Global COE Hi-Stat Discussion Paper Series 255

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

The AMU Deviation Indicators Based on
the Purchasing Power Parity andAdjusted by the Balassa-Samuelson Effect

Hi-Stat Discussion Paper

Eiji Ogawa
Zhiqian Wang

November 2012

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

The AMU Deviation Indicators Based on the Purchasing Power Parity
and Adjusted by the Balassa-Samuelson Effect*

October 8, 2012
Eiji Ogawa a and Zhiqian Wang b
Abstract
This paper investigates how the AMU Deviation Indicators for surveillancemeasurements among East Asian currencies are improved by changing their benchmark
rates from the constant rates in 2000-2001 to time-varying rates based on their
Purchasing Power Parities (PPPs). The Consumer Price Indexes (CPIs) are used to
calculate their PPPs as a time-varying benchmark for the AMU Deviation Indicators.
Because the CPIs include prices of non-tradable goods, the PPPs based on the CPIs
have a problem related with the Balassa-Samuelson effect. For the reason, the PPPs
adjusted by the Balassa-Samuelson effect should be used to calculate the AMU
Deviation Indicators when the CPIs are used as price data. This paper compares the two
types of the PPP-based AMU Deviation Indicators and the PPP-based AMU Deviation
Indicators adjusted by the Balassa-Samuelson effect. We conclude that both the PPPbased AMU Deviation Indicators and the PPP-based AMU Deviation Indicators
adjusted by the Balassa-Samuelson effect are also useful in making surveillance over
overvaluation or undervaluation of the intra-regional exchange rates of East Asiancurrencies.
Key words: Asian Monetary Unit, AMU Deviation Indicators, Purchasing Power Parity,
Balassa-Samuelson Effect, Regional Monetary Cooperation
JEL classification codes: F31, F33, F36*

The authors are grateful to the participants at seminars of RIETI (Research Institute of
Economy, Trade and Industry) for helpful comments.
a
Eiji Ogawa, Professor, Graduate School of Commerce and Management, Hitotsubashi
University and Faculty Fellow of Research Institute of Economy, Trade and Industry. Email:eiji.ogawa@r.hit-u.ac.jpbZhiqian Wang, Graduate Student, Graduate School of Commerce and Management,
Hitotsubashi University.
11.

Introduction
In the aftermath of the East Asian currency and financial crisis in 1997, the needfor surveillance over intra-regional exchange rates among East Asian currencies forcrisis prevention has been propounded by some policymakers and scholars. Among the
propositions, in order to strengthen the regional monetary cooperation in East Asia, theChiang Mai Initiative (CMI) was established by the members of ASEAN, Japan, China
and Korea (ASEAN+3). Under the CMI, the monetary authorities have developed acooperative relationship in the field of bilateral and multilateral currency swap
arrangements. At the same time, in order to make surveillance over macroeconomic
performance of each member country of ASEAN+3, the Economic Review and Policy
Dialogue (ERPD) was executed at the Finance Deputy Ministers Meeting of ASEAN+3.
However, the CMI is an agreement that was arranged for the purpose of managing acrisis. Therefore, it may be useful once a currency crisis happens. On one hand, the
ERPD is a surveillance system only focusing on the performance of each country’s
macroeconomic variables such as GDP and inflation rate. To prevent a currency crisis in
the future and enhance surveillance within ASEAN+3, it is necessary to incorporate
intra-regional exchange rates into the surveillance process, and the monetary authorities
are expected to establish a surveillance system to monitor fluctuations andmisalignments of each currency of ASEAN+3.
In the context of the increasing needs for coordination of exchange rate policies
among East Asian countries, Ogawa and Shimizu (2005, 2006a) have proposed a new
surveillance measurement called the Asian Monetary Unit (AMU). The AMU iscalculated by the same method used to calculate the European Currency Unit (ECU).
AMU Deviation Indicators of component currencies of the AMU are useful formonitoring deviations of East Asian currencies from the benchmark rate. The AMU
Deviation Indicators include two types, namely, the Nominal AMU Deviation Indicatorand the Real AMU Deviation Indicator, depending on their purposes.
On the basis of previous studies about the AMU and the AMU Deviation
Indicators, we point out that the benchmark rate should be not constant but varying overtime especially for currencies of East Asian countries with higher productivity growth.
We improve the AMU Deviation Indicators by changing the benchmark rate from a
constant rate into a time-varying rate based on the Purchasing Power Parity (PPP). The
Consumer Price Indexes (CPIs) are used to calculate their PPP as a time-varying
benchmark for AMU Deviation Indicators because of data constraints for somecountries. Because the CPIs include prices of non-tradable goods, the PPP based on the
CPIs have a problem such as the Balassa-Samuelson effect. For the reason, the PPP
2adjusted by the Balassa-Samuelson effect should be used to calculate the AMU
Deviation Indicators when the CPIs are used as price data.
Thus, we also calculate the Balassa-Samuelson effect on each currency in order toeliminate the Balassa-Samuelson effect from the benchmark rate based on the PPP. We
compare the two types of the AMU Deviation Indicators based on the PPP and the PPP
adjusted by the Balassa-Samuelson effect. Our comparisons between both of them havea result that the PPP-based AMU Deviation Indicator and the PPP-based AMU
Deviation Indicator adjusted by the Balassa-Samuelson effect can be used as subsidiaryindexes to complement the original AMU Deviation Indicators.
This paper has the following contents. In section 2, we will begin by reviewing
the advanced research about the AMU and the AMU Deviation Indicators. In section 3,
we estimate the AMU Deviation Indicator by using the benchmark rate which is
calculated by the PPP. In section 4, a simple model which is used to explain the BalassaSamuelson effect will be clarified. The Balassa-Samuelson effect of each country of theASEAN6+3 will be calculated according to the model. We use the results to indicate
impacts of each variable on the calculation of the Balassa-Samuelson effect. The PPPbased AMU Deviation Indicator adjusted by the Balassa-Samuelson effect will beworked out at last. In section 5, we conclude that it is a useful way to use the revised
AMU Deviation Indicators as well as the original AMU Deviation Indicators tostrengthen the regional monetary cooperation within ASEAN+3.
2.

Asian Monetary Unit and AMU Deviation Indicators
In terms of a common currency basket in East Asia, which is expected to enforce

surveillance over intra-regional exchange rates, it is believed that the monitoring effort
within the framework of ASEAN+3 is the most efficient. Ogawa and Shimizu (2005)
advocated a new type of currency basket called the Asian Monetary Unit that is aweighted average of the currencies of ASEAN+3. The AMU is calculated by the same
method used to calculate the European Currency Unit (ECU) under the European
Monetary System (EMS) prior to the introduction of the euro in 1999. Weights on eachcurrency in the currency basket is based on the share of GDP measured in terms of PPP
and trade volumes (the sum of exports and imports), which respectively is the
proportion of one country to the others. Since both the United States and the EU areimportant trading partners of ASEAN+3, the official exchange rate of the AMU is set up
in terms of a weighted average of the U.S. dollar and the euro. On the basis of the East
Asian countries’ trade volumes with the United States and the euro-zone, the weights of
the U.S. dollar and the euro are set 65% and 35%, respectively.
3

It is necessary to determine a benchmark in order to calculate the AMU Deviation
Indicators from the benchmark period level. Depending on the comparisons of the total
trade balance of the member countries, the total trade balance of the member countries
with Japan, and the total trade balance of the member countries with the rest of world,which a period relatively close to zero is selected as the benchmark period. Also, thebenchmark exchange rate is selected with reference to the most balanced period of
trading. On the basis of trade accounts of ASEAN+3 from the beginning of the 1990s
until recently, the trade accounts of the 13 countries were closest to balance in 2001.
Assuming a one-year time lag before changes in exchange rates affect trade volumes,
2000 and 2001 are chosen as the benchmark period. The exchange rate of the AMU iscalculated by the following equation: 1
USD & EUR
USD & EUR
USD & EUR
USD & EUR
= 0.0040 ×+ 6.2017 ×+ 3.0765×
CNY
AMU
BND
KHR
USD & EUR
USD & EUR
+ 472.2701×+ 26.5817 ×
IDR
JPY
USD & EUR
USD & EUR
+124.1471×+ 9.4017 ×
KRW
LAK
USD & EUR
USD & EUR
+ 0.1729 ×+ 0.0208×
MYR
MMK
USD & EUR
USD & EUR
+ 0.9247 ×+ 0.1165×
PHP
SGD
USD & EUR
USD & EUR
+1.9639 ×+ 298.7892 ×
THB
VND
where USD denotes the U.S. dollar, EUR denotes the euro, BND denotes the Brunei
dollar, KHR denotes the Cambodian riel, CNY denotes the Chinese yuan, IDR denotes
the Indonesian rupiah, JPY denotes the Japanese yen, KRW denotes the Korean won,
LAK denotes the Laos kip, MYR denotes the Malaysian ringgit, MMK denotes the
Myanmar kyat, PHP denotes the Philippine peso, SGD denotes the Singapore dollar,
THB denotes the Thai baht, VND denotes the Vietnamese dong.
A Nominal AMU Deviation Indicator is useful in order to monitor the deviationsof how far one currency’s exchange rate in terms of the AMU per national currency isaway from the benchmark rate in real time. The Nominal AMU Deviation Indicator is
calculated by the following equation: 2

