What Impact HIPAA? State Regulation and Private Health Insurance Coverage Among Adults.

by

Deborah J. Chollet, Ph.D.
Mathematica Policy Research

Kosali Ilayperuma Simon, Ph.D.
Michigan State University

and

Adele M. Kirk, M.A.
University of California at Los Angeles

Submitted to
the Office of the Assistant Secretary for Planning and EvaluationU.S. Department of Health and Human Services
in fulfillment of Contract HHS-10098-0014

October 20, 2000

This research was conducted with partial funding from the Robert Wood Johnson Foundation to the Academy for Health Services Research and Health Policy.

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Executive Summary

This study investigates the impact of the states’ access regulation on health insurance coverage among adults in worker families. We offer several methodological improvements on past estimates of this type: (1) We estimate coverage among only adults in worker families, minimizing potential error in the measurement of key variables. (2) We introduce continuous measures of allowed rate variation and waiting periods for coverage of preexisting conditions. (3) We estimate the impact of regulations individually, rather than in “bundles.” (4) We introduce measures of market structure to control for supply-side variables that may influence the price of insurance (which is unobserved) and therefore the rate of private coverage.

Our results related to market structure are new to the literature. We find that states with more concentrated group insurance markets enjoyed higher rates of small employer coverage, all else being equal. This finding is consistent with diseconomies of small scale in states with more fragmented insurance markets, but also with potential differences in regulatory oversight that may be endogenous to market structure.

We also find that very few forms of regulation affected small-employer coverage independent of their primary impacts on market structure. However, all-product guaranteed issue (as HIPAA requires) significantly raised the probability of coverage among adults in worker families. Our earlier research indicated that all-product guaranteed issue also improved competition in the group market, increasing the number of insurers that participated.

We find that the impact of rate regulation on coverage was significant and relatively complex in the group market, but insignificant in the individual market. In states with a narrow composite rate band (or lacking a composite band, narrow bands on both age and health rating), small employer coverage was significantly higher among adults associated with workers in firms with fewer than 100 employees (although we found no impact on workers in firms with fewer than 25 employees). However, when both narrow composite rate bands and guaranteed issue were imposed, their combined effect on coverage was negative: employer coverage was significantly lower among adults associated with workers in firms with fewer than 100 workers.

We could discern no significant impact of rate regulation on coverage among workers in the smallest firms. This pattern is consistent with the conventional economic theory of price discrimination: if insurers enjoy some degree of monopoly pricing power and “larger” small firms demonstrate lower price elasticity of demand for insurance compared to the smallest firms, insurers may raise prices more for larger small firms than for the smallest firms for which average premium levels generally are higher.

Most significant for anticipating the possible results that HIPAA may have on coverage, we observed no negative impact on employer coverage either from guaranteed issue or shorter waiting periods on coverage for preexisting conditions. Indeed, it is likely that HIPAA’s provision requiring all-product guaranteed issue in the small-group market helped to extend insurance coverage to a greater number of employees and their dependents, all else being equal.

In the individual market, some states have implemented regulation that is much more extensive than HIPAA’s very modest provisions, but we found no evidence that most of the states’ access reforms in the individual market have affected the rate of individual coverage. Specifically, neither constraints on rating nor shorter preexisting condition exclusions had any apparent impact on coverage among adults in worker families with neither employer nor public coverage. In contrast, regulation requiring guaranteed issue of all products in the individual market may have substantially reduced individual coverage, although the statistical significance of this finding was weak. The availability of a high-risk pool — an alternative to guaranteed issue to ensure access in the individual market — had no significant impact on individual coverage.

Further research in this area, corroborating our findings and those of earlier studies, is essential. In this research as in earlier studies of this type, only a few observations of change drive all of the estimates of regulatory impact. Thus, it is essential that more recent years (after the states implemented regulations to comply with HIPAA) be studied to corroborate any conclusions about the impacts of access regulation on coverage.

Also, our results regarding the impact of market structure on coverage suggest that diseconomies of small scale in the supply of health insurance may be a problem warranting further investigation of insurance supply and its impact on coverage. Such research should examine further the extent of competition and scale economies in the production of health insurance as well as differences in the administration of regulations that may be endogenous to market structure.

1. Introduction

The literature examining the impact of state regulation on health insurance coverage has expanded significantly in the last decade. The earliest research investigates the effect of state benefit mandates on employer offer of coverage and, later, on coverage of the population.¹ More recent research examines the impact of other types of regulation -- guaranteed issue and renewal, and rate regulation. All of these studies investigate the impact of the states’ access regulation prior to implementation of the federal Health Insurance Portability and Accountability Act (HIPAA).

Despite advocates’ hopes that health insurance access laws might improve overall rates of health insurance coverage, the preponderance of the research literature finds that comprehensive reforms have little net impact on the general rate of coverage among the population. However, such regulation may affect the risk composition of the insured population (that is, older people and people with health problems may gain coverage while younger, healthier people may drop coverage), and different forms of regulation may have different levels and direction of impact.

This research attempts to improve on past studies of regulatory impacts on coverage in two ways. First, in contrast to past studies of the impact of regulation on coverage, we construct continuous variables to measure the restrictiveness of rate regulation and statutory limits on the length of preexisting condition exclusions in the group and individual health insurance markets, and we consider each market separately. By constructing continuous regulation variables where possible, we are able to overcome some of the statistical difficulties that have constrained other research studies of health insurance regulation. Specifically, we are able to understand the impacts of different types of regulation and also the impact of less or more restrictive regulation in each market.

Second, we look at the potential role of competition among insurers in explaining changes in health insurance coverage. In an earlier research paper (Chollet, Kirk and Simon, 2000), we investigated the change in the structure of health insurance markets that followed a change in regulation. That research found some impact of selected types of regulation on the number of insurers and concentration in the group market. In the individual market, regulation appeared to affect industry concentration and commercial insurers’ market share. These findings suggest that access regulation may affect insurance prices — and therefore coverage — both indirectly (by affecting some aspects of market structure) and directly (as insurers anticipate or experience changes in revenues or medical losses due to regulation). In this paper, we extend existing research by investigating the impact of regulation on group and individual health insurance coverage, controlling for the potential effects of market structure.

The paper is organized as follows. In Section 2, we offer a brief review of the recent literature that estimates the impact of state regulation on the probability of employer group coverage among workers or among the entire population under age 65. In Sections 3, we describe our research design, data and methods. In sections 4 and 5, we present our findings related to the small-group market and the individual market, respectively. We summarize the paper and present our conclusions in Section 6.

2. Recent Literature on Health Insurance Reforms and Coverage

Prior studies of health insurance regulation and coverage have examined either the impact of small-group reforms on employer offer of coverage or the impact on coverage among workers or other segments of the population under age 65. Of course, all of these studies examine impacts of state reform prior to HIPAA. As a result, they are able to observe effects related to differences in some types of regulation (for example, guaranteed issue) that for groups of 2 to 50 are now uniform among the states.

All studies of the impacts of state regulation on either group or individual insurance coverage have measured various types of reform as simple dummy variables. Because few states legislated only one type of reform, use of dummy variables to measure reforms creates serious problems of multicollinearity, leading some researchers to “bundle” reforms for empirical analysis. However, studies that bundle reforms have been able to identify the effects of various reforms only in combination; they cannot parse whether these effects may be due to a particular reform or to all reforms in combination.²

Among the studies that examine employer offer, one examines specific reforms (Jensen and Morrissey, 1999); two others consider bundles of reforms (Hing and Jensen, 1999; Monheit and Schone, 1998). Each of these studies discovered effects on employer offer across all firm sizes, despite the fact that the states’ reforms typically applied only to firms of fewer than 50 lives. Also, each found patterns of results that contradicted expectations.

Jensen and Morissey concluded that, among reforms considered alone and in combination (but excluding rating reforms, due to multicollinearity), only limits on preexisting condition exclusion periods affected employers’ decisions to offer coverage. The imposition of limits on preexisting condition exclusions raised the likelihood of employer offer. Early results from work conducted by Monheit and Schone (1999) indicate that community rating (but not limits on preexisting condition exclusions) raised the probability of employer offer.³ Finally, while Hing and Jensen concurred that the probability of employer offer (aggregating firms of all sizes) was greater in reform states ( and especially in “recent” reform states rating constraints were imposed, but not guaranteed issue), they found that employee enrollment was lower in these states, suggesting that employee contributions for coverage in these states may have increased.⁴

A larger number of studies have addressed the impact of state insurance on the probability of insurance coverage, typically considering the probability of group coverage or any private coverage (group and individual) as well as the probability of being insured at all. Some studies also have considered the impact on public coverage. Three studies examined market reforms individually, and found different impacts in the small-group market than in the individual market. All used the same basic data (the March CPS), and while their results were generally consistent, they varied across subsets of the population.

• Sloan and Conover (1998) examined the impact of state reforms (considered individually) on the probability of coverage, estimated at the person level with CPS data. They concluded that small-group community rating laws had no impact on private coverage among general population, but raised coverage among older adults. In the individual market, regulation requiring community rating reduced the likelihood of being insured.
• Zuckerman and Rajan (1999) considered the impact state reforms both individually and in combination on state-level rates of health insurance coverage. They detected no impact of small-group reform (alone or in combination) on average rates of employer coverage. However, in the individual market, Zuckerman and Rajan concluded (consistent with Sloan and Conover) that comprehensive individual market reforms (in any combination) reduced both private coverage and total (private plus public) coverage. Individually, only guaranteed issue in the individual decreased total coverage (but, oddly, it had no significant impact on the rate of private coverage).
• Browne and Frees (2000) considered the impact of small-group rating reforms, and then any restriction on underwriting (including guaranteed issue or renewal) on coverage among single adults (aged 15-64) and then on all adults employed in small firms (of less than 10). They concluded that constraints on rating affected coverage among the segments of the working population would most likely be affected. Specifically, constraints on health rating reduced group coverage among workers who reported no work-related disability, but raised group coverage among those with work-related disability. Constraints on gender rating increased coverage among women and reduced coverage among men. Constraints on age rating increased coverage among older workers (but had no discernable impact on younger workers). Browne and Frees found no significant effects from underwriting restrictions in the individual market.

