Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis

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The analysis employs a Nonlinear Autoregressive Distributed Lag (NARDL) model to capture potential asymmetric relationships between the explanatory variables and household consumption, allowing for different effects of positive and negative changes over the short and long run. The findings reveal that in the long run, positive changes in VAT significantly reduce household consumption, while negative changes lead to an increase, indicating an asymmetric and inverse relationship. Similarly, positive changes in interest rates significantly reduce household consumption, whereas negative changes lead to an increase in consumption, indicating an asymmetric relationship. Similarly, positive and negative changes in household disposable income are associated with corresponding increases and decreases in household consumption, respectively, and these relationships are statistically significant in the long run. Economic growth, despite exhibiting an asymmetric pattern, is also found to be insignificantly related to household consumption in the long term. In the short run, both positive and negative VAT and interest rate shocks result in a decline in household consumption, with the effects of positive and negative changes being statistically significant. A positive change in household disposable income causes a rise in household consumption in the short run, which is statistically significant. Additionally, a negative change in household disposable income causes a drop in household consumption in the short run. This study suggests that the South African government should exercise caution when adjusting VAT and interest rates as increases may suppress household consumption, particularly among low-to middle-income households. Additionally, efforts to enhance disposable income through targeted fiscal measures may support consumption and promote overall economic stability." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1056/v1", "name": "Nonlinear Effects of VAT on Household Consumption in South Africa:..." } } ] } Home Browse Nonlinear Effects of VAT on Household Consumption in South Africa:... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Takentsi S and Mah G. Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.12688/f1000research.167056.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] Siyakudumisa Takentsi https://orcid.org/0000-0002-7708-1131 1 , Gisele Mah https://orcid.org/0000-0002-8729-9935 1 Siyakudumisa Takentsi https://orcid.org/0000-0002-7708-1131 1 , Gisele Mah https://orcid.org/0000-0002-8729-9935 1 PUBLISHED 09 Oct 2025 Author details Author details 1 Economic Sciences, North West University Faculty of Economic and Management Sciences, Potchefstroom, North West, South Africa Siyakudumisa Takentsi Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Gisele Mah Roles: Funding Acquisition, Project Administration, Supervision, Validation, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract This study empirically investigates the nonlinear effects of value added tax (VAT), interest rate, household disposable income, and economic growth on household consumption in South Africa, using annual data from 1994 to 2023 obtained from the South African Reserve Bank (SARB) and World Bank. The analysis employs a Nonlinear Autoregressive Distributed Lag (NARDL) model to capture potential asymmetric relationships between the explanatory variables and household consumption, allowing for different effects of positive and negative changes over the short and long run. The findings reveal that in the long run, positive changes in VAT significantly reduce household consumption, while negative changes lead to an increase, indicating an asymmetric and inverse relationship. Similarly, positive changes in interest rates significantly reduce household consumption, whereas negative changes lead to an increase in consumption, indicating an asymmetric relationship. Similarly, positive and negative changes in household disposable income are associated with corresponding increases and decreases in household consumption, respectively, and these relationships are statistically significant in the long run. Economic growth, despite exhibiting an asymmetric pattern, is also found to be insignificantly related to household consumption in the long term. In the short run, both positive and negative VAT and interest rate shocks result in a decline in household consumption, with the effects of positive and negative changes being statistically significant. A positive change in household disposable income causes a rise in household consumption in the short run, which is statistically significant. Additionally, a negative change in household disposable income causes a drop in household consumption in the short run. This study suggests that the South African government should exercise caution when adjusting VAT and interest rates as increases may suppress household consumption, particularly among low-to middle-income households. Additionally, efforts to enhance disposable income through targeted fiscal measures may support consumption and promote overall economic stability. READ ALL READ LESS Keywords Value Added Tax, Household Consumption, NARDL, South Africa. Corresponding Author(s) Siyakudumisa Takentsi ( [email protected] ) Close Corresponding author: Siyakudumisa Takentsi Competing interests: No competing interests were disclosed. Grant information: The researchers received funding form North West University The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Takentsi S and Mah G. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Takentsi S and Mah G. Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.12688/f1000research.167056.1 ) First published: 09 Oct 2025, 14 :1056 ( https://doi.org/10.12688/f1000research.167056.1 ) Latest published: 09 Oct 2025, 14 :1056 ( https://doi.org/10.12688/f1000research.167056.1 ) 1. Introduction and Background The impact of value added tax (VAT) on household consumption is a critical issue in South Africa’s fiscal and economic landscape. Household consumption plays a pivotal role in the country’s economy, accounting for a significant portion of the nation’s Gross Domestic Product (GDP) ( Roos et al., 2020 ), contributing approximately 64.6% in 2020, 64.7% in 2021, 64.7% in 2022, 64.4% in 2023, and 64.1% in 2024 ( Macrotrends.net , 2024; Theglobaleconomy.com , 2024). Therefore, understanding the factors that influence consumption patterns is crucial for formulating effective economic policies. One such factor is VAT, a consumption-based tax that serves as a significant revenue source for the South African government ( SARS, 2023 ). However, VAT’s regressive nature, whereby lower-income households spend a larger proportion of their income on VAT inclusive goods and services, raises concerns about its impact on household welfare and consumption behavior ( IMF, 2024 ). In 2025, the South African government held policy discussions centered on the proposed increases in the VAT rate to address fiscal deficits. As a result, the government of South Africa announced a two-step increase during the same period: the VAT rate will rise from 15% to 15.5% on May 1, 2025, and further to 16% on April 1, 2026 ( SARS, 2025 ). This increase has raised concerns that it could worsen economic inequality and place unnecessary financial strain on low- and middle-income households, which are already grappling with rising living costs and stagnant wages ( National Treasury, 2025 ). The regressive impact of VAT is particularly pronounced in South Africa, where nearly half of the population lives below the poverty line and social grant increases have not kept pace with inflation ( Stats SA, 2024 ). On the other hand, Van Oordt (2015) , Stacey et al. (2017) , Roos et al. (2020) , and Omodero et al. (2023) argue that VAT has negative effects on household consumption in South Africa and emphasize the need for further analysis, particularly regarding its impact on households, to inform the development of sustainable VAT policies. However, these studies lack an exploration of the asymmetric effects of VAT changes, that is, whether increases or decreases in VAT rates have different impacts on household consumption. This study seeks to fill this gap by employing the Nonlinear Autoregressive Distributed Lag (NARDL) model to analyze the asymmetric effects of VAT on household consumption in South Africa from 1994 to 2023. By capturing both short- and long-run dynamics, this study provides nuanced insights into how VAT adjustments influence consumption patterns in South Africa. The findings of this study have significant policy implications. Understanding the asymmetric effects of VAT on consumption can inform more equitable tax policies that mitigate adverse impacts on vulnerable populations. As South Africa continues to navigate economic challenges, such insights are essential for balancing fiscal objectives with social equity considerations ( Heinrich Böll Stiftung, 2023 ). 