Nonparametric estimation of the coefficient of overlapping—theory and empirical application

The coefficient of overlapping OVLOVL measures the amount of agreement of two probability distributions. Statistical inference for OVLOVL has been mainly investigated in a parametric framework. Five strongly consistent nonparametric estimators for OVLOVL based on kernel density estimation are suggested. A Monte-Carlo simulation investigates bias and standard deviation of the estimators in finite samples. Results of an empirical application to German labor income data of men and women (based on GSOEP data) are presented. It is shown that there is much more agreement of labor income distributions of men and women in East Germany (new Federal States) than in West Germany (old Federal States).