Three other studies have attempted to consider the impact of bundles of reforms. These studies use different population survey data and also different data identifying state regulation. Probably due to differences in both data and methods, they found inconsistent results.

• Using March CPS data, Marsteller et al. (1998) concluded that some small-group reforms (any combination of guaranteed issue, guaranteed renewal, limits on preexisting condition exclusions or portability) raised rates of total (private plus public) coverage, but only when all were present did they raise the rate of private coverage. The combination of guaranteed issue and rating reforms in the individual market reduced rates of total coverage but, oddly, their impact on private coverage was weak.
• Also using CPS data but a different database describing state reforms, Simon (1999a) considered three “bundles” of small-group reforms — states with full reform, partial reform, or only a waiver of mandated benefits.⁵ Simon concluded that full group reforms (but in general, not partial reforms) decreased employer coverage among small- firm workers (especially among demographically low-risk populations), even when age and gender were allowed rating factors.⁶ Constraints on rating for age and gender magnified the negative effects of other group reforms. In a later paper using a somewhat different estimation method, Kaestner and Simon (2000) concluded that even partial reforms reduced coverage among groups “vulnerable” to insurance loss (low-educated employees and young, unmarried employees without children).
• Using employer-survey data to examine the effects of state regulation on the price of group insurance to employers and employees, Simon (1999b) concluded that full reform increased premiums (by about 4 percent), and that most of the increase was passed on to workers as higher employee contributions for coverage. Presumably responding to higher contribution levels, full reform did not affect the rate of employer offer but decreased the rate of employer coverage (implicitly, employee take-up). Again, partial reform had no significant impact on either employer offer or coverage.

Appendix 1 provides a summary of the measures and methods used in each of these studies.

Differences in these studies’ models and the populations they consider appear to drive many of the inconsistencies in their findings. However, Simon (1999a and 199b) — arguably the most reliable investigations of the impacts of regulation⁷ — discovers the same general result using different databases and methods in each of her studies. Specifically, some reforms in combination apparently discourage employer coverage, probably because insurers respond by raising insurance prices and employers then increase employee contributions. Among the several reforms most states had enacted before HIPAA compliance, guaranteed issue in combination with rating reform may be the most likely cause of a price-contribution-coverage sequence: constraints on rating with guaranteed issue, may force broader increases in premiums (affecting more groups and individuals) than guaranteed issue alone.

Finally, these studies rarely consider individual market reforms. Those that do consider individual market reforms combine them in a general analysis of population coverage (employer coverage and individual coverage) and find a contradictory pattern of effects — for example, an impact on total (private plus public) coverage but no significant impact on private coverage. Others have noted that most of these studies suffer from specification problems which may produce both biased and inefficient estimates of effects (Nichols, 1999).

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:

(1) ERCOV_ist = f (GMARKET_st, SOCIOECON_ist, STATE_s, YEAR_t),

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.

(2) ERCOV_ist = f (GMARKETA_st, GREGULATION_st, SOCIOECON_ist, STATE_s, YEAR_t),

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).

(3) ERCOV_ist = f (GMARKET_st,GREGULATION_st ,SOCIOECON_ist, STATE_s, YEAR_t),

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:

(4) INDIV_ist = f (IMARKET_st, SOCIOECON_ist, STATE, YEAR),

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.

(5) INDIV_ist = f (IMARKETA_st, REGULATION_st, SOCIOECON_ist, STATE, YEAR),

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.

(6) INDIV_ist = f (MARKET_st, REGULATION_st, SOCIOECON_ist, STATE, YEAR),

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.

4. Results: Small Employer Coverage

The results reported in Tables 2 and 3 indicate that both market structure and regulation may affect the rate of employer coverage among small-firm employees. In general, our results find fewer impacts of access regulation on coverage (controlling for the strictness of regulation, not just the fact the regulation). However, they also implicate market structure as having a marginal impact on small employer coverage. Also, consistent with some other studies, we find a greater apparent impact of rate regulation on coverage among workers in firms with fewer than 100 workers (many of whom were not subject to rate regulation) than among workers in firms with fewer than 25 employees (all of whom were subject to rate regulation). This anomalous result may reflect a stratification of supply within the small-group market (and, therefore, differences in products and pricing to very small employers) that is not discernable in our market structure data.

The impact of market structure on small employer coverage

Most measures of market structure appear to have little impact on coverage among workers in firms with fewer than 100 employees. Specifically, the number of insurers in the state has no significant impact, suggesting that competition — all else being equal — does not affect the price of insurance enough to drive higher rates of coverage (indeed, the coefficient on this variable in our third and preferred specification is negative). Similarly, the distribution of the market among BCBS plans, HMOs and commercial insurers had no significant impact on coverage when we also controlled for market concentration.

However, among workers in firms with fewer than 100 employees and with even greater statistical significance among workers in firms with fewer than 25 employees, greater market concentration among the largest insurers (the “top five”) drove higher rates of coverage. This result is consistent with either or both of two potential explanations: (1) insurance markets with greater concentration benefit from greater economies of scale; and/or (2) state insurance departments review the prices of large insurers more critically and, in states with greater market concentration, a larger share of the entire market is subject to more critical review. In either case, price levels may be lower in states with greater market concentration. The magnitude of the ultimate impact of market concentration on coverage was surprisingly large: all else being equal, a ten-percentage point increase in the collective market share of the largest five insurers drove a 2 to 4 percentage point increase in small employer coverage.

The impact of regulation on small employer coverage

Controlling for market structure (as well as individual characteristics), we also find impacts of state regulation on small employer coverage, and these impacts are in general consistent with earlier research. First, guaranteed issue of all products has a positive impact on coverage, all else being equal raising the probability of small employer coverage by as much as 26 percent. This result was consistent across both firm sizes, although its statistical significance was moderate (90 to 95 percent). In contrast, states that enacted guaranteed issue of only some products did not affect coverage significantly.

Second, among workers in firms with fewer than 100 employees, composite rate bands affected small employer coverage. In the absence of guaranteed issue, narrower composite rate bands (in the limit, pure community rating) raised the likelihood of coverage by as much as 15 to 17 percentage points. However, when coupled with guaranteed issue, very narrow composite rate bands had no appreciable net impact on coverage: that is, the positive effects of either guaranteed issue or very narrow rate bands disappear when implemented together. These offsetting effects are not apparent among workers in the smallest firms: among workers in firms with fewer than 25 employees, guaranteed issue of all products raised the probability of coverage, and any offsetting (negative) impact of rate constraints was statistically insignificant. The difference that we observed between effects of regulation on employees in “larger” small firms versus very small firms is consistent with insurers practicing conventional price discrimination in the small-group market (where demand among very small employers is arguably more price-elastic than demand among “larger” small employers).

In summary, we conclude that guaranteed issue of all products in the small-group market, such as HIPAA requires, probably has expanded small employer coverage. States that also enacted rate constraints may have offset the coverage impact of guaranteed issue on larger small groups (25 to 99 employees), but on net, coverage in those states was not lower than in the absence of regulation. Among workers in the smallest firms, all-product guaranteed issue had an especially strong positive impact on coverage, and the impact of very strict rate regulation was insignificant. Notably, other forms of access regulation — shortened preexisting condition exclusion periods, guaranteed renewal, narrower rate bands on either age or health (but not both, and either alone or in combination with guaranteed issue) — had no significant impact on coverage.

5. Results: Individual Coverage

The results of our analysis of individual coverage among adults in working families with neither employer coverage nor public coverage are reported in Table 4, and described below.

The impact of market structure on individual coverage

No measure of individual market structure had a statistically significant impact on the probability of coverage. However, the pattern of estimated coefficients is of some interest, in that it is in general consistent with the same potential diseconomies of small scale that we observed in the small-group market. That is, a larger number of insurers in the individual market, and therefore presumably greater competition, did not raise the probability of private coverage. Instead, in states with a greater number of insurers and lower market concentration, individuals in worker families without either employer or public coverage were generally less likely (in all specifications of the model) to be privately insured — although these effects were not statistically significant.

When we control for some, but not all measures of market structure (and for all types of regulation), we found that the average individual-product loss ratio among commercial insurers was negatively related to coverage, although the statistical significance of this relationship was weak. The negative sign of the coefficient is unexpected, but might be explained in terms of an intervening, unobserved relationship between insurer loss ratios and insurance prices. That is, in markets where price levels are high (and coverage is low), insurers may accept higher loss ratios rather than raise prices still further; in these markets, insurers may be especially cautious about triggering adverse selection and extinguishing demand.

The impact of regulation on individual coverage

Unlike our results among adults in the small-group market, guaranteed issue of all products in the individual market reduced the likelihood of individual coverage — by about 9 percentage points, all else being equal. However, the statistical significance of this relationship was weak. No other measure of regulation (including guaranteed renewal, narrower constraints on rating or shortened preexisting condition exclusions) significantly affected coverage.

6. Summary and Conclusions

The literature describing the impact of state regulation on health private health insurance is fraught with methodological difficulties and contradictory findings. This study offers several improvements on this literature: (1) We estimate coverage among only adults in worker families, minimizing potential error in the measurement of key variables. (2) We introduce continuous measures of regulations that govern how insurers price coverage and how they set waiting periods for coverage of preexisting conditions. (3) We estimate the impact of regulations individually, rather than in “bundles.” (4) We introduce measures of market structure to control for supply-side variables that may influence the price of insurance (which is unobserved) and therefore the rate of private coverage.

Our results related to market structure are new to the literature. We find that states with more concentrated group insurance markets enjoyed higher rates of small employer coverage, all else being equal. This finding is consistent with diseconomies of small scale in states with more fragmented insurance markets, but also with potential differences in regulatory oversight that may be endogenous to market structure.

With respect to regulatory impacts, we find that very few forms of regulation affected small employer coverage independent of their primary impacts on market structure. However, all-product guaranteed issue (as HIPAA requires) significantly raised the probability of coverage among adults in worker families. Our earlier research indicated that all-product guaranteed issue also improved competition in the group market, increasing the number of insurers that participated.

