The goal of this research is to evaluate the efficiency of GDP in Thailand from the past years and provide suggestions for government and policy-makers on ways to manage inputs and improve outputs in the future while enhancing the GDP of Thailand. The paper analyzed the data collected from Office of the National Economic and Social Development of Thailand through a period of 25 years ranging from 1993 to 2017. The results show that the year 2017 was the worst years in terms of efficiency. In order to achieve the research goal, data envelopment analysis (DEA) was used. Theoretically, research has found that evaluation of GDP can be improved by eradicating the negative values of slack movement. In economic terms, the research proposed the promotion of export-led growth, business incubators, and entrepreneurship to boost not only the inputs but also the GPD of the country. In general, the GDP of Thailand is quite efficient. This research can provide strategic advice for Thai Government to improve the Gross Domestic Product thoroughly

1. International Journal of Modern Research in Engineering & Management (IJMREM)
||Volume|| 1 ||Issue|| 5 ||Pages|| 35-41 || December 2018 || ISSN: 2581-4540
www.ijmrem.com IJMREM Page 35
 Efficiency Evaluation of Thailand Gross Domestic Product Using
DEA
Achirawee Kuntano
1
, Xu Haiyan
1
College of Economics and Management
Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
------------------------------------------------------------ABSTRACT----------------------------------------------------------
The goal of this research is to evaluate the efficiency of GDP in Thailand from the past years and provide
suggestions for government and policy-makers on ways to manage inputs and improve outputs in the future while
enhancing the GDP of Thailand. The paper analyzed the data collected from Office of the National Economic and
Social Development of Thailand through a period of 25 years ranging from 1993 to 2017. The results show that the
year 2017 was the worst years in terms of efficiency. In order to achieve the research goal, data envelopment
analysis (DEA) was used. Theoretically, research has found that evaluation of GDP can be improved by
eradicating the negative values of slack movement. In economic terms, the research proposed the promotion of
export-led growth, business incubators, and entrepreneurship to boost not only the inputs but also the GPD of the
country. In general, the GDP of Thailand is quite efficient. This research can provide strategic advice for Thai
Government to improve the Gross Domestic Product thoroughly.
KEYWORDS: Gross domestic product, Thailand, Data envelopment analysis, Efficiency
---------------------------------------------------------------------------------------------------------------------------------------
Date of Submission: Date,5 December 2018 Date of Publication: Date 15 December 2018
----------------------------------------------------------------------------------------------------------------------------------------
I. INTRODUCTION
Across the last four decades, Thailand has made extraordinary progress in social and economic development,
shifting from a low-income country to an upper-income country in fewer than a generation. Thailand has been one
of the extensively cited development success stories, with sustained strong growth and effective poverty
reduction, particularly in the 1980s [1]. Economic growth of a country depends on the market value of all goods
and services produced within the boundaries of the nation, generally called the Gross Domestic Product or GDP.
The good economic stability can be seen from the perspective of GDP. According to the IMF, Thailand had a GDP
of 15.450 trillion baht (US$455 billion) in 2017, ranking the 8th largest economy of Asia [2]. Thailand, who is the
the second biggest economy in ASEAN after Indonesia, is an upper middle-income country with an open
economy. Its economy is massively export-dependent, with exports accounting for more than two-thirds of its
gross domestic product (GDP). Thailand is the 28th largest export destination for the United States. Two-way
trade of goods and services in 2016 averaged $40 billion, with $29.5 billion in Thai exports to the U.S. and $10.5
billion in U.S. exports to Thailand. Among countries in Asia, Thailand ranks as the United States’ 9th largest
export destination after China, Japan, Hong Kong, South Korea, Singapore, Taiwan, India, and Malaysia [3]. GDP
is considered one of the principal indicators in determining the health of the economy [4].
