DP
RIETI Discussion Paper Series 15-E-031
Productivity, Firm Size, Financial Factors, and Exporting Decisions:
The case of Japanese SMEs
OGAWA Kazuo
Institute of Social and Economic Research, Osaka University
TOKUTSU Ichiro
Graduate School of Business Administration, Kobe University
The Research Institute of Economy, Trade and Industry
http://www.rieti.go.jp/en/
RIETI Discussion Paper Series 15-E-031
March 2015
Productivity, Firm Size, Financial Factors, and Exporting Decisions:
The case of Japanese SMEs*
OGAWA Kazuo
Institute of Social and Economic Research, Osaka University
TOKUTSU Ichiro
Graduate School of Business Administration, Kobe University
Abstract
This study is an empirical attempt to compare the exporting behavior of small and medium-sized
enterprises (SMEs) with large firms from the viewpoints of export market participation decision
(extensive margin) and export volume decision (intensive margin), using firm-level panel data. We find
that firm size is an important determinant of both extensive margin and intensive margin decision for
SMEs as well as large firms. In contrast, productivity affects only the intensive margin of export for both
SMEs and large firms. Quantitatively, the contribution of productivity to export volume is much larger for
large firms. Financial factors are also important determinants of export. Liquidity reserve has positive
effects on the extensive margin of export for SMEs and large firms. Moreover, financial institutions play
an important role in supporting the export activities of SMEs.
Keywords: Export, Extensive margin, Intensive margin, Total factor productivity, Price-cost margin,
Financial constraint, Firm size
JEL classification: E44, F14, O30
RIETI Discussion Papers Series aims at widely disseminating research results in the form of
professional papers, thereby stimulating lively discussion. The views expressed in the papers are
solely those of the author(s), and neither represent those of the organization to which the author(s)
belong(s) nor the Research Institute of Economy, Trade and Industry.
* This research is conducted as a part of the Project “Study on Corporate Finance and Firm Dynamics” undertaken at
the Reserach Institute of Economy, Trade and Industry (RIETI). This research is financially supported by
Grant-in-Aid for Scientific Research (#25285068) from the Japanese Ministry of Education, Culture, Sports, Science
and Technology. The authors would like to thank Shin-ichiro Ono and Kazumi Suzuki for helpful comments and
suggestions on data issues. The authors are also grateful to Masahisa Fujita, Shin-ichi Fukuta, Kaoru Hosono, Keiko
Ito, Daisuke Miyakawa, Jun-ichi Nakamura, Hiroshi Ohashi, Etsuro Shioji, Iichiro Uesugi, Nobuyoshi Yamori and
seminar participants at Development Bank of Japan and RIETI for extremely helpful comments and suggestions. Any
remaining errors are the sole responsibility of the authors.
1. Introduction
Japanese small and medium-sized enterprises (SMEs) export much less than large
listed firms. Figure 1 compares the proportion of firms that engage in exporting
activities between small and medium-sized manufactures and large counterparts in the
period of 1995 to 2009. The SMEs in manufacturing sector are defined as the
enterprises whose number of employees is less than 300 or equity capital is less than
300 million yen. 1 Around 60 % of large firms export during this period. On the other
hand, the proportion of exporting SMEs is at most 28.2% in 2009 although it exhibits an
increasing trend. Figure 2 shows the share of SMEs in terms of export values of the total
manufacturing sector. The share of SMEs increases over time, but it still stands at 6.5%
in 2009.
Why do SMEs export less than large firms? Three factors have been pointed out.
First, new exporters face significant start-up costs. For example, they set up an export
department, make market research of overseas market, develop marketing channels,
retool and redesign products for foreign customers. Most of these costs are sunk cost, so
that only large firms cover sunk entry costs. Moreover, large firms also benefit from
lower variable costs such as trading costs due to scale economy. Large firms can attain
lower trading costs due to bulk shipping and extensive overseas network. Thus large
firms have cost advantage in export activities. 2
Second, it is well established that productivity is one of the driving engines of
export. 3 Positive relationship between productivity and export has been found in many
countries since the seminal work of Bernard and Jensen (1995). 4 Heterogeneity in firm
productivity can explain why not all firms engage in exporting activities. As large firms
have higher productivity than SMEs, SMEs export less than large firms. Third, large
firms have advantage in financing export activities. It is because asymmetry in
information between lenders and borrowers is less severe for large firms. Therefore
large firms face lower costs in raising trade finance than SMEs. 5
1
The definition of SMEs comes from Small and Medium Enterprises Basic Act.
Wagner (1995, 2001) emphasizes the importance of firm size in exports.
3
For example, see Roberts and Tybout (1997), Bernard et al. (2003), Melitz (2003) and Greenaway
and Kneller (2007).
4
For example, positive relationship between productivity and export has been found in US by
Bernard and Jensen(1995,1999,2004a,2004b) and Bernard et al.(2007), in Canada by Baldwin and
Gu(2003), in European countries by Bernard and Wagner(2001) and Mayer and Ottaviano(2007), in
Colombia, Mexico and Morocco by Clerides, Lach and Tybout(1998), in Asian countries by Aw,
Chung and Roberts(2000) and Hallward-Driemeier et al.(2002) and in Japan by Kimura and
Kiyota(2006), Tomiura(2007), Wakasugi, Ito and Tomiura(2008), Todo(2011) and Yashiro and
Hirano(2010).
5
See, for example, Chaney (2005), Amiti and Weinstein (2011) and Manova (2013) for the
2
2
The importance of three factors in comparing the exporting behavior of large firms
with that of SMEs has been well documented, but there are few attempts to evaluate
quantitatively the relative importance of three factors in exporting activities of SMEs
and large firms. The purpose of this study is to compare quantitatively the relative
importance of three factors: firm size, productivity and financial factors in exporting
activities between SMEs and large firms, using firm-level data in Japan. We use Basic
Survey of Japanese Business Structure and Activities (BSJBSA) collected by the
Ministry of Economy, Trade and Industry. The virtue of this survey is to include SMEs
as well as large firms. In fact this survey covers enterprises with 50 or more employees
and whose equity capital is over 30 million yen. The proportion of SMEs in this survey
is 83% in 2009.
We focus on firms in the four leading exporting industries: general machinery,
electrical machinery, transport equipment and precision instrument. 6 The sample period
covers from 1995 to 2009. This period includes decade-long stagnation in 1990s, the
recovery phase of the Japanese economy from “lost decade” in 2000s and global
financial crisis triggered by the massive non-performing subprime loans. The main
feature of this study is to compare the exporting behavior of SMEs with that of large
firms from two aspects. First, we examine the determinants of whether a firm exports or
not (extensive margin). Second, we examine the determinants of export volume
(intensive margin). Two equations corresponding to extensive margin and intensive
margin decisions are consistently derived from firms’ profit maximizing behavior.
Let us preview our main findings. We find that both firm size and financial factors are
important determinants of extensive margin for both SMEs and large firms. However,
marginal effects of firm size on export market participation decision are large for SMEs.
Surprisingly, the profitability of export, measured by the ratio of export price to unit
production cost (price-cost margin), has no effects on extensive margin of export.
Price-cost margin as well as firm size is an important determinant of intensive margin of
export. In addition, lending attitude of financial institutions, a proxy of financial factors,
has a positive effect on exports of SMEs and world income has a positive effect on
exports of large firms. Decomposing the contribution of each factor to representative
firm’s exports during 1999 to 2007, we find that the contribution of firm size to export
is quite large for both SMEs and large firms. The contribution of productivity to export
volume is as large as that of firm size for large exporters, but the contribution of
productivity to export volume is relatively small for SMEs. On the other hand, the
importance of credit constraints in international trade.
6
The average export share by these four industries in BSJBSA amounts to 83% during 1995-2009.
3
contribution of lending attitude of financial institutions to export is relatively large for
small and medium-sized exporters, although it is small for large exporters.
The remainder of the paper is organized as follows. In Section 2 we characterize
the exporting behavior of a firm in partial equilibrium context. We describe our dataset
and present some descriptive statistics in Section 3. Section 4 shows our empirical
results. Section 5 compares quantitatively the relative contribution of each determinant
to export volume between SMEs and large firms. The last section concludes.
2. Characterization of Firm-level Exporting Behavior
2.1 Basic Model of Exporting Behavior
We construct a market equilibrium model of firms that sell their products in both
domestic and overseas markets. Our model is in line with the recent trade theory
developed by Melitz(2003), Melitz and Ottaviano(2008) and Bernard et al.(2003) that
stresses firm heterogeneity. Consider a profit-maximizing firm that sells its product in
both domestic and overseas markets. The firm faces a downward-sloping demand curve
in domestic and overseas market, respectively. We assume that there are N firms in the
market. Downward-sloping demand curve in overseas market is given by
𝑃
−𝜂
𝑄𝐸 = 𝐸 �𝑒𝑃𝐸 �
𝑤
(1)
where 𝑄𝐸 : demand for exports
𝑃𝐸 : export price on a yen basis
𝑃𝑤 : world price on a dollar basis
𝑒 : exchange rate (yen per dollar)
𝜂 : price elasticity of overseas demand and
𝐸 : factors that shift export demand
The inverse demand curve is expressed as
−
𝑃𝐸 = 𝑒𝑃𝑊 𝐵𝑄𝐸
1
𝜂
(2)
1
where B = 𝐸 𝜂
Similarly, Downward-sloping demand curve in domestic market and the inverse
domestic demand curve are given by eqs.(3) and (4), respectively.
4
𝑄𝐷 = 𝐻𝑃𝐷−𝜃
(3)
where 𝑄𝐷 :domestic demand
𝑃𝐷 :domestic price
𝜃: price elasticity of domestic demand and
𝐻:factors that shift domestic demand
−
𝑃𝐷 = 𝐽𝑄𝐷
1
𝜃
(4)
1
where J = 𝐻 𝜃
The i-th firm maximizes its profit, defined by (5), with respect to overseas sales (𝑄𝑖𝑖 )
and domestic sales (𝑄𝑖𝑖 ).
𝜋 = 𝑝𝐸 𝑄𝑖𝑖 + 𝑝𝐷 𝑄𝑖𝑖 − 𝐶𝑖 (𝑇𝑖 , c𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 )(𝑄𝑖𝑖 + 𝑄𝑖𝑖 ) − 𝜙(𝐴𝑖 )𝑄𝑖𝑖 − 𝐹𝑖
where 𝑃𝐸 = 𝑒𝑃𝑊 𝐵(∑𝑁
𝑖=1 𝑄𝑖𝑖 )
−
1
𝜂
,
(5)
1
−
𝜃
𝑃𝐷 = 𝐽(∑𝑁
𝑖=1 𝑄𝑖𝑖 )
𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 ):unit cost function with
𝜕𝐶𝑖
𝜕𝑇𝑖
𝜕𝐶
𝜕𝐶
𝜕𝐶
< 0, 𝜕𝑐 𝑖 > 0, 𝜕𝑤𝑖 > 0, 𝜕𝑝 𝑖 > 0,
𝑖
𝑖
𝑀𝑀
𝑇𝑖 : total factor productivity (TFP)
𝑐𝑖 : rental cost of capital
𝑤𝑖 : wage rate
𝑝𝑀𝑀 : material price
𝜙(𝐴𝑖 ): unit trading cost with 𝜙′(𝐴𝑖 ) < 0
𝐴𝑖 : total assets and
𝐹𝑖 : start-up cost of export
It is assumed that production technology is linearly homogeneous, so that the unit cost
function does not depend on the level of output. The trading cost includes tariff and
transportation cost. We assume that the unit trading cost is a decreasing function of firm
size, measured by total assets. 7 We assume that a firm pays fixed cost 𝐹𝑖 to start up
export.
