Abenomics, Yen Depreciation, Trade Deficit
and Export Competitiveness§
SHIMIZU Junko† and SATO Kiyotakaǂ
Abstract
The sharp depreciation of the yen from the end of 2012 was expected to have a
positive impact on the Japanese trade balance, since Japan had recorded large trade deficits
since the Great East Japan Earthquake in March 2011. Trade balance tends to deteriorate at the
beginning due to the J-curve effect. However, the Japanese trade balance has not shown any
signs of improvement, even though one year has passed since the start of the yen depreciation.
There is a growing concern that Japanese firms might lose export competitiveness in the global
market. This paper empirically shows that Japanese firms expanded overseas production after
the sharp appreciation of the yen from 2008 to 2012, which resulted in the increase in Japanese
imports of intermediate inputs as well as finished products. The empirical result of an
auto-regressive distributed lag (ARDL) model also indicates that the long-run impact of yen
depreciation has weakened in recent years. In addition, Japanese manufacturing export prices in
terms of the contract (invoice) currency have not changed in response to the large exchange rate
fluctuations of the yen, which is empirically confirmed by the time-varying parameter
estimation of the exchange rate pass-through analysis. Finally, a comparative analysis of the
industry-specific exchange rate between Japan and Korea shows that the recent depreciation of
the yen has improved the export price competitiveness of the Japanese manufacturing sectors.
JEL Classification: F23, F31, F33
Keywords: J Curve Effect, Exchange Rate Pass-Through, PTM (pricing-to-market)、Export
Competitiveness, Industry-specific Real Effective Exchange Rate
§
This study is conducted as a part of the Project “Research on Exchange Rate Pass-Through”
undertaken at Research Institute of Economy, Trade and Industry (RIETI). The authors are grateful
for helpful comments and suggestions by Discussion Paper seminar participants at RIETI. The
authors would also appreciate the financial support of the JSPS (Japan Society for the Promotion of
Science) Grant-in-Aid for Scientific Research (A) No. 24243041, (B) No. 24330101, and (C) No.
24530362.
† Corresponding author. Faculty of Economics, Gakushuin University. Email:
junko.shimizu@gakushuin.ac.jp
ǂ
Department of Economics, Yokohama National University.
1
1. Introduction
The ‘Abenomics’ successfully changed the recent trend of the yen appreciation that
started in 2008. A rapid and large depreciation of the yen from the end of 2012 was expected to
have a positive impact on the Japanese trade balance. While trade balance tends to deteriorate at
the beginning due to the J-curve effect, Japanese trade balance has not yet shown any sign of
improvement, even though one year has already passed since the start of yen depreciation in the
end of 2012. Accordingly, there is a growing concern that Japanese firms might lose export
competitiveness in the global market.
In opposition to the above view, this paper empirically demonstrates why Japanese
trade deficit continues to grow despite the substantial depreciation of the yen. First, Japanese
firms have expanded overseas production network after the sharp appreciation of the yen from
2008 to 2012, which results in the increase in Japanese imports of intermediate inputs as well as
finished goods. In fact, Japanese imports of machinery goods have grown at a rapid rate for the
first three quarters in 2013: 16.4 percent in general machinery, 19.8 percent in electrical
machinery and 15.6 percent in transport equipment, which are much higher than in
mineral-related fuels (8.3 percent). Thus, since Japanese machinery firms have become more
integrated into global (especially Asian) production network, Japanese exports of finished goods
tend to induce more imports of intermediate inputs than before, which likely weakens the
positive impact of yen depreciation on Japanese trade balance. This observation is empirically
supported by the estimated results of an auto-regressive distributed lag (ARDL) model.
Second, casual observation of the export price data published by the Bank of Japan
shows that Japanese machinery export price in terms of the contract (invoice) currency has not
changed in response to the large exchange rate fluctuations of the yen. However, by conducting
the time-varying parameter estimation of the exchange rate pass-through in Japanese machinery
exports, we demonstrate that Japanese firms pursue different pass-through behavior in response
to asymmetric exchange rate changes in the yen. During the yen appreciation after the Lehman
Brothers collapse, Japanese firms increased the degree of exchange rate pass-through, while
Japanese firms pursue the pricing-to-market (PTM) behavior in response to the rapid yen
depreciation from the end of 2012. It means that the recent depreciation of the yen does not
cause the export price reductions in the destination countries, which weakens the positive
impact of yen depreciation on trade balances.
Finally, a comparative analysis of the industry-specific real effective exchange rates
between Japan and Korea shows that the recent depreciation of the yen improved the export
price competitiveness of the Japanese machinery industries. In particular, it is confirmed that
both electrical machinery and transport equipment industries in Japan have rapidly recovered
2
their export price competitiveness compared with the Korean counterparts. Thus, the slow
recovery of Japanese trade balance in response to the yen depreciation can be explained by the
Japanese firms’ pricing behavior as well as the active overseas operation caused by the
unprecedented level of yen appreciation before Abenomics. It is more important to look at
income balance as well as trade balance.
The remainder of this paper is organized as follows. Section 2 describes the current
characteristics Japanese trade by data. Section 3 conducts the empirical analysis of the yen
depreciation impact on Japanese trade balance by using an auto-regressive distributed lag
(ARDL) model. Section 4 observes the export price data published by the Bank of Japan and
conducts the time-varying parameter estimation of the exchange rate pass-through in Japanese
machinery exports. Section 5 indicates the change of export competitiveness between Japan and
Korea by using the industry-specific real effective exchange rates. Finally, Section 6 concludes.
2. What are the main factors that caused Japanese trade deficit?
Trade deficit has become almost a norm in Japan since the Great East Japan
Earthquake that occurred in March 2011. In 2013, the trade deficit reached 11 trillion yen, the
highest since 1979. It is said that the main cause is the increased import of liquefied natural gas
(LNG) for generating thermal power prompted by the suspension of nuclear power plants, in
addition to the stagnated production due to disruption of parts supply network by the earthquake,
sudden appreciation of the yen, and export slump due to global economic slowdown.
Figure 1 shows the changes in the trade balance and the dollar yen exchange rate.
Japan recorded a trade deficit in January 2011 after the exchange rate stayed around 80 to 85
yen in the second half of 2010. Since then, the trade deficit trend became prominent, especially
after April 2011 due to the Great East Japan Earthquake in March.
Although the yen began rapidly depreciating in response to “Abenomics” launched in
late 2012 by the new Liberal Democratic Party regime, the trade deficit trend has continued. It
had increased by 1.6 times to 4.8 trillion—the highest in the comparable records since 1979—in
the first half of the fiscal year 2013 to the same period in the previous year. This is because the
yen depreciated against the dollar by more than 20% from the same period in the previous year
and caused the increase of import value in term of the yen while the import of LNG remained
high. It looks an early stage of the J-curve effect in which the trade deficit initially increases
when the yen depreciates.
To further analyze the trade deficit, let us first look at the import trends by item since
2010 based on the Ministry of Finance’s Trade Statistics of Japan. In addition to the increase in
3
volume after the Great East Japan Earthquake, the import of mineral fuels calculated in yen has
drastically increased since the yen began depreciating at the end of 2012. As shown in Figure 2,
mineral fuels account for 34% of the total imports—the highest percentage than any other
item—at the end of September 2013. However, while the growth rate of import value from the
January-September period in 2012 to the same period in 2013 was 8% for mineral fuels, it was
16%, 19%, and 15% for general machinery, electric equipment, and transport equipment,
respectively, indicating that the increase in imported industrial products is actually another
major cause for the increased trade deficit (Figure 3).
Looking at more detailed item data on the import of manufacturing products, we see
that the value of imported parts has grown since the beginning of 2013 (Figure 4). The growth
rate of import value from the January-September period in 2012 to the same period in 2013 was
35% for electric components (including semiconductors) and 13% for microchips (both electric
equipment parts), while the value of imported auto parts (transport equipment parts) grew by
20%. In particular, the volume of imported auto parts is also steadily trending up because
Japanese automobile and parts manufacturers are importing more parts from their overseas
subsidiaries in Asia (Figure 5). According to Mori (2001), many Japanese automobile and parts
makers began importing from their overseas subsidiaries after the 1997 Asian financial crisis in
order to actively take advantage of competitively priced parts made in Asia, although those
subsidiaries used to focus on the production for the local market during the 1990s. In particular,
in China, where the labor cost is low, and in Thailand, where the automotive industry is most
clustered, have become the international centers for auto parts supply for Japan.
