Estimating and Imputing Missing Tax Loss Carryforward Data to Reduce Measurement Error

Malte M. Max, Jacco L. Wielhouwer and Eelke Wiersma

European Accounting Review, 2023, vol. 32, issue 1, 55-84

Abstract: The ability to reduce current and future taxable income with prior years' taxable losses is highly relevant for explaining firms' effective tax rates. Compustat data on the tax loss carryforward (TLCF) are, however, often missing. We propose a method to estimate values for the missing TLCF data instead of the common practice in the literature of imputing zero values. In order to assess the accuracy of our method, we compare our estimated TLCFs with both a random selection of 10-K data and Compustat data for firm-years where Compustat data is available. The results show that our estimated values align very closely with the reported data. We re-analyze two existing studies using these estimated values. With the first, we show that imputing our estimated values instead of zeros leads to a large decrease in measurement error. This reduces the risk that firms with missing data and low effective tax rates are incorrectly classified as tax aggressive. The second re-analysis shows that using our estimated TLCFs leads to economically and statistically different conclusions compared to imputing zeros. Using our estimated values thus increases the probability of correct inferences in studies that use Compustat TLCF data. The estimated values are available from https://doi.org/10.34894/N9J1WE.

Date: 2023
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DOI: 10.1080/09638180.2021.1924812

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