Dynamic Price Competition in Auto-Insurance Brokerage

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1 Dynamic Price Competition in Auto-Insurance Brokerage Luis H. B. Braido Bruno C. A. Ledo March 5, 2015 Abstract We access Brazilian data on auto-insurance and document that: (a) about 20 percent of all policies are sold without brokerage commission; (b) over 40 percent of all policies are sold with the highest brokerage fee allowed for that contract; and (c) the remaining policies are associated with a spread-out distribution of positive fees. We develop a dynamic model of price competition with switching costs which reproduces these empirical findings. The equilibrium structure is used to estimate the model parameters and identify the distribution of switching costs. We also compute brokers life-time expected earns when dealing with regular and new customers. Keywords: Brokerage, fees, price competition, structural estimation, autoinsurance. JEL Classification: D4; L1. We are thankful for comments from Rodrigo de Losso Bueno, Marcelo Medeiros, Humberto Moreira, Mauricio Moura, Heleno Pioner, Bernard Salanié, Marcelo Sant Anna, and seminar participants at the EUI, FGV-SP, FUCAPE, IPEA-RJ, PUC-Rio, UFRJ, USP-RP, USP-SP, and at the following meetings: Econometric Society (Malaga 2012 and Shanghai 2010), European Economic Association (Glasgow 2010), and SBE (Salvador 2010). We are also grateful to Antero Alves Neto for computing support. FGV/EPGE, Rio de Janeiro, Brazil; University of Sao Paulo, Ribeirao Preto, Brazil; 1

2 1 Introduction This paper analyzes the brokerage activity in the Brazilian auto-insurance market. The standard auto-insurance policy lasts for one year. Automatic extensions are not available, and new contracts need to be signed after expiration. All contracts are required to be sold through a certified broker. 1 Auto-insurance brokers are considered experts who advise the insurees on details of the contract. They quote prices with different insurance companies and suggest the best deal for each consumer. They also represent the insuree during the policy duration, helping them to claim the contracted benefits in case of accidents. There are tens of auto-insurance companies and thousands of certified brokers. The typical broker runs a small local business, but the brokerage service is also offered by bank branches and internet companies. Each insurance company provides a standardized software program for brokers. This program computes the baseline insurance price for each given set of characteristics of the insuree, the insured vehicle, and the insurance contract. Brokers add a fee to the baseline price. Brokerage fees must not exceed a ceiling level defined by the insurance company. Ceilings vary from 15 to 50 percent of the policy final price, depending on observed and unobserved characteristics of the market and the contract. Our data document the coexistence of policies being sold with zero and positive brokerage fees. About 20 percent of all contracts are sold without brokerage commission. Positive fees vary smoothly within an interval that ranges from zero to over 900 BRL (equivalent to about 450 PPP USD). They reach a ceiling level for over 40 percent of the contracts. It is challenging to rationalize the economics behind this evidence. On the one hand, the mass of zero brokerage fees suggests that the brokerage market is very competitive. On the other hand, the mass of fees at ceiling levels suggests exactly the opposite. Moreover, our regression analysis shows that the variability in brokerage fees is poorly related to a large set of observable characteristics, suggesting that randomness may be an important element in brokerage pricing. We recall that mixed strategy is sometimes used to model equilibrium price dis- 1 This legislation is enforced under the supervision of a strong national association of insurance brokers with offices and elected representatives in all states of the country. 2

3 persion that is not related to observables see Varian [1980], Stahl [1989], Sharkey and Sibley [1993], and Braido [2009]. We notice however that Nash equilibrium requires that prices in the support of each player s mixed strategy generate the same expected payoff, given the other players strategies. Therefore, the usual static games do not generate zero and positive fees being simultaneously played in equilibrium. We introduce switching costs into a model of price competition. Costs to switch brokers generate an intertemporal value for brokerage. When dealing with a new client, brokers are tempted to set low fees in order to increase the probability of making a sale and becoming matched to the consumer for the following period. This type of loss leading generates a mass of contracts without brokerage commission. When dealing with a regular customer, brokers take advantage of the switching cost and only offer contracts with positive fees. They face a tension between extracting consumer surplus and risking to loose the customer to a competitor. This game has a symmetric stationary Nash equilibrium in which mixed strategies replicate the observed price dispersion. We use the equilibrium mixed-strategy distribution to derive a theoretical data generating distribution. We then perform a maximum likelihood estimation of the model parameters. This structural econometric procedure allows us to compute the brokerage present value of dealing with matched and unmatched insurees. These are important concepts in industrial organization which are difficult to obtain from data on prices. They are useful for analyzing mergers and acquisitions as they allow us to compute the value of a portfolio with regular customers and a stable flow of new potential clients. The remaining of this paper is organized as follows. We discuss related papers in Section 2 and describe the data recorded by the Brazilian Superintendence of Private Insurance (SUSEP) in Section 3. This thorough database conveys information about the population of auto-insurance contracts in the country. Section 4 formally describes our dynamic game of price competition and derives a symmetric stationary Nash equilibrium. In Section 5, we start to take the model to the data. We stress that the database does not record all quotations but only the contracted fees. We take the mixed-strategy equilibrium distribution and the individual decision rule to derive the theoretical data generating distribution. This distribution is used in Section 6 to identify the parameters of the model through a maximum 3

