Discussion Papers In Economics
And Business

A Panel Study of Zombie SMEs in Japan:
Identification, Borrowing and Investment Behavior

Kentaro Imai
Discussion Paper 13-16-Rev.

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANA Panel Study of Zombie SMEs in Japan:
Identification, Borrowing and Investment Behavior

Kentaro Imai
Discussion Paper 13-16-Rev.
September 2014

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANA Panel Study of Zombie SMEs in Japan:
Identification, Borrowing and Investment Behavior
Kentaro Imai✝
(Abstract)
Using a panel dataset of firms for the period 1999-2008, we estimated theprevalence of zombies among Japanese Small- and Medium-sized enterprises (SMEs)
and their borrowing and investment behaviors. We observe that 4-14% of SMEs werezombie firms during the period 1999-2008.
Analysis of borrowing behavior indicates that zombie firms could not reduce
their loans. Reductions in the land values of SMEs did not lead to a decrease in the
borrowing of zombie firms due to ever-greening. We also observe that the
profitability of investment, measured by marginal q, did not increase investment
among zombie firms because evergreen loans increased investment in less productive
and profitable projects.

Keyword; zombie firms, ever-greening, SMEs, borrowing, investment
JEL classification G21 E22 and E44



We thank Kazuo Ogawa and the participants of the poster session at the 2012 Spring Meeting of theJapanese Economic Association for their helpful comments and suggestions. We are responsible for any
remaining errors.
✝

Graduate School of Economics Osaka University, Machikaneyama-cho, Toyonaka Osaka, 560-0043,

Japan
E-mail address: fge002ik@mail2.econ.osaka-u.ac.jp

1. Introduction
This paper explores the extent to which zombie SMEs existed in Japan and their borrowing andinvestment behavior using firm panel data. The dataset we use is a balanced dataset of non-consolidated
financial statements of 4,090 SMEs from 1998 to 2008 that were edited by Tokyo Shoko Research.
Many studies have analyzed listed zombie firms in Japan, but far less research explores the existence of
SME zombies in Japan. The misallocation of bank lending has been implicated in the ever-greening ofloans. Caballero, Hoshi and Kashyap (2008) (hereafter, CHK) argue that the existence of zombie firms, thatis, firms that should exit the market but continue to operate through bank assistance1, has negative effects
on the firm performance, productivity and investment of healthy firms. CHK conclude that the existence ofzombie firms is responsible for stagnation in the Japanese economy. Fukuda and Nakamura (2011)(hereafter, FN) argue that CHK include excellent firms in their definition of zombie firms. Thus, FN
identify zombie firms using a unique method. Additionally, FN consider why most of these firms recoveredand did not experience bankruptcy primarily using listed firm data. However, CHK and FN consider only
listed firms.
However, the Japanese economy dominantly consists of SMEs rather than listed firms. The 99.7% offirms operated in non-primary industry is SMEs and the 62.7% of full-time workers are employed in SMEs
in 2012. 2Moreover, SMEs may have been more affected by the banking sector than listed firms because
the SMEs mainly rely on bank loans and do not issue bonds.
Therefore, it is important to analyze how extensively the banking sector influenced SMEs. No researchhas examined the emergence of ever-greening and zombie SMEs, using the analytical methods employedby CHK and FN. Moreover, very few studies estimate the borrowing and investment functions of SMEs
using micro data. In particular, no research has shed light on the borrowing and investment behaviors ofzombie SMEs. For these reasons, we examine the characteristics of zombie SMEs in Japan and theirborrowing and investment behaviors.
The remainder of this paper is organized as follows. In section 2, we review previous research on theidentification of zombie firms and borrowing and investment behaviors in Japan. In section 3, we estimate
the extent to which zombie SMEs existed in Japan by modifying the methods developed by CHK and FN.
In section 4, we present our estimated model. In section 5, we present descriptive statistics, and in section 6,
we report the estimation results. Section 7 concludes.

2. A Literature Review of Zombie Firm Identification and their Behavior
In this section, we review the previous research that addresses the identification of zombie firms,
ever-greening as well as the borrowing and investment behaviors of Japanese SMEs.

2.1 Identification of Zombie Firms

1This assistance includes interest rate exemption and ever-greening.
See 2012 Economic Census reported by Ministry of Internal Affairs and Communications andMinistry of Economy, Trade and Industry.
2

1

Before addressing zombie identification using the method developed by FN, we identify zombie firms
using the CHK method. The CHK criterion is defined as the minimum required interest payment for each∗firm each year,𝑅𝑖,𝑡, as follows:
1R∗𝑖,𝑡 = rs𝑡 ∙ 𝐵𝑆𝑖,𝑡−1 + (5 ∑4𝑗=0 𝑟𝑙𝑡−𝑗 ) ∙ 𝐵𝐿𝑖,𝑡−1 + min(𝑟𝑐𝑏𝑡−4 , … , 𝑟𝑐𝑏𝑡 ) ∙ 𝐵𝑜𝑛𝑑𝑠𝑖,𝑡−1 3(1)where 𝐵𝑆𝑖,𝑡−1 , 𝐵𝐿𝑖,𝑡−1 , and 𝐵𝑜𝑛𝑑𝑠𝑖,𝑡−1 represent short-term bank loans (less than one year), long-termbank loans (more than one year), and total bonds outstanding (including convertible bonds (CBs) andwarrant-attached bonds), respectively, for firm i at the end of year t .

Additionally, rs𝑡 , 𝑟𝑙𝑡 ,and min(𝑟𝑐𝑏𝑡−4 , … , 𝑟𝑐𝑏𝑡 ) represent the average short-term prime rate in year t, average long-term prime
rate in year t, and minimum observed coupon rate for convertible corporate bond issued over the last five
years, respectively.
The CHK criterion compares the actual interest payments made by firm 𝑅𝑖,𝑡 with a hypothetical lower∗
bound 𝑅𝑖,𝑡
. The CHK criterion normalizes the interest payment difference using the total outstanding ofborrowing at the beginning of the period, (B𝑖,𝑡−1 = 𝐵𝑆𝑖,𝑡−1 + 𝐵𝐿𝑖,𝑡−1 + 𝐵𝑜𝑛𝑑𝑠𝑖,𝑡−1 + CP𝑖,𝑡−1 ), whereCP𝑖,𝑡−1 represents the commercial paper outstanding for firm i at the beginning of period t, so that theunits are comparable to the interest rate. Using the CHK criterion, the firm is considered zombie if∗∗(𝑅𝑖,𝑡 − 𝑅𝑖,𝑡)/𝐵𝑖,𝑡−1 = 𝑟𝑖,𝑡 − 𝑟𝑖,𝑡
< 0,.