1
2

The share and the weight on each country in the AMU were revised in October 2011.
N.C. stands for National Currency.
4

The Nominal AMU Deviation Indicator (%)
ActualBenchmark AMU 
 AMU −
N.C. 
N.C. 
=× 100
Benchmark AMU 
 N.C. 
The Nominal AMU Deviation Indicator is expected to act as an index for eachcountry to monitor the volatility of foreign exchange rates on a daily basis. If theNominal AMU Deviation Indicator is positive, the value of the currency is overvalued.
On one hand, if the Nominal AMU Deviation Indicator is negative, the value of thecurrency is undervalued.
In contrast, a Real AMU Deviation Indicator is more appropriate for conducting
surveillance of the effects of foreign exchange rates on the real economy which includes
international trade and trade balances. The Real AMU Deviation Indicator is calculated
by taking into account inflation rate differentials. It can be worked out according to thefollowing equation:
The Real AMU Deviation Indicator (%)= the Rate of Change in Nominal AMU Deviation Indicator of Country i− (P− P )
AMU

iwhere PAMU is the inflation rate in ASEAN+3 and Pi is the inflation rate of country i .
In summary, the Nominal AMU Deviation Indicator is more useful in monitoring
the intra-regional exchange rates in terms of frequency and time lag. In contrast, theReal AMU Deviation Indicator is more effective in investigating the effects of exchange
rates on real economic variables such as trade volumes or real GDP.
3.

PPP-based AMU Deviation Indicator
Both the Nominal and Real AMU Deviation Indicators are expected to be used ascomplementary measures for the surveillance over intra-regional exchange rates among
East Asian currencies. However, there is a question whether it is appropriate to use aconstant benchmark rate over time to show overvaluation or undervaluation of East
Asian currencies with higher productivity growth. Because the benchmark rate of the
AMU Deviation Indicators is the average rate of 2000 and 2001, the Nominal and RealAMU Deviation Indicators reflect spreads between an actual exchange rate and thebenchmark rate. Along with the remarkable economic growth with higher productivity
improvements in East Asia and the structural changes in foreign exchange policies in
China and Malaysia, there is a possibility that the current AMU Deviation Indicators5

might not be sufficient to observe foreign exchange rate conditions of each country
appropriately. Therefore, it is necessary to take into account equilibrium exchange rates
or the PPP to observe the changes in exchange rates within ASEAN+3 adequately.
Furthermore, it is also important to strengthen the functions of the AMU
Deviation Indicators because the AMU Deviation Indicators play an important role inregional currency cooperation among ASEAN+3. On the basis of previous studies about
the AMU Deviation Indicators, a new approach to the AMU Deviation Indicators is
introduced by taking into account a time-varying benchmark rate based on the PPP. The
year of 2001 is selected as the benchmark year because the trade accounts of ASEAN+3
in 2001 are the most balanced as Ogawa and Shimizu (2005) pointed out. According to
the relative PPP, the PPP of country i in time t can be calculated as the following
equation:i×
S tPPP ,i = S 2001

AMU
Pt AMU P2001i
Pt i P2001

(3-1)

i
where S 2001
is the exchange rate of country i in 2001, Pt AMU is the CPI of the AMU

AMU
area in time t , P2001
is the CPI of the AMU area in 2001, Pt i is the CPI of country i in

itime t , and P2001
is the CPI of country i in 2001.

According to the idea of the AMU Deviation Indicators, the PPP of currency i interms of the AMU per national currency will be used in place of the benchmark rate in
the case of calculation of the PPP-based AMU Deviation Indicator:
Actual AMU 
 AMU −
N.C. 
N.C. 
PPP − based AMU Deviation Indicator (%) =PPP AMU 
 N.C. 

PPP×100 (3-2)

If the PPP-based AMU Deviation Indicator is positive, it means that the actual
exchange rate in terms of the AMU per national currency is overvalued than the PPP. On
one hand, if the PPP-based AMU Deviation Indicator is negative, it means the actual
exchange rate in terms of the AMU per national currency is undervalued than the PPP.
The sample for our empirical analysis is from the period of January 2000 torecently. We employ data from database of the Research Institute of Economy, Tradeand Industry (RIETI), and International Financial Statistics (IFS) to calculate the PPP-

6

based AMU Deviation Indicators. 3 The calculation results of the PPP-based AMU
Deviation Indicators are shown in figure 3-2. It is clear that the higher inflation rates are,the PPP-based AMU Deviation Indicators are the more overvalued, and vice versa.
Inflation rates in each of country of ASEAN+3 and the AMU area are shown in figure3-1. It shows that the PPP-based AMU Deviation Indicator in such high inflationary
country as Indonesia and Laos is always overvalued. On one hand, the PPP-based AMU
Deviation Indicator in such a deflationary country as Japan has a tendency to beundervalued. Furthermore, fluctuations of the PPP-based AMU Deviation Indicators
have widened since 2005. Specifically after the bankruptcy of Lehman Brothers, manyof the ASEAN+3 currencies plunged into the situation of undervaluation. When wecompare the PPP-based AMU Deviation Indicators with the Nominal AMU Deviation
Indicators in figure 3-3, it is obvious that the diverging spreads between both of themtend to be broadening in high inflationary countries. On the other hand, the Real AMU
Deviation Indicators and the PPP-based AMU Deviation Indicators have a similar trendof fluctuations for the lower inflationary countries which include China, Japan, Korea,
and Singapore.
4.

PPP-based AMU Deviation Indicator Adjusted by the Balassa-Samuelson
Effect4-1. The Balassa-Samuelson Effects on ASEAN6+3
Due to data constraints that only the CPI is available across the countries, the CPI
is used in the calculation of the AMU Deviation Indicator. There are some possibilities
that the PPP of each currency diverges from an exchange rate that the law of one priceholds especially for tradable goods because the CPI includes not only prices of tradable
goods but also those of non-tradable goods. The PPP-based AMU Deviation Indicator ismodified after we clarify a problem of the divergences between the PPP calculated bydata on the CPI and the exchange rate based on the law of one price for tradable goods.
In general, a growth rate of productivity in the tradable good sectors is higher
than that of the non-tradable good sectors. In the situation, inflation rates in prices of the
tradable goods tend to be lower than those of the non-tradable goods. Therefore, the
PPP-based on the CPI differs from the exchange rate based on the law of one price forthe tradable goods. The difference between them is known as the Balassa-Samuelson
effect.
3

For the calculation of the PPP, the benchmark rate of each currency in terms of the AMU pernational currency is from the AMU database in RIETI (Research Institute of Economy, Tradeand Industry); the CPI is from International Financial Statistics (IMF).
7

A simple model is used to explain the Balassa-Samuelson effect according to
Ogawa and Sakane (2006). Under an assumption of two countries (home and foreigncountries) both of them have a tradable good sector ( T ) and a non-tradable good sector( N ). The home country is assumed to be a small open economy, which means that thedomestic economy gives no effects on the foreign economy. Labor is freely mobile
between the tradable good sector and the non-tradable good sector while it is completely
immobile across the border between both of the two countries. Under the assumption offull mobility of labor, a nominal wage rate ( W ) is equal between the tradable good
sector and the non-tradable good sector in the home country. Similarly, a nominal wage
rate ( W ∗ ) is equal between the tradable good sector and the non-tradable good sector in
the foreign country.
For simplicity, a price of the tradable good ( PT ) is assumed by a quotient of thenominal wage rate ( W ) in terms of labor productivity of the tradable good sector ( α T )while a price of the non-tradable good ( PN ) is assumed by a quotient of the nominal
wage rates ( W ) in terms of labor productivity of the non-tradable good sector ( α N ). As
well, prices of the tradable good and the non-tradable good in the foreign economy are
assumed by the same way as the domestic economy.
Based on the above assumptions, the prices of the tradable good ( PT ) and thenon-tradable good ( PN ) in the domestic economy are represented as following:
PT =
PN =

