Limao and Venables (2001) investigated the dependence of transportation costs on geography and infrastructure. Transportation costs and trade volumes depend on many complex details of geography, infrastructure, administrative barriers, and the structure of the shipping industry. The authors used several sources of evidence to explain transportation costs and trade flows in terms of geography and the infrastructure of the trading countries. Two different data
sources for transportation costs were used. The first is shipping company quotes for the cost of transporting a standard container from Baltimore, Maryland, to selected destinations. A second data set used a cross section of the ratio of carriage, insurance, and freight (CIF) to free on board (FOB) values that the International Monetary Fund (IMF) reported for bilateral trade between countries. Analysis of bilateral trade data confirmed the importance of these variables in
determining trade and enabled the computation of estimates of the elasticity of trade flows with respect to transportation costs. They found that this elasticity is large, with a 10% increase in transportation costs typically reducing trade volumes by approximately 20%. They also found that the deterioration of infrastructure from the median to the 75th percentile raised transportation costs by 12% and reduced trade volumes by 28%. In addition, the authors extended the
quantitative implications of their findings by applying them to the Sub-Saharan African trade, a real case study.
Behar and Venables (2010) studied not only the impact of transportation costs on the volume and nature of international trade but also the determinants of international transportation costs. Transportation costs also influence modal choice, the commodity composition of trade, and the organization of production, particularly as ‘just-in-time’ methods get extended to the global level. The authors found that transportation costs affect international trade and vice versa.
Both are influenced by considerations of geography, technology, infrastructure, fuel costs, and policy towards trade facilitation. Distance is not the only significant geographical factor. Being landlocked increases trade costs by 50% and reduces trade volumes by 30-60%. Over time, technical change and the price of fuel have influenced transportation costs and trade volumes.
Binkley and Harrer (1981) explained that trade volume is of approximately equal
rates suggests that ship size and trade volume. The authors developed a cross-section model to investigate the determinants of ocean freight rates for grain. Large ships reduce ocean
transportation costs, but larger ships appear to incur higher port costs such as loading and unloading costs. They found that policies to improve shipping technology and increase trade volume can lead to lower rates, reduce geographic differences among exporters, and generate more competitive markets. This implies that the role of transportation in trade analysis should not be ignored.
Park and Koo (2004) evaluated structural changes and price differentials in ocean freight rates for grain shipments from US ports to various, major importing countries using a cross-sectional econometric model. Ocean freight rates fluctuate widely because of unbalanced traffic, low probability of backhaul shipments, and a lake of economic regulations in ocean
transportation industries. The authors found that not only cost factors, such as distance and the ship size, but also the geographical location of the port play an important role in determining ocean freight rates. Ocean freight rates depend on types of commodities. In addition, there were seasonality and changes in structure for grain shipments during the 1987-1998 period.
Hummels (2007) used regression analysis to investigate the role of cost shocks and technological and compositional change in shaping the time series in transportation costs and then draw out implications of these trends for the changing nature of trade and integration. The author concentrated on international shipping trends of ocean and air transportation from 1950 to 2004. He found that ocean shipping constituted 99 percent of world trade by weight and a
majority of world trade by value also experienced a technological revolution in the form of container shipping, but dramatic price declines are not in evidence. Instead, prices for ocean shipping exhibited little change from 1952–1970, substantial increases from 1970 through the
mid-1980s, followed by a steady 20-year decline. Trade using container lowers shipping costs from 3% to 13%. However, ocean freight costs began to increase with the rising cost of crude and port congestion at the end of 1980s.
Korinek and Sourdin (2010) attempted to investigate the role that maritime freight costs play in determining ocean–shipped agricultural imports by using the newly-compiled
Organization for Economic Co-operation and Development (OECD) Maritime Transportation Costs database. The authors found that transportation costs significantly and negatively impact agricultural imports, even after controlling for shipping distance. Analysis of the new dataset on maritime transportation costs underscored the importance of shipping in determining agricultural trade flows. The cost of shipping represented 10% of the overall cost of importing goods
worldwide in 2007, and maritime transportation costs are even higher for some products, for example grains and oilseeds, and some countries, particularly small, developing countries. Lower income, net food-importing countries paid particularly dearly for imports of staple foods. The shipping costs of importing grains to some of these countries were 20–30% of their 2008 import value.
Koo and Uhm (2008) applied the theory of rail rates for cargo shipments to the United States and Canadian grain movements for both domestic and export destinations. The authors attempted to analyze that grain freight rates in US are significantly determined by distance, shipment size, frequency of shipments, intermodal competition, and geographical characteristics of route origins and destinations. For domestic grain, the rail-rate equation was estimated on the basis of 523 origin-destination routes where grain flows are heavy. 200 observations for wheat and 323 observations for corn and soybean movements were used. The total number of
187 observations were used for wheat and 245 observations were used for corn and soybean movements. A comparison of US export rates with Canada's statutory rate revealed that US rate levels, in 1979, were 4.3 and 2.9 times higher for hauling distances of 200 and 1,000 miles respectively in the lowest-rate route; while it was about 7.8 and 7.5 times higher for the same mileage in the highest-rate route. It is concluded that if deregulation stimulates competition between rail and other inland transportation modes and among the railroads themselves, the expectation is of a lowering of freight rates in the Corn Belt, the Eastern, and Southern states. In contrast, in the Northern Plains, where railways face only limited competition from barge and truck transportation freight rates were already the highest in the United States. There was not seasonality on the demand for grain for domestic and export markets, although seasonal rates might be beneficial to both producers and consumers if they have the effect of moderating seasonal fluctuations in demand.