An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc.

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1 [Tye text] [Tye text] [Tye text] ISSN : Volume 10 Issue 12 BioTechnology 2014 An Indian Journal FULL PAPER BTAIJ, 10(12), 2014 [ ] Panel cointegration analysis of exort facilitation and comarative advantages: The case of Chinese low-technology manufactures Yu Hong 1,2, Jiayue Wang 1, Hongwei Su 2 and Xiaowei Mu 2 * 1 Jilin University of Finance and Economics, , (P.R.CHINA) 2 College of Humanities and Sciences, Northeast Normal University, , (P.R.CHINA) ABSTRACT This aer attemts to investigate the nexus between the comarative advantages and the net exort caability by emloying anel Granger non-causality analysis technique on a dataset of the Chinese low-technology manufactures for the eriod of The results show that the net exort caabilities significantly surass the comarative advantages. However, there is no sign that the enhanced net exort caabilities as the results of exort facilitation have any statistically significant effect on the imrovement of the Chinese comarative advantages in the low-technology manufactures. On the oosite, the comarative advantages Granger cause the net exorts in both short-run and long-run. Further examination indicates that the short-run effect is negative while the long-run effect is ositive, imlying that in the short-run, the exort facilitation targets at saving the deteriorating comarative advantages, and the long-run net exort caabilities are essentially based uon the comarative advantages. At least for the low-technology manufactures, the Chinese exort facilitation aims at emloyment and economic growth instead of the imrovement of comarative advantages. KEYWORDS Comarative advantage; Exort facilitation; Trade olicy; Panel cointegration analysis; Granger non-causality test. Trade Science Inc.

2 BTAIJ, 10(12) 2014 Xiaowei Mu et al INTRODUCTION It is controversial whether exort facilitation can imrove the comarative advantages of industries [1,2]. Dynamic comarative advantage theories and strategic trade theories state the answer should be yes, because develoing countries may be loced in a comarative advantage tra without being able to udate their low-technological labor-intensive industries [3,4]. On the other hand, the freetrade roonents believe that olicy intervention in trade will only distort the factor marets, arguing that exort facilitation is one of the manifestations of trade rotectionism, which is not confined to the form of imort limitation [5,6]. Behind these academic controversies, there is still an even more fundamental question left to be answered: is it really advantageous for a country to imrove the comarative advantage of a secific industry? Even according to the former strand of literatures, the exort facilitation of a develoing country should target at imroving the international cometitiveness of higher technology or strategically otential domestic industries by taing advantage of the effects of increasing returns [7,8]. However, a country may also have lenty of incentives to develo labor-intensive industries with a mere urose of emloyment enhancement. In this case, deteriorating comarative advantages may adversely encourage the government of a develoing country to facilitate exorts in these labor-intensive industries to generate more woring oortunities. This is esecially true when the country is in transition of an urban-rural dual economy, which is in face of the roblems of absorbing the comaratively excessive rural labor forces. Chinese exeriences in the recent years add evidences that government can lay a crucial role in trade develoment and economic growth [9]. There are three main causes for China s heavy deendence on exorts. Firstly, the domestic demand has been limited in relation to the suly side, driving China to see external demand from the world maret. Secondly, the need to absorb the redundant rural labor force has made structural changes in industries. The relatively chea labor cost has guaranteed the develoment of Chinese exorts in labor-intensive manufactures. Thirdly, Chinese government has taen active measures to facilitate exorts to dynamic comarative advantage. In short words, China may have facilitated its exorts in both higher-technology and low-technology labor-intensive industries with essentially different olicy targets. Does the Chinese government aggressively facilitate her exort in low-technology roducts? What is the olicy target of the Chinese exort facilitation in low-technology roducts? How is the erformance of the trade olicy? This study addresses these questions by emirically testing for the Granger causal relation between the indices of net exort ratio and revealed symmetric comarative advantage. Our findings from anel data shed a new light on the understanding of the erformance of Chinese strategic trade olicy. MEASUREMENTS AND DATA Our basic idea is to measure the degree of a country s exort facilitation, or the roensity for divergence of trade attern, by deducting the comarative advantage from the net exort caability of a secific roduct. Net exort ratio Net exorts caability in a roduct is measured by y = NX = ( X M ) ( X + M ) (1) where y (or NX ) stands for the net exorts ratio of roduct ; X and M reresent exorts and imorts. This indicator consequently catures the ercentage of the trade balance in the total exorts and imorts. The value interval of y is [-1, 1] with a mean of zero. y >0 indicates trade surlus in roduct,

