Optimising Eswatini’s Value-Added Tax (VAT) Threshold: Balancing Revenue Efficiency and Compliance Costs

by | Jul 3, 2025 | AMTJ, AMTJ 2025 I1

Optimising Eswatini’s Value-Added Tax (VAT) Threshold: Balancing Revenue Efficiency and Compliance Costs

Authors: Masuku Phindile T; Mamba Lwemvelo

ISSN: 2709-8575
Affiliations: Manager Research – Research, Strategy and Statistics Division at the Eswatini Revenue Service; Senior Analyst – Research, Strategy and Statistics Division at the Eswatini Revenue Service
Source: African Multidisciplinary Tax Journal, Volume 5, Issue 1 (2025), p. 276–295
https://doi.org/10.47348/AMTJ/V5/i1a13

Abstract

Value-added tax (VAT), a consumption tax levied on value added at every stage of production in the value chain, was introduced in Eswatini in April 2012 to replace general sales tax (GST). At its introduction, it was set at a rate of 14%, and since 2012, the VAT registration threshold has been set at E500 000. However, due to inflation and the time value of money, E500 000 in 2012 is no longer the equivalent of E500 000 in 2024, hence the need to review and determine the optimal VAT threshold. This study uses a method based on the idea of collecting the most amount of VAT revenue from the least number of taxpayers to approximate the optimal level of a VAT threshold for Eswatini. This optimal level creates administration and compliance cost-efficiencies for both the tax administration and the taxpayer, respectively. The findings from the study show that the marginal changes to the number of registered taxpayers and the VAT to be collected from them converge at a VAT threshold in the range of E800 000 to E900 000 for Eswatini; at this level, 99% of VAT revenue collected comes from only 54% of VAT-registered taxpayers. Therefore, based on the methodology, the study recommends that the VAT threshold should be revised from E500 000 to E900 000 to allow for the cost-efficient collection of VAT in the country.

The Role of Electronic Tax Stamps System on Revenue Collection in Tanzania

by | Jul 3, 2025 | AMTJ, AMTJ 2025 I1

The Role of Electronic Tax Stamps System on Revenue Collection in Tanzania

Authors: Innocent Nyamfulula; Cornel Joseph; August O. Kessy; Elly H. Mloso

ISSN: 2709-8575
Affiliations: Institute of Tax Administration, Tanzania Revenue Authority; Mkwawa University College of Education, University of Dar es Salaam; Institute of Tax Administration, Tanzania Revenue Authority; Institute of Tax Administration, Tanzania Revenue Authority
Source: African Multidisciplinary Tax Journal, Volume 5, Issue 1 (2025), p. 296–310
https://doi.org/10.47348/AMTJ/V5/i1a14

Abstract

This study examined the Electronic Tax Stamps (ETS) System’s role in revenue collection in Tanzania with a focus on cigarettes, beer and spirits. The results from a trend analysis show an increase in respective revenue in the immediate period after the introduction of the ETS system. Moreover, the estimated results from the regression with Newey-West standard errors show that the coefficient associated with the ETS is positive and statistically significant. Thus, the study concludes that ETS plays a critical role in improving excise revenue performance and fostering a more transparent and efficient fiscal system in Tanzania. The government and policymakers should continuous improvement of the ETS system so that it can contribute significantly to revenue collection. Also, the ETS System should be backed by a well-designed system of enforcement so as to realize a more positive contribution to revenue collection.

Digitalising Tax Compliance and Elevating Revenue Forecasting in Rwanda: Evidence from Statistical Modelling and Machine Learning

by | Jul 3, 2025 | AMTJ, AMTJ 2025 I1

Digitalising Tax Compliance and Elevating Revenue Forecasting in Rwanda: Evidence from Statistical Modelling and Machine Learning

Author: Clement Uwizeye

ISSN: 2709-8575
Affiliations: Wageningen University & Research (WUR)
Source: African Multidisciplinary Tax Journal, Volume 5, Issue 1 (2025), p. 311–330
https://doi.org/10.47348/AMTJ/V5/i1a15

Abstract

This paper explores the dual role of digitalisation and advanced forecasting models in enhancing tax compliance and revenue prediction in Rwanda. It investigates the impact of electronic billing machine (EBM) adoption on tax performance, the comparative accuracy of traditional econometric models (Bayesian VAR) versus machine learning models (NNAR and Ensemble), and the policy implications of these findings. Using data from 2010 to 2023, the analysis reveals that while EBM usage has expanded significantly, its impact on tax revenue is limited due to enforcement and implementation challenges. Regression results indicate that trade openness, financial development and effective governance positively influence tax revenue, whereas corruption and remittance inf lows pose challenges. Forecasting models indicate a moderately optimistic outlook for tax-to-GDP and trade integration, with NNAR outperforming other models in predictive accuracy. The study concludes with key policy recommendations focused on strengthening digital compliance infrastructure, addressing corruption, supporting financial sector development and leveraging machine learning for more accurate fiscal forecasting. These insights are vital for designing evidence-based tax reforms and achieving sustainable domestic resource mobilisation in Rwanda.

Forecasting Tax Revenue Using Arima and Vector Autoregressiive (VAR) Modelling in Tanzania

by | Jul 3, 2025 | AMTJ, AMTJ 2025 I1

Forecasting Tax Revenue Using Arima and Vector Autoregressiive (VAR) Modelling in Tanzania

Author: Masoud Mohammed Al-biman

ISSN: 2709-8575
Affiliations: Lecturer, Institute of Tax Administration (ITA), Dar-es-Salaam, Tanzania
Source: African Multidisciplinary Tax Journal, Volume 5, Issue 1 (2025), p. 331–352
https://doi.org/10.47348/AMTJ/V5/i1a16

Abstract

This article intends to examine whether times series approaches of ARIMA and VAR are effective in forecasting tax revenue. It also compares the two approaches to evaluate which is the more effective forecasting method. Quarterly data from 1996Q1 to 2016Q4 (21 years or 84 observations) are used to forecast the tax revenue for the period 2017Q1 to 2017Q4. Five common types of taxes are selected due to their significant contributions to Tanzania’s total tax revenue collected by the Tanzania Revenue Authority (TRA). Generally, the results reveal that both time series approaches are effective and demonstrate strong predicting power in short-horizon tax revenue forecasting. However, in most cases that the VAR model outperforms ARIMA modelling, especially based on forecasting criteria. However, we suggest that both methods to be applied by the TRA in forecasting tax revenue as their forecasting errors differ only slightly.