W

αT
W

αN

(4-1)
(4-2)

The prices of the tradable good ( PT∗ ) and the non-tradable good ( PN∗ ) in theforeign economy are represented as following:
PT∗ =
PN∗ =

W∗

α T∗
W∗

α N∗

(4-3)
(4-4)

Furthermore, a general price level is defined by a weighted average of the pricesof the tradable good and the non-tradable good. General price levels of the domestic andthe foreign economy ( P and P* ) can be expressed as following:
P = PTwT ⋅ PNwN

8

(4-5)

wT∗

P ∗ = PT∗ ⋅ PN∗

w∗N

(4-6)

where wT is a weight on the tradable good in the general price level of the domestic
economy, wN is a weight on the non-tradable good in the general price level of thedomestic economy, wT∗ is a weight on the tradable good in the general price level of theforeign economy, and wN∗ is a weight on the non-tradable good in the general price levelof the foreign economy.
Under the law of one price on the tradable good, prices of the tradable good areequalized between the domestic and foreign economies. Given an exchange rate whichis expressed in terms of home currency units per foreign currency as S LOP , the law of
one price is expressed as following:
PT = S LOP PT∗ (4-7)
where S LOP is an exchange rate on the law of one price.
On one hand, the PPP is expressed by a ratio of the domestic general price level interms of the foreign general price level as following:
S PPP =

P
P∗

(4-8)

By substituting equations (4-5) and (4-6) into equation (4-8), the PPP is rewritten
in terms of the prices of tradable and non-tradable goods as following:
S PPP=

PTwT ⋅ PNwN
P=∗∗P∗ PT∗ wT ⋅ PN∗ wN(4-9)

Moreover, by substituting equations (4-1) to (4-4) and (4-7) into equation (4-9)
and taking logarithm of the derived equation, equation (4-9) is rewritten as following:(log S PPP = log S LOP + wN ⋅ (log α T − log α N ) − wN∗ ⋅ log α T∗ − log α N∗

) (4-10)

By making differentiation of equation (4.10), the PPP is expressed in terms of therate of change as following:(

S PPP = S LOP + wN (α T − α N ) − wN∗ α T∗ − α N∗

Accordingto(equation(4-11),S PPPis) (4-11)largerthan

S LOPif)wN (α T − α N ) − wN∗ α T∗ − α N∗ > 0 . That is, the PPP is changing to be undervalued

compared with the exchange rate based on the law of one price. On one hand, S PPP is

9smaller than S LOP if wN (α T − α N ) − wN∗ (α T∗ − α N∗ ) < 0 . In this case, the PPP is changing
to be overvalued compared with the exchange rate based on the law of one price.
Specifically, in the case where a country has a higher growth rate of productivity in the
tradable good sectors, the PPP has a tendency to be undervalued compared with theexchange rate based on the law of one price.
4-2. Data
The above simple model is used to conduct a simulation of the PPP based AMU
Deviation Indicators and those adjusted by the Balassa-Samuelson effect. We have tolimit six countries of ASEAN (Singapore, Indonesia, Thailand, Malaysia, thePhilippines, and Vietnam), Japan, China, and Korea to conduct the simulation becauseof data constraints. 4
In order to calculate the productivity in both a tradable good sector and a nontradable good sector for each country of ASEAN6+3, industrial origins of each country
are defined as below. For all the members of ASEAN6+3, the tradable good sectors
include agriculture, livestock, forestry, fishery, mining, quarrying and manufacturing.
On the other hand, the non-tradable good sectors include construction, utilities,
wholesale, retail trade, hotels, restaurants, transport, storage, communications, financial
services, business services, real estate services, community services, social services,personal services and other service industries. 5
We define the productivities both the tradable good sectors and the non-tradable
good sectors as a quotient of real GDP in terms of employment. The data of real GDP
and employment of each sector are from the department of statistics, and statistical
yearbook of each country. For Japan, the data of real GDP is from Japan Statistical
Yearbook and Cabinet Office, Government of Japan, and employment is from OECD
Structural Analysis Statistics and Ministry of Internal Affairs and Communications. For
China, the data both real GDP and employment are from China Statistical Yearbook andNational Bureau of Statistics of China. For Korea, the data of real GDP is from Korea
Statistical Yearbook and Statistics Korea, and employment is from OECD Structural
Analysis Statistics and Ministry of Employment and Labor. For Singapore, the data of
4

The total weights of the other four countries (Brunei, Cambodia, Laos and Myanmar) in the
AMU area are smaller than 1%. Therefore, there is no problems by neglecting the four
countries when we limit the ASEAN6+3 to calculate economic variables in the AMU area.
5
Based on the classification by General Statistics Office of Vietnam, the data of construction isissued with manufacturing, so the constructing industry in Vietnam is classified into the
tradable good sectors.
10real GDP is from Yearbook of Statistics Singapore and Department of Statistics
Singapore, and employment is from Ministry of Manpower. For Indonesia, the data both
real GDP and Employment are from Statistical Yearbook of Indonesia and Statistics
Indonesia. For Thailand, the data of real GDP is from Thailand Statistical Yearbook andNational Statistical Office, and employment is from Office the National Economic andSocial Development Board. For Malaysia, the data both real GDP and employment arefrom Yearbook of Statistics Malaysia and Department of Statistics Malaysia. For
Vietnam, the data both real GDP and employment are from Statistical Yearbook of
Vietnam and General Statistics Office of Vietnam. For the Philippines, the data of real
GDP is from Philippine Statistical Yearbook and National Statistical Coordination
Board, and employment is from Bureau of Labor and Employment Statistics. The
sample for our empirical analysis is from 2000 to 2010. 6
4-3. Empirical Results of the Balassa-Samuelson Effect
In general, if a country has a higher growth rate of productivity in the tradable
good sectors, its currency’s PPP calculated by the CPIs tends to be undervalued
compared with the exchange rate based on the law of one price of tradable goods. As
shown in equation (4-11), the weight on the non-tradable good sector as well as growth
rates of productivities is also a key factor on determining the Balassa-Samuelson effect.
The simulation results show that there is a tendency that growth rates of productivity inthe tradable good sectors are increasing during the analytical period excluding 2009 formost countries of ASEAN6+3. It might be said that the PPPs are undervalued withrespect to the growth rate of productivity in the tradable good sectors for most countriesof ASEAN6+3.
The Balassa-Samuelson effect on each currency is affected not only bydifferentials in the growth rates of productivities but also by the changing weight on the
non-tradable good sectors. It means that changes in the industrial structure are an
important factor in considering the Balassa-Samuelson effect within the area of
ASEAN6+3. Thus, the Balassa-Samuelson effect is much affected by the variables of
the relevant country in the case of a country that has a larger weight in the non-tradable
good sectors than the AMU area like Singapore. On one hand, it seems that the rates ofchange of the Balassa-Samuelson effects tend to be negative and the currency tends tobe overvalued in the case of a country that the growth rate of productivity is higher thanthe AMU area while the weight on the non-tradable good sectors is smaller than the
6 Because there are time lags in data publication, we have to limit our empirical period to 2010.