Such increases in the imports of industrial products and intermediate goods were
brought by the increased relocation of Japanese manufacturing companies to overseas
production networks in recent years. According to the results of the FY2012 Annual Survey of
Corporate Behavior by the Cabinet Office (Figure 6), 67% of manufacturing companies are
producing overseas. The figure is particularly high for the processing and manufacturing
industry at over 70%. Furthermore, while the percentage of overseas production for the entire
manufacturing industry is 17%, the figure for the processing manufacturing industry is as high
as 24% and increasing fast. These increases in the imported industrial products are the result of
efficient corporate activities of importing inexpensive industrial products and parts
manufactured with the right material in the right place within the production network of
Japanese companies around the world and selling final products in Japan or exporting again to
overseas after the value added. With the international division of labor within Asia further
strengthened by Japanese companies, increased export of industrial products from Japan today
is accompanied by increased import of parts from overseas subsidiaries, implying that we have
now built a structure in which trade balance is unlikely to be improved by yen's depreciation.
4
Next, let us look at the export trends. As Figure 7 indicated, the export value from the
January-September period in 2012 to the same period in 2013 grew most for mineral fuels, by
45%, followed by chemical products (17%) and raw materials (17%). In contrast, exports
among major manufacturing industries in Japan have not grown much; electric equipment,
transport equipment, and general machinery grew only by 3%, 4%, and 0.1%, respectively. As
indicated by Figure 8, which shows the changes in the export volume of transportation
equipment, there is no significant change in 2013. To summarize the above results, as of
September 2013, the yen that began depreciating at the end of 2012 has only shown a notable
effect of inflating the import value. Unfortunately, the positive effect of increasing exports has
not been observed.
Meanwhile, the income balance trends as well as the trade balance trends have become
important, considering the international business development of Japanese companies. Besides
the international division of labor within Asia, Japanese companies have been strengthening the
production and sales systems in the United States and European countries. For example,
Japanese automobile companies have shifted their focus from exporting finished vehicles to
producing and selling there. Because part of the profits from the overseas production and sales
activities is remitted to the head office in Japan, it shows as an increase in the income balance.
In fact, as Figure 9 showed, the growth rate of direct investment income listed under income
balance for the period January-September 2013 was 26%, which is substantially higher than
14% growth rate of securities investment income. Undoubtedly, increasing GDP would be a
policy issue from the standpoint of domestic employment recovery; however, if we were to
consider the international business activities of Japanese companies, it is important to make
decisions based on both trade and income balances.
3. Empirical analysis of J-Curve effects
A devaluation of a country's currency affects its trade balance. In the case of Japan, the
effect of yen's devaluation is an increase in the trade deficit in the short run, though this will
shift into an increased international demand for Japanese export goods due to a lowered yen's
exchange rate and will improve the trade balance in Japan later. Such a J-Curve effect was
observed in Japanese economy several times. For example, just after the Plaza Accord in 1985,
Japanese trade surplus expanded temporarily and continued to expand till 1988 despite of the
strong yen, and then it started to reduce in 1989. It is considered as an inverted J-curve effect.
Similarly, at the time of a strong yen from 1990 to 1995, Japanese trade surplus expanded at
first followed by the reduction of trade surplus later. Furthermore, after hitting the postwar
5
highest yen against the US dollar at 79 in April 1995, the dollar yen exchange rate turned to
depreciate. At that time, the J-curve effect which Japanese trade surplus decreased in 1996 and
then improved in 1998 was observed.
There are numerous empirical studies exploring whether currency depreciation leads
to trade balance improvement in the long run, and if so whether a J-curve pattern occurs1. As a
representative study on the trade between US and Japan, Rose and Yellen (1989) used quarterly
data for the period between 1960 and 1985,however they could not find both short-run and
long-run relationship between bilateral real exchange rates and trade flows. Bahmani-Oskooee
and Brooks (1999), on the other hand, employed ARDL model which use both cointegration
relationship and error-correction model between US and its trading partners. As a result, they
found that long-run effects of real depreciation of the dollar improved the US trade balance
while the short-run effects do not follow the J-curve pattern. Bahmani-Oskooee and Goswami
(2003) applied ARDL model by using quarterly bilateral data over 1973 to 1998 period between
Japan and its nine major trading partners and confirmed evidence of the J-curve between Japan
and Germany as well as between Japan and Italy2.
In this section, we conduct the empirical analysis of the effects of monthly yen's real
effective exchange rate change on Japanese aggregate trade balance by using ARDL model as
well as the above previous studies over January 1985 to September 2013 period which includes
the sharp yen's depreciation due to Abenomics. Our purpose is to compare the effects of yen's
REER change on Japan's trade balance between the former period which includes 1985 to 1998
when the J-curve effects were empirically confirmed and the later period. For the partition of the
sample period, we decide the period between January 1985 and December 1998 as the former
and the period between January 1999 and September 2013 as the latter from following two
reasons: First, as Figure 6 indicated, the overseas production ratio of Japanese manufacturing
companies exceeded 10 percent in the end of 1998. Second, the revised Foreign Exchange Law
was changed to totally liberalize cross-border transactions in April 1998 and so Japanese firms'
practice of exchange rate risk management was changed. Accordingly, we suppose that the
effects of exchange rate movement on the trade flows might be different before and after this
period.
3-1. Model
Following Rose and Yellen (1989) and other previous studies cited above, we employ
1 Bahmani-Oskooee and Ratha (2004) provide the detailed literature review for J-curve related empirical
papers.
2 They also found that real depreciation of the yen has favorable long-run effects in the cases of Canada,
UK and US.
6
the following log-linear equation model as a long-run relationship between trade balance and
real exchange rate
ln
a
,
b ∙ ln
c ∙ ln
,
,
d ∙ ln
ε
,
(1)
where TBJapan,t is a measure of trade balance in Japan, YJapan,t is a measure of Japan's income,
Yforeign,t is a measure of foreign countries' income, and REERJapan,t is the real effective exchange
rate of the Japanese yen3. As a measure of trade balance, we adopt the ratio of Japan's real
exports to the world over her real imports from the world as follows4.
⁄
,
As a measure of income, we use industrial production index (IPI) in order to conduct our
analysis on monthly basis. As for a measure of aggregate foreign countries' income, we use
OECD countries IPI published by OECD5. Based on the definition of the above variables, we
expect the estimated sign of coefficients as b<0, c>0 and d<0 if real depreciation of the Japanese
yen improves the trade balance in Japan. In addition, we put the dummy variable of the Great
East Japan Earthquake in the later sample period, since it is obvious that the earthquake in
March 2011 causes the recent trade deficit tendency in Japan6.
In order to extract both long-run and short-run effects of real depreciation of the yen,
we follow Pesaran et al. (2001) , which places both the levels and the first differences of each
variable in a single-equation Error Correction Model (ECM). We specify equation (1) as an
ARDL form with lag lengths n as follows.789
∆ln
∙ ∆ln
,
∑
,
∙∆
, −1
,
4·
∙∆
,
∙ ln
,
, −1
∙
∙
,
,
∙
5·
(2)
3
In this analysis, we use BIS's REER of Japan (narrow indices).
We use real exports and real import data published by Bank of Japan.
5 For the purposes of accuracy, we have to make weighted average of Japanese trading partner countries'
IIP. We will try to do so in the revised version of this paper.
6 The Great East Japan Earthquake dummy takes 1 after March 2011, and takes 0 otherwise.
7 Pesaran et al. (2001) call this model "Conditional ECM Model".
8 The ARDL/Bounds Testing methodology of Pesaran et al. (2001) has some advantages over
conventional cointegration testing as follows; it can be used with a mixture of I(0) and I(1) data, it
involves just a single-equation set-up, making it simple to implement and interpret, and different variables
can be assigned different lag-lengths as they enter the model.
9 We select the lag lengths of each variable by Akaike Information Criterion (AIC) and the Schwarz
Criterion (SC).
4
7
In this setup, we focus on the sign and significance of the estimated coefficient
, which
indicates the short-run effects of REER on trade balance, and the estimated coefficient
,
which indicates the long-run effects of REER on trade balance. If negative values were obtained
followed by positive values for
for
, the J-curve phenomenon will be confirmed. Before
estimating equation (2), we check the unit root test and cointegration test for all variables. As
Table 1 summarizes the results, we confirm that all variables are I (1) and there are at least one
cointegration relationship among them in both former and latter sample period.
The estimation results are summarized at Table 2 (Jan. 1985-Dec. 1998) and Table 3
(Jan. 1999-Sept. 2013). At first, we have to perform both "Wald test" and "Bounds F-test"
proposed by Pesaran et al. (2001) of the hypothesis, H0:
=
=
=
=0 ; against the
alternative that H0 is not true, since a rejection of H0 implies that we have a long-run relationship.
