Journal of Chemical and Pharmaceutical Research, 2014, 6(5): Research Article

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1 Available online Journal of Chemical and Pharmaceutical Research, 204, 6(5): Research Article ISSN : CODEN(USA) : JCPRC5 Exort facilitation and comarative advantages of the Chinese low-technology manufactures: A anel granger causality analysis Yu Hong, Wanjun Yang, Jiayue Wang and Hongwei Su 2 Jilin University of Finance and Economics, Changchun, China 2 College of Humanities and Sciences, Northeast Normal University, Changchun, China ABSTRACT This study emirically examines the causal relationshi between the changing comarative advantages and the net exorts of the Chinese low-technology manufactures, with an attemt to answer the question of whether the governmental exort facilitation efforts can effectively imrove the comarative advantage of secific industries. By emloying anel data analysis of the Chinese trade in low-technology manufactures ranging from 987 to 20, we find that ) the Chinese net exort caabilities are significantly above the comarative advantage, strongly indicating a tendency of the Chinese trade rotection which is featured with exort facilitation in the ast decades; 2) comarative advantage Granger causes net exorts in long-run, which is consistent to the Ricardian rediction that comarative advantage determines net exorts of an industry; 3) the short-run effect of comarative advantage uon the net exorts of the Chinese low-technology manufactures is significantly negative, imlying that a dro in the comarative advantage does encourage the Chinese government to tae exort facilitation measures; and 4) the Granger causal relationshi is uni-directional. Secifically, we find no evidence that exort facilitation can effectively imrove the comarative advantages of the Chinese low-technology manufactures. A ossible exlanation is that the exort facilitation mainly targets at imroving the domestic emloyment instead of the technology-based comarative advantages. Key words: Trade attern, Exort facilitation, Panel data analysis, Cointegration, Granger non-causality test INTRODUCTION It is controversial whether exort facilitation can imrove the comarative advantages of industries []-[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]. On the other hand, the free-trade 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. 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 [4]-[5]. 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 [6]. 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 580

2 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. EXPERIMENTAL SECTION Our basic idea is to measure the degree of a country s exort facilitation, or the roensity for divergence of trade atterns, 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 net exort ratio y = NX = X M ) ( X + M ) () ( 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 [-, ] with a mean of zero. y >0 indicates trade surlus in roduct, and the extreme of y = 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 = X / X ) ( X / X ) (2) i ( i i w w where RCA is the revealed comarative advantage of roduct exorts [7]; 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. In order to comare with y, this study follows Dalum (998) transformation technique [8] to normalize the RCA index by x RSCA = ( RCA ) ( RCA + ) (3) = where x is the revealed symmetric comarative advantage. The value interval of x is [-, ] with a mean of zero, which is exactly identical to the distribution of y. Note that when RCA =, 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 > and x <0 is equivalent to RCA <. 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 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 = n ( h w ), w = ( X + M ) / ( 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 58

3 imorts of the category. Data We follow Lall (2000) [9] to classify the Standard International Trade Classification Revision 2 (SITC Rev.2) three-digit roducts. As shown in table I, there are two sub-categories of low-technology manufactures: the category of textile, garment and footwear (LT) includes 20 roducts 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 992. Tab. reorts the classification scheme. Tab. Product codes of low-technology manufactures Category SITC Rev. 2 three-digit roduct codes LT 6, 62, 63, 65, 652, 654, 655, 656, 657, 658, 659, 83, 842, 843, 844, 845, 846, 847, 848, 85 LT2 642, 665, 666, 673, 674, 675, 676, 677, 679, 69, 692, 693, 694, 695, 696, 697, 699, 82, 893, 894, 895, 897, 898, 899 A reliminary observation of the weight averaged indicators of the low-technology category shows that the Chinese comarative advantage in the 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 990 and records in 20, 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). Tab. 2 resents the summary of the results for y and x. Table 2 Panel unit root test for net exort ratio and revealed symmetric comarative advantage y x y t x t CT C CT C N N C N CT CT C N LLC (0.00) (0.09) Breitung (0.95) (.00) (.00) (0.67) (0.92) (0.48) IPS (0.00) (0.0) ADF PP (0.27) (0.09) Note: C stands for individual intercet, T for trend and N for no exogenous variable; robabilities are in arentheses. Pedroni anel cointegration test For both y and x 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. 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. Tab. 3 Panel cointegration test results Panel v Panel rho Panel PP Panel ADF Grou rho Grou PP Grou ADF CT 0.72 (0.23) (0.9) (0.00) (0.00) 2.05 (0.98) -.8 (0.04) -7.5 (0.00) C.92 (0.03) (0.02) (0.00) (0.00).2 (0.87) (0.7) (0.23) N -.58 (0.94) 0.07 (0.53) -.68 (0.05) -.26 (0.0) 2.68 (.00) -3.0 (0.00) -.69 (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. 582

