Budgeting for Sdgs: Quantitative Methods to Assess the Potential Impacts of Public Expenditure

Abstract

Using a novel large-scale dataset that links thousands of expenditure programs to the Sustainable Development Goals for over a decade, we analyze the impact of public expenditure on more than 100 different development indicators. Contrary to the single-dimensional view of evaluating expenditure in terms of overall economic growth, we take a multi-dimensional approach. Then, we assess the effectiveness of three quantitative methods for capturing expenditure effects on development: (1) regression analysis, (2) machine learning techniques, and (3) agent computing. We find that, under the existing data, approaches (1) and (2) are unable to disentangle sector-specific effects, which is consistent with results in previous empirical research. In contrast, by applying a micro-founded agent-computing model of policy prioritization, we can provide empirical evidence about potential impacts and bottlenecks across a high-dimensional policy space. Our findings suggest that, in the discussion of budgeting for SDGs, one should be careful about the `hype' for purely data-driven approaches and consider alternative methods that are richer in terms of incorporating explicit causal mechanisms and being scalable to a large set of indicators.

Keywords: Public Finance, Sustainable Development Goals, Regression Analysis, Machine Learning, Agent-based Models.

JEL Classification: C54, C63, H50, O23,Q01