4 likelihood estimation. We compute the distribution of the switching costs and also find an average switching cost of BRL (approximately PPP USD). Additional implications of this structural analysis are presented in Section 7. We find, for instance, that brokers expected lifetime discounted profit is BRL when dealing with a regular (matched) customer and BRL when facing a new (unmatched) insuree. These values are economically meaningful. Concluding remarks are presented in Section 8. 2 Related Literature In frictionless competitive markets, all units of any given homogeneous good should be sold at the same price. Transaction costs for searching prices as well as for switching suppliers have being used to rationalize the fact that identical goods are sometimes sold at very different prices. We use this section to review some contributions in these areas. Search Costs In a seminal paper, Stigler [1961] pointed out the relation between price dispersion and the fact that consumers spend resources to gather information about prices. Since them, many authors in labor economics and industrial organization have contributed to this topic. In an important work, Varian [1980] develops a model of sales in which identical oligopolistic firms set prices for a homogeneous good produced at a constant marginal cost. There are two types of consumers, informed and uninformed. Informed consumers buy at the lowest available price, while uninformed consumers buy from a randomly selected firm. Firms face a tradeoff between lowering prices to increase the probability of making a sale to informed consumers and increasing prices to extract rent from randomly selected uninformed consumers. In order to balance these forces, firms end up using mixed strategies in equilibrium. This generates random sales and ex-post price dispersion. A natural concern about Varian s model is that uninformed consumers should be able to search for price information. To address this issue, Stahl [1989] introduces a searching model in which a fraction of (informed) consumers face zero search costs, while another fraction faces positive search costs. Similarly to Varian [1980], firms use mixed strategy to balance the tradeoff between selling to informed customers 4

5 and extracting rent from consumers with positive search costs. There is also a large empirical literature on the importance of searching costs to explain price dispersion in different markets such as: auto-insurance in Alberta, Canada (Dahlby and West [1986]); prescription drugs in New York drugstores (Sorensen [2000]); life insurance in the US (Brown and Goolsbee [2002]); retail items (such as refrigerator, chicken, coffee, and flour) selected from the Israeli CPI (Lach [2002]); hedge funds in the US (Hortaçsu and Syverson [2004]); on line books (Hong and Shum [2006]); and mortgage contracts (Allen et al. [2013]). There are also recent papers by De Los Santos et al. [2012] and De Los Santos et al. [2013] accessing the empirical relevance of non-sequential search models, as initially conceived by Stigler [1961]. These empirical works study environments with differentiated goods, where consumers hold preferences for brands or characteristics of the seller. Unlike them, we account for search in a fashion that is similar to Varian [1980]. We stress however that the static nature of the search friction is not able to rationalize the coexistence of zero and positive fees in equilibrium. The switching cost is the key element behind this equilibrium outcome in our approach, as it introduces an intertemporal value for selling today without a brokerage commission in order to become matched to the insuree for the next period. Switching Costs In a pioneering paper, Klemperer [1987] introduced a model in which two firms set prices over two periods of time, and consumers bear a fixed cost for switching from one firm to another in the second period. Prices are restricted to be the same for regular and new consumers, and the authors focus on computing a stationary mixed-strategy Nash equilibrium. Thus, the second-period pricing strategies depend on the firm s stock of regular customers. In equilibrium, firms charge identical prices and capture the same share of the market in each period. In other important contributions, Padilla [1995] and Anderson et al. [2004] extend this analysis to an environment with overlapping generations and infinitely many periods. 2 Like in Klemperer [1987], the two firms cannot price discriminate between regular and new consumers, and the equilibrium pricing depend on each 2 See Farrell and Shapiro [1988] and Beggs and Klemperer [1992] for alternative infinite-horizon frameworks. 5