The lower columns of Table 2 and Table 3 show the result of Wald test and our values of former
and latter period are 5.701 and 3.778, respectively. Due to the Table C1 (iii) on page 300 on
Pesaran et al. (2001), the lower and upper bounds for the F-test statistic at the 1%, 5%, and 10%
significance levels are [4.29, 5.61], [3.23, 4.35], [2.72, 3.77], respectively. As the value of our
F-statistic exceeds the upper bound at the 1% significance level in the former period and 10 %
in the later period. In addition, the t-statistics of
in former and latter period are -4.129 and
-3.578, respectively and they also exceed the upper bound at the 1%, 5%, and 10% significance
levels the outside of the critical value of [-3.43, -4.37], [-2.86, -3.78], [-2.57, -3.46], respectively.
Accordingly, we can conclude that there is evidence of a long-run relationship between trade
balance, REER and IIP of Japan and OECD. When we look the coefficient estimates of the
lagged first-differenced REER, some of them are positive and significant. These results indicate
the existence of the J-curve effects. Comparing the results each other, the coefficient estimate of
in the former period (-0.209) is larger than in the later period (-0.061). Furthermore, the
simultaneous to 9th lagged coefficient estimates of the first-differenced REER in the former
period are all positive, which indicates the J-curve effects very clearly, while some of those in
the latter period are negative.
In order to confirm the above results, we conduct the usual ECM model as well.
Assuming that the bounds test leads to the conclusion of cointegration, we can meaningfully
estimate the long-run equilibrium relationship between the above variables:
ln
,
∙ ln
,
∙ ln
,
∙ ln
,
∙
ε
(3)
as well as the usual ECM Model:
8
∆ln
∙
,
∙ ∆ln
∙ ∆ln
∙∆
,
∙ ∆ln
,
,
,
(4)
where
is the error correction term which is constructed as the residual series of equation (3).
We confirm that the coefficient of the error-correction term,
, is negative and significant. This
is what we'd expect if there is cointegration between trade balance, IIP in Japan and OECD
countries, and REER of the Japanese yen. Tables 4 and 5 summarize both results in the former and
latter period, respectively. From the estimated results, we can write the long-run equilibrium
relationship between trade balance and other variables as follows (figure in parentheses are
standard deviation):
Jan 1985 to Dec 1998:
ln
,
4.588
1.134 ∙ ln
,
(0.255) (0.076)
0.314 ∙ ln
,
(0.057)
0.235 ∙ ln
,
ε
(0.034)
Jan 1999 to Sept 2013:
ln
,
9.681
(0.819)
0.708 ∙ ln
(0.106)
0.129 ∙
,
2.717 ∙ ln
(0.189)
,
0.074 ∙ ln
,
(0.057)
ε
(0.020)
All coefficient estimates except for REER in the latter period are same as the expected sign and
significant. In addition, the ECM results also indicate that the coefficients of the simultaneous and
11th lagged REER are positive and significant in the former period. It is clear again that there is
evidence of the J-curve phenomenon only in the former period.
Summarizing the above results, our empirical analysis confirms that the exchange rate
fluctuations in the 2000s had a weaker impact on the trade balance than in the middle of 1980s
to 1990s.
4. Does the Export Price Decline in Response to Yen Depreciation?
4-1. Exchange Rate Changes and Export Price Index
The J-curve effect suggests that Japanese export price in terms of the destination
9
currency will decline in response to the yen depreciation, which gradually increases the export
volume and, hence, finally results in the improvement of Japanese trade balance. Let us first
check whether Japanese export price has declined in practice to investigate what has impeded
the improvement of Japanese trade balance even after the large depreciation of the yen..
The Bank of Japan (BOJ) publishes the monthly series of the industry/commodity
breakdown data on export price indices. The BOJ collects the export price data when cargo is
loaded in Japan at the customs clearance stage, and the free on board (FOB) prices at the
Japanese port of exports are surveyed. In addition, the BOJ reports export price indices both on
a yen basis and on a contract (invoice) currency basis. As long as traded in a foreign currency,
the sample prices are recorded on the original contract currency basis, and finally compiled as
the “export price index on the contract currency basis”. To compile the “export price index on
the yen basis”, the sample prices in the contract currency are converted into the yen equivalents
by using the monthly average exchange rate of the yen vis-à-vis the contract currency.10
Figure 10 shows not only the nominal exchange rate of the yen vis-à-vis the US dollar
that is converted into the index (2005=100) but also the Japanese export price index (all
industries).11 First, while the level of the exchange rate fluctuated to a large extent from 2000,
the export price on the contract currency basis appears to be quite stable at the level of 100 until
the end of 2013, which suggests that Japanese exporters tend to stabilize the export price in
terms of the destination currency and, hence, to conduct the pricing-to-market (PTM) strategy.
Third, during the yen depreciation period starting from the end of 2012, the export
price index on the contract currency basis declined only slightly from 101.0 in December 2012
to 99.2 in December 2013. Thus, the magnitude of export price changes on the contract currency
basis is far smaller than that of the yen depreciation.
Next, let us observe possible difference in export price movements across industries.
Figure 11 presents the export price indices of Japanese three major machinery industries:
general machinery, electric machinery and transport equipment.12 In the general machinery and
transport equipment, the export price indices on the contract currency basis do not exhibit
declining trend from 2000 to 2013, and the indices rose to a small extent after the collapse of
Lehman Brothers in 2008. Even from the end of 2012 when the yen started to depreciate rapidly,
these two indices declined only slightly. The export price indices of the electric machinery
10
See the BOJ website (https://www.boj.or.jp/en/statistics/pi/cgpi_2010/index.htm/) for further details.
Not the nominal effective exchange rate but the bilateral nominal exchange rate of the yen vis-à-vis the
US dollar is used in Figure 10. As discussed below, about 90 percent of Japanese exports are invoiced in
the yen and US dollar as of 2013.
12
The BOJ substantially revised the industry classification when starting to publish the 2010 base year
data. “General machinery” denotes the “general purpose, production & business oriented machine; and
“electric machinery” denotes “electric & electronic products”. As of 2010, 66.6 percent of Japanese
exports are accounted for by the sum of the three industries: general machinery, electric machinery and
transport equipment.
11
10
exhibit steady downward movements over the sample period, due to the global decline of
electronics prices. But, the export price index on the yen basis moves differently from the export
price on the contract currency basis in response to the large exchange rate fluctuations.
4-2. Time-Varying Parameter Estimation of Exchange Rate Pass-Through in Japanese
Exports
As discussed in the previous sub-section, the BOJ’s export price index measured in
contract (or invoicing) currency shows that Japanese export prices have been stable since 2000,
indicating that Japanese exporters have been maintaining their export prices in overseas markets
regardless of exchange rate fluctuations, a phenomenon referred to as the PTM behavior. To
confirm the possible PTM behavior, we conduct a more rigorous empirical analysis of the
exporter’s pricing strategy after allowing for the choice of contract or invoicing currency.
There have so far been a large number of studies on the exchange rate pass-through.
The following single-equation model is typically used in the literature, such as Campa and
Goldberg (2005):
 ln Pt X  0  1 ln Et   2  ln Pt D   t ,
(5)
where P X denotes the export price index on a yen basis, P D represents the domestic input
price index, E indicates the nominal exchange rate of the yen vis-à-vis the importer’s currency,

denotes the white-noise residuals, and  represents the first-difference operator. Our
primary interest is in  1 that indicates the degree of exchange rate pass-through to export
prices. This coefficient is also called the “PTM elasticity”. If  1 is statistically significant and
equal to one, the degree of exchange rate pass-through is zero and the PTM is complete. If  1
is not statistically different and, hence, equal to zero, the degree of exchange rate pass-through
is complete and no PTM.
Both the export price index and the input price index are obtained from the BOJ.
The input price index exhibits the weighted average prices of the intermediate input goods (i.e.,
raw and intermediate materials, fuel, and energy) and services to produce the products of
respective industries.13
Since the destination-specific export price index is not published by the BOJ, it is
better to employ the nominal effective exchange rate to analyze the degree of exchange rate
13
The weights are based on the input values of goods (i.e., raw and intermediate materials, fuel, and
energy) and services for the manufacturing industry at purchasers' prices in the I-O Tables during the base
year 2005, published by the Ministry of Internal Affairs and Communications.
