Asymmetric information in the Chilean Private Health Insurance Market

Asymmetric information in the Chilean Private Health Insurance Market Dolores de la Mata Universidad del Rosario Matilde P. Machado Universidad Carlos III de Madrid and CEPR M. Nieves Valdés Universidad Adolfo Ibáñez 1 PRELIMINARY AND INCOMPLETE VERSION PLEASE DO NOT QUOTE OR CIRCULATE March 15, 2014 1 This research was developed while M. Nieves Valdés was an assistant professor in the Department of Economics at Universidad de Santiago de Chile, institution whose researchers have the legal access to the Administrative Data on ISAPREs provided by the Superintendencia de Salud del Gobierno de Chile.

Abstract Since Rothschild and Stiglitz (1976) that asymmetric information is considered a potential setback in insurance markets particularly so in health insurance. The recent literature establishes that failure to nd evidence of asymmetric information based on traditional tests may be due to its multidimensionality. In this paper we assess the existence of a double source of asymmetric information in the Chilean private health insurance market; on the one hand, in the relationship between beneciaries and their private insurers and, on the other hand, in the relationship between the private insurers and the Regulator. The former exercise is somewhat standard in the literature while the latter is identied using the implementation of a law (Ley 19.966, Garantías Explicitas de Salud ) that grants quality of treatment and eective insurance to the severely ill. We use claim data for the universe of the privately insured for the year 2007. Our tests of asymmetry of information are based on the correlation between risk (measured by frequency of usage or expenditures) and coverage conditional on observables (Chiappori and Salanié, 2000 and follow-ups). Importantly, our measure of risk, based on hospitalizations, allow us to isolate adverse selection from moral hazard. Keywords: Testing Asymmetric Information, Adverse Selection, Insurance Markets, Moral Hazard and Asymmetric Information, Chilean Health Insurance, GES, ISAPREs, Private Health Insurance. JEL Classication: I13, L13.

1 Introduction Since Rothschild and Stiglitz (1976) (hereafter R&S) that asymmetric information is considered a potential setback in insurance markets particularly in health insurance; the premise is that clients have better information than insurance companies regarding their risk. Insurers are thought to react to their clients' private information by exerting both risk selection and price discrimination, which causes ineciencies in the way private insurance works and may lead to situations where some groups of the population are left uninsured. Policy interventions are often used to ameliorate this situation either in the form of public insurance provision or in the form of regulation (e.g. insurance mandates). The objective of the papers is to assess the existence of a double source of asymmetric information in the Chilean private health insurance market: on the one hand, in the relationship between beneciaries and their private insurers and, on the other hand, in the relationship between the private insurers and the Regulator. The former exercise is somewhat standard in the literature while the latter is identied using the implementation of a law (Ley 19.966, Garantías Explicitas de Salud ) intended to ensure quality of treatment and eective insurance to the severely ill. We use discharge data for the universe of the privately insured for the year 2007 in all exercises. Our tests of asymmetric information are based on the correlation between measures of clients' risk such as frequency or usage of medical services (e.g. hospitalizations) or medical expenditures and the level of insurance coverage, conditional on client characteristics used for pricing by the insurer as in Chiappori and Salanié (2000). This correlation was rst established by R&S and is robust to several modications of the original model's assumptions (Chiappori and Salanié, 2006). The idea is the following: in an insurance market with asymmetric information, the competitive equilibrium involves the higher risk individuals to self-select into high coverage and expensive contracts and the less risky individuals to selfselect into less expensive but suboptimal coverage contracts. From this equilibrium emerges a positive correlation between risk and insurance coverage in the presence of asymmetric 1

