Original Research

Development and comparison of payment behaviour prediction models for two South African state departments

Erika Fourie, Tanja Verster, Gary W. van Vuuren
South African Journal of Economic and Management Sciences | Vol 20, No 1 | a1701 | DOI: https://doi.org/10.4102/sajems.v20i1.1701 | © 2017 Erika Fourie, Tanja Verster, Gary W. van Vuuren | This work is licensed under CC Attribution 4.0
Submitted: 17 November 2016 | Published: 10 October 2017

About the author(s)

Erika Fourie, Centre for Business Mathematics and Informatics, North West University, South Africa
Tanja Verster, Centre for Business Mathematics and Informatics, North West University, South Africa
Gary W. van Vuuren, Centre for Business Mathematics and Informatics, North West University, United Kingdom


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Abstract

Background: No credit rating methodology currently exists for any of South Africa’s sub nationals.

Aim: To develop a generic, quantitative credit rating methodology for the Department of Health and the Department of Education combined, as well as specific quantitative credit rating methodologies, for each department, individually.

Setting: A comparison between generic and specific subnational credit rating methodologies to assess which fits the South African subnational environment best. Studies and results obtained from other nations were used to construct the approach.

Methods: In a typical credit rating methodology, both quantitative and qualitative information is considered. In South Africa (as a developing economy), the quantitative information equates to a smaller portion of the final credit rating. A generic quantitative credit rating methodology, as well as specific credit rating methodologies, was developed. The appropriateness of these generic and specific models was tested with regards to prediction accuracies using Red, Amber or Green (RAG) statuses on a traffic light series. An illustration of the predicted versus actual ranks is provided, as well as an example to illustrate how model-predicted RAG statuses, based on quantitative information, may be overlaid with more recent qualitative information to derive a final ranking.

Results: A generic, quantitative credit rating methodology for the Departments of Health and the Department of Education combined was developed, as well as specific credit rating methodologies for each department separately. The specific subnational credit rating methodology outperformed the generic methodology considerably; more precisely, the generic models predicted a maximum of 50% of the new cases correctly as opposed to the specific Health and Education models’ 78%.

Conclusion: The primary contribution of this study was to develop and compare generic and specific subnational credit rating methodologies. A further contribution was to test the appropriateness of these models’ prediction accuracies using RAG statutes. The specific subnational credit rating methodology was found to outperform the generic methodology considerably.


Keywords

subnational governments; credit ratings; linear regression; prediction accuracy

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