1
An evaluative study for the ASEAN in predicting factors that affect GDP per capita using four
macroeconomic variables: inflation, exchange, interest and unemployment rates.
Junius W. Yu
De La Salle University
junius.yu@dlsu.edu.ph
Abstract
The Association of South East Asian Nations (ASEAN) is among several regional blocs that
have improved in terms of economic integration. The basic premise for economic integration has
been the idea behind the possibility of having a single monetary unit in the future. The paper
reviews the nominal GDP per capita with a comparison of four macroeconomic variables namely
inflation rate, exchange rate, interest rate and unemployment rate. The theoretical framework is
patterned from the Maastricht Criteria and the conceptual framework of Kabir and Salim’s into
an operational framework of ASEAN convergence criteria index. The research findings for the
correlation coefficients were used in identifying intertwining variables for the four
macroeconomic factors. Based on the results, Singapore has shown tremendous economic
sustainability and a potential for a future anchor currency on the region. However, there are no
empirical proofs that sustain a single monetary unit currency in the region but can be a potential
monetary policy in the future.
Keywords: ASEAN, economic integration, Maastricht Criteria, inflation, interest rate, exchange
rate, unemployment rate
2
I. Introduction
“The poor people don’t have money to spend, while people who have money to spend are
not confident about the future, (hence) no one consumes anything,” said Mr. Sommai Phasee,
Thailand Finance Minister.
ASEAN monetary policy is quite robust compared to other parts of Asia except China
and Japan. “Income which is derived from the goods market has a considerable influence on the
demand for money in the money market while interest rate has significant effects on planned
investment in the goods market.” ASEAN region has a tricky regional currency since the US
dollar act as an anchor currency in the area. Hence interest rates are related with the exchange
rate due to the relative free flow of goods within the region. This argument present an inverse
relationship compared to the European Union, since the inter-connectedness are attributed to
geographic locations against an archipelago. The region’s monetary policies are quite different
from one another since the diversity of the region prevents a regional cohesiveness that could
ascertain a parallel monetary policy (Mundell 2003). Monetary policy has been an economic
theory that emphasized macroeconomic effect on the money supply and the role of a central
bank. However, Milton Friedman, a renowned economist statistician, argued the Keynesian
economic policy wherein the role of government can increase employment rate by increasing
aggregate demand via a natural flow of unemployment rate that eventually would lead to
stagflation. The excessive expansion of money supply can accentuate inflation (Yoshimi 2014).
The research question arises on the factors that affect GDP per capita against interest
rates, exchange rates, inflation rates and unemployment rates. The research findings will
illuminate the potential for providing empirical proof on whether a single monetary unit is
sustainable with significant or no significant factors.
3
II. Review of Related Literature
The roadmap for ASEAN 2020 has been the forefront vision of the Association of
Southeast Asian Nations. Three themes has been the community’s integral initiative for the
ASEAN integration for the development progress namely ASEAN political security community,
ASEAN economic community and ASEAN socio-cultural community (Hew 2005).
Hew (2005) postulated that the ASEAN region’s ultimate goal should be the creation of a
fully integrated market wherein a common market for labor, capital, service and goods are
essential without trade limits for free trade and continuous mobilization with the primary target
by 2020. ASEAN has made significant advancement in the development of the region via intraregional relationships like the ASEAN Free Trade Area (AFTA) in order to enhance competitive
advantages especially economic efficiency and productivity of its member nations. Other
ASEAN initiatives include the ASEAN Surveillance Process, Monitoring Capital Flows, Early
Warning Systems, ASEAN swap arrangement, bilateral swaps and repurchase agreements, the
Chiang Mai Initiative of the ASEAN Finance Ministers and more.
A monetary future can be achieved via criteria undertaken similar to the Maastricht
Criteria (Plummer and Wignaraja 2007). Plummer (2006) conducted a qualitative and
quantitative study on the effect of policy on the integration process for the European Union with
a clear relation between EU policy and the trade flow. A creation of a “custom union plus” by
2020 in the ASEAN region can be a feasible idea for a deeper financial cooperation.
In order for the ASEAN region to have a stronger economic currency, a single monetary
unit is feasible. However, discriminatory bilateral trade policies seem to be the barrier for such
undertaking especially in the context of anchor currency like the US dollar when the Chinese
R.M.B is stronger (Sally and Sen 2011).
4
Review on the study of RMB exchange in relation to the US real economy via
unemployment rate was done by Li, Bao, Wang and Cheng. This was accentuated with a
cointegration test and VECM model in order to explain the relationship that may exist between
the Chinese RMB exchange rate, bilateral trade and the US unemployment rate. Their findings
pointed that there is a negative correlation between exchange rate and unemployment rate, while
an impulse response analysis discloses RMB appreciation and decrease in US-China bilateral
trade and increase in US unemployment rate (Li, Bao, Wang and Cheng 2013).
The cointegration method from Johansen-Juselius model was already done from previous
studies relative to the ASEAN countries namely for Malaysia, Singapore and Philippines using
unemployment rate as a key macroeconomic variable. Since the primary economic objective of
any country is to reduce unemployment rate, Subramaniam and Baharumshah from Malaysia
were able to deduce from their research findings the existence of long-run cointegration
relationship between unemployment for the three countries: Malaysia (exports and foreign direct
investments as determinants), Philippines (government spending and exports are inversely
related to unemployment) and Singapore (exports as a significant factor). One of their major
findings is that Singapore shows the rapid speed of adjustment when encountering shocks
compared to other ASEAN countries (Subramaniam and Baharumshah 2011).
