Description: Using data from the Mexican Social Security Institute for labor risks, this paper classifies the severity of sequels in three categories: mild, moderate and high, and attempts to relate this level with the characteristics of the worker and his activities at work. Toward this goal, a model was built using a multilogistic regression, which allowed predicting the severity of physical sequels caused by a labor risk. From Actuarial Research Clearing House, 2006.

Description: This report provides insights into statistical methods for imputing race and ethnicity in the context of insurance industry disparities. It discusses various imputation techniques, performance metrics, and the evolution of methods over time. The aim is to address the lack of consistent data collection on race and ethnicity in insurance datasets.