Abstract

BackgroundWith the rapid population aging, the payment crisis of China’s pension insurance fund is increasing yearly. The government adjusts fertility policy to alleviate population aging and improve the solvency of pension insurance fund. On January 1, 2016, China’s fertility policy was adjusted from “selective two-child policy” to universal two-child policy.MethodsThis paper establishes actuarial models to analyze how fertility policy adjustment influences the pension insurance fund.Results(1) if the “one-child policy” were still employed, the accumulated deficit of pension insurance fund would appear in the year of 2076; (2) if all couples that satisfy the rules of “selective two-child policy” bear the second child, the time of accumulated deficit of pension insurance fund would be postponed by about 9 years; (3) after implementing the universal two-child policy, the time of accumulated deficit of pension insurance fund would delay under different situations of fertility intentions, if more than 54% of the qualified couples bear a second child, the accumulated deficit of pension insurance fund would not appear before 2090. The above conclusions have passed the sensitivity tests.ConclusionTherefore, “two-child policy” can alleviate the payment pressure of pension insurance fund. If the government wants to solve the payment crisis of pension insurance fund, fertility intentions should be improved.

Highlights

  • Introduction and literature reviewAt this stage, most countries in the world have become the aging society, and China is no exception

  • This paper sets the actuarial model to simulate the impact of adjusting policies on financial operation of pension fund

  • This paper finds that (1) if China would continually implement “one-child policy”, cumulative deficit would appear in 2076

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Summary

Methods

This paper establishes actuarial models to analyze how fertility policy adjustment influences the pension insurance fund.

Conclusion
Introduction and literature review
Findings
Conclusion and policy recommendations
Full Text
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