Tanzania Growth Diagnostic. Godius Kahyarara, Senior Lecturer, Department of Economics, and Director of Investment . and Resource Mobilization, University of Dar es Salaam

The production of the constraints analyses posted on this website was led by

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the content presented therein.

2014-001-1569-02

Tanzania Growth

Diagnostic

Partnership for Growth (2011)

A Joint Analysis for the Governments of the United Republic of Tanzania and the United States of America

Wehavealonganddifficultwaytogobeforeachievingthekindofdevelopmentthatwedesire.Itisimportantthatwegivedueattentiontothequestionofpromotingeconomicgrowthinourcountriesandbringingaboutdevelopmenttoourpeople.

- PresidentJakayaMrishoKikwete,AddresstotheAfricanUnion,February3,2009Tounleash transformationalchange,we’reputtinganewemphasison themostpowerful force theworldhaseverknownforeradicatingpovertyandcreatingopportunity…TheforceI’mspeakingaboutisbroad‐basedeconomicgrowth.

- PresidentBarackObama,September22,2010

The Growth Diagnostic working group is composed of the following main contributors:

Government of United Republic of Tanzania: Steering Committee:

Peniel M. Lyimo, Permanent Secretary, Prime Minister's Office, & Chairperson of the PFG Steering Committee Dr. Philip I. Mpango, Executive Secretary, President's Office, Planning Commission, Coordinator of PFG Steering Committee Members from Both Tanzania Mainland and Zanzibar Technical Committee:

Obey N. Assery, Director, Prime Minister’s Office Theonest Bagandanshwa, Assistant Director, Prime Minister’s Office – Regional Administration and Local Government Joseph Kiraiya, Senior Economist, Ministry of Finance Emmanuel Achayo, Director of Policy & Planning, Ministry of Agriculture, Food Security, and Cooperatives Eng. Othman S. Omar, Principal Irrigation Engineer, Ministry of Agriculture, Food Security, and Cooperatives Dr. Consolatha D. Ishebabi, Assistant Director, Ministry of Industry, Trade, and Marketing Eng. Fintan Kilowoko, Assistant Director, Ministry of Works Rose Ambrose, Economist, Ministry of Energy & Minerals Aunyisa B. Meena, Principal Transport Officer, Ministry of Transport Nsima Longin, Principal Livestock Statistician, Ministry of Livestock and Fisheries Development Lister Kongola, Assistant Director (Water Resources), Ministry of Water Moris Oyuke, Director, National Bureau of Statistics Dr. Godius Kahyarara, Senior Lecturer, Department of Economics, and Director of Investment and Resource Mobilization, University of Dar es Salaam Dr. Elibariki Msuya, Senior Lecturer, Sokoine University of Agriculture Omary H. Juma, Principal Economist, President's Office, Planning Commission Julieth S. Magambo, Principal Economist, President's Office, Planning Commission Mwita Mgeni, Commission for Economic Development, Zanzibar Ahmed H. Makame, Commissioner for Budget, Zanzibar Dr. Rahma S. Mahfoudh, Senior Economist, President’s Office, Finance and Economic Development, Poverty Reduction, Zanzibar Ramadhan Kh. Juma, Senior External Finance Officer, Zanzibar

United States Government:

Rachel Bahn, Program Economist, United States Agency for International Development Dr. David Garber, Economist, United States Agency for International Development Dr. Theresa Osborne, Lead Economist, Millennium Challenge Corporation Al Wood, Energy Analyst, Department of State Thanks are extended to the many individuals who assisted this work, including Ashley Edwards and Sonia Koshy at MCC, Mitch Ginsberg at United States Trade Representative, Elizabeth Pelletreau at the State Department, and Molly Dunn and Ben Linkow at USAID, as well as all individuals who commented on earlier drafts.

Contents

1.IntroductionandMainConclusions........................................................................................................................1

GeneralTrendsinandComponentsofGDP.........................................................................................................5

B.TrendsinGDP–Zanzibar...............................................................................................................................8

C.ExportsandTradePerformance.................................................................................................................10

D.TheAgriculturalSector...................................................................................................................................12

E.InvestmentPerformance................................................................................................................................15

F.GovernmentExpenditureandForeignAssistance.............................................................................17

G.GrowthinHouseholdIncomes....................................................................................................................19

3.IsthePrimaryConstrainttoGrowththeLackofAccesstoFinance?....................................................22

A.IntroductionandReformHistory...............................................................................................................22

B.IndicatorsofSupplyandDemandforInvestmentFinance.............................................................23

a.PriceIndicators..........................................................................................................................................23 b.Quantity/AccessIndicators..................................................................................................................25

C.Conclusion............................................................................................................................................................34

4.MacroeconomicRisksandDistortions..............................................................................................................36

A.Inflation.................................................................................................................................................................36

B.FiscalBalanceandDeficit..............................................................................................................................40

a.GovernmentRevenue..............................................................................................................................40 b.PublicExpenditure...................................................................................................................................41

C.ExternalPosition...............................................................................................................................................42

a.PublicExternalDebt................................................................................................................................44 b.RiskandCosttoReachFinancialMaturity....................................................................................48 c.CurrentAccount.......................................................................................................................................49

D.ExchangeRate....................................................................................................................................................51

E.Conclusions..........................................................................................................................................................53

5.WeakMicro‐AppropriabilityofReturns............................................................................................................54

A.LandPolicyandImplementation................................................................................................................55

a.HistoricalContext.....................................................................................................................................57 b.ContemporaryRegime...........................................................................................................................58 c.TestsofLandRegimeasaBindingConstraint................................................................................59

B.Taxes.......................................................................................................................................................................65

a.TaxRates......................................................................................................................................................65 b.TheTaxBaseandCollectionEfficiency...........................................................................................68 c.TaxesonAgriculture................................................................................................................................71

C.RegulatoryEnvironmentforPrivateBusinessandTrade................................................................71

a.Overview.....................................................................................................................................................72 b.TheInformalEconomy...........................................................................................................................75 c.CircumventionofRegulation...............................................................................................................78

D.MarketAccessandOpennesstoTrade....................................................................................................80

E.Conclusions.........................................................................................................................................................86

6.MarketFailuresinInnovation................................................................................................................................88

A.SpilloversinInnovationandIntellectualPropertyRights...............................................................88

B.ProductSophisticationIndexversusGDPCapita.................................................................................90

C.BusinessSophistication,TechnologicalReadiness,R&DLevels....................................................92

D.RegistrationofNew/ImportedTechnologies........................................................................................95

E.IntellectualPropertyandInformationExternalities..........................................................................95

a.TrademarkApplications........................................................................................................................95 b.PatentApplicationsandE‐Filing.......................................................................................................96 c.IntellectualPropertyRightsIndicators...........................................................................................97

F.MonopolyPowerandLackofCompetition.............................................................................................98

G.Conclusions.......................................................................................................................................................100

7.LackofInfrastructure.............................................................................................................................................102

A.GeneralOverview...........................................................................................................................................102

B.ThePowerSector...........................................................................................................................................102

a.PowerGeneration..................................................................................................................................103 b.PrivateInvestmentinPowerGeneration.....................................................................................103 c.PowerUsageandTransmissionandDistributionSystemCoverage................................104 d.PowerOutages........................................................................................................................................106 e.PrivateGeneratorUsage.....................................................................................................................106 f.GDPGrowthinYearsofPowerCrisis.............................................................................................107 g.PolicyandInstitutionalChallenges...............................................................................................108 h.EnergyPoverty.......................................................................................................................................109

C.Transport...........................................................................................................................................................110

a.Roads...........................................................................................................................................................110 AssessmentofDemand(Traffic)andShadowValueof(Poor)RoadCondition.......................112 c.RoadManagement.................................................................................................................................115 d.TransportationCostsinAgriculture..............................................................................................116 e.InstitutionalandPolicyIssuesforRoads.....................................................................................118 f.UrbanTraffic............................................................................................................................................119 g.Ports............................................................................................................................................................119 h.Rail...............................................................................................................................................................122 i.AirTransport...........................................................................................................................................126

D.ICTInfrastructure/Provision....................................................................................................................128

E.WaterInfrastructure.....................................................................................................................................129

a.Irrigation...................................................................................................................................................131

F.Conclusions.......................................................................................................................................................134

8.LackofHumanCapital.............................................................................................................................................135

A.EducationandSkills......................................................................................................................................135

a.SupplySide:EducationandSkillsandTrainingSectorReformsandResults............136 b.DemandforSkillsandtheSocialandPrivateReturnstoHumanCapital......................146

B.LackofHealthandNutritionalStatus....................................................................................................157

a.TrendsinNutritionalStatus..............................................................................................................157 b.HealthandDiseaseStatus..................................................................................................................158

C.Conclusions.......................................................................................................................................................163

9.NaturalCapital.........................................................................................................................................................165

A.AccesstoSeaandDistancetoMarkets..................................................................................................165

B.MineralWealth................................................................................................................................................165

C.LandResources..............................................................................................................................................167

D.WaterResourceWealth................................................................................................................................170

E.OtherNaturalandCulturalResources..................................................................................................172

F.ClimateandClimate‐RelatedVulnerability.........................................................................................172

G.GeographicallyDeterminedPrevalenceofHumanandLivestockDisease.................................173

H.Conclusions.......................................................................................................................................................175

10.SummaryofConclusions.......................................................................................................................................176

AnnexI–Data......................................................................................................................................................................177

Chapter3........................................................................................................................................................................177

Chapter4........................................................................................................................................................................179

a.Inflation‐MoneySupplyCorrelation..............................................................................................179 b.RealExchangeRateMovements.....................................................................................................179

Chapter7........................................................................................................................................................................180

PowerTariffCategories...................................................................................................................................180

Chapter8........................................................................................................................................................................181

References.............................................................................................................................................................................182

Figures

Figure1.1:GrowthDiagnosticFramework...................................................................................................................3 Figure2.1:RealGDPGrowth...............................................................................................................................................6 Figure2.2:EvolutionofGDPinTanzania.......................................................................................................................7 Figure2.3:SharesofGDPbySub‐Sector........................................................................................................................7 Figure2.4:DetailedSub‐SectorGrowthRates.............................................................................................................8 Figure2.5:RealGDPGrowth,Zanzibar...........................................................................................................................8 Figure2.6:ZanzibarGDP,bySector..................................................................................................................................9 Figure2.7:Zanzibar’sIndustrialSub‐SectorGrowth................................................................................................9 Figure2.8:Zanzibar’sServicesSub‐SectorGrowth...................................................................................................9 Figure2.9:PercentageShareoftheAgriculturalSectorinZanzibar’sGDP..................................................10 Figure2.10:ExportsasaPercentageofGDP.............................................................................................................10 Figure2.11:GDPandExportValueGrowth,Tanzania,2000‐2010.................................................................11 Figure2.12:ContributionofCommoditiestoTotalExportsEarnings(2008/09).....................................12 Figure2.13:LaborProductivityofAgriculture,inComparison.........................................................................13 Figure2.14:CropProductionandYield,Tanzania,1980‐2009.........................................................................14 Figure2.15:LandUnderCerealProductionandCerealYield............................................................................14 Figure2.16:FertilizerUse,Tanzania,2002‐2007....................................................................................................15 Figure2.17:InvestmentRatesofSelectedCountries,1990‐2009....................................................................16 Figure2.18:PrivateInvestmentRates,SelectedCountries.................................................................................17 Figure2.19:InvestmentRatesbySector(Detailed),2004‐2009......................................................................17 Figure2.20:GDPExpendituresShares1999‐2009.................................................................................................18 Figure2.21:DevelopmentAssistanceasaPercentofGDP,1970‐2006.........................................................19 Figure2.22:ForeignAssistanceasShareofGovernmentBudgetandPublicInvestment.....................19 Figure3.1:LendingandInflationRates,1989–2009...........................................................................................24 Figure3.2:InterestRatesversusInvestmentasaPercentageofGDP,1989‐2006...................................24 Figure3.3:CorrelationbetweenRealInterestRatesandInvestmentRate,2000‐2006.........................25 Figure3.4:BankMargins(Percent),TanzaniaandSelectedCountries..........................................................25 Figure3.5:DomesticCreditasaPercentageofGDP...............................................................................................26 Figure3.6:PrivateSectorDomesticCredit/GDP......................................................................................................27 Figure3.7:UseofBanksbyRegisteredFirms...........................................................................................................27 Figure3.8:PercentageofBankCredittoAgriculture.............................................................................................28 Figure3.9:PercentageofFirmsNamingAccesstoFinanceasaMajorConstraint...................................29 Figure4.1:InflationbySelectedSub‐Components..................................................................................................37 Figure4.2:GrowthofGDPandInflation,TanzaniaMainland.............................................................................37 Figure4.3:GDPGrowthRateandInflation,Zanzibar............................................................................................38 Figure4.4:InflationandMajorImports.......................................................................................................................39 Figure4.5:InflationandMonetaryMeasures............................................................................................................39 Figure4.6:ComparativeGovernmentRevenue........................................................................................................40 Figure4.7:ComparativeGovernmentExpenditures..............................................................................................41 Figure4.8:Revenue/ExpendituretoGDP...................................................................................................................42 Figure4.9:FiscalBalance...................................................................................................................................................43

Figure4.10:ExternalPerformanceIndicators..........................................................................................................44 Figure4.11:TotalStockofPublicDebt........................................................................................................................45 Figure4.12:PublicDebtRatios.......................................................................................................................................46 Figure4.13:ExternalDebtServicetoGDP..................................................................................................................46 Figure4.14:PublicExternalDebt...................................................................................................................................47 Figure4.15:ComparativeGrossGovernmentDebttoGDPRatio.....................................................................47 Figure4.16:ComparativeRiskPremia.........................................................................................................................49 Figure4.17:ComparativeCurrentAccountBalances............................................................................................50 Figure4.18:ComparativeTradeBalance....................................................................................................................50 Figure4.19:GrowthofExportValue,VolumeandExchangeRate...................................................................52 Figure4.20:RealExchangeRate(2000‐2009).........................................................................................................53 Figure5.1:ProceduretoAcquireInvestmentLandfromVillageHoldings...................................................60 Figure5.2:TaxRatesFacingFirms................................................................................................................................66 Figure5.3:TaxExemptionsasaPercentofGDP......................................................................................................68 Figure5.4:ExemptionsGrantedbyRecipientCategory(2008/09‐2009/10)............................................69 Figure5.5:Tax‐to‐GDPRatio,Tanzania,2001‐2008..............................................................................................69 Figure5.6:TaxRevenue‐to‐GDPRatiobyPerCapitaIncome............................................................................70 Figure5.7:MajorComponentsofTaxRevenueCollectedinTanzania,2000‐2010..................................70 Figure5.8:NumberofNewBusinessesRegistered,2001‐2010.......................................................................73 Figure5.9:RegulatoryQuality(2009)basedonWorldwideGovernanceIndicators...............................74 Figure5.10:BusinessFreedomIndex...........................................................................................................................75 Figure5.11:InformalEconomyasPercentageofGDP(2004/2005)..............................................................76 Figure5.12:CostofBusinessStart‐UpProcedures.................................................................................................78 Figure5.13:ExportsandRegulatoryQuality.............................................................................................................80 Figure5.14:ExportsofAgriculturalvs.ManufacturedGoods............................................................................81 Figure5.15:CosttoExport(WBDB)..............................................................................................................................81 Figure5.16:TradeFreedomIndexinComparison..................................................................................................83 Figure5.17:AverageImportTariffsPaidbyCountry,2010...............................................................................84 Figure5.18:StructureofAgriculturalExports1999‐2008..................................................................................85 Figure5.19:LivestockProductionIndex,TanzaniaandotherLivestockProducers................................85 Figure5.20:ValueofTotalCropProductionandValueAdded..........................................................................86 Figure6.1:ExportDiversification...................................................................................................................................89 Figure6.2:RatesofExportDiversification.................................................................................................................89 Figure6.3:RelationshipbetweenPerCapitaGDPandEXPY,2005.................................................................91 Figure6.4:ComparativeExportSophistication........................................................................................................91 Figure6.5:RelationshipbetweenPerCapitaGDPandOpenForest,2005...................................................92 Figure6.6:BusinessSophistication&InnovationIndices...................................................................................93 Figure6.7:BusinessSophistication–ComparativeRanking..............................................................................93 Figure6.8:TechnologicalReadiness–ComparativeRanking............................................................................94 Figure6.9:Innovation–ComparativeRanking........................................................................................................94 Figure6.10:FirmAdoptionofForeignTechnology................................................................................................95 Figure6.11:TrademarkApplications(Total)............................................................................................................96 Figure6.12:TrademarkApplications(ResidentandNon‐Resident)..............................................................97

Figure6.13:IntellectualPropertyRightsandProtections...................................................................................98 Figure6.14:DegreeofCompetitionFacedbyLargeFirms..................................................................................99 Figure6.15:ComparativePerformance–GoodsMarketEfficiency..............................................................100 Figure6.16:ChangesinMarketConcentrationandLaborProductivity.....................................................100 Figure7.1:PercentageofFirmsIdentifyingElectricityasaMajorConstraint.........................................103 Figure7.2:ElectricityProductionperCapita..........................................................................................................104 Figure7.3:ElectricityGenerationbyFuel................................................................................................................105 Figure7.4:PercentageofUrbanandRuralPopulationwithAccesstoElectricity..................................106 Figure7.5:PercentofFirmsOwningorSharingaGenerator..........................................................................107 Figure7.6:PercentofElectricityfromGenerator.................................................................................................108 Figure7.7:PowerProductionandPercentageGDPGrowthovertheYears2000‐2007.....................108 Figure7.8:PercentofFirmsIdentifyingTransportationasaMajorConstraint......................................110 Figure7.9:PercentofProductsLostinTransitDuetoBreakageandSpillage........................................112 Figure7.10:AverageAnnualDailyTraffic,UnpavedRoadNetwork............................................................113 Figure7.11:AverageAnnualDailyTraffic,SecondaryRoadNetwork.........................................................113 Figure7.12:UnpavedSecondaryRoadNetworkwith>300AADT..............................................................114 Figure7.13:TanzaniaRoadNetwork(TrunkandRegionalRoads)..............................................................118 Figure7.14:ContainerTrafficatDSMandMombasa..........................................................................................120 Figure7.15:TotalTrafficatDSMandMombasa...................................................................................................120 Figure7.16:PerformanceIndicatorsatthePortofDaresSalaam................................................................121 Figure7.17:TotalTonnageatZanzibar’sMalindiPort(2000‐2010)...........................................................122 Figure7.18:QualityofRailroadInfrastructure.....................................................................................................123 Figure7.19:RaidFreightonTanzania’sTwoMainRailwayLines................................................................124 Figure7.20:TanzaniaversusKenyaRailways,GoodsTransported.............................................................124 Figure7.21:AirTransport,CityPairsServed(2007)..........................................................................................127 Figure7.22:AirTransport,AnnualSeatsDomesticandInternational........................................................128 Figure7.23:MobileTelephoneSubscribers,per100Inhabitants.................................................................129 Figure7.24:InternetUsers,per100People...........................................................................................................129 Figure7.25:ImprovedWaterSourceAccessbyRuralandUrbanPopulation.........................................130 Figure7.26:ImprovedWaterSources,PercentofPopulationwithAccess...............................................131 Figure7.27:CumulativeAreaunderIrrigatedAgriculture...............................................................................132 Figure7.28:IrrigationEquippedAreasaPercentageofCultivatedArea...................................................133 Figure7.29:PercentageofIrrigationPotentialRealized...................................................................................133 Figure8.1:TrendsinEnrolmentRatesbyLevels(Primary,SecondaryandHigher

Education)...................................................................................................................................................................136 Figure8.2:EnrollmentTrendsinTertiaryPrograms,Tanzania.....................................................................138 Figure8.3:PrimaryCompletionRatebyCountry2000‐2008(Source:WDI)..........................................138 Figure8.4:EducationLevelinTanzaniabyRural/Urban2007......................................................................139 Figure8.5:PassRateinFormFourExaminationbyDivision..........................................................................140 Figure8.6:QualityofPrimaryEducation.................................................................................................................140 Figure8.7:QualityofMathandScienceEducation..............................................................................................141 Figure8.8:QualityoftheEducationalSystem........................................................................................................142 Figure8.9:PupilMeanTestScores,SACMEQ.........................................................................................................142

Figure8.10:AbsoluteReadingAchievementbyLevelinComparisonSACMEQIII................................143 Figure8.11:IndicatorsofAbsoluteCognitiveAbility:Mathematics.............................................................143 Figure8.12:TeacherReadingScores.........................................................................................................................144 Figure8.13:PercentageofStudentswithOwnReadingTextbook...............................................................145 Figure8.14:SchoolResourcesIndex..........................................................................................................................145 Figure8.15:AdultUnemployment..............................................................................................................................146 Figure8.16:PercentageofFirmsIdentifyingLaborSkillLevelasaMajorConstraint.........................154 Figure8.17:PercentageofFirmsIdentifyingLaborSkillLevelasaMajorConstraint.........................154 Figure8.18:IndicatorsofFormalEmployer‐ProvidedTraining....................................................................155 Figure8.19:ExtentofStaffTraining...........................................................................................................................156 Figure8.20:LocalAvailabilityofSpecializedResearchandTrainingServices........................................156 Figure8.21:PrevalenceofChildMalnutrition.......................................................................................................158 Figure8.22:PercentageofIndividualsReportingIllnessorInjuryinthePastFourWeeks..............160 Figure8.23:HIV/AIDSPrevalenceRates..................................................................................................................160 Figure8.24:AmountofTimeLostbyEducationalAttainment.......................................................................162 Figure8.25:BusinessImpactofHIV...........................................................................................................................163 Figure9.1:AnnualGrowthRatesMiningandQuarryingYear1999‐2009(%).....................................166 Figure9.2:TotalValueforMineralExports:'000USD'Year2000‐2009...................................................167 Figure9.3:ArableLandasPercentageofTotalLandAreaperCountry.....................................................168 Figure9.4:ArableLandPerCapita,SelectedCountries.....................................................................................168 Figure9.5:PerCapitaAgriculturalLandbyCountry(HectaresperPerson)............................................169 Figure9.6:LandUnderProductionandCerealYieldinTanzania(1980–2008).....................................169 Figure9.7:FreshwaterCapacityandWithdrawal................................................................................................170 Figure9.8:FreshwaterWithdrawalsbySector.....................................................................................................171 Figure9.9:PerCapitaAnnualRenewableWaterResources(cu.m/annum).............................................172 Figure9.10:NumberofLivestockDeaths(Reported)byDisease,2000‐2010......................................174

Tables

Table2.1:ChangesinPerCapitaIncomebyIncomeQuintile,2001‐2007....................................................20 Table3.1:SectorSharesofCommercialBankLending.........................................................................................29 Table3.2:FractionofEnterprisesRespondingFinancialConstraintsAreSevere.....................................30 Table3.3:ExperienceWithandAccesstoNon‐BankCredit...............................................................................32 Table3.4:ResponsestoWhyHaveYouNeverAppliedForaLoan?................................................................33 Table3.5:WhatTypeofSecurityWasRequestedofLoanApplicants?..........................................................34 Table4.1:GovernmentofTanzaniaPublicExpenditureFinancing.................................................................43 Table4.2:AverageTimetoRe‐FixingandMaturityforSelectedAfricanCountries.................................48 Table4.3:ComparativeSovereignRiskRatings.......................................................................................................48 Table4.4:TradeBalance,1995‐2010...........................................................................................................................51 Table5.1:WBDBRegisteringPropertyRank,2010‐2011...................................................................................55 Table5.2:TICRegisteredProjectsbySectorThrough2006...............................................................................62 Table5.3:TaxRatesforMajorPlayersacrossCountries......................................................................................66 Table5.4:WBDBPayingTaxesRanking......................................................................................................................66 Table5.5:ASampleofTaxRatesinTanzania(2010‐2011)................................................................................67 Table5.6:ConstraintstoRuralEnterpriseEmploymentGrowth.....................................................................77 Table5.7:MostProblematicFactorsforDoingBusinessinComparison(Global

CompetitivenessSurvey2010)..............................................................................................................................79 Table5.8:CostofCustomsProcedures........................................................................................................................82 Table6.1:MajorExportProducts....................................................................................................................................90 Table6.2:InnovationandCompetition........................................................................................................................98 Table7.1:PowerOutages................................................................................................................................................106 Table7.2:BenchmarkedRoadIndicators................................................................................................................111 Table7.3:TanzaniaRoadConditionandTrafficIndicators(AICD)..............................................................115 Table7.4:RoadConditionunderTANROADSandDistrictCouncils.............................................................116 Table7.5:EastAfrica:AStudyoftheRegionalMaizeMarketandMarketingCosts..............................117 Table7.6:BenchmarkedPortIndicators..................................................................................................................121 Table7.7:IrrigationUseandPotential.....................................................................................................................132 Table8.1:TotalGraduatesinVocationalTrainingCoursesbyGenderandYear...................................137 Table8.2:UnemploymentRatebyEducationalAttainmentandArea,2006............................................147 Table8.3:LaborForceActivityStatus.......................................................................................................................148 Table8.4:LaborForceParticipationRate15+YearsbyEducationalAchievement..............................149 Table8.5:MedianRealMonthlyEarningsbyEducationLevel2001‐2006...............................................151 Table8.6:EstimatedReturnstoSkillsAssociatedwithSchoolinginTanzania,2001and

2006ConditionalonWageorSelf‐Employment*.......................................................................................151 Table8.7:ComparativeRatesofReturnstoEducation......................................................................................152 Table8.8:TrendsinNutritionalStatusofChildren.............................................................................................158 Table8.9:TrendsinEarlyChildhoodMortalityRates........................................................................................159 Table8.10:LostDaysofWorkorSchool..................................................................................................................161 Table8.11:BusinessImpactsofDisease..................................................................................................................162

Acronyms

AADT AnnualAverageDailyTrafficAgCLIR AgriculturalCommercial,Legal,andInstitutionalReformAICD AfricaInfrastructureCountryDiagnosticARIPO AfricanRegionalIntellectualPropertyOrganizationASF AfricanSwineFeverBCF BillionCubicFeetBEMP BasicEducationMasterPlanBEST BusinessEnvironmentStrengtheningforTanzaniaBFI BusinessFreedomIndexBOT BankofTanzaniaBRELA BusinessRegistrationsandLicensingAgencyBSE MadCowDiseaseCBPP ContagiousBovinePleuropneumoniaCCPP ContagiousCaprinePleuropneumoniaCCRO CertificateofCustomaryOccupancyCOMESA CommonMarketforEasternandSouthernAfricaCRDB CooperativeRuralDevelopmentBankEAC EastAfricanCommunityEPZ ExportProcessingZoneEPZA ExportProcessingZoneAuthorityEU EuropeanUnionEWURA EnergyandWaterRegulatoryAuthorityEXPY IndexofexportsophisticationrelativetonationalincomeEXZ ExportProcessingZonesFAO FoodandAgricultureOrganizationFDI ForeignDirectInvestment

FMD FootandMouthDiseaseGCS GlobalCompetitivenessSurveyGDP GrossDomesticProductIEA InternationalEnergyAgencyGFCF GrossFixedCapitalFormationGOT GovernmentoftheUnitedRepublicofTanzaniaHBS HouseholdBudgetSurveyHIPC HeavilyIndebtedPoorCountriesHPAI HighlyPathogenicAvianInfluenzaHRV Hausmann,Rodrik,Velasco(seeReferences)ICF InvestmentClimateFacilityICT InformationandCommunicationsTechnologyIFEM Inter‐BankForeignExchangeMarketIFMS IntegratedFinancialManagementSystemILD InstituteforLibertyandDemocracyILFS IntegratedLabourForceSurvey

IPP IndependentPowerProducersIPR IntellectualPropertyRightsIPTL IndependentPowerTanzaniaLtdITN InsecticideTreatedNetLA LandActLIC LowIncomeCountryLMIC LowMiddleIncomeCountriesLSD LumpySkinsDiseaseLTD LargeTaxpayersDepartmentMAFSC MinistryofAgriculture,FoodSecurity,andCooperativeMCC MillenniumChallengeCorporationMDRI MultilateralDebtReliefInitiativeMEM MinistryofEnergyandMineralsMFI MicrofinanceInstitutionMOW MinistryofWorksMOWI MinistryofWaterandIrrigationMTEF MediumTermExpenditureFrameworkNBC NationalBankofCommerceNBS NationalBureauofStatisticsND NewcastleDiseaseNHC NationalHousingCorporationNIC NationalInsuranceCorporationNIMP NationalIrrigationMasterPlanNMB NationalMicroFinanceBankNPF NationalProvidentFundNTB Non‐TariffBarriersOLS OrdinaryLeastSquaresPER PublicExpenditureReviewPPF ParastatalProvidentFundPFG PartnershipforGrowthPMO PrimeMinister’sOfficeRER RealExchangeRateREER RealEffectiveExchangeRateRICA RuralInvestmentClimateAssessmentRVF RiftValleyFeverSACMEQ Southern and Eastern African Consortium for Monitoring

EducationalQualitySADC SouthernAfricanDevelopmentCommunitySME SmallorMediumEnterpriseSSA Sub‐SaharanAfricaTANESCO TanzaniaEnergySupplyCorporationT‐VET Technical‐vocationaleducationandtrainingTAD Trans‐boundaryAnimalDiseasesTHB TanzaniaHousingBankTIB TradeInvestmentBankTPB TanzaniaPostalBankTRA TanzaniaRevenueAuthorityTRC TanzaniaRailwayCorporationTRL TanzaniaRailways

TSH TanzanianShillingTSIP TransportSectorInvestmentProgramTTCL TanzaniaTelecommunicationsCompanyURT UnitedRepublicofTanzaniaUSAID UnitedStatesAgencyforInternationalDevelopmentVAPW ValueAddedPerAgriculturalWorkerVLA VillageandLandActWBDB WorldBankDoingBusinessWDI WorldDevelopmentIndicatorsWEF WorldEconomicForumWGI WorldGovernanceIndicatorsWNV WestNileVirus

1

1. IntroductionandMainConclusions

Inearly2011,theUnitedStatesGovernmentchosetheUnitedRepublicofTanzaniaasoneoffourcountries to join its Partnership for Growth.1 The Partnership for Growth (PFG) is based on theprinciples set forthbyPresidentObama’sPresidentialPolicyDirectiveonGlobalDevelopmentofSeptember2010andrepresentsanefforttotransformthecharacteroftheUnitedStates’bilateralrelationshipswithaselectsetoftop‐performinglow‐incomecountries.Thegoalofthiseffortistoassist those countries to accelerate and sustain broad‐based economic growth, themost provendriver of poverty reduction. It also seeks to transform the bilateral relationships through anemphasisonpartnership,countryownership,andjointprioritization.Finally,thePFGinitiativewasdesigned to leverageUSG engagement formaximum impact and focus on catalytic policy changeandinstitutionalreform.

ThefirststepindevelopingaJointCountryActionPlan(JCAP)forthisPartnershipistoconducta“GrowthDiagnostic”(alsoknownas“ConstraintsAnalysis”).ToimplementtheGrowthDiagnostic,inMarch 2011 the United Republic of Tanzania (GOT) and the United States Government (USG)establishedajointteamof20TanzaniantechnicalexpertsandfourUSGeconomiststoidentifythetwoorthreemostbindingconstraintstobroad‐basedeconomicgrowthinTanzania.

TheGrowthDiagnosticmethodologywasproposedina2005workingpaperbyRicardoHausmann,Dani Rodrik, and Andrès Velasco (HRV) to identify those constraints to growth which, whenloosened, would contribute the most to accelerating broad‐based economic growth. Whereas acountry like Tanzania faces many economic and development challenges, clearly not all suchchallengesareequallyrestrictivetogrowth.Reformandinvestmenteffortsarelimitedbycapacity,commitment,andotherresources. Moreover, it isnotpossibletoquantifyallofthedynamicandindirecteffectsoflooseningagivenconstraint.Therefore,asHRVassert,agrowthstrategyfocusedonalleviatingthoseconstraintswhicharemostbindingwouldinprinciplehavethegreatestimpactonprivatesectorinvestmentandproductivity.Thepurposeofagrowthdiagnosticisnottoidentifyall of the economic, social, and institutional problems that a country faces in achieving itsdevelopment goals. Nonetheless, the policy implications are highly relevant to a developmentstrategythatplaceseconomicgrowthatitscore.

The HRV approach is to use indicators, data, and other contextual information and analysis toestablishwhetherthe‘market’forthefactorinquestionisprimarilysupplyconstrainedordemandconstrained;and,ifsupplyconstrained,toassessthelikelymagnitudeofthatconstraintgiventheeconomy’sstructureandtrends.

Hausmann,Klinger,andBailey(2008)suggestfourtestsforthepresenceofsymptomsofabindingconstraint. Whether or not thesemay be conductedwould depend on the growth factor beingassessedandthedataavailable.Nonetheless,asawayofsiftingthroughtheevidence,oneshouldjudgetheconstraintinquestionmorebindingif:

1TheotherthreecountriesareGhana,ElSalvador,andThePhilippines.

2

(1)Theshadowpriceoftheconstrainedfactorishigh;2 (2)Theavailabilityofaconstrainedfactoriscorrelatedwithinvestmentorgrowth;(3)Economicagentsareincurringhighcoststocircumventtheconstraint;and(4)Firmswhichwouldrelyheavilyonthefactorarenotobservedintheeconomy.

Toassesswhetherafactorisscarceoftenrequiresthatacomparisonorbenchmarkingexercisebedone against other countries. To be informative, the comparison countries should be somewhatsimilar in geographyand income levelsand, in the caseofTanzania, shouldbeeither among themoremarket‐oriented countries or thosewith successful recent growthhistories. TheUSG‐GOTteam selected as a core set of comparison countries Ghana, Kenya,Mozambique, andUganda, inaddition toalldevelopingSub‐SaharanAfricancountriesandLow Incomecountries,asavailable.MauritiusandvariousEastAsianTigersand,insomecases,low‐middleincomecountrieswerealsosometimes used as ‘goal’ benchmarks. Depending on the topic, other countries may have beendeemedappropriatecomparisonsaswell.

HRV present the framework for the growth diagnostic analysis as a ‘tree,’ as shown in slightlyamendedformfromHRV(2005),inFigure1.1.Thisframeworkallowsforasequentialapproachtoanalyzingwhichbranchesaremajordriversof investmentandgrowthand for theeliminationofparts of the tree at higher nodes, before focusing on the details within a given box of issues.Problemsandconstraintsat the individual firmlevelcanbe foundinalmostallareasonthetreeandwilldifferbysector.However,theseproblemsandconstraintsmustbeseenwithaviewofthebroaderforcesimpactingprivatesectorinvestmentandproductivitygrowth.

TheUSG‐GOTteamdividedthetreeintothetopicsrepresentedintheboxesshown,withgroupsofanalysts firstcollectingandanalyzingdata topermitasequentialapproachto the finaldiagnosis.Building on the data analysis performed, the USG‐GOT team reached a broad consensus on thethreemostbindingconstraintstoinvestmentandgrowthinTanzania.

1. Lackofkeyinfrastructure:

Inparticularareliableandadequatesupplyofelectricalpower.Theevidenceofthisasabindingconstrainttogrowthwasoverwhelming.

In addition, an inadequate rural road network is a binding constraint, particularly forconnectinghighpotentialagriculturalproductionareastomarkets.

2Ashadowpriceisthemarginalvaluetotheentireeconomyoftheopportunitycostofanadditionalunitofthefactor.

3

Figure1.1:GrowthDiagnosticFramework

2. Lackofappropriabilityofreturns:

Inparticular,accesstosecurelandrightsonthepartofinvestorsseekingtoinvestoutside

thesmallholdervillage‐customarysystem.Theabilityofaninvestortoacquiresufficientlysecure land use rights is severely constrained in Tanzania, given the high cost and lowlikelihoodofsuccessinenteringintoasecurelandleasecontract.

The USG‐GOT analysis also identified additional constraints to investment and growth. Theseconstraintsare:

Lackofotherkeytransportinfrastructure, inparticularthepoorqualityandreliabilityof rail service andport capacity inDar es Salaam, largely due to poor infrastructure andrelatedinstitutionalcapacities.

Lackofvocational, technical,andprofessionalskills currently demanded in the labormarket,largelyduetoalackoffinancingforsuchtrainingandincompleteimplementationoftheGovernment’stechnical‐vocationaltrainingstrategy.

Growth Diagnostic Framework

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What Constrains P rivate Investment and E nt repreneurship?

Low Returns to Economic Activities

Low Appropriability

Macro Risks

Micro Risks

Market Failures

Innovations

Low Intrinsic Returns

Natural Capital

Human Capital

Infrastructure

High Cost of Finance

Costly Local Finance

Low Savings

Costly intermediation

Costly Foreign Finance

Sou rce : Ad ap ted fr om H au sma nn , Rod rik, an d Velasc o (2 00 5, 27 ), “Gr ow th Diag no stics. ”

4

Lack of access to finance, in particular for micro, small, and medium enterprises andagriculture.

Relativelylowqualityregulationofbusinessandtrade.Abroadsetofissuesinthisareaappear toweakenaccess tomarketsbyproducers, inparticular forexporters,and inhibitgreaterproductivitygrowthintheeconomy.

Therearesomekeycross‐cuttingconsiderationsunderlyingtheconstraints,whichare:(1)varyingquality of regulation as it relates to the binding constraints; (2) incomplete and inconsistentimplementation of the Government’s reform strategies; and (3) weak institutional and financialarrangementsforprovidingandmaintainingthekeyfactorsthatarelacking.Tacklingtheseissuesis more difficult than identifying them and, while the purpose of this report is not to providedetailedpolicyprescriptions,generalpolicyrecommendationswillbemadeineachsectionofthereport.

The rest of the report is structured as follows: Chapter Two begins with the recent historicalcontextandcurrentgrowthandinvestmenttrendsinTanzaniaasawaytoframethesubsequentdiagnosisofconstraints;ChaptersThreethroughNinepresenttheresultsundereachtopicshownon the analytical tree, with more detailed conclusions and general policy recommendations onmanyofthemajorissuesidentified;ChapterTenconcludes.

5

2. RecentEconomicTrends

TheUnitedRepublicofTanzania(URT)hasregisteredimpressiveeconomicgrowthoverthepastdecade, averagingapproximately6.8percentover themost recent fiveyearperiod (2005‐2010).Atthesametime,acceleratingpopulationgrowthhasmeantalowerrateofpercapitaGDPgrowth,at3.8percentperannum. Between2001and2007,thelatestyear forwhichtherearedata,realpercapitaconsumptionisestimatedtohavegrownbylessthanonepercentperyear.Similarly,thereductioninthemeasuredheadcountpovertyratehasbeenadisappointingtwopercentagepoints,fromapproximately37percentto35percent.3Eveniftheseestimatesunderstatethereal impactonpoverty,asislikely,itisclearthatanaccelerationofbroadbased,private‐sectordrivengrowthisnecessarytoachievemorerapidandsustainedpovertyreduction.

A. GeneralTrendsinandComponentsofGDP

Sinceitsformationin1964,theURThasundergoneroughlythreedistinctperiodswithrespecttoitseconomicpoliciesandperformance.Followingthe1967ArushaDeclaration,Tanzaniaadoptedasocialistmodelinvolvingwidespreadstateownershipandintervention.Thesepoliciesarecitedasthecauseofthedeclinesseeninagriculturalproductionandexports,theinefficientmanagementofpublicenterprises,largebudgetdeficits,andforeignexchangeandimportsshortagesovermuchofthe period. At the same time, between 1970‐1980, collapsing commodity prices, the oil priceshocksof1973and1979,andwarwithUganda in1979‐1980adversely impacted theeconomy.4From1986through1995,stateownershipandgovernmentinterventionwerereduced,andlimitedmarketallocationofresourceswasallowed.Thesuccessofthesereformsatbringingaboutrobusteconomic performancewas at firstmodest. However, reformshave subsequently deepened andTanzaniahasregistered large increases inexports,aswellasprivateand foreign investmentandgrowth (Nordetal., 2009). Tanzania’s public finances have also improved, putting it on amoresustainablemacroeconomic course.Nonetheless, sustaining high rates of investment and growthhasprovenachallenge.Growthintheearlypartofthelastdecadehasbeendriveninsubstantialpartbypublic investments, financed inpart throughdonorassistance,andbyprivate investmentwhichhasslowedover thepast fewyears,andwith itgrowth.Asshown inFigure2.1,economicgrowthacceleratedfrom1992through2004,buthasdeceleratedoverthepastfewyears.

3CalculatedfromHouseholdBudgetSurveys.Itisalargeresearchundertakingtoresolvediscrepanciesinthenational accounts andhousehold surveydata.This typeofdiscrepancy isnotunique toTanzania, andhasbeen noted and partially explained for many fast‐growing developing countries, including India andMozambique.Furtherdiscussionisfoundlaterinthischapter.4TanzaniaalsodevotedconsiderableresourcestosouthernAfricanliberationmovementswithinthisperiod.Despite these factors, Tanzania was able to achieve universal primary education and among the highestliteracyratesinAfricaat85percent.

6

Figure2.1:RealGDPGrowthAn understanding of theunderlyingstructureofgrowthis important to inform thesearch forbindingconstraints.Figure2.2showsthetrendsinreal GDP (measured inconstant Tanzanian Shillings ‐Tzs) between 1999 and 2009,disaggregated by primary(agriculture, fishing andforestry), secondary(manufacturing, construction,and refining) and tertiary(services)sectors.Notbeinga

directreflectionofprivatesectoractivity,thegovernmentservicesactivityhasbeenseparatedoutfrom the tertiary category and,with rapid expansionof the gold sector,mining andquarrying isseparated from the secondary sector. Figure 2.3 shows that growth in the secondary sector hasbeen well above the overall GDP growth rate. This is an important indicator of economicdiversification,especiallyasitisoccurringindependentlyoftheminingsector’sexpansion.Atthesametime,therehasbeenacontinuousincreaseinprimarysectorGDP,albeitatalowerratethanthat in other sectors. As shown in Figure 2.3, agriculture’s share of GDP has declined fromapproximately23percentto19percent,withtheshareoflivestock,forestry,andfishingdecreasingfrom10percentto7.5percent.Whilethetertiarysectorasawholehasnotexhibitedparticularlyhigh growth rates relative to other sectors of the economy, as illustrated in Figure 2.4, thecommunications sub‐sector has experiencedunprecedented and continuously increasing rates ofgrowth,reachingover20%by2009.

Alsoillustratedistheboominconstructionoverthedecade,fueledbyforeignassistanceandurbandevelopment, and the very high real GDP growth rates in the mining and quarrying sector ofbetween14percentand16percentfrom2000through2006(Figure2.4),duetoexpansionofthegoldsector.5

5Alargeinflowofforeignassistanceisoftenassociatedwithaboominnon‐tradeablessuchasconstruction,which could lead to Dutch Disease effects that would adversely impact export producers and may harmoverallgrowth.However,asofrecently,theTanzanianeconomyhasnotexhibitedotherhallmarksymptomsofDutchDisease,inparticularanover‐valuedRealEffectiveExchangeRate.