information that is commonly known as adverse selection. Although it may seem obvious that asymmetric information is indeed prevalent in insurance markets, the empirical literature that followed R&S does not always nd evidence of its existence (see Cohen and Siegelman, 2009 for an excellent review on evidence of asymmetric information in dierent insurance markets and Einav and Finkelstein, 2011 for a simple set up). For example, Chiappori and Salanié (2000) do not nd evidence of asymmetric information in the french car insurance market, 1 and perhaps more surprisingneither do studies using health and long-term insurance data for the US (Carlton and Hengel, 2001; Bundorf et al. 2012; and Finkelstein and McGarry, 2006). Other studies, however, using somewhat dierent methodologies and/or data, do nd evidence of asymmetric information in the form of adverse selection in health insurance markets (e.g. Olivella and Vera-Hernández (forthcoming), Bolhaar et al. 2012, and the reviews of Cutler and Zeckhauser, 2000 and Cohen and Siegelman, 2009). One explanation for the lack of a positive correlation in Carlton and Hengel ( 2001)'s study may be the idiosyncrasies of the American health insurance market, namely its collective nature where plans and premiums oered are, by and large, the result of bargaining between employers and insurance companies. The possibility of risk pooling at the rm level and the little role of each individual employee in the bargaining outcome may justify the observed irrelevance of asymmetric information in the US health insurance market. An alternative explanation, which has gained a lot of preponderance in the literature, is the presence of multi-dimensional private information which is consistent with zero, positive or even negative risk-coverage correlations (de Meza and Webb, 2001; Chiappori et al., 2006; Finkelstein and McGarry, 2006; Cutler et al. 2008; Fang et al., 2008; Keane and Stavrunova, 2011, Bolhaar et al., 2012). Fang et al. (2008) and Keane and Stavrunova (2011), for example, nd that cognitive ability and income, which are typically not observed by insurers, are systematically 1 Cohen (2005) points out that Chiappori and Salanié's (2000) result may be due to the restriction of the sample to relatively inexperienced drivers. In Cohen (2005)'s estimations for the Israeli market, she nds plenty of evidence of asymmetric information amongst more experienced drivers. 2

correlated with Medigap coverage and reduced risk. Finkelstein and McGarry ( 2006) and Cutler et al. (2008) nd that risk aversion increases the likelihood of buying long-term care insurance but decreases the likelihood of using long-term care. In de Meza and Webb ( 2001)'s model, the negative correlation between risk aversion and precautionary behavior i.e. (exante) moral hazard, may lead to an equilibrium where the riskier individuals are uninsured. Hence, in these papers, asymmetric information regarding preferences, i.e. risk aversion, or any individual characteristics that are simultaneously positively (negatively) correlated with insurance coverage and negatively (positively) correlated with risk, i.e. cognitive ability, constitutes a source of advantageous selection which may oset the positive correlation produced by adverse selection. Additional reasons for the nonexistence of a positive riskcoverage correlation, such as the inability or unwillingness of clients to use private information or the lack of information of the econometrician, are described in Cohen and Siegelman (2009). 2 Unfortunately, despite the dierent factors that may counteract or neutralize the correlation between risk and coverage, nding a positive correlation is not irrefutable evidence of adverse selection. In contrast, nding a negative correlation is irrefutable evidence of advantageous selection. A positive correlation may be the result of adverse selection more risk implies more coverage as well as moral hazard more coverage implies higher usage i.e more risk. In the case of health services utilization, the presence of moral hazard is well established among researchers (e.g. Manning et al., 1987; Cameron et al., 1988; Newhouse, 1993; Coulson et al., 1995; Holly et al., 1998; Vera-Hernández, 1999; Savage and Wright, 2003; Barros et al., 2008; Keane and Stavrunova, 2011), which complicates the identication of adverse selection. Some papers, typically making use of exclusion restrictions for identication, test simultaneously for selection and moral hazard (e.g. Carlton and Hengel 2001; Holly et al. 2002; 2 Olivella and Vera-Hernández (forthcoming) extend the R&S model to accommodate a National Health Service where private insurance is complementary. In their model, under symmetric information (i.e. in the absence of adverse selection) and whenever both private and public health systems are active, only the low risks take up private insurance. Hence, this form of advantageous selection into private insurance occurs not because of unobserved characteristics of the individuals but rather because of a characteristic of the supply side of the market. 3

