Journal of Health Politics, Policy and Law

Political and Economic Determinants of Insurance Regulation in Mental Health David A . Lambert, University of Southern Maine, and Thomas G . McGuire, Boston University

Abstract. This article studies determinants of two important sets of laws regulating insurance coverage for mental health care: mandated inclusion of minimum coverage for psychotherapy, and mandated coverage for psychologist services, the so-called freedom of choice (FOC) laws. Political market models are developed and estimated to examine the passage of mandates and FOC laws among all fifty states from 1968 through 1983. Findings indicate that a number of groups influence whether these laws are passed, including psychologists and the state, which acts both in its own interests as a direct provider of services and to protect the public’s interest. A state’s political system and socioeconomic environment also influence the likelihood of passage of these regulations. Our findings run counter to the assumption often made by policymakers and researchers that regulations exclusively serve the interests of providers.

Introduction Most research on insurance regulation has focused on the effect of regulations on the cost, prices, and volume of services provided in a market. A major problem with such studies is that they assume causality to be unidirectional-from the regulations to the market. As a number of researchers have pointed out, the economic interests of buyers and sellers may be important determinants of whether regulations are adopted in the first place; without taking these relations into account, research on the impact of regulations may be misleading (Feldstein 1983). The purpose of this article is to study the determinants of two important sets of laws regulating insurance coverage for mental health care: mandated inclusion of minimum coverage for psychotherapy, and mandated coverage for psychologist services, the so-called freedom of choice (FOC) laws. These laws have important economic effects on the demand for professionals’ services. The likely economic

Research for this article was supported by grant number MH 37293 from the National Institute of Mental Health. Journal of Health Polifics, Policy and Law, Vol. 15, No. 1, Spring 1990. Copyright 0 1990 by Duke University. 1. See, for example, McGuire and Montgomery (1982) or Frank (1982).

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Table 1. Number of States Passing a Mental Health Mandate (by Type) by 1973, 1978, and 1983 (Cumulative Count)" Number

Year

Availability Compulsory Total

1 2 3

1973

Availability Compulsory Total

7 I0 17

1978

Availability Compulsory Total

13 13 26

1983

Type

a. Count is for the year indicated. Some states first passed an availability mandate and later amended it to include compulsory coverage. Source: Survey of state laws concerning mental health insurance coverage, Public Policy Department, National Mental Health Association, November 1983.

effects of the regulations are used to develop market measures of the economic interests of the affected parties. We use lagged values of these market measures to minimize the problem of simultaneously determined endogenous variables. of particular interest in this research is the dual role of the state. The state government represents a level of decisionmaking that may influence the likelihood of a law's passage. In addition, many states are important direct providers of mental health care, and a law that forces insurance companies to cover mental health services may have favorable impacts on the state budget. The presence of professional regulation has usually been explained in terms of either protecting the public or serving the economic interests of professionals. The framework we develop broadens the study of the determinants of passage of mental health insurance regulations to include the political process through which regulation is secured, the role of the state in protecting the public, the self-interest of providers, and the interests of other relevant actors.

Background on the passage of mandates and FOC laws During the 1970s, state governments began to pass compulsory insurance laws requiring that health insurance plans include coverage of specific benefits. These laws, usually referred to as state mandates, have been passed in a number of areas of care. One of the most controversial mandates has been for mental health coverage, which has been strongly opposed by private insurance carriers. As shown in Table 1, 26 states had passed mental health mandates by the end of 1983. Half the state mandates require that plan enrollees be covered for mental health care; the other half require only that enrollees be offered the option of mental health

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coverage. The typical mandate requires a minimum of $500 coverage for outpatient care, and some inpatient care. Usually the mandate only applies to group insurance policies. State community mental health center associations and local mental health advocates have led the fight at the state level for the passage of mental health mandates. Community mental health centers (CMHCs) were originally funded through eight-year federal staffing grants that called for decreased government funding each year and fiscal self-sufficiency by the end of the eighth year. As these grants have declined, mandates have become an increasingly important source of potential revenue. Since 1978 a network of advocates for passage of mental health mandates, led by the National Mental Health Association, has provided strong technical support to groups trying to pass mandates in their states. Psychiatrists and psychologists have been relatively uninvolved in the efforts to pass mandates. While generally in favor of mandates, these two providers have tacitly opposed them in a few states. Since psychiatrists are usually covered for some services under voluntary insurance provisions, they stand to gain less than other groups from mandates. In addition, psychiatrists have been concerned that minimum benefit provisions in mandate laws may become de fact0 maximum benefit provisions. Psychologists have most actively pursued passage of FOC laws, which promote coverage for their services, leaving to other groups the major responsibility for fighting for passage of mandates. In those states where FOC laws have not been passed, psychologists have generally withheld their support for (or tacitly opposed) mandates, since it is not in their interest to have increased coverage for their competitors. The most significant resistance to mental health mandates has come through the courts. Insurance companies have challenged the authority of states to pass mental health mandates on several grounds. These challenges have resulted in a series of suits argued before state and appellate courts and the Supreme Court. The right of a state to pass a mandate has been affirmed at every level, most recently by the Supreme Court in June 1985.’ Thirty-seven states had passed FOC laws by the end of 1983 (see Table 2). Psychologists have actively lobbied for passage of FOC laws in every state since 1968, when the Committee on Health Insurance was established within the American Psychological Association (APA). The heart of the argument for FOC laws advanced by state psychological associations centers on the competence of psychologists to deliver psychotherapy independent of psychiatrists, and projected savings associated with the substitution of psychologists for psychiatrists. Insurance companies have been less resistant to FOC laws than to mandates. In 1976 the APA Committee on Health Insurance reached agreement with the Health Insurance

2. Metropolitan Life Insurance Company v. Massachusetts, Supreme Court of the United States,

NO.84-325.

