The Problem of Data Aggregation in People Metrics

This analysis is a reaction to Kansas City Star article, “Asian-Americans narrow wealth gap, new studies show,” which oversimplifies income and race trends. It aggregates “Asian-Americans” into a group and tells the story of averages. This is not uncommon in major coverage of demographics and Asian Americans. In demonstrating issues with disaggregation, data from U.S. Census dataset from UCI Machine Learning Library, here and here, are compared with the findings from a St. Louis (STL) Federal Reserve paper on The Demographics of Wealth. Demographic data aggregation tells the wrong story of income and race in the United States. There are cases where metrics should be aggregated but in those cases the advantages must be laid out.

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