More than 40 million Americans are uninsured. Policymakers and analysts widely agree that low incomes and high premiums are a primary cause. Thus, most proposals for reform include subsidies or public program expansions to reduce these barriers (e.g., Pauly 2001; Davis and Schoen 2003). The Bush administration proposed a new tax credit for those who do not have access to employer-sponsored insurance, which received broad political support (Cunningham 2002b). Because the tax system subsidizes the purchase of employer group coverage, some analysts argue that providing tax subsidies to those who are not offered group plans is an equitable approach to reducing the problem of the uninsured (Kendall 2000; Buffer 1999; Pauly and Hoff 2002).
However, others believe that tax credits may lead to an unraveling of the employment-based system for health insurance that could lead to a reduction in overall coverage (Aaron 1999). This would occur if employees found they were better off purchasing in the individual market and dropped their employer plan. Employers' decisions to offer insurance may also be affected if healthy members leaving the group leads to an increase in premiums or an inability to meet group size requirements.
Central to designing a tax credit is information about how a change in the price of individual insurance will affect decisions to purchase it. We need information about the price response and how it varies for different subgroups to determine the necessary size of the tax credit. We also need this information to determine how many workers covered by group plans might switch to the individual market to assess the effects of a tax credit on the employment-based system. Despite the considerable recent interest in tax subsidies and credits, there is relatively little empirical evidence about the price elasticity of demand for individual insurance. Our goal is to help fill this information gap.
PREVIOUS LITERATURE
Few studies have specifically examined the effect of price on demand for individual insurance. Estimating this response is hampered by the difficulty in obtaining an appropriate price measure. In the individual market, prices are often based on the individual's characteristics, and so the premium paid by an individual is endogenous (Blumberg and Nichols 2001). Second, a measure of price is often unavailable for those who did not purchase insurance.
Previous studies, summarized in Table 1, have used a variety of approaches to overcome these difficulties. These approaches include: linking a price list from a major insurer in the individual market to individuals based on residence, age, and gender (Marquis and Long 1995); responses to hypothetical insurance offers (Marquis and Buchanan 1992); reservation prices based on expected health care spending (Pauly and Herring 2001), and a sliding scale subsidy schedule from the Washington State Basic Health Plan (Long and Marquis 2002). Gruber and Poterba (1994) use the Tax Reform Act of 1986 (PL99-514), which reduced the after-tax price of individual insurance for the self-employed, to measure the price response. Their estimates are sensitive to the specification, but suggest a somewhat greater demand response than found in the other studies.
Several analyses that simulate the effects of tax credits make implicit behavioral assumptions about how employees' decisions to participate in a group plan will be altered by reductions in the price of individual insurance. The simulations suggest that even fairly substantial credits of $1,000 for an individual and $2,000 for families would induce fewer than 5 percent of employees to switch to the individual market (Gruber 2000; Blumberg et. al. 2002). However, we are not aware of any studies that explicitly model how employee decisions about enrolling in an employer group plan are affected by the price of individual insurance.
Estimates of price response from the existing literature are often restricted to certain population groups, such as the self-employed or workers, and not to the full population that may be affected by tax credits. Moreover, heterogeneity in price response may be important in predicting the effectiveness of alternative credits and distributional consequences. Some of the studies, as noted in Table 1, explore differences by income or by marital status. But they seldom explore interactions between price and many of the other variables believed to be important. Our objective is to add to the existing literature by estimating the price elasticity of demand for individual coverage among persons in a market who lack access to group insurance, examining switching behavior of those with group insurance, and exploring heterogeneity in these responses.
DATA AND METHODS
Data
Our study focuses on decisions about participation in the individual insurance market by people in California. We limit it to a single state because cooperation from insurers was necessary to obtain detailed information about the benefits and premiums of plans offered. We need to observe decisions among consumers who face different premiums and different options in order to estimate how decisions are affected by these characteristics. Therefore, California is a good state for our study because it is a large state with in-state variation in premiums charged. In addition, changes in the products offered over time, including a revision in the slate of products offered by one participating plan in January 2001, produced variation over time in the premiums facing consumers as well as the extent of choice in the market.
California accounts for nearly 15 percent of all individual insurance products sold. It is also quite competitive; a recent study of markets in 26 states found the California market to be less concentrated than all other states although three carriers account for most individual products sold (Chollet, Kirk, and Ermann 1997). If policies are adopted that lead to a growth in the size of the individual market, markets elsewhere may well become more competitive. Thus the California experience is a good one to study, even if the results currently do not generalize to markets that are now less competitive.
The data for our study come from several sources: the Current Population Survey (CPS) for 1996-2002, the Survey of Income and Program Participation (SIPP) for 1996-1999, the National Health Interview Survey (NHIS) for 1997-2001, the 1997 Robert Wood Johnson Foundation (RWJF) Employer Health Insurance Survey, data from the three largest carriers selling individual insurance in California, and a number of extant databases that provide information about health care markets in California.
The CPS is a monthly survey of about 50,000 households conducted by the Bureau of the Census for the Bureau of Labor Statistics. A supplement to the CPS administered in March of each year includes questions on health insurance coverage for each family member. The NHIS is a cross-sectional household interview survey conducted by the National Center for Health Statistics to monitor trends in the nation's health. The SIPP is a longitudinal survey conducted by the Bureau of the Census to gather information about the economic and demographic characteristics of the U.S. population. The 1996 panel was a four-year panel. We selected these surveys because they provide information over time, they include a large sample in California, and they measure insurance coverage.
The study team abstracted detailed benefit and premium information about all individual and family health insurance products offered by the three participating carders over the 1996 to 2002 period using brochures and other documents provided by carriers. About 80 percent of subscribers in the individual market in California enroll in one of the products included in the study. The Actuarial Research Corporation (ARC) used the abstracted data to develop measures of the actuarial value of each plan by simulating what each insurance product would pay for the health care services incurred by each person in a standardized population. (1) The premium data were linked to respondents in the surveys based on the age of the person, time, and the county of residence.
