Finding solutions to methodological challenges through interdisciplinary collaboration
11 October 2021 - 12h15-13h15
Online - Registration mandatory
The Data Science Competence Center (CCSD) of the University of Geneva is pleased to invite you to the third session of the Data Science Clinics.
The objective of the Data Science Clinics is to allow researchers to share with their peers some of the concrete challenges they encounter in the analysis of their research data. This sharing of methodological questions aims to collectively shape solutions to these challenges and to pave the way to the development of mutually beneficial collaborations between researchers.
12h15-12h45: Giedre Lideikyte Huber, Faculty of Law, and Marta Pittavino, Geneva School of Economics and Management.
"Do tax deductions encourage charitable giving behavior? Evidence from panel data of the Canton of Geneva, Switzerland"
Under the current Swiss law, taxpayers can deduct charitable donations from their taxable income (individuals) or taxable profits (corporations) subject to a threshold of 20% of taxable income or profits. This threshold was introduced on January 1, 2006 as part of a larger reform of the Swiss federal tax law, replacing the previous threshold which was 10%.
The efficiency of the above reform, and more generally the existing system of tax deductions for charitable giving in Switzerland has never been evaluated. Using a unique data set shared by the Tax Administration of the Canton of Geneva for this purpose (approximately 3 million tax returns, concerning every taxpayer in Geneva for the given period of time), we provide statistical analysis about taxpayers’ charitable giving behavior in Geneva from 2001 to 2011. In particular, we study the changes in the volume of deductions during the selected period of time and also analyze patterns of giving behavior by income and wealth. We divided the taxpayers into the following segments: Low (income or wealth) – 25% of taxpayer’s population; middle-low – 25%, middle -25%; midle-high – 20%; high – 4%, very high – 0.99%, very very high - 0.01% (Saez/Zucman 2014). The aim of this work is to provide as many insights as possible into both the effects of the 2006 reform that increased the deductible threshold for charitable donations, as well as into the patterns of giving and deducting by different classes of taxpayers by income and wealth. This paper seeks to provide both Swiss and foreign academics and policymakers with new research and policy insights.
Would be a single regression model for each year worth it, given that the « income » is variable “fiscally” linked with the deductions ? A longitudinal follow-up of the 0.01% wealthy people in the sample size, would it still be statistically significant?
12h45-13h15: Frédérique Lisacek, Faculty of Sciences.
"Sugars as mediators of cell-cell communication"
The pandemic and the studies on Sars-Cov-2 are an inexhaustible source of modelling based on epidemiological, genomic and other data. A much less visible category of new questions has emerged in a much smaller volume of data, but one that is equally inspiring in attempting to understand the virus. The problem can be summarised in an image showing the usual representation of the Sars-Cov-2 surface protein on the left and a very likely model of its in vivo appearance on the right:
Access to the image >>
This bushy coating is made of sugar molecules whose role beyond camouflaging the virus, is not clearly understood. These molecules are described from very incomplete data, and their enumeration is as speculative as the enumeration of genes in the human genome before it was sequenced, with variations of 10^3 to 10^8. This presentation will illustrate the breadth and heterogeneity of the issues surrounding the study of those sugars that line the surface of cells.
Our main challenge is the deconvolution of interactions involving sugars because these molecules are complex tree structures, their quantification at each protein attachment site (tens of possibilities per site and multiplicity of sites) is an experimentally-hard question and more sugar decoration on "sugar-reader" proteins, such as antibodies, needs to be deciphered. In any case, a lot of graph and combinatorics issues to be discussed.