The problem of too many infrastructure investment proposals for too few funds keeps so many policymakers up at night that it would take a whole series of blogs to discuss the range of tools that have emerged in response. However, for many countries—Belarus included—the challenge is more complex: too many investment proposals for too little funds with limited information and/or time to make a sophisticated analysis.
Belarus is looking at an estimated $3.58 billion in infrastructure investment before 2020. Within its public investment cycle, the government receives hundreds of municipal project proposals competing for limited funding. Complex resource allocation decisions are made with as little information as a list with project names, brief justifications, and estimated costs. In this context, getting the most output from every ruble spent on infrastructure becomes an imminent necessity.
But achieving systemic improvements requires going down a long road of introducing regulatory changes and building institutional capacity. The government needs a practical and feasible method for choosing where to invest scarce public funds—today.
This is where the World Bank's Infrastructure Prioritization Framework (IPF) comes into play. Unlike other project appraisal methods (including cost-benefit analysis), using IPF is possible even when sophisticated project-level information is simply not available. It also allows the use of information that does not necessarily translate into a monetized indicator.
Making smarter investment decisions with the IPF requires following a five-step process, starting with deciding what criteria determine a "good" project in the context of policy priorities and sector requirements.
The next step requires sourcing all available information to reflect these criteria. It’s essential to be prepared to make several iterations of these two steps over one IPF cycle.
The IPF Process
What makes the process considerably easier is that the statistical analysis behind the IPF has been fully automated and can be performed by a Microsoft Excel add-in. Once the project data is collected and consolidated in a database, calculating composite indices and visualizing comparative advantages of different projects becomes as easy as clicking a few buttons. From Excel rows and columns you are taken to an interface where project proposals are ranked across the two socio-environmental and financial-economic indicators and then mapped in a scatterplot. Projects that rank high on both these parameters are categorized as "high priority" and recommended for implementation.
The IPF Matrix:
The IPF pilot in Belarus
The IPF tool was introduced to Belarus under the umbrella of the World Bank's broader public financial management reform program. As a data-driven approach, it enhances objectivity of the investment planning process.
To illustrate potential benefits of the IPF it was applied to the $244 million pool of 83 water supply and sanitation projects competing for $145 million in funding. The pilot was designed as a demonstration exercise to walk the Ministry of Housing and Utilities (which volunteered to participate) and other stakeholders through the process.
The tool is very useful to identify potential constraints to data-driven investment decision-making. It triggers important policy discussions on project proposal preparation, data quality, and criteria for project selection. This held true for the Belarus pilot.
In the future there will be several important questions to consider:
Should national programs shape funding priorities?
There’s a strong case for relying on strategic planning and a programmatic approach in infrastructure policymaking, both across and within sectors. In this pilot, some of the criteria were derived from Belarus national development programs and strategic documents. Usually, aligning project funding criteria with policy priorities outlined in national programs should increase regulatory transparency, coherence, and predictability of public financial management systems. At the same time, it’s important to consider stakeholder consensus and whether national programs are outdated or not tailored enough to a situation at hand.
What data will Belarus need?
National strategic documents allowed us to define criteria such as service coverage, continuity of water supply, quality of networks, water potability, affordability, energy efficiency, utility operations' efficiency, and financial sustainability for the water supply subsector. Criteria for the sanitation subsector included similar parameters, with additional emphasis on water use efficiency and the impact of water discharge on natural waters. But the ability to apply these criteria to prioritization is ultimately defined by available data. IPF pilots serve as a tool to define data gaps so that policymakers can start recording and accounting for more essential information.
For example, . To prioritize between respective investments it will need to account for water quality indicators and information on current wastewater treatment levels and discharges.
Should Belarus invest in better preparation of project proposals?
When there’s little information about the features of the actual projects, any data-driven prioritization method will rely on utility-, municipality- or even national-level data. This means that most projects submitted by the same utility (or even several utilities within the same territory) are likely to have less variation in terms of their socio-environmental and financial and economic impact. So as much as it holds true that IPF is designed to accommodate situations when only minimum important information is available, the robustness of the analysis and reliability of results do improve with more project-level data. Another important policy question raised by the pilot was whether a project should meet certain preparation criteria before being considered for funding.
Should IPF become part of Belarus' infra-planning process?
Although IPF received great interest from the government from day one, now there is solid evidence that this methodology could become part of the country’s public financial management system.
Lessons learned from the pilot created enough food for thought to start discussing how to institutionalize the framework and the necessary steps to ease constraints to data-driven infrastructure investment planning.
Can good infrastructure decisions be made with little information?
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