Price Endogeneity with Unobserved Sales Effort Evidence from Pharmaceutical Retailing

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1 Price Endogeneity with Unobserved Sales Effort Evidence from Pharmaceutical Retailing Morten Sæthre January, Abstract I propose a general empirical model of non-price actions undertaken by firms to influence demand. When such actions are unobserved and correlate with the profit margins of the firm, standard methods for estimating demand will yield biased estimates. I present an application to the market for prescription drugs, where I use the model to infer non-price actions of the firms. I find that controlling for pharmacy chain influence on consumers has important effects on estimates of a drug choice model, and that a large part of the generic market share can be attributed to pharmacies incentives to dispense these drugs. These findings suggest that the estimation bias arising from ignoring non-price actions by firms can be important for applied studies. Introduction In many markets, sellers influence consumer choice through channels other than price, such as different kinds of marketing measures. When this is the case, the validity of standard instrumental variable approaches to obtain consistent estimates of demand is questionable. A valid instrument for price will necessarly affect the profit margin of a product, which in general will influence a seller s incentive to perform other actions to sell the product. When these actions are unobservable, they constitute unobserved determinants of demand which the instrument in general will be correlated with, thus making our estimates biased. Consider the following stylized example: A retailer currently sells two products, deciding on prices and a certain level of additional services for each product. If the cost of obtaining one of the products increases exogenously, the retailer increases the price and reduces the profit margin. The retailer also reduces the amount of services on the product that has become less profitable in relative terms. Both the increase in price and the decrease in services will reduce demand. Given that service is unobserved, estimated price elasticities of demand will be overstated when the cost of obtaining the product is used as Norwegian School of Economics, Department of Economics, Helleveien, NO- Bergen. morten.saethre@nhh.no. I thank Steve Berry, Pierre Dubois, Ben Handel, Jeroen Hinloopen, Lars Sørgard, Ragnhild Balsvik, Kurt Richard Brekke and Aline Bütikofer for valuable suggestions and comments. The paper has also benefited from comments of seminar participants at Toulouse School of Economics and Bergen Center for Competition Law and Economics.

2 an instrument for price, as the reduction in demand will be attributed to the increase in price following the increase in cost. This paper suggests a framework for dealing with this problem, allowing us to obtain consistent estimates of demand when unobserved supply side actions have an important influence on demand. I refer to such actions as sales effort, or simply effort. As the example above illustrates, it is difficult to imagine a solution to the identification problem involving instrumental variable approaches alone in such a situation. My proposal involves direct estimation of a general model of optimal sales effort. The model is based on the intuitive idea that firms would like to sell more of goods for which they have higher margins, everything else equal. The innovation of the empirical model is to directly link the firm s profit margins to its incentive to exert sales effort. This is a desirable feature, as correlation between profit margins and sales effort is the fundamental problem for validity of instruments for price when firms exercise unobserved sales effort to influence demand. Clearly, there is no way to say how important non-price competition is in general, without undertaking substantial empirical investigations and collecting much more detailed data on the practices of firms than is the standard today. As a solution in face of lack of data, I propose a fairly general empirical framework that can be used to estimate and predict behavior in markets where non-price competition is important. The most fundamental part of the approach, however, is to remove the bias in demand estimates arising from the influence of unobserved supply side actions. Demand estimation is a cornerstone of empirical market studies, since predicting demand is crucial for many problems in applied economics, such as evaluating mergers, valuing new goods, inferring market conduct, and evaluating regulation. Dealing with possible endogeneity is crucial for the credibility of the results and recommendations generated by applied market studies. A central problem is the endogeneity of prices, which is usually solved using an instrumental variable approach. For any instrument for price to be relevant, it must have a significant effect on prices. When firms engage in practices that influence demand, it is likely that the instrument will be invalid, in the sense that it is correlated with unobserved determinants of demand. I present an application of the proposed model to the pharmacy market using data on sales of prescription drugs in Norway, where I show that demand is quite responsive to pharmacy effort. For the same market and data, Brekke, Holmås, and Straume () have found evidence that relative margins of substitute drugs within the pharmacy chains have an impact on sales. The application in this paper is complementary to their work, as I estimate a structural model of demand and supply suitable which directly incorporates such mechanisms. See e.g,. Fan (), who studies the US daily newspaper market; Houde (), for an application to a Canadian gasoline market; Björnerstedt and Verboven (), for an ex-post evaluation of a merger in the Swedish analgesic market; and Nevo (), who studies the market for ready-to-eat cereals. See e.g., Gentzkow (7); Petrin (); Hausman (99). See e.g., Bonnet, Dubois, Villas-Boas, and Klapper (); Bonnet and Dubois (); Villas-Boas (7); Bresnahan (97). See e.g., Villas-Boas (9); Brenkers and Verboven (); Goldberg and Verboven (). See the survey by Ackerberg, Benkard, Berry, and Pakes (7) for an in-depth discussion of strategies for dealing with endogeneity of prices.

