Bayesian Learning and Regulatory Deterrence: Evidence from Oil and Gas Production

Peter Maniloff

No 2016-04, Working Papers from Colorado School of Mines, Division of Economics and Business

Abstract: This paper proposes a Bayesian learning model of regulatory enforcement. Firms exert compliance effort based on their belief about a regulator's effort level. Firms use regulatory actions to learn about the regulator and update their own compliance efforts accordingly. This theoretical model suggests that deterrence will be most effective when regulators have discretion or when firms are inexperienced. Econometric analysis of inspections of Pennsylvania oil and gas wells supports these hypothesis. This work provides a causal mechanism for the commonly observed phenomenon of general deterrence in which regulatory actions towards one firm lead other fims to increase their own compliance.

Keywords: enforcement; deterrence; reputation oil and gas; hydraulic fracturing (search for similar items in EconPapers)
JEL-codes: D22 K32 L51 L71 Q58 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2016-04
New Economics Papers: this item is included in nep-ene, nep-law and nep-reg
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http://econbus-papers.mines.edu/working-papers/wp201604.pdf First version, 2016 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:mns:wpaper:wp201604

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