Transport Policy 29 (2013) 145–153

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Transport Policy

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Physical infrastructure and shipment consolidation efficiency driversin Brazilian ports: A two-stage network-DEA approach

Peter F. Wanke n

Center for Studies in Logistics, Infrastructure and Management, COPPEAD Graduate Business School-Federal University of Rio de Janeiro, Rua Paschoal Lemme355, 4o Andar, Cidade Universitária, Rio de Janeiro, CEP 21949-900, Brazil

a r t i c l e i n f o

Keywords:PortsDEATwo-stageBrazilIntermediate measureCentralized

0X/$ - see front matter & 2013 Elsevier Ltd. Ax.doi.org/10.1016/j.tranpol.2013.05.004

fax: +55 21 25989896.ail address: peter@coppead.ufrj.br

a b s t r a c t

Port efficiency has been widely studied using standard DEA (Data Envelopment Analysis) models and itsvariations. As a matter of fact, these models do not account for the internal structure relative to measurescharacterizing port operations performance. In this paper, efficiency in Brazilian ports is measured usinga two-stage process. In the first stage, called physical infrastructure efficiency, assets (number of berths,warehousing area, and yard area) are used to accomplish a certain shipment frequency per year. In thesecond stage, called shipment consolidation efficiency, these movements allow solid bulk and contain-erized cargoes to be handled. The network-DEA centralized efficiency model is adopted here to optimizeboth stages simultaneously. Results indicate that a private administration exerts a positive impact onphysical infrastructure efficiency levels, while the hinterland size and the operation of both types ofcargoes have a positive impact on shipment consolidation efficiency levels. Policy implications for thenew regulatory framework on the Brazilian port sector are also derived.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

In the past few years, accelerated economic growth hasincreased the demands for port services in Brazil. Between 2006and 2010, the physical aggregate throughput handled in Brazilianports—measured in tons/year—grew at an average rate of 10% peryear (CEL, 2009). As well as accelerated growth in cargo tonnage,these years also saw a significant increase in value added, mainlydue to the rising prices of several commodities exported by Brazil(Sá, 2009). This increasing demand for reliable services has placedenormous pressure on the infrastructure of Brazil's ports. Althoughpublic investments in infrastructure expansion have remained atminimal levels for the last 3 decades, the comparison of severalports in terms of their overall efficiency has become an essentialpart of the Brazilian microeconomic reform agenda for sustainingeconomic growth based on foreign trade (Fleury and Hijjar 2008).

In recent papers, the capacity shortfall issue in Brazilian portterminals has been studied by Wanke et al. (2011) and Wanke(2012). Both papers corroborate previous studies on Brazilian ports(Rios and Maçada, 2006) and anecdotal evidence (Agência Brasil,2004). Not only do the vast majority of Brazilian ports presentincreasing returns-to-scale (Rios and Maçada, 2006; Wanke et al.,2011; Wanke, 2012), but also the only alternative for dealing withthe rapid demand growth in the short term is to increase shipmentconsolidation levels. In other words, the only feasible alternative is

ll rights reserved.

to load an increased amount of cargo in each shipment. Addition-ally, the available infrastructure slacks, to meet future demandgrowth was found to be negligible, especially in terms of berths.

Despite the increased use of DEA to measure the efficiency ofports in recent years (e.g. Panayides et al., 2009; Hung et al., 2010),there are still few studies that explicitly recognize that someintermediate outputs are produced and consumed between portproduction and service processes (Yun et al., 2011). While the DEAstudies cited above provide meaningful insights regarding portperformance, some recent developments in DEA enable the studyof the port industry through examination of the internal relationsof the factors related to their performance (Liang et al., 2008; Zhu,2011).

More precisely, although each port is usually treated as adecision-making unit (DMU) in the presence of multiple inputsand outputs, in many cases factors can be classified as eitherinputs or outputs because they are intermediate measures (Bichou,2011). In most real situations, the DMUs may perform severaldifferent functions and can also be separated into differentcomponents that play important roles in producing outputsthrough the use of intermediate outputs obtained from theirprevious components (Bichou, 2011; Yun et al., 2011, Yu, 2010).Recently, DEA has been extended to examine the efficiency of two-stage processes, where all the outputs of the first stage areintermediate measures that constitute inputs to the second stage(Chen et al., 2010; Shahroudi et al., 2011).

