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‘Matching’ has become second nature to western millennials. Be that Uber rides or Tinder profiles, the way we interact with our ‘matches’ has become a way of life and one that we shouldn’t take for granted. But how are these ‘matches’ determined? What theories are at play? And what perks do we gain from them? From dating apps to ‘wise guys’; what is the current state of the Italian mafias in modern times and how far afield does their money and influence stretch?
Whom Matches with Whom? (And How?)
In economics, matching theory provides a useful framework for understanding how individuals form mutually beneficial relationships. The theory can be applied in a variety of matching contexts: firms and workers in labour markets, potential partners using dating apps and drivers and passengers using rideshare apps. We present results from laboratory experiments that help us answer two fundamental questions. First, which matches are more likely when there are many candidates for consideration? Second, how do matched individuals bargain over and share the benefits arising from their relationship?
Contemporary Scenarios of Italian Mafias Around the World Today
Everyone knows the Italian mafias, they are one of the main images of gangsterism and organised crime across the world, thanks to movies, TV series and books all throughout the last century. This talk will look at the reality of Italian mafias today, especially the Calabrian ’Ndrangheta, a peculiar organisation whole markets, activities, networks and money reach all parts of the globe, from Canada to Australia, from the UK to Germany.
How Different are the Genders? Labour Market Outcomes and Lab Experiments
Why do women earn less than men for doing the same job? Preferences for competition and risk appear to be a reason but they are not innate and compensation for work can be rewarded differently. This talk will look at how more competitive and risk taking workers tend to earn higher wages, how that affects gender differences in pay, and what can be done about remuneration to address gender gaps in pay.