Courts, Caps, and Medical Malpractice Insurance

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1 Courts, Caps, and Medical Malpractice Insurance Paola Bertoli University of Bologna Veronica Grembi Catholic University of Milan First Draft: June Please do not quote without authors permission Abstract We assess the impact of introducing a tiered caps system (i.e. schedules) of noneconomic damages on commercial insurers decisions related to the medical liability market. The novelty of our approach is twofold: we provide an evaluation of tiered caps, which differ from flat caps, usually adopted in the U.S. institutional system, and we control for the performance of the judiciary system, measured as the level of backlog of Courthouses. We use the Italian case study to assess the policy impact, because in Italy schedules of tiered caps were adopted by different Courts in different years and Courts are heterogeneously performing. The institutional framework allows to implement a Difference-in-Differences strategy to estimate the effect of caps adoption on premiums and number of insures in the reference market. To cope with omitted variables problems we exploit the partial overlapping between Courts districts and healthcare providers districts. JEL Classification: I10, K32, K40 Keywords: Tiered Cap of Non Economic Damages, Courts, Medical Malpractice Insurance, Difference-in-Differences We thank Rosa Ferrer, Matteo Lippi Bruni, Tommaso Nannicini, Neil Rickman, Matteo Rizzolli, the participants of the 2012 Italian Health Economics Association Annual Meeting, the 2012 Italian Association of Law and Economics Annual Meeting, the 2013 workshop The Future of Law and Economics, and of the seminars at Bocconi University, Catholic University of Milan, and Free University of Bozen for their insightful comments on previous versions of this work. We are grateful to the Italian Ministry of Justice (Laura Malgieri) for kindly providing the data on Courts, and to the Italian Ministry of Health for providing the data on the health care sector. Francesco Esposito provided excellent assistantship with the data on malpractice claims. Usual caveats apply. 1

2 1 Introduction Commercial insurers withdrawals from the market of medical liability have been regular at least in the U.S. [Danzon (2000); Danzon et al. (2004);Mello et al. (2003); Mello (2006)]. In some cases, insurers decide to operate only in restricted markets, either selecting States (i.e. geographical bounds) or selecting the type of coverage (i.e. medical specialties bounds). 1 The decision is mainly justified by the lack of predictability of losses due to malpractice claims. The explanations in the literature are mixed: some contributions stress a long run perspective on the coverage crisis according to which 1) there is no strong evidence to define a clear cut link between trends of malpractice claims and compensations and the behavior of insurers [GAO (2003a) and (2003b)], 2) malpractice insurance market periodically experiences insurance cycle common to other long-tailed lines of liability insurance [Danzon et al.(2003)]. Contributions related to a short run perspective stress the importance of policies able to lower down the level and variance of compensations, the most effective being the introduction of flat caps on damages [Avraham (2010); Kessler (2011)]. 2 A flat cap is an upper bound to compensations for non-economic damages related to personal injuries, and it changes across states in the U.S.. The effects of this kind of cap on insurers is a reduction of losses, although effects on premiums are not univocal [Viscusi and Born (1995) and (2005); Born et al. (2009)]. 3 Alternatively, systems of tiered caps can be designed and implemented. Tiered caps are different from flat caps, cause they do not set an upper bound for compensations of all injuries, but they adjust compensations to the type of damages and, in some cases, to victims characteristics, increasing the level of vertical equity in compensation. So while a well known flat cap sets a maximum of 250,000 dollars limit to non-economics damages of any kind, a tiered caps system allows to calculate a benchmark compensation for a certain degree of injury, for instance 40,000 dollars for a 10% permanent invalidity of a 50 years old. The use of tiered caps of non-economic damages (i.e. schedules), which is common and refers to all types of personal injuries in countries like the U.K., France, and Belgium among the others, has been discussed among alternative policy tools to curb medical malpractice costs also in the U.S., in the form of standardized evaluation scenarios and NAIC Severity of Injury Scale, among the others [Mello and Kachalia (2010)]. However empirical evidence on the effects of tiered caps is lacking. In this work we aim at filling this gap. Overall the novelty of our contribution is twofold: we provide an empirical analysis of tiered caps and we assess tiered caps impact taking 1 Hereafter when we mention insurance and insurance premium we refer to the market for medical liability insurance, unless differently stated. 2 We refer only to the case of caps on non economic damages and not to caps on punitive damages. 3 Using the same source of data National Association of Insurance Commissioner (NAIC) data Thorpe (2004) estimates the effect of tort reforms on aggregate premium income at the state level from 1985 to 2001 in the U.S.. This study has the added merit to extend the analysis also to the aggregate premium revenues per physician and control for several state characteristics, such as the level of concentration of the reference market in each state and the number of medical practitioners. Thorpe concludes that caps on noneconomic damages imply both lower loss ratios and lower premium revenues for insurance companies. Specifically, a reduction of premium income between 13 and 17 percent is associated with caps on non-pecuniary damages. 2