7
Forslid and Okubo(2011) find that the unit trading cost is a decreasing function of firm size due to
scale economy.
5
Extensive Margin Export Decision
A firm exports if current revenue is greater than cost or
𝑝𝐸 𝑄𝑖𝑖 − 𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 )𝑄𝑖𝑖 − 𝜙(𝐴𝑖 )𝑄𝑖𝑖 − 𝐹𝑖 > 0
(6)
This inequality is written as
𝑝
𝐸
�𝐶 (𝑇 ,𝑐 ,𝑤
,𝑝
𝑖
𝑖 𝑖
𝑖
𝑀𝑀
𝐹
− 1� 𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 ) − 𝜙(𝐴𝑖 ) − 𝑄 𝑖 > 0
)
𝑖𝑖
In other words, a firm is more likely to export when the price-cost margin (PCM),
𝑝𝐸
, is higher and firm size is larger. Large firms attain lower unit trading cost,
𝐶𝑖 (𝑇𝑖 ,𝑐𝑖 ,𝑤𝑖 ,𝑝𝑀𝑀 )
𝐹
𝜙(𝐴𝑖 ) and fixed cost per export, 𝑄 𝑖 as export amount and total assets are positively
𝑖𝑖
correlated. Existence of sunk costs generates hysteresis in export markets. Once a firm
enters the export market by paying fixed cost, the firm is more likely to stay in the
export market. To sum up, start-up decision of export depends on firm size, measured by
total assets, price-cost margin and the firm’s status in the export market in the previous
period. We employ a binary response model to specify the export market participation
decision described above. Let us define a latent variable 𝑌𝑖∗ as
(7)
𝑌𝑖∗ = 𝑓�𝐴𝑖,−1 , 𝑃𝑃𝑃𝑖,−1 , 𝑌𝑖,−1 , 𝜖𝑖 �
where 𝑃𝑃𝑃𝑖−1 : price-cost margin in the previous year
𝑌𝑖,−1=1 if a firm exported in the previous year and 0 otherwise
We observe
𝜖𝑖 : disturbance term
1 𝑖𝑖 𝑌𝑖∗ > 0
𝑌𝑖 = �
0 𝑖𝑖 𝑌𝑖∗ ≤ 0
Intensive Margin Export Decision
The first order condition of export volume and domestic sales is given by (8) for all
𝑖 = 1,2, ⋯ , 𝑁.
1
− −1
𝑁
𝜂
1
𝐵𝐵𝑃𝑊 �− � �� 𝑄𝑖𝑖 �
𝑄𝑖𝑖 + 𝑝𝐸 − 𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 ) − 𝜙(𝐴𝑖 ) = 0
𝜂
𝑖=1
6
(8)
1
− −1
𝑁
𝜃
1
𝑃𝐷 �− � �� 𝑄𝑖𝑖 �
𝑄𝑖𝑖 + 𝑝𝐷 − 𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 ) = 0
𝜃
𝑖=1
(𝑖 = 1,2, ⋯ , 𝑁)
Using the total export demand and domestic demand, eq.(8) is re-written as follows.
1 𝑄𝑖𝑖
𝑃𝐸 �− �
+ 𝑝𝐸 = 𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 ) + 𝜙(𝐴𝑖 )
𝜂 𝑄𝐸
1 𝑄
(9)
𝑃𝐷 �− � 𝑖𝑖 + 𝑝𝐷 = 𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 )
𝜃 𝑄
𝐷
Thus the i-th firm’s share in total export and domestic sales is given by eq.(10).
𝑄𝑖𝑖
𝑄𝐸
= 𝜂 �1 −
𝑄𝑖𝑖
𝐷
𝐶𝑖 (𝑇𝑖 ,𝑐𝑖 ,𝑤𝑖 ,𝑝𝑀𝑀 )
= 𝜃 �1 −
𝑃𝐸
−
𝜙(𝐴𝑖 )
𝑃𝐸
�
(10)
𝐶𝑖 (𝑇𝑖 ,𝑐𝑖 ,𝑤𝑖 ,𝑝𝑀𝑀 )
𝐷
�
The i-th firm’s share in total export depends upon the price-cost margin and real unit
trading cost. The firm with higher price-cost margin may attain higher share of export.
The price-cost margin is an increasing function of TFP and a decreasing function of
wage rate, rental price of capital and material price, so that the firm’s export share
increases when the firm raises its TFP and faces lower input prices. The firm may also
increase its export share by lowering real unit trading cost. Larger firm may increase its
export share since it faces lower trading cost due to scale economy.
From eq.(10) the export function is written as
𝑄𝑖𝑖 = 𝑓 �𝑄𝐸 ,
𝐶𝑖 (𝑇𝑖 ,𝑐𝑖 ,𝑤𝑖 ,𝑝𝑀𝑀 ) 𝜙(𝐴𝑖 )
,
�
𝑃𝐸
𝑃𝐸
7
(11)
Note that 𝑄𝐸 is a function of real exchange rate
𝑃𝐸
𝑒𝑒𝑊
and shift factors E of export
demand function, as is given by (1). An important ingredient of shift parameter is world
income. To sum up, the export function is expressed as
𝑄𝑖𝑖 = 𝑓 �𝑦𝑊 ,
𝑃𝐸
𝑒𝑒𝑊
,
𝐶𝑖 (𝑇𝑖 ,𝑐𝑖 ,𝑤𝑖 ,𝑝𝑀𝑖 )
where 𝑦𝑊 : world income
𝑃𝐸
, 𝐴𝑖 �
(12)
This is the basis export volume equation to be estimated.
2.2 Equilibrium Export Price
Aggregating the first order condition of export given by eq.(9) across firms, we obtain
the following equation.
𝑁
𝑁
1 ∑𝑁
𝑖=1 𝑄𝑖𝑖
𝑃𝐸 �− �
+ 𝑝𝐸 𝑁 = � 𝐶𝑖 (𝑇𝑖 , 𝑐𝑖 , 𝑤𝑖 , 𝑝𝑀𝑀 ) + � 𝜙(𝐴𝑖 )
𝑄𝐸
𝜂
𝑖=1
𝑖=1
(13)
Using the market clearing condition ∑𝑁
𝑖=1 𝑄𝑖𝑖 = 𝑄𝐸 , we can solve eq.(13) in terms of
as
𝑃𝐸 =
∑𝑁
1
𝑖=1 𝐶𝑖 (𝑇𝑖 ,𝑐𝑖 ,𝑤𝑖 ,𝑝𝑀𝑀 )
�
1
1− �𝜂𝜂
𝑁
+
∑𝑁
𝑖=1 𝜙(𝐴𝑖 )
𝑁
�
(14)
Yen-denominated export price is therefore described as a function of the average unit
cost and unit trading cost multiplied by the mark-up ratio. A rise in TFP will lower
Japanese export price relative to world price and hence increases overseas demand for
Japanese exports.
2.3 Role of Financial Factors in Exporting
It is implicitly assumed that exporters do not face liquidity constraints in
characterizing the firms’ exporting behavior above. However exporters might face
higher effective borrowing rate with external finance premium added on when capital
market is imperfect. This is especially so for SMEs since the SMEs have less financial
assets and have limited access to capital market. Recent empirical studies find that
exporters might be liquidity-constrained. Amiti and Weinstein (2011) demonstrate that
8
trade finance provided by the financial institutions played an important role in exporting
behavior of Japanese listed firms. They show that bank health was important in
providing trade finance with exporters and hence contributed to export increase. 8
We extend both firms’ export market participation decision and intensive margin
export function by including the firm health and bank health variables. As for the firm
health variable, we use the liquidity ratio, defined as the current assets less current
liabilities over total assets. We use as a proxy of bank health the lending attitude
diffusion index (DI) of financial institutions that measures easiness of providing
external finance with exporters. Lending attitude DI is defined as the difference between
the proportion of the firms feeling the lending attitude to be accommodative and that of
the firms feeling the lending attitude to be severe. The larger the lending attitude DI, the
healthier the banks are since healthier banks can supply more credit to their customers.
The extended export market participation decision is specified as
1 𝑖𝑖 𝑌𝑖∗ > 0
𝑌𝑖 = �
0 𝑖𝑖 𝑌𝑖∗ ≤ 0
where 𝑌𝑖∗ = 𝑓�𝐴𝑖,−1 , 𝑃𝑃𝑃𝑖,−1 , 𝐿𝐿𝐿𝐿𝑖,−1 , 𝐿𝐿𝐿𝐿𝑖 , 𝑌𝑖,−1 , 𝜖𝑖 �
𝐿𝐿𝐿𝐿𝑖,−1: liquidity ratio in the previous year
(15)
𝐿𝐿𝐿𝐿𝑖 : lending attitude DI of financial institutions
The extended intensive margin export function is written as
𝑄𝐸,𝑖 = 𝑓 �𝑦𝐸 , �
𝑃𝐸
� ,
𝑒𝑃𝑤 𝑖
𝐶𝑖 (𝑇𝑖 ,𝑐𝑖 ,𝑤𝑖 ,𝑝𝑀 )
𝑝𝐸
, 𝐴𝑖,−1 , 𝐿𝐿𝐿𝑖,−1 , 𝐿𝐿𝐿𝐿�
(16)
3. Data Description
This section describes the data sources and the procedures to construct variables used
in empirical analysis. We also overview the characteristics of Japanese exporters during
the sample period.
3.1 Data Construction
8
A number of researchers have examined the role of trade finance or external finance in exporting
behavior. For example, see Kletzer and Bardhan(1987), Ronci(2005), Muûls(2008), Bricogne et
al.(2012), Iacovone and Zavacka(2009), Feenstra et al. (2014), Haddad et al. (2010), Levchenko et al.
(2010), Chor and Manova(2012) and Manova et al.(2011).
9
Three key variables in this study are: total factor productivity, price-cost
margins, and real exports. This section describes how these variables are
constructed. We provide detailed explanations about the data sources and the
method to construct variables in Data Appendix. Our analysis focuses on the
machinery-manufacturing firms since these firms play a major role in exporting
activities. The data for individual corporations are all from BSJBSA in the period of
1995 to 2009.
The first variable, total factor productivity for firm i in period t, 𝑇𝑖𝑖 , is
constructed by using the multilateral productivity index number developed by Caves
et al. (1982) as follows.