However, FN warn that high-performing firms that receive low interest rates might be considered
zombies according to the CHK criterion. According to the FN, firms should satisfy both a profitability
criterion and a financial support criterion in order to qualify as zombies.
The profitability criterion holds if the earnings before interest and taxes (EBIT)4 fall below the minimum
required interest payment. The following basic concept underpins this criterion: if a firm cannot pay theminimum required interest, a firm is non-performing.
The financial support criterion holds if firms either satisfy the CHK criterion or can obtain new loans. If
current year borrowing exceeds the previous year borrowing (with a threshold for long-term borrowing of 1year), it implies that the firm was able to obtain new loans. When a firm cannot pay the minimum required
interest payment or receives new loans, the firm experiences ever-greening.
FN state that the zombie ratio of listed firms decreases after 2005. However, only a few firms declaredbankruptcy; therefore, most listed zombie firms recovered and became healthy firms5.
We basically identify zombie firms and estimate the zombie ratio using the methods developed by
CHK and FN. However, the FN method is also inadequate because healthy firms that experience temporary

3

CHK use the long-term prime rate, short-term prime rate and coupon rate on convertible corporate bondon the previous fiscal year base. However, we use current year interest rates. As FN presume that CHK
organize financial data and interest rates by calendar year, we organize the database by fiscal year. We thenuse these interest rates for the current year base period.
45

We calculate EBIT as follows: current profit + interest expense – interest income.
FN note that listed zombie firms recovered by downsizing employees and assets.

2

decreases in profits might be identified as zombies. We therefore devised an original zombie identification
method (i.e., a modified FN method). This method is discussed in details in section 3.
We now describe the previous research that addresses ever-greening among SMEs.
Fukuda et al. (2006) analyze whether ever-greening exists in unlisted Japanese firms and argue thatever-greening exists when the debt-asset ratio is greater than 3. They note that only a few firms exhibit
debt-asset ratios greater than 3 and conclude that ever-greening seldom occurs among SMEs.
Sakai et al. (2010) conclude that no unnatural selection or ever-greening occurs among SMEs because theexpected borrowing cost of surviving firms falls below that of the exiting firms and the expected ROA of
the surviving firms exceeds that of the exiting firms.
However, Fukuda et al. (2006) consider only the profit rate and debt-asset ratio of the borrowing firms anddo not consider other firm characteristics. Moreover, they do not identify firms as zombies or non-zombies.
While Sakai et al. (2010) compared surviving firms with exiting firms, they do not analyze remaining
zombie firms that should have exited, such as those considered by CHK.
Therefore, it is important to identify zombie SMEs directly using their financial statements.

2.2 Borrowing and Investment Behaviors of SMEs
In this section, we review the previous research that addresses the borrowing and investment behavior of
SMEs during the recent years in Japan.
A considerable amount of research addresses recent firm investment in Japan. However, no research
investigates recent borrowing and investment behaviors among SMEs from the viewpoint of changes inbank lending, and the soft budget problem. We take account of the bank’s capital adequacy ratio and ratio
of nonperforming loans to proxy for bank soundness, which intrinsically affects the behavior of their client
firms through bank lending.
Ogawa (2003) estimates the firm investment function using firm micro data, and he indicates that bankwillingness to lend affected firm investment significantly and that the severe lending attitudes of financial
institutions deterred investment among small firms. However, the measure of bank willingness to lend is
not constructed from micro data and cannot represent individual banks’ willingness to lend.
In a subsequent study, Ogawa (2008) uses micro data and indicates that a bank nonperforming loan ratio
negatively affects lending attitudes toward their client SMEs and subsequently the rate of change in fixedtangible assets of their client SMEs. However, this study did not utilize the change or changing rate of banklending to client firms.
Fukuda et al. (2007) estimate investment function for SMEs using recent micro data. They use the capital
adequacy ratio and nonperforming loans ratio of the firms’ main banks and conclude that the soundness ofthe banks affected client investment. However, these authors do not consider changes in bank lending.
These studies indicate that a capital crunch occurred and it decreased recent investment, especially among
SMEs. Bank financial health and firm land values affected firm investment through the lending of theirmain banks. However, these investigations did not consider change or changing rate of bank lending
directly for recent years in Japan using SME micro data. Bank soundness would have affected their client3

firms through bank lending. Therefore, it is important to estimate the borrowing and investment functions,
including the change in bank lending, using SME micro data.

3. The Identification of Zombie SMEs
Before estimating the borrowing and investment functions, we first estimate the number of zombie SMEs
in Japan. We identify zombie firms using both the CHK and FN criteria. If we use only the FN criteria, wemight mistakenly identify non-zombie firms as zombie firms if the non-zombie firms experienced
temporary profit decreases6. Similarly, we might mistakenly identify zombie firms as non-zombie firms if
the zombie firms experienced temporary profit increases.
Therefore, we use the following modified FN profit criterion to identify zombie firms (.i.e., we do notchange the FN financial support criterion in this paper).
Where the firms satisfy the following dynamic condition, we say that the firms satisfy the modified FN
profit criterion:∗∑𝑇𝑚=0(𝐸𝐵𝐼𝑇𝑖,𝑡−𝑚 − 𝑅𝑖,𝑡−𝑚
)<0

(2)

Note that when T equals 0, this condition corresponds to the FN profit criterion.
If firms satisfy the modified FN profit and financial support criteria (the CHK criterion that the actualinterest payment falls below the theoretical minimum interest payment or that the firm can obtain a newloan), we consider them zombies. In so doing, we excluded firms that capitalized more than 10 billion yen
to focus on SME zombies.
We identify zombie firms by the CHK, FN and modified FN with T=1-9 criterion, respectively and thencalculate the zombie ratios. The results are displayed in Table 1. Using the CHK criterion, between 30-40%
of SMEs are zombie firms. Using the FN criteria, we regard approximately 14% of firms as zombies in1999 and thereafter the zombie ratio exhibits a downward trend. Moreover, the zombie ratios after T=3
hovers around 4-7% stably. Therefore, when SMEs satisfy both modified FN profit criterion for T=3 andthe financial support criterion, we consider these SMEs to be zombie firms by modified FN criterion7.
Judging from the proportion of zombie firms in Table 1, we can confirm that between 4-7% of SMEs werezombies even if we estimate zombie firms conservatively. This result is quite different from the previous
studies, which reported that there were only a few zombie firms among SMEs. Figures 1 to 4 compares the
FN and modified FN criteria zombie firm ratios by industry and equity capital.
Figure 1 provides the FN zombie ratio by equity capital and Figure 2 provides the modified FN
zombie ratio by equity capital. These figures indicate that there are many zombie firms among SMEs
capitalized at less than 10 million yen. Between 9-20% of the SMEs capitalized at less than 10 million yen
are zombie firms by the modified FN zombie criteria. Using the modified FN zombie criteria, thezombie ratio of SMEs capitalized at between 10 million and 100 million yen is approximately 5%.
Additionally, the zombie ratio of SMEs capitalized at between 100 million 1 billion yen is in the range of
6

For example, non-zombie firms may experience temporary losses.
In the subsequent section it turns out that the estimation results of borrowing and investment functions
were unaltered regardless of the choice of T in identifying FN zombie firms. We therefore present only
modified FN zombie firms for T=3.
74

