Abstract
What has been the effect of competition from parallel imports on prices of locally sourced on-patent drugs? Did the 2002 Swedish mandatory substitution reform increase this competition? To answer these questions, we carried out difference-in-differences estimation on monthly data for a panel of all locally sourced on-patent prescription drugs sold in Sweden during the 40 months from January 2001 to April 2004. On average, facing competition from parallel imports caused a 15–17 % fall in price. While the reform increased the effect of competition from parallel imports, it was only by 0.9 %. The reform, however, did increase the effect of therapeutic competition by 1.6 %.
Notes
Own calculations based on the data used in this study.
Parallel imports have considerable market shares ranging from ~8 to ~28 % in 2006 with an average of 18.40 % in key destination countries in Europe namely UK, Sweden, Denmark, Germany, Netherlands, and Norway (Kanavos and Kowal [23]).
Theoretical literature on reference pricing includes Brekke et al. [5], Mestre-Ferrandiz [33], Miraldo [34]; empirical literature on reference pricing and substitution reform includes Aronsson et al. [2], Bergman and Rudholm [3], Brekke et al. [4], Buzzelli et al. [6], Granlund [16], Granlund and Rudholm [17], Kaiser et al. [20], Kanavos et al. [22], Pavcnik [38], Puig-Junoy [41]. The survey of studies on the effect of the introduction of reference pricing policies concludes that reference pricing was generally associated with a decrease in the prices of the drugs subject to the policy (Galizzi et al. [12]).
Läkemedelsverket—The Medical Products Agency (MPA)—defines a product as a substitute if it has the same active substance, strength, and form (e.g., pills or fluid) as the prescribed product, and if its package size is approximately the same as that of the prescribed one.
If the physician prohibits the substitution for medical reasons, the consumer is still reimbursed based on the full price of the more expensive prescribed drug. Physicians only prohibited substitution for a few percent of the prescriptions (Granlund [15]).
In order to be able to take the natural logarithm, we defined Longevity it equal to 0.5 the first month a product was sold, and so on. Lnlong it is the natural logarithm of a variable truncated at 108.5 months due to lack of older data.
Separate effects of Mpi it and Timepi it were identified by data on drugs changing from facing pi-competition to not facing it, or vice versa, at different times during the study period. For the whole sample, the partial correlation between these variables are 0.80, while for drugs that faced pi-competition none or all months of the study period, Mpi it and Timepi it are perfectly correlated.
The share of drugs facing therapeutic competition is statistically significantly higher among the drugs facing competition from parallel imports than those not facing such competition at all, but the difference is small in size: only 5 percentage points. After removing the effects of the time variables (Time t , Ref t , Month t ) and the fixed effects, the partial correlation between Picomp it and Thcomp it is not statistically significant. However, there is a statistically significant partial correlation of 0.024 between Picomp it and Thgencomp it .
The mean of Mpi it is statistically significantly larger after the reform than before. As shown in Table 2, this difference is large for drugs facing pi-competition.
Ching [8] provides evidence for the role of consumer learning on the diffusion of generics in the market. Using U.S. aggregated market share data for 14 drugs, he found that the generics’ market share would be much larger right after patent expiration if there were no uncertainty at all about the quality of generics and unless it slowly resolves. No such study is done for parallel imports, but using data on on-patent prescription drugs sold in the county of Västerbotten, Sweden, during 2003-2006 (see Granlund and Rudholm [18] for details of the dataset), we found that patients were statistically significantly less likely to oppose substitution by a parallel import the larger Mpi it was. Controlling for Mpi it , however, the patients became more likely to oppose substitution over time. Since Mpi it is correlated with sales volume of the parallel import, we estimated the fixed-effects IV regression including the market share of parallel imports, but got similar results regarding Mpi it , suggesting that this is not the explanation to its effect.
Wooldridge [47] suggests that instruments can be generated by interacting predictions of an endogenous variable with exogenous variables and proves the consistency of the estimator using generated instruments. Wooldridge ([48], pp. 262–268) discusses an example of this approach. For an empirical application, see e.g., Giles and Yoo [14].
As mentioned above, Lnlong it is the natural logarithm of a variable truncated at 108.5 months due to lack of older data. Including a dummy variable for those with a value of 108.5 or higher did not contribute to explaining Picomp it , however, so this dummy variable was not included as an instrument.
The chairperson of Läkemedelshandlarna, an association consisting of ten parallel traders, estimates this time to be 1–2 years (source: an email from the chairperson to us on March 13, 2012).
As an example, the differential dlnP/d (Ref*Picomp) was calculated as a linear combinations of the estimates of β5 and β7 that is: b5 + b7*31.31797, where b5 and b7 are the estimates of β5 and β7 and where 31.31797 is the mean of Ref*Mpi when Ref*Picomp equals one. The Stata command lincom is used to calculate the differentials. Point estimates are used even they are not statistically significantly different from zero.
