DECOMPOSITION ANALYSIS OF GLOBAL VALUE CHAIN S IMPACT ON THAI ECONOMY

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1 DECOMPOSITION ANALYSIS OF GLOBAL VALUE CHAIN S IMPACT ON THAI ECONOMY Punyawich Sessomboon Faculty of Economics Thammasat Univesy 2 Pachan Road, Phanakon, Bangkok 10200, Thailand Punyawichsessomboon@gmail.com Abstact A eview of leatues concening the measuement of a county s expot abily shows that moe compehensive famewoks ae equied to accuately account fo goss expot value. One of such famewoks is the decomposion of value into 3 main categoies, namely domestic value-added, foeign value-added and pue double counted expots. Economic data of thity-two Thai industies duing 2000 and 2011 demonstates that even though computes, electonics and optical equipment wee among industy sectos that geneated the highest goss expot value; such figues did not deive fom domestic valueadded component. As a esult, a pocess of deducing expot abily fom goss tem of expot would geneate misleading consequences. To coectly measue the expot abily of Thai industies in global value chain, this study pesents the compaative examination of Reveal Compaative Advantage (RCA) indices and constucts panel egessions including fixedeffects and Two Stage Least Squaes (2SLS) fixed-effect based on the expot-led gowth stategy. The esults show that e-computing RCA is a moe accuate indicato to measue compaative advantage of Thai industies in the global value chain compaed to the conventional RCA. In addion, constucted panel egessions demonstate that among thee categoies of goss expot, domestic value-added has the most significant impact on a county s economic gowth. Hence, this study suggests that policy-makes should encouage wholesale and etail tade and epais, since they ae among industies that have the highest degee of competiveness and could geneate the highest domestic value-added expots, as indicated by e-computing RCA and panel egessions espectively. Keywods: Decomposion analysis, Global value chain, Compaative advantage, Expot-led gowth, Panel egession 1.1 Statement of the poblem 1. INTRODUCTION Thailand has employed the expot-led gowth as the main gowth stategy fo ove a decade which then leads to a continuous gowth. Tang et al. (2015) defined expot-led gowth as a suation whee a county gowth follows s abily to expot. Even though the paticipation of Thai poduces and economy in the global value chain has inceased ove a peiod of time, thei net expot 1 has not impoved as much as thei goss expot. Figue 1.1 shows that the shae of net expot pe GDP is lowe than the goss expot s shae in evey yea. Fo this eason, the contibution of expot-led expansion to local economy needs to be pecisely measued since the quantative measue of the impact and gain fom conventional expot-led gowth stategy ae misleading that is they ty to stimulate only the total amount of goss expot whout consideing expot components. 1 Net expot equals goss expot minus goss impot.

2 2 When a county expots s poducts, the amount of goss expot, which can be divided into thee categoies, including: Domestic Value-Added in Goss Expot (DVAING), Foeign Value-Added in Goss Expot (FVAING), and Pue Double Counted in Goss Expot (PDCING) should be consideed. Theefoe, in this study, the potential policy fomulation fo enhancing competiveness and value added fom global value chain paticipation is examined. Figue 1.1: Shae of Goss Expot and Net Expot to GDP fo Thai Economy 80% 70% 60% 61% 65% 72% 65% 67% 73% 50% 40% 30% 20% 10% 0% -10% 42% 9% 10% 6% 3% 2% -7% -1% Goss Expot's shae Net Expot's Shae Souce: Autho s calculation based on OECD 1.2 Objectives of the Study To decompose the value of goss expot of Thai industies into Domestic Value- Added, Foeign Value-Added and Pue Double Counted in ode to exploe the embedded components To compae the Revealed Compaative Advantage indices between conventional tade and new tade appoaches of Thai industies in ode to examine the bette measuement of expot pefomance in the global value chain To exploe the linkage of Thai industies in the global value chain based on the degee of Vetical Specialization in ode to quantify the impact of the global value chain on Thai industies as well as apply them wh the expot-led gowth stategy To popose the economic policy fo a bette Thai expot-led gowth stategy based on the egession model showing contibution of Domestic Value-Added, Foeign Value-Added and Pue Double Counted. 1.3 Scope of Study This study focuses on expot-led gowth stategy which employs two methods: Panel Fixed-Effect and Panel Two Stage Least Squae (2SLS) Fixed-Effect. The empiical models ae based on the panel data of Thai economy in five yeas: 2000, 2005, 2009, 2010 and 2011; the main souce of data is OECD Inte-County Input-Output Tables, 2015.

