The Children in Care and Adoption Performance Tables show, against 15 key indicators, how each local authority is performing. The data we have used is already available. We want the tables to help generate debate, discussion and, above, all action. We’ll be updating the tables as new data becomes available. And we will be talking to local authorities and other partners about how the tables can be developed and extended.

A table is provided for each of the indicators mainly based on a three year rolling average. Where available, data are provided for 2011, 2012, 2013. The only exceptions are the indicators on absence from school and schools performing below the floor target for which only two years and one year of data are available.

The system is on a two-year cycle that gives a "base" score for the first year and a "growth" score in the second year. The Base API, which is usually released in the spring (for example, 2013), comes from the previous spring's test scores (2012). The Growth API, released in October (2013), comes from 2013 spring test scores.

Abstract

In July 2012, representative stratified samples of public primary schools, head teachers, teachers and pupils were surveyed in the six Nigerian states where the DFID/UKaid-funded Education Sector Support Programme in Nigeria works.

The ESSPIN Composite Survey (CS) process serves two main functions: periodically assessing the effects of ESSPIN's integrated School Improvement Programme (SIP), and reporting on selected indicators of the quality of education in the six ESSPIN-supported states. The CS addresses five Output pillars of the SIP, namely teacher competence, head teacher effectiveness, school development planning, school based management committee functionality and inclusive practices in schools. It also provides estimates of one Outcome indicator-school quality; and one Impact indicator-pupil learning achievement. The CS is wide-ranging but not exhaustive: it complements other ESSPIN/state monitoring and evaluation processes in areas such as institutional development, school enrolments and infrastructure. It brings together into a single exercise baseline surveys that were conducted by ESSPIN in 2010, hence 'composite' survey.

Four data collection methods were used to complete ten questionnaires: interviews, record schedules, observation and oral/written tests. The total sample covered 595 schools/head teachers/SBMCs, 2,975 teachers and 9,520 pupils. Enumerators drawn from State School Improvement Teams and education officials were trained and then mobilised to collect the data over a six week period, with field supervision by NPC and ESSPIN. Data entry, cleaning and checking took longer than intended due to several technical problems. Each indicator of education quality was underpinned by a variety of objectively observable criteria. Estimates (values drawn from a sample to describe the population as a whole) are shown within 95% confidence intervals. In the case of Kano (and to a lesser extent Kaduna) some values are insufficiently precise to include in programme-wide aggregates. Mean estimates for ESSPIN-supported schools and non-ESSPIN supported schools are compared, and said to be significantly different at the 0.05 level (ie, where there is at least a 95% probability that the values for Phase 1 and Control Schools are actually different from one another). For certain numeracy measures, a comparison of the difference between 2010 and 2012 values for Phase 1 and Control Schools is possible. In most cases, such 'difference in differences' calculations will have to wait until the CS is repeated in 2014 and beyond. Although those CS 2012 results which show a significant difference between Phase 1 and Control Schools cannot necessarily be ascribed to 'the ESSPIN effect' (since other characteristics of schools in those categories could actually determine the difference), in the absence of evidence for an alternative cause it is reasonable to suppose that ESSPIN interventions are having the intended effect. This is particularly true of the Output and Outcome indicators but less likely with respect to Impact (children's learning outcomes) at this stage in the programme. The basis of allocation of schools to Phase 1 in each state is reported, to aid critical consideration of any selection bias.

Geographic coverage

Six Nigerian states - Enugu, Jigawa, Kaduna, Kano, Kwara, and Lagos

Analysis unit

School; Pupil; Teacher

Universe

Schools in the six ESSPIN states - Enugu, Jigawa, Kaduna, Kano and Lagos

Kind of data

Sample survey data [ssd]

Sampling procedure

This section outlines the sampling strategy and target sample sizes for each unit of observation for the 2012 ESSPIN composite survey conducted in the six focus states: Enugu, Jigawa, Kaduna, Kano, Kwara and Lagos.

1. Aim of sampling design

The analysis requires estimation of several indicators for each of the units of observation and where the 2010 MLA data and documentation allow it, attribution of any observed changes in the outputs and outcomes of interest over time to corresponding ESSPIN programme interventions; therefore, the sample of units was selected with rigorous scientific procedures in order that selection probabilities are known.

