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Utilities Policy

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Efficiency and productivity of container terminals in Brazilian ports(2008–2017)Beatriz López-Bermúdez∗, María Jesús Freire-Seoane, Fernando González-LaxeUniversity of A Coruña, Spain

A R T I C L E I N F O

Keywords:EfficiencyProductivityBrazil

A B S T R A C T

After the economic recession in which Brazil was immersed during the years 2015 and 2016, signs of recoveryare perceived through port activity. The objective of this research is to analyze the efficiency and productivity of20 container terminals in Brazilian ports for the 2008 to 2017 period. The methodology used is stochasticfrontier analysis and operational port indicators including variables such as the frequency of calls. The mostsignificant findings obtained from the efficiency analysis reveal that private terminal operators are more effi-cient. The average technical efficiency level was 0.66 in 2008 and in the last year 0.51.

1. Introduction

In recent years, proposals have been presented to reactivate globalgrowth, the most significant of which were put forward by theInternational Monetary Fund (FMI, 2018) referring to trade, pro-ductivity, inclusive growth policies, gender equality, and debt man-agement.

In 2016, the Latin American continent has experienced a generalrecession due mainly to the performance of very important economiesin the region, including Brazil, which between 2015 and 2016 experi-enced a fall in Gross Domestic Product of 3.6%. A report by EconomicCommission for Latin America and the Caribbean (ECLAC, 2018) in-dicates a growth in the country's Gross Domestic Product (henceforthGDP) in 2017. According to the FMI (2018), growth prospects for 2018are estimated at around 1.5%. While consumption and investment re-main at very low levels, exports reach significant levels that favor re-covery and reflect a surplus in the balance of goods and services, gen-erating a reduction in the current account deficit.

UNCTAD estimates (2017) show that 80% of world trade is trans-ported by sea. Researchers (Stopford, 2002; Rodrigue et al., 2016;López-Bermúdez et al., 2018) point out that investments in port infra-structures have a direct impact on national economies. Maritimetransport, and containerized merchandise in particular (cargo) presentsthe greatest added value in terms of commercial exchange and is afaithful economic indicator. Foreign trade is very important for Brazil;in 2014, foreign trade represented 19.34% of the Gross National Pro-duct (Tadeu and Nobre, 2015). Bottasso et al. (2018) confirm the link

between international trade and GDP for the period 2009–2012 inBrazil, revealing that maritime infrastructure investment realized overthe period generated an increase of about 14% for export and 11% forimport flows.

Brazil's growth before the world economic crisis put severe stress onthe transport system, with a registered increase in inefficiency (Barroset al., 2015) and critical conditions in the main transport infrastructure.Since seaports are the main gateway to foreign markets, maritime-re-lated infrastructure is of particular importance supporting the growthprocess (Bottasso et al., 2018).

The objective of this research is to analyze the efficiency and pro-ductivity of 20 terminals of containerized goods in Brazil between 2008and 2017. During this period, there have been changes in the legislationof the port sector seeking to favor an increase in private capital in-vestments in the country. In addition, in terms of containerized goodstrade, these terminals have gone from moving 7 million TEUs (Twenty-foot Equivalent Units) in 2008 to 9.2 million in 2015 and 8.8 million in2016; ECLAC estimates that 9.8 million would be reached in 2017.

This article is structured as follow: Section 2 summarizes thebackground on studies of port efficiency and productivity; Section 3presents an analysis of the concepts of efficiency and productivity;Section 4 develops the applied methodology; Section 5 provides ananalysis of the results; Section 6 collects the discussion about the re-sults; and finally, the last two sections contain the conclusions andbibliography.

https://doi.org/10.1016/j.jup.2018.11.006Received 10 September 2018; Received in revised form 26 November 2018; Accepted 26 November 2018

∗ Corresponding author.E-mail addresses: beatriz.lopez2@udc.es, beatrizlopezbermudez@gmail.com (B. López-Bermúdez), maje@udc.es (M.J. Freire-Seoane),

laxe@udc.es (F. González-Laxe).

Utilities Policy 56 (2019) 82–91

0957-1787/ © 2018 Elsevier Ltd. All rights reserved.

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2. Background

The first investigations on port efficiency are carried out by Roll andHayuth (1993), who explained how to apply the data envelopmentanalysis (DEA) to calculate the factors that determine efficiencythrough mathematical programming techniques. Liu (1995) demon-strated that private property is more efficient than public enterprisebased on principal-agent theory. Tongzon (1995) analyzed the factorsthat determine efficiency for 23 international ports, focusing on theefficiency of the terminals. Millington (1998) performed a stochasticcost frontier analysis at twelve coal terminals in Australia, establishingthat the use of labor increases over time, but that service quality canimprove without increasing labor.

Coto-Millán et al. (2000) analyzed efficiency in Spanish ports duringthe period 1985 to 1995. The results suggest that an increase in ad-ministrative autonomy means an improvement in efficiency. Estacheet al. (2002) analyzed efficiency gains through reforms for Mexicanports in the period 1996 to 1999, indicating improvement with theprivatization of services but not with infrastructures. Cullinane et al.(2002) studied the efficiency of port terminals in Asia for the period1989 to 1998 and showed that the level of port system regulation af-fects port efficiency. They corroborated that there are various relevantvariables and that a high degree of privatization or deregulation isclosely related to improvement in productive efficiency. Tongzon andHeng (2005) investigated the factors that cause port terminals to retaintheir competitive advantage, concluding that a degree of privatizationimproves port efficiency, but not total privatization. González andTrujillo (2009), examined the situation in nine Spanish ports, and theresults show the capacity of the port authorities to capture traffic andthe difficulty they have in adjusting inputs. They demonstrated thepositive effects of legislative reforms and technological advances andsuggested reforms on the liberalization of the market proposed by theEuropean Union.

