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Incentives of a Monopolist for Innovation under Regulatory Threat

Saglam, Ismail (2022): Incentives of a Monopolist for Innovation under Regulatory Threat.

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In this paper, we investigate whether a natural monopoly with private cost information can reduce the likelihood of regulatory threat by investing, in the ex-ante stage, in cost-reducing R&D to generate process innovations and whether such an investment can yield Pareto gains in welfare. We model the regulatory process using a sequential game where a benevolent regulator makes the first move by announcing the probability that the monopolist will be optimally regulated. The monopolist, hearing this announcement, chooses the optimal level of its R&D investment. We numerically compute the subgame-perfect Nash equilibrium of this game for a wide range of model parameters. Our results show that the monopolist invests more in R&D if the regulatory threat is less certain. Anticipating this response, the regulator makes her threat less certain if she puts more weight on the monopolist's welfare. Moreover, we find that regulation with uncertainty can be Pareto superior to regulation with certainty if the welfare weight of the monopolist is sufficiently, but not extremely, high or if the cost of R&D is sufficiently small.

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