Causal nexus between the transport and logistics sector and trade: The case of Australia

ARTICLE IN PRESS Transport Policy 17 (2010) 135–146

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Causal nexus between the transport and logistics sector and trade: The case of Australia$ Hong-Oanh Nguyen a,n, Jose Tongzon b,1 a b

National Centre for Ports and Shipping, Australian Maritime College, University of Tasmania, Locked Bag: 1397, Launceston, TAS 7250, Australia Graduate School of Logistics, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon 402751, South Korea

a r t i c l e in fo

abstract

Available online 12 January 2010

Although a number of studies have already been conducted on the economic impact of the development of the transport and logistics sector and international trade, these are regarded as two separated topics, and little has been done so far to study in depth the relationship between them. This paper seeks to shed light on this issue in the context of Australia. To this end, the vector autocorrelation (VAR) framework is employed to explore the causal relationship between Australia–China trade and the development of the Australian transport and logistics sector. This framework is then extended to allow for the effect of Australia’s trade with the US, Japan, the rest of the world and other variables. Based on the analysis results, implications for the transport and logistics sector are discussed. & 2009 Elsevier Ltd. All rights reserved.

Keywords: International trade Transport and logistics Australia China US Japan Time series analysis Vector autocorrelation (VAR) Causality

1. Introduction It is well known that development of the transport and logistics sector is conducive to economic growth through its effect on production, consumption and trade. Investment in transport infrastructure, advancement in information and communication technology, shipbuilding, cargo handling and tracking lower the costs of production, exporting and importing (Rietveld, 1989; Berndt and Hansson, 1992; Alokan, 1995). The issue of transport and logistics facilitating trade can also be seen in the broader context of infrastructure development and its role in economic growth. Not only does development of transport and logistics affect production and consumption directly, but it also creates many direct and indirect externalities, and involves large flows of expenditure, thereby stimulating growth through the multiplier effect (Gramlich, 1994; Kessides, 1996). On the other hand, international trade could affect the transport and logistics sector

$ This research was funded by the Institutional Grant Scheme (IGS) 2007, the Australian Maritime College, University of Tasmania. A previous version of the paper was presented at the International Association of Maritime Economists (IAME) Conference, Dalian, China, 2–4 April 2008. This paper benefited from the helpful comments of two anonymous referees from the above conference and two others from the Transport Policy journal. The authors remain responsible for all the errors and mistakes. n Corresponding author. Tel.: + 613 6335 4762; fax: + 613 6335 4720. E-mail addresses: [email protected], [email protected] (H.-O. Nguyen), [email protected] (J. Tongzon). 1 Tel.: + 8232 860 8234; fax: + 8232 860 8226.

0967-070X/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2009.12.005

because it directly contributes to higher demand for transport services, thereby creating more opportunities for business expansion and investment (Brooks, 1994). These imply that the causal relationship between trade and the transport and logistics sector (herein after ‘‘the transport sector’’) may be bidirectional (Kessides, 1996; Fritsch and Prud’homme, 1997). Economies like those of Singapore and Hong Kong have grown rich partly because their past investments in superior transport have facilitated trade (Carruthers et al., 2003), while economies like that of China continue to be driven by their exports, which have brought substantial changes to the logistic network with new flows of raw materials, parts and final products (Lee and Rodrigue, 2006; Frankel, 1998). Although the above causal nexus generally may run both ways in theory, whether this is true in practice is not clear, and results of empirical studies on this topic are far from robust (Kessides, 1996). This ambiguity is attributable to a number of factors. First, the effect of the development of the transport sector on international trade could be dampened by the adverse effects of other factors, such as increases in the costs of fuel, financial funds, political upheavals, and protectionism of domestic production. Second, demand constraint can deter investment in transport and logistics from realising scale economies and efficiency. As population density is one of the key determinants of demand for transport and logistics, for countries with low population density, investment in transport infrastructure often cannot be justified unless contribution to demand by other factors is significant (Rietveld and Boonstra, 1995). Fig. 1 shows a relationship

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Fig. 1. Population density and road pavement percentage. Source: World Bank (2007).

between population density and development of the transport sector represented by the percentage of roads paved for OECD countries. As can be seen, countries with low population density tend to experience a lower rate of transport infrastructure development. Third, growth in international trade may fail to encourage the development of the transport industry if the latter is poorly managed, it has access to only an insignificant part of the market dominated by foreign-owned companies, or there is lack of contact with the market (Alokan, 1995; Kennedy et al., 2005). Although much has already been written on the economic impacts of international trade (Tongzon and Nguyen, 2008; Antkiewicz and Whalley, 2005; Australian Department of Foreign Affairs and Trade, 2005; Boske and Cuttino, 2003; Balaguer and Cantavella-Jorda, 2001; Thornton, 1997); little in-depth study has been done so far on its dynamic relationship with the transport sector. Especially, it is not known how the transport sector responds to growth in trade, and how the two variables interact with each other. The direction of causation between the transport sector and international trade can have important policy implications. For example, unidirectional causation running from the transport sector to trade would mean that trade and economic growth can benefit from investment in transport and logistics, and therefore investment in transport and logistics is probably an effective way to promote economic development and trade. In addition, the focus for policy making under this condition may be to improve market access and other factors affecting exports. Conversely, if the direction of causation runs from trade to the transport sector, there may be a better way to promote international trade and economic development than investment in transport and logistics. Furthermore, development policies should be based on further analysis and research into the causes of such unidirectional causality. However, if causality runs in both directions between transport and logistics expansion and international trade, it is both necessary and feasible to develop both sectors at the same time. Thus, the study of the causal link between the transport sector and trade not only contributes to a better understanding of the relationship between these sectors, but may also be beneficial for governments in developing more effective economic policies. This paper seeks to examine the dynamics of the causal relationship between trade and the sector, especially in the context of Australia’s trade with China and the Australian transport and logistics sector. Australia is selected because its population density is the lowest among the OECD countries. As explained earlier, this may impose a constraint on demand for transport and logistics, thereby inhibiting the effect of this sector on trade. In addition, as shown below, Australia’s trade with China has burgeoned recently, allowing for an interesting case study of

Fig. 2. Australia’s trade with China, Japan and the US. Source: Australian Bureau of Statistics (2006).

the dynamic relationship between the development of the transport sector and international trade. Fig. 2 shows the trends in the values of Australia’s trade with its main trading partners, China, Japan and the US, for the period Q1:1988 to Q3:2006. Until Q3:2004, the US was Australia’s second largest trading partner with an average trade value of about 3.5% of Australian GDP. Until Q2:1993, Australia’s trade with China was less than 1% of Australian GDP. However, from Q4:2004, China has taken over the position of the US as Australia’s second largest trading partner. By Q3:2006, China is closely following Japan as Australia’s largest trading partner with the values of Australia– China and Australia–Japan trade being 5.1% and 5.5% of Australian GDP, respectively. Over the whole period of study, the average growth rate of Australia’s trade with China was 4.2%, while the average growth rate of Australia’s trade with Japan, the US and the rest of the world were only 1.4%, 1.3% and 1.9%, respectively. Twoway merchandise trade between Australia land China has doubled since 2001–2002 to be more than AU$41 billion in 2005–2006, and China now is Australia’s largest trading partner. Table 1a and b show the trends in Australia’s top-ten exports to and imports from China for the 2001–2004 period. The goods are ranked according to their trade value in 2004. The last column shows the enormous growth rates of exports and imports over the whole period. Australia’s main merchandise exports to China include agricultural and mineral products, and some high-valued manufactures. China’s main merchandise exports to Australia are manufactured goods, and it also invests in Australian resource, processing, manufacturing and services sectors. It is expected that China’s demand for Australia’s exports of energy and resource commodities will remain strong in the future due to its continuing economic growth and industrialization (Australian Department of Foreign Affairs and Trade, 2005). The figures in the tables suggest the benefit from export growth for both countries. The above-mentioned growth of the Australia–China trade relationship implies increasing demand for transport and logistics services, a critical element in the Australian transport and logistics sector. This sector currently contributes about 9.2% to the national economy (Logistics Association of Australia, 2004), with the transport and storage sector alone accounting for about 4.5% of GDP and 4.6% of total employment (Bureau of Transport and Regional Economics, 2006). Each year, the transport and logistics sector accomplishes an international freight task of about six trillion tkm and a domestic freight task of about 443 billion tkm, transporting more than 2.3 billion tons of freight around the country. To study the causal nexus between Australia’s trade with China and the Australian transport sector, the vector autocorrelation (VAR) framework is employed, and the Granger-causality tests are conducted to check if Australia–China trade Granger-causes the

