Global Initiative on Out-of-School Children

Global Initiative on Out-of-School Children TANZANIA VERIFICATION OF THE OUT-OF-SCHOOL CHILDREN STUDY MARCH 2018 The United Republic of Tanzania Ministry of Education Science and Technology

Background 1 1. Background 1.3 million Primary school age children (7 13 years) 3.6 million children out of school in Tanzania 2.3 million Secondary school age children (14 17 years) In the year 2015 through 2016, the Ministry of Education Science and Technology (MOEST), in collaboration with UNICEF Tanzania, conducted a study to establish the profile of the out-of-school children in terms of who they are, where they are, and what they are doing. The study also established the factors and practices that keep children out of schools. The study was guided by five dimensions of exclusions which focused on: Children of pre-primary school age who were not in pre-primary or primary Children of primary school age who were not in primary or secondary school Children of lower secondary school age who were not in primary or secondary school Children who were in primary school but were at risk of dropping out Children who were in lower secondary school but were at risk of dropping out. The methodology for this study was mainly desk review, whereby existing data from the 2012 Population and Housing Census; the 2011/12 Household Budget Survey and Tanzania mainland administrative data on education (BEST), was used. Based on the analysis of this data, it was revealed that approximately 3.5 million primary and secondary school age children were out of school, and more than 1.7 million pre-primary school age children had not yet enrolled in school by 2012. Using the population projection from the 2012 Census data, it was estimated that, in 2015 there were 2.2 million out-of-school children at the primary school level (aged 7 13), and 1.7 million out-of-school children at the lower secondary school level (aged 14 17). This meant that a total of 3.9 million children of school age 7 17-year-olds, were expected to be out of school by 2015. At the pre-primary school level, about 1 million five-year-olds, and about 900 000 six-year-olds, attend neither pre-primary nor primary school. Moreover, Demographic Health Survey and Malaria Indicator Survey (2015/16 TDHS-MIS) data shows that more than 21.6 per cent of primary school age children, and 7.1 per cent of secondary school age children have never attended school. In addition, the 2016 EMIS data shows that 1.33 million (14.4%) primary school age children (7 13 years) are out of school. Furthermore, EMIS data shows that 2.3 million (57%) of secondary school age children (14 17 years) are out of school. This makes a total of 3.6 million children out of school in Tanzania. These findings provide indicative data on the number and percentage of out-ofschool children in Tanzania based on the estimated population growth rate of 2.7 per cent (2012 Census). However, since 2012 to 2016 there have been a number of initiatives to address the problem of out-of-school children in the country including Literacy and Numeracy Education Support (LANES) under GPE programme; Fee Free Basic Education Policy, and other non-formal training programmes (COBET and Secondary Education through ODL). These initiatives have contributed significantly to addressing the problem of out-ofschool children though it still persists. Since the quantitative data presented above, was based on 2012 population estimate data, there was a need to conduct a verification study to establish the relationship between the estimated data, and the actual situation on the ground, as experienced from the field. Objectives of the verification study The purpose of the verification study was to establish whether the findings reported in the out-of-school study of 2016, which used the 2012 census data, revealed the reality, as compared to the actual field data as observed in schools and in the community. Specifically, the study intended to: i. Establish the percentage of the outof-school children as compared to the projected population at village, district and regional level, in order to develop a strategy to address the problem ii. Find out factors associated with the out-of-school children in selected areas so as to establish the contextual strategy for intervention.

2 Verification of the out-of-school children study Rwanda Uganda Lake Victoria Kenya Burundi Mwanza Democratic Republic of the Congo Tabora Dodoma Dar es Salaam Zambia Malawi Mozambique TABLE 1 Sample size S/N Regions Districts Wards Villages/Mtaa Primary Schools Secondary Schools 1. Dar es Salaam Temeke Azimio Toangoma Azimio kusini Mji Mpya Masaki Toangoma Tandika Mwangaza Toangoma Mzinga Kichanga Toangoma 2. Tabora Kaliua Kazaroho Ugunga Imalamihayo Kazaroho Tuombe Mungu Mkuyuni Imalamihayo Kazaroho Tuombe Mungu Mkuyuni Kazaroho Ugunga 3. Mwanza Sengerema Sima Chifunfu Ishishang holo Sima Nyakahako Chifunfu Ishishang holo Sima Nyakahako Bugumbikisu Sima Chifunfu 4. Dodoma Chemba Paranga Sanzawa Paranga Paranga Paranga Kelema Balai Kelema balai Kelema Balai Motto Motto Sanzawa Sanzawa Motto

