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A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity

Emir Malikov (University of Nevada, Las Vegas, Las Vegas, NV, USA)

Shunan Zhao (Oakland University, Rochester, MI, USA)

Jingfang Zhang (Alcorn State University, Lorman, MS, USA)

Essays in Honor of Subal Kumbhakar

ISBN: 978-1-83797-874-8, eISBN: 978-1-83797-873-1

Publication date: 5 April 2024

Abstract

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

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Acknowledgements

Acknowledgments

We are most grateful to the editors of this volume, Chris Parmeter, Mike Tsionas, Hung-Jen Wang and Dan Millimet, and an anonymous referee for many insightful comments and suggestions that helped improve this chapter.

Citation

Malikov, E., Zhao, S. and Zhang, J. (2024), "A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity", Parmeter, C.F., Tsionas, M.G. and Wang, H.-J. (Ed.) Essays in Honor of Subal Kumbhakar (Advances in Econometrics, Vol. 46), Emerald Publishing Limited, Leeds, pp. 211-263. https://doi.org/10.1108/S0731-905320240000046009

Publisher

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Emerald Publishing Limited