Logistics Costs and Competitiveness
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Course: | BUS606: Operations and Supply Chain Management |
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Date: | Saturday, September 7, 2024, 7:35 PM |
Description
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?
Executive Summary
This paper examines the issue of measuring logistics costs from an applied
trade policy research perspective, as well as identifying logistics-intensive
sectors. It focuses on currently available data at the macro- and firm-levels.
Data sources considered include national accounts, national input-output
tables, the International Comparison Project, firm-level data, and production
and trade data. Although current data exhibit a number of weaknesses
compared with "custom" logistics costs data - notably in terms of sectoral
definition - they nonetheless make it possible to conduct some preliminary
empirical analysis that can inform future measurement efforts. First, the paper
finds that there is little systematic evidence of a link between the size of the
logistics sector and economic outcomes, such as trade openness. Second, the
relationship between the size of the logistics sector and logistics performance
is non-monotonic. Third, the size of the logistics sector only increases in per
capita income up to a certain point, before the relationship turns negative.
These findings suggest that measures of sectoral size - such as logistics costs
relative to GDP - may be of limited use to researchers and policymakers
because they do not have an unambiguous interpretation in terms of
performance or economic outcomes. Fourth, however, direct indicators of
price and performance are more clearly related to economic outcomes, and
have a more straightforward relation with per capita income. The emphasis
going forward should therefore be on compiling data that capture logistics
performance most accurately, rather than sector size. Finally, the paper uses
input-output data to identify logistics-intensive sectors, and finds suggestive
evidence that improvements in logistics performance could lead to sectoral
reallocations in favor of relatively heavy industries in developing countries,
which is consistent with the goal of export diversification
Source: Ben Shepherd, https://openknowledge.worldbank.org/handle/10986/26724 This work is licensed under a Creative Commons Attribution 3.0 IGO License.
Introduction
Despite the commercial importance of the logistics sector in helping firms
complete import and export transactions, international trade practitioners
have only recently come to focus on it in any detail. There are two main ways
in which logistics intersects with the trade policy agenda. First, logistics covers
a number of sectors that are subject to ongoing liberalization discussions at
regional and multilateral levels in the context of trade in services. Examples
include transport and distribution. Some regional initiatives, such as ASEAN,
have recognized the importance of logistics by treating it as an independent "cluster" for negotiation and liberalization purposes, even though it cuts across
a number of pre-existing sectoral definitions in the ISIC and GATS
classifications. There is thus a strong linkage between the logistics sector and
trade policy in services.
The second area in which linkages between trade and logistics emerge is in the
context of trade facilitation. Although the WTO has adopted a narrow working
definition of trade facilitation - focusing essentially on import and export
procedures - many other forums, such as APEC, have adopted a much broader
approach. More generally, trade facilitation can be considered as including the
full range of policies that tend to reduce the transaction costs affecting
international movements of goods. Improving logistics performance is in fact
at the core of the private sector trade facilitation agenda, and is an important
complement to public sector measures such as reducing red tape, and
improving infrastructure quantity and quality.
Although there is now an extensive body of analytical work on the links between trade facilitation - using both the broad and narrow definitions - and trade flows, there is as yet relatively little analytical work dealing specifically with the trade effects of logistics sector performance. Until recently, the data constraints involved in doing such work have proved formidable. However, a number of recent initiatives, such as the World Bank's Logistics Performance Index, have started to loosen that constraint.
Against that background, this paper has two main aims. First, it provides a first
overview of currently available data relevant to logistics, and suggests some
preliminary applications. Although data availability is limited in terms of
country coverage and sector specificity, it is useful to analyze freely-available data to see whether expected relationships appear to exist. Examining data in
this way can also provide important insights into the types of data that could
be collected in the future. Such exercises have not previously been conducted
in the literature. Clearly, though, a major caveat in relation to the analysis
undertaken here is that it necessarily relies on proxies for the logistics sector,
and does not purport to capture the full range of logistics activities considered
by more micro-level, industry-specific studies. Nonetheless, there is a tradeoff
to be made in terms of data availability versus specificity, and a number of
important insights arise from the basic analysis presented here.
