CESSA WP 2015-01

Industry-specific Exchange Rate Fluctuations, JapaneseExports, and Financial Constraints:
Evidence from Panel VAR Analysis

Shajuan Zhang

Yokohama National University
April 2015

Center for Economic and Social Studies in Asia (CESSA) Working Paper
Downloadable from:http://www.econ.ynu.ac.jp/cessa/publication/workingpaper.html

Center for Economic and Social Studies in Asia, Department of Economics
Yokohama National University

Industry-specific Exchange Rate Fluctuations, Japanese Exports, and Financial
Constraints: Evidence from Panel VAR Analysis
Shajuan Zhang

Abstract
This study employs the industry-breakdown data of financial constraints
obtained from the Bank of Japan, Tankan (Short-Term Economic Survey of Enterprises
in Japan) to empirically investigate whether and how Japanese firms’ financial
constraints (internal and external) influence the response of Japanese sectoral exports toan exchange rate shock using a Panel VAR approach. Furthermore, we use theindustry-specific real effective exchange rate data developed by Sato et al. (2013) toallow for different movements of real effective exchange rates across industries. It isfound that financial constraints have a significant influence on Japanese exports in
response to exchange rate shocks. Japanese exporters with either lower internal financial
constraints (higher liquidity ratio) or external financial constraints (accommodative
lending attitudes of financial institutions) are less affected by the yen’s appreciation. In
addition, if firms face high internal financial constraints, then reducing the externalfinancial constraints can help them mitigate the impact of the yen’s appreciation on theirexports. Thus, an accommodative financial environment plays an important role inalleviating the impact that the yen’s appreciation has on Japanese exports.

JEL Classification: F31, F33, F15
Keywords: Industry-specific real effective exchange rate, Internal financial constraints,
External financial constraints, Japanese exports, Panel VAR

 Corresponding author. International Graduate School of Social Sciences, Yokohama National

University, 79-4 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan.
Email address: zhangshajuan@gmail.com. Tel. 81-90-9767-4374

1

1. Introduction
Financial constraints can play a crucial role in firms’ export performance;
however, only a few studies have so far investigated how financial constraints affect the
impact that exchange rate fluctuations have on exports.1 Dekle and Ryoo (2002) built atheoretical model and presented empirical results using Japanese firm-level data from1982 to 1997.2 They found that keiretsu firms, which can be characterized as having
fewer financial constraints due to their strong relationship with banks, were lessresponsive to exchange rate fluctuations than non-keiretsu firms. This finding suggeststhat firms or industries that are less financially constrained tend to have lower exchange
rate elasticities of exports. Strasser (2013) noted that the degree of exchange ratepass-through for financially constrained firms is almost twice as high as that forunconstrained firms. Moreover, the export volumes of firms with financial constraints
are about twice as sensitive to exchange rate fluctuations as those of firms without
financial constraints. Although interesting findings, these studies basically focus on thebanks’ lending attitudes, which can be characterized as external financial constraints. In
the context of Japanese exporting firms, however, it is more important to consider theinternal as well as external financial constraints.
Figure 1 indicates how the two types of financial constraints have changed
from 2001 to 2013: one concerns the lending attitude of Japanese financial institutions(a measure of external financial constraints) and the other is the liquidity ratio ofJapanese firms (a measure of internal financial constraints). In Japan, banks’ lending
attitudes exhibited large fluctuations from 2002 to 2003 and again from 2009 to 2010
when Japanese firms faced severe external financial constraints. Furthermore, thelending attitudes tend to fluctuate at a relatively low level (sever lending attitude) after
the Lehman shock. In contrast, firms had very high liquidity ratios during the post-crisis
period, which indicate that Japanese firms on average had ample internal funds. The
degree of external and internal financial constraints may each differ across industries.
Thus, it is interesting to examine how and to what extent financial constraints influence
the response of Japanese exports to exchange rate shocks and whether the response
1See, among others, Amiti and Weinstein (2011) and Manova (2012).
Given inadequate data on financial constraints, they conducted two empirical schemes toinvestigate the role of such constraints on exchange rate elasticities of exports: First, they compared
the export elasticities of non-keiretsu firms with those of keiretsu firms, which can be characterized
as having fewer financial constraints due to their strong relationship with banks. Further, they
compare actual export elasticities with hypothetical export elasticities under the assumption that afirm hedges completely (fewer financial constraints).

