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March 08, 2021 12:00 AM

Morningstar turns detective to find gender from 5500s

Robert Steyer
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    What's in a name? Morningstar spends a lot of time trying to find out.

    Poring over Form 5500s to determine the gender of defined contribution plan administrators, Morningstar Inc. researchers usually had a relatively easy time distinguishing male vs. female.

    There was a strong probability that names like Jeffrey and Thomas are male and names like Cynthia and Angela are female, according a Morningstar report, "Wonder Women: Why Defined Contribution Plans Benefit From Female Plan Administrators." But what about Dana? Or Lee? Or Kelly? Or Leslie? That required some detective work.

    Researchers checked "a dataset that includes information about baby names from Social Security applications for the year 1965," said the Morningstar report linking the quality of plan administration to the gender of the administrator.

    Why 1965? Even though a Form 5500 doesn't cite an administrator's age, Morningstar chose 1965 because "it results in an average assumed age over the period of approximately 50," the report said. "Name/gender combinations are relatively static over time."

    By checking names in the Form 5500s between 2000 through 2017, Morningstar estimated an administrator's gender by assigning a probability score. The higher the score, the greater likelihood the administrator is male; the lower the score, the greater likelihood the administrator is female.

    Thus, male probability scores include 99.77% for Jeffrey, 99.65% for Thomas and 99.56% for Michael. (Luckily for Morningstar's researchers, the actress Michael Learned, best known for her role as Olivia Walton in the TV series "The Waltons," isn't a defined contribution plan administrator.)

    Female probability scores include 0.22% for Angela, 0.26% for Donna and 0.2% for Susan. (However, none of the administrators was the model for the 1969 Johnny Cash song, "A Boy Named Sue.")

    Having decided that a probability score of 95% or higher identifies a male and a probability score of 5% or lower identifies a female, the researchers were left with 5.66% of names that were classified as "undefined." They were excluded from the report.

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