We find that the impact of rate regulation on coverage was significant and relatively complex in the group market, but insignificant in the individual market. In states with a narrow composite rate band (or lacking a composite band, narrow bands on both age and health rating), small employer coverage was significantly higher among adults associated with workers in firms with fewer than 100 employees (although we found no impact on workers in firms with fewer than 25 employees). However, when both narrow composite rate bands and guaranteed issue were imposed, their combined effect on coverage was negative: employer coverage was significantly lower among adults associated with workers in firms with fewer than 100 workers.

We could discern no significant impact of rate regulation on coverage among workers in the smallest firms. This pattern is consistent with the conventional economic theory of price discrimination: if insurers enjoy some degree of monopoly pricing power and “larger” small firms demonstrate lower price elasticity of demand for insurance compared to the smallest firms, insurers may raise prices more for larger small firms than for the smallest firms for which average premium levels generally are higher.

Most significant for anticipating the possible results that HIPAA may have on coverage, we observed no negative impact on employer coverage either from guaranteed issue or shorter waiting periods on coverage for preexisting conditions. Indeed, it is likely that HIPAA’s provision requiring all-product guaranteed issue in the small-group market has helped to extend insurance coverage to a greater number of employees and their dependents, all else being equal.

In the individual market, some states have implemented regulation that is much more extensive than HIPAA’s very modest provisions. Our analysis produced no evidence that most of the states’ access reforms in the individual market have affected the general rate of individual coverage. Specifically, neither constraints on rating nor shorter preexisting condition exclusions had any apparent impact on coverage among adults in worker families without employer or public coverage.

In contrast, regulation requiring guaranteed issue of all products in the individual market appears to have substantially reduced individual coverage, although the statistical significance of this effect was weak. The availability of a high-risk pool — an alternative to guaranteed issue to ensure access in the individual market — had no significant impact on individual coverage.

Despite its statistical weakness, the impact of all-product guaranteed issue on individual coverage is of some interest in considering the potential impact of HIPAA’s more modest provision for guaranteed issue to HIPAA-eligible workers and dependents. Others (Pollitz et al., 2000) have observed that, in states where the absence or looseness of rate regulation permits, the premiums charged to HIPAA-eligibles can be extremely high, circumventing the coverage protection that HIPAA intended. Our results suggest that insurers may in fact substantially raise price in the individual market when constrained by guaranteed issue, producing lower rates of individual coverage.

Finally, some mention of the need for further research in this area is in order, both with respect to the impacts of regulation and with respect to the potential importance of insurance market structure. Both our the results and those of all other empirical studies of health insurance regulation to date rely on observation of very few changes in state regulation prior to 1997. Thus, in most cases, only a few observations of change drive all of the estimates of regulatory impact. It is essential that more recent years (after the states implemented regulations to comply with HIPAA) be studied to corroborate the conclusions reached here or in other studies of regulatory impacts.

Also, our results regarding the impact of market structure on coverage suggest that diseconomies of small scale in the supply of health insurance may be a problem. In the small-group market, we observed a positive effect of greater market concentration on employer coverage. In the individual market, we observed no impact of statistical significance, but the signs of key market structure variables were consistent with diseconomies of small scale. Further investigation of insurance supply and its impact on coverage is warranted — including specifically the extent of competition and scale economies in the production of health insurance, and differences in the administration of regulations that may be endogenous to market structure.

References

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Chollet, D.J. (Spring I 1994). “Employer-Based Health Insurance in a Changing Work Force,” Health Affairs 13(1): 315-326.

Chollet, D.J., A.M. Kirk and M.E. Chow (in press). “Mapping State Health Insurance Markets: Structure and Change in the Group and Individual Health Insurance Markets, 1995-1997.” Report to The Robert Wood Johnson Foundation’s State Coverage Initiatives Program. Washington, DC: The Academy for Health Services Research and Health Policy.

Chollet, D.J., A.M. Kirk and K.I. Simon (June 2000). “The Impact of Access Regulation on Health Insurance Market Structure.” Report to the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services (contract HHS-10098-0014).

Hing, E. and G.A. Jensen (September 1998). “Health Insurance Portability and Accountability Act of 1996: Lessons from the States.” Medical Care 37(7): 692-705.

Jensen, G.A. and J.R. Gabel (1992). “State Mandated Benefits and the Small Firm’s Decision to Offer Insurance.” Journal of Regulatory Economics 4(4): 379-404.

Jensen, Gail A. and M.A. Morrisey (Summer 1999). “Small Group Reform and Insurance Provision by Small Firms, 1989-1995.” Inquiry 36: 176-187.

Kaestner, R. and K.Ilayperuma Simon (2000). “Labor Market Consequences of State Health Insurance Regulation.” Working paper.

Marsteller, J.A., L.M. Nichols, A. Badawi et al. (June 1998). “Variations in the Uninsured: State and County Level Analyses” (Washington, DC: Urban Institute).

Monheit, A. and B.S. Schone (1998). “How Has Small Group Market Reform Affected Employee Health Insurance Coverage?” (unpublished AHCPR working paper).

Nichols, L. (November 1999). “What Do We Know About the Effects of Health Insurance and Market Reforms in the United States?” Presentation to the American Public Policy and Management Association, Washington, DC.

Pollitz, K., N. Tapay, E. Hadley and J. Specht (July/August, 2000). “Early Experience with ?New Federalism’ in Health Insurance Regulation. Health Affairs 19(4): 7-22.

Simon, K.I. (March 1999a). “Did Small-Group Health Insurance Reforms Work?” (unpublished).

Simon, K.I. (October 1999b). “The Impact of Small-Group Health Insurance Reform on the Price and Availability of Health Benefits” (unpublished).

Sloan, F.A. and C.J. Conover (Fall 1998). “Effects of State Reforms on Health Insurance Coverage of Adults.” Inquiry 35: 280-293.

Zuckerman, S. and S. Rajan (Spring 1999). “An Alternative Approach to Measuring the Effects of Insurance Market Reforms.” Inquiry 36: 44-56.

Tables

Table 1

VARIABLE DEFINITIONS
Group market characteristics	G_NUMINS	Number of insurers in the market
	G_TOPFIVE	Share of business accounted for by top 5 firms (percent)
	G_HMO	Share of business accounted for by HMOs
	G_COM	Share of business accounted for by commercial companies
	G_COMLR	Average commercial loss ratio
Individual market characteristics	I_NUMINS	Number of insurers in the market
	I_TOPFIVE	Share of business accounted for by top 5 firms (percent)
	I_HMO	Share of business accounted for by HMOs
	I_COMM	Share of business accounted for by commercial companies
	I_COMLR	Average commercial loss ratio
State characteristicsa	POPMIL	State population, in millions
Small group market regulation	GISOMALL	Guaranteed issue of some or all plans (dummy)
	G_ALL	Guaranteed issue of all plans (dummy)
	G_RATEH	Interaction of all product guaranteed issue and rate band for health (continuous variable)
	G_RATEA	Interaction of all product guaranteed issue and rate band for age (continuous variable)
	G_RATEC	Interaction of all product guaranteed issue and composite rate band (continuous variable)
	G_PREEX	Waiting period for coverage of preexisting conditions, in months
	G_HEALTH	Rate band on health (continuous variable)
	G_AGE	Rate band on age (continuous variable)
	G_COMP	Composite rate band (continuous variable)
	GR	Guaranteed renewal (dummy)
Individual market regulation	I_SOMALL	Guaranteed issue of some or all plans (dummy)
	I_GIALL	Guaranteed issue of all plans (dummy)
	I_GR	Guaranteed renewal (dummy)
	RISKDUM	High risk pool (dummy)
	I_COMP	Composite rate band (continuous variable)
	I_HEALTH	Rate band on health (continuous variable)
	I_AGE	Rate band on age (continuous variable)
	I_PREEX	Waiting period for coverage of preexisting conditions, in months
Sociodemographic and employment characteristics	AGEYR	Age in years
	AGEYR2	Age squared
	FAMINC	Family income (in $10,000s)
	FAMINC2	Family income squared
	GEWAGE	Wage of greatest earner (in $10,000s)
	GEWAGE2	Wage of greatest earner squared
	GEFT	Greatest earner works full time
	GSELFEM	Greatest earner is self employed
	IND1	Greatest earner works in agriculture
	IND2	Greatest earner works in mining
	IND3	Greatest earner works in construction
	IND4	Greatest earner works in durable manufacturing
	IND5	Greatest earner works in nondurable manufacturing
	IND6	Greatest earner works in public utilities
	IND7	Greatest earner works in wholesale trade
	IND8	Greatest earner works in retail trade
	IND9	Greatest earner works in finance, insurance and real estate
	IND10	Greatest earner works in business and repair service
	IND11	Greatest earner works in personal services — household
	IND12	Greatest earner works in entertainment and recreation services
	OCC1	Greatest earner works as an executive, administrative or managerial worker
	OCC2	Greatest earner works in a professional specialty
	OCC3	Greatest earner works as a technician and/or related support
	OCC4	Greatest earner works in sales
	OCC5	Greatest earner works in administrative support, including clerical
	OCC6	Greatest earner works in a private household
	OCC7	Greatest earner works in a protective service
	OCC8	Greatest earner works in a service, except protective and household
	OCC9	Greatest earner works in farming, forestry and/or fishing
	OCC10	Greatest earner works in precision production, craft and repair
	OCC11	Greatest earner works as a machine operator, assembler, and/or inspector
	OCC12	Greatest earner works in transport and material moving equipment
	FAMCHILD	Child under age 18 in family
	EDUC1	Greatest earner completed less than 7th grade
	EDUC2	Greatest earner completed 7th or 8th grade
	EDUC3	Greatest earner completed 9th grade
	EDUC4	Greatest earner completed 10th grade
	EDUC5	Greatest earner completed 11th grade
	EDUC6	Greatest earner completed 12th grade
	EDUC7	Greatest earner completed some college
	EDUC8	Greatest earner a college graduate, no graduate education
	FEMALE	Female
	FMARRIED	Married female
	FWIDDIV	Wiowed or divorced female
	FSEP	Separated female
	MARRIED	Married
	WIDDIV	Widowed or divorced
	SEP	Separated
	WHITE	White
	BLACK	Black
	ASIAN	Asian
	HEA1	Excellent health
	HEA2	Very good health
	HEA3	Good health
	HEA4	Fair health
	SIZE2	Greatest earner works in firm with 10-25 workers
	SIZE3	Greatest family earner works in firm with 25-99 workers
Year fixed effects	YEAR96	1996 CPS
	YEAR97	1997 CPS
a - All specifications of the model also include state dummy variables.