Robert Mundell’s OCA (Optimum Currency Area) has come out from as a major
theoretical assessment from the review of related literature. However, one paper accentuated the
generalized purchasing power parity theory as a potential for assessment in the ASEAN sector
during a period containing significant structural breaks. Using three base countries as a foci of
comparison namely US, Japan and China along with 5 ASEAN countries, the research findings
posit the relevance of considering breaks using the Johansen method that support long-run G-
5
PPP for the ASEAN5 along with the Big 3 as an OCA, compared for only the Big 3 standalone
OCA. Their findings conclude that financial stability can occur in regards in the addition of more
countries under the currency blocks making the assumption on whether the ASEAN bloc is
already an Optimum Currency Area (Nusair 2011).
The Panel Seemingly Unrelated Regression Kapetanios Shin Snell or simply the panel
SURKSS are thoroughly used as metric analysis for the purchase power parity. Several related
literature has point out in Europe, Latin America and even the ASEAN bloc. The findings on the
theoretical and empirical aspects of the PPP and the real exchange rate were done by Chang,
Zhang and Liu (2010). They postulated from the Wu and Lee model (2009) in the investigation
of the properties of long-run Purchasing Power Parity using the panel SURKSS tests. Empirical
results posit that several panel based unit root test indicate that PPP does not hold except when
the US dollar is used as an anchor currency (Chang, Zhang and Liu 2010).
Other approaches are explored using the Copula approach, like the research findings done
by Chaiboonsri and Chaitip in determining the dependent measures of the Thai Baht exchange
rate among other ASEAN currencies during the period of 2008 – 2011. A Dynamic Copula
Approach was also tested to investigate correlation movement based from Pearson linear
correlation having a positive relationship except for Vietnam. Their findings posit that the Thai
Baht has a dependent structure to other ASEAN currencies that are essential in determining the
ASEAN financial market (Chaiboonsri and Chaitip 2012).
GARCH was also used for the investigation on the links between inflation, uncertainty
and economic growth in five countries from 1980 onwards for the causal relationship with
findings: uncertainty increases to positive inflation, inflationary shocks affect uncertainty from
the Friedman-Ball hypothesis and inflation affects growth negatively (Mohd et al 2013).
6
A. Theoretical Framework
HCIP Inflation
Budget Deficit
Euro
Convergence
Criteria
Debt to GDP ratio
Exchange Rates
Interest Rates
Figure 1: Maastricht Criteria 1996
In the 7th of February 1992, European countries signed a mutual agreement at
Maastricht, Netherlands for the integration of European countries within the region. This led to
the European Union as well as a single monetary currency called the Euro €(Afxentiou 2000).
The original Euro Convergence Criteria allows for only four variables namely inflation
rate, government finances, exchange rates and interest rates as defined by Article 121 from the
treaty. The primary purpose of this criterion is to maintain stability of price within the European
zone context, even with the addition of new member states.
Under the Treaty of the Functioning of European Union, Article 140 expanded the
criteria for the fiscal criterion to include both the debt criterion and the deficit criterion.
European member states are obliged to the strict adherence to the Stability and Growth Pact
(SGP). The pact simply stated that members should maintain the stability of the European
Economic and Monetary Union (EMU) as well as facilitate growth for member nations. This
points out to the budget deficit to GDP and debt to GDP ratio as economic indicators.
7
HICP Inflation – Harmonized Index of Consumer Prices is a consumer price index
provided by the European Central Bank (ECB) as an indicator for inflation and price stability. It
is an economic indicator that utilized a weighted average of price indices of member states with
the Euro € as a primary currency. The ECB maintain price stability by keeping a threshold of 2%
for the medium term and control short-term interest rate through the EONIA (European
overnight index average) that affect market expectations.
For the Euro Convergence Criteria, the twelve months average of yearly rates is used in
the calculation of the last month of observed data. The criterion is pegged at “the un-weighted
arithmetic average of HICP inflation rates in the three EU member states with the lowest HICP
inflation plus 1.5%.”
Budget Deficit – under the agreement, each European country should not exceed 3% for
the ratio of the annual general government deficit relative to the market price of the gross
domestic product (GDP) at the end of the fiscal period. There are exemptions from the rule
provided that the deficit ratio has declined substantially and continuously prior to the 3% limit;
or the small deficit ratio was caused by exceptional circumstances with a temporary stature.
Countries in violation of the agreement shall abide to Article 126 (6) upon the
recommendation of the Council of the European Union.
Debt to GDP ratio – Government debt pertains to the nominal value outstanding at the
end of the fiscal year among the consolidation of sectors from government deficit. The 60% limit
was applied to the ratio of government debt relative to GDP at market price.
A satisfactory pace is allowed for the new debt reduction benchmark rule imposed last
December 2011 as amended by the European Commission. The commission can grant an
exemption in accordance to Article 126 (6) for “exemption compliance”.
8
Exchange Rates – European countries should have maintained the currency rates for the
past two years upon application. Hence, applicant countries using the central rate of the euro
pegged currency relative to their own currency does not diminish in value during the previous
two years with a premise that their own currency is stable without “severe tensions”.
Countries are expected to adhere to the Exchange Rate Mechanism (ERM/ ERM II)
under the strict regulations set by the European Monetary System (EMS) for two consecutive
years. The key element here is the adoption period that would be set by the Council of the
European Union following approximately 1.5 month after the publication of the convergence
report was made.
Long-term Interest Rates – Average yields for a 10-year government bonds from the past
year are taken into consideration. The standard rate is less than 2% than the un-weighted
arithmetic average for similar 10-year government bond yields in three European Union states
with the lowest HICP inflation.
Economic and Monetary Policy – the consolidated version of the treaty on the
functioning European Union was presented with focuses on economic and monetary policy.