The research in this area has evolved to include aspects such as thederegulation of stowage (Díaz et al., 2008), the factors that make a portcompetitive (Martagan et al., 2009), technological advances (Kim andSachish, 1986), the effects of the financial crisis (Wilmsmeier et al.,2013), the analysis of efficiency around environmental factors, such asair pollution, incorporating the concept of environmental efficiency(Liu and Lim, 2017), and other factors indirectly related to efficiency inport terminals, such as ship accidents in ports (Paul and MacDonald,2017).

Currently, efficiency is also being investigated over the entire lo-gistics chain, as is the case with inland navigation (Wiegmans andWitte, 2017), rail (Andrade and Stow, 2017) and the specific legislationthat regulates other modes (rail transport) in the European Union(Smith et al., 2018).

Predominantly, stochastic frontiers analysis (SFA) and data envel-opment analysis (DEA) are used as the main methodologies in efficiencystudies. However, these methods of analysis applied to the same dataset produce different conclusions (Serebrisky et al., 2016; Suárez-Alemán et al., 2016). SFA is a parametric method, while DEA is anonparametric method. In addition, the DEA method is used for itsability to contain multiple inputs and outputs and, because it is notnecessary to specify the production function, it is usually used whenthere are inaccurate or incomplete data in the sample. Cullinane andWang (2006) conclude that, in general, the functional approach (SFA)provides better results, especially when the estimation is specific andpanel data are used.

Researches on port efficiency and productivity-focused only onBrazilian ports are very scarce, but among all, we can highlight Wankeet al. (2011), who analyzed 25 terminals in Brazil from 2002 to 2008with the DEA method; this research was expanded in Wanke (2013).

In terms of productivity, Barros et al. (2012) analyzed the pro-ductivity of ports applying the Malmquist index for the period 2004 to2010. However, research related to port infrastructure in Brazil has

proliferated in the areas such as the international logistics chain (Tadeuand Nobres, 2015; Barros et al., 2015); port legislation (Galvão et al.,2013, 2017); and investment on infrastructure (Garcia-Escribano et al.,2015; Vieira et al., 2015).

3. Efficiency and productivity

The measurement of efficiency is directly related to the measure-ment of productivity. Although the concepts are not synonymous, someresearch treats them as equivalent, especially when the focus is oncomparing company performance. The idea that supports the use ofboth concepts in a similar way is that companies improve their per-formance as they improve their efficiency and productivity.

The use of efficiency and productivity in an equivalent way stemsfrom approximating the measurement of the efficiency of companiesusing partial productivity indicators, which are ratios between theproduct and a factor. To calculate productivity, two fundamental ap-plications are derived. One consists in obtaining its temporal evolution,and the other in breaking down the growth in productivity into its maindetermining factors, where changes in efficiency play a relevant role.From a different perspective, efficiency in production rests on thecomparison between a company's actual and optimal performance; thatis, it is obtained by comparing the observed values of products andfactors with their optimal values. That optimum target that the com-pany could attain is based on factual evidence on optimal results ob-tained by other companies.

Finally, it can be concluded that efficiency differs from productivityin that while the study of the latter focuses fundamentally on changes ofthat magnitude over time; efficiency analysis makes special reference tocross-sectional variations; that is, comparisons between companies(Schmidt and Pesaran, 1997). The former requires introducing tech-nical progress in the sector, while the latter consists of introducinggreater efficiency in the industry. In this research we use the concept ofefficiency to refer to the objective of each terminal to maximize profitsthrough minimizing inputs; whereas, productivity expresses the amountof output per input consumed (Cullinane et al., 2002; Barros et al.,2012).

A report by United Nations Conference on Trade and Development(UNCTAD, 1976) divides port performance indexes into two categories:financial and operational. The financial indicators are: tonnage worked;berth occupancy revenue per ton of cargo; cargo-handling revenue perton of cargo; labour expenditure per ton of cargo; capital equipmentexpenditure per ton of cargo; and contribution per ton of cargo. Theoperational indicators are: late arrival; waiting time; service time;tonnage per ship; fraction of timer berthed ships worked; number ofgangs employed per ship per shift; tons per ship-hour in port; tons pership hour at berth; tons per gang-hour; and, fraction of inactive gangtime. Tongzon (1995) classified productivity and efficiency indicators.Among the productivity indicators are: the frequency of calls; portcharges; economic activity; and the number of containers loaded andunloaded per berth hour. The efficiency indicators are: the mix ofcontainers (proportion of TEU and Forty-Foot Equivalent Unit -FEU-);delays in trade and during stevedoring; crane efficiency; vessel size andcargo exchange. Port performance indicators are used internally in mostof the Port Authorities at the international level; De Langen et al.(2007) compiles the indicators used by the Port of Rotterdam, Rabo-bank, ECORYS-NEI. Self-assessment of ports and port authorities makesuse of a number of indicators: cargo transfer product (throughput vo-lumes; value added of port; investment level in port, market shares inhinterland regions; number of first port of call services; electronic datainterchange -EDI-, use in port; modal split hinterland traffic; index ofport dues at real prices; custom revenues from port); port logisticsproduct (warehouse area; time to major consumer markets); portmanufacturing product (value added in port-related manufacturing;investments in port manufacturing; number of chemicals productsavailable in the port); characteristics of the port as a whole (value

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added of port; investment level in the port; certification of managementprogrammes; average wage for port industries compared to regionaleconomy; number of environmental accidents; water quality in port;employment in the port region; emissions of greenhouse gasses; porteconomic impact).

