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Review of Industrial Organization

Erschienen in:

03.05.2016

The Staggers Act and Firm Performance: Long-Run Evidence

verfasst von: Lee Pinkowitz, Rohan Williamson

Erschienen in: Review of Industrial Organization | Ausgabe 2/2016

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Abstract

One of the primary purposes of the Staggers Act of 1980 was to increase the profitability of the railroad industry. The paper examines the industry’s financial performance from 1963 to 2013 and provides evidence that railroads outperformed most other industries from pre- to post-deregulation using accounting-based measures. However, using a market-based measure, the railroad industry underperforms two-thirds of industries. Additionally, the post-Staggers accounting performance improvements were not at the expense of firms from commodity groups that are reliant on rail transportation. Importantly, there exists considerable skewness and heterogeneity across measures, which affects the effectiveness of inferences that are based on the mean of the distribution to reflect economic reality.

Vorheriger Artikel Modernizing U.S. Freight Rail Regulation

Nächster Artikel Regulation in a ‘Deregulated’ Industry: Railroads in the Post-Staggers Era

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The findings in Sect. 2 of the Staggers Act state: “(6) earnings by the railroad industry are the lowest of any transportation mode and are insufficient to generate funds for necessary capital improvements…(8) failure to achieve increased earnings within the railroad industry will result in either further deterioration of the rail system or the necessity for additional Federal subsidy…”. Moreover one of the goals specified in Sect. 3 is “to assist the rail system to remain viable in the private sector of the economy.” Staggers Rail Act (1980).

The Staggers Act applied to the freight rail industry and not to the passenger rail industry. Throughout this paper, when we discuss the rail industry (which for brevity we often refer to as Rail), we mean only the freight rail industry.

Staggers Rail Act §3 (2) and (3).

It is critical to note that data limitations make it impossible to assign causality to our results. Deregulations do not occur randomly to particular industries, and they do not occur randomly in time. Our empirical methodology attempts to control for other factors that could affect firm and industry performance over the long-run period that we examine. However, we acknowledge that our results should be interpreted as correlations rather than causal. For brevity in the writing, we do not continually make this disclaimer and often use phrasing such as “the impact of deregulation”.

The telecommunications industry experienced major changes and deregulation, but the event was not as clean as the other three industries. AT&T was charged with violations of the Sherman Act in 1974, but the break-up of the Bell System didn’t occur until 1984. Further, deregulation didn’t occur until 1996 with the passage of the Telecommunications Act of 1996; see, for instance, Mayer-Schonberger and Strasser (1999). As such, we exclude telecom (SIC codes 4812 and 4813) from our analyses. We thank the editors and an anonymous referee for pointing this out. Our results are robust to the inclusion of Telecom and the assumption that its deregulation occurred within our deregulation period.

Our main results are robust to this choice. None of our analyses at the median or with quantile regression are changed by the decision to winsorize. However, the raw numbers are disparately affected by firms with large losses and relatively small sales. The mean operating margin without winsorizing is −2.18 with a standard deviation of 107 and a kurtosis of 40,544. Winsorizing reduces the mean to 0.014 with a standard deviation of 0.276 and a kurtosis of less than 10.

The STB method for estimating WACC changed several times across that time period, which perhaps makes it more surprising that our technique obtains such close estimates.

We use only market value of common equity in our variable. Fewer than 20 % of our firm years have non-zero preferred stock, and the magnitude is typically considerably smaller than common equity value. The correlation between MB measured using the value of common equity versus common plus preferred equity is 0.99.

CPI data are obtained from the St. Louis FRED database (data code CPIAUCNS) and adjusted to January 2000 dollars.

We also drop firm years where assets or sales are negative, zero, or missing.

Compustat SIC codes are used, but some companies’ SIC codes change through time. Additionally, in certain years, the SIC codes also differ from those found in the CRSP database. As such, we assign Railamerica SIC code 4011. While SIC codes were replaced by NAICS in 1997, Compustat and CRSP continue to report SIC codes for all years and for consistency, we use those data after 1997.

If we eliminate the period 1975–1985, our results are similar, although the relative outperformance of the rail industry increases. If we exclude only the 1978–1980 period, our results are nearly identical to those reported.

Operating losses are more prominent in smaller firms. More than half of the firm years where real assets are less than $25 mm exhibit losses, while firms above $150 mm in real assets have losses only 12 % of the time.

Deregulated firms had a median decrease of 1.4 percentage points (0.076–0.062). This is 1.5 percentage points higher than the 2.9 percentage point decrease at the median for not-deregulated firms (0.088–0.059).

Staggers Rail Act §3(3).

See Wilson (2001), Grimm and Winston (2000), and Prater et al. (2010).

Full results of the tests in this subsection are available upon request from the authors.

With a linear trend assumption, of the 49 OLS industry trend coefficients only 4 are significantly different at the 5 % level from RAIL for operating margin, while 14 differ for SROI. However, among those 14, 2 are significantly negative while 12 are positive. However, all 49 of the OLS trend coefficients for MB are positive, and 38 are significant.

With a log trend assumption, only 2 of the 49 operating margin trend coefficients are significantly different at the 5 % level and 12 differ for SROI (11 positive and 1 negative). For MB, 30 are significant (29 positive and 1 negative). One of the five negative coefficients is significant.

See for example Adam and Anderson (1985), Babcock et al. (1985), Fuller et al. (1987), MacDonald (1987), MacDonald (1989), Bitzan et al. (2003).

For brevity and ease of exposition, we hereafter refer to firms in commodity groups that ship by rail as “shippers”, even though we have only aggregate data on freight revenue by commodity group and thus can’t determine which firms are actually shipping by rail.

While the STCC was designed to be compatible with SIC codes, they are not identical. For a history of the STCC, see https://www.railinc.com/rportal/documents/18/260769/STCCHistory.pdf.

An added benefit of comparing all industries to rail is that while the previous section examined the commodity groups that shipped the most by rail, we are unable to know which industry paid the shipping expenses. For instance, even though Coal is the most shipped product, it is often the case that the freight charges are paid by customers and not the coal industry. Providing results for all industries allows us to remain agnostic toward which industries are considered to be “top shippers”. We thank participants at the Research Colloquium on the Economics and Regulation of the Freight Rail Industry for pointing this out.

If any preferred stock or other hybrid securities exist in the capital structure, they are ignored for our calculation of WACC.

If NP, DD1, or DLT is missing it is assumed to be zero. If the sum of the debt is negative it is assumed to be zero.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

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Titel: The Staggers Act and Firm Performance: Long-Run Evidence
verfasst von: Lee Pinkowitz
Rohan Williamson
Publikationsdatum: 03.05.2016
Verlag: Springer US
Erschienen in: Review of Industrial Organization / Ausgabe 2/2016
Print ISSN: 0889-938X
Elektronische ISSN: 1573-7160
DOI: https://doi.org/10.1007/s11151-016-9522-3

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