Relationships between economic growth, foreign

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Relationships between economic growth, foreign direct investment and trade: evidence from China Xiaohui Liu , Peter Burridge & P. J. N. Sinclair Published online: 04 Oct 2010.

To cite this article: Xiaohui Liu , Peter Burridge & P. J. N. Sinclair (2002) Relationships between economic growth, foreign direct investment and trade: evidence from China, Applied Economics, 34:11, 1433-1440, DOI: 10.1080/00036840110100835 To link to this article: http://dx.doi.org/10.1080/00036840110100835

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Applied Economics, 2002, 34, 1433 ±1440

Relationships between economic growth, foreign direct investment and trade: evidence from China X I A O H U I L I U * , P E T E R B U R R I D G E z and P . J. N . S I N C LA I R

Downloaded by [112.253.13.170] at 09:15 22 March 2014

Department of Marketing and Internationa l Business, Luton Business School, University of Luton, UK, z Department of Economics, City University, UK and } Department of Economics, University of Birmingham, UK

This study investigates the causal links between trade, economic growth and inward foreign direct investment (FDI) in China at the aggregate level. The integration and cointegration properties of quarterly data are analysed. Long-run relationships between growth, exports, imports and FDI are identi®ed in a cointegration framework, in which this paper ®nds bi-directional causality between economic growth, FDI and exports. Economic development, exports and FDI appear to be mutually reinforcing under the open-door policy.

I. INTRODUCTION Since starting the process of economic reform, and opening up to the outside world in 1979, China has become one of the fastest growing economies in the world. External trade and GDP grew on average by about 15% and 9% p.a., respectively, from 1979 to 1997 and China is now the largest recipient of foreign direct investment (FDI) in the developing world.1 Many studies have investigated the relations between China’s inward FDI and other aspects of the Chinese economy. However, there has been little detailed empirical study of causal links between FDI, trade and economic growth in China, especially in a multivariate framework. Understanding the causal connections between these phenomena is important for development strategies in China and other developing countries. Previous research in this area falls roughly into three groups. The ®rst group of studies tests the main determinants of inward FDI in China using either time-series or panel data (for example, Wang and Swain, 1997; Liu, et al. 1997), the extent to which FDI can be explained by economic growth and external trade being the focus of attention. These studies, which assume one-way causality from openness and economic growth to FDI, appear to indicate

that the rapidly expanding Chinese economy and a high level of openness to the outside world, proxied by external trade, are the main economic determinants of inward FDI. Papers in the second group examine the e ects of trade and FDI on growth. Empirical results are mixed. Wei (1995) and Wei et al. (2001) ®nd that exports and FDI in¯uence economic growth. Dees (1998) ®nds that FDI has a positive e ect on economic growth through its in¯uence on technical change, while Woo (1995) argues that FDI does not have a signi®cant impact on growth, Wei’s (1995) estimates overstating the contribution of FDI to growth because FDI is correlated with total factor productivity growth. Although, taken together, these papers consider both possible directions of causality, none of them tests explicitly for bi-directional links. The third group of studies attempts to test the causal relationship between trade and growth, that is, whether China’s trade expansion is caused by its rapid economic growth or, alternatively, whether the higher level of openness leads to higher economic growth. Liu et al. (1997) test the direction of causality between external trade (exports and imports) and GNP. Shan and Sun (1998) test for causality between exports and real industrial output. They all ®nd bi-directional causality between trade and economic

* Corresponding author. E-mail: [email protected] 1 China Statistical Yearbook (1997) and The People’s Daily, Oversees, Edition, 1 February (1998). Applied Economics ISSN 0003±6846 print/ISSN 1466±4283 online # 2002 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080 /0003684011010083 5

1433

X. Liu et al.


1434 growth, implying that China’s economic growth and trade reinforce each other. However, FDI is not included in these latter studies. Two limitations of the work cited above are that (a) bivariate causality tests may be seriously biased if relevant covariates are omitted, and (b) test outcomes from equations estimated in levels may be unreliable if data are non-stationary. The contribution of this paper, therefore, is to examine the causal relationship between economic growth, trade and FDI in China by using multivariate Granger causality tests in a cointegration framework. This study tests the integration properties of the data, then employs the Johansen procedure to detect the number of cointegrating vectors, and then tests causality in the resulting restricted VARECM. The next section summarizes the methodology, Section III describes the data, Section IV presents empirical results and Section V concludes.

