RegData 2.2: a panel dataset on US federal regulations

McLaughlin, Patrick A.; Sherouse, Oliver

doi:10.1007/s11127-018-0600-y

Cited by 33 publications

(18 citation statements)

References 15 publications

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“…Using the updated version of this per capita income panel, we derive the average real per capita income and its natural log. Finally, the updated educational attainment panel (an extension of Frank, 2009), equals tion algorithm used by RegData, see McLaughlin and Sherouse (2019). 16 Many of the studies cited at the beginning of Section 4 of this article utilize RegData.…”

Section: Data Descriptionmentioning

confidence: 99%

Federal Regulation and Mortality in the 50 States

Broughel

Chambers

2021

Risk Analysis

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Previous research speculates that some regulations are counterproductive in the sense that they increase (rather than decrease) mortality risk. However, few empirical studies have measured the extent to which this phenomenon holds across the regulatory system as a whole. Using a novel U.S. state panel data set spanning the period 2000–2014, we estimate the effect of U.S. federal regulation on state‐level mortality. We find that a 1% increase in federal regulation of state economies is associated with an increase in an index of state mortality of between 0.53% and 1.35%. These findings are robust to the form of mortality measure, choice of covariates, and the inclusion/exclusion of various regions, states, and industries. We also provide an update of the “cost‐per‐life saved cutoff,” which is the counterproductive risk threshold for expenditures. We find that expenditures in excess of $38.6 million (2019 dollars) per life saved can be expected to increase mortality risk. This article fills an important gap in the empirical literature and boosts the credibility of mortality risk analysis, whereby public policymakers weigh both the expected lives saved and lost due to a proposed regulation or other policy.

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Section: Data Descriptionmentioning

confidence: 99%

Federal Regulation and Mortality in the 50 States

Broughel

Chambers

2021

Risk Analysis

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Section: Regdata: Australia and The New Regulatory Analyticsmentioning

confidence: 99%

“…RegData is a recent project aimed at quantifying regulation using textual analytics (Al‐Ubaydli & McLaughlin, 2017; McLaughlin, Potts, & Sherouse, 2019; McLaughlin & Sherhouse, 2019). RegData ‘is both a methodology and a database that quantifies regulations by industry, by regulatory agency, and over time’ (McLaughlin & Sherouse, 2019, p. 43). RegData has become more sophisticated over time, progressing from its initial release in 2012, using human‐assisted algorithms to its current version relying on machine learning (McLaughlin & Sherouse, 2019).…”

Section: Regdata: Australia and The New Regulatory Analyticsmentioning

confidence: 99%

“…RegData ‘is both a methodology and a database that quantifies regulations by industry, by regulatory agency, and over time’ (McLaughlin & Sherouse, 2019, p. 43). RegData has become more sophisticated over time, progressing from its initial release in 2012, using human‐assisted algorithms to its current version relying on machine learning (McLaughlin & Sherouse, 2019).…”

Section: Regdata: Australia and The New Regulatory Analyticsmentioning

confidence: 99%

“…In Section 4 we introduce RegData: Australia. RegData is a method and database measuring the number of ‘restrictiveness clauses’ contained within regulations and legislation – such as ‘must’, ‘cannot’ and ‘shall’ (see Al‐Ubaydli & McLaughlin, 2017; McLaughlin & Sherouse, 2019). We explore some of the benefits and limitations of this method as it relates to the political economy of regulatory reform, and see how this new measurement enables comparative regulatory analytics.…”

Section: Introductionmentioning

confidence: 99%

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The political economy of Australian regulatory reform

et al. 2020

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The problem of regulatory accumulation has increasingly been recognised as a policy problem in its own right. Governments have then devised and implemented regulatory reform policies that directly seek to ameliorate the burdens of regulatory accumulation (e.g. red tape reduction targets). In this paper we examine regulatory reform approaches in Australia through the lens of policy innovation. Our contributions are twofold. We first examine the evolutionary discovery process of regulatory reform policies in Australia (at the federal, intergovernmental, and state levels). This demonstrates a process of policy innovation in regulatory mechanisms and measurements. We then analyse a new measurement of regulatory burden based on text analytics, RegData: Australia. RegData: Australia uses textual analysis to count ‘restrictiveness clauses' in regulation – such as ‘must’, ‘cannot’ and ‘shall’ – thereby developing a new database (RDAU1.0). We place this ‘restrictiveness clauses’ measurement within the context of regulatory policy innovation, and examine the potential for further innovation in regulatory reform mechanisms.

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