The current study applies the network-DEA centralized effi-ciency model proposed by Liang et al. (2008) and Zhu (2011) to aset of 27 Brazilian ports, in order to simultaneously optimize

P.F. Wanke / Transport Policy 29 (2013) 145–153146

physical infrastructure and shipment consolidation efficiencylevels by considering shipment frequency per year as the criticalintermediate output. This research contributes by taking intoaccount this number of movements as the cornerstone intermedi-ate output that establishes the link between longer and shorterterm perspectives on two relevant issues of port productionprocesses: physical infrastructure (Alderton, 2008) and shipmentconsolidation (Wanke et al., 2011), respectively. A final contribu-tion of this research lies in its empirical application to Brazilianports. Inspired by the current debate in the Brazilian port sector, inwhich anecdotal evidence suggests a capacity shortfall, differentcontextual variables that characterize port operations (connectiv-ity, cargo scope, hinterland size, and administration type) aretested as drivers for increased efficiency levels in these twoproduction stages.

The remainder of the paper is organized as follows: Section 2presents the literature review. Section 3 presents the data and themodel. The empirical results are presented and discussed in termsof policy implications in Section 4. Conclusions follow in Section 5.

2. Literature review

Traditionally, the performance of ports and terminals has beenvariously evaluated by numerous attempts at calculating andseeking to optimize the operational productivity of cargo handlingat the berth and in the terminal area (Cullinane et al., 2006). Aninvestigation of the literature on port efficiency indicates that theexisting research encompasses the three scientific methods ofquantitative efficiency analysis, namely ratio analysis, the econo-metric frontier and DEA (see Barros et al., 2012 for a comprehen-sive list of references).

More precisely, approaches such as DEA, FDH or Free DisposalHull (Cullinane et al., 2005), and SFA or Stochastic FrontierAnalysis (Cullinane and Song, 2003; Cullinane et al., 2006) havebeen increasingly utilized to analyze production and performanceof ports and terminals. It must be noted, however, that FDH andSFA are less frequently used than DEA, the technique that presentsthe largest amount of applications in this sector (see Panayideset al. (2009) for a comprehensive list of references). Within portoperations, the use of DEA is though increasing while the applica-tion of FDH remains low (Wanke et al., 2011).

According to Barros et al. (2012), when comparing the above-mentioned research – and the reference therein-with that under-taken in other fields, port efficiency is one of the main fields ineconomics in which frontier models have been applied, with suchdiverse methods that range from DEA to econometrics, displayingan openness to different approaches that is not so apparent inother fields. However, as discussed by these authors, there are toomany papers that replicate previous research, while offering scantmethodological improvements.

Following the same line, Panayides et al. (2009) presented acritical assessment of DEA applications to port efficiency measure-ment. The authors identified some major gaps that should beaddressed in future research: (i) model choice, (ii) study focus/DMU selection, and (iii) variable selection/data reduction. They arediscussed further and were considered for designing the metho-dology of this research, as detailed in Section 3.

First, there is the need to use different DEA models, enablingresearchers to address particular limitations attributed to specificDEA models and improve the validity of the results. In thisresearch, the network-DEA centralized efficiency model, originallypresented in Liang et al. (2008), is adopted to optimize physicalinfrastructure and shipment consolidation simultaneously. One ofthe major advantages of this model is that the intrinsic trade-offbetween two subsequent production stages—where one stage

should lower its efficiency level to the detriment of the other-isaddressed by means of a cooperative game approach whereefficiency scores for both individual stages and the overall processcould be obtained (Zhu, 2011).