In each of the six focus states, the intended sample for the 2012 CS was 105 primary schools, except in Enugu where phase 2 schools had not been identified at the time of the survey and the intended sample was 70 schools. This gives a total sample size of 595 schools. In each school the head teacher (N~595) and five other teachers who had received ESSPIN-sponsored training (N~2,975) and five other teachers who had not received such training (N~2,975) were expected to be interviewed except in cases where a sample school had fewer than five teachers (of either category) in which case all teachers were interviewed. Four primary 2 pupils were to be assessed in literacy and four primary 2 pupils in numeracy in each school, and similarly for primary 4 pupils (N~ 9,520).

1. Construction of sampling frame

The school sample frame was constructed using information on school ESSPIN and 2010 MLA survey participation and school size from the Education Management Information System (EMIS). To enable the planned analyses a multi-stage sampling design was used as shown in Figure A.1 in the CS1 report.

The lines connecting the units of observation in Figure A.1 represent sampling stages. The six survey states were pre-determined as the ESSPIN programme operates in these states. In each focus state, public primary schools were selected (first stage), and then within each sample school, teachers and grade 2 and grade 4 pupils respectively (second stage) were selected. In the first sampling stage, there is stratification in order to allow the observation of a minimum number of units in each stratum of various types of analytical importance such as ESSPIN phase 1, ESSPIN phase 2, and control (no ESSPIN interventions) schools. The total intended sample across the six states was 595 public primary schools

1. Drawing of the samples for the baseline survey

Selection of schools The major sampling strata (hereafter denoted with the subscript h) are the schools' participation in the ESSPIN programme: ESSPIN phase 1 schools, ESSPIN phase 2 schools, and control (no ESSPIN intervention) schools in each of the six states with the exception of Enugu, where there are no phase 2 schools. Each of the major strata is divided into two sub-strata, respectively composed of the schools selected and not selected for the 2010 MLA survey.

2010 MLA schools were selected in one of two ways depending on the total number of 2010 MLA schools in the 2010 MLA school sub-strata. If there were more than 17 MLA schools, 17 were selected using systematic equal probability sampling and if there were fewer than 17 MLA schools, all were selected with certainty.

The reason for using systematic equal probability sampling was that this method was used to select the school sample for the 2010 MLA survey combined with the need for a minimum number of 2010 MLA schools to be contained within the 2012 sample in order to enable analysis over time of any changes in pupil learning as measured by the MLA Selection of teachers

The head teacher was interviewed in all sample schools. Five ESSPIN-trained and five non-ESSPIN-trained teachers were selected in each sample school using simple random sampling. The teacher sampling was conducted in schools by the enumerators who used a special form and random number tables.

The teacher and pupil sampling was conducted in the field. The sampling selections delegated to the enumerators were conducted as a part of interviewing processes that had broader objectives. For this reason the selection processes were not supported by stand-alone forms but were instead integrated with the survey questionnaires and used as follows for pupils (the same procedure was used for teacher sampling):

• First, the enumerator used the school's pupil register to write pupil codes next to each pupil name starting with 1 for the first pupil listed up until the last pupil listed, which provided the largest pupil code.
• Second, the interviewer wrote down the largest pupil code in a box on the questionnaire.
• Third, the interviewer scanned the provided random number table according to the instructions provided to find the pupil codes of the eligible pupils to be selected.

Selection of pupils Four grade 2 pupils and four grade 4 pupils were selected for each of the literacy and numeracy assessments respectively in each sample school using simple random sampling. The pupil sampling was conducted in schools by the enumerators who used a special form and random number tables similar to the teacher sampling.

The teacher and pupil sampling was conducted in the field. The sampling selections delegated to the enumerators were conducted as a part of interviewing processes that had broader objectives. For this reason the selection processes were not supported by stand-alone forms but were instead integrated with the survey questionnaires and used as follows for pupils (the same procedure was used for teacher sampling):

• First, the enumerator used the school's pupil register to write pupil codes next to each pupil name starting with 1 for the first pupil listed up until the last pupil listed, which provided the largest pupil code.
• Second, the interviewer wrote down the largest pupil code in a box on the questionnaire.
• Third, the interviewer scanned the provided random number table according to the instructions provided to find the pupil codes of the eligible pupils to be selected.