Table 2

Regression Results for Small Employer Coverage
Firms with <100 workers
Dependent = Any employer coverage

	Model 1		Model 2		Model 3
Parameter	Estimate	Std Error	Estimate	Std Error	Estimate	Std Error
INTERCEPT	-0.0869	0.0883	0.6041	0.3894	0.3181	0.4128
G_NUMINS	0.0007	0.0007			-0.0002	0.0009
G_TOPFIV	0.0008	0.0008			0.0021 **	0.0010
G_HMO	-0.0417	0.0426	-0.0747 ***	0.0419	-0.0420	0.0446
G_COMM	-0.0246	0.0586	-0.0269	0.0628	-0.0152	0.0633
G_COMLR	-0.0498	0.0394	-0.0327	0.0388	-0.0658	0.0423
GISOMALL			0.0068	0.0166	0.0077	0.0167
G_ALL			0.2130 ***	0.1098	0.2569 **	0.1125
G_RATEH			0.3637	0.2980	0.1447	0.3222
G_RATEA			-0.5013	0.4208	-0.1702	0.4590
G_RATEC			-0.4741 ***	0.2560	-0.4702 ***	0.2560
G_PREEX			0.0001	0.0005	0.0000	0.0006
G_HEALTH			0.0021	0.1175	0.0330	0.1188
G_AGE			0.9076	0.6567	0.5587	0.6889
G_COMP			0.1457 ***	0.0862	0.1724 **	0.0895
GR			-0.6317	0.3878	-0.4986	0.3976
AGEYR	-0.0001	0.0009	-0.0001	0.0009	-0.0001	0.0009
AGEYR2	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
FAMINC	0.0193 *	0.0009	0.0193 *	0.0009	0.0193 *	0.0009
FAMINC2	-0.0004 *	0.0000	-0.0004 *	0.0000	-0.0004 *	0.0000
GEWAGE	0.0375 *	0.0013	0.0375 *	0.0013	0.0376 *	0.0013
GEWAGE2	-0.0010 *	0.0000	-0.0010 *	0.0000	-0.0010 *	0.0000
GEFT	0.0844 *	0.0050	0.0844 *	0.0050	0.0845 *	0.0050
GSELFEM	-0.1145 *	0.0045	-0.1145 *	0.0045	-0.1145 *	0.0045
IND1	-0.0034	0.0140	-0.0034	0.0140	-0.0034	0.0140
IND2	-0.0237	0.0210	-0.0234	0.0210	-0.0231	0.0210
IND3	-0.0705 *	0.0070	-0.0703 *	0.0070	-0.0703 *	0.0070
IND4	0.0021	0.0074	0.0020	0.0074	0.0020	0.0074
IND5	-0.0089	0.0088	-0.0087	0.0088	-0.0087	0.0088
IND6	-0.0330 *	0.0088	-0.0329 *	0.0088	-0.0329 *	0.0088
IND7	0.0162 **	0.0082	0.0162 **	0.0082	0.0164 **	0.0082
IND8	-0.0645 *	0.0063	-0.0644 *	0.0063	-0.0643 *	0.0063
IND9	0.0012	0.0080	0.0012	0.0080	0.0014	0.0080
IND10	-0.0515 *	0.0067	-0.0513 *	0.0067	-0.0513 *	0.0067
IND11	-0.0518 *	0.0099	-0.0520 *	0.0099	-0.0518 *	0.0099
IND12	-0.0495 *	0.0127	-0.0497 *	0.0127	-0.0495 *	0.0127
OCC1	0.0975 *	0.0096	0.0975 *	0.0096	0.0974 *	0.0096
OCC2	0.0841 *	0.0108	0.0840 *	0.0108	0.0841 *	0.0108
OCC3	0.1338 *	0.0138	0.1338 *	0.0138	0.1336 *	0.0138
OCC4	0.0740 *	0.0100	0.0740 *	0.0100	0.0741 *	0.0100
OCC5	0.1140 *	0.0103	0.1139 *	0.0103	0.1140 *	0.0103
OCC6	-0.0190	0.0186	-0.0187	0.0186	-0.0188	0.0186
OCC7	0.0177	0.0243	0.0173	0.0243	0.0175	0.0243
OCC8	-0.0399 *	0.0101	-0.0400 *	0.0101	-0.0400 *	0.0101
OCC 9	-0.0257	0.0160	-0.0260	0.0160	-0.0261	0.0160
OCC10	0.0821 *	0.0092	0.0820 *	0.0092	0.0821 *	0.0092
OCC11	0.0375 *	0.0109	0.0372 *	0.0109	0.0372 *	0.0109
OCC12	0.0550 *	0.0108	0.0546 *	0.0108	0.0546 *	0.0108
FAMCHILD	-0.0143 *	0.0015	-0.0142 *	0.0015	-0.0142 *	0.0015
EDUCG1	-0.1957 *	0.0112	-0.1958 *	0.0112	-0.1957 *	0.0112
EDUCG2	-0.1819 *	0.0125	-0.1818 *	0.0125	-0.1815 *	0.0125
EDUCG3	-0.1510 *	0.0126	-0.1508 *	0.0126	-0.1508 *	0.0126
EDUCG4	-0.0956 *	0.0115	-0.0956 *	0.0115	-0.0957 *	0.0115
EDUCG5	-0.0670 *	0.0098	-0.0670 *	0.0098	-0.0670 *	0.0098
EDUCG6	-0.0149 ***	0.0077	-0.0148 ***	0.0077	-0.0147 ***	0.0077
EDUCG7	0.0218 *	0.0076	0.0218 *	0.0076	0.0218 *	0.0076
EDUCG8	0.0308 *	0.0077	0.0309 *	0.0077	0.0309 *	0.0077
FEMALE	0.0454 *	0.0062	0.0454 *	0.0062	0.0454 *	0.0062
FMAR	-0.0528 *	0.0073	-0.0528 *	0.0073	-0.0528 *	0.0073
FWIDDIV	-0.0194 ***	0.0109	-0.0195 ***	0.0109	-0.0195 ***	0.0109
FSEP	-0.0150	0.0196	-0.0148	0.0196	-0.0150	0.0196
MAR	0.1280 *	0.0057	0.1279 *	0.0057	0.1279 *	0.0057
WIDDIV	0.0115	0.0082	0.0115	0.0082	0.0115	0.0082
SEP	0.0435 *	0.0144	0.0434 *	0.0144	0.0436 *	0.0144
WHITE	0.0590 *	0.0179	0.0589 *	0.0179	0.0590 *	0.0179
BLACK	-0.0087	0.0187	-0.0090	0.0187	-0.0089	0.0187
ASIAN	-0.0079	0.0195	-0.0081	0.0195	-0.0081	0.0195
HEA1	0.1667 *	0.0146	0.1666 *	0.0146	0.1666 *	0.0146
HEA2	0.1482 *	0.0145	0.1481 *	0.0145	0.1480 *	0.0145
HEA3	0.1130 *	0.0146	0.1127 *	0.0146	0.1128 *	0.0146
HEA4	0.0755 *	0.0160	0.0754 *	0.0160	0.0754 *	0.0160
SIZE2	0.0851 *	0.0042	0.0852 *	0.0042	0.0852 *	0.0042
SIZE3	0.1659 *	0.0041	0.1660 *	0.0041	0.1660 *	0.0041
YEAR96	0.0245 *	0.0052	0.0252 *	0.0053	0.0269 *	0.0058
YEAR97	0.0270 *	0.0044	0.0280 *	0.0046	0.0286 *	0.0046
POPMIL	0.0096	0.0100	0.0073	0.0161	0.0120	0.0171
n	80,177		80,177		80,177
R2	0.2524		0.2525		0.2526
F-value	237.16 *		221.69 *		218.16 *

Table 3

Regression Results for Small Employer Coverage
Firms with <25 workers
Dependent = Any employer coverage

