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Transportation uncertainty and international trade
Transport Policy 18 (2011) 156–162
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Transportation uncertainty and international trade$ Xiaoyun Liu a,b, Xian Xin a,b,n a b
China Agricultural University, Beijing, 100094 PR China Department of Economics, The University of Western Ontario, London, Ontario, Canada N6A 5C2
a r t i c l e in f o
a b s t r a c t
Available online 4 August 2010
This paper uses a numerical framework to demonstrate that uncertainty in the arrival time of foreign goods can substantially reduce the demand for foreign goods. It further reveals that the impacts of falling transport costs and shipment time on international trade growth could be discounted, if uncertainty arises in the arrival time of the imported goods. This in turn suggests that reduced uncertainty, which is possibly the results of transportation improvements, might have contributed to the growth experienced in world trade growth over the past several decades. Thus, neglecting the roles of improvements in international transportation arrangements and reduced uncertainty will lead to underestimating the contribution of transportation improvements to trade growth. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Trade Uncertainty Transportation
World trade grew twice as fast as world GDP growth in the last decades. A rapidly growing literature has attempted to explain the causes of this phenomenal international trade growth. Krugman (1995) offers a comprehensive survey of this issue and invites further inquiries into the matter. Among the standard explanations provided for the fast growth of world trade, trade liberalization (reduction of tariff rate), transport costs reduction, income convergence and economic growth are the four main factors often cited in trade literature (Helpman, 1987; Hummels and Levinsohn, 1995; Whalley and Xin, 2007). It has also been found that outsourcing (vertical specialization or disintegration of production) plays an important role in the surge of international trade flows (Yi, 2003; Feenstra, 1998; Hummels et al., 1998). Furthermore, preference changes are also found to significantly affect world trade flows, and it is said to have contributed to about 27% reduction in the volume of world trade (Whalley and Xin, 2007). Though many factors affect trade growth, the factor of principal interest here is the contribution of transportation improvements. Many studies have documented that transport costs have significantly negatively affected trade volumes and patterns (McCallum, 1995; Wei, 1996; Lima~ o and Venables, 2001;
Wolf, 2000; Baier and Bergstrand, 2001; Head and Mayer, 2002; Helliwell and Verdier, 2001; Evans and Harrigan, 2003; Hillberry and Hummels, 2003; Anderson and van Wincoop, 2003 and 2004). However, in recent years, the performance of transport has substantially improved both in terms of direct transport costs and time costs (Lima~ o and Venables, 2001; Hummels, 2001; Rietveld and Vickerman, 2004; Anderson and van Wincoop, 2004)1 . But researchers have reported different values for the response of transport cost to trade volume. For instance, the elasticity of trade with respect to transport costs obtained from Anderson and van Wincoop (2003) model is approximately 0.8, which implies that a 10% fall in transport costs will lead to an 8% rise in trade volume. But Lima~ o and Venables (2001) report an elasticity of 2.5, which means that a 10% reduction in transport costs will lead to a 25% rise in trade volumes2 The findings in Baier and Bergstrand (2001) further suggest that reductions in transport costs explain 8% of trade growth in OECD countries. These findings suggest that improvement in transportation has substantial impact on trade volume. However, this paper argues that the contribution of transportation improvements to trade growth might have been underestimated in the trade literature. There are two reasons. One reason is that in the trade literature, the impacts of transport costs on trade flows are usually drawn
$ The authors appreciate financial support from CN Post-doctoral Fellowships in Transportation Policy and part of the work was conducted, while the two authors worked in the University of Western Ontario in 2005–2006. The authors would like to thank John Whalley for discussions. n Corresponding author at. China Agricultural University, Beijing 100094, PR China. Tel./fax:+ 86 10 62738710. E-mail address: [email protected] (X. Xin).
1 A number of studies suggest that international transport costs have declined, while some studies argue no substantial changes; and others nevertheless argue that international transport costs have been on the rise (see Disdier and Head (2004), Hummels (1999), Leamer and Levinsohn (1995), Carrer and Schiff (2004), Rose (1991)). 2 Please refer to Linders (2004) for an overview of the impacts of distance on trade flows.
1. Introduction
0967-070X/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2010.07.005
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
from the regression results of the gravity model, in which transport costs are conventionally assumed proportional to distance3. Recent studies began to realize that distance is not an appropriate measure of transport costs, because transport costs do not only depend on pure monetary costs, but also on the time costs (Hummels, 2001; Rietveld and Vickerman, 2004; Anderson and van Wincoop, 2004; Evans and Harrigan, 2003; Harrigan and Venables, 2004). Hummels’s (2001) estimate shows that each day saved in shipping time is worth 0.8% ad-valorem for manufactured goods, and provides an estimate that each additional day in ocean transit reduces the probability that a country will export to the U.S. by 1% for all goodsand 1.5% for manufactured goods. Therefore, neglecting the role of time costs associated with distance may produce incorrect estimate of the elasticity of trade with respect to transport costs, which in turn conveys incorrect message about the contribution of transportation improvement to trade growth. The other reason is that the traditional transport costs neglect uncertainty associated with long distance shipments and border delay. Recent studies indicate that uncertainty at the arrival of imports involves further costs (Lima~ o and Venables, 2001; Mazner, 2001; Macdonald, 2002; Cudmore and Whalley, 2003; Evans and Harrigan, 2003; Taylor et al., 2003; Rietveld and Vickerman, 2004; Harrigan and Venables, 2004; Huang and Whalley, 2006a and 2006b)4 . Uncertainty might occur from ports to ports as well as at the national border. The world’s average distance of import-related trade is estimated to be around 5000 km (Carrer and Schiff, 2004), and distance introduces uncertainty into the completion of trades (Harrigan and Venables, 2004). Transportation uncertainty may also arise from border delays, transport coordination problems, security reasons, etc., which create excess planning time, higher insurance costs, higher scheduling costs and higher extra inventory-carrying costs. Of the transportation-related uncertainties, border delays are very common. Evidences indicate that border delays associated with maritime transport mode at African customs are 12 days, 7 days in Latin America, 3.5 days in North America, 5.7 days in Asia, 4 days in Central and Western Europe, 2.4 days in East Europe and 5.4 days in Former Soviet Union. For some African countries, border delays are much longer, e.g., 18 days in Nigeria, 20 days in Cameroon and 30 days in Ethiopia (Clark et al., 2004). Border delays may also reach as high as 3–6 months in some African economies (WTO, 1999), while customs clearance in Commonwealth of Independent States (CIS) economies may be in the range of weeks or months (Cudmore and Whalley, 2003). Also, crossborder shipments attribute to trade delays and lead to increase in trade cost. For instance, cross-border shipments required voluminous paperwork and faced interminable delays at the E.U. member borders in the mid 1980s (EUC, 1996). Border delays are also very common even within the free trade areas such as North American Free Trade Agreement (NAFTA). The variability of the border delay in NAFTA ranges from 10 min up to 4 h at the Canada–U.S. border and the Mexico–U.S. border (OCC, 2004; Lakshmanan and Anderson, 1999). Furthermore, Mazner (2001) finds that paperwork and inspection costs add up to about 13% of the cost of goods moved across NAFTA borders, and that longer delays since the 9/11 are adding another 3%. In addition, customs clearance and compliance is estimated to cost consumers a
3 This may be due to that the direct measure of transport costs data is limited by its private nature (Anderson and van Wincoop, 2004). The imputed transport costs data from bilateral trade values is notoriously bad, and thus should not be disqualified from use as a measure of transport costs in even-semi-careful studies (Hummels, 1999). 4 For a comprehensive literature review of an uncertainty in trade models, other than international transportation uncertainty, please refer to Pomery (1985).
157
hidden surtax of 5–7% (Macdonald, 2002). Taylor, Robideaux and Jackson (2003) offer an estimate of around US$2.53 billion in 2001 for this uncertainty related costs at the U.S.–Canada border— accounting for about 25% of the total U.S.–Canada border related costs. The higher costs associated with an uncertainty in the arrival of imports may affect international trade through many channels. Firms may shift from sourcing foreign goods to using home goods in expectations of uncertainty, especially in just-in-time production and logistics (Evans and Harrigan, 2003; Harrigan and Venables, 2004; Rietveld and Vickerman, 2004). Transport uncertainty may also result in excess capacity building, inventory and crossing time. Furthermore, transport uncertainty may also impact transport agents involved in moving goods from exporters to importers, because delivery schedules may be disrupted, and returnable containers end up out of position. These in turn may lead to lower trade flows. For example, Taylor et al. (2003) find that imports from Canada to the U.S. fell 10.8%, due to U.S. industrial buyers’ concerns about border delays and uncertainty introduced by the 9/11. Although much work has been done on the impact of uncertainty resulting from border delay, however, systematic investigations of the impacts of uncertainty related to arrival time of foreign goods on trade flows are scarce. One of the most important reasons is that the precise measure of transport uncertainty is unfortunately poorly documented in the literature, and there exists little systematic information on the reliability, scheduling, and comfort of transport (Cudmore and Whalley, 2003; Rietveld and Vickerman, 2004). Advancement in transportation technology, however, indicates that international shipping time has substantially declined over the past half century. That is, the introduction of containerization in the late 1960s and early 1970s is believed to have resulted in a doubling of the average speed of ocean fleet. For example, Hummels (2001) shows that an average shipping time fell from 40 days in 1950s to 10 days in 1998. The falling shipping days might have reduced the uncertainty involved, which in turn, might have resulted in a surge of trade flows. The surge of free trade agreements, crossborder procedural changes, and application of advanced technologies also accelerated a reduction in border delays. For instance, even traditionally efficient shippers usually experienced paperwork delay of 4–5 h at the U.S. border before the implementation of NAFTA, but these border delays have now been decreased to 12.4 min (Kirk and Frittelli, 2004; USDT, 2010). Thus, neglecting the trade-creation effects of reduced uncertainty in the arrival time of foreign goods will lead to underestimating the contribution of transportation improvements to trade growth. In this paper, therefore, we assess the impacts of international transportation uncertainty on trade flows, using a numerical model that incorporates time costs into the transport costs into our model. Our simulation results suggest that international transportation uncertainty can significantly reduce international trade flows. The results further suggest that reduced uncertainty—which is possibly the result of improvements in transportation, and leads to falling transport cost, might have contributed to the world trade growth experienced over the last several decades. However, the impacts of falling transport costs on trade growth could be offset if uncertainty in the arrival time of imported goods arises. Therefore, ignoring the role of reduced uncertainty might underestimate the significance of improvements in transportation and overestimate the role of other factors. Our model thus incorporates the role of reduced uncertainty, in order to properly capture its effect on world trade growth. The rest of the paper is organized as follows. Section 2 describes the set up of a numerical model, Section 3 presents the simulation results of the impacts of international transportation
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uncertainty on trade flows. Concluding remarks are drawn in Section 4.