3 This allows one to perform subsequent analysis of the implications of the model in terms of competition and counterfactual responses to changes in the market environment. Combining data on the pharmacy chains costs of obtaining drugs and regulatory features that constrain the pricing, I present reduced form evidence that margins affect sales when prices are fixed. This represents a credible source of exogenous variation for identification of the structural model. I assess the predictive performance of the model in an out-of-sample setting featuring a change in a regulatory price ceiling, showing that it outperforms a benchmark BLP-type model which does not account for unobserved sales effort. Illustrating the importance of the mechanism, I use the estimates to show that percentage points of the overall generic market share can be attributed to the incentives of pharmacies to sell them. In section, I present a model for supply and demand in an oligopolistic market where retailers influence consumer choices. In section, I present a Monte Carlo simulation of the model displaying the bias using standard instrumental variable approaches in a market featuring retailer sales effort. Section describes the institutional features of the Norwegian pharmaceutical market. Section describes the data and presents some evidence on the importance of retailer response to profit margins in this market. Section presents the estimation strategy and results. Section 7 concludes. A model of sales incentives The general model consists of retailers having two actions available to maximize profits; prices and sales effort. I consider a market with multi-product retailers, who choose unobservable effort that has the effect of increasing demand. I motivate the problem of demand estimation under a richer model of firm behavior with a simple example, before I describe the main idea for constructing an effort variable, as well as clarifying what variation one can use to identify the effects of effort.. Illustration of the problem Consider the following model of firm behavior: Let the profits of a single product firm be given by π = q(p, e)(p c) γ(e), where q(p, e) is the demand function, which is a function of price p and sales effort e. Further, we have c, which is the unit cost per sale, and γ(e) which is the cost of effort. I assume that γ( ) is increasing and convex in this example. Let demand in the market behave as we would expect, that is, decreasing in price and increasing in effort. The firm chooses price and effort to maximize profits, such that π p = and π p=p,e=e e =, () p=p,e=e where p and e denotes optimal price and effort respectively. In general, it is not possible to sign dp de dc and dc without further assumptions on demand. For the sake of the example, let us assume that demand follows the

4 linear specification q = β αp + β e e + ε, () where β is an intercept and ε is an idiosyncratic demand innovation. Then the comparative statics evaluate to dp ( ) dc de = αγ (e ) βe αγ (e ) βe. αβ e dc It is clear that an increase in costs will have the effect of decreasing effort, as a decrease in the profit margin will reduce the incentive to expend resources on increasing demand, but the effect on price could go both ways. If we assume that demand is not too responsive to sales effort, we will have the classic case of prices increasing in marginal cost, although the effect will be somewhat offset by the decrease in effort compared to the same model without effort. 7 It is apparent that not accounting for the firm s choice of effort will lead to a model mis specification: Let us assume that the firm observes ε, or at least part of it, when setting prices, such that we have the traditional case where E[pε]. A common solution is to use suitable instrumental variables, for instance cost shifters, under the assumption that the instruments affect price but do not correlate with the demand side unobservable. If we omit effort from the empirical specification, the estimated model is q = β αp + u, () where the error u = β e e + ε. We see that the cost shifters no longer satisfy the exclusion restriction, as we according to the previous arguments would believe that E[cu] = E[cβ e e] + E[cε] = β e E[ce] <. In this example, the price parameter will be estimated with a negative bias when cost shifters are employed as instruments, i.e., overstating the true demand response to changes in price. In general, it will be unclear in which direction the bias goes. The bias will depend on the instruments used, the true model of sales effort and the variation in the data. As a second example, say we have an observable, θ, excluded from the demand specification, which measures substitutability of competing products, for example, similarity of characteristics of products owned by competitors. It then <, i.e., the firm reduces its price as consumers have better substitutes available. Furthermore, assume that e θ <. Using the same demand specification as in Equation (), we immediately see that estimating Equation () using θ as instrument leads to a positive bias in the estimated price coefficient, thereby understating the demand response to price changes. seems reasonable to assume that p θ Note that the denominator is required to be positive by the second order condition. 7 In the case where β e < α < βe, the reductions in effort due to a cost increase will make price decreases optimal, since demand falls very steeply relative to price sensitivity as a result. There does not appear to be an equally intuitive way of determining whether the firm exerts more or less effort when substitutability increases. There are, however, appealing reasons to believe that effort should decrease when competition becomes fiercer, as profit margins should decrease.

