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Although the majority of Americans receive their health insurance through an employment-based health plan, that number is declining as health insurance premiums rise, the share of the premium that is passed on to employees rises, and fewer firms offer coverage to their workers (Fronstin 2003). In this article, we examine changes in insurance coverage between 1999 and 2002 and consider the implications of the downward trend in employer-sponsored insurance (ESI) coverage on the future insurance status of low- and middle-income workers. Specifically, we use data from the 1999 and 2002 National Survey of America's Families (NSAF) to answer three questions:

• How did insurance coverage, especially ESI coverage, change between 1999 and 2002 for low- and middle-income workers?
• What explains the change in ESI coverage during this period?
• What are the implications for workers' insurance status in the future if ESI is no longer an option?

Methods

We use a range of descriptive and multivariate methods to address the three questions outlined above. In addressing the first question—how insurance coverage has changed over time—we provide descriptive statistics that show how insurance coverage in 2002 compares with coverage in 1999 for all workers and for workers by income group. In addressing the second question—what explains the changes in ESI coverage between 1999 and 2002—we examine how ESI offer and take-up patterns have changed during this period, using regression-based decomposition methods to determine how much of the changes are shifts in the characteristics of workers over this period versus shifts in the effect of these and other factors not captured by the model on ESI coverage over time. In addressing the third question on the implications of a loss of ESI coverage for workers, we first explore how health insurance options beyond ESI have changed over the period. We then use multivariate analysis to simulate a currently covered worker's insurance status if ESI from his or her own employer were no longer an option.

Before presenting our findings, we provide some background on several of the measures used in the study.

• Insurance Status and ESI Offer. We use information on reported health insurance coverage at the time of the survey to classify each individual into one of five mutually exclusive groups, ordered hierarchically. The order of the hierarchy is: (1) ESI through own employer, (2) ESI through another family member (e.g., spouse or parent), (3) Medicaid, SCHIP, or any state-specific program, (4) coverage through the nongroup market, and (5) uninsured. To identify individuals working in a firm that offers ESI coverage, we constructed an offer status measure based on whether the individual reported having ESI through his or her own employer, or if he or she answered "yes" to the survey question, "Does your current employer offer health insurance to workers in the same position as yours?"¹
• Estimation of Nongroup Premiums. In our analysis of workers' insurance options in the absence of ESI, we estimate the share of workers who would have access to affordable nongroup coverage. We define nongroup coverage as "affordable" if the premium is less than 10 percent of family income. Because NSAF does not collect information on premiums and observed premiums in the market are not likely to reflect the potential nongroup premiums faced by individuals who do not currently purchase nongroup coverage, we apply the framework for generating nongroup premium estimates developed by Hadley and Reschovsky (2003) for the Community Tracking Survey (CTS) to our NSAF data. Hadley and Reschovsky generated estimates of nongroup premiums for their CTS sample through a two-step process. First, they estimated a model of nongroup premiums using data from the CTS, which collects information on nongroup premiums for individuals in the sample with nongroup coverage. Because the CTS collected premium amounts of actual nongroup benefit packages, the data reflect the distribution of premiums and benefit packages in the real nongroup market. Hadley's and Reschovsky's model captures the effect of worker demographics (including health status), family structure, job characteristics, nongroup market regulations in the state, and local market characteristics on the individual's nongroup premium, while controlling for the unobservable factors that led the individual to select the nongroup market (i.e., they employed a Heckman selection model). In the second step, Hadley and Reschovsky used the coefficient estimates from the model to predict the nongroup premiums faced by all members of their sample (regardless of insurance status). In estimating nongroup premiums for our sample, we use the coefficient estimates from Hadley's and Reschovsky's model and replicate their second step using NSAF data to obtain estimates of the nongroup premiums faced by each NSAF worker in our sample.
• Eligibility for Public Programs. Our measure of eligibility for Medicaid, SCHIP, and other public programs is obtained from a microsimulation model developed at the Urban Institute (Davidoff et al. 2004). Eligibility for each worker is based on the program rules for Medicaid, SCHIP, and, if relevant, other state programs in place in the worker's state of residence.

How Did Insurance Coverage Change between 1999 and 2002?