7

Figure2.2:EvolutionofGDPinTanzania6

Figure2.3:SharesofGDPbySub‐Sector

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008r

2009p

Share of GDP

Shares of GDP Tanzania(1999‐2009), GoT National Accounts

agriclture

livestock, forestry, fishing

mining and quarrying

manufacturing

construction

other secondary sector

trade and repairs

transportation

communications

hotels and restaurants

public administration

all other services

6“r”denotes“revised”,“p”denotesprojected. AllURTnationalaccountsdatacomesfromthemostcurrentnationalaccountspublication.

8

Figure2.4:DetailedSub‐SectorGrowthRates

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%20

00

2001

2002

2003

2004

2005

2006

2007

2008r

2009p

Growth Rate

GDP Annual Growth(1999‐2009), GoT National Accounts

agriclture

livestock, forestry, fishing

mining and quarrying

manufacturing

construction

communications

hotels and restaurants

public administration

all other services

Total GDP

B.TrendsinGDP–Zanzibar

Zanzibar’sGDPgrowthratesince2000hasbeensomewhatlowerthanthecountry’sasawhole.Itrosesharplyfrom3.6percentin2000to9.3percentin2001,beforereturningtoratesinthe5‐6percentrange.

Figure2.5:RealGDPGrowth,Zanzibar

Source:OfficeofGovernmentStatistician,Socio‐EconomicSurvey2010The services sector contributes the largest share to Zanzibar’s total GDP, averaging 44 percentbetween2005 and2009. Althoughbased largely on tourism, between2005‐2009, the strongestgrowthwithintheservicesectorwas intransportandcommunications. Industry’sshareofGDPhas been relatively low at about 14 percent, while agriculture has contributed 28 percent on

9

averagebutonaslightincreasingtrend.Inrecentyears,constructionandmanufacturinghaveseenthemostsubstantialgrowthwithinindustry.7

Figure2.6:ZanzibarGDP,bySector Figure2.7:Zanzibar’sIndustrialSub‐SectorGrowth

Source:OfficeofGovernmentStatistician,Socio‐EconomicSurvey2009

Figure2.8:Zanzibar’sServicesSub‐SectorGrowth

Source:OfficeofGovernmentStatistician,Socio‐EconomicSurvey2009

7Zanzibar’sindustrialsectorisdominatedbymanufacturing,construction,miningandquarrying,andsupplyofelectricity,gas,andwater.

10

Figure2.9:PercentageShareoftheAgriculturalSectorinZanzibar’sGDP

Source:OfficeofGovernmentStatistician,Socio‐EconomicSurvey2009;WorldDevelopmentIndicators

C.ExportsandTradePerformance

Exportsareakeydriverofgrowthandexportperformanceisanindicatorofacountry’sabilitytocompete in world markets. After a decline following liberalization of the economy, Tanzania’sexport performance has grown dramatically, and the real value of goods exported hasapproximatelytripledoverthepastdecade.Nonetheless,exportsasashareofGDP,recentlyat23percent, have not kept pace with those of Mozambique, another fast‐growing economy in theregion, and remains below those of all three comparator economies with access to the sea, asshowninFigure2.10.

Figure2.10:ExportsasaPercentageofGDP

Source:WorldDevelopmentIndicators

11

GrowthinexportvaluesarealsobroadlycorrelatedwithyearonyearGDPgrowth(Figure2.11),suggestingthatexportperformanceisakeyfactorfortheeconomy’sgrowth,whetherdeterminedbylocalfactorssuchasweather,productivity,orbyglobaldemand.

Figure2.11:GDPandExportValueGrowth,Tanzania,2000‐2010

Source:InternationalTradeCentreandWorldDevelopmentIndicators

Tanzania’s composition of exports by value, shown in Figure 2.12, is currently split betweenprimaryproductsat37percentofthetotal,followedbygoldat31.8percent,andmanufacturingat25percent.Althoughtheriseofthemineralssectorcanexplainsomeoftheexportgrowthintheearly part of the last decade, aggregate growth in exports has been largely driven by growth inexportsofmanufacturedgoods,whichhasgrownfrom7percentoftotalexportvaluein2004to25percentin2009.Meanwhiletheshareoftotalexportvaluecomprisedofagriculturehasfallenfrom66percentin1989,ofwhich‘traditional’exportshavefallenfrom20percentin2004to16percentin2008. InadditiontoTanzania’s traditionalexportcrops–coffee, tea,cashew,cotton, tobacco,cloves,andsisal,Tanzania’smostimportantagriculturalexportsincludepyrethrum,maize,wheat,cassava,fruits,vegetables,andlivestock.

12

Figure2.12:ContributionofCommoditiestoTotalExportsEarnings(2008/09)

D.TheAgriculturalSector

Growth of Tanzania’s agriculture sector, at approximately four percent per annum, has not keptpace with aggregate growth trends. This is partly the outcome of the normal structuraltransformationwhichtendstoaccompanyeconomicdevelopment. Laborhasbeenshiftingoutofagriculture. A declining share of theworking population – currently approximately 75 percent –earnsapartof its income in theagricultural sector (USAID,2010). At the same time, increasingnumbers of rural households, most recently estimated at one third, also operate non‐farmbusinesses(ILFS,2006).Nonetheless, improvedproductivityofagriculture isanessentialelementofsustainedgrowth. Improvedproductivitydeterminestheabilitytocompeteinexportmarkets;theleveloffoodpricesforconsumers,whichisaprimarycomponentofinflation;andagriculturalincomes,whichimpactdemandforotherdomesticallyproducedgoodsandservices.

As shown in Figure 2.13, Labor Productivity Growth in TanzanianAgriculture (Value Added perAgriculturalWorker)hascaughtupwiththatofotherlowincomecountriessince1992,surpassingthatofMalawi,Mozambique,Uganda,andZambia.Nonetheless,itremainslowerthanthatofSub‐SaharanAfricaandhasnotkeptpacewiththatoflow‐middleincomecountries.

13

Figure2.13:LaborProductivityofAgriculture,inComparison

Source:WorldDevelopmentIndicatorsMuchofthisproductivitygrowthhasbeenduetoanincreaseincultivatedlandpercapita.Asonewouldexpectforarelativelyland‐abundantcountry,theareaundercultivationofbothcerealsandnon‐cerealshasexpandedoverthepasttenyears(seeFigure2.14).AsdiscussedinChapterNine,there are limits to expansion into new lands. At the same time,whereas non‐cereal yields haveincreased, cereals yields have largely not improved (see Figure 2.15). Average fertilizer use hasremained low relative to Sub‐SaharanAfrica and low incomecountries, as shown inFigure2.16.Thissuggeststhattheprivatereturntoinputintensificationforcerealsislowrelativetotherisksandcosts,thusdiscouragingfurtherinvestmentinTanzanianagriculture.Atthesametime,thereisgrowingevidencethatreturnsintheregion(i.e.,KenyaandMalawi)arehighforfertilizeruse,andthat thereason forsub‐optimalusemay lie in theseasonalityofcash flowand fertilizerdelivery,andthefact thatfarmersmakeinputusedecisionswhichare largelydeterminedbytoday’swell‐being,withoutsufficientconsiderationfornextyear’sincome(Duflo,Kremer,andRobinson,2010).

Exportcrops,includingcotton,sugarcane,coffee,andtobacco,havealsomadeupasignificantpartofagriculturalproductionandhaveexperiencedfastgrowth(nearlytenpercentannually)between2000 and 2007 (Pauw and Thurlow, 2010) in part due to increased world prices for thesecommodities.

14

Figure2.14:CropProductionandYield,Tanzania,1980‐2009

Source:FAOStatisticsFigure2.15:LandUnderCerealProductionandCerealYield


15

Figure2.16:FertilizerUse,Tanzania,2002‐2007

Source:FAOStatisticsTanzaniaisSub‐SaharanAfrica’sthirdlargestproduceroflivestock,accountingforbetween15and18percent of primary sectorGDPbetween1999and2009 (TanzaniaNationalAccounts), and iscurrentlyestimatedatfourpercentofGDP.Thisproductionisalmostentirelybysmallholdersandsemi‐nomadicpastoralists,whoproduce97‐99percentofTanzania’s livestockoutput,75percentofwhichiscattle(USAID,2010).Yetgrowthratesinlivestock,forestry,andfishinghavegenerallybeenthelowestofallsub‐sectors(Figure2.4).

Similartrendsprevail inZanzibar,wherecropproductionhasgrowninimportanceoverthepastdecade,while livestock and fishing have held constant. Forestry and hunting have declined andcontribute0.3percentonaveragetoagriculturalGDP.

E.InvestmentPerformance

Investmentperformanceprovidesatleasttwoimportantpiecesofinformationaboutaneconomy.Higher private investment rates are a positive reflection of the underlying characteristics of theeconomyandapredictoroffuturegrowth.

AsshowninFigure2.17,sincethereformsofthe1990s,investmentasafractionofGDProse,butbegantoslowstartingin2003andwasbelowthatofthecomparatorcountriesin2006.

16

Figure2.17:InvestmentRatesofSelectedCountries,1990‐2009

Series: Gross capital formation (% of GDP)

Source: World Bank, World Development Indicators

Atthesametime,theshareoftotalgrossfixedcapitalformationarisingfromtheprivatesectorhasrisenfrom60percentofthetotaltoapproximately73percentinrecentyears.Publicinvestment,largely financed by foreign grants, has comprised at least 20 percent of the total, peaking at 33percentin2003(2009StatisticalAbstractofTanzania).

Thenetresultofthis,asshowninFigure2.18,isthatprivateinvestmentinTanzaniahasfallenasapercentofGDPfrom11.4percentin2000to9.8percentin2008,whileforeigndirectinvestment(FDI) fell from 5.1 percent to 3.6 percent of GDP.8 Yet for all comparison countries exceptMozambique, aswell as low income and Sub‐SaharanAfrican countries, private investment roseover this timeframe. In addition, FDI has been consistently low in the agricultural sector atapproximately2‐3percentofGDP,despitelargetaxincentivesofferedtoagriculturalinvestors.

,which shows the compositionof investments over time, similarly showsa very low investmentrate inagriculture,butalso inrealestate, finance,andbusinessservices,andahighandgrowingshareofinvestmentinconstruction.

8Thisisnotduetoaninvestmentspikeinminingin2000,assuchspikesoccurredin1999andagainin2002.The year 2005 represented a peak in FDI across the non‐mining economy: All sectors other thanmining,agriculture, and construction exhibited relatively high levels of FDI that year, which have since dropped(NationalAccounts(notshown)).

17

Figure2.18:PrivateInvestmentRates,SelectedCountries

Source:LittleDataBookonPrivateSectorDevelopment(WorldBank2010).

Figure2.19:InvestmentRatesbySector(Detailed),2004‐2009

0.0%

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15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

Share

Gross Fixed Capital FormationSector Shares (2004‐2009), GoT National Accounts

1. Agriculture and Fishing

2. Mining and Quarrying

3. Manufacturing

4. Electricity and WaterSupply

5. Construction

6. Wholesale and RetailTrade and Hotels andRestaurants 7. Transport, Storage andCommunication

8. Financial intermediation,Real Estate and B.S

9. Public Administration,Education, Health and O.S

Source:2009StatisticalAbstractofTanzania

F.GovernmentExpenditureandForeignAssistance

AnexaminationoftheexpendituresharesofGDP,asshowninFigure2.20,revealsarisingshareofgovernment expenditure over the past decade, to a substantial degree financed by inflows of

18

foreignassistance.Muchofthishasbeendirectedateconomicgrowth,andhumandevelopment.9OverseasdevelopmentassistanceasapercentageofGDPpeakedat26percentofGDPin1992andsubsequentlydeclinedover the1990sandbegin risingagain to reach15percent in2008. Thispattern of aid receipts is similar to that of other developing countries, although since 2005Tanzaniahasreceivedarelativelyhigheramount(seeNordetal.2008).

Figure2.20:GDPExpendituresShares1999‐2009

As shown in Figure 2.22, grants and basket support comprised as high as 40 percent of publicrevenuein2004/2005,althoughtheshareofgrantsupporthassincedeclinedtoreach25percentby 2009, and have financed over 100 percent of public investment. The composition of foreignassistancehas evolved fromprimarilyproject support towardsan increasing share comprisedofbudget support. Since2002at least 50percent of all foreign assistancehasbeen in the formofgeneral budget support, up from 30 percent in 1997. Debt service relief under the IMF HighlyIndebtedPoorCountry(HIPC)debtinitiativeandtheMultilateralDebtReliefInitiative(MDRI)hasincreasedtobetween5and11percentinrecentyears.

9MajordonorsincludetheWorldBank,theUnitedStates,theUnitedKingdom,theAfricanDevelopmentBank,theEuropeanUnion,theInternationalMonetaryFund,theGlobalFund,Norway,Denmark,andSweden,TheWorldBankcurrentlyoperatesmorethan$2.6billionofprojectsinTanzania,withafocusontransportandurban development aswell as a Poverty Reduction Support Credit which provides budget support. TheUnitedStatesDepartmentofStateandtheUnitedStatesAgencyforInternationalDevelopment(USAID)have,inthepastsixyears,deliveredmorethan$2.3billioninforeignassistance.Morethan$1.9billionhasbeenwithin the health sector including programs to target diseases like AIDS and malaria. A $698 millionMillenniumChallengeCorporationcompactwithTanzaniaissupportinginvestmentsinwater,electricity,andruralroadssystemsonthemainlandandinZanzibar.

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Figure2.21:DevelopmentAssistanceasaPercentofGDP,1970‐2006

Figure2.22:ForeignAssistanceasShareofGovernmentBudgetandPublicInvestment

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

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160.0%

180.0%

Share

Foreign Assistance

% of DevelopmentExpenditure fromForeign Funds

Grants and BasketSupport as % ofGovernment Revenue(inc. grants)

Grants and BasketSupport as % of PublicInvestment

G.GrowthinHouseholdIncomes

Accordingtorecentestimatesusinghouseholdbudgetsurveys,therelativelyhighratesofgrowthrecordedover thepast decadehavenot translated to similarlyhigh increases in incomes for theTanzanianpopulation, inparticularforthepoor. EstimatesofrealGDPgrowthfromthenationalaccountsdonotcorrespondwithrealconsumptionincreasesfoundinthe2007HouseholdBudgetSurvey (HBS), and many are concerned that growth has not been sufficiently broad‐based.HoogeveenandRuhinduka(2009)arguethat,otherthanthepoorestandwealthiestincomedeciles,

20

Tanzanianhouseholdsbenefitedminimallybutequitablyfromaggregategrowth,asshowninTable2.1.10

Table2.1:ChangesinPerCapitaIncomebyIncomeQuintile,2001‐2007

Source:HoogeveenandRuhinduka

Thereareseveralpotentialexplanationsforthisdisappointingpicture,butitisnotyetclearwhichof theseare theprimaryones. Somegrowth inGDPrepresents foreignprofits,whichwouldnotaccruetodomestichouseholds.Moreimportantly,bothsourcesofdataprobablymis‐measurenethousehold incometosomeextent. Householdbudgetsurveysmeasurecurrentconsumptionasaproxyforincome,withoutcapturingandadjustingincomeestimatesforsavingsandinvestment.Asshown in Figure 2.20, an increased share of GDP has been devoted to government expenditure,investmentandinventories,andtheshareoffinalhouseholdexpenditureintotalGDPhasdeclinedsignificantly from approximately 80 to 70 percent over the period. Investments by households,includingthepoor,indurables,smallbusinesses,andeducation,forexample,appeartohaverisen.Householdsurveysalso tend tounder‐sample therichestsegmentof theeconomy,and thereforeunder‐estimate mean incomes, as well as inequality. National accounts statistics can also beinaccurateforavarietyofreasons,especiallywhengrowthaccelerates(see,e.g.,DeatonandKozel2005, and Ravallion 2001). The price deflator used to calculate real consumption can be amisleading estimate of the price level for some segments of the population in either of the twoyears. Moreover,consumptioncapturedin2007maynotberepresentativeofthebroadertrend.Thesurveyyear(2007)closelyfollowedadroughtyear,andonemightexpectconsumptiontobelowerafteratemporaryeconomicdownturn.Finally,althoughsomepartofthestoryappearstoliewithmeasurementissues,thegrowthmaynothavebeensufficientlybroad‐basedorhighenoughtoliftmoreofthepooroutofpoverty.Themajorityoftheruralworkingpopulationearnsatleastpart of its living through the agricultural sector (according to Hoogeveen and Ruhinduka, 70percentofmenin2009),andinpercapitatermsagriculturalgrowthhasnotkeptpacewithothersectors. Mkenda et al. (2010) estimate that the four sectors with the highest growth rates10Since2001,moreover,ownershipofconsumerdurables,mostnotably televisions,mosquitonets, radios,and bicycles, has increased (Uwazi and Twaweza, 2010). Thismay be due to changes in relative prices,ratherthanareflectionofincreasedrealincomes.

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collectivelyemploylessthan10percentofthelaborforce.11Howeverthisquestionisresolved,therealincomesofmanyofthepoordoappeartohaveimproved,butmoremodestlythanrequiredtoreducepovertyat thedesired level. Basedon theevidenceon internationalgrowthandpovertyreduction, the solution is to adopt policies and make the most critical investments required toaccelerateandsustainbroad‐basedgrowth.

11TheycitedatafromtheIntegratedLaborForceSurveyof2006showingthattheindustryandconstructionsectorseachemployroughly2.2percentoftheworkforcewhiletheservicessectoremploys16percent.Inaddition,mining and quarrying employ roughly 1 percent of thework force. The communications sector,having experienced themost pronounced rate of growth in the economyas shownabove, employs only3percentoftheTanzanianworkforce.

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3. IsthePrimaryConstrainttoGrowththeLackofAccesstoFinance?

A.IntroductionandReformHistory

As shown in theHausmann, Rodrik andVelasco (2005)GrowthDiagnostics ‘tree’ (Figure 1.1), ahigh cost of finance can present a binding constraint to growth if it prevents a large share ofprofitable investments frombeingundertaken. Thediagnostic frameworkrequiresananswer tothe question of whether inadequate investment and growth are due primarily to a high cost offinance or to the lack of private investment opportunities with attractive returns. The analysispresented in this chapter indicates that, while access remains limited for some sectors of theeconomy,accesstofinanceisnotamongthemostbindingconstraintstogrowth.Astherestofthereportwilldemonstrate,lowprivatereturnsdissuadeprivateinvestmentandreducebroad‐basedeconomicgrowth.

Financial sectors in all countries exhibit market failures and inefficiencies due largely to theinabilityof lenders tobeassuredof repayment. A lackof access to external financewill tend toslowfirmgrowth;firmsmaybeforcedtomakesmallerinvestmentsandoperateatalessefficientscale. Indeed, smaller rural enterprises in Tanzania generate less income per worker in mostsectors(WorldBank2007).Atthesametime,theexperienceofaconstraintattheindividualfirmleveldoesnotnecessarilytranslatetolessinvestmentoverall;whileindividualfirmscannotexpandfaster,otherfirmsmaybeabletodoso,withlittletonoimpactontotalinvestmentandproductionlevels.12

Afterindependencein1961,theGovernmentofTanzaniaundertookeffortstointegratepeopleintothe financial system inorder to redress the lackof accessby the local population to bank creditfrom under the colonial system. At the time of independencemuch of bank credit, particularlythrough the National Bank of Commerce (NBC), was directed to the public sector agencies,including cropmarketing andnon‐marketingparastatal enterprises. Theprivate sector receivedtheresidual,andaccessbysmall–scaleborrowerswasminimal.Between1967and1990financialinstitutions were mostly state owned and limited in number. The scope for competition wasrestricted,asstateownedbanksspecializedbysectorandroleinmobilizingsavings.TheNBCwasthe only commercial bank at the time and accounted for over 95 percent of the total domesticdepositbase. TheCooperativeRuralDevelopmentBank (CRDB)was statutorilyprohibited fromtaking deposits. Other financial institutions specialized in financing medium to long term

12Avarietyoffinancialservicesprovidedtohouseholdsandindividualscanbeimportantfortheirwelfare–for facilitatingpayments,providing savings and investment vehicles, dealingwith risksand fluctuations inincome,andoptimizingsavingandconsumptionover the lifecycle. Indeed,according toFinscope (2009),more people save than wish to borrow in Tanzania. Since this report is concerned with the question ofwhetherornotahighcostoffinanceisakeyconstrainttoeconomicgrowth,itdoesnotattempttodescribeall features and shortcomings, nor assess all welfare losses, associated with all inefficiencies within thefinancialsector.

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investments in housing, industry and agriculture. Loan advances were priced using regulatedinterestratesandoftenguaranteedbythegovernment.Inaddition,thetypesoffinancingprovidedweremostlyshort–term,suchasoverdraftsandrediscountedcommercialbills.

Theeconomicliberalizationbegunintheearly1990susheredinanewerainthefinancialsector’sdevelopment.TheBankingandFinancialInstitutionsActNo.12of1991allowedprivatebanksandotherfinancialintermediariestooperateandcompetewiththelargelystateownedinstitutionsandcalledforarestructuringofthestateownedbanksincludingtheCRDB,NBCandTanzaniaHousingBank (THB), whichwere suffering from asset quality problems. Since then, Tanzania’s bankingsystemhasbeenexpandingsteadily.Therearenow41commercialbanks,19ofwhichareforeignowned;13 seven small domestic community bankswhose operations are restricted to particulargeographicalareas;andthreeremaininggovernment‐ownedfinancialinstitutions(TanzaniaPostalBank (TPB), Tanzania Investment Bank (TIB), and Twiga). A few entrants are expected to startoperations soon, including EcoBank, a large regional player that has the potential to increasecompetitioninthesector,whichtendstoenhanceaccesstopreviouslyunder‐servedmarkets.

B.IndicatorsofSupplyandDemandforInvestmentFinance

GiventheachievementsTanzaniahasattainedfromreformsthusfar,isthelackorcostoffinanceabindingconstrainttogrowth?Inordertoassessthisquestion,thenextsectionexaminesthe‘price’of finance,or the interest rate,quantityof supply,andaccess issues,disaggregatingamongrural,urban,andsmall,medium,andlargefirms.

a.PriceIndicators

Apart from a few years of negative real interest rates during periods of high inflation, the reallendingrateonaveragehasbeendecliningsincethemid‐1990s.Ifoneexaminestherelationshipbetween real lending rates and gross fixed capital formation as a percentage ofGDP, taking intoaccountallyearsforwhichtherearedata,oneseesadownwardslopingcurve,asshowninFigure3.2.

This inverse relationship is consistent with a shifting supply side constraint. When supplyincreases, interest rates fall, and investment rises. However, this result is being driven by therelatively anomalous periods of negative real interest rates. Further analysis suggests that thedegree towhich the cost of finance has constrained investment has declined alongwith positivedevelopmentsinthefinancialsector.Whenearlieryearsofnegativeinterestratesareexcluded,therelationship shifts to a positive sloping one, as shown in Figure 3.3. Inmost recent years, themarketpriceandvolumeoflendinghavebeendeterminedprimarilybydemand‐sideshifts;supplyis no longer the primary determinant ofmarket outcomes. At the same time, as the sector hasdeveloped, banks have becomemore efficient and able to reduce lending margins, as shown inFigure3.4.

13Foreignbankshaveownershipstakesintwo(NMB(Rabobank)andNBC(ABSA)).

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Figure3.1:LendingandInflationRates,1989–2009

Source:WorldDevelopmentIndicators(WDI)

Figure3.2:InterestRatesversusInvestmentasaPercentageofGDP,1989‐2006

Bankmargins(lendingrateminusdepositrate)havenarrowedsignificantlyonanabsolutebasis,from14.19percent in2000to6.3percent in2010,andrelativetothebenchmarkcountriesusedhere ‐‐Mauritius11percent,Uganda9percent,Kenya8.5percent,andMozambique7.3percent.Thismarginremainswidebyglobalbankingstandards,buthigherriskspreadsaretypicalforSub‐SaharanAfrica.

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Figure3.3:CorrelationbetweenRealInterestRatesandInvestmentRate,2000‐2006

Source:WDIFigure3.4:BankMargins(Percent),TanzaniaandSelectedCountries

Sources:WorldDevelopmentIndicatorsandBankofTanzania

b.Quantity/AccessIndicators

Interestratesintheformalfinancialsectorareonlyindicativeofthepriceforthoseabletoaccessbankloans. However,accesstotheformalfinancialsectormaybeseverelylimited. Whilesemi‐formalor informal sourcesof financingmaybeavailable, informal creditmarketsare segmentedfromtheformalfinancialsector,andthesesourcesofcreditoftenentailahighercost.Therefore,itisequallyimportanttoexamineindicatorsofaccesstofinancebothwithinandoutsidetheformalfinancialsector.

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AsshowninFigure3.5,domesticcreditcontractedasaresultofreformsinthe1990s,butbegantoexpandasapercentageofGDPbeginningin2002.Despitethis,asof2008TanzaniastillrankedlowrelativetobenchmarkcountriesintermsofdomesticcreditasapercentofGDP.Whileshowingahigheravailabilityofcredit intheeconomythaninMozambiqueandUganda,Tanzania’s financialsectorlagsbehindthatofGhana,Kenya,Sub‐SaharanAfricaandlowincomecountriesasawhole.14Whereas the level of domestic credit as a fraction of GDP remains relatively low, Tanzania’sinvestment rate as a percentage of GDP has also been on average lower than in comparisoncountries,aspreviously illustrated inChapter2 (Figure2.17). Similarly,asapercentageofGDP,lendingtotheprivatesectorisonparwiththatofMozambiqueandUganda,butlowerthanthatofGhana,Kenya,andSub‐SaharanAfrica(seeFigure3.7).

Figure3.5:DomesticCreditasaPercentageofGDP

Source:WorldBank/IMFIn 2006 relatively few Tanzanian firms reported using external financing for investment (WorldBankEnterpriseSurveys).Tanzaniaranksatthebottomofcomparisoncountriesinthisindicator,as shown in Figure 3.6. However, a substantially higher percentage used banks to finance theiroperations.

Asinmostdevelopingcountries,credittoagricultureinTanzaniaislowrelativetoitsshareoftheeconomyduetothegreaterrisksinvolved,includingweather,pests,andthedifficultyofobtainingsufficientcollateral.AsshowninFigure3.9,lendingtoagricultureasapercentageoftotallendinginTanzaniahasnonethelessincreasedsince2000,althoughithasleveledoutatanaverageof9.9percent(1990‐2009)oftotallendingand19percentoflendingtotheprivatesector.AsshowninFigure3.9personalfinancerepresentsasubstantialshareoftotallending.Manufacturingreceivessubstantialsharesofbanklending,albeit–aswithagriculture–lessthanitsshareofGDP.15Atthe

14Inaddition,giventheirrelativelackofexperienceandsmallbalancesheets,Tanzanianbankshavenotbeenabletocompetewith foreign institutions insomekeysectors,suchasmining.However, theopeningof thesectortoforeigninstitutionshasgreatlyalleviatedthisconstraint.15ThefullsharesbysectorareshownintheDataAppendixforthischapter.

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same time, given the slow growth of agriculture, total lending to agriculture as a percentage ofagriculturalGDPhasrisenwiththeexpansionofthefinancialsector.Inthemostrecentyearsforwhich there are data, lending to agriculture expanded from 54millionUSD in 2006/2007 to 94millionUSDin2008/2009,anincreaseof73percentoveratwoyearperiod(BankofTanzania).

Figure3.6:PrivateSectorDomesticCredit/GDP

Source:WorldBank/IMF

Figure3.7:UseofBanksbyRegisteredFirms

Source:WorldBankEnterpriseSurvey(respectiveyearsinparentheses)

RuralaccesstofinancialservicesisgenerallylimitedinTanzania.Banksoperate303branchesand181ATMswitha totalof2millionclients. Semi‐formal sourcesof financingarealso limitedbutexpanding. As of 2009, there were over 3,577 savings and credit cooperatives (SACCOS) with

28

approximately429,240members(Finscope,2009).Savingsandcreditassociations(villagesavingsand loan associations) numbered 146 with total membership of 4,197 (70 percent women),whereas micro‐finance institutions were estimated to have only 220,000 active borrowers.Physical presence indicators (shown in theData Appendix for this chapter) reveal that, in someareasof the country, thenearest branch canbe very far. However, physical accessdidnot rankamongthetopreasonsfornotaccessingbankingservices(Finscope,2009).

Figure3.8:PercentageofBankCredittoAgriculture

Source:BankofTanzania

Indicatorsofwhetherlackoffinanceisasevereormajorconstrainttodoingbusinessareavailablefrom the 2006 World Bank Enterprise Survey, in which a substantial 40 percent of firms inTanzaniarankedthehighcostorlackofaccesstofinanceasamajorconstraint.However,thisisalower share than for other comparison countries, including developing Sub‐Saharan Africa as awhole(Table3.1).Moreover,withcreditexpansionsince2006,onewouldexpectthissituationtohaveimproved.

Constrained access appears to be a larger problem formicro and small firms. Table 3.2 belowshows responses by size of firm and for domestic versus foreign ownership to questions on theseverityofaccesstofinanceasaconstrainttobusiness.Firms’ownassessmentoftheconstraintstheyfaceshowsthatlackofaccesstofinanceisnotamongthetoptwoconstraints.Microandsmallenterprises inparticular reported thataccessing financewasamajoror severeobstacle,but thiswasstillnotoneofthetoptwoobstaclestotheirbusiness.Thevastmajorityofmicroenterprisesdidnotapply fora loanand,of these,nearlyhalfexpresseddifficultaccessas thereason(i.e.,29percent cited the reason fornon‐application tobe complex approvalprocedures, and20percentsaid that collateral requirementswere unattainable). Only 12 percent cited unfavorable interest

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rates,whichwerehigheratthetimeofthesurveythantoday.Onlyeightpercentreportedtheyhadnoneedforaloan.16

Table3.1:SectorSharesofCommercialBankLending

Commercial Bank Lending by Sector, Percent of Total Domestic Loans

2006 2007 2008 2009 2010

Agriculture 11 10.8 9.6 10.4 10.7

Financial intermediaries 3.8 3.2 2.6 2.2 2.3

Mining and Quarrying 1.5 1.1 1 0.5 0.5

Manufacturing 20.4 18.6 15.1 12.7 13.8

Building and construction 4.2 4.1 3.6 3 3.1

Real estate 2.4 2 2 2.1 2.6

Transport and communication 8.2 8 8.3 8.1 9.4

Trade 22.8 22.2 20 17.2 18.1

Tourism 2.7 1.5 0.6 0.6 0.6

Hotels and Restaurant 3.5 4.1 3.9 4 4.2

Personal (Private) 12.7 15.8 19.9 21.2 21.6

Source: Bank of Tanzania

Figure3.9:PercentageofFirmsNamingAccesstoFinanceasaMajorConstraint

16Thefractionoffirmsmakingfixedassetpurchaseswasnotparticularlyhighin2005.Ofmicroenterprises(with less than five employees) surveyed, 37 percent did so (compared to 58 percent for manufacturingenterprisesand46percentforretailandinformationtechnologyfirms).

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Thisresultcontrastssomewhatwiththeresultofa2005RuralInvestmentClimateAssessment(RICA)survey,inwhichfirmsrankedlackofaccesstofinanceasthemostsevereconstrainttheyfaced,followedbyaccesstoutilitiesserviceandtransportation(WorldBank2007).Accessinruralareasappearedtobesignificantlymoreproblematic,atleastatthattime,duetoalackofgeographicpenetrationbyfinancialinstitutionsandknowledgeandexperiencewithbanks.Nonetheless,theRICAreportalsopointsoutthatruralenterpriseearningsarelargelydeterminedbydemand–i.e.,agriculturalearningsinthearea–andseasonality.Thus,thedegreetowhichtheexpansionoffinancewouldenhancebroad‐basedgrowthwithoutraisingagriculturalproductivityisunclear.

Table3.2:FractionofEnterprisesRespondingFinancialConstraintsAreSevere

FractionofSub‐SampleRespondingasFollowstoSeverityofFinancialConstraintsbySizeofFirmandDomesticversusForeignowned(NumberofEmployeesinParentheses)

(Source:2006WorldBankEnterpriseSurvey) Micro

Enterprise(<5)

Smallenterprise(5‐19)

MediumEnterprise(20‐99)

LargeEnterprise(>100)

DomesticOwnership70percentorabove

>30percentForeignOwned

MostSeriousObstacle

7.7% 10.0% 5% 2.7% 8.7% 2%

SecondMostSeriousObstacle

6.2% 17% 10.7% 2.7% 16.3% 2%

MajororVerySevereObstacle

50.4% 42.6% 37.3% 27% 32.5% 31.9%

Thesurveyalsoshowsthatallclassesoffirms,frommicroenterprisestolargefirms,relyverylittleon formal bank lending to finance investment. The percentage of fixed asset purchases in 2005financedbybankswasonly4.2percentformicroenterprises,1.6percentforsmallenterprises,14percentformediumenterprises,and27percentbylargeenterprises,withtheremainingamountsbeingfinancedalmostentirely frominternalresources. AsHausmannetal. (2008)pointout, ifamissingfactorposesabindingconstraint,oneshouldobservefirmsgoingtosomelengthandcosttocircumvent this constraint. However,almostno firms in the2006survey, irrespectiveof size,reportedusinginformalsourcesoffinance–familyorfriends,ornon‐bankfinancialinstitutions–to finance the purchase of fixed assets, although these alternative sourceswere used to financeworkingcapital.

Collateral requirements reported in the same survey are high, but not unusually so.Microenterpriseswithbankloanspledgedonaverage151percentcollateraltoloanvalue,whichisvery similar to the value reported as required by manufacturing firms (148 percent), butsignificantly more than for retail and IT (108 percent on average). These collateralizationrequirementsarefairlytypicalindevelopingcountries.

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Furtherinsightsonthedemandforfinancingbyhouseholdandmicroenterprisesareprovidedbythe Finscope 2009 Survey for the Demand for and Barriers to Accessing Financial Services inTanzania. This survey explores households’ participation in, attitudes toward, and need forfinancial services. The rate of utilization of the formal financial sector expanded from 2006 to2009 from 9.1 to 12.4 percent of the population. With informal and semi‐formal access usecomprisingapproximately32percentofthepopulation,56percentofthepopulationisestimatedtohavenoaccesstoanysourceoffinance.17Asafractionofhouseholds,veryfew(1.2percent)hadever taken bank loans, with the fraction similar in rural and urban areas. Thorough approvalproceduresandcollateralrequirementsarenecessarypartsofprudentiallending,andthereforeitisunclearwhatfractionoftheserespondentswouldqualifyforloansorbeviableborrowersevenifaccesswereexpanded.Moreinformalorsemi‐formalarrangementsaremorelikelytobeeffectiveforreachingsuchbusinessesthanexpansionofformalinstitutions.

TheFinscopeSurveyalsosuggeststhatlowaccesstothefinancialsectorisnotalwaysamenabletosimple supply‐side solutions, such as branch network expansion. Only 5.7 percent of thosereportinganinterestinopeningabankaccountrespondedthatareasonwastobeabletoaccessaloanforbusinesspurposes.Forexample,approximately75percentofhouseholdsrespondedthatthe reason they did not have a bank account was that they did not have regular employment;another20percentcitedthelackofanyemployment.Thesefractionswereverysimilarforruraland urban households (74 and 19 for rural and 76 and 25 for urban populations, respectively).Only23percentofruralrespondentscitedphysicalaccess–thedistancetoabankfromtheirhouse–asareason. Inaddition, somehouseholdssaid that in‐kind lendingwasmoreadvantageous insomecircumstances,presumablybecauseofreducedtransactioncostsandrisks.

Manyhouseholdsalsohadaccesstoothersourcesofexternalfinancing,whichcouldbeeithermoreorlessadvantageoustothemthantraditionallending.However,basedontheFinscopedata,itdoesnotappearthatextremeeffortswerebeingmadetousethesesourcestocircumventaconstrainttoinvestment.Only22percentofhouseholdsparticipated ingroupsavingsand loanarrangements,withaslightlylowerfractioninruralareas(20percent),andonlyasmallfractionofthesepeopleclaimtousethemforbusinessfinancepurposes.Only25.8percentofthosewhodonotbelongtosuch a group do not ‘know about’ them, indicating a small fraction of the other 74 percent areutilizingsuchmeanstoaccessfinancingforbusiness.Inaddition,asignificantpercentageofpeoplehadaccesstocreditfromfamily/friends,kiosks,andotherformalandinformalnon‐banksources,asshowninTable3.3.Thelevelofpreviousexperience(‘usedtohave’)issubstantiallyhigherthancurrentparticipationforallloansources,aswouldbeexpectedifcurrentdemandforloansbythesameindividualsdoesnotexceedhistoricalusage.

17Theproportionofpeoplewithoutanyaccesstofinancialservicesatallrosefrom53.7percentin2006to56.0percentin2009overthesameperiod,althoughthisdifferencemaynotbestatisticallysignificant.

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Table3.3:ExperienceWithandAccesstoNon‐BankCredit

ExperiencewithSourcesofNon‐BankCreditFinscopeSurvey2009 Rural Urban Total PersonalloanfromSACCO Currentlyhave 2.0% 1.9% 2.0% Usedtohave 1.2% 2.7% 1.7% Neverhad 96.8% 95.3% 96.4% Loanfromamicrofinanceinstitution Currentlyhave 0.67% 2.25% 1.10% Usedtohave 0.70% 3.70% 1.53% Neverhad 98.63% 94.06% 97.37% LoanfromanASCA Currentlyhave 0.5% 0.5% 0.5% Usedtohave 1.2% 1.2% 1.2% Neverhad 98.3% 98.3% 98.3% Loanfromfamily/friendswithoutsecurity Currentlyhave 4.9% 4.1% 4.7% Usedtohave 29.1% 29.5% 29.2% Neverhad 66.0% 66.4% 66.1% Loanfromfamily/friendswithsecurity(kuwekaakiturehani) Currentlyhave 1.0% 0.8% 0.9% Usedtohave 7.8% 7.8% 7.8% Neverhad 91.3% 91.4% 91.3% Loanfromaninformalmoneylender/(watubinafsiwanaokopeshakwariba) Currentlyhave 0.6% 0.3% 0.5% Usedtohave 1.8% 2.9% 2.1% Neverhad 97.6% 96.9% 97.4% Creditfromakiosk Currentlyhave 10.4% 7.3% 9.6% Usedtohave 34.9% 33.2% 34.5% Neverhad 54.6% 59.5% 56.0% Non‐monetaryloans–e.g.,livestock,bicycle,radio,agri‐stock,sharecropping Currentlyhave 3.1% 3.2% 3.1% Usedtohave 9.6% 6.2% 8.6% Neverhad 87.4% 90.7% 88.3% Note:Dataonanyloansourcewhere‘neverhad’iscloseto99percentisnotpresentedinthisTable.Thisincludessupplierandbuyercredit.

In addition, as shown in Table 3.5, of those householdswhich have never applied for a loan, 73percent in rural areas and 84 percent in urban areas report reasons which indicate a lack ofdemand– forexample, a fearof inability to repay, lackofneed,oropposition topaying interest.‘Toughconditions’deterred15percentofnon‐applicants,andlackofcollateralanothersixpercent.

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Someofthoserespondentsmaynotqualifyforcreditifavailable.Forthoserespondentswhohadapplied for a loan, in fact, only 23.3 percent were asked for collateral, presumably becauseapplicationswerenotmadetobanks.Ofthoseaskedforsecurity,25percentwereaskedforatitletolandorhome.

Table3.4:ResponsestoWhyHaveYouNeverAppliedForaLoan?

Responsesto‘whyhaveyouneverappliedforaloan?’(Multiplementionspossible) Rural Urban TotalI fear that Imay not have enoughmoney to repaytheloan 35.6% 35.6% 35.6%Ihaveneverneededit 27.1% 34.0% 28.9%Idon’tknowwheretogetaloan 22.9% 11.3% 19.8%Idon’thaveenoughmoney 17.4% 22.7% 18.8%ToughConditions 14.2% 16.4% 14.8%Thereisnoplacenearbytogotogetaloan 10.7% 2.6% 8.5%Idon’tbelieveinpayinginterest 6.4% 8.3% 6.9%Idon’thaveanycollateral 5.4% 8.4% 6.2%Idon’thaveaguarantor/referee 3.5% 4.1% 3.7%Theychargetoomuch 3.2% 5.0% 3.7%Iamtooyoungtoqualify 3.0% 4.5% 3.4%I don’t have identification or the right documenta‐tion 1.4% 2.2% 1.6%Myspouse/partnerwon’tallowit 1.0% 1.5% 1.2%Others 0.9% 1.4% 1.0%

Source:Finscope2009TanzaniaSurvey.Under perhaps the most costly source of alternative financing, money kiosks, lenders utilizerepeated interactions to establish creditworthiness of borrowers on small loans at initially veryhigh interest rates, which they reduce once the reliability of the borrower is better known.18Unfortunately,thesurveydidnotascertainwhetherfundsaccessedthroughthisorotherchannelswereusedtoserveshorttermconsumptionorbusinessneeds.

18Anecdotally,ona loanofTsh20,000foroneweek, initiallyaborrowerwouldhavetorepayTsh25,000,which is a tremendously high implied rate of interest, duepresumably to thehigh risk of lendingwithoutguaranteeorcollateral.

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Table3.5:WhatTypeofSecurityWasRequestedofLoanApplicants?

ResponsestoWhatTypeofSecuritywasRequestedofLoanApplicantsaskedforSecurity(Multiplementionspossible)

Rural Urban TotalOwnlandtitle/housetitle 23.3% 28.0% 24.9%Letterofcredit 25.3% 18.9% 23.0%Ownhouseholdgoods 14.8% 31.2% 20.5%Guaranteefromemployer 22.0% 14.5% 19.4%Third party securities – guarantee or documents fromsomeoneelse 17.9% 19.0% 18.3%Guaranteefromvillageexecutive/councilor 17.3% 18.3% 17.7%Cashdeposit(Dhamanambadala) 16.9% 14.5% 16.0%Groupguarantee 14.1% 12.6% 13.6%OwnbusinessItems 8.0% 18.1% 11.5%CompulsorysavingsSACCOs 8.0% 13.8% 10.0%Guaranteefromspouse 8.3% 5.2% 7.2%Other 6.8% 0.6% 4.6%Ownmachinery,tools 1.0% 9.0% 3.8%Ownbusinessstock 1.5% 0.7% 1.2%Owncarregistrationcard 0.4% 1.5% 0.8%Assetpurchasedusingtheloan 0.4% 1.4% 0.7%

Source:Finscope2009TanzaniaSurvey.

C.Conclusion

Since 1991, the Government of Tanzania has made strides with its reforms to create a sound,market‐orientedfinancialsector. Accessto finance isrelatively limited inTanzania,buthasbeenexpandingrapidly.

Additional constraints to further expansion and reach of the sector are related to the currentstructure of the economy anddevelopment of other sectors. These constraints include a lack ofbasicinfrastructureinruralareasincludingelectricityandroads,althoughtherecentexpansionofmobile technologies have lowered such barriers to providing financial services; a lack of creditinformationandexpertise;weakorcostlyaccesstomarketsindistantruralareaswithlowdensityof potential customers; a limited framework for using fixed andmovable assets as collateral forlending;andalowlevelofformalfinancialliteracy.