	Model 1		Model 2		Model 3
Parameter	Estimate	Std Error	Estimate	Std Error	Estimate	Std Error
INTERCEPT	-0.1073	0.1100	0.7165	0.4803	0.2230	0.5101
G_NUMINS	0.0008	0.0009			-0.0003	0.0011
G_TOPFIV	0.0023 **	0.0010		0.0037 *		0.0013
G_HMO	-0.0577	0.0537	-0.1145 **	0.0528	-0.0578	0.0562
G_COMM	-0.0806	0.0730	-0.0737	0.0783	-0.0497	0.0789
G_COMLR	-0.0653	0.0492	-0.0219	0.0486	-0.0782	0.0529
GISOMALL			0.0038	0.0203	0.0053	0.0204
G_ALL			0.1883	0.1351	0.2646 ***	0.1387
G_RATEH			0.4027	0.3683	0.0245	0.3992
G_RATEA			-0.6137	0.5192	-0.0422	0.5679
G_RATEC			-0.4701	0.3150	-0.4642	0.3150
G_PREEX			-0.0001	0.0007	-0.0003	0.0007
G_HEALTH			0.0454	0.1470	0.0998	0.1486
G_AGE			1.0175	0.8096	0.4169	0.8503
G_COMP			0.0905	0.1066	0.1362	0.1107
GR			-0.6896	0.4783	-0.4613	0.4905
AGEYR	-0.0022 ***	0.0012	-0.0022 ***	0.0012	-0.0022 ***	0.0012
AGEYR2	0.0000 *	0.0000	0.0000 *	0.0000	0.0000 *	0.0000
FAMINC	0.0220 *	0.0011	0.0220 *	0.0011	0.0220 *	0.0011
FAMINC2	-0.0004 *	0.0000	-0.0004 *	0.0000	-0.0004 *	0.0000
GEWAGE	0.0342 *	0.0015	0.0343 *	0.0015	0.0343 *	0.0015
GEWAGE2	-0.0009 *	0.0000	-0.0009 *	0.0000	-0.0009 *	0.0000
GEFT	0.0725 *	0.0058	0.0723 *	0.0058	0.0725 *	0.0058
GSELFEM	-0.1189 *	0.0049	-0.1190 *	0.0049	-0.1189 *	0.0049
IND1	-0.0153	0.0165	-0.0153	0.0165	-0.0155	0.0165
IND2	0.0084	0.0313	0.0074	0.0313	0.0083	0.0313
IND3	-0.0749 *	0.0088	-0.0746 *	0.0088	-0.0750 *	0.0088
IND4	0.0045	0.0105	0.0045	0.0105	0.0043	0.0105
IND5	0.0125	0.0129	0.0127	0.0129	0.0125	0.0129
IND6	-0.0464 *	0.0116	-0.0463 *	0.0116	-0.0464 *	0.0116
IND7	0.0028	0.0107	0.0026	0.0107	0.0026	0.0107
IND8	-0.0802 *	0.0080	-0.0801 *	0.0080	-0.0802 *	0.0080
IND9	-0.0005	0.0101	-0.0005	0.0101	-0.0004	0.0101
IND10	-0.0474 *	0.0083	-0.0470 *	0.0083	-0.0473 *	0.0083
IND11	-0.0678 *	0.0116	-0.0682 *	0.0116	-0.0680 *	0.0116
IND12	-0.0532 *	0.0157	-0.0541 *	0.0157	-0.0535 *	0.0157
OCC1	0.1014 *	0.0124	0.1015 *	0.0124	0.1013 *	0.0124
OCC2	0.0866 *	0.0139	0.0868 *	0.0139	0.0865 *	0.0139
OCC3	0.1298 *	0.0180	0.1301 *	0.0181	0.1299 *	0.0181
OCC4	0.0860 *	0.0128	0.0862 *	0.0129	0.0860 *	0.0129
OCC5	0.1126 *	0.0135	0.1126 *	0.0135	0.1126 *	0.0135
OCC6	-0.0054	0.0208	-0.0047	0.0208	-0.0051	0.0208
OCC7	0.1324 *	0.0352	0.1319 *	0.0352	0.1315 *	0.0352
OCC8	-0.0224 ***	0.0130	-0.0224 ***	0.0130	-0.0224 ***	0.0130
OCC9	-0.0063	0.0195	-0.0063	0.0195	-0.0063	0.0195
OCC10	0.0794 *	0.0117	0.0794 *	0.0118	0.0794 *	0.0118
OCC11	0.0298 **	0.0151	0.0298 **	0.0151	0.0296 **	0.0151
OCC12	0.0516 *	0.0141	0.0515 *	0.0141	0.0514 *	0.0141
FAMCHILD	-0.0144 *	0.0019	-0.0144 *	0.0019	-0.0144 *	0.0019
EDUCG1	-0.1822 *	0.0139	-0.1820 *	0.0139	-0.1820 *	0.0139
EDUCG2	-0.1821 *	0.0152	-0.1820 *	0.0153	-0.1814 *	0.0153
EDUCG3	-0.1492 *	0.0156	-0.1488 *	0.0156	-0.1489 *	0.0156
EDUCG4	-0.1005 *	0.0142	-0.1002 *	0.0142	-0.1004 *	0.0142
EDUCG5	-0.0673 *	0.0121	-0.0670 *	0.0121	-0.0673 *	0.0121
EDUCG6	-0.0217 **	0.0095	-0.0215 **	0.0095	-0.0215 **	0.0095
EDUCG7	0.0206 **	0.0094	0.0207 **	0.0094	0.0206 **	0.0094
EDUCG8	0.0349 *	0.0095	0.0351 *	0.0095	0.0350 *	0.0095
FEMALE	0.0463 *	0.0079	0.0462 *	0.0079	0.0463 *	0.0079
FMAR	-0.0482 *	0.0093	-0.0481 *	0.0093	-0.0481 *	0.0093
FWIDDIV	-0.0258 ***	0.0134	-0.0259 ***	0.0134	-0.0260 ***	0.0134
FSEP	-0.0170	0.0244	-0.0167	0.0244	-0.0168	0.0244
MAR	0.1255 *	0.0071	0.1254 *	0.0071	0.1254 *	0.0071
WIDDIV	0.0025	0.0100	0.0025	0.0100	0.0027	0.0100
SEP	0.0633 *	0.0178	0.0631 *	0.0178	0.0632 *	0.0178
WHITE	0.0747 *	0.0224	0.0751 *	0.0224	0.0748 *	0.0224
BLACK	0.0065	0.0235	0.0068	0.0235	0.0067	0.0235
ASIAN	0.0003	0.0244	0.0006	0.0244	0.0003	0.0244
HEA1	0.1593 *	0.0176	0.1591 *	0.0176	0.1591 *	0.0176
HEA2	0.1332 *	0.0174	0.1332 *	0.0174	0.1330 *	0.0174
HEA3	0.0980 *	0.0176	0.0978 *	0.0176	0.0978 *	0.0176
HEA4	0.0508 *	0.0193	0.0506 *	0.0193	0.0505 *	0.0193
SIZE2	0.0851 *	0.0044	0.0851 *	0.0044	0.0851 *	0.0044
YEAR96	0.0188 *	0.0066	0.0176 *	0.0068	0.0204 *	0.0073
YEAR97	0.0267 *	0.0056	0.0271 *	0.0058	0.0282 *	0.0059
POPMIL	-0.0071	0.0125	0.0006	0.0202	0.0088	0.0214
n	54,248		54,248		54,248
R2	0.2196		0.2196		0.2197
F-value	134.82 *		125.89 *		123.93 *

Table 4

Regression Results for Individual Private Coverage
Dependent = Private (Individual) Coverage