the following equations:
2. Analytical framework
A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b ð1aÞ x2 1=s
In this section, we first set up a basic model with no uncertainty involved in the arrival time of imported goods, and then extend it by allowing for the possibility of arrival delays of imported goods. Product market is assumed to be competitive and firms employ intermediate inputs (composite of home and foreign goods, substitutes, but imperfect) and capital inputs (composite of physical capital and labor) to produce a finished good. The production function is assumed to be a two-stage nested function. The first-stage function is a Cobb–Douglas form, in which firms use intermediate and capital inputs to produce the final goods with b as the output elasticity of the intermediate inputs. For simplicity, we assume that capital inputs are fixed in the production period and only intermediate inputs are variable. Following Armington’s (1969) formulation of product differentiation by country, we use the Constant elasticity of substitution (CES) function to describe the relationship between intermediate inputs (composite of goods) and home and foreign goods (inputs). The final production function can be represented by Q ¼ A½a1 x1 s1=s þð1a2 Þx2 s1=s bs=s1
ð1Þ
where Q is output, A is the unit term which also captures fixed capital inputs, x1 and x2 are home and foreign goods, a1 and 1 a1 are share parameters of home and foreign goods, s is the elasticity of substitution between home and foreign goods, and b is the output elasticity of intermediate inputs ranging 0–1. Klier (1999) as well as Harrigan and Venables (2004) find that firms usually locate close to final suppliers who are within a day’s drive. In this sense, we assume that a firm can acquire home goods continuously, and that home goods supplier bear the costs involved in the transit of goods. Costs associated with the transit of imported goods include fixed costs of each ordering of shipment, tariff, and non-tariff barriers, tariff equivalent, freight rates and insurances, and inventory-carrying costs. Following the procedure of Baumol and Vinod (1970), costs can be broken down into C ¼ rT þutT þ
a wsT þ s 2
¼ w2 þ rw2 þutw2 þ
1 1=2 ð2aww2 Þ1=2 x2 2
ð5Þ
ð6Þ
Conceptually, with two unknown variables and two equations one can derive the optimal demand for home and foreign goods from Eqs. (5) and (6). Unfortunately, it is not generally possible to derive a simple expression of the demand for home and foreign goods. However, we could use other mathematics tools (such as GAMS) to solve Eqs. (5) and (6) given values of parameters in the next section5. We then extend the model by introducing the possibility of arrival delay of foreign goods into the above framework. In the presence of uncertainty, we will investigate two cases. One is that the firm can resort to an alternative costly air mode. The second case is that no alternative transport mode is available. Assume the probability of arrival delay of foreign goods is p. In determining arrival delay, the firm is assumed to instantly order another shipment of s x2 w2 via an alternative air mode with cost of u s x2 w2 . Evidences indicate that air shipping requires only a day or less to most destinations (Hummels, 2001), so we assume no delays associated with the air mode. For simplicity, we further assume that an arrival delay can happen at most once in the whole production period and that the firm can costlessly adjust the next ordering of shipment. The expected total costs, thus, becomes a wsT a wsT C e ¼ ð1pÞ rT þ utT þ þ þ p rT þ utT þu sTustT þ þ s 2 s 2 ð7Þ The optimal s is obtained by setting the first derivative of C e with respect to s to zero 1=2 2a ð8Þ se ¼ wT þ 2pTðu utÞ The profit of the representative firm is therefore
pe ¼ PQ w1 x1 w2 x2 rw2 x2 utw2 x2 ½2aðw þ 2pðu utÞÞ1=2 ðw2 x2 Þ1=2 ð9Þ
ð2Þ
where C is the total transit costs associated with foreign goods, T is the total value of imported goods in one production period (one year in this paper), r is the per unit cost of tariff and non-tariff barrier, t is the transit time (in days) required from exporting locations to firm locations, u is the shipping cost per unit per day, s is the average time between shipments, a is the fixed costs per ordering of shipment, and w is the inventory-carrying cost per unit per production period. Assuming firms minimize C given T, the optimal value of s can be obtained by setting the first derivative of C with respect to s to zero, 1=2 2a s¼ ð3Þ wT Assume further that the representative firm knows this information and thus Eq. (3) enters into the firm’s profit maximization decision making as
p ¼ PQ w1 x1 w2 x2 rw2 x2 utw2 x2 ð2awÞ1=2 ðw2 x2 Þ1=2
A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b a x1 1=s ¼ w1
ð4Þ
where w1 and w2 are prices of home and foreign goods, respectively. Applying the first-order condition to Eq. (4) yields
Applying the same procedure yields the optimal amount of home and foreign goods in which Eq. (5) is left unchanged, while Eq. (6) becomes A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b ð1aÞ x2 1=s ¼ w2 þ rw2 þutw2 þ
1 1=2 ½2aðw þ 2pðu utÞÞw2 1=2 x2 2
ð10Þ
In the absence of an alternative model, the firm may increase its carrying inventory, thereby increasing its carrying-inventory costs. Assuming that the arrival delay can occur only once in the whole production period, the firm holds an extra inventory of sT till the last shipment comes. The total cost is therefore a wsT a wsT C u ¼ ð1pÞ rT þ utT þ þ þ p rT þ utT þ þ þ wðs1ÞT s 2 s 2 ð11Þ
5 The General Algebraic Modeling System (GAMS) is specifically designed for modeling linear, nonlinear and mixed integer optimization problems. It allows users to concentrate on the modeling problem by making the setup simple. The GAMS language is formally similar to commonly used programming languages. For more information please refer to GAMS (2010).
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
while the optimal s becomes 1=2 2a su ¼ wð12pÞT
ð12Þ
The profit of the representative firm is, thus,
p ¼ PQ w1 x1 w2 x2 rw2 x2 utw2 x2 pww2 x2 ð2awð12pÞÞ1=2 ðw2 x2 Þ1=2 u
ð13Þ Applying the first-order condition yields A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b ð1aÞ x2 1=s 1 1=2 ¼ w2 þ rw2 þ utw2 þ pww2 þ ð2awð12pÞw2 Þ1=2 x2 2
ð14Þ
By far, we have set up the numerical framework to investigate the impacts of transportation uncertainty on trade inflows. In Section 3, we will conduct a series of experiments to demonstrate the impacts of transportation uncertainty on trade inflows.