3 into account the role played by the judicial system, in charge for caps enforcement. The empirical literature on caps does not consider the role of the judicial system and generally assess the impact of torts reform on insurers (as well as the other players of a malpractice case) independently from this institutional element. We argue that the judiciary plays an important role which has to be included in the analysis. We focus on one aspect of the judicial system, its performance, measured as the level of backlog affecting each Court. We associate backlog to Court inefficiency, and we control for this element when tiered caps are adopted. Although Bovjerg et al. (1989) have warned of the advantages of extending scheduling from workers compensation plans and disability plans to all tort damages, there is a lack of a comprehensive theoretical framework of the expected impact of tiered caps. Mello and Kachalia (2010) are the only exception, to our knowledge, even though they state that much of the expected impact is due to the level of the tiers. We use the Italian institutional framework, where, from the Nineties, Courts of general jurisdictions Courts of the First Instance (CFI) started to adopt systems of tiered caps of non-economic damages (tabelle per il risarcimento del danno non economico). Tiered caps, so called schedules, provide guidelines to judges belonging to CFI districts to compensate non-economic losses related to all kind of personal injuries, according to quite detailed parameters, as the victim s age and the severity of the injury. As such, schedules aim at improving both the vertical and horizontal equity of compensations, while reducing the discretion of judges on individual cases. However, Italy is characterized by high heterogeneity when judicial performance is at stake, both across country and within each regional territory. Given that CFI adopted the policy in different years, we rely on a Difference-in-Differences (DD) approach to identify the impact of tiered cap on insurers behavior. To address possible omitted variables problems, due to the staggered process of treatment (i.e. policy) adoption, we identify the effect of tiered caps, exploiting the not perfect territorial overlapping between Courts districts and the competence of the local healthcare providers. 4 The Italian health care system is mainly public and and it is organized on a territorial base. Regions equivalent to States are in charge for delivering healthcare services to their residents through a system of Local Health Units (LHU))(Aziende Sanitarie Locali). Each Region is divided in LHU, which are in charge to provide healthcare plans to the residents of a pre-defined territory, directly through their own hospitals or indirectly through a system of independent hospitals (i.e. trusts) placed in their district. 165 Courts districts divide 20 Regions, that are divided (according to the years from ) in LHUs and covered by independent hospitals. Hence, hospitals under the competence of a same LHU, in a same Region, might be held liable before different Courts. On the other hand, a same Court district might cover more than one LHU district, so that the caseload of one Court is not univocally determined by the same territory of one health care provider. The definition of LHU districts and of the Courts districts belongs to two different authorities: healthcare providers competences are defined by Regional governments in agreement with the 4 The approach is similar to the concept of territorial congruence used to assess the impact of information on political accountability in Snyder and Stromberg (2010). 3

4 Ministry of Health, Courts districts have been defined by the Ministry of Justice. We use this territorial differences to assess the impact of tiered caps conditional to Courts performance. Our analysis run on a unique dataset: a sample of 856 public procurement procedures for insurance contracts dated from 2000 to 2010 and involving only public healthcare providers dealing with private insurers. This sample is representative of insurance contracts for hospitals personnel. 5 Our findings show that the introduction of tiered caps increases the number of insurers interested in the market of medical liability both in absolute and relative terms, when the judicial system is inefficient. A decrease in premiums paid by healthcare providers is registered although it is not statistically significant. The paper is organized as follows. Section 2 describes the main institutional elements on medical liability and insurance, healthcare system, tiered caps, and Courts in Italy. Section 3 introduces the econometric strategy, while section 4 describes the data we use for the results discussed in section 5. Section 6 concludes. 2 Institutional Framework In this part we provide the main background institutional elements concerning the Italian organization of Courts, tiered caps/schedules system, the malpractice liability system, and the market of medical liability insurance for healthcare providers. 2.1 Courts Italy counts 165 Courts of First Instance (CFI), which are the default Courts for both civil and criminal general jurisdiction. 6 A 1941 Royal Decree defined the borders of CFI districts and since then they have not changed substantially. CFI, as the entire Italian legal system, rank poorly in the World rankings of the judiciary performance [e.g. World Bank (2009)] with a significant degree of within country heterogeneity. One of the parameter use to rank different legal systems is the average duration of a case in the first instance. Between 2000 and 2007 a civil case took 977 days (2.7 years) on average to be closed with a Court decision. 7 Scarcity of human resources or lack of funding are not among the strongest rationales of such poor result [CEPEJ (2009)]. Several studies on the Italian case have shown that the main reason of the so-defined judicial inefficiency relies in bad organization of judges work within each Court [Coviello et al. (2012a) and (2012b)]. For instance, in 2005, the average duration of a case in Turin was 174 days and 324 in Milan. The difference is striking notwithstanding common socio-economic characteristics of the two Courts and the higher number of new cases filed in Turin during the same year [Coviello et al. (2009)]. 5 Physicians can also have extra insurance coverage. Information on private insurance contracts between physicians and private insurers are not available. 6 Decisions of CFI can be appellated to a Court of Appeal and there is a last appeal to the Court of Cassation, supreme Court of both civil and criminal jurisdictions. 7 The median duration was equal to 907 days (2.5 years). The information at the district level is available through the National Institute of Statistics only till

