Journal of Transport Geography 19 (2011) 1222–1234

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Journal of Transport Geography

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Monetary values of freight transport quality attributes: A sample of Tanzanian firms

Luca Zamparini a,⇑, John Layaa b,c, Wout Dullaert b,d

a Department of Law, Faculty of Social, Political and Regional Sciences, University of Salento, Italyb Institute of Transport and Maritime Management Antwerp (ITMMA), University of Antwerp, Belgiumc Dar es Salaam Maritime Institute, Dar es Salaam, Tanzaniad Antwerp Maritime Academy, Antwerp, Belgium

a r t i c l e i n f o

Keywords:Freight transportStated preferenceQuality attributesMonetary valuesInfrastructural investmentsTanzania

0966-6923/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.jtrangeo.2011.01.002

⇑ Corresponding author. Address: Dipartimento di SSalento, Via per Monteroni, snc, 73100 Lecce, Italy. Te0832 298450.

E-mail address: luca.zamparini@unisalento.it (L. Z

a b s t r a c t

This paper presents the findings of a stated preference research conducted in Tanzania (East Africa). Theobjective of the survey was to determine the relative importance as well as monetary values attached tofreight transport quality attribute by shippers in this region. In-depth interviews with the logistic man-agers of companies that produce and ship goods were conducted. The freight transport quality attributesconsidered in this survey were flexibility, frequency, loss and damage, reliability, and transit time. Themonetary values of these attributes have been computed as willingness to pay for their improvementas well as willingness to accept compensation for a decrease in their quality. The results show that ship-pers in this region consider travel time, loss and damage and frequency as the most important qualityattributes. This may have relevant implications for the infrastructural transport policies to be imple-mented in the country.

� 2011 Elsevier Ltd. All rights reserved.

1 In the case of reliability, the coefficient of variation is measured as the ratio

1. Introduction

The analysis of the economic value of freight transport qualityattributes is currently one of the most relevant research topicsamong transport economists. Traditionally, the attention ofresearchers was devoted to the value of travel time savings that,in freight transport, can be considered as the marginal utility at-tached to a unit reduction in the time that is necessary to movea determined shipment from an origin to destination point. Areduction in travel times may imply the possibility for transportfirms to concentrate production and distribution processes in few-er locations, and to benefit from the consequent economies ofscale, to deploy tighter scheduling processes and to extend thegeographical dimension of their markets (McKinnon, 1995).

However, researchers have increasingly considered furthertransport quality attributes in their studies (i.e. frequency, flexibil-ity, loss and damage, and reliability). Frequency is related to thenumber of shipments offered by a transport company, or anyfreight forwarding agent, in a determined period of time. Flexibilityconsiders the number of unplanned shipments that are executedwithout excessive delay. Loss and damage may refer to the per-centage of the commercial value of shipped goods that is lost be-cause of thefts, damages or losses (see, among others, Witlox and

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amparini).

Vandaele, 2005). Reliability has been defined in several heteroge-neous ways by the authors that have considered this transportquality attribute. It has, alternatively, been defined as the absolutevariation in transit times, as the relative variation (measured as thecoefficient of variation1), or as the percentage of consignments thatarrive within scheduled time (see, among others, Shinghal and Fow-kes, 2002). This latter definition of reliability will be used in theempirical sections of the paper.

The importance of these transport quality attributes has raisedthe attention of transport economists, that have provided a varietyof theoretical models and of empirical estimations of the monetaryvalue of freight transport quality attributes, that will be surveyedin Section 3. Moreover, freight transport firms have a strong inter-est in considering these attributes. For example, Fowkes et al.(2004) have surveyed the reasons why the freight transport indus-try may value reliability by considering both the demand side andthe supply side.2 Lastly, public administrations, at local, national,and international levels, may be interested in the monetary valuesof freight transport quality attributes as they can be part of CostBenefit, cost effectiveness, and other analyses aimed at optimising

between the standard deviation and the average of the transit times of a determinedamount of shipments in a given period of time (see Winston, 1981).

2 On the demand side, Fowkes et al. (2004) considered: just in time practices,deadlines for arrivals at ports, quick response operations for deliveries, and hub andspoke operations. On the supply side, efficiency of logistics operations, consolidationof heterogeneous deliveries, order management, and warehousing regimes werementioned.

L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234 1223

transport infrastructure policies and strategies. The large majorityof the empirical studies that have considered quality attributes offreight transport have been conducted in the European Unionand in the United States. Fewer studies have analysed this topicin developing countries (see Shinghal and Fowkes, 2002 in the caseof freight transport in India and DFID, 2005 for value of time in thecase of passenger transport in Tanzania). The objective of the pres-ent paper is to determine the relative importance and the mone-tary value of the abovementioned freight transport qualityattributes for a sample of firms in Tanzania. It is based on a statedpreference design and on a subsequent preference disaggregationby use of partial utility functions. The results of the survey mayprovide guidance with respect to the infrastructural investmentpolicies that this country may promote in order to reduce the prox-imity gap that characterises Tanzania, as well as several other Afri-can countries (Naudé, 2009).

The structure of the paper is as follows. After this introduction, adescription of the Tanzanian transport system and a theoreticaland empirical review of freight transport quality attributes are pre-sented. The Section 4 summarises the adopted research methodol-ogy and the experimental design. The Section 5 provides theestimated monetary values of freight transport quality attributesfor the surveyed firms and discusses these empirical results. Thelast section proposes some policy implications for transport policydecisions and concludes.

2. Tanzania transport system

Tanzania is a developing low income country located in sub-Saharan Africa (Fig. 1). Poor infrastructure, imperfect informationand corruption hamper the seamless process of moving goods insub-Saharan Africa. This involves significant costs associated withlengthy and uncertain delivery times (Christ and Ferantino, 2009).

By comparing the level, quality and density of transport infra-structure for Tanzania and other countries from Eastern and South-ern Africa, it is possible to identify the degree of development of itstransport networks and the gaps with the other surveyed coun-tries. Table 1 illustrates that the total length and density of theroad network in Tanzania is among the worst in the region. OnlyDR Congo and Kenya have a lower percentage of paved roadwaysthan Tanzania. As transport service providers in Tanzania chargemore for transport on unpaved roads compared to transport onpaved roads (World Food Program, 2008), the low quality of theroad network implies high transport costs.

