TRIO2002: New Developments in Empirical International Trade“Globalizing Activities and the Rate of Survival:
Panel Data Analysis on Japanese Firms “*December 2002
February 2003 (Revised)

Fukunari KIMURA(Faculty of Economics , Keio University)
andTakamune FUJII(Faculty of Business Administration, Aichi University)*

The authors would like to thank the participants in the Pre-conference and theConference of TRIO 2002 for comments and suggestion. The assistance provided by
Mitsuyo Ando is gratefully acknowledged.
The MITI database was prepared and analyzed in the cooperation with theApplied Research Institute, Inc. and the Research and Statistics Department, Minister’s
Secretariat, Ministry of International Trade and Industry (currently Ministry of
Economy, Trade, and Industry), Government of Japan. However, opinions expressedin this paper are those of the authors.
1

Abstract

This paper conducts the Cox-type survival analysis of Japanese corporate firms
with using the census-coverage data collected by METI in the mid-1990s.

The

analysis with careful treatment of exiting firms confirms a number of peculiar featuresof Japanese firms claimed in the academic and semi-academic literature.
First, we find that excessive internalization in corporate structure andconducted activities seems to be harmful for corporate survival.
establishments and affiliates is no good for corporate survival.

Having too many

Active concentrationon core competence by using outsourcing contracts increases the probability of survival.
This finding may depend on the historical background and the market condition thatJapanese firms in the mid-1990s must be confronted with.
Second, global commitment seems to help Japanese firms be more competitiveand more likely to survive.

However, the channels or types of global commitment

must carefully be selected with considering the size of firm.

Small firms can benefit

from exporting activities though having foreign affiliates or conducting foreign
outsourcing may rather aggravate their performance. Large firms, on the other hand,
can utilize the channels of foreign direct investment and foreign outsourcing andenhance the probability of survival.
Third, we find that corporate performance matters in the choice of exits foraffiliate firms, but it does not in the survival/exit of independent firms.

We must cast

doubt on the possible malfunctioning of market mechanism in the exits of independent
firms.

With observing the low level of turnover ratios in Japan, we definitely need toprovide economic environment where corporate turnovers are easier and efficient.
Fourth, we do not find any statistically significant evidence that firms withforeign asset holdings are more likely to exit.

After controlling other factors, ourregression results indicate that little evidence exists for foot-loose behavior of foreigncompanies.

2

1. Introduction

The era of the 1990s are said to be the lost decade for the Japanese economy.
After the series of hard debates among economists, we now share the view that issues
are not simply cyclical along the business cycle but are related to the existence ofserious structural problems that have driven the long-term recession.

The financial

sector and macroeconomic management have obviously had fatal problems.

In

addition, Japanese corporate firms, once being praised as the core of “Japanese
economic system,” also seem to suffer from crucial structural impediments to adjusting
new economic environment in the 1990s.
The recent academic and semi-academic literature has reached a loose
consensus that three stylized facts exist on Japanese corporate firms in the 1990s.

First,

Japanese firms excessively expanded their boundaries and internalized too many
activities in the late 1980s.

To take advantage of economies of scope and risk pooling,

many firms got into new fields and diversified their products.

In the course ofdiversification, they founded a number of establishments and affiliates in both domestic
and foreign locations in order to launch new enterprises.

Furthermore, firms wereactive in developing tight intra-firm-group networks and long-term inter-firm
relationship.

The wide scope of internalized activities within a firm as well as

extended intra-group/inter-firm relationship were regarded as an essential component oflong-term efficiency in the context of so-called Japanese economic system.
However, once the Japanese economy fell into a big slump as well as facing
foreign competition in the 1990s, a drastic reversal came over.

The excessive

expansion of corporate activities and inter-firm relationship suddenly became a sourceof inefficiency, and Japanese firms were forced to reduce the scope of activities, toreorganize establishments and affiliates, and to critically review the old inter-firm
relationship.

The old type of corporate structure and inter-firm relationship seemed towork adversely on corporate performance in this period.
Second, excessive adaptation to high growth period resulted in rigid industrial
structure and low turnover ratios of firms. Cross-shareholding and other types of longterm inter-firm relationship made the cost of firms’ exit extraordinary high.

3Thecooperative labor relations as well as various government regulations also became an
obstacle to efficient turnovers.

We did not observe many mergers and acquisitions(M&As) and hardly experienced hostile takeovers.1

Inactive turnovers obviously

delayed necessary adjustments of industrial structure and prolonged poor economic
situation.
Third, even in such stagnant situation, the global commitment of firms
worked as a crucial element for enhancing efficiency.
external activities.

Good firms tend to develop

At the same time, with the opposite arrow of causality, various

types of global commitment such as exporting activities, foreign direct investment, andforeign outsourcing seem to improve corporate performance by accelerating efficientreformulation of corporate structure and inter-firm relationship.
These stylized facts have not been fully proved by formal economic analysis.
In particular, no serious empirical study of survival and exit of firms withcomprehensive statistics has been done for the Japanese economy because we did nothave any census-coverage statistics that provided stable data over time.2 However, we
now have the METI’s firm-level survey in a certain length of time series and are readyto conduct formal survival/exit analysis.
Because M&As are not observed quite often in Japan, we can primarily
interpret the exit of a firm as an indication of poor performance. If so, we would liketo confirm whether over-internalization of corporate structure truly makes a firm prone
to exit and whether global commitment helps a firm survive.

In addition, if the cost of

exiting matters in the turnover of firms in Japan, we may find differences between thecases of affiliates of other firms and those of independent firms when we investigate therelationship between corporate performance and the probability of survival/exit.

This

paper focuses on the characteristics specific to Japanese firms in terms of corporate
1

Shimizu (2001, p. 88) reports that listed companies at Tokyo Stock Exchange that
conducted mergiers are only 71 out of all listed companies during the period of 19491998 (1273 in his sample). The Fair Trade Commission (FTC), Government of Japan(2002, p. 220) presents that the number of mergers reported to FTC is only 151, 170,
and 127 in 1999 F/Y, 2000 F/Y, and 2001 F/Y, respectively.
2
Honjo (2000) conducted survival analysis for the manufacturing firms located inTokyo with using the data bank of Tokyo Shoko Research (TSR). Shimizu (2001)
analyzed the corporate survival in terms of the listing at Tokyo Stock Exchange.

4

structure, inter-firm relationship, and globalizing activities and examines how thesefactors affect the survival of firms.

The empirical study is based on the survival

analysis using Cox’s proportional hazard model with the panel data of Japanese firms
for the period between 1994 F/Y and 1999 F/Y.
Cox’s proportional hazard model was originally developed in order to analyze
the survival of living animals in the field of biology and medical science and started
being applied for the survival analysis of corporate firms and establishments in the mid
1990s. The first application of Cox’s model was the survival analysis of the U.S. firms
and establishments.