3 3 2. RESEARCH METHODOLOGY Figue 2.1 illustates the basic concept of tade in value-added by assuming that thee ae fou steps of the value chain, including: aw mateial extaction, pocessing, manufactuing, final demand, and fou paticipating counties (county A to county D). Accoding to Figue 2.1, county A extacts aw mateial wh the value of $2 then expots to county B, esulting in county B ceating value-added of $24 in pocessing. Afte that, county B can futhe expot the total output of $26, which includes double counted fom county A ($2), to county C. In tems of a manufactuing pocess, county C can ceate value-added of $46 and expot $72 which includes double counted fom both counties A ($2) and B ($24) to county D. In this scenaio, the amount of $72 becomes the final demand fo county D. Fom this basic concept of tade in value-added explained above, the conventional tade appoach concludes that the total amount of wold expot equals to $100 (the sum of $2, $26 and $72 fom goss expots of county A to county C, espectively); howeve, measuing in this way can geneate the misleading poblem since $28 of total double counted is also included in the equation. Theefoe, the amount of $72 of domestic value-added expot (the sum of $2, $24 and $46 fom domestic value-added expot of county A to county C, espectively) is employed as a new appoach in measuing the expot value since can povide moe accuate measuement fo expot value and can diminish a misleading poblem. Figue 2.1: Basic Concept of Tade in Value-Added Souce: UNCTAD 2.1 Poduction Shaing and Tade in Value-Added The infomation in this section is associated wh a model poposed by Koopman et al. (2014). In this model, is assumed that thee ae two counties (a home county and a foeign county) in the wold; each county has only one secto which poduces a single poduct. The poduct in each secto can be diectly consumed as final goods o indiectly used as an intemediate input. In addion, each county can expot both intemediate and final goods to

4 4 othe counties. The goss output poduced by county s (x s ) is classified as intemediate and final goods fo both home and foeign counties. Thus, the goss output of county s (x s ) can be wten as the following equation: x a x a x y y,, s 1,2 (1) s ss s s ss s Accoding to the equation, y s is the final demand of county which impots goods fom county s while a s is the coefficient of input-output that descibes one un of intemediate goods in which county impots fom county s to poduce the same un of output in s own county. Hence, the total amount of intemediate goods which county impots fom county s is a s x. In addion to the goss output of county s, the poduction of two counties can be shown by tansfoming equation (1) into a matix fom specified in Equation (2): x1 a11 a12 x1 y11 y12 x a a x y y (2) Afte e-aanging equation (2), equation (3) is deived as follows: 1 1 I a11 a b11 b12 1 x y y y x a I a y y b b y (3) Matix B is Leontief invese o the total equiement coefficients of input-output matix. Fo example, if b 11 is an amount of county 1 s goss output that used to poduce an exta un of final goods in s own county then this can contibute to domestic consumption and county 2 impot. The othe coefficients in matix B can be similaly intepeted. In ode to poduce one un of county 1 s goods, poduces have to use a 11 un of domestic intemediate goods and a 21 un of impoted intemediate goods. Theefoe, the atio of value-added of an output fo a paticula secto whin county 1 (the domestic value added in county 1 is v 1 =1 a 11 a 21. Similaly, county 2 s atio of value-added to output fo a secto is: v 2 =1 a 12 a 22. As a esult, v 1 and v 2 can be wten in a 2 2 value-added coefficient matix as follows: V v1 0 0 v2 (4) If the matix V fom equation (4) is multiplied by the Leontief invese B fom equation (3), a 2 2 matix of value-added shae (VB) which is the measuement of valueadded shaes by a souce of poduction is deived. v b VB v b v b v b (5) Fom the equation (5), v 1 b 11 and v 2 b 22 stand fo domestic value-added shaes of county 1 and county 2 espectively; wheeas v 2 b 21 and v 1 b 12 stand fo value-added shaes of the same types of goods of a foeign county. Since the value-added comes fom ehe domestic o foeign counties, the summation of a column has to be equal to one:

5 5 v1b 11 v2b21 v1b 12 v2b22 1 (6) 2.2 Accounting of Goss Expots The goss expot of county 1 which is the combination of final and intemediate goods expots can be wten as the following equation: e12 y12 a12 x2 (7) By multiplying equation (7) wh equation (6), equation (8) is deived as follows: e v b v b y a x v b y v b y v b a x v b a x v b y v b y v b y v b y v2b21a12 x2 (8) v b a x Futhemoe, the value of county 1 s intemediate goods expot and s value of double counted fom a total 100 pecent can be incopoated into an accounting equation. When combining equations (1) and (7) togethe, this geneates x 1 =y 11 +a 11 x 1 +e 12 and x 2 =y 22 +a 22 x 2 +e 21, which can be eaanged to get equation (9) as follows: x1 a11 y11 a11 e x2 1 a22 y22 1 a22 e21 (9) Substuting equation (9) into equation (8) yields equation (10) as follows: e v b e v b e v b y v b y v b y v b a a y v b a a e v2b21 y12 v2b21a12 1 a22 y22 v2b21a12 1 a22 e21 (10) All of the eight tems on the ight-hand side of the equation (10) ae goss expot combinations of county 1 which coesponds to Figue 2.2 and 2.3 as listed below:

