437
The Impact of Macroeconomic Factors on Government Spending: Evidence of
Lao PDR
Souliyadeth CHANTHAVONG*, Vilayphone SOMSAMONE,
Vatthanaly SIPHADA and Pheng HER
1Department of Economics, Faculty of Economics and Tourism, Souphanouvong University, Luang Prabang Province, Lao PDR
[email protected]
บทคัดยอ่
บทความนี้มีวัตถุประสงค์เพอ่ื วิเคราะห์ผลกระทบของปัจจัยเศรษฐกิจมหภาคต่อการใช้จ่ายของรัฐบาลลาวโดยใช้ข้อมูล
อนุกรมเวลาตั้งแต่ปี 2533-2563 และบอกเปน็ นัยด้วยรูปแบบการรวมและการแก้ไขขอ้ ผิดพลาด ผลการศึกษาพบว่าตัวแปร
ท้งั หมดอยกู่ ับที่ท่ีความแตกตา่ งท่ี 1 สาหรับการวิเคราะหเ์ ชงิ ประจักษพ์ บว่ามีความสัมพันธ์แบบเป็นเหรียญกษาปณ์ แตไ่ ม่
มรี ูปแบบการแก้ไขขอ้ ผิดพลาด เนอ่ื งจากคา่ สัมประสทิ ธ์ิดเี ทอร์มแิ นนต์ “การปรับความเรว็ ” สูงมากหรอื สูงกวา่ -1 ซึง่ ขดั
กับสภาวะของแบบจาลองการแก้ไขข้อผิดพลาดซ่งึ อาจเกิดจากการสุ่มตวั อย่างเพียงเลก็ นอ้ ยหรือตัวแปรสาคัญบางตวั ทา
ไม่รับโมเดลน้ี เช่น เครื่องบ่งชี้การทุจริต หน้ีสาธารณะ การลงทุนโดยตรงจากต่างประเทศ เป็นต้น ดังน้ัน โมเดลน้ีจงึ มี
ความสัมพันธ์ระยะยาวเทา่ นัน้ ซ่งึ รายได้จากภาษสี าคญั ที่สดุ รองลงมาคือ ปริมาณเงินและดชั นรี าคาผูบ้ ริโภคทมี่ ีนยั สาคญั
ทางสถติ เิ ทา่ กบั 1 % ระดับ.
คำสำคญั : แบบตาลอง Error Correction, การใชจ้ า่ ยของรัฐบาล, รายได้ภาษี
Abstract
The paper aim to analysis the impact of macroeconomic factors on the Lao government spending by
using the time series data from 1990- 2020 and implied with cointegration and error correction model.
The results found that all variable stationary at 1st differences. For empirical analysis found that there
are cointegration relationship but no error correction model because the coefficient determinant
“speed adjustment” is very high or higher than -1 which against the error correction model’s condition
which may cause from the small of sampling or some important variables do not take in this model
likes corruption indicator, public debt, foreign direct investment, etc. Therefore, this model only has
long-run relationship which tax revenue is the most important, follows by money supply and consumer
price index at the statistically significance of 1% level.
Keywords: Error Correction Model, Government Spending, Tax Revenue
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
438
Introduction
In the Economic development of the nation, the monetary policy and fiscal policy plays important role.
Government spending is a fiscal tool coupled with government revenue generation to bring about
economic change both in terms of promoting growth and equitable distribution of income. Government
spending is a factor that is very important to the country's administration, especially public spending
which directly increase productivity are expenditures for government investment because it will help
the government to have capital goods and increased production capacity, resulting in the country's
ability to sell products in the future and is a factor that helps the country's economy to grow rapidly.
Expenditures that increase the efficiency of direct production are the construction of roads, irrigation
systems and research. The global financial or economic crisis has resulted in many governments facing
public debt and massive budget deficits ( Presbitero, 2010) . The problem of public debt and budget
deficit is also a problem that both developed, developing and underdeveloped countries are facing.
Labonte (2012) & Cottarelli and Schaechte (2010) have argued that short-term budget deficits result in
increased unemployment and lead to the problem of continuous accumulation of public debt and
beyond the level that is said to be sustainable, causing difficulties in adjusting policies or macro
variables, especially affecting the gross domestic product. To solve this problem, the government
should reduce expenses or increase income sources and income sources that have a balanced budget
or a low dividend.
Although Lao PDR's economy is growing rapidly at an average of 7% per year, according to the collected
data and the annual report of the Ministry of Finance, it is found that the Lao government is experiencing
continuous and chronic budget deficit problems. Due to increased spending and affected by various
crisis issues. Since the spread of the COVID-19 disease at the end of 2019, the economy of the Lao PDR
has been severely affected. The state budget revenue in 2020 was 21,846 billion kip, equal to 12.7% of
GDP, which decreased by 13. 8% compared to 2019 while the state budget expenditure in 2020 was
30,858 billion kip, which was 1.5% of GDP and increased by 1.3% compared to 2019, which in 2020 had
a budget deficit of 9,012 billion kip of GDP, which was 5. 2% ( Ministry of Finance, 2020) . The
macroeconomic situation of the Lao PDR during 2021 is still highly uncertain, partly due to the impact
of the second round of the Covid-19 epidemic within the country, which affects the economy in a wide
circle. causing the government to implement strict and strict preventive measures that will affect the
implementation of the 2021 state budget plan. The implementation of budget revenue for the 9 months
of 2021 reached 16,149- billion- kip, equivalent to 8. 8% of GDP, while the implementation of budget
expenditure reached 16,957- billion- kip, equivalent to 9. 3% of GDP, due to the normal administrative
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
439
expenditure decreased by 2.6% and investment expenditure decreased by 32.5%, expressed a budget
deficit of 807 billion kip (Ministry of Finance, 2021).
Tools/Methodology
Data Collection:
This paper is an empirical analysis by testing the validity of Wagner's law. This paper used time series
data from 1990- 2020, which was collected from the website of the Bank of the Lao PDR, including:
government expenditure (GE), gross domestic product (GDP), money supply (M2), tax revenue (TAX) and
consumer price index (CPI).
Model:
According to the Wagner’s law, the researcher developed the model as following:
LnGEt = 0 + 1LnTAX t + 2LnM 2t + 3LnGDPt + 4LnCPIt + t (1)
Where, : the logarithms of government spending at period t
: the logarithms of tax revenues at period t
2 : the logarithms of money supply at period t
: the logarithms of gross domestic product at period t
: the logarithms of consumer price index at period t
: the error term.
Hypothesis:
- 1 0 This means that tax revenues and government spending have positive
relationship
- 2 0 showing that the amount of money supply has a positive relationship with
government spending.
- 3 0 indicated that changes in government spending affect changes in gross domestic
product and are related in the same direction.
- 4 0 Meaning that government spending and inflation rate have negative
relationship.
Unit Root Test:
Unit root tests can be used to determine if trending data should be first differenced or regressed on
deterministic functions of time to render the data stationary. Moreover, economic and finance theory
often suggests the existence of long- run equilibrium relationships among nonstationary time series
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
440
variables. Therefore, before running the model all variables were tested by the augmented Dickey–
Fuller (ADF) tests and have the equations below:
None p (2)
(3)
X t = X t−1 + iX t−1 + t (4)
i=1
Intercept p
X t = + X t−1 + iX t−1 + t
i=1
p
Intercept &Trend Xt = + T + X t−1 + iX t−1 + t
i=1
Hypothesis: 0: = 0 Stationary, 1: < 0 non-Stationary
Co-integration Test:
For testing the model whether there are cointegration or not? The Engle and Granger ( 1987) test was
used which has the hypothesis as following:
0: there are no cointegration
1: there are cointegration
If there are cointegration, the cointegration model were written as:
LnGEt = 0 + 1LnTAX t + 2 LnM 2t + 3LnGDPt + 4 LnCPIt + t (5)
The Error Correction Model (ECM):
LnGEt = 0 + 1LnTAX t + 2LnM 2t + 3LnGDPt + 4LnCPIt + ECM t + t (6)
: Error Correction Term
: The speed of adjustment towards the long-run equilibrium, where −1 < < 0
Results
Table 1: Unit root Test at 1st difference
ADF Unit root test at First Difference
t-statistic
Variables model Critical Value P-Value Concluded
-2.749963***
-2.605605** 1% 5% 10%
-2.233418**
LnGE None -2.641672 -1.952066 -1.610400 0.0076 Stationary
LnGDP None 0.0109 Stationary
LnM2 None -2.641672 -1.952066 -1.610400 0.0268 Stationary
-2.641672 -1.952066 -1.610400
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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LnTAX None -1.840358* -2.641672 -1.952066 -1.610400 0.0632 Stationary
LnCPI None -7.676254*** -2.644302 -1.952473 -1.610211 0.0000 Stationary
Notes: ***,** the statistically significance level of 0.01 and 0.05 respectively.
From the results of unit root test f of the table 1 above we found that all variables non-stationary, but
after the 1st differences they are stationary, meaning that we can use it to conduct the cointegration
and error correction model.
Table 2: The Results of Cointegration Test
Dependent Variable: LNGE
Method: Fully Modified Least Squares (FMOLS)
Date: 10/18/22 Time: 14:45
Sample (adjusted): 1990 2020
Included observations: 31 after adjustments
Cointegrating equation deterministic: C
Long-run covariance estimate (Bartlett kernel, Newey-West fixed
bandwidth = 4.0000)
Variable Coefficient Std. Error t-Statistic Prob.
LNCPI -0.139063** 0.065990 -2.107322 0.0445
LNGDP 0.083143ns 0.181729 0.457509 0.6510
LNM2 0.235506** 0.093286 2.524565 0.0178
LNTAX 0.536225*** 0.149978 3.575359 0.0013
C 2.107398*** 0.718986 2.931069 0.0068
R-squared 0.995833 Mean dependent var 8.079412
Adjusted R-squared 0.995215 S.D. dependent var 1.971551
S.E. of regression 0.136374 Sum squared resid 0.502140
Long-run variance 0.014645
Notes: ***,**,* the statistically significance level of 0.01, 0.05 and 0.1 respectively.
The results of Co- integration evaluation found that the R- Squared is equal to 0. 995833. It shows that
the independent variables used in the model can explain the government expenditure by 99.58% while
the other 0.42% is other factors. The coefficient of tax revenue is equal to 0.536225 and has a statistical
significance level of 0. 01, showing that tax revenue has a positive effect on the Lao PDR government's
expenditure, which means that if the tax revenue increase by 1% (other factors constant), it will affect
the Lao government's expenditure increasing by 53. 62% which match the set hypothesis. For money
supply is equal to 0.235506 and has a positive statistical significance level of 0.05, indicating that if the
money supply increase by 1% with other factors constant, it will affect the Lao government expenditure
to adjust of 23.55% and is contrary to the established hypothesis. By referring to the theory of monetary
policy to solve the problem of the money supply, the amount of money in the economic system
increases, it will result in a decrease in government spending, which is the reason for the analysis of the
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บัณฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
442
amount of money in the economic system increases, and it affects the government's expenditure in the
same way, because the monetary policy of the Bank of Lao PDR to control inflation is still not able to
solve the problem, in addition, Laos is in the period of development of the country from agriculture to
industrialization, so it will spends more. For Consumer Price Index, has a negative statistical significance
level of 0.05, showing that if the consumer price index is adjusted to increase by 1% with other factors
constant, it will affect the government expenditure decrease by 13.90%.