ESI coverage dropped about 3 percentage points between 1999 and 2002 for both workers with income below 200 percent of the federal poverty level, or FPL (hereafter referred to as low-income workers), and workers with incomes between 200 and 400 percent of FPL (hereafter referred to as lower-middle-income workers) (table 1). For low-income workers, the decline in ESI coverage was offset almost entirely by an increase in public coverage, leaving uninsurance unchanged between 1999 and 2002.

The drop in ESI coverage for lower-middle-income workers was the result of a significant reduction in own ESI coverage (> 4 percentage points). Lower-middle-income workers in 2002 were much less likely than similar workers in 1999 to have an ESI offer (down 2.5 percentage points) and, when they had an offer, to take up ESI coverage (down 2.6 percentage points). Unlike with low-income workers, this drop in ESI coverage was not offset by any increase in public coverage. Thus, uninsurance rate among lower-middle-income workers increased by 2.2 percentage points.

For workers with higher incomes (above 400 percent of the FPL), there was virtually no change in overall ESI coverage and actually a slight reduction in uninsurance. Therefore for the rest of our analysis, we focus on the low- and lower-middle-income workers that faced changes in their insurance status over the period.

What Explains the Change in ESI Coverage during This Period?

The level of ESI coverage in 2002 reflects the probability that the workers' employers offer coverage and the probability that the workers take up that coverage. We begin by examining shifts in the characteristics of workers and their jobs over the 1999 to 2002 period.

As shown in table 2, we see an increase in the share of workers with characteristics known to be associated with a lower likelihood of ESI coverage (Shen and Zuckerman 2003). These include increases in the shares of low- and lower-middle-income workers who are of Hispanic origin, who are noncitizens, and who have low levels of educational attainment. Similarly, we see an increase in the share of workers in jobs that have a lower likelihood of ESI coverage (Chollet 1994; Gruber 2000). Between 1999 and 2002, there were increases in the shares of low- and lower-middle-income workers who were working part-time, who had been at their job for less than one year, and who were working for small firms. There was also a shift away from jobs in manufacturing industries to jobs in wholesale and retail trade, which are less likely to offer ESI coverage to their workers.

In table 3, we use regression-decomposition methods to determine what share of the changes in ESI coverage that occurred between 1999 and 2002 is explained by the changes in the characteristics of the workers and their jobs over the period, as opposed to other factors. We examine changes in the probability of an ESI offer and changes in ESI take-up for those with an offer. Since there was not a significant change in the probability of having an ESI offer for low-income workers, we do not attempt the regression decomposition for that outcome for those workers.

The first row of table 3 shows the total difference in the probability of workers' having an ESI offer or taking up ESI coverage between 1999 and 2002. The next two rows separate the total differences into what can be attributed to differences in worker characteristics (e.g., demographic characteristics, hours of work and job tenure) and job characteristics (e.g., size and industry of firm) over time and what is the result of changes in other unobserved factors. The bottom half of the table separates the differences due to changes in worker and job characteristics into the component parts: differences due to changes in demographic characteristics, changes in employment and job characteristics, changes in the work status of the worker's spouse (if present), and changes in the characteristics of the worker's county of residence.

As shown in the top panel of table 3, the regression decomposition yields very different patterns for ESI offer and ESI take-up. We find that changes in the probability of an ESI offer are driven largely by changes in the observed characteristics of workers and their jobs, while the changes in ESI take-up are mainly driven by shifts in the structure of the market over time and unobserved factors. In fact, based on the changes in the characteristics of the workers and their jobs, we would have expected an even greater reduction in the probability of an ESI offer than was observed (-3.5 percentage points versus the -2.5 percentage points). As shown in the bottom panel of table 3, changes in employment characteristics and job characteristics were the primary factors behind the drop in ESI offer for the lower-middle-income workers. Changes in employment characteristics include changes in full-time work and changes in job tenure, while the changes in job characteristics include changes in industry and changes in firm size.

