Analysis of Overtime Behavior in the Iranian labor Market with Decision-Tree Approach

Understanding the behavior of the workforce in cases involving individuals' decision to work overtime, is crucial to accurately assessing the effects of employment policies; Given that the issue of overtime in the analysis of human resources and the labor market system in macroeconomics, has been somewhat ignored, and on the other hand, changing the approach to the use and management of overtime in certain circumstances can be an unavoidable option in the labor market, this article examines the feasibility of this issue by analyzing the behavior of the Iranian labor force using the cost and income statistics of urban households for the period 2005-2020 and the factors that influence the individual's decision regarding Examines effective overtime. The technique used is one of the data mining techniques, and in particular, the Decision Tree algorithm, which allows us to study their behavior by realizing the underlying distribution of the data obtained from the set of subjects under study. The results suggest that overtime is difficult to define by individual-level characteristics such as age, education, gender, work attitudes, or any other invisible factor represented by these variables, but it can be defined as job attribute, the structural features of the labor market, as well as the cost decile in which the household is located, so that if these results are further supported, there will be significant consequences for both individuals and policymakers for workforce optimal allocation