This paper focuses on the technical characteristics of containerterminals; the indicators analyzed are related to the loading andtransferring of cargo. The indicators chosen are: throughput (volume ofcontainers handled per day) and crane productivity (volume of con-tainers handled per crane and per hour).

4. Methodology

4.1. Variables

The variables used in the analysis of efficiency and productivityconcern 20 terminals of containerized goods in Brazil between 2008and 2017. These variables are: output, input, exogenous and determi-nants of inefficiency.

The most important limitation of type of research is the availabilityand veracity of the data throughout the period analyzed. The choice ofvariables used are backed by numerous scientific investigations: thecontainerized handled TEUs' variable (Nguyen et al., 2017; Wiegmansand Witte, 2017; Serebrisky et al., 2016; Suárez-Alemán et al., 2016);the frequency of calls (Jahn and Scheidweiler, 2018; Karsten et al.,2017); the existence of cranes for the operation of handling the con-tained goods (Wiegmans and Witte, 2017; Serebrisky et al., 2016); and,the detailed analysis of different port governance systems in differentcountries (Van De Voorde and Verhoeven, 2017; Notteboom and Yang,2017; Wilmsmeier and Sánchez, 2017; Castillo-Manzano et al., 2017;Nguyen et al., 2017; Monios, 2017; Caldeirinha et al., 2017). Othervariables included in the analysis as exogenous variables are importantcharacteristics of the terminals. These include: the draft, which de-termines the vessel sizes; the location, belonging to a port cluster orother; and the port infrastructure quality index, which analyzes andquantifies the development and efficiency of infrastructures in ac-cordance with international standards (Chang et al., 2008; González-Laxe et al., 2015; WB, 2018).

As the output variable of the production function of port services,we introduce the volume of containerized goods expressed in TEUs(ECLAC, 2018). The inputs that are part of this function are the fre-quency of calls (AIS, 2017) and the number of gantry cranes and mobilecranes in the different terminals1.

Table 1 shows the average values for each of these variables for theports in our analysis. The data obtained reveal that the highest averagevolume of TEUs moved in the period analyzed corresponds to the portterminals of Tecon Santos (1.41 million), BTP (935 thousand), RioGrande do Sul (653 thousand), Libra Santos (617 thousand) and DPWorld Santos (518 thousand).

The frequency of calls is defined as the number of times a con-tainership calls at a terminal each of the years analyzed. The highestvalues correspond to Tecon Santos (309), BTP (229), Rio Grande do Sul(136), DP World Santos (132) and Libra Santos (141).

Regarding the average number of gantry and mobile cranes, it isobserved that of a total of 20 terminals, 14 have gantry cranes and 11mobile cranes. The port terminals with the largest numbers of gantrycranes are Tecon Santos (13), BTP (8), Libra Santos (9) and Rio Grandedo Sul (6.5). Moreover, the ports with the highest number of mobilecranes are Pecem (4.6), Itajaí in the terminal operated by APMTerminals (4.2), MultiRio (3) and Rodrimar (3).

The exogenous variables include several dummies (RTG, Rio, Santos,

and draft) as well as a variable expressed as the quality index of portinfrastructures (World Bank, 2018). The following variables have beentaken as binary variables:

• The existence of container stacking cranes with rubber tires (RubberTired Gantry Cranes -RTG-). The variable takes the value 1 whenthere are this type of cranes, and 0 otherwise.1

• If the terminals are part of the Rio de Janeiro port (Libra Rio,MultiRio and Sepetiba Tecon) it takes the value 1, and 0 otherwise.1

• If the terminals are part of the Santos port (BTP, DP World Santos,Ecoporto, Libra Santos, Rodrimar, and Tecon Santos), they take thevalue 1, and 0 otherwise.1

• If the draft at the terminals is equal to or greater than 15m(Postpanamax ships berthing), it takes the value 1, and 0 otherwise.1

Finally, the possible determinants of inefficiency that are analyzedare whether they belong to the private terminal operators groupsTecon, Libra, APM or to those that are owned by the State as public, orthey do not belong to them. With this information four dummies arebuilt, which take the value of 1 when one of these terminalists operatesat the terminal (Tecon, Libra, APM Terminals, or Public), and 0otherwise.1

4.2. Modelling

Researches on port efficiency have focused on two methods. Thefirst, based on econometric techniques called stochastic frontier ana-lysis (SFA) and the second, based on mathematical programming calleddata envelopment analysis (DEA).

Cullinane and Wang (2006), analyze the different data structures inthe DEA and SFA and conclude that the panel data structure (SFA) willbe the most adequate to perform efficiency analysis in container ports.González and Trujillo (2009) carry out a systematic analysis of theexisting studies that evaluate the economic efficiency and productivityof the sector, identifying the applied methodology and the variablesused. Its main contribution is that it is necessary to identify and isolatethe port activity on which the analysis is being carried out, through theproduction function (SFA). Almost all the investigations about thissubject conclude that the DEA analysis tends to be very sensitive to the

Table 1Average values of the output and input variables (2008–2017).

