EDUCATION PLANS AND PLANNING

There are two distinct classifications of simulation models, depending on either the model’s specificity (generic vs. country-specific) or its approach (demographic vs. financial model). These classifications, however, should not exclude the variety of many other subcategories that can exist in between.

Generic vs. country-specific models: Generic models are sometimes called “ready-to-use” models, and country-specific models are often called “tailor-made” models.

The generic approach is used in designing simulation models which contain components common to a majority of education systems. They do not correspond, therefore, to any particular country’s education system. With careful adaptation, such models can however make it possible to approximate the pedagogical, physical, and financial consequences of a policy.

The country-specific approach is used in order to assess the consequences of a particular country’s education policy options, and to identify, in greater detail, the development actions required to implement the stated policy. Country-specific models take into account the structure and specific details of an individual country’s education system.

Generic models have the advantage of being operational as soon as the baseline data and main objectives are inputted, but have a limited power as detailed programming tools. In contrast, “tailor-made” simulation models, designed on the basis of close collaboration between decision-makers and specialists reflect a particular country’s situation and its educational policy, but such models require a much longer time to prepare and to verify.

Demographic vs. financial models: The second classification is between “demographic” and “financial” models, with multiple variants in between. These two types of models are designed according to two opposite methodological approaches: financial models use public spending on education as the decision variable, and demographic models estimate educational expenditure as the result of the simulation.

In the financial or budgetary models, one is first concerned with determining an acceptable budget ceiling for education as a proportion of the State’s overall budget. The computer calculates backwards to obtain likely enrolment targets. In the demographic models, the enrolment-related targets (independent variables) are laid down a priori and the computer calculates the resultant, necessary financial resources.

In reality, the principal simulation parameters ─ the enrolment and budget parameters ─ are interdependent. Whichever modelling approach is used, the options concerned in the initial scenario undergo several changes before leading to a balanced result. The search for a scenario that corresponds to the policy, leads the planner to repeatedly test different options for the two types of principal variables which are considered either as causes or consequences. The final decision is made by considering the implications of each parameter, and the scenario to be adopted finally results from a reasoned choice of the possible variables to be applied, both upstream and downstream in the course of the calculations.

For instance, in demographic models, the financial resources calculated as requirements for achieving the enrolment targets may prove unsustainable within a certain macro-economic and fiscal framework. In this case, one needs to review the parameters of input mix (student/teacher ratio, class size, textbook/student ratio, etc.) until one achieves a feasible expenditure level. With financial models, the budgetary ceiling specified as the decision variable for a certain level of education may not make it possible to achieve a policy goal, such as an adequate teachers’ training. In this case, one would adjust the aforementioned parameters of input mix in order to reduce the education costs per pupil to train more teachers.