Calendrier du 16 novembre 2021
Virtual Development Economics Seminar
Du 16/11/2021 de 17:00 à 18:15
On line
ROSENZWEIG Mark (Yale University)
Democratization, Development and Elite Capture
PSI-PSE (Petit Séminaire Informel de la Paris School of Economics) Seminar
Du 16/11/2021 de 17:00 à 18:00
Salle R1.14, Campus Jourdan
CAKIR Bayram (PSE)
Automation, Skill Premium and Factor Shares: Labor Will Be Back
Paris Trade Seminar
Du 16/11/2021 de 14:45 à 16:15
Sciences Po, 28 rue des Saints-Pères, 75007 Paris (M° Saint Germain des Prés), SALLE H 405
DEMIR PAKEL Banu (Oxford)
O-Ring Production Networks
écrit avec C. Fieler, D. Xu, and K. Yang
Applied Economics Lunch Seminar
Du 16/11/2021 de 12:30 à 13:30
R1.09 Jourdan
ZABROCKI Leo (PSE)
Why Acute Health Effects of Air Pollution Could Be Inflated
écrit avec Vincent Bagilet (Columbia University)
Accurate and precise measurements of the short-term effects of air pollution on health play a key role in setting air quality standards. Yet, statistical power calculations are rarely—if ever—carried out. We first collect estimates and standard errors of all available articles found in the standard epidemiology and causal inference literatures. We find that nearly half of them may suffer from a low statistical power and could thereby produce statistically significant estimates that are actually inflated. We then run simulations based on real data to identify which parameters of research designs affect statistical power. Despite their large sample sizes, we show that studies exploiting rare exogenous shocks such as transport strikes or thermal inversions could have a very low statistical power, even for plausibly large effect sizes. Our simulation results indicate that the observed discrepancy in the literature between instrumental variable estimates and non-causal ones could be partly explained by the inherent imprecision of the two-stage least-squares estimator. We also provide evidence that subgroup analysis on the elderly or children should be implemented with caution since the average number of events for an health outcome is a major driver of power. Based on these findings, we build a series of recommendations for researchers to evaluate the design of their study with respect to statistical power issues