tanzania bukombe dc cwiq 2006
TRANSCRIPT
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PMO-RALG
BUKOMBE DC CWIQ
Survey on Poverty, Welfare andServices in Bukombe DC
December 2006
Implemented by:
EDI (Economic Development Initiatives)
PO Box 393, BukobaTanzania
Telephone and Fax: +255-(0)28-2220059Email:
www.edi-africa.com
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ACKNOWLEDGEMENTS
This research was commissioned by the Prime Ministers Office Regional
Administration and Local Governance (PMO-RALG) and implemented by EDI(Economic Development Initiatives). It is part of an effort to conduct CWIQ surveys in
34 districts across Tanzania. The project Director is Joachim De Weerdt. Field workoperations are being co-coordinated by Respichius Mitti and Francis Moyo. Fieldsupervision was in the hands of Matovu Davies, Wilson Kabito, Henry Kilapilo, Henry
Lugakingira, Josephine Lugomora, George Musikula, and Neema Mwampeta. The listing
team was formed by Felix Kapinga and Benjamin Kamukulu. Interviewers were Dativa
Balige, Geofrey Bakari, Rukia Charles, Abbanova Gabba, George Gabriel, JamaryIdrissa, Felix James, Sampson Mutalemwa Gloria Joseph, Placidia Josephat, Justina
Katoke, Makarius Kiyonga, Faustine Misinde, Jesca Nkonjerwa, Kamugisha Robert,
Resti Simon, Pius Sosthenes, Aissa Soud, Adella Theobald, and Honoratha Wycliffe. Thedata processing software was written by Jim Otto and Neil Chalmers. The data entry team
consisted of Mary Stella Andrew and Alieth Mutungi, and was supervised by Thaddaeus
Rweyemamu. Formatting the final document layout was in the hands of Amina Suedi.The data analysis and report writing were undertaken by Luis Barron, John Ibembe,
Ngasuma Kanyeka, Ezekiel Kiagho, and Teddy Neema under the supervision of Manuel
Barron. Assistance from Charles Citinka and Howard Clegg from PMO-RALG is
acknowledged.
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DEFINITIONS
General
Accessible Village Within a district, accessible villages are villages
located closer to the district capital, all-weather
roads, and public transport.
Remote Village Within a district, remote villages are villages
located villages located farther from the district
capital, all-weather roads, and public transport.
Socio-economic Group The socio-economic group of the household is
determined by the type of work of the mainincome earner.
Poverty Predictors Variables that can be used to determine
household consumption expenditure levels innon-expenditure surveys.
Basic Needs Poverty Line Defined as what a household, using the food
basket of the poorest 50 percent of the
population, needs to consume to satisfy its basic
food needs to attain 2,200 Kcal/day per adultequivalent. The share of non-food expenditures
of the poorest 25 percent of households is then
added. The Basic Needs Poverty Line is set atTZS 7,253 per 28 days per adult equivalent unit
in 2000/1 prices; households consuming lessthan this are assumed to be unable to satisfy theirbasic food and non-food needs.
Education
Literacy Rate The proportion of respondents aged 15 years or
older, who identify themselves as being able toread and write in at least one language.
Primary School Age 7 to 13 years of age
Secondary School Age 14 to 19 years of age
Satisfaction with Education No problems cited with school attended.
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Gross Enrolment Rate The ratio of all individuals attending school,irrespective of their age, to the population of
children of school age.
Net Enrolment Rate The ratio of children of school age currently
enrolled at school to the population of childrenof school age.
Non-Attendance Rate The percentage of individuals of secondary
school-age who had attended school at some
point and was not attending school at the time ofthe survey.
Health
Need for Health Facilities An individual is classed as having experiencedneed for a health facility if he/she had suffered
from a self-diagnosed illness in the four weeks
preceding the survey.
Use of Health Facilities An individual is classed as having used a health
facility if he/she had consulted a health
professional in the four weeks preceding thesurvey.
Satisfaction with Health
Facilities
No problems cited with health facility used in the
four weeks preceding the survey.
Vaccinations BCG: Anti-tuberculosis
DPT: Diphtheria, Pertussis3, Tetanus
OPV: Oral Polio Vaccination
Stunting Occurs when an individuals height is
substantially below the average height in his/her
age-group.
Wasting Occurs when an individuals weight is
substantially below the average weight forhis/her height category.
Orphan A child is considered an orphan when he/she haslost at least one parent and is under 18 years.
Foster child A child is considered foster if neither his/herparents reside in the household
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Employment
Working Individual An individual who had been engaged in any type
of work in the 4 weeks preceding the survey.Underemployed Individual An individual who was ready to take on more
work at the time of the survey.
Non-working Individual An individual who had not been involved in any
type of work in the 4 weeks preceding the
survey.
Unemployed Individual An individual who had not been engaged in any
type of work in the 4 weeks prior to the survey
but had been actively looking for it.
Economically Inactive
Individual
An individual who had not been engaged in any
type of work in the 4 weeks prior to the surveydue to reasons unrelated to availability of work
(e.g. Illness, old age, disability).
Household duties Household tasks (cleaning, cooking, fetchingfirewood, water, etc.) that do not entail payment
Household worker A household worker performs household dutiesbut received payment.
Household as employer A person is said to be employed by his/her
household if he/she does domestic/household
work for the household they live in (e.g. ahousewife or a child that works on his/her
parents fields or shop). It does not include
people whose main job was domestic work for
other households (private sector).
Welfare
Access to Facilities A household is considered to have access to
facilities if it is located within 30 minutes of
travel from the respective facilities.
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TABLE OF CONTENTS
1. INTRODUCTION.. 1
1.1 The Bukombe District CWIQ... 1
1.2 Sampling... 1
1.3 Constructed variable to disaggregated tables.... 2
1.3.1 Poverty Status.... 21.3.2 Cluster Location..... 4
1.3.3 Socio-economic Group...... 4
2 VILLAGE, POPULATION AND HOUSEHOLDS CHARACTERISTICS.......... 7
2.1 Introduction... 7
2.2 Main Population Characteristics... 7
2.3 Main Household Characteristics....... 9
2.4 Main Characteristics of the Heads of Household.......... 11
2.5 Orphan and Foster Status...... 14
3 EDUCATION.. 17
3.1 Overview Education Indicators........ 173.1.1 Literacy...... 17
3.1.2 Primary School Access Enrolment and Satisfaction.......... 17
3.1.3 Secondary School Access, Enrolment and Satisfaction......... 20
3.2 Dissatisfaction....... 21
3.3 Non-Attendance.... 22
3.4 Enrolment and Drop Out Rates......... 23
3.5 Literacy..... 24
4 HEALTH...... 27
4.1 Health Indicators....... 27
4.2 Reasons for Dissatisfaction... 28
4.3 Reasons for Not Consulting When Ill....... 304.4 Type of Illness... 31
4.5 Health Provider..... 31
4.6 Child Deliveries.... 32
4.7 Child Nutrition...... 34
5 EMPLOYMENT...... 39
5.1 Employment Status of Total Adult Population......... 39
5.1.1 Work Status........ 39
5.1.2 Employment of Household Heads......... 40
5.1.3 Youth Employment.... 40
5.2 Working Population...... 41
5.3 Underemployment Population.......... 43
5.4 Unemployed Inactive Population.......... 45
5.5 Household Tasks... 46
5.6 Child Labour..... 48
6 PERCEPTIONS ON WELFARE AND CHANGES WITHIN COMMUNITIES 49
6.1 Economic Situation....... 49
6.1.1 Perception of Change in the Economic Situation of the Community.............. .. 49
6.1.2 Perception of Change in the economic Situation of the Household.............. 51
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6.2 Self- reported Difficulty in Satisfying Household Needs............. 52
6.2.1 Food Needs.... 53
6.2.2 Paying School Fees.... 55
6.2.3 Paying House Rent. 57
6.2.4 Paying Utility Bills..... 57
6.2.5 Paying for Healthcare......... 58
6.3 Assets and Household Occupancy Status......... 58
6.3.1 Assets Ownership... 59
6.3.2 Occupancy Documentation ... 59
6.4 Agriculture.... 60
6.4.1 Agriculture Inputs...... 60
6.4.2 Landholding....... 62
6.4.3 Cattle Ownership.... 62
6.5 Perception of Crime and Security in the Community........... 63
6.6 Household Income Contribution....... 64
6.7 Other House Items........ 65
7 HOUESHOLD AMENITIES... 67
7.1 Housing Materials and Typing of Housing Unit.......... 67
7.2 Water and Sanitation..... 70
7.3 Type of Fuel...... 73
7.4 Distance to Facilities..... 74
7.5 Anti -Malaria Measures........ 76
8 GOVERNANCE...... 79
8.1 attendance at Meeting... 79
8.2 Satisfaction with Leaders.. 79
8.3 Public Spending.... 81
9 CHANGES BETWEEN 2004 AND 2006... 83
9.1 Household Characteristics......... 84
9.2 Education...... 84
9.3 Health.... 84
9.4 Households Assets and Perception of Welfare............................................................. 86
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LIST OF TABLES
Table 1.1 Variables used to predict on consumption expenditure in Shinyanga Region....... 1
Table 1.2 Predicted vs. actual poverty, Shinyanga Region, 2000/......... 2
Table 1.3 Cluster location................... 3
Table 1.4 Socio-economic group............ 3
Table 1.5 Socio-economic group and gender of household............ 4
Table 1.6 Socio-economic group and main economic activity........... 5
Table 2 1 Percent distribution of total population by gender and age............ 7
Table 2.2 Dependency ratio .............. ......... 8
Table 2.3 Percent distribution of households by number of household members...... 8