ThischapterprovidesindicationsthatalackofaccesstofinanceconstrainsSMEsandagriculturalinvestors in particular. Some micro‐ and household businesses utilize imperfect and costlyinformal financing,andsuchmeansare likely inadequate toundertake larger investments. Smallandmedium enterprises and agricultural investors appear to have theweakest access, although

35

access is expanding through private commercial and semi‐formal channels. However, on anaggregatelevelalackoffinancialsupplyisnotthemostconstrainingfactortoinvestment.Thereisnosignoffirmsusingcostlymeanstocircumventtheconstraint:BuyerorsuppliercreditisnotasignificantphenomenoninTanzania,asonewouldexpectifbusinessesattemptingtotransactwereprimarily constrained by a lack of financial access by their customers and suppliers.19 Informalsources of credit are only used by small household‐based businesses and, although manymicroenterprisesreportthatfinanceisamajorconstraint,theyreportotherconstraintstobemoresevere.

Assuch,althoughaccessto finance isaconstraint for importantsectorsoftheeconomy, itcannotberankedamongthemostbindingconstraintstoeconomicgrowth.

Basedonthe findingsabove, theGovernment’s financialsectorreformshavebeensuccessfulandshouldbesustainedanddeepened.

19NeithertheFinscopeSurveynortheEnterpriseSurveyfoundmorethan1percentofrespondentstoutilizesuchcredit.

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4. MacroeconomicRisksandDistortions

While macroeconomic stability is not sufficient to drive investment and economic growth,macroeconomic disorder is clearly detrimental to them. A government cannot provide neededpublicservicesifahighshareofitsresourcesmustbedirectedtodebtrepayment.Persistentfiscaldeficitslimittheabilityofagovernmenttoundertakecounter‐cyclicalfiscalpolicyinthefaceofaneconomicdownturn. Unstableordistortedexchangeratescanreduceexportsandgrowth. Andinflationincreasesrisksanddeterseconomicactivitybybothbusinessesandlenders. Tanzania’srecent economic reforms undertaken by the government with assistance from its developmentpartners, including the International Monetary Fund (IMF), have paid off in terms of reducinginflation, increasing government revenue collection, improving resource availability forinfrastructuredevelopment,andultimatelyboostingprivateinvestmentinflows.

A.Inflation

Inflationdistortspricesignalsbetweenbuyersandsellers,borrowersandsavers,andreducesthewealthofsaversheldinmonetaryinstruments.Itredistributesincomeandwealthinitiallyinfavorofborrowersand,byharmingcreditorsandsavers,deters savingand thereby restrictsaccess tofinance.Italsoimpactsagovernment’sfiscalspacebyreducingtherealvalueofanytaxcollectedinfixed‐currencyterms,reducingthevalueof its localcurrencydebt,and increasingthevalueof itsforeigncurrencydebt.

Since theearly1990s,when inflationreachedover30percent, theGOThassuccessfully reducedandstabilizedinflationthrougheffectivefiscalandmonetarypolicies.Fiscalimbalances,themaindriverofhighinflation,werebroughtintoline,andinflationslowedgraduallyandfelltosingledigitlevelsinthe2000s.

InflationinTanzaniaisnowmainlydrivenbyfoodandoilpriceshocks.Fluctuationsinfoodpricestendtoimpacttheoverallinflationratesignificantly,asfooddominatesthebasketofcommoditiesusedtoestimateinflation,withaweightingforfoodandnon‐alcoholicbeveragesof47.8percentforTanzania (mainland) and 57.4 percent for Zanzibar. Inflation rises during the dry season andperiodsofdrought,andfallswhenthereisanabundantharvest.

The impactof foodproductionon inflation isshown inFigure4.1. Foodprice inflationexceedednon‐food inflation until 2005, when the two converged due to a good harvest. Then food priceinflationroseto7.0percentin2006duetoprolongeddroughtinthe2006/07agriculturalseasons,globalfoodshortagesandlocaldrought,thecombinedeffectofwhichwastodriveinflationbacktodouble digit levels (10.2) in 2008/09. The average annual headline inflation rate rose to 12.1percentin2009,duetofoodpriceinflation,whichincreasedto17.5percentfrom12.7percentin2008. When the country experienced favorable weather conditions during the 2009/10 cropseason, an abundant food supply led inflation to slow from 12.7 percent (October 2009) to 6.4percent(January2011).

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Like theTanzanianmainland, theZanzibareconomyexperiencedadramatic increase in inflationlevelsinthelate2000s,withinflationrisingfromaten‐yearaverageof9.6percentto20.6percentin2008beforedroppingbackto8.9percentin2009and6.1percentin2010.

Figure4.1:InflationbySelectedSub‐Components

‐5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

2003 2004 2005 2006 2007 2008 2009 2010

Percen

t

Inflation by Selected Sub‐Component

Transportation Fuel, Power and Water Food

Source:NationalBureauofStatisticsOnewould expect that higher inflationwould be associatedwith lower real GDP growth for theusualreasonslistedabove,aswellaswheninflationisdrivenbyagriculturalproductionthereisanadditional driver for a negative correlation. As shown in Figure 4.2, inflation and GDPmove asexpected,inoppositedirections.ThesameistrueforZanzibar.20

Figure4.2:GrowthofGDPandInflation,TanzaniaMainland

Source:UnitedRepublicofTanzania(URT),EconomicSurvey,2009

20Forafullerdiscussion,seeAnnex:MacroeconomicRisksandDistortions.

0

2

4

6

8

10

12

14

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Percen

tage

Growth of GDP and Inflation‐ Tanzania Mainland

GDP Growth Inflation

38

Figure4.3:GDPGrowthRateandInflation,Zanzibar

Source:RevolutionaryGovernmentofZanzibar,ZanzibarEconomicSurvey2009

Inflation rates in Tanzania are also influenced by the price of energy products. Figure 4.4demonstratestherelationshipbetweenTanzania’srecentinflationperformanceandthechangesinimportsofmajorfoodandoil‐basedproducts.Inflation,asreadalongtheright‐handaxis,appearstobeconsiderablylessvolatilethanimportsuntilapproximately2007.In2007,however,inflationbegantoincreaseconsiderably,drivenbyongoingincreasesinimportsofthelargestimporttothecountry–mineralfuelsandoils.Inflationfellslightlyfrom2008to2009,whilemostmajorimportsinto the country fell in value, and began to increase again from 2009 as most major importsrecovered.

That inflation inTanzania isno longerdrivenbymonetaryphenomena is evidencedbya simplecorrelationmatrix between the growth inmoney supply (across theM1 andBroadMoney –M2aggregates) and inflation since 2000 to 2010. Though there seems to be a positive relationshipbetweenmoneysupplyandinflation,itisweak,asdemonstratedbythelowcorrelationcoefficientof.17.Infact,thedivergenttrendsfrom2007to2009depictedinFigure4.5suggestthattheBankofTanzaniamaintainedaconstantgrowthinthemoneysupplyinordernottoexacerbateimportedinflation,butresumedmoneysupplygrowthfrom2009asinflationfellbacktomorenormallevels.

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Figure4.4:InflationandMajorImports

Source:IMF,WorldEconomicOutlook(October2010)andInternationalTradeCentre/Comtrade

Figure4.5:InflationandMonetaryMeasures


0

5

10

15

20

25

30

35

40

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Percent Change

Inflation and Monetary Measures

Money (M1)

Money Plus Quasi‐MoneyM2

Inflation

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B.FiscalBalanceandDeficit

Thefiscalbalancecanimpacteconomicgrowththroughavarietyofchannels.Largefiscaldeficitscandriveinflationhigher,andmaycauseanincreaseininterestratesandleadtocrowdingoutofproductive private investment. While fiscal deficitsmaybe financed by foreigndonors, in somecasesthiscancauseexcessivecurrencyappreciationattheexpenseoftradablegoodssectors. Atthesametime,ifdeficitsarenotfinancedbyoutsidelending,thenpublicinvestmentsinareassuchas infrastructure or health and education programs may be too low to stimulate productiveinvestmentandeconomicgrowth.

a.GovernmentRevenue

Overthepasttenyears,theGovernmentofTanzaniaundertookmassiveeffortstostrengthenandbroaden its domestic revenue collections by rationalizing tax administration and structures.However, in recentyearsestimatedrevenuesstarted to fall shortof targets. Theslowingrateofimprovement in revenue collections is partly explained by lagging implementation of reformsacrossdifferenttaxcategories,suchaspetroleumproductsforminingcompanies,andbytheglobalfinancial crisis. Thebroadergrowthof theeconomyseems tohavehad little impacton revenuecollections.

Tanzania’srecentperformanceinrevenuecollectionisimproving,althoughtheratioofrevenuetoGDPfallsbelowtheaverageforSub‐SaharanAfricaandbelowtheratesofcomparatorcountries.21ArelativelylowlevelofrevenuesuggeststhatTanzaniahaslimitedresourcesforpublicspendingorfinancingofabudgetdeficitandlimitedfiscalspace.Figure4.6:ComparativeGovernmentRevenue

Source:IMFWorldEconomicOutlookandMinistryofFinance

21DataforcomparatorsaredrawnfromtheIMFWorldEconomicOutlook.DataforTanzaniaaredrawnfromtheGovernmentofTanzania,MinistryofFinanceofficialstatistics.

41

b.PublicExpenditure

Duringthepast tenyears,Tanzania’sgovernmentexpenditurepolicies focusedonenhancingandsustaining sound financial management in order to achieve the national objectives of economicgrowthandpovertyreduction.ThePublicFinanceandProcurementActsof2001andtheMediumTermExpenditureFramework(MTEF)were implementedeffectively,and thePublicExpenditureReview (PER), Cash Budgeting, and Integrated Financial Management System (IFMS) wereintroduced to combat misuse of public funds and enhance efficient resource allocation andmanagement. Generally, there has been a substantial improvement in expenditure absorptioncompared to the 1990s. Budget deficits have been low, signaling sound fiscalmanagement. Theoveralldeficit,inclusiveofgrantreceipts,hasbeenbelow6.0percentofGDPforthepasttenyears,averaging3.1percentannually.

Compared to other countries in the region, Tanzania performs relatively well on fiscalexpenditure.22 ItsratioofexpendituretoGDPaveraged20.9percentovertheprevioustenyears,which is below the Sub‐Saharan Africa average of 27.4 percent. That said, Tanzania has nearlydoubled government expenditure as a percentage of GDP over the past decade, overtakingexpenditurelevelsinMauritiusandUganda.Itisnotclearifthisincreasingtrendwillcontinueorwill stabilize, and thus what the implications may be for long‐term fiscal sustainability of thegovernment.Figure4.7:ComparativeGovernmentExpenditures

Source:IMFWorldEconomicOutlookandMinistryofFinance

AsseeninFigure4.8,Tanzaniais increasingboththeratioofrevenuetoGDPandexpendituretoGDP.Expendituresare,however,risingmorequicklythanrevenues.

22DataforcomparatorsaredrawnfromtheIMFWorldEconomicOutlook.DataforTanzaniaaredrawnfromtheGovernmentofTanzania,MinistryofFinanceofficialstatistics.

42

Figure4.8:Revenue/ExpendituretoGDP

Source:UnitedRepublicofTanzania,EconomicSurvey2009

A widening gap between government revenues and public expenditures has implications forsustainabilityofgovernmentprogramsiffinancingsourcesarenotreliable.Infact,theGOTreliesheavilyonexternalfundingtofinanceitsprimaryfiscaldeficit,atanaverageof99.9percentoverthepastsixfiscalyears(90.7percentif2007/08isexcludedasanoutlier).Donorgrantsandloanshave been the largest components of total deficit funding, at an average of approximately 82.5percentoverthesameperiod.23

Though progress has been achieved in recent years to reduce donor dependency, the trenddepicted inFigure4.9suggestsa riskof reversing this trend,asdonor financing isused tooffsetwidening fiscal deficits. Such reliance can place the economy in a precarious position, shouldforeignassistancefailtomaterializeduringaperiodwhenfiscalstimulusisneeded.

C.ExternalPosition

A country’s external position signals to the private sector the level of risk of future macroinstability. A countrymustobtain funding to runanexternaldeficit,whether to cover a currentaccount deficit, fiscal deficits, or interest payments on debt, or it may face painful adjustmentsthroughthe tradeorcapitalaccounts. Anexternaldeficitmaybesustainable ifoffsetting foreigninvestment or donor assistance flows into the country, though with potential impacts on theexchangerateandthecompetitivenessofexport‐and import‐orientedsectors. If foreign lendingand assistance are insufficient, the governmentmight borrowby sale of bonds, treasury bills, orother debt issuance. However, this borrowing may drive up interest rates, to the detriment of

23The recent move to allow Government borrowing from the market may reduce the need somewhat tofinancethebudgetfromdonorgrantsandloans.

43

privatefirmsseekingfinance.Agovernmentthatoperatesalargeandgrowingexternaldeficitmayeven deter foreign investors, who fear that the government will turn to printing money andinflationtoserviceitsdebts.

Table4.1:GovernmentofTanzaniaPublicExpenditureFinancing

2004/2005 2005/2006 2006/2007 2007/2008 2008/2009 2009/2010

Actual Actual Actual Actual Actual Budge ted

FINANCING 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

External Sources 96.1% 86.8% 97.3% 146.3% 87.4% 85.7%

Grants 67.3% 56.5% 56.0% 99.9% 49.9% 57.3%

Basket Support 10.6% 4.5% 2.7% 12.7% 7.5% 7.9%

Import Support / OGL Loans 4.4% 13.9% 15.4% 23.2% 13.2% 10.1%

Project Loans 20.9% 17.8% 25.0% 13.3% 19.4% 11.9%

Amortization (Foreign) -7.3% -5.8% -1.7% -2.9% -1.1% -1.5%

Internal Sources 3.9% 13.2% 2.7% -46.3% 12.6% 14.3%

Non-Bank Borrowing 9.8% 11.9% 12.2% -1.3% 0.0% 5.7%

Bank Borrowing 0.0% 7.0% 1.5% -20.1% 8.4% 82.2%

Proceeds from Privatization 0.0% 1.8% 0.0% 0.0% 1.8% 0.4%

Payment of Arrears 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

NBC Bonds 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Recapitalization 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Adjustment to Cash 3.6% 2.0% -3.5% -5.0% 10.9% 0.0%

Amortization (Local) 0.0% -1.0% 0.0% -0.9% 0.0% 0.0%

Expenditure Float -9.5% -8.5% -7.6% -19.0% -8.6% 0.0%

Source:Authors’calculationsbasedonUnitedRepublicofTanzania,EconomicSurvey2009

Figure4.9:FiscalBalance

44


Figure4.10showsthatTanzania’sexternalperformancehas fluctuatedoverthepast15years,asmeasuredbyitsfiscalbalance,currentaccountbalance,andtradeingoodsandservicesbalance.Figure4.10:ExternalPerformanceIndicators

Source:IMF,WorldEconomicOutlook(October2010)andWorldDevelopmentIndicators

a.PublicExternalDebt

Tanzania’sstockofpublicdebt,comprisedofexternalanddomesticdebt,stoodat10.5billionUSDasofendJune2010,24anincreaseof2.9billionUSD(38.5percent)fromJune2001and3.5billionUSD(49.6percent)fromJune2008(seeFigure4.11).

24Thisfiguredoesnotincludeinterestarrearsonexternaldebt.

45

Figure4.11:TotalStockofPublicDebt

Source:MinistryofFinanceandEconomicAffairsandBankofTanzania

Whereas therehasbeenan increase in totalpublicdebtsince June2001,debtasapercentageofGDPhasdeclinedatanaggregatelevelfrom65.2percentin2000/01to34.0percentin2006/07,largely due to debt relief under the Heavily Indebted Poor Countries (HIPC) Initiative andMultilateralDebtReliefInitiative(MDRI).However,publicdebtreboundedslightlyinthefollowingyears to reach 42.4 percent in 2009/10 due primarily to new loans for development projects,especiallyinfrastructure,whiledomesticdebtasapercentageofGDPincreasedfrom10.3percentin2000/01to20.5percentin2009/10.

Over thesameperiod,Tanzania’sexternaldebtperformance improvedsignificantly. Theratioofexternaldebt–thelargershareofpublicdebt–toGDPdeclinedfrom55.6percentin2000/01to22.5percent in 2009/10.Public external debt fell sharply from38.5percent in2005/06 to 17.2percentinyear2007/08,mainlyduetoMDRI.Concessionalmultilateralloanshavebeenthemajorsourceof external financingover the last decade (85.9percent), followedbybilateral debt (11.5percent)andcommercialandexportcredits(2.6percent)asofend‐June2010.Tanzaniahassuccessfullyreduceditsexternaldebtservice,bothasapercentageofGDPandasapercentage of domestic revenue. The debt service‐to‐revenue ratio fell from 20.7 percent in2000/01to3.5percentin2009/10,whileitsdebt‐to‐GDPratiofellfromahighof118.1percentin1995to21.4percentin2009.Thisreductioninpublicexternaldebtoverthepast15yearsbroadlymirrors a trend seen across benchmark countries and aggregates. Thedramatic improvement inpublicexternaldebtratios,seenacrossallcomparators,reflectstheroleofHIPCandMDRI.Sincethose initiatives, however, Tanzania’s debt ratio has risen above those of Sub‐Saharan Africa,Ghana,Mauritius,andUganda,asshowninFigure4.15.

46

Figure4.12:PublicDebtRatios

Source:MinistryofFinanceandBankofTanzania

Figure4.13:ExternalDebtServicetoGDP

Source:MinistryofFinanceandBankofTanzania

On the broader measure of total public debt – aggregating both domestic and external debt –Tanzania continues to perform largely in alignment with the benchmark countries (see Figure4.14).Tanzaniahassteadilyreduceditstotalpublicdebtburden,fromnearly90percenttoslightlymorethan40percentover thepastdecade.The improvingratiosignifies that thecountry is inabetterpositiontoserviceitstotaldebtstockand,withalowerdebt‐to‐GDPratio, isat lessriskofdefault.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.020

00/0

1

2001

/02

2002

/03

2003

/04

2004

/05

2005

/06

2006

/07

2007

/08

2008

/09

2009

/10

Per

cen

t

Public Debt Ratios

Public Debt as % of

GDP

External Debt as % of

GDP

Domestic Debt as %

of GDP

47

Figure4.14:PublicExternalDebt

Source:WorldDevelopmentIndicatorsFigure4.15:ComparativeGrossGovernmentDebttoGDPRatio

0

20

40

60

80

100

120

140

160

180

200

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Percent

Comparative Gross Government Debt to GDP Ratio

Ghana

Kenya

Mauritius

Mozambique

Tanzania

Uganda

Sub‐Saharan Africa

Source:IMF,WorldEconomicOutlook(October2010)

48

b.RiskandCosttoReachFinancialMaturity

Given the concessional nature of most of its debt, Tanzania enjoys relatively long repaymentperiods for its public debt, as reflected in the Average Time to Maturity (ATM) of the overallportfolioof15.7years,nearlydoublethefigureforcomparatorcountriesthatreportsimilardata.Thissuggeststhatfinancingrisksaremanageable.

Table4.2:AverageTimetoRe‐Fixing25andMaturityforSelectedAfricanCountries

Ghana Kenya Tanzania ZambiaAverageTimetoRefixing(ATR) 7.8 8.3 15.2 6.7AverageTimetoMaturity(ATM) 8.0 8.3 15.7 6.9

Source:MediumTermDebtManagementStrategyforTanzaniaPreliminaryDraft

Tanzania compares roughly on par with benchmark countries on the risk premium applied toTanzanianloans,withsomefluctuationsbothinabsoluteandrelativeterms(seeFigure4.17).Overtheperiod1995to2005,Tanzaniamovedfromthelowestriskpremiumamongcomparatorstothehighestandbackagain.Anincreaseintheriskpremiumfrom2007‐2009ispartlyassociatedwithan increase in debt stock fromboth concessional andnon‐concessional loans, as a result of highdemand to meet infrastructure project obligations. Therefore, this increase is expected to be ashort‐termissue.

Tanzania performs on par with the benchmarkcountriesonitssovereignriskmeasureasreportedbytheEconomistIntelligenceUnit.Tanzania’sriskratingof “B” places it firmly in line with every nationalcomparator, exceptingUgandawith its superior “BB”rating.26

Based on the forgoing, Tanzania seems to haveexperiencedlowdistressfromdebtrisksoverthepastdecade;however,anincreasingtrendindebtovernexttwoyearsisanissuetobemonitored.

25Average Time to Re‐fixing (ATR) gives information on the exposure of the debt portfolio to changes ininterestrates,i.e.,theaveragetimetakentore‐fix.26Riskratingsrun indescendingorder fromAAAtoD. Ariskratingof “BB” isdefinedasnon‐investmentgrade.Ariskratingof“B”isdefinedashighlyspeculative.

Table4.3:ComparativeSovereignRiskRatings

SovereignRisk

Tanzania B

Ghana B

Kenya B

Mozambique B

Uganda BB

Source:EconomistIntelligenceUnit

49

Figure4.16:ComparativeRiskPremia


c.CurrentAccount

Thecurrentaccountbalance,measuredasashareofGDP,canindicatewhetheracountryisonanunsustainable external financing pathwhichmay ultimately createmacroeconomic instability orrequirecostlymacroeconomicadjustments.Whileatradedeficitshouldnotnecessarilybeseenasanegativeoutcome, tradedeficitsmustbeoffsetbynet investment,remittances,orother inwardtransfers of capital, including official (aid) transfers, in order for the external accounts to be inbalance.

Tanzania’scurrentaccountbalanceasapercentofGDPhasbeeningrowingdeficitoverthepasttenyearsmainlyduetoanincreasingtradedeficit(withnetincometransfersrangingfrom1.0‐3.6percentofGDP). Whereasexportshavecontinuallyexpanded, as shown inTable4.4,Tanzania’strade balance showed a lower deficit than any of the individual benchmark countries by 2006,when ithad reduced thedeficit from17percent to less than sixpercent. Since then,Tanzania’stradedeficithas increasedtoa levelmorecomparable to thebenchmarkcountries, importshaveexpanded more rapidly as the economy has grown. Intermediate goods in particular havecontributed to widen the current account deficit. Intermediate goods imports rose from 529.7millionUSD in2000to2.7billionUSD in2010, representingan increaseofover300percent–ahealthysymptomofagrowingeconomy.

50

Figure4.17:ComparativeCurrentAccountBalances

Source:IMF,WorldEconomicOutlook(October2010)

Figure4.18:ComparativeTradeBalance

-30

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-5

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1995

1996

1997

1998

1999

2000

2001

2002

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2009

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cent

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DP

Comparative Trade Balances Tanzania

Ghana

Kenya

Low Income

Mauritius

Mozambique

Sub-Saharan Africa

(developing only)Uganda


Tanzania’stradebalanceshowedalowerdeficitthananyoftheindividualbenchmarkcountriesby2006, when it had reduced the deficit from 17 percent to less than six percent. Since then,Tanzania’stradedeficithasincreasedtoalevelmorecomparabletothebenchmarkcountries.

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Table4.4:TradeBalance,1995‐2010

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Exports 854.1 1,040.0 1,234.9 1,157.4 1,201.8 1,361.0 1,766.7 1,899.7 2,168.6 2,615.3 2,971.7 3,445.7 4,102.3 5,577.6 5,149.3 6,388.3

Goods 255.1 232.8 752.6 636.1 601.5 733.6 851.3 979.6 1,220.9 1,481.6 1,702.5 1,917.6 2,226.6 3,578.8 3,294.6 4,296.8Services 599.0 807.2 482.4 521.3 600.3 627.3 915.4 920.1 947.8 1,133.6 1,269.2 1,528.1 1,875.7 1,998.8 1,854.6 2,091.5

Imports ‐1,263.8 ‐1,202.1 ‐1,948.2 ‐2,337.5 ‐2,210.4 ‐2,050.0 ‐2,209.7 ‐2,143.9 ‐2,659.2 ‐3,457.6 ‐4,204.9 ‐5,113.4 ‐6,274.3 ‐8,661.2 ‐7,543.2 ‐8,974.7Goods ‐551.9 ‐493.9 ‐1,148.0 ‐1,382.1 ‐1,415.4 ‐1,367.6 ‐1,560.3 ‐1,511.3 ‐1,933.5 ‐2,482.8 ‐2,997.6 ‐3,864.1 ‐4,860.6 ‐7,012.3 ‐5,834.1 ‐7,125.1Services ‐711.9 ‐708.2 ‐800.2 ‐955.3 ‐795.0 ‐682.4 ‐649.3 ‐632.5 ‐725.7 ‐974.7 ‐1,207.3 ‐1,249.3 ‐1,413.7 ‐1,648.9 ‐1,709.1 ‐1,849.6

TradeBalance ‐409.6 ‐162.1 ‐713.3 ‐1,180.1 ‐1,008.7 ‐689.0 ‐443.0 ‐244.2 ‐490.5 ‐842.3 ‐1,233.2 ‐1,667.8 ‐2,172.0 ‐3,083.6 ‐2,393.9 ‐2,586.4

TanzaniaTradeBalance,1995‐2010MillionUSD

Note:2010figuresbasedonprovisionaldata.Beginning2006,exportedgoodsareadjustedtoincludedataforunrecordedcross‐bordertrade. Source:BankofTanzania.

D.ExchangeRate

Thelevelandvolatilityofexchangeratesareaprimarydeterminantofthereturnsforexport‐andimport‐orientedbusinesses.Anovervaluedexchangeratemakesexportsrelativelymoreexpensiveon the world market, decreasing the competitiveness of domestic firms vis‐à‐vis foreigncompetitorsandreducingtheir incentives to investorexpandproduction. Import‐oriented firmswouldconverselybenefit fromanovervaluedexchangerate. Avolatileexchangerate introducesgreaterriskintotheinvestmentdecisionsthatfirmsmustmake,whileamorestableexchangerateallowsthemtoforecastreturnsoninvestmentwithmorecertainty.

TheTanzanianshillingexchangeremainsmarketdetermined.TheBankofTanzaniacontinues toparticipate in the Inter‐Bank Foreign Exchange Market (IFEM), primarily to meet liquiditymanagementobjectives,whilefosteringorderlymarketdevelopments.Overthepasttenyears,theTanzanianshillingnominallydepreciatedagainstmostothercurrenciesintheworld.Theaverageshilling‐U.S. dollar exchange rate rose from 744.9:1 in 1999 to 1392.9:1 in 2010, representing anominal depreciation of more than 100 percent during that period. The general trend ofdepreciationisduetoasubstantialextenttodifferentialinflationratesandslowingprivatecapitalinflows.ThetradegaphasbeenfallingrelativetoGDP,andincreasingofficial/aidtransferstendtopushtheexchangeratedown(appreciatethecurrency).

ThereisnoevidencethatamisalignedexchangeratehasdepressedTanzania’sexportperformance.As shown below, over the past several years, the rate of export growth has been negativelycorrelatedwithchangesinthenominalexchangerate:

52

Figure4.19:GrowthofExportValue,VolumeandExchangeRate


Since increasing demand for exports should tend to reduce the exchange rate (appreciate thecurrency) and produce a negative correlation between exports and the exchange rate, thecorrelation seen in Figure 4.20 suggests that the exchange rate is driven by export demand. Asdemand for exports increases (upward movement in green and blue lines of the graph), theTanzanianshillingbecomesmorevaluableorappreciates(downwardmovementinredlineofthegraph).Thisisconsistentwithstrongdemandforexportproductsthatmakeupalargepercentageoftheexportbasket–forexample,goldandotherminerals.

Justasthenominalexchangerateimpactseconomicgrowththroughtradeandinvestment,therealexchangerate(RER,adjustedforinflation)andtherealeffectiveexchangerate(REER,adjustedforinflationandtrade‐weighted)alsoaffectthesedriversofgrowth. AccordingtoHobdari(2008:3‐11),Tanzania’sREERsignificantlydepreciatedbetweenend‐2000andmid‐2006,thoughitdidrisemodestlybetweenmid‐2006andmid‐2007.Hobdariappliesthreemethodstocalculateover‐andunder‐valuation of the Tanzanian shillingwith respect to its equilibrium real exchange rate andfindsthat,asofmid‐2007,theREERoftheshillingwasaboveitsequilibriumlevel,orunder‐valued.Barring any significant, real appreciation of the Tanzanian shilling since mid‐2007, it does notthereforeappearthattheexchangerateisdampeninggrowththroughtheexportschannel.

53

Figure4.20:RealExchangeRate(2000‐2009)

Source:Authors’calculationsbasedPennWorldTables

Inaddition,thevalueoftheTanzanianshillingoverthepasttenyearshasbeenstablecomparedtoothercountriesintheregion,whencalculatedbasedonamethodusedbyRodrik(2008).27Therealexchangeratemovementsince2000wasbelowthebaseyeartrenduntil2007,whenthetrendroseabove the baseline slightly. Ghana, Kenya, Mauritius, and Uganda all experienced considerablygreatermovement in the real exchange rate, as evidenced by largermovements above the 2000baseline. Figure 4.20 suggests that the Tanzanian economy has enjoyed relative exchange ratestability,whichshouldfosterlong‐runinvestmentintheTanzanianeconomy.

E.Conclusions

Based on the evidence presented above, it does not appear that any elements of the Tanzanianmacro‐economy currently pose a binding constraint to private investment and, thereby, broadeconomicgrowth.Relativelylowratesofinflation,areasonablefiscaldeficitandmanageabledebtburden, and a real exchange rate at equilibrium currently impose a low burden on the privatesector.Noneof theevidenceprovidedsuggests that theprivatesector ismakingcostlyefforts toavoidexcessivemacroeconomicrisks.AnyremainingmacroeconomicrisksanddistortionsarenotsufficienttosubstantiallyimpedeappropriabilityofreturnsinTanzania.

In fact, themacroeconomicreformsundertaken inTanzaniaover thepast twodecadesappear tohavereducedtherisksanddistortionsfacedbytheprivatesector.

27SeeAnnex:MacroeconomicRisksandDistortions.

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5. WeakMicro‐AppropriabilityofReturns

AsindicatedintheHRVframework,returnstoaneconomyasawholefrominvestingmaybehigh,but theabilityofprivateentrepreneurs toappropriate thosereturnsmaybe limitedbyrisksanddistortions at themicro‐economic level. Among themany issues thatwould increase the riskofinsufficient appropriabilityordriveawedgebetweensocial andprivate returnsare excessiveorcostlyregulatoryobstacles;thestructure,level,andadministrationoftaxes;politicalrisk,crimeorinsecurity;restrictionsonortaxationoftrade;andinadequateprotectionofall formsofpropertyandcontractualrights.

Giventhevastspaceinwhichmicro‐economicappropriabilityissuescanarise,theanalysisbeganwith an overview of the available international business and investment climate indicators,including the World Bank Doing Business Indicators (WBDB), World Bank Enterprise Surveys,GlobalCompetitivenessSurveys,andWorldwideGovernance Indicators.28 While these indicatorscannot on their own constitute definitive evidence of the existence and severity of a particularconstraintto investmentandgrowth, intheareaswhereTanzaniaeitherrankedconsistently lowagainstcomparatorcountriesacrossavarietyof indicatorsources,orparticularly lowonamorespecific indicator, theshadowvalueof the ‘constraint’would tend tobehigh. Inaddition, if theconstrainttendstoimpactabroadswathoftheeconomy,itwouldhaveamoresignificantimpacton growth. Thus, a survey of the available indicators was used, along with a ‘size of potentialimpact’testasaguideforfocusinganalysisonareaswhichmostwarrantfurtherinvestigation.

Four somewhat related issues emerged from this process as potentially acute problems in thecountry’sinvestmentenvironment.Thoseareasare:(1)thesystemforaccessingandsecuringlanduserights–Tanzaniaranksparticularlylowonaccesstoandsecurityoflandrights,akeyfactorofproductionalmosteconomy‐wide;(2)taxratesandadministration–Tanzania’staxregimeappearstobebasedonhigherthanaveragetaxrates,andproceduresareburdensomeasindicatedbythenumberof annual taxpayments required; (3) thequalityof regulationofprivate sectorbusinessandtrade–Tanzaniaranksbelowcomparatorcountriesonindicatorsofregulatoryqualityandthisisanareawhichappearstoimpactallmajorsectorsoftheeconomy,especiallyexports;andgivenTanzania’s relatively weaker performance in exportmarkets (Chapter 2) and the importance ofexports to the larger economy; and (4) the country’s system of tariff and non‐tariff barriers tointernationaltrademayposeanobstacletogrowth.

Although there undoubtedly remain issues and challenges in other areas affecting micro‐appropriability, therewasnocompellingevidence thatpolitical instability, contractenforcement,corruption,orcrimeandinsecuritypresentbindingconstraintstoeconomicgrowth.29

Based on the analysis presented, this chapter concludes that the lack of sufficientmicroeconomicappropriabilityofreturnsposesakeybindingconstrainttoinvestmentandgrowth inTanzania.Theprimary andmostbinding constraint in this area is the lackof

28Intheinterestsofbrevity,notallindicatorsarepresentedinthischapter.29 This is based on the World Bank Doing Business Indicators, as well as responses to the World BankEnterpriseSurveys(2006)andWorldwideGovernanceIndicatorrankingsagainstcomparatorcountries.

55

Table5.1:WBDBRegisteringPropertyRank,2010‐2011Registering Property Rank

Country 2010 2011 Country

Ghana 31 36 Ghana

Korea, S. 72 74 Korea, S.

Average SSA‐SA 122.3478 122.5 Average SSA‐SA

Kenya 130 129 Kenya

Tanzania 148 144 Mozambique

Uganda 150 150 Uganda

Mozambique 153 151 Tanzania

World Bank Doing Bus iness Survey

efficientand timelyaccess tosecure landrights.While currentpolicyaims to fully recognizecustomary tenure systems and bring them into a larger national framework of property rights,Tanzania’s institutional capacity to implement the policy comprehensively remains weak. Theincomplete application of current land policy has led to ambiguities on use rights, subsequentlyendangering the livelihoods of traditional land users, leading to land conflicts, and creating anenvironment characterized by uncertainty that impedes investment. The procedural costs thatcharacterizethelanddeliverysystemareaparticularlyacuteconstrainttopotentialinvestors.

Theanalysisalsoshows that,althoughTanzaniahas implemented importantpro‐marketreformsfor the regulatory environment and achieved greater openness to trade since the early 1990s,furtherprogressinimprovingthequalityofthepolicyandregulatoryenvironmentforfacilitatingbusiness and commerce has been challenging. Regulatory quality constitutes a constraint toeconomic growth in Tanzania; though not of the same consequence today as the three bindingconstraintsidentified.Furtherresearchintothesignificantongoingchallengeposedbyregulatoryqualitytoassesstheextentofitsimpactoneconomicgrowthwouldbebeneficial.

The rest of the chapter presents the relevant analysis, background, and tests to identify bindingconstraints. Itstartswiththe landregime, thenproceedswiththetaxregime, thenthecountry’sregulatoryqualityforprivatesectorbusinessandtrade,andfinallybarrierstointernationaltrade.

A.LandPolicyandImplementation

Thelevelofsecurityofrightstouse,own,andtransferlandiswidelybelievedtoplayadeterminingrole in the willingness of a local or foreign producer to invest in land‐based capital and otherassets.30 In many developing countries, particularly in Sub‐Saharan Africa, however, propertyrightsareweak. Tanzania’scurrentsystemappearsparticularlyproblematic. AsshowninTable5.1, the WBDB indicator on time to registerproperty ranks Tanzania at the bottom of thecomparisongroup,althoughwithintherangeofitssoutheastAfricanneighbors.31Onaverageittakesmore than 70 days to register property in urbanareasofTanzania,currentlythelongestamongtheindividual comparative countries,but shorter thantheSSAorLICaverages. Thecompleteprocessofsale and transfer of land, including rural land,however,takesmuchlongeratmorethan380daysonaverage(MkurabitaDiagnosticReport,ILD,2005).

30Notethattheemphasisison“secure,”ratherthanonaparticulartypeoflandrightssystem.Inadditiontothecontemporaryconceptoftitle‐basedprivateownership,customarytenuresystems(thatincludecommonownership)canbesecureaswellasconducivetoproductiveinvestmentgivenanappropriatesetoflanduseregulations.31AccordingtoUSAID(2011)Tanzaniaranked127onthelandregistrationWBDBindicatorin2009,alongwithTable,confirmingaworseningtrend.

56

Where property rights are incompletely defined, enforced, or transferable, the property systemmayactasaconstraintoninvestmentandhenceeconomicgrowththroughatleastfourchannels:

(1) Lowappropriabilityduetoinsecurepropertyrights:

Wherepropertyrightsareweak,thosewhoholdrightstolandorotherpropertyfacethepossibilitythattheirclaimswillbechallengedorextinguished.Investmentsthatarefixedinlandwillalsobelostiftheclaimtotheunderlyinglandorpropertyislost.Byincreasingtheperceivedriskandthusreducing the expected returns to potential investments, the lack of tenure security weakensinvestmentincentives.ThispresumedcausalrelationshipisappropriatelydescribedinHornberger(2007)asan“investmentdemandeffect.”

(2) Lowappropriabilityduetoconstrainedlandmarkets:

Incompletelandmarketsalsoreducetheexpectedreturnstoinvestment.Wherelandcanbefreelyboughtandsold,investorsknowthattheycanpotentiallyrecoupthevalueoftheirinvestmentsbysellingthe improvedproperty inthefuture,whatBrasselleetal. (2002)call “realizability.” Intheabsence of landmarkets, investors do not have this assurance, as the associated benefit streamcannotbemonetizedandtransferred.

Investment is further constrainedwhen firms and individuals are restricted in their capacity tofreely acquire land. In agricultural settings, the consequence is that producers are not able tooptimizethesizeoftheirlandholdingstoproduceatthemostefficientscalepossible.Inabilitytoaccess land will hinder firms and individuals as they try to reach optimal production in anycommercialsectorthatuseslandasaninput.Theresultingdecreaseinexpectedreturnsservestodiscourageinvestment.

Arelativelyfreelandmarketallowsthosewhocanusethelandmostproductivelytoacquireitintheappropriatequantity. Fromaneconomy‐wideperspective,significantconstraintsonthesale,purchase,andtransferoflandresultininefficientallocation.

(3) Highcostoffinance:

Theabilitytocollateralizeassetsisanimportantmeansbywhichinvestorsaccessfinance.Bankswillnotacceptassetsascollateraliftheowner’sclaimsareinsecureorcannotbetransferredtothebank in the event of default. Hence, insecure or non‐transferable property rights limit thewillingnessofbankstolend.

(4) Reducedvalueofnaturalcapital:

Renewablenaturalresourcesmaybevulnerabletoover‐exploitationora‘tragedyofthecommons,’if access to those resources is open and lacks a set of understood and enforceable rules thatprevents such exploitation. For example, open access to pastureland can lead to overgrazing,compromisingthevalueoftheresourceinthelongrun.Likewise,deforestationisamajorconcern,particularlyforrelativelyopenaccessresources.

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a.HistoricalContext

Tanzania’s land tenure system in its current form is best described as unstable. Onemay evenarguethatinstabilityhasbeenthegeneralstateofTanzanianlandtenurepolicysincethecountry’sindependencein1961.Instability,however,doesnotequatetoalackofofficialeffortstoprovideasecureformoflandrightstousers.TheTanzanianGovernmenthasmadetangibleeffortsoverthepast two decades to introduce a system of clear use and ownership rights into policy, one thatemphasizes equitable recognition of both customary and contemporary forms of tenure. Theproblem arises from the confusion that has been created as an expected aspect of institutionaltransition,butalsofromthedifficultiesofreconcilingcustomaryandcontemporarytenuresystemswithinonenationalframework.

LandpolicyinTanzaniaoverthepastcenturybeginswiththeLandOrdinancepassedbyTanzania’scolonialpower,GreatBritain, in1923. UndertheOrdinance,all landsweredeclaredpubliclandsunder the protectorate of the Governor. It recognized two types of rights: granted rights ofoccupancythatwereissuedandregisteredbytheGovernorandalmostexclusivelyissuedtonon‐natives;and“deemed”rightsofoccupancy,correspondingtoprevailingcustomarylaw.Thelattercategoryofrightswasnotregistered.32

Although the precedence of customary land tenure systems initially returned with the post‐Independence rise in 1961 of President Julius Nyerere, the socialist and nationalist politicalphilosophiesthatlateremergedasexpressedthroughthe1967ArushaDeclarationshapedensuinglandpolicyquitedifferently.Customarylandrightsandchieftainshipswereabolished,replacedbyasystemofvillageanddistrictgovernance(USAID,2011).

Oneofthemostdramaticanddefiningpoliciesoftheimmediatepost‐colonialperiodbeganin1973whenOperationVijiji,amass“villagization”plan,wasimplemented.Anestimated75percentoftheTanzanianpopulationwasrelocatedfromscatteredhomesteadsintocommunal“Ujamaa”villagesof 2,000‐4,000 residents. Village councils were responsible for land allocation and investment.Stein andAskew (2009) argue that, “villagization fundamentally alteredpeoples’ relations to thelandand forcefullydemonstrated thepowerof thestate.” Villagizationwasverypoorlyreceivedacrossthecountry,withmanyTanzaniansrefusingtoabidebytherelocationorder(Lawi,2007).Itwasalsoalikelyfactorintheeconomiccrisisthatbeganinthemid‐to‐late1970s(Odgaard,2006).The policywas abandoned in 1977, andmany households returned to their original homestead,withtheresultthatlandconflictensuedbetweenusersofthesamepieceoflandundertheVijijiandpriortenuresystems.

Under Ali Hassan Mwinyi’s presidency, beginning in 1985 the government gave attention toaddressingtheriseoflandconflictandthegenerallackoftenuresecuritythroughoutthecountry.A Land Commission (called the “Shivji Commission” after its director, Professor Issa Shivji)undertook a three‐year studyon land tenure, releasing its report in1994. The report called forvillageassemblyownershipofvillagelandandnationalassemblyownershipofstateland(USAID,

32Odgaard (2006) argues that the broad definition of customary rights and right holdersmade it easy forcolonialauthoritiestousetheOrdinancetoalienatelandheldundernativelawandcustom.

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2010).ThenewLandPolicy,spelledoutbetween1995and1997,setoutsomeofthefundamentalprinciplesofwhatwouldbecometheprevailingpolicytoday.

b.ContemporaryRegime

Tanzania’sofficiallandpolicy,ascodifiedbytheLandandVillageLandActsof1999(LAandVLA,respectively),attemptstopreservelocalcustomarylandtenuresystemswhilebringingthemintoalarger national framework that includes use regulations for non‐village lands.33 The Land Actdefines three categories of land in Tanzania: Village Land, Reserved Land, and General Land.Village Land is defined as land within village boundaries which were agreed in the 1970s and1980s. ReservedLand includesnationalparks,gamereserves, forest reserves,andothernaturalreserves.GeneralLandisallotherland,includingurbanandsomeperi‐urbanland,andisthereforeextremelyimportanteconomically.VillageLand,estimatedtocomprise70percentoftotallandinthecountry, isgovernedby locallyprevailingcustomary law,subject tocertainrequirementsandapprovals by other levels of the Government. Village Councils,made up of elected officials, arelargely given managerial control of Village Land and have wide latitude in defining, granting,withdrawing,administering,andcontrollinglandrights,landuse,andlandtransactions.TheymustfollowlocalcustombutarenotrequiredtoanswertotheVillageAssembly,exceptinthecaseofanumber of specific types of transactions, such as the proposed lease of five hectares or greater(Hoekema,2010).34

Under the Village Land Act two types of rights are available. One can apply for a Certificate ofCustomaryRightofOccupancy(CCRO),whichmaybeheldindefinitely.Holdersofthesecertificatescan then lease out their plots in the form of a derivative right of occupancy, but this requiresapprovalof theVillageCouncil. Inaddition,VillageCouncilsmaysetaside land in thevillage forpublic use, preventing individuals from applying for occupancy rights. Villages have the right todefinetheirboundariesandobtaincertificatesasproofoftheserights. Yetrelativelyfewvillageshavecompletedthesesteps. AsUSAID(2011)notes,asof2009,10,397villageswereregistered,butonly753ofthemhadobtainedcertificates.