Second, the majority of studies are focused on container ports/terminals, while other non-containerized cargoes have beenunderstudied. According to Panayides et al. (2009), conclusionsso far are largely biased towards containerized cargoes. Besides,although there have been no limitations in the selection of ports/terminals, it would be preferable if the selected ports could beregarded as direct rivals, like those located within the samecountry or at the same hinterland. This would accentuate theimportance of ranking their relative efficiency and would providemore useful conclusions. In this paper, both Brazilian containerand bulk terminals are considered in the analysis.

At last, advances in the work on variable reduction should findapplications in the seaport decision-making in the future. In thispaper, reduction variable techniques are employed on contextualvariables as a first step to establish the efficiency drivers. There-fore, the present research observes the major research gaps foundin the literature review, besides presenting a practical contributionon the port efficiency issue in Brazil.

3. Methodology

3.1. Two-stage network DEA model

The network structure that links different production stageswith intermediate inputs/outputs in a series of activity processeswas first introduced by Färe (1991), and was extended in Färe(1991), Färe and Whittaker (1995), Färe and Grosskopf (1996a,1996b, 2000), Tone and Tsutsui (2009, 2010). A particular case ofthis multi-stage, the two-stage network DEA model was developedby several scholars (Liang et al., 2006, 2008; Yang, 2006; Kao andHwang, 2008, 2011; Kao, 2009; Chen et al., 2009a, 2009b, 2010,2013; Zha and Liang, 2010). The potential conflict between the twostages arising from the intermediate measure is a common issue tobe addressed in these models (Kao and Hwang, 2008; Liang et al.,2008, Zhu, 2011).

Therefore, differently from traditional DEA models, and inorder to introduce the two-stage network DEA model originallypresented in Liang et al. (2008), the notation for intermediatemeasures needs to be included. It is assumed that DMUj

(j¼ 1;2; :::;n) has D intermediate measures zdj ðd¼ 1;2; :::;DÞ,besides the initial inputs xij ði¼ 1;2; :::;mÞ and the final outputsyrj ðr¼ 1;2; :::; sÞ – cf. Fig. 2. Also considering vi;wd;and urasunknown non-negative weights, the two-stage network DEAmodel is given as the following linear program:

θGlobalo ¼Max ∑s

r ¼ 1uryro

s:t:

∑s

r ¼ 1uryrj− ∑

D

d ¼ 1wdzdj ≤0; j¼ 1;2; :::n;

∑D

d ¼ 1wdzdj− ∑

m

i ¼ 1vixij ≤0; j¼ 1;2; :::n;

∑m

i ¼ 1vixio ¼ 1;

wd≥0; d¼ 1;2; :::D; vi≥0; i¼ 1;2; :::m; ur≥0; r¼ 1;2; :::s; ð1Þ

where θGlobalo is overall (global) efficiency level of the two-stageprocess for DMUo. Assuming that model (1) yields a uniquesolution, the efficiencies for the first and second stages are

Fig. 1. A map of the major Brazilian ports researched.

Fig. 2. Port two-stage DEA model.

P.F. Wanke / Transport Policy 29 (2013) 145–153 147

P.F. Wanke / Transport Policy 29 (2013) 145–153148

respectively given next:

θ1;Physical inf rastructureo ¼ ∑D

d ¼ 1wn

dzdo; ð2Þ

and

θ2;Shipment consolidationo ¼ ∑

s

r ¼ 1un

r yro= ∑D

d ¼ 1wn

dzdo: ð3Þ

Since a unique solution was assumed, it is possible to express thefollowing relationship:

θGlobalo ¼ θ1;Physical inf rastructureo � θ2;Shipment consolidation

o ; ð4Þthat is, the overall efficiency level is equal to the product of theindividual efficiency levels for each stage.

3.2. The data

Secondary data for a sample of 27 Brazilian ports were obtainedfrom the statistical database provided by the ANTAQ website(http://www.antaq.gov.br), encompassing the year of 2011. Thissample of 27 DMUs is comparable to similar DEA applications, asdiscussed in Panayides et al. (2009). A map of Brazil is given inFig. 1 in order to illustrate the geographic location of the portspresent in the sample.