6 firm s market share. In a second front, there are papers analyzing environments with price discrimination. Chen [1997] and Taylor [2003] present models in which two firms set different prices for regular and new consumers over a finite number of periods. 3 Consumers face different switching costs in each period, according to i.i.d. draws from an exogenous distribution function. In equilibrium, the two firms offer the same price for their own regular customers and the same discount price for their rival s consumers. The exogenous distribution of switching costs determines the fraction of consumers who switch suppliers. 4 There are several differences between our work and those in the switching cost literature. 5 The most important is the type of price differentiation allowed in our model. Insurance brokers usually deal to one consumer at a time. It is then natural in our setting to allow for price differentiation between regular and new consumers as well as across insurees within each of these groups. Our equilibrium mixed strategies depend on whether the insuree is matched or not to the broker. They are unrelated to the broker s market share. Since brokers set prices to each consumer at a time, identical consumers end up facing different prices. 3 Data We use data recorded by the Brazilian Superintendence of Private Insurance (SUSEP), a federal institution responsible for regulating the insurance market in the country. Auto-insurance companies are commanded to provide detailed information on all active policies. The SUSEP database conveys cross-section information about the population of auto-insurance policies that have been active (for at least one day) during each semester. We use data relative to one-year policies that were active in the first semester of We focus our analysis on comprehensive 3 Fudenberg and Tirole [2000] and Villas-Boas [2006] also study dynamic models of price discrimination based on consumer s purchasing history. 4 In a relatively different context, Chen and Rosenthal [1996] derive a mixed-strategy equilibrium for a dynamic duopoly in which consumers are loyal to firms, but loyalty varies over time according to firms realized prices and an exogenous stickiness rule. 5 We refer the reader to Farrell and Klemperer [2007] for an extensive survey on this topic. 6

7 policies contracted by individuals. 6 For each policy, we access information about the characteristics of the insuree (such as gender and age), the insured vehicle (such as model and year), and the insurance contract (such as the insurance company, type of coverage, and deductible values). The insurance premium and the brokerage fee embedded on that price are also available. 7 broker. Table 1 defines all variables used along the paper. [Table 1] There is no information about the The insurance policies are associated with a wide range of brokerage fees. This feature is very robust, and a similar pattern is observed in different subsamples. Figure 1 presents histograms for nominal brokerage fees expressed in BRL, and Figure 2 displays the broker s percentage fees as a fraction of the policy final price. We report results for the following six different groups. Subsample A contains the fees paid to brokers across all insurance contracts in the country. Subsample B focuses on contracts in the metropolitan area of Sao Paulo (the largest in the country). Subsample C presents fees on policies covering nine comparable compact cars with identical power train in the metropolitan area of Sao Paulo. 8 Subsample D refines subsample C to include only contracts with positive bonus discount. Insurees with positive bonus discount are particularly interesting because they must have bought an insurance policy before and, then, they most likely conform our model assumption of being initially matched to a broker. (The insurees carry over the bonus status after changing the insurance company or the broker.) Subsamples E and F further refine subsample D to include only new compact cars and new Volkswagen Gol 1.0L, respectively. This vehicle model is the most popular within its class. On average, about 20 percent of the insurance policies are sold with zero fees. Furthermore, there is large dispersion in both nominal and percentage fees. The shapes of the histograms are surprisingly similar across the different subsamples. This suggests that there might be some relevant economic feature behind the dispersion in brokerage fees paid by consumers. 6 We do not analyze: (i) policy endorsements and cancelations; (ii) collective policies covering multiple vehicles; (iii) policies contracted by firms; and (iv) noncomprehensive contracts. 7 We also access files containing information on reported accidents, other claims, and disbursements. However, these files are not useful for the purpose of this paper. 8 The vehicle models are: Peugeot L, Clio 1.0L, Gol 1.0L, Fiesta 1.0L, Ka 1.0L, Uno 1.0L, Palio 1.0L, Corsa 1.0L, and Celta 1.0L. 7

