Determinants_of_Unemployment_Rate_in_Sel

DETERMINANTS OF UNEMPLOYMENT RATE IN SELECTED ASEAN
(ASSOCIATION OF SOUTHEAST ASIAN NATIONS) MEMBER STATES:
A PANEL DATA ANALYSIS
A Thesis Manuscript
Presented to the Faculty of the
Department of Economics
College of Management and Economics
Visayas State University
ViSCA, Baybay City, Leyte
In Partial Fulfillment of the Requirements for the Degree of
Bachelor of Science in Economics
CLAIRE LARZEN ZAMORA TELLO
April 2015

TRANSMITTAL
The undergraduate thesis attached hereto, entitled DETERMINANTS OF
UNEMPLOYMENT RATE IN SELECTED ASEAN (ASSOCIATION OF
SOUTHEAST ASIAN NATIONS) MEMBER STATES: A PANEL DATA
ANALYSIS, prepared and submitted by Claire Larzen Z. Tello in partial fulfillment of
the requirements for the degree of Bachelor of Science in Economics is hereby accepted.
MOISES NEIL V. SERIÑO
Thesis Adviser
__________________
Date
Accepted in partial fulfillment of the requirements for the degree of
BACHELOR OF SCIENCE IN ECONOMICS.
BRENDA M. RAMONEDA __________________
Member Date
Student Research Committee
MA. SALOME B. BULAYOG __________________
Member Date
Student Research Committee
MA. SALOME B. BULAYOG __________________
Head, Department of Economics Date

College of Management and Economics
Visayas State University
VisCA, Baybay, Leyte
GENERAL EVALUATION OF STUDENT THESIS
Title: DETERMINANTS OF UNEMPLOYMENT RATE IN SELECTED ASEAN
(ASSOCIATION OF SOUTHEAST ASIAN NATIONS) MEMBER STATES: A PANEL
DATA ANALYSIS
General comments of the adviser and the department head.
(Please check the appropriate box or blank)
Thesis strongly recommended for publication.
_______ As approved
_______ Needs a little revision
_______ Needs to be rewritten
Thesis may be published.
_______ As approved
Thesis should be integrated with related studies in the department.
_______ As approved
Thesis not recommended for publication. Give reasons why not. If one of those reasons
is objectable methodology, explain further why it was not corrected or improved and
why the thesis was not accepted.
…………………………………………………………………………………………….
…………………………………………………………………………………………….
MOISES NEIL V. SERIÑO MA. SALOME B. BULAYOG
Thesis Adviser Head, Department of Economics
________________________ ________________________
Date Date

ACKNOWLEDGEMENT
“Dreams are milestones. Inspiration keeps you going. Hard work gives you success.”
To the Creator, the giver of life, who made all things possible.
To her parents, who supported her all throughout life’s journey. Whatever
success, the author may achieve, know that it will always be for you.
To the author’s relatives most especially to her aunts, uncles, cousins, and
grandparents, thank you for always having faith in the author’s strengths and
capabilities.
To Auntie Jessica, her second mother, thank you for the love and support.
To Dr. Pedro T. Armenia, the first in the Department of Economics who believed
that the author could actually finish her thesis work and for providing the
necessary references to the author.
To Dr. Moises Neil V. Seriño, who assumed the position of thesis adviser when
Dr. Armenia had to retire. Without his advices and critics, the author could not
have possibly finished her thesis work in time.
To Dr. Ma. Salome B. Bulayog and Prof. Brenda M. Ramoneda, for serving as
thesis committee members of the author and for being the author’s favorite
teachers in her trudge as a BSEcon student.
To the faculty, staff, and students of the Department of Economics, thanks for all
the memories.
To the BSEcon students batch 2015, Ian Dave, Imae, KC, Karl, Te Jinky, Te
Vilma, Te Sherlyn, Kuya Elmer, Kuya Rolen, Leira, Mikee, Abel, Irene, Jessa,
Jovil, and Daffodil; for all the times they stood by the author in good times and in
bad, the friendship, the memories, and the wonderful experience, thank you so
much.
To Mr. Raymund Igcasama, for all his support, advices, and friendship.

TABLE OF CONTENTS
TITLE PAGE
TRANSMITTAL ii
GENERAL EVALUATION OF STUDENT THESIS iii
ACKNOWLEDGEMENT iv
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES viii
LIST OF APPENDICES ix
ABSTRACT x
CHAPTER I: INTRODUCTION 1
Nature and Importance of the Study 1
Statement of the Problem 3
Objectives of the Study 6
Scope and Limitation 6
CHAPTER II: REVIEW OF RELATED LITERATURE 7
Unemployment and Education 7
Unemployment and Inflation 8
Unemployment and Gross Domestic Product 8
Unemployment and Foreign Direct Investment 9
Unemployment and Interest Rate 11
Unemployment and Population 12
CHAPTER III: THEORETICAL FRAMEWORK 14
Panel Data Analysis 14
Fixed Effects Approach 16
Random Effects Approach 17
CHAPTER IV: METHODOLOGY 19
Coverage of the Study 19
Data Collection 20
Empirical Model 20
Definition of Variables 22
Data Analysis 26
CHAPTER V: RESULTS AND DISCUSSION 27
Summary of Macroeconomic Indicators of the 5 ASEAN States in 2010 27

Unemployment and Real Per Capita GDP across time 34
Heterogeneity of Unemployment & Real Per Capita GDP across countries 36
Heterogeneity of Unemployment & Real Per Capita GDP across years 38
Means of the Variables 40
Determinants of Unemployment across Each ASEAN State 42
Determinants of Unemployment in the 5 ASEAN States 48
Evidence of Phillips Curve in the 5 ASEAN States 56
CHAPTER VI: SUMMARY, CONCLUSION, POLICY IMPLICATIONS, 57
RECOMMENDATION
Summary of Findings 57
Conclusion 58
Policy Implications 60
Recommendation 61
LITERATURE CITED 62
APPENDICES 67

LIST OF FIGURES
FIGURE TITLE PAGE
1 Sample of a Panel Dataset (Torres-Reyna, 2013) 16
2 Map of Southeast Asia (USFUNDS, 2013) 19
3 Unemployment Rate Trends Overlay from 1980-2011, 5 ASEAN States 35
4 Real Per Capita GDP Trends Overlay from 1980-2011, 5 ASEAN States 35
5 Heterogeneity across countries, Unemployment Rate 37
6 Heterogeneity across countries, Real Per Capita GDP 37
7 Heterogeneity across years, Unemployment Rate 39
8 Heterogeneity across years, Real Per Capita GDP 39

LIST OF TABLES
TABLE TITLE PAGE
1 Summary of the Macroeconomic Indicators of the 5 ASEAN States for 2010 33
2 Means of the Variables 41
3 Regression estimates across countries through OLS, real GDP used to
capture national output
46
4 Regression estimates, sectoral approach 47
5 LSDV regression: Equation 1 and Equation 2 55

LIST OF APPENDICES
APPENDIX TITLE PAGE
1 Graph of FDI net inflows 67
2 Graph of inflation rate 67
3 Graph of real interest rate 68
4 Graph of import volume index 68
5 Graph of literacy rate 69
6 Graph of population 69
7 Graph of export volume index 70
8 Graph of gross capital formation 70
9 Graph of household final consumption expenditures 71
10 Graph of real wages 71
11 Graph of general government final consumption expenditures 72
12 Test for heteroscedasticity 72
13 Test for multicollinearity 72
14 Wooldridge test for autocorrelation in panel data 72
15 Test for cross-sectional independence 72
16
17
18
19
20
Hausman Test
Regression estimates in 4 approaches, using real GDP to capture
national output
Regression estimates in 4 approaches, sectoral approach
Actual regression from STATA (Equation 1 using Real GDP to capture
national output)
Actual regression from STATA (Equation 2 using sectoral approach)
72
73
74
76
79

ABSTRACT
TELLO, CLAIRE LARZEN Z. Visayas State University (VSU), VisCA,
Baybay City, Leyte. 2015. “DETERMINANTS OF UNEMPLOYMENT RATE IN
SELECTED ASEAN (ASSOCIATION OF SOUTHEAST ASIAN NATIONS)
MEMBER STATES: A PANEL DATA ANALYSIS”.
Major Thesis Adviser: DR. MOISES NEIL V. SERIÑO
This study investigates the determinants of unemployment in selected ASEAN
states: Indonesia, Malaysia, Philippines, Singapore, and Thailand; employing the
methods of panel data analysis. Specifically, this study aimed to address the following:
(1) identify and compare the determinants of unemployment rate; (2) investigate if there
is evidence of a short run tradeoff between inflation and unemployment commonly
known as the Phillips Curve; (3) provide analysis on the determinants of unemployment
rate in each country; (4) present trends of relevant macroeconomic indicators; and (5)
provide policy recommendations that will address the unemployment problems in the 5
ASEAN states. This study made use of secondary time series data of the 9
macroeconomic variables of the 5 ASEAN states from 1980-2011. In this study, the
economic model employed is that unemployment rate is a function of real GDP, inflation
rate, population, real interest rate, foreign direct investment, real wage, and literacy rate.
Results of the least squares dummy variable (LSDV) regression shows that real
GDP, inflation rate, real interest rate, and real wage are negatively associated with the
unemployment rate. Population shows a positive relationship to unemployment.

Furthermore, results suggest that Phillips Curve is relevant in the 5 ASEAN states
indicating that the higher the inflation rates, the lower the unemployment in the short-run.
Trends of the macroeconomic indicators used in the study is presented giving emphasis
on the real GDP and unemployment rate to see the disparities between each ASEAN
member state and see the changes of the variables over time.
This study also found the determinants of unemployment rate in each of the
selected ASEAN member states through employing the ordinary least squares (OLS)
regression. More importantly, this study aims to provide possible policy
recommendations that will help in minimizing unemployment incidence in the selected
ASEAN states in preparation for the forthcoming changes to be brought about by the
ASEAN Economic Community in 2015.