Methodology 3 2. Methodology Sample design The study was conducted in four regions of Tanzania Mainland, namely, Dar es Salaam, Dodoma, Mwanza and Tabora. The selection of these regions considered the reported number of out-of-school children (OOSC) in the 2015/2016 OOSC study. In each selected region, one district, two wards, four villages, a minimum of 40 households, two primary schools and one secondary school from each ward were sampled for this study. Table 1 provides a summary of the sample size for the verification. Four regions, two with high (Tabora and Mwanza) and one with low (Dodoma) out-of-school children rates (OOSC profile study), and Dar es Salaam as a special case, considering representation of zones, were taken into account in the verification study. Sampling procedures Data collection tools The data collection tools, which included interviews, questionnaires (open and closed) and checklists, were used. Prior to the field verification study, a letter was sent to the Regional Administrative officers (RASs), informing them about the verification exercise. They then passed down the information to the respective councils for the same. The councils informed the respondents before the researchers arrived. Upon the arrival of the researchers, discussions were held on the best modality to get appropriate information. During data collection, the selected wards, schools, villages and households were visited. The choice of schools ensured that at least one school was typically rural and/or represented main economic activities of the community such as fishing. The selection of households captured different types of households including those headed by children and the elderly. Relevant documents such as admission books, duty books and attendance registers were reviewed. Interviews with respective officers, heads of schools, village leaders and heads of households were conducted. In some cases, it was 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 school and 57.7% in secondary school) as indicated in the OOSC study report. Mwanza had a slightly lower percentage (19.5 in primary school and 35 in secondary school) but was selected due to high absolute numbers of out-of-shool children (103,060). Dodoma, with lower (17.4 %) OOSC at primary school but slightly higher OOSC at secondary school (37.3 %), was included in the sample as one of the education disadvantaged regions. Dar es Salaam, with 8.6 per cent and 38.1 per cent for primary and secondary schools respectively, was selected as the most urbanized setting with a fair representation of a diverse population from different backgrounds. The districts were purposively selected considering some socio-economic conditions such as major economic activities and poverty index in the districts. Wards were also purposively selected, one from urban and another from rural settings in each district. Two villages from each ward and 40 households per district were randomly selected upon the arrival of the teams in the areas. At least one primary and one secondary school were also randomly selected where there was more than one school per ward. Places deemed to attract out-of-school children such as fishing and mining areas were also considered. Sources of data No. Source Responsible 1. Village register 2. School Census and attendance registers Village Executive Officers Schools 3. Ward Level reports Ward Executive Officers 4. Households Heads of households (Identified through stratified sampling) of single, child, elderly households etc. No. Source Tool for data collection 1. Households Questionnaire 2. Schools Questionnaire 3. Ward level Checklist and interview 4. Village level Checklist 5. District level Questionnaire 6. Regional level Checklist

