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| Solvency, the Life Cycle, and the Dynamic Adjustment Mechanism of Pensions |
| ZHANG Yong
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| Lingnan College, Sun Yat-sen University |
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Abstract Population aging and the slowdown of economic growth are challenging the solvency of pension funds. To maintain a sustainable pension system and coordinate the solvency of the pension funds and benefits for retirees, it is necessary to optimize the current adjustment mechanism for pension payments. On the one hand, if the funds have insufficient solvency, pension systems will be unsustainable. On the other hand, if the solvency is increased by simply reducing the benefits, the retirees will be unable to maintain a basic standard of living.
We directly use solvency as an endogenous variable and add it to the pension adjustment model, so that the pension payments dynamically adjust to the changes in solvency. When a pension fund is insolvent, the growth rate of the pension payments is automatically reduced. Based on this idea, we construct four pension adjustment modes. The first mode is the current policy, the second mode only adjusts the growth rate of payments during the retirement period, the third mode only adjusts the contribution index during the working period, and the fourth mode adjusts both the growth rate of payments and the contribution index. The payments of the latter three modes are dynamically and automatically adjusted according to the change in solvency, and the modes operate as self-feedback systems that can correct the prediction bias.
Solvency is the key index in this paper, and is equal to the ratio of assets to liabilities of a pension fund over some future period. The index needs to be evaluated each year and is used to adjust the pension amounts. When the assets are less than the liabilities, the pension funds are insolvent. Thus, a reasonable level of solvency should be maintained. If the solvency is too high, the contributions will be used inefficiently, as a large proportion will not be used to pay pensions. Therefore, a reasonable adjustment strategy should strive to make the solvency index tend to 1, which will not only maintain the financial sustainability of the fund, but also provide retirees with the greatest degree of security.
To determine the assets, liabilities, and solvency of pension funds, we need to calculate the income and expenditure, which means we have to forecast the future values of some parameters, such as the GDP growth rate, birth rate, inflation rate, and life expectancy. The data for 2016 and before are drawn from the National Bureau of Statistics of China. After 2016, we use the “2017 Revision of World Population Prospects” published by the United Nations Population Office, which provides population forecasts for 2017-2100. We collect the GDP grow rate data for 2017-2100 from the Pardee Center for International Futures at Denver University.
As we do not know the exact future values of the parameters, we use the scenario analysis method to test the robustness of the model, which is widely used in the banking and insurance industry. We choose 18 scenarios after analyzing the economic, population, and policy variables. We then analyze, compare, and rank the effects of the four adjustment modes under each scenario, and test whether the ranking is robust. Finally, we identify the most advantageous mode.
The main conclusions are as follows. First, if the current pension adjustment mode is not optimized, the degree of insolvency will continue to increase under the trend of population aging and slowing economic growth. Second, compared with the current policies and existing models, our adjustment model can effectively alleviate the degree of insolvency and reduce the large fluctuations in the benefits for retirees. Third, adjusting the pension according to life cycle theory not only improves the solvency better than the other models, but also extends the time for paying retirees and improves their ability to deal with risks. Finally, when introducing policies such as delaying the retirement age, multiple factors need to be considered, such as the adjustment mode, growth rate of pensions, the birth rate, and the solvency of pension funds.
This paper makes the following contributions. First, we use solvency as an endogenous variable and add it to the “pay-as-you-go” pension system, whereas the current automatic balance mechanism (ABM) is applicable only to a fully funded pension system. Second, our model is a self-feedback system that can correct the prediction bias, which improves the robustness and operability of the model. If there is no forecast, the solvency of the pension fund cannot be calculated, whereas if there is no correction, the forecast deviation will continue to increase. Third, compared with other models, such as the ABM, our model can reduce the risk of excessive fluctuations of benefits and provide a more stable income for retirees, although it does require some public financial support. The dynamic effect of public financial support on the solvency of the pension fund is a topic for future research.
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Received: 26 October 2018
Published: 27 September 2019
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