Using Machine Learning to Predict Firms’ Tax Perception

I investigate whether a machine learning model can reliably predict firms’ tax rate perception. While standard models assume that decision-makers in firms are perfectly informed about firms’ tax rates and tax implications, also their tax rate perception influences the way in which they incorporate taxes into their decision-making processes. However, studies examining firms’ tax rate perception and its consequences remain scarce, mostly due to a lack of observations of firms’ tax rate perception. Using a dataset of German SMEs, I apply machine learning in the form of Extreme Gradient Boosting, to predict firms’ tax rate perception based on firm and personal characteristics of the decision-maker. The results show that Extreme Gradient Boosting outperforms traditional OLS regression. The model is highly accurate, as evidenced by a mean prediction error of less than one percentage point, produces reasonably precise predictions, as indicated by the root mean square error being comparable to the standard deviation, and explains up to 23.2% of the variance in firms’ tax rate perception. Even based on firm characteristics only, the model maintains high accuracy, albeit with some decline in precision and explained variance. Consistent with this finding, Shapley values highlight the importance of firm and personal characteristics such as tax compliance costs, tax literacy, and trust in government for the prediction. The results show that machine learning models can provide a time- and cost-effective way to fill the information gap created by the lack of observations on firms’ tax rate perception. This approach allows researchers and policymakers, to further analyze the impact of firms’ tax rate perception on tax reforms, tax compliance, or business decisions.