Agent Computing and AI to Achieve the 2030 Agenda: New Methods to Infer Policy Priorities from Open Fiscal Data and Sustainable Development Indicators

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How can we reach the Sustainable Development Goals (SDGs) by 2030? This is the most recurrent question in most international forums, and a central factor in how governments are formulating policy priorities around the world. However, how can we know if those priorities are conducive to the SDGs? Will developing countries repeat the same mistakes from adopting the Millennium Development Goals (regarded by many scholars as a failed agenda)? How can we improve the way in which governments formulate policy priorities? This project will harness novel data on public expenditure and development indicators, cutting-edge machine learning techniques, and state-of-the-art computational simulation methods to tackle these questions. In doing so, it will produce profound insights into how governments prioritise policy issues, and redraw the landscape of questions and methods that guide evidence-based policymaking.The project is structured in three pillars: (1) linking public expenditure data to SDGs, (2) identifying development indicators that are susceptible of direct policy interventions, and (3) modelling the process of policy prioritisation to assess development strategies. The first pillar builds on the growing movement of open fiscal data. The idea is to classify public expenditure programmes into the SDGs through deep learning. To train this classifier, I will employ a novel dataset from Mexico (unique in the world), in which government experts have assigned SDG labels to 4,000 expenditure programmes. This is the same technology used by Netflix to classify movies. Since hiring movie experts to categorise every movie is economically unfeasible, the company uses a sample of expert classifications, and exploits the texts describing the plots to train an algorithm and predict labels. In a similar way, I will exploit the texts describing Mexican expenditure programmes in order to assign SDG labels to unclassified data.The second pillar consists of identifying development indicators that are 'instrumental'. These are indicators susceptible of being intervened by specific policies that receive dedicated resources. For example, a vaccination campaign (a policy with resources) is designed to transform the indicator of incidence of measles (the indicator). Interestingly, there exist several development indicators that are not instrumental. For example, GDP per capita is a composite measure of various factors and no government has a specific policy to directly intervene on it. Thus, identifying instrumental indicators is key to understand and evaluate policy priorities, as governments only allocate resources to those development issues with policy instruments. I propose conducting an online survey across policymakers and experts who will be asked to identify instrumental indicators from a random sample. With the support of the UNDP and GIFT, this survey will be administered to UNDP functionaries and government officials around the world. Through this survey, I expect to classify approximately 100 to 150 development indicators.The third pillar builds on 1 and 2 in order to calibrate an agent-computing model of policy prioritisation. I have previously developed a similar model and validated it throughout various publications, for example, by estimating policy priorities, policy resilience, policy coherence, ex-ante policy evaluation, and the effectiveness of the rule of law. A distinctive feature of my model is that policy priorities (in the form of resource allocations across development indicators) emerge endogenously from an adaptive policymaking process that takes into account the complex network of interlinkages between SDGs (a central topic in the sustainability literature). Thus, these priorities can be defined over instrumental indicators, and the model can be calibrated to match the empirical expenditure patterns estimated in pillar 1. There is currently no tool that can achieve this.