3. Numerical simulations This section presents the simulation results of our numerical model. We first provide the values of the model parameters in Table 1 as the base scenario. In performing the simulation, we assume that in a costless-trading world, firms are neutral in choosing home and foreign goods, and then set the share parameter of home goods in the CES production function to 0.5. Output elasticity of intermediate goods is set to 0.6, since intermediate inputs usually account for 60% of an output value (Liu et al., 2010). The size of the elasticity of substitution between home and foreign goods in the trade literature is considerably controversial and varies drastically in different studies. Anderson and van Wincoop (2004) suggest that an order of five to ten is more plausible. We first set this substitution parameter to 10, and then proceed to conduct sensitive analysis by shifting this parameter to 8 and 5 as suggested in Anderson and van Wincoop (2004). The non-air mode of shipping time is then assumed to be 20 days based on the results of Hummels (2001) and Anderson and van Wincoop (2004). Shipping cost per unit per day comprises of time cost and direct transport costs. In accordance with Hummels (2001), the time cost is set to 0.8% per unit per day. However, other works (Anderson and van Wincoop, 2004; Rietveld and Vickerman 2004) estimated direct transport costs are to be around 10%. The direct transport cost per unit per day is thus set to 0.5% given 20 days shipping time, and 10% of the sub-total of the direct transport costs. The shipping cost per unit per day is thus set to 1.3%, which comprises of 0.8% Table 1 Model parameter values of base scenario. Parameters Value
Notes
A
Unit parameter of firm production function Share parameter of home goods in firm production function Output elasticity of intermediate goods Elasticity of substitution between home and foreign goods Price of home good (input) Price of foreign good (input) Per unit tariff and non-tariff barriers tariff equivalents Shipping cost per unit per day Transit time
a1 b
s w1 w2 r u t a w u*
5 0.5 0.6 8 5 4 8% 1.3% 20 days 5% 20% 44%
Fixed costs per ordering of shipment Per unit inventory-carrying cost Per unit shipping cost of air mode
159
of time cost and 0.5% of direct transport costs. Tariffs and nontariff barriers tariff equivalents are set to 8% as in Anderson and van Wincoop (2004). We further proceed with experiments to assess the impacts of reductions in policy barrier (the sum of tariffs and non-tariff barriers tariff equivalents) as well as transportation costs on trade flows. Per unit inventory-carrying costs ratio is set to 20% (Coyle et al., 2009; Bowersox et al., 1986). Per unit shipping cost incurred from an alternative air mode is assumed to be 44% according to Rietveld and Vickerman (2004). The possibility of an arrival delay of imported goods is set to zero in base scenario, and then set to 0.1, 0.2, and 0.4 for comparisons. With these set-out values, we can calibrate, with the aid of GAMS, the optimal demand for home and foreign goods in the base scenario as well as in the presence of uncertainty. The simulation results of the alternative mode un-available are reported in Table 2. Compared with the base scenario, in which there is no uncertainty in the arrival of imported goods, the presence of uncertainty substantially reduces the demand for foreign goods. As the simulation results indicate, a 10%, 20%, and 40% possibility of arrival delays would reduce the demand for foreign goods by 8.3%, 15.1%, and 24.3%, respectively. Previous research has shown that ocean shipping costs per unit decreased by approximately 60% between 1930 and 1990, 50% for rail shipping costs between 1950 and 2000, and 50% for road transport costs between 1950 and 2000 (Rietveld and Vickerman, 2004). These figures roughly suggest that non-air mode transport costs fall by 10% every ten years. So, we now conduct simulations to assess the impacts of a 10% fall in transport costs on trade flows. The simulation results of transport costs reduction, with or without the possibility of arrival delays of foreign goods are, thus, reported in Table 2. The simulation results suggest that the tradecreation effects of falling transport costs could be offset drastically if uncertainty arises. Without uncertainty in the arrival time, the demand for foreign goods will increase by 17.1% as transport costs reduce by a 10% point. This implies an estimate of 1.71 elasticity of trade with respect to transport costs, which lies between the elasticity estimated by Anderson and van Wincoop (2003), and that by Lima~ o and Venables (2001). The presence of uncertainty at 10% level could reduce the demand for foreign goods by 9.4%. If the possibility of arrival delay rises to 40%, the combined effects of falling transport costs and rising uncertainty result into an even negative effect on trade volumes. We also conduct simulations to investigate the impacts of reductions in trade barriers associated with an uncertainty in arrival delays on trade flows. The results suggest that halving tariff and non-tariff barriers tariff equivalents will generate a 27.4% increase in trade flows. However, the presence of a 10% possibility of arrival delays will offset approximately 10% of the gain, while a 20% possibility of arrival delays will further cut down another 9% points. Thus, the presence of a 40% possibility of arrival delays may offset all the trade growth resulting from reductions in trade barriers. According to Hummels (2001), the average ocean shipment time was halved from 40 days in 1950 to 20 days by 2000. This then leads us to investigate how an uncertainty of arrival delays impacts trade flows, given another 50% drop in shipment time. The results in Table 2 indicate that uncertainty of arrival delays still has a significant trade-reducing effect, even though the shipment time is halved from 20 to 10 days. This implies that the trade-reducing effects of uncertainty still matters if an uncertainty of arrival delays persists given reductions in international shipment time. When an alternative transportation mode is available, the presence of arrival delay of foreign goods still has significant trade-reducing effects (even though smaller). These results imply that reduced uncertainty of arrival delays has significant
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X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
Table 2 Uncertainty in the arrival time of imported goods and international trade flows. Without uncertainty
Uncertainty in arrival time of imported good Alternative mode un-available
Alternative mode available
Base scenario
p ¼ 0.1
0.2
0.4
p¼ 0.1
p¼ 0.2
p¼ 0.4
Home goods Foreign goods Changes in imported goods compared with base scenario (%) 10% points reductions in transport costs Home goods Foreign goods Changes in imported goods compared with base scenario (%)
1.091 0.350 —
1.116 0.321 8.3
1.142 0.297 15.1
1.177 0.265 24.3
1.097 0.338 3.4
1.108 0.327 6.6
1.130 0.307 12.3
1.019 0.410 17.1
1.055 0.377 7.7
1.086 0.349 0.3
1.128 0.309 11.7
1.034 0.396 13.1
1.047 0.384 9.7
1.072 0.361 3.1
Halve tariff and non-tariff barriers tariff equivalents Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.982 0.446 27.4
1.029 0.410 17.1
10.53 0.379 8.3
1.099 0.336 4.0
0.996 0.433 23.7
1.007 0.422 20.6
1.029 0.402 14.9
Base scenario 10 days of transit time Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.721 0.710 —
0.768 0.661 6.9
0.811 0.616 13.2
0.881 0.545 23.2
0.739 0.690 2.8
0.756 0.672 5.4
0.787 0.640 9.9
Without uncertainty
Uncertainty in arrival time of imported good
Table 3 Sensitivity analysis with the elasticity of substitution equal to 8.
Alternative mode un-available
Alternative mode available
Base scenario
p ¼0.1
0.2
0.4
p ¼ 0.1
0.2
0.4
Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.918 0.376 —
0.943 0.353 6.1
0.964 0.333 11.4
0.994 0.305 18.9
0.928 0.367 2.4
0.936 0.359 4.5
0.951 0.345 8.2
10% points reductions in transport costs Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.870 0.422 12.2
0.897 0.396 5.3
0.920 0.374 0.5
0.954 0.342 9.0
0.881 0.412 9.6
0.89 0.403 7.2
0.907 0.386 2.7
Halve tariff and non-tariff barriers tariff equivalents Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.844 0.448 19.1
0.871 0.421 12.0
0.895 0.398 5.9
0.932 0.363 3.5
0.853 0.439 16.8
0.861 0.431 14.6
0.876 0.416 10.6
Base scenario 10 days of transit time Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.661 0.636 —
0.691 0.606 4.7
0.721 0.574 9.7
0.770 0.523 17.8
0.672 0.626 1.6
0.685 0.613 3.6
0.707 0.589 7.4
trade-creation effects. If an uncertainty in the arrival of foreign goods decreases along with falling transport costs and shipment time, ignoring reductions in the uncertainty of arrival delays – as most of the current literature do – will result in underestimating the contribution of transportation improvement to trade growth. The authors also conducted two other sets of simulations to examine the sensitiveness of trade flows to the parameter of the elasticity of substitution. The magnitude of the elasticity of substitution in the CES production function captures the difficulty of substitution between the two inputs, and significant changes in the magnitude of the elasticity parameter will lead to significant changes in demand for imports as well as home goods6. We set the elasticity parameter to 8 and 5 for the demand for import and
6 The significant changes in the estimated trade barriers to different substitution elasticity parameters are also found in Anderson and van Wincoop (2004).
home goods, and then apply similar procedure to assess the impacts of uncertainty on trade flows. The results presented in Tables 3 and 4 suggest that when home and foreign goods are less substitutable, the impacts of uncertainty on trade flows become smaller, but still significant. In conclusion, the simulation results suggest that an uncertainty in the arrival time of foreign goods can substantially reduce imports. Note that in above framework and experiments, the authors assume only a single arrival delay in the entire production period. If arrival delays occur on multiple occasions and the probability of arrival delays are identically independently distributed, we could conceptually write the expected profit equation and solve the optimization problems. It is, however, no longer generally possible to present a simple analytical expression of the optimal value of s, which complicates the expressions of optimal demand for home and foreign goods. Furthermore, if the firm employs multistage fabrications in producing final goods, the uncertainty in the arrival time of imported goods could have much larger impacts.
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
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Table 4 Sensitivity analysis with the elasticity of substitution equal to 5. Without uncertainty
Uncertainty in arrival time of imported good Alternative mode un-available
Alternative mode available
Base scenario
p ¼0.1
0.2
0.4
p ¼ 0.1
0.2
0.4
Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.588 0.332 —
0.598 0.321 3.3
0.608 0.311 6.3
0.621 0.296 10.8
0.592 0.328 1.2
0.596 0.323 2.7
0.603 0.315 5.1
10% points reductions in transport costs Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.568 0.354 6.6
0.579 0.342 3.0
0.588 0.332 0.0
0.603 0.316 4.8
0.573 0.349 5.1
0.577 0.344 3.6
0.585 0.336 1.2
Halve tariff and non-tariff barriers tariff equivalents Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.557 0.367 10.5
0.568 0.355 6.9
0.578 0.343 3.3
0.593 0.327 1.5
0.561 0.362 9.0
0.565 0.358 7.8
0.571 0.35 5.4
Base scenario 10 days of transit time Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.481 0.458 —
0.493 0.442 3.5
0.505 0.428 6.6
0.523 0.406 11.4
0.487 0.45 1.7
0.493 0.443 3.3
0.504 0.429 6.3
4. Concluding remarks This paper is motivated by the ignorance of international transportation uncertainty in the trade literature—although this uncertainty prevails in both long distance shipments and border crossing. With the use of a numerical framework, the authors demonstrate in this paper that uncertainty in the arrival time of foreign goods can substantially reduce the demand for foreign goods. Moreover the impacts of falling transport costs and shipment time on international trade growth could be offset, if uncertainty arises in the arrival time of the imported goods. This in turn suggests that reduced uncertainty, which is possibly the results of improvements in transportation, might have contributed to the growth experienced in world trade over the last several decades. On the other hand, ignoring the role of improvements in international transportation arrangements and uncertainty will inevitably underestimate the role of transportation in the growth of trade. However, since the framework is simplified by assuming a one-stage production process as well as only one chance of arrival delay of imported goods, our results very likely underestimate the impacts of uncertainty on international trade flows. As many are aware, world trade collapsed sharply in 2008 and 2009, following the global financial crisis. Studies suggest that this collapse has been accompanied by a marked increase in protectionism. Tariff increases have been relatively rare, while countries have been proactive in implementing protectionist nontariff measures, such as antidumping, anti-subsidy, and safeguard tariffs (Bhagwati, 2009; Evenett, 2009; Fung, 2009; Gamberoni and Richard, 2009; Krueger, 2009; Yi, 2009; Kee et al., 2010; WTO, 2010). The number of new anti-dumping investigations in 2008 was 31% up compared with that of 2007, and the number of antidumping measures actually applied increased by 19% in 2008 compared with that of 2007 (Bown, 2009). The revived non-tariff protectionist measures may highly be possibly introduced into the uncertainty of arrival delays of imports, which may have compounding effects on world trade collapse. References Anderson, J.E., van Wincoop, E., 2003. Gravity with gravitas: a solution to the border puzzle. American Economic Review 93 (1), 170–192.
