Family, labour and fertility

Family, labour and fertility

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Age and Gender Discrimination in Job Opportunities

Francesco Maura, Paola Profeta and Olivia Masi 

This study investigates how gender and age biases affect managerial decisions in Italian small and medium enterprises. Analyzing survey data from 827 managers, we find little evidence of bias in task assignments, but younger employees - particularly younger women - are favored for training program allocation. These findings reveal that while explicit gender or age bias is limited in some areas, implicit preferences persist in others, especially age preferences, shaping workforce development and reward distribution.

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Do women matter in household portfolio allocation?

Francesco Maura

This work investigates the stereotype toward households' portfolio allocation where the man is the only decision maker. I develop a portfolio choice model where households who declare to manage their finances jointly decide about stock market participation and then choose the optimal allocation. The model accounts for household risk tolerance, measured as a weighted sum of the household members' risk preferences. I study the determinants of the household portfolio controlling for selection into participation: the results show that household risk preferences affect portfolio allocation but not stock market participation. Last, I compare the goodness of fit of two models, the one that accounts for the preferences of all the household members and the one that accounts for the preference of only the male decsion-makers. I show that the former approach fits significantly better the data, thus women's preferences matter in household portfolio allocation. Moreover, I find that the role of wives in financial choices may be underestimated.

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Automating Inequality: Gender Bias in AI-mediated Labor Markets

Kenza Elass, Germain Gauthier, Debora Nozza, Paola Profeta 

The rise of Generative Artificial Intelligence (GenAI) has led to the widespread adoption of automated CV generation services for job seekers and CV screening services for employers. We study the consequences of using GenAI for such tasks. We find that GenAI systematically reproduces and can even amplify gender biases. It generates gender-stereotyped CVs and recommends lower earnings for female job seekers. We also run a series of online experiments inspired by classical correspondence studies, which measure hiring discrimination by comparing employer responses to equivalent CVs that differ only by gender. Our results show that GenAI penalizes female candidates during CV screening in male-dominated occupations. These patterns persist even when models are explicitly instructed to remain gender-neutral. Our findings provide the first direct evidence that GenAI can reinforce structural labor market inequalities, potentially disadvantaging women at every stage of the job search.

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Partners’ Risk Perception and Household Portfolio Allocation

Francesco Maura