Chapter 5 Show Me the Money
In 2015, SDSN, ODW, and partners estimated the cost of producing a representative set of SDG indicators in 77 of the World Bank’s International Development Association (IDA) and “blend” countries to be between US$13.5 and US$14.2 billion dollars over the period 2016 to 2030 (SDSN 2015). That included US$11.4 to US$12.1 billion for surveys, censuses, and improvements to CRVS and education management information systems (EMIS). The recommended set of surveys and the unit costs of surveys and censuses were provided by institutions and experts familiar with the instruments and data collection process. The remaining US$2.1 billion was for real sector statistics and the development of geospatial and environmental monitoring capabilities. The cost estimation methodology is described in Data for Development (SDSN 2015).
In 2016, partners of the GPSDD updated the above estimates to include additional surveys for monitoring gender violence and literacy levels, time-use modules in labor force surveys, annual agricultural surveys, and improvements to health management information systems (HMIS), bringing the total to US$17.0 billion, of which US$14.9 billion was for surveys, censuses, CRVS, EMIS, and HMIS. The 2016 update is documented in The State of Development Data Funding (GPSDD 2016).
Based on the above estimates, SDSN and partners identified a data financing gap of at least US$500-600 million per annum, of which at least US$200 million was required of the international community (SDSN 2015). In 2019, PARIS21 and ODI recalculated these figures to include additional investments in statistical capacity based on the recommended actions in the CTGAP, and estimated the international community needs to provide an additional US$700 million per annum (ODI 2019).
The Sustainable Development Goals (SDGs) place high importance on using data to monitor and ultimately achieve sustainable development, and include such commitments as disaggregating data by income, gender, age, race, ethnicity, migratory status, disability, and geographic location to ensure we leave no one behind (Target 17.18) (UN 2015). The SDGs also include specific targets to increase availability of data for management and monitoring of sustainable development and to build the capacity of countries to use it (17.18 and 17.19). But in spite of these compelling objectives, the international community has been reluctant to fill the funding gap for data and statistics.
The result is acute data gaps, issues of data timeliness, and concerns about accuracy. As of 2019 the IAEG-SDGs reported that approximately half of SDG indicators do not have available data, while 88 indicators had no defined methodology and a further 34 had a methodology, but data was not yet being collected and reported for them in most countries (IAEG-SDGs 2019). “That means that even relatively sophisticated national statistical offices may have hands-on familiarity with only some 40% of the eventual full range of SDG indicators” (Rogerson 2019).
Furthermore, there are huge numbers of people who still go uncounted, such as the 25.4 million refugees in the world who are missing from national statistics (UN 2018). Overcoming the systematic under-investment in data requires a coordinated, concerted approach consisting of three pillars: advocacy, coordination, and new funding mechanisms (as identified by the Bern Network on Financing for Development Data or “Bern Network” in January 2019, see Box 11).
A. Build a Case for Investing in High-value Data
When advocating for increased investments in data, the international data community needs to show not only the social and environmental benefits, but also the economic incentives and the return on investment that can be derived from well-functioning data systems. For example, it has been estimated that the worldwide economic benefit of the US-funded Landsat earth observation program is equivalent to US$2.19 billion per year (as of 2011), and there are huge cost savings per annum from recurrent investment, ranging from US$350 million to US$436 million for federal and state governments, non-governmental organizations, and the private sector (Espey 2018a). Likewise, a valuation report found that the New Zealand census returns NZ$5 to the national economy for every NZ$1 invested (Dahmm 2018b). The Philippines has invested into a new ID system and expects to see resulting taxation efficiency savings of 2% of GDP over the next five years, equivalent to US$6 billion (Espey 2018b). Meanwhile, the BudgIT project in Nigeria has exposed corruption scandals, such as a 41 million Naira (approximately US$110,000) investment that claimed to be funding a non-existent youth center in Kebbi State (see Box 9 and Espey 2018c). Such examples should be used in a coordinated and strategic advocacy campaign that not only appeals to national governments and multilateral investors, but also to private and philanthropic investors looking to build systems with maximum social, environmental, and economic returns.
