| Наименование статьи | Development of Tax Support for Agriculture in the Context of Enhancing the Effectiveness of Various Tax Regimes |
|---|
| Страницы | 123-140 |
|---|
| Аннотация | The paper proposes our own methodological approach based on the data of financial statements of agricultural organizations and allowing us to assess the impact of taxation regimes on the results of their activities. The subject of the study is the system of statistical indicators of agricultural organizations characterizing the level of economic production. The aim of the work is to substantiate the architecture of the tax incentive system for the industry, as well as to design further directions for the development of tax support for agriculture. Research methods include typical grouping, machine learning models (decision tree, random forest and gradient boosting). As a result, the methodological approach was tested and significant differences in the performance indicators of agricultural organizations depending on the choice of taxation systems were substantiated, and net profit forecasting models were built for each of them. The constructed models allow us to identify the nature of the influence of tax factors on the performance of agricultural entities. Recommendations for improving the system of tax incentives for the industry are presented. The developed methodological approach helps to assess the differences in taxation systems using the grouping method and machine learning methods, as well as to build high-quality forecasting models. The scientific novelty of the study consists in developing a set of proposals for improving tax incentives for the industry, taking into account (1) the optimal architecture of the tax support system at the macro level and (2) systemic problems of applying industry tax incentives at the micro level. Proof of the optimality of the architecture of the tax incentive system for agriculture was revealed using our methodology for assessing the impact of the tax regime on the performance indicators of agricultural producers, based on the use of machine learning methods
|
|---|
| Ключевые слова | tax regime, tax forecasting, agriculture, tax factor, machine learning methods, decision tree, random forest, gradient boosting
|
|---|
| Журнал | Economic and social changes: facts, trends, forecast |
|---|
| Номер выпуска | 2 |
|---|
| Автор(ы) | Tikhonova A. V., Gerasimova A. E. |
|---|