Table 2.4 Percent distribution of total population by relation to head of household.............. 9
Table 2.5 Percent distribution of the total population age 12 and above by marital status............ 10
Table 2.6 Percent distribution of the total population age 5 and above by socio-economic group.... 10
Table 2.7 Percent distribution of the total population age 5 and above by highest level of education.. 11
Table 2.8 Percent distribution of heads of households by marital status................ 12
Table 2.9 Percent distribution of heads of households by socio-economic group.......... 12
Table 2.10 Percent distribution of heads of household by highest level of education .............. ..... 13
Table 2.11 Percent distribution of children under 18 years old who have lost their mother and /or father... 14
Table 2.12 Percent distribution of children under 18 years old living without mother and/or father.... 15
Table 3.1 Education indicators............... 18
Table 3.2 Percentage of students currently enrolled in school with reasons for dissatisfaction... 19
Table 3.3 Percentage of children 7-9 years who ever attended school by reasons not currently attending 21
Table 3.4 Primary School enrolment and drop out rates by age and gender.............. 22
Table 3.5 Secondary school enrolment and drop out rates by age and gender... 23
Table 3.6 Adult literacy rates by age and gender (persons age 15 and above)............... 24
Table 3.7 Youth literacy rates by age and gender (persons age 15-24).............. 25
Table 4.1 Health Indicators............. .... 27
Table 4.2 Percentage of persons who consulted a health provider in the 4 weeks proceeding the survey
and were not satisfied, and the reasons for dissatisfaction....................... ............. ............... ...... 28
Table 4.3 Percentage of persons who did not consulted a health provider in the 4 weeks preceding
the survey and the reasons for not consulting.... 29
Table 4.4 Percentage of population sick or injured in the 4 weeks preceding the survey, and those sick or
injured the percentage by type of sickness/injury.......................................................................... 30
Table 4.5 Percentage distribution of health consultation in past 4 weeks by type of health provider
consulted..................................................................................................................................... 31
Table 4.6 Percentage of women aged 12-49 who had a live birth in the year proceeding the survey by age
of the mother and the percentage of those births where the mother received pre-natal care......... 32
Table 4.7 Percentage distribution of births in the five years preceding the survey by place of birth..... 33
Table 4.8 Percentage distribution of births in the five years preceding the survey by person who assisted
in delivery of the child................. .............. ............... ............... ............... ............... .............. ........... 34
Table 4.9 Nutrition status indicators and program participating rates........ 35
Table 4.10 Percent distribution of children vaccination by type of vaccination received.......... 36Table 4.11 Percent distribution of children vaccinated by source of information...... 37
Table 5.1 Percentage distribution of the population by working status (age 15 and above)...... 39
Table 5.2 Principal labour force indicators (persons age 15 and above)........ 40
Table 5.3 Percentage distribution of the population by work status (age 15 -24).. 41
Table 5.4 Percentage distribution of the working population by type of payment in main job......... 41
Table 5.5 Percentage distribution of the working population by employer....... 42
Table 5.6 Percentage distribution of the working population by activity............... 42
Table 5.7 Percentage distribution of the working population by employer, sex and activity............. 43
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Table 8.2 Distribution of leaders' satisfaction ratings and reasons for dissatisfaction... 80
Table 8.3 Percentage distribution of households who received financial information in the past 12
months............................................................................................................................................. 81
Table 8.4 Satisfaction with public spending and reasons for dissatisfaction...... 82
Table 9.1 Household Characteristics...... 83
Table 9.2 Education.... 84
Table 9.3 Health.............. ............... ............... ............. ............... .............. ............... ............... ............. ............. 85Table 9.4 Household assets and perception of welfare................. ............... .............. ............... ............... ....... 86
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Generic Core Welfare Indicators (2006)
Total
Margin of
error* Accessible Remote Poor Non-poor
Household characteristics
Dependency ratio 1.3 0.1 1.3 1.2 1.8 1.1
Head is male 88.1 2.2 87.1 89.4 81.8 89.3
Head is female 11.9 2.1 12.9 10.6 18.2 10.7Head is monagamous 57.8 2.6 61.2 53.6 63.8 56.7
Head is polygamous 23.9 2.7 18.7 30.5 15.1 25.6
Head is not married 18.2 2.3 20.1 15.9 21.1 17.7
Household welfare
Worse now 69.8 4.3 65.0 75.9 79.1 68.0
Better now 17.1 2.7 19.6 14.1 5.0 19.5
Worse now 29.2 6.0 26.6 32.6 33.0 28.5
Better now 43.2 4.7 44.9 41.0 27.7 46.2
Food 37.3 4.2 30.1 46.3 64.5 32.0School fees 0.6 0.5 0.3 1.1 0.0 0.7
House rent 0.8 0.7 1.3 0.2 0.0 1.0
Utility bills 1.0 0.8 1.8 0.0 0.0 1.2
Health care 15.8 3.0 12.2 20.4 24.4 14.1
Agriculture
Less now 2.1 0.8 2.2 2.0 4.6 1.7
More now 1.6 0.5 1.2 2.1 0.0 1.9
Less now 8.6 2.0 7.7 9.9 5.7 9.2
More now 7.2 1.2 6.3 8.3 4.0 7.8
Yes 38.5 4.0 39.6 37.0 20.9 41.9
Fertilizers 59.6 5.8 53.0 68.5 43.1 61.2
Improved seedlings 57.3 8.7 61.2 51.9 48.7 58.1
Fingerlings 0.0 0.0 0.0 0.0 0.0 0.0
Hooks and nets 0.0 0.0 0.0 0.0 0.0 0.0
Insecticides 22.5 5.4 15.7 31.7 23.8 22.4
Other 0.0 0.0 0.0 0.0 0.0 0.0
Household infrastructure
Secure housing tenure 9.5 3.8 14.5 3.1 1.2 11.1
Access to water 85.3 3.3 91.1 77.8 73.7 87.5
Safe water source 77.8 4.6 76.3 79.6 64.7 80.3Safe sanitation 3.4 1.5 6.1 0.0 0.0 4.0
Improved waste disposal 19.0 5.1 18.6 19.6 8.9 21.0
Non-wood fuel used for cooking 0.0 0.0 0.0 0.0 0.0 0.0
Ownership of IT/Telecommunications Equipment
Fixed line phone 0.6 0.5 1.0 0.0 0.0 0.7
Mobile phone 14.4 4.1 23.0 3.5 0.0 17.2
Radio set 60.2 4.0 71.8 45.5 16.3 68.7
Television set 1.1 0.9 2.0 0.0 0.0 1.4
Household economic situation compared to one year ago
Difficulty satisfying household needs
Use of agricultural inputs
Neighborhood crime/security situation compared to one year ago
Land owned compared to one year ago
Cattle owned compared to one year ago
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EmploymentEmployer in the main job
Civil service 0.8 0.5 1.4 0.0 0.0 0.9
Other public serve 0.0 0.0 0.0 0.0 0.0 0.0
Parastatal 0.0 0.0 0.0 0.0 0.0 0.0
NGO 0.0 0.0 0.0 0.0 0.0 0.0
Private sector formal 2.2 0.9 3.7 0.4 0.6 2.6Private sector informal 53.6 3.9 57.5 48.8 53.6 53.7
Household 36.2 3.1 29.7 44.5 41.3 35.1Activity in the main job
Agriculture 52.9 6.8 37.7 71.9 69.2 49.3
Mining/quarrying 3.1 2.3 5.3 0.3 2.8 3.2
Manufacturing 0.1 0.1 0.1 0.0 0.0 0.1
Services 1.8 0.9 3.3 0.0 1.1 2.0Employment Status in last 7 days
Unemployed (age 15-24) 0.0 0.0 0.0 0.0 0.0 0.0
Male 0.0 0.0 0.0 0.0 0.0 0.0
Female 0.0 0.0 0.0 0.0 0.0 0.0
Unemployed (age 15 and above)) 0.0 0.0 0.0 0.0 0.0 0.0
Male 0.0 0.0 0.0 0.0 0.0 0.0
Female 0.0 0.0 0.0 0.0 0.0 0.0Underemployed (age 15 and above) 18.0 2.0 18.5 17.4 19.5 17.7
Male 27.8 4.2 29.0 26.3 29.8 27.4Female 9.0 1.3 9.1 9.0 10.4 8.7
EducationAdult literacy rate
Total 62.1 2.8 67.2 55.7 57.7 63.1
Male 74.3 2.8 79.0 68.4 68.9 75.4
Female 50.9 3.3 56.5 43.6 47.8 51.6
Youth literacy rate (age 15-24)
Total 69.0 3.7 71.3 66.3 64.0 70.2
Male 76.7 3.3 75.8 77.6 68.3 79.1
Female 63.2 5.7 67.9 57.4 59.7 63.9Primary school
Access to School 73.4 6.5 83.3 59.7 60.5 79.0
Primary Gross Enrollment 88.7 3.5 86.7 91.5 71.5 96.2
Male 91.3 6.1 84.6 99.3 68.0 103.7
Female 86.6 3.1 88.2 84.3 75.2 90.8
Primary Net Enrollment 67.6 2.9 69.4 65.2 55.5 73.0
Male 63.8 3.7 61.0 67.3 48.8 71.8
Female 70.7 3.4 75.5 63.3 62.4 73.8
Satisfaction 50.8 5.6 57.8 41.6 41.8 53.7
Primary completion rate 7.7 2.1 5.7 10.4 6.7 8.1
Secondary school
Access to School 16.7 5.0 27.3 6.8 2.4 21.3Secondary Gross Enrollment 4.7 2.2 5.9 3.7 1.6 5.7
Male 7.2 3.3 7.3 7.1 2.5 9.4
Female 2.1 2.1 4.4 0.0 0.0 2.6
Secondary Net Enrollment 4.1 2.0 4.5 3.7 1.1 5.1
Male 6.0 3.3 4.7 7.1 1.7 7.9
Female 2.1 2.1 4.4 0.0 0.0 2.6
Satisfaction 80.7 14.9 71.7 94.0 100.0 78.9
Secondary completion rate 1.0 1.0 2.1 0.0 0.0 1.4
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Medical servicesHealth access 34.4 7.3 44.7 20.5 14.6 40.2
Need 14.6 0.9 13.4 16.3 15.0 14.5Use 18.7 1.7 18.0 19.6 20.1 18.3
Satisfaction 89.4 2.1 88.3 90.8 87.6 90.0
Consulted traditional healer 4.4 1.4 2.0 7.5 4.4 4.5
Pre-natal care 96.9 2.3 97.6 95.5 94.5 97.6
Anti-malaria measures used 70.9 4.5 77.7 62.3 44.7 76.0
Person has physical/mental challenge 1.0 0.2 1.3 0.6 1.8 0.8
Child welfare and healthOrphanhood (children under 18)
Both parents dead 1.0 0.4 1.3 0.6 0.1 1.4
Father only 5.3 1.5 3.5 7.9 9.8 3.7
Mother only 2.2 0.9 3.4 0.6 0.5 2.8
Fostering (children under 18)Both parents absent 10.2 1.7 10.9 9.2 12.0 9.6
Father only absent 12.7 2.3 11.9 13.9 19.2 10.4
Mother only absent 3.4 1.2 4.2 2.2 2.1 3.8
Children under 5
Delivery by health professionals 49.1 5.7 53.9 41.3 37.2 52.7
Measles immunization 62.7 4.0 59.9 67.3 47.9 67.1
Fully vaccinated 24.4 3.0 27.2 19.8 15.0 27.2
Not vaccinated 15.6 4.0 17.2 13.1 25.0 12.9
Stunted 25.2 2.7 20.7 32.1 38.9 21.3
Wasted 1.2 0.6 0.4 2.4 0.5 1.3
Underweight 8.4 2.3 6.2 11.8 11.1 7.7
* 1.96 standard deviations
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Estimate SE Signif.
