Reexamining Staggered Boards and Shareholder Value

Alma Cohen is Professor of Empirical Practice, and Research Director of the Laboratory for Corporate Governance, at Harvard Law School; Charles C.Y. Wang is the Glenn and Mary Jane Creamer Associate Professor of Business Administration at Harvard Business School. This post is based on their recent article, Reexamining Staggered Boards and Shareholder Value, recently published in the Journal of Financial Economics. Related program research includes The Costs of Entrenched Boards by Lucian Bebchuk and Alma Cohen and How Do Staggered Boards Affect Shareholder Value? Evidence from a Natural Experiment by Alma Cohen and Charles Wang.

The Journal of Financial Economics has recently published our article, Reexamining Staggered Boards and Shareholder Value, which seeks to contribute to understanding how staggered boards affect shareholder value.

In an article published in the Journal of Financial Economics in 2013, How Do Staggered Boards Affect Shareholder Value? Evidence from a Natural Experiment (CW2013), we provided evidence that market participants perceive staggered boards to be, on average, value-reducing. In a subsequent article, Do Staggered Boards Harm Shareholders?, Amihud and Stoyanov (2016) (AS2016) contest our findings, arguing that excluding some observations or amending certain specifications renders our results statistically insignificant (though they largely retain their sign). In our new article, we address the concerns raised by AS2016.  We show that the evidence is overall consistent with the main conclusion of CW2013: market participants view staggered boards as value-reducing on average.

CW2013 examined two rulings by the Delaware courts, which affected the antitakeover force of staggered boards, in the case of Air Products & Chemicals Inc. v. Airgas, Inc. We found that the rulings were accompanied by abnormal stock returns that are statistically significant and consistent with the view that staggered boards are value-decreasing. After replicating the CW2013 results, AS2016 reports that excluding some observations from our sample yields results that largely have the same sign as in CW2013 but are not statistically significant. When an event study is not based on a large sample, the statistical significance of its results is often sensitive to the removal of a small number of observations. However, our new article shows that, in the case of the Airgas rulings, a wide range of additional tests yields results that are significant and reinforce the CW2013 conclusions.

The analysis of our new article proceeds as follows. We begin by describing the results of CW2013 and the analysis of AS2016. Given the robustness concerns raised by AS2016, we also discuss two alternative definitions of treated companies (the set of companies affected by the rulings) to improve robustness. We do so by expanding the set of treated companies to include companies that are affected less strongly by the rulings. We show that these alternative specifications yield results that are consistent with those produced when using CW2013’s definition of treated companies.

We next focus on AS2016’s central claim that the results of CW2013 become statistically insignificant when excluding a handful of very small companies and, thus, cannot inform the assessment of how staggered boards affect value in normal-size companies. We first show that, when imposing the same sample filters recommended by AS2016, the results are statistically significant using the two alternative definitions of treated companies. We then demonstrate that, using each of the three definitions of treated companies (the one used by AS2016 and the two alternative definitions), the results of CW2013 are robust to excluding all companies with market capitalization below $500 million or $1 billion, instead of excluding the handful of small companies suggested by AS2016. These findings are inconsistent with the claim that the CW2013 results are driven by small companies and that they do not hold when such companies are excluded.

Turning to examine the source of the non-significance results presented in AS2016, we show that they are not due to a differential size effect. Instead, these results are due to the happenstance that some of the companies excluded by AS2016 have large returns that go in one direction; that is, the sample restrictions of AS2016 happen to remove extreme observations asymmetrically, from one side of the return distribution. After excluding large returns symmetrically from both sides of the distribution, we obtain an array of results (across various alternative specifications and samples excluding small companies) that are consistent with the results and conclusions of CW2013.

We then examine the AS2016 claim that the results of CW2013 are unduly driven by a few particular observations with extreme returns. We first show that when excluding the observations suggested by AS2016, our results still retain their significance using the two alternative definitions of treated companies. Furthermore, when excluding extreme returns in a symmetric fashion, we obtain results that are statistically significant and consistent with the findings of CW2013 under each of the three definitions of treated firms (the one used by AS2016 and the two alternative definitions).

Finally, we consider the sensitivity of the CW2013 results to our use of industry fixed effects based on six-digit Global Industry Classification Standard (GICS-6). AS2016 suggests using four-digit GICS (GICS-4), as opposed to GICS-6, and argues that doing so renders our results not statistically significant. We show that the results retain their significance even when using GICS-4 fixed effects under each of the three definitions of treated companies. Furthermore, under each of these three definitions, the results retain their significance when no industry fixed effects are used, as is commonly done in event studies.

We conclude that the wide array of results from our reexamination of the data are consistent with the view that staggered boards are, on average, viewed by market participants as value-reducing. Our results thus reinforce the conclusions of CW2013.

Our article is available for download here.

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