tanzania education sector analysis: beyond primary education, the

TanzaniaBeyond Primary Education, the Quest for Balanced

and Efficient Policy Choices for Human Developmentand Economic Growth

EDUCATION SECTOR ANALYSIS

Regional Bureaufor Education in Africa

TanzaniaBeyond Primary Education, the Quest for Balanced and

Ecient Policy Choices for Human Developmentand Economic Growth

EXECUTIVE SUMMARY

EDUCATION SECTOR ANALYSIS


2011

The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of theExecutive Director of UNESCO or the Government of Tanzania.

Tanzania Education Sector Analysis4

ContentForeword 14Acknowledgments 20Abbreviations 22Executive Summary 26

CHAPTER 1THE CONTEXT OF THE EDUCATION SECTOR 56The Demographic and Social Contexts 58The Macroeconomic Context 61Government Finance 63Total Government Education Expenditure 67Prospects for Increased Public Education Expenditure 71Key Findings 73

CHAPTER 2ENROLLMENT AND INTERNAL EFFICIENCY 76The Structure of the Tanzanian Education System 78Enrollment Dynamics by Education Level 83School Coverage 96Out-of-School Children 107Key Findings 111

CHAPTER 3EDUCATION COST AND FINANCING 116Public Education Expenditure 120Household Education Spending 129Public Recurrent Spending Per Student (Unit Costs) 132Key Findings 154

CHAPTER 4QUALITY AND LEARNING OUTCOMES 158Internal Efficiency of the Education System 160Learning Outcomes 165Key Findings 194

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CHAPTER 5EQUITY IN SCHOOLING 202Equity in Schooling Patterns 204Education Supply and Demand Factors 214Equity in the Distribution of Public Education Resources 228Key Findings 234

CHAPTER 6EXTERNAL EFFICIENCY 240Education and Human Development 242Relevance of Education to the Labor Market 246Key Findings 263

CHAPTER 7PRIMARY AND SECONDARY EDUCATIONMANAGEMENT ISSUES 268Primary Level Administrative Management 270Secondary Level Administrative Management 288Pedagogical Management 299Key Findings 301

CHAPTER 8MANAGEMENT OF HIGHER, TECHNICALAND VOCATIONAL EDUCATION AND TRAINING 306Higher Education 308Technical and Vocational Education and Training (TVET) 317Key Findings 330

CHAPTER 1 ANNEXES 335CHAPTER 2 ANNEXES 338CHAPTER 3 ANNEXES 341CHAPTER 4 ANNEXES 344CHAPTER 5 ANNEXES 363CHAPTER 6 ANNEXES 370CHAPTER 7 ANNEXES 373CHAPTER 8 ANNEXES 390

References 398

Tanzania Education Sector Analysis 5


List of Figures

Figure 1.1 GDP Trends, (FY) 1998/99-2008/09 and Projections 63

Figure 1.2 Trends in Domestic Revenues (Not Including Grants), (FY) 1998/99-2008/09and Projections 64

Figure 1.3 Domestic revenues (Not Including Grants), Selected Countries and Subregions,2008 or MRY 64

Figure 1.4 Recurrent Expenditures After Debt Service and Domestic Revenue,(FY) 1998/99-2009/10 67

Figure 1.5 Share of Education in Government Recurrent Expenditure after Debt Service,Selected Countries and Subregions, 2006 or MRY 70

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Figure 2.1 The Structure of the Tanzanian Education System 79

Figure 2.2 Primary Level Additional Enrollment Intake, over Sets of Two ConsecutiveSchool Years, 2000/01-2008/09 85

Figure 2.3 O-Level Enrollment Trend and Share of Private Sector, 2000-09 86

Figure 2.4 O-Level Enrollment Intake, over Sets of Two Consecutive School Years,2000/01-2008/09 86

Figure 2.5 Distribution of University Students, by Type of Qualification, Academic Year2009/10 90

Figure 2.6 Cross-country Comparison of the Relationship between the Developmentof Higher Education and the Share of Female Students, 2006 or MRY 95

Figure 2.7 Transversal Schooling Profile, 2003-09 99

Figure 2.8 Age Distribution of Standard I New Entrants, 2000, 2004 and 2006 100

Figure 2.9 Comparison of Various African LICs According to their Primary Access andCompletion Rates, 2008 or MRY 101

Figure 2.10 Share of People Having Ever Attended Primary School, by Age, 2006 102

Figure 2.11 Probabilistic Schooling Profile, 2006 102

Figure 2.12 Education Pyramids, for SSA and Tanzania, 2009 or MRY 106

Figure 2.13 School Life Expectancy, Various African Low-income Countries, 2009 or MRY 107

Figure 2.14 Incidence of Out-Of-School Children (Aged 7-13 Years), by SocioeconomicCharacteristic, 2006 108

Figure 2.15 Probability of Being Out of School, by Household Characteristic, 2006 109

Figure 2.16 Frequency of Reasons Cited for Nonattendance, Children Aged 7-13 Years, 2006 109

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Figure 3.1 Total Public Education Expenditure, by Implementing Institution,FY2000/01-FY2008/09 119

Figure 3.2 Real Public Education Expenditure, by Nature, (FY) 2000/01-2008/09 121

Figure 3.3 The Primary Cycles Allocation of Public Recurrent Education Expenditure,by PCR, Tanzania and Comparable African Countries, 2006 or MRY 126

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Figure 3.4 The Secondary Cycles Allocation of Public Recurrent Education Expenditure,by PCR, Tanzania and Comparable African Countries, 2006 or MRY 127

Figure 3.5 Higher Educations Allocation of Public Recurrent Education Expenditure, Sample of African Low-Income Countries, 2006 or MRY 128

Figure 3.6 TVETs Allocation of Public Recurrent Education Expenditure, by Coverage,Tanzania and Comparable African Countries, 2006 or MRY 129

Figure 3.7 International Comparison of Household Spending on Education, by Level,2009 or MRY 131

Figure 3.8 Direct Household Spending per Student, by Level, (FY) 2000/01 and 2007/08 132

Figure 3.9 Secondary Education Public Unit Costs, (FY) 2000/01-2008/09 134

Figure 3.10 Cross-Country Comparison of Public Higher Education Unit Costs, 2006 or MRY 139

Figure 3.11 Other Charges Direct Subsidy per Student (OC Unit Cost), for Selected PublicTechnical Training Institutions, by Subject Area, 2008/09 140

Figure 3.12 Economies of Scale in University Other Charges per Student, 2008/09 141

Figure 3.13 Composition of VET Management-Related Costs, 2009 145

Figure 3.14 TVET Public Recurrent Unit Costs, Selected African Low-income Countries,2006 or MRY 148

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Figure 4.1 Primary Level Repetition Trends, 2000-09 161

Figure 4.2 Proportion of Primary and Secondary Repetition, by Subsector and Grade, 2009 162

Figure 4.3 Proportion of Primary and Secondary Repetition, Various African Countries,2006 or MRY 162

Figure 4.4 Probability of Adult Literacy (22-44 Years), by Highest Grade Completed, 2004 165

Figure 4.5 Number of PSLE Candidates and Share of Female Candidates, 2000-09 167

Figure 4.6 PSLE Grade Distribution, Core Subjects, 2009 168

Figure 4.7 PSLE Grade Distribution, Core Subjects, by Gender, 2009 169

Map 4.1 PSLE Pass Rate, by Region, 2009 169

Figure 4.8 SACMEQ Reading and Mathematics Scores, 2007 171

Figure 4.9 Distribution of SACMEQ Reading (Kiswahili) and Mathematics Results,by Level, 2000 and 2007 172

Figure 4.10 Share of Students Reaching the Minimum Level (Level 4) in Reading (Kiswahili)and Mathematics, by Socioeconomic Characteristic, 2000-07 173

Figure 4.11 Comparison of Teachers and Pupils SACMEQ Reading and MathematicsScores, 2007 177

Figure 4.12 Distribution of the Effect of Pupil, Teacher and School Characteristics on PupilsSACMEQ Reading and Mathematics Scores, 2007 178

Figure 4.13 Number of CSEE Candidates and Share of Female Candidates, 2000-09 179

Figure 4.14 CSEE Pass Rates, by Type of Candidate, 2000-09 180

Figure 4.15 Distribution of CSEE Pass Grades, by Type of Graduate and Gender, 2009 181

Figure 4.16 Distribution of School Candidates CSEE Grades, by Core Subject, 2009 182

Figure 4.17 Number of ACSEE Candidates, and Share of Female and Private Candidates,2000-09 185

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Figure 4.18 ACSEE Pass Rates, by Type of Candidate, 2000-09 186

Figure 4.19 Distribution of ACSEE Pass Grades, by Type of Graduate and Gender, 2009 186

Figure 4.20 VET Pass Rates, for Long Course Tests, 2001-08 189

Figure 4.21 Distribution of Technical Education Examination Pass Scores, by Award, 2008 190

Figure 4.22 Distribution of Higher Education Pass Scores, by Award type, 2008 192

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Figure 5.1 Probabilistic Profiles by Gender, Location, and Income Group, 2006 207

Figure 5.2 Regional Disparities in Primary Access and Retention Probabilities, 2006 211

Figure 5.3 Regional Disparities in Primary Retention and Primary-Secondary TransitionProbabilities, 2006 212

Figure 5.4 Schooling Disparities, EAC and LIC Countries, 2006 or MRY 213

Figure 5.5 Odds Ratios for Primary Access, Primary Retention and Secondary Access, 2006 215

