HIGH-LEVEL PROJECTIONS FOR EDUCATION (HIPE) A Resource Planning Tool for Effective Policy Makers Collaborative Africa Budget Reform Initiative meeting Accra, Ghana February 25-27, 2013

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Data Management Data Visualization Data Analysis One of the largest online databases for education data from around the world www.epdc.org Administrative and household survey data Learning outcomes from achievement studies Data Visualization A state-of-the-art resource in online data visualization: www.epdc.org Publication quality country education profiles Data Analysis Research on key issues in education (e.g.: orphans and vulnerable children, out-of-school) Education trend analysis and projections of future enrollment

Education projections

Introduction Projections are a management and planning tool for researchers and policy makers in different fields Demography Environment Finance Health Education Projections in education are used to set expectations of pupil enrollment and resource needs within an area of interest

FHI 360 EPDC projections expertise Education trend projections for multilateral and bilateral agencies (USAID, UNESCO, UNICEF, AusAID, GPE) Target-driven projections for local governments and education authorities (Pakistan, Namibia) Each set of projections has its rationale and assumptions Projections are only as good as the baseline data and the realism of the assumptions that drive them

Stage 1: Pupil enrollment projections Use available current and historical pupil enrollment data Apply known or target rates of new intake, repetition, and dropout, to model the flow of cohorts and calculate total number of pupils in each grade Disaggregate by geographic area, level of education, gender, etc Use pupil/teacher ratio, pupil/ classroom ratio, pupil/ textbook ratio data to estimate total need for teachers and classrooms

EPDC projections: the Cohort method EPDC employs the Cohort method, where pupils are traced through a time series, starting from latest historical year Intake, repetition and dropout rates are applied to the available population and pupil data to model cohort movement Grade 3 Students, Year 3 Grade 2 Students, Year 2 Grade 1 Students, Year 1 New entrants, Year 0

General principles Projections of pupil enrollment are driven by a set of manipulable parameters: Gross intake rate (i.e. proportion of pupils in Grade 1 over the population of starting age) Repetition rates, by grade and gender if possible Dropout rates, by grade and gender if possible Pupil-teacher, pupil-classroom, pupil textbook ratios Training costs, if available Therefore, the assumptions about the expected change in these key indicators determines the outcome, in terms of pupil enrollment

Constant method: Fix the parameter at most recent available historical value

Linear Trend Method

Target method: Reach 100% by 2025

Flexible trend: EPDC model for GPE

Flexible trend: EPDC model for GPE

Once the driver parameter assumptions are set: Pupil projections Once the driver parameter assumptions are set: For each year in the projection period (e.g. 2012-2020), a value for GIR and flow rates is calculated The resulting value for each year is applied towards relevant population and pupil data to obtain a projection of pupils

Pupil cohort projections in a nutshell Grade 1 projection = New entrants + Repeaters in G1 New entrants = Gross intake rate * Relevant population Repeaters = Repetition rate * Relevant Enrollment in G1 Grade 2 projection= Promoted pupils+ Repeaters in G2 Promotion= Enrollment from G1, minus repetition (those who stay in G1), minus dropout (those who leave the system permanently) Repeaters = Repetition rate * Relevant Enrollment in G2 Grade 3 and above: Same method as for Grade 2 (promotion from previous grade + repetition from same grade)

Results: Total primary enrollment

Education cost projections

Stage 2: Cost projections Enrollment projections are transformed into projections of resource needs: Teachers: total enrollment / pupil-teacher ratio (PTR) New teacher need is estimated as the difference between existing teacher force and total teacher need Teachers to be trained (if available, based on proportion of teachers known to be untrained) Classrooms: total enrollment / pupil classroom ratio Textbooks: total enrollment / pupil textbook ratio Unit costs are then applied to convert resource needs into anticipated expenditures

Costing it out: Teacher salaries = Projected Teachers X Annual teacher salary (can be set as target or kept constant ) Teacher salary cost teacher salary

Costing it out: Teacher training costs (South Sudan HIPE module only) Teacher training need = % Trained teachers X Total teachers projected Training may be disaggregated by type of training: in-service or pre-service, set by the user (in-service may be higher cost)

Costing it out: Teacher training cost Cost of pre-service training Cost of in-service training X Proportion of teachers that need training

Costing it out: Classroom costs Classroom costs consist of: New classroom construction cost Classroom maintenance cost (usually a percentage of construction cost) Total classroom need = Enrollment / PCR New construction need = Total need – Existing classrooms All existing classrooms should be included in maintenance costs

Costing it out: Classroom costs Classroom construction may be further broken down by type of classroom: permanent, semi-permanent, or other, as in this example:

Costing it out: Classroom costs Even as the total number of classrooms needed decreases in South Sudan, the new classroom building costs increase as the Ministry aims to build more permanent classrooms

Costing it out: Textbook costs = Textbook need X Cost per textbook Total textbook costs can be set by the user – held constant, or vary across future years

Costing it out: Textbook costs In the South Sudan HIPE model, textbook costs were projected separately for English, Math, and other subjects, based on target costs set for future years

Costing it out: Total expenditure and gap estimation Combine all costs: teacher salary costs teacher training costs (if estimated) classroom construction and maintenance costs textbook costs Add other recurrent cost projection (set as % of salaries, or broken out by individual line items) IF IMPORTANT: Separate out PUBLIC expenditure Project budget availability Education budget as % of total government budget, by level (primary and secondary)

Summing it up for South Sudan

Summing it up for South Sudan

Flexibility and richness of HIPE projection modeling Can be adapted to any context and scenario Is flexible and driven by user-set parameters, which can be either aspirational (target) or trend-based Can span multiple national and subnational units: For South Sudan, the national model combines several state-specific modules, each with state-level data and projections A model developed for Pakistan develops projections within one province, at the district level A model for UNESCO and GPE spans across multiple countries

More information is available upon request: epdc@fhi360.org THANK YOU More information is available upon request: epdc@fhi360.org Carina Omoeva, Ph.D. Education Policy and Data Center Global Learning Group FHI 360