms’ financial status remain
the same. However, macro-level and some industry-specific variables are more sensitive to the
44
Following Zingales (1998) the coverage is determined as log(1+coverage) and if a firm’s cash flow, earnings before
interest and taxes, is negative or zero the interest coverage is set to equal zero.
45
The results of the corresponding analysis for the period 1994-2000 are provided upon a request.
21
change of the study period. This may be due to the relatively short study period which covers
mainly the years of recovery and boom.
TABLE 6 Cox-regression results of hazard confronting plants in Finnish manufacturing
Variable
(1)
(2)
(3)
(4)
(5)
(6)
GDP growth
-3.187*** -2.950***
-6.582***
-3.343*
-5.098***
-4.130***
M2 to GDP
-1.253*** -1.111**
-1.978***
-1.361*
Interest rate
0.026**
0.028**
0.004
0.042**
Relative
-1.238**
change in
credit
Relative
-0.233
change in M2
Investment
-4.040***
-3.439***
-2.898**
-3.230***
-4.141*** -4.196***
rate
Wages
-0.048*** -0.050***
-0.043***
-0.042***
-0.029**
-0.035**
Capital
0.003***
0.004***
0.004***
0.005***
0.004***
0.004***
intensity
Export
-0.205**
-0.089
-0.254**
-0.130
-0.229*
-0.198
intensity
Entry rate
1.067***
1.141***
1.375***
1.446***
1.231***
1.331***
MES
0.000***
0.000***
0.000***
0.000***
0.000***
0.000***
Net sales
-0.408**
-0.386**
-0.212
-0.277
-0.323*
-0.290
growth
Plant size
-0.407***
-0.455***
-0.492***
-0.489***
-0.490***
Foreign
-0.008***
-0.006***
-0.005**
-0.005**
-0.005**
ownership
Production
0.183***
0.293***
0.255***
0.267***
0.257***
unit
Multi-plant
0.429***
0.537***
0.401***
0.388***
0.402***
Profit
-0.067***
-0.063**
-0.061**
-0.061**
Fixed to total
-0.328***
-0.546***
-0.567***
-0.561***
assets
Debt to assets
0.876***
0.902***
0.894***
0.884***
Coverage
-0.166***
-0.154***
-0.155***
-0.156***
Current ratio
0.013***
Lagged
-0.004
-0.004
-0.004
current ratio
No. of plants
6377
6377
5515
4946
4946
4946
Log
-25076
-24915
-16637
-12460
-12460
-12461
Likelihood
Notes: *, **, and *** indicate significance at 10, 5, and 1 per cent level, respectively.
(7)
0.041***
-3.404***
-0.037***
0.005***
-0.169
1.354***
0.000***
-0.348*
-0.492***
-0.005**
0.264***
0.400***
-0.062**
-0.558***
0.889***
-0.156***
-0.004
4946
-12461
6.2 Discrete-time Hazard Model and Unobserved Heterogeneity
The previous sub-section analysed plant survival in the framework of a continuous time
proportional hazard model. However, due to the nature of the data used the discrete time version of
the hazard model may be argued to be more suitable for the survival analysis of the study. The data
is reported on annual basis and thus the exact instant of failure and start-up times is not observed.
This section introduces a discrete time version of the model similar to that estimated in Sub-section
6.1. Furthermore, the question of unobserved heterogeneity is analysed. Although the model
specifications of the survival analysis include some plant-specific covariates, it is likely that they
cannot account for all the observation-specific effects. The inference from model estimations
ignoring unobserved differences between plants may be problematic. As Jenkins (2001) points out,
the estimation results of such models may (i) over-estimate (under-estimate) the degree of negative
22
(positive) duration dependence in the baseline hazard,46 and (ii) attenuate the proportionate response
of the variation in each regressor at any survival time, i.e. the estimate of positive (negative) βi may
under-estimate (over-estimate) the “true” estimate. The presence of unobserved heterogeneity
implies also that the proportionate effect of a given regressor on the hazard rate is no longer
constant and independent of survival time.