2. Theoretical literature review The literature review examined three principal hypotheses related to VAT and household consumption. The first is Keynesian consumption theory, developed by John Maynard Keynes in 1936, which argues that household consumption is primarily determined by current income levels. An increase in taxes may reduce disposable income and, consequently, decrease consumption, particularly among lower-income households that spend a larger proportion of their income on consumption ( Keynes, 1936 ; Owusu-Sekyere, 2020 ). The theory supports the notion that VAT increases can suppress consumption, whereas increases in household disposable income encourage household consumption. The second hypothesis is the Life-Cycle Hypothesis (LCH), proposed by Modigliani and Brumberg in 1954 . This theory posits that individuals plan their consumption and saving behavior over their lifetime, aiming to smooth consumption despite income fluctuations. Increases in tax rates may disrupt this plan, especially if they are unexpected or not offset by future tax relief or income growth. Asymmetric VAT effects align with the LCH, as households may respond differently to tax increases than to tax decreases depending on their stage in the life cycle ( Modigliani & Brumberg, 1954 ; Afonso & de Sá Fortes Leitão Rodrigues, 2025 ). Lastly, the third hypothesis is rooted in behavioral economics and loss aversion, particularly Prospect Theory, developed by Kahneman and Tversky in 1979 . This theory suggests that individuals react more strongly to losses than to equivalent gains ( Kahneman & Tversky, 1979 ; Omodero et al., 2023 ). In the context of VAT, a tax increase perceived as a loss may lead to a sharper consumption reduction than the corresponding increase in consumption resulting from a tax cut. 2.1 Empirical review Numerous studies have explored the complex relationship between value-added taxes and household consumption, revealing varied and often contradictory findings. Early contributions, such as those of Tagkalakis (2014) , estimated the relationship between VAT revenue and economic activity in Greece using quarterly data from Q1 2000 to Q3 2012. The results suggest that consumption patterns remained largely unchanged, even when lower VAT rates were applied, implying a limited influence of VAT changes on household spending behavior. This aligns with Zídková (2014) , who analyzed VAT gaps in the EU using OLS estimations for 2002 and 2006, confirming that the VAT gap increased with higher final consumption. Both studies suggest that VAT policy alone may not strongly alter consumer behavior. However, other studies contradicted this view. Střílková and Široký (2015) , examining Czech households between 2007 and 2014, found that reductions in VAT led to increases in household consumption, suggesting that consumers respond positively to tax relief on their expenditure baskets. Similarly, Van Oordt (2015) , through a detailed survey-based analysis of South Africa, argued that increases in VAT significantly affect consumption, particularly among low-income households, reinforcing the regressive nature of indirect taxation. Further debate emerged in the findings of Erero (2015) , who applied a CGE model to South Africa and concluded that VAT increases would not negatively impact poor households, a result that stands in contrast to that of Stacey et al . (2017) . Using the same model framework, Stacey et al. demonstrated that VAT hikes during 2010–2011 had a harmful effect on the consumption patterns of low-income households, emphasizing the potential inequity of VAT policies. Adding to this discourse, Terfa et al. (2017) , using stratified random sampling in Ethiopia, found a negative but statistically insignificant effect of VAT on consumption in 2014. This nuanced result suggests potential context-specific dynamics that influence how households adjust to tax changes. Ogneru et al. (2019) further support the argument that VAT may not always translate into reduced consumption is further supported by Ogneru et al . (2019) . Their VAR model analysis of Romania (2007–2018) found no direct link between VAT revenue and consumption, contradicting earlier studies that suggested clear behavioral responses to VAT changes. Conversely, Otemu (2020) , analyzing data from Nigeria (2000–2018), found strong evidence that VAT influences consumption patterns. These findings echo those of Al Gahtani et al. (2020) , who modelled the Saudi Arabian context using an error correction framework (1970–2017), estimating a −0.3% one-time impact of VAT on consumption, suggesting a measurable, though modest, effect of VAT introduction. More recent studies have reinforced the consumption-suppressing potential of VAT. Roos et al. (2020) confirmed that VAT increases negatively affect household consumption by applying a multiregional model to South Africa. However, Hammour and McKeown (2022) found no significant effect of VAT on consumption in the United Arab Emirates based on survey responses from 2019, suggesting that in higher-income contexts or in the short term, VAT may be less impactful on spending behavior. Notably, Adegbite (2023) , using the Autoregressive Distributed Lag (ARDL) model for Nigeria (1990–2021), reported a significant negative relationship between VAT and household consumption, bolstering the argument that VAT erodes disposable income and curbs expenditures. These findings are extended by Omodero et al. (2023) , who employed a panel ARDL model for Nigeria and South Africa (1994–2021) and discovered that while VAT has a short-run positive effect on household consumption, its long-run effect is significantly negative, which underscores the temporal complexity of VAT’s impact. In conclusion, the literature suggests that the relationship between VAT and household consumption is not uniform. While some studies find insignificant or even positive short-run effects, the broader consensus, particularly in developing countries, leans toward the view that VAT increases tend to suppress household consumption, especially among low-income groups. 3. Methodology 3.1 Nonlinearity test The nonlinearity test is one of the crucial tests that needs to be conducted on the time series data before the estimation of the NARDL technique, as it can identify the nature of the relationship between the series ( Narayan, 2005 ; Rahman & Ahmad, 2019 ). Therefore, this study used the BDS linearity test to check for nonlinearity in the model. 3.2 The BDS linearity test The BDS linearity test is an appropriate test to capture nonlinearity in time-series data, as recommended by many authors such as Broock et al. (1996) and Monamodi (2021) . The following data-generating process (DGP) was used to demonstrate the BDS linearity test: (1) Y t = ϑ ( p t − 1 , δ , u t ) Where Y t is a stationary variable that results from the DGP, p t − 1 = ( Y t − 1 , Y t − p ) , δ represents an unobserved set of parameters, u t is the unmeasured disturbance term that exhibits finite variance and a mean of zero in a normal distribution, and does not rely on Y t − 1 at every time instance t . As suggested by Monamodi (2021) , the BDS model specification includes Equation (2) as part of the DGP: (2) Y t = f ( p t − 1 , δ T , u ̂ t ) t = 1 , … , T where u ̂ is the estimated residual, δ T is the consistent estimator of δ , the limit of u ̂ t = u t is the limit, and is serially independent. u ̂ t was normally distributed when δ T was equal to δ . In this study, for each embedding dimension ( m ), the correlation integral is computed, which measures the probability that two points in the phase space are within a certain distance ( ε ) from each other. The correlation integral takes the following form. (3) C m ( ϵ ) = 2 N ( N − 1 ) ∑ i < j I ( ‖ X i − X j ‖ < ϵ ) Where I is an indicator function that is 1 if the condition is true and 0 otherwise, X i and X j are points in the phase space, and N is the number of points. Furthermore, the BDS statistic was calculated by comparing the correlation integral for the original time series with that of a surrogate dataset generated under the null hypothesis of linearity ( Rahman & Ahmad, 2019 ). (4) W m ( ϵ ) = N ( C m ( ϵ ) − C 1 m ( ϵ ) σ ̂ m , ϵ ) Where C 1 m ( ϵ ) represents the correlation integral for the embedded series with dimension 1 raised to the power m , and σ ̂ m , ϵ is an estimate of the standard deviation of C m ( ϵ ) − C 1 m ( ϵ ) . The null hypothesis is that the time-series data have no significant nonlinearity, while the alternative hypothesis states that there is nonlinearity in the time-series data. A significant BDS statistic with a p-value of less than 5% suggests the presence of nonlinearity in the series ( Kim et al., 1998 ; Mitchell & McKenzie, 2011 ). 3.3 Data sources The data for the variables examined in this study were extracted from South African Reserve Bank (SARB) and World Bank databases. Due to the limited quarterly data available in South Africa, this study used annual data from 1994 to 2023 with 30 observations. This study period was chosen to check the impact of value-added tax on household consumption after the first South African democratic election and after the economic devastation caused by the Covid-19 pandemic, hence, using the estimation of the NARDL regression model to check the effect of value-added tax on household consumption during the specified study period. 3.4 Nonlinear stationarity testing To run a NARDL method, all the conditions within its framework must be met, and all the variables must be integrated either in order I(0) or I(1), or mixed, but not in order I(2), as for the ARDL approach ( Ahmad et al., 2018 ; Ur Rahman et al., 2019 ). Hence, this study estimated the KSS ( Kapetanios et al., 2003 ) nonlinearity unit root test before applying the NARDL technique. 