Article 120 – 126 stipulated the economic policy, whilst Article 127 – 133 stipulated the
monetary policy for both chapters 1 and 2. Chapters 3 to 5 specified the institutional provisions
on Article 135 – 136, additional provisions on Article 136 – 138 and transitional provisions on
Article 139 – 144 pursuant to a European Council decision.
European Central Bank (ECB) published a convergence report at least every two years
for countermeasures on the member states. The latest convergence report was published last June
2014 for compliance checking in the period of May 2012 to April 2014. Hence, the Euro
Convergence Criteria has been used as a compliance check for member states.
9
B. Conceptual Framework
Inflation
ASEAN
convergence
criteria
Interest
Exchange Rates
Figure 2: Kabir & Salim Conceptual Framework 2014
Shahriar Kabir and Ruhul A. Salim conducted a research paper with the title “Regional
Economic Integration in ASEAN, how far will it go?” Their paper highlighted the ASEAN
regional bloc with a potential for monetary union in the future. They provided an overview of
trade performance and a comparison of three macroeconomic variables namely inflation, interest
and exchange rates (Kabir & Salim 2014).
Inflation – Kabir and Salim postulated that Laos has a relative high inflation rate
followed closely by Indonesia and Myanmar. This posits that the three countries not only have a
high inflation rate but exemplify a very unstable inflation on their economy. On the other hand,
Philippines writhed with high inflation as well, but a comparative stable economy. Their research
follows the Maastricht Criteria in determining the standards set for the evaluation of the inflation
criterion. Only four countries qualified under the criterion namely Brunei, Malaysia, Singapore
and Vietnam (Kabir and Salim 2014).
From the period of 2001 to 2012, only Myanmar is left with a very high inflation rate
despite the normalcy of the other countries in the region for stabilization, while Brunei
Darussalam incurred at an average of 0.64 with the lowest inflation rate (Kabir and Salim 2014).
10
Interest Rates – During the 1980s, there are three countries that exemplified very high
interest rates namely Indonesia, Philippines and Thailand. No data were available for Laos and
Vietnam for observation. During the 1990s, Laos, Myanmar and Vietnam incurred high interest
rates and joined the other three previously mentioned in an average of double figure rates. In the
2000s, only three countries still maintained high interest rates namely Myanmar, Indonesia and
Vietnam. Using the Maastricht Criteria, no countries from the ASEAN region would qualify
compared against European standards (Kabir and Sahim 2014).
Exchange Rates – According to the Maastricht Criteria, the ASEAN region is quite
unstable on their currency respective to the country. This was promulgated by the 1997 Asian
Financial Crisis that saw the devaluation of the Thai Baht and affected the entire region with a
domino effect. The anchor currency being used by the region is the US dollar, since it is the
currency widely accepted on the region compared to any other ASEAN currency. Robert
Mundell, a Canadian economist and 1999 Nobel Prize Winner for Economics, whose work on
the monetary dynamics and optimum currency areas (OCA) has provided insights on the
possibility for an Asian Currency Area. He postulated that the Theory of Optimum Currency
Areas can be achieved via a single currency for a maximum economic efficiency. The ASEAN
region is a prime location for a possible singular currency in the future (Mundell 2003).
Kabir and Sahim (2014) accentuated that the strong currency of the Brunei dollar and the
Singaporean dollar have been stable throughout the observation period. The stability of the
currency can be attributed to the three countries with a managed float on a relatively large
exchange rate fluctuation namely Indonesia, Cambodia and Laos, while Thailand, Philippines
and Vietnam have a relatively smaller exchange rate fluctuation. A useful scenario in explaining
the currency management policies of ASEAN economies were constructed (Kawai 2008).
11
C. Operational Framework
Inflation
ASEAN
convergence
index
GDP per
capita
Interest Rates
Exchange Rates
Unemployment
Figure 3: ASEAN convergence index based from Maastricht Criteria
Kabir and Salim was the first to use the Maastricht Criteria for the ASEAN region,
however their study involves only three macroeconomic variables namely inflation, interest and
exchange rates. Based on the Maastricht Criteria, government deficit and debt to GDP also plays
a vital role in the assessment of countries economic stability.
Asian Development Bank has made numerous research papers about the feasibility of the
Asian Currency Unit (ACB). Masahiro Kawai, dean and CEO of Asian Development Bank
Institute, wrote “The Role of an Asian Currency Unit for Asian Monetary Unit”, “Toward a
regional exchange rate regime in East Asia” and more. He postulated that a single monetary
currency can be feasible with a primary concern on economic stability (Kawai 2009).
This argument presented a case for the inclusion of GDP as a proxy variable in the
calculation for the ASEAN convergence index. Since the Gross Domestic Product represents the
total market value of a country’s output within a given time period. The dilemma usually arises
on whether it is calculated via the expenditure approach or the income approach as well as the
nominal versus real GDP that became the reason for the omission (Kawai 2008).
12
ASEAN convergence index will provide a similar criterion to the Euro convergence
criteria as prescribed by the Maastricht Criteria. However, unlike the Maastricht Criteria, it has
five variables. Kabir and Salim patterned their framework from the Maastricht Criteria with foci
of variables on the interest, exchange rates and inflation. The argument of the Purchase Power
Parity also comes into as a moderating variable but research findings would focus more on the
GDP per capita using current dollars as the dependent variable. However, many economists like
Simon Kuztnets to Frank Shostak would argue using nominal GDP as a metric of production not
taking into consideration price level.
The counter argument by other economist believe that using nominal GDP could be a
good indicator since inflation is already taken to account in the model. A real GDP has been
adjusted to take into account inflation and such presentation could create multicollinearity
between adjusted inflation and with actual inflation. However, the premise of using real GDP can
be a continuing study for future research since any findings done from this paper would serve as
a stepping stone in understanding the dynamics of the operational framework in testing for
coefficient of determination of model fitting.