Box 9: The Return on Investment from Data Systems
An SMS-based system called mTRAC, implemented in Uganda, has supported significant improvements in the country’s health system – including halving of response time to disease outbreaks and reducing medication stock-outs, the latter of which contributed to a reduction in malaria-related deaths.
NASA’s and the US Geological Survey (USGS)’s Landsat program – satellites that provide imagery known as earth observation data – is enabling discoveries and interventions across the science and health sectors, and provided an estimated worldwide economic benefit as high as US$2.19 billion per year as of 2011.
BudgIT, a civil society organization making budget data in Nigeria more accessible to citizens through machine-readable PDFs and complementary online/offline campaigns, is empowering citizens to partake in the federal budget process, and is helping to minimize waste and corruption.
International nonprofit BRAC is ensuring mothers and infants in the slums of Bangladesh are not left behind through a data-informed intervention combining social mapping, local censuses, and real-time data sharing. BRAC estimates that from 2008 to 2017, 1,087 maternal deaths were averted out of the 2,476 deaths that would have been expected based on national statistics.
Atlantic City, New Jersey police are developing new approaches to their patrolling, community engagement, and other activities through risk modeling based on crime and other data, resulting in reductions in homicides and shooting injuries (26%) and robberies (37%) in just the first year of implementation.
In 2013, the Philippines merged multiple data producing agencies into a single institution: the Philippine Statistics Authority. The creation of the Philippine Statistics Authority has improved timeliness of national and regional accounts; opened up national statistical data, including microdata; innovated the way the Philippines conducts household survey and censuses; and is deriving value from a new national identification system.
According to a 2014 study, the New Zealand census returns to the national economy NZ$5 for every NZ$1 invested. The census’s contributions to other areas, such as inclusion and empowerment of the Māori, are documented in this case study.
Household surveys are a powerful analytical tool that can shed light on how households interact with services and how interventions affect their wellbeing. This case study evaluates the return on investment from the Living Standard Measurement Study – for example, helping to improve the beneficiary targeting of the Malawi Farm Input Subsidy Program (FISP) and to investigate the impacts of FISP on smallholder agriculture.
Civil registration and vital statistics are the backbone of effective national service delivery. CRVS data is also key to monitoring 12 of the 17 Sustainable Development Goals and 67 of the 230 SDG indicators. This case study shows the immense value that can be derived from CRVS investment for governments and for society at large.
With two-thirds of the world’s population facing water scarcity at some point during the year, increasing the reliability of water access is essential to sustainable development. The sensor-driven Smart Handpump project showcases one data technology that is revolutionizing the way water services can be delivered.
Full case studies are available at: [sdsntrends.org/valueofdata].
B. Gain Support Through Shared Priorities to Implement the SDG Agenda
In addition to a coordinated advocacy campaign featuring examples of the value of investing in data, the data community may do well to consider some more quantifiable, public goals that help the global community to track progress in building national data systems. It has been said that the Millennium Development Goals (the predecessors to the SDGs) “broke new ground […] catching the attention of millions of policy makers at national and international level [sic]” due to their simplicity and ability to focus resource investments (Solberg 2016). For the international data community, a short-list of 8 to 10 clear, compelling goals that draw upon the CTGAP, the targets in the SDGs, and priorities articulated through the UN Statistical Commission could be a powerful rallying tool to focus energies and investments and communicate objectives more effectively. Goals might focus on themes such as:
leaving no one behind through investments in the census (e.g. increase investments in the 2020 Census and support to all countries to improve their interim population estimates using new methodological approaches, such as those identified by POPGRID);
improvements in disaggregated data (e.g. Target 17.18: "By 2020 increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts”) (UN 2015);
advances in civil registration and vital statistics (CRVS) system coverage (e.g. increase investments in the 100 low- and middle-income countries that lack functional CRVS systems and that under-record or completely fail to record vital events of specific populations) (PMNCH 2012);
advances in data openness and transparency (e.g. via a measure of the Open Data Inventory (ODW 2019b);
new data partnerships for innovation (e.g. a target number of countries to have established multi-stakeholder partnerships in order to fill key data gaps in national statistics, either for SDG monitoring or high-frequency data for policy- and decision-making, drawing upon new data sources such as big data and telecommunications data);
the widespread utilization of geospatial imagery (e.g. all countries are utilizing geospatial imagery and other earth observation data for improved environmental monitoring and geographic disaggregation, in partnership with national earth observation agencies and teams);
leveraging the use of citizen science (e.g. contributing data to the SDG monitoring framework but also mobilizing action by engaging citizens in the implementation of the SDGs; this could help building awareness on societal challenges, promoting behavior change and thus delivering the transformations needed to achieve the SDGs); and
supportive governance frameworks (e.g. all countries have a governance framework or statistical law that enables the utilization of third-party data, including that provided by private companies and academic institutions).