LandintheGeneralandReservedcategories,comprising2and28percentoftotallandinTanzania,respectively,isgovernedbytheMinistryofLands,Housing,andHumanSettlementsDevelopment.Twotypesof individualuserightsareavailable. OnecanapplyforaGrantedRightofOccupancythatmay last toamaximumof99yearsand is subject toannual rent. Holdersof this rightmaysubsequently lease out the land. Written contracts, registration, and district level approvals arerequiredforuserightsarrangementsforGeneralLand.Incontrast,theequivalentrightsforVillageLand, when allocated to village residents, may be written or oral and need not be registered.However, allocationofVillageLand tooutsidersof the village is subject to special residencyandother requirements, and likeotherallocationsmustbeapprovedby theVillageCouncil. All landover250hectarestobeacquiredforinvestmentbyanon‐villageentity,oranysizeparcelacquired

33These laws are considered relatively progressive for their attention to gender equity: Land transactions,evenwithinVillageLandasmandatedintheVLA,areexplicitlyforbiddeniftheyinvolvegenderbias.34Villageassembliesarecomprisedofalladultcitizensofavillage.

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by any foreign entity,must first be converted toGeneral Land. In practice, enforcement of landcontractspertainingtobothVillageLandandGeneralLandhasbeendifficultanduncertain.

Ultimately,theLandActconfersallfinallandownershiptothenation,withthepresidentactingastrusteefortheTanzanianpeople.Thus,finalownershipofalllandrestswiththestateratherthanacurrentholderofoccupancyrights.ThegovernmentalsohastherighttoconvertVillageland,eventhatheldunderaCCRO,toGeneralLandwhenitisdeemedintheurgentnationalinterest,whichisinterpretedtoincludeinvestmentinterestsimportanttothenationaleconomy.

Although theLandPolicyprovides a favorablebalancebetween respecting customary rights andfosteringmoremarket‐basedlandtransactions,implementationoftheLandActshasbeenslowandhas failed to foster the development of landmarkets and the tenure security required for land‐intensive investments. In addition, some reviews of the land laws have identified specific issueswith the laws themselveswhichmay inhibit efficient implementation and the development of afullyfunctioninglandmarket(seeUSAID(2011),Odgaard(2006),Hoekema(2010)).35In2008only30 percent of urban land was registered (USAID, 2011), with only 5‐11 percent of total landregistered(USAID2011,ILD2005).Thiscomparesunfavorablywith30percentand18percentforKenyaandUganda,respectively.Thetransactioncostsofacquiringlandareprohibitivelyhigh,aswillbeelaboratedfurtherbelow,andformalizationdoesnotultimatelyresult infullyenforceableuserights.

In addition, the attempt to integrate two types of tenure systems has created ambiguities forcustomary land users. Village Land users, including traditional local authorities, are not wellinformedabout their rightsunder theActs, andhavenot taken advantageof benefits granted tothem. Few village residents have acquired the Certificate of Customary Rights to Occupancy(CCRO).Pederson(2010)reportsthatonly110,000CCROshadbeengrantedby2010.Ofthese,25percent were in Mbozi District, a region that has been the focus of more than one pilot titlingprojecttoreduceandfacilitatetheissuanceofthesecertificates.Asaresult,thelivelihoodsofsmalllandholders,who lackknowledgeof and/oraccess to the system, canbe jeopardized. Moreover,thisambiguityhas resulted inmounting landconflicts involving investors in land‐basedprojects,withtheresultthatinvestorsseekingtoaccesslandultimatelyfaceinsecureandunenforceableuserights.Whereassomeinvestorshaveattemptedtomaneuverthroughthesystemandothershaveusedmoreinformalmeanstoaccessland,neitherroutepromiseseffectivesecurityoftenure.

c.TestsofLandRegimeasaBindingConstraint

Thefirsttestofwhetheraconstraintisbindingisthatithasahighshadowprice,i.e.,relaxingtheconstraint should have a high marginal value to the economy. The primary evidence of this istwofold– first is thehighcostofacquiring land through the formalprocess, incombinationwithevidentdemandandthelossofsignificantpotentialinvestmentsinlargesegmentsoftheeconomy;andsecond,thelackofsecuritythatresultsfromeitherformalorinformalmeansofaccesshaveled

35Whereas inprinciplerightsofoccupancycanbebought,sold, leasedandmortgaged, inpractice, the landmarket is constrained by the need for approvals at several levels of government, the complexity involveddependentuponthelandclassificationandsizeofparcel(seeUSAID2011).

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to increasing land conflictswhich are costly to resolve and disincentivize investments requiringsecurelandrights.36

The high cost of acquiring land resultsfromthecomplicatedandcostlymulti‐stepprocess of searching for, transferring, andregistering land, which for those whoactually complete the process does notcurrentlyresultinanenforceableuseright.Numerous reports from investorsregarding the process of searching,acquiring, and registering land show thatthewaiting time can be between one andthreeyearsorlonger,withanaveragetimefor transferring and registration of 380days (Institute forLibertyandDemocracy(ILD), 2005). In urban areas, the processby which a business registers alreadypurchased land takes 73 days and ninesteps, on average. A 2007 World Bankstudy for the Tanzania Business Councildemonstrated that, across the country,transferring land to a business andobtaining secure title is the most time‐consuming process in starting a business(WorldBank,2007).Withcostsofstartingabusinessrangingfrom90‐145percentofGDP per capita, depending upon theregion,thisrepresentsasignificantbarriertoentry(WorldBank,2007b). Utz(2008)reports that the total cost to registerproperty represents 12.2 percent ofproperty value, a significantly higher ratethan the 4.7 percent for OECD countries.

Hoekema(2010)describesan“awesomeseriesofbureaucraticstepswhichtakesmorethanayearof full timeworkanda lotofmoney.” Similarly,a feasibilitystudyforexpansionof theTanzaniaInvestmentCentre(TIC)LandBank,conductedbytheInvestmentClimateFacilityofAfrica(ICF)in2008 claims that potential investors in General Lands are faced with 72 forms to complete aGeneral Land transaction, and 50 forms for Village Lands. The ILD claims that titling andregistrationalonecosts$937inadministrativepaperwork.

36 Ideally, one could also obtain data on land prices for legally registered versus unregistered land, andcontrolforcharacteristicsofthelandandlocationtotestfortheshadowvaluedirectly. Unfortunately,thedatanecessarytoundertakesuchananalysiswerenotavailableatthetimeofthisstudy.

Figure5.1:ProceduretoAcquireInvestmentLandfromVillageHoldings

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Figure5.1,fromSulleandNelson(2009),illustratesatypicaltimelineforaforeigncompany(inthiscase,aproducerofbiofuels)toacquireatitledderivativerightofoccupancy.37TheprocessforSunBiofuelsbeganinFebruary2006withitsapplicationforandissuanceofaCertificateofIncentiveswith the Tanzania Investment Center (TIC), as the law requires for all foreign investors. Threeyearslater,inFebruary2009,afteralongseriesofnegotiationsandadministrativesteps,thelandwastransferredfromVillageLandtoGeneralLand.

TheTICwasestablishedunderthe1990InvestmentActtoencourageandfacilitatelargedomesticandforeigninvestmentinthecountry.Foreigninvestorsarerequiredtopassallproposedprojectsthrough the TIC. Domestic investors are not required to do so, but they are usually drawn toinitiatinginvestmentthroughtheCentrebythesubstantialtaxincentivesthatareoffered.In1997alandbankwasestablishedwithintheTIC(USAID,2011)whosegoalwastoacquirelanditself,actas the primary holder of the certificate of granted right of occupancy, and thereby shorten theusuallylongtimerequiredforalargerinvestortoidentifyandnegotiateacquisitionoflandonitsown. Foravarietyofreasons, including lackof financing, theBankhashaddifficultiesbecomingmore than a pilot project. Today its role is confined to connecting potential investors withpotentiallyavailableland;theinvestorisstillrequiredtomaneuverthroughthewebofproceduresillustratedinFigure5.1.

An additional indication of the high shadow price of the constraint posed by the current landdeliverysystem is theapparentlysubstantialunmetdemand for landbypotential investors,whoappearinitiallywillingtoundertakethecostlyprocess.AccordingtotheICFfeasibilitystudy,from2004‐2007 therewere440 applications for landwith theTICLandBank requiring a total of 9.6millionhectares.Since2007,accordingtoarepresentativeoftheTIC,theLandBankhasmadelessthanfivedirecttransfersoflandannuallytoinvestors. Similarly,accordingtoUSAID(2011),theLandBankhastransferredonly50,000hectaresoflandtoinvestorsovertheperiod2004to2009.While the vision for the LandBankwas for it tobe theprimary supplier of land for foreign andothercommercialinvestors,asof2007ithasonlybeenabletograntderivativerightsofuseto13oftheseapplicationstotaling6,920hectares,orlessthan1percentoftherequestedland.TheTICestimatesthatonlyonefourthofseriousinvestorswouldbeabletoeventuallyaccesslandthroughthecurrentlanddeliverysystem.

Table5.2showsthedistributionofthetypeofTIC‐registeredprojectsregisteredthroughDecember2006(ICF).AccordingtotheICF,80percentoftheseprojectsrequirelargelandparcels.Whilethesector representing the highest level of capital and number of projects is manufacturing,agriculturalprojectshaverequired themost landat roughlyeightmillionhectares.Thepotentialinvestments represent both domestic and foreign investors. Among the potential foreign directinvestments, 21 percent of total capital value is in the manufacturing sector, ten percent incommercialbuilding,andninepercentineachofagriculture,construction,tourism,andtransport(ICF,2008). Althoughit isnotpossibletoknowtheextent, it is likelythatthe lackofclarityandweakenforcementandimplementationofthecurrentlandactsdiscourageasignificantamountof

37 Currently a foreign investor is not permitted to hold a granted right of occupancy, the form of tenureclosest to fullpermanentownership. Rather theTICholds thegrantedrightofoccupancy, leasing it totheforeigninvestorasaderivativeright.

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investorswhoneverevenregisterwiththeTIC.Furtheranecdotalevidencethataccesstolandisamajor impediment to larger investors is provided by the growing interest in Tanzania’s biofuelsdevelopmentpotential,largelyblockedbythelandregime.By2009,only640,000hectaresofthefourmillionhectaresoflandbeingsoughtbybiofuelscompanieshadbeengranted.38TheFELISACompanyhadobtainedonly4,300hectaresoutof10,000hectaresneededtoestablishapalmoilindustry. Thesamecompanyranintodifficultieswhen,attheendoftheprocessofsecuring350hectaresofvillageland,oneofthetwovillagesattemptedtoretracttheland,havingallocatedittosomeoneelse(SulleandNelson,2009).

Table5.2:TICRegisteredProjectsbySectorThrough2006Sector Number of Projects Value TZS Million Value USD Million Agriculture and Livestock Developme 275 1,757,879 2,343.84 Natural Resources 163 465,586 620.78 Tourism 880 1,748,885 2,331.85 Manufacturing 1,582 4,227,466 5,636.62 Petroleum & Mining 115 525,752 701.00 Construction 179 1,793,900 2,391.87 Commercial Building 265 1,958,625 2,611.50 Transportation 325 1,755,613 2,340.82 Services 162 798,597 1,064.80 Computer 19 13,140 17.52 Financial Institutions 59 1,434,605 1,912.81 Telecommunication 56 1,577,628 2,103.50 Energy 11 302,525 403.37 Human Resources 88 171,423 228.56 Economic Infrastructure 21 1,206,723 1,608.96 Broadcasting 9 244359 325.81 Geographical Development 1 535056 713.408 Total 4,210 20,517,762 27,357.02 Thedifficultyforevengovernment‐sponsoredExportProcessingZones(EPZs)toacquiresufficientlandisfurtherevidencethatthecurrentlanddeliverysystemdetersinvestment.AnEPZAuthority(EPZA) was established in 2006 to attract investors through the provision of generous taxincentivesand infrastructure. Fifteenzoneswere initially identifiedrepresentingroughly35,000hectareswith thegoalof establishinganEPZ inevery regionof the country;however, asofMay2011, only a small zone inDar es Salaamwas able to start operations due in part to difficultiesobtainingland.ThefourteenremainingEPZs,alllocatedonprivatelyoccupiedGeneralLandratherthanonVillageLand(andthustheoreticallyeasierthanacquiringvillageland),arebeingheldupinthe land acquisitionprocess. According to a source at theTIC, several investorshave expressedinterest in establishing a presence in the zones and are waiting for the completion of the landtransferprocess.TheEPZAreportsthattheDaresSalaamEPZisalreadyatfulloccupancy,andatleast14potentialinvestorshavebeenturnedawayduetolackoflandaccessinthatEPZ.

The lackof awell‐functioning landmarketposesabarrier forbothurbanandrural investments.From1995‐2002,74percent(314)ofapplicationsforlandforinvestmentpurposeswasforruralland, theremainder(110) forurban land(Lugoe2008). Urban land investmentsarecentered incoastalregions(DaresSalaam,Coast,andTanga),followedbyArusha,Morogoro,andMtwara.The

38Thefocushereisonthedifficultyoflandaccessratherthanthemeritsofthebiofuelsindustry.

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situation is detrimental to small enterprises as well as large: small enterprises have greaterdifficultypayingthehighcostsofseekingformalrightstotheirownpremises.

Similarly,provisionoflotsforresidentialpurposeshasbeenslow.Between1999and2001,DaresSalaamauthoritiesreceivedover240,000applicationsforlandplots.The“20,000Plots”program,initiatedin2002,aimedatproviding20,000newresidentialplotsoflandannuallyinurbanareas.Onaverage,theprogramhasonlybeenabletoprovide6,000plotsannually(Lugoe,2008).

Thereislessevidencethatthelackofafunctioningtitle‐basedlandtenurepolicyisamostbindingconstraintforsmallholderinvestment.InTanzaniathereappeartobemorebindingconstraintstoinvestmentsbysmallholders,includingthelackofaccesstoinputandoutputmarketsandalackofbasicinfrastructure.Fenske(2009),inapaperthatexaminesthestrengthofthelinkagebetweensmallholder tenure security and smallholder investment in Africa, reports that the causal linkbetweensecuretitleandagriculturalinvestmentbysmallholdersissignificantonlyforinvestmentssuchasfallowandtreeplanting(long‐terminvestments),butislessrobustforinputswithashortertimehorizon(fertilizer,manure,labor).Moreover,morewidespreadtitlingisunlikelytoleadtoawholesale expansion of formal credit provision to smallholder farmers. As USAID (2011) notes,bankshavelittletrustinthevillagelevelcertificateofcustomaryoccupancy.Similarly,asHoekema(2010)states,“Bankshavenointerestinthiskindofsmallloan,nor(sic)thecapacitytoadministerthese.Ortheyfearnonpaymentwhileevictionsarenotfeasible.”

Nonetheless,given theapparentstrongdemand for landby investors in ruralareas fromoutsidethe village system as well as in urban areas, where the economy is growing most rapidly, thisimpedimentaffectsalargesegmentoftheeconomydirectly.Largerscalebusinessesrequiringlandto operate – whether in urban or rural areas, agriculture or manufacturing – are critical toachievingdiversification,jobcreation,andeconomicgrowth.

Inaddition, theshadowpriceof theambiguities in landuserightscreatedbyoverlappingtenuresystemsandincompleteimplementationofthecurrentlandlawscanbemeasuredbythehighandrisingcostoflandconflicts.AsHuberetal.(2007)state,theincreasingnumberoflandconflictsinTanzaniaisevidenceofarisingandunfulfilleddemandforland.Thepriceoflandconflictsincludesinjuriesandliveslost.Italsoincludeslostinvestmentaspotentialinvestorsavoidregionsandcountriesthatdemonstrateahistoryoflandconflict,astheseareconsideredtooriskyforcommercialinvestment.

Aneffectivelandregimemustfacilitatelargerinvestmentsbynon‐villagerswhileequippingsmallerlandholdersandvillageleaderswiththeinformationandtoolsneededtoreaptheappropriatelevelof benefit from an exchange of their resources. On this front, the URT land system is alsoparticularlyweak.Theabsenceofanactivecampaigntosensitizevillageresidentsand leaderstotheproceduresandtheirrightsundertheLandActshaspreventedtheirappropriatevaluationofland andhas contributed to land conflictswith investors. For example, Sulle andNelson (2009)recountasituationinwhichtheRufijiDistrictLandUseCommitteefoundthatvillagesweresigningcontractswithinvestorsthatgaveawaymostoftheirVillageLandstothecompanySEKABBT.Inoneextremeexample,leadersinUtungeVillagesignedacontractgivingaway72percentofitsland.Village leaders claim that investors often offer unwritten promises, not understanding that such

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promisesmust be written in a contract to be legally enforced. In addition, conflicts have oftenoccurredwhen,duringtheextendedperiodbetweenlocallevelnegotiationsandthefinaltransferofVillageLandtoGeneralLand,somelocalresidentsbegintooccupytheparcelinordertobenefitfromimprovementsthattheinvestormightmake.Someconflictsareviolent.39Conflictsareontherise,andtheabilityofthesystemtoresolvetheminatimelymannerislimited.

There exist several difficulties in implementing the Land Acts at the village level to avoid suchconflicts. Among these are ambiguities surrounding the village demarcation process, lack ofknowledgeof the lawby traditionalvillage leaders,and lackof feasibleaccess to theregistrationsystembysmalllandholders.WhereastheVillageLandActgivesauthoritytotheVillageCouncilstomaintaincustomaryuserightsinthevillage,andlandusersmayobtainaCCROfromtheVillageCouncil, in practice few villages have the capacity to administer the certification program.Applications are frequently sent to a higher‐level office outside of the immediate area, withoutcopiesofrecordsbeingprovidedtothelanduser.Becauseobtainingsuchacertificateiscostlyinprocedural termsandmanyvillage landholders fearunfairadministrationof land issues, theydonotapplyforofficiallandrights(Hoekema,2010).

Alsocentraltoasuccessful implementationoftheLAandVLAisthevillagedemarcationprocess,whichdefinestheboundarybetweenVillageLandandGeneralLand.Villagesareencouraged,butnotrequired,toobtainCertificatesofVillageLandthatofficiallyregisteravillage’slandandconferupontheVillageCounciltheauthoritytomanagetheland.Thereisnospecificprovisiontoverifytheboundariesofthevillagepriortothelandcertificationprocess.Thiscreatesthepotentialforconflictwhen landperipheral to a villagemay seemunused and is classified asGeneral Landbycentralauthorities,butisviewedatthelocallevelasaproductivepartofthevillage(Sundet,2005).

Thesecond“test”whichispossibletoconducttodeterminewhetheraconstraintshouldbecharacterizedasbindingisanassessmentoftheextenttowhichhighcosteffortsaremadeto circumvent the constraint. Given the costly land search, registration, transfer, and titlingprocesses,severalextralegalorinformalwaystodocumentlandownershipandtransactionshaveemerged, including in urban areas (ILD2009). In fact,most applicants for landhave reportedlyturned to informal mechanisms, a sign that investors seek ways to circumvent the constraint(Lugoe,2008).Kimati(2010)reportsthat,inasurveycarriedoutinDaresSalaam,104outof154parcelsvisited therewere illegallyacquired. Lugoe (2008)reports that70percentof theDaresSalaampopulation resides in informal (unplanned) settlements. In a recent survey on small andmediumenterprises,only5percentofbusinessownerssaidtheyheldtitletothelandonwhichthebusiness is situated. Yet costly landdisputes are frequent, and the extralegal documents areoflimitedvaluetoobtainmortgagesandcredits.

39InMeruDistrictinArushain2009,squadronsofenragedresidentsinvadedtheestatesatSing’isilocationinMeruDistrict,settingfiretoabout20houses,immobilizingafarmtractoranddestroyingcloseto50acresofland. In2001,30membersof theAsian community living in theKiruValley (Babati) fledafter their farmestateswereinvadedandsetonfirebyangrylocalresidents,leavingbehindroughlyfivebillionTshoflosses.Victims includedaBritish investorandamemberof theTanzanianParliament. Conflictshavealsobrokenoutbetweenpastoralistsandotherlandusers,whichmayincreasewiththespreadofirrigation.

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Similarly, USAID (2011) states that potential investors have chosen to avoid the complicatedbureaucracy, choosing to negotiate for land directly with village leaders. Villages are alsoincentivized to use this avenue in order to avoid the complicated official land transfer andcompensationprocessandcollectrentdirectlyfromthetenant.Suchinformalarrangementsdonotprovidesufficientsecurityinlanduserightsforanon‐localinvestor,however,astherightswouldnotbeenforceableinthecourts.

ThetwoothertestsproposedbyHausmannetal.aredifficulttoconductforland‐relatedissuesinTanzania without better data. First, relaxing a binding constraint should result in significantchangesininvestmentorproductioninanaggregatemeasureofeconomicactivity.Unfortunately,datatoconductthistestarenotavailableand,inanyevent,thereisrelativelylittlevariationovertime in the measured cost of accessing land. One could also map out investment projectsundertakenwithinthecountrywhichrequire largeinvestments inthelandor in immobileassetslinked to the land (such as tall or permanent structures) alongside the level of integration of aregioninthecurrentnationalLandActframework.Iftheconstraintisbinding,onewouldexpecttosee a concentration of commercial investments in regions with a history of land titling, secureaccessandalowincidenceoflandconflict.Dataoncomparativelandpricesandinvestmentratesin and near the areas where pilot projects have been implemented to facilitate the issuance ofCCRO’s(particularlyMboziDistrict)couldalsobeusedtotestthisconstraint.ThefinaltestofthelandregimeasabindingconstrainttogrowthwouldbetoassesswhetherthereisarelativelackofenterprisesinTanzaniarequiringsecurelandrights–i.e.,enterprises‘intensive’intheconstrainedfactor. One couldbenchmarkTanzania’s levelof investment in fixedor land‐basedassetsby theprivatesectoragainstothercomparatorcountriesand,assuggestedabove,within thecountry totheextentthereisvariationintheeffectivenessofthelandregime. Unfortunatelysuchdatawasnotavailableforthisanalysiseither.Basedontheindicatorsthatareavailable,however,ofaveryhighshadowprice,widespreadeconomicimpact,andthecostsincurredofavoidingtheformal land registry system, the current state of Tanzania’s land regime appears toconstituteabindingconstrainttoeconomicgrowth.

B.Taxes

Taxesarenecessary to generate revenueswhichcanbeutilized toprovidekeypublicgoodsandinfrastructureandtherebyimprovethereturnstoprivateinvestment.Iftaxcollectionsaretoolowto closeunsustainable fiscaldeficits, growth canbeharmed throughmacroeconomic channels asdiscussedinChapter4.Atthesametime,taxesmayalsointroducedistortionstoefficientallocationof resources in production. If unduly burdensome, taxes would severely reduce the expectedprivate return on investment, and thus investment and growth. Finally, if fulfilling taxrequirements requires high costs or risks, the system of tax administration itself can beburdensomeanddeterinvestmentorfullformalizationofenterprises.

a.TaxRates

Figure 5.2 provides a comparative overview of various producer tax rates in Tanzania, Kenya,Uganda, and South Korea in 2011. It is clear from this that Tanzania’s tax environment is not

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particularlybusiness‐friendly.Forthefivetypesoftaxesanalyzed,therateinTanzaniaisneverthelowest.40Tanzaniahas thesecondhighest total tax rateasapercentageofprofitsamong the fourcountriesshown,aswellasthehighestrateoflabortaxandcontributions.

Figure5.2:TaxRatesFacingFirms

Source:WBDB

40TheWBDBIndicatorsclaimthatprofittaxrateissecondlowesttoSouthKorea’s,givenanestimatedprofittaxrateof20Percent.Itisunclearhowthe20percentwasderived,giventhatthecurrentcorporatetaxrateinTanzaniais30percentwhichwouldplaceTanzaniainasimilarrangeasKenya.

Table5.4:WBDBPayingTaxesRanking

Paying Taxes Ranking

Country 2010 2011 Country

Korea, S. 48 49 Korea, S.

Uganda 63 62 Uganda

Ghana 80 78 Ghana

Mozambique 98 101 Mozambique

Average SSA‐SA 115.4565 116.4565 Average SSA‐SA

Tanzania 116 120 Tanzania

Kenya 163 162 Kenya

World Bank Doing Bus iness Survey

Table5.3:TaxRatesforMajorPlayersacrossCountries

CorporateIncomeTaxMozambique 32%

specialrateof10%agricultureandanimalhusbandry

Tanzania 30%Uganda 30%Kenya 30%forresident

37.5%fornonresidentGhana 25%(forAccraandTema)

18.75%(forAllotherRegionalCapitals)

12.5%(OutsideotherRegionalCapitals)

S.Korea 24.2%Mauritius 15%Source:WorldTaxRates2010/11(http://www.taxrates.cc/index.html)

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Table5.5:ASampleofTaxRatesinTanzania(2010‐2011)TaxType Rate NotesCorporateIncomeTax 30% Residentornon‐residentCorporateIncomeTax,NewlyListedCompanies

25% Reduce rate for 3 years if at least 30% ofsharesarepubliclyissued

Payroll–SocialSecurity 20%ofpay 20% rate shared 10%by employee and 10%employer41

Payroll–SkillsandDevelopmentLevy 6%ofpay Agriculturalemploymentexempt;5%inZanzibar

Capitalgains–individualasset 10%/20% Resident/non‐residentCapitalgains–company 30% Resident or non‐resident; exemptions for

private residence and small agriculturallandholder

ValueAddedTax 18%Import Duty – raw materials,pharmaceuticals, capital goods, agriculturalinputs,anygoodsfrommemberEACstates

0%

ImportDuty–semi‐finishedgoods 10%Import Duty – final consumer goods orfinishedcommercialgood

25% Rates of higher than 25% are charged forsensitive products including yogurt milk andcream, cane or beet sugar and solid formsucrose, sacks and bags intended forpackaging of goods, and worn clothing andotherarticles

ExciseDuty 7‐120% Wine, spirits, beer, soft drinks, cigarettes,tobacco,petroleumproducts,plasticbags

FuelLevy Tsh200/LExportTax 15%/20% Rawcashewnuts/hidesandskins

Source:TanzaniaRevenueAuthority(2010)

Table 5.3 similarly shows that Tanzania’s corporate tax rates are comparable to those ofneighboring countries, albeit significantly higher than in Ghana, Mauritius, and South Korea. Inaddition, paying taxes in Tanzania requires, on average, the highest number of tax payments byfirms,whichraisesthetransactioncoststobusinessofmeetingtheseobligations.Enterprisesandinvestors have indicated in four surveys (1999, 2003, 2006, and 2009) that high tax rates areamong the top four constraints to business. TheWBDB indicator on Paying Taxes, as shown inTable5.3,ranksTanzanialowerthantheSub‐Saharanaverageandonlyhigher(albeitsignificantlyso)thanKenya.MoredetailsofthecurrenttaxrateschedulerelevanttofirmsareshowninTable5.5.

Thereareafewnotablefeatures.First,importsfromEastAfricanCommunity(EAC)membersareduty‐free. Second, the taxation of payroll, at 26 percent (with 10 percent of this paid by theemployee) is relatively high and may be a significant disincentive to formal wage employment.Finally,someagriculturalinputsareexemptfromimportduties,inparticularforlargerinvestors.42

41Notethatalthoughtheemployeronly‘pays’10percentofthis,incompetitivelabormarketsaccordingtostandardeconomictheory,whopaysdoesnotaffecttheultimatewageoremploymentlevel:thewedgeof20percentbetweenthecostofemploymenttofirmsandthewageremainsthesame.42 It isunclearwhetheragricultural inputs importedbylargeragricultural inputsupplyfirmsservingsmallproducerswouldqualifyforthisexemption.

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Figure5.3:TaxExemptionsasaPercentofGDP

However,exportsofrawcashewnuts,hides,andskinsaresubjecttoanexporttax.Inaddition,alldomestically consumed goods and services are taxed through a Value Added Tax (VAT), withexemptions for export goods and services and businesses with less than Tsh 40million annualturnover.

b.TheTaxBaseandCollectionEfficiency

Asmall taxbase, togetherwithhighexemptionrates,mayresult in theneed forhigher taxrates,whichsubsequentlymayleadtoalargerinformalsectorandanevensmallertaxbase.AccordingtoAgCLIRTanzania(2010):

“AsmalltaxbaseismostdefinitelyaprobleminTanzania,wherethetaxbaseissosmallthatmostofthegovernment’sincomeispaidbyarelativehandfuloftaxpayers.TheTanzaniaRevenueAuthority

(TRA)itselfestimatesthatinacountry with a population ofnearly 40 million persons,there are only about 400,000registered (business) tax‐payers.”

In addition,manybusinessesmay enjoy partial tax exem‐ption.Tanzaniahas thehigh‐est tax exemption‐to‐GDPRatio in East Africa, but it isdeclining substantially, asshown. There is some con‐cern that the mining sectorreceivesaparticularlygener‐

ous level of tax incentives. About 7.5 percent of all tax exemptions during2008/09 – 2009/10periods were granted to mining companies. Moreover, some argue that some of the incentivesprovidedarenotnecessary,andtheinvestmentswouldmostlikelyhavetakenplaceregardlessoftheseincentives.

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Figure5.4:ExemptionsGrantedbyRecipientCategory(2008/09‐2009/10)

Source:UwaziPolicyBriefTZ.12/2010E

Figure5.5:Tax‐to‐GDPRatio,Tanzania,2001‐2008As shown in Figure 5.5,sincetheyear2000thetax‐to‐GDP ratio has increasedby five percentage points(fromNordetal.,2009).Asisalsoshown,nearlyhalfofthe increase in revenue hascome from the Tax andRevenue Authority’s (TRA)Large TaxpayersDepartment (LTD), whichcollects taxes from onlyabout400entities.43

43LRD denotes “large taxpayer division”, while DRD denotes “domestic revenue division” and handles allotherincometaxes.

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Figure5.6:TaxRevenue‐to‐GDPRatiobyPerCapitaIncome

Althoughthecorporatetaxbaseisrelativelysmall,andahighshareofgovernmentexpenditureisfinancedbydonors,improvementsoverthelastdecadeinTanzania’sabilitytocollecttaxeshavebroughtthecountrywithinrangeofothercountriesintheregion,relativetoGDPpercapitaasshownin

Figure5.6(Nordetal.,2009).

Figure5.7 shows themajor sourcesof tax revenueasapercentageof total collections, for2000‐2010.VATcomprisesthelargestshareofallsources,althoughitssharehasdroppedbeginningin2005/06asapercentageofthetotalasthesharerepresentedbyexcisetaxesincreased.IndividualspayagreatershareoftotalincometaxesthanbusinessesinTanzania,atapproximately57percentofthosetaxesversus42percentpaidbybusinesses(BankofTanzania).44

Figure5.7:MajorComponentsofTaxRevenueCollectedinTanzania,2000‐2010

44Datanotshown.

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c.TaxesonAgriculture

As with all productive sectors, taxation of agriculture can present a potential disincentive toproductionandinvestment.ArecentImpactAssessmentofTaxReformsStudyconductedin2007(MinistryofAgriculture,FoodSecurity,andCooperative–MAFSC2009)findsthattheagriculturaltaxregimeisoneofanumberoffactorsexplaininglowagriculturalinvestment. Althoughseveraltaxreforms favoring theagriculturalsectorhavebeenadopted, thestudyrevealedsomereformswhichhavenegativelyimpactedagriculture.

First, thedutyon imported inputs can reduceprofitability, especiallywhencombinedwithothertaxes.Althoughlargeagriculturalenterprisesbenefitfromazerodutyonimportedinputs,smallerentitiesdonot. Moreover,spareparts(whichcompriseahigherproportionofoperationalcosts)arechargedthenormalrates.Inaddition,thecorporateincometaxrateof30percentisappliedtoallproductionsectorsinTanzania,includingagriculture. AsshowninTable5.3,Mozambique,forexample, stratifies its corporate tax system, applying a 10percent rate to the agricultural sectorratherthanthe32percentrateappliedtoallothersectors.Ghanahasattemptedtoattractprivateinvestment to rural areas through a reduction in the corporate tax rate applied to ruralestablishments. The MAFSC (2009) tax study recommends that the corporate tax rate in theTanzanianagriculturalsectorbereducedto5‐10percentofnetprofits.

Currentlythe6percentSkillsandDevelopmentLevyfacingproducersexemptsemployersoffarmworkersbutnotemployersoffactoryworkers.However,particularlyfromtheperspectiveofsomepotentialagricultural investors, farmand factorymayoperateasan integratedentity. Thus, theMAFSCstudyrecommendsreducingthistaxfortheentireagriculturalsector(farmandfactory)tonomorethan1percentofgrosswages.

Whilelocaltaxesfacingagriculturalproductionhavebeennearlyeliminated,theproduce‘cesstax’remainsandhasbecomeincreasinglycontroversial.Localandregionalauthoritiesassessthe‘cess’taxas5percentofthevalueofproduction,but implementationisnotconsistentacross localities.Sincethesetaxesarenotleviedonprofits,theyareapotentialdisincentivetoproductionofhighervaluecrops,whichtypicallyentailbothhigherrevenuesandcosts.Assuch,ataxbasedonsalesorrevenuesresultsinahigherdefactotaxonprofitsforhighervalueaddedproduction.

Despitetheseissues,thereisnoindicationthattheshadowpriceoftaxesandtaxpaymentsis exceptionally high, and there is no indication that Tanzania’s tax system or ratesconstituteabindingconstrainttoeconomicgrowth.

C.RegulatoryEnvironmentforPrivateBusinessandTrade

Inanyeconomy,thesetof laws,policies,regulationsandtraditionalpracticesthataffectbusinesscostsandrisksand,therefore,investmentandgrowthconstitutewhatisknownasthe“regulatoryenvironment” or “business enabling environment.” Regulatory quality therefore makes a greatdifferencetosustainable,broad‐basedeconomicgrowth.RecentanalysesbytheWorldBankGroup

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and other third party or private sector organizations suggest that the quality of Tanzania’sregulatory environment for business and trade has improved in some aspects and, in otherrespects,requirescontinuedcollaborationamongtheGOT,developmentpartners,andtheprivatesector to reduce adverse impacts on growth. Ongoing GOT reforms make policy‐marketrelationshipsdynamic. Thedataused in these analyses areusually retrospective and thus therehave been some improvements in regulatory quality not fully reflected in available indicators.Nonetheless, it is useful to examine the most recent data to develop as accurate a picture aspossibleregarding theextent towhich theregulatoryenvironment isconducive toprivatesectorinvestment.

RegulatoryqualityconstitutesaconstrainttoeconomicgrowthinTanzania;thoughnotofthesameconsequencetodayasthethreebindingconstraintsidentified.Furtherresearchintothesignificantongoing challenge posed by regulatory quality to assess the extent of its impact on economicgrowth would be beneficial. Firms bear a significant cost of remaining informal, but otherconstraints to firm growth may be more severe. Business registration and trade appear to besomewhatresponsivetoreducedregulation,andregulatoryqualityandtradeopennessappeartobe correlated with aggregate export performance and production in the most affected goodscategories.

a.Overview

Tanzania’s economic liberalization and transition from socialismhave significantly improved thelegalandregulatoryframeworkforprivatesector‐ledgrowth,withnotableresults. Itisgenerallybelievedthatthecountry’sgrowthoverthepastdecadesisdueinlargeparttothisliberalizationprocess. At the same time, establishing a consistent, conducive regulatory environment forbusinessatthenational,regional,andlocallevelsrequiressustainedengagementonmanyfronts,including on the complex task of strengthening implementing institutions. The Government ofTanzaniahascontinuedtoengageinsuchefforts.

TherecentBusinessEnvironmentStrengtheningforTanzania(BEST)programbeganin2003withthe support of the World Bank and bilateral donors (DANIDA, SIDA, DFID, and the RoyalNetherlandsEmbassy). BESTwasdesignedtoaddresskeyconstraintsinthelegalandregulatoryenvironmentforbusinessandoutlinedthemosteffectivemeasuresusingfourstrategicpillars:(1)reducing theburdenofdoingbusinessbyachievingbetter regulationandeliminatingproceduraland administrative barriers; (2) reducing the complexity, cost and time taken to process andresolvecommercialdisputes;(3)changingthecultureofgovernment,aimingatimprovingservicedeliveryby thegovernment to theprivate sector; and (4) strengthening theadvocacy roleof theprivate sector. These steps include efforts to strengthen implementation of the land laws. ThegovernmenthasachievedseveraloftheBESTprogram’sgoals.IndeedthesubstantialprogressonregulatoryreformafterimplementationoftheoriginalBESTprogramwashighlightedbytheWorldBank Doing Business indicators (2007) which ranked the country as 10th best in the world onimprovingconditionsfordoingbusiness.Despitethemanypositivestepsthatthegovernmenthastakentoattainamorepro‐businessregulatoryenvironment,manychallengesremain.

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The government is now implementing the Investment Climate Roadmap,which is also aimed atimprovingtheregulatoryenvironmentforbusiness,includinglandlaws.

Since 2004, the cost of starting a business in Tanzania has dropped. One can see a jump in thenumber of firms registering after 2007 (Figure 5.8) in apparent response to liberalization andreduced regulatoryburden. By2010,Tanzania is ranked122nd in theworld,better thanKenyaandUganda inbusinessstart‐upcosts. In termsof thecostofclosingabusiness,Tanzaniaranks113thintheworldintheDoingBusinessIndicators,comparableinrankingtoGhanaandsuperiortoMozambique. Tanzania ranksevenbetteron investorprotectionat 93rd in theworld (WorldBank,2011a). Incontractenforcement, it ranks32nd,with thetimetoenforceacontract lowestamongcomparatorsatapproximately450calendardaysfromthefilingofthelawsuitincourtuntilthefinaldeterminationand,inappropriatecases,payment.

Figure5.8:NumberofNewBusinessesRegistered,2001‐2010

Otherdatasuggestthatimportantchallengesremainforstrengtheningandsustainingaregulatoryenvironmentconduciveforbroad‐basedeconomicgrowth.AsshownbelowinFigure5.9.Tanzaniarankedbelowthecomparatorcountriesin2009onqualityofregulation.45

4590percentconfidenceintervalsareshowninthefigure;and,asshown,onecannotrejectthehypothesisthatTanzania’srankingisnolowerthanforKenya,Uganda,andMozambique.

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Figure5.9:RegulatoryQuality(2009)basedonWorldwideGovernanceIndicators

One obtains a similar picture using the World Bank Ease of Doing Business (WBDB) indicator,which covers a rangeof legal, regulatory, and institutional issues that affect theease and costofdoing business. In this composite ranking, Tanzania ranks 128th in the world. The BusinessFreedomIndex(BFI),anindexderivedfromsimilardataoftheabilitytostart,operate,andcloseabusiness, is intended to capture the overall burden of regulation as well as the efficiency ofgovernment in the regulatory process. On this index, too, Tanzania’s scores and rankings from2011reflectconsiderableopportunitiestoimprovetoalevelmoreconsistentwithitscomparatorcountries.46 It is important to note that Tanzania’s ongoing reform effortsmay generate betterratingsandrankingsinfuturesurveys.Still,significantworkremains,ifonlytosustainmomentumalreadygeneratedbyreformstodate.

46 The business freedom score for each country is calculated as a number between 0 and 100, with 100equalingthefreestbusinessenvironment.Thescoreisbasedontenfactors,allweightedequally,usingdatafromtheWorldBank’sDoingBusiness study. These factors include;1)numberofproceduresrequired tostartabusiness,2)timeindaystostartabusiness,3)costtostartabusiness,4)minimumcapitalrequiredtostartabusiness,5)numberofproceduresrequiredtoobtainalicense,6)timeindaystoobtainalicense,7)costtoobtainalicense,8)timeinyearstocloseabusiness,9)costtocloseabusiness,and10)recoveryratesforclosingabusiness.

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Figure5.10:BusinessFreedomIndexFurther examination of thecomponent parts of theseindicators is required tobetter understand whereTanzania’s regulatoryquality has the greatestadverse impacts onbusiness. While a detailedregulatoryqualityreviewisbeyond the scope of thepresent diagnostic,continuing regulatorychallenges can be grouped

into the following themes. First is themultiplicity of steps and requirements for adhering to thelegal and regulatory framework for conducting business. Second is the fact that the rules andproceduresdonotappear tobe implemented in a consistent, facilitative, andnon‐discriminatorymanner.47Third, certain labor regulationsand their implementationmayadverselyaffectgrowthandemployment.Given the importance of regulatory quality to any economy, the section below assesses certainaspects of Tanzania’s regulatory environment against criteria that give some indication of theongoing challenges to growth posed by that environment. However, further investigation andapplicationofsomeofthefourtestsproposedbyHausmannetal(2008)wouldbeneededinordertoassesstheextenttowhichTanzania’sregulatoryenvironmentconstrainseconomicgrowth.Thetests undertaken below rely on correlations and patterns using retrospective information andindicativedata,notrigoroustestsofcausality.

b.TheInformalEconomy

When informal economic activity is defined as in Schneider (2007) tomean all activitywhich isdeliberately concealed from public authorities to escape either taxation, social securitycontributions, labor market standards and regulation, and/or compliance with certainadministrativerequirements,TanzaniaranksamongthemostinformaleconomiesinSub‐SaharanAfrica, as shown in Figure 5.11. In the IFC’s Regulatory Capacity Review of Tanzania, 2010, it isestimatedthatapproximately55percentofTanzania’sGDPisgeneratedwithintheinformalsector,andmorethan95percentofenterprisesinTanzaniaareestimatedtobeinformaltosomedegree.

47AmorerecentEnterpriseSurveywouldbeusefultoassesschangessince2006.

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Figure5.11:InformalEconomyasPercentageofGDP(2004/2005)

Source:Schneider(2007).

The 2006 Integrated Labor Force Survey showed that 40 percent of all households inmainlandTanzania engaged in informal sector activities. The concentrationof informal sector activities ishigherinurbanhouseholds(55percent)thaninruralones(33percent).Moreover,thehouseholdenterprisesector,whichisoverwhelminglyinformal,contributesmorethan40percentofthetotalurbanlaborforceandmorethan50percentofruralnon‐agriculturelaborforce,andisthefastestgrowingprimaryemploymentsource in thecountry. Themainreasonssurveyrespondentsgaveforstartinghouseholdenterprises,apart fromtheneed foradditional income,are the inability tofind otherwork (36 percent forwhom the enterprisewas themain activity and 18 percent forwhom it was a secondary activity). Other common reasons cited were the fact that the sectorprovidesgoodincomeopportunitiesandthatthetypesofbusinessesonecanengageininformallydonotrequiremuchcapital(ILFS2006).