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Table 1 Australia’s top-ten exports/imports. Source: Adapted from Australian Department of Foreign Affairs and Trade, 2005 2001 (a) Australia’s top-ten exports to China (US$ million) Iron ore 945 Alumina 523 Wool 639 Crude oil 154 Coal 28 Wheat 8 Gases 74 Aluminium 96 Barley 211 Manganese ores 46 2001 (US$ mil.) (b) Australia’s top-ten imports from China ADP machines 303 Video and digital cameras 67 Women’s suits 191 Office equipment 100 Toys 177 TV and videos 47 Footwear 141 Travel goods 144 Furniture 69 T-shirts 119

2002

2003

2004

Growth rate (2001–2004) (%)

995 589 682 242 146 10 87 135 229 56

1632 998 588 445 208 1 127 196 133 104

3346 1103 900 467 387 364 273 261 239 227

254 111 41 203 1282 4450 269 172 13 393

2002 (US$ mil.)

2003 (US$ mil.)

2004 (US$ mil.)

Growth rate (2001–2004) (%)

476 104 212 169 205 74 183 157 102 123

development of the Australian transport sector, or the latter Granger-causes the former, or both. The impulse responses are also analysed to obtain information on the dynamic relationship between the variables. Next, the standard VAR framework is extended to incorporate the effect of other variables that may be influential to trade and transport. The resulting model is expected to give a further insight into the relationship between the transport and logistics sector, international trade and other variables. The paper has six sections. The next section is a review of relevant literature. The third section discusses the econometric methods used in the data analysis. The fourth section presents the analysis results, and the fifth section discusses policy implications followed by the conclusion.

2. Literature review Existing literature has recognised the relationship between the transport sector and economic growth (Rietveld, 1989; Gramlich, 1994; Kessides, 1996). Aschauer (1990) found that investment in non-military public infrastructure, especially ‘‘core’’ infrastructure such as roads, airports, sewers and water supply, contributed to economic growth and total factor productivity in the US for the period 1949–1985. Subsequently, Berndt and Hansson (1992) explored the case of Sweden and found an inverse relationship between infrastructure investment and private sector cost. Using time series data for the US economy and cointegration analysis, Lau and Sin (1997) found a long-term relationship between output growth and investment in public infrastructure. Using cross-section data, Fritsch and Prud’homme (1997) found the contribution of transport infrastructure to output and productivity growth in France. It is interesting to note that even though the relationship between transport infrastructure and economic growth has attracted a lot of research effort and attention from economists, policy makers and politicians in the early 1990s (Gramlich, 1994), it remains essentially unclear whether the direction of causation is from transport infrastructure to economic growth or vice versa or both. The same is also true regarding the causal relationship

735 250 272 229 246 122 210 191 156 163

1273 501 324 298 297 280 265 252 245 219

320 648 70 198 68 496 88 75 255 84

between the transport and logistics sector and international trade. Kessides (1996) noted that one of the main shortcomings of research on the economic impact of transportation infrastructure is that it has so far not adequately accounted for simultaneity of effects—economic growth can lead to development of the transport system as well as result from it. Studies, such as Aschauer (1990), Munnell and Cook (1990), Fritsch and Prud’homme (1997) and Bougheas et al. (1999), could not confirm the direction of causation between the development of the transport sector and economic growth. Even where efforts are made to identify the direction of causality, the results are often inconclusive (Kessides, 1996) or even spurious (Tatom, 1993). Studies so far offered little information on causation between the variables and factors affecting the direction of causation. Based on the results of cointegration analysis, Lau and Sin (1997) reject the endogenous growth model for the US economy, but offered no further explanation or analysis on this finding in relation to the effect of transport infrastructure. Thus, so long as the ambiguity on the causal relationship between transport infrastructure and economic growth remains, the benefits of investment in transport infrastructure are questionable. Previous studies focus on the effect of transportation infrastructure rather than the transport and logistics sector. Most studies used public infrastructure capital stock as a proxy for the transport infrastructure variable, implying that public funds are the main source of contribution to the development of the transport sector. There are four issues arising from this approach. First, public infrastructure capital includes expenditure on transport infrastructure, schools, education and hospitals. Since this affects not only transport costs but also human capital and labour stock, the use of public capital as a proxy for transport infrastructure tends to exaggerate the effect of transport infrastructure. Second, while it is known that transport infrastructure helps to improve the capacity of the transport and logistics sector, most of the available research does not examine the efficiency of transport infrastructure, that is, the flow of services actually generated from the investment expenditure (Kessides, 1996). Third, despite its importance, transport infrastructure is not the only determinant of transport and logistics development. Other

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variables, such as governance, financing and neighbourhoods (Kennedy et al., 2005), population density (Rietveld and Boonstra, 1995), investment in information and communication technology (Banister and Stead, 2004; Capineri and Leinbach, 2004) and port efficiency (Sanchez et al., 2003), are also important to transport and logistics development. Using disaggregate trade panel data for various subsectors in Spain, Martinez-Zarzoso et al. (2008) investigated the impact of transport costs on trade and estimated the elasticity of trade with respect to transport costs for each sector. Their analysis results suggest that the quality of door-todoor services, transport infrastructure, port efficiency and availability of different transport modes are among the key variables affecting transport costs and international trade especially in high value-added sectors. In contrast to other studies’ results, they found the effect of infrastructure variables on transport costs is in most cases not significant for high value-added sectors. Fourth, the approach employed in the previous studies of the effect of transport and logistics development tend to ignore the contribution of the private sector to the development of the transport and logistics sector, including private investment in information and communication technology (ICT), transport vehicles, warehouses and storage, human resources, etc. This suggests other channels, through which transport and logistics affect international trade and economic activities, are also important but have not been adequately covered in the current literature. For example, Banister and Stead (2004) and Capineri and Leinbach (2004) show that the costs of cargo transportation, cargo distribution, exporting and importing have been reduced thanks to ICT investment. In addition, ICT makes market information available at less cost, and at the same time minimises face-to-face communication that is often costly in international trade as a result of travelling. Little focus has been directed to the study of the relationship between international trade and development of the transport and logistics sector. Vasiliauskas and Barysiene_ (2008), Frankel (1998) and Lee and Rodrigue (2006) have shown that, because trade is one of the determinants of demand for transport and logistics, growth in international trade stimulates the growth in transport and logistics. According to Vasiliauskas and Barysiene_ (2008), recent increases in containerised trade have caused higher demand for container terminal development and logistics services. Trade also indirectly affects the transport and logistics sector by promoting competition within this sector. Yap et al. (2006) found that steady economic and trade growth in East Asia has increased competition among container terminals in this region. Frankel (1998), and Lee and Rodrigue (2006) have postulated that the reorientation of Korean trade caused by the ‘‘China effect’’, especially the growth and expansion of transYellow Sea supply chains, is the main factor behind recent changes and differential port growth in the Korean port system. In this context, international trade can be considered as an explanatory condition for examining the development of regional port systems. On the other hand, other studies (e.g. Wilson et al., 2003; Carruthers et al., 2003) offer some evidence that improvements in transport and logistics would lead directly to improvements in export performance and that development of transport and logistics precedes the growth in international trade. Wilson et al., 2003 have shown that differences in the quality of logistics and trade facilitation are significantly related to trade performance and concluded that substantial growth in trade could be accomplished by improving their logistics performances. This study attempts to address some of the issues highlighted above. In particular, it aims to investigate the dynamics of the causal relationship between the transport and logistics sector and trade, using time series data for Australia and econometric analysis methods. It focuses on the effect of the transport and