Anderson, J.E., van Wincoop, E., 2004. Trade costs. Journal of Economic Literature 42 (3), 691–751. Armington, P.S., 1969. A theory of demand for products distinguished by place of production. International Monetary Fund Staff Papers 16 (1), 159–178. Baier, S.L., Bergstrand, J.H., 2001. The growth of world trade: tariffs, transport costs and income similarities. Journal of International Economics 53 (1), 1–27. Baumol, W.J., Vinod, H.D., 1970. An inventory theoretical model of freight transport demand. Management Science 16 (7), 413–421. Bhagwati, J., 2009. The lithium president: fight protectionism with more passion. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Bowersox, D.J., Closs, D.J, Helferich, O.K., 1986. Logistics Management third ed. Macmillan, New York. Bown, C.P., 2009. Protectionism is on the rise: antidumping investigations. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Carrere, C.M. Schiff, 2004. On the geography of trade: distance is alive and well, World Bank Policy Research Working Paper 3206. Clark, X., Dollar D., Micco A., 2004. Port efficiency, maritime transport costs and bilateral trade, NBER Working Paper 10353, National Bureau of Economic Research, Cambridge. Coyle, J.J., Langley, C.J., Gibson, B., Novack, R.A., Bardi, E., 2009th. Supply Chain Management: A Logistics Perspective eighth ed. South Western Educational Publishing. Cudmore, E., and Whalley J., 2003. Border delay and international trade, NBER Working Paper 9485, National Bureau of Economic Research, Cambridge, USA. Disdier, A., Head, K., 2004. The Puzzling Persistence of the Distance Effect on Bilateral Trade, Mimeo, Sauder School of Business. University of British Columbia. EUC (European Commission), 1996. The impact and effectiveness of the single market, Communication from the Commission to the European Parliament and Council, 30 October 1996. Evans, C.L.Harrigan J., 2003. Distance, time, and specialization, NBER Working Paper 9729, National Bureau of Economic Research, Cambridge, USA. Evenett, S.J., 2009. Crisis-Era Protectionism One Year after The Washington G20 Meeting. In: Baldwin, R. (Ed.), The Great Trade Collapse: Causes, Consequences and Prospects. A VoxEU.org Publication. Feenstra, R.C., 1998. Integration of trade and disintegration of production in the global economy. Journal of Economic Perspectives 12 (4), 31–50. Fung, V.K., 2009. Jobs, global trade and the perils of protectionism: the imperative of restoring confidence. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Gamberoni, E., Richard, N., 2009. Trade protection: incipient but worrisome trends. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. GAMS (General Algebraic Modeling System), 2010. An Introduction to GAMS, http://www.gams.com/docs/intro.htm. Harrigan, J., Venables, A.J., 2004. Timeliness, trade and agglomeration, NBER Working Paper 10404, National Bureau of Economic Research, Cambridge, USA.
162
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
Head, K., Mayer, T., 2002. Illusory Border Effects: Distance Mis-measurement Inflates Estimation of Home Bias in Trade. University of British Columbia, Mimeo. Helliwell, J.F., Verdier, G., 2001. Measuring internal trade distances: a new method applied to estimate provincial border effects in Canada. Canadian Journal of Economics 34 (4), 1024–1041. Helpman, E., 1987. Imperfect competition and international trade: evidence from fourteen industrial countries. Journal of the Japanese and International Economics 1 (1), 47–62. Hillberry, R.H., Hummels, D., 2003. Intra-national home bias: some explanations. Review of Economics and Statistics 85 (4), 1089–1092. Huang, H., Whalley, J., 2006a. Baumol–Tobin and the Welfare Costs of National Security Border Delays. University of Western Ontario, Mimeo. Huang, H., Whalley, J., 2006b. The Costs of Uncertainty in Arrival Time of Orders due to Border Delays. University of Western Ontario, Mimeo. Hummels, D., 1999. Have International Transportation Costs Declined?. University of Chicago Mimeo. Hummels, D., 2001. Time as a trade barrier, GTAP Working Paper #17, Perdue University. Hummels, D., Levinsohn, J., 1995. Monopolistic competition and international trade: reconsidering the evidence. Quarterly Journal of Economics 100 (3), 799–836. Hummels, D., Rapoport, D., Yi, K., 1998. Vertical specialization and the changing nature of world trade. Federal Reserve Bank New York Economic Policy Review 4, 79–99. Kee, H.L., Neagu C.Nicita A., 2010. Is protectionism on the rise? Assessing national trade policies during the crisis of 2008, World Bank Policy Research Working Paper 5274. Kirk, R.S., Frittelli J.F., 2004. North American free trade agreement (NAFTA) implementation: the future of commercial trucking across the Mexican Border, CRS Report for Congress, RL31738. Klier, T., 1999. Agglomeration in the U.S. auto supplier industry. Federal Reserve Bank of Chicago Economic Perspectives Issue QI, 18–34. Krueger, A., 2009. Protectionism and the crisis. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Krugman, P., 1995. Growing world trade: causes and consequences. Brookings Papers on Economic Activity 1995 (1), 327–377. Lakshmanan, T.R., Anderson W.P., 1999. Trade and transportation integration: lessons from North American Experience, Paper Prepared for Presentation at World Bank/UNESCAP Technical Workshop on Transport and Transit Facilitation Bangkok, Thailand, April 19–21, 1999. Leamer, E.E., Levinsohn, J., 1995. International trade—the evidence. In: Grossman, G.M., Rogoff, K. (Eds.), Handbook of International Economics, vol. 3; 1995.
Lima~ o, N., Venables, A.J., 2001. Infrastructure, geographical disadvantage, transport costs and trade. World Bank Economic Review 15, 451–479. Linders, G.M., 2004. Distance Decay in International Trade Patterns: a Metaanalysis. Department of Spatial Economics, Vrije Universiteit, De Boelelaan 1105, 1081 HV and Tinbergen Institute, Amsterdam, The Netherlands. Liu, X.Y., Whalley, J., Xin, X., 2010. Non-tradable goods and border effect puzzle. Economic modeling 27 (5), 909–914. Macdonald, I., 2002. It’s time to open up the border. The Montreal Gazette B3 July 2. Mazner, M., 2001. Michigan tells U.S. customs it needs help staffing canadian crossings. MLive-Com, 2001 October 18. McCallum, J., 1995. National border matters: Canada–U.S. Regional Trade Patterns, American Economic Review 85 (3), 615–623. OCC (Ontario Chamber of Commerce), 2004. Cost of Border Delays to Ontario,. Pomery, J., 1985. Uncertainty in trade models. In: Jones, R.W., Kenen, P.B. (Eds.), Handbook of International Economics, vol. 1; 1985. Rietveld, P., Vickerman, R., 2004. Transport in regional science: the ‘death of distance’ is premature. Papers in Regional Science 83 (2), 229–248. Rose, A.K., 1991. Why has trade grown faster than income? Canadian Journal of Economics 24 (2), 417–427. Taylor, J.C., Robideaux D.R.Jackson G.C., 2003. The U.S.–Canada border: cost impacts, causes, and short to long term management options, Report for the Michigan Department of Transportation, U.S. Department of Transportation, New York State Department of Transportation, Grand Valley State University, Michigan. USDT (U.S. Department of Transportation), 2010. Border Crossing Delay, http://ops. fhwa.dot.gov/freight/border_crossing.htm. Wei, S.J., 1996. Intra-national versus international trade: how stubborn are nations in global integration, NBER Working Paper W5531, National Bureau of Economic Research, Cambridge, USA. Whalley, J., and Xin X., 2007, Regionalization, changes in home bias, and the growth of world trade, NBER Working Papers W13023, National Bureau of Economic Research, Cambridge, MA. Wolf, H.C., 2000. Intra-national home bias in trade. Review of Economics and Statistics 82 (4), 555–563. WTO (World Trade Organization), 2010. World Trade 2009: Prospects for 2010, Press/598. WTO (World Trade Organization), 1999. Trade policy review: TOGO 1999. WTO Publications Switzerland. Yi, K.M., 2003. Can vertical specialization explain the growth of world trade? Journal of Political Economy 111 (1), 52–102. Yi, K.M., 2009. The collapse of global trade: the role of vertical specialization. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication.
