Internet use during the pandemic

The COVID-19 outbreak changed several aspects of our everyday lives. During the widespread lockdowns, the internet came to play a crucial role, as many activities that used to be carried out in person had to be moved online, including working, shopping, accessing services, and talking to family and friends. While information technology helped to mitigate the negative consequences of the outbreak for the general population; for the elderly population, the use of the internet represented a challenge. According to data from Eurostat (2021), in- ternet use is less popular among the older than the younger generations: before the COVID-19 outbreak, 98% of individuals aged 16–24 used the internet, com- pared to 61% of individuals aged 65–74. However, this figure varied across the EU member states. The countries with the highest shares of individuals aged 65–74 using the internet were Denmark (94%), followed by Luxemburg and Sweden (91%); while the countries with the lowest shares of individuals aged 65–74 using the internet were Bulgaria (25%) and Croatia (28%)

The multifaceted aspects of inequality: health and labour market issues

2017 - 2018Inequalities have been the subject of keen economic interest due to ethical and policy implications for the society. In fact, there is wide agreement about the fact that living in a more egalitarian society leads to better social outcomes. However, on the other side, since most of the inequality is generated within the labour market there are discording opinions about the possibility of public intervention in the contemporary market economies. To make this picture even more multifaceted, related to economic inequality there is overwhelming evidence on the socioeconomic gradient in health. Indeed, economists have deeply investigated the two-way relationships between income and health and tried to disentangle the main mechanisms of transmissions. However, most of the findings rely on descriptive studies or analysis “at the mean”. With the aim of providing causal evidence and contributing to this long-standing strand of literature, this thesis analyses distributional aspect of income, focusing on the top tail of income distribution and in particular on those individuals who get high earnings in the labour market: the so-called working super-rich. Moreover, it investigates the socioeconomic gradient in health both in the top tails and in the rest of the distribution. The thesis consists of three chapters. The first chapter focuses on the determinants that allow some individuals to receive extraordinary earnings in labour markets compared to their peers, i.e. CEO or the superstars of sport, music and cinema. Specifically, it empirically analyses the effects of performance, popularity and bargaining power on the earnings of the universe of football players of Italian Serie A. The reasons are multiple. First, exploiting the possibility of having a perfect match employer-employee. Second, a longitudinal dataset with detailed information about the football players of Italian Serie A. Lastly, the fact that in Italy football players and managers represent the 20% of top 500 earners. On a methodological point of view, unlike previous analyses that are essentially based on cross-sectional data, disposing of panel data allows to investigate the returns of performance, popularity and bargaining power while controlling for players and team unobserved heterogeneity. Moreover, I employ the Unconditional Quantile Regression approach developed by Firpo, Fortin and Lemieux (2009) to estimate the impact of a marginal change in the determinants of earnings on their entire distribution. This is relevant because players’ earnings exhibit a Author Francesco Principe large dispersion around the mean and investigating the role of the determinants is especially interesting at the top of the earnings distribution, where superstar effects should more clearly manifest. Main results show that all the aforementioned factors significantly affect the players’ earnings. However, the analysis “beyond the mean” reveals that the role played by popularity increases at the top of earnings distribution being the main determinant of the “superstars”. These results challenge the interpretations of extraordinary earnings based only on very talented workers who “win and take all”. As mentioned above, labour market seems a fertile ground for the escalation of contemporary society’s extreme inequalities. Thus, understanding how the “working super-rich” respond to health shocks can be a key element of these dynamics. The second chapter of this thesis aims at providing evidence of the relationship between health shocks and labour market outcomes, focusing on those in the top tail of earnings distribution. Therefore, the dataset presented above has been enriched with data about the nature and the incidence of the injuries suffered by the universe of Serie A football players. In particular, in this chapter, I exploit traumatic injuries as exogenous variation in professional football players’ health to provide estimates of the causal impact of a health shock on two main labour market outcomes: the annual net wages and the probability of changing the terms of the contract between the employer (the club) and the employee (the player). The empirical approach employs panel fixed effects models combined with an IV strategy, which uses the average number of yellow cards received by the team as an instrument. Main results show that working super-rich are not immune to economics consequences of health shocks. In fact, injuries reduce the net wage in the following season by around 12%. This result is mainly driven by precautionary reasons due to the club’s concern about depreciation in the player’s human capital rather than by a direct effect of the shock on the player’s productivity. Thus, the second chapter contributes to the existing literature by providing original findings that can be summarised as follows. Firstly, it provides causal evidence of the relationship between health shocks and labour market outcomes. Secondly, it focuses on the consequences of health shocks for those on top incomes, for whom there is scant evidence so far. Lastly, it allows for a deeper analysis of the main mechanisms of the health shock, disentangling the effect mediated through the player’s performance and the one generated by human capital depreciation, inducing the club, to offer a lower wage for precautionary reasons. The third and last chapter carry on the emphasis on inequality and its multifaceted aspects by moving the focus from the top tail to the whole population. In fact, it investigates how people heterogeneously respond to public education policies delivering health information by using as case study the 2015 WHO warning about the carcinogenic effects of red meat consumption. Importantly, it exploits high frequency data, i.e. monthly data, about Italian households’ expenditures to identify the effect of the warning in the long vs. short run, for which there is no previous evidence in the literature and to document the response of households differing due to educational levels and health awareness. In order to identify such effect, I employ a Difference-in-Difference model,, which exploits the strong seasonality patterns in meat consumption in Italy, mainly associated with culinary traditions in occasion of catholic holidays celebrations. The results show a general misinterpretation of the warning and a short-term fall in red meat consumption by around 5%. However, a long-lasting and consistent shift in red meat consumption is documented only among households with higher educational levels and health awareness. These findings highlight a brand-new driver of health-education gradient and a potential source of health inequalities. Finally, they have strong policy implications about the successful design of public health-information policies, suggesting that they should be designed in a way that expose the individual to a constant flow of information. [edited by author]XVII n.s. (XXXI ciclo

Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications

We propose a simple yet powerful test statistic to quantify the discrepancy between two conditional distributions. The new statistic avoids the explicit estimation of the underlying distributions in highdimensional space and it operates on the cone of symmetric positive semidefinite (SPS) matrix using the Bregman matrix divergence. Moreover, it inherits the merits of the correntropy function to explicitly incorporate high-order statistics in the data. We present the properties of our new statistic and illustrate its connections to prior art. We finally show the applications of our new statistic on three different machine learning problems, namely the multi-task learning over graphs, the concept drift detection, and the information-theoretic feature selection, to demonstrate its utility and advantage. Code of our statistic is available at https://bit.ly/BregmanCorrentropy.Comment: manuscript accepted at IJCAI 20; added additional notes on computational complexity and auto-differentiable property; code is available at https://github.com/SJYuCNEL/Bregman-Correntropy-Conditional-Divergenc

Three are better than one: plasminogen receptors as cancer theranostic targets.

Activation of plasminogen on the cell surface initiates a cascade of protease activity with important implications for several physiological and pathological events. In particular, components of the plasminogen system participate in tumor growth, invasion and metastasis. Plasminogen receptors are in fact expressed on the cell surface of most tumors, and their expression frequently correlates with cancer diagnosis, survival and prognosis. Notably, they can trigger multiple specific immune responses in cancer patients, highlighting their role as tumor-associated antigens. In this review, three of the most characterized plasminogen receptors involved in tumorigenesis, namely Annexin 2 (ANX2), Cytokeratin 8 (CK8) and alpha-Enolase (ENOA), are analyzed to ascertain an overall view of their role in the most common cancers. This analysis emphasizes the possibility of delineating new personalized therapeutic strategies to counteract tumor growth and metastasis by targeting plasminogen receptors, as well as their potential application as cancer predictors

THERAPY-RELATED MYELOID NEOPLASMS: CONSIDERATIONS FOR PATIENTS’ CLINICAL EVALUATION

Therapy-related myeloid neoplasms (t-MNs) encompass a specific sub-group of myeloid malignancies arising after exposure to radio/cytotoxic agents for the treatment of unrelated diseases. Such malignancies present unique features, including advanced age, high comorbidities burden, and unfavorable genetic profiles. All these features justify the need for a specific diagnostic work-up and dedicated treatment algorithms. However, as new classification systems recognize the unique clinical characteristics exhibited by t-MN patients, how to assess fitness status in this clinical setting is largely unexplored. Optimizing fitness assessment would be crucial in the management of t-MN patients, considering that factors usually contributing to a worse or better outcome (like age, comorbidities, and treatment history) are patient-specific.In the absence of specific tools for fitness assessment in this peculiar category of AML, the aim of this review is to describe all those factors related to patient, treatment, and disease that allow planning treatments with an optimal risk/benefit ratio