Explaining Central Government’s Tax Revenue Categories through the Bradley-Terry Regression Trunk Model

Alessio Baldassarre, Antonio D’Ambrosio and Claudio Conversano

Statistics and Public Policy, 2024, vol. 11, issue 1, 2356507

Abstract: The Bradley-Terry Regression Trunk (BTRT) model combines the log-linear Bradley-Terry model, including subject-specific covariates, with a particular tree-based model, the so-called regression trunk. It aims to consider simultaneously the main effects and the interaction effects of covariates on data expressed as paired comparisons. We apply this model to financial data expressed as rankings and then transformed into paired comparisons. Tax revenues differentiated by category represent the statistical units of the analysis (i.e., taxes on income, social security contributions, taxes on property, and taxes on goods and services). We combine data from OECD, World Bank, and IMF databases for the year 2018 to investigate the effect size of socio-economic covariates and their interaction on the composition of tax revenues for a set of 100 countries worldwide. We also present a comparison with a more established method proposed in tax determinants literature and with two alternative models used for matched pairs. Finally, we discuss the implications of reported results for stakeholders and policymakers.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/2330443X.2024.2356507 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:usppxx:v:11:y:2024:i:1:p:2356507

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uspp20

DOI: 10.1080/2330443X.2024.2356507

Access Statistics for this article

Statistics and Public Policy is currently edited by Eric Sampson

More articles in Statistics and Public Policy from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().