Keane and Stavrunova, 2011; Shane and Trivedi, 2012, Bolhaar et al. 2012). The results are mixed: Keane and Stavrunova (2011), for example, nd a strong moral hazard eect while adverse selection is only found after controlling for many variables, such as cognitive ability and income among others; Bolhaar et al. 2012, on the other hand, nd evidence of advantageous selection but no evidence of moral hazard. In the rst part of this paper, we test for asymmetric information in the Chilean private health insurance market. Private and public insurance coexist in the Chilean health system. Insurance is mandatory for employed individuals but individuals must choose between public coverage, oered by the National Health Fund (FONASA), or private coverage oered by ISAPREs (from the Spanish Instituciones de Salud Previsional ). There is, therefore, no dual coverage in the Chilean market. 3 The Chilean market is ideal to test for the presence of asymmetric information because regulation limits the number of variables on which private insurers premiums can vary and all these variables are contained in our dataset. More concretely, the premium on any private plan can only vary according to three client characteristics: age; gender; and the inclusion of dependents to be covered by the plan. Our unique dataset consists of individual-level data on the universe of ISAPRE beneciaries for the year 2007 which was provided by the Superintendencia de Salud del Gobierno de Chile. For our test of asymmetric information between beneciaries and their private insurers, we construct a measure of risk that can be safely assumed to be free of moral hazard. Consequently, the nding of a positive risk-coverage correlation would constitute strong evidence in favor of adverse selection. Our choice for the measure of risk relies on results from other researchers which show that hospitalizations do not suer from (patients') moral hazard (e.g. Manning et al., 1987; Chiappori et al, 1998; Gardiol et al., 2005; Olivella and Vera-Hernández, 3 An exception of dual coverage is the disease-specic health insurance, which are, to certain extent, common in the population. Women, for example, would tend to contract insurance against breast cancer. These insurances which are oered either by ISAPREs, banks, or insurance companies are complementary private insurance that cover expenses such as copayments and hospitalizations, up to some amount, in the event of pre-specied catastrophic illnesses. This complementary coverage is compatible with either private or public insurance. 4

forthcoming). Hence, our baseline measure of risk is an indicator function that takes a value one if the individual has been hospitalized at least once during the year 2007 and zero otherwise. Our measure of coverage is not an indicator of private insurance, since all individuals in our sample are privately insured, but an indicator of coverage above 90% for expenditures on ambulatory and hospitalizations. Crucially, we can control for all variables that can be legally used for pricing purposes. Preliminary results show that the risk-coverage correlation estimate, after controlling for observable characteristics used for pricing purposes, is always positive ranging from 0.037 to 0.129 and strongly statistically signicant in the subsample of individuals holding individual or family plans throughout 2007. Hence, our results constitute strong evidence of adverse selection in the Chilean private insurance market during that year. When restricting to observations where there is collective policy negotiation at the level of the rm (as opposed to individual or family plans) our estimates of the risk-coverage correlation, in line with the results obtained by Carlton and Hengel (2001) for the US, are closer to zero, ranging from 0.010 to 0.077 and not always statistically signicant at 5% signicance level, showing a much lower level of adverse selection. For our test of asymmetric information between the private insurers and the regulator, we use the implementation of GES (Ley 19.966 of September 3, 2004) which establishes mandatory coverage both in the private and public systems for a list of health problems publishable by the Ministry. The privately insured patient who becomes eligible for GES coverage may nonetheless reject it and remain with her current plan. When an eligible beneciary opts for GES coverage, however, the costs to its ISAPRE may increase substantially. In order to compensate for these extra costs, the Regulator runs a supra-insurance for all ISAPREs. The insurance's compensation for GES patients, however, only adjusts for gender and age, leaving lots of room for ISAPREs' asymmetric information regarding the future costs of each of their beneciaries. We believe ISAPREs make use of this private information in order to convert to GES only the least costly of the eligible individuals. Similarly to our rst test of asymmetric information, we construct a coverage variable which takes value 1 whenever an eligible patient becomes GES and 0 otherwise and a risk variable that takes value 1 whenever the eligible GES beneciary is hospitalized in the period prior to accepting GES coverage. 5