TEUs Frequency ofcalls

Gantrycranes

Mobilecranes

Belem 21,622.10 13.57 0 0BTP: APM

Terminalsa935,310.60 229.62 8 0

DP World Santosa 518,741.20 132.05 6 0ECOPORTO 297,420.70 79.31 3 8Fortaleza 71,191.31 30.66 0 2Itajaí Public 69,531.80 19.68 2.8 1.9Itajaí: APM

Terminals296,510.50 69.83 1.8 4.2

Libra Rio 188,220.20 44.86 3.6 1Libra Santos 617,823.40 141.01 9 0MultiRio 226,077.30 54.05 2.3 3Natal 29,014.10 8.08 0 0Pecem 168,446.60 42.49 0 4.6Rio Grande do Sul

Tecom653,561.80 136.96 6.5 2

Rodrimar 116,915.10 28.81 0 3Salvador Tecon 257,115.60 61.56 6 0Sepetiba Tecon 276,247.50 55.95 4 2Suape Public 73,814.70 17.95 0 0Suape Tecon 316,702.20 68.74 6 0Tecon Santos 1,413,615.90 309.96 13 1Vila do Conde 48,997.30 19.41 0.8 0

a Annual average during the period of operations from 2013 to 2017.

1 (ANTAQ, 2018; Suape, 2018; Rio Grande, 2018; SepetibaT, 2018; CODESP,2018; MultiRio, 2018; LibraTerminais, 2018; Itajaí, 2018; CODEBA, 2018;Ecoporto, 2018; DPWorld, 2018; BTP, 2018).

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number of variables of the chosen sample, show statistical incon-sistency, biased results and a questionable inference process (Simar andWilson, 1998, 2000).

The methodology used is a stochastic frontier analysis and opera-tional port indicators. Through this analysis, the technical efficiencylevels of the terminals are studied, as well as the possible determinantsof inefficiency. In addition, the study of operational port indicatorsevaluates the evolution of the volume of containerized goods in portsand the number of containers moved by cranes per hour.

4.2.1. Port efficiency: stochastic frontier analysisThe stochastic frontier analysis (SFA) is a parametric approach to

estimate technical efficiency in those cases where the productionfunction is specified. This method calculates the economic inefficiencyof the agents based on distribution assumptions, so that different portsmay have different efficiencies.

Battese and Coelli (1995) formulate a stochastic frontier analysis,where exogenous factors exist in the inefficiency distribution function,and construct a model with panel data. This model takes into account,simultaneously, endogenous and exogenous factors. The authors definethe stochastic frontier production function of panel data as:

Yit = exp (xit β +Vit + Uit)

Where:

• Yit indicates the production for the i-th company in the t-th ob-servation;

• xit is a vector (1 • k) of the values of the known functions of theproduction inputs and other explanatory variables associated withthe i-th company in the t-th observation;

• β is a vector of unknown parameters to be estimated (k • 1);• Vit are assumed to be idd N(0, σv2) random error variable, in-dependently distributed to the Uit;

• Uit are non-negative random variables, associated with the technicalinefficiency of production, which are assumed to be independentlydistributed, such that Uit is obtained by truncation (at zero) of thenormal distribution with mean zitδ and variance σu2;

• zit is a (1 • m) vector of the explanatory variables associated with thetechnical inefficiency of production over time, and δ is an (m • 1)vector of unknown coefficients;

• σv2 is the variance of the noise term;• σu2 is the variance of the inefficiency term;• σ2 is the variance of the error term.• i represent each company• t: represents each year of the analysis (2008–2017)

In the above equation, the stochastic frontier production function isspecified in terms of the original production values. However, the ef-fects of technical inefficiency are:

• Uit, which is a function of a set of explanatory variables;• zit, which is an unknown vector of δ coefficients.

The explanatory variables of the inefficiency of the model may in-clude some input variables in the stochastic frontier, and the expectedeffects of the inefficiency are stochastic. If the first variable z has avalue of 1, and all the other variables z are 0, this case represents themodel specified by Stevenson (1980) and Battese and Coelli (1988,1992). If all the elements of vector δ are equal to 0, then the effects oftechnical inefficiency are not related to the z variables so that thenormal mean distribution originally specified in Aigner et al. (1977) isobtained.

The effect of technical inefficiency Uit in the stochastic frontiermodel is specified as:

Uit = zit δ + wit

Where the random variable, wit, is defined by the truncation of thenormal distribution with zero mean and variance σu2, so that thetruncation point is zitδ; that is, wit > zitδ. These assumptions areconsistent with Uit, being a non-negative truncation of the N(zitδ, σu2)distribution.

The maximum-likelihood method is used for the simultaneous es-timation of the parameters of the stochastic frontier and for the effectsof technical inefficiency (Battese and Coelli, 1995).

The probability function is expressed in terms of the parameters ofthe variance:

σ 2= σ 2v + σ2u

Where:

γ= σ2u/ σ2

The technical production efficiency for the i-th port at the t-th timeinstant is defined by the equation:

TEit = exp(-Uit)= exp (-zit δ -Wit)

The prediction of technical efficiency is based on the conditionalexpectation, given the assumptions of the model.

In this research, the functional form used is the translogarithmicfunction, considering it as port efficiency (Liu, 1995; Coto-Millán et al.,2000, Tongzon and Heng, 2005; Cullinane and Wang, 2006; Núñez-Sánchez and Coto-Millán, 2012; Trujillo et al., 2013; Serebrisky et al.,2016).

