Surveying the difference of TFP between group of taiwan korea and group of thailand – malaysia; and their TFP growths determinants

Problem statement

Highincometrapisaconceptwhichwasi n i t i a l l y referredbyWorldBankec on om i st s HomiKharas,andIndermitGillin2007inthebooknamedAnEastAsianRenaissance,I d e a s f o r E c o n o m i c G r o w t h A l t h o u g ht h i s c o n c e p t i s s t i l l c o n t r o v e r s i a l a m o n g economistsitsphenomenonintermsofstatisticaspectisveryclear.

Abramovitz (1986) suggested that developing countries could catch up with wealthier nations, leading to GDP per capita convergence However, data reveals a different reality; as noted by Jesus Felipe (2012), the number of low-income countries increased from 47 in 1980 to 48 in 2001, with only eight nations successfully transitioning to higher income levels The outlook for middle-income countries is similarly bleak, with 52 classified as such in 2010, reflecting little change from 1980's 56 countries Successful transitions to high-income status have been rare, with only a few examples like South Korea and Taiwan (Foxley & Sossdorf, 2011) Felipe (2012) highlighted that countries achieving an average growth rate of 3.5% per annum over 14 years could escape the higher middle-income trap, raising questions about the sources of sustainable growth.

Thisp a p e r trytoan a l y z e t h e s o u r ce o f g r o w t h o f t h e f o u r c o u n t r i e s o f w h i c h t h e t w o nations KoreaandTaiwanaretheonessuccessfullyshiftinguptoadvancedeconom ies; andthetworemainingareinuppermiddleincomegroup 1 ,intermsofsupply sideaspectspecializinginT F P l e v e l , a n d i t s d e t e r m i n a n t s T h i s w o u l d b e l i k e l y somer e f e r r a l e x p e r i e n c e t o V i e t n a m ( t h e c o u n t r y w h o h a s somec o m m o n t o t h e f o u r mentionedi n d i f f e r e n t asp ects)inupcomingtimewhenitconvergestouppermiddleincomefraction.

K o r e a , T ai w an , Thailand,andMalaysiaduring1962and2010.

No Item Korea Taiwan Malaysia Thailand

Surveyingt h e ta blea t t h e it em of G D P we c a n see Korea is thebiggest scale a n d the smallesto n e isMalaysia.BothKoreanandTaiwanhavethehigherGDP’saverageg r o w t h r a t e i n c o m p a r e w i t h T h a i l a n d a n d Malaysiaw h i l e t h e l a t t e r h a v e t h e h i g h e r p opulationgrowthrates.

1 As JesusFelipe(2012),MalaysiaisthecountrywhichtrappedwhileThailandisnot.However,inrelativewiththespeedingofKor ea,andTaiwanfortransitioningtohighincomegroup,Thailandcouldbecalledintrappedsomehow.

2 Author ofPTW7.1appliedPPPConvertedsystematyear2005constantpricesforGDP,andGDPpercapita

GDPpercapitaatthestartingpointalsoshowsthedeparturelevelofTaiwanandKoreaisbi ggerthantherestones.ThoseconsequentlygivetheadvantageofthegrowthrateofG D P perc apitatoTaiwanandKoreafortakingoffinthewhile.Allthethingsseemedtob ethatitishardtosay anythingaboutthereasonwhyTaiwan,andKoreacanshiftupthea d v a n c e d economysoonertha nMalaysia,andThailand.However,itwouldbeinterestingi f w e knowthatexceptingThailandbecamelowermiddleincomein

1976,Taiwanjoinedi n lowermiddleincomegroupin1967,andonlytwoyearslaterbothMalaysia andKoreaa l s o p a r t i c i p a t e d i n 1 9 6 9 B u t e v e r y t h i n g g o e s s i g n i f i c a n t l y differe ntt h e r e a f t e r K o r e a a n d T a i w a n n e e d 19yearst o moveu p t o u p p e r middlei n c o m e w h i l e T h a i l a n d a n d Malaysiagetstuckwiththegroupfor28years,and27yearsrespect ively.Themoving- upspeedo f K o r e a a n d T a i w a n i s a c c e l e r a t e d w i t h only7 yearsf o r transmittedt o h i g h incomecountriesthatleftMalaysia,andThailandfarawayinuppermiddleincomebeingo f 16year,and8yearsrespectively 3Jesus Filipe(2012).

