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Global Initiative on Out-Of-School Children

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Global Initiative on Out-Of-School Children The United Republic of Global Initiative on Tanzania Out-of-School Children Ministry of Education Science and Technology TANZANIA VERIFICATION OF THE OUT-OF-SCHOOL CHILDREN STUDY MARCH 2018 Background 1 1.3 million Primary school age 3.6 children (7—13 years) million children out of school in Tanzania 1. 2.3 million Secondary school age Background children (14—17 years) In the year 2015 through 2016, the The methodology for this study was mainly school children in Tanzania based on Ministry of Education Science and desk review, whereby existing data from the estimated population growth rate of Technology (MOEST), in collaboration the 2012 Population and Housing Census; 2.7 per cent (2012 Census). However, with UNICEF Tanzania, conducted the 2011/12 Household Budget Survey and since 2012 to 2016 there have been a a study to establish the profile of Tanzania mainland administrative data on number of initiatives to address the the out-of-school children in terms education (BEST), was used. problem of out-of-school children in the of who they are, where they are, and country including Literacy and Numeracy what they are doing. The study also Based on the analysis of this data, it was Education Support (LANES) under GPE established the factors and practices revealed that approximately 3.5 million programme; Fee Free Basic Education that keep children out of schools. The primary and secondary school age Policy, and other non-formal training study was guided by five dimensions of children were out of school, and more programmes (COBET and Secondary exclusions which focused on: than 1.7 million pre-primary school Education through ODL). These age children had not yet enrolled in initiatives have contributed significantly • Children of pre-primary school school by 2012. Using the population to addressing the problem of out-of- age who were not in pre-primary projection from the 2012 Census data, it school children though it still persists. or primary was estimated that, in 2015 there were Since the quantitative data presented 2.2 million out-of-school children at the above, was based on 2012 population • Children of primary school age primary school level (aged 7—13), and 1.7 estimate data, there was a need to who were not in primary or million out-of-school children at the lower conduct a verification study to establish secondary school secondary school level (aged 14—17). the relationship between the estimated This meant that a total of 3.9 million data, and the actual situation on the • Children of lower secondary school children of school age 7—17-year-olds, ground, as experienced from the field. age who were not in primary or were expected to be out of school by secondary school 2015. At the pre-primary school level, Objectives of the verification study about 1 million five-year-olds, and • Children who were in primary school about 900 000 six-year-olds, attend The purpose of the verification study but were at risk of dropping out neither pre-primary nor primary school. was to establish whether the findings Moreover, Demographic Health Survey reported in the out-of-school study of • Children who were in lower and Malaria Indicator Survey (2015/16 2016, which used the 2012 census data, secondary school but were at TDHS-MIS) data shows that more than revealed the reality, as compared to the risk of dropping out. 21.6 per cent of primary school age actual field data as observed in schools children, and 7.1 per cent of secondary and in the community. Specifically, the school age children have never attended study intended to: school. In addition, the 2016 EMIS data shows that 1.33 million (14.4%) primary i. Establish the percentage of the out- school age children (7—13 years) are out of-school children as compared to the of school. Furthermore, EMIS data shows projected population at village, district that 2.3 million (57%) of secondary and regional level, in order to develop school age children (14—17 years) are out a strategy to address the problem of school. This makes a total of 3.6 million children out of school in Tanzania. ii. Find out factors associated with the out-of-school children in selected These findings provide indicative data on areas so as to establish the contextual the number and percentage of out-of- strategy for intervention. 