Figure 5.6 Primary Access in Relation to the Distance to a Primary School, by Region, 2006 216

Figure 5.7 Secondary Access in Relation to the Distance to a Secondary School,by Region, 2006 217

Figure 5.8 Age Distribution of Standards I and VII Students, by Area of Residence, 2006 221

Map 5.1 Impact of Supply or Demand Factors in Primary Access, by Region, 2006 224

Map 5.2 Impact of Supply or Demand Factors in Primary Retention, by Region, 2006 225

Map 5.3 Impact of Supply or Demand Factors in Secondary Access, by Region, 2006 226

Map 5.4 Location of HLIs, by Region, 2010 228

Figure 5.9 Lorenz Curve for Tanzania, 2009 230

Figure 5.10 Share of Public Resources Absorbed by the 10 Percent Most Educated,Various African Countries, 2009 or MRY 230

Figure 5.11 Disparity in the Distribution of Public Education Resources, by Level of Income,Area of Residence, and Gender, Various SSA Countries, 2009 of MRY 233

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Figure 6.1 Relationship between Education, Income and Behavior 242

Figure 6.2 Comparison of Higher Education Enrollment Trends and Projections,Tanzania and Regional Pattern, 2006-25 252

Figure 6.3 Distribution of Surveyed VET Graduates, by Employment Sector, 2010 258

Figure 6.4 Employment Rate of VET Graduates, by Sector, 2010 259

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Figure 7.1 Primary Level PTRs, by School Type, 2000-09 275

Figure 7.2 Average PTRs in Government Primary Schools, SADC Countries, 2007 or MRY 276

Map 7.1 Government School Pupil-Teacher Ratios, by Region, 2000 277

Map 7.2 Government School Pupil-Teacher Ratios, by Region, 2009 278

Figure 7.3 Over and Under Supply of Government School Teachers at the District Level,by Region, 2009 279

Figure 7.4 Shares of Qualified and Female Teachers in Government Primary Schools,by Region, 2009 281

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Figure 7.5 Coherence in the Allocation of Primary Teachers among GovernmentSchools, 2007 282

Figure 7.6 Degree of Randomness (1-R) in Government Primary School TeacherAllocation, Subsample of African Countries, 2006 or MRY 283

Figure 7.7 Availability of English Books, by Region, 2009 285

Figure 7.8 Coherence in the District-Level Availability of English Books, for Public PrimarySchools, 2009 286

Figure 7.9 Public Secondary Pupil-Teacher Ratios, Various African Countries, 2009 or MRY 292

Map 7.3 O-Level Pupil-Teacher Ratios (Government schools), by Region, 2009 293

Map 7.4 O-Level Pupil-Qualified Teacher Ratios (Government schools), by Region, 2009 294

Figure 7.10 Coherence in the Allocation of O-Level Teachers among Government Schools,2009 295

Figure 7.11 Degree of Randomness (1-R) in Public O-Level Teacher Allocation,Various African Countries, 2006 or MRY 296

Figure 7.12 Degree of Randomness (1-R) in O-Level Government school TeacherAllocation, by Region, 2009 296

Figure 7.13 Relationship between SACMEQ Scores and Primary Level Unit Costs, 2009 299

Figure 7.14 Relationship between CSEE Pass Rates and Secondary Level Unit Costs, 2009 300

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Figure 8.1 Distribution of HLI Teaching Staff, by Category, 2009/10 314

List of Tables

Table 1.1 Demographic Trends in Tanzania, 1967-2002 and Projections through 2020 59

Table 1.2 International Comparison of Demographic and Social Trends, 2008 or MRY 60

Table 1.3 Gross Domestic Product, (FY) 1998/99-2008/09 and Projections 62

Table 1.4 Overall Government Revenue, (FY) 1998/99-2009/10 65

Table 1.5 Trends in Government Expenditure, (FY) 1998/99-2009/10 66

Table 1.6 Actual Public Education Expenditure, (FY) 2000/01-2008/09 68

Table 1.7 Actual Public Education Expenditure in Macroeconomic Perspective, (FY)2000/01-2009/10 and projections 69

Table 1.8 Scenarios of Educations Share of Recurrent Expenditure, FY 2019/20 Projections 72

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Table 2.1 Enrollment by Level, 2000-09 84

Table 2.2 Enrollment in Technical Institutions, by Subject Area, 2006/07 and 2009/10 88

Table 2.3 University Enrollment Trends, the Share of Nongovernmental Institutions andthe Share of Science Courses, 2003/04-2009/10 89


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Table 2.4 Enrollment Growth Rates, by Level/Subsector, 2000-09 92

Table 2.5 Share of Students Enrolled in Nongovernmental Institutions, 2000-09 93

Table 2.6 International Comparison of the Share of Private Sector Enrollment, 2006 or MRY 94

Table 2.7 Share of Female Student Enrollment, 2000-09 95

Table 2.8 Schooling Coverage, by Level, 2003-09 97

Table 2.9 International Comparison of Enrollment, by Level, 2008 or MRY 98

Table 2.10 Evolution of the Primary Completion Rate, 2003-09 100

Table 2.11 Primary to A-Level Effective Transition Rates, by Level, 2002/03-2008/09 103

Table 2.12 Trends in Pass and Transition Rates, 2000-09 104

Table 2.13 Distribution of Out-of-School Children (Aged 7-13 Years), by SocioeconomicCharacteristic, 2006 108

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Table 3.1 Actual Public Education Expenditure, by Nature, (FY) 2000/01-2008/09 120

Table 3.2 Distribution of Actual Public Education Expenditure, by Nature and Subsector,(FY) 2000/01-2008/09 123

Table 3.3 Reclassification of Public Recurrent Education Expenditure amongPostsecondary Levels, FY 2008/09 124

Table 3.4 Comparison of the Allocation of Public Recurrent Education Expenditure,by Cycle, Tanzania and Selected African Countries Average, 2006 or MRY 125

Table 3.5 Household Spending on Education, by Level, FY 2008/09 130

Table 3.6 Public Spending per Student, by Level, (FY) 2000/01 and 2008/09 133

Table 3.7 HESLB Loans Disbursed, by Level, FY 2008/09 135

Table 3.8 Reconstructed Public Recurrent Expenditure for Higher and TechnicalEducation, by Level, Source, and Type of Expense, FY 2008/09 136

Table 3.9 Higher and Technical Education Public Unit Costs, by Level and Composition,FY 2008/09 137

Table 3.10 Other Charges Direct Subsidy per Student in Technical Institutions, by SubjectArea, FY 2008/09 138

Table 3.11 Social Expenditures, by Level and Type of Institution, FY 2008/09 142

Table 3.12 Distribution of Folk Education Public Recurrent Expenditure, by Key Item,FY 2008/09 144

Table 3.13 Value and Distribution of VETA Public Recurrent Expenditure, by Key Item,2001 and 2009 145

Table 3.14 VETA Income, by Source, 2001 and 2009 146

Table 3.15 Vocational Education and TVET Public Recurrent Unit Costs, FY 2008/09 147

Table 3.16 Composition of Basic Education Public Recurrent Expenditure, by Subsector,FY 2008/09 149

Table 3.17 Capitation Grants per Student, for Primary and Secondary Schools,(FY) 2004/05-2009/10 150

Table 3.18 Average Salaries and Personnel Emoluments, According to the TeacherSalary Scale, 2009 151


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Table 3.19 Average Primary and Secondary Teachers Salary Ranges and Level, by Qualification, 2009 152

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Table 4.1 Primary and Secondary Schooling Internal Efficiency Coefficients, 200009 163

Table 4.2 Primary and Secondary Schooling Internal Efficiency Coefficients, VariousAfrican LICs, by Cycle and Level, 2009 or MRY 164

Table 4.3 PSLE Candidates and Pass Rate, by Gender, and Gender Parity Index, 2000-09 167

Table 4.4 SACMEQ Reading (Kiswahili) and Math Scores and Share of Pupils ReachingMinimum Skill Levels, 2000-2007 170

Table 4.5 The Effect of Socioeconomic Factors on SACMEQ Scores, 2007 175

Table 4.6 CSEE Pass Rate, by Type of Candidate and Gender, 2006-09 180

Table 4.7 School Candidates CSEE Pass Rates and Score Distribution, by Type of School,2009 181

Table 4.8 Main Determinants of CSEE Pass Rates, 2009 183

Table 4.9 Number and Proportion of VET Long Course Learners Completing their Year,by Gender, 2006-08 187

Table 4.10 Number and Proportion of VET Long Course Learners Completing their Year,by Gender and Type of Training Center, 2007 188

Table 4.11 Number and Share of VET Exam Candidates, by Test Entered, 2005-08 189

Table 4.12 Technical Education Examination Finalists, Graduates, and Pass Rates, by Typeof Award and Gender, 2008 190

Table 4.13 Distribution of Technical Education Pass Results, by Award Type, Gender andOwnership, 2008 191

Table 4.14 Higher Education Examination Finalists, Graduates and Examination Pass Rates,by Award, 2008 192

Table 4.15 Distribution of Higher Education Pass Scores, by Award Type and Gender, 2008 193

Table 4.16 Potential Measures to Improve Basic Education Learning Achievements,and their Related Impact and Cost 196

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Table 5.1 Gross Enrollment Ratios and Parity Indexes, by Gender, Area of Residence,and Level of Income, 2006 206

Table 5.2 Cumulated Disparities in Schooling Profiles, by Extreme Group, 2006 208

