1. Aid effectiveness in sustainable development: A multidimensional approach.
- Author
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Guerrero, Omar A., Guariso, Daniele, and Castañeda, Gonzalo
- Subjects
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ARTIFICIAL intelligence , *DEVELOPMENT economics , *PUBLIC spending , *ECONOMIC development - Abstract
• A new (artificial intelligence) method to estimate the impact of aid flows at a disaggregate level (per indicator per country) • A nuanced picture of aid effectiveness across 146 countries during 2000–2013 • A multidimensional account for the impact of aid flows through the Sustainable Development Goals (SDGs) across 70 + development indicators • A novel approach that takes into account the complex networks of interdependencies between development dimensions • A unique application using big data on aid flows • A new impact metric that accounts for the non-linear dynamics of development indicators • A theoretical backbone based on well-known aid-effectiveness drivers such as government expenditure, fungibility, ineffective public governance, incentive misalignments, and multi-level causal chains • Consistency with results from prior studies and validation through quantitative and qualitative evidence 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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