4-7%. These ratios are relatively low and have similar values. In addition, using the modified FN criteria,
the zombie ratio of firms capitalized at more than 1 billion yen is in the range of 5-13%. The zombie ratio
for these large firms is relatively high.
Interestingly, the results above indicate that ever-greening occurred even in SMEs. Therefore, itsuggests that the Regional Banks, Second Association of Regional Banks, Sinkin Banks and Credit Unions,known as regional financial agencies, may have protected their client firms. Moreover, the credit-guarantee
system (Sinyo Hosho Seido) of local governments would have protected regional SMEs. Hoshi and
Kashyap (2010) note that the Japanese government required banks receiving public capital to increase
lending to SMEs. This forced lending may have increased non-performing loans. A white paper on SMEs inJapan (1999) observed that smaller firms faced more permissive lending in 1998 as a Japanese government
took countermeasures against the credit crunch. This policy might explain why many SMEs
capitalized at less than 10 million yen are identified as zombies.
Figures 3 and 4 depict the zombie ratios by equity capital using the FN criteria and modified FN criteria,
respectively. Many zombie firms existed in the finance and real estate industries. The figures hint that
disposition of non-performing loans did not progress in these industries. The zombie ratio in the financeindustry increased after the Lehman Brothers bankruptcy. However, the zombie ratio in the constructionindustry is relatively low. It seems that in the construction sector, banks bankrupted many non-performing
firms, and ever-greening did not occur. Moreover, we also observe high zombie ratios in other industries.
These industries exhibit zombie ratios in the range of 3-12%, which decrease over time, as indicated in
Figure 4.
Note that the zombie ratio is extremely high in March 1999, as indicated in Figures 1 and 3. The special
credit-guarantee system, established for saving troubled SMEs, might have temporarily increased thenumber of zombie firms8. The special credit-guarantee system was accessed on over 1,720,000 occasions
for a total of 29 trillion yen. Matsuura and Hori (2003) demonstrated that firms that used the special
credit-guarantee system tended to suffer from excessive debt and low profits. Many of these firms might beidentified as zombies9. The zombie ratio exhibits a downward trend until 2007, which indicates that many
zombie firms eventually became healthy firms.
There is a caveat in interpreting our results. Our dataset is a balanced panel consisting of surviving firms,
so that these data do not include failed firms by definition, which were likely zombie firms prior tobankruptcy10. A dataset that included failed firms would have enabled us to analyze why banks protect
rather than bankrupt their non-performing clients. If the information of whether firms had used the special
credit-guarantee system were available, we could estimate the effect of this policy measure on zombie ratio
and bankruptcies.

89This was kindly pointed out by Yasuo Goto.
Access to the firm micro data used by Matsuura and Hori (2003) would enable them to calculate thezombie ratio of firms that utilized the special-credit guarantee system.
10
If the data had included failed firms, we could have checked whether the zombie ratio was higher thanthe current estimate. Hoshi (2006) states that listed zombie firms tend to exit.

54.

Models of Borrowing and Investment Behaviors of SMEs

To investigate the borrowing and investment behaviors of SMEs, notably zombie SMEs, we estimate thefollowing borrowing and investment equations, separately for all firms, FN zombie firms and modified FN
zombie firms.

4.1 Specification of Borrowing Function
The borrowing function to be estimated is specified as follows:
𝜋𝑖,𝑡𝐷𝑒𝑏𝑡𝑖,𝑡−1
𝐿𝑎𝑛𝑑𝑖,𝑡−1∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡⁄𝐴𝑠𝑠𝑒𝑡= 𝛼0 + 𝛼1 ∆𝑙𝑛𝑆𝑖,𝑡 + 𝛼2
+ 𝛼3
+ 𝛼4
𝑖,𝑡−1
𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1
𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1
𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡−1+𝛼5 𝐵𝑎𝑛𝑘𝐶𝑎𝑝 + 𝛼6 𝐵𝑎𝑛𝑘𝑁𝑃𝐿 + 𝛼7 𝑦𝑒𝑎𝑟𝑑𝑢𝑚𝑚𝑦 + 𝛼8 𝐼𝑛𝑑𝑢𝑠𝑡𝑖𝑎𝑙𝑑𝑢𝑚𝑚𝑦 + 𝑢𝑖,𝑡(3)

where 𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡 is the sum of the real value of long-term and short-term borrowing.11 𝑆𝑖,𝑡 , 𝜋𝑖,𝑡 ,Debt 𝑖,𝑡 , 𝐿𝑎𝑛𝑑𝑖,𝑡 and, 𝑢𝑖,𝑡 represent the real values of sales, real values of current profit, long- andshort-term borrowing and bonds, land values, and error terms.
𝐵𝑎𝑛𝑘𝐶𝑎𝑝 and 𝐵𝑎𝑛𝑘𝑁𝑃𝐿 are the capital adequacy ratios and non-performing loans (NPL) ratios The
firms located in the same prefecture take the same values of 𝐵𝑎𝑛𝑘𝐶𝑎𝑝 and 𝐵𝑎𝑛𝑘𝑁𝑃𝐿 since we canidentify the firm location, but we cannot identify the main bank of individual firms. Therefore we use thecapital adequacy ratios and non-performing loans (NPL) ratios of the Regional Banks and Second
Association of Regional Banks that are available on prefecture basis.12.
We include the land-borrowing ratio as one of the explanatory variables because firm land is often used
as collateral when firms borrow from banks, especially in SME financing. 13 Land collateral mitigates
asymmetric information between firms and banks. Therefore we expect the sign of 𝛼4 to be positive for all
firms. However, we expect that 𝛼4 is negative or statistically insignificant for zombie firms because adecrease in the collateral value might cause a soft budget problem. According to Berglof and Roland (1997),
hard budget constraint equilibrium occurs when collateral value exceeds net verifiable returns fromrefinancing. On the other hand, when collateral value falls below net verifiable return refinancing, the bankhas no incentive to activate the client and refinance a poor project.
We expect that 𝛼1 is positive because firms require borrowing to expand their economic activities. We
expect that 𝛼2 is negative. When an asymmetric information problem exists, the internal financing cost
will be lower than the external financing cost. Hence, the firms would raise the funds internally rather thanborrowing from banks.
Comparison of the value of 𝛼3 between all firms and zombie firms might be interesting because itcorresponds to the comparison of the severity of over-leveraging between the entire sample and zombie
firms.
11

We use total borrowing rather than bank borrowing since the latter figure is not available in our datasetof SMEs.
12
Details on the construction of capital adequacy ratios and NPL ratios on prefecture basis are provided inAppendix 3.
13
Appendix 2 shows the procedure to calculate land value.