Seminal works were Audretsch (1995) and Audretsch and

Mahmood (1994, 1995), being followed by Agarwal (1998), Klepper and Simons(2000), Agarwal and Audretsch (2001) and others.
Europe.

Similar studies were conducted in

Mata and Portugal (1994) and Mata, Portugal and Guimaraes (1995) on

Portuguese firms were in the first cohort, and a number of studies are conducted withthe data of Germany (Harhoff, Stahl and Woywode, 1998), Italy (Audretsch, Santerelli
and Vivarelli, 1999), Norway (Tveteras and Eide, 2000), and others.

These studies

primarily found that the size and technological level of a firm seemed to positively
affect its survival.

However, the relationship of corporate structure including

establishments and affiliates with survival/exit has not fully been explored yet.
Furthermore, few studies analyze the connection of global commitment with theirsurvival.3 Our study has a unique focus in this regard.
The paper plan is as follows: the next section explains statistical data that weuse, and section 3 presents our analytical methodology.

Section 4 summarizes our

hypotheses, and section 5 discusses our analytical results. The last section concludes.

2. Data

Our dataset is constructed from the firm-level micro data of Kigyou Katsudou

However, their datasets are far from census-coverage statistics.
3
Li (1995) and McCloughan and Stone (1998) analyze the exit of foreign affiliates fromthe viewpoint of host country. However, their studies do not directly examine theglobal commitment of firms.
5

Kihon Chousa (Basic Survey of Business Structure and Activity).

This survey was first

conducted in 1991 F/Y, then in 1994 F/Y, and annually afterward.

The prime purposeof the survey is to capture the overall structure of Japanese corporate firms in light of
their diversification, internationalization, inter-firm linkages, and strategies on R&D andinformation technology though financial information is barely included.

It covers all

firms that have more than 50 workers, capital of more than 30 million yen, and an
establishment in mining, manufacturing, wholesale/retail trade, or restaurants.

We

constructed a longitudinal data set by connecting annual firm-level data from 1994 F/Y
to 1999 F/Y.
The Basic Survey has several attractive features.
data.

First, it provides firm-level

A common form of firm-related statistics in the world is rather on theestablishment basis, not on the firm basis, and thus most of the related empirical studies
in the United States, Canada, and others have used establishment-level longitudinal data.
In case of Japan, establishment-level micro data are also available on the basis ofKougyou Toukei Hyou (Census of Manufactures).

Establishment-level data are usefulin analyzing production activities but are not perfectly appropriate to examine corporate
activities as a whole.

A corporate firm is an individual economic agent that makes

economic decisions.

When we would like to investigate the structure, performance,and strategies of corporate firms, firm-level data provided by the Basic Survey provide
clear advantages.
The second strength of the Basic Survey is its frequency.

Statistics withcensus coverage tends to be conducted only once in several years because of the hugeamount of cost and labor required in processing.4 However, more data points areneeded in time series in order to precisely identify the nature of entry and exit ofcorporate firms. The Basic Survey collects every year’s data, which provide precious
information on the survival of firms.
Third, relatively high ratios of effective questionnaire returns are also thestrength of the Basic Survey.

Statistics conducted by the Government of Japan islegally classified into two categories: designated statistics (shitei toukei) and approved4

For example, the seminal paper of the literature, Dunne, Roberts and Samuelson(1989), uses the U.S. manufacturing censuses that are conducted once in five years.
6statistics (shounin toukei). The Basic Survey is the first type, and thus firms in thesurvey must return the questionnaires under the Statistics Law.5

The actual ratios ofeffective questionnaire returns are not disclosed but are probably about 90% to 95%.
More importantly, the preciseness of the firm list itself is well known, which is notnecessarily the case in previous studies in other countries.

Hence, we can be confident

that the distortion due to low effective returns is relatively small.
Even with such a date set of quality, though, we must have a great care in
defining the exit of firms. In particular, because turnover ratios of Japanese firms are
known to be very low, the data handling could be very sensitive. A weak point of the
Basic Survey in the context of survival analysis is that it does not include areconfirmation process to check whether a firm truly exits from the market or not.
Therefore, to identify whether a firm exits from the market or not must depend solely on
the information on whether the concerned firm shows up in the data set or not.
In general, there would be various reasons why a firm gets out of the data set.
Such a case occurs when a firm does not return the questionnaire by chance, when afirm geographically relocates headquarters, when a firm switches the industry it belongs
to, when mergers and acquisitions (M&As) occur, and others.

The permanent firm

numbering system in the Basic Survey could deal with most of the industry changes andgeographical relocation.6 However, when a firm changes the contents of activities andloses establishments covered by the survey, for example, the firm gets out of the data set.
Furthermore, some firms may drop from the sample set because of the shrinkage in size;
the Basic Survey has a cut-off line in size as mentioned above.
To avoid erroneous interpretation as far as possible, this paper treats firms
dropping from the survey in two sequent years as those that get out of the market.
Because the data from 1994 F/Y to 1999 F/Y are available, our data set consists ofcorporate firms that are in business in 1994 F/Y, 1995 F/Y, 1996 F/Y, and/or 1997 F/Y
so that we can identify whether the firms survive or not. In addition, considering a

5“Approved” statistics is not accompanied with strong legal enforcement so that
effective return ratios tend to be low.
6
Kimura and Kiyota (2000) find that a substantial number of firms covered by the Basic
Survey switch industries over time. This suggests that the survey follows industry
switching pretty well.
7possibility of relatively small firms dropping from the data set due to the shrinkage insize, we conduct regressions not only with the whole sample set but also with sample
sets for firms with different cutoffs, which is discussed in details in the Appendix.

3. Methodology: the proportional hazard modelThis section presents the proportional hazard model that we utilize in oursurvival analysis of corporate firms.
The analysis of survival and exit of corporate firms requires carefulconsideration on methodology.

If we collect data only for firms exiting from themarket and conduct OLS regressions, serious sampling bias occurs.

Although it ispossible to treat survival and exit as discrete choices and conduct logit or probit analysis,
we cannot take into account over-time changes of each firm.

To completely avoid

these problems, we would have to observe all firms from entry to exit, which is virtually
impossible in most of the studies. The sample period typically ends before most of thefirms get out of the market.

We must confront with such a serious censored data

problem.
The issue is how to utilize the information on censored firms in survival.
One way to deal with this task is to conduct the event history analysis by using a modelsuch as the proportional hazard model.
The event history analysis examines what happens in a time span before someevent occurs; in our case, “some event” is the exit of a firm.

It specifies the survival

function that describes the probability of a firm’s survival until a certain time period.
By using a hazard function, the probability of a firm’s exit at a certain time period isexpressed.
The survival function is specified as follows:(1)

S ( t) = Pr(T ≥ t) ,where T is the duration of survival of a firm and t is a certain time point. The function
presents the probability of a firm’s survival at time t as a function of t.

