February 28, 11.00 Jérôme Adda (Bocconi University)
"TBA"

March 7,11.00 Antonio Cabrales (Universidad Carlos III de
Madrid)
"TBA"

March 14,11.00 David Craininch (IESEG School of Management)
"TBA"

March 28,11.00 David de la Croix (Université Catholique de Louvain)
"TBA"

April 11,11.00 Michael Burda (Humboldt Universität zu Berlin)
"TBA"

Past seminars 2021-2022

November 15,11.00 Etienne Billette de Villemeur (Université de Lille)
"Assessing Inequality Assessments:A General Representation of Inequality Indices"

We propose a unifying representation of all normalized inequality indices, regardless of their properties. The key concept behind our results is that of negative extreme transfers (NETs), which are transfers from the poorest individuals to the richest individuals. This concept alone is rich enough to describe the entire space of income distributions. Indeed, our first result (Lemma 1) is that any income distribution can be obtained as an expansion from the equal distribution by applying a sequence of NETs. In other words, NETs constitute a mathematical basis of the space of income distributions. Our main representation theorem (Theorem 1) builds upon the NET decomposition of income distributions to describe any given inequality index based on the weight it attaches to all possible NETs. Accordingly, desirable properties (axioms) of inequality indices translate into properties of the weight function that are easy to check. We express well-known inequality indices according to this decomposition. We define the λ -NET Criterion, the requirement that the weighting function is greater than some parameter λ >0 . The stringency of the λ -NET Criterion filters out inequality indices as λ increases, so that one can therefore rank inequality measures on the basis of the λ -NET Criterion they satisfy. According to this ranking, we find that the Pietra index is the unique measure to satisfy the maximal λ.

October 11,11.00 Pierre Dubois (Toulouse School of Economics)
"Bargaining and International Reference Pricing in the Pharmaceutical Industry"

The United States spends twice as much per person on pharmaceuticals as European countries, in large part because prices are much higher in the US. This fact has led policymakers to consider legislation for price controls. This paper assesses the effects of a US reference pricing policy that would cap prices in US markets by those offered in reference countries as proposed in the H.R.3 Lower Drug Costs Now Act of 2019. We estimate a structural model of demand and supply for pharmaceuticals in the US and reference countries like Canada where prices are set through a negotiation process between pharmaceutical companies and the government. We then simulate the counterfactual international reference pricing equilibrium, allowing firms to internalize the cross-country externalities introduced by this policy. We find that such a policy results in a slight decrease in US prices and a substantial increase in reference countries prices. The magnitude of these effects depends on the number, size and market structure of references countries. Overall, we find modest consumer welfare gains in the US but substantial losses in reference countries suggesting that this policy may not be the best way to introduce price controls in the US.

September 14, 11.00 Friederike Mengel (University of Essex)
"Non-Bayesian Statistical Discrimination"

Models of statistical discrimination typically assume that employers make rational inference from (education) signals. However, there is a large amount of evidence showing that most people do not update their beliefs rationally. We use a model and two experiments to show that employers who are conservative, in the sense of signal neglect, discriminate more against disadvantaged groups than Bayesian employers. We find that such irrational statistical discrimination deters high-ability workers from disadvantaged groups from pursuing education, further exacerbating initial group inequalities. Excess discrimination caused by employer conservatism is especially important when signals are very informative. Out of the overall hiring gap in our data, around 40% can be attributed to rational statistical discrimination, a further 40% is due to irrational statistical discrimination, and the remaining 20% is unexplained or potentially taste-based.