The paper is structured as follows: Section 2 describes the Chilean Health System; Section 3 describes our dataset; Section 4 describes the methodology; Section 5 describes and discusses our results and Section 6 concludes. The Appendix contains tables and gures. 2 The Chilean Health System The Chilean Health Insurance market is characterized by the coexistence of public and private insurance. Insurance is mandatory for employed individuals but individuals must choose between public coverage or private coverage. There is a single public insurance provider, FONASA, and, in 2007, 14 private insurers or ISAPREs (from the Spanish Instituciones de Salud Previsional ). In 2007, FONASA covered 11, 740, 688 individuals or 70.4% of the population whereas ISAPREs covered 2, 776, 912 individuals or 16.6% of the population. 4 Unfortunately, our data is restricted to beneciaries from ISAPREs. FONASA classies its clients into 4 categories, known as classes A, B, C, and D. Classes cover progressively richer individuals where the coinsurance rate is 0, 0, 10, and 20 percent respectively for classes A, B, C and D. Individuals in classes other than A must pay 7% of their gross wages (with a cap) as a premium for the basic plan which covers the same services in FONASA and ISAPREs. For extra coverage, individuals may pay higher premiums. FONASA cannot deny coverage to any individual. Hence classes A and B are highly subsidized through general taxation. Not surprisingly, FONASA shows an over-representation of beneciaries from the lower income quantiles (Paraje and Vásquez, 2011). ISAPREs plans can be classied into three categories. The free-choice plans (from the Spanish plan de elección libre) allow beneciaries to choose any private health care provider and be reimbursed according to the copayment specied in their insurance contract. 5 closed plans (from the Spanish plan cerrado) only cover expenses with providers listed in the insurance contract. Finally, the preferred provider plans (from the Spanish plan con 4 Source: (http://www.fonasa.cl/wps/wcm/connect/internet/sa-general/informacion+corporativa/estadisticas+institu 5 ISAPRE beneciaries have access to the public providers only in the event of an emergency. 6 The

proveedor preferente) are a combination of the previous two, that is, they allow beneciaries to choose any health care provider but they enjoy lower copayments at a pre-specied provider of their choice which must be listed in the insurance contract. Once an ISAPRE releases a plan, it cannot modify its copayment structure and, in theory, the plan is available to any beneciary. The plan's copayment structure, which may depend on the medical procedure, the physician's specialty, and the provider, is describe in the insurance contract. Each plan also establishes a maximum amount that is covered by the insurance for each service which implies that the beneciary must pay the dierence between this cap and the price charged by the provider. These caps vary by procedure and specialty and are most common for ambulatory procedures and visits. Importantly, these caps are reached easily for the average plan. Each contract also establishes a cap on the total annual expenditures that are covered by the ISAPRE, but contrary to service caps, these are usually suciently high and often not binding. The pricing of private plans is subject to very simple regulation. There is a reference premium (from the Spanish precio base) for each plan which the ISAPRE can modify annually and unilaterally. The premium paid by each beneciary for a given plan depends directly on the reference premium and on a factor load which is a function of the policyholder's age, gender, and the inclusion of dependents in the policy. 6 ISAPREs may oer collective plans for employees of selected institutions (private or public)in 2007 only 17.4% of the privately insured individuals had a plan negotiated or oered by their employer. These collective plans, although not as common in Chile as in the US for example, oer more coverage and/or lower prices than comparable individual or family 6 Some exceptions are family plans that have lower eective premiums for dependents (children and spouses) when the dependents' income is below the minimum legal wage. Other examples are plans that oer reduced coverage for specic events such as childbirth, and physician visits. For these events the coinsurance rate may reach 75%, but the plan is cheaper than a comparable plan without reduced coverage. 7

