Estimating Vat Refunds the Case of South Africa

This study intends to estimate VAT refund levels in South Africa in an ideal situation where there are well-equipped, incorruptible officials and a proper VAT system is in place. Understanding the dynamics behind the behaviour of VAT and its main drivers is crucial and could have a huge benefit to the country’s economy with regards to closing the tax gap related to this tax type. Using the data from various sources (VAT refunds and some macroeconomic variables), a Vector Autoregression (VAR) model was used to estimate the level of VAT refunds in South Africa. The model estimates VAT refunds for the period 2021/22 to be R242.7 billion, while the VAT refunds forecast for the period 2022/23 and 2023/24 amounts to R254.6 billion and R267.3 billion, respectively. Furthermore, VAT refunds contribute on average 17.5% to the total tax for the forecast period of 2021/22-2023/24. The study also indicates that the growth in VAT refunds is influenced by the growth in domestic VAT collections, increasing employment rate and the growth in both agriculture and construction GDP. The estimated level of VAT refunds can serve as an important consideration in the national budgeting processes in South Africa. Adequate provisions can be made to enable proper planning and distributions to government departments. To our knowledge, this study is the first of its kind for South Africa. In summary, the South African tax authority should not deviate from the primary goal of building sound VAT systems based on improved voluntary compliance through effective systems of self-assessment

Impact of tourism development on inclusive growth: A panel vector autoregression analysis for African economies

The persistent debate among policy makers and academics around combating the high rates of poverty and income inequality can be further illuminated by understanding how tourism contributes to inclusive growth, especially in developing economies. Tourism sector can be regarded as one of the key contributors to inclusive growth and where it has the capacity to generate prospects for productive employment. The goal of this article is thus to investigate the link between inclusive growth and tourism in the African context. To do this, we utilized a recent panel vector autoregression (pVAR) and data for 45 African countries spanning the period 1995 to 2019. Thus, by the error variance decomposition and impulse response functions, our results showed a weak positive effect of international tourism arrivals and the composite tourism indicator on inclusive growth, while tourism receipts and tourism expenditure insignificantly decreases inclusive growth in the sampled African economies. Our result is further supported by the panel system generalized method of moments (GMM). We provide some policy implications from our findings.

THE CONNECTEDNESS BETWEEN THE SENTIMENT INDEX AND STOCK RETURN VOLATILITY UNDER COVID-19: A TIME-VARYING PARAMETER VECTOR AUTOREGRESSION APPROACH

We analyze the connectedness between the sentiment index and the return and volatility of the crude oil, stock and gold markets by employing the time-varying parameter vector autoregression model vis-à-vis the coronavirus disease (COVID-19) epidemic. Our sentiment index is constructed via text mining technology. We also employ a network to visualize and better understand the structure of the connectedness. The results confirm that the sentiment index is the net pairwise directional connectedness receiver, while the infectious disease equity market volatility tracker is the transmitter. Furthermore, the impact of the COVID-19 pandemic on the total connectedness of volatility is unprecedented.

Technological progress spillover effect in Lithuanian manufacturing industry

Research background: Various methods for technological progress assessment and evaluation exist in the context of economic development. Each of the methods possesses distinct advantages and disadvantages in analysis of technological progress fluctuations. For most neoclassical growth theories, technological progress measures are included as exogenous variables, thus excluding evaluation of factors influencing technological progress variation throughout time. Purpose of the article: The aim of this article is to offer improvements on classical technological progress evaluation methodologies for manufacturing industries, separating effect of intersectoral technological progress spillover effect from internal factors influencing technological progress growth and perform analysis in the case of Lithuanian manufacturing industry. Methods: Earlier research papers used linear time series regression and vector autoregression methods to assess technological progress values and define equations explaining effect of different manufacturing level indicators on technological progress measure growth. This research paper uses results of previously mentioned methods and performs simulation analysis applying agent-based modelling framework. Findings & value added: The conducted vector autoregression analysis has showed that two variables which influence technological progress most significantly are labor productivity measure and gross profit value. Sensitivity analysis emphasizes that effect of these two variables on technological progress growth is substantially different. Increase in gross profit value affects technological progress growth for wider range of sectors from Lithuanian manufacturing industry (15 out of 18 analyzed sectors? technological progress measure values are affected by changes in gross profit, while changes in labor productivity influence technological progress values in the case of 9 sectors). Rising gross profit values also produce intersectoral technological progress spillover effect more significantly, while growth in labor productivity measure has stronger effect on technological progress fluctuations for sectors which are able to exploit this effect. Presented research suggests improved methodology for intersectoral technological progress spillover effect assessment in the context of manufacturing industries.

An Empirical Study of Carbon Emission Impact Factors Based on the Vector Autoregression Model

It is important to effectively reduce carbon emissions and ensure the simultaneous adjustment of economic development and environmental protection. Therefore, we used Kaya identity to screen the factors influencing carbon emissions and conducted preliminary qualitative analyses, including grey relation analysis and linear regression analysis, on important variables to establish a vector autoregression (VAR) model based on their annual data to empirically analyze the influencing factors of carbon emissions. The results showed that economic growth effect, energy intensity effect and embodied carbon in foreign trade were the key factors affecting carbon emissions, among which the economic growth effect contributed the most. Accordingly, we propose countermeasures including technological innovation to reduce energy intensity, the development of new energy sources to improve energy structure, acceleration of industrial structure transfer, and optimization of trade structure.

Macroeconomic Variables and Sukuk Outstanding in Indonesia

Sukuk or Sharia bonds are one of the investment instruments in Indonesia. Since the 19th century, Sukuk has become popular with investors. Several previous studies found contradictory results that macroeconomic variables have a relationship and influence on Sukuk by observing the year before the pandemic. This study uses a quantitative descriptive method with a Vector Autoregression (VAR) approach. Through the optimum lag value, namely, lag 3, statistically it was found that there was a significant relationship between the variables of GDP, interest rates, and the exchange rate on Sukuk. In addition, several analysis results found a causal relationship between these variables.