Table 3: The Results of Error Correction Test
Dependent Variable: D(LNGE)
Method: Least Squares
Date: 10/18/22 Time: 15:32
Sample (adjusted): 1990 2020
Included observations: 31 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(LNCPI) -0.061513ns 0.042993 -1.430790 0.1644
D(LNDGP) 0.499269** 0.213480 2.338714 0.0273
D(LNM2) -0.294545** 0.132360 -2.225333 0.0349
D(LNTAX) 0.672687*** 0.167534 4.015239 0.0004
ECM(-1) -1.002124*** 0.160226 -6.254434 0.0000
R-squared 0.828733 Mean dependent var 0.182905
Adjusted R-squared 0.802384 S.D. dependent var 0.210765
S.E. of regression 0.093694 Akaike info criterion -1.750883
Sum squared resid 0.228241 Schwarz criterion -1.519595
Log likelihood 32.13869 Hannan-Quinn criter. -1.675489
Durbin-Watson stat 2.108372
Notes: ***,**,* the statistically significance level of 0.01, 0.05 and 0.1 respectively.
For Error Correction Model above found that the independents variables can explain the dependent
variable by 82.87% and the ECM has negative significance at 1% level, but it was higher than 1 suggesting
that it against the condition of ECM, so I conduct to check the residual distributed.
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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7
6 Series: Residuals
Sample 1990 2020
Observations 31
5 Mean 0.011447
Median 0.009327
4 Maximum 0.152945
Minimum -0.138744
3 Std. Dev. 0.086444
Skewness -0.110226
2 Kurtosis 2.115081
1 Jarque-Bera 1.074254
Probability 0.584425
0
-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15
Figure 1. Residual Distributed
For residual distributed checking by Jurque- Bera test found that there is normally distribution shown
that the model is accurate in forecasting. Then I continue to test heteroskedasticity by Breusch-Pagan-
Godfrey.
Table 4: The Results of Error Correction Test
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.082050 Prob. F(5,25) 0.3943
Obs*R-squared 5.515173 Prob. Chi-Square(5) 0.3563
Scaled explained SS 2.111275 Prob. Chi-Square(5) 0.8335
For Breusch-Pagan-Godfrey test found that P (Chi- Square (5)= 0.8335>0.05 indicated that we cannot
rejected null hypothesis, meaning that this model is appropriate. After the diagnostic check I concluded
that this model does not have error correction model or short-run model because its coefficient against
the theory and condition which may cause from small sampling.
Discussion
This results against with the study of Molefe Kagiso and Ireen Choga (2017). They found that there is a
negative long-run relationship between government expenditure and economic growth in South Africa.
Furthermore, the estimate of the speed of adjustment coefficient found in this study has revealed that
49 per cent of the variation in GDP from its equilibrium level is corrected within of a year. Furthermore,
the study discovered that the causality relationship run from economic growth to government
expenditure. This implied that the Wagner’ s law is applicable to South Africa since government
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
444
expenditure is an effect rather than a cause of economic growth. Ismael Sanz and Francisco J. Velázquez
( 2002) also studied on the determinants of the composition of government expenditure by functions
and found that income and prices, institutional factors, population density and its age structure have
significant effects on the composition of government expenditure.
However, it according with the studies of Muhammad I. J. A and Attiya Y. Javed ( 2013) . They indicated
that in the short run, the rate of inflation does not affect the economic growth but government
expenditures do so. Suchit Limpocharoenchai ( 2003) suggested that government expenditure is
governed by two important factors likes national development strategy and the country progressed
economically which the lag one period of tax to GDP and economy openness are positively related to
the level of public expenditure while as income per capita exhibits negative relationship with the level
of public expenditure which is contrary to the finding of most developed economies since World War
II.
As the above debates, this topic has to continue to analyze in the future. Therefore, the variables of
corruption index, investment, interest rate, public debt, ect. , should be considered by further
researchers and should use another method to analyze such as vector error correction model ( VECM)
because it can express both unidirectional and bidirectional relationships.
Conclusion
For the empirical analysis by using cointegration and error correction model to analyze the impact of
macroeconomic factors on Lao government spending found that there are cointegration relationship
but no error correction model because the coefficient determinant “speed adjustment” is very high or
higher than -1 which against the error correction model’s condition which may cause from the small of
sampling or some important variables do not take in this model likes corruption indicator, public debt,
foreign direct investment, etc. Therefore, this model only has long- run relationship which tax revenue
is the most important, follows by money supply and consumer price index.
Acknowledgments
I would like to thank all teachers and advisors in Souphanouvong University for they continuous
guidance, support, opinion and encouragement in the preparation of this paper. My thanks are not
enough for their continuous guidance.
Furthermore, I also would like to thanks, the Maejo University to offer this opportunity to me and I
would like to thank all of my friends for their endless support and encouragement in my life.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
445
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Andrea Filippo Presbitero, (2010). Total public debt and growth in developing countries. European
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Carlo Cottarelli and Laura Jaramillo (2012). Walking hand in hand: Fiscal policy and growth in
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Ismael Sanz and Francisco J. Velázquez (2002). Determinants of the composition of government
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Ministry of Finance, (2021). Annual report of financial implementation.
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Economics and Behavioral Studies (ISSN: 2220-6140), 9 (4):164-172.
Muhammad I.J.A and Attiya Y.Javed (2013). Inflation, economic growth and government
expenditure of Pakistan: 1980-2010. Procedia Economics and Finance, 5: 58-67
Suchit Limpocharoenchai (2003). Determinants of growth of public expenditure in Thailand
during 1960-2000. Department of Economic. Dhurakijpundit University.
Wagner, Richard E.; Weber, Warren E. (March 1977). "Wagner's Law, Fiscal Institutions, and the Growth
of Government". National Tax Journal. 30 (1): 59-68.
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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ປັດໄຈທຳງດຳີ້ ນເສດຖະກດິ ລະຫວ່ື ຳງປະເທດທ່ື ມຜນົ ຕື່ ກຳນຄຳີ້ ລະຫວ່ື ຳງປະເທດຂອງ ສປປ ລຳວ
International Economic Factors Affecting International Trade of Lao PDR
Chanmany CHANHTANGEUN
Department of Economics Faculty of Economics and Tourism Souphanouvong University, Laos
Email: [email protected]
Abstract
The topic of this research is to study about " International Economic Factors Affecting
International Trade of Lao PDR " . The aims to study the relationship between international
economic factors and international trade of Lao PDR from 2005- 2020. And analyze the
international economic factors affecting international trade of Lao PDR. The data is used in
the study were secondary data. The analysis was used correlation values ( Correlation) to
analyze the relationship between international economic factors and international trade and
using multiple regression with minimal squared estimation method through SPSS version 22 to
determine the importance of international economic factors affecting international trade of
Lao PDR.
The results of the study were found that increasing gross domestic product ( GDP ) of Lao
PDR, gross domestic product ( GDP ) of the major trading partners, the value of foreign direct
investment ( FDI ) played a significant role in the growth of international trade in 80. 9 % to
99. 1% ; Foreign exchange and inflation played a significant role in the growth of international
trade from 17. 3 % to 59. 9 %. For the international economic factors that affect the growth
of international trade of Lao PDR, there are 4 factors: the gross domestic product increase 1
million US dollars will be affected the value of international trade increase 0. 361 million US
dollars at the reliability statistics level 99%. Gross domestic product of major trading partners
increase 1 million US will be affected international trade value increased 13. 898 million US
dollars, foreign investment value in Lao PDR increased 1 million US dollars will be affected t
in increasing on the value of international trade 0.258 million US dollars, exchange rate (Kip/US
$ ) increase 1 Kip/ US dollar will be affected the value of international trade increase 0. 579
million US dollars, particularly, it effected on increasing Lao PDR's goods export as an increase
in the exchange rate will weaken the local currency by making the value of Lao PDR exports
lower in foreign exchange. At the reliability statistics level of 95% which is based on the
theories and assumptions were set.
Keywords: International trade, international economic factors and correlation.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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Introduction
International trade is important in the development of every country in the world because of
the exchange of goods and services between countries. If any country tries to produce all
goods and services by itself without importing and exporting, that country will develop slowly,
including the standard of living of the people will also be low. Therefore, the issue of
international trade is very necessary for the development of various countries, especially
developing countries that want to raise the standard of living of the people, public utilities and
things in the country to a higher level. It is necessary to give more importance to international
trade. The benefits and importance of international trade will be as follows: It is important to
help the development of economic growth for developing countries, especially emphasizing the
issue of exports, which will help to grow the economy of that country, generate more domestic
demand, and produce more goods for export. create employment and generate income for the
people; able to use domestic resources cost- effectively; The domestic market is growing
because there is more production in the country; There are various knowledge developments,
Develop work skills, develop technology and move funds between countries from developed
countries to developing countries, resulting in developing countries having funds to circulate
to manage the country's economy. The Lao PDR is one of the ASEAN member countries that
has a prominent point in connecting land and water transport routes with countries such as
China, Burma, Thailand, Vietnam, Cambodia, which is considered to be an economic and
commercial stimulus for the Lao PDR. In the current economic situation, economic growth in
Lao PDR is expected to recover at a rate of 6.5% in 2019, increasing from 6.3% in 2018. (Lao
PDR Economic Report, Department of Macroeconomics, Trade and Investment, Asia Pacific
Region, August 2019). But due to the spread of the Covid-19 pneumonia epidemic, economic
activity has decreased significantly. Economic growth will drop to 3. 3% in 2020, from 5. 46%
last year, which is due to the decline in the service sector. For 2021, the economy will continue
to grow at a rate of 3.8% to achieve the goal of sustainable economic recovery, which recovery
is expected to be driven by the construction sector supported by large- scale infrastructure
construction projects and the service sector that will continue to grow, such as wholesale and
retail trade that can still grow. In 2021, there will be a gradual recovery, in line with the
government's forecast that the economy will grow at the level of 3.8%, driven by the ongoing
large-scale infrastructure projects and various exports, such as the Vientiane-Bu Ten highway
construction project, the Lao- China railway construction project, which is planned to be
completed and open for service in December 2021, which will be a commercial and investment
facility in the country to improve; Meanwhile, the export sector is expected to gradually adjust
according to the economic recovery of the partner countries ( especially China, Vietnam and
Thailand) . Regarding the export development progress, the electricity purchase agreement
signed before the spread of the Covid- 19 pneumonia epidemic will be a guarantee of income
in the export of electricity; Meanwhile, global aggregate demand and domestic investment
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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climate will help raise revenue from mineral exports. (Lao PDR Economic Report, Bank of Lao
PDR, 2021).
From the above information, it can be seen that the economic factors between various
countries have a role and importance and are a catalyst for international trade, especially the
export aspect of Lao PDR. Therefore, the study and analysis of various economic variables
that affect the international trade of the Lao PDR will be useful to determine and improve the
international trade policy of the government to be in line with the current economic situation;
It is also useful for business operators involved in international trade so that they can use it
as a guide to conduct business in accordance with the current economic situation.
In order to make the conduct of this research to be on target and convenient for the research,
the group of researchers has the objectives of the research as follows:
1) Study the relationship between international economic factors and international
trade of Lao PDR from 2005-2020.
2) Analyze the economic factors that affect the international trade of the Lao PDR
Equipment and methods
The tools used in the research are: In this research study, it will be a quantitative study, By
using secondary data obtained from the compilation of various related documents such as:
Annual economic reports, relevant research articles, information based on reliable internet
sites, etc. are used in the study. The explanation of the results of this study is using the
description format with the presentation of numerical values; including other relevant theories
to include in explaining the causes and results of the study.
Collection of data: The data collected by the research team is mainly collected from the
Ministry of Industry and Trade, the Export- Import Promotion Department, the National
Statistics Center and the Bank of the Lao PDR.