Population has been taken into consideration as one of the macroeconomic variable that
could also be potential in the understanding of the dynamics of the economic model. However,
related literature points to GDP per capita as a better indicator taken into account two things
simultaneously namely GDP and population for wealth distribution to avoid redundancy.
The study would include GDP per capita as the dependent proxy variable in order to
make the convergence index stronger relative to the understanding of the criterion. In this way, it
would enhance GDP in the ability of a country to produce an aggregate of goods relative to their
production capacity that provide added value to the region relative to the population.
13
ASEAN monetary policy is quite robust compared to other parts of Asia except China
and Japan. “Income which is derived from the goods market has a considerable influence on the
demand for money in the money market while interest rate has significant effects on planned
investment in the goods market.” ASEAN region has a tricky regional currency since the US
dollar act as an anchor currency in the area. Hence interest rates are related with the exchange
rate due to the relative free flow of goods within the region. This argument present an inverse
relationship compared to the European Union, since the inter-connectedness are attributed to
geographic locations against an archipelago. The region’s monetary policies are quite different
from one another since the diversity of the region prevents a regional cohesiveness that could
ascertain a parallel monetary policy.
Inflation represents an increase in overall price level. Even though not all price increase
constitutes inflation, there are many way to determine the prices of individual goods and services
like the interaction of many buyers and many sellers from an economic stand point of view. The
most popular fixed weight price index being used is the consumer price index (CPI). The
measurement can help ascertain the inflation variable further in relation to the convergence index
as an independent variable predictor. Exchange rate would focus primarily on the LCU (Local
Currency Unit) of each ASEAN currency. Interest rate would focus primarily on the bank
deposit interest rate as ascertained by the local central bank from each respective location.
Unemployment rate has been added to the economic mix and as an integral variable that
makes this paper differ from the norms using the Maastricht criteria. The argument presented
here indicates the Keynesian economics that link unemployment highly as a key indicator of the
economy’s wealth and concepts like sticky wages, minimum wage laws, efficiency wage theory
to imperfect information could be a good future research intertwine with this phase 1 study.
14
III. Methodology
Data were gathered from data.worldbank.org for economic indicators namely GDP per
capita, inflation rate, exchange rate, interest rate and unemployment rate. The availability of data
depends on the country’s information, this was vital to exclude Myanmar from the study due to
the unavailability of the country’s data. Most research findings exclude Myanmar, formerly
known as Burma; from the study due to the political ramifications the country is having that may
not be a good measure in the comparison with other ASEAN countries.
The period taken into consideration was from 1980 up to 2013. As of writing the research
paper, 2014 economic indicators is not yet available but would be updated as soon as the data
can incorporated with the existing calculations. The numerical sample from each study could
account to 34 samples per variable with 1700 data sets (34 x 5 variables = 170 x 10 countries =
1700) available for data mining and analysis.
However the dilemma arises for some countries with no pertinent data available
especially during the 1980s. This is attributed to the political instability during that period like
the Vietnam War, the independence of Brunei, the EDSA revolution in the Philippines and more.
During the 1990s, new members like Vietnam, Laos, Cambodia and Myanmar were introduced
to the regional bloc. Hence, the proxy means would be used to supplement missing data
especially during the 1980s. Future research would exclude the 1980s when the sample reach
three decades of observation, but for the mean time a proxy mean can be substituted for the
calculation and interpretation of the results.
Descriptive studies were implemented along with Pearson rho in ascertaining bivariate
correlations. A multiple regressions were performed with facets of the four macroeconomic
indicators namely inflation, exchange, interest and unemployment relative to GDP per capita.
15
IV. Results
Country
BRUNEI
CAMBODIA
INDONESIA
LAOS
MALAYSIA
PHILPPINES
SINGAPORE
THAILAND
VIETNAM
GDP pc
INF
EXC
INT
UNE
20876.10
1.71
1.72
0.71
3.72
498.42
5.25 3317.78
3.82
1.18
1207.70
10.21 5238.25
13.66
6.90
529.40
21.31 4873.28
11.90
1.86
4443.39
3.05
3.01
5.24
3.32
1142.47
8.83
32.97
9.59
8.72
23072.67
2.26
1.72
2.95
3.27
2355.68
4.25
30.69
7.25
1.56
625.76
7.25 12176.01
8.94
2.34
Table 1: ASEAN mean scores for all measures from 1980 - 2013
(Note: calculated from data.worldbank.org)
According to the World Bank for the year 2013, Singapore ranked 12th overall worldwide
with a GDP per capita at $55,182. Based from the descriptive statistics, Singapore also ranked 1st
in the ASEAN region with mean scores of $23,072.67 from 1980 up to 2013. Brunei ranked 2nd
overall in the ASEAN region with an average GDP per capita at $20,876 and $38,563 in 2013
that placed them 27th overall worldwide. Followed by Malaysia with an average GDP per capita
at $4,443.39 and $10,538 with 68th ranking worldwide. Thailand, Indonesia and Philippines are
clustered in the middle and are ranked 93rd, 119th and 130th respectively worldwide.
The mean scores for inflation rate points to Brunei, Singapore and Malaysia that
maintained the price of goods and services relative to economy from 1980 up to 2013. At the
middle of the pack are Thailand, Cambodia, Vietnam and Philippines that provided a gradual
increase in their price level. While Indonesia and Laos provided a high increase of inflation rate
over the time period, this is attributed after 1997 financial crisis wherein both countries suffered
tremendous increase with an inflation rate as high as 58% and 128% respectively (World Bank).
Using inflation and GDP per capita as a measure, Singapore and Brunei are segmented into one
group with Malaysia catching up with the big two in terms of economic sustainability.