4 Verification of the out-of-school children study 3. The Findings Temeke District could be attributed to the fact that Toangoma 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 was no data at ward level concerning the number of children attending or not attending school. This section presents the findings from four selected districts. In order to get a clear picture of the situation, various information was collected at district, ward, village or household, and school levels so as to ascertain the authenticity of the data. Ward information The analysis of data regarding school attendance at ward level has shown that an average of 71.3 per cent of school age children were attending school. The ward level analysis shows variation across wards, as shown in Table 2. Sengerema (55.2%) recorded the lowest percentage of children attending school compared with Kaliua (86.9%). Further analysis at ward level showed that Chifunfu Ward in Sengerema District, was the lowest (51.6%), while Toangoma Ward in Temeke District recorded the highest percentage of children attending school (93.8%). It is worth noting that the main economic activity in Chifunfu Ward is fishing and this was one of the reasons identified as a leading cause of poor attendance and dropout in the area. The notable difference between Toangoma and Azimio in Household information Data from the households revealed that 62.4 per cent of students (excluding Kaliua District) were attending school. To be specific, Toangoma Masaki in Temeke District, indicated the highest rate (88.9%), while Sanzawa in Chemba District had shown the lowest rate of attendance (30.8%). A summary of the household rate of attendance is illustrated in Table 3. On average only 60.1 per cent of all school aged children are attending school as per the household data, which means about 40 per cent are out of school. As shown in Table 3, Sanzawa Village has the lowest rate of children attending school (30.8%). Based on the verification study, major reasons for low attendance is distance from home to school (approximately 8 11 kilometres), accompanied by economic activities, especially animal rearing. In addition, there is serious lack of meals for students while at school and experience shows that even at the homestead, families go for a single meal a day. The poverty index in this village is also a factor for non-school attendance, especially for girls, who are sometimes used for house chores, and also when they miss their personal belongings. On top of that, there are some settlements where the distances from school range from 7 to 14 kilometres, TABLE 2 Number of attending primary school children in selected villages by ward Attending (%) Attending Council Ward Population M F Total % Kazaroho 2814 85.1 81.6 2345 83.3 Kaliua Ugunga 3907 91.2 87.6 3493 89.4 Total 8721 88.6 85.1 5838 86.9 Azimio 13403 57.0 63.0 8077 60.3 Temeke Toangoma 11637 93.3 94.3 10919 93.8 Total 25040 77.4 77.1 18996 75.9 Chifunfu 10713 47.2 55.6 5524 51.6 Sengerema Sima 2844 93.7 55.4 1966 69.1 Total 13557 54.9 55.5 7490 55.2 Grand Total 45318 71.0 71.6 32324 71.3 Note: No data was available from Dodoma on attendance and population at ward level.

The Findings 5 TABLE 3 Percentage distribution of school age population attending school by district/village District Temeke Chemba Sengerema Village Children attending school (%) 62.9 Azimio mji mpya 47.8 Azimio Kusini 49.0 Toangoma Masaki 88.9 Toangoma 87.8 54.8 Paranga 73.3 Kalama Barai 63.2 Motto 57.1 Sanzawe 30.8 62.7 Mnazi mmoja 78.4 Chifunfu 45.5 Nyakahako 66.0 Ishishang holo 68.8 Sima Kati 65.5 Majengo 78.8 where the children are supposed to be enrolled in Sanzawa primary school. The parents have refused to enrol their children because of the distance, and also, in between, there is a seasonal river which hinders the children from attending school regularly. It is clear from the village information that the number of children attending school is less than 65 per cent on average for the visited villages. This information corroborates with the data from other sources in the current study, showing that more than 20 per cent of the school going age children were not attending school. It could be concluded that on average, about 35 per cent of the school going age children were not in school. Primary school dropout information The dropout rate in the surveyed primary schools has demonstrated a significant variation for the year 2015, ranging from 0.5 per cent in Temeke to 16.4 per cent in Chemba districts. Data for 2015 has been used to make this analysis because Chemba District has not yet compiled the 2016 data (see Table 4). In 2016, the dropout rates vary between 2.2 per cent in Sengerema District and 14.2 per cent in Kaliua District. Although Sengerema District shows the lowest percentage of dropout in 2016, Ishishang holo Primary School recorded the highest dropout rate of 23.2 per cent of all surveyed schools. Other primary schools which recorded high dropout rates are Imalamihayo (20.5%) and Mkuyuni (16.5%) in Kaliua District. It is clear from Table 4 that the number of dropouts is progressively decreasing for both boys and girls from 6.6 per cent and 5.3 per cent in 2014 to 3.8 per cent and 3.3 per cent in 2016, respectively. The sharp decrease in 2016 could be attributed to the government s Fee Free Basic Education Policy, introduced in January 2016. For detailed District Data see annex 1 Secondary school level information Table 5 shows the distribution of secondary school age population by district, year, and number of registered dropped out and proportion of dropouts. The table covers Chemba, Kaliua, Sengerema and Temeke districts. The dropout rate in the surveyed secondary schools ranges between 11 per cent in Kaliua District and 21.4 per cent in Chemba District in 2015. In 2016, Temeke District recorded one per cent and Kaliua District had 7.3 per cent. Table 5 shows that the number of dropouts has decreased for boys and girls from 7.2 per cent and 7.4 per cent in 2014 to 1.8 per cent and 1.5 per cent in 2016, respectively. This significant decrease in 2016 could be attributed to the government s Fee Free Basic Education Policy, introduced in January 2016, as push factors may have been significantly reduced by the policy. It is, however, important to note that 410 boys and 390 girls were out of school, as having dropped out in the four selected districts in 2016. Key observations Data availability and management Data management regarding children who dropped out from school at ward and village levels was not readily available. The main reason given was that the villages and wards no longer maintained village registers. Similarly, information about never attended school was systematically missing in all villages and wards. When probed further on the availability of the number of children, ward and village leaders directed the researchers to the heads of schools in their respective areas. Heads of schools were contacted but they pointed out clearly that schools only captured information of children who were enrolled at schools and not otherwise. It is conclusive and logical to point out that data for never attended school children remains unclear because it was not captured at all. However, according to the NBS (2016) about 21.6 per cent of children aged 7 to 13 years have never attended school.

