Paper Spotlight: Disregarding the Shoulders of Giants: Inferences from Innovation Research

David Reeb

Wanli Zhao

Empirical research typically builds on the findings of prior studies. New tests rely on previously identified determinants of the outcome variable studied as controls. Authors of a forthcoming RCFS paper, “Disregarding the Shoulders of Giants: Inferences from Innovation Research,” David Reeb and Wanli Zhao observe this process is often ad hoc. Using past research on corporate innovation as a platform, authors show that only a small subset of proposed determinants of innovation provide material, independent information about patents and citations. To do so, they use a data-driven approach – i.e., utilize various machine learning techniques to evaluate these covariates that have been argued or shown to affect innovation. After identifying the subset of determinants with high explanatory power (HEP), the authors show how including these HEP variables would have changed inferences in recent empirical studies and discuss how excluding them might affect future research. Commonly used econometric methods, including fixed effects and plausible shocks, do not change their findings on these previously identified innovation determinants. This important paper offers researchers a framework to select control variables for future research, rather than arbitrarily choosing a subset of control variables from prior studies.

Spotlight by Isil Erel
Photos courtesy of
David Reeb and Wanli Zhao

Paper Spotlight: Agency Conflicts and Investment: Evidence from a Structural Estimation

Redouane Elkamhi

Daniel Kim

Chanik Jo

Marco Salerno







Agency problems between managers and shareholders affect both investment and capital structure decisions at firms. In “Agency Conflicts and Investment: Evidence from a Structural Estimation,” Redouane Elkamhi, Daniel Kim, Chanik Jo, and Marco Salerno develop and structurally estimate a dynamic model to understand the real and financial implications of such agency problems.

The manager in the model is pulled into two different directions. On the one hand, there is the quiet life hypothesis. The manager derives private benefits out of the firm’s net income, which provides an incentive to reduce leverage. Because investment projects are often financed with debt, this effect leads to underinvestment relative to the first-best level. On the other hand, the manager’s compensation can depend on the size of the firm, which provides an empire-building incentive to overinvest.

Which of these two effects is more important in the real world? The authors structurally estimate the model on a sample of large US public firms over the period 1993 to 2019. The structural parameters are chosen to match the leverage, Tobin’s q, and managerial compensation from the data. The average firm in the sample overinvests by about 8% compared to a world with no agency conflicts. There is heterogeneity across firms based on the characteristics of the CEO, with investment being higher for firms with younger CEOs but somewhat paradoxically also firms with longer-tenured. Overall, the results suggest that the empire-building motive dominates the incentives to underinvest.

Spotlight by Uday Rajan
Photos courtesy of Redouane Elkamhi, Daniel Kim, Chanik Jo, and Marco Salerno

Paper Spotlight: Do Security Analysts Discipline Credit Rating Agencies?

Kingsley Fong

Harrison Hong

Marcin Kacperczyk

Jeffrey D Kubik







Credit ratings agencies are important players in financial markets and they could contribute to efficient resource allocations through information generation. Often, though, expectations of this positive impact are not met in reality because of well-documented biases that credit rating agencies suffer from. Such issues can be due to revolving doors, home biases, and political beliefs of credit analysts. These biases can ultimately have a significant nefarious effect on debt prices.

One big question is the source of these biases. This is an important question because if we want to move closer to a state where markets work efficiently we need to eradicate or limit such biases.

In the paper “Do Security Analysts Discipline Credit Rating Agencies?” Kingsley Fong, Harrison Hong, Marcin Kacperczyk, and Jeffrey D. Kubik investigate one important force that could be driving these credit rating agencies biases: information spillovers from equity markets to credit ratings. The paper argues that credit rating agencies find it harder to issue high grades for a firm’s debt when the firm’s informational environment is strong due to the presence of equity analyst reports that are informative about a firm’s distance-to-default. The authors’ argument is predicated on two related arguments: first, theory shows that a firm’s price of both debt and equity claims should be based on the same underlying fundamental value, and, second, evidence shows that equity prices often lead credit ratings and are crucial in the determination of credit spreads.

Using a diff-in-diff framework around an exogenous drop in analyst coverings, the authors show that a decrease in information generated by security analysts increases the subsequent debt ratings of a firm by around a half-rating notch. There is, in other words, greater optimism-bias in credit ratings when there is a lower presence of security analysts, especially for firms with little bond analyst coverage to begin with, and for firms that are close to default. This coverage-induced shock also leads to less informative ratings for future default and downgrading events, and bond-level mispricings.

Spotlight by Andrew Ellul
Photos courtesy of Kingsley Fong, Harrison Hong, Marcin Kacperczyk, and Jeffrey D Kubik