11
pass-through. However, this paper constructs the industry-specific nominal effective exchange
rate weighted by the share of contract (invoicing) currency, developed by Ceglowski (2010),
which is obtained by dividing the industry-specific export price on the yen basis by the
corresponding export price on the contract currency basis. Different from the conventional
effective exchange rate, the increase (decrease) in the nominal effective exchange rate on the
contract currency basis represents depreciation (appreciation) of the yen.
Before performing the OLS estimation of equation (5), we conduct both the ADF
(Augmented Dicky-Fuller) and PP (Phillips-Perron) tests for unit-root. Although not reported in
this paper, it is confirmed that all variables are non-stationary in level but stationary in
first-differences. To ensure the stationarity of variables, we employ the first-difference model
for the OLS estimation.
Table 6 presents the estimated result of the degree of exchange rate pass-through. For
the whole sample period from January 1980 to December 2013, the estimated coefficient of
exchange rate pass-through ranges from 0.68 to 0.82, implying the low pass-through rate (i.e.,
high PTM). By dividing the whole sample into two sub-samples, we compare the degree of
exchange rate pass-through between two periods and have found that the pass-through rate
increased to some extent from 2000 to 2013. This evidence suggests that Japanese exporters
tend to pass through more exchange rate changes to importers after 2000.
To make more rigorous examination on possible changes in the pass-through behavior,
we employ the Kalman filter technique to estimate the time-varying parameter of the
pass-through equation. Equation (5) can be reformulated into the observation equation (6) and
the state equation (7), respectively:
 ln Pt X   0,t  1,t  ln Et   2,t  ln Pt D   t
(6)
 i ,t   i ,t 1   i ,t
(7)
i = 0, 1, 2
where  i ,t and  i ,t indicate, respectively, the time-varying coefficient and the Gaussian
disturbances with zero mean.
Our major interest is in the time-varying pass-through coefficient of the nominal
effective exchange rate on the contract currency basis, 1,t , which indicates that the degree of
the exchange rate pass-through is small (large) if 1,t is high (low). Figure 12 presents the
12
estimated result of the time-varying pass-through coefficient. First, the result of all
manufacturing shows that the pass-through coefficient fluctuates within a range from 0.6 to 1.0.
This evidence implies that Japanese exporters generally lower the degree of pass-through and,
hence, tend to conduct the PTM behavior. 14 Second and more noteworthy is that the
pass-through coefficient exhibits a sharp decline from the beginning of 2010 and reaches the
lowest value (0,236) as of January 2012. The yen appreciated further in 2010 and 2011 when the
European countries suffered from the serious budget crisis. The yen went as high as 75.32 as of
October 31, 2011. In response to the continuing strong yen, Japanese exporters started to change
the pricing behavior by increasing the degree of exchange rate pass-through into export prices.
Third, Japanese exporters started to lower the exchange rate pass-through again after the sharp
depreciation of the yen from the end of 2012. As long as choosing the PTM behavior and
stabilizing the export price in terms of the contract currency, Japanese exporters enjoyed foreign
exchange profit due the substantial depreciation of the yen.
Finally, the share of yen invoicing has to do with the improvement of trade balance
during the yen depreciation period. As long as exports are invoiced in the yen, the importer’s
currency value of yen exports will be declining in response to the yen depreciation. Figure 13
shows that the share of yen invoicing in Japanese total exports declined for the last few years
from 41 percent in 2010 to 35.6 percent in 2013. The recent decline of yen-invoicing exports
mitigated the positive impact of yen depreciation on Japanese trade balance.
5. Japanese Export competitiveness and Industry-Specific REER
How do we describe the change of a country's export competitiveness? It is well
known that a real effective exchange rate (REER), not bilateral nominal exchange rates, is a
better measurement to consider the export firms’ competitiveness in the global market. In
considering the empirical importance of the exchange rate on the exporter’s price
competitiveness and producer profits in specific industries, Sato et. al. (2012, 2013a, 2013b)
constructs a new data set of the industry-specific real effective exchange rate (REER) for
Japan, China and Korea15.
Currently, it is widely known that Japanese manufacturing companies face a severe
competition with Korean manufacturing companies in some particular industries. There are a lot
14
The averaged pass-through rate (i.e., the PTM elasticities) from February 1990 to April 2010 is 0.722,
which indicates that if the nominal effective exchange rate appreciates by 1 percent, the export price on
the contract currency basis increases by only 0.278 percent.
15 Please see Sato et al. (2012) for the details of the data construction of Industry-specific REER. These
data are published on the website of RIETI (http://www.rieti.go.jp/users/eeri/index.html).
13
of factors to affect their competitiveness, but the movement of yen/won exchange rate might be
one of the most important factors. The Korean won fell precipitously in US dollar terms relative
to the Japanese yen and Chinese yuan after the Lehman Brothers collapse , helping to preserve
the competitiveness of Korean exports, while the strong yen dampened export competitiveness
of Japanese firms especially in the automobile and electric industries. Since the end of 2012,
however, the sentiment of exchange rate market has dramatically changed and the Japanese yen
has started to depreciated while the Korean won has turned to appreciate. Such a yen's sudden
slide has revived talk of a global currency war in which nations competitively devalue their
currencies to get a trade advantage.
Figure 14 shows the Japanese industry-specific REER we calculated. The black bold
line stands for the weighted average of REERs of all industries (henceforth, the aggregate
REER). First and the most notable feature is a large difference in the level of REERs across
industries. Specifically, as discussed in Sato, Shimizu, Shrestha and Zhang (2012), the extent of
difference in the level of REERs started to widen after the collapse of Lehman Brothers. Second,
the Electrical Machinery REER fluctuates at the lowest level, while the REERs of Non-metallic
Mineral products and Paper products are at the highest level. Let us focus on the General
Machinery, Electrical Machinery and Transport Equipment that are Japan’s main industry in
terms of export amounts. The Transport Equipment fluctuates somewhat above the aggregate
REER, while the General Machinery moves just below the aggregate REER. Third, from around
November 2012, the REERs of all industries started to fall sharply, which may reflect the
nominal depreciation of the yen. As of 14 January 2013, the Electrical Machinery REER reaches
to 79.0, almost the same level as in 2007. Thus, the nominal exchange rate changes are likely to
have large influences on the REER movements.
Sato, Shimizu, Shrestha and Zhang (2013a, 2013b) analyze the comparison of export
competitiveness between Japan and Korea by using Industry-specific REER. Similarly, Figure
15 is a comparison of the trends in the real effective exchange rates of the Japanese yen and the
Korean won for the electric machinery and transport equipment industries. Each
industry-specific REER is the index based on 2005 equal 100 and an increase (decline) means
appreciation (depreciation) of the REER, in other words, the loss of export competitiveness (the
gain of export competitiveness). Both the Electrical Machinery and Transport Equipment REER
in Japan rose sharply after the collapse of Lehman Brothers in 2008 due to the sudden
appreciation of the Japanese yen, while those in Korea fall due to the depreciation of the Korean
won. During the strong yen period at around 80 yen per dollar which continued until the end of
2012, the Electrical Machinery and Transport Equipment REER in Japan stayed around the
same level, which means that they had struggled to bring their cost down in order to compete in
the overseas market. In the same period, on the other hand, Korean Electrical Machinery and
14
Transport Equipment companies enjoyed their strong export competitiveness.
As the yen started to depreciate from the late 2012, the graphs indicate that both
Japanese electrical machinery and transport equipment industries have rapidly recovered their
export price competitiveness compared with the Korean counterparts. Thus, we can confirm that
the rapid depreciation of the yen since the onset of Abenomics has enabled Japanese companies
to narrow the gap with their South Korean rivals significantly in terms of export price
competitiveness. This is also supported by the fact that Japanese transport equipment
manufacturers reported strong earnings results for the half-year period through September 2013,
achieving robust growth in sales.
6. Conclusion
Against the backdrop of the recent increase in Japanese trade deficit under the
depreciation of the yen since the end of 2012, this paper makes the following three arguments.
First, a sharp appreciation of the yen following the collapse of Lehman Brothers prompted many
Japanese companies to enhance the cross-border division of labor by expanding their production
networks in other Asian countries. The result is a structure where much of the export-boosting
effect of a weaker yen is negated because an increase in Japan’s export of industrial products is
accompanied inevitably by an increase in the import of parts and components produced by
Japanese overseas subsidiaries. Indeed, our empirical analysis of the J-curve effect found that
exchange rate fluctuations in the 2000s had a weaker impact on the trade balance than in the
middle of 1980s to 1990s.