plans. 78 It is likely that ISAPREs compete among each other for beneciaries but it is also likely that they have market power. Olivella and Vera-Hernández (2007) have a model of competition among health plans with asymmetric information which is probably a good proxy for the Chilean market. They show that rms have a higher expected prot from the low risk than from the high risk and that there is cross subsidization between both types of patients. However, there are at least two reasons why it is hard to fully adapt their model to our setup. First, because both high an low risks in their model pay the same premium although they choose dierent plans. Second, their model assumes the number of health plans to be given exogenously. This is hardly the case in the Chilean private insurance market where in 2007 there are 37, 248 plans chosen by at least one beneciary. 9 Finally, we believe the IS- APREs continuously create new plans as a price discrimination mechanism that circumvents the pricing regulation. 2.1 GES and the Compensating Mechanism The law 19.966 of September 3, 2004, establishes mandatory coverage both in the private and public systems for a list of health problems publishable by the Ministry. The mandatory coverage, denoted by GES (from the Spanish Garantías Explicitas de Salud ) is enacted in July 1, 2005 with the objective of guaranteeing adequate treatment and insurance to patients diagnosed with one or more of the 25 health problems listed. 10 For each health problem, 7 Note that these collective plans are not exactly equal to the US plans subscribed by employers for their employees. In the collective plans in Chile, the individual can choose whether to subscribe the collective plan usually oered by one specic ISAPRE, or to buy an individual plan from any ISAPRE in the market. 8 We believe that the greater weight of the individual in selecting health plans in the private sector in Chile generates equilibria where asymmetric information is more preponderant than in the US although it also leaves more room to the insurance company to adapt the menu of contracts to the characteristics of the individual. 9 Source: Administrative data of the universe of the private insurances companies in Chile (ISAPREs) provided by the Superintendencia de Salud del Gobierno de Chile for the year 2007. 10 Initially GES was denoted as AUGE (from the Spanish Plan de Acceso Universal de Garantías Explicitas). 8

the law describes the adequate treatment (access), the maximum waiting time to obtain treatment (opportunity), the certication of the treatment provider (quality), the thresholds below which there is a 20% copayment and beyond which treatment is provided for free (nancial protection). Importantly, the certication of providers (guarantee of quality) is still not in place by 2007 (Castillo de Desal et al. 2008), which may lead to strategic assignment of GES patients to low quality providers. The list of health problems is extended to 40 in July 1, 2006 and to 56 problems in July 1, 2007. The latter extension occurs right in the middle of our sample period. When diagnosed with one of the 56 health problems, the provider has the obligation to notify the patient of her entitlement to GES status and coverage (article 24 of Law 19.966). Upon receiving notication, privately insured patients, however, may opt out of GES and remain with their current health plan. When a notied patient opts for GES coverage, the cost to the ISAPRE is likely to increase substantially due to low copayments and the thresholds imposed by the law. Aware of the low incentives that ISAPREs and providers alike have to notify their patients about GES eligibility, the regulator established a monitoring program. Results for the 2011 monitoring program, for example, show that only 31.1% of the 45 providers analyzed noties its patients satisfactory (Informe de Fiscalización, Superintendencia de Salud, 2012). Alternatively to not notifying the patient, the ISAPRE may either select a low cost provider to serve GES patients in order to oset (at least partially) the increase in costs or select a low quality provider to discourage eligible patients to choose GES status. The report by Castillo del Desal et al. (2008) shows indirect evidence that these strategies may be taking place. They show, using a dierent data source from ours, that there is a lot of heterogeneity across ISAPREs in the uptake of GES and a lot of variation within ISAPREs across health problems. For example, there is a large percentage of eligible cataract patients that take GES status compared to other health problems. We believe that the treatment of cataracts is quite standard and hence there is low variance across patients and relatively inexpensive. For other health problems, where patients' future costs may vary substantially, we believe ISAPREs have the incentive to only notify of their eligibility and promote the least costly patients to GES. 9