Readers should note the presence of five riverine ports in thissample: Macapá, Porto Velho, Santarém, Vila do Conde, and Belém.Most of them are specialized in handling and moving soybeans onfrom producers, located at middle-eastern inland states, to foreignmarkets or to the closest road/railway in order to reach the majorexport terminals, located at the ports Santos and Paranaguá. Theyalso serve as hubs to the transport of goods between cities locatedin the Amazon basin.

According to Alderton (2008), one major characteristic thatshould be considered when distinguishing operating advantages/disadvantages between a seaport and a riverine port is whetherthe later is located: on a tidal estuary on a river delta. In manycases, dredging can be one of the major costs and in estuarialports; in particular any new structure on the river banks or withinthe water may cause serious situation problems. The Amazon riverand basin, however, are very peculiar. The width of the Amazonriver ranges between 1.6 and 10 km at low stage but expandsduring the wet season to 48 km or more. The river enters theAtlantic Ocean in a broad delta (about 240 kmwide). The mouth ofthe main stem is 80 km large. Because of its vast dimensions, it issometimes called “The River Sea”.

As regards the input/output variables used, readers shouldrecall one of the aims of the paper, which is to establish the links

Table 1Summary statistics for the sample.

Model variables Me

Inputs Number of berths 9.0Warehousing area (sq m) 43,Yard area (sq m) 108

Intermediate Input/Output Solid bulk frequency (shipments/year) 238Container frequency (shipments/per year) 624

Outputs Solid bulk throughput (tons/year) 6,7Container throughput (containers/year) 180

Contextual variables Privateadministration(1¼yes/0¼no)

Hinterland(sq km)

Number ofhighway accesses

Riv(1¼

Mean 0.11 952,264.61 2.15 0.4Standard deviation 0.32 941,249.77 1.10 0.5Coefficient of variation 2.88 0.99 0.51 1.2

between physical infrastructure and shipment consolidation effi-ciency levels by means of the number of movements or shipmentfrequency per year, while looking at the ports as a sequence ofproductive processes. In the first productive stage, the number ofberths, the warehousing area (sq m), and the yard area (sq m) areinputs to the solid bulk and to the container shipment frequencies,the two intermediate measures considered. The goal is to mini-mize the physical infrastructure required to achieve a certain levelof shipment frequency per year. In the second productive stage,these movements per year are used as a resource to produce notonly a certain solid bulk throughput (in tons per year) but also acontainer throughput (in TEUs per year). The two-stage processconsidered in this research is depicted in Fig. 2.

With respect to the relationship between the number of DMUsand the number of inputs/outputs used in the analysis, the ratioproposed by Cooper et al. (2001) was observed, that is, the numberof DMUs should be at least three times higher than the number ofinputs and outputs. Correlation analyses indicate significant posi-tive relationships between the single input and the three outputvariables, which are, therefore, isotonic and thus justify theirinclusion in the model (Wang et al., 2011). All these data relateto 2011 and their descriptive statistics are presented in Table 1.

In addition, contextual variables were collected to explain differ-ences in the efficiency levels of both productive stages. They are alsopresented in Table 1 and relate to the presence of a port privateadministration, whether yes (1) or no (0); the presence of riverine andrailroad access, whether yes (1) or no (0); the port hinterland (sq km);the number of highway accesses; the number of accessing channels;and whether (1) or not (0) the port handles both container and solidbulk cargoes. In order to justify some of the contextual variablescollected, it should be highlighted that Turner et al. (2004) identified asignificant positive effect of railroad connectivity on port efficiency.With respect to the governance structure, Cullinane and Song (2003)and Wang and Cullinane (2006) found a positive association betweenefficiency levels and the private control of terminals.

Recently, Xiao et al. (2012) modeled the effects of port owner-ship on capacity investment and pricing structure, and theimplications of these changes on port service level and socialwelfare. These authors argues that capacity investment and pri-cing are significantly influenced by a port's ownership form, andthe different levels of government involved. Ownership was also akey focus when comparing the relative efficiency of dry ports inIndia by Ng and Tongzon (2010).

