Spatial concentration and location dynamics in logistics : the
case of a Dutch province
Citation for published version (APA):
Heuvel, van den, F. P., Langen, de, P. W., Donselaar, van, K. H., & Fransoo, J. C. (2011). Spatial concentration and location dynamics in logistics : the case of a Dutch province. (BETA publicatie : working papers; Vol. 355). Technische Universiteit Eindhoven.
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Spatial concentration and location dynamics in logistics:
the case of a Dutch province
Frank P. van den Heuvel, Peter W. de Langen,
Karel H. van Donselaar, Jan C. Fransoo
Beta Working Paper series 355
BETA publicatie
WP 355 (working
paper)
ISBN
ISSN
NUR
804
Spatial concentration and location dynamics in logistics:
the case of a Dutch province
Frank P. van den Heuvela,∗, Peter W. de Langena, Karel H. van Donselaara, Jan C. Fransooa
aSchool of Industrial Engineering, Eindhoven University of Technology,
P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
Abstract
To better understand spatial concentration of logistics establishments, this paper analyzes loca-tion dynamics in relaloca-tion to spatial clusters. Such an analysis is relevant for both decision makers within logistics firms and regional policy makers, as both co-located logistics firms as well as so-ciety as a whole can benefit from co-location of logistics firms. For this analysis, longitudinal empirical data on logistics establishments in a Dutch province are used. Six general conclusions are drawn on spatial concentration over time and location decisions of logistic firms in relation to spatial concentration in logistics as well as the proximity to intermodal terminals. First, logistics employment spatially concentrates in particular areas, called Absolute and Relative Concentration areas (AREC areas). Second, logistics establishments that relocated within the province locate relatively more in AREC areas than in other areas; new logistics establishments do not. Third, larger logistics establishments locate relatively often in AREC areas. Fourth, logistics establish-ments that came from AREC areas are more likely to relocate in AREC areas than establishestablish-ments that came from non-AREC areas. Hence, experience matters in location decisions of logistics es-tablishments. Fifth, transport establishments locate relatively often in newly formed AREC areas. Finally, data on employment growth show that intermodal container terminals attract logistics employment, in their direct vicinity as well as on a municipal level.
Keywords: Spatial concentration, Co-location, Location dynamics, Transportation, Warehousing, Intermodal terminal
1. Introduction
The location decisions of logistics firms have a huge impact on the demand for freight transport. Locations of logistics firms strongly influence the choice of freight transport modes (Bowen, 2008). In many supply chains, given locations of different companies constrain the realization of potential supply chain cost reductions. Understanding location decisions for logistics firms is especially
∗Corresponding author
relevant as the demand for ’logistics floor space’ is expected to grow substantially in advanced economies. In the U.K., logistics floor space has grown substantially in the last decade, in contrast with ’industrial floor space’, which has been constant and is expected to decline (McKinnon, 2009). The expected growth in logistics floor space is one of the drivers of the expected growth of freight transport volumes; the European Commission (2011), for example, assumes a freight transport growth of around 82% in the E.U. to 2050. Location decisions for new logistics firms shape the (additional) demand for freight transport and deeply influence the feasibility of a shift of freight transport towards more sustainable modes of transport such as rail and barge (as advocated by the European Commission (2011)). Hence, better understanding of location decisions of logistics firms is helpful for policy makers, that may aim to attract logistics activities while minimizing congestion, as well as companies, such as logistics real estate developers (that develop and lease warehousing facilities, e.g. Prologis), and logistics park developers (that develop large scale logistics parks, and lease land on these parks, e.g. Abertis).
Notwithstanding this societal relevance, academic literature that deals specifically with loca-tion decisions of logistics firms is limited (see Jing and Cai (2010) for an analysis of the spatial distribution of the logistics sector in China). Hesse and Rodrigue (2004) legitimately remark that research into freight transport and logistics is generally underrepresented in regional science. A substantial body of literature develops models for the location of logistics firms (see Melo et al. (2009) for an overview), but these models are hardly tested empirically, while empirical observa-tions suggest that factors that are generally not included in facility location models (e.g. synergy due to the presence of other logistics facilities) are relevant in practice. Although literature about the relationship between location decisions and co-location of e.g. R&D companies abounds (e.g. Martin and Ottaviano, 1999; Alc´acer and Chung, 2007; Suire and Vicente, 2008), not much is known about this relationship for logistics firms.
This paper adds to the emerging body of literature that empirically examines location choices of logistics firms, taking into account potential benefits through co-location. Thus, these location choices are analyzed at a small geographical scale. Even though an understanding of synergies through co-location would provide informed support for the location decisions of logistics firms, and would also be instrumental for policy decisions regarding spatial and infrastructure development, very little research has been done on this topic. Such research is likely to become increasingly relevant, for at least three reasons. First, shippers as well as logistics service providers increasingly look beyond their own supply chains and explore opportunities for cooperation across different supply chains (Cruijssen et al., 2007a,b), possibly through co-location. Second, investments in logistics real estate are increasingly being made by firms that do not provide logistics services, but lease logistics warehouse space. Such firms do not base a location decision on the optimization of a specific supply chain, but on the market value of logistics property. This value may be relatively
high in concentration areas for logistics. Third, policies to influence the location patterns of logistics firms may become more relevant as land and infrastructure become increasingly scarce.