6 6 Figue 2.2: Decomposion Analysis of Goss Expot Goss Expot (EXP) Domestic Value-Added (DVAING) Foeign Value-Added (FVAING) Pue Double Counted (PDCING) (1) DV in diect final goods expots (2) DV in intemediate expots absobed by diect impotes (3) DV in intemediate eexpoted to thid counties (4) DV in intemediate that etuns via final impots (5) DV in intemediate that etuns via intemediate impots (6) FV in final goods expots (7) FV in inteme diate goods expots (8) Double counted intemediate expots poduced at home (9) Double counted intemedia te expots poduced aboad Souce: Adapted fom Koopman et at. (2014) Note: DV (3) on Figue 2.2 only appeas in a minimum numbe of the thee county model, but does not appea in a two county model. The fist two tems, v1b 11 y12 and v1b 12 y 22 ((1) and (2) in Figue 2.2), ae defined as value-added expots of final and intemediate goods of county 1 espectively. The thid tem, v1b 12 y 21 ((4) in Figue 2.2), is the domestic value added in intemediate goods expots of county 1 of which is etuned home as pat of the final goods impot. The fouth tem, 1 1 v b a a y ((5) in Figue 2.2), is domestic value-added in intemediate expots of county 1 that ae etuned home as pat of the impots of intemediate goods used to poduce final goods that ae absobed at a home county. Addionally, the fifth tem, 1 1 v b a a e ((8) in Figue 2.2), is a pue double counted tem poduced at home. This tem only appeas if both counties expot intemediate goods. The sixth tem, v2b21 y 12 ((6) in Figue 2.2), is the foeign value-added in final goods expot of county 1. The seventh tem, 1 1 v b a a y ((7) in Figue 2.2), is foeign value-added in intemediate goods expoted out of the county 1. They both finally etun to a foeign county and ae consumed thee. Lastly, the eighth tem, 1 1 v b a a e ((9) in Figue 2.2), is anothe pue double counted tem in county 1 s goss expots being poduced aboad. Simila to the fifth tem, the eighth tem only appeas if both counties expot intemediate goods.

7 7 Figue 2.3: The Schematic Diagam of Intenational Poduction Chain Souce: Adapted fom Baldwin and Lopez-Gonzalez (2015) Note: DV is Domestic Value-Added DDC is Domestic Double Counted FV is Foeign Value-Added FDC is Foeign Double Counted (Numbe in paenthesis is coesponded to Figue 2.2 s numbe) 2.3 The Compaison between Conventional Revealed Compaative Advantage (RCA) and New Revealed Compaative Advantage (NRCA) Indices One of the most inteesting issues of the quantative measuement of impact and gain fom global value chain is the Revealed Compaative Advantage index (RCA) as shown in the conventional fomula in equation (11). Conventional RCA is the measuement fo the compaative advantage of a paticula secto in a paticula county in the wold economy. Given that thee ae N commodies and G counties, the conventional RCA can be calculated * using goss expot value of goods i in county ( n * E ) pe total expot of county ( i E ) i1 i G then dividing by wold expot of good i ( E * ) pe total wold expot ( n G * i ). i E i TRCA E * G i i n n G Ei i1 i E * * * i E i (11) Koopman et al. (2014) poposed using a new method in measuing compaative advantage called New Revealed Compaative Advantage (NRCA). This NRCA can be calculated using the same fomula as RCA uses, but is equied to change the vaiable fom goss expot to Domestic Value-Added in Goss Expot ( DVAING ), which is the sum of the fist to the fifth i tems in Figue 2.2 o equivalent to the sum of the fist fouth tems in the equation (10). As a esult, equation (11) will be tansfomed to equation (12). The eason NRCA should be consideed using in the model instead of conventional RCA is because NRCA does not include Foeign Value-Added (FVAING) and Pue Double

8 8 Counted (PDCING) in Goss Expot, which is the sum of (6) to (9) tems in Figue 2.2 o equivalent to the sum of the fifth to the eighth tems in the equation (10). These two tems also do not eflect the abily of competion in the global value chain. Fo this eason, NRCA is employed in the equation (12) instead of conventional RCA. NRCA DVAING G i i n n G DVAINGi i1 i DVAING i DVAING i (12) 2.4 Vetical Specialization (VS) Index VS index claifies the degee of impoted content in a county s expot o the degee of linkage to global value chain which was intoduced by Hummels et al. (2001). Koopman et al. (2014) exploed the idea of VS index and found that VS index is the sum of foeign valueadded in final goods expots, foeign value-added in intemediate goods expots and double counted intemediate expots poduced aboad, which ae shown as tems numbe (6), (7), and (9) espectively in Figue 2.2, divided by goss expot. This can also be intepeted as the equivalent of the sum of the sixth to eighth tems in the equation (10) divided by goss expot. 2.5 Regession Analysis Based on Expot-led Gowth Stategy Accoding to Tang et al. (2015), the souce of gowth equation in the bivaiate model that epesents the oveall effect of expot-led gowth can be specified as follows: LnY 0 1LnEXP 1 (13) In ode to identify the patial effect of expot-led gowth, this study exploes thee addional cases based on Figue 2.2 (Decomposion Analysis of Goss Expot), consisting of: Domestic Value-Added in Goss Expot-led Gowth (DVAING-led Gowth), Foeign Value-Added in Goss Expot-led Gowth (FVAING-led Gowth) and Pue Double Counted in Goss Expotled Gowth (PDCING-led Gowth) as shown below: Domestic Value-Added in Goss Expot-led Gowth: LnY 0 1LnDVAING 2 (14) Foeign Value-Added in Goss Expot-led Gowth: LnY 0 1LnFVAING 3 (15) Pue Double Counted in Goss Expot-led Gowth: LnY 0 1LnPDCING 4 (16) Futhemoe, the analysis of the pevious bivaiate model can be extended to the ti-vaiate one which includes domestic investment as an addional explanatoy vaiable (Wha, 2004). Hence, the new souce of gowth equation can be specified as follows: LnY LnEXP LnINVEST (17)