logistics sector rather than transport infrastructure. This helps to avoid the issues arising from choosing a proxy for transport infrastructure as mentioned earlier. In addition, the study explores the causal relationship between transport and logistics on the one hand and trade on the other hand that has not been given adequate attention in the existing literature.

3. Methodology The behaviour of many economic variables could be studied using univariate econometric models. Yet one of the most fertile areas of econometric analysis concerns multivariate models, which allows for the analysis of simultaneous interactions among variables using a system of equations. The vector autocorrelation (VAR) framework developed by Sims (1980, 1986) allows for the study of not only simultaneous equations but also the dynamic relationship among variables, through impulse response functions and variance decomposition. As noted by Glen and Martin (2005), the VAR method is relatively new to the study of transport, and has only recently been applied to this field. In particular, Veenstra (1999) and Glen and Martin (2005) applied the VAR framework to test the efficient shipping market hypothesis for various sectors of the shipping market. The null hypothesis of an efficient market was first transformed into joint cross-equation restrictions on the VAR system which could then be tested using the Wald test. The authors found that the null hypothesis of an efficient market could not be rejected for the panamax-size dry bulkers and tankers of all ship sizes when measured in world scale. The overall results of the study provided confirmatory evidence that the VAR approach can generate a reasonable model of the dynamic relationship between variables (Glen and Martin, 2005). Using the Granger causality test and impulse response function obtained from the VAR system, Yao (2005) examined the linkages between freight transportation and economic fluctuations. The author found the evidence that freight movements are predictive of economic recessions. Another application of the VAR framework was Cariou and Wolff’s (2006) study of the causal relationship between the bunker adjustment factor (BAF) and bunker price, and between liner tariffs and the charter rate on the Europe/Far East container trade. They found that BAF did respond to changes in the average bunker price, and the charter rate Granger-causes tariff, but not the other way around, suggesting that tariffs are mainly driven by cost considerations, as claimed by shipping lines. No study so far has been found to apply the VAR or a similar approach, e.g. vector error correction (VEC), to examine the relationship between trade and transport services. The current study applies the VAR approach to study the causal and dynamic relationship between the growth of Australia’s trade with China and the development of the Australian transport sector. The Granger-causality tests are also conducted to see if growth in trade Granger-causes the development of the transport sector, or the latter Granger-causes the former, or both. Once the causal direction has been identified, impulse responses will be analysed to obtain more knowledge on the dynamic behaviour of the variables. In addition, the simultaneous system will be extended to incorporate other variables for a more comprehensive analysis. Since the study utilises time series data, it is important that series are tested for unit roots. The unit root test procedure developed by Dickey and Fuller (1979, 1981) is based on the following equation:

Dyt ¼ a0 þ a1 t þ ryt1 þ

pX þ1 j¼1

gj Dytj þ et ;

ð1Þ

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where yt is the series to be tested for a unit root. The parameter to be tested is r. Under the null hypothesis that the series has a unit root, the value of r is zero. The critical values of the test statistic depend on the sample size and whether the intercept or drift term a0 and/or the deterministic time trend t are included in the regression equation.2 These values are non-standard and are used to compare with the t-statistic of the parameter r to test for the null hypothesis of r =0 against the alternative of r o0. The number of lags of Dy can be chosen using the Akaike information criterion (AIC) and/or Schwartz Bayesian criterion (SBC) to make sure the residual et is white noise. If the variables are found to be stationary, the standard VAR framework will apply as outlined below. However, if the variables are found to be non-stationary, a more appropriate approach to study their dynamic relationship is the vector error correction (VEC) framework, which can be regarded as a special case of VAR for non-stationary variables (Hamilton, 1989). If the variables are non-stationary and their linear combination is stationary, the variables are said to be cointegrated or have a long-term relationship, and there also exists an ‘‘error correction mechanism’’ (ECM), in which the short-term dynamics of the variables are influenced by the deviation from their long-term relationship. Since the unit root test results shown in the next section suggest that all variables are stationary, the VAR framework is applied and is presented as follows: dQtrant ¼ a10 þa11 ðLÞdQtrant1 þa12 ðLÞdTChnt1 þe1t

ð2aÞ

dTChnt ¼ a20 þ a21 ðLÞdQtrant1 þ a22 ðLÞdTChnt1 þ e2t

ð2bÞ

where t is the time period, dQtran is the growth rate of the transport and logistics sector, dTChn is the growth rate of trade between Australia and China, and aij(L) are the polynomials in the lag operator L. The error terms e1 and e2 are assumed to have zero means, constant variances and are individually serially uncorrelated. The essential feature of the above system is that the error terms e1 and e2 are not independent. However, as Hamilton (1989) noted, because the right-hand side of the Eqs. (2a) and (2b) contain only predetermined variables and the error term being serially uncorrelated with constant variance, each equation can be estimated using the least square method. In addition, the specification of the above system shows that the current value of each variable is influenced by the past values of both variables. The values of the coefficients in the polynomials, a11, a12, a21 and a22 show the magnitude of the causal relationships between the variables. Therefore, to see whether the growth of Australia’s trade with China causes the development of the Australian transport and logistics sector, we test the significance of the coefficients on the lagged growth rates of Australia–China trade contained in the polynomial a12. Similarly, to see if the development of the Australian transport and logistics sector helps to boost Australia’s trade with China, we test the significance of the coefficient on the lagged growth rates of the Australian transport sector’s output, which are included in the polynomial a21. These tests are called ‘‘Granger causality’’ tests (Granger, 1969). Once the direction of the causality has been established, it is possible to learn further about the dynamic relationship between the variables by analyzing the graphs of their impulse response functions, which show how a single-period shock to one variable affects itself and the others, at different time horizons. In particular, if causality runs in only one way, e.g. trade Grangercauses transport but not vice versa, Choleski decomposition could