3.4.1 KSS nonlinear unit root test Kapetanios et al. (2003) developed a KSS unit root test capable of capturing unit roots in the presence of nonlinear adjustments. Erdas (2019) believes that the KSS nonlinear unit root test is based on the Exponential Smooth Transition Autoregressive (ESTAR) model. Equation (5) represents the ESTAR model: (5) ∆ yt = γ Y t − 1 [ 1 − exp ( − θ Y t − 1 2 ) ] + ∑ i = 1 p δ i ∆ Y t − i + ε t Where Y t is the time series variable, ∆ is the first difference operator, γ and θ are parameters, p denotes the lag structure, and ε t is the residual term. The equation below represents a nonlinear transformation. (6) ∆ yt = δ 0 + δ 1 t + γ Y t − 1 3 + ∑ i = 1 p δ i ∆ Y t − i + ε t To capture the nonlinear adjustment, Equation (6) must be applied for nonlinear transformation to the series. According to Erdas (2019) , the model specification used in the KSS nonlinear unit root test can be expressed as follows: (7) y t = βy t − 1 + γy t − 1 Θ ( θ ; y t − d ) + ε t where β and γ indicate the unknown parameters and y t is the zero-mean stochastic process. The exponential form of the transmission function is expressed as: (8) Θ ( θ ; y t − d ) = 1 − exp ( − θy t − d 2 ) Null hypothesis: the series has a unit root. Alternative hypothesis: the series is nonlinear stationary; therefore, (9) H 0 : γ = 0 (10) H 1 : γ < 0 Kapetanios et al. (2003) suggested that the computed test statistics and critical values must be compared. If the computed t-statistics is less than the critical value of either −2.82 or −2.22 or −1.92 at a significant level of 1%, 5% or 10%, respectively, dismiss the null hypothesis in favour of the alternative hypothesis at the specified significance level. 3.5 NARDL cointegration bounds testing approach NARDL cointegration bounds testing is essential for determining at least one cointegrating asymmetric vector in the series ( Shin et al., 2014 ; Rezitis, 2019 ). Kapetanios et al., (2006) believed that the NARDL bounds testing approach is a robust method for detecting cointegration in the presence of nonlinearity. Hence, this study estimates the following asymmetric cointegration equations to capture cointegration before analyzing the asymmetric effects of the VAT tax on household consumption: (11) ∆ HC t = α 0 + ∑ i = 1 p α 1 ∆ HC t − i + ∑ i = 0 q α 2 + ∆ LVAT t − i + + ∑ i = 0 q α 2 − ∆ LVAT t − i − + ∑ i = 0 q α 3 + ∆ INT t − i + + ∑ i = 0 q α 3 − ∆ INT t − i − + ∑ i = 0 q α 4 + ∆ HDI t − i + + ∑ i = 0 q α 4 − ∆ HDI t − i − + ∑ i = 0 q α 5 + ∆ GDP t − i + + ∑ i = 0 q α 5 − ∆ GDP t − i − + λ 1 HC t − 1 + λ 2 + LVAT t − 1 + + λ 2 − LVAT t − 1 − + λ 3 + INT t − 1 + + λ 3 − INT t − 1 − + λ 4 + HDI t − 1 + + λ 4 − HDI t − 1 − + λ 5 + GDP t − 1 + + λ 5 − GDP t − 1 − + ε t (12) ∆ LVAT t = α 0 + ∑ i = 1 p α 1 ∆ LVAT t − i + ∑ i = 0 q α 2 + ∆ HC t − i + + ∑ i = 0 q α 2 − ∆ HC t − i − + ∑ i = 0 q α 3 + ∆ INT t − i + + ∑ i = 0 q α 3 − ∆ INT t − i − + ∑ i = 0 q α 4 + ∆ HDI t − i + + ∑ i = 0 q α 4 − ∆ HDI t − i − + ∑ i = 0 q α 5 + ∆ GDP t − i + + ∑ i = 0 q α 5 − ∆ GDP t − i − + λ 1 LVAT t − 1 + λ 2 + HC t − 1 + + λ 2 − HC t − 1 − + λ 3 + INT t − 1 + + λ 3 − INT t − 1 − + λ 4 + HDI t − 1 + + λ 4 − HDI t − 1 − + λ 5 + GDP t − 1 + + λ 5 − GDP t − 1 − + ε t (13) ∆ INT t = α 0 + ∑ i = 1 p α 1 ∆ INT t − i + ∑ i = 0 q α 2 + ∆ HC t − i + + ∑ i = 0 q α 2 − ∆ HC t − i − + ∑ i = 0 q α 3 + ∆ LVAT t − i + + ∑ i = 0 q α 3 − ∆ LVAT t − i − + ∑ i = 0 q α 4 + ∆ HDI t − i + + ∑ i = 0 q α 4 − ∆ HDI t − i − + ∑ i = 0 q α 5 + ∆ GDP t − i + + ∑ i = 0 q α 5 − ∆ GDP t − i − + λ 1 INT t − 1 + λ 2 + HC t − 1 + + λ 2 − HC t − 1 − + λ 3 + LVAT t − 1 + + λ 3 − LVAT t − 1 − + λ 4 + HDI t − 1 + + λ 4 − HDI t − 1 − + λ 5 + GDP t − 1 + + λ 5 − GDP t − 1 − + ε t (14) ∆ HDI t = α 0 + ∑ i = 1 p α 1 ∆ HDI t − i + ∑ i = 0 q α 2 + ∆ HC t − i + + ∑ i = 0 q α 2 − ∆ HC t − i − + ∑ i = 0 q α 3 + ∆ LVAT t − i + + ∑ i = 0 q α 3 − ∆ LVAT t − i − + ∑ i = 0 q α 4 + ∆ INT t − i + + ∑ i = 0 q α 4 − ∆ INT t − i − + ∑ i = 0 q α 5 + ∆ GDP t − i + + ∑ i = 0 q α 5 − ∆ GDP t − i − + λ 1 HDI t − 1 + λ 2 + HC t − 1 + + λ 2 − HC t − 1 − + λ 3 + LVAT t − 1 + + λ 3 − LVAT t − 1 − + λ 4 + INT t − 1 + + λ 4 − INTR t − 1 − + λ 5 + GDP t − 1 + + λ 5 − GDP t − 1 − + ε t (15) ∆ GDP t = α 0 + ∑ i = 1 p α 1 ∆ GDP t − i + ∑ i = 0 q α 2 + ∆ HC t − i + + ∑ i = 0 q α 2 − ∆ HC t − i − + ∑ i = 0 q α 3 + ∆ LVAT t − i + + ∑ i = 0 q α 3 − ∆ LVAT t − i − + ∑ i = 0 q α 4 + ∆ INT t − i + + ∑ i = 0 q α 4 − ∆ INT t − i − + ∑ i = 0 q α 5 + ∆ HDI t − i + + ∑ i = 0 q α 5 − ∆ HDI t − i − + λ 1 GDP t − 1 + λ 2 + HC t − 1 + + λ 2 − HC t − 1 − + λ 3 + LVAT t − 1 + + λ 3 − LVAT t − 1 − + λ 4 + INT t − 1 + + λ 4 − INTR t − 1 − + λ 5 + HDI t − 1 + + λ 5 − HDI t − 1 − + ε t where ∆ represents the first-order difference operator; p and q represent lag orders; α 0 indicates the intercept; α 1 , α 2 + , α 2 − , α 3 + , α 3 − , α 4 + , α 4 − , α 5 + , α 5 − are the short-run coefficients; λ 1 , λ 2 + , λ 2 − , λ 3 + , λ 3 − , λ 4 + , λ 4 − , λ 5 + , λ 5 − are the long-run coefficients; and ε t is the error term. The NARDL bounds test involves testing the null hypothesis of no asymmetric cointegration against the alternative hypothesis that at least one λ is not equal to zero (i.e., asymmetric cointegration exists): (16) H 0 : λ 1 = λ 2 + = λ 2 − = λ 3 + = λ 3 − = λ 4 + = λ 4 − = λ 5 + = λ 5 − = 0 (17) H 1 : λ 1 = λ 2 + ≠ λ 2 − ≠ λ 3 + ≠ λ 3 − = λ 4 + ≠ λ 4 − ≠ λ 5 + ≠ λ 5 − = 0 The decision rule is that, if the computed F-statistic is greater than the upper bound critical value, the null hypothesis is rejected. Additionally, if the F-statistic is less than the lower-bound critical value, it fails to reject the null hypothesis. If the F-statistic falls between the lower- and upper-bound critical values, the result is inconclusive ( Pesaran et al., 2001 ). 3.6 NARDL model specification The NARDL framework can be adopted after the BDS linearity test confirms that the relationship between variables is nonlinear ( Bahmani-Oskooee & Fariditavana, 2016 ). This study modified the subsequent model, which was developed by Shin et al. (2014) and applied by Gohar et al. (2022) , who examined the short- and long-run effects of income and price changes on consumption expenditures in seven emerging countries over the period 1991 to 2020. The NARDL model specifications are as follows. (18) ∆ InCP kt = β 0 + β 1 In CP kt − 1 + β 2 InNI kt − 1 + + β 3 InNI kt − 1 − + β 4 InPR kt − 1 + + β 5 InPR kt − 1 − + β 6 InIR kt − 1 + ∑ i = 0 n θ 1 ∆ In CP kt − i + ∑ i = 0 n ( θ 2 + ∆ InNI kt − 1 + + θ 3 − ∆ InNI kt − 1 − ) + ∑ i = 0 n ( θ 4 + ∆ InPR kt − i + + θ 5 − ∆ InPR kt − i − ) + ∑ i = 0 n θ 6 + ∆ InIR kt − i + ε t where the long-run coefficients are represented by β 1 - β 6 and short-run coefficients are represented by θ 1 - θ 6 . IR kt , PR kt , NI kt and CP kt indicate the interest rate, price, income, and consumption for country k at time t , respectively. ln within each variable denotes that the variables are used with the natural logarithm, whereas ∆ is the difference operator indicating the short-term impact. This study modified Equation (18) by excluding variables that were not reviewed in the current study, such as NI income and PR prices. To achieve the objectives of the study, these variables were replaced with VAT, household disposable income, and economic growth. The following variables were used: household consumption is abbreviated as HC , and interest rate as INT . The following section presents the NARDL model, estimated to examine the effect of VAT on household consumption. In line with Equation (18) , the long-run NARDL model estimated in this study to determine the asymmetric and nonlinear effects of VAT on household consumption in South Africa is presented in Equation (19) : (19) HC t = β 0 + β 1 VAT + kt − 1 + β 2 VAT − kt − 1 + β 3 INT + kt − 1 + β 4 INT − kt − 1 + β 5 HDI + kt − 1 + β 6 HDI − kt − 1 + β 7 GDP + kt − 1 + β 8 GDP − kt − 1 + μ t After long-run estimation, the following short-run NARDL model was estimated to shed light on the full dynamics of the NARDL framework, especially when the effects of positive and negative changes differ. This study finds it important to not overlook key short-term dynamics. (20) ∆ HC t = θ 0 + ∑ i = 1 n 1 θ 1 ∆ HC kt − 1 + ∑ i = 1 n 2 θ 2 + ∆ LVAT + kt − i + ∑ i = 1 n 3 θ 3 + ∆ LVAT − kt − i + ∑ i = 1 n 3 θ 3 + ∆ INT + kt − i + ∑ i = 1 n 4 θ 4 + ∆ INT − kt − i + ∑ i = 1 n 5 θ 5 + ∆ HDI + kt − i + ∑ i = 1 n 6 θ 6 − ∆ HDI − kt − i + ∑ i = 1 n 7 θ 7 + ∆ GDP + kt − i + ∑ i = 1 n 8 θ 8 − ∆ GDP − kt − i + μ t Where HC stands for household consumption, VAT denotes value-added tax, INT is interest rate, HDI is household disposable income, and GDP signifies economic growth. The NARDL error correction model (ECM) is as follows: (21) ∆ HC t = θ 0 + ∑ i = 1 n 1 θ 1 ∆ HC kt − 1 + ∑ i = 1 n 2 θ 2 + ∆ LVAT + kt − i + ∑ i = 1 n 3 θ 3 + ∆ LVAT − kt − i + ∑ i = 1 n 4 θ 4 + ∆ INT + kt − i + ∑ i = 1 n 5 θ 5 − ∆ INT − kt − i + ∑ i = 1 n 6 θ 6 + ∆ HDI + kt − i + ∑ i = 1 n 7 θ 7 − ∆ HDI − kt − i + ∑ i = 1 n 8 θ 8 + ∆ GDP + kt − i + ∑ i = 1 n 9 θ 9 − ∆ GDP − kt − i + λECT kt − 1 + μ t where λ is the error correction term (ECT) coefficient, which measures the speed of adjustment toward the long-run equilibrium, ECT kt − 1 , is the error correction term from the long-run equation, representing the deviation from the long-run equilibrium. The NARDL error-correction model captures both the short- and long-run asymmetric dynamics between the variables. ECT plays a critical role in adjusting deviations from the long-run equilibrium, and the short-run coefficients allow for the modelling of different responses to positive and negative changes in the independent variables. A favorable error correction term must have a significant negative coefficient. 