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Management Decision Sustainability and supply chain infrastructure development Anthony D. Ross Hamieda Parker Maria del Mar Benavides-Espinosa Cornelia Droge Article information: To cite this document: Anthony D. Ross Hamieda Parker Maria del Mar Benavides-Espinosa Cornelia Droge, (2012),"Sustainability and supply chain infrastructure development", Management Decision, Vol. 50 Iss 10 pp. 1891 - 1910 Permanent link to this document: http://dx.doi.org/10.1108/00251741211279666 Downloaded on: 21 December 2014, At: 02:48 (PT) References: this document contains references to 57 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 1231 times since 2012* Users who downloaded this article also downloaded: Dr Stefan Schaltegger, Prof Roger Burritt, Stefan Schaltegger, Roger Burritt, (2014),"Measuring and managing sustainability performance of supply chains: Review and sustainability supply chain management framework", Supply Chain Management: An International Journal, Vol. 19 Iss 3 pp. 232-241 http:// dx.doi.org/10.1108/SCM-02-2014-0061 Dr Stefan Schaltegger, Prof Roger Burritt, Philip Beske, Stefan Seuring, (2014),"Putting sustainability into supply chain management", Supply Chain Management: An International Journal, Vol. 19 Iss 3 pp. 322-331 http://dx.doi.org/10.1108/SCM-12-2013-0432 Craig R. Carter, Dale S. Rogers, (2008),"A framework of sustainable supply chain management: moving toward new theory", International Journal of Physical Distribution & Logistics Management, Vol. 38 Iss 5 pp. 360-387 http://dx.doi.org/10.1108/09600030810882816 Access to this document was granted through an Emerald subscription provided by 549148 [] For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download. Downloaded by SELCUK UNIVERSITY At 02:48 21 December 2014 (PT)

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Page 1: Sustainability and supply chain infrastructure development

Management DecisionSustainability and supply chain infrastructure developmentAnthony D. Ross Hamieda Parker Maria del Mar Benavides-Espinosa Cornelia Droge

Article information:To cite this document:Anthony D. Ross Hamieda Parker Maria del Mar Benavides-Espinosa Cornelia Droge,(2012),"Sustainability and supply chain infrastructure development", Management Decision, Vol. 50 Iss 10pp. 1891 - 1910Permanent link to this document:http://dx.doi.org/10.1108/00251741211279666

Downloaded on: 21 December 2014, At: 02:48 (PT)References: this document contains references to 57 other documents.To copy this document: [email protected] fulltext of this document has been downloaded 1231 times since 2012*

Users who downloaded this article also downloaded:Dr Stefan Schaltegger, Prof Roger Burritt, Stefan Schaltegger, Roger Burritt, (2014),"Measuring andmanaging sustainability performance of supply chains: Review and sustainability supply chain managementframework", Supply Chain Management: An International Journal, Vol. 19 Iss 3 pp. 232-241 http://dx.doi.org/10.1108/SCM-02-2014-0061Dr Stefan Schaltegger, Prof Roger Burritt, Philip Beske, Stefan Seuring, (2014),"Putting sustainability intosupply chain management", Supply Chain Management: An International Journal, Vol. 19 Iss 3 pp. 322-331http://dx.doi.org/10.1108/SCM-12-2013-0432Craig R. Carter, Dale S. Rogers, (2008),"A framework of sustainable supply chain management: movingtoward new theory", International Journal of Physical Distribution & Logistics Management, Vol. 38 Iss5 pp. 360-387 http://dx.doi.org/10.1108/09600030810882816

Access to this document was granted through an Emerald subscription provided by 549148 []

For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald forAuthors service information about how to choose which publication to write for and submission guidelinesare available for all. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The companymanages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well asproviding an extensive range of online products and additional customer resources and services.

Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committeeon Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archivepreservation.

*Related content and download information correct at time of download.

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Sustainability and supply chaininfrastructure development

Anthony D. RossDepartment of Supply Chain Management, Eli Broad College of Business,

Michigan State University, East Lansing, Michigan, USA

Hamieda ParkerGraduate School of Business, University of Cape Town, Cape Town,

South Africa

Maria del Mar Benavides-EspinosaDepartment of Business, Universitat de Valencia, Valencia, Spain, and

Cornelia DrogeDepartment of Marketing, Eli Broad College of Business,Michigan State University, East Lansing, Michigan, USA

Abstract

Purpose – This study aims to examine logistics infrastructure, trade differences, and environmentaland social equity factors, for a set of 89 countries.

Design/methodology/approach – Following recent work which uses secondary data sources forsupply chain research at the country-level, data were obtained from the World Bank and InternationalMonetary Fund databases. Data envelopment analysis (DEA) was used to compute country-levelefficiencies and ANOVA was used to do regional comparisons.

Findings – The analysis shed light on country-level dimensions of logistics infrastructure and tradeperformance. It also provided insights regarding environmental (e.g. CO2 emissions) and social equity(e.g. health expenditure) dimensions for different regions.

Research limitations/implications – Panel data rather than longitudinal data were used to drawthe conclusions. A more exhaustive study could consider a multi-year timeframe. A limited number ofdimensions were examined. As the study was exploratory, further work could consider a moreextensive number of dimensions.

Practical implications – The study has important implications for policy makers, since theattractiveness of various resource endowments like those considered here (environmental, social,supply chain logistics) can be seen to be associated with trade performance.

Originality/value – This is one of the few studies to explore efficiency differences (enacted throughDEA and ANOVA analyses), differentiating the research from the usual country clusteringapproaches. It also contributes to the understanding of differences between countries from a macroperspective, which provides insights for firms intending to expand their supply chains.

