The United Republic of Global Initiative on

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

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. 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 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%). Ward information A summary of the household rate of attendance is illustrated in Table 3. On average only 60.1 per cent of all school aged The analysis of data regarding school attendance at ward children are attending school as per the household data, level has shown that an average of 71.3 per cent of school which means about 40 per cent are out of school. age children were attending school. The ward level analysis shows variation across wards, as shown in Table 2. As shown in Table 3, Sanzawa Village has the lowest rate of children attending school (30.8%). Based on the Sengerema (55.2%) recorded the lowest percentage of verification study, major reasons for low attendance children attending school compared with Kaliua (86.9%). is distance from home to school (approximately 8 – 11 Further analysis at ward level showed that Chifunfu Ward kilometres), accompanied by economic activities, especially in Sengerema District, was the lowest (51.6%), while animal rearing. In addition, there is serious lack of meals for Toangoma Ward in Temeke District recorded the highest students while at school and experience shows that even percentage of children attending school (93.8%). at the homestead, families go for a single meal a day. The poverty index in this village is also a factor for non-school It is worth noting that the main economic activity in Chifunfu attendance, especially for girls, who are sometimes used Ward is fishing and this was one of the reasons identified as for house chores, and also when they miss their personal a leading cause of poor attendance and dropout in the area. belongings. On top of that, there are some settlements where The notable difference between Toangoma and Azimio in 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 Other primary schools which recorded high dropout rates are Percentage distribution of school age population Imalamihayo (20.5%) and Mkuyuni (16.5%) in Kaliua District. attending school by district/village It is clear from Table 4 that the number of dropouts is Children attending progressively decreasing for both boys and girls from 6.6 District Village school (%) per cent and 5.3 per cent in 2014 to 3.8 per cent and 3.3 per 62.9 cent in 2016, respectively. The sharp decrease in 2016 could Azimio mji mpya 47.8 be attributed to the government’s Fee Free Basic Education Temeke Azimio Kusini 49.0 Policy, introduced in January 2016. Toangoma Masaki 88.9 For detailed District Data see annex 1 Toangoma 87.8 54.8 Secondary school level information Paranga 73.3 Chemba Kalama Barai 63.2 Table 5 shows the distribution of secondary school age Motto 57.1 population by district, year, and number of registered Sanzawe 30.8 dropped out and proportion of dropouts. The table covers 62.7 Chemba, Kaliua, Sengerema and Temeke districts. Mnazi mmoja 78.4 Chifunfu 45.5 The dropout rate in the surveyed secondary schools ranges

Nyakahako 66.0 between 11 per cent in Kaliua District and 21.4 per cent in Sengerema Ishishang’holo 68.8 Chemba District in 2015. In 2016, Temeke District recorded Sima Kati 65.5 one per cent and Kaliua District had 7.3 per cent. Majengo 78.8 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 where the children are supposed to be enrolled in Sanzawa 2014 to 1.8 per cent and 1.5 per cent in 2016, respectively. primary school. The parents have refused to enrol their This significant decrease in 2016 could be attributed to the children because of the distance, and also, in between, there government’s Fee Free Basic Education Policy, introduced in is a seasonal river which hinders the children from attending January 2016, as push factors may have been significantly school regularly. reduced by the policy. It is, however, important to note that 410 boys and 390 girls were out of school, as having It is clear from the village information that the number of dropped out in the four selected districts in 2016. children attending school is less than 65 per cent on average for the visited villages. This information corroborates with the data Key observations from other sources in the current study, showing that more than 20 per cent of the school going age children were not attending Data availability and management school. It could be concluded that on average, about 35 per cent of the school going age children were not in school. Data management regarding children who dropped out from school at ward and village levels was not readily Primary school dropout information available. The main reason given was that the villages and wards no longer maintained village registers. The dropout rate in the surveyed primary schools has Similarly, information about ‘never attended school’ was demonstrated a significant variation for the year 2015, systematically missing in all villages and wards. When ranging from 0.5 per cent in Temeke to 16.4 per cent in probed further on the availability of the number of children, Chemba districts. Data for 2015 has been used to make this ward and village leaders directed the researchers to analysis because Chemba District has not yet compiled the the heads of schools in their respective areas. Heads of 2016 data (see Table 4). In 2016, the dropout rates vary schools were contacted but they pointed out clearly that between 2.2 per cent in Sengerema District and 14.2 per cent schools only captured information of children who were in Kaliua District. enrolled at schools and not otherwise. It is conclusive and logical to point out that data for never attended school Although Sengerema District shows the lowest percentage of children remains unclear because it was not captured at all. dropout in 2016, Ishishang’holo Primary School recorded the However, according to the NBS (2016) about 21.6 per cent highest dropout rate of 23.2 per cent of all surveyed schools. 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

2014 2015 2016 District M F M F M F Registered 931 1036 956 1105 968 1099 Kaliua Dropout (%) 9.0 10.1 11.0 11.7 16.3 12.4 Registered 1118 1199 1145 1204 1094 1311 Chemba Dropout (%) 21.5 11.2 22.0 11.0 NA NA Registered 2962 3201 3547 7254 3728 3929 Temeke Dropout (%) 2.7 2.1 1.2 0.7 1.6 1.1 Registered 2514 2710 2677 2821 3008 3191 Sengerema Dropout (%) 3.6 4.7 3.3 2.9 3.9 4.1

GRAND Registered 7525 8146 8325 8837 8798 9530 TOTAL 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