Contents lists available at ScienceDirect
Transport Policy journal homepage: www.elsevier.com/locate/tranpol
Transportation uncertainty and international trade$ Xiaoyun Liu a,b, Xian Xin a,b,n a b
China Agricultural University, Beijing, 100094 PR China Department of Economics, The University of Western Ontario, London, Ontario, Canada N6A 5C2
a r t i c l e in f o
a b s t r a c t
Available online 4 August 2010
This paper uses a numerical framework to demonstrate that uncertainty in the arrival time of foreign goods can substantially reduce the demand for foreign goods. It further reveals that the impacts of falling transport costs and shipment time on international trade growth could be discounted, if uncertainty arises in the arrival time of the imported goods. This in turn suggests that reduced uncertainty, which is possibly the results of transportation improvements, might have contributed to the growth experienced in world trade growth over the past several decades. Thus, neglecting the roles of improvements in international transportation arrangements and reduced uncertainty will lead to underestimating the contribution of transportation improvements to trade growth. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Trade Uncertainty Transportation
World trade grew twice as fast as world GDP growth in the last decades. A rapidly growing literature has attempted to explain the causes of this phenomenal international trade growth. Krugman (1995) offers a comprehensive survey of this issue and invites further inquiries into the matter. Among the standard explanations provided for the fast growth of world trade, trade liberalization (reduction of tariff rate), transport costs reduction, income convergence and economic growth are the four main factors often cited in trade literature (Helpman, 1987; Hummels and Levinsohn, 1995; Whalley and Xin, 2007). It has also been found that outsourcing (vertical specialization or disintegration of production) plays an important role in the surge of international trade flows (Yi, 2003; Feenstra, 1998; Hummels et al., 1998). Furthermore, preference changes are also found to significantly affect world trade flows, and it is said to have contributed to about 27% reduction in the volume of world trade (Whalley and Xin, 2007). Though many factors affect trade growth, the factor of principal interest here is the contribution of transportation improvements. Many studies have documented that transport costs have significantly negatively affected trade volumes and patterns (McCallum, 1995; Wei, 1996; Lima~ o and Venables, 2001;
Wolf, 2000; Baier and Bergstrand, 2001; Head and Mayer, 2002; Helliwell and Verdier, 2001; Evans and Harrigan, 2003; Hillberry and Hummels, 2003; Anderson and van Wincoop, 2003 and 2004). However, in recent years, the performance of transport has substantially improved both in terms of direct transport costs and time costs (Lima~ o and Venables, 2001; Hummels, 2001; Rietveld and Vickerman, 2004; Anderson and van Wincoop, 2004)1 . But researchers have reported different values for the response of transport cost to trade volume. For instance, the elasticity of trade with respect to transport costs obtained from Anderson and van Wincoop (2003) model is approximately 0.8, which implies that a 10% fall in transport costs will lead to an 8% rise in trade volume. But Lima~ o and Venables (2001) report an elasticity of 2.5, which means that a 10% reduction in transport costs will lead to a 25% rise in trade volumes2 The findings in Baier and Bergstrand (2001) further suggest that reductions in transport costs explain 8% of trade growth in OECD countries. These findings suggest that improvement in transportation has substantial impact on trade volume. However, this paper argues that the contribution of transportation improvements to trade growth might have been underestimated in the trade literature. There are two reasons. One reason is that in the trade literature, the impacts of transport costs on trade flows are usually drawn
$ The authors appreciate financial support from CN Post-doctoral Fellowships in Transportation Policy and part of the work was conducted, while the two authors worked in the University of Western Ontario in 2005–2006. The authors would like to thank John Whalley for discussions. n Corresponding author at. China Agricultural University, Beijing 100094, PR China. Tel./fax:+ 86 10 62738710. E-mail address: [email protected] (X. Xin).
1 A number of studies suggest that international transport costs have declined, while some studies argue no substantial changes; and others nevertheless argue that international transport costs have been on the rise (see Disdier and Head (2004), Hummels (1999), Leamer and Levinsohn (1995), Carrer and Schiff (2004), Rose (1991)). 2 Please refer to Linders (2004) for an overview of the impacts of distance on trade flows.
1. Introduction
0967-070X/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2010.07.005
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
from the regression results of the gravity model, in which transport costs are conventionally assumed proportional to distance3. Recent studies began to realize that distance is not an appropriate measure of transport costs, because transport costs do not only depend on pure monetary costs, but also on the time costs (Hummels, 2001; Rietveld and Vickerman, 2004; Anderson and van Wincoop, 2004; Evans and Harrigan, 2003; Harrigan and Venables, 2004). Hummels’s (2001) estimate shows that each day saved in shipping time is worth 0.8% ad-valorem for manufactured goods, and provides an estimate that each additional day in ocean transit reduces the probability that a country will export to the U.S. by 1% for all goodsand 1.5% for manufactured goods. Therefore, neglecting the role of time costs associated with distance may produce incorrect estimate of the elasticity of trade with respect to transport costs, which in turn conveys incorrect message about the contribution of transportation improvement to trade growth. The other reason is that the traditional transport costs neglect uncertainty associated with long distance shipments and border delay. Recent studies indicate that uncertainty at the arrival of imports involves further costs (Lima~ o and Venables, 2001; Mazner, 2001; Macdonald, 2002; Cudmore and Whalley, 2003; Evans and Harrigan, 2003; Taylor et al., 2003; Rietveld and Vickerman, 2004; Harrigan and Venables, 2004; Huang and Whalley, 2006a and 2006b)4 . Uncertainty might occur from ports to ports as well as at the national border. The world’s average distance of import-related trade is estimated to be around 5000 km (Carrer and Schiff, 2004), and distance introduces uncertainty into the completion of trades (Harrigan and Venables, 2004). Transportation uncertainty may also arise from border delays, transport coordination problems, security reasons, etc., which create excess planning time, higher insurance costs, higher scheduling costs and higher extra inventory-carrying costs. Of the transportation-related uncertainties, border delays are very common. Evidences indicate that border delays associated with maritime transport mode at African customs are 12 days, 7 days in Latin America, 3.5 days in North America, 5.7 days in Asia, 4 days in Central and Western Europe, 2.4 days in East Europe and 5.4 days in Former Soviet Union. For some African countries, border delays are much longer, e.g., 18 days in Nigeria, 20 days in Cameroon and 30 days in Ethiopia (Clark et al., 2004). Border delays may also reach as high as 3–6 months in some African economies (WTO, 1999), while customs clearance in Commonwealth of Independent States (CIS) economies may be in the range of weeks or months (Cudmore and Whalley, 2003). Also, crossborder shipments attribute to trade delays and lead to increase in trade cost. For instance, cross-border shipments required voluminous paperwork and faced interminable delays at the E.U. member borders in the mid 1980s (EUC, 1996). Border delays are also very common even within the free trade areas such as North American Free Trade Agreement (NAFTA). The variability of the border delay in NAFTA ranges from 10 min up to 4 h at the Canada–U.S. border and the Mexico–U.S. border (OCC, 2004; Lakshmanan and Anderson, 1999). Furthermore, Mazner (2001) finds that paperwork and inspection costs add up to about 13% of the cost of goods moved across NAFTA borders, and that longer delays since the 9/11 are adding another 3%. In addition, customs clearance and compliance is estimated to cost consumers a
3 This may be due to that the direct measure of transport costs data is limited by its private nature (Anderson and van Wincoop, 2004). The imputed transport costs data from bilateral trade values is notoriously bad, and thus should not be disqualified from use as a measure of transport costs in even-semi-careful studies (Hummels, 1999). 4 For a comprehensive literature review of an uncertainty in trade models, other than international transportation uncertainty, please refer to Pomery (1985).
157
hidden surtax of 5–7% (Macdonald, 2002). Taylor, Robideaux and Jackson (2003) offer an estimate of around US$2.53 billion in 2001 for this uncertainty related costs at the U.S.–Canada border— accounting for about 25% of the total U.S.–Canada border related costs. The higher costs associated with an uncertainty in the arrival of imports may affect international trade through many channels. Firms may shift from sourcing foreign goods to using home goods in expectations of uncertainty, especially in just-in-time production and logistics (Evans and Harrigan, 2003; Harrigan and Venables, 2004; Rietveld and Vickerman, 2004). Transport uncertainty may also result in excess capacity building, inventory and crossing time. Furthermore, transport uncertainty may also impact transport agents involved in moving goods from exporters to importers, because delivery schedules may be disrupted, and returnable containers end up out of position. These in turn may lead to lower trade flows. For example, Taylor et al. (2003) find that imports from Canada to the U.S. fell 10.8%, due to U.S. industrial buyers’ concerns about border delays and uncertainty introduced by the 9/11. Although much work has been done on the impact of uncertainty resulting from border delay, however, systematic investigations of the impacts of uncertainty related to arrival time of foreign goods on trade flows are scarce. One of the most important reasons is that the precise measure of transport uncertainty is unfortunately poorly documented in the literature, and there exists little systematic information on the reliability, scheduling, and comfort of transport (Cudmore and Whalley, 2003; Rietveld and Vickerman, 2004). Advancement in transportation technology, however, indicates that international shipping time has substantially declined over the past half century. That is, the introduction of containerization in the late 1960s and early 1970s is believed to have resulted in a doubling of the average speed of ocean fleet. For example, Hummels (2001) shows that an average shipping time fell from 40 days in 1950s to 10 days in 1998. The falling shipping days might have reduced the uncertainty involved, which in turn, might have resulted in a surge of trade flows. The surge of free trade agreements, crossborder procedural changes, and application of advanced technologies also accelerated a reduction in border delays. For instance, even traditionally efficient shippers usually experienced paperwork delay of 4–5 h at the U.S. border before the implementation of NAFTA, but these border delays have now been decreased to 12.4 min (Kirk and Frittelli, 2004; USDT, 2010). Thus, neglecting the trade-creation effects of reduced uncertainty in the arrival time of foreign goods will lead to underestimating the contribution of transportation improvements to trade growth. In this paper, therefore, we assess the impacts of international transportation uncertainty on trade flows, using a numerical model that incorporates time costs into the transport costs into our model. Our simulation results suggest that international transportation uncertainty can significantly reduce international trade flows. The results further suggest that reduced uncertainty—which is possibly the result of improvements in transportation, and leads to falling transport cost, might have contributed to the world trade growth experienced over the last several decades. However, the impacts of falling transport costs on trade growth could be offset if uncertainty in the arrival time of imported goods arises. Therefore, ignoring the role of reduced uncertainty might underestimate the significance of improvements in transportation and overestimate the role of other factors. Our model thus incorporates the role of reduced uncertainty, in order to properly capture its effect on world trade growth. The rest of the paper is organized as follows. Section 2 describes the set up of a numerical model, Section 3 presents the simulation results of the impacts of international transportation
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uncertainty on trade flows. Concluding remarks are drawn in Section 4.
