Using life insurance holdings by age, sex, and marital status, we infer how individuals value consumption in different demographic stages. We estimate equivalence scales and bequest motives simultaneously within a fully specified model where agents face US demographics and save and purchase life insurance. Our findings indicate that individuals are very caring for dependents, that economies of scale are large, that children are very costly (or yield very high marginal utility), that wives with children produce lots of home goods, and that females display habits from marriage, while men do not. These findings contrast sharply with standard equivalence scales.

Theory suggests that, in the presence of local bias, the price of a stock should be decreasing in the ratio of the aggregate book value of firms in its region to the aggregate risk tolerance of investors in its region. Using data on U.S. states and Census regions, we find clear-cut support for this proposition. Most of the variation in the ratio of interest comes from differences across regions in aggregate book value per capita. Regions with low population density—e.g., the Deep South—are home to relatively few firms per capita, which leads to higher stock prices via an “only-game-in-town” effect.

We find that technology's effect on employment varies greatly across manufacturing industries. Some industries exhibit a temporary reduction in employment in response to a permanent increase in TFP, whereas many more industries exhibit an employment increase in response to a permanent TFP shock. This raises serious questions about existing work that finds a labor productivity shock has a strong negative effect on employment. There are tantalizing and interesting differences between TFP and labor productivity. We argue that TFP is a more natural measure of technology because labor productivity reflects shifts in the input mix as well as in technology.

We attempt to measure the effect of competition on bias in the context of analyst earnings forecasts, which are known to be excessively optimistic because of conflicts of interest. Our natural experiment for competition is mergers of brokerage houses, which result in the firing of analysts because of redundancy (e.g., one of the two oil stock analysts is let go) and other reasons such as culture clash. We use this decrease in analyst coverage for stocks covered by both merging houses before the merger (the treatment sample) to measure the causal effect of competition on bias. We find that the treatment sample simultaneously experiences a decrease in analyst coverage and an increase in optimism bias the year after the merger relative to a control group of stocks, consistent with competition reducing bias. The implied economic effect from our natural experiment is significantly larger than estimates from OLS regressions that do not correct for the endogeneity of coverage. This effect is much more significant for stocks with little initial analyst coverage or competition.

We investigate the welfare properties of the one-sector neoclassical growth model with uninsurable idiosyncratic shocks. We focus on the notion of constrained efficiency used in the general equilibrium literature. Our characterization of constrained efficiency uses the first-order condition of a constrained planner’s problem. This condition highlights the margins of relevance for whether capital is too high or too low: the factor composition of income of the (consumption-) poor. Using three calibrations commonly considered in the literature, we illustrate that there can be either over- or underaccumulation of capital in steady state and that the constrained optimum may or may not be consistent with a nondegenerate long-run distribution of wealth. For the calibration that roughly matches the income and wealth distribution, the constrained inefficiency of the market outcome is rather striking: it has much too low a steady-state capital stock.