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The purpose of this paper is to present and validate a large-scale methodology for risk assessment and management in cultural heritage sites, taking into account their specific tangible or intangible values. Emphasis is given to historic centres that are key resources in building resilience to disasters but are also highly vulnerable due to several factors, such as the characteristics of the built environment, the community and social life, the lack of risk awareness and maintenance and finally the poor regulatory framework for their management and valorisation.

The multi-step procedure starts from the assessment of the attributes of cultural heritage in order to identify priorities and address the analysis. Then, it evaluates the primary and secondary hazards in the area, the vulnerabilities and threats of the site and the impacts of the chain of events. Finally, it allows for calibrating a site-specific set of mitigation, preparedness, response and recovery measures.

The application to two case studies in the Italian peninsula, the historic centres of San Gimignano and Reggio Calabria, allows for identifying research gaps and practical opportunities towards the adoption of common guidelines for the selection of safety measures.

By providing a qualitative assessment of risks, the research points out the potentialities of the methodology in the disaster risk management of cultural heritage due to its capacity to be comprehensive and inclusive towards disciplines and professionals.

While the economic literature mostly tackled discrimination looking at labour costs, this work focuses on its relation to labour productivity, arguing that discrimination may worsen the performance of female employees. In this view, it represents a source of allocative inefficiency, which contributes to reducing output.

Female discrimination is both a social and an economic problem. In social terms, consolidated gender stereotypes impose constraints on women’s behaviour, worsening their overall well-being. In economic terms, women face generally worse labour market conditions. Using long-run Italian data spanning from 1861 to 2009, the authors propose a novel measure of female discrimination based on the observed frequency of discriminating epithets. Following social capital theory, the authors distinguish between structural and voluntary discrimination, and use Data Envelopment Analysis for time series data to assess the extent of inefficiency that each component of discrimination induces in the production process.

The results draw the trajectory of female discrimination in Italy and provide evidence in favour of the idea that female discrimination reduces productive efficiency. In particular, the structural component of female discrimination, although less sizeable than the voluntary component, plays a major role, especially in recent years, where more stringent beauty standards fuel looks-based discrimination.

The contribution of this work is twofold. First, based on contributions from social sciences different from economics, it proposes a novel theoretical framework that explores the effect of discriminatory language on labour productivity. Second, it introduces a novel and direct measure of female discrimination at the country level, based on the bidirectional link between language and culture. The indicator is easily understood by policymakers and may be used to evaluate the effectiveness of anti-discrimination policies.