Information, Misallocation, and Aggregate Productivity

By J. David, Hugo A. Hopenhayn, Venky Venkateswaran. 2016

229 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/84f4683d51aa1ac054771b468355c6e5523d569e

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In Economics and Econometrics

Chuanwang Sun

Citations in the corpus

Factor market distortion correction, resource reallocation and potential productivity gains: An empirical study on China's heavy industry sector (2018, background): “David et al. (2016) investigated the reduced aggregate productivity and output resulting from the misallocation of factors across heterogeneous firms due to informational frictions in China, India and the US.”
Big Data as a Governance Mechanism (2019, unknown): “48 Prior literature has found that learning from market prices contributes little to market resource allocation (David et al. 2016).”
Firm performance and macro forecast accuracy (2018, unknown): “1 See, for example, classic works including Abel and Blanchard (1986) ; Caballero (1997) ; Chirinko (1993) ; Nickell (1978) or Dixit and Pindyck (1994) . https://doi.org/10.1016/j.jmoneco.2019.02.008 0304-3932/© 2019 Elsevier B.V.”
Incomplete Information in Macroeconomics: Accommodating Frictions in Coordination (2016, unknown): “crises are the product of “coordination failures”, such as Diamond (1982), Bryant (1983), Cooper and John (1988), Diamond and Dybvig (1983), Calvo (1988), and Obstfeld (1986, 1996).”
Competition and the Welfare Gains from Transportation Infrastructure: Evidence from the Golden Quadrilateral of India (2018, unknown): “Prominent papers in this large literature include Arkolakis, Costinot, Donaldson, and Rodríguez-Clare (2015), de Blas and Russ (2015), Dhingra and Morrow (2014), Edmond, Midrigan, and Xu (2015), Epifani and Gancia (2011), Feenstra (2014), Feenstra and Weinstein (Forthcoming), Holmes, Hsu, and Lee (2”
The Sources of Capital Misallocation (2019, unknown): “…( 2016 ) focus on longer time horizons ( they analyze 3-year time intervals . ignoring , This might lead one to conclude that other factors, as David, Hopenhayn and Venkateswaran ( 2016 ) do, leads to negligible bias in the estimate of uncertainty. that But, this is not a general result and…”