the following equations:
2. Analytical framework
A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b ð1aÞ x2 1=s
In this section, we first set up a basic model with no uncertainty involved in the arrival time of imported goods, and then extend it by allowing for the possibility of arrival delays of imported goods. Product market is assumed to be competitive and firms employ intermediate inputs (composite of home and foreign goods, substitutes, but imperfect) and capital inputs (composite of physical capital and labor) to produce a finished good. The production function is assumed to be a two-stage nested function. The first-stage function is a Cobb–Douglas form, in which firms use intermediate and capital inputs to produce the final goods with b as the output elasticity of the intermediate inputs. For simplicity, we assume that capital inputs are fixed in the production period and only intermediate inputs are variable. Following Armington’s (1969) formulation of product differentiation by country, we use the Constant elasticity of substitution (CES) function to describe the relationship between intermediate inputs (composite of goods) and home and foreign goods (inputs). The final production function can be represented by Q ¼ A½a1 x1 s1=s þð1a2 Þx2 s1=s bs=s1
ð1Þ
where Q is output, A is the unit term which also captures fixed capital inputs, x1 and x2 are home and foreign goods, a1 and 1 a1 are share parameters of home and foreign goods, s is the elasticity of substitution between home and foreign goods, and b is the output elasticity of intermediate inputs ranging 0–1. Klier (1999) as well as Harrigan and Venables (2004) find that firms usually locate close to final suppliers who are within a day’s drive. In this sense, we assume that a firm can acquire home goods continuously, and that home goods supplier bear the costs involved in the transit of goods. Costs associated with the transit of imported goods include fixed costs of each ordering of shipment, tariff, and non-tariff barriers, tariff equivalent, freight rates and insurances, and inventory-carrying costs. Following the procedure of Baumol and Vinod (1970), costs can be broken down into C ¼ rT þutT þ
a wsT þ s 2
¼ w2 þ rw2 þutw2 þ
1 1=2 ð2aww2 Þ1=2 x2 2
ð5Þ
ð6Þ
Conceptually, with two unknown variables and two equations one can derive the optimal demand for home and foreign goods from Eqs. (5) and (6). Unfortunately, it is not generally possible to derive a simple expression of the demand for home and foreign goods. However, we could use other mathematics tools (such as GAMS) to solve Eqs. (5) and (6) given values of parameters in the next section5. We then extend the model by introducing the possibility of arrival delay of foreign goods into the above framework. In the presence of uncertainty, we will investigate two cases. One is that the firm can resort to an alternative costly air mode. The second case is that no alternative transport mode is available. Assume the probability of arrival delay of foreign goods is p. In determining arrival delay, the firm is assumed to instantly order another shipment of s x2 w2 via an alternative air mode with cost of u s x2 w2 . Evidences indicate that air shipping requires only a day or less to most destinations (Hummels, 2001), so we assume no delays associated with the air mode. For simplicity, we further assume that an arrival delay can happen at most once in the whole production period and that the firm can costlessly adjust the next ordering of shipment. The expected total costs, thus, becomes a wsT a wsT C e ¼ ð1pÞ rT þ utT þ þ þ p rT þ utT þu sTustT þ þ s 2 s 2 ð7Þ The optimal s is obtained by setting the first derivative of C e with respect to s to zero 1=2 2a ð8Þ se ¼ wT þ 2pTðu utÞ The profit of the representative firm is therefore
pe ¼ PQ w1 x1 w2 x2 rw2 x2 utw2 x2 ½2aðw þ 2pðu utÞÞ1=2 ðw2 x2 Þ1=2 ð9Þ
ð2Þ
where C is the total transit costs associated with foreign goods, T is the total value of imported goods in one production period (one year in this paper), r is the per unit cost of tariff and non-tariff barrier, t is the transit time (in days) required from exporting locations to firm locations, u is the shipping cost per unit per day, s is the average time between shipments, a is the fixed costs per ordering of shipment, and w is the inventory-carrying cost per unit per production period. Assuming firms minimize C given T, the optimal value of s can be obtained by setting the first derivative of C with respect to s to zero, 1=2 2a s¼ ð3Þ wT Assume further that the representative firm knows this information and thus Eq. (3) enters into the firm’s profit maximization decision making as
p ¼ PQ w1 x1 w2 x2 rw2 x2 utw2 x2 ð2awÞ1=2 ðw2 x2 Þ1=2
A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b a x1 1=s ¼ w1
ð4Þ
where w1 and w2 are prices of home and foreign goods, respectively. Applying the first-order condition to Eq. (4) yields
Applying the same procedure yields the optimal amount of home and foreign goods in which Eq. (5) is left unchanged, while Eq. (6) becomes A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b ð1aÞ x2 1=s ¼ w2 þ rw2 þutw2 þ
1 1=2 ½2aðw þ 2pðu utÞÞw2 1=2 x2 2
ð10Þ
In the absence of an alternative model, the firm may increase its carrying inventory, thereby increasing its carrying-inventory costs. Assuming that the arrival delay can occur only once in the whole production period, the firm holds an extra inventory of sT till the last shipment comes. The total cost is therefore a wsT a wsT C u ¼ ð1pÞ rT þ utT þ þ þ p rT þ utT þ þ þ wðs1ÞT s 2 s 2 ð11Þ
5 The General Algebraic Modeling System (GAMS) is specifically designed for modeling linear, nonlinear and mixed integer optimization problems. It allows users to concentrate on the modeling problem by making the setup simple. The GAMS language is formally similar to commonly used programming languages. For more information please refer to GAMS (2010).
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
while the optimal s becomes 1=2 2a su ¼ wð12pÞT
ð12Þ
The profit of the representative firm is, thus,
p ¼ PQ w1 x1 w2 x2 rw2 x2 utw2 x2 pww2 x2 ð2awð12pÞÞ1=2 ðw2 x2 Þ1=2 u
ð13Þ Applying the first-order condition yields A½a1 x1 s1=s þ ð1a2 Þx2 s1=s bss þ 1=s1 b ð1aÞ x2 1=s 1 1=2 ¼ w2 þ rw2 þ utw2 þ pww2 þ ð2awð12pÞw2 Þ1=2 x2 2
ð14Þ
By far, we have set up the numerical framework to investigate the impacts of transportation uncertainty on trade inflows. In Section 3, we will conduct a series of experiments to demonstrate the impacts of transportation uncertainty on trade inflows.