9 9 In ode to examine the patial effect of expot-led gowth, equation (17) can be classified into thee cases using a simila method in the bivaiate model in equation (13) that involves Domestic Value-Added in Goss Expot-led Gowth (DVAING-led Gowth), Foeign Value- Added in Goss Expot-led Gowth (FVAING-led Gowth) and Pue Double Counted in Goss Expot-led Gowth (PDCING-led Gowth) as stated in the equations (18) to (20). Domestic Value-Added in Goss Expot-led Gowth: LnY LnDVAING LnINVEST Foeign Value-Added in Goss Expot-led Gowth: (18) LnY LnFVAING LnINVEST Pue Double Counted in Goss Expot-led Gowth: (19) LnY LnPDCING LnINVEST (20) Lastly, the multivaiate model is constucted using domestic investment and Vetical Specialization index (VS index) as addional explanatoy vaiables. Hence, the new souce of gowth equation can be specified as follows: LnY LnEXP LnINVEST VSindex (21) In ode to exploe the patial effect of expot-led gowth, equation (21) can be classified into thee cases using a simila method in the bivaiate model in equations (13) and ti-vaiate model in equation (17) that involve Domestic Value-Added in Goss Expot-led Gowth (DVAING-led Gowth), Foeign Value-Added in Goss Expot-led Gowth (FVAINGled Gowth) and Pue Double Counted in Goss Expot-led Gowth (PDCING-led Gowth) as indicated in the equations (22) to (24). Domestic Value-Added in Goss Expot-Led Gowth: LnY LnDVAING LnINVEST VSindex Foeign Value-Added in Goss Expot-led Gowth: (22) LnY LnFVAING LnINVEST VSindex Pue Double Counted in Goss Expot-led Gowth: (23) Whee; LnY LnPDCING LnINVEST VSindex (24) LnY is gowth ate of GDP of industy i at peiod t LnEXP is gowth ate of Goss Expot of industy i at peiod t LnDVAING is gowth ate of Domestic Value-Added in Goss Expot of industy i at peiod t LnFVAING is gowth ate of Foeign Value-Added in Goss Expot of industy i at peiod t LnPDCING is gowth ate of Pue Double Counted in Goss Expot of industy i at peiod t LnINVEST is gowth ate of Domestic Investment of industy i at peiod t VSindex is Vetical Specialization index of industy i at peiod t

10 Agicultue Mining Food Textiles Wood pape petoleum chemical Rubbe & plastics non-metallic Basic metals Fabicated Machiney Compute & Elec Electical machiney Moto vehicles Othe tanspot Manufactuing Electicy Constuction Wholesale & etail Hotels and est Tanspot and sto Telecommunications Financial inte Real estate Renting Compute & elated R&D Education Health Othe communy RESULTS AND DISCUSSION 3.1 Decomposion of Goss Expot fo Thai Industies The esults fom decomposion of goss expots fo Thai industies ae based on Koopman et al. (2014). Figue 3.1 demonstates the amounts of Domestic Value-Added in Goss Expot (DVAING) and Goss Expot (EXP) fo all thity-two Thai industies in Accoding to Figue 3.1, is obvious that compute, electonic and optical equipment industies have goss expot values thee times highe than thei DVAING. Similaly, whole sale and etail tade and epais industies have about the same goss expot value as the pevious industies do; howeve, in tem of DVAING, these industies have explicly highe value than compute, electonic and optical equipment industies do. Figue 3.1: Goss Expot and Domestic Value-Added in Goss Expot in 2011 Million Dolla 30,000 25,000 20,000 15,000 10,000 5,000 0 EXP DVAING Souce: Autho s calculation based on Koopman et al. (2014) In tems of Foeign Value-Added in Goss Expot (FVAING) as stated in Figue 3.2 and Pue Double Counted in Goss Expot (PDCING) as stated in Figue 3.3, these values ae elatively high in compute, electonic and optical equipment industies, but ae elatively low in whole sale and etail tade and epais industies. It is appaent that having highe FVAING in such an industy means that a county employs highe value-added fom foeign industies in a poduction pocess but ceates less of s own domestic value-added. Fom Figue 2.2, PDCING can be divided into two pats: double counted intemediates expots poduced at home (the eighth tem) and double counted intemediates expots poduced aboad (the ninth tem). Having a lage amount of PDCING in such an industy means that a county uses moe intemediate input fom ehe domestic o intenational souces to poduce goss expot. Hence, wh all of these easons, the abily to expot cannot be diectly deduced by employing goss tems of expot as this could esult in misleading poblems.