Such goals would not only make it easier for us to communicate what we want to donors but also to our own governments, and make it easier for the disparate global data community to pull in the same direction. As John F. Kennedy once said, “By defining our goal more clearly, by making it seem more manageable and less remote, we help all people to see it, to draw hope from it, and to move irresistibly towards it” (Kennedy 1963). The High-Level Group for Partnership, Coordination and Capacity-Building for Statistics for the 2030 Agenda for Sustainable Development (HLG-PCCB) is perhaps best placed to devise such a list, given that it is comprised of a representative group of Member States and has representation from regional and international agencies, and should look to do so fast, to focus attention on data in the run up to the 2019 SDG Summit under the auspices of the UN General Assembly.
C. Build a Coalition Around a Set of Commitments Involving All Stakeholders
Exacerbating the problem of underfunding for data is the fragmentation of data funders and funds. The data landscape is undermined by many of the same problems affecting development financing in other sectors, including the fact that funding coming from multiple sources – domestic, bilateral, philanthropic, and multilateral, including loans (Rogerson 2019). Funding is also limited to relatively few donors – according to Rogerson, “just five, four of which are multilaterals supplying over two-thirds” of official development assistance (Rogerson and Calleja 2019). Loan financing accounts for 38% of all development data funding, which is much higher than the percentage of loan financing used to fund global education (4%) or health (15%), for example (Ibid). Of the multilateral funding, multi-donor trust funds account for approximately US$150 million of disbursements per annum, coming from the World Bank, the African Development Bank, and International Monetary Fund (ODW 2016). There are more than 50 different instruments bring used, varying in size and scope from under US$10 million per disbursement to upwards of US$50 million per disbursement (Ibid).
To ease the burden on countries looking to access international assistance, current funding arrangements need to be streamlined and coordinated around a set of common principles. They need to be provided with full transparency so that geographical allocations can be tracked and we can avoid the problems of certain countries being over-funded, while others are left behind. In general, aid for statistics should adhere to the Paris Declaration on Aid Effectiveness and the Accra Agenda for Action, as well as the subsequent Busan Partnership for Effective Develop Co-operation (OECD 2005; OECD 2008; OECD 2011). Donors should commit to, at a minimum:
ownership: supporting recipient countries to set their own strategies and prioritize their investments;
alignment: aligning investments to the national strategy for statistics, using local systems to channel resources;
harmonization: coordinating among donors, simplifying procedures and sharing information;
results: a clear focus on long-term, quantifiable outcomes and results;
mutual accountability: accountability on both sides – recipients and donors – for the success of their investments;
inclusive partnerships: inclusion of all partners, bilateral, multilateral, foundations, et al. in discussions on investments and coordination; and
capacity development: a strong emphasis on building the capacity of national statistical and financial systems so that countries can mobilize the necessary resources domestically over the medium to long term.
In addition, the Paris Declaration and the Accra Agenda call for donors to disclose their plans for donations over a three- to five-year window. If this were adhered to, it would be far easier to coordinate pooled investment and to ensure fair, balanced system and geographic investments.
