Scoping Education PPPs is tough. These four steps will help you do it right.
As I have written previously here, PPPs have the potential to break the cycle of invest and neglect in education infrastructure that erodes efforts to tackle inequalities in coverage and quality of education in developing countries. Experience in Canada, Australia, Latin America and in my Scottish homeland show us that it can be done.
In the course of Caledonian Economics’ recent education PPP assignments in West Asia, Central Asia, Southeast Europe and Latin America, it has become apparent to us that the education sector needs a different approach to PPP project identification and selection compared to the classic infrastructure sectors (such as transport, energy, municipal services).
The textbook approach, as described in the APMG PPP Certification Guide is to use a Multi Criteria Analysis structure to pick the best PPP candidates. It asks: is the project of a suitable size, is it legal, is there a market, and so on? Each candidate project is given a score based on a basket of criteria, and the least suitable ones are weeded out. Then, a pre-Feasibility Study or Outline Business Case is produced to confirm viability and affordability of a very limited shortlist.
This approach works fine in the classic infrastructure sectors because the list of ideas to be sifted is generally self-evident and limited. For example, a highway is needed between two major towns, or a region’s power grid is failing, or the capital city’s water treatment system can’t cope with its growing population.
Social infrastructure – especially education buildings – is quite different. The challenges are multi-faceted and diffuse, especially in developing countries. A city of 1 million people will have several hundred schools, colleges and universities of many different designs and ages, some will be in terrible condition, some will be operating with two or even three shifts of pupils, while others may have spare capacity. Although the problems are clear for all to see, the best configuration of buildings and services to include in a PPP are seldom immediately obvious.
Identifying the best candidates for PPP requires a systematic approach, the main steps of which are described below.
Step 1 – get reliable data
Decision making in a complex and dynamic environment requires reliable data. If we are to avoid spending money in schools that are too big, or too small, or in the wrong places we need a reliable database that contains the following information on each building:
- size (gross floor area and theoretic number of pupils)
- condition of the building (a simple A-D rating system is fine – is the building weathertight, in good decorative order, with functioning heating and safe drinking water)
- suitability for education (A-D rating for factors such as classroom size, facilities, telecoms)
- population (age cohort) forecasts for the area covered by the school
- number of teachers, vacant positions. This data allows the best use to be made of the space that already exists – the first step in managing the education portfolio efficiently.
Step 2 – Tackle the worst first
This means prioritising the worst buildings or the biggest gaps between forecast supply and demand, or where there is greatest economic or social need. This does not necessarily imply using PPP where need is greatest (PPP may be problematic in areas with high risk of natural disasters, for example), but it will guide the overall investment strategy. In Scotland our long term strategy is to have all schools at least ‘B’ in terms of Suitability and Condition. Our first phase of PPPs tackled the buildings rated as ‘D’, especially those in areas of economic deprivation.
Step 3 – Know what works best for PPP
Some developments are more suitable than others for PPP. Large high schools, greenfield projects, and uncomplicated sites make more attractive PPPs than small buildings, brownfield sites and renovations. Regions with good transport links and established construction and building maintenance businesses are also likely to be more attractive.
I like to visualise this by placing buildings on a graph, with ‘Need’ on the y-axis and ‘Suitability for PPP’ on the x-axis.
Schools that are placed in Zone A are low priority. Schools in Zone B are not suitable for PPP and the most urgent ones should be addressed using other sources of finance. The most suitable candidates for including in a PPP will lie in Zone C.
Step 4 – Assemble viable bundles
The minimum ‘viable’ size (usually expressed as capital cost) of a PPP is a subject of discussion. The absolute value will be affected by local economic conditions, maturity of the market and availability of finance, but in my experience a reasonable rule of thumb would be at least 5,000 classroom places.
Therefore the final stage in the project selection process should be to identify a group (or groups) of schools within Zone C that share common features. This could mean schools of a similar size (so that standard designs can be used), or within a region (efficiency of logistics). Confidence in the local construction and building maintenance sector should also be a consideration and will reduce the risk of procurement failure.
An example of a PPP pipeline emerging from this kind of analysis in a developing country might be to launch pilot projects in a country’s two largest cities of between 5,000 and 10,000 pupils, perhaps one for public schools (grades 1-11) and one for VET colleges. A second phase of PPPs might involve a project in a smaller city for kindergartens, and one to create public schools in a vulnerable rural region. Subsequent phases would learn from the pilot models, improving and expanding their use.
In conclusion, the diffuse nature of the education sector requires some adaptation to the standard approach to PPP project identification and selection.
The challenge when selecting projects for education PPPs is to balance the competing considerations of social need with deliverability.
A systematic approach which identifies the most suitable candidate schools, then creates PPP projects bundles based on common features will maximise prospects of a positive outcome.
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