𝑚
1
��𝚥𝚥
��� �ln 𝑋𝑖𝑖𝑖 − �������
ln𝑇𝑖𝑖 = �ln 𝑌𝑖𝑖 − ������
ln 𝑌𝑡 � − ��𝑆𝑖𝑖𝑖 + 𝑆
ln 𝑋𝚥𝚥 �, 𝑡 = 1
2
𝑗=1
𝑚
1
��� �ln 𝑋𝑖𝑖𝑖 − �������
ln 𝑌𝑡 � − ��𝑆𝑖𝑖𝑖 + �𝑆�𝚥𝚥
ln 𝑋𝚥𝚥 �
ln𝑇𝑖𝑖 = �ln 𝑌𝑖𝑖 − ������
2
𝑗=1
𝑡
𝑡
𝑠=2
𝑠=2
𝑚
1
������
����
������� ������� ����������
+ ��ln
𝑌𝑠 − ��������
ln 𝑌𝑠−1 � − � ��𝑆
𝚥𝚥 + 𝑆𝚥,𝑠−1 � �ln 𝑋𝚥,𝑠 − ln 𝑋𝚥,𝑠−1 �, 𝑡 > 1,
2
𝑗=1
where Y it is the real output of firm i in period t, X ijt is the input j of firm i in period t, S ijt
is the cost share of input j of firm i in period t, and the variables with bar (-) and without
subscript i indicate that they are industrial average.
The first and second terms of the right-hand side is the discrepancy of log of output
and factor input of firm i in period t from those of the hypothetical and representative
firm of the corresponding industry in the corresponding period. The third and fourth
terms are the accumulated value of the period-to-period change of the log of T of the
representative firm. That is to say, the index indicates the log of T relative to the
hypothetical and representative firm of the corresponding industry in the period 1 (base
year). This is why the index has no first and second term for the period 1.
The hypothetical and representative firm is defined as that with the arithmetic
averages of cost shares and geometric averages of output and inputs of the sample firms
of the corresponding industry in the corresponding period. It is clear that log of T of the
representative firm is 0 in the period t.
The second variable, the price-cost margin, is calculated as the value of output
divided by the total cost, where the total cost (TC) is the sum of labor, material,
10
and capital cost: 9
𝑇𝑇 = 𝑤𝑤 + 𝑐𝑐 + 𝑃𝑀 𝑀
where w: wage rate
L: labor input
c: rental price of capital
K: capital stock
𝑃𝑀 : intermediate input price
M: intermediate input
The cost shares, SK, SL, and SM, used in constructing TFP is obtained by
dividing each factor cost by the total cost.
Finally, our third key variable, real exports, is obtained by deflating the value of
exports (pEQE) by the price index of exports (pE).
3.2 Descriptive Statistics of Exporters
Table 1 shows the sample median of major variables in the manufacturing sector
for four types of firms: large exporters, large non-exporters, small and medium-sized
exporters and small and medium-sized non-exporters. Firm size is largest for large
exporters, second largest for large non-exporters, third largest for small and
medium-sized exporters and smallest for small and medium-sized non-exporters,
whatever measure is used for firm size: number of employees, real sales or total assets.
The large exporters attain best performance in price-cost margin, sales growth rate, ratio
of R&D investment to sales, growth rate of total assets, while small and medium-sized
exporters ranks second in all these performance measures and small and medium-sized
non-exporters ranks last. As for the financial variables, the liquidity ratio is largest for
small and medium-sized exporters and second largest for large exporters. Debt-asset
ratio is largest for small and medium-sized non-exporters and smallest for large
exporters.
Figure 3 compares dynamic firm performance among four types of firms in the
manufacturing sector. Continuous exporter in year t is defined as the firm that has
exported for four consecutive years in year t. New exporter in year t is defined as the
9
The value of output and total cost in calculating the price-cost margin includes both
overseas and domestic values since separate figures for overseas and domestic sales and
costs are unavailable. Our procedure might be justified by the linear homogenous
production technology.
11
firm that has not exported for three consecutive years prior to year t and started export
in year t. Exit exporter in year t is defined as the firm that exits from export markets in
year t, although it exported for three consecutive years prior to year t. Continuous
non-exporter in year t is defined as the firm that has not exported for four consecutive
years in year t. Figure 3 depicts the average of the same variables used in Table 1 for
four types of firms defined above over the past three years and future three years during
1997 To 2009. Firm size is largest for continuous exporters, second largest for exit
exporters, third largest for new exporters and smallest for continuous non-exporters,
whatever measure is used for firm size: number of employees, real sales or total
assets. 10 We observe the same rank order for the ratio of R&D investment to sales and
liquidity ratio. One exception is the price-cost margin. The price-cost margin of
continuous exporters is highest, but the price-cost margin of new exporters exceeds that
of exit exporters in year t when new exporters start exporting. The price-cost margin of
new exporters rises faster than exit exporters prior to start-up years. We test the null
hypothesis that the mean of the variables is the same across four types of firms for each
year by multi-way analysis of variance. Table 2 shows the test statistics of analysis of
variance and the null hypothesis is decisively rejected for all the variables and years. 11
Table 3 compares the average growth rate of employment, real sales and total assets
over the past three years and the future three years across four types of firms defined
above. The average growth rate of employment, real sales and total assets over the past
three years is highest for new exporters and lowest for exit exporters. Moreover the null
hypothesis that the mean growth rate is the same across four types of firms is decisively
rejected, as is shown in Table 2. However, there is no statistical difference in the mean
growth rate of real sales and total assets over the future three years.
In the subsequent section we examine the firms’ exporting behavior empirically,
using the panel data of firms in four leading export industries: general machinery,
electrical machinery, transport equipment and precision instrument. Therefore we show
some descriptive statistics characterizing the firms in these industries. Table 4 shows the
proportion of exporting firms in these four industries by firm size. More than 80% of
large firms export in general machinery (83.5%) and precision instrument (87.8%). The
proportion of small and medium-sized exporters is also high. In particular, the
proportion of SME exporting firms is 53.0% and 43.2% for precision instrument and
general machinery, respectively. Figure 4 shows the median of price-cost margin during
1996 to 2009 for four types of firms: large exporters, large non-exporters, small and
10
11
In Table 4 the firm size of continuous non-exporter is normalized as unity in year t.
See the row of export status in Table 2.
12
medium-sized exporters and small and medium-sized non-exporters by industry. The
price-cost margin is largest for large exporters and smallest for small and medium-sized
non-exporters in all the four industries but general machinery. The price-cost margin
exhibits an increasing trend in the 2000s and reaches its peak in 2006. It falls sharply
thereafter, reflecting the aftermath of global financial crisis. Figure 5 shows the median
of log of TFP during 1996 to 2009 for four types of firms by industry. Large exporters
have the highest TFP in the 2000s for all the industries, followed by large non-exporters.
Small and medium-sized non-exporters have lowest TFP. The log of TFP of each
industry turns to a stable increasing trend around 2001 to 2002 except for transport
equipment, where the log of TFP takes a U-shaped path in the 2000s.
4. Estimation Results and Implications
4.1 Determinants of Extensive Margin of Export
We first examine the determinants of export market participation by estimating a
binary response model specified in Section 2 by employing random probit model. The
estimation results (marginal effects) of SMEs and large firms are presented in columns
(1) to (3) and columns (4) to (6) of Table 5a, respectively. The dependent variable takes
unity when a firm exports in year t and zero otherwise. The columns (1) for SMEs and
(4) for large firms show the results of basic static model where the explanatory variables
are logarithm of lagged total assets, lagged price-cost margin and two financial
variables: lagged liquidity ratio and lending attitude of financial institutions.
We modify the basic specification in two directions. First, we replace lending
attitude of financial institutions by year dummies. Note that lending attitude is not a
firm-specific variable since it takes common value for the firms in the same size group
of the same industry. The estimation results are shown in the columns (2) for SMEs and
(5) for large firms of Table 5a.
Second, we modify the lag structure of price-cost margin. It takes a good amount of
time to prepare for entering export market since the firm sets up an export department,
make overseas market research, develop marketing channels, retool and redesign
products for foreign customers before the firm starts exporting. Therefore we use the
average price-cost margin over the past three years instead of one-year lagged value.
The estimation results are shown in the columns (3) for SMEs and (6) for large firms of
Table 5a.
The estimation results are now in order. Firm size has significantly positive effects
on participation decision of SMEs in all four industries, irrespective of specification.
Liquidity ratio has also significantly positive effects on participation decision of SMEs
13
in all four industries but electrical machinery, irrespective of specification. Lending
attitude of financial institutions has significantly positive effects on participation
decision of SMEs only in general machinery and transport equipment. Price-cost margin
does not have significantly positive effects on participation decision of SMEs in any
industries, irrespective of specification. As for the large firms, we cannot find any
explanatory variables that are statistically significant across specifications.
Now we turn to the dynamic specification of export market participation decision.
The estimation results are shown in Table 5b. In the dynamic specification hysteresis in
export market is taken into consideration. The estimation results are improved to a large
extent by including the firm’s lagged status in export market. 12 The coefficient estimate
of lagged status in export market is significantly positive for all industries except for
large firms in precision instrument, irrespective of firm size and specification. This
indicates the importance of fixed costs in entering export market. Firm size exerts a
significantly positive effect on export market participation decision for all industries
except for large firms in general machinery and precision instrument, irrespective of
firm size and specification Comparing the magnitude of the coefficient estimates of firm
size between SMEs and large firms, they are larger for SMEs. Therefore firm size is
much more important for SMEs in export market entry decision. Liquidity ratio is an
important determinant for export market participation for both SMEs and large firms in
all industries except precision instrument. On the other hand, the coefficient estimate of
lending attitude of financial institutions is significantly positive only for SMEs in
general machinery.
It should be noted that the price-cost margin does not have significantly positive
effects on participation decision of SMEs or large firms in any industries, irrespective of
specification. This implies that productivity, by way of price-cost margin, does not
affect the export market participation decision.
4.2 Determinants of Intensive Margin of Export
We estimate the intensive margin export function derived in Section 2 under two
specifications. We have six explanatory variables in the first specification. They are
price-cost margin, world income, real exchange rate, lagged total assets and two
financial factors: liquidity ratio and lending attitude of financial institutions. The basic
export function to be estimated is given by
12
Most of the explanatory variables are still insignificant for large firms in precision instrument.
This is possibly due to the paucity of the firms that do not export. In fact we have only 105
observations that do not export for the whole sample period.
14
𝑝
log(𝑄𝐸 )𝑖𝑖 = 𝛼0 + 𝛼1 𝑙𝑙𝑙(𝑃𝑃𝑃)𝑖𝑖 + 𝛼2 𝑙𝑙𝑙(𝑦𝐸 )𝑡 + 𝛼3 𝑙𝑙𝑙 �𝑒𝑒𝐸 � + 𝛼4 𝑙𝑙𝑙(𝐴)𝑖,𝑡−1 +
𝛼5 𝑙𝑙𝑙(𝐿𝐿𝐿𝐿)𝑖,𝑡−1 + 𝛼6 𝐿𝐿𝐿𝐿𝑡 + 𝜈𝑖 + 𝑢𝑖𝑖
𝑤
𝑡
(17)
where 𝜈𝑖 : firm-specific term and
𝑢𝑖𝑖 : disturbance term
World income, real exchange rate and lending attitude are industry-specific and we do
not include year dummies as explanatory variables in eq.(17). In the second
specification we replace lending attitude of financial institutions by year dummies to see
the robustness of the effects of price-cost margin, firm size and liquidity ratio on export.
