Search Results (12)

Due to tough economic conditions, more retailers are relying on lay-by agreements to maintain revenue. Lay-by agreements are thus part of their business models and are included in their forecasting and budgeting strategies. As part of financial planning, decisions need to be made based on financial information to achieve organisational goals. A recent South African income tax amendment regarding lay-by agreements resulted in three possible income tax interpretations. This study analysed and evaluated the implications of these amendments for South African deferred tax. The study utilised a doctrinal approach in an interpretive paradigm. The results show that the amendment in the South African Income Tax Act relating to lay-by agreements has an impact on deferred tax calculations, depending on the tax interpretation used. The resulting ambiguity and diversion in the practice of the deferred tax treatment may potentially lead to less useful financial information, contrary to the objectives of the International Accounting Standards Board for effective decision making. This study recommends that the National Treasury should clarify this ambiguity, through either legislative amendments or an interpretation note. This will create the necessary certainty for organisations to plan their finances. Full article

Corporate taxation assumes a pivotal role in all economies, as it constitutes a substantial source of revenue for governmental agencies tasked with fulfilling social obligations. Nonetheless, modifications in financial policies and the unpredictability of macroeconomic factors result in a significant decline in this vital revenue source for the government. This study examines the financial determinants influencing corporate tax revenue in Nigeria from 1990 to 2022. In this analysis, the broad money supply, access to credit by the private sector, borrowing costs, and exchange rates are utilized as independent variables, while corporate tax revenue serves as the dependent variable. Data pertinent to this investigation on corporate income tax are sourced from the Federal Inland Revenue Service, whereas information regarding the broad money supply and credit extended to the private sector is acquired from the Central Bank of Nigeria. Additionally, statistical data on interest and exchange rates are gathered from the World Bank. This investigation applies autoregressive distributed lag and error correction models, acknowledging the existence of a long-term relationship within the series. The significant findings indicate that the broad money supply positively and significantly affects corporate income tax in the short run, but this effect diminishes to a positively insignificant level in the long run. Additionally, the interest rate is shown to have a significant harmful effect on corporate tax income in the short run, while it becomes negatively insignificant over the long term. Other financial policy factors do not significantly account for changes in corporate income tax. This study suggests the formulation of financial policies that are advantageous to corporate organizations, particularly through the reduction in borrowing costs, to facilitate business growth and enhance the government’s ability to collect substantial corporate tax revenue. The originality of this research is apparent in its utilization of financial policy instruments to illustrate the effectiveness of financial guidelines on corporate tax receipts and to argue for particular amendments that are essential when these guidelines prove detrimental to business activities. Full article

The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of the constituent entities of the Russian Federation and more than 93% of the structure of tax revenues from special tax regimes. The purpose of this study is to identify and assess the factors influencing tax revenues from the tax levied in connection with applying the simplified system of taxation (taxable object—income reduced by the amount of expenses). The objective of this study is to determine a set of factors used by economists to model the level of tax revenues and to conduct a corresponding econometric analysis of the influence of the selected factors on the dependent variable to identify characteristics of the simplified taxation system functioning in the Russian Federation. The object of this study is the per capita tax revenue from the tax levied in connection with applying the simplified system of taxation (the object of taxation is income reduced by expenses) in the Russian Federation. The subject of the research is a set of economic relations, which arise because of tax-legal relations between tax authorities and taxpayers in relation to the calculation of the tax levied in connection with the application of the simplified taxation system. This study’s hypothesis is that the amount of tax revenues is influenced by factors characterizing the economic situation and development of small and medium businesses in the constituent territories of the Russian Federation. This study was conducted in 83 constituent territories of the Russian Federation in 2020–2022. The research methods are statistical analysis and econometric modeling on panel data. During this study, six econometric models were constructed. Based on the results of specification tests, the least squares dummy variables model was selected. The results of the modeling show that the tax rate, the number of taxpayers, and the real average per capita monetary income of the population have a statistically significant impact on the per capita tax revenue under the simplified tax system (the object of taxation is income reduced by the number of expenses). As a result, the focus of economic policy at both macro and meso levels should be on the support of small and medium-sized enterprises in the early stages of their life cycle, as well as on the increase of the purchasing power of the population. Based on the results obtained, it is possible to forecast the revenue side of the budgets of the constituent entities of the Russian Federation. Full article

