Key determinants of tax revenue in Zimbabwe: assessment using autoregressive distributed lag (ARDL) approach

Chamisa, Moses G.; Sunde, Tafirenyika Article Key determinants of tax revenue in Zimbabwe: assessment using autoregressive distributed lag (ARDL) approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Chamisa, Moses G.; Sunde, Tafirenyika (2024) : Key determinants of tax revenue in Zimbabwe: assessment using autoregressive distributed lag (ARDL) approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-20, https://doi.org/10.1080/23322039.2024.2386130 This Version is available at: https://hdl.handle.net/10419/321560 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Key determinants of tax revenue in Zimbabwe: assessment using autoregressive distributed lag (ARDL) approach Moses G. Chamisa & Tafirenyika Sunde To cite this article: Moses G. Chamisa & Tafirenyika Sunde (2024) Key determinants of tax revenue in Zimbabwe: assessment using autoregressive distributed lag (ARDL) approach, Cogent Economics & Finance, 12:1, 2386130, DOI: 10.1080/23322039.2024.2386130 To link to this article: https://doi.org/10.1080/23322039.2024.2386130 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 13 Aug 2024. Submit your article to this journal Article views: 1745 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Key determinants of tax revenue in Zimbabwe: assessment using autoregressive distributed lag (ARDL) approach Moses G. Chamisa a and Tafirenyika Sunde b,c a Midlands State University Gweru, Gweru, Midlands Province, Zimbabwe; b Namibia University of Science and Technology, Windhoek, Khomas Region, Namibia; c Trade and Development Department, North-West University, South Africa ABSTRACT The study investigates the determinants of tax revenue in Zimbabwe using the ARDL approach for the period 1980 to 2022. This study aims to offer a thorough summary of the different factors that influence tax revenue within the framework of economic and social factors. The variables included in the analysis are GDP growth, the share of agriculture in GDP, private consumption expenditure, inflation, foreign direct investment, real interest rates, trade openness, shadow economy and population growth. The results indicate that private consumption expenditure and share of agriculture in GDP negatively and significantly impact tax revenue in the long run. GDP growth, inflation, foreign direct investment and real interest rates exhibit a positive but insignificant impact on tax revenue. Trade openness, shadow economy and population growth are negatively and insignificantly related to tax revenue. The short-run analysis indicates that lagged tax revenue, GDP growth, private consumption expenditure, inflation, and trade openness significantly impact tax revenue, while the share of agriculture in GDP and the shadow economy significantly hinder tax revenue. Real interest rates and population growth have positive but insignificant impacts on tax revenue. The study’s findings provide valuable guidance to policymakers in formulating policies and strategies that enhance tax revenue collection and support the government’s domestic resources mobilisation agenda by uncovering the relationships between tax revenue and its determinants. IMPACT STATEMENT This research paper provides an in-depth analysis of the determinants of tax revenue in Zimbabwe from 1980 to 2022 using the Autoregressive Distributed Lag (ARDL) approach. By examining various economic and social factors, such as GDP growth, private consumption expenditure, the share of agriculture in GDP, inflation, foreign direct investment, and the shadow economy, the study uncovers both short-term and long-term relationships between these variables and tax revenue. The findings have significant implications for policymakers, offering valuable guidance on enhancing tax revenue collection and supporting Zimbabwe’s domestic resource mobilisation agenda. SIGNIFICANCE The research offers crucial insights into the factors influencing Zimbabwe’s tax revenue performance, which is vital for economic stability and government prosperity. By identifying both the positive and negative impacts of various economic and social factors on tax revenue, the study provides policymakers with a robust foundation for designing effective fiscal policies. The findings emphasise the need for targeted strategies to enhance tax compliance, broaden the tax base, and address challenges the shadow economy poses. Additionally, the research contributes to the broader understanding of tax revenue dynamics in developing countries, making it a valuable resource for economists, analysts, and policymakers working in similar contexts. ARTICLE HISTORY Received 16 May 2024 Revised 27 June 2024 Accepted 11 July 2024 KEYWORDS Tax revenue; determinants of tax revenue; economic growth; ARDL approach; economic and social variables; share of agriculture in GDP; FDI; inflation; shadow economy SUBJECTS Economics; Econometrics; Public Finance CONTACT Tafirenyika Sunde [email protected] Namibia University of Science and Technology, Windhoek, Khomas Region, Namibia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2386130 https://doi.org/10.1080/23322039.2024.2386130 1. Introduction Tax