2

2

varies across firms’ different levels of financial constraints.
This paper differs from existing studies in three respects. First, in contrast to
previous studies, the effects of both external and internal financial constraints are
empirically investigated for the first time. By fully utilizing financial data from the
Bank of Japan’s Tankan (Short-Term Economic Survey of Enterprises in Japan)
Database, the effects that external and internal financial constraints have upon therelationship between exchange rate fluctuations and Japanese exports are separately andjointly estimated. Second, this study uses a novel approach by focusing on differencesin the short-run effect of exchange rate shocks on exports when exporters face various
levels of financial constraints both internally and externally, whereas existing studies
have investigated to what extent the financial constraints can affect the impact of
exchange rate fluctuation on exports (Dekle and Ryoo, 2007; Stress, 2013). Third, theyen’s industry-specific real effective exchange rate developed by Sato et al. (2012) is
employed to consider the heterogeneity in exchange rate movements across industries asshown in Figure A1 in the Appendix. As pointed out by Byrne et al. (2008), estimatesfrom sectoral studies may be biased by using aggregated exchange rates; using an
industry-specific exchange rate can eliminate this bias.
Following the approach in Loayza and Raddatz (2007) and Towbin and Weber(2013), the present study estimates a Panel VAR and augments it with interaction terms
that allow the VAR coefficients to vary with financial constraints (internal and external).
This technique enables us to directly analyze how the responses of exports to exchange
rate shocks vary with both internal and external financial constraints. While theliterature typically uses interaction terms in a single equation regression, only a few
studies employ interaction terms in a VAR model. The use of interaction terms in Panel
VARs is a novel approach that will allow for deterministically varying coefficients
across time and industries.
This study reports three main findings: First, Japanese exports negatively andsignificantly respond to exchange rate shocks. Second, financial constraints (internaland external) have significant influences on how Japanese exports respond to exchange
rate changes. Japanese exporters with lower internal or external financial constraints areless affected by exchange rate shocks. Third, lowering external financial constraints canhelp exporters with relatively higher internal financial constraints become less affectedby exchange rate shocks. This result suggests that an accommodative financial
environment can help Japanese exporters alleviate the impact of the yen’s appreciation
on exports when facing internal financial difficulties.
The rest of the paper is organized as follows: Section 2 surveys the theoretical3

relationship between exchange rates, financial constraints, and exports. Section 3
describes the data. Section 4 explains the estimation methodology. Section 5 discusses
the main results, and Section 6 concludes.
2. Exchange Rate Fluctuations, Financial Constraints, and Exports
After the collapse of the Bretton Woods agreements in the early 1970s,
exchange rate fluctuations have been a central concern for policymakers and academics.
However, no consensus exists regarding the impact of exchange rate fluctuations onexports, though there is a growing body of literature on this issue. The traditional viewholds that a change in the exchange rate may affect relative prices between domestic
and foreign countries, which results in a shift in expenditures between domestic andforeign goods (Obstfeld, 2002). Thus, exchange rate appreciation may reduce exports byincreasing the consumer price of export goods in the destination country. However, as
shown in Figure 2, despite large fluctuations in the yen’s nominal effective exchange
rate, the export price index in the contract currency basis is quite stable, which suggeststhat Japanese exporters pursue pricing to market behavior.3 Given such an incomplete
exchange rate pass-through, Japanese exports might not be affected by exchange rate
fluctuations.4
Accordingly, the question becomes one of how financial constraints affect therelationship between exchange rate fluctuations and exports when such an incomplete
exchange rate pass-through exists. One possible mechanism is that exchange rate
fluctuations affect exporters’ profits, which leads to changes in working capital. For
example, following appreciation of the yen, Japanese exporters do not completely passthrough the exchange rate change to the price of export goods, likely due to competitive
pressure in the export market. Even though neither the price of export products in thedestination country nor export demand change, appreciation can cause exporters’current earnings to decline, as the price of export goods denominated in yen decreases.
As shown in Table 1, most industries have a negative correlation between exchange ratechanges and current profits, which supports the expectation that the yen’s appreciation
may cause exporters’ current earnings to drop. If exporters face difficulties in financing
internally or borrowing from external financial markets to produce export goods, a3This pricing behavior is empirically investigated by Shimizu and Sato (2015).
Existing studies have mainly explored three channels leading to incomplete pass-through, namely,
pricing to market (Dornbusch, 1987; Atkeson and Burstein, 2008), local currency pricing (Engel, 2003),
and local distribution margins (Campa and Goldberg, 2010).