3 6042 Panel cointegration analysis of exort facilitation and comarative advantages BTAIJ, 10(12) 2014 and the extreme of y =1 imlies there are only exorts. Similarly, y <0 indicates trade deficit in roduct. Revealed symmetric comarative advantage Comarative advantage is measured by the Balassa index of RCA i = ( X i / X i ) ( X w / X w ) (2) where RCA is the revealed comarative advantage of roduct exorts [10] ; X i is the total exorts of the concerned country i; X w is the world exorts of roduct and X w is the total world exorts. The value interval of RCA is [0, ], with a median of 1. In order to comare with y, this study follows Dalum (1998) transformation technique [11] to normalize the RCA index by x = RSCA = ( RCA 1) ( RCA + 1) (3) where x is the revealed symmetric comarative advantage. The value interval of x is [-1, 1] with a mean of zero, which is exactly identical to the distribution of y. Note that when RCA =1, we have x =0, imlying that the secialization of country i in roduct is identical to the world average. Similarly, x >0 reflects to RCA >1 and x <0 is equivalent to RCA <1. Proensity for divergence of trade attern This study defines divergence of trade atterns as the relative difference between the net exort caability and the current comarative advantage. Hecscher-Ohlin-Ricardo aroach redicts that a country tends to secialize in and to exort the comarative advantage roducts. The higher the comarative advantage, the more roduct exorts by the country. Assuming in equilibrium, y is strictly in accordance with x, the condition of trade attern equilibrium is thus given by y =x. Define h = y x (4) as the roensity for trade attern divergence. The h-index measures the difference between y and x. It has a symmetric distribution with a mean of zero. h =0 indicates a trade attern equilibrium, while h >0 reveals a roensity for "ositive divergence" which is featured by excessive net exorts in relation to the temorary comarative advantage. This form of trade attern divergence may reflect a mercantilist tendency inherent in the ossible strategic trade olicy which targets at the comarative advantage imrovement of a secific industry, by means of exort romotion or/and imort rotection. When it involves a category of n roducts, we use H = n = 1 ( h w ), w = ( X + M ) / n = 1 ( X + M ) (5) to measure the weighted divergence roensity for trade attern. w is the weight of roduct in the total value of exorts and imorts of the category. Data We follow Lall (2000) [12] to classify the Standard International Trade Classification Revision 2 (SITC Rev.2) three-digit roducts. As shown in TABLE 1, there are two sub-categories of lowtechnology manufactures: the category of textile, garment and footwear (LT1) includes 20 roducts

4 BTAIJ, 10(12) 2014 Xiaowei Mu et al while the category of other low-technology roducts contains 24 roducts. All of the trade data are comiled from UN Comtrade database. We eliminate Iron, steel hoo, stri (code 675) from our samles because China has involved no trade of this roduct since TABLE 1 reorts the classification scheme. TABLE 1 : Product codes of low-technology manufactures Category SITC Rev. 2 three-digit roduct codes LT1 611, 612, 613, 651, 652, 654, 655, 656, 657, 658, 659, 831, 842, 843, 844, 845, 846, 847, 848, 851 LT2 642, 665, 666, 673, 674, 675, 676, 677, 679, 691, 692, 693, 694, 695, 696, 697, 699, 821, 893, 894, 895, 897, 898, 899 A reliminary observation of the weight averaged trade atterns of the low-technology category shows that the Chinese comarative advantage in low-technology roducts has been stable while the net exort ratio has exhibited an aarently increasing trend. As a result, the H index has been ositive since 1990 and records in the year of 2011, imlying a strong roensity for trade attern divergence. PANEL COINTEGRATION ANALYSIS Panel unit root test In order to avoid surious regression which arises from the using non-stationary anel data, we conduct anel unit root tests to examine the stationarity of y and x series. Five alternative methods are available, among which LLC and Breitung assume common unit root rocess, while IPS, ADF-Fisher and PP-Fisher assume individual unit root rocess. We carry out lag selection via Schwarz criterion (SC). TABLE 2 resents the summary of the results for y and x. For both y and x level series, the method of Breitung can not reject the null of common unit root, imlying that neither is stationary. Both are stationary uon taing first-difference, maing it ossible and necessary for anel cointegration tests. TABLE 2 : Panel unit root test for net exort ratio and revealed symmetric comarative advantage LLC Breitung IPS ADF PP y x Δy t Δx t CT C CT C N N C N CT CT C N (0.00) (0.00) (0.00) (0.00) (0.00) (0.09) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.95) (1.00) (1.00) (0.00) (0.00) (0.67) (0.92) (0.48) (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.27) (0.09) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Note: C stands for individual intercet, T for trend and N for no exogenous variable; robabilities are in arentheses. Pedroni anel cointegration test Considering the heterogeneity across individual of the anel members, this study uses Pedroni s method to test for the cointegration relationshi between y and x. Among the seven available statistics, Panel v, Panel rho, Panel PP and Panel ADF are based on ooling the residuals of the regression along the within-dimension, while Grou rho, Grou PP, Grou ADF are based on ooling the residuals of the regression along the between-dimension. TABLE 3 shows the results of three ossible model secifications.

