How do you measure uncertainty of SSA policy scores?

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As an analogy, we can think of policy scores as the coefficient (an intended causal effect) in a regression of a policy output (such as the balance or cost rate) on the treatment variable (whether or not the proposed policy is adopted) plus an error term. SSA offers no uncertainty estimates for this estimated causal effect, although of course some causal effects are likely to be better estimated or better known than others. Sometimes by known ex ante assumptions we may think the effects are known with a high degree of certainty. However, causal effects are never observed in the real world; only the policy outputs are ever observed. To empirically estimate what will happen in the real world if a policy is adopted, or to evaluate a claim about a causal effect’s size or its uncertainty in a way that makes oneself vulnerable to being proven wrong, we must rely on forecasts under present law and forecasts under the counterfactual condition of the policy being adopted. It is the uncertainty of the forecast under present law that our papers show how to estimate using the observed forecast errors. In this evaluation, we find that most of what could be observable from the impact of the causal effects are swamped by these uncertainty estimates. For example, the most recent SSA evaluation of a policy proposal gives a graphic illustration in Figure 1 which plots the point estimate of the Trust Fund Ratio for each year in the future, under both present law and a proposed law under consideration; each of these lines has uncertainty at least as large as we estimate in our paper. There is also additional uncertainty, over and above forecast errors, because we do not know exactly what would happen if the policy were actually changed, and how all the workers, beneficiaries, government officials, and others would respond under the new regime.