his paper examines statins competition in the Spanish pharmaceutical market, where prices are highly regulated, and simulates a situation in which there is unrestricted price competition. A nested logit demand model is estimated with a panel of monthly data for pharmaceuticals prescribed from 1997 to 2005. The simulation indicates that the regulation of prices is similar in its effects to cooperation among producers, since the regulated prices are close to those that would be observed in a scenario of perfect collusion. Freedom to set prices and a regulatory framework with appropriate incentives would result in a general reduction in prices and may make the current veiled competition in the form of discounts to pharmacists become more visible. The decrease in prices would be partially offset by an increase in consumption but the net effect would be an overall decrease in expenditure. The counterfactual set-up would also lead to important changes in the market shares of both manufacturers and active ingredients, and a reversal of generic drugs. Therefore, pro-competitive regulation would be welfare-enhancing but would imply winners and losers.
How do monopolistically competitive industries react to shocks in the context of a New Keynesian macro model? I bridge macroeconomics and trade theory by considering market dynamics. I use an analytically tractable closed-economy model with endogenous entry of firms and show the implications of markets structure for the transmission of real shocks on aggregate variables and welfare. Shock sources become crucial for the results: traditional productivity shocks cause an extensive effect on production; shocks on innovation cause an intensive impact. More patient populations bring the economy to a richer market, although it cushions the extensive effect after an innovation shock.
An increasing number of studies have sprung up in recent years seeking to identify individual inventors from patent data. Different heuristics have been suggested to use their names and other information disclosed in patent documents in order to find out “who is who” in patents. This paper contributes to this literature by setting forth a methodology to identify them using patents applied to the European Patent Office (EPO hereafter). As in the large part of this literature, we basically follow a three-steps procedure: (1) the parsing stage, aimed at reducing the noise in the inventor’s name and other fields of the patent; (2) the matching stage, where name matching algorithms are used to group possible similar names; (3) the filtering stage, where additional information and different scoring schemes are used to filter out these potential same inventors. The paper includes some figures resulting of applying the algorithms to the set of European inventors applying to the EPO for a large period of time.