8 [Figures 1-2] The class width of the histograms in Figure 2 is 0.50 percentage point, and there are contract fees in virtually all classes between zero and 42 percent. We notice a higher frequency of fees equal to 1, 5, 10, 15, 20, 25, 30, and 35 percent of the contract s final price. In addition to the fact that brokers round some numbers, the insurance companies impose percentage caps to brokerage fees. These percentage caps vary across companies and withing contracts of a given company. The most frequent caps are 15, 20, 25, 30, 35 percent. We stress however that policies associated with nonround percentage fees such as 12.9 and percent are not rare (they account, for instance, for 13.5 percent of subsample E). What Brokers Say We conducted structured interviews with 20 registered brokers in order to improve our understanding about institutional details of this market. The computer programs used by brokers record the contract final price, the brokerage nominal fee, and the percentage fee. Brokers argued they use these three pieces of information to make their pricing decisions. In many times, they round the percentage fee, the final price, or the monthly installment (when the sale is financed). They also mentioned that insurance companies impose ceilings on brokerage fees expressed in percentage terms. Selected Subsample We use subsample E in our quantitative analyses throughout the paper. The choice of analyzing a more homogeneous subsample is justified by the fact that we attempt to understand the economics behind the price dispersion that is not related to consumer heterogeneity. We recall that subsample E contains policies signed by insurees with positive bonus discount (and therefore not new in the market), covering new compact cars with identical power train, in the metropolitan area of Sao Paulo. This is the largest metropolitan area in the country and, according to SUSEP s categories, it includes 29 connected cities. Figure 3 lists these 29 cities with their respective zip codes. We stress that the city of Sao Paulo itself is indicated with number 1. It is subdivided in 99 neighborhoods in Figure 4. [Figures 3-4] 8

9 We perform reduced-form OLS regressions (using subsample E) for four different dependent variables: the insurance final price; the nominal brokerage fee; the percentage brokerage fee; and a dummy variable indicating whether the policy was sold without brokerage commission. Our econometric models use independent variables describing the policy coverage, the insuree s gender, age, and bonus category. Moreover, we divide the metropolitan area in 158 regions according to the first 3 digits of the zip code and introduce fixed effects for these subregions as well as for the vehicle model and the insurance company. The results are presented in Table 2. Its column zero lists the independent variables used in the estimation. The regression in column 1 analyzes the insurande final price. The R-squared in this regression shows that about 50 percent of the variability in the final price is explained by our model. This is a good fit considering that the insurance companies may use different models for pricing and are likely to access additional information that is not required to be reported to SUSEP. Next, we pursue a similar analysis with respect to the variables describing different aspects of the brokerage commission. The regressions presented in columns 2 to 4 show that our control variables account for: about 14% of the variation in brokerage fees; only 5% of the variation in the percentage fees; and about 15% of why policies are sold without brokerage commission. We conclude that differences in the observable variables are more relevant to explain the dispersion in the insurance price than the variability in the three variables measuring different aspects of the brokerage commission. This supports our view that an important part of the dispersion in brokerage fees is exogenously random. [Table 2] 4 A Recursive Model of Price Competition There are a few distinguishing features in the auto-insurance market. Policies last for one year and must be sold through an insurance broker. There is no automatic extension, and long-term contracts are not enforceable. Insurance companies define a positive baseline price b for each policy. Brokers add a fee q to that price. Insurance companies require that the brokerage fees do not exceed a percentage cap τ 9

10 of the policy final price p := b + q. The condition q τp is therefore equivalent to q r, where r := τ 1 τ b is a nominal ceiling for brokerage fees. Remark 1. The baseline price b and the percentage cap τ are random variables, in the sense that they take different values for each observation in the data. We assume brokers are well-informed and take as given the values of b and τ associated with each insuree. Game Structure Brokers are allowed to set a different fee for each quotation. This is as if they competed for each insuree at a time. We therefore model an environment with a single ensuree, a continuum of identical brokers, and an infinite sequence of periods t T := {0, 1,...}. The insuree demands one insurance policy every period and seeks to minimize the expected lifetime discounted costs. He starts each period matched to a broker and faces a fixed cost c > 0 for switching to another broker. 9 The switching cost c is heterogeneous across insurees, but it is taken as given by brokers when dealing with each insuree. In each period, the insuree faces an i.i.d. shock on the oportunity cost of time. With probability µ (0, 1), he is too busy to shop around and only accesses the price quotation from the matched broker. Alternatively, with probability (1 µ), he costlesly accesses another price quotation from an unmatched broker, randomly selected from the continuum set of brokers. Brokers select a price offer for each period they are called to play. They cannot commit to prices in the future. They maximize expected lifetime discounted profits, and share the same intertemporal discount factor δ (0, 1). Brokerage fees cannot exceed the ceiling r > 0. In principle, fees are not allowed to be negative. However, when dealing with a new insuree, the broker can offer assistance services that reduces the consumer switching cost. To represent this possibility, we allow the unmatched broker to set negative fees down to c. Hence, brokerage fees q must lie in the interval [0, r] when the broker deals with a regular (matched) insuree, and in [ c, r] when the broker deals with a new (unmatched) insuree. The timing of the game is as follows. Nature moves first. It selects an unmatched broker out of the continuum of brokers and determines whether the insuree meets 9 In period t = 0, the matched broker is randomly selected. After that, the insuree stays matched to the last broker he purchased from. 10