Thatsituationprovedthatthereshouldbesomethingshouldbeanalyzedandmadecleara b o u t t h e sourcesofthesecountries’growthintermsofsupplyside.Wewillcheckthesec o u n t r i e s w iththesamemodelsderivedfromCobbDouglasfunctionandgoindeepofTFPb r e a k d o w n F o r c a l c u l a t i n g a n d c o n t e m p l a t i n g T F P l e v e l I u s e d t h e p e r i o d f r o m 1 962to2010becauseitisthedurationplausibleenoughwhenthefourcountriesstartedind ustrializationu n d e r t h e e x p o r t - l e d o r i e n t a t i o n r e p l a c i n g t h e i m p o r t s u b s t i t u t i o n instead,yetwithdifferentlevelofd eparture.

Researchobjectives

(2) Calculatingandcontemplating themovingofthe TFP, and its part:Technology aso t h e r factorstocapital(K)andlabor(L) ofeachgroupbytimeperiodfrom1962–2 0 10

Researchquestions

(1) WhatisthedifferenceinmovementofTFP’scontributionof thesecountries? Andh o w muchthedifferenceis?IsthereanyabnormalshiftofTFPlinesofKorea andTaiwaninrelativewithThailandandMalaysiaoritisjustabouttimetocatchup?

Researchhypothesis

 Hypotheses5:Patentasapositiveresultofcountries’R&Dactivitieswouldbe al so significanteffectonTFP.

Studyscopeanddata

Mainmethodswhicha r e usedinthisresearchare GLS andFEM r e g r e s s i o n Da tacombinedfroms o u r c e s o f T h e C o n f e r e n c e B o a r d T o t a l E c o n o m y

T A T A i s u s e d ford e p l o y i n g dataandprocessedthesample fromtheperiodof1962– 2010for thefourc o u n t r i e s However,duetotheavailabilityofdatathatIexpoilted,thedataof GDP( Y ) , Capitalstock(K),andlaborforce(L)isusedtoappliedeconometricregressiont o c a l c u l a t e T F P l e v e l , a n d t h e contributiono f i t s growthr a t e tot h e econo micg r o w t h ra te w o u l d be from1962t o 2010while thedataset o f TFP’sdetermina ntsc o u l d beonlycollectedfrom1980–2010.

Middleincometrap and the linkwithTFP

Middleincometrapisjustatoddlerdefinitionthatstillbedebatedaroundtheworld.T h i s so-callnamehas beenreferredbyWorldBank’seconomistsIndermitGill,HomiK h a r a s , e t a l

( 2 0 0 7 ) a n d s u p p o r t e d bys o m e o t h e r s u c h a s K e n i c h i O h n o ( 2 0 0 9 , 20 10),A l e j a n d r o Foxley andF er na nd o S o s s d o r f ( 2 0 1 1 ) , J e s u s F e l i p e (2012), AnnaJankowska,ArneJ.NagengastandJoseRamonPerea(2012).Thedefinition cre atesm a n y supportingoragainstideasmainlyoccurredinAsiaandSouthAmericabecausether earealotofcountriesoftheregionsarereferredtrappedorso-calledname.

Althought h e d e f i n i t i o n i s u n d e r d i s c u s s e d , e v e r y o n e h a s o b s e r v e d t h e r e a l phenomenont h a t t h e r e a r e s i g n i f i c a n t l y d i f f e r e n t b e t w e e n t h e c o u n t r i e s w h o h a v e successfullymoveduptohi gh - i nc ome ec o n o m y likeT a i w a n , K o r e a , or some th ei rr e g i o n a l h o o d p r e c e d e n t s l i k e J a p a n , H o n g k o n g , o r S i n g a p o r e a n d t h e c o u n t r y like Malaysiainterm softhedurationofbeingintheuppermiddleincomegroup.