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Table 2 . Number of States Passing FOC Laws by 1973, 1978, and 1983 (Cumulative Count) Number

Year

I2

1973 1978 1983

31 37 Source: See Table 1 .

Association of America (HIAA) on a model law that established direct recognition of psychologists for reimbursement. Despite this joint resolution, insurers in some states resisted complying with FOC laws. In 1979 the Federal Trade Commission reported that in nine states Blue Shield was not in compliance with FOC laws that had been passed (Stone et al. 1979). As shown in Table 3, some of the same forces seem to affect the passage of both mandates and FOC laws. States which pass one type of insurance regulation also tend to pass the other.

Previous research on the determinants of passage of economic regulations Two theories have dominated the economic study of regulation. The first (called public interest theory) states that regulation is a response to market inefficiencies. The second (of which there are two versions, called monopoly theory and capture theory) holds that regulation is introduced in response to the demands of interest groups who seek to maximize the incomes of their members. The public interest model was developed in the late 188Os, when government regulation of various industries first came into prominence (Posner 1974). Beginning in the 1950s, research questioned the connection between regulation and the presence of identifiable externalities, or with the inefficiencies associated with monopolistic market structure. Nor did regulation appear to improve existing market inefficiencies (Stigler and Friedland 1962). Within the growing literature on regulation, a number of explanations emerged to account for the discrepancy between the assumptions of the public interest theory and what was actually being observed in regulated industries. Among the most common explanations were that (1) there are inherent

Table 3 . Number of States Passing Mental Health Mandates (Any Type) and FOC Laws by 1983 Mandates

No mandate Mandate Total xz = 5 . 8 8 8 , ~= .015

No FOC Law

FOC Law

Total

10 3 13

14 23 37

24 26 50

Source: See Table 1.

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difficulties in administering and enforcing the regulatory function; (2) when regulations fix prices or restrict entry into an industry, regulated firms may increase profits by restricting the quantity of their services; and (3) regulators eventually become the pawns of, or are captured by, the industries they were intended to regulate (Posner 1974). Studies advancing the second explanation have loosely labeled it the monopoly theory of regulation; those studies advancing the third explanation have called it the capture theory of reg~lation.~ In a landmark article, Stigler (1971) explained the presence of regulation in certain industries by stating that industries seek to acquire regulation in order to further their economic interests. This theory contrasts with the public interest model’s assumption that industries have regulation imposed upon them. Although sharing the capture model’s assumption that regulation benefits the private interests of regulated firms, Stigler’s theory differs significantly because it assumes that such benefits are the outcomes of rationally acting groups seeking to further their self-interest, not the result of a breakdown of the regulatory mechanism. Stigler presents a model of the supply and demand for regulation, in which government (including political and bureaucratic actors) is viewed as the supplier of regulatory services to firms in given industries. Regulatory services are supplied by government actors in exchange for political support from regulated firms. Although Stigler’s article was heralded as having recast the way regulation should be studied, efforts to test and refine Stigler’s theory have lagged. A major obstacle has been the specification of the political market model central to Stigler’s theory. Few empirical studies of how regulations are obtained within political markets appeared until the 1 9 7 0 ~Several .~ studies of congressional voting found the ideology of members of Congress to be significant in explaining their voting and, in many instances, to be more important than the economic interests of their constituents (Kau and Rubin 1979; Kalt 1981). Peltzman (1984) argues that the significant role of ideology found in these and other studies may result from researchers failing to consider the association between the economic interests of constituents and the ideological preferences of legislators. Peltzman contends that economic interests may affect ideological beliefs, and that the measures employed in these studies may thus be proxies for (unmeasured) economic interests. What

3. The concept of vested interests capturing regulators can be attributed to earlier work by political scientists. See, for example, Truman (1951). 4. Stigler’s theory was tested in two doctoral dissertations during the early 1970s. McPherson ( 1972), following Olson (1965), assumed that very small groups would be more likely than large groups to lobby for and secure tariff protections. Using data from 1954 and 1963, McPherson regressed group size (measured by market concentration) and various measures of lobbying resources against the nominal tariff rates in 1954 and 1963. The results did not substantiate McPherson’s original hypothesis. Neither market concentration nor any of the measures of lobbying resources were significant. Pincus (1975) found considerably more support for Stigler’s theory in a study of which occupations were able to secure favorable tariffs in 1824: a variety of measures of occupational size, concentration, and resources explained over 60 percent of the variance in an occupation’s nominal tariff level.

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is needed, he contends, are political market models that conceptualize more broadly the role of principal agent^.^ The political economy of regulation has been actively studied in the health area (Feldstein and Melnick 1984; Mueller 1986). In case studies of the regulation of clinical laboratory personnel and nurses, White (1979, 1983) sought to extend the models used by Stigler, Peltzman, and Posner to examine the “dynamics of the process” by which influence is exerted to introduce (or block) regulation. Whether proposed regulation will be adopted is seen as the outcome of the activities of five groups in supporting or opposing regulation: consumers, members of licensed occupations, members of regulated occupations, government bureaucrats, and taxpayers. By adding members of related occupations and government bureaucrats and including economic factors, White proposed a broader model than had been previously developed to study the introduction of regulation of professionals. Begun and Rldman (1981) studied the degree of regulation of optometrists by three groups of factors: interest group power, political system characteristics, and socioeconomic characteristics. Drawing on the political science literature on state policy outcomes, the authors develop a path model in which regulation of optometry is assumed to be affected directly by socioeconomic factors through their impact on interest groups and the political system. The authors’ findings confirm the importance of political-system-level factors in affecting regulatory outcome.