The second objective of this paper is to frame the issue of logistics cost
measurement and data collection in terms of the types of inputs needed for
applied trade policy research. As will be shown, the needs of trade researchers
are fundamentally different from those of industry groups: the latter can make
use of data that effectively measure sector size for political economy purposes,
but trade researchers need to focus more on issues of performance as
measured by cost relative to some output price. Once such data become
available, however, a number of interesting research avenues are available. On
the one hand, logistics performance is expected to be an important
determinant of bilateral trade flows, and there is already some empirical
evidence to support that view. In addition, logistics performance combined
with sectoral logistics intensities can also be expected to have a significant
impact on the global pattern of production, exports, and specialization. The
cross-sectoral implications of logistics performance have as yet received only
cursory attention in the literature, but are likely to be the source of major gains
going forward. This paper is the first to sketch out a data-driven research
agenda for trade and logistics in this way.
The paper is organized as follows. The next section presents an overview of
possible directions in applied trade policy research using logistics data. Section
3 examines existing data sources that can be used to measure domestic
logistics costs, focusing on the national accounts, input-output tables, price
comparisons, and firm-level data. Section 4 presents a new methodology for
measuring international trade costs, and identifies the proportion of those
costs due to logistics. Section 5 uses input-output data to identify logistics-
intensive sectors in a range of countries. Section 6 concludes.
Logistics and Trade Policy Research: What Are the Connections?
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.
Measuring Domestic Logistics Costs
This section outlines a number of macro-level methodologies that could be
used to measure various aspects of domestic logistics costs. The emphasis is
on exploiting existing data sources. The first subsection discusses the
treatment of logistics in the national accounts, and provides some
approximate data on the size of the logistics sector relative to GDP in a
number of countries. The second subsection presents data relevant to logistics
from the International Comparison Program, focusing on both the size of the
sector relative to GDP and prices. The third subsection uses firm-level data
from the World Bank’s Enterprise Surveys dataset to analyze productivity in the
logistics sector across a range of countries.
National Accounts Data
As noted above, recent analysis of the logistics sector has focused on
producing aggregate measures of sector size, such as the level of logistics
costs relative to GDP. Existing efforts deal with one country at a time, and are
difficult to compare across countries because of different methodologies and
data sources. An alternative approach that is more easily applied on a cross-
country basis is to use national accounts data to obtain an estimate of the size
of the logistics sector relative to GDP. Clearly, data obtained in this way will
not be directly comparable with work such as that of Bowersox et al. for
two reasons: differences in sectoral classifications mean that what is intended
by the term "logistics" will inevitably differ between the two approaches; and
the national accounts approach can only compare the value added by the
logistics sector relative to other sectors in the economy, not the total amount
spent on logistics, including internal costs, such as inventories. Internal
logistics costs can be substantial, especially in low income countries.
Nonetheless, national accounts data can provide a useful point of comparison
with previous work.
Treatment of Logistics in the National Accounts
Internationally comparable national accounts data follow the International
Standard Industrial Classification at a sectoral level. The ISIC system does not
identify logistics as a separate sector. However, a number of ISIC Rev.3 sectors
are potentially relevant to work on logistics. Table 1 summarizes relevant ISIC
Rev.3 sectors according to narrow, medium, and broad definitions of the range
of activities included in logistics. The narrow definition of logistics limits the
sector to transport and related activities, of which a number in sector 63 fall
into the core of logistics services. The medium definition includes in addition
wholesale trade, which captures the core of distribution activities. The broad
definition also includes retail trade, in order to cover a wider range of
distribution activities.
A number of caveats are required in relation to these definitions of logistics. First, as previously noted, they differ somewhat from the commercial definition of logistics activities. The differences go in both directions, i.e. there are some activities that are considered to be part of logistics in the commercial sphere, but which are not included in the ISIC definitions, but at the same time, the ISIC definitions include some activities that are not considered to be logistics from a commercial standpoint. Second, the ISIC definitions are not strictly limited to freight activities, but also include passenger activities within the context of transport. Although it is in principle possible to distinguish between the two by using the three digit level of the ISIC scheme, the cross- country data source used here includes two digit sector definitions only. It is therefore left to future research to return to national sources and develop logistics indicators using ISIC three digit data. The results presented here should be interpreted as rough orders of magnitude only.