An empirical application of efficiency was found in many fields including healthcare, education, banking,
manufacturing, and even in logistics. The evaluation also emphasized efficiency. Efficiency is a measure of
whether input is well used for an expected task or function (output) [5]. It is desired by an efficient system in
producing [7], or a person who makes greater achievements (outcome, output) related to the inputs (resources,
time, money) spent to do something [6]. In this perspective, one of the commonly used method to evaluate the
efficiency of a system or entity is known as Data Envelopment Analysis (DEA). Data envelopment analysis
(DEA) is widely used to estimate the efficiency, it is employed in evaluating the efficiency of many different kinds
of entities engaged in different activities in many contexts in different countries. There also have various
applications of DEA, such as hospitals, US Air Force wings, universities, cities, courts, business firms, and others.
Data envelopment analysis is a non-parametric method-based technique for measuring the relative efficiency of a
set of similar units, usually used to empirically measure the efficiency of decision-making units (or DMUs) [8].
The DMU is collective and reliable, which is convert multiple inputs into multiple outputs. An important decision
in DEA modeling is the selection of inputs and outputs that are included in the specification, as different
inputs/outputs combinations will produce different efficiency rankings. Dolores E. Luna et al. (2012) [9] the
Using Data Envelopment Analysis (DEA) to Assess Government Web Portals Performance.

2. Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
www.ijmrem.com IJMREM Page 36
The methodology has applications in science, such as Serrano-Cinca (2005) [10] applied to measure DEA
efficiency in Internet companies. In this regard, this highlights that DEA can be employed in any research area
involving the measurement of any performing system with regards to its efficiency like in our case of Efficiency
of A study by Afonso et al. (2003) [11]. Examine the efficiency of public spending using a non-parametric
approach by constructing a public sector efficiency (PSE) index for 23 industrialized countries. Herrera and Pang
(2005) [12] apply DEA to assess the efficiency of public expenditure of 140 developing countries between 1996
and 2002. They find that efficient spending is correlated with lower expenditure level. Other studies on
government expenditure efficiency used FDH analysis together with data envelopment analysis (DEA) by Afonso
and Aubyn (2004) [13]. Richard Dutu and Patrizio Sicari(2016)[14] utilizes data envelopment analysis (DEA) to
evaluate the capability of welfare spending in a specimen of OECD countries around 2012, They are forecasting
on health care, secondary education and general public services. Huge of research focusing on efficiency by using
DEA model.
This paper showed the measurements to evaluate the efficiency of GDP in Thailand using DEA, and then discuss
how to improve the efficiency by controlling inputs based on the DEA results. Data Envelopment Analysis (DEA)
is used to gauge the efficiency of the Gross Domestic Product in Thailand for the past years and understand the
value of the Gross Domestic Product in Thailand’s economy. It’s can be regarded as the most cost-effective way
of improving the competitiveness of the GDP.
II. BACKGROUND FOR EFFICIENCY EVALUATION
2.1 Introduction to DEA Model: For this research, one of the objectives is to evaluate the efficiency of GDP in
Thailand from the years of 2008 to 2017. For this determination, DEA was taken as it measures the performance of
a decision-making unit (DMU) in terms of efficiency or productivity. This letter can be determined as the ratio
total weighted outputs to total weighted inputs. The efficiency of the DMUs’ in this sample is the years are
indicated a score ranging between 0 to 1, with the best of DMU is getting a score of 1. The DEA formulation for
mth DMU under consideration is given as follows (Vaidya, 2014):
For output-oriented DEA,
∑
Subject to,
∑
∑ ∑
Whereas,
is the efficiency of mth
DMU.
is the jth
output of mth
DMU.
is the weight of jth
output.
is the ith
input of the mth
DMU.
is the weight of the ith
input.
are the jth
output and ith
input of the nth
DMU.
2.2 Data Collection: To perform analysis, this data envelopment analysis model (DEA) is considered, where
GDP is the output variable and the dependent variables are expressed as a function of various Microeconomics
measures of growth. Then 6 inputs and 1 output was selected for the measurement in this study, all the various
factors have impact on the output. To find out whether the GDP efficient or not, all factors have been considered.
