Daniel Kim

Postdoktorstipendiat - Institutt for finans


I am an Assistant Professor of Finance at BI Norwegian Business School.

I graduated with a Ph.D. in Finance from The Wharton School at the University of Pennsylvania. My research interests are in topics related to empirical corporate finance, public finance and applied econometrics.

Please see my homepage for further information.


Elkahmi, Redouane; Kim, Daniel, Jo, Chanik & Salerno, Marco (2022)

Agency Conflicts and Investment: Evidence from a Structural Estimation

The Review of Corporate Finance Studies Doi: 10.1093/rcfs/cfac019

We develop a dynamic capital structure model to study how agency conflicts between managers and shareholders affect the joint determination of financing and investment decisions. We show that there are two agency conflicts with opposing effects on a manager’s choice of investment: first, the consumption of private benefits channel leads managers not only to choose a lower optimal leverage, but also to underinvest, and second, compensation linked to firm size may lead managers to overinvest. We fit the model to the data and show that the average firm slightly overinvests, younger CEOs invest more than older ones, while CEOs with longer tenure overinvest more than CEOs with shorter tenure

Chalak, Karim; Kim, Daniel, Miller, Megan & Pepper, John (2022)

Reexamining the evidence on gun ownership and homicide using proxy measures of ownership

Journal of Public Economics, 208 Doi: 10.1016/j.jpubeco.2022.104621

Limited by the lack of data on gun ownership in the United States, ecological research linking firearms ownership rates to homicide often relies on proxy measures of ownership. Although the variable of interest is the gun ownership rate, not the proxy, the existing research does not formally account for the fact that the proxy is an error-ridden measure of the ownership rate. In this paper, we reexamine the ecological association between state-level gun ownership rates and homicide explicitly accounting for the measurement error in the proxy measure of ownership. To do this, we apply the results in Chalak and Kim (2020) to provide informative bounds on the mean association between rates of homicide and firearms ownership. In this setting, the estimated lower bound on the magnitude of the association corresponds to the conventional linear regression model estimate whereas the upper bound depends on prior information about the measurement error process. Our preferred model yields an upper bound on the gun homicide elasticity that is nearly three times larger than the fixed effects regression estimates that do not account for measurement error. Moreover, we consider three point-identified models that rely on earlier validation studies and on instrumental variables respectively, and find that the gun homicide elasticity nearly equals this upper bound. Thus, our results suggest that the association between gun homicide and ownership rates is substantially larger than found in the earlier literature.

Chalak, Karim & Kim, Daniel (2019)

Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt

Journal of Econometrics Doi: 10.1016/j.jeconom.2019.08.001 - Fulltekst i vitenarkiv

Chalak, Karim & Kim, Daniel (2019)

Measurement Error Without the Proxy Exclusion Restriction

Journal of business & economic statistics Doi: 10.1080/07350015.2019.1617156 - Fulltekst i vitenarkiv

This article studies the identification of the coefficients in a linear equation when data on the outcome, covariates, and an error-laden proxy for a latent variable are available. We maintain that the measurement error in the proxy is classical and relax the assumption that the proxy is excluded from the outcome equation. This enables the proxy to directly affect the outcome and allows for differential measurement error. Without the proxy exclusion restriction, we first show that the effects of the latent variable, the proxy, and the covariates are not identified. We then derive the sharp identification regions for these effects under any configuration of three auxiliary assumptions. The first weakens the assumption of no measurement error by imposing an upper bound on the noise-to-signal ratio. The second imposes an upper bound on the outcome equation coefficient of determination that would obtain had there been no measurement error. The third weakens the proxy exclusion restriction by specifying whether the latent variable and its proxy affect the outcome in the same or the opposite direction, if at all. Using the College Scorecard aggregate data, we illustrate our framework by studying the financial returns to college selectivity and characteristics and student characteristics when the average SAT score at an institution may directly affect earnings and serves as a proxy for the average ability of the student cohort.

Akademisk grad
År Akademisk institusjon Grad
2019 The Wharton School, University of Pennsylvania Ph.D.
År Arbeidsgiver Tittel
2019 - Present BI Norwegian Business School Assistant professor