44decline in current earnings leads to a decrease in working capital and, hence, to a fall in
production. Therefore, if firms can rely on easy access to external finance, exchange
rate changes will have less impact on exports, a dynamic that has been empirically andtheoretically examined by Dekle and Ryoo (2007), who show that less financiallyconstrained firms tend to have lower exchange rate elasticity of exports. Thismechanism is also rationalized by Aghion et al. (2009), who assume that firms’
borrowing ability is proportional to their current earnings, with a higher multiplier
implying a better developed financial market in the economy. When the export price isinflexible, firms’ current earnings denominated in domestic currency will fall in
response to exchange rate appreciation. This will reduce their ability to borrow tosurvive an idiosyncratic liquidity shock and thereby their ability to innovate in thelonger term. Therefore, relaxing external financial constraints can weaken the negative
effect of currency appreciation on their access to external finance and thereby exports.
3. Data Description
This study uses the quarterly series of the Japanese industry-breakdown datafrom the first quarter of 2001 through the third quarter of 2013. The industry
classification is based on the International Standard Industry Classification Revision 3(ISIC, Rev.3). Twenty-one manufacturing sectors at the two-digit level are aggregatedinto 13 industries, following Sato et al. (2013). The selection of industries is limited by
the exchange rate data. See Table A1 in the Appendix for details.
The data used to measure the financial constraints are from Tankan(Short-Term Economic Survey of Enterprises in Japan), which is widely used in Japan.5
The Tankan database reports data collected through a statistical survey by the Bank ofJapan, which is performed to provide an accurate picture of business trends ofenterprises in Japan. The survey is carried out quarterly in March, June, September, andDecember and comprises four groups: (1) “Judgment Survey,” (2) “Annual Projections,”(3) “Inflation Outlook of Enterprises,” and (4) “Number of New Graduates Hired(surveyed only in June and December).” Sample enterprises are selected from a
population (approximately two hundred and ten thousand private enterprises excluding
financial institutions) based on the Ministry of Internal Affairs and Communications’“Establishment and Enterprise Census of Japan” (conducted in October 2006) having at
5

Detail of the Tankan statistics can be found at:https://www.boj.or.jp/en/statistics/outline/exp/tk/extk03.htm/#p01

5

least 20 million yen in capital. Manufacturing and nonmanufacturing are split into 17
and 14 categories, respectively. This study only focuses on the manufacturing sector,
and 17 categories are aggregated into 13 industries as described above. Since theTankan database does not provide financial data for the rubber and plastics industry, it
will be excluded from the analysis.
The proxy for internal financial constraints is the liquidity ratio, which isdefined as the ratio of a firm’s quarter-end balance of cash, deposits, and securities
listed as liquid assets to monthly average sales during the fiscal year. A higher valuemeans the firm can access a greater amount of internal finance, which can becharacterized as the firm having fewer internal financial constraints. External financial
constraints are measured by the lending attitude of financial institutions, which is adiffusion index from the “Judgment Survey.” In the survey, the responding enterprises
are asked to choose one of three answers: 1) Accommodative, 2) Not so severe, 3)
Severe, to describe the lending attitude they face from financial institutions. These
responses are then compiled into a Diffusion Index (DI) by industry as follows:
DI (%) = Percentage share of enterprises responding Answer One minus Percentage
share of enterprises responding Answer Three.
Therefore, this is a direct measurement of external financial constraints, where a higher
value means they experience a more accommodative lending attitude from Japanesefinancial institutions, which can be characterized as these firms having lower externalfinancial constraints. As shown in Figure A2 in the Appendix, a big difference exists inliquidity ratio movements and the lending attitude of financial institutions acrossindustries. These sectoral variations can be exploited to identify the exchange rate’s
effect on Japanese exports for different levels of financial constraints, both internal andexternal.
Regarding exchange rate data, this study uses the yen’s industry-specific realeffective exchange rate developed by Sato et al. (2012), which captures the difference inexchange rate fluctuations across industries. An increase in the exchange rate means
appreciation. As shown in Figure A1 in the Appendix, the most striking feature of thisapproach is the large difference in the level of I-REER across industries, especially after
the sharp appreciation following the collapse of Lehman Brothers in September 2008. If
we ignore this heterogeneity in movements across industries by using aggregated REER,
which is widely used by existing studies, the results will be biased as suggested by
Byrne et al. (2008).
6

