Logistics Costs and Competitiveness

As noted above, there are a number of connections between logistics and trade policy that have yet to be fully exploited in the literature. One direction in which research could move is to focus on the links between logistics performance and trade intensity (i.e., the intensive margin of trade). Arvis et al. present descriptive statistics suggesting a positive association between logistics performance and important outcome indicators, such as trade openness. Hoekman and Nicita push the analysis further by including the LPI in a gravity model of trade. They find that there is a significant positive association between logistics performance and trade intensity, and that the effect is quantitatively important: increasing the average low income country's LPI score to the middle income average would increase trade by around 15%, which is much stronger than the other reform scenarios considered by the authors, including reductions in traditional trade barriers such as tariffs. Considering logistics as part of the broad trade facilitation agenda, this result sits well with previous work such as Wilson et al., which consistently finds that the potential gains from improved trade facilitation are significantly larger than those from improvements in traditional market access constraints.

The trade facilitation literature has recently expanded to consider the extensive margin of trade as well, i.e. exporting new products and dealing with new markets. The data strongly suggest that better trade facilitation is linked with a more diversified export bundle in both the sectoral and geographical dimensions. However, there is as yet no specific evidence on the extensive margin trade effects of logistics performance. Future research could examine questions such as whether better logistics make it more likely that production networks can be formed among a range of countries. The policy implications of such research are clear for countries in Asia and elsewhere that are interested in promoting further integration into regional and international production networks.

Most of the studies referred to above focus on total trade flows, and do not deal in depth with issues of cross-sectoral heterogeneity. However, some sectors are likely to be much more intensive in their use of logistics services than others (see further below), which suggests that they may respond more strongly to improvements in performance. Saslavsky and Shepherd (Forthcoming) present some of the first evidence on this point, focusing on the case of parts and components. Since those products are often traded within international production networks that are based on low inventories and just- in-time management, logistics would seem to play a crucial role in facilitating this kind of trade. Indeed, the data suggest that this is the case: trade in parts and components is nearly 50% more sensitive to improvements in logistics performance than is trade in final goods.

There is clearly great scope for future work to examine the issue of cross- sectoral heterogeneity more closely. It is likely, for example, that time sensitive products such as perishable agricultural goods are more sensitive to logistics performance than non-perishable goods; however, there is as yet no evidence on this point. Future work in this area could also follow one strand of the trade facilitation literature in examining not only the potential for logistics performance to boost trade, but its impact on the pattern of sectoral specialization across countries. Djankov et al., for example, show that countries with low export times tend to be relatively specialized in the export of time-sensitive goods. There is as yet no comparable evidence for logistics, but similar results could be expected. This line of research would have important policy implications in areas such as competitiveness and export diversification.

An additional area that has only just started to be explored in the trade facilitation literature is the use of firm-level data. In line with the broader trade literature, the use of firm-level data is attractive for two reasons. First, firm- level models do not suffer from omitted variables bias in the country dimension, since those variables are constant across all firms. Omitted local variables can still be an issue, of course, but variance within countries is much less of a problem than variance across countries, which is the issue that plagues standard cross-country regressions. The second advantage of firm- level data is that enables analysts to identify particular causal paths and economic mechanisms more precisely. For instance, although the cross- country evidence on openness and growth is mixed - see Dollar and Kraay versus Rodriguez and Rodrik - there is highly consistent and generally accepted evidence that firms in open sectors tend to be more productive and grow faster.

There are a number of recent examples of firm-level data being used in the trade facilitation literature. Shepherd uses firm-level data to show that poorer trade facilitation as measured by longer lead times to export and import is associated with higher reported levels of trade-related corruption, as poor performance gives firms an incentive to flout the rules by paying "speed money". More generally, Dollar et al. use firm-level data to show that a variety of business environment constraints affect trade performance and integration into international markets. Li and Wilson similarly show that time to export is an important determinant of firm-level trade behavior.

The possible research directions for trade and logistics discussed in this section are suggestive of a number of priorities for data collection efforts going forward. First, from a trade research point of view, the crucial data element is the relationship between logistics performance and trade costs. The emphasis in collecting data on logistics should therefore be on performance, rather than on alternative data points such as sector size. Existing work on the logistics sector tends to aggregate total logistics costs and express them relative to some economic baseline, such as GDP. Although this approach is useful in giving an overall idea of the size of the sector, it is not necessarily relevant for doing trade research. The reason is that it does not automatically follow that larger (or smaller) sectors perform better, i.e. provide a given output at lower cost. So although it is useful to track the evolution of logistics costs relative to GDP over time - as initiatives in a number of countries do - it is important not to lose sight of the limited policy-relevant information contained in such estimates. Indeed, this paper shows that the relationship between sector size and performance is non-monotonic in a large sample of countries. Measures such as the LPI do not suffer from this problem, and can easily be used in cross- country regression frameworks.

