In the 2001 U.S. Survey of Consumer Finances (SCF), 27% of households report simultaneously revolving significant credit card debt and holding sizeable amounts of low-return liquid assets; this is known as the \\credit card debt puzzle". In this paper, I quantitatively evaluate the role of liquidity demand in accounting for this puzzle: households that accumulate credit card debt may not pay it off using their money in the bank, because they anticipate needing that money in situations where credit cards cannot be used. I characterize the puzzle in survey data, and calibrate a dynamic stochastic heterogeneous-agent model of household portfolio choice, where consumer credit and liquidity coexist as means of consumption and saving, where households consume a cash good and a credit good, and where cash consumption is subject to uncertainty. The model accounts for between 44% and 56% of the households in the data who hold consumer debt and liquidity simultaneously, and for 100% of the liquidity held by a median such household. Under reasonable calibration alternatives, the model can capture the entire puzzle group size as well. One-half of money demand in the model is precautionary.

Many individuals simultaneously have significant credit card debt and money in the bank. The credit card debt puzzle is as follows: given high interest rates on credit cards and low rates on bank accounts, why not pay down debt? While some economists go to elaborate lengths to explain this, we argue it is a special case of the rate of return dominance puzzle from monetary economics. We extend standard monetary theory to incorporate consumer debt, which is interesting in its own right since developing models where money and credit coexist is a long-standing challenge. Our model is quite tractable—for example, it readily yields nice existence and characterization results—and helps put into context recent discussions of consumer debt.

Reverse mortgage loans (RMLs) allow older homeowners to borrow against housing wealth without moving. Despite rapid growth in this market, only 1.9% of eligible homeowners had RMLs in 2013. In this paper, we analyze reverse mortgages in a calibrated life‐cycle model of retirement. The average welfare gain from RMLs is $252 per homeowner, and $1,770 per RML borrower. Bequest motives, uncertainty about health and expenses, and loan costs account for low demand. According to the model, the Great Recession's impact differs across age, income, and wealth distributions, with a threefold increase in RML demand for lowest income and oldest households.