On the Identification of Gross Output Production Functions

By Amit J. Gandhi, S. Navarro, D. Rivers. Journal of Political Economy, 2020

330 citations recorded by the source corpus.

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.

Subfields: Development

View Paper: https://www.semanticscholar.org/paper/84ab1459451b20f30ec8cb07da320deb9a46ff99

Citations in the corpus

• Venting Out: Exports During a Domestic Slump (2018, neutral): “34 See among, many others, Wolf (2000) for the United States; Garmendia et al. (2012) for Spain; Helliwell (1996) for Canada; Combes, Lafourcade, and Mayer (2005) for France; Poncet (2005) and Xing and Li (2011) for China; Daumal and Zignago (2010) for Brazil; and Volker (2002) and Helble (2007) for”