_2. Existing tools for SDG benchmarking_

# **1. Introduction**

The UN 2030 Agenda and the Sustainable Development Goals (SDGs) are comprehensive and holistic in nature, as they include 17 goals and 169 targets covering economic, social, and environmental dimensions of development. The highly ambitious agenda stresses that all individuals shall collectively benefit from the fruits of development and that no one shall be left behind. In this context, policy makers face the challenge of translating the global agenda into feasible yet sambitious development plans and policies. Hence, there is a need to develop methodologies that can assist during different steps in this process: from data measurement and benchmarking, to prioritization of targets and monitoring of progress. Tailored to their individual national development plans and under the auspices of the global agenda, these methods should be transparent and replicable.

This paper presents a methodology that can assist policy makers to prioritize targets related to their SDG development plans. The methodology uses extensively a global database on SDG progress constructed mainly with data from the United Nations and the World Bank. The methodology proposes a set of metrics that indicate the probability of becoming an over‐achiever in a particular SDG indicator, a probability that is _conditional_ on progress on the rest of the SDGs.

The method is based on the notion that existing patterns of development outcomes can reveal information about the commonalities that exist in the _unobservable_ SDG‐ delivery mechanisms that exist within countries. It is natural to think that countries have a diversity of _capacities_ in the form of skills, culture, infrastructure, roads, natural resources, technology, institutions, systems of government, religion, etc. In addition, through a process of collective action, countries’ _SDG_ ‐delivery mechanisms are (intentionally and non‐intentially) created, and their effectiveness is reflected in the achievement of _observable_ development outcomes.

The approach used in this paper is largely based on the basic notions of economic complexity (Hartmann, Guevara, Jara‐Figueroa, Aristarán, & Hidalgo, 2015; Hausmann et al., 2013; Hausmann & Klinger, 2006; Hidalgo, Klinger, Barabasi, & Hausmann, 2007). Our hypothesis is that if a set of positive development outcomes are observed frequently co‐ existing across countries, then there are strong commonalities in the mechanisms of SDG‐ delivery that make possible these positive development outcomes. For instance, secondary and primary completion rates are two indicators that are strongly correlated – and it can clearly be seen that the first indicator is conditional on the later and that _some_ (but not all) of the capacities used to over‐perform in primary completion can be re‐used for completion in secondary education. _Our hypothesis simply implies that the probability of succeeding in an SDG target can be estimated conditionally on the observed progress on all other targets._ To assess this hypothesis, we built measures of SDG progress at the detailed indicator level, largely based on the methodology of Gable, Lofgren, & Osorio‐ Rodarte, (2015), and propose a set of metrics based on network theory and economic complexity to test our hypothesis. Examples of questions that will be addressed are as followed:

- How are the SDGs interconnected?

- What is the probability of over‐performing in a particular SDG, given progress in others?

- Does achievement in some targets tend to promote progress on others?

The paper begins in Section 2 by assessing recent efforts to create similar SDG‐related benchmark and scorecard tools. Section 3 discusses the compiled SDG data, its coverage, and caveats, and presents our methodological framework, which includes a metrics 2