Panel component CS1 forms the baseline survey with the aim to visit the same schools during future rounds.

Sampling deviation

One

Additional file 1: Supplementary Table 1. Characteristics of students accepted in the academic year 2012-2013.

Abstract

With a budget of nearly $20 million, the Formal Technical Education Sub-Activity was designed to strengthen technical and vocational educational institutions in the Northern Zone of El Salvador. By improving schools and offering scholarships, the sub-activity financed efforts to increase youths' access to high-quality technical education in the region, thus increasing their achievement levels, secondary (and post-secondary) school graduation rates, and prospects for gainful employment. By 2012, the Formal Technical Education Sub-Activity was scheduled to invest $3.8 million in scholarships for students enrolled in secondary and post-secondary technical schools in the Northern Zone. According to preliminary budgets, the sub-activity would also provide $9 million to improve 20 technical secondary schools in the Northern Zone with infrastructure investments and additional teacher training programs. In addition, the sub-activity was scheduled to invest $7 million to strengthen ITCHA, an existing post-secondary institute in the Northern Zone.

In conducting the evaluation of the Formal Technical Education Sub-Activity-which includes secondary and post-secondary school improvements and scholarships-Mathematica will address the following research questions regarding Sub-Activity investments from 2009 to 2012:

1.Program design/implementation. How were the secondary school strengthening and scholarship programs, and the ITCHA strengthening program designed and implemented? Did implementation meet original targets and expectations? Why or why not?

2.Description of participants. What are the characteristics (age, gender, initial household income, etc.) of scholarship recipients? What are the basic characteristics of secondary school and ITCHA students?

3.Impact/Results. What is the impact of FOMILENIO's strengthening secondary school program on students' education and labor market outcomes, including secondary school enrollment, grade completion, graduation, and further education, employment, and income? What is the impact of the offer of scholarships in some programs within strengthened schools on student educational and labor outcomes? Did ITCHA graduates obtain jobs and experience increased income following graduation? Did ITCHA students who graduated from secondary school MEGATEC programs have better academic and labor market outcomes than students who did not attend secondary school MEGATEC programs?

4.Impacts/Results by key target subgroups. Were impacts/results different for girls versus boys? What types of participants experienced positive impacts?

5.Explanation for impact findings and results. What was the ex-post statistical power, and can this explain the lack of impacts (in cases where no impacts are found)? What aspects of implementation could explain the impacts/results? If impacts/results were different for girls versus boys, why?

6.Sustainability. Are secondary school improvements and scholarships being maintained? Are ITCHA improvements being maintained? Are they likely to be maintained in the medium to long term?

To answer all research questions regarding the design, implementation, and sustainability of the strengthening efforts and scholarships (Topics 1, 2, 5, and 6), Mathematica will use a mixed-methods evaluation design that uses qualitative and quantitative methods (see Table III.1). With this approach, researchers will use qualitative methods-namely, qualitative interview data and programmatic reports-to help understand processes and activities, provide information on setting or context, and communicate the perspectives and experiences of key participants through direct quotes. In addition, Mathematica will use quantitative information on program outputs and costs, participant characteristics, and budget outlays to summarize the intervention, describe its participants, and analyze the sustainability of its original investments.

To answer research questions regarding impacts and results (3, 4, and 5), Mathematica will use a variety of designs. To determine the impact of secondary school scholarships, researchers designed and implemented a random assignment design, by which some eligible applicants were randomly selected to receive scholarships. To determine the impact of secondary school strengthening investments, Mathematica designed and implemented a matched comparison group approach using propensity score methods, by which students at the 20 strengthened schools are compared to students at 20 similar non-strengthened schools. Finally, to measure key results of the ITCHA intervention-including graduation and employment rates-researchers used a mixed-methods approach that featured a follow-up survey of ITCHA students.

All impact and results analyses rely on in-person surveys, including panel surveys of scholarship applicants, cross-sectional baseline and follow-up surveys of secondary school students, and a follow-up survey of ITCHA students.