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be applied to obtain the graphs of the variables’ impulse response functions. However, if causality runs both ways, structural decomposition is required using methods proposed by Sims (1986), Bernanke (1986), or Blanchard and Quah (1989). Although the standard VAR approach is unique and powerful in studying the dynamics of multivariate relationship, it is limited to only the systems with identical variables on the right-hand sides of all equations. Such specification requirement may not be consistent with economic theory. The VAR model for trade and transport specified in Eqs. (2a) and (2b) includes only the lags of these variables and ignores their relationship with other variables. This limitation could be overcome by incorporating in the system other variables that may be influential to trade and transport. The following system of Eqs. (3a) and (3b) is an extension of the standard VAR system. In particular, Eq. (3a) attributes the growth of the Australian transport and logistics sector to Australia’s trade with not only China (dTChn) but also with Japan (dTJap) and US (dTUS), and the rest of the world (dTothers), as well as (the growth of) its manufacturing sector (dQman). Regarding the inclusion of the manufacturing output, dQman, it is argued here that demand for transport and logistics services depends on trade as well as manufacturing activities. Yao (2005) found evidence that for-hire freight movements are even more closely related to activities in manufacturing than those in the trade sector. The Eq. (3b) with the dependent variable being the growth of Australia–China trade now includes two additional variables, the GDP growth rates of Australia (dGDPAus) and the GDP growth rates of China (dGDPChn). The inclusion of these variables is essentially based on the well-known gravity law of international trade. dQtrant ¼ a10 þ a11 ðLÞdQtrant1 þa12 ðLÞdTChnt1 þb11 ðLÞdTJapt1 þb12 ðLÞdTUSt1 þ b13 ðLÞdTotherst1 þ b14 ðLÞdQmant1 þe1t ;

ð3aÞ

dTChnt ¼ a20 þ a21 ðLÞdQtrant1 þ a22 ðLÞdTChnt1 þb21 ðLÞdGDPAust1 þb22 ðLÞdGDPChnt1 þe2t ;

ð3bÞ

Note that unlike the standard VAR model, the above system has two different sets of right-hand side variables. Therefore, the seemingly unrelated regressions (SUR) method could be exploited to improve the estimation efficiency (Doan, 2004, p. 333). The econometric analysis in this study utilises quarterly time series data covering the period from the first quarter 1988 to the third quarter 2006. Data for international trade, GDP, output of the transport and logistics sector and the manufacturing sector were collected from the Australian Bureau of Statistics (2006). In particular, data for international trade were calculated as the sum of merchandise exports (FOB) value and imports (custom) value. To be consistent, data for both GDP and the output of the transport and logistics sector and the manufacturing sector are original chain volume measures of gross value added by industry (Australian Bureau of Statistics, 2006; Table 6). Because data for the transport and logistics sector are unavailable (Bureau of Transport Economics, 2001), data for the transport and storage services sector provided by Australian Bureau of Statistics (2006) were used as a proxy. Due to the unavailability of high-frequency data for China’s GDP, yearly data provided by National Bureau of Statistics of China (2006) were used after being converted into quarterly data using the interpolation method.3Table A1 in Appendix A shows all the variables’ descriptive statistics. Note that because the main focus is on the growth of trade and the

2 Dickey and Fuller (1981) also provide three alternative F-statistics called F1, F2, F3, and the associated critical values to test the joint hypotheses involving the

coefficients under different specifications. Only the first approach is applied in this study.

3 For more detail about methods used to change data frequencies, see Doan (2004, Chapter 2).

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transport sector, data in levels are necessarily converted into growth rates. This reduces the number of observations by one and results in a total of 74 observations used in econometric analysis.

4. Analysis results As explained earlier, the first step in data analysis is to test if the variables are stationary. This step is necessary to ensure the appropriate analytical approach will be applied to avoid spurious analysis results. The test was conducted based on Eq. (1) with the lag length being selected using the Akaike information criterion (AIC) and the Schwartz Bayesian criterion (SBC). Given the nature of the quarterly data and a relatively small sample size of only 74 observations, only four or eight lags would be suitable for the unit root tests. Because growth rates of variables were used, no time trend is expected in Eq. (1)—this can be checked by inspection of the line charts of variables (to be available on request). Table 2 reports the values of AIC and SBC for these two lag length options for all the variables. It is clear that both the AIC and SBC for four lags are smaller than those for eight lags for all variables, except the growth rate of Chinese GDP, dGDPChn, which has AIC for four lags slightly larger than that for eight lags, but SBC for four lags significantly smaller than that for eight lags. Thus, the values of both AIC and SBC strongly suggest that four lags are appropriate for the unit root tests. The results of the unit root tests based on Eq. (1) without the time trend t are provided in Table 3. For more comprehensive results, tests without the intercept term were also conducted. The test results are reported in Table 3 with the critical values given in the row below the reported values of the test statistic. The results of the tests with the intercept term suggest that the null hypothesis of a unit root is rejected at the 1% significance level for (the growth rate of) transport output (dQtran), Australia’s trade with China (dTChn), with Japan (dTJap), with the US (dTUS), and with the rest of the world (dTothers). The null hypothesis of a unit root is also rejected at the 5% significance level for (the growth rate of) Australian GDP (dGDPAus), Chinese GDP (dGDPChn), and manufacturing output (dQman). When the unit root tests were conducted without the intercept term, the values

of the test statistic reported in the lower row of Table 3 indicate that the null hypothesis of a unit root is rejected at the 1% significance level for all variables. The conclusion of stationary variables is consistent with the variables’ data construction process. Because their values are measured in growth rates, which are constructed by taking the first difference of the natural logarithms of their original values in levels, the obtained series are stationary even if the original data (in levels) are integrating of the first order. Since variables are stationary, the VAR framework is applicable. Following the methodology explained in Section 2, the least squares method was first used to estimate Eqs. (2a) and (2b). One of the key issues in the estimation of the system concerns the lag length incorporated in the polynomials a11, a12, a21 and a22. As mentioned earlier, given the nature of the study using quarterly data, either four lags or eight lags would be appropriate. Because the test for the most appropriate lag length involves crossequation restrictions, the individual F-tests on lags in each equation are not appropriate. Instead, the proper test for crossequation restrictions is a likelihood ratio test with the following likelihood ratio statistic (Enders, 2004): X  X Tðln9 4 ln 8 9Þ;

ð4Þ

P where T is the number of usable observations, and 9 49 and P 9 89 are the determinants of the variance-covariance matrices of the residuals obtained from regression on the system with four and eight lags, respectively. The test statistic has the asymptotic w2 distribution with degrees of freedom equal to the number of restrictions in the system, which is 16 (four lags for two variables for two equations). The resulting value of the likelihood ratio statistic is 22.289865. Given the sixteen degrees of freedom, the corresponding P-value is 0.13407636. Thus, the four lag restriction is not binding, and four lags can be used in estimation of the VAR system. Table 4a and b provide the estimation results with the numbers in parentheses indicating the number of lag periods. For example, dTChn(1) means one-period lagged dTChn. Since quarterly data were used, seasonal dummies are needed to capture the seasonal effect of variables. Most of the seasonal dummies, D to D(2), in both tables are significant suggesting

Table 2 Lag selection using AIC and SBC. Variable

dQtran dTChn dTJap dTUS dTothers dGDPAus dGDPChn dQman

Akaike information criterion (AIC)

Schwartz Bayesian criterion (SBC)

Lags= 4

Lags =8

Lags = 4

Lags= 8

 289.22869  75.34136  130.86163  109.21805  170.87124  322.28619  803.36044  300.39924

 287.43564  71.60195  127.06335  101.93133  167.10231  320.16332  804.20534  298.41616

 276.56345  62.67612  118.19638  96.55281  158.206  309.62094  790.69519  287.734

 266.3269  50.49321  105.95461  80.82259  145.99357  299.05458  783.0966  277.30742

Table 3 Results of unit root tests. dQtran

dTChn

dTJap

dTUS

dTothers

dGDPAus

dGDPChn

dQman

With intercept

 4.45185  4.37264  3.65955  4.667 Critical values: at 1%=  3.531; at 5% =  2.906; at 10%=  2.590