Net Enrolment RatePrimary School 73.3 67.6 -5.7 4.7 -15.1 3.8
Secondary School 6.8 4.1 -2.7 2.1 -5.1 3.4ate o
Dissatisfaction with
School 70.9 46.3 -24.6 12.1 ** 5.0 -48.4
Reasons for Dissatisfaction
Books/Supplies 75.4 15.3 -60.1 13.0 *** -64.0 -12.2
Poor Teaching 17.7 12.2 -5.5 4.2 ** 0.7 17.4Lack of Teachers 82.6 86.0 3.4 14.6 * -1.7 56.7
d Condition of Facilities 38.5 29.9 -8.6 8.7 *** 12.5 47.3
Overcrowding 7.8 21.2 13.4 7.1 ** 1.5 29.8
Health Facility
Consulted
Private hospital 6.2 8.4 2.2 2.7 -3.3 7.7
Government hospital 54.5 48.7 -5.8 6.9 -19.5 7.9
Traditional healer 3.3 4.4 1.1 1.8 -2.5 4.8Pharmacy 22.5 36.5 14.0 9.9 -5.8 33.8
ate o
Dissatisfaction with
Health Facilities 33.3 10.6 -22.7 4.9 *** -33.2 -13.4
Reasons for Dissatisfaction
Long wait 15.1 11.2 -3.9 7.1 -17.5 10.9
of trained professionals 47.4 16.7 -30.7 12.8 ** -54.1 -2.8Cost 41.5 5.1 -36.4 9.7 *** -55.3 -16.6
No drugs available 51.2 21.9 -29.3 15.7 * -58.6 4.7
nsuccessful treatment 18.2 42.4 24.2 8.5 ** 4.4 38.6
2004 2006 Change
95% Confidence Interval
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Water and Sanitation
Piped water 0.0 5.1 5.1 4.1 -3.0 13.2
Protected well 50.2 77.5 27.3 7.5 *** 11.4 41.2
No toilet 4.7 7.6 2.9 2.6 -1.3 9.1
Flush toilet 2.4 3.4 1.0 1.7 -1.0 5.9
Covered pit latrine 85.9 82.2 -3.7 4.5 -13.1 4.9
Uncovered pit latrine 7.1 6.8 -0.3 3.2 -6.6 6.3
Child Delivery
Hospital or Maternity 72.0 41.4 -30.6 8.3 *** -47.4 -14.3
Delivery Assistance
Doctor/Nurse/Midwife 54.4 42.2 -12.2 9.8 -31.8 7.5
TBA 27.2 15.5 -11.7 8.0 -27.7 4.3
Self-assistance 18.4 41.9 23.5 6.8 *** 9.8 37.1
Child Nutrition
Stunted 31.4 25.2 -6.2 6.7 -20.5 6.2
Severely Stunted 13.9 6.1 -7.8 6.9 * -26.0 1.7
Wasted 3.0 1.2 -1.8 1.8 -6.2 1.1
Severely Wasted 1.1 0.0 -2.2 1.5 -5.1 0.7
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1 INTRODUCTION
1.1 The Bukombe District
CWIQ
This report presents district level analysisof data collected in the Bukombe DistrictCore Welfare Indicators Survey using theCore Welfare Indicators Questionnaire
instrument (CWIQ).
The survey was commissioned by thePrime Ministers Office Regional
Administration and Local Governance andimplemented by EDI (EconomicDevelopment Initiatives), a Tanzanian
research and consultancy company. Thereport is aimed at national, regional and
district level policy makers, as well as theresearch and policy community at large.
CWIQ is an off-the-shelf survey packagedeveloped by the World Bank to produce
standardised monitoring indicators ofwelfare. The questionnaire is purposivelyconcise and is designed to collectinformation on household demographics,
employment, education, health andnutrition, as well as utilisation of andsatisfaction with social services. An extrasection on governance and satisfaction
with people in public office was addedspecifically for this survey.
The standardised nature of thequestionnaire allows comparison between
districts and regions within and acrosscountries, as well as monitoring change ina district or region over time.
This survey was the second of its kind tobe administered in Bukombe DC, located
in Shinyanga region, the first one havingbeen administered in 2004. Chapter 9 ofthis report analyses changes between thetwo surveys.
Although beyond the purpose of thisreport, the results of Bukombe CWIQcould also be set against those of otherCWIQ surveys that have are being
implemented at the time of writing inother districts in Tanzania: Bariadi DC,Bukoba DC, Bunda DC, Chamwino DCDodoma MC, Hanang DC, Karagwe DC,Kasulu DC, Kibondo DC, Kigoma DC,
Kilosa DC, Kishapu DC, Korogwe DC,
Kyela DC, Ludewa DC, Makete DC,Maswa DC, Meatu DC, Kahama DC,Mbulu DC, Morogoro DC, Mpwapwa DC,
Muheza DC, Musoma DC, Ngara DC,Ngorongoro DC, Njombe DC, Rufiji DC,
Shinyanga MC, Singida DC, Songea DC,Sumbawanga DC, Tanga MC, TemekeMC. Other African countries that haveimplemented nationally representative
CWIQ surveys include Malawi, Ghanaand Nigeria.
1.2 Sampling
The Bukombe District CWIQ was
sampled to be representative at districtlevel. Data from the 2002 Census wasused to put together a list of all villages inthe district. In the first stage of the
sampling process villages were chosen
Basic Variables Household Assets
Household size Radio
Level of education of the household head Bicycle
Consumption of meat Iron
Main source of income WatchProblems satisfying food needs Wheelbarrow
Number of meals Sewing machine
Main activity of the household head Bed
Household Amenities
Material in the walls
Material in the floor
Type of toilet
Source: HBS 2000/2001 for Shinyanga Region
Table 1.1 Variables Used to Predict Consumption Expenditure in Shinyanga Region
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1 Introduction
proportional to their population size. In asecond stage the sub-village (kitongoji)
was chosen within the village throughsimple random sampling. In the selectedsub-village (also referred to as cluster orenumeration area in this report), all
households were listed and 15 householdswere randomly selected. In total 450households in 30 clusters were visited. Allhouseholds were given statistical weights
reflecting the number of households thatthey represent.
A 10-page interview was conducted inach of the sampled households by an
portant to highlight that the
ata entry was done by scanning the
ructed variables
eportill be disaggregated by certain categories
Status
usehold isbtained by measuring its consumption
household consumptionxpenditure data allows more extensive
core set of variables that arecorporated in the majority of surveys.
ose of this report, the data
ollected in the Household Budget Survey
Table 1.2 : Predicted and Observed Poverty
Rates, Shinyanga Region, 2000/01
Non-Poor Poor Total
Non-Poor 58.3 15.1 73.4
Poor 9.2 17.4 26.6
Total 67.5 32.5 100.0Source: HBS 2000/01 for Shinyanga Region
ObservedPredicted
eexperienced interviewer trained by EDI.
The respondent was the most informedperson in the household, as identified bythe members of the household. A weightand height measurement was taken by the
interviewers for each individual under theage of 5 (60 months) in the surveyedhouseholds.
Finally, it is im
dquestionnaires, to minimise data entryerrors and thus ensure high quality in thefinal dataset.
1.3 Const
to disaggregate tables
The statistics in most tables in this rwof individuals or households. Some ofthese variables have been constructed by
the analysts and, in the light of theirprominence in the report, deserve moreexplanation. This chapter discusses some
of the most important of these variables:poverty status, cluster location and socio-
economic group.
1.3.1 Poverty
The poverty status of a hooexpenditures and comparing it to a povertyline. It is, however, difficult, expensive
and time consuming to collect reliablehousehold consumption expenditure data.
One reason for this is that consumptionmodules are typically very lengthy. Inaddition, household consumption patternsdiffer across districts, regions and seasons;
hence multiple visits have to be made to
the household for consumption data to bereliable.
However,eand useful analysis of patterns observed insurvey data and renders survey outcomes
more useful in policy determination.Because of this, the Tanzaniangovernment has become increasinglyinterested in developing ways of using
non-expenditure data to predict householdconsumption and, from this, povertymeasures.
There is ainThese variables inform on household
assets and amenities, level of education ofthe household head, amount of land ownedby the household and others. By observingthe relation between these variables and
consumption expenditure of the householdin an expenditure survey, a relationshipcan be calculated. These variables arecalled poverty predictors and can be used
to determine household expenditure levelsin non-expenditure surveys such asCWIQ. This means that, for instance, ahousehold that is headed by an individual
who has post secondary school education,with every member in a separate bedroomand that has a flush toilet is more likely tobe non-poor than one where the household
head has no education, a pit latrine is usedand there are four people per bedroom.This is, of course, a very simplified
example; however, these are some of thevariables used to calculate the relationshipbetween such information and the
consumption expenditure of thehousehold.
For the purp
c
2000/01 (HBS) was used to select thepoverty predictors and determine thequantitative relationship between these
and household consumption. The five-yeargap is far from ideal, but the data itself isreliable and is the most recent source ofinformation available. Work was thendone to investigate the specific
characteristics of Bukombe in order toensure that the model developed
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accurately represents this particulardistrict.Some caveats are in order when tabulatingvariables used as poverty predictors on
poverty status. Poverty status is defined as
dicted, it isompared to the Basic Needs Poverty Line
assumed to be unable to satisfy their basicfood and non-food needs1.
ine whether aouseholds monthly consumption per
icted to be
determining the accuracy of
es of each type the model makes.do this the poverty predictor model is
a weighted average of the povertypredictors; hence it should come as nosurprise that poverty predictors are
correlated to them. For instance, educationof the household head is one of thevariables included in the equation used to
calculate household consumption. The
relationship is set as a positive one,consequently when observing the patternsin the data this relationship may be
positive by construction. Table 1.1 liststhe variables that have been used tocalculate predicted householdconsumption expenditure.