revenue is crucial for government prosperity, as it funds public services and infrastructure development and enhances economic stability. Taxation has a historical significance and enhances social order (Jiang, 2017). Insufficient tax revenue leads to spending shocks and increased borrowing costs for the government. In addition to raising revenue, taxation helps establish a social contract between citizens and the government based on service provision in exchange for taxes paid. Recently in Zimbabwe, the combination of global economic challenges, declining official development assistance, and environmental catastrophes such as climate change and COVID-19 has amplified the importance of tax revenue as a government’s priority. The government has implemented various initiatives to improve tax revenue collection to support its developmental goals. These efforts encompassed launching programs like the Zimbabwe Agenda for Sustainable Socio-Economic Transformation (ZIMASSET) in 2013, implementing the Transitional Stabilisation Programme (TSP) in 2019, and introducing National Development Strategy 1 (NDS1) in 2020. Additionally, reform measures involved instituting new taxes, including an Intermediated Money Transfer Tax, a Digital Services Tax, and a VAT on E-Commerce. These measures were aimed at expanding the revenue base, enhancing tax enforcement, and mobilising resources from various sectors of the economy (Saungweme & Odhiambo, 2018). However, the nation’s socio-economic conditions have worsened and are marked by a high inflationary environment, low utilisation of industrial capacity, and decreases in GDP. The financial flexibility is limited due to weak tax revenue performance amidst increasing current spending and a shrinking tax foundation. Additionally, the country is burdened with significant debt due to its inability to secure international capital and investment inflows. The country faces relatively low tax-to-GDP ratios compared to most Sub-Saharan African countries. The tax-to-GDP ratio measures the extent to which taxes are collected relative to the available economic capacity. It reflects both the effectiveness of tax policy design and the efficiency of tax administration. According to Modica et al. (2018), levels of taxation in an economy indicate the resources accessible for funding government activities. The considerable fluctuations in Zimbabwe’s tax-to-GDP ratios impact its ability to finance public services. For example, from 1980 to 2022, there was a drop in the tax-to-GDP ratio from 16.3% in 1980 to 14.9% in 2020, with significant declines between 2005 and 2008 caused by worsened economic conditions. Volatility in tax revenue performance stems from various factors that need careful examination by policymakers, economists, and analysts alike as it illuminates influences on revenue generation and suggests potential remedies for issues related to collecting taxes. Numerous investigations have examined the correlation between tax revenues and changes in GDP, revealing a positive association (Abate, 2013; Gupta, 2007; Velaj & Prendi, 2014). However, these studies overlooked other important determinants of tax revenues, such as additional macroeconomic variables, social factors, and demographic characteristics. Research on the determinants of tax revenue specific to Zimbabwe is limited. Most analyses that included Zimbabwe used panel data, making isolating the country’s unique behaviour regarding tax revenue determinants challenging. Other studies, such as those by Ndedzu et al. Peter, Binha, Jeketera and Chamisa attempted to explore tax performance in Zimbabwe. Nevertheless, Ndedzu et al. (2013) and Jeketera and Chamisa (2022) focused solely on the buoyancy of tax revenues without identifying specific impacting factors, and Peter (2017) and Binha (2020) did not include key socio-economic variables. This study aims to offer a thorough summary of the different factors that influence tax revenue within the framework of economic indicators (such as GDP growth, private consumption spending, the share of agriculture in GDP, inflation, foreign direct investment, real interest rates and trade openness) and social indicators (shadow economy and population growth). The null hypothesis is that economic and social indicators negatively influence tax revenue, while the alternative hypothesis is that they positively affect tax revenue. By consolidating existing research and empirical data, the study will pinpoint crucial elements that consistently impact tax revenue performance while emphasising areas requiring additional exploration. The structure of this article is as follows: Section 2 provides a literature review that discusses the determinants of tax revenue. Section 3 outlines the research methodology, detailing the econometric model specifications used. Section 4 presents the analysis of the data and discusses the findings. Finally, Section 5 concludes the paper by synthesising the main findings and their implications for policymakers, economists and academics interested in fiscal policy and economic development. 