Table 1: Isic Rev.3 Sectors Relevant to Logistics (Various Definitions).
ISIC Rev. Sector |
Narrow Definition |
Medium Definition |
Broad Definition |
---|---|---|---|
60-62: Land, water, and air transport |
✓ | ✓ | ✓ |
63: Supporting and auxiliary transport activities (cargo handling; storage and warehousing; supporting transport activities; travel; tour, and transport agencies). |
✓ | ✓ | ✓ |
51: Wholesale trade. |
✓ | ✓ | |
52: Retail trade |
✓ |
Most countries currently use the ISIC Rev.3 classification for their national
accounts. In 2008, a new ISIC Rev.4 classification was released, but it has not
yet been widely implemented. It adopts a generally similar approach to the
sectors of most interest here, the only significant differences being in the
replacement of "supporting and auxiliary transport activities" with
"warehousing and support activities for transportation". The new sectoral
definition focuses more closely on core logistics activities, such as freight
forwarding - the word "logistics" is even used in the explanation of class
5229 - and excludes tour and transport agencies. As a result, measurement of
logistics activities using national accounts data can be expected to improve
marginally in the coming years with implementation of the ISIC Rev.4 scheme.
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).
The International Comparison Program
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.
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 |
Measuring International Logistics Costs
The gravity model is the workhorse of empirical international trade. Typically,
it is used to obtain econometric estimates of the sensitivity of trade flows with
respect to particular trade cost factors, and to run counterfactual simulations
based on those estimates. Novy turns the gravity model on its head to
develop a methodology for inferring trade costs based on the observed pattern
of trade and production. He starts from a variety of theory-based gravity
models, and uses simple algebra to derive a theory-consistent expression for
bilateral trade costs between two countries. His approach has been applied in a
number of recent papers, such as: Jacks et al. on trade costs over the
1870-2000 period; Shepherd, who uses the methodology to assess the
effectiveness of trade facilitation programs in APEC and ASEAN; Brooks and
Ferrarini on trade costs between India and China; Duval and Utoktham on trade costs in the Asia-Pacific; Miroudot et al. on trade costs
in international services markets; and Olper and Raimondi on trade
costs in food industries.
There are three main advantages to the Novy methodology. First, it is
"top down", in the sense that it provides an all-inclusive measure of trade
costs, covering all factors - even unobservables - affecting exports and
imports. Second, its data requirements are limited to the value of domestic
and international shipments, which can be approximated using commonly
available data from national accounts and standard trade databases. It is not
necessary to have policy data on the full range of trade costs in order to
properly account for them using this approach. Third, the methodology is
theory-based, and relies on an identity relationship rather than econometric
estimation. There is thus no risk of omitted variable bias, or other problems
that typically plague econometric estimates of gravity models.
Of course, the cost of relying heavily on theory is that if it is incorrect, then the
decomposition might also be erroneous. However, Novy shows that the
approach used here can be applied successfully to a variety of theoretical
models of trade; it obviously captures a deep regularity in the relationship
between trade costs, production, and trade flows. He also shows that it is
highly robust to the possibility of measurement error.
In ad valorem equivalent terms, Novy's measure takes the following
form:
where: is the geometric average of trade costs facing exports from
country i to country j and those facing exports from country j to country i; k and t index sectors and time periods respectively;
is the cost of shipping
goods from country i to country j relative to the cost of shipping them within
country i;
is the value of goods shipped within country i relative to the
value of those shipped from country i to country j; and s is a model parameter,
usually the elasticity of substitution among product varieties within a sector.
The basic interpretation of equation (1) is straightforward: as the ratio of
international trade relative to domestic shipments increases, trade
costs fall. In other words, trade costs must be lower when countries exhibit a
greater tendency to trade with each other rather than with themselves. The
precise relationship between trade costs and the ratio of trade to domestic
shipments depends on how substitutable the goods in question are: in more
homogeneous sectors, the effect on trade costs of a given change in the ratio
is dampened.
However, it is important to be clear on a number of other aspects of the
interpretation of . First, it represents average trade costs in both directions
between i and j. The structure of the model is such 38 that it is not possible to derive expressions for unidirectional trade costs in
terms of observables. From a policy perspective, it is therefore important to
interpret changes in cautiously: they might be caused by policy changes
in country i, in country j, or in both simultaneously.