Data analysis: For the study of the relationship between economic growth and international
trade of the Lao PDR, the method of multiple correlation coefficient analysis is used to find
the correlation value to match with the theory of economic growth and international trade
theory; Explain and then present data using Descriptive Statistics To describe the
characteristics of various data such as correlation coefficients and percentage values. For the
analysis of the economic factors that affect the international trade of the Lao PDR is used
with the estimation method Ordinary Least Squares: OLS With a confidence level of 90% ,
95% and 99% by analyzing through a statistical program ( SPSS Version 22) by setting the
statistical significance level ∗sig ≤ 0.1 ∗∗sig ≤ 0.05 ∗∗∗sig ≤0.01
Models used in data analysis: TV = f( GDPlaos, GDPpartner, FDI, Exc, Inf)
TV = β0 + β1GDPlaos + β2GDPpartner + β3FDI + β4Exc + β5Inf + εt ………….(1)
In the model showing the relationship between economic factors that affect the growth of
trade between Lao PDR countries from 2005-2021 shown in Table 1.
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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Research results
Through the analysis of the relationship between the international economic factors and the
international trade of the Lao PDR, it was found that: after the Lao PDR has adjusted its
economic structure since 1986, combined with the fact that the Lao PDR became a member
of ASEAN in 1997 and opened the year of tourism in 2000, with the goal of entering the
international market. As a result, the Lao PDR has a rapidly growing economy until now. Based
on the analysis of economic growth data from 2005 to 2021, it can be seen that: The gross
domestic product of the Lao PDR has been continuously increasing at an increasing rate during
the period 2005- 2012 at an average rate of 15. 57% per year and the economic growth rate
will be relatively stable at an average rate of 7. 69% per year; Especially in 2012, the gross
domestic product increased the most to 2,253.20 million US dollars, which is equal to 30.66%.
But there will be a continuous increase in the rate of decline starting from 2013 at an average
rate of 8.81% per year and an average economic growth rate of 6.14% per year. In this period
from 2019- 2021, there is the lowest rate of increase: the average economic growth rate is
4.33% per year. In 2021, the gross domestic product increased to only 92.7 million US dollars,
which is equal to 0.46% compared to 2020. This is due to the epidemic of pneumonia, Covid-
19, which has affected the health and well- being of people all over the world, resulting in the
lowest global economic recession in many years. including the economy of the Lao PDR. For
the value of foreign investment, there will be irregular ups and downs and high volatility during
the period 2005- 2015. In 2009, the value of direct investment from abroad will be equal to
4,312.89 million US dollars, which is an increase of 2.54 times the value of investment in 2008.
which is the year with the highest increase in foreign investment value compared to other
years and the year 2010 is worth 1,402. 21 million US dollars which is a decrease of 67. 49%
compared to 2009 and it is considered to be the year in which the value of foreign investment
has dropped in the highest percentage compared to other years as well. For the years 2016-
2021, the change in the value of foreign investment is quite low, that is, there is an increase
and a decrease at a low level, which in 2017 the value of foreign direct investment is equal to
7,215. 74 million US dollars, which is an increase of 40. 52% compared to 2016. and it is the
year with the highest investment value compared to other years. And it is the year with the
highest investment value compared to other years.
Inflation is seen to be highly fluctuating, with irregular increases and decreases. On average,
the economic condition of Lao PDR will have an average inflation rate of 4. 27% per year.
Because Lao PDR mainly import goods from foreign countries such as Thailand, China, Vietnam
and which need to be paid in foreign currency. While the foreign currency exchange rate of
the Lao PDR is quite normal and has been going up and down steadily all along. The
international trade situation of Lao PDR in the period from 2005- 2022 is continuously
increasing from 2005- 2019, which is the year with a large increase in the period from 2016-
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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2021. The international trade situation of Lao PDR in the period from 2005- 2022 is
continuously increasing from 2005- 2019, which is a year with a large increase in the period
from 2016- 2021, which in 2016, the value of international trade increased by 1,615. 36 million
US dollars, which is 20. 19% compared to 2015, due to the PDR becoming a member of the
World Trade Organization from 2010 onwards. The policy of reducing import duties on goods
from abroad and the policy of reducing import duties on Lao PDR's export goods from trading
partner countries resulted in a gradual increase in trade value and a decrease in 2020. which
is worth 592. 54 million US dollars, which is 4. 91% compared to 2019, which is caused by the
spread of pneumonia from the end of 2019 onwards many sectors of the economy face this
crisis and are unable to operate, especially business units related to production, which causes
the output to be exported to decrease, combined with many countries closing the entry and
exit of the country, which results in the inability to export and import goods, causing the value
of trade to decrease. But by 2021, the value of international trade has increased to 2,099. 49
million US dollars, which is equal to 18. 28% compared to 2020. However, if you look at the
foreign trade balance of Lao PDR, you can see that there has been a deficit since 2005-2019.
The reason is that the production of domestic goods cannot meet the needs of the society
and the Lao PDR is still lacking in capital goods, making it necessary to import a large amount
of capital goods with high value while most of the exports are agricultural products with a
relatively low value, the value of the export of goods is lower than the value of the import of
the goods itself. But by 2020-2021, Lao PDR has a trade balance because at the end of 2020,
the Tariff Management Committee of the Chinese National Assembly announced that the
import tariff from Lao PDR will be reduced to 0% for goods exported to China in the amount
of 8,256 items, which is equivalent to 97% of all Lao PDR goods exported to China, allowing
Lao PDR to be able to export more goods.
For the analysis of the relationship between international economic factors and the value of
international trade, it is a study of the level of the relationship between the economic factors
between countries and the international trade of Lao PDR. From the results of the analysis, it
is found that: the gross domestic product of Lao PDR is related to the value of international
trade at a ratio of 15.68% and with a correlation coefficient equal to 0.991 or 99.1%, which is
considered to be at a high level and related in the same direction. That means that if the gross
domestic product of Lao PDR increases, it will result in the international value of Lao PDR
increasing as well and vice versa. The gross domestic product of trading partner countries is
related to the value of international trade at a ratio of 76.71% and with a correlation coefficient
equal to 0. 946 or 94. 6% which is considered to be at a high level and related in the same
direction. That means that if the gross domestic product of trading partner countries
increases, it will result in the international value of Lao PDR increasing as well and vice versa.
The value of direct investment from abroad is related to the value of international trade at a
ratio of 0.64% and with a coefficient of 0.809 or 80.9% which is considered high and related
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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in the same direction. This means that if the value of direct investment from abroad increases,
it will result in the international value of Lao PDR increasing as well and vice versa. The foreign
exchange rate ( kip/ dollar) is related to the value of international trade. In the opposite
direction, the correlation coefficient is equal to 0. 173 or 17. 3%, which is considered to be at
a low level. That means that if the foreign exchange rate ( Kip/ Dollar) increases, the
international value of Lao PDR will decrease and vice versa. The inflation rate is related to
the value of international trade. In the opposite direction, with a correlation coefficient equal
to 0.599 or 59.9%, which is considered to be at a moderate level, which means that if there is
an increase in the inflation rate, it will result in the international value of Lao PDR falling and
vice versa. The results of the multiple regression model analysis of the economic factors
affecting the international trade of the Lao PDR through the analysis of the correlation
coefficient of the independent variables with each other found: the correlation test of the
variable with the Durbin- Watson value is equal to 2. 5 which is between 1. 5- 2. 5 which is
considered that there is no correlation problem of the independent variables used in the
analysis. Therefore, it can be concluded that the economic factors affecting international trade
that have been identified for use in this study are highly independent and can be used in the
next level of analysis.
From the results of the analysis, it was found that: the statistical value of the ANOVA test or
the variable variance test (F = 292.428) and with a statistical significance level (Sig = 0.00)
or a 99% confidence level shows that: The model of economic factors affecting the
international trade of the Lao PDR can be applied and depends on the identified factors at
least 1 factor. And from the analysis of the modified decision coefficient (Adjusted R Square)
the value is equal to 0.989 which means that the independent variables included in this model
can explain the economic change up to 98. 9% and the remaining 1. 1% depends on other
factors or other independent variables that are not included in the study. Therefore, it can be
said that the model is more economically viable and consistent with economic theory.
From examining the features of the economic factor relationship model that affects the
international trade of the Lao PDR, we can create an economic model as follows:
TV = −8121.882 + 0.361GDP + 13.898 GDP + 0.258 FDI + 0.579 EXR
From the above model can explain the result of the mean difference test of variables with test
statistic (t-test) can explain the result as follows:
1. For the factors of gross domestic product of Lao PDR ( GDP Laos) there is a test
statistic value (t-test = 3.669) and a statistical significance value (Sig = 0.004) which
means that the economic growth rate can explain the change in international trade
value at a 99% confidence level and has a regression coefficient equal to 0.361 which
can be explained as: The gross domestic product value of the Lao PDR has a
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
452
relationship in the same direction with the value of international trade, that is: the gross
domestic product value of the Lao PDR increases by 1 million US dollars will result in
an increase in the value of international trade by 0. 361 million US dollars and vice
versa. which is based on the assumption that the educator defined.
2. For the factor of gross domestic product of the main trading partner country
( GDPpartner) there is a statistical test value ( t- test = 2. 702) and a statistical
significance value (Sig = 0.021) which means that the economic growth rate can explain
the change in the value of international trade at a 95% confidence level and has a
regression coefficient equal to 13.898 which can be explained as: The gross domestic
product value of the main trading partner countries has a relationship in the same
direction as the international trade value, that is: an increase in the gross domestic
product value of the main trading partner countries by 1 million US dollars will result in
an increase in the international trade value of 13,898 million US dollars. And on the
other hand, there is a particular effect on the export of goods of the Lao PDR. This is
because when the trading partner countries, especially the trading partner countries of
the Lao PDR, have an increase in the value of the gross domestic product, it means
that the purchasing power of the people in that country is higher, which has a good
effect on the export of goods of the Lao PDR, which is according to the set theory and
hypothesis.
3. For the value factor of foreign investment (FDI) there is a test statistic value (t-test =
2.416) and a statistical significance value (Sig = 0.034) which means that the value of
foreign investment can explain the change in the value of international trade at a 95%
confidence level and has a regression coefficient equal to 0.258 which can be explained
as: The value of foreign investment has a relationship in the same direction as the
value of international trade, that is: when the value of foreign investment in Lao PDR
increases by 1 million US dollars, it will result in an increase in the value of international
trade by 0.258 million US dollars and vice versa. with a confidence level of 95%. which
is according to the set hypothesis.
4. For the exchange rate factor (EXR) there is a test statistic value (t-test = 2.397) and
a statistical significance value (Sig = 0.035) which means that the exchange rate can
explain the change in international trade value at a 95% confidence level and has a
regression coefficient equal to 0. 579 which can be explained as: The exchange rate
(Kip/US$) has a relationship in the same direction with the value of international trade,
that is: when the exchange rate (Kip/US$) increases by 1 kip/US dollar, it will result in
an increase in the value of international trade by 0. 579 US dollars and on the other
hand, it will especially affect the export of Lao PDR products, because the increased
exchange rate will weaken the value of the domestic currency, making the goods
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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exported from the Lao PDR have a low price in the eyes of foreign countries, which is
according to the theory and hypothesis set.
5. For the inflation factor ( INF) there is a statistical test value ( t- test = - 1. 557) and a
statistically significant value ( Sig = 0. 148) and a regression coefficient equal to -
143.889 which can be explained that: the inflation rate has a relationship in the opposite
direction with the value of international trade, that is: when the exchange rate (Kip/US$)
increases by 1%, it will result in a drop in the value of international trade by - 143. 889
million US dollars and vice versa. but not statistically significant.