The data was collected in Thailand government publishing statistic data from the Office of the National Economic
and Social Development Board. The data was the secondary data. It’s from 1993-2017.

3. Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
www.ijmrem.com IJMREM Page 37
TABLE 1: Thailand government publishing statistic data from 1993-2017
year PFCE GFCE GFCF CI Exports Imports GDP (Y)
1993 1,695,657 359,018 1,274,845 440,029 1,201,505 1,335,682 3,263,439
1994 1,923,156 407,728 1,475,626 454,224 1,410,786 1,586,561 3,689,090
1995 2,163,675 474,507 1,742,772 485,736 1,751,674 2,033,895 4,217,609
1996 2,398,572 537,348 1,932,208 461,440 1,809,910 2,099,234 4,638,605
1997 2,496,496 569,186 1,630,306 404,865 2,272,115 2,205,119 4,710,299
1998 2,429,535 614,234 1,041,478 322,919 2,723,953 1,988,906 4,701,559
1999 2,547,645 649,934 977,604 409,392 2,703,308 2,120,348 4,789,821
2000 2,744,483 688,277 1,093,810 456,603 3,287,284 2,862,304 5,069,823
2001 2,993,349 720,212 1,201,576 454,509 3,380,750 3,047,574 5,345,013
2002 3,211,203 759,991 1,264,206 468,763 3,499,004 3,134,265 5,769,578
2003 3,514,398 816,546 1,454,995 471,099 3,886,566 3,485,273 6,317,302
2004 3,885,693 911,963 1,729,126 477,557 4,587,869 4,272,712 6,898,771
2005 4,251,880 1,039,643 2,110,153 626,933 5,208,464 5,288,296 7,614,409
2006 4,574,251 1,134,277 2,255,290 434,587 5,769,171 5,494,996 8,400,655
2007 4,769,451 1,263,961 2,310,486 424,334 6,223,911 5,536,630 9,076,307
2008 5,125,445 1,543,460 2,232,017 181,909 6,932,341 5,295,925 9,658,656
2009 5,639,208 1,707,775 2,593,166 568,143 6,223,911 7,185,922 10,808,151
2010 5,988,241 1,825,136 2,921,294 528,716 7,185,922 8,011,498 11,306,894
2011 6,544,063 2,020,794 3,335,698 548,069 8,011,498 8,619,918 12,357,338
2012 6,746,875 2,113,370 3,278,324 688,529 8,619,918 8,797,311 12,915,162
2013 6,933,447 2,238,086 3,262,777 322,507 8,797,311 9,165,180 13,230,301
2014 7,012,571 2,352,615 3,369,939 116,119 6,444,693 7,861,679 9,747,495
2015 7,260,410 2,461,539 3,484,345 1 3,950,612 7,804,666 11,533,495
2016 5,206,738 1,392,163 2,567,261 593,384 9,950,612 6,932,341 9,706,932
2017 10,538,202 2,532,020 3,580,036 370,108 10,533,101 10,533,101 10,959,495
III. ANALYSIS FOR THAILAND'S GDP USING DEA
3.1 Design DMU, Inputs and Outputs: To measure the efficiency of each year in the period of 1993 to 2017, the
production technology set is defined as follows:
T = ( ) ( ) ( )
From the table 1, The year in this paper will be considered as a DMU and the input variable considered in this
paper for efficiency analysis are Private final consumption expenditure (PFCE) as x1, General government final
consumption expenditure (GFCE) as x2, Gross fixed capital formation (GFCF) as x3, Change in inventories (CI)
as x4, Exports as x5, and Import as x6 .And the output variable is considered A gross domestic product (GDP) as
y1. The inputs and outputs of the DEA system are summarized in table 2 along with the maximum and minimum
variations of each for the said time period.

4. Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
www.ijmrem.com IJMREM Page 38
TABLE 2: Descriptive statistics of inputs and outputs (1993-2017)
Parameters Unit Min Max Mean
Inputs 1. Private final consumption expenditure
(PFCE)
Thai baht 1,695,657 7,538,202 4,616,929.5
2. General government final consumption
expenditure (GFCE),
Thai baht 359,018 2,532,020 1,445,519
3. Gross fixed capital formation (GFCF) Thai baht 977,604 3,580,036 2,278,820
4. Change in inventories (CI) Thai baht -420,724 267,804 -76,460
5. Exports Thai baht 1,201,505 10,533,101 5,867,303
6. Import Thai baht 1,335,682 8,492,423 4,914,052.5
Outputs 1. Gross Domestic Product (GDP) Thai baht 3,263,439 15,450,107 9,356,773
3.2 Evaluation for Efficiency: The introduced models in section 2 were implemented to evaluate the efficiency
of Thailand GDP from the years from 1993 to 2017. In the Table 3, below describes the constant returns to scale
technical efficient (crste) and the variable returns to scale technical efficiency (vrste). This both is scale efficiency
(crste/vrste) which are successively measured of these years starting from 1993 to 2017. With the output,
orientation is chosen because the GDP of Thailand is lower than 8 by Human Development Index (HDI). So, the
GDP (output) need to increase. As it is shown, the GDP of Thailand was efficient in the years 1993-1999, 2003,
2006-2009, 2013, and 2015 when constant return to scale (CRS) is assumed. These years of full efficiency are
called peers. Nevertheless, when variable return to scale (VRS) is considered, this sector was efficient in the years
1993-1999, 2002, 2003, 2006-2009, 2011-2013, 2015 and 2016. This highlights that it is more normal for
Thailand GDP to achieve an efficient level under the variable return to scale (VRS)rather than under constant
return to scale (CRS). The reason for this is that constant return to scale (CRS)should only be considered if the
entire efficiency of GDP in Thailand was operating at optimal scale during all these years. The use of the VRS
specification allows us to accomplish the calculation of the technical efficiency of GDP in Thailand throughout
these years, devoid of scale efficiency (SE) effects. In other, words the variable return to scale (VRS) provides us
pure technical efficiencies of GDP in Thailand in these years.
TABLE 3: The efficiency performance from 2008-2017
Year crste vrste scale
1993 1.000 1.000 1.000
1994 1.000 1.000 1.000
1995 1.000 1.000 1.000
1996 1.000 1.000 1.000
1997 1.000 1.000 1.000
1998 1.000 1.000 1.000
1999 1.000 1.000 1.000
2000 0.983 0.988 0.995
2001 0.975 0.976 0.998
2002 0.998 1.000 0.998
2003 1.000 1.000 1.000
2004 0.984 0.993 0.991

5. Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
www.ijmrem.com IJMREM Page 39
2005 0.931 0.977 0.953
2006 1.000 1.000 1.000
2007 1.000 1.000 1.000
2008 1.000 1.000 1.000
2009 1.000 1.000 1.000
2010 0.983 0.988 0.994
2011 0.984 1.000 0.984
2012 0.994 1.000 0.994
2013 1.000 1.000 1.000
2014 0.805 0.828 0.972
2015 1.000 1.000 1.000
2016 0.967 1.000 0.967
2017 0.711 0.828 0.859
Average 0.973 0.983 0.988
From the table 4, it’s show the DEA evaluation of the year from 1993-2017, that is to say within a period of 25
years. As we can see, the value for Technical efficiency is close to 1. This depicts that efficiencies within these
years are high. The variables comprise one (1) output and six (6) inputs. In order to improve the efficiency of GDP
in Thailand, the Slack Movement has to be taken into account. In other words, if the value of the slack movement
is high negative value, the value of the related input needs to be reduced in order to increase the GDP.
