Journal of Economic Modeling Research

Collective pension funds have many advantages including larger risk pool and the possibility of interpersonal and intergenerational risk sharing, as well as economies of scale and lower administrative costs. For decades, however, this has been achieved through mandatory participation, while this traditional and mandatory form of contribution is no longer commensurate with the future of work. In this regard, many countries have implemented a combinatorial policy in the form of auto-enrolment pensions and then the granting of opting out authority. However, the sustainability of these schemes will depend on people's motivation to participate or leave. This article tries to examine the motivations of individuals to exit the Iran Social Security Organization (ISSO) pension fund, assuming that the insureds are given the opportunity to opt out once in a certain time. For this purpose, the method of option pricing is used. Findings show that insureds will accept even a 60 percent deficit in fund’s long-term liabilities for the only reason to take advantage of investment income of their predecessors funds or interpersonal and intergenerational risk sharing. It was also observed that an increase in the funding ratio, lower liabilities, a rise in assets and a higher rate of return on investments encourage participation and reduce the incentive to exit. A decline in accrual rate, increase in the contribution rate, higher retirement age, accelerating the adjustment rate of fund deficit due to their detrimental effect on the insureds have a direct negative effect on the incentive to participate and stimulate withdrawal. It should be noted, however, that these factors will also reduce liabilities and increase the funding ratio, thereby contributing to the sustainability of the plan may ultimately reduce the exit incentives.

The pension coverage of the Iranian Social Security Organization for self-employed workers is offered at three contribution rates of 12, 14 and 18 percent, but looking at the statistics shows that the demand for these types of insurances is low. This research investigates the characteristics of these insured groups by using data mining and applying two machine learning algorithms, decision tree and random forest, and predicts their behavior by providing a classification model. This will help the Social Security Organization to improve customer relationship management. For this purpose, the information of 1286174 insured persons of self-employed in 2020 was used, which includes the characteristics of age, gender, average monthly income, the years of service, and the type of self-employed pension scheme. The obtained results show that women mainly apply for the scheme with 12 percent contribution, while men tend to be covered by schemes with contribution rates of 14 and 18 percent due to the burden of supporting the family. Also, for men, the demand for schemes of 14 and 18 percent increases with the increase of age, income and years of service, but there are no such trends for women. According to the obtained results, years of service and then gender are decisive in choosing the type of pension scheme in such a way that according to the prediction of the model, people with less than 4.5 years of service are known as definite applicants for 12 percent self-employed pension scheme.