Second, casual observation of the export price data published by the Bank of Japan
shows that Japanese machinery export price in terms of the contract (invoice) currency has not
changed in response to the large exchange rate fluctuations of the yen. However, by conducting
the time-varying parameter estimation of the exchange rate pass-through in Japanese machinery
exports, we demonstrate that Japanese firms pursue different pass-through behavior in response
to asymmetric exchange rate changes in the yen. During the yen appreciation after the Lehman
Brothers collapse, Japanese firms increased the degree of exchange rate pass-through, while
Japanese firms pursue the pricing-to-market (PTM) behavior in response to the rapid yen
depreciation from the end of 2012. It means that the recent depreciation of the yen does not
cause the export price reductions in the destination countries, which weakens the positive
impact of yen depreciation on trade balances.
Finally, a comparative analysis of the industry-specific real effective exchange rates
between Japan and Korea shows that the recent depreciation of the yen improved the export
15
price competitiveness of the Japanese machinery industries. In particular, it is confirmed that
both electrical machinery and transport equipment industries in Japan have rapidly recovered
their export price competitiveness compared with the Korean counterparts. Thus, the slow
recovery of Japanese trade balance in response to the yen depreciation can be explained by the
Japanese firms’ pricing behavior as well as the active overseas operation caused by the
unprecedented level of yen appreciation before Abenomics. It is more important to look at
income balance as well as trade balance.
What specific policy implications can we draw from those observations? First, an
increase in the import of mineral fuels due to the shutdown of nuclear power plants following
the Great East Japan Earthquake will continue to be a major factor contributing to Japan’s trade
deficit, and a weaker yen will translate into a greater yen value of imports. If a weaker yen fails
to deliver its conventionally expected export-increasing effect—i.e., the positive section of the
J-curve—under this situation, Japan’s trade deficit may become chronic. In order to prevent this
from happening, it is imperative for Japan to reexamine its long-term energy policy to explore
more affordable energy sources and promote the development of new energy sources.
Second, in order to offset a decrease in exports resulting from manufacturing
offshoring by increasing income surplus, it is necessary to maintain the flows of overseas
earnings repatriated to Japan. A rise in the share of overseas sales has been prompting the
offshoring of research and development (R&D) activities, a function that has been typically
retained at the headquarters in Japan. Against this backdrop, Japanese companies are becoming
inclined to keeping more of their earnings overseas, giving rise to the concern that Japan’s
income surplus may turn into a downward trend. In order to prevent such a turn of events, the
government needs to implement measures designed to encourage companies to undertake R&D
activities in Japan and remove tax impediments to the repatriation of overseas earnings, namely,
by expanding the scope of application of tax exemption for foreign income.
As aforementioned, our analysis found that Japanese export prices have been stable
when measured in contract currency, and the implication of this finding is that Japanese
exporting companies have strived to keep their prices unchanged, even at the cost of lower
margins, each time they were challenged by a sharp appreciation of the yen. Those companies
having their export prices set in Japanese yen would not be directly impacted by foreign
exchange fluctuations. However, in order to be able to increase the proportion of
yen-denominated exports, a company must be highly competitive in export markets. Meanwhile,
those having their export prices set in foreign currencies would be hit directly by a sharp
appreciation of the yen. When foreign exchange losses become unbearable, they would move
their manufacturing bases to overseas locations and cease to export affected products from
Japan.
16
Our estimation using the time-varying parameter model shows that the pass-through
rate of exchange rate changes into Japanese export prices increased amid a historic appreciation
of the yen in the period following the Lehman collapse. This suggests that many Japanese
companies must have raised their export prices in response to a higher yen. At the same time,
however, those unable to do so were left with no choice but to shift to overseas production. In
fact, some electric machinery products and telecommunications equipment such as mobile
devices, which suffered a significant decline in their export competitiveness in the post-Lehman
high-yen period, are now being produced entirely overseas. It may be the case that as a result of
this prolonged high-yen trend, which drove manufacturing offshoring to what appears to be an
excessive extent, Japanese manufacturers are now focusing on areas of core competitive
advantage. Indeed, those capable of producing highly competitive products are the ones that
have been able to enjoy huge foreign exchange gains brought by the rapid yen depreciation
under Abenomics. And it may be said that the yen depreciation policy has been effective, at least
to some extent, in stemming the excessive exodus of Japanese manufacturing. Going forward, it
is hoped that the government will put its growth strategy, the third arrow of Abenomics, into
concrete action as quickly as possible so as to foster export competitiveness across a broader
spectrum of industrial sectors.
17
References
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the J-Curve Phenomenon: Japan versus Her Major Trading Partners," Journal of Economics and
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Bahmani-Oskooee, Mohsen. and Artatrana Ratha, 2004, “The J-curve: A Literature Review,”
Applied Economics, 36, pp.1377-1398.
Campa, Jose Manuel and Linda S. Goldberg, 2005, “Exchange Rate Pass-Through into Import
Prices,” Review of Economics and Statistics, 87(4), pp.679-690.
Ceglowski, Janet, 2010, “Has pass-through to export prices? Evidence for Japan,” Journal of the
Japanese and International Economies, 24(1), pp.86-98.
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Pesaran, M. Hashem, Yongcheol Shin and Richard J. Smith, 2001, “Bounds Testing
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Economics, 24, pp.53-68.
Sato, K., Shimizu J., Shrestha N., and Zhang Z., 2012, "The Construction and Analysis of
Industry-specific Effective Exchange Rates in Japan," RIETI Discussion Paper, 12-E-043.
Sato,
Kiyotaka,
Junko
Shimizu,
Nagendra
Shrestha
and
Shajuan
Zhang,
2013a,
“Industry-specific Real Effective Exchange Rates and Export Price Competitiveness: The Cases
of Japan, China and Korea,” Asian Economic Policy Review, 8(2), pp.298-321.
Sato, Kiyotaka, Junko Shimizu, Nagendra Shrestha and Shajuan Zhang, 2013b, “Exchange Rate
Appreciation and Export Price Competitiveness: Industry-specific real effective exchange rates
of Japan, Korea, and China,” RIETI Discussion Paper Series 13-E-032.
18
Figure 1. Japan's Trade Balance and Yen/Dollar Exchange Rate (Jan 2010 to Sep 2013)
（Yen/Dollar）
（millions of Yen）
1,500,000 105
1,000,000 100
500,000 95
0 90
(500,000)
85
(1,000,000)
80
(1,500,000)
75
(2,000,000)
70
Source: Trade Statistics (Ministry of Finance), the Bank of Japan.
Figure 2. The Import Value by Industry (Jan 2010 to Sep 2013)
（millions of Yen）
8,000,000 Foodstuff
General machinery
Raw materials
Electrical machinery
Mineral‐related fuels
Transport equipment
Chemicals
Others
Manufactured goods
7,000,000 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 0 Source: Trade Statistics (Ministry of Finance).
19
Figure 3. The Import Value of Main Manufacturing Industry (Jan 2010 to Sep 2013)
（millions of Yen）
3,000,000 General machinery
Electrical machinery
Transport equipment
Others
2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 Source: Trade Statistics (Ministry of Finance).
Figure 4. The Import Value of Parts Products (Jan 2010 to Sep 2013)
（millions of Yen）
600,000 Parts of computer
Semiconductors ETC
IC
Parts of motorvehicles
500,000 400,000 300,000 200,000 100,000 0 Source: Trade Statistics (Ministry of Finance).
20
Figure 5. The Import Quantity of Parts Products (Jan 2010 to Sep 2013)
（computer, motorvehicles）
80,000,000 Parts of computer
Parts of motorvehicles
Semiconductors ETC(IC)
（IC）
2,500,000,000 70,000,000 2,000,000,000 60,000,000 50,000,000 1,500,000,000 40,000,000 1,000,000,000 30,000,000 20,000,000 500,000,000 10,000,000 0 0 Source: Trade Statistics (Ministry of Finance).
Figure 6. Overseas Production of Japanese Firms
（％）
80
Ratio of Firms Performing Overseas
Production (%)
Manufacturing Industries Total
Material-Type
Processing-Type
Others
（％）
30
Overseas Production Ratio of
Japanese Manufacturing Industries (%)
Manufacturing Industries Total
Material-Type
Processing-Type
Others
25
70
20
60
50
15
40
10
30
5
20
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Source: FY2012 Annual Survey of Corporate Behavior (Cabinet Office).
21
Figure 7. The Export Value by Industry (Jan 2010 to Sep 2013)
Foodstuff
General machinery
Raw materials
Electrical machinery
Mineral-related fuels
Transport equipment
Chemicals
Others
Manufactured goods
（millions of Yen）
7,000,000
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
Source: Trade Statistics (Ministry of Finance).