Once the consumption level of ahousehold has been pre
o
c
set by National Bureau of Statistics (NBS)on the basis of the 2000/01 HBS. TheBasic Needs Poverty Line is defined bywhat a household, using the food basket of
the poorest 50 percent of the population,needs to consume to satisfy its basic foodneeds to attain 2,200 Kcal/day per adultequivalent. The share of non-food
expenditures of the poorest 25 percent ofhouseholds is then added. With this
procedure, the Basic Needs Poverty Lineis set at TZS 7,253 per 28 days per adultequivalent unit in 2000/01 prices.Households consuming less than this are
The Bukombe 2006 CWIQ uses poverty
predictors to classify households as pooror non-poor, i.e. to determhadult equivalent unit is below or above the
Basic Needs Poverty Line. This binaryapproach generates two types of mistakesassociated with the prediction:
1. A poor household is predicted to benon-poor2. A non-poor household is pred
poor
One way ofthe poverty predictors is to see how many
mistakTapplied to the actual consumptionexpenditure data. Results of this exercise
are presented in Table 1.2. The modelwrongly predicts a non-poor household tobe poor in 9.2 percent of the cases, andvice versa in 15.1 percent of the
households. This gives an overallpercentage of correct predictions of 75.7percent.
Remote ClustersAccessible
Clusters
Socio-Economic Group
Employees 0.0 67.8 32.2
Self-Employed Agriculture 20.0 38.1 61.9
Self-Employed Other 10.0 79.7 20.3
Other 4.6 44.7 55.3Source: CWIQ 2006 Bukombe DC
Poverty Rate
1 The exact procedure by which this line has
been set is described in detail in the 2000/01
HBS report: National Bureau of Statistics,2002, 2000/2001 Tanzania Household
Budget Survey.
Table 1.3: Cluster Location
District
Capital
All-Weather
Road
Public
Transport
Cluster Location
Remote 60.0 30.0 240.0 19.7 38,445
Accessible 20.0 10.0 120.0 12.8 38,040
Source: CWIQ 2006 Bukombe DC
Median Time (in minutes) to:
Poverty Rate
Estimated
Number of
Households
Table 1.4: Socio-economic Group, Poverty Rate, and Location
Percentage Living in
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1 Introduction
When the model is applied to the CWIQ2006 data for Bukombe DC, the share of
households living in poverty is 16 percent,similar to the figure for Bukombe 2004CWIQ (16 percent). These rates are lower
, such large scalerveys have insufficient number of
sis of self-reported travel time of theons: the
ort, thearest all-weather road and the district
cessible villages. Whereas the poverty
that aon the
t of the household head.t the report heads employed in
e private sectors, formally or informally,
cent. In turn,
overty is lowest for households where the
e-headedouseholds is lowest for the employees at
activity in the district isgriculture, to which 61 percent of the
Table 1.5: Socio-eco
Gender of
Male Female Total
Socio-economic Group
Employees 93.0 7.0 100.0
Self-Employed Agriculture 88.2 11.8 100.0
Self-Employed Other 87.2 12.8 100.0
Other 87.5 12.5 100.0
Total 88.1 11.9 100.0
Source: CWIQ 2006 Bukombe DC
than 33 percent estimated for Shinyaga
Region. However, it must be kept in mindthat the aim of the model is not estimatingpoverty rates, but determining the
characteristics of the poor population.Hence, the accuracy of the model does nothinge on the closeness between theestimated and actual poverty rate; but on
the percentage of correct predictions asindicated in Table 1.2.
Expenditure surveys, such as the
2000/2001 Household Budget Survey, aremuch better suited for informing onpoverty rates. Howeversu
observations to inform on district-leveltrends. The Bukombe CWIQ, on the otherhand, is sufficiently large to allow detailed
district-level analysis. The accuracy withwhich households can be classified bypoverty status using the CWIQ givescredence to the use of predicted povertylevel as a variable throughout this report.
1.3.2 Cluster Location
Cluster Location is constructed on thebahousehold to three different locati
nearest place to get public transpnecapital. Travel time is probed for by the
households most commonly used form oftransport. For each household, the averagetravel time is taken across these three
locations. For each cluster, the median ofthe 15 means is calculated. All clusters arethen ranked according to this median. The15 clusters with the lowest median are
labelled as accessible and the 15 clusterswith the highest median are labelled asremote. Table 1.3 shows the median of
each of the variables used to construct thecluster location.
Table 1.3 shows that the poverty ratesdiffer substantially by cluster location:households in remote villages are more
likely to be poor than households in
acrate in accessible villages is 13 percent,the rate in remote villages is 20 percent.
1.3.3 Socio-economic
Group
The socio-economic grouphousehold belongs to depends
employmenhroughouT
thas well as Government and Parastatal
employees are categorised asEmployees. Self-employed individuals
are divided into two groups, depending onwhether they work in agriculture (Self-employed agriculture) or in trade orprofessional sectors (Self-employed
other). Finally, those who worked inother activities or who had not beenworking for the 4 weeks preceding thesurvey are classed as other.
Table 1.4 shows that the poverty rate ishighest for households whose mainincome earner is self-employed in
agriculture, at a rate of 20 per
pmain income earner is an employee. Inaddition, households from the former
group are the most likely to be located inaccessible villages, at 62 percent, whereasthe self-employed in non-agriculturalactivities are the most likely to be located
in remote villages, at 80 percent.
The gender composition of the socio-economic group is shown in Table 1.5.Almost 9 out of 10 households are headedby a male. The share of femalh
7 percent.
Table 1.6 shows the breakdown of socioconomic groups by main activity of the
household heads. As expected, the maineconomicahousehold heads is dedicated. Employees
are mostly dedicated to mining,manufacturing, energy or construction,with a share of 91 percent. The self-
nomic Group of the Household and
the Household Head
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employed in non-agricultural activities aremostly dedicated to services (99 percent).
More than half of the other category ismainly concentrated in agriculture (57percent) with the rest almost evenly splitbetween services, household duties and
other activities (17, 12 and 14 percent,
respectively).
Table 1.6: Socio-economic Group of the Household and Main Economic Activity of the Household Head
Agriculture
Mining
ManufacturingEne
rgy Construction
Private and
Public Services
Household
DutiesOther Total
Socio-economic Group
Employees 9.1 90.9 0.0 0.0 0.0 100.0
Self-Employed Agriculture 84.0 0.4 13.7 1.4 0.5 100.0
Self-Employed Other 0.0 0.0 98.5 1.5 0.0 100.0
Other 56.9 0.0 16.9 12.2 14.0 100.0
Total 60.7 2.7 34.3 1.6 0.7 100.0
Source: CWIQ 2006 Bukombe DC
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2 VILLAGE, POPULATION AND
HOUSEHOLD CHARACTERISTICS
2.1 Introduction
This chapter provides an overview of the
Bukombe DC households and populationcharacteristics. The main population
characteristics are presented in sectiontwo. Section three presents the maincharacteristics of the households, such asarea of residence, poverty status, number
of members, and dependency ratio. Thesame analysis is then conducted for thehousehold heads in section four. Anexamination of orphan and foster status in
the district concludes the chapter.
2.2 Main Population
Characteristics
Table 2.1 shows the percent distribution ofthe population by cluster location andpoverty status, by gender and age. Overall,
the districts population is young. Forinstance, 4 percent of the population isover 60 years old, whereas 53 percent isunder 15 years old. The remaining 43
percent is between 15 and 59 years old.Poor households and households in
accessible villages have higher shares inthe 0-14 group than non-poor households
or households in remote villages, but thedifference by poverty status is wider.
The dependency ratio of the districtshouseholds is shown in Table 2.2. Thedependency ratio is the number ofhousehold members under 15 and over 64
years old (the dependant population) overthe number of household members aged
between 15 and 64 (the working agepopulation). The result is the average
number of people each adult at workingage takes care of.
The mean dependency ratio is 1.3,
meaning that one adult has to take care ofmore than 1 person. On average poorhouseholds and households in remotevillages present higher dependency ratios
(1.8 and 1.3, respectively) than non-poorhouseholds and households fromaccessible villages (1.1 and 1.2,
respectively).
The dependency ratio increases with thenumber of household members, from 0.2
for households with 1 or 2 members, to1.7 for households with 7 or moremembers. The breakdown by socio-economic group of the household shows
that the self-employed in agriculture reportthe highest dependency ratio (1.3),whereas the other socio-economic groupreports the lowest (1.1).
The breakdown by gender of thehousehold head shows that thedependency ratio in male-headed
households is slightly higher than in
female-headed households, at 1.3 and 1.2,respectively.
Table 2.3 shows the percent distribution ofhouseholds by number of household
members. The mean household size is 5.2individuals. Households with at most twoindividuals only represent 17 percent of allhouseholds in the district. The figure for
households with 7 or more members is 31percent.