 4.22298

 2.91355

 3.04722

 3.34929

Without intercept

 4.49660  3.65955  3.69806  4.62343 Critical values: at 1%=  2.598; at 5% =  1.945; at 10% =  1.618

 4.23492

 2.93552

 2.79088

 3.33916

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Table 4 VAR estimated by least squares. Variable number

Variable name

Coefficient

Standard error

T-statistic

Significance level

(a) Dependent variable: dQtran 1 2 3 4 5 6 7 8 9 10 11 12

dQtran(1) dQtran(2) dQtran(3) dQtran(4) dTChn(1) dTChn(2) dTChn(3) dTChn(4) Constant D D(1) D(2)

 0.010077907  0.176820563  0.245624335 0.047479145  0.023960968  0.055826602 0.005181614 0.020208411 0.026349435  0.003410659  0.045028033 0.004145547

0.139772013 0.138828879 0.133319592 0.13472848 0.025152729 0.025211721 0.026685794 0.023427589 0.008981707 0.013689735 0.012817872 0.01272709

 0.0721  1.27366  1.84237 0.35241  0.95262  2.21431 0.19417 0.86259 2.93368  0.24914  3.51291 0.32573

0.94277277 0.20795383 0.07062404 0.72583341 0.34480546 0.03082185 0.84673216 0.39197756 0.00481849 0.80414819 0.00087508 0.74582425

(b) Dependent variable: dTChn 1 2 3 4 5 6 7 8 9 10 11 12

dQtran(1) dQtran(2) dQtran(3) dQtran(4) dTChn(1) dTChn(2) dTChn(3) dTChn(4) Constant D D(1) D(2)

 1.279832353 0.218711028  0.225756521  0.438651195  0.33607602  0.488735271  0.315757323  0.123945403 0.225981305  0.076246467  0.185246046  0.182297606

0.776334906 0.771096467 0.740496261 0.74832164 0.139705664 0.140033321 0.148220757 0.130123725 0.049887043 0.076036821 0.071194234 0.070690004

 1.64856 0.28364  0.30487  0.58618  2.4056  3.49014  2.13032  0.95252 4.52986  1.00276  2.60198  2.57883

0.10474009 0.77771674 0.76157414 0.56006872 0.01941334 0.00093874 0.03747374 0.34485539 0.00003065 0.32021607 0.01179033 0.01251968

strong existence of seasonality in trade and transport. Note that the use of the first differences of (the natural logarithms of) the variables tend to weaken the analysis results because of the loss of information caused by differencing. Despite this, there appears to be some effect of the growth in Australia–China trade on the Australian transport sector, as shown in Table 4a, with the coefficient for the two-period lagged Australia–China trade, dTChn(2), significant at 5%. The Granger-causality test, which tests the significance of the lagged variables dTChn(1) to dTChn(4), was conducted, and the resulting values, with F-statistic of 2.1610 and the P-value of 0.0849862, confirm the significance of the causal effect of Australia–China trade on the transport sector at the 10% significance level. However, the results reported in Table 4b show that effect of the transport sector on Australia–China trade is insignificant. This is confirmed by the causality test for the coefficients on the lagged variables dQtran(1) to dQtran(4) with the value of the F-statistic of 0.7137 and the P-value of 0.5860307. Thus, the above results indicate that the growth in Australia’s trade with China causes the growth in its transport and logistics, but not vice versa. To gain more insight into the causal relationship between the variables, further analysis will be conducted using an extended framework. In addition, we could learn more about the dynamic relationship between the two variables through their response functions. Given the unidirectional causation running from trade to transport, Eqs. (2a) and (2b) with the polynomial a21 excluded were re-estimated using the seeming unrelated regressions (SUR) method. The estimation results (given in Tables A2(a) and (b) in Appendix A) were then used to obtain the impulse response functions4. Fig. 3 shows the graphs of the response functions of Australia’s trade with China and transport to one percent increase in the value of the former. Both variables respond positively and instantaneously to the shock, but the response of

4 For more detail about Choleski decomposition and obtaining impulse response functions, see Doan (2004, pp. 339–350) and Hamilton (1989).

Fig. 3. Response to trade with China.

the transport sector is significantly smaller in magnitude. The two variables’ responses weaken in the following two quarters and then rebound in the third and fourth quarters, reflecting the seasonality in both series. It is interesting to note that the response of transport in the fourth and fifth periods after the shock, 0.14 and 0.30, are stronger than that of trade, 0.05 and 0.07. The response of both series declines substantially in the third year and dies away after that (the values of the response functions are provided in Table A3 in Appendix A). Next, Eqs. (2a) and (2b) are extended to incorporate other variables that are influential to trade and the transport and logistics sector. As explained earlier, the new variables added to Eq. (3a) are Australia’s trade with Japan, dTJap, with the US, dTUS, and with the rest of the world, dTothers, and manufacturing output, dQman. The new variables added to Eq. (3b) are the growth rate of Australian GDP, dGDPAus, and the growth rate of Chinese GDP, dGDPChn. Table 5a and b report the estimation results for the extended system. The addition of new variables appears to bring some improvement to the system. In the first equation, the coefficient for dQtran(3) is now significant at 1%, and those for dTChn(2) and dTChn(4) are significant at 5% and 1%, respectively. It is interesting to note from Table 5a that one coefficient for

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Table 5 Simultaneous system estimated by SUR. Variable number

Variable name

(a) Dependent variable: dQtran 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Constant D D(1) D(2) dQtran(1) dQtran(2) dQtran(3) dQtran(4) dTChn(1) dTChn(2) dTChn(3) dTChn(4) dTJap(1) dTJap(2) dTJap(3) dTJap(4) dTUS(1) dTUS(2) dTUS(3) dTUS(4) dTothers(1) dTothers(2) dTothers(3) dTothers(4) dQman(1) dQman(2) dQman(3) dQman(4)

(b) Dependent variable: dTChn 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Constant D D(1) D(2) dQtran(1) dQtran(2) dQtran(3) dQtran(4) dTChn(1) dTChn(2) dTChn(3) dTChn(4) dGDPAus(1) dGDPAus(2) dGDPAus(3) dGDPAus(4) dGDPChn(1) dGDPChn(2) dGDPChn(3) dGDPChn(4)

Coefficient

0.021203126  0.074834986 0.008266568 0.038873428  0.123153877  0.093305932  0.313775858  0.061132524  0.013662441  0.059118677 0.026136765 0.065695333  0.058249159  0.042956213  0.105719193 0.011704338 0.082726427 0.042767413  0.01156135  0.099665289  0.06879567 0.061315918 0.054729066  0.011189439 0.075060267 0.315037231 0.197133865  0.119117942 0.39709398  0.51257004  0.49477999  0.03633206  0.71838642 0.28003812 0.16240065  0.20698344  0.41504819  0.42423657  0.36784056  0.13289568  1.26064138 1.68505313 1.38684942  2.05150886  24.99933059 35.84688004 8.3129584  20.22420051

Australia–Japan trade, dTJap(3), and two coefficients for Australian-US trade, dTUS(1) and dTUS(4), are significant, while none of the coefficients for Australia’s trade with the rest of the world, dTothers, is significant. This suggests that, while Australia’s trade with the rest of the world is much larger than its trade with the main trading partners, China, Japan and the US, it has insignificant impact on the transport sector. The significance of the manufacturing output variables, dQman(2) and dQman(3), at 1% also confirms a strong causal relationship between manufacturing and transport. The reason that the same variable dQtran is influenced by different lag variables of different countries could be due to the difference in the seasonal characteristics of the products that Australia exports to or imports from these countries, as can be seen from Fig. 2. The estimation results reported in Table 5b show no improvement in the coefficients for the dQtran variable. Together with the results from Table 5a, these confirm the causal relationship