Therea r e q u i t e m a n y empiricals t u d i e s c o n c e r n i n g toa n a l y z i n g theses u c c e s s f u l c o u n t r i e s likeTaiwan a n d Ko rea intermsof so u r c e of g r o w t h b u t t h e r e are f ew tosurveythedifferencesbetweentheabovementionedandthemiddlei ncomeretainers( i e theconnectingwithmiddleincometrap).Theanalysismethodsmo stlyareusedi n thelatterpapersarestatisticdescription,qualitative,combinedwithcasestudy. Theu p d at ed m e t h o d h a s b e e n u s e d i s P r o d u c t s p a c e mapd e p l o y e d f r o m t h e p a p e r o f Hi d alg o etal.,

(2007)ofAnnaJankowska,ArneJ.Nagengast,andJoseRamonPerea(2012)i n w h i c h t h e a u t h o r s p r o v i d e d a comparingt w o mapso f a l l t r a d e d goodsp r e s en ti n g proximityo r similaritya m o n g commodities,thatofbetweenanovercomingcountry, andatrappedcountry,inordertoshowthedifferencebetweent h e c o u n t r i e s ’ structuretransformation.Thismethodis one ofuseful analytical tool tod e e p e n onemajorfactoreffectingonthesuccessfulescapingfrommiddleincometrapo f K

6 orea,andTaiwan.KeninchiOhnoreferredinhispapersmanyqualitativev a r i a b l e s l i k e policymakers’w i s e , b u s i n e s s momentums,n a t i o n a l s p i r i t , e t h n i c ’ s natureordifficulttoquantifiedliketherenovationtoworking- hardness 4or private- s e c t o r dynamic.WhileJesusFelipe(2012)minedstatisticdescriptionof125c o u n t r i e s ina h u n d r e d yearstosupplyt h r e s h o l d s t a n d a r d s t o t h e middlei n c o m e r e m a i n i n g onesbasedontheexperiencesofsuccessfulpredecessors,AlejandroFo xleyandFernandoSossdorf(2011)combinedthesimilarmethodwithcasestudiest o w i t h d r a w n t h e d i f f e r e n c e s a m o n g T h e s e p a p e r s a n y w a y g o t o t h e sam ei s s u e c o n c e r n i n g t o t h e r o l e o f T F P a s t h e s h i f t parametera s t h e n e o c l a s s i c a l theory’sa s s u m p t i o n ofdiminishingreturnoftheinputs.

Albeitt h e r e h a v e b e e n a n immenseo c e a n o f p a p e r d i s c u s s i n g a b o u t T F P a n d t h e s o u r c e ofgrowthoriginatedfromexogenousmodelofSolow(1957)toen dogenous modelofRomer(1986)andtheirlaterfollowers,papersembodiedthe directconnectionbetweenTFP,middleincometrap, andTFP’sdecompositionarequitefewrelatively.H o w e v e r , t h e a c c u m u l a t i o n o f p a p e r s a b o u t s o u r c e o f g r o w t h h a s contributedgoodenoughtoserveinthestudy.Thetw orecentgoodworksconcerningt o t h e t o p i c a r e A i y a r , D u v a l , Puy,W u , Z h a n g (

2 0 1 3 ) , a n d D a u d e , A r i a s ( 2 0 1 0 ) Amongtheoutcomesi ntheirpaper,Chris tianDaudeandEduardo Fernandes Ariaspointedou tt ha tt heg ap ofincomebe t we en typicalco un tr y inLa ti n Americanan d

U.SwasdeterminedbythegapofTFP.TheIMF’seconomist5w h i l s tgodeeperwiththed e t e r m i n a n t s o f g r o w t h s l o w d o w n o f w h i c h T F P c o n t r i b u t e d q u i t e l a r g e proportion.

TFPandDeterminantstoTFPlevel

TFP

The debateabout TFPand TFP growthconcept and measurement hasbeenlasting fordecadesw h e n i t s a b o u t mentionedw a s i n t r o d u c e d bySolows( 1 9 5 7 ) a n d a n i ndependentworkofAbramovitz(1956),thoughitsroletothegrowthissupported 6by alargea mountofpapersprevailingovertheoppositeideasofKimandLau(1993)

4 Using somevariablespresentingforhumancapitalthatarecommonlyinempiricalpaperscouldleadthereaderstom i s u n d e r s t a n d i n g Besides,thelong-termsurveyforhundredyearswouldbealsoobstacle.