Table 2.1: Percent distribution of total population by gender and age
0-14 15-59 60+ Total 0-14 15-59 60+ Total 0-14 15-59 60+ Total
Total 23.5 20.2 2.4 46.0 29.7 22.4 1.9 54.0 53.1 42.6 4.2 100.0
Cluster Location
Accessible 23.3 20.3 1.3 44.9 31.2 22.0 1.8 55.1 54.5 42.4 3.1 100.0
Remote 23.8 20.0 3.8 47.6 27.5 23.0 1.9 52.4 51.3 43.0 5.8 100.0
Poverty Status
Poor 28.9 15.8 1.6 46.3 33.9 18.2 1.6 53.7 62.9 34.0 3.2 100.0
Non-poor 21.9 21.5 2.6 45.9 28.4 23.7 2.0 54.1 50.3 45.1 4.6 100.0
Source:CWIQ 2006 Bukombe DC
Male Female Total
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2 Village, population and household characteristics
The breakdown by cluster location shows
that households in remote villages tend to
roups, themployees have the highest mean
household size, at 6.4, and the self-
olds: thermer have 5.4 members in average,
Table 2.2: Dependency ratio
0-4 years 5-14 years 0-14 years 15-64 years 65+ years Total
Dependency
ratio
Total 1.1 1.7 2.8 2.3 0.1 5.2 1.3
Cluster Location
Accessible 1.2 1.7 2.9 2.3 0.1 5.4 1.3
Remote 0.9 1.6 2.6 2.3 0.1 5.0 1.2
Poverty Status
Poor 1.5 3.0 4.6 2.6 0.1 7.3 1.8
Non-poor 1.0 1.4 2.4 2.3 0.1 4.8 1.1
Household size
1-2 0.0 0.0 0.1 1.4 0.2 1.7 0.2
3-4 0.7 0.7 1.5 2.1 0.1 3.6 0.8
5-6 1.4 1.7 3.1 2.3 0.1 5.5 1.4
7+ 1.7 3.4 5.1 3.1 0.1 8.3 1.7
Socio-economic Group
Employee 1.7 1.5 3.1 2.9 0.3 6.4 1.2
Self-employed - agriculture 1.1 1.7 2.8 2.3 0.1 5.2 1.3
Self-employed - other 1.1 1.6 2.7 2.3 0.1 5.1 1.2
Other 1.0 1.6 2.6 2.7 0.3 5.5 1.1
Gender of Household Head
Male 1.2 1.7 2.9 2.4 0.1 5.4 1.3
Female 0.5 1.4 1.9 1.8 0.2 4.0 1.2
Source:CWIQ 2006 Bukombe DC
Table 2.3: Percent distribution of households by number of household members
1-2 persons 3-4 persons 5-6 persons 7+ persons Total
household
size
Total 17.4 26.0 25.4 31.2 100.0 5.2
Cluster Location
Accessible 12.8 29.3 25.2 32.7 100.0 5.4
Remote 23.2 21.8 25.6 29.4 100.0 5.0
Poverty Status
Poor 2.5 7.6 26.9 63.1 100.0 7.3
Non-poor 20.3 29.4 25.2 25.1 100.0 4.8
Socio-economic Group
Employee 0.0 25.5 38.6 35.9 100.0 6.4
Self-employed - agric 19.8 23.5 25.1 31.6 100.0 5.2
Self-employed - other 14.2 32.9 23.5 29.5 100.0 5.1
Other 12.0 25.6 31.3 31.2 100.0 5.5
Gender of Household Head
Male 14.0 26.4 27.0 32.6 100.0 5.4
Female 42.6 22.7 13.5 21.2 100.0 4.0
Source:CWIQ 2006 Bukombe DC
be larger than households in accessiblevillages, with means of 5.4 and 5.0members, respectively. The difference by
poverty status is more pronounced, withpoor households reporting a meanhousehold size of 7.3 members, and non-poor households reporting 4.8.
Regarding socio-economic ge
employed in non-agricultural activities
have the lowest at 5.1 members.
Finally, households headed by males are
larger than female-headed househfowhereas the latter have 4.0 members. Thisdifference partly owes to the fact that, as
shown in Section 2.4, female householdheads rarely have a spouse.
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2.3 Main Household
Characteristics
Table 2.4 shows the percent disttotal population by relationship tof household.
ribution ofo the head
us shows that the shares of
hild and other relative are higher in
e head of the household. The category
ercent,spectively. In turn, females are more
, 30 percent of theopulation has never been married. In
addition, 41 percent is married and
monogamous, and 17 percent is marriedand polygamous. Despite only 1 percentreported to be officially divorced, 5
y to be monogamous or informal unions.
lygamous marriage.
d-
onogamous is the most common
women are widowed and a further 7
on
Other Not
Head Spouse Child Parents relative related Total
Total 19.2 16.1 51.6 0.7 12.1 0.3 100.0
Cluster Location
Accessible 18.6 15.6 52.1 0.8 12.7 0.2 100.0
Remote 20.0 16.8 50.9 0.6 11.2 0.6 100.0
Poverty Status
Poor 13.8 10.9 57.0 0.7 17.2 0.3 100.0
Non-poor 20.7 17.6 50.0 0.7 10.6 0.3 100.0
Age
0- 9 0.0 0.0 84.0 0.0 15.7 0.3 100.0
10-19 0.6 4.0 76.1 0.0 19.0 0.3 100.0
20-29 23.4 54.0 14.0 0.0 7.5 1.1 100.0
30-39 57.2 36.5 2.4 0.7 3.1 0.0 100.0
40-49 59.2 38.8 0.5 0.0 1.5 0.0 100.0
50-59 64.1 31.9 0.0 1.4 2.5 0.0 100.0
60 and abov
No particular trends emerge by analysingby cluster location. However, the analysisby poverty stat
cpoor households, whereas non-poorhouseholds report higher shares of headand spouse.
The age breakdown shows that after theage of 30, most of the population is eitherhead of their own household or spouse toth
other relative peaks for the 10-19 cohortat 19 percent, whereas the shares for theolder cohorts are under 10 percent.
The gender split-up shows that males aremore likely to be household heads than
females, with shares of 37 and 4 prelikely to be spouses to the household headthan males, at rates of 30 and less than 1
percent, respectively.
Table 2.5 shows the percent distribution ofthe population age 12 and above by
marital status. Overallp
percent of the population is unofficially
separated. Informal unions constitute 3percent of the population and 4 percent iswidowed.
The breakdown by cluster location showsthat people of remote villages are morelikely to be never married or polygamous
than people in accessible villages, who aremore likel
by relationship to head of household
in
The breakdown by poverty status showsthat members of poor households are morelikely to have never been married, whereasmembers of non-poor households are more
likely to be in a poThe age breakdown shows that thepolygamous-married category tends to
increase with age, peaking for the 40-59groups, roughly at 35 percent. For thepopulation after 20 years old, marrie
mcategory. Neither divorced nor separatedshow a trend but, as would be expected,
widowed increases with age. Nevermarried also shows correlation with age,decreasing as the population gets older.
Around 33 percent of the men has neverbeen married, but for women the figure isonly 28 percent. While 7 percent of
Table 2.4: Percent distribution of total populati
70.3 9.2 0.0 13.5 6.9 0.0 100.0
Gender
Male 36.7 0.4 51.4 0.2 11.1 0.2 100.0
Female 4.2 29.6 51.7 1.2 12.9 0.4 100.0
Source:CWIQ 2006 Bukombe DC
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2 Village, population and household characteristics
percent separated, the shares for males are2 and 1 percent, respectively.
Table 2.6 shows the percent distribution of
the population age 5 and above by socio-economic group. Overall, 26 percent ofthe population is self-employed in
agriculture, with 62 percent in other
ricultural activities, and less likely to be
t, thenecreases to 24 percent for the population
able 2.7 shows the percent distribution of
Table 2.5: Percent distribution of th tu
Never Married Married Informal,
married monog polyg loose union Divorced Separated Widowed Total
Total 30.2 41.4 16.7 2.5 0.6 4.5 4.1 100.0
Cluster Location
Accessible 28.7 43.8 13.2 4.5 1.0 4.8 4.2 100.0
Remote 32.1 38.4 21.0 0.1 0.2 4.2 4.0 100.0
Poverty Status
Poor 40.9 39.7 8.9 0.0 0.4 6.6 3.5 100.0
Non-poor 27.7 41.7 18.6 3.1 0.7 4.0 4.2 100.0
Age
12-14 100.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
15-19 83.0 11.4 0.8 2.7 0.0 2.1 0.0 100.0
20-24 27.1 53.1 8.6 6.9 0.7 3.2 0.5 100.0
25-29 1.8 70.3 14.6 4.6 1.1 7.1 0.5 100.0
30-39 1.7 61.0 26.3 1.5 0.9 5.7 3.0 100.0
40-49 0.5 55.6 33.5 2.3 0.0 3.3 4.7 100.0
50-59 1.0 46.9 35.6 4.3 0.0 1.4 10.8 100.0
60 and above 0.0 30.7 23.8 0.0 2.5 15.9 27.1 100.0
Gender
Male 32.7 43.5 17.6 2.7 0.0 2.2 1.4 100.0
Female 28.0 39.5 15.8 2.4 1.2 6.6 6.5 100.0
Source:CWIQ 2006 Bukombe DC
activities. Individuals living in accessible
villages seem to be more likely to be self-employed in agriculture, or in non-ag
in the other group than remotehouseholds. In turn, poor households aremore likely to be self-employed inagriculture on in the other group than
non-poor households, who are more likelyto be self-employed in agriculture.
The analysis of the age-groups isparticularly interesting. The share of self-
employed in agriculture tends to increasewith age, peaking at 71 percent for the 60+group. On the contrary, the categoryother tends to decrease with age,
showing a sharp decrease between 15-19and 20-29, from 81 to 43 percendaged 40+.
The gender breakdown shows that malesare more likely to be self-employed in
agriculture or in non-agricultural activities
than women. In turn, females are morelikely to be in the other category, with a
share of 73 percent against 50 percent forthe males.
T
the population aged 5 and above byhighest level of education. Around 42percent of the population has noeducation, 29 percent has some primary,
and 24 percent has completed primary.
e total population age 12 an above by marital sta s
Table 2.6: Percent distribution of the total population age 5 and
above by socio-economic group
Self-em
Employee Agric
Total 1.3 26
Cluster Location
Accessible 2.0 23.5 16.3 58.3 100.0
Remote 0.5 30.1 2.1 67.3 100.0
Poverty StatusPoor 0.0 23.0 4.7 72.3 100.0
Non-poor 1.7 27.3 11.7 59.3 100.0
Age
5- 9 0.0 0.0 0.4 99.6 100.0
10-14 0.0 0.9 1.2 97.9 100.0
15-19 1.0 15.0 2.6 81.4 100.0
20-29 1.3 36.0 21.3 41.4 100.0
30-39 2.8 49.0 28.1 20.0 100.0
40-49 5.5 57.6 12.6 24.2 100.0
50-59 2.7 65.1 8.6 23.6 100.0
60 and abov
ployed Self-employed
ulture Other Other Total
.4 10.1 62.2 100.0
e 0.0 71.0 5.3 23.7 100.0
Gender
Male 2.2 34.4 13.3 50.0 100.0
Female 0.6 19.2 7.3 72.9 100.0Source:CWIQ 2006 Bukombe DC
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Bukombe DC CWIQ 2006
The remaining levels have shares of atmost 2 percent each.
The breakdown by cluster location shows
rt a higher share of
opulation with completed primary.
