Understanding wealth is central for uncovering the barriers to wealth-building and designing policies that unlock opportunities for everyone. However, household wealth data at the local level are generally not widely available, especially statistics disaggregated by race and ethnicity.
In this research report, we document how we use machine learning to estimate net worth and emergency savings data at the local, city, state, and national levels. We also disaggregate our estimates by racial and ethnic groups at the city, state, and national levels. Using a random forest model, we predict whether households in the American Community Survey have $2,000 in emergency savings and their net worth. We then aggregate this household-level data to produce statistics at different geographic levels and by racial and ethnic groups.
What We Found
- This report demonstrates a feasible methodology for using detailed wealth information from smaller sample size surveys to impute wealth in large sample size surveys.
- Using this approach, we are able to estimate median net worth and emergency savings in areas as small as the census’s Public Use Microdata Areas (PUMAs).
- For larger areas, such as bigger cities and states, we produce wealth statistics by racial and ethnic groups.
- We found stark differences in both emergency savings and net worth by racial and ethnic groups. For example, we estimate a typical household’s wealth in the richest area of Chicago and Cook County is 206 times higher than a typical household’s wealth in the poorest area. Many of these differences are driven by structural racism and compounded disadvantages that Black Americans face most acutely.
Lack of Liquid Assets Used for Emergencies Is Concentrated in Black Communities in Chicago and Cook County
Lack of Net Worth Is Concentrated in Black Communities in Chicago and Cook County