	Model 1		Model 2		Model 3
Parameter	Estimate	Std Error	Estimate	Std Error	Estimate	Std Error
INTERCEPT	0.0611	0.1253	0.1472 **	0.0599	0.0539	0.1304
I_NUMINS	-0.0008	0.0012	-0.0015	0.0009	-0.0008	0.0012
I_TOPFIV	0.0009	0.0010			0.0009	0.0010
I_HMO	0.0358	0.0370	0.0208	0.0332	0.0277	0.0380
I_COMM	0.0411	0.0572			0.0200	0.0607
I_COMLR	-0.0140	0.0131	-0.0130 ***	0.0132	-0.0125	0.0133
I_SOMALL			0.0333	0.0326	0.0318	0.0330
I_GIALL			-0.0944 ***	0.0541	-0.0949 ***	0.0551
I_GR			0.0105	0.0128	0.0112	0.0128
RISKDUM	-0.0334	0.0229	-0.0329	0.2859	-0.0332	0.0230
I_COMP			-0.0058	0.0295	-0.0051	0.2864
I_HEALTH			0.0027	0.3036	0.0039	0.0298
I_AGE			0.0519	0.0002	0.0563	0.3040
I_PREEX			0.0002	0.0010	0.0002	0.0002
AGEYR	-0.0038 *	0.0010	-0.0037 *	0.0000	-0.0038 *	0.0010
AGEYR2	0.0001 *	0.0000	0.0001 *	0.0011	0.0001 *	0.0000
FAMINC	0.0222 *	0.0011	0.0221 *	0.0000	0.0222 *	0.0011
FAMINC2	-0.0004 *	0.0000	-0.0004 *	0.0026	-0.0004 *	0.0000
SGEWAGE	0.0041	0.0026	0.0041	0.0001	0.0041	0.0026
SGEWAGE2	-0.0002 **	0.0001	-0.0002 **	0.0119	-0.0002 **	0.0001
SGEFT	0.0965 *	0.0119	0.0965 *	0.0497	0.0965 *	0.0119
GSELFEM	0.1029 **	0.0497	0.1045 **	0.0379	0.1040 **	0.0497
SIND1	0.1745 *	0.0379	0.1745 *	0.0649	0.1744 *	0.0379
SIND2	0.2530 *	0.0649	0.2531 *	0.0209	0.2534 *	0.0649
SIND3	0.0563 *	0.0209	0.0560 *	0.0270	0.0562 *	0.0209
SIND4	0.0608 **	0.0270	0.0606 **	0.0332	0.0609 **	0.0270
SIND5	0.1306 *	0.0332	0.1298 *	0.0284	0.1303 *	0.0332
SIND6	0.0253	0.0284	0.0250	0.0279	0.0254	0.0284
SIND7	0.0815 *	0.0279	0.0807 *	0.0207	0.0810 *	0.0279
SIND8	0.0547 *	0.0207	0.0546 *	0.0253	0.0548 *	0.0207
SIND9	0.0432 ***	0.0253	0.0429 ***	0.0195	0.0433	*** 0.0253
SIND10	0.0407 **	0.0195	0.0405 **	0.0243	0.0407 **	0.0195
SIND11	0.1468 *	0.0243	0.1462 *	0.0302	0.1463 *	0.0243
SIND12	0.0609 **	0.0302	0.0608 **	0.0479	0.0606 **	0.0302
SOCC1	-0.0415	0.0479	-0.0427	0.0499	-0.0425	0.0479
SOCC2	-0.0401	0.0499	-0.0416	0.0664	-0.0411	0.0499
SOCC3	0.0493	0.0664	0.0483	0.0487	0.0490	0.0664
SOCC4	-0.0211	0.0487	-0.0224	0.0516	-0.0223	0.0487
SOCC5	-0.0088	0.0516	-0.0098		-0.0096	0.0516
SOCC7	-0.2070 ***	0.1074	-0.2074 ***	0.0500	-0.2081 ***	0.1074
SOCC8	-0.1128 **	0.0500	-0.1143 **	0.0589	-0.1139 **	0.0500
SOCC9	-0.0567	0.0589	-0.0576	0.0483	-0.0570	0.0589
SOCC10	-0.0975 **	0.0483	-0.0987 **	0.0558	-0.0984 **	0.0483
SOCC11	-0.0702	0.0558	-0.0713	0.0516	-0.0709	0.0558
SOCC12	-0.0406	0.0516	-0.0423	0.0020	-0.0421	0.0516
GEWAGE	-0.0069 *	0.0020	-0.0069 *	0.0001	-0.0069 *	0.0020
GEWAGE2	0.0001	0.0001	0.0001	0.0055	0.0001	0.0001
GEFT	-0.0464 *	0.0055	-0.0465 *	0.0180	-0.0465 *	0.0055
IND1	-0.0357 **	0.0180	-0.0359 **	0.0267	-0.0359 **	0.0180
IND2	-0.0638 **	0.0267	-0.0649 **	0.0093	-0.0650 **	0.0267
IND3	-0.0526 *	0.0093	-0.0527 *	0.0091	-0.0527 *	0.0093
IND4	-0.0515 *	0.0091	-0.0514 *	0.0098	-0.0514 *	0.0091
IND5	-0.0520 *	0.0098	-0.0521 *	0.0102	-0.0521 *	0.0098
IND6	-0.0598 *	0.0102	-0.0599 *	0.0114	-0.0599 *	0.0102
IND7	-0.0256 **	0.0114	-0.0254 **	0.0073	-0.0254 **	0.0114
IND8	-0.0393 *	0.0073	-0.0393 *	0.0107	-0.0393 *	0.0073
IND9	0.0009	0.0107	0.0010	0.0087	0.0009	0.0107
IND10	-0.0374 *	0.0087	-0.0373 *	0.0109	-0.0373 *	0.0087
IND11	-0.0361 *	0.0109	-0.0358 *	0.0149	-0.0358 *	0.0109
IND12	-0.0141	0.0149	-0.0140	0.0105	-0.0140	0.0149
OCC1	0.0148	0.0105	0.0147	0.0116	0.0147	0.0105
OCC2	0.0356 *	0.0116	0.0357 *	0.0156	0.0357 *	0.0116
OCC3	0.0391 **	0.0156	0.0391 **	0.0099	0.0392 **	0.0156
OCC4	0.0348 *	0.0099	0.0348 *	0.0100	0.0349 *	0.0099
OCC5	0.0246 **	0.0100	0.0246 **	0.0187	0.0246 **	0.0100
OCC6	-0.0033	0.0187	-0.0035	0.0219	-0.0035	0.0187
OCC7	-0.0273	0.0219	-0.0278	0.0093	-0.0274	0.0219
OCC8	0.0007	0.0093	0.0007	0.0180	0.0007	0.0093
OCC9	0.0112	0.0180	0.0113	0.0089	0.0113	0.0180
OCC10	0.0111	0.0089	0.0110	0.0103	0.0111	0.0089
OCC11	-0.0096	0.0103	-0.0097	0.0108	-0.0096	0.0103
OCC12	-0.0051	0.0108	-0.0049	0.0017	-0.0049	0.0108
FAMCHILD	-0.0019	0.0017	-0.0019	0.0126	-0.0019	0.0017
EDUCG1	-0.2611 *	0.0126	-0.2611 *	0.0136	-0.2610 *	0.0126
EDUCG2	-0.2463 *	0.0136	-0.2466 *	0.0138	-0.2466 *	0.0136
EDUCG3	-0.2232 *	0.0138	-0.2232 *	0.0131	-0.2232 *	0.0138
EDUCG4	-0.2125 *	0.0131	-0.2124 *	0.0120	-0.2124 *	0.0131
EDUCG5	-0.1924 *	0.0120	-0.1923 *	0.0105	-0.1924 *	0.0120
EDUCG6	-0.1788 *	0.0105	-0.1787 *	0.0105	-0.1787 *	0.0105
EDUCG7	-0.1268 *	0.0105	-0.1268 *	0.0108	-0.1268 *	0.0105
EDUCG8	-0.0564 *	0.0108	-0.0565 *		-0.0565 *	0.0108
FEMALE	0.0137 **	0.0058	0.0137 **	0.0058	0.0137 **	0.0058
FMAR	-0.0028 *	0.0076	-0.0028	0.0076	-0.0028	0.0076
FWIDDIV	0.0415 *	0.0107	0.0414 *	0.0107	0.0414 *	0.0107
FSEP	-0.0248	0.0187	-0.0252	0.0187	-0.0252	0.0187
MAR	0.0480 *	0.0058	0.0478 *	0.0058	0.0479 *	0.0058
WIDDIV	-0.0268 *	0.0080	-0.0269 *	0.0080	-0.0269 *	0.0080
SEP	-0.0186	0.0133	-0.0187	0.0133	-0.0186	0.0133
WHITE	0.0732 *	0.0186	0.0734 *	0.0186	0.0734 *	0.0186
BLACK	0.0150	0.0192	0.0151	0.0192	0.0152	0.0192
ASIAN	0.0056	0.0201	0.0057	0.0201	0.0056	0.0201
HEA1	0.1277 *	0.0149	0.1279 *	0.0149	0.1278 *	0.0149
HEA2	0.0957 *	0.0148	0.0958 *	0.0148	0.0957 *	0.0148
HEA3	0.0676 *	0.0149	0.0676 *	0.0149	0.0675 *	0.0149
HEA4	0.0204	0.0163	0.0203	0.0163	0.0202	0.0163
YEAR96	0.0265 *	0.0065	0.0271 *	0.0070	0.0269 *	0.0070
YEAR97	0.0171 *	0.0052	0.0187 *	0.0054	0.0183 *	0.0054
POPMIL	0.0064	0.0115	0.0093	0.0101	0.0050	0.0118
n	44905		44905		44905
R2	0.2241		0.2242		0.2242
F-value	93.02 *		89.82 *		88.60 *

