Calendrier du 21 mars 2022
Régulation et Environnement
Du 21/03/2022 de 17:30 à 19:00
Online
SHAPIRO Joseph (UC Berkeley)
Regulating Untaxable Externalities: Are Vehicle Air Pollution Standards Effective and Efficient?
What is a feasible and efficient policy to regulate air pollution from vehicles? A Pigou-vian tax is technologically infeasible. Most countries instead rely on exhaust standards that limit air pollution emissions per mile for new vehicles. We assess the effectiveness and efficiency of these standards, which are the centerpiece of US Clean Air Act regulation of transportation, and counterfactual policies. We show that the air pollution emissions per mile of new US vehicles has fallen spectacularly, by over 99 percent, since standards began in 1967. Several research designs with a half century of data suggest that exhaust standards have caused most of this decline. Yet exhaust standards are not cost-effective in part because they fail to encourage scrap of older vehicles, which account for the majority of emissions. To study counterfactual policies, we develop an analytical and a quantitative model of the vehicle fleet. Analysis of these models suggests that tighter exhaust standards increase social welfare and that increasing registration fees on dirty vehicles yields even larger gains by accelerating scrap, though both reforms have complex effects on inequality.
Econometrics Seminar
Du 21/03/2022 de 16:00 à 17:15
DOVONON Prosper (Concordia University)
Specification Testing for Conditional Moment Restrictions under Local Identification Failure
écrit avec Co-author: Nikolay Gospodinov
In this paper, we study the asymptotic behavior of the specification test in conditional moment restrictions model under first-order local identification failure with dependent data. More specifically, we obtain conditions under which the conventional specification test for conditional moment restrictions remains valid when first-order local identification fails but global identification is still attainable. In the process, we obtain some novel intermediate results that include extending the first- and second-order local identification framework to models defined by conditional moment restrictions, characterizing the rate of convergence of the GMM estimator and the limiting representation for degenerate U-statistics under strong mixing dependence. Simulation and empirical results illustrate the properties and the practical relevance of the proposed testing framework.
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Roy Seminar (ADRES)
Du 21/03/2022
Salle R2.21 - Campus Jourdan 75014 Paris
LIANG Annie (Northwestern University)
Algorithmic Design: Fairness Versus Accuracy
écrit avec joint with Jay Lu and Xiaosheng Mu
Algorithms are increasingly used to guide consequential decisions, such as who should be granted bail or be approved for a loan. Motivated by growing empirical evidence, regulators are concerned about the possibility that the errors of these algorithms differ sharply across subgroups of the population. What are the tradeoffs between accuracy and fairness, and how do these tradeoffs depend on the inputs to the algorithm? We propose a model in which a designer chooses an algorithm that maps observed inputs into decisions, and introduce a fairness-accuracy Pareto frontier. We identify how the algorithm's inputs govern the shape of this frontier, showing (for example) that access to group identity reduces the error for the worse-off group everywhere along the frontier. We then apply these results to study an ``input-design" problem where the designer controls the algorithm's inputs (for example, by legally banning an input), but the algorithm itself is chosen by another agent. We show that: (1) all designers strictly prefer to allow group identity if and only if the algorithm's other inputs satisfy a condition we call group-balance; (2) all designers strictly prefer to allow any input (including potentially biased inputs such as test scores) so long as group identity is permitted as an input, but may prefer to ban it when group identity is not.