5 The average length of civil trials is highly correlated with the backlog index. 8 The backlog index of Court j=1, 2,...,165, at year t, can be written as: Backlog jt = New Cases jt+p ending Cases jt Closed Cases jt As such the level of backlog provides a measure of not solved cases per year of Court activity: it is the ratio of new open cases plus pending cases at the beginning of each new year (i.e. not solved at year t 1) and the closed cases. The minimum value of a backlog index is 1, since 1 means that all the entering cases at time t were solved during the same period. On average, between 2000 and 2010, the backlog of Italian Courts was equal to 3.62, with a median value of The 95th percentile was equal to The worst performers according to this index are Southern Regions, with an average of However, within each Region there are substantial differences, as apparent by Figure Tiered Caps System Awarding non economic damages is a quite recent trend in the Italian legal system. Legal scholars began to address their relevance during the Sixties (1962). 9 Till then victims of personal injuries were entitled also to non-economic damages compensation only if the injuries were the results of a criminal act. The pressure of a raising number of car accidents and of injuries not related to any criminal act started to modify judges perception of fairness in compensations. During the Seventies, attempts to introduce an independent-from-thecriminal-law right to non-economic damages generated a jungle of different approaches and criteria, which varied widely both across and within courts. 10 At the beginning of the Eighties ( ), the Court of Cassation intervened as court of last appeal for both criminal and civil jurisdictions, ruling the possibility to compensate damages even when not related to patrimonial contents. Once an independent right to noneconomic damages was granted, the big issue was how to calculate compensations, given the lack of strictly economic benchmarks. To address this stance (judges within) Courts started to elaborate cap schedules using the experience of other European countries (i.e. France). 11 In 1986, the Constitutional Court ruled the constitutionality of the use of schedules (tabelle per il danno biologico) to settle non economic damages. 12 From that moment, CFIs opted 8 The correlation coefficient is 0.78 for the period Yet, a 1967 decision of the Court of First Instance in Florence ruled the impossibility to grant compensation to an injured retired 70 years old on the base that there might be people without value according to the law [Nuovi orientamenti per la determination del danno, (1989)]. 10 Reasons for these differences mainly rely on ideology and political believes. See Nuovi orientamenti per la determinazione del danno, The definition of the value of the percentage point of disability has been assigned to medical experts [Comandé (2005)]. In order to guarantee the consistency of Courts decisions, these monetary values have been defined with respect to previous cases compensations [Sella (2005)]. 12 In the sentence n.184/1986, the Constitutional Court recognizes the validity of schedules by identifying as fundamental criteria for the assessment of non-economic damages those criteria at the basis of schedules themselves. Specifically, these criteria are (i) the specification of monetary values of general application; and (ii) the possibility to adapt these values according to the severity of the injury suffered by the victim. 5

6 for cap schedules schemes on the base of their judges decision: judges belonging to a same CFI should vote for the adoption of the scheme. 13 Once the adoption of a schedule has been voted, the schedule is in place. 14 If adopted, schedules of tiered caps apply to all kind of injuries, from car accidents consequences to work accidents, as well as medical malpractice. 15 In most cases provides a range of compensation also for the victim s relatives, in case of victim s death. Schedules set a value for each percentage point of permanent damage suffered by victims. Among the most successful scheme, the one adopted by the court of Milan has gained the greatest consensus. Specifically, the Milan method sets the monetary point value on the basis of the severity of the harm suffered and the age of the injured. In particular, this mechanism foresees the simultaneous application of two criteria: (i) a progressive criterion for the determination of the monetary point values of the disability percentages; and (ii) a regressive criterion with respect to the age of the injured party. According to the first criterion the compensation varies unevenly and more rapidly with the increasing severity of the injury. Differently, the regressive criterion reflects the fact that, considering the average possible lifetime of a person, a victim who has been harmed at a younger age, would bear the consequences of the physical impairment for a longer period than an older victim (see Table A1). 16 The Court of Cassation has not only recognized the validity of the Milan schedule, it has also pointed out the great effectiveness of the Milan method in avoiding an unequal treatment of victims and assuring the predictability of the sentences (Court of Cassation 748/2000). As a result, the Milan schedule, developed for the first time in 1995, has been soon taken as main reference by other courts to the extent that nowadays it has prevailed becoming the most widely used mechanism for the assessment of noneconomic damages given also that the Italian Supreme Court, in its recent sentence 12408/2011, has recognized the Milan method as the basic criterion to compensate physical damages [Negro (2011)]. The adoption of tiered caps has been common across the country as shown in Figure 1. In 1996, 8% (14) of CFI relied on caps, in 2000, 45% (75) and by %(125) of CFI used them. The adoption took place both in more efficient and less efficient Courts, when efficiency is measured as the CFI level of civil backlog. Figure 1, about here 13 Judges can waive the schedules but in that case they must justify their choice. If they waive, judges have always to standardize as much as possible the criteria for the assessment of non economic damages to average compensations granted in the previous cases (Court of Cassation, May 24, 2001, n.7048; Court of Cassation, May 8, 2001, n. 6396; Court of Cassation, November 6, 2000, n ; Court of Cassation, August 11, 2000, n ; Court of Cassation, May 19, 1999, n. 4852). As a consequence, schedules are a quite enforced calculation mechanism on all kinds of injuries once they are in place. 14 The Legislator tried to face the territorial differences by imposing a uniform noneconomic damages schedule for the first nine percentage points of disability (the so-called micropermanenti) (Law 57/2001) with respect to auto accident personal injury damages. However, even in this case, Courts have remained responsible for the determination of the damages for the most serious cases. 15 As it happens in other countries where tiered caps system are adopted in the civil system, like France, the U.K., or Belgium. 16 De Paola and Avigliano (2009). 6