For ESI take-up, only between 5 and 27 percent of the 1999-2002 difference is the result of changes in the characteristics of the workers and their jobs. The remaining differences were because of shifts in how these observed characteristics affected a worker's likelihood of taking up an ESI offer over time and shifts in other factors not captured in the model. It is likely that one of those other factors was rising premiums for ESI coverage. (Because changes in observed characteristics explain so little of the change in ESI take-up for the low-income workers, we do not attempt to separate the explained component into particular worker or job characteristics.)

Despite the differences in the role of worker and job characteristics in explaining changes in ESI offer and ESI take-up over time, changes in the effect of employment and job characteristics on the probability of ESI offer and ESI take-up were both highly significant when we perform a joint F-test on the coefficient shifts between 1999 and 2002 (results not shown). This suggests that the changes in the characteristics of the workers and their jobs and the returns to those characteristics in terms of the likelihood of having ESI coverage were important factors in the change in ESI coverage between 1999 and 2002.

What Happens to Workers' Coverage when ESI Is No Longer an Option?

Given the decline in ESI coverage for both low- and lower-middle-income workers, we next consider what is likely to happen to workers if ESI is no longer an option. Understanding the implications of a loss in ESI coverage on insurance status requires that we understand the alternative insurance options available to workers. Without ESI, an individual's coverage options narrow to obtaining coverage through a family member's job, purchasing nongroup insurance, or enrolling in public programs (for those who are eligible). The availability of these three options to low- and lower-middle-income workers is summarized in table 4.

Not surprisingly, the potential coverage options available to low- and lower-middle-income workers are quite distinct. Using our standard of "affordability" for nongroup coverage described above, we find that, in the absence of ESI, only 50 percent of low-income workers would likely have any insurance options in 2002, with public coverage (19 percent) and nongroup coverage (23 percent) the two most likely sources of coverage. In contrast, 84 percent of lower-middle-income workers would have at least one potential coverage option in the absence of ESI in 2002. For these workers, coverage through a spouse (31 percent) and nongroup coverage (69 percent) are the most likely sources.

Next we examine how these insurance options have changed over this period. The third and fourth columns of table 4 show that between 1999 and 2002, the options available to both low- and lower-middle-income workers dropped significantly, primarily because nongroup coverage became less affordable in 2002. The share of workers with nongroup premiums that were less than 10 percent of income dropped about 10 percentage points for both low- and lower-middle-income workers. However, the drop is partly offset by the share of workers eligible for public programs, which increased by nearly 6 percentage points for low-income workers and 3 percentage points for lower-middle-income workers. There is little change in the share of low-income workers whose spouses have an ESI offer, while there is a decline of 2 percentage points in the share of lower-middle-income workers likely to have access to ESI coverage through a spouse.

While low-income workers are more vulnerable overall, both low- and lower-middle-income workers faced substantial declines (5 percentage points overall) in their coverage options between 1999 and 2002.

Given the limited set of options available to many of the workers, we would expect uninsurance to rise if employers stopped offering ESI coverage or if ESI coverage became unaffordable. Using multivariate models of worker behavior, we simulate a currently covered worker's insurance status if ESI from his or her own employer were not an option. Our model assumes the worker chooses among coverage through a spouse, public coverage, nongroup coverage, and uninsurance as a function of his or her individual and family characteristics, employment characteristics, and characteristics of the state and local labor and health care markets. We base our simulations of worker behavior in the absence of ESI on the behavior of similar workers in firms that currently do not offer coverage, with some adjustments for preferences for insurance coverage. Since our simulation model is based on actual worker behavior, not all workers with access to coverage through a spouse, eligibility for public coverage, or facing "affordable" nongroup coverage will choose to be insured.

Our simulation work shows that 48 percent of low-income workers and 31 percent of lower-middle-income workers would become uninsured in the absence of ESI (table 5). Among the lower-middle-income workers who would retain insurance coverage, nearly all would rely on either ESI coverage through their spouse's job or nongroup coverage. Only a very small share would be eligible for and choose to participate in a public insurance program.

The coverage patterns would be substantially different for the low-income workers who retained insurance in the absence of ESI. In particular, nearly half would enroll in a public insurance program. The remaining workers with coverage would split between coverage through the spouse's job and nongroup coverage.