The overall conditions of the rail network is rather poor, withremarkable implications in terms of inefficient and unreliable ser-vices. Road is therefore the dominant mode of transport in Tanza-nia (Wood, 2004), also to haul goods from the seaports to thehinterland. A recent briefing report on the performance of the sea-port of Dar es Salaam shows that, in 2009, 90% of the cargo fromport was cleared by road. The remaining 10% was handled by rail(Tanzania Ports Authority, 2010).

Tanzanian rivers are not navigable (CIA Fact Book, 2010) andwater transport is therefore limited to the Victoria, Tanganyikaand Nyasa lakes. The lakes host eight inland ports: Musoma andNansio on Lake Victoria; Kigoma and Kasanga on Lake Tanganyikaand Itungi, Mbamba Bay, Liuli and Manda on the shores of LakeNyasa. Intermodal transport (rail – inland navigation) comes intoplay mainly in the transportation of transit cargo to and from theland-locked countries such as Uganda, Burundi and DR Congo.

Maritime transport is relatively more developed than inlandtransport as Tanzania has four major seaports which are Tanga,Dar es Salaam, Mtwara and Zanzibar. Zanzibar is under the juris-diction of the Zanzibar Port Corporation while the ports of Tanga,Dar es Salaam and Mtwara are governed by the Tanzania Ports

Authority. The seaports are the gateway for international tradefor Tanzania and its neighbouring land-locked countries. WhileZanzibar is the local hub for international freight of the firms lo-cated in the islands of Zanzibar and Pemba, Dar es Salaam is themajor seaport handling the bulk of international and coastal freightof Tanzania. The seaport of Dar es Salaam is facing stiff competitionfrom the seaports of Kenya, Mozambique and South Africa. Theport has a reputation for long delays and corruption (Wood,2004) which add to port user costs in terms of monetary and timecost. The situation is worsened by the underdeveloped and costlyhinterland transport system. Despite its geographic advantage asa gateway to its land locked neighbours, there is clear evidencethat Tanzania’s lack of infrastructure is acting as a constraint onexpansion of trade and economic activity in both the country andthe region as a whole (Minassian et al., 2008).

Starting from the degree of development of the transport net-work briefly described in this section, the following parts of the pa-per will ascertain, on the basis of a stated preference methodology,the monetary values that a sample of Tanzanian firms attach to thetransport quality attributes such as frequency, transit time, reli-ability, flexibility, loss and damage and transportation costs. Thesemonetary values reflect the level of importance that the users ofthe transport services attach to these quality attributes and mayconstitute a possible support towards setting priorities related totransport infrastructure planning and investments.

3. Survey of studies on freight transport quality attributes

The importance of the transport quality attributes for both thestrategies of private freight firms and the investment policies ofpublic administrations has led part of transport economic researchto analyse this topic, to propose theoretical models and to provideempirical estimations. In this context, it is important to stress thatthe studies display a sensible heterogeneity in terms of the consid-ered decision maker, of the proposed utility (or cost) function, andof the analysed quality attributes (Table 2). One of the first eco-nomic models was proposed by Winston (1981) who studied thedemand for intercity freight transportation in the United States,by analysing the possible choices of the receiver of the shipment.Making use of a random utility model, Winston considered notonly the characteristics of the firm and of the transported com-modity but also two important quality attributes (transit timeand reliability).

In the last decade, several studies have reconsidered this topicin various European countries (Belgium, Italy, Sweden, Switzer-land, and United Kingdom), in India, and in Australia (Table 2).No study, among these ones, has considered the receiver of theshipment as the focus of analysis. Three studies (Wigan et al.,2000; Shinghal and Fowkes, 2002; Fowkes et al., 2004) have beenbased on freight shippers/forwarders. Others researchers (Bolisand Maggi, 2003; Witlox and Vandaele, 2005; Massiani, 2008) havetaken into account industrial firms and the related internal orexternal logistics service providers. Almost all studies have madeuse of utility functions (weighted, non-linear, and random logit)while the paper by Bolis and Maggi (2003) was based on a costfunction.

All recent surveyed studies have considered the two freighttransport quality attributes (time and reliability) taken into ac-count by Winston (1981). Moreover, there has been a tendency to-wards the inclusion of further important attributes. The studies byWigan et al. (2000) and INREGIA (2001) have also analysed the rel-evance of damage, while the paper by Shinghal and Fowkes (2002)mentioned the frequency of service. Bolis and Maggi (2003) in-cluded frequency and flexibility. The two more recent studies (Wit-lox and Vandaele, 2005 and Massiani, 2008) are the ones that

Fig. 1. Tanzania transport system. Source: http://commons.wikimedia.org/wiki/File:Tanzania_transport_map-fr.svg

Table 1Quality and density of roadways and railways. Source: The CIA Fact Book (updated in April 2010) and own calculations.

Country Roadways Railways

Paved Unpaved Total Paved Density Total length Density(km) (km) (km) % of Total km/100 km2 (km) km/100 km2

Burundi 1286 11,036 12,322 10.4 45 – –DR Congo 2794 150,703 153,497 1.82 6.8 4007 0.18Kenya 11,273 166,527 177,990 6.3 31 2778 0.49Malawi 6956 8495 15,451 45.0 16 797 0.85Mozambique 5685 24,715 30,400 18.7 3.8 4787 0.61Rwanda 2662 11,346 14,008 19.0 57 – –South Africa 73,506 288,593 362,099 20.3 30 20,872 1.72Tanzania 6808 72,083 78,891 8.62 8.9 3689 0.42Uganda 16,272 54,474 70,746 23.0 36 1244 0.63Zambia 20,117 71,323 91,440 22.0 12 2157 0.29

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include the wider number of freight transport quality attributes.They consider time, reliability, loss and damage, frequency, andflexibility.

The present paper, in line with the latter surveyed ones, willstudy five quality attributes, identified according to the followingdefinitions. Flexibility will be expressed as the percentage of un-planned shipments with respect to the total ones. Frequency will

be quantified as the number of freight transport services offeredper month by a shipping company or any freight forwarding agent.Loss and damage will be expressed as the percentage of the com-mercial value of the total freight shipped that is lost or damaged.Reliability will be associated to the percentage of timely deliveries.Lastly, travel time will be related to freight transit time and it willinclude the loading and unloading procedures.

Table 2Main characteristics of theoretical models related to freight transport quality attributes.

