Jonathan Skinner of Dartmouth University reviews “Why Not Better and Cheaper? Healthcare and Innovation” by James B. Rebitzer and Robert S. Rebitzer. The Econlit abstract of this book begins: “Considers why the US health-care system is becoming neither better nor more affordable over time, contending that the health sector generates the wrong kinds of innovation by making it easy to profit from low-value innovations and difficult to do the same from innovations that reduce care costs.”

Panel data suggest that women who subsequently divorce increase their labor supply in the three years prior to separation. A simultaneous model of future divorce probability and current labor supply is estimated for married women. The results support the hypothesis that divorce probabilities increase labor supply. Thus, the recent rise in the frequency of divorce may account for one-third of the unexplained increase in women's postwar labor force participation.

The focus of the paper is to measure how consumption responds to changes in the interest rate. The equivalence between the effect of the interest rate, and the effect of mortality probabilities, on consumption is used to gain an estimate of the intertemporal elasticity of substitution between current and future consumption. Seemingly unrelated regressions in a cross-sectional model of consumption, earnings, and assets are used to provide efficient estimates of the intertemporal parameter. The regression results suggest that the elasticity is somewhat higher than previously thought.

In the United States, health care technology has contributed to rising survival rates, yet health care spending relative to GDP has also grown more rapidly than in any other country. We develop a model of patient demand and supplier behavior to explain these parallel trends in technology growth and cost growth. We show that health care productivity depends on the heterogeneity of treatment effects across patients, the shape of the health production function, and the cost structure of procedures such as MRIs with high fixed costs and low marginal costs. The model implies a typology of medical technology productivity: (I) highly cost-effective “home run” innovations with little chance of overuse, such as anti-retroviral therapy for HIV, (II) treatments highly effective for some but not for all (e.g., stents), and (III) “gray area” treatments with uncertain clinical value such as ICU days among chronically ill patients. Not surprisingly, countries adopting Category I and effective Category II treatments gain the greatest health improvements, while countries adopting ineffective Category II and Category III treatments experience the most rapid cost growth. Ultimately, economic and political resistance in the United States to ever-rising tax rates will likely slow cost growth, with uncertain effects on technology growth.

One of the more startling demographic trends of the 1970's has been the sharp rise in lifespan in the United States. Between 1970 and 1980, the life expectancy of 25year-old men and women increased by at least two years. While longevity has been steadily rising for many decades, the recent spurt has hastened the trend; the increase for both men and women in the 1970's more than doubled the improvement of the 1960's.1 The 1970's were also characterized by a decline in labor force participation of older workers. Although rising income and increased disability payments may have explained some of the decline (Donald Parsons, 1980), it seems clear that expanding longevity has not been strongly associated with deferred retirement (Daniel Hamermesh, 1984).2 Thus the retirement years have assumed greater importance in financial planning for the consumer as he or she retires earlier and lives longer. The traditional life cycle model would predict greater savings rates as consumers increase accumulation of assets for their retirement. Consumption while young would drop, and there might be an increase in labor supply as individuals substitute leisure while young for greater levels of life cycle consumption. Even under a Social Security system, as long as the current generation's tax liabilities reflect their longer lifespan, total savings (private plus public) should also rise.3 The predicted increase in savings is substantial. A simple example using the Modigliani-Ando-Brumberg life cycle model can illustrate the extent of the savings shift. Assume that earnings and consumption are constant over working years and lifespan, respectively, and that the interest rate is zero. Consumption is then a constant proportion k of earnings, where k is the ratio of working years to one's lifespan. If consumers made plans based on average lifespan, the representative 25-year-old would have saved 18 percent of earnings in 1970 (1-40/49 when he plans to retire at age 65 and expects to live until age 74). In 1980, after a rise in life expectancy of two years, the representative 25-year-old would have increased savings to 21 percent of earnings, or a net rise in the savings rate (per worker) of 17 percent. That is, this simple life cycle model implies that savings in the last decade should have risen (other things held constant) by approximately 17 percent, or about 80 billion dollars per year. The evidence from aggregate time-series data appears to contradict the life cycle model's prediction of rising savings rates. The net private savings rate in the United States declined from 7.8 percent of NNP in 1970 to 6.2 percent in 1980. While Alan Auerbach (1982) has suggested that some of the reported decline in savings may be caused by mismeasurement, it seems clear that there has been no substantial rise in the savings rate. There are numerous explanations for *Department of Economics, University of Virginia, Charlottesville, VA 22901. I am grateful to Maxim Engers, Daniel Hamermesh, William R. Johnson, Laurence J. Kotlikoff, John Strauss, and especially James Davies and John R. Wolfe for helpful suggestions and comments. 'Life table data can be found in Daniel Hamermesh (1985), the Life Insurance Fact Book (various years), or in unpublished form from the National Center for Health Statistics. 2John Wolfe (1983) provides an ingenious argument for why the Social Security benefit schedule has induced those with shorter life expectancies to retire early. We might therefore expect some rise in the average retirement age as those expecting to live longer defer retirement until age 65. 3If the Social Security taxes do not rise as expected lifespan increases (perhaps because current tax proceeds are paid out as current benefits, rather than retained in a trust fund), young workers will save more to offset the lower expected benefits in the future. 4In a two-period model, the percentage change in savings by the young is equal to the percentage change in aggregate savings.

We draw on macroeconomic models of diffusion and productivity to explain empirical patterns of survival gains in heart attacks. Using Medicare data for 2.8 million patients during 1986-2004, we find that hospitals rapidly adopting cost-effective innovations such as beta blockers, aspirin, and reperfusion, had substantially better outcomes for their patients. Holding technology adoption constant, the marginal returns to spending were relatively modest. Hospitals increasing the pace of technology diffusion ("tigers") experienced triple the survival gains compared to those with diminished rates ("tortoises"). In sum, small differences in the propensity to adopt effective technology lead to wide productivity differences across hospitals.

Catastrophic medical expenses are an important economic risk facing the elderly. Little is known about the persistence of such out-of-pocket medical costs. We measure the time-series property of medical costs using information on medical deductions from a panel of tax returns. During the period of analysis, 1968-73, taxpayers could deduct medical expenses above 3 percent of income. We correct for the resulting censoring bias using multivariate Tobit estimated with a variant of the smoothed simulated maximum likelihood (SSML) method. The data suggest that the burden of out-of-pocket medical expenses is substantially larger for lower income families. Furthermore, the estimated coefficients suggest substantial time-persistence in out-of-pocket medical care costs; a $1 increase in out-of-pocket medical spending is predicted to increase future spending by an additional $2.80. These results may shed light both on the social value of catastrophic health insurance as well on aggregate saving behavior.