Political market models of mental health manhtes and FOC laws We take the position that the passage of mental health mandates and FOC laws is the outcome of the support of (or opposition to) these laws by providers, private third-party payers, and the state, acting both as an agent of the public and as a direct provider of services. The influence these groups have on the passage of mandates and laws depends on the magnitude of the benefits (costs) likely to be experienced by the group and the group’s political power. The nature of the state political system through which the economic interests of these groups are expressed and their power exerted affects whether a state passes or does not pass a mandate and an FOC law. Finally, a state’s socioeconomic environment influences the outcome of mandates and FOC laws by predisposing the state to pass or oppose these laws.

5 . Kalt and Zupan (1984) criticize kltzman’s argument on the grounds that he employs a methodology fundamentally different from those used in the studies he discusses. The exchange between Rltzman and Kalt and Zupan brings to full circle the problem initially anticipated by Stigler concerning to what degree an economic theory of regulation needs to include, and is capable of including, noneconomic (e.g., political) institutions and factors. kltzman’s view is that, given the underdevelopment of theory of political markets, economists should stick to models with which they are most experienced-those that include economic variables and relegate noneconomic factors to taste variables. Kalt and Zupan hold that if economists take up studying political markets, they cannot assume away political factors.

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To specify the influence of relevant actors on the passage of mental health mandates and FOC laws, it is necessary to describe the likely economic effects of these regulations for these actors.

Economic effects of mental health mandates. Compulsory mandates and availability mandates should have economic effects in the same general direction. However, the effects of compulsory mandates will generally be stronger than the effects of availability mandates. In this discussion, we will consider only the effects of compulsory mandates. Passage of a mandate will decrease the out-of-pocket price of psychotherapy to consumers, and thus increase demand for psychiatrist services. In the short run, the effect of increased demand for psychotherapy on psychiatrists’ fees and workloads depends on several factors: (1) the sensitivity of consumer demand to increased insurance coverage, (2) the willingness of psychiatrists to increase their caseloads, given the potential for higher revenues per visit, and (3) the degree to which a mandate increases demand for the services of substitute providers. In the long run, the effect of a mandate on psychiatrists depends on the supply response to increased demand. New psychiatrist providers are likely to be attracted to states with higher fees, thus forcing fees down toward the equilibrium price that existed before the mandate was passed.6 Conceptually, the effect of a mandate on the demand for psychologists is the same as its effect on the demand for psychiatrists. Supply elasticity should be higher for psychologists than for psychiatrists (there is likely to be a larger pool of licensed, but nonpracticing , psychologists than psychiatrists). A critical factor mediating the magnitude of the effect of a mandate on demand for psychologists is whether psychologists have won (or are likely to win) the right to be reimbursed directly for services through FOC legislation. Economic effects ofFUC laws. An FOC law will decrease the price of psychologist services to consumers, resulting in an increase in demand for psychologist services. Demand for psychiatrist services will decrease as the price of the substitute service is reduced. An FOC law will significantly diminish the potential demand-increasing effects of a mandate for psychiatrists. In the long run, the joint presence of an FOC law and a mandate may decrease demand for psychiatrist services. The effect of an FOC law on the state and on third-party payers is more ambiguous than the effect of a mandate. An FOC law may save the state money by fostering competition and lowering price^.^ The state may also favor an FOC law because it believes that it may increase choice for consumers. Third-party ‘payers

6. For a general discussion of the market for psychotherapy, see McGuire (1981). 7. Frank (1982) finds evidence for such a competitive effect of FOC laws.

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should not be as adversely affected by an FOC law as by a mandate. If an FOC law increases competition and lowers prices, third-party payers could, in theory, save money. However, if they deemed it beneficial, third-party payers could have chosen to extend coverage to psychologists even without (or before) passage of an FOC law. An FOC law restricts a payer’s options, and is therefore likely to be opposed by third-party payers.

Model specijication. Dependent variables. Passage of a mental health mandate is measured in terms of a dichotomous variable, MANDl, which is scored 0 if no mandate has been passed by 1983 and scored 1 if a mandate has been passed. Another specification of the mandate variable takes into account whether the mandate passed requires compulsory mental health coverage under insurance or only that mental health coverage be made available. Under this specification, the variable MAND2 is scored 0 if no mandate has been passed, 0.5 if a mandate has been passed requiring that coverage be made available, and 1 if a mandate has been passed requiring that coverage be compulsory. We assume that an “availability” law is a partial victory for the pro-mandate forces. Passage of an FOC law is measured by a dichotomous variable, FOC, which is scored 0 if no law had been passed by 1983, and 1 if a law had been passed. As we have described, there is likely to be an interactive effect between the passage of an FOC law and the passage of a mandate. We have not included an interactive effect in our model because the data do not appear to be strong enough to support the more detailed investigation that this would require. Interest group injuence. Three provider groups-community mental health centers (CMHCs), psychologists, and psychiatrists-are assumed to influence the passage of mandates. Where possible, we measured the economic importance of these groups before 1973, the beginning of the period with the most activity in passing this legislation. The role of CMHCs in passing mandates (variable CMHC) is measured by the total receipts of community mental health centers in a state in 1976, divided by state population. The higher CMHC is, the more likely a state is to pass a mandate. The influence of psychologists in passing mandates is measured by the variables LOBBY, DUES, PHD, and RATIO. LOBBY is the percentage of a state psychological association’s budget allocated to lobbying in 1976. DUES is the total revenue from the 1976 annual dues of members of the state psychological association, divided by the state’s population. We divide total dues revenue by population to control for the size of the state. PHD is the number of Ph.D. psychologist service providers per million in 1972. The influence of psychiatrists is measured by the number of psychiatrist providers per million in 1970 (MD). The influence of psychologists relative to psychiatrists is measured by the ratio of psychologist to psychiatrist service providers (RATIO). We expect that measures of provider influence will be positively associated with the probability of passage of a mandate.