Keywords – Supply chain management, Logistics infrastructure, Sustainability,Data envelope analysis, Efficiency, Distribution management, Cross-cultural studies

Paper type Research paper

1. IntroductionRising consumer sophistication, product proliferation, and convergence of consumertastes in seemingly disparate geographic regions have led many multinationalenterprises or corporations (MNEs/MNCs) to adopt new strategies in sourcing,

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0025-1747.htm

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pp. 1891-1910q Emerald Group Publishing Limited

0025-1747DOI 10.1108/00251741211279666

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producing and delivering products. As a result, global supply chains have emerged ashighly integrated corporate structures comprised of layers of suppliers and customersall connected (loosely or tightly) as networks of entities operating within the marketenvironment of a given industry (Cohen and Mallik, 1997; Geum et al., 2011; Smolarskiand Kut, 2011). Management practices and decisions increasingly span supply chainechelons in multiple functions, firms and geographic regions. The actors in theseindustries tend to concentrate and consume physical and human resource inputsavailable in a given country while impacting its natural environment. As industrialactivity increases within a country, the demand for resource inputs increases, whichcan increase inefficiencies and uncertainties in supply chains (Wu and Lin, 2008;Wakkee et al., 2010; Franco and Haase, 2010). Electronic integration allows for superiorperformance in the supply chain, that is to say, it makes products and servicesavailable to consumers in the right amount at the right time (Vakharia, 2002; Tolstoy,2010; Ullah et al., 2010). Firms today view sustainability as a critical factor forcompetitiveness.

Over the last 15 years, supply chain management has been the subject of continuedmanagement research interest. Many aspects of supply chain management, such asdemand planning, inventory management, lean practices, capacity management,facility location, and others have been studied by academics for decades (Everett et al.,2010; Sebora and Theerapatvong, 2010; Tihula and Huovinen, 2010).

Recently, focusing on preserving the social and environmental capital for futuregenerations, sustainability has become a twenty-first century mantra for businessexecutives, non-government organisations (NGOs) and decision makers (Lee et al.,2011). Firms today are under constant pressure to “go green,” and sustainability hasbecome a critical factor in corporate competitiveness for many. Customers, investors,employees, and other stakeholders want to know the environmental and social impactsof corporate activities (Hormiga et al., 2011; Kautonen and Palmroos, 2010; Linan et al.,2011; Sommer and Haug, 2011). Managers would like to make more sustainablechoices, but the relentless demand for financial performance can make them hesitant todo so. Some sustainability decisions appear to be self-evident: reducing the use ofmaterials, becoming more energy efficient, recycling, among other aspects. However,most choices are far more challenging and can pit financial results and social impactsdirectly against each other (Epstein and Yuthas, 2012). While there is no clearconsensus on a definition of sustainability and what it means for supply chain researchtoday (Carter and Rogers, 2008), sustainability implies that economic developmentmust be integrated with ecological stewardship and social well-being (Clift, 2003;Elkington, 1997).

2. Linkages between logistics, environment, and developmentDicken (2007) observes that growth in transportation and communicationinfrastructure has shrunk “distances” between countries and MNCs operating there,but this has not solved all problems associated with sourcing, making, and movinggoods or services. Investment policy and resource allocation should make efforts toincorporate, develop and employ information technologies that foster collaboration.Certain technologies, especially those associated with inventory management and themanagement of information shared with customers, are beginning to lose theireffectiveness as enablers of competitive advantages, as they no longer represent a

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necessity for maintaining such advantages. In particular, three phenomena may havesignificant implications for strategic supply chain orientations:

(1) physical infrastructure capabilities (such as, modes of transportation andcommunication);

(2) preserving the environment (such as public policies concerning theenvironment); and

(3) the national trade policies of a country.

This suggests inter-relationships in the aggregate among adequate supply chaininfrastructure, sustainability, and economic growth.

Three prominent macro-theoretical lenses have been proposed to examineenvironmental issues at the country level. First, the pollution haven hypothesis(PHH) maintains that public policies on the environment have an effect on industriallocation by domestic and multinational firms; moreover, this type of policy is playingan increasingly relevant role (Rust et al., 2009). Second, the Environmental Kuznetsview (accorded to Simon Kuznets, 1955) suggests that, like the pattern of inequalityand gross domestic income per capita, the poorest and richest countries have thecleanest environments, and that middle-income countries are the most-polluted. Thuspollution and gross domestic income have a U-shaped relationship. Third, the eclecticparadigm (Dunning, 1958) is a framework that has been used by scholars to explain thelevel and structure of foreign value-creating activities of multinational enterprises(including foreign and domestic firms, both importing and exporting).

Research trends do suggest, however, a growing interest in socio-environmentalperformance and inter-organisational activities related to the supply chain (Korhonenand Luptacik, 2004; Akbostanci et al., 2004; Bergh et al., 2011), particularly inexamining specific logistics infrastructure, trade, the environment and social equityfactors, with the country level serving as the unit of analysis (Kinra and Kotzab, 2008),and in increasing social variables for improving quality of life and the optimisation ofresources allocated to social improvement (Ghosh, 2011).

Tirschwell (2007) observed that domestic and global conditions that promote tradeand commerce are clashing with obsolete infrastructure. Lowering unit sourcingand/or manufacturing costs does not necessarily translate directly into lower totallanded costs; policy makers’ decisions may facilitate trade expansion but increasedtrade volume can increase congestion and environmental pressures, thus lessening theefficiency of the logistics infrastructure.

