{'fraction_non_alphanumeric': 0.018433179723502304, 'fraction_numerical': 0.013824884792626729, 'mean_word_length': 5.633587786259542, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Getting the Incentives Right: Backfilling and Biases in Executive Compensation Data

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.