Table 7 presents the results of the discrete time model regressions. For comparison the first column
provides the estimation results of the corresponding specification of the Cox continuous time
proportional hazard model. The second column displays the results of the discrete time model
estimation without controlling for the unobserved heterogeneity. The estimation approach used is
the method developed by Prentice and Gloeckler (1978). It is similar to the method used in the Cox
model in the sense that the baseline hazard is non-parametric, i.e. there are no assumptions made
about the shape of the baseline hazard. The comparison of the results of Estimation 1 and 2 shows
that the inference from both discrete and continuous time model is similar. Most coefficients
increase in absolute magnitude and their significance improves.
Following Jenkins the non-parametric specification for the baseline hazard is estimated by creating
interval-specific dummy variables (D2-D11, D1 reference group) and incorporating these dummies
into the model. The coefficient estimates interval-specific dummies show that the baseline hazard
decreases non-monotonically, i.e. the degree of negative duration dependence increases nonmonotonically with duration. These results are consistent with those of Nurmi (2003).
The unobserved heterogeneity is analysed in the framework of discrete time models with nonparametric baseline hazard. The advantage of this modelling approach is that conclusions about the
effects of unobserved heterogeneity are more reliable if a flexible specification for the baseline
hazard is used. A usual approach to take into account the unobserved differences between
observations is to model the pattern of heterogeneity using a parametric functional form. For
discrete time proportional hazard models gamma distributed47 specifications are commonly used.
However, as the result may be sensitive to the choice of the distribution, an alternative distribution,
normal (Gaussian) distribution48, is also used.
The results of the specifications with unobserved heterogeneity are presented in the last two
columns of Table 7. Controlling for unobserved heterogeneity does not change any of the findings
about the effects of the covariates on hazard. On the contrary, the estimated coefficients are
strengthened compared to most of the corresponding estimates in Specifications (1) and (2). This
result is consistent with Nurmi (2003) who also finds that although there is evidence on the
presence of unobserved heterogeneity accounting for it does not change her results.
It is, however, interesting to note that the estimated effect of firms’ profits in the specifications with
unobserved heterogeneity has the same sign, but is clearly smaller than that in discrete time
specification without unobserved heterogeneity. In the former specifications the magnitude of the
estimated coefficients is closer to that of continuous time Cox specification.
46
According to Jenkins (2002) this is called the selection effect.
The model used is based on Prentice and Gloecker (1978) incorporating a gamma mixture distribution to summarize
unobserved heterogeneity, as proposed by Meyer (1990). The model is estimated in the framework of Stata with the
‘pgmhaz’ program written by Stephen Jenkins.
48
The model used is the random effects complementary log-log model.
47
23
TABLE 7 Regression results of discrete proportional hazard models with unobserved heterogeneity
Variable
(1) Cox’s
(2) Discrete PH
(3) With gamma
(4) With normal
continuous PH
model
heterogeneity
heterogeneity
model
D2
-0.529***
-0.901***
-0.510***
D3
-0.496***
-0.732***
-0.470***
D4
-0.778***
-0.923***
-0.747***
D5
-0.629***
-0.810***
-0.598***
D6
-0.689***
-0.850***
-0.627***
D7
-0.789***
-0.941***
-0.751***
D8
-0.719***
-0.969***
-0.674***
D9
-0.687***
-0.889***
-0.641***
D10
-0.520***
-0.625***
-0.464**
D11
-0.891***
-1.013***
-0.792**
GDP growth
-3.343*
-4.624**
-8.344***
-4.447**
M2 to GDP
-1.361*
-1.852*
-4.814***
-1.905**
Interest rate
0.042**
0.051**
0.078***
0.052**
Investment rate
-3.439***
-4.030***
-3.294**
-4.096***
Wages
-0.042***
-0.050***
-0.042***
-0.048***
Capital intensity 0.005***
0.005***
0.005***
0.006***
Export intensity
-0.130
-0.141
-0.223
-0.177
Entry rate
1.446***
1.553***
1.557***
1.613***
MES
0.000***
0.000***
0.000***
0.000***
Net sales growth -0.277
-0.297*
-0.301
-0.300*
Plant size
-0.492***
-0.525***
-0.489***
-0.542***
Foreign
-0.005**
-0.005**
-0.005**
-0.005**
ownership
Production unit
0.255***
0.287***
0.283***
0.290***
Multi-plant
0.401***
0.430***
0.483***
0.456***
Profit
-0.063**
-0.464***
-0.072***
-0.066**
Fixed to total
-0.546***
-0.577***
-0.577***
-0.628***
assets
Debt to assets
0.902***
0.932***
0.734**
0.984***
Coverage
-0.154***
-0.127***
-0.178***
-0.173***
Lagged current
-0.004
-0.004
-0.006
-0.004
ratio
Constants
0.280
0.887**
0.428
No. of obs
15658
15658
15658
Log Likelihood
-12460
-4869
-4390
-4880
Notes: *, **, and *** indicate significance at 10, 5, and 1 per cent level, respectively.