64.2 Specification of Investment Function
Now we turn to estimation of the marginal q-type investment function specified as follows:𝐼𝑖,𝑡∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡𝐶𝑎𝑠ℎ𝑓𝑙𝑜𝑤𝑖,𝑡𝐷𝑒𝑏𝑡⁄𝐾= 𝛽0 + 𝛽1⁄𝐴𝑠𝑠𝑒𝑡+ 𝛽2 𝑀𝑞𝑖,𝑡 + 𝛽3 𝐾
+ 𝛽4 𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1
𝑖,𝑡−1
𝑖,𝑡−1
𝑖,𝑡−1
𝑖,𝑡−1
+𝛽5 𝑦𝑒𝑎𝑟𝑑𝑢𝑚𝑚𝑦 + 𝛽6 𝐼𝑛𝑑𝑢𝑠𝑡𝑖𝑎𝑙𝑑𝑢𝑚𝑚𝑦 + 𝑢𝑖,𝑡(4)

where 𝐼𝑖,𝑡 , 𝐾𝑖,𝑡 , 𝑀𝑞𝑖,𝑡 , and 𝐶𝐹𝐿𝑂𝑊𝑖,𝑡 are investment, capital stock, marginal q and cash flow,
respectively14.
We calculated 𝑀𝑞𝑖,𝑡 as follows. We assumed that the discount rates and profit rates of the firm follow
the random walk.
𝑟𝑖,𝑡+1 = 𝑟𝑖,𝑡 + 𝑢𝑖,𝑡+1
𝜋𝑖,𝑡+1 = 𝜋𝑖,𝑡 + 𝑣𝑖,𝑡+1(5)where 𝜋𝑖,𝑡 , and 𝑟𝑖,𝑡 , are the profit rates and discount rates, respectively, and 𝑢𝑖,𝑡+1 and 𝑣𝑖,𝑡+1 represent
stationary white noise. The discount rate was calculated by dividing the interest paid and the discount on
note by the outstanding of long- and short-term borrowing, bonds and discount bills at the end of previous
year. The profit rate was calculated by dividing the operating profit by the capital stock at the end of
previous year.
Given the above assumptions, 𝑀𝑞𝑖,𝑡 is expressed as follows:

Mqi ,t  i ,t 1  ri ,tPt I ri ,t  (6)where δ and 𝑃𝑡𝐼 represent the capital depreciation rate, which is also used to construct capital stock series
as reported by Hayashi and Inoue (1991) and the investment goods deflator reported by the Bank of Japan.
The cash flow is calculated as current income plus depreciation minus directors' bonus minus interim
dividend. We expect that 𝛽1 , 𝛽2 , 𝛽3 are positive for the entire sample of firms. When marginal q,borrowing and cash flow increase, investment would expand. When the Modigliani-Miller theorem is held,
borrowing and cash flow do not affect investment. However, when asymmetric information exists between
firms and banks, banks cannot monitor their clients perfectly and thus the external financing premium does
exist. Moreover, we expect that

𝛽1 is statistically insignificant for FN and modified FN zombie firms

because new loans would be allocated to inefficient use, not efficient investment under ever-greening
among zombie firms.

5. Descriptive Statistics
Tables 2 to 4 depict the descriptive statistics for all variables. We excluded sample firms whose absolutevalues of ∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 , ∆𝑙𝑛𝑆𝑖,𝑡 and 𝜋𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 exceeded 1, 𝐼𝑖,𝑡 ⁄𝐾𝑖,𝑡−1 exceeded 2,
14

The calculations for investment and capital stock series are detailed in Appendix 1.

7

𝑀𝑞𝑖,𝑡 exceeded 20, and 𝐿𝑎𝑛𝑑𝑖,𝑡−1 ⁄𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡−1 exceeded 10 as outliers. Moreover, we excluded
firms capitalized at more than 10 billion yen as large firms.
First, we show the descriptive statistics of the explanatory variables of the borrowing function. Notethat ∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 decreases over time for all firms. To the contrary, for FN and modified
FN zombie firms, ∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 exhibited an increasing trend because they satisfied thefinancial support criterion and experienced ever-greening.
As for 𝐿𝑎𝑛𝑑𝑖,𝑡−1⁄𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡−1, it decreased for all firms as well as FN and modified FN zombie
firms. These results indicate that the collateral value of land decreased. Comparing the𝐿𝑎𝑛𝑑𝑖,𝑡−1 ⁄𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡−1 of FN and modified FN zombie firms with that of all firms, the former isapproximately 30-50% of the latter. This fact might suggest that zombie firms borrowed more than thecollateral land value or received ever-greening. The soft budget theory proposed by Berglof and Roland(1997) implies that ever-greening occurs when the value of collateral decreases. As the value of collateraldecreases, the liquidation value decreases. This reduces lenders’ incentives to bankrupt non-performing
firms and leads to ever-greening. These results imply that zombie firms might experience soft budget
problems. If the soft budget hypothesis is held, the land-borrowing ratio of bankrupt firms would be higher
than that of zombie firms.
For all firms, ∆𝑙𝑛𝑆𝑖,𝑡 and 𝜋𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 are positive, while for FN and modified FN zombie firms,
these values are negative. These results indicate that healthy firms increased sales and that profit rates are
positive while non-performing zombie firms could not increase sales and profit rates are negative15.
Notably, the debt-asset ratios for FN and modified FN zombie firms were over 40% and did not decrease.
The debt-asset ratio for all firms was approximately 30% in 2000 and exhibits a decreasing trend. These
results suggest that healthy SMEs succeeded in reducing their debt; however, zombie firms are stillburdened with debt.
Next, we outline the descriptive statistics for investment. 𝐼𝑖,𝑡 ⁄𝐾𝑖,𝑡−1 is relatively stable, irrespective offirm type. We do not observe differences in the 𝐼𝑖,𝑡 ⁄𝐾𝑖,𝑡−1 for either the entire sample or the zombie firmsubsample, except for FN zombie firms in 2000 and 2001. These results indicate that there was noremarkable difference in investment rate across firms over time.
The marginal q and cash flows for all firms and zombie firms are very different. The marginal q and cash
flow of zombie firms are lower than those of all firms. In particular, the marginal q and cash flow of FN
zombie firms are extremely low. It might reflect our definistion of zombie firms because we imposed a
profit criterion on zombie firms and marginal q and cash flow are calculated from the operating profit. In
particular, FN zombie firms are considered zombie firms when their EBIT is under the minimum required
interest payment even for only one year, in which case, the low profit firms are most likely concentrated
within FN zombie firms.

6. Estimation Results

15

We imposed a profit criterion on zombie firms, and thus 𝜋𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 of zombie firms are low.