Author Country Decision maker Function Quality attributes

Winston (1981) USA Receiver Random utility Transit time, reliabilityWigan et al. (2000) Australia Freight shippers – Travel time, reliability, damageINREGIA (2001) Sweden – – Time, reliability, damageShinghal and Fowkes (2002) India Exporters, freight forwarders, and

industrial firmsWeighted utility Time, reliability, and frequency of service

Bolis and Maggi (2003) Switzerland – Italy Industrial firm using logistics servicesas an input

Cost function Time, reliability, frequency and flexibility

Fowkes et al. (2004) United Kingdom Shippers, hauliers and third partylogistics operators

Weighted Utility Time, reliability

Witlox and Vandaele (2005) Belgium Industrial firms and logistics serviceproviders

Non-linear utility Time, loss and damage, frequency,reliability, flexibility

Massiani (2008) Italy Logistics managers of industrial firms Random logit Travel time, reliability, loss and damage,frequency, flexibility

L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234 1225

4. Research methodology and design of the experiment

In order to gather data for their economic analyses, researcherscan either employ revealed preferences or stated preferences(Zamparini and Reggiani, 2007). By using the revealed preferencemethodology, researchers model behaviour of economic agents onthe basis of their actual choices. Data obtained through revealedpreferences have a high acceptability because they presumably re-flect real choices that are made under real market situations whichtake into account existing market constraints (Louviere et al., 2000).However the revealed preference methodology has a number ofdraw-backs. Firstly, it is necessary to have a sufficiently largenumber of people who reflect the real trade-offs made amongstthe attributes that affect choice. This is practically impossible in realmarket situations. Secondly, high collinearity among multiple attri-butes is common among revealed preference data (Greene, 2007;Louviere et al., 2000; Freeman, 2003; Hensher et al., 2005) makingit difficult to separate attribute effects. Thirdly, revealed preferencemodels are generally suitable for short-term forecasting of smallchanges in choice behaviour with respect to prevailing state ofaffairs (Louviere et al., 2000). Lastly, most often attributes underinvestigation show very little variability in real markets making itdifficult to rely on revealed preference data to estimate a changeof behaviour given a change in the attributes (Hensher et al., 1998).

In stated preference methodology, consumer preferences areobtained using hypothetical, rather than actual scenarios. As such,stated preference methodologies have been criticised as depictingbehaviour which is not observed and thus fail to take into consid-eration certain types of real market constraints (Cummings et al.,1986; Mitchell and Carson, 1989; Louviere et al., 2000). However,stated preference methods allow the estimation of demand fornew products/services to which revealed preference data is notavailable. Moreover, collinearity can be avoided in stated prefer-ence methods by careful design of the stated preference experi-ment. The experiment can also be designed in such a way thatthe variability of the attributes is in such a way that the derivedchange of behaviour of the consumers can reliably be estimated gi-ven the corresponding change in the attributes.

In this research, the stated preference methodology has beenadopted coherently with recent transport economic literature(Shinghal and Fowkes, 2002; Bolis and Maggi, 2003; Witlox andVandaele, 2005) for a series of reasons. First, it was necessary todetermine partial utility functions of the form:

DU ¼ aDfrequencyþ bDtimeþ cDreliabilityþ kDflexibility

þxDLoss & Damageþ qDcost: ð1Þ

associated with shippers’ freight mode choice. In order to get reli-able values of the parameters in a revealed preference setting, a

very large sample of respondents would have been required. Sec-ondly, information on attributes like frequency, reliability and flex-ibility are not usually kept by freight transporters, making itimpossible to get reliable data with respect to such attributes.Thirdly, given the low development in transport infrastructure inAfrica over time (see Section 2), little variability of the attributesis observed. This makes it difficult to determine a change of behav-iour of freight transporters given a change in the attributes in a re-vealed preference setting. Lastly, anticipated high collinearity in theattributes like frequency, reliability and flexibility required the useof stated preference methodology. These attributes depend verymuch on the quality of transport infrastructure in such a way thatimprovement in the quality of transport infrastructure results inthe increase in frequency, reliability and flexibility simultaneously.In this case obtaining reliable values of the parameters in revealedpreference setting would be unlikely.

A stated preference questionnaire was thus designed for thepurpose of conducting the state preference survey. The question-naire was tested using five companies in Tanzania. In-depth inter-views were conducted with the logistic managers of thesecompanies so as to determine the suitability of the questionnaire.The shortfalls identified in the questionnaire were then correctedin its final version. The questionnaire was divided into two parts.The first part aimed at deriving information on characteristics ofthe companies and the second part contained questions that wouldallow to derivate the necessary information for the stated prefer-ence analysis.

The first part of the questionnaire was composed by three setsof questions. The first one was designed to capture informationon the general characteristics of the surveyed company (location,sector, number of employees and yearly turnover). The secondset of questions was related to the organisation of transport inthe company and to the accessibility to different types of transportinfrastructure or transport modes. The third set of questions aimedat acquiring information on characteristics of the transport per-formed by the company (transportation cost, modal distributionof goods shipped, spread of trade volume of the company by geo-graphical distance, nature of goods shipped and the average com-mercial value of the goods shipped in US dollars per kilogram).

The second part of the questionnaire was constituted by twosets of questions and a ranking exercise. The first set of questionssought information on the typical goods flow to be considered asa reference for the stated preference experiment. The respondentwas asked to give the type (or short description) of the goods, dis-tance covered, yearly loaded tonnage, frequency of shipment permonth, average size of shipment and modal distribution. More-over, with the second set of questions, the respondent was alsoasked to give the relative importance of each of the determiningfactors that affect the choice of the transport service. These factors

1226 L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234

were transportation cost and the quality attributes described inSection 3 (flexibility, frequency, loss and damage, reliability, andtransit time). The respondent was also asked to rank the attributesin order of importance and to give the relative weight of each ofthem. The respondents were then asked to indicate whether theywere considering making a change of transport mode for one ormore of their goods flow. Lastly, respondents were asked to giveinformation as to whether a modal shift on their part would bepossible if the preferred alternative could be made availablethrough a mode different from the one that is being used.

5. Descriptive statistics of the dataset

After the development and testing of the questionnaire, 125Tanzanian firms were contacted; out of which 24 interviews con-taining all the necessary information for the stated preferenceanalysis were successfully conducted in 2008. The answers pro-vided by the surveyed companies to the first part of the question-naire are summarised in Table 3.