3. Related literature3.1 Country-clusteringCountry-clustering has been widely used in the business marketing literature as onelens to assess emerging market potential. Some clustering methods (Cavusgil et al.,2004; Huszagh et al., 1985) and market potential weighted indexing methods (Cavusgil,1997) have been used to develop macro-level insights to emerging markets.

Since these early studies, the clustering methodology has become a prevalentmethodology in the marketing domain (e.g. Huszagh et al., 1985; Cavusgil et al., 2004).Despite strengths, there are several shortcomings to the clustering approach. Theyinclude:

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. the exclusive reliance on marketing indicators (Douglas and Craig, 1983;Papadopoulos and Denis, 1988) and environmental macro-factors (Kinra andKotzab, 2008); and

. the exclusive reliance on secondary market-oriented data (Papadopoulos andDenis, 1988).

While indexing provides the potential for grouping the countries, the method isgenerally silent on the explanation of productivity differences between the groups andthe members within a group, despite intense interest in this area (Dyckhoff and Allen,2001; Williams et al., 2011; Cassia and Colombelli, 2010; Krasniqi, 2010). Theseshortcomings provide the motivation in the current research for:

. proposing alternative methodologies in the current research; and

. clustering countries on a priori criteria.

3.2 Macro-level logistics costA second, but much smaller stream of research is found in the supply chain domainand concerns macro-level logistics expenditure for a country. The global logisticsexpenditure literature estimated global logistics costs based on four components:

(1) total gross domestic product (GDP);

(2) government sector product;

(3) industrial sector product; and

(4) total trade ratio (Bowersox et al., 2003).

Recent refinements to the estimation method either introduced an artificial neuralnetwork model, or expanded the dataset by including infrastructure variables relatedto cost and information and communication systems. To achieve this integration, thecurrent research accesses secondary data and applies the methodology of dataenvelopment analysis (DEA).

4. Data and methodology4.1 Secondary dataQuantifying foreign market opportunity is a primary concern for academics,practitioners and policy makers, and a diverse set of approaches and data sources havebeen reported (Cavusgil et al., 2004; Wood and Lenzen, 2009). Moreover, the use ofsecondary data sources at the country-level has received renewed attention as a viableand much needed research area (Bhattacharyya et al., 2010).

Following up recent work (Wu and Lin, 2008; Calantone and Vickery, 2010) in theuse of secondary data sources for supply chain research at the country-level, data wasobtained from the World Bank and International Monetary Fund were matched bycountry for the year 2004. This was the most recent year for which complete data onour social and environmental equity dimensions were available. The variables aredefined in Table I.

The literature has identified a variety of dimensions for assessing macroperformance (Bhattacharyya et al., 2010; Seppala et al., 2005; Park and De, 2004). To setthe stage for our analysis, the countries were clustered into the five World Bank incomegroup designations, from low-income countries to high-income Non-OECD

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(Organisation for Economic Co-operation and Development). Group means for thesocial and environmental equity factors as well as logistics cost overhead burden arereported in Table II. Several relationships emerged based on comparing group meansfor the country clusters. First, there is a positive relationship between per capita CO2emissions and wealth. Non-OECD countries emit CO2 at the highest rates, with therichest countries emitting more than three times as much as upper-middle incomecountries.

Second, we observed the lowest logistics burden costs in richer countries, with anearly three-fold increase for poor countries. Third, workers’ remittances were highestin high-income, non-OECD countries; they were lowest in upper-middle countries andin high income OECD countries. Finally, we observed that high-income countries arecharacterised by more public health care expenditures than poor countries, withsignificantly less healthcare expenditures per capita by governments in the low-incomecountries.

Mean SD Min Max

Input dimensionsLn(Xdays): Number of days required to clear shippingcontainers through customs 2.795 0.556 1.609 4.06LogsOH: Estimated logistics cost overhead rate 0.0697 0.151 0.002 1.38Remittances: Workers’ remittances and compensation ofemployees as a % of GDP 5.244 911.80 0.018 100C02: Volume of CO2 emissions in metrics tons per capita 5.849 6.681 0.01 40HealthCost: Per capita spending on public health care 951.76 1351.7 13 5636

Output dimensionsAirFreight: Air freight shipments in million of tons per km 8.18 16.86 0.009 82.79TransportSvc: Ratio of exported-related vs import-relatedtransportation as a % of commercial services 0.985 0.823 0.051 4.89CommSvc: Ratio of exports vs imports in current US dollars(2004) 8.593 41.67 0.004 381.75Food Ratio: Ratio of food exports to imports as % ofmerchandise exports/imports 1.065 0.487 0.147 2.529Fuel Ratio: Ratio of fuel exports to imports as % ofmerchandise exports/imports 1.679 1.656 0.009 9.945Hitech: High-technology exports as a % of manufacturingexports 12.237 13.49 0.123 73.597AgriE2i: Agriculture raw materials exports vs imports 2.363 2.484 0.125 12Cduty: Customs and other import duties as % of total taxrevenue 11.083 21.96 1.99 182.281

Table I.Descriptive statistics for

the performancedimensions

Income group CO2 LogsOH Remittances HealthCost

Low 0.357 12.67 6.44 25.00Lower middle 1.963 11.81 6.62 102.85Upper middle 4.454 5.05 1.636 269.91High income, Non OECD 16.44 4.33 16.61 871.22High income OECD 9.13 2.62 2.391 2832.33

Table II.Social and environmentalequity means by income

group

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The next section develops the methodological lens adopted in this research. We focuson efficiency analyses as a fruitful line of inquiry because performance efficiency hasreceived renewed attention regarding eco-efficiency measurement (Brattebo, 2005).