8

We provide the estimation results of the borrowing function in Table 5. We estimated the borrowing
function using fixed effects, random effects, OLS and robust OLS methods. The various statistics, such as F
test, Hausman test and Blues Pegan Lagrange Multiplier (BPLM) test, suggest that the fixed effect results
are acceptable for all firms, FN zombies and modified FN zombies. Thus, we present only the fixed
effects results.
The estimated coefficient of ∆𝑙𝑛𝑆𝑖,𝑡 is significant and positive for the entire sample of firms. Moreover,the coefficients of 𝜋𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 and 𝐷𝑒𝑏𝑡𝑖,𝑡−1⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1are significant and negative. The coefficientsof ∆𝑙𝑛𝑆𝑖,𝑡 and 𝜋𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 satisfy the expected signs.
As for the coefficient of 𝐿𝑎𝑛𝑑𝑖,𝑡−1 ⁄𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡−1, it is positive and significant at the 5% level for all
firms, while it is insignificant for FN and modified FN zombies. These results demonstrate that decreases
in the value of land collateral reduced borrowing from banks among healthy firms 16. Note that theland-borrowing ratios of zombie firms are lower than those of all firms. These results for the zombies
support the soft budget hypothesis proposed by Berglof and Roland (1997). The value of land collateral for
non-performing firms decreased, and the liquidation value decreased, so that their lenders could not recovera profit by selling the land collateral. The banks therefore had no incentive to bankrupt firms. If the softbudget hypothesis is held, the value of land collateral of bankrupt firms, which is not included in ourdataset, increases. As these liquidation values increased, banks could eliminate non-performing loans andgained profits by selling the land. Therefore, we could not obtain a significant coefficient ofland-borrowing ratio for FN and modified FN zombies.
In Table 6, we show the estimation results of the Mq-type investment function. We used the panel
instrument variable method. The endogenous variable is ∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1. The instruments are
the explanatory variables of the investment function lagged by one year and the current andone-year-lagged explanatory variables for the borrowing function. The F test and Hausman test statistics
indicate that the random effects instrumental variable method is appropriate for the entire sample of firms
and the FN zombie firms, while the fixed effects instrumental variable method is adopted for modified FN
zombies.
First, note that the coefficient estimate of ∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 is positive and significant at the
1% level for all firms, FN zombies and modified FN zombies. Moreover, the absolute value of thecoefficient is much larger for all firms. According to the soft budget hypothesis, most of the ever-greening
received by zombie firms would be allocated to repay their main banks and ever-greening would notaffect investment so much. Thus our results support the soft budget hypothesis. Note that for all firms,∆𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡 ⁄𝐴𝑠𝑠𝑒𝑡𝑖,𝑡−1 and 𝐿𝑎𝑛𝑑𝑖,𝑡−1 ⁄𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡−1 exhibits a decreasing trend. These results
imply that the decrease in land values for healthy firms decreased borrowing and investment.
As for the effect of marginal q on investment, the coefficient estimate of the marginal q is insignificant
for modified FN zombie firms. Moreover, the coefficient estimate of the marginal q is negative andNote that 𝐿𝑎𝑛𝑑𝑖,𝑡−1 ⁄𝐵𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔𝑖,𝑡−1 for all firms and zombie firms decreases over time as indicated inTable 3.
16

9significant for FN zombies17. These results indicate that ever-greening received by modified FN zombie
firms increased investment but these investments were not necessarily profitable or productive18.
For all firms, the coefficients of marginal q and cash flow are positive and significant at the 1% level. The
coefficients of cash flows are also significant and positive for FN and modified FN zombie firms.
As for the effect of debt-asset ratio on investment, the effects are mixed. The coefficient of the debt-asset
ratio is insignificant for FN zombies, while it is positive and significant for all firms and modified FN
zombies. The absolute value of the coefficient is much larger for modified FN zombies and it suggests thatmodified FN zombie firms aggressively allocated debt to investment.
7. Concluding Remarks
We estimated the extent to which zombie SMEs existed in Japan and examined their borrowing andinvestment behavior using balanced panel data of SMEs edited by Tokyo Shoko Research.
We confirm that many zombie firms existed among SMEs, especially among firms capitalized at less than
10 million yen at a modest estimate. One reason for this observation may be that regional financial agencies
and local government credit-guarantee systems (Sinyo Hosho Seido) protected non-performing SMEs ineach region.
Furthermore, recent decreases in land values induced a credit crunch in healthy firms. However, thiscrunch did not lead to a decrease of borrowing among zombie firms. This result suggests that theever-greening of zombie firms was prevalent. Moreover, the credit crunch reduced productive or profitable
investment among healthy firms, while ever-greening induced non-productive andnon-profitable

investment among zombie firms.
Given these results, it is a task for economists to analyze how ever-greening among zombie firms affected
their productivity compared to the productivity of healthy firms19. In particular, we should investigate thedeterminants of the productivity of zombie firms. Hayashi and Prescott (2002) claim that the productivityof the Japanese economy slowed down during the lost decade. This slowdown might be attributable tonon-efficient investment induced by ever-greening and zombie SMEs.
If we could obtain data from bankrupt firms and identify each firm’s major lenders, we could analyze
why banks protected zombie firms or bankrupted non-performing firms. In addition, we would be able tocalculate the zombie ratio of SMEs by incorporating the bankrupt incidents of SMEs. It would be importantto account for the factors, such as the credit-guarantee system (Sinyo Hosho Seido), bank type, bank17

According to FN criterion firms that experienced temporarily low profits and were otherwise healthy areclassified as zombie firms, so that a low marginal q might be correlated with high investment for FN
zombies.
18
One might cast doubt on the reliability of computed marginal q for FN zombie firms because they reflect
temporarily low evaluation. However, we excluded firms that experienced temporary losses from themodified FN zombie category. Therefore, the criticism above on marginal q is not held for the modified
FN zombie.
19
Ahearne and Shinada (2005) state that total borrowing in the non-traded goods sectors, such as
construction, retail and wholesale, rose sharply during the 1990s and these sectors experienced low
productivity growth. They conclude that ever-greening of zombie firms in non-traded goods sectors
induced low productivity growth. However, they did not identify zombie firms from financial statements.

10

soundness, and trade terms with main lenders. Moreover, if the soft budget theory is held, fall of land value
induced ever-greening. Thus, land values determine the choice of ever-greening or bankruptcy. It is one ofthe policy goals that regional financial agencies and governments protect local client firms to support
regional economies20. Exploration into the banks’ decision to bankrupt or ever-greening client SMEs is our
future avenue for research.

20

Recent SME data would allow us to calculate the effect of the Financial Service Agency’s circular notice,which requested that banks protect their clients after the Lehman bankruptcy.

11

Appendices

Appendix 1. Construction of Investment and Capital Stock Series

We calculated investment series as follows. First, we calculated the real capital stock by dividing thecapital stock by the capital goods deflator. Capital goods deflators are included in the “Index by Stage of
Demand and Use” published by the Bank of Japan. We represent capital stock as 𝐾𝑡𝑟𝑒𝑎𝑙 .
Second, we compute 𝐼𝑡 as follows:𝑟𝑒𝑎𝑙
𝐼𝑡 = 𝐾𝑡𝑟𝑒𝑎𝑙 − 𝐾𝑡−1
+ 𝐶𝐼𝑃𝑡 + 𝐷𝑒𝑝𝑡where CIP represents construction in progress and Dep represents depreciation.
Finally, we obtained the capital stock series following the perpetual inventory method:
𝐾𝑡 = (1 − 𝛿)𝐾𝑡−1 +𝐼𝑡
where 𝛿 is the depreciation rate reported by Hayashi and Inoue (1991). Benchmark stock is the capitalstock in 1998.

