Meta-analysis categorization of EU countries in the context of corporate income tax

Andrejovská, Alena; Mihóková, Lucia; Martinková, Slavomíra

doi:10.1016/j.cya.2017.04.002

Cited by 6 publications

(4 citation statements)

References 19 publications

(20 reference statements)

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“…As one of the goals of the countries' tax strategy is perhaps attracting foreign investment to increase the tax revenues (Clausing, 2007), and once there is a tendency of countries to decrease the tax rate, several authors investigated the relationship between the nominal tax rate and foreign investment. Alena et al (2017) confirmed "that between countries there are still significant differences in the level of nominal and effective corporate tax burden, especially among a group of old and new EU Member States." Bénassy-Quéré et al (2000) concluded that foreign investment is sensitive to differences in tax rates.…”

Section: Nominal Ratementioning

confidence: 94%

The relationship between the effective tax rate and the nominal rate

Dias

Reis²

2018

CyA

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<p class="Pa7">The main goal of this investigation is to understand the relationship between the nominal rate and the effective tax rate and to evaluate if the differences between them depend on the value of the nominal rate. Based on a sample of 1,530 companies from 5 countries members of the European Union (Denmark, Slovenia, Finland, Luxembourg and the United Kingdom) there’s evidence that the effective tax rate is positively related to the nominal rate. The effective tax rate was calculated through the ratio between the value of the tax paid over the result before tax. When the nominal tax rate increases, the effective rate increases equally but with a slower growth. This relationship is softened if we take into account the value of the nominal tax rate, which shows that companies have the ability to manage the results in order to increase savings in tax.</p>

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Section: Nominal Ratementioning

confidence: 94%

The relationship between the effective tax rate and the nominal rate

Dias

Reis²

2018

CyA

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“…The majority of the clustering algorithms are based on two popular techniques known as hierarchical and partitional clustering [41][42][43]. In the present research, two clustering methods were used: hierarchical clustering and k-means clustering.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Dependence of GHG Emissions on the Support and Taxes in the EU Countries

2021

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The reduction of GHG emissions is one of the priorities of the EU countries. The majority of studies show that financial support and environmental taxes are one of the most effective measures for the mitigation of the negative consequences of climate change. The EU countries employ different environmental support measures and environmental taxes to reduce GHG emissions. There is a shortage of new studies on these measures. The aim of the present study is to compare the effectiveness of the environmental support measures of the EU countries with the effectiveness of environmental taxes in relation to the reduction of GHG emissions. This study is characterized by the broad scope of its data analysis and its systematic approach to the EU’s environmental policy measures. An empirical study was performed for the EU countries with the aim of addressing this research problem and substantiating theoretical insights. A total of 27 EU member states from 2009 to 2018 were selected as research samples. The research is based on a cause-and-effect relationship, where the factors affecting environmental pollution (environmental taxes and subsidies) are the cause, and GHG emissions are the effect. Statistical research methods were used in the empirical study: descriptive statistics, the Shapiro–Wilk test, one-way analysis of variance (ANOVA), simple regression and cluster analysis. The results show that the older member countries of the EU, which had directed the financial measures of environmental policy towards a reduction in energy consumption, managed to achieve a greater reduction in GHG emissions compared to the countries which had not applied those measures. The Central and Eastern European countries are characterized by lower environmental taxes and lower expenditure allocated to environmental protection. The countries with a higher GDP per capita have greater GHG emissions that the countries with lower GDP per capita. This is associated with greater consumption, waste, and energy consumption. The study conducted gives rise to a discussion regarding data sufficiency in the assessment and forecasting of GHG emissions and their environmental consequences.

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“…Once all the parameters are obtained, a categorization is carried out using an unsupervised model known as c-means [37,38]. This model is widely recognized for generating accurate categorizations in comparative studies due to its fuzzy characteristics in categorization, which extends the concept of unsupervised artificial intelligence [39][40][41]. The c-means classification method was implemented in Python, specifically making use of the fuzzy-c-means library [42].…”

Section: Non-supervized Fuzzy Categorizationmentioning

confidence: 99%

Fuzzy classification of countries according to their sustainable economic potential: application of the Solow-Swan and Green Solow models.

Quintero,

Gomez,

Gaitan

2023

Preprint

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The pollution and the progressive depletion of resources are global problems that affect all humanity, but their approach is affected by the different sociopolitical and economic conditions present in the different regions. Thus, the objective of this work is to implement and analyze a methodological structure based on the Solow/Green-Solow models that facilitates the identification and organization of countries according to their economic capacity and the polluting emissions associated with their development, with the purpose of promoting the application of more appropriate and effective solutions, considering the diversity of challenges faced by different economies. To achieve this objective, a preliminary guide is proposed to compare and group viable strategies in different territories, evaluating them according to the parameters established by the Solow-Swan and Green Solow models. For the recognition of the parameters, the Levenberg-Marquardt algorithm has been used, the result of which is classified using the fuzzy artificial intelligence method known as c-means, which provides a category for the different countries. The key findings of this article highlight how the particularities of countries are influenced not only by their geographic location, but also by their level of income and the calculated elasticity related to product exports, together with their ecological potential. It also challenges the widely held belief that enrichment is linked to increased carbon emissions.

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Meta-analysis categorization of EU countries in the context of corporate income tax

Cited by 6 publications

References 19 publications

The relationship between the effective tax rate and the nominal rate

The relationship between the effective tax rate and the nominal rate

Assessment of the Dependence of GHG Emissions on the Support and Taxes in the EU Countries

Fuzzy classification of countries according to their sustainable economic potential: application of the Solow-Swan and Green Solow models.

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