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Green licenses and environmental corruption: a random matching model

Angelo Antoci, Simone Borghesi () and Gianluca Iannucci ()

No 1116, SEEDS Working Papers from SEEDS, Sustainability Environmental Economics and Dynamics Studies

Abstract: This paper studies environmental corruption via a random matching evolutionary game between a population of firms and a population of bureaucrats who have to decide whether to release a “green” license to the firms. A firm obtains the license if the bureaucrat checks that it complies with environmental regulations, otherwise it is sanctioned. The model assumes that there are two types of bureaucrats (honest and dishonest), two types of firms (compliant and non-compliant), and two possible crimes (corruption and extortion). Corruption occurs when a dishonest bureaucrat accepts a bribe from a non-compliant firm, while extortion occurs when a dishonest bureaucrat claims a bribe from a compliant firm. When there is no dominance of strategies, we show that there exist two bistable regimes, in which two attractive stationary states exist, and two regimes with an internal stable equilibrium, corresponding to the mixed strategy Nash equilibrium of the one-shot static game, surrounded by closed trajectories. From comparative statics analysis performed on the latter two dynamic regimes, it emerges that policy instruments may help the Public Administration reduce both corruption and extortion, although increasing sanctions and detection probability do not always get the desired results.

Keywords: Bureaucratic corruption; Evolutionary games; Environmental regulations; Economics of crime (search for similar items in EconPapers)
JEL-codes: C73 D21 D73 K42 Q52 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2016-11, Revised 2016-11
New Economics Papers: this item is included in nep-env, nep-gth and nep-law
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http://www.sustainability-seeds.org/papers/RePec/srt/wpaper/1016.pdf First version, 2016 (application/pdf)
http://www.sustainability-seeds.org/papers/RePec/srt/wpaper/1016.pdf Revised version, 2016 (application/pdf)

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