2 M. G. CHAMISA AND T. SUNDE 2. Brief literature review 2.1 Studies that utilised the ARDL approach Several empirical works on the determinants of tax revenue have been conducted, and the literature analysis reveals varying results. Below is a brief review of the empirical work on this critical topic. A study by Mawejje and Munyambonera (2016) in Uganda found that the dominance of the agricultural and informal sectors poses the most significant impediments to tax revenue performance, while the two-period lagged values of GDP and tax revenues had a positive effect on tax revenue. Azime et al. (2017) studied the responsiveness of tax revenue to the economy’s broad sectors, including the agriculture and industry sectors, as well as services and components of government public expenditure. The results indicated that the dominance of the agricultural sector impeded tax revenue performance. Ikhatua and Ibadin (2019) investigated the determinants of tax revenue in Nigeria using time series data covering the period from 1980 to 2015. The results of the study indicated that the shares of the agriculture and tourism sectors in GDP, trade openness, and human capital development had a positive and significant influence on tax revenue. However, productivity in the manufacturing and telecommunication sectors, as well as capital flight, had significant deleterious effects on tax revenue. Akintoye et al. (2019) evaluated the influence of political variables jointly with economic variables on tax revenue in Nigeria. The study’s results showed a significant relationship between political stability and the absence of violence or terrorism and tax revenue. Furthermore, insignificant positive relationships were found between control variables (shares of agriculture and industry in GDP, trade openness, and inflation) and tax revenue. Adesanya (2020) analysed the impact of macroeconomic variables on corporate tax revenue in Nigeria. The results revealed that foreign direct investment and exchange rate were significant positive determinants of corporate tax revenue, while GDP per capita and unemployment rate significantly reduced it. Other variables such as trade openness, public debt, corporate tax rate, and inflation were not significant determinants of corporate tax revenue. A study by Soro (2020) examined the effect of institutional quality on tax revenue in Cote d’Ivoire from 1984 to 2016 and found that low-quality institutions, high informality (shadow economy), and trade openness hinder tax revenue mobilisation. On the other hand, GDP per capita, official development assistance, the share of services in GDP, and income distribution were positively related to tax revenue. In analysing the effect of exchange rate volatility on Ghana’s tax revenue from 1984 to 2014, Ofori et al. (2020) concluded that exchange rate volatility and trade openness negatively impacted tax revenue. However, control variables such as GDP per capita and inflation positively influenced tax revenue. AL-Qudah (2021) studied the determinants of tax revenue in Jordan and revealed that per capita GDP, fiscal deficit, and government expenditure positively impacted tax revenues. Foreign aid had a significant negative impact on tax revenues. Industrial sector value-added and trade openness exhibited positive significance in the short run while having a positive insignificant impact in the long run. Atolagbe and Abiodun (2022) assessed the impact of trade liberalisation on tax revenue mobilisation in Nigeria. The study found that trade liberalisation, coupled with a share of petroleum in GDP, positively impacted tax revenue, while foreign direct investment, inflation, real GDP per capita, and the share of agriculture in GDP negatively influenced it. Sike and Anyanwu (2022) examined the relationship between economic growth and tax revenue in Nigeria from 1970 to 2021 and found that economic growth is a significant positive determinant of tax revenue. Mohammed et al. (2021) analysed the impact of the shadow economy and corruption on Algeria’s tax revenue from 1996 to 2020. The shadow economy and corruption were found to have a negative effect on tax revenue, while positive shocks to real GDP resulted in high tax revenue. Similarly, Hallunovi and Vangjel (2023) investigated the impact of the shadow economy on tax revenue in Albania for the period from 1996 to 2019 and found that the shadow economy erodes tax revenue. Other variables, such as economic growth and tax rate, also negatively affected tax revenue. Tarawalie and Hemore (2021) investigated the determinants of Sierra Leone’s tax revenue from 1990 to 2020. The results indicated that real GDP growth, trade openness, and official development assistance were the main positive determinants of tax revenue. Like most literature, inflation was negatively related to tax revenue. Jeza et al. (2016) analysed the effect of foreign direct investment on tax revenue in Ethiopia for the period from 1974 to 2014. The study found that both foreign direct investment and GDP had a negative impact on tax revenue. Ali and Audi (2018) found a negative relationship between Pakistan’s tax revenue, foreign direct COGENT ECONOMICS & FINANCE 3 investment, and inflation. Kiang et al. (2021) examined the determinants of tax revenue in Malaysia from 1989 to 2018 and found that public debt, trade openness, and foreign direct investment