GiventhemagnitudeoftheinformalsectorwithinTanzania’seconomy,theabilityoftheinformalsector to operate efficiently – or conversely, the costs of their inability to operate formally – issignificant.Manyotherfactors,includingaccesstoinputs,alsoimpactthebusinesscommunityandit is not possible to attribute the high rate of informality principally to the quality of regulation.

The shadow price of the constraint appears to bemoderately high, but depends on thesector. Firms will remain informal as long as the costs of formality – i.e., interacting with theformalregulatoryenvironment–arehigherthanthecostsofinformality,whicharemanifestedaslostproductivityorprofits.Informalitycanreduceproductivitybyinhibitingaccesstoinputssuchas utilities such as electricity andwater, formal finance, business premises and land, as well asachievementof amore efficientdivisionof labor and access to expandedmarkets. Asdiscussed

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elsewhereinthisreport,thereareothersevereobstaclesforsmallenterprisesandothereconomicactorstoaccessinginputssuchaselectricityandwater,land,andfinanceinTanzania.Thus,itisnotpossible toattributeTanzania’shigh rateof informalityprincipally to thequalityof regulationofbusinessandcommerce.48

Nonetheless,analysisgloballyregardingtheinformaleconomyindevelopingcountriesshowsthatinformalitymakesaccessingkeyproductionfactorsmoredifficultandrisky,andgreaterformalityis associated with greater profitability. Data collected in Tanzania in 2004 as part of a RuralInvestment Climate Assessment (World Bank 2007) showed that, with the exception of theconstruction sector, formal firms in rural areashadhigher salesperworker. Thisoutcome is inspiteofthefactthatlargerfirmshadlowersalesperworkerthansmallerfirms.Atthesametime,54percentofsurveyedenterprisesclaimedthattheprimaryreasonfornotregisteringwasthatthiswasnotrequiredaslongastheydidnotintendtoexpand.Expansiontoincludemovingbeyondthelocal market would mean incurring higher transaction costs, including those of registration,managingamore complexorganizational structure, and improved (andmoreexpensive) service,production,ortradingmethods. TheRuralInvestmentClimateAssessmentshowedastatisticallysignificantcorrelationatthecommunitylevelbetweenincreasedbusinessregistrationandannualemploymentgrowth,asshowninTable5.6,althoughthesecorrelationsdonotestablishcausality.The correlation between employment growth and business registration appears particularlysignificantrelative to thecostof regulatoryreform,especiallywhen thecostsofalleviatingothersignificant barriers that correlate to employment growth, such as roads and electricity, areconsidered.

Table5.6:ConstraintstoRuralEnterpriseEmploymentGrowth

The conclusion that formality is potentially beneficial for firm profitability is indicated by theapparentresponsebybusinessestoareductioninoneaspectofregulatorycosts.Since2004,the

48Skof (2008) notes that informal operators cite the unaffordability of permanent premises for theirbusinessesastheirtopobstacletodoingbusiness,followedbyalackaccesstocredit.Inaddition,althoughitis adiminishingproblem, informaloperatorshave reportedharassment, anddemolitionor confiscationofpropertybylocalauthorities.

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Figure5.12:CostofBusinessStart‐UpProcedures

costofstartingabusinessinTanzaniahasdropped, as shown in Figure 5.12. By2010, Tanzania ranked 122nd in theworld, better than Kenya and Uganda inthese particular costs of formality. Onecan see a jump in the number of firmsregistering after 2007, possibly inresponse to this reduced regulatoryburden. This jump suggests that theshadowpriceof regulatoryburdensmayhavebeenhighwithrespecttostartingabusiness. If the opportunity cost ofremaining regulatory burdens aresimilarly high, continued GOT progresson regulatory reform could deliversignificantprivatesectorresponse.

c.CircumventionofRegulation

Tanzania has a fairly well developed system of written agreements that allows for some semi‐formal governance of commerce and property rights. This system includes informal villagerepositories or registries of documents, calledMwenyekiti, used to track contractual agreementsand property in the event of a dispute. These agreements are largely enforced through socialpressures which are effective only within the community, and enforceability would not extendbeyondthelocality.49

This semi‐formal contractual system lacks mechanisms for creating distinct legal entities, forpartitioning assets between a firm and its owner(s), and for amore flexible and, in some cases,efficient division of labor. The system also fails to provide the legal means for enterprises tooperateinmarketsrequiringinspection,regulation,orpermits,ortoaccessinputsandfactorsonacontractual basis vianetworksbeyond familymembers and socialnetworks. Finally, the systemdoes not facilitate full transferability andmovement of property rights to themost efficient use.Giventhewidespreaduseof thissemi‐formalcontractualsystemtorecordcontractsandregisterproperty, Tanzania might benefit from an assessment of this system to determine costs to theprivatesectorandtheirimpactongrowthinordertoinformfuturestepstoimprovetheregulatoryenvironment.

Anotherwayinwhichfirmsmaypaythecostsofregulatoryconstraintsisbymakingsidepaymentsto facilitate or circumvent regulatory requirements. In general, corruption, likepoor regulatoryquality, can arise from bureaucratic discretion and a lack of accountability by implementinginstitutions. In any country, a rising or persistently high level of corruption suggests poor oruneven regulatory quality and poor or uneven regulatory quality can facilitate corruption. As

49The exception is transactions involvingVillageLandapprovedby theVillageCouncil,which are formallyrecognized.

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shownbelowinTable5.7,corruptionisratedaslessofaprobleminTanzaniathaninKenyaandUganda, but it ranks as themost frequent response on the 2010Global Competitiveness Survey(GCS)(WorldEconomicForum)to thequestionofwhatare the fivemostproblematic factors fordoingbusinessinTanzania,upfromsecondplacein2008.50

Table5.7:MostProblematicFactorsforDoingBusinessinComparison(GlobalCompetitivenessSurvey2010)

Ghana Kenya Mauritius Mozambique Tanzania Uganda Average

Corruption 8.5 21.7 7.0 17.2 17.4 21.9 15.6

AccesstoFinancing 21.1 12.9 9.1 18.9 15.1 15.3 15.4

InadequateSupplyofInfrastructure 12.5 9.5 15.5 7.3 13.3 13.0 11.9

TaxRates 8.5 7.2 1.0 4.4 9.0 8.9 6.5

TaxRegulations 4.1 5.0 4.0 4.0 7.9 4.4 4.9

CrimeandTheft 3.5 8.0 4.2 5.7 6.3 3.1 5.1

InefficientGovernmentBureaucracy 8.6 12.2 15.8 12.2 6.2 6.7 10.3

Inflation 12.2 7.5 4.7 9.1 6.0 6.3 7.6

PoorWorkEthicinNationalLaborForce 7.9 3.1 12.2 2.4 4.0 7.1 6.1

InadequatelyEducatedWorkforce 3.9 1.1 14.3 5.4 3.9 5.0 5.6

RestrictiveLaborRegulations 1.0 2.6 4.9 3.2 3.6 0.8 2.7

ForeignCurrencyRegulations 3.0 1.8 2.5 6.4 3.2 2.0 3.2

PoorPublicHealth 0.7 0.9 2.1 1.9 2.6 2.7 1.8

PolicyInstability 4.1 3.4 2.4 1.8 1.2 2.4 2.6

GovernmentInstability/Coups 0.4 2.9 0.4 0.0 0.2 0.5 0.7Calculationsareweightedhistoricalaverageswhichplacehigherweightoncurrentresponses.Firms’TopFiveResponsesareweightedbytheir1‐5Ranking

Inagriculture,theAgCLIR(2010)reportconcursthatthelevelofcorruptioninTanzaniaisnothighbyregionalstandardsandthatitdoesnotposea“majorobstacletoinvestment”.GOTpoliciesandpartnerships that mitigate corruption and continue efforts to improve regulatory consistencyshouldhelpdrawinvestmentandincreasethebenefitsofbeingintheformalsector,suchasaccesstocredit,andtherebyfacilitateexpansionoftheformalsector.

50Thissurveyisadministeredtoexecutivesofarandomsampleoffirmsacrossallsectorsoftheeconomyandresponses are weighted by the sector’s share in the economy. Therefore, these responses should not beskewedorbiasedbyafewsector‐specificexamples.Thequestionaskedoffirmswasforarankingofthetopfive most problematic factors, and each response was weighted using its rank. A similar ranking ofcorruption as a problem across countries is evidenced using Transparency International’s CorruptionPerceptionsIndex.

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Ifa lackofquality regulatoryqualitywereaconstraint,onewouldalsoexpect that thosesectorsmostimpactedbytheregulatoryenvironmentwouldperformlesswellthanothers,andchangesintheconstrainedfactorwouldtendtomovetheeconomyoverall.

AccordingtotheIFCInvestmentClimateSurvey(2004),businessregulationdoesnotvarygreatlybyspecificproductivesectorinTanzania.However,thesurveyconcludedthatbureaucraticburdenassociatedwithregulationwasmuchhigherforexportersthanfornon‐exporters.Asshownbelow,thereappearstobeacorrelationbetweenTanzania’sWGIaggregateindicatorofregulatoryqualityandexports.Whenonecontrolsforthetimetrend,therelationshipbetweenregulatoryqualityandexportsisstatisticallysignificantatthe10percentlevel.

Figure5.13:ExportsandRegulatoryQuality

Source:WGIandWorldDevelopmentIndicators

D.MarketAccessandOpennesstoTrade

Policy or regulatory obstacles to domestic or international trade canmakemarket accessmoreriskyorcostlyforproducers,inhibitspecializationandgainsfromtrade.Inthecaseofbasicfoodgrainmarkets, such obstacles can hinder the achievement of food price stability, access to foodmarkets, and higher real incomes, all of which are key ingredients to enhanced food security.Regulationwhichimpactsagriculturalmarketingandexportsisaparticularareaofconcernraisedby a number of studies of the Tanzanian economy (e.g., AgCLIR (2010), Baregu and Hoogeveen(2009),Minot(2009),Nyange(2005),WorldBank(2009)).

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Figure5.15:CosttoExport(WBDB)

Figure5.14:ExportsofAgriculturalvs.ManufacturedGoods

Source:WDIAs isdiscussedinChapter7,thelackofadequateroadinfrastructurerepresentsa substantial hindrance to marketaccess, particularly for more remoterural producers in Tanzania. At thesame time, regulatory and policyobstacles also appear to increase thecosts and risks of market access,especially for output markets inagriculture.Althoughthesemarketsarein principle fully liberalized, thereappear to be ongoing obstacles toachievement of competitive markets.51According to the World Bank (2009),based on a survey of total domesticmarketing costs, the price differentialbetweenfarm‐gateandcapitalwholesalemarketsaveragedUS$54pertoninUganda,US$80perton in Kenya, and US$91 per ton of grain in Tanzania. While the majority of this difference isprobably explainedbydirect transport costs, asdiscussed inChapter7, itmightbepossible thatsomeofthedifferenceisexplainedbyTanzanianregulatoryhurdles.

51 According to AgCLIR (2010), domestic wholesale markets for agricultural produce are often nottransparent andopen, reducingpotential earnings for farmers. Further investigationmaybewarranted tounderstandthisissueingreaterdetail.

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Tanzania’sregulationsimposedoneligiblebuyersofexportcrops(cotton,maize,andtobacco)havetendedtolimitcompetitionamongbuyers,therebyreducingfarm‐gateprices.52 Recentanecdotalinformation suggests thatmarket reforms and the spread of cell phone technology appear to beleadingto improvedcompetitionandmarket informationtransmission inmanymarkets,but thishasnotyetbeenconfirmedwithreliabledata.

Inefficient customs regulations and procedures canalsoraisethecostsofaccessinginternationalmarkets.As shown above in Figure 5.15, Tanzania ratesfavorably in the cost of exporting relative to Ugandaand Kenya; these costs are similar to those forMozambique, and higher than for Ghana and othercomparators,accordingtotheWBDBindicators.53Thecosts of importing reflect a similar pattern. However,according to recent firm‐level surveys, such as theGlobal Competitiveness Survey, Tanzania’s customsprocedures are rated the least efficient of thecomparatorcountries,asshowninTable5.8.

Finally,intentionalpoliciestorestrictortaxtradedrivea wedge between producers and potentially moreprofitable markets. This can impede productivity‐enhancingcompetition,asdiscussedinChapter6.

52 Although these markets have been liberalized, market access difficulties and imperfect competition byagricultural buyers, particularly of traditional export crops in Tanzania, are described by the WB TradeDiagnosticIntegrationStudy2005,BizCLIR,andBareguandHoogeveen2010.53Compared to LIC’s and SSA, where many countries are landlocked, Tanzania also has lower costs ofimportingandexporting.

Table5.8:CostofCustomsProcedures

Country 2007 2008 2009

Korea,Rep. 5.89 5.03 4.55

Mauritius 4.45 4.57 4.59

SSA(dev.) 3.29 3.27 3.63

Ghana 3.37 3.44

Kenya 3.27 3.15 3.28

Lowincome 3.08 3.04 3.37

Uganda 3.31 3.11 3.38

Tanzania 3.00 2.66 2.97

Mozambique 2.93 2.78 3.12Source:WEFGlobalCompetitivenessSurvey; (1=extremely inefficient to7=extremelyefficient)

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Figure5.16:TradeFreedomIndexinComparison

Tanzania’sopennesstotradehasimprovedsubstantiallysincethetransitionfromsocialisminthe1990s,andthishasaccompaniedastrong increase inexportsuntil thepast fewyears. However,since 2005, Tanzaniahas become relativelymore closed to trade,according to theHeritage Foundation’sIndexofTradeFreedom,now ranking belowcomparator countries inthe region. Ghana is theonly comparator coun‐tryrankingmorepoorlyon this measure, asshown in Figure 5.16.The Heritage Founda‐tion determines theextent and severity ofnon‐tariff barriers(NTBs), using bothqualitative and quanti‐tative information.54 In addition, the Index captures regulation, policies, and governmentinterventionsnotstrictlyrelatedtotradepolicybutwhichimpacttrade.AlthoughthecomparisoncountrieswithintheEastAfricanCommunity(Tanzania,Kenya,Uganda)haveacommonexternaltariff, Tanzaniamakes greateruseof allowable exceptions and, therefore, on average,Tanzania’simport duties on agricultural products are slightly higher than for other countries in the EastAfricanCommunity,and isalsohigher than forcomparisoncountriesnotpartof theEastAfricanCommunity.

Given the Government’s understandable concerns over food security and food prices, Tanzaniaperiodicallyimposesbansontheexportofcommoditiesitviewsasvitalforthefoodsecurityofitspeople, including for example,maize.55 Both GOT policymakers andUSG partners recognize thatpoliciesundertakeninresponsetoimmediateandurgentfoodsecurityconcernsmayimposehighcostsonthedynamicforcesthatreducethelikelihoodandseverityoffuturecrises.GOTpoliciestoprovide short‐term relief and address food security concerns include food assistance and exportbans. The discussion of export bans below highlights the consequences that trade‐restrictingpolicies canhaveon investment andgrowth. Given the importanceof establishing apredictable

54TheTradeFreedomIndexisacompositemeasureofthetariffandnon‐tariffbarriersthataffectimportsandexports of goods and services. The categories considered for calculating NTBs include: 1) quantityrestrictions, 2) price restrictions, 3) regulatory restrictions, 4) investment restrictions, 5) customsrestrictions,and6)governmentinterventions.55Maize exports are only allowed once each region of the country is declared to have sufficient maizeproductiontofeeditspopulation.The2011banwasannouncedonMay17andreportedonMay20,2011byBusinessDailyAfrica.http://www.businessdailyafrica.com

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policy environment that creates incentives for agricultural growth, the GOT and USG have anopportunitytocollaborateinthesearchforapolicyframeworkthatensuresfoodsecuritywhilenotunderminingthebasisforruralinvestmentandagriculturaldevelopmentandgrowth.

Economic reasoning suggests that exportbans createuncertainties in access tomarkets forbothfoodproducersandconsumers,therebyweakeningincentivestoenhanceproductivity.Inthecaseofmaize,usuallythebandoesnotstopmaizefromflowingacrossthebordersentirely; insteaditaltersthedistributionofbenefitsinthevaluechainandincreasestransactioncosts,whichmightbetothedisadvantageof farmers.Infact,availabledataseemstoindicatethatanexportbanisonlypartiallyprohibitive.Inyearsinwhichanexportbanonrawmaizeisimposed,theresponsivenessof exports to production drops from approximately 6 percent to 4 percent or lower56. Thecombined effect of Tanzania’s export bans with maize import duties (reportedly 50 percent insurplusyears(USAID,2008),alongwithbansandrestrictionsbyotherneighboringeconomies,onthe agricultural economy and food security is unclear. The likely impact is to reduce producerprices inmaize surplus areas (creatingdisincentives for production in future years andpossiblyincreasing the likelihood of future shortages),while increasing average consumer prices in food‘deficit’ areas of the country (seeMinot 2009) in addition to increasingprice variability. Amorerigorousstudywhichcaptures farmers’ response to risk,dynamic incentives,and theannualandspatial variation in rainfall and yields across Tanzania and the East African region would helppolicymakers formulate policies and regional agreements which could enhance agriculturalproductivityandfoodsecurity.

Figure5.17:AverageImportTariffsPaidbyCountry,2010

Source:InternationalTradeCenter,MarketAccessMap

56Author’scalculationsusingFAOSTATdata.

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Figure5.18:StructureofAgriculturalExports1999‐200857


Figure5.19:LivestockProductionIndex,TanzaniaandotherLivestockProducers

Source:WDI

57Itshouldbenotedthatadecliningshareinagivencropdoesnotnecessarilymeanadecreaseinthevalueofexportsofthatcrop.

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Tanzania’s agricultural productivity trends have been positive, especially for non‐cereals.However,productivitygrowthhasnotbeensufficientlyrobusttocatchupwiththatoflowermiddleincomecountries.TrendsinValueAddedperAgriculturalWorker(VAPW)andexpansionofcropproduction for Tanzania, low income countries, and lowmiddle income countries are shown inFigure5.20.In1992,productivityperworkerinTanzaniawasslightlylowerthanforlowincomecountriesasawhole,andcountriescurrentlyclassifiedaslowermiddleincomecountries(LMICs)hadappreciablyhigheragriculturalproductivityatthetime. VAPWforcurrentLMICswasinfacthigher in1980at291 constant year2000USD, than isTanzania’s today, at283USD. VAPW forLMICs today is over 600 USD (constant 2000 USD), given their more rapid productivity gains.WhereasTanzania’sVAPWhas increased faster than thatof low incomecountriesasawhole, asshown inChapter 2, this is largelydue to increases in total area cropped. These increaseshavebeguntoslow,andincentivestoinvestinotheryield‐enhancinginputswillbemoreimportantforsustainingincomeandproductivitygainsinagricultureinthecomingyears.

Figure5.20:ValueofTotalCropProductionandValueAdded


E.Conclusions

There are a variety of microeconomic issues that affect productivity and economic growth inTanzania. The most important micro‐appropriability issue, which appears to present abinding constraint to economic growth, is the lack of an efficient and secure land rightssystem. Securetitletolandisnecessarytoattractland‐dependentinvestmentsthatcandirectlycontribute to economic growth through increased production and job creation, and to facilitate

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small and medium enterprise establishment and expansion. In addition, efficient and securetransferabilityisrequiredforlandmarketstodevelopwhichpermithigherreturninvestments.Atthesametime,smallscalelandusersandvillageleadersrequireasufficientlevelofknowledgeofthe Land Acts to receive fair value for their resource and to avoid land conflicts which can bedestabilizingtotheeconomyanddisincentivizeinvestment.

Whereas the principles of Tanzania’s land policy appear sound, further reviewof the impacts ofspecificaspectsofthelegalframeworkmaybeadvisable,alongwithanassessmentoftherisksandbenefitsofanychangestothe1999LandActandVillageLandActoraccompanyingimplementingregulations.Inaddition,theimplementationoftheActshasbeenincomplete,creatingambiguitiesforbothlandbuyersandsellers.Theproceduralcostsforregisteringlandarehigh,asarethosetoacquirelandforinvestment,aprocesscharacterizedbyHoekema(2010)asan“awesomeseriesofbureaucraticstepswhichtakesmorethanayearoffulltimeworkandalotofmoney.”Therateofregistration of use rights is very low, and interest by investors – interest which is very oftenfrustratedbythe lackof identified land–appearsstrong. Landholdersandinvestorsalikeopttouse informalchannels toconduct land transactionsdespite the lackofsecure tenure thatresults.Thesituationhas ledtoanincreasedfrequencyofcostly landdisputeswhichadverselyaffecttheinvestmentenvironment.

While the GOT has made progress in improving the business environment, there isconsiderable evidence that suggests serious challenges continue to impede investment.These challengeshavenotbeendetermined tobebinding constraints to growthbutnonethelessposechallengestoTanzania’seffortstoaccelerateandsustainbroad‐basedeconomicgrowth.Themost compelling evidence suggests that regulatory quality has adversely impacted exportperformanceinparticular.AsdiscussedinChapter6,totheextentthatregulatorybarrierstoentryor tomarket access reduce competition in key sectors of the economy, this also tends to reduceinnovation and productivity growth. The government may benefit from further analysis of thesignificant constraints to growth that emanate from uneven quality of Tanzania’s regulatoryenvironmentwithaviewtowardfacilitatingprioritizationanddeterminationbythegovernmentasto how to invest its efforts and funds to continue targeting those regulatory issues that, ifaddressed,wouldprovidethegreatestimpactoneconomicgrowth.

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6. MarketFailuresinInnovation

Technologicalchangeandinnovationareamongthekeycausesoftherapideconomicgrowththatthecurrentrichcountriesexperiencedoverthepastfewcenturies.Technologicalinnovationisalsoan importantmeans to sustain growth in standards of living once the diminishing returns frombetter institutions, infrastructure, macroeconomic stability, and improved human capital areexhausted(WorldEconomicForum,2010).TheyareincreasinglycriticalforTanzania’ssustainedgrowth prospects, as the major gains from improved allocative efficiency spurred by economicliberalizationmayhavebeenlargelyexhausted.

Eveninthepresenceofanotherwiseadequateinvestmentclimate,marketfailuresresultingfromweak ‘learning by doing’ or technological spillovers, coordination failures, or inadequateappropriability of the gains from innovation can inhibit investments which would be viable forprivate investors. By examining a number of indicators, as well as the totality of evidencepresentedinthisreport,wecandeterminewhethersuchmarketfailuresactasabindingconstraintto private sector investment and therefore economic growth in Tanzania. If other areas ofweakness cannot explain poor performance in innovation, then it is possible that an economy’sgrowth and development are significantly impeded by these market failures. In that instance,deeper investigation into where these failures may lie, and whether there are policy or otherweaknessesunderpinningthem,maybeinorder.

A.SpilloversinInnovationandIntellectualPropertyRights

Export performance and diversity is one measure of an economy’s ability to innovate in theproductionofgoodsandservices.WhileTanzaniahasbeenabletogrowrapidlyinrecentyears,asubstantialpartofthisgrowthmaybeduesimplytothetransitiontoamoreefficientorintensiveuse of existing factors of production, without necessarily signaling substantial technologicalprogress.

Tanzania’s performance over the past decade in this area is mixed. Export diversification hasincreased,andnotonlyintoprimarycommoditiesandminerals.Between2001and2010,Tanzaniasuccessfullyincreasedthenumberofdistinctproductlinesexportedfromthecountry,asmeasuredatthe4‐digitand6‐digitlevelsoftheHarmonizedCommodityDescriptionandCodingSystem(HS),standardized across 170 countries. A more diverse export base is also increasing in value, asmeasuredbythenumberofexportlinesvaluedatmorethan50,000USD.

Compared to other similar countries Tanzania comes second only to Uganda in exportdiversificationoverthepastdecade,asmeasuredbytherateofincreaseof4‐digitexportproduct

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lines(totalandthoseexceeding50,000USDinvalue).58ThissuggeststhatTanzaniahasarelativeabilitytoadaptandinnovateascomparedtoothercountries.

Figure6.1:ExportDiversification

Source:InternationalTradeCentreandCOMTRADE

Figure6.2:RatesofExportDiversification

Source:InternationalTradeCentreandCOMTRADEAs shown in Tanzania’s export product rankings for 2001 and 2010, there are several productcategories at the HS‐4 level that have increased in the rankings – i.e., manganese ores andconcentrates,furnishingarticles,driedvegetables,andfertilizers–andthatcannotbeclassifiedas

58Available InternationalTradeCentre export statistics forGhana cover theperiod2003‐2009; forKenya,Mauritius,Mozambique,andUganda,theperiod2001‐2009.

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unprocessed primary commodities. Despite the fact that Tanzanian exports continue to bedominatedbycommodityexportsandrawmaterials,thislistsuggeststhatTanzaniahasbeenableto avail itself of opportunities to produce new, higher value added products closely related toexistingcomparativeadvantage.

Table6.1:MajorExportProducts

ProductCategory ExportRank2001

ExportRank2010

Gold(unwroughtorinsemi‐manufacturedforms) 1 1Fishfilletsandpieces(fresh,chilled,frozen) 2 8Brazilnuts,cashewnuts,andcoconuts 3 6Coffee 4 7Preciousmetaloresandconcentrates 5 2Tobacco(unmanufacturedandrefuse) 6 5Tea 7 15Diamonds(notmountedorset) 8 Cotton(notcardedorcombed) 9 11Preciousandsemi‐preciousstones(notstrung) 10 20 Manganeseoresandconcentrates 3Copperwasteandscrap 4Driedvegetables(shelled) 9Furnishingarticles 10Petroleumgases 12Mineralorchemicalfertilizers 13

Oilseeds 14Source:InternationalTradeCentre,TradeMap

B.ProductSophisticationIndexversusGDPPerCapita

Anexportsophisticationindex–alsoknownasEXPY–calculatedbyHausmann,Hwang,andRodrik(2005)suggeststhatTanzaniahasalowlevelofexportsophisticationrelativetoitsGDPpercapita.Figure 6.3 displays the relationship between GDP per capita and export sophistication formorethan150countries,basedon2005exportfigures;Tanzania,shownasthesinglereddiamondmark,fallsbelowtheregressionlinewithinthescatterplot.

Figure 6.4 depicts Tanzania’s performance relative to similar countries in Africa. Since 2000,Tanzania has performed worse than the comparators available, mainly Uganda and Mauritius.Moreover, the flatteningordecliningcurvesince1999relative toUganda isapossible indicationthatTanzaniaisnotabletodevelopitscomparativeadvantageinmoresophisticatedproducts.

Asimilarindicator–‘openforest’–measuresthedistance‐adjustedlevelofincomeassociatedwithallpotentialnewexport goods,or theeconomicvalueofproductswhichare close toa country’sexisting products in terms of ease and likelihood of entry from existing production patterns.Hausmann andKlinger (2006) show that this indicator strongly predicts the speed of structural

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transformationofaneconomyasmeasuredbygrowthinthelevelofsophisticationofexports.Onthismeasure,Tanzania(onceagainthereddiamondmark) isslightlybetterplacedrelativeto itsGDPpercapitathanmanycountriesinitsunexploitedpotentialtodiversifyproductionanddevelopnewareasofcomparativeadvantage.

Figure6.3:RelationshipbetweenPerCapitaGDPandEXPY,2005

Source:Hausmann,Hwang,Rodrik(2005)

Figure6.4:ComparativeExportSophistication

Source:RicardoHausmann

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Figure6.5:RelationshipbetweenPerCapitaGDPandOpenForest,2005

Source:RicardoHausmann

C.BusinessSophistication,TechnologicalReadiness,R&DLevels

Tanzania’s ability to move to new, higher value‐added products depends in part upon itstechnologicalreadinessandbusinesssophistication,whichincreaseefficiencyandopportunityforinnovation as well as the quality of firms’ operations and strategies.59According to the GlobalCompetitivenessReport,Tanzaniaperformsatroughlythesamelevelsascomparatorcountriesonbroadindicesofinnovationandbusinesssophistication.Basedonanaggregatescorerangingfrom1‐7,onlyKenyaperformsbetteronthemeasureofbusinesssophistication;KenyaandMozambiquebothout‐performTanzaniaontheinnovationindex.

A variety of sub‐measures are used to calculate business sophisticationmeasures, including thequality and quantity of local suppliers and sophistication of the production process. Figure 6.6showsTanzania’s relative rankingonbusinesssophistication,whereahighernumber representsrelatively poorer performance, suggests that Tanzania’s performance is in line with mostcomparatorcountries.Itsperformanceonlocalsupplierquantityandquality,however,arenotablypoorascomparedtoeverycountryexceptMozambique.

TheGlobalCompetitivenessReportoftechnologicalreadinessattemptstomeasuretheagilitywithwhichaneconomyadoptsexisting technologiestoenhancetheproductivityof its industries,withspecific emphasis on its capacity to use information and communication technologies in dailyactivities and production processes for increased efficiency and competitiveness. Tanzaniacompares poorly to comparator countries on each sub‐measure used to assess technologicalreadiness, including availability of latest technologies, foreign direct investment (FDI) andtechnologytransfer,andtheabsorptionoftechnologyatthefirm‐level.Figure6.8showsTanzania59GlobalCompetitivenessReport2010‐2011.

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astheworstperformeramongthebenchmarkcountriesonallsub‐indicators.Itisunclearwhetherthelackofbroadbandsubscriptionsandinternetbandwidthisduetopoorinfrastructureversusalackofdemand,althoughifthespreadofmobilephonenetworksisanyindication,alackofinterestisnotaseriousbarriertothespreadofInternetinthecountry.

Figure6.6:BusinessSophistication&InnovationIndices

Source:WorldEconomicForum,GlobalCompetitivenessReport2010‐2011

Figure6.7:BusinessSophistication–ComparativeRanking


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Figure6.8:TechnologicalReadiness–ComparativeRanking


The Global Competitiveness Report also rates the level of investment in innovation acrosscountries, including company spending on research and development (R&D), collaborationbetween universities and industry inR&D, and the availability of scientists and engineers in thedomesticeconomy.Figure6.9suggeststhatTanzania’sperformanceisfavorablerelativetothatofcomparatorcountries,despitepoormeasuresof thequalityofscientific research institutionsandthe availability of scientists and engineers; the country does relatively well on capacity forinnovationandcompanyspendingonR&D.Unfortunately,amoredetailedlookintotheavailabilityofscientistsandengineersisnotpossible,asharddataonthenumberofscientistsandtechniciansperformingresearchanddevelopmentarenotavailableforTanzaniaoritscomparatorcountries.60

Figure6.9:Innovation–ComparativeRanking


60WorldBank,WorldDevelopmentIndicatorsreturnnodataforanyofthecomparatorcountriesorregionsfor“ResearchersinR&D(permillionpeople)”or“TechniciansinR&D(permillionpeople).”

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D.RegistrationofNew/ImportedTechnologies

AccordingtothemostrecentavailabledatafromtheWorldBankEnterpriseSurveys,14.7percentofTanzanianfirmssurveyedreportedthattheylicensetechnologyfromforeignfirms.ThisplacesTanzania behind Mozambique and Kenya, but ahead of Ghana, Mauritius, and Uganda on thismeasure. Due to limiteddataavailability forallof thesecountries,however, it isnotpossible todeterminetheirrelativeperformanceovertime.

Figure6.10:FirmAdoptionofForeignTechnology

Source:WorldBankEnterpriseSurveys

E.IntellectualPropertyandInformationExternalities

Market failures can arise through ‘information externalities’, whereby others can imitate newproducts, services, or innovations and learn from others’ success in the marketplace. If theseexternalitiesareimportant,entrepreneurswouldbedeterredbylowerbarrierstoentryandahighdegree of competition; other potential investors would enter and compete for profits, possiblylowering them to within a range that does not compensate the investor for his investment ininnovation. Given thisproblem,governmentsworldwideattempt toprotect intellectualpropertybyissuingtrademarksandpatentssothattheinnovatorcancapturealargershareofthereturns.AccordingtotheGlobalCompetitivenessReportandothersimilarstudies,Tanzaniademonstratesarelativelylowdegreeof localcompetitioninthemarket(seeFigure6.15). Whilethisreducestheneed to innovate, it also suggests that Tanzanian entrepreneurs should face a relatively lowerinformationexternalitythanincomparatorcountries.

a.TrademarkApplications

Trademarks are used by an individual or business to identify and distinguish its products andservices. Tanzaniaprovides legalprotection for trademarksunder theTradeandServiceMarksActNo.12of1986. Underthe law,trademarksareregisteredforaperiodofsevenyears,witha

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renewal period of ten years. Tanzanian ‘common law’ also extends limited protections tounregisteredtrademarks.

Dataonapplicationstoregisteratrademarkwithanationalorregionalintellectualpropertyofficecan be informative regarding the degree of innovation, product branding, and/or level ofintellectual or technological property protection in an economy. In absolute numbers, relativelyfewtrademarkapplicationsare filed inTanzaniaascomparedwiththetwobenchmarkcountriesforwhichdataareavailable,KenyaandMozambique(Figure6.11,Figure6.12).

Figure6.11:TrademarkApplications(Total)


b.PatentApplicationsandE‐Filing

Patentsconferuponaninventororinnovatorthesolerighttomake,use,andsellthatinventionfora set period of time and is another means of IP protection that fosters business innovation.TanzaniaprovideslegalprotectionforpatentsunderthePatentsActNo.1of1987.Underthelaw,invention and utility patents are protected for a period of 20 years. However, according to theWorldTradeOrganization(2006:A2‐168),“AlthoughTanzaniacangrantpatents,ithasneverdoneso;ithasregisteredpatentsgrantedelsewhere.” Instead,TanzaniahasreliedonitsmembershipwithintheAfricanRegionalIntellectualPropertyOrganization(ARIPO)forthereviewofallpatentapplicationsreceived.Ghana,Kenya,andUgandaarealsomembersofARIPO.

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Figure6.12:TrademarkApplications(ResidentandNon‐Resident)


c.IntellectualPropertyRightsIndicators

According to Tanzania’s Ministry of Industry, Trade and Marketing, the country’s intellectualpropertyprotectionsmaybeinsufficientinpracticeduetolackofadherencetothelaw;difficultiesinsecuringenforcementinthecommercialcourts;anddeficienciesintheappropriatelawsandactsthemselves.TheWorldTradeOrganization,initslastTradePolicyReviewofTanzania,notedthatthe country lacks the resources toproperly enforce intellectual property rights (IPR) (2006:A2‐169).DespitethechallengeofestablishingandenforcingintellectualpropertyrightsinTanzania,itsperformance falls in themiddle of comparator countries according to the International PropertyRightsIndexandtheGlobalCompetitivenessIndex.Thecountry’sperformanceonbothindicators,whichmeasure legalprotectionsaswellaseffectiveenforcement,results in lowerrankings(withlower being stronger rights and protections) than those of Kenya, Mozambique, Uganda, andVietnam. This suggests that IPR protections are not an undue impediment to business andinvestmentinTanzania.

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Figure6.13:IntellectualPropertyRightsandProtections

Source:GlobalCompetitivenessIndicators,2010‐2011;InternationalPropertyRightsIndex2011

F.MonopolyPowerandLackofCompetition

Although technological spillovers and learning‐by‐doing effects can create market failure andreduceinvestmentininnovationintheabsenceofadequate intellectual property protection,imperfect competition and barriers to entry canalso reduce the incentives to innovate. Firmsenjoying relatively un‐competitive domesticmarkets for their goods and serviceswill be lesslikely to turn to export markets, which requirecontinual investment in productivity‐enhancements.

UndertheFairCompetitionActof2003,Tanzaniaprohibits the abuse of dominantmarket position(though not dominant market position per se),anti‐competitive agreements, misuse of marketpower,andmergersandacquisitionsthatcreateorstrengthenapositionofmarketdominanceinaspecificmarket. Tanzania, likeKenya andUganda, is subject by law to theprotocols of theEastAfricanCommunity(EAC),whichprovidesthelegalbasisforcompetitionpolicy.Unfortunately,nostructured analysis is available to determine comparative application and performance inimplementingtheserulesamongthesethreecountries.

Competition is generally correlatedwith investment in newproducts or processes in the region,includingTanzania,asshowninTable6.2.Althoughonecannotnecessarilyconcludefromthisthatincreasedcompetitioncauses innovation inTanzania, there isagrowingbodyofresearch linkingproductivitygrowthtoincreasedcompetition,especiallyatlowlevelsofcompetition.Forexample,Aghionetal. (2008) find thathighermark‐upshaveanegative impactonproductivitygrowth in

Table6.2:InnovationandCompetition

PercentofFirmsIntroducingNewProductsorProcesses

0‐1competitor

>2competitors

Burundi 40.00% 57.89%Kenya 70.00% 80.62%Rwanda 77.78% 70.83%Tanzania 53.33% 81.05%Uganda 70.00% 83.65%Source:WorldBankEnterpriseSurveys

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South African manufacturing firms, and in a 56‐country sample of firms. The increase inproductivitygrowthassociatedwith10percenthigherprofitmarginsis2‐3percent,whichishighrelativetomedianproductivitygrowthof1‐2percent.

Tanzania’s economy has recently shown a higher degree of market concentration and a lesserdegreeofcompetitionamonglargefirms.InthemostrecentWorldBankEnterpriseSurvey(2006),which ask detailed questions of a representative sample of registered forms, Tanzanian firmsreportedaparticularlyhighfocusondomesticmarkets,relativetoneighboringcountrieswhichare,inthreeoutofthe fourcomparisoncases, land‐locked. Inaddition, firmsreportfacingrelativelylittlecompetitionintheirmain(domestic)market,asshowninFigure6.14

Figure6.14:DegreeofCompetitionFacedbyLargeFirms

AccordingtodatafromtheTanzaniaAnnualSurveyofIndustrialProduction,however,mostofthemanufacturing sub‐sectors have become less concentrated over time. With the exceptions oftextiles, apparel, coke, and other non‐metallic mineral products, where concentration hasincreased, the level of concentration inmanufacturing decreased between 2001‐2002 and 2006‐2007(WorldBankbackgroundnote,unpublished).

Inaddition,GlobalCompetitivenessSurveyindicatorsofmonopolypowershowthattheTanzanianeconomy may be becoming more competitive overall. On a measure of intensity of localcompetition, a higher ranking (on a scale of 1‐7) indicates that local entrepreneurs judgecompetition tobe intense inmost industries. Ahigherscoreon theextentofmarketdominanceindicatorreflectscorporateactivitythatisspreadacrossmanyfirms,asopposedtofew.Ahigherscore on effectiveness of anti‐monopoly policymeans that policy ismore effective at promotingcompetition.Tanzaniaperformsroughlyinthemiddleofcomparatorcountriesontwoofthethreemeasures,amongtightly‐clusteredscores.ThesescoressuggestthatwhiletheTanzanianeconomyisnotstronglymarkedbymonopolyoverall–perhapsduetoreasonablyeffectiveanti‐monopolypolicies –neither is the business environment marked by exceptional competition among firms.Largefirmsappeartodominatesomesectors,whereasothershavebecomemorecompetitive.

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Figure6.15:ComparativePerformance–GoodsMarketEfficiency

COMPARATIVEPERFORMANCE‐ GOODSMARKETEFFICIENCY(Scale1=poorestrating;7=best)

IntensityofLocalCompetition

ExtentofMarketDominance

EffectivenessofAnti‐MonopolyPolicy

Tanzania 4.3 3.6 4.0 Ghana 4.8 4.2 4.0Kenya 5.1 4.0 4.3Mozambique 4.0 3.1 3.6Uganda 4.9 3.0 3.8


Greater competition is generally associatedwith increased factorproductivity inTanzania. Thecorrelationbetweenconcentrationandlowersalesperworkerisaproxyforlaborproductivity,asshowninFigure6.16.

Figure6.16:ChangesinMarketConcentrationandLaborProductivity

Source:UnpublishedbackgroundnoteusingTanzaniaAnnualSurveyofIndustrialProduction.

G.Conclusions

Economichistoryclearlyshowsthat innovationisessentialtogrowthanddevelopment.Thedataand indicatorspresented above, in tandemwith the investment rates of benchmark countries aspresentedinChapterTwo,presentclearevidenceregardingtheimportanceofmarketfailuresthatimpedetechnologicalinnovationandexploitationofcomparativeadvantage.

Tanzania’sperformanceininnovationandtechnologicalreadinessismixed.Ithasarelativelylowlevelofexportsophistication.Havingimproveditsperformanceoverthepast,improvementshavetapered off somewhat, and there remains considerable unexploited potential to enhance

‐0.15

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technologicalsophistication levelandGDP. Tanzaniaranksbroadly in themiddleof comparatorcountries on indicators of business sophistication and innovation, though its relatively poorperformanceontechnologicalreadinesssuggestsaseriousbarriertotheimportandapplicationoftechnology. Tanzaniademonstratesgenerallyweakprotectionof intellectualproperty rights,butthisdoesnotappeartobesubstantiallyworsethaninbenchmarkcountries.Reliabledatadonotexist to demonstrate a correlation between improved (de facto) IPR protections and the level ofinvestment in Tanzania, or to demonstrate whether firms take costly and evasive measures toprotect their intellectual property and other investments. Tanzanian firms enjoy somewhat lesscompetition than comparator countries, although market concentration appears to be decliningoverall. Altogether,theevidenceavailablesuggests,whilemarketfailuresininnovationmaybeachallenge,theydonotimposeasignificantcostonprivateinvestorsinTanzania,andthuscannotbeconsideredabindingconstrainttoprivateinvestmentinthecountry.

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7. LackofInfrastructure

A.GeneralOverview

ThepoorqualityofinfrastructureinTanzaniaisroutinelycitedasaconstrainttoeconomicgrowthand investment. The following analysis uses a wide variety of indicators to assess Tanzania’selectricity, transportation, telecommunications, and water infrastructure in order to determinewhetheranyweaknessesfoundarebindingconstraintstogrowth.Tanzaniandataisbenchmarkedagainst ten countries (Botswana, Ethiopia, Ghana, Kenya,Malawi,Mozambique, Senegal, Uganda,Zambia, and Zimbabwe) as well as the average for Sub‐Saharan Africa (SSA) and low incomecountrieswhenavailable.

WhileeverysectorofTanzania’sinfrastructurefacesseriouschallenges,thefocusofthisreportison identifying the key bottlenecks in Tanzania’s infrastructure that are currently bindingconstraints to growth. Thepoorprovisionofelectricity isone such constraint. Tanzania’swell documented electricity problems are by far the most important infrastructureconstrainttoinvestmentandeconomicoutput.Inaddition,thepoorstateofruralroadsthatconnect high production agricultural areas tomarkets is a binding constraint to growth.Whilethisreportfocusesonthephysicalconstraintswithininfrastructurenetworks, institutionalissuesarealsoimportant.AcommonthemethatrunsthroughTanzania’sinfrastructurechallengesisinsufficientfundingduetounderpricing,unaccountedlosses,andinefficientcollectionefforts,aswellasweakinstitutionalarrangementforgoverninginfrastructureservices.