Geographic coverage

The Northern Zone of El Salvador

Analysis unit

Individuals, Schools

Kind of data

Sample survey data [ssd]

Research instrument

This study features student-level questionnaires. Two primary versions of these questionnaires were developed--one for secondary school students and one for post-secondary school students. The secondary school questionnaire was administered to students who applied for scholarships and students who attended the 40 secondary schools in the secondary school evaluation. The post-secondary school questionnaire was administered to students of the Chalatenango Technical Institute (ITCHA). Both questionnaires asked students (or former students) about enrollment, academic performance, progression and graduation, as well as their employment and income in the previous 12 months.

In 2012 a national assessment was carried out, which was commissioned by the Flemish Government, to assess whether pupils have sufficient skills to acquire and process information at the end of primary education. Data were collected through two written tests: a test on working with tables and graphs and a test on working with plans and drawings. A representative sample of 2383 pupils from 92 Flemish primary schools participated. In addition, the extent to which pupils have mastered certain ICT competencies was assessed through a ICT prformance assessment. A subsample of six pupils from each school participated in this performance assessment. Tests were supplemented with background questionnaires for pupils, parents, teachers and ICT coordinators. The pupil and parent questionnaires collected, among other things, information on socio-economic background, diagnosis of disabilities and circumstances that may stimulate learning. The questionnaire for teachers collected, among other things, information on their teaching experience, the use of educational materials and ICT during the classes. The questionnaire for the ICT coordinator asked about the ICT policy and infrastructure at school. The response rates to these questionnaires were almost 100% for the pupil questionnaire, 93% for the parent questionnaire, 95% for the teacher questionnaire and 93% for the ICT coordinator. In 2012 vond een peiling plaats in opdracht van de Vlaamse overheid om na te gaan in welke mate leerlingen op het einde van het basisonderwijs beschikken over voldoende vaardigheden om informatie te verwerven en verwerken. De gegevens werden verzameld door middel van twee schriftelijke toetsen: een toets over het werken met tabellen en grafieken en een toets over het werken met plannen en tekeningen. Deze toetsen werden afgenomen bij een representatieve steekproef van 2383 leerlingen uit 92 Vlaamse basisscholen. Daarnaast werd ook nagegaan in hoeverre leerlingen bepaalde ICT-competenties beheersen door middel van een praktische proef. Deze werd afgenomen bij een deelsteekproef van zes leerlingen per school. Naast de toetsen werden ook vragenlijsten afgenomen bij leerlingen, ouders, leerkrachten en ICT-coördinatoren. De leerling- en oudervragenlijsten verzamelden onder meer informatie over de sociaal-economische achtergrond, eventuele diagnoses van beperkingen en omstandigheden die het leren kunnen stimuleren. Leerkrachtvragenlijsten verzamelden informatie over hun diploma, hun onderwijservaring, het gebruik van leermiddelen en ICT tijdens de lessen. Vragenlijsten voor de ICT-coördinator gingen over het ICT-beleid en de infrastructuur op school. De respons op deze vragenlijsten was bijna 100% voor de leerlingen, 93% voor de ouders, 95% voor de leerkrachten en 93% voor de ICT-coördinator.

Abstract

With a budget of nearly $20 million, the Formal Technical Education Sub-Activity was designed to strengthen technical and vocational educational institutions in the Northern Zone of El Salvador. By improving schools and offering scholarships, the sub-activity financed efforts to increase youths' access to high-quality technical education in the region, thus increasing their achievement levels, secondary (and post-secondary) school graduation rates, and prospects for gainful employment. By 2012, the Formal Technical Education Sub-Activity was scheduled to invest $3.8 million in scholarships for students enrolled in secondary and post-secondary technical schools in the Northern Zone. According to preliminary budgets, the sub-activity would also provide $9 million to improve 20 technical secondary schools in the Northern Zone with infrastructure investments and additional teacher training programs. In addition, the sub-activity was scheduled to invest $7 million to strengthen ITCHA, an existing post-secondary institute in the Northern Zone.

In conducting the evaluation of the Formal Technical Education Sub-Activity-which includes secondary and post-secondary school improvements and scholarships-Mathematica will address the following research questions regarding Sub-Activity investments from 2009 to 2012:

1.Program design/implementation. How were the secondary school strengthening and scholarship programs, and the ITCHA strengthening program designed and implemented? Did implementation meet original targets and expectations? Why or why not?

