On estimating firm-level production functions using proxy variables to control for unobservables

By Jeffrey M. Wooldridge. 2009

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In Economics and Econometrics

Citations in the corpus

Deconfounding Reinforcement Learning in Observational Settings (2018, background): “Fortunately, it is possible, because a number of works have shown that one can use the knowledge inferred from the joint distribution between proxy variables and confounders to adjust for the hidden confounders (Louizos et al., 2017; Edwards et al., 2015; Kuroki and Pearl, 2014; Miao et al., 2018; P”
TOTAL FACTOR PRODUCTIVITY ESTIMATION: A PRACTICAL REVIEW (2012, positive): “Unlike the fixed effects estimator, IV methods do not rely on strict exogeneity of the inputs for consistent estimation (Wooldridge, 2009).”
High-growth firms and productivity: evidence from the United Kingdom (2015, methodology): “The modification allows both identifying variable inputs in the first stage of the estimation, and obtaining robust standard errors in a more straightforward fashion (see Wooldridge (2009) for the details).”
R&D and Productivity: Estimating Endogenous Productivity (2013, methodology): “In contrast, the previous literature either relies on a two-stage procedure (OP, LP, and ACF) or jointly estimates a system of equations (Wooldridge 2009).”
The Multiprod project: A comprehensive overview (2017, methodology): “…pointed out that the identification of the coefficient on the variable input is likely to be problematic in Levinsohn and Petrin (2003).24 More recently, Wooldridge (2009) developed a technique that builds upon the Levinsohn and Petrin (2003) method but also overcomes the identification problem.”
Production Function Estimation in Stata Using the Olley and Pakes Method (2008, methodology): “See Arellano and Bond (1991), Arellano and Bover (1995), Blundell and Bond (1998, 2000), Griliches and Mareisse (1998), Levinsohn and Petrin (2003), Pavcnik (2002), and Wooldridge (2005) for further information.”
The walking dead? Zombie firms and productivity performance in OECD countries (2018, methodology): “Our results are robust to other measures of MFP (Table A12), including a measure derived from an IV estimation of the production function, following the method proposed by Wooldridge (2009).”