2.4 Main Characteristics of
the Heads of Household
married and monogamous, 15 percentivorced, separated or widowed, 24
amous increases. The
la
evel of education
Nursery Some Completed Some Completed Post
None school primary primary secondary secondary secondary Total
Total 42.0 1.9 28.5 24.3 1.5 0.2 1.5 100.0
Cluster Location
Accessible 38.7 2.8 27.6 26.6 1.8 0.4 2.2 100.0
Remote 46.3 0.7 29.8 21.3 1.2 0.0 0.6 100.0
Poverty Status
Poor 48.8 1.0 31.5 18.6 0.0 0.0 0.0 100.0
Non-poor 40.1 2.1 27.7 25.9 2.0 0.3 1.9 100.0
Age
5- 9 77.5 7.1 15.4 0.0 0.0 0.0 0.0 100.0
10-14 17.0 1.0 80.9 1.0 0.0 0.0 0.0 100.0
15-19 23.2 0.0 38.4 35.0 3.3 0.0 0.0 100.0
20-29 30.4 0.0 16.8 48.2 3.7 0.0 1.0 100.0
30-39 25.7 0.0 11.2 59.7 1.5 0.0 1.9 100.0
40-49 39.5 0.0 17.2 31.5 4.5 2.5 4.8 100.0
50-59 60.1 0.0 18.1 18.5 0.0 0.0 3.3 100.0
60 and above 68.8 0.0 18.1 2.9 0.7 0.0 9.5 100.0
Gender
Male 36.3 2.6 28.5 27.8 2.0 0.3 2.4 100.0
Female 47.0 1.2 28.6 21.2 1.2 0.1 0.7 100.0
Source:CWIQ 2006 Bukombe DC
that remote clusters report a higher share
with no education and a lower share ofcomplete primary than accessibleclusters. In turn, the breakdown by
poverty status shows that poor householdsreport higher shares of population with noeducation or some primary than non-poorhouseholds, who repo
pThe age breakdown shows that 77 percentof the children between 5 and 9 have no
formal education, but 81 percent of thechildren 10-14 have at least some primary.Rates of no education are lowest for thepopulation 10-14 (17 percent) and higher
for the older groups. In the groupsbetween 20 and 39 years old, the mostcommon is completed primary.
The gender breakdown shows that femaleshave a higher share of uneducatedpopulation than males: 47 against 36percent, while the share of males reporting
complete primary is higher than that offemales (28 and 31 percent, respectively).
Table 2.8 shows the percent distribution of
household heads by marital status.O erall, 58 percent of the household heads
tion age 5 and above by highest
visd
percent is married and polygamous, 1percent has never been married and a 3percent lives in an informal union.
The breakdown by cluster location andpoverty status shows that accessiblevillages and poor households report highershares of married and monogamous
household heads, whereas remote villagesand non-poor households report highershares of married and polygamoushousehold heads.
Analysis by age-groups shows thatmarried-monogamous is the category with
the highest share of household heads after20 years old, whereas the heads in the 15-19 age-groups are concentrated in nevermarried (78 percent) and informal union
(23 percent). The married-monogamouscategory decreases with age, as the sharein married-polyg
share of divorced, widowed or separatedhousehold heads peaks at 36 percent of the60+ age-group.
Table 2.7: Percent distribution of the total popu
l
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2 Village, population and household characteristics
Most female household heads aredivorced, separated or widowed (87
percent), whereas for males, this categoryroughly represents 5 percent. Most malehousehold heads are married,
monogamous or polygamous (91 percent).
Table 2.9 shows the percent distribution ofhousehold heads by socio-economic
group. It is worth remembering that thesocio-economic group of the household isdetermined by the type of employment ofthe main income earner of the household,
old by marital status
Divorced
who not always the household head. Asexpected, the great majority of the
districts household heads belongs to theself-employed in agriculture, with a shareof 68 percent. The self-employed in non-
agricultural activities represent 25 percentof the household heads, the othercategory (unemployed, inactive, unpaid,and household workers) represents 3
percent, and the employees are a further 3
eh
M rried Informal, Separated
1.2 53.6
overty Status
percent.
ygamous loose u nion Widowed Total
23.9 2.9 14.5 100.0
18.7 5.0 14.5 100.030.5 0.1 14.5 100.0
Table 2.8: Percent distribution of heads of hous
Never Married
married monogamous pol
Total 0.9 57.8
Cluster Location
Accessible 0.6 61.2Remote
a
P
Poor 2.9 63.8 15.1 0.0 18.2 100.0
Non-poor 0.5 56.6 25.7 3.4 13.8 100.0
Age
15-19 77.5 0.0 0.0 22.5 0.0 100.0
20-29 2.1 74.9 9.8 6.5 6.6 100.0
30-39 0.0 66.2 22.0 0.6 11.2 100.0
40-49 0.3 55.4 29.6 3.9 10.8 100.0
50-59 0.0 43.8 35.1 6.7 14.4 100.0
60 and above 0.0 36.0 28.3 0.0 35.7 100.0
Gender
Male 0.9 65.0 26.1 3.3 4.7 100.0
Female 0.5 4.6 7.7 0.0 87.1 100.0Source:CWIQ 2006 Bukombe DC
Table 2.9: Percent distribution of heads of household by socio-economic group
Employed Self-employed Self-employed Other
Agriculture Other Total
Total 3.9 68.1 24.7 3.3 100.0
Cluster Location
Accessible 5.5 53.4 39.6 1.5 100.0
Remote 2.0 86.7 5.8 5.6 100.0
Poverty Status
Poor 0.0 83.8 15.3 0.9 100.0
Non-poor 4.7 65.0 26.6 3.8 100.0
Age15-19 0.0 100.0 0.0 0.0 100.0
20-29 2.1 58.8 39.0 0.0 100.0
30-39 4.3 57.4 36.8 1.6 100.0
40-49 7.8 69.7 16.0 6.6 100.0
50-59 4.2 81.6 10.0 4.2 100.0
60 and above 0.0 90.4 3.4 6.2 100.0
Gender
Male 4.1 68.1 24.5 3.3 100.0
Female 2.3 67.7 26.5 3.4 100.0
Source:CWIQ 2006 Bukombe DC
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The analysis by location shows that theshare of household heads self-employed inagriculture in remote villages is higher
than in accessible villages, with shares of87 and 53 percent, respectively. Inaccessible villages, household heads aremore likely to be in the self-employed
other group than heads of households inremote villages, with shares of 40 and 6percent, respectively.
he breakdown by age of the household
ent for
e 20-29 group and then decreases
Table 2.10 shows the percent distributionof the heads of household by highest levelof education. Overall, only around 9
percent of the household heads has anyeducation after primary. Around 31percent of the household heads has noeducation, 17 percent some primary and
45 percent has completed primary.
wn by cluster location showsat household heads in remote villages
. Thisould be no surprise, since education of
eme, whereasirtually no head of poor households has
e
Total
4 5.2 100.0
50 100.0
38.9 19.8 37 100.0
Poverty Status
Poor 30.2 23.7 46.1 0.0 0.0 0.0 100.0
Non-poor 30.8 15.2 44.1 3.1 0.7 6.2 100.0
Age
15-19 22.5 0.0 77.5 0.0 0.0 0.0 100.0
20-29 22.9 14.7 57.1 2.6 0.0 2.6 100.0
30-39 17.0 13.4 64.7 1.6 0.0 3.4 100.0
40-49 29.4 16.0 37.6 7.6 2.9 6.5 100.0
50-59 50.6 20.6 24.6 0.0 0.0 4.2 100.0
60 and above 59.0 24.4 4.1 1.0 0.0 11.5 100.0
Gender
Male 27.0 16.8 47.3 3.0 0.6 5.3 100.0
Female 57.2 14.6 23.9 0.0 0.0 4.3 100.0
Source:CWIQ 2006 Bukombe DC
Heads of poor households belong to theself-employed agriculture group more
frequently than non-poor households, at84 and 65 percent, respectively. On the
other hand, the heads of non-poorhouseholds belong to the employee orself-employed other groups more often
than the heads of poor households.
T
head shows interesting insights. For allage-groups, self-employed agriculture isthe most important category, representingat least 3 out of 5 household heads in each
age-group, and increasing with age. Theemployee category peaks at 8 percent forthe 40-49 age-groups. The self-employed other category starts at 39 perc
thsteadily down to 3 percent for the cohortaged 60 and above. The other category ishigher for the 40+ cohorts.
The breakdown by gender of thehousehold head shows no strongdifferences.
hold by highest level of education
pleted Some Completed Post
ary secondary secondary secondary
.5 2.6 0.5
The breakdo
.2 3.3 1.0 7.6
.3 1.8 0.0 2.1
th
are more likely to have no education or just some primary than the ones from
accessible villages, who in turn are morelikely to have completed primary or have
post-secondary education.
Poverty status is correlated with the
education of the household headsshthe household head is one of the poverty
predictors used to define poverty status.However, the difference is still important:while whereas 24 percent of heads of poorhouseholds have only some primary, the
share for non-poor households is 15percent. At the other extrvpost-secondary education, the figure for
non-poor households is higher, at 6percent.
The age breakdown shows that 59 percent
of household heads aged 60 or over has noeducation, and a further 24 percent justsome primary. Completed primary is themost common category for the groups
between 20 and 49.
Table 2.10: Percent distribution of heads of hous
Some Com
None primary prim
Total 30.6 16.5 4
Cluster Location
Accessible 24.1 13.9
Remote
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2 Village, population and household characteristics
The analysis by gender shows that femalehousehold heads are more likely to haveno education than males, with rates of 57and 27 percent, respectively. Almost half
the male household heads (47 percent) hascompleted primary, against 24 percent offemales.
2.5 Orphan and Foster
Status
ildren who
mother only
Children who
lost father only
both father &
mother
2.2 5.3 1.0
3.4 3.5 1.3
Table 2.11 shows the percent distributionof children under 18 years old who havelost at least one parent. Overall, about 1percent of children under 18 lost both
parents, 2 percent lost only their motherand 5 percent lost only their father. This
ounts to 8 percent of all children under
e time
tus is correlated with cluster
cation and poverty status, with children
he children between 15 and7 years lost a parent, and 14 percent of
s old by foster status is shown in
able 2.12. A child is defined as living in
e table shows that
he analysis of age-groups shows that the
Table 2.11 - Orphan status
Ch
lost
Children who lost
Total
Cluster Location
Accessible
Remote 0.6 7.9 0.6
Poverty Status
Poor 0.5 9.8 0.1
Non-poor 2.8 3.7 1.4
Age
0-4 0.0 2.7 0.0
5-9 3.5 4.0 0.9
10-14 3.3 8.2 0.9
15-17 3.3 13.7 5.9
Gender
Male 1.4 5.6 1.2
Female 2.8 5.2 0.9Source:CWIQ 2006 Bukombe DC
am
18 who lost at least one parent at thof the survey.