5 It isreferredtoShekharAiyar,RomainDuval,DamienPuy,YiqunWu,LongmeiZhang

Total Factor Productivity (TFP) has been recognized as a significant factor in the economic growth of East Asia, particularly during the East Asian Miracle, as noted by Chen (2002) Despite its importance, there is a lack of consensus in academic literature regarding TFP, its growth, and measurement methods, leading to various estimation approaches derived from growth theory These methods explore how to elevate a country's output amidst diminishing returns on individual inputs The ongoing debates among economists aim to uncover the sources of economic growth, resulting in a categorization of TFP literature into two main paths: the exogenous models initiated by Solow (1956) and Swan (1956), and the related endogenous models developed by Arrow (1962), Lucas (1988), Romer (1990), and Aghion and Howitt (1992) While these models differ in their views on TFP—whether as an intrinsic improvement or an externality—they both acknowledge TFP's critical role as a driver of growth.

Unlike the neoclassical model, which assumes a general production function represented as Y = F(At, Kt, Lt), where output growth is attributed to capital (Kt), labor (Lt), and technological progress (At) independently, this framework treats At as an exogenous factor that can enhance output levels In his 1962 work, Arrow argued that At evolves during the investment process concerning capital and labor As workers supplement capital, they become more skilled, efficient, and innovative, leading to improvements in At through a phenomenon known as "learning by doing." This process expands knowledge diffusion across economies, driven by the movement and generation of a skilled workforce The model is initiated with the so-called AK model.

Lucas(1986)introducedconceptofhumancapitalwherebyRomer(1990)developedt h i s intoaprocesssuchcalledspill- overeffectinhisexcellentpaper.Romersplittedhumancapitalinto twokinds thatoneis publicand theother isrestrictedbyrivalryore x c l u d a b i l i t y orboth.R&Dmodelismorespecificassemb ledwiththreecorebasesincluding( i ) m u t u a l effect betweentechnology andca pitalaccumulation;

R&D activities within firms are driven by market motivations and are treated as public goods or non-rival excludable goods This perspective serves as a distinctive argument against exogenous thinking, as previous research has failed to address all three bases, even in the context of endogenous precedents established by Arrow (1962) and further developed by Schumpeter (1942) and Aghion and Howitt (1992, 2002) In modeling R&D, the production process is divided into two stages, where ideas are generated as input goods for subsequent processes Overall, these insights can be summarized through specific assumptions regarding the nature of innovation and its economic implications.

- Labori s d i v i d e d i n t o L A(labor f o r R & D activity)a n d LY(laborf o r p r o d u c i n g co nsumption goods)thatisL=LA+LY;

- Newinnovation(technology)𝐴̇basedonpastknowledgeassembledinlabor workingincreativitythatis:𝐴̇=𝛿� 𝐴w i t h 𝛿istheproductivity(rateofgeneratin gideas);and𝛿=𝛿𝐴 𝜑 ;withλ ,φF = 0.0000 ln_rgdp Coef Std.Err t P>|t| [95%Conf.Interval] ln_K 5319382 0380974 13.96 0.000 4562949 607581 ln_L 8267362 1522913 5.43 0.000 5243583 1.12911 _cons 9624191 6553164 1.47 0.145 -.338727 2.26356 sigma_u 34305407 5 sigma_e 06842775 rho 96173558 (fraction ofvariancedue to u_i)

Fixed-effects(within)regression Numberofobs = 98

R-sq: within = 0.9906 Obs per group: min = 49 between= 1.0000 avg = 49.0 overall= 0.8750 ma x = 49

F(2,94) = 4951.88 corr(u_i,Xb) = -0.1820 Prob>F = 0.0000 ln_Yperw Coef Std.Err t P>|t| [95%Conf.Interval] ln_Kperw 5319376 0380975 13.96 0.000 4562941 60758 ln_L 3586767 1146176 3.13 0.002 1311008 586252 _cons 9624045 6553175 1.47 0.145 -.3387437 2.26355 sigma_u 34305541 3 sigma_e 06842785 rho 96173576 (fraction ofvariancedue to u_i)

F(2,94) = 8548.30 corr(u_i,Xb) = -0.4651 Prob>F = 0.0000 ln_rgdp Coef Std.Err t P>|t| [95%Conf.Interval] ln_K 7851124 0341031 23.02 0.000 7174 852824 ln_L 1535635 103422 1.48 0.141 -.0517833 358910 _cons 1.731462 3405035 5.09 0.000 1.055384 2.40753 sigma_u 19960958 9 sigma_e 07044321 rho 88925098 (fraction ofvariancedue to u_i)

xtreg ln_Yperwln_Kperwln_L,fe

Fixed-effects(within)regression Numberof obs = 98

Groupvariable:country_code Numberof groups = 2

R-sq: within =0.9857 Obspergroup:min= 49 between=1.0000 avg= 49.0 overall=0.9549 max= 49

