Volume 6, Issue 1

The release of clarified Food and Drug Administration (FDA) guidelines and independent changes in consumer behavior provide an opportunity to study the effects of direct-to-consumer advertising (DTCA) in the prescription drug market alongside the effects of various physician-oriented promotions. We examine the effects of DTCA and detailing for brands in five therapeutic classes of drugs, using monthly aggregate U.S. data from August 1996 through December 1999. In terms of impact of DTCA on demand, we provide evidence on two issues: (1) do increases in DTCA increase the market size of an entire therapeutic class? and (2) does DTCA increase within-class market share of advertised drugs? Our findings suggest that, for these classes of drugs, DTCA has been effective primarily through increasing the size of the entire class. Overall, we estimate that 13 to 22 percent of the recent growth in prescription drug spending is attributable to the effects of DTCA.

This paper examines why health insurance coverage fell despite the lengthy economic boom of the 1990s. I show that insurance coverage declined primarily because fewer workers took up coverage when offered it, not because fewer workers were offered insurance or were eligible for it. The reduction in take-up is associated with the increase in employee costs for health insurance. Estimates suggest that increased costs to employees can explain the entire decline in take-up rates in the 1990s.

Prior research on health insurance expansions has ignored the content of coverage, yet the nature of coverage offered is likely to affect both take-up by the uninsured and the public policy – relevant consequences of the expansion. This paper uses the Medical Expenditure Panel Survey, the Survey of Program Dynamics, and the Rand Health Insurance Experiment to show that uninsured people are likely to value certain types of coverage more than others. Using a simulation model of the value of coverage expansions, I show that front-end coverage with a low-benefit maximum is likely to be perceived as more valuable than catastrophic coverage by low-income uninsured people. Some high-deductible coverage may make uninsured people subjectively worse off.

In March 2001, the longest economic expansion in U.S. history ended, and an economic recession began. This paper seeks to provide a better understanding of the historical relationship between macroeconomic variables and health insurance coverage.We use data from two nationally representative samples: the Survey of Income and Program Participation (SIPP) and the National Longitudinal Survey of Youth (NLSY). The longitudinal nature of our data allows us to remove individual-specific, time-invariant heterogeneity and to focus on changes in health insurance status in response to changes in macroeconomic variables.The results confirm our prediction that the probability of any health insurance coverage is negatively associated with unemployment rate. We find that a one percentage point increase in the state unemployment rate is associated with a decrease in the probability of health insurance coverage, through any source, of 0.62 percent for men, 0.54 percent for women, and 1.1 percent for children. However, our prediction that an indicator variable for national recession would be negatively correlated with the probability of health insurance coverage is not supported by the data. We find that changes in employment status explain roughly one-quarter of the correlation between health insurance coverage and unemployment rates. Our estimates imply that 440,000 men, 436,000 women, and 494,000 children have lost health insurance coverage during the current recession.

We used the first five waves of the Health and Retirement Study (HRS) to study three distinct but possibly interrelated phenomena: smoking cessation, changes in alcohol consumption, and changes in weight. The HRS is well suited for our study because it contains smoking and drinking behavior measures; weight; detailed financial, demographic, and health data; and health conditions existing at baseline and those newly occurring. Men who quit smoking within two years before the interview reduced daily alcohol consumption by about 0.1 to 0.15. Smoking cessation did not affect alcohol consumption for women. Unlike men, for whom there was no interaction between smoking cessation and problem drinking, female problem drinkers who quit smoking during the last two years reduced daily alcohol consumption by about 0.3 to 0.4 drinks per day on average, but the effect was only temporary. Quitting or starting heavy drinking had no effect on smoking cessation for either gender. Smoking cessation led to an increase in body mass index (BMI), both for men and for women. Furthermore, the effect increased with duration of smoking cessation. For men, BMI increased by 0.28 in the first two years after smoking cessation, but by almost 0.7 among male smokers who had quit more than two years previously. For females, the short-run effect of smoking cessation was larger, but the long-run effect was about the same as for men. A 0.7 increase in BMI is equivalent to about a five-pound increase in weight for a person who is 5 feet, 11 inches tall. Overall, our longitudinal analysis of HRS data shows that smoking cessation is negatively associated with alcohol consumption and positively associated with weight gain. The specific nature of the link between smoking cessation and alcohol consumption differs between the genders.