We take the endogeneity of price-cost margin into consideration explicitly in
estimating export function. Price-cost margin is one of the important determinants of
export in our model. However, the price-cost margin variable is constructed only
from the information contained in balance sheet and profit-and-loss statements of
firms. Thus unobservable important information such as the values of overseas
network is not reflected on our price-cost margin variable. Then the observable
price-cost margin might include measurement errors. Straight application of
conventional panel regression might yield downward bias of the estimates. In this
case the instrumental variable (IV) estimator is a legitimate procedure to allow for
endogeneity. Candidates for instrument are ingredients of cost function; which are
log of input prices (real wage, real capital cost), TFP, debt-asset ratio and year dummy
variables, as will be explained in the next section. The preliminary estimation, however,
reveals that if we adopt all the explanatory variables in the cost function as instruments,
the Sargan test decisively rejected the overidentification restrictions, so that we use only
part of the instruments that do not violate the overidentification restrictions. Therefore,
we use the log of TFP and lagged debt-asset ratio as valid instruments for price-cost
margin that do not violate the overidentification restrictions. The Hausman specification
test is applied for selection between fixed-effect model and random-effect model.
The estimation results of SMEs and large firms are presented in Table 6a for simple
panel estimation and Table 6b for panel IV estimation. In both tables columns (1) and
(2) correspond to SMEs, while columns (3) and (4) to large firms. Firm size has a
significantly positive effect on export volume of both SMEs and large firms,
irrespective of industry and estimation method. Contrary to the estimation results of
export market participation decision, price-cost margin exerts a significantly positive
15
effect on export volume of both SMEs and large firms, irrespective of industry and
estimation method. Moreover, the IV estimates are much larger than those of simple
panel regression, suggesting that the price-cost margin is indeed endogenous. Our
finding of positive relationship between price-cost margin and export is consistent with
De Loecker and Warzynski (2012). They find that exporters have on average higher
markups for Slovenian firms. Comparing the coefficient estimates of price-cost margin
between SMEs and large firms, price-cost margin has much larger effect on export
volume of large firms. This implies that large firms are more sensitive to profitability in
export markets.
As for the effects of financial factors on export volume, lending attitude of financial
institutions is a significant determinant of export volume for both SMEs and large firms.
The estimated coefficient of lending attitude of financial institutions is significantly
positive in all four industries for SMEs under IV regression, while it is significantly
positive only in general machinery and electrical machinery for large firms. Our finding
is consistent with Amiti and Weinstein (2001) finding that trade finance provided by the
financial institutions affects exports of Japanese firms. World income is also an
important determinant of export of large firms. It exerts a significantly positive effect on
export volume of large firms in all four industries, irrespective of estimation method.
5. Role of Productivity, Firm Size and Financial Factors in Exporting: Quantitative
Evaluation
In this section we calculate the extent to which each determinant of intensive
margin of export contributed to export surge in the 2000s. In so doing we make a
quantitative comparison of the relative importance of productivity, firm size and
financial factors in expanding export volume between SMES and large firms during
1999 to 2007 when export surged.
5.1 Price-Cost Margin Equation
The price-cost margin equation is important since it is used for evaluating
quantitatively the contribution of TFP and other determinants of the cost function to
export volume. The price-cost margin equation to be estimated is written as
Log(𝑃𝑃𝑃)𝑖𝑖 = 𝛽0 + 𝛽1 𝑙𝑙𝑙 �𝑤�𝑃 � + 𝛽2 𝑙𝑙𝑙 �𝑐�𝑃 � + 𝛽3 𝑙𝑙𝑙(𝑇)𝑖𝑖 +
𝐸 𝑖𝑖
𝐸 𝑖𝑖
𝛽4 𝑙𝑙𝑙(𝐷𝐷𝐷𝐷)𝑖,𝑡 + ∑𝐾
𝑘=1 𝛽5𝑘 𝐷𝐷𝑘𝑘 + 𝜈𝑖 + 𝑢𝑖𝑖
16
(18)
Where 𝐷𝐷𝐷𝐷𝑖𝑖 : debt-asset ratio
𝐷𝐷𝑘𝑘 : year dummies (𝑘 = 1996, ⋯ ,2009)
𝜈𝑖 : firm-specific term
𝑢𝑖𝑖 : disturbance term
We add the debt-asset ratio and year dummies to the list of explanatory variables.
Note that the material price is common to all the firms in the sample, so that it is
subsumed into the year dummies. Equation (18) is estimated using the sample of
exporting firms. The estimation results of SMEs and large firms are presented in
columns (1) and (2) of Table 7, respectively.
The coefficient estimates of factor prices are all significantly negative, irrespective
of industry and firm size. There is no discernible difference in the magnitude of
coefficient estimates across firm size in the same industry. The TFP variable has a
significantly positive effect on the price-cost margin, irrespective of industry and firm
size. One-percent rise in TFP increases the price-cost margin by 0.778 % (SMEs in
transportation equipment) to 0.967% (SMEs in precision instrument).
5.2 Quantitative Evaluation of Export Determinants: Productivity, Firm Size and
Financial Factors
Now we calculate the contribution of each determinant of export volume: firm
size, two financial factors, world demand, real exchange rate and components of
price-cost margin: wage rate, rental price of capital and TFP to export variations during
1999 to 2007. We use the estimates of the intensive margin export function as well as
those of the price-cost margin equation. The contribution of firm size to export is the
proportion of the rate of change in total assets that explains the rate of change in export.
𝛼4 �𝑙𝑙𝑙(𝐴)𝑖,𝑡+𝑇−1 −𝑙𝑙𝑙(𝐴)𝑖,𝑡−1 �
𝑙𝑙𝑙(𝑄𝐸 )𝑖,𝑡+𝑇 −𝑙𝑙𝑙(𝑄𝐸 )𝑖𝑖
(19)
Similarly, the contribution of world income, real exchange rate and lending attitude
of financial institutions to export is calculated, using the corresponding coefficient
estimates of the intensive margin export equation. The contribution of each component
of the price-cost margin can be also obtained by using the coefficient estimates of the
intensive margin export function and the price-cost function. For example, the
contribution of TFP to export is given by
17
𝛼1 𝛽3 �𝑙𝑙𝑙(𝑇)𝑖,𝑡+𝑇 −𝑙𝑙𝑙(𝑇)𝑖𝑖 �
𝑙𝑙𝑙(𝑄𝐸 )𝑖,𝑡+𝑇 −𝑙𝑙𝑙(𝑄𝐸 )𝑖𝑖
(20)
The contribution of the variables to export is calculated during 1999 to 2007 for all
the firms that existed during this period. The first panel of Table 8 shows the median of
the frequency distribution of the contribution of each variable across firms. We use the
IV coefficient estimates of the intensive margin export function where world income,
real exchange rate and lending attitude of financial institutions are explicitly taken into
consideration.
There is a large difference in the relative contribution of export determinants to
export volume between SMEs and large firms. The contribution of firm size to export is
sizable for large firms in electrical machinery (56.2%), transport equipment (54.7%) and
precision instrument (41.3%). The contribution of TFP to export is also large for large
firms. TFP can explain 19.8%, 28.7% and 35.5% of the total variations of export
volume in general machinery, electrical machinery and precision instrument,
respectively.
The contribution of firm size to export is also large for SMEs. The contribution of
firm size to export is 30.7%, 15.4% and 34.0% in electrical machinery, transport
equipment and precision instrument, respectively. In contrast, the contribution of TFP is
much smaller for SMEs. The contribution of TFP to export is large for SMEs in
electrical machinery (17.3%), but the contribution of TFP is much smaller in other
industries. 13 Financial factors, especially lending attitude of financial institutions, play
more important role in expanding export activities of SMEs. The contribution of lending
attitude of financial institutions is large in general machinery (17.0%) and moderately
large in other industries, ranging from 5.4% in precision instrument to 7.1 % in
electrical machinery and transport equipment. This implies that financial support by
financial institutions is important for SMEs to expand export activities.
We also calculate the contribution of firm size and TFP to export volume under
different specification of intensive margin export function to see the robustness of the
results obtained above. We use the IV estimate coefficients of export function where
year dummies replace world income, real exchange rate and lending attitude of financial
institutions. We use the same price-cost margin equation as above. The second panel of
Table 8 shows the median of the frequency distribution of the contribution of each
variable across firms under this specification. The results remain essentially unaltered.
13
The contribution of TFP to export volume is 5.2% for general machinery and 2.0% for precision
instrument. The contribution of TFP is nil for transport equipment.
18
The contribution of firm size is large for SMEs as well as large firms, irrespective of
industry. The contribution of TFP to export is also non-negligible for large firms except
for transport equipment. Thus TFP is important as a driving force of export for large
firms. On the other hand, the contribution of TFP to export is much smaller for SMEs
except for electrical machinery.
6. Concluding Remark
This study is an empirical attempt to compare the exporting behavior of SMEs with
large firms from the viewpoints of export market participation decision (extensive
margin) and export volume decision (intensive margin), using the firm-level panel data.
We find that firm size is an important determinant of both extensive margin and
intensive margin decision for SMEs as well as large firms. Marginal effect of firm size
on the export market participation decision is larger for SMEs, suggesting that SMEs
are more likely to cover fixed start-up cost as firm size gets larger. Furthermore, SMEs
as well as large firms benefit from scale economies. In contrast, productivity affects
only intensive margin of export for both SMEs and large firms. Quantitatively the
contribution of productivity to export volume is much larger for large firms. Financial
factors are also important determinants of export. Liquidity reserve has positive effects
on extensive margin of export for SMEs and large firms. Moreover financial institutions
play an important role in expanding export activities of SMEs, suggesting that financial
institutions help SMEs export by providing a variety of services including trade finance.
Our empirical findings yield important policy implications to activate export of
SMEs. One big obstacle to SMEs’ export market participation is start-up cost, such as
obtaining basic knowledge of legal systems and business practices in export markets,
overseas marketing to acquire customers and reliable partners, developing marketing
channels, retooling and redesigning products for foreign customers. Large firms have
already accumulated expertise to facilitate export due to scale economies, but it will
take a lot of efforts and cost for the SMEs to prepare such arrangement prior to export.
Even if a SME has a potential technology with high TFP, the SME cannot utilize this
technology advantage in exporting without painstaking initial preparation of exporting.
Instead of SMEs, policymakers should play more vital role in providing a variety of
information that facilitates SMEs’ participation in export markets. 14
14
Small and Medium Enterprise Agency (2014) supports our policy recommendations. In fact it
presents results of an interesting survey (Questionnaire Survey into the Actual Conditions of Overseas
Expansion by SMEs) that asks what the efforts that enterprises considered to be the most important are
19
We also find that SMEs need abundant liquidity reserves for entry into export
markets. Financial support at start-up of export as well as maintaining export activities
is also inevitable. The estimation results reveal that TFP is lower for SMEs.
Policymakers should give SMEs preferential treatment on tax deduction of R&D and
ICT investment to promote innovative activities that enhance productivity.
Future avenue of extending our current work is to incorporate the decision of
production location by SMEs, i.e. foreign direct investment, in addition to sales decision
between domestic market and overseas market.
in achieving success in exports. Many enterprises responded, “securing sales destinations” followed
by “securing reliable business partners/advisors,” and “assessment of local market needs and trend.”
20
Data Appendix
The data for individual corporations are all from Basic Survey of Japanese Business
Structure and Activities (hereafter abbreviated as BSJBSA) of Ministry of Economy,
Trade, and Industry of the government of Japan form 1995 to 2009. Complementary
aggregate time series, such as exchange rate, prices and working hours, are from the
Bank of Japan online data base and the website of the Cabinet Office of the Government
of Japan.