Global real income is proxied by the trade-weighted average of the partners’
real GDP for each industry. Following Sato et al. (2013), 28 of Japan’s trading partners
are included in the calculation as shown in Table A2 in the Appendix. The GDP data are
obtained from International Financial Statistics (IFS)-IMF. The export data used to
calculate the industry-breakdown export weight of each partner are from STAN Bilateral
Trade in Goods by Industry and End-use (BTDIxE), ISIC Rev.3, OECD.
The Japanese export data are from Trade Statistics of Japan Ministry of
Finance, which is monthly data based on nine-digit HS classifications. Then, the export
data is converted into ISIC rev.3 two-digit industries by using the concordance codefrom World Integrated Trade Solution (WITS) and further transformed into quarterly
data for 13 industries.
4. Empirical methodology
4.1 Model
Several authors (Stress, 2013; Dekle and Ryoo, 2007) have argued that anegative relationship exists between financial constraints and the reaction of exports in
response to exchange rate variability. However, most of these studies focus on theextent to which financial constraints can affect the impact that exchange rate
fluctuations have on exports. By contrast, this study tries to investigate the short-run
effect of an exchange rate shock at a different level of financial constraint. In addition,in contrast with existing studies, this study focuses not only on the external financial
constraints but also on internal financial constraints. This study marks the first empiricalinvestigation of the joint effect of these two types of constraints on the response ofJapanese exports to an exchange rate shock.6
This study employs a structural Panel Vector Autoregression (Panel VAR)
model augmented by adding interaction terms with financial constraints. The standard
Panel VAR is as follows:q

A0Yi ,t   AlYi ,t  l  Di  ei ,t ,

(1)

l 1

6

The financial constraints defined in existing studies can be characterized as external financial
constraints. For example, the financial constraints variable defined in Strasser (2013) is a judgment ofcredit provided by banks, with three possible answers “accommodating,” “neutral,” and “restrictive,”
which is quite similar to the proxy for external financial constraints used in this study.

7where i denotes industry, t represents time, and q stands for the number of lags.
Yi ,t is a vector that contains three variables: (1) the weighted average of trading
partner’s GDPs is a proxy for global real income (external variable), (2) Japanese export
volumes (real export), and (3) I-REER. D i denotes industry-specific effects. To allow
the coefficients to vary with both internal and external financial constraint levels,interaction terms with financial variables ( Fi.t ), including both internal and externalfinancial constraints, are added into the Panel VAR model:

q

q

l 1

l 1

A0Yi ,t  B0 Fi ,t * Yi ,t   AlYi ,t  l   Bl Fi.t * Yi ,t  l  Fi ,t Di  vi ,t , (2)which can be rewritten in a more compact form:q i ,t ,0Yi ,t    i ,t ,lYi ,t  l  X i ,t   i ,t ,
l 1

(3)

where X i ,t is a vector of industry-specific effect ( D i ) and single terms of financial
variables ( Fi.t ), which contains internal financial constraints ( InFi.t ), external financial
constraints ( ExFi.t ), and interaction terms of internal and external financial constraints( InFi.t * ExFi.t ). The matrices  i ,t , g ( g  0,1,..., q ) with time t and industry subscript icontain time-varying coefficients defined as  i ,jkt , g , which are combinations of
B g Fi ,t

,

Ag

and

g  0 ,1,..., q . Thus, each of these coefficients is a function of financial

constraints for different lags including contemporaneous ones.  i,t ’s are the structural
errors, which are i.i.d. with zero means and a diagonal variance-covariance matrix Σ.
The identification assumption used in this study is a combination of strict
exogenous and contemporaneous zero restrictions. First, real global income is restricted
to being exogenous with respect to two domestic variables, Japanese export volume and
I-REER. Therefore, domestic shocks have neither contemporaneous nor lagged effects
on real global income. Second, contemporaneous zero restrictions are imposed on two
domestic variables: export volumes cannot be contemporaneously affected by an
I-REER shock, but the I-REER can be contemporaneously affected by export volumes.
This assumption is motivated by the fact proposed in Clark et al. (2004) that exporters
8

are unlikely to respond immediately to exchange rate changes but will rather take a“wait and see” approach. These assumptions are equivalent to imposing the following
structures in the  i ,t ,0 and  i ,t ,l matrices:

 1
0
 21
 i , t ,0  i , t ,01
31
32
i , t ,0 i , t ,0


0

0
1

 l11
0
0 
 21
23 
 i ,t ,l  i ,t ,l i22
, t , l i , t , l , l  1,2,..., p 31  32  33 
 i,t ,l i,t ,l i,t ,l ;

