Abstract

Previous studies have established that social capital plays a significant role in individual tax-related behaviors, including inclinations toward tax evasion and compliance. This study seeks to extend the understanding of tax morale in Spain using data from the Public Opinion and Fiscal Policy Survey (CIS, Study 3332). We use factor analysis with maximum likelihood extraction and Varimax rotation to identify key social capital variables and tax attitudes. We identify profiles based on their social capital and tax compliance using cluster analysis. We will apply hierarchical clustering with Ward's chaining and k-means clustering. The robustness of the resulting profiles will be confirmed by discriminant analysis and a multilayer perceptron neural network, which will look for higher rates of correct classification as an indicator of improved profile consistency. Our findings suggest that identifying Spanish tax citizens' profiles helps analyze social capital in tax policy. After our analysis, we have determined that enhancing the accumulation of social capital variables leads to better tax adherence.

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