Volume 13, Issue 1

Cash income offers an incomplete picture of the resources available to finance household consumption. Most American families are covered by an insurance plan that pays for some or all of the health care they consume. Only a comparatively small percentage of families pays for the full cost of this insurance out of their cash incomes. As health care has claimed a growing share of consumption, the percentage of care that is financed out of household incomes has declined. Because health care consumption is more important for some groups in the population than others, the growth in spending and changes in the payment system for medical care have reduced the value of standard income measures for assessing relative incomes of the rich and poor and the young and old. More than a seventh of total personal consumption now consists of health care that is purchased with government insurance and employer contributions to employee health plans. This paper combines health care spending and insurance reimbursement data in the Medical Expenditure Panel Study and money income and health coverage data in the Current Population Survey to assess the impact of health insurance on the distribution of income. Our estimates imply that gross money income significantly understates the resources available to finance household purchases. The estimates imply that a more complete measure of resources would show less inequality than the income measures that are currently used. The addition of estimates of the value of health insurance to countable incomes reduces measured inequality in the population and the income gap between young and old. If the analysis were extended over a longer period, it would show a sizeable impact of insurance on inequality trends in the United States.

As per capita incomes in developed countries have grown over the past three decades, over-nutrition leading to obesity and elevated health risks for cardiovascular disease, diabetes and some forms of cancer has occurred. We use economic and econometric models to identify the impact of food prices on the aggregate demand for calories and the supply of health, as reflected in mortality rates. Our models are fitted to unique panel data for 18 developed countries over 1971-2001, a period when the relative price of food first rose and then declined steadily. Some findings, using de-trended data, are that a lower real price of food, of other purchased consumer goods and of time increase the demand for calories, one cause of energy imbalance, and the supply of mortality associated with obesity. These prices do not affect the rate of non-obesity-related mortality. Caloric intake is a normal good, contributing to energy imbalance as income increases, but higher incomes do reduce mortality risk. However, higher labor force participation rates, largely associated with rising numbers of working women, and a higher child dependency ratio lead to a higher rate of obesity-related mortality. An implication of our results is that further reductions in the price of food in developed countries can be expected to have a net negative impact on health as reflected in a higher mortality rate due to diseases that are linked to obesity—diabetes, cardiovascular diseases and most forms of cancer.

About 7.4 million children were covered by the State Children’s Health Insurance Program (SCHIP) at some point during fiscal year 2008. Many of these children would probably have had private coverage in the absence of SCHIP; recent estimates of the extent of “crowd-out” associated with SCHIP are about 60 percent (Gruber and Simon 2008). The high rate of crowd-out means that the program is not as effective as it could be at reducing the number of uninsured children and has been a political liability for the program. Both political concerns and policy research focusing on crowd-out in SCHIP build on more than a decade of similar attention to the crowd-out associated with the Medicaid expansions of the late 1980s and early 1990s. While there is little doubt that expanding eligibility for public insurance to children who are not poor will lead some to substitute public for private coverage, this substitution should increase total resources available to these households in two ways. First, those who drop private coverage in order to enroll their children in SCHIP can take whatever they had been spending on health insurance and spend it on something else. Second, public insurance requires less cost-sharing than a typical private insurance policy, providing first-dollar coverage with minimal co-payments. This means that switching from private to public coverage reduces a family’s out-of-pocket medical spending, freeing up even more of the family’s resources for other uses. From the perspective of a low-income family, then, crowd-out is a windfall. In this paper we ask: what do these families do with the additional resources? We address this question with an empirical analysis of consumption data from the Consumer Expenditure Survey. We find that eligibility for SCHIP is associated with an increase in overall expenditure, and most of this increase is allocated to consumption of transportation or saving for retirement. These results suggest that the SCHIP expansions substantially improved the material well-being of the low-income families it is intended to assist – including those who had previously been paying for their own coverage. This evidence should allow a better accounting of the benefits and not just the costs of recent expansions in public health insurance programs.

While much has been written about the distinctions between biologics and small molecules in terms of their scientific, manufacturing and regulatory experiences, relatively little has been published comparing their clinical and commercial experiences. Employing a data base encompassing all 96 biologics and 212 small molecules newly launched in the U.S. between 1998Q1 and 2008Q4, we compare their downstream clinical and commercial characteristics. Substantial heterogeneity occurs across therapeutic classes. Biologics are more concentrated than small molecules in their therapeutic class composition, but have obtained FDA indication approvals in 13 of 15 classes. While average delays between FDA approval and first observed sales revenues are similar, biologics are twice as likely as small molecules to be Orphan Drugs, are slightly more likely to be designated FDA priority rather than standard review status, and gain slightly more supplemental indication approvals. Although 9.4% of new small molecules permanently exited the market for a variety of reasons, 7.3% of new biologics exited, but 26% of biologics had black box warnings compared to 20% of small molecules. Both biologics and small molecules take 21-22 quarters from launch to reach $100 million in real revenues. Small molecules have an initially more rapid uptake, but thereafter biologics’ mean revenues tend to be slightly greater than for small molecules. While launch prices for biologics are commonly perceived as being greater than for small molecules, price growth per standard unit is generally greater for small molecules than biologics, with rates of price growth increasing for small molecules in the first five years since launch, and decreasing thereafter. We conclude that the market dynamics of biologics differ substantially from those of small molecules, although therapeutic class composition plays a major role.