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
Cross-country Comparison of Logistics Sectors
In principle, national accounts data with some level of sectoral disaggregation
are available for a wide range of countries from local sources. To give a first
idea of the type of analysis that could be conducted using national accounts
data, however, it makes sense to look first at data that have already been
cleaned and harmonized by an international agency. The OECD's STAN
database provides such data for OECD members (national accounts by sector),
and a number of non-members (input-output tables).
Both sources provide information on value added by sector, which can then be
compared with total value added in the economy (GDP). Although there are
some discrepancies between the national accounts and input-output tables,
they are generally small, and data from the two sources remain relatively
comparable. The major difference between the two is that the national
accounts data are more disaggregated, which enables application of all three
potential ISIC Rev.3 definitions of logistics, as discussed above. The input-
output tables, by contrast, are only detailed enough to make it possible to
distinguish between the narrow and broad definitions.
Table 2 presents logistics sector data from the STAN database, covering 45
countries (latest year). OECD members account for 34 observations, with the
remaining 11 coming from non-member countries including the BRICs,
Indonesia, and South Africa. Applying the narrow definition of logistics
suggests that the sector accounts on average for about 5% of GDP, although
the range is quite large across the countries included in the sample (2%-12%).
The medium definition increases the estimated size of the sector substantially,
to an average of 11% of GDP. Application of the broad definition results in
another substantial increase, to around 17% of GDP on average. Comparing
these three sets of numbers with existing work on logistics costs as a
percentage of GDP tends to suggest that the medium and broad definitions
may include too many non-logistics activities, thereby resulting in substantial
over-estimates of the size of the sector. Numbers based on a narrow definition
tend to accord better with existing work, particularly taking into account the
fact that the data presented here are based on value added (netting out
intermediate inputs) rather than gross production (the equivalent of total
logistics costs). As a rule of thumb, if the numbers presented here are
measuring the same activities as in existing measurements of logistics costs
relative to GDP, they should be one-third to one-half as large as previous
estimates due to the intermediate inputs problem.
Table 2: Logistics Sector Value Added as a Percentage of Gdp; Alternative Definitions.
Country | Year | Narrow | Medium | Broad | Source |
---|---|---|---|---|---|
Argentina | 1997 | 5.61 | 17.78 | Input-Output | |
Australia | 2006 | 5.19 | National Accounts | ||
Austria | 2009 | 4.20 | 10.68 | 15.23 | National Accounts |
Belgium | 2008 | 5.89 | 12.81 | 17.04 | National Accounts |
Brazil | 2005 | 4.96 | 18.74 | Input-Output | |
Canada | 2005 | 4.06 | 9.36 | National Accounts | |
Chile | 2003 | 7.42 | 16.66 | Input-Output | |
China | 2005 | 5.68 | 12.50 | Input-Output | |
Czech Republic |
2009 | 7.72 | 13.19 | 17.69 | National Accounts |
Denmark |
2009 | 4.32 | 11.18 | 14.63 | National Accounts |
Estonia | 2008 | 7.53 | 14.72 | 19.48 | National Accounts |
Finland | 2009 | 5.94 | 10.54 | 14.18 | National Accounts |
France | 2008 | 4.41 | 8.72 | 12.79 | National Accounts |
Germany | 2008 | 3.96 | 8.86 | 12.72 | National Accounts |
Greece | 2008 | 6.72 | 16.34 | 22.88 | National Accounts |
Hungary | 2007 | 5.08 | 10.43 | 15.12 | National Accounts |
Iceland | 2008 | 4.62 | 10.00 | 14.46 | National Accounts |
India | 2003/04 | 6.42 | 18.52 | Input-Output | |
Indonesia | 2005 | 4.18 | 15.72 | Input-Output | |
Ireland | 1995 | 3.00 | National Accounts | ||
Israel | 2008 | 4.19 | 8.63 | 11.63 | National Accounts |
Italy | 2009 | 5.28 | 10.18 | 14.38 | National Accounts |
Japan | 2006 | 4.39 | 13.22 | 17.47 | National Accounts |
Korea | 2008 | 4.38 | National Accounts | ||
Luxembourg |
2009 | 4.19 | 10.84 | 14.28 | National Accounts |
Mexico | 2008 | 6.53 | 22.08 | 22.65 | National Accounts |
Netherlands | 2008 | 4.40 | 12.27 | 15.49 | National Accounts |
New Zealand |
2006 | 4.25 | National Accounts | ||
Norway | 2008 | 5.00 | 9.91 | 12.81 | National Accounts |
Poland | 2007 | 4.79 | 11.85 | 18.82 | National Accounts |
Portugal | 2006 | 4.01 | 9.29 | 13.61 | National Accounts |
Romania |
2005 | 8.21 | 19.24 | Input-Output | |
Russia |
2000 | 8.96 | 39.51 | Input-Output | |
Slovakia | 2009 | 4.66 | 12.81 | 19.26 | National Accounts |
Slovenia | 2009 | 4.84 |
10.70 | 15.35 | National Accounts |
South Africa |
2005 | 5.59 | 18.57 | Input-Output | |
Spain | 2008 | 4.54 |
8.74 | 13.45 | National Accounts |
Sweden | 2008 | 5.67 | 11.28 | 15.01 | National Accounts |
Switzerland | 2008 | 3.55 | 10.88 | 15.69 | National Accounts |
Taiwan | 2006 | 3.16 | 25.10 | Input-Output | |
