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Contribution of VAT to economic growth: A dynamic CGE analysis

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Erero J. L.
Journal of Economics and Management
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2021
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vol. 43
22-51
EN
Aim/purpose–This study sought to assess the impact of an increased historical fixed VAT rate of 14% to the current rate of 15% on the South African economy. Design/methodology/approach–The method applied in this study was based on a Dynamic Computable General Equilibrium (CGE) model to evaluate the impact of both the VAT rate of 14% and a new rate of 15% on the South African economy. The CGE model has been proven over the years to be a suitable model when evaluating the impact assessment of any shock within an economy. Enhancements were made by the researcher to the direct and indirect tax section of the model, i.e., the direct tax section was disaggregated, such that for both firm and household revenues, a dividend income stream is separated from other income streams. The main reason is to facilitate a detailed analysis of Corporate Income Tax (CIT) and Personal Income Tax (PIT), as well as the latest implemented Dividend Tax (DT).Findings–When VAT was increased from 14% to 15%, the immediate reaction of the shock from the Dynamic CGE model indicates that the Gross Domestic Product (GDP) declined by 0.0002% in 2018, but increased by 0.0028% in the following year (2019). The trend continued until 2021, hence the 1% increase in the VAT tax rate will increase the expected forecast of VAT collection by approximately R3.2 billion on average. Research implications/limitations–The findings of this study will be implemented by the South African government, which will use a dynamic CGE model to assess South Africa’s VAT contribution to the economy. The database of the CGE model was limited to the Social Accounting Matrix (SAM) for 2015. Originality/value/contribution–The study recommends the use of this method for assessing the impact of tax policy changes to the South African economy. The CGE model seems to be the best model as far as the impact assessment of a shock in the econ-omy is concerned. This will assist the South African authorities with their decision mak-ing regarding future VAT revenue.
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Predicting South African personal income tax – using Holt–Winters and SARIMA

100%
Makananisa M. P., Erero J. L.
Journal of Economics and Management
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2018
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vol. 31
24-49
EN
Aim/purpose – Over estimation and under estimation of the Personal Income Tax (PIT) revenue results in an unstable economy and unreliable statistics in the public domain. This study aims to find a suitable SARIMA and Holt–Winters model that suits the sample monthly data for PIT well enough, from which a forecast can be generated. Design/methodology/approach – This study uses the aspects of time series model (Holt–Winters and SARIMA) and regression models with SARIMA errors to simulate the structure which followed the historical actual realization of PIT. The quarterly data were obtained from quarter1, 2009 to quarter 1, 2017 for the purpose of modelling and forecasting. The data were divided into training (quarter 1, 1995 to quarter 1, 2014) and testing (quarter 2, 2014 to quarter 1, 2017) data sets. The forecast from quarter 2, 2017 to quarter 1, 2020 were also derived and aggregated to annual forecast. Findings – Holt–Winters, SARIMA and Time Series Regression models fitted captured the movement of the historical PIT data with higher precession. Research implications/limitations – The generated forecast is recommended to avoid several model revisions when locating the actual PIT realisation. However, monitoring of this model is crucial as the prediction power deteriorate in a long run. Originality/value/contribution – The study recommends the use of these methods for forecasting future PIT payments because they are precise and unbiased when forecasting are made. This will assist the South African authorities in decision making for future PIT revenue.
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