Family, labour and fertility

Family, labour and fertility

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Too Old to Train? Age Gaps in Employer-Provided Training: Experimental Evidence from Italian SMEs

Francesco Maura, Olivia Masi, Paola Profeta

Older workers are among the most expensive employees on firms’ payrolls — yet they are systematically the least likely to receive employer-provided training. This paperinvestigates the managerial decisions that generate this paradox through two complementary surveys conducted among Italian small and medium enterprises (SMEs). Thefirst survey documents a strong, monotonic age gradient in training allocation: employees’probability of being selected for training decreases sharply with age. Motivated bythese findings, the second survey introduces a randomized information treatment: managersreceive information on labor-force aging, rising retirement ages, and the remainingworking life of older workers. This information does not reduce managers’ age gradientin training allocation, suggesting that this bias is sustained by mechanisms other thansimple horizon-based considerations.

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Parental Leave Uptake in Denmark: Fathers and Workplace Constraints

Sina Smid, Sigrid Koob

Following Denmark’s parental leave reform, which earmarked 11 weeks for each parent, fathers’ leave uptake has increased substantially. Yet most fathers still do not use their full entitlement. This paper looks into which fathers do not take the full leave and examines the workplace and labour market barriers behind this. Combining register and survey experimental data, it analyses uptake patterns and explores fathers’ experiences, including workplace norms, barriers and discrimination. The findings aim to inform policies and workplace practices that promote a more equal sharing of care and improve the effective use of parental leave rights.

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Survival and Financial Literacy in Investment Decisions in Later LIfe

Chiara Dal Bianco, Francesco Maura, Francesca Parodi and Guglielmo Weber

We document that the underestimation of subjective survival probabilities negatively affects investments in risky assets in the UK context. We then explore the role of cognitive skills in explaining this negative relationship. More precisely, we disentangle a direct effect of cognitive skills on investments in risky assets through entry costs and an indirect effect through survival probability underestimation. We calibrate a life-cycle model to quantify the importance of survival probability underestimation on suboptimal ownership of risky assets. Using subjective survival curves instead of life table probabilities results in a lower asset accumulation and lower investment in risky assets. These findings suggest that policy interventions aimed at increasing financial literacy are likely to affect the decision to own risky assets not only by reducing entry costs but also by attenuating survival risk underestimation.

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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