To encourage ISAPREs conversion of their eligible patients to GES, there exists also a compensation mechanism denoted Fondo de Compensación (FC from hereafter) which acts as an insurance for the ISAPREs regarding GES. The FC is managed by the regulator. The ISAPREs pay a xed amount per beneciary 11 to the FC denoted by community premium (from the Spanish prima comunitaria). This amount does not depend on any of the beneciaries characteristics or expected cost. The FC computes also a risk-adjusted premium for each beneciary according to gender and age. An ISAPRE receives a compensation when the total risk-adjusted amount for the GES beneciaries is higher than the sum of the community premiums for that population and it pays a compensation to other ISAPREs when the reverse occurs. We believe the risk adjustment by age and gender is insucient to encourage ISAPREs to convert all their eligible GES patients into GES status. The idea is that the ISAPREs hold very precise information on its own beneciary's expected future costs which certainly go beyond age and gender. Hence, it is likely that the ISAPREs have much better information on each eligible GES beneciary than the regulator and it will use this asymmetric information to try to inuence which eligible GES patients actually choose GES. In Section 4 we discuss the general test of asymmetric information and in Section 5.2 we discuss the results and the exact specication for the test of asymmetric information between the ISAPREs and the regulator. 3 Data We use data from the universe of beneciaries of private insurance companies in Chile (IS- APREs) provided by the Superintendencia de Salud del Gobierno de Chile for the year 2007. This constitutes unique individual-level data compiled from administrative records from all private health insurers in Chile. It contains all claims to ISAPREs made by their beneciaries during the year. Crucial to our analysis, the data contains, for each beneciary, all the variables that 11 This fee is charged directly to the beneciaries through an increase in their insurance premium. 10

ISAPREs are allowed to use for pricing their plans age, gender, and the existence of dependents. Additionally, we also observe other individual characteristics such as whether the individual is the main policyholder, the type of policyholder (e.g. employed, self-employed, retired, or voluntary contributor), the individual's state of residence and, in the case the individual is employed, we may infer income. The individual characteristics also allow us to match each dependent with the main policyholder and observe their relationship (e.g. spouse, children, etc.). The dataset also contains information on the main characteristics of the insurance contract: identity of the insurer (ISAPRE), the type of plan (individual, family, or collective), and whether the plan has reduced coverage for birth delivery, and/or for physician fees. For each claim, we observe whether or not a preferred provider is chosen. Finally, the dataset contains plan specic summary information that the regulator requires from all ISAPREs such as the average copayment in case of ambulatory services, and the average copayment in case of hospitalization. We use these two variables in the denition of one of our main dependent variables. We restrict our working sample to individuals who remained insured throughout 2007 and who have individual or family plans although we also run separate regressions for individuals who choose collective plans oered by their employers. Our baseline measure of risk is an indicator function that takes value one if the individual has been hospitalized at least once during 2007 and zero otherwise. This measure of risk has two advantages. First, we minimize the chance that a correlation between risk and coverage arises due to ex-post moral hazard. Second, it is unlikely that individuals forgo claiming a hospitalization event to the insurance company since, on average, these events are relatively expensive. This second point is important because, in general, claim data from insurance companies is a noisy measure of individual real risk since the decision to convert a health event into a claim (i.e., going to the doctor or asking for reimbursement) is an individual choice sensitive to the characteristics of the insurance contract (Chiappori and Salanié, 2000; Cohen and Siegelman, 2009). Table 1 shows the distribution of beneciaries across ISAPREs in 2007. We may conclude that the private insurance market in 2007 is highly concentrated with six of the largest IS- 11

APREs concentrating more than 92% of the beneciaries and the two largest accounting for more than 45%. In the last column of Table 1 we classify the ISAPREs as open for those cases where the ISAPREs accept any individual as a potential beneciary or as closed for those that only accept beneciaries that fulll some condition, usually individuals that are employed by a particular employer. Most ISAPREs in the system are open, and the closed ISAPREs account for only 5% of the beneciaries. Since the closed ISAPREs do not compete for clients, this increases slightly the relevant concentration in the market. In our analysis to detect the presence of asymmetric information we restrict the analysis to the open ISAPREs. Table 1: Distribution of beneciaries across ISAPREs ISAPRE Number of Percentage of Type of beneciaries beneciaries ISAPRE 1 3,865 0.2 closed 2 5,522 0.3 closed 3 5,752 0.3 open 4 11,015 0.5 closed 5 23,711 1.1 closed 6 32,941 1.5 closed 7 36,077 1.7 closed 8 39,555 1.8 open 9 124,806 5.8 open 10 199,768 9.2 open 11 338,279 15.6 open 12 351,736 16.2 open 13 458,638 21.2 open 14 534,461 24.7 open Total 2,166,126 100.0 12