2.Description of participants. What are the characteristics (age, gender, initial household income, etc.) of scholarship recipients? What are the basic characteristics of secondary school and ITCHA students?

3.Impact/Results. What is the impact of FOMILENIO's strengthening secondary school program on students' education and labor market outcomes, including secondary school enrollment, grade completion, graduation, and further education, employment, and income? What is the impact of the offer of scholarships in some programs within strengthened schools on student educational and labor outcomes? Did ITCHA graduates obtain jobs and experience increased income following graduation? Did ITCHA students who graduated from secondary school MEGATEC programs have better academic and labor market outcomes than students who did not attend secondary school MEGATEC programs?

4.Impacts/Results by key target subgroups. Were impacts/results different for girls versus boys? What types of participants experienced positive impacts?

5.Explanation for impact findings and results. What was the ex-post statistical power, and can this explain the lack of impacts (in cases where no impacts are found)? What aspects of implementation could explain the impacts/results? If impacts/results were different for girls versus boys, why?

6.Sustainability. Are secondary school improvements and scholarships being maintained? Are ITCHA improvements being maintained? Are they likely to be maintained in the medium to long term?

To answer all research questions regarding the design, implementation, and sustainability of the strengthening efforts and scholarships (Topics 1, 2, 5, and 6), Matheatica will use a mixed-methods evaluation design that uses qualitative and quantitative methods (see Table III.1). With this approach, researchers will use qualitative methods-namely, qualitative interview data and programmatic reports-to help understand processes and activities, provide information on setting or context, and communicate the perspectives and experiences of key participants through direct quotes. In addition, Mathematica will use quantitative information on program outputs and costs, participant characteristics, and budget outlays to summarize the intervention, describe its participants, and analyze the sustainability of its original investments.

To answer research questions regarding impacts and results (3, 4, and 5), Mathematica will use a variety of designs. To determine the impact of secondary school scholarships, researchers designed and implemented a random assignment design, by which some eligible applicants were randomly selected to receive scholarships. To determine the impact of secondary school strengthening investments, Mathematica designed and implemented a matched comparison group approach using propensity score methods, by which students at the 20 strengthened schools are compared to students at 20 similar non-strengthened schools. Finally, to measure key results of the ITCHA intervention-including graduation and employment rates-researchers used a mixed-methods approach that featured a follow-up survey of ITCHA students.

All impact and results analyses rely on in-person surveys, including panel surveys of scholarship applicants, cross-sectional baseline and follow-up surveys of secondary school students, and a follow-up survey of ITCHA students.

Geographic coverage

The Northern Zone of El Salvador

Analysis unit

Individuals, Schools

Kind of data

Sample survey data [ssd]

Research instrument

This study features student-level questionnaires. Two primary versions of these questionnaires were developed--one for secondary school students and one for post-secondary school students. The secondary school questionnaire was administered to students who applied for scholarships and students who attended the 40 secondary schools in the secondary school evaluation. The post-secondary school questionnaire was administered to students of the Chalatenango Technical Institute (ITCHA). Both questionnaires asked students (or former students) about enrollment, academic performance, progression and graduation, as well as their employment and income in the previous 12 months.

Abstract

Education Quality Improvement Programme in Tanzania (EQUIP-T) is a Government of Tanzania programme, funded by UK DIFD, which seeks to improve the quality of primary education, especially for girls, in seven regions of Tanzania. It focuses on strengthening professional capacity and performance of teachers, school leadership and management, systems which support district management of education, and community participation in education.

The independent Impact Evaluation (IE) of EQUIP-T is a four-year study funded by the United Kingdom Department for International Development (DFID). It is designed to: i) generate evidence on the impact of EQUIP-T on primary pupil learning outcomes, including any differential effects for boys and girls; ii) examine perceptions of effectiveness of different EQUIP-T components; iii) provide evidence on the fiscal affordability of scaling up EQUIP-T post-2018; and iv) communicate evidence generated by the impact evaluation to policy-makers and key education stakeholders.