16
The anchor currency used for the ASEAN region is the US dollar as attributed with the
GDP per capita and the LCU (local currency unit). This means local central bank in the region
primarily used US dollar as a currency commodity with trade transactions in the region. The key
indicator was accentuated during the 1997 financial crisis. Thailand baht jumped with a 40%
decrease in value that resulted to a domino effect in Asia. Indonesian rupiah was drastically
affected with 83% decrease in value, while Malaysian ringgit and Philippine peso decrease to an
average of 39% value reduction. From 2000 onwards, the stability of the ASEAN currency was
maintained and with parallel value of the Singaporean and Brunei dollar still exemplified a
strong valuation against the US dollar (World Bank).
In terms of interest rates, Brunei has the lowest followed by Singapore and Cambodia.
However, Indonesia and Laos still showcased high mean scores on interest rates due to the 1997
financial crisis. As of 2013, three countries still exemplify more than 5 basis points namely Laos,
Indonesia and Vietnam (World Bank).
With reference to unemployment, the Philippines still has the highest mean score of
unemployment in the ASEAN region followed closely by Indonesia. Currently, the Philippines
have 7.1% and Indonesia at 6.3%. This is attributed to the growing population for the two
countries with Philippines estimated to have 101 million and Indonesia at 256 million in 2015.
Both countries represent around 58% of the total population in the ASEAN region. Vietnam
currently is ranked 2nd overall in 2013 with a 7% unemployment rate and a growing population
of 93 million. Brunei and Singapore have maintained their unemployment rate at a mean of 3%,
but the countries represent less than 1% of the population in the region. However, the growing
population in the ASEAN region can no longer sustain with respect to the availability of jobs
making the job market relatively more competitive each year.
17
A. Bivariate Correlations
SINGAPORE
Descriptive Statistics and Inter-Correlations for All Measures
µ
σ
INF
EXC
INT
UNE
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
23072.67
2.26
1.72
2.95
3.27
0.219185
-0.408721*
-0.045109
-0.504319**
X
14783.27 0.058641
2.26
X
0.30
2.74
0.91
-0.901556**
-0.043564
X
-0.760663**
0.427870*
0.691142**
X
Table 2: Singapore Bivariate Correlations
GDP per Capita is significantly correlated with exchange rate and interest rate. This
implies that the negative correlation occurs when GDP per capita increases, the Singaporean
dollar becomes stronger in terms of valuation against the US dollar and interest rate goes down
that could result to more expenditure. Exchange rate shows a significant relation with interest
rate. Inflation on the other hand shows minimal significant correlation with interest rates and
unemployment. Unemployment shows a negative significant relationship with inflation and
interest rate that could explain when rates went down then unemployment goes up.
BRUNEI
µ
σ
INF
EXC
INT
UNE
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
20876.10
1.71
1.72
0.71
3.72
8955.97
2.11
0.30
0.36
0.39
-0.098639
X
-0.593125**
0.218900
X
-0.589366**
0.442796**
0.886545**
X
-0.559974**
0.176737
0.646912**
0.587982**
X
Table 3: Brunei Bivariate Correlations
GDP per capita is significantly correlated with exchange rate, interest rate and
unemployment rate, however the negative correlations implies that when GDP per capita
increases then inflation rate, exchange rate and unemployment rate goes down. Inflation rate is
positively correlated with interest rate. Exchange rate is also significantly correlated with interest
and unemployment. However, the results of 0.88 correlation between interest rate and exchange
rate implies that the Brunei dollar maintain valuation despite having minimal changes in interest.
18
MALAYSIA
µ
σ
INF
EXC
INT
UNE
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
4443.39
3.05
3.01
5.24
3.32
2669.64
1.98
0.58
2.47
0.38
-0.232043
X
0.442301**
-0.373438*
X
-0.623091**
0.547167**
-0.640177**
X
-0.147695
-0.029417
0.116623
-0.148935
X
Table 4: Malaysia Bivariate Correlations
GDP per capita is significantly correlated with exchange rate and interest rate. This
exemplify when GDP increases, exchange rate devalues whilst interest rate decreases. Inflation
rate on the other hand is significantly correlated with exchange rate and interest rate as well,
however with a negative relationship with exchange rate and positive relationship with interest
rate. Interest rate has an inverse relationship with exchange rate. Results showed that there is no
relationship with unemployment with any of macroeconomic variable.
THAILAND
µ
σ
INF
EXC
INT
UNE
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
2355.68
4.25
30.69
7.25
1.56
1451.18
3.70
7.05
4.58
0.75
-0.261847
X
0.314004
-0.433807*
X
-0.688482**
0.460276**
-0.762973**
X
-0.843317**
0.304603
-0.265410
0.622346**
X
Table 5: Thailand Bivariate Correlations
GDP per capita is significantly correlated with interest rate and unemployment with an
inverse relationship. This implores that when GDP increases, interest rate and unemployment
rate goes down. Inflation rate is significantly correlated with exchange rate and interest rate.
Interest rate has a negative relationship with exchange rate and unemployment.
INDONESIA
µ
σ
INF
EXC
INT
UNE
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
1207.70
10.21
5238.25
13.66
6.90
924.94
9.26
3970.32
7.18
2.26
-0.281965
X
0.621292**
0.139177
X
-0.491628**
0.625141**
-0.136443
X
0.105402
-0.069712
0.472618**
-0.436802**
X
Table 6: Indonesia Bivariate Correlations
19
GDP per capita is significantly correlated with exchange rate and interest rate. It has a
positive relationship with exchange rate that explore when GDP increases, exchange rate
devalues against the US dollar. It has a negative relationship with interest rate that implore when
GDP increases, interest rate decreases. Inflation is significantly correlated with interest rate as
well, while unemployment is positively correlated with exchange rate and negatively correlated
with interest rate.
