Improving pension funds’ performance using data envelopment analysis considering government regulations

Abstract

Pension fund managers operate in an investment environment with strict government regulations and a unique taxation system. Also, low birth rates, together with a higher average age of the population and an increase in general life expectancy provide further motivation for investigating pension funds’ performance. Adding to the study by Badrizadeh and Paradi (2020) in which a new model was presented for evaluating pension funds’ performance considering the effects of invisible variables, this study introduces a new methodology based on data envelopment analysis (DEA) which evaluates the pension funds’ performance by considering the importance of different variables based on an expert’s judgements as well as borrowing useful information from the mutual funds’ dataset. Similar variables between pension funds and mutual funds are included. The correlation between mutual fund variables is extracted and tested statistically. Then, these regressions are used to define trade-offs in the pension funds’ model. When these trade-offs and expert’s opinions are added, the results show that the discriminatory power of the DEA increases. Furthermore, three different target levels are defined for inefficient pension plans. This research is applied to Canadian pension funds and mutual funds but could be utilized in similar problems in industry and government