BUS606: Operations and Supply Chain Management

Another useful data source for conducting cross-country analysis in relation to the logistics sector is the International Comparison Program (ICP). The ICP is a worldwide statistical partnership to collect comparative price data and compile detailed expenditure values of countries' GDPs, and to estimate purchasing power parities (PPPs) of the world's economies. Although the ICP does not identify logistics as a separate sector, it does provide data on the size of the transport sector and the level of transport prices in 155 countries. These measures can be taken as rough proxies for the size of the logistics sector and its price level, on the assumption that transport activities represent an important part of the overall concept of logistics. Again, results need to be interpreted cautiously due to the difference between this sectoral definition and the understanding of logistics that is common in the sector-specific literature.

Figure 6 repeats the analysis in Figure 3 above, namely the relationship between sector size and logistics performance as measured by the World Bank's LPI 2010. The connection between the two variables is more nuanced than in the smaller sample - primarily composed of OECD members - considered above, using national accounts data. In this case, there is a positive relationship between sector size and performance up to a certain point - around 7% or 8% of GDP - after which it turns negative. Increasing the size of a very small transport sector therefore tends to be associated at the margin with improved logistics performance, but above a critical point, performance improvements tend to be associated with decreases in sector size. The two figures can be reconciled by noting that the restricted sample considered in Figure 3 generally has strong logistics performance, so the regression line only captures the right hand part of the full-sample regression curve in Figure 6.

Figure 6: Non-parametric Regression of Logistics Performance on the Size of the Transport Sector.

Note: Data sourced from the International Comparison Program (transport data), and the 2010 Logistics Performance Index.

Again, attempting to extend the analysis to trade openness gives poor results, despite the link - albeit non-monotonic - between sector size and logistics performance (Figure 7). As was the case using national accounts data, there is no systematic relationship between the size of the transport sector and the level of openness to the international economy: countries with larger transport sectors are not systematically more open.

Figure 7: Non-parametric Regression of Trade Openness on the Size of the Transport Sector.

Note: Data sourced from the International Comparison Program (transport data), and the World Development Indicators (trade openness). Two outliers (Hong Kong, China and Singapore) have been excluded from the sample.

The ICP data can also be used to analyze the relationship between the size of the transport sector and per capita income. As was the case for the national accounts data, Figure 8 shows that the relationship is non-monotonic: richer countries tend to have larger transport sectors until an income level of around $20,000 is reached, at which point the transport sector appears to contract. The inflection point is considerably higher than in the national accounts - at around the income level of Portugal or Greece - but the same general relationship between the two variables is apparent.

Figure 8: Non-parametric Regression of the Size of the Transport Sector on Per Capita Income.

Note: Data sourced from the International Comparison Program (GDP data), and the World Development Indicators (per capita income). Two outliers (Luxembourg and Qatar) have been excluded from the sample.

In addition to sector size, the ICP dataset also provides information on prices in the form of an index number (world = 100). Figure 9 examines the relationship between transport prices and logistics performance. Interestingly, there is a strong, positive relationship: higher prices are generally associated with stronger performance. At first, this result might appear surprising because technological improvements linked to superior performance can sometimes drive prices lower, not higher. However, there are a number of economic mechanisms at play to explain the positive relationship seen in these data. First, the Balassa-Samuelson effect suggests that prices are generally higher in more developed economies, which also tend to have stronger logistics performance. The figure is partly capturing this relationship. Second, high prices and high performance might be indicative of the fact that end users of logistics services are prepared to pay a premium for good, reliable service. Technology improvements that increase service level but also costs might therefore still be attractive to end users optimizing their supply chain performance.

Figure 9: Non-parametric Regression of Logistics Performance on the Price of Transport Services.

Note: Data sourced from the International Comparison Program (price data), and the Logistics Performance Index 2010.

Figure 10 examines the relationship between transport sector prices and trade openness. Although the regression line is relatively flat through much of the sample - which is suggestive of a weak, and possibly insignificant relationship - there is some evidence of an overall negative relationship between the two variables: countries with higher transport prices tend to be less open to the world economy, particularly at relatively low levels of transport costs. As transport costs increase above a threshold - roughly the world average - the negative relationship more or less disappears. The first finding is in line with expectations, but its contingent nature highlights the fact that countries with very high levels of transport costs need to make significant improvements before major changes in economic outcomes will be apparent. The need for a "big push" in this area has similarly been recognized in recent work on logistics performance

Figure 10: Non-parametric Regression of Trade Openness on the Price of Transport Services.

Note: Data sourced from the International Comparison Program (price data), and the World Development Indicators (trade openness). Two outliers (Hong Kong, China and Singapore) have been excluded from the sample.

Finally, Figure 11 examines the relationship between transport prices and GDP per capita. Although the regression suggests a non-linear relationship - particularly at very low levels of income - the overall relationship is positive: richer countries tend to have more expensive transport services. As noted above, a number of factors could support such a conclusion. First, transport services obviously involve a higher level of technological inputs in high-income countries than in low-income ones. Higher prices would thus reflect the provision of a different level of service. Second, this finding might be a manifestation of the much more general Balassa-Samuelson effect, due to the fact that the bulk of transport services take place within a country and thus are not traded internationally in the conventional sense. Such trade can take place via GATS Mode III (commercial presence), but the economic mechanisms involved are quite different. In light of these sorts of mechanisms, it is not surprising that logistics performance but also prices should be higher in more developed economies.

Figure 11: Non-parametric Regression of the Price of Transport Services on Per Capita Income.

Note: Data sourced from the International Comparison Program (price data), and the World Development Indicators (per capita income). Two outliers (Luxembourg and Qatar) have been excluded from the sample.

As an additional exercise, ICP data were also used in an attempt to test the hypothesis that logistics performance can be an important determinant of price gaps across countries. Price data in sectors such as food products and clothing were used as the dependent variable, with logistics performance proxied by the LPI as the independent variable. Results, however, were not in line with expectations: higher prices were consistently associated with higher LPI scores. The most likely explanation for this finding is that prices (due to the Balassa-Samuelson effect) and logistics performance are both strongly positively correlated with per capita income. The regressions therefore just pick up the association between development level and logistics performance, rather than saying anything specific about price differences across countries. For this reason, results are not discussed in detail at this point. The potential impact of logistics on price gaps is left as an issue for future research to examine using more detailed data.