Orphan sta
lofrom remote villages and poor householdsreporting higher shares having lost their
father.
The age breakdown shows that orphanstatus is correlated with age: as can beexpected older children are more likely tobe orphans than younger children. Around
23 percent of t1the children in that age-group lost their
father. There does not seem to be a gendertrend in orphan status.
The percent distribution of children under18 year
Ta nuclear household when both parentslive in the household and as living in anon-nuclear household when at least one
parent is absent from the household. Note
that this makes it a variable defined at thelevel of the child, rather than thehousehold (a household may be nuclear
with respect to one child, but not withrespect to another). Th26 percent of children under 18 wereliving in non-nuclear households at the
time of the survey.
There is no strong relation between clusterlocation and foster status, but children
from poor households tend to be fosteredmore often than children from non-poorhouseholds (with shares of 33 and 24percent, respectively). The main
difference arises from children living withtheir mother only: 19 percent for poorhouseholds and 10 percent for non-poor
households.
Tshare of children living in non-nuclear
households increases steadily with age,from 17 percent for children between 0and 4 years old, to 50 percent for childrenbetween 15 and 17 years old.
There appears to be no strong correlationbetween gender and foster status.
of children under 18 years old
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15
Table 2.12 - Foster status of children under 18 years old
Children living
with mother only
Children living
with father only
Children living
with no parents
Children living in
non-nuclear
households
Total 12.7 3.4 10.2 26.3
Cluster Location
Accessible 11.9 4.2 10.9 27.0
Remote 13.9 2.2 9.2 25.4
Poverty Status
Poor 19.2 2.1 12.0 33.4
Non-poor 10.4 3.8 9.6 23.8
Age
0-4 12.8 1.3 3.1 17.2
5-9 10.0 3.4 13.1 26.5
10-14 12.2 5.7 13.1 31.0
15-17 23.7 5.6 20.9 50.2
Gender
Male 12.4 3.8 10.1 26.3
Female 13.0 3.0 10.3 26.4
Source:CWIQ 2006 Bukombe DC
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3 EDUCATION
This chapter examines selected educationindicators in Bukombe DC. These include
literacy rate, access to schools, satisfaction
rate, dissatisfaction rate and enrolment.
The first section presents an overview on
selected education indicators. The secondsection provides information ondissatisfaction and non-attendance alongwith the reasons behind them. School
enrolment and drop-out rates are presentedin the fourth section. These give a pictureon the enrolment patterns according to theage of pupils. The final section of the
chapter gives information on adult andyouth literacy status within the district.
3.1 Overview of theEducation indicators
3.1.1 Literacy
Table 3.1 shows the main educationindicators for the district. Literacy is
defined as the ability to read and write inany language, as reported by therespondent. Individuals who are able toread but cannot write are considered
illiterate. The adult literacy rate1 is 62
percent. Literacy rates differ betweenaccessible and remote villages at 67 and56 percent respectively. Likewise, the
literacy rate among non-poor householdsis higher than that of poor households at63 and 58 percent respectively.
The breakdown by socio-economic groupof the household shows that literacy ratesare higher among households where themain income earner is an employee (80
percent) than in the remaining categories.
The gender breakdown shows animportant literacy rate gap between men
and women. The literacy rate among menis 23 percentage points higher than that of
women at 74 percent and 51 percentrespectively.
Orphaned children have a literacy rate of
68 percent, whereas the rate for non-
1 The Adult literacy rate is defined for the
population aged 15 and over.
orphaned children is 5 points higher, at 73percent. Finally, 79 percent of non-
fostered children are literate compared to
40 percent of fostered children.
3.1.2 Primary School
Access
Primary school access rate is defined as
the proportion of primary school-agechildren (7 to 13 years) reporting to livewithin 30 minutes of the nearest primaryschool. Overall, 73 percent of primary
school-age children live within 30 minutesof a primary school. Primary school access
is remarkably higher in accessible clustersthan in remote clusters, at 83 and 60
percent respectively.
The majority (79 percent) of the childrenaged 7 to 13 living in non-poor
households lives within 30 minutes of thenearest primary school compared to 61percent of those living in poor households.
The breakdown by socio-economic groupshows that virtually all children living in
households belonging to the employeecategory live within 30 minutes of thenearest primary school compared to 75
percent of the children living inhouseholds where the main income earnerbelongs to the other category and 65percent of children living in householdsbelonging to the self-employed
agriculture category.
Non-orphaned children have a higheraccess rate to primary schools than
orphaned, at 74 and 66 percentrespectively. Similarly, 75 percent of non-fostered children has access to primaryschools, whereas the rate for fostered
children is 47 percent. Finally, gender
does not show strong correlation withprimary school access.
Enrolment
The two main measures of enrolment, theGross Enrolment Rate (GER) and the NetEnrolment Rate (NER) are analysed in thissection. GER is defined as the ratio of all
individuals attending school, irrespective
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3 Education
Table 3.1: Education indicators
gross net gross net
access enrollment enrollment satisfaction access enrollment enrollment satisfaction
Total 62.1 73.4 88.7 67.6 50.8 6.3 4.7 4.1 80.7
Cluster Location
Accessible 67.2 83.3 86.7 69.4 57.8 13.1 5.9 4.5 71.7
Remote 55.7 59.7 91.5 65.2 41.6 0.0 3.7 3.7 94.0
Poverty Status
Poor 57.7 60.5 71.5 55.5 41.8 0.0 1.6 1.1 100.0
Non-poor 63.1 79.0 96.2 73.0 53.7 8.4 5.7 5.1 78.9
Socio-economic Group
Employee 80.0 100.0 85.6 79.1 79.2 0.0 81.1 66.2 81.7
Self-employed - agriculture 57.5 64.7 88.7 65.2 51.0 4.9 1.1 0.9 49.7
Self-employed - other 73.5 94.1 90.0 75.6 45.2 16.9 7.0 7.0 100.0
Other 48.6 74.5 82.6 50.0 54.6 0.0 0.0 0.0 0.0
Gender
Male 74.3 72.7 91.3 63.8 54.6 6.1 7.2 6.0 75.3
Female 50.9 73.9 86.6 70.7 47.5 6.5 2.1 2.1 100.0
Orphan statusOrphaned 68.1 65.9 106.7 69.9 61.7 10.9 3.4 3.4 100.0
Not-orphaned 72.9 74.4 86.8 67.5 49.2 5.1 3.1 3.1 87.0
Foster status
Fostered 40.0 47.1 65.4 50.8 77.1 9.2 4.6 4.6 100.0
Not-fostered 79.3 75.1 88.7 68.8 48.1 4.8 2.5 2.5 83.1
Source:CWIQ 2006 Bukombe DC
1. Literacy is defined for persons age 15 and above.
2. Primary school:
Access is defined for children of primary school age (7-13) in households less than 30 minutes from a primary school.
Enrollment (gross) is defined for all persons currently in primary school (Kindergarden, Grade 1 to Grade 8) regardless of age.
Enrollment (net) is defined for children of primary school age (7-13) currently in primary school (Kindergarden, Grade 1 to Grade 8 ).
Satisfaction is defined for all persons currently in primary school who cited no problems with school.
3. Secondary school:
Access is defined for children of secondary school age (14-19) in households less than 30 minutes from a secondary school.
Enrollment (gross) is defined for all persons currently in secondary school (Form 1 to Form 5) regardless of age.
Enrollment (net) is defined for children of secondary school age (14-19) currently in secondary school (Form 1 to Form 5).
Satisfaction is defined for all persons currently in secondary school who cited no problems with school.
Primary Secondary
Adult Literacy
rate
of their age, to the population of school-age children. If there are a largeproportion of non-school-age individuals
attending school, the GER may exceed100 percent. Primary school GER informson the ratio of all individuals in primary
school to the population of individuals ofprimary school-age (7 to 13 years) in thedistrict.
NER is defined as the ratio of school-agechildren enrolled at school to thepopulation of school-age children.
Therefore, primary school NER is the ratioof children between the ages of 7 and 13years in primary school to the populationof children in this age-group in the district.
The NER provides more information foranalysis than the GER. While trends in the
actual participation of school-age childrenin formal education are in part captured bythe NER, the GER, at best provides a
broad indication of general participation ineducation and of the capacity of theschools. The GER gives no preciseinformation regarding the proportions of
individuals of school and non-school-agesat school, nor does it convey any
information on the capacity of the schoolsin terms of quality of education provided.
The primary school GER was 89 percent
at the time of the survey. This figureindicates that all individuals who were atprimary school constitute 89 percent of allchildren of primary school-age in the
district. The NER further shows that 68percent of all primary school-age childrenwere attending school.
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Table 3.2: Percentage of students currently enrolled in school by reasons for dissatisfaction
Total 46.3 15.3 12.2 86.0 2.0 21.2 29.9 0.0 4.1
Cluster Location
Accessible 39.0 13.7 16.7 82.4 1.1 29.0 30.0 0.0 6.6
Remote 56.6 16.9 7.8 89.5 2.8 13.7 29.8 0.0 1.6
Poverty Status
Poor 57.4 17.0 8.4 87.1 0.0 12.3 22.2 0.0 5.3
Non-poor 43.0 14.7 13.6 85.6 2.7 24.7 32.9 0.0 3.6
Socio-economic Group
Employee 16.8 0.0 20.0 80.0 0.0 80.0 26.7 0.0 0.0
Self-employed - agriculture 48.0 19.3 12.7 82.7 0.4 10.9 31.3 0.0 6.0
Self-employed - other 48.0 8.7 10.2 92.9 6.4 39.5 30.5 0.0 0.0
Other 50.0 0.0 12.4 100.0 0.0 45.9 0.0 0.0 0.0
Gender
Male 43.0 18.8 10.0 89.0 2.5 22.3 28.3 0.0 4.6
Female 49.2 12.7 13.7 83.8 1.5 20.4 31.0 0.0 3.6
Type of school
Primary 49.2 15.5 11.9 86.5 2.0 20.9 29.3 0.0 4.2
Government 49.6 15.5 11.9 86.5 2.0 20.9 29.3 0.0 4.2
Private 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Secondary 19.3 31.3 0.0 68.7 0.0 56.3 87.6 0.0 0.0
Government 22.2 31.3 0.0 68.7 0.0 56.3 87.6 0.0 0.0
Private 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other 14.9 0.0 30.8 69.3 0.0 19.6 30.7 0.0 0.0
Government 24.1 0.0 36.8 82.7 0.0 23.4 17.3 0.0 0.0
Private 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Other 33.2 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0
Source:CWIQ 2006 Bukombe DC
1. Base for column 1 is enrolled students. For columns 2 to 9, dissatisfied students
Other
Reasons for dissatisfaction
Percent
dissatisfied
Books/
supplies
Poor
Teaching
Lack of
teachers
Facilties in bad
condition High fees
Teachers
absent
Lack of
space
While the GER for households located inremote clusters is 92 percent, the share forhouseholds located in accessible clusters is
87 percent. In contrast, NER forhouseholds in accessible clusters is higherthan that of households in remote clustersat 69 and 65 percent respectively.
