2010
DOI: 10.1016/j.ijindorg.2010.02.004
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Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration

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Cited by 157 publications
(109 citation statements)
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References 38 publications
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“…Specifications 2 and 3 suggest that this is primarily caused by the effect NotMD has when parallel imports are prescribed. The estimates for the four variables controlling for the number of months parallel imported products have been available are consistent with the results of Mott and Cline (2002) and Ching (2010), which showed that the time a product of a drug has been available has a positive effect on the probability that it is bought. The only exception is the negative estimate for lnNmpi_subs in specification 4.…”
supporting
confidence: 86%
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“…Specifications 2 and 3 suggest that this is primarily caused by the effect NotMD has when parallel imports are prescribed. The estimates for the four variables controlling for the number of months parallel imported products have been available are consistent with the results of Mott and Cline (2002) and Ching (2010), which showed that the time a product of a drug has been available has a positive effect on the probability that it is bought. The only exception is the negative estimate for lnNmpi_subs in specification 4.…”
supporting
confidence: 86%
“…The variables lnNmpi_drug, Nmpi_drug_tr, lnNmpi_subs and Nmpi_subs_tr are included since the results of Mott and Cline (2002) and Ching (2010) indicate that the time a product has been available has a positive effect on the probability that it is bought. Note that since the natural logarithm of 1 is 0, none of these variables changes value the first time a parallel import is bought, which means that there is no risk of within-observation endogeneity.…”
Section: The Market Share For Parallel Importsmentioning
confidence: 99%
“…To take the potential endogeneity problem of detailing into account, we extend the estimation method proposed by Ching (2000;2008b), which does not require solving manufacturers' (dynamic or static) optimization problem. This method uses a reduced form approach to model detailing as a function of observed and unobserved state variables that determine demand, and then jointly estimate this pseudo-detailing policy function with the demand side model.…”
mentioning
confidence: 99%
“…Another set of studies takes a structural modeling approach to study how uncertainty about drug qualities affects demand (e.g., Ching 2000;2008a;2008b, Narayanan et al 2005, Mukherji 2002). In particular, Narayanan et al (2005) and Mukherji (2002) use the framework of Erdem and Keane (1996) to investigate the effects of detailing on demand, in which they assume manufacturers use detailing to convey noisy signals about the true quality of their products to physicians.…”
mentioning
confidence: 99%
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