11

AMU area like China and Vietnam. The detail of the simulation results are as following.
(1) Japan
In Japan, the growth rates of productivity both the tradable good sectors and thenon-tradable good sectors have fallen into a sluggish pace especially from the end of2008 to 2010. The growth rate of productivity in the tradable good sectors is relatively
higher than that of the non-tradable good sectors. For the reason, it might be consideredthat the PPP of the Japanese yen is undervalued. On one hand, the growth rate ofproductivity in the tradable good sectors is higher than that in the non-tradable good
sectors in the AMU area. Accordingly, a differential in the growth rates of productivity
is positive in the AMU area. When we compare the differentials in growth rates of
productivity between Japan and the AMU area, we can find that the growth rates of
productivity in Japan are smaller than those in the AMU area in many years. When we
focus on weights in Japan and the AMU area, it can be said that the rate of change of theBalassa-Samuelson effect of the Japanese yen is not only influenced by the domestic
factors of Japan but also factors of the AMU area. Accordingly, the rate of change of the
PPP of Japanese yen was undervalued before 2004, and then it has turned to be
overvalued.
(2) China
In China, the growth rates of productivity in both the tradable good sectors andthe non-tradable good sectors had increased steadily since around 2000. They dropped
substantially after the bankruptcy of Lehman Brothers. Moreover, because the growthrate of productivity in the tradable good sectors is higher than that in the non-tradable
good sectors, it might be said that the PPP of Chinese yuan is undervalued when we
focus only on the domestic economy. On one hand, the weight on the non-tradable good
sectors in China has grown since 2000, but it has not been over 40% in 2010. It meansthat the main industries are still the tradable good sectors in China. When we compare
differentials in the growth rates of productivity between China and the AMU area, thedifferentials in growth rate in China is higher than those in the AMU area. Because of
the lower weight in the non-tradable good sectors, the rate of change of the BalassaSamuelson effect of the Chinese yuan is seriously affected by the factors of the AMU
area. Therefore, it is clear that the rate of change of the Balassa-Samuelson effect of theChinese yuan is negative. It means the rate of change of the PPP of Chinese yuan isovervalued.

12(3) Korea
In Korea, the growth rates of productivity both the tradable good sectors and thenon-tradable good sectors have kept increasing in the last ten years, excluding 2008 and2009. Based on the growth rate of productivity in the tradable good sectors is higher
than the non-tradable good sectors, it might be said that the PPP of Korean won isundervalued from the aspects of domestic economy. However, the weight on the nontradable good sectors has decreased since 2000 though it is still higher than the AMU
area. By comparing differentials in the growth rates of productivity between Korea andthe AMU area, there is a tendency that differentials in the growth rate of productivity in
Korea are higher than those in the AMU area. Because of the greater weight on the nontradable good sectors and the higher differentials in growth rate of productivity in Korea,
the rate of change of the Balassa-Samuelson effect of the Korean won is consistently
positive. It means that the rate of change of the PPP of Korean won is undervalued.
(4) Singapore
As a member of the newly industrializing economies, Singapore had a positive
growth rate of productivity in the tradable good sectors before 2008. Furthermore, since
Singapore is one of the world’s major financial centers, the growth rate of productivity
in the non-tradable good sectors is also kept at a steady level. Because the differentials
in growth rates of productivity between the tradable good sectors and the non-tradable
good sectors tend to be positive, it seems that the PPP of Singapore dollar isundervalued from the viewpoint of domestic factor. The weight on the non-tradable
good sectors in Singapore is larger than that in the AMU area. When we compare thedifferentials in growth rates of productivity between Singapore and the AMU area, thedifferential in Singapore is also larger than that the AMU area during most of analytical
period. Because of the larger weight on the non-tradable good sectors and the larger
differentials in growth rates of productivity in Singapore, the rate of change of theBalassa-Samuelson effect of the Singapore dollar tends to be positive. It means that therate of change of the PPP of Singapore dollar is undervalued within the framework of
AMU.
(5) Indonesia
Indonesia has no tendency to show both the growth rates of productivity of the
tradable good sectors and the non-tradable good sectors. However, the differential in
growth rates of productivity in Indonesia tends to be near zero or negative. It means that
the PPP of Indonesian rupiah might be overvalued. Although the weight on non-tradable
13

good sectors was smaller than 50% at the beginning of 2000, it has reached a level at
55% in 2010. Based on the changes of weight in the non-tradable good sectors, it can besaid that the main industries of Indonesia have shifted from the tradable good sectors to
the non-tradable good sectors. On one hand, when we compare the differential in
growth rates of productivity between Indonesia and the AMU area, the differential in
growth rate of productivity in Indonesia is smaller than the AMU area during most ofanalytical period. For the reasons, the rate of change of the Balassa-Samuelson effect of
the Indonesian rupiah has a tendency to be negative. It means that the rate of change of
the PPP of Indonesian rupiah is overvalued.
(6) Thailand
In Thailand, both the growth rates of productivity of the tradable good sectors andthe non-tradable good sectors have kept increasing during most of analytical period. The
differential in growth rates of productivity also tends to be positive. Thus, the domestic
factor might cause undervaluation of the PPP of Thai baht. The weight on the nontradable good sectors in Thailand is around 50% and smaller than that in the AMU area.
When we compare the differential in growth rate of productivity in Thailand with that in
the AMU area, the differentials have varied from year to year. Because the weight on
the non-tradable good sectors in the AMU area is around 60%, the rate of change of theBalassa-Samuelson effect of the Thai baht might be substantially affected by the factors
in the AMU area. The analytical results show that the rate of change of the BalassaSamuelson effect of the Thai baht tends to be negative. It means that the rate of changeof the PPP of Thai baht is overvalued.
(7) Malaysia
In Malaysia, both the growth rates of productivity of the tradable good sectors andthe non-tradable good sectors tend to be increasing during the whole analytical period
excluding 2009. The growth rate of productivity in tradable good sectors was higher
than that in the non-tradable good sectors before 2005 while it has been lower after
2006. It is considered that the PPP of Malaysian ringgit was undervalued before 2005
and has been overvalued since 2006. However, the weight on the non-tradable good
sectors in Malaysia has grown since 2001, and surpassed the AMU area in 2007. On one
hand, the differential in the growth rates of productivity in Malaysia was higher thanthat in the AMU area before 2004 while it has been lower from 2005 to recently.
Therefore, the rate of change of the Balassa-Samuelson effect on the Malaysian ringgit
was positive before 2004 and has been negative since 2005. It means that the rate of14

change of the PPP of Malaysian ringgit has turned to be overvalued since 2005.
(8) Vietnam
Although the growth rates of productivity in the tradable good sectors and thenon-tradable good sectors are increasing steadily in Vietnam, the pace is slower thanother ASEAN members. Based on a higher growth rate of productivity in the tradable
good sectors, it might be said that the PPP of Vietnamese dong is undervalued from the
aspect of domestic factors. On one hand, the weight on the non-tradable good sectors in
Vietnam is around 40%, and smaller than the AMU area. When we compare thedifferentials in growth rates of productivity between Vietnam and the AMU area, thedifferential in the growth rates of productivity in Vietnam has been increasing relatively
while the growth rate of productivity in the non-tradable good sectors in Vietnam is nearto zero or negative. Therefore, the rate of change of the Balassa-Samuelson effect of theVietnamese dong tends to be positive. It means that the rate of change of the PPP ofVietnamese dong is undervalued in most years of the analytical period.
(9) The Philippines
In the Philippines, both the growth rates of productivity in the tradable good
sectors and the non-tradable good sectors are increasing during most of analytical period.
However, the growth rate of the tradable good sectors is not as high as that of the nontradable good sectors. Therefore, it might be regarded that the PPP of Philippine peso is
overvalued because of the domestic factors. On one hand, the weight on the nontradable good sectors has grown since 2000. The weights have been close to each otherbetween the Philippines and the AMU area in recent years. As mentioned above, thegrowth rate of productivity in the tradable good sectors of the Philippines was lower
than the non-tradable good sectors before 2005. Accordingly, the differential in thegrowth rates of productivity was negative. The differential in the growth rates of
productivity has turned into being positive because of an uptrend of productivity in the
tradable good sectors since 2006. Furthermore, because the differential in the growthrate of productivity in the Philippines is smaller than the AMU area, the rate of changeof the Balassa-Samuelson effect on the Philippine peso tends to be negative. It meansthat the rate of change of the PPP of Philippine peso is overvalued in many of observing
years.
4-4. PPP-based AMU Deviation Indicator adjusted by the Balassa-Samuelson effectAs previously mentioned, the benchmark rate of the PPP-based AMU Deviation
15

Indicator is calculated by the exchange rate in 2001 and the CPIs. However, we shouldtake into account the Balassa-Samuelson effect in using CPIs to calculate PPPs. The
PPP as a benchmark rate itself may be overvalued or undervalued due to the BalassaSamuelson effect. It is necessary to eliminate the Balassa-Samuelson effect from thebenchmark in order to secure the accuracy of the benchmark rate in the calculation of
the AMU Deviation Indicators. It means that the exchange rate on the law of one priceshould be used as a benchmark rate.
On the basis of the definition about the AMU Deviation Indicators, the PPP-based
AMU Deviation Indicator adjusted by the Balassa-Samuelson effect ( DI PPP Adjusted by BS )can be expressed as below:DI