F(2,94) = 3234.90 corr(u_i,Xb) =0.3908 Prob>F = 0.0000 ln_Yperw Coef Std.Err t P>|t| [95%Conf.Interval] ln_Kperw 7851116 034103 23.02 0.000 7173993 85282 ln_L -.0613222 0702129 -0.87 0.385 -.2007317 078087 _cons 1.731452 3405032 5.08 0.000 1.055375 2.40752 sigma_u 19961204 9 sigma_e 07044319 rho 88925346 (fraction ofvariancedue to u_i)

Remark:Equation(a’)r u n withFixedEffect(FE)methodcouldnotfittoGroup2

xtregln_rgdpln_Kln_Lt,fe

R-sq: within =0.9976 Obs pergroup:min = 49 between=1.0000 avg = 49.0 overall=0.9433 max = 49

F(3,93) = 13079.3 corr(u_i,Xb) =-0.4715 Prob>F = 0.0000 ln_rgdp Coef Std.Err t P>|t| [95%Conf.Interval] ln_K 1767478 0512474 3.45 0.001 0749807 278514 ln_L 1.270944 1270517 10.00 0.000 1.018644 1.52324 t 0246095 0029325 8.39 0.000 0187861 030432 _cons 3.3713 5739406 5.87 0.000 2.231568 4.51103 sigma_usigm 2 a_e rho

(fraction ofvariancedue to u_i) Ftestthatallu_i=0: F(1,93)= 66.34 Prob>F=0.0000

xtregln_Yperwln_Kperwln_Lt,fe

F(3,93) = 5762.86 corr(u_i,Xb) = -0.2383 Prob> F = 0.0000 ln_Yperw Coef Std.Err t P>|t| [95%Conf.Interval] ln_Kperw 1767467 0512475 3.45 0.001 0749793 27851 ln_L 4476945 0875725 5.11 0.000 2737929 621596 t 0246095 0029325 8.39 0.000 0187861 03043 _cons 3.371287 5739417 5.87 0.000 2.231553 4.51102

4022093 8 0518967 9836241 (fraction of variancedue to u_i) Ftestthatallu_i=0: F(1,93)= 66.34 Prob> F = 0.0000

xtregln_rgdpln_Kln_Lt,fe

F(3,93) = 7222.44 corr(u_i,Xb) = -0.4044 Prob>F = 0.0000 ln_rgdp Coef Std.Err t P>|t| [95%Conf.Interval] ln_K 5902221 0487886 12.10 0.000 4933376 687106 ln_L 175822 092027 1.91 0.059 -.0069253 358569 t 0149643 0029354 5.10 0.000 0091351 020793 _cons 4.958357 7016247 7.07 0.000 3.565069 6.35164

5 sigma_u 09049476 sigma_e 06261118 rho 67627297 (fraction ofvariancedue to u_i) Ftestthatallu_i=0: F(1,93)= 1.33 Prob>F=0.2523

xtregln_Yperwln_Kperwln_Lt,fe

F(3,93) = 2738.54 corr(u_i,Xb) =0.4876 Prob>F = 0.0000 ln_Yperw Coef Std.Err t P>|t| [95%Conf.Interval] ln_Kperw 590222 0487886 12.10 0.000 4933376 687106 ln_L -.2339537 0710024 -3.30 0.001 -.3749505 -.09295 t 0149643 0029354 5.10 0.000 0091351 020793 _cons 4.958338 7016254 7.07 0.000 3.565049 6.35162 sigma_u 09049726 7 sigma_e 06261123 rho 67628474 (fraction ofvariancedue to u_i)

