Abstract
The dramatic improvement in railroad safety since the 1970s has been accompanied by a substantial increase in safety regulation and a substantial reduction in economic regulation after 1980. We assess the effects of both regulatory changes on railroad safety with the use of RegData: a new data set that was developed by one of the authors that measures the amount of regulation that is imposed by specific regulatory agencies on specific industries. We find that partial economic deregulation is associated with improved safety. Safety regulation was most closely associated with improved railroad safety during the period when economic regulation curtailed railroads’ incentives to operate safely.
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Notes
Figures calculated by the authors based on the dataset used in this paper.
See Fig. 1 below and accompanying text for derivation of this figure.
RegData is a database that quantifies regulation by industry over time using text analysis software. It was created by Omar Al-Ubaydli and Patrick A. McLaughlin, and is broadly described in their recent journal article (Al-Ubaydli and McLaughlin 2015). It is also described and freely distributed on the website www.regdata.org.
Some exclusion rules were also applied, in order to avoid false positives. See McLaughlin and Sherouse (forthcoming).
Railroads are required to report accidents to the FRA if an incident (which could be a collision, derailment, or other event that causes equipment damage) causes damages to equipment in excess of the reporting threshold, or if an incident causes an injury or death. The threshold is updated from year to year. In 2002, it was $6700, while by 2010, it had been raised to $9200 (FRA 2013).
Accident rate = 2487 [0.00] – 1167 × Staggers [0.000] + 666 × 1/Years since Staggers [0.007] − 12.9 × Time trend [0.034] + (Railroad dummies omitted to conserve space) (p values in brackets).
Accident rate = 3700 [0.00] + 328 × Staggers [0.192] – 435 × 1/Years since Staggers [0.028] + (Railroad dummies omitted to conserve space) (p values in brackets).
This is still an issue under the current regulatory system. See TRB (2015, p. 147).
Cothen et al. (2005) document the history of negotiated rulemaking at the FRA.
The accident rate is conventionally presented as accidents per million miles; accidents per hundred million miles is just a scaling factor that allows us to avoid superfluous decimal places in the regression coefficients.
As robustness checks, we employed several phase-in variables described below; the results for our regulatory variables of interest remained unchanged.
49 CFR 1201 Subpart A §1-1.
Operating and financial data were supplied by the Association of American Railroads.
The regression results reported below are similar when we omit Conrail.
We also performed Fisher-type unit root tests on accidents per hundred million miles, including from one to five lags, and soundly rejected the null hypothesis that all panels contain unit roots in each test. Fisher-type unit root tests combine p values from augmented Dickey-Fuller tests performed separately on each individual panel.
We could not include the merger variables and the railroad-specific Staggers and time trend variables in the same regression, because many variables were dropped due to collinearity.
The value of 1/(Years since Staggers) is 0.03 in 2013. Thus, using figures from the regression in column 1, the calculated reduction in accidents associated with Staggers would be 1873 − 1139 × 0.03 = 1839.
Regression results are omitted here to conserve space but are available from the authors.
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Acknowledgments
The authors would like to thank Tyler Richards for research assistance; Oliver Sherouse for steadfast development of new versions of RegData; Richard Schmalensee, Lawrence J. White, Wesley Wilson, three anonymous referees for this journal, three anonymous referees at the Mercatus Center at George Mason University, and participants at the Georgetown University’s Research Colloquium on the Economics and Regulation of the Freight Rail Industry and at the Association of American Railroads brown bag lunch for helpful comments. The authors also thank Brenda Moscoso of AAR for facilitating our gathering of financial and operational data from AAR, and Andy Martin of the Federal Railroad Administration for helping find data from FRA’s safety database.
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Ellig, J., McLaughlin, P.A. The Regulatory Determinants of Railroad Safety. Rev Ind Organ 49, 371–398 (2016). https://doi.org/10.1007/s11151-016-9525-0
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DOI: https://doi.org/10.1007/s11151-016-9525-0