11 Agicultue Mining Food Textiles Wood pape petoleum chemical Rubbe & plastics non-metallic Basic metals Fabicated Machiney Compute & Elec Electical machiney Moto vehicles Othe tanspot Manufactuing Electicy Constuction Wholesale & etail Hotels and est Tanspot and sto Telecommunications Financial inte Real estate Renting Compute & elated R&D Education Health Othe communy Agicultue Mining Food Textiles Wood pape petoleum chemical Rubbe & plastics non-metallic Basic metals Fabicated Machiney Compute & Elec Electical machiney Moto vehicles Othe tanspot Manufactuing Electicy Constuction Wholesale & etail Hotels and est Tanspot and sto Telecommunications Financial inte Real estate Renting Compute & elated R&D Education Health Othe communy 11 Figue 3.2: Goss Expot and Foeign Value-Added in Goss Expot in 2011 Million Dolla 30,000 25,000 20,000 15,000 10,000 5,000 0 EXP FVAING Souce: Autho s calculation based on Koopman et al. (2014) Figue 3.3: Goss Expot and Pue Double Counted in Goss Expot in 2011 Million Dolla 30,000 25,000 20,000 15,000 10,000 5,000 0 EXP PDCING Souce: Autho s calculation based on Koopman et al. (2014) 3.2 The Compaison between Conventional Revealed Compaative Advantage (RCA) and New Revealed Compaative Advantage (NRCA) Indices fo Thai Industies Thee ae two cases in a compaison between RCA and NRCA of Thai industies in the global value chain. In the fist case, compaative advantage is inceased due to a change fom RCA to NRCA. These industies include agicultue, hunting, foesty and fishing; mining and quaying; food poducts, beveages and tobacco; textiles, textile poducts, leathe and footwea; wood, poducts of wood and cok; chemicals and chemical poducts; ubbe and plastics poducts; othe non-metallic mineal poducts; electicy, gas and wate supply;

12 12 wholesale and etail tade and epais; hotels and estauants; tanspot and stoage; post and telecommunications; financial intemediation; eal estate activies; enting of machiney and equipment; compute and elated activy; R&D and othe business activies; education; and othe communy, social and pesonal sevices. Fo the second case, compaative advantage is deceased due to the change fom RCA to NRCA. These industies include pulp, pape, pape poducts, pinting and publishing; coke, efined petoleum poducts and nuclea fuel; basic metals; fabicated metal poducts; machiney and equipment; compute, electonic and optical equipment; electical machiney and appaatus; moto vehicles, tailes and semi-tailes; othe tanspot equipment; manufactuing and ecycling; constuction; and health and social wok. Coupled wh the analysis fom ADB (2015), the abily of expot o competiveness of Thai industies in the global value chain was measued using conventional RCA index. The esults suggest that Thailand stimulate the top expoting industies such as compute, electonic and optical equipment; moto vehicles, tailes and semi-tailes; and machiney and equipment since these industies not only have high expot value but also high competiveness (eflected by conventional RCA index: , and espectively). Howeve, this eseach study suggests that dawing such a conclusion may not be completely accuate because the abily of expot o competiveness in global value chain in those top expoting industies could be wose if is measued by NRCA instead of conventional RCA (0.8791, and espectively). In addion, these industies cannot yield high DVAING values compaed wh thei high goss expot values. Futhemoe, the othe top seven expoting industies that wee mostly elied on the past infomation, including: wholesale and etail tade and epais; food poducts, beveages and tobacco; tanspot and stoage; and chemicals and chemical poducts seem bette in tems of the abily to expot o competiveness in the global value chain wh the use of NRCA measuement (fom , , and to , , and espectively) because they can ceate high DVAING values compaed wh thei goss expot values. Table 3.1: Compaison between Conventional RCA and New RCA in 2011 Industies RCA NRCA Status Agicultue, hunting, foesty and fishing Incease Mining and quaying Incease Food poducts, beveages and tobacco Incease Textiles, textile poducts, leathe and footwea Incease Wood, poducts of wood and cok Incease Pulp, pape, pape poducts, pinting and publishing Decease Coke, efined petoleum poducts and nuclea fuel Decease Chemicals and chemical poducts Incease Rubbe and plastics poducts Incease Othe non-metallic mineal poducts Incease Basic metals Decease Fabicated metal poducts Decease Machiney and equipment Decease Compute, electonic and optical equipment Decease Electical machiney and appaatus Decease Moto vehicles, tailes and semi-tailes Decease