This paper contributes to the understanding of spatial concentration of logistics firms by ana-lyzing location decisions in relation to spatial clusters. The method developed by Van den Heuvel et al. (2011) is used to identify areas in North Brabant in which the logistics sector is concentrated. North Brabant is a province in the south of the Netherlands, located between Europe’s two largest seaports (Rotterdam and Antwerp) and large consumer markets in Germany and France. Due to this location, many European Distribution Centers (EDCs) are located in the region, and the logistics sector is relatively large. Hence, an analysis into the location dynamics of logistics firms is especially relevant in this province. The location of logistics firms in this province is analyzed for the period 1996 to 2009. This analysis addresses spatial concentration over time and location decisions of logistics firms in relation to spatial clusters as well as the proximity to multimodal container terminals.
The remainder of this paper is organized as follows. Section 2 discusses relevant issues in logistics location decisions; more specifically, the extent of geographical concentration of logistics establishments and the relevance of intermodal terminals in location decisions. Section 3 of the paper continues with the analysis of location dynamics of the logistics sector in North Brabant. Section 4 discusses the outcomes and presents opportunities for further research.
2. Understanding spatial concentration and location dynamics in logistics
Co-location of establishments (spatial clustering) is studied extensively (e.g. Krugman, 1991; Porter, 2000; Malmberg and Maskell, 2002; Rosenthal and Strange, 2003; Devereux et al., 2004). In logistics, such research is limited, mostly to clustering of port and maritime industries (De Lan-gen, 2004; De Langen and Visser, 2005; Brett and Roe, 2010). Furthermore, a fair amount of research has been done on intermodal terminals, dry ports, and freight villages (e.g. Konings, 1996; Tsamboulas and Dimitropoulos, 1999; Tsamboulas and Kapros, 2003; Bottani and Rizzi, 2007; Woxenius, 2007). In this research, intermodal transport chains are center-stage, not the spatial concentration of logistics establishments. This lack of research is at least partly explained by the focus of most supply chain research on specific supply chains and location decisions in spe-cific supply chains. Synergies through co-location of different supply chain activities are seldom taken into account.
The seminal work on agglomeration economies is the work of Marshall (1956), who described three sources of agglomeration economies, namely labor market pooling, knowledge spillovers, and inputs sharing. Labor market pooling provides co-located firms with better access to specialized labor, because of an inflow of labor, better (on the job) training, and a more flexible labor market.
locate in a spatial cluster for both firms and workers, because of better labor utilization. This is highly likely for logistics firms operating in different supply chains, since these firms serve different markets.
Marshall (1956) also refers to knowledge spillover effects. The general idea is that geography plays a fundamental role in innovation and learning. Innovations are generally created through collaboration of different firms, and, everything else being equal, the costs of exchanging infor-mation increases with the distance between the firms (Malmberg and Maskell, 1997). This is not caused by communication costs (Laserre, 2008), but by the need to create trust and understand-ing between the cooperative firms, which in turn depends on language, shared values, and culture (Cruijssen et al., 2007b).
Finally, Marshall’s input sharing is related to the broad local supplier base, that reduces costs and increases flexibility. In the logistics sector, input sharing can be beneficial in different ways. First, the most apparent benefit for logistics firms that co-locate is combining transport flows, which often saves transport costs. Cooperation between co-located transport firms results in a denser network that can result in lower transport costs (Jara-D´ıaz and Basso, 2003), due to less repositioning of trucks (Ergun et al., 2007), a decrease in empty mileage (Cruijssen et al., 2007a), and a decrease of the average distance between customers (Van Donselaar et al., 1999; Wouters et al., 1999). This also has a positive environmental impact. Second, co-location of logistics firms may lead to supply of flexible storage capacity by third parties, because of the presence of several firms with demand for short-term storage capacity. Third, co-location in logistics may lead to sufficient scale for multimodal services. Multimodal transport can hardly compete with road transport unless relatively large volumes can be bundled. When logistics firms co-locate, freight volumes increase and can enable the development of multimodal transport services.
Bowen (2008) researched the relationship between the location of warehouses and the ac-cessibility of these warehouses via several kinds of transportation networks in the U.S. Results indicated that air and highway accessibility matter more than rail and especially sea accessibility. Bowen (2008) concludes that the significant speed advantages air and road transport have over maritime and rail transport are highly important. However, Bowen (2008) only included sea-ports in his measure of maritime transport accessibility, not intermodal terminals. Furthermore, while airports are very important for freight transport in the U.S., this is not the case in Europe. In Europe, barge and rail transport are expected to become more important, due to the recent trends of sustainability and containerization (Notteboom and Rodrigue, 2009). Especially in the Netherlands, container transport via barges has grown substantially in the past years (CBS, 2007). Bowen (2008) also found positive correlations between the growth in warehouses from 1998 to 2005 and the air and road accessibility, and between the growth in large warehouses over time and rail accessibility. In addition, O’Connor (2010) found a disproportionate large share of activity in
regions with multiple air- and seaports worldwide, and an increase in that share over time. These findings can be explained by what Mori and Nishikimi (2002) call economies of transport density. All the above-mentioned factors influence the location decision of (logistics) firms. While location decisions of companies are influenced by spatial concentration, spatial concentration emerges and evolves because of location decisions of companies. Empirical research on the location decisions of new and already existing firms in relation to spatial clusters can provide new insights about the development of these clusters (Cidell, 2010).