3.7 NARDL long- and short-run asymmetric effects This study finds that it is crucial to determine the existence of asymmetric linkages among the series. Asymmetry means that the response of one variable to another differs depending on the direction or magnitude of change ( Verheyen, 2013 ; Mosikari & Eita, 2020 ; Hossain et al., 2021 ). Not accepting either long-run symmetry or short-run symmetry will result in long- or short-run asymmetry. This study estimated asymmetric cointegration prior to estimating the long- and short-run asymmetries using the NARDL model. After confirming one cointegrating asymmetric vector among the series, the study estimated the long-run asymmetries by examining the coefficients λ 2 + , λ 2 − , λ 3 + and λ 3 − : (22) HC t − 1 = λ 2 + LVAT t − 1 + + λ 2 − LVAT t − t − + λ 3 + INT t − 1 + + λ 3 − INT t − t − + λ 4 + HDI t − 1 + + λ 4 − HDI t − t − + λ 5 + GDP t − 1 + + λ 5 − GDP t − t − + ε t The estimation of Equation (21) was based on the Wald test to check if λ 2 + = λ 2 − , λ 3 + = λ 3 − . Rejecting the null hypothesis indicates a long-run asymmetry. This study examined short-run dynamics by examining the coefficients α 2 + , α 2 − , α 3 + and α 3 − : (23) ∆ HC t = ∑ i = 0 q α 2 + ∆ LVAT t − i + + ∑ i = 0 q α 2 − ∆ LVAT t − i − + ∑ i = 0 q α 3 + ∆ INT t − i + + ∑ i = 0 q α 3 − ∆ INT t − i − + ∑ i = 0 q α 4 + ∆ HDI t − i + + ∑ i = 0 q α 4 − ∆ HDI t − i − + ∑ i = 0 q α 5 + ∆ GDP t − i + + ∑ i = 0 q α 5 − ∆ GDP t − i − + ε t The Wald test was conducted to check if α 2 + = α 2 − , α 3 + = α 3 − , α 4 + = α 4 − . The rejection of the null hypothesis indicates short-run asymmetry. We also estimated the following diagnostic tests to ensure the robustness of the model: the homoscedasticity test, autocorrelation test, residual normality assessment, and Ramsey RESET test. 4. Empirical results 4.1 Linearity tests This study used the BDS test for linearity to assess nonlinearity in the variables under review. The estimated BDS Linearity results are listed in Table 1 . Table 1. BDS Linearity test results. Variable Dimension BDS test statistic Probability HC 2 0.038323 0.0015 *** 3 0.054666 0.0000 *** 4 0.038084 0.0000 *** 5 0.019536 0.0003 *** 6 0.014244 0.0000 *** LVAT 2 0.044650 0.0000 *** 3 0.070921 0.0000 *** 4 0.089881 0.0000 *** 5 0.091713 0.0000 *** 6 0.102875 0.0000 *** INT 2 0.126622 0.0000 *** 3 0.219953 0.0000 *** 4 0.267731 0.0000 *** 5 0.281185 0.0000 *** 6 0.270588 0.0000 *** 2 0.126622 0.0000 *** HDI 2 0.044183 0.0152 ** 3 0.079963 0.0073 *** 4 0.063312 0.0842 * 5 -0.0014653 0.7104 6 -0.002820 0.9429 GDP 2 0.049959 0.0000 *** 3 0.044809 0.0048 *** 4 0.031276 0.0126 ** 5 0.024503 0.0048 *** 6 0.021604 0.0001 *** * Statistically significant at 10% level. ** Statistically significant at 5% level. *** Statistically significant at 1% level. In Table 1 , the findings from the BDS test for linearity show that the null hypothesis of linearity on household consumption, value-added tax, interest rate, household disposable income, and economic growth are rejected at the 1%, 5%, and 10% levels of significance. In summary, the results of the BDS test for linearity indicate that all the variables under review have nonlinearity characteristics. It is worth noting that the BDS results provide insights into the appropriate methodology for examining the linkages between household consumption, value added tax, household disposable income and economic growth. This relationship can only be effectively modelled using a nonlinear approach (NARDL). 4.2 Nonlinear stationarity testing This study estimates the KSS nonlinear unit root test to check the stationarity of the series. Table 2 presents the findings of the KSS test for nonlinearity in the unit root hypothesis at the level. Table 2. KSS nonlinear unit root test results at level. Variable T-Statistics Critical value Conclusion HC -4.191 *** −2.82 Nonlinear stationary process VAT -3.980 *** −2.82 Nonlinear stationary process INT -3.714 *** −2.82 Nonlinear stationary process HDI -5.776 *** −2.82 Nonlinear stationary process GDP -4.397 *** −2.82 Nonlinear stationary process * Statistically significant at 10% level [−1.92] ** Statistically significant at 5% level [−2.22] *** Statistically significant at 1% level [−2.82] It is clear from Table 2 that all the variables–household consumption, value-added tax, interest rate, household disposable income, and economic growth–are nonlinearly stationary at level, with t-statistics of -4.191, -3.980, -5.776, and -4.397, respectively, which are less than the KSS critical value of −2.82 at the 1% significance level. This study did not estimate the KSS nonlinear unit root test at the first difference because all the variables were stationary at level. Therefore, this study employed the NARDL approach because it is effective when the time series exhibits nonlinear stationarity. In addition, this study is of the view that the data properties that suit the NARDL model are nonlinearity, nonlinearly stationarity, and asymmetric cointegration. 4.3 NARDL optimal lag length selection Before the empirical analysis of the NARDL model, this study first determines the optimal lag length. The lag section criteria were selected based on AIC and ARDL frameworks, as illustrated in Figure 1 . Figure 1 illustrates the lag length suggested by AIC in the NARDL (1, 2, 2, 2, 2, 2, 2) model. The lags remain constant across all positive and negative partial breakdowns of the explanatory and response variables. Figure 1. Optimal lag structure. Source: Author’s computation using Eviews 10. After establishing the optimal lag structure for the model, the NARDL bounds test for asymmetric cointegration is performed using the chosen lag specification. 4.4 NARDL bound test to asymmetric cointegration As observed in the preceding section, each variable in the NARDL model was evaluated for asymmetric cointegration using endogenous testing methods. This is conducted to identify at least one cointegrating relationship with asymmetry in the series since the NARDL model functions effectively even in the presence of a single asymmetric long-run equilibrium relationship. The results of the NARDL bound tests are presented in Table 3 . Table 3. NARDL bound test results. Model F-statistic Conclusion HC = f ( LVAT ± , INTR ± , HDI ± , GDP ± ) 5.820890 *** Asymmetric cointegration LVAT = f ( HC ± , INT ± , HDI ± , GDP ± ) 1.029333 No asymmetric cointegration INT = f ( HC ± , LVAT ± , HDI ± , GDP ± ) 1.091261 Asymmetric cointegration HDI = f ( HC ± , LVAT ± , INT ± , GDP ± ) 3.235941 * No asymmetric cointegration GDP = f ( HC ± , LVAT ± , INT ± , HDI ± ) 1.077285 No asymmetric cointegration * Statistically significant at 10% level 1.99 2.94 ** Statistically significant at 5% level 2.27 3.28 *** Statistically significant at 1% level 2.88 3.99 In Table 3 there are two asymmetric cointegrating vectors, which are the main model and one of the subsidiary models, with F statistics of 5.820890 and 3.235941, which is above the critical value at the I(1), at the 1% and 10% significance level, respectively. Therefore, this study continues to estimate the NARDL long- and short-run relationships between the variables of interest, as presented in the following section. 4.5 NARDL long-run model estimates Having determined the existence of two asymmetric cointegration vectors in the previous section and the lag length selection criteria of the NARDL model based on AIC, this study uses the chosen lag length to estimate the long-run relationship between value-added tax and household consumption. The results are presented in Table 4 . Table 4. Long run estimates for NARDL Model. Variable Coefficient Standard error T-statistics Probability LVAT (POS) -14.47224 5.175307 -2.796402 0.0267 ** LVAT (NEG) -14.69062 6.717559 -2.186898 0.0650 * INT (POS) -0.842311 0.331234 -2.543912 0.0148 ** INT (NEG) -0.614892 0.289765 -2.121456 0.0389 ** HDI (POS) 1.031451 0.223413 4.616797 0.0024 *** HDI (NEG) 0.935190 0.383924 2.435871 0.0450 ** GDP (POS) 0.256819 0.309150 0.830728 0.4335 GDP (NEG) 0.406983 0.541896 0.751036 0.4771 R-Squared 0.991514 Adj R-Squared 0.968481 P(F-Statistic) 0.000018 *** * Statistically significant at 10% level. ** Statistically significant at 5% level. *** Statistically significant at 1% level. In Table 4 , a positive change in value-added tax in the long run causes a decrease of 14.47 units in household consumption in South Africa and is statistically significant at the 5% level, since it has a p-value of 2.67%. In other words, in the long run, an increase in value-added tax leads to a decline in household consumption. In Table 4 , a negative change in value-added tax has a negative coefficient of -14.69062, indicating that value-added tax has a negative relationship with household consumption. A negative coefficient indicates that the variables are in the opposite direction. Therefore, a negative change in value-added tax leads to an increase in household consumption, and is statistically significant at the 10% level, with a p-value of 