Gender and the Labour Market: An International Perspective and the Case of Italy

This paper provides an overview of the literature on international differences in the gender wage gap. It then focuses on the Italian case and analyzes differentials in gender wage and employment gaps across regions. The cross-regional variation reproduces the negative correlation between gender wage and employment gaps observed at the cross-country level. Using the methodology in Olivetti and Petrongolo (2008), the paper shows the importance of regional differentials in sample selection induced by non-employment in accounting for this phenomenon.

Change in Women's Labor Force Participation: The Effect of Changing Experience

Over the past two decades married women's labor force participation has shown a considerable increase in the US. In particular, both the cross sectional and the life cycle behavior of married women's hours worked has undergone a substantial change. I show that a key factor underlying this trend is the change in behavior for married women with children. In particular, while in the past married women of childbearing age used to specialize in childrearing and homeproduction activities at the expense of engaging in market work, they now do not curb their labor participation. What gives rise to this change in behavior? In this paper I focus on relative changes in returns to experience as an explanation. In particular, I quantitatively assess the contribution of changes in the return to experience to the change in married women's life-cycle profiles of hours worked. I build a life-cycle model with human capital accumulation and home production in which the basic unit of analysis are married couples with children, and calibrate it using data from the 1970s and the 1990s. I show that changes in returns to experience can account for a large part of observed changes. I also demonstrate that decreases in the gender wage gap cannot account for much of the change in the shape of life cycle profiles for women.

Gender Gaps across Countries and Skills: Supply, Demand and the Industry Structure

The gender wage gap varies widely across countries and across skill groups within countries. Interestingly, there is a positive cross-country correlation between the unskilled-to-skilled gender wage gap and the corresponding gap in hours worked. Based on a canonical supply and demand framework, this positive correlation would reveal the presence of net demand forces shaping gender differences in labor market outcomes across skills and countries. We use a simple multi-sector framework to illustrate how differences in labor demand for different inputs can be driven by both within-industry and between-industry factors. The main idea is that, if the service sector is more developed in the US than in continental Europe, and unskilled women tend to be over-represented in this sector, we expect unskilled women to suffer a relatively large wage and/or employment penalty in the latter than in the former. We find that, overall, the between-industry component of labor demand explains more than half of the total variation in labor demand between the US and the majority of countries in our sample, as well as one-third of the correlation between wage and hours gaps. The between-industry component is relatively more important in countries where the relative demand for unskilled females is lowest.gender gaps, education, demand and supply, industry structure

Gender Gaps Across Countries and Skills: Supply, Demand and the Industry Structure

The gender wage gap varies widely across countries and across skill groups within countries. Interestingly, there is a positive cross-country correlation between the unskilled- to-skilled gender wage gap and the corresponding gap in hours worked. Based on a canonical supply and demand framework, this positive correlation would reveal the presence of net demand forces shaping gender differences in labor market outcomes across skills and countries. We use a simple multi-sector framework to illustrate how differences in labor demand for different inputs can be driven by both within-industry and between-industry factors. The main idea is that, if the service sector is more developed in the US than in continental Europe, and unskilled women tend to be over-represented in this sector, we expect unskilled women to suffer a relatively large wage and/or employment penalty in the latter than in the former. We find that, overall, the between-industry component of labor demand explains more than half of the total variation in labor demand between the US and the majority of countries in our sample, as well as one-third of the correlation between wage and hours gaps. The between-industry component is relatively more important in countries where the relative demand for unskilled females is lowest.gender gaps, education, demand and supply, industry structure

MEG Decoding Across Subjects

Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. The extreme difficulty is related to the structural and functional variability across the subjects. We call this approach "decoding across subjects". In this work, we address the problem of decoding across subjects for magnetoencephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. Third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one

The sound motion controller: a distributed system for interactive music performance

We developed an interactive system for music performance, able to control sound parameters in a responsive way with respect to the user’s movements. This system is conceived as a mobile application, provided with beat tracking and an expressive parameter modulation, interacting with motion sensors and effector units, which are connected to a music output, such as synthesizers or sound effects. We describe the various types of usage of our system and our achievements, aimed to increase the expression of music performance and provide an aid to music interaction. The results obtained outline a first level of integration and foresee future cognitive and technological research related to it