The activities of the surveyed firms are very heterogeneousencompassing raw materials (i.e. cement and steel), oil, chemicals,semi-finished goods (i.e. clothing materials and batteries) and sev-eral finished goods (i.e. mattress, spirits, slippers, and exercisebooks). The survey has thus covered a broad spectrum of the Tan-zanian industrial and marketing activities. With respect to thedimension of the firms, it is possible to use, as a benchmark, oneof the identification criteria of small, medium and large enterprisesprovided by the European Union with the Recommendation 2003/

Table 3Surveyed firms’ descriptive statistics.

Employees(2006)

Total yearlytonnage

Goods’ v

Cement production 354 700000 Less tha

Chemicals 53 120 Less thaCigarettes 700 4800 Between

Clothing materials 727 4200 Less tha

Dry batteries 112 3876 Less thaElectric cables 45 1254 Greater

Exercise books 180 2000 Less thaManufacturing of plastics, cosmetics, etc. 600 12200 Between

Manufacturing of wheat flour 50 18000 Less thaMattress manufacturing 1 150 1500 Less thaMattress manufacturing 2 131 1260 Less thaOil marketing 200 176000 Less tha

Paints production 324 1800 Less thaPharmaceuticals 1 48 410 Between

Pharmaceuticals 2 600 600 Less tha

Pipes – 2653 Greater

Printing and publishing 161 20 Less tha

Production and distribution of opaque beer 85 10800 Less thaSlippers 250 2700 Less thaSoap production 30 – Less thaSpirits 83 25200 Less thaSteel production 400 2400 Greater

Steel structuring-trading 30 3000 Between

Textile manufacturing 1600 210 Less tha

361 in terms of the number of employees (a small firm employsless than 50 workers, a medium firm employs between 50 and250 workers, large enterprises employ more than 250 workers).

It is possible to notice that, out of the 24 interviewed firms, fourenterprises are of small dimension (electric cables, pharmaceuti-cals1, soap production, and steel structuring-trading), nine firmsare of medium dimension, ten firms are large enterprises (for oneof the surveyed firms, it was not possible to retrieve the numberof employees in 2006). The total yearly tonnage produced andshipped by the firms is extremely varied. Some firms are character-ised by a yearly production that is lower than one thousand tons(chemicals, pharmaceuticals, printing and publishing, and textilemanufacturing), others produce larger quantities and there aretwo firms (cement production and oil marketing) that manage700,000 and 176,000 tons respectively. The value density of thegoods is generally less than 6 $/kg. Four firms (cigarettes, manufac-turing of plastics and cosmetics, pharmaceuticals1, and steel struc-turing-trading) produce and ship goods that have a valuecomprised between 6 $/kg and 35 $/kg and only three firms (elec-tric cables, pipes, and steel production) deal with goods that have avalue higher than 35 $/kg. It appears that the results obtained interms of monetary values of freight qualitative attributes may beinfluenced by the value density of the shipped goods. However, itis quite likely that similar shares of firms shipping low value den-sity goods would be obtained in Western countries samples. Forexample, in the United States, the average value density of railtransport is about 0.2 $/kg and the average value density of roadtransport is slightly less than 1 $/kg (McKinnon, 2010).

alue Export (%) Average distancetravelled (km)

Mode

n $6/kg 0 200 Road 97.5%Rail 2.5%

n $6/kg 0 150 Road 100%$6/kg and $35/kg 20 800 Road 85%

Maritime 15%n $6/kg 18 800 Road 95%

Maritime 5%n $6/kg 10 580 Road 100%than $35/kg 3.4 300 Road 90%

Rail 10%n $6/kg 0 200 Road 100%

$6/kg and $35/kg 15 550 Road 90%Maritime 10%

n $6/kg 0 500 Road 100%n $6/kg 0 300 Road 100%n $6/kg 0 500 Road 100%n $6/kg – 690 Road 60%

Rail 40%n $6/kg 0 850 Road 100%

$6/kg and $35/kg 0 260 Road 98%Air 2%

n $6/kg – 500 Road 90%Short sea shipping 10%

than $35/kg 0 825 Road 80%Maritime 20%

n $6/kg 0 200 Road 95%Maritime 4%Air 1%

n $6/kg 0 100 Road 100%n $6/kg 15 700 Road 100%n $6/kg 1 700 Road 100%n $6/kg – 1720 Road 100%than $35/kg 100 2500 Road 95%

Maritime 5%$6/kg and $35/kg 0 450 Road 95%

Rail 5%n $6/kg 3 260 Road 100%

Table 4Quantitative values of firms’ freight transport attributes (weights in parentheses).

Flexibility Frequency (per month) Loss and damage Price in US$ (per ton/km) Reliability Time (hours)

Cement production 90% (5) 26 (4) 2% (3) 0.03 (48) 90% (10) 4 (30)Chemicals 90% (50) 4 (5) 1% (6) 0.10 (7) 90% (21) 3 (11)Cigarettes 80% (19) 30 (19) 1% (12) 0.06 (12) 90% (19) 10 (19)Clothing materials 90% (20) 2 (10) 1% (5) 0.03 (50) 90% (10) 12 (5)Dry batteries 90% (7) 3 (8) 1% (5) 0.034 (50) 80% (20) 8 (10)Electric Cables 3% (4) 15 (4) 1.2% (5) 0.023 (10) 65% (70) 3 (7)Exercise books 90% (6) 24 (11) 1% (6) 0.04 (6) 90% (60) 5 (11)Manufacturing of plastics, cosmetics, etc. 40% (5) 24 (10) 2% (5) 0.06 (10) 90% (20) 8 (50)Manufacturing of wheat flour 90% (5) 30 (5) 1% (5) 0.03 (5) 90% (50) 12 (30)Mattress manufacturing 1 80% (6) 30 (6) 1% (6) 0.13 (70) 80% (6) 4 (6)Mattress manufacturing 2 80% (5) 30 (3) 1% (1) 0.028 (1) 90% (10) 6 (80)Oil marketing 40% (30) 30 (6) 10% (4) 0.08 (10) 80% (40) 12 (10)Paints production 10% (16) 3 (5) 8% (11) 0.013 (26) 35% (16) 12(26)Pharmaceuticals 1 50% (6) 1.5 (8) 1% (6) 0.154 (54) 65% (10) 6 (16)Pharmaceuticals 2 75% (8) 10 (10) 1% (6) 0.168 (6) 75% (50) 7 (20)Pipes 90% (2) 3 (10) 1% (5) 0.3 (60) 80% (20) 24 (3)Printing and publishing 90% (5) 4 (2) 1% (3) 0.458 (5) 85% (20) 4 (65)Production and distribution of opaque beer 75% (6) 15 (15) 10% (4) 0.013 (35) 90% (25) 7 (15)Slippers 20% (19) 3 (16) 5% (10) 0.014 (16) 30% (23) 9 (16)Soap production 50% (7) 3 (4) 10% (20) 0.056 (60) 80% (5) 12 (4)Spirits 80% (15) 30 (16) 1% (9) 0.11 (26) 90% (21) 72 (13)Steel Production 60% (15) 2 (15) 1% (5) 0.052 (25) 50% (20) 24 (20)Steelstructuring-trading 80% (5) 10 (5) 3% (5) 0.019 (10) 90% (50) 7 (25)Textile manufacturing 60% (11) 15 (11) 2% (7) 0.06 (20) 80% (11) 4 (40)