DETERMINANTS OF UNEMPLOYMENT RATE IN SELECTED ASEAN
(ASSOCIATION OF SOUTHEAST ASIAN NATIONS) MEMBER STATES:
A PANEL DATA ANALYSIS1
CLAIRE LARZEN ZAMORA TELLO
CHAPTER I
INTRODUCTION
Nature and Importance of the Study
The Association of Southeast Asian Nations (ASEAN) has been established since
1967 to foster economic development, unity, and stability within its member nations:
Indonesia, Malaysia, Philippines, Singapore, and Thailand. In the succeeding years since
its foundation, Brunei Darussalam, Cambodia, Myanmar, Laos and Vietnam signed
allegiance to ASEAN as well seeing its benefits (Martin, 1987). With the organization’s
guiding motto, “One Vision, One Identity, One Community”, it aims to improve the
general welfare condition of the citizens and of the state as a whole. In fact, it is one of
the most powerful political organizations located in the Pacific Basin and has helped its
member states in so many ways such as patron-client relationship with big powers, war
1/ Thesis manuscript presented in partial fulfillment of the requirements for
graduation with the degree of Bachelor of Science in Economics from the Visayas State
University, Visca, Baybay City, Leyte on ______________. Contribution no.______.
Prepared in the Department of Economics under the guidance and supervision of Dr.
Moises Neil V. Seriño.

and conflict prevention, international relations, avenue for intellectual exchange and
goodwill, and of course, economic cooperation (Martin, 1987).
Since 1967, ASEAN has come a long way and has done its member states and
citizens much good but there are still problems within the individual member states that
remain unanswered. Some of these problems are poverty, unemployment, managing
macroeconomic and financial stability, economic convergence and equitable growth,
forging a competitive and innovative region, and lastly, nurturing natural resources and
sustaining the environment (Martin, 1987).
As the organization moves toward solving the aforementioned problems and
reaching its goals, it can still be observed that out of the 10 member states, only 2 are
considered as developed countries namely Singapore and Brunei Darussalam in terms of
Human Development Index (HDI). Meanwhile, Malaysia, Vietnam, Thailand, Indonesia
and the Philippines are all considered as developing countries and the other three,
Cambodia, Myanmar, and Lao PDR are considered underdeveloped. (HDR-UNDP, 2013)
HDI is a macroeconomic indicator generated by the Human Development Reports
of the United Nations Development Programme (HDR-UNDP) to measure human
development in terms of income, health, and education. As the HDI value gets closer to
1.0, the higher the level of human development; if the index gets close to 0, it indicates
low level of human development. Here are the HDIs of the 10 ASEAN member states as
of 2013: Singapore (0.901, 9th
in the world); Brunei Darussalam (0.852, 30th
in the
world); Malaysia (0.773, 62nd
in the world); Thailand (0.772, 89th
in the world);
Indonesia (0.684, 108th
in the world); Philippines (0.660, 117th
in the world); Vietnam

(0.638, 121st
in the world); Cambodia (0.584, 136th
in the world); Lao PDR (0.569, 139th
in the world); and Myanmar (0.524, 150th
in the world). (HDR-UNDP, 2013)
In this study, one of the major problems of ASEAN member states is analyzed
through identifying the determinants of unemployment rate in five countries: Indonesia,
Malaysia, Philippines, Singapore, and Thailand. The econometric approach used the
mechanism of panel data analysis in investigating how unemployment is influenced by
relevant macroeconomic indicators such as real gross domestic product, inflation rate,
import volume index, population, literacy rate, real interest rate, foreign direct
investment, and export volume index over time.
Statement of the Problem
Unemployment is one of the major problems in almost all countries of the world.
It has been the most consistent problem which is faced by all industrially-advanced
nations and especially the poor countries. Unemployment is defined as the condition of
having no job or being out of work or proportion of people which are able to work and
actively searching jobs but they are unable to find it (Chowdhury & Hossain, 2014).
According to the report of the International Monetary Fund (IMF) in 1998,
“unemployment is measured annually as the percentage of a labor force that can’t find a
job”. The International Labor Organization (ILO) in 2001 defined unemployment as (1) a
situation of being out of work or in need of a job and continuously searching for it in the
last four weeks or (2) unemployed ( age 16 or above) but available to join work in the

next two weeks. People who voluntarily do not want to work, full time students, retired
people and children are not included in the unemployed category. (Rafiq et al., 2009)
Dramatic increase in the level of unemployment is a major problem especially in
less developed countries in particular and even in advanced countries in general. A
number of social problems are driven by high growth of unemployment. For example,
Rafiq et al (2009) studied unemployment and social problems and they concluded that
unemployment gave rise to crimes, suicides and poverty rates. Unemployment suffers
workers, workers’ families and even countries because loss of job means loss of income
both at the individual level and national level.
Although ASEAN member states are doing their very best to achieve their
political and economic goals, it cannot be denied that unemployment has been a problem
for most member states. In fact, high unemployment levels are one of the root causes that
hinder their economic growth and stability.
In 2015, the ASEAN Economic Community (AEC) would be launched. The
(AEC) shall be the goal of the regional economic integration by 2015. AEC envisages the
following key characteristics: (a) a single market and production base, (b) a highly
competitive economic region, (c) a region of equitable economic development, and (d) a
region fully integrated into the global economy. The AEC areas of cooperation include
human resources development and capacity building; recognition of professional
qualifications; closer consultation on macroeconomic and financial policies; trade
financing measures; enhanced infrastructure and communications connectivity;
development of electronic transactions through e-ASEAN; integrating industries across
the region to promote regional sourcing; and enhancing private sector involvement for the

building of the AEC. In short, the AEC will transform ASEAN into a region with free
movement of goods, services, investment, skilled labour, and freer flow of capital.
(ASEAN Secretariat, 2014)
This integration will definitely affect the labor market in all of its member states.
The dynamics of the general labor market in each member states are very unpredictable
that is why unemployment cannot be disregarded. Therefore, the need to investigate the
determinants of unemployment rate in selected ASEAN member states namely:
Indonesia, Malaysia, Philippines, Singapore, and Thailand, is essential so that the labor
markets of each member states can take advantage of the ASEAN Economic Integration.
The 5 aforementioned countries were specifically selected to be covered in this study due
to the following reasons: (1) these countries have the most complete data on the variables
to be used in this study from the year 1980-2011; (2) these are the 5 pillars or the
founding members of ASEAN; and (3) these countries have distinct characteristics in
terms of unemployment and the other macroeconomic indicators to be included as
variables in this study.
In this regard, this study aimed to address the following questions: (1) What are
the determinants of unemployment rate in the 5 ASEAN member states? (2) Does
inflation and unemployment have a short run trade off in these 5 countries? (3) What are
the determinants of unemployment rate in each country? (4) Are there trends that can be
observed of the different macroeconomic indicators to be used in the study? (5) What are
the possible policy recommendations that could be addressed to help lessen the
unemployment incidence in the 5 ASEAN countries?

Objectives of the Study
The general objective of this study is to identify, compare, and analyze the
determinants of unemployment rate in the selected ASEAN member states.
The study specifically aimed:
1. To identify and compare the determinants that affect unemployment rates in
Indonesia, Malaysia, Philippines, Singapore, and Thailand;
2. To investigate if there is evidence of a short run tradeoff between inflation and
unemployment commonly known as the Phillips Curve;
3. To provide analysis of the determinants of unemployment rate in each country;
4. To present trends of the macroeconomic indicators to be used in the study; and
5. To provide policy recommendations that will address the issue of unemployment
in the selected ASEAN states.
Scope and Limitation
For this research study, secondary data of the five pioneer ASEAN member
economies namely Indonesia, Malaysia, Philippines, Singapore, and Thailand were used
and the data were taken from the Worldbank’s official website. These five countries are
the only ones among the 10 ASEAN member states with the most complete statistics of
the different macroeconomic indicators to be used in this study as gathered by national
surveys within each of the 5 states then further forwarded and published in the official
website of the Worldbank.

CHAPTER II
REVIEW OF RELATED LITERATURE
There have been several studies about the issue of unemployment not just in
ASEAN context but also worldwide. This chapter will present some highlights in the
literature about relevant macroeconomic variables influencing unemployment.
Unemployment and Education
The linkages among education, poverty, unemployment, income inequality and
economic growth in an economy have been discussed in many studies. Authors like
Heckman and Klenow (1997), Michaelowa (2000), Yesufu (2000), Abiodun (2002),
Dahlin (2005), Bakare (2006), Todaro and Smith (2007), Larocque (2008), Ajakaiye and
Fakiyesi (2009), and Dauda (2010) have made notable contributions to knowledge in this
area. According to Larocque (2008) education, as a key component of human capital
formation is recognized as being vital in increasing the productive capacity and living
standard of people. In their own views Dahlin (2005), Heckman and Klenow (1997) and
Michaelowa (2000) discovered that education, especially at the higher level, contributes
directly to economic growth by making individual workers more productive and
indirectly by leading to the creation of knowledge, ideas, and technological innovation.
They affirm that the higher the level of education, the greater the opportunity to secure
viable jobs and earn higher wages.

Unemployment and Inflation
Aside from the relationship of unemployment to education and poverty, there is
also a need to provide literatures that relate unemployment and inflation. To strengthen
the affirmations on the relationship of unemployment and inflation, a thorough citation of
the Phillips Curve is presented. The Phillips curve originated by Sir A. W. Phillips in
1958 and was named after him, plotted 95 years of data of UK wage inflation against
unemployment. It seemed to suggest a short-run trade-off between unemployment and
inflation. The theory behind this was fairly straightforward. Falling unemployment might
cause rising inflation and a fall in inflation might only be possible by allowing
unemployment to rise. If the government wanted to reduce the unemployment rate, it
could increase aggregate demand but, although this might temporarily increase
employment, it could also have inflationary implications in labor and the product
markets. In fact, Phillips conjectured that the lower the unemployment rate, the tighter the
labor market and, therefore, the faster firms must raise wages to attract scarce labor. At
higher rates of unemployment, the pressure abated. Phillips’ “curve” represented the
average relationship between unemployment and wage behavior over the business cycle
(Phillips, 1958). It showed the rate of wage inflation that would result if a particular level
of unemployment persisted for some time.
Unemployment and Gross Domestic Product
According to the expenditure approach, gross domestic product (GDP) is the sum
of household final consumption expenditure (C), gross capital formation (I), general

government final consumption expenditure (G), and the difference of exports and imports
(X-M). GDP or national output is one of the most significant indicators to determine
economic performance of a country. One of the relationships to be tested in the
econometric model to be used in this paper is that between unemployment and GDP
which economists can associate with the Okun’s Law. Villaverde and Maza (2009)
analyze Okun’s law for the Spanish regions over the period 1980-2004. They found that
an inverse relationship between unemployment and output holds for most of the regions
and for the whole country. However, the quantitative values of Okun’s coefficients are
quite different, a result that is partially explained by regional disparities in productivity
growth. These differences imply that, when it comes to policy issues, conventional
aggregate demand or supply management policies should be combined with region-
specific policies. Furthermore, Prachowny (1993) found that changes in output will result
in changes in efficiency of production. Other important determinants of output include
the amount of time worked and exploitation of facility space. Although the empirical
study of Okun’s law has indeed blossomed since the publication of Prachowny’s paper
(1993), most of it only deals with data at national level.
Unemployment and Foreign Direct Investment
Another factor to be considered is the Foreign Direct Investment (FDI) which is
presumed to have a negative relationship with unemployment. Baharom et al. (2008)
carried out a study to examine the role of trade openness and foreign direct investment in
influencing economic growth in Malaysia during 1975-2005, using the Bounds testing

