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DTSTART;TZID=UTC:20240129T090000
DTEND;TZID=UTC:20240205T121500
DTSTAMP:20260711T102915
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SUMMARY:Biases\, Discrimination\, and Fairness\, Arthur Charpentier (Université du Québec à Montréal)
DESCRIPTION:  \n\n\n\n  \n  \nSCHEDULE\n  \nMonday\n  \n29th January 2024 \n5th February 2024\n  \nFrom 9:00 to 12:15\n  \nRoom 2033\n\n\n  \nThursday\n  \n1st February 2024\n  \nFrom 9:00 to 12:15\n  \nRoom 2033\n\n\n\nAims and objectives\nThis course will provide a state-of-the-art\, on fairness and discrimination\, in the context of insurance pricing (and more generally\, predictive models). As explained by Avraham et al. (2014) “‘insurance companies are in the business of discrimination. Insurers attempt to segregate insureds into separate risk pools based on the differences in their risk profiles\, first\, so that different premiums can be charged to the different groups based on their differing risks and\, second\, to incentivize risk reduction by insureds. This is why we let insurers discriminate. There are limits\, however\, to the types of discrimination that are permissible for insurers. But what exactly are those limits and how are they justified?”. First\, we will come back to the specificities of predictive models in insurance. We will come back to the different places where a potential discrimination can intervene\, by insisting on the possible biases in the data\, in the models. We will present in particular the regulations in Europe and North America. In a second step\, we will see how to quantify a possible discrimination\, insisting on the main measures of “group-fairness”\, before discussing the individual approach\, in particular in relation with the causal approaches. Indeed\, the central question of discrimination is “would the price have been different if this person had been a man instead of a woman”. We will see how to build a counterfactual allowing to quantify a possible discrimination. Finally\, we will see how to correct a discrimination\, insisting on the in-processing (throught penalized models) and post-processing approaches (using optimal transport). \n
URL:https://crest.science/event/biases-discrimination-and-fairness-arthur-charpentier-universite-du-quebec-a-montreal/
LOCATION:2033
CATEGORIES:Actuarial Science
ORGANIZER;CN="Christian-Yann%20Robert":MAILTO:christian-yann.robert@ensae.fr
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DTSTART;TZID=Europe/Helsinki:20240131T121500
DTEND;TZID=Europe/Helsinki:20240131T133000
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SUMMARY:Enrico Rubolino (Lausanne) "Market Externalities of Tax Evasion"
DESCRIPTION:Applied Seminar \nTime: 12:15 pm – 13:30 pm\nDate: 31th of January\nRoom : 3001 \n  \nEnrico Rubolino (Lausanne) “Market Externalities of Tax Evasion” \nAbstract : This paper shows that tax evasion significantly distorts market competition and firm growth. Using administrative data on the universe of firms in Italy\, we compute a tax evasion proxy as the fraction of individual firms who manipulate their revenue to meet eligibility criteria for several preferential tax regimes. This measure widely varies geographically\, across industries\, and over time within municipality-industry cells\, our fine definition of a market. We examine the impact of market-level variations in tax evasion on competitor firms\, defined as those non-eligible for preferential tax regimes and operating in the same market. To address endogeneity concerns\, we use policy-induced changes in tax notches’ size to predict the fraction of non-compliant firms in each market. Our findings indicate that as the proportion of non-compliant firms in a market increases\, their competitors suffer significant revenue losses. This unfair competition erodes firms’ productivity and reduces their survival rates. Firms respond to these losses by shifting some of the burden to workers. Our findings suggest that cracking down on tax evasion not only increases tax revenue and promotes tax fairness\, but can also enhance market efficiency by leveling the playing field. \nOrganizer: Pierre BOYER \n
URL:https://crest.science/event/enrico-rubolino-lausanne-market-externalities-of-tax-evasion/
CATEGORIES:Applied Seminar,Seminars
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