PHILIPPINES
µ
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
1142.47
8.83
32.97
9.59
8.72
σ
600.75
8.84
14.98
5.24
1.60
INF
EXC
INT
UNE
-0.390661*
X
0.573386**
-0.487565**
X
-0.760242**
0.730407**
-0.730768**
X
-0.392895*
-0.164682
0.363004*
0.029005
X
Table 7: Philippines Bivariate Correlations
GDP per capita is highly significant for exchange rate and interest rate and also
significantly correlated with inflation and unemployment with an inverse relationship. All four
macroeconomic variables in the Philippines affect GDP per capita with an inverse relationship
except for the exchange rate. This implies that when GDP increases, then there is a decrease in
inflation, unemployment and interest rate while exchange rate devalues against the US dollar.
Inflation rate is also negatively correlated with exchange rate and positively correlated with
interest rate. Exchange rate on the other hand has an inverse relationship with interest rate and
significant relationship with unemployment. Basing on the data, interest rate shows a mean score
of 0.73 and highly significant for all factors except unemployment.
VIETNAM
µ
σ
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
625.76
7.25
12176.01
8.94
2.34
505.49 0.544060**
5.88
X
6197.44
4.15
0.32
Table 8: Vietnam Bivariate Correlations
INF
EXC
INT
UNE
0.694038**
0.332385
X
-0.316576
0.102732
-0.307227
X
-0.358500*
-0.244750
-0.194241
0.268383
X
20
GDP per capita is significantly correlated with inflation and exchange rate, while
negatively correlated with unemployment. This accentuated the fact that Vietnam was recovering
from “the Vietnam War” at the start of 1980s and the transition to a command economy.
Vietnam shows the highest correlation of inflation rate to GDP with 0.54 correlation score
among all ASEAN countries. However, the data provided no significantly relationship with other
various factors except for the GDP per capita.
LAOS
µ
σ
INF
EXC
INT
UNE
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
529.40
21.31
4873.28
11.90
1.86
389.44
30.00
4340.85
8.12
0.43
-0.402052*
X
0.439183**
-0.429037*
X
-0.486667**
0.624770**
-0.863091**
X
-0.699645**
0.226737
-0.609961**
0.458553**
X
Table 9: Laos Bivariate Correlations
GDP per capita is significantly correlated with all four macroeconomic variables. It also
shows that there is an inverse relationship to all except exchange rate. This implies that when
GDP increases, all the other factors decreases while exchange rate relative to dollar devalues.
Inflation rate is significantly correlated with interest rate (positive) and exchange rate (negative).
Exchange rate is highly significant with interest rate and unemployment with an inverse
relationship, while interest rate is also significant with unemployment.
CAMBODIA
µ
σ
INF
EXC
INT
UNE
GDP per Capita
Inflation
Exchange Rate
Interest
Unemployment
498.42
5.25
3317.78
3.82
1.18
250.73
6.15
1128.84
2.96
0.85
0.289439
X
0.704291**
0.321282
X
-0.837523**
-0.275802
-0.898477**
X
-0.450511**
-0.057354
0.188708
0.101554
X
Table 10: Cambodia Bivariate Correlations
GDP per capita is significantly correlated with interest rate and unemployment with an
inverse relationship. Exchange rate is significant with GDP per capita as well as interest rate,
while all factors shows no signs of significance.
21
B. Multiple Regressions
R2
BRUNEI
0.440022
CAMBODIA 0.863275
INDONESIA 0.742770
LAOS
0.597714
MALAYSIA
0.473364
PHILIPPINES 0.799874
SINGAPORE 0.882181
THAILAND
0.772043
VIETNAM
0.641918
Adj R2
0.362784
0.844416
0.707290
0.542226
0.400725
0.772270
0.865930
0.740600
0.592527
INF
0.336839
0.902656
0.541872
0.362762
0.280526
0.488499
0.015378
0.528145
0.006834
EXC
0.959883
0.030410
0.000000
0.086449
0.612959
0.005184
0.000009
0.238363
0.000490
INT
0.201739
0.092952
0.000386
0.106174
0.001235
0.020836
0.013272
0.016423
0.191295
UNE
0.109225
0.000006
0.000144
0.000029
0.069644
0.000036
0.419980
0.000031
0.296684
Table 11: Multiple Regressions: Predictors of GDP per Capita with four factors using p value
A series of multiple regressions were used in order to test the extent on which
macroeconomic variables provide a better predictor in terms of GDP per capita. Among all the
ASEAN countries in this study, only Brunei showed no significant predictors relative to changes
in GDP per capita.
Two countries accentuated the model of GDP per capita relative to the four
macroeconomic variables with a high adjusted coefficient of determination namely Singapore
and Cambodia with more than 80% predictability. This is followed by Philippines, Thailand and
Indonesia around 70% or more. Vietnam and Laos still generated more than 50% adjusted
coefficient of determination, but Malaysia and Brunei resulted to a low adjusted R square
making the model variable still inadequate and need further testing with other variables.
Inflation showcased as a better predictor for two countries namely Singapore and
Vietnam. However, looking back with the correlation results Vietnam’s inflation is not correlated
with any other variable except GDP per capita which is inversely correlated. This is attributed to
the long recovery of the country from the 80s and 90s toward the new millennium as GDP
increases, inflation rate has subsided. Singapore on the other hand shows no correlation with
inflation and GDP, but has an association with interest rate and inversely with unemployment.
