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This paper provides some initial results of long-term trends in economic inequality in Catalonia from 1400-1800 ca. These first findings show that the evidence collected for Catalonia matches quite well with some hypotheses suggested previously in the literature. Namely, the high inequality levels prevalent across pre-industrial Europe; an inequality gradient that linked urban, more populated, and wealthier communities with greater inequality and vice versa; and the importance of the trends followed by the share owned by the wealthy as good predictors of economic inequality trends. However, at this stage, one of the most appealing propositions—the idea that economic inequality grew for the whole of Europe during the early-modern period, shaping a long left side of a “super Kuznets curve”—does not seem to be fully confirmed for Catalonia. From the mid-17th century, inequality growth seems to go hand-in-hand with growth in per capita GDP. In earlier periods, though, the inequality trend seems to be unrelated to economic growth and even, during the second half of the 16th century, there is some evidence of inequality decline coupled with economic growth.

This article provides a general chronology of Italian famines, incorporating earlier chronologies as well as recent research on preindustrial mortality crises and covering the whole period from circa 1250 to 1810. Hypotheses about the occurrence of famines are tested using the largest-existing database of time series of burials, covering northern Italy and part of central Italy, as well as a database of time series of wheat prices covering the whole of the Peninsula. The role played by food provisioning institutions is briefly detailed and a summary discussion of the causative factors of famines is provided. We argue that the majority of the most severe medieval and early modern famines happen when a situation of high demographic pressure on the available resources couples with periods of meteorological instability of the kind unfavorable to wheat crops, and the crisis is so widespread that institutions are unable to provide effective remedies.

This article explores opinions and semantic orientation around fertility and parenthood by scrutinizing filtered Italian Twitter data. We propose a novel methodological framework relying on Natural Language Processing techniques for text analysis, which is aimed at extracting sentiments from texts. A manual annotation for exploring sentiment and attitudes to fertility and parenthood was applied to Twitter data. The resulting set of tweets (corpus) was analysed through sentiment and emotion lexicons in order to highlight how affective language is used in this domain. It emerges that parents express a generally positive attitude towards their children and being and become parents, but quite negative sentiments on children’s future, politics and fertility and also parental behaviour. Exploiting geographical information from tweets, we find a significant correlation between the prevalence of positive sentiments about parenthood and macro-regional indicators for both life satisfaction and fertility levels.