L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234 1227

The destination markets of the goods produced by the surveyedfirms are predominantly domestic. Only one firm (steel produc-tion) of the sample is 100% export oriented. Eight firms have an ex-port share that is in the range between 1% and 20%. Twelve firmshave no exports.3 The destination markets of the goods influencethe average distance travelled by the goods shipped by the firms,which is generally lower than 1000 kms, except for two firms (spir-its and steel production). Among the other firms, 10 have an aver-age lower than 500 kms and twelve cover a mean distancebetween 500 and 1000 kms.

It was found out during the survey that the main mode used bythe companies in freight transportation is road transport. Twelve ofthem use it exclusively while the other ones have a predominantshare of road transport but also use other means of transport. Fivefirms make use of maritime transport (with shares between 5% and20%), four firms utilize rail transport (with shares between 2.5%and 40%), one firm employs air transport (2%), one firm makesuse of air (1%) and maritime transport (4%), and one employs shortsea shipping (10%). When asked whether they would be eager toshift from road transport to a different mode, fifteen firms statedthat they would increase the percentage of rail transport used incase of an increase of efficiency of this latter transport mode. Thismay constitute an important feature to consider when examiningthe results of the survey given that, as it is shown in Table 1, Tan-zania has the highest railway density in the area.

With respect to the provision of transport services, the sampleconsiders 12 firms that outsource more than 50% of its transportrequirements, and by other 12 firms that are characterised bymainly internal transport services. As will be shown in Section6.6, this feature has important effects in terms of the monetary val-ues of qualitative attributes considered in the survey.

As it was mentioned in Section 4, the second part of the ques-tionnaire contained questions that were aimed to gather the neces-sary information for the stated preference exercise. The answers ofthe respondents are listed in Table 4. For each firm, the quantita-tive values of the various freight transport attributes (flexibility,frequency, loss and damage, price, reliability, and time) are re-

3 For the further three firms, it was not possible to ascertain the export share.

ported jointly with the relative weights that the firms attach toeach one of the attributes.

With respect to flexibility, most firms seem to have a percent-age of unplanned shipments with respect to the total ones that isvery high. The relative weight of the parameter is generally quitelow. Only the oil marketing and the chemical firm have weightsof 30% and 50% respectively.

The frequency of freight transport services offered per month bythe surveyed firm ranges between 2 (clothing material firm) and30 (cigarettes, manufacturing of wheat flour, mattress manufactur-ing, oil marketing, and spirits firms). The relative weight attachedto this parameter by firms is low throughout the sample, as no firmdisplays a value higher than 20%.

The amount of loss and damage, quantified as the percentage ofthe commercial value of the total freight shipped that is lost ordamaged, does not seem to be relevant in the surveyed firms. Onlythree firms (oil marketing, production and distribution of opaquebeer and soap production) have a value equal to 10%. One firm(paints production) has 8% of loss and damage. All other firms havevalues ranging from 1% to 5%. The soap production firm shows thehighest relative weight (20%) for this parameter. For all the otherfirms this parameter does not seem to be very relevant, and itranges from 1% to 11%.

The price of the transport services, quantified per ton/km, spansfrom 0.013 US$ (paints production) to 0.458 US$ (printing andpublishing). The latter value is due to the fact that this firm has avery low quantity of the average consignment size that is equalto 0.2 tons. For eleven firms in the sample, price is the most rele-vant parameter. Only the chemical and the second mattress man-ufacturing firm have values lower than 10% for this parameter.

The performance in terms of reliability, expressed as the per-centage of timely deliveries, of the sampled firms is quite highfor most firms. Eighteen firms have a reliability ratio that rangesbetween 80% and 90%. Only two firms (paints production and slip-pers) display reliability ratios lower than 50%. The importanceattributed to this attribute by the firms is very heterogeneous asit ranges from 5% (soap production) to 70% (electric cables). How-ever, half of the firms have relative weights close or equal to 20%.

Travel time (related to freight transit time and including theloading and unloading procedures) is connected to the average

1228 L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234

distance of shipments. For the majority of the firms it is lower thanor equal to 10 h. Two firms (pipes and steel production) employ24 h for their freight transit time and one firm (spirits), that shipsgoods at an average distance of 1720 kms, is characterized by atransit time of 72 h.

In the following section, the evidence summarised in Table 3and described in the previous paragraphs will be used in order toperform a stated preference analysis. This will be aimed at ascer-taining the monetary values of the qualitative attributes of trans-port for the sample of Tanzanian firms.

6. Monetary values of transport quality attributes

Analysis of the data was carried out by the use of the MUSTARD(Multi-criteria Utility-based STochastic Aid for Ranking Decisions)software. The software is based on utility additive (UTA) multicri-teria method proposed by Jacquet-Lagréze and Siskos (1982).

The MUSTARD software makes use of all the variants of the UTAmodel and at the same time it is capable of computing equivalentmoney value of the transport quality alternatives given one of thequality criteria is in monetary terms. The performance of the soft-ware was reviewed and found to be robust (Siskos, 2002) and hasbeen used by a number of researchers (see, for example, Beutheand Scannella, 2001; Scannella and Beuthe, 2001; Beuthe et al.,2003; Witlox and Vandaele, 2005).