Appendices

Appendix 1

Recent Empirical Studies of the Effects of State Health Insurance Reform

Author/Title	Reforms considered	Data and methods	Findings
Buchmueller, T.C. and G.A. Jensen (Fall 1997). “Small Group Reform in a Competitive Managed Care Market: The Case of California, 1993-1995.” Inquiry 34: 249-263.	Small group reforms in combination: Guaranteed issue (GI) Guaranteed renewal (GR) Preexisting condition exclusion (PreX) limits Prohibited rate factors Limits on rating HIPC	Data: Employer surveys conducted for UC-Irvine; sample included only independent firms with 3-99 employees. Estimation: Examination of changes in percent of small firms offering health insurance and change in premiums. (Supporting multinomial logit analysis not reported.) Dependent variable(s): employer offer of insurance, group premium.	Employer familiarity with reforms was moderate (56-57 percent of firms; 59-62 percent of firms offering insurance); 24 percent were aware of HIPC. High growth in insurance offer among firms with 3-9 employees (significant at 0.01) was probably related to reforms. No change among larger firms; no impact on median premium. Increase in managed care relative to indemnity insurance was potentially due to reforms. Decline in highest premiums paid for coverage was likely due to reforms (note: data included no control for plan design).
Buchmueller, T.C. and J. DiNardo (September 1998). “Did Community Rating Induce an Adverse Selection Death Spiral? Evidence from New York, Pennsylvania and Connecticut.” Working paper, University of California at Irvine.	Small group reforms: Pure community rating GI	Data: 1987-1996 Current Population Survey in three states (1986-1995 coverage). Estimation: Difference in difference (DD) and difference-in-difference-in difference (DDD) estimation. Control groups were workers in PA and CT, and workers in large firms in all states. Dependent variable(s): Rate of group health coverage; prevalence of HMO vs. indemnity coverage.	NY’s small-group market reforms were not associated w/ decreased coverage relative to PA or CT, states that did not have community rating laws. Reforms altered structure of the small group market, increasing managed care penetration relative to PA and CT.
Schriver, M.L. and G.M. Arnett (August 1998). “Uninsured Rates Rise Dramatically in States with Strictest Health Insurance Regulations.” Backgrounder (Washington, DC: The Heritage Foundation).	States that had passed any type of small-group and any type of individual-market reform by 1995 (n=16)	Data: Calculations based on 1991-1997 CPS (1990- 1996 coverage). Estimation: Observation of differences between state groups (without control variables or tests of significance). Dependent variable(s): Percent of the state’s nonelderly population that is uninsured.	Uninsured rate grew faster or declined less in reform states between 1990 and 1996 Employer-based coverage declined in reform states, but grew slightly in nonreform states. Individual coverage declined faster in reform states than in nonreform states. Medicaid coverage grew faster in reform states than in nonreform states.
Sloan, F.A. and C.J. Conover (Fall 1998). “Effects of State Reforms on Health Insurance Coverage of Adults.” Inquiry 35: 280-293.	Number of mandates Mandate waiver Employer tax credit MSAs High risk pool Mandatory, voluntary reinsurance Purchasing alliances GI, GR (all products, some products) PreX limitations Rate limits (for health status, age, gender)	Data: 1990-95 Current Population Survey, persons aged 18-64, unduplicated persons (1989-1994 coverage) Estimation: OLS (private and group coverage specifications contingent on any and private coverage, respectively) Dependent variable(s): Among the nonelderly population: probability of being insured (private or public), probability of private coverage contingent on any coverage, probability of employer group coverage contingent on private coverage.	Greater number of mandates reduced private coverage, and group coverage in particular. Small-group community rating raised the likelihood of group coverage among persons over age 55 Individual community rating reduced the likelihood of any private coverage Limits on individual PreX increased likelihood of any coverage (public or private) among single persons.
Marsteller, J.A., L.M. Nichols, A. Badawi et al. (June 1998). “Variations in the Uninsured: State and County Level Analyses” (Washington, DC: Urban Institute).	Small group access reform combinations of GI, GR, PreX and portability Small group rating restrictions Individual GI and rating restrictions Any individual market reform Benefit mandates for drug and alcohol treatment	Data: 1990-1996 Current Population Survey (1989- 1995 coverage) Estimation: Logit with state fixed effects. Dependent variables are ESI, private coverage, overall coverage. Dependent variable(s): Percent of the state’s nonelderly population that is uninsured.	Small group access reforms (GI, GR, PreX limits or portability) were associated with higher rates of coverage overall. But only when all were present did they increase rates of private coverage. Group premium restrictions were associated with lower rates of private coverage and lower rates of coverage overall. When both access and rating reforms are present, magnitude of effects were fully offsetting. Individual market GI and rating reforms were associated with lower rates of coverage overall. Results related to private coverage were consistent but weak. Benefit mandate for drug and alcohol treatment decreased coverage overall (but statistical significance of this result was weak). Presence of an open high risk pool increased coverage.
Hing, E. and G.A. Jensen (September 1998). “Health Insurance Portability and Accountability Act of 1996: Lessons from the States.” Medical Care 37(7): 692-705.	Small group reforms: HIPAA+rate regulation, recent HIPAA+rate regulation, 2+years HIPAA only, recent HIPAA only, 2+years Also: Number of mandated benefits Mandate waiver	Data: 1994 National Employer Health Insurance Survey (NEHIS): 17,818 private establishments employing 1-50 or fewer workers (self-insured and Hawaii excluded); 1993 coverage and worker characteristics; all firms and separately, “red-lined” firms (establishment reported that insurer could refuse to cover within plan) Estimation: Logit and regression models. Dependent variable(s): Employer offer of insurance, proportion of employees enrolled	Likelihood of firms offering coverage greater if full or partial reform states (greater effect from earlier vs. recent reforms) Among very small firms (1-9 employees) more recent reforms (typically rating reforms, no GI) reduced probability of offer. Percent of employees covered within offering firms lower in full or partial reform states (regardless of duration of reforms) Among red-lined firms, reforms (early/recent, full/partial) raised probability of coverage more than among all firms. Full or partial reforms decreased employee enrollment among establishments offering insurance (but mean value lower in reform states). Greater number of mandated benefits increased employee enrollment, for all small firms and for the smallest firms (1-9). Reforms generally reduced reported redlining, especially among very small firms, however, rating reforms without GI (recent partial reforms) increased redlining.
Monheit, A. and B.S. Schone (1998). “How Has Small Group Market Reform Affected Employee Health Insurance Coverage?” (AHCPR revised working paper as reported in Simon (1999a).	Alternative configurations of reform “packages” including: GI GR Community rating vs. rate bands Any reform vs. no reform	Data: 1987 NMES and 1996 MEPS Estimation: DD Dependent variable(s): Employer offer of insurance.	Community rating increases insurance offer rates. The presence of any reform increases offer rates among young single workers, but reduces offer rates among high-risk groups.
Jensen, Gail A. and M.A. Morrisey (Summer 1999). “Small Group Reform and Insurance Provision by Small Firms, 1989-1995.” Inquiry 36: 176-187	Separate reforms: GI GR Portability PreX limits Mandated benefit waiver Number of mandated benefits All reforms in combination (including rate reform)	Data: HIAA and KPMG Peat Marwick surveys of employers. Pooled cross-sections totaled 2,472 unique businesses with fewer than 50 employees, from 1989 to 1995. Estimation: Logit model, estimated for separately for firms with fewer than 10 employees and 10 or more employees. Analysis omitted rate reform from the specific regulation analysis to resolve multicollinearity. Dependent variable(s): Employer offer of insurance.	Only PreX restrictions affected firms’ decision to offer insurance, raising the likelihood of offer (significant at 0.10). # of mandated benefits significant and reduced firms’ offer of insurance (0.05). Effect was insignificant for firms of 1-9.
Zuckerman, S. and S. Rajan (Spring 1999). “An Alternative Approach to Measuring the Effects of Insurance Market Reforms.” Inquiry 36: 44-56.	Separate small group reforms: GI GR Rating restrictions PreX restrictions Portability Small group reform combinations: All five reforms All reforms but GI GI and rating restrictions Other combinations Separate individual market reforms: GI GR Rating restrictions PreX restrictions Individual reform combinations: All reforms Other combinations	Data: 1990-1996 Current Population Survey (1989- 1995 coverage) Estimation: OLS regression Dependent variable(s): Percent of the state’s nonelderly population that is: uninsured privately insured Medicaid	No combination of small-group reforms had an effect on coverage (coverage rate fell with comprehensive reforms and with GI and rating reforms, but these results were statistically insignificant). Comprehensive individual market reforms (in any combination) were associated with lower coverage rates overall, and lower private coverage rates in particular. Coefficients were quantitatively large. No small group reform, estimated individually, was significantly related to private coverage or coverage overall. Individual market GI, estimated individually, decreased coverage overall, but had no significant effect on private coverage.
Simon, K.I. (March 1999a). “Did Small-Group Health Insurance Reforms Work?” (unpublished).	Full reform Partial reform (excluding “portability only”) Bare bones law only	Data: 1992-1997 Current Population Survey (1991- 1996 coverage), full-time workers aged 16-65 employed in private establishments and who worked at least 13 weeks during the year. Sample excluded persons living in Hawaii. Estimation: DDD with alternative specifications (linear and probit), large-group employees as a control group Dependent variable(s): Probability of employer coverage	Full group reforms decreased coverage rates among small-firm workers, especially among low-risk (single men under age 36) workers, even when age and gender are allowed rating factors. Full group reforms increased coverage rates among some high- risk workers (e.g., married women of childbearing age with children). Constraints on rating by age and gender magnify the negative effects of other group reforms. Partial group reforms had a negative but usually insignificant impact on group coverage. Bare bones laws had no significant impact on coverage.
Simon, K.I. (October 1999b). “The Impact of Small-Group Health Insurance Reform on the Price and Availability of Health Benefits” (unpublished).	Full reform (both GI and rating reform) Partial reform (rating reform but no GI)	Data: 1993 National Employer Health Insurance Survey (NEHIS) and 1996 Medical Expenditure Panel Survey Insurance Component (MEPS-IC). Data exclude self-insured small groups and reflect only the largest-enrollment plan among employers that offered multiple plans. Estimation: matrix-algebra calculation of OLS coefficients and DD method comparing health insurance outcomes in small states that reformed before and after reform. (Method reflects confidentiality constraints on use of NEHIS and MEPS-IC.) Dependent variable(s): Premiums, employee contributions, employer offer, employee eligibility, medical underwriting, conversion to HMO coverage, conversion to self-insurance	Full reform increased premiums (by about 4 percent), and most of the increase was passed on to workers as increased employee contributions for coverage. Full reform decreased the rate of employer coverage (by more than 2 percentage points), but did not decrease the rate of employer offer. Full reform decreased the probability that small-group plans could exclude selected individuals or impose a PreX waiting period (by about 5 percentage points). Partial reform had no significant impact on offer or coverage, but was weakly associated with lower premiums and lower employee contributions in high risk firms (variously defined). No compelling evidence that regulation increased the prevalence of managed care or self-insured plans.
Browne, M. J. and E.W. Frees (January 2000). “Prohibitions on Health Insurance Underwriting: A Means of Making Health Insurance Available or a Cause of Market Failure?” Working paper (University of Wisconsin, Madison).	Separate small group rating reforms for: gender age health status Any restriction on underwriting (including rate reforms or GI/GR)	Data: 1989-1995 Current Population Survey (1988- 1994 coverage). Single-person households aged 15-64; all persons and persons employed in firms of less than 10. Estimation: Multinomial logit, fixed effects (year and state). Dependent variable(s): Probability of group coverage vs. no coverage; probability of group coverage vs. individual coverage	Limitations on health rating reduced group coverage among nondisabled persons and raised group coverage among persons who reported disability (i.e., self-reported condition limiting the respondent’s type or amount of work). Limitations on gender rating increased group coverage among women and reduced group coverage among men. Limitations on age rating raised group coverage among older workers. Persons with disability, older workers and women were more likely to have public insurance in states that prohibit underwriting on health, age, gender. Effects insignificant w/ respect to individual coverage.
Chollet, D.J., A.M. Kirk and K.I. Simon (June 2000). “The Impact of Access Regulation on Health Insurance Market Structure.” Draft Report to the Office of the Asst. Secretary for Planning and Evaluation (DHHS)	Small group market reforms: PreX limits GI (some/all products) GI (all products) GR Rating restrictions on age, health, composite Individual market reforms: PreX limits GI (some/all products) GI (all products) GR Rating restrictions on age, health High risk pool	Data: 1995-1997 Health Insurer Database (HMO, BCBS and commercial carrier state filings; supplemental survey of commercial insurers). Estimation: OLS regression; group market and individual market models estimated separately. Dependent variable(s): Number of insurers, BCBS market share, HMO market share, commercial insurer market share, market concentration (Herfindahl, percent of market held by largest 5 insurers), commercial insurer loss ratios.	Group market: All-product GI was associated with more insurers in the market and less market concentration. Shorter PreX waiting periods were associated with greater market concentration. Tighter composite rate bands (compared to looser or no composite rate bands) were associated with more insurers in the market, but tighter health rate bands fully offset this effect. On net, a state with full community rating had about the same number of insurers as states with no constraints on rating, all else equal. None of the forms of regulation investigated affected HMO, BCBS or commercial market share. Individual market: Some-product GI was associated with greater market concentration, but all-product GI drove less concentration and lower commercial insurer market share. On net, all-product GI states had much less concentrated markets (but not significantly more insurers) than some-product GI states, all else equal. Tighter rate bands on health were associated with greater BCBS market share, lower commercial market share and greater market concentration.
Kaestner, R. and K.I. Simon (2000). “Labor Market Consequences of State Health Insurance Regulation.” Working paper.	Number of mandated benefits, collectively in “high-cost” categories Full reform (GI, rating, GR, PreX and portability) Partial reform (rating but no GI)	Data: 1989-1998 Current Population Survey (1988- 1997 coverage). Employees in firms with fewer than 100 workers or 25 workers, variously. Estimation: OLS regression. Dependent variable(s): Hours worked per week; weeks worked per year; hourly wages; employment in a small v. large firm; private coverage; coverage from own job.	Full reform associated with slight decline in employer coverage among workers in firms with fewer than 25 employees. Partial reform reduces coverage among groups “vulnerable” to insurance loss: low-educated employees and young, unmarried employees without children. Small group reforms are unrelated to other labor market outcomes.