5 If one were to use several instruments, it would be very hard to assess the bias from not observing sales effort without having an instrument that does not affect firms effort decision. In the following section, I describe a model for an oligopolistic market where multi-product firms set optimal prices and effort. The model shows that the results can be incorporated quite naturally into demand estimation.. Retailing and effort The effort in the subsequent application of this paper is most readily thought of as a scheme for sales personnel persuasion, but the model could fit well as a representation of a marketing mix with respect to things like product availability, product placement, local advertisement and product information. I model this by a choice variable e rj which retailer r sets for each product j, incurring a cost γ(e rj, q rj ). This cost represents the firm s alternative cost of the marketing policy given by e rj, which I allow to depend on the quantity sold of the product, q rj. 9 The profits of retailer r are given by π r = j G r [(p j c j )q j γ(e j, q j )], where the summation is over the set of goods G r that retailer r sells, q j is quantity sold of good j, p j price, and c j is the per-unit costs of supplying the good. Assuming that we have a case of static Bertrand competition with differentiated products, the vectors of first order conditions for retailer r s optimal price and effort becomes and q r + p q r ( p r c r γ q (e r, q r ) ) = () e q r ( p r c r γ q (e r, q r ) ) γ e (e r, q r ) =, () where the notation y r denotes a vector of variables y rj, j G r for retailer r, and p r and e r denotes the optimal vector of prices and sales effort for retailer r. The symbols p q r and e q r denotes the gradients of retailer r s vector of quantities with respect to prices and effort respectively, while γ q and γ e are the vectors of partial derivatives of the cost of effort with respect to quantity sold and effort respectively. The gradient of quantity will follow from the assumptions one makes about how sales effort enters demand, and the parametric (or nonparametric) specification of demand. 9 It would be possible to let the cost function depend on other variables as well, though I will not consider this possibility in this paper. In most cases, I will just denote these as p r and e r for notational simplicity, as choice variables of the firms are mostly referred to as their equilibrium counterparts. The gradient is just the transpose of the jacobian, i.e., pq r (D pq r ), where the Jacobian is defined as usual, that is, row j contains the derivative of the quantity q rj with respect to the price vector p r. Here I assume that the cost of effort for a product j is only a function of the quantity sold and effort of the same product. Even though it would be possible, I will not explore alternative specifications in this paper, as it would increase analytical complexity substantially.

6 To see the intuition for how sales effort is determined, consider the element corresponding to the derivative of profits with respect e k in the left hand side of Equation (): ( ) p j c j γ q j (e j, q j ) γ (e k, q k ). q j e k e k j G r The first term is the marginal revenue of effort for good k, which is the sum of the profit margin on each product j times the derivative of demand for product j with respect to effort for good k. At the optimal sales effort for good k, e k, this weighted sum of profit margins will be equal to the marginal cost of sales effort, given by the second term. Note that the profit margin also includes the incremental cost of effort for good j from an increase in the quantity sold. We would expect increases in effort for a product to induce substitution towards this product, such that qj e is positive when j = k and negative otherwise. The firm k will thus weigh the gain in profits on product k against the loss on all others. The substitution patterns of consumers in the market will thus be crucial in determining the firm s incentive to influence consumer choice in response to changes in margins. I return to the issue of capturing substitution patterns when discussing the application of the model in Section. Given a demand function, Equations () and () define the optimality conditions for a vector of price and effort for retailer r. Often, we observe prices but not costs, in which case these two equations will allow us to identify the unobserved vectors of cost and effort. An intuitive way of interpreting this, is that the shape of demand together with an assumption on the market equilibrium (e.g., non-cooperative Nash equilibrium) identifies the optimal profit margin of the firm by p r c r γ q (e r, q r ) = ( ) p q r qr, while the optimal effort is mainly identified by a relation between profit margins and sensitivity of demand to effort, which can be expressed as γ e (e r, q r ) = e q r ( p q r ) qr. One can thus think of identification of the effect of effort on demand in terms of relating effort to the firm s profit margins. To illustrate the intuition behind utilizing the model for estimation, assume for the moment that we observe margins and know how firms compete. In this case, one can solve for sales effort from Equation () as a function of data and parameters governing e q r, γ e and γ q. Effort can then be introduced into the estimating equations for demand, which will then allow estimation of the parameters of demand and cost of producing effort. This allows us to obtain a direct estimate of how important the incentives captured by this model are in a given setting. However, if we do not know the true mode of conduct in this market, we will have to make an assumption on this, such as Nash-pricing Note that variation in margins can provide the information needed to identify the importance of sales effort here, thus relieving the need to rely on functional form for identification. This points to the possibility of non-parametrically estimating the effect of margins on demand, though in markets with many products this would likely be precluded by the implied data requirements.

7 with differentiated products. Even if we do not observe costs, it is feasible to calculate them from the first order condition for prices, as is standard in many empirical market studies. This points to several possibilities for estimating effort, depending on how much information we have about the market. Simulation evidence To show the possible extent of the bias when endogenous effort is not accounted for, and how well the procedure for correcting for effort works, I perform a Monte Carlo study. I simulate and estimate an explicitly aggregated mixed parameters logit discrete choice model with heterogenous consumers, as described by Berry, Levinsohn, and Pakes (99). The steps in the simulation are as follows:. Generate exogenous variables for each market, taken to be product characteristics, costs and unobserved demand and supply shifters.. Solve for equilibrium prices and effort in each market, giving the equilibrium market shares.. Estimate the GMM objective, with and without calculating the effort variable.. Model fundamentals and data generation To keep the model simple, I simulate samples of markets with three firms selling a single good. The single product case is sufficient to illustrate the estimation bias using standard instrumental variables, and how my suggested approach reduces this. 7 Each sample consists of markets, and I simulated a total of samples. The goods have one characteristic and one cost shifter, as well as an unobserved characteristic ξ and unobserved cost component ω. The observed characteristic and cost shifter were generated by a procedure ensuring strong correlation between them and bounded support. The unobserved components are generated by draws from ( ( ( ) ( ) ξ.. N, ω) )... This assumption is in principle testable. See e.g., Nevo (). See e.g., Nevo (); Verboven (99); Berry et al. (99). The simulation is carried out using Python, employing the NumPy and SciPy packages. Code is available from the author upon request. 7 In the multi-product case, the margin on all products of a firm will have a direct effect on the optimal effort for any one of them. The exact procedure was generating two series from a uniform random distribution with zero mean and unit variance, i.e., U(, ( ) ), multiplying the two series by the Cholesky.9 decomposition of and adding a number to each of the series such that the distribution starts from for the characteristic and for the cost shifter. This gives a uniform.9 distribution for the characteristic and a triangular distribution for the cost shifter. This was done to reduce the probability of a firm becoming very dominant in the market, that is, having both a large value of the characteristic and very low costs, which seemed to reduce some problems I encountered initially when solving for market equilibria in some of the simulations. 7

