Summersemester 2026
What is the best way to reform Social Security? Academic literature offers diverging advice. There is a well-known result that the optimal size of Social Security is zero, implying it is best to phase the program out. Other studies argue that much can be gained by redesigning the program, given its current size. We provide a unified analysis that examines how the optimal size of Social Security depends on the key features of its design. We first develop a theoretical decomposition tracing the program's welfare effects to (i) income redistribution, (ii) distortions on the annuitization level, and (iii) intertemporal distortions. We then quantitatively assess the role of these channels. We show that the zero-optimal-size result arises because Social Security is too distortive and not redistributive enough. Once these design flaws are corrected, it is even optimal to increase the size of the program.
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This paper quantifies why households save and work using a life-cycle model that incorporates wage risk, endogenous labor supply of both spouses, marital transitions, health, medical expenses, mortality, and bequest motives at the death of the first and last household member. We estimate it using PSID and HRS data and conduct counterfactuals to assess the quantitative role of individual mechanisms. Precautionary saving against wage risk is smaller than in models that abstract from labor supply and within-household insurance. Bequest motives and medical expenses remain important drivers of wealth, while marriage and divorce generate large but offsetting effects across household types.
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We present a new approach to formulating and solving heterogeneous agent mod- els with aggregate risk. We replace the cross-sectional distribution with low-dimensional prices as state variables and let agents learn equilibrium price dynamics directly from sim- ulated paths. To do so, we introduce a structural reinforcement learning (SRL) method which treats prices via simulation while exploiting agents’ structural knowledge of their own in- dividual dynamics. Our SRL method yields a general and highly efficient global solution method for heterogeneous agent models that sidesteps the Master equation and handles models traditional methods struggle with, like those with nontrivial market-clearing con- ditions. We illustrate the approach in the Krusell-Smith model, the Huggett model with aggregate shocks, and a HANK model with a forward-looking Phillips curve, all of which we solve globally within minutes.
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