7 Conclusions
Despite of the increasing empirical research on entry and exit dynamics, there are few studies
examining plant and firm-specific characteristics of entering and exiting plants. In particular, little
attention has been paid to financial characteristics of firms owning entering and exiting plants. This
paper analyses these characteristics across industries and over time in Finnish manufacturing and
compares the differences between entering, exiting and existing plants. Furthermore, the paper
provides an analysis on the effects of firms’ financial status and macro-level financial distress on
plant survival using both continuous and discrete time proportional hazard model. In addition, the
paper addresses the question of unobserved heterogeneity between plants.
The comparison of the firm-specific characteristics shows that exiting plants are on average owned
by relatively more indebted, less profitable and more liquidity constrained firms. Entering plants
also seem to be owned by firms that are more financially constrained than their incumbents. This
finding may be explained by the fact that firms establishing new plants often need external funds to
24
finance the opening of a new plant. Furthermore, these constraints are often relaxed in the years
following the entry.
The analysis of the plant-level characteristics supports the approach that entry and exit dynamics
reflect the so-called selection process where less productive and profitable plants are forced to exit
and new more productive and profitable plants enter the market. On average exiting plants grow
clearly slower and are less profitable than their incumbents. The entrants are not on average more
productive than their incumbents staying in the market. However, during the years following the
entry their labor productivity growth increases. In addition, they grow faster measured in terms of
value added and net sales.
The preliminary results of the survival analysis support the view that financial conditions are
important determinants of plant survival. Firm-level financial fragility affects the likelihood of
survival. In particular, low profitability and degree of liquidity, as well as, high degree of
indebtedness tend to hinder survival. Recessions are also found to reduce the probability of survival.
The effects of macro-level financial distress are less clear. According to the results of the study high
interest rates increase the probability of failure. In addition, the M2 to GDP ratio seems to have a
negative effect on the probability of hazard. However, due to the potential problem of
multicollinearity this result should be interpreted with caution.
25
References
Audretsch, D. B. (1995) “Innovation, Growth and Survival”, International Journal of Industrial
Economics, 13, pp. 441-457.
Audretsch, D. B., and T. Mahmood (1994) “The Rate of Hazard Confronting New Firms and Plants
in U.S. Manufacturing”, Review of Industrial Organization, 9(1), pp. 41-56.
Audretsch, D. B., and T. Mahmood (1995) “New Firm Survival: New results Using a Hazard
Function”, The Review of Economics and Statistics, 77, pp.97-103.
Baldwin, J. R., and P. K. Gorecki (1991) “Firm Entry and Exit in the Canadian Manufacturing
Sector, 1970-1982”, Canadian Journal of Economics, XXIV, No. 2, pp.301-323.
Baldwin, J., L. Bian, R. Dupuy, and G. Gellatly (2000) “Failure Rates for New Canadian Firms:
New Perspectives on entry and Exit”, Cataloque No. 61-526-XPE, Ottawa, Statistics Canada.
