How Large are Pre-Default Costs of Financial Distress? (with Redouane Elkamhi and Marco Salerno), Management Science (2023) view
Presented at University of Toronto seminar, AFA 2021, EFA 2020, MFA 2020, NFA 2020
How Large are Pre-Default Costs of Financial Distress? (with Redouane Elkamhi and Marco Salerno), Management Science (2023) view
Presented at University of Toronto seminar, AFA 2021, EFA 2020, MFA 2020, NFA 2020
Abstract: We estimate the costs of financial distress prior to default (pre-default costs) separately from the loss incurred at default (the loss given default) using a dynamic trade-off model of capital structure. We show that pre-default costs account for a large fraction of total distress costs, approximately 64.1%. We demonstrate that the expected pre-default costs of financial distress vary significantly across industries with a range between 3.2% and 8.3%, and are higher for small firms relative to larger ones.
Agency Conflicts and Investment: Evidence from a Structural Estimation (with Redouane Elkamhi, Chanik Jo, and Marco Salerno), Review of Corporate Finance Studies (2022) view
Abstract: We develop a dynamic capital structure model to study how manager-shareholders agency conflicts 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: (a) the consumption of private benefits channel leads managers not only to choose a lower optimal leverage, but also to underinvest, (b) 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, small firms underinvest, while CEOs with longer tenure overinvest more than CEOs with smaller tenure.
Reexamining the Evidence on Gun Ownership and Homicide Using Proxy Measures of Ownership (with Karim Chalak, Megan Miller, and John V. Pepper) Journal of Public Economics, Volume 208 (2022). view
Presented at Southern Economic Association 2021
Abstract: 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.
Measurement Error Without the Proxy Exclusion Restriction (with Karim Chalak) Journal of Business & Economic Statistics, Volume 39(1) (2021), 200-216. view
Presented at New York Camp Econometrics XII 2017, Asian Meeting of the Econometric Society 2017, North American Summer Meetings of the Econometric Society 2017, European Meeting of the Econometric Society 2017, West Coast Experiments 2017, SEA 2017, Boston College 2016, University of Virginia 2016, The Lebanese Econometric Study Group 2016, SEA 2015, Econometrics Society 2013
Abstract: This paper 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 coefficients on the latent variable, the proxy, and the covariates are not identified. We then derive the sharp identification regions for these coefficients 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.
Measurement Error in Multiple Linear Equations: Tobin's q and Corporate Investment, Saving and Debt (with Karim Chalak) Journal of Econometrics, Volume 214 (2) (2020), 413-432 . view
Presented at Africa Meeting of the Econometric Society 2019, North American Summer Meeting of the Econometric Society 2018, Wharton PhD Finance Seminar 2018, Western Economic Association 2018
Abstract: We econometrically characterize the identification regions for the coefficients in a system of linear equations under the classical measurement error assumptions. We demonstrate the identification gain that results from jointly considering the equations, as opposed to separately. We apply this framework to COMPUSTAT data and analyze the effects of cash flow on the investment, saving, and debt of firms when Tobin's q is used as an error-laden proxy for marginal q. The linear regression estimates suggest that cash flow affects investment positively, saving positively, and debt negatively. These results are incompatible with some economic theories and the literature, sometimes, attributes this discrepancy to the measurement error in Tobin’s q. Using our framework, we document a considerable identification gain that results from analyzing the investment, saving, and debt equations jointly. Further, the measurement error in Tobin’s q can reconcile the discrepancy with the theories if, and only if, Tobin's q is a noisy proxy for marginal q. If a researcher maintains that Tobin’s q is a moderately accurate proxy for marginal q, then a more elaborate theory or specification must be considered.