Figure 8. The Export Quantity of Transport Equipment (Jan 2010 to Sep 2013)
（bus&truck, autocycles）
100,000 Bus&Truck
Motorcycles, Autocycles
Car
（car）
600,000 90,000 500,000 80,000 70,000 400,000 60,000 50,000 300,000 40,000 200,000 30,000 20,000 100,000 10,000 0 0 Source: Trade Statistics (Ministry of Finance).
22
Figure 9. Breakdown of Income Balance (Jan 2010 to Sep 2013)
（hundred millions of Yen）
25,000
Direct investment income
Portfolio investment income (Income on debt)
Portfolio investment income (Income on equity)
Other investment income
20,000
15,000
10,000
5,000
0
‐5,000
Source: The Balance of Payments Statistics (Ministry of Finance)
Figure 10. Yen/Dollar Exchange Rate and Export Price Index of Japan (all industry,
2005=100)
130
120
110
100
90
80
70
60
All (JPY)
All (Contract)
JPY/USD
Jun‐13
Nov‐12
Apr‐12
Sep‐11
Jul‐10
Feb‐11
Dec‐09
Oct‐08
May‐09
Aug‐07
Mar‐08
Jan‐07
Jun‐06
Apr‐05
Nov‐05
Sep‐04
Jul‐03
Feb‐04
Dec‐02
Oct‐01
May‐02
Mar‐01
Jan‐00
Aug‐00
50
Notes: Monthly series (2005=100) from January 2000 to February 2013. All (JPY) indicates the Japanese
yen based Export Price Index (All Industries), All (Contract) indicates the contract currencies based
Export Price Index (All Industries). JPY/USD is the monthly average of daily JPY/USD exchange rates.
All values are converted into the indices based on 2005=100.
Source: Bank of Japan, IMF, International Financial Statistics, CD-ROM.
23
Figure 11. Export Price Indices of Main Japanese Manufacturing Industries (2005=100)
130
120
110
100
90
80
70
GM(JPY)
TR(JPY)
EL(Contract)
60
EL(JPY)
GM(Contract)
TR(Contract)
Jun‐13
Apr‐12
Nov‐12
Feb‐11
Sep‐11
Jul‐10
Dec‐09
Oct‐08
May‐09
Mar‐08
Jan‐07
Aug‐07
Jun‐06
Apr‐05
Nov‐05
Sep‐04
Jul‐03
Feb‐04
Dec‐02
May‐02
Oct‐01
Mar‐01
Jan‐00
Aug‐00
50
Notes: Monthly series (2005=100) from January 2000 to February 2013. GM (JPY) and GM (Contract)
indicate the export price index of "General Machinery" based on the Japanese yen and contract currencies,
respectively. EL (JPY) and EL (Contract) indicate the export price index of "Electrical Machinery" based
on the Japanese yen and contract currencies, respectively. TR (JPY) and TR (Contract) indicate the export
price index of "Transport Equipment" based on the Japanese yen and contract currencies, respectively.
Source: Bank of Japan
24
‐0.20
1.40
‐0.20
Chemical
General
Transport
‐0.40
2SE(Low)
2SE(Low)
2SE(Low)
2SE(Low)
2SE(Up)
1.20
1.20
1.00
1.00
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
0.00
0.00
(3) Chemical
‐0.40
2SE(Up)
0.00
(5) General Machinery
‐0.40
2SE(Up)
0.00
(7) Transport Equipment
‐0.40
2SE(Up)
1.20
1.00
0.00
‐0.20
1.40
1.20
1.20
1.00
1.00
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
‐0.20
1.40
1.20
1.20
1.00
1.00
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
‐0.20
1.20
‐0.20
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
All Mnf.
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
1.40
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
1.40
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
(1) All Manufacturing
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
‐0.20
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
1.40
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
‐0.20
1980M02
1981M06
1982M10
1984M02
1985M06
1986M10
1988M02
1989M06
1990M10
1992M02
1993M06
1994M10
1996M02
1997M06
1998M10
2000M02
2001M06
2002M10
2004M02
2005M06
2006M10
2008M02
2009M06
2010M10
2012M02
2013M06
Figure 12. Exchange Rate Pass-Through Estimation —Time-Varying Parameter Model—
(2) Textile
1.40
Textile
Metal
Electric
Others
2SE(Low)
2SE(Low)
2SE(Low)
2SE(Low)
2SE(Up)
(4) Metal
‐0.40
2SE(Up)
0.00
(6) Electric Machinery
‐0.40
2SE(Up)
0.00
(8) Other Manufacturing
‐0.40
2SE(Up)
1.00
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
0.00
‐0.40
Note: Authors' calculation
25
Figure13. Share of Invoice Currency in Japanese Exports: 1980-2013 (Percent)
Yen
US Dollar
2004
2006
2008
2010
2012
2006
2008
2010
2012
2002
2000
1998
1996
1994
1990
1992
Yen
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
2012
2010
2008
2006
0
2004
10
0
2002
20
10
2000
30
20
1998
40
30
1996
50
40
1994
60
50
1992
70
60
1990
80
70
1988
90
80
1986
100
90
1984
US Dollar
(d) To Asia
100
1982
1988
Yen
(c) To EU (EC)
1980
1986
1980
2012
2010
2008
2006
2004
2002
US Dollar
2004
Yen
2000
0
1998
10
0
1996
20
10
1994
30
20
1992
40
30
1990
50
40
1988
60
50
1986
70
60
1984
80
70
1982
90
80
1980
100
90
1984
(b) To the United States
100
1982
(a) To World
US Dollar
Notes: The data for 1999 is not available. The September data is used for 1992-97, the March data for 1998, the 2nd
half of the year data for 2000-2012, and the 1st half of the year data for 2013.
Sources: Bank of Japan, Yushutsu Shinyojo Tokei (Export Letter of Credit Statistics); MITI, Yushutsu Kakunin Tokei
(Export Confirmation Statistics); MITI, Yushutsu Hokukosho Tukadate Doko (Export Currency Invoicing Report);
MITI, Yushutsu Kessai Tsukadate Doko Chosa (Export Settlement Currency Invoicing); the website of Japan
Customs.
26
Figure 14. Industry Specific REER for Japanese yen (2005=100)
140
Food
120
Textile
Wood
Paper
Petroleum
Chemical
Rubber
100
Non-Metal
Metal
General Machinery
Electrical Machinery
Optical Instruments
Transport Equipment
80
Manufacturing All
60
2005/1/3
2006/1/3
2007/1/3
2008/1/3
2009/1/3
2010/1/3
2011/1/3
2012/1/3
2013/1/3
Note: Monthly series (2005=100) from January 2001 to February 2013. By definition, an increase
(decline) means appreciation (depreciation) of the I-REER. “All Manufacturing” denotes the I-REER of
all thirteen industries.
Source: RIETI Website（http://www.rieti.go.jp/users/eeri/index.html）
Figure 15. Industrial-Specific REER of Japan and Korea
Industry-Specific REER of Japan and Korea
（Electrical Machinery）
120
Industry-Specific REER of Japan and Korea
(Transport Equipment)
120
100
100
80
80
Appreciation
Appreciation
Depreciation
Depreciation
60
60
Electrical Machinery (Japan)
Transport Equipment (Japan)
Electrical Machinery (Korea)
Transport Equipment (Korea)
Jul-13
Jul-12
Jan-13
Jul-11
Jan-12
Jul-10
Jan-11
Jul-09
Jan-10
Jul-08
Jan-09
Jul-07
Jan-08
Jul-06
Jan-07
Jul-05
Jan-06
40
Jan-05
Jul-13
Jul-12
Jan-13
Jul-11
Jan-12
Jul-10
Jan-11
Jul-09
Jan-10
Jul-08
Jan-09
Jul-07
Jan-08
Jul-06
Jan-07
Jul-05
Jan-06
Jan-05
40
Note: See Figure 14.
Source: RIETI Website（http://www.rieti.go.jp/users/eeri/index.html）
27
Table 1．Descriptive Statistics, Unit-root Test and Cointegration Test
Descriptive Statistics of variables
TB
IPJAPAN
IPOECD
89.80
78.48
REER
Mean
0.7664
95.57
Median
0.7532
96.6
75.1
96.95
Maximum
1.0713
116.4
109.3
143.23
Minimum
0.4901
48.4
46.3
68.01
Std. Dev.
0.1246
16.6672
18.4357
15.6563
Unit Root Test (level)
Unit Root Test (1st Difference)
-2.3627
-20.9350
-2.4509
-14.8879
***
***
-2.3990
-13.1398
***
-1.1486
-6.7131
***
Note: Trade Balance is calculated as Real Export/Real Import.