Table 5.3 Disparities in Primary and Secondary Access Probabilities, by SocioeconomicGroup, 2000 and 2006 209

Table 5.4 Disparities in Retention and Transition Probabilities in Primary and SecondaryEducation, 2006 212

Table 5.5 Distance to the Closest Primary School, by Area of Residence, 2006 216

Table 5.6 Distribution of School-Aged Children According to the Distance to the ClosestSecondary School, by Area of Residence, 2000 and 2006 217

Table 5.7 Number of O-Level Schools per 100,000 School-Aged Children (13-24 Years),and Supply Growth, by Region, 2004 and 2009 219

Table 5.8 Main Reasons for Dropout, Primary and Secondary Levels, by Gender and Areaof Residence, 2006 222


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Table 5.9 Distribution of VTCs by Region, 2008 227

Table 5.10 Distribution of Public Education Resources among a Theoretical Cohort of100 School-Aged Individuals, 2008-09 229

Table 5.11 Distribution of the School-Aged Population (6-30 Years), by SocioeconomicStatus, Location, Gender, and Highest Level Attained, 2006 231

Table 5.12 Benefit Incidence of Public Education Resources, by Level of Income, Area ofResidence, and Gender, 2009 232

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Table 6.1 Simulated Net Impact of Education on Social Behavior in Tanzania, 2004-05 243

Table 6.2 Relative Impact of Primary and Secondary Education Levels on SocialBehaviors, by Indicator and Strength of Impact, 2004-05 245

Table 6.3 Human Development Related Cost-Efficiency of Education, by Level, 2004-05 246

Table 6.4 Employment, Unemployment and Inactivity, with Ratios, 2001 and 2006 247

Table 6.5 Distribution of Employment, by Sector, 2001 and 2006 248

Table 6.6 Education Profile of the Labor Force, by Highest Level Attained andAge-Group, 2001 and 2006 249

Table 6.7 Employment Status of the Labor Force (25-35 Years), by Level of Education, 2006 250

Table 6.8 Projected Higher Education Enrollment Growth, by Catch-up Scenario, 2015,2020 and 2025 253

Table 6.9 Workers Average Income and Years of Schooling (15-60 Years), Salaried andSelf-Employment, 2006 254

Table 6.10 Annual Income, by Education Attainment and Employment Sector, 2006 255

Table 6.11 Long Course TVET Enrollment and Potential Demand, 2009 256

Table 6.12 Reasons Stated by VET Graduates for Unemployment, 2010 260

Table 6.13 Expected Earnings of VET Graduates, and Share below the Poverty Line, bySector, 2010 261

Table 6.14 Comparison of VET Graduates and Self-Employed Income, by Sector, 2006 262

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Table 7.1 Primary School Teacher Characteristics, by School Type, 2000-09 272

Table 7.2 Attrition and its Main Causes, Primary School Teachers, by Gender and TeacherQualification, 2008 274

Table 7.3 Ranking of Regions by Average PTR, Government Primary Schools,2000 and 2009 279

Table 7.4 Ranking of Regions According to the Share of Primary Government SchoolQualified Teachers, 2000 and 2009 281

Table 7.5 Textbook Availability in Government Primary Schools, by Grade, 2009 284

Table 7.6 Secondary School Teacher Characteristics, by School Type, 2000-09 289

Table 7.7 Diploma-Level Teacher Trainees in TTCs, by Type of Training, 2000-09 290

Table 7.8 Share of Secondary Teacher Subject Specializations, by Gender and School

Ownership, 2000-09 290

Table 7.9 Secondary Level PTRs and PqTRs, by School Type, 2000-09 291


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Table 7.10 Secondary Level PTRs and PqTRs, by Subsector and School Type, 2009 292

Table 7.11 Textbook Availability at O-Level, by Type of School and Subject, 2009 297

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Table 8.1 Distribution of Student Loans, by Amount Granted, 2009/10 311

Table 8.2 Age Distribution of HLI Teaching Staff, 2009/10 312

Table 8.3 Teaching and Administrative Staff Numbers, and Share of Female,by HLI Type and Name, 2009/10 313

Table 8.4 Student-Teacher and Student-Administrative Staff Ratios,by HLI Type and Name, 2009/10 316

Table 8.5 Registration and Accreditation Status of HLIs, 2009 319

Table 8.6 Distribution of TE Registered Teaching Staff, by Qualification, 2008/09 322

Table 8.7 Age Distribution of Technical Teaching Staff, 2008/09 322

Table 8.8 Number of VTCs by Registration Status, 2008 326

Table 8.9 Distribution of VET Training Centers, by Type, Ownership and Region, 2008 327



Foreword

This education sector analysis (ESA) for mainland Tanzania is a detailed analyticaldocument that offers a comprehensive picture of mainland Tanzanias educationsector. The main purpose of an ESA (also known as Country Status Report, or CSR)is to provide an evidence-based diagnosis of an education sector, to enabledecision-makers to orient national policies. It also provides relevant analytical

information to nourish the dialogue between the government and education sectorstakeholders, including development partners. In the current development context, markedby the necessity for countries to develop sound, sustainable and credible strategies andplans in which education is embedded, ESAs represent a valuable and essential tool.

This is the second ESA for Tanzania; the first one having been conducted in 2001. Althoughits main objective is to provide a comprehensive picture of the education system in 2009(the last year for which statistics were available), it also provides some analysis of theevolution of the system over the decade, when feasible and relevant. This second report isalso more than an update. It provides more in-depth analysis on certain aspects of thesystem: detailed unit costs by subsector, external efficiency, quality and out-of-school, andtechnical education and vocational training and higher education in particular. It provideskey monitoring and evaluation inputs on the education sector as a whole, that areparticularly valuable in the framework of the implementation of the Education SectorDevelopment Programme.

This 2011 ESA was carried out between February 2009 and November 2010 by a multi-ministerial national team with the support of the Ple de Dakar (UNESCO/BREDA) and theUNESCO Institute of Statistics. It was part of the activities conducted under the EducationSector Management Information System (ESMIS) Programme,1 one goal of which is tosupport the development of capacities in data analysis using data generated by the ESMISand other sources to strengthen sector-wide planning and policy reforms. The ESA processcontributed to the strategy for building capacities in data analysis through a combinationof: (i) learning-by-doing, through a series of workshops, and (ii) theoretical training sessions,offered in parallel to the workshops by the Bureau of Educational Research and Evaluationof the University of Dar es Salaam (BERE/UDSM), based on the SAMES2 materials providedby the Ple de Dakar.

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The analyses presented in this ESA were made possible by using existing data andinformation from multiple sources, and more particularly: school administrative surveysconducted by the Ministry of Education and Vocational Training (BEST, TCU and NACTEdata); household budget, labor force, demographic and health surveys conducted by theNational Bureau of Statistics; and SACMEQ data on learning achievements, includingexamination data from NECTA. Macroeconomic data and government finance statistics wereprovided by MoFEA, and specific data were made available from VETA and the HESLB.Obtaining timely (household surveys, SACMEQ, and payroll data) and reliable key data (EMISdata were fraught with flaws) was a major constraint that has heavily limited the scope ofsome analyses. Nevertheless, some important conclusions have been reached, both on theachievement front, and on the major challenges faced by the education system.

The 2011 ESA has highlighted some interesting achievements, including:

Sustained economic growth and greater public resources have translated into a relativelyhigher education budget. The government spent 4.3 percent of GDP on education inFY 2008/09 (from a low 2.5 percent in FY 2000/01), much more than countries withsimilar levels of development. Education has also been given high budget priority. Thesector benefited from 26.5 percent of recurrent government expenditure after debtservice in FY 2008/09, well above the African low-income countries average of 21.4percent;

Tanzania is on track to achieve the millennium development goal of universal primaryeducation. Access is almost universal and the primary completion rate is close to 90percent. The fee-free primary education policy has had a positive impact by boostingboth access and retention. Tanzanias preprimary gross enrollment ratio is close to 37percent, compared with just 20 percent on average for comparable African countries.Tanzanias administration of this level, using similar teaching approaches as for theprimary cycle and similar school premises, has helped to lower unit costs and increaseenrollment;

Enrollment has increased for all cycles, and particularly in higher education, allowingTanzania to rapidly catch up with the levels of comparable developing countries: in 2009,the number of higher education students in Tanzania was 36 percent lower than theaverage, down from 50 percent in 2006. This trend is likely to continue as a directconsequence of the expected development of secondary education;

The Tanzanian higher education and TVET sectors are well positioned to adequatelymanage the development and diversification of supply. Existing policies and regulatorybodies provide a sufficient, solid and modern institutional framework for the system tobuild upon for its future development;

Education has a significant impact on social and human development, particularly onliteracy, poverty, fertility, and maternal and child health. Primary education is the levelthat has the greatest impact on social outcomes: it contributes to almost 60 percent ofthe total impact, which further reinforces the justification for sustained efforts to ensurethat all Tanzanian children complete at least the primary cycle; and

Education responds to labor market needs. Greater levels of education lead to higherincomes. The wage premium for workers with secondary education is particularlysignificant, suggesting that there is a severe shortage of individuals with secondaryqualifications. There is also a strong connection between vocational training andgraduates employment. In general, the income of VET graduates compares favorablywith that of self-employed individuals with primary education or O-Level secondary.

