National scorecards alone cannot capture sustainable development; stronger global metrics and data systems are essential for collective progress
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As 2030 approaches, a critical milestone for the global SDG agenda is on the horizon. This agenda has contributed to the evolving discourse on how progress can be evaluated holistically. The SDG initiative relies on benchmarks defining optimal performance at the national level. Assessment of countries’ overall performance, as well as their performance across the indicators subsumed under the 17 SDG goals, utilises a four-colour-band framework—a traffic-light system classifying countries based on performance. These colours represent relative distances from relevant benchmarks (the goalposts). The SDG Index underlies this classification system. Although this national-level appraisal of progress is necessary, there is merit in considering whether it is sufficient.
One of the dimensions of the ‘sufficient’ nature of SDG measurement is informed by the recognition that sustainable development is, in many ways, a global public good. Climate action, pandemic risk mitigation, peace and international stability are obvious dimensions of sustainable development as a global public good. The recent COVID pandemic highlighted the collective risk confronting global health systems. Focusing on the intricate relationship between these dimensions and SDG goals reiterates the framing of SDGs as a global public good. Inputs generated by a nuanced understanding of this relationship can be valuable for multilateralism and global governance.
One of the dimensions of the ‘sufficient’ nature of SDG measurement is informed by the recognition that sustainable development is, in many ways, a global public good.
Given the ‘public good’ nature of SDGs at the global level, is it enough to measure progress merely at the national level? Is a global aggregate metric of sustainable development warranted? Even if the relevant institutions aspire to construct such a metric, it would require a sophisticated and robust data infrastructure. The actual demand for the nature and scale of data would be ascertained by the mathematical definition of this metric and its relationship with the national-level SDG index.
A simple or weighted average oversimplifies the complex interactions underlying sustainable development trajectories across countries and goals. Such an average is therefore unlikely to capture the core of a global composite metric. However, can a measure be conceived that, in broad terms, balances individual welfare, the significance of country-level contributions to global public goods and transnational gains (such as climate change mitigation and adaptation and biodiversity conservation), as well as development gains accruing to sovereign states (such as peace and political stability)? These elements point to a broader and coherent set of first principles on which a global SDG aggregate would need to be anchored.
Over the decade since the launch of the 2030 Agenda, the adequacy of data infrastructure has improved significantly. In 2017, OECD countries had measurement capacity that allowed, on average, assessment of 57 percent of SDG targets. However, this masked wide variation across goals and countries, with coverage falling below 40 percent in several cases. Currently, measurement capacity allows data coverage of at least 85 percent of targets in some SDGs.
Before constructing a global aggregate of sustainable development, the most immediate objective is near-complete data coverage of targets across goals and countries. This measurement gap adversely affects countries that are already struggling with development. Data availability is roughly positively associated with levels of development. SDG monitoring and related global governance initiatives are a classic case of underproduction of a public good.
Before constructing a global aggregate of sustainable development, the most immediate objective is near-complete data coverage of targets across goals and countries.
If multilateralism pays greater attention to data infrastructure in these countries, the gains are likely to be more than proportionate to the investments made. Investing in SDG monitoring would involve strengthening national statistical systems, household surveys, administrative data structures, geospatial and satellite monitoring, international databases, metadata standardisation and data-sharing platforms. Such investments generate positive externalities beyond governance, benefiting the private sector, academia and civil society. This is one dimension of the more-than-proportionate returns to investment. Another aspect is the steep development deficits characterising less-developed regions, which such investments can help bridge.
Other concerns around data quality and availability include the timeliness of data collection and publication, the need for a more nuanced, integrated, and non-fragmented operationalisation of targets, and greater granularity in SDG monitoring to address sectoral data gaps.
Financing SDG data infrastructure is acknowledged as a priority by IFIs and development organisations. However, it is often relegated to a secondary concern relative to socioeconomic priorities such as education, health, and food security. The trade-off between financing SDG monitoring architecture and spending on these primary needs is essentially a short-term phenomenon. In the long run, investment in data infrastructure is complementary to such spending. While this trade-off can be understood as a problem of constrained optimisation under finite resources, it continues to pose a challenge for the financing of sustainable development.
The construction of a global measure of sustainable development is more than a statistical enterprise, and its success depends on addressing questions of institutional capacity and political agreement. This, in turn, points to the capacity of contemporary multilateralism to meet the demands of measuring sustainable development at a global scale. Currently, SDG 17 serves as a partial measure of such capacity regarding financial assistance, collaboration, and technology transfer, among other dimensions. However, is it comprehensive enough to capture the complexities of the relationship between sustainable development and multilateralism? As it stands, the internal coherence of the targets subsumed under SDG 17 is weak, with a Cronbach’s Alpha of just 0.27[1]. It may therefore be beneficial to conceive of ‘multilateral capacity’ as the latent variable that underlies and binds the targets of SDG 17, and to allow it to guide a rethinking of the goal.
A global aggregate measure of sustainable development could aspire to replace this myopic understanding of progress and address some of its limitations.
As 2030 approaches, building a parsimonious meta-framework that provides normative guidance for strengthening data capacity and multilateral cooperation in pursuit of sustainable development may prove valuable. Such a meta-framework may also lay the foundation for developing a global measure of sustainable development. Beyond this, such a measure will require technical work on definitions, performance benchmarks, weighting methods, data standardisation, and reporting protocols.
Global economic growth, or GDP, continues to dominate collective attention as a measure of human progress. A global aggregate measure of sustainable development could aspire to replace this myopic understanding of progress and address some of its limitations.
The burden of crossing the finish line on the SDGs cannot be borne solely by national scorecards. It requires a vision for collective action supported by a global assessment framework.
Renita D’Souza is a Consultant-Data Analysis with ORF.
[1] Guillaume Lafortune, Grayson Fuller, Jorge Moreno, Guido Schmidt-Traub, and Christian Kroll, SDG Index and Dashboards: Detailed Methodological Paper (SDSN, 2018), p. 26.
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Dr. Renita D’Souza is Consultant- Data Analysis with ORF. She is a data researcher with experience in data-driven analysis, statistical interpretation, and research-oriented problem solving. Her ...
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