The SDGs’ Implicit Theoretical Assumptions and Their Limitations

Authors: Erica Di Ruggiero, University of Toronto

In 2015, the United Nations released the 17 Sustainable Development Goals (SDGs) with the aim of peace and prosperity for people and the planet, and a commitment to tackle global social, economic, and health inequities. Unlike their predecessor, the Millennium Development Goals (MDGs), this 2030 Agenda explicitly calls upon all nations (and not just low- and middle-income countries) to leave no one behind. The 17 SDGs include 169 targets and 232 indicators to monitor progress toward the achievement of social, economic, and environmental goals before the end of 2030. The Sustainable Development Solutions Network (SDSN) created the SDG Index to benchmark country performance across the SDGs. Yet, this preoccupation with measuring progress and tracking indicators is embedded in assumptions and premises that may not be widely shared, leaving other conceptions, interpretations, and perspectives out of the picture. Without reliable and meaningful contextually sensitive data, we cannot measure complex problems and solutions, track progress, and course correct. Not surprisingly, the MDGs’ technocratic approach to measurement was carried through into the SDGs. Is this a good approach? What might it be missing?

Fundamentally, this largely quantitative measurement enterprise may inadvertently convey the impression that progress is not without contestation and tensions within and across goals, and between targets and related indicators. An example I have used in previous scholarship reflects on the inherent tension within SDG 8 (“to promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all’.) ^[1] The goal states the relationship between employment and economic growth without really calling into question the potential consequences of increased growth on the achievement of decent work. This framing does not sufficiently question the systems of power and knowledge structures that underlie this relationship and reproduce precarity through the continuous neoliberal restructuring of labour markets, and weakening social protections

Let’s take another example related to SDG 5 (gender equality). The relationships between gender (SDG 5), social class, race/ethnicity, and other dimensions of inequalities run the risk of being eclipsed in their implementation if an intersectional approach to measurement is not adopted. As it’s currently framed, SDG 5 has been critiqued for adopting a binary conceptualization of gender and perpetuating forms of gender erasure. Yet gender conceptualized as a social construct shaped by social norms and power relations must be measured through multiple contextually sensitive indicators, many of which may be proxies for gender (in)equality. This is where theory(ies) and conceptual frameworks such as poststructural feminism and intersectionality, if chosen and applied well, have an explanatory potential to deepen understanding and interpretation of such complex phenomena.

Efforts to assess progress against an overwhelming number of sub-goals, targets, and indicators for each of the SDGs may also thwart the meaningful measurement of collective global impact across SDGs. SDG indicators have been criticized based on data availability and the reporting burden placed on countries, especially those with limited capacity. Conversely, indicators with extensive data availability will inevitably skew the construction of what is considered “success” toward achieving progress on the SDGs at global and country levels. Are there indicators that should be “retired” from use because they are diverting unnecessary attention and resources away from what may be even more important to capture, or are they excluding marginalized populations? What additional kinds of capacity are needed to shift measurement away from being a purely technocratic exercise?

To inform future dialogue on SDG measurement, we need to also engage in reflections on what phenomena we might not be measuring at all, or well enough. First, are we predominantly measuring various “problems” (e.g., unemployment rates) in reductionist ways, unduly focusing on problem-based measurement and therefore not shifting measurement toward intersectoral policy, program, governance, and system-level solutions to the problem?

Second, research needs to be further oriented toward valuing and measuring the co-benefits, trade-offs, and synergies across SDGs rather than measuring only siloed impacts related to individual SDGs. For example, can the health co-benefits of climate change mitigation efforts be more meaningfully captured by treating “ecosystems and social systems as interdependent and mutually reinforcing”? ^[2]

Third, context matters. Context broadly refers to a “local mix of conditions and events … which characterise open systems … whose unique confluence in time and space selectively activates …  causal powers … in a chain of reactions that may result in very different outcomes depending on the dynamic interplay of conditions and mechanisms over time and space.”^[3] In-depth measurement of context needs to be embraced and not controlled for, given our choice of methods. Context should not be “blamed for intervention failure” or lack of intervention fidelity. Better deployment of qualitative methods of inquiry such as empirical approaches to narrative storytelling and case studies together with quantitative methods is needed to investigate the richness of context.

Finally, measurement is inherently political, and its governance is key. Existing governance structures can, however, further perpetuate epistemic injustices and so these structures need to better contend with issues of power and privilege, including whose voices are represented, and excluded. As we approach 2030, we need to reimagine how decisions are made about targets and indicators to more meaningfully measure progress and impact toward SDGs and future goals to ensure no one is actually left behind.

“As we approach 2030, we need to reimagine how decisions are made about targets and indicators to more meaningfully measure progress and impact toward SDGs and future goals to ensure no one is actually left behind.”

– Erica Di Ruggiero, University of Toronto

References

^[1] Erica Di Ruggiero, “Global Health Governance in the Sustainable Development Goal Era,” in Global Health and Global Health Ethics, 2^nd ed. edited by Solomon Benatar and Gillian Brock (Cambridge University Press, 2021).

^[2]  Éloi Laurent, Alessandro Galli, Fabio Battaglia, Giorgia Dalla Libera Marchiori, and Lorenzo Fioramonti, “Toward Health-environment Policy: Beyond the Rome Declaration,” Global Environmental Change 72 (2022). https://www.sciencedirect.com/science/article/abs/pii/S0959378021001977

^[3]  Peter Craig, Erica Di Ruggiero, Katherine L. Frohlich, Eric Mykhalovskiy, and Martin White, “Taking Account of Context in Population Health Intervention Research: Guidance for Producers, Users and Funders of Research,” Canadian Institutes of Health Research-National Institute for Health Research on behalf of the Canadian Institutes of Health Research (CIHR)–National Institute for Health Research (NIHR), April 2018.  https://www.ncbi.nlm.nih.gov/books/NBK498645/

About the Faculty Mentor Paper Series

This paper is part of the Reach Alliance faculty reflection series, Reimagining the Future of Sustainable Development, in response to Mariana Prado’s Sustainable Development Goals: The End of Theory? Featuring contributions from leading scholars across the Reach Alliance global academic consortium, the series opens a timely dialogue on the evolving role of universities in shaping the future of sustainable development theory and practice. Developed as part of Reach’s commitment to advancing research-to-impact and fostering interdisciplinary collaboration, these reflections aim to engage higher education professionals in shaping the future of the Sustainable Development Goals.