positively impacted tax revenue. GDP per capita, inflation, and manufacturing had a negative impact. Desta et al. (2022) studied the factors affecting tax revenue in Ethiopia from 1996 to 2020 and found that inflation, the share of service in GDP, political stability, and tax reforms were positive influencers of tax revenue. However, the share of agriculture in GDP and corruption impeded tax revenue. In their study on the impact of foreign direct investment on tax revenue mobilisation in South Africa, Jemiluyi and Jeke (2023) revealed that foreign direct investment significantly reduces tax revenue while GDP growth, financial development, trade openness, and consumption expenditure positively impact it. However, Musah et al. (2024) established a positive and significant relationship between foreign direct investment and tax revenue in the long run, while in the short run, its influence is insignificant. Economic factors such as economic growth, inflation, and financial development also exhibited a positive relationship with tax revenue. Using data for Turkey from 2006 to 2022, G€ okpinar (2023) found that broad money supply, industrial production index, interest rate, and export services positively affect tax revenue while unemployment and exchange rate decrease it. 2.2. Studies that utilised other approaches Peter (2017) used the OLS approach to analyse the potential impact of the shadow economy on tax revenues in Zimbabwe and found a positive and significant relationship between the shadow economy and tax revenue. The study also indicated a compelling, significant influence of foreign direct investment, government expenditure, real interest rate, and inflation on tax revenue. Macha et al. (2018) used the same approach to examine tax ratios and efforts in Kenya and Malawi. The results revealed that GDP per capita, the share of agriculture in GDP, and the share of industry in GDP influenced tax revenue in Kenya, while in Malawi, all variables under study explained changes in tax revenue. Ade et al. (2018) employed panel analysis to investigate the determinants of tax revenue performance in all 15 SADC states from 1990 to 2010. The study found that tax rates and policies influence tax revenue performance in the region. Andrejovska and Pulikova (2018) quantified the impact of macroeconomic indicators on tax revenue in 28 EU countries. The study revealed that tax revenue is strongly related to the employment rate, followed by foreign direct investment and gross domestic product. Boukbech et al. (2018) explored the determinants of tax revenues in developing countries from 2001 to 2014 using panel analysis. The results showed that per capita GDP and the value-added of agriculture are significantly and positively related to tax revenue, while trade openness had a positive and insignificant effect on tax revenue. The effect of population growth was found to be negative and significant. Using the OLS approach, Okonkwo (2018) examined the determinants of tax revenue in Nigeria and concluded that tax revenue was significantly influenced by real GDP, broad money supply, interest rate, and inflation. In Tanzania, employing multiple regression analysis, Bitababaje (2020) found that GDP, reforms, and corruption are positively related to tax revenue. Binha (2020) utilised the OLS regression to investigate the impact of foreign direct investment on tax revenue in Zimbabwe. The study’s results revealed that foreign direct investment significantly enhances tax revenue while foreign aid and GDP per capita impede it. Puspita et al. (2020) analysed the role of population, inflation, and economic growth on local tax revenue in East Java Province, Indonesia. The study showed that the population positively and significantly affected local tax revenue, while inflation and economic growth had no effect. Mueni et al. (2021) studied the effects of political risks on tax revenue in Kenya. The study showed that enhanced bureaucracy quality, institutional efficiency, and democratic accountability significantly led to increased tax revenue, while internal conflicts had deleterious effects on tax revenue. Chigome and Robinson (2021) utilised the stochastic tax frontier and unbalanced panel data approaches to investigate the drivers of tax capacity and tax effort in SADC. The results concluded that economic growth, trade openness, inflation, financial deepening, political stability, and population growth positively drove tax capacity and effort. Chikwede (2021) found similar results in his study on SADC countries. Harahap et al. (2018) used panel data analysis to determine the effects of various economic factors on tax revenue in Indonesia, finding that inflation, GDP, exchange rates, and interest rates positively influenced tax revenue. Sari and Aswitari (2020) employed the same approach for the Regency/City of Bali Province, Indonesia. They discovered that household consumption negatively impacted locally generated revenue, 4 M. G. CHAMISA AND T. SUNDE while economic growth had a positive impact, and investment did not affect the locally generated revenue. Using the Johansen Cointegration and VECM estimation techniques, Prowd and Kollie (2021) found that tax revenue was positively influenced by