This chapter presents analyses of WITS and Quantec data gathered for Germany, United States, South Africa, Australia and Venezuela in contributing towards a better understanding of the relationship between the composition of imports and a country’s import cif/fob ratios.
United States is one of the largest traders on the globe, a complex economy with an effective national statistical agency that assures a quality trade reporting system and data that is reliable.
Through the analysis of its trade data, the United States provided evidence that a rise in the proportion of a country’s high-valued manufactured imports contributes to the decline in that country’s imports cif/fob ratios, all things being equal (ceteris paribus), and an increase in the proportion of a country’s low-value imports composition, equally contributes to a rise in that country’s imports cif/fob ratios, ceteris paribus. Therefore, it would be inaccurate to ignore or assume a change in a country’s imports composition, as insignificant to a change, in the variation of that country’s import cif/fob ratios. The United States data seemingly provided similar evidence that when a country’s trade data are correct and reliable, a country’s imports composition of trade has a significant and substantial effect on that country’s imports cif/fob ratios. Hence, the ratio cannot be relied on or be used as a measure of a country’s direct shipping costs (ad valorem shipping costs) without the context of the country’s imports composition.
In the case of Australia, Germany and United States, the correlation analyses shows that their high- valued imports from SITC 5 to SITC 9, and their imports cif/fob ratios, were negative and significantly correlated (note: except for Germany’s SITC 9 (0.037), USA SITC 6 (.891**) and SITC 7 (-0.26, though negative but statistically insignificant), Australia’s SITC’s 7,8 and 9 (which are all negative but statistically insignificant) and SITC 6 (.880**) while their low-valued imports SITC 0 to SITC 4 were positive and some statistically correlated, with the exception of Australia’s SITC 3 and US SITC 4, which are negative and statistically insignificant. See table 4.4 in chapter 4). The data analysis of Australia, USA and Germany shows that a change in imports composition of a country has a significant change in that country’s import cif/fob ratios variation.
Venezuela’s high-value imports from correlation analyses in SITC 8 and SITC 9 are negative and statistically significant except for SITC 5, which is negative and statistically insignificant, its SITC
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7 is positive and statistically significant while SITC 6 is positive and insignificant. While its low- value imports in SITC’s 1, 2 and 3 are positive and statistically significant with exception of SITC 4 which is positive and insignificant and SITC 0 which is negative and insignificant. However, due to data imputation by the IMF, the results from the analyses of Venezuela shows that the cif/fob ratio, measure is inaccurate and unreliable and should not be used as a direct measure of shipping cost. This study has shown, in the case of Venezuela, that imports cif/fob ratio cannot be relied on as a direct measure of international transport cost, as the country’s imports cif/fob ratio is consistent at 11% and 10 % ad valorem, evidence of possible IMF staff data imputations and manipulations, as was found in the similar study by Chasomeris (2006:80) for the case on Malawi.
South Africa’s correlation analyses (table 4.4) shows evidence of a statistically significant relationship between the country’s cif/fob ratios and the country’s composition of imports, though not consistent with those of Germany, USA and Australia, where data are considered more accurate and reliable. South African data cannot be relied on as a direct measure for shipping costs, due to some data manipulation and deliberate misclassification during the country’s period of economic sanctions.
In comparison, analyses of Venezuela and South Africa provided evidence that data inaccuracy and unreliability of a nation’s trade data are synonymous with unreliability and inaccuracy of the country’s imports cif/fob ratios, which, without doubt, leads to inconsistencies, inaccuracies and misinterpretation of the actual ad valorem shipping costs of a country.
The correlation analyses presented for the five countries provide insight into the particular nature of imports cif/fob ratios and its flaws as a proxy for a country’s direct ad valorem shipping costs.
The correlation result for Germany, Australia and United States showed that changes in the proportion of the country’s high-value imports and low-valued imports have substantial significant effect on the variation of the country’s cif/fob ratios with a rise or fall in imports composition of trade leading to a corresponding decrease or increase in the country’s cif/fob ratios.
