EXTENDED ABSTRACT This study first deals with the tax structure of 31 transition economies in the period 2010-2019, which consists of tax rates, tax tariffs, and tax revenues. In this context, to evaluate the components of the tax...
moreEXTENDED ABSTRACT This study first deals with the tax structure of 31 transition economies in the period 2010-2019, which consists of tax rates, tax tariffs, and tax revenues. In this context, to evaluate the components of the tax structure, an evaluation was made by relating tax rates and tax response, economic freedom with tax revenues, and finally corruption and GDP per capita. Then, the factors that add to the corruption index of 24 transition economies in the 2010-2019 period were analyzed with panel data analysis. While corruption is the dependent variable in the model, tax revenues (control variable), GDP per capita, commercial openness, economic freedom, corporate tax rate, income tax rate, and excise tax rate are included as independent variables. In this context, the scope of the study is to examine the tax structure of transition economies in the 2010-2019 period and then analyze the institutional determinants of the tax structure of 24 transition economies. In this context, the study aims to investigate whether the tax system is rationalized as financing the transition process. In other words, the first purpose is to examine the relationship of the tax structure in these countries with tax response, economic freedom, and corruption. Secondly, in countries where free-market institutions are not developed, factors affecting the problem of corruption, such as bribery as a substitute for taxes, are examined in the tax system. In the study, tax rates, tax revenues, and tax tariffs are analyzed with descriptive and predictive statistical methods. Descriptive statistics, which is one of these methods, converts numbers and observations into descriptive indices. It is used to summarize, organize, and reduce many observations. Descriptive statistics are the best way to summarize data and interpret research results. On the other hand, inferential statistics are used to draw conclusions and make predictions in determining the similarities between the population and the sample. Additionally, many research questions require predictions about the universe from the sample. According to the results, corruption is high in countries with a low economic freedom index. Similarly, GDP per capita is low in economies with high corruption. It can be said that bribery in transition economies is seen as a substitute for tax due to reasons such as high tax burden, low justice in income distribution, and the institutional weakness of tax administrations. Within this framework, according to the findings of static analysis, a one-unit increase in income per capita in transition economies positively affects corruption at 0.0371%, openness at 0.0134%, economic freedom at 0.0394%, and income tax rate at 0.0122%. According to the findings of the dynamic analysis, two variables negatively affect corruption. Among these, economic freedom corruption negatively affects 0.073% and excise taxes 0.108%. On the other hand, tax revenues (0.064), per capita income (0.056), trade deficit (0.073), and income tax rate (0.063) positively affect corruption. The corruption perception index was used in the study to examine the institutionalization of the tax system. The relationship between economic freedom, which was examined in the first part, corruption, and GDP per capita was analyzed in the second part with panel data analysis. In this context, panel data analysis has been done with static and dynamic models. This is because the dynamic analysis does not neglect the periodic effect and the static analysis detects the annual effect. In static analysis, first, the F test, LR test, LM test, and ALM test were used to determine estimators. As a result of these tests, it was concluded that the pooled least squares method (PLSM) is invalid, and the random-effects (RE) model is valid. Then, the Hausman test was conducted to test the random effects model against the fixed effects (FE) model and the fixed effects estimator was decided upon. Variance, autocorrelation, and inter-unit correlation tests were performed for the three basic assumptions of the fixed effects estimator, and it was found that all three questions were present. For this reason, the Driscoll / Kraay fixed effects regression test was conducted, which gives consistent results against these problems. Additionally, the Arellano and Bover / Blundell and Bond system generalized moments (System GMM) estimators were used for dynamic panel analysis. The existence of autocorrelation in the model was checked by AR (1) and AR (2) statistics and it was concluded that there was no second-order autocorrelation. Using the Sargan test in the prediction tested the validity of the vehicle variables and it was concluded that it is appropriate. ÖZ Tarihsel mirasında kurumsal vergi sistemine sahip olmayan ekonomiler bağımsızlıklarıyla birlikte vergi sistemi tesisi arayışına girmişlerdir. Böyle bir sistemin esas unsuru olan vergi yapısı ise geçiş ekonomilerinin serbest piyasa reformlarının finansmanı anlamına gelmektedir. Çalışmanın amacı, 31 geçiş ekonomisinin mevcut vergi yapısını değerlendirmektir. Ek olarak, vergi yapısı ve vergi sisteminin kurumsal yapısının etkinliğini değerlendirmek amacıyla 24 geçiş ekonomisinde yolsuzluk ile toplam vergi yükü, kişi başına düşen GSYH, dışa açıklık, ekonomik özgürlük, kurumlar-gelir-tüketim vergisi oranları ilişkisi incelenmiştir. Bu amaçla statik ve dinamik panel veri analizi yapılmıştır. Sonuçlarına göre ekonomik özgürlük endeksi düşük olan ülkelerde yolsuzluk yüksek seyretmektedir. Benzer şekilde yolsuzluğun yüksek olduğu ekonomilerde kişi başına düşen GSYH düşüktür. Vergi yükünün yüksek, gelir dağılımında adaletin düşük ve vergi idarelerinin kurumsal zayıflığı gibi nedenlerden dolayı geçiş ekonomilerinde rüşvet, verginin ikamesi olarak görüldüğü söylenebilir. Bu çerçevede statik analiz bulgularına göre geçiş ekonomilerinde kişi başına düşen gelirde bir birimlik artış yolsuzluğu %0,0371, dışa açıklık %0,0134, ekonomik özgürlük %0,0394 ve gelir vergisi oranı %0,0122 pozitif etkilemektedir. Dinamik analizin bulgularına göre yolsuzluğu negatif etkileyen iki değişken bulunmaktadır. Bunlardan ekonomik özgürlük yolsuzluğu %0,073 ve tüketim vergileri %0,108 negatif etkilemektedir. Diğer taraftan vergi gelirleri (0,064), kişi başına düşen gelir (0,056), ticari açıklık (0,073) ve gelir vergisi oranı (0,063) yolsuzluğu pozitif etkilemektedir. Anahtar Kelimeler: Geçiş ekonomileri, Vergi yapısı, Yolsuzluk, Sabit etkiler, Sistem GMM