Location decisions of logistics firms are extensively modeled (see Melo et al. (2009) for an overview). The basic idea in these models is to minimize the distance to suppliers and customers, but do not consider benefits through co-location of activities across different supply chains. In addition, while some studies analyze location decisions of firms in relation to agglomeration benefits (e.g Head et al., 1995), these studies do not pay attention to transportation costs. Bhatnagar and Sohal (2005) legitimately remark that there are many qualitative variables in the location decisions of firms, besides general cost factors, that are mostly not considered in these models. Especially for logistics firms, the inclusion of the presence of other logistics firms, the existence of multimodal terminals, and the land value are relevant characteristics to consider while studying location decisions. On the one hand, logistics firms have an incentive to locate in or close to spatial clusters and/or multimodal terminals, due to agglomeration economies described above. On the other hand, the land value is expected to increase in logistics clusters and around multimodal terminals (Tsutsumi and Seya, 2008).
This paper empirically researches these location decisions of logistics firms in relation to the concentration areas for logistics. The purpose of this paper is threefold. First, the question is whether and where logistics activities co-locate in the province North Brabant and whether these co-location patterns are stable over time. Second, can the location decisions of logistics firms be linked to the concentration areas identified and to the locations of intermodal terminals? Third, as multimodal terminals play an increasingly important role in the logistics sector in North Brabant, does logistics employment grow faster around these terminals than in the rest of the province? If these concentration areas for logistics and multimodal terminals indeed attract an increasing amount of logistics firms and employment, policy makers would be interested to know, since this heavily influences policy decisions related to infrastructure developments.
3. Spatial clusters and location dynamics in logistics in a Dutch province 3.1. Material used
This paper empirically studies location dynamics of logistics establishments1in North Brabant, one of the southern provinces of the Netherlands, from 1996 to 2009. The logistics sector is one of the most important sectors in North Brabant. During the years 1996 to 2009, logistics employment was equal to on average 10.5% of total employment in this province. Furthermore, this percentage increased over this period until the economic crisis in 2008. The analysis is based on annual establishment statistics. The establishments related to the logistics sector are selected based on the industry codes as used by Statistics Netherlands and based on employment size, as described in detail in AppendixA. After a standard clean-up of the annual datasets, on average 3125 logistics establishments were located in North Brabant, with a standard deviation of 94, a minimum of 2984 in 1996, and a maximum of 3270 in 2000. The average logistics employment was equal to 83912, with a standard deviation of 7349, a minimum of 68654 in 1996, and a maximum of 94207 in 2008. In total, the database contains 2344 logistics establishments that made a location decision (i.e. were new in or relocated within the province).
3.2. Spatial concentration of logistics over time
This section uses the locational Gini coefficient (Krugman, 1991) to measure how the spatial concentration of logistics employment in four-digit postal code areas developed over time in North Brabant. For logistics employment, this coefficient is on average equal to 0.299 (on a scale from 0 to 0.5) for the years 1996 to 2009. Based on a comparison with other industries (Guillain and Le Gallo, 2010), this is relatively high. The standard deviation is 0.007, which shows that the spatial concentration is relatively stable over time.
Next, it is analyzed where logistics employment concentrated in different years. Absolute and Relative Employment Concentration areas (AREC areas) for logistics were identified in all years from 1996 to 2009. As the name suggests, AREC areas are areas in which there is both absolute and relative employment concentration of a particular industry (in this case logistics). Especially for the logistics sector, analyzing absolute or relative concentration results in different conclusions (Jing and Cai, 2010). Hence, this paper uses a method in which these two concentration measures are combined. Van den Heuvel et al. (2011) developed the method to identify AREC areas, presented in AppendixB. The method is applied on the 502 four-digit postal code areas in North Brabant.
Figure 1 shows the AREC areas for logistics in 1996 and in 2009. In 1996, there were 16 AREC areas, consisting of 37 four-digit postal code areas, compared to 19 AREC areas consisting of 44
1Note that the former sections discussed the spatial concentration of logistics firms. As firms can consist
of multiple establishments at different locations, the remainder of the paper will analyze locations of logistics establishments.
postal code areas in 2009. Although the exact size of AREC areas changes over time, the AREC areas are relatively robust and grow over time. In 1996, 27% of the logistics establishments and 47% of logistics employment were located in AREC areas presented in figure 1(a), while in 2009, 33% of the logistics establishments and 54% of logistics employment were located in AREC areas presented in figure 1(b). Hence, the number of logistics establishments located in AREC areas grew with almost 29%, while the total number of logistics establishments in the province only grew with 8%. Similarly, logistics employment located in AREC areas grew with 52%, while the logistics employment in the province only grew with 32%.
3.3. Location patterns related to logistics concentration areas
This section analyzes whether logistics establishments locate more in or close to AREC areas compared to elsewhere in the province. For this analysis, the postal addresses of logistics estab-lishments of year t were compared to year t − 1, with t ∈ {1997, . . . , 2009}. From the database of logistics establishments of year t the addresses were selected that were not in the database of year t − 1. This resulted in two different sets of logistics establishments:
I. New establishments in North Brabant: These are the logistics establishments in year t that were not present in the database of year t − 1. In total, 1465 new logistics establishments chose to locate in North Brabant (113 establishments on average per year).