(4)While the exogenous restriction of real global income should be rational instudies of small economies, this might be rather controversial in studies on countries
having a relatively large economy, such as Japan. In fact, a standard Granger causality
test cannot reject the hypothesis that I-REERs and export volume cannot Granger cause
real global income, which rationalizes the assumption for real global income.
The formula of the coefficients  i ,jkt , g in the matrices  i ,t , g is given by

i ,jkt , g   gjk   gjk,1InFi ,t   gjk,2 ExFi ,t   gjk,3 InFi ,t * ExFi ,t ,(5)where internal financial constraints ( InFi.t ) is proxied by the liquidity ratio, and externalfinancial constraints ( ExFi.t ) is proxied by the lending attitude of financial institutions.
This structure allows Japanese exports to respond differently to exchange rate shocks
for different levels of financial constraint. To control for the endogeneity problem offinancial constraints in that their current value may depend on exports, all financial
variables enter the estimation with a one time period lag.
For a comparison with existing studies, the first step is to examine the dynamic
effects of exchange rate shocks conditional on internal financial constraints and externalfinancial constraints separately by setting either  gjk,2   gjk,3  0 or  gjk,1   gjk,3  0 . Then,the most general case in which all coefficients are unrestricted can be examined. Whilethe domestic variables, export volumes, and I-REERs are allowed to be dependent on
financial constraints (internal and external), the external variable of real global income
is restricted to being independent of domestic financial constraints. Note that l11 in i ,t ,l , l  1,2,..., p , is without time t and industry subscript i.
The use of Panel VARs, which allow heterogeneity in coefficients by including9

interaction terms, together with the corresponding restrictions on the parameters, iscommon in recent literature that estimates the impact of exogenous shocks on different
macroeconomic variables (Loayza and Raddatz, 2007; Towbin and Weber, 2013). Thisapproach has the advantage of being able to test how country or industry characteristics
affect the economy’s response to external shocks.
4.2 Implementation
Under the identification assumptions described above, error terms are assumed
to be uncorrelated across equations. Thus, the Panel VAR in (3) can be efficiently
estimated equation by equation using OLS, and the finance crisis dummy is included ineach equation to control for the disturbance caused by the Lehman collapse. Allvariables enter the Panel VAR model in terms of the first differences of the logarithmic
form. The number of lags is chosen following the Schwartz information criterion, andthe optimal number of lags is two. The confidence intervals for the impulse response
function (IRF) are estimated by parametric bootstrapping following the procedure
mentioned in Towbin and Weber (2013).
To test whether the response of Japanese exports to an exchange rate shock isaffected by financial constraints (internal and/or external), three different Wald tests areapplied. The first one tests for the joint significance of all interactions and triple terms.
The null hypothesis is that internal and external financial constraints cannot explain theheterogeneity in the response of Japanese exports:
H 0 :  gjk,1   gjk,2   gjk,3  0, g  0,1,2

The second one tests for a significant role for internal financial constraints. The null
hypothesis is that internal financial constraints cannot improve the overall explanatory
power:
H 0 :  gjk,1   gjk,3  0, g  0,1,2 .

The last test determines whether external financial constraints play a significant role.
The null hypothesis is that the external financial constraints cannot improve the overall
explanatory power:
10

H 0 :  gjk,2   gjk,3  0, l  0,1,2 .

As shown in Table 2, the Wald statistics of these three tests are 4.89, 4.88, and4.50, respectively, which indicates that financial constraints (internal and external) do
play a role in explaining the relationship between exchange rate fluctuations andexports.
Japanese exports are allowed to have varying responses to an exchange rate
shock with internal financial constraints and external financial constraints, which arecaptured by i ,jkt , g . Although these coefficients are time-varying, we can evaluate themusing a specific value of financial constraint (internal and external). To analyze theeffect of financial constraints, this study compares the cumulative impact of exchange
rate shocks on Japanese exports at different levels of financial constraint. Specifically,
two levels of financial constraint are specified, namely, a relatively higher level offinancial constraint measured at the 25th percentile value and relatively lower level offinancial constraint measured at 75th percentile value.7 Comparing a given shock’s
impact at these different levels provides a clear picture of the extent to which financial
constraints serve to amplify or dampen the impact of exchange rate shocks.
5. Empirical Results
5.1 Response to an exchange rate shock without interactions
For comparison purposes, a three-variable Panel VAR is first estimated, which includes
real global income, Japanese export volumes, and I-REERs without any interaction
terms. Figure 3 shows the cumulative effect of a one unit shock in terms of exchange
rate (appreciation) on Japanese export volumes. The dotted lines in the figure show 10%
and 90% confidence bands estimated by parametric bootstrapping. In addition, it
indicates that Japanese export volumes have a significantly negative response to an
exchange rate shock, which indicates the yen’s appreciation will reduce export volumes.
This result differs from those of existing studies, which support the idea that exchange
The higher the level of measured financial constraints (internal and external), thelower the actual financial constraints are; see Section 3.1.
711