The research priorities for the midline IE are captured in a comprehensive midline evaluation matrix (see Annex B in the 'EQUIP-Tanzania Impact Evaluation. Midline Technical Report, Volume I: Results and Discussion' under Reports and policy notes). The matrix sets out evaluation questions linked to the programme theory of change, and identifies sources of evidence to answer each question-either the quantitative survey or qualitative research, or both. It asks questions related to the expected results at each stage along the results chain (from the receipt of inputs to delivery of outputs, and contributions to outcomes and impact) under each of the programme's components. The aim is to establish: (i) whether changes have happened as expected; (ii) why they happened or did not happen (i.e. whether key assumptions in the theory of change hold or not); (iii) whether there are any important unanticipated changes; and (iv) what links there are between the components in driving changes.

The main IE research areas are: - Impact of EQUIP-T on standard 3 pupil learning in Kiswahili and mathematics. - Impact of EQUIP-T on teacher absence from school and from classrooms. - Impact of EQUIP-T on selected aspects of school leadership and management.

The IE uses a mixed methods approach that includes: - A quantitative survey of 100 government primary schools in 17 programme treatment districts and 100 schools in 8 control districts in 2014, 2016 and 2018 covering: - Standard three pupils and their parents/caregivers; - Teachers who teach standards 1-3 Kiswahili; - Teachers who teach standards 1-3 mathematics; - Teachers who teach standards 4-7 mathematics; - Head teachers; and - Standard two lesson observations in Kiswahili and mathematics.

• Qualitative fieldwork in nine research sites that overlap with a sub-set of the quantitative survey schools, in 2014, 2016 and 2018, consisting of key informant interviews (KIIs) and focus group discussions (FGDs) with head teachers, teachers, pupils, parents, school committee (SC) members, region, district and ward education officials and EQUIP-T programme staff.

The midline data available in the World Bank Microdata Catalog are from the EQUIP-T IE quantitative midline survey conducted in 2016. For the qualitative research findings and methods see 'EQUIP-Tanzania Impact Evaluation. Midline Technical Report, Volume I: Results and Discussion' and 'EQUIP-Tanzania Impact Evaluation. Midline Technical Report, Volume II: Methods and Supplementary Evidence' under Reports and policy notes.

Geographic coverage

The survey is representative of the 17 EQUIP-T programme treatment districts. The survey is NOT representative of the 8 control districts. For more details see the section on Representativeness in 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume I: Results and Discussion' and 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume II: Methods and Technical Annexes' under Reports.

The 17 treatment districts are: - Dodoma Region: Bahi DC, Chamwino DC, Kongwa DC, Mpwapwa DC - Kigoma Region: Kakonko DC, Kibondo DC - Shinyanga Region: Kishapu DC, Shinyanga DC - Simiyu Region: Bariadi DC, Bariadi TC, Itilima DC, Maswa DC, Meatu DC - Tabora Region: Igunga DC, Nzega DC, Sikonge DC, Uyui DC

The 8 control districts are: - Arusha Region: Ngorongoro DC - Mwanza Region: Misungwi DC - Pwani Region: Rufiji DC
- Rukwa Region: Nkasi DC - Ruvuma Region: Tunduru DC - Singida Region: Ikungi DC, Singida DC - Tanga Region: Kilindi DC

Analysis unit

• School
• Teacher
• Pupil
• Lesson (not sampled)

Kind of data

Sample survey data [ssd]

Sampling procedure

Because the EQUIP-T regions and districts were purposively selected (see 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume I: Results and Discussion' under Reports and policy notes), the IE sampling strategy used propensity score matching (PSM) to: (i) match eligible control districts to the pre-selected and eligible EQUIP-T districts (see below), and (ii) match schools from the control districts to a sample of randomly sampled treatment schools in the treatment districts. The same schools are surveyed for each round of the IE (panel of schools) and standard 3 pupils will be interviewed at each round of the survey (no pupil panel).

Identifying districts eligible for matching

Eligible control and treatment districts were those not participating in any other education programme or project that may confound the measurement of EQUIP-T impact. To generate the list of eligible control and treatment districts, all districts that are contaminated because of other education programmes or projects or may be affected by programme spill-over were excluded as follows:

• All districts located in Lindi and Mara regions as these are part of the EQUIP-T programme but implementation started later in these two regions (the IE does not cover these two regions);
• Districts that will receive partial EQUIP-T programme treatment or will be subject to potential EQUIP-T programme spillovers;
• Districts that are receiving other education programmes/projects that aim to influence the same outcomes as the EQUIP-T programme and would confound measurement of EQUIP-T impact;
• Districts that were part of pre-test 1 (two districts); and
• Districts that were part of pre-test 2 (one district).