8 Let x j denote the characteristic for product j, p j the price and e j the sales effort. I allow consumers to be heterogenous with respect to their utility of the characteristic and their sensitivity to price. The utility consumer i derives from consuming product j can then be written u ij = β + β i x j + α i p j + β e e j + ξ j + ɛ ij, where β is a constant utility term, β i and α i are an individual specific coefficients for the characteristic and price, β e is the utility parameter of sales effort, ξ j is an unobserved product characteristic, and ɛ ij is an iid random utility term following a standard Gumbel distribution. The individual specific coefficients are given independent uniform distributions, with means β and α, and spreads σ x and σ p. 9 It is helpful to decompose utility into δ j = β + βx j + αp j + β e e j + ξ j, which is the market mean utility for product j, and µ ij = σ x ν x i x j + σ p ν p i p j, which is the individual deviations from mean utility. The given distribution of the individual coefficients means that ν i = (νi x, νp i ) is a vector of individual taste characteristics with components independently distributed U(, ). An outside good is added to the model, which is taken to yield zero utility. The demand for product j expressed in terms of market share is then s j = e δj+µij + k eδ k+µ ik df (ν i ), i.e., the integral over individual specific choice probabilities with respect to the distribution of individual taste characteristics, where the individual choice probabilities take the multinomial logit form due to the extreme value distribution of the idiosyncratic random utility term ɛ ij. Since the purpose of this paper is not to investigate properties or identification of preference heterogeneity specifically, the distribution of coefficients is not simulated during data generation, but rather approximated by quadrature. This is in line with Judd and Skrainka (), although their focus is on the use of quadrature rules to approximate the integrals used in the estimation procedure. To match the distributions, I choose a uniform quadrature, with seven nodes in each dimension. This can be thought of as the preference distribution being discrete over a 9-point grid with a special structure. I use the same quadrature for the estimation procedure as well, which implies that there will be no approximation error in the market share integration. Denoting the points on the grid by ν r, where r is the grid index with R = 9 points in total, market shares are given by s j = R r e δj+σxνx r xj+σpνp r pj + k eδ k+σ xν x r x k+σ pν p r p k 9 We can thus write β i U(β σ x, β + σ x), which has the obvious reformulation β i β + σ xν i, where ν i U(, ).

9 The supply side model is given by firms maximizing profits with respect to prices and effort in a non-cooperative static Nash game. The profits of firm j is given by π j = s j (p j c j ) γ(e j ), where, for simplicity, γ is defined as a function of effort alone, i.e. not depending on quantity produced. Market size is set to in all markets, such that s j = q j. Marginal cost, c j, is defined as c j λ + ηw j + ω j, where λ is a constant cost common to all firms, w j is the cost shifter with parameter η, and ω j is the unobserved marginal cost component.. Equilibrium solution The Nash equilibrium is then calculated as the vector of prices and effort in each market which satisfies the system s j (p j c j ) + s j = p j s j (p j c j ) γ (e j ) =, e j where the effort cost function is given the form γ(e j ) = e j. There is no analytical solution to this system of equations, due to non-linearities and the integral defining the market shares, which means that it must be solved numerically. After a candidate equilibrium given by a vector satisfying the first order conditions has been found, I verify the second order conditions by numerically checking whether the Hessian of each firm s profit function, evaluated at the candidate equilibrium vector, is negative definite. Markets where either the algorithm fails to converge to a solution to the first order conditions or the second order conditions do not hold are simply disregarded from further analysis. It should be pointed out that the check of second order conditions, which is not commonplace in these kinds of simulations as noted by Knittel and Metaxoglou (), leads to the rejection of about a third of the simulated markets. The existence or uniqueness of equilibria for general combinations of parameters and exogenous variables is, as far as the author is aware, not guaranteed when demand has a mixed multinomial logit form. The issue of equilibrium in the usually assumed Bertrand pricing game has to some extent been investigated by Allon, Federgruen, and Pierson (), though there have been no studies of this when there is competition along the effort dimension as well. To improve the numerical search for the equilibrium vector, I adapt the advantageous transformation proposed by Morrow and Skerlos () to the above system. The start point for the search algorithm is at price equal to marginal cost and zero effort, which seems to ensure stable and swift convergence. This design feature saves computing time trying to find a market equilibrium which might not exist, and saves researcher time in trying to design a program that is more robust to such occurrences. Moreover, it did not seem to alter the data generating process of the exogenous variables in any discernable way. I have no proof that the equilibrium of the model proposed here is unique, though my admittedly sparse numerical tests have failed to find evidence of multiple equilibria in markets where an equilibrium is actually found. 9