Table 2 shows little preponderance of one gender over the other in the private health insurance market. However, looking closer at the age distribution of beneciaries by gender (Figure 1) we see that females of childbearing age (20 to 40 years old) are under-represented in this market. The reason is likely to be the premiums that ISAPREs charge to females in this age group which can be up to 3 times higher than those charged to males of the same age. The distribution of the beneciaries' age follows closely the scheme of plan-specic prices by age: prices go down until the age of 20, for the 20 40 age bracket approximately prices dier by gender (lower for males than females), and increase steadily above 50 years old. Because premiums are signicantly higher for the elderly, people turn to the public insurance (FONASA) when they grow older. This selection, means that the typical ISAPRE client is relatively young, around 30 years old, while the numbers of children under 3, of women of childbearing age, and of seniors older than 64 years are disproportionately low. Finally, 87% of the beneciaries are employees, and only 4% are voluntary contributors. Figure 1: Distribution of beneciaries' age by gender. % 0.2.4.6.8 1 1.2 1.4 1.6 1.8 2 0 10 20 30 40 50 60 70 80 90 100 110 Age Male Female 13

Table 2: Beneciaries' demographic and economic characteristics Variable mean sd min max male 0.52 0.50 0.00 1.00 age 30.29 18.48 0.00 107.00 younger3 0.04 0.20 0.00 1.00 fem_fert 0.16 0.37 0.00 1.00 older64 0.04 0.19 0.00 1.00 income 1,394.59 836.24 0.00 22,093.81 employee 0.87 0.34 0.00 1.00 self_employed 0.03 0.18 0.00 1.00 pensioner 0.05 0.22 0.00 1.00 voluntary contributor 0.04 0.21 0.00 1.00 Rows description: males: proportion of beneciaries that are males; younger3: proportion of beneciaries that are younger than 3 years old; fem_fert: proportion of beneciaries that are women between 20 and 40 years old; older64: proportion of beneciaries that are older than 64 years old; income: mean income in dollars of 2007; employee: proportion of beneciaries that are employees; self_employed: proportion of beneciaries that are self employed; pensioner: proportion of beneciaries that are pensioners; voluntary contributor: proportion of beneciaries that are voluntary contributor; 14

The main characteristics of insurance contracts are presented in Table 3. The majority of contracts are individual or family contracts and only 17% are collective contracts i.e. contracts that are acquired through the beneciary's employer. In terms of coverage, the average contract is relatively generous with 73% of coverage for ambulatory medical care usage, and 85% of coverage for hospitalizations. Approximately 80% of the contracts do not have coverage restrictions for any specic medical care. Table 3: Main Characteristics of insurance contracts Variable mean sd collective 0.17 0.38 cov_a 73.05 18.16 cov_h 84.75 23.84 cov_gen 0.79 0.41 Rows description: collective: proportion of beneciaries that have a collective plan; cov_a: mean coverage for ambulatory procedure or MD visits; cov_h: mean coverage for hospitalizations; cov_gen: proportion of plans without reduced coverage for specic services. In Table 4 we can see that 7% of beneciaries are hospitalized at least once during 2007. The average beneciary spends US$ 671 on 14 medical care services during 2007, and pays US$ 216 as copayments. The average beneciary uses a preferred provider in 12% of the medical care events. 3.1 Descriptive statistics by ISAPRE Table 5 shows that there exists a selected group of very small ISAPREs (denoted by 1, 3, 5, 6 in the Table, respectively) with more than 95% collective plans. A plausible explanation is that these ISAPREs were created by worker unions that oered medical insurance to their aliates before the law of ISAPREs was passed (May 1981). After the law, these organizations were restructured as ISAPREs to continue functioning. 15 Looking at Figure 3.1 we can see that