6 Verification of the out-of-school children study TABLE 4 Number of registered primary school age children and dropout by district in selected years District Kaliua Chemba Temeke Sengerema GRAND TOTAL 2014 2015 2016 M F M F M F Registered 931 1036 956 1105 968 1099 Dropout (%) 9.0 10.1 11.0 11.7 16.3 12.4 Registered 1118 1199 1145 1204 1094 1311 Dropout (%) 21.5 11.2 22.0 11.0 NA NA Registered 2962 3201 3547 7254 3728 3929 Dropout (%) 2.7 2.1 1.2 0.7 1.6 1.1 Registered 2514 2710 2677 2821 3008 3191 Dropout (%) 3.6 4.7 3.3 2.9 3.9 4.1 Registered 7525 8146 8325 8837 8798 9530 Dropout (%) 6.6 5.3 5.8 4.2 3.8 3.3 TABLE 5 Number of registered primary school age children and dropout by District in selected years District Kaliua Chemba Temeke Sengerema GRAND TOTAL 2014 2015 2016 M F M F M F Registered 372 188 331 197 373 233 Dropout (%) 11.8 20.7 10.9 11.2 8.0 10.3 Registered 1676 2175 1476 2315 1618 2513 Dropout (%) 3.8 5.2 2.1 2.0 2.3 2.7 Registered 6322 6295 21074 22289 19911 22284 Dropout (%) 8.3 8.1 1.5 1.1 1.5 1.2 Registered 312 307 304 313 511 881 Dropout (%) 18.7 10.1 15.1 11.2 8.1 5.5 Registered 9420 9291 23555 25313 22821 25911 Dropout (%) 7.2 7.4 5.4 3.6 1.8 1.5 School based data Data at school level was available but not well kept, or easily accessible, in some schools. In most of the schools, data entry was incorrectly done and not consistently updated which caused a lot of work for the team in order to make sense of it. For example, in one school, progression of repeated cases was difficult to ascertain whether or not some pupils were still in school or had dropped out of school. School infrastructure School infrastructure in most of the visited schools was unfavourable with the exception of teachers houses which were in a fair condition though not adequate. In some schools, for example, the number of toilets and classrooms were well below the minimum recommended standard. Some of these were in pathetic condition as shown in figure 1. At one school in one ward, for example, there are five and six toilet pit holes for 326 boys and 342 girls, making a ratio of 1:81 and 1:57 respectively. According to the National Basic Standards for toilets in primary school, 20 girls are required to use one pit hole while 25 boys can use one pit hole for toilet services. Community economic activities Economic activities such as fishing, animal rearing and petty business affect school attendance in different ways; as for example in fishing communities. Firstly, children s daily attendance suffers as they engage in fishing activities as a source of income. In one ward in Mwanza region some children were found at the lake shore processing fish and doing other related activities. These children were of different ages, as shown in Figure 3.