II. Logistics establishments that relocated within North Brabant: These are logistics estab-lishments with different addresses within the province in year t and year t − 1. Excluding the relocation of establishments within the same four-digit postal code area, these are 842 relocations of logistics establishments (on average 65 per year).
The following subsections analyze these two categories separately. The analysis of new es-tablishments is based on data from year t, since this is the only data available. The analysis of relocated establishments is based on data from year t − 1, since the location decision was most likely made in year t − 1.
3.3.1. New logistics establishments
This section analyzes whether new logistics establishments (category I) chose relatively often to locate in AREC areas. The percentage of logistics establishments that on average locate in AREC areas is used as a reference throughout the paper. In total 1,465 new logistics establishments located in the province over the years 1997 to 2009; 401 (=27.37%) of these establishments chose for a location in an AREC area (as identified in year t − 1), while 1,064 chose for another location. Based on a binomial distribution test, this percentage is significantly lower (α = 0.003) than the
(a) AREC areas for logistics in 1996
(b) AREC areas for logistics in 2009
average percentage logistics establishments located in AREC areas (30.72% on average between 1996 and 2008). 2
A possible explanation can be the lack of space available in AREC areas. Hence, new estab-lishments (have to) go to areas close to AREC areas. Locating in these areas is probably also somewhat cheaper than in AREC areas. To analyze this, the AREC areas as identified in 2009 were used. However, this is not the case: only 28.67% of the new logistics establishments chose for a location in these 2009 AREC areas. This percentage is also significantly lower than the reference percentage of logistics establishments located in AREC areas in 2009, which is equal to 32.73%.
Table 1 gives an overview of the effect of the size of new logistics establishments on the decision to locate in AREC areas. There is a difference between large establishments and small ones; the larger the establishments are, the more they chose for AREC areas. For establishments with more than five employees, the percentage that chose for AREC areas as defined in year t − 1 is significantly higher than 30.72% (α ≤ 0.000) and the percentage that chose for AREC areas as defined in 2009 is significantly higher than 32.73% (α = 0.001). The large share of small establishments in the dataset of new establishments (45%) thus explains why no concentration of new establishments in AREC areas is observed.
Table 1: Locations of new logistics establishments, classified based on establishment size Number of Went to Went to
establishments AREC areas t − 1 AREC areas 2009 All (> 1 employee) 1,465 27.37%†† 28.67%‡‡ > 5 employees 811 36.74% ∗∗ 37.98% ?? > 10 employees 600 40.17% ∗∗ 42.17% ?? > 50 employees 91 62.64% ∗∗ 71.43% ?? > 100 employees 34 67.65% ∗∗ 73.53% ?? > 250 employees 8 37.50% 75.00%
∗∗ Significantly higher than 30.72% (α ≤ 0.05);††Significantly lower than 30.72% (α ≤ 0.05) ?? Significantly higher than 32.73% (α ≤ 0.05);‡‡Significantly lower than 32.73% (α ≤ 0.05) Could not be statistically tested; too small sample size.
Based on Montgomery and Runger (2003, page 119), n should be larger than 0.30725 ≈ 17 and larger than 0.32735 ≈ 16 for a test based on AREC areas as defined in t − 1 and 2009, respectively.
The price of land may explain the difference between small and large firms. It seems plausible that land prices in AREC areas for logistics are higher than in other areas. Relatively small new establishments may not be able to afford these locations (yet). This is a relevant issue for further research.
2For this and all other statistical tests in this paper, normal approximations to the binomially distributed
variables were used. According to Montgomery and Runger (2003, page 119 and 311) this is valid as long as np > 5 and n(1 − p) > 5. Whenever this is not the case, no statistical test can be conducted.
3.3.2. Relocated logistics establishments
This section focuses on the location decisions of the logistics establishments that relocated from one location in the province to another (category II). Table 2 presents the percentages of logistics establishments relocated to AREC areas, both as identified in the year before the establishments relocated and in 2009. Both the percentages of logistics establishments that relocated into AREC areas are significantly higher than the reference percentages. This again indicates that establish-ments chose to relocate to or close to concentration areas. In addition, also for establishestablish-ments relocated within the province, the percentage that chose for AREC areas is generally higher if establishments are larger.
Table 2: Locations of relocated logistics establishments, classified based on establishment size Number of Relocated to Relocated to
establishments AREC areas t − 1 AREC areas 2009 All (> 1 employee) 842 33.37% ∗ 37.05% ?? > 5 employees 610 39.18% ∗∗ 43.28% ?? > 10 employees 459 42.70% ∗∗ 45.32% ?? > 50 employees 59 57.63% ∗∗ 61.02% ?? > 100 employees 19 52.63% ∗∗ 57.89% ?? > 250 employees 5 60.00% 60.00%
∗Significantly higher than 30.72% (α ≤ 0.1) ∗∗ Significantly higher than 30.72% (α ≤ 0.05) ?? Significantly higher than 32.73% (α ≤ 0.05)
Could not be statistically tested; too small sample size.
In addition, tables 3 and 4 present all relocations classified into establishments coming from AREC areas and establishments coming from other areas. The percentage relocations from an AREC area to an AREC area is relatively high. A plausible explanation would be that establish-ments coming from AREC areas already experienced the advantages of being located in this kind of areas and want to have a similar location in the future.