22
Exchange rate is intertwined with five countries namely Cambodia, Indonesia,
Philippines, Singapore and Vietnam as a predictor for GDP per capita. However the country that
showed the highest correlation is Singapore with 90% negative correlation of exchange rate and
GDP per capita. Connecting all the results exemplified that Singapore is ranked 12th overall in
terms of GDP per capita and as they increase GDP, the Singaporean dollar becomes stronger in
valuation relative to the US dollar. This is an empirical proof that the Singapore dollar has the
potential to be an anchor currency in the region to mitigate currency losses outside the region.
While the four countries showed a positive correlation between exchange rate and GDP per
capita, this implies that the countries increase GDP, but the LCU exchange rate devalues.
Interest rate is a significant predictor relative to GDP per capita for five countries namely
Indonesia, Malaysia, Philippines, Singapore and Thailand with an inverse relationship. Singapore
and Philippines exemplified the highest correlation with GDP per capita (-0.76 average), while
Malaysia and Vietnam showcased a moderate (-0.65 average) and a low correlation for Indonesia
at (-0.49). This is relevant to the concept of expansionary monetary policy wherein government
spending is increased with the primary goal of increasing aggregate output while maintaining
interest down to keep the money supply out of banks.
Unemployment is a significant predictor relative to GDP per capita for five countries
namely Cambodia, Laos, Indonesia, Philippines and Thailand. All countries exemplified a
negative correlation with GDP per capita except for Indonesia. This is attributed to Indonesia
large population in the region, as aggregate output increases then unemployment also increases
with few jobs available in the market. Thailand epitomizes the highest inverse relationship of
GDP per capita and unemployment wherein GDP increases then unemployment rate goes down
due to the country’s employment rate clustered about 40% in the agricultural sector.
23
V. Discussions
Since its inception in 1967, the ASEAN regional bloc has encountered dilemmas,
challenges and opportunities. 30 years of culminating progress and struggles along with trials
and tribulations, the region has remained steadfast in turning their economic fortunes especially
during the 1997 financial crisis. Ngo (2013) presented empirical evidences on the benefits, costs
and feasibility of a monetary union for the ASEAN. Her research findings are accentuated by the
Granger Causality Tests and the OLS for the ASEAN countries using macroeconomic variables
from nominal interest rates, inflation growth, budget deficit/GDP and public debt/GDP.
One of the major discussions from this research paper is the high inflation and high
unemployment that occurs. This highlights a stagflation that may loom in the horizon if the
government doesn’t see the economic down turn from the economic trends that occurs. This was
essentially noted after the 1997 financial crisis wherein countries like Indonesia suffered
tremendous devaluation of the Indonesian Rupiah, high inflation rate and high unemployment
rate. The region would try to avoid such similar shocks that could lead to the regions
destabilization. Such an argument can be pointed out to the regions results especially on the
dependency on the US dollar as an anchor currency. Fluctuations can be noted from the results
and shocks are felt with velocity.
Perhaps when the region is ready a singular currency to avoid such pitfalls the region
may occur. One of the strongest economies in the region falls on Singaporean dollar as a primary
anchor currency rather than the US dollar. Singapore economy is intertwined with the region, but
at the same time with high recovery that is eminent and conclusive with reluctance. There is no
clear empirical evidence that exemplify that the region could sustain a single monetary unit
based on the Maastricht criterion and overall coefficient of determination.
24
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Appendix
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.77312
R Square
0.597714
Adjusted R
Square
0.542226
Standard Error
247.6317
Observations
34
LAOS
26
ANOVA
df
Regression
Residual
Total
4
29
33
SS
2642220
1778322
4420542
Intercept
inf
exc
int
une
Coefficients
2468.46
-1.83482
-0.04127
-16.2706
-763.769
Standard
Error
425.116
1.984266
0.023253
9.757318
154.4233
SUMMARY OUTPUT
MS
F
660555.1 10.77201
61321.45
t Stat
5.806555
-0.92468
-1.77469
-1.66753
-4.94595
Significance
F
0.000018
Upper
95%
P-value
Lower 95%
0.000003 1598.99963 3337.919
0.362762 -5.8930957 2.223464
0.086449 -0.08882511 0.006291
0.106174 -36.2265818 3.685328
0.000029 -1079.60063 -447.938
BRUNEI
Regression Statistics
Multiple R
0.663342
R Square
0.440022
Adjusted R
Square
0.362784
Standard Error
7149.181
Observations
34
ANOVA
df
Regression
Residual
Total
4
29
33
Intercept
inf
exc
int
