Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey

To make progress, we have to understand why children are not in school.

One major reason is violence in the world’s ongoing conflict areas, including Syria, Yemen, Sudan, and Nigeria. Half of all out-of-school children live in conflict-affected countries.2

The other large barrier – often closely intertwined with conflict – is poverty.3

In low-income countries, public finances for education are very low: the annual spending in a high-income country like Austria is more than 200 times higher per student than in a low-income country like the Democratic Republic of Congo.

In the worst cases, poverty requires children to work – most commonly on smallholder farms – and this means they leave school early or never enter school in the first place.

If we want to make progress on education, then we will need to continue the developments that reduce conflict and poverty.

These macro changes are essential but can seem intangible in the short term. Targeted policies can make a difference in such situations. One policy with a long and well-established track record is to provide free meals in schools. School meals achieve two goals at the same time: They offer children a better diet, and they provide an incentive for parents to send their children to school. Research studies have shown that school meals increase school attendance and have a long-lasting impact over the child’s lifetime. A study in Sweden showed that pupils who received meals in school in the 1960s had 3% higher lifetime incomes.4 They have even been shown to have intergenerational benefits: the children of mothers who received school meals when they were children also benefit from school meal programs.5

The data contain information on 837 low-cost for-profit private schools (LCPS) from three districts in Punjab, Pakistan: Faisalabad, Gujranwala, and Sialkot. The past few decades have seen an exponential increase in the growth of these LCPS globally, and in countries like Pakistan and India, the private sector now commands a large and quickly increasing share of the market. Over forty percent of primary school enrolment in Pakistan is now in LCPS, and students in private schools in Pakistan far out-perform those in public schools. Yet, firm innovation and expansion is constrained for private schools, likely due to a range of supply-side and market level failures. The main research questions this study and the uploaded dataset seek to answer are: (1) To what extent are schools constrained by finance, and does the type of financing vehicle (loan vs equity) matter? (2) Is LCPS quality improvement constrained by a lack of access to appropriate quality-enhancing products and services, i.e. educational support services (ESS)? (3) Is there a positive interaction between access to finance and the provision of appropriate innovative investment opportunities? The dataset includes topics such as school administration, facilities, fees, enrolment, student population, finances, and financial expectations and literacy. Schools are uniquely identified using the variables mauza (administrative district) code and school code. While most of the variables are school-level, there are a few individual-level data pieces that were collected from the school owner. For each school we interviewed only one owner, therefore both schools and school owners are identified using the same mauza code and school code ID.

Most interventions to improve education in developing countries require spending significant amounts of money on improving the quality of the inputs to the education system. While this is often a useful approach, in countries with weak governments and low tax collection, little resources are available to invest in schools. In these settings, such as in Pakistan, private schools have provided an alternative to the low quality public schools, and parents are willing to pay for the improved quality, and so even in many remote rural areas, parents can pick from sending their child to the public school or one of several private schools in the village. This variety of schools has prompted us to study education markets instead of the inputs to the production of learning, applying theories from studying Small and Medium Enterprises (SMEs) to private schools. Instead of going to schools and telling them which inputs they should focus on, we tend to ask them what prevents them from expanding in quality and quantity. Over the past decade, our research team led by Tahir Andrabi (Pomona College), Jishnu Das (World Bank), and Asim Ijaz Khwaja (Harvard University) has studied the education markets in Pakistan. Despite the tremendous growth in the low cost private school (LCPS) sector (rising from 3,300 schools in 1983 to over 70,000 in 2011) and relatively better quality than the public sector (LCPS are 1.5 years ahead in learning outcomes relative to government schools), there is also evidence of substantial untapped innovation potential in this sector. The team has gathered both primary data and implemented randomized controlled trials (RCTs) that reveal constraints to growth and quality improvement for LCPS. Two factors that contribute to this innovation constraint are the lack of financing (a financial market failure) and access to affordable educational support services (ESS) (an input market failure), which together create a very challenging context for school owners. The current project is a RCT that seeks to explain how alleviating these constraints one at a time or simultaneously would affect learning outcomes, enrolment and school profitability. The randomized component means that schools are randomly allocated to either receiving offers of a loan product or an equity product to alleviate financial constraints, and/or receive access to buying ESS such as teacher training, improved curricula or student testing services. The controlled component of the trial means that some - randomly chosen - schools do not get any of these treatments, which allows us to compare the treatment outcomes with the counterfactual. The two financial products were developed together with one of Pakistan's largest microfinance banks. The equity product represents an innovation in the type of financial product offered to SMEs, and it is particularly relevant to LCPS since its revenue-contingent interest rate (if the school earns more, it will pay a higher interest rate) effectively shifts some of the risk of an investment over to the bank, and LCPS tend to have to make more lumpy investments than other SMEs. Our theory is that a less risky financial product would allow schools to take on more risky investments, such as investments in...