Getting the Incentives Right: Backfilling and Biases in Executive Compensation Data

Stuart Gillan; Jay C. Hartzell; Andrew Koch; Laura T. Starks

doi:10.1093/rfs/hhx061

Getting the Incentives Right: Backfilling and Biases in Executive Compensation Data

Stuart Gillan¹; Jay C. Hartzell²; Andrew Koch³; Laura T. Starks²

¹ Terry College of Business, University of Georgia · ² McCombs School of Business, The University of Texas at Austin , · ³ Katz Graduate School of Business, University of Pittsburgh

Review of Financial Studies 2018

We document that backfilling in the ExecuComp database introduces a data-conditioning bias that can affect inferences and make replicating previous work difficult. Although backfilling can be advantageous due to greater data coverage, if not addressed, the oversampling of firms with strong managerial incentives and higher subsequent returns leads to a significant upward bias in abnormal compensation, pay-for-performance sensitivity, and the magnitudes of several previously established relations. The bias also can lead to one misinterpreting the appropriate functional form of a relation and whether the data support one compensation theory over another. We offer methods to address this issue. Received May 12, 2014; editorial decision May 10, 2016 by Editor David Hirshleifer.

DOI: 10.1093/rfs/hhx061
Volume: 31 (4)
Pages: 1460-1498
Language: en
Export: BibTeX
Sources: crossref openalex