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 andsave 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.

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 andsave 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.

The standard life-cycle models of household portfolio choice have difficulty generating a realistic age profile of risky share. These models not only imply a high risky share on average but also a steeply decreasing age profile, whereas the risky share is mildly increasing in the data. We introduce age-dependent, labor market uncertainty into an otherwise standard model. A great uncertainty in the labor market—high unemployment risk, frequent job turnovers, and an unknown career path—prevents young workers from taking too much risk in the financial market. As labor market uncertainty is resolved over time, workers start taking more risk in their financial portfolios.

The standard life-cycle models of household portfolio choice have difficulty generating a realistic age profile of risky share. These models not only imply a high risky share on average but also a steeply decreasing age profile, whereas the risky share is mildly increasing in the data. We introduce age-dependent, labor market uncertainty into an otherwise standard model. A great uncertainty in the labor market—high unemployment risk, frequent job turnovers, and an unknown career path—prevents young workers from taking too much risk in the financial market. As labor market uncertainty is resolved over time, workers start taking more risk in their financial portfolios.

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 andsave 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.

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 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.