GDP growth and inflation, while social development from agriculture and mining, the real exchange rate, and population growth exhibited negative impacts. To analyse the determinants of tax revenue in upper-middle-income countries, Tsaurai (2021) utilised the generalised Methods of Moments (GMM), pooled OLS, and fixed and random effects models. The study indicated that foreign direct investment, economic growth, financial development, urbanisation, human capital development, and population growth significantly and positively impacted tax revenue, while trade openness and the exchange rate reduced it. Hanrahan (2021) used panel data analysis in OECD countries and found that GDP per capita, trade openness, and the share of agriculture in GDP positively impacted tax revenue. Foreign direct investment, government debt, population growth, and digitalisation negatively affected tax revenue. Andrejovsk a and Golva (2023) employed panel regression analysis to investigate the determinants of corporate income tax revenue in 27 EU member states. The results showed that GDP per capita, public debt, and corruption negatively affected corporate income tax revenue, while the nominal tax rate was an insignificant determinant, and the effective tax rate significantly enhanced tax revenue. Corporate tax revenue was also positively influenced by the harmonised index of consumer prices, foreign direct investment, and trade openness. Ihuarulam et al. (2021) analysed the macroeconomic determinants of tax revenue in 6 ECOWAS countries using the panel data approach. They found that GDP and inflation significantly and positively influence tax revenue, while the effect of the exchange rate is positive but insignificant. Unemployment and trade openness have a negative but insignificant impact on tax revenue. Other studies utilised the panel data analysis approach on developing countries. Lompo (2021) found that in a developed financial sector, GDP growth, trade openness, natural resources rents, and policy significantly and positively impact tax revenue, while the share of agriculture in GDP impedes it. Dale (2022), in his study on five East African countries, revealed that economic growth and political stability positively drive tax revenue, while population growth and corruption pose deleterious effects. Saptono et al. (2022) used the instrumental variable (IV) and System Generalized Method of Moments (SGMM) techniques in 79 developing countries. They found that GDP per capita, trade openness, industry share in GDP, control of corruption, governance index, and trust in politicians enhance tax revenue. Employing the Generalized Method of Moments (GMM) approach on 83 countries from 1990 to 2012, Zarra-Nezhad et al. (2016) found that trade openness positively impacts tax revenue. Other factors that significantly influence tax revenue are the GDP growth rate, the share of agriculture in GDP, the official exchange rate, urbanisation, and democracy. Using panel data analysis, Gnangnon (2023) investigated the effects of the shadow economy on tax revenue. The results indicated that the shadow economy, GDP per capita, inflation, governance quality, and the share of natural resource rents in GDP undermine tax revenue. Trade openness and unemployment exhibited a positive influence on tax revenue. This extensive review of empirical studies across diverse geopolitical and economic contexts demonstrates factors influencing tax revenue. These studies, utilising various econometric approaches such as ARDL, OLS, and panel data analysis, collectively underscore the significant influence of economic growth, sectoral contributions (notably agriculture), foreign direct investment, and institutional quality on tax revenue dynamics. Although occasionally contradictory, the results reflect the different fiscal environments and the varying impacts of economic determinants. This comprehensive analysis advances our understanding of tax revenue determinants and highlights the adaptability and robustness of econometric methods in capturing economic relationships. 3. Research methodology 3.1. Choice of methodology The study utilised the Autoregressive Distributed Lag (ARDL) approach to determine the long and short-run impact of selected macroeconomic and social factors on tax revenue. Pesaran and Shin (1998) introduced and developed the ARDL approach, while Pesaran et al. (2001) refined it a few years later. Several studies (Atolagbe & Abiodun, 2022; Desta et al., 2022;G € okpinar, 2023; Musah et al., 2024; Sike & COGENT ECONOMICS & FINANCE 5 Anyanwu, 2022; Soro, 2020) have adopted this approach to investigate the determinants of tax revenue in various countries. The ARDL approach amalgamates both autoregressive and distributed lag models. Therefore, under the ARDL approach, a time series is a function of its lagged values and the function of current and lagged values of one or more explanatory variables. The ARDL approach was used because it has several advantages that include (i) the ability to capture both the dynamic effects of the lagged dependent variable and lagged exogenous variables; (ii) elimination of autocorrelation in the error term if enough lags of both dependent and explanatory variables are included; (iii) use of only a single reduced form equation to estimate long-run relationships; (iv) handling mixed order of integration of I(0), I(1) or both; (v) applicable to limited sample data while still providing robust and consistent results. 