Furthermore, while GER for non-poorpoor households is 96 percent, the sharefor poor households is 72 percent.Similarly, NER for non-poor households
is higher than that of poor households at73 and 56 percent respectively.
GER is highest among people living inhouseholds belonging to the self-employed other category at 90 and NER
is highest among households where themain income earner is an employee at 79percent. On the other hand, GER and NERare lowest among households where the
main income earner belongs to the othercategory at 83 and 50 percent respectively.
Furthermore, while GER for males is 91percent, the share for females is 87percent. In contrast, females have higher
NER than males at 71 and 64 percentrespectively.
Surprisingly, the breakdown by orphan
status shows higher GER and NER fororphaned children. On the other hand non-fostered children have higher GER thanfostered children at 89 and 65 percent
respectively. Likewise, while NER fornon-fostered children is 69 percent, the
share for fostered children is 51 percent. Itis worth remembering the small samplesize in the orphaned and fostered category(see chapter 2), as well as that foster and
orphan status is strongly correlated withage: orphaned and fostered children havehigher mean ages than non-orphaned andnon-fostered children.
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3 Education
Satisfaction
The satisfaction rate informs on theproportion of primary school pupils who
cited no problems with their schools.Information on satisfaction was obtained
by asking respondents to identifyproblems they faced with school.
Around half (51 percent) of all primaryschool pupils were satisfied with school. A
higher share of pupils living in accessibleclusters reported to be satisfied with theirschools than pupils living in remoteclusters, at 58 and 42 percent respectively.
Likewise, while 54 percent of pupils livingin poor households reported to be satisfiedwith school, the share for pupils living innon-poor households is 42 percent.
The breakdown by socio-economic group
of the household shows that the employeeshave the highest rate of satisfaction with
their primary schools at 79 percent, whilepupils living in households where themain income earner is self-employed innon-agricultural activities have the lowest
satisfaction rate at 45 percent.
Furthermore, 62 percent of orphaned
children reported to be satisfied withprimary school compared to 49 percent ofnon-orphaned children. Likewise, the
percentage of fostered children who reportto be satisfied with school is higher thanthat of non-fostered, at 77 and 48 percent
respectively.
Finally, the percentage of boys whoreported to be satisfied with primaryschool is higher than that of girls at 55 and
48 percent respectively.
3.1.3 Secondary School
Access
Secondary school access rate is defined asthe proportion of secondary school-age
children (14 to 19 years) reporting to livewithin 30 minutes of the nearest secondaryschool.
Only 6 percent of all pupils in secondary
school live within 30 minutes of thenearest secondary school. While 13percent of pupils living in accessiblevillages live within 30 minutes of thenearest secondary school, the share for
pupils living in remote villages is virtuallynull. Similarly, 8 percent of pupils livingin non-poor households live within 30minutes of the nearest secondary school,
whereas the share for pupils living in poorhouseholds is virtually null.
The socio-economic status of thehousehold seems to be strongly correlatedwith the rate of access to secondaryschool. While pupils living in householdsbelonging to the self-employed other
category have the highest rate of access tosecondary school at 17 percent, followedby those who belong to the self-employedagriculture category (5 percent), the share
for the other and employee categoriesis virtually null.
Gender does not show strong correlationwith secondary school access, but theaccess rate for orphaned children is 11
percent, higher than that for non-orphanedchildren, at 5 percent. Likewise, while 9percent of fostered children live within 30minutes of the nearest secondary school,
the share for non-fostered children is 5percent.
Enrolment
As explained before, Gross EnrolmentRate (GER) is defined as the ratio of all
individuals attending school, irrespectiveof their age, to the population of school-age children while the Net Enrolment Rate
(NER) is defined as the ratio of school-agechildren enrolled at school to thepopulation of school-age children. Thesecondary school-age is between 14 and19 years old.
The GER and NER at secondary schoolare very low compared to primary schoollevel. Overall, GER was 5 percent andNER was 4 percent. While secondary
school NER does not show strongcorrelation with cluster location, thesecondary school GER for householdslocated in accessible clusters is 2
percentage points higher than that ofhouseholds located in remote clusters.Both secondary GER and NER are higher
in non-poor households than in poorhouseholds, with a difference of 4percentage points.
The breakdown by socio-economic groupof the household shows that employees arethe category with highest GER and NER
at 81 and 66 percent respectively, whereas
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Table 3.3: Percentage of children 6-17 years who ever attended school by reason not currently attending
Percent not
attending
Completed
school Distance Cost Work Illness Pregnancy
Got
married
Useless/
uninteresting
Failed
exam
Awaits
admission Dismi
Total 16.2 31.7 0.0 11.0 3.7 5.5 0.8 8.5 18.2 22.4 22.6 0.Cluster Location
Accessible 13.4 36.8 0.0 8.9 2.5 8.5 0.0 10.5 19.7 21.1 17.2 0.
Remote 19.5 27.5 0.0 12.7 4.7 3.1 1.5 6.8 17.1 23.6 27.0 1.
Poverty Status
Poor 17.7 15.6 0.0 0.0 9.9 6.2 0.0 2.1 36.2 16.2 28.1 0.
Non-poor 15.7 37.6 0.0 15.0 1.4 5.3 1.1 10.8 11.7 24.7 20.5 1.
Socio-economic Group
Employee 13.2 68.4 0.0 31.6 0.0 0.0 0.0 0.0 0.0 0.0 68.4 0.
Self-employed - agric 17.4 32.5 0.0 10.6 3.6 6.3 0.0 11.4 22.5 21.2 20.0 0.
Self-employed - other 9.1 35.4 0.0 16.3 8.3 6.9 0.0 0.0 0.0 36.3 13.1 0.
Other 35.2 7.8 0.0 0.0 0.0 0.0 8.4 0.0 16.1 21.8 37.4 8.
Gender
Male 15.8 19.7 0.0 5.6 2.5 6.8 0.0 0.0 17.6 24.2 31.2 0.
Female 16.6 41.6 0.0 15.4 4.7 4.5 1.5 15.5 18.8 21.0 15.4 1.
Age
7-13 3.1 19.3 0.0 7.0 8.4 6.2 0.0 0.0 48.3 0.0 19.3 6.
14-19 40.9 33.5 0.0 11.6 3.0 5.5 1.0 9.7 14.0 25.6 23.0 0.
Source:CWIQ 2006 Bukombe DC
1. Base for column 1 is school-age children. For columns 2 to 13, not enrolled school children
Reasons not currently attending
the shares for the other category is
virtually null. GER is higher among malethan female-headed households, with adifference rate of 5 percentage points.
Similarly, the NER rate is 4 percentagepoints higher among males than females.
Finally, the GER and NER rates do notshow important differences amongorphaned and non-orphaned children. Onthe other hand, while the GER and NERfor fostered children is 5 percent, the share
for non-fostered children is 3 percent.
Satisfaction
Roughly four fifths (81 percent) of thepopulation enrolled in secondary school issatisfied with school. 19 percent of this
population reports to be dissatisfied withthe secondary schools they attend. This
satisfaction rate is higher than in primaryschools (51 percent). The satisfaction rate
is noticeably higher among people livingin remote clusters than that of peopleliving in accessible clusters, at 94 and 72percent respectively. On the other hand,
virtually all pupils living in poorhouseholds reported to be satisfied withtheir secondary schools, compared to 79
percent of those living in non-poorhouseholds.
The breakdown by socio-economic group
shows that virtually all pupils living inhouseholds belonging to the self-employed other category are satisfied
with secondary school, while the share forthose living in households where the mainincome earner belongs to the other
category is virtually null.
Virtually all female pupils were satisfiedwith their school compared to 75 percentof males.
Among the individuals enrolled insecondary schools, orphaned childrenwere more satisfied with their schools than
non-orphaned children. While virtually all(100 percent) orphaned children aresatisfied with their schools, the share fornon-orphaned children is 87 percent.
Similarly, virtually all fostered childrenreports to be satisfied with their secondary
schools compared to 83 percent of non-fostered children.
3.2 DissatisfactionOne of the aims of the survey is to informon perceptions of quality of servicesreceived among individuals for whom
these are provided. To obtain thisinformation, primary and secondary
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3 Education
Table 3.4: Primary school enrollment and drop out rates by gender
Male Female Total Male Female Total
Total 63.8 70.7 67.6 0.6 2.1 1.4
7 25.0 37.5 30.3 0.0 0.0 0.0
8 38.3 58.3 51.3 0.9 1.2 1.1
9 88.2 75.5 80.9 0.0 1.6 0.9
10 80.2 87.9 83.4 0.0 2.7 1.1
11 81.2 82.5 82.0 4.9 0.0 2.1
12 76.9 83.7 81.6 0.0 0.0 0.0
13 90.4 77.8 83.8 0.0 12.9 6.8
Source:CWIQ 2006 Bukombe DC
1. Base for table is primary school-age population (age 7-13)
Drop out ratesNet enrollment rates
school students who were not satisfied
with school at the time of the survey wereasked to provide reasons for theirdissatisfaction. Complaints regarding lackof books and other resources were
allocated into the Books/Suppliescategory, while those relating to quality of
teaching and teacher shortages weregrouped into the Teaching category. The
Facilities category incorporatescomplaints regarding overcrowding and
bad condition of facilities. The results ar