Abstract
Classic public choice skepticism about the regulatory state, based on theories of rent-seeking, rent extraction and regulatory capture, is based on the unrealistic, and usually unstated, assumption of a monopolist regulator. In practice, the regulatory state is polycentric, involving numerous quasi-independent agencies with overlapping responsibilities. This has led to a more optimistic picture based on the idea of regulatory arbitrage: when firms can, to some extent, pick and choose their preferred regulator, regulatory agencies are constrained to deliver relatively efficient regulatory policies. In our view, this optimism is also unrealistic. We build a family of models that explores the possible regulatory outcomes, and use some aspects of Gordon Tullock’s critique of the common law as a conceptual foundation for the analysis of the efficiency of a polycentric regulatory system.
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Notes
The problem can be ameliorated by so-called patent trolls that buy packages of many patents and act as intermediaries between patent users and the original patent holders. Without the patent anticommons problem, such “patent trolls” simply would raise production costs without any economic benefits. In the presence of the patent anticommons trolls actually may increase economic efficiency.
See Geradin and McCahery (2004) for an overview of the evidence, including a contrarian argument claiming that the possibility of races to the bottom still needs to be taken seriously.
The tables were produced using data from RegData 2.2. RegData provides several datasets, including one identifying which federal agency produced each piece of regulation (identified by year, title, part) and others estimating the probability that each piece of regulation affects each specific industry (three industry datasets are available, based on the NAICS 2017 industry identification codes, 2-digit, 3-digit, and 4-digit). We used the 3-digit identification industry dataset, and, for each piece of regulation, we kept only the industries that had an assigned probability greater than 90%. For more details about how RegData’s probabilities are calculated, see Al-Ubaydli and McLaughlin (2015) and Goldschlag and Tabarrok (2018). The R code for producing all of the industry-agency interaction tables (similar to Tables 1, 2, 3 and 4) is available at: https://github.com/vladtarko/regulation-of-industry.
Health concerns are used as a justification (Guy 2016).
The regulation component of the index is an average of the following: (A) Credit market regulations: (i) Ownership of banks; (ii) Private sector credit; (iii) Interest rate controls/negative real interest rates; (B) Labor market regulations: (i) Hiring regulations and minimum wage; (ii) Hiring and firing regulations; (iii) Centralized collective bargaining; (iv) Hours regulations; (v) Mandated cost of worker dismissal; (vi) Conscription; (C) Business regulations: (i) Administrative requirements; (ii) Bureaucracy costs; (iii) Starting a business; (iv) Extra payments/bribes/favoritism; (v) Licensing restrictions; (vi) Cost of tax compliance.
See the Appendix in Gwartney et al. (2018, pp. 213–225) for the full explanation of their sources.
The result is similar to how Bertrand competition can generate the same outcome as perfect competition even with only two firms.
As noted by Tullock (1980a), the amount paid by rent-seekers ultimately depends on the ease of entry in the rent-seeking market (see also Higgins et al. 1988). Aligica and Tarko (2014) also argue that “crony capitalism” is built on this logic, limiting access to rent-seeking based on “crony” relations, in order to secure larger rents. If the number of rent seekers affects the individual rent-seeking investment in accordance with the formula \(r\left( {N - 1} \right)/N\) (see Mueller 2003, pp. 331–338, for a discussion), which we weight by probability ψ, we obtain the revenue \(R = \psi r\left( {N - 1} \right)\) instead of \(R = \psi rN\).
For a critique of Tullock’s view on common law see Shughart II (2018).
Tullock has a particularly iconoclastic and negative view of trial by jury (2005a: 345–346) and is especially critical of any claim that the average juror is adequately equipped to determine “factual or other information” (2005a: 350). This side of his argument is not particularly relevant for our purposes here.
Error costs are the sum of the costs associated with Type 1 error (the Court incorrectly finds a party to a dispute liable) and Type 2 error (the Court incorrectly fails to impose liability on a party to a dispute). Administrative costs are the sum of the various costs associated with legal proceedings (Zywicki 2008: 39–40).
Needless to say, Tullock views the “nonuse of the jury system by people who have a choice at the time they write their [arbitration] contracts” to provide strong evidence that juries are not highly valued by the parties to the contract (83).
One can adopt a less radical position: in the same way that a small subset of the ASME requirements have been adopted as mandatory state codes, we might still maintain mandatory minimal state codes for medicine, taken as a subset from the requirements created by the certification companies.
Aligica et al. (2019) discuss the issue of market power as a few certification companies may come to dominate the market similar to how ASME dominates the certification market for industrial safety.
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We thank Jerry Ellig, Patrick McLaughlin, Matt Mitchell, and William Shughart II for very useful feed-back and suggestions.
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Tarko, V., Farrant, A. The efficiency of regulatory arbitrage. Public Choice 181, 141–166 (2019). https://doi.org/10.1007/s11127-018-00630-y
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DOI: https://doi.org/10.1007/s11127-018-00630-y