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Warning: Too Much Reliance on Data Can Undermine the UN’s SDGs


A German-government financed project for the UN Development Program’s climate-change adaptation work includes protecting coasts in Grenada, above. The country, a Caribbean island heavily reliant on tourism, is especially vulnerable to rising sea levels. UNDP

At the United Nations, the buzz around a “data revolution” has taken the role of numeric indicators to new heights. Once thought to be a technical issue for statisticians, data questions were a recurring theme in conversations among the thousands of politicians, officials, activists and researchers who gathered in mid-July at the UN’s High-Level Political Forum to review the implementation of the 17 Sustainable Development Goals, or SDGs.

The SDG indicator framework now dominates the review, as exemplified by the progress report submitted to the forum by UN Secretary-General António Guterres, which provided a compilation of data trends rather than an analytical narrative with a statistical annex. Quantitative evidence is undeniably essential, but with data gaps and inherent limits to measuring development, the SDG indicator framework in its current state risks giving an unbalanced and incomplete picture, particularly losing sight of the integrated concept of Agenda 2030 and its promise of “leaving no one behind.”

A more holistic approach is needed, using qualitative as well as quantitative data.

Gaps in the SDG indicator framework

The SDGs pose huge measurement challenges, and it is a tribute to the global statistical community that a consensus framework with 232 indicators has been adopted. But the framework is still a work in progress. Only a few countries, if any, currently collect all 232 indicators, or even the 82 well-established statistical measures categorized as Tier I. Work has only started on developing data and methodologies for the remaining 150 indicators, which are more innovative but lack data coverage (Tier II), or have not yet been defined (Tier III).

The gaps in data are particularly serious for the new elements of the SDG agenda, such as environment, governance, inequality and means of implementation. While the socioeconomic goals (poverty, gender, education, health) have many Tier I indicators, Tier II and Tier III indicators predominate for most of the environment-related goals; for targets on systemic issues in global partnerships (17); and for many of the “means of implementation” targets under different goals. Another critical gap is in developing disaggregated data, which is essential to monitoring progress on the commitment to “leave no one behind.”

Apart from data gaps, there are also deficiencies in the framework where indicators reinterpret the intent of the target or goal. For example, the indicator for reducing fisheries subsidies (target 14.6) is illegal fishing.

Another example is technology. The agenda reflects the important role of technology from both public and private sectors in driving progress and includes targets in several areas. But the indicators that were selected focus mostly on the diffusion of the Internet and public-sector research, neglecting critical challenges for the agenda, such as green technology and private-sector research.

As a result of these gaps, the indicator framework provides an incomplete and potentially unbalanced picture of progress, leaving out many of the most innovative elements of the 2030 Agenda that depart from past paradigms of development. Compounding the gaps is the need to be selective, given that no report can include an overview of all 232 indicators.

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Sakiko Fukuda-Parr, the author.

Indeed, Guterres’s 2017 SDG progress report has encountered sharp criticisms from civil-society commentators. A recent blogpost from the Center for Economic and Social Rights noted: “The ambitious spirit of the 2030 agenda would be undermined by the weakness of the ‘official’ monitoring and reporting arrangements,” and points out a number of ways in which the report does not include some of the most transformative propositions of the agenda.

Reviewing the 2016 and 2017 reports of the UN secretary-general, I find that data availability drove the focus while some apparently arbitrary choices were made in selecting which indicators to report on. Thus, socioeconomic outcome indicators of the MDG era continue to populate the reports. By contrast, new issues are neglected because of lack of data.

For example, of the 13 indicators for Goal 5 — on gender equality — the 2016 report included four indicators (three Tier I and one Tier II), while the 2017 report included seven indicators (all tiers). All were outcome indicators, and there was no reporting on “means of implementation,” which refer to women’s access to technology and economic resources.

Of the 11 indicators in Goal 10 — on inequality — the 2016 report included three (Tier I) outcome indicators. The 2017 report included five Tier I indicators, of which three were focused on means of implementation. But a notable indicator left out of both reports was the labor share of GDP — arguably a central issue in contemporary debates about inequality, as the loss of manufacturing jobs and wage stagnation seem to be a factor behind the new politics of populism.

Indicators vs. the transformative nature of the 2030 Agenda

The data gaps and imperfections of the SDG indicator framework aside, there is a fundamental tension between a review process structured around indicators and the concept of SDGs as an integrated agenda.

The adoption of the 2030 Agenda was a significant advance in thinking about development that took the concept to the realities of the 21st century. The SDGs go beyond the MDGs’ focus on poverty to incorporate environment, economic growth and transformation, sustainable production and consumption, inequality, governance and partnerships — including a more important role for civil society and the private sector.

But the difference is more fundamental. The 2030 Agenda reconceptualizes development as a universal priority regardless of levels of income. While the MDGs promoted development as a simple process driven by investment and technology to meet basic needs, the SDGs are built on the premise that development is complex, multifaceted, unequal and prone to reversals, and that the goals must take into account the ecological limits of the earth and remedy systemic causes of poverty, environmental destruction, inequality and exclusion.

Thus the SDGs include both outcomes and policy change under “means of implementation.” While all 17 SDGs and 169 targets are individually important, they are also interrelated and should be viewed as a package. Though each goal has been debated for decades, it is the first time that they are all integrated into a single agenda.

The datacentric approach structures the review into silos of goals and targets and works against keeping sight of the broader concept and ambition as well as the most innovative elements of the agenda. As an IISD blog post on the High-Level Political Forum reflected: “Even a dedicated session on interlinkages did not give participants the chance to break down silos, hone down on specifics, or disentangle complexities, such as the finer differences between ‘coordination,’ ‘coherence,’ ‘interconnections’ and ‘interlinkages.’ Despite these limitations, delegations at the HLPF recognized again and again that the world’s new development agenda is integrated, interlinked, and ultimately indivisible.”

The point is not to reject the SDG indicator framework but to warn against over-reliance on data. SDG datasets should not be the only source of information to assess progress. Indicators are merely a representation of a social phenomenon, and not all social phenomenons can be translated into a single number. The SDGs as a concept is larger than the list of 17 goals, 169 targets and 232 indicators. Agenda 2030 needs a holistic review with proper attention to key matters of coherence, leaving no one behind and means of implementation.

Data and measurement are technical issues requiring expertise to ensure they are legitimate and reliable. They are objective in and of themselves, but the selection of which data to collect and analyze is a subjective decision. It is also a deeply political step. While data are essential, we also need to ask what they do not show and the trends that they mask.

This essay is adapted from a statement by the author as keynote speaker at the opening session of the UN High-Level Political Forum, July 20, 2017.


Sakiko Fukuda-Parr is a professor at The New School’s Julien J. Studley Graduate Program in International Affairs, where she specializes in development. From 1995 to 2004, she led the United Nations Development Program’s Development Report Office and headed the UNDP’s bureau in West Africa from 1992-1994. She started her career with the World Bank in the Young Professionals Program in 1973. She has written numerous books and has an M.A. from the University of Sussex; an M.A. and MALD from the Fletcher School of Diplomacy and Law at Tufts University; and a B.A., with honors, from Cambridge University.

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Warning: Too Much Reliance on Data Can Undermine the UN’s SDGs
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