7 2.3 Medical Malpractice Liability In malpractice cases, physicians face both civil and criminal liabilities, while hospitals can only be sued for civil negligence. 17 Malpractice claims are decided according to a fault system. The statute of limitations is six years for a criminal case and ten years for a civil case. 18 Healthcare providers are liable if the action or the lack of action (i.e. omission) of one of their employees is the cause of damages. The majority of litigation concerning medical liability (86% of malpractice claims) takes place in Courts [Council of Europe (2009)]. When a claim is against a hospital the Court in charge of the claim is the one competent over the municipality in which the hospital is located. The non economic part of the compensation is on average equal to the 70% of the final award. 2.4 Hospitals Insurance The Italian healthcare system is mainly public. Healthcare services are delivered through a local organized health insurance system made up of hospitals trusts (independent hospitals, IHs) and health authorities (local health authorities, LHUs), which do or do not manage their own hospitals. In 2010, the public system was employing 646,236 units of personnel which represented 93% of the personnel employed in the overall (public and private) Italian healthcare system. Physicians working in the public system are de facto civil servants. As such, they are entitled to have an insurance coverage for medical liability as part of their employment contract. 19 Even though both hospital trusts and health authorities supply such coverage, being public entities they are not allowed to go to the market to freely select an insurer. Healthcare providers need to contract out their risk through public auctions. Since the process is expensive, it is not surprising that the average duration of an insurance contract is 36 months. 17 To obtain compensation, an injured patient can freely choose whether to opt for persecution in a criminal court or a civil action. In any case, the plaintiff can always decide to transfer the compensation request from the civil to the criminal court if the civil action has not come to a final decision and the statute of limitations to bring the civil action in the criminal proceeding is not expired. Otherwise, the civil and criminal procedures remain separate and continue to take place in their respective court. 18 A physician may be prosecuted in a criminal court when it is possible to identify a serious error in his conduct and there is a casual relationship between medical error and the injuries, thus the subsequent damages, suffered by the patient. In this case, the physician is accused of a crime for negligent personal injuries (lesioni personali colpose), that under the article 43 of the national penal code is described as an event that, even if it happened against the intention, occurred due to negligence, imprudence, unskillfulness or failure to comply with laws, regulations, orders and disciplines. In this case, the criminal prosecution can be initiated by the injured party, that is the patient itself. Additionally, a doctor is criminally liable also in the case of manslaughter, but differently from the hypothesis of negligent personal injuries, the criminal persecution may be initiated only by a prosecutor. Italian patients opt for a prosecution in a criminal court in the 3-4 percent of malpractice cases and among criminal cases the 52% of the claims concern manslaughter [Marsh, (2011); Rasini Viganó, (2011)]. For further details on medical malpractice liability in Italy see Grembi and Garoupa (2013). 19 Recently, Law 27/2012 introduced the obligation for all prefessionals (including doctors) to be covered by liability insurance by 13 August

8 LHUs and IHs might decide to open a joint auction to minimize the administrative costs of the procurement procedure and they might jointly open calls to contract out more than one service: an auction can include more auctioneers and relate to more lots (e.g. different layers of medical liability insurance, or legal expenditures). However, in case of a joint auction (e.g. more than one LHU), the winner stipulates individual contracts with each auctioneer. When more than one service is jointly auctioned, there might be more winners, one for each lot included in the call. Providers can rely on brokers, private operators acting on behalf of the auctioneers in carrying out the procurement procedure. Specifically, this type of intermediary supports providers in preparing and running the tendering process, as well as in managing the insurance contract once a winner has been selected. 20 Between 2000 and 2010, we counted 32 insurance companies providing coverage to 336 Italian healthcare providers and their employees: 11 operating in the three areas (i.e. North, Center, South), 8 in only two areas, and 13 in one area. Overall, Northern regions are covered by 26 insurers, Central regions by 16 insurers, and 20 insurers covered providers in Southern regions (Table A2). 21 If we consider insurers providing coverage other than for medical liability to the same providers during the same period, we can count 41 companies in the North, 25 in the Center, and 30 in the South. 18 insurers operate in all areas, 8 only in two areas, and 26 only in one area of the country (Table A3). Insurers have both national and international identities. Overall, from 2000 to 2010, in Italy there were 52 insurance companies dealing with healthcare providers risk, therefore 52 potential competitors in the market for medical liability, of which 32 were actually covering the malpractice risk of the providers Empirical Strategy and Data We use a Difference-in-Differences (DD) estimator to evaluate the impact of adopting tiered caps on two outcomes related to insurers: 1) the medical liability market attractiveness measured both as absolute and weighted number of insurers interested in providing hospitals coverage, and 2) malpractice premiums at the healthcare trust and authority level. As in Autor et al. (2008) our identification relies on the exogeneity of the treatment year respect to our outcome variables. 23 In our case, this is plausible for two reasons. First, the outcomes of 20 Auctions can be awarded following three procedures: 1) open procedure; 2) restricted procedure; or 3) negotiated procedure [Buzzacchi and Gracis (2006)]. Bidders win the auction either because they provide the service according to the lowest price criterion or because they meet the requirements of the most economically advantageous criterion (MEAT), a bidding package. For instance, the paid premium could be the same in two bids, but bidder n.1 provides a total coverage while bidder n.2 offers a coverage with deductibles: according to MEAT, bidder 1 should win.for legal claims related to the auction procedure, the competent authority is the Administrative Court (Tribunali Amministrativi Regionali) and not the Court of First Instance. 21 When an insurance company operates through one or more subsidieres, only the holding companing has been counted. 22 We control that the 52 companies, winners of insurance contracts for professional liability to risk of fire, were offering medical malpractice insurance as part of their services. 23 Autor et al. (2006) assess the impact of states supreme courts adoption of rule limiting the possibility of firing. 8