PPP Adjusted by BS

S Actual − S LOP=
S LOP(4-12)

where S Actual is the actual exchange rate in terms of the AMU per national currency, andS LOP is the benchmark exchange rate on the law of one price.
Equation (4-12) can be expressed in terms of logarithm:

DI PPP Adjusted by BS ≈ log S Actual − log S LOP (4-13)
According to equation (4-10), 7 the exchange rate on the law of one price can also be
expressed by log S

LOP(= log S PPP − wN ⋅ (log α T − log α N ) + wN∗ ⋅ log α T∗ − log α N∗

) , soequation (4-13) can be rewritten as below:(

DI PPP Adjusted by BS ≈ log S Actual − log S PPP + wN ⋅ (log α T − log α N ) − wN∗ ⋅ log α T∗ − log α N∗)

(4-14)
Based on equation (4-14), the rate of change of the PPP-based AMU Deviation
Indicator adjusted by the Balassa-Samuelson effect can be expressed in terms oflogarithmic differentiation as following:(⊿DI PPP Adjusted by BS ≈ S Actual − S PPP + wN (α T − α N ) − wN∗ α T∗ − α N∗) (4-15)

Because the PPP-based AMU Deviation Indicator is defined by equation (3-2), 8
the PPP-based AMU Deviation Indicator can also be expressed in terms of logarithm
7(log S PPP = log S LOP + wN ⋅ (log α T − log α N ) − wN∗ ⋅ log α T∗ − log α N∗
Actual AMU 
 AMU −
N.C. 
N.C. 
8
PPP − based AMU D.I . (%) =PPP AMU 
 N.C. 
16

PPP

×100

)

( DI PPP ≈ log S Actual − log S PPP ). By making differentiation of the PPP-based AMU
Deviation Indicator, the rate of change of the PPP-based AMU Deviation Indicator canbe expressed by a differential between the rate of change of an actual exchange rate andthe exchange rate based on the PPP ( ⊿DI PPP ≈ S Actual − S PPP ).
So equation (4-15) can be rewritten as below:(⊿DI PPP Adjusted by BS ≈ ⊿DI PPP + wN (α T − α N ) − wN∗ α T∗ − α N∗) (4-16)

Hence, equation (4-16) shows that the rate of change of the PPP-based AMU
Deviation Indicator adjusted by the Balassa-Samuelson effect is expressed by the rate ofchange of the PPP-based AMU Deviation Indicator and the rate of change of theBalassa-Samuelson effect.
The above model is used to estimate the PPP-based AMU Deviation Indicator
adjusted by the Balassa-Samuelson effect. The fluctuations of the PPP-based AMU
Deviation Indicators adjusted by the Balassa-Samuelson effect are similar to thefluctuations of the PPP-based AMU Deviation Indicators as shown in figure 4-2.
Currencies of Inflationary countries tend to be overvalued while a currency of
deflationary country tends to be undervalued. 9 Comparison of the analytical results
among the countries makes it clear that there is a disparity between the PPP-based AMU
Deviation Indicator and the PPP-based AMU Deviation Indicator adjusted by theBalassa-Samuelson effect.
However, figure 4-3 shows that the PPP-based AMU Deviation Indicator adjusted
by the Balassa-Samuelson effect has a tendency to be undervalued for the Japanese yen,
the Chinese yuan, and the Malaysian ringgit while it has a tendency to be overvalued for
the Korean won, the Indonesian rupiah, the Thai baht, the Vietnamese dong and thePhilippine peso. Regarding the fluctuation of the PPP-based AMU Deviation Indicator
adjusted by the Balassa-Samuelson effect, it can be said that the asymmetric diversity
on the foreign exchange rate within the AMU area is still an important issue on theprocess of regional monetary cooperation in East Asia.
5.

Conclusion
This paper investigated how the AMU Deviation Indicator should be revised byusing the PPP adjusted by the Balassa-Samuelson effect instead of an average of
exchange rates in 2000 and 2001 as the benchmark rate. It pointed out that thebenchmark rate should be changing over time if fundamentals of exchange rates such as
9

The Balassa-Samuelson effect on each currency is transformed from yearly to monthly by
linear interpolation.
17

the PPP are changing over time and that there is a possibility that the benchmark itself isovervalued or undervalued because the PPPs are calculated based on the CPIs which
include prices of non-tradable goods. By taking into account the Balassa-Samuelson
effect of each currency, we calculated the PPP-based AMU Deviation Indicator adjusted
by the Balassa-Samuelson effect.
When we compared the four types of the AMU Deviation Indicators which
include the original Nominal and Real AMU Deviation Indicators, the PPP-based AMU
Deviation Indicators, and the PPP-based AMU Deviation Indicators adjusted by theBalassa-Samuelson effect, it is clear that the trend of fluctuation is similar with one
another although the PPP-based AMU Deviation Indicators and the PPP-based AMU
Deviation Indicators adjusted by Balassa-Samuelson effect have different movementswith the original Nominal AMU Deviation Indicators.
Each type of the AMU Deviation Indicator has its own merit. The Nominal AMU
Deviation Indicator can be calculated at real time. For the reason, it can be used as areal-time indicator to monitor daily exchange rate movements. Because the Real AMU
Deviation Indicator can only be calculated by monthly and there are time lags on thedata, it is useful in estimating impacts of exchange rates on the macroeconomic
variables of concern. On the other hand, the PPP-based AMU Deviation Indicator and
the PPP-based AMU Deviation Indicator adjusted by the Balassa-Samuelson effect also
have a disadvantage on time lags in collecting the data of price, real GDP and
employment. However, they are useful in evaluating whether the exchange rates are in
an appropriate level compared with such fundamentals as PPPs and growth rates of
productivities.
Both the PPP-based AMU Deviation Indicators and the PPP-based AMU
Deviation Indicators adjusted by the Balassa-Samuelson effect are expected to act as
sub-indexes to judge of overvaluation or undervaluation for each of East Asiancurrencies. In the case of Japan, the Japanese yen is undervalued by approximately 35%
in terms of the Real AMU Deviation Indicator since 2008. In contrast, it is undervalued
by approximately 25% in terms of both the PPP-based AMU Deviation Indicator and
the PPP-based AMU Deviation Indicator adjusted by the Balassa-Samuelson effect. The
Chinese yuan tends to be overvalued in terms of the Real AMU Deviation Indicator
after the bankruptcy of Lehman Brothers. However, it is undervalued in terms of boththe PPP-based AMU Deviation Indicator and the PPP-based AMU Deviation Indicator
adjusted by the Balassa-Samuelson effect.
Over ten years have passed since the regional monetary cooperation started in
East Asia and some positive results on the cooperation have been reached as the CMI
18

Multilateralization (CMIM) and the ASEAN+3 Macroeconomic Research Office(AMRO). Moreover, the AMU and the AMU Deviation Indicators would be a symbol ofthese achievements if the monetary authorities of East Asian countries as well as the
AMRO strengthened surveillance over intra-regional exchange rates. The PPP-based
AMU Deviation Indicators and the PPP-based AMU Deviation Indicators adjusted by
the Balassa-Samuelson effect are also expected to act as a supplementary tocomplement the role of the original AMU Deviation Indicators. The surveillance overthe intra-regional exchange rates should be an important factor in the regional monetary
cooperation in East Asia after we have experienced currency turmoil in the global
financial crisis and the European fiscal crisis as well as the Asian currency crisis.

19

References
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Ogawa, Eiji and Junko Shimizu, 2006b, “Progress toward a common currency basket
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Asian currencies,” RIETI Discussion Paper Series, 09-E-018.
Samuelson, Paul A., 1964, “Theoretical notes on trade problems,” The Review of
Economics and Statistics, Vol. 46, No. 2, 145-155.