In this stated preference exercise, the monetary values attachedto transport quality attributes are related to the money equivalentthat a firm is willing to pay (WTP) for a 10% improvement of theattribute or the money equivalent that the same firm is willingto accept (WTA) as compensation for a 10% worsening of the attri-bute. By using the MUSTARD software, the equivalent money val-ues for a given transport were calculated using Eqs. (2) and (3)below (Witlox and Vandaele, 2005):

WTP ¼ Cþ10% � C0

lðC0Þ � lðC�10%Þ

" #lðAþ10%Þ � lðA0Þ

Aþ10% � A0

" #ð2Þ

and

WTA ¼ C0 � C�10%

lðCþ10%Þ � lðC0Þ

" #"lðA0Þ � lðA�10%Þ

A0 � A�10%

#ð3Þ

where C0 = current transportation cost (status quo), C+10% = trans-portation cost increased by 10%, C�10% = transportation cost de-creased by 10%, A0 = current value of the attribute (status quo),A+10% = value of the attribute increased by 10%, A�10% = value ofthe attribute decreased by 10%, and l(⁄) are the utility levels ofthe various parameters.

The monetary values of freight transportation quality attributeshave been computed in terms of willingness to pay for a unit in-crease in quality of the quality attribute (WTP) and willingnessto accept compensation in case of a unit decrease in quality ofthe quality attribute (WTA). Monetary values have been computedusing Eqs. (2) and (3) above.

In the case that a firm is risk neutral, the WTP and the WTA areequivalent, given that the utility function is linear and, conse-quently, decrease in utility by decreasing the parameter value by10% is equal to the increase in utility by increasing the parameterby 10%. WTP and WTA are instead heterogeneous in the case of riskaversion given that the utility function is concave and so the WTPis lower than the WTA. The following subsections present andcomment on the monetary values of the freight transport qualityattributes both for the risk neutrality case and the risk aversionone. The monetary values are as shown in Figs. 2–6 below and de-tailed estimates are provided in Appendix. Moreover, a further sub-section presents the impact that management (internal or

outsourced) of transport services and the value density of shippedgoods have on the monetary values of freight transport qualityattributes. A last subsection will provide some general remarkson the results that will be presented in the next subsections.

6.1. The monetary values of flexibility

Flexibility appears to be the least appealing attribute for thesample of Tanzanian firms. The enterprises that are more interestedin increasing the possibility of undertaking unforecasted transportservices are the chemicals and oil marketing ones, that value it(in the case of risk neutrality) 0.008 US$/ton km and 0.006 US$/ton km respectively. This may be due to the variability of prices ofthe products that they trade. It is thus possible that it may be eco-nomically valuable to them to have a very flexible monthly sche-dule of transport services. Other firms that have a comparativelyrelevant value of flexibility are the printing and publishing, electriccables, pharmaceuticals 2, and mattress manufacturing 2.

For all other firms, the value of flexibility is less than 0.002 US$/ton km; which implies the irrelevance of this attribute for them.

6.2. The monetary values of frequency

Frequency, meant as the amount of transport services permonth, is in an intermediate position among the qualitative attri-butes of transport in the considered sample. For this, as well asfor the further analysed attributes, the firm that values it most isthe printing and publishing one, given the large unit costs of trans-port that this firm faces (0.458 US per ton/km).

The other firms that value frequency more than 0.01 US$/ton km are the two pharmaceutical firms, the chemicals one, thepipes one, and the steel production one. All other firms’ stated pref-erences imply a value of frequency lower than 0.01 US$/ton km.The firm that produces and distributes opaque beer, one of thetwo mattress manufacturing firms, and the cement productionfirm are characterised by a negligible value of frequency.

6.3. The monetary values of loss and damage

Loss and damage is the second most important attribute for thesample of Tanzanian firms. Even if, according to their answers tothe questionnaire, the share of the commercial value of the totalfreight shipped that is lost or damaged is very low for most ofthe firms, some of them have a WTP/WTA (in the case of risk neu-trality) for a 10% reduction of this attribute that is higher or close to0.1 US$/ton km (printing and publishing, pharmaceuticals 2, chem-icals, and cigarettes firms).

Other firms that display a relatively significant monetary valueof loss and damage are the exercise books, spirits, manufacturing ofwheat flour, pipes and mattress manufacturing 2. For most of theremaining firms, the value of loss and damage appears to be almostirrelevant.

6.4. The monetary values of reliability

From the stated preference exercise, it emerges that, among thesampled Tanzanian firms, reliability is one of the least relevantqualitative attribute of transport services. In the case of risk neu-trality, no firm has a value of reliability remarkably higher than0.02 US$/ton km and 22 out of 24 firms evaluate a 10% increasein reliability less than 0.01 US$/ton km.

The two firms that have a WTP/WTA close to 0.02 in the riskneutrality case are the printing and publishing and the pharmaceu-ticals 2 firms. Several firms (paints production, dry batteries, mat-tress manufacturing1, production and distribution of opaque beer,cement production, clothing materials, and soap production) have

Fig. 2. Monetary values of flexibility.

Fig. 3. Monetary values of frequency.

L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234 1229

a negligible economic interest in a 10% increase/decrease inreliability.

6.5. The monetary values of travel time

In the case of the value of travel time, the printing and publish-ing firm appears to have a WTP for a 10% reduction in time that isequal to 1.5 US$/ton tkm.

Another firm that shows a remarkable value of time is the mat-tress manufacturing 2 firm which has an economic interest in a10% improvement in this quality attribute that is equal to0.33 US$/ton km. For all other firms, the value of a 10% reductionin travel time is lower than 0.1 US$/ton km (see Table A5 in Appen-dix). Among them, the pharmaceuticals 2, the chemicals, the man-ufacturing of plastics and cosmetics, the textile manufacturing, theexercise books, and the manufacturing of wheat flour firms are theones that have a value of travel time higher than 0.01 US$/ton km.For the spirits, pipes, soap production, and clothing materials firmsthe value of a 10% reduction in travel time is negligible.

6.6. Transport provider, density value of shipped goods, and monetaryvalues of freight transport quality attributes

Given the answers to the questionnaire, it was possible topartition the surveyed firms according to the transport provider(internal to the firm or outsourced) and to density value ofshipped goods in order to ascertain whether these characteris-tics impact on the monetary values of freight transport qualityattributes.

Table 5 presents the results for internal and external transportproviders. It emerges clearly that this feature has a remarkable im-pact on the evaluation of the qualitative attributes considered inthe study.

Firms that outsource transport display higher monetary val-ues for all considered attributes. This may probably be due tothe fact that they have higher expectations given their relianceon professional transport firms. They thus require improved ser-vices than the ones that they would be able to provide on theirown.

