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

40 Pages Posted: 10 May 2022

See all articles by Daniele Guariso

Daniele Guariso

The Alan Turing Institute

Omar A Guerrero

The Alan Turing Institute

Gonzalo Castañeda Ramos

University of the Americas, Puebla - Department of Economics

Date Written: May 4, 2022

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

Suggested Citation

Guariso, Daniele and Guerrero, Omar A and Castañeda Ramos, Gonzalo, Budgeting for Sdgs: Quantitative Methods to Assess the Potential Impacts of Public Expenditure (May 4, 2022). Available at SSRN: https://ssrn.com/abstract=4100793 or http://dx.doi.org/10.2139/ssrn.4100793

Daniele Guariso (Contact Author)

The Alan Turing Institute ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

Omar A Guerrero

The Alan Turing Institute ( email )

96 Euston Road
London, NW1 2DB
United Kingdom

Gonzalo Castañeda Ramos

University of the Americas, Puebla - Department of Economics ( email )

Sta. Catarina Martir
Cholula, Puebla 72820 72810
Mexico

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
95
Abstract Views
428
Rank
501,346
PlumX Metrics