Standard error

T-statistic

Significance level

0.015544995 0.024415218 0.023889209 0.023859465 0.116909949 0.114502136 0.109166677 0.112825423 0.023860209 0.024759444 0.025479552 0.023127128 0.045480394 0.046315459 0.044228311 0.040452558 0.036103234 0.034565397 0.037607653 0.042019943 0.082053176 0.070098988 0.075854789 0.054905972 0.106420985 0.108056408 0.107645786 0.116520115

1.36398  3.0651 0.34604 1.62927  1.05341  0.81488  2.87428  0.54183  0.5726  2.38772 1.02579 2.84062  1.28075  0.92747  2.39031 0.28933 2.29139 1.23729  0.30742  2.37186  0.83843 0.8747 0.7215  0.20379 0.70531 2.91549 1.83132  1.0223

0.17257257 0.002176 0.72931436 0.1032566 0.29215403 0.41513885 0.00404947 0.58793364 0.56691312 0.01695314 0.30498881 0.00450262 0.20028038 0.3536824 0.01683434 0.77232509 0.02194113 0.21597951 0.75852361 0.01769895 0.40179044 0.38173458 0.47060328 0.83851546 0.48061452 0.00355132 0.06705281 0.30664125

3.63559  2.79259  2.71577  0.20263  1.10507 0.44776 0.27456  0.33939  3.24019  3.49926  2.94172  1.1772  1.21529 1.40842 1.11045  1.84482  0.95476 0.54872 0.13101  0.83621

0.00027734 0.00522882 0.00661211 0.83942257 0.26913101 0.65432358 0.78365187 0.73431237 0.00119451 0.00046655 0.00326398 0.23911421 0.22425577 0.15900771 0.26680655 0.06506321 0.33969768 0.58319443 0.89577034 0.40303603

0.10922399 0.18354657 0.18218748 0.17930059 0.65008465 0.62541466 0.5914874 0.60986046 0.12809388 0.12123607 0.12504279 0.11289098 1.03731793 1.19641658 1.2489112 1.11203527 26.18381374 65.3276286 63.4546562 24.18551403

between Australia’s trade and its transport sector. It is also clear that Australia’s trade with China is strongly influenced by its lags, dTChn(1) to dTChn(3) being significant at 1%. Moreover, trade between the two countries is also affected by the growth of the Australian economy, dGDPAus(4). However, there is no evidence that the growth of the Chinese economy affects trade between the two countries. This could be due to the use of quarterly data converted from annual data for the Chinese GDP series. Given the exploratory results concerning the significance of the relationships between the variables, it is possible to restructure the system of Eqs. (3a) and (3b) leaving out the insignificant variables. This results in a considerable reduction of the number of variables from 48 to 36. The re-estimation results provided in Table 6a and b are consistent with and very close to those obtained earlier. While the significance of most variables remains the same, the number of significant variables, including the intercept and

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Table 6 Simultaneous system estimated by SUR. Variable number

Variable name

Coefficient

Standard error

T-statistic

Significance level

(a) Dependent variable: dQtran 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Constant D D(1) D(2) dQtran(1) dQtran(2) dQtran(3) dQtran(4) dTChn(1) dTChn(2) dTChn(3) dTChn(4) dTJap(1) dTJap(2) dTJap(3) dTJap(4) dTUS(1) dTUS(2) dTUS(3) dTUS(4) dQman(1) dQman(2) dQman(3) dQman(4)

0.0138  0.06886 0.01998 0.048606  0.09911  0.0894  0.34277  0.00871  0.00885  0.04849 0.031597 0.055117  0.08693  0.03574  0.0815 0.007451 0.074015 0.058444 0.011423  0.07026 0.052763 0.327148 0.141869  0.16979

0.015159 0.024134 0.023281 0.022517 0.114208 0.109686 0.10219 0.106814 0.022549 0.022431 0.023561 0.020639 0.03676 0.037126 0.037128 0.036012 0.029122 0.028862 0.030951 0.03085 0.106967 0.10165 0.10164 0.107533

0.91035  2.8533 0.8582 2.15869  0.86783  0.8151  3.35424  0.08156  0.39263  2.16194 1.34106 2.67059  2.36478  0.96271  2.19511 0.20691 2.5416 2.02497 0.36905  2.2776 0.49327 3.21838 1.3958  1.579

0.36264 0.004327 0.390785 0.030874 0.38549 0.415016 0.000796 0.934998 0.694596 0.030623 0.179902 0.007572 0.018041 0.335695 0.028156 0.836079 0.011035 0.042871 0.71209 0.02275 0.621825 0.001289 0.162776 0.114337

(b) Dependent variable: dTChn 25 26 27 28 29 30 31 32 33 34 35 36

Constant D D(1) D(2) dTChn(1) dTChn(2) dTChn(3) dTChn(4) dGDPAus(1) dGDPAus(2) dGDPAus(3) dGDPAus(4)

0.375269  0.56614  0.44885  0.03232  0.38435  0.34454  0.2899  0.17567  1.64695 1.408224 0.947572  2.94121

0.106048 0.184998 0.170245 0.181127 0.113496 0.118235 0.115063 0.101989 1.020007 1.052872 1.044886 1.039052

3.53869  3.06026  2.63648  0.17846  3.38648  2.91405  2.51945  1.72245  1.61465 1.33751 0.90687  2.83067

0.000402 0.002211 0.008377 0.85836 0.000708 0.003568 0.011754 0.084988 0.106387 0.181057 0.364477 0.004645

dummies has increased from 15 in Table 5a and b to 19 in Table 6a and b, despite a smaller number of variables in the latter. In particular, the coefficients for variables dTJap(1), dTUS(1), dTUS(2) and dTChn(4) are now significant. In addition, the seasonal effects remain significant for both trade and transport. However, the coefficient for dQman(3) is now insignificant.

5. Implications The analysis in the last section found evidence that Australia’s trade with its main trading partners, especially China, contributed to the growth of the Australian transport and logistics sector. These imply the importance of Australia’s international trade to the development of its transport sector—for an already open and accessible economy like Australia, increasing international trade competitiveness should be its transport and logistics priority, as pointed out by Carruthers et al. (2003). The econometric analysis based on Granger (1969) causality tests provided evidence that Australia’s transport failed to cause its international trade, suggesting this sector might have been lagging behind its trade. As explained earlier, this is attributable to the following factors:

 adverse social and economic changes, for example increases in the costs of fuel, financial funds, political upheavals,

 

protectionism of domestic production and other barriers to trade; transport demand constraint, for example low population density, production and trade; governance and regulation of competition in the transport sector.

Australia’s low population density, in fact the lowest among the OECD countries,5 would impose a constraint on demand for transport and logistics that inhibits this sector from achieving economies of scale. In relative terms, it would be less economically viable to invest in transport and logistics in areas with a small population. However, the effect of this constraint could be reduced when there is sufficient contribution to demand by other factors such as trade, manufacturing, mining, etc. Australia’s recent intensive participation in international trade and exports of raw materials and mineral products are among the key factors contributing to the development of its transport and logistics sector. The resulting causation running from trade to transport and logistics has been confirmed by the results of the causality tests.