The hazard

function describes the probability of the risk of occurring some event.

When we8

denote the probability density function of event occurrence as f ( t) , the hazard function
can be written ash ( t) =f ( t).
S ( t)(2)

The hazard function is in general specified as follows:Pr( t ≤ T ≤ t + ∆t | T ≥ t),∆t → 0∆th ( t) = lim

where T is the duration of a firm and t denotes time.

(3)
This function presents theprobability that the event (exit) occurs in a fraction of time Δt, conditional on no
occurrence of the event until time t (i.e., the firm survives by time t). However, it isempirically difficult to specify the functional form of hazard function in our case due tothe difficulty in specifying probability distribution and others.7
The extended version of the proportional hazard model proposed by Cox(1972, 1975) analyzes the relationship between the probability of event occurrence andvarious covariates, based on the concept of hazard function. It imposes the conditionof “hazard proportionality” and makes the analysis of covariates possible without
specifying a hazard function itself. “Hazard proportionality” is the assumption that the
proportion of two kinds of hazard is constant over time.

The model treats eachsample’s hazard rate hi ( t) as a function of a number of covariates. It conceptually
defines the baseline hazard ( h0 ( t) ) that is not influenced by any covariate and treats the
proportion of hi ( t) and h0 ( t) as constant based on the hazard proportionality
assumption. Hence, the proportion is interpreted as a function of covariates.
If we denote the vector of covariates (explanatory variables) as xi, we canwritehi ( t) / h0 ( t) = exp(βx i )(4)hi ( t) = h0 ( t)exp(βx i ) .

(5)This is the proportional hazard model. By taking logarithm, we obtainlog hi ( t) = log h0 ( t) + βx i .
7(6)

In the case of durable time analysis of machines, for example, we can specify thesurvival function or hazard function because we a priori know the distribution ofdurable time as the Weibull distribution. It however is not the case when we conduct
survival analysis of corporate firms.
9

In this model, we investigate the factors that explain the height of hazard rates.
Thus, a negative coefficient means that the explanatory variable is associated withhigher survival probability while a positive coefficient suggests that the explanatory
variable accelerate the exit of firms.
Although the baseline hazard, h0 ( t) , is not obtained ex ante because the
distribution of hazard is not known it can be estimated ex post.8,

Figure 1 presents thebaseline survival function S0(t) calculated from the estimated baseline hazard h0(t).9This function indicates the survival pattern of sample firms when any covariates do notaffect to the survival of firms, which is specified as
S0 ( t) = exp{− H 0 ( t)} ,(7)

where H0(t) is the cumulative function of baseline hazard, h0(t).

This curvaturesuggests that the probability of exit is higher in younger period before covariates aretaken into account.

The deviation of actual hazard from the baseline hazard (h0(t)) is

explained by covariates.10<Figure 1>

4. Explaining the probability of exits
To estimate parameter β, we use the partial likelihood estimation method. When wedenote the set of firms that have not experienced the event (exit) at time t as R(t), Risk
Set, we estimate the parameter of covariates,β, by maximizing the partial likelihoodmexp(βx i ). Then, we do not have to specify the baseline hazard
estimator, L = ∏∑ exp(βx k )i= 1
8k ∈ R ( ti )function, h0 ( t) . For further explanation, please refer to Cox (1972, 1975), Kiefer(1988), or Kalbfleisch and Prentice (2002).
The baseline hazard h0(t) is obtained from model 1 in table 2.
10
Figure 1 shows the baseline survival function because it is convenient to interpret thesurvival pattern of sample firms. However, baseline hazard function h0(t) is used for
estimating the proportional hazard model. The relationship between h0(t) and S0(t) is
derived from equation (2) as follows;d (log( S ( t))h0 ( t) = −
.
dt
9

10

In general, the exit of a firm can take various forms for various reasons. For
example, M&As are one of the typical forms of a firm’s exit, where poor corporate
performance is not necessarily a trigger.11 However, in case of Japan in the 1990s,
hostile takeovers were quite rare, and thus the exit of a firm can largely be interpreted asa result of bad performance.

In the following, we discuss the expected sign of thecoefficient for each explanatory variable based on such intuition.

In addition, there is apossibility that a firm is an affiliate of another firm and exits as a part of the corporate
restructuring.

We will take care of such cases by separating our data set into affiliatesof other firms and independent firms.
The explanatory factors that possibly affect the survival and exit of firms are
categorized into four groups: (i) variables related to individual corporate performance,(ii) variables representing firms’ competitiveness and technology, (iii) variables
expressing internalization patterns and global commitment of firms, and (iv) industry
dummies at the 2-digit level of the Basic Survey.

The list of variables with the

expected signs (except industry dummies) is summarized in Table 1.12

<Table 1>
The variables related to individual corporate performance include the size andthe capital intensity of firms.

As prior studies found, firm size, expressed by thenatural logarithm of the number of regular workers, would have a positive relationshipwith the firms’ survival.13

Capital-labor ratio represents the quality of production

11

McGuckin and Nguyen (1995), for example, found that M&As are more likely tooccur for establishments with higher labor productivity in the U.S. manufacturing sector
in 1977-1987 though the opposite is observed for establishments with more than 250
workers.
12Note that all variables are for each corporate firm that includes its establishments but
does not include its affiliates.
13
Jovanovic (1982) theoretically demonstrated a strong positive relationship between
firm size and firm performance, as opposed to the stochastic growth rate hypothesis
regardless of firm size along the intuition of Gibrat’s law. Many of the previous
empirical studies on the survival of firms. including Audretsch and Mahmood (1994,
1995), Mata and Portugal (1994), and Mata, Portugal and Guimaraes (1995), also founda positive relationship between firm size and the survival of firms.
11equipments or the efficiency in production, and thus a firm with higher ratio would havestronger competitiveness to survive.

Operating surplus ratio, which is operating

surplus divided by total sales, is also included.
coefficient of these variables.

The expected signs are negative for the

The expected sign of the coefficient for value added

ratio is not sure after controlling operating surplus ratio.

The expected sign of thecoefficient for wage ratio is positive; heavy personnel payments would be a burden forfirms in survival.
The variables presenting firms’ competitiveness and technological intensity
include R&D dummy and advertisement cost ratio, the latter of which is the ratio ofadvertisement cost to operating cost.14

As Audretsch and Mahmood (1994, 1995)

emphasized, R&D intensity would have a positive effect on the firms’ survival.
Advertising cost ratio is a proxy variable for product differentiation in the literature ofindustrial organization.

In general, producers of differentiated goods would enjoystronger competitiveness than those of standardized goods would do.

