Recent work by Smith and Wenger (JPAM 32:3, 2013) finds that both average and maximum weekly unemployment insurance (UI) benefit amounts, as ratios to the average weekly wage, are higher in states and in years with more highly solvent UI trust funds. Smith and Wenger further find that because UI program benefits and financing are not as tightly coupled as in other social insurance programs in the United States, such as Medicaid, the countercyclacity of the UI program is dampened. An important policy implication of this finding is that a more tightly coupled state UI program (which could realign the program’s countercylicality) that continues to rely on prefunding would require a mechanism by which optimal UI trust balances are forecast. There are currently no theoretical or empirical insights on estimating optimal state UI trust fund balances, however.

This paper proposes a Monte Carlo simulator for estimating optimal state unemployment insurance trust fund balances under a myriad of economic circumstances and policy regimes. Specifically, the simulator estimates UI trust fund failure rates, as measured by annual deficits (flows) and trust fund depletions (stocks) in the UI program, given variation in the unemployment rate and plausible policy changes in the taxable wage base and the weekly benefit amount. We also estimate the impact of hypothetical policy interventions, such as the creation of a federal unemployment reinsurance policy that would cover benefits for the unemployed when the unemployment rate exceeds a particular threshold.