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The effect of a state acting in its interests as a direct provider of services is measured by the state mental health expenditures per capita in fiscal years 1975 and 1976 (MHEXP). MHEXP should be positively related to the passage of a mandate because the higher the percentage of total state health expenditures devoted to mental health, the more a state may benefit by shifting mental health costs to private payers. The proclivity of the state to regulate in the public interest is measured by the number of consumer protection laws a state had passed by 1974 (CONSUM74). The higher CONSUM74, the more likely a state was to pass a mandate. The final interest group is private third-party payers, who are assumed to oppose passage of a mandate. A single large payer may be more effective politically than many small payers. Blue Cross market share (BCMS) in a state in 1978 is used to measure the concentration of power of third-party payers. Political system characteristics. Two measures of a state’s tendency to adopt new programs are considered. WALKER is a score developed by Walker (1969) of a state’s general willingness over time to adopt new social and economic programs. It is based on the amount of time it took a state to adopt 88 different programs across 12 areas. MANSUM is the number of insurance mandates a state had passed in 8 areas, other than mental health. Both WALKER and MANSUM should be positively related to the passage of regulation. Party competition (PARCOM), developed by Ranney (1976), is expected to be related to passage of an FOC law. In states in which party competition is more vigorous, competition for support of interest groups should increase the likelihood of special interest legislation.8 Socioeconomicfactors. A state’s socioeconomic environment may predispose it to pass or not to pass insurance regulation. The effect of socioeconomic environment on passage of a mandate may be direct and/or indirect (through its effect on the political system or interest group behavior). Richer, more populated states with higher levels of education are likely to pass new social and economic programs. We expect that these factors will be positively related to the passage of regulation. Income is personal income per capita, measured in thousands (INCOME). Education (EDUC) is measured by the percentage of a state’s population over 18 years of age who have completed four years or more of college. Population (POP) is measured in thousands. The effects of socioeconomic factors must be interpreted with caution. Socioeconomic variables in political outcome studies tend to have effects that are very

8. It is possible to specify and interpret political system variables more narrowly in an economic model of the political market. Variables such as the dominance of political parties, the duration of a legislative session, and the voter-to-legislator ratio may be viewed in terms of how they relate to the cost of passing legislation. In specifying our model, we have taken a broader view of the role of a state’s political system in determining regulatory outcome.

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sensitive to the specification of the model. These variables are also likely to be serving as proxies for other factors. Definitions and data sources for all of the variables appear in Appendix 1.

Findings Different specifications of the model were considered to predict the passage of mental health mandates and FOC laws. Logit regression was used to take account of the form of the dependent variables. We first report our findings when the model is used to predict the passage of mandates, and then report our findings when it is used to predict the passage of FOC laws.

Mandates. Statistical results. Variations in the model for predicting passage of a mandate include using MANDl and MAND2 as the dependent variable; using psychological association dues (DUES), psychological association lobbying budget (LOBBY), and number of psychologist providers (PHD) as measures of psychologist power; using the number of insurance mandates (MANSUM) and the Walker innovation score (WALKER) to measure political system factors; and using per capita personal income (INCOME), education (EDUC), and population (POP) to measure socioeconomic factors. The significance of the variables was generally consistent across the specifications considered. The best estimate-in terms of the overall fit of the model and the number of significant variables-is obtained using MAND2 as the dependent variable, DUES as the measure of psychologist power, MANSUM to measure political system factors, and INCOME and POP to measure socioeconomic factors.’ Th? results are reported in Table 4. Estimates using MAND2 as the dependent variable are presented on the left side of the table, and estimates using the MANDl specification are presented on the right side. The estimate using MAND2 as the dependent variable is the primary specification of interest. The first column reports the beta coefficients for each independent variable. The significance of these coefficients is given by the Wald maximum-likelihood estimate, chi-square, reported in the second column. The chi-square statistic is calculated by taking the square of the ratio of the regression coefficient estimate to the standard error estimate. The associated probability of significance of chi-square, assuming one degree of freedom, is given in the third column.

9. Education and income were considered a priori to be equally good measures of consumer demand for a mandate. There are several possible ambiguities in interpreting education and income. Education and income may tap aggregate, state-level behavior and not individual-lev4 demand. Education and income are also relatively highly correlated ( r of EDUC, INCOME = .%). In preliminary estimates, EDUC was entered separately and with INCOME. EDUC was not significant in any of these estimates. INCOME was significant when entered separately from EWC, and barely missed significance when entered with EDUC. It was on this basis that we decided to include INCOME and not E W C in the final estimate. It should be emphasized that this decision was made on empirical and not theoretical grounds.