The 2011 ESA also points to key challenges in the coming years for the development of theeducation sector in Tanzania, including:

Achieving greater efficiency gains (or implementing cost-saving strategies) in the use ofpublic education resources. Indeed, it is unlikely that the current level of budget prioritygiven to the education sector will be maintained over the next decade, due to competingdemands by health, agriculture and infrastructure;

Increasing the public resources allocated to secondary education. Tanzanias secondarycycle receives 35 percent less funding than countries who are equally close to achievinguniversal primary education. This situation should be carefully reviewed to avoidaffecting quality as the sector expands. Secondary schools already display high pupil toteacher ratios (49 to 1);

Ensuring children enter primary school at the right age. Approximately 13 percent ofprimary school-aged children were still out of school in 2006, 88 percent of which hadnever attended. Although poverty is a constraint, age appeared to be the main reasonfor nonattendance. Late primary entry is common (only 36 percent of Standard Istudents were of official school age _ seven years _ in 2006) and is known to have adetrimental impact on schooling paths;


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Improving access to and retention in secondary cycles. Although considerableimprovements in access to secondary school have been noted, especially at O-Level,they are still limited. In 2009, half of children had access to O-Level and 23 percent wereable to reach the last grade of the cycle, up from just eight percent in 2003. A-Levelaccess is still strikingly low, at five percent. Whereas lack of supply is a major hindranceto O-Level and A-Level access, economic difficulties and cultural issues among certainpopulation groups also contribute to fragile school demand. The policy to have asecondary school in each ward has had a very positive impact on secondary access andon primary retention rates;

Supporting pro-poor schooling. Important disparities in access exist according to genderand area of residence, and they increase with successive levels of education, but themost discriminatory factor in schooling patterns is families level of income. It has alsobeen shown that households contributions to education are still significant at theprimary level (equivalent to a quarter of public resources), despite the fee-free primaryeducation policy. Furthermore, disadvantages tend to be cumulative. Poor rural girls facethe worst access and retention conditions;

Taking affirmative action to enhance girls participation in school to ensure gender parityat postprimary levels. Insistence on girls fulfilling their traditional role in society, earlymarriage and pregnancy all favor dropout. Trends could be reversed by: (i) awarenessraising campaigns to sensitize parents on the value of educating girls beyond primary,and on the negative impact of early marriage and pregnancy on schooling and femalehealth; (ii) greater numbers of female teachers and the provision of community-basedhostels to avoid girls the long journeys to and from school, addressing security concerns;and (iii) scholarships and cash transfers targeting bright girls, reducing direct andopportunity costs, mirroring the governments programme targeting the most talentedprimary graduates from poorer backgrounds;

Improving pedagogical management to raise the quality of basic education. Althoughthe improvement dynamic observed in primary education learning outcomes between2000 and 2007 is very encouraging, and better than in neighboring countries, learningachievements are still modest by international standards. In addition, nationalexamination pass rates are dropping, and the results of those who graduate are low,especially at primary and O-Level;

Reducing disparities between regions, districts and schools, that persist despitedecentralization, highlighting the need for effective planning and monitoring tools toallocate education inputs more efficiently. A decentralized information and monitoringsystem could help by providing decision makers with timely, accurate and reliable data onthe education sector. In addition to an EMIS system, financial and human resourcemanagement systems would improve fiscal management and accountability. A firstresponse to this challenge was given in 2009, with the development of a pilot decentralizedBasic-Education Management Information System (BE-MIS). Tested in 28 district councilsin 14 regions, the BE-MIS is to be scaled up to all councils nationwide by 2014; and


Adequate planning of TVET and higher education expansion. The increase in primaryand secondary school enrollments is already placing much strain on secondary, TVETand higher education institutions. An urgent response is required to ensure the smoothand manageable development of these subsectors.

The challenges faced by higher education are of particular importance:

It is essential that funding mechanisms be improved. Higher education is blatantlyinefficient, paying little attention to potential economies of scale. In addition,approximately 28 percent of the levels budget is devoted to badly targeted socialexpenditures, particularly loans transferred directly to students: 48 percent of studentsbenefit from a loan, yet less than 10 percent come from the poorest quintiles, whichcalls for an improvement in the loan targeting mechanisms; and

Students career objectives and the distribution of graduates by subject area must beadjusted, to achieve better relevancy of higher education programmes to the labormarket and enable Tanzania to keep abreast of rapid technological development andneeds. Science subjects in particular attract too few students (only 24 percent ofstudents for the 2007/08 academic year, down from 34 percent in 2003/04). Adequateanalytical tools should be implemented, such as labor market tracer surveys.

Technical education and vocational training will also be key to Tanzanias development.Some of the key required actions that this ESA highlights for the subsector include:

Strengthening the subsectors coordination mechanisms. Although regulatory andquality assurance bodies provide important guarantees for the controlled developmentof the TVET subsector, it still faces a series of challenges, including: (i) the diversity oftraining demand linked to the heterogeneity of the target population; (ii) the institutionalfragmentation of technical education, under the umbrella of various ministries; (iii) thefragmentation of vocational education and training service delivery, involving twoministries and a parastatal agency; and (iv) the practical continuity between vocationaland technical curricula and programmes, although theoretically bridges do exist, asdefined by the national qualifications framework;

Revising subsector budget trade-offs. The Tanzanian TVET system as a whole is not asunderfunded as in many other African countries. However, technical nonhighereducation absorbs almost 57 percent of all TVET resources, against just 37 percent forvocational training, and six percent for folk education. This funding imbalance shouldbe reduced in order to scale-up vocational education and training activities; and


Fore

wor

d

Defining a funding formula to rationalize the allocation of resources among technicalinstitutions. Surprisingly, it has been noticed that planning and welfare courses are twiceas expensive as health and allied science courses. However, even for a given subjectarea, and among institutions with comparable levels of enrollment, variations in theresources allocated are sizeable. This situation merits an improved funding formula andfor more coordination in planning and budgeting among parent ministries.

More broadly, this ESA offers valuable and comprehensive resources to anyone interestedin the education sector in Tanzania. It is however a snapshot of the system at a particulartime. As the sector makes progress in implementing its sector plan, this reports findingsare therefore likely to become outdated, although many features will remain valid. It is thehope of both the Ministry of Education and development partners that this document willbe of use to all stakeholders in the education sector.


1 The Education Sector Management Information System (ESMIS) Programme is implemented by the government of Tanzania withthe financial and technical support of development partners (the European Union, UNESCO, UNICEF, and UNFPA), within theoverall framework of the Education Sector Development Programme for 2008-17. The UNESCO Institute of Statistics is providingtechnical assistance through its permanent Dar es Salaam cluster office.

2 The Sectoral Analysis and Management of the Education System (SAMES), also known as the PSGSE (Politiques Sectorielles etde Gestion des Systmes Educatifs) is a masters degree offered by the University Cheikh Anta Diop of Dakar (Senegal) with thesupport of the Ple de Dakar, targeting Ministry of Education staff and other actors working in the field of education in Africa.The training is currently available in French. An English course is currently under development with the University of The Gambia.For the purpose of this ESA, all training modules were translated into English and made available to BERE.

Dr. ShukuruKawambwa (MP)Minister of Education andVocational TrainingTanzania

Vibeke JensenDirector

and RepresentativeUNESCO Dar es Salaam

Office for Comoros,Madagascar,

Mauritius, Seychellesand Tanzania

Ann Therese Ndong-JattaDirector


UNESCO


AcknowledgmentsThis Education Sector Analysis was prepared through a close collaborative effort by thegovernment of Tanzania, the Ple de Dakar (UNESCO/BREDA), the UNESCO Institute ofStatistics, and the UNESCO Dar es Salaam cluster office.

The government team consisted of staff from the different ministries in charge of education,led by the Ministry of Education and Vocational Training (MoEVT), as well as other ministriesand departments, including the Ministry of Community Development, Gender and Children(MCDGC), the Ministry of Finance and Economic Affairs (MoFEA), the Prime Ministers Officefor Regional Administration and Local Government (PMO-RALG), the National ExaminationsCouncil of Tanzania (NECTA), the National Council for Technical Education (NACTE), theTanzania Commission for Universities (TCU), the Vocational Education and Training Authority(VETA), the National Bureau of Statistics (NBS) and the Bureau for Educational Research andEducation of the University of Dar es Salaam (BERE/UDSM), which was instrumental infacilitating all theoretical workshops.

The government team was successively led by Cyprian Miyedu, former Chief of theMonitoring and Evaluation (M&E) Section, Department of Policy and Planning of MoEVT,the late George Maliga, Chief of the M&E Section of MoEVT, and Muhwela Kalinga, ActingChief, M&E Section, under the overall leadership of Professor H.O. Dihenga, the PermanentSecretary of MoEVT. Related administrative issues were handled by Mr Malili and Ms Levira.For Chapters 1 and 3, the government ESA team consisted of Ms Baitwa (Chapters head,Budget and Finance Division, MoEVT), Ms Elinzu (NBS), Mr Kitali (PMO-RALG), Ms Luena(EMIS, MoEVT), Mr Minja (Administration and Personnel, MoEVT), Mr Mtyama (MoEFA), MsOmolo (TMC-DPLO/LGA Temeke District Council) and Mr Zullu (Administration andPersonnel, MoEVT). Mr Pambe (Chapters head, Primary Education, MoEVT), Ms Kiisheweko(TCU), Ms Levira (Adult Education, MoEVT), Mr Maiga (Adult Education, MoEVT), MrMchunguzi (Higher Education, MoEVT), Ms Sigwejo (NACTE), Mr Saro (FDC, MCDGC) andMr Wilberforce (EMIS, MoEVT) constituted the government team for Chapters 2 and 5. Theteam for Chapter 6 included Mr Mhagama (Chapter head, VETA Division, MoEVT), MrMisana (Technical Education, MoEVT), Mr Malili (Higer Education, MoEVT), Mr Mwakapalala(NBS), Mr Ndamgoba (FDC, MCDGC), Mr Petro (EMIS, MoEVT) and Mr Sunday (MIS,MCDGC). The government team for Chapters 4, 7 and 8 was composed of Mr Mwenda(Chapters head, Secondary Education, MoEVT), Mr Gabriel (LGA Bagamoyo, PMO-RALG),

Ack

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Mr Kinunda (Higher Education, MoEVT), Mr Nzoka (Teacher Training, MoEVT), Mr Mbowe(NECTA), Ms Mrigo (Administration and Personnel, MoEVT), Mr Pambe (Primary Education,MoEVT), Mr Ponera (EMIS, MoEVT) and Mr Shauri (Primary Education, MoEVT). Chapter 7received additional inputs from staff from the Inspection Department of MoEVT.