9 interest refer to medical malpractice, while tiered caps apply to every case of personal injury, and the levels of compensation were initially set based on compensations to cars accidents. Second, our identification relies neither on early birds (courts treated before 2000) nor on late comers (courts treated after 2010) but on a 30% of Courts shifting to the treatment between 2000 and As a consequence, the heterogeneity among courts should not be so relevant. However, since there might be time-varying confounding factors leading to omitted variables bias, we exploit the partial overlap between Courts districts and LHU s districts to address this concern. Partial overlapping works in two directions: a LHU district can be ruled by more than one court, and a Court district can cover more than one LHU district, as shown in Figure 2 for two representative regions, Piedmont and Sardigna in both 2000 and Within the borders of one region, municipalities are grouped in LHUs and in Courts districts by two different authorities, the Ministry of Justice, for Courts districts, and Regional government, for the LHU borders. Partial overlapping between the two territorial competences means that the caseload of a Court and what happens at the LHU level are not perfectly correlated. Figure 2, about here To take into account the overlapping, we construct an index, λ pjt, that weighs the relative importance of Court j at time t for provider p, with specific reference to the level of civil backlog, Backlog, and the adoption of caps, Caps. Since they are weighted by λ, they correspond to the following equivalences: Backlog pt = N pt j=1 (λ pjtbacklog jt ) Caps pt = N pt j=1 (λ pjtcaps jt ) with j=1, 2, and λ pjt [0, 1]. The basic intuition underneath λ can be explained through a first (rough) approximation. Assume LHU p (IH p ) manages 5 hospitals, 2 placed in Court 1 district and 3 placed in Court 2 district. Hence, Court 1 indexes (i.e., backlog and schedule) are weighed 2 and Court 5 2 indexes are weighed 3. The insurer facing the decision 5 to provide coverage to LHU p should be affected by the indexes of two Courts, combined as the sum of the weighted indexes of Court 1 and Court 2. Yet, it might be the case that hospitals placed in Court 2 are more important (e.g. they treat more patients) than those placed in Court 1. In this case the probability to end up before Court 2 is higher. Consequently the relative importance of Court j has to be approximated, being aware that the activity level of a hospital might be influenced by malpractice pressure, as pointed out by the literature on defensive medicine [Kessler and MacClellan (1996) and (2002); Currie and MacLeod (2008)]. Therefore, we rely on a weight given by the number of beds for each hospital out of the total number of available beds for each LHU, so that if hospitals in Court 1 manage the 70% of the beds managed by LHU p, Court 1 indexes will play 9

10 a heavier role. 24 These measures, as the borders of local health authorities, tend to change over time due to public finance constraints or political decisions over the management of the health care system. 25 Hence λ has an its own time dimension. For each outcome of interest, we estimate the impact of adopting tiered caps using the specifications described in the following subsections. 3.1 Insurers: Participation and Premiums We use data from the insurance contracts to evaluate how the adoption of tiered caps affects insurers decisions. 26 We approximate the attractiveness of the malpractice insurance market using both an absolute and a relative measure. Using data from the auction process needed to contract out the risk, we recover information on the number of bidders for each auction. 27 We consider the number of bidders as a first absolute value of attractiveness. From 2000 to 2010, on average per auction 2.16 commercial insurers bade in auctions involving medical liabilities (2.65 if only auctions with at least 1 bidder). We then weigh this first measure on the number of potential bidders, given by insurers operating in the same territorial market and providing insurance services to health care providers at time t and that list medical malpractice insurance as one of their field of action. On average this second, relative, measure is equal to 0.07, which means that the 7% of potential bidders generally showed up to the contracting process when medical liability was involved. Insurers cpt, for contract c of healthcare provider p at time t is estimated with equation 1: Insurers cpt = θbacklog pt + DCaps pt + ωbacklog pt Caps pt + α p + γ a + ρ t + X ptβ + Z ctπ + ε cpt (1) where Insurers pct is either the absolute number of bidders or the relative number of bidders on potential bidders measure of attractiveness, α p are healthcare providers fixed effects, γ a are the geographical area fixed effects (i.e. North West, North East, Center, and the South), and ρ t controls for yearly shocks.to take into account also demographic 24 The number of beds per thousand inhabitants, as well as the number of doctors, is decided at the central level by the Government, that over time has intervened several times implementing different decrees and laws for the reorganization of the national healthcare system (e.g Law 595/85 or Law 412/91). 25 From 2000 to 2010 the number of LHUs changed, from 197 in 2000 to 145 in 2010, managing a total number of 617 hospitals. The decreasing trend in the number of LHUs is due to an attempt to improve competition in the public healthcare system breaking the vertical integration between insurer and provider. As a consequence, in the same period the number of IHs decreased from 98 in 2000 to 64 in We collect the data from the Official Journal of the European Union at the Health Care provider level. Insurance contracts have to fulfill transparency and advertising obligations required for public procurement, for this reason the Official Journal constitutes the most valuable source to collect data on tendering processes carried out by the public sector in Europe. We integrate and check this data with information provided by both the Italian Authority for the Supervision of Public Contract and a private firm, Telemat, specialized in managing information on public contracts. 27 The identity of the bidders is not available in any of the used dataset. At the auction level only the identity of the winner is available. 10

11 characteristics which affect the probability to commit a medical error, the probability to claim, and the probability to get a better compensation, we also control with X pt for a group of socio-economic variables at the healthcare provider level, as the covered population, the part of the population older than 65, the proportion of foreigner population, and the average income level. 28 Z ct groups control variables at the contract level, such as the awarding mechanism (i.e. Open tender and MEAT ) and the involvement of brokers. We use the same model to test the impact of tiered caps on P remiums cpt. Since the 22% of the signed insurance contracts were auctioned jointly by more than one healthcare providers, as robustness check we estimate equation 1 also dropping the cases of joint contracting. It might be the case that the joint work of more providers can achieve more convenient prices, having more contractual power. 4 Descriptive statistics and Results We recover 856 tenders for insurance services for the period for a total of 1296 observations (Table 1). 29 Overall, the analyzed contracts refer to 336 different health care providers, which represent the 93% of the entire Italian providers population. During the same period 36 were treated, switching from not having caps to deal with a schedule cap system, and 300 were not treated for never having to operate under a cap system (56) or for dealing with schedule before 2000 (244). Table 1, about here On the overall sample, on average for every closed case, civil CFI are unable to close 3.56 cases for each year and 80% of the CFI in the sample adopt a cap system. 80% of tenders are run with an open procedure, relying on the help of a broker in the 39% of cases. Two or more health care providers join together and carry out a common procurement procedure in the 22% of the time, while they choose flexible awarding procedures, MEAT, in the 45% of tenders. The average duration of a contract is 36 months (see Table A4). Besides the descriptives statistics on the overall sample, in Table 2 we provide descriptives for the treated and the control. As a matter of fact we have two types of control during the period : providers which never signed a contract operating under tiered caps schedules, and providers which were covered by a schedule for the entire period. Although the overall mean values of the outcomes of interest differs, this per se is not a violation of the common trend hypothesis needed to identify the policy impact using a DD approach. Since Courts are switching to the treatment in different years, the usual graphical tests are not suitable on our dataset. 28 At the same time, we also control for those policies at the local level that may influence the behavior of LHUs towards medical malpractice, such as, for instance, a monitoring system on medical malpractice claims, that has been implemented in some regions [Amaral Garcia and Grembi (2012)]. 29 One contract might refer to more than one insurance service (i.e. lots>=2) or it can gather more than one auctioneer. 11