The stochastic frontier translogarithmic production equation isspecified as follows:

LTit = β0 + β1·LFEit + β2·LGr1it + β3·LGr2it + β4·[LFEit]2 +β5·[LGr1it]2 + β6·[LGr2it]2 + β7·LFEit·LGr1it + β8·LFEit·LGr2it +β9·LGr1it·LGr2it + β10·RTG + β11·LCa + β12·rio + β13 ·santos +β14·postpana + β15trend + vit + uit

Where:

• LTit: natural logarithm of the volume of containers in TEUs in the i-th terminal in the th-th year (ECLAC, 2017);

• LFEit: natural logarithm of the frequency of calls of ships in the ithterminal in the th-th year (AIS, 2017);

• Gr1it: natural logarithm of the number of gantry cranes in the i-thterminal in the th-th year;

• Gr2it: natural logarithm of the number of mobile cranes in the i-thterminal in the th-th year;

• RTG: dummy variable that takes value 1 when there are containerstacking cranes with rubber tires in the terminals, and 0 otherwise;

• LCa: natural logarithm of the quality index of the port infra-structures of the country;

• rio: dummy variable that takes value 1 when the terminal belongs tothe port of Rio, and 0 otherwise;

• santos: dummy variable that takes value 1 when the terminal be-longs to the port of Santos, and 0 otherwise;

• postpana: dummy variable that takes value 1 when the draft depthallows the entry of Containers of Postpanamax characteristics, and 0otherwise;

• trend: trend variable;• i: each of the terminals analyzed;• t: represents each year of the analysis (2008–2017);• vit is the random error term independent from uit;• uit is a random variable that follows a normal truncated distributionassociated with technical inefficiency.

The production function used assumes that the production factorshave to be introduced logarithmically. For the variables Cranes1 andCranes2 it is necessary to carry out a conversion since in many casesthey take the value 0. In this case, the methodology proposed by Battese

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(1997) and by Serebrisky et al. (2016) is used, so that the variablesbecome:

Gr1=Max (G1, DG1), where DG1=1 if G1= 0, and DG1=0 ifG1>0

Gr2=Max (G2, DG2), where DG2=1 if G2= 0, and DG2=0 ifG2>0

On the other hand, the determinants of inefficiency analyzed areintroduced into the equation, so that:

uit = δ1·tecon+ δ2·libra+ δ3·apm + δ3· public + wit

Where variables tecon, libra, apm, and public are dummy variables thattake the value 1 when the terminal belongs to that operator, and 0otherwise.

4.2.2. Benchmarking indicators: productivityBenchmarking is a process that evaluates the strengths and

weaknesses of an organization and its advantages over its main com-petitors, identifying best practices by creating a strategic plan aimed atachieving a dominant position over its competitors and finally, with aprocess of post-evaluation (Cuadrado et al., 2004; Hokey and Jong,2006; Tovar and Rodríguez-Déniz, 2015; Rodríguez et al., 2017). In-formation on port management and operations is needed to analyzeport performance (Doer and Sánchez, 2006).

Port performance indicators are relatively simple to calculate andunderstand based on financial or operating conditions. Given the dif-ficulty of access to financial information in ports, we opted to use onlyoperational indicators.

The approach comes from research by East (1973) and Plumlee(1975) but has evolved to more precise studies by Wang et al. (2002),Cullinane et al. (2004), and Doer and Sánchez (2006).

As ports are service providers, different inputs can be used to definethe production function of SFA, such as the frequency of calls andnumber of cranes. Operational indicators such as the volume of con-tainers handled in the ports per day and volume of containers handledby each crane per hour allow for measuring the productivity of eachproduction factor and the development strategy of the port.

The volume of containers handled at the ports daily is obtained fromthe volume of containers moved (in TEUs) divided by 365 days, whichis the total of operational days of the ports at the international level, sothat:

Daily volume = (TEUs / 365)

The indicator of the volume of containers handled by each crane perhour is obtained by dividing the container volume of each terminalexpressed in TEUs by the number of cranes and the total operationalhours in the ports at international level (365×24). It should be notedthat, when differentiating between gantry and mobile cranes (Doer andSánchez, 2006), it is established that a gantry crane represents 0.6mobile cranes. Based on this conversion, the different cranes aregrouped to perform the calculations. Thus:

Volume per crane and hour= [(TEUs / nº cranes) / 8760]

5. Estimation results

Table 2 shows the results of the maximum-likelihood estimation ofthe stochastic frontier analysis. The sample consists of 20 terminals overthe period 2008 to 2017, with a total of 200 observations.

The estimation is done with a Translog function (1), which includesthe analysis of inefficiency factors, as well as the Translog function (2),which excludes these factors. The purpose of these two estimates is toverify that there are no significant differences between the standarderror coefficients, e.g., β1 the difference is 0.0023; β2 is 0.0171; β10 is0.02, …; this supports the robustness of the estimation. In addition, themodel to be estimated takes into account variations of the technologyover time and the panel data structure that reduces the probability ofthe presence of heteroscedasticity.

The results obtained are presented for both functions, but it is es-sentially those of the Translog function (1) which are discussed here.The variables that reveal themselves as significant with a 95% con-fidence level are the frequency of calls (LFE), the gantry cranes (LGr1),the existence of RTG cranes, the quality index of the port infrastructures(LCa), belonging to the port of Santos, and presenting enough draftlevel for Postpanamax ships.

A 1% growth in the frequency of calls in the terminals of Brazilduring one year represents a rise of 1.58% in the volume of TEUshandled. This effect is relevant and an increase is observed in the size ofthe container ships; that is, very large ships in the fleet are a reality inBrazil's containerized goods terminals.

The variable ‘existence of gantry cranes’ is significant, but it shows anegative sign. This result may be due to the composition of the sample,

Table 2Stochastic frontier estimation.