rate fluctuations generally have an insignificant impact on exports for developed
countries (Hooper and Kohlhagen, 1978; Grier and Smallwood, 2007). However,previous studies used an aggregated exchange rate, which may lead to some bias on theestimates, as pointed out by Byrne et al. (2008). Another explanation proposed by Dekle
and Ryoo (2007) is that the negative impact of exchange rate fluctuations can be
attributed to changes in financing constraints that are correlated with exchange rate
fluctuations. In their working paper version, Dekle and Ryoo (2007) show thatexchange rate depreciation is an average related to a relaxation of financing constraints
in 10 out of 14 industries. In addition, Table 1 shows that the yen’s appreciation isnegatively correlated with current profit in most industries. When the exchange rate
appreciates, exporters with tightened financial constraints cannot produce more goods
for export even when foreign prices are inflexible. Consequently, exchange rate
appreciation has a negative impact on exports.
5.2 Response to exchange rate shocks conditional on external financial constraints
Next, external financial constraints are added to the Panel VAR model. Figure 4 shows
the response of Japanese exports to an exchange rate shock conditional on different
levels of external financial constraints, which are proxied by the lending attitude offinancial institutions. The relatively lower level of financial constraints is measured by
75 percentile point (relatively accommodative lending attitude by financial institutions),
and the relatively higher level of financial constraints is measured by 25 percentile point(relatively severe lending attitude by financial institutions). We can observe thedifferent response of Japanese export volumes to an exchange rate shock across these
two levels of external financial constraint: exporters show a smaller response to an
exchange rate shock when facing relatively low external financial constraints. This
result is consistent with the findings of existing studies (Dekle and Ryoo, 2007; Stress,2013) that support the view that financially constrained exporters are more sensitive to
exchange rate fluctuations. However, it is surprising that the difference is negligible.
5.3 Response to an exchange rate shock conditional on internal financial constraints
Figure 5 shows the cumulative impact of exchange rates conditional on
different levels of internal financial constraints, which is proxied by the liquidity ratio.
Similar to previously, a relatively low level of internal financial constraint is measured
by the 75 percentile point (higher liquidity ratio) and a relatively high level of internal
12financial constraint is measured by the 25 percentile (lower liquidity ratio). It is very
striking that, compared to external financial constraints, the difference in the response ofJapanese export volumes to an exchange rate shock across the two levels of internalfinancial constraints is quite large. An exchange rate shock has quite a large impact onexports, and this effect continues for a long period under relatively higher internalfinancial constraints, while the impact is almost insignificant at relatively lower internalfinancial constraints. This finding may suggest that the internal financial constraints do
appear to have a significant influence on the impact that exchange rate shocks have onexports.
5.4 Response to an exchange rate shock conditional on both internal and externalfinancial constraints
The main interest of this study lies in the triple interaction term between
exchange rate fluctuations, internal financial constraints, and external financial
constraints. Figure 6 shows the impulse response of export volumes to an exchange rate
shock for four combinations of internal financial constraints and external financial
constraints, i.e.,., (1) low internal financial constraints (high liquidity ratio) and low
external financial constraints (accommodative lending attitude); (2) low internalfinancial constraints and high external financial constraints (severe lending attitude); (3)high internal financial constraints (low liquidity ratio) and low external financial
constraints; and (4) high internal financial constraints and high external financial
constraints. As shown in Figure 6, an exchange rate shock has no significant effect on
Japanese exports when both internal and external financial constraints are relatively low.
By contrast, when both internal and external financial constraints are relatively high, an
exchange rate shock has a much larger impact on Japanese exports. We can also observethat easing either internal or external financial constraints does appear to mitigate theimpact of exchange rate shocks. When external financial constraints are relatively high,the negative impact of an exchange rate shock on Japanese exports becomesinsignificant by easing internal financial constraints (relatively lower internal financial
constraints). This result may imply that by increasing its liquidity ratio, a firm canalleviate at least some of the negative impact of exchange rate fluctuations. However, an
excessively high liquidity ratio is not good for capital efficiency. As shown in the upper
part of Figure 6, even when internal financial constraints are relatively high, easing
external financial constraints leads to a smaller response by Japanese exports to an
exchange rate shock. This suggests that an accommodative financial environment can
13help firms with high internal financial constraints alleviate the impact that yen
appreciation has on their exports.
6. Conclusion
This study is motivated by the fact that Japanese firms are facing a quite
different financial environment from that experienced prior to the Lehman collapse.
Financial institutions in Japan became relatively severe in their lending attitudes, andliquidity ratios became relatively higher after the collapse. This study employed a Panel
VAR approach to empirically investigate whether and how internal and externalfinancial constraints influence the response of Japanese exports to an exchange rate
shock. Internal financial constraints are proxied by a firm’s liquidity ratio, and externalfinancial constraints are proxied by the lending attitude of financial institutions. All
industry-breakdown data of financial constraints are obtained from Bank of Japan,
Tankan (Short-Term Economic Survey of Enterprises in Japan). To consider any
possible bias that may be caused by using aggregated exchange rates, this study
employs the industry-specific real effective exchange rate data developed by Sato et al.
(2013) to allow for the different movements of real effective exchange rates acrossindustries.
It is concluded that first, Japanese exports negatively and significantly respondto exchange rate shocks. Under the existence of an incomplete pass-through, one
explanation for this result proposed by Dekle and Ryoo (2007) is that the
responsiveness of Japanese exports to exchange rate fluctuations can be attributed to atightening of balance sheet constraints. Second, financial constraints (both internal andexternal) have significant influences on the response of Japanese exports to an exchange
rate shock. Japanese exporters with lower internal or external financial constraints areless affected by an exchange rate shock. Finally, an accommodative financial
environment can help firms with higher internal financial constraints alleviate theimpact of the yen’s appreciation on their exports.
Although our finding suggests that increasing the liquidity ratio can help firms
absorb some impact of an exchange rate shock, capital efficiency can be harmed if theliquidity ratio becomes excessively high. As highlighted by Sher (2014), large cash
holdings may be a potential obstacle to the success of Abenomics by impeding growthin wages, investment, and dividend payments. Furthermore, it is shown that financial
constraints are one of the factors contributing to positive cash accumulations by
Japanese nonfinancial firms. Therefore, a policy that improves firms’ access to14

