What are the causes of educational inequality and their evolution over time? Evidence from PISA

This paper provides evidence on the sources of differences in inequalities in educational scores in European Union member states, by decomposing them into their determining factors. Using PISA data from the 2000 and 2006 waves, the paper shows that inequalities emerge in all countries and in both period, but decreased in Germany, whilst they increased in France and Italy. Decomposition shows that educational inequalities do not only reflect background related inequality, but especially schools' characteristics. The findings allow policy makers to target areas that may make a contribution in reducing educational inequalities

Long Run and Short Run Constraints in the Access to Private Health Care Services: Evidence from Selected European Countries

This paper aims at distinguishing long-run and short-run constraints in the access to private health care services. To this end, we apply the methodology proposed by Carneiro and Heckman (2003) to the SHARE database, a survey conducted in a number of European countries, involving some 22,000 individuals over the age of 50. Micro-data includes information on health and health consumption, and socioeconomic variables (like income and wealth). Our results show that the problem of short-run constraints in the access to private health care services could be real, especially in Italy, Greece, and to some extent Spain. Moreover, there appear to be differences in the role of credit constraints, both considering more specific services, and gender differences

What Explains the Redistribution Achieved by the Italian Personal Income Tax? Evidence from Administrative Data

We analyze the Italian personal income tax (PIT) in the light of the different tools available to the government to achieve income redistribution. Wefocus in particular on three mechanisms: marginal tax rates, deductions, and tax credits. Exploiting an extended version of the standard Pfahler decomposition, we estimate the contribution of each of these three tools to the overall redistributive effect of the PIT using administrative data on more than 1.3 million individual tax returns. Our estimates suggest that more than half of the total PIT redistributive effect is due to the two most important tax credits (the tax credit for employment and the tax credit for retirement income), while the marginal rates schedule contribution is about 40 percent. On the contrary, most of the itemized expenditures do not show any sizable impact on redistribution

Feature Selection for Classification with QAOA

Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution space that grows combinatorically. In this work, we consider in particular a quadratic feature selection problem that can be tackled with the Quantum Approximate Optimization Algorithm (QAOA), already employed in combinatorial optimization. First we represent the feature selection problem with the QUBO formulation, which is then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of finding the ground state of this Hamiltonian, which corresponds to the optimal selection of features. In our experiments, we consider seven different real-world datasets with dimensionality up to 21 and run QAOA on both a quantum simulator and, for small datasets, the 7-qubit IBM (ibm-perth) quantum computer. We use the set of selected features to train a classification model and evaluate its accuracy. Our analysis shows that it is possible to tackle the feature selection problem with QAOA and that currently available quantum devices can be used effectively. Future studies could test a wider range of classification models as well as improve the effectiveness of QAOA by exploring better performing optimizers for its classical step

Good or Bad? Understanding the Effects Over Time of Multigrading on Child Achievement

Multigrading represents the practice of mixing children of different ages in the same classroom. This paper examines the effect of attending a multigrade class in Grade 2 on students’ academic achievement in Grades 2, 5, and 8, respectively, considering Italy as a case study. To address the issue of endogeneity of multigrading (and class size), we adopt an IV identification strategy based on a law that disciplines class composition. We show that multigrading has a positive (16 percent of a standard deviation) short-term effect on academic achievements. However, this effect diminishes over time and becomes negative (-10 percent of a standard deviation) if students spend several years in a multigrade class. Mechanism analysis indicates the fundamental role of teachers and suggests that the negative long-term effect of multigrading is not statistically different from zero when multigrade classes are taught by more experienced teachers. These findings, based on longitudinal data, reconcile contrasting results in the literature, which are based on cross-sectional data and on the short-term effects of multigrading

Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances

In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit structure by adding and removing gates, and optimize their parameters during the training. Several Adaptative VQAs, based on heuristics such as circuit shallowness, entanglement capability and hardware compatibility, have already been proposed in the literature, but there is still lack of a systematic comparison between the different methods. In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline. In order to compare these algorithms to traditional VQAs, we also include the Quantum Approximate Optimization Algorithm (QAOA) in our analysis. We apply these algorithms to QUBO problems and study their performance by examining the quality of the solutions found and the computational times required. Additionally, we investigate how the choice of the hyperparameters can impact the overall performance of the algorithms, highlighting the importance of selecting an appropriate methodology for hyperparameter tuning. Our analysis sets benchmarks for Adaptative VQAs designed for near-term quantum devices and provides valuable insights to guide future research in this area