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Table 4. Maximum Likelihood Logit Equations: Determinants of Passage of Mental Health Mandate by 1983 MAND 1

MAND2 Variable DUES CMHC MHEXP CONSUM74 MANSUM BCMS INCOME POP

L3 ,490 - .334 12.717 .441 .486 1.370 - 1.499 - .00033

1” 2a Intercept Model x2 (PI x2 Percent predicted correctly

U

X2

x2

B

~~

0.49 5.59 10.30 8.87 3.90 0.48 7.17 5.83

.027 .018 .001 .003 .048 .489 .007 .016

9.620 ,393 .432 2.384 - 1.595 - .00016

-

33.64 p

3.68 1.21 4.67 3.87 2.49 0.75 5.29 I .27

.587

- .249

2.511 0.435

CY

CY

< .ooOol 84.2

P .055

.272 .036 .049 .114

.387 .020 .259

2.84 27.39 p < .0006 87.5 ~~

~

a. Using logistic regression with nonbinary dependent variables, SAS output reports alpha estimates in lieu of an intercept.

The model is significant, as shown by the model chi-squares. The percentage of cases predicted correctly is high for both specifications (84.2 percent predicting MAND2, 87.5 percent predicting MANDl), suggesting a good fit of the overall model. All variables except for Blue Cross market share (BCMS) are significant when MAND2 is the dependent variable. The two state variables, MHEXP (x’ = 10.30, p < .001) and CONSUM74 (x’ = 8.87, p < 0.001), significantly predict whether a state will pass a mandate. The state, acting in its interests as a provider of mental health services and acting in the public interest, has a strong impact on whether a mandate is passed. These effects are relatively independent of each other, suggested by a very low correlation (r = .02) between MHEXP and CONSUM74 and the statistical significance of each variable when the other is omitted from the regression (data not shown). BCMS is not significant, and the sign of the coefficient is the opposite of what we expected. This finding is surprising. Third-party payers have strongly resisted the passage of mental health mandates. The nonsignificant effect of BCMS may be because this variable is a poor proxy of the power of third-party payers, or it may be that the resistance of third-party payers does not significantly affect whether a mandate is passed.”

10. Researchers have had considerable difficulty in developing good measures of insurance carrier

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The two provider interest variables (CMHC and DUES) are significant. However, these estimates are not robust. When POP is omitted, the significance of both DUES and CMHC drops appreciably, especially DUES (data not shown). One explanation for this result is the possibility of collinearity between POP and DUES, suggested by a relatively high correlation between the variables (r = .71). The sign of the coefficient for CMHC is negative, the opposite of what we had predicted. A plausible explanation is that the lower the community mental health center receipts per capita, the more community mental health centers in a state are in need of a mental health mandate and the harder they may work toward that end. MANSUM has a significant effect (x’ = 7.17, p < .Ol), suggesting the importance of a state’s political system in influencing whether a mandate will be passed. The two socioeconomic variables, INCOME and POP, have significant effects (x’ = 7.17, p < .01; x2 = -5.83, p = .016). Socioeconomic factors may be proxies for individual level demand and may also be proxies for aggregate, state-level behavior. The negative coefficient of INCOME suggests that consumers in states with lower income will have higher demand for a mental health mandate. This is because lower-income persons stand to benefit more from a mandate than higher-income persons, who are more likely to have higher mental health coverage through their employer and are also more likely to be able to pay for out-of-pocket costs for treatment. States with larger populations have different mental health markets and political systems. No interpretation of POP is made beyond this. When the model was estimated measuring the dependent variable as whether or not a mandate was passed, regardless of the type of mandate (MANDl), results were similar, but somewhat weaker. Community mental health center receipts (CMHC) and population (POP) were not significant, while they were significant using the MAND2 specification. Blue Cross market share remained insignificant. We believe that MAND2 is a better measure of the passage of a mandate than MANDl and that that is why stronger estimates are obtained when MAND2 is used. Relative importance of the factors affecting passage of a mandate. To assess the relative importance of each factor affecting the passage of a mandate, we simulated the change in probability of passage of a mandate when each of the independent variables was increased one at a time by 20 percent while holding the other variables constant. These results are reported in Appendix 2. We report the simulated change in probability of passage of a mandate of any kind, rather than by specific type, because the simulations are likely to be more stable using a dichotomous dependent variable than a three-category nominal dependent variable.

power. BCMS may reflect a regional bias in that Blue Cross and Blue Shield associations are strongest in northeastern states. Given the uncertainty of how good a proxy BCMS is of the power of thirdparty payers, alternative measures should be developed and tested before concluding that the influence of third-party payers is not a significant factor in whether a state passes a mandate.

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The probability of passage of a mandate, based on the actual characteristics of each variable, is .576." When the mean value of state mental health expenditures per capita (MHEXP) is increased by 20 percent, the probability of passage of a mandate increases to .729, an increase of 26.6 percent. A 20 percent increase in the mean value of the number of consumer protection laws passed (CONSUM74) results in a similar increase (28.5 percent) in the probability of passage of a mandate. A 20 percent increase in psychological association dues (DUES) increases the probability of passage by 14.3 percent. A 20 percent increase in INCOME results in a very substantial decrease (81.2 percent) in the probability of passage of a mandate. Apparently this large a change in a state's personal income per capita accounts for most of the range of probability that a state will pass a mandate. As we have discussed, INCOME may be reflecting other effects. We also simulated the change in probability of passage of a mental health mandate of specific type (data not shown). Results were similar to those just described.