11 this broker or not. This latter event occurs with probability 1 µ. It is private to the insuree and cannot be credibly revealed to the matched broker. Second, the two selected brokers choose their pricing strategies. Then, the insuree purchases one policy. The broker who makes the sale becomes matched to the insuree, and the game ends for the other broker. An identical game starts next period. Strategies Brokers payoffs depend on their matched/unmatched statuses. Conditional on this status, the payoffs do not directly depend on the history of the game or on the broker s name. Although strategies could still depend on this information, we will focus on equilibria in which all players select the same stationary strategy (σ m, σ u ), where σ m is a probability distribution over [0, r] to be used when playing under the matched status, and σ u is a distribution over [ c, r] to be used when unmatched. Brokers Payoff Functions For a given strategy σ u followed by the unmatched broker, we denote by π m (q, σ u ) the probability that the matched broker sells the policy when she poses the fee q. The expected life-time discounted payoff of a broker facing a matched insuree is implicitly defined by the following equation: V m (σ m, σ u ) = r 0 π m (q, σ u ) (q + δv m (σ m, σ u )) dσ m (q), where δv m (σ m, σ u ) is the equilibrium continuation value of becoming matched to the consumer next period, after selling a policy today. 10 Similarly, for a given strategy σ m, we let π u (q, σ m ) represent the probability that the unmatched broker after being selected to play sells the contract when her fee is q. The expected life-time discounted payoff of a broker selected to play under the unmatched status is V u (σ m, σ u ) := r c π u (q, σ m ) (q + δv m (σ m, σ u )) dσ u (q). We impose a tie-breaking rule that favors the matched broker in case of a tie. 10 Throughout the paper, we use θ θ to represent the Lebesgue integral over the set of real numbers larger than θ and smaller than θ. In cases where θ < θ, this set is empty and the integral is zero. 11

12 When the distribution σ u is atomless, we obtain π m (q, σ u ) = µ + (1 µ)(1 σ u (q c)) (1) and π u (q, σ m ) = 1 σ m (q + c). (2) In equation (1), the fraction µ stands for the probability that the insuree has no other option but to take the offer from his matched broker, and (1 µ)(1 σ u (q c)) is the probability that the insuree meets a second (unmatched) broker but this broker poses a fee that does not beat offer q posed by the matched broker. In equation (2), the term 1 σ m (q + c) is the probability that the fee offered by the matched broker exceeds q + c and turns to be less attractive than the fee q posed by the unmatched broker. Remark 2. The unmatched broker infers that she only plays after nature has promoted a meeting with the insuree. For this reason, the probability (1 µ) does not affect definition of π u and V u. The value of being called to play under the unmatched status before nature decides whether the insuree will or will not access the unmatched offer is (1 µ)v u (σ m, σ u ). Definition 1 (Equilibrium Concept). A symmetric stationary Nash equilibrium is described by a strategy (σ m, σ u ) for which there is no profitable one-period deviation that is, there is no pair of probability distributions σ m over [0, r] and σ u over [ c, r] such that either V m (σ m, σ u ) < r 0 π m (q, σ u ) (q + δv m (σ m, σ u )) dσ m(q) or V u (σ m, σ u ) < r c π u (q, σ m ) (q + δv m (σ m, σ u )) dσ u(q). Remark 3. We focus on independent one-period deviations and this is without loss of generality. In a stationary equilibrium, players strategies do not relate their current actions to past information that is not summarized by their current matching status. In principle, deviations are allowed to depend on it, but no broker can profit from conditioning behavior on the history of this game given that no other player does 12