TABLE 4: The Projection summery from 1993-2017
Results for firm 25
Technical efficiency 0.828
Scale efficiency 0.859
Variable Original Value Radial
Movement
Slack
Movement
Projected
Value
Output 10,959,495 2,270,806 0.000 13,230,301
Input 1 10,538,202 0.000 -3,604,755 6,933,447
Input 2 2,532,020 0.000 -293,934 2,238,086
Input 3 3,580,036 0.000 -317,259 3,262,777
Input 4 370,108 0.000 -47,601 322,507
Input 5 10,533,101 0.000 -1,735,790 8,797,311
Input 6 10,533,101 0.000 -1,367,921 9,165,180
Projection Summary
Peer Lambda weight
4 1.000
IV. RESULT ANALYSIS
From the table 3, we realize that the year 2014 and 2017 had the worst technical efficiency from variable returns to
scale with 0.828(VRS) score. Since our objective here is directed towards an output orientation, which means
keeping the inputs constant while increasing outputs, the efficiency of GDP in Thailand has to be increased by
17.2% (when CRS is assumed) or by 19.5% (under VRS) in 2014 and the year of 2017 by 28.9% .One of the main
solutions is to re-allocate our six inputs (x1,x2,x3,x4,x5,x6 ) in order to boost outputs (Direct contribution to GDP)
seeing that these inputs must remain constant. That is to say, the capital must be invested in assets that encourage.
Finally, the worst scale efficient was recorded in 2017 with the value of 0.859 (Scale). The high value of the scale
efficiency demonstrate that the GDP in Thailand was not at optimal scale.

6. Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
www.ijmrem.com IJMREM Page 40
Due to the fact, the overall performance of the GDP in Thailand for the studied period can be described as
efficient, for most of the years, the obtained score for the technical efficiency and scale efficiency nearly reached
the ideal value of 1. From the average scale efficiency, it can be deduced that the scale efficiency of GDP in
Thailand can further be improved by 14.1% by removing inefficiency causing reasons.Considering the fact that,
the worst efficiency was obtained in 2017, it would be necessary to evaluate the criteria that affect the GDP of this
year, in order to find the reasons behind this inefficiency. To do so, table 4 can be used. In the table 4, we will
focus on the slack movement to analyze inefficiencies. As we can, a negative value of -3,604,755 is recorded for
the slack movement of input1 (Private final consumption expenditure) and for all the other inputs in 2017. These
negative values can be interpreted as a sign of inefficiency. In this case, in order to make the GDP of this year
efficiency, all negative slack values for these inputs must be raised by the same value in order to reach the value of
zero. For instance, the slack movement for input 1 must be raised by (+) 3,604,755 and the slack movement for
input 2 (General government final consumption expenditure) can increased by (+) 293,934. The same process
should be done for all the other inputs whose slack values are negative. By putting the emphasis on the Technical
efficiency value, it can be concluded that the efficiency value in 2017 was 0.828 under variable return to scale and
of 0.711 under constant return to scale. This designates inefficiency under both constant and variable return to
scale despite the degree of closeness of these efficiency values to the value 1 which represents the minimum value
to reach an efficient level. In other words, if value for the efficient is 1 under constant return to scale we can
conclude that the GDP for the year 2017 is efficient under constant return to scale. Likewise, if under variable
return to scale, if the efficiency value rich the value of 1, we can also conclude that the GDP for the year 2017 is
efficient.
V. CONCLUSIONS AND FUTURE WORK
The study has pointed out the counter measures to evaluate and improve efficient of the GDP of Thailand based on
the results of efficiency evaluation that covers a period of 25 years based on data envelopment analysis (DEA).
After the efficiency evaluation of the previous years, we concluded the GDP for the years 2000, 2001, 2004, 2005,
2010, 2014, and 2017 was not efficient, while GDP the year 2017 had the worst inefficiency level. Taken these
into account, the research has provided solutions to eradicate the constraining factors that causes these
inefficiencies by focusing on the values for the lack movement of each input. Scholars have identified various
ways to boost the GDP of several countries like Thailand. Among those ways we can cite export-led growth,
promotion of business incubators, entrepreneurship and investment, the enhancement of industrial development
zones among others.