FOC laws. Statistical results. Different specifications of the model considered to predict the passage of FOC laws included psychological association dues (DUES), psychological association legislative budget (LOBBY), the number of psychologist service providers (PHD), and the ratio of psychologist to psychiatrist service providers (RATIO) to measure psychologist power; Walker innovation score (WALKER)and party competition (PARCOM) to measure political system factors; and personal income per capita (INCOME), education (EDUC), and population (POP) to measure socioeconomic environment. Estimates were not as consistent across specifications as they were in the model for mandates. PHD was chosen to measure the strength of psychologists' interests. CONSUM74 was insignificant and was dropped from the model. PARCOM and WALKER were selected as measures of state political system factors, and INCOME and POP were selected to measure socioeconomic factors. Logistic regression estimates of the model are presented in Table 5. The equation on the left side does not include POP; the equation on the right side includes POP as a control variable. The second estimate (including POP) is the specification of primary interest. The model chi-square is significant, using both estimates (p < .001). A very high percentage of cases are predicted correctly using both estimates (94 percent), suggesting a strong fit of the overall model. PHD has a significant effect on the probability of passage of an FOC law (x' = 5.50, p = .019). MHEXP is not significantly related to the probability that an FOC law will be passed. This is not surprising, in that the potential cost savings to a state of an FOC law are indirect and are likely to occur (if they do) in the

11. The estimated probability of passage of a mandate was calculated by the formula P = ez /( 1 e'), where z equals the sum of the constant and the mean values of the independent variables multiplied by their estimated coefficients. We simulated the relative influence of each independent variable by increasing its mean value by 20 percent.

+

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Table 5. Maximum Likelihood Logit Equation: Determinants of Passage of an FOC Law by 1983 Not Including POP

Variable PHD MHEXP WALKER PARCOM BCMS INCOME POP

s ,139 6.420 .039 14.603 12.429 - 1.190

Intercept Model x2 (PI x2 Percent predicted correctly

Including POP

x2

R

5.40 2.12 5.12 6.85 5.15 2.98

.020 .145 .024 .009 ,023 .080

P .140 6.455 .034 14.180 11.133 - 1.090 - .o001

- 30.19

33.67 p < .001 94.0

X2 5.19 2.24 2.86 6.38 3.49 2.23 0.17

P .022 .134 .090 .Ol 1 .061 .135 .679

28.20 33.85 p < .001 93.6

-

long run. In contrast, a state may achieve immediate cost savings by shifting costs to third-party payers through a mental health mandate. How soon, relative to other states, a state adopts a new program (WALKER) is positively but not significantly related to the probability that it has an FOC law. The more interparty competition within a state (PARCOM), the more likely it is to have an FOC law. BCMS does not have a statistically significant effect. (When POP is omitted from the model, the effects of WALKER and BCMS become statistically significant.) The sign of BCMS is positive, the opposite of what we hypothesized. Interpreting the effect of Blue Cross market share is difficult. The positive coefficient of BCMS may reflect the association between high Blue Cross market share and a large health and mental health market, with a larger mental health market associated with passage of an FOC law. A better proxy of third-party payer influence than BCMS is required before the effect of third-party payer influence may be determined. INCOME has a statistically insignificant effect. The variable POP is not associated with the passage of an FOC law. Although the fit of the model is stronger when estimating the passage of FOC laws than when estimating passage of a mandate, the performance of individual variables with respect to predicted sign and significance is better in estimating the passage of mandates than FOC laws. This may be because of the distribution of the dependent variable: only 13 states had not passed an FOC law by 1983. It may be that these are the states where there is not a sufficiently large market for the services of psychologists to result in the passage of an FOC law. It also may be that the states not passing an FOC law have a strong bias against passing such legislation. Relative importance of the factors affecting passage of an FOC law. We simulated the change in probability of passage of an FOC law when each of the in-

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dependent variables is increased one at a time by 20 percent, while holding the other variables constant. We used the same method as we did to simulate changes in the probability of passage of a mandate. These results are reported in Appendix 3. The probability of passage of an FOC law, based on the actual characteristics of each variable, is .648. Increasing the mean value of the number of psychologist service providers (PHD) by 20 percent increases the probability of passage of an FOC law to 89.3 percent, an increase of 37.9 percent. A 20 percent increase in the Walker innovation score (WALKER) and in party competition (PARCOM) resulted in similarly large increases of 49.3 and 39.9 percent, respectively, in the probability of passage of an FOC law. A 20 percent increase in INCOME results in a 59.7 percent decrease in the probability of passage.

Assumption of equilibrium of political market. An assumption of the model we have developed is that the political market in which mental health regulations are demanded and supplied had reached equilibrium by 1983. In the six years since 1983, only one state, Hawaii (1988), has passed a new mental health mandate.12 Hawaii’s law requires mandatory coverage. Since 1983, three states have passed FOC laws: Indiana (1989, Wyoming (1983, and South Dakota ( 1986).13The fact that only one state passed a mandate and only three states passed FOC laws supports our assumption that political markets for mental health regulations were in equilibrium across the fifty states. Conclusion Our findings indicate that a number of groups influence whether or not mental health mandates and FOC laws are passed. Most groups act in their own interest, and some groups act in the public interest. The state, acting in its economic interest as a direct provider of services and acting as an agent of the public, had a strong impact on whether or not a mental health mandate was passed. The political activity of psychologists, the need of community mental health centers for additional revenues, and the history of a state in passing insurance mandates in other areas had estimated effects bordering on or close to the conventional level of significance. The interest of third-party payers, as measured by Blue Cross market share, did not have a significant effect. However, this finding may reflect the absence of a

12. State lawmakers and advocates of mandates in states without mandates may have suspended their efforts to pass mandates while awaiting the Supreme Court ruling in Metropolitan Life v. Massachusetts. In 1985, six states considered legislation to pass a mandate; in 1986, similar legislation was considered in six other states. Personal communication with the National Conference of State Legislatures, August 1989, and Patterson (1986). 13. Information from the American Psychological Association’s List and Description of States with Freedom of Choice Laws (December 1986) and from personal communication with the National Conference of State Legislatures, August 1989.