Appendix 2. Construction of Land Stock Series

We calculated the current value of land from book values of land as follows. First, we estimated the bookvalues of land per representative firm for four groups categorized by equity capital, using Surveys for the
Financial Statements Statistics of Corporations (Hojin Kigyo Tokei Nenpo) reported by Ministry of
Finance. Our four firm groups categorized by equity capital are those less than 10 million yen, thosebetween 10 million yen and 100 million yen , those between 100 million yen and 1 billion yen and thosemore than 1 billion yen .

Second, we estimated the current value per representative firm for each firmgroup by utilizing the Corporations Survey on Land (Hojin Tochi Kihon Chosa) compiled by the Ministryof Land, Infrastructure, Transport and Tourism. As of January 1, the data for 1998, 2003 and 2008 areavailable. Thus, we use the 1998 survey to compute the 1998-2000 current values. Similarly, we use the2003 survey to compute the 2001-2005 current values and the 2008 survey to compute the 2006-2007
current values.
Third, we evaluated the current book value ratio for each firm group as:
current book value ratio𝑡 =𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒𝑡
𝑏𝑜𝑜𝑘 𝑣𝑎𝑙𝑢𝑒𝑡

Finally, we calculated each firm’s land assets at current value by multiplying each firm’s book value ofland by the current book value ratio above.

Appendix 3 Construction of the Capital Adequacy Ratio and12Non-Performing Loan Ratio by Prefecture

We calculated a measure for capital adequacy ratio and non-performing loan ratio of the Regional Banks(RB) and Second Association of Regional Banks (SARB) by prefecture.
We calculated each prefecture’s capital adequacy ratio as follows:
capital adequacy ratio by prefecture𝑡 =∑𝑛𝑖=1({𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑎𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑟𝑎𝑡𝑖𝑜 − 𝐵𝐼𝑆 𝑟𝑎𝑡𝑖𝑜) × 𝐿𝑜𝑎𝑛}𝑖,𝑡∑𝑛𝑖=1 𝐿𝑜𝑎𝑛𝑖,𝑡where i is the bank, t is the year and n is the number of RBs and SARBs in each prefecture. The
𝐵𝐼𝑆 𝑟𝑎𝑡𝑖𝑜 is 8% when the bank is a bank operating international business. The 𝐵𝐼𝑆 𝑟𝑎𝑡𝑖𝑜 is 4% when thebank is operating domestic business only.
We calculate each prefecture’s non-performing loan ratio as follows:.
Non Performing Loan ratio by prefecture𝑡 = ∑=𝑁𝑜𝑛 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑖,𝑡𝐿𝑜𝑎𝑛𝑖,𝑡(× 𝑛)∑𝑖=1 𝐿𝑜𝑎𝑛𝑖,𝑡𝐿𝑜𝑎𝑛𝑖,𝑡𝑖=1
𝑛∑𝑛𝑖=1 𝑁𝑜𝑛 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑖,𝑡∑𝑛𝑖=1 𝐿𝑜𝑎𝑛𝑖,𝑡A 𝑁𝑜𝑛𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛 is a risk-management loan.

13Reference
Ahearne, A. G. and N. Shinada (2005) “Zombie Firms and Economic Stagnation in Japan”, International
Economics and Economic Policy, Vol. 2, pp. 363-381.
Berglof, E. and G Roland (1997) “Soft Budget Constraints and Credit Crunches in Financial Transition”,
European Economic Review. Vol. 41, pp. 807-817.
Bernanke, B. and A. Blinder (1998) “Credit, Money, and Aggregate Demand”, American Economic Review.
Vol. 78, pp 435-439.
Caballero, R., T. Hoshi and A. Kashyap (2008) “Zombie Lending and Depressed Restructuring in Japan”,
American Economic Review. Vol. 98, pp.1943-1977.
Fukuda, S., M. Kasuya and J, Nakajima (2006) “Bank Health and Lending in Japan: Evidence from
Unlisted Companies under Financial Deteriorating Distress”, Journal of the Asia Pacific Economy. Vol. 11,
pp. 482-501.
--,-- and -- (2007) “Hijyoujyou Kigyou no Setsubitohshi no Kettei Yohin : Kin-yu Kikan no Kenzensei
Oyobi Kajyou Saimu Mondai no Eikyou”, Kin-yu no Kinohuzen. Hayashi., F., eds. Keiso Shobo.(InJapanese.)
Hoshi, T., A. Kashyap and D. Scharfstein (1991) “Corporate Structure, Liquidity, and Investment: Evidence
from Japanese Industrial Group”, Quarterly Journal of Economics. Vol.106,pp.33-60.
Fukuda, S. and J. Nakamura (2011) “Why Did 'Zombie' Firms Recover in Japan?” World Economy. Vol. 34,
pp. 1124-1137.
Hayashi, F. and T. Inoue (1991) “The Relation between Firm Growth and Q with Multiple Capital Goods:
Theory and Evidence from Panel Data on Japanese Firms”, Econometrica Vol.59, pp.731-753.
Hayashi, F. and E.C. Prescott (2002) “The 1990s in Japan: A Lost Decade. Review of Economic Dynamics”,
Vol.5,pp.206-235.
Hoshi, T. and A. Kashyap (2004) “Japan’s Financial Crisis and Economic Stagnation”, Journal of
Economic Perspectives, Vol.18, pp.3–26.
Hoshi, T.(2006) “Economics of the Living Dead”, Japanese Economic Review. Vol. 57, pp. 30-49.
--and -- (2010) “Will the U.S. Bank Recapitalization Succeed? Eight Lessons from Japan”, Journal of
Financial Economics. Vol. 97, pp. 398-417.
Hosono, K. and A. Masuda(2005) “Bank Health and Small Business Investment: Evidence from Japan”,
RIETI Discussion Paper Series 05-E-030.
Matsuura, K. and M. Hori (2003) “Special Credit Guarantee and Restructuring of Small Firms”, ESRI
Discussion Paper Series No.50. (In Japanese. With English summary.)
Minamihashi, N.(2011) “Credit Crunch Caused by Bank Failures and Self-Selection Behavior in Lending
Markets” Journal of Money, Credit, and Banking. Vol. 43, pp. 133-161.
Motonishi, T. and H. Yoshikawa (1999) “Causes of the Long Stagnation of Japan during the 1990s:
Financial or Real?”, Journal of the Japanese and International Economies. Vol. 13, pp. 181-200.
Nagahata, T. and T. Sekine (2005) “Firm Investment, Monetary Transmission and Balance-Sheet Problems
in Japan: An Investigation Using Micro Data”, Japan and the World Economy. Vol. 17, pp. 345-369.
Ogawa,K., K. Kitasaka, H. Yamaoka, and Y. Iwata (1996) “Borrowing Constraints and the Role of Land