Bartelsman, E., S. Scarpetta, and F. Schivardi, (2003): Comparative Analysis of Firm
Demographics and Survival: Micro-Level Evidence for the OECD countries. OECD, Economic
Department Working Papers, No. 348.
Boeri, T., and L. Bellman (1995) “Post-entry Behaviour and the Cycle: Evidence from Germany”,
International Journal of Industrial Economics, 13, pp.483-500.
Brandt, N. (2003) “Business Dynamics in Europe”, Mimeo.
Caballero, R. J., and M. L. Hammour (1994) “The Cleansing Effects of Creative Destruction”,
American Economic Review 84, pp. 1350-1368.
Cox, D. R. (1972) “Regression Models and Life-Tables”, Journal of Royal Statistical Society Series
B, 34, pp. 187-220.
Disney, R., J. Haskel, and Y. Heden (2000) “Restructuring and Productivity Growth in UK
Manufacturing”, CEPR Discussion Paper Series, No. 2463.
Dunne, T., M. Roberts, and L. Samuelson (1988) “Patterns of Firm Entry and Exit in U.S.
Manufacturing Industries”, RAND Journal of Economics, Vol. 19, Issue 4, pp. 495-515.
Foster, L., J. Haltiwanger, and C. J. Krizan (1998) “Aggregate Productivity Growth: Lessons from
Microeconomic Evidence”, NBER Working Paper No. 6803.
Geroski, P. A. (1991) “Domestic and Foreign Entry in the United Kingdom: 1983-1984”, in Entry
and Market Contestability – An International Comparison, ed. by P. A. Geroski, and J. Schwalbach,
pp. 63-88, Basil Blackwell, Oxford.
Geroski, P. A. (1995) “What Do We Know about Entry?”, International Journal of Industrial
Economics, 13, pp. 421-440.
Greene, W. H. (2003) ”Econometric Analysis”, Prentice Hall.
26
Fotopoulos, G., and N. Spence, (1998), “Entry and exit from manufacturing industries: symmetry,
turbulence and simultaneity – some evidence from Greek manufacturing industries 1982-1988”,
Applied Economics, 30, pp. 245-262.
Fotopoulos, G., and H. Louri (2000) “Determinants of Hazard Confronting New Entry: Does
Financial Structure Matter?”, Review of Industrial Organization, 17, pp.285-300.
Ilmakunnas, P., M. Maliranta, and J. Vainiomäki (2001), ”Linked Emloyer-Employee Data on
Finnish Plants for the Analysis of Productivity, Wages and Turnover”, Tampere Economic Working
Papers, Net Series.
Ilmakunnas, P. and J., Topi (1999): “Microeconomic and Macroeconomic Influence on Entry and
exit of Firms”, Review of Industrial Organization, 15, pp.283-301.
Jenkins, S. P. (2001): ”Introduction to the analysis of spell duration data”, Lecture notes of the
courses at University of Essex.
Khemani, R. S., and D. Shapiro (1986) “The Determinants of New Plant Entry in Canada”, Applied
Economics, 18, pp.1243-1257.
Kiander, J., and P., Vartia (1996) “The Great Depression of the 1990s in Finland”, Finnish
Economic Papers, 9, pp. 72-88.
Maliranta, M., (2002), “Factor Income Shares and Micro-Level Restructuring”, The Research
Institute of the Finnish Economy Discussion papers No. 796.
Mata, J. (1991) “Sunk Costs and Entry by Small and Large Plants”, in Entry and Market
Contestability – An International Comparison, ed. by P. A. Geroski, and J. Schwalbach, pp. 49-62,
Basil Blackwell, Oxford.
Mata, J. (1993) “Entry and Type of Entrant – Evidence from Portugal”, International Journal of
Industrial Economics, 11, pp.101-122.
Mata, J., P. Portugal, and P. Guimarães (1995) “The Survival of new Plants: The Start-up
Conditions and Post-entry Evolution”, International Journal of Industrial Economics, 13, pp.459481.
