Topic:Identification of Gross Output Production Functions with a Nonseparable Productivity Shock

Lecturer: Assistant Professor Qingsong Pan, School of Economics, Shandong University

Time: 12:15-13:15p.m. October 19th, 2023

Venue: B321,Zhixin Building, Central Campus

Abstract:We study the nonparametric identification of gross output production function with a nonseparable productivity shock. Our nonseparable specification relaxes the traditional assumption of Hicks neutrality that has been shown to be inconsistent with a number of data sets. It can thus capture the bias in technical change, which recent research has found relevant to many important economic questions. We first generalize the identification approach of Gandhi et al. 2020 to nonseparable models and show the identification of output elasticities. To identify the entire production function, we then impose a homogeneity assumption, which is supported by the data. Given the fact that our nonseparable models nest Hicks-neutral models, we are able to document the misspecification bias of the latter. Using Chilean and Colombian plant-level data, our estimates suggest that Hicks-neutral models overestimate returns to scale, overestimate output elasticities of labor, and generate biased estimates of capital intensity. Our estimates also indicate that technological change is predominantly biased toward capital over labor and intermediate inputs.