Healthcare is a huge business sector in many countries, focusing on the social function of delivering quality health when people develop illness. The system is essentially financed by public funds based on the solidarity principle. With a large financial outlay, the sector must use economic evaluation methods to achieve better efficiency. The objective of our study was to evaluate and to understand how health economics is used today, taking Belgium as an example of a high-income country. The evaluation started with a historical view of healthcare development and ended with potential projections for its future. A literature review focused on country-specific evaluation reports to identify the health economic methods used, with a search for potential gaps. The first results indicated that Belgium in 2021 devoted 11% of its GDP, 17% of its total tax revenue, and 30% of the national Social Security Fund to health-related activities, totalizing EUR 55.5 billion spending. The main health economic method used was a cost-effectiveness analysis linked to budget impact, assigning reimbursable monetary values to new products becoming available. However, these evaluation methods only impacted at most 20% of the money circulating in healthcare. The remaining 80% was subject to financial regulations (70%) and budgeting (10%), which could use many other techniques of an economic analysis. The evaluation indicated two potentially important changes in health economic use in Belgium. One was an increased focus on budgeting with plans, time frames, and quantified treatment objectives on specific disease problems. Economic models with simulations are very supportive in those settings. The other was the application of constrained optimization methods, which may become the new standard of practice when switching from fee-for-service to pay-per-performance as promoted by value-based healthcare and value-based health management. This economic refocusing to a more constrained approach may help to keep the healthcare system sustainable and affordable in the face of the many future challenges including ageing, climate change, migration, pandemics, logistical limitations, and financial instability. Full article

This research aims to explore the fiscal and public finance viability on climate physical risk externalities cost for building social-economic-environmental sustainability. It analyzes climate physical risk impact on the real business cycle to change the macroeconomic output functions, its regressive cyclic impact alters tax revenue income and public expenditure function; This research also analyzes that the climate physical risk escalates social-economic inequality and change fiscal-financial policy functions, illustrates how the climate damage cost and adaptation cost distorts fiscal-finance cyclical and structural equilibrium function. This research uses binary and multinomial logistic regression analysis, dynamic stochastic general equilibrium method (DSGE) and Bayesian estimation model. Based on the climate disaster compensation scenarios, damage cost and adaptation cost, analyzing the increased public expenditure and reduced revenue income, demonstrates how climate physical risk externalities generate binary regression to financial fiscal equilibrium, trigger structural and cyclical public budgetary deficit and fiscal cliff. This research explores counterfactual balancing measures to compensate the fiscal deficit from climate physical risk: effectively allocating resources and conducting the financial fiscal intervention, building greening fiscal financial system for creating climate fiscal space. Full article

Tax revenue represents an essential budget source for most countries around the world. Accordingly, the modernization of relevant technological infrastructure has become a key factor of tax administration strategy for improving tax collection efficiency. In particular, the fiscal consolidation of the Kingdom of Saudi Arabia has been supported by considerable development in tax policy and administration, aimed at raising more taxes from non-oil activities. In fact, non-Saudi investors are liable for income tax in Saudi Arabia. On the other hand, Saudi citizen investors (and citizens of the GCC countries) are liable for Zakat, an Islamic assessment. Typically, taxpayers are in charge of preparing and accurately reporting their Zakat declaration. This allows tax authorities to overview and audit their business activities. However, despite administration efforts to increase taxpayer compliance, considerable revenue remains at under-reporting risk. In this paper, we introduce a novel intelligent approach to support tax authority efforts in detecting under-reporting among Zakat payer declarations. In particular, the proposed solution aims at improving detection accuracy and determining the fraud cases that correspond to a higher revenue at risk. Specifically, we formulate Zakat under-reporting detection as a supervised machine learning task through the design of a deep neural network that performs simultaneous classification and regression tasks. In particular, the proposed network contains an input layer, five hidden layers, and two output layers for classification and regression. Zakat declarations are mapped into the predefined “under-reporting” or “actual declaration” classes. Moreover, the revenue at risk caused by the predicted fraud cases is learned by the designed model. This allows the proposed approach to prioritize the auditing of specific Zakat payers based on the corresponding predicted revenue at risk. A real dataset including 51,919 Zakat declarations was used to validate and assess the designed model. Further, the Synthetic Minority Oversampling Technique (SMOTE) boosted the proposed model performance in terms of classification and prioritization. Full article

We seek to explore whether local economic characteristics affect the collection of profit-seeking enterprise (PSE) income tax in Taiwan, by adopting panel data from 2001 to 2020 collected in its counties and cities. The results of this analysis of spatial econometric modeling indicate that the increase in sales of profit-seeking enterprises (SPSE) has a positive and significant direct effect on the collection of PSE income tax in this county and city. In terms of spatial spillover effects, when the number of profit-seeking enterprises (NPSE) in neighboring regions increases and the percentage of employees working in industrial sectors (PEI) increases, they will then impact the increase in PSE income tax collection in any particular county and city. We find that the amount of PSE income tax collection relates to the agglomeration economy. The findings of this study may be provided as a reference for local governments to conduct administrative construction on the formulation of PSE income tax collection. Full article

