BUS606: Operations and Supply Chain Management

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.

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).