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An empirical analysis of the multimarket contact theory in pharmaceutical markets


Multimarket contact theory predicts that firms will optimally reduce prices in markets where collusive prices are sustainable and allocate the slack of the corresponding incentive compatibility to increase prices in markets where collusion is not sustainable. Binding price caps in collusive markets will have different effects over the multimarket contact mechanism depending on the severity of the cap. Setting a price cap close to the unregulated case will increase the size of the redistribution of market power whereas stronger regulation will even reduce prices in unregulated markets. Therefore, price regulations aiming at capping prices in a specific market will also affect markets that are not subject to specific mandatory price regulations. We find evidence of the theory predictions using information for nine OECD countries for pharmaceutical markets. Unregulated US markets are shown to respond to the redistribution effect; Canadian markets, known to be subject to soft price regulations, with respect to the former, are shown to be consistent with a stronger redistribution effect. EU markets and Japan are either consistent with the effect of a medium regulation or strong regulation. In this last case multimarket contact cannot explain prices, and these are expected to be lower compared to the unregulated benchmark.

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  1. Other theoretical works on the multimarket contact structure of industries are Spagnolo [19] and Matsushima [14]. In practice, as such a strategy implies firms pricing above competitive prices in some markets and reducing prices in others, the average multimarket effect over prices can be ambiguous.

  2. This functional form is taken from an unpublished paper by Gimeno and Woo [12].

  3. This approach was also present in Jans and Rosembaum [13]; however, they did not intend to test for the market power redistribution hypothesis. Nevertheless, the latter found that prices are expected to be higher because of contacts across markets whenever the concentration is higher in a market.

  4. The data set gathers information from the 4th quarter of each year, apart from 2003, for which the information is provided for the 2nd quarter.

  5. We are indebted to Guillem López (UPF) and Vicente Ortún (UPF) and Félix Lobo (UC3M) for helpful advice on this regard.

  6. The ATC classification is supported and maintained by the World Health Organization Collaborating Center for Drug Statistics Methodology with a base at the Norwegian Institute of Public Health.

  7. Note that whenever a firm is a monopolist in a product market, the variable takes the value of zero.

  8. In any case, it would be a never-ending exercise to identify marginal costs of production for a large set of products like the one we consider in our sample.

  9. See Berndt et al. [2, 3], Cockburn and Anis [6], and Suslow [21].

  10. Tables 12 and 13 in the “Appendix” show IV-GMM estimations for Eqs. (9) and (10), respectively, where lags of the multimarket contact variables are used as instruments. The estimated marginal effects for the multimarket contact specification do not change significantly with respect to our baseline estimation strategy shown above. Furthermore, for the great majority of countries the instruments pass the Sargan test of over-identifying restrictions. We take this result as indicative that lagging the multimarket contact variables provides consistent estimates of the marginal effects of interest.

  11. The complete set of results is available from the author upon request.


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Early research for this paper was supported by an unrestricted educational grant from the Merck Company Foundation, the philanthropic arm of Merck & Co., Inc., Whitehouse Station, NJ. Partial funding was also obtained from the Spanish Ministry of Science and Technology under projects ECO2008-06395-C05-01 and ECO2011-30323-C03-02. We thank MSD and IMS for providing the data and conference participants at IDEI/U.Toulouse, AES/A Corunha and Swiss IO Day conferences and seminar audiences at UPF and UC3M. The helpful suggestions of Antonio Cabrales, Félix Lobo, Guillem López, Vicente Ortún, Jaume Puig, Fran Ruiz-Aliseda and Joel Shapiro are gratefully acknowledged. We are specially grateful to Patricia Danzon for her kind suggestions on critical issues in the data analysis.

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Correspondence to Sergi Jiménez-Martín.



See Tables 9, 10, 11, 12, 13, 14, and 15.

Table 9 Mean and SD (in parentheses) for variables in sample by country
Table 10 Pricing regressions for molecule markets: average multimarket effect
Table 11 Pricing regressions for molecule markets: multimarket contact redistribution effect
Table 12 Pricing regressions for average multimarket effect: IV regressions with lagged multimarket variables as instruments
Table 13 Pricing regressions for multimarket redistribution effect: IV regressions with lagged multimarket variables as instruments
Table 14 Pricing regressions for molecule markets: ATC-1 fixed effects
Table 15 Pricing regressions for molecule markets: IV regressions with lagged controls as instruments

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Coronado, J., Jiménez-Martín, S. & Marín, P.L. An empirical analysis of the multimarket contact theory in pharmaceutical markets. Eur J Health Econ 15, 623–643 (2014).

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