21

MMK
(Year of 2000 = 100)

690

590

490

390

290

190

90

22

2009/1

2009/7

2010/1

2009/7

2010/190

2009/1100

2008/7110

2008/7120

2008/1130

2008/1140

2007/7150

2007/7160

2007/1170

2007/1

PHP(Year of 2000 = 100)

2006/790

2006/7110

2006/1130

2005/7150

2006/1170

2005/7

190

2005/1

210

2005/1

LAK(Year of 2000 = 100)

2004/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/790

2004/792

2004/1

94

2004/1

96

2004/1

JPY(Year of 2000 = 100)

2003/798

2003/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

CNY(Year of 2000 = 100)

2003/7100

2003/1102

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

BND(Year of 2000 = 100)

2003/790

2003/1110

90
2002/7130

95

2002/7150

100

2002/7

170

105

2002/7190

110

2002/1

210

115

2002/1120

2002/1

230

2002/1

125

2001/790

2001/7100

90

2001/7110

92

2001/794

2000/7

96

2001/1150

2001/1

2000/1

98

2000/7160

100

2001/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

170

102

2001/1

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

104

2000/7

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

180

2000/7

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

106

2000/7

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1FIGURE 3-1. THE INFLATION RATES OF ASEAN+3 AND THE AMU AREA
KHR
(Year of 2000 = 100)

140
130

120

IDR
(Year of 2000 = 100)

KRW
(Year of 2000 = 100)

140

135

130

125

120

115

110

105

100

95

90

MYR
(Year of 2000 = 100)

130

125

120

115

110

105

100

95

90

Source: International Financial Statistics (IMF).

Complete results are available from the authors.

23

2009/1
2009/7
2010/1

2009/7
2010/1

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2008/790

2009/1

95

2008/1100

2008/7105

2007/7110

2008/1115

2007/1120

2007/7125

2006/7

AMU(Year of 2000 = 100)

2007/1130

2006/7

2006/190
2005/7110

2005/1130

2004/7

VND(Year of 2000 = 100)

2004/1150

2001/7

SGD(Year of 2000 = 100)

2003/7170

2003/1

190

2002/7

210

2002/190
2001/1

95

2001/7100

2000/7

110

2001/1

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

115

2000/7

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

120
135
T HB
(Year of 2000 = 100)

130

125

105
120

115

110

105

100
95

90

FIGURE 3-2. THE PPP -BASED AMU DEVIATION INDICATORS
60

%

BND

KHR

CNY

IDR

JPY

KRW

LAK

MYR

PHP

SGD

50

T HB

VND

IDR

40
30
KRW
20
T HB
10

PHP
VND

0

CNY
SGD
MYR

-10
JPY

-20

2000/1
2000/4
2000/7
2000/10
2001/1
2001/4
2001/7
2001/10
2002/1
2002/4
2002/7
2002/10
2003/1
2003/4
2003/7
2003/10
2004/1
2004/4
2004/7
2004/10
2005/1
2005/4
2005/7
2005/10
2006/1
2006/4
2006/7
2006/10
2007/1
2007/4
2007/7
2007/10
2008/1
2008/4
2008/7
2008/10
2009/1
2009/4
2009/7
2009/102010/1
2010/4
2010/7
2010/10
2011/1
2011/4
2011/7
2011/10-30

Note: The PPP-based AMU Deviation Indicator of Myanmar is drastically higher thanthe other countries; therefore, it is excluded from the figure of 3-2.
Source: RIETI (Research Institute of Economy, Trade and Industry) online database.
International Financial Statistics (IMF).
Complete results are available from the authors.

24

25
10 %

2011/7
2011/7%40

2011/1

2011/7

2011/1

IDR

2011/1%

2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2011/7

2011/1

2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

KHR

2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/1

2001/7%

2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

LAK

2005/720

2005/140

2004/780

2004/1-30

2004/1-20-40

2003/7-30

2003/7

10

2003/1

Nominal D.I.
PPP Base D.I.
Real D.I.

2003/1-15

2002/7-10

2002/7

5

2002/7

10

2002/1100

2002/1-40

2002/1-30-25

2001/1-20-20

2001/7-15

2001/7-10

2000/70-10

2001/1

10

2001/1-5

2001/7-40
2000/120

02000/730

5

2001/1

Nominal D.I.
PPP Base D.I.
Real D.I.

2000/1

2011/7

2011/1

2010/7

2010/1

10

2000/7-20

2000/1

2011/7

Nominal D.I.
PPP Base D.I.
Real D.I.

40

2000/7

2011/7

2011/1

2010/7

Nominal D.I.
PPP Base D.I.
Real D.I.

2000/1

2011/7100 %2011/1

JPY

2011/1%

2010/7

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

BND

2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7CNY

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/115 %2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1%

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/120

2000/715

2000/1FIGURE 3-3. THE NOMINAL AMU DEVIATION INDICATOR, THE REAL AMU DEVIATION
INDICATOR AND THE PPP-BASED AMU DEVIATION INDICATOR
Nominal D.I.
PPP Base D.I.
Real D.I.

Nominal D.I.
PPP Base D.I.
Real D.I.

80

60

0
40

-5
20

-20
0

-40

KRW
Nominal D.I.
PPP Base D.I.
Real D.I.

30

0

20

-10

10

-10
0

MYR
Nominal D.I.
PPP Base D.I.
Real D.I.

60
5

0

0
-5

-20

-10

-15

2011/7

100 %2011/1

International Financial Statistics (IMF).

Complete results are available from the authors.

26
2011/7

2011/7

2011/1

2010/7PHP

2011/130

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1%

2010/735 %2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7-5

2004/1

5

2003/7

10

2003/1

Nominal D.I.
PPP Base D.I.
Real D.I.

2001/7-30

2002/7-20

2002/1

0-100

2001/1

400

2001/7500

2000/730

2001/1-10

2000/1

Nominal D.I.
PPP Base D.I.
Real D.I.

2000/7

2011/7

2011/1

2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

100

2000/1

2011/7

SGD

2011/115 %2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

MMK

2010/7

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1600 %Nominal D.I.
PPP Base D.I.
Real D.I.

20

300
10

200

-10

0

THB
Nominal D.I.
PPP Base D.I.
Real D.I.

25

20

0
15

10
5

-5
0

-10

VND

80
Nominal D.I.
PPP Base D.I.
Real D.I.

60

40

20

-20
0

-40

-60

Source: RIETI (Research Institute of Economy, Trade and Industry) online database.

TABLE 4-1-1. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (Japan)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

62.96

60.06

12.48

7.12

9.68

4.59

5.36

5.09

0.32

2001

63.96

60.44

-10.88-10.32

-4.08

-3.20

-0.56

-0.88

0.17

2002

64.61

60.68

0.05

-2.38

6.39

3.86

2.43

2.54

0.03

2003

64.01

60.14

14.33

8.53

10.95

6.46

5.81

4.49

1.02

2004

63.36

59.52

13.27

7.26

12.64

4.51

6.00

8.13

-1.03

2005

63.10

59.02

2.26

-0.67

9.54

6.16

2.93

3.38

-0.15

2006

62.58

58.05

-3.51

-5.60

9.84

7.53

2.09

2.31

-0.03

2007

61.92

57.05

2.18

-0.56

12.34

11.21

2.74

1.13

1.05

2008

61.93

56.82

14.10

12.13

11.42

9.24

1.97

2.17

-0.01

2009

65.41

59.10

-3.85

5.83

1.26

1.86

-9.68

-0.60

-5.98

2010

62.82

57.12

22.63

7.14

18.81

9.56

15.48

9.25

4.44

Source: Japan Statistical Yearbook.
OECD Structural Analysis Statistics.
Cabinet Office, Government of Japan.
Ministry of Internal Affairs and Communications.
Complete results are available from the authors.

27)

TABLE 4-1-2. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (China)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

35.03

60.06

8.15

6.27

9.68

4.59

1.88

5.09

-2.40

2001

35.61

60.44

6.87

8.24

-4.08

-3.20

-1.37

-0.88

0.05

2002

36.06

60.68

7.80

6.33

6.39

3.86

1.46

2.54

-1.01

2003

35.97

60.14

11.19

6.48

10.95

6.46

4.72

4.49

-1.00

2004

35.87

59.52

13.53

4.80

12.64

4.51

8.73

8.13

-1.71

2005

36.39

59.02

14.44

10.44

9.54

6.16

4.00

3.38

-0.54

2006

37.10

58.05

17.75

14.54

9.84

7.53

3.21

2.31

-0.15

2007

37.81

57.05

20.99

20.33

12.34

11.21

0.66

1.13

-0.39

2008

38.08

56.82

21.76

17.99

11.42

9.24

3.77

2.17

0.20

2009

38.68

59.10

12.49

9.19

1.26

1.86

3.30

-0.60

1.63

2010

38.55

57.12

14.18

9.06

18.81

9.56

5.12

9.25

-3.31

Source: China Statistical Yearbook.
National Bureau of Statistics of China.
Complete results are available from the authors.