The Findings 7 Secondly, the fishing activities affect school activities due to the seasonal moving of parents to other areas considered to have more fish during the low season. This practice affects children s school activities in two ways. In some cases the entire family relocates to new fishing areas making children leave school; and there was no evidence to suggest that these children were transferred to other schools when they were relocated. A similar situation is experienced in pastoralist communities who move with their animals to seek new grazing lands. Reasons given by schools for dropping out of school The verification teams used different methods to find out reasons given as the cause to why children dropped out of school. The list below provides what school children and community members identified as leading causes of children dropping out of school. Distance Distance from school was identified as one of the major reasons for keeping children from enrolling in school. Pre and Grade 1 school going age were still at home because they were not able to walk long distances to and from school. In some districts school children were expected to cover more than six kilometres per day.?? Confusion of Fee Free Basic Education Policy In some households, school children were not able to attend school due to lack of school requirements such as uniforms and meals, which were initially catered for by parents. With a fee free policy parents no longer feel responsible to provide for such items as they were made to understand that the government would provide them. Pregnancy Pregnancy is still a major cause of drop out in some schools. Deaths Death of children is also a cause of drop out of children from school. Parental restrictions In all districts covered by the verification study it was found that parents restricted their children from attending school in order for them to undertake domestic chores. Grazing in pastoralist communities, fishing in fishing communities, petty business in both urban and rural settings were identified as reasons for keeping children away from school. Truancy tendencies Truancy was also identified as a major reason causing drop out of children. Bad youth groups Youth groups which lacked role models for the importance of education, attracted some of the truant children to join them. In some cases parents were not aware that their children were not attending school as they left in the morning and came back later in the day when school had ended. Some children become involved in smoking bhang.

8 Verification of the out-of-school children study Recommendations The verification exercises trigger the following recommendations: 1. Proper record keeping, crucial for organisational planning. Correct data should be timeously accessible at all levels. There is need for every village to have an updated register that comprises of types of households and ages of all household members. The village register should be available to schools in the village especially during the registration of pre-primary and Std I children. Schools should maintain daily attendance and update admission registers when necessary. Wards should consolidate information from schools as well as from the village to have cumulative information regarding the ward. 2. The mismatch of infrastructure with numbers of learners has contributed significantly to children not attending school or dropping out. It is therefore recommended that proper projections should be made regularly, in order to address the challenges of dropping out and not attending school. 3. Distance from home to school, and some other geographical factors contribute to barriers for children not attending school. It is recommended that satellite centres be established in such areas. 4. School meals have proved to be among the factors that motivate learners to attend school. On introduction of the Fee Free Basic Education Policy some parents have been reluctant to contribute to meals for their children due to the misconception of the policy. It is recommended that the policy should be adequately publicised to the community at large.

Annexes 9 References National Bureau of Statistics (2016), Tanzania Demographic and Health Indicators Survey and Malaria Indicator Survey (2015/2016 TDHS-MIS) Annexes ANNEX 1 Primary school children attending school by ward Population Attending Council Ward M F Total M F Total Kazaroho 1402 1412 2814 1193 1152 2345 Percentages 85.1 81.6 83.3 Kaliua Ugunga 1954 1953 3907 1782 1711 3493 Percentages 91.2 87.6 89.4 Total 3356 3365 6721 2975 2863 5838 Percentages 88.6 85.1 86.9 Azimio 6099 7304 13403 3476 4601 8077 Percentages 57.0 63.0 60.3 Temeke Toangoma 5643 5994 11637 5264 5655 10919 Percentages 93.3 94.3 93.8 Total 11742 13298 25040 8740 10256 18996 Percentages 74.4 77.1 75.9 Chifunfu 5137 5576 10713 2424 3100 5524 Percentages 47.2 55.6 51.6 Sengerema Sima 1019 1825 2844 955 1011 1966 Percentages 93.7 55.4 69.1 Total 6156 7401 13557 3379 4111 7490 Percentages 54.9 55.5 55.2 GRAND TOTAL 21254 24064 45318 15094 17230 32324 Percentages 71.0 71.6 71.3 Note: No data was available from Dodoma on attendance and population at ward level.