Translog (1) Translog (2)

β0 -.2852097* .4265953*Std. Err .1107569 .1875267

LFE β1 1.588769* 2.355023*Std. Err. .1991847 .1767881

LGr1 β2 -.9405093* −1.101807*Std. Err. .1898807 .2069857

LGr2 β3 .0908612 -.0451706Std. Err. .3153275 .2510216

LFE2 β4 -.0146844 -.2616558*Std. Err. .0303746 .0402931

LGr12 β5 .5503577* .0682371Std. Err. .1230081 .1088755

LGr22 β6 .0169846 -.1592296Std. Err. .13336056 .1171801

LFE·LGr1 β7 -.2922165* .3248049*Std. Err. .1405651 .0916647

LFE·LGr2 β8 -.2383328 .0903577Std. Err. .1518838 .0835806

LGr1·LGr2 β9 .5519225* -.0237723Std. Err. .2135007 .1384161

RTG β10 -.560226* -.230406**Std. Err. .1071532 .1301252

LCa β11 3.400487* 2.681166*Std. Err. .2351843 .1136828

rio β11 -.0190117 -.145953Std. Err. .0482637 .105787

santos β12 .3754775* .0639805Std. Err. .0997147 .1023922

Postpana β13 -.2777556* -.5511022*Std. Err. .0621943 .0814736

trend β14 .0308813* .0054821Std. Err. .0036945 .0117198

tecon δ1 −4.007292*Std. Err. .8415065

libra δ2 −1.457515*Std. Err. .3106549

apm δ3 −1.537357*Std. Err. .2911526

public δ4 .2552148Std. Err. .2033808

σ2u 5.258287 10.99396*Std. Err. 5.580132

σ2v .0647807* .1960593*Std. Err. .0090216 .022901

Log Likelihood −92.4048 −146.8077Wald Chi2 56758.89 13571.86Prob > Chi2 0.0000 0.0000Observations 200 200No of ports 20 20

Calculations made in STATA 13.*p < 0.05, **p < 0.10.

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since there are terminals with a large number of this type of cranes thathave seen their merchandise volume reduced due to the appearance ofnew terminals (competitors) with captive traffic, which would havebeen the case of BTP (APM Terminals) and DP World Santos since 2013.

The variable representing RTGs, which facilitate the handling op-erations and contribute to a slight automation at the terminals, is sig-nificant with a negative sign. These investments have been made inthose new terminals which have increased the volume of merchandise

considerably in a short period of time, and which were equipped withthis type of cranes from the beginning of the operations; therefore, it isnot relevant with an effect of −0.56%.

The index for the quality of port infrastructures, developed andpublished by the World Bank, is significant with a positive sign. Thus, a1% rise in this indicator implies an effect of 3.40%. This is a high valuethat must be interpreted with caution since the infrastructure qualityindex ranges between 1 and 7, and a minimum increase in this indexresults in major improvements in infrastructure.

The analysis includes two dummies variables, rio and santos, whichgroup in one case the terminals of the port of Rio and in the other, theport of Santos. Only the variable santos is significant and with a positivesign. Therefore, the fact that a terminal belongs to the conglomerate ofthe port of Santos produces an effect of 0.37% on the volume of TEUs.

The draft level needs important infrastructures that allow the entryof larger Postpanamax-type ships. This variable is significant but with anegative sign of −0.27%.

We also analyzed the effect of privatization on port efficiency. TheTecon, Libra, and APM Terminals as private operators; are the only onesthat are significant and with a negative sign, indicating less in-efficiency, with a greater effect for Tecon versus the Libra and APMTerminals.

Technical efficiency takes values between 0 and 1, where theaverage values of containerized goods terminals in Brazil for the periodfrom 2008 to 2017 range between 0.66 in 2008 and 0.51 in 2017. Fig. 1shows that these values remain between 0.63 and 0.69 in the periodfrom 2008 to 2015, while between 2016 and 2017 they decrease to 0.49and 0.51. A possible interpretation of this decline may be found in thefall in merchandise volume in these years along with the appearance ofnew terminals (competitors).

Table 3 shows the analysis of the technical efficiency of eachterminal. With the information obtained it is observed that of the 20terminals considered, 14 have an average value higher than 0.50, and 7above 0.80. The highest average value of technical efficiency is that ofthe Rio Grande do Sul Tecon terminal (0.9770), which is located withina port complex where it is the only terminal specialized in containerizedcargo; Salvador Tecon (0.9765) is in second place followed by SuapeTecon (0.9759). Next comes Santos Tecon (0.9758), in the port ofSantos, Sepetiba Tecon (0.9737) in the port of Rio, and BTP (0.8384) inthe port of Santos, operated by APM Terminals.

Table 4 shows the results of the movements of TEUs handled in the

Fig. 1. Evolution of the average technical efficiency in container terminals inBrazil (2008–2017).

Table 3Average technical efficiency per terminal (2008–2017).

No Terminal TE No Terminal TE

1 Rio Grande do Sul Tecon 0.9770 11 Itajaí: APM Terminals 0.71542 Salvador Tecon 0.9765 12 Itajaí: Public 0.60103 Suape Tecon 0.9759 13 Belem 0.56944 Santos Tecon 0.9758 14 Ecoporto 0.55695 Sepetiba Tecon 0.9737 15 Natal 0.36436 BTP: APM Terminals 0.8384 16 Fortaleza 0.26677 Libra Santos 0.8090 17 Pecem 0.26058 MultiRio 0.7696 18 Rodrimar 0.25169 Libra Rio 0.7218 19 Vila do Conde 0.170510 DP World Santos 0.7158 20 Suape: Public 0.1364

Table 4Movement of containers (TEUs per day).