approach used by Pesaran and company in the year 2001 (Baharom et al., 2008). The
empirical results demonstrate that trade openness is positively associated and statistically
significant determinant of growth, both in short run and the long run. The result also
suggested that foreign direct investment is positively associated in the short run and
negatively associated in the long run, both significantly. In addition, Aktar, Ozturk and
Demirci (2008) examined the impact of foreign direct investment, export, economic
growth and total fixed investment on unemployment in Turkey for the period of 1987-
2007. The empirical findings suggest that there are two cointegrating vectors, indicating
the presence of a long run relationship, though all the variables were found to affect the
unemployment rate significantly. In another study, Ozturk and Aktar (2009) took a
comprehensive approach to unemployment by using variance decomposition and impulse
response function analysis. They were interested in studying the interrelationship among
foreign direct investment, export, gross domestic product and unemployment in Turkey
for the period of 2000-2007. They found only two counteracting vectors in the system,
showing long run relationship. They concluded that foreign direct investment did not lead
to reduce unemployment in Turkey. Shu-Chen Chang (2006) also applied variance
decomposition and impulse response function analysis for studying relationship among
economic growth, trade, foreign direct investment, and unemployment in Taiwan. The
result showed that export and economic growth affect FDI inflow positively however
export expansion has negative impact on FDI outflow. The study confirmed no
relationship between FDI and unemployment whereas a negative relationship between
unemployment and economic growth was confirmed.

Unemployment and Interest Rate
Interest rate is one of the most influential variables that affect different sectors of
the economy from the micro level up to the macro level. In line with this study, the
following literatures show relationship of unemployment and interest rate as well. When
the nominal rate binds to zero, a low or negative inflation rate implies a real rate that is
too high compared to the equilibrium value that clears the market (Albertini & Poirier,
2013). As shown by Hall (2011), the excess supply shows up as diminished output, lower
employment, and higher unemployment. But how much of the rise in unemployment is
due to the Zero Lower Bound (ZLB)? How does the labor market behave when the
nominal interest rate hits the ZLB? A paper by Albertini and Poirier (2013) investigated
how the labor market behaves at the ZLB. Its framework takes into account explicitly the
non-linearities induced by the ZLB which cannot be accurately studied when the model is
solved using linear-approximation methods. It showed that matching frictions matters for
inflation dynamics at the ZLB and have nontrivial consequences for the policy analysis.
Furthermore, it showed that when the economy enters in a liquidity trap (a situation when
expansionary monetary policy (increase in money supply) does not increase the interest
rate, income and hence does not stimulate economic growth), the unemployment rate can
increase dramatically which amplify the deflation, consistent with recent observations. In
the spirit of Blanchard and Gali (2010) they analyze the role of unemployment rigidities.
As a result, it showed they have strong effects on the way variables respond to shock if
the economy is at the ZLB. It influences quantitatively and qualitatively the government
spending and taxes multipliers. At the ZLB, the government spending multipliers is about
two times higher than in normal times. If the labor market is flexible it is about three but

still less than unity. The taxes multipliers are found to be very low. In an economy
characterized by search and matching frictions, tax cuts on labor income reduce, not
increase, output (Blanchard and Gali, 2010). Furthermore, Aleksander et al. (2009)
focused on studying long run relationships among money supply, interest rate and
unemployment. They concluded that these variables are positively related at low
frequencies.
Unemployment and Population
To complete the specifications of the relationships of the variables to be used in
the econometric model of this paper, the correlations between population and
unemployment based on published literatures will be discussed. Rafiq et al. (2009)
identified the determinants of unemployment in Pakistan with the following result: high
levels of population have a direct relationship to unemployment. Based on this paper, it is
highly recommended that government should give proper attention to rapid increases of
population. Rapid increase of population creates unemployment pressure along with other
problems. Deda and Luku (2013) also stated the same results for the case of Kosovo.
The related literatures cited above are just some of the many studies that
investigated the relationships of unemployment to factors such as education, inflation,
gross domestic product, foreign direct investment, interest rate, and population. The
findings made by the mentioned authors serve as motivation to develop this study.
Though the literature on determinants of unemployment is comprehensive; still there is
missing information on the influence of important macroeconomic indicators such as

export volume indexes and literacy rates to unemployment especially for the case of
selected Southeast Asian nations namely Indonesia, Malaysia, Philippines, Singapore,
and Thailand. Furthermore, this study will contribute to the literature by using panel data
analysis with more macroeconomic variables included in the model compared to those
used by the above mentioned authors. This study used panel data analysis and compares
the results of different panel approaches such as fixed effects and random effects to see
whether the impact of the variables varies over time. The method of panel data analysis
has not been widely explored in the literatures as compared to time series analysis in
investigating the determinants of unemployment.

CHAPTER III
THEORETICAL FRAMEWORK
There have been many studies as to what are the determinants that affect
unemployment. With the aforementioned literatures, this study aimed to identify whether
there is a significant correlation between unemployment and relevant macroeconomic
variables of the selected ASEAN member states. To address the objectives of the study,
the economic model is postulated as follows: unemployment is a function of real GDP,
inflation rate, real interest rate, population, real wage, foreign direct investment, and
literacy rate.
Panel Data Analysis
Panel data (also known as longitudinal or cross-sectional time-series data) is a
dataset in which the behavior of entities is observed across time. These entities could be
states, companies, individuals, countries, etc. (Torres-Reyna, 2013). A longitudinal, or
panel, data set is one that follows a given sample of individuals over time, and thus
provides multiple observations on each individual in the sample. (Hurlin, 2010)
Here is an example of a multiple linear regression model for individual i = 1, … ,
N that is observed at several time periods t = 1, … ,T
Yit = 𝛼 + 𝑋′
it 𝛽 +𝑍’i 𝛾 + Ci + µit
Where Yit is the dependent variable, X’it is a K-dimensional row vector of time-
varying explanatory variables and Z’i is a M-dimensional row vector of time-invariant

explanatory variables excluding the constant, α is the intercept, β is a K-dimensional
column vector of parameters, γ is a M-dimensional column vector of parameters, Ci is an
individual-specific effect and µit is an idiosyncratic error term (Schmidheiny, 2014). The
above equation is an example of a regression model in a panel data setup.
Figure 1 shows an example of a panel dataset of 3 countries covering 3 years. As
seen in the figure above, the key feature of panel data that distinguishes it from a pooled
cross section is the fact that the same cross-sectional units (individuals, firms, or counties
in the above examples) are followed over a given time period. Because panel data require
replication of the same units over time, panel data sets, especially those on individuals,
households, and firms, are more difficult to obtain than pooled cross sections. Not
surprisingly, observing the same units over time leads to several advantages over cross-
sectional data or even pooled cross-sectional data. The benefit that we will focus on is
that having multiple observations on the same units allows us to control certain
unobserved characteristics of individuals, firms, and so on. The use of more than one
observation can facilitate causal inference in situations where inferring causality would
be very difficult if only a single cross-section were available. A second advantage of
panel data is that it often allows us to study the importance of lags in behavior or the
result of decision making. This information can be significant since many economic
policies can be expected to have an impact only after some time has passed. Most books
at the undergraduate level do not contain a discussion of econometric methods for panel
data. However, economists now recognize that some questions are difficult, if not
impossible, to answer satisfactorily without panel data. Considerable progress could be

made with simple panel data analysis, a method which is not much more difficult than
dealing with a standard cross-sectional data set. (Wooldridge, 2012)
There are two main approaches to Panel Data analysis: (1) The fixed effects (FE)
approach, and (2) the random effects (RE) approach. In the fixed effects model, the
individual-specific effect is a random variable that is allowed to be correlated with the
explanatory variables. While on the other hand, in the random effects model, the
individual-specific effect is a random variable that is uncorrelated with the explanatory
variables (Schmidheiny, 2014).
Figure 1. Sample of a Panel Dataset
(Torres-Reyna, 2013)
Fixed Effects Approach
When using fixed effects, we assume that something within the individual may
impact or inflict bias on the predictor or outcome variables and we need to control for
this. This is the rationale behind the assumption of the correlation between entity’s error

term and predictor variables. FE removes the effect of those time-invariant characteristics
from the predictor variables so we can assess the predictors’ net effect. (Torres-Reyna,
2013)
The equation for the fixed effects model could be expressed in the form:
Yit = β1Xit + αi + uit
Where:
Yit is the dependent variable
β1 is the regression coefficient of the independent variable
Xit is the independent variable
αi (i=1….n) is the unknown intercept for each entity (n entity-specific intercepts)
uit is the error term
Furthermore, the least square dummy variable model (LSDV) provides a good
way to understand fixed effects. The effect of the independent variable is mediated by the
differences across countries. By adding the dummy for each country we are estimating
the pure effect of the independent variable (by controlling for the unobserved
heterogeneity). Each dummy is absorbing the effects particular to each country. (Torres-
Reyna, 2013)
Random Effects Approach
The rationale behind random effects model is that, unlike the fixed effects model,
the variation across entities is assumed to be random and uncorrelated with the predictor
or independent variables included in the model. If there is a reason to believe that

differences across entities have some influence on your dependent variable then you
should use random effects. An advantage of random effects is that you can include time
invariant variables (i.e. gender). (Torres-Reyna, 2013)
The random effects model is:
Yit = β1Xit + αi + Uit + εit
Where:
Yit is the dependent variable
β1 is the regression coefficient of the independent variable
Xit is the independent variable
αi (i=1….n) is the unknown intercept for each entity (n entity-specific intercepts)
εit is the within-entity error
Uit is the between-entity error
Random effects assume that the entity’s error term is not correlated with the
predictors which allows for time-invariant variables to play a role as explanatory
variables. In random-effects you need to specify those individual characteristics that may
or may not influence the predictor variables. The problem with this is that some variables
may not be available therefore leading to omitted variable bias in the model. (Torres-
Reyna, 2013)

CHAPTER IV
METHODOLOGY
Coverage of the Study
The study covered the 5 founding members of the Association of Southeast Asian
Nations namely Indonesia, Malaysia, Philippines, Singapore, and Thailand. Figure 2
shows the map of the Southeast Asian region as well as the flags of each ASEAN
member state.
Figure 2. Map of Southeast Asia (USFUNDS, 2013)