12 Table 2, about here Tables 3 and 4 present the main results on Insurers. We cluster the errors at the regional level given that the median duration of contracts is equal to 36 months, which means we have few observations per provider. For each variable we run the same model on the entire dataset and on the dataset without joint contracts (i.e. more than LHU or IH) as robustness checks. To interpret the results we need to remember that while cap can be set equal to zero, the same does not hold for backlog. The minimum value of civil backlog is 1, which corresponds to absence of backlog. Therefore, while θ can be interpreted independently from ω, the impact of Cap is always the sum of D and ω. To interpret the coefficients, we simulate different scenarios. For instance, we derive the impact of a change of a standard deviation of civil backlog (i.e. 1.14), given Caps=0, 0.81, and 1, 0.81 being the mean on Caps in our sample. When we focus on Caps, we simulate the impact of standard deviation increase (i.e. 0.38), when Backlog is equal to 1, 2, 3, 3.56, 4.13, and 5, being 3.56 its mean value, and 4.13 and 5, the 75th and the 90th percentiles of Backlog distribution. Tables 3 and 4, about here Table 3 reports incidence rate ratios values, so that values higher than 1 identify coefficients with a positive effect on the reference outcome, while values lower than 1 stand for a negative effect. The main effect is on the number of bidders. They decrease when the civil backlog is high, but once an inefficient context adopts schedules then the interest of insurers strongly increases. Similarly, if we take into consideration only the tenders where there is already at least one bidder, a high judicial inefficiency negatively impacts on the number of participants, therefore a more inefficient courts deters insurers from offering medical professional liability. Conversely, the introduction of a cap system in an inefficient context makes the medical malpractice liability market more attractive increasing the number of insurance companies making a bid. To provide an idea of the degree of the effects, an increase of a standard deviation of Backlog decreases the number of insurers of 8% in the sample of all contracts and of 4% in the sample of contracts with only one contractor. The impact of an increase of a standard deviation of Cap (i.e. 0.38), when Backlog is equal to 3.56 (its mean value of the sample), is a 23% increase of bidders in the all sample and a 50% increase in the single contracts sample. As shown in Table 4, a bad performing judiciary makes less attractive the malpractice insurance market since, out of all potential competitors, a lower number of insurers takes part to a tendering process. On the contrary, the number of bidders out of all potential competitors increases when an inefficient court implements caps. However the trend of the impact provides interesting information, as shown in Figure 3. When we consider the entire sample, we have that for values of Backlog lower than 3, so basically when the judiciary is efficient, in relative terms the introduction of caps decreases the attractiveness of the malpractice insurance market.when Backlog is equal 1 (i.e. no inefficiency) introducing tiered caps schedules reduces the relative number of bidders by 28%, which becomes -6% when Backlog is equal 2. When Backlog is equal 3, introducing Cap increases the number of 12

13 weighed bidders by 17%. This result is consistent with the combined expected effect of Caps and Backlog on the expected number of errors and their severity. In other words, when the judiciary is efficient, physicians do have incentives to take efficient level of precaution and introducing a tiered cap system decreases the malpractice pressure on them, since decreases uncertainty and it might decrease the level of due compensation (it depends on the level of tiers). Therefore the cap schedules effect might go to contrast the efficient court system effect. As the judiciary becomes less deterrent, because the system is more inefficient, the introduction of caps schedule even though decreases the pressure on physicians, increases the certainty level for insurers. There are not significant effects on paid premiums. This is quite consistent with what expected since it takes time before insurance price are adjusted after the introduction of caps (e.g. Currie and MacLeod, 2008). However, we plot the simulated effects per different levels of Backlogs in Figure 4. Figures 3 and 4, about here 5 Concluding remarks We provide an empirical evaluation of the introduction of tiered caps schedules controlling for the efficiency level of the judiciary using the Italian case study. Italy provides an interesting institutional framework. Since the end of the Nineties Courts have started to adopt tiered caps schedules in different years. However, the judiciary in Italy shows high degree of heterogeneity when performance is at stake. Focusing on the period we identify the impact of adopting schedules on two proxies of the attractiveness for the medical malpractice insurance market, the absolute and relative number of insurers attending insurance contract auctions of public healthcare providers, and paid premiums. Our analysis shows that while paid premiums are not affected, insurers tend to increase the participation rate to the market. The lack of an impact on premiums is probably due to the adjustment time of prices respect to the paid compensations. The more inefficient is the judiciary the longer will take for the premiums to be readjusted. 13

