The Trend over Time of the Gender Wage Gap in Italy

We analyse gender wage inequalities in Italy in the mid-1990s and in the mid-2000s. In this period important labour market developments occurred: institutional changes have loosened the use of flexible and atypical contracts; the female employment rates and educational levels have substantially increased. We identify the time trends of different components of the gender wage gap by estimating wage distributions in the presence of covariates and sample selection and by counterfactual microsimulations. We find that women swam against the tide: whilst the trend in female qualifications slightly reduced the gender wage gap, the gender relative trends in the wage structure significantly increased it.gender wage gap, counterfactual distributions, decompositions, hazard function, labour market reforms

Gender Wage Gap : A Semi-parametric Approach with Sample Selection Correction

Sizeable gender differences in employment rates are observed in many countries. Sample selection into the workforce might therefore be a relevant issue when estimating gender wage gaps. This paper proposes a new semi-parametric estimator of densities in the presence of covariates which incorporates sample selection. We describe a simulation algorithm to implement counterfactual comparisons of densities. The proposed methodology is used to investigate the gender wage gap in Italy. It is found that when sample selection is taken into account gender wage gap widens, especially at the bottom of the wage distribution. Explanations are offered for this empirical finding.gender wage gap, hazard function, sample selection, glass ceiling, sticky floor

The Trend over Time of the Gender Wage Gap in Italy

We analyse gender wage inequalities in Italy in the mid-1990s and in the mid-2000s. In this period important labour market developments occurred: institutional changes have loosened the use of flexible and atypical contracts; the female employment rates and educational levels have substantially increased. We identify the time trends of different components of the gender wage gap by estimating wage distributions in the presence of covariates and sample selection and by counterfactual microsimulations. We find that women swam against the tide: whilst the trend in female qualifications slightly reduced the gender wage gap, the gender relative trends in the wage structure significantly increased it.gender wage gap, counterfactual distributions, decompositions, hazard function, labour market reforms

Improved synthesis of the hypoxia probe 5-deutero-5-fluoro-5-deoxy-azomycin arabinoside (FAZA) as a model process for tritium radiolabeling

Peer reviewedPublisher PD

On the properties of the Lambda value at risk: robustness, elicitability and consistency

Recently, financial industry and regulators have enhanced the debate on the good properties of a risk measure. A fundamental issue is the evaluation of the quality of a risk estimation. On the one hand, a backtesting procedure is desirable for assessing the accuracy of such an estimation and this can be naturally achieved by elicitable risk measures. For the same objective, an alternative approach has been introduced by Davis (2016) through the so-called consistency property. On the other hand, a risk estimation should be less sensitive with respect to small changes in the available data set and exhibit qualitative robustness. A new risk measure, the Lambda value at risk (Lambda VaR), has been recently proposed by Frittelli et al. (2014), as a generalization of VaR with the ability to discriminate the risk among P&L distributions with different tail behaviour. In this article, we show that Lambda VaR also satisfies the properties of robustness, elicitability and consistency under some conditions

The Rhombi-Chain Bose-Hubbard Model: geometric frustration and interactions

We explore the effects of geometric frustration within a one-dimensional Bose-Hubbard model using a chain of rhombi subject to a magnetic flux. The competition of tunnelling, self-interaction and magnetic flux gives rise to the emergence of a pair-superfluid (pair-Luttinger liquid) phase besides the more conventional Mott-insulator and superfluid (Luttinger liquid) phases. We compute the complete phase diagram of the model by identifying characteristic properties of the pair-Luttinger liquid phase such as pair correlation functions and structure factors and find that the pair-Luttinger liquid phase is very sensitive to changes away from perfect frustration (half-flux). We provide some proposals to make the model more resilient to variants away from perfect frustration. We also study the bipartite entanglement properties of the chain. We discover that, while the scaling of the block entropy pair-superfluid and of the single-particle superfluid leads to the same central charge, the properties of the low-lying entanglement spectrum levels reveal their fundamental difference.Comment: 12 pages, 11 figure

Dissecting the 3D structure of elliptical galaxies with gravitational lensing and stellar kinematics

The combination of strong gravitational lensing and stellar kinematics provides a powerful and robust method to investigate the mass and dynamical structure of early-type galaxies. We demonstrate this approach by analysing two massive ellipticals from the XLENS Survey for which both high-resolution HST imaging and X-Shooter spectroscopic observations are available. We adopt a flexible axisymmetric two-component mass model for the lens galaxies, consisting of a generalised NFW dark halo and a realistic self-gravitating stellar mass distribution. For both systems, we put constraints on the dark halo inner structure and flattening, and we find that they are dominated by the luminous component within one effective radius. By comparing the tight inferences on the stellar mass from the combined lensing and dynamics analysis with the values obtained from stellar population studies, we conclude that both galaxies are characterised by a Salpeter-like stellar initial mass function.Comment: Proceedings of the IAU Symposium 309, Contributed Talk, Vienna, July 2014; 4 pages, 2 figure

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

The relevance of oil in the world economy explains why considerable effort has been devoted to the development of different types of econometric models for oil price forecasting. Several specifications have been proposed in the economic literature. Some are based on financial theory and concentrate on the relationship between spot and futures prices (“financial” models). Others assign a key role to variables explaining the characteristics of the physical oil market (“structural” models). The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static and dynamic forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings can be summarized as follows. Financial models in levels do not produce satisfactory forecasts for the WTI spot price. The financial error correction model yields accurate in-sample forecasts. Real and strategic variables alone are insufficient to capture the oil spot price dynamics in the forecasting sample. Our proposed mixed models are statistically adequate and exhibit accurate forecasts. Different data frequencies seem to affect the forecasting ability of the models under analysis.Oil Price, WTI Spot And Futures Prices, Forecasting, Econometric Models