Automatic SDG Budget Tagging: Building Public Financial Management Capacity through Natural Language Processing

26 Pages Posted: 10 Mar 2023

See all articles by Daniele Guariso

Daniele Guariso

The Alan Turing Institute

Omar A Guerrero

The Alan Turing Institute

Gonzalo Castaneda

Centro de Investigación y Docencia Económica

Date Written: March 6, 2023

Abstract

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.

Keywords: Public Financial Management, Sustainable Development Goals, Natural Language Processing, Machine Learning, International Development

JEL Classification: C45, H61, H83, O20, Z18

Suggested Citation

Guariso, Daniele and Guerrero, Omar A and Castaneda, Gonzalo, Automatic SDG Budget Tagging: Building Public Financial Management Capacity through Natural Language Processing (March 6, 2023). Available at SSRN: https://ssrn.com/abstract=4379856 or http://dx.doi.org/10.2139/ssrn.4379856

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 Castaneda

Centro de Investigación y Docencia Económica ( email )

Carretera Mexico-Toluca 3655
Lomas de Santa Fe
Mexico City, 01210
Mexico
5557279800 (Phone)

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