Publications

Three features make sustainable development very challenging to governments and analysts: multidimensionality, interconnectedness, and complexity. It is multidimensional because one needs to consider a wide variety of policy issues. It is interconnected because these many dimensions are interdependent. It is complex because the macro-level development data used in performance evaluation emerges from micro-level interventions, so one needs to consider vertical causal channels. Despite the growing availability of government spending data across various policy dimensions, approaches developed in econometrics and machine learning offer limited solutions because expenditure data and development indicators are not big enough or sufficiently detailed to meet the technical requirements of these methods.This book develops a novel computational approach to handle these challenging features. It builds on a well-founded socioeconomic theory and provides a parsimonious modelling framework to explain the link between public expenditure and development. It walks the reader through a detailed construction of the analytic toolkit and, then, acquaints them with a diverse set of empirical applications using publicly available data.

What is the impact of international aid? We answer this question by linking disaggregated aid-flows data to a large set of indicators classified into the Sustainable Development Goals (SDGs). Since such linkage is not perfect (due to the nature of the data), we deploy an artificial intelligence model of the causal process through which changes in aid flows contribute to the dynamics of individual indicators. The model accounts for salient features of real-world development such as multidimensionality, complex interconnections between indicators, heterogeneous aid-to-expenditure ratios, rationally-bounded bureaucracies, fungibility, and the temporal structure of contemporary aid flows across development dimensions. The model does not require cross-country pooled data, so we calibrate its parameters for each of the 146 aid-recipient countries in our sample, preserving important contextual information of each nation. By producing counterfactual simulations where aid is removed, we obtain nuanced estimates of the impact of international assistance during the first decade of the 21st century, at the level of each country, SDG, and indicator. We validate our results using a sector-specific study with similar–but more aggregate–findings. Such a large and detailed picture of the multidimensional impact of aid has not been documented before.

The “budgeting for SDGs”–B4SDGs–paradigm seeks to coordinate the budgeting process of the fiscal cycle with the Sustainable Development Goals (SDGs) set by the United Nations. Integrating the Goals into Public Financial Management systems is crucial for an effective alignment of national development priorities with the objectives set in the 2030 Agenda. Within the dynamic process defined in the B4SDGs framework, the step of SDG budget tagging represents a precondition for subsequent budget diagnostics. However, developing a national SDG taxonomy requires substantial investment in terms of time, human, and administrative resources. Such costs are exacerbated in least-developed countries, which are often characterized by a constrained institutional capacity. The automation of SDG budget tagging could represent a cost-effective solution. We employ well-established text analysis and machine-learning techniques to explore the scope and scalability of automatic labelling budget programs within the B4SDGs framework. The results show that, while our classifiers can achieve great accuracy, they face limitations when trained with data that is not representative of the institutional setting considered. These findings imply that a national government trying to integrate SDGs into its planning and budgeting practices cannot just rely solely on AI tools and off-the-shelf coding schemes. Our results are relevant to academics and the broader policymaker community, contributing to the debate around the strengths and weaknesses of adopting computer algorithms to assist decision-making processes.

We develop a bottom-up causal framework to study the impact of public spending on high-dimensional and interdependent policy spaces in the context of socioeconomic and environmental development. Using data across 140 countries, we estimate the indicator-country-specific development gaps that will remain open in 2030. We find large heterogeneity in development gaps, and non-linear responses to changes in the total amount of government expenditure. Importantly, our method identifies bounds to how much a gap can be reduced by 2030 through sheer increments in public spending. We show that these structural bottlenecks cannot be addressed through expenditure on the existing government programs, but require novel micro-policies intended to affect behaviors, technologies, and organizational practices. One particular set of bottlenecks that stands out relates to the environmental issues contained in the sustainable development goals 14 and 15.

From a public finance point of view, achieving sustainable development hinges on two critical factors: the subnational implementation of public policies and the efficient allocation of resources across regions through vertical intergovernmental transfers. We introduce a framework that links these two mechanisms for analyzing the impact of reallocating federal transfers in the presence of regional heterogeneity from development indicators, budget sizes, expenditure returns, and long-term structural factors. Our study focuses on the case of Mexico and its 32 states. Using an agent-based computational model, we estimate the development gaps that will remain by the year 2030, and characterize their sensitivity to changes in the states’ budget sizes. Then, we estimate the optimal distribution of federal transfers to minimize these gaps. Crucially, these distributions depend on the specific development objectives set by the national government, and by various interdependencies between the heterogeneous qualities of the states. This work sheds new light on the complex problem of budgeting for the Sustainable Development Goals at the subnational level, and it is especially relevant for the study of fiscal decentralization from the expenditure point of view.

An increasing number of researchers and practitioners advocate for a systemic understanding of the Sustainable Development Goals (SDGs) through interdependency networks. Ironically, the burgeoning network-estimation literature seems neglected by this community. We provide an introduction to the most suitable estimation methods for SDG networks. Building a dataset with 87 development indicators in four countries over 20 years, we perform a comparative study of these methods. We find important differences in the estimated network structures as well as in synergies and trade-offs between SDGs. Finally, we provide some guidelines on the potentials and limitations of estimating SDG networks for policy advice.

