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

Review of Financial Studies 2018 31(4), 1460-1498
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.

Choosing the Precision of Performance Metrics

Journal of Financial and Quantitative Analysis 2018 53(4), 1911-1935
There is a standard trade-off in compensation contracts between the provision of incentives and insurance. We hypothesize that this trade-off influences the precision with which firm performance is measured. We find that firm outcomes are measured less precisely when chance plays a large role in these outcomes. Further, this precision is determined through the choice of shares outstanding. This has several novel implications. Nominal stock prices can remain constant over time, and firms with unpredictable cash flows should have more shares and lower stock price levels, all else equal. We find evidence consistent with these implications.

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

Review of Financial Studies 2018 31(4), 1460-1498
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.