Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/298299 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
NBB Working Paper No. 428
Verlag: 
National Bank of Belgium, Brussels
Zusammenfassung: 
Productivity is influenced by several firm-level factors, often latent. When unexplained, this latent heterogeneity can lead to the mismeasurement of productivity differences between groups of firms. We propose a flexible, semi-parametric extension of current production function estimation techniques using finite mixture models to control for latent firm-specific productivity determinants. We establish the performance of the proposed methodology through a Monte Carlo analysis and estimate export premia using firm-level data to demonstrate its empirical applicability. We apply our framework to assess export productivity premia and their robustness with respect to latent heterogeneity. Our results highlight that latent heterogeneity distorts export premia estimates and their contribution to aggregate productivity growth. The proposed approach delivers robust estimates of productivity differences between firm groups, regardless of the availability of productivity determinants in the data.
Schlagwörter: 
finite mixture model
productivity estimation
productivity distribution
latent productivity determinants
JEL: 
C13
C14
D24
L11
Dokumentart: 
Working Paper

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