Tax policy stance over the business cycle: evidence from Europe

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Empirical evidence on the difference between the effective and the statutory tax rates for listed groups in Germany

The difference between the statutory and effective tax rate for listed groups is a complex variable influenced by a variety of factors. This paper aims to analyze whether this difference exists for listed groups in the German market and tests which factors have an impact on it. Thus the sample consists of 130 corporations listed in the three major German stock indices. The findings suggest that the companies that pay less than the statutory rate clearly outweigh the ones that pay more, and that the income earned from associated companies has a significant impact on this difference.

Reporting Income Taxes in the Banking Sector In Angola - Accounting, Tax Regulation and Financial Reporting Evidences

This research provides an insight into income taxes reporting in Angola, based on hand collected data from the annual reports of banks. Empirical studies on Angolan companies are scarce, in part due to the limited access to data. The results show that income taxes’ reporting has improved over the years 2010-2013, becoming more reliable and understandable. The Angolan Government is boosting the economic growth through tax benefits in the investment in public debt, which cause a reduction in the banks’ effective tax rate. The new income tax law will reduce the statutory tax rate from 2015 onwards and change the taxable income, resulting in shifting the focus to promoting private investment.

Progressivity in the Portuguese personal income tax system

This Work Project analyzes the evolution of the Portuguese personal income tax system’s progressivity over the period of 2005 through 2013. It presents the first computation of cardinal progressivity measures using administrative tax data for Portugal. We compute several progressivity indices and find that progressivity has had very modest variations from 2005 to 2012, whilst from 2012 to 2013 there has been a relatively stronger decrease, excluding the impact of the income tax surcharge of the years 2012 and 2013. When this latter is included, progressivity of 2012 and 2013 decreases considerably. Analyzing the effective average tax rates of the top income percentiles in the income scale, we find that these rates have increased over the period 2010–2013, suggesting that an analysis of effective tax rates is insufficient to assess progressivity in the whole tax scheme.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.