Table 3: Locations of relocated logistics establishments (moving into AREC areas t − 1), classified based on establishment size and former location
Coming from AREC areas t − 1 Not coming from AREC areas t − 1 Number of Relocated to Number of Relocated to establishments AREC areas t − 1 establishments AREC areas t − 1 All (> 1 employee) 221 46.15% ∗∗ 621 28.82% > 5 employees 179 49.72% ∗∗ 431 34.80%∗∗ > 10 employees 143 51.05% ∗∗ 316 38.92%∗∗ > 50 employees 29 58.62% ∗∗ 30 56.67%∗∗ > 100 employees 11 63.64% 8 37.50% > 250 employees 3 100.00% 2 0.00%
∗∗ Significantly higher than 30.72% (α ≤ 0.05)
Could not be statistically tested; too small sample size.
whole-Table 4: Locations of relocated logistics establishments (moving into AREC areas 2009), classified based on establishment size and former location
Coming from AREC areas 2009 Not coming from AREC areas 2009 Number of Relocated to Number of Relocated to establishments AREC areas 2009 establishments AREC areas 2009 All (> 1 employee) 221 51.13%?? 621 32.05% > 5 employees 179 53.63%?? 431 38.98%?? > 10 employees 143 53.15%?? 316 41.77%?? > 50 employees 29 62.07%?? 30 60.00%?? > 100 employees 11 63.64% 8 50.00% > 250 employees 3 100.00% 2 0.00%
?? Significantly higher than 32.73% (α ≤ 0.05)
Could not be statistically tested; too small sample size.
sale trade firms (the two largest types of logistics establishments in the database). As AppendixA describes, the logistics sector only includes wholesale trade establishments with ten or more em-ployees. Hence, for an analysis of location choice differences between different kinds of establish-ments, all wholesale trade establishments with more than ten employees are compared to transport establishments with more than ten employees. The two subsets of transport and wholesale trade establishments with more than ten employees are similar in the average and variation of the num-ber of employees per establishment. An analysis based on AREC areas defined in year t − 1 does not result in a difference in location decisions of these two types of logistics establishments. For AREC areas as defined in 2009, this is different. Transport establishments locate relatively often in areas that later became AREC areas. 36.73% of the 422 new and 43.35% of the 316 relocated wholesale trade establishments chose for AREC areas, compared to 43.56% of the 101 new and 47.32% of the 112 relocated transport establishments. Again, land prices are a possible explana-tion. Since transport establishments generally need many square meters of land, the price of land is a major constraint for these establishments. Furthermore, transport establishments do not have to be in AREC areas to have benefits of co-location. Just outside already existing concentration areas, land prices may (still) be somewhat lower, while these locations are still close to transport establishments’ co-located customers.
3.4. Location patterns related to multimodal container terminals
This section focuses on the areas in the province in which multimodal terminals are located. In the analyses, only container terminals are considered, since bulk terminals are mostly dedicated to one specific customer, while container terminal operators generally serve many different customers. Both barge and rail terminals are taken into account. The analysis based on the location of multimodal terminals is done on several geographical aggregation levels: the four-digit postal code level and the municipality level. AppendixC gives an overview of the 11 areas within the
2009). The expectation is that the logistics employment growth around multimodal terminals is higher than in the province in total.
Let xi(t) be the logistics employment in area i in year t. Furthermore, mi(t) is used to indicate
whether an area i has a multimodal container terminal in year t or not: mi(t) is equal to 1 if there
is at least one multimodal container terminal in area i in year t and is equal to 0 if there is no multimodal terminal in area i in year t. Next, M (t) and N (t) are respectively defined as the sets of areas with and without at least one multimodal container terminal: M (t) = {i|mi(t) = 1} and
N (t) = {i|mi(t) = 0}. The average annual growth in logistics employment in the period 1996 to
2009 in areas with at least one multimodal terminal is denoted as Gmand similarly, the average
annual growth in logistics employment in the period 1996 to 2009 in areas with no multimodal terminal is denoted as Gn. The annual growth in areas with and without multimodal container
terminals will be calculated using the following formulas:
Gm= P2009 t=1997 1 |M (t−1)| P j∈M (t−1)[xj(t) − xj] P2009 t=1997 1 |M (t−1)| P j∈M (t−1)xj(t − 1) and Gn= P2009 t=1997 1 |N (t−1)| P j∈N (t−1)[xj(t) − xj] P2009 t=1997 1 |N (t−1)| P j∈N (t−1)xj(t − 1)
Gm(and Gn) can be interpreted as the average logistics employment growth in all areas with at
least one multimodal terminal (no multimodal terminals). It corrects for the fact that the number of areas with at least one multimodal terminal (no multimodal terminal) changes over time, by dividing the sum of the logistics employment per year and the sum of the logistics employment growth per year by the number of areas with at least one multimodal terminal (no multimodal terminals). Table 5 presents the values for logistics employment in North Brabant, both on the level of four-digit postal code areas and on the level of municipalities. Both on the postal code level and on the municipality level, logistics employment grew more in areas with at least one multimodal terminal than in areas without one. Comparing the levels of spatial aggregation with each other, on the municipality level, a multimodal terminal has more influence on the logistics employment growth than on the postal code level. This coincides with the results of Kim and Van Wee (2011), that present break-even distances of drayage of intermodal freight transport systems ranging from 25 to 200 kilometers, while the average surface of a four-digit postal code area in North Brabant is equal to 10 km2 and that of a municipality in North Brabant is equal to 74 km2.
