Modeling Fiscal Implications of Education Policies Part 2 Modeling Fiscal Implications of Education Policies Sajitha Bashir April 25, 2007 Public Finance Analysis and Management Course, World Bank

Structure of the Presentation Why model fiscal implications of education policy? Structure of models Choice of Scenarios Examples – DRC and Benin Limitations

What will NOT be covered… How to build a model Building a model is a technical exercise; takes time and care. But it is a tool which can be done by a technician For the PER author, what is important is to understand how to use this tool

Why Undertake Fiscal Modeling? PER analysis should reveal areas where public resources are not aligned with government objectives; not used efficiently; do not promote equity Government Education Plan/Strategy sets out objectives and strategies Usually no costs, especially costs No implementation schedule

Usefulness of Fiscal Modeling in PER Identifies fiscal impact of measures recommended to improve efficiency Which measures create more fiscal space? Which measures are under control of policy maker? Assess realism and feasibility of proposed plan, its objectives, strategies and implementation Fiscal sustainability; Managerial feasibility Has impact on policy discussions, especially with Ministry of Finance

Different Modeling Approaches Aggregate Fiscal Discipline Sectoral Expenditure Envelope set by MOF (3-5 years); usually as part of MTEF Within Sector: determine priorities, objectives, strategies (PER; sectoral analysis) Cost Strategies Is it consistent with resource availability? Iterations – alternative strategies; suggest savings Simulation Model Set Sectoral Objectives and Strategies (PER/sectoral analysis/sectoral plan) Estimate costs Check macro/budget implications Estimate domestic resource gap – compare with external financing Iterations – come up with realistic resource gap

Simulation Model Purpose Method Results Evaluate tradeoffs required to arrive at fiscally sustainable and technically sound educational strategy consistent with government objectives for coverage, quality, equity Method Develop different scenarios with varying assumptions Results Evolution of expenditures by type Evolution of education system (pupil numbers, staff, schools, classes…)

Structure of Model Spreadsheet – all quantifiable variables of education system are linked to each other Five categories of elements Base year data Objectives Assumptions about macro environment Policy parameters Results

Simple or Complex Models? Model whole education sector? Usually desirable to see sub-sectoral trade-offs Level of complexity should be determined by purpose of exercise and results of sectoral analysis If focus is on primary, more detailed strategies at primary level

Building Scenarios Input data are fixed However many other things can vary: Assumptions regarding macro environment Policy Objectives Policy parameters

Limit Number of Scenarios Macro Assumptions x Objectives x Policy Parameters = potentially scores of scenarios Choose 3 –5 scenarios! Judgment is required – base selections on PER/sectoral analysis What are the critical decisions confronting the government?

Macro Assumptions Usually invariant across scenarios Economic growth Determines public receipts, public expenditures Demographic growth Determines growth of child population entering primary school Usually invariant across scenarios

Years by which objectives are to be achieved can also vary Sector Objectives Pre-primary Population coverage Primary Entry and completion rates (usually 100%) Secondary and higher Transition rates Years by which objectives are to be achieved can also vary

Key Policy Parameters (1) Internal efficiency Repetition and drop out rates Service delivery targets (access/quality) School availability (proximity to habitation) and size Teacher pay (by category of teacher) Pupil-teacher ratio Ratio of teachers to non-teaching staff Use of multigrade teaching Spending on non-salary items Year for attainment of target is also variable

Key Policy Parameters (2) Construction Type of construction (community?) Financing % of enrolment in private sector (residual determines maximum for public financing) Set public financing as ratio of domestic resources Household financing in public sector (by category of expenditure and sub-sector) – reasonable in relation to household income? External financing (by category of expenditure and sub-sector) - realistic?