14

Asset in Japanese Corporate Investment Decision”, Journal of the Japanese and International Economies.
Vol. 10, pp. 122-149.
Ogawa, K. and Suzuki, K. (1998) “Land Value and Corporate Investment: Evidence from Japanese PanelData” Journal of the Japanese and International Economies. Vol. 12, pp. 232-249.
--and --(2000) “Demand for Bank Loans and Investment under Borrowing Constraints: A Panel Study ofJapanese Firm Data” Journal of the Japanese and International Economies. Vol.14, pp.1-21.
Ogawa, K. and S. Kitasaka (2000) “Bank Lending in Japan: Its Determinants and Macroeconomic
Implications. Crisis and change in the Japanese financial system”, Hoshi, T., Patrick, H., eds., Innovationsin Financial Markets and Institutions: Kluwer Academic. pp. 159-199.
Ogawa, K. (2003) “Financial Distress and Corporate Investment: The Japanese Case in The 90s” ISER
Discussion Paper 584.
--(2008) “Main Bank Health and Activities of Firm Customers: An Empirical Study Based on Micro Data
of Small Firms”, Economic Review (Keizai Kenkyu). Vol. 59, pp. 1-15. (In Japanese. With English
summary.)
Peek, J. and E.S. Rosengren (2005) “Unnatural selection: Perverse incentives and themisallocation of credit in Japan”, American Economic Review Vol. 95, pp.1144–1166.
Sekine, T. and K. Kobayashi and Y.Saita (2003) “Forbearance Lending: The Case of Japanese Firms”,
Monetary and Economic Studies, Vol. 21, pp.69–92.
Sakai, K., I., Uesugi, and T. Watanabe(2010) “Firm Age and the Evolution of Borrowing Costs: Evidence
from Japanese Small Firms” Journal of Banking and Finance. Vol. 34, pp. 1970-1981.
Small and Medium Enterprise Agency in Japan (1999) ‘A white paper on SMEs in Japan (1999)’
Suzuki, K. (2001) Setsubi Tohshi to Kin-yu, Tokyo University Press. (In Japanese.)

15

Figure 1
FN's Zombie Ratios by Equity Capital
0.3
0.25
0.2
0.15
0.1
0.05
0
1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Less than ￥10million
Between ￥10million and ￥100million
Between ￥100million and ￥1billion
More than ￥1billion

Figure 2
Modified FN's Zombie Ratios by Equity Capital
0.3
0.25
0.2
0.15
0.1
0.05
0
2002

2003

2004

2005

2006

Less than ￥10million
Between ￥10million and ￥100million
Between ￥100million and ￥1billion
More than ￥1billion

16

2007

2008

Figure3
FN's Zombie Ratios by Industry
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1999

2000

2001

2002

2003

2004

2005

construction

manufacturingtransportation

retailesalse

financereal estate

2006

2007

2008

wholesalse

Figure 4
Modified FN's Zombie Ratios by Industry
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2002

2003

2004

2005

2006

construction

manufacturingtransportation

retailesalse

financereal estate

17

2007

2008

wholesalse

Table 1 Estimates of Zombie Ratios by Three Criteria
year
CHK's criterion T=0(FN's criterion) T=1
T=2
T=3(Modified FN's criterion) T=4
T=5
T=6
T=7
T=8
T=9
1999
0.41900.1409 .
.
.
.
.
.
.
.
2000
0.39360.0890 0.1050
.
.
.
.
.
.
.
2001
0.42500.0807 0.0732 0.0816 .
.
.
.
.
.
.
2002
0.42900.1039 0.0836 0.0752
0.0794 .
.
.
.
.
.
2003
0.42980.0744 0.0833 0.0734
0.0678 0.0702 .
.
.
.
.
2004
0.40370.0551 0.0622 0.0645
0.0575 0.0523 0.0542 .
.
.
.
2005
0.39500.0490 0.0433 0.0490
0.0537 0.0518 0.0457 0.0471 .
.
.
2006
0.40300.0484 0.0442 0.0404
0.0456 0.0484 0.0470 0.0437 0.0432 .
.
2007
0.40500.0590 0.0492 0.0468
0.0468 0.0445 0.0515 0.0482 0.0459 0.0435 .
2008
0.37590.0727 0.0610 0.0521
0.0460 0.0460 0.0455 0.0511 0.0483 0.0450 0.0427
Total
0.40790.0773 0.0672 0.0604
0.0567 0.0522 0.0488 0.0475 0.0458 0.0443 0.0427

18

Table 2 Descriptive statics 1⊿Borrowing/Asset,t-1⊿lnS
π /Asset,t-1
All Firms
FN's
Modified FN's All Firms
FN's
Modified FN's All Firms
FN's
Modified FN's
year
ZombieZombieZombieZombieZombieZombie1999 Mean
0.01000.0198-0.0371-0.08700.0241-0.0162
S.D.
0.07570.08440.14180.16220.04080.03402000 Mean
0.00500.02160.0116-0.0401
0.0305-0.0105
S.D.
0.08940.12450.13530.15180.04030.0226
2001 Mean-0.0009
0.0186
0.04870.01160.0326-0.0152
S.D.
0.08400.08850.14320.15600.0441
0.0611
2002 Mean
0.00090.03350.0234-0.0215-0.0759
0.00370.0254-0.0193-0.0089
S.D.
0.08160.1006
0.09970.14970.17940.18300.04100.0472
0.0533
2003 Mean-0.0126-0.0011-0.01280.0136-0.0248-0.00450.0303-0.0140-0.0017
S.D.
0.07900.11450.10810.14460.17570.18030.03950.03220.0388
2004 Mean-0.01440.0063-0.0060
0.0282-0.0273
0.00640.0354-0.0127
0.0032
S.D.
0.09100.07340.09040.14390.16410.1771
0.04420.03130.0326
2005 Mean-0.02130.0044-0.0230
0.0358-0.0361
0.01190.0399-0.01080.0112
S.D.
0.0816
0.06670.08580.14000.15520.16150.04230.02870.0467
2006 Mean-0.01400.01020.00690.0281-0.02390.01000.0416-0.0171
0.0022
S.D.
0.08310.10420.08020.12660.11530.10480.04400.05210.0551
2007 Mean-0.01130.0083-0.0036
0.0289-0.02650.00140.0418-0.0185-0.0048
S.D.
0.08530.11490.11720.13770.17090.20500.04840.0466
0.05742008 Mean-0.00220.01430.01560.0188-0.0312-0.00440.0378-0.0175-0.0095
S.D.
0.08140.08700.10320.13180.11090.10100.04300.03950.0522
Total
Mean-0.0059
0.01590.00070.0151-0.04360.00330.0337-0.0154-0.0015
S.D.
0.08380.09830.09970.14200.16090.1661
0.04320.0406
0.048619