The expected

signs for the coefficients of these two variables are thus negative.
The variables that we would like to highlight on in our analysis are those
representing internalization and global commitment of firms. Outsourcing dummy, thenumber of establishments, owning affiliates dummy are intended to capture the degreeof internalization. Outsourcing is in general much more foot-loose form of inter-firm
relationship than the traditional long-term subcontracting system.

The expected signof the coefficient for outsourcing dummy is negative because outsourcing indicatesparsimony in specifying internalized activities.

The number of establishments andhaving affiliates present the extensiveness of internalized activities, which means thatthe expected signs of the coefficients are positive.
Multiple forms of global commitment are expressed as foreign sales ratio,
foreign procurement ratio, foreign outsourcing dummy, and owing foreign affiliates
dummy.15

The expected signs are negative, except for foreign procurement ratio,

14

The ratio of R&D expenditures to total sales provides basically the same results as
R&D dummy does. Because the large number of firms do not have any R&D
expenditure, the results with R&D dummy is shown in the following.
15
Foreign sales and foreign procurement are slightly different from exports and imports
because they include sales and procurement of establishments located abroad. It does12

because global commitment is supposed to make a firm more likely to survive.16 In thecase of foreign procurement ratio, we are not sure about the sign of coefficient because
purchasing commodities and sell them in the domestic market really provides
competitive environment; rather, the recession of domestic economy may adversely
affect such firms.
Foreign ownership ratio indicates whether firms are affiliates of foreign firms
or not and how strong foreign managerial control is.17 Foreign firms may make adecision on the exit of their affiliates in Japan more severely and quickly than Japaneseindigenous firms may do if the performance of their affiliates in Japan deteriorates.
We hence expect a positive coefficient for foreign ownership.
Affiliate firm dummy is introduced to check whether affiliate firms owned byother firms and independent firms have different probability to survive.

If the exiting

cost is high, the exit of an affiliate would be easier than that of an independent firm.
We thus expect a positive sign for the coefficient of affiliate firm dummy.

5. Results

This section presents the results of our hazard model analysis and discusses
their implication. Table 2 provides the results of analysis with all firms in our data set.
The results are fairly stable no matter whether we include industry dummies or not andmostly confirm our intuition.

<Table 2>

First, consistent with the previous literature, firm size and R&D dummy havenot make much difference, however, since the number of establishments located abroad
is limited.
16
Lewis and Richardson (2001) make comprehensive surveys on the empirical evidenceof the connection between global commitment and corporate performance in the case of
the United States and conclude that the positive relationship is almost decisive.
17
Note that the Basic Survey simply collects total foreign ownership ratios, and thus
“foreign ownership” includes both foreign direct investment and portfolio investment.
13

negative coefficients, which means that larger firms and firms that conduct R&D are
more likely to survive. The coefficients for advertisement cost ratio unexpectedly havepositive signs. Signs of these three variables are unaltered even if we change thesample set in the following analysis.

Capital labor ratio, operating surplus ratio, value

added ratio, and wage ratio are sensitive to the sample set so that we will discuss later.
Second, excessive internalization is proved to be a serious problem. Boththe number of establishments and owning affiliates dummy have significantly positive
signs while outsourcing dummy has a negative sign.

After being controlled by othervariables, the compact design of corporate structure concentrating on core competence
is important for enhancing the probability of survival.
Third, global commitment seems to be important for survival though theresult is mixed for some variables.

Foreign sales dummy has a negative coefficient,which is consistent with our intuition that exporting activities are positively correlatedwith the likelihood of survival.

However, foreign outsourcing dummy and owningforeign affiliates dummy have positive coefficients in these regressions, as opposed toour prior belief.

Actually, the size of firms matter for the signs of these coefficients;

we will discuss this issue more in detail below.
Fourth, the sign of coefficient for foreign ownership ratio is positive but is not
significantly different from zero.

This means that the public belief blaming foot-loose

behavior by foreign companies is not statistically warranted.
Fifth, affiliate firm dummy has a strongly positive coefficient, which meansthat affiliates of other firms are more likely to exit than independent firms. As shownin Appendix Table A1, the “exit ratio” of affiliates firms is 6.4% while that ofindependent firms is 5.6%.18 Even after controlling other factors, the probability ofexiting is different.
Related to the last point, we separate our sample set into two, affiliate firms
and independent firms, and conduct regressions again. The results are shown in Tables
3 and 4.

Most notable is that the signs of the coefficients for operating surplus ratio,

value added ratio, and wage ratio are negative, negative, and positive for affiliates firms
while signs are insignificant for independent firms.
18This means that whether an

As for the definition of “exit ratio,” please refer to the Appendix.
14

affiliate is closed or not strongly depends on its performance while such natural
selection mechanism does not work for independent firms. The exit of affiliates can bea part of the restructuring of corporate structure, and thus the cost of exiting may belower than usual exits, taking into account possible relocation of released resources. In
other words, the cost of exiting is high for independent firms so that it cannot get out of
the market even if the performance is poor. Or, an alternative interpretation is thatindependent firms exit regardless of their performance due to financial crunch and otherfactors external to the firms themselves.

<Table 3><Table 4>

Tables 5 and 6 present regression results when we separate our sample into
firms with affiliates and firms without.

As shown in Appendix Table A1, the “exitratio” of firms with affiliates (4.6%) is much lower than that of firms without (7.6%).
However, both firm groups pretty much share common factors that affect the probabilityof exiting.

<Table 5><Table 6>

Because over-internalization issue seems to strongly influence survival andexit, we separate our sample into different employment size categories and conduct
regressions.

Then, as shown in Table 7, very clear-cut results are obtained for global

commitment variables.

Foreign sales dummy has a significant negative coefficientwhen firms are small, but the significance is diluted as firms become larger.

On theother hand, owning foreign affiliates dummy switches the sign of its coefficient frompositive to negative as firm size goes up.

Exporting activities seem to be a proper formof global commitment for small firms while having foreign affiliates costs them too
much.

Large firms are affordable enough to hold foreign affiliates in order to takeadvantage of global commitment. Foreign outsourcing dummy also changes the sign15

from positive to negative (though not significantly different from zero) as firm size
increases.

Foreign procurement dummy has a significantly positive coefficient whenfirms are small but looses its significance as firm size goes up.

We can thus conclude

that global commitment improves the probability of survival if the channel is properlychosen, particularly considering the size of firms.

<Table 7>

6. Conclusion

This paper conducts the survival analysis of Japanese corporate firms withusing the census-coverage data collected by METI in the mid-1990s.

The analysis

with careful treatment of exiting firms confirms our intuition on the three stylized facts
listed in the introduction.

Our findings are summarized as follows.

First, excessive internalization in corporate structure and conducted activities
seems to be harmful for corporate survival.