Illustration – Democratic Republic of Congo Challenges: limited public resources; high dependence on private financing; low coverage even at primary level but rapid growth at other levels; inefficiency in public spending Policy issues: expansion of post primary levels; abolition of fees; raising teacher salaries EFA plan sets ambitious objectives and strategies which are not costed

DRC- Common Assumptions of Scenarios

Key Policy Choices Reflected in Scenarios Universal pre-school? School feeding ? Change some service delivery parameters (staffing norms etc)? Trade-off between rapid quantitative expansion and quality improvement in post-primary

DRC- Cost Saving Measures of Scenarios 3 and 4

DRC- Impact on Education Indicators

DRC- Expenditure Requirements (FC and 2001US $ )

DRC - Preliminary Conclusions Universal pre-school is not feasible Staffing rationalization/use of multigrade teaching yields considerable savings Reducing transition rates in post primary education is still required Scenario 4 is most acceptable: Examine relative unit costs and composition of expenditures to further assess suitability

Other trade-offs are possible… Eliminate school feeding - expensive even when targeted to 30 % of pupils What is its objective ? (increase attendance? improve student attentiveness?) Are resources better used elsewhere – e.g., to raise teachers’ salaries? Raise pupil-teacher ratio Stagger construction Raise private financing share in higher education (but equity trade-off)

Benin – Issues Primary GER – 97 % but high disparities between regions, gender and social groups Quality very low – less than 10 percent of 3rd graders could read with comprehension Primary completion rate – 46 % Repetition rate – 36 % in final primary grade Less than 2 % of domestic education budget on books and teacher training Very rapid growth in higher education (mainly private, but also public)

Benin – Objectives of Modelling Not to evaluate policy choices for a plan Simulation model, used in context of PER, was used to identify the main issues to be addressed by education policy

Large Differences in Salaries of Primary Teachers Salary per teacher in $ (% of per cap GDP) % of total teachers Primary teachers 1 860 (4.9) 100 Permanent 3 011 (7.9) 56.1 Contractual 750 (2.0) 19.9 Community 300 (0.8) 23.9 Francophone Africa (6.3) Anglophone Africa (3.6)

Distribution of schools by pupil-teacher ratio in grade 1

Objectives for Primary Education Goal Target Impact Primary completion 100 % (1.76 millions pupils by 2015) Classrooms/ teachers Quality Pupil-teacher ratio : 40 Teachers Internal efficiency Repetition rate : 10 Reduces rooms/teachers

Expenditure requirements – policy trade-offs Number of teachers will need to double Using civil servant teachers at current salary levels: expenditure needs will multiply by 4 Using contractual/community teachers: expenditures will multiply by 3 A new statute for teachers? Post primary transition rate will also need to be reduced

Example: Algeria Simulation model used in context of public expenditure analysis Government Plan proposed massive expansion of higher education Focus on infrastructure building; continue existing policy of free student food and accommodation Modelling identified large impact on recurrent budget of cost of student accommodation/food No hard budget constraint – oil windfall ! Helped to identify policy focus: should government be focusing on managing student dormitories or quality improvement and institutional reform of higher education?

Limitations (1) Powerful tool for decision-making However, the most important first step is to formulate the policy choices Hence, not a substitute for analysis and decision

Limitations (2) Model links only variables that can be quantified Outcomes related to quality improvement (eg learning achievement) cannot be modelled How do you model 1 million “better educated” students versus 1 million “less educated”students? We model the inputs associated with better quality, hence focus is on costs , rather than outcomes

Major Reforms to Improve Quality – Difficult to Model Example: Benin – French as language of instruction from class 1 may be impeding quality Alternatives are: use local language (18 in Benin) use small groups in classes 1 and 2 (teaching aides?) Use radio instruction to reinforce learning How do you model costs? Use some key cost drivers (eg additional teachers; teacher support; additional materials)

Limitations (3) Many policy parameters are set as policy objectives (eg reducing repetition rate or drop-out rate) But they are not necessarily under the control of the policy maker We don’t clearly understand how provision of inputs (schools, teachers, etc) impacts on repetition (make implicit assumptions) Many demand side factors affect parameters

Limitations (4) Policy parameters with greatest fiscal impact often the most difficult to change (e.g., teacher pay policy, ratios of teachers to administrative staff) Often difficult to link with budget preparation

Conditions for Effective Use of Simulation Models Sound technical team Policy makers and managers who use it as a tool for decision-making Are results communicated for decision-making? Institutional mechanisms for negotiating resources (for example, with Ministry of Finance, donors) AND technical capacity Model should be updated regularly Validation and documentation