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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
Efficiency Evaluation of Thailand Gross Domestic Product Using DEA
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Efficiency Evaluation of Thailand Gross Domestic Product Using DEA

  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.
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