The Impact of Access Regulation on Health Insurance Market Structure. A. Insurer Response to Regulation


The types of regulation that states have imposed in the small group market, and to a lesser extent in the individual market, reward greater insurer size, especially if insurers already are operating with increasing economies of scale.5 In general, one would expect that regulation which reduces insurers’ ability to deny risk or to rate risk appropriately would enhance rewards to scale by allowing insurers to defray the implicit subsidy to high-cost groups and individuals over a larger base of insured lives. Moreover, it is unlikely that regulation favoring market entry by high-risk groups and individuals (guaranteed issue or renewal, restrictions on preexisting condition exclusions and limits on rate variation for age, health or other factors) expands the overall size of the market (Nichols, 2000). Instead, it is likely that the number of covered lives might decline, given the price elasticity of demand for coverage in these markets (Marquis and Long, 1995), and indeed that is the sense of the emerging literature linking health insurance coverage (usually among workers in small groups) and insurance market regulation. In markets with increasing economies of scale and no net growth in demand for coverage, insurance regulation is likely to increase price; and competition among insurers may force merger or exit to recapture lower average cost and to increase market share.

Number of insurers. We hypothesize that the number of insurers writing coverage will decline in states with regulation that would require insurers to accept risk that they would otherwise deny or to rate risk in ways that subsidize high-cost insureds. This decline would occur either by insurers exiting the market (ceding market share), or by insurers merging with their competitors to increase the surviving insurer’s market share and gain economies of scale.

To test this hypothesis, we estimate a fixed effects model of the following general forms:

(1) NUMINSst =f (Xst, REGst)

(2) NUMINSst =f (Xst, REGst, BCBSst)

(3) NUMINSst =f (Xst, REGDUMst, BCBSst)

where NUMINS is the number of insurers in state s and year t, Xst is a vector of control variables, REGst is a vector of continuous and categorical regulation variables, and REGDUMst is a vector of regulation variables expressed as categorical variables only (precise variable definitions are provided in Table 8). We tested regulatory variables as both continuous and categorical, the latter to test for a “shock effect” of regulation unrelated to the stringency of the requirement. Also, note that the latter two models control for the Blues’ market share as an exogenous variable; in these specifications we entertain the hypothesis that the Blues’ market share in many states is an artifact of their history and unique position in state regulation, not principally a result of short-term market dynamics.

Market share. We hypothesize that very large insurers, with constant economies of scale, are more likely to thrive in highly regulated markets than small insurers with increasing economies of scale, and that insurer types with characteristically different cost economies systematically gain or lose market share in more highly regulated states. Because different insurer types may have different scale economies,6 we first estimate the relationship between states’ regulatory variables and market share for each of the major types of insurers. To reflect the very nonexclusive rules that BCBS plans in some states have adopted to qualify as HMOs in state law, we include BCBS HMOs as BCBS insurers. Specifically, we estimate the following general forms, again as fixed-effect models and again introducing BCBS market share in some specifications as an independent (exogenous) variable:

(4) BCBSst =f (Xst, REGst)

(5) HMOst =f (Xst, REGst)

(6) HMOst =f (Xst, REGst, BCBSst)

(7) COMMst =f (Xst, REGst)

(8) COMMst =f (Xst, REGst, BCBSst)

Market concentration. A significant net exit of insurers, all else being equal, will increase market concentration, reducing competition among insurers. We estimate the impact of state regulation on two conventional measures of market concentration: (a) a Herfindahl index;7 and (b) recognizing the problems of the Herfindahl index in describing highly skewed markets, the share of the market held by the largest (arbitrarily, the largest five) competitors in the state.

(9) HERF =f (Xit, REGit)

(10) TOPFIVE = f (Xit, REGit)

The price of insurance. Because health insurance products are nonstandard, it is very difficult to measure price in the usual sense. Risk-averse consumers are willing to pay a price in excess of the actuarially fair price (that is, the expected cost of their health care estimated over a class of similar risks), and the price of insurance is measured as the loading that consumers are willing to pay in excess of the actuarially fair price. We express the loading on the insurance policy as its inverse: the insurer’s medical loss ratio (i.e., medical claims paid per premiums earned). In more competitive markets, it is presumed that the price of health insurance is lower – and insurers’ loss ratios are higher.

Insurance regulation complicates several aspects of this conventional model of competitive markets. Most insurance regulation is intended to increase pooling by insurers, forcing them to develop more heterogeneous classes of risk and to cross-subsidize higher-risk members of the class. With more heterogenous risk classes, higher loss ratios may correlate not only with greater competition (measured by the number of insurers and/or market concentration)8 but also with the entry of higher-risk groups or individuals into the health plan.

We hypothesize that average loss ratios are higher in states with insurance regulations that attempt to force insurers to develop more heterogeneous risk classes, both accepting greater risk and limiting rate variation. The model controls for the number of insurers in the market and for market concentration (as measures of competition) as well as the usual vector of control variables defined in Table 10. We estimated the model with alternative measures of market concentration as follow:

(11) LRATIOst =f (Xst, REGst, NUMINSst, HERFst)

(12) LRATIOst =f (Xst, REGst, NUMINSst, TOP5st)