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good measure of third-party payer influence. Ftr capita income was negatively and significantly related to the passage of a mandate, suggesting the importance of consumer demand for a mandate. States with smaller populations were significantly more likely to pass a mandate than states with larger populations. Professional regulation has usually been explained in terms of either protecting the public or serving the economic interests of providers. Our findings demonstrate that mental health mandates and FOC laws are passed as a result of the interests of both providers and the public. Passage of these laws is the outcome of the interests of diverse groups and not the overwhelming power of a single interest provider group, as is often assumed in empirical studies of regulation. A state’s political system and socioeconomic environment also influence the likelihood of passage of these regulations. As we hypothesized, the greater the tendency of a state to adopt new programs, the more likely the passage of mental health insurance regulations. We can speculate on more specific interpretations of the political system factors. The importance of these factors may be more narrowly interpreted in terms of reducing the cost of passing legislation to specific interest groups. A broader interpretation of these same factors would suggest that the more favorable a state’s political system is for adopting new programs, the greater the opportunity for different interest groups to act in concert to pursue and achieve their interests. This would suggest that regulatory outcome may reflect the activity of coalitions of groups. This article should also be evaluated in terms of how reasonably the political market model we developed has held up and how well the empirical results square with what is known about how mandates and FOC laws have come to be passed. A major task in developing the model was to incorporate both political and economic factors so that they would be interpretable. Our results suggest that this has been accomplished. The empirical results obtained in this paper are consistent with the history of different groups’ efforts to pass or block mandates and FOC laws. Our findings that mandates and FOC laws are enacted, at least in part, in response to consumer demand for regulation run counter to the assumption made by many policymakers that regulations exclusively serve the interests of providers. Consequently, one cannot assume categorically that these regulations are “good” or “bad” because they theoretically serve the interests of a particular group. U1timately, policy makers will need better research on the effects of these regulations. Meanwhile, the political process by which these regulations are enacted is apparently responsive to enough diverse interests (provider, consumer, and state) to suggest that the current way in which regulatory decisions are made is a satisfactory second-best method.

References Begun, J . , and R. Feldman. 1981. A Social and Economic Analysis of Professional Regulation in Optometry. Washington, DC: U.S. Department of Health and Human Services.

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Feldstein, I? 1983. Health Care Economics. New York: Wiley. Feldstein, l? , and G. Melnick. 1984. Congressional Voting Behavior on Hospital Legislation: An Exploratory Study. Journal of Health Politics, Policy, and Law 8 (4): 686701. Frank, R. 1982. Freedom of Choice Laws: Empirical Evidence of Their Contribution to Competition in Mental Health Care Delivery. Health Policy Quarterly 2: 79-97. Kalt, J. 1981. The Economics and Politics of Oil Price Regulation. Cambridge, MA: MIT Press. Kalt, J., and M. Zupan. 1984. Capture and Ideology in the Economic Theory of Politics. American Economic Review 74 (3): 279-300. Kau, J., and F? Rubin. 1979. Self-Interest Ideology and Log Rolling in Congressional Voting. Journal of Law and Economics 22 (2): 365-84. McGuire, T. 1981. Financing Psychotherapy. Cambridge, MA: Ballinger. McGuire, T., and J. Montgomery. 1982. Mandated Mental Health Benefits in Private Health Insurance Policies. Journal of Health Politics, Policy and Law 7 (2): 380-406. McPherson, C. 1972. Tariff Structures and Political Exchange. Ph.D. dissertation, University of Chicago. Mueller, K. 1986. An Analysis of Congressional Health Policy Voting in the 1970s. Journal of Health Politics, Policy and Law 11 (1): 117-35. Olson, M. 1965. The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge, MA: Harvard University Press. Patterson, Andrea. 1986. Mandated Mental Health Insurance: A Complex Case of Pros and Cons. Denver: National Conference of State Legislatures. Peltzman, S. 1984. Constituent Interest and Congressional Voting. Journal of Law and Economics 27: 181-210. Pincus, J. 1975. Pressure Groups and the Pattern of Tariffs. Journal of Political Economy 83: 757-78. Posner, R. 1974. Economic Theories of Regulation. Bell Journal of Economics 4:335-58. Stigler, G. 1971. The Theory of Economic Regulation. Bell Journal of Economics andManagement Science 2 (1): 3-21. Stigler, G., and C. Friedland. 1962. What Can Regulators Regulate? The Case of Electricity. Journal of Law and Economics 5: 1-16. Stone, A., et al. 1979. Medical Participation in Control of Blue Shield and Certain Other Open-Panel Medical Prepayment Plans. Staff Report to the Federal Trade Commission and Proposed Trade Regulation Rule. Washington, DC: U.S. Federal Trade Commission. Truman, D. 1951. The Governmental Process. New York: Knopf. White, W. 1979. Public Health and Private Gain: The Economics of Licensing Clinical Laboratory Personnel. New York: Methuen. . 1983. Labor Market Organization and Professional Regulation: A Historical Analysis of Nursing Licensure. Journal of Law and Human Behavior 7 (2/3): 157-70.