10 . Estimation Using the first order condition for price, we can estimate the cost parameters from ( ) s j p j + s j = c j λ + ηw j + ω j. p j In the simulation estimates, I will assume that the effort cost function, γ(e j ) = e j, is known. Thus, conditional on the parameters of demand, effort can be calculated from the first order conditions of the firm as e j = ( ) sj sj e j p sj j and substituted into the expression for δ j directly. The equations governing demand are constructed by solving for the vector of δ j in each simulated market using the contraction procedure of Berry et al. (99), conditional on the parameters governing preference heterogeneity, σ x and σ p. These equations will be separable in the unobserved characteristic, ξ j, such that we can use standard linear instrumental variable methods to estimate the parameters of demand. The full vector of parameters, θ = ( β, β, α, β e, σ x, σ p, λ, η ), is obtained by estimating the simultaneous equations describing demand and supply, constructing moments using an instrument set consisting of the characteristic, the cost shifter, their squares, and rivals sum of the characteristic and cost shifter. I estimate the vector of parameters θ using GMM with empirical moments m D (θ) = Z T ξ(θ) for demand and m S (θ) = Z T ω(θ) for supply, where Z is the matrix of instruments and exogenous variables. Letting m(θ) = (m D (θ), m S (θ) be the stacked vector of moments for supply and demand, the non-linear GMM estimator is θ = arg min m(θ) W m(θ), () θ where W is the weighting matrix for the moments. I use the optimal weighting matrix (Hansen, 9), calculated as W = (m(ˆθ)m(ˆθ) ), where ˆθ is a consistent initial estimate of the parameter vector using W = (Z Z) in the GMM objective function in Equation (). The results are reported in Table. The results confirm that the bias in the estimates when one does not account for effort can be substantial. Using the proposed procedure for estimating effort performs very well, leaving just a negligible bias stemming from the small sample properties of the estimator. Figure shows the distribution of the estimated coefficients, where the true value is marked with a vertical line. For the demand parameters, the estimates not controlling for effort can be very far off, even to such an extent that the entire distribution of estimated coefficients can be situated to one side of the true parameter value. This should make us worry that demand estimates, even using instruments that are widely regarded as sound, can yield predictions and policy counterfactuals that are biased. Due to the way the data are generated and the features of the underlying model, the cost shifter is a very powerful instrument. This explains the heavy negative bias on the price coefficient, as the variation in the cost instrument in general will drive prices and effort in separate directions, making the price coefficient pick up much of the variation in the unobserved effort as well. The bimodal distribution of the estimated spread in price sensitivity when one does not correct for effort might seem peculiar. The most common positive bias is

11 Table : Bias and efficiency Excluding effort Including effort True Bias Bias (%) RMSE Bias Bias (%) RMSE β e α σ p β σ x β η λ due to margins and effort being lower for firms capturing a lot of highly price sensitive consumers, while the opposite is true for firms capturing a lot of less price sensitive consumers. The negative bias will arise when firms are very similar, especially with high value products, such that competition drives down margins and effort, thus making less price sensitive consumers look more price sensitive. The particular configuration and most important driving variation in each market will decide which way the bias goes. The bias in the coefficient for the product characteristic is driven by products with high values of the characteristic being more valued on average, thus giving scope for higher markups which in turn drives unobserved effort up. The positive bias in the spread of the characteristic valuation distribution can be explained by the fact that effort does not perfectly correlate with the value of the characteristic. This is because differences in costs and characteristics of rivals products will impact the scope for profitable effort across the markets in each sample. This will make consumers seem more heterogenous with respect to their valuation of product characteristics than they really are, as this parameter is also picking up variability in the sales effort across products. The positive bias in the constant term β is just picking up the average effect of effort, which gives the inside goods higher value. The parameters of the cost function, η and λ, exhibit much less dramatic bias. This is likely due to the specification of the effort cost function as a fixed cost of effort, which implies that it does not enter the true marginal cost. Thus, the bias in the cost function will only be driven by the bias in markup, as calculated from the consumer model, which will have a second-order effect on the estimates of marginal cost. Application and institutional setting To illustrate the usage and performance of the model, I use the pharmacy market of Norway as an example. In the Norwegian market for prescription drugs, the specific brand prescribed by the doctor can be substituted when alterna-

12 Density.. Density. Density (a) ˆα (b) ˆβ (c) ˆβ Density Density (d) ˆσ α (e) σˆ x Density Density (f) ˆσ α (g) σˆ x Figure : Densities for estimated parameters from GMM estimation on simulated data. Estimated parameter density from model including effort indicated by solid line, while estimated parameter density from model excluding effort indicated by dashed line. True parameter value indicated by vertical line.

