Estimating networks of sustainable development goals

Ospina-Forero, Luis; Castañeda, Gonzalo; Guerrero, Omar

doi:10.1016/j.im.2020.103342

Cited by 35 publications

(32 citation statements)

References 45 publications

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“…The context specificity of the network means that it should be estimated for individual countries, without pooling cross‐national data. Ospina‐Forero et al (2020) review several methods that can be used for this purpose. For the analysis presented here, we employ the networks that Castañeda et al (2018) have already estimated.…”

Section: Methodsmentioning

confidence: 99%

“…the United Nations Development Programme (UNDP) and the World Bank) extended their agenda from PCD to policy coherence for sustainable development in 2014, they aimed at integrating economic, social and environmental dimensions of development across all levels of domestic and international policy‐making through their complex interdependencies. Unfortunately, the demand for identifying interactions between policies has exposed severe technical limitations in qualitative approaches; in particular, their heavy dependence on expert knowledge in highly specific fields, which precludes scalability and introduces conflicting biases (see Ospina‐Forero et al (2020) for a review). This makes it an essential endeavour to develop systematic, quantitative and scalable frameworks.…”

Section: On the Coherence Of Policy Prioritiesmentioning

confidence: 99%

“…Measures of policy coherence aiming to guide real‐world policies should consider country‐specific spillover networks. Recent developments in network science make it possible to estimate the complex structure of synergies and trade‐offs from highly restrictive datasets (see Ospina‐Forero et al, 2020, for a review). This does not mean that the network alone is enough to assess policy coherence, since the data from which it is typically built (development indicators) do not account for implementation inefficiencies.…”

Section: On the Coherence Of Policy Prioritiesmentioning

confidence: 99%

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Quantifying the coherence of development policy priorities

Guerrero

Castañeda

2020

Development Policy Review

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Over the last 30 years, the concept of policy coherence for development has received especial attention among academics, practitioners and international organizations. However, its quantification and measurement remain elusive. To address this challenge, we develop a theoretical and empirical framework to measure the coherence of policy priorities for development. Our procedure takes into account the country-specific constraints that governments face when trying to reach specific development goals. Hence, we put forward a new definition of policy coherence where context-specific efficient resource allocations are employed as the baseline to construct an index. To demonstrate the usefulness and validity of our index, we analyze the cases of Mexico, Korea and Estonia, three developing countries that, arguably, joined the OECD with the aim of coherently establishing policies that could enable a catch-up process. We find that Korea shows significant signs of policy coherence, Estonia seems to be in the process of achieving it, and Mexico has unequivocally failed.Furthermore, our results highlight the limitations of assessing coherence in terms of naive benchmark comparisons using development-indicator data. Altogether, our framework sheds new light in a promising direction to develop bespoke analytic tools to meet the 2030 agenda.

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Section: Methodsmentioning

confidence: 99%

Section: On the Coherence Of Policy Prioritiesmentioning

confidence: 99%

Section: On the Coherence Of Policy Prioritiesmentioning

confidence: 99%

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Quantifying the coherence of development policy priorities

Guerrero

Castañeda

2020

Development Policy Review

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“…3 This is so because policy interventions take place at a micro-level, while development indicators are typically macro-level variables. Thus, in the absence of highly granular multilevel data, vertical causal mechanisms cannot be properly addressed by traditional statistical tools (Casini and Manzo, 2016;Ospina-Forero et al, 2020). Instead, a production account of causation is more adequate and, once again, Complexity Science has developed the relevant tools.…”

Section: On the Complexity Of Developmentmentioning

confidence: 99%

“…This is consistent with conditional dependencies. A further discussion of why SDG networks-by themselves-cannot capture causal relations is provided by Ospina-Forero et al (2020).…”

Section: Sdg Network and Spilloversmentioning

confidence: 99%

Policy priority inference: A computational framework to analyze the allocation of resources for the sustainable development goals

Guerrero

Castañeda

2020

Data & Policy

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We build a computational framework to support the planning of development and the evaluation of budgetary strategies toward the 2030 Agenda. The methodology takes into account some of the complexities of the political economy underpinning the policymaking process: the multidimensionality of development, the interlinkages between these dimensions, and the inefficiencies of policy interventions, as well as institutional factors that promote or discourage these inefficiencies. The framework is scalable and usable even with limited publicly available information: development-indicator data. However, it can be further refined as more data becomes available, for example, on public expenditure. We demonstrate its usage through an application for the Mexican federal government. For this, we infer historical policy priorities, that is, the non-observable allocations of transformative resources that generated past changes in development indicators. We also show how to use the tool to assess the feasibility of development goals, to measure policy coherence, and to identify accelerators. Overall, the framework and its computational tools allow policymakers and other stakeholders to embrace a complexity (and a quantitative) view to tackle the challenges of the Sustainable Development Goals.

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What Data and Evidence Can You Build System Maps From?

Barbrook-Johnson

Penn

2022

Systems Mapping

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This chapter takes a step back from individual systems mapping methods and considers what evidence and data we might use to underpin the design of system maps. It presents four broad types—data from participatory processes, qualitative data, existing evidence, and quantitative data—and outlines the pros and cons of each, considers how you can use them, and makes a call, ideally, for using them in combination and in creative ways.

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Estimating networks of sustainable development goals

Cited by 35 publications

References 45 publications

Quantifying the coherence of development policy priorities

Quantifying the coherence of development policy priorities

Policy priority inference: A computational framework to analyze the allocation of resources for the sustainable development goals

What Data and Evidence Can You Build System Maps From?

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