This finding may depend on the historicalbackground and the market condition that Japanese firms in the mid-1990s must be
confronted with. In the 1980s, the Japanese economic system was praised, and one of
the essential components was extensive internalization of various activities in a
corporate firm as well as the construction of concerted long-term inter-firm relationship.
In the 1990s, however, extensive internalization became rather an obstacle to stay alivein stagnant economic environment. In addition, we have to point out that international
competition became by far intense in the 1990s even in the sectors, such as electronic
machinery, in which Japanese firms had competitiveness.

Having too many

establishments and too many affiliates is no good for corporate survival.
Concentration on core competence by using outsourcing contracts seems to enhance theprobability of survival.

The challenge that Japanese firms confront with is whether theefficient reorganization of corporate structure and inter-firm relationship is realized or
not.

16

Second, global commitment seems to help Japanese firms be more competitiveand more likely to survive.

However, the channels or types of global commitment

must carefully be selected with considering the size of firm.

Small firms can benefit

from exporting activities though having foreign affiliates or conducting foreign
outsourcing may rather aggravate their performance. Large firms, on the other hand,
can utilize the channels of foreign direct investment and foreign outsourcing andenhance the probability of survival.

Kimura and Kiyota (2000) found that global

commitment accelerates corporate restructuring, but we here add a caveat that proper
degree of internalization must be chosen even in the context of global commitment.
Third, we find that corporate performance matters in the choice of exits foraffiliate firms, but it does not in the survival/exit of independent firms.

Taking into

consideration that M&As are not a common form of exits, we must cast doubt on thepossible malfunctioning of market mechanism in the exits of independent firms. One
possibility is that the cost of exiting is too high for independent firms so that they stay inthe market for long even if the performance is bad.

Or, the selection of survival or exit

is done regardless of each firm’s performance because of financial crunch withincomplete information.

With observing the low level of turnover ratios in Japan, we

definitely need to provide economic environment where corporate turnovers are easierand efficient.
Fourth, we do not find any statistically significant evidence that firms withforeign asset holdings are more likely to exit.

There is a long-lasting debate onwhether accepting inward foreign direct investment is beneficial or not. Some express
concern about foot-loose behavior of foreign companies. However, after controllingother factors, our regression results indicate that little evidence exists for such tendency.
The analysis conducted in this paper just utilizes a small part of the information
carried by the micro data but already proves to be very effective in investigating what
happened in the long-lasting recession in Japan at the micro level.
studies using micro data sets should be encouraged in future research.

17

More empiricalReferences
Agarwal, Rajshree, Small Firm Survival and Technological Activity, Small BusinessEconomics 11.3, 1998, pp. 215-224
Agarwal, Rajshree and David B. Audretsch, Does Entry Size Matter?: The Impact of theLife Cycle and Technology on Firm Survival, Journal of Industrial Economics
49.1, 2001, pp. 21-43
Audretsch, David B., Innovation, Growth and Survival, International Journal ofIndustrial Organization 13.4, 1995, pp. 441-57
Audretsch, David B., Enrico Santarelli and Marco Vivarelli, Start-up Size and Industrial
Dynamics: Some Evidence from Italian Manufacturing, International Journalof Industrial Organization 17.7, 1999, pp. 965-983
Audretsch, David B. and Talat Mahmood, The Rate of Hazard Confronting Firms andPlants in U.S. Manufacturing, Review of Industrial Organization 9.1, 1994, pp.
41-56
Audretsch, David B. and Talat Mahmood, New Firm Survival: New Results Using a
Hazard Function, Review of Economics and Statistics 77.1, 1995, pp. 97-103
Cox, David R., Regression Models and Life Tables, Journal of the Royal StatisticalSociety Series B. 34.2, 1972, pp. 187-220
Cox, David R., Partial Likelihood, Biometrica 62.3, 1975, pp. 269-75
Dunne, Timothy, Mark J. Roberts and Larry Samuelson, Plant Turnover and Gross
Employment Flows in the U.S. Manufacturing Sector, Journal of Labor
Economics 7.1, 1989, pp. 4-71
The Fair Trade Commission (FTC), Government of Japan, Heisei 13 nendo Kousei
Torihiki Iinkai Nenji Houkoku (Annual Report of the Fair Trade Commission,
2001 F/Y), Tokyo: Fair Trade Commission, 2002
Harhoff, Dietmar, Konrad Stahl and Michael Woywode, Legal Form, Growth and Exitof West German Firms: Empirical Results for Manufacturing, Construction,
Trade and Service, Journal of Industrial Economics 46.4, 1998, pp. 453-488
Honjo, Yuji, Business Failure of New Firms: An Empirical Analysis Using a
Multiplicative Hazards Model, International Journal of Industrial
Organization 18.4, 2000, pp. 557-574
Jovanovic, Boyan, Selection and the Evolution of Industry, Econometrica 50.3, 1982,
pp. 649-670
Kalbfleisch, John D. and Ross L. Prentice, The Statistical Analysis of Failure Time Data
(2nd edition), New York; John Wiley and Sons, 2002
Kiefer, Nicholas M., Economic Duration Data and Hazard Functions, Journal of
Economic Literature 26.2, 1988, pp. 646-679
Kimura, Fukunari and Kozo Kiyota, Exports and Foreign Direct Investment Accelerate
Corporate Reforms: Evidence from the Japanese Micro Data, prepared for
Analytical Issues in the Trade, Foreign Direct Investment, andMacro/Financial Relations of the United States and Japan Pre-conference
Meeting at University of Michigan (Ann Arbor), Ann Arbor, Michigan, 2000.
Forthcoming in Robert M. Stern, ed., Analytical Studies in U.S.-Japan
International Economic Relations, Cheltenham: Edward Elgar Publishing.
Klepper, Steven and Kenneth L. Simons, The Making of an Oligopoly: Firm Survival
and Technological Change in the Evolution of the U.S. Tire Industry, Journal of18Political Economy 108.4, 2000, pp. 728-760
Lewis, III, Howard and J. David Richardson, Why Global Commitment Really Matters!
Washington, D.C.: Institute for International Economics, 2001
Li, Jiatao, Foreign Entry and Survival: Effects of Strategic Choices on Performance inInternational Markets, Strategic Management Journal 16.5, 1995, pp333-351
Mata, Jose and Pedro Portugal, Life Duration of New Firms, Journal of Industrial
Economics 42.3, 1994, pp. 227-245
Mata, Jose, Pedro Portugal and Paulo Guimaraes, The Survival of New Plants: Start-up
Conditions and Post-Entry Evolution, International Journal of Industrial
Organization 13.4, 1995, pp. 459-81
McCloughan, Patrick and Ian Stone, Life Duration of Foreign Manufacturing
Subsidiaries: Evidence from UK Northern Manufacturing Industry 1970-93,
International Journal of Industrial Organization 16.6, 1998, pp. 719-747
McGuckin, Robert H. and Sang V. Nguyen, On Productivity and Plant Ownership
Change: New Evidence from the Longitudinal Research Database, RAND
Journal of Economics 26.2, 1995, pp. 257-276
Shimizu, Takashi, Gappei Koudou to Kigyou no Jumyo: Kigyokoudou heno Atarashii
Apurouchi (Mergers and Firm Longevity: A New Approach to Firm Behavior),
Tokyo; Yuhikaku, 2001 (in Japanese)
Tveteras, Ragnar and Geir Egil Eide, Survival of New Plants in Different Industry
Environments in Norwegian Manufacturing; A Semi-Proportional Cox Model
Approach, Small Business Economics 14.1, 2000, pp. 65-82