13 tives with the same active ingredients are available. This is known as generic substitution when the patent on the drug has expired. The drug can then be manufactured by other companies, whose product is then a generic drug, as opposed to the originator drug of the original patent holder. The decision on whether to substitute or not will take place at the pharmacy level, where a pharmacist or trained pharmaceutical personnel is responsible for offering substitute drugs to the consumers. This could be both parallel import drugs or generic varieties. The final choice is made by the consumer, who can accept a cheaper alternative, or stick with the usually more expensive originator product. Pharmacies thus have a clear opportunity to influence consumer choice between substitutable alternatives. In this sector, we could expect that the possibility of influencing consumer choice will be exacerbated through the informational advantage of pharmacy personnel and expert status of pharmacists. In addition, extensive price regulation in the market might also affect the incentives to exert sales effort. A natural interpretation of effort in this market is therefore the extent to which the personnel in the pharmacy will more or less directly convince the consumer to choose a certain alternative. Another possible interpretation is that the effort in my model captures logistics and availability of drugs in individual pharmacies, if firms choose these factors based on differences in profitability of each drug.. The Norwegian pharmaceutical market The supply side of the market for prescription drugs consists mainly of three large pharmacy retail chains, which are vertically integrated with each of their upstream wholesaler. The three largest chains cover % of all pharmacies, while public hospital pharmacies (%), a smaller retail chain (%), and independent pharmacies (%) make up the rest. The Norwegian market for drugs is subject to a wide array of regulations. The Norwegian Medicines Agency, a governmental organization under the Ministry of Health and Care Services, is the main regulatory body for drug affairs, in charge of marketing authorization, drug classification, market vigilance, price regulation, reimbursement regulation, and providing information on drugs to prescribers and the public... Regulation All prescription drugs sold on the Norwegian market will be subject to a price cap, set by the Norwegian Medicines Agency. The price cap is set as the average of the three lowest among market prices in a fixed group of European comparison countries, consisting of Sweden, Finland, Denmark, Germany, United Kingdom, Netherland, Austria, Belgium and Ireland. The price caps should normally not change more than once every year. Reconsideration of the price caps will be initiated by the Norwegian Medicines Agency, where selection is based on sales volume over the past months. In cases where the patient has a long term ailment, defined as demanding treatment for at least three months, and the drug under question has been The shares are calculated from my own data and checked against data I have obtained from the Norwegian Medicines Agency. The numbers correspond exactly to official statistics reported by the Norwegian Medicines agency and the Norwegian Pharmacy Association.

14 judged to have sufficient effect compared to the costs, government reimbursement is available. The prescribing physician is responsible for deciding if the patient satisfies the criteria for treatment length, while the Norwegian Medicines Agency decides if a drug is efficient and cheap enough to be put on the list of drugs approved for reimbursement. When patients get reimbursed, they face a co-insurance of % of the total price, capped at NOK in (approximately EUR ) per three months. There is an out-of-pocket maximum for drugs and health care of NOK yearly in (approximately EUR ). For drugs that are on patent, government reimbursement is based on the full cost of the drug to the patient. The patient pays % of the price up to the threemonth co-insurance cap of NOK. When the drug is off-patent and generic drugs have entered the market, the reimbursement rate will be reduced. There have been several ways of implementing this reduction, but the most prevalent regime now is the so-called step price regulation. Under this regime, the reimbursement rate declines to 7% of the price cap of the originator drug at the time when the first generic drug becomes available in pharmacies. Thereafter the reimbursement will be subject to additional fixed reductions as percentages of the original price cap after and months. The generic varieties will be regarded as equal to the originator drug, and be subject to generic substitution at the pharmacy. This implies that, even when the physician has prescribed the originator drug, the pharmacy is obliged to offer the patient the cheapest alternative. Should the offer be refused, the patient will have to pay the full difference between the list price of the prescribed drug and the maximum reimbursement, including the co-insurance for the amount up to the maximum reimbursement. Data I use registry data on sale of prescription drugs from the Norwegian Directorate of Health and the Norwegian Institute of Public Health, as well as data on regulation from the Norwegian Medicines Agency. The data contains all transactions for a sample of drug classes, where, for each transaction, I observe an identifier for the individual, an identifier for the specific drug package sold, an identifier for the pharmacy selling the drug and the price at which it was sold. The identifier for individual allows me to track individuals over time as well as identify their age and gender. The identifier for package uniquely determines the active ingredient, the amount of the active ingredient, the size of the package and the producer. The identifier for pharmacy uniquely determines the location of the pharmacy and its owner, which allows for identification of chain affiliation. I also observe the price paid by the three full-line wholesalers to the producers for each specific drug package in each month. In addition, I observe the regulation that applies for each package of drugs, which includes the price cap, maximal reimbursement and which packages that can be substituted for each other at the pharmacy. In this application, I study the market for statins in the time period from -7. Statins are a group of drugs for cholesterol reduction. They constitute a large market, being used by approximately % of the population. Statins Due to the vertical integration between the domestic wholesalers and pharmacy chains, I will by wholesale price refer to the price paid by the wholesalers to the producers.