market-based financing (external finance) can help firms mitigate the negative impact of
exchange rate shocks and also encourage them to use their cash holdings.

Acknowledgements
The earlier version of this paper was presented at the 14th International Convention on
the East Asian Economic Association (EAEA) in Bangkok, Thailand, November 1–2,
2014, and also at the RIETI-IWEP-CESSA Joint-Workshop in Beijing, China,
December 12–14, 2014. The author is grateful for helpful comments and suggestions
provided by Kiyotaka Sato, Eiji Ogawa, Junko Shimizu, Kentaro Kawasaki, Taiyo
Yoshimi, Willem Thorbecke, Qiyuan Xu, Jianwei Xu, Xiaoqin Li, and conference andworkshop participants. Furthermore, the author would appreciate the financial supportof the JSPS (Japan Society for the Promotion of Science) Grant-in-Aid for Scientific
Research (B) No. 24330101 and Yokohama National University.

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16

Table 1 Correlations between Changes in Exchange Rate and Current Profits
Industry Description
All Including from Industry 1 to Industry 13
1
Food, Beverage, and Tobabco
2
Textiles, Textile Products, Leather, and Footwear
3
Wood Products(excl. furniture)
4
Paper, Paper Products, and Printing and publishing
5
Coke, Refined Petroleum Products, and Nuclear Fuel6
Chemicals and Chemical Products
8
Non-metallic Mineral Products
9
Basic Metals and Fabricated Metal Products
10
Machinery and Equipment n.e.c.
11Electrical Machinery and Apparatus n.e.c.
12
Optical Instruments
13
Transport Equipment

NEER-0.227-0.030-0.162-0.260-0.1350.017-0.247-0.419-0.291-0.364-0.3440.088-0.268REER-0.233-0.022-0.222-0.006-0.202-0.134-0.337-0.390-0.418-0.359-0.3240.074-0.289Note: All variables entered in the first difference of log terms. Current profit is the half
yearly data from the first half of 2001 to the first half of 2013.
Data source: the Bank of Japan, Tankan (Short-Term Economic Survey of Enterprises inJapan).