Remark:Equation(b)runwithFEmodeldidnotfitGroup1whenhascoefficiento f Labor>

xtregln_rgdpln_Kln_Lln_t,fe

F(3,93) = 7842.92 corr(u_i,Xb) = -0.3735 Prob>F = 0.0000 ln_rgdp Coef Std.Err t P>|t| [95%Conf.Interval] ln_K 5655472 040117 14.10 0.000 4858827 645211 ln_L 5471411 1933681 2.83 0.006 1631503 931131 ln_t 0615609 0271269 2.27 0.026 0076922 115429 _cons 2.687189 9944429 2.70 0.008 7124227 4.66195 sigma_u 20774251 6 sigma_e 06696548 rho 90587215 (fraction ofvariancedue to u_i)

xtregln_Yperwln_Kperwln_Lln_t,fe

F(3,93) = 3448.72 corr(u_i,Xb) =-0.0459 Prob>F = 0.0000 ln_Yperw Coef Std.Err t P>|t| [95%Conf.Interval] ln_Kperw 5655466 0401171 14.10 0.000 485882 645211 ln_L 1126901 1559842 0.72 0.472 -.1970636 422443 ln_t 061561 027127 2.27 0.026 0076923 115429 _cons 2.687178 9944445 2.70 0.008 7124086 4.66194

8 sigma_u 20774355 sigma_e 06696558 rho 90587275 (fraction ofvariancedue to u_i)

xtregln_rgdpln_Kln_Lln_t,fe

R-sq: within =0.9949 Obspergroup:min= 49 between= 1.0000 avg= 49.0 overall= 0.9779 max= 49

F(3,93) = 6047.90 corr(u_i,Xb) =-0.4717 Prob> F = 0.0000 ln_rgdp Coef Std.Err t P>|t| [95%Conf.Interval] ln_K 7295419 0394439 18.50 0.000 6512141 807869 ln_L 2022022 1021498 1.98 0.051 -.000647 405051 ln_t 0545875 0210572 2.59 0.011 0127721 096402 _cons 2.199861 376748 5.84 0.000 1.451714 2.94800 sigma_u 21226872 8 sigma_e 06839296 rho 90595075 (fraction ofvariancedue to u_i) Ftestthatallu_i=0: F(1,93)= 6.59 Prob> F = 0.0119

xtregln_Yperwln_Kperwln_Lln_t,fe

R-sq: within =0.9866 Obspergroup:min = 49 between=1.0000 avg = 49.0 overall=0.9505 max = 49 corr(u_i,Xb) =0.3784

= ln_Yperw Coef Std.Err t P>|t| [95%Conf.Interval] ln_Kperw 7295408 0394438 18.50 0.000 6512131 807868 ln_L -.0682542 0682218 -1.00 0.320 -.2037291 067220 ln_t 0545879 0210572 2.59 0.011 0127725 096403 _cons 2.199855 3767476 5.84 0.000 1.451709 2.94800

9 (fraction ofvariancedue to u_i) Ftestthatallu_i=0: F(1,93)= 6.59 Prob>F=0.0119

Loglikelihood = 112.9088 Prob>chi2 = 0.0000 ln_rgdp Coef Std.Err z P>|z| [95%Conf.Interval] ln_K 7295331 0095848 76.11 0.000 7107471 74831 ln_L 026064 0266653 0.98 0.328 -.026199 078327 _cons 4.389026 137236 31.98 0.000 4.120048 4.65800

Loglikelihood = 112.9086 Prob> chi2 = 0.0000 ln_Yperw Coef Std.Err z P>|z| [95%Conf.Interval] ln_Kperw 7295332 0095849 76.11 0.000 7107472 748319 ln_L -.2444029 0194292 -12.58 0.000 -.2824834 -.206322 _cons 4.389024 1372362 31.98 0.000 4.120046 4.65800

Loglikelihood = 120.1835 Prob>chi2 = 0.0000 ln_rgdp Coef Std.Err z P>|z| [95%Conf.Interval] ln_K 8623323 0090633 95.15 0.000 8445685 88009 ln_L -.0871881 0134266 -6.49 0.000 -.1135037 -.060872 _cons 2.48365 1160618 21.40 0.000 2.256173 2.71112

Loglikelihood = 120.1835 Prob >chi2 = 0.0000 ln_Yperw Coef Std.Err z P>|z| [95%Conf.Interval] ln_Kperw 8623324 0090633 95.15 0.000 8445687 880096 ln_L -.224856 0088267 -25.47 0.000 -.2421561 -.207556 _cons 2.48365 1160619 21.40 0.000 2.256173 2.71112

Remarks:Equation(a)runwithGLSmethoddidnotfitwithGroup1. ln_rgdp Coef Std Err z P>|z| [95% Conf Interval] ln_Yperw Coef Std Err z P>|z| [95% Conf Interval]

