United States

Data-driven approaches, for planning and programming, results monitoring and evaluation, are deeply rooted in United States (US) government agencies that manage development co-operation. A large share of the United States’ official development assistance (ODA) for data and statistics is targeted to low-income and fragile states and more than half aims to strengthen health data and statistics. Activities span a wide range of types of data and potential users. They include, for instance, the United States Agency for International Development’s (USAID) Demographic and Health Surveys (DHS) Program, which has supported more than 400 household surveys in over 90 countries; the strengthening of partner countries’ health management information systems; and initiatives aimed at advancing local capacities to create and use data, including new sources of data.

Data-driven approaches for planning and programming, results monitoring and evaluation, are deeply rooted in US government agencies that manage development co-operation, including USAID, the Millennium Challenge Corporation (MCC) and others. The 2018 Joint Strategic Plan FY 2018-2022 of the two agencies notes effectiveness, accountability, learning and transparency as the central principles that drive their use of evidence and data to achieve their objectives.

According to OECD data and research,1 US government agencies supported data production and use in developing countries with an average of close to USD 70 million per year (in 2018 prices) from 2017 to 2019 (Figure 1), with a significant increase observed between 2010 and 2015. Around 50 countries benefit from US ODA to data and statistics in any given year. While the largest share of US support is targeted to countries in sub-Saharan Africa, Afghanistan and Haiti have been major beneficiaries as well. More than 80% of US ODA for data and statistics is targeted to fragile states. Health and population data are a key focus of US support. A significant proportion of US bilateral assistance is managed by two agencies, USAID and the MCC, whose strategies and programmes in support of data for development are discussed below.

Using data for improved decision making is at the core of USAID’s mission and is reflected in its use of country metrics, collated on the International Data and Economic Analysis (IDEA) website, ForeignAssistance.gov and others. These country metrics are used in planning, policy dialogue and context monitoring during implementation to support partner country governments and other actors to achieve locally sustained results.

Initiated in the 1980s, USAID’s flagship Demographic and Health Surveys (DHS) Program has since supported over 90 countries to collect, analyse and disseminate data on population, health, HIV, nutrition and other domains through more than 400 household surveys. Between 2017 and 2019, the programme accounted for nearly 50% of US investment in data and statistics in its partner countries. Other major USAID-sponsored projects also often focus on health data and statistics, especially health information management systems (e.g. in Eswatini, Haiti and Kenya).

USAID’s mission to be data-driven is not only about improving and increasing the amount of data, but supporting the digital ecosystem that underpins USAID’s capacity for better data collection, analysis, dissemination and use. This is reflected in USAID’s Digital Strategy, a five-year (2020-24), agency-wide strategy that includes initiatives aimed at advancing partner countries’ capacity to create and use data to advance development and humanitarian assistance outcomes. Efforts aim to: address cybersecurity and data-privacy issues; improve digital literacy; close the gender digital divide; focus on vulnerable populations (who are often under-represented in data collection and are the least likely to benefit from gains in the technology sector); and incorporate the Principles for Digital Development in all USAID-funded programmes. This includes partnering with the private sector, local partners and government across a number of initiatives that support governments to better understand, analyse and use data responsibly for improved development outcomes. USAID primarily invests in the following types of data for development programmes and activities:

Assessing the data ecosystem landscape: Many of the governments with which USAID partners have some of the elements necessary for a robust data ecosystem, but lack some of the scaffolding elements for mature and inclusive data ecosystems. USAID works with partner governments to increase literacy, production and use of data. For example, USAID completed a Data Ecosystem Rapid Assessment to understand the landscape of data stakeholders and systems in Djibouti, as well as potential opportunities to drive co-ordination and improved data use for better effectiveness and impact of humanitarian assistance efforts. It plans to expand the assessment to other countries.

Supporting government data systems: Understanding the critical need of government data systems, USAID supports the development and deployment of secure, interoperable, standards-based data systems. For example, USAID works with the governments of Guinea, Liberia and Sierra Leone to support the development of national strategic health information system investment plans, to equip government workers to use health data through digital communication platforms like mHero, and to maintain data system investments through local technical expertise like the West African Health Informatics Team. The team developed the ECOWAS COVID-19 Dashboard and is working to improve health information systems and capacity to collect, analyse and use data in post-Ebola West Africa.

Supporting national data governance and policies: USAID published Considerations for Using Data Responsibly at USAID, providing a framework for identifying and understanding risks associated with collecting, sharing and using data. These considerations provide a basis for USAID to support partner governments as they build good data governance practices and policies. For example, USAID supports the Eurasia Foundation’s TAPAS (Transparency and Accountability in Public Administration and Services Activity) programme in Ukraine, which aims to improve the efficiency and effectiveness of government and reduce corruption through transparent, open electronic processes. It includes an open platform that allows civil society and citizens to monitor government procurement and to raise protests around questionable procurements. The Ukrainian government reports that e-procurement saved it more than USD 4.4 billion between January 2017 and October 2020.

Improving data use: USAID seeks to improve responsible data use to improve programmatic outcomes as well as to monitor the context in which USAID operates. For example, in Malawi, USAID is supporting a Rapid Feedback Monitoring System that integrates Catholic Relief Service’s Measurement Indicators for Resilience Analysis and the World Bank’s Survey of Well-Being through Instant and Frequent Tracking in Malawi. This system uses a methodology to collect necessary data quickly and efficiently, allowing stakeholders and communities to better measure and understand resilience and well-being, and in turn improve and refine resilience programming.