Data Collection
Secondary data was employed in this study and all the data for the 8
macroeconomic indicators were gathered from the official website of the Worldbank. The
time series data from 1980-2011 for the unemployment rate, real gross domestic product
(GDP), real inflation rate, population, import volume index, real interest rate, foreign
direct investment, export volume index, gross capital formation, household final
consumption expenditures, general government final consumption expenditures, and
literacy rate were gathered from the mentioned source and the following data of the
mentioned macroeconomic indicators was aggregated into a panel dataset encoded in
STATA.
Empirical Model
To address the objectives of the study, the following regression models were
used: Equation 1 (using the real GDP variable to capture national output); and Equation 2
(using the sectoral approach to disaggregate the effect into different components).
(Equation 1)
𝑈𝑁𝐸𝑀𝑃𝑖𝑡 = 𝛽0 + 𝛽1 𝑙𝑟𝑒𝑎𝑙𝐺𝐷𝑃𝑖𝑡 + 𝛽2 𝐼𝑛𝑓𝑙𝑎𝐴𝑁𝑝𝑟𝑖𝑡 + 𝛽3 𝑙𝑃𝑂𝑃𝑈𝐿𝑖𝑡 + 𝛽4 𝐼𝑁𝑇𝑅𝑖𝑡
𝛽5 𝑙𝑟𝑒𝑎𝑙𝐹𝐷𝐼𝑛𝑒𝑡𝑖𝑛𝑓𝑖 𝑡 + 𝛽6 𝐿𝐼𝑇𝑖𝑡 + 𝛽7 𝑟𝑒𝑎𝑙𝑤𝑎𝑔𝑒𝑖𝑡 + 𝑎𝑖 + µ𝑖𝑡
(Equation 2)
(Equation 2)
𝑈𝑁𝐸𝑀𝑃𝑖𝑡 = 𝛽0 + 𝛽1 𝑙𝐻𝐶𝑜𝑛𝐸𝑥𝑝𝑖𝑡 + 𝛽2 𝑙𝐺𝑟𝐶𝑎𝑝𝐹𝑜𝑟𝑚𝑖𝑡 + 𝛽3 𝑙𝐺𝑜𝑣𝐸𝑥𝑝𝑖𝑡 +
𝛽4 𝐸𝑉𝐼𝑖𝑡 + 𝛽5 𝐼𝑉𝐼𝑖𝑡 + 𝛽6 𝐼𝑛𝑓𝑙𝑎𝐴𝑁𝑝𝑟𝑖𝑡 + 𝛽7 𝑙𝑃𝑂𝑃𝑈𝐿𝑖𝑡 +
𝛽8 𝐼𝑁𝑇𝑅𝑖𝑡 + 𝛽9 𝑙𝑟𝑒𝑎𝑙𝐹𝐷𝐼𝑛𝑒𝑡𝑖𝑛𝑓𝑖 𝑡 + 𝛽10 𝐿𝐼𝑇𝑖𝑡 + 𝛽11 𝑟𝑒𝑎𝑙𝑤𝑎𝑔𝑒𝑖𝑡
+ 𝑎𝑖 + µ𝑖𝑡

Where:
Dependent Variable:
UNEMPit= unemployment rate
Independent Variables:
lrealGDPit= logarithm of real GDP
lHConExpit= logarithm of household final consumption expenditure
lGrCapFormit= logarithm of gross capital formation
lGovExpit= logarithm of government expenditure
EVIit= export volume index
IVIit= import volume index
InflaANprit= inflation rate
lPOPULit= population
INTRit= real interest rate
lrealFDInetinfit= real foreign direct investment
LITit= literacy rate
realwageit= real wage
𝑎𝑖= country-specific intercept
µ𝑖𝑡 = error term

Definition of Variables
The unemployment rate is recorded as the percentage of the total labor force that
is unemployed; since this data is a national estimate, definitions of labor force and
unemployment differs by country (Worldbank, 2015).
In equation1, the real gross domestic product (GDP) is adjusted for inflation using
2005 as the base year. The US dollar figures of the real GDP are converted from
domestic currencies using the year 2000 official exchange rates (Worldbank, 2015). Real
GDP is expressed in the logarithmic form for interpretability.
In equation 2, the real GDP variable is omitted and expressed using a sectoral
approach. The components of GDP, as prescribed by the expenditure approach, is the
sum of household final consumption expenditures (C), gross capital formation (I), general
government final consumption expenditures (G), and the difference of exports and
imports (X-M), were used to capture national output to determine which components of
GDP affect unemployment.
The lHConExp variable stands for the household final consumption expenditure
(formerly private consumption) which is the market value of all goods and services,
including durable products (such as cars, washing machines, and home computers),
purchased by households. It excludes purchases of dwellings but includes imputed rent
for owner-occupied dwellings. It also includes payments and fees to governments to
obtain permits and licenses. Here, household consumption expenditure includes the
expenditures of nonprofit institutions serving households, even when reported separately
by the country. Data are in constant 2005 U.S. dollars. (Worldbank 2015)

The lGrCapForm variable stands for the Gross capital formation (formerly gross
domestic investment) which consists of outlays on additions to the fixed assets of the
economy plus net changes in the level of inventories. Fixed assets include land
improvements (fences, ditches, drains, and so on); plant, machinery, and equipment
purchases; and the construction of roads, railways, and the like, including schools,
offices, hospitals, private residential dwellings, and commercial and industrial buildings.
Inventories are stocks of goods held by firms to meet temporary or unexpected
fluctuations in production or sales, and “work in progress”. According to the 1993 SNA,
net acquisitions of valuables are also considered capital formation. Data are in constant
2005 U.S. dollars. (Worldbank, 2015)
The lGovExp variable stands for the general government final consumption
expenditure (formerly general government consumption) which includes all government
current expenditures for purchases of goods and services (including compensation of
government employees). It also includes most expenditure on national defense and
security, but excludes government military expenditures that are part of government
capital formation. Data are in constant 2005 U.S. dollars (Worldbank, 2015).
Export Volume Indexes (EVI) are derived from UNCTAD’s (United Nations
Conference on Trade and Development) volume index series and are at the ratio of the
export value indexes to the corresponding unit value indexes. Unit value indexes are
based on data reported by countries that demonstrate consistency under the UNCTAD
quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade
values at the Standard International Trade Classification three-digit level as weights. To
improve data coverage, especially for the latest periods, UNCTAD constructs a set of

average prices indexes at the three-digit product classification of the Standard
International Trade Classification revision 3 using UNCTAD’s Commodity Price
Statistics, international and national resources, and UNCTAD secretariat estimates and
calculates unit value indexes at the country level using the current year’s trade values as
weights. (Worldbank, 2015)
Import volume indexes (IVI) are derived from UNCTAD’s volume index series
and are the ratio of the import value indexes to the corresponding unit value indexes. Unit
value indexes are based on data reported by countries that demonstrate consistency under
UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous
year’s trade values at the Standard International Trade Classification three-digit levels as
weights. To improve data coverage, especially for the latest periods, UNCTAD constructs
a set of average prices indexes at the three-digit product classification of the Standard
International Trade Classification revision 3 using UNCTAD’s Commodity Price
Statistics, international and national sources, and UNCTAD secretariat estimates and
calculates unit value indexes at the country level using the current year’s trade values as
weights. (Worldbank, 2015)
The variable InflaANpr represents inflation as measured by the consumer price
index (CPI) which reflects the annual percentage change in the cost to the average
consumer of acquiring a basket of goods and services that may be fixed or changed at
specified intervals (in this case, yearly) using the Laspeyres formula. (Worldbank, 2015)
The variable lPOPUL is the logarithm of population wherein total population is
based on the de facto definition of population, which counts all residents regardless of

legal status or citizenship---except for refugees not permanently settled in the country of
asylum, which are generally considered part of the population of their country of origin.
The total population values are midyear estimates (Worldbank, 2015). This variable is
expressed in the logarithmic form for interpretability.
Real interest rate is the lending interest rate adjusted for inflation as measured by
the GDP deflator. (Worldbank, 2015)
In this study, human capital is perceived in terms of literacy rate of adults (people
aged 15 and up). According to the Worldbank (2015), the variable “literacy rate, adult
total (% of people ages 15 and above)”, is the percentage of the population ages 15 and
above who can, with understanding, read and write a short, simple statement, on their
everyday life. Generally, “literacy” also encompasses “numeracy”, the ability to make
simple arithmetic computations. This indicator is calculated by dividing the number of
literates aged 15 years and over by the corresponding age group population and
multiplying the result by 100. This indicator was selected to be employed in this study
since it encompasses the percent of the labor force (people aged 15 and above) who are
literate, to be able to test the relationship of education to unemployment rates.
The lrealFDInetinf variable, it records foreign direct investments that are the net
inflows of investment to acquire a lasting management interest (10 percent or more of
voting stock) in an enterprise operating in an economy other than that of the investor. It is
the sum of equity capital, reinvestment of earnings, other long-term capital, and short-
term capital as shown in the balance of payments (Worldbank, 2015). The data is
adjusted for inflation using GDP deflator from the Worldbank.

Lastly, the realwage variable is the average monthly wages of workers in local
currency deflated through the official exchange rates and then expressed in terms of U.S.
dollars. The average monthly wages of workers (in local currency) were taken from the
labor statistics of the International Labor Organization (ILO) website. To adjust for
inflation and to convert the data into U.S. dollars, official exchange rates (from the
Worldbank) were employed to generate the necessary data used in the study.
Data Analysis
To estimate the determinants of unemployment in each of the countries, OLS was
employed. Furthermore, descriptive statistics in the form of tables and graphs were
employed to summarize the data.
The econometric model was regressed via STATA. The Hausman Test was
conducted to determine what type of panel data approach is more applicable. Since there
is a significant correlation between the independent variables and the country-specific
effect, it implies that fixed effects approach is more appropriate compared to random
effects. The fixed effects estimators were derived using two techniques: (1) the Least
Squares Dummy Variable (LSDV) Model; and (2) the N-entity Specific Intercepts
Model. The study also used Ordinary Least Squares (OLS) and Random Effects Models
to generate comparisons of coefficients. Post-estimation tests were also conducted to
check the validity of the regression estimates; these tests were employed: (1) test for
heteroskedasticity; (2) test for multicollinearity; (3) test for cross-sectional independence;
and the (4) test for autocorrelation.

