论文标题
老年社会保障的最佳规模和渐进性
The Optimal Size and Progressivity of Old-Age Social Security
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
Almost every public pension system shares two attributes: earning deductions to finance benefits, and benefits that depend on earnings. This paper analyzes theoretically and empirically the trade-off between social insurance and incentive provision faced by reforms to these two attributes. First, I combine the social insurance and the optimal linear-income literature to build a model with a flexible pension contribution rate and benefits' progressivity that incorporates inter-temporal and inter-worker types of redistribution and incentive distortion. The model is general, allowing workers to be heterogeneous on productivity and retirement preparedness, and they exhibit present-focused bias. I then estimate the model by leveraging three quasi-experimental variations on the design of the Chilean pension system and administrative data merged with a panel survey. I find that taxable earnings respond to changes in the benefit-earnings link, future pension payments, and net-of-tax rate, which increases the costs of reforms. I also find that lifetime payroll earnings have a strong positive relationship with productivity and retirement preparedness, and that pension transfers are effective in increasing retirement consumption. Therefore, there is a large inter-worker redistribution value through the pension system. Overall, there are significant social gains from marginal reforms: a 1% increase in the contribution rate and in the benefit progressivity generates social gains of 0.08% and 0.29% of the GDP, respectively. The optimal design has a pension contribution rate of 17% and focuses 42% of pension public spending on workers below the median of lifetime earnings.