Corruption is an endemic societal problem with profound implications in the development of nations. In combating this issue, cross-national evidence supporting the effectiveness of the rule of law seems at odds with poorly realized outcomes from reforms inspired in the academic literature. This paper provides an explanation for such contradiction. By building a computational approach, we develop three methodological novelties into the empirical study of corruption: (1) modeling government expenditure as a more adequate intervention variable than traditional indicators, (2) generating large within-country variation by means of bottom-up simulations (instead of cross-national data pooling), and (2) accounting for all possible interactions between covariates through a spillover network. Our estimates suggest that, the least developed a country is, the more difficult it is to find the right combination of policies that lead to reductions in corruption. We characterize this difficulty through a rugged landscape that governments navigate when changing the total budget size and the relative expenditure towards the rule of law. Importantly our method helps identifying the—country-specific—policy issues that complement the rule of law in the fight against corruption.

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.

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.

We provide two methodological insights on ex ante policy evaluation for macro models of economic development. First, we show that the problems of parameter instability and lack of behavioral constancy can be overcome by considering learning dynamics. Hence, instead of defining social constructs as fixed exogenous parameters, we represent them through stable functional relationships such as social norms. Second, we demonstrate how agent computing can be used for this purpose. By deploying a model of policy prioritization with endogenous government behavior, we estimate the performance of different policy regimes. We find that, while strictly adhering to policy recommendations increases efficiency, the nature of such recipes has a bigger effect. In other words, while it is true that lack of discipline is detrimental to prescription outcomes (a common defense of failed recommendations), it is more important that such prescriptions consider the systemic and adaptive nature of the policymaking process (something neglected by traditional technocratic advice).

This paper studies the resilience of public policies that governments design for catalyzing economic development. This property depends on the extent to which behavioral heuristics and spillover effects allow policymakers to attain their original goals when a particular policy cannot be funded as originally planned. This scenario takes place, for example, when unanticipated events such as natural disasters or political turmoil obstruct the use of resources to advance certain policy issues, e.g., infrastructure or labor reforms. Here, we analyze how the adaptive capacity of the policy-making process generates resilience in the face of disruptions. In order to estimate the allocation of resources across policies, we employ a computational model that accounts for diverse social mechanisms, for example, coevolutionary learning and network interdependencies. In our simulations, we use a data set of 117 countries on 79 development indicators over an 11-year period. Then, we calculate a resilience score corresponding to each development indicator via counter-factual analysis of policy disruptions. Next, we assess whether some development strategies produce resilient/fragile policy profiles. Finally, by studying the relationship between policy resilience and policy priority, we determine which issues are bottlenecks to economic development.

Determining policy priorities is a challenging task for any government because there may be, for example, a multiple objectives to be simultaneously attained, a multidimensional policy space to be explored, inefficiencies in the implementation of public policies, interdependencies between policy issues, etc. Altogether, these factors generate a complex landscape that governments need to navigate in order to reach their goals. To address this problem, we develop a framework to model the evolution of development indicators as a political economy game on a network. Our approach accounts for the –recently documented– network of interactions between policy issues, as well as the well-known political economy problem arising from budget assignment. This allows us to infer not only policy priorities, but also the effective use of resources in each policy issue. Using development indicators data from more than 100 countries over 11 years, we show that the country-specific context is a central determinant of the effectiveness of policy priorities. In addition, our model explains well-known aggregate facts about the relationship between corruption and development. Finally, this framework provides a new analytic tool to generate bespoke advice on development strategies.

En este reporte se realiza un análisis de la asignación presupuestaria de los ODS para la República de Colombia a partir de una metodología novedosa: la Inferencia de Prioridades de Política (IPP), la cual hace uso de un modelo computacional basado en agentes. Desde el punto de vista teórico, el modelo describe el problema de presupuestación que existe entre la autoridad central, que distribuye los recursos públicos, y los distintos servidores públicos, que tienen el mandato de usarlos con el fin de mejorar el des- empeño de los indicadores. En esta formulación se plantea que los objetivos de los servidores no necesariamente coinciden con los de la autoridad central, lo que da lugar a un problema de economía política, al dispendio de recursos, y a la necesidad de implementar mecanismos que pro- curen la buena gobernanza.

En este reporte se realiza un análisis de la presupuestación de los ODS para Bogotá D.C. a partir de una metodología novedosa, la Inferencia de Prioridades de Política (IPP), y de un modelo computacional basado en agentes. Desde el punto de vista teórico, el modelo plantea el problema de presupuestación que existe entre la autoridad central, que distribuye los recursos públicos, y los distintos servidores públicos, que tienen el mandato de usarlos con el fin de mejorar el desempeño de los indicadores. En esta formulación se plantea que los objetivos de los servidores no necesariamente coinciden con los de la autoridad central, lo que da lugar a un problema de economía política, al dispendio de recursos, y a la necesidad de implementar mecanismos de gobernanza.

