Measuring the tax performance of provinces by spatial analysis method

The fact that classical OLS estimators don't include neighborhood relations in the modeling may prevent us from making accurate analyzes in the World where the boundaries are removed. Although spatial econometrics, in which countries, regions an deven cities are affected by each other and these effects are included in the models, emerged in the second half of the 20th century, it's more common in social sciences thanks to the technological developments in recent years. In this thesis, a spatial analysis has been made to increase taxes, which have been the most important income source of states throughout history, with the help of technology and different policies applied geographically. As a result of the test, the Spatial Durbin Model (SDM), which is the most suitable model fort he data, was chosen and as a result of the analysis, openness and per capita income from the independent variables affect tax revenues positively, while the ratio of agriculture to GDP affects negatively.