Marta Pittavino

This chapter examines the shift from a data-driven culture to an AI-driven enterprise as a transformation in how firms build knowledge, interpret evidence and govern decisions. It argues that statistical literacy is not a residual technical skill, but a methodological foundation for managerial judgment, data quality, control and governance. Thus, it highlights the centrality for future organizations of statistical and soft skills: understanding quantitative evidence requires statistical grounding integrated with interpretive, communicative and organizational capabilities.

This study conducts the first large-scale analysis of tax incentives for charitable giving in Switzerland, focusing on regular donors and using innovative statistical forecasts. For the first time, these estimates are validated with cumulative data from Geneva authorities. Using unique panel data from Geneva taxpayers (2001–2011), this study examines income tax deductions, donation frequency, and donor characteristics, alongside forecasts for future charitable deductions and donor trends. This period also included a legal reform that raised the deduction ceilings. Findings reveal that 29% of donors deducted only once, while 71% deducted multiple times. Regular donors are not significantly influenced by tax incentive ceilings, but their donations increase over time. Wealth plays a crucial role in donation frequency, as shown by linear regression estimates. Time-series forecasting models predict total deductions steadily reaching CHF135 million by 2022, with results validated against actual data. The donor population was also forecasted to grow, reaching 72,349 by 2022, four times the 2001 figure. Based on these empirical findings, the study suggests policy measures to enhance charitable giving, aligning with legislative tax reforms. These projections offer valuable insights into optimizing tax incentives.

In this study, dietary and serum measurements of vitamin-B6 and folate from two nested case–control studies within the European Prospective Investigation into Cancer and Nutrition study were integrated in a Bayesian framework to explore the data measurement error structure and relate dietary exposures to cancer risk. A Bayesian hierarchical model was developed, including: an exposure model, to define the unknown true intake distribution; a measurement model, to relate true intake to observed assessments; and a disease model, to estimate exposure/cancer relationships. Serum and plasma levels of vitamin-B6 and folate were inversely associated with kidney and lung cancer risk, while dietary assessments of these vitamins were not associated with kidney and lung cancer risk. The Bayesian synthesis of these data suggests a protective effect but with substantial uncertainty in the effect size.

The varying-thresholds model (VTM) is a novel methodology proposed by Tutz ( Flexible predictive distributions from varying-thresholds modelling. https://doi.org/10.48550/arXiv.2103.13324, arXiv:2103.13324 2021) capable of estimating the whole conditional distribution of a response variable in a regression setting. It can be used for continuous, ordinal and count responses. In this study, conditional quantiles and prediction intervals estimated through VTM are compared with those of quantile regression. The comparison is based on a set of data-generating models to assess the performance of the two methodologies regarding the coverage and width of prediction intervals. The simulation study encompasses settings with several functional forms and types of errors. In addition, a discrete version of the continuous ranked probability score is proposed as a tool to choose the best link function for the binary models used in the fitting of VTM. In summary, the varying-thresholds model is a flexible methodology that can be broadly applied with light assumptions; it is advantageous over quantile regression when the conditional quantile function is misspecified.

Objectives: We provide recent and detailed estimates of the prevalence of kinlessness (i.e., people lacking [close] kin) among older individuals in 27 countries. We add to the literature by considering a broad range of kinship ties and examining within-country variabilities by age, sex, education, and rurality of the residential area. Methods: Using data from Wave 8 (2019-2020) of the Survey of Health, Ageing, and Retirement in Europe (SHARE), we estimated the prevalence of kinlessness among individuals aged 65 and over in 26 European countries and Israel. We considered different definitions of kinlessness, from a less restrictive (based on the absence of both a partner and children) to a more restrictive one (absence of partner, children, grandchildren, parents, and siblings). Results: Kinlessness varied considerably across countries. The percentage of people aged 65 and over who lacked a partner and children ranged between 2-3.5% in the Czech Republic, Romania, Israel, and Bulgaria to more than 8% in Switzerland, Spain, Belgium, and Malta. The percentage of older people lacking all close kin considered (partner, children, grandchildren, parents, and siblings) ranged from 0.1 to 4.1%. In addition, there was substantial heterogeneity in kinlessness in some countries by age, sex, and rurality of the residential area, whereas, except for a few cases, we did not find significant differences by education. Discussion: Understanding the prevalence of kinless older adults is critical for policymakers and healthcare providers to design appropriate support systems for this potentially vulnerable group of people.

The present study is the first large-scale analysis of tax incentives for charitable giving in Switzerland for constant donors. Using unique panel data of the entire Geneva taxpayers’ population from 2001 to 2011, which includes the rare information on individual wealth of donors, we analyze tax incentives for charitable giving: income tax deductions, focusing on the frequency of donations and the characteristics of regular donors. The latter are not primarily driven by the ceilings for income tax incentives, even though their donations rise over time. However, we find that wealth is an important variable characterizing the donations’ frequency based on linear regression estimates.