Table 5: Average annual logistics employment growth in 1996 to 2009 in areas with and without multimodal terminals
Postal code areas Municipalities Areas with (a) multimodal terminal(s) (Gm) 2.31% 2.81%
One of the reasons for the concentration of logistics employment in areas with multimodal terminals could be that the number of new establishments that choose for these areas is relatively large. Hence, based on a distinction between postal code areas (municipalities) with and without at least one multimodal terminal, the movements of logistics establishments are analyzed. Table 6 (Table 7) gives an overview of the new establishments in North Brabant and the establishments relocated within North Brabant which chose for a postal code area (municipality) with at least one multimodal terminal. Again only the establishments that relocated beyond their own postal code area (municipality) are considered. Now the percentages are compared to the percentage of logistics establishments in postal code areas (municipalities) with at least one multimodal container terminal, being equal to 8.10% (31.55%) on average for 1996 to 2008.
On a postal code level, both new and relocated logistics establishments with more than five employees chose relatively more often for areas with a multimodal terminal than for areas without one. On the level of municipalities, this also holds for new logistics establishments, but not for logistics establishments that relocated within the province. The conclusion about the size of the establishments is similar to the conclusion based on AREC areas: also for areas with multimodal terminals it holds that these generally attract more larger establishments, both on the level of postal code areas and on the level of municipalities.
Table 6: New logistics establishments and relocated logistics establishments related to postal code areas with (a) multimodal terminal(s)
New establishments Relocated establishments Number Relocated to Number Relocated to
of postal code of postal code establishments area with terminal establishments area with terminal All (> 1 employee) 1465 7.71% 842 8.67% > 5 employees 811 10.85% ∗∗ 610 10.82%∗∗ > 10 employees 600 12.33% ∗∗ 459 11.55%∗∗ > 50 employees 91 17.58% ∗∗ 59 13.56% > 100 employees 34 17.65% 19 10.53% > 250 employees 8 12.50% 5 20.00%
∗∗ Significantly higher than 8.10% (α ≤ 0.05)
Could not be statistically tested; too small sample size.
Based on Montgomery and Runger (2003, page 119), n should be larger than 0.08105 ≈ 62.
4. Conclusions
To better understand spatial concentration of logistics establishments, this paper analyzed location decisions in relation to spatial clusters. For this analysis, longitudinal empirical data on logistics establishments in North Brabant were used. North Brabant is a Dutch province with a large logistics sector, due to its strategic position between Europe’s two most important seaports
Table 7: New logistics establishments and relocated logistics establishments related to municipal-ities with (a) multimodal terminal(s)
New establishments Relocated establishments Number Relocated to Number Relocated to
of municipality of municipality establishments with terminal establishments with terminal All (> 1 employee) 1465 30.99% 345 28.12% † > 5 employees 811 36.87%∗∗ 256 31.64% > 10 employees 600 38.17%∗∗ 206 31.55% > 50 employees 91 48.35%∗∗ 38 28.95% > 100 employees 34 55.88%∗∗ 10 20.00% > 250 employees 8 52.50% 1 0.00%
†Significantly lower than 31.55% (α ≤ 0.1) ∗∗ Significantly higher than 31.55% (α ≤ 0.05)
Could not be statistically tested; too small sample size.
Based on Montgomery and Runger (2003, page 119), n should be larger than 5
0.3155 ≈ 16.
into the location dynamics of logistics establishments was conducted for the period 1996 to 2009. It resulted in six general conclusions on spatial concentration over time and location decisions of logistic firms in relation to spatial clusters as well as the proximity to intermodal terminals.
First, logistics employment spatially concentrates in particular areas. After showing that lo-gistics employment in North Brabant is indeed spatially concentrated, using the locational Gini coefficient (Krugman, 1991), it was identified in which four-digit postal code areas logistics em-ployment was spatially concentrated, using the method developed by Van den Heuvel et al. (2011). The areas in which logistics employment is spatially concentrated are called Absolute and Relative Employment Concentration areas (AREC areas). In North Brabant, the AREC areas contained on average about 31% of the logistics employment, while these areas only represented 8% of the total number of four-digit postal code areas. On average over time, the AREC areas were robust and grew, both in the number of postal code areas per AREC area and in the percentage of logistics employment located in AREC areas.
Second, logistics establishments that relocated within the province locate relatively more in AREC areas than in other areas; new logistics establishments do not. This can be one of the explanations for the growth of the AREC areas over time. Logistics establishments already located in the province relocated relatively often in AREC areas. However, this is not the case for new logistics establishments in the province, which may be caused by the relatively high percentage of small establishment in this dataset. These small new establishments probably are really new firms, while the larger ones probably are establishments from larger organizations, that can better afford the probably rather expensive locations in AREC areas.
Third, larger logistics establishments locate relatively often in AREC areas. In general, it is found that all logistics establishments with more than five employees that have to make a new
location decision choose relatively often to locate in AREC areas. The most apparent explanation is the price of the land. Although not tested, it seems plausible that land prices in AREC areas are higher than in other areas, since demand for land is higher in these areas. Relatively small firms cannot afford these rather expensive locations (yet), while larger firms probably can.