13 so. In this, we follow Duffie et al. [1994]. 4.1 Mixed Strategy Equilibrium Consider a deviation strategy in which the matched broker always charges the ceiling fee r. Since the insuree meets no other broker with probability µ > 0, the lifetime expected payoff associated with this strategy is at least t=0 (δµ)t µr = µr 1 δµ. In economies with µr 1 δµ c, there is a pure-strategy equilibrium in which σ m degenerates to min {(1 δ)c, r} and σ u degenerates to min{ δc, r c}. The matched { } r broker sells the policy every period and earns a lifetime payoff of min c, 1 δ. 11 All others earn zero from this insuree. This pure strategy is not a Nash equilibrium when the matched broker can deviate and collect analyze. µr 1 δµ > c. We take this case to Assumption 1. The model parameters are such that δ (0, 1), µ (0, 1), and µr > (1 δµ)c > 0. Let σ u be an atomless probability distribution with support given by [ a, r c), where c < a < r c. Similarly, let σ m be a probability distribution with support given by [c a, r], which is atomless in [c a, r) and has a mass point at r. The lifetime expected payoff associated with an action q [0, r] taken by the matched broker is v m (q) := [µ + (1 µ)(1 σ u (q c))] (q + δv m (σ m, σ u )). (3) Analogously, the lifetime expected payoff associated with a fee q [ c, r] for the broker called to play under the unmatched status is v u (q) := (1 σ m (q + c)) (q + δv m (σ m, σ u )). (4) Strategy when Unmatched We define v m := µr 1 δµ (5) 11 Recall that our tie-breaking rule favors the matched broker. 13

14 and set σ u ( ) to be such that v m (q) = v m, q [c a, r]. (6) If we take v m (q) and v m from equations (3) and (5) and solve condition (6) for σ u, we obtain the following expression for the strategy of brokers under the unmatched status: σ u (q) = 1 µ ( ) r 1 µ δµr + (1 δµ)(q + c) 1, q [ a, r c). (7) By setting σ u ( a) = 0, we obtain a = c (1 δ)µr 1 δµ. (8) It is simple to verify that c < a < r c, 0 σ u ( ) 1, and lim σ u(q) = 1. q r c Strategy when Matched define σ m to be such that Using an analogous reasoning, we take v u > 0 and v u (q) = v u, q [ a, r c). (9) We set σ m (c a) = 0 and evaluate equation (9) at the point q = a. By using equations (4), (5), and (8), we obtain v u = µr c. (10) 1 δµ It follows from Assumption 1 that v m > v u > 0. From our previous derivation, we also obtain σ m (q) = 1 µr (1 δµ)c δµr+(1 δµ)(q c), for q [c a, r); 1, for q = r. Notice that σ m (c a) = 0 as initially set. The distribution σ m is continuous at any q < r and has a mass at the point r. We also have 0 σ m ( ) 1. Moreover, (11) 14

15 thanks to Assumption 1, we have µr (1 δµ)c 0 < 1 lim σ m (q) = < 1. (12) q r r (1 δµ)c Proposition 1. The strategy (σ m, σ u ) defined by conditions (7), (8), and (11) is a symmetric stationary Nash equilibrium under Assumption 1. Proof. The matched broker is indifferent to any action in supp σ m = [c a, r]. Moreover, it follows from conditions (3), (5), and (8) that fees smaller than c a generates a payoff smaller than v m. Analogously, the unmatched brokers are also indifferent to any action in supp σ u = [ a, r c), and fees outside this interval cannot increase this broker s payoff. Remark 4. Notice from equation (8) that c a > 0. Therefore, the fees posed by the matched broker are always positive. When a < 0, there is a positive probability that the unmatched broker offers assistance services to alleviate the switching cost. 5 Taking the Model to Data In our recursive model, brokers pose prices for each insuree at a time. They share the same discount factor δ (0, 1) and are assumed to know the values of r, c, and µ associated with each insuree. When taking the model to data, we assume that µ is a constant parameter in (0, 1) and let r and c vary across insurees. We recall that r := τ b. (13) 1 τ The insurance baseline price b takes a different value for each observation, and these realizations are available from the data. Unfortunately, this is not the case for the percentage ceiling τ. The realizations of τ are known by the brokers, but they are not recorded in our data. We model τ as a latent random variable following a conditional distribution ϕ(τ x), where x represents a vector of observed characteristics. We also notice that, since our parameters are connected to each other through Assumption 1, we can write the random variable c as follows: c = α µr 1 δµ, (14) 15