[1] Nominal output (pX) and real output (X)
The output is defined as the sum of [218:Sales] 15 and net increase of inventories
[170:Inventory at the end of period] - [169:Inventory at the beginning of period]. It
should be noted, however, that inventories are evaluated at factor costs, while sales
amount are at market prices. Accordingly, we adjust this conceptual inconsistency by
multiplying the ratio of [218:Sales] to [225:Cost of sales] to the net increase of
inventories above.
As for the output price (p), we apply the industry output price commonly to the
firms within the same industry. Industry output price is the [Input-Output Price Index of
the Manufacturing Industry by Sector (2005 base)/ Output Price Index/ Manufacturing
industry sector] of Bank of Japan. We will explain the data on prices later in detail. The
detailed explanation of output price index will be provided in [10].
[2] Total cost (TC) and price cost margin (PCM)
Total cost (TC) is the sum of [225: Cost of sales], net increase of inventories
[170:Inventory at the end of period] - [169:Inventory at the beginning of period],
[227:General and administrative expenses], and [242: Interest and discount expense].
Price cost margin (PCM) is defined as the ratio of output to total cost, pX / TC.
[3] Total asset (ASSET), total debt (DEBT), and related variables.
Total asset is [180:Total asset] and total debt is [181:Total debt]. Debt / Asset ratio
(DEBT) is the ratio of total debt (DEBT) to total asset (ASSET). Liquidity ratio (LIQR)
is defined as the ratio of the net of current asset [168:Current asset] - [182:Current
liabilities] to total asset (ASSET).
15
In what follows the number in the parenthesis indicates the item number in BSJBSA.
21
[4] Labor cost (wL), labor input (L), and wage rate (w)
Labor cost (wL) is the sum of [238: Amount of salary payment] and [240: welfare
expense]. Labor input (L) is in yearly working hours and defined as the products of [89:
Number of regular employees] and [Hours worked by employed persons classified by
economic activity] in Annual Report on National Account, Cabinet Office of the
Government of Japan. Accordingly, wage rate (w) is defined as the hourly wage and
obtained by dividing the labor cost (wL) by total working hours (L).
[5] Capital cost (cK), real capital stock (K), and the rental price of capital (c)
It is not easy to construct the gross capital stock for individual firms based on the data in
the Survey. For the sake of convenience, we construct the capital stock (K) by deflating
[172:Tangible fixed asset] by the price index for investment good (q) in [14]. That is to
say, we evaluate the capital stock by the re-purchasing price at the corresponding
period.
Rental price of capital (c) is defined as follows.
𝑐 = 𝑞(𝑟 + 𝑑) − 𝑞̇ ,
where q is the price of investment good, r is the interest rate, and d is the depreciation
rate. Interest rate (r) is the ratio of [242: Interest and discount expense] to the average of
current and the preceding year’s [181:Total debt]. Interest bearing debt, such as [184:
Short-term bank loan]，[188: Long-term bank loan] are also available in the Survey, but
there are many missing cases for these items. This is the reason for our convenient
treatment of interest rate. Depreciation rate (d) is the ratio of [239: Depreciation cost] to
the average of the current and the preceding year’s [172: Tangible fixed asset]. Capital
cost (cK) is the product of real capital stock (K) and the rental price (c).
[6] Cost of intermediate inputs (p M M) and real intermediate input (M)
Cost of intermediate goods (p M M) is obtained by subtracting the labor cost (wL)
and capital cost (cK) from the total cost (TC). Real intermediate input (M) is obtained
by deflating p M M by intermediate input price (p M ). As for the intermediate input price
(p M ), as in the case of output price, we apply the industry intermediate input price
commonly to the firms within the same industry. Industry output price is the
[Input-Output Price Index of the Manufacturing Industry by Sector (2005 base) / Input
Price Index / Manufacturing industry sector] of Bank of Japan. The detailed explanation
of input price index will be provided in [11].
[7] World income (Yw)
22
World income is defined as the weighted average of GDP in constant dollars in eight
regions: Asia, Middle East, Western Europe, Russia and Eastern Europe, North America,
Central and South America, Oceania and Africa. The weighs are the proportion of
export to each region to each industry’s total export from Japan, and thereby the world
income thus obtained varies across four industries.
[8] Nominal export amount (p E X E ) and real export amount (X E )
As the nominal export amount, we have [251:Amount of direct foreign export] in
the Survey and real export amount is obtained by deflating it by export price (p E ). We
also apply the industry export price commonly to the firms within the same industry.
Industry export price is the [Input-Output Price Index of the Manufacturing Industry by
Sector (2005 base) / Output Price Index/ Manufacturing industry sector (Exports)] of
Bank of Japan. The detailed explanation of export price index will be provided in [12].
[9] Research and Development intensity (RD)
The ratio of [525: In-house Research and Development expense] to [218:Sales] in
BSJBSA.
[10] Output price index (p)
[Input-Output Price Index of the Manufacturing Industry by Sector (2005 base) / Output
Price Index / Manufacturing industry sector]. As for the sector of electrical machinery,
the sector is subdivided into “Electrical machinery”, “Information and communication
electronics equipment” and “Electronic components” after 2000. Accordingly, in order
to maintain the time-series consistency, we aggregate the three sectors based on the
weights of 2005 (base-year) after 2000. The weights are 55.092 for “Electrical
machinery”, 38.294 for “Information and communication electronics equipment” and
56.384 for “Electronic components.” We connect the series of 2000 and 2005 base years
and construct the price index of 2005 base year. Concordance of the name of the data
series in Bank of Japan is as follows.
Concordance of code number of data series in BOJ
General machinery
Electrical machinery
Info. & comm. electronics equipment
Electronic components
Transportation equipment
Precision instruments
Before 1999
PR’PRIO_8A0030010
PR’PRIO_8A0030011
PR’PRIO_8A0030012
PR’PRIO_8A0030013
23
After 2000
PR’PRIO05_8A0030010
PR’PRIO05_8A0030011
PR’PRIO05_8A0030012
PR’PRIO05_8A0030013
PR’PRIO05_8A0030014
PR’PRIO05_8A0030015
[11] Intermediate input price (p M )
[Input-Output Price Index of the Manufacturing Industry by Sector (2005 base)/ Input
Price Index / Manufacturing industry sector]. As for the sector of electrical machinery,
the sector is subdivided into “Electrical machinery”, “Information and communication
electronics equipment” and “Electronic components” after 2005. Accordingly, in order
to maintain the time-series consistency, we aggregate the three sectors based on the
weights of 2005 (base-year) after 2000. The weights are 30.19 for “Electrical
machinery”, 4.71 for “Information and communication electronics equipment” and
73.809 for “Electronic components.” We connect the series of 1995, 2000 and 2005
base years and construct the price index of 2005 base year. Concordance of the name
of the data series in Bank of Japan is as follows.
Concordance of code number of data series in BOJ
General machinery
Electrical machinery
Info. & comm.
Electronics equipment
Electronic components
Transportation
equipment
Precision instruments
Before 1999
PR’PRIO95_6C123001
PR’PRIO95_6C133001
2000 to 2004
PR’PRIO_6C0030012
PR’PRIO_6C0030013
After 2005
PR’PRIO05_6C0030012
PR’PRIO05_6C0030013
PR’PRIO05_6C0030014
PR’PRIO95_6C143001
PR’PRIO_6C0030014
PR’PRIO05_6C0030015
PR’PRIO05_6C0030016
PR’PRIO95_6C153001
PR’PRIO_6C0030015
PR’PRIO05_6C0030017
[12] Export price (p E )
[Corporate Goods Price Index (2005 Base)/ Export Price Index (yen basis)] in BOJ data
base.
The code number of data series in BOJ database
General machinery and equipment
Electric & electronic products
Transportation equipment
Precision instruments
PR’PRCG_1400420001
PR’PRCG_1400520001
PR’PRCG_1400620001
PR’PRCG_1400720001
As for the export price, [Input-Output Price Index of the Manufacturing Industry by
Sector (2005 base) / Output Price Index/ Manufacturing industry sector (Exports)] is
also available in BOJ database. For consistency with the import price index in contract
currency base, we apply the [Corporate Goods Price Index (2005 Base) / Export Price
24
Index (yen basis)]
[13] Import price (p W )
[Corporate Goods Price Index (2005 Base) / Import Price Index (contract currency
basis)] in BOJ data base.
The code number of data series in BOJ database
General machinery and equipment
Electric & electronic products
Transportation equipment
Precision instruments
PR’PRCG_1500720001
PR’PRCG_1500820001
PR’PRCG_1500920001
PR’PRCG_1501020001
[14] Investment good price (q)
[Corporate Goods Price Index (2005 Base) / Index by Stage of Demand and Use
(reference) Investment goods] (PR’PRCG_28K1020005) in BOJ database.
[15] Exchange rate (ex)
[Nominal / Effective Exchange Rates (2010=100)] (ST'FX180110001) in BOJ database.
The series is converted to 2005 base year.
[16] Lending attitude of financial institution (LEND)
[D.I./Lending Attitude] in Bank of Japan's (BOJ) National Short-Term Economic Survey
of Enterprises in Japan (known for short as Tankan). The indices are classified by firm
size as in the following table. As can be seen from the table, the firms with equity
capital below 20 million yen are excluded after 2004. We adopt the series for industrial
machinery as the index for general machinery sector.
Classification by firm-size
Large enterprises
Before 2003
(By number of employees)
More than 1000
After 2004
(By share capital)
More than billion yen
Medium-sized enterprises
More than 300 and below 1000
Small -sized enterprises
Below300
More than 100 million yen and
below billion yen
More than 20 million yen and
below 100 million yen
25
Code number of data series in BOJ database
Industrial machinery
Electrical machinery
Transportation
equipment
Precision instruments
Large enterprises
CO’COAEF1140612
GCQ01000@
CO’COAEF1150612
GCQ01000@
CO’COAEF1160612
GCQ01000@
CO’COAEF1200612
GCQ01000@
Medium enterprises
CO’COAEF1140612
GCQ02000@
CO’COAEF1150612
GCQ02000@
CO’COAEF1160612
GCQ02000@
CO’COAEF1200612
GCQ02000@
26
Small enterprises
CO’COAEF1140612
GCQ03000@
CO’COAEF1150612
GCQ03000@
CO’COAEF1160612
GCQ03000@
CO’COAEF1200612
GCQ03000@
References
Amiti,M. and D.E. Weinstein(2011). “Exports and Financial Shocks,” Quarterly
Journal of Economics 126, pp.1841-1877.
Aw, B. Y., Chung, S. and M.J.Roberts(2000). “Productivity and Turnover in the Export
Market: Micro-level Evidence from the Republic of Korea and Taiwan (China),” World
Bank Economic Review 14, pp.65-90.
Baldwin, J.R. and E. Gu(2003). “Export-Market Participation and Productivity
Performance in Canadian Manufacturing,” Canadian Journal of Economics 36,
pp.634-657.
Bernard, A.B. and J.B. Jensen(1995). “Exporters, Jobs, and Wages in U.S.
Manufacturing: 1976-87,” Brookings Papers on Economic Activity: Microeconomics,
pp.67-112.