15 are mostly used by people above the age of, with about % being on statin treatment in the age group 7. I use data on individual purchases, their expenditure for each purchase, price charged by the pharmacy chain, and wholesale price for each package sold by each retailer, all in terms of defined daily doses (DDD). I do the analysis for Simvastatin and Pravastatin, which are the two statins where generics were either present or entered early in the period I study, and where step price regulation for reimbursement was in place. Before generic entry, there will be no substitution at the pharmacy level, except for the possibility of parallel imports. In this application, I choose to focus on the pharmacy incentives in substitution between originator and generic drugs. The pharmacies, for most periods, sell only the product of one specific generic producer within each segment, where segment is defined as packages with a specific concentration of a specific active ingredient. 7 Thus, I aggregate generic sales within each segment, and take the input price of the most prominent generic producer as the relevant one for the pharmacy chain. This corresponds almost perfectly to the generic producer with the lowest reported price to the wholesaler, and is almost unanimously the only one having non-negligible sales in any given period within the corresponding chain. The reason for defining segments in this fashion, is that for the statin class of drugs only drug packages featuring the same concentration and active ingredient will be substitutable at the pharmacy. This implies that the pharmacy can take an active role in influencing the consumer only within these segments. For the consumer, it is the case that a choice will be made together with the physician during the prescription stage, regarding which statin and concentration to use. The different types of statins are, as far as I understand, to a large degree substitutes in a therapeutic situation, and it is thus conceivable that economic motives might enter into a decision between several alternatives. It might be a worry that such therapeutic substitution will affect my estimates in non-transparent ways, confounding the effect of the pharmacies incentives from margin differences on generic substitution. In the following section, I present a preliminary analysis which suggests that this is not the case at least on the margin we care about. This leads me to believe that the details of the prescription, that is, the choice of prescribed active ingredient and concentration, is based mostly on medical considerations, or at least that it is not very sensitive to the economic variables that are of interest to this study. I will take the number of individuals buying, the segment of statin they buy, and the amount they purchase as given. If it is the case that included regressors are related to either the decision to engage in statin therapy, the decision on which statin to consume, or the amount to consume, this will generate a bias in the estimates. As mentioned, substitution between statin segments based The reason for this selection is that I wish to focus on generic substitution at the pharmacy level, and that the reimbursement regulation represented by the step price regime is plausibly exogenous. Lovastatin had generic presence in this period, but was not on step price regulation, and I therefore decided to drop it. As it represents less than % of the statin market in Norway within any month during the sample period, I do not see this as a big concern. After generic entry, sales of parallel imports are negligible in the market for these statins, with total market share well below %, even for finely defined segments within each active ingredient based on the amount of active ingredient in each tablet and package size. 7 This corresponds to how the Norwegian Medicines Agency defines groups of substitutable products.

16 Simvastatin, mg Simvastatin, mg 9 Price DDD 7 ' DDD Price/DDD Simvastatin, mg Pravastatin, mg ' DDD 7 Price/DDD Jul Jan Jul Jan Jul Jan 7 Jul 7 Jul Jan Jul Jan Jul Jan 7 Jul 7 Figure : Aggregate weekly quantities and prices for included segments of Simvastatin and Pravastatin from 7. Quantity in s of Daily Defined Dose ( DDD) on the left axis and price in NOK/DDD on the right axis. on observable economic factors does not seem to be an important issue. If the number of individuals buying or the amount they buy is influenced by, say, price, this will imply that estimated price elasticities in general will be biased towards zero, as I do not account for the substitution towards outside options they might have. In Figure, I show sales and prices aggregated on a weekly basis for each segment included in the analysis. Sales exhibit clear seasonal patterns, e.g., being low during Easter, summer vacation and the period around Christmas and New Year. There is a clear tendency for sales to be very low in the beginning of the year and very high towards the end of the year, which is driven by expenses towards the out-of-pocket maximum being counted by calendar year. This implies that December will be the last opportunity to buy drugs without incurring co-insurance for individuals who have reached the out-of-pocket maximum, and, as a consequence, many individuals will have made their necessary purchases for some period at the beginning of the year. This time-varying purchase pattern will not be problematic for my estimates as long as it can be regarded as exogenous to the factors I study, in particular, that it is not driven by changes in prices or effort. Across the years, there is a clear upwards trend in the sales of Simvastatin and a downward trend in the sales of Pravastatin. This goes together with a downward trend for aggregate prices, where the peculiar pattern of price changes is caused entirely by discrete changes in the regulatory prices (see Figure ). The total number of individuals using statins increase from about Even though explaining choice between statins and the lack of therapeutic substitution based on prices would be interesting to study from a medical and an agency perspective, this would clearly be beyond the scope of this article.