une
Coefficients
52402.28
708.0629
-536.039
-7271.9
-5636.54
SUMMARY OUTPUT
Regression Statistics
SS
MS
F
1.16E+09 2.91E+08 5.696938
1.48E+09 51110783
2.65E+09
Standard
Error
13832.98
725.0212
10564.99
5567.077
3410.962
CAMBODIA
t Stat
3.788214
0.97661
-0.05074
-1.30623
-1.65248
P-value
0.000709
0.336839
0.959883
0.201739
0.109225
Significance
F
0.00164647
Lower 95%
24110.6641
-774.771912
-22143.8678
-18657.8525
-12612.7388
Upper
95%
80693.89
2190.898
21071.79
4114.047
1339.661
27
Multiple R
R Square
Adjusted R
Square
Standard Error
Observations
0.929126
0.863275
0.844416
96.46561
34
ANOVA
df
4
29
33
SS
1703895
269862.8
1973758
Coefficients
537.8339
0.456744
0.071425
-20.9739
-171.128
Standard
Error
135.7271
3.701887
0.031381
12.07293
31.05238
Regression
Residual
Total
Intercept
inf
exc
int
une
SUMMARY OUTPUT
MS
F
425973.7 45.77599
9305.614
t Stat
3.962612
0.123381
2.27604
-1.73727
-5.51094
Significance
F
0.000000
P-value
Lower 95%
0.000443 260.240771
0.902656 -7.11446541
0.030410 0.00724313
0.092952 -45.665839
0.000006 -234.636986
Upper
95%
815.4271
8.027954
0.135607
3.718002
-107.619
INDONESIA
Regression Statistics
Multiple R
0.861841
R Square
0.74277
Adjusted R
0.70729
Square
Standard Error
500.4171
Observations
34
ANOVA
df
Regression
Residual
Total
4
29
33
SS
20969809
7262100
28231909
Intercept
inf
exc
Coefficients
3113.968
-7.90522
0.187312
Standard
Error
503.8032
12.80671
0.025429
Significance
F
3.3148E-08
MS
F
5242452 20.93487
250417.3
Upper
95%
t Stat
P-value
Lower 95%
6.180921 0.000001 2083.57459 4144.361
-0.61727 0.541872 -34.0978846 18.28745
7.366189 0.000000 0.13530472 0.23932
28
int
une
-72.4092
-262.03
SUMMARY OUTPUT
18.04085
59.91241
-4.01362 0.000386 -109.306884
-4.37355 0.000144 -384.564335
-35.5115
-139.495
MALAYSIA
Regression Statistics
Multiple R
0.688015
R Square
0.473364
Adjusted R
Square
0.400725
Standard Error
2066.644
Observations
34
ANOVA
df
Regression
Residual
Total
4
29
33
Intercept
inf
exc
int
une
Coefficients
13725.64
239.3407
414.8827
-758.769
-2201.09
SUMMARY OUTPUT
SS
MS
F
1.11E+08 27832673 6.516637
1.24E+08 4271018
2.35E+08
Standard
Error
5031.256
217.6501
811.3041
211.9612
1168.39
t Stat
2.728073
1.099659
0.511378
-3.57975
-1.88387
P-value
0.010705
0.280526
0.612959
0.001235
0.069644
Significance
F
0.00072033
Lower 95%
3435.56223
-205.803603
-1244.42051
-1192.27849
-4590.71949
PHILIPPINES
Regression Statistics
Multiple R
0.894357
R Square
0.799874
Adjusted R
0.77227
Square
Standard Error
286.6841
Observations
34
ANOVA
df
Regression
Residual
Total
4
29
33
SS
9526270
2383445
11909716
MS
F
2381568 28.97715
82187.77
Significance
F
0.000000
Upper
95%
24015.71
684.4851
2074.186
-325.26
188.5309
29
Intercept
inf
exc
int
une
Coefficients
2992.707
6.356392
18.829
-53.8923
-235.077
SUMMARY OUTPUT
Standard
Error
343.867
9.059374
6.227344
22.04963
48.24977
t Stat
8.703096
0.701637
3.0236
-2.44414
-4.87207
Upper
95%
P-value
Lower 95%
0.000000 2289.42023 3695.994
0.488499 -12.1721067 24.88489
0.005184 6.09264938 31.56535
0.020836 -98.9888447 -8.79573
0.000036 -333.758371 -136.395
SINGAPORE
Regression Statistics
Multiple R
0.939245
R Square
0.882181
Adjusted R
Square
0.86593
Standard Error
5412.974
Observations
34
ANOVA
df
Regression
Residual
Total
Intercept
inf
exc
int
une
4
29
33
Coefficients
76448.97
1393.052
-31747.7
-1994.83
1289.267
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.87866
R Square
0.772043
Adjusted R
0.7406
Square
Standard Error
739.1054
Observations
34
SS
MS
F
6.36E+09 1.59E+09 54.28514
8.5E+08 29300286
7.21E+09
Standard
Error
8518.626
540.9146
5897.562
756.2114
1575.957
THAILAND
t Stat
8.97433
2.575365
-5.38319
-2.63792
0.818085
Significance
F
0.000000
P-value
Lower 95%
0.000000 59026.4208
0.015378
286.75774
0.000009 -43809.5866
0.013272 -3541.45149
0.419980 -1933.92614
Upper
95%
93871.51
2499.347
-19685.8
-448.2
4512.46
30
ANOVA
df
Regression
Residual
Total
4
29
33
Intercept
inf
exc
int
une
Coefficients
6433.993
25.37274
-37.8666
-150.506
-961.125
SUMMARY OUTPUT
SS
MS
F
53653507 13413377 24.55418
15842026 546276.8
69495533
Standard
Error
1194.912
39.73673
31.45331
59.08595
194.9942
t Stat
5.38449
0.638521
-1.2039
-2.54723
-4.92899
P-value
8.72E-06
0.528145
0.238363
0.016423
0.000031
Significance
F
5.9573E-09
Upper
95%
Lower 95%
3990.12298 8877.862
-55.8979844 106.6435
-102.195884 26.4626
-271.349994 -29.6613
-1359.93297 -562.317
VIETNAM
Regression Statistics
Multiple R
0.801198
R Square
0.641918
Adjusted R
Square
0.592527
Standard Error
312.8703
Observations
34
ANOVA
df
Regression
Residual
Total
Intercept
inf
exc
int
une
4
29
33
SS
5088893
2838746
7927639
Coefficients
656.9512
41.23279
0.033857
-12.9453
-232.159
Standard
Error
531.8242
14.1575
0.008625
9.675144
218.4599
MS
F
1272223 12.99675
97887.8
t Stat
1.235279
2.912433
3.925197
-1.338
-1.06271
Significance
F
3.5149E-06
P-value
Lower 95%
0.226641 -430.751492
0.006834 12.2774413
0.000490 0.01621557
0.191295 -32.7332294
0.296684 -678.95982
Upper
95%
1744.654
70.18814
0.051498
6.842552
214.6413