II. METHODOLOGY This study is interested in the interplay of four variables, GDP, FDI, Imports, and Exports; to set the scene, therefore, consider a VAR involving four variables, W ; X; Y ; and Z. In a stationary setting, a Granger causality test in such a structure would be carried out (following Ghartey, 1993) via the following regressions: W t ˆ ¯0 ‡ ‡

m X iˆ1

m X lˆ1

‡

lˆ1

Yt ˆ ³ 0 ‡ ‡

iˆ1

lˆ1

lˆ1

¯3k Yt¡k

kˆ1

…1† n X

’1i Xt¡i ‡

jˆ1

’2j Yt¡j ‡

n X

¢Z

’3k Zt¡k

kˆ1

³1i Yt¡i ‡

p X jˆ1

…2† ³2j Xt¡j ‡

p X

iˆ1

®1i Zt¡i ‡

³3k Zt¡k

kˆ1

®4l Wt¡l ‡ u4t

t ˆ 1; 2; . . . ; N

…3†

q X jˆ1

®2j Yt¡j ‡

q X

t

¬41 ¬42 2 3 "1 6 7 6 "2 7 6 7 ‡6 7 6 "3 7 4 5 "4

³4l Wt¡l ‡ u3t

q X

q X

jˆ1

m X

’4l Wt¡l ‡ u2t

p X

p X

Z t ˆ ®0 ‡ ‡

iˆ1

n X

¯2j Zt¡j ‡

¯4l Xt¡l ‡ u1t

n X

Xt ˆ ’0 ‡

m X

¯1i Wt¡i ‡

where the lag lengths, (m; n; p; q) are determined so that u1t ; u2t ; u3t and u4t are serially uncorrelated. The null hypothesis `Y does not Granger cause X, given W and Z’ is tested via a standard F-test, being rejected if the ’2j in Equation 2 are jointly signi®cant. Similarly, if the ³2j in Equation 3 are jointly signi®cantly di erent from zero, the null hypothesis that X does not Granger cause Y, given W and Z, is rejected and so on. However, if the series under test contain unit root non-stationarity , it becomes important to determine whether or not they are cointegrated, as this a ects the sampling distributions of the causality tests as well as the interpretation of the test equations. Toda and Phillips (1993) show that levels of autoregressions are an unreliable basis for inference about causality in the nonstationary case, since the sampling distributions of the test statistics depend on the ranks of certain sub-matrices in the cointegrating space. They thus favour the use of the VARECM framework of Johansen (1988) in which the necessary information about the cointegrating space is available, and where causality test statistics will usually have asymptoti c Chi-square distributions. This study adopts this approach. This study thus tests for unit roots, ®ts a VAR, selecting lag length via the usual combination of residual diagnostics and coe cient F-tests, tests for cointegrating rank, and ®nally tests for causality. Anticipating slightly, since it is found that the data are cointegrated with rank equal to 2, ignoring the higherorder dynamics and the seasonal pattern, Equations 1±4 above can be re-written in VARECM form: 2 3 2 3 2 3 W ¢W ¬11 ¬12 6 7 6 7" #6 7 6 ¢X 7 6 ¬21 ¬22 7 11 12 13 14 6 X 7 6 7 6 7 6 7 6 7 ˆ6 7 6 7 6 ¢Y 7 6 ¬31 ¬32 7 21 22 23 34 6 Y 7 4 5 4 5 4 5

®3k Yt¡k

kˆ1

…4†

Z

t¡1

…5†

t

where the parameters of interest are in the long-run (i.e. cointegrating) vectors, ij , and the adjustment coe cients (loading factors), ¬ij . Hall and Milne (1994; 600±1) introduce the notion of the absence of weak causality to denote the situation in which the long-run level of one or more variables is una ected by the levels of others. In Equation 5, this is testable via zero restrictions on ¬ij which are equivalent to weak exogeneity. Following Hall and Milne it is noted that if weak non-causality is rejected, then Granger non-causality, which in addition involves the remaining higher-order short-run dynamics also is rejected. Thus bi-directional causality can be explored by estimating

Economic growth, foreign direct investment and trade the full VARECM proposed in Equation 5, and testing restrictions on the long-run and short-run adjustment coef®cients.