An important underlying assumption on contextual variablesconsidered by all these authors is that these contextual variablesor efficiency drivers are exogenous, that is, they affect efficiencylevels without being affected by them. Here, connectivity issues

an Standard deviation Coefficient of variation

4 9.66 1.07243.41 103,072.01 2.38,730.70 215,822.08 1.98.67 393.62 1.65.13 931.40 1.4905,914.70 16,897,645.31 2.52,547.56 365,517.32 2.02

erine access?yes/0¼no)

Railroad access?(1¼yes/0¼no)

Number of accessingchannels

Both containerand solid bulk(1¼yes/0¼no)

1 0.67 1.19 0.590 0.48 0.48 0.503 0.72 0.41 0.84

P.F. Wanke / Transport Policy 29 (2013) 145–153 149

and the type of port administration represent, therefore, decisionvariables based on the Brazilian Government's discretion ratherthan endogenous variables generated within the ambit of anefficiency model or a production process.

3.3. Bootstrapped truncated regression

The approaches to the statistical treatment of the variations inthe efficiency estimates produced using DEA have evolved over theyears; see, for example, Banker (1993) and Simar and Wilson(2007). As a depiction of this evolution, Cooper et al. (2007) pointto the growing number of studies that combine DEA scoresobtained in a first stage with those of multivariate data analysis(such as regression analysis) in a second stage, when the scores areincorporated in the form of the dependent variable. According toFried et al. (2002), such two-stage DEA approaches are animportant recognition that environmental factors or contextualvariables can significantly influence efficiency scores.

Turner et al. (2004) advocate the use of Tobit regression on DEAscores. In general, the basic model for Tobit regression is similarto that for OLS. However, Simar and Wilson (2007) arguethat truncated regression combined with bootstrapping as are-sampling technique best overcomes the unknown serial corre-lation complicating the analysis in two stages, where DEA scoresare generated first and subsequently analyzed under a stochasticmodel. The adequacy of the functional form to the data is aprevalent problem and a common critique of stochastic frontiermodels (Kumbhakar and Lovell (2003)). In this research, the Simarand Wilson (2007) approach is employed and the followingregression specification is assumed and tested:

θj ¼ aþ Zjδþ εj; j¼ 1; ::::;n; ð5Þ

which can be understood as the first-order approximation of theunknown true relationship. In Eq. (5), a is the constant term, εj is

Fig. 3. Efficiency d

statistical noise, and Zj is a row (vector) of observation-specificvariables for DMU j that is expected to be related to the DMU'sefficiency score, θj.

Specifically, noting that the distribution of εj is restricted by thecondition εj≥1−a−Zjδ (since both sides of (5) are bounded by unit),Simar and Wilson (2007) is followed here and it is assumed thatthis distribution is truncated normal with zero mean (beforetruncation), unknown variance, and (left) truncation point deter-mined by this very condition

Furthermore, replacing the true but unobserved regressand in(5), SEj, by its DEA estimate, θ j, the econometric model formallybecomes:

θ j≈aþ Zjδþ εj; j¼ 1; ::::;n; ð6Þ

where

εj �Nð0; s2ε Þ; such that; εj≥1−a−Zjδ; j¼ 1; :::;n; ð7Þ

which is estimated by maximizing the correspondent likelihoodfunction, with respect to ðδ; s2ε Þ, given the data collected. Para-metric bootstrap for regression can be employed to construct thebootstrap confidence intervals for the estimates of parametersðδ; s2ε Þ, which incorporates information on the parametric structureand distributional assumption. For the sake of brevity, readersshould refer to Simar and Wilson (2007) for the details of theestimation algorithm. The respective computations were carriedout with R codes developed by the authors, built upon the censRegpackage (Henningsen, 2012).

4. Results and discussion

The physical infrastructure and shipment consolidation effi-ciency levels calculated using the two-stage network DEA modelfor each DMU are given in Fig. 3. In 2011, just 7 out of 27 (25.9%)

ecomposition.