The conclusions and remarks made in this paper can be used by policy-makers in Thailand government for
purposes of adopting policies that may have significant impacts on the development of the country. These can
have a positive impact on the living standards of the citizens of the Thailand and may therefore attract more
entrepreneurs to considerably invest in the country.This paper can give an impetus to other studies on the
efficiency of GDP of Thailand. Further studies can be done to gauge the efficiency of this country’s GDP by
employing different approaches or methods such as econometrics, gray system theory (GST), or other
mathematical programming methods.
ACKNOWLEDGEMENTS
The authors are thankful to Thailand government publishing statistic data from the Office of the National
Economics and Social Development Board to provide a use full data and thankful for everyone involve for support
in this research. Without their superior knowledge and experience, this paper would like in quality of outcomes.
REFERENCES
[1] The world banks. “The World Bank in Thailand Overview.” The World Bank Group. Last modified
September 2018 and accessed November 13,
2018.https://www.worldbank.org/en/country/thailand/overview
[2] "Thailand: Gross domestic product, current prices". International Monetary Fund. Retrieved 10 May
2018.
[3] Export.gov. “Thailand - Market Overview.” The U.S. Department of Commerce’s International Trade
Administration. Last modified June 1, 2018 and accessed June 1,
2018.https://www.export.gov/article?id=Thailand-market-overview
[4] Callen, Tim. "What Is Gross Domestic Product?" Finance & Development (2008).

7. Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
www.ijmrem.com IJMREM Page 41
[5] Wikipedia. “Efficiency.” Wikimedia Foundation, Inc., a non-profit organization. Last modified
November 13, 2017 and accessed November 15, 2017. https://en.wikipedia.org/wiki/Efficiency
[6] Professor Dr. Yaseen Hayajneh Website. “Effectiveness, Efficiency, and Efficacy.” Hayajneh.org.
Accessed November 15, 2017.
http://hayajneh.org/glossary/vocabulary/e/EfficiencyEffectivenessEfficacy.html
[7] Wikipedia. “Data envelopment analysis.” Wikimedia Foundation, Inc., a non-profit organization. Last
modified August 31, 2017 and accessed November 15, 2017.
https://en.wikipedia.org/wiki/Data_envelopment_analysis
[8] Yishi Zhang; Anrong Yang; Chan Xiong; Teng Wang; Zigang Zhang (2014). "Feature selection using
data envelopment analysis". Knowledge-Based Systems. ELSEVIER. 64: 70–80.
[9] Luna, Dolores E., J. Ramon Gil-Garcia, Luis F. Luna-Reyes, Rodrigo Sandoval-Almazán, and Abel
Duarte-Valle. "Using data envelopment analysis (DEA) to assess government web portals performance."
In Proceedings of the 13th annual international conference on digital government research, pp. 107-115.
ACM, 2012.
[10] Serrano-Cinca, Carlos, Yolanda Fuertes-Callén, and Cecilio Mar-Molinero. "Measuring DEA efficiency
in Internet companies." Decision Support Systems 38, no. 4 (2005): 557-573.
[11] Afonso, A, Schuknecht, L & Tanzi, v 2003, “Public Sector Efficiency: An International Comparison”,
European Central Bank Working Paper Series, No. 242.
[12] Herrera, S. and G. Pang (2005), “Efficiency of public spending in developing countries: An efficiency
frontier approach”, World Bank Policy Research Working Paper, No. 3645, World Bank, Washington
D.C.
[13] Afonso, A &Aubyn, MSt 2004, “Non-parametric Approaches to Education and Health Expenditure
Efficiency in OECD Countries”, Department of Economics of School of Economics and Management of
Technical University of Lisbon Working Papers Series, No. 2004/01.
[14] Richard Dutu and Patrizio Sicari(2004). “Public spending efficiency in the OECD: Benchmarking Health
Care, Education and General Administration.