Meyer, B., D. (1990): “Unemployment Insurance and Unemployment Spells”, Econometrica, 58,
757-782.
Nurmi, S. (2003): “The Determinants of Plant Survival in Turbulent Macroeconomic Conditions”,
in Essays on Plant Size, Employment Dynamics and Survival, Doctoral Dissertation Series A-230,
Helsinki School of Economics.
Nurmi, S. (2003): “Sectoral Differences in Plant Start-up Size in the Finnish Economy”, in Essays
on Plant Size, Employment Dynamics and Survival, Doctoral Dissertation Series A-230, Helsinki
School of Economics.
27
Prentice, R., and L., Gloeckler (1978): “Regression Analysis of Grouped Survival Data with
Application to Breast Cancer Data”, Biometrics, 34, 57-67.
Orr, D., (1974), “The Determinants of Entry: A Study of the Canadian Manufacturing Industries”,
Review of Economics and Statistics, 56, pp.58-66.
Scarpetta, S., P. Hemmings, T. Tressel, and J. Woo, (2002) ”The role of Policy and Institutions for
Productivity and Firm Dynamics: Evidence from Micro and Industry Data”, OECD, Economic
Department Working Papers, No. 329.
Scherer, F. M., and D. Ross (1990) “Industrial Market Structure and Economic Performance” 3rd
ed., Houghton Mifflin, Boston.
Shapiro, D., and R. S. Khemani (1987) “The Determinants of Entry and Exit Reconsidered”,
International Journal of Industrial Economics, 5, pp.15-26.
Siegfried, J. J., and L. B. Evans (1994) “Empirical Studies of Entry and Exit: A Survey of the
Evidence”, Review of Industrial Organization, 9, pp.121-155.
Tveterås, R., and G. E. Eide (2000) “Survival of new Plants in Different Industry Environments in
Norwegian Manufacturing: A semi-Proportional Cox Model approach”, Small Business Economics,
14, pp.65-82.
Winter, J. K. (1999) “Does Firms’ Financial Status Affect Plant-Level Investment and Exit
Decisions? Mimeo.
Yamawaki, H. (1991) “The Effects of Business Conditions on Net Entry: Evidence from Japan”, in
Entry and Market Contestability – An International Comparison, ed. by P. A. Geroski, and J.
Schwalbach, pp. 168-186, Basil Blackwell, Oxford.
Zingales, L. (1998) “The Survival of the Fittest or the Fattest: Exit and Financing in the Trucking
Industry” Journal of Finance, 53, pp. 905-938.
28
APPENDIX I
Description of the Process of Linking the Databases
The process of linking the LDPM (Longitudinal Database on Plants in Finnish Manufacturing), the
Business Register on plants and the Financial Statements Statistics was conducted as follows. First,
the two plant-level databases were merged using unique identification codes. This resulted in data
set with 121124 observations for the period of 1988-2001.49 This data set includes observations that
existed in both of the databases, but also observations appearing only in one of the databases.
Although the LPDM is constructed using a survey based on the Business Register, there are, in
practice, some differences in these databases. The main reasons why there is not a perfect match
between plants in the LDPM and the Business Register are the following: (i) There may be delay in
including plants in the survey, although some plants may be in the survey before they appear in the
Business Register. (ii) Some plants that are in the Business Register considered as two separate
plants located in the same address are in the survey considered as one plant.
The firm-level financial information is included in the data set by linking the Financial Statements
Statistics with the above-discussed plant-level data set. This is carried out using firm identification
codes. Both the LDPM and the Business Register include information on the identification codes of
the owner firms of plants. Thus, if the Financial Statements Statistics include data on the owner firm
of a specific plant, it is possible to link the information on each plant in the data set with its owner
firm. However, due to the limited coverage of small firms in the Financial Statements Statistics
before 199450 the matching between firms’ financial information and firms owning plants in the
data set is not perfect. 84789 of 121124 observations in the final data set include in formation on the
firm-level financial variables. In order to check whether the results of the survival analysis in
Section 6 are affected by the missing values of financial variables the study period is split into years
1989-1993 and 1994-2000. The latter period is used to examine the same model specifications as in
Section 6. The results concerning the effect of firms’ financial status are similar to the
corresponding results with the study period 1989-2000.