3. Numerical simulations This section presents the simulation results of our numerical model. We first provide the values of the model parameters in Table 1 as the base scenario. In performing the simulation, we assume that in a costless-trading world, firms are neutral in choosing home and foreign goods, and then set the share parameter of home goods in the CES production function to 0.5. Output elasticity of intermediate goods is set to 0.6, since intermediate inputs usually account for 60% of an output value (Liu et al., 2010). The size of the elasticity of substitution between home and foreign goods in the trade literature is considerably controversial and varies drastically in different studies. Anderson and van Wincoop (2004) suggest that an order of five to ten is more plausible. We first set this substitution parameter to 10, and then proceed to conduct sensitive analysis by shifting this parameter to 8 and 5 as suggested in Anderson and van Wincoop (2004). The non-air mode of shipping time is then assumed to be 20 days based on the results of Hummels (2001) and Anderson and van Wincoop (2004). Shipping cost per unit per day comprises of time cost and direct transport costs. In accordance with Hummels (2001), the time cost is set to 0.8% per unit per day. However, other works (Anderson and van Wincoop, 2004; Rietveld and Vickerman 2004) estimated direct transport costs are to be around 10%. The direct transport cost per unit per day is thus set to 0.5% given 20 days shipping time, and 10% of the sub-total of the direct transport costs. The shipping cost per unit per day is thus set to 1.3%, which comprises of 0.8% Table 1 Model parameter values of base scenario. Parameters Value
Notes
A
Unit parameter of firm production function Share parameter of home goods in firm production function Output elasticity of intermediate goods Elasticity of substitution between home and foreign goods Price of home good (input) Price of foreign good (input) Per unit tariff and non-tariff barriers tariff equivalents Shipping cost per unit per day Transit time
a1 b
s w1 w2 r u t a w u*
5 0.5 0.6 8 5 4 8% 1.3% 20 days 5% 20% 44%
Fixed costs per ordering of shipment Per unit inventory-carrying cost Per unit shipping cost of air mode
159
of time cost and 0.5% of direct transport costs. Tariffs and nontariff barriers tariff equivalents are set to 8% as in Anderson and van Wincoop (2004). We further proceed with experiments to assess the impacts of reductions in policy barrier (the sum of tariffs and non-tariff barriers tariff equivalents) as well as transportation costs on trade flows. Per unit inventory-carrying costs ratio is set to 20% (Coyle et al., 2009; Bowersox et al., 1986). Per unit shipping cost incurred from an alternative air mode is assumed to be 44% according to Rietveld and Vickerman (2004). The possibility of an arrival delay of imported goods is set to zero in base scenario, and then set to 0.1, 0.2, and 0.4 for comparisons. With these set-out values, we can calibrate, with the aid of GAMS, the optimal demand for home and foreign goods in the base scenario as well as in the presence of uncertainty. The simulation results of the alternative mode un-available are reported in Table 2. Compared with the base scenario, in which there is no uncertainty in the arrival of imported goods, the presence of uncertainty substantially reduces the demand for foreign goods. As the simulation results indicate, a 10%, 20%, and 40% possibility of arrival delays would reduce the demand for foreign goods by 8.3%, 15.1%, and 24.3%, respectively. Previous research has shown that ocean shipping costs per unit decreased by approximately 60% between 1930 and 1990, 50% for rail shipping costs between 1950 and 2000, and 50% for road transport costs between 1950 and 2000 (Rietveld and Vickerman, 2004). These figures roughly suggest that non-air mode transport costs fall by 10% every ten years. So, we now conduct simulations to assess the impacts of a 10% fall in transport costs on trade flows. The simulation results of transport costs reduction, with or without the possibility of arrival delays of foreign goods are, thus, reported in Table 2. The simulation results suggest that the tradecreation effects of falling transport costs could be offset drastically if uncertainty arises. Without uncertainty in the arrival time, the demand for foreign goods will increase by 17.1% as transport costs reduce by a 10% point. This implies an estimate of 1.71 elasticity of trade with respect to transport costs, which lies between the elasticity estimated by Anderson and van Wincoop (2003), and that by Lima~ o and Venables (2001). The presence of uncertainty at 10% level could reduce the demand for foreign goods by 9.4%. If the possibility of arrival delay rises to 40%, the combined effects of falling transport costs and rising uncertainty result into an even negative effect on trade volumes. We also conduct simulations to investigate the impacts of reductions in trade barriers associated with an uncertainty in arrival delays on trade flows. The results suggest that halving tariff and non-tariff barriers tariff equivalents will generate a 27.4% increase in trade flows. However, the presence of a 10% possibility of arrival delays will offset approximately 10% of the gain, while a 20% possibility of arrival delays will further cut down another 9% points. Thus, the presence of a 40% possibility of arrival delays may offset all the trade growth resulting from reductions in trade barriers. According to Hummels (2001), the average ocean shipment time was halved from 40 days in 1950 to 20 days by 2000. This then leads us to investigate how an uncertainty of arrival delays impacts trade flows, given another 50% drop in shipment time. The results in Table 2 indicate that uncertainty of arrival delays still has a significant trade-reducing effect, even though the shipment time is halved from 20 to 10 days. This implies that the trade-reducing effects of uncertainty still matters if an uncertainty of arrival delays persists given reductions in international shipment time. When an alternative transportation mode is available, the presence of arrival delay of foreign goods still has significant trade-reducing effects (even though smaller). These results imply that reduced uncertainty of arrival delays has significant
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Table 2 Uncertainty in the arrival time of imported goods and international trade flows. Without uncertainty
Uncertainty in arrival time of imported good Alternative mode un-available
Alternative mode available
Base scenario
p ¼ 0.1
0.2
0.4
p¼ 0.1
p¼ 0.2
p¼ 0.4
Home goods Foreign goods Changes in imported goods compared with base scenario (%) 10% points reductions in transport costs Home goods Foreign goods Changes in imported goods compared with base scenario (%)
1.091 0.350 —
1.116 0.321 8.3
1.142 0.297 15.1
1.177 0.265 24.3
1.097 0.338 3.4
1.108 0.327 6.6
1.130 0.307 12.3
1.019 0.410 17.1
1.055 0.377 7.7
1.086 0.349 0.3
1.128 0.309 11.7
1.034 0.396 13.1
1.047 0.384 9.7
1.072 0.361 3.1
Halve tariff and non-tariff barriers tariff equivalents Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.982 0.446 27.4
1.029 0.410 17.1
10.53 0.379 8.3
1.099 0.336 4.0
0.996 0.433 23.7
1.007 0.422 20.6
1.029 0.402 14.9
Base scenario 10 days of transit time Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.721 0.710 —
0.768 0.661 6.9
0.811 0.616 13.2
0.881 0.545 23.2
0.739 0.690 2.8
0.756 0.672 5.4
0.787 0.640 9.9
Without uncertainty
Uncertainty in arrival time of imported good
Table 3 Sensitivity analysis with the elasticity of substitution equal to 8.
Alternative mode un-available
Alternative mode available
Base scenario
p ¼0.1
0.2
0.4
p ¼ 0.1
0.2
0.4
Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.918 0.376 —
0.943 0.353 6.1
0.964 0.333 11.4
0.994 0.305 18.9
0.928 0.367 2.4
0.936 0.359 4.5
0.951 0.345 8.2
10% points reductions in transport costs Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.870 0.422 12.2
0.897 0.396 5.3
0.920 0.374 0.5
0.954 0.342 9.0
0.881 0.412 9.6
0.89 0.403 7.2
0.907 0.386 2.7
Halve tariff and non-tariff barriers tariff equivalents Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.844 0.448 19.1
0.871 0.421 12.0
0.895 0.398 5.9
0.932 0.363 3.5
0.853 0.439 16.8
0.861 0.431 14.6
0.876 0.416 10.6
Base scenario 10 days of transit time Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.661 0.636 —
0.691 0.606 4.7
0.721 0.574 9.7
0.770 0.523 17.8
0.672 0.626 1.6
0.685 0.613 3.6
0.707 0.589 7.4
trade-creation effects. If an uncertainty in the arrival of foreign goods decreases along with falling transport costs and shipment time, ignoring reductions in the uncertainty of arrival delays – as most of the current literature do – will result in underestimating the contribution of transportation improvement to trade growth. The authors also conducted two other sets of simulations to examine the sensitiveness of trade flows to the parameter of the elasticity of substitution. The magnitude of the elasticity of substitution in the CES production function captures the difficulty of substitution between the two inputs, and significant changes in the magnitude of the elasticity parameter will lead to significant changes in demand for imports as well as home goods6. We set the elasticity parameter to 8 and 5 for the demand for import and
6 The significant changes in the estimated trade barriers to different substitution elasticity parameters are also found in Anderson and van Wincoop (2004).
home goods, and then apply similar procedure to assess the impacts of uncertainty on trade flows. The results presented in Tables 3 and 4 suggest that when home and foreign goods are less substitutable, the impacts of uncertainty on trade flows become smaller, but still significant. In conclusion, the simulation results suggest that an uncertainty in the arrival time of foreign goods can substantially reduce imports. Note that in above framework and experiments, the authors assume only a single arrival delay in the entire production period. If arrival delays occur on multiple occasions and the probability of arrival delays are identically independently distributed, we could conceptually write the expected profit equation and solve the optimization problems. It is, however, no longer generally possible to present a simple analytical expression of the optimal value of s, which complicates the expressions of optimal demand for home and foreign goods. Furthermore, if the firm employs multistage fabrications in producing final goods, the uncertainty in the arrival time of imported goods could have much larger impacts.
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
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Table 4 Sensitivity analysis with the elasticity of substitution equal to 5. Without uncertainty
Uncertainty in arrival time of imported good Alternative mode un-available
Alternative mode available
Base scenario
p ¼0.1
0.2
0.4
p ¼ 0.1
0.2
0.4
Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.588 0.332 —
0.598 0.321 3.3
0.608 0.311 6.3
0.621 0.296 10.8
0.592 0.328 1.2
0.596 0.323 2.7
0.603 0.315 5.1
10% points reductions in transport costs Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.568 0.354 6.6
0.579 0.342 3.0
0.588 0.332 0.0
0.603 0.316 4.8
0.573 0.349 5.1
0.577 0.344 3.6
0.585 0.336 1.2
Halve tariff and non-tariff barriers tariff equivalents Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.557 0.367 10.5
0.568 0.355 6.9
0.578 0.343 3.3
0.593 0.327 1.5
0.561 0.362 9.0
0.565 0.358 7.8
0.571 0.35 5.4
Base scenario 10 days of transit time Home goods Foreign goods Changes in imported goods compared with base scenario (%)
0.481 0.458 —
0.493 0.442 3.5
0.505 0.428 6.6
0.523 0.406 11.4
0.487 0.45 1.7
0.493 0.443 3.3
0.504 0.429 6.3
4. Concluding remarks This paper is motivated by the ignorance of international transportation uncertainty in the trade literature—although this uncertainty prevails in both long distance shipments and border crossing. With the use of a numerical framework, the authors demonstrate in this paper that uncertainty in the arrival time of foreign goods can substantially reduce the demand for foreign goods. Moreover the impacts of falling transport costs and shipment time on international trade growth could be offset, if uncertainty arises in the arrival time of the imported goods. This in turn suggests that reduced uncertainty, which is possibly the results of improvements in transportation, might have contributed to the growth experienced in world trade over the last several decades. On the other hand, ignoring the role of improvements in international transportation arrangements and uncertainty will inevitably underestimate the role of transportation in the growth of trade. However, since the framework is simplified by assuming a one-stage production process as well as only one chance of arrival delay of imported goods, our results very likely underestimate the impacts of uncertainty on international trade flows. As many are aware, world trade collapsed sharply in 2008 and 2009, following the global financial crisis. Studies suggest that this collapse has been accompanied by a marked increase in protectionism. Tariff increases have been relatively rare, while countries have been proactive in implementing protectionist nontariff measures, such as antidumping, anti-subsidy, and safeguard tariffs (Bhagwati, 2009; Evenett, 2009; Fung, 2009; Gamberoni and Richard, 2009; Krueger, 2009; Yi, 2009; Kee et al., 2010; WTO, 2010). The number of new anti-dumping investigations in 2008 was 31% up compared with that of 2007, and the number of antidumping measures actually applied increased by 19% in 2008 compared with that of 2007 (Bown, 2009). The revived non-tariff protectionist measures may highly be possibly introduced into the uncertainty of arrival delays of imports, which may have compounding effects on world trade collapse. References Anderson, J.E., van Wincoop, E., 2003. Gravity with gravitas: a solution to the border puzzle. American Economic Review 93 (1), 170–192.
