What Impact HIPAA?

State Regulation and Private Health Insurance Coverage among Adults

by
Deborah J. Chollet, Ph.D.
Kosali Ilayperuma Simon, Ph.D.
Adele M. Kirk, M.A.

3. Research Design: Data and Methods

We consider the impact of state regulation of the small-group and individual insurance markets, respectively, on two segments of the population:

• Adults aged 18-64 in families of workers who are employed in small private-sector firms (for group market reforms); and
• Adults aged 18-64 in families of workers who are employed in private-sector firms of any size, and who have neither employer-based coverage nor public coverage (for individual market reforms).

By selecting these two groups for analysis we hope to discern impacts that might be undetectable among the entire population and also to eliminate variation for which dummy variables (e.g., worker or nonworker family) may be inadequate controls. Moreover, by selecting only the adult population we hope to eliminate some problems that may result from under-reporting of public program coverage; eligibility for public coverage is much less common among adults than among children. The years of analysis are 1995 through 1997; all estimation is done at the person-level using general linear model regression.

Our estimates rely on three sources of data:

• The March 1996, 1997, and 1998 Current Population Surveys. Within CPS families we identified the “greatest earner” as the individual in the family who (a) reported nonzero earnings during the year; and (b) reported greater earnings than any other adult family member. All family members were then matched to their greatest earner. Earlier research (Chollet, 1994) determined that the employment characteristics of the greatest family earner predict employer-based coverage among family members more reliably than individuals’ own employment characteristics.
• Verified data describing state regulation. These data were compiled primarily from an Alpha Center survey of state Departments of Insurance, conducted in 1998 with funding from the National Association of Insurance Commissioners (NAIC). Responses were verified against a database of state small-group insurance regulation that had been compiled by Simon (1999a), those data were adopted where the Alpha Center survey was unable to obtain information or obtained information that was suspect. Similarly, where our survey information about individual market regulation were missing or suspect, we obtained information from the Georgetown University Health Policy Center survey of individual market regulation. All of our regulatory variables reflect verified, full-year implementation of statutes, not enactment.
• A health insurer database compiled by Alpha Center with funding from the Robert Wood Johnson Foundation. We compiled the health insurer database from insurers’ annual reports to the states (assembled by NAIC), and supplemented these data with a survey of every commercial insurer that reported selling health insurance coverage (of any type) in 1995, 1996 or 1997. These data identify (in the group and individual markets, separately) the type of insurer (HMO, commercial or BCBS), the insurer’s earned premiums associated with primary major medical coverage (by state), and the insurer’s medical losses on this business. (An extensive description of this database is provided in Chollet et al., 2000).

Employer coverage

We estimate two sets of models to identify the potential effects of small-group regulation on employer coverage among adults in worker families, reflecting the firm size definitions available in the 1996-1998 CPS. First, we estimate coverage among adults in families of workers in firms of under 100 workers. Although workers in firms of more than 50 workers (in some states more than 25) were outside the scope of states’ small-group statutes, the 1996-1998 CPS public use data do not allow us to isolate workers in firms of 50 or fewer employees. Thus, we estimate potential impacts on workers in firms of less than 100, and anticipate weaker results than we might find were we able to isolate the target population more precisely. Second, we estimate coverage among adults in families of workers in firms of under 25; all states’ reform statutes applied to workers in these firm sizes.

For both populations we estimate three alternative specifications of a linear fixed-effects model, as follow:

where:

• ERCOV_ist is a dummy variable indicating whether individual i has employer coverage either from his/her own employer or as a dependent;
• GMARKET_st is a vector of variables which varies by state and year, and describe the number of insurers that sold group major medical coverage, group market concentration, the group market shares of HMOs and commercial insurers, and the average loss ratio of group commercial insurers in the state;
• SOCIOECON_ist is a vector of variables that include the sociodemographic characteristics of the individual and the employment characteristics of the individual’s greatest family earner; and
• STATE and YEAR are vectors of dummy variables controlling for the state and year.

where ERCOV, SOCIOECON, STATE and YEAR are as defined above, and

• GMARKETA_st is a vector of variables which varies by state and year, and includes only the variables that in our earlier work (Chollet et al., 2000) were insensitive to access regulation in the group market (HMO and commercial insurer market share, and average commercial insurer loss ratios); and
• GREGULATION_st is a vector of categorical and continuous variables identifying whether in its small-group market the state has implemented guaranteed issue (of some or all products, and separately, all-products), the maximum waiting period for coverage of preexisting conditions, and the narrowness of bands on rating for health and for age (if any), and the narrowness of the composite rate band (if any).

where all vectors are as defined above. Table 1 includes definitions of all of the variables included in the specifications.

The inclusion of the GMARKET and GMARKETA variables in this analysis warrants additional explanation. Differences among states in the structure of their group (and individual) health insurance markets are striking.⁸ Large-population states characteristically have many fewer insurers per capita — and much larger premium volume per insurer — than small population states. Moreover, commercial insurers in either market have characteristically low average premium volume. The literature estimating economies of scale in health insurance, while scant, uniformly suggests that insurers experience increasing economies of scale (i.e., declining marginal cost) over an extensive range of production, and only the largest insurers experience even constant economies of scale. Thus, all else being equal, one might expect markets with greater concentration to have lower average costs of production, lower prices, and greater coverage. Conversely, more insurers, all else being equal, may foster greater price competition. These competing hypotheses underlie our expectation that measures of market structure may affect (via unobserved prices for insurance) coverage in the small-group market, and also in the individual market.

The alternative specifications of our employer-coverage model differ only in whether GMARKET (a full vector of market variables) or GMARKETA (a partial vector) was entered, and whether small-group regulation variables (GREGULATION) were considered. The first specification of our model omits regulation variables to test solely for the potential influence of market structure on insurance prices (which are unobserved) and on employer coverage. The second specification includes a full array of regulatory variables, but omits market variables that are sensitive to regulation.⁹ We anticipate that the results of this specification will most closely approximate those of earlier research which did not control for market structure. The third and final specification includes the full array of both market and regulatory variables. We argue that this is the most appropriate specification for estimating the immediate impacts of regulation, controlling for the intervening effects of market structure.

Individual coverage

We estimate similar specifications of the same general model to measure the impacts of state regulation of individual insurance on coverage among adults without coverage either from an employer or a public program. These are as follow:

where INDIV is private (non-employer) coverage; MARKET is a vector of variables that reflect individual (nongroup) market structure; and SOCIOECON, STATE and YEAR are defined as above.

where IMARKETA is a vector of variables that control for dimensions of individual market structure (HMO market share and average commercial insurer loss ratios) that were insensitive to regulation in our earlier results; and SOCIOECON, STATE and YEAR are defined as above.

where all vectors are as defined above.

We estimate all models using current-year measures of regulation, market structure and coverage.¹⁰ Descriptive statistics of all variables are provided in Appendix 2 and 3.

8 - These differences are described in Chollet, Kirk and Chow (in press) and were summarized in our earlier report under this contract (Chollet, Kirk and Simon, 2000).

9 - In our earlier paper (Chollet et al., 2000) we concluded that some small-group regulation affected market structure. Specifically, all-product guaranteed issue, shorter exclusion periods for preexisting conditions and some forms of rate regulation appeared to drive differences in the number of insurers in the market and in market concentration, respectively.

10 - We estimated the models with leading and lagging variables for both regulation and socioeconomic characteristics, but found that the regulatory impacts detectable in the current-year specifications either vanished or did not differ appreciably when lead-lag specifications were used. Therefore, we report only current-year specifications.