5 6044 Panel cointegration analysis of exort facilitation and comarative advantages BTAIJ, 10(12) 2014 TABLE 3 : Panel cointegration test results Panel v Panel rho Panel PP Panel ADF Grou rho Grou PP Grou ADF CT 0.72 (0.23) (0.19) (0.00) (0.00) 2.05 (0.98) (0.04) (0.00) C 1.92 (0.03) (0.02) (0.00) (0.00) 1.12 (0.87) (0.17) (0.23) N (0.94) 0.07 (0.53) (0.05) (0.10) 2.68 (1.00) (0.00) (0.05) Note : C stands for individual intercet, T for trend and N for no exogenous variable; Automatic selection of maximum lags is based on Schwarz information criterion. When the model secification allows for individual intercet and deterministic time trend, anel PP, Panel ADF, Grou PP, Grou ADF reject the null of no cointegration at 0.05 confidence level. We thus can come to the conclusion that there is a cointegration relationshi between y and x. MODEL SPECIFICATIONS FOR PANEL VECM We intend to emloy vector error correction model (VECM) to conduct anel Granger noncausality tests. A crucial ste is to secify the VECM as well as the cointegrating equation. Panel cointegrating equation We assume there are three ossible tyes of anel models for the cointegrating equation. Let y t be the deendent variables, the unrestricted anel model is given by y = α + β x + e t t t (6) where α stands for intercets, β reresents the arameters for estimation, and e is the residuals. By allowing individual intercets and arameters, this is a variable-coefficient model with fixed effect. If we imose the restriction of β 1 =β 2 = =β =β, where β is a common coefficient, we can therefore have a variable-intercet anel regressional model y = α + β x + e t t t (7) By further imosing the restriction on the intercets of α 1 =α 2 = =α =α, we can obtain a mixedool model in the form of y = α + β x + e t t t (8) which requires a common intercet as well as a common coefficient for all cross-sections of the ooled anel model. The multile ossibilities imly that any re-assumtion of the secification of the otimal model would be imrecise or even dangerous. We therefore emloy F-tests to determine which is the otimal model Model secification tests Let S 1 be the sum of squared residuals of variable-coefficient model (6), S 2 be that of variableintercet model (7), and S 3 be that of mixed-ool model (8). Using S 1, S 2 and S 3, we can obtain F 1 and F 2 statistics. F 1 statistic is ( S2 S1) /[( n 1) K] F1 = S /[ nt n( K + 1)] 1

6 BTAIJ, 10(12) 2014 Xiaowei Mu et al which is asymtotically distributed as an F-statistic with (n-1)k and n(t-k-1) degrees of freedom, where n is the number of cross-sections, T is the number of samle eriods and K is the number of indeendent variables. The F 1 statistic comares the (6) and (7), with a null hyothesis of β 1 =β 2 = =β =β. A significant F 1 statistic rejects the null of variable-intercet model. In ste three, we obtain F 2 statistic by ( S3 S1)/[( n 1)( K + 1)] F2 = S /[ nt n( K + 1)] 1 which is asymtotically distributed as an F-statistic with (n-1)(k+1) and n(t-k-1) degrees of freedom. The F 2 statistic comares the (6) and (8), with a null hyothesis of α 1 =α 2 = =α =α and β 1 =β 2 = =β =β. A significant F 2 statistic rejects the null of mixed-ool model, imlying that the otimal model should allow for individual fixed-effects. Moreover, the otimum model may either contain deterministic time trend(s) or not. TABLE 4 reorts the F-test results for both occasions. TABLE 4 : Secification Test for the Panel Cointegrating Equation S 1 S 2 S 3 F 1 F 2 SC No trend (0.00) (0.00) Trend (0.00) (0.00) F 1 and F 2 statistics are both significant, suggesting that the variable-coefficient model is otimal. When comaring the SC statistics for the two variable-coefficient models (one has individual time trends and one has no trend), we conclude that the otimal model have deterministic time trend as shown in y t = C t + τ Trend + β x + ε t (9) where τ is the individual arameter of time trend (Trend). We estimate the model in this form and mae the residuals for further use. Panel vector error correction model There are also three ossible basic secifications for the anel VECM, each has or has no individual time trend. TABLE 5 reorts the F-test results searately, by allowing the maximum lags to be u to four. TABLE 5 : Secification test for anel VECM Lag No trend Deterministic Trend S 1 S 2 S 3 F 1 F 2 SC S 1 S 2 S 3 F 1 F 2 SC (0.84) 0.86 (0.82) (0.99) 0.69(0.99) (0.00) 1.34 (0.00) (0.07) 1.10(0.17) (0.00) 1.59 (0.00) (0.02) 1.16(0.06) (0.00) 2.90 (0.00) (0.00) 1.63(0.00) 0.87