The analysis of Australia, United States and Germany affirmed that imports composition of trade (composition of imports) of a country do contribute to the variation in a country’s imports cif/fob ratios and that it would be incorrect to believe that a change in a country’s imports cif/fob ratios
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will reveal the actual difference in a country’s direct shipping costs rather than the commodity mix effect for the country. On the other hand, it was established through the data analyses and the correlation analyses, that using a country’s import cif/fob ratios to measure a country’s direct costs of transportation may be misleading, misinterpreted and misrepresent the country’s direct shipping cost. It is important when examining a country’s import cif/fob ratios to view the ratios in context and understanding measurement analyses of that country’s composition of imports.
The interpretation and measurement of international transport costs figures of trade are of great interest to both countries and merchants. According to Chasomeris (2009:149), “In the absence of direct measures, researchers have used an indirect measure of international transport costs - a country’s import cif/fob ratios”. Therefore, the objectives of this study are:
• To source, compile, calculate and compare the country cif/fob ratios for South Africa, United States, Germany, Venezuela and Australia from year 1980 to 2012 (see results in table 4.2)
• To establish using correlation analysis, the relationship between a country cif/fob ratio and a country’s SITC trade data at HS (Harmonized System) revision 2-digit code 1 (see results table 4.4)
• To examine the use of imports cif/fob ratios as a measurement for international transport costs.
In the analysis of a comparative study on the use of country import cif/fob ratios to measure international transport costs, one of the main challenges faced is that of obtaining reliable data.
This is because the cif/fob ratio data are believed to be unreliable and error ridden and susceptible to manipulations (Chasomeris, 2006; Hummels and Lugovskyy, 2006). However, notwithstanding this drawback, the study was able to provide substantial evidence and explanation in the comparative study on the use of a country’s imports cif/fob ratios to measure international transport costs.
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The literature review chapter deals with lots of key definitions important to this study, like imports cif/fob ratio, giving more insight in what international trade represents and international transport costs as a whole.
The data from the five countries analysed provided enough evidence that import cif/fob ratios alone are not sufficient to be used as a proxy for direct shipping cost. Hummels and Lugovskky (2003) believed that IMF cif/fob ratios are error-ridden and contains no beneficial information for cross commodity or time series variations. Users should be aware because IMF databases are the major data hub for almost all imports cif/fob ratios computed.
The study shows that in the case where data are reliable and accurate, a country’s composition of imports in its high-value and low-valued imports have a significant effect in the variation of the country’s imports cif/fob ratios, such as the case of Australia, Germany and the United States.
There is a relationship between the two variables, that is, a country’s imports cif/fob ratio and the country’s composition of imports. Chasomeris (2006:68) explained, “a rise in the proportion of a country’s high-valued imports contributes to a decline in that country’s imports cif/fob ratios, ceteris paribus and a rise in the proportion of a country’s low-valued imports equally contributes to a rise in that country’s import cif/fob ratios, ceteris paribus”.
The study showed the case of South Africa and Venezuela; where trade data are unreliable and inaccurate, and as such, the import cif/fob ratio computed from the data have no economic significance. Therefore, ratios were unable to reveal the country’s direct transport costs or the ad valorem shipping costs. This was evident in the lack of significant correlation between South Africa’s imports composition of trade (composition of imports) and the country’s imports cif/fob ratios. As well as South Africa and Venezuela’s lack of directional sign when compared to countries such as Australia, Germany, and the United States.
The study shows that uneven development has a possible impact on trade flows, pattern and freight transportation flows, and undoubtedly, the results of each country’s imports cif/fob ratios.
In total, the study has shown that
• the composition of imports of a country do have a significant contribution to the variation in imports cif/fob ratios of the country;
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• it would be unwise to assume that a change in a country’s imports cif/fob ratio would reveal the true or exact difference in direct shipping costs of that country, rather than the commodity mix effect of the country;
• the measurement of a country’s imports cif/fob ratios result in the misrepresentation or misinterpretation of that country’s direct international transport costs; and
• where trade data are accurate and more reliable, like in Germany, the USA and Australia, statically significant relationships are observed between a country’s composition of imports and the country’s imports cif/fob ratios.