13 13 Industies RCA NRCA Status Othe tanspot equipment Decease Manufactuing and ecycling Decease Electicy, gas and wate supply Incease Constuction Decease Wholesale & etail tade and epais Incease Hotels and estauants Incease Tanspot and stoage Incease Post and telecommunications Incease Financial intemediation Incease Real estate activies Incease Renting of machiney and equipment Incease Compute and elated activies Incease R&D and othe business activies Incease Education Incease Health and social wok Decease Othe communy, social and pesonal sevices Incease Souce: Autho s calculation based on Koopman et al. (2014) and OECD 3.3 Vetical Specialization Index (VS index) of Thai Industies This study exploes the linkage of Thai industies in the global value chain using Vetical Specialization index (VS index) which is the sum of foeign value-added in final goods expot, foeign value-added in intemediate expot, and double counted intemediates expots poduced aboad (the sum of ems numbe (6), (7) and (9) in Figue 2.2) divided by goss expot. Figue 3.4 shows that compute, electonic and optical equipment industy yields the highest degee of linkage in global value chain (VS index is equal to 68%). This can be intepeted as these industies employ 0.68 un of impoted intemediate input fom othe counties in global value chain in ode to expot one un. On the contay, the eal estate activies industy equies the lowest amount of impoted content in expot (VS index is equal to 5%), meaning that the degee of linkage in the global value chain is the lowest in accodance wh the Vetical Specialization index. Addionally, the analysis of VS index can be incopoated into the top seven expoting industies, and categoized into two main goups. Fist, the top seven expoting industies in which DVAING shae is lowe than 50%, including, compute, electonic and optical equipment (68%); moto vehicles, tailes and semi-tailes (56%); and machiney and equipment (56%). These industies have highe VS index than anothe goup because they have to significantly ely on the foeign maket fo expoting poducts. Second, the top seven expoting industies in which DVAING shae is highe than 50%, including, wholesale and etail tade and epais (11%); food poducts, beveages and tobacco (24%); tanspot and stoage (31%); and chemicals and chemical poducts (41%). These industies have lowe VS index compaed wh the fist goup because they ely heavily on thei own makets fo expoting poducts.

14 Agicultue Mining Food Textiles Wood pape petoleum chemical Rubbe & plastics non-metallic Basic metals Fabicated Machiney Compute & Electonic Electical machiney Moto vehicles Othe tanspot Manufactuing Electicy Constuction Wholesale & etail tade Hotels and estauants Tanspot and stoage Telecommunications Financial intemediation Real estate Renting Compute & elated R&D Education Health Othe communy 14 Figue 3.4: Vetical Specialization Index in % 70% 60% 50% 40% 30% 20% 10% 0% 43% 24% 26% 23% 18%16% 57% 41% 38% 38% 63% 61%56% 68% 53% 56% 48% 50% 35% 46% 31% 25% 30%28% 20% 11% 12% 17% 17% 10%5% 11% Souce: Autho s calculation based on Koopman et al. (2014) 3.4 Regession Analysis Based on Expot-led Gowth Stategy Panel Fixed-Effect Regession The esult fom Panel Fixed-Effect egession is claified in Table 3.2. Thee ae thee models involved in the egession. Fist, wh a bivaiate model, the findings show that the oveall effect of expot-led gowth in accodance wh the goss tems of expot has a posive impact on economic gowth (0.6475%), meaning that if poduces incease thei expots by 1%, then economic gowth is aised by %. Moeove, the patial effect fom Domestic Value-Added in Goss Expot contibutes to the highest impact on economic gowth (0.6653%) compaed to othe goss expot combinations, including, Foeign Value-Added in Goss Expot (0.5835%) and Pue Double Counted in Goss Expot (0.5309%). Second, a tivaiate model is used to claify that among those goss expot combinations, Domestic Value-Added in Goss Expot can povide the highest impact on economic gowth (0.6205%) compaed to Foeign Value-Added (0.5492%) and Pue Double Counted in Goss Expot (0.4957%). In addion, this ti-vaiate model is used to examine the effect of domestic investment on economic gowth. The finding shows that domestic investment can also geneate economic gowth fo % but has less impact on economic gowth than goss expot (0.6060%). Thid, a multivaiate model is used to claify the new souce of a gowth equation that includes Vetical Specialization index as an addional explanatoy vaiable. The findings suggest that thee ae thee conclusive issues; the fist issue is associated wh Domestic Value-Added in Goss Expot which can geneate the highest impact on economic gowth again (0.5869%) compaed to Foeign Value-Added in Goss Expot (0.5625%) and Pue Double Counted in Goss Expot (0.5140%); the second issue is associated wh domestic investment which can also posively affect economic gowth (0.1031%) but still has less impact than goss expot (0.5880%); and the last issue is associated wh model 2. The esults fom the last issue shows that the Domestic Value-Added in Goss Expot tend to have the highe degee of linkage in the global value chain (eflected by VS index) and can posively affect economic gowth (1.91), meaning that when paticipation of poduces in the global value chain inceases by one un then economic gowth is aised by 1.91%. Subsequently,

15 15 findings fom the use of models 3 and 4, which stimulate Foeign Value-Added and Pue Double Counted in Goss Expots, show that VS indices have a negative impact on the economic gowth though is not significant. This finding implies that poduces have to significantly ely on foeign makets that can hampe the economic gowth. Table 3.2: Panel Fixed-Effect Regession (1) Bivaiate Model Model1 Model2 Model3 Model4 LOG_EXP (0.0560)*** LOG_DVAING (0.0625)*** LOG_FVAING (0.0422)*** LOG_PDCING (0.0371)*** CONST (0.4239)*** (0.4484)*** (0.2520)*** (0.1674)*** R-squaed F(1,31) Pob > F Obs (2) Tivaiate Model Model1 Model2 Model3 Model4 LOG_EXP (0.0568)*** LOG_DVAING (0.0626)*** LOG_FVAING (0.0430)*** LOG_PDCING (0.0388)*** LOG_INVEST (0.0452)** (0.0455)** (0.0423)** (0.0427)** CONST (0.3777)*** (0.3987)*** (0.2503)*** (0.2400)*** R-squaed F(2,31) Pob > F Obs (3) Multivaiate Model Model1 Model2 Model3 Model4 LOG_EXP (0.0634)*** LOG_DVAING (0.0629)*** LOG_FVAING (0.0568)***