As part of the Decade of Action (2020-2030), the United Nations system began im- plementing the global initiative to strengthen national financing frameworks for the SDGs, known as Integrated National Financing Frameworks or INFF. A significant part of the analysis of all sources of financing for the SDGs is the public budget. This document presents guidelines for (i) aligning public budgets (and development coo- peration) to the SDGs, and (ii) presents relevant inputs for developing local SDG ta- xonomies to explore public budget and private sector contribution to SDG Financing. The guidelines are presented in a step-by-step way to aid users in tailoring to local circumstances the analysis of SDG Financing and are based on the implementation of SDG budget tagging by the INFF-Colombia Joint Program.

This chapter presents the Policy Priority Inference model. It describes how this model, through the identification of positive and negative spillovers between policy dimensions within and between countries, as well as agent- based modelling, can inform policy and budgeting decision-making processes to accelerate development. It also describes how the model, developed by academia, was adapted to the SDGs and piloted with real- world data in Mexico. Building on the outcomes of the pilot project, the chapter reflects on the various historical and prospective analyses the tool can generate to help governments make evidence-based decisions to optimise progress on the SDGs.

El choque exógeno provocado por la pandemia de el COVID-19 ha hecho que la sociedad mexiquense, y la humanidad en su conjunto, tengan que enfrentar importantes retrocesos en materia socioeconómica que requieren ser encauzados mediante políticas públicas pertinentes y basadas en evidencia. En particular, la severidad del problema económico causado por las políticas de ‘distanciamiento social y confinamiento’ para abatir la curva de contagios, hace imprescindible la reasignación de los recursos presupuestales en comparación con el escenario pre contingencia. Ante este reto, la gran interrogante de los gobiernos estatales, como el del Estado de México, consiste en determinar cuáles deben ser las prioridades de política que ayudarían a restablecer la normalidad en el mediano plazo, o bien a adoptar una trayectoria de desarrollo que se ajuste a un nuevo entorno.

El segundo documento es una aplicación para el caso de México. En éste, se describen los indicadores utilizados y se muestran los resultados de los análisis retrospectivo y prospectivo. En cuanto al análisis retrospectivo, se da cuenta de las prioridades inferidas a nivel federal. Prospectivamente, se evalúan diferentes modos de desarrollo. Por ejemplo, se evalúa la factibilidad de que México alcance los modos de desarrollo de cada uno de los países que integran la Organización para la Cooperación y el Desarrollo Económicos (OCDE). Es decir, utilizando los valores de los indicadores de los países de la OCDE como metas, se evalúa qué tan factible y en cuánto tiempo México podría alcanzarlos. Adicionalmente, se lleva a cabo un análisis de aceleradores, en donde se recomienda priorizar nodos con efectos multiplicadores positivos en las diferentes dimensiones del desarrollo.

El tercer documento de esta serie es una aplicación para el caso subnacional de México que da cuenta de las prioridades retrospectivas de las entidades federativas y de los resultados del análisis de factibilidad de metas aportadas por funcionarias y funcionarios públicos de cinco estados: Chiapas, Jalisco, Estado de México, Nuevo León y Yucatán.

En el Reporte metodológico, primero de la serie, se describe el tratamiento que se hace de los datos para su uso en el modelo, las ecuaciones del juego conductual de economía política y los algoritmos del modelo. Asimismo, se describe a detalle el modelaje del comportamiento de la burocracia y de los incentivos que enfrentan la autoridad central y el funcionariado implementador de programas.

The United Nations 2030 Agenda of the Sustainable Development Goals (the SDGs) has turned into a useful framework to coordinate international efforts across a variety of development issues. In our experience from academic and policy work, we find that the SDGs serve an - often not obvious - function of crucial importance to technical teams working on PFMs, thus enabling the application of Artificial Intelligence (AI) to understand the impact of budgeting strategies and policy priorities. As an interface between PFM and AI, the SDGs offer a tremendous potential to fully exploit existing datasets on public spending and development indicators; a potential that traditional statistical and benchmarking analyses are unable to tap into.

Since 2015, national and local governments around the world have been constructing new development indicators to track their progress towards the United Nations 2030 Agenda of the Sustainable Development Goals (SDGs). In the international community, this has become a common practice inherited from previous global initiatives such as the Human Development Report and the Millennium Development Project. Development indicators are typically used to –ex-post– evaluate outcomes. However, governments could take greater advantage of them, for example, to –ex-ante– understand how some policy priorities are more or less effective in reaching specific development goals. This particular problem, often referred to as policy prioritisation, is especially challenging due to complex interconnections between policy issues, uncertainties in the policymaking process, and a variety of incentives and behaviours that give place to inefficiencies in the use of public funds.

Since their establishment in 2015, the Sustainable Development Goals (SDGs) have become the leading international agenda to promote social, economic and environmental development. The 2030 Agenda has encouraged the construction of numerous development indicators through which governments can track and evaluate their progress towards the SDGs.

Desde su establecimiento en 2015, los Objetivos de Desarrollo Sostenible (ODS) se han convertido en la agenda internacional lí­der para promover el desarrollo social, económico y ambiental. La Agenda 2030 ha fomentado la construcción de numerosos indicadores de desarrollo a través de los cuales los gobiernos pueden evaluar y darle seguimiento al progreso hacia los ODS.