Table 4: Statistics of medical care usage (by beneciary). variable mean sd min max hosp 0.07 0.26 0.00 1.00 tothosp 0.10 0.54 0.00 79.00 totexp 671 3,167 0.00 621,462 totcop 216 1,206 0.00 462,998 mdvisit 3.50 4.38 0.00 104.00 hemogram 0.38 0.92 0.00 142.00 bloodext 0.61 1.30 0.00 58.00 rxsimple 0.39 0.96 0.00 31.00 ins_use 13.52 20.99 0.00 1284.00 pref 11.85 28.71 0.00 100.00 Rows description: hosp: proportion of beneciaries there were hospitalized at least once during 2007; tothosp: number of hospitalizations; totexp: total amount of expenditures in dollars of 2007; totcop: total amount of copayments in dollars of 2007; mdvisit: number of visits to the physician; hemogram: number of hemograms; bloodext: number of blood extractions; rxsimple: number of rx; ins_use: number of times the insurance was used during 2007; pref: mean percentage of health services provided by a preferred provider;. 16

collective plans are concentrated among beneciaries of 50 years old and more, another fact consistent with the union origin of these ISAPREs. Comparing the distributions of age and gender of beneciaries with and without collective plans, we conclude that ISAPREs with collective plans do not seem to pool the risk of beneciaries as clearly as other ISAPREs. Also in Table 5 we can see that there are important dierences in beneciaries' risk across ISAPREs, with the percentage of beneciaries that have at least one hospitalization ranging from 2% to 14% across all ISAPREs and from 3% to 12% across Open ISAPREs. 17

Table 5: Descriptive statistics: selected information by ISAPRE. 18 ISAPRE part male younger3 fem_fert older64 income collective cov_a cov_h cov_gen pref hosp 1 0.18 0.54 0.04 0.10 0.02 794.35 1.00 75.03 90.13 1.00 0.00 0.10 2 0.25 0.51 0.03 0.11 0.05 998.55 0.00 97.21 100.00 1.00 68.74 0.13 3 0.27 0.50 0.03 0.10 0.03 1443.86 0.99 70.46 89.76 1.00 0.00 0.06 4 0.51 0.53 0.03 0.15 0.03 520.87 0.47 79.83 99.62 1.00 0.00 0.02 5 1.09 0.45 0.02 0.10 0.24 926.83 0.95 83.52 99.97 1.00 0.16 0.09 6 1.52 0.49 0.03 0.10 0.04 1138.14 0.97 96.47 99.13 1.00 66.44 0.03 7 1.67 0.51 0.03 0.12 0.08 0.00 0.62 90.26 76.74 0.99 78.06 0.14 8 1.83 0.53 0.05 0.15 0.02 818.67 0.33 99.96 100.00 0.97 26.18 0.05 9 5.76 0.50 0.04 0.17 0.06 763.98 0.00 72.51 84.84 0.71 8.13 0.12 10 9.22 0.51 0.05 0.18 0.02 817.62 0.13 81.63 99.19 0.46 0.14 0.09 11 15.62 0.58 0.03 0.13 0.02 680.48 0.16 72.31 92.34 0.84 24.21 0.03 12 16.24 0.50 0.05 0.18 0.05 834.71 0.28 74.88 73.33 0.83 1.65 0.06 13 21.17 0.51 0.04 0.17 0.03 665.77 0.11 71.45 91.17 0.81 1.32 0.05 14 24.67 0.53 0.04 0.16 0.04 704.77 0.09 65.16 73.82 0.78 17.61 0.10 Total 100.00 0.52 0.04 0.16 0.04 728.94 0.17 73.05 84.75 0.79 11.85 0.07 Columns description: part: market participation (%); males: proportion of beneciaries that are males; younger3: proportion of beneciaries that are younger than 3 years old; fem_fert: proportion of beneciaries that are women between 20 and 40 years old; older64: proportion of beneciaries that are older than 64 years old; income: mean income in thousands of chilean pesos; collective: proportion of beneciaries that have a collective plan; cov_a: mean coverage for ambulatory procedure or MD visits; cov_h: mean coverage for hospitalizations; cov_gen: proportion of plans without reduced coverage for specic services; pref: mean percentage of health services provided by a preferred provider; hosp: proportion of beneciaries there were hospitalized at least once during 2007.