28)

TABLE 4-1-3. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (Korea)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

65.31

60.06

16.92

5.71

9.68

4.59

11.20

5.09

4.26

2001

65.97

60.44

-8.83

-11.68

-4.08

-3.20

2.85

-0.88

2.41

2002

66.25

60.68

11.03

6.10

6.39

3.86

4.93

2.54

1.73

2003

65.85

60.14

11.55

6.40

10.95

6.46

5.15

4.49

0.69

2004

64.23

59.52

16.81

2.80

12.64

4.51

14.01

8.13

4.16

2005

63.67

59.02

18.94

12.99

9.54

6.16

5.95

3.38

1.79

2006

62.92

58.05

16.89

9.15

9.84

7.53

7.75

2.31

3.54

2007

62.48

57.05

11.34

5.35

12.34

11.21

5.99

1.13

3.10

2008

62.26

56.82

-9.95

-13.48

11.42

9.24

3.53

2.17

0.97

2009

62.93

59.10

-13.08-14.05

1.26

1.86

0.97

-0.60

0.96

2010

60.80

57.12

22.05

12.12

18.81

9.56

9.93

9.25

0.75

Source: Korea Statistical Yearbook.
OECD Structural Analysis Statistics.
Statistics Korea.
Complete results are available from the authors.

29)

TABLE 4-1-4. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (Singapore)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

73.74

60.06

25.42

4.81

9.68

4.59

20.61

5.09

12.14

2001

76.36

60.44

-21.51

-9.02

-4.08

-3.20

-12.49

-0.88

-9.01

2002

75.38

60.68

11.32

2.76

6.39

3.86

8.56

2.54

4.92

2003

75.55

60.14

4.24

4.61

10.95

6.46

-0.37

4.49

-2.98

2004

74.47

59.52

19.11

8.34

12.64

4.51

10.76

8.13

3.18

2005

74.07

59.02

18.94

6.25

9.54

6.16

12.69

3.38

7.40

2006

73.35

58.05

7.62

4.25

9.84

7.53

3.37

2.31

1.13

2007

74.01

57.05

10.78

15.21

12.34

11.21

-4.42

1.13

-3.92

2008

75.72

56.82

-0.30

9.21

11.42

9.24

-9.51

2.17

-8.43

2009

76.54

59.10

-0.91

-4.97

1.26

1.86

4.06

-0.60

3.46

2010

73.58

57.12

39.06

13.01

18.81

9.56

26.05

9.25

13.88

Source: Yearbook of Statistics Singapore.
Department of Statistics Singapore.
Ministry of Manpower.
Complete results are available from the authors.

30)

TABLE 4-1-5. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (Indonesia)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

47.22

60.06

-7.41

0.80

9.68

4.59

-8.21

5.09

-6.94

2001

47.79

60.44

-15.24-16.64

-4.08

-3.20

1.40

-0.88

1.20

2002

48.15

60.68

12.08

14.99

6.39

3.86

-2.90

2.54

-2.94

2003

48.82

60.14

11.66

17.98

10.95

6.46

-6.32

4.49

-5.79

2004

49.75

59.52

1.58

-7.34

12.64

4.51

8.92

8.13

-0.40

2005

50.69

59.02

-7.86

0.01

9.54

6.16

-7.86

3.38

-5.98

2006

51.57

58.05

8.98

14.62

9.84

7.53

-5.64

2.31

-4.25

2007

52.77

57.05

2.59

5.48

12.34

11.21

-2.89

1.13

-2.17

2008

53.96

56.82

-2.60

-6.70

11.42

9.24

4.10

2.17

0.98

2009

54.68

59.10

-4.78

-5.07

1.26

1.86

0.29

-0.60

0.51

2010

55.64

57.12

17.78

17.34

18.81

9.56

0.44

9.25

-5.04

Source: Statistical Yearbook of Indonesia.
Statistics Indonesia.
Complete results are available from the authors.

31)

TABLE 4-1-6. THE RATE OF CHANGE OF EACH VARIABLE
The Rate of Change of Each Variable (Thailand)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)

Weight (%)
Year(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

52.24

60.06

-1.77

-4.15

9.68

4.59

2.39

5.09

-1.81

2001

52.43

60.44

-8.65

-13.13

-4.08

-3.20

4.48

-0.88

2.88

2002

52.20

60.68

6.31

5.32

6.39

3.86

0.99

2.54

-1.02

2003

50.43

60.14

14.69

2.25

10.95

6.46

12.45

4.49

3.58

2004

50.64

59.52

9.56

3.60

12.64

4.51

5.96

8.13

-1.82

2005

50.99

59.02

3.30

2.52

9.54

6.16

0.78

3.38

-1.60

2006

50.70

58.05

9.80

11.26

9.84

7.53

-1.45

2.31

-2.08

2007

50.62

57.05

14.19

13.15

12.34

11.21

1.04

1.13

-0.11

2008

49.96

56.82

6.63

1.59

11.42

9.24

5.04

2.17

1.28

2009

51.16

59.10

-7.08

-7.61

1.26

1.86

0.53

-0.60

0.63

2010

49.84

57.12

20.80

10.92

18.81

9.56

9.88

9.25

-0.36

Source: Thailand Statistical Yearbook.
National Statistical Office.
Office the National Economic and Social Development Board.
Complete results are available from the authors.

32)

TABLE 4-1-7. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (Malaysia)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

54.30

60.06

10.03

-0.03

9.68

4.59

10.07

5.09

2.41

2001

56.04

60.44

0.38

0.11

-4.08

-3.20

0.26

-0.88

0.68

2002

56.42

60.68

6.88

0.50

6.39

3.86

6.38

2.54

2.06

2003

55.50

60.14

6.60

-0.63

10.95

6.46

7.23

4.49

1.31

2004

55.12

59.52

9.20

3.26

12.64

4.51

5.94

8.13

-1.56

2005

55.84

59.02

4.39

5.79

9.54

6.16

-1.40

3.38

-2.78

2006

56.31

58.05

4.55

8.95

9.84

7.53

-4.40

2.31

-3.82

2007

58.08

57.05

10.74

12.71

12.34

11.21

-1.97

1.13

-1.79

2008

59.56

56.82

6.76

7.86

11.42

9.24

-1.10

2.17

-1.89

2009

62.06

59.10

-8.00

-7.79

1.26

1.86

-0.21

-0.60

0.23

2010

61.85

57.12

15.80

14.66

18.81

9.56

1.14

9.25

-4.58

Source: Yearbook of Statistics Malaysia.
Department of Statistics Malaysia.
Complete results are available from the authors.

33)

TABLE 4-1-8. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (Vietnam)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

41.30

60.06

5.11

-6.36

9.68

4.59

11.47

5.09

1.68

2001

41.00

60.44

-3.51

-4.64

-4.08

-3.20

1.13

-0.88

1.00

2002

40.79

60.68

5.60

1.44

6.39

3.86

4.16

2.54

0.16

2003

40.45

60.14

5.85

-2.03

10.95

6.46

7.88

4.49

0.49

2004

40.25

59.52

4.91

0.53

12.64

4.51

4.38

8.13

-3.08

2005

40.27

59.02

5.94

1.34

9.54

6.16

4.60

3.38

-0.14

2006

40.29

58.05

5.57

1.51

9.84

7.53

4.06

2.31

0.30

2007

40.44

57.05

5.78

2.28

12.34

11.21

3.50

1.13

0.77

2008

40.84

56.82

2.34

1.43

11.42

9.24

0.90

2.17

-0.86

2009

41.35

59.10

-1.72

-1.15

1.26

1.86

-0.57

-0.60

0.12

2010

41.63

57.12

-3.57

-8.43

18.81

9.56

4.86

9.25

-3.26

Source: Statistical Yearbook of Vietnam.
General Statistics Office of Vietnam.
Complete results are available from the authors.