1435 Equation 5 with seasonal dummies and further lagged differences on the RHS. Testing for cointegration


III. DATA Quarterly exports (EX) and imports (IM) from 1981:1 to 1997:4 are obtained from the International Financial Statistics Yearbook. We treat exports and imports separately to allow for the possibility that their in¯uence is asymmetric. Quarterly inward FDI data for the same period are acquired from various sources, including the China State Statistical Bureau and Journal of International Trade (in Chinese). These series are de¯ated using the GDP de¯ator …1990 ˆ 100†. No quarterly or monthly GDP statistics for China are available, only monthly gross industrial output (GIO) at 1990 constant prices, so this study must construct an estimated quarterly series. Following Liu et al. (1997), it has been found that the annual growth pattern of GDP is similar to that of GIO, and the GDP estimate constructed as follows. Let gt , t ˆ 1981; 1982; . . . be the annual GDP/GIO ratio, and GIOt:q be the quarterly value of GIO (sum of three consecutive months), then the quarterly GDP data are calculated by the following equation: GDPt:q ˆ gt £ GIOt:q ;

q ˆ 1; . . . ; 4

…6†

From Figs 1 and 2, it can be seen that the GDP, export and import series have a regular seasonal pattern since production and demand are seasonal. The FDI series also has a quite regular seasonal pattern in the earlier part of the record. A possible reason for this is that in the early period FDI ¯ows just re¯ected accounting ¯ows associated with exports, while later on they contained a signi®cant amount of new capital, and hence the seasonal pattern is swamped. To accommodate these regular patterns, seasonal dummies are introduced to this model when unit roots, cointegration and causality are tested. For the FDI equation, the seasonal dummy is only present in the ®rst part of the record.

IV. EMPIRICAL RESULTS Testing for integration Table 1 gives the results of ADF unit root tests with lag length chosen by downward search (t-test on the longest lag). This study works throughou t with the logarithms of the variables, so that ®rst di erences correspond to growth rates. The null hypothesis of a unit root in the logarithm is not rejected for any of the four variables. However, each of the logged series is stationary in ®rst di erences, so all the variables are integrated of order one. Therefore, the causality tests in this paper are based on estimation of

After a general-to-speci ®c search starting with a system with 4 lags, constant and seasonal dummies, a model with constant and seasonal dummies constrained to lie in the cointegrating space was chosen. Three lagged di erences are required since the reduction of lag length from 3 to 2 was rejected by the residual diagnostics. The ®tted series and residuals, and the residual diagnostics for the maintained model are shown in Figs 1±2 and in Table 3. The test for cointegration rank described by Johansen and Juselius (1990) is reported in Table 2. Because the tabulated critical values of the rank tests are not reliable in small samples (tending to overstate the number of cointegration vectors), it is thought that rank ˆ 2 is most plausible. This is borne out by the eigenvalues, of which two are large, and two small (0.96, 0.53, 0.33 and 0.19). Maintaining a cointegration rank of two, two normalizing and two exclusion restrictions for identi®cation are introduced; that is, this study normalizes on LGDP in Equation 7 below, and on LFDI in Equation 8, and excludes LFDI from Equation 7 and LGDP from Equation 8, which leads to the cointegrating relations below: LGDP ˆ ¡3:6 ‡ 0:55LEX ‡ 0:02LIM ‡ SD LFDI ˆ ¡8:43 ‡ 2:31LEX ¡ 1:04LIM ‡ SD

…7† …8†

where SD represents seasonal dummy variables. As is apparent from Fig. 1, there is a common rising trend, and the most plausible interpretation of Equation 7 is that GDP and external trade are broadly proportional. Equation 8 re¯ects the relationship between FDI, exports and imports. Under the policy of export requirements, foreign invested enterprises (FIEs) will tend to be export-orientate d ®rms. FIEs are allowed to access world markets and are able to operate with the minimum of administrative and other restrictions after they set up in China. As a result, FIEs have made a great contribution to the growth of Chinese exports. After 1992, the policy of export requirements was relaxed, and foreign ®rms were allowed, wholly or partially, to target the domestic market. This may be one of the reasons why a substitution e ect exists between FDI and imports as shown in Equation 8. Testing for weak exogeneity and causality Tables 4 and 5 report the weak exogeneity and causality tests, in which, in the notation of Equation 5, Wt ˆ LFDI ; Xt ˆ LGDP; Yt ˆ LIM and Zt ˆ LEX. Notice that the four weak exogeneity (weak causality ± after Hall and Milne) tests (that each successive row of the matrix, ¬, is zero) massively reject the null hypothesis for

X. Liu et al.

1436

Log(FDI) actual, fitted value and residuals 6

Fitted

Lfdi

4

2

2

1985

1990

1995

1985

1990

1995

rLfdi


1 0 -1 -2

Log(GDP) actual, fitted value and residuals 7

Fitted

Lgdp

.5 6 .5

3

1985

1990

1995

1985

1990

1995

rLgdp

2 1 0 -1 -2

Fig. 1. Log(FDI) and Log(EDP) actual, ®tted value and residuals

Economic growth, foreign direct investment and trade

1437

Log(EX) actual, fitted value and residuals Fitted

Lex

5

4

3

2

1985

1990

1995

1985

1990

1995

1985

1990

1995

1985

1990

1995

rLex


1 0 1 2

Log(IM) actual, fitted and residuals Fitted

Lim

5

4

3

2

rLim

1 0 -1 -2

Fig. 2. Log(EX) and Log(IM) actual, ®tted value and residuals

X. Liu et al.

1438 Table 1. ADF test for unit root Null hypothesis: LGDP, LFDI, LEX and LIM contain a unit root. Variables LFDI LGDP LIM LEX

