Development agencies frequently talk of transforming from “owning development” to “catalyzing development” – what does this mean in the context of World Development Information Day? The past three decades have witnessed a steady expansion in global development aid. For instance, ODA reached a new peak of USD 142.6 billion in 2016, double that of the figure in 20001. However, in parallel, external aid as a fraction of GDP of developing countries has been falling over the same period. In 2015, it was 8.7% for low income countries, 0.8% for lower middle-income countries, 0.2% in middle-income countries, and 0.1% in upper middle-income countries2. External aid therefore continues to be a vital source of development support in some countries (especially in situations where state fragility is high).
More Effective Aid: A Standard Ecosystem for Information Exchange
Catalyzing development refers to identification and pilot testing of the most efficient, effective and scalable intervention pathways for a given development objective. These intervention pathways are then shared with the primary development agent – the national (or subnational) government, for scale up and monitoring. The Millennium Development Goals (MDGs) and the Sustainable Development Goals (SDGs) are critical milestones in this transformation. Both have attempted to create a common framework and objective set for development agencies globally, cutting across the government – non-government barrier. The current homogenized reporting on development indicators across the world, however critiqued it might be, is largely thanks to such standardization.
Building on this, the time is now ripe to implement a standard architecture of development information to facilitate learning across geographical and agency boundaries. Such standardization is needed on three levels:
A comprehensive map of where funding flows from, and where it flows to is the first step towards facilitating multi-stakeholder collaboration on development information. Funding flows reflect a great deal of research which goes into assessment of needs, criticality and estimation of required resource intensity, which can help development stakeholders reach out to peers to understand and learn from earlier interventions, and plan for new initiatives based on already committed resources. Relatively, this is the most mature area of information sharing, with established platforms such as aiddata3 and the Aid Management Program4 providing granular data and analysis on where (thematically and geographically) development aid has been moving, and from who
While existing frameworks do a good job of defining what to measure (the SDGs for example identify ~ 250 outcome indicators to be tracked across its 17 goals), there is a vacuum of development information on the question of “how to measure” these outcomes. Given that statistical capacities of even national governments are quite heterogenous in developing countries, implementing the SDG framework on outcome data is challenging given that a significant part of the required data needs to be captured at sub-national level. A positive development in this space is the emergence of global partnerships on development data (the data4sdg network5, for example). This seeks to integrate not only traditional “development agencies”, but stakeholders in the larger development information/data ecosystem (not just development data – Facebook, for instance is a part of the data4sdg network), appreciating the systemic benefits which such disruptive agents can bring in. Successful knowledge sharing from such partners can bring significant clarity on the most cost-effective ways of collecting development indicators
The third, and most critical space for development information sharing is on intervention pathways. This is also where non-governmental stakeholders can add the most, as this is essentially about identifying the most catalytic pathways which work in specified environments for achieving a given development outcome. A great initiative in this space is 3ie’s impact map6, which capture evidence on interventions which have moved the needle on each of the SDG areas. However, evidence is currently limited to high quality studies and research, and misses out on narratives, case studies and importantly, Government programs which are a large source of knowledge on what has worked and what has not. This limits the output of creative analytics tools7 which are now available to explore relationships and identify trends from development data.
Achieving coordination and learning on such intervention pathways (for both data and outcomes) will require standardization of intervention log-frames across all development funders. This is a significant challenge, as such standardization risks limiting usage of creative and hitherto unexplored intervention pathways, and disruptive innovations. However, there is value in creating an organically evolving master intervention frame, which learns on the go, and expands a shared understanding of potential interventions which have been attempted, whatever be the outcome. While this means swallowing the bitter pill of opening failure stories to the world, it is very much in line with the dialogue on global partnerships, enshrined in SDG17 (revitalize global partnerships for sustainable development). A comprehensive and flexible development information sharing framework (like the SDGs) which continues to learn over time, could massively increase value for money of interventions by traditional (governments, multilateral and bilateral donors) and upcoming (social impact funds, private foundations and CSR) agents.