Criticize the results
For the results of the analysis of the relationship between international economic
factors and the growth of international trade of the Lao PDR, it is a study of the level of the
relationship between the various economic factors between the countries that are related to
the international trade of the Lao PDR in which direction and at what level, which from the
results of the analysis found: The gross domestic product of the Lao PDR, the gross domestic
product of trading partner countries, the value of direct investment from abroad is related to
the value of international trade in the same direction with a correlation coefficient of 80.9% -
99.1% which is considered high; The foreign exchange rate (Kip/Dollar) and the inflation rate
are related to the international trade value in the opposite direction with a correlation
coefficient of 17.3% - 59.9%. In which the gross domestic product of trading partner countries
is related to the value of international trade at the highest ratio of 76. 71% , because the
operation of international trade activities, especially the export of goods of the Lao PDR will
increase, must rely on the purchasing power of the trading partner countries, that is, if the
trading partner country's income increases, it will result in the ability to import goods from the
Lao PDR, which will result in the value of international trade of the Lao PDR increasing
accordingly. Which is in line with the study of Sunida Saigeya Chongtu ( 2017) analyzing the
relationship between the foreign exchange rate and the export and import of Lao PDR. That
the foreign exchange rate has a relationship in the same direction with the value of import
goods but has a relationship in the opposite direction with the value of export goods of Lao
PDR.
For the analysis of international economic factors that affect international trade, it is found
that: the factors of the gross domestic product of Lao PDR, the gross domestic product of
the main trading partner countries, the value of foreign investment and the foreign exchange
rate have an effect on international trade in the same direction: When the gross domestic
product of the Lao PDR, the gross domestic product of the main trading partners, the value
of foreign investment and the foreign exchange rate increase will result in an increase in the
value of international trade. Which is in line with the study of Sung A Ting (2020) which studies
the factors that affect the export of goods from Xayabuli province to Thailand that the factors
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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of gross domestic product of the province and gross domestic product of trading partner
countries and the value of foreign investment have an effect on the value of exports of goods
from Xayabuli province to Thailand in the same direction; In accordance with the research of
Thongchan Chonglisongtongsu 2017 (2017). Analyze the factors that affect the export of Lao
PDR goods to the Kingdom of Thailand that the factor of the total value of Thailand's products
has an effect on the value of the export of Lao PDR goods and is also in line with the research
results of Jun and Taylor (2013) who studied the relationship between foreign economic growth
and the export growth of the United States of America that the export growth of the United
States depends on the economic growth of the trading partner countries. And the research of
Piyaporn Changsarn and Kanokporn Chaiprasit (2016) in the title The Relationship of internal
and external factors affecting cassava's product export to China. The foreign exchange rate
that is traded in the United States dollar increases will result in an increase in the volume of
Thai cassava exports to China and the factor of the total mass product of China has an effect
on the export value in one direction with a significance level of 0.05.
Summary of research results
The results of the study on the international economic factors affecting the international trade
of the Lao PDR, focusing on the study of the relationship between the international economic
factors and international trade and analyzing the international economic factors affecting the
international trade of the Lao PDR. From the analysis, it can be seen that: international
economic factors have a moderate to high relationship with international trade, in which the
gross domestic product of trading partner countries has a relationship with the value of
international trade in the highest ratio of 76.71% with a correlation coefficient equal to 0.991
or 99. 1% ; followed by the gross domestic product of the Lao PDR is related to the value of
international trade at a rate of 15.68% with a correlation coefficient equal to 0.946 or 94.6%;
The value of foreign investment is related to the value of international trade at a rate of 0.64%
with a correlation coefficient equal to 0.173 or 17.3%. This is due to the Covid-19 pneumonia
epidemic at the end of 2019, which has affected the health and well- being of people all over
the world, resulting in the lowest global economic recession in many years, making investors
unable to invest in Lao PDR. For the analysis of the factors affecting the growth of
international trade of Lao PDR, there are 4 factors that affect the growth of international trade
value of Lao PDR in the same direction: gross domestic product of Lao PDR, gross domestic
product of trading partner countries, value of foreign investment in Lao PDR and foreign
exchange rate; The inflation factor has no effect on the growth of international trade value of
Lao PDR. From the results of this research, the government sector should implement
economic development policies in a manner that is beneficial for more investment in the
production of domestic goods and increase cooperation with the main trading partner
countries, especially the surrounding neighboring countries, so as to be able to become a
catalyst for the export of Lao PDR's goods to foreign countries in the future.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
455
Acknowledgments
This scientific research paper has been successfully completed. I would like to express my
thanks and gratitude to Dr. Bounthavy SIPHANTHONG, Advisor and Soutthavone
SIOUDOMPHAN, assistant consultant, He gave advice, as a consultant, gave comments and
helped throughout the period of writing this scientific research paper to achieve complete
results.
I would like to express my thanks and gratitude to the parties that provided information such
as the Bank of Lao PDR, the World Bank, the Lao Statistics Bureau for providing assistance
in searching for information and responding to the information needed to write this article.
I would like to express my gratitude and gratitude to my father, mother, relatives and friends
who encouraged, encouraged, helped to advise and provided support in writing this research
paper successfully. However, in writing this scientific research paper, there are mistakes and
shortcomings that may occur. The researcher is solely responsible and is happy to listen to
the advice from all of you who have come to study to be useful in the development of the
research in the next stage.
Finally, I would like to wish all of you who participated in the success of this event happiness,
prosperity, good health and success in all your work.
Reference
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to Thailand. Department of Economics, Faculty of Economics and Tourism. LuangPrabang:
Bachelor's Degree Program, International Business, Suphanouvong University.
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and the economic growth of Huaphan Province. Department of Economics, Faculty of
Economics and Tourism. LuangPrabang: Bachelor's Degree Program, International Business,
Suphanouvong University.
Drim simi. ( 2017) . Analyze the relationship between the value of international trade and the
economic growth of Sayaburi. Department of Economics, Faculty of Economics and Tourism.
Luang Prabang: Bachelor's Degree Program, International Business, Suphanou vong University.
Vongdao tongsavath. ( 2017) . Analyze the relationship between international trade value and
the economic growth of LuangNamtha. Department of Economics, Faculty of Economics and
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sunida saikoeyachongtua. ( 2017) . Analyze the relationship between foreign exchange rates
and Lao PDR's exports and imports. Department of Economics, Faculty of Economics and
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
456
Tourism. Luang Prabang: Bachelor's Degree Program, International Business, Suphanou vong
University.
Thongchan chonglisongtongsu. (2017). Analyze the factors affecting the export of goods from
Lao PDR to the Kingdom of Thailand. Department of Economics, Faculty of Economics and
Tourism. Luang Prabang: Bachelor's Degree Program, International Business, Suphanu Vong
University.
Piyaporn Changsarn and Kanokporn Chaiprasit ( 2016) . The Relationship of internal and
external factors affecting cassava’s product export to China. Journal of management science,
Chiang Rai Rajabhat University year 11 issue 2 (July-December 2016). p118-132
Bank of Lao PDR (2020). Annual Economic Report 2000-2020. Bank of the Lao PDR. Available:
https://www.bol.gov.la/annualreports.
World Bank ( 2020) . Annual Economic Report 2000- 2020. World Bank. Available:
https://www.worldbank.org/en/country
Lao Statistics Bureau (2020). Annual economic statistics 2016-2020. - Lao Statistics
Bureau. Available: https://laosis.lsb.gov.la
Table 1: International economic variables used in the model
Order variable Variable mean relationship Unit Resource
1 TV Value of international trade Million Bank of the
of Lao PDR US$ Lao PDR
2 GDPLaos Gross domestic product of + % Bank of the
the Lao PDR. Lao PDR
3 GDPpartner he gross domestic product + Billion World Bank
of the main trading partner US$
countries
4 FDI Foreign investment value + Million Bank of the
US$ Lao PDR
5 Exc Exchange rate - Kip/US$ Bank of the
+ Lao PDR
6 Inf Inflation rate - % Lao Statistics
Bureau
0: constant value and 1 − 5 The coefficient of each independent variable or of each factor
Table 2: Shows the value and percentage of gross domestic product, foreign direct investment,
exchange rate and inflation rate in Lao PDR from 2005-2022.
year GDP GDP FDI (%) of Exchange Inflation
(%) of growth rate rate
(million US$) GDP FDI
(%) (million US$) Kip/US$ (%)
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
457
2005 3,595.28 - 7.3 1,245.31 - 10,802.50 7.16
2006 4,165.56 15.86 8.3 2,699.69 116.79 9,658.90 6.81
2007 4,646.13 11.54 6.8 1,136.91 -57.89 9,423.33 5.57
2008 5,145.37 10.75 7.5 1,215.54 8,501.03 3.17
2009 4,967.68 -3.45 7.6 4,312.89 6.92 8,501.03 3.92
2010 6,386.19 28.55 8.1 1,402.21 254.81 8,501.03 5.76
2011 7,347.98 15.06 2,734.46 -67.49 8,001.21 7.57
2012 9,601.18 30.66 8 1,813.86 95.01 7,997.54 4.26
2013 11,043.29 15.02 1,712.96 -33.67 8,016.78 6.37
2014 12,564.35 13.77 7.9 2,441.14 8,057.56 4.13
2015 13,794.35 9.79 8.03 4,859.35 -5.56 8,150.56 1.28
2016 15,209.29 10.26 7.61 5,135.10 42.51 8,179.55 1.6
2017 16,552.71 8.83 7.27 7,215.74 99.06 8,348.73 0.83
2018 17,931.06 8.33 7.02 5,451.01 5.67 8,481.03 2.04
2019 19,136.12 6.72 6.85 5,249.62 40.52 8,796.79 3.32
2020 20,307.29 6.12 6.29 4,220.26 -24.46 9,380.95 5.07
2021 20,400.00 0.46 5.46 5,620.00 -3.69 9,475.00 3.8
3.28 -19.61
3.5 33.17
Source: Annual economic summary of the Bank of the Lao PDR, 2005- 2021 and calculations
with Microsoft Excel 2017
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
458
Graph showing the percentage change of economic variables of Lao PDR from
2005-2021
300
250
200
150
100
50
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
-50
-100
(%) of GDP (%) of GDP growth (%) of FDI (%) of Exchange rate (%) Inflation rate
Source: From calculations with Microsoft Excel 2017, year 2022
Table 3: Shows export value, import value, trade balance, trade value and percentage change
in trade value of Lao PDR from 2005-2022.
Unit: 1 million US dollars
Years Export value Import value Trade Value Change % Change Trade
value balance
2005 553.08 881.97 1,435.05 507.14 35.34 (328.89)
2006 882 1,060.19 1,942.19 45.13 2.32 (178.19)
2007 1,064.63 1,987.32 507.75 25.55 (141.94)
2008 922.69 1,403.17 2,495.07 18.73 0.75 (311.27)
2009 1,091.90 1,461.10 2,513.80 1,297.00 51.6 (408.4)
2010 1,052.70 2,064.40 3,810.80 466.04 12.23
2011 1,746.40 2,422.86 4,276.84 1,048.95 24.53 (318)
2012 1,853.98 3,055.12 5,325.79 19.09 0.36 (568.88)
2013 2,270.67 3,080.94 5,344.88 1,588.37 29.72 (784.45)
2014 2,263.94 4,271.23 6,933.25 1,068.53 15.41
2015 2,662.02 5,232.80 8,001.78 (817)
2,768.98 (1,609.21)
(2,463.82)
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
459
2016 4,244.77 5,372.37 9,617.14 1,615.36 20.19 (1,127.60)
2017 4,873.16 5,667.32 10,540.48 923.34 9.6 (794.16)
2018 5,407.82 6,314.64 11,722.46 1,181.98 11.21 (906.82)
2019 5,805.95 6,271.92 12,077.87 355.41 3.03 (465.97)
2020 6,114.93 5,370.40 11,485.33 (592.54) (4.91) 744.53
2021 7,699.61 5,885.21 13,584.82 2,099.49 18.28 1,814.40
Source: Annual Economic Summary of the Bank of the Lao PDR, 2005-2021
Table 4: Shows the relationship between international economic factors and the value of
international trade of Lao PDR.