Anderson, J.E., van Wincoop, E., 2004. Trade costs. Journal of Economic Literature 42 (3), 691–751. Armington, P.S., 1969. A theory of demand for products distinguished by place of production. International Monetary Fund Staff Papers 16 (1), 159–178. Baier, S.L., Bergstrand, J.H., 2001. The growth of world trade: tariffs, transport costs and income similarities. Journal of International Economics 53 (1), 1–27. Baumol, W.J., Vinod, H.D., 1970. An inventory theoretical model of freight transport demand. Management Science 16 (7), 413–421. Bhagwati, J., 2009. The lithium president: fight protectionism with more passion. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Bowersox, D.J., Closs, D.J, Helferich, O.K., 1986. Logistics Management third ed. Macmillan, New York. Bown, C.P., 2009. Protectionism is on the rise: antidumping investigations. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Carrere, C.M. Schiff, 2004. On the geography of trade: distance is alive and well, World Bank Policy Research Working Paper 3206. Clark, X., Dollar D., Micco A., 2004. Port efficiency, maritime transport costs and bilateral trade, NBER Working Paper 10353, National Bureau of Economic Research, Cambridge. Coyle, J.J., Langley, C.J., Gibson, B., Novack, R.A., Bardi, E., 2009th. Supply Chain Management: A Logistics Perspective eighth ed. South Western Educational Publishing. Cudmore, E., and Whalley J., 2003. Border delay and international trade, NBER Working Paper 9485, National Bureau of Economic Research, Cambridge, USA. Disdier, A., Head, K., 2004. The Puzzling Persistence of the Distance Effect on Bilateral Trade, Mimeo, Sauder School of Business. University of British Columbia. EUC (European Commission), 1996. The impact and effectiveness of the single market, Communication from the Commission to the European Parliament and Council, 30 October 1996. Evans, C.L.Harrigan J., 2003. Distance, time, and specialization, NBER Working Paper 9729, National Bureau of Economic Research, Cambridge, USA. Evenett, S.J., 2009. Crisis-Era Protectionism One Year after The Washington G20 Meeting. In: Baldwin, R. (Ed.), The Great Trade Collapse: Causes, Consequences and Prospects. A VoxEU.org Publication. Feenstra, R.C., 1998. Integration of trade and disintegration of production in the global economy. Journal of Economic Perspectives 12 (4), 31–50. Fung, V.K., 2009. Jobs, global trade and the perils of protectionism: the imperative of restoring confidence. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Gamberoni, E., Richard, N., 2009. Trade protection: incipient but worrisome trends. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. GAMS (General Algebraic Modeling System), 2010. An Introduction to GAMS, http://www.gams.com/docs/intro.htm. Harrigan, J., Venables, A.J., 2004. Timeliness, trade and agglomeration, NBER Working Paper 10404, National Bureau of Economic Research, Cambridge, USA.
162
X. Liu, X. Xin / Transport Policy 18 (2011) 156–162
Head, K., Mayer, T., 2002. Illusory Border Effects: Distance Mis-measurement Inflates Estimation of Home Bias in Trade. University of British Columbia, Mimeo. Helliwell, J.F., Verdier, G., 2001. Measuring internal trade distances: a new method applied to estimate provincial border effects in Canada. Canadian Journal of Economics 34 (4), 1024–1041. Helpman, E., 1987. Imperfect competition and international trade: evidence from fourteen industrial countries. Journal of the Japanese and International Economics 1 (1), 47–62. Hillberry, R.H., Hummels, D., 2003. Intra-national home bias: some explanations. Review of Economics and Statistics 85 (4), 1089–1092. Huang, H., Whalley, J., 2006a. Baumol–Tobin and the Welfare Costs of National Security Border Delays. University of Western Ontario, Mimeo. Huang, H., Whalley, J., 2006b. The Costs of Uncertainty in Arrival Time of Orders due to Border Delays. University of Western Ontario, Mimeo. Hummels, D., 1999. Have International Transportation Costs Declined?. University of Chicago Mimeo. Hummels, D., 2001. Time as a trade barrier, GTAP Working Paper #17, Perdue University. Hummels, D., Levinsohn, J., 1995. Monopolistic competition and international trade: reconsidering the evidence. Quarterly Journal of Economics 100 (3), 799–836. Hummels, D., Rapoport, D., Yi, K., 1998. Vertical specialization and the changing nature of world trade. Federal Reserve Bank New York Economic Policy Review 4, 79–99. Kee, H.L., Neagu C.Nicita A., 2010. Is protectionism on the rise? Assessing national trade policies during the crisis of 2008, World Bank Policy Research Working Paper 5274. Kirk, R.S., Frittelli J.F., 2004. North American free trade agreement (NAFTA) implementation: the future of commercial trucking across the Mexican Border, CRS Report for Congress, RL31738. Klier, T., 1999. Agglomeration in the U.S. auto supplier industry. Federal Reserve Bank of Chicago Economic Perspectives Issue QI, 18–34. Krueger, A., 2009. Protectionism and the crisis. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication. Krugman, P., 1995. Growing world trade: causes and consequences. Brookings Papers on Economic Activity 1995 (1), 327–377. Lakshmanan, T.R., Anderson W.P., 1999. Trade and transportation integration: lessons from North American Experience, Paper Prepared for Presentation at World Bank/UNESCAP Technical Workshop on Transport and Transit Facilitation Bangkok, Thailand, April 19–21, 1999. Leamer, E.E., Levinsohn, J., 1995. International trade—the evidence. In: Grossman, G.M., Rogoff, K. (Eds.), Handbook of International Economics, vol. 3; 1995.
Lima~ o, N., Venables, A.J., 2001. Infrastructure, geographical disadvantage, transport costs and trade. World Bank Economic Review 15, 451–479. Linders, G.M., 2004. Distance Decay in International Trade Patterns: a Metaanalysis. Department of Spatial Economics, Vrije Universiteit, De Boelelaan 1105, 1081 HV and Tinbergen Institute, Amsterdam, The Netherlands. Liu, X.Y., Whalley, J., Xin, X., 2010. Non-tradable goods and border effect puzzle. Economic modeling 27 (5), 909–914. Macdonald, I., 2002. It’s time to open up the border. The Montreal Gazette B3 July 2. Mazner, M., 2001. Michigan tells U.S. customs it needs help staffing canadian crossings. MLive-Com, 2001 October 18. McCallum, J., 1995. National border matters: Canada–U.S. Regional Trade Patterns, American Economic Review 85 (3), 615–623. OCC (Ontario Chamber of Commerce), 2004. Cost of Border Delays to Ontario,. Pomery, J., 1985. Uncertainty in trade models. In: Jones, R.W., Kenen, P.B. (Eds.), Handbook of International Economics, vol. 1; 1985. Rietveld, P., Vickerman, R., 2004. Transport in regional science: the ‘death of distance’ is premature. Papers in Regional Science 83 (2), 229–248. Rose, A.K., 1991. Why has trade grown faster than income? Canadian Journal of Economics 24 (2), 417–427. Taylor, J.C., Robideaux D.R.Jackson G.C., 2003. The U.S.–Canada border: cost impacts, causes, and short to long term management options, Report for the Michigan Department of Transportation, U.S. Department of Transportation, New York State Department of Transportation, Grand Valley State University, Michigan. USDT (U.S. Department of Transportation), 2010. Border Crossing Delay, http://ops. fhwa.dot.gov/freight/border_crossing.htm. Wei, S.J., 1996. Intra-national versus international trade: how stubborn are nations in global integration, NBER Working Paper W5531, National Bureau of Economic Research, Cambridge, USA. Whalley, J., and Xin X., 2007, Regionalization, changes in home bias, and the growth of world trade, NBER Working Papers W13023, National Bureau of Economic Research, Cambridge, MA. Wolf, H.C., 2000. Intra-national home bias in trade. Review of Economics and Statistics 82 (4), 555–563. WTO (World Trade Organization), 2010. World Trade 2009: Prospects for 2010, Press/598. WTO (World Trade Organization), 1999. Trade policy review: TOGO 1999. WTO Publications Switzerland. Yi, K.M., 2003. Can vertical specialization explain the growth of world trade? Journal of Political Economy 111 (1), 52–102. Yi, K.M., 2009. The collapse of global trade: the role of vertical specialization. In: Baldwin, R., Evenett, S. (Eds.), The Collapse of Global Trade, Murky Protectionism, and The Crisis: Recommendations for The G20. A VoxEU.org Publication.