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The Effects of the Interstate Commerce Act on Transport Costs: Evidence from Wheat Prices

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Abstract

There is significant debate over the effect of the Interstate Commerce Act (ICA) on the cost of rail transport to shippers. Taking price differences across locations as proxy for transport costs, we use data on wheat prices before and after the implementation of the ICA to see if the Act led to smaller differences in wheat prices across American cities relative to a control group of European cities. We find that the ICA had no effect on US transport costs; however, it reduced their volatility substantially. This evidence supports the view that the ICA helped stabilize cartel prices after a period of significant price wars.

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Notes

  1. See Porter (1983) for an interesting theoretical and empirical analysis that confirms switches in industry behavior from cartel pricing to non-cooperative oligopoly pricing in the years immediately preceding the ICA.

  2. See Aitchison (1937) for a detailed description of the ICA.

  3. He also notes a memo from an ICC Commissioner, Aldace F. Walker, who resigned after his first 2 years to take another position, indicating the Commissioner’s opinion that the ICC was more effective at maintaining cartel prices than the railroads’ own “pool” prior to the ICA.

  4. Relatedly, the “Wabash decision” in 1886 was a court ruling that prohibited states from regulating railroads on interstate transport, setting the stage for the passage of the ICA to establish federal regulation of interstate transport. This is viewed by many as a substantial gain for railroad interests as well, because it eliminated many state regulations that had become quite severe in some circumstances.

  5. Spann and Erickson (1970) estimate from their limited survey that rates fell 15–30 % on short-haul routes and went up only slightly on long-haul routes. MacAvoy (1965) provides a more detailed, and complicated, picture about how rates changed. Posting of public rates subsequent to the passage of the ICA appears to have led to the elimination of “special”, discriminatory rates for certain customers, tending to keep rates higher. Yet, even within the first year, there were examples of competitive rate decreases on the longer haul routes.

  6. Dennis (1999) is an example of the estimation of the impact of railroad deregulation on volatility of transport costs in another (more modern) setting: steam coal rates after the 1980 deregulation.

  7. We are unable to find data for only wheat shipments for this general period, even from Interstate Commerce Commission reports, but others (e.g., Fogel 1964) note that wheat and corn were the two main grain shipments at the time.

  8. See Table 2 in Jacks (2006) and related online data appendices at http://www.sfu.ca/~djacks/data/publications/publications.html for further details on data sources and measurement.

    Table 1 Summary statistics
    Full size table

  9. Further details on the econometrics of threshold regression techniques can be found in Balke and Fomby (1997), Hansen (1997), and Hansen and Seo (2002)

  10. A difference-in-differences specification simply estimates the relative effect of a “treatment” on a treated group vis-à-vis a non-treated (or control) group with respect to a particular outcome (here, wheat transport costs). This relative difference-in-difference effect is typically estimated by an interaction of a variable that indicates a “treated” observation and a variable indicating the period of treatment. This is exactly how our ICA \(_{ijt}\) variable is defined in Eq. (6), where the “treatment” is the ICA and our treatment group is the US city-pairs in our sample. See Meyer (1995) and Angrist and Pischke (2008) for general discussion of the methodology.

  11. Jacks reports that results are robust to weighting by other related measures, such as the standard error or p value of the estimated transport costs. Saxonhouse (1976) provides the econometric theory for the application of such weights when using a generated dependent variable.

  12. The continuous variables that we express in log form are the transport cost, distance, distance squared, rail \(\times \) distance, exchange rate volatility, and ad valorem tariff.

  13. Our first difference model imposes the constraint that the slope coefficients are common across US and non-US city pairs. In unreported results, we have experimented with a more flexible model specification that also includes interaction terms between the regression variables and the indicator for US domestic city-pairs. However, the ICA estimate remains insignificant.

    Table 3 First difference estimation
    Full size table

  14. In unreported results, we also investigate the effect of ICA on the rate of wheat price adjustment (i.e., \(\rho \) parameters in the system of Eqs. 3) and (4). Data on the speed of adjustment is available from the same source: Jacks (2005, 2006). A high degree of market integration is associated with a rapid convergence in wheat prices within a city-pair. While we are agnostic about the potential impact of ICA on the speed of price adjustment, our estimation results suggest that the new regulatory regime introduced by the ICA had no direct effect on the rate of price convergence.

  15. In a related specification, we replace the continuous distance measure with an indicator variable for short- versus long-haul routes. City–pairs that are located within a 500-mile radius are classified as short-distance routes; the remaining city-pairs are in the category of long-haul routes. In unreported estimations, we find similar results: The ICA has no significant effect on transport costs between proximate cities, but leads to a fall in transport costs of 17 % (relative to short-haul routes) on long-distance routes.

  16. We have also experimented with city-specific (rather than city-pair) ICA effects in order to see whether the insignificant effect of the ICA is due to opposing effects in monopolized versus competitive markets. The city-specific ICA coefficients turn out to be insignificant in most cases, except for a positive effect for Ithaca and negative effect for San Francisco.

  17. We have also examined the change in estimates when we exclude all of the city–pairs that involve Ithaca, which is a considerably smaller market compared to the rest of the sample cities. We found no qualitative difference between those estimates and the main results that are reported in this paper.

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Acknowledgments

We thank David Jacks for sharing his data with us, and Mitch Johnson for excellent research assistance. We also thank Lawrence White, Wes Wilson and anonymous referees for their comments.

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Authors and Affiliations

  1. Department of Economics, 1285 University of Oregon, Eugene, OR, 97403-1285, USA

    Bruce A. Blonigen & Anca Cristea

  2. National Bureau of Economic Research, Eugene, OR, 97403-1285, USA

    Bruce A. Blonigen

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  1. Bruce A. Blonigen
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  2. Anca Cristea
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Correspondence to Bruce A. Blonigen.

Appendix

Appendix

See Table 5.

Table 5 Country coverage
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Blonigen, B.A., Cristea, A. The Effects of the Interstate Commerce Act on Transport Costs: Evidence from Wheat Prices. Rev Ind Organ 43, 41–62 (2013). https://doi.org/10.1007/s11151-013-9394-8

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