Real Export and Real Import are taken from Bank of Japan, Industrial Production Index of Japan and OECD countries
are taken from OECD, and REER (narrow base) is taken from BIS.
Unit Root Test shows Augmented Dickey-Fuller test statistic.
Cointegration (Johansen) Test
Lags interval: 1 to 3
January 1985- December 1998
Variables: Trade balance, REER of the Japanese yen, IPIJapan and IPI OECD
Data Trend:
Test Type
None
No Intercept
No Trend
Trace
Max-Eig. Value
None
Intercept
No Trend
1
0
Linear
Intercept
No Trend
2
2
Linear
Intercept
Trend
1
1
Quadratic
Intercept
Trend
1
1
1
1
January 1999 - September 2013
Variables: Trade balance, REER of the Japanese yen, IPIJapan and IPI OECD
Data Trend:
Test Type
Trace
Max-Eig. Value
None
No Intercept
No Trend
None
Intercept
No Trend
0
0
Linear
Intercept
No Trend
1
0
Linear
Intercept
Trend
1
0
Quadratic
Intercept
Trend
1
0
2
0
Note: Selected (0.05 level*) Number of Cointegrating Relations by Model
Critical values based on MacKinnon-Haug-Michelis (1999)
28
Table 2.
Conditional ECM Model of Pesaran et al. (2001)
January 1985- December 1998
Explained variable：　⊿log(Real Export/Real Import)
Method: Least Squares (Included observations: 168)
Variable
Constant
⊿log(Real Export(-1)/Real Import(-1))
⊿log(Real Export(-2)/Real Import(-2))
⊿log(IPIJapan)
⊿log(IPIJapan(-1))
⊿log(IPIOECD)
⊿log(IPIOECD(-1))
⊿log(REER)
⊿log(REER(-1))
⊿log(REER(-2))
⊿log(REER(-3))
⊿log(REER(-4))
⊿log(REER(-5))
⊿log(REER(-6))
⊿log(REER(-7))
⊿log(REER(-8))
⊿log(REER(-9))
⊿log(REER(-10))
⊿log(REER(-11))
log(Real Export(-1)/Real Import(-1))
log(IPIJapan(-1))
log(IPIOECD(-1))
log(REER(-1))
Std. Error
Coefficient
1.423
-0.327
-0.246
-0.544
0.087
1.074
0.031
0.192
0.208
0.257
0.128
0.109
0.147
0.188
0.291
0.170
0.068
-0.070
0.383
-0.395
-0.311
0.194
-0.209
Adjusted R-squared
Durbin-Watson stat
F-statistic
Prob(F-statistic)
0.358
2.021
5.241
0.000
Wald Test (H0: δ_1=δ_2=δ_3=δ_4=0)
F-statistic
Prob(F-statistic)
5.701
0.000
***
(0.461)
(0.123)
(0.097)
(0.291)
(0.247)
(0.793)
(0.837)
(0.123)
(0.122)
(0.130)
(0.131)
(0.156)
(0.128)
(0.149)
(0.156)
(0.133)
(0.149)
(0.128)
(0.124)
(0.096)
(0.113)
(0.056)
(0.048)
t-Statistic
Prob.
3.089
-2.651
-2.542
-1.872
0.354
1.353
0.037
1.564
1.715
1.982
0.971
0.698
1.149
1.264
1.867
1.281
0.455
-0.547
3.103
-4.129
-2.760
3.485
-4.320
0.002
0.009
0.012
0.063
0.724
0.178
0.971
0.120
0.089
0.049
0.333
0.487
0.252
0.208
0.064
0.202
0.650
0.585
0.002
0.000
0.007
0.001
0.000
***
(Authors' calcuulation)
Note1) Please see Table C1 (iii) on page 300 on Pesaran et al. (2001) for the lower and upper bounds for the Ftest statistic.
Note2) We chose lag lengths of each variables by Akaike Information Criterion and the Schwarz Criterion.
Note3) ***, **,and * denote the 1 percent, 5 percent and 10 percent significance level, respectively.
29
Table 3.
Conditional ECM Model of Pesaran et al. (2001)
January 1999 - September 2013
Explained variable　⊿log(Real Export/Real Import)
Method: Least Squares (Included observations: 177)
Variable
Constant
⊿log(Real Export(-1)/Real Import(-1))
⊿log(Real Export(-2)/Real Import(-2))
⊿log(Real Export(-3)/Real Import(-3))
⊿log(IPIJapan)
⊿log(IPIJapan(-1))
⊿log(IPIJapan(-2))
⊿log(IPIJapan(-3))
⊿log(IPIOECD)
⊿log(IPIOECD(-1))
⊿log(REER)
⊿log(REER(-1))
⊿log(REER(-2))
⊿log(REER(-3))
⊿log(REER(-4))
⊿log(REER(-5))
⊿log(REER(-6))
⊿log(REER(-7))
⊿log(REER(-8))
⊿log(REER(-9))
⊿log(REER(-10))
⊿log(REER(-11))
log(Real Export(-1)/Real Import(-1))
log(IPIJapan(-1))
log(IPIOECD(-1))
log(REER(-1))
Shinsai Dummy
Coefficient
Std. Error
t-Statistic
-1.331
-0.503
-0.212
-0.125
0.173
0.657
0.271
0.106
1.311
0.506
-0.102
0.054
0.039
-0.133
0.222
0.167
0.252
-0.014
-0.077
0.073
0.032
0.158
-0.212
-0.294
0.640
-0.061
-0.043
(0.674)
(0.086)
(0.092)
(0.078)
(0.141)
(0.144)
(0.129)
(0.130)
(0.496)
(0.515)
(0.112)
(0.113)
(0.114)
(0.107)
(0.107)
(0.106)
(0.104)
(0.102)
(0.100)
(0.101)
(0.100)
(0.100)
(0.059)
(0.083)
(0.195)
(0.036)
(0.013)
-1.976
-5.868
-2.288
-1.597
1.227
4.570
2.093
0.818
2.643
0.983
-0.919
0.482
0.338
-1.243
2.076
1.580
2.426
-0.135
-0.769
0.726
0.314
1.569
-3.578
-3.544
3.288
-1.704
-3.295
Adjusted R-squared
Durbin-Watson stat
F-statistic
Prob(F-statistic)
0.418
2.072
5.852
0.000
Wald Test (H0: δ_1=δ_2=δ_3=δ_4=0)
F-statistic
Prob(F-statistic)
3.778
0.006
＊
Prob.
0.050
0.000
0.024
0.113
0.222
0.000
0.038
0.414
0.009
0.327
0.360
0.631
0.736
0.216
0.040
0.116
0.017
0.893
0.443
0.469
0.754
0.119
0.001
0.001
0.001
0.091
0.001
*
(Authors' calcuulation)
Note1) Please see Table C1 (iii) on page 300 on Pesaran et al. (2001) for the lower and upper bounds for the F-test
statistic.
Note2) We select lag lengths of each variables by Akaike Information Criterion and the Schwarz Criterion.
Note3) ***, **,and * denote the 1 percent, 5 percent and 10 percent significance level, respectively.
Note4) The Great East Japan Earthquake (Shinsai) dummy takes 1 after March 2011, and takes 0 otherwise.
30
Table 4.
ECM Model with Error Collection Term
January 1985- December 1998
＜Long-term＞
Explained variable：　log(Real Export/Real Import)
Method: Least Squares (Included observations: 180)
Variable
Coefficient
Constant
log(IPIJapan)
log(IPIOECD)
log(REER)
Adjusted R-squared
Durbin-Watson stat
F-statistic
Prob(F-statistic)
4.588
-1.134
0.314
-0.235
0.677477
0.734579
126.3333
0
＜Short-term＞
Explained variable：　⊿log(Real Export/Real Import)
Method: Least Squares
Variable
Coefficient
C
0.000
ECT(-1)
-0.229
⊿log(Real Export(-1)/Real Import(-1))
-0.418
⊿log(Real Export(-2)/Real Import(-2))
-0.262
⊿log(IPIJapan)
-0.683
⊿log(IPIJapan(-1))
-0.054
⊿log(IPIOECD)
0.468
⊿log(IPIOECD(-1))
-0.975
⊿log(REER)
0.234
⊿log(REER(-1))
0.048
⊿log(REER(-2))
0.096
⊿log(REER(-3))
-0.050
⊿log(REER(-4))
-0.119
⊿log(REER(-5))
-0.044
⊿log(REER(-6))
0.061
⊿log(REER(-7))
0.087
⊿log(REER(-8))
0.061
⊿log(REER(-9))
-0.067
⊿log(REER(-10))
-0.181
⊿log(REER(-11))
0.201
Adjusted R-squared
0.306
Durbin-Watson stat
1.982
F-statistic
5.137
Prob(F-statistic)
0.000
Std. Error
***
***
***
***
(0.255)
(0.076)
(0.057)
(0.034)
Std. Error
***
**
***
***
**
**
*
(0.004)
(0.092)
(0.100)
(0.083)
(0.292)
(0.269)
(0.756)
(0.743)
(0.118)
(0.130)
(0.131)
(0.132)
(0.129)
(0.130)
(0.130)
(0.129)
(0.129)
(0.128)
(0.128)
(0.118)
t-Statistic
Prob.