Second, as the first part of equation (1) indicates, depends on the ratio of
international trade costs to domestic trade costs
. One aspect of this connection is that some kinds of "behind-the-border" trade costs
are effectively cancelled out in the final measure of average trade costs,
namely those that affect domestic and foreign producers in exactly the same
way. However, many behind-the-border measures discriminate in fact, if not in
law, in the sense that it is more costly for foreign producers to obtain
information on procedures, or navigate a path through domestic regulations
and institutions. These kinds of differences are captured in
. However,
when comparing trade costs across countries, it is impossible to separately
identify international versus domestic trade costs.
Third, is an all-inclusive measure of trade costs, in the sense that it takes
account of the full range of transaction costs affecting exports and imports. It
thus takes account of logistics performance. It is not a measure of protection,
like the World Bank's Trade Restrictiveness Indices. It takes account of tariff
and non-tariff barriers to trade, but also includes a wide range of other trade
cost factors typically captured in gravity models. Examples include
geographical distance, and cultural or historical links. As a result,
is
generally much larger in magnitude than the rates of protection trade
economists are used to dealing with in measures such as the Overall Trade
Restrictiveness Index (OTRI) or average applied tariffs.
Once the Novy trade cost measure has been calculated for a range of
countries, it is possible to use an econometric decomposition to assess the
impact of different factors on the overall level of trade costs. Shepherd
adopts this approach to examine the impact of logistics performance on total
trade costs in the Maghreb region (Table 5). Logistics costs are captured by a
rescaled version of the LPI, 39
in which a higher score indicates poorer performance. Results show that
logistics performance is clearly an important determinant of trade costs in this
sample of countries: increasing logistics performance by 10% would tend to
decrease trade costs by 6.5% in manufacturing and 8% in agriculture.
Table 5: Regression Results Using Log(Trade Costs) as the Dependent Variable, 2007 Only.
(1) Manufacturing |
(2) Agriculture |
(3) Energy |
|
---|---|---|---|
Log(Logistics Costs) |
0.653*** (0.000) |
0.808*** (0.000) |
-0.061 (0.668) |
Log(Tariff) |
1.943 (0.415) |
-2.786* (0.100) |
-115.840*** (0.002) |
Log(Distance) | 0.397*** (0.000) |
0.467*** (0.000) |
0.372*** (0.000) |
No Common Border |
0.207** (0.037) |
0.282** (0.011) |
0.225* (0.057) |
No Common Language |
0.190** (0.011) |
0.126 (0.228) |
-0.038 (0.704) |
No Colonial Relationship |
0.426** (0.001) |
0.050 (0.652) |
0.192 (0.278) |
No Common Colonizer |
0.055 (0.564) |
-0.186 (0.312) |
-0.344 (0.191) |
Constant | -3.961*** (0.000) |
-3.476*** (0.000) |
-1.582*** (0.002) |
R2 Observations |
0.620 336 |
0.579 448 |
0.357 322 |
To illustrate the relative importance of the various factors as determinants of
overall trade costs, Chen and Novy (2010) suggest a variance decomposition
approach. The percentage of the observed variance in trade costs accounted
for by logistics, for example, is given by the following expression:
where is the relevant partial regression coefficient. Applying this approach
to the model for manufacturing (Table 5, column 1) shows that logistics
accounts for just over 15% of the observed variation in total trade costs.
Tariffs, by comparison, account for only 0.6% of the variation in trade costs,
but distance accounts for over one-third of the total. Although these are little
more than "back of the envelope" calculations, it is clear that as far as policy-
related impediments to trade are concerned, logistics is an issue of major
quantitative importance. This result lines up well with the existing literature,
which tends to suggest that the gains from reforming non-tariff measures -
and in particular trade facilitation and logistics - outweigh the gains from
comparable tariff reductions.
Clearly, it will be important for future research to expand the country sample
used for this analysis to include a broader range of countries. Inclusion of LPI
scores for 2007 and 2009 will make it possible to control for a range of country-
specific factors using fixed effects, thereby reducing the risk of omitted
variables bias. Nonetheless, it seems likely that the basic results presented
here will be confirmed, namely that logistics is a very important determinant
of bilateral trade costs, accounting for perhaps as much as 15% of the total.