6.50%. This means that a decline in value-added tax results in an increase in household consumption by 14.69 units in the long run. This concurs with Van Oordt (2015) , who used a different technique and found that an increase in VAT caused a decrease in household consumption in South Africa. Similarly, Sekwati and Malema (2011) found that VAT has a negative connection with household consumption in Botswana. In Table 4 , a positive change in the interest rate in the long run leads to a decrease of 0.84 units in household consumption in South Africa and is statistically significant at the 5% level, with a p-value of 1.84%. In other words, in the long run, an increase in the interest rate results in a decline in household consumption. Similarly, Table 4 shows that a negative change in the interest rate is associated with a coefficient of -0.614892, indicating an inverse relationship between interest rates and household consumption. This negative coefficient implies that a decline in interest rate leads to an increase in household consumption. This relationship was also statistically significant at the 5% level, with a p-value of 3.89%. In practical terms, a decrease in the interest rate results in a 0.61-unit increase in household consumption in the long run. These findings are consistent with those of Kozlov (2023) and Ekong et al. (2020) , who also found that an increase in the interest rate causes a decline in consumption. Table 4 clearly shows that a positive change in household disposable income in the long run causes an upsurge of 1.031 units in household consumption in South Africa and is statistically significant at the 1% level, since it has a p-value of 0.24%. On the other hand, a negative change in household disposable income has a positive coefficient of 0.935190, indicating that household disposable income has a positive relationship with household consumption ( Table 4 ). A positive coefficient indicated that the variables were in the same direction. Therefore, a negative change in household disposable income causes a decrease in household consumption in the long run, as shown in Table 4 . In other words, in the long run, a decrease in household disposable income causes a decline of 0.935 units in household consumption in South Africa and is statistically significant at the 5% level, since it has a p-value of 4.50%. Consistent with Sulistyowati et al. (2017) and Aron and Muellbauer (2006) , disposable income is found to be positively related to household consumption. Table 4 indicates that positive changes in economic growth are insignificant, since the p-values are more than the 1%, 5%, and 10% levels of significance in the long run. As indicated in Table 4 , negative changes in economic growth are insignificant since the p-values are greater than the significance level in the long run. These results are aligned with those of Koyuncu and Ünal (2020) , who find that growth insignificantly affects household consumption. In Table 4 , the R-squared value is 0.991514, which means that about 99.15% of the variance in household consumption is explained by value-added tax, household disposable income, and economic growth in the model. This high value indicated an excellent fit of the model to the data. Furthermore, an adjusted R-squared of 0.968481 means that 96.85% of the variance in household consumption is explained, considering the number of predictors and sample size. The slight decrease from R-squared to adjusted R-squared suggests that the model is still excellent, as shown in Table 4 . It is clear from Table 4 that the P-value of the F-statistic is very low, with a value of 0.000018, implying that the overall model is statistically significant at the 1% significance level. 4.6 NARDL short-run model estimates After estimating the NARDL long-run model, it is conventional to generate the NARDL short-run method and error correction term. Table 5 presents the short-run NARDL results. Table 5. Short run estimates for NARDL Model. Variable Coefficient Standard error T-statistics Probability D (LVAT) POS -11.51443 4.458872 -2.582365 0.0363 ** D (LVAT) NEG 15.63948 4.504950 3.471620 0.0104 *** D (INT) POS -0.432145 0.144321 -2.993200 0.0052 *** D (INT) NEG -0.309456 0.118054 -2.621984 0.0126 ** D (HDI) POS 1.180393 0.122879 9.606139 0.0000 *** D (HDI) NEG 1.138582 0.141721 8.033958 0.0001 *** D (GDP) POS -0.133012 0.130491 -1.019321 0.3420 D (GDP) NEG 0.081081 0.137031 0.591700 0.5727 ECT -0.637294 0.089200 -7.143876 0.0000 * * Statistically significant at 10% level. ** Statistically significant at 5% level. *** Statistically significant at 1% level. In the short run, a positive change in value-added tax shows a significant relationship with household consumption with a probability of 3.63%, below the 5% level of significance, as shown in Table 5 . Additionally, a positive change in value-added tax in the short run caused a decline of 11.51 units in household consumption in South Africa. These results are consistent with the long-run outcomes and the empirical literature that includes Alm and El-Ganainy (2013) and Al Gahtani et al . (2020) , who found that the value-added tax and consumption of households are negatively related. It is worth noting that a negative change in value-added tax has a positive coefficient of 15.63948, which means that the value-added tax has a positive relationship with household consumption, as indicated in Table 5 . A positive coefficient indicated that the variables were in the same direction. Therefore, a negative change in value-added tax leads to a decrease in household consumption, and is statistically significant at the 1% level, with a p-value of 1.04%. In other words, a decrease in value-added tax causes a 15.63 unit fall in household consumption; these results are contrary to the long-run findings. In the short term, these results could be caused by economic stress, such as a high rate of unemployment in South Africa. Empirical evidence from other countries, such as studies by Obiakor et al. (2015) , Ajibola and Segun (2017) and Taiwo and Morufu (2016) , is of the view that value added tax and consumption of households are positively related. In South Africa, Omodero et al . (2023) and Erero (2015) find that an increase in VAT would not negatively affect households. In the short run, a positive change in the interest rate leads to a decrease of 0.43 units in household consumption in South Africa and is statistically significant at the 5% level, as indicated by a p-value of 0.52%, as shown in Table 5 . In other words, an increase in the interest rate results in a decline in household consumption in the short term. Table 5 also shows that a negative change in the interest rate has a coefficient of -0.309456, indicating a negative relationship between interest rates and household consumption. This negative coefficient implies that the variables move in the opposite direction. Therefore, a decrease in the interest rate leads to an increase in household consumption and is statistically significant at the 5% level with a p-value of 1.26%. This means that a decline in the interest rate results in a 0.30 units increase in household consumption in the short run. These findings are consistent with those of Owusu-Sekyere (2017) , who also identified a negative relationship between interest rates and household consumption. Table 5 shows that a positive change in household disposable income causes an increase of 1.180 units in household consumption in the short run, and is statistically significant at the 1% level, since it has a p-value of 0%. Table 5 indicates that a negative change in household disposable income has a positive coefficient of 1.138582, which means household disposable income has a positive relationship with household consumption, as indicated in Table 5 . A positive coefficient indicated that the variables were in the same direction. Therefore, a negative change in disposable income has a p-value of 0.01%, which is statistically significant at the 1% level ( Table 5 ). In addition, a negative change in disposable income results in a 1.138 unit drop in household consumption in the short run. These findings concur with the long-term results. These results are in line with Habanabakize (2021) and Bohlmann and Inglesi-Lotz (2021) , who find a positive link between disposable income and household consumption in South Africa. Table 5 shows that positive changes in economic growth are statistically insignificant in the short run. As demonstrated in Table 5 , negative changes in economic growth are statistically insignificant, and these results are in line with the long-run findings. The error correction term has a negative significant coefficient with a p-value of 0%, which is less than the 1% significance level. This implies that the model adjusts substantially toward an equilibrium steady state. This result is satisfactory because the coefficient is negative and statistically significant. 