CHAPTER V
RESULTS AND DISCUSSION
This chapter discusses the summary of the data used in the study, descriptive
statistics, trends of the variables; regression results, post-estimation results, and
comparative analysis on the different panel approaches are presented.
Summary of Macroeconomic Indicators of the 5 ASEAN States in 2010
Table 1 shows the performance of each ASEAN state in different aspects of the
economy as of the year 2010. The year 2010 was selected as the year to be discussed due
to the following reasons: (1) this study used panel data across the 5 ASEAN States from
the year 1980-2011; (2) year 2010 shows the recent economic performance of the
ASEAN States in different sectors of the economy allowing for comparisons; and (3)
2010 is the year that most countries are starting to recover after the financial crisis of
2007-2008.
Unemployment Rate. Some economists believe that if a nation’s unemployment
rate is less than 4%, then the economy is assumed to be in-full swing in terms of the labor
sector. On the other hand, there are also economists who believe in the concept of the
Non-Accelerating Inflation Rate of Unemployment (NAIRU), which refers to the
possibility that periods of high unemployment tend to increase the rate of unemployment
below which inflation begins to accelerate; NAIRU is also known as hysteresis or even
the “long run Phillips Curve” (Investopedia, 2015). Using the assumption that if
unemployment rates are less than 4%, then the labor market is considered in full swing,

as seen in Table 1, then only Indonesia and the Philippines have unemployment problems
at 7.10 and 7.30, respectively in the year 2010.
The indications of the 2010 unemployment rates of the 5 ASEAN States are as
follows: for Indonesia, 7.10% of the total labor force is unemployed; for Malaysia, 3.70%
of the total labor force is unemployed; for the Philippines, 7.30% of the total labor force
is unemployed; for Singapore, 3.10% of the total labor force is unemployed; and for
Thailand only 1.5% of the total labor force is unemployed.
Real Per Capita GDP. According to Investopedia (2015), real per capita GDP is
a measure of the total output of a country that takes the gross domestic product (GDP)
and divides it by the number of people in the country, adjusted for inflation. The per
capita GDP is especially useful when comparing one country to another because it shows
the relative performance of the countries. A rise in per capita GDP signals growth in the
economy and tends to translate as an increase in productivity. The gross domestic product
(GDP) is one of the primary indicators of a country's economic performance. It is
calculated by either adding up everyone's income during the period or by adding the
value of all final goods and services produced in the country during the year. Per capita
GDP is sometimes used as an indicator of standard of living as well, with higher per
capita GDP being interpreted as having a higher standard of living.
As seen in Table 1, there is a big disparity among the 5 ASEAN States in terms of
real per capita GDP. For Indonesia, the real per capita GDP is US$1,570.57; for
Malaysia, real per capita GDP is US$6,441.09; for the Philippines, real per capita GDP is
US$1,401.90; for Singapore, a very high real per capita GDP of US$34,668.19; and for

Thailand, real per capita GDP is US$3,162.54. As seen in Table 1, the Philippines has the
lowest real per capita GDP; this fact also indicates that among the 5 ASEAN States it can
be assumed that the Philippines has the lowest standard of living. Furthermore, it can be
assumed that since the GDP calculation through the expenditure approach is GDP= C + I
+ G + (X-M), where G is the estimate for government expenditures intended primarily for
the provision of social services; hence, the Philippines in terms of social services
provided by the government has the least quality among the 5 ASEAN states. For the
case of Indonesia, despite it being a member of the G-20 major economies, it has a
relatively low real per capita GDP compared to Thailand, Malaysia, and Singapore since
Indonesia has a very high population of 240 million. Also this indicates that Indonesia
has a lesser standard of living compared to Thailand, Malaysia, and Singapore. Indonesia
and the Philippines’ real per capita GDP values are relatively close to each other
compared to the other states.
Indonesia and the Philippines are almost at par in terms of standard of living as
indicated by the human development index (HDI), wherein both states are classified to be
of medium human development in terms of health, income, and education. Likewise, in
terms of real per capita GDP, Indonesia and the Philippines have close values. For the
case of Malaysia, it comes second in rank in terms of real per capita GDP; hence it is
second also in rank in terms of standard of living among the 5 ASEAN States. Lastly, for
the case of Singapore, despite it being a small country, it has the highest real per capita
GDP value among the 5 ASEAN states. Indeed, Singapore has the highest standard of
living in ASEAN and it is listed in the top 10 rank in terms of nominal per capita GDP

according to IMF; and in terms of HDI, Singapore is ranked 9th
in the world indicating a
high level of human development.
Inflation Rate. In terms of inflation rate, the 5 ASEAN states have relatively low
inflationary levels, Indonesia (5.13), Philippines (3.79), Thailand (3.27), Singapore
(2.80), and Malaysia (1.71). The 5 ASEAN have performed well in controlling
inflationary problems on consumer prices for basic commodities in 2010.
Population. In terms of population, it can be observed that there is a huge
disparity among the 5 ASEAN states. The state with the highest population is Indonesia
with over 240 million, followed by the Philippines at around 93.5 million, Thailand at
around 66.5 million; Malaysia with almost 28 million, and then Singapore at around 5.1
million. The analysis of population is more relevant if discussed in terms of population
density (the measure of population per unit area). The population density of each ASEAN
state in the year 2010 is as follows: 84 (Malaysia), 312 (Philippines), 125 (Indonesia),
129 (Thailand), 7089 (Singapore). The population densities indicate the ratio of the ideal
number of people living in one square kilometer. The analysis is if population density is
high, then it means that in every square kilometer there are more people competing for
natural resources, capital resources, basic needs, and even job opportunities. With the
mentioned population density values Singapore has the highest, followed by the
Philippines, Thailand, Indonesia, and lastly, Malaysia.
Real Interest Rate. The real interest rates of the 5 ASEAN states for 2010 are
relatively low, for most countries especially Thailand (2.19), Philippines (3.31),
Indonesia (4.59), and Singapore (5.16). Meanwhile only Malaysia has a relatively higher

real interest rate compared to the 4 ASEAN states with a real interest rate exceeding 10%.
Malaysia’s real interest rate is almost 12%, as of 2010.
Export Volume Index and Import Volume Index. In terms of EVI, Singapore
has the highest at 235.86; followed by Thailand (187.36), Philippines (145.30), Malaysia
(132.45), and lastly, Indonesia (109.06). As for IVI, Thailand has the highest value for
2010 at 217.47, followed by Indonesia (178.14), Singapore (177.35), Malaysia (155.91),
and lastly the Philippines at 121.22. To analyze whether how the 5 ASEAN States are
performing in trade, there is a need to compute for the Terms of Trade (TOT). TOT is
defined by Investopedia (2015) as the value of a country's exports relative to that of
its imports. It is calculated by dividing the value of exports by the value of imports, then
multiplying the result by 100. If a country's TOT is less than 100%, there is more
capital going out (to buy imports) than there is coming in. A result greater than 100%
means the country is accumulating capital (more money is coming in from exports).
Using the TOT concept, the following are the TOT values of the 5 ASEAN states in
2010: Indonesia (61.22), Malaysia (84.95), Philippines (119.86), Singapore (133), and
Thailand (86.15). Basing on the assumptions of TOT, the Philippines and Singapore are
accumulating capital since their TOT values are greater than 100; thus making them net
exporters for 2010. Meanwhile, Indonesia, Malaysia, and Thailand are net importers and
more capital is going out as indicated by their TOT values that are less than 100, for the
year 2010.
Foreign Direct Investment. In terms of net inflows of FDI, Singapore has the
highest at more than 55 billion U.S. dollars; followed by Indonesia (15.3 billion U.S.

dollars); Malaysia (9.1 billion U.S. dollars); and lastly, the Philippines (1.07 billion U.S.
dollars).
Literacy Rate. The data on literacy rates used in this study was interpolated since
the 5 ASEAN states have incomplete data on this particular variable. In terms of literacy
rates of the labor force, the 5 ASEAN states scored high indicating that more than 90% of
their labor forces can read and write and perform basic mathematical computations.
Singapore has the highest literacy rate at 95.86, followed by Philippines (95.42), Thailand
(91.38), Malaysia (93.12), and lastly Indonesia (92.19).

Table 1. Summary of the Macroeconomic Indicators of the 5 ASEAN States for 2010
Macroeconomic Indicator
ASEAN State
Indonesia Malaysia Philippines Singapore Thailand
Unemployment Rate 7.10 3.70 7.30 3.10 1.50
Real Per Capita GDP 1,570.57 6,441.09 1,401.90 34,668.19 3,162.54
Inflation Rate 5.13 1.71 3.79 2.80 3.27
Population 240,700,000 27,790,324 93,444,322 5,076,700 66,402,316
Real Interest Rate 4.59 11.78 3.31 5.16 2.19
Export Volume Index 109.06 132.45 145.30 235.86 187.36
Import Volume Index 178.14 155.91 121.22 177.35 217.47
Foreign Direct Investment 15,300,000,000 10,885,613,792 1,070,386,940 55,075,864,345 9,103,993,910
Literacy Rate* 92.19 93.12 95.42 95.86 93.51
Note: *some of the data used were interpolated values derived by getting the mean literacy rate of each state

Unemployment and Real Per Capita GDP across time
Figure 3 shows the unemployment rate trends of the 5 ASEAN states from 1980-
2011. Basing on the graph, the Philippines has the highest unemployment rate levels
among the 5, followed by Indonesia, Malaysia, Singapore, and lastly, Thailand. It can
also be observed that unemployment rate trends of Malaysia, Singapore and Thailand are
below 5% while as for Philippines and Indonesia; it is above 5% and at some point has
exceeded the 10% range.
Figure 4 shows the real per capita GDP trends of the 5 ASEAN states. As seen
above, Indonesia, Malaysia, Philippines, and Thailand have real per capita GDP values
below the 10,000 US$ bracket. Only Singapore has real per capita GDP higher than the
10,000 US$ bracket, even almost reaching up to 40,000 US$ in 2011. It can be observed
that Singapore’s per capita GDP is moving in an increasing trend through the years.
Although Malaysia is also moving in an increasing trend through the years, the increase
in real per capita GDP of Malaysia is not as high as Singapore’s. Thailand is also moving
in an increasing trend although it’s lower than that of Malaysia’s. For the case of
Philippines and Indonesia, their real per capita GDP values through the years are very
close to one another as indicated by their overlapping graphical trends. It can be observed
however that despite their almost similar values, Indonesia has a slightly higher real per
capita GDP compared to the Philippines especially in 2010-2011.
To see the time dimensions of the other variables used in this study, please see
Appendices 1-11.