2 Verification of the out-of-school children study Uganda Lake Kenya Victoria Rwanda Mwanza Burundi Tabora Democratic Republic Dodoma of the Congo Dar es Salaam Zambia Malawi Mozambique TABLE 1 Sample size S/N Regions Districts Wards Villages/Mtaa Primary Schools Secondary Schools Azimio kusini Tandika Mwangaza Azimio Kichanga Mji Mpya Dar es 1. Temeke Masaki Salaam Toangoma Toangoma Toangoma Toangoma Mzinga Imalamihayo Imalamihayo Kazaroho Kazaroho Kazaroho Kazaroho 2. Tabora Kaliua Tuombe Mungu Tuombe Mungu Ugunga Ugunga Mkuyuni Mkuyuni Ishishang’holo Ishishang’holo Sima Sima Sima Sima 3. Mwanza Sengerema Nyakahako Nyakahako Chifunfu Chifunfu Chifunfu Bugumbikisu Paranga Paranga Paranga Paranga Kelema Balai Kelema balai Kelema Balai 4. Dodoma Chemba Motto Motto Sanzawa Motto Sanzawa Sanzawa Methodology 3 2. Data collection tools The data collection tools, which included interviews, questionnaires (open and closed) and checklists, Methodology were used. Prior to the field verification study, a letter was sent to the Regional Administrative officers (RASs), informing Sample design them about the verification exercise. They then passed down the information to the respective councils for the The study was conducted in four regions of Tanzania same. The councils informed the respondents before the Mainland, namely, Dar es Salaam, Dodoma, Mwanza researchers arrived. Upon the arrival of the researchers, and Tabora. The selection of these regions considered discussions were held on the best modality to get the reported number of out-of-school children (OOSC) appropriate information. in the 2015/2016 OOSC study. In each selected region, one district, two wards, four villages, a minimum of During data collection, the selected wards, schools, 40 households, two primary schools and one secondary villages and households were visited. The choice of school from each ward were sampled for this study. Table 1 schools ensured that at least one school was typically provides a summary of the sample size for the verification. rural and/or represented main economic activities of the community such as fishing. The selection of households Four regions, two with high (Tabora and Mwanza) and captured different types of households including those one with low (Dodoma) out-of-school children rates headed by children and the elderly. Relevant documents (OOSC profile study), and Dar es Salaam as a special such as admission books, duty books and attendance case, considering representation of zones, were taken registers were reviewed. Interviews with respective into account in the verification study. officers, heads of schools, village leaders and heads of households were conducted. In some cases, it was Sampling procedures necessary to take some photographs. Tabora region was selected as a region with the highest percentage of out-of-school children (44.3% in primary Sources of data school and 57.7% in secondary school) as indicated in the OOSC study report. Mwanza had a slightly lower No. Source Responsible percentage (19.5 in primary school and 35 in secondary Village Executive 1. Village register school) but was selected due to high absolute numbers Officers of out-of-shool children (103,060). Dodoma, with lower School Census and (17.4 %) OOSC at primary school but slightly higher 2. Schools attendance registers OOSC at secondary school (37.3 %) , was included in the sample as one of the education disadvantaged regions. 3. Ward Level reports Ward Executive Officers Dar es Salaam, with 8.6 per cent and 38.1 per cent for Heads of households primary and secondary schools respectively, was selected (Identified through as the most urbanized setting with a fair representation of 4. Households stratified sampling) a diverse population from different backgrounds. of single, child, elderly households etc. The districts were purposively selected considering some socio-economic conditions such as major economic No. Source Tool for data collection activities and poverty index in the districts. Wards were 1. Households Questionnaire also purposively selected, one from urban and another from rural settings in each district. Two villages from 2. Schools Questionnaire each ward and 40 households per district were randomly 3. Ward level Checklist and interview selected upon the arrival of the teams in the areas. At least one primary and one secondary school were also 4. Village level Checklist randomly selected where there was more than one school 5. District level Questionnaire per ward. Places deemed to attract out-of-school children 6. Regional level Checklist such as fishing and mining areas were also considered. 4 Verification of the out-of-school children study Temeke District could be attributed to the fact that Toangoma 3. is a new settlement area with mixed culture, and economic status is moderate. However, information from wards in newly formed Chemba District is not available because there The Findings was no data at ward level concerning the number of children attending or not attending school. Household information This section presents the findings from four selected districts. In order to get a clear picture of the situation, Data from the households revealed that 62.4 per cent of various information was collected at district, ward, students (excluding Kaliua District) were attending school. village or household, and school levels so as to ascertain To be specific, Toangoma Masaki in Temeke District, the authenticity of the data. indicated the highest rate (88.9%), while Sanzawa in Chemba District had shown the lowest rate of attendance (30.8%).
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