The Ple de Dakar (UNESCO/BREDA) team consisted of Borel Foko (Team Leader, EducationPolicy Analyst) and Diane Coury (Education Policy Analyst), under the overall guidance ofJean-Pierre Jarousse (former Head of the Ple de Dakar) and Mohammed Bougroum (Headof the Ple de Dakar). Inputs were also provided by the Ple members Alain Patrick NkengneNkengne, Mireille Harivola Ravelojaona and Ibrahima Dao.

The team received constant support from the UIS team of the UNESCO Dar es Salaam clusteroffice, which consisted of Marc Bernal (UIS Regional Advisor for Eastern and SouthernAfrica), Criana Connal (former EMIS Programme Specialist) and Erick Makoye and AbdulatifMin-Hajj (IT specialists). Special thanks are due to Marc Bernal and Criana Connal whoprovided strong support and facilitated the policy dialogue throughout the process.

The UNESCO Dar es Salaam cluster office was also instrumental in the effective elaborationof the ESA. The team would particularly like to thank Min Jeong Kim (Education ProgrammeSpecialist) who helped complete the process and Flora Rusenene and Rahma Islem for theirconstant administrative support. Special thanks are due to Barnaby Rooke for the editingwork and Regis LHostis for the graphic design.

The team received valuable comments from the peer reviewers Criana Connal, Jean-PierreJarousse, Jean-Marc Bernard, Agripina Habicht, Monica Githaiga, and Joseph Vere, as wellas from the development partner groups led by Corey Huntington (Canadian HighCommission).

The preparation of this report was funded by the Education Management InformationSystem (EMIS) Programme, financially supported by multiple donors, under theadministrative responsibility of the UIS/UNESCO-Dar es Salaam cluster office, and by thePle de Dakar (UNESCO/BREDA).


AbbreviationsACSEE Advanced Certificate of Secondary Education Examination

A-Level Advanced Level

AE/NFE Adult Education and Nonformal Education

AKU Aga Khan University

ARU Ardhi University

BE-MIS Basic Education - Management Information System

BEST Basic Education Statistics in Tanzania

CBET Competence-Based Education and Training

COBET Complementary Basic Education in Tanzania

CPRS Contrats Programme de Russite Scolaire (School Performance Contract)

CSEE Certificate of Secondary Education Examination

DbyD Decentralization by Devolution

DEO District Education Officer

DSE Department of Secondary Education of MoVET

DUCE Dar es Salaam University College of Education

EAC East African Community

EFA Education For All

ECCD Early Childhood Care and Development

EMAC Educational Material Approval Committee of MoEVT

EMIS Education Management Information System

FBO Faith-Based Organization

FDC Folk Development College

FY Fiscal Year

GDP Gross Domestic Product

GER Gross Enrollment Rate

GPI Gender Parity Index

HBS Household and Budget Survey

HE Higher Education

HEDP Higher Education Development Programme

HESLB Higher Education Student Loan Board

HKMU Hubert Kairuki Memorial University

HLI Higher Learning Institution

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ns


IAE Institute of Adult Education

ICBAE Integrated Community-Based Adult Education

ICT Information and Communication Technology

IEC Internal Efficiency Coefficient

IIEP International Institute for Educational Planning

IMF International Monetary Fund

IMTU International Medical & Technological University

IUCO Iringa University College

KCK Postbasic Literacy (ICBAE component)

KCM Basic Literacy (ICBAE component)

KCMC Kilimanjaro Christ Medical College

LGA Local Government Authority

LGRP Local Government Reform Programme

LIC Low-Income Country

LOITASA Language of Instruction in Tanzania and South Africa - A research project

MCDGC Ministry of Community Development, Gender and Children

MCST Ministry of Communication, Science and Technology

MDAs Ministries and Department Agencies

MDRI Multilateral Debt Relief Initiative

MEM Ministry of Energy and Minerals

MHA Ministry of Home Affairs

MHEST Ministry of Higher Education, Science and Technology

MHSW Ministry of Health and Social Welfare

MICS Ministry of Information, Culture and Sports

MID Ministry of Infrastructure Development

MITM Ministry of Industry, Trade and Marketing

MJCA Ministry of Justice and Constitutional Affairs

MLFD Ministry of Livestock and Fisheries Development

MLHHSP Ministry of Lands, Housing and Human Settlements Development

MMU Mount Meru University

MNRT Ministry of Natural Resources and Tourism

MoEVT Ministry of Education and Vocational Training

MoFEA Ministry of Finance and Economic Affairs


MRY Most Recent Year

MUCCOBS Moshi University College of Cooperative and Business Studies

MUCE Mkwawa University College

MUCO Makumira University College

MUHAS Muhimbili University of Health & Allied Sciences

MUM Muslim University of Morogoro

MWUCE Mwenge University College

MU Mzumbe University

NABE National Business Examinations

NACTE National Council for Technical Education

NBS National Bureau of Statistics

NECTA National Examinations Council of Tanzania

NGO Nongovernmental Organization

NTA National Technical Awards

ODL Open Distance Learning

OUT Open University of Tanzania

O-Level Ordinary Level

PASEC Programme on the Analysis of Education Systems (Programme d'Analysedes Systmes Educatifs de la CONFEMEN Confrence des MinistresdEducation des Pays Ayant le Franais en Partage)

PCR Primary Completion Rate

PEDP Primary Education Development Plan

PETS Public Expenditure Tracking Survey

PIRLS Progress in International Reading Literacy Study

PMO-RALG Prime Ministers Office - Regional Administration and Local Government

PO-PSM Presidents Office - Public Service Management

PSLE Primary School Leaving Examination

PTR Pupil-Teacher Ratio

RUCO Ruaha University College

SACMEQ The Southern and Eastern Africa Consortium for Monitoring EducationalQuality

SADC Southern African Development Community

SAUT St. Augustine University of Tanzania

SEDP Secondary Education Development Plan

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SEKUKO Sebastian Kolowa University College

SJUT St. John's University of Tanzania

SMC School Management Committee

SMMUCO Stefano Moshi Memorial University College

SSA Sub-Sahara Africa

STHEP Science, Technology and Higher Education Project

SUA Sokoine University of Agriculture

SUZA State University of Zanzibar

TASAF Tanzania Social Action Fund

TCU Tanzania Commission for Universities

TDHS Tanzania Demographic and Health Survey

TDMS Teacher Development and Management Strategy

TEKU Teofilo Kisanji University

THMIS Tanzania HIV/AIDS and Malaria Indicator Survey

TIE Tanzania Institute of Education

TIMSS Trend in International Mathematics and Science Study

TSD Teachers Service Department

TT Trade Test

TTC Teacher Training College

TUDARCO Tumaini University Dar es Salaam College

TVET Technical and Vocational Education and Training

UCEZ University College of Education Zanzibar

UDOM University of Dodoma

UDSM University of Dar es Salaam

UoA University of Arusha

UPE Universal Primary Education

URT United Republic of Tanzania

VETA Vocational Education and Training Authority

VTC Vocational Training Center

WBUCHS Weill Bugando University College of Health Sciences

ZU Zanzibar University

Executive Summary1. In a context of high demographic pressure, Tanzania has mobilized important

public resources to adequately address the growing demand for education.

The total population is expected to grow by 32 percent between 2010 and 2020. Over thesame period, the under 15 years age-group will remain constant at 44.2 percent of the total.The primary school-aged population (seven to 13 years) is projected to reach 10.2 million by2020, corresponding to an additional 1.8 million children compared with 2009.

The government has given high budget priority to the education sector: in FY 2008/09,education was allocated about 26.5 percent of government recurrent expenditure after debtservice, higher than the East African Community average (25.1 percent), and than the otherAfrican low-income countries average (21.4 percent). In terms of GDP, the increase issignificant: from 2.5 percent of GDP in FY 2000/01 to 4.3 percent of GDP in FY 2008/09, avalue that is also higher than the average for all African low-income countries (3.3 percent).

Population (million)

Annual Growth Rate (%)

Sex Ratio (number of boys per 100 girls)

Population Under 15 Years (% of total)

Urban Population (% of total)

Demographic Trends and Projections, 1967-2020

12.3

n.a.