14 References Avraham, Ronen (2007). An Empirical Study of the Impact of Tort Reforms on Medical Malpractice Settlement Payments, The Journal of Legal Studies, Vol. 36(2), pp Born, Patricia H., W. Kip Viscusi and Tom Baker (2009). The Effects of Tort Reform on Medical Malpractice Insurers Ultimate Losses, The Journal of Risk and Insurance, Vol. 76(1), pp Buzzacchi, Luigi and Camilla Gracis (2008). Meccanismi allocativi per il rischio sanitario nelle Aziende Sanitarie pubbliche italiane, Mecosan 66, pp Carnevale, Aldo and Generoso Scarano (2010). Il danno alla persona. Aspetti giuridici e medico-legali, Cedam, Padova. Chindemi, Domenico (2011). Il danno alla persona patrimoniale e non patrimoniale, Maggioli Editori, Santarcangelo di Romagna. Coviello, Decio, Andrea Ichino, and Nicola Persico (2012a). Time Allocation and Task Juggling, mimeo, available at: progress/continuous_giudici.pdf. Coviello, Decio, Andrea Ichino, and Nicola Persico (2012b). Giudici in affanno, Annuario di diritto comparato e di studi legislativi, Currie, Janet, and W. Bentley MacLeod (2008). First Do No Harm? Tort Reform and Birth Outcomes. Quarterly Journal of Economics, Vol. 123, pp Danzon, Patricia (1984). The Frequency and Severity of Medical Malpractice Claims. Journal of Law and Economics, Vol. 27, pp Danzon, Patricia (1986). The Frequency and Severity of Medical Malpractice Claims: New Evidence. Law and Contemporary Problems, Vol. 9, pp Danzon, Patricia M. (1991). Liability for Medical Malpractice, Journal of Economic Perspective, Vol. 5(3), pp Danzon, Patricia, Andrew J. Epstein and Scott Johnson (2004). The Crisis in Medical Malpractice Insurance, in Richard Herring, Litan Robert E., editors. Brookings-Wharton Papers on Financial Services: Washington, DC: Brookings Institution Press; pp De Paola, Gabriele and Lina Avigliano (2009). La liquidazione del danno nell infortunistica stradale, Guida al Diritto, Il Sole24 Ore, Milano. 14

15 Decarolis Francesco, Cristina Giorgiantonio and Valentina Giovanniello (2010). L affidamento dei lavori pubblici in Italia: un analisi dei meccanismi di selezione del contraente privato, Bank of Italy, Questioni di Economia e Finanza n. 83. Durrance, Christine P. (2009). Noneconomic Damage Caps and Medical Malpractice Claim Frequency: A Policy Endogeneity Approach, The Journal of Law, Economics, & Organization, Vol. 26(3), pp European Observatory on Health Care Systems (2001). Health Care Systems in Transition - Italy. Grembi, Veronica and Nuno Garoupa (2012). Delays in Medical Malpractice Litigation in Civil Law Jurisdictions: Some Evidence from the Italian Court of Cassation, forthcoming in Health Economics, Policy and Law. General Accounting Office (2003a). Medical Malpractice: Multiple Factors Have Contributed to Premium Rate Increases. Discussion Paper GAO , Washington DC. General Accounting Office (2003b). Medical Malpractice: Multiple Factors Have Contributed to Premium Rate Increases. Testimony before the Subcommittee on Wellness and Human Rights. Washington DC. Kessler, Daniel P. (1996). Institutional Causes of Delay in the Settlement of Legal Disputes, Journal of Law, Economics, and Organization, Kessler, Daniel P. (2006). The Determinants of the Cost of Medical Liability Insurance, mimeo. Kessler, Daniel P. (2011). Evaluating the Medical Malpractice Systems and Options for Reform, Journal of Economic Perspectives, Vol. 25(2), pp Kessler, Daniel P. and Mark MacClellan (1996). Do Doctors Practice Defensive Medicine?, Quarterly Journal of Economics, Vol. 111(2), pp Kessler, Daniel and Mark McClellan (1997). The Effects of Malpractice Pressure and Liability Reforms on Physicians Perceptions of Medical Care, Law and Contemporary Problems, Vol. 60(1), pp Kilgore, Meredith L., Michael A. Morrisey and Leonard J. Nelson (2006). Tort Law and Medical Malpractice Insurance Premiums, Inquiry, Vol. 46, pp Letiter, Andrea, Magdalena Thöni and Hannes Winner (2012). Evaluating Human Life Using Courts Decisions on Damages for Pain and Suffering, International Review of Law and Economics, Vol. 32, pp Marsh (2011). MedMal Claims Italia. 15

16 Mello, Michelle M. (2006). Understanding medical malpractice insurance: A primer, Princeton, NJ, The Robert Wood Johnson Foundation, Research Synthesis Report n. 8. Mello, Michelle M. and Allen Kachalia (2010). Evaluation of Options for Medical Malpractice System Reform, A Report to the Medicare Payment Advisory Commission (MedPAC). Negro, Antonella (2011). La vittoria delle tabelle milanesi, in Trattato dei nuovi danni, P. Cedon, Cedam, Padova. Nye, Blaine F. and Alfred E. Hofflander (1987). Economics of Oligopoly : Medical Malpractice Insurance as a Classic Illustration, Journal of Risk and Insurance 54(3), pp Rasini Viganò (2011). Mappatura del Rischio del Sistema Sanitario Regionale, Regione Lombardia Direzione Generale Sanità, Milano. Scarso, Alessandro P. (2009). Punitive Damages in Italy, in Punitive Damages: Common Law and Civil Law Perspective, H. Kuziol and V. Wilcox, Springer, Wien/New York. Sella, Mauro (2005). I sistemi tabellari, available at: Sloan, Frank A. (1990). Experience Rating: Does it Make Sense for Medical Malpractice Insurance?, American Economic Review 80(2): Snyder, James M. and David Stromberg (2010). Press Coverage and Political Accountability, Journal of Political Economy, University of Chicago Press, Vol. 118(2), pp Studdert, David M., Yang Y.T. and Michelle M. Mello (2005). Options for Rational Scheduling and Valuation of Noneconomic Damages, Report to the Washington State Noneconomic Damages Task Force. Thorpe, Kenneth E. (2004). The Medical Malpractice Crisis: Recent Trends and the Impact of State Tort Reforms, Health Affairs, Suppl Web Exclusives: W4, pp Traina, Francesco (2008). Medical Malpractice, Clin Orthop Relat Res, Vol. 467(2), pp Viscusi, Kip W. and Patricia H. Born (1995). Medical Malpractice Insurance in the Wake of Liability Reform, Journal of Legal Studies, Vol. 24, pp Viscusi, Kip W. and Patricia H. Born (2005). Damages Caps, Insurability, and the Performance of Medical Malpractice Insurance, The Journal of Risk and Insurance, Vol. 72(1), pp

