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Stochastics and Statistics Seminar

Is quantile regression a suitable method to understand tax incentives for charitable giving? Case study from the Canton of Geneva, Switzerland

April 29 @ 11:00 am - 12:00 pm

Giedre Lideikyte Huber and Marta Pittavino, University of Geneva


Abstract: Under the current Swiss law, taxpayers can deduct charitable donations from their individual’s taxable income subject to a 20%-ceiling. This deductible ceiling was increased at the communal and cantonal level from a previous 5%-ceiling in 2009. The goal of the reform was boosting charitable giving to non-profit entities. However, the effects of this reform, and more generally of the existing Swiss system of tax deductions for charitable giving has never been empirically studied. The aim of this work is to provide as many taxation insights and deducters characteristics as possible into both the effects of the 2009 reform, as well as into the patterns of giving and deducting by different classes of deducters by income and wealth.

Using unique panel data, shared by the Geneva Tax Administration, for a time framework of 11 years: 2001-2011, an in-depth statistical analysis was conducted. The overall taxpayers population has been described, dividing them into six categories according to the income distribution. We studied the changes in the volume of deductions between categories. Quantile regressions models for each year has been fitted to underlying the different income behaviors toward deductions. Moreover, a specific subset of deducters more sensitive to the deductible ceiling for their donations was identified and studied in detail. The overall net income, gross wealth, together with the year of birth, were the main covariates of interest. Standard linear regression and robust regression models were performed and significant variables, which help answering the questions of taxpayers’ charitable giving behavior, were identified.

Income has resulted the most significant variable, driving donations, and robust regressions the statistical techniques better incorporating the data peculiarity, without giving too much weight to outliers, and with an excellent model fitting. This paper seeks to provide both Swiss and foreign academics and policymakers with new research and policy insights.

Bios: Giedre Lideikyte Huber is a Senior lecturer at the Faculty of Law and a Swiss National Science Foundation researcher. She specializes in tax law, and more specifically in taxation of philanthropy, corporate taxation and sustainable tax systems (including gender and climate issues in taxation). She has received numerous academic awards and grants, awarded by the Swiss National Science Foundation (FNS), the Fondation Zdenek et Michaela Bakala, the University of Geneva (Subside Tremplin) and Centre Maurice Chalumeau en sciences des sexualités. (see more)

Marta Pittavino is a Senior Lecturer and a Senior Research Associate at the Research Center for Statistics of the Geneva School of Economics and Management (GSEM), within the University of Geneva. She is scientific coordinator and manager of the Master of Science in Business Analytics.  Marta holds a PhD in Biostatistics and Epidemiology from the University of Zurich. Before joining the GSEM, she was a post-doctoral scientist, applied statistician, at the International Agency for Research on Cancer, part of the World Health Organization, in Lyon, France. Her research interests lie in the applied statistics field: data analysis, forecasting and regression methods, with a focus on the development of Bayesian hierarchical models applied to epidemiological studies



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