49
Only plants with at least 5 employees were included the data set. This cut-off limit is due to the restriction of the plant
size in the LDPM. The Business Register has a broader coverage of smaller plants. However, the smaller the size of a
plant the less reliable the observation may be, i.e. such observations may be plants with no real activity.
50
For the years 1986-1993 this database is compiled such that it contains all the firms with at least 100 employees and a
sample of the smaller firms. From 1994 onwards it includes 95 to 99 percent of all the firms in the Business Register.
29
APPENDIX II
TABLE A1 Explanatory variables
Variable
Real GDP growth
Credit to private sector
M2
Interest rates
Industry investment rate
Industry capital intensity
Industry wages
Industry export intensity
Industry entry rate
Industry exit rate
MES
Industry growth of sales
Firm debt-to-equity ratio
Firm interest coverage
Firm current ratio
Firm fixed assets to total
assets
Firm profit
Plant investment rate
Plant capital intensity
Plant hourly wages
Plant export intensity
Plant price-cost margin
Plant growth of labor
productivity
Plant growth of sales
Plant size
Foreign ownership
Production unit
Multi-plant
51
52
Description
Relative change in the real gross domestic product
Credit to private sector relative to GDP
M2 relative to GDP
Twelve-month money market rate (Helibor before 1999 and Euribor
from 1999 onwards
Industry average investment rate (gross investment/gross output)
Industry average capital intensity (capital-labor ratio)
Industry average hourly wages (Wages/hours worked)
Industry average export intensity (exports/gross output)
Industry entry rate (number of entrants/total number of plants)
Industry exit rate (number of plant failures/total number of plants)
Mean size of the largest plants in each industry accounting for one half
of the industry gross output
Relative change in the industry net sales
Firm’s Long-term liabilities/shareholders’ equity
Firm’s operating profit/interest expenses51
Firm’s current assets/current liabilities
Firm’s fixed assets/total assets
Firm’s net profit/total assets
Plant gross investment/gross output
Plant capital-labor ratio
Plant wages/hours worked
Plant exports/gross output
Plant (value added-wages-material)/value added
Relative change in the plant labor productivity
(Real value added/hours worked)
Relative change in the plant net sales
Number of employees in a plant52
Percentages of a plant’s equity held by non-Finnish residents
Dummy variable with value one if a plant is a production unit
Dummy variable with value one if a plant is owned by multi-plant firm
In the Cox-regression analysis the explanatory variable of interest coverage is defined as log(1+coverage).
In the Cox-regression analysis the explanatory variable of plant size is defined as log(plant size).
30
FIGURES A1-A6 Total number of plants according to two-digit industry level using SIC (Standard
Industrial Classification) adopted in 1995, 1988-2001
FIGURE A1
1400
1200
1000
Food & beverages
800
Textiles
Textile products
600
Footwear & leather
400
200
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
0
FIGURE A2
1200
1000
Wood & cork products
800
Paper
600
Printing and publishing
400
200
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
0
31
FIGURE A3
700
600
500
Petroleum & nuclear fuel
400
Chemicals
Rubber & plastics
300
Other non-metallic mineral
products
200
100
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
0
FIGURE A4
1600
1400
1200
1000
Basic metals
Fabricated metal products
800
Machinery
600
400
200
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
0
32
FIGURE A5
350
300
250
Office
200
Electrical apparatus
150
Radio, television &
communication
Instruments
100
50
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
0
FIGURE A6
250
200
150
Motor vehicles
100
Other transport
equipment
50
00
99
20
19
98
19
97
19
96
19
95
19
94
19
93
19
92
19
91
19
90
19
89
19
19
88
0
33