In Ethiopia, USAID conducted phone surveys to understand the food security and socio-economic impacts of the COVID-19 crisis. USAID/Ethiopia developed a community of practice with the United Kingdom’s Foreign, Commonwealth & Development Office; non-governmental organisation partners; the World Bank; and the International Food Policy Research Institute to harmonise data collection efforts to monitor the effects of the COVID-19 pandemic on their beneficiaries and understand changes in the context that needed to be addressed when activity implementation restarted.

In Uganda, a USD 7.5 million, five-year Feed the Future activity, experimented with different digital feedback loops. They used a phone-based survey to enable field agents to share feedback with each other in near real-time and used interactive voice response and digital network mapping to capture feedback on whether their activities were reshaping the purchasing habits of agricultural suppliers. These digital feedback loops helped the team test hypotheses more easily and inexpensively and enabled them to iterate rapidly on their programmatic approaches. In the end, this furthered the activity’s impact.

Since its inception in 2004, the MCC has championed the use of data in the development of its compact and threshold programmes. Support for the collection and use of data is integrated through nearly all of its investments. Examples include support for statistical systems in Niger as part of the MCC's USD 437 million compact designed to promote the sustainable use of natural resources for agricultural production while improving market access; investment in access to information in Togo; and a proposed health systems strengthening project in Lesotho, which will invest in data collection and use throughout the health system. Data and statistical capacity (ecosystem) assessments are also concurrently being conducted in early-stage partner countries like Côte d’Ivoire, Malawi and Kenya to better understand statistical capacity and data use.

The MCC has also prioritised strengthening data use by implementing a USD 21.8 million interagency agreement funded by the President’s Emergency Plan for AIDS Relief (PEPFAR). The Data Collaboratives for Local Impact (DCLI) programme aims to empower individuals and communities to use data to improve lives through better policy, programme and resource allocation decisions, and improved transparency. Under the DCLI programme, the MCC and PEPFAR supported various initiatives aimed at strengthening local data skills and data use in the United Republic of Tanzania (since 2016) and Côte d’Ivoire (since 2018).

The MCC’s experience with investments in data at different stages of its investment cycle has led to several lessons learnt:

  • Leverage local data systems: Collection of data during programme design can be ineffective and may even be counterproductive if it obviates local data systems (i.e. is conducted mainly to support donor-related investments). To the extent possible, it is important to leverage existing data sources.

  • Data use at the local/subnational level: Local data use is important during implementation to optimise and sustain programme impact. For example, investments in modern mass transit systems that leverage Big Data need to be developed in concert with and managed by local staff within the ministry of transport who are knowledgeable of these types of data sources. Also, while data are often collected at the subnational level by the government or donors, once aggregated, they are not always made available for reuse at the subnational level. Additional investment is needed to close this gap and ensure that local authorities in partner countries can access the data that would allow them to propose their own, tailored development priorities.

  • Appropriate data governance structures: With increasingly sophisticated data and digital development solutions, there is a need for appropriate data governance structures that balance a country’s use of innovative sources (in addition to the traditional public sources of data) with the need for interoperability, security and data ethics that protect citizens’ right to privacy. Improved data governance frameworks will require a foundational understanding of data use, data policies and practices at all levels of government (e.g. staff within ministries, but also ministers themselves) and, at a minimum, a citizenry that is aware of such issues.

  • Gender-balanced data efforts: It is important to invest in gender-balanced data efforts; that is, not only in collecting gender-disaggregated data, but in ensuring that women are encouraged and enabled to access economic opportunity through digital tools, digital savvy and data skills.

More than half (54%) of US ODA to data and statistics aims to improve gender equality; 38% aims to improve reproductive, maternal, newborn and child health; 26% aims to improve nutrition outcomes; and 21% aims to address environmental issues (Figure 2). In line with the importance of the DHS Program in the United States’ overall financial support of data and statistics and its focus on maternal and child health, 57% of US financial aid to data and statistics aims to strengthen health statistics. Just under a fifth (18%) aims to strengthen population statistics and 12% to strengthen agricultural statistics.

Between 2017 and 2019, around 70 developing countries directly benefited from US ODA for data and statistics (Figure 3). While Haiti was among the top recipients in this area, overall, the geographic focus of US support was on Africa (51% of the total and 72% of all ODA to data and statistics that could be assigned to a specific country), especially West, East and Southern Africa. With nearly 9% of the total, Tanzania, where the MCC and PEPFAR support the DCLI-funded Tanzania Data Lab, was the top recipient over this time period (Figure 4).

Of the 63% of US support to data for development that can be assigned to specific countries, 53% was targeted to low-income countries and 40% to lower middle-income countries. In 2019, close to 90% of US ODA to data and statistics was disbursed to countries currently classified as fragile, a share that has increased significantly in recent years (Figure 5).

Between 2017 and 2019, the large majority of US bilateral ODA to data and statistics, around 97%, was delivered in the form of project-type interventions, many of which include technical assistance as a component (Figure 6). The remainder is mostly accounted for by funding of experts and other technical assistance outside of the project-type interventions. Technical assistance accounted for nearly 9% in 2015 and has since decreased outside of project-type interventions.

A large share of US ODA to data and statistics, 63% between 2017 and 2019, was channelled through the private sector (Figure 7). For example, the DHS Program is implemented under USAID contract with ICF International. Twenty-three per cent was channelled through US public sector entities, 10% through research and teaching institutions, and 2-3% through non-governmental organisations.


← 1. The analysis in this profile is based on official data reported by members to the OECD’s Creditor Reporting System. It is published under the responsibility of the OECD. OECD analysts mined the database using a text search with manual curation. Where relevant, members contributed additional data to fill gaps. Please see the methodological annex for further details on the data analysis.

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