Three Essays in Economics of Gender

During recent decades, economists' interest in gender-related issues has risen. Researchers aim to show how economic theory can be applied to gender related topics such as peer effect, labor market outcomes, and education. This dissertation aims to contribute to our understandings of the interaction, inequality and sources of differences across genders, and it consists of three empirical papers in the research area of gender economics. The aim of the first paper ("Separating gender composition effect from peer effects in education") is to demonstrate the importance of considering endogenous peer effects in order to identify gender composition effect. This fact is analytically illustrated by employing Manski's (1993) linear-in-means model. The paper derives an innovative solution to the simultaneous identification of endogenous and exogenous peer effects: gender composition effect of interest is estimated from auxiliary reduced-form estimates after identifying the endogenous peer effect by using Graham (2008) variance restriction method. The paper applies this methodology to two different data sets from American and Italian schools. The motivation of the second paper ("Gender differences in vulnerability to an economic crisis") is to analyze the different effect of recent economic crisis on the labor market outcome of men and women. Using triple differences method (before-after crisis, harder-milder hit sectors, men-women) the paper used British data at the occupation level and shows that men suffer more than women in terms of probability of losing their job. Several explanations for the findings are proposed. The third paper ("Gender gap in educational outcome") is concerned with a controversial academic debate on the existence, degree and origin of the gender gap in test scores. The existence of a gap both in mean scores and the variability around the mean is documented and analyzed. The origins of the gap are investigated by looking at wide range of possible explanations.

Visual-somatosensory interactions in mental representations of the body and the face

The body is represented in the brain at levels that incorporate multisensory information. This thesis focused on interactions between vision and cutaneous sensations (i.e., touch and pain). Experiment 1 revealed that there are partially dissociable pathways for visual enhancement of touch (VET) depending upon whether one sees one’s own body or the body of another person. This indicates that VET, a seeming low-level effect on spatial tactile acuity, is actually sensitive to body identity. Experiments 2-4 explored the effect of viewing one’s own body on pain perception. They demonstrated that viewing the body biases pain intensity judgments irrespective of actual stimulus intensity, and, more importantly, reduces the discriminative capacities of the nociceptive pathway encoding noxious stimulus intensity. The latter effect only occurs if the pain-inducing event itself is not visible, suggesting that viewing the body alone and viewing a stimulus event on the body have distinct effects on cutaneous sensations. Experiment 5 replicated an enhancement of visual remapping of touch (VRT) when viewing fearful human faces being touched, and further demonstrated that VRT does not occur for observed touch on non-human faces, even fearful ones. This suggests that the facial expressions of non-human animals may not be simulated within the somatosensory system of the human observer in the same way that the facial expressions of other humans are. Finally, Experiment 6 examined the enfacement illusion, in which synchronous visuo-tactile inputs cause another’s face to be assimilated into the mental self-face representation. The strength of enfacement was not affected by the other’s facial expression, supporting an asymmetric relationship between processing of facial identity and facial expressions. Together, these studies indicate that multisensory representations of the body in the brain link low-level perceptual processes with the perception of emotional cues and body/face identity, and interact in complex ways depending upon contextual factors.

Role of gender in transthyretin-related amyloidosis: an international perspective based on the Transthyretin Amyloid Outcomes Survey (THAOS)

Background. Transthyretin amyloidosis (ATTR) is an underdiagnosed disease caused by destabilization of transthyretin (TTR) due to pathogenic mutations (ATTRm) or aging (ATTRwt). We explored the role of gender in determining clinical picture using the largest available database on ATTR, the ongoing Transthyretin Amyloid Outcomes Survey (THAOS) international registry. Methods. Data through 1st April 2019 were explored. Symptomatic ATTRm (n=3737), asymptomatic ATTRm (n=644) and ATTRwt (n=874) patients were studied. Results. Male prevalence was 61% in the entire registry, 53% in ATTRm and 95% in ATTRwt. In the overall cohort, cardiac phenotype was more frequent in males (30.7% vs 10.5%, p<0.001). Among ATTRm, 72.3% of patients with amyloidotic cardiomyopathy (ATTR-CM) were males (p<0.001) but echocardiographic features showed no substantial gender differences. Sensory abnormalities (70.1% vs 64.1%, p<0.001), autonomic abnormalities (60% vs 48.5%, p<0.001) and walking disabilities were more frequent among ATTRm males. Carpal tunnel syndrome was more frequent in ATTRm males (18.6% vs 15.5%, p=0.014). In ATTRwt cohort, females had a more pronounced (but anyhow mild) walking disability. Male-to-female ratio varied within genotype, from 0.61 in Val30Met to 11.11 in ATTRwt; furthermore, males’ imbalance was more evident among symptomatic patients rather than in asymptomatic ones. Male gender, age at presentation and specific genotype were independently associated with the presence of ATTR-CM. Conclusions. In ATTR, cardiac involvement is more frequent in men, supporting the hypothesis that some biologic characteristics may “protect” from myocardial amyloid infiltration in women. Further investigations are needed to identify possible underlying protective mechanism and orient the research for innovative, gender-tailored therapeutic approaches.

