Elementos del diseño

To test whether disclosure of executive compensation is good or bad, I perform an event study for early adopters and firms that hide pay in 2004. The event study measures the average effect of the Law on shareholder value because the treatment group consists of all firms with hidden pay in fiscal year 2004, no matter whether they switch to observable pay afterwards (Hypothesis 1A versus 1B). For the identification of the average effect, I

26 _{Note that the subsamples have lower mean values for institutional ownership and higher mean values for}

excess returns than the respective group means in column 2. The reason is that the mean values in column 2 include all companies with hidden pay in 2004 while the mean values in the last two columns exclude companies for which data on the disclosure policy are not available in 2006.

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estimate difference-in-differences (diff-in-diff) in excess returns by adding dummy variables to Carhart’s (1997) four factor model:

Excess returni,t = α + β1 × Hidden payi + β2 × All eventst

+ β3 × Hidden payi × All eventst

+ β4 × RMRFt + β5 × SMBt + β6 × HMLt + β7 × WMLt + εi,t (II.1)

The four factor model attributes the daily Excess returni,t to the three Fama and French

(1993) factors RMRFt, SMBt, and HMLt, and to Carhart’s (1997) momentum factor WMLt.

The sample period is from August 2004 to July 2005. The dummy variables Hidden payi,

All eventst, and the interaction term Hidden payi times All eventst establish the diff-in-diff

approach. Hidden payi equals one if company i hides pay for individual executives in 2004.

This dummy controls for time-invariant differences between firms with hidden pay and early adopters, such as differences in governance characteristics that may affect excess returns (see Gompers, Ishii, and Metrick (2003)). All eventst equals one on the seven days

when investors receive news about the Law. This dummy controls for confounding macroeconomic and political events that affect stock prices from both subsamples and are not fully captured by the market factor RMRFt.27 The interaction term Hidden payi times

All eventst is the main variable of interest. Its coefficient β3 is the diff-in-diff estimate and

27 _{This interpretation assumes that the Law does not create spill-over effects on early adopters, or that any}

spill-over effects average out. Positive spill-over effects may arise if companies with hidden pay create a negative externality in the competition for managerial talent before Law enactment. For example, companies with hidden pay may drive up the price for managerial talent if they allow the CEO to extract rents. Negative spill-over effects may arise if mandatory disclosures yield more efficient compensation designs in companies with hidden pay. Efficiency gains may be detrimental for early adopters. For example, companies that have to switch from hidden to observable pay may drive up competition in the product market if their CEOs are better incentivized after Law enactment.

Positive spill-over effects on well-governed companies also arise in models, in which companies with good and poor corporate governance co-exist (see Acharya and Volpin (2010) and Dicks (2012)). Negative spill- over effects do not arise in these models because corporate governance and incentive pay are substitutes. Poorly governed companies grant their CEOs higher incentive pay. In contrast, the managerial power view assumes that CEOs grants themselves lower incentive pay.

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measures the average abnormal return for firms with hidden pay. The average abnormal return is the difference in excess returns between firms with hidden pay and early adopters on event days relative to non-event days. If β3 is positive, disclosure is good (Hypothesis

1A). If β3 is negative, disclosure is bad (Hypothesis 1B). I calculate cumulative abnormal

returns as 7 × β3 (number of event days times average abnormal return) to measure the

average effect of the Law on shareholder value.

To draw statistical inference, I perform two tests. The first test is the common two sided t- test that β3 differs from zero. For this test, I cluster standard errors by day and company

(see Petersen (2009)). Standard errors clustered by day allow for cross-sectional correlation of contemporaneous residuals. Standard errors clustered by company allow for serial correlation of residuals. The second test is a one-sided test that a positive estimate of β3 is

higher or a negative estimate of β3 is lower than the average diff-in-diff estimate on non-

event days. Under the null of no effect of the Law, β3 comes from the same distribution as

any diff-in-diff estimate on non-event days. Thus, if β3 measures the positive (negative)

effect of the Law, it should be significantly higher (lower) than the average diff-in-diff estimate on non-event days. I assess the significance of β3with a bootstrap p-value. I run

the regression above 5000 times with placebo event dummies instead of the true event dummy and save the resulting diff-in-diff estimate. Each time, I randomly draw seven non- event days from the estimation period to recalculate the placebo event dummy. The bootstrap p-value represents the fraction of the 5000 diff-in-diff estimates that are higher than β3 if β3 is positive, or that are lower than β3 if β3 is negative. Since I compare the diff-

in-diff estimate on event days with diff-in-diff estimates on non-event days, this test is a variant of a difference-in-difference-in-differences (diff-in-diff-in-diff) approach.28

28 _{Thus, my motivation for bootstrap differs from prior work. Prior work motivates bootstrap to allow for}

serial correlation (see, e.g., Bertrand, Duflo, and Mullainathan (2004)) or cross-sectional correlation (see, e.g., Black and Kim (2012)) in diff-in-diff regressions with a low number of clusters. A low number of

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The All events dummy is the sum of seven event dummies, each of which equals one on one event day. I re-estimate the regression with a vector of seven event dummies instead of the aggregated All events dummy to analyze whether certain events drive the overall effect. The seven diff-in-diff estimates indicate the abnormal return on each event day separately. Diff-in-diff estimates identify the average effect of the Law on shareholder value only if excess returns from the two subsamples follow parallel trends (see, e.g., Angrist and Pischke (2009)). To check for global trends, I graph trend lines for excess returns on the portfolio of firms with hidden pay and on the portfolio of early adopters. To check for local trends, I graph diff-in-diff estimates for event windows around each event day. If diff-in- diff estimates on event days are not part of a local trend, they are local maxima or local minima. This identification strategy allows for stock price run-ups and post-announcement drifts as long as they are smaller in magnitude than the stock price reaction on the event day itself. Such a graphical identification, however, fails if an event coincides with the beginning or end of a trend. I therefore perform a more formal test that diff-in-diff estimates for pre-event and post-event periods are equal to zero. This test assumes that there are no price-run ups or post-announcement drifts related to an event.