ADF (incl. trend) Level ¡1.05 ¡0.67 ¡1.90 ¡0.54

(3) (3) (1) (1)

ADF (intercept only) Di erence ¡3.59*** (2) ¡3.57*** (3) ¡2.80* (3) ¡5.41*** (1)


Notes: (1) ***, ** and * denote signi®cance at the 1%, 5% and 10% levels, respectively. (2) Figures in parentheses are the number of lags used.

each of the four variables. Therefore, this study concludes that each is weakly caused by the other three, which implies in turn that there is a bi-directional Granger causality between all pairs of variables. This conclusion is reinforced by the results in Table 5. There this study reports Wald tests of the hypothesis that all higher-order lagged coe cients in the VARECM, on each variable in turn, are zero ± that is, the second set of restrictions implied by Granger non- causality (see again Hall and Milne, 1994; 600, Equation 14). These Wald tests reveal bi-directional causal links in the short-run dynamics between GDP, Exports and FDI, but only a one-way causal link appears running from these three variables to Imports. Taken together, these results are consistent with endogenous growth in which export promotion or openness, which includes attracting FDI, can generate permanent e ects on the level of GDP (Ben-David and Loewy,

1997). As a fast growing country, China has become a manufacturing centre for labour intensive goods.2 In 1978, China accounted for only 0.75% of total world exports, but by 1995 this share had increased to 3.0% which is equivalent to that of the four member countries of ASEAN: Indonesia, Thailand, Malaysia and the Philippines (Naughton, 1996). Thus export expansion and growth go hand in hand.

Further interpreting the empirical results There is intense interest in the causal connections, if any, between economic growth, FDI, exports and imports, especially in the context of development strategies. The results reported in Tables 4 and 5 appear to con®rm the existence of causal connections in both directions for three variables, GDP, FDI and exports. These results are consistent with previous ®ndings that FDI takes place in China because growth and foreign trade prospects have made the country more attractive to foreign investors, re¯ected here in the causal link from growth, and exports to FDI (Broadman and Sun, 1997; Sun, 1998). On the other hand, two-way causal links between these variables also imply FDI a ects growth as found not only in the case of the UK and Germany (Barrell and Pain, 1997), but also in 69 developing countries (Borensztin et al., 1998). However, it would be a mistake to infer from this that there is evidence that FDI `causes’ growth. It remains possible that FDI is a close proxy for the openness of the macroeconomic policy stance of the Chinese government, and there is no evidence in Figs 1±2, that the surge in FDI in the early 1990s has yet been

Table 2. Cointegration rank Null

Alternative

¶max

95% CV

Trace

95% CV

Rank ˆ 0 Rank µ 1 Rank µ 2 Rank µ 3

r¶1 r¶2 r¶3 rˆ4

106** 39.33** 21.6 11.5

28.1 22.0 15.7 9.2

178.2** 75.21** 32.88 11.5

53.1 34.9 20.0 9.2

** denotes rejection of the null hypothesis at the 5% signi®cance level. Table 3. Model diagnostics

AR 1-4F(5, 45) Normality ARCH 4 F(4, 42) Vector AR 1-5 F(80, 108)

2

LFDI (probability)

LGDP

LEX

0.35 2.73 0.70 0.95

1.55 (0.19) 3.13 (0.21) 0.62 (0.65) Vector normality

1.00 0.98 1.19 5.04

(0.88) (0.25) (0.60) (0.59)

In 1994, China produced 18% of all world exports of labour-intensive products (Naughton, 1996).

LIM (0.44) (0.61) (0.33) (0.75)

0.99 (0.43) 0.19 (0.91) 0.18 (0.95)

Economic growth, foreign direct investment and trade Table 4. Results from weak exogeneity/causality tests

V. CONCLUSION

Lag length of VAR ˆ 3 Sample period: 1981 Q1±1997 Q4


1439

System exogeneity tests: X2(2)

LR test

P-value

LGDP weakly exogenous to system LFDI weakly exogenous to system LEX weakly exogenous to system LIM weakly exogenous to system

50.49 22.27 93.68 68.00