Table 3 Descriptive statics 2
Debt,t-1/Asset,t-1
Land,t-1/Borrowing,t-1
All Firms
FN's
Modified FN's All Firms
FN's
Modified FN's
year
ZombieZombieZombieZombie1999 Mean
0.34860.47050.9391
0.4926
S.D.
0.22270.24561.3641
0.66352000 Mean
0.35890.5096
0.96810.5729
S.D.
0.22150.26691.40511.1452
2001 Mean
0.35090.4951
0.95880.5822
S.D.
0.21550.27351.36640.8998
2002 Mean
0.34150.45580.48330.70620.43830.4558
S.D.
0.21600.25090.2593
1.1594
0.94290.80392003 Mean
0.34920.45210.50630.78980.46270.4573
S.D.
0.21720.26620.2752
1.22280.67940.7184
2004 Mean
0.34470.42670.47540.74250.48250.4718S.D.
0.21400.28500.27771.13020.80271.04132005 Mean
0.33230.44270.45990.80700.66570.4296
S.D.
0.21110.26110.2672
1.15281.40750.9862
2006 Mean
0.31730.4656
0.41580.87830.50270.4470S.D.
0.20940.25500.27561.28510.89800.8831
2007 Mean
0.30390.46730.44530.86800.48570.4506
S.D.
0.20510.26050.2736
1.3122
0.79280.83782008 Mean
0.29040.42660.45400.8882
0.49390.2734
S.D.
0.20390.26400.27251.38020.93320.4543Total
Mean
0.33450.46450.46730.85510.51020.4322
S.D.
0.21490.26120.2712
1.28460.90460.837120

Table 4 Descriptive statics 3
I/K,t-1
Mq
Cashflow/K,t-1
All Firms
FN's
Modified FN's All Firms
FN's
Modified FN's All Firms
FN's
Modified FN's
year
ZombieZombieZombieZombieZombieZombie1999 Mean
0.2016
0.19232.6224-1.10900.2238-0.1344
S.D.
0.26920.2502
4.60744.44730.51250.6787
2000 Mean
0.18350.2856
3.3348-0.65710.28740.0639
S.D.
0.23290.3443
4.57963.72900.3621
0.46232001 Mean
0.19430.33043.4147-0.83360.21950.0909
S.D.
0.26030.39524.55673.56690.53780.8797
2002 Mean
0.17620.20020.21232.7992-1.4366
0.08980.1997-0.0752
0.1137
S.D.
0.22770.22430.24334.58364.47754.03340.52900.93460.8378
2003 Mean
0.15240.13920.14093.2103-0.43850.47340.22490.07980.1172
S.D.
0.17900.14140.15294.43084.22544.33870.44130.80940.9021
2004 Mean
0.16000.15660.13833.5763-0.7429
0.34370.2467-0.00110.0745
S.D.
0.20570.1416
0.16544.60864.71693.58210.36020.40050.29932005 Mean
0.16540.16180.11893.9471-0.5545
0.48310.2334-0.01340.0805
S.D.
0.19770.15650.1611
4.67723.01213.32170.51400.40730.4227
2006 Mean
0.17340.18690.1424
3.7869-1.5599-0.20960.2346-0.1505-0.0661
S.D.
0.19810.24180.18754.85434.66414.70440.5274
0.9683
0.9762
2007 Mean
0.16950.13530.16233.5753-0.1604
0.51680.2413-0.1054-0.0605
S.D.
0.18650.11800.18994.6351
3.38875.47310.5123
1.21651.42202008 Mean
0.16000.10830.13043.4731-0.4290-0.1306
0.23800.03220.0724
S.D.
0.18630.09490.24204.66163.80344.23140.3104
0.46370.5235Total
Mean
0.17380.19590.15443.3679-0.83800.24090.2347-0.02520.0602
S.D.
0.21750.24880.19704.63394.09404.22150.46890.77950.836921

Table 5 The Estimation Results of Borrowing Function
All firms
FN's zombie firms
Fixed effect
Fixed effect
b/t/p
b/t/p
⊿lnS
0.09550.1171[21.28]***[6.26]***
0.000
0.000
π /Asset,t-1-0.3965-0.3944[-19.82]***[-4.39]***
0.000
0.000
Debt,t-1/Asset,t-1-0.3459-0.4729[-41.2]***[-12.38]***
0.000
0.000
Land,t-1/Borrow,t-1
0.0018-0.0015[2.14]**[-0.25]0.033
0.806
BankCap,t-1-0.0003-0.0004[-0.37][-0.11]
0.711
0.914
BankNPL,ｔ-1
0.000-0.0005[0.711][0.914]
0.854
0.659
N
19282
1667
group
2185
757
r2
0.137
0.2073r2_a0.0259-0.4755
F testF(2184, 17082) = 2.19
F(756, 895) =1.41
Prob > F = 0.0000
Prob > F = 0.0000
Hausman testchi2(15) =2494.64
chi2(15) = 138.17
Prob>chi2 = 0.0000
Prob>chi2 =0.0000
BPLM testchi2(1) = 155.49
chi2(1) = 20.33
Prob > chi2 = 0.0000
Prob >chi2 =0.0000*Significant at 10%.
**Significant at 5%.
***Significant at 1%

Modified FN's zombie firms
Fixed effectb/t/p0.1001[4.4]***
0.000-0.2798[-3.66]***0.000-0.5986[-12.09]***0.000
0.0062[0.68]
0.499
0.0021[0.27]
0.787-0.001[0.787]
0.537
947
376
0.2854-0.2093
F(375, 559) =1.98
Prob > F = 0.0000
chi2(12) = 193.05
Prob>chi2 =0.0000
chi2(1) =10.36
Prob > chi2 = 0.0006

22

Table 6 The Estimation Results of Investment Function
All firms
FN's zombie firms
Random effectRandom effect
b/t/p
b/t/p
⊿Borrowing/Asset,t-1
1.97771.3865[14.92]***[5.08]***
0.000
0.000
Mq
0.0049-0.0053[7.08]****[-1.91]
0.000
0.056
Cashflow/K,t-1
0.06690.0669[9.47]***[4.08]***
0.000
0.000
Debt,t-1/Asset,t-1
0.09710.0052[6.07]***[0.10]
0.000
0.918
N
8281
587
group
1527
328
r2
r2_a
F testF(1526,6742) =1.51
F(327,247) =1.24
Prob > F = 0.0000
Prob > F = 0.0353
Hausman testchi2(12) = -40.70
chi2(12) = -177.35

Modified FN's zombie firms
Fixed effectb/t/p
0.8466[3.19]***
0.001
0.0031[0.91]
0.362
0.0407[2.20]**
0.028
0.6829[2.67]***
0.008
394
187
F(186,197) =1.40
Prob > F = 0.0105
chi2(10) = 20.93
Prob>chi2 =0.0216

Instrumented: ⊿Borrowing/Asset,t-1
Instruments: ⊿lnS π /Asset,t-1 Land,t-1/Borrow,t-1 BankCap,t-1 BankNPL,ｔ-1⊿lnS,t-1 π ,t-1/Asset,t-2 Land,t-2/Borrow,t-2 BankCap,t-2 BankNPL,ｔ-2 a time lag of 1 of explanetary variables*Significant at 10%.
**Significant at 5%.
***Significant at 1%23