Fourth, logistics establishments that came from an AREC area choose more often for new locations in AREC areas than logistics establishments that came from non-AREC areas. Hence, experience matters for the location decisions of logistics establishments. A plausible explanation would be that firms coming from AREC areas already experienced the advantages of being located in this kind of areas and choose to have a similar location in the future.
Fifth, new and relocated logistics establishments have a major role in the formation of new AREC areas. Within the logistics sector, especially transportation establishments locate relatively often in areas that become AREC area in the future. One possible explanation is that in already existing AREC areas no space is available anymore and establishments move close to these areas. Another explanation again can be the land price. If this indeed is higher in AREC areas, especially transport establishments may decide to locate close to, instead of in, an AREC area. These establishments in general need much land and do not have to be in an AREC area to already have benefits of co-location, they tend to choose for locations close to these areas.
Finally, data on employment growth show that multimodal container terminals attract logistics employment, in their direct vicinity as well as on a municipal level. Logistics employment grows faster in areas with at least one multimodal container terminal than in areas without one. One of the explanation for the higher growth of logistics employment around these terminals than else-where in the province is that large logistics firms move relatively often to these areas. In addition, also for areas with a multimodal container terminal, it holds that the larger establishments are, the more these establishments move to these areas.
This paper gave an overview of the extent of spatial concentration of logistics activities and location dynamics related to areas in which logistics co-locate. The general conclusion is that concentration areas for logistics grow and that especially larger logistics firms and logistics firms that already located in concentration areas before tend to locate relatively often in concentration areas. Hence, there is something about these areas that attracts logistics firms. Although some plausible explanations for these location dynamics have been given, most are based on expected benefits of co-location for logistics firms, and although these are based on general literature about agglomeration economics, this is not yet specifically tested for logistics firms. Hence, based on this paper, an interesting topic for future research would be to analyze what benefits co-located logistics firms have over other logistics firms, i.e. what determines that logistics firms choose to co-locate.
Acknowledgements
The authors are grateful to the Provincie Noord-Brabant for financial support, provision of data, and helpful discussions.
Appendices
AppendixA. Definition of logistics establishments
To classify identify logistics establishments, the standard Dutch industry classification code, the Standaard BedrijfsIndeling (SBI) code, is used. The SBI is developed by Statistics Netherlands and categorizes economic activities based on five digits. The first four digits correspond to the categorization of the European Union (NACE: Nomenclature statistique des activit´es ´economiques dans la Communaut´e europ´eenne), with a small number of exceptions. The first two digits of the SBI and the NACE correspond to the categorization of the United Nations (ISIC: International Standard Industrial Classification of All Economic Activities).
Table A.1: SBI codes (2008) for the industries used in this paper Wholesale trade
G 46 Wholesale trade and commission trade (except of motor vehicles and motorcycles)
Excluding 461 Wholesale on a fee or contract base 4623 Wholesale of live animals
4651 Wholesale in computers, computer peripheral equipment, and software
Freight transport, storage, and other logistics establishments H 4920 Freight transport via railways
H 4941 Freight transport by road (except for removal transport) H 50401-50403 Inland water ways freight transport
H 5121 Air freight transport H 52101-52109 Storage and warehousing
H 52242, 52291-52292 Other supporting transport activities
The in table A.1 presented SBI codes were used for the definition of the logistics sector in this paper. The logistics industry is defined to consist of the following industries: wholesale trade, freight transport, cargo handling, storage and warehousing, and other supporting transport activities. From all establishments characterized as wholesale trade establishments based on the SBI code (46), the categories wholesale on a fee or contract basis (SBI = 461), wholesale of live animals (4623), and wholesale in computers, computer peripheral equipment, and software (4651) are excluded. It is clear that indeed the establishments in the first two categories are no part of the logistics industry. In the last category mentioned, the wholesale in software is very dominant, definitely being no logistics, and hence, this category is also excluded from the analysis. In addition, to exclude wholesale trade establishments that are only responsible for the
administrative part of trade and not the physical part, all wholesale trade establishments with less than ten employees were deleted from the database. Administrative trade establishments are mostly relatively small and hence, this seems to be a valid method to exclude these establishments. Furthermore, all establishments in the above-described logistics categories with only one employee were excluded, since for these establishments it generally holds that the establishment’s address is equal to the owner’s address, which does not have anything to do with location decisions.
To check the validity of the method of selecting logistics establishments, a list of European Distribution Centres (EDCs) in Noord-Brabant in 2008 was used. Most of these establishments (73%) are defined as logistics establishments in the way described above and for the remainder of them it is plausible that their primary activity is something else than logistics, mainly production. Hence, it is concluded that the logistics activity selection method is valid.
AppendixB. AREC area identification method
Figure B.1: The AREC area identification method (Van den Heuvel et al., 2011)
Van den Heuvel et al. (2011) propose to use 90th percentiles for the cut-off values used in the method. The cut-off values used in this paper were determined based on the 90th percentiles of the absolute employment and the location quotients in 1996. These are kept constant over time, to assure that the definition of AREC areas is constant over time.