16 16 (3) Multivaiate Model (Continued) Model1 Model2 Model3 Model4 LOG_PDCING (0.0578)*** LOG_INVEST (0.0472)** (0.0469)** (0.0441)** (0.0437)** VS_INDEX (0.7814) (0.7035)** (0.8430) (0.9766) CONST (0.3240)*** (0.3987)*** (0.1977)*** (0.1804)*** R-squaed F(3,31) Pob > F Obs Souce: Autho s calculation Note: ***, ** and * ae significant at 1%, 5% and 10% espectively; numbe in paenthesis is Robust Standad Eo and dependent vaiable is LOG_GDP Panel 2SLS Fixed-Effect Regession Accoding to the expot-led gowth model, the findings imply that expots can have an impact on the economic gowth and that the expot gowth may also be geneated by the economic gowth. Thus, this simultaneous effect can lead to an endogeney poblem (Spout and Weave, 1993 & Wizaat and Lau, 2013). In ode to pevent such a poblem, this study employs the panel 2SLS Fixed-Effect egession. The esults fom Panel 2SLS Fixed-Effect egession 2 fo bivaiate, ti-vaiate and multivaiate models ae simila to the esults fom Panel Fixed-Effect egession in which Domestic Value-Added in Goss Expot has the stongest impact on the economic gowth compaed to Foeign Value-Added and Pue Double Counted in Goss Expot. Similaly, domestic investment can geneate economic gowth but still has a lesse impact than goss expot as illustated in Table 3.3. Table 3.3: Panel 2SLS Fixed-Effect Regession (1) Bivaiate Model Model1 Model2 Model3 Model4 LOG_EXP (0.0781)*** LOG_DVAING (0.1216)*** LOG_FVAING (0.0468)*** LOG_PDCING (0.0524)*** 2 Instument Vaiables (IVs) in this study wee selected following two main ceions. Fist (weak instuments test), IVs have to be stongly coelated wh the endogenous vaiable; second (ove identification test), IVs do not have to coelate wh the eo tem of the stuctual equation.

17 (1) Bivaiate Model (Continued) Model1 Model2 Model3 Model4 CONST (0.5616)*** (0.8033)*** (0.3622)*** (0.3795)*** R-squaed Wald chi2(1) Pob > chi Obs (2) Tivaiate Model Model1 Model2 Model3 Model4 LOG_EXP (0.0841)*** LOG_DVAING (0.1164)*** LOG_FVAING (0.0442)*** LOG_PDCING (0.0431)*** LOG_INVEST (0.0425)** (0.0406)* (0.0336)*** (0.0455)*** CONST (0.6091)*** (0.7321)*** (0.4121)*** (0.3863)*** R-squaed Wald chi2(2) Pob > chi Obs (3) Multivaiate Model Model1 Model2 Model3 Model4 LOG_EXP (0.1676)*** LOG_DVAING (0.2173)*** LOG_FVAING (0.1947)*** LOG_PDCING (0.2106)*** LOG_INVEST (0.0463)* (0.0443)* (0.0380)* (0.0470)* VS_INDEX (1.5078) (1.5295) (2.1991) (2.5625) CONST (0.8247)*** (1.2105)** (0.5950)*** (0.4270)*** R-squaed Wald chi2(3) Pob > chi Obs Souce: Autho s calculation Note: ***, ** and * ae significant at 1%, 5% and 10% espectively; numbe in paenthesis is Robust Standad Eo and dependent vaiable is LOG_GDP. 17