4 MODEL SPECIFICATION FOR PANEL VECM We intend to emloy vector error correction model (VECM) to conduct anel Granger non-causality 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 =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 =α 2 = =α =α, we can obtain a mixed-ool 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 the otimal model is. Model secification tests Let S be the sum of squared residuals of variable-coefficient model (6), S 2 be that of variable-intercet model (7), and S 3 be that of mixed-ool model (8). Using S, S 2 and S 3, we can obtain F and F 2 statistics. F statistic is F ( S = S 2 S) /[( n ) K] /[ nt n( K + )] which is asymtotically distributed as an F-statistic with (n-)k and n(t-k-) degrees of freedom. n is the number of cross-sections, T is the number of samle eriods and K is the number of indeendent variables. The F statistic comares the (6) and (7), with a null hyothesis of β =β 2 = =β =β. A significant F statistic rejects the null of variable-intercet model. For the next ste we obtain the F 2 statistic by ( S3 S) /[( n )( K + )] F2 = S /[ nt n( K + )] which is asymtotically distributed as an F-statistic with (n-)(k+) and n(t-k-) degrees of freedom. The F 2 statistic comares the (6) and (8), with a null hyothesis of α =α 2 = =α =α and β =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. Nevertheless, the otimum model may either contain deterministic time trend(s) or not. We allow for all these ossibilities in the tests to avoid arbitrariness. Tab. 4 reorts the F-test results for both occasions. Tab. 4 Secification Test for the Panel Cointegrating Equation S S 2 S 3 F F 2 SC No trend (0.00) (0.00) Trend (0.00) (0.00) Both F and F 2 statistics are significant, suggesting that the variable-coefficient model is otimal. By comaring the SC statistics for the two variable-coefficient models (one with individual time trends and one without), we conclude the otimal model has deterministic time trend as shown in y t = C t + t Trend + β x + e t (9) 583

5 where τ is the individual arameter of time trend (Trend). We estimate the model and mae the residuals for further use. Panel vector error correction model There are three ossible basic secifications for the anel VECM, each has or has no individual time trend. Tab. 5 gives the F-test results searately, by allowing the maximum lags to be u to four. Tab. 5 Secification test for anel VECM Lag Without Trend With Deterministic Trend S S 2 S 3 F F 2 SC S S 2 S 3 F F 2 SC (0.84) (0.82) (0.99) (0.99) (0.07) (0.7) (0.02) (0.06) 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 and 2 lags, while a variable-coefficient model is adequate when the lag is 3 or 4. We estimate all of the ossible models and obtain the SC statistics. SC statistics indicate that t 2 ( y y t + x xt ) + θ Trend + c y = r e + + µ t = 2 et + t t + = ( y y + x x ) + θ Trend + c x t = r µ () are the otimal anel VECMs with deendent variables to be y t. and x t resectively. The symbol of ( ) stands for first difference; the subscrit stands for lags; e t- is the error correcting term or the residuals of (9); c is a common intercet; r, y, x and θ are arameters for estimation and µ is the residual term. The asteriss in () distinguish the related arameters in (0). PANEL GRANGER NON-CAUSALITY TESTS Methods of short-run and long-run Tests The error correcting terms (e t- ) in (0) and () reflect the long-run relationshi between y t and x t and the lags of the first differences ( ( )) contain the short-run information. Following revious studies, we argue that the ey to identify short-run and long-run effects is whether the other conditions eeing unchanged [0]. We test for Wald restrictions of the null of x =x 2 =0 for (0) and y =y 2=0 for () 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 r =0 for (0) and r =0 for () to see whether the long-run equilibrium relation imroves the exlanatory ower of the models. Second, we test for the null of r =x =x 2 =0 for (0) and r =y =y 2=0 for () to chec whether the lagged differences of the indeendent variable exert significant effects on the deendent variables via e t-. Test results Both short-run and long-run Granger non-causality test results are resented in Tab. 6. Tab. 6 Panel Granger non-causality test results Short-run Effects Long-run Effects y t-, y t-2 x t-, x t- e t- e t-, y t-, y t-2 e t-, x t-, x t-2 y t 8.03 (0.00) 29.7 (0.00) (0.00) x t.45 (0.24) 2.06 (0.5).84 (0.4) Note: The first column indicates deendent variables of the anel VECMs and the values resented are F-statistics. We identify a uni-directional Granger causal relationshi running from x t to y t. In other words, revealed symmetric comarative advantage is the short-run determinant of net exort caability. When examining the estimation in (0), we find x = 0.57 and x 2 = 0.43, imlying that NX has negative short-run effects uon RSCA. In the long-run, the error correcting term (e t- ) as well as its combination with x t- and x t- Granger causes y t (0) 584

6 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 35.3, with a mean of This indicates that the long-run effect of comarative advantage uon net exort caability is ositive. 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 987, 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 tell an interesting and thought worthy story: The 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. Acnowledgement This wor is financially suorted by grant 202BS22 from Jilin Provincial Social Science Fund and grant 203GXS4D2 from National Soft Science Project of China. REFERENCES [] T Iwanow; C Kiratric. World. Dev. 2009, 37(6), [2] D Lederman; M Olarreaga; L Payton. J. Dev. Econ. 200, 9(2), [3] AP D'Costa. Sci. Tech. Society. 2002, 7(), [4] E Helman; PR Krugman. Maret Structure and Foreign Trade: Increasing Returns, Imerfect Cometition, and the International Economy. Cambridge, MA, MIT Press, 985. [5] PR Krugman. Rethining International Trade, Cambridge, MA, MIT Press, 994. [6] B Hoeman; A Nicita. World. Dev. 20, 39(2), [7] B Balassa. J. Politic. Econ. 964, 72(6), [8] B Dalum; K Laursen; G Villumsen. Int. Rev. Al. Econ. 998, 2(3), [9] S Lall. Oxford. Dev. Stud. 2000, 28(3), [0] Y Hong; Z Song; J Wang; H Su. J. Chem. Pharm. Res. 204, 6(),