Prima Facie: four facets of gender bias in personnel selection.

Recent findings have highlighted a ‘perfection bias’, that is women being evaluated on more criteria than men in the workplace (Moscatelli et al., 2020; Prati et al., 2019). However, these studies have not considered faces as stimuli, even if facial first impressions can affect several real-world outcomes (Todorov et al., 2015). On this basis, the present research aimed to verify the presence of a perfection bias at face perception level, employing for the first time all the four facets of the fundamental dimensions of social judgments (i.e., competence, dominance, morality, sociability; Abele et al., 2016) and attractiveness (Hosoda et al., 2003) as evaluation criteria of applicants’ hireability. Four experiments were conducted (total N = 645), employing a gender-neutral position (Study 1) as well as managerial positions (Study 2, 3, 4) and recruiting Italian and British students (Study 1, 2) as well as British workers (Study 3, 4). Results of Study 1 confirmed that male applicants were evaluated only on their facial competence, while female applicants were evaluated on all the other facial traits. However, the other three studies showed a different and unexpected pattern: besides facial attractiveness and competence considered equally important for both male and female applicants, facial dominance was considered as more important in evaluating women, while facial morality and sociability were considered as more important in evaluating men. Hence, results highlighted a sort of ‘deficit bias’, so that counter stereotypic traits in which men and women are believed weak (Fiske, 1998) were more relevant for their hireability.

Embedded representations of social interactions

Social interactions have been the focus of social science research for a century, but their study has recently been revolutionized by novel data sources and by methods from computer science, network science, and complex systems science. The study of social interactions is crucial for understanding complex societal behaviours. Social interactions are naturally represented as networks, which have emerged as a unifying mathematical language to understand structural and dynamical aspects of socio-technical systems. Networks are, however, highly dimensional objects, especially when considering the scales of real-world systems and the need to model the temporal dimension. Hence the study of empirical data from social systems is challenging both from a conceptual and a computational standpoint. A possible approach to tackling such a challenge is to use dimensionality reduction techniques that represent network entities in a low-dimensional feature space, preserving some desired properties of the original data. Low-dimensional vector space representations, also known as network embeddings, have been extensively studied, also as a way to feed network data to machine learning algorithms. Network embeddings were initially developed for static networks and then extended to incorporate temporal network data. We focus on dimensionality reduction techniques for time-resolved social interaction data modelled as temporal networks. We introduce a novel embedding technique that models the temporal and structural similarities of events rather than nodes. Using empirical data on social interactions, we show that this representation captures information relevant for the study of dynamical processes unfolding over the network, such as epidemic spreading. We then turn to another large-scale dataset on social interactions: a popular Web-based crowdfunding platform. We show that tensor-based representations of the data and dimensionality reduction techniques such as tensor factorization allow us to uncover the structural and temporal aspects of the system and to relate them to geographic and temporal activity patterns.

Deploying and processing neural representations of signals

Neural representations (NR) have emerged in the last few years as a powerful tool to represent signals from several domains, such as images, 3D shapes, or audio. Indeed, deep neural networks have been shown capable of approximating continuous functions that describe a given signal with theoretical infinite resolution. This finding allows obtaining representations whose memory footprint is fixed and decoupled from the resolution at which the underlying signal can be sampled, something that is not possible with traditional discrete representations, e.g., grids of pixels for images or voxels for 3D shapes. During the last two years, many techniques have been proposed to improve the capability of NR to approximate high-frequency details and to make the optimization procedures required to obtain NR less demanding both in terms of time and data requirements, motivating many researchers to deploy NR as the main form of data representation for complex pipelines. Following this line of research, we first show that NR can approximate precisely Unsigned Distance Functions, providing an effective way to represent garments that feature open 3D surfaces and unknown topology. Then, we present a pipeline to obtain in a few minutes a compact Neural Twin® for a given object, by exploiting the recent advances in modeling neural radiance fields. Furthermore, we move a step in the direction of adopting NR as a standalone representation, by considering the possibility of performing downstream tasks by processing directly the NR weights. We first show that deep neural networks can be compressed into compact latent codes. Then, we show how this technique can be exploited to perform deep learning on implicit neural representations (INR) of 3D shapes, by only looking at the weights of the networks.