Logistics Costs and Competitiveness
Read this article. The document examines issues and costs related to domestic and international logistics. Sections 3 and 4 are most applicable here. What are the unique challenges facing domestic and global logistics?
Measuring Domestic Logistics Costs
Firm-level Data
The recent international trade literature has become heavily focused on firm-
level phenomena (see Bernard et al., 2007 for a review). Although most firm-
level work in international trade focuses on a single country, the World Bank's
Enterprise Surveys dataset makes it possible to do cross-country work at the
firm-level as well. As Table 3 shows, the 2001-2005 Enterprise Surveys dataset
covers services as well as manufacturing, and has at least some observations
on firms active in logistics-related sectors such as wholesale and retail trade,
and transport. The sectoral coverage of the Enterprise Surveys data essentially
mirrors the broad definition of logistics used in the analysis of national
accounts (see above). For this reason, caution is again required in interpreting
results due to differences in sectoral definitions between the national accounts
and commercial reality, in particular as regards the inclusion of passenger
services in the definition of transport.
Table 3: Availability of Enterprise Surveys Firm-level Data (2001-2005).
Sector | Number of Countries |
Total Observations |
---|---|---|
Wholesale and Retail Trade (51-52) |
98 | 10,188 |
Transport (60-63) |
70 | 1,456 |
The primary interest in firm-level data as a descriptive tool lies in the
possibility of estimating firm- and sector-level productivity for logistics
providers. These measures can in principle provide detailed information on
sector performance. As an example, I calculate simple labor productivity
measures using the Enterprise Surveys data referred to in Table 3; attempts to
estimate total factor productivity using the Levinsohn-Petrin methodology ran
into numerical difficulties, and will need to be left for future research. To
enable cross-country comparisons, I average the labor productivity estimates
by country.
Figure 12 presents a non-parametric regression of logistics performance, as
measured by the LPI, and labor productivity in the transport sector as captured
in the Enterprise Surveys data. Although the sample is relatively small, there is
a clear positive association between transport productivity and logistics
performance: countries with more productive transport sectors tend to have
higher overall logistics performance. Figure 13 repeats the analysis using
productivity in wholesale and retail trade as the independent variable, with
similar results. Although the relationship is weaker, there is still a noticeable
positive association between productivity and logistics performance. The
difference in strength between the associations evident in Figures 12 and 13 is
perhaps due to the fact that transport plays a larger role in what is commonly
referred to as the logistics sector than do wholesale and retail trade activities.
Figure 12: Non-parametric Regression of Logistics Performance on Labor Productivity in Transport.
Note: Data sourced from Enterprise Surveys (productivity data), and the Logistics Performance Index 2010.
One outlier (Lebanon) has been excluded from the sample.
Figure 14 presents results of a non-parametric regression of labor productivity
in transport on GDP per capita. Figure 15 repeats the regression using labor
productivity in wholesale and retail trade, rather than transport. Results in
both cases are in line with expectations: countries at higher income levels tend
to have more productive logistics sectors. As was the case for the LPI as
dependent variable, the relationship appears to be stronger for the transport
sector than for wholesale and retail trade.
Figure 13: Non-parametric Regression of Labor Productivity in Transport on Per Capita Income.
Note: Data sourced from Enterprise Surveys (productivity data), and the World Development Indicators (per
capita income). One outlier (Lebanon) has been excluded from the sample.
Figure 14: Non-parametric Regression of Labor Productivity in Wholesale and Retail Trade on Per Capita Income.
Note: Data sourced from Enterprise Surveys (productivity data), and the World Development Indicators (per
capita income). One outlier (Lebanon) has been excluded from the sample.
More surprising are results in Figures 16 and 17, where the dependent variable
is trade openness. In both cases, the data suggest that higher productivity in
logistics is associated with a lesser degree of openness, which is contrary to expectations. The reasons for this result are as yet unclear. One possibility is
that labor productivity is only a very approximate measure, and that results
using total factor productivity might be different. Another possibility is that
the data are primarily capturing the characteristics of domestic logistics firms,
not those involved directly in international transactions. Presumably,
productivity in international logistics operations would be positively associated
with openness. However, these questions will need to be examined further in
future research.
Figure 15: Non-parametric Regression of Trade Openness on Labor Productivity in Transport.
Note: Data sourced from Enterprise Surveys (productivity data), and the World Development Indicators
(openness). One outlier (Lebanon) has been excluded from the sample.
Figure 16: Non-parametric Regression of Trade Openness on Labor Productivity in Wholesale and Retail Trade.
Note: Data sourced from Enterprise Surveys (productivity data), and the World Development Indicators
(openness). One outlier (Lebanon) has been excluded from the sample.
The above analysis has only exploited one part of the Enterprise Surveys
dataset, namely surveys undertaken between 2001 and 2005. Future research
can exploit similar data from later surveys (Table 4). These new data offer the
advantage of being disaggregated according to a more precise sectoral
definition following the ISIC scheme. It will therefore be possible to examine
the relationship between productivity in individual components of the logistics
sector, and important economic outcomes, as well as overall logistics
performance.
Table 4: Availability of Enterprise Surveys Firm-level Data (2006-2010).
Sector | Number of Countries |
Total Observations |
---|---|---|
Wholesale Trade (51) |
72 | 1,194 |
Retail Trade (52) |
104 | 8,867 |
Land Transport (60) |
65 | 600 |
Water Transport (61) |
16 | 40 |
Air Transport (62) |
24 | 35 |