19

Appendix: “Exit” of a firm

As discussed in section 2, the Basic Survey does not include a reconfirmation
process to check whether a firm truly exits from the market or not. To avoid erroneousinterpretation as far as possible, our study treats the “exit” of a firm as a drop of a firmfrom the survey in two sequent years.
Table A1 counts the number of observations and exit firms in our data set for
regressions. If a firm survives, say, throughout the sample period of 1994 F/Y-1997
F/Y, it is counted as four observations. Hence, “exit ratio” shown in this table is muchhigher than the proportion of exit firms in one year.

<Table A1>

Table A2 presents the number of firms that dropped from the sample and“returned” later.

These tables show that a considerable number of firms did return tothe sample; about 30% of firms once disappeared from the sample returned the nextyear.

For example, among 1,552 firms that disappeared in the 1995 F/Y survey, forinstance, 448 firms re-appeared in 1996 F/Y.

This suggests that to treat two-year

sequent disappearance from the sample as a criterion of exit effectively reduces possibly
erroneous determination of “exit.” In addition, if a firm returned to the sample in morethan two years, we treat the firm as “no exit.”<Table A2>

It is obvious that the “return” of firms is mostly due to ineffective response to
the questionnaire.

The cut-off line in size the Basic Survey applies would be anotherfactor that induce the “return” of firms, but we believe that the problem is not very
serious.

Table A3 shows descriptive statistics of annual changes in absolute values ofthe size of firms, i.e., the number of workers for the full sample, and Table A4 does for
only firms with less than 300 workers.

Both tables are of course for firms that exist in

the sample in two sequent years, so we must be careful that these figures are somewhat

20understated by not including firms dropped from the sample. In the case of full sample,
the mean is around 30, and the standard deviation is about 150 while median is 7 to 8.
When looked at the sample for small and medium sized firms, the mean is 10 to 11, andthe standard deviation is about 25.

These imply that while some large firms alter thenumber of workers by a larger amount, smaller firms do not very much change thenumber of workers.

We can thus guess that the cut-off line in size does not distort ourstudy very much. Although not reported, we conducted regressions with different size
cut-off and found that the regression results are fairly robust.

<Table A3><Table A4>

21

Table 1 The list of independent variables

Independent variables

Definition

Firm size

Number of total regular workers (natural logarithm)

Expected sign-

Capital labor ratio

Tangible fixed assets / total regular workers-

Operating surplus ratio

Operating surplus / total sales-

Value added ratio(Total sales-total procurement) / total sales?

Wage ratio

Total wage / operating cost+

R&D dummy

1 for firms with R&D expenditure; 0 for firms without-

Advertisement cost ratio

Advertisement cost / operating cost-

Foreign sales dummy

1 for firms with foreign sales; 0 for firms without-

Foreign procurement dummy

1 for firms with foreign procurement; 0 for firms without?

Outsourcing dummy

1 for firms with outsourcing; 0 for firms without-

Foreign outsourcing dummy

1 for firms with outsourcing to firms abroad; 0 for firms without-

Foreign ownership ratio

Foreign ownership ratio+

Number of establishments

Number of establishments owned by each firm+

Affiliate firm dummy

1 for firms that are affiliates of other firms; 0 for independent firms+

Owning affiliates dummy

1 for firms with affiliate(s); 0 for firms without+

Owning foreign affiliates dummy

1 for firms with foreign affiliate(s); 0 for firms without-

Summary statistics of independent variables

Firm size

Mean

s.d.

Minimum

Maximum

402

1079

50

53584

Firm size (in natural logarithm)

5.280

0.996

3.912

10.889

Capital labor ratio

9.634

15.6610.000

962.275

Operating surplus ratio

0.020

0.450

-89.032

0.860

Value added ratio

0.431

0.346

-30.427

1.000

Wage ratio

0.169

0.107

0.001

1.000

R&D dummy

0.393

0.488

0.000

1.000

Advertisement cost ratio

0.006

0.018

0.000

0.626

Foreign sales ratio

0.256

0.436

0.000

1.000

Foreign procurement ratio

0.242

0.428

0.000

1.000

Outsourcing dummy

0.506

0.500

0.000

1.000

Foreign outsourcing dummy

0.030

0.170

0.000

1.000

Foreign ownership ratio

0.013

0.095

0.000

1.000

Number of establishments

9.109

27.5440.000

997

Affiliate firm dummy

0.345

0.475

0.000

1.000

Owning affiliates dummy

0.565

0.496

0.000

1.000

Owing foreign affiliates dummy

0.175

0.380

0.000

1.000

Data source: The MITI database.
Note: the following observations are dropped from the sample;(1) firms with more than 100 affiliates(2) firms with more than 1000 establishments(3) firms with outsourcing cost larger than operating cost(4) firms with R&D expenditure larger than operating cost(5) firms with advertisement cost larger than operating cost(6) firms with total wage larger than operating cost

22

Table 2 Results of Cox regressions: the case of all firms (base case)

Model 1Model 2Model 3Model 4

Independent variables

Firm size

Capital labor ratio

Operating surplus ratio

Value added ratio

Wage ratio

R&D dummy

Advertisement cost ratio

Foreign sales dummy

Foreign procurement dummy

Outsourcing dummy-0.809***-0.823***-0.851***-0.864***

0.025

0.025

0.026

0.026

-0.001-0.001-0.00002-0.00001

0.001

0.001

0.001

0.001

-0.022-0.017-0.025-0.024

0.020

0.021

0.021

0.021

0.031

0.014

0.029

0.025

0.048

0.039

0.046

0.043

0.882***

0.736***

0.435**

0.422**

0.156

0.148

0.174

0.172

-0.165***-0.189***-0.093**-0.101**

0.039

0.038

0.041

0.041

4.284***

4.397***

4.207***

4.170***

0.594

0.582

0.635

0.633

-0.183***-0.261***-0.141***-0.211***

0.050

0.052

0.052

0.054

0.342***

0.265***

0.340***

0.264***

0.048

0.050

0.049

0.051

-0.119***-0.102***

0.034

0.040

Foreign outsourcing dummy

Foreign ownership ratio

Number of establishments

Affiliate firm dummy

Owning affiliates dummy

0.282***
0.105

0.084

0.179

0.215

0.296

0.181

0.182

0.182

0.183

0.005***

0.005***

0.005***

0.004***

0.00040.0005

0.001

0.001

0.997***

1.000***

0.982***

0.985***

0.034

0.033

0.034

0.034

0.083**

0.081**

0.035

0.035

Owning foreign affiliates dummy

Industry dummies

0.361***
0.104

Not included

0.331***

0.332***

0.059

0.059

Not included

Included

IncludedLog-likelihood-37027.82-37014.18-36855.40-36841.47

Chi-squared

2344.88***2372.17***2689.73***2717.59***

N

67970

67970

67970

67970

Note:

Standard errors are presented below the estimates of coefficients.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

23

Table 3 Results of Cox regressions: the case of affiliate firms

Model 5Model 6

Model 7

Model 8

Independent variables

Firm size

Capital labor ratio

Operating surplus ratio

Value added ratio

Wage ratio

R&D dummy

Advertisement cost ratio

Foreign sales dummy

Foreign procurement dummy

Outsourcing dummy-0.634***-0.649***-0.656***-0.707***

0.040

0.039

0.041

0.040

-0.001-0.0020.00001-0.001

0.002

0.002

0.002

0.002

-0.333***-0.314***-0.342***-0.330***

0.090

0.093

0.093

0.096

-0.345***-0.408***-0.332**-0.338**

0.141

0.139

0.150

0.149

1.277***

1.338***

0.943***

1.082***

0.262

0.259

0.278

0.275

-0.141**-0.183***-0.049-0.075

0.065

0.064

0.070

0.069

5.940***

6.136***

5.490***

5.908***

0.776

0.763

0.804

0.800

-0.159*-0.175*-0.143-0.158*

0.088

0.091

0.091

0.093

0.426***

0.406***

0.400***

0.381***

0.084

0.085

0.086

0.087

-0.153***-0.048

0.056

0.066

Foreign outsourcing dummy

Foreign ownership ratio

Number of establishments

Owning affiliates dummy-0.0050.233-0.094-0.020

0.001

0.070

0.198

0.199

0.200

0.200

0.004***

0.005***

0.004***

0.004***

0.001

0.001

0.001

0.001

-0.312***-0.347***

0.063

0.064

Owning foreign affiliates dummy

Industry dummies-0.0010.231

Not included-0.158-0.143

0.129

0.130

Not included

Included

IncludedLog-likelihood-12221.72-12237.64-12174.42-12189.64

Chi-squared

555.19***523.34***649.79***619.34***

N

23456

23456

23456

23456

Note:

Standard errors are presented below the estimates of coefficients.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

24

Table 4 Results of Cox regressions: the case of independent firms (not affiliate firms)

Model 9Model 10Model 11

Model 12

Independent variables

Firm size

Capital labor ratio

Operating surplus ratio

Value added ratio

Wage ratio

R&D dummy

Advertisement cost ratio

Foreign sales dummy

Foreign procurement dummy

Outsourcing dummy-0.926***-0.924***-0.976***-0.971***

0.033

0.033

0.034

0.034

-0.001-0.0002-0.0001

0.0003

0.002

0.001

0.001

0.001

-0.041-0.031-0.027-0.026

0.027

0.026

0.027

0.026

0.169

0.100

0.066

0.053

0.107

0.100

0.091

0.075

0.726***

0.511**

0.214

0.080

0.239

0.234

0.244

0.238

-0.153***-0.148***-0.110**-0.093*

0.049

0.048

0.051

0.051

2.263**

2.425**

2.447**

2.366**

0.991

0.966

1.085

1.084

-0.175***-0.285***-0.132**-0.238***

0.061

0.063

0.063

0.066

0.280***

0.167***

0.284***

0.179***

0.058

0.061

0.059

0.062

-0.093**-0.124**

0.044

0.050

Foreign outsourcing dummy

Foreign ownership ratio

Number of establishments

Owning affiliates dummy

0.345***
0.118-0.038-0.077

0.112

0.058

0.534

0.537

0.535

0.537

0.006***

0.006***

0.005***

0.005***

0.001

0.001

0.001

0.001

0.302***

0.313***

0.044

0.044

Owning foreign affiliates dummy

Industry dummies

0.446***
0.116

Not included

0.521***

0.525***

0.068

0.068

Not included

Included

IncludedLog-likelihood-22267.73-22256.43-22127.24-22120.94

Chi-squared

1189.94***1212.54***1470.93***1483.52***

N

44514

44514

44514

44514

Note:

Standard errors are presented below the estimates of coefficients.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

25

Table 5 Results of Cox regressions: the case of parent firms with affiliate(s)Model 13Model 14Model 15

Model 16

Independent variables
Firm size

Capital labor ratio

Operating surplus ratio

Value added ratio

Wage ratio

R&D dummy

Advertisement cost ratio

Foreign sales dummy

Foreign procurement dummy

Outsourcing dummy-0.822***-0.845***-0.850***-0.871***

0.033

0.033

0.034

0.034

-0.001-0.001-0.0001-0.0003

0.002

0.002

0.002

0.002

-0.039-0.026-0.019-0.013

0.035

0.035

0.036

0.036

0.202

0.131

0.068

0.034

0.127

0.125

0.135

0.135

1.099***

1.010***

0.612*

0.606*

0.294

0.294

0.316

0.315

-0.133**-0.154***-0.100*-0.113**

0.055

0.054

0.058

0.058

3.821***

4.218***

4.128***

4.201***

1.217

1.206

1.263

1.266

-0.071-0.179***-0.033-0.132*

0.065

0.068

0.068

0.071

0.272***

0.166**

0.277***

0.177***

0.063

0.066

0.064

0.067

-0.104**-0.151**

0.052

0.059

Foreign outsourcing dummy

Foreign ownership ratio

Number of establishments

Affiliate firm dummy

0.363***
0.125-0.089-0.013

0.017

0.087

0.357

0.359

0.356

0.358

0.004***

0.004***

0.004***

0.004***

0.001

0.001

0.001

0.001

0.672***
0.057

0.686***
0.057

0.660***
0.058

0.671***
0.058

Owning foreign affiliates dummy

Industry dummies

0.263**
0.126

Not included

0.300***

0.299***

0.067

0.067

Not included

Included

IncludedLog-likelihood-15069.47-15056.36-14988.80-14979.56

Chi-squaredN

1000.30***
38424

1026.52***38424

1161.65***38424

1180.12***
38424

Note:

Standard errors are presented below the estimates of coefficients.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

