Essays in Empirical Political Economics

This thesis takes two perspectives on political institutions. From the one side, it examines the long-run effects of institutions on cultural values. From the other side, I study strategic communication, and its determinants, of politicians, a pivotal actor inside those institutions. The first chapter provides evidence for the legacy of feudalism - a set of labor coercion and migration restrictions -, on interpersonal distrust. I combining administrative data on the feudal system in the Prussian Empire (1816 – 1849) with the geo-localized survey data from the German Socio-Economic Panel (1980 – 2020). I show that areas with strong historical exposure to feudalism have lower levels of inter-personal trust today, by means of OLS- and mover specifications. The second chapter builds a novel dataset that includes the Twitter handles of 18,000+ politicians and 61+ million tweets from 2008 – 2021 from all levels of government. I find substantial partisan differences in Twitter adoption, Twitter activity and audience engagement. I use established tools to measure ideological polarization to provide evidence that online-polarization follows similar trends to offline-polarization, at comparable magnitude and reaches unprecedented heights in 2018 and 2021. I develop a new tool to demonstrate a marked increase in affective polarization. The third chapter tests whether politicians disseminate distortive messages when exposed to bad news. Specifically, I study the diffusion of misleading communication from pro-gun politicians in the aftermath of mass shootings. I exploit the random timing of mass shootings and analyze half a million tweets between 2010 – 2020 in an event-study design. I develop and apply state-of-the-art text analysis tools to show that pro- gun politicians seek to decrease the salience of the mass shooting through distraction and try to alter voters’ belief formation through misrepresenting the causes of the mass shootings.

New perspectives of genetic disorders in cattle

In the last decades a negative trend in inbreeding has accompanied the evident improvement in productivity and performance of bovine domestic population, predisposing to the occurrence of recessively inherited disorders. The objectives of this thesis were: a) the study of genetic diseases applying a “forward genetic approach” (FGA); b) the estimation of the prevalence of deleterious alleles responsible for eight recessive disorders in different breeds; c) the collection of well-characterized materials in a Biobank for Bovine Genetic Disorders. The FGA allowed the identification of seven new recessive deleterious variants (Paunch calf syndrome - KDM2B; Congenital cholesterol deficiency - APOB; Ichthyosis congenita - FA2H; Hypotrichosis - KRT71; Hypotrichosis - HEPHL1; Achromatopsia - CNGB3; Hemifacial microsomia – LAMB1) and of seven new de novo dominant deleterious variants (Achondrogenesis type II - two variants in COL2A1; Osteogenesis imperfecta - COL1A1; Skeletal-cardio-enteric dysplasia - MAP2K2; Congenital neuromuscular channelopathy - KGNG1; Epidermolysis bullosa simplex - KRT5; Classical Ehlers-Danlos syndrome - COL5A2) in different breeds, associated with a large spectrum of phenotypes affecting different systems. The FGA was based on the sequence of a clinical, genealogical, gross- and/or histopathological and genomic study. In particular, a WGS trio-approach (patient, dam and sire) was applied. The prevalence of deleterious alleles was calculated for the Pseudomyotonia congenita, Paunch calf syndrome, Hemifacial microsomia, Congenital bilateral cataract, Ichthyosis congenita, Ichthyosis fetalis, Achromatopsia and Hypotrichosis. A particular concern resulted the allelic frequency of 12% for the Paunch calf syndrome in Romagnola cattle. In respect to the Biobank for Bovine Genetic Diseases, biological materials of clinical cases and their available relatives as well as controls used for the allelic frequency estimations were stored at -20 °C. Altogether, around 16.000 samples were added to the biobank.

An Embedded In-Cabin Lightweight High-Performance 3D Gaze Estimation System

Gaze estimation has gained interest in recent years for being an important cue to obtain information about the internal cognitive state of humans. Regardless of whether it is the 3D gaze vector or the point of gaze (PoG), gaze estimation has been applied in various fields, such as: human robot interaction, augmented reality, medicine, aviation and automotive. In the latter field, as part of Advanced Driver-Assistance Systems (ADAS), it allows the development of cutting-edge systems capable of mitigating road accidents by monitoring driver distraction. Gaze estimation can be also used to enhance the driving experience, for instance, autonomous driving. It also can improve comfort with augmented reality components capable of being commanded by the driver's eyes. Although, several high-performance real-time inference works already exist, just a few are capable of working with only a RGB camera on computationally constrained devices, such as a microcontroller. This work aims to develop a low-cost, efficient and high-performance embedded system capable of estimating the driver's gaze using deep learning and a RGB camera. The proposed system has achieved near-SOTA performances with about 90% less memory footprint. The capabilities to generalize in unseen environments have been evaluated through a live demonstration, where high performance and near real-time inference were obtained using a webcam and a Raspberry Pi4.