Box 10: Promising Examples of Country Ownership and Donor Alignment
Since its establishment in 1999, PARIS21 has been a key advocate of in-country donor coordination, which they suggest can promote “transparency, alignment and cost effectiveness” for both donors and recipients. In a presentation to the HLG-PCCB in May 2019, the group cited a number of countries taking positive steps to better align their donors behind their National Strategies for the Development of Statistics (NSDS) and/or other government-determined investment priorities (PARIS21 2019). For example, Bhutan’s NSO convened a series of multi-stakeholder taskforces and workshops, some with participation from donors, to inform and ensure alignment with their new NSDS for 2018-2019. In Tanzania and Rwanda, the governments have set up additional mechanisms such as statistical coordination committees comprised of members of the NSO, key ministries, and their leading donors, and in doing so has incentivized more streamlined basket funding measures.
Source: PARIS21 (2019)
Discussions through the Bern Network (see Box 11) have clarified that although the data agenda for the SDGs is a priority, it would be unlikely for any of the bilateral and multilateral donors to come forward with significant contributions to set up a new general funding facility, aimed at strengthening weak foundational statistical systems around the world. However, there seems to be interest in funding data systems for certain sectors. Also, other opportunities have been identified to get to more and better funding for data. Based on stakeholder consultations, six areas look promising for donors and partner countries to come together and agree on a set of common actions and commitments:
Encouraging national governments to increase allocation of domestic resources for the data agenda and, hopefully, to make a public commitment to invest more in data.
Engaging with bilateral donors to promote use of national systems for monitoring and evaluation where research shows a significant amount of resources are allocated, and to create efficiency gains by promoting shared and open data.
Leveraging multilateral development funds such as IDA, which is envisaged to complement domestic resource allocations to fund significant investments in statistical capacity-building.
Setting up mechanisms to improve cooperation and coordination among donors and recipient countries based on the principles noted earlier, e.g. through a clearing house system or mapping the supply and demand for data.
Establishing a data funding pooling arrangement supported by OECD Development Assistance Committee (DAC) and non-DAC donors for systems that have been underfunded through other mechanisms.
Sharing knowledge and leveraging investments in sectoral data, where donors are making significant targeted investments in strengthening particular data sectors such as health or agriculture. The aim is to broaden the objectives to also contribute to improving foundational statistical systems, not just certain parts of the systems.
The articulations of these six initiatives and the commitments built around them should be released and endorsed by the data and donor community at the 2020 UN World Data Forum. Given the scale of resource gaps and the multiplicity of donors (each with a targeted set of sectors or countries), the multilateral and bilateral donors should agree on a set of consolidated operating principles and an information-sharing platform to help coordinate all of the diverse actors at play. The Bern Network and the upcoming World Data Forum would be an opportunity for a global commitment around these common set of principles.
Box 11: The Bern Network on Financing for Development Data
The Bern Network on Financing for Development Data is an open, multi-stakeholder collaboration with the objective of supporting the 2030 Agenda for Sustainable Development by promoting more and better financing for data. Composed of aid and development agencies, national statistical offices and ministries, international organizations, private sector partnerships and civil society groups, its aim is to advance the implementation of the CTGAP and work towards a robust funding framework to be presented at the UN World Data Forum 2020 in Bern, Switzerland.
The High-level Group for Partnership, Coordination and Capacity-Building for Statistics for the 2030 Agenda for Sustainable Development, working with the Global Partnership for Sustainable Development Data, should coordinate the international data for development community to shortlist a set of 8 to 10 clear, compelling goals that focus attention and investment on clear priorities. In support of this, they should develop and showcase compelling evidence of the return on investment from data systems.
Countries should take charge to improve donor coordination at the country level. A common set of principles for aid alignment, and using tools such as country project inventories to minimize duplication and proliferation of funding approaches, should be pursued as soon as possible among partner groups such the Bern Network on Financing Data for Development.