Dynamic OLG
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View a technical overview of PWBM's Dynamic OLG Model
The dynamic version of PWBM is based on an overlapping-generations (OLG) model where households maximize their welfare in a forward-looking manner. Households respond to policy changes by altering how much to work and save, given wages and interest rates. These choices are made given the amount of income, time, assets, technology, and skills that households possess and the prices, wages, interest rates and uncertainties that households face, both today and in the future. These feedback effects can change the size of the economy, economic growth, distribution of income and federal revenues.
PWBM’s dynamic model has unique features. First, the OLG model includes numerous types of households that vary by income as well as key demographics. Households face uncertainty today and in the future about their income and longevity. Second, the model carefully analyzes key features of changes to tax, Social Security and immigration policy. Finally, the PWBM model allows for unbalanced reforms that increase or decrease government debt.
Our model is calibrated to empirical measures of the responsiveness of labor and savings to changes in after-tax wages and interest rates. The responsiveness of labor to changes in after-tax wages’ default value is 0.5. The responsiveness of savings to changes in interest rates’ default value is 0.5. Users can choose the openness of the U.S. economy to international capital flows. The openness of the U.S. economy to international capital flows’ default value is 40 percent. The return to capital’s default value is the risk-free rate of return.
Integration between PWBM’s dynamic model and static model is achieved by first running the OLG model in “static” mode, in which households do not alter their economic choices, and then running the model in “dynamic” mode, in which households are allowed to alter their economic choices. The differences between the two are then layered on top of the static microsimulation results. This approach captures the richness of detail in the microsimulation model along with the behavioral changes observed in the OLG model.
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