Correlations
Trade GDPLaos GDPpartner FDI Exchange Inflation
Trade value Pearson 1 .991** .946** .809** -0.173 -0.599*
Correlation
GDPLaos Pearson 1 .950** .770** -0.183 -0.570*
Correlation
GDPpartner Pearson 1 .674** -0.371 -0.499*
Correlation
FDI Pearson 1 -0.162 -0.717**
Correlation
Exchange Pearson 1 0.408
Correlation
Inflation Pearson 1
Correlation
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Source: Analysis results with statistical data analysis program SPSS version 22
Table 5: shows the criteria for explaining the correlation coefficient results
Correlation coefficient Criteria for explaining the results
Pearson Correlation ≥ ±0.80 There is a high degree of correlation
±0.50 ≤ Pearson Correlation < ±0.80
Pearson Correlation < ±0.50 There is a moderate correlation
There is a low level of correlation
Table 6. Shows the results of multiple regression analysis and regression coefficients of
various variables.
Coefficientsa
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บัณฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
460
Model Unstandardized Standardized t Sig.
Coefficients Coefficients
1 )Constant(
GDPLaos B Std .Error Beta
GDPpartner
FDI -8121.882 2711.311 0.531 -2.996 0.012**
EXR 0.364 3.669 0.004***
INF 0.361 0.098 0.120 2.702 0.021**
0.108 2.416 0.034**
13.898 5.144 -0.073 2.397 0.035**
-1.557
0.258 0.107 0.148
0.579 0.242
-143.889 92.399
a .Dependent Variable :Trade value
R Square = 0.993
Adjusted R Square = 0.989
Durbin-Watson = 2.5
Sum of Squares = 273077692.408
F = 292.428
Sig = 0.000
Source: Analysis results with statistical data analysis program SPSS version 22
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
461
Predict the Crude Oil Price Volatility by ARCH and GARCH Models
Phetsavanh SOUPHAPHONE*, Thongphanh CHANTHAVONE and Pheng HER
Department of Economics, Faculty of Economics and Tourisms, Souphanouvong University
E-mail: [email protected]
ABSTRACT
Oil price increases are generally thought to increase inflation and reduce economic growth. In terms of
inflation, oil prices directly affect the prices of goods made with petroleum products. As mentioned
above, oil prices indirectly affect costs such as transportation, manufacturing, and heating. The increase
in these costs can in turn affect the prices of a variety of goods and services, as producers may pass
production costs on to consumers. Therefore, the objective of this research is to forecast the volatility
of crude oil price by ARCH and GARCH models. The results indicated that the ARCH model is significant
at the statistically 1% level, the time- varying volatility includes a constant plus a component which
depends on past errors .ˆt2−1 Z- statistics of the 1st order coefficient ( 0.46) suggests a significant ARCH
( 1) coefficient. All coefficients of the conditional variance specification meet 0 but 0 1 11
meaning that the equation is stability. For GARCH model, the coefficients of the constant variance term,
the ARCH and GARCH parameters are statistically significant at 1% and 5% level but the coefficient of
GARCH model in lag 2 order is negative which against the condition. Based on the model’s select criteria
guideline as Hossain Academy found that ARCH model has the lowest AIC and SIC value thus it is a
fitted model and we used it to predict the volatility of the crude oil price volatility. The forecast result
seen that root mean squared error, mean absolute error and mean absolute percent error are small
meaning that actual price and its forecasted, they are moving closely indicated that the predictive
power of our ARCH model's predicting are very accuracy and the bad news will cause crude oil price
increasing. However, in reality it is not only depend on its volatility but also depends on other factors
such as supply, political, etc. Therefore, further researchers can use others methods to predict,
especially the ARIMAX model and GRACH Model.
Keywords: Crude oil price, Volatility, ARCH model, GARCH Model
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
462
Introduction
Oil price increases are generally thought to increase inflation and reduce economic growth. In terms of
inflation, oil prices directly affect the prices of goods made with petroleum products. As mentioned
above, oil prices indirectly affect costs such as transportation, manufacturing, and heating. The increase
in these costs can in turn affect the prices of a variety of goods and services, as producers may pass
production costs on to consumers. Barsky and Kilian ( 2001) indicated that the 1970s stagflation was
solely induced by monetary policy. Blanchard and Gali ( 2007) suggested that increased flexibility in
labor markets, monetary policy improvements, and a bit of good luck ( meaning the lack of concurrent
adverse shocks) have also contributed to the decline of the impact of oil shocks on the economy. Sill
( 2007) said that high oil prices also can reduce demand for other goods because they reduce wealth,
as well as induce uncertainty about the future. Fernald and Trehan ( 2005) pointed out the effects of
higher oil prices is to think about the higher prices as a tax on consumers.
Higher oil prices will lead to an improvement in the current account position of oil exporters like OPEC
countries. It will lead to a deterioration in the current account position of oil importers. Oil exporters
will see an increase in foreign currency reserves which they could use to purchase foreign assets. In the
2020s, higher oil prices will encourage consumers to look into buying electric cars which don’t need oil.
Energy companies are wary about environmental pressures which make oil less attractive than it used
to be. Governments may bring in higher carbon taxes or directly encourage less use of oil. Therefore,
high oil prices may not cause the surge in investment that we saw back in the 1980s. Bernanke et al.
(1997) found that a majority of the real effects of oil price shocks are not caused directly by the shock
but by the subsequent tightening of monetary policy. OECD pointed out commodity prices have risen
considerably since mid-2010. Oil prices (Brent) have risen by about 40%, with the bulk of the increase
having taken place since December. Prices of food and agricultural commodities, metals and minerals
have risen faster than in the summer of 2008.
Form above reasons, we conclude that oil price shocks affect the economy through both in the supply
side, the demand side and the terms of trade. Supply suffers as production costs rise in the wake of an
oil price shock. On the demand side, oil price shocks drive up the general level of prices, which
translates into lower real disposable incomes and thus reduces demand. Therefore, the objective of
this research is to forecast the volatility of crude oil price by ARCH and GARCH models.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บัณฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
463
Materials and Methods
This paper the researchers used the monthly data of crude oil price since January 2010 – December
2020 from the West Texas Intermediate (WTI) benchmark to forecast by ARCH and GARCH model:
1 ARCH Model
Generally, we assume that there are no heteroscedasticity in our time series but in reality some series
may have the heteroscedasticity problems in the time series analysis which the residual volatility from
the model will depends on its past volatility, it was introduced by Robert F. Engel (1982) and known as
the Autoregressive Conditional Heteroscedasticity (ARCH) model, written as:
Mean Equation: Oilt = + 1Oilt−1 + t …(1)
Where Oilt : the mean equation of crude oil price (unit: USD/barrel)
: mean or constant and 1 is the coefficient of lag order
Inft−1 : the inflation rate in lag 1 order
t : a white noise error term.
Let the error variance be time varying, that is heteroscedasticity and called ht therefore we get the
variance equation as:
ht = b0 + i 2 ….(2)
t −1
ut iid N (0; 2 ), ht = t = b0 , and
t t
b0 0,0 bt 1
2 : Variance, b0 : constant and t is the variance changes over time.
t
And the ARCH (q) model can write as: n …(3)
ht = b0 +
i 2
t −i
i=1
Forecast Variance:
ℎ +1 = 0 + 1( − ̂ )2……(4)
Where, ℎ̂ = ( − ̂ ), the estimate error in time t can be used obtain the estimated
conditional variance.
2 GARCH model
The General Autoregressive Conditional Heteroscedasticity ( GARCH) model was introduced by Tim
Bollerslev (1986) which GARCH models provide a parsimonious alternative to high order ARCH models.
GARCH (p,q) model could be written as following:
qp iht−1 …(5)
ht = + i 2 +
t −i
i=1 k =1
: constant
t : coefficient of the lagged squared error term, bi 0
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
464
i : coefficient of the past variances
ht : the conditional variance at time t
ht−1 : past value of the conditional variance
2 : the lagged squared error term
t −i
p: lagged term of the squared error
q: terms of the conditional variances
0, 0 & 0: to guarantee positive variance.
qp
0 i + i 1: to have a decaying variance.
i=1 k =1
For Stationarity bi + i 1. If >1 and integrated GARCH process has occurred and if p= 0 and q= 2
reduces to ARCH ( q) . From the estimation of GARCH ( 1,1) the long run variance or volatility mean is
computed as (Roman Kozhan, 2010).
1−1 − b1
Results
Based on the econometrics theories before running the ARCH model the crude oil price should be
stationary. In testing found it non- stationary therefore the researcher converted by the 1st difference
(Figure 1) and see that the periods of low volatility tend to be followed by periods of low volatility for
a prolonged period and periods of high volatility is followed by periods of high volatility for a prolonged
period, thus we continue the regression and check the residual of this model ( Figure 2) . In figure 1
expressed that the crude oil price was fluctuated during the period which very low on March 2020 and
highest on May 2020. So, a researchers concluded that periods of low volatility tend to be followed by
periods of low volatility for a prolonged period and periods of high volatility is followed by periods of
high volatility for a prolonged period [Benoit Mandelbrot (1963)], this result indicated the researchers
have all justification to run ARCH family models. To ensure, we can check the whole by appointing
ARCH test whether we should imply ARCH family model or not. Specially to check the
heteroskedasticity, result in p-value=0.000 is less than 5% level of statistical meaning that we can reject
null hypothesis concluded that there is ARCH effect, meaning that we can run the ARCH model. For the
estimation ARCH model, the researchers tried with lag 1 to lag 2 which found that lag 1 has lowest AIC
and SIC and significance therefore I decided to use ARCH (1) model and get the equation as following:
Mean equation: ̂ = 76.19 + 0.93 ̂ −1 (1)
(9.33)* (46.95)*
Forecast mean equation give the estimated mean return that it does not change over time.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
465
Variance equation: hˆt = 28.77 + 0.46ˆt2−1 (2)
(4.82)* (2.76)*
R2 = 90.76; S.E = 6.77
Log likelihood= -472.96
D.W=1.78; AIC=6.706; SIC=6.658
Note: the value in parentheses are z-statistic and *,**,*** is the statistically significant at 1%,5% and
10% respectively.
The result also indicated that the ARCH model is significant at the statistically 1% level, the time-
varying volatility includes a constant plus a component which depends on past errors
.ˆt2−1 Z- statistics of the 1st order coefficient ( 0. 46) suggests a significant ARCH( 1) coefficient. All
coefficients of the conditional variance specification meet 0 but 0 1 11 meaning that the
equation is stability.
From equation (2) we can written forecast variance as following:
hˆt = 28.77 + 0.46(Oilt − 76.19)2 …(3)
For the estimation of GARCH model, the research tried to test with GARCH (1,1), GARCH (1,2) and GARCH
(2,1) but found that GARCH (1,2) is the most suitable due to it all coefficients are positive and significant,
therefore in this paper the researchers expressed only the results of GARCH (1,2) model and others are
not considered. The estimation of GARCH (1,1) result in.
The mean Equation:
̂ = 78.24 + 0.91 ̂ −1 (4)
(12.27)* (54.88)*
The coefficients are positive and statistically significant at 1% which the average exchange rate of
crude oil price is 78.24 and its past value significantly forecasts the present series by 0.91 points or
units.