Measuring International Logistics Costs
The gravity model is the workhorse of empirical international trade. Typically,
it is used to obtain econometric estimates of the sensitivity of trade flows with
respect to particular trade cost factors, and to run counterfactual simulations
based on those estimates. Novy turns the gravity model on its head to
develop a methodology for inferring trade costs based on the observed pattern
of trade and production. He starts from a variety of theory-based gravity
models, and uses simple algebra to derive a theory-consistent expression for
bilateral trade costs between two countries. His approach has been applied in a
number of recent papers, such as: Jacks et al. on trade costs over the
1870-2000 period; Shepherd, who uses the methodology to assess the
effectiveness of trade facilitation programs in APEC and ASEAN; Brooks and
Ferrarini on trade costs between India and China; Duval and Utoktham on trade costs in the Asia-Pacific; Miroudot et al. (2010) on trade costs
in international services markets; and Olper and Raimondi on trade
costs in food industries.
There are three main advantages to the Novy methodology. First, it is
"top down", in the sense that it provides an all-inclusive measure of trade
costs, covering all factors - even unobservables - affecting exports and
imports. Second, its data requirements are limited to the value of domestic
and international shipments, which can be approximated using commonly
available data from national accounts and standard trade databases. It is not
necessary to have policy data on the full range of trade costs in order to
properly account for them using this approach. Third, the methodology is
theory-based, and relies on an identity relationship rather than econometric
estimation. There is thus no risk of omitted variable bias, or other problems
that typically plague econometric estimates of gravity models.
Of course, the cost of relying heavily on theory is that if it is incorrect, then the
decomposition might also be erroneous. However, Novy (2010) shows that the
approach used here can be applied successfully to a variety of theoretical
models of trade; it obviously captures a deep regularity in the relationship
between trade costs, production, and trade flows. He also shows that it is
highly robust to the possibility of measurement error.
In ad valorem equivalent terms, Novy's measure takes the following
form:
where: is the geometric average of trade costs facing exports from
country i to country j and those facing exports from country j to country i; k and t index sectors and time periods respectively;
is the cost of shipping
goods from country i to country j relative to the cost of shipping them within
country i;
is the value of goods shipped within country i relative to the
value of those shipped from country i to country j; and s is a model parameter,
usually the elasticity of substitution among product varieties within a sector.
The basic interpretation of equation (1) is straightforward: as the ratio of
international trade relative to domestic shipments increases, trade
costs fall. In other words, trade costs must be lower when countries exhibit a
greater tendency to trade with each other rather than with themselves. The
precise relationship between trade costs and the ratio of trade to domestic
shipments depends on how substitutable the goods in question are: in more
homogeneous sectors, the effect on trade costs of a given change in the ratio
is dampened.
However, it is important to be clear on a number of other aspects of the
interpretation of . First, it represents average trade costs in both directions
between i and j. The structure of the model is such 38 that it is not possible to derive expressions for unidirectional trade costs in
terms of observables. From a policy perspective, it is therefore important to
interpret changes in cautiously: they might be caused by policy changes
in country i, in country j, or in both simultaneously.
Second, as the first part of equation (1) indicates, depends on the ratio of
international trade costs to domestic trade costs
. One aspect of this connection is that some kinds of "behind-the-border" trade costs
are effectively cancelled out in the final measure of average trade costs,
namely those that affect domestic and foreign producers in exactly the same
way. However, many behind-the-border measures discriminate in fact, if not in
law, in the sense that it is more costly for foreign producers to obtain
information on procedures, or navigate a path through domestic regulations
and institutions. These kinds of differences are captured in
. However,
when comparing trade costs across countries, it is impossible to separately
identify international versus domestic trade costs.
Third, is an all-inclusive measure of trade costs, in the sense that it takes
account of the full range of transaction costs affecting exports and imports. It
thus takes account of logistics performance. It is not a measure of protection,
like the World Bank's Trade Restrictiveness Indices. It takes account of tariff
and non-tariff barriers to trade, but also includes a wide range of other trade
cost factors typically captured in gravity models. Examples include
geographical distance, and cultural or historical links. As a result,
where is the relevant partial regression coefficient. Applying this approach
to the model for manufacturing (Table 5, column 1) shows that logistics
accounts for just over 15% of the observed variation in total trade costs.