26

Table 6 Results of Cox regressions: the case of firms without affiliates

Model 17

Model 18

Model 19

Model 20

Firm size-0.826***0.041-0.829***0.041-0.877***0.042-0.880***0.042

Capital labor ratio-0.0020.002-0.001
0.002-0.0001
0.002

0.00001
0.002

Operating surplus ratio-0.1180.087-0.0740.090-0.147*0.086-0.144*0.087

Value added ratio

0.109
0.091

0.061
0.093

0.142
0.090

0.138
0.090

Wage ratio

0.559***
0.210

0.477**
0.212

0.273
0.226

0.287
0.226

R&D dummy-0.229***0.057-0.256***0.056-0.121**
0.060-0.126**0.059

Advertisement cost ratio

4.167***
0.703

4.272***
0.688

0.983***
0.764

3.974***
0.762

Foreign sales dummy-0.326***0.081-0.339***0.081-0.298***0.083-0.301***0.083

Foreign procurement dummy

0.401***
0.073

0.374***
0.074

0.384***
0.075

0.359***
0.076

Outsourcing dummy-0.151***
0.046

Independent variables

Foreign outsourcing dummy-0.0590.054
0.348*
0.186

0.328*
0.188

Foreign ownership ratio

0.107
0.214

0.162
0.215

0.253
0.216

0.281
0.216

Number of establishments

0.007***
0.001

0.007***
0.001

0.006***
0.001

0.006***
0.001

Affiliate firm dummy

1.194***
0.043

1.191***
0.043

1.182***
0.044

1.183***
0.044

Industry dummies

Not included

Not included

Included

IncludedLog-likelihood-19257.04-19260.81-19158.01-19157.20

Chi-squared

1229.25***

1221.73***1427.31***1428.95***

N

29546

29546

29546

29546

Note:

Standard errors are presented below the estimates of coefficients.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

27

Table 7 Results of Cox regressions: the case by firm size (number of regular worker)
Model 21

Model 22

(Firm size:50-99)(Firm size:100-199) (Firm size:200-299) (Firm size:300-499) (Firm size:500-999) (Firm size:1000 or more)

Model 23

Model 24

Model 25

Model 26

Firm size-0.237***0.120-0.521***
0.168-0.347
0.475

0.283
0.411

0.502
0.385

-0.455**
0.199

Capital labor ratio-0.003*0.002-0.0004
0.002

0.003
0.002

-0.021**
0.008-0.041***
0.013

0.004
0.007

Operating surplus ratio-0.216***0.077-0.215*
0.112

0.072
0.086

0.391
1.412

-0.1290.174-1.243
2.114

Value added ratio

0.212***
0.079-0.281*0.159-0.314
0.288

0.535
0.338

0.076
0.412

0.604
0.520

Independent variables

Wage ratio

R&D dummy

Advertisement cost ratio

Foreign sales dummy

Foreign procurement dummy

Foreign outsourcing dummy

Foreign ownership ratio

Number of establishments

Affiliate firm dummy

Owning foreign affiliates dummy

Industry dummies

0.441**

1.015***

1.366**-0.472

1.788**

1.830*

0.218

0.349

0.058

0.800

0.778

0.987

-0.070-0.236***-0.272**-0.371***-0.421**-0.013

0.055

0.077

0.125

0.140

0.175

0.241

5.047***

3.664***

3.790

3.991*-11.427*

2.879

0.773

1.208

2.841

2.355

6.711

4.547

-0.171**-0.325***-0.342*-0.256-0.280-0.136

0.074

0.105

0.175

0.185

0.248

0.294

0.365***
0.069

0.171*
0.103

0.318*
0.167

0.210
0.177

-0.165
0.232

0.121
0.260

0.289*

0.332

0.420

0.600**

0.468-0.300

0.160

0.216

0.322

0.299

0.371

0.526

0.148

0.163

0.294

0.404

0.624

-0.711

0.320

0.360

0.571

0.485

0.685

0.706

0.008

0.013**

0.002

0.010***

0.005**

0.002***

0.008

0.005

0.006

0.003

0.002

0.001

0.997***

0.917***

0.958***

1.051***

1.185***

0.944***

0.046

0.069

0.117

0.128

0.163

0.215

0.396***

0.563***

0.096

0.235

-0.048-0.580**

0.093

0.116

0.183

0.177

0.229

0.265

Not included

Not included

Not included

Not included

Not included

Not included

Log-likelihood-17572.40-7766.92-2421.34-1898.48-1154.60-683.38

Chi-squared

1327.22***233.84***90.23***

107.51***129.41***62.67***

N

19761

20241

8871

7789

6250

5058

Note:

Standard errors are presented below the estimates of coefficients.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

28

Figure 1: Survival probability and the age of firm
Survival probability (S0(t))
1

0.5

0
1

25

50

75

100

Age of firm

Note: survival probability (S0(t): baseline survival function) is obtained as follows:

So ( t) = exp{−H 0 ( t)}where H0(t) is the cumurative function of baseline hazard h0(t), which is estimated by the proportional hazard model,hi ( t) = h0 ( t)exp(βx i ) .

29

Appendix Table A1: Number of exit firms

Total observations Exit firms

Exit ratio (%)

Independent firms

44514

2485

5.58

Afffiliate firms

23456

1511

6.44

Total

67970

3996

5.88

Firms with affiliates

38424

1764

4.59

Firms without affiliates

29546

2232

7.55

Total

67970

3996

5.88

Firm size: 50-99

19761

2175

11.01

Firm size: 100-149

12345

624

5.05

Firm size: 150-199

7896

319

4.04

Firm size: 200-249

5133

183

3.57

Firm size: 250-299

3738

147

3.93

Firm size: 300 or more

19097

548

2.87

Total

67970

3996

5.88

Notes:(1) "Exit firms" are defined in our analysis as those who dropped from the surverys in
two sequent years or more and also never returned to the survey once they dropped
from the sample.
(2) The figures for total observations show the number of firm samples showed up in
our panel dataset. Those showed up in the sequent surveys from 1994 to 1997, forinstance, are counted as 4 observations. On the other hand, the figures for "exit firms"
show the number of exit firms as defined above. Thus, "exit ratio" is obtained by
dividing the number of "exit firm" by the number of total sample firms.

30Appendix Table A2: Number of "returned" firms: the case of firms that appeared in
the 1994 F/Y Survey

Dropped in 1995

Dropped in 1996

Dropped Firm Total

1552

1070

Returned in 1996

448

Returned in 1997

115

324

Returned firm total

563

324

Returned firm %

36.3

30.3

Note: Samples with missing data are included.

Appendix Table A3: Changes in firm size for the full sample (surviving firms only)(Number of regular worker, absolute value)
1994-1995

1995-1996

1996-1997

Mean

31.6

30.7

29.1

Median

8

7

7

145.5

157.3

131.8

s.d.

Appendix Table A4: Changes in firm size for firms with less than 300 workers(surviving firms only)(Number of regular worker, absolute value)
1994-1995

1995-1996

1996-1997

Mean

11.2

10.4

10.7

Median

5

5

5

26.6

24.7

26.5

s.d.

31