In this study, an assessment of precision of poverty indicators is made with a view to improving its performance. A multiplicative bias reduction density function is used in estimating the poverty indicators and compared to the uniform, normal, and the nearest neighbor density estimators. Simulation results shows the practical potential of the multiplicative density estimator over its usual competitors especially when the sample size is large.