Bernard, A.B. and J.B. Jensen(1999). “Exceptional Exporter Performance: Cause, Effect,
or Both?” Journal of International Economics 47, pp.1-25.
Bernard, A.B. and J.B. Jensen(2004a). “Exporting and Productivity in the USA,” Oxford
Review of Economic Policy 20, pp.343-357.
Bernard, A.B. and J.B. Jensen(2004b).“Why Some Firms Export?” Review of
Economics and Statistics 86, pp.561-569.
Bernard, A.B. and J. Wagner(2001). “Export Entry and Exit by German Firms,”
Weltwirtschaftliches Archiv/Review of World Economics 137, pp.105-123.
Bernard, A.B., Eaton, J., Jensen, J.B. and S. Kortum(2003). “Plants and Productivity in
International Trade,” American Economic Review 93, pp.1268-1291.
Bernard, A.B., Jensen, J.B., Redding, S.J. and P.K. Schott(2007). “Firms in International
Trade,” Journal of Economic Perspective 21, pp.105-130.
27
Bricogne, J.C., Fontagne, L., Ganlier, G., Taglioni, D. and V. Vicard(2012). “Firms
and the Global Crisis: French Exports in the Turmoil,” Journal of International
Economics 87, pp.134-146.
Caves, D.W., Christensen, L.R. and W. E. Diewert(1982). “Multilateral Comparisons of
Output, Input and Productivity Using Superlative Index Numbers,” The Economic
Journal 92, pp.73-86.
Chaney, T.(2005). “Liquidity Constrained Exporters,” mimeographed, Massachusetts
Institute of Technology.
Chor, D. and K. Manova(2012). “Off the Cliff and Back? Credit Conditions and
International Trade during the Global Financial Crisis,” Journal of International
Economics 87, pp.117-133.
Clerides,S., Lach, S. and J.Tybout(1998). “Is Learning by Exporting Important?
Micro-dynamic Evidence from Columbia, Mexico and Morocco,” Quarterly Journal of
Economics 113, pp.903-947.
De Loecker, J. and F. Warzynski(2012). “Markups and Firm-level Export Status,”
American Economic Review 102, pp.2437-2471.
Feenstra, R.C., Li, Z. and M. Yu(2014). “Exports and Credit Constraints under
Private Information: Theory and Evidence from China,” The Review of
Economics and Statistics 96, pp.729-744.
Forslid, R. and T. Okubo(2011). “ Are Capital Intensive Firms the Biggest
Exporters?” RIETI Discussion Paper 11-E-014.
Greenaway, D., Guariglia, A. and R. Kneller(2007). “Financial Factors and Exporting
Decisions,” Journal of International Economics 73, pp.377-395.
Haddad, M., A. Harrison and C. Hausman(2010) “Decomposing the Great
Trade Collapse: Products, Prices and Quantities in the 2008-2009 Crisis,” NBER
Working Paper w16253.
28
Hallward-Driemeier, M., Iarossi, G. and K.L. Sokoloff(2002). “Exports and
Manufacturing Productivity in East Asia: A Comparative Analysis with Firm-level Data,”
NBER Working Paper 8894.
Iacovone, L. and V. Zavacka(2009). “Banking Crises and Exporters: Lesson from the
Past,” World Bank Policy Research Working Paper 5016.
Kimura, F. and K. Kiyota(2006). “Exports, FDI and Productivity of Firm: Dynamic
Evidence from Japanese Firms,” Review of World Economics 142, pp.695-719.
Kletzer, K. and P. Bardhan(1987). “Credit Markets and Patterns of International
Trade,” Journal of Development Economics 27, pp. 57-70.
Kwon, H., Kim, Y. and K. Fukao(2008). “Why Did the Japanese TFP Growth Rate
Recover? An Empirical Analysis Based on Basic Survey of Japanese Business Structure
and Activities,” (in Japanese) RIETI DP Series 08-J-050
Levchenko, A., Lewis, L. and L. Tesar(2010). “The Role of Financial Factors in
the Great Trade Collapse: A Skeptic’s View,” University of Michigan,
mimeographed.
Manova, K.(2013). “Credit Constraints, Heterogeneous Firms, and International Trade,”
Review of Economic Studies 80, pp.711-744.
Manova, K., Wei, S-J, and Z. Zhang(2011). “Firm Exports and Multinational Activity
under Credit Constraints,” NBER Working Paper 16905.
Mayer, T. and G.I.P. Ottaviano(2007). The Happy Few: The Internationalisation of
European Firms, Bruegel Blueprint Series.
Melitz, M.J.(2003). “The Impact of Trade on Intra-Industry Reallocations and
Aggregate Industry Productivity,” Econometrica 71, pp.1695-1725.
Melitz, M.J. and G.I.P. Ottaviano(2008). “Market Size, Trade and Productivity,” Review
of Economic Studies 75, pp.295-316.
29
Muûls, M.(2008). “Exporters and Credit Constraints: A Firm Level Approach,”
mimeographed.
Roberts, M. and J. Tybout (1997). “The Decision To Export in Colombia: An Empirical
Model of Entry with Sunk Costs,” American Economic Review 87, pp.545–564.
Ronci, M.(2005) “Trade Finance and Trade Flows: Panel Data Evidence from 10
Crises,” in Access to Trade Finance in Times of Crisis, ed. Wang, J-Y. and M. Ronci
(Washington, DC: International Monetary Fund)
Small and Medium Enterprise Agency (2014). 2014 White Paper on Small and Medium
Enterprises in Japan
Todo,Y.(2011).“Quantitative Evaluation of Determinants of Export and FDI: Firm-Level
Evidence from Japan,” The World Economy 34, pp.355-381.
Tomiura,E.(2007). “Foreign Outsourcing, Exporting and FDI: A Productivity
Comparison at the Firm Level,” Journal of International Economics 72, pp.113-127.
Wagner, J.(1995). “Exports, Firm Size, and Firm Dynamics,” Small Business Economics
7, pp.29-39.
Wagner, J.(2001). “A Note on the Firm Size – Export Relationship,” Small Business
Economics 17, pp.229-237.
Wakasugi, R., Ito, B. and E. Tomiura(2008). “Offshoring and Trade in East Asia: A
Statistical Analysis,” Asian Economic Papers 7, pp.101-124.
Yashiro, N. and D. Hirano(2010). “Evaluating Japanese Exporters’ Performance and
Investment Behavior during the 2002-2007 Export Boom,” RIETI Policy Discussion
Paper Series 10-P-005. (in Japanese)
30
70
60
50
%
40
30
20
10
Data source: Basic Survey of Japanese Business Structure and Activities
Figure 1. Proportions of exporters by firm size
31
2009
2008
2007
2006
2005
2004
2003
2001
2000
1999
1998
1997
1996
1995
2002
SMEs
Large firms
0
10
9
8
7
5
4
3
2
1
Data source: Basic Survey of Japanese Business Structure and Activities
Figure 2. SME’s share of export amount
32
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
1995
%
6
Data source: Basic Survey of Japanese Business Structure and Activities
Figure 3. Dynamic performance of firms by export status
33
Data source: Basic Survey of Japanese Business Structure and Activities
Figure 3. (cont.) Dynamic performance of firms by export status
34
Figure 4. Price-cost margin by firm size and export status
35
Figure 5. Log of TFP by firm size and export status
36
Table 1. Sample median of firm characteristics by firm size and export status
(1)
(2)
(3)
SMEs
L
PCM
RS
RD
DEBT
A
LIQR
NO
(4)
(5)
(6)
(7)
Sub total
Total
Large firms
NonExporters Sub total NonExporters
exporters
exporters
No. of employees
113
147
120
597
807
(Growth rate)
-0.93
-0.49
-0.83
-0.81
-1.02
Price cost margin
1.011
1.021
1.013
1.020
1.030
Sales amount
2,574
3,940
2,848
22,736
33,611
(Growth rate)
-0.57
0.58
-0.32
0.40
1.32
R&D / Sales ratio
0.06
0.79
0.17
0.60
2.38
Debt /Asset ratio
74.68
68.01
73.09
64.98
56.04
Total asset
2,185
3,955
2,528
19,130
36,549
(Growth rate)
-0.19
1.05
0.10
0.38
1.52
Liquidity ratio
11.36
18.71
13.16
5.45
14.94
No. of observations
130,120
41,044
171,164
11,448
19,046
723
-0.94
1.025
28,686
0.93
1.59
58.97
28,262
1.11
11.68
30,494
Unit: Millions of yen in 2005 constant price for RS and A. Growth rates and ratios are in percentage.