P.F. Wanke / Transport Policy 29 (2013) 145–153150

ports achieved 100% efficiency in the first stage of physicalinfrastructure efficiency. Most of these DMUs are large portsimportant for both solid bulk and container operations: RioGrande, São Francisco do Sul, Suape, Tubarão, and Praia Mole. Thisresult indicates that most Brazilian ports were not efficient inusing their physical infrastructure to generate ship movementsthroughout the year. On the other hand, only four out of 27 (14.8%)ports achieved 100% efficiency in the second stage of shipmentconsolidation efficiency. Among them, two important ports forcontainer operations located in Brazil's Southwestern region:Santos and Vitória. It is noteworthy that the median value forphysical infrastructure efficiency was higher than that for ship-ment consolidation—0.45 vs. 0.38—thus suggesting that Brazilianports tend to be comparatively less efficient in turning shipmovements into cargo flows than in turning physical infrastruc-ture into ship movements, probably due to physical constraints inchannel depth affecting the maximum size of ships that are able toapproach the quay area. The empirical distributions of theseefficiency estimates are illustrated in Fig. 4.

4.1. Exploring group differences

An exploratory analysis, considering the median values forphysical infrastructure and shipment consolidation efficiencylevels as the cut-off points, was performed on the efficiencyestimates, on the inputs/outputs, and on the remaining contextualvariables. According to Fig. 3, four different quadrants or groupswere delimited. Descriptive statistics were computed for each ofthese groups. These are given in Table 2 and discussed below.

DMUs located in Group no. 1 are reasonably infrastructured,large ports with both high physical infrastructure and shipmentconsolidation efficiency levels, most of them located in the southof Brazil, with the exceptions of ports of Suape, Belém, andSalvador. This group of eight ports account for more than 90% ofthe container throughput share, 85% of the solid bulk throughputshare, and 60% (90%) of the solid bulk (container) frequency shareof Brazilian ports, which explains, in part, the high efficiency levelsencountered in both stages.

On the other hand, DMUs located in Group no. 2 consist ofrelatively infrastructured, mid-sized ports with both low physicalinfrastructure and shipment consolidation efficiency levels. All ofthem are public administered ports located in northern Brazilianregions, with scarce opportunities for increasing cargo flows in the

Fig. 4. Empirical distribution

short-term. They account for only 4% of the solid bulk throughputshare, 2% of the container throughput share, and 9% (4%) of thesolid bulk (container) frequency share of Brazilian ports, whichpartly explains why efficiency levels are so low in both stages.

Group no. 3, in turn, handles a significant amount of the solidbulk frequency share of Brazilian ports (20%), albeit only 3% itsthroughput. Their container operations are negligible. Since theiroperation focuses solely on small vessels for grain transportationdue to physical constraints in channel depth and width, theirshipment consolidation efficiency levels are low in contrast tophysical infrastructure ones. Lastly, Group no. 4 represents mid-sized infrastructured ports scattered throughout the country;some are located in the north of the country(e.g. Santarém andCabedelo) and consist of auxiliary routes for soy-bean exportsfrom mid-western regions. They account for a significant move-ment and throughput share of Brazilian ports, when compared toGroups nos. 2 and 3, which partly explains their high flightconsolidation efficiency levels.

Looking at these four groups, there are various short/medium-term actions that could be implemented in order to increase portproduction outputs in both stages. Ports located in Group no.2 could benefit, for instance, from a private administration in orderto increase their attractiveness and, consequently, expand theiraverage hinterland size. On the other hand, ports located in Groupno. 3 could be targeted for emergency investments in accesschannels, thus allowing the operation of large vessels and theincrease of shipment consolidation levels. The higher physicalinfrastructure efficiency levels of ports located in Group no. 1 maysuggest it is time for an effective capacity expansion, as shipmentconsolidation efficiency levels are also becoming dangerouslyhigh.

4.2. Regression results

The R package was used to carry out the previously discussedbootstrapped truncated regression on this dataset, testing forsignificant differences in these two-stage efficiency levels given aset of context-related variables. Before proceeding with thisanalysis, however, a factor analysis with Varimax standardizedrotation was first of all conducted on the contextual variables inorder to deal with multicollinearity issues. Results are consideredonly for load factors greater than 0.50 and eigenvalues greater

s of efficiency estimates.