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Appendix 1. Definition of Variables Variable Name

Definition

Dependent variables MANDl Dummy variable, scored 1 if state had passed a mental health insurance mandate of any kind, and scored 0 if no mandate had passed by 1983 MAND2

FOC

Dummy variable, scored 1 if state had passed a mental health insurance mandate requiring compulsory coverage, scored 0.5 if mandate required that coverage only be made available, and scored 0 if no mandate had passed by 1983 Dummy variable, scored 1 if state had passed an FOC law by 1983, and scored 0 if no FOC law had passed by 1983

Independent variables Interest group influence CMHC Total receipts of community mental health centers, 1976, divided by state population (in 1,OoOs) Total 1976 dues of state DUES psychological association, divided by state population LOBBY

krcent of 1976 state psychological association budget allocated for lobbying

PHD

Ph.D. psychologist service providers per million, 1972

MD

Psychiatrist service providers per million, 1970

Source Survey of state laws concerning mental health insurance coverage, Public Policy Department, National Mental Health Association, November 1983 Survey of state laws concerning mental health insurance coverage, Public Policy Department, National Mental Health Association, November 1983

Survey of state laws concerning mental health insurance coverage, Public Policy Department, National Mental Health Association, November 1983

Survey of community mental health centers, National Institute of Mental Health, 1976 Survey of state psychological associations, Professional Affairs Office, American Psychological Association, 1976 Survey of state psychological associations, Professional Affairs Office, American Psychological Association, 1976 G. D. Gottfredson survey of psychologist and psychiatrist service providers, 1976 G. D. Gottfredson survey of psychologist and psychiatrist service providers, 1976

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Variable Name

Definition

Source

RATIO

Ratio of Ph.D. psychologist service providers per million, 1972, to psychiatrist service providers per million, 1970 State mental health expenditures per capita, FY75-FY76, in cents, divided by total state health and hospital expenditures, in cents, 1975 Number of consumer protection laws passed by a state, 1974 Number of persons covered for hospital service by Blue Cross, 1978, divided by number of persons covered for hospital service, all insurance companies, 1978

G. D. Gottfredson survey of psychologist and psychiatrist service providers, 1976

MHEXP

CONSUM74 BCMS

Political system variables MANSUM Number of insurance mandates in other areas state had passed

WALKER

Walker Innovation Score

PARCOM

Interparty Competition Index

Socioeconomic factors INCOME Total personal income, per capita, 1975 Percent population with 4 years EDUC or more of college POP

Population (in 1,OOOs)

H. C. Schnibbe, Funding Sources and Expenditures for State Mental Health Agencies, National Association of Mental Health Program Directors, 1980 Book of States, 1976 Sourcebook of Health Insurance Data, 1979

J. Larsen, Mandated Health Insurance Mechanisms, report to the Bureau of Insurance, State of Virginia, 1979 J. Walker, The Diffusion of Innovations Among the American States, American Political Science Review 63: 880-99, 1969 A. Ranney, Parties in State Politics, in Politics in the American States, pp . 51-91, Boston: Little, Brown, 1976

Statistical Abstracts of the United States, 1978 Statistical Abstracts of the United States, 1978 Statistical Abstracts of the United States, 1978

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-658

.082

14.3

Simulated probability of passage, given a 20% increaseh

Difference between actual probability and simulated probability

Percentage difference between actual and simulated probabilities'

7.9

.046

.530

.576

CMHC

26.6

.I53

.729

,576

MHEXP

28.5

.I64

.740

.576

CONSUM74

9.5

.054

.630

.576

MANSUM

~

+

9.6

.055

.63 1

.576

BCMS

81.2

.468

.I08

,576

INCOME

6.0

.034

.542

.576

POP

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a . The estimated probability of passage of a mandate was calculated by the formula P = e'/l e'; where z equals the sum of the constant and the mean values of the independent variables multiplied by their estimated coefficients. b. Each simulated probability represents the impact of a 20 percent increase in the mean value of the independent variable when all other variables are held constant. c . The elasticity of each variable may be estimated by dividing by 20 percent.

.576

Actual probabi 1it y of passage"

DUES

Variable

Appendix 2. Simulations of Effect of a 20% Increase in Each Independent Variable on Probability That a Mental Health Mandate (Any Kind) Will Be Passed

fu,

:

4 0

00 00

w

.245

37.9

Difference between actual probability and simulated probability

Percentage difference between actual and simulated Drobabilities'

~~

.893

Simulated probability of passage, given a 20% increaseb

14.9

.097

.745

.648

MHEXP

49.3

.320

.968

.648

WALKER

39.9

.258

.906

.648

PARCOM

30.3

196

.844

.648

BCMS

59.7

.387

.261

.648

INCOME

3.1

.020

.628

.648

POP

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a. The estimated probability of passage of a mandate was calculated by the formula P = e'/l + e'; where z equals the sum of the constant and the mean values of the independent variables multiplied by their estimated coefficients. b. Each simulated probability represents the impact of a 20 percent increase in the mean value of the independent variable when all other variables are held constant. c. The elasticity of each variable may be estimated by dividing by 20 percent.

.648

Actual probability of passage"

PHD

Variable

Appendix 3. Simulations of Effect of a 20% Increase in Each Independent Variable on Probability That an FOC Law Will be Passed

Political and economic determinants of insurance regulation in mental health.

This article studies determinants of two important sets of laws regulating insurance coverage for mental health care: mandated inclusion of minimum co...
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