17 Feb Aug Feb Aug Apotek mg Boots mg Vitus mg mg mg mg 9 9 mg Feb Aug Feb 7 Aug 7 7 mg mg mg Feb Aug Feb Aug mg Feb Aug Feb 7 Aug Feb Aug Feb Aug mg Feb Aug Feb 7 Aug Generic share Selling price of generic Buying price of generic Selling price of originator Buying price of originator Figure : Simvastatin (CAA) Market share of generics (left axis) and prices in NOK (right axis) for each segment of Simvastatin in each pharmacy chain over time. Shows the price to consumers (selling price) and the price the wholesaler pays to producers (buying price). in to about in 7, where the entire net increase comes from a substantial increase in the users of Simvastatin from to in this period. All other statin drugs had a decrease in the number of users over the same period. The explanation for this can largely be attributed to a change in the regulation of statin prescriptions introduced in June. The regulation required that Simvastatin was to be prescribed for all new cases requiring statin treatment, while present users were to be put on treatment with Simvastatin within a year, unless medical considerations dictate otherwise. 9 The regulation was motivated by reducing expenditure for the Norwegian National Insurance Administration. The large increase of statin usage together with the general decrease in prices during this period is still consistent with treatment choice being responsive to the price of treatment, something that should be kept in mind when one interprets some of the results. Even though there is a negative correlation between aggregate prices and sales of Simvastatin in this period, it is likely that a lot of this is driven by regulatory change and changes in medical practice for prescription of statins, especially seeing that a large part of the increase happens during the two-year period, when aggregate prices stay flat. It is therefore difficult to assess the impact of price on total demand in this market, though the lack of response in total demand during, when prices fell by approximately two thirds, is indeed striking. This at least suggests that aggregate demand for statins is not very responsive to price. Figures and show the evolution of prices, pharmacy costs and generic market shares over time within each segment and each chain. The large and concerted jumps in prices are mainly due to changes in price regulation, which 9 More details on this regulatory change can be found in Sakshaug et al. (7). It should also be noted that the prices here are measured in nominal terms, such that the implied decrease in real prices adds to the plausibility of prices driving the observed increase in total demand, even though the increase of % in sales of Simvastatin between and is too large to plausibly be explained by real price depreciation alone. 7

18 .. Apotek mg Boots mg Vitus mg.. mg mg mg Feb Aug Feb Aug Feb Aug Feb 7 Aug 7 Feb Aug Feb Aug Feb Aug Feb 7 Aug 7 Feb Aug Feb Aug Feb Aug Feb 7 Aug 7 Generic share Selling price of generic Buying price of generic Selling price of originator Buying price of originator Figure : Pravastatin (CAA) Market share of generics (left axis) and prices in NOK (right axis) for each segment of Pravastatin in each pharmacy chain over time. Shows the price to consumers (selling price) and the price the wholesaler pays to producers (buying price). mg mg 9 mg 7 mg mg 9 7 mg Feb Aug Feb Aug Feb Aug Feb 7 Aug 7 Feb Aug Feb Aug Feb Aug Feb 7 Aug 7 Price ceiling Step price Price, ORG Apotek Price, GEN Apotek Price, ORG Boots Price, GEN Boots Price, ORG Vitus Price, GEN Vitus Price ceiling Step price Price, ORG Apotek Price, GEN Apotek Price, ORG Boots Price, GEN Boots Price, ORG Vitus Price, GEN Vitus (a) Simvastatin (b) Pravastatin Figure : Prices of generic and originator in each pharmacy chain for the indicated statin. Price ceiling and step price (regulation for maximal reimbursement) are also indicated.

19 happen at discrete points in time. The pattern for this is shown more clearly in Figure. We see that generics are always present in the market for Simvastatin, with the exception of mg tablets, and that generics enter in the market for Pravastatin in the beginning of. There are also some clear patterns over the time regarding the share of generics, which is generally increasing for Simvastatin and somewhat decreasing for Pravastatin with the exception of the initial jumps as generics enter. Since step price regulation was enacted for both of these drugs early in my sample period, and generics entered in the beginning of for Pravastatin, I drop for Simvastatin and the period before July for Pravastatin from the sample for estimation purposes.. Preliminary results To show some first evidence of pharmacy incentives actually playing a role, I utilize the regulatory prices, together with the fact that for drugs on step price regulation, the pharmacy is required to have at least one drug package available priced no higher than the maximum reimbursement, given by the step price. I determine quasi-experiments in my data, where the prices of the originator package and its generic substitute are constrained in at least three consecutive periods within a pharmacy chain. Here, constrained prices imply that the originator package is priced at the price ceiling, while the generic package is priced at the reimbursement limit. In these cases, the prices that consumers face stay fixed due to the regulatory features. When the price a pharmacy chain pays for acquiring a drug changes, the margin will change without any corresponding response in the consumer price. Any changes in sales during such a period must either be due to the arguably exogenous changes in price regulation, or, keeping prices fixed while margins change, due to margins and sales having a separate connection besides prices. Given the reimbursement scheme, the selling price and the price actually paid by consumers will generally differ, according to the schedule p C = min{τp S, τp R } + max{p S p R, }, where p C denotes the price paid by the consumer, p S is the selling price earned by the pharmacy, p R is the maximal reimbursement and τ is the copayment. This means that the consumer will pay the share τ of prices up to the maximal reimbursement, and potentially the full amount in excess of p R. The results of two regression specifications in the periods with constrained prices are shown in Table, one featuring prices only and one with margins added. I add separately the price of the originator and the generic within a segment, though I constrain them to have equal coefficients. To investigate the extent of therapeutic substitution, I also add variables capturing prices and margins for drugs with the same active ingredient that are not substitutable at Due to a low number of observed sales, as well as the entry pattern for Simvastatin mg, I drop mg Simvastatin and mg Pravastatin from the sample. This is because the pharmacies are required to provide customers with at least one substitute that does not exceed the reimbursement limit. When a particular drug is available with different concentrations and package sizes, there should be at least one such substitute available for each category. Over the sample period of -7, τ =.. Note that the regressions are in logs, and there are generally substantial asymmetries in the shares of originator and generic. Allowing for different elasticities does not change the results qualitatively, though the cross price elasticity is higher for originator varieties, and I settled on the reported specification for brevity. 9