Figure 3. Unemployment Rate Trends Overlay from 1980-2011, 5 ASEAN States
Figure 4. Real Per Capita GDP Trends from 1980-2011, 5 ASEAN States

Heterogeneity of Unemployment and Real Per Capita GDP across Countries
Figure 5 shows the comparisons between the unemployment rates of each
ASEAN state to their unemployment rate means (UNEMP_mean). Through this figure, it
can be observed that across countries, the data is very heterogeneous. As seen above, the
Philippines has the highest unemployment rate values, followed by Indonesia, Malaysia,
Singapore, and lastly, Thailand. Thus, it can be assumed that unemployment is more
prevalent in the Philippines from 1980-2011 compared to that of Indonesia, Malaysia,
Singapore, and Thailand.
Figure 6 shows the comparisons between the real per capita GDP values of each
ASEAN state to their real per capita GDP means (realPCGDP_mean). Through this
figure, it can be observed that data across countries is very heterogeneous. As seen above,
Singapore has the highest per capita GDP values, followed by Malaysia, Thailand, and
lastly, Indonesia and the Philippines. Thus, it can be assumed that Singapore has the
highest standard of living among the 5 ASEAN states and that the nation is more
progressive compared to the rest.

Figure 5. Heterogeneity across countries, Unemployment Rate
Figure 6. Heterogeneity across countries, Real Per Capita GDP

Heterogeneity of Unemployment and Real Per Capita GDP across Years
Figure 7 shows the comparisons between unemployment rates across years, to the
unemployment rate means across years (UNEMP_mean1). As seen above, it can be
observed that the trends of the unemployment rate means across time have been moving
up and down between 4-6%. Thus, it can be assumed that across time unemployment
rates have not been very volatile since there are no erratic movements that could be
observed.
Figure 8 shows the comparisons between real per capita GDP values across years,
to the real per capita GDP means across years (realPCGDP_mean1). As seen above, it
can be observed that the trends of the real per capita GDP means across time have been
increasing, at an exponential trend. Thus, it can be assumed that across time real per
capita GDP has continued to increase for all the 5 ASEAN states since 1980-2011. Real
per capita GDP is perceived to have been increasing across years due to increasing trends
of the components of GDP across time. The following components of GDP: household
final consumption expenditures (C), gross capital formation (I), general government final
consumption expenditures (G), and exports (X) as indicated by the export volume index
have gradually increased across time causing an increase in national output. The graphs
of the trends of the 4 GDP components are presented in the appendices (Appendix 7, 8, 9,
and 11).

Figure 7. Heterogeneity across years, Unemployment Rate
Figure 8. Heterogeneity across years, Real Per Capita GDP

Means of the Variables
Table 2 shows the means of the variables used in the study. The means of the
variables were computed in each country to present the average values of the variables
from 1980-2011. The grand means are the means of the variables across the 5 ASEAN
states from 1980-2011. As seen in Table 2, the following variables’ means were
computed: unemployment rate, real GDP, household final consumption expenditures,
gross capital formation, general government final consumption expenditures, export
volume index, import volume index, real foreign direct investment, population, real
interest rate, literacy rate, real wage, inflation rate.

Table 2. Means of the Variables
Variable ASEAN State Grand Mean
(5 ASEAN States)
Unemployment Rate 5.45 4.06 8.14 3.43 2.1 4.63
Real GDP 209,000,000,000 94,600,000,000 78,600,000,000 83,600,000,000 121,000,000,000 117,000,000,000
*Household Final
Consumption
Expenditures (C)
129,000,000,000 42,300,000,000 55,300,000,000 32,800,000,000 69,700,000,000 65,800,000,000
*Gross Capital
Formation (I)
58,600,000,000 25,700,000,000 17,100,000,000 24,300,000,000 41,500,000,000 33,400,000,000
*General Government
Final Consumption
Expenditures (G)
17,800,000,000 10,500,000,000 8,640,000,000 8,250,000,000 15,100,000,000 12,100,000,000
*Export Volume Index
(X)
69.44 69.29 66.19 90.74 79.21 74.98
*Import Volume Index
(M)
93.65 74.02 71.98 81.39 93.61 82.93
Real Foreign Direct
Investment
2,870,000,000 3,750,000,000 1,030,000,000 13,100,000,000 3,580,000,000 4,860,000,000
Population 195,000,000 20,800,000 70,700,000 3,664,492 59,000,000 69,900,000
Real Interest Rate 6.51 3.97 4.95 4.77 6.74 5.39
Literacy Rate 86 83.56 91.50 90.44 91.38 88.57
Real Wage 151.35 268.11 210.51 518.76 139.59 257.66
Inflation Rate 10.52 3.12 9.18 2.19 4.36 5.88
Note: *components of Gross Domestic Product through the expenditure approach (C + I + G + X – M)

Determinants of Unemployment across Each ASEAN State
One of the objectives of this study is to analyze the determinants of
unemployment in each of the 5 ASEAN states. Ordinary Least Squares (OLS) regression
controlling for time trend was employed per country to estimate the determinants of
unemployment.
Generally, Ordinary Least Squares method is used for estimating the unknown
parameters in a linear regression model, with the goal of minimizing the differences
between the observed responses in some arbitrary dataset and the responses predicted by
the linear approximation of the data (visually this is seen as the sum of the vertical
distances between each data point in the set and the corresponding point on the regression
line - the smaller the differences, the better the model fits the data).
There are 2 empirical models used in the study: (1) Equation 1 which uses real
GDP to capture national output; and (2) Equation 2 which uses a sectoral approach
disaggregating national output to consumption, investment, government expenditures,
and net exports. Table 3 shows the regression estimates across each ASEAN state
employing real GDP to capture national output and Table 4 shows the regression
estimates across each ASEAN state employing a sectoral approach.
Indonesia. As seen in Table 3, inflation is perceived to have a positive
relationship with unemployment, indicating that as inflation increases, unemployment
also increases. In a sectoral approach, the determinants of unemployment in Indonesia (as
seen in Table 4) are general government final consumption expenditures (significant at
1%), import volume index (significant at 1%), inflation rate (significant at 1%),

population (significant at 1%), and real FDI (significant at 5%). The time variable is also
significant at 1% indicating that unemployment varies across time when all other
variables are constant.
The general government final consumption expenditures (GGFCE) is seen to have
a negative relationship with unemployment, indicating that as GGFCE increases,
unemployment tends to decline. Import volume index and population are also perceived
to have a negative relationship to unemployment. There is a positive relationship between
inflation and unemployment, indicating that as inflation increases, unemployment also
tends to increase. Real FDI is also perceived to have a positive relationship with
unemployment. The R-squared values for equation 1 and 2 are 0.854 and 0.971
respectively, implying that the data fits the models at 85.4% (for equation 1) and 97.10%
(for equation 2).
Malaysia. The determinants of unemployment in Malaysia are inflation, real FDI,
real GDP, and population; all significant at 1% as seen in Table 3. The time variable is
also significant at 1% indicating that unemployment varies across time when all other
variables are constant. Real GDP, inflation, and population are perceived to have a
negative relationship with unemployment. While real FDI is perceived to have a positive
relationship with unemployment.
In a sectoral approach, as seen in Table 4, the determinants of unemployment are
gross capital formation (significant at 5%) and inflation (significant at 10%). There is a
negative relationship between GCF and inflation to unemployment; indicating that as
these variables increase, unemployment rates decline. The R-squared values for equation

1 and 2 are 0.733 and 0.767 respectively, implying that the data fits the models at 73.30%
(for equation 1) and 76.70% (for equation 2).
Philippines. The determinants of unemployment in the Philippines are inflation,
population, real FDI and real interest rate as seen in Table 3. Real GDP, real interest rate
and real FDI are significant at 5%; and population is significant at 1%. The time variable
is also significant at 5% indicating that unemployment varies across time when all other
variables are constant. Real GDP, inflation, and real FDI are perceived to have a negative
relationship with unemployment; while population is perceived to have a positive
relationship with unemployment. In a sectoral approach, only population is significant at
5% still indicating a positive relationship. The time variable is again significant at 5%
indicating that unemployment varies across time when all other variables are constant.
The R-squared values for equation 1 and 2 are 0.731 and 0.785 respectively,
implying that the data fits the models at 73.1% (for equation 1) and 78.5% (for equation
2).
Singapore. The determinants of unemployment rate are real GDP (significant at
1%), inflation (significant at 1%) and real wage (significant at 10%). Real GDP, inflation,
and real wage are perceived to have a negative relationship with unemployment. The time
variable is again significant at 1% indicating that unemployment varies across time when
all other variables are constant.
In a sectoral approach, only the household final consumption expenditures
(HFCE) variable is significant at 10%. A negative relationship is suggested between
HFCE and unemployment, indicating that at higher levels of HFCE, it generates a
decrease in unemployment. The R-squared values for equation 1 and 2 are 0.835 and

0.831 respectively, implying that the data fits the models at 83.50% (for equation 1) and
83.10% (for equation 2).
Thailand. The determinants of unemployment rate are real GDP (significant at
1%), population (significant at 1%), and real wage (significant at 5%) as seen in Table 3.
Population and real wage are perceived to have a positive relationship to unemployment,
indicating that at higher levels of population and real wage, unemployment also
increases. Meanwhile, real GDP is perceived to have a negative relationship with
unemployment rate; indicating that at higher levels of real GDP, unemployment tends to
decline. In a sectoral approach, the lone significant variable to be considered as a
determinant of unemployment rate in Thailand is population (significant at 5%) as seen in
Table 3. The R-squared values for equation 1 and 2 are 0.776 and 0.791 respectively,
implying that the data fits the models at 77.60% (for equation 1) and 79.10% (for
equation 2).