95.2

6.4

34.4

3.0

96.0

46.5

23.1

43.2

2.9

96.9

44.4

26.3

Census-Years

1967 2002 2010 2020

NBS-Projections

57.1

2.8

98.6

44.2

29.7

2000/01

2004/05

2008/09

Percentage of Actual Public Expenditure Allocated to Education

16.6

6.1

1.19

0.56

20.4

18.2

RecurrentExpenditure

DevelopmentExpenditure

TotalExpenditure

% of Total

24.3

23.3

26.5

% of Total,(after debt)

2.5

3.1

4.3

% of GDP % of GDP % of Total % of GDP

4.3

4.9

Source: NBS data and projections (NBS, 2006; URT, 2005); and authors estimates.

Source: Authors calculations based on Tables 1.3, 1.4, 1.5 and 1.6.


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Improved government capacities to mobilize significant resources directly from nationalincome. This has indeed allowed domestic revenues to increase from 9.2 percent ofGDP in FY 1998/99 to 15.9 percent of GDP in FY 2008/09. It is imperative that thegovernment supports this favorable trend in domestic revenue collection, to reduceits dependency on foreign aid, which has represented almost 40 percent of total publicresources since FY 2002/03.

2. The allocation and use of public education resources is still not optimal however.

The increase in recurrent public education expenditure has been followed by significant changesin subsector allocations. Over the decade, the share of primary education (including preprimaryeducation) has decreased from 58 percent to 48 percent, a level similar to that of othercountries close to achieving universal primary education. Most primary education savings havebenefited higher education, whose share of resources has increased to 27 percent of the totaleducation budget, making the subsector one of the best financed among African countries.

Secondary education continues to be heavily underfunded. In 2008/09, it absorbed 13.5percent of education public resources; a level far below countries that are equally close toachieving universal primary education.

500,000

450,000

400,000

350,000

300,000

250,000

200,000

1998

2000

2002

2004

2006

2008

2009

**

2010

**

2014

**19

9920

0020

0220

0420

0620

08

2009

**

2010

**

2014

**

Constant 2001 T Sh

GDP per Capita Annual GDP Growth Rate

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0Percent

GDP Trends (FY) 1998/99-2008/09 and Projections

Source: Based on Table 1.3.Note: **Projections.

Along with the high budget priority given to the education sector, this positive evolutionwas also made possible following:

Impressive and sustained economic growth registered over the last decade. Over the2000-08 period, the average annual economic growth rate was estimated at 7.1percent, a higher figure than the African low-income countries average, of 6.2 percent.This trend is likely to strengthen given that average GDP per capita (about US$ 565 in2010) remains lower than the African low-income countries average (US$ 800).



This situation has led to a sharp 50 percent reduction in public spending per student at thesecondary level, while it has increased in all other subsectors. The Tanzanian secondary unitcost is only two-thirds of the African LIC average, while the higher education unit cost (theaverage for university and higher technical education) is 20 percent higher. While thegovernments strategy to expand secondary education is not matched by current budgettrade-offs within the sector, options to increase secondary education funding must beexplored to ensure the quality of the service delivered is not harmed. Although it may not bepossible to reallocate funds from higher education to secondary, the government should lookfor efficiency gains and/or potential cost-saving measures within the higher education sector.

The TVET system is better funded than in many African countries, receiving seven percent ofpublic education resources, against five percent on average for the latter. However, mainallocation issues stem from funding imbalances amongst its different subsectors. Indeed,while technical nonhigher education absorbs almost 57 percent of all TVET resources,vocational training receives just 37 percent, against a low six percent for folk education. Thisfunding imbalance should be reduced in order to scale-up vocational education and trainingactivities. For technical education, the high level of randomness in resource allocation amonginstitutions is a problem, that is mainly linked to striking unit costs in specific programs.

Source: Tables 3.2 and 3.3 and authors calculations based on MoFEA and EMIS data for Tanzania; and Ple de Dakar/UNESCO-BREDA for other countries.Note: * Based on countries with similar primary school duration (7 years) and closer to UPE; ** Based on countries with similar secondaryschool duration (7 years) and closer to UPE; *** Based on the averages of all African low-income countries for which data were available.

Primary

Secondary

TVET

Technical Nonhigher

VETA

Folk Education

Higher Education

University Education

Technical Higher

Other

Preprimary

Teacher Training

Adult and Nonformal Education

Total

Mainland Tanzania (2008/09) Comparable African Countries Average

Comparison of the Allocation of Public Recurrent Education Expenditure,by Cycle, Tanzania and Selected African Countries Average, 2006 or MRY

44.2

13.5

7.0

4.0

2.6

0.4

26.9

23.6

3.4

8.3

4.5

2.5

1.3

100.0

43.6

26.3

5.0

20.8

4.8

100.0

*

**

***

***

***

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The way resources are used highlights potential room for improvement. Indeed, evidenceshows that:

Basic education focuses too little on spending that directly improves the quality of theservice delivered;

In secondary education, capitation grant spending is 40 percent lower than the norm,and student meals absorb four times as much of the budget;

Teacher training colleges also overspend on student meals, to the tune of 90 percentof nonsalary expenditures;

Preprimary and primary pupil to teacher ratios are excessively high, partly because highsalaries constitute a constraint to further recruitment. Secondary PTRs are also wellabove par, due to a quantitative and qualitative shortage of teachers; and

In higher education, social spending is excessive (28 percent of higher education unitcosts not including scholarships for study abroad), and inequitable (almost 48 percentof students receive a loan, although less than 10 percent are from the poorest quintiles).

3. Households and the private sector contribute considerably to the cost ofschooling, at varying degrees according to the level of education.

Households contribute significantly to education funding; their spending is equivalent to 32.1percent of public education expenditure. This is however comparatively lower than in otherLICs (48 percent on average). Despite the fee-free primary policy, household contributionsremain important: a quarter of primary public education costs are covered by households.This raises some concern as for the poorest households, as it might be a major obstacle tosend their children to schools. At the higher education level, the cost-sharing mechanismseems to be effective, reducing the governments financial burden. But its effectiveness overthe long run will very much depend on the capacity of the HESLB to recover loans.

Secondary Education Public Unit Costs, (FY) 2000/01 - 2008/09

280

260

240

220

200

180

160

140

120

100

2000

/01

2001

/02

2002

/03

2003

/04

2004

/05

2005

/06

2006

/07

2007

/08

2008

/09

T Sh

(00

0s)

136

269

248

Thousands of Constant 2008/09 T Sh

Source: Authors calculations based on MoFEA and BEST and EMIS data.


International Comparison of Household Spending on Education, by Level, 2009 or MRYPercentage Equivalent of Public Recurrent Education Expenditure

Primary Secondary Higher/Tertiary Average

90

80

70

60

50

40

30

20

10

0

48

3230

53

83

21

41

26

Mainland Tanzania

African LICs

The role of the private sector varies greatly across sectors. On the one hand it is marginal atthe preprimary and primary levels (where expansion has mainly been supported by the publicsector), and decreasing at O-Level and to a lesser extent at A-Level, following the governmentspolicy of increasing secondary access. On the other hand, the expansion of the teacher trainingand higher education subsectors increasingly relies on cost-sharing, favoring the developmentof private sector contributions. In 2009, 39 percent of students were enrolled in private TeacherTraining Colleges, against five percent in 2004. In technical education, all folk developmentcourses are government-run, but those delivered through vocational centers are increasinglyprivate, reflecting the ministrys policy of diversification to promote the subsector.

Source: Table 3.5 for Tanzania; Rwanda CSR, 2010 and Brossard et al., 2008 for 17 African low-income countries.Note: 18 African low-income countries are considered here: Benin, Burkina Faso, Cameroon, Chad, Congo, Cte dIvoire, Djibouti,Guinea Bissau, Madagascar, Malawi, Mali, Mauritania, Niger, Rwanda, Senegal, Sierra Leone, Togo and Uganda.

Source: BEST, NACTE, TCU, various years; authors computations for Tanzania. World Bank and Ple de Dakar/UNESCO-BREDA forother countries.Note: * Refers to NACTE-registered institutions.

Preprimary

Primary

O-Level

A-Level

Teacher Training

Technical Education *

VET (VTC Long Courses)

Higher Education

Share of Students Enrolled in Nongovernmental Institutions, 2004-09Percent

1.3

0.6

38.0

48.6

5.4

7.4

2.3

1.0

26.6

38.6

9.3

15.5

67.8

19.4

7.8

1.3

14.2

36.4

23.7

16.2

23.9

Tanzania

2004 2006 2008 2009 2009 or MRY

Average LIC

5.0

1.5

10.8

32.3

38.6

28.2

16.7

20.4

27.7

19.5

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4. School enrollment has increased at all levels.

The preprimary sector is comparatively well developed. The policy to mainstream the provisionof preprimary teaching through primary schools (thus controlling unit costs) has enabled agrowing number of young children to benefit from this level. Coverage at the preprimary levelreached 37 percent in 2009, up from 26 percent in 2004. This is a very reasonable level ofpreschool attendance compared with the 20 percent average of other countries in the region.

Source: Table 2.1, and census projections for Tanzania.Note: * TVET includes VTC and FDC long courses, and nonhigher technical education; ** Higher education includes universities,university colleges and higher technical education.

Tanzania is on the way to reaching universal primary education, but late entry still remainsa major challenge and many children are still out of school. Access to Standard I is almostuniversal, although 5.5 percent of children did not have access to primary school in 2006.The primary completion rate has steadily increased over the past decade, to reach at least89 percent in 2009. The fee-free primary education policy and extensive classroomconstruction have had positive impacts on both primary access and retention levels. Thesystem is still marked by considerable late entry however: only 36 percent of Standard Istudents were of official school-age in 2006.