The main role of local development policy is to create a favorable business environment and new jobs, thus contributing to economic development. Creating a positive business environment to act as a pull factor for new businesses is of great importance, because entrepreneurship conduces to the rise in the supply of goods and to an increase in citizens’ income and revenues of local budgets. This paper therefore examines the main goals of economic development in the towns and municipalities of the Republic of Croatia, as well as the tools used by local governments to encourage entrepreneurship and new businesses. Using a questionnaire, mayors and deputies of towns and municipalities were surveyed, and the sample covered 131 towns and municipalities. The research methodology included a descriptive analysis and the Kruskal–Wallis test. The results indicate that improving the quality of life of the residents, creating new jobs, and retaining the residents in the town or municipality were identified as the most important goals of economic development. To encourage entrepreneurship and new jobs, towns and municipalities most often simplify regulations, reduce local taxes, and introduce various benefits and incentives. The contribution of the paper is manifested in providing useful guidance to local governments to improve the business environment. Full article

Detecting tax fraud is a top objective for practically all tax agencies in order to maximize revenues and maintain a high level of compliance. Data mining, machine learning, and other approaches such as traditional random auditing have been used in many studies to deal with tax fraud. The goal of this study is to use Artificial Neural Networks to identify factors of tax fraud in income tax data. The results show that Artificial Neural Networks perform well in identifying tax fraud with an accuracy of 92%, a precision of 85%, a recall score of 99%, and an AUC-ROC of 95%. All businesses, either cross-border or domestic, the period of the business, small businesses, and corporate businesses, are among the factors identified by the model to be more relevant to income tax fraud detection. This study is consistent with the previous closely related work in terms of features related to tax fraud where it covered all tax types together using different machine learning models. To the best of our knowledge, this study is the first to use Artificial Neural Networks to detect income tax fraud in Rwanda by comparing different parameters such as layers, batch size, and epochs and choosing the optimal ones that give better accuracy than others. For this study, a simple model with no hidden layers, softsign activation function performs better. The evidence from this study will help auditors in understanding the factors that contribute to income tax fraud which will reduce the audit time and cost, as well as recover money foregone in income tax fraud. Full article

The availability of open government data has expanded considerably in recent years. This expansion is expected to generate significant benefits not just for increasing government transparency, but also for the economy. The aim of this study is to illustrate the use of open government data in estimating personal income levels for all 3181 municipalities, towns, and communes in Romania. The novelty of our work comes from the high granularity of the estimates obtained. We use tax revenues collected by local governments in Romania on vehicles and buildings owned by natural persons, as well as data on energy subsidies. The classification is conducted using the k-means clustering algorithm. We find three distinct clusters of communities, which we map. The results can benefit both businesses and policymakers. The former can use the income level estimates for market intelligence purposes, while for the latter, these may aid in determining the financial sustainability of local governments and a better allocation of central government resources at the subnational level. Full article

This study uses a Computable General Equilibrium model to analyze policy scenarios for a carbon tax on greenhouse gas emissions from petroleum fuels and kerosene in Ethiopia. The carbon tax starts at $5 per ton of carbon dioxide in 2018 and rises to $30 per ton in 2030; these rates are translated into taxes on the different energy types covered, depending on their carbon contents. Different scenarios examine the impacts with revenue recycling through a uniform sales tax reduction, reduction of labor income tax, reduction of business income tax, direct transfer back to households, and use by the government to reduce debt. Because petroleum fuels and kerosene are a relatively small part of the Ethiopian economy, the carbon tax has small impacts on overall economic activity and greenhouse gas emissions. In proportional terms, however, the impact on greenhouse gas emissions from these energy sources is notable, depending on the recycling scenario. The assumed carbon tax trajectory also can raise significant revenue—up to $800 million per year by 2030. The impacts on the poor through increased cost of living are not that large, since the share of the poor in total use of the taxed energy types is small. In terms of induced income effects through employment changes, urban households tend to experience more impacts than rural households, but the results also depend on the household skill level and the revenue recycling scenario. Full article

In November 2016, Washington State voters were presented with a ballot initiative (Initiative 732) advancing the first carbon tax on production and use of fossil fuels in the United States. Initiative 732 promised to reduce fossil fuel consumption by taxing carbon emissions, while remaining revenue-neutral by lowering taxes on businesses, consumers, and working families. In promising revenue-neutrality, Initiative 732 sought support beyond environmentalists and similarly sympathetic voters. It failed to pass, achieving 41.2 percent of votes cast. To investigate this initiative’s failure at the ballot, we analyzed zip code-level voting patterns and demographic data. Relying on a two-step LASSO (Least Absolute Shrinkage and Selection Operator) + OLS (Ordinary Least Squares) procedure, our results suggest that the framing of revenue-neutrality did not sufficiently satisfy moderate right-leaning voters regarding perceived costs of the carbon tax. We also found evidence suggesting not only that some voting segments may have opposed revenue-neutrality, but that those facing higher climate change risk did not appear to see the initiative’s value net of expected costs. Full article