Table 2Differences between groups.

Variables Groups of ports

Both high physicalinfrastructure andshipment consolidationefficiency levels

Both low physicalinfrastructure andshipment consolidationefficiency levels

High physicalinfrastructure efficiencylevel but low shipmentconsolidation efficiencylevel

Low physicalinfrastructure efficiencylevel but high shipmentconsolidation efficiencylevel

Initial inputs Number of berths 12.88 9.00 3.60 8.40Wareshousing area 109,263.88 28,081.78 180.00 7965.00Yard area 262,832.38 51,403.89 20,680.00 53,407.00

Final outputs Solid bulk throughput 19,145,686.75 725,639.22 1,014,953.80 3,257,736.20Container throughput 333,117.50 5280.67 − 35,259.00

Intermediate inputs/outputs

Solid bulk shipmentfrequency

509.88 62.11 262.20 99.00

Container shipmentfrequency

1122.63 41.56 − 126.20

Efficiency levels Global 0.66 0.03 0.11 0.32Physical infrastructure 0.95 0.12 0.84 0.22Shipment consolidation 0.71 0.18 0.12 0.65

Number of cases 8 9 5 5

Port type within eachgroup

Private administration? 13% 0% 20% 20%Hinterland (sq km) 1,330,502.10 468,579.16 680,257.48 1,489,725.58Number of highwayaccesses

2.13 2.67 1.80 1.60

Riverine access? 25% 44% 40% 60%Railroad access? 75% 67% 60% 60Number of accessingchannels

1.25 1.11 1.00 1.40

Both container and solidbulk?

88% 56% 0% 80%

Group relevance Solid bulk frequencyshare

63% 9% 20% 8%

Container frequencyshare

90% 4% 0% 6%

Solid bulk throughputshare

85% 4% 3% 9%

Container throughputshare

92% 2% 0% 6%

Berth share 42% 33% 7% 17%Yard share 72% 16% 4% 9%

Ports within each group Paranaguá Fortaleza Aratu ImbitubaRio Grande Natal Areia Branca Vila do CondeSão Francisco do Sul Forno Pelotas VitóriaSantos Ilhéus Porto Velho SantarémSalvador Itaqui Praia mole CabedeloBelém MacapáSuape MaceióTubarão Porto Alegre

Recife

P.F. Wanke / Transport Policy 29 (2013) 145–153 151

than 1. Thus, three main factors represent the original set ofcontextual variables, interpreted as follows:

�

Contextual variables hinterland and both container and solidbulk make up factor 1, simply interpreted as hinterland size andcargo diversity.

�

The contextual variable number of highway accesses makes upfactor 2, simply interpreted as highway connectivity.

�

The contextual variable private administration makes up factor 3,which was interpreted similarly to the previous two factors.

The results presented in Table 3 confirm the positive impact ofa private administration on physical efficiency levels, thus corro-borating previous studies. On the other hand, hinterland size andcargo diversity also merit attention. They have a significant and

positive impact on shipment consolidation efficiency levels. Theunderlying hypothesis is that these variables may act as demanddrivers, thus enabling a better fit to be achieved between cargovolume and ship capacity.

4.3. Policy implications

Results presented in Table 3 for physical infrastructure andshipment consolidation efficiency levels have some policy impli-cations that should be deeply examined in light of the regulatoryissues of the Brazilian port sector.

Recently, the Brazilian government reopened the debate on theregulatory agenda. Further improvements in physical infrastruc-ture efficiency levels are at a standstill point, as legal barriers to

Table 3Regression results.

Independent variables Physicalinfrastructure

Shipmentconsolidation

Constant 0.517nnn 0.403nnnHinterland size and cargo diversity 0.040 0.168nnHighway connectivity −0.046 0.098Private administration 0.147n 0.068Variance of the model 0.040 0.026nnnTotal number of observations 1000nnn 1000

nnn Statistical significance at 1%.nn Statistical significance at 5%.n Statistical significance at 10%.