Fig. 4. Monetary values of loss and damage.

Fig. 5. Monetary values of reliability.

1230 L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234

On the other hand, partitioning firms according to the valuedensity of the shipped goods does not allow to obtain unambigu-ous results (Table 6). However, firms that ship goods with a valuelower than $6/kg have the highest valuation of almost all of theconsidered attributes (flexibility, loss and damage, reliability, andtime).

It is not possible to draw strong conclusions among firms whosegoods have a value density between $6/kg and $35/kg and thosewho handle goods with a value greater than $35/kg. These latterfirms do display the highest valuation of the frequency attribute.It should be noted that the large share of firms shipping goods witha low value density may be one of the key determinants to the gen-erally low monetary values of the qualitative attributes displayedin the preceding subsections.

6.7. General remarks

The conjoint analysis of all the attributes, allows to ascertainthat travel time, loss and damage, and frequency are the most

economic relevant characteristics of transport services for a largeshare of the sampled firms. Consequently, these firms should in-vest in logistics strategies that allow them to reduce travel timeand loss and damages and to increase frequency of shipments.The improvements in reliability and flexibility do not stand askey issues for the Tanzanian firms. This is likely due to their per-ception of a good performance with respect to these attributes(see Table 3). There is a large heterogeneity in the economic valuesattributed to the various parameters by the firms. Two remarkableoutliers are represented by the values of time for the mattressmanufacturing 2 and printing and publishing firms. Moreover,the values of the quality attributes for the firms are not clusteredaccording to the type of produced and shipped goods. It results thatfirms that outsource transport services do, on average, have highermonetary values of freight transport quality attributes. The parti-tioning of firms according to the density value of shipped goodsdoes not allow us to obtain clear-cut results, although firms thatship goods with a value density lower than $6/kg exhibit the high-est valuation of almost all of the considered attributes.

Fig. 6. Monetary values of travel time.

Table 5Transport provider and monetary values of freight transport attributes.

Flexibility Frequency (per month) Loss and damage Reliability Time

Outsourced transport 0.00212 0.00919 0.04457 0.00409 0.12302Internal transport 0.00057 0.00461 0.01962 0.00138 0.03891

Table 6Value density of shipped goods and monetary values of freight transport attributes.

Flexibility Frequency (per month) Loss and damage Reliability Time

Greater than $35/kg 0.0012 0.0110 0.0144 0.0015 0.0026Between $6/kg and $35/kg 0.0006 0.0055 0.0236 0.0010 0.0154Less than $6/kg 0.0016 0.0068 0.0387 0.0035 0.1152

L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234 1231

7. Conclusions

As was shown in the literature review, several quality attributesmay impact on the performance of the freight transport servicesadopted by firms. The relative influence of the various parametersdepends particularly on the regional context and on the nature ofthe firm. In the last decade, the economic literature that has ana-lysed this topic has tried to encompass in the estimations the larg-est possible number of relevant attributes. This research has takeninto account flexibility, frequency of service, loss and damage, reli-ability, and travel time in Eastern Africa. According to the statedpreferences expressed by the surveyed Tanzanian firms, it appearsthat the improvements in travel time, loss and damage and fre-quency have a higher economic value than the upgrading of flexi-bility and of reliability. A possible explanation of these results canbe obtained by considering the World Bank’s Logistics PerformanceIndex (World Bank, 2010). It emerges that Tanzania ranks 95 out of155 countries in the world which means it is below the median.However, the country’s performance is above the mean perfor-mance of low-income countries as well as sub-Saharan Africa.These figures show that as far as logistics performance indicatorsare concerned, the country’s quality of trade and transport relatedinfrastructure is below the mean of both low-income countries andsub-Saharan countries. This basically means that the quality oftransport infrastructure in Tanzania is very low leading low speedin freight transportation and perhaps high rate of loss and damageand low service frequency.

When transport mode is considered, it emerges that the largemajority of shipments are performed through road transport. Thismay mostly be due to the fact that the destination markets of theproduced and shipped goods are, for a very large share, domesticand that the average travel distance is in general much lower than1000 kms. It appears that the dimension of the firm (in terms ofemployees) does not influence the average travelled distance(and consequently the dimension of the market) and the adoptedtransport mode. However, when asked whether they would bewilling to opt for a modal shift, 15 firms out of 24 stated that theywould be willing to move to rail transport (at least for a certainshare of their overall shipped goods) if such alternative transportmode would increase its degree of efficiency.

The abovementioned findings may provide some importantguidance with respect to transport infrastructure planning andtransport policy in Tanzania. Were these results to be confirmedin a larger sample study, Tanzanian transport policy should fosteroptions that allow it to implement a reduction in transit times andin losses and damages and an increase in frequency of the suppliedservices. It does not appear that a large share of freight transportmay be shifted to transport modes that are alternative to roadtransport. However, an improvement of the quality of rail servicesmay be important to reach a relatively more balanced freighttransport across modes. Future direction of research of this studyshould be aimed at increasing the considered sample and at imple-menting econometric techniques in order to strengthen the con-clusions obtained through this study.

1232 L. Zamparini et al. / Journal of Transport Geography 19 (2011) 1222–1234

Appendix A

See Tables A1–A5.

Table A1Monetary values of flexibility.

WTP in case of risk aversion WTA in case of risk aversion WTP and WTA in case of risk neutrality

Cement production 0.00003 0.00005 0.00004Chemicals 0.00694 0.01263 0.00772Cigarettes 0.00109 0.00186 0.00120Clothing materials 0.00012 0.00021 0.00013Dry batteries 0.00005 0.00008 0.00005Electric cables 0.00284 0.00479 0.00307Exercise books 0.00042 0.00067 0.00044Manufacturing of plastics. cosmetics, etc. 0.00072 0.00113 0.00078Manufacturing of wheat flour 0.00031 0.00057 0.00033Mattress manufacturing 1 0.00013 0.00022 0.00014Mattress manufacturing 2 0.00152 0.00420 0.00152Oil marketing 0.00556 0.00937 0.00600Paints production 0.00073 0.00127 0.00080Pharmaceuticals 1 0.00031 0.00054 0.00034Pharmaceuticals 2 0.00280 0.00448 0.00299Pipes 0.00010 0.00018 0.00011Printing and publishing 0.00473 0.00872 0.00509Production and distribution of opaque beer 0.00003 0.00005 0.00003Slippers 0.00076 0.00137 0.00084Soap production 0.00012 0.00020 0.00013Spirits 0.00074 0.00124 0.00080Steel production 0.00048 0.00082 0.00052Steelstructuring-trading 0.00011 0.00018 0.00012Textile manufacturing 0.00051 0.00084 0.00056

Table A2Monetary values of frequency.