3.2. Data sources This study utilises time series secondary data from 1980 to 2022, chosen primarily based on data availability. The primary variable of interest, total tax revenue (TTR), is measured as a percentage of GDP, representing the government’s tax collection efficiency relative to the overall economy size. Data on TTR were sourced from the Government Finance Statistics (GFS) database of the International Monetary Fund (IMF). The independent variables were obtained from various reputable international and local institutions, including the World Bank Development Indicators (WBDI) and the Zimbabwe Revenue Authority (ZIMRA). The dataset includes annual data points, providing a total of 43 observations for each variable. This extensive period allows for a thorough analysis of trends and the impact of various economic and social factors on tax revenue in Zimbabwe. The data from IMF GFS and the World Bank’s database are freely accessible online for download. For data from ZIMRA, formal requests were made, involving direct contact with the authority to explain the study’s purpose and request the necessary data, which was subsequently provided. 3.2.1. Relationship between independent and dependent variables Table 1 below summarises the variables, expected signs and data sources. In Equation (1), tax revenue is examined in relation to various economic and social variables, each hypothesised to exert either a positive or negative influence. GDP growth is expected to impact tax revenue positively; as the economy expands, the increased business activity, employment, and incomes typically enhance collections from income, corporate, and consumption taxes. This growth broadens the tax base, leading to an overall increase in tax revenue (Besley & Persson, 2014;C¸i gdem & Altaylar, 2021). Similarly, higher private consumption expenditure implies more spending on goods and services, likely resulting in greater collections from sales taxes and value-added taxes (VAT), positively affecting tax revenue (Silva, 2018). Conversely, a larger share of agriculture in GDP often negatively impacts tax revenue due to significant tax exemptions for agricultural outputs and inputs, combined with lower profitability and the Table 1. Summary of variables, expected signs and data sources. Variable Measurement Expected sign Data source Total tax revenue (TTR) Total tax revenue (% GDP) Dependent variable (The lagged value can either be þ/−) IMF GFS, WBDI, ZIMRA GDP growth (GDPGROWTH) Proxy for economic development þWBDI Private consumption expenditure (PCE) Private consumption expenditure (% GDP) þWBDI Share of agriculture in GDP (AGRIC) Agriculture sector (% GDP) −WBDI Foreign direct investment (FDI) Foreign direct investment (% GDP) þWBDI Inflation (INFL) Annual inflation rate (deflator of GDP) −WBDI Real interest rates (RINTR) Real interest rates (proxy of real lending rates) −WBDI Openness Level (OPP) Exports and imports (% GDP) þWBDI Shadow Economy (SE) Dynamic general equilibrium model-based (DGE) estimates of informal output (% of official GDP) −WB Informal Economy Statistics Population Growth (POPGROWTH) Rate of population growth þWBDI 6 M. G. CHAMISA AND T. SUNDE prevalence of small-scale farming, which often escapes formal taxation (Brun & Diakate, 2016; Hanrahan, 2021; Soro, 2020). On the other hand, foreign direct investment (FDI) tends to positively influence tax revenue through various channels, such as corporate taxes from profits generated by foreign enterprises and taxes on employee wages, enhancing the overall tax intake (Gul & Naseem, 2015; Tsaurai, 2021). Inflation generally has a negative effect on tax revenue as it can erode the real value of money, leading to lower real tax collections if the tax system is not indexed to inflation. Furthermore, inflation can alter consumer and business behaviour, pushing them towards non-taxable or less-taxed goods and services, thereby reducing tax revenue (Kitessa & Jewaria, 2018; Muchiri, 2014). Higher real interest rates may also negatively impact tax revenue by slowing economic activities, as increased borrowing costs can suppress both investment and consumption, thus reducing income and sales tax collections (Huang & Frentz, 2014; Onakoya et al., 2020; Tanzi, 1989). A greater degree of trade openness is associated with a positive impact on tax revenue. Open economies typically experience increased business activities and receive higher income from customs and tariffs, fostering overall economic growth and enhancing tax revenue (Binha, 2020; Ikhatua & Ibadin, 2019; Minh Ha et al., 2022). However, a substantial shadow economy usually indicates untaxed economic activities, which drain potential tax revenues as transactions in this sector typically evade formal taxation systems, negatively impacting official tax revenue (Chelliah, 1971; Chikwede, 2021; Gnangnon, 2023). Finally, population growth is expected to positively influence tax revenue by increasing the number of taxpayers and potentially boosting consumption and overall economic activity, thereby enhancing tax collection from various sources (Khujamkulov, 2016; Jiang, 2017; Puspita et al. (2020). 