AppendixC. Multimodal terminals in North Brabant
Table C.1: Postal code areas with (a) multimodal container terminal(s) in North Brabant during the year 1996 to 2009
Postal code Municipality First year of terminal Last year of terminal 4612 Bergen op Zoom 1998 > 2009 4705 Roosendaal < 1996 > 2009 4782 Moerdijk 2003 > 2009 4906 Oosterhout 2000 > 2009 5015 Tilburg 1998 > 2009 5145 Waalwijk 2001 > 2009 5222 ’s Hertogenbosch < 1996 > 2009 5347 Oss < 1996 > 2009 5466 Veghel 2005 > 2009 5651 Eindhoven 2001 > 2009 5704 Helmond 1996 2007
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Working Papers Beta 2009 - 2011
nr. Year Title Author(s)
355 354 353 352 351 350 349 348 347 346 345 344 343 342 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011
Spatial concentration and location dynamics in logistics: the case of a Dutch provence
Identification of Employment Concentration Areas
BOMN 2.0 Execution Semantics Formalized as Graph Rewrite Rules: extended version
Resource pooling and cost allocation among independent service providers
A Framework for Business Innovation Directions The Road to a Business Process Architecture: An Overview of Approaches and their Use Effect of carbon emission regulations on transport mode selection under stochastic demand
An improved MIP-based combinatorial approach for a multi-skill workforce scheduling problem An approximate approach for the joint problem of level of repair analysis and spare parts stocking
Joint optimization of level of repair analysis and spare parts stocks
Inventory control with manufacturing lead time flexibility
Analysis of resource pooling games via a new extenstion of the Erlang loss function
Vehicle refueling with limited resources Optimal Inventory Policies with Non-stationary Supply Disruptions and Advance Supply Information
Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo
Frank P. van den Heuvel, Peter W. de Langen, Karel H. van Donselaar, Jan C. Fransoo
Pieter van Gorp, Remco Dijkman
Frank Karsten, Marco Slikker, Geert-Jan van Houtum
E. Lüftenegger, S. Angelov, P. Grefen
Remco Dijkman, Irene Vanderfeesten, Hajo A. Reijers
K.M.R. Hoen, T. Tan, J.C. Fransoo G.J. van Houtum
Murat Firat, Cor Hurkens
R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten
R.J.I. Basten, M.C. van der Heijden, J.M.J. Schutten
Ton G. de Kok
Frank Karsten, Marco Slikker, Geert-Jan van Houtum
Murat Firat, C.A.J. Hurkens, Gerhard J. Woeginger
341 339 338 335 334 333 332 331 330 329 328 327 326 325 2011 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010
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Analysis of the dial-a-ride problem of Hunsaker and Savelsbergh
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Flexible Heuristics Miner (FHM)
An exact approach for relating recovering surgical patient workload to the master surgical schedule
Efficiency evaluation for pooling resources in health care
The Effect of Workload Constraints in Mathematical Programming Models for Production Planning
Using pipeline information in a multi-echelon spare parts inventory system
Reducing costs of repairable spare parts supply systems via dynamic scheduling
Identification of Employment Concentration and Specialization Areas: Theory and Application
A combinatorial approach to multi-skill workforce scheduling
Stability in multi-skill workforce scheduling
Maintenance spare parts planning and control: A framework for control and agenda for future research
Near-optimal heuristics to set base stock levels
Kurtulus Baris Öner, Alan Scheller-Wolf Geert-Jan van Houtum
Joachim Arts, Gudrun Kiesmüller
Murat Firat, Gerhard J. Woeginger
Murat Firat, Cor Hurkens
A.J.M.M. Weijters, J.T.S. Ribeiro
P.T. Vanberkel, R.J. Boucherie, E.W. Hans, J.L. Hurink, W.A.M. van Lent, W.H. van Harten
Peter T. Vanberkel, Richard J. Boucherie, Erwin W. Hans, Johann L. Hurink, Nelly Litvak
M.M. Jansen, A.G. de Kok, I.J.B.F. Adan
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H.G.H. Tiemessen, G.J. van Houtum
F.P. van den Heuvel, P.W. de Langen, K.H. van Donselaar, J.C. Fransoo
Murat Firat, Cor Hurkens
Murat Firat, Cor Hurkens, Alexandre Laugier
M.A. Driessen, J.J. Arts, G.J. v. Houtum, W.D. Rustenburg, B. Huisman
324 323 322 321 320 319 318 317 316 315 314 313 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010
in a two-echelon distribution network
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Teaching Retail Operations in Business and Engineering Schools
Design for Availability: Creating Value for Manufacturers and Customers
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Osman Alp, Tarkan Tan
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R.A.C.M. Broekmeulen, C.H.M. Bakx
310 2010 The state of the art of innovation-driven
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309 2010 Design of Complex Architectures Using a Three
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308 2010 Effect of carbon emission regulations on
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K.M.R. Hoen, T. Tan, J.C. Fransoo, G.J. van Houtum
307 2010 Interaction between intelligent agent strategies
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Martijn Mes, Matthieu van der Heijden, Peter Schuur
306 2010 Internal Slackening Scoring Methods Marco Slikker, Peter Borm, René van den
Brink 305 2010 Vehicle Routing with Traffic Congestion and
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304 2010 Practical extensions to the level of repair
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281 2009 The link between product data model and
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279 2009 Co-Evolution of Demand and Supply under
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272 2009
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271 2009 Distributed Decision Making in Combined
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270 2009
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267 2009 Vehicle routing under time-dependent travel
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266 2009 Restricted dynamic programming: a flexible
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