Abstract
Economists use event studies to evaluate the impact of new regulations before there are enough data to empirically estimate the effects. This research investigates how accurately event studies and financial markets predict the benefits associated with a new law. Specifically, I utilize a change in US antidumping law known as the Byrd Amendment to compare the benefits predicted by event study methodology with the actual benefits accruing to individual firms under the law. The results illustrate why researchers who utilize event studies should be cautious when interpreting their results as estimates of the true impact of a regulatory change.
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Notes
1. For examples of how this method has been used, see Thompson [1993] and Mutti et al. [2000]. Similarly, Hartigan et al. [1989] use event study methodology to estimate the impact of the imposition of antidumping duties on firms.
2. The other benefits to the firm from the imposition of antidumping tariffs were not changed by the law.
3. See Liebman and Reynolds [2006] for a more complete description of the passage of the Byrd Amendment.
4. Byrd Amendment funds will continue to be distributed through 1 October, 2007.
5. Qualified expenditures include money spent on manufacturing facilities, raw materials, personnel training, equipment, and research and development, among other things.
6. Steel imports were restricted during this time period not just by high antidumping duties, but by safeguards put in place by the US government between 2001 and 2003.
7. Passage of the Byrd Amendment may change government and firm behavior, which would make predicting future levels of protection even more difficult. For example, Collie and Vandenbussche [2006] predict that the Byrd Amendment will lower the level of antidumping protection imposed by the US government.
8. This model is particularly useful if the researcher would like to aggregate abnormal returns over multiple event windows.
9. For example, the estimated sum of cumulative abnormal returns for all 42 Byrd recipients in my sample over the period from October 4 to 6 October, 2000 was −0.1338 using equation (3) and −0.1338 using the residual method.
10. See Binder [1985a, 1985b] and Thompson [1993] for further discussion of the MVRM.
11. Recall that all of the Byrd revenue collected by these 42 companies in 2001 and 2002 resulted from antidumping petitions filed prior to passage of the Byrd Amendment. Investors were more likely to be able to predict the impact of the Byrd Amendment on these firms compared to those firms that benefited from the Byrd Amendment due to antidumping petitions filed after 2000.
12. I chose this as the event-window with the largest number of significant firm-specific cumulative abnormal returns, both positive and negative. Results from other specifications are available from the author upon request.
13. Firms are defined by the “header SIC code” reported in the CRSP database.
14. It is interesting to note that the negative and significant abnormal return of Commercial Metals is similar in magnitude to the insignificant, average return to the other firms in the industry.
15. American Italian Pasta announced the purchase of Muellers pasta brand on October 5. As an agribusiness, ADM was likely impacted by a number of provisions besides the Byrd Amendment in the Agriculture appropriations bill.
16. Federal-Mogul's CEO resigned in October 2000.
17. Pearson's correlation coefficient value is 0.1539, with a significance level of 0.3632.
18. Cutler and Summers [1988], for example, calculate the total change in the value of Texaco and Pennzoil that resulted from litigation over Getty Oil Company by multiplying estimates of abnormal returns over particular event periods by the outstanding equity of the firm. Thompson [1993] notes that abnormal returns estimates suggest that the value of individual industries, particularly paper, lumber and primary metals, changed by as much as 4 percent when the US-Canada FTA was reached; she uses this to suggest that the FTA could change the cash flows accruing to firms within these industries by as much as 16 percent.
19. I use a discount rate of 0.1.
20. Correlation coefficients and significance levels are available from the author.
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Reynolds, K. Anticipated vs Realized Benefits: Can Event Studies be used to Predict the Impact of New Regulations. Eastern Econ J 34, 310–324 (2008). https://doi.org/10.1057/palgrave.eej.9050036
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DOI: https://doi.org/10.1057/palgrave.eej.9050036