5 Australian population density is 2.65 people per square km (World Bank, 2007).

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Looking further, we note that the capital-intensive nature of the transport and logistics sector tends to inhibit its ability to quickly adjust and respond to shocks in the market. This effect on the Australian transport and logistics sector could even be amplified as it is lagging behind international trade. Our findings seem to be consistent with what is currently happening to the Australian transport and logistics sector, ‘‘At a time of unprecedented prosperity and in the midst of an international resources boom, there could be no more potent images of lost opportunity, than the sight of queues of up to 50 vessels off three of our major ports’’ (Standing Committee on Transport and Regional Services, 2007, p. vii). Thus, from a policy perspective, there is a need for the sector to improve its capacity to respond more expeditiously to the changing business environment and trade. It is interesting to note that costly congestions happened mainly to Australian ports, but not to other chains in the transport network. In planning for more investments in transport and logistics infrastructure, it is important to identify and assess the underlying factors responsible for the inadequacy of Australia’s transport and logistics infrastructure before adopting appropriate policies and strategies. Further, transport planners should also identify the regions in need and/or those where this further infrastructure investment can provide better returns in terms of trade. The above analysis also suggests a more holistic view of transport and logistics development. As trade growth brought about by recent booms in the Australian resources sector causes the growth of the transport and logistics sector, national policies may be needed to stimulate and support the resources sectors. Furthermore, as Kessides (1996) noted, while attention and effort has been given mainly to transport infrastructure, the inefficiency relating to the utilisation of the transport and logistics system remains to be solved. Fig. 4 shows the quarterly output, as percentage of GDP, of the transport sector from Q1:1988 to Q3:2006. Note that the sector’s contribution to GDP remains below 5% throughout the period, while Australian international trade has increased to more than double, from 17% to 37% over the same period. There are many reasons why this occurred. First, demand for transport and logistics services is derived from not only trade but also manufacturing activities as already substantiated by the analysis results. While the growth of international trade has been relatively strong, contribution to GDP of the Australian manufacturing sector has declined significantly from 14% to about 10% over the same period. This implies that international trade is increasingly important to the development of the transport and logistics sector. Second, the increasing gap between trade and logistics could be due to increases in inhouse logistics, whose output is not reflected in the national

accounts. As argued by the Australian Logistics Council (2007) and the Logistics Association of Australia (2004), Australian in-house logistics could contribute to more than 50% of the sector’s total output. Third, transport and logistics productivity has increased significantly due to economies of scale, massive investment in more efficient cargo handling equipment, transportation vehicles as well as information management systems. Together with strong competition among transport and logistics companies, this enables the sector to be able to handle a much greater amount of freight without increasing the costs. It was found that growth in Australia’s trade with China, Japan and the US Granger-caused the development of its transport and logistics sector, while its trade with the rest of the world has no effect on this sector, even though it accounts for more than 60% of the total value of Australian international trade. This result further highlights the role of the main trading partners. Because the econometric analysis is only concerned with the growth rate or short-term changes from one period to the next, the value of trade with a country or a group of countries (as a percentage of the total trade value) is not expected to interfere with the variable’s growth rate. The above result could also be due to the fact that, even though individual countries are highly heterogeneous in trade structure and orientation, their impacts are cancelled out by the aggregation, which therefore would not reveal anything about major trading partners.

6. Conclusion Although much has been written on the effect of transport infrastructure on economic growth, it remains unclear whether the direction of causation runs from transport infrastructure to economic growth or vice versa or both. The existing literature also fails to provide an unequivocal proposition about the direction of causation between the transport sector and international trade. This paper seeks to study the causal relationship between growth in the transport sector and trade in the context of Australia. Using the VAR framework, we found growth in Australia’s trade with China causes the development of the Australian transport sector but not the other way around. Australia’s trade with its other main trading partners, Japan and the US, also causes the growth in its transport sector. However, Australia’s trade with the rest of the world has insignificant effect on its transport sector, even though it accounts for more than 60% of Australia’s total trade value. In addition, growth in the transport sector is affected by both international trade and the manufacturing sector. The analysis results highlight the influential role of Australia’s trade with large economies, namely China, Japan and US, in relation to the development of its transport sector. The inability of Australia’s transport sector to cause trade growth implies that this sector is lagging behind its trade. Given demand constraint due to the country’s relatively low population density, investment in transport infrastructure may not be the best way to promote economic growth and trade. A better alternative may be to focus on improving the efficiency of the transport sector. For example, this could be to improve the utilisation rate of the existing transport infrastructure system. In addition, national transport policies should be more oriented to exports, and there is a need to ensure that investment is directed to areas or regions that need it most.

Appendix Fig. 4. The transport and logistic sector (GDP %). Source: Australian Bureau of Statistics (2006).

See Tables A1–A3.

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Table A1 Variables’ descriptive statistics. Variable name

Number of observations

Mean

Standard error

Minimum

Maximum

dQtran dTChn dTJap dTUS dTothers dYAus dYChn dQman

74 74 74 74 74 74 75 74

0.009786 0.041874 0.013728 0.0128 0.019492 0.008598 0.023097 0.004394

0.03523 0.12941 0.072972 0.079006 0.063695 0.059002 0.006886 0.055357

 0.08799  0.28522  0.13662  0.21229  0.11723  0.11009 0.006409  0.12587

0.063254 0.372647 0.183337 0.182928 0.149014 0.090421 0.036809 0.080076

Table A2 VAR Estimated by SUR. Variable number

Variable name

Coefficient

Standard error

T-statistic

Significance level

(a) Dependent variable: dQtran 1 2 3 4 5 6 7 8 9 10 11 12

Constant D D(1) D(2) dQtran(1) dQtran(2) dQtran(3) dQtran(4) dTChn(1) dTChn(2) dTChn(3) dTChn(4)

0.02336577  0.002269437  0.042791564 0.009246316 0.073542906  0.190983286  0.233753097 0.077720688  0.029225147  0.055024516 0.008755181 0.017747244

0.007687719 0.011594844 0.011039361 0.010869767 0.116911283 0.116134028 0.111124297 0.112619712 0.022331664 0.022525503 0.023693411 0.020880733

3.03936  0.19573  3.87627 0.85065 0.62905  1.64451  2.10353 0.69012  1.30869  2.44277 0.36952 0.84993

0.00237079 0.84482298 0.00010607 0.3949664 0.52931709 0.10007141 0.03541962 0.49012111 0.19064058 0.01457522 0.71174043 0.39536179

(b) Dependent variable: dTChn 13 14 15 16 17 18 19 20

Constant D D(1) D(2) dTChn(1) dTChn(2) dTChn(3) dTChn(4)

0.176260557  0.05175034  0.148938861  0.100967377  0.425718977  0.468198492  0.269372201  0.155509414

0.028359988 0.03573883 0.040577265 0.035413091 0.117768128 0.123154651 0.123301856 0.111105685

6.21511  1.44801  3.6705  2.85113  3.61489  3.80171  2.18466  1.39965

0.000000 0.14761303 0.00024208 0.0043564 0.00030047 0.0001437 0.02891404 0.16161723

Table A3 Response to Australia–China trade.