From a trade research point of view, it is important to distinguish three ways in which logistics costs can be measured or proxied. The first is logistics costs as a percentage of total firm costs. This measure essentially captures logistics intensity: those sectors that have relatively high levels of logistics costs relative to total costs are relatively intensive in logistics services. Logistics intensity is an important concept for two reasons. First, identifying logistics intensive sectors makes it possible to foreshadow the sectoral impacts of improvements in logistics performance: logistics intensive sectors should be more sensitive to performance improvements than other sectors. Second, logistics intensity combined with logistics performance is likely to be an important determinant of the sectoral composition of production and trade across countries. As a country's logistics performance improves, it is likely to become relatively more specialized in the production of goods that are logistics intensive. These issues are discussed further in Section 5 below.

A second alternative is to aggregate expenditures into a measure of total logistics costs, and then to express it relative to some economic aggregate such as GDP. This approach effectively measures the size of the logistics sector, but does not necessarily indicate anything about performance. Although there is some evidence of a link between the two in the data, the relationship is non-monotonic, which means that it is difficult to draw solid conclusions on performance based only on sector size.

See further below, where it is shown that, in general, sector size is not strongly associated with trade outcomes of interest. A further problem with expressing logistics costs relative to GDP is that the final number is likely to be greatly inflated as a true measure of size because intermediate inputs in the logistics sector do not appear to be netted out. That is, total logistics expenditures must equal total logistics sector value added plus the value of all inputs used in the production process. The number is therefore much closer to gross production than value added. Since GDP is the sum of value added in the economy - not gross production - there is strong cause to be skeptical of numbers such as those produced by Bowersox et al., which indicate that logistics accounts for about 10% of total economic activity in the USA

The third approach is to proxy logistics costs by using a performance variable, such as the World Bank's Logistics Performance Index. This approach differs fundamentally from the other two in that it does not produce a direct measure of cost. Nonetheless, techniques are available for converting the LPI into a cost-like measure, for instance by calculating total trade costs as an ad valorem equivalent and using econometric methods to identify the part of them that is due to logistics (see Section 4, below). The advantage of a performance measure like the LPI is that it is likely to be strongly linked to trade costs, which are the fundamental variable of interest for applied trade policy work. By contrast, measures such as sector size (logistics costs to GDP ratio) or logistics intensity (logistics costs to total costs ratio) are informative of the characteristics of the sector, but do not have any direct link to trade performance and international economic integration. Another data collection effort that goes in this direction is Hansen and Hovi (2008), in which logistics costs are expressed as a percentage of total export value.

One of the contributions of this paper is to perform a number of external validity exercises using the LPI, and to show that it is correlated with other measures of logistics sector size, performance, and price. Although the focus of the paper is on measurement issues, it is useful to briefly highlight the international trade side of the analysis at this point. As a first step, Figure 1 shows the relationship between merchandise trade openness and specialization in exports of transport services, as a proxy for logistics services. (Due to lack of data availability, it is impossible to measure trade in logistics services as such). A weak positive association is in evidence until a threshold is reached when transport services exports account for around 30% of the total, after which the relationship flattens out. The data therefore provide some support for the view that specialization in logistics-related services can be important for trade outcomes, though only up to a certain point.

In addition, logistics performance is expected to be associated with trade in services, and in particular with specialization in trade in logistics-related services such as transport. Figure 2 shows that, as expected, countries with stronger logistics performance generally tend to see a higher percentage of their overall services exports accounted for by transport. The effect greatly diminishes, and the relationship thus flattens out, above a certain level of performance (an LPI score of 3.25). Although this result should be interpreted cautiously due to the conventions with which services data are recorded, as well as their relatively poor quality compared with goods trade data, Figure 2 is very much consistent with specialization according to comparative advantage in a logistics-related sector.

Figure 1: non-parametric regression of merchandise trade openness on the Percentage of transport services exports in total services exports.

Figure 2: non-parametric regression of the percentage of transport services exports In total services exports on logistics performance.

Regardless of which approach is taken to measurement, a key requirement for trade research focusing on logistics is the need for comparable data across a variety of countries and time periods. Cross-country regressions such as the gravity model remain the workhorse of applied international trade research. Similarly, research on the pattern of production and specialization across countries relies heavily on cross-country frameworks. Standardized methodologies and results frameworks for the collection of data on logistics costs are absolutely necessary from a trade research point of view.

Firm-level data on logistics could also be useful for the research agenda going forward. However, they would need to be combined with data on firm characteristics (size, basic financial variables, et.c and trade performance (exporters vs. non-exporters, etc.) in order to make it possible to draw policy conclusions. Again, it would be important to focus on measuring logistics performance rather than intensity or sector size.

In the remainder of the paper, the issues discussed in this section are addressed in greater detail in the context of data-based examples during on macro- and firm-level sources.