Sampling frame

To be able to select an appropriate sample of pupils and teachers within schools and districts, the sampling frame consisted of information at three levels:

• District;
• School; and
• Within school.

The sampling frame data at the district and school levels was compiled from the following sources: the 2002 and 2012 Tanzania Population Censuses, Education Management Information System (EMIS) data from the Ministry of Education and Vocational Training (MoEVT) and the Prime Minister's Office for Regional and Local Government (PMO-RALG), and the UWEZO 2011 student learning assessment survey. For within school level sampling, the frames were constructed upon arrival at the selected schools and was used to sample pupils and teachers on the day of the school visit.

Sampling stages

Stage 1: Selection of control districts Because the treatment districts were known, the first step was to find sufficiently similar control districts that could serve as the counterfactual. PSM was used to match eligible control districts to the pre-selected, eligible treatment districts using the following matching variables: Population density, proportion of male headed households, household size, number of children per household, proportion of households that speak an ethnic language at home, and district level averages for household assets, infrastructure, education spending, parental education, school remoteness, pupil learning levels and pupil drop out.

Stage 2: Selection of treatment schools In the second stage, schools in the treatment districts were selected using stratified systematic random sampling. The schools were selected using a probability proportional to size approach, where the measure of school size was the standard two enrolment of pupils. This means that schools with more pupils had a higher probability of being selected into the sample. To obtain a representative sample of programme treatment schools, the sample was implicitly stratified along four dimensions:

• District;
• PSLE scores for Kiswahili;
• PSLE scores for mathematics; and
• Total number of teachers per school.

Stage 3: Selection of control schools As in stage one, a non-random PSM approach was used to match eligible control schools to the sample of treatment schools. The matching variables were similar to the ones used as stratification criteria: Standard two enrolment, PSLE scores for Kiswahili and mathematics, and the total number of teachers per school.

The midline survey was conducted for the same schools as the baseline survey (a panel of schools) and the endline survey in 2018 will cover the same sample of schools. However, the IE does not have a panel of pupils as a pupil only attends standard three once (unless repeating). Thus, the IE sample is a repeated cross-section of pupils in a panel of schools.

Stage 4: Selection of pupils and teachers within schools Pupils and teachers were sampled within schools using systematic random sampling based on school registers. The within-school sampling was assisted by selection tables automatically generated within the computer assisted survey instruments.

Per school, 15 standard 3 pupils were sampled. For the teacher development needs assessment (TDNA), in the sample treatment schools, up to three teachers of standards 1 to 3 Kiswahili, up to three teachers of standards 1 to 3 mathematics; and up to three