16 where α is a constant parameter in (0, 1). Under this modeling strategy, we end up with three parameters (δ, µ, α) and one conditional distribution ϕ(τ x) to estimate. 5.1 Data Generating Probability We do not observe all price quotations but only the contracted fees. Our theoretical data generating probability must then combine the equilibrium strategy (σ m, σ u ) with the insurees selection rule. According to our model, each insuree observes a matched offer q m drawn from σ m. Moreover, with probability (1 µ), he also observes an unmatched offer q u drawn from σ u. When observing two offers, the insuree accepts the fee q m if q m q u + c, and he takes q u otherwise. Let y R + denote the brokerage fee recorded in the data (i.e., the winning offer). The conditional probability Pr (y b, τ) represent the data generating distribution of y given (b, τ). Recall that negative fees represent the situation in which the unmatched broker charges zero and offers assistance services to alleviate the switching costs. Since assistance services are not recorded in the data, our observations of y are censored at zero. Moreover, nonpositive fees must be drawn from the distribution σ u. Each insuree accesses an unmatched offer with probability 1 µ and takes it whenever it exceeds the quotation posed by the matched broker plus the switching cost. This implies Pr (0 b, τ) = (1 µ) min{0,r c} a (1 σ m (q + c)) dσ u (q). 12 (15) The distribution Pr (y b, τ) has no mass point over the open interval (0, r). For y (0, r), we have: y Pr (y b, τ) = (1 µ) a (1 σ m (q + c)) dσ u (q) + (1 µ) y c a (1 σ u (q c)) dσ m (q) + µσ m (y). (16) The first two terms in this equation are associated with the scenario in which the insuree accesses two offers. This occurs with probability 1 µ. In this case, with 12 Notice from equation (8) that a < r c. Moreover, recall from footnote 10 than this integral is zero when a > 0. 16

17 probability 1 σ m (q +c), the insuree takes the offer posed by the unmatched broker. Alternatively, with probability 1 σ u (q c), the insuree takes the offer drawn from the matched-broker strategy σ m. The last term in equation (16) accounts for the fact that, with probability µ, the insuree accesses only one offer q < y drawn from the matched-broker strategy σ m. We also recall that: σ m (q) = dσ m (q) = 0, q c a; (17) and dσ u (q) = 0, q r c. (18) This means that fees in (0, c a) are never generated from the distribution σ m which support is [c a, r], while fees in (r c, r) are never generated from the distribution σ u which support is [ a, r c). We conclude by pointing out that the distribution Pr ( b, τ) has a second mass point at the ceiling fee r := τ 1 τ b. Since the support of σ u is [ a, r c), fees equal to r can only be taken by insurees who do not meet an unmatched broker. This is to say that ( ) µr (1 δµ)c 1 lim Pr (y b, τ) = µ. 13 (19) y r r (1 δµ)c 6 Estimation Procedure We estimate the the parameters (α, δ, µ) in two steps. We first model the unobserved percentage ceiling imposed by the insurance company as a latent random variable τ. We use an ordered logit model to represent the conditional distribution ϕ(τ x) given the observed characteristics x. Next, we use this distribution to construct a likelihood model and estimate the structural parameters (α, δ, µ). 6.1 Ordered Logit for ϕ(τ x) We accessed documents from the insurance companies and reports from the broker s association mentioning ceilings on brokerage fees at five different per- 13 We notice that r c (1 σm(q + c)) dσu(q)+ r (1 σu(q c)) dσm(q) = 1. This is because, a c a for any independent realizations of q u and q m, respectively draw from σ u and σ m, we have that q m > q u + c if and only if q u < q m c. This implies r c (1 σm(q + c)) dσu(q) = r σu(q c)dσm(q). a c a 17

