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Cited by 367

The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.

On the Identification of Gross Output Production Functions

Gandhi, Amit, Navarro, Salvador, Rivers, David A.·2020·Journal of Political Economy
DOI 10.1086/707736
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Abstract

We study the nonparametric identification of gross output production functions under the environment of the commonly employed proxy variable methods. We show that applying these methods to gross output requires additional sources of variation in the demand for flexible inputs (e.g., prices). Using a transformation of the firm’s first-order condition, we develop a new nonparametric identification strategy for gross output that can be employed even when additional sources of variation are not available. Monte Carlo evidence and estimates from Colombian and Chilean plant-level data show that our strategy performs well and is robust to deviations from the baseline setting.

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Metadata

Authors
Gandhi, A., Navarro, S., & Rivers, D. A.
Venue
Journal of Political Economy
Publisher
University of Chicago Press
Published
2020-08
Volume
128
Issue
8
Pages
2973-3016
ISSN
  • 0022-3808 (ISSN-L)
  • 1537-534X
DOI
10.1086/707736
OpenAlex
W2887758799
Cited by
367
References
48
FWCI
76.00
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: On the Identification of Gross Output Production Functions. https://app.cochat.ai/papers/10.1086/707736
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Gandhi, A., Navarro, S., & Rivers, D. A. (2020). On the Identification of Gross Output Production Functions. Journal of Political Economy. https://doi.org/10.1086/707736

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.