Last week, we shared our schedule for this week’s Data for Development Festival, hosted by the Global Partnership for Sustainable Development Data (GPSDD) in Bristol, UK. The event brings together a vibrant community of governments, foundations, private sector and nonprofit organizations, galvanized by the belief that data, used well, can drive better development outcomes and achieve the Sustainable Development Goals (SDGs). But the question remains, are we doing enough? And are there sufficient (and sufficiently well-targeted) resources available to realize the potential of data for development?
According to the best estimates, there is a $350+ million annual funding gap – on top of the existing ~$300 million per year being disbursed by funders – standing between us and data of sufficient quality to effectively monitor the SDGs. While efforts to close this financial gap are critical, I believe we must complement new money with practical and creative thinking on how we can better make use of existing resources by answering three simple questions: What money? What data? What purpose?
Firstly, what money is currently being spent on data?
While the $350 million per year funding gap seems to loom large, and is perhaps unlikely to be met purely through new commitments, what if there was an existing source of funding for data left untapped?
In 2016, the development community disbursed $142.6 billion in total Official Development Assistance (ODA). Of this, approximately 2-3% ($2.85-$4.28 billion) went towards project monitoring and evaluation (M&E). For most projects, M&E spending focuses heavily on collecting project-specific data that are rarely used, and almost never shared for reuse. Beyond financial cost alone, the reality that the burden of M&E data collection often falls upon local providers, at the expense of their other service delivery and/or data collection activities, presents a strong opportunity cost.
Thus, one could argue that the development community spends more money hampering ($2+ billion) government capacity to generate SDG data than it does helping ($300 million). In the following, I present two constructive ways in which the data for development community can engage the results community to flip this narrative and close the funding gap.
First: Results-based financing (RBF) programs (in which implementers, often governments, deliver services and are reimbursed based upon their work’s verified results) constitute just a small slice of overall ODA (no exact figure is available, but our best estimates are roughly $5 billion per year). Instead, spending on data is much heavier, at approximately 10% of total spending ($500 million per year).
RBF programs come in many forms, but research shows that when government systems are used (and funded) through RBF, error rates in data are “significantly diminished” within about two years, as the role and understanding of use of data improves. For instance, projects funded with the World Bank’s Program for Results use government administrative data for about 25% of the work of verifying results, and only 1% use support from National Statistical Offices (most use independent audits or other third-party verification methods). If these funders (primarily the World Bank, UK’s Department for International Development, the Inter-American Development Bank, and Norway) shift from 25% to 50% spending on the use of administrative data systems, an additional $125 million for data for development could be unlocked.
Second: Current M&E approaches typically work on a project-by-project basis, in cycles of 5-year programs that create, scale, and finally tear down their systems at project closeout. But to achieve real results, development agency country offices must shift how they think about using resources allocated to M&E in individual projects. Specifically, these resources should be used across their wider portfolio to support core country data systems. For example, one DFID country office told us that lacking solid population and demographic data at the subnational level was hampering their ability to monitor the impact of all of their programs. Following their significant frustration,they decided to fund the country’s first census in 30 years. This is a good example of how we can use M&E funding to support strong country systems, which can benefit all programs, rather than project-specific systems that limit the sharing of data, sap country capacity, and provide no long-term impact.
We encourage the GPSDD community to broaden its aperture, engage with results professionals and policymakers, and unlock more and better data that can fit the needs, rather than tax the resources, of on-the-ground service delivery and implementation staff.
If an organization with an existing culture of learning and adaptation gets lucky, and an innovative funding opportunity appears, the result can be a perfect storm for changing everything. The Results Data Initiative was that perfect storm for DG. RDI confirmed that simply building technology and supplying data is not enough to ensure data is actually used. It also allowed us to test our assumptions and develop new solutions, methodologies & approaches to more effectively implement our work.
From our experience understanding data use, the primary obstacle to measuring and organizational learning from feminist outcomes is that development actors do not always capture gender data systematically. What can be done to change that?
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