Tariffs, by comparison, account for only 0.6% of the variation in trade costs,
but distance accounts for over one-third of the total. Although these are little
more than "back of the envelope" calculations, it is clear that as far as policy-
related impediments to trade are concerned, logistics is an issue of major
quantitative importance. This result lines up well with the existing literature,
which tends to suggest that the gains from reforming non-tariff measures -
and in particular trade facilitation and logistics - outweigh the gains from
comparable tariff reductions.
Clearly, it will be important for future research to expand the country sample
used for this analysis to include a broader range of countries. Inclusion of LPI
scores for 2007 and 2009 will make it possible to control for a range of country-
specific factors using fixed effects, thereby reducing the risk of omitted
variables bias. Nonetheless, it seems likely that the basic results presented
here will be confirmed, namely that logistics is a very important determinant
of bilateral trade costs, accounting for perhaps as much as 15% of the total.
Identifying Logistics-intensive Sectors
As noted above, an important question from a trade policy perspective relates
to the impact of improved logistics performance on the pattern of sectoral
specialization. At its most basic, trade theory suggests that as the price of
logistics services falls relative to other goods and services in the economy,
those sectors that use logistics particularly intensively will tend to undergo a
relative expansion. We therefore expect improvements in logistics
performance to affect relative sector size, and thus the pattern of
specialization across countries.
To undertake a detailed analysis of the impacts of logistics performance on
sectoral patterns of specialization, it would be necessary to incorporate the
sector into a fully-specified general equilibrium model, such as the Global
Trade Analysis Project (GTAP). GTAP currently includes a transport sector,
which could be used as a first proxy for logistics. The model could therefore
provide a platform for examining possible changes in the sectoral composition
of production and trade by modeling improvements in logistics performance
as reductions in transport costs. To do so, however, it would first be necessary
to obtain an econometric estimate of the relationship between logistics
performance and transport costs. Such work has not yet been undertaken, but
future research focusing either on direct measures of transport costs or
omnibus measures such as the Novy index discussed above could make an
important contribution to a better understanding of this area.
Although the relationship between logistics and sectoral composition is a
complex one, it is possible to use basic input-output data to provide some
initial information on sectors in developing countries that are likely to be
particularly sensitive to logistics performance. The OECD's STAN database
input-output tables provide sectorally disaggregated data on intermediate
input use, from which it is possible to construct measures of logistics intensity
using the narrow and broad definitions discussed above; the medium
definition cannot be used due to a lack of necessary sectoral detail in the input-
output tables. “Logistics intensity” is defined simply as the percentage by
value of total intermediate input use accounted for by logistics services.
Table 5 lists the five most logistics-intensive sectors in 11 non-OECD countries, using the latest available input-output data from OECD STAN. The first stylized fact that emerges is clearly that each country is different when it comes to logistics intensity in production: some sectors that are strongly logistics intensive in some countries (e.g., agriculture in South Africa) do not display that characteristic in most other countries. Second, it is nonetheless apparent that some sectors are logistics-intensive in a number of economies, which suggests that modes of production are relatively similar across countries. Mining and minerals are examples. Third, a number of relatively "heavy" industries are logistics intensive in a range of countries. Boosting production and trade in such sectors relative to the rest of the economy would be consistent with the goal of export diversification in many developing countries. Recent cross-country empirical evidence indeed suggests that improved trade facilitation - of which logistics performance is an important component - can help boost export diversification.
Table 6: Top Five Logistics-intensive Manufacturing Sectors Based on Input-output Data; Non-oecd Countries.