Loglikelihood = 127.0594 Prob> chi2 = 0.0000 ln_K 4529043 0490007 9.24 0.000 3568647 5489438 ln_L 2897943 0515025 5.63 0.000 1888513 3907374 t 0211999 003701 5.73 0.000 013946 0284539 _cons 6.973163 4665093 14.95 0.000 6.058822 7.887505

Loglikelihood = 127.0592 Prob> chi2 = 0.0000 ln_Kperw 4529043 0490008 9.24 0.000 3568645 5489441 ln_L -.2573014 016967 -15.16 0.000 -.290556 -.2240468 t 0212 0037011 5.73 0.000 013946 0284539

Loglikelihood = 134.3552 Prob >chi2 = 0.0000 ln_rgdp Coef Std.Err z P>|z| [95%Conf.Interval] ln_K 6106301 044599 13.69 0.000 5232177 698042 ln_L 0761498 0307688 2.47 0.013 0158441 136455 t 0158858 0027709 5.73 0.000 0104549 021316 _cons 5.472755 530969 10.31 0.000 4.432074 6.51343

Loglikelihood = 134.3551 Prob> chi2 = 0.0000 ln_Yperw Coef Std.Err z P>|z| [95%Conf.Interval] ln_Kperw 6106305 044599 13.69 0.000 5232181 69804 ln_L -.3132202 0172021 -18.21 0.000 -.3469356 -.2795048 t 0158858 0027709 5.73 0.000 0104549 021316 _cons 5.472751 5309696 10.31 0.000 4.43207 6.51343

Remarks:Both(b)and(b’)runwithGLSmethodarefitwithbothgroup1,andg r o u p 2.

Loglikelihood = 125.5313 Prob >chi2 = 0.0000 ln_rgdp Coef Std.Err z P>|z| [95%Conf.Interval] ln_K 6530831 0165523 39.46 0.000 6206411 685525 ln_L 0885221 0261733 3.38 0.001 0372234 139820 ln_t 1061687 0197852 5.37 0.000 0673905 144946 _cons 5.032661 170128 29.58 0.000 4.699217 5.36610

xtgls ln_Yperw ln_Kperwln_Lln_t

Loglikelihood = 125.5312 Prob>chi2 = 0.0000 ln_Yperw Coef Std.Err z P>|z| [95%Conf.Interval] ln_Kperw 653083 0165524 39.46 0.000 6206409 685525 ln_L -.2583948 017279 -14.95 0.000 -.292261 -.2245286 ln_t 106169 0197852 5.37 0.000 0673907 144947 _cons 5.032662 1701282 29.58 0.000 4.699217 5.36610

Loglikelihood = 123.0401 Prob>chi2 = 0.0000 ln_rgdp Coef Std.Err z P>|z| [95%Conf.Interval] ln_K 8146214 0215505 37.80 0.000 7723832 856859 ln_L -.0557685 0183812 -3.03 0.002 -.091795 -.019742 ln_t 0513963 02119 2.43 0.015 0098646 09292

xtgls ln_Yperw ln_Kperwln_Lln_t

Loglikelihood = 123.0401 Prob >chi2 = 0.0000 ln_Yperw Coef Std.Err z P>|z| [95%Conf.Interval] ln_Kperw 8146212 0215505 37.80 0.000 772383 856859 ln_L -.2411474 0108909 -22.14 0.000 -.2624933 -.219801 ln_t 0513967 02119 2.43 0.015 009865 092928 _cons 2.969585 2298801 12.92 0.000 2.519028 3.42014

Remarks: Equation (c)runwithGLSmethoddidnotfitgroup2fornegativecoefficiento f Labor;als oEquation(c’)didnotfitgroup1fornegativecoefficientofLabor.