Table 3. Regression estimates across countries through OLS, real GDP used to capture
national output
VARIABLES
ASEAN State
Equation 1 Equation 1 Equation 1 Equation 1 Equation 1
(OLS) (OLS#) (OLS) (OLS) (OLS#)
Unemployment
Rate
Unemployment
Rate
Unemployment
Rate
Unemployment
Rate
Unemployment
Rate
Real GDP -6.152 -10.12*** 1.335 -9.195*** -12.45***
(-6.395) (-2.635) (-8.105) (-2.39) (-3.359)
Inflation Rate 0.327** -0.318*** -0.128** -0.190*** -0.0134
(-0.122) (-0.0846) (-0.0511) (-0.0563) (-0.0709)
Population -2.357 -6.049*** 86.22*** 3.405 42.16***
(-40.9) (-1.925) (-28.28) (-2.26) (-12.69)
Real FDI -0.337 0.385*** -0.619** 0.248 -0.0741
(-0.461) (-0.112) (-0.261) (-0.267) (-0.368)
Real Interest
Rate
-0.0445 -0.0287 -0.169** 0.0472 0.0374
(-0.0836) (-0.0427) (-0.0663) (-0.0381) (-0.078)
Literacy Rate -0.0179 0.0273 0.0297 -0.0716 0.0732
(-0.11) (-0.0468) (-0.253) (-0.0664) (-0.119)
Real Wage 0.00231 0.00176 -0.00365 -0.00634* 0.0161**
(-0.00432) (-0.002) (-0.00636) (-0.0031) (-0.00643)
Time 0.629 0.634*** -1.977** 0.547*** 0.155
(-0.513) (-0.175) (-0.812) (-0.142) (-0.1)
Constant 204.2 341.3*** -1,539** 178.1** -446.2**
(-680.4) (-76.37) (-618.9) (-64.37) (-172.3)
Observations 27 32 31 31 32
R-squared 0.854 0.733 0.731 0.835 0.776
Note: Standard errors in parentheses
# OLS regression made use of robust standard errors
*** Significant at 1%
** Significant at 5%
* Significant at 10

Table 4. Regression estimates, sectoral approach
VARIABLES
ASEAN State
Equation 2 Equation 2 Equation 2 Equation 2 Equation 2
(OLS) (OLS#) (OLS) (OLS) (OLS#)
Unemployment
Rate
Unemployment
Rate
Unemployment
Rate
Unemployment
Rate
Unemployment
Rate
1
Household Final
Consumption
Expenditures (C)
0.686 3.692 7.864 -7.259* -1.747
(-2.859) (-5.555) (-17.71) (-3.478) (-6.055)
1
Gross Capital
Formation (I)
4.718 -4.478** 1.662 -1.645 -3.6
(-2.735) (-1.579) (-2.425) (-1.488) (-2.816)
1
General Government
Final Consumption
Expenditures (G)
-21.40*** -1.032 -8.437 4.238 3.887
(-4.763) (-3.151) (-6.672) (-3.174) (-6.024)
1
Export Volume Index
(X)
-0.0343 -0.0268 0.0152 0.00484 0.00962
(-0.0508) (-0.0306) (-0.0393) (-0.0132) (-0.0435)
1
Import Volume Index
(M)
-0.104*** -0.00857 -0.024 -0.0257 0.0134
(-0.0226) (-0.0219) (-0.0377) (-0.0282) (-0.0415)
Inflation Rate 0.265*** -0.248* -0.0895 -0.0322 -0.106
(-0.0708) (-0.125) (-0.0686) (-0.0955) (-0.112)
Population -240.2*** -3.69 107.5** 2.169 40.66**
(-42.49) (-7.346) (-42.61) (-3.403) (-16.22)
Real FDI 1.017** 0.429 -0.385 0.0695 -0.148
(-0.379) (-0.256) (-0.32) (-0.3) (-0.471)
Real Interest Rate 0.0771 -0.0802 -0.118 -0.00531 -0.0407
(-0.048) (-0.0509) (-0.0869) (-0.0571) (-0.101)
Literacy Rate 0.0258 0.067 -0.111 -0.0547 -0.0312
(-0.0822) (-0.0448) (-0.272) (-0.0791) (-0.305)
Real Wage -0.00278 0.00331 -0.0147 -0.00612 0.0141
(-0.00226) (-0.00366) (-0.204) (-0.00488) (-0.0103)
Time 5.290*** 0.234 -2.489** 0.305 -0.621
-0.854 -0.341 -1.143 -0.233 -0.437
Constant 4,862*** 92.52 -1,913** 92.25 -672.6*
(-808.2) (-205.3) (-855.7) (-70.51) (-326.9)
Observations 27 32 31 31 32
R-squared 0.971 0.767 0.785 0.831 0.791
Note: Standard errors in parentheses
# OLS regression made use of robust standard errors
1
Components of GDP (C + I + G + X – M)
* Significant at 10%

Determinants of Unemployment in the 5 ASEAN States
There are many studies that aimed to find the determinants of unemployment rate
in certain countries since unemployment is a major problem for many economies all
around the globe. With the ASEAN Economic Community, this study aimed to
investigate the determinants of unemployment rate among the 5 founding members of
ASEAN namely: Indonesia, Malaysia, Philippines, Singapore, and Thailand. To identify
these determinants, the following methods were employed and then compared: (1)
Ordinary Least Squares (OLS) Method; (2) Fixed Effects using the Least Squares
Dummy Variable (LSDV) Estimator; (3) Fixed Effects using n-entity specific intercepts;
and (4) Random Effects model.
Two fixed effects approaches were used to analyze the impact of variables that
vary over time. Fixed effects models are designed to study the causes of changes within
an entity (in this case, an ASEAN state). A time-invariant characteristic cannot cause
such a change since it is constant for each ASEAN state. The 2 fixed effects approaches
employed in this study are the LSDV and the n-entity specific intercepts. These two
approaches will generate the same regression coefficients although they will have
differences due to the presence of dummy variables in the LSDV.
These 4 approaches were employed for the purpose of making a comparison as to
which approach is most effective in this study. To evaluate the acceptability and the
validity of the results, the regression results were subjected to the following post-
estimation procedures: (1) test for heteroscedasticity; (2) test for multicollinearity; (3) test
for autocorrelation; and (4) test for cross-sectional dependence. Furthermore, to

determine if which panel data approach is more effective for this study, Hausman test
was conducted.
Post-estimation test results. The following are the derived conclusions from the
different post-estimation tests conducted: (1) there is a problem with heteroscedasticity as
determined by the Breusch-Pagan/Cook-Weisberg test; (2) there is no multicollinearity
problem as determined by the vif test; (3) there is an autocorrelation problem in the panel
dataset as determined by the Wooldridge test; (4) there is no problem with cross-sectional
dependence as determined by the Pesaran’s test. Lastly, the Hausman Test results indicate
that fixed effects approach is more effective compared to random effects. The fixed
effects approach using the least squares dummy variable (LSDV) model is the most
appropriate model for the case of this study as seen in Table 5.
Table 5 shows the regression estimates of the determinants of unemployment rate
in the 5 ASEAN states (Indonesia, Malaysia, Philippines, Singapore, and Thailand)
already corrected for heteroscedasticity problem by employing robust standard errors
using the Least Squares Dummy Variable estimator (LSDV) of the fixed effects
approach. Table 5 shows the LSDV regression estimates of equation 1 and equation 2.
Appendix 17 and 18 shows the regression estimates of the 4 regression
approaches (OLS, Fixed Effects using the LSDV estimator, Fixed Effects using the N-
entity specific intercepts, and the Random Effects). Appendix 17 shows the regression
estimates of equation 1 (which uses real GDP to capture national output) and Appendix
18 shows the regression estimates of equation 2 (which uses a sectoral approach to
capture national output). The regression estimates are already corrected for
heteroscedasticity using robust standard errors for OLS and LSDV. While the fixed

effects and random effects approach is corrected of the heteroscedasticity and
autocorrelation problem using clustered robust standard errors since Torres-Reyna (2013)
suggested that if autocorrelation and heteroscedasticity is a problem in panel data,
clustered robust standard errors should be employed.
Least Squares Dummy Variable (LSDV) Estimator. Table 5 shows the
regression estimates of the determinants of unemployment in the 5 ASEAN states in
Model 1 (where real GDP was employed to capture national output) and Model 2
(sectoral approach). As seen in Table 7, the following are the determinants of
unemployment rate: real GDP (significant at 1%), inflation rate (significant at 10%),
population (significant at 1%), real interest rate (significant at 10%), and real wage
(significant at 1%).
In Table 5, it can be observed that the following variables have a negative
relationship with unemployment rate: real GDP, inflation rate, real interest rate, and real
wage. This indicates that there is an inverse relationship among the variables, meaning
(for example) as real GDP increases, unemployment rate tends to decline. Empirically,
there is a -0.02176 decrease in unemployment rate, per one percent change in real GDP.
There is a -0.0619 decrease in unemployment rate, per one unit change in inflation rate.
There is a-0.0766 decrease in unemployment rate, per one unit change in real interest
rate. There is a -0.00588 decrease in unemployment rate, per one unit change in real
wage. Furthermore, there is a positive relationship between population and
unemployment rate; empirically, there is a 0.06338 increase in unemployment rate, per
one percent change in population.

The negative relationship between real GDP and unemployment coincides with
the literature indicating that as national output tends to increase, unemployment decreases
due to increase in production which can be correlated with the fact that increase in
production means increase in the number of people employed in the economy.
This negative relationship between inflation and unemployment coincides with
the literature indicating that as inflation tends to increase, unemployment decreases. This
negative relationship coincides with the literatures especially on the Phillips Curve
theory, which suggests a short-run tradeoff between inflation and unemployment.
According to this theory, unemployment tends to decline when inflation increases due to
increase of money supply in circulation which can be correlated with increase of number
of people earning income through many sources, including employment. The more
money supply in circulation, it tends to push prices upward creating a short-run inflation.
To mitigate inflation, there is a pressure to push money supply downwards through many
measures, and one of which is controlling the levels of unemployment. When
unemployment levels reaches the Non-Accelerating Inflation Rate of Unemployment
(NAIRU), inflation and unemployment levels are balanced once again.
There is a negative relationship between real interest rate and unemployment
indicating that at higher levels of real interest rate, unemployment decreases. This
negative relationship coincides with the literature indicating that as real interest rate tends
to decrease, unemployment increases and vice versa.
The regression estimates controlled country-specific characteristics by including
country dummy variables in the LSDV regression.