18 centage levels, namely:.15,.20,.25,.30, and.35 of the policy final price. about 99.88% of observations in subsample E, the observed percentage fees do not exceed the.35-percentage cap. There are, however, 15 observations with percentage fees in (.35,.42]. To account for that, we also include the value.42 as a percentage ceiling. According to our model, the conditional probability of observing a percentage fee equal to τ is given by ϕ(τ x) (1 lim y r Pr (y b, τ)). By replacing equation (14) into (19), we find that this mass point is given by For 1 lim y r Pr (y b, τ) = 1 α 1 αµ µ2. (20) Since the term 1 α 1 αµ µ2 is constant across all observations, we can estimate the distribution ϕ(τ x) by using the subsample of observations for which the percentage fee reach one of the possible ceiling levels τ {.15,.20,.25,.30,.35,.42}. Subsample E has 6,200 observations with percentage fees equal to one of those percentage caps. We use this reduced subsample to estimate an ordered logit model for ϕ(τ x). The results from this estimation appear in Table 3. We then take these estimates to predict values for ˆϕ τ,j for all observations of Subsample E (including those not used in this estimation step). These values are used next to estimate the parameter vector (α, δ, µ) through a maximum likelihood procedure using the entire subsample E. [Table 3] 6.2 Maximum Likelihood Estimation for α, δ, and µ We adapt the maximum likelihood procedure for censored data described in Meeker and Escobar [1994] to estimate the parameter vector (α, δ, µ). Each observation j is associated with one realization of the vector of characteristics x j and baseline price b j. Moreover, each observation j is generated from one of six possible games, indexed by τ. We fix an observation j and a game τ to define an auxiliary likelihood function L j,τ (α, δ, µ) as follows. For policies sold without brokerage commission (censored observation), we take 18

19 equation (15) and define L j,τ (α, δ, µ) := Pr (0 b j, τ). (21) Similarly, for censored observations associated with a brokerage fee at the percentage ceiling τ, we take equation (20) and write L j,τ (α, δ, µ) := 1 α 1 αµ µ2. (22) Next, we take equations (13) and (14) and write r j,τ = τ 1 τ b j and c j,τ = α µr j,τ 1 δµ. For observations associated with a brokerage fee y j (0, r j,τ ), we derive equation (16) to find a density probability and write L j,τ (α, δ, µ) := (1 µ) (1 σ m (y j + c j,τ )) dσ u (y j ) + [(1 µ) (1 σ u (y j c j,τ )) + µ] dσ m (y j ). (23) We stress that conditions (8) and (17) imply σ m (y j ) = dσ m (y j ) = 0, (24) for every y j (1 δ)µr j,τ 1 δµ. Moreover, condition (18) implies dσ u (y j ) dy = 0, (25) for every y j r j,τ c j,τ. Given the assumption that each τ is independently withdrawn with probability ϕ(τ x), the likelihood function associated with a given observation j is L j (α, δ, µ) = τ ˆϕ τ,j L j,τ (α, δ, µ). (26) To conclude, we write our final likelihood function as L(α, δ, µ) = j L j (α, δ, µ), (27) 19

20 and the associated log-likelihood function as lnl(α, δ, µ) = j ln (L j (α, δ, µ)). (28) 6.3 Numerical Estimation Our optimization problem is not simple since the log-likelihood function is not continuous. We have managed to reduce the model to depend only on three parameters (α, δ, µ), each of them lying in the unit interval (0, 1). This allows us to set a comprehensive grid search and considerably reduces concerns about global optimality. We use the software Wolfram Mathematica to compute closed-form solutions for the integral in equation (15), used in definition of the function (21). The optimization code is written in Matlab. We evaluate the log-likelihood function over a grid with 8 million points. More precisely, our grid includes 200 equally spaced points for each unit interval of the domain. Each block with a million points take about 11 hours to be computed in a high performance server. We select the 50,000 highest values obtained from the grid computation and use their respective parameter values as initial seeds for the Matlab patternsearch optimization algorithm. This algorithm performs a numerical search using fixeddirection vectors (GPS method) and randomly-selected vectors (MADS method). For robustness, we also make this same optimization for 50,000 random initial seeds. Our results are presented in Table Brokers discount factor is This factor accounts for the intertemporal cost of money as well as for the probability that the brokerage relationship is interrupted for unmodeled motives: for instance, when the insuree moves to another city. Our estimate for µ is This means that about percent of the insurees see a price quotation from a new (unmached) broker in addition to the quotation from the regular (matched) broker. The estimated value for α is.4997, which implies an average switching cost of BRL (i.e., approximately PPP USD), where the average is taken over different models indexed by τ and observations indexed by j. Moreover, Figure 5 displays the distribution of switching costs across insurees (averaged over τ). 14 The optimal parameter values were rounded to 4 decimal places. 20

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