Conclusion:Choosingtheequationswhichthatfittobothgroupsintermsofbothag greg ate and percapitamodel,thatare(b),(b’)

Loglikelihood = 202.7011 Prob> chi2 = 0.0000 g_Y Coef Std.Err z P>|z| [95%Conf.Interval] g_K 432764 0896445 4.83 0.000 2570641 60846 g_L 8015684 157082 5.10 0.000 4936934 1.10944 _cons 0128891 0081458 1.58 0.114 -.0030765 028854

Loglikelihood = 203.3227 Prob >chi2 = 0.0000 g_Yperw Coef Std.Err z P>|z| [95%Conf.Interval] g_Kperw 4361735 0903809 4.83 0.000 2590302 613316 g_L 2272552 1402668 1.62 0.105 -.0476627 502173 _cons 0122865 0081918 1.50 0.134 -.003769 028342

Loglikelihood = 188.7607 Prob>chi2 = 0.0000 g_Y Coef Std.Err z P>|z| [95%Conf.Interval] g_K 6501268 0969156 6.71 0.000 4601757 84007 g_L 4679094 1301705 3.59 0.000 21278 723038 _cons 0051572 0080752 0.64 0.523 -.0106698 020984

Loglikelihood = 190.0581 Prob> chi2 = 0.0000 g_Yperw Coef Std.Err z P>|z| [95%Conf.Interval] g_Kperw 6660256 0967247 6.89 0.000 4764487 855602 g_L 118602 1467454 0.81 0.419 -.1690137 406217 _cons 0040956 008078 0.51 0.612 -.0117369 019928

Remarks:equation(6)ismorebetterthanequation(7)intermsofclosenesstotheestimatedvalueofK,andrejectionofnullhypothesis.

A5.8.Equation(8a)and(8b)runwithFixedEffectmethodRun Mod el(8a):

xtregln_TFPln_eduln_FDICPIln_patentln_export,fe

F(5,113) = 376.82 corr(u_i,Xb) = 0.5807 Prob>F = 0.0000 ln_TFP Coef Std.Err t P>|t| [95%Conf.Interval] ln_edu 5363595 2522038 2.13 0.036 0366983 1.03602 ln_FDI 0103647 0125649 0.82 0.411 -.0145286 03525 CPI -.0018455 0020826 -0.89 0.377 -.0059716 002280 ln_patent 050792 0111345 4.56 0.000 0287326 072851 ln_export 1480144 0473492 3.13 0.002 0542072 241821 _cons 4.408151 2091204 21.08 0.000 3.993846 4.82245 sigma_u 77794242 6 sigma_e 07934768 rho 98970376 (fraction ofvariancedue to u_i)

xtregln_TFPln_eduln_exportln_patent,

Fixed-effects(within)regression fe

R-sq: within =0.9423 Obspergroup:min = 30 between=0.8983 avg = 30.5 overall=0.6977 max = 31

F(3,115) = 626.42 corr(u_i,Xb) =0.5992 Prob>F = 0.0000 ln_TFP Coef Std.Err t P>|t| [95%Conf.Interval] ln_edu 4738333 2078085 2.28 0.024 0622047 88546 ln_export 1666449 0353763 4.71 0.000 0965713 236718 ln_patent 0547197 0107895 5.07 0.000 0333478 076091 _cons 4.38205 2038735 21.49 0.000 3.978216 4.78588

4 sigma_u 76750724 sigma_e 07940359 rho 98941011 (fraction ofvariancedue to u_i)

xtregln_TFPln_eduln_FDICPIln_patentln_export,re

R-sq: within =0.7066 Obspergroup:min= 30 between= 0.9819 avg= 30.5 overall= 0.9272 max= 31

Randomeffectsu_i~ Gaussian Waldchi2(5) = 1477.03 corr(u_i,X) =0 (assumed) Prob> chi2 = 0.0000 ln_TFP Coef Std.Err z P>|z| [95%Conf.Interval] ln_edu 1620227 2239583 0.72 0.469 -.2769275 600972 ln_FDI -.1520429 0339727 -4.48 0.000 -.2186282 -.0854576 CPI 0118793 0063733 1.86 0.062 -.0006122 024370 ln_patent 3406275 0202882 16.79 0.000 3008635 380391 ln_export -.0319778 0746258 -0.43 0.668 -.1782417 114286 _cons 7.05014 5308704 13.28 0.000 6.009653 8.09062 sigma_us 7 igma_e rho

Tiêu đề	Surveying The Difference Of TFP Between Group Of Taiwan - Korea And Group Of Thailand - Malaysia; And Their TFP's Determinants
Tác giả	Duong Khanh Toan
Người hướng dẫn	Dr. Truong Dang Thuy
Trường học	University of Economics Ho Chi Minh City
Chuyên ngành	Development Economics
Thể loại	thesis
Năm xuất bản	2013
Thành phố	Ho Chi Minh City

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