4.2 Methodology and efficiency evaluation through data envelope analysis (DEA)Data envelopment analysis (DEA) is the linear programming technique used tocompute country-level efficiency scores in this research (Charnes et al., 1978; Sharmaand Yu, 2010). Since DEA’s introduction, the literature on this methodology hasproliferated rapidly across a variety of problem contexts (Cooper et al., 2000;Kuosmanan and Kortelainen, 2007).

A country is classified as efficient if its index score is 1.0. This implies that it is notpossible to increase (or decrease) any of its outputs (or inputs) with increasing (ordecreasing) any of the inputs (or outputs). A country is inefficient if its score is lessthan 1.0. Scores for all countries in the dataset are bounded between 0.00 and 1.00. Anefficiency frontier envelopment is comprised of all efficient countries. Those countriesthat do not lie on the frontier are inefficient and will have an inefficiency scorecomputed.

The shape of the frontier is partly determined by whether constant returns to scale(CRS) versus variable returns to scale (VRS) are assumed (Banker et al., 1984). The VRSmodel creates a frontier using the convex hull and thus provides efficiency scores thatare bounded from below by those from the CRS model. Moreover, the VRS modelensures that inefficient countries are compared only to similarly situated countries inthis particular dataset (Avkiran and Rowlands, 2007). We calculate CRS and VRSscores in order to compare scale efficiencies.

Before the DEA problem is solved, the orientation of the formulation must bespecified. An input orientation focuses on how much the inputs can be reduced whilemaintaining the same level of output, while an output orientation focuses on how muchthe outputs can be increased while holding the inputs constant. Given the strategicnature of macro investments and our interest in the performance implications of certaindimensions that should be held to a minimum, an input orientation was selected. Thischoice does not compromise the implications that can be drawn for business,government, and non-government policy makers.

The input-oriented DEA model specifications follow below; we adopted theapproach accorded to Cooper et al. (2000). The model formulations are in dual form,which is generally preferred when working with datasets containing a small number ofvariables relative to the number of decision making units (DMUs; or countries, in ourcase). This simply provides solutions to the models in less computation time. Theinput-oriented CRS model formulation in dual form is:

Minulu

s:t:

2y0 þ Yl $ 0

uxo 2 Xl $ 0

l; u $ 0

ð1Þ

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In equation (1), X and Y are the vector of input and output variables respectively fromTables I and II, while l is the vector of multiplier weights derived during the solutionprocess. We let x0 and y0 represent the setup of inputs and outputs for the DMU underevaluation during the solution process. In general, if there are m inputs and n outputsthen there should be at least (m þ n) *3 DMUs to evaluate. The value of u obtained willbe the efficiency score for DMU0.

The VRS model is an extension to equation (1) above and accounts for convexity inthe constraint sets. It is specified as:

Minulu

s:t:

2y0 þ Yl $ 0

uxo 2 Xl $ 0

el $ 0

l; u $ 0

ð2Þ

The additional constraint:

el ¼Xk

j¼1

lj ¼ 1

ensures that the feasible solution space is smaller than that of CRS, ensuring that VRSscores are always bounded from below by CRS scores.

5. Results and discussionThe following sections contain the results of several analyses as well as interpretationsof the macro level linkages between supply chain logistics, environmental sustainability,and development. Where appropriate, we refer to three approaches mentioned earlier:

(1) the pollution haven hypothesis (PHH), which maintains that publicenvironmental policies affect industrial location;

(2) the Environmental Kuznets view (accorded to Simon Kuznets, 1955) whichsuggests a U-shaped relationship between pollution and wealth; and

(3) the eclectic paradigm (Dunning, 1958) explaining the level and structure offoreign value activities of multinational enterprises.

Four export/import ratios (food, fuel, commercial services, and high-techmanufacturing) can be viewed as proxies for foreign value activities since theyreflect aggregate levels of activity in the corresponding industries.

5.1 Country comparisons of efficiencyModels (1) and (2) above were used to derive the slacks-based technical efficiencyscores under CRS and VRS assumptions, respectively. Determining the extent to whicha country was operating at its most productive scale size, known as scale efficiency,was also of interest. This is computed as:

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ScaleEff ¼ CRSscore=VRSscore ð3Þ

Scale efficiency scores help in understanding the extent to which country differences arecaused by the fact that some countries are operating under less than optimal conditions.

Table III reports the VRS and the CRS efficiency scores, as well as the tabulation ofScaleEff (equation 3) for each country. If a country is fully efficient in both VRS andCRS models, such as Algeria and Argentina, then ScaleEff ¼ 1 and it is operating at itsmost productive scale size. In total, 56 countries were scale-efficient and at their mostproductive scale sizes.

For example, European countries of France, Austria, Belgium, Bulgaria, Hungary,Portugal, the UK, Italy and Spain face increasing returns (average scale efficiency of0.788), so further development/investment in the input variables can lead to larger thanproportional improvements in trade balance due to increasing returns to scale. Bycomparison, investment in scale efficient countries in this region such as Germany andDenmark would lead only to proportional returns at best. We do not assign any relativepriority ranking among these countries, nor do we suggest which specific factorswarrant such attention.

Efficiency performance was then examined by country clusters. The countries wereclassified in two ways:

(1) as either landlocked or coastal (Table IV); and

(2) according to their geographic region as per World Bank classification method(Table V).