Appendix 2

Small Employer Coverage
Descriptive Statistics

Variable	Mean	Std Deviation	Min	Max
ER	0.5628	0.4892	0.0000	1.0000
G_NUMINS	64.0709	17.7127	13.0000	94.0000
G_TOPFIV	63.4946	12.4153	37.8000	97.0000
G_HMO	0.1465	0.1595	0.0000	0.7317
G_COMM	0.4379	0.1040	0.0352	0.6553
G_COMLR	0.3529	0.0582	0.4654	0.9069
GISOMALL	0.7461	0.4484	0.0000	1.0000
G_ALL	0.7080	0.4906	0.0000	1.0000
GR	0.4501	0.2915	0.0000	1.0000
G_RATEH	0.9033	0.4319	0.0000	1.0000
G_RATEA	0.5056	0.2681	0.0000	1.0000
G_RATEC	0.1180	0.3377	0.0000	1.0000
G_HEALTH	0.2512	0.3184	0.0000	1.0000
G_AGE	0.6029	0.2714	0.0000	1.0000
G_COMP	0.1267	0.3348	0.0000	1.0000
G_PREEX	0.2819	16.2605	0.0000	72.0000
AGEYR	14.5198	11.9765	18.0000	64.0000
AGEYR2	38.1350	958.9352	324.0000	4096.0000
FAMINC	1601.7600	5.7221	-1.7498	83.7825
FAMINC2	5.2783	205.2309	0.0000	7019.5100
GEWAGE	61.5265	4.3318	0.0000	57.6372
GEWAGE2	3.1670	131.6683	0.0000	3322.0500
GEFT	0.8749	0.3263	0.0000	1.0000
GSELFEM	0.8749	0.4117	0.0000	1.0000
IND1	0.2248	0.2203	0.0000	1.0000
IND2	0.0527	0.0736	0.0000	1.0000
IND3	0.0056	0.3459	0.0000	1.0000
IND4	0.1436	0.2764	0.0000	1.0000
IND5	0.0860	0.2117	0.0000	1.0000
IND6	0.0484	0.2159	0.0000	1.0000
IND7	0.0505	0.2346	0.0000	1.0000
IND8	0.0602	0.3697	0.0000	1.0000
IND9	0.1691	0.2272	0.0000	1.0000
IND10	0.0562	0.2886	0.0000	1.0000
IND11	0.0946	0.1986	0.0000	1.0000
IND12	0.0423	0.1242	0.0000	1.0000
OCC1	0.0161	0.3671	0.0000	1.0000
OCC2	0.1662	0.3023	0.0000	1.0000
OCC3	0.1050	0.1448	0.0000	1.0000
OCC4	0.0220	0.3388	0.0000	1.0000
OCC5	0.1367	0.2633	0.0000	1.0000
OCC6	0.0773	0.1089	0.0000	1.0000
OCC7	0.0123	0.0663	0.0000	1.0000
OCC8	0.0045	0.2972	0.0000	1.0000
OCC9	0.1010	0.2163	0.0000	1.0000
OCC10	0.0507	0.3751	0.0000	1.0000
OCC11	0.1754	0.2305	0.0000	1.0000
OCC12	0.0580	0.2288	0.0000	1.0000
EDUC1	0.0571	0.1882	0.0000	1.0000
EDUC2	0.0378	0.1501	0.0000	1.0000
EDUC3	0.0237	0.1485	0.0000	1.0000
EDUC4	0.0232	0.1706	0.0000	1.0000
EDUC5	0.0309	0.2360	0.0000	1.0000
EDUC6	0.0610	0.4704	0.0000	1.0000
EDUC7	0.3502	0.4376	0.0000	1.0000
EDUC8	0.2696	0.3455	0.0000	1.0000
HEA1	0.1433	0.4416	0.0000	1.0000
HEA2	0.2776	0.4931	0.0000	1.0000
HEA3	0.5007	0.3704	0.0000	1.0000
HEA4	0.1700	0.1935	0.0000	1.0000
FAMCHILD	0.0401	1.1387	0.0000	9.0000
FEMALE	0.8486	0.4928	0.0000	1.0000
FMAR	0.4840	0.4528	0.0000	1.0000
FSEP	0.3019	0.1176	0.0000	1.0000
MAR	0.0144	0.4840	0.0000	1.0000
WIDDIV	0.5955	0.3159	0.0000	1.0000
SEP	0.1161	0.1593	0.0000	1.0000
WHITE	0.0268	0.3288	0.0000	1.0000
BLACK	0.8726	0.2692	0.0000	1.0000
ASIAN	0.0811	0.1906	0.0000	1.0000
SIZE2	0.0389	0.4140	0.0000	1.0000
SIZE3	0.2284	0.4625	0.0000	1.0000
YEAR96	0.3267	0.4641	0.0000	1.0000
YEAR97	0.3310	0.4660	0.0000	1.0000
POPMIL	0.3365	8.5322	0.4163	29.4019

Appendix 3

Individual Private Coverage
Descriptive Statistics

Variable	Mean	Std Deviation	Min	Max
INDIV	0.1971	0.3925	0.0000	1.0000
I_NUMINS	22.6511	9.5893	2.0000	42.0000
I_TOPFIV	82.9843	8.3406	67.0000	100.0000
I_HMO	0.2176	0.1987	0.0000	0.6482
I_COMM
I_COMLR	0.7102	0.2217	0.1658	2.1058
I_SOMALL	0.1762	0.3759	0.0000	1.0000
I_GIALL	0.1372	0.3394	0.0000	1.0000
I_GR	0.2673	0.4366	0.0000	1.0000
RISKDUM	0.4803	0.4929	0.0000	1.0000
I_HEALTH	0.1756	0.3586	0.0000	1.0000
I_AGE	0.1286	0.3059	0.0000	1.0000
I_PREEX	31.6422	25.5597	3.0000	72.0000
AGEYR	36.0030	12.0485	18.0000	64.0000
AGEYR2	1445.3800	945.9810	324.0000	4096.0000
FAMINC	3.8811	4.4668	-1.7498	61.2428
FAMINC2	35.5639	141.0197	0.0000	3750.6800
GEWAGE	2.1728	3.1443	0.0000	57.6372
GEWAGE2	14.8796	87.6686	0.0000	3322.0500
GEFT	0.8355	0.3657	0.0000	1.0000
GSELFEM	0.1963	0.3919	0.0000	1.0000
IND1	0.0578	0.2302	0.0000	1.0000
IND2	0.0050	0.0698	0.0000	1.0000
IND3	0.1257	0.3270	0.0000	1.0000
IND4	0.0824	0.2712	0.0000	1.0000
IND5	0.0623	0.2385	0.0000	1.0000
IND6	0.0588	0.2321	0.0000	1.0000
IND7	0.0406	0.1948	0.0000	1.0000
IND8	0.2159	0.4059	0.0000	1.0000
IND9	0.0468	0.2083	0.0000	1.0000
IND10	0.0926	0.2860	0.0000	1.0000
IND11	0.0566	0.2279	0.0000	1.0000
IND12	0.0193	0.1357	0.0000	1.0000
OCC1	0.1071	0.3051	0.0000	1.0000
OCC2	0.0766	0.2623	0.0000	1.0000
OCC3	0.0177	0.1302	0.0000	1.0000
OCC4	0.1312	0.3330	0.0000	1.0000
OCC5	0.0803	0.2681	0.0000	1.0000
OCC6	0.0140	0.1161	0.0000	1.0000
OCC7	0.0072	0.0837	0.0000	1.0000
OCC8	0.1543	0.3564	0.0000	1.0000
OCC9	0.0590	0.2325	0.0000	1.0000
OCC10	0.1621	0.3635	0.0000	1.0000
OCC11	0.0765	0.2622	0.0000	1.0000
OCC12	0.0612	0.2365	0.0000	1.0000
SGEWAGE	0.4388	2.1938	0.0000	39.1163
SGEWAGE2	5.1375	56.4463	0.0000	1530.0800
SGEFT	0.1639	0.3652	0.0000	1.0000
SIND1	0.0223	0.1458	0.0000	1.0000
SIND2	0.0008	0.0285	0.0000	1.0000
SIND3	0.0367	0.1855	0.0000	1.0000
SIND4	0.0066	0.0796	0.0000	1.0000
SIND5	0.0035	0.0580	0.0000	1.0000
SIND6	0.0113	0.1044	0.0000	1.0000
SIND7	0.0089	0.0928	0.0000	1.0000
SIND8	0.0317	0.1728	0.0000	1.0000
SIND9	0.0119	0.1070	0.0000	1.0000
SIND10	0.0228	0.1474	0.0000	1.0000
SIND11	0.0099	0.0975	0.0000	1.0000
SIND12	0.0050	0.0695	0.0000	1.0000
SOCC1	0.0445	0.2034	0.0000	1.0000
SOCC2	0.0240	0.1510	0.0000	1.0000
SOCC3	0.0014	0.0363	0.0000	1.0000
SOCC4	0.0371	0.1865	0.0000	1.0000
SOCC5	0.0059	0.0755	0.0000	1.0000
SOCC6	0.0000	0.0000	0.0000	0.0000
SOCC7	0.0003	0.0174	0.0000	1.0000
SOCC8	0.0130	0.1117	0.0000	1.0000
SOCC9	0.0213	0.1424	0.0000	1.0000
SOCC10	0.0353	0.1820	0.0000	1.0000
SOCC11	0.0032	0.0560	0.0000	1.0000
SOCC12	0.0090	0.0934	0.0000	1.0000
EDUC1	0.0603	0.2349	0.0000	1.0000
EDUC2	0.0355	0.1825	0.0000	1.0000
EDUC3	0.0336	0.1777	0.0000	1.0000
EDUC4	0.0419	0.1976	0.0000	1.0000
EDUC5	0.0758	0.2611	0.0000	1.0000
EDUC6	0.3645	0.4748	0.0000	1.0000
EDUC7	0.2498	0.4271	0.0000	1.0000
EDUC8	0.1041	0.3013	0.0000	1.0000
HEA1	0.2724	0.4392	0.0000	1.0000
HEA2	0.4766	0.4927	0.0000	1.0000
HEA3	0.1921	0.3887	0.0000	1.0000
HEA4	0.0454	0.2054	0.0000	1.0000
FAMCHILD	0.7641	1.1278	0.0000	9.0000
FEMALE	0.4601	0.4917	0.0000	1.0000
FMAR	0.2338	0.4175	0.0000	1.0000
FSEP	0.0177	0.1301	0.0000	1.0000
MAR	0.4586	0.4916	0.0000	1.0000
WIDDIV	0.1396	0.3419	0.0000	1.0000
SEP	0.0349	0.1812	0.0000	1.0000
WHITE	0.8114	0.3859	0.0000	1.0000
BLACK	0.1306	0.3324	0.0000	1.0000
ASIAN	0.0499	0.2148	0.0000	1.0000
YEAR96	0.3284	0.4633	0.0000	1.0000
YEAR97	0.3335	0.4651	0.0000	1.0000
POPMIL	11.2982	8.9255	0.4163	29.4019