Data source: Basic Survey of Japanese Business Structure and Activities
37
141
-0.85
1.015
3,538
-0.10
0.34
70.97
3,138
0.28
12.93
201,658
Table 2. Multi-way analysis of variance
Period
Export status
Industry
Year
Industry×export status
Industry×year
Export status×year
(1)
(2)
(3)
(4)
(5)
(6)
(7)
t-3
t-2
t-1
t
t+1
t+2
t+3
Number of employees
9.6
326.0
334.6
329.8
280.3
240.0
208.8
2.5
6.7
7.3
10.7
7.4
7.0
6.3
2.3
1.7 
1.6 
1.5 
1.0 
1.0 
1.0 
1.8
27.9
31.6
34.2
31.3
28.3
25.6
1.1 
0.3 
0.3 
0.2 
0.2 
0.2 
0.2 
0.7 
4.9
4.6
3.9
2.9
2.1
1.5 
612.5
21.5
1.0 
43.3
0.3 
0.7 
Export status
Industry
Year
Industry×export status
Industry×year
Export status×year
540.3
105.1
11.6
23.0
3.0
2.9
Export status
Industry
Year
Industry×export status
Industry×year
Export status×year
195.6
485.2
12.5
115.3
5.2
12.8
3.8
22.6
0.6 
3.0
1.1 
2.9
Export status
Industry
Year
Industry×export status
Industry×year
Export status×year
Debt / Asset ratio
520.7
198.5
103.5
114.4
91.7
16.8
12.6
6.9
8.3
7.1
1.6 
8.6
4.9
5.5
3.4
24.8
4.1
2.4
2.7
2.3
0.4 
0.6 
0.6 
0.7 
0.7 
0.9 
1.0 
0.6 
0.6 
0.6 
Export status
Industry
Year
Industry×export status
Industry×year
Export status×year
Real total asset
148.4
495.1
473.3
458.0
380.1
320.1
273.9
29.1
15.6
12.7
13.1
9.1
6.9
5.8
1.7 
1.5 
1.7 
1.8 
1.5 
1.4 
1.4 
3.0
24.1
24.1
24.4
21.1
18.6
16.4
0.7 
0.5 
0.7 
0.7 
0.6 
0.5 
0.4 
1.6
1.0 
0.9 
1.1 
1.0 
0.9 
1.0 
Export status
Industry
Year
Industry×export status
Industry×year
Export status×year
18.9
6.8
31.0
2.9
6.2
2.8
15.1
4.1
4.7
2.1
1.7
0.9 
Price-cost margin
20.2
29.0
21.2
4.4
8.9
5.4
5.1
8.6
7.6
2.3
3.5
2.2
1.9
3.1
3.1
1.2 
1.8
1.9
Export status
Industry
Year
Industry×export status
Industry×year
Export status×year
27.1
6.1
9.2
2.5
3.6
2.5
136.1
26.8
4.7
2.5
0.6 
1.7
Liquidity ratio
61.6
63.1
12.6
14.9
2.9
4.4
1.3 
1.4 
0.6 
0.6 
1.0 
1.2 
38
50.0
11.7
2.9
1.1 
0.6 
1.1 
(9)
Past
Future
3-year
growth
rate
3-year
growth
rate
18.9
6.8
31.0
2.9
6.2
2.8
16.0
6.0
38.4
2.1
4.9
4.0
15.2
31.4
25.6
2.9
15.5
3.6
0.1 
28.1
47.8
2.5
23.0
3.2
15.4
45.7
8.3
1.9
11.7
1.9
1.8 
50.9
11.5
2.4
14.1
1.6
42.5
9.6
15.6
3.5
5.7
3.9
Real sales amount
595.9
597.7
620.9
507.6
421.1
358.9
22.1
19.7
20.0
16.5
12.6
10.8
0.7 
0.8 
1.0 
0.7 
0.9 
1.1 
43.4
46.1
49.2
41.3
35.3
30.7
0.2 
0.3 
0.4 
0.4 
0.3 
0.4 
0.7 
0.5 
0.8 
0.7 
0.7 
0.7 
R&D / Sales ratio
354.8
343.1
257.7
88.2
78.4
68.3
11.2
7.4
5.8
17.8
18.1
15.1
2.1
2.1
1.4
2.9
1.9
1.9
(8)
208.7
154.5
61.7
47.3
6.2
1.0 
14.0
10.8
1.2 
0.6 
2.7
1.5 
75.0
5.7
2.4
1.9
0.6 
0.5 
39.1
9.3
2.4
0.9 
0.6 
0.8 
60.7
4.3
1.5 
1.7
0.6 
0.6 
29.4
7.9
1.7 �
0.8 
0.6 
0.7 
Table 3. Past and future average growth rates of firm size by export status
(1)
(2)
Number of employees
Past
3 years
Future
3 years
(3)
(4)
(5)
(6)
Real sales amount
Real total assets
Past
3 years
Past
3 years
Future
3 years
Future
3 years
Mean
Continuous non-exporters
New exporters
Exit exporters
Continuous exporters
-0.99
-0.41
-2.50
-1.32
-0.69
-0.50
-1.37
-0.92
0.68
3.26
0.20
2.06
0.48
1.01
-0.06
1.05
1.48
3.97
1.43
2.62
0.93
1.16
0.55
1.73
Total
-1.09
-0.76
1.09
0.64
1.83
1.16
Median
Continuous non-exporters
New exporters
Exit exporters
Continuous exporters
-1.19
-0.87
-1.86
-1.24
-0.87
-0.76
-1.25
-0.66
-0.34
1.33
-0.12
1.03
-0.36
0.12
-0.44
0.53
0.33
1.93
1.05
1.74
-0.06
0.54
0.14
1.21
Total
-1.20
-0.81
0.03
-0.13
0.75
0.31
39
Table 4. Proportion of exporting firms by firm size
(1)
SMEs
Large firms
Total
Non-exporters
Exporters
Total
Proportion of exporters (%)
Non-exporters
Exporters
Total
Proportion of exporters (%)
Non-exporters
Exporters
Total
Proportion of exporters (%)
(2)
General
machinery
11,971
9,099
21,070
43.2
575
2,919
3,494
83.5
12,546
12,018
24,564
48.9
40
Electrical
machinery
16,424
7,727
24,151
32.0
1,920
4,097
6,017
68.1
18,344
11,824
30,168
39.2
(3)
Transportation
equipment
10,735
3,060
13,795
22.2
1,285
2,563
3,848
66.6
12,020
5,623
17,643
31.9
(4)
Precision
instruments
2,145
2,421
4,566
53.0
105
757
862
87.8
2,250
3,178
5,428
58.5
Table 5a. Estimated result of extensive margin decision: Basic specification
（1）
dy/dx
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
-0.2187 **
(2.12)
0.3900 ***
(7.73)
0.1738 ***
(2.74)
0.0016 ***
(3.36)
14,227
-0.0092
(1.20)
0.0224 ***
(4.27)
0.0047
(1.38)
0.0000
(0.86)
16,020
-0.0015
(1.43)
0.0018 **
(2.40)
0.0015 **
(2.25)
0.0000
(1.55)
9,466
-0.0557
(0.21)
0.5640 ***
(2.84)
0.6510 ***
(2.58)
-0.0013
(0.86)
3,017
（2）
SMEs
dy/dx
-0.2129 **
(2.03)
0.3659 ***
(6.33)
0.1019
(1.63)
14,227
（3）
（4）
dy/dx
dy/dx
General machinery
-0.0005
(0.61)
-0.0470
(0.20)
0.4872 ***
0.0005 *
(5.86)
(1.86)
0.2266 **
0.0002
(2.26)
(0.41)
0.0019 ***
0.0000
(2.84)
(0.18)
9,596
2,669
Electrical machinery
-0.0073
(1.12)
0.0033
(0.26)
0.0253 *** 0.0225 ***
0.0059
(4.23)
(4.08)
(1.54)
0.0054
0.0008
0.0060
(1.46)
(0.21)
(1.23)
0.0000
0.0000
(0.30)
(0.82)
16,020
10,534
4,521
-0.0055
(0.74)
Transportation equipment
-0.0226
(0.40)
-0.0091
(1.34)
0.0061 *** 0.0068 **
0.0324 *
(3.00)
(2.54)
(1.74)
0.0049 *** 0.0072 **
0.0587
(2.61)
(2.37)
(1.46)
0.0000 *
-0.0001
(1.86)
(0.45)
9,466
6,441
3,106
-0.0053
(1.50)
Precision instruments
-0.0001
(0.24)
0.1582
(0.36)
0.4095 *** 0.4216 **
0.0004
(3.43)
(2.37)
(0.54)
0.4843 *** 0.6684 **
0.0003
(2.72)
(1.99)
(0.47)
-0.0011
0.0000
(0.70)
(0.47)
3,017
1,958
631
0.0012
(0.01)
41
（5）
Large firms
dy/dx
（6）
dy/dx
0.0000
(0.15)
0.0002
(1.51)
0.0001
(0.58)
2,669
-0.0027
(1.03)
0.0007 *
(1.73)
0.0021
(1.35)
0.0000
(1.02)
1,917
-0.0058
(0.90)
0.0069
(1.56)
0.0067
(1.26)
4,521
0.0017
(0.23)
0.0039 *
(1.90)
0.0048
(1.47)
0.0000
(0.82)
3,269
0.0008
(0.02)
0.0331
(1.57)
0.0514
(1.33)
3,106
0.0410
(0.82)
0.0143
(1.38)
0.0411
(1.32)
-0.0001
(0.88)
2,347
0.0000
0.0000
0.0000
631
0.0005
(0.36)
0.0007
(1.08)
0.0001
(0.10)
0.0000
(0.74)
463
Table 5b. Estimated result of extensive margin decision: Dynamic specification
（1）
dy/dx
EXD -1
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
EXD -1
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
EXD -1
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
EXD -1
PCM -1
������−3
𝑃𝑃𝑃
log(A) -1
LIQR -1
LEND
no.
1.1581 ***
(85.7)
-0.1283
(1.61)
0.0800 ***
(9.94)
0.1114 ***
(3.64)
0.0011 **
(2.42)
14,227
0.9955 ***
(66.9)
-0.0487
(0.77)
0.0550 ***
(9.91)
0.0639 ***
(3.12)
-0.0005
(0.99)
16,020
0.6725 ***
(35.1)
-0.0191
(0.24)
0.0585 ***
(9.84)
0.0907 ***
(3.84)
0.0001
(0.18)
9,466
1.1654 ***
(40.2)
0.1729
(1.13)
0.0806 ***
(4.49)
0.0775
(1.26)
-0.0003
(0.29)
3,017
（2）
SMEs
dy/dx
1.1603 ***
(85.5)
-0.1189
(1.46)
0.0797 ***
(9.89)
0.1057 ***
(3.44)
14,227
0.9969 ***
(66.7)
-0.0255
(0.44)
0.0574 ***
(10.2)
0.0661 ***
(3.21)
16,020
（3）
（4）
dy/dx
dy/dx
General machinery
1.2021 ***
0.3201 ***
(71.6)
(8.25)
-0.0731
(1.20)
0.0013
(0.01)
0.0763 ***
0.0040
(7.39)
(0.87)
0.1169 ***
0.0928 ***
(3.02)
(3.01)
0.0006
-0.0001
(1.14)
(0.34)
9,596
2,669
Electrical machinery
1.0263 ***
0.8559 ***
(55.6)
(30.7)
-0.1307
(1.21)
0.0265
(0.26)
0.0670 ***
0.0218 ***
(9.08)
(3.13)
0.0689 ***
0.0720 **
(2.62)
(2.06)
-0.0013 **
-0.0013
(1.97)
(1.54)
10,534
4,521
Transportation equipment
0.6716 *** 0.7291 ***
0.9239 ***
(35.0)
(30.0)
(27.2)
-0.0117
-0.1232
(0.24)
(0.40)
-0.0298
(0.35)
0.0587 *** 0.0607 ***
0.0348 ***
(9.88)
(7.72)
(3.59)
0.0904 *** 0.0669 **
0.2711 ***
(3.86)
(2.17)
(3.78)
0.0008
-0.0002
(1.47)
(0.23)
9,466
6,441
3,106
1.1833 ***
(39.5)
0.2174
(1.39)
0.0795 ***
(4.52)
0.0760
(1.22)
3,017
Precision instruments
1.1759 ***
0.0655
(32.0)
(1.18)
-0.0060
(0.19)
0.1464
(0.58)
0.0695 ***
0.0173
(2.98)
(1.55)
0.0595
0.0067
(0.77)
(0.36)