Coastal countries and landlocked countries (means in Table IV) appear to be similar inscale efficiencies so the anticipated returns will generally be of an increasing nature.However, this may be biased due to the difference in group sample size.

In terms of regional differences in scale efficiency, Table V’s mean scores suggestthat greater returns to scale improvements may be required for countries in the ESCA,MENA and NALAC regions than for the Asian and African countries represented inour dataset. To some extent, this finding seems at odds with other views on thepotential benefits of investing in the African region. We caution against drawing adefinitive conclusion based solely on this dataset and suggest that additionalconfirmatory examination may be worthy of pursuit.

5.2 Analysis of landlocked versus coastal clustersLand-locked countries are dependent on surface movement of goods across severalcountry borders, or on movement via airfreight. Coastal countries have oceans as part oftheir national borders, or can be island nations. Table VI lists the dependent variables,the means and standard deviations (SD) for landlocked vs. coastal countries, and the Fand p-values from t-tests for equality of means (conducted using SPSS q version 17).

There are only two significant (p , 0:05) differences between landlocked versuscoastal countries: in shipping container clearance through customs (ln(Xdys)) and intransport logistics cost overhead burden (logsOH). For customs clearance times, theaverage time was 3.09 days for landlocked countries and 2.74 days for coastalcountries. For transport logistics cost overhead burden in landlocked countries, theaverage overhead burden was 14.71 per cent, while coastal countries’ average was 5.74

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Country VRS CRS ScaleEff Country CRS VRS ScaleEff

Albania 0.438 0.357 0.816 Latvia 1 1 1Algeria 1 1 1 Lithuania 0.927 0.879 0.949Argentina 1 1 1 Malaysia 1 1 1Armenia 0.458 0.324 0.708 Mexico 1 1 1Australia 1 1 1 Moldova 1 1 1Austria 0.593 0.409 0.689 Mongolia 1 1 1Azerbaijan 1 1 1 Morocco 1 1 1Bangladesh 1 1 1 Mozambique 1 1 1Belarus 1 1 1 Nepal 1 1 1Belgium 0.577 0.367 0.635 The Netherlands 1 1 1Bolivia 1 0.749 0.749 NewZealand 1 1 1Brazil 1 1 1 Nigeria 1 1 1Bulgaria 0.515 0.481 0.934 Norway 1 1 1Canada 0.523 0.359 0.688 Pakistan 1 1 1Chile 1 1 1 Panama 1 1 1China 1 1 1 Paraguay 1 1 1Colombia 0.774 0.726 0.938 Peru 0.724 0.556 0.768CostaRica 1 1 1 Philippines 1 1 1Croatia 1 1 1 Poland 0.745 0.539 0.723CzechRep 1 0.677 0.677 Portugal 0.557 0.419 0.753Denmark 1 1 1 Romania 1 1 1Dom.Repc 1 1 1 Russia 1 1 1Ecuador 0.779 0.709 0.910 SaudiArabia 1 1 1Egypt 1 1 1 Senegal 1 1 1ElSalvador 0.558 0.392 0.701 Singapore 1 1 1Estonia 1 1 1 SlovakRep 1 1 1Finland 1 1 1 Slovenia 0.585 0.503 0.859France 1 0.642 0.642 SouthAfrica 0.634 0.522 0.824Georgia 1 1 1 SouthKorea 0.415 0.376 0.908Germany 1 1 1 Spain 0.585 0.519 0.887Ghana 1 1 1 SriLanka 0.546 0.353 0.647Greece 1 1 1 Sweden 1 0.707 0.707Guatemala 1 0.679 0.679 Switzerland 1 1 1Honduras 0.583 0.391 0.672 Syria 1 1 1HongKong 1 1 1 Thailand 1 1 1Hungary 0.815 0.781 0.958 Tunisia 0.852 0.842 0.988India 1 1 1 Turkey 1 1 1Indonesia 1 0.661 0.661 UAE 1 1 1Ireland 1 1 1 Ukraine 1 1 1Israel 0.619 0.403 0.651 UK 0.646 0.563 0.872Italy 0.554 0.345 0.622 Uruguay 1 1 1Japan 1 1 1 Venezuela 1 1 1Jordan 0.375 0.312 0.831 Vietnam 1 1 1Kenya 1 1 1 Yemen 0.495 0.243 0.491Kuwait 0.441 0.401 0.909

Notes: VRS ¼ variable returns to scale DEA model; CRS ¼ constant returns to scale DEA model;ScaleEff ¼ scale efficiency as per Equation 3 in the text. Analysis excludes the USA

Table III.Country-level

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per cent. We infer from this that there is more sourcing, production, and transportationcomplexity (time and cost) among landlocked countries than coastal countries.