WTP in case of risk aversion WTA in case of risk aversion WTP and WTA in case of risk neutrality

Cement production 0.00009 0.00015 0.00010Chemicals 0.01625 0.02727 0.01806Cigarettes 0.00291 0.00495 0.00320Clothing materials 0.00274 0.00475 0.00300Dry batteries 0.00165 0.00287 0.00181Electric cables 0.00057 0.00096 0.00061Exercise books 0.00292 0.00450 0.00311Mattress manufacturing 1 0.00034 0.00059 0.00037Mattress manufacturing 2 0.00249 0.00653 0.00249Oil marketing 0.00148 0.00250 0.00160Paints production 0.00079 0.00127 0.00087Pharmaceuticals 1 0.01387 0.02388 0.01521Pharmaceuticals 2 0.02625 0.04200 0.02800Pipes 0.01524 0.02632 0.01667Plastics and cosmetics 0.00231 0.00391 0.00250Printing and publishing 0.04089 0.08179 0.04404Production and distribution of opaque beer 0.00034 0.00058 0.00037Slippers 0.00424 0.00778 0.00467Soap production 0.00114 0.00196 0.00124Spirits 0.00207 0.00358 0.00226Steel production 0.01432 0.02467 0.01568Steel structuring–trading 0.00091 0.00143 0.00099Textile manufacturing 0.00204 0.00338 0.00224Wheat flour 0.00093 0.00171 0.00100

Table A3Monetary values of loss and damage.

WTP in case of risk aversion WTA in case of risk aversion WTP and WTA in case of risk neutrality

Cement production 0.00080 0.00158 0.00088Chemicals 0.07500 0.13636 0.08333Cigarettes 0.05455 0.09474 0.06000Clothing materials 0.00263 0.00494 0.00288Dry batteries 0.00298 0.00559 0.00326Electric cables 0.00852 0.01557 0.00920Exercise books 0.03750 0.06000 0.04000

Table A3 (continued)

WTP in case of risk aversion WTA in case of risk aversion WTP and WTA in case of risk neutrality

Manufacturing of plastics. cosmetics, etc. 0.01333 0.02438 0.01440Manufacturing of wheat flour 0.02571 0.05571 0.02769Mattress manufacturing 1 0.01016 0.01757 0.01114Mattress manufacturing 2 0.01867 0.08400 0.01867Oil marketing 0.00296 0.00500 0.00320Paints production 0.00062 0.00111 0.00068Pharmaceuticals 1 0.01561 0.02686 0.01711Pharmaceuticals 2 0.15750 0.25200 0.16800Pipes 0.02195 0.04105 0.02400Printing and publishing 0.22900 0.52343 0.24662Production and distribution of opaque beer 0.00014 0.00024 0.00015Slippers 0.00159 0.00292 0.00175Soap production 0.00171 0.00295 0.00187Spirits 0.03408 0.06171 0.03723Steel production 0.00904 0.01733 0.00990Steel structuring-trading 0.00281 0.00515 0.00304Textile manufacturing 0.00927 0.01688 0.01020

Table A4Monetary values of reliability.

WTP in case of risk aversion WTA in case of risk aversion WTP and WTA in case of risk neutrality

Cement production 0.00006 0.00011 0.00007Chemicals 0.00294 0.00525 0.00327Cigarettes 0.00097 0.00165 0.00107Clothing materials 0.00006 0.00011 0.00007Dry batteries 0.00016 0.00027 0.00017Electric cables 0.00229 0.00387 0.00248Exercise books 0.00417 0.00667 0.00444Manufacturing of plastics. cosmetics, etc. 0.00123 0.00208 0.00133Manufacturing of wheat flour 0.00298 0.00595 0.00321Mattress manufacturing 1 0.00013 0.00022 0.00014Mattress manufacturing 2 0.00259 0.00778 0.00259Oil marketing 0.00370 0.00625 0.00400Paints production 0.00021 0.00036 0.00023Pharmaceuticals 1 0.00040 0.00069 0.00044Pharmaceuticals 2 0.01750 0.02800 0.01867Pipes 0.00114 0.00197 0.00125Printing and publishing 0.01924 0.03849 0.02072Production and distribution of opaque beer 0.00009 0.00016 0.00010Slippers 0.00062 0.00111 0.00068Soap production 0.00006 0.00009 0.00006Spirits 0.00091 0.00155 0.00100Steel production 0.00075 0.00133 0.00083Steelstructuring-trading 0.00098 0.00165 0.00106Textile manufacturing 0.00038 0.00063 0.00042

Table A5Monetary values of time.

WTP in case of risk aversion WTA in case of risk aversion WTP and WTA in case of risk neutrality

Cement production 0.00426 0.00740 0.00469Chemicals 0.04667 0.08485 0.05185Cigarettes 0.00873 0.01516 0.00960Clothing materials 0.00024 0.00041 0.00026Dry batteries 0.00078 0.00134 0.00085Electric Cables 0.00511 0.00863 0.00552Exercise books 0.01400 0.02240 0.01493Manufacturing of plastics. cosmetics, etc. 0.03472 0.05859 0.03750Manufacturing of wheat flour 0.01339 0.02679 0.01442Mattress manufacturing 1 0.00254 0.00439 0.00279Mattress manufacturing 2 0.31111 0.93333 0.31111Oil marketing 0.00617 0.01042 0.00667Paints production 0.00099 0.00172 0.00108Pharmaceuticals 1 0.00694 0.01194 0.00760Pharmaceuticals 2 0.07500 0.12000 0.08000Pipes 0.00061 0.00105 0.00067Printing and publishing 1.33311 2.66621 1.43565Production and distribution of opaque beer 0.00074 0.00128 0.00080Slippers 0.00141 0.00259 0.00156Soap production 0.00028 0.00049 0.00031Spirits 0.00071 0.00123 0.00078Steel production 0.00157 0.00278 0.00172Steelstructuring-trading 0.00633 0.01069 0.00684Textile manufacturing 0.02727 0.04688 0.03000

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