4.7 NARDL long and short run asymmetric effects The estimated results for the long- and short-run asymmetric linkages are discussed in this section. Table 6 presents the outcomes of the Wald test assessing asymmetry in both long-term and short-term dynamics. Table 6. Long and short run asymmetric effects results. Asymmetric null hypothesis Long run Short run T-statistics Probability T-statistics Probability LVAT + = LVAT − -0.700669 0.5061 2.640464 0.0334 ** INT + = INT − -2.621984 0.0126 ** -2.212390 0.0302 ** HDI + = HDI − 0.754248 0.4753 -0.902337 0.3969 GDP + = GDP − -2.869365 0.0240 ** 1.985794 0.0874 * * Statistically significant at 10% level. ** Statistically significant at 5% level. *** Statistically significant at 1% level. Over the long term, the findings in Table 6 indicate no support for asymmetric impacts on the interplay between value-added tax, household disposable income, and household consumption. In other words, the long-run relationships between value-added tax, household disposable income, and household consumption are revealed in this study as symmetric and well represented within the ARDL model. It is clear from Table 6 that both interest rates and economic growth have asymmetric effects on household consumption in the long run. Table 6 also indicates that the interest rate and economic growth have asymmetric effects on household consumption in the short run. Table 6 shows that the short-run relationship between the value-added tax and household consumption shows evidence of an asymmetric effect. Household disposable income has a symmetric effect in both the short and long run, as demonstrated in Table 6 . In the short run, the relationship between value-added tax and household consumption is most effectively modelled using the ARDL specification. The following section presents the estimation of a graphical asymmetric analysis of the value-added tax and household consumption. 4.8 NARDL dynamic multiplier graph This study used a dynamic multiplier graph, and the results are presented in Figure 2 . Figure 2. NARDL dynamic multiplier graph. Source: Author’s computation using Eviews 10. Figure 2 confirms the results in Table 6 , which show that the value-added tax has an asymmetric effect on household consumption in the short run since the asymmetric plot is away from zero between periods 1 and 3.5. Hence, it is concluded that the asymmetry effect is significant in the short run, whereas in the long run, the asymmetry effect is insignificant because the asymmetry plot moves close to the zero line starting from period 3.5 15. Figure 2 indicates that the negative shocks to value-added tax have a positive effect on household consumption, since the negative multiplier line for value-added tax lies on the positive side above the zero line. The positive multiplier line for value-added tax lies below zero on the negative side, as demonstrated in Figure 2 , which implies that positive shocks to value-added tax harm household consumption. 4.9 NARDL residuals diagnostics tests This study ensured the robustness of the NARDL model through diagnostic tests. Table 7 presents the results of the serial correlation, heteroscedasticity, normality, and Ramsey reset test. Table 7. Residual diagnostic test results. Test Null hypothesis P-value Conclusion Breusch-Godfrey No serial correlation 0.2941 There is no serial correlation Breusch-Pagan-Godfrey No heteroscedasticity 0.8699 There is no heteroscedasticity Jarque-Bera Residuals are normally distributed 0.57148 Residuals are Normally distributed Ramsey Reset test No misspecification 0.4430 No misspecification in the model Table 7 shows the results of the Breusch-Godfrey test, with a p-value of 29.41%, suggesting that there is no evidence of serial correlation. Consequently, the null hypothesis of no serial correlation could not be rejected at the 5% significance level. It is clear from Table 7 that the Breusch-Pagan-Godfrey test was utilized in this study to check for heteroscedasticity in the model. The findings from the test results in Table 7 indicate that the null hypothesis of homoscedasticity, which asserts that the variance of the residuals is constant, cannot be rejected. This is because the probability of 86.99% exceeded the 5% significance threshold. Furthermore, the normality of the error terms was assessed using the Jarque-Bera test statistic. As shown in Table 7 , the p-value for the Jarque-Bera statistic was 57.14%. Thus, we accept the null hypothesis that the residuals follow a normal distribution as the p-value exceeds the 5% significance level. The results of the Ramsey RESET test displayed in Table 7 indicate no evidence of model misspecification, as the p-value of 44.30% exceeds the 5% level of significance. Thus, the null hypothesis, which asserts that the NARDL model is correctly specified, was not rejected. This test is followed by the CUSUM and CUSUM of Squares tests. 4.10 NARDL dynamic stability test This thesis also examines the stability of the NARDL model at the 5% significance level. To assess stability, recursive estimations of the NARDL model were performed using the CUSUM and CUSUMQ tests, and the outcomes are presented in Figure 3 . Figure 3. NARDL dynamic stability test results. Source: Author’s computation using Eviews 10. The findings presented in Figure 3 indicate that the NARDL model exhibits stability, as evidenced by both CUSUM and CUSUMQ lines remaining within the 5% significance threshold. Consequently, the model instability hypothesis is rejected. 5. Conclusions, summary, and policy implications Using the NARDL model, this study examines the asymmetric effects of VAT, interest rate, household disposable income, and economic growth on household consumption in South Africa from 1994 to 2023. The key findings reveal significant asymmetries in the long-run relationships: positive changes in VAT and interest rate reduce household consumption, whereas negative changes boost household consumption. Similarly, household disposable income exhibits a symmetric yet significant influence on positive changes in household consumption, leading to increases, whereas negative changes lead to declines. Interestingly, although economic growth displays an asymmetric pattern, the impact of positive and negative shocks on household consumption is statistically insignificant in the long term. In the short run, both positive and negative shocks to VAT and interest rates reduce household consumption, whereas changes in disposable income encourage household consumption. This study contributes to the growing literature on consumption behavior by emphasizing the importance of accounting for asymmetries in macroeconomic policy variables. It advances empirical modelling by applying the NARDL framework to the South African context, uncovering nuanced insights often overlooked in linear models. The theoretical implications suggest that conventional symmetric models may misrepresent the true dynamics of consumption behavior, particularly in economies with structural and fiscal rigidities. These findings support behavioral and nonlinear macroeconomic theories, which allow for differing consumer responses to positive and negative economic stimuli. From a practical perspective, the results highlight the sensitivity of household consumption to fiscal interventions, especially changes in VAT, interest, and disposable income insights that are valuable for businesses and financial institutions to forecast consumer demand. This study argues that the asymmetric impact of VAT changes implies that tax increases have a stronger and more damaging effect on household consumption than tax reductions have a stimulating effect. Therefore, policymakers should exercise caution when adjusting VAT, especially during periods of economic fragility. Strengthening disposable household income through targeted fiscal support, wage policies, or social transfers could serve as a more effective lever for sustaining household consumption. Based on these findings, this study recommends that policymakers prioritize income-enhancing policies and carefully consider the negative effects of VAT and interest rate increase on household consumption. Ethical considerations Ethical approval and consent were not required, as the study used publicly available secondary data. Data availability statement The data supporting the findings of this study are available in the following repositories. The data were sourced from credible sources such as, South African Reserve database [Time series codes: Household consumption: KBP6007Z; Value Added Tax: KBP4431J; Household Disposable Income: KBP6272Z; Gross Domestic Product: KBP6270Z] Link: https://www.resbank.co.za/en/home/what-we-do/statistics/releases/online-statistical-query and the data for Interest Rate was retrieved from World Bank [License: CC BY-4.0] Link: https://data.worldbank.org/indicator/FR.INR.RINR?locations=ZA . Acknowledgements The authorship contributions for this manuscript are as follows: Siyakudumisa Takentsi participated conceptualized the study, conducted formal analysis, and contributed to the methodology, original draft preparation, review, editing, and visualization. Gisele Mah contributed to investigation, original draft preparation, review, editing, and visualization. All authors have read and agreed to the final version of the manuscript. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 09 Oct 2025 ADD YOUR COMMENT Comment Author details Author details 1 Economic Sciences, North West University Faculty of Economic and Management Sciences, Potchefstroom, North West, South Africa Siyakudumisa Takentsi Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Gisele Mah Roles: Funding Acquisition, Project Administration, Supervision, Validation, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The researchers received funding form North West University The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 09 Oct 2025, 14:1056 https://doi.org/10.12688/f1000research.167056.1 Copyright © 2025 Takentsi S and Mah G. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Takentsi S and Mah G. Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.12688/f1000research.167056.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 09 Oct 2025 Views 0 Cite How to cite this report: Berezvai Z. Reviewer Report For: Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.5256/f1000research.184132.r440165 ) The direct URL for this report is: https://f1000research.com/articles/14-1056/v1#referee-response-440165 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 02 Jan 2026 Zombor Berezvai , Corvinus University of Budapest, Budapest, Hungary Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.184132.r440165 The manuscript investigates the effects of value-added tax, interest rate, household disposable income, and economic growth on household consumption in South Africa. The results are generally in line with expectations. The manuscript is interesting, although there are some shortcomings that ... Continue reading READ ALL The manuscript investigates the effects of value-added tax, interest rate, household disposable income, and economic growth on household consumption in South Africa. The results are generally in line with expectations. The manuscript is interesting, although there are some shortcomings that need correction. Theoretical literature review: 1. It is sometimes not clear whether the papers cited investigated a VAT increase or a decrease. For example, “discovered that while VAT has a short-run positive effect on household consumption, its long-run effect is significantly negative” (p. 4). It is unclear whether a VAT increase or decrease was investigated. Furthermore, it would be useful to shed light on how a VAT increase can lead to higher consumption in the short-run or how a VAT decrease can lead to a decline in consumption in the long-run. Methodology: 2. In the NARDL model specification, it is not clear what NI+, NI-, PR+ and PR-, etc., mean. In the case of ΔNI and ΔPR, the + and – differentiation makes sense, but in the case of the level variables, these do not make sense to me (income and price cannot be negative). 3. The effect of VAT or interest rate might be visible only after a threshold is reached; therefore, a threshold cointegration model might have been more useful. This should be added, at the very least, as a robustness test. 4. The descriptive statistics of the variables are missing; please add a table for that. 5. Please indicate whether nominal or real values were used for household consumption and disposable income. I suggest using real values. 6. I assume VAT is not changing very often in South Africa (at least, it is not modified frequently in other countries). However, the number of changes in VAT during the investigated time period (1994–2023) is a crucial identification assumption. If VAT changed only a few times (once or twice) and the changes were only minor, then the conclusions drawn from these changes might not be relevant at all. The manuscript should include a detailed introduction of the changes in VAT in South Africa in the investigated time period. 7. It is essential to analyze the tax shifting of the VAT changes. A 0.5%-point increase in VAT might not alter prices at all, as it might not be passed on to consumers. However, over shifting can also happen. Anyway, it is incorrect to assume a complete tax shifting in all cases; further empirical evidence is needed. Empirical results: 8. Figure 1 is completely meaningless in its current form. Please redo the graph or drop it. 9. “positive change in VAT” – it is misleading as a positive change would mean a tax reduction for the majority of the population; therefore, I prefer using language like “increase in VAT”. The same applies to “negative change in VAT”, which should be replaced with “decrease in VAT”. 10. It would be interesting to see the R-square and the adjusted R-square if VAT is eliminated from the model, i.e., what is the added value of including VAT for R-square. This is crucial to understanding the real impact (effect size) of VAT on household consumption. 11. “In the short run, the relationship between value-added tax and household consumption is most effectively modelled using the ARDL specification” (p. 14). – I believe this is true for the long-run relationship between VAT and HC. 12. Please define what a “dynamic multiplier” is. 13. Please also elaborate more on the dynamic stability test. Conclusions, summary, and policy implications: 14. The general conclusion of the study is very generic: tax increases reduce consumption, while increasing disposable income and social transfers increase consumption. These were very well-known stylized facts even before this study. I’m struggling to find the added value of this research from an academic point of view. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Retailing, Empirical industrial organisation, micromobility, food systems, public health I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Berezvai Z. Reviewer Report For: Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.5256/f1000research.184132.r440165 ) The direct URL for this report is: https://f1000research.com/articles/14-1056/v1#referee-response-440165 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Zídková H. Reviewer Report For: Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.5256/f1000research.184132.r422876 ) The direct URL for this report is: https://f1000research.com/articles/14-1056/v1#referee-response-422876 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 Nov 2025 Hana Zídková , Prague University of Economics and Business, Prague, Czech Republic Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.184132.r422876 The article deals with the very interesting and important topic of the impact of changes in VAT rates on final household consumption. The authors base their analysis on an idea arising from behavioral economics, particularly the assumption that changes in ... Continue reading READ ALL The article deals with the very interesting and important topic of the impact of changes in VAT rates on final household consumption. The authors base their analysis on an idea arising from behavioral economics, particularly the assumption that changes in VAT rates do not necessarily have a symmetrical impact on final household consumption. The result of the article, that a downward change in the VAT rate does not have a positive effect on final consumption in the short term, unlike an upward change in the rate, which reduces final consumption immediately, is important for tax policymakers, but I would not recommend the article for indexing in this form. First of all, it is clear from the entire text that it is based on a diploma thesis (it is even mentioned in the text on page 15). The paper contains a very detailed methodological section at the expense of the literature review, discussion, and conclusion. In my opinion, a detailed description of the model and all the tests performed is appropriate for an econometric journal, where the main topic of the contribution is the econometric model and its correct use. I recommend amending the literature review with articles that are then mentioned in the discussion of the results (e.g., Kozlov (2023) mentioned on page 11, Alm and El-Ganainy (2013) mentioned on page 12, and many others). The studies in the literature review seem to be selected somewhat randomly; not all of them focus on the effect of VAT rates on consumption, and they often examine the opposite relationship. It would be useful to look more closely at the models used in the cited articles, as their results often contradict each other. This may also be due to poor quantitative methods used in those articles. Since the manuscript that I review is very focused on a new dynamic model that allows for the study of nonlinear and asymmetric time series, it would be appropriate to focus on the methods used in the review of the articles. Overall, the literature review is incomplete and not well structured. It would also be appropriate to mention in the literature review the concept of tax incidence, which has an effect on the transfer of increased and reduced rates to the prices of goods and services. This can also affect final consumption. This should be considered in the analysis. In this context, I would perhaps recommend leaving a variable expressing prices in the model. But the authors excluded them because it is not a variable of their interest, as they explain on p. 7. I would also suggest reworking part of the methodology, definitely shortening it, and including the results of individual tests only in the appendix. The results of the NARDL models themselves, on the other hand, should be described better. They should also include an economic interpretation. Why, for example, does final household consumption react asymmetrically to a change in the VAT rate in the short term? Currently, the coefficients and their meaning are only mechanically described, but no economic context is provided. It is also necessary to expand the conclusion, where attention should be paid to the economic and social impacts of changes in VAT rates, and recommendations for tax policymakers should be formulated in more detail. Currently, there is only one general paragraph in the conclusion recommending greater caution in changing VAT rates and the preferential use of other tax instruments. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: VAT, tax evasion, tax policy. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Zídková H. Reviewer Report For: Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.5256/f1000research.184132.r422876 ) The direct URL for this report is: https://f1000research.com/articles/14-1056/v1#referee-response-422876 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 09 Oct 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 09 Oct 25 read read Hana Zídková , Prague University of Economics and Business, Prague, Czech Republic Zombor Berezvai , Corvinus University of Budapest, Budapest, Hungary Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Berezvai Z. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Jan 2026 | for Version 1 Zombor Berezvai , Corvinus University of Budapest, Budapest, Hungary 0 Views copyright © 2026 Berezvai Z. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript investigates the effects of value-added tax, interest rate, household disposable income, and economic growth on household consumption in South Africa. The results are generally in line with expectations. The manuscript is interesting, although there are some shortcomings that need correction. Theoretical literature review: 1. It is sometimes not clear whether the papers cited investigated a VAT increase or a decrease. For example, “discovered that while VAT has a short-run positive effect on household consumption, its long-run effect is significantly negative” (p. 4). It is unclear whether a VAT increase or decrease was investigated. Furthermore, it would be useful to shed light on how a VAT increase can lead to higher consumption in the short-run or how a VAT decrease can lead to a decline in consumption in the long-run. Methodology: 2. In the NARDL model specification, it is not clear what NI+, NI-, PR+ and PR-, etc., mean. In the case of ΔNI and ΔPR, the + and – differentiation makes sense, but in the case of the level variables, these do not make sense to me (income and price cannot be negative). 3. The effect of VAT or interest rate might be visible only after a threshold is reached; therefore, a threshold cointegration model might have been more useful. This should be added, at the very least, as a robustness test. 4. The descriptive statistics of the variables are missing; please add a table for that. 5. Please indicate whether nominal or real values were used for household consumption and disposable income. I suggest using real values. 6. I assume VAT is not changing very often in South Africa (at least, it is not modified frequently in other countries). However, the number of changes in VAT during the investigated time period (1994–2023) is a crucial identification assumption. If VAT changed only a few times (once or twice) and the changes were only minor, then the conclusions drawn from these changes might not be relevant at all. The manuscript should include a detailed introduction of the changes in VAT in South Africa in the investigated time period. 7. It is essential to analyze the tax shifting of the VAT changes. A 0.5%-point increase in VAT might not alter prices at all, as it might not be passed on to consumers. However, over shifting can also happen. Anyway, it is incorrect to assume a complete tax shifting in all cases; further empirical evidence is needed. Empirical results: 8. Figure 1 is completely meaningless in its current form. Please redo the graph or drop it. 9. “positive change in VAT” – it is misleading as a positive change would mean a tax reduction for the majority of the population; therefore, I prefer using language like “increase in VAT”. The same applies to “negative change in VAT”, which should be replaced with “decrease in VAT”. 10. It would be interesting to see the R-square and the adjusted R-square if VAT is eliminated from the model, i.e., what is the added value of including VAT for R-square. This is crucial to understanding the real impact (effect size) of VAT on household consumption. 11. “In the short run, the relationship between value-added tax and household consumption is most effectively modelled using the ARDL specification” (p. 14). – I believe this is true for the long-run relationship between VAT and HC. 12. Please define what a “dynamic multiplier” is. 13. Please also elaborate more on the dynamic stability test. Conclusions, summary, and policy implications: 14. The general conclusion of the study is very generic: tax increases reduce consumption, while increasing disposable income and social transfers increase consumption. These were very well-known stylized facts even before this study. I’m struggling to find the added value of this research from an academic point of view. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Retailing, Empirical industrial organisation, micromobility, food systems, public health I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Berezvai Z. Peer Review Report For: Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.5256/f1000research.184132.r440165) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1056/v1#referee-response-440165 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Zídková H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Nov 2025 | for Version 1 Hana Zídková , Prague University of Economics and Business, Prague, Czech Republic 0 Views copyright © 2025 Zídková H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The article deals with the very interesting and important topic of the impact of changes in VAT rates on final household consumption. The authors base their analysis on an idea arising from behavioral economics, particularly the assumption that changes in VAT rates do not necessarily have a symmetrical impact on final household consumption. The result of the article, that a downward change in the VAT rate does not have a positive effect on final consumption in the short term, unlike an upward change in the rate, which reduces final consumption immediately, is important for tax policymakers, but I would not recommend the article for indexing in this form. First of all, it is clear from the entire text that it is based on a diploma thesis (it is even mentioned in the text on page 15). The paper contains a very detailed methodological section at the expense of the literature review, discussion, and conclusion. In my opinion, a detailed description of the model and all the tests performed is appropriate for an econometric journal, where the main topic of the contribution is the econometric model and its correct use. I recommend amending the literature review with articles that are then mentioned in the discussion of the results (e.g., Kozlov (2023) mentioned on page 11, Alm and El-Ganainy (2013) mentioned on page 12, and many others). The studies in the literature review seem to be selected somewhat randomly; not all of them focus on the effect of VAT rates on consumption, and they often examine the opposite relationship. It would be useful to look more closely at the models used in the cited articles, as their results often contradict each other. This may also be due to poor quantitative methods used in those articles. Since the manuscript that I review is very focused on a new dynamic model that allows for the study of nonlinear and asymmetric time series, it would be appropriate to focus on the methods used in the review of the articles. Overall, the literature review is incomplete and not well structured. It would also be appropriate to mention in the literature review the concept of tax incidence, which has an effect on the transfer of increased and reduced rates to the prices of goods and services. This can also affect final consumption. This should be considered in the analysis. In this context, I would perhaps recommend leaving a variable expressing prices in the model. But the authors excluded them because it is not a variable of their interest, as they explain on p. 7. I would also suggest reworking part of the methodology, definitely shortening it, and including the results of individual tests only in the appendix. The results of the NARDL models themselves, on the other hand, should be described better. They should also include an economic interpretation. Why, for example, does final household consumption react asymmetrically to a change in the VAT rate in the short term? Currently, the coefficients and their meaning are only mechanically described, but no economic context is provided. It is also necessary to expand the conclusion, where attention should be paid to the economic and social impacts of changes in VAT rates, and recommendations for tax policymakers should be formulated in more detail. Currently, there is only one general paragraph in the conclusion recommending greater caution in changing VAT rates and the preferential use of other tax instruments. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise VAT, tax evasion, tax policy. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Zídková H. Peer Review Report For: Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :1056 ( https://doi.org/10.5256/f1000research.184132.r422876) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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