2). The Variance Equation:
hˆt = 42.5 + 0.17ˆt2−1 + 0.43hˆt−1 − 0.56hˆt−2 (5)
(3.67)* (2.25)** (2.56)** (-2.34)**
R2 = 90.59; S.E = 6.48
Log likelihood= -471.85
D.W=1.71; AIC=6.76; SIC=6.68
The coefficients of the constant variance term, the ARCH and GARCH parameters statistically significant
at 1% and 5% level. But the coefficient of GARCH model in lag 2 order is negative which against the
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
466
condition. Based on the model’s select criteria guideline as Hossain Academy found that ARCH model
has the lowest AIC and SIC value thus it is a fitted model and we used to predict the volatility of the
crude oil price volatility. Therefore, we have continued diagnostic checking serial correlation, residual
distribution and heteroscedasticity and results indicated that the p- value of all lags is higher than the
statistical 5% level, we cannot reject null hypothesis meaning that there is no serial correlation for the
model and. For the heteroscedasticity by ARCH LM test found that the p- value equal to 0.355, higher
than the statistical 5% level we cannot reject null hypothesis meaning that there is no
heteroscedasticity. For the residual distribution test by Jarque-Bera method found that the p-value=0.01
is less than the statistical 5% level indicate we reject null hypothesis, meaning that the residual is not
normally distributed but many researchers argue that is a very weak problem ( Samulson & Nordhaus,
2001) . So, this model able to predict the volatility of the crude oil price volatility of West Texas. And
we have the forecast result ( Figure 3) that root mean squared error, mean absolute error and mean
absolute percent error are small meaning that actual price and its forecasted, they are moving closely
indicated that the predictive power of our ARCH model's predicting are very accuracy. In the checking
of the moving of actual and forecasting volatility indicated that ability for predict our estimated ARCH
model is satisfactory ( Figure 4) and indicated that the bad news will cause crude oil price increasing,
especially the decrease of OPEC supplier, war, etc.
Discussion
Based on the empirical, our finding against the research of Yildirim (2017). He studied on “ARCH-GARCH
model on volatility of crude oil” by using daily price. He found that there is an arch effect on crude oil
prices and the best model is GARCH (1,1). After determining the model, ARCH LM test was applied for
GARCH ( 1,1) and results indicate that there is no arch effect among error terms. Furthermore, when
crude oil prices are controlled graphically, crude oil has sharp volatility since Rusia, Ukrania, Greece, Iran
and Iraq are seem as geopolitical risk. A deal which OPEC members and decision which play an essential
role on crude oil also constitute market price for crude oil. Especially, commodity such as crude oil
has trend in direction of FED and China. Any news or progress about macroeconomic variables or
decision led to volatility. For this reason, crude oil price has fluctuation by climbing peak and de
decreasing the deepest point from beginning of 2015 to end of 2016 years. Furthermore, Shen ( 2021)
used the ARCH family model to predict the oil price of Zhuochuang (China) from the period 02 January
2014- 28 February 2019. The results indicated that the ARCH effect test on the oil price residual
sequence, it was found that the sequence has a significant “spike tail” phenomenon, and the residual
sequence has a high-order ARCH effect. After further analysis, it is found that the residual sequence also
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
467
has asymmetry. This phenomenon is very significant, so the TARCH (1, 1) model is established for the
sequence. Hassan and Regassa (2021) studied on “Asymmetric behavior of volatility in gasoline prices
across different regions of the United States” by using GARCH model and the results show evidence of
high persistence of shocks to volatility and signs of an asymmetric behavior in volatility across regions
which imply that gas prices may react differently to good news relative to bad news. Salisu and Fasanya
(2012). They researched on “Comparative Performance of Volatility Models for Oil Price”, they find that
oil price was most volatile during the global financial crises compared to other sub samples and based
on the appropriate model selection criteria, the asymmetric GARCH models appear superior to the
symmetric ones in dealing with oil price volatility. This finding indicates evidence of leverage effects in
the oil market and ignoring these effects in oil price modelling will lead to serious biases and misleading
results. In addition, Zhang, Zhang and Zhang (2015) studied on “A novel hybrid method for crude oil
price forecasting” by using the ensemble empirical mode decomposition (EEMD) method to decompose
international crude oil price into a series of independent intrinsic mode functions (IMFs) and the residual
term. Then, the least square support vector machine together with the particle swarm optimization
(LSSVM–PSO) method and the generalized autoregressive conditional heteroskedasticity (GARCH) model
and the results showed that, the newly proposed hybrid method has a strong forecasting capability for
crude oil prices, due to its excellent performance in adaptation to the random sample selection, data
frequency and structural breaks in samples. Furthermore, the comparison results also indicate that the
new method proves superior in forecasting accuracy to those well- recognized methods for crude oil
price forecasting. Zhang and Tu ( 2016) also researched on “ The effect of global oil price shocks on
China's metal markets” and implied with the autoregressive conditional jump intensity ( ARJI) model,
combining with the generalized conditional heteroscedasticity (GRACH) model. The outcomes indicated
that crude oil price shocks have significant impacts on China's metal markets and the impacts are
symmetric. When compared with aluminum, copper is more easily affected by oil price shocks.
Conclusion
According to the empirical analysis, indicated that the ARCH model is significant at the statistically 1%
level, the time- varying volatility includes a constant plus a component which depends on past errors
.ˆt2−1 Z- statistics of the 1st order coefficient ( 0. 46) suggests a significant ARCH( 1) coefficient. All
coefficients of the conditional variance specification meet 0 but 0 1 11 meaning that the
equation is stability. For GARCH model, the coefficients of the constant variance term, the ARCH and
GARCH parameters are statistically significant at 1% and 5% level but the coefficient of GARCH model
in lag 2 order is negative which against the condition. Based on the model’s select criteria guideline as
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
468
Hossain Academy found that ARCH model has the lowest AIC and SIC value thus it is a fitted model
and we used it to predict the volatility of the crude oil price volatility.
Based on these findings, the predict of this model is accuracy due to the root mean squared error and
mean absolute error are lower and closely with the actual value. However, in reality it is not only
depend on its volatility but also depends on other factors such as supply, political, etc. Therefore,
further researchers can use others methods to predict, especially the ARIMAX model and GRACH Model.
Conflict of Interest
We certify that there is no conflict of interest with any financial organization regarding the material
discussed in the manuscript.
Acknowleggement
We would like to express our sincere thank to Faculty of Economics and Tourisms at Souphanouvong
University for support in writing, editing and commenting on the paper.
References
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International Journal of Energy Economics and Policy. Vol. 2, No. 3, 2012, pp.167-183. ISSN: 2146-4553
Hassan, A. , & Regassa, H. ( 2021) . Asymmetric behavior of volatility in gasoline prices across different
regions of the United States. Journal of Finance and Accountancy.
Zhang, C., & Tu, X. (2016). The effect of global oil price shocks on China's metal markets. Energy Policy,
90:131-139
Yildirim, H. ( 2017) . ARCH- GARCH model on volatility of crude oil. International Journal of Discipline
Economics & Administrative Science Studies, 13(1):17-22. ISSN:2587-2168
Engel, F. R. ( 1982) . Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of
United Kingdom Inflation, Econometrica, 5:987-1007.
Zhang L. J., Zhang, J. Y., & Zhang, L. (2015). A novel hybrid method for crude oil price forecasting. Energy
Economics, 49: 649-659
Kozhan, R. (2010). Financial Econometric with Eviews.
Samulson, P. A., Nordhaus, W. D. (2001). Handbook of Economics, seventeenth edition, McGraw-Hill,
USA.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
469
Shen, S. ( 2021) . Empirical analysis of ARCH family models on oil price fluctuations. School of
Mathematics and Statistics, Qinghai Nationalities University, Xining, China. Vol.12 No.4, April 2021
Bollerslev, T. ( 1986) . Generalised Autoregressive Conditional Heteroscedasticity with Estimates of the
Variance of United Kingdom Inflation, Econometrica, 3:307-327
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
470
Oil D_OIL
120 0.8
0.6
100
0.4
80 0.2
0.0
60
-0.2
40 -0.4
-0.6
20
-0.8
0 -1.0
10 11 12 13 14 15 16 17 18 19 20 21 10 11 12 13 14 15 16 17 18 19 20 21
Figure 1. Oil Price Volatility before and after 1st difference
120
100
80
60
30 40
20 20
10 0
0
-10
-20
-30
10 11 12 13 14 15 16 17 18 19 20 21
Res idual A c t ual Fitted
Figure 2. The Residual Volatility
160
Forecast: D_OILF
120 Actual: D_OIL
Forecast sample: 2010M01 2021M12
Adjusted sample: 2010M02 2021M12
80 Included observations: 143
Root Mean Squared Error 6.750591
40 Mean Absolute Error 4.971984
Mean Abs. Percent Error 8.577922
0 Theil Inequality Coefficient 0.046482
Bias Proportion 0.002744
-40 Variance Proportion 0.046629
10 11 12 13 14 15 16 17 18 19 20 21
Covariance Proportion 0.950627
D_OILF ± 2 S.E.
400
300
200
100
0
10 11 12 13 14 15 16 17 18 19 20 21
Forecast of Variance
Figure 3. Crude Oil Price Forecast
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
471
120
100
80
60
40
20
0
10 11 12 13 14 15 16 17 18 19 20 21 22
Oil FORECAST
Figure 4. Oil Forecasting
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
472
ກຳນປ່ື ຽນແປງປ່ື ຳໄມ ີ້ ແລະ ກຳນປົກຫຸມ້ີ ພນີ້ ທ່ື ໃນເຂດປ່ື ຳຜະລດິ ແຫ່ື ງຊຳດ ປະທມຸ ພອນ, ເມອງ
ປະທຸມພອນ, ແຂວງຈຳປຳສກັ , ສປປ ລຳວ ແຕ່ື ປ 2007 ຫຳ 2021
Changes in forest and land cover in the Pathumphone National Production
Forest Area, Pathumphone District, Champasak Province, Lao PDR from
2007 to 2021
ສຸວນັ ທອນ ດວງພະຈນັ 1 ແລະ ຄຳດ ຈ່ື ຳງຊງົ ຕວົ 2
Souvanthone Douangphachanh1 and Khamdee Changxongtua2
1Department of Environmental Science, Faculty of Agriculture and Environment, Savannakhet University, Savannakhet
Province, Lao PDR
[email protected]
2Forest Inventory and Planning Division, Department of Forestry, Ministry of Agriculture and Forestry, Vientiane Capital, Lao
PDR
[email protected]
Abstract
A study on changes in the state of forest and land cover change during the years 2007– 2017
and 2017–2021 using data on the allocation of productive forests was obtained from the forest
inventory and planning division, DoF(department of forestry), MAF (ministry of agriculture and
forestry), Lao PDR, and Sentinel 2 satellite imagery from January to March 2021 obtained from
Google earth engine. Image classification was defined as five types of land cover, including
forest land, wetland land, agriculture land, built- up land, and roads. For the accuracy
assessment of image classification, 51 points according to each land use class were included
in the field survey. All satellite image data were analyzed using ArcGIS 10.8.1, QGIS 3.20, and
Microsoft Excel. The results of the study revealed that with the change in land cover, the
accuracy assessment of classification by using the Kappa Coefficient was 90. 20 % and 0. 81
%. And, according to the conversion of forest and land cover, forest land increased by 1,220.2
hectares between 2007 and 2017. whereas wetland types decreased by 727. 2 hectares, and
forest land decreased by 7,319.8 hectares between 2017 and 2021. Agricultural land increased
rapidly by 7,121.2 hectares, followed by roads (93.4 hectares), wetlands (70.4 hectares), and
built-up land (34.8 hectares) respectively. Therefore, over the last fifteen years (2007–2017–
2021), the study area has shown that forest land has increased from 2007 to 2017. Whereas,
during the year from 2017 to 2021, forest land declined. Due to the production forest area
closely adjacent to the residential land, there is also some human activity ( cutting down the
trees, clearing the land for agriculture) that impacts the type of forest and land cover in the
Pathumphone National Production Forest Area.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
473
Keywords: Forest Land Cover Change, Production Forest, Geographic Information System,
Remote Sensing, Savannakhet province, Lao PDR
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
474
Introduction
Lao PDR is a country rich in valuable forest resources that are the treasure of the
national board and a source of forest resources for national economic and social development.
