Research Database

We describe a model for calculating the optimal quantity of debt and then apply it to the U.S. economy. The model consists of a large number of infinitely-lived households whose saving behavior is influenced by precautionary saving motives and borrowing constraints. This model incorporates a different role for government debt than the standard representative agent growth model and captures different trade-offs between the benefits and costs of varying its level. Government debt enhances the liquidity of households by providing additional assets for smoothing consumption (in addition to claims to capital) and effectively loosening borrowing constraints. By raising the interest rate, government debt makes assets less costly to hold and more effective in smoothing consumption. However, the implied taxes have wealth distribution, incentive, and insurance effects. Further, government debt crowds out capital (via higher interest rates) and lowers per capita consumption. Our quantitative analysis suggests that the crowding out effect is decisive for welfare. We also describe variations of the model which permit endogenous growth. It turns out that even with lump sum taxes and inelastic labor, government debt as well as government consumption have growth rate effects, thereby implying large welfare gains from reducing the level of debt.

The money demand literature presents much conflicting evidence on this question. For example, Lucas (1988) reports unrestricted money demand regressions which seem to imply that long-run money demand elasticities are highly unstable across subsamples. At the same time, he also presents evidence from money demand regressions with the income elasticity restricted to unity which seem to suggest stability. We conduct a formal analysis which weighs these apparently conflicting facts to determine which hypothesis is more plausible; the hypothesis that money demand is stable, or the hypothesis that money demand is unstable. We find that the stability hypothesis is the more plausible one. Thus, according to our data set, the answer to the question in the title is "yes".

This paper reexamines the conventional wisdom that commercial banking is an industry in severe decline. We find that a careful reading of the evidence does not justify this conclusion. It is true that on-balance sheet assets held by commercial banks have declined as a share of total intermediary assets. But this measure overstates any drop in banking, for three reasons. First, it ignores the rapid growth in commercial banks' off-balance sheet activities. Second, it fails to take account of the substantial growth in off-shore C&I lending by foreign banks. Third, it ignores the fact that over the last several decades financial intermediation has grown rapidly relative to the rest of the economy. We find that after adjusting the measure of bank assets to account for these considerations there is no clear evidence of secular decline. To corroborate these findings, we also construct an alternative measure of the importance of banking, using data from the National Income Accounts. Again, we find no clear evidence of a sustained declined. At most the industry may have suffered a slight loss of market share over the last decade. But as we discuss, this loss may reflect a transitory response to a series of adverse shocks and the phasing in of new regulatory requirements, rather than the beginning of a permanent decline.

This paper integrates and extends some recent computational advances in Bayesian inference with the objective of more fully realizing the Bayesian promise of coherent inference and model comparison in economics. It combines Markov chain Monte Carlo and independence Monte Carlo with importance sampling to provide an efficient and generic method for updating posterior distributions. It exploits the multiplicative decomposition of marginalized likelihood into predictive factors, to compute posterior odds ratios efficiently and with minimal further investment in software. It argues for the use of predictive odds ratios in model comparison in economics. Finally, it suggests procedures for public reporting that will enable remote clients to conveniently modify priors, form posterior expectations of their own functions of interest, and update the posterior distribution with new observations. A series of examples explores the practicality and efficiency of these methods.