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Tax Morale and Perceived Intergenerational Mobility: a Machine Learning Predictive Approach

Caferra, Rocco and Morone, Andrea (2019): Tax Morale and Perceived Intergenerational Mobility: a Machine Learning Predictive Approach.

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Abstract

The purpose of this paper is to investigate the linkage between the perceived intergenerational mobility and the preferences for tax payment. Unfortunately, we do not have a unique dataset, however missing data might be predicted by employing di�erent methods. We compare the efficiency of k-nearest-neighbors (kNN), Random Forest (RF) and Tobit-2-sample-2-Stage (T2S2S) techniques in predicting the perceived inter- generational mobility, hence we exploit the predicted values to estimate the relation with tax morale. Results provide evidence of a strong negative relation between perceived mobility and tax cheating, suggesting that fairness in tax payment has also to be seen on the light of the perceived efficiency of the welfare state in providing more opportunities across generations.

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