Agreatdealofeconomicliteratureisdedicatedtothelinkagebetweeninfrastructureandgrowthindeveloping countries. The majority of studies estimate a positive causal relationship betweeneconomicgrowthandthephysical indicatorsof infrastructurequalityandprovision inacountry.Ingeneral,thestrongestrelationshipsbetweeninfrastructureandgrowthhavebeenidentifiedwithtelecommunications, roads, and electricity (Ter‐Minassian et al., 2008: 5). The relationshipbetweenpubliclyfundedinfrastructureandacountry’sgrowthislessclear,withdifferentstudiesarriving at different results (Straub, 2008). The key determinants of the effectiveness of publicspending on infrastructure seem to be how the investments are financed (excessive debt andtaxation can crowdout otherprivate sector economic activity); thequalityofproject evaluation;andthepresenceofcomplementaryinputs(Ter‐Minassianetal.,2008:6).

B.ThePowerSector

Inadequate and unreliable electricity is the most commonly cited infrastructure challenge forTanzanian firms,bothon themainlandand inZanzibar. In2006,88percentofTanzanian firmsconsidered inadequate electricity to be a major constraint to their operations, the highestpercentageofanycountryintheWorldBank’sEnterpriseSurveys. Morerecentsurveydatafromthe World Economic Forum rank the quality of Tanzania’s electricity supply 122nd out of 139

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countries.Frequentandsustainedpoweroutages,lowlevelsofpowercoverage,andahighlevelofgeneratoruse inbothmainlandandZanzibarallpoint toelectricitybeingabindingconstraint togrowth.Figure7.1:PercentageofFirmsIdentifyingElectricityasaMajorConstraint


a.PowerGeneration

WhilepowergenerationinTanzaniahasgrownbyabout6percentperyearsince2000,ithasnotkeptpacewithdemand. Total installed capacity on themain grid amounts to1,051mega‐watts(mW),whichiswellbelowtheper‐capitaaverageforsub‐SaharaAfrica. Hydropowerconstitutes561mW,or55.7percentoftotalinstalledcapacity.Thermalandgasgeneratingcapacityformstherest,mainlyfromindependentpowerproducers(IPPs). Distributionalandtransmissionlossesofalmost20percenthaveexacerbatedtheproblemandaretwicetheaverageforSub‐SaharanAfrica.

b.PrivateInvestmentinPowerGeneration

TheGovernmentofTanzaniahassoughttoattractprivatesectorinvestmentinthesector,includingthrough enactment of the Electricity Act of 2008 and the introduction of Power PurchaseAgreements. There are two main IPPs in the country contributing about 289 mW of nationalinstalled capacity: Independent Power Tanzania Ltd. (IPTL) with 100 mW (diesel based) ofinstalled capacity and SONGAS with 189 mW (natural gas based) capacity (Tanzania ElectricitySupplyCorporation,TANESCO,2010).61BothsystemsselltheirpowertoTANESCOfordistributionthrough the national grid. The importance of the IPPs to national power production was

61Smalldieselgeneratingplants invariousregionsof thecountryareconnectedto thegridandhavetotalinstalledcapacityof80MW,ofwhichonlyfiveMWiscurrentlybeingusedwhiletherestisduefordisposalduetoobsolescenceandhighmaintenancecosts.Otherisolateddieselgeneratorshaveinstalledcapacityof31MW. TheArtumasGroupLtdpowerplantbased in southernTanzania supplieseightMWofelectricityfromgasfoundinMnaziBay.

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highlightedduring thepowerproblems createdby the 2006and2010droughts and subsequentdrops in hydro‐power production. The situation would have been much worse without thecontributionofSONGASpowergeneration, aswell asgas‐based rentalpowerplantsoperatedbythegovernment,andelectricityimportsfromUgandaandZambia.62

Figure7.2:ElectricityProductionperCapita


TheIPTLIPPhasbeenplaguedbyalegaldisputewiththegovernmentovercapacitycharges,theunitpriceofelectricity,operatinglevels,andotherissues.Negotiationsarenowunderwayforthegovernmenttoassumecontroloftheproject.AkeychallengeforthisandanyotherIPPsellingtoTANESCOisthatthepriceofelectricityTANESCOcanchargeconsumers,whichissetbytheEnergyandWaterRegulatoryAuthority(EWURA),isoftenbelowthecontractedpriceTANESCOmustpayIPTLperkWhforgenerationalone.

c.PowerUsageandTransmissionandDistributionSystemCoverage

Aswithgeneration,Tanzania’spowersystemhassufferedfromunder‐investmentintransmissionanddistributionaswell. Thenationalgridcoversa relativelysmallpartof thecountry,andanymajor increase ingenerationcapacitymustalsobemetbyupgrades to thegrid.Accordingto theInternationalEnergyAgency’s(IEA)ElectricityAccessData,Tanzania’selectrificationratein2009wasjust13.9percent(14.5percentin2010accordingtoTANESCO),whichisoneofthelowestintheworldandwellbelowthatofallcomparatorcountriesexceptMalawiandMozambique,aswellasthe30.5percentaverageforSSA(IEA,2011)(Figure7.4). Inruralareasonly2percentofthepopulationhasaccesstoelectricity.

62Atthetimeofpreparingthisreport,TANESCOannouncedthat,duetomaintenanceandinspectionofthegassupplyfacilitiesthatfeedSONGAS,theelectricitysupplytomuchofthecountrywouldbecutofffor15hoursperday(8amto11pm)foratleastoneweekinlateMay.

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Zanzibar electric power is fully dependent on a single sub‐marine electric cable connectingZanzibar(onUngujaisland)withmainlandTanzania.Problemswiththiscableledtoathree‐monthpoweroutagebetweenDecember2009 andMarch2010.Obviously, thepowerblackout affectedproductivityacrosstheislandsthatcompriseZanzibar,andhencefluctuationsinthegrowthrate.Thecablehasaloadcapacityof45mW,whichisjustsufficientforthecurrentconsumptionlevelof44mWbut far belowwhat is necessary for future demand. A project funded by theMillenniumChallengeCorporation(MCC)isbeingimplementedtoprovideanewelectricsubmarinecablewithacapacityofabout100mW.Theprojectisexpectedtobecompletedinlate2012.

Figure7.3:ElectricityGenerationbyFuel

Source:IEAwebsite(http://www.iea.org/stats/pdf_graphs/TZELEC.pdf)

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Figure7.4:PercentageofUrbanandRuralPopulationwithAccesstoElectricity

Source:IEAElectricityDatabase2008

d.PowerOutages

Poweroutages,exacerbatedbydrought, imposeahighcostontheeconomy. Amongcomparatorcountries,Tanzaniaranks first in termsof thenumberofpoweroutagespermonth in theWorldBank’s Enterprise Survey Data.63 While Ghana and Uganda both experience more total time ofpoweroutagespermonth,Tanzania’s94.66hoursofoutagesiswellabovetheSSAaverageof65.29hourspermonth.Tanzanianfirmsalsoincurahighlossvalueduetopoweroutages,secondonlytoUganda among comparator countries andwell above the SSA average. TheAfrica InfrastructureCountryDiagnostic(AICD)initiatedbytheWorldBankestimatedtheeconomiccostofoutagesinTanzaniaisoneofthehighestinAfrica(WorldBank,2010:8).

Table7.1:PowerOutages

Country(year)Number of PowerOutagesinaTypicalMonth

Average Total Time ofPower Outages perMonth

ValueLostDuetoPower Outages(%ofSales)

Tanzania(2006) 12.00 94.66 9.62

SSAAverage 10.80 65.29 6.44Source:WorldBankEnterpriseSurvey2006

e.PrivateGeneratorUsage

Ifthelackofreliableelectricityfromthenationalgridisabindingconstrainttogrowth,weshouldseefirmsattemptingtoovercomethatconstraintbyinvestinginoff‐gridelectricitysources,suchas63ItisimportanttonotethatthedatainTable7.1werecollectedin2006,whenwidespreadpoweroutageswerecommonduetolowrainfall.

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back‐upgenerators. While fewenterprisesreliantuponelectricitycouldbeprofitablegeneratingthe bulk of their own electricity, investing in backup power generation allows firms to hedgeagainstthethreatofcostlypoweroutagesthatdisrupttheirbusinessoperationsandreduceprofits.Butthisinvestmentiscostly,astheelectricityproducedbysmallgeneratingunitsisgenerallymuchmoreexpensivetoperkWhthanpurchasingelectricityfromthenationalgridduetotheeconomiesof scale in power production. Foster and Steinbuks (2009) estimate the total average cost ofprivategenerationinTanzaniaatmorethanthreetimesthepriceofgridpower.

Figure7.5:PercentofFirmsOwningorSharingaGenerator


AsshowninFigure7.6,WorldBankEnterpriseSurveydata indicatethat45.7percentof firms inTanzania own a generator, which is above the SSA average of 41.7 percent but not the highestamong comparator countries. Tanzanian firms, however, receive the highest percentage of theirelectricity fromgeneratorsamongourcomparatorcountries(seeFigure7.6). Thefact that firmsarewillingtobearthehighcostofgenerator‐producedelectricitysuggestsahighshadowpriceforelectricityandprovidesstrongevidencethatelectricityisabindingconstrainttoeconomicgrowth.

f.GDPGrowthinYearsofPowerCrisis

When a binding constraint is tightened,we should expect to see lower levels of investment andeconomicgrowthintheaffectedsectors. Regionaldroughtsin2003and2006reducedelectricityproductionatTanzania’shydropowerfacility.Whiledirectcausalityisdifficulttoprove,2003and2006were the only two years since 2000 inwhich GDP growth slowed from the previous year,whichsuggeststheseelectricityproblemshadadirectimpactongrowth(Figure7.9).Whilepartofthe decline in GDP growth can be attributed to the direct effect of lower rainfall on agriculturaloutput, there were also temporary declines in manufacturing and construction growth in 2006,whenadrought‐induceddropinhydroelectricproductionwasparticularlysevere.

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Figure7.6:PercentofElectricityfromGenerator

Source:WorldBankEnterpriseSurvey(respectiveyearsinparentheses)Figure7.7:PowerProductionandPercentageGDPGrowthovertheYears2000‐2007


g.PolicyandInstitutionalChallenges

Institutional challenges explain part of the challenge in expanding and maintaining the powersector inTanzania. TheAICDestimatesthat ‘hiddencosts’ inthepowersectordueunderpricing,poorcollection,anddistributionallossesamountedtoasmuchas2.1percentofTanzania’sGDPin

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2008. Thesearehidden fiscalcostswhichultimately thegovernment,asTANESCO’sowner,mayhavetocompensate.Underpricingofelectricityrelativetoproductioncostsconstitutesthelargestshare of TANESCO’s losses. Electricity prices are well below historical costs, meaning thatTANESCOstrugglessimply tomaintaincurrentoperations, leaving littleorno fundsavailable forcapital improvements. Last year TANESCO requested an increase of 36.4 percent in electricitytariffs, but was only granted 18 percent increase.64 The Energy and Water Regulatory Agency(EWURA) cited insufficient justification and documentation fromTANESCO in its decision not toapprovethefull increaserequestedinTANESCO’srateapplication. Inaddition,ahistoryofweakplanningandgovernanceofthesectorappearstobeanunderlyingcauseofthesector’sdifficulties.Issues,includingboardgovernanceatTANESCOandtheinabilitytoattractsufficientfinancingandprivateinvestmenttothesector,meritfurtherin‐depthinvestigation.

h.EnergyPoverty

The AICD uses the term ‘hidden costs’ to mean lost income by a service provider, in this caseTANESCO.However,theactualeconomiccostsofinadequatepowersupplyaremuchhigherthanthese revenue losses suggest – the shadow value of electricity substantially exceeds the costs ofproduction. Consumers, includingthepoor,wholackaccesstoelectricitypayamuchhighercostfor substitutes, includingkerosene lighting, candles,andsolar systems. Modern formsofenergy,likeelectricity,makeupasmall fractionof the totalenergyconsumption inTanzania. In fact,90percentoftotalenergyconsumptioninTanzaniaisbiomass(fuelwoodandcharcoal).Commercialenergy (petroleum, hydropower, natural gas, and coal) represents about9.2percent,while solarandwindaccountforlessthan1percent(MinistryofEnergyandMinerals,2010).

TheInternationalEnergyAgency(IEA)hasdevelopedanEnergyDevelopmentIndextomeasureacountry’s transition to the use of modern fuels. The EDI is modeled after the UN’s humandevelopmentindexandusesfourindicatorstomeasureacountry’s“energypoverty”,namely,per‐capitacommercialenergyconsumption,per‐capitaelectricityconsumptionintheresidentialsector,shareofmodernfuelsintotalresidentialsectorenergyuse,andtheshareofpopulationwithaccesstoelectricity.Usingtheseinputs,theIEArankedTanzania60thoutofthe64developingcountriesinitsdatabase(InternationalEnergyAgency,2010:264).

This low level of modern energy is not due to a lack of natural resources. The country hasestimatedhydropowerpotentialof4,700MWcomparedtoacurrentinstalledcapacityof561MW.Tanzaniaalsohas4,636billioncubicfeet(bcf)ofprovennaturalgasreserves,whichrepresents24years of reserves at current levels of production (more than 100 years if probable reserves areincluded). In fact, with abundant energy resources, the AICD concludes that Tanzania has thepotentialtobeasubstantialpowerexportertotheEastAfricanCommunityinthelongterm.

64AfullexplanationofTanzania’selectricitytariffsisintheAnnex.

Figure 7.9: Percent of Products Lost in Transit Due to Breakage and Spillage

Source: World Bank Enterprise Survey Data

Only 7 percent of Tanzania’s roads are paved, one of the lowest percentages in Sub-Saharan Africa, leaving 93 percent of the network unpaved (gravel or earth) and susceptible to erosion. Three quarters of the paved kilometers are trunk roads. The GoT has a stated goal of paving all of the country’s main trunk roads by year 2018. The projects are behind schedule, with only 5,166 km of paved roads, leaving 7,260 km unpaved. The primary challenge for Tanzania has been in extending the reliable road networks to rural areas. Only 24 percent of Tanzania’s rural population lives within two kilometers of an all-weathered road, which makes the flow of goods and services to and from the rural areas difficult and expensive.

Assessment of Demand (Traffic) and Shadow Value of (Poor) Road Condition The high shadow price of the constraint is first demonstrated by breakage and spillage of goods in transit, which represents a direct loss of revenue for Tanzanian firms and the economy as a whole. Tanzania compares poorly on this indicator of the cost of relatively poor overall road quality, as shown in Figure 7.12, in comparison to other African countries. Yet, as shown in Table 7.2, demand for road transport is high. Traffic levels for both paved and unpaved roads are quite high compared to other low income countries. Similarly, using aggregate

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percentofthemaintrunkroadnetworkwaspavedand90percentofthetrunkroadswereingoodor fair condition. There is very little evidence that the main trunk roads in Tanzania areconstraininggrowth.

Table7.2providesbenchmarkeddataonthequantityandqualityofTanzania’sroadsasreportedintheAICDreportforTanzania(WorldBank,2010).Measuresofthequantityofroads(roaddensity)inTanzaniacomparepoorlytootherlowincomecountries,whilemeasuresofroadqualitycomparefavorably.Thedensityofpavedroadsiswellbelowtheaverageforotherlowincomecountries,butthedensityofunpavedroadsisroughlycomparable.

Table7.2:BenchmarkedRoadIndicators

Indicator Unit LIC Tanzania

PavedRoadDensity km/1000km2ofarableland 86.6 47.1

UnpavedRoadDensity km/1000km2ofarableland 504.7 482.6

RuralAccessibility% of rural population within 2kmofall‐seasonroad

21.7 24

PavedRoadTraffic AverageAnnualDailyTraffic 1,049.6 1,797.0

UnpavedRoadTraffic AverageAnnualDailyTraffic 62.6 99.8

PavedNetworkCondition %ingoodorfaircondition 80 94.7UnpavedNetworkCondition %ingoodorfaircondition 57.6 69.1

Source:AfricaInfrastructureCountryDiagnostic2010(datafrom2008)

Figure 7.9: Percent of Products Lost in Transit Due to Breakage and Spillage

Source: World Bank Enterprise Survey Data

Only 7 percent of Tanzania’s roads are paved, one of the lowest percentages in Sub-Saharan Africa, leaving 93 percent of the network unpaved (gravel or earth) and susceptible to erosion. Three quarters of the paved kilometers are trunk roads. The GoT has a stated goal of paving all of the country’s main trunk roads by year 2018. The projects are behind schedule, with only 5,166 km of paved roads, leaving 7,260 km unpaved. The primary challenge for Tanzania has been in extending the reliable road networks to rural areas. Only 24 percent of Tanzania’s rural population lives within two kilometers of an all-weathered road, which makes the flow of goods and services to and from the rural areas difficult and expensive.

Assessment of Demand (Traffic) and Shadow Value of (Poor) Road Condition The high shadow price of the constraint is first demonstrated by breakage and spillage of goods in transit, which represents a direct loss of revenue for Tanzanian firms and the economy as a whole. Tanzania compares poorly on this indicator of the cost of relatively poor overall road quality, as shown in Figure 7.12, in comparison to other African countries. Yet, as shown in Table 7.2, demand for road transport is high. Traffic levels for both paved and unpaved roads are quite high compared to other low income countries. Similarly, using aggregate

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cross‐country data from the World Bank’s Africa Infrastructure Database, traffic levels onTanzania’s unpaved roads are relatively high (Figure 7.13) and traffic on Tanzania’s secondaryroads is above the average for our comparator countries (Figure 7.14). The percentage of theunpavedsecondarynetworkinTanzaniawithahighleveloftraffic(greaterthan300vehiclesperday) is thehighestamongourcomparatorcountries (Figure7.15). Taken together, theseresultssuggestarelativelyhighlevelofdemandforroadservicesinTanzania’ssecondaryroadnetwork.Bycomparison, thegeneral traffic levelsonTanzania’sprimarynetworkareroughlyequal to theaverageofourcomparatorcountries.

Figure7.10:AverageAnnualDailyTraffic,UnpavedRoadNetwork

Source:WorldDevelopmentIndicators,AfricaInfrastructureDatabaseFigure7.11:AverageAnnualDailyTraffic,SecondaryRoadNetwork

Source:WorldDevelopmentIndicators,AfricaInfrastructureDatabase

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Onecanalsoexamine traffic levelsonroads in relativelypoor conditionasan indicatorof firms’willingness to incurahighcost. Roads inpoorconditionwithhightrafficsuggestahighshadowcost for transportation services and potentially high return investments. Data from the AfricaInfrastructureCountryDiagnosticgivesaclearpictureof the traffic levels forTanzania’sprimary(trunk) and secondary (regional) roads, and classifies each road segment by condition.Unfortunately,therewasnoreliabledataavailablefortrafficlevelsonTanzania’stertiaryroads.

Figure7.12:UnpavedSecondaryRoadNetworkwith>300AADT

Source:WorldDevelopmentIndicators,AfricaInfrastructureDatabase

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Table7.3:TanzaniaRoadConditionandTrafficIndicators(AICD)

Secondaryonly Primaryonly

Km

PercentofRoadswithKnownCondition Km

PercentofRoadswithKnownCondition

Datacomprises 15,209

9,973 Ofwhich:

KnownCondition 12,154 100%

8,177 100%

PoorconditionwithAADT>50 889 7%

404 5%

PoorconditionwithAADT>150 297 2.4%

225 2.7%

PoorconditionwithAADT>300 106 0.9%

156 1.9%

Poor,orFairconditionwithAADT>500 557 4.6%

838 10.2%

PoororFairConditionwithAADT>1500 92 1%

151 2%

UnknownconditionwithAADT>300 136 n/a

518 n/a

Note:NoDataAvailableonTertiaryRoadNetworkTable7.3indicatesthatarelativelysmallfractionofTanzania’sprimaryandsecondaryroadshaveboth a high level of traffic and are in poor condition, suggesting that the existing problemswithprimaryandsecondaryroadsareconcentratedinafewkeyroadsthathaveahighshadowprice.Forinstance,297kmofsecondaryroads(2.4percent)haveaveragetrafficlevelsofmorethan150vehiclesperday,yetare listed inpoorcondition. 557kmof secondaryroads(4.6percent)haveaveragetrafficlevelsofmorethan500vehiclesperday,yetarelistedasfairorpoorcondition.

c.RoadManagement

Financing transport infrastructure projects in Tanzania has long been a challenge for theGovernment.In2007,theGovernmentofTanzaniaprovidedUSD6.2billiontofinancePhaseOneofitsten‐yearTransportSectorInvestmentProgram(TSIP),whichsoughttosignificantlyimprovetransportinfrastructureinthecountry.ByJune2009,escalatingroadcostshadledtolessthan40percent of the prioritized projects being implemented, while 64 percent of the budget for theprojectshadbeenspent.Asaresult,TSIPPhaseOneisunlikelytobecompletedasplannedbyJune2012.

The biggest distinction in the quality of roads indicators is between the roads managed by thecentral government and the roadsmanaged by local government authorities. Table 7.4 indicatesthatonly10percentofroadsunderthemanagementofTANROADSareinpoorcondition,versus

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nearly half of the roads under localmanagement. Most of the rural roads that providemarketaccesstotheagriculturalsectorareunderthemanagementoflocalgovernmentauthorities.

Table7.4:RoadConditionunderTANROADSandDistrictCouncils

TYPE CONDITION(%) NETWORKLENGTH(km) GOOD FAIR POORA:TANROADS(CentralGovernment)TrunkPaved 76 21 3 5,478.0TrunkUnpaved 60 30 10 7,308.0RegionalPaved 85 13 2 840.0RegionalUnpaved 55 32 13 20,265.0OverallCondition(weightedforlength) 61 29 10 33,891.0B:RoadsUnderDistrictCouncilsPaved 54 23 23 745.7Gravel 38 47 15 12,049.55Earth 18 31 51 45,241.88OverallCondition(weightedforlength) 21 32 47 58,037.13NB:CouncilroadsareDistrict,Feeder,andTown/Municipalroads.

Source:TANROADS;PrimeMinister’sOffice,RegionalAdministration&LocalGovernment(PMO‐RALG)

d.TransportationCostsinAgriculture

Estimates for transportation’s shareof agriculturalproductioncostsvarywidely. For traditionalexport crops (coffee, tea, cotton, tobacco, cashew, sisal), transportation, at least until recently,appearstohaverepresentedarelativelysmallfractionofthetotalmarketingcost.Ina2005study,Nyangefoundthattransportationrepresentedabout20percentoftotalmarketingcostsforcotton,tobacco, and cashews; for coffee, that figure was around 5 percent (Utz, 2008: 120). Theserelativelylowcostslikelyrepresenteitheradecisionbyfarmerstogrowexportcropsinareasthatareclosetoexportroutesandhavegoodqualityroads,oradecisionbypreviousgovernmentstoimprove roads serving important export sourcemarkets. Given the increase in fuel prices since2005whenthisanalysiswasconducted,thesepercentageshavealmostcertainlyincreased.

Transport seems toaccount foramuchhigher fractionofmarketingcosts forgrains,however.AWorldBankstudyonthemaizesectorestimatesthattransportationcostscomprise83percentoftotalmarketingcostsformaizeinTanzania(ZoryaandMahdi,2009).OtherWorldBankresearchfindsthatthecostofgettingmaizefromthefarmgatetotheprimarymarketishigherinTanzaniathaninKenyaorUganda(WorldBank,2009:49).Transportationcosts(USD/ton‐km)formaizeinthis first stageof transport (from the farm to the firstprimarymarket) are33percenthigher inTanzania than in Kenya; costs to move maize from the primary market through the secondarymarketandontothewholesalemarketarecomparabletoKenyaandlowerthanUganda,suggestingthatthekeybottleneckisinthefirststageoftransport(seeTable7.5).MuchofthedifferenceintheoverallcostoftransportingmaizeisduetothelongerdistancesinTanzaniaversustheothertwocountries. On average, maize in Tanzania must travel 461 km to reach a wholesale market,compared to 373 km in Kenya and 133 km in Uganda. Since these distances cannot change,transportcostsarealwayslikelytorepresentalargerfractionofmarketingcostsinTanzaniathan

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in theother twocountries. Improvingtheruralroadconnections tohighproductionagriculturalareaswillhelpreducethesecosts.

Table7.5:EastAfrica:AStudyoftheRegionalMaizeMarketandMarketingCosts

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Figure7.13:TanzaniaRoadNetwork(TrunkandRegionalRoads)

e.InstitutionalandPolicyIssuesforRoads

Tanzania receives high marks in the AICD on the quality of the institutional reforms it hasundertaken in the road sector. The creation of the independent roads agency, the TanzaniaNationalRoadsAuthority(TANROADS),helpedboostbudgetexecutionratesformajortrunkroadprojects fromaround50percent in2002‐03 to over75percent in2006‐07. Execution rates forregionalandlocalroadrehabilitationprojectsremainaround40percent.

Tanzania’s road fund meets all of the criteria of a properly designed fund for financing roadmaintenanceputforwardbytheAfricaTransportPolicyProgram.Onpaper,thefundshouldcollectenoughinfueltaxestopayforallmaintenanceneedsinthecountry.However,only39percentofthetaxesduearecollected,whichisoneofthelowestcollectionratesinAfrica(WorldBank,2010:15).ThiswillmakeitextremelychallengingforTANROADStofundmaintenanceonnewlyupgraded

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roads. It also reduces the funding available to the local government authorities tomaintain theroadnetworksinruralareas.TheGOTrecognizestheproblemsandisexploringwaystoincreasecollection,includedthecollectionoffueltaxesatthepointofentryoffuelsintothecountry.

Anotherareawhere thegovernmenthasadoptedsoundpoliciesbutwhereexecutionhasbeenaproblem in is the weighbridges, which impose fees and penalties to prevent the overloading oftrucksonthemaintrunkroads. Overloadingdamagesroadsandincreasesmaintenancecosts,sothisisanimportantpolicy.However,truckerscomplainoflongwaittimesandunnecessarystops.IntheiranalysisofTanzania’scentralcorridor,NathanandAssociatesestimatethatimprovingtheoperationoftheweighbridgesandothercheckpointscouldcuttraveltimesbetweenDaresSalaamandKigalibyafullday(NathanAssociates,2011b:35).

f.UrbanTraffic

IfthereisaplaceinTanzaniawherethereisclearlyahighlevelofdemandforroads,itisinDaresSalaam.Trafficcongestionisagrowingchallengefortheflowofpeople,good,andservicesinDares Salaam aswell as Arusha,Mwanza andMbeya. Excessive commute times reduce the overallproductivityoftheworkforceandincreasefuelconsumptionincities.

WhilethereisverylittlehardtrafficdataforDaresSalaamtoadequatelyquantifytheextentoftheproblemorallowbenchmarkingagainstothercitiesintheregion,anecdotalevidencesuggeststhatitisnotunusualforsomeDaresSalaamresidentstospendseveralhoursperdaycommutingtoandfrom work in the city center. The population of Dar es Salaam has increased from 2.5 millionpeople in 2002 to about four million in 2011 without a comparable increase in transportationinfrastructure.TheavailabledataontrafficinDaresSalaamsuggeststhatthenumberofcarsinthecityincreasedbymorethan12percentfrom2004to2006,whilethekilometersofroadsincreasedby less than5percentand thekilometersofpavedroads increasedbyabout2percentover thatsameperiod(MinistryofInfrastructure,2010).

g.Ports

In addition to the primary port in Dar es Salaam (DSM), Tanzania has coastal ports servingZanzibar,Tanga (in thenorth), andMtwara (in the south). There are also two lakeports in thewest,atMwanzaonLakeVictoriaandatKigomaonLakeTanganyika.Thisanalysiswillfocusonthe port at DSM,which handles roughly three quarters of Tanzania’s overseas trade (EconomistIntelligenceUnit,2008:13).

Total tonnage through DSM increased at 8.7 percent per year between 2000 and 2009, whilecontainertrafficincreasedat12.2percent,indicatingagrowinglevelofdemandforportservices.In2009, imports constituted82percentof the total cargo (in tons). Transit traffic (importsandexports) constituted about 40 percent of total trade, with the port serving markets in Zambia,Rwanda,Burundi,Ugandaand theeasternregionof theDemocraticRepublicof theCongo(DRC)(NathanAssociates,2011a:36).Figure7.17andFigure7.18displaythesetrends.

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Figure7.14:ContainerTrafficatDSMandMombasa

Source:NathanAssociates

Figure7.15:TotalTrafficatDSMandMombasa


ThegrowingdemandsontheDSMporthaveincreasedcongestionandwaittimes.Asaresult,someof the traffic has been diverted to the port of Mombasa in Kenya, which is evidence of firmsattempting to overcome a constraint (Economist Intelligence Unit, 2008). Analysis by NathanAssociatesestimatesthatimportingorexportinga20‐footlightcontainerthoughDSMis7percentmore expensive and 25 percent slower than the port atMombasa in Kenya (Nathan Associates,2011a:49,66).Butinthekeyindicatorsofportperformance,DSMgenerallycomparesfavorablytootherportsintheregionasshowninTable7.6.

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Table7.6:BenchmarkedPortIndicators

EfficiencyIndicatorDSM(Tanzania)

Mombasa(Kenya)

Maputo(Mozambique) Sudan

CapeTown(SouthAfrica)

Averagecontainerdwelltimeinterminal(days) 7 5 22 28 6Averagetruckprocessingtimeforreceiptanddeliveryofcargo(hours) 5 4.5 4 24 4.8Averagecontainercraneproductivity(containerloaded‐unloadedpercranehour) 20 10 11 8 18Averagegeneralcargocraneproductivity(Tonsloaded‐unloadedpercraneworkinghour) 20 20.82 11 8 15


A concerted effort in recent years to improve the operation of the port at DSM, including theexpandeduseofinlandcontainerdepotstoreduceportcongestion,hasimprovedtheperformanceindicatorsof theport (Figure7.19)butmorecouldbedone(NathanAssociates,2011a:37).Asaresult of the port’s relatively good performance indicators, it does not appear to be a bindingconstrainttoTanzania’soveralleconomy.

Figure7.16:PerformanceIndicatorsatthePortofDaresSalaam

Source:USAIDHowever,absentimprovementsinportcapacity,theDSMportcouldbecomeabindingconstrainttogrowthastradeandeconomicoutputcontinuetoincrease.TheportofDSMfacesphysicalandopperational constraints that willhinder Tanzania’s international trade if not addressed in thecomingyears.Thedepthoftheharborlimitsthesizeofshipsthatcanusetheport.Theporthas

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limitedspacetogrowastradeexpands,suchthattheGOTisconsideringoptionstobuildanotherdeep‐water port along the coast. Institutional improvements are also likely to be important toreducingthecostofusingthisport.ShippingexpertshaverecommendedthattheTanzanianPortAuthorityadopta“landlordmodel”ofportmanagement,wherethegovernmentregulatorallowsprivatefirmstohandlemostportoperations,therebyinjectingsomecompetitionintoprovisionofport services. This model has worked well in many parts of the world, but has not beenimplementedwidelyinAfrica(WorldBank,2010:13).

Zanzibar’smainmaritime transport facility is theMalindi Portwhich handles domestic, regionalandinternationaltraffic.About95percentofZanzibarimportsandexportspassthroughMalindi.The port has the busiest passenger terminal in East Africa, handling an average of 1.2 millionpeopleperyear.Theportoperationsimprovedconsiderablybetween2008and2010afteramajorrehabilitation project thatwas funded by EU and completed in 2008. The number of containershandledforthelasttwoyearswasabovetheport’sofficialcapacity,anddemandforportservicesisexpectedtocontinuetoincrease.

Figure7.17:TotalTonnageatZanzibar’sMalindiPort(2000‐2010)

‐

100,000

200,000

300,000

400,000

500,000

600,000

700,000

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Source:ZanzibarPortsCorporation

h.Rail

Tanzania’s rail systemsaregenerallydescribedasunreliable andpoorlymaintained. TheWorldEconomic Forum’s Global Competitiveness Report scores Tanzania better than most of itscomparatorcountriesinthequalityofitsrailinfrastructure(Figure7.21).However,thisismoreacommentontherailinfrastructureinSub‐SaharanAfrica,giventhatallscoresfallbelow2.5outofseven,exceptforthatofSouthAfrica.

Theprincipalindicatorofpoorrailsystemperformanceisthatmostcompaniesnowusetheroadsystemtomovetheirgoods,includingheavyandbulkgoodsthatwouldbegoodcandidatesforrailshipment. In2009,only5percentofthecontainersclearedfromtheportofDaresSalaamweretransportedbyrail,theresttraveledbyroad(NathanAssociates,2011a). Thisdiversionofgoodsawayfromtherailwayhasincreasedtrafficandmaintenancecostsfortheroads.Whilediversionof

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cargo to roadsmay indicate businesses attempting to get around a constraint, firmsmay simplyfavortheflexibilityofroadtransport.

Therearetwomainraillinesinthecountry.TheTAZARAline,jointlyownedbytheTanzanianandZambian governments, connects the port at Dar es Salaam to the copper producing regions ofZambia,andhasatotallengthof1,860kmofmainline,ofwhich960kmareinTanzania.ThelargerTanzaniaRailways(TRL)systemconsistsofabout2,600kmoftrack. This“centralline”connectsDar es Salaam to Kigoma in the far west of the country, with connections to Mwanza on LakeVictoria,thenorthernportofTanga,andArushanearKilimanjaro.In2007,aconcessiontooperatetheTRLsystemwasawardedtotheIndianfirmRITESunderajointventurewiththeGovernmentof Tanzania, called the Tanzanian Railway Corporation (TRC). The deal included guaranteedfinancingfromtheWorldBank/IFC(WorldBank,2010:17).Unfortunately,declinesinoperationaland financial performance indicators continued under the concession, leading to mutualrecriminationandGOTassumptionofcontrolovertheTRCsystemin2010.

Figure7.18:QualityofRailroadInfrastructure

Source:GlobalCompetitivenessReport,2010‐2011

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Figure7.19:RaidFreightonTanzania’sTwoMainRailwayLines

Source:MinistryofTransportationFigure7.22showsrailfreightvolumesovertheperiod1995‐2009.Therelativelyhighlevelofrailfreightreachedin2002and2003mayindicatethatdemandforrailservicescouldbehigheriftherailsystemperformedbetter. Duringbriefperiods inthemid‐1980sandtheearly1990s, freightexceeded 2000 ton‐km, more than four times the current usage. However, those peaks werefollowedbysharpdropsinrailusage,suggestingdifficultiesinmaintaininghighlevelsofusageonthe 100‐year‐old system (Figure 7.23). Poor track conditions mean that speed restrictions areimposedonmanysectionsoftherailnetwork,leadingtolongdelays.The1,229kmtripfromDaresSalaamtoMwanzatakesanaverageof120hours,whichworksouttoanaverageofroughly10kmperhour. Waittimesonthetriprangefromtwodaystotendays(NathanAssociates,2011a:67).

Figure7.20:TanzaniaversusKenyaRailways,GoodsTransported


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Thedropinrailservicessince2003ontheTRLlinehascoincidedwithanimprovementinthemaintrunkroadsthat followroughly thesameroute.The large‐scaleswitching fromrail toroadsoverthelastsevenyearshasoccurreddespitegenerallyhigherfuelprices.

Given the efforts to accelerate and broaden investment and growth, and given projections forcontinued increases in domestic and transit traffic in Tanzania, the existing state of Tanzania’srailwaysystem,whilenotcurrentlyabindingconstrainttogrowth,doesappeartobeconstrainingeconomicgrowth.

A2009studyconductedbyBNSFrailwayfortheUSTradeandDevelopmentAgencyconcludedthatupgradingtheraillinebetweenDaresSalaamandIsaka(aonebilliondollarproject)isfinanciallyand economically viable. BNFS’s “most likely” scenario projects that a rehabilitated rail linebetweenIsakaandDaresSalaamwouldseetrafficincreasefrom1.5millionmetrictonsin2012to22.4millionmetrictonsin2031.BNSFalsoconcludedthattherewillbecontinuedannualgrowthindemandalongTanzania’scentralcorridorof6percentregardlessofwhetherthecentralraillineisrehabilitated.Thequalityandoverallcostofrailservicewilldeterminewhetherthisadditionaltrafficusestherailortheroad.

BNSFReportp.67

BNSFestimatestheeconomicrateofreturnforupgradingtheraillinebetweenDaresSalaamandIsakaat31.5percentinits”mostlikely”scenario,whilefinancialratesofreturnontheprojectvaryfrom11.9percentinthelowgrowthscenarioto18.4percentinthehighgrowthscenario.

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BNSFReportp.85BNSFcautions,however,thattheoperatoroftherailwayislikelytosustainfinanciallossesforthefirstfouryearsaftertheupgradeiscompleted,whichwouldnecessitateadditionalborrowing(BNSFp83).TheprojectedincreasesinfreightrailalsoassumethattherearesignificantcapacityexpansionsattheportofDaresSalaamtohandleadditionalfreighttraffic(BNSFp31).Otherriskstoinvestorsincludeunanticipatedchangesintaxandregulatorylaws,and potential for passenger service demands that impact the ability to provide high quality freight operations(BNSFp85).Despitetheserisks,BNSFconcludesthatthetotaleconomicbenefitsofupgradingthelinefromIsakatoDaresSalaamaresubstantial. A separate 2008 study conducted by DB International GmbH (DBI) assesses the feasibility ofupgradingtheraillinebetweenIsakaandDaresSalaamaswellasconstructingnewraillinesintoRwandaandBurundi. Thetotalinvestmentcostfortheprojectwasestimatedat$4.7billion:onebilliondollarstoupgradethelinefromDaresSalaamtoIsaka(roughlyequaltoBNSF’sestimate)andanadditional$3.7billion toconstructnewrail lines intoRwandaandBurundi (substantiallyhigher thanBNSF’s estimateof $2.6billion). The study concluded that theprojectwouldhaveasufficientfinancialreturntoattractprivatefinancingifthethreegovernmentscanagreetoaproperinstitutionalframeworkandundertakeothermeasurestolimittheriskstoinvestors(DBIp9‐10).

i.AirTransport

TanzaniahasthreeinternationalairportslocatedatDaresSalaam,Kilimanjaro,andZanzibarandanumberofsmalldomesticairportsthroughoutthecountry.WhileTanzania’sairtransportsystemrankslowintheWEFglobalcompetitivereport(118thof139),Tanzaniaisoneofthefewcountriesin Sub‐Saharan Africa to have adopted reformswhich allow real competition in its domestic airtransportationmarket,whichisoneofthelargestinSSA.Tanzaniarateswellagainstcomparator

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countries in thenumber of citypairs servedbothdomestically and internationally (Figure7.24),andhasseenasignificantincreasebetweeninthetotalnumberofseatsavailable(Figure7.25).

Yet there are still challenges. The airport terminal in Dar es Salaam is operating beyond itspassengercapacity,althoughexpansionisunderway.Manyrunwaysatthesmalldomesticairportsarenotpaved,andallairportsrequireupgradingtoimproveflightsafety(WorldBank,2010:17).Improvingsafetyatitsinternationalairportswouldallowformoreinternationalflightstosupporttourism. In the meantime, Dar es Salaam is a Federal Aviation Administration “Category Two”airport,meaningtherecanbenodirectflightsbetweentheUnitedStatesandTanzania.

Basedontheavailable indicators,Tanzania’sair transport infrastructuredoesnotappear tobeabindingconstrainttogrowthintheTanzanianeconomy.Improvingtheperformanceandsafetyoftheairtransportsystemwillbeimportant,however,tosupportingfuturegrowth,especiallyinthetourismindustry.

Figure7.21:AirTransport,CityPairsServed(2007)

Source:WorldBankAfricaInfrastructureDatabase

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Figure7.22:AirTransport,AnnualSeatsDomesticandInternational

Source:WorldBankAfricaInfrastructureDatabase

D.ICTInfrastructure/Provision

Improvements in the telecommunications sector have been an important contributor to GDPgrowthover the lastdecade(WorldBank,2010:1),with themost impressiveresults seen in themobile sector. Tanzania has introduced significant institutional reforms in themobile sector tocreateanappropriateregulatoryframeworkandfostercompetition.Asaresulttherefourprivateoperatorsinthecountry,i.e.,MICTanzaniaLtd.,VodacomTanzaniaLtd.,Airtel,andZantel,plusonepublic operator, Tanzania Telecommunications Company (TTCL). Tanzania’sHerfindahl index isamong the lowest in Sub‐Saharan Africa, which demonstrates a high level of competition in themobile sector. Among selected comparator countries, Tanzania seems about average with 20percentofthepopulationhavingcellphonesubscriptionsasof2007,wellabovethe15.1averageforotherlow‐incomecountries.

Other telecom indicators tell a different story. Tanzania has a low level of both internet andlandlinesubscribers.DatafromtheWorldBank’sEnterpriseSurvey(2006)suggeststhatTanzaniaisaboutaverageamong itscomparatorcountries for thepercentageof firmsthathavetheirownwebsiteandthepercentageoffirmsusingemailtocommunicatewithclientsandsuppliers.Withthismixedevidence,itdoesnotappearthatthestateofthetelecomsectorisabindingconstrainttogrowthinTanzania.

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Figure7.23:MobileTelephoneSubscribers,per100Inhabitants

Source:WorldBankEnterpriseSurveysFigure7.24:InternetUsers,per100People


E.WaterInfrastructure

UnlikemanyAfricannations,Tanzaniahassufficient freshwaterresources tomeet itsneeds. Yetduring the last twenty years, Tanzania has experienced little improvement in the access toimproved water for Tanzanian homes and businesses. According to data from the Ministry ofWater (Budget Speech for FY 2010/2011), access to improved water supply for the ruralpopulationincreasedfrom53.7percentin2005to58.7percentin2009,whileaccesstoimprovedwaterfortheurbanpopulationincreasedfrom78percentin2005to84percentin2009.ForDaresSalaam,accesshasbeenmaintainedat68percent.

Historicdata fromtheWorldBankshows thataccess to improvedwater inurbanareasdeclinedfrom94percent in1990to80percent in2008. Amongthecomparatorcountries,onlyEthiopiahada lowerpercentageof thepopulationwithaccess to improvedwater (38percent). Tanzania

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alsorankedlowestamongcomparatorsintheWorldBank’sEnterpriseSurveydataonthenumberof incidents of water insufficiency in a given month. AICD analysis cites an inability to collectrevenue,lowtariffs,anddistributionallossesofupto50percent(comparedto33percentinotherlow income African nations) as the main problems limiting the expansion of service coverage.(WorldBank,2010:20)

PipedwaterinDaresSalaamisprovidedbytheDaresSalaamWaterandSewageCorporation.TheEnergyandWaterRegulatoryAuthority(EWURA)setswaterprices, includingthepricesthatcanbechargedbythemany“waterkiosks”inthecity.A2010surveyconductedbyUwaziatTwawezafoundthatmanyof thewaterkioskschargeuptoseventimestheofficialrateofoneshillingperliter,indicatingthatconsumersarewillingtopaymorethantheregulatoryceilingpriceforwater.