17 Zuckerman, Stephen, Randall R. Bovbjerg, and Frank Sloan Effects of Tort Reforms and Other Factors on Medical Malpractice. Inquiry, Vol. 27(2), pp

18 Tables and Figures Table 1: Treated and Non-treated Advertised Procurement Procedures per Year Year Awarding Procedures Contracts Treated Non-treated Treated Non-treaded Total Notes: Awarding procedures= Public procurement procedures to award MM insurance; Contracts= MM insurance contracted out through public procurement. T reated=the awarding authority running the procurement procedure is located in a Court that adopts a cap system; Non treated=the awarding authority running the procurement procedure is located in a Court that does not adopt a cap system. 18

19 Table 2: Descriptive Statistics (Treated vs. Control) Variable Treated Control Never Always Cap Cap N Bidders (1.98) (2.57) (2.73) N Bidders> (1.91) (2.53) (2.81) Bidders on Competitors (0.07) (0.08) (0.08) Premium p* 2, , , (4,494.23) (10,638.99) (17,621.53) Premium ms** 4, , , (6,998.72) (16,989.10) (30,240.90) Notes: Mean values reported. Standard deviations in parenthesis. N Bidders= The number of insurance companies submitting an offer; Bidders on Competitors=Number of insurers bidding out of potential insurers represented by all the insurers dealing with healthcare providers that can offer MM insurance;p remium= The MM insurance premium paid by health care providers. Cap=Cap Schedule system on noneconomic damages, Never Cap= Healthcare structures located in courts that have never applied a cap schedule of noneconomic damages during the period of observation ; Always Cap= Healthcare structures located in courts that have always applied a cap schedule of noneconomic damages during the period of observation * =normalized on the personnel of the health care provider; ** =normalized on medical personnel of the health care provider 19

20 Table 3: Number of Bidders All Bidders>0 All Single All Single Contracts Contracts Contracts Contracts Backlog 0.833** 0.756*** 0.757* 0.801*** (0.065) (0.045) (0.124) (0.049) Caps (0.341) (1.175) (0.287) (1.062) Caps*Backlog 1.516*** 1.738*** 1.792*** 1.712*** (0.243) (0.334) (0.353) (0.289) Years FE Yes Yes Yes Yes Contractors FE Yes Yes Yes Yes Area FE Yes Yes Yes Yes Contracts Controls Yes Yes Yes Yes LHU Controls Yes Yes Yes Yes Obs Notes: Number of Bidders=Number of insurance companies that take part to the tender for MM insurance;backlog= Civil Backlog; Caps=Tiered caps system of noneconomic damages. Contracts Controls include Open T ender, MEAT, Broker, Duration, and Joint Contracts. LHU Controls include LHU popres, LHU income, LHU old, and LHU foreigners. Poisson regressions. Coefficients represent incidence-rate ratio. Standard errors clustered at the regional level in parenthesis. Significance at the 10% level is represented by *, at the 5% level by **, and at the 1% level by ***. 20

21 Table 4: Bidders per Potential Insurers All Contracts Single Contracts Backlog ** *** (0.009) (0.010) Caps ** (0.038) (0.058) Caps*Backlog 0.047*** 0.060*** (0.013) (0.018) Years FE Yes Yes Contractors FE Yes Yes Area FE Yes Yes Contracts Controls Yes Yes LHU Controls Yes Yes Obs R-squared Notes: Bidders competitors=number of insurers bidding out of potential insurers represented by all the insurers dealing with healthcare providers that can offer MM insurance; Backlog= Civil Backlog; Caps=Tiered caps system of noneconomic damages. Contracts Controls include Open T ender, MEAT, Broker, Duration, and Joint Contracts. LHU Controls include LHU popres, LHU income, LHU old, and LHU foreigners. OLS regressions. Standard errors clustered at the regional level in parenthesis. OLS regressions. Standard errors clustered at the regional level in parenthesis. Significance at the 10% level is represented by *, at the 5% level by **, and at the 1% level by ***. 21

22 Table 5: Paid Premia Per Personell Per Medical Personell All Single All Single Contracts Contracts Contracts Contracts Backlog ( ) ( ) ( ) ( ) Cap -1, , , , (2, ) (2, ) (3, ) (3, ) Caps*Backlog ( ) ( ) ( ) ( ) Years FE Yes Yes Yes Yes Contractors FE Yes Yes Yes Yes Area FE Yes Yes Yes Yes Contracts Controls Yes Yes Yes Yes LHU Controls Yes Yes Yes Yes Obs R-squared Notes: P er P ersonnel= paid premium normalized by the employed personnel; P er Medical P ersonnel= paid premium normalized by the employed physicians and nurses; Backlog= Civil Backlog; Caps=Tiered caps system of noneconomic damages. Contracts Controls include Open T ender, MEAT, Broker, Duration, and Joint Contracts. LHU Controls include LHU popres, LHU income, LHU old, and LHU foreigners. OLS regressions. Standard errors clustered at the regional level in parenthesis. Significance at the 10% level is represented by *, at the 5% level by **, and at the 1% level by ***. 22

23 Figure 1: Caps Adoption and Civil Backlog per Court District ( ) Notes: In red the Regional border. In blue the Courts districts. Courts adopting a schedule of non economic damages are colored in violet. 23

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