Source: DHS, 2004; HBS, 2000/01 and 2007; authors computations.

Age at Standard I

Perc

ent

1.0 0.45.0 3.8

11.0

17.0

22.6

36.0

23.0

30.7

22.019.0

20.5

14.017.0

11.89.0 9.0

5.12.0

5.03.0 2.0

5.02.1 1.0

Age Distribution of Standard I New Entrants, 2000, 2004 and 2006Percent

5 6 7 8 9 10 11 12 13+

40353025201510

50

2000

2004

2006

3.0

2003

2004

2005

2006

2007

2008

2009

Preprimary PrimarySecondary

O-Level

GER (%) Per 100,000 inhabitants

A-Level AllTVET * Higher

Education **

Schooling Coverage, by Level, 2003-09Percent, and Students per 100,000 inhabitants

26.3

29.3

29.8

34.4

36.7

36.6

104.5

109.5

113.1

115.9

117.6

115.4

112.4

10.5

12.8

15.2

19.0

28.3

33.0

38.6

1.9

2.2

2.3

3.0

3.4

3.6

3.9

7.8

9.5

11.2

14.0

20.5

23.8

27.7

235

252

250

174

291

335

2003

2004

2005

2006

2007

2008

2009

Preprimary PrimarySecondary

O-Level

GER (%) Per 100,000 Inhabitants

A-Level AllTVET * Higher

Education **

Schooling Coverage, by Level, 2003-09Percent, and Students per 100,000 Inhabitants

26.3

29.3

29.8

34.4

36.7

36.6

104.5

109.5

113.1

115.9

117.6

115.4

112.4

10.5

12.8

15.2

19.0

28.3

33.0

38.6

1.9

2.2

2.3

3.0

3.4

3.6

3.9

7.8

9.5

11.2

14.0

20.5

23.8

27.7

235

252

250

174

291

335


This situation tends to inflate out-of-school statistics. Indeed, among the 925,000 estimatedout-of-school (representing 13 percent of primary school-aged children in 2006), 88 percenthad never attended. Should all children enter on time, the number of children estimated tonever attend school would drop to 425,500. Given its detrimental impact on schoolingpaths (exposing them to greater risk of early dropout), ensuring that children attend schoolat the correct age should be a priority. MoEVT may address both supply and demandconstraints, for instance through sensitization campaigns to alter parents perceptions aboutthe appropriate age for school attendance, assisted further by the expansion of ECCDprogrammes.

School coverage at secondary and higher education levels is still low compared with otherAfrican countries, but is rapidly increasing, especially at the higher education level. Schoolcoverage is particularly low at A-Level, where only four out of 100 school-aged childrenwere enrolled in 2009, one of the lowest rates of all African low-income countries. Thesituation is less problematic at O-Level, for which the GER reached 39 percent in 2009, upfrom a low 10.5 percent in 2003.

Considerable emphasis has been put on higher education, to adequately meet the growingdemand from secondary school leavers and produce skills relevant to current and futureeconomic growth. University enrollment has grown at an average annual rate of 30 percentover 2005-09, among the highest annual growth rates registered for all subsectors (althoughit started with lower enrollment), allowing Tanzania to rapidly catch up with the levels ofcomparable developing countries. In 2009, the number of higher education students inTanzania was 36 percent lower than the average, down from 50 percent in 2006. However,university and technical higher education coverage remains low, at 335 students per 100,000inhabitants in 2009/10, against 381 students per 100,000 in other low-income countries.

Source: Table 2.8 for Tanzania; World Bank and Ple de Dakar/UNESCO-BREDA for other countries.Note: To allow for international comparisons: * TVET includes VET and FDC long courses and NACTE-registered technical nonhighereducation; and ** Higher education includes universities, university colleges and technical higher education.

Tanzania (2008)

Burundi

Kenya

Rwanda

Uganda

East African Community Average

African Low-income Countries

Average

Min Max

Preprimary Primary LowerSecondary

GER (%) Per 100,000 inhabitants

UpperSecondary

TVET * Higher Education **

International Comparison of Enrollment, by Level, 2008 or MRYPercent, and Students per 100,000 inhabitants

36.7

5.4

54.0

18.0

3.7

23.5

20.4

0.8 141

115.4

115.3

114.7

151.0

120.7

122.8

103.1

56.9 157.7

33.0

22.9

94.6

28.0

28.1

42.4

43.4

15.9 94.6

3.6

6.0

39.6

9.0

10.3

13.7

17.2

2.6 39.6

252

156

74

440

115

212

228

35 484

291

243

359

474

329

337

381

61 1009

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Tanzania (2008)

Burundi

Kenya

Rwanda

Uganda



Average

Min Max



UpperSecondary


International Comparison of Enrollment, by Level, 2008 or MRYPercent, and Students per 100,000 Inhabitants

36.7

5.4

54.0

18.0

3.7

23.5

20.4

0.8 141

115.4

115.3

114.7

151.0

120.7

122.8

103.1

56.9 157.7

33.0

22.9

94.6

28.0

28.1

42.4

43.4

15.9 94.6

3.6

6.0

39.6

9.0

10.3

13.7

17.2

2.6 39.6

252

156

74

440

115

212

228

35 484

291

243

359

474

329

337

381

61 1,009

Tanzania (2008)

Burundi

Kenya

Rwanda

Uganda



Average

Min Max



UpperSecondary


International Comparison of Enrollment, by Level, 2008 or MRYPercent, and Students per 100,000 Inhabitants

36.7

5.4

54.0

18.0

3.7

23.5

20.4

0.8 141

115.4

115.3

114.7

151.0

120.7

122.8

103.1

56.9 157.7

33.0

22.9

94.6

28.0

28.1

42.4

43.4

15.9 94.6

3.6

6.0

39.6

9.0

10.3

13.7

17.2

2.6 39.6

252

156

74

440

115

212

228

35 484

291

243

359

474

329

337

381

61 1,009


TVET education coverage in Tanzania is higher than in other low-income countries (250students per 100,000 inhabitants in 2009, compared with 228 students per 100,000).Seventy percent of TVET students are registered on vocational courses (in VTCs and FDCs),whereas 30 percent are in nonhigher technical learning streams. The sector still falls short ofthe huge needs in TVET programmes for primary and secondary school leavers. The currentannual flow of students into vocational education represents less than five percent of thepotential demand for VET services, while technical nonhigher education covers about 22percent of potential demand. This underlines the urgency for the diversification of TVETprovision, offering more short and tailor-made courses to enhance productivity and the qualityof products and services.

The number of teacher trainees has increased over the decade, with the exception of the2007-08 period that registered a decrease in TTC trainees (places were more limited as aresult of the extension of the curricula from one to two years in 2006). However, given thegrowing demand for teachers at all levels, the pursuit of the expansion of teacher training isto be closely monitored and planned, so as to not jeopardize the development of the primaryand secondary school system.

Literacy programmes cover just a quarter of the target population. Similarly, COBETprogrammes only cater for a small fraction of out-of-school children, and their efficiency inmainstreaming childrens return to school is weak.

Access to postprimary levels still remains challenging for many children. Although strongimprovements in access to secondary have been noted, especially at O-Level, they are stilllimited. In 2009, half of children had access to O-Level and 23 percent were able to reachthe last grade of the cycle, against just eight percent in 2003. A-Level access is still strikinglylow, at five percent. Whereas lack of supply is a major hindrance to O-Level and A-Levelaccess, economic difficulties and cultural issues among certain groups also contribute tofragile school demand. With respect to the former, the policy to have a secondary school ineach ward has had a very positive impact on secondary access and on primary retentionrates. The pursuit of the policy is expected to improve both O-Level and A-Level access andretention in the coming years.


The increase in primary and secondary school enrollments is already putting a lot of strainon secondary, TVET and higher education institutions, and enrollment at these levels isexpected to grow more rapidly still over the coming years. An urgent and well-plannedresponse is required to ensure the smooth and manageable development of the system andthat it remains in line with labor market needs. This raises both financial and practicalchallenges (teacher requirements, classroom supply). A sectorwide financial simulationmodel may help to explore policy options, assessing both facilities and required resources.

5. Dropout is still a problem at postprimary levels however, despite generally goodinternal efficiency levels.

While internal efficiency is generally good, dropout remains a problem, particularly atpostprimary levels. Tanzanias education system is comparatively efficient at both primary andO-level, and its A-Level efficiency is in line with the African low-income countries average.The primary IEC was estimated at 88 percent in 2007, implying that 12 percent of resourcesare wasted due to repetition or dropout. Repetition being generally low (2.4 percent in primaryand under two percent in secondary, on average in 2009), dropout is the main source of

Education Pyramid for Tanzania, 2009

23%

3%

5%

55%

108%

108%

Upp

erSe

cond

ary

Higher Education:335 Students

per 100,000 inhabitants

TVET:6% of Secondary

GER = 4%

GER = 39%

GER = 112%

Low

erSe

cond

ary

Prim

ary

49%

33%

Source: Tables 2.8 and 2.11 and Figure 2.7.Note: TVET refers to technical non-higher education and VET courses (both VETA and NACTE-registered).