7 6046 Panel cointegration analysis of exort facilitation and comarative advantages BTAIJ, 10(12) 2014 If no trend involves, mixed-ool model is otimal for one lag while a variable-coefficient model is suitable for 2, 3 and 4 lags. When assuming the resence of deterministic time trend, a mixed-ool model is otimal for 1 and 2 lags, while a variable-coefficient model is adequate when the lag is 3 or 4. We estimate all of the eight ossible models and obtain the SC statistics. SC statistics indicate that Δy t t 1 2 ( ψ Δyt + ξ Δxt ) + θ Trend+ c μ = ρ e + + = 1 (10) * * * * ( ψ Δy + ξ Δx ) + θ Trend+ c μ 2 t 1 + t t + = 1 Δxt = ρ e * (11) are the otimal anel VECMs with deendent variables to be Δy t. and Δx t resectively. The symbol of Δ( ) stands for first difference; the subscrit stands for lags; e t-1 is the error correcting term or the residuals of (9); c is a common intercet; ρ, ψ, ξ and θ are arameters for estimation and μ is the residual term. The asteriss in (11) distinguish the related arameters in (10). PANEL GRANGER NON-CAUSALITY TESTS Methods of short-run and long-run Tests e t-1 in (10) and (11) reflects the long-run relationshi between y t and x t and the lags of the first differences (Δ( )) contain the short-run information. Following revious literatures, we argue that the ey to identify short-run and long-run effects is the assumtion of other conditions eeing unchanged [13]. We test for the Wald restrictions of the null of ξ 1 =ξ 2 =0 for (10) and ψ * 1=ψ * 2=0 for (11) to examine the short-run Granger non-causality because the tests involve no error correcting term. We examine the long-run effects by two aroaches. First, we test ρ =0 for (10) and ρ * =0 for (11) to see whether the long-run equilibrium relation imroves the exlanatory ower of the models. Second, we test for the null of ρ * =ξ 1 =ξ 2 =0 for (10) and ρ * =ψ * 1=ψ * 2=0 for (11) to chec whether the lagged differences of the indeendent variable exert significant effects uon the deendent variables via e t-1. Test results Granger non-causality test results are shown in TABLE 6. We identify a uni-directional Granger causal relationshi running from Δx t to Δy t. In other words, revealed symmetric comarative advantage is the short-run determinant of net exort caability. When examining the estimation in (10), we find ξ 1 = and ξ 2 = 0.143, imlying that NX has negative short-run effects uon RSCA. In the long-run, the error correcting term (e t-1 ) as well as its combination with Δx t-1 and Δx t-1 Granger causes Δy t uni-directionally. Because the cointegrating equations are the residual series of the variable-coefficient models, we aggregate the coefficients (β ) in (9), which is the long-run equilibrium cointegrating equation, to generate a sum of 35.13, with a mean of This indicates that the longrun effect of comarative advantage uon net exort caability is ositive. TABLE 6 : Panel Granger non-causality test results Short-run Effects Long-run Effects Δy t-1, Δy t-2 Δx t-1, Δx t-1 e t-1 e t-1, Δy t-1, Δy t-2 e t-1, Δx t-1, Δx t-2 Δy t 8.03 (0.00) (0.00) (0.00) Δx t 1.45 (0.24) 2.06 (0.15) 1.84 (0.14) Note: The first column indicates deendent variables of the anel VECMs and the values resented are F-statistics.