34)

TABLE 4-1-9. THE RATE OF CHANGE OF EACH VARIABLE

Weight (%)
Year

The Rate of Change of Each Variable (the Philippines)
Growth Rate Gap ofGrowth Rate of Productivity (%)
Productivity (%)(α T − α N ) (α T∗ − α N∗ )Rate of Change ofBalassa-Samuelson Effect (%)(

wN (α T − α N ) − wN∗ α T∗ − α N∗

wN

wN∗

α T

α N

α T∗

α N∗

2000

51.58

60.06

-5.46

-8.90

9.68

4.59

3.44

5.09

-1.28

2001

52.15

60.44

-19.45-19.72

-4.08

-3.20

0.28

-0.88

0.68

2002

52.44

60.68

1.66

2.06

6.39

3.86

-0.40

2.54

-1.75

2003

52.70

60.14

-5.01

-3.87

10.95

6.46

-1.14

4.49

-3.30

2004

53.47

59.52

2.53

4.83

12.64

4.51

-2.30

8.13

-6.07

2005

53.99

59.02

2.43

3.37

9.54

6.16

-0.94

3.38

-2.51

2006

54.39

58.05

11.96

11.84

9.84

7.53

0.12

2.31

-1.27

2007

54.88

57.05

16.36

16.33

12.34

11.21

0.03

1.13

-0.62

2008

54.79

56.82

8.55

4.02

11.42

9.24

4.53

2.17

1.25

2009

56.01

59.10

-7.44

-10.50

1.26

1.86

3.06

-0.60

2.07

2010

55.76

57.12

12.31

10.10

18.81

9.56

2.21

9.25

-4.05

Source: Philippine Statistical Yearbook.
National Statistical Coordination Board.
Bureau of Labor and Employment Statistics.
Complete results are available from the authors.

35)

FIGURE 4-1. THE COTRIBUTION OF EACH VARIABLE

35%

Japan (T)Japan (N)AMU Area (N)

B.S.(JPY)AMU Area (T)
%

25

8

35

6

25

%

China (T)China (N)AMU Area (N)

B.S.(CNY)AMU Area (T)
%

8
6

4

4

15

15
2

2

5

5
0
2000

-5

2001

2002

2003

2004

2005

2006

2007

2008

2009

0

2010

2000

-5

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

-2

-2

-15

-15
-4

-4

-25

-6

-25

-6

-35

-8

-35

-8

35

%

Korea (T)

Korea (N)AMU Area (N)

B.S.(KRW)AMU Area (T)
%

25

8

35

6

25

%

Singapre (T)Singapore (N)AMU Area (N)

B.S.(SGD)AMU Area (T)
%

15

10

4
15

15

5
2
5

5
0
-5

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

0

2010

-5

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

-2
-5
-15

-15
-4
-25

-6

-25

-35

-8

-35

36

-10

-15

35

%

Indonesia (T)Indonesia (N)AMU Area (N)

B.S.(IDR)AMU Area (T)
%

25

8

35

6

25

%

Thailand (T)Thailand (N)AMU Area (N)

B.S.(THB)AMU Area (T)
%

8
6

4

4

15

15
2

2

5

5
0
2000

-5

2001

2002

2003

2004

2005

2006

2007

2008

2009

0

2010

2000

-5

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

-2

-2

-15

-15
-4

-4

-25

-6

-25

-6

-35

-8

-35

-8

35

%

Malaysia (T)Malaysia (N)AMU Area (N)

B.S.(MYR)AMU Area (T)
%

25

8

35

6

25

%

Vietnam (T)Vietnam (N)AMU Area (N)

B.S.(VND)AMU Area (T)
%

6

4

4

15

15
2

2

5

5
0

-5

8

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

0

2010

-5
-2

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010
-2

-15

-15
-4

-4

-25

-6

-25

-6

-35

-8

-35

-8

37

35

%

Philippines (T)Philippines (N)AMU Area (N)

B.S.(PHP)AMU Area (T)
%

8
6

25

4
15
2
5
0
-5

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010
-2

-15
-4
-25

-6

-35

-8

Note: Left scale is the rate of change of each variable; Right scale is the rate of change of the Balassa-Samuelson effect.
Source: The same as table 4-1-1 to 4-1-9.
Complete results are available from the authors.

38

FIGURE 4-2. THE PPP-BASED AMU DEVIATION INDICATORS ADJUSTED BY THE BALASSASAMUELSON EFFECT
60

CNY

JPY

%

KRW

SGD

IDR

T HB

MYR

VND

PHP

50
40
KRW
30

IDR

20
PHP

T HB
10

VND

0

SGD

CNY
MYR

-10
-20
JPY
-30

Source: RIETI (Research Institute of Economy, Trade and Industry) online database.
International Financial Statistics (IMF).
Table 4-1-1 to 4-1-9.
Complete results are available from the authors.

39

2010/1

2009/7
2009/10

2009/4

2008/7

2008/10
2009/1

2008/4

2007/7

2007/10
2008/1

2007/4

2006/7

2006/10
2007/1

2006/4

2005/7

2005/10
2006/1

2005/1
2005/4

2004/7

2004/10

2004/1
2004/4

2003/7

2003/10

2003/1
2003/4

2002/7

2002/10

2002/1
2002/4

2001/7

2001/10

2001/1
2001/4

2000/10

2000/4
2000/7

2000/1

-40

40

80

2010/1

2009/7

2009/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

KRW

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1%

2010/1-40

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7CN Y

2009/7-20

2003/115 %2009/1-10-20

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7JPY

2008/70%

2008/140

2007/750

2007/1100 %2006/7-15

2006/7-10-15

2006/1-10

2006/1-5

2005/70-5

2005/7

5

2005/10

2004/7

KRW

2005/1

5

2002/7CN Y

2004/7

10

2001/1

JPY

2004/7

10

2004/1-30

2004/1-20-30

2004/1-20

2003/7-10

2003/70-10

2003/7

10

2003/10

2003/120

10

2003/130

20

2002/730

2002/740

2002/7

PPP Base D.I.
BS Adjusted D.I.

2002/1-12

2002/1-10

2002/1-4

2002/1-2

2001/7-40

2001/7-35-30

2001/7-25

2001/7-20

2000/7-15-15

2001/1-10

2001/1-6

2000/1-5-10

2000/72

2000/1-5

2000/7

2010/1

2009/70

2001/1

2000/1

2010/1

2009/1

2008/7

5

2000/7

2010/1

2009/7

PPP Base D.I.
BS Adjusted D.I.

15

2001/1

2000/1

IDR

2010/1SGD

15 %2009/7

2009/1

PPP Base D.I.
BS Adjusted D.I.

2000/7IDR

PPP Base D.I.
BS Adjusted D.I.

2009/7SGD
PPP Base D.I.
BS Adjusted D.I.

2009/1

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7-20

2000/1

2010/1

2009/7%

2009/1%

2009/1%

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1%

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/1

10

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1%

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/760

2001/1

15

2000/7

2000/140

2000/7

2000/1

4

2000/715

2000/1FIGURE 4-3. THE AMU DEVIATION INDICATORS OF ASEAN 6 + 3
Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

10
5

0

-25

-30

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

0

10
5

-5

0

-8

-10

-15

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

30

60

20

40

10

20

0

THB

THB

35 %

PPP Base D.I.
BS Adjusted D.I.

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

35 %

30

30

25

25
20

20

15
15

10
10

MYR

MYR
PPP Base D.I.
BS Adjusted D.I.

10 %2010/1

2009/7

2009/1

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

10 %

5

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7-10
2001/1-5

2000/7-5
2000/1002000/7

5

5

VND
50 %

PPP Base D.I.
BS Adjusted D.I.

2010/1

2009/7

2009/1

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

VND

20 %

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1-20
2002/7-20

2002/1-15

2001/7-10-15

2001/1-10

2000/70-52000/10-52000/7

5

40

15

30
10

20

5

10
0

0

-10

-5

-20
-10

-30

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2000/1

2001/1

2010/1

2009/730 %2009/1

PPP Base D.I.
BS Adjusted D.I.

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1PHP
30 %2000/7-40-15

Nominal D.I.
PPP Base D.I.
Real D.I.
BS Adjusted D.I.

PHP

25

20

20
15

10

10

0

5
0

-10

-5
-10

-20

-15

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/1

2010/1

2009/7

2009/1

2008/7

2008/1

2007/7

2007/1

2006/7

2006/1

2005/7

2005/1

2004/7

2004/1

2003/7

2003/1

2002/7

2002/1

2001/7

2001/1

2000/7

2000/1

2000/7-30-20

Note: Left side is the graph on the comparisons of the PPP-based AMU Deviation
Indicator and the PPP-based AMU Deviation Indicator adjusted by the BalassaSamuelson effect; Right side is the graph on the comparisons of the Nominal
AMU Deviation Indicator, the Real AMU Deviation Indicator, the PPP-based
AMU Deviation Indicator and the PPP-based AMU Deviation Indicator adjusted
by the Balassa-Samuelson effect.
Source: RIETI (Research Institute of Economy, Trade and Industry) online database.
International Financial Statistics (IMF).

41

Table 4-1-1 to 4-1-9.
Complete results are available from the authors.

42