The runaway taxpayer

In order to analyse the determinants of tax evasion, the existing literature on individual tax compliance typically takes a prior-to-audit point of view. This paper focuses on a post-audit, post-detection -so far unexplored- framework, by investigating what happens after tax evasion has been discovered and noncompliant taxpayers are asked to pay their debts. We fi rst develop a two-period dynamic model of individual choice, considering an individual that has been already audited and detected as tax evader, who knows that Tax Authorities are looking for her to cash the due amount. We derive the optimal decision of running away in order to avoid paying the bill, and show that the experience of a prior tax notice reduces the probability to behave as a scofflaw. We then exploit information on post-audit, post-detection tax compliance provided by an Italian collection agency for the period 2004-2007 to empirically test the effectiveness of the prior notice against scofflaws. The evidence from alternative logit model speci cations supports our theoretical prediction: experiencing a tax notice reduces the probability of running away by about 10%. However, this may prove to be insufficient to discourage some individuals to runaway in order to avoid paying their dues

Molecular Profiling of Lymphatic Endothelial Cell Activation In Vitro

The lymphatic vascular system plays a key role in cancer progression. Indeed, the activation of lymphatic endothelial cells (LECs) through the lymphangiogenic process allows for the formation of new lymphatic vessels (LVs) that represent the major route for the dissemination of solid tumors. This process is governed by a plethora of cancer-derived and microevironmental mediators that strictly activate and control specific molecular pathways in LECs. In this work we used an in vitro model of LEC activation to trigger lymphangiogenesis using a mix of recombinant pro-lymphangiogenic factors (VFS) and a co-culture system with human melanoma cells. Both systems efficiently activated LECs, and under these experimental conditions, RNA sequencing was exploited to unveil the transcriptional profile of activated LECs. Our data demonstrate that both recombinant and tumor cell-mediated activation trigger significant molecular pathways associated with endothelial activation, morphogenesis, and cytokine-mediated signaling. In addition, this system provides information on new genes to be further investigated in the lymphangiogenesis process and open the possibility for further exploitation in other tumor contexts where lymphatic dissemination plays a relevant role

The effect of co-payments on the take-up of prenatal tests

Noninvasive prenatal screening tests help identify genetic disorders in a fetus, but their take-up remains low in several countries. Using a regression discontinuity design, we test the causal effect of a policy that eliminated co-payments for noninvasive screening tests in Italy. We identify the treatment effects by a discontinuity in women's eligibility for a free test based on their conception date. We find that the policy increases the probability of women's undergoing noninvasive screening tests by 5.5 percentage points, and the effect varies by socioeconomic status. We do not find evidence of substitution effects with more expensive and riskier invasive diagnostic tests. In addition, the increase in take-up does not affect pregnancy termination or newborn health. We find some evidence of positive effects on mothers’ health behaviors during pregnancy as measured by reductions in mothers’ weight gain and hospital admissions during pregnancy, but these are statistically significant only at the 10 percent level

Good or Bad? Short- versus Long-Term Effects of Multigrading on Child Achievement

This paper studies the effect of multigrading—mixing children of different ages in the same classroom—on students’ short- versus long-term academic achievement in Italy. We cope with the endogeneity of multigrading (and class size) through an instrumental variable identification strategy based on a law that disciplines class composition. By relying on longitudinal data that follow a cohort of Italian students over their compulsory school career, we show that multigrading has a positive short-term effect on achievements. This effect fades away over time to become negative in the long run if students spend several years in a multigrade class. The analysis of mechanisms points to the fundamental role of teachers and suggests that no negative long-term effect arises when multigrade classes are taught by more experienced and motivated teachers. These results reconcile contrasting findings in the literature based on cross-sectional data and a short-term focus