2014 2015 2016 District M F M F M F Registered 372 188 331 197 373 233 Kaliua Dropout (%) 11.8 20.7 10.9 11.2 8.0 10.3 Registered 1676 2175 1476 2315 1618 2513 Chemba Dropout (%) 3.8 5.2 2.1 2.0 2.3 2.7 Registered 6322 6295 21074 22289 19911 22284 Temeke Dropout (%) 8.3 8.1 1.5 1.1 1.5 1.2 Registered 312 307 304 313 511 881 Sengerema Dropout (%) 18.7 10.1 15.1 11.2 8.1 5.5

GRAND Registered 9420 9291 23555 25313 22821 25911 TOTAL Dropout (%) 7.2 7.4 5.4 3.6 1.8 1.5

School based data shown in figure 1. At one school in one ward, for example, there are five and six toilet pit holes for 326 boys and Data at school level was available but not well kept, or easily 342 girls, making a ratio of 1:81 and 1:57 respectively. accessible, in some schools. In most of the schools, data According to the National Basic Standards for toilets in entry was incorrectly done and not consistently updated primary school, 20 girls are required to use one pit hole which caused a lot of work for the team in order to make while 25 boys can use one pit hole for toilet services. sense of it. For example, in one school, progression of repeated cases was difficult to ascertain whether or not Community economic activities some pupils were still in school or had dropped out of school. Economic activities such as fishing, animal rearing School infrastructure and petty business affect school attendance in different ways; as for example in fishing communities. School infrastructure in most of the visited schools was Firstly, children’s daily attendance suffers as they unfavourable with the exception of teachers’ houses engage in fishing activities as a source of income. In which were in a fair condition though not adequate. In one ward in Mwanza region some children were found some schools, for example, the number of toilets and at the lake shore processing fish and doing other related classrooms were well below the minimum recommended activities. These children were of different ages, as standard. Some of these were in pathetic condition as shown in Figure 3. The Findings 7

Secondly, the fishing activities affect school activities Reasons given by schools for dropping out of school due to the seasonal moving of parents to other areas considered to have more fish during the low season. The verification teams used different methods to find This practice affects children’s school activities in two out reasons given as the cause to why children dropped ways. In some cases the entire family relocates to new out of school. The list below provides what school fishing areas making children leave school; and there children and community members identified as leading was no evidence to suggest that these children were causes of children dropping out of school. 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.

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 Ugunga 1954 1953 3907 1782 1711 3493 Kaliua 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 Toangoma 5643 5994 11637 5264 5655 10919 Temeke 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 Sima 1019 1825 2844 955 1011 1966 Sengerema 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 5 4 4 8 9 T 0 0 13 81 16 14 57 47 27 36 29 29 58 54 1.3 411 1.6 8.9 2.6 6.6 572 106 790 3 5 2 7 4 8 6 F 0 0 0 13 18 35 14 28 50 20 24 28 1.5 68 5.5 1.2 176 2.7 389 269 10.3 Dropout 5 5 5 2 2 9 9 0 0 0 15 31 19 M 16 16 10 22 38 30 30 1.5 8.1 235 1.8 2.3 8.0 303 401 2016

T 81 101 139 233 557 219 103 861 169 410 509 442 881 1707 606 9742 4131 8729 1006 11363 12361 43114 48732 F

35 41 75 93 36 69 151 531 219 271 633 124 511 346 233 5261 4621 1003 5802 2513 6600 25911 22654 Registered

M 95 67 64 66 40 171 211 373 259 330 140 100 704 373 290 370 5561 5761 4481 4108 1618 22821 20460 1 3 7 9 T 0 31 12 12 18 16 41 16 27 24 81 80 90 58 77 121 261 2.0 553 4.4 13.1 13.1 11.0 9420 1 5 5 7 4 6 9 8 9 F 0 0 13 17 38 50 35 40 20 22 46 3.6 109 2.0 238 11.2 11.2 11.2 9420 Dropout 3 5 7 4 2 8 6 9 0 0 11 18 71 M 16 31 24 42 50 36 46 152 2.1 315 5.4 15.1 15.1 10.9 9420 2015

T 92 135 171 161 355 136 127 214 219 104 862 668 617 780 528 1481 3791 11257 11220 10247 10639 43932 48868

F

61 37 43 58 49 44 68 89 138 374 122 313 445 628 868 197 5234 5569 5762 5724 2315 25313 22488 Registered

M 93 87 77 67 84 82 97 48 153 613 217 335 234 331 294 304 5013 5495 5496 5070 1476 23555 21444 5 7 2 0 0 T 13 31 21 91 43 34 42 28 26 83 83 7.5 7.3 516 176 198 140 4.6 177 13.4 1373 14.8 1030 3 5 7 9 F 0 0 0 11 13 12 18 19 74 31 82 67 94 20 39 7.7 5.2 258 7.4 114 508 10.1 692 20.7 Dropout 2 2 2 9 6 0 0 13 15 31 12 14 M 24 94 29 66 52 63 44 7.2 258 104 7.4 3.8 522 681 11.8 16.7 2014 T 57 72 151 116 219 122 168 274 454 610 564 748 902 619 3217 1637 560 3102 3222 3076 3851 18711 13681 F 13 54 58 77 44 67 26 135 191 156 418 458 322 977 188 1561 307 1682 1492 1560 2175 6621 9291 % dropout % dropout % dropout % dropout % % dropout % Registered M 72 44 84 64 46 118 114 319 419 142 330 312 242 444 372 660 1584 1542 1656 1540 1676 9420 7060

Total Total Total Total Form I Form I Form I Form I Grade Form II Form II Form II Form II Form V Form III Form Form III Form Form III Form Form III Form Form IV Form VI Form IV Form IV Form IV

Kaliua Council Temeke Chemba Sengerema Overall grand total 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 chil- dren 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

The United Ministry of Education Republic of Science and Technology Tanzania