Figure7.25:ImprovedWaterSourceAccessbyRuralandUrbanPopulation

Source:MinistryofWater,2011

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Figure7.26:ImprovedWaterSources,PercentofPopulationwithAccess

Source:WorldBankWorldDevelopmentIndicators

Anecdotalevidencesuggests thatTanzaniansarespendingtime,effort,and financialresources toovercome a lack ofwater. There is rising production of bottledwater inTanzania and a risingnumber of firms drilling privatewells.Many houses and businesses across Tanzania havewatertanks. Justasfirmsmightuseageneratortogetaroundanelectricitycontraint,awatertankatabusinessthathasaccesstopipedwaterrepresentsanefforttocircumventaconstraintarisingfrominadequate public water service. The difference between the two is that the marginal cost tooperate awater tank is low incomparison. Although thewaterservicesector inTanzania facesclearchallenges,accesstoimprovedwaterserviceisnotabindingconstrainttoeconomicgrowthinTanzania.

a.Irrigation

Tanzaniahassubstantialirrigationpotential.About44millionhectaresoflandareainTanzaniaisclassified as arable land, and the National IrrigationMaster Plan (NIMP) 2002 identified a totalirrigation development potential area of 29.4 million hectares with varying potential levels ofirrigation development. About 2.3million hectares of this potential area are categorized as highirrigation potential, 4.8 million hectares as medium potential, and 22.3 million hectares as lowpotential. The FAO lists Tanzania’s total potential for irrigation at 2.1million hectareswhich isroughlyequaltotheNIMP’sdefinitionofhighpotentialland.AsofJune2010,331,490hectareshadbeenequippedforirrigationanddrainage.Thedevelopedirrigationareaiscomprisedof276,261hectaresundersmallholderfarmersand55,229hectaresundermediumandlargescalecommercialfarmerscultivatingcashcropsliketea,coffee,sugarcane,flowers,vegetables,andpaddyrice.

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Table7.7:IrrigationUseandPotential

Tanzania Kenya Uganda Mozambique Ghana

Potential area for Irrigationdevelopment(ha)

2,132,000 353,060 90,000 3,072,000 1,900,000

Full or partial controlirrigation:equippedarea(ha)

331,490 103,203 5,580 118,120 30,900

Total water withdrawal (106m3/yr)

5184 2,735 300 635 982

‐irrigation(106m3/yr) 4425 2,165 120 550 652‐livestock(106m3/yr) 207 470 0 0 0‐municipalities 527 100 134 70 235

‐industry 25 87 46 15 95

perinhabitant(m3/yr) 143 87 12 36 50Source:FAO‐AQUASTAT

Figure7.27:CumulativeAreaunderIrrigatedAgriculture

Source:MinistryofWaterandIrrigation(MOWI)

Tanzania’s use of irrigation compares favorably to comparator countries onmostmeasures. At331,490hectaresofirrigatedlands,TanzaniahasmorethanthreetimesasmuchirrigatedlandasKenya. It also leads the comparator countries in totalwaterwithdrawals for irrigation and percapitawaterwithdrawals,andtheareaundercultivationhasgrownsubstantiallysince2001(Table7.7). Tanzania compares relatively poorly in the percentage of irrigation potential realized,however,givenitsgreaterpotential(Figure7.27).

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Figure7.28:IrrigationEquippedAreasaPercentageofCultivatedArea

Source:FAO‐AQUASTATFigure7.29:PercentageofIrrigationPotentialRealized

Source:FAO‐AQUASTAT

Giventhesemostlypositiveoutcomescitedabove,itisdifficulttoconcludethatalackofirrigationinfrastructure is a binding constraint on the overall Tanzanian economy. The Government ofTanzania has identified several constraints to the greater use of irrigation in the country,whichinclude the availability of adequate financial resources for irrigation investments, a low capacityand low level of participation by the private sector, and inadequate capacity of irrigationinstitutionsbothatthenationallevelandwithinlocalgovernmentauthorities.

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F.Conclusions

WhilethisreportcitesmanyproblemswithTanzania’sinfrastructure,basedontheanalysistwosectors‐electricityandruralroads–arebindingconstraintstoeconomicgrowth. Theevidence to support the conclusion that the electricity sector is a binding constraint isoverwhelming.Lessthan15percentofthepopulationhasaccesstopower,whichisabouthalftheaverageforSub‐SaharanAfrica.Highgeneratorusageisevidenceoffirmsattemptingtoovercomea constraint, and the high cost of fuel for those generators suggests a high shadow price forelectricity. Declines in GDP growth across several sectors of the economy during recent powercrises provide further evidence that theproblems in the power sector arebinding constraints togrowth. Whereas the root causes of this constraint require further detailed investigation,underpricing and inadequate governance of the sector (including in long range planning, utilitygovernance,andtheregulatoryprocess)havebeencitedbysectorexpertsandtheAICDasissues.

Evidence of the need for improvements in rural roads is strong but not overwhelming, in part,because data are lacking particularly on the tertiary road network. Nonetheless, there arecompelling indications that poor rural feeder and tertiary roads constrain market access foragriculturalproducers.Thisweakenstheroleofpricesignalstothefarmgateandthereliabilityoffoodmarkets, which are required to specialize in the most profitable crops, raise incomes, andachieveimprovedfoodsecurity.RelativelyhighlevelsoftrafficonsecondaryandunpavedroadsinTanzaniaprovideevidenceofhighdemandforroadtransportinruralareas. Alowpercentageofthepopulationlivingnearanall‐weatherroadandhighlevelsofspoilageandbreakageintransportalsopointtopoorruralroadsasacostlyproblemforruralagriculture.RecentWorldBankanalysissuggeststhatthecostformovingmaize,awidelyproducedgraininTanzania’sruralareas,fromthefarmtothefirstmarketissignificantlyhigherforTanzaniathanitsneighbors(KenyaandUganda).Moreover, the fact thatTanzanian farmers arewilling topay thisprice suggestsa relativelyhighshadowpricefortransportationservicesinruralareas. Theprimaryissueinthisareaappearstobea lackof fundingmechanisms formaintenanceand investmentat the local anddistrict levels,although institutional capacity for managing the sector and project implementation are alsohighlightedbyexpertsworkinginthesector.

This analysis also finds that the state of Tanzania’s rail and port services could become bindingconstraintstogrowthifotherconstraintsarerelaxed,andifnotimprovedoverthecomingyears.ThedeteriorationofTanzania’srailwayshascontributedtoaswitchfromrailtotheroadformanyshippers,despitethehighercostofroadtransport. ExpectedincreasesintrafficalongTanzania’scentralcorridor(especially inheavybulkgoods)will leadtoincreasedcongestionanddamagetoroads absent improvements to the rail system. Independent feasibility studies suggest thatupgradingTanzania’srailsystemwouldbeeconomicallyviable.However,therarityofsuccessesinAfricanrailsystemsprovidesacautionarynoteonthecomplexityofthefinancialandinstitutionalarrangementsrequiredtoalleviatethisconstraint.TheportofDaresSalaamislikelytobecomeabindingconstraint toeconomicgrowth if theoperationalefficiencyandphysical infrastructureoftheportarenotimprovedtohandletheexpectedincreasesininternationaltradeandtransittrafficinthecomingyears.

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8. LackofHumanCapital

Withoutadequateavailabilityofhumancapital–theskillsandproductivecapacityofthecountry’slabor force – the productivity of other factors of production will be low, as will returns oninvestment. Despite Tanzania’s dramatic progress in expanding the supply of education andprogress in improvinghealth, there appear tobe significant gaps in the availabilityof vocationalandtechnicalskillsprovidedbytheTanzanianlaborforcerelativetodemand.

Thereisnostrongevidencethatamoregeneral lackofskillsoreducationcurrentlyconstitutesabindingconstrainttoeconomicgrowthinTanzania. ReturnstoschoolinginTanzania,asof2006,appeartobeonparwiththoseofKenya,Uganda,andGhanawhenadjustedfortheprobabilityofemployment, but higher than the returns of comparator countries of Kenya, Mauritius, Uganda,Ghana andVietnam. A lackof demand for educated labor relative to supply is demonstratedbyhighunemploymentratesamongtheskilledworkingagepopulation,especiallyinurbanareasandamongyouth,andarelativelymodestsocialandprivatereturntoeducation.

Tanzania also hasmade significant improvements over the past several years in the health andnutritional status of its population. While thewelfare andproductivity costs of poor health andnutritionaloutcomesinTanzaniaremainhighforaffectedindividualsandhouseholds,thereisnocompellingevidencetosuggestthatpoorhealthandnutritionaldeficienciesarebindingconstraintstoinvestmentorgrowthoftheeconomyasawhole.

A.EducationandSkills

Thesubsequentsectionspresentananalysisofthesupplyanddemandforcognitiveandtechnicalskills relyingprimarilyoneducationalattainment. It is important todistinguishbetweenhumancapital, defined broadly, and educational attainment, measured as years of schooling or highestgrade completed (see, e.g. Hanushek andWoesmann (2008), Knight and Sabot (1990)). Humancapitalisacompositeofcognitiveskillsandotherskillsandcharacteristics,whicharelikelytobecorrelated with, but not entirely caused by, years of schooling. In fact, cross country evidenceshowsthatcognitiveskillsaresignificantlycorrelatedwitheconomicgrowthinacrosssectionofcountries much more strongly than indicators of educational attainment (Hanushek andWoesmann,2008).

Apartfromtheimportanceofinvestingincognitiveskillsdevelopment,thepolicyimplicationsforTanzaniaofthisfindingarenotclear.First,thisisanaveragefinding,andtheimpactongrowthiscontext and country‐specific. Second, there is a complex causal relationship between nutritionvery early in life, health status, other parental inputs, social environment, and school quality onlearning,schoolingattainment,andlabormarketoutcomes.Forinstance,crosscountryevidencesuggests that cognitive skills are complementary with other features of the economy whichpromote investment and growth, in particular, openness to trade and institutional quality(HanushekandWoesmann,2008).Ifreturnstoeducationandtraining,forinstance,arerelativelylowinTanzaniatoday,itwouldbeequivalenttoalowshadowpriceforthisfactorandthusastrong

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indication that there are other, more binding constraints to economic growth than inadequatehumancapital.

a.SupplySide:EducationandSkillsandTrainingSectorReformsandResults

In 1995, the Government of Tanzania initiated a series of education sector reforms with theobjectives of expanding access and enhancing the quality and efficiency of formal education andadultliteracyprograms.Inearly1997,theGovernmentdevelopedaBasicEducationMasterPlan(BEMP)toguidedevelopmentinbasiceducationprovision.ThestructureoftheFormalEducationandTraining System inTanzania constitutes two years of pre‐primary education, seven years ofprimaryeducation,fouryearsofJuniorSecondary(ordinarylevel),twoyearsofSeniorSecondary(advanced level),andup to threeormoreyearsofTertiaryEducation,which includesvocationaleducation.65 Measures to reform higher education included encouragement of private sectorprovisionofeducation,andtheprivatesectorisnowactiveintheeducationsector.Between1995and 1999, first year enrolments in the two state universities of Dar es Salaam and Sokoineincreasedby50percent(MbeleandGalabawa,2000).Bytheyear2000,eightprivateuniversitieswereestablishedandthenumberofuniversitiesrosefromtwototenfrom1990to2000(UnitedRepublic of Tanzania, 2002). The reforms resulted in increased enrolment, reaching over 100percentgrossenrolmentinprimary.Enrolmentinpre‐primaryreached33percent,ascomparedtoaSub‐SaharanAfricaaverageof27percent,by2009(UNESCOInstituteforStatistics).

Enrolment rates for secondary and higher education have also increased (Figure 8.1) withsecondaryenrolmentreachingover30percentin2010.66

Figure8.1:TrendsinEnrolmentRatesbyLevels(Primary,SecondaryandHigherEducation)

Source:Compiledusingvariouseconomicsurveys,officialeducationstatisticsandStatisticalAbstract.

65Specifically,theeducationsystemhasthreelevels,namely:Basic,SecondaryandTertiaryLevels. Basicorfirstleveleducationincludespre‐primary,primaryandnon‐formaladulteducation.SecondaryhasOrdinaryand Advanced levels, while Tertiary includes programs and courses offered by vocational and highereducationinstitutions.66Enrolmentratesareforbothprivateandpubliceducationalinstitutions.

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Tertiary enrollment remains low at only 1.5 percent, as compared to much higher rates forcomparisoncountriesinearlieryears:3.24percentforUgandaand3.52percentforKenyain2001,Botswanaat4.69andSouthAfricaat15.05percentin2002.Theexpansionoftechnical/vocationaleducationandtraining(T‐VET)hasbeenrapidrelativetoanextremelylowbase.Thetotalnumberof short and long course training centershas grown from36 in1995 to819 centers and annualtrainingcapacityofstudentshasgrownfrom39,200in1995to100,653asof2005/2006(VETADGpaper, Sea Cliff Nov. 2006). The number of distinct skills training programs offered has also

increased from 36 to 93 in thesame timeframe. 67 The numberof graduates from public andprivate vocational traininginstitutions has increased to73,851 as of 2005, and today’senrolment capacity is 120,000,which is very limited relative tothe over one million primaryschool leavers annually.Enrolment in tertiary educationhas increased,asshowninFigure8.2withenrolmentsthehighestinlaw, education, business, scienceandICT,andmedicalscience.

Enrolment and other patterns are similar in Zanzibar. Recent analyses (e.g., by the AfricanDevelopment Bank (2010) andWort et al. 2008) show a relatively under‐developed vocationaleducationalsystem,withonlyanestimatedthreevocationaltraininginstitutionswithanacceptablequality. Thereareapproximately209,000primaryschoolpupils,andtheenrolmentcapacityforthesecondarylevelisjustapproximately78,000.

Primarycompletionrateshaveimproveddramaticallyvis‐à‐viscomparatorcountries,asshowninFigure 8.3. As shown in Figure 8.4, there are significant disparities between rural and urbanpopulations in the fraction of the population with no education, at 28 percent for the ruralpopulation versus approximately 10 percent for the urban population. As shown in Figure 8.4,therehavebeenmodest increasesinprimaryandjuniorsecondarycompletionratesfortheruralareas, junior secondary completion for ‘other urban’ areas, and for the increased proportion ofpeoplewithtertiarydegrees,whichtripledfrom0.2to0.6percent.

67 The greatest training capacity is found in carpentry/joinery, electrical installation, masonry and brickmaking, welding and fabrication, tailoring and motor vehicle mechanics. Other popular programs areplumbing,fittermechanics,secretarialandcomputer,machineryfitter,cateringandhotelservices,computer,printingandsignwriting,andautoelectric.

Table8.1:TotalGraduatesinVocationalTrainingCoursesbyGenderandYear

LongVocationalTrainingCoursesYear Male Female Total2002 17459 12384 288432003 18331 13004 313352004 18331 13004 313352005 21748 16241 37989 ShortVocationalTrainingCourses2002 17298 12384 288432003 18161 19412 375732004 18161 19412 375732005 17569 18266 35862Source:CompiledusingofficialeducationstatisticsandStatisticalAbstract.

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Figure8.2:EnrollmentTrendsinTertiaryPrograms,Tanzania

Source:BasicEducationStatisticsFigure8.3:PrimaryCompletionRatebyCountry2000‐2008(Source:WDI)

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Figure8.4:EducationLevelinTanzaniabyRural/Urban2007

Source:HouseholdBudgetSurvey(2007)

There is a tradeoff between quality and access to education, and the quality of Tanzania’seducationalinstitutionsremainsanimportantissue.Averagestudent‐teacherratiosinprimaryandsecondary schools have risen to 52 percent recently. There is a decrease in the percentage ofstudentspassingtheirprimaryschoolleavingexaminations.Insecondaryschools,thepassrateislessthan50percent,andanincreasingfractionofstudentsobtainthelowestpassingresults(i.e.,Divisionzero)ontheirexams,asshowninFigure8.7.

In addition, executives of firms operating in Tanzania rate the quality of the educational systempoorly. The Global CompetitivenessReport (2010‐2011) ranks the quality of Tanzania’s primaryschools as 115th in the world, and below all comparators except Mozambique, as shown in.RespondentsalsorankedTanzania’squalityofmathandscienceeducationparticularlypoorly,thelowestamongcomparatorcountriesandamongthelowestofthe139countriessurveyed,asshowninFigure8.9. Inaddition,whenaskedtorate theabilityof theeducationalsystemasawhole to“meettheneedsofacompetitivecountry,”firmsrateTanzaniaat99thoutof139countries,worsethanthecomparatorcountries,asshowninFigure8.5.

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Figure8.5:PassRateinFormFourExaminationbyDivision

Source:BasicEducationStatistics(2010)

Figure8.6:QualityofPrimaryEducation

Source:TheGlobalCompetitivenessReport2010‐2011

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Figure8.7:QualityofMathandScienceEducation


AlthoughTanzania’seducationalsystemfacesissuesofqualityandrelevancetothelabormarket,according to recent indicators of cognitive achievement in Tanzania’s primary education system,Tanzania’scurrentpupilsfaresurprisinglywellonaverageinbothmathematicsandreadingskillson standardized tests,with thehighestmean scores in reading among the Southern andEasternAfricaConsortiumonMonitoringEducationQuality(SACMEQ)group,andamongthehighestmeanscoresinmathematics,afterMauritiusandKenya(SACMEQIII2007).

Asmeasuredagainstabsolutecompetencystandards,as shown inFigure8.12,Tanzania ranks inthemiddleofthegroupinthefractionofstudentsachievingthemeasuredcompetencies.Whereasmost comparison countries had higher percentages of Level 1 students showing pre‐numeracyskills, by Level 5 Tanzania had the highest percentage of students showing mathematicalcompetency,duepresumably tomoreconsistentqualityofeducation. Nonetheless, forall thesecountries very few students (apart from those in Mauritius) had abstract problem solvingcompetencyinLevel8,includingTanzaniaatonlyonepercent.

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Figure8.8:QualityoftheEducationalSystem


Figure8.9:PupilMeanTestScores,SACMEQ

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Figure8.10:AbsoluteReadingAchievementbyLevelinComparisonSACMEQIII

Figure8.11:IndicatorsofAbsoluteCognitiveAbility:Mathematics

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Tanzanian students’ relatively higher cognitive achievement occurs in spite of relatively lowteacherreadingscores(Figure8.14)andalowownershipofreadingtextbooks(

Figure8.15).WhileTanzania’sschoolfacilityrankingswerewellbelowthemeanforSouthernandEastern Africa, for rural areas they ranked higher than for all Eastern African countries exceptKenya on the basis of an index of school resources (apart from the low ranking for Zanzibar)(Figure8.16).68 Whereasschoolqualitymayhave increasedalong thesedimensionssince2000,this improvement would not explain the discrepancy as Tanzanian students’ test scores wererelativelyhighinthe2000SACMEQtestingroundaswell(dataavailableatwww.sacmeq.org).

Figure8.12:TeacherReadingScores

Source:SACMEQ2000

68TheSchoolResources Indexcompiles information fromsurveysofpupils, teachers,andschoolheadsandtakesaccountof68categoriesofschoolresourcesandconditions,includingteachingresources,materialsandequipment,physicalfacilitiesandtheircondition.

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Figure8.13:PercentageofStudentswithOwnReadingTextbook

Source:SACMEQ2000

Figure8.14:SchoolResourcesIndex

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b.DemandforSkillsandtheSocialandPrivateReturnstoHumanCapital

Thesupplyof laborwithall levelsofeducationhasexpandedsince1990asithasinSub‐SaharanAfricagenerally.Toassesswhetherthereisacriticalshortageofskillsrelativetomarketdemand,the premium received in the labor market for education and skill acquisition (or ‘returns toeducation’)isakeyindicator.69Ifalackofskillsisabindingconstrainttogrowth,onewouldexpecttoseehighandrisingreturns toskillsandeducationandrelatively lowunemploymentofskilledandeducatedindividuals.

1)UnemploymentandLaborForceParticipation

Expected returns to additional education or training depend on the individual’s ability to obtainemployment, as well as the compensation premium he or she obtains for periods employed.Therefore, it is important toquantify theprobabilityofbeingemployed forpeoplewithdifferentlevels of education. In addition, unemployment by skill level is an important indicator of thescarcityofhumancapitalrelativetodemand.Forexample,ifunemploymentratesdeclinerapidlybyskilllevel,andreturnsforthoseemployedincrease,thisisanindicationofapotentiallybindingconstraint to growth. Using the strict international definition of unemployment – i.e., excludingfromthe labor force individualswhoarenotactivelyseekingemployment–unemploymentratesfellbetween2000and2003,thenrosein2006,ayearofslowergrowth,andthenappeartohavestabilizedsomewhat.70

Figure8.15:AdultUnemployment

69Social returns to educationmaybe eitherhigheror lower thanprivate returns, because individualsmayseek additional education for a variety of reasons. Education may help a given individual compete withothersforcertaindesirablejobs,inwhichcaseeducationispartlya‘signaling’deviceandtheimpactontheeconomyasawholeislessthanthereturnstotheindividual. Additionally,arapidexpansionofeducationmaysimplylowerthereturnstoeducationforeveryone. Atthesametime,privatereturnsmayunderstateeconomic returns to society throughpositiveknowledgeor technologyspillovers. TheGrowthDiagnosticmethodologyisbasedontheacknowledgmentthatourabilitytoestimatespilloversandotherindirecteffectsofreleasingagivenconstraintislimited,andassumesthatalleviatingtheconstraintswiththelargestdirectimpactswillalsohavelargesttotalimpactontheeconomy.Inthiscase,thatmeansfocusingontheprivatereturnstoeducationasourkeyindicator.70Allsectorsarecoveredinestimatesofthelaborforceandunemploymentrates,andindividualsengagedinagriculturearecountedasemployed.

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Table8.2:UnemploymentRatebyEducationalAttainmentandArea,2006

DaresSalaam OtherUrban Rural Total

Noschooling 38.1 14.2 7.5 9.0

Primaryonly 32.4 16.7 7.5 12.0

SecondaryandAbove

26.6 17.8 8.2 17.3

Total 31.3 16.5 7.5 11.7

Source:IntegratedLabourForceSurvey2006However,thenationaldefinitionofunemploymentisdeemedbytheNationalBureauofStatisticsofTanzania to be the most informative for assessing labor market conditions, as it also includesindividualswhoarediscouraged fromactively seekingwork, and it counts asunemployed thosewho,althoughworkingonthedayofthesurvey,wereunsureabouttheavailabilityofworkatanacceptable rate of pay the following day (Analytical Report on 2006 ILFS).71 By this definition,thereisasignificantfractionofpeoplewitheducationandskillstrainingwhodonothavesteadywork. Moreover, the level of unemployment is actually increasingwith the level of educationalattainment,asshowninTable8.2.

Unemploymentofpeoplewithsecondaryeducationandabovenationwide is17.3percent,and issignificantlyhigherinDaresSalaamandotherurbanareas. Thisfactisastrongindicationthatalack of skills obtained through education (or cognitive skills which are highly correlated witheducation)isnotcurrentlyabindingconstrainttoeconomicgrowthinTanzania.

Table 8.3 shows a more disaggregated view of labor force activity and employment status byeducationalattainmentandbyagegroup,usingthestandardinternationaldefinitionandnationaldefinitionsofunemployment,respectively.Thistableshowsthatfortheyoungworkforce,withtheexception of ‘tertiary non‐university’ educated people (i.e., people with vocational or technicaldegrees),unemploymentratesrisewiththelevelofeducationalattainment,andareveryhighforuniversityeducatedworkersat18.6percentunderbothdefinitions. Thelevelofinactivityisalsovery high, in the 40 percent range, for Senior Secondary and University graduates, although asignificant fraction of these younger individuals may be still studying. For more mature orexperiencedworkers,unemploymentlevelsaremuchlower,andunemploymentratesdecreaseasindividuals gainmore secondary education, andnon‐university tertiary education, but again risewithtertiaryeducation.72

71Sincethelaborforcesurveyisahousehold‐basedsurvey,alltypesofworkarecaptured,includinginformalwork.72Cautionmustbeusedinusingthenumbersfortertiarygraduates,giventhatthesamplesaremuchsmallerfortheselevelsofeducation.

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Table8.3:LaborForceActivityStatus

StandardDefinitionofUnemployment NationalDefinitionofUnemployment

Employed Unemployed Inactive Employed Unemployed InactiveYouthage15‐29

STD0‐3 88.3% 3.5% 8.2% 81.9% 9.9% 8.2%STD4 75.5% 3.1% 21.4% 70.8% 7.7% 21.4%STD5‐6 60.9% 2.0% 37.1% 57.6% 5.3% 37.1%STD7‐8(primarycomplete) 85.1% 7.6% 7.3% 78.7% 14.0% 7.3%FORM1‐3 43.1% 9.3% 47.6% 40.0% 12.5% 47.6%FORM4(Jr.sec.complete) 69.7% 17.3% 13.0% 64.3% 22.6% 13.0%FORM5‐6(Sr.sec.complete) 37.8% 22.7% 39.6% 34.5% 26.0% 39.6%Tertiarynon‐Univ. 46.8% 4.3% 48.9% 42.6% 8.5% 48.9%University 22.8% 18.6% 58.6% 22.8% 18.6% 58.6%Total 77.4% 7.3% 15.3% 71.8% 12.9% 15.3%Age30+

STD0‐3 91.2% 1.1% 7.8% 84.0% 8.3% 7.8%STD4 87.7% 1.4% 10.9% 81.9% 7.2% 10.9%STD5‐6 94.1% 1.6% 4.2% 86.6% 9.2% 4.2%STD7‐8(primarycomplete) 93.9% 3.0% 3.1% 86.4% 10.6% 3.1%FORM1‐3 90.1% 4.6% 5.3% 81.0% 13.8% 5.3%FORM4(Jr.sec.complete) 92.5% 3.9% 3.6% 86.8% 9.6% 3.6%FORM5‐6(Sr.sec.complete) 95.4% 1.8% 2.8% 92.6% 4.6% 2.8%Tertiarynon‐Univ. 96.1% 0.0% 3.9% 96.1% 0.0% 3.9%University 91.9% 3.7% 4.4% 91.9% 3.7% 4.4%Total 92.8% 2.7% 4.6% 85.7% 9.7% 4.6%Note:STD=primaryeducation.FORM=secondary

Thesepatternscannotbeexplainedbyhigher laborforceparticipationratesforeducatedpeople.Infact,individualswithsecondaryeducationandaboveparticipateatlowerratesthanthosewhonever attended school or who completed primary education (see Table 8.4). Labor forceparticipation rates are actually lower for the more educated (with some drop in ‘primaryincomplete’ratesvis‐à‐visprimary‘complete’).Moreover,highinactivitylevelsarenotdrivenbylower female labor force participation rates or other gender differences. Using the definition of‘active’thatanindividualwaseitheremployedforat leastonehourduringthepreviouscalendarweek,wastemporarilyabsentfromworkbuthadajobattachment,orwasavailableforwork,therates of labor force participation (i.e., employed and unemployed) are similar for males and

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females,at90.5percentformalesand88.8percentforfemales,withthegendergapinparticipationrates highest for thosewith secondary and above education (82.2 percent versus 74.2 percent).Thispatternholds forDaresSalaam,otherurbanandrural areas. It alsosuggests that femalesmoreoftenoptoutoffemalelaborforceparticipationwhentheyaremoreabletomeethouseholdconsumption needs without the additional income, although cultural factors are undoubtedlyimportanttothesepatterns(ILFS2006).73

Table8.4:LaborForceParticipationRate15+YearsbyEducationalAchievement

EducationLevel DaresSalaam OtherUrban Rural Total

Neverattended 80.6 80.5 89.2 88.1

Primarynotcomplete

67.6 72.5 80.1 78.2

Primarycomplete 94.5 95.5 97.7 96.8

Secondaryandabove

75.8 79.1 81.6 78.8

Total 85.8 87.2 90.8 89.6

Source:IntegratedLabourForceSurvey2006Asthesedatashow,employmentgrowthhasbeentooslowtoabsorbtheinfluxofeducatedlaborsupplied,particularlyinurbanareasandforyoungeragegroups.Someindividualshaveinvestedin household andmicro enterprises, but others appear to be either discouragedby the availableopportunitiesortheirskills–eventertiarydegrees–arenotindemand.74

Theunemploymentpatterns,however,doindicatethatworkerswithvocational‐technicaltrainingare in greaterdemandrelative to supply, signaling theneed forgreater investment invocationalandtechnicaltrainingatthatlevel.

Thesituation inZanzibar issimilar to themainland. Basedonaskillsassessmentby theAfricanDevelopment Bank, there is a shortage of vocational and technical skills relative to demand,specificallyrelatedtothemanagementofagriculture,horticulture,andpoultry,hospitality,andthecreativearts.

2)EstimatesofReturnstoEducation

Estimating returns to education is complicated by several issues, and estimates of returns toschoolinginTanzaniavarydramaticallydependingonthemethodologyanddataused.Thereareat

73Manyofthesefemalesmaybeworkingathomeondomesticandchildcareduties;however,theseactivitiesdonotcountaseconomicactivity.74Anecdotally,manyunemployed individualsareable torelyupon familyand friends for their living,as iscommoninSub‐SaharanAfrica.

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least two major issues. First, there is the commonly observed problem of likely correlationbetween schooling and ability or other attributes correlatedwith both schooling attainment andsubsequent income,butwhichcannotbemeasured. Theseattributesmaybeacquiredthroughavarietyofmeansapart fromeducation, includingparental inputs,workhabits, and innateability.Thiscorrelationwill tendto introduceanupwardbias in thesimplestestimatorusedtoquantifyreturnstoschooling–ordinaryleastsquares(OLS)–andthis‘returns’estimatecannotbeusedtoderivethe‘treatment’effectofsupplyingmoreeducation.Moreover,inthecaseofTanzania,suchupwardbiasmaybemorepronouncedformoreadvancedlevelsofeducation,giventhataminorityofthepopulationacquirespost‐primaryeducation.

In addition, the number of years of schooling is only a proxy for skills and abilitieswhich raiseproductivity but aremuchmore difficult tomeasure. The only attempt to directly estimate thereturnstomeasuredcognitiveskillsusingcognitivetestdatainTanzania(KnightandSabot1990)showedthatanincreaseofastandarddeviationinmeasuredcognitiveskillsincreasedincomesinTanzaniaby13percent,which at the timewas low relative toKenya at 19‐22percent, or SouthAfricaat34‐38percent.Usingeducationalattainmentasaproxy,KnightandSabotalsofoundthatsecondary education increasedwages by 25 percent in urban areas in Tanzania. Assuming fouryearsofsecondaryeducation,thisissimilartorecentestimatesbyBarroandLee(2010)showingthat, for Sub‐Saharan Africa as a whole, an additional year of schooling increases wages byapproximately6.6percent incrosscountryregressions,butthisdoesnot includereturnsinruralareas.75

UsingTanzanian data onmanufacturing employees – an unrepresentative sample relative to theentirepopulation—Soderbom,Teal,Wambugu,andKahyarara(2006)showthatincomeofyoungmanufacturingemployeesrisesbyapproximately10percentforeveryadditionalyearofschooling,when one controls in a plausible way for unobserved factors.76 The study also shows that thisreturn is very similar to the return to schoolingof oldermanufacturingworkers inKenya in thesameyear.77 Moreover,incontrasttotheKenyancase,estimatedreturnstoanadditionalyearofschooling of young Tanzanian manufacturing workers do not increase as years of schoolingincrease.However,thepopulationofmanufacturingworkersislikelytodiffersystematicallyfromthe population at large, and these estimates could over‐state the average market returns toeducation.78Forallindividualsemployedoutsideofagriculture,Table8.5belowshowsmedianrealearningsbyeducationlevelusingthe2001and2006IntegratedLabourForcesurveys:

75Thisestimateusesparentaleducationasaninstrumentforyearsofschooling,butthisapproachislikelytobe flawed since parental education also has a direct effect on student achievement aswell as on years ofschooling.76Theapproachtheseauthorstake–toutilizeparents’educationasan‘instrument’–hasbeenshowntobeinvalidinotherstudiesusingUSdata.77Thestudiesusedifferentestimationmethods,butbothuseparentaleducationasanexclusionrestrictiontodealwithendogeneityissues.78TheevidencealsosuggeststhatthereturntoschoolingofmanufacturingworkersinKenyahasfallenoverthe1990s,whereasithasriseninTanzania,asonewouldexpect.

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Table8.5:MedianRealMonthlyEarningsbyEducationLevel2001‐2006

MedianRealMonthlyEarningsbyEmployeesinNon‐AgriculturebyEducationLevel2001‐2006(TanzanianShillings)

Surveyyear 2001 2006NoEducation 15,000 18,7501‐4Years 28,000 27,0545‐8Years 30,000 30,6039‐14Years 70,000 62,50015+years 245,000 120,313*Source:KerrandQuinn(2010)usingILFSurveys2001and2006Note:Dataarenotweightedgivenlackofweightsin2001survey.*Differencesforhigherlevelsofeducationmaybeduetosmallsamplesizes.

Thisshowslittletonoincomegainsoverthefive‐yearperiodforanypositivelevelofeducationalattainment.

Education can increase entrepreneurship outside of agriculture. Using data from a pilot ruralinvestment climate survey, theWorld Bank (2007) reports that the probability of engaging in anon‐farm rural enterprise – an activity which is strongly associated with increased income inTanzania– ismuchmore likely for individualswith someprimaryeducation;75percentof such

enterprises wererunbyanindividualwith at least someeducation, althoughonly11percenthadcompleted primary.Eachyearofschool‐ing was associatedwith a 2.3 percentincrease in theprobability of start‐ingaruralbusiness.These results can‐notbeusedtoimplycausation, however.Moreover, otherconstraints to busi‐ness were estima‐

ted to be strongly associated with these outcomes, including demand arising from agriculturalsectorgrowth,accesstofinance,andtransport.79

79World Bank (2007). Tanzania Pilot Rural Investment Climate Assessment: Stimulating non‐FarmEnterpriseGrowth.

Table8.6:EstimatedReturnstoSkillsAssociatedwithSchoolinginTanzania,2001and2006ConditionalonWageorSelf‐Employment*

2001 2006

Average Percent Increased income perYearofSchooling

14% 12%

Bylevel:

STD7(Primarycomplete) 13.5% 13.0%

FORM4(OLevelsor11years) 16.0% 19%

*Sample has more education and higher income than average Tanzanianpopulation,andisestimatedusingonlyemployednon‐agriculturalworkers.

Source:Kerr(2011)UsingIntegratedLabourForceSurveys2001and2006

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AlthoughOLSestimatesofreturnsarelikelytobebiased,withappropriatecaveats,theystillmayprovidesome indicationof themagnitudeofreturns. Becausethedegreeofestimationbiasmaynot be constant across countries, it may be somewhat misleading to benchmark returns acrosscountriesusingmethodswhicharenotguaranteedtobeunbiased.OnecaninterpretOLSestimatesofreturnstoeducationasthereturntoallhumancapital,attributes,andopportunitieswhicharecorrelatedwitheducation.80 Thiscanbecalled the ‘schooling‐correlatedabilityandacquisitionofskills’(henceforthSCAAS).Thisprovideslesspolicyguidance.TheappropriateinterventionintheeventthatalackofSCAASconstitutesaprincipalconstrainttogrowthmightbegreaterinvestmentin early childhood education, school vouchers, parental or community training, de‐worming,nutritionalinterventions,orchangestoteachersupervisionorcompensationsystems.

Using the ILFS for 2006and 2001 and the simpleOLS estimator of returnstoSCAAS,oneobtainstheestimates shown below(Kerr and Quinn 2010,unpublished). Althoughthereturnof12percentisslightly higher than theglobalmeanestimatedbyBarro and Lee, it has notrisen since 2001 despiterapid economic growthover the decade. Inaddition, the estimateuses only employedindividuals and must bemultiplied by theprobability of beingemployed, which fallswith educational attain‐

ment. Since unemployment rates rise with the level of education in Tanzania, making thisadjustmentwillbringthereturnstosecondaryandhigherSCAASclosertotheregionalmeansforSub‐SaharanAfrica. If one compares thesenumbers – adjustingdownward appropriately for thefact that theestimatesare likely tobeoverstated forTanzania inparticular– to returns inothercountries,oneobservesgenerallysimilarreturnstothoseinKenyaandUganda,andhigherreturnsthanforGhanaandVietnam,althoughtheyearsandmethodologiesdiffer.

80This isonlytrueifoneiswillingtomaketheassumptionthatcurrent incomedoesnotcauseeducationalattainment.

Table8.7:ComparativeRatesofReturnstoEducation

PrimaryEducation

SecondaryEducation

TertiaryEducation

Tanzania 13.0% 19.0% N/A

Ghana 5% 8% 18%

Uganda 30.20% 11.50% 24.20%

Vietnam 2.33% 7.65% 10.7%

Kenya 7.90% 17.20% 32.50%

Notes:ThereturnstoeducationinTanzaniaareestimatedusingonlyindividualswhoareemployedinnon‐agriculturalactivitiesin2006,anddonotaccountforreturnsinagricultureordifferentialunemploymentrates.

GhanaestimatesarebasedonGhanaLivingStandardSurveyof2000,UgandadatausedareNationalHouseholdSurvey2000,Vietnamestimatesarebasedonthe2004LaborForceSurvey.Kenyaestimatesarefromthe2007WelfareMonitoringSurvey.

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3)FirmBehaviorandSurveyResponses

Additional indications of excess demand for skills can be provided by firms’ perceptions andbehavior.AccordingtothemostrecentGlobalCompetitivenessSurvey(2010/11),alackofskilledlaborinTanzania,rankedonlythe10thmostfrequentlycitedanswertothemostproblematicfactorout of 15 possible responses. Similarly, in the most recentWorld Bank Enterprise Survey forTanzania (2006), respondents rated several other constraints more severe. Approximately 12percent of firms said that inadequately educated laborwas amajor or severe constraint to theirbusiness.Thisfractionishigherfornon‐microenterprises.Amongmanufacturingfirms,19percentrateda lackofskillasamajorconstraint todoingbusiness, for ‘other’ typesofbusinesses,23.68percent rated this as one of the top three constraints, but for retail and information technologyfirms,thefractionwasonly11percent.

AsshowninFigure8.19moreTanzanianfirmsratealackofskilledlaborasamajorconstraintthanfirms in Uganda and Ghana, and Kenya, but less than in Malawi and Mauritius, and similar toNamibiaandMozambique.Exportersmoreoftenratealackofskillsasamajorconstraintthandonon‐exporters.81

Efforts by existing firms to train theirworkers is another potential indication ofwhether or notunusual or costly efforts are beingmade to relax a constraint that they facewith respect to theavailabilityoftheseskills.Basedonresponsesbyformalmanufacturingfirms,Tanzaniahasarateofemployer‐providedformaltrainingslightlyhigherthanthemeanforSub‐SaharanAfrica,higherthanGhana,similar toUganda,butnotashighasKenya,Mozambique,Malawi,andNamibia.Thepercentageofemployeesofferedformaltrainingbytheiremployersisreasonablyhigh,butnotashigh as Namibia, Mozambique, Kenya, or Uganda, and the fraction of manufacturing firmrespondents offering training is higher than Uganda and Ghana. Information from the GlobalCompetitivenessReportSurveyissimilar,asshownbelowinFigure8.16.

81Thedataalsoshowthatmediumsizedfirmsratetheconstraintmoreseverely,asdoforeignownedfirms.

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Figure8.16:PercentageofFirmsIdentifyingLaborSkillLevelasaMajorConstraint

Figure8.17:PercentageofFirmsIdentifyingLaborSkillLevelasaMajorConstraint


FirmsinTanzaniainvestintrainingtheiremployeesdirectlytoalesserdegreethanthecomparisoncountries, except forMozambique. This could be in part due to the 6 percent levy theypay onpayroll which is intended to fund vocational and technical training. Nonetheless, if this was a

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crucial binding constraint, onewould expect firms to bewilling to investmore in training theirworkersintherequiredskills.82

Figure8.18:IndicatorsofFormalEmployer‐ProvidedTraining

Source:WorldBankEnterpriseSurveys,mostrecentyears.

Giventherelativelylowunemploymentratefortechnicallyorvocationallytrainedindividuals,ofalllevelsofeducationthelackofspecializedpracticalandtechnicalskillsappearstoposethelargestconstraintfortheprivatesector.FirmsrateTanzaniarelativelypoorlyintheavailabilityofrelevantandqualitytrainingserviceslocally,asshowninFigure8.20.Thisisanindicationthatemployersmayfeelrelativelylittle‘return’ontherelativelyhighpayrolllevy(6percent)theypay.

Another means of getting around the constraint would be to hire foreign workers. Tanzanianenterprises appear to employ foreign workers with a more appropriate mix of skills, but thenumberswerenotavailableforthisreport.Theonlyestimateavailablewasfortheapproximately16,000 foreignworker permits obtained during the 2001‐2005 period, approximately 4,000 peryear. This isarelativelyhighnumberascomparedwithT‐VETgraduatesinthoseyears,but isalownumberascomparedwiththeeducatedlaborforce.Morerecentlywiththeintroductionofthefreemovementofworkerswithin theEastAfricanCommunity, thecostsof importingskillshavebeenreducedsubstantially. Nonetheless,thisindicator,combinedwiththelowqualityofgeneraleducationandthelowunemploymentrateforvocational/technicallytrainedworkersindicatethatthis is a seriousproblem, and that theTanzanianpopulationmaynothave access to thekind ofrelevanttrainingthatisneededfortheirfullparticipationintheeconomy.

82Theproblemofcapturingthebenefitsoftrainingworkerswhomayleavetotakeemploymentelsewhereiscommonacrosscountriesandshouldnotaffecttheserankings.

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Figure8.19:ExtentofStaffTraining

Figure8.20:LocalAvailabilityofSpecializedResearchandTrainingServices

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B.LackofHealthandNutritionalStatus

Anotherimportantformofhumancapitalishealthandnutritionalstatus.Thereisanabundanceofevidence that higher income andwealth levels generally lead to improved health and nutrition.However,evidenceofthereverse–thatimprovedhealthandnutritionraiseincomes‐islessclear.In principle, poor health or nutrition could impact sectors such as agriculture ormanufacturingwherestrengthandendurancearerequired,but there is relatively littleempiricalevidenceofaneffect,exceptatextremelevelsofdeprivationandillness.83 Atthesametime,thereis littledoubtthat preventable illness and poor nutrition cause welfare and economic losses in developingcountries,which tend to be located in latitudesmost prone to virulent contagious diseases (e.g.,malaria, tsetse, schistosomiasis).84 Estimates of the direct and indirect costs ofmalaria, TB, andHIV/AIDS, including lostproductivity, travel time for treatmentandpreventativeandtherapeutictreatments, total approximately 1‐2 percent of aggregate income, but these estimates do notconsiderbroadereconomicimpacts,includingsubstitutionoflaborresourcesthatmaybeinexcesssupply,lostschooldaysorlearning,deterrentstoinvestment,orothereffects.SachsandMalaney(2002)broadlyestimatethelosstothelevelofGDPinSub‐SaharanAfricancountriestobehighatan average of 10percent, through the combined effects of reduced investment andproductivity;however,theyadmitthattheirestimatesaresomewhatspeculative.85

Thereislittledebatethatincomehasamajorimpactonnutritionalandhealthoutcomes,andthoseoutcomesarealsolikelytoimpactincomeatthehouseholdlevel.Thequestionforthisdiagnosticstudy iswhetherpoorhealthand/ornutritionaloutcomes inTanzaniatodayconstituteabindingconstrainttoproductiveinvestmentandbroad‐basedgrowthoftheeconomy.

a.TrendsinNutritionalStatus

RatesofmalnutritionhavebeendeclininginTanzaniaalongwitheconomicgrowthinrecentyears.Therateofstuntinginchildrenaccordingtothe2010DemographicandHealthSurvey(DHS)hasdroppedslightlyoverthepreviousfiveyearsto35percentasshowninTable8.8.