References

Period

Australia–China Trade

Transport and logistics

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1  0.425718977  0.286961845 0.052113885 0.071336677 0.088733832  0.040588165  0.051586212 0.005968594 0.01874602 0.00943271  0.006378174  0.0076789 0.000799212 0.003506237 0.001193487  0.001170849  0.001129104 0.000162126 0.00058942 0.000159393  0.000211908  0.0001684 3.63096E-05

0.39162859  0.137298615  0.326909833 0.141194689 0.29611742  0.024809515  0.180189687  0.078959906 0.090292449 0.083983802  0.018256432  0.056960599  0.013671907 0.026298142 0.019343696  0.006688845  0.013425616  0.002111318 0.00644249 0.00388374  0.001817594  0.002862071  0.000239443 0.001417339

Alokan, O.O., 1995. The road freight industry in Nigeria: new challenges in an era of structural adjustment. Transport Reviews 15 (1), 27–41. Aschauer, D.A., 1990. Why is infrastructure important?, Conference Proceedings, Conference series no. 34, Federal Reserve Bank of Boston. Antkiewicz, A., Whalley, J., 2005. China’s new regional trade agreements. World Economy 28 (10), 1539–1557. Australian Bureau of Statistics, 2006. Australian National Accounts: National Income, Expenditure and Product, Canberra. Australian Department of Foreign Affairs and Trade, 2005. Australia-China Free Trade Agreement Joint Feasibility Study, Canberra. Australian Logistics Council, 2007. Contribution of Transport and Logistics to the Economy: Dispelling the Myths, Apelbaum Consulting Group Pty Ltd. Balaguer, J., Cantavella-Jorda, M., 2001. Examining the export-led growth hypothesis for Spain in the last century. Applied Economics Letters 8 (10), 681–685. Banister, D., Stead, D., 2004. Impact of information and communications technology on transport. Transport Reviews 24 (5), 611–632. Bernanke, B., 1986. Alternative explanations of money-income correlation. Carnegie-Rochester Conference Series on Public Policy 25, 49–100. Berndt, E.R., Hansson, B., 1992. Measuring the contribution of public infrastructure capital in Sweden. Scandinavian Journal of Economics 94, 151–168. Blanchard, O., Quah, D., 1989. The dynamic effects of aggregate demand and supply disturbancies. American Economic Review 79, 655–673. Boske, L.B., Cuttino, J.C., 2003. Measuring the economic and transportation impacts of maritime-related trade. Maritime Economics and Logistics 5 (2), 133–157. Bougheas, S., Demetriades, P.O., Morgenroth, E.L.W., 1999. Infrastructure, transport costs and trade. Journal of International Economics 47, 169–189.

ARTICLE IN PRESS 146

H.-O. Nguyen, J. Tongzon / Transport Policy 17 (2010) 135–146

Brooks, M.R., 1994. The impact of NAFTA on transportation companies: a Canadian point of view. Transport Reviews 14 (2), 105–117. Bureau of Transport and Regional Economics, 2006. Australian Transport Statistics, Department of Transport and Regional Services, Canberra. Bureau of Transport Economics, 2001. Logistics in Australia: A preliminary analysis, Working paper, 49, Oct. Capineri, C., Leinbach, T.R., 2004. Globalization, e-economy and trade. Transport Reviews 24 (6), 645–663. Cariou, P., Wolff, F.-C., 2006. An analysis of bunker adjustment factors and freight rates in the Europe/Far East market. Maritime Economics and Logistics 8, 187–201. Carruthers, R., Bajpai, J.N., Hummels, D., 2003. Trade and logistics: an East Asian perspective. In: Krumm, K., Kharas, H. (Eds.), East Asia Integrates: A Trade Policy Agenda for Shared Growth. The World Bank, Washington DC. Fritsch, B., Prud’homme, R., 1997. Measuring the contribution of road infrastructure to economic development in France. In: Quinet, E., Vickerman, R. (Eds.), The econometrics of major transport infrastructures. Macmillan, London. Dickey, D., Fuller, A.W., 1979. Distribution of the estimates for autoregressive time series with a unitroot. Journal of the American Statistical Association 74, 427–431. Dickey, D., Fuller, A.W., 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057–1072. Doan, T., 2004. RATS User’s Manual, Estima, Evanston, Ill. Enders, W., 2004. Applied Econometric Time Series, 2. John Wiley & Sons, Inc. New York. Frankel, E.G., 1998. China’ maritime developments. Maritime Policy and Management 25 (3), 235–249. Glen, D.R., Martin, B.T., 2005. A survey of the modelling of dry bulk and tanker markets. In: Cullinane, K. (Ed.), Shipping Economics. Elsevier, Amsterdam. Gramlich, M.E., 1994. Infrastructure Investment: a review essay. Journal of Economic Literature 32, 1176–1196. Granger, W.J.C., 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438. Hamilton, J.D., 1989. Time Series Analysis. Princeton University Press, Princeton. Kennedy, C., Miller, E., Shalaby, A., Maclean, H., Coleman, J., 2005. The four pillars of sustainable urban transportation. Transport Reviews 25 (4), 393–414. Kessides, C., 1996. A review of infrastructure’s impact on economic development. In: Batten, D., Karlsson, C. (Eds.), Infrastructure and the Complexity of Economic Development, Chapter 12, pp. 213–230. Lau, S.-H.P., Sin, C.-Y., 1997. Public infrastructrue and economic growth: timeseries properties and evidence. Economic Record 73 (221), 125–135.

Lee, J.Y., Rodrigue, J.P., 2006. Trade reorientation and its effects on regional port systems: the Korea–China link along the Yellow Sea Rim. Growth and Change 37 (4), 597–619. Logistics Association of Australia, 2004. Characteristics, Strategies and Trends for 3PL/4PL in Australia, Sydney. Martinez-Zarzoso, I., Perez-Garcia, E.M., Suarez-Burguet, C., 2008. Do transport costs have a differential effect on trade at the sectoral level? Applied Economics 40, 3145–3157. Munnell, A.H., Cook, L.M., 1990. How does public infrastructure affect regional economic performance. New England Economic Review September/October, 11–32. National Bureau of Statistics of China, 2006. China’s Statistical Yearbook 2006. Rietveld, P., 1989. Infrastructure and regional development: a survey of multiregional economic models. Annals of Regional Science 23 (4), 225–274. Rietveld, P., Boonstra, J., 1995. On the supply of network infrastructure: highways and railways in European regions. Annals of Regional Science 29 (2), 207–220. Sanchez, R.J., et al., 2003. Port efficiency and international trade: port efficiency as a determinant of maritime transport costs. Maritime Economics and Logistics 5 (2), 199–218. Sims, C., 1980. Macroeconomics and reality. Econometrica 48, 1–49. Sims, C., 1986. Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review, 3–16. Standing Committee on Transport and Regional Services, 2007. The Great Freight Task: Is Australia’s Transport Network up to the Challenge, The Parliament of the Commonwealth of Australia, Canberra. Tatom, J.A., 1993. The spurious effect of public capital formation on private sector productivity. Policy Studies Journal 21 (2), 391–395. Thornton, J., 1997. Exports and economic growth: evidence from nineteenth century Europe. Economics Letters 55 (2), 235–240. Tongzon, J., Nguyen, H.O., 2008. China’s economic rise and its implications for logistics: the Australian Case, International Forum on Shipping, Ports and Airports conference proceedings, 25–28 May, Hong Kong. Vasiliauskas, A.V., Barysiene_ , J., 2008. An economic evaluation model of the logistics system based on container transportation. Transport 23 (4), 311–315. Veenstra, A.W., 1999. Quantitative Analysis of Shipping Markets. Delft University Press, Delft. Wilson, J., Mann, C., Otsuki, T., 2003. Trade facilitation and economic development, World Bank Policy Research Working Paper 2988, Washington. World Bank, 2007. World Development Indicator, Washington D.C. Yao, V.W., 2005. The causal linkages between freight and economic fluctuations. International Journal of Transport Economics 32 (2), 143–159. Yap, W.Y., Lam, J.S.L., Notteboom, T., 2006. Developments in container port competition in East Asia. Transport Reviews 26 (2), 167–188.