P.F. Wanke / Transport Policy 29 (2013) 145–153152

the contracting of private terminal operators still exist, the mainone being the bureaucracy faced by the state-owned companyCompanhia Docas to perform the various steps that precede suchcontracting; indeed, the entire process can take years (Goldberg,2009). The idea now is to speed up capacity expansion projects tobetter serve Brazil's major hinterlands. It is likely that the landlordport model (Farrell, 2011) adopted in Brazil will be enhanced toalso encompass the funding of private ports.

It is noteworthy, however, that the control of crucial decisionson port concession agreements that affect the shipment consoli-dation efficiency levels-such as the types of ports and cargoes thatshould be prioritized within each region and how port connectiv-ity to access channels, highways, and railroads could be improved-will still be held by the federal government. It seems that thisdichotomous regulatory approach on the different stages of theport production process—physical infrastructure and shipmentconsolidation-may leave some missing links behind.

For instance, the lack of vision as regards the concept of a“logistics corridor”, while connectivity investments still belong tofederal government, may produce a situation where port physicalresources and demands are unbalanced. According to Rodrigue(2012), the development of a “logistics corridor” is a fundamentalcomponent of the port production process. From an economicperspective, the function of a corridor is to promote both internaland external trade by providing more efficient connectivity interms of transport and logistics services within an hinterland.

The main reason for designating a “logistics corridor” is to focusattention on improving not only the capacity of the routes, butalso the quality of the transport and other logistic services alongthe corridor that serves a given hinterland. Quality is measuredin terms of the transit time and costs for shipments alongthe corridor and the reliability and flexibility of the transportservices offered on multimodal routes. The lack of significance ofthe highway connectivity variable in Table 3, as well the absenceof other transportation modes within this construct, may sug-gest that the regulatory agenda should also encompass portconnectivity issues, besides a more integrated approach as regardsthe scope and responsibility on funding projects for capacityexpansion.

5. Conclusions

This paper analyzed efficiency drivers in Brazilian ports bymeans of a two-stage network-DEA approach. Up to now, applica-tions of two-stage DEA models in the port industry sphere havebeen scarce. One contribution of this paper is to regard thenumber of movements as the cornerstone intermediate outputthat establishes the link between longer- and shorter-termperspectives on two relevant issues of port production

processes: physical infrastructure and shipment consolidation.Results, initially discussed in light of different groups of ports,indicate that different efficiency drivers, such as administrationtype and hinterland size and cargo diversity, respectively, impactphysical infrastructure and shipment consolidation efficiencylevels in different ways.

This paper also contributes to the literature by helping Brazilianport authorities to foster efficient future growth in cargo traffic bymaking use of currently available information regarding theimpact of contextual variables on efficiency levels of differentstages of the port production process. More precisely, it can helpdecision-making regarding the funding of port improvementprojects, including establishing a list of priorities within eachgroup of ports. This is relevant because, since the early 1990s, incontrast with several Asian countries, Brazil has seen relativelylittle investment in new ports, not only because of federal budgetconstraints, but also because it has been possible to obtainadditional capacity by improving existing ports through terminalprivatization, deregulation etc.

The current debate on the regulation of the port sector in Brazilalso merited attention. Future research should keep assessingbetter ways to use the limited resources of Brazilian ports undera production process perspective with several stages, while thenew regulatory framework is still being designed. It is deemednecessary to develop a perspective on the “logistics corridors” andon the connectivity issues within each hinterland in order to fostera more effective expansion of physical infrastructure levels. Inorder words, future research endeavors on the Brazilian portsector should address connectivity issues within the “logisticscorridors” as a means for achieving a better fit between physicalinfrastructure and shipment consolidation efficiency levels.

Acknowledgments

The author would like to thank the editors and the reviewersfor their helpful comments on this paper.

This research was supported by FAPERJ (Fundação CarlosChagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro).Project ID: E-26/103.286/2011.

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