5.3 Analysis of regional clusters: countries grouped into geographic regionsAs surrogates for supply chain infrastructure capability, three factors were used:customs clearance export time (LN(xdays), logistics transport overhead cost (LogsOH)

VRS CRS ScaleEff

Coastal Mean 0.882 0.824 0.915n ¼ 76 SD 0.193 0.252 0.137Landlocked Mean 0.865 0.813 0.918n ¼ 13 SD 0.232 0.279 0.125Total Mean 0.879 0.822 0.915n ¼ 89 SD 0.198 0.255 0.134

Table IV.Landlocked versuscoastal countries

Landlockedcountries: Coastal countries:

Dependent variables Mean SD Mean SD F (from t-tests) p-value

Ln(Xdys) 3.09 0.766 2.744 0.505 4.527 0.036logsOH 0.1471 0.370 0.0574 0.062 4.014 0.048Remittances 5.842 7.474 5.142 12.50 0.038 0.846CO2 3.777 2.822 6.204 7.13 1.456 0.231HealthCost 879.0 1740.78 964.21 1297.1 0.043 0.836AirFreight 1.854 3.535 9.263 18.09 2.148 0.146TransportSvc 1.322 1.408 0.9275 0.681 2.559 0.113CommSvc 1.0385 1.799 9.886 45.27 0.158 0.692Food Ratio 1.0143 0.419 1.073 0.503 0.000 0.989Fuel Ratio 1.674 1.801 1.681 1.65 0.492 0.485Hitech 7.358 8.035 13.071 14.17 1.993 0.162AgriE2i 7.391 9.125 11.716 23.58 1.638 0.204Cduty 3.179 3.762 2.223 2.218 0.423 0.517

Table VI.Descriptive statistics andT-tests for landlockedversus coastal countries

Region (World Bank classification) VRS CRS ScaleEff

East Asia Pacific (EAP) Mean 0.955 0.926 0.967n ¼ 13 SD 0.162 0.189 0.095Europe, South and Central Asia (ESCA) Mean 0.867 0.801 0.904n ¼ 41 SD 0.202 0.261 0.137North America, Latin America and Caribbean (NALAC) Mean 0.886 0.809 0.895n ¼ 18 SD 0.178 0.245 0.139Middle East and North Africa (MENA) Mean 0.798 0.745 0.897N ¼ 11 SD 0.260 0.328 0.174SubSaharan Africa (SSA) Mean 0.939 0.920 0.970n ¼ 6 SD 0.149 0.195 0.072Total Mean 0.879 0.822 0.915n ¼ 89 SD 0.198 0.255 0.134

Table V.Five geographic regions

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and tonnage of goods transport (AirFreight). Worker remittances back tohome-country family members (Remittances) are one proxy for potential (demandfor) trade, and we viewed this variable, along with HealthCost as socio-economicindicators. Finally, CO2 was the environmental factor used. The means and the SD forthe dependent variables (as Tables I and II) are in Tables VII-IX, organized by region.

To determine whether the means are statistically significantly different acrossregions, ANOVAs were run (along with Duncan Range tests). The SSA region wasdropped from this analysis because of its small sample size (n ¼ 6). The results are inFigure 1; regions not significantly different from one another are underlined, asexplained below.

Of the 13 macro factors defined in Tables I and II, there were significant differencesamong the four tested regions for ten of them. The non-significant factors werecontainer export clearance time (Ln(Xdays),commercial services (CommSvc), andagricultural products trade (AgriE2i). Since these were non-significant, no multiplerange tests were performed. The ten that were significant are reported in Figure 1, with

AirFreight TransportSvc Fuel CommSvc

EAP Mean 25.646 0.787 2.700 0.891SD 29.338 0.3096 7.216 0.321

ESCA Mean 6.258 1.301 1.530 1.173SD 14.958 1.067 4.384 0.4783

NALAC Mean 3.730 0.660 4.77 0.975SD 5.002 0.337 9.065 0.543

MENA Mean 5.294 0.754 45.888 1.119SD 8.616 0.566 113.908 0.635

SSA Mean 2.127 0.654 12.719 0.865SD 3.429 0.395 27.919 0.2808

Total Mean 8.181 0.985 8.593 1.0646SD 16.952 0.828 41.914 0.4897

Table VIII.Summary descriptive

statistics by region

Ln(Xdys) LogsOH Remittances CO2 HealthCost

EAP Mean 2.822 0.1546 6.699 6.23 860.77SD 0.614 0.368 10.199 4.585 1086.75

ESCA Mean 2.666 0.0364 2.832 6.103 1516.49SD 0.626 0.0236 4.429 4.222 1665.84

NALAC Mean 2.893 0.0584 4.609 3.222 369.39SD 0.5118 0.0379 5.812 4.479 673.12

MENA Mean 2.860 0.0791 15.072 11.00 380.90SD 0.2660 0.0409 28.948 14.422 473.72

SSA Mean 3.210 0.1419 2.47 1.733 83.67SD 0.1936 0.1806 2.424 3.578 147.85

Total Mean 2.796 0.0705 5.245 5.849 951.76SD 0.559 0.1517 11.869 6.719 1359.41

Table VII.Summary descriptive

statistics by region

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the first column naming the factor and reporting the F and p-values from ANOVA.There were significant differences at p , 0:05 for remittances received fromexpatriates, carbon dioxide emissions, per capita health expenditures, airfreight cargomoved, transportation services consumed, the export and import ratios for food and forfuel, and high technology products. At p , 0:10, logistics cost overhead rate andgovernment receipts of customs duties were also significant.

Next, the Duncan multiple range test for each dimension in Figure 1 is displayed asfollows. First, the four means for a particular factor are ordered from smallest to largestand each mean is labelled with its geographic area. For example, for logistics overheadcost (logsOH), the means in rank order are 0.036, 0.058, 0.079, and 0.1546, andunderneath each mean, the region is identified. Second, the means are grouped in setsthat are not significantly different by underlining. For example, for logsOH, the meansfor ESCA, NALAC and MENA are underlined with one line (forming one grouping),and the means for MENA and EAP are underlined, forming another grouping of meansthat are not significantly different from one another. In this example, MENA can beassigned to more than one grouping, a not uncommon outcomes of multiple range tests.These results are discussed next.