10 Verification of the out-of-school children study ANNEX 2 Sampled secondary schools registered and dropout information 2014-2016 2014 2015 2016 Council Grade Registered Dropout Registered Dropout Registered Dropout M F T M F T M F T M F T M F T M F T Form I 330 418 748 14 20 34 335 445 780 7 9 16 704 1003 1707 22 35 57 Form II 660 977 1637 24 67 91 613 868 1481 11 20 31 373 633 1006 5 8 13 Chemba Form III 242 322 564 12 9 21 234 628 862 5 13 18 211 346 557 9 18 27 Form IV 444 458 902 13 18 31 294 374 668 8 4 12 330 531 861 2 7 9 Total 1676 2175 3851 63 114 177 1476 2315 3791 31 46 77 1618 2513 4131 38 68 106 % dropout 3.8 5.2 4.6 2.1 2.0 2.0 2.3 2.7 2.6 Form I 114 54 168 9 19 28 93 68 161 3 0 3 140 93 233 0 4 4 Form II 142 77 219 31 12 43 87 49 136 9 7 16 100 69 169 15 14 29 Kaliua Form III 72 44 116 2 3 5 84 43 127 18 9 27 67 36 103 5 0 5 Form IV 44 13 57 2 5 7 67 37 104 6 6 12 66 35 101 10 6 16 Total 372 188 560 44 39 83 331 197 528 36 22 58 373 233 606 30 24 54 % dropout 11.8 20.7 14.8 10.9 11.2 11.0 8.0 10.3 8.9 Form I 118 156 274 29 13 42 97 122 219 24 17 41 171 271 442 16 20 36 Form II 64 58 122 15 11 26 82 89 171 16 8 24 95 124 219 9 5 14 Sengerema Form III 84 67 151 6 7 13 48 44 92 2 5 7 64 75 139 5 3 8 Form IV 46 26 72 2 0 2 77 58 135 4 5 9 40 41 81 0 0 0 Total 312 307 619 52 31 83 304 313 617 46 35 81 370 511 881 30 28 58 % dropout 16.7 10.1 13.4 15.1 11.2 13.1 8.1 5.5 6.6 Form I 1656 1561 3217 104 94 198 5496 5724 11220 42 38 80 5761 6600 12361 19 28 47 Form II 1584 1492 3076 258 258 516 5495 5762 11257 152 109 261 5561 5802 11363 235 176 411 Form III 1542 1560 3102 94 82 176 5070 5569 10639 71 50 121 4108 4621 8729 16 13 29 Temeke Form IV 1540 1682 3222 66 74 140 5013 5234 10247 50 40 90 4481 5261 9742 31 50 81 Form V 319 135 454 0 0 0 217 138 355 0 0 0 290 219 509 0 0 0 Form VI 419 191 610 0 0 0 153 61 214 0 1 1 259 151 410 2 2 4 Total 7060 6621 13681 522 508 1030 21444 22488 43932 315 238 553 20460 22654 43114 303 269 572 % dropout 7.4 7.7 7.5 15.1 11.2 13.1 1.5 1.2 1.3 Overall grand total 9420 9291 18711 681 692 1373 23555 25313 48868 9420 9420 9420 22821 25911 48732 401 389 790 % dropout 7.2 7.4 7.3 5.4 3.6 4.4 1.8 1.5 1.6

Annexes 11 FIGURE 1 A side view of a classroom in Kaliua, Tabora FIGURE 2 A grass roofed classroom at Kaliua, Tabora FIGURE 3 Some out-of-school children along the shores of Lake Victoria, ready for a fish processing job

12 Verification of the out-of-school children study FIGURE 5 An out-of-school girl doing petty business along the shores of Lake Victoria FIGURE 4 School toilet buildings which do not correspond with the number of pupils FIGURE 6 A cross section of OOSC Study Verification Team talking to children doing fish business at Lake Victoria

Ministry of Education Science and Technology The United Republic of Tanzania