3.3. Model specification and estimation technique The study models tax revenue as a function of macroeconomic and social variables. TR ¼f Economic Variables, Social Variables ðÞ (1) where: TR ¼Tax revenue Economic variables ¼(GDP Growth (GDPGrowth), private consumption expenditure (PCE), share of agriculture in GDP (AGRIC), inflation (INFL), foreign direct investment (FDI), real interest rates (RINTR) and trade openness (OPP)). Social Variables ¼ðshadow economy ðSEÞand population growth ðPOPGROWTHÞÞ After intercept, error term and transforming all variables in the form of natural logarithms, Equation (1) has the new form specified as: LnTRt¼a0þb1LnGDGROWTHtþb2LnPCEtþb3LnAGRICtþb4LnINFLtþb5LnFDItþb6LnRINTRt þb7LnOPPtþb8LnSEtþb9LnPOPGROWTHtþet(2) The study adopted the approaches of Amin et al. (2014), Ahmad et al. (2016), and Hassan et al. (2016) to develop its ARDL models. The ARDL models used to test for long-run relationships of variables in Equation (2) are presented below: a1þX n i¼0 b1DLnTRt-iþX n i¼0 b2DLnGDGROWTHt-iþX n i¼0 b3DLnPCEt-iþX n i¼0 b4DLnAGRICt-i þX n i¼0 b5DLnINFLt-iþX n i¼0 b6DLnFDIt-iþX n i¼0 b7DLnRINTRt-iþX n i¼0 b8DLnOPPt-iþX n i¼0 b9DLnSEt-i þX n i¼0 b10DLnPOPGROWTHt-iþk1LnTRt-iþk2LnGDPGROWTHt-iþk3LnPCEt-iþk4LnAGRICt-i þk5LnINFLt-iþk6LnFDIt-iþk7LnRINTRt-iþk8LnOPPt-iþk9LnSEt-iþk10LnPOPGROWTHt-iþe1t (3) COGENT ECONOMICS & FINANCE 7 Hasan (2010) and Puspita et al. (2020) but opposes Mahdavi (2008), Boukbech et al. (2018) and Tsaurai (2021). Finally, the significant and near-unity error correction term indicates a rapid adjustment of tax revenue towards its long-term equilibrium following shocks, highlighting the resilience of the tax system and the efficacy of current tax policies in maintaining stability. 4.6. Diagnostic tests Various diagnostic tests were carried out to check the robustness of the estimated model. The BreuschGodfrey Serial Correlation LM Test was employed to test for autocorrelation, while the BreuschPagan-Godfrey and ARCH tests were employed for heteroscedasticity. The Jarque-Bera test was used to test for normality. Finally, the Ramsey RESET test was applied to test the model specification. Table 9 presents the results of the diagnostic tests. The null hypothesis of the autocorrelation, heteroscedasticity, normality and functional form misspecification test is accepted since the p-values of their respective tests are insignificant at the 5% level for the estimated model. The stability of the estimated model was examined through the cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMSQ) test. The graphical representation of the results is presented in Figure 1. The results reveal that the model’s coefficients are stable over time as they fall within the 5% significance level. Therefore, the estimated results are reliable. 5. Conclusion, policy implications and future research The novelty and contribution of this article lie in its comprehensive examination of Zimbabwe’s tax revenue determinants from 1980 to 2022 using the ARDL approach. This method uniquely captures both short-run and long-run dynamics, integrating a broad spectrum of economic and social variables, such as GDP growth, private consumption expenditure, agriculture’s GDP share, inflation, FDI, real interest rates, trade openness, the shadow economy, and population growth. The results indicate that in the Table 9. Diagnostic test results. Diagnostic test Type of test F-statistics p-value Autocorrelation test Breusch-Godfrey Serial Correlation LM Test 0.395158 0.6796 Heteroscedasticity test Breusch-Pagan-Godfrey Test 1.144799 0.3856 ARCH Test 0.193117 0.6638 Normality test Jarque-Bera Test 1.177878 0.554916 Functional misspecification test Ramsey RESET Test 0.611241 0.5487 Source: Authors’computation. Figure 1. Stability test results. Source: EViews output. 14 M. G. CHAMISA AND T. SUNDE long run, private consumption expenditure and the share of agriculture in GDP significantly impede tax revenue, while GDP growth, inflation, foreign direct investment, and real interest rates have insignificant positive effects. Trade openness, the shadow economy, and population growth have insignificant negative impacts. In the short run, total tax revenue, GDP growth, private consumption expenditure, inflation, and trade openness positively and significantly influence tax revenue, while the share of agriculture in GDP and the shadow economy significantly erode tax revenue. The impacts of real interest rates and population growth are positive but insignificant. These insights provide crucial guidance for policymakers aiming to enhance tax revenue collection and support domestic resource mobilisation in Zimbabwe. The positive relationship between GDP growth and tax revenue implies that more tax revenue is collected because of economic growth. Thus, policymakers should develop and implement prudent policies to enhance economic growth and support the domestic resources mobilisation agenda. Foreign direct investment exhibited a positive influence on tax revenue, indicating the impact of its positive spillover, which enhances