As seen in Table 5, the R-squared value of the LSDV estimator (in equation 1) is
at 0.694, indicating that this model fits the data at 69.40%. Due to the R-squared value,
LSDV estimator is the most effective model to be used in this study since the other
models have lower R-squared values than that of the LSDV. The R-squared values of the
other models are the following: (1) OLS= 0.263; (2) Random effects= 0.077; and (3)
Fixed effects using n-entity specific intercepts= 0.235.
Equation 2 shows LSDV regression estimates that omitted the real GDP variable
and used a sectoral approach. The following are the determinants of unemployment rate
when sectoral approach is employed: gross capital formation, export volume index,
import volume index, population, and real wage. In equation 1, the real GDP variable is
significant and in equation 2, it can be observed that I, X, and M components of real GDP
are significant. The variable, gross capital formation is significant at 1%, export volume
index is significant at 1%, import volume index is significant at 5%, population is
significant at 1%, and real wage is significant at 10%.
As seen in Table 5, there is a negative relationship between gross capital
formation, export volume index, and real wage to unemployment. This means that when
one of these 3 variables increase, unemployment tends to decline. Meanwhile, import
volume index and population have a positive relationship with unemployment, indicating
that when either of these 2 variables increases, unemployment also increases.
Theoretically, when investments are high, more jobs are created due to an
increase in the number of firms searching for employees. The increase in labor demand
due to increase in investments lowers down unemployment. In this study, the gross
capital formation (which stands for investment or I in the GDP computation) coefficient

coincides with the theory as it shows a negative relationship to unemployment rate. Per
one percent change in gross capital formation, it generates a -0.0294 decrease in
unemployment rate.
Exports are seen as inflows to an economy. When export levels are high, it can be
assumed that there are more people employed in various industries that export goods and
services, hence causing an increase in production and an increase in the number of people
employed in the export industries. There is a negative relationship between exports and
unemployment. With this hypothesis, as exports (as indicated by the export volume
index) increases, unemployment tends to decline. As seen in Table 5, the regression
coefficient is -0.0259 indicating that per one unit change in export volume index, it
generates a -0.0259 decrease in unemployment rate.
On the other hand, imports are seen as outflows to an economy. When import
levels are high, it can be assumed that there is a revenue loss to local business due to
international competition, which slows down demand of locally produced goods, which
can eventually force local business to minimize production and lay-off workers or worse,
shutdown production leading to increased unemployment. There is a positive relationship
between imports and unemployment. With this hypothesis, as imports (as indicated by the
import volume index) increases, unemployment also increases. As seen in Table 5, the
regression coefficient indicates that per one unit change in import volume index, it
generates a 0.0229 increase in unemployment rate.
As seen in Table 5, the R-squared value of the LSDV estimator (of equation 2) is
at 0.726, indicating that this model fits the data at 72.60%. Due to the R-squared value,
LSDV estimator is the most effective model to be used in this study since the other

models have lower R-squared values than that of the LSDV. The R-squared values of the
other models are the following: (1) OLS= 0.617; (2) Random effects= 0.206; and (3)
Fixed effects using n-entity specific intercepts= 0.316.
Ultimately, through the LSDV regression estimates, it can be concluded that the
determinants of unemployment in the 5 ASEAN states are: real GDP, inflation, real
interest rate, population, and real wage. Furthermore, the components of real GDP, that
makes it a significant determinant of unemployment, are the following: gross capital
formation (investments), exports (as indicated by the export volume index), and imports
(as indicated by the import volume index).

Table 5. LSDV regression: Equation 1 and Equation 2
VARIABLES
Equation 1 Equation 2
LSDV LSDV
Unemployment
Rate
Unemployment
Rate
Real GDP -2.176*** -
(-0.746) -
1
Household Final Consumption
Expenditure (C)
- 1.648
- (-1.782)
1
Gross Capital Formation (I) - -2.904***
- (-0.657)
1
General Government Final Consumption
Expenditure (G)
- -0.872
- (-1.076)
1
Export Volume Index (X) - -0.0259***
- (-0.00743)
1
Import Volume Index (M) - 0.0229**
- (-0.0101)
Inflation -0.0619* -0.0428
(-0.032) (-0.0334)
Population 6.338*** 6.013***
(-1.813) (-1.896)
Real FDI -0.118 -0.0678
(-0.2) (-0.168)
Real Interest Rate -0.0766* -0.0454
(-0.0402) (-0.0432)
Literacy Rate 0.00831 0.034
(-0.0838) (-0.069)
Real Wage -0.00588*** -0.00366*
(-0.00212) (-0.00216)
_ICountry_MA 11.27*** 11.70***
(-3.565) (-3.616)
_ICountry_PH 6.163*** 5.841***
(-1.626) (-1.796)
_ICountry_SG 22.87*** 22.76***
(-6.729) (-6.939)
_ICountry_TH 2.58 3.532
(-2.057) (-2.138)
Constant -55.78*** -59.97**
(-19.78) (-26.47)
Observations 154 154
R-squared 0.694 0.726
Note: Robust standard errors in parentheses
1
Components of GDP (C + I + G + X – M)
* Significant at 10%

Evidence of Phillips Curve in the 5 ASEAN States
The regression estimates using the least squares dummy variable (LSDV)
estimator have indicated that per one unit change in inflation rate, it creates a decrease of
unemployment rate valued at -0.0619. The inflation rate variable is significant at 10%.
This -0.0619 regression coefficient implies an inverse relationship between
unemployment and inflation. Basing on this regression result and the definition of the
Phillips Curve (which postulates a short-run tradeoff between unemployment and
inflation), it could be concluded that there is empirical evidence of the existence of the
Phillips Curve in the 5 ASEAN states (Indonesia, Malaysia, Philippines, Singapore, and
Thailand) covered in this study. Therefore, the higher the inflation rates in the 5 ASEAN
states, the lower the unemployment and vice versa. There is a need for inflation to be
stabilized so as to not generate an erratic change in unemployment across the ASEAN
states. One of the goals of the ASEAN Economic Community (AEC) is a more stabilized
labor market by opening job opportunities of ASEAN citizens across the 10 ASEAN
member states. ASEAN policy makers should always take into account the effect of
inflation to unemployment given that this study has proven inflation to be a significant
determinant of unemployment and that there is a tradeoff among these two
macroeconomic indicators.

CHAPTER VI
SUMMARY, CONCLUSION, POLICY IMPLICATIONS, RECOMMENDATION
This study used secondary data from Worldbank’s official website to investigate
the determinants of unemployment rate of the 5 founding members of ASEAN:
Indonesia, Malaysia, Philippines, Singapore, and Thailand. The data used the following
macroeconomic indicators as variables: unemployment rate, real GDP, household final
consumption expenditure, gross capital formation, general government final consumption
expenditure, export volume index, import volume index, foreign direct investment,
inflation rate, population, real interest rate, real wage, and literacy rate from 1980-2011.
The study was conducted to (1) identify and compare the determinants of unemployment
rate; (2) investigate if there is evidence of a short run tradeoff between inflation and
unemployment commonly known as the Phillips Curve; (3) provide analysis on the
determinants of unemployment rate in each country; (4) present trends of relevant
macroeconomic indicators; and (5) provide policy recommendations that will address the
unemployment problems in the 5 ASEAN states. Among the 4 regression models, it can
be concluded that for this study, fixed effects using the LSDV estimator is the most
effective to use after subjecting the 4 regression models (OLS, LSDV, Fixed effects using
the N-entity specific intercepts, and Random effects) to post-estimation tests.
Summary of Findings
Determinants of unemployment rate. The determinants of unemployment rate
in the 5 ASEAN states after conducting several regression analyses in 4 different

approaches are the following: real GDP, inflation rate, population, real interest rate, and
real wage. Furthermore, the components of real GDP, that makes it a significant
determinant of unemployment, are the following: gross capital formation (investments),
exports (as indicated by the export volume index), and imports (as indicated by the
import volume index).
Phillips Curve in the 5 ASEAN states. Based on several regression analyses,
evidence suggests that Phillips curve exists in the 5 ASEAN states. In this study, the most
effective method is the fixed effects using the LSDV estimator, and in this method it
reflects that the inflation variable is significant with a negative coefficient. The negative
regression coefficient implies an inverse relationship or a tradeoff between inflation and
unemployment. Therefore, there is empirical evidence of a short-run tradeoff between
unemployment and inflation in the 5 ASEAN states.
Conclusion
The negative relationship between real GDP and unemployment implies that one
percent change in real GDP creates a significant impact in lessening the levels of
unemployment. The presence of the Phillips curve in the 5 ASEAN states is also an
important factor to consider in making unemployment regulatory policies and even in
setting the annual inflation rates. The positive relationship between population and
unemployment is given emphasis in the regression results suggesting that per one percent
change in population, it causes a significant impact in increasing levels of unemployment

due to excess labor supply in comparison to labor demand; given that there is a very high
population. The inverse relationship of unemployment rate and real interest rate suggest
that if there are high real interest rates, it creates a decrease in unemployment levels, and
that at the liquidity trap or even when real interest rates are really low, unemployment
increases. Real wages are also a significant determinant of unemployment; the higher the
real wages, the lower the unemployment rate.
Furthermore, the components of real GDP that significantly affect unemployment
are gross capital formation, exports (as indicated by the export volume index), and
imports (as indicated by the import volume index). Gross Capital Formation (GCF) or
investment has an inverse relationship with unemployment, at higher levels of GCF;
unemployment tends to decline due to more firms offering labor opportunities. Exports
play a very significant role in lessening unemployment across the 5 ASEAN states since
the export volume index is perceived to have an inverse relationship with unemployment
rate, for the case of this study. This indicates that at higher levels of export,
unemployment declines due to an increase in the supply of labor opportunities in the
export market. Also, imports have a positive impact on unemployment, thus, the higher
the import volume index, unemployment also tends to increase. Therefore, there is a need
to mitigate importation of goods especially if the things a nation imports, could be
produced domestically.
The 5 ASEAN states: Indonesia, Malaysia, Philippines, Singapore, and Thailand
should monitor the impact of real GDP, inflation, real interest rate, population, and real
wages to regulate the levels of unemployment since these five macroeconomic indicators
have been proven to be the determinants of unemployment among these states.

Policy Implications
This study suggests that monetary policies should be given due importance by the
5 ASEAN states since real interest rate and inflation rate have highly significant impacts
to the levels of unemployment. There is a need to control erratic changes in real interest
rates and inflation rates to avoid volatile changes in unemployment. Exports and trade
policies should be given emphasis by the governments of the 5 ASEAN states. Population
should be regulated through population mitigating measures since higher populations
tend to have a high impact on the increase of unemployment due to labor supply
exceeding labor demand. Lastly, these 5 ASEAN economies should strive to increase
their outputs so as to mitigate the unemployment problem. With higher levels of real
GDP, there is an increase in production efficiency which creates a multiplier effect to an
increase in the demand for laborers, hence generating more employment opportunities.
The ASEAN Economic Community (AEC) should strive to improve monetary policies
intra-ASEAN and at the national level. Trade policies such as the ASEAN Free Trade
Area and the ASEAN Single Window should really be given priority since gains in trade
from exports (as implied by the export volume index) will increase national output and
mitigate unemployment. Minimum wage laws should also be standardized across
ASEAN and at the national level; it should always maintain a priority of the state to
ensure that workers get their due. Better wages encourage people to remain employed
within the country and not migrate someplace else; hence lessening the unemployment
problem and increasing national output since more people are generating income.
Investments from local sources should be given top priority and the national governments

should encourage people to startup businesses to create a positive multiplier effect to the
economy.
Recommendation
This study could be improved by focusing the analysis at the individual (country)
level so as to generate comparisons among the 5 ASEAN states. Time series analysis per
country could be employed using the same empirical model used in this study provided
that there will be a longer time period covered to generate credible results. Furthermore,
the inclusion of the other ASEAN states namely Brunei Darussalam, Myanmar, Vietnam,
Laos, and Cambodia will really improve the study to be able to identify the determinants
of unemployment in ASEAN that will serve as basis on policy creation.

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