Calendrier du 13 janvier 2023
Du 13/01/2023 de 12:00 à 13:00
Salle R1.13
MANDEL Antoine (Paris 1)
The network structure of global tax evasion evidence from the Panama papers
écrit avec Fernando Garcia Alvarado
This paper builds on recent insights from network theory and on the rich dataset made available by the Panama Papers in order to investigate the micro-economic dynamics of tax-evasion. We model offshore financial entities documented in the Panama Papers as links between jurisdictions in the global network of tax evasion. A quantitative analysis shows that the resulting network, far from being a random collection of bilateral links, has key features of complex networks such as a core-periphery structure and a fat-tail degree distribution. We argue that these structural features imply that policy must adopt a systemic perspective to mitigate tax evasion. We offer three sets of insights from this perspective. First, we identify through centrality measures tax havens that ought to be priority policy targets. Second, we show that efficient tax treaties must contain exchange information clauses and link tax-havens to non-haven jurisdictions. Third, we show that the optimal deterrence strategies for a social-planner facing a strategic tax-evader in a Stackelberg competition can be characterized using the notion of Bonacich centrality.
Texte intégral
Du 13/01/2023 de 12:00 à 13:00
Salle R1.13
MANDEL Antoine (Paris 1)
The network structure of global tax evasion evidence from the Panama papers
écrit avec Fernando Garcia Alvarado
This paper builds on recent insights from network theory and on the rich dataset made available by the Panama Papers in order to investigate the micro-economic dynamics of tax-evasion. We model offshore financial entities documented in the Panama Papers as links between jurisdictions in the global network of tax evasion. A quantitative analysis shows that the resulting network, far from being a random collection of bilateral links, has key features of complex networks such as a core-periphery structure and a fat-tail degree distribution. We argue that these structural features imply that policy must adopt a systemic perspective to mitigate tax evasion. We offer three sets of insights from this perspective. First, we identify through centrality measures tax havens that ought to be priority policy targets. Second, we show that efficient tax treaties must contain exchange information clauses and link tax-havens to non-haven jurisdictions. Third, we show that the optimal deterrence strategies for a social-planner facing a strategic tax-evader in a Stackelberg competition can be characterized using the notion of Bonacich centrality.
EU Tax Observatory Seminar
Du 13/01/2023 de 12:00 à 13:00
Salle R1.13
MANDEL Antoine (Paris 1)
The network structure of global tax evasion evidence from the Panama papers
écrit avec with Fernando Garcia Alvarado
This paper builds on recent insights from network theory and on the rich dataset made available by the Panama Papers in order to investigate the micro-economic dynamics of tax-evasion. We model offshore financial entities documented in the Panama Papers as links between jurisdictions in the global network of tax evasion. A quantitative analysis shows that the resulting network, far from being a random collection of bilateral links, has key features of complex networks such as a core-periphery structure and a fat-tail degree distribution. We argue that these structural features imply that policy must adopt a systemic perspective to mitigate tax evasion. We offer three sets of insights from this perspective. First, we identify through centrality measures tax havens that ought to be priority policy targets. Second, we show that efficient tax treaties must contain exchange information clauses and link tax-havens to non-haven jurisdictions. Third, we show that the optimal deterrence strategies for a social-planner facing a strategic tax-evader in a Stackelberg competition can be characterized using the notion of Bonacich centrality.
Job Market Seminar
Du 13/01/2023 de 12:00 à 13:15
R2-21
KUANG Yizhou (Cornell University)
Robust Bayesian Estimation and Inference for Dynamic Stochastic General Equilibrium Models
This paper introduces a new algorithm to conduct robust Bayesian estimation and inference in dynamic stochastic general equilibrium models. The algorithm combines standard
Bayesian methods with an equivalence characterization of model solutions. This algorithm
allows researchers to perform the following analysis: First, find the complete range of
posterior means of both the deep parameters and any parameters of interest robust to the
choice of priors in a sense I make precise. Second, derive the robust Bayesian credible
region for the model parameters. I prove the validity of this algorithm and apply this
method to the models in Cochrane (2011) and An and Schorfheide (2007) to achieve robust
estimations for structural parameters and impulse responses. In addition, I conduct a
sensitivity analysis of optimal monetary policy rules with respect to the choice of priors
and provide bounds to the optimal Taylor rule parameters.