Zobrazeno 1 - 10
of 951
pro vyhledávání: '"Peter Arcidiacono"'
Publikováno v:
Journal of Political Economy Microeconomics.
Publikováno v:
Journal of Labor Economics. 40:133-156
Publikováno v:
Journal of Political Economy. 128:4475-4522
Using data from Duke University undergraduates, we make three main contributions to the literature. First, we show that data on earnings beliefs and probabilities of choosing particular occupations...
Publikováno v:
American Economic Journal: Applied Economics. 12:175-206
Coupling weekly grocery transactions with the exact location and opening date of Walmarts over an 11-year period, we examine how Supercenter entry affects prices and revenues at incumbent supermarkets. We find that entry within 1 mile of an incumbent
Author(s): Arcidiacono, Peter; Muralidharan, Karthik; Shim, Eun-young; Singleton, John D. | Abstract: In this paper, we use a unique two-stage experiment that randomized access to school vouchers across both markets and students in rural India to est
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::402b771385d544497c0faee258d35748
https://doi.org/10.3386/w29077
https://doi.org/10.3386/w29077
Autor:
Robert A. Miller, Peter Arcidiacono
Publikováno v:
Quantitative Economics. 10:853-890
The estimation of nonstationary dynamic discrete choice models typically requires making assumptions far beyond the length of the data. We extend the class of dynamic discrete choice models that require only a few‐period‐ahead conditional choice
Detecting racial discrimination using observational data is challenging because of the presence of unobservables that may be correlated with race. Using data made public in the SFFA v. Harvard case, we estimate discrimination in a setting where this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32d3bc25cebb70f235c049cbff9cd144
https://doi.org/10.3386/w27068
https://doi.org/10.3386/w27068
Publikováno v:
SSRN Electronic Journal.
Detecting racial discrimination using observational data is challenging because of the presence of unobservables that may be correlated with race. Using data made public in the SFFA v. Harvard case, we estimate discrimination in a setting where this