This paper examines the relation between CEO inside debt holdings (pension benefits and deferred compensation) and corporate tax sheltering. Because inside debt holdings are generally unsecured and unfunded liabilities of the firm, CEOs are exposed to risk similar to that faced by outside creditors. As such, theory (Jensen and Meckling [1976]) suggests that inside debt holdings negatively impact CEO risk‐appetite. To the extent that corporate tax shelters are likely to result in high cash flow volatility in the future, we expect that inside debt holdings will curb CEOs from engaging in tax shelter transactions. Consistent with the prediction, we document a negative association between CEO inside debt holdings and tax sheltering. Additional analyses suggest that the effect of inside debt on tax sheltering is more (less) pronounced in the presence of high default risk and liquidity threats (cash‐out options in pension packages). Overall, our results highlight the importance of investigating the implication of CEO debt‐like compensation for corporate tax policies.

We examine the impact of distance on internet search, and the effect of the “local bias” in search on the stock market response around earnings announcements. We find significant local bias in search behavior. Motivated by theories explaining local bias, local information advantage, and familiarity bias, we predict and find that firms with higher local bias in search experience higher bid-ask spreads, lower trading volumes, and lower earnings response coefficients at the time of earnings announcements, consistent with non-local investors relying more than locals on public information announcements. Consistent with local information advantage, we find that in the week prior to the announcement, firms with higher local bias have higher bid-ask spreads, higher trading volumes, and returns that are more predictive of the coming earnings surprise. Consistent with familiarity bias, firms with higher local bias in search experience stronger post-earnings announcement drift. We use unique predictions, propensity score matching, and two-stage least squares to identify the effects of local bias separately from the effects of overall visibility. Overall, we show there is significant local bias in search, and that this local bias has a significant impact on the market response around earnings announcements.