To place our estimates in context, if the employers of 10 percent of currently insured low- and lower-middle-income workers were to drop ESI coverage, at least 1.3 million workers (500,000 low-income workers and 850,000 lower-middle-income workers) would be added to the ranks of the uninsured.² Although the projected uninsurance rate is lower for lower-middle-income workers, their larger share of the population means more lower-middle-income workers would be affected than low-income workers if the same share of workers were to lose their ESI offer. The actual loss of coverage is likely even higher than we estimate since uninsurance would also go up among family members who had been covered under the worker's ESI policy and were unable to obtain other coverage.

Our projected levels of uninsurance vary widely across subgroups of low- and lower-middle-income workers (results not shown). Although the projected uninsurance rate for lower-middle-income workers overall is lower than that for low-income workers, certain subgroups of lower-middle-income workers would actually fare worse than low-income workers. In particular, noncitizens and workers with disabilities would have higher uninsurance rates than their low-income counterparts in the absence of ESI coverage, because they are less likely to be covered by public program and less likely to purchase insurance from the nongroup market.

Discussion

We draw several conclusions from this analysis.

• While low-income workers are more likely to be uninsured and are most vulnerable to the loss of ESI coverage, many lower-middle-income workers are also in a precarious position. Uninsurance for lower-middle-income workers increased between 1999 and 2002, reflecting a loss in ESI coverage and a decline in the share of workers with coverage options outside ESI.
• The changes in ESI coverage for low- and lower-middle-income workers between 1999 and 2002 were driven largely by changes in the nature of their jobs, shifts in the structure of the insurance market, and changes in other factors not captured in the model.
• Many low- and lower-middle-income workers have few coverage options in the absence of ESI. This is particularly problematic for low-income workers, as nearly half of them have no viable alternatives outside the ESI market. Moreover, the affordability of nongroup insurance has declined sharply between 1999 and 2002: the shares of low- and lower-middle-income workers that would face nongroup premiums that are less than 10 percent of their income have dropped 10 percentage points over this period.
• When workers lose ESI, many are likely to become uninsured. Although dependent ESI coverage, public coverage, and nongroup coverage would all increase substantially if ESI were no longer an option, the overall levels of each would remain relatively low. This is true for both low- and lower-middle-income workers: we estimate that 48 percent of low-income workers and 31 percent of lower-middle-income workers would become uninsured if ESI were no longer available to them. Moreover, lower-middle-income workers who were disabled or noncitizens actually would fare worse than their low-income counterparts, because the lower-middle-income workers do not have the safe haven of public programs.

These findings make clear that policies shoring up the ESI insurance system are important for both low- and lower-middle-income workers, as both are vulnerable to a loss of insurance coverage in the absence of ESI. While the expansion of public coverage provided some protection from that increase for low-income workers, lower-middle-income workers lost ground over the period.

Notes

1. The NSAF offer question seeks information about the type of job that the person holds, not about the person's particular experience. Consequently, individuals can answer "yes" to the question even if they themselves are not eligible to enroll in their employer's plan. It is important to keep this in mind, because offer rates produced using the NSAF will be higher than those based on data sets that focus on whether a particular individual received an offer. Additionally, offer rates based on the NSAF will be lower than those computed from surveys that ask if an individual's employer offers health insurance to any workers at the firm where the individual is employed. However, since the offer question was asked consistently in the 1999 and 2002 NSAF surveys, it provides an accurate measure of changes in offer probabilities over time.

2. There were about 18.6 million low-income workers and 34.7 million lower-middle-income workers in 2002. Among them, 55.4 percent of low-income and 78.6 percent of lower-middle-income workers currently have an ESI offer. Based on table 5, we project that 4.8 percent and 3.1 percent of low- and lower-middle-income workers, respectively, would lose ESI coverage if 10 percent of their employers stop offering ESI, which results in 500,000 low-income workers and 850,000 lower-middle-income workers added to the uninsured population.

References

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Acknowledgments

This research was funded by the Robert Wood Johnson Foundation. The opinions expressed are those of the authors and do not represent the views of the Urban Institute, its trustees or sponsors.

Note: This report, including all tables and charts, is available in its entirety in the Portable Document Format (PDF).

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