5.4 Economic dimensions: logistics infrastructure and trade dimensionsThere are seven economic and infrastructure factors considered. First, the estimatedlogistics overhead rates among the countries fall into two overlapping groupings,which shows that the EAP cluster has much different rates than ESCA and NALACclusters. Next, for freight transportation movements, there are two distinct subsets.EAP countries move significantly more ton-kms of air freight than any other region.Third, for export-import transportation services, there are two overlapping groups,with ESCA significantly higher than NALAC.

The next set of results concern food and fuel export/import ratios. The fourth setsays that the ratio of food exports to imports for all regions can be placed into threesubsets, with overlap across two of them. MENA countries’ (subset one) 0.73 ratio offood exports to imports is significantly lower than that for EAP countries (2.05) andNALAC countries (3.33). Only NALAC countries occupy subset 3 and they have

Food Hitech AgriE2I Cduty

EAP Mean 2.0496 26.804 6.270 2.92SD 1.8119 23.139 5.641 3.947

ESCA Mean 1.141 10.498 6.651 1.67SD 0.8441 8.445 8.7583 1.247

NALAC Mean 3.331 10.0598 9.490 3.083SD 2.3609 11.060 9.554 2.981

MENA Mean 0.7340 6.356 16.357 2.400SD 0.5643 7.728 22.417 2.695

SSA Mean 1.341 9.867 46.925 3.667SD 1.001 13.045 68.877 2.562

Total Mean 1.679 12.237 11.084 2.36SD 1.666 13.563 22.0827 2.498

Table IX.Summary descriptivestatistics by region

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Figure 1.ANOVAs by geographic

region

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significantly higher ratios of food exports/imports than any other region. Fifth,according to the results for the ratio of fuel exports to imports (Fuel), the regions can beplaced into two distinct subsets: MENA countries have significantly higher levels offuel export/import ratios (45.89) than the other three regions (who are not significantlydifferent from one another).

The sixth economic result is for the ratio of high technology-related exports toimports (HiTech). There are two distinct, non-overlapping subsets. EAP regioncountries have Hitech ratios (26.8) that are significantly higher than any other region.All other regions are in a different subset one and have HiTech ratios that are notsignificantly different from one another. Finally, for customs duties as a percent of taxrevenue (CDuty), there are two overlapping subsets, with MENA countries havingsignificantly higher duties than both EAP and ESCA countries.

The eclectic paradigm offers perspective on trade factors and logistics cost resultsin Figure 1. First, we observe that the Americas region (north, central and south) are byfar responsible for higher levels of food-based trade activity, while East Asiancountries lead in high-technology products sectors. This appears to be consistent withthe higher volumes of airfreight movements in Asian countries (25.65 million ton-kms)than other countries in the dataset (from 3.37-6.26 million ton-kms). From a secondperspective, that of logistics cost, Asia’s apparent dominance in the high-technologysector brings with it higher logistical process complexity and transportation overheadcost (15.46 per cent), despite the high dispersion in cost among this block of countriesas compared to others country blocks. It is also interesting to observe the magnitude ofactivity concentrations for fuel, where it seems that Middle-Eastern and North Africancountries differ from other regions by upwards of ten-fold in fuel trade activity.

5.5 Environmental equity dimensions: CO2 emissionsWe considered CO2 emissions as an environmental variable. Results for CO2 emissionsshow that all regions can be placed into two overlapping subsets. CO2 emissions forMENA countries’ emissions are higher than other countries. However, it can also beconcluded that and EAP countries’ emissions are not significantly different from eachother; MENA countries are different from the ESCA and NALAC countries. In the DEAliterature, dimensions of this type can also be classified as undesirable outputs because ofthe negative environmental impact. DEA models with undesirable outputs require modelspecifications vastly different from the scope of this work and are left to future research.We observe that the dispersion in CO2 emissions per capita was highest among MiddleEastern and North African countries, while it was lowest among North American andLatin American countries at 14.4 metric tons and 3.22 metric tons/capita, respectively.This seems consistent with the reported higher levels of both fuel production andtransportation export/import activity for MENA countries and worker remittancesreceived by consumers in these same countries. Finally, the mean and dispersion of CO2emissions for East Asian Pacific countries were similar to that for ESCA countries.

5.6 Social equity dimensions: remittances and healthcare expendituresWe considered two relevant social variables. Workers’ remittances as a percent ofnational GDP were placed into two very distinct subsets. Regions ESCA, NALAC andEAP fall into one subset and are not significantly different from one another. MENA(15.07 per cent of GDP) is in a separate subset and the mean is significantly higher than

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the other regions. Second, according to the results for public spending on healthcare, allregions can be placed into two overlapping subsets. The first subset consists ofNALAC, MENA and EAP countries while the second comprises EAP and ESCA. Thuswe conclude that healthcare expenditures are much higher in ESCA countries than inNALAC and MENA countries.

5.7 Recommending paths to efficiency improvement: analysis by regionOur next analysis focuses on technical inefficiency using our computations of VRS andCRS efficiency scores. The mean values were aggregated by region, across all fiveregions, since the analyses above have already shown that comparisons by region arefruitful. Since our primary focus lies on sustainability and we used input-oriented DEAmodel specifications, we compute improvement paths for the five input performancedimensions only (that is, Ln(Xdays), logsOH, Remittances, CO2, and HealthCost; seeTables I and II). The results for the VRS and CRS models are shown in Tables X and XI.