the productivity of local firms. Therefore, policymakers should create and nurture a sustainable environment that attracts foreign direct investment directed to the productive sectors of the economy. On the other hand, policymakers should not be waylaid by the insignificant positive influence of foreign direct investment as its effects on tax revenue are long-term. The long-run negative impact on private consumption expenditure tax revenue implies a shift in the citizenry’s consumption towards basic non-taxable or lower-taxed goods and services as they endure the prevailing economic hardships and an increase in informalisation outside the tax net. Hence, policymakers are encouraged to develop policies that encourage consumption and investment in taxable goods and services and promote formal economic activities that are easy to tax. Trade openness is negatively associated with tax revenue, indicating the negative effects of tariff reductions and unfair competition associated with trade liberalisation. Policymakers should try to balance the non-revenue advantages and revenue disadvantages of trade openness to achieve successful trade reforms. This may mean revising trade policies to improve collections from expanded trade. A negative and significant association between the share of agriculture in GDP was revealed, implying the difficulty of collecting taxes from the sector due to its heavy subsistence nature. The sector also enjoys significant tax exemptions, which hinder tax revenue collection. Policymakers should work on modernising and transforming subsistence agriculture into commercial agriculture and collect tax from the outputs or the sector’s value added. The long-run and short-run impact of population growth on tax revenue is negative. This calls for policymakers to invest in policies that will alleviate the negative effects of population growth on tax revenue. Inflation and real interest rates positively influence tax revenue, but the influence is insignificant, albeit significant for inflation in the short run. This implies that although these are not critical factors for now in increasing tax revenue, policymakers should aim to keep them at reasonable and acceptable levels to ensure macroeconomic stability. However, the shadow economy negatively impacts tax revenue, reflecting its huge size, estimated at around 60%. Thus, policymakers must develop and implement policies to reduce shadow economy activities and enhance tax revenue collection. In addressing the gaps identified in this research, future studies should consider a more extensive array of macroeconomic variables, such as technological advancements and their impact on tax compliance and collection efficiency. Additionally, incorporating structural changes, such as major policy reforms or significant economic events, would provide a deeper understanding of their effects on tax revenue dynamics. Exploring qualitative factors, like governance quality and taxpayer attitudes, could also enrich an understanding of the complexities involved in tax revenue generation. By broadening the analytical framework in these ways, subsequent research can offer better guidance for policymakers aiming to enhance tax revenue collection in contexts similar to Zimbabwe’s or other settings with comparable economic challenges. Informed consent statement Not applicable. COGENT ECONOMICS & FINANCE 15 Author contributions Conceptualisation, M.G.C. and T.S.; methodology, M.G.C. and T.S.; software, M.G.C. and T.S.; validation, T.S.; formal analysis, MGC.; investigation, MGC.; resources, M.G.C. and T.S; writing-original draft preparation, M.G.C.; writingreview and editing, M.G.C. and T.S.; supervision, T.S.; project administration, M.G.C. Both authors have read and agreed to the published version of the manuscript. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This research received no external funding. About the authors Moses G. Chamisa is a PhD candidate at Midlands State University, Zimbabwe, and holds a Master’s degree from the National University of Science and Technology (NUST), Zimbabwe. With experience as a researcher, tax administration expert, and consultant, his expertise spans various areas, including quantitative and qualitative research, tax and economic policy design, data analytics, and more. He specializes in enhancing tax compliance, managing tax arrears, and reforming tax and customs administration. Tafirenyika Sunde is an Associate Professor of Economics at the Namibia University of Science and Technology (NUST), formerly known as the Polytechnic of Namibia. He is also an Extraordinary Associate Professor at South Africa’s North-West University (NWU). Before joining the then Polytechnic of Namibia in 2008, he worked as a Teaching Assistant at the University of Zimbabwe (UZ) and as a lecturer at Midlands State University (MSU). His research interests include macroeconomics, energy economics, econometrics, and public policy. Sunde has published several research articles in peer-reviewed local and international journals. He holds a DLitt et Phil in Economics from the University of South Africa and an MSc and a BSc from the University of Zimbabwe. 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