Labor market analysis through transformations and robust multivariate models. (February 2021)

• Record Type: Journal Article
• Authors: Corbellini, Aldo; Magnani, Marco; Morelli, Gianluca
• Is Part Of: Socio-economic planning sciences. Number 73(2021)
• Publication Date: 2021-02
• Subjects: Labor share -- Robust regression -- Data transformation; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2020.100826
• Languages: English
• ISSNs: 0038-0121

Abstract

Abstract: The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation. Highlights: Forward Search and ACE transformation allow outlier and data structure identification. We provide insights on the interactions of firm labor share determinants. Capital and labor are substitute implying a negative effect of capital deepening. Firm market power measured by ROS, has a negative impact on the labor share.