In addition, it has also been a source of food supply and income for the community in the
countryside, such as the use of wood for house construction, firewood, fiber, medicine, food,
traditional beliefs, and so on for a long time. the National Forestry Conference in 1989, which
opened for the first time to review the results of the implementation of the forest policy, and
the problem of forest utilization that caused the decline of forest cover, which agreed on the
steps to be taken to prevent forest destruction and set a strategy for the area of forest cover
to cover 70% or 16.5 million hectares (Ministry of Agriculture and Forestry, 2015).
The Prime Minister issued Decree in 2 0 0 2 in terms of Sustainable Management of the
Production of Forests, such as timber and non- timber forest products, that is permanent and
sustainable and enhances multi- stakeholder participation in the conservation, management,
and sustainable use of forest resources. During the same period from 2 0 0 4 to 2 0 0 6 , the
distribution of land and forest to the people was completed according to plan in about 5 0
percent of all villages throughout the country (Prime Minister's Office, 2002). Between 2006
and 2 0 0 8 , We have 5 1 national production forests totaling 3 ,0 8 9 ,1 7 7 hectares were
established. In 2 0 1 7 , the National Production Forest Management Survey was completed
throughout the country in collaboration with the Laos government and international partners
(Department of Forestry, 2009).
Pathumphone production Forest has an area of 2 7 , 0 4 4 . 7 hectares and was established
according to the Prime Minister's Office, (2006), which has organized rural management and
development work through people's participation since 2 0 0 4 with promotion and
encouragement, such as the establishment of a committee responsible for forest management
at the village level, creation of forest management tools and budgets for the people involved
in village management, legislation, and budgeting.
Along with the socio- economic development of the country to get out of the underdeveloped
country, the government has paid attention to many aspects, especially the use of policies to
attract investment to develop the people's lifestyle in many activities, some of which are given
to the people to provide materials, such as planting the cassava to support to factories and
promoting other agriculture to provide local food. The continuous promotion of cassava
cultivation in such a way as to generate good income for the people coupled with the
unemployment of the local people has led to more demand for agricultural land. Therefore,
there has been an expansion of agricultural land into forest land, especially in the Pathumphone
production forest area (Agriculture and Forestry Office, Pathumphone District, 2021).
Therefore, this study is interested in studying the number of areas that have changed in forest
land during the years 2007-2017 and during the years 2017-2021 in the Pathomphone national
production forest area.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
475
Methodology
Research Design
In this study, we used the images of Landsat 5 & 8 and Sentinel 2 by using ArcGIS software
and QGIS. See the figure below:
Figure 1: Research Design and methods
Landsat 05 Landsat 08 Sentinel 2
2007 2017 2021
Pre-Processing
Image Classification
Accuracy Assessment
Change
LC2007 LC2017 LC2021
FLCC (2007 to 2017) and (2017-
Forest and land cover Map
Data collection
Satellite image
The data on Pathumphone's productive forest area was obtained from the Forest Inventory
and Planning Division-FIPD in 2007 and 2017 (Department of Forestry, 2017). In this case, the
Landsat 5 TM and the Landsat 8 OLI were obtained from USGS
(https://earthexplorer.usgs.gov/), which includes: wood production areas; agricultural-forestry
areas; wood plantation areas; and forest uses which this study has identified as forest cover
changes and the land use classification.
Satellite image data is using the method of downloading “Sentinel 2” images from Google Earth
Engine (https://code.earthengine.google.com.) by using a script that includes the reduction of
cloud-fog coverage and the interval of satellite images set from January to March 2021.
Site Selection
Figure 2: Location map of Pathumphone- NPFA, Pathumphone District, Champasak Province,
Lao PDR
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บัณฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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Typically, the Champasak province includes
3 production forests, namely: Vang Wern
production forest area ( VPFA) , Pathumphone
production forest area ( PPFA) , and Nong
Tangong production forest area ( NPFA) , which
in this study was selected as the Pathumphone
production forest area. On the other hand,
Pathumphone Forest is located in Pathumphone
District, Champasak Province, Lao PDR,
covering a total area of 27,044. 7 hectares
( Fig. 1) . It lies between longitudes 105°56'00"
and 106°12'30" East and latitudes 14°41'25"
and 14°54'10" North see the figure 2.
Data analysis
Image Classification
The land use classification of Pathumphon's
productive forest area in 2007 and 2017 was
obtained from FIPD. Therefore, the "Sentinel 2" satellite images from Google Earth Engine in
2021 were used by the method of digitizing ( Toolbox) in ArcGIS 10 ver. 8. 1 for land use and
land cover classifications in the year 2021 through visual observation (Campbell, 1987). Which
type of land use in production forest follows the table below (Table 2).
Table 2. Characterization and interpretation of satellite images
No. Land Cover Type Satellite images
1 Forest Land (FL)
2 Wetland (WL)
3 Agriculture Land (AL)
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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No. Land Cover Type Satellite images
4 Built-up Land (BL)
5 Road (R)
Simple Size
Firstly, we have to find the number of points in the field survey to verify the accuracy of
the land use classes obtained from the interpretation of satellite images in 2021 ( Fitzpatrick-
Lins, 1981). It is detailed below:
2( )( ) 22(85)(15) = 51
= 2 = 102
= Total Simple Size
= The percentage of accuracy required in the overall map is 85%.
= 100 -
= acceptable margin of error 10%
= 2 from a standard deviation of 1.96 / for a 95% confidence level
• Weight ratio
=
r = weight ratio of land type (%)
n = size of the sample group
• Sample group size distribution:
Sample group size of land type = total sample size × weight ratio
Table 3: Weighting by land use type
Land Cover Coordinat
e
No. Types Area (ha) Percentage (%)
66.93 34
1 Forest Land 15,163.9 1.03
1
2 Wetland 233.7
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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Land Cover Area (ha) Percentage (%) Coordinat
No. Types 7,130.6 31.47 e
3 Agriculture Land 0.15
4 Built-up Land 34.8 0.41 14
5 Road 93.4 100.00
22,656.4 1
Total
1
51
Random sampling
In this case, Create Random Points tool in Program GIS 10 ver. 8.1 to help distribute points
in each land use type using the WGS_1984_ UTM_Zone_48N system to distribute points to land
type information.
Clsssification Accuracy Assessment
Use the method of field survey by using the Program Avanza maps tool as an indicator of
the direction of each point and record the change of land cover. Evaluate the accuracy of
satellite image classification in 2021: Producer Accuracy Calculation, User Accuracy
Calculation, Overall Accuracy, and Kappa Coefficient (Congalton, 1999). Accuracy assessment
of the satellite images classification, after completing the waypoint from the field survey, then
we used the waypoint to calculate the forest and land cover change ( Congalton, 1999) with
the following formula:
= × 100%
OA = percentage of accuracy
Tc = sum of correct points
Ta = sum of all points
Kappa Coefficient: ̂ = ∑ =1 −∑ =1( × )
2 −∑ =1( × )
k ̂ = Kappa coefficient
N = total number of checkpoints
x_i = the sum of the reference values in each row
x_ii = the sum of the reference values in each row
x_j = the sum of the reference values in each row
r = the sum of the reference values in each row
According to Landis and Koch (1977), the Kappa Coefficient is used to evaluate the accuracy
of the satellite image classification, including the following 6 values:
Table 4. the level of Kappa Coefficient
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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No. Kappa Coefficient Level
1 <0.0 No agreement
2 0.0 to 0.20 Slight
3 0.21 to 0.40 Fair
4 0.41 to 0.60 Moderate
5 0.61 to 0.80 Substantial
6 0.81 to 1.00 Perfect
Comparison of forest and land cover changes
Using data from each type of forest and land cover change between 2007 and 2017, as
well as 2017 and 2021.
Result
Classification Accuracy Assessment
Overall classification accuracy was taken from the probability of correctly mapped locations
with the ground survey and user accuracy comparing the map with the data of the ground
survey. Producers’ assessment was compared between ground survey data and maps. In
addition to this study, the ground survey data was collected by using Global Positioning
Systems (GPS). The result of the accuracy assessment indicated that the overall classification
accuracy of the map was 90.20 % and Kapa Coefficient was about 0.81%.
Table 5: Accuracy assessment of land cover classification
Land Cover Ground truth
Types
Agriculture Built-up Total UA(%)
Land Road
Forest Land Wetland Land
Forest Land 31 1 2 0 0 34 91.18
Wetland 0 1 0 0 0 1 100.00
Agriculture 0 2 12 0 0 14 85.71
land
Built-up Land 0 0 0 1 0 1 100.00
Road 0 0 0 0 1 1 100.00
Total 31 4 14 1 1 51
PA (%) 100.00 25.00 85.71 100.00 100.00
Overall Classification Accuracy: 90.20
Kapa Coefficient: 0.81
Status of Forest and Land cover in 2007
According to shape file data on production forest in the year 2007 was obtained from the
forest inventory and planning division, ( Department of Forestry, 2007) . The result found that
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑติ ศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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the attention to 2 classes distribution namely forest land and wetland which the total area was
21,247 ha (78.56%) and 4,881.3 ha (18.05%) see figure 3.
No Land Cover Area (ha) (%) Status of Forest and Land cover in 2017
1 Forest Land 21,247.0 78.56
2 Wetland 3.39 Based on data on production forest ( 2017)
3 Non-Forest 916.4 18.05 was obtained from the forest inventory and
Total 4,881.3 100.00
27,044.7 planning division, ( DoF, 2007) . The result found
that the attention to 2 classes distribution
Figure 3: Forest and land cover classification in the year namely forest land and wetland which the total
2007
area was 22,467.2 ha (83.77 %) and 4,881.3 ha
(16.23 %) see figure 4.
Status of Forest and Land cover in 2021
The interpretation of the Sentinel 2
satellite image from Google Earth Engine in
2021. The result found that forest land was
15,147. 4 hectares accounting for ( 56. 01% ) ,
Wetland 259. 6 hectares ( 0. 96% ) , agricultural
land 7,121. 2 hectares ( 26. 33% ) , Built- up Land
34.8 hectares accounting for (0.13%) and 93.4
hectares of communal land accounting for
(0.35%).
Discussion
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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Changes in forest and land cover during the
years 2007-2017 and 2017-2021 Changes in land
cover during 2007–2017 saw forest land increase
by 1,220. 2 hectares and wetlands decrease by
727. 2 hectares. The Landsat 5 TM satellite,
which was used in 2007, has a resolution of 30
meters when compared to the Landsat 8 OLI
satellite, which was used in 2017 and has a
resolution of 15 meters. Between 2017 and 2021,
the result saw forest land decreased by 7,319. 8
hectares, which was caused by people using
forest land, especially the forest land that was
cleared by human activities such as the
expansion of the land for agricultural land to
No. Land Cover Area (ha) (%) plant cassava, about 7,121. 2 hectares, which is
consistent with the report of the Agriculture and
1 Forest Land 22,467.2 83.07
2 Wetland 189.2 0.70 Forestry Office of Pathumphone District
regarding the name list of villages that have
3 Non-Forest 4,388.3 16.23 invaded and destroyed forests to plant the tree
Total: 27044.7 100.00
Figure 5: Forest and land cover classification in the year
Fiignureth4e: Fporreosdt uancdtilvanedfco2or0ve2es1r tclaasrseifaica(tiAongirnicthueltyueraer and Forestry Office, Pathumphone District, 2021) .
