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

Abstract

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.

Does Expenditure in Public Governance Guarantee Less Corruption? Non-linearities and Complementarities of the Rule of Law

“…We develop an agent-computing model along the lines of Guerrero and Castañeda (2021), Guerrero and Castañeda (2020), Castañeda et al (2018). The model consists of a political economy game where the central authority (the government) sets policy priorities in terms of resource allocations, while public servants (the agents) are in charge of implementing such policies.…”

Subnational Sustainable Development: The Role of Vertical Intergovernmental Transfers in Reaching Multidimensional Goals

“…The model is based on Guerrero and Castañeda (2021b), who study the feasibility of the SDGs worldwide. Guerrero and Castañeda (2020) provide a thorough motivation of a closely related model, and an application to the national case of Mexico. Earlier works using similar models can be traced back to , who focus on socioeconomic indicators.…”

“…Finally, we perform a manual classification of the indicators into 'instrumental' and 'collateral'. This is a classification created in earlier applications of related models, and allows identifying those indicators that can be directly impacted by existing government programs (instrumental), and those that are too aggregate to receive such directed impacts (collateral) (Guerrero and Castañeda, 2020). Policy priorities can only be defined over instrumental indicators, so collateral ones improve through spillover effects and inertial factors.…”

“…This appendix is a transcription of the model explanation provided in Guerrero and Castañeda (2021b). This explanation spares the reader from interpretations, motivations, and references to the literature, since these can be found in Guerrero and Castañeda (2020).…”

“…J o u r n a l P r e -p r o o f fashion, we allow the topology of each state's network to exert an influence on the simulation's outcomes. and Guerrero and Castañeda (2020) show that removing the spillovers from the analysis results in a lower variation of inefficiency across policy issues.…”