Thailand | 2005 |
4.28 | 27.83 | Input-Output | |
Turkey | 2002 | 12.26 | 26.15 | Input-Output | |
UK | 2007 | 4.36 | 8.27 | 13.43 | National Accounts |
USA | 2007 | 2.95 | National Accounts |
||
Vietnam | 2000 | 2.21 | 12.93 | Input-Output |
The OECD STAN data can be combined with information on other economic
variables to provide a first indication of the possible links between the size of
the logistics sector and important outcomes of interest. To ensure maximum
data coverage, I use the narrow definition of logistics in all cases. To allow for
maximum flexibility in examining the possible relationships among variables, I
use a non-parametric regression technique - the Locally Weighted Scatterplot
Smoother (Lowess) - rather than the more standard parametric OLS
approach. Lowess proceeds by conducting a separate OLS regression using
each data point as the center of a reduced sample (80% of the full sample), and
estimating response parameters for each regression function.
The first question of interest is whether the size of the logistics sector as
measured by its weight in GDP is systematically associated with logistics performance, as measured by the World Bank's Logistics Performance Index.
Data for the most recent year of the LPI are used (2010), even though the GDP
data correspond to a variety of previous years. Due to data limitations, it is
impossible to achieve an exact correspondence, which means that results
should be interpreted cautiously. Nonetheless, Figure 3 shows a clear negative
relation between the size of the logistics sector and performance: the larger
the logistics sector, the worse is performance, on average. The reason is likely
linked to technological change: as technology improves, it becomes possible
to achieve a given level of service for a lesser amount of expenditure.
Offsetting this effect is increased demand for logistics services as the price falls
(or quality rises), but these data suggest that it is the technological
improvement effect that dominates, at least in the limited country sample
used in this first analysis (mostly OECD members).
Figure 3: Non-parametric Regression of Logistics Performance on the Size of the Logistics Sect or.
Note: Data sourced from the OECD STAN database and input-output tables (logistics data), and the 2010
Logistics Performance Index. Two outliers (Vietnam and Turkey) have been excluded from the sample.
In light of the apparently strong link between sector size and performance in
these data, it is surprising that an important economic variable of interest -
trade openness, defined as the sum of merchandise exports and imports
relative to GDP - does not appear to have any strong association with sector
size. Figure 4 shows that there is little evidence of a systematic relationship
between openness and the size of the logistics sector: the regression line is
essentially flat throughout most of the sample. For example, there is no
systematic evidence that countries with larger logistics sectors tend to be
more open to international trade. The reason for this finding is perhaps that
openness is dependent on a wide range of factors, of which logistics
performance is only one. Since sector size is really being used here as a proxy
for performance, the link between the two tends to be weakened, in this case
to the point of insignificance.
Figure 4: Non-parametric Regression of Trade Opennessthe Size of the Logistics Sector.
Note: Data sourced from the OECD STAN database and input-output tables (logistics data), and the World
Development Indicators (openness). One outlier (Turkey) has been excluded from the sample.
A third hypothesis of interest concerns the relationship between per capita
income and the size of the logistics sector. It might be thought, for example,
that richer countries tend to have larger logistics sectors. One reason for this
effect might be that outsourcing takes place at a greater rate as countries
develop. Figure 5 provides a much more nuanced picture, however. There is
indeed a positive relationship between sector size and per capita income in
relatively poor countries, but an inflection point is reached at around $10,000
in PPP terms. Once country income exceeds the level of, for example,
Argentina or Mexico, there is an inverse relation with the size of the logistics
sector. One possible explanation is that improvements in technology in upper-
middle- and high-income countries tend to dominate increased demand for
outsourced logistics services. However, this is a point that would need to be
researched in more detail in the future. For the present, it is simply important
to note that richer countries do not systematically have a larger logistics
sector. This finding is indeed consistent with the first one, to the effect that a
larger sector tends to be correlated with worse performance.
Figure 5: Non-parametric Regression of the Size of the Logistics Sector (Narrow Definition) on Per Capita Income.
Note: Data sourced from the OECD STAN database and input-output tables (logistics data), and the World
Development Indicators (per capita income).