Data is sourced from various education resources. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Four reports exist for this dashboard:1. Matric Pass RatesMatric Pass rates (2016-2019); filter by year & province - Province and gender - count of Wrote and -Passed, %Pass average and Total pass rate Matric Pass Rates Western Cape (2016-2020); filter by year & province - Province and gender - count of Wrote and -Passed, %Pass average and Total pass rate Percentage of high schools attaining 60% and higher pass (2016-2020); filter by year & Region (province) - Region (Province) - count of schools, count of schools with 60% pass rate and higher, and actual pass rate (percentage) Percentage of high schools attaining 60% and higher pass in Western Cape (2016-2020); filter by year - count of schools, count of schools with 60% pass rate and higher, and actual pass rate (percentage)2. Tertiary EducationEligibility Bachelor Degree (2012-2018); filter by year and region - region, count B-degree passes, count grade 12 who wrote exams, % B-degree passesEligibility Bachelor Degree in Western Cape (2012-2018); filter by year - year, count B-degree passes, count grade 12 who wrote exams, % B-degree passesUniversity admission eligibility rate (2015-2018); filter by year - year, count B-degree passes, count grade 12 who wrote exams, % B-degree passesPercentage 25 years plus educational attainment (2018-2020); filter by year - Percentage 25 years and older tertiary education attainment by yearPercentage 25 years and older educational attainment by year and level (primary, secondary, NSC/Grade12, tertiary), province Percentage 25 years plus educational attainment Western Cape (2010-2020); filter by year - Percentage 25 years and older tertiary education attainment by yearPercentage 25 years and older educational attainment by year and level (primary, secondary, NSC/Grade12, tertiary), province Percentatge 20 years plus tertiary education in the Western Cape (2017-2020); filter by year - Percentage 20 years and older educational attainment by year by level (primary, secondary, NSC/Grade12, tertiary, other)3. Subject Pass RateMathematics and Physical Science pass rate (2012-2018); filter by year and region - Pass rate for Mathematics and Physical Science in Grade 12 by region with pass rates of >30% for maths literacy, maths, and physical sciencePass rate for Mathematics and Physical Science in Grade 12 by region with pass rates of >30% and >40% for maths literacy, maths, and physical scienceMathematics and Physical Science pass rate in Western Cape (2012-2018); filter by year and region - Pass rate for Mathematics and Physical Science in Grade 12 by year with pass rates of >30% for maths literacy, maths, and physical sciencePass rate for Mathematics and Physical Science in Grade 12 by year with pass rates of >30% and >40% for maths literacy, maths, and physical sciencePercentage Language and Mathematics systemic test pass rate for Grade 3, 6, 9 (2013-2019); filter by year, grade, measure (all, language. maths) - Pass rate for Language and Mathematics by year and pass rates grades 3, 6, and 9 language and maths4. LearnersSchool sport participation per 100K in WC (2014/15-2016/17); filter by years - count participants by year (high- or primary school); Primary and High School - count of participants, educators/volunteers trained to assist, Neighboring school participantsLearner retention rate in WC (2004-2020); filter by years - retention rate Grade 10 to 12 by year; retention count Grade 1 to 12 by year; retention rate by year for Grade 10 to 11 and Grade 11 to 12Count of learners in no-fee schools (2012/13-2020/21); filter by years - count of learners in no-fee public ordinary schools by year; count of no-fee learners, count of learners, % learners benefitting from no-fee by yearPublication Date20 January 2023LineageData from various education resources used to create dynamic dashboards reflecting the Outcome Indicators as in the Outcome Indicator Release: Percentage of Grade 3 learners in the Western Cape achieving a pass rate for Mathematics systemic tests; Language systemic testsPercentage of Grade 6 learners in the Western Cape achieving a pass rate for Mathematics systemic tests; Language systemic testsPercentage of Grade 9 learners in the Western Cape achieving a pass rate for Mathematics systemic tests; Language systemic testsMatric pass rate achievedMatric pass rate achieved in the Western CapePercentage of high schools attaining a 60% or higher pass rate for the matric examinationsPercentage of high schools attaining a 60% or higher pass rate for the matric examinations in the Western CapeMathematics pass rate; Physical Science pass rateMathematics pass rate; Physical Science pass rate in the Western CapeUniversity admission eligibility rate for learners completing grade 12University admission eligibility rate for learners completing grade 12 in the Western CapeLearner retention rate between Grade 8 and 12; and Grade 10 and12Percentage of learners who complete Grade 12 out of learners who entered Grade 10 two years prior in the Western CapeThe number of learners in no fee schools or benefitting from fee-exemptionPercentage of population (aged 25 years and older) who have completed a tertiary qualificationPercentage of population (aged 25 and older) who have completed a tertiary qualification in the Western CapePercentage of population (aged 20 and older) who have completed matric or equivalent in the Western Cape; Grade 7 or equivalent (literacy rate)Data SourcesWCED Annual Report 2018/19-2019/20, WCED APP 2018/19-2019/20;WCED Media Release January 2020 and March 2022;NSC Examination Report 2017-2021; DBE;Table reproduced from WCED Annual Performance Plan 2022/23; retention rates are own calculations based on tableGHS 2016-2020

This table covers data published in the Welsh Government's annual "Examination Results" release. It provides information on the number of GCSE entries into each subject group and the percentage of those entries achieving each GCSE grade. For more information see the Weblinks. Note that this year, the definition of this table has changed. This table now includes entries taken in previous years, and discounted exams are excluded. This is so that the table is consistent with the rest of the key performance indicators. Figures should be treated with caution - it is possible for pupils to have entered more than one exam within a small number of subject groups.