Table 2 Results of Wald tests
Null hypothesis

Wald statistics

External Financial Contraints:

H 0 :  l ,jk2   l ,jk3  0

4.89***

Internal Financial Constraints:

H 0 :  l ,jk1   l ,jk3  0

4.88***

External & Internal Financial Constraints:

H 0 :  l ,jk1   l ,jk2   l ,jk3  0

4.50***

17

Figure 1 Lending Attitude of Financial Institutions and Liquidity Ratios for All
Manufacturing Sectors
25

1.8

20

1.6

15

1.4
1.2

10

1

5

0.8

0

0.6

-5

0.4

-10

0.2

-15

0
Lending Attitude

Liquidity Ratio

Data source: the Bank of Japan, Tankan (Short-Term Economic Survey of Enterprises inJapan), 2001Q1–2013Q3.

Figure 2 Export Price Index for All Industries
120.000(2005=100)
110.000
100.000
90.00080.00070.000EPI_JPY
60.0001/2001

1/2003

EPI_Contract
1/20051/2007

REER(All)
1/2009

Data source: the Bank of Japan, from Jan 2001 to Oct 2014.

18

1/20111/2013

Figure 3 Responses to an Exchange Rate Shock Without Interactions

Note: Bands are 90 % confidence intervals computed by bootstrapping.

Figure 4 Response to an Exchange Rate Shock Conditional on External Financial
Constraints

Note: Bands are 90 % confidence intervals computed by bootstrapping.

19

Figure 5 Response to an Exchange Rate Shock Conditional on Internal Financial
Constraints

Note: Bands are 90 % confidence intervals computed by bootstrapping.

20

Figure 6 Response to an Exchange Rate Shock Conditional on Both Internal andExternal Financial Constraints

Note: Bands are 90 % confidence intervals computed by bootstrapping.

21

Appendix
Table A1 Industry Classification
Code
1
2
3
4
5
6
7
8
9
10
11
12
13

ISIC.rev3 Industry Name
Description
15-16 Food
Food, Beverage, and Tobabco
17-19 Textile
Textiles, Textile Products, Leather, and Footwear
20
Wood
Wood Products(excl. furniture)
21-22 Paper
Paper, Paper Products, and Printing and publishing
23
Petroleum
Coke, Refined Petroleum Products, and Nuclear Fuel24
ChemicalChemicals and Chemical Products
25
RubberRubber and Plastics Products
26
Non-Metal
Non-metallic Mineral Products
27-28 Metal
Basic Metals and Fabricated Metal Products
29
General Machinery Machinery and Equipment n.e.c.
30-32 Electric Machinery Electrical Machinery and Apparatus n.e.c.
33
Optical Instruments
34-35 Transport Equipment Transport Equipment

Table A2 Partner Countries
1.
2.
3.
4.
5.
6.
7.

Asia
China
India
Indonesia
Korea
Malaysia
Philippine
Singapore

8. Taiwan
9. Thailand
Europe10. Belgium
11. Canada
12. Denmark
13. Finland
14. France

15. German
16. Greece
17. Ireland
18. Italy
19. Netherlands
20. Norway
21. Russia
22. Spain

22

23. Sweden
24. Switzerland
25. UK
Other
26. Australia
27. New Zealand
28. USAFigure A1 Industry-specific Real Effective Exchange Rates in Japan
150.0

130.0

140.0

120.0

130.0
110.0

120.0
110.0

100.0

100.0

90.0

90.0
80.0

80.0
70.0
2001M01 2003M07 2006M01 2008M07 2011M01 2013M07

70.0
2001M01 2003M07 2006M01 2008M07 2011M01 2013M07

Food

TextileNon-Metal

Metal

Wood

Paper

General Machinery

Electrical Equipment

Petroleum

ChemicalOptical Instruments

Transport Equipment

Manufacturing All

Manufacturing All

Data source: Research Institute of Economy, Trade & Industry, IAA (RIETI), January
2001–July 2014.

23

Figure A2 Lending Attitude of Financial Institution and Liquidity Ratio for Each Sector

Figure A2 Lending Attitude of Financial Institution and Liquidity Ratio for Each Sector(Continued)
24

Data source: the Bank of Japan, Tankan (Short-Term Economic Survey of Enterprises inJapan), 2001Q1–2013Q3.

25