Under VRS assumptions, SSA (SubSaharan Africa) countries require the largestreduction in Ln(Xdys) since 21.68 per cent was the largest number in absolute value inthe first column of the table (it is italicised). It was somewhat noteworthy to find thatMENA (Middle East and North Africa) countries seem to require the largest reductionsin the remaining equity input dimensions (214.57 per cent for logsOH, 231.16 per centfor Remittances, 231.9 per cent for CO2, and 222.08 per cent for HealthCost).

These VRS results were not inconsistent with the results using CRS assumptions(Table XI), although there were some differences. MENA countries required the largest

Region Ln(Xdys) logsOH Remittances CO2 HealthCost

EAP 21.04% 23.38% 26.92% 24.4% 26.75%ESCA 20.45% 211.74% 222.5% 214.8% 217.04%NALAC 20.45% 29.19% 218.71% 212.9% 215.92%MENA 21.09% 2 14.57% 2 31.16% 2 31.9% 2 22.08%SSA 2 1.68% 23.87% 215.25% 28.9% 2 0.83%Mean 2 .699% 2 9.82% 2 20.06% 2 14.6% 2 14.84%

Notes: The regions are defined in Table V as per the World Bank; the performance input equityvariables are defined in Tables I and II. Means of each column are italicised; also, the largest number inabsolute value in each column is italicised

Table X.Input performance

improvement paths toefficiency: requiredreduction in equity

variables – VRS model

Region Ln(Xdys) logsOH Remittances CO2 HealthCost

EAP 25.98% 28.26% 29.47% 211.76% 29.22%ESCA 27.47% 220.26% 227.07% 221.59% 2 27.59%NALAC 28.24% 220.42% 221.02% 220.85% 224.94%MENA 2 11.24% 2 23.69% 2 32.45% 2 32.72% 227.19%SSA 28.76% 29.19% 26.26% 212.48% 23.16%Mean 2 7.88% 2 17.54% 2 21.55% 2 20.2% 2 21.64%

Notes: The regions are defined in Table V as per the World Bank; the performance input equityvariables are defined in Tables I and II. Means of each column are italicised; also, the largest number inabsolute value in each column is italicised

Table XI.Input performance

improvement paths toefficiency: requiredreduction in equity

variables – CRS model

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proportional reductions on four of the five input dimensions (exception was healthcareexpenditure). For healthcare, ESCA (Europe, South and Central Asia) countriesrequired slightly higher reductions, with ESCA at 227.59 per cent and MENA at227.19 per cent. Note that under CRS assumptions, SSA had the largest reduction onLn(Xdays) at 21.68 per cent while MENA came second at 21.09 per cent; under VRSassumptions, MENA had the largest reduction on Ln(Xdays) at 211.24 per cent whileSSA came next at 28.76 per cent.

6. Concluding remarksThis paper examined infrastructure and trade differences for a set of 89 countries alongsome important categories reported in the literature. Some recent extensions to thewell-known DEA methodology were used to compute relative efficiency and scaleefficiency. Then the countries were clustered a priori by region, and also based onbeing landlocked or coastal. The analysis explored not only country-level dimensionsof logistics and trade performance, but also differences across some environmental andsocial equity categories. The joint use of DEA and post hoc statistical analysis of theensuing country clusters offered insights to regional differences. One of the clearoutcomes of this study is the importance of the territory factor and the relevance of thegeographical concentration of certain variables that characterize regions. Thesignificant difference between countries with coastal regions and those that arelandlocked have become apparent. It is necessary to develop cooperation capabilitiesand institutional agreements to improve the infrastructures and internalcommunication systems of supply chains. Improvements in infrastructures shouldbe accompanied by the development of competencies, particularly those related tohuman resources (Caceres et al., 2011; Mathew, 2010; Ojala and Heikkila, 2011; Ramırezet al., 2010). Understanding differences from a macro perspective is important tocompanies contemplating foreign investment of technical know-how, new products,and other assets because of the unique challenges and opportunities present in acountry. From a global systemic approach, the supply chain represents more than asimple concern for minimizing production costs, and firms are now concentrating onspecialization and flexibility aimed at obtaining advantages in emerging economies, aswell as looking to reduce other costs. These costs may be associated with thecoordination of different firms and institutions to reduce unforeseen circumstancesregarding conflicting interests that can arise from the supply chain. The developmentof infrastructures is more closely linked to coordination between pressure groups(government and private sector) to enable the supply of integrated chainsencompassing a multitude of customers-suppliers. It is important to policy makerssince the attractiveness of various resource endowments like those considered here(environmental, social, supply chain logistics cost/time) can influence import andexport activity.

We found that 33 of the 89 countries were not operating at their most productivescale and thus should seek to increase the scale of their infrastructure investments. Wealso found similarities among some of regions for several other factors considered.However, the data reflect the aggregate level of export/import trade performance, CO2emissions, and logistics cost rather than tapping any particular industry or firm. Thuswe do not offer insights to a particular industry, or a specific subset of areas within thecountries considered (e.g. presence of technology parks, related industries). Nor do we

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provide insight to a specific set of government policies regulating or de-privatizing anindustrial sector.

As with any study, there are several limitations. First, panel data rather thanlongitudinal data were used to draw our conclusions. A more exhaustive study couldconsider a multi-year timeframe. Second, we do not suggest that the dimensionsconsidered here are the only definitive ones to be used. Rather they are illustrative ofwhat is available from the non-governmental organizations whose focus is globaldevelopment. The consideration of other datasets containing a different set of factorswas not the focus here, but other studies on this issue are encouraged. Finally, each ofthese limitations represents opportunities for future research.

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Corresponding authorHamieda Parker can be contacted at: [email protected]

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