In recent years, borders between countries have been opened gradually thanks to globalization, which is reflected in minimal barriers to the movement of persons and capital. This situation could be potentially abused by taxpayers willing to shift the capital to preferential tax jurisdictions. Due to facts aforementioned, several instruments for tax administrators have been introduced. Bilateral and multilateral instruments are concluded with particular countries for the purpose of obtaining information about foreign residents staying abroad but also to avoid double taxation or double non-taxation. In recent years there has been an increased number of companies in the Czech Republic whose owners come from preferential tax jurisdiction from 12,676 up to 13,167. This paper is focused on the Czech taxpayers' reaction on concluding agreements concerning exchange of information in tax matters with preferential tax jurisdictions, the so-called "Tax havens". The Difference-in-Differences Method was carried out to predict the taxpayers' behavior. The model shows that the agreements work well as a preventive tool. If the Czech Republic concludes the agreement with the tax haven, the taxpayers lose their anonymity. This results in their relocation into tax havens that are not covered by the agreement in order to keep their anonymity.

VAT Gap Estimation-Czech Republic Case Study The paper deals with the estimation of the Value Added Tax Gap in the Czech Republic during the years 2009-2014. In the introduction, the term of Value Added Tax Gap and related terms are defined following available approaches and different methods of the Value Added Tax Gap estimation are presented. In the analytical part of the paper, the Czech Value Added Tax Gap is measured using three applicable methods-the decomposition of the gap on the VAT, the identification of transactions subject to VAT and the adjustment of GDP. In this part the exact procedure for possible replication of the research is declared and the comparsion of the results reached for the Czech Republic using different methods of Value Added Tax Gap estimation is carried out.

There are many studies focusing on VAT (value added tax) tax gap but very few relevant studies that deal with the corporate income tax loss. The studies vary particularly in their methodology, databases and interpretation. In the case of the Czech Republic the studies resulted in a range between CZK 57 billion tax gap and CZK 12.5 billion corporate tax revenue gain caused by the tax planning. The main aim of the paper is to calculate the corporate income tax effi ciency rate for the Czech Republic and compare it with other member states. The indicator of corporate income tax effi ciency is important for the calculation of the tax revenue without profi t shifting (RWS) indicator and then the subsequent corporate income tax gap estimation for 2013-2015, which is the second goal of the paper. The RWS indicator gives an overview of the Czech Republic´s amount of loses/ gains relating to the corporate tax base erosion and corporate profi t shifting. In the case when the actual corporate income tax revenue takes a higher value than the revenue without profi t shifting indicator the jurisdiction benefi ts from the profi t shifting operations. The opposite situation results in tax revenue losses caused by profi t shifting to other "more attractive" tax jurisdictions. Authors' study re-estimation results in approximately CZK 9.404 billion tax gap caused by base erosion and profi t shifting instead of 12.5 billion CZK that shows EPRS's study for period 2013. The third aim of the paper is to deal with the difference between input data from Eurostat database and offi cial data from General Financial Directorate.

Frequent price discounts have become one of the features of retail chains in the Czech Republic. Discounts are most often provided for products popular with customers. One of the products that is subject to frequent discounts is milk. Several expert opinions estimate that up to 50% of milk is sold with frequent price discounts. Price pressure on farmers and food producers owing to frequent price promotions is supported by the purchasing power of retailers and, in extreme cases, leads to sub-cost purchasing prices. These facts set up the framework to assess the impact of frequent price discounts on policymaking, including tax administration. One of the effects of selling goods at discounts is lower VAT collection from shops, and thus lower revenue to the state budget. This paper attempts to estimate one of the potential impacts of frequent price discounts, namely the loss of VAT for the state budget due to low retail prices of milk. Theoretical estimation of the effect of frequent price discounts on VAT can help to assess policy that touches on mark-ups in specific markets, such as food. The estimation is based on data obtained from Czech Statistical Office, FADN, and Orbis databases and employs Monte Carlo simulation to capture the stochastic element of retail markups. Sub-cost prices of producers of milk have a more significant negative effect on VAT revenue than prices of other supply chain participants. The theoretical effect on VAT revenue is estimated to be in the range from a negative effect of 14.9 billion CZK to a positive effect of 7.4 billion CZK. Values of zero VAT effect points (mark-ups of producers, dairies, and retailers) are shown, as well as the critical value of price elasticity of demand (−0.1715), at which the effect of frequent price discounts on VAT revenue is zero.

The COVID-19 pandemic outbreak has upended the educational system worldwide, possibly with severe long-term consequences as most training institutions were forced to move to an online environment. Given the sudden transition to remote education, the main objective of this contribution is to evaluate the impact of distance education on examination results. We investigated the examination results of tax related subjects collected at the Czech University of Life Sciences in Prague during the period from 2014 to 2020. The sample consists of examination results of 120 different classes within 6 years with a total amount of 7268 observations. Firstly, we pivoted the data into the long format and performed binary logistic regression. Our findings suggest that the odds that student successfully passes the exam increases if the student was examined online compared to in-person. Additionally, we used KNN regression which enables us to predict success rate for an upcoming semester. According to our analysis, it is expected that on average 82 students out of 100 will successfully pass the exam. The model was calibrated using cross-validation to choose optimal K.