Earliermeasuresobtained fromWDI,whichallow for international comparison, showTanzania’srateofchildmalnutritionatsimilarratestobenchmarkcountries(Ghana,Kenya,andUganda),as

83 Empirical studies have found some support for a causal linkage between nutrition and wages oragriculturalproductivityespeciallyatverylownutritionlevels(seee.g.FosterandRosenzweig(1994))butonthewholecastdoubtonthenutrition‐wageefficiencymodels whicharepredicatedonthisrelationship(Binswanger and Rosenzweig [1984], Rosenzweig [1988], Strauss (1996)). In fact, maintaining close torecommendedcaloricintakesisinexpensiverelativetowagesineveninsomepooreconomiessuchasruralIndia(SubramianandDeaton[1996]andSwamy[1998]).ThenutritionalstatusoftheIndianpopulation,forinstance,isamongtheworstamongdevelopingcountries,despitedecadesofgrowthandincreasingincomes,despitehavingenjoyedrapidincreasesinagriculturalproductivity.Thereissomeevidenceofadirectimpactonschoolingandcognitiveattainment(seeAlderman,Hoogeveen,andRossi2008),andthattherearehigheconomic return interventions available to improve these outcomes (Berhman, Alderman, and Hoddinott2004).ForareviewseeStraussandThomas(1998).84ThelinktonaturalcapitalandgeographyisdiscussedbrieflyinthechapteronNaturalCapital.85Otherresearchpointstoanimportantlongrunimpactthroughthedevelopmentofpro‐growthinstitutionsAcemoglu,D.,S.Johnson,andP.Robinson(2001).

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shown in Figure 8.21, but lower than for Sub‐Saharan Africa (developing) and low incomecountries.

Figure8.21:PrevalenceofChildMalnutrition

Table8.8:TrendsinNutritionalStatusofChildrenPercentage of children under five years classified as malnourished according to threeanthropometricindicesofnutritionalstatus:height‐for‐age,weight‐for‐height,andweight‐for‐age,bybackgroundcharacteristics,Tanzania2010

Surveyyear

Height‐for‐agePercentagebelow‐3SDPercentagebelow‐2SD

Weight‐for‐heightPercentagebelow‐3SDPercentagebelow‐2SD

Percen‐tagebelow+2SD

Weight‐for‐agePercentagebelow‐3SDPercentagebelow‐2SD

2004‐05TDHS

12.8 37.7 0.4 3.0 3.0 3.7 21.8

2010TDHS 12.4 35.4 0.5 4.0 3.2 3.8 20.7

Note:Tableisbasedonchildrenwhosleptinthehouseholdthenightbeforetheinterview.Eachoftheindicesisexpressedinstandarddeviationunits(SD)fromthemedianoftheNCHS/CDC/WHOreference.Tableisbasedonchildrenwithvaliddatesofbirth(monthandyear)andvalidmeasurementofbothheightandweight.

b.HealthandDiseaseStatus

ThewelfareandeconomiccostsoftropicalandotherdiseasesinTanzaniaandtropicalSub‐SaharanAfrica generally are high. Themost costly diseases in terms of lost lives and productivity areTuberculosis,Malaria, andHIV/AIDS, butdiarrhea, anemia andother conditions related to livingconditionsandpovertyalsoexactatoll.Malariaremainstheleadingcauseofdisease‐relateddeathinchildrenunderfiveinTanzania.

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Theeconomicburdenofthesediseasesislikelytobesignificant. Onestudy(JowettMiller2000)estimatesthatexpendituresontreatmentandpreventionofmalariain1998totaledapproximately1.1percentofGDP,with71percentofthatbeingbornebyhouseholds.FortheindividualsaffectedbyTBinparticular,theeconomiccostscanbeashighas100percentofannualincome,accordingtosomestudies.

Inthepastdecade,Tanzaniahasmadesignificantstridesinreducingdiseaseprevalence,bothformalaria andHIV/AIDS. Ownershipofmosquito nets increased from56percent in2007/8 to75percentin2010,andownershipofinsecticidetreatednetsfrom23percentin2004/5to64percentin2010.Moreover,thepercentageofchildrenundertheageoffivewhosleptunderaninsecticidetreatednet(ITN)thenightbeforethehouseholdwassurveyedincreasedfrom16percentin2004‐05to26percentin2007‐08andto64percentin2010. Prevalencerateswerehalvedinthepastdecadetoapproximately18percentamongchildren(2007/08NBS).InZanzibar,prevalenceratesare reported tohavedropped from35percent toonly1percent recently. InDaresSalaam, thereductioninallprevalencehasbeendramatic,from24percentin2004tojust4percentin2008.

Table8.9:TrendsinEarlyChildhoodMortalityRatesUsing data from the 2007Household budget survey tocompare with the survey from2000/01,asshowninFigure8.22one sees a slight drop in thepercentage of individualsreporting an illness or injury inthe past four weeks. People inrural areas appear to be slightlymore affected by illness (27

percent)thanthoseinurbanareas(24percent).

Similarly, theprevalenceofHIV/AIDShasdeclinedovertimeto5.6percent in2009,asshowninFigure8.23,butnotnearlyasquicklyasithasinUganda.Nonetheless,asof2011,Tanzaniaranked4thintheworldamongcountriesinabsolutenumbersofAIDSrelateddeaths(CIAFactbook2011)at86,000.

Malaria,other fevers,andchronic illnessessuchascancer,heartdisease,andTBare theprimaryself‐reported causes of illness, although malaria is generally believed to be somewhat over‐diagnosed.

TrendsinearlychildhoodmortalityratesInfantandunder‐fivemortalityrates,Tanzania,1996‐2010

Surveyyear

Approximatecalendarperiod

Infantmortality

Under‐fivemortality

1996 1992‐1996 88 1372004/5 2000‐2004 68 1122007/8 2003‐2007 58 912010 2006‐2010 51 81Note:DatarefertothefiveyearspriortoeachsurveySource:TanzaniaDemographicandHealthSurvey2010PreliminaryReport

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Figure8.22:PercentageofIndividualsReportingIllnessorInjuryinthePastFourWeeks

Figure8.23:HIV/AIDSPrevalenceRates

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Table8.10:LostDaysofWorkorSchoolAs shown in Table 8.10, in 2007 approximately 9percentofallworkingageindividuals lostoveroneweekofschoolorworkoutof thepast fourweeks.The fractions are somewhat higher in rural areas,but are also high in urban areas, as shown. Thecosts to individuals and households experiencingillness are significant. Indeed, of individuals whoare currently unemployed but who had beenemployed,22percentreportbeingunemployeddueto illness(2007HBS). Another18percentofsuchindividualsareunemployedduetodisability.Basedonestimatesofthedirectandindirectcoststhatcan

bemeasuredofthethreediseasesofTB,malaria,andHIVapproximately1‐2percentofhouseholdincomeislosteachyear.86Thisestimateexcludesthecostsoflostlife,theeffectsoccurringthroughanemia, educational attainment and cognitive development, and any economic impact throughreducedforeignorotherinvestment.

Aggregate lost productivity is difficult to quantify directly, given the possibility of substitutingbetween workers and through time. Whereas there is excess labor supply in urban areas,individualswithmorespecializedskillsor firm‐specificknowledgewouldnotbereplaceable,andformalsectoremployersreplacingthemwouldbeobligatedbylawtopayemployeeswhentheyareunable to work due to illness. Agricultural workers may not be able to locate adequatereplacementlaboratcriticaltimesaswell.

Lostwork time is generallydecreasing in the level of educational attainment. Whethernot thisimposesahighcosttoinvestmentandproductivitydependssomewhatonthelaborregulationsandpracticesoffirms.

ThecostsofpoorhealthtobusinessappeartobesignificantinTanzania. AccordingtotheGlobalCompetitivenessRankings,Tanzaniaranksamongthelowestcountriesintheworldintermsofthebusiness impact of malaria (131st out of 139), and slightly better on the business impact ofTuberculosis(129th)andHIV/AIDS(125th),wherefirmsareaskedtoratetheiranticipatedimpactson the company through death, disability, medical and funeral expenses, productivity andabsenteeism,recruitmentandtrainingexpenses,andrevenues,asshowninTable8.25.Anticipatedimpactsareabouthalfwaybetween‘severe’and ‘none.’ GivenTanzania’soverallcompetitivenessrankingof113th,theseindicatorsareclearlypullingdowntheoverallrating.

Similarly,Figure8.25 shows thebusiness impactofHIV/AIDSonTanzaniancompanies,with theimpactratedatthemidpointbetweensevereandnone,whichisatthemeanforSub‐SaharanAfrica(population‐weighted).

86Studies suchasRussell (2004)andMashaetal. (2007) tally thecostsperepisodeanddonotappear tomultiplybytheprevalencerateofthoseepisodesintheirsurveypopulations.

WorkingAgeIndividuals(Ages15‐65)

Source:HouseholdBudgetSurvey2007 Urban RuralNone 86.3% 81.7%1weekorless 7.5% 9.1%1to2weeks 3.4% 5.3%morethan2weeks 2.7% 3.8%Nonresponse 0.0% 0.0%

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Nonetheless, in this same survey, public health was rated the 13th most problematic issue forbusiness by the same companies, out of 15 possible problem areas, below education and laborregulation.Onewouldalsoexpectthat,ifpoorpublichealthwereabindingconstrainttoprivatesector investment, over the past five years when disease prevalence has improved, investmentwould accelerate. So far, no such improvement in private investment rates has occurred as aresponsetoimprovedpublichealth.

Figure8.24:AmountofTimeLostbyEducationalAttainment

Table8.11:BusinessImpactsofDisease

GlobalCompetitivenessRankings,outof139Countries

Ghana Kenya Mozambique Tanzania Uganda

BusinessImpactof:

Malaria 126 119 128 131 136

TB 103 126 130 129 128

HIV/AIDS 109 127 130 125 134

Source:GlobalCompetitivenessReport2010

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Figure8.25:BusinessImpactofHIV

C.Conclusions

WhileaseriousproblemforsomeinvestorsandpotentiallyforsomeTanzanianlaborers,thelackofhumancapitaldoesnot appear to rankasabindingconstraint tobroad‐basedeconomicgrowth.Increasing the supply of skills, health, and nutrition, while socially worthwhile, would not besufficient on its own to accelerate growth. Whereas public health and poor nutrition remainimportant issues and challenges, there is no strong indication that alleviating these constraintswould lead to an acceleration of private sector investment and economic growth. Raising theincome level of the populationwould, however, go a longway towards improving nutrition andhealth. It is difficult to estimate how many more investors would invest or how much moreproductiveself‐employedandexistingenterpriseswouldbewitheachexpansionofhumancapital,buttheavailableevidencepresentedinthisreportstronglyindicatesthatproducersandbusinessesfaceother,muchmorebindingconstraintsatpresent.

Thereareissuestobeaddressedtoensurethatalackofhumancapitaldoesnotbecomeabindingconstraint,aneventualitythatismorelikelyifthebindingconstraintsarereleased.Anaccelerationofgrowthwouldincreasethedemandforhumancapitalandincreasethereturnstoskillandlabor,all else equal. One challenge is the quality of education. As in many low income and Africancountries,thelevelofskillsacquisitionofprimaryandsecondarystudentslagsthatofmiddleand

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upperincomecountriesmarkedly.Thisispartly,butnotonly,duetothequalityoftheeducationalsystem.

The second challenge is to improve the effectiveness and reach of the current T‐VET system. Ashortageoramismatchoftheskillssuppliedrelativetothosedemandedintheeconomyappearstobemostsevereforvocational‐technicalskills.Thegovernment’sT‐VETpolicy,codifiedthroughtheVocationalandTrainingAct(VTA)of1994,aimsatfacilitatingthedevelopmentofaflexibleskillstraining system that is responsive to the skills demandsof theprivate sector andoffers trainingsuitable toworkerswithvaryingeducationalbackgrounds. TheVTAestablishedanautonomoustrainingauthoritywhichwastoallocatefundingtotrainingcentersinademand‐drivenmanner,incoordination and consultation with the private sector. The skills training levy of 6 percent ofpayroll expenses was instituted to finance these training programs. Although the revenuescollectedthroughtheskillstraininglevymaybeadequatetoexpandcapacity,thereareindicationsthattheactualfundingflowstotheVTAmayhavefallenshortofleviescollected.87Moreover,thelevywasdesigned tobeused forvocational trainingonly. There isno sustainable financing fortechnicaltrainingcentersorforstudentswishingtoattendsuchcenters.Inaddition,theabsenceofa levy drawback provision leads to potential double ‘taxation’ of firms who may find itadvantageoustodirectlyfinanceandselecttheirownemployees’trainingprograms.

87Anecdotal.Thishasnotbeenconfirmed.

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9. NaturalCapital

A lack of natural resources or unfavorable geographic attributes can limit viable investmentopportunitiesandmake rapideconomicgrowthmoredifficult toachieve. A lackofwater, land,vegetation, mineral or soil wealth would reduce the productivity of other factors of production(capitalandlabor)andcurtailinvestmentandwealthcreation.Tanzaniafacesnaturaladvantagesand geographic disadvantages. It is endowedwith relatively high natural capital in the form ofwater and mineral resources, wildlife and scenery, and a long coastline on the Indian Ocean.However,Tanzanialiesinatropicalzonepronetodisease,betweenlatitudes10and120Southandlongitudes290and410East. Tanzaniamaybeincreasinglypronetodroughtandclimatechange‐relatedincreasedvolatilityinrainfallandtemperature.Tanzania’sneighborsarealsoclassifiedaslow income ‐‐ Kenya to the north, Uganda to the northwest, Rwanda, Burundi and DemocraticRepublicofCongotothewestandZambia,MalawiandMozambiquetothesouth.Thisgeographicpositionmakesaccess torichermarketsmorecostly. Basedontheavailable indicators,a lackofnatural capital is not presently a binding constraint to growth of the Tanzanian economy.However, improving themanagement and exploitation of natural resources – in particular, soil,vegetation, and water resources necessary for livestock and crop production –will becomeincreasinglyimportantforenhancinggrowthoverthemediumterm.

A.AccesstoSeaandDistancetoMarkets

Likemanydevelopingcountries,Tanzaniaisgeographicallydistantfromitslargesttradingpartnersof Europe, India, and China. Major trade is done by sea using international maritime shippingvesselswhiletransittradeisbyrailandroadtransport.Tanzania’sportofDaresSalaamisamajorregional port providing a transit route for goods to and from land‐locked countries of Uganda,Burundi,Rwanda, easternDRC,ZambiaandMalawi. Othermajor seaports includeTanga to thenorthandMtwaratothesouth.TheportscompetewithMombasainKenyaandNacalaandBeirainMozambiquefortransitgoodsfromandtoUganda,Burundi,theDRCandMalawi,respectively.

B.MineralWealth

Tanzaniaisendowedwithgreatmineralresources,includinggold,basemetals,diamonds,ferrousmineralsandawidevarietyofgemstones,someofwhichareunique,suchastanzanite.Moreover,coal, natural gas, uranium, and various industrial minerals such as soda, kaolin, tin, gypsum,phosphate and dimension stones are available at attractive economic rates. This conducivegeologicalenvironment,incombinationwithmajoreconomicreforms,hasenabledarapidgrowthof minerals exploration and exploitation over the past ten years.88 Today, the mining sectoremploysaround1percentofwageearners,andthesectorpresentsbothchallengesandeconomicopportunities.88Reforms undertaken since the mid 1980’s include the Mineral Policy of 1997, and enactment ofinternationallycompetitivefiscalandlegalregimesforthemineralsector.

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Goldhasreplaceddiamondsastheprimarysourceofgovernmentmining‐relatedrevenues,andhasbecomethelargestmineralexportofTanzania,at93.7percentoftotalmineralexports.Inthemostrecentyearforwhichdataareavailable,thetotalvalueofmineralexportsincreasedtoUSD1,217.3millionin2009fromUSD1,098.8milliontheyear2008,equivalenttoanincreaseof10.8percent.ExportsincreasedfromUSD992.8millionin2008toUSD1,140.7millionin2009,or14.9percent.Atthesametime,diamondexportsalesincreasedonly1.5percentinthesameyear,fromUSD22.4milliontoUSD22.73million in2009. Whereasproductionofdiamondsdecreased22.8percent,from237,676caratsto181,874carats,bettersalespriceswereobtainedbyauctioningintheworldmarketratherthandirectsaletoDTC,asubsidiarycompanyoftheDeBeers.Therehavebeennonew substantial investments in replacement equipment or newmines in recent years. DiamonddepositsatWilliamsonDiamondsLimitedmine,exploitedsincethe1950s,mayhavebeendepleted,andnonewmajordiamonddiscoverieshavebeenfoundsinceNewAlamasiminein1975.

Figure9.1:AnnualGrowthRatesMiningandQuarryingYear1999‐2009(%)

Source:TheEconomicSurvey2007,2008,and2009(MinistryofFinance)

Thereare some indications that theTanzaniangovernmenthasnot enjoyedaveryhigh shareofminingroyalties,levies,orprofitsfromthelargegoldminingcompanies.Thisappearstobedueinparttounfavorablecontracttermsbetweeninvestorsandthegovernmentonbehalfofthepublic(see Chapter 5). Whereas mining companies are expected to contribute to local developmentthrough the provision of schools, health facilities, or local infrastructure, there has been littlereinvestment of mining profits in other productive enterprises within Tanzania. If investedeffectively ashasbeendone in countries such as SouthAfrica,Ghana andBotswana,whichhaveavoided the ‘resourcecurse’, increasedpublicrevenues fromgoldexploitationcouldhelp financepublicinvestmentsinpublicgoodsandinfrastructure.89

89The resource curse refers to the paradox that countries and regions with an abundance of naturalresources, specifically non‐renewable resources like minerals and fuels, tend to have slower economic

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Figure9.2:TotalValueforMineralExports:'000USD'Year2000‐2009

Source:TheEconomicSurvey2000‐2009(MinistryofFinance)

C.LandResources

Tanzania’s surface area is 94.3 million hectares, of which 22 million hectares (23 percent) isallocated to reserves (4.2 million hectares of National Parkland, 7.7 million hectares of gamereserves,and10.1millionhectaresofforestreserves).Agriculturalland,includingarableandnon‐arable agricultural land, represents about 36million hectares, or 38.6 percent of total land, andtotalarablelandareais19.1millionhectares,or20.3percentoftotalland.Thisincludes5.1millionhectaresofgrossareacultivatedannually,or5percentofTanzania’ssurfacearea,and10millionhectaresofarableuncultivatedland–muchofwhichisusedforlivestockgrazing.90

Tanzaniaisabundantinarablelandpercapita,asshowninFigure9.4,butmostrecentlyfellbelowthe Sub‐Saharan African average of arable land per capita, given the country’s high populationgrowth rate. The per capita endowment of arable land is declining particularly in the Arusha,Kilimanjaro,MbeyaandKageraregions,duetorisingpopulation.

AsshowninFigure9.5,Tanzaniaisalsorelativelyabundantintotalagriculturallandcomparedtothebenchmarkcountries,althoughZambiaandNamibiaareevenmorelandabundant.

growthandworsedevelopmentoutcomesthancountrieswithfewersuchresources.Thisishypothesizedtohappenfordifferentreasons,includingadeclineinthecompetitivenessofothereconomicsectors,volatilityofrevenuesfromthenaturalresourcesector,mismanagementofgovernmentresources,orweak,ineffectual,unstableorcorruptinstitutions.

90 Agricultural land refers to arable land plus land that is under permanent crops or permanent pasture.Arablelandincludeslandundertemporarycrops,temporarypastures,landundermarketorkitchengardens,andlandwhichistemporarilyfallow,excludinglandabandonedasaresultofshiftingcultivation.

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Figure9.3:ArableLandasPercentageofTotalLandAreaperCountry

Source:www.Tanzania.go.tz/lands.html,WDI

Figure9.4:ArableLandPerCapita,SelectedCountries

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Figure9.5:PerCapitaAgriculturalLandbyCountry(HectaresperPerson)

Source:WorldBankDevelopmentIndicators

If a lackofarable landwerepresentlyabindingconstraint togrowth,onewouldexpect to seeatrend of increased application of other factors of production to the land and consequent risingyields.Infact,oneseestheoppositepattern–growthincerealsoutputhasbeendrivenprimarilybyexpansionofcultivatedarea,asshowninFigure9.6.

Figure9.6:LandUnderProductionandCerealYieldinTanzania(1980–2008)

Inaddition,iflackoflandwereabindingconstraint,onewouldexpecttoseepercapitaagriculturalproduction falling, due to increasing labor intensity in agriculture. This is not evidenced in thecurrent trends shown in Figure 2.9 (Chapter 2), nor in the trends in yields per hectare, whichremainfairlyflat.

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D.WaterResourceWealth

Tanzaniaisendowedwithabundantwaterresources(89km3annuallyrenewablewaterresources,including three of Africa’s largest freshwater lakes, rivers, springs and groundwater (40 km3)).While it faceschallenges inapplyingamorestrategicapproachtoexploiting theseresourcesandadopting sustainable management practices, a lack of water resources does not appear to be abindingconstrainttoeconomicgrowth.

Figure 9.7 shows the internal freshwater capacity and water withdrawal for five countries.MauritiusandSouthKoreautilizetheirwaterresourcesmorefully,aswouldbeexpectedformoredevelopedeconomies.Tanzania’swaterwithdrawalishighrelativetootherlowincomecountriesshown, although it still has plenty of room to develop and utilize its water resources withoutconstraining the resource. As shown inFigure9.8,Tanzaniauses a relativelyhigh shareof totalwaterwithdrawalsforirrigatedagriculture,andarelativelylowshareondomesticandindustrialuse. The lowusage for industrialproduction inparticularmaybe an indicator thatpoorurbanwater delivery constrains investments in water intensive non‐agricultural production, includinglightindustryandagriculturalprocessing.

Figure9.7:FreshwaterCapacityandWithdrawal

Source:WorldBankDevelopmentIndicators

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Figure9.8:FreshwaterWithdrawalsbySectorFigure 9.10 below showsa declining trend in percapita annual renewablewater resources.According to the WorldBusiness Council forSustainableDevelopment,water stress applies tosituations where there isnot enough water for alluses, whetheragricultural, industrial ordomestic. When annualper capita renewable

freshwater availability is less than1,700 cubicmeters, countriesbegin to experienceperiodic orregular water stress. Below 1,000 cubic meters, water scarcity begins to hamper economicdevelopment and human health and well–being. At current trends, Tanzania will reach thethresholdvalueof1,700cubicmetersbytheyear2018.By2025,theannualpercapitarenewablewater resources value is estimated to reach 1,488. Additional water resource managementchallenges including control of pollution and overexploitation of groundwater (in, for example,Singida and Arusha towns), which can permanently damage the water aquifers. Given theexpandinghumanpopulation,expandingeconomicactivitiessuchas irrigation, industrial,miningandhydropowergeneration,climatechangeandpotentialforandactualwaterpollution,therewillbe growing competition for water which will require robust integrated water resourcemanagement practices, including river basin and demandmanagement. Currently, Tanzania ispreparingbasinIntegratedWaterResourcesManagementandDevelopmentPlans(IWRMDPlans)thatwill bind different users to allocatedwater resources for a period of five years. In cases ofscarcity,priorityisdomesticwatersupply,theenvironment,andothersocialeconomicactivities.

Giventhatwateravailabilityisstillabovethisscarcitythreshold,waterresourceavailabilityisnotpresentlyabindingconstrainttoeconomicgrowth.Moreover,theconstraintcanbealleviatedoverthemediumandlongrunbysustainableexploitationpracticesandimprovedmanagementofwaterresources.

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Figure9.9:PerCapitaAnnualRenewableWaterResources(cu.m/annum)

Source: WorldBankDevelopmentIndicators,NationalBureauofStatistics–PopulationEstimates

E.OtherNaturalandCulturalResources

Tanzaniaiswellendowedwithnaturalresourceswhichcanattracttourismandhuntingrevenues.TanzaniacontainsMountKilimanjaro,Africa'shighestpeak,pristinesandybeaches,manylargeandecologically significant wildlife parks, including the world famous Ngorongoro Crater, SerengetiNationalPark,SelousGameReserve,MikumiNationalPark,LakeMunyaraNationalPark,TarangireNational Park, and Gombe National Park, known as the site of Dr. Jane Goodall's studies ofchimpanzee behavior. Other important resources for tourism are Kalambo waterfalls in thesouthwesternregionofRukwa,thesecondhighestwaterfallinAfrica.Tanzaniaalsoshareswithitsneighbors the Great Lakes of Victoria, Africa's largest; Tanganyika, Africa's deepest; and LakeNyasa.SheishosttopreciousRamsarsiteswithsignificantanduniquebiodiversityofinteresttotourists, aswell as conservationists and harvesters of forest and fisheries products. It containsOlduvaiGorge,excavatedbyLouisandMaryLeakeyinthemid‐1950sandknownastheCradleofMankind, in northernTanzania. And, of course, islands such as Zanzibar offer a unique culturalheritage and beach destinations on the Indian Ocean. Although it is difficult to benchmarkTanzania’s natural advantage as a tourism destination, Tanzania clearly possesses the requisitenaturalassetsforgrowthofthetourismsector,assumingthatitcancompeteinserviceprovisionandthatinternationaltourismdemandcontinuestogrow.

F.ClimateandClimate‐RelatedVulnerability

Tanzaniaisvulnerabletoincreasingincidenceofdroughtsandfloodsduetoclimatechange.Theincidenceoftheoccurrenceofdroughtshasincreasedfromoneintentooneinfouryearsoverthepast50 years (GovernmentofTanzania). TheFoodandAgricultureOrganization (FAO)defines

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droughtasalongperiodofextremelydryweatherwhenthereisnotenoughrainforthesuccessfulcropcultivationor thereplenishmentofwatersupply;oranextendedperiodofmonthsoryearswhenaregionnotesadeficiencyin itswatersupply. Greater investment inwaterstorage,dams,and irrigation could be used to cope with some drought conditions, although this is somewhatlimited by the spatial allocation ofwater resources, downstreamwater use rights, and the cost‐benefitrelationshipsofsuchinfrastructure.Forexample,whereastheavailabilityoflakewaterisanatural advantage, the distances to cultivated areas may make irrigation from these sourcesinfeasible. Inaddition,about30to40percentofTanzania’swaterresourcesare trans‐boundary(Nile/LakeVictoria,Zambezi,Ruvuma,LakesTanganyika,NyasaandRukwaandtheLakesChala‐JipeandUmbariversystem),andtherefore itmustadhere to InternationalTreaties/ConventionsgoverningtheresourceswherethedevelopmentofsuchresourceshastobedoneinaccordancetotheseTreaties’rightsandobligations.Theserightsandobligationsrequireensuringwatersecurityandavoidanceofanysignificantharmtodownstreamriparianusers. ThequestionofwhetheralackofwaterinfrastructureconstitutesabindingconstrainttogrowthisaddressedinChapter7.

G. Geographically Determined Prevalence of Human and LivestockDisease

Givenitstropicalclimateandgeography,Tanzaniaissubjecttomanyofthetropicalzonediseasesimpacting livestock productivity and human health. Trans‐boundary Animal Diseases (TADs) inparticularcanposesignificantbarrierstotheexportoflivestockandtheirproducts,inparticulartohighervaluemarketswhichrequirecompliancewiththesanitarymeasuresoutlinedbytheWorldOrganization for Animal Health (OIE). Common TADs found in the country include ContagiousBovinePleuropneumonia(CBPP),Rabies,FootandMouthDisease(FMD)andContagiousCaprinePleuropneumonia (CCPP). Emerging TADs, including Bovine Spongiform Encephalopathy (BSE),RiftValleyFever(RVF),WestNileVirus(WNV)andHighlyPathogenicAvianInfluenza(HPAI),arealso important. Other TADs include Newcastle Disease (ND), which accounts for more than 90percentmortality of rural chickens, Lumpy Skins Disease (LSD), and African Swine Fever (ASF).Rinderpesthasnowbeeneradicatedandthenationhasanemergencypreparednessplaninplaceinordertopreventre‐introductionofthedisease.AnewvaccinehasbeendevelopedforNewcastleDiseasecontrolandhasledtoimprovedlocalchickenproduction.

Asshownbelow,theincidenceofdisease‐relatedlivestockdeathshastrendeddownwardoverthepastdecadeduetoimprovedveterinaryhealthservicesandanimalhusbandryinthecountry,apartfromthetemporaryoutbreaksofRiftValleyFeverandBovineTheileriosisin2007.

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Figure9.10:NumberofLivestockDeaths(Reported)byDisease,2000‐2010

Tanzania’s current livestockwealth consists of 21.3million cattle, 15.2million goats. 6.4millionsheep, 35 million local chicken, 23 million commercial chicken and 1.9 million pigs (NationalCensus of Agriculture 2007/20080), which contributes 4 percent of the national GDP (TheEconomicSurvey2009).

Whilelackofgreaterlivestockdiseasecontroldeniesthecountryaccesstosomeexportmarketsforbeef with stringent standards, othermarkets are accessible to Tanzania, including COMESA andSADCcountries.Inadditiontoimproveddiseasecontrolandveterinaryservices,greaterlivestockproductivity would require improved livestock husbandry, including pasture and water sourcemanagement.Givenmeatgradingandmarketstandardswhichrewardlargerandyoungeranimals,strongerlanduserightswhichenableimprovedcommunitymanagementofthecommonsmaybecrucialtoexploitingtheseopportunities.Onealternativewouldbetheallocationofindividuallandtitlesorlongtermprivateuserights,whichwouldleadtoproperlandmanagementconsistentwithitscarryingcapacity.

Tanzaniaisalsosubjecttotropicalhumandiseaseswhichtakeaseveretollonhealthandhumancapital accumulation (see Chapter 9). Malaria is prevalent inmost parts of the country exceptZanzibar where it has been almost eradicated, with only a 5 percent prevalence rate. Affectedzoneshaveexpanded tohighlandareaspreviouslyunaffected,presumablydue toclimatechangeandgreatermovementofpeoplewhocantransmitthedisease,aswellasresistanceoftheparasitestotreatmentdrugs.Inadditiontomalaria,schistosomiasisisespeciallyprevalentinfloodareas.

In some sense geography, and in particular Tanzania’s tropic disease environment, may explainTanzania’s low income status, whichmeans its low average growth rate over the past hundred

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yearsandmore. Tanzaniahas the twindisadvantagesofbeing located inapoorregionand inatropicalzone.Thetropicsentailhighdiseaseprevalenceandpotentiallyincreasingclimate‐relatedrisks,andmanagingtheseconditionsimposesahighcosttotheeconomy,discouragesinvestment,andmayhavecreatedalongrundynamicunfavorabletoeconomicgrowth.91

H.Conclusions

Tanzania enjoys the advantages of access to the sea, mineral and wildlife endowments, andadequate landandwater resources innon‐droughtperiods. Manyother tropical countries, fromAsia toLatinAmerica,haveseen longperiodsofsustainedeconomicgrowthand improved livingstandards. Althoughnaturalcapital isnotaconstraint toeconomicgrowthatpresent,Tanzaniacan benefit more economically from its endowment of natural resources. Some areas forimprovementwhicharesuggestedbytheanalysisinthisreportare:

Adoptingimprovedstrategiesandlegalframeworksforenteringintomorebeneficialvaluesharing on mineral contracts with private companies and for the management anddisposition of the resulting fiscal inflows, with Botswana as one successful and relevantmodel.

Strengthening institutions forwater resourcemanagement to copewith the challengesofpopulationgrowth,catchmentdegradation,andpollutionduetoincreasedhumanactivities.

Investingin infrastructuretoimproveaccesstotourismsitesandupgradingofhospitalityskills.

91Accordingtosomeeconomists(EasterlyandLevine2003,Acemoglu, JohnsonandRobinson2001,2002)whohave takenahistorical perspectiveon long rungrowth, thedisease environmenthas conditioned thequalityofinstitutionsintroducedbycolonization,andtheseinstitutionsmaysignificantlyimpactacountry’sabilitytogrowanddevelopeconomically.

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10.SummaryofConclusions

Although the policies of the Government of the United Republic of Tanzania have been highlysuccessful in stimulating economic growth over the post‐liberalization period, the more recentdecelerationofprivate investmentandgrowthsuggeststhatthehighgrowthratesrecordedoverthepastdecadecannotbesustainedwithoutaddressing the factorsmostconstraining togrowth,particularly those identified in this diagnostic report. Private returns to investment across theeconomy are generally low and uncertain, with the apparent exception of construction,communications, and relatively less competitive goods sectors served by large enterprises. Lowsocial returns are due primarily to a lack of key infrastructure services, and private returns arereducedevenfurtherbyweakoruncertainappropriabilityofthosereturns.

Morespecifically,basedontheavailableevidence,themostbindingconstraintstogrowthidentifiedinthisreportare(1)thelackofadequateandreliablesupplyofelectricalpower;(2)thelackofacceptable secondaryand tertiary roads to connect rural producers tomarkets, and (3)alackofsupportiveconditions foraneffective landmarket,accessto landby investors,andforsecurityoftenure.

A skills mismatch in the labor market (in particular, a lack of specific vocational and technicalskills),accesstofinance,keytransportbottlenecksincludingrailandtheportinDaresSalaam,anda relativelyweak environment forprivate sectorbusiness and trade, either constitute significantconstraints to broad based growth, orwill likely emerge as binding constraints if not effectivelyaddressedoverthemediumterm.

Addressingthebindingconstraintswillrequireamoredetailedreviewoftheprimarycausesandthe potential solutions likely to prove most effective. Based on the analysis conducted for thisreport, the Government’s broad policy direction appears generally favourable across theconstrainedandunconstrainedsectors.Acommonthemewhichemergedfromtheanalysisistheneed for improved and consistent application and implementation of existing policies. Thisobservation applies to earlier reforms to the enabling environment for investment in the powersector, the T‐VET system, to the land regime, and the operationalization of the Road Fund.Successful implementation may require, in some cases, refinements to the specific legal,governance, or regulatory frameworks, as well as improved institutional capacity to implementthem.

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AnnexI–Data

Chapter3

Table:A‐3‐1CommercialBankLendingtoSomeSectors,%ofTotalDomesticLoans

2006 2007 2008 2009 2010

Agriculture 11 10.8 9.6 10.4 10.7

Fish 1 0.8 0.5 0.4 0.6

Forest 0.2 0.3 0.2 0.3 0.4

Hunting 0.3 0 0 0 0

Financialintermediaries 3.8 3.2 2.6 2.2 2.3

MiningandQuarrying 1.5 1.1 1 0.5 0.5

Manufacturing 20.4 18.6 15.1 12.7 13.8

Buildingandconstruction 4.2 4.1 3.6 3 3.1

Realestate 2.4 2 2 2.1 2.6

Leasing 0.3 0 0.1 0.1 0.1

Transportandcommunication 8.2 8 8.3 8.1 9.4

Trade 22.8 22.2 20 17.2 18.1

Tourism 2.7 1.5 0.6 0.6 0.6

HotelsandRestaurant 3.5 4.1 3.9 4 4.2

WarehousingandStorage 0 0.1 0.2 0.2 0

Electricity 4.7 4.6 4.6 4 3.3

Gas 1.2 0.7 0.5 0.9 1.5

Water 0.1 0.1 0 0 0.1

Education 0.7 1 1.1 1.2 1.2

Health 0.2 0.3 0.4 0.5 0.3

OthersServices 10.4 3.5 8.7 9 5.7

Personal(Private) 12.7 15.8 19.9 21.2 21.6


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TABLE1:REGIONALBRANCHDENSITYMEASURE(RBD)SQ1000KM/BRANCH/PERSON.

S/no

Geographical

Area

Number of

institution's

Branches

Number of

ATMs

LAND

AREAS/ SQ.

KM

LAND

AREAS/ SQ.

1,000 KM

BRANCH

DENSITY/S

Q 1,000KM

BRANCHES

+ ATMS

B+A

DENSITY

pop.

Density

(sq.km)

pop.

Density/

sq.1000Km

branch

coverage

area/person total pop.

1 Arusha 33 74 36,486 36.5 1.1 107 0.3 35 0.04 31.6 1,277,010

2 Coast 9 19 32,407 32.4 3.6 28 1.2 27 0.03 133.4 874,989

3 DSM 155 367 1,393 1.4 0.01 522 0.003 1793 1.79 0.01 2,497,649

4 Dodoma 12 39 41,311 41.3 3.4 51 0.8 41 0.04 84 1,693,751

5 Iringa 18 41 56,864 56.9 3.2 59 1 26 0.03 121.5 1,478,464

6 Kagera 12 21 28,388 28.4 2.4 33 0.9 72 0.07 32.9 2,043,936

7 Kigoma 6 10 37,037 37 6.2 16 2.3 45 0.05 137.2 1,666,665

8 K/njaro 22 49 13,309 13.3 0.6 71 0.2 104 0.1 5.8 1,384,136

9 Lindi 9 12 66,046 66 7.3 21 3.1 12 0.01 611.5 792,552

10 Manyara 10 15 45,820 45.8 4.6 25 1.8 23 0.02 199.2 1,053,860

11 Mara 13 19 19,566 19.6 1.5 32 0.6 70 0.07 21.5 1,369,620

12 Mbeya 23 51 60,350 60.4 2.6 74 0.8 34 0.03 77.2 2,051,900

13 Morogoro 21 46 70,799 70.8 3.4 67 1.1 25 0.03 134.9 1,769,975

14 Mtwara 10 14 16,707 16.7 1.7 24 0.7 68 0.07 24.6 1,136,076

15 Mwanza 35 67 19,592 19.6 0.6 102 0.2 150 0.15 3.7 2,938,800

16 Rukwa 6 10 68,635 68.6 11.4 16 4.3 17 0.02 672.9 1,166,795

17 Ruvuma 9 19 63,498 63.5 7.1 28 2.3 18 0.02 392 1,142,964

18 Shinyanga 12 27 50,781 50.8 4.2 39 1.3 55 0.06 76.9 2,792,955

19 Singida 6 13 49,341 49.3 8.2 19 2.6 22 0.02 373.8 1,085,502

20 Tabora 10 23 76,151 76.2 7.6 33 2.3 23 0.02 331.1 1,751,473

21 Tanga 16 29 26,808 26.8 1.7 45 0.6 61 0.06 27.5 1,635,288

22 Pemba 3 5 906 0.9 0.3 8 0.1 428 0.43 0.7 387,768

23 Unguja 14 25 1,554 1.6 0.1 39 0 700 0.7 0.2 1,087,800

Total 464 995 847,263 35,079,928

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Chapter4

a.Inflation‐MoneySupplyCorrelation

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

INFL(annualpercentchange)

6.0 5.1 4.3 5.3 4.7 5.0 7.3 7.0 10.3 12.1 7.2

MO2(annualpercentchange)

23.0 11.8 21.3 21.1 24.8 26.3 22.8 23.8 22.6 23.2 23.6

SimpleCorrelation(E‐View)

INFL MO2

1.000000 0.173494

0.173494 1.000000

b.RealExchangeRateMovements

The calculation of real exchange rates for Tanzania and comparator countries is based on anapproachusedbyRodrik(2008).Datainputs–theofficial,nominalexchangeratetotheU.S.dollar(XRAT) specified per country (i) and per year (t) and the purchasing‐power parity conversionfactors(PPP)–aredrawnfromthePennWorldTables,version7.0.Theprincipalequationis:

Theinterpretationoftheresultisasfollows:AnRERgreaterthanone(1.0)indicatesthatthevalueofacurrencyislower–ormoredepreciated–thanisindicatedbypurchasing‐powerparity.

180

Chapter7

PowerTariffCategories

TariffCategory Definition

DomesticLowUsageTariff(D1)

This category covers domestic customers who on average have aconsumption pattern of 50 kWh. The 50 kwh are subsidized byTANESCO and are not subjected to a service charge. Under thecategory, any unit exceeding 50 kwh is charged a higher rate up to283.4kWh. In this tariff category,power is suppliedata lowvoltage,singlephase(230V).

GeneralUsageTariff(T1)

This segment is applicable for customerswho use power for generalpurposes, including residential, small commercial and light industrialuse, public lighting, and billboards. In this category, the averageconsumption ismore 283.4 kWh permeter reading period. Power isgivenatlowvoltagesinglephase(230),aswellasthreephase(400V).

Low Voltage Maximum Demand(MD)UsageTariff(T2)

Applicable for general use where power is metered at 400V andaverage consumption is more than 7,500 kWh per meter readingperiodanddemanddoesn’texceed500KVApermeterreadingperiod.

High Voltage Maximum Demand(MD)UsageTariff(T3).

Applicableforgeneralusewherepowerismeteredat11KVandabove.

EnergyTariffs(Tshs)

DomesticLowUsage(D1)

GeneralUsage(T1)

LowVoltageMax(T2)

HighVoltageMax(T3)

Zanzibar

LowEnergy(0‐50kWh)‐perkWh 60.00 157.00 94.00 84.00 83.00

ServiceChargeperMonth 2,303.00 8,534.00 8,500.00 8,534.00

DemandChargeperKVA 9,347.00 8,669.00 4,755.00

EnergyChargeperkWh 129.00 85.00 79.00 75.00

181

Note:AllchargesexcludeVATandEWURA.

Source:TANESCO

Chapter8

LevelofEducationAchievedasPercentageofTanzanianPopulationAged15andAbove

LevelAchieved

DaresSalaam OtherUrban Rural MainlandTanzania

91/92 00/01 2007 91/92 00/01 2007 91/92 00/01 2007 91/92 00/01 2007

NoEducation 9.0 7.6 7.9 13.0 13.1 12.1 28.0 29.0 28.5 24.9 25.2 23.6

Adulteducationonly

1.2 0.9 0.4 1.3 1.1 0.7 3.7 2.3 1.2 3.3 2.1 1.1

Primary1–4 8.6 6.4 5.2 14.3 9.8 7.9 15.8 12.8 12.3 15.2 11.9 10.9

Primary5–8 57.0 60.6 57.0 58.8 57.6 58.9 49.0 52.5 52.4 50.7 53.8 54.0

Form1–4 17.4 14.9 16.6 8.9 12.7 13.7 2.1 2.2 4.1 3.9 4.6 7.0

Form5–6 1.4 1.7 2.4 1.0 0.9 1.0 0.1 0.2 0.2 0.3 0.4 0.6

Diploma/university

1.6 2.9 2.6 0.4 0.7 0.9 0.0 0.1 0.3 0.2 0.4 0.6

Courseafterprimary

0.2 1.6 2.0 1.1 1.4 1.4 0.8 0.4 0.5 0.8 0.6 0.8

CourseafterSecondary

2.3 2.7 4.8 0.6 2.2 2.8 0.2 0.2 0.4 0.4 0.7 1.1

CourseafterformVI

n.a n.a 0.8 n.a n.a 0.4 n.a n.a 0.0 n.a n.a 0.2

Othercertificate 1.3 0.8 1.1 0.6 0.6 0.4 0.2 0.2 0.1 0.3 0.3 0.2

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Notes:Adultsareaged15yearsandabove.‘Noeducation’includespre‐schoolin2000/01and2007;pre‐schoolwasnotincludedasacategoryin1991/92.

182

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Documents

Tanzania Growth Diagnostic. Godius Kahyarara, Senior Lecturer, Department of Economics, and Director of Investment . and Resource Mobilization, University of Dar es Salaam