18 18 4. CONCLUSIONS AND RECOMMENDATIONS To conclude, this study exploes fou main issues. Fist, the decomposion analysis of Thailand s goss expot that can be gouped into thee majo categoies: Domestic Value- Added in Goss Expot (DVAING), Foeign Value-Added in Goss Expot (FVAING) and Pue Double Counted in Goss Expot (PDCING). Inteestingly, compute, electonic and optical equipment; moto vehicles, tailes and semi-tailes; and machiney and equipment anking in the top seven of expoting industies, which wee significantly elied on the past infomation, tend to be in accuately measued given that DVAING is used instead of goss expot. It is appaent that DVAING shae of these industies associated wh the goss expot in 2011 ae 32%, 44% and 44% espectively as they have to heavily ely on othe counties value-added (FVAING) and an intemediate use (PDCING) to poduce thei goss expot. In contast, wholesale and etail tade and epai; food poducts, beveages and tobacco; tanspot and stoage; and chemicals and chemical poducts anking in the top seven expoting industies ae explicly diffeent in tem of DVAING shae associated wh goss expot, esulting in 89%, 76%, 69% and 59% espectively in This implies that the industies have to significantly ely on thei own makets to ceate high DVAING shae. As a consequence, expot pefomance should not be deduced by employing goss expot because can possibly geneate the misleading poblem (ove expot value) and then distots the abily of expot as well as the economic gowth. The second issue is the compaison between conventional Revealed Compaative Advantage (RCA) and New Revealed Compaative Advantage (NRCA) indices. The compaison suggests that NRCA povide moe accuacy in measuing expot abily o competiveness of a paticula industy in a paticula county in the wold economy. This study classifies the esults in 2011 into two goups. Fo the fist goup, the compaative advantage is inceased as a esult of the change fom RCA to NRCA. These industies include agicultue, hunting, foesty and fishing; mining and quaying; food poducts, beveages and tobacco; textiles, textile poducts, leathe and footwea; wood, poduct of wood and cok; chemicals and chemical poducts; ubbe and plastics poducts; othe non-metallic mineal poducts; electicy, gas and wate supply; wholesale and etail tade and epais; hotels and estauants; tanspot and stoage; post and telecommunications; financial intemediation; eal estate activies; enting of machiney and equipment; compute and elated activy; R&D and othe business activies; education; and othe communy, social and pesonal sevices. Fo the second goup, the compaative advantage is deceased as a esult of the change fom RCA to NRCA. These industies in this goup include pulp, pape, pape poducts, pinting and publishing; coke, efined petoleum poducts and nuclea fuel; basic metals; fabicated metal poducts; machiney and equipment; compute, electonic and optical equipment; electical machiney and appaatus; moto vehicles, tailes and semi-tailes; othe tanspot equipment; manufactuing and ecycling; constuction; and health and social wok. Coupled wh the analysis fom ADB (2015), is suggested that Thailand stimulate the industies that have lage expot value and the high conventional RCA indices such as the industies in compute, electonic and optical equipment; moto vehicles, tailes and s es; and machiney and equipment since they do not only have a lage amount of goss expot but also have high competiveness (thei conventional RCA indices in 2011 ae , and espectively). Howeve, this study poves that such suggestions can distot the economic policy because once e-computed RCA o NRCA is used to measue expot abily o competiveness in the global value chain instead of using conventional RCA, expot abily o competiveness of those top expoting industies as mentioned ealie can become wose (thei NRCA indices in 2011 ae , and espectively) since they cannot ceate high DVAING values compaed wh thei high goss expot values. Thus,

19 19 the policymakes should suppot the othe top expoting industies which can ceate high DVAING values compaed wh thei goss expot values such as the industies in wholesale and etail tade and epais; food poducts, beveages and tobacco; tanspot and stoage; and chemicals and chemical poducts because thei expot abily o competiveness in the global value chain tend to be bette in tems of e-computed RCA o NRCA (thei NRCA indices in 2011 ae , , and espectively). The thid issue is elevant to the exploation of Vetical Specialization index (VS index) that epesents the degee of linkage to the global value chain. The study finds that in 2011, the analysis of VS index can be incopoated into the top seven expoting industies, and categoized into two main goups. Fist, the top seven expoting industies in which DVAING shae is lowe than 50%, including, compute, electonic and optical equipment (68%); moto vehicles, tailes and semi-tailes (56%); and machiney and equipment (56%). These industies have to impot a lage amount of intemediate use fom foeign makets to poduce thei lage amount of expots. Second, the top seven expoting industies in which DVAING shae is highe than 50%, including wholesale and etail tade and epais (11%); food poducts, beveages and tobacco (24%); tanspot and stoage (31%); and chemicals and chemical poducts (41%). These industies have a lowe VS index compaed wh the fist goup because they have to employ a lage amount of intemediate input fom thei own maket in ode to poduce the lage goss of expots. The final issue is the egession analysis based on the expot-led gowth stategy which can lead to the conclusive esults which ae as follows. The findings of this study suggest that policymakes should stimulate the industies and/o sectos that have high DVAING athe than only concentating on high goss expot value. Fo example, wholesale and etail tade and epais; food poducts, beveage and tobacco; tanspot and stoage; chemicals and chemical poducts; and agicultue, hunting, foesty and fishing (see Figue 3.1: Goss Expot and Domestic Value-Added in Goss Expot of Thai Industies). Moeove, this study has shown that industies and/o sectos which gain benef fom high DVAING pe un of goss expot should be suppoted since one un incease of thei expots can geneate a geate magin of DVAING; fo instance, eal estate activies; financial intemediation; education; wholesale and etail tade and epais; post and telecommunications; mining and quaying; enting of machiney and equipment; compute and elated activies; agicultual, hunting, foesty and fishing; and hotel and estauants of which can gain ove 80% of DVAING pe un. 5. ACKNOWLEDGEMENT I would like to expess my deepest gatude to thee pesons: D. Nattapong Puttanapong who widened my vision to ealize the technique of Decomposion analysis of global value chain by using substantially inteesting data Global IO; D. Monthien Satimanon who gave advices about Econometics fo Regession unning; D. Chaospon Chalemtiaana who povided pecious advices in doing my eseach study especially wing until my eseach study has been pefectly completed. 6. REFERENCES ADB (2015). Thailand: Industialization and Economic Catch-Up. Asian Development Bank. Alfao, L., Antàs, P., Cho, D., & Conconi, P. (2015). Intenalizing Global Value Chains: A Fim-Level Analysis: National Bueau of Economic Reseach.

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