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Belem 122 81 103 79 29 49 68 59 2 0BTP: APM Terminals 153 2019 3124 3556 3960DP World Santos 194 1498 1725 1825 1864Ecoporto 867 762 1089 1321 1406 1277 995 423 1 8Fortaleza 146 139 162 156 161 214 244 219 245 264Itajaí: APM Terminals 0 0 1055 982 1013 1008 1021 538 300 583Itajaí: Public 0 539 171 159 97 119 57 0 0 0Libra Rio 574 482 583 594 581 607 541 469 398 327MultiRio 591 471 580 677 809 791 661 599 538 476Natal 26 33 55 65 65 81 88 108 120 153Pecem 392 356 436 529 420 411 535 494 469 574Rio Grande do Sul Tecon 1663 1719 1773 1693 1675 1716 1860 1991 1939 1876Rodrimar 482 498 500 557 415 454 234 60 2 1Salvador Tecon 600 596 641 664 700 731 752 758 807 795Santos Libra 2444 2002 2095 2198 2070 1769 1506 1361 417 1064Santos Tecon 3479 2861 3713 4073 4749 4963 3781 3600 3907 3605Sepetiba Tecon 771 566 454 838 867 998 671 736 801 866Suape Tecon 641 666 782 879 755 709 995 1023 1020 1206Suape: Public 326 303 106 265 323 375 150 68 50 57Vila do Conde 72 76 98 93 56 86 117 155 277 313

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terminals per day. With the information available, it can be seen thatRio Grande do Sul, Santos Libra and Santos Tecon show values above1000 TEUs per day throughout the period analyzed. Specifically, RioGrande do Sul Tecon remains with values of 1663 in 2008 and 1876 in2017, Santos Libra with 2444 in 2008 and 1064 in 2017, and in SantosTecon the indicator ranges from 3479 in 2008 to 3605 in 2017.

Since 2014, the terminals of BTP, DP World Santos and Suape Teconshow values higher than 1000 TEUs per day; specifically, BTP showsvalues that range from 2019 in 2014 to 3960 in 2017.

Fig. 2 shows the productivity values of the TEUs moved in the portsper crane and hour. The terminals with the highest ratio are Rio Grandedo Sul Tecon, which between 2008 and 2011 increased productivityfrom 13.33 to 13.57 TEUs/crane/hour, Santos Tecon, which between2012 and 2014 worsened its ratio from 14.55 to 11.58 TEUs/crane/hour, and BTP, which from 2015 to 2017 has improved productivityfrom 16.27 to 20.63 TEUs/crane/hour. This terminal reached thehighest values with a rapid growth (Annex Table A1).

Table 5 shows the efficiency values and the productivity indicatorsof the Brazilian terminals in 2017. The difference between efficiencyand productivity is clearly observed. A terminal with a high level oftechnical efficiency does not necessarily present the best level of pro-ductivity.

The highest values of TEUs per day and TEUs/crane/hour

correspond to BTP (APM Terminals) with 3960 TEUs per day and 20.63TEUs/crane/hour and an efficiency level of 0.6108, above the averageof 2017.

When analyzing the classification of the ports according to theirefficiency and TEUs per day ratios, it is observed that Tecon Santos hasan efficiency level of 0.9481 and its TEUs per day ratio is 3,605, RioGrande do Sul with 1876 TEUs per day and a technical efficiency levelof 0.9710, and Suape Tecon with a ratio of 1203 TEUs per day and anefficiency level of 0.9833. These three terminals belong to the Tecongroup.

6. Discussion

The SFA shows that the input frequency of calls and gantry cranesare variables that determine the output (volume of containers) in the 20port terminals of Brazil. This is not the case of mobile cranes becausenew investments have been made in gantry cranes and ports withmobile cranes have become obsolete or their volumes of cargo are re-latively low to justify making a large investment in superstructure.

Others exogenous variable significant to the Brazilian terminals arethe existence of RTG, the quality index of port infrastructure, and thelevel of draft, but only the infrastructure quality index shows a positivesign. Therefore, the existence of RTG and the draft level forPostpanamax vessels does not contribute to an increase in the volume ofcontainers. Although the phenomenon of gigantism has proliferated inthe container sector, it seems that it is not the target of these terminals.

Tecon, Libra and APM as inefficiency determinants show significantand with a negative sign, which means that are factors that reduceinefficiency in the terminals in which these companies are operating.However, the levels of average technical efficiency are relatively low,from 2008 to 2015, varying between 0.63 (2009) and 0.69 (2014), butin 2016 and 2017 these values are reduced to 0.49 and 0.51. As long asthe values of average technical efficiency decreased, the average valuesof container volume per day for the total of the terminals raised upannually, going from 660 (TEUs per day) in 2008 to 900 (TEUs/day) inthe year 2017.

The ports with the highest volume of containers handled per day areTecon Santos with an annual average from 2008 to 2017 of 3873 TEUsper day, followed by BTP with 2562 TEUs per day (from 2013 to 2017).

Fig. 2. Productivity of the cranes expressed in TEUs per hour.

Table 5Efficiency and productivity of the terminals in the year 2017.

Terminal TechnicalEfficiency

TEUs perday

TEUs/crane/hour

Suape Tecon 0.9833 1203 8.38Salvador Tecon 0.9828 795 5.52Rio Grande do Sul Tecon 0.9710 1876 7.67Sepetiba Tecon 0.9669 866 6.94Tecon Santos 0.9481 3605 11.04Libra Santos 0.7876 1064 4.03Itajaí:APM Terminals 0.7285 583 4.85Libra Rio 0.6483 327 2.96MultiRio 0.6152 476 4.13BTP: APM Terminals 0.6108 3960 20.63Vila do Conde 0.5600 313 6.52DP World Santos 0.4015 1864 12.95Fortaleza 0.2874 264 9.18Pecem 0.2699 574 7.98Natal 0.2351 153Suape Public 0.0761 57Rodrimar 0.0171 1 0.02Belem 0.0131Ecoporto 0.0055 8 0.04Itajaí: Public 0.0002

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If the results of the two methods are compared, stochastic frontieranalysis and port performance index, it is observed that the ports thathave a greater volume of containerized goods, not necessarily, are thosethat have higher levels of technical efficiency. This is the case of BTPthat in 2017, was the terminal that moved the highest volume of con-tainers per day (3960 TEUs per day), however, in terms of technicalefficiency it was in position 10 with a value of 0.6108; followed byTecon Santos that in 2017, positioned as the second terminal with thehighest volume of containers per day (3605 TEUs per day), which interms of technical efficiency is ranked 5th (0.9481).