0.0004
-0.0004
(0.27)
(1.07)
1,958
631
42
（5）
Large firms
dy/dx
0.3120 ***
(7.92)
-0.0754
(1.25)
0.0045
(1.04)
0.0942 ***
(3.15)
2,669
0.8565 ***
(30.4)
-0.0873
(0.80)
0.0213 ***
(3.04)
0.0691 **
(1.98)
4,521
0.9299 ***
(26.7)
-0.1105 *
(0.35)
0.0345 ***
(3.58)
0.2820 ***
(3.92)
3,106
0.0032
(0.34)
0.0004
(0.19)
0.0012
(0.36)
0.0010
(0.36)
631
（6）
dy/dx
0.3243 ***
(6.88)
-0.0976
(1.17)
0.0018
(0.34)
0.1164 ***
(3.06)
-0.0003
(0.70)
1,917
0.8355 ***
(24.9)
-0.1001
(0.65)
0.0189 **
(2.39)
0.0966 **
(2.34)
-0.0020 *
(1.92)
3,269
0.9088 ***
(22.9)
0.0148
(0.03)
0.0313 ***
(2.95)
0.2909 ***
(3.43)
-0.0009
(0.92)
2,347
0.1213 *
(1.95)
0.0353
(0.53)
0.0225 **
(2.56)
-0.0050
(0.15)
-0.0008
(1.50)
463
Table 6a. Estimated results of intensive margin decision: Simple panel regression
(1)
(2)
SMEs
General
machinery
log (PCM)
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
Electrical
machinery
log (PCM)
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
Transportation
equipment
log (PCM)
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
Precision
instruments
log (PCM)
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
FE
0.9469 ***
(6.78)
0.1714
(1.16)
-0.4704 ***
(2.80)
0.7257 ***
(12.3)
0.1254
(0.99)
0.0073 ***
(7.45)
0.2408 / 7,153
FE
1.0114 ***
(7.07)
0.7218 ***
(12.3)
0.0316
(0.25)
0.2354 / 7,153
FE
1.0301 ***
(7.29)
1.7169 ***
(8.83)
-0.3841 **
(2.05)
0.8095 ***
(14.7)
-0.2678 **
(2.48)
0.0075 ***
(5.91)
0.2469 / 6,030
FE
1.0396 ***
(7.28)
0.7526 ***
(13.2)
-0.2796 ***
(2.60)
0.2436 / 6,030
RE
0.7355
(1.52)
0.0057
(0.02)
-0.1766
(0.25)
1.0683 ***
(16.5)
0.4603 **
(2.18)
0.0055 ***
(2.67)
0.2347 / 2,477
RE
0.9891 **
(2.00)
0.9995 ***
(14.4)
0.3697
(1.74)
0.2349 / 2,477
RE
0.2914 **
(2.39)
0.2328
(0.72)
-0.1429
(0.34)
0.9376 ***
(13.8)
0.3862 **
(2.30)
0.0037 **
(2.10)
0.1420 / 1,871
RE
0.3081 **
(2.54)
0.9075 ***
(13.1)
0.3151 *
(1.87)
0.1459 / 1,871
43
(3)
(4)
Large firms
FE
FE
1.4527 ***
1.4245 ***
(6.64)
(6.27)
0.6236 ***
(3.35)
-0.5732 ***
(2.84)
0.8655 ***
0.8819 ***
(10.7)
(10.8)
-0.1556
-0.2575
(0.91)
(1.54)
0.0030 ***
(2.86)
0.5255 / 2,431
0.5269 / 2,431
FE
1.3461 ***
(6.30)
1.9791 ***
(8.56)
-0.4733 **
(2.20)
0.8342 ***
(12.0)
-0.2018
(1.40)
0.0056 ***
(3.80)
0.5668 / 3,359
FE
0.5989
(1.17)
1.4816 ***
(6.33)
-0.0702
(0.13)
1.3370 ***
(17.0)
0.5740 ***
(2.90)
0.0031 **
(2.38)
0.6664 / 2,198
RE
1.7677 ***
(4.02)
0.9866 **
(2.19)
-1.0895 **
(2.05)
1.1022 ***
(11.6)
0.4613
(1.61)
0.0002
(0.09)
0.3738 / 608
RE
1.3146 ***
(6.18)
1.1510 ***
(26.2)
-0.4381 ***
(3.18)
0.5740 / 3,359
RE
1.2819 **
(2.55)
1.2506 ***
(22.4)
0.3755 **
(2.07)
0.6681 / 2,198
RE
1.9491 ***
(4.33)
0.9419 ***
(9.41)
0.4129
(1.47)
0.3757 / 608
Table 6b. Estimated results of intensive margin decision: IV panel regression
(1)
(2)
SMEs
General
machinery
log (PCM)
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
Sargan χ2 (p-value)
Electrical
machinery
log (PCM)
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
Sargan χ2 (p-value)
Transportation log (PCM)
equipment
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
Sargan χ2 (p-value)
Precision
instruments
log (PCM)
log (Yw)
log (P E /eP W )
log (A) -1
LIQR -1
LEND
overall R2 / no.
Sargan χ2 (p-value)
FE
1.2501 ***
(7.30)
0.2120
(1.42)
-0.4875 ***
(2.82)
0.7362 ***
(12.1)
0.0953
(0.72)
0.0069 ***
(6.71)
0.2311 / 6,876
3.838 (0.050)
FE
1.4872 ***
(9.07)
1.6667 ***
(8.33)
-0.3571 *
(1.86)
0.8203 ***
(14.7)
-0.3379 ***
(2.86)
0.0072 ***
(5.59)
0.2479 / 5,811
0.056 (0.814)
RE
2.4577 ***
(3.35)
-0.0424
(0.14)
-0.3663
(0.49)
1.0783 ***
(16.3)
0.5611 **
(2.53)
0.0048 **
(2.24)
0.2341 / 2,354
7.712 (0.006)
FE
0.3337 **
(2.46)
0.0849
(0.25)
-0.1908
(0.44)
0.9630 ***
(10.7)
0.4430 **
(2.30)
0.0034 *
(1.86)
0.1420 / 1,871
3.081 (0.079)
44
FE
1.2603 ***
(7.20)
0.7320 ***
(12.1)
-0.0135
(0.10)
0.2317 / 6,876
8.686 (0.003)
RE
1.3658 ***
(8.40)
0.9364 ***
(24.2)
-0.3364 ***
(3.42)
0.2532 / 5,811
2.084 (0.149)
RE
2.0138 ***
(2.86)
1.0071 ***
(14.2)
0.4592 **
(2.06)
0.2341 / 2,354
10.254 (0.001)
RE
0.3377 **
(2.52)
0.9051 ***
(12.9)
0.3005 *
(1.74)
0.1524 / 1,871
0.243 (0.622)
(3)
(4)
Large firms
FE
RE
1.6667 ***
1.6349 ***
(6.63)
(6.14)
0.6443 ***
(3.49)
-0.5296 ***
(2.67)
0.8573 ***
1.1718 ***
(10.7)
(22.5)
-0.2178
-0.0724
(1.32)
(0.45)
0.0026 **
(2.44)
0.5182 / 2,390
0.5264 / 2,390
0.634 (0.426)
4.073 (0.044)
RE
RE
1.9333 ***
1.6941 ***
(7.37)
(6.54)
1.1155 ***
(5.49)
-0.3649 ＊
(1.65)
1.2220 ***
1.2154 ***
(28.8)
(28.1)
-0.3823 ***
-0.3472 **
(2.68)
(2.43)
0.0046 ***
(3.05)
0.5739 / 3,253
0.5755 / 3,253
4.038 (0.045)
3.945 (0.047)
RE
RE
3.1911 ***
2.6105 ***
(3.99)
(3.58)
1.1065 ***
(4.79)
0.3352
(0.60)
1.4533 ***
1.3191 ***
(28.6)
(24.7)
0.4340 **
0.3995 **
(2.29)
(2.13)
0.0018
(1.26)
0.6657 / 2,156
0.6690 / 2,156
0.655 (0.418)
2.389 (0.122)
RE
RE
2.6935 ***
2.1996 ***
(4.78)
(3.98)
0.7893 ＊
(1.76)
-0.7093
(1.33)
1.1886 ***
1.0268 ***
(12.5)
(10.1)
0.4388
0.4056
(1.54)
(1.45)
0.0001
(0.04)
0.3660 / 600
0.3771 / 600
0.639 (0.424)
2.281 (0.131)
Table 7 Estimated result of price cost margin equation
(1)
General machinery
log(w)
log(c)
log(T)
log(DEBT)
overall R2 / No.
Electrical machinery
log(w)
log(c)
log(T)
log(DEBT)
overall R2 / No.
Transportation equipment
log(w)
log(c)
log(T)
log(DEBT)
overall R2 / No.
Precision instruments
log(w)
log(c)
log(T)
log(DEBT)
overall R2 / No.
45
(2)
Large
firms
FE
SMEs
FE
Fixed
-0.1553 ***
(113.6)
-0.0171 ***
(24.6)
0.9138 ***
(291.5)
-0.0016
(1.09)
0.9278 / 6,876
-0.1488***
(67.7)
-0.0240***
(20.3)
0.9213***
(166.4)
0.0000
(0.02)
0.9459/ 2,390
FE
-0.1578 ***
(94.2)
-0.0170 ***
(20.9)
0.9323 ***
(263.2)
-0.0091 ***
(4.67)
0.8844 / 5,811
FE
-0.1250***
(53.5)
-0.0305***
(25.9)
0.8617***
(147.8)
-0.0016
(0.70)
0.8872/ 3,253
FE
-0.0957 ***
(45.9)
-0.0186 ***
(17.3)
0.7780 ***
(90.5)
-0.0068 **
(2.03)
0.7711 / 2,354
FE
-0.1135***
(47.2)
-0.0196***
(19.0)
0.7793***
(84.1)
-0.0089***
(3.66)
0.8124/ 2,156
FE
-0.2089 ***
(67.0)
-0.0207 ***
(12.1)
0.9671 ***
(246.7)
-0.0018
(0.50)
0.9621 / 1,807
RE
-0.1991***
(48.8)
-0.0169***
(8.86)
0.9504***
(96.0)
-0.0054**
(2.07)
0.9586/ 600
Table 8. Decomposition of export growth during 1999-2007
(1)
(2)
General machinery
SMEs
Large
firms
(3)
(4)
Electrical machinery
SMEs
Large
firms
(5)
(6)
Transport equipment
SMEs
(7)
(8)
Precision instrument
Large
firms
SMEs
Large
firms
0.0270
0.0512
-0.0306
0.5469
0.0041
0.0340
-0.0347
0.0107
0.0146
0.0014
0.0078
0.0043
0.0247
0.3397
0.0197
0.0543
-0.0111
0.0011
0.0204
0.0001
0.1469
0.0382
0.1013
0.4129
-0.0053
0.0016
-0.0978
-0.0007
0.3554
0.0021
Without year dummy variables
log(PCM)
log(Yw)
log(P E /eP W )
log(A) -1
LIQR -1
LEND
log(w)
log(c)
log(T)
log(DEBT)
0.0358
0.0221
0.0701
0.0692
0.0012
0.1699
-0.0113
0.0039
0.0520
0.0001
0.1234
0.1174
0.1343
0.1743
0.0104
0.0856
-0.0437
0.0058
0.1976
0.0000
0.0097
0.1935
0.0100
0.3065
-0.0041
0.0713
-0.0991
-0.0023
0.1733
0.0010
0.0022
0.1372
0.0118
0.5617
0.0074
0.0350
-0.1241
-0.0087
0.2870
0.0001
0.0065
-0.0011
0.0194
0.1539
0.0025
0.0710
-0.0002
0.0034
-0.0047
-0.0001
With year dummy variables
log(PCM)
log(Yw)
log(P E /eP W )
log(A) -1
LIQR -1
LEND
log(w)
log(c)
log(T)
log(DEBT)
0.0361
0.1210
0.0089
0.0019
0.0053
0.0220
0.0079
0.1199
0.0688
-0.0002
0.2383
0.0034
0.3499
-0.0041
0.5587
0.0067
0.1437
0.0020
0.4964
0.0037
0.3193
0.0134
0.3567
-0.0049
-0.0114
0.0039
0.0524
0.0001
-0.0429
0.0056
0.1938
0.0000
-0.0911
-0.0021
0.1592
0.0009
-0.1088
-0.0076
0.2515
0.0001
-0.0002
0.0028
-0.0039
-0.0001
-0.0284
0.0088
0.0120
0.0011
-0.0112
0.0011
0.0206
0.0001
-0.0798
-0.0006
0.2903
0.0018
46