Since the dependent variable is in logarithmic form, the exact change in price (in percent) should be calculated using the formula 100∗[exp(β)-1].
For observations with Picomp equal to one, the average values for Mpi and Ref * Mpi are 27.26 and 15.49, respectively. The Mpi-variables thus account for more than 75 % of the estimates for dlnP/dPicomp in all three estimations.
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Appendix: robustness analysis and instrument regressions
Appendix: robustness analysis and instrument regressions
As noted earlier, the identifying assumption for the effect of the mandatory substitution reform on the price-effect of pi-competition is that no excluded variable influence the price-effect of facing pi-competition differently before and after the reform. By including the interaction variable between time trend and dummy for facing pi-competition (Timepi it ), we allowed drugs facing such competition to have a different time trend relative to those not facing it, without this biasing the estimator of how the reform affected the effect of facing pi-competition. Still, this estimator might be biased if factors not accounted for in the regressions affected the two groups differently, and if these factors increased or decreased over time in an unstable manner so that their effects could not be captured by Timepi it , for example, if something affecting the two groups differently occurred only during a certain part of the study-period. To test the importance of this problem, we ran regression 2 for different periods: January 2000–April 2004, January 2001–June 2003, and using the normal study period (January 2001–April 2004) but excluding observations from April 2002, when the law regarding mandatory substitution was passed by parliament, through October 2002. Besides functioning as sensitivity analyses, the latter regressions were designed to give an idea whether firms started to adjust their prices even before the reform came into effect. The results from these regressions, presented in Table 5, indicate that the key estimates are stable to changes in the study period and there is no evidence of firms adjusting prices before the reform came into effect.
When predicting Picomp it , we used only data from the period January 2001 through April 2004. Thus, only variations in Mpi it within this period could be predicted for each product. With fixed effects, subtracting a product specific constant (i.e., the value at Mpi it in December 2000) from Mpi it do not affect the estimates for this variable. However, this prevented us from including Mpi it nonlinearly, e.g., \(Mpi_{it}^{2}\). Another constraint on the specification is that year-month dummies cannot be included since this would prevent us from using SEK/Euro t which has no cross-sectional variation as a basic instrument. We have studied the effect of not including \(Mpi_{it}^{2}\) and year-month dummies using OLS regression. More precisely, we conducted an OLS estimation including \(Mpi_{it}^{2}\) and Ref* \(Mpi_{it}^{2}\) as well as an estimation including 40 year-month dummies instead of 11 month dummies (Month t ), the time trend (Time t ) and the dummy for the reform (Ref t ). Comparing estimation 8, presented in Table 6, with estimation 1 in Table 3, we see that including \(Mpi_{it}^{2}\) and Ref* \(Mpi_{it}^{2}\) reduced dlnP it /dPicomp it by about 0.5 percentage point and dlnP it /d (Ref*Picomp it ) by about 0.1 percentage point in absolute terms. Similarly, estimation 9 shows that including year-month dummies reduced the average estimated effect of pi-competition by about 0.6 percentage point, but changed the estimate for dlnP it /d (Ref*Picomp it ) by less than 0.1 percentage point. Thus, Time and Ref seem to have captured changes over time common to all drugs sufficiently well that such changes have little effects on the key results.
To check whether the instrumental variable results are affected by either of the basic instruments having a direct effect on the prices, we include SEK/Euro t and Lnlong it , respectively, as explanatory variables in estimations which are otherwise identical with estimation 4. We can do this since when we for example include SEK/Euro t as an explanatory variable, the five generated instruments are still not a linear combination of the included variables, which is enough for identification. Comparing the key results from these estimations, which are presented as estimations 10 and 11 in Table 6, with those for estimation 4 in Table 3, we see that differentials are largely unaffected by controlling for either SEK/Euro t and Lnlong it . This indicates that, at most, a very minor part of the estimated effects for the differentials of main interest could be explained by a direct effect of either of the basic instruments on the prices. In other words, the instruments identify the causal effect of pi-competition, by capturing the exogenous variation generated on the profitability of parallel import.
The results from the regressions used to generate the instruments are presented in Table 7 while the results from the first-stage regressions are given in Tables 8, 9 and 10.
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Granlund, D., Koksal-Ayhan, M.Y. Parallel imports and a mandatory substitution reform: a kick or a muff for price competition in pharmaceuticals?. Eur J Health Econ 16, 969–983 (2015). https://doi.org/10.1007/s10198-014-0646-9
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DOI: https://doi.org/10.1007/s10198-014-0646-9