7. Conclusions

This research focuses on the analysis of containerized goods term-inals in Brazil, which is currently in full development of its infra-structure, as gateway ports with a high potential for becoming hubports. Gateway ports have the greatest impact at the level of theeconomies of the countries, and, in the particular case of Brazil, theyare expected to contribute to a sustained recovery based on interna-tional trade (ECLAC, 2017; International Monetary Fund, 2018).

The research is based on a stochastic frontier analysis, throughwhich the production function of the port industry is estimated and thepossible determinants of efficiency are identified. In addition, to carryout a comparative analysis between efficiency and productivity, twoindicators have been used to evaluate the selected terminals.

Among the most significant conclusions, it should be first noted thatBrazil modified its port legislation in 2013, in the same year privateterminalists such as BTP (APM) and DP World started operations; sincethen, they are showing high volumes of cargo handling. Second, someterminals such as BTP or DP World Santos, indicate operating indexeswith very significant values in the first four years of operation, since in2017, BTP handled 3960 TEUs per day (20 containers per hour and

crane) and DP World Santos handled 1864 TEUs per day (12 containersper hour and crane).

The determinants of efficiency studied reveal that the terminals runby Tecon, Libra, and APM Terminals as private operators are more ef-ficient. Of the total of the 20 terminals, 5 belong to the Tecon group, 2to Libra, and 2 to APM Terminals.

Third, the average technical efficiency levels at the terminals in theperiod from 2008 to 2017 are intermediate values. Thus, this value is0.66 in the year 2008; in 2014 the maximum value of the period isreached with 0.69; but in 2016 and 2017 the lowest values are reachedwith 0.49 and 0.51, respectively. This is a consequence of the drop inefficiency in the public terminals due to the appearance of new com-petitors, especially in 2013 (Annex 2).

Fourth, when analyzing the technical efficiency per port, it can beobserved that the three terminals with the highest levels are Rio Grandedo Sul Tecon, Salvador Tecon, and Suape Tecon. These terminals arethe only ones with containerized merchandise in the port where theyare located and, if they have a competitor, it is a public one.

Lastly, the highest value of the operational indicators of port pro-ductivity has been reached by the BTP terminal in Santos, operated byAPM Terminals since 2013, with a ratio of 3960 TEUs per day and20.63 TEUs/crane/hour, and an efficiency level of 0.6108, above the2017 average.

The main limitation presented in this research on efficiency andproductivity is the availability and veracity of the data. There is a lackof transparency and accessibility in relation to the characteristics ofport infrastructure and superstructure, the number of workers in theterminal, number of stevedores, costs, and other variables of interest.

Potential future research lines could advance the estimation ofeconomic and environmental management efficiency for ports, as wellas improve the estimation methodology.

Annex. 1

Table A1Productivity of the cranes (gantry and mobile) in hours in the Brazilian port terminals (TEUs per hour).

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BelemBTP: APM Terminals 0.8 10.52 16.27 18.52 20.63Ecoporto 4.63 4.07 5.82 7.06 7.51 6.82 5.31 2.26 0.01 0.04EMBRAPORT: DPWorld 1.34 10.4 11.98 12.68 12.95Fortaleza 5.05 4.83 5.62 5.42 5.61 7.44 8.49 7.59 8.49 9.18Itajaí: APM Terminals 8.79 8.18 8.44 8.4 8.51 4.48 2.5 4.85Itajaí: Public 5.34 1.7 1.58 0.96 1.18 0.57Libra Rio 6.64 5.58 6.75 6.87 5.27 5.5 4.9 4.25 3.61 2.96Libra Santos 14.55 11.92 12.47 13.08 12.32 6.7 5.7 5.16 1.58 4.03MultiRio 8.79 7.01 6.36 7.43 8.87 6.86 5.74 5.2 4.67 4.13NatalPecem 6.8 6.18 7.57 9.18 5.83 5.71 7.43 6.86 6.51 7.98Rio Grande do Sul Tecon 13.33 13.78 14.21 13.57 13.42 7.01 7.6 8.13 7.92 7.67Rodrimar 11.16 11.54 11.57 12.9 9.59 10.52 5.41 1.39 0.04 0.02Salvador Tecon 4.17 4.14 4.45 4.61 4.86 5.08 5.22 5.26 5.6 5.52Sepetiba Tecon 6.18 4.54 3.64 6.71 6.94 7.99 5.38 5.9 6.42 6.94Suape Tecon 4.45 4.63 5.43 6.11 5.24 4.92 6.91 7.1 7.09 8.38Suape: PublicTecon Santos 10.66 8.76 11.38 12.48 14.55 15.2 11.58 11.03 11.97 11.04Vila do Conde 2.44 3.23 5.77 6.52

Source: own elaboration

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Annex. 2

Grafic A2. Technical efficiency level by terminal.Source: own elaboration.*PR: private terminal with dash line; PB: public terminal with continuous lines.

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