Country |
Year |
Narrow Definition | Broad Definition |
---|---|---|---|
Argentina |
1997 |
Wood products; Mining and quarrying; Minerals; Food products; Radio, television, and communications equipment. |
Wood products; Office, accounting, and computing machinery; Metal products, Iron and steel; Minerals. |
Brazil |
2005 |
Mining and quarrying (energy and non-energy); Pharmaceuticals; Iron and steel; Minerals. |
Mining and quarrying (energy and non-energy); Pharmaceuticals; Minerals; Textile products. |
China |
2005 |
Mining and quarrying (energy and non-energy); Minerals; Rubber and plastic products; Wood products. |
Minerals; Rubber and plastic products; Mining and quarrying (energy and non-energy); Wood products. |
India |
2003/04 | Medical, precision, and optical instruments; Minerals; Mining and quarrying; Textile products; Paper products. |
Textile products; Iron and steel; Minerals; Medical, precision, and optical instruments; Food products. |
Indonesia |
2005 |
Wood products; Other manufacturing; Radio, television, and communication equipment; Pharmaceuticals; Medical, precision, and optical instruments. |
Wood product; Other manufacturing; Radio, television, and communication equipment; Pharmaceuticals; Medical, precision, and optical instruments. |
Romania |
2005 |
Mining and quarrying, Coke and petroleum products; Minerals; Medical, precision, and optical instruments; Iron and steel. |
Mining and quarrying, Food products; Motor vehicles; Medical, precision, and optical instruments; Minerals. |
Russia |
2000 |
Mining and quarrying; Minerals; Wood products; Iron and steel; Coke and petroleum products. |
Coke and petroleum product; Minerals; Mining and quarrying; Iron and steel, Wood products. |
South Africa |
2005 |
Mining and quarrying; Agriculture; Rubber and plastic products; Coke and petroleum products. | Mining and quarrying; Agriculture; Textile products; Food products. |
Taiwan |
2006 |
Minerals; Wood products; Pulp and paper products, Other manufacturing, Machinery and equipment. | Wood products; Pulp and paper products, Agriculture, Minerals; Transport equipment. |
Thailand |
2005 |
Mining and quarrying (energy and non-energy); Minerals; Wood products; Pharmaceuticals. | Mining and quarrying; Wood products; Pharmaceuticals; Agriculture; Pulp and paper products. |
Vietnam |
2000 |
Wood products; Coke and petroleum products; Mining and quarrying (energy and non-energy); Building and repairing of ships and boats. |
Pulp and paper products; Textile products; Motor vehicles; Electrical machinery; Medical, precision, and optical instruments |
Conclusion
This paper has explored a number of different data sources and methodologies
in an effort to move forward on the analysis of logistics costs from a trade
policy research perspective. In the future, it will be important to distinguish
between data collection efforts that are industry-driven - such as estimates of
total logistics costs in GDP - and those that are research-driven. The former
are useful in establishing the size of the sector and in attracting attention from
researchers and policy analysts. However, the results presented here suggest
that they may be of limited use from a trade research point of view. The reason
is that measures of sector size exhibit little systematic relationship with
economic outputs and inputs in a cross-country regression framework.
Moreover, the relationship between sector size and performance appears to be
non-monotonic, which makes it difficult to draw meaningful policy conclusions
based on size alone. By contrast, performance measures such as prices
generally display a more significant relationship with important economic
variables.
The work presented here has three important implications for future research
and data collection work in this area. First, the data and analysis presented
here has relied on descriptive statistical techniques only. There is clearly major
scope to exploit data sources such as national accounts, input-output tables,
and firm-level data within the framework of a fully-specific regression
problem. Such an approach could properly account for intervening causes, and
establish more robust results than those presented here. In tandem with future
data collection efforts, it will be important to make better use of existing data
sources too.
Second, it is important that future data collection efforts emphasize
performance measures rather than size measures. Data on logistics
expenditures is important in either case, but the choice of denominator is
crucial in terms of making the resulting data most useful for applied trade
policy researchers. Ideally, logistics costs should be converted into an ad
valorem equivalent - i.e., a percent of the landed price of 45 traded goods -
which is the measure trade economists most commonly work with in their
models. Alternatively, "pure" performance measures like the LPI can also be
used to estimate ad valorem equivalents by applying the Novy
methodology.
Third, measures of logistics intensity should also be part of the data and
analysis framework moving forward. Some existing work has already focused on logistics costs as a percentage of total costs, which is essentially a measure
of intensity. Moving further in this direction will help fuel research that
identifies sectors in particular countries that are most sensitive to
improvements in logistics performance, and which therefore will tend to
expand relative to other sectors in the face of logistics sector reforms. From a
policy and political economy point of view, it will be important to identify such
sectors and make them aware of the potential role logistics can play in
facilitating their growth.