The comment sta20t1e7d that the change in forest land is caused by the promotion of
planting (Casava) that the state has given to businessmen as the implementers of the
project together with the people, resulting in the expansion of agricultural land (Cassava)
into forest land without approval from the relevant government organizations and
following the comments of village-level organizations. He said that the people have
unstable occupations and are unemployed when the income from the promotion of
cassava cultivation leads to good family development, thus, they expand the agricultural
land into forest land, which is in line with education. Therefore, it causes people to have
many jobs; Followed by the increase of 93.4 hectares of road, which in the past was not
allocated for transportation land to create a plan for allocating Pathumphon production
forest, and the other part was caused by the construction of new roads. The increase of
70.4 hectares of land around the water is caused by several people blocking the water
channel and expanding the forest land into fish ponds for personal use and village fish
ponds. For built-up land, 34.8 hectares were caused by expansion into forest land due to
the development of the village's infrastructure which is in line with the results of the
study Changes in land use and land cover in the water catchment area. A case study of
Ban Khonglong Wild Animal Hunting Area, Buang Kan Province, Thailand to distinguish
land use and land cover and evaluate the change in land use and land cover. It was
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
482
found that the land use and land cover in 2002 included forest 6, 0 95 hectares, rice
fields 4, 6 34 hectares, rice fields 3, 2 01 hectares, land 28, water park 3, 1 hectare 2,8,
1,094 hectares of orchards, 745 hectares of housing and 2,978 hectares of other lands;
In 2013 from evaluating land use of land users and 44 hectares, the rice, the fruit of the
socio-economic and the expansion of the rubber area more and more ( Jiladeth
Machandaeng, 2016). The details of changes in forest and land cover are shown in Table
6. below :
Table 6: Forest and land use change from 2007 to 2017 and 2017 to 2021
Area (ha) Land Cover
No. Land Cover Change
Types
2007 2017 2021 2007- 2017-
2017 2021
1 Forest Land 21,247.0 22,467.2 15,147.4 1,220.2* 7,319.8**
2 Wetland 916.4 189.2 259.6 727.2** 70.4*
3 Agriculture Land 0.0 0.0 7,121.2 0.0 7,121.2*
4 Built-up Land 0.0 0.0 34.8 0.0 34.8*
5 Road 0.0 0.0 93.4 0.0 93.4*
Total 22,163.4 22,656.4 22,656.4 493.0* 0.0
Noted: * Increased / ** Decreased Conclusion
Change in forest and land cover between 2007 and 2021 in the Pathumphone national
production forest area The results revealed that forest land was converted to other land uses,
particularly forest converted to agricultural land (cassava, cultivation). On the other hand, the
change of forest land from the investment promotion management process to land in
agriculture to produce raw materials (casava) combined with the unemployment of the people
who are connected to the forest land has caused negative effects in many aspects, such as
the process of forest management, people's participation, living, redeveloping the land into an
area covered by forest, finding difficulties in the future and so on.
Acknowledgments
We would like to thank Maejo University, Chiangmai Province, Thailand for allowing us to
participate at this time. Thank you to the faculty of agriculture and environment at
Savannakhet University in Lao PDR and the Provincial Agriculture and Forestry Department,
the U. S. Geological Survey ( USGS) websites for making available land- based data such as
Landsat- 5 Thematic Mapper ( TM) and Landsat- 8 Operational Land Imager ( OLI) and Google
Earth Engine regarding sentinel satellite images and thanks to the forest inventory and planning
division, department of forestry, Ministry of agriculture and forestry, regarding the data of land
cover in 2007 and 2017.
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บัณฑิตศึกษา ประจําปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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References
Agriculture and Forestry Office, Pathumphon District. (2021). The report on the list of
villages that have invaded and destroyed the forest to plant in the production forest
area in 2018-2019. Agriculture and Forestry Office, Pathumphon District: Lao PDR,
Champasak Province.
Congalton, R. G. (1999). Assessing the Accuracy of Remotely Sensed Data Principles and
Practices. Boca Raton: CRC Press Taylor and Francis Group.
Department of Forestry. (2007). Summarize the plan for the allocation and management of
the Pathomphon production forest. Lao PDR, Vientiane: Forest Inventory and
Planning Division.
Department of Forestry. (2009). The proposal of the program for the allocation of productive
forests and rural development from 2009 to 2011. Lao PDR, Vientiane: Program for
the allocation of sustainable productive forests and rural development.
Fitzpatrick-Lins, K. (1981). Comparison of Sampling Procedures and Data Analysis for a
Land-Use and Land-Cover Map. Florida: U.S. Geological Survey.
Jiladeth Machandaeng. (2016). Changes in land use and land cover in the catchment area, a
case study of the Ban Kong Forest Hunting Area. Buang Kan Province, Thailand:
Faculty of Humanities and Sociology, Mahasaram University.
Office of the Prime Minister. (2002). Decree on sustainable production forest management.
Lao PDR, Vientiane: Ministry of Agriculture and Forestry.
Landis, J. R., and Koch, G.G. (1977). The measurement of observer agreement for
categorical data. Literature, 159-174.
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
484
Creativity and Innovation Skills for the 21at Century of Year 4 Bachelor Students
Majoring in Tourism at Savannakhet University, Lao PDR
Ms. Thipphachanh KEOSAVANH , Asst. Prof. Dr. Vijittra Vonganusith2 , Mr. Brendan D. McKell3 ,
Ms. Yatawee Chaiyamat4
1Lecturer, Faculty of Business Administration, Savannakhet University, Lao PDR
A Ph.D. student from Doctor of Philosophy Degree Program in Research of Curriculum and Instruction, Faculty of Education,
Sakon Nakhon Rajabhat University, Sakon Nakhon, Thailand
2Lecturer, Faculty of Education, Sakon Nakhon Rajabhat University
3-4Lecturer, Faculty of Humanities and Social Sciences, Sakon Nakhon Rajabhat University
Corresponding author: [email protected]
Abstract
The Government of Lao People’s Democratic Republic put the education for the first step in
socio-economic development. Savannakhet University is one of four university in Lao PDR to play
important roles for human resource development to respond the labor market in harse competitive
era. Therefore, learning and teaching is controversial due to the fact is qualified of education. The
need for creativity and innovation skills for the 21st century of Year 4 Bachelor Students Majoring in
Tourism at Savannakhet University in Lao PDR which composes of three main components such as
creative thinking, working with others creatively and successful innovation.
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บณั ฑติ ศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
485
Introduction
In the age of rapid change, it is very noticeable that tourism is a major force in the economic
sector for many countries and regions around the world. Tourism is the major income source of
national development, especially in the least developing countries. The education sector is one of
the important sectors in a country's development (Collins, 2014; MOES, 2015). While education
prepares for quality human resources, tourism is meant to be the source of income for the human
resources who have related educational backgrounds and training in tourism services and enterprises.
In Laos, the government has focused on education development and new mechanism economics by
changing the landlocked country to be a land linking country by promoting tourism. Therefore, the
Ministry of Education and sports, PDR takes priority in education for tourism which starts in vocational
schools and higher education institutions. However, in the 21st Century, in education sectors, the
tourism curriculum needs to reconsider the era development for creativity and innovation skills of the
human resources to create employability and make tourism sustainably.
National policy on human resources development
The government of Lao PDR Laos considers the development of education and sports as well
as the development of human resources as the key to the country's economic and social
development to lift the country out of its status as a least developed country by 2020 and gradually
move towards an industrial and modern country. The 8th National Economic and Social Council,
which is the resolution of the Ninth Party Central Committee, emphasizes the important role of
education and sports by emphasizing the need to follow the four steps to achieve the goals and
objectives of the nation. Education and sports need to strengthen human resources, especially
workers’ skills and abilities, promotion of discipline, patience, the number of professionals and
professionals, and the technical and professional capacity of the public and private sectors over the
next five years.
Tourism programs at higher education institutions
In the past, higher education has seen a significant increase in the quantity and quality of
teaching and learning, scientific research, academic administration, and the preservation of national
culture, which is the main mission of higher education in particular. At the same time, the party and
the government in general, especially the Ministry of Education and Sports, have focused on
investment In addition, the government has invested heavily in the development of higher education.
In particular, the establishment and renovation of four state universities, including the construction of
new facilities, facilities for teaching, scientific research, and technical services Bachelor degree of
การประชมุ สัมมนาทางวชิ าการ ไทย ลาว ครงั้ ท่ี ระดบั บัณฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
486
tourism By implementing the seventh party parliamentary resolutions VIII and IX on the government's
policy of industrialization and modernization to lead the country out of the least developed countries
by 2020, the creation of the country's human resources of quality and fourth. of the party Party
policies and policies.
To implement the Higher Education Development Plan, but 2015 and 2020 are related to the
strategy of reforming the education system for more than 2 years 2015-2020, focusing on the quality
of education in vocational and higher education. Savannakhet University College said that Branch
Executive Travel is an important branch of the Lao PDR to help solve the problem of shortage of
resources. Human knowledge about social tourism, Therefore, Savannakhet University has established
a curriculum management curriculum issued national curriculum standards revised by the Ministry of
Education and Sports to Help the party and government to build a bachelor's degree in management,
field trips to support the development-economic society of the national government, but this year,
2015, 2020 and prepare for the ASEAN community in 2015 Tourism and Hospitality Studies Program
Bachelor of Arts To meet the development - economy and society at the national level, but 2020 is a
strategy developed to study the country towards industrialization and modernization. The course aims
to create students in management and tourism to become knowledgeable. Have technical skills,
qualifications, morality, ethics, patriotism, high occupation, can apply the knowledge learned to apply
according to the reality of each locality for maximum benefits for themselves and the needs of the
nation's society.
The current Tourism curriculum in Laos PDR’s educational institutions is divided into two
programs, which are the Bachelor’s degree level and Certificate level. The teaching processes of
Bachelor’s degrees are focused on theoretical and research approaches. While at the certificate level,
the focus is placed upon practical rather than theoretical knowledge. Problems that are currently
faced by these programs include 1) the majority of instructors assigned to tourism and hotel
management programs do not hold qualifications from such programs, 2) the lacked budget for buying
rather than theoretical tourism and hotel equipment, 3) relatively short-term work placement for
students and finally, 4) the limited numbers of available work placements offered by hotels and
operators in the tourism industry.
The current Tourism curriculum in a university in Savannakhet, Lao PDR has been improved to
integrate various sciences toward modern professional courses emphasizing teaching both theory and
practice. The public education policy in a higher education level Savannakhet University (SKU), Lao
PDR addresses the desired attributes of graduates covering morality, ethics, service mind, and social
responsibility according to professional ethics, knowledge, and ability. The tourism program is also
followed the national education policy and the university’s focus on students’ expertise in hotel and
การประชุมสัมมนาทางวชิ าการ ไทย ลาว ครงั้ ที่ ระดบั บณั ฑิตศึกษา ประจาํ ปี ການປະຊຸມສໍາມະນາທາງວຊິ າການ ໄທ ລາວ ຄງັ້ ທີ ລະດບັ ປະລນິ ຍາໂທ ແລະ ປະລນິ ຍາເອກ ປະຈາໍ ປີ
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Submitted Letter of teachers and students to attend the conference at MAEJO Universiity
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Submitted Letter of teachers and students to attend the conference at MAEJO Universiity
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Submitted Letter of teachers and students to attend the conference at MAEJO Universiity
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