International Journal of Modern Research in Engineering & Management (IJMREM) ||Volume|| 1 ||Issue|| 5 ||Pages|| 35-41 || December 2018 || ISSN: 2581-4540
ď€ Efficiency Evaluation of Thailand Gross Domestic Product Using DEA 1
Achirawee Kuntano , 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.
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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA 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.
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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA TABLE 1: Thailand government publishing statistic data from 1993-2017 year
PFCE
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.
GFCE
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.
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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA TABLE 2: Descriptive statistics of inputs and outputs (1993-2017) Parameters Inputs
Unit
Min
Max
Mean
Thai baht
1,695,657
7,538,202
4,616,929.5
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
1. Gross Domestic Product (GDP)
Thai baht
3,263,439
15,450,107
9,356,773
1. Private final consumption expenditure (PFCE) 2. General government final consumption expenditure (GFCE),
Outputs
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
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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
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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA 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 Output 10,959,495 Input 1 10,538,202 Input 2 2,532,020 Input 3 3,580,036 Input 4 370,108 Input 5 10,533,101 Input 6 10,533,101 Projection Summary Peer Lambda weight 4 1.000
IV.
Radial Movement 2,270,806 0.000 0.000 0.000 0.000 0.000 0.000
Slack Movement 0.000 -3,604,755 -293,934 -317,259 -47,601 -1,735,790 -1,367,921
Projected Value 13,230,301 6,933,447 2,238,086 3,262,777 322,507 8,797,311 9,165,180
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.
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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA 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.
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