8 BTAIJ, 10(12) 2014 Xiaowei Mu et al CONCLUSION The Chinese industrialization and urbanization has attracted a large number of rural labor forces to move to the non-agricultural industries. On one hand, this rocess has generated abundant suly in the labor maret, enabling China to gain comarative advantages in the labor-intensive low-technology manufactures. On the other hand, it has also brought tight ressure of emloyment, which has romted the Chinese government to see for even stronger comarative advantages by means of exort facilitation. Using anel data of , this aer emirically studies the dynamic relation between Chinese net exort ratio and revealed symmetric comarative advantage. Firstly, we find that the net exort ratio of the Chinese low-technology manufactures as a category has et increasing since 1987, while the revealed symmetric comarative advantage has exhibited no obvious trend. The inconsistent time aths have given rise to a ositive roensity for trade attern divergence, where the net exort ratio ees uwardly diverged from the revealed symmetric comarative advantage. This henomenon may be much a result of governmental exort facilitation. Secondly, Granger causality runs form revealed symmetric comarative advantage to net exort ratio in both short-run and long-run. However, revealed symmetric comarative advantage exerts ositive effect uon net exort ratio only in the long-run, while the short-run effect is negative. In other words, a dro in the comarative advantage will encourage exort facilitation in short-run. Last but not least, we can not ignore the fact that net exort ratio has no significant effect on comarative advantage. This imlies that the erformance of government exort facilitation is very oor in terms of imroving the comarative advantage of Chinese low-technology manufactures. These evidences suggest a story that Chinese government does tae measures to facilitate the exorts of the low-technology manufacturing industries with a olicy target of imroving the domestic emloyment. The net exort caabilities of these industries are based uon the comarative advantages, although there are indications that the Chinese government has an exectation that the exort facilitation efforts can level u the comarative advantages at the exense of maret distortion. The facilitation efforts, however, are virtually in vain. ACKNOWLEDGEMENT This wor is financially suorted by 2013GXS4D112 from National Soft Science Project of China and 14BJY233 from National Social Science Fund of China. REFERENCES [1] T.Iwanow, C.Kiratric; Trade facilitation and manufactured exorts: Is Africa different? World Develoment, 37(6), (2009). [2] D.Lederman, M.Olarreaga, L.Payton; Exort romotion agencies: Do they wor? Journal of Develoment Economics, 91(2), (2010). [3] M.Cimoli; Loc-in secialization (Dis)advantages in a structuralist growth model, in J.Fagerberg, B.Versagen, Von N.Tunzelmann (Eds), The dynamics of technology trade and growth, Aldershot: Edward Elgar, (1994). [4] A.P.D'Costa; Exort growth and ath deendence: The locing-in of innovations in the software industry. Science Technology and Society, 7(1), 51-89, 2002 (2002). [5] D.Pang, Y.Hong; Measuring distortions of trade atterns: An alication to China. IEEE International Conference on Service Oerations and Logistics and Informatics, (2010). [6] Y.Hong, H.Su; A test of dynamic comarative advantage hyothesis using anel data of the Chinese trade in medium-technology roducts International Conference on Management Science and Engineering (ICMSE), (2010). [7] E.Helman, P.R.Krugman, Maret structure and foreign trade: Increasing returns, imerfect cometition, and the international economy. Cambridge, MA: MIT Press, (1985).

9 6048 Panel cointegration analysis of exort facilitation and comarative advantages BTAIJ, 10(12) 2014 [8] P.R.Krugman; Rethining international trade, Cambridge, MA: MIT Press, (1994). [9] B.Hoeman, A.Nicita; Trade olicy, trade costs, and develoing country trade. World Develoment, 39(12), (2011). [10] B.Balassa; The urchasing-ower arity doctrine: A rearaisal. The Journal of Political Economy, 72(6), (1964). [11] B.Dalum, K.Laursen, G.Villumsen; Structural change in OECD exort secialisation atterns: Desecialisation and 'sticiness'. International Review of Alied Economics, 12(3), (1998). [12] S.Lall; The technological structure and erformance of develoing country manufactured exorts, Oxford Develoment Studies, 28(3), (2000). [13] Y.Hong; A study of changes and distortions of Chinese commodity trade attern. Ph.D. dissertation of Jilin University, (in Chinese), (2009). [14] Y.Hong, Z.Song, J.Wang, H.Su; Comarative advantage of food exorts, rural-urban income disarity and agricultural emloyment: Emirical evidence from China. Journal of Chemical and Pharmaceutical Research, 6(1), (2014).