17.981
-14.984
5.494
-6.870
t-Statistic
0.000
0.000
0.000
0.000
Prob.
-0.121
-2.485
-4.187
-3.147
-2.338
-0.202
0.620
-1.312
1.988
0.368
0.730
-0.378
-0.921
-0.335
0.468
0.676
0.475
-0.521
-1.409
1.696
0.904
0.014
0.000
0.002
0.021
0.841
0.536
0.192
0.049
0.713
0.467
0.706
0.359
0.738
0.641
0.500
0.636
0.603
0.161
0.092
(Authors' calcuulation)
Note1) We select lag lengths of each variables by Akaike Information Criterion and the Schwarz Criterion.
Note2) ***, **,and * denote the 1 percent, 5 percent and 10 percent significance level, respectively.
31
Table 5.
ECM Model with Error Collection Term
January 1999 - September 2013
＜Long-term＞
Explained variable：　log(Real Export/Real Import)
Method: Least Squares (Included observations: 177)
Variable
Coefficient
Constant
log(IPIJapan)
log(IPIOECD)
log(REER)
Dummy Shinsai
Adjusted R-squared
Durbin-Watson stat
F-statistic
Prob(F-statistic)
-9.681
-0.708
2.717
0.074
-0.129
0.743257
0.442606
128.3777
0
Std. Error
***
***
***
***
(0.819)
(0.106)
(0.189)
(0.057)
(0.020)
t-Statistic
-11.826
-6.664
14.389
1.295
-6.597
Prob.
0.000
0.000
0.000
0.197
0.000
＜Short-term＞
Explained variable：　⊿log(Real Export/Real Import)
Method: Least Squares
Variable
Coefficient
Std. Error t-Statistic
Prob.
C
0.001
(0.003)
0.432
0.667
**
ECT(-1)
-0.148
(0.060)
-2.459
0.015
⊿log(Real Export(-1)/Real Import(-1))
(0.089)
-5.560
0.000
-0.497 ***
⊿log(Real Export(-2)/Real Import(-2))
-0.156
(0.094)
-1.651
0.101
⊿log(Real Export(-3)/Real Import(-3))
-0.096
(0.080)
-1.197
0.233
⊿log(IPIJapan)
0.228
(0.144)
1.579
0.116
⊿log(IPIJapan(-1))
(0.148)
4.370
0.000
0.645 ***
⊿log(IPIJapan(-2))
0.136
(0.127)
1.069
0.287
⊿log(IPIJapan(-3))
-0.042
(0.127)
-0.330
0.742
⊿log(IPIOECD)
(0.506)
2.160
0.032
1.093 **
⊿log(IPIOECD(-1))
0.207
(0.520)
0.398
0.692
⊿log(REER)
-0.148
(0.114)
-1.303
0.195
⊿log(REER(-1))
-0.017
(0.114)
-0.149
0.882
⊿log(REER(-2))
-0.010
(0.114)
-0.085
0.933
⊿log(REER(-3))
(0.109)
-2.353
0.020
-0.256 **
⊿log(REER(-4))
0.129
(0.109)
1.179
0.240
⊿log(REER(-5))
0.141
(0.108)
1.307
0.193
⊿log(REER(-6))
(0.105)
1.952
0.053
0.205 *
⊿log(REER(-7))
-0.058
(0.103)
-0.563
0.575
⊿log(REER(-8))
-0.118
(0.102)
-1.152
0.251
⊿log(REER(-9))
0.075
(0.103)
0.725
0.470
⊿log(REER(-10))
0.035
(0.103)
0.344
0.731
⊿log(REER(-11))
0.126
(0.101)
1.247
0.214
Shinsai Dummy
-0.010
(0.007)
-1.486
0.140
Adjusted R-squared
0.381
Durbin-Watson stat
1.869
F-statistic
5.684
Prob(F-statistic)
0
(Authors' calcuulation)
Note1) We select lag lengths of each variables by Akaike Information Criterion and the Schwarz Criterion.
Note2) ***, **,and * denote the 1 percent, 5 percent and 10 percent significance level, respectively.
Note3) The Great East Japan Earthquake (Shinsai) dummy takes 1 after March 2011, and takes 0 otherwise.
32
Table 6. Exchange Rate Pass-through Estimation of Japanese Export by Industry
(a) 1980M1-2013M12
All Industry
Textile
Chemical
Metal
General
Machinery
Electric
Machinery
Tranport
Equipment
Others
Constant
-0.0001
(0.0001)
0.0001
(0.0001)
-0.0001
(0.0002)
0.0002
(0.0002)
0.0000
(0.0001)
-0.0005 **
(0.0001)
0.0003 **
(0.0001)
0.0000
(0.0001)
NEER
0.69
(0.02)
0.71
(0.04)
0.70
(0.04)
0.79
(0.04)
0.81
(0.02)
0.68
(0.02)
0.82
(0.03)
0.78
(0.03)
**
**
**
**
**
**
**
**
Input Price
0.41 **
(0.05)
0.36 **
(0.09)
0.69 **
(0.05)
1.09 **
(0.11)
-0.03
(0.08)
0.42 **
(0.09)
0.17
(0.16)
0.34 **
(0.08)
R^2
0.77
D.W.
2.04
NOBS
407
0.58
2.00
407
0.64
1.47
407
0.59
1.18
407
0.76
1.92
407
0.73
1.99
407
0.72
2.15
407
0.72
1.86
407
Input Price
0.26 **
(0.09)
0.26 **
(0.09)
0.55 **
(0.09)
0.30 **
(0.13)
-0.11
(0.09)
0.34 **
(0.11)
0.21
(0.20)
0.06
(0.09)
R^2
0.79
D.W.
1.90
NOBS
239
0.59
1.63
239
0.65
1.23
239
0.65
0.87
239
0.78
1.88
239
0.77
1.82
239
0.75
2.26
239
0.81
1.98
239
R^2
0.75
D.W.
2.29
NOBS
168
0.56
2.26
168
0.63
1.75
168
0.61
1.48
168
0.73
1.98
168
0.69
2.24
168
0.67
2.03
168
0.63
1.89
168
(b) 1980M1-1999M12
All Industry
Textile
Chemical
Metal
General
Machinery
Electric
Machinery
Tranport
Equipment
Others
Constant
0.0000
(0.0001)
0.0000
(0.0001)
-0.0003
(0.0002)
-0.0001
(0.0002)
0.0000
(0.0001)
-0.0003 **
(0.0001)
0.0005 **
(0.0001)
0.0000
(0.0001)
NEER
0.73
(0.03)
0.67
(0.04)
0.71
(0.04)
0.85
(0.04)
0.84
(0.03)
0.67
(0.03)
0.85
(0.03)
0.81
(0.03)
**
**
**
**
**
**
**
**
(c) 2000M1-2013M12
All Industry
Textile
Chemical
Metal
General
Machinery
Electric
Machinery
Tranport
Equipment
Others
Constant
-0.0002 *
(0.0001)
0.0002
(0.0002)
0.0001
(0.0003)
0.0003
(0.0003)
0.0000
(0.0001)
-0.0008 **
(0.0001)
0.0001
(0.0002)
0.0000
(0.0002)
NEER
0.66
(0.04)
0.74
(0.06)
0.67
(0.07)
0.74
(0.06)
0.78
(0.04)
0.71
(0.05)
0.78
(0.04)
0.71
(0.05)
**
**
**
**
**
**
**
**
Input Price
0.50 **
(0.07)
0.54 **
(0.19)
0.73 **
(0.07)
1.74 **
(0.17)
0.12
(0.15)
0.53 **
(0.14)
0.12
(0.27)
0.73 **
(0.15)
Note: **, and * denote the 1 percent and 5 percent significance level, respectively.
Figures inside the parentheses below each coefficient represent the standard deviation.
33