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Improving retention will necessarily require addressing both supply and demand constraints.This could entail:

Alleviating schooling direct and opportunity costs. Although most of the interventionscited above (regarding the expansion of secondary education for instance) should alsofavor primary school retention, special attention should be given to costs borne byparents, that increase with successive grades and levels. School feeding programmesand cash transfer programmes are being implemented to compensate, but furthercost-benefit analysis is necessary before expanding them, mainly because of theirnotoriously high cost;

Further improving school supply. Schools with incomplete cycles are known tonegatively affect retention. Although this issue appears to be marginal in Tanzania,scope for improvement nevertheless exists at the primary level: satellite schools, knownto offer incomplete cycles, could possibly be converted into full-cycle schools throughmultigrade teaching. At postprimary levels, building more schools will prove decisive;and

inefficiency, especially at O-Level and A-Level. More efforts are needed to reduce dropout inorder to improve the overall internal efficiency of the system, and reduce resource wastage.

Source: BEST, various years.Note: * Not provided as 2009 primary schooling patterns are highly affected by the multicohort phenomenon, which tends tounderestimate dropout; ** Because 2007 A-level repetition data were not available, the proportion observed in 2009 was assumedto have remained constant over the period. The change in the A-Level IEC is therefore only related to the rise in dropout.

Primary

Internal Efficiency Coefficient

Dropout-Related (no Repetition)

Repetition-Related (no Dropout)

Years Required to Completion

O-Level





A-Level





Primary and Secondary Schooling Internal Efficiency Coefficients, 2000-09

67

69

97

10.5

82

83

98

4.9

88

92

96

7.9

83

85

98

4.8

83

84

99

2.4

2000 2007 2009

81

82

98

5.0

72

73

99

2.8

**

**

*

Percent and Number of Years


At the primary level, closely monitoring repetition would be helpful, especially forStandard I, that has the highest proportion of repeaters. However, as ECCDprogrammes expand and the school preparedness of children improves, this issueshould resolve itself. Assessing the relevance and quality of teaching would beworthwhile, as dropout is often justified by a lack of interest in school.

6. Important disparities persist in access to formal schooling according to gender,area of residence and especially families income levels; and, they tend to becumulative.

Beyond the primary level, girls participation in education is systematically lower than that ofboys. Gender parity indexes decrease from 1.04 (girls enrollment is greater than boys) inprimary school to 0.65 at the higher/tertiary level. TVET is still slightly gender-oriented: malestudents accounted for 55 percent of trainees in 2008. At the higher education level, femaleenrollment has barely reached 34 percent: girls are doubly prejudiced by their lower chancesof reaching secondary school, and by their comparatively lower results in the ACSEE exam.

Schooling inequalities are particularly unfair to children from rural areas. Children fromurban areas have better access probabilities to all levels of education than their rural peers,in part due to the inadequate supply of rural schools. The gap in the probability of accessreaches 23 percentage points for O-Level entry, and eight percentage points for A-Levelentry.

Gender

Male

Female

Gender Parity Index (Female/Male)

(Memo: Index, 2000)

Area of Residence

Urban

Rural

Location Parity Index (Rural/Urban)

(Memo: Index, 2000)

Income Group

Q5 (The wealthiest)

Q1 (The poorest)

Wealth Parity Index (Q1/Q5)

(Memo: Index, 2000)

Total Tanzania

GERs and Parity Indexes, by Socioeconomic Characteristic, 2006

29.9%

27.2%

0.91

0.89

45.9%

23.8%

0.52

0.53

48.1%

23.0%

0.48

0.21

28.6%

114.6%

118.8%

1.04

0.95

119.6%

115.8%

0.97

0.79

125.3%

117.1%

0.93

0.82

116.6%

31.7%

30.2%

0.95

1.13

56.6%

21.9%

0.39

0.13

64.8%

19.1%

0.30

0.23

30.9%

7.2%

6.0%

0.83

0.95

16.2%

2.6%

0.16

0.09

26.8%

1.6%

0.06

0.19

6.6%

2.9%

1.9%

0.65

0.75

n.a. *

7.9%

0.0%

0.00

0.15

2.4%

Preprimary Primary O-Level A-Level Higher

Source: HBS, 2007, authors calculations.Note: The location parity index is irrelevant to higher learning institutions, that are all located in urban areas. Reading Note: A gender parity index of 0.83 (2006, A-Level) indicates that for every 100 boys enrolled, there were 83 girls.

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The unavailability of a school nearby is often a major hindrance (in some rural areas, 22percent of children live over five kilometers away). There is clearly potential to build moreschools in underserved areas, compensating the cost by offering multigrade teaching underclose supervision. Lack of interest in school is also a major reason for nonattendance(mentioned by 12 percent) that might be counter arrested by improving the relevancy andquality of teaching.

Disparities in access increase sharply with successive levels of education, especially thoserelated to income. Wealth parity indexes decrease from 0.94 in primary school to 0.09 atA-Level, and are virtually nil at the higher/tertiary level. Retaining the poorest students inprimary schools and ensuring their transition to postprimary cycles is a major challenge.Although the abolition of school fees has been a major measure in alleviating educationexpenses, the poorest households still face prohibitive schooling costs (uniforms, stationery,books, and so on). Interventions specifically targeting these households, such as cashtransfers, may help to remove economic and financial barriers. Better coverage of thescholarship grants and remedial classes should make schooling more equitable for the poor.

Furthermore, disadvantages tend to be cumulative. Poor rural girls face the worst accessconditions, and disparities tend to broaden as of the end of primary (for every 100 rich urbanboys completing primary, only 53 poor rural girls do). They then explode at postprimary levels,leaving poor rural girls with virtually no opportunities to pursue secondary education.

Finally, literacy programmes targeted at parents should give positive results, mainly bygradually overcoming cultural barriers to education. The encouragement of families andschools to ensure that all children have birth certificates (although not strictly an educationsector intervention), may also have a positive impact on school access and retention.

Access disparities by region are equally marked. For instance, primary access and retentionare particular issues in Rukwa, Tabora and Dodoma regions. Beyond school supplyconstraints, economic, cultural and environmental issues (agro-pastoral activities, culturalbeliefs, tobacco production and climate conditions) shape demand and keep children outof school. In 2006, secondary access probabilities were as low as four percent in one region,and were just 16 percent in five others, well below the national average of 27 percent.Extensive primary and secondary school construction has contributed to loosen school supplyconstraints in many of those regions since.

Source: HBS, 2007: authors calculations.

Primary Access

Primary Completion

O-Level Access

O-Level Completion

A-Level Access

A-Level Completion

Cumulated Disparities in Schooling Profiles, by Extreme Group, 2006Percent

98.8

94.2

55.4

36.5

21.3

12.8

Male/Urban/Q5 Female/Rural/Q1 Parity Ratio

92.5

50.1

7.1

1.1

0

0

0.94

0.53

0.13

0.03


Socioeconomic Status

Q1

Q2

Q3

Q4

Q5

Area of Residence

Rural

Urban

Gender

Girls

Boys

Benefit Incidence of Public Education Resources, by Level of Income, Area of Residence, and Gender, 2009

Percent, and Appropriation Index

27.0

23.8

20.0

17.3

11.9

74.0

26.0

52.3

47.7

Share of thePopulation

(%)

(a)

12.7

15.4

21.1

18.0

32.8

47.1

52.9

45.7

54.3

Public Resources

Absorbed (%)

(b)

0.5

0.6

1.1

1.0

2.8

0.6

2.0

0.9

1.1

AppropriationRatio

(b)/(a)

1.0

1.4

2.2

2.2

5.9

1.0

3.2

1.0

1.3

AppropriationIndex

TVET and higher education opportunities are also unequal across areas and regions. Justfive regions (Dar es Salaam, Iringa, Arusha, Kilimanjaro and Mwanza) are home to almost55 percent of VTCs. HLIs are also particularly present in cities and the eastern part of thecountry. The expansion of open distance learning will be crucial in breaking the urban/ruralfracture.

Regional Disparities in Primary Access and Retention Probabilities, 2006

Primary Access Probability (%)

Prim

ary

Rete

ntio

n Pr

obab

ility

(%) 110

100

90

80

70

60

50

40

88 90 92 94 96 98 100 102

TaboraKigoma

ArushaKilimanjaro Dar

IringaMaraRuvuma

KageraMbeyaTanga

Mwanza

ShinyangaLindi Morogoro

MtwaraSingindaDodoma

Manyara Rukwa

Pwani

Source: Authors calculations based on probabilistic profiles using HBS, 2007 data.

Source: Authors calculations based on Annex Table 5.8.

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Regional Disparities in Primary Access and Retention Probabilities, 2006

Primary Access Probability (%)

Prim

ary

Rete

ntio

n Pr

obab

ility

(%) 110

100

90

80

70

60

50

40

88 90 92 94 96 98 100 102

D A

C B

TaboraKigoma

ArushaKilimanjaro Dar

IringaMaraRuvuma

KageraMbeyaTanga

Mwanza

ShinyangaLindi Morogoro

MtwaraSingindaDodoma

Manyara Rukwa

Pwani

Lake Natron

Arusha45 Kilimanjaro

37

Tanga54

Manyara52

Singida56

Tabora68

Shinyanga73

Mara62

Lake Victoria

Kagera61

Kigoma59

Rukwa65

Mbeya55

LakeTangyanika

Dodoma56

Pwani42

DSM49

Lindi55

Mtwara52

Ruvuma48

Iringa45

Morogoro48


The distribution of public education resources is therefore unequal, benefiting the mostprivileged students. Indeed, the 10 percent most educated benefit from 47 percent of publiceducation resources, in line with the LIC average. The benefit incidence analysis furthershows that boys benefit from 30 percent more public education expenditure than girls. Dueto longer schooling, 33 percent of public resources ar

tanzania education sector analysis: beyond primary education, the

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