New data reveals 60 years of rising local wealth inequality across the United States

Using ensemble machine learning techniques, we build a new dataset capturing the spatial distribution of wealth in the US between 1960 and 2020, enabling researchers to pose new questions about wealth inequality, with implications for economic, social and epidemiological outcomes.
New data reveals 60 years of rising local wealth inequality across the United States

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Wealth inequality – defined as disparities in household assets net of debts – has been sharply rising in the United States, and across many other high-income countries. Various studies suggest that the richest 1 percent of families in the United States now possess almost 35 percent of total national wealth, a share that has grown by more than 50 percent since 1980. These trends are concerning because of the adverse effects of wealth inequality on social, economic, political, and health outcomes, disproportionately affecting non-white households.

Initially, we wanted to know: where in the United States wealth inequality has increased the most? And, which communities have been left behind? However, we found that there was no straightforward way to answer these questions.

Answers are important because wealthier cities and towns have greater resources to invest in schools, health care, transportation and other infrastructure; we know that differences in these public goods confer benefits that can span generations and improve life chances for children born into poverty. Even in places where wealth exists, if it is distributed unequally, it can result in underinvestment in essential local public goods. A lack of wealth or extreme wealth inequality can also fuel resentment, leading to support for anti-system politics. Moreover, changes in wealth, especially housing wealth, shape consumer behavior, which can have major effects on the availability and quality of local jobs

Why do we know so little about the geography of wealth? Few data sources report on personal assets and debts, and those that do are highly restrictive due to confidentiality concerns. Researchers have access to data on housing markets, but home ownership is only one channel among several by which wealth can vary across locations. In practice, home values and net wealth are only moderately correlated across American households, since the latter also includes households’ financial assets like stocks and bonds, as well as various forms of debt - credit cards, student loans, and so on.  

In our new article in Scientific Data, we introduce the first comprehensive dataset detailing the changing geography of wealth inequality from 1960 to 2020. This data compendium, referred to as the Spatial Wealth Inequality Database (“GEOWEALTH-US”), utilizes a novel machine-learning-based method that can estimate distributions of wealth at various scales, from households to regions.

The GEOWEALTH-US relies on ‘ensemble’ learning techniques to generate predictive models of household wealth, using rich survey information from the Federal Reserve's Survey of Consumer Finances (SCF). We then use these models to impute wealth among households in Census population surveys that include geographical identifiers. The end result is a dataset that permits description of variation in average wealth between places (‘geography of wealth’), as well as how wealth is distributed within cities and regions (‘local wealth inequality’). Our paper reports on extensive validation we undertake against a wide range of published data sources, providing confidence in the reliability of our underlying approach. 

As an example of this validation, Figure 1 compares our estimates of the share of national wealth held by top wealth holders against state-of-the-art estimates produced by other researchers, including: Piketty, Saez and Zucman (2018); Saez and Zucman (2020); and Smith, Zidar and Zwick (2022). In this figure, validation comes from the strong alignment between our approach (visualized in orange) and these and other leading methods.

Figure 1. Comparing our national top wealth share estimates against the existing state of the art 

Of course, unlike these estimates providing a snapshot of the national situation, the main virtue of our approach is that it enables us to track wealth and wealth inequality at relatively fine-grained spatial scales, including public-use microdata areas (PUMAs), commuting zones, and states.

Our initial exploration of subnational geographical patterns in the data reveals three key facts. 

First, Figure 2 below demonstrates that the distribution of wealth between regions has become meaningfully more unequal since 1970. US wealth holdings have become increasingly concentrated in a smaller set of regions. 

Figure 2 also shows that inter-regional wealth disparities have grown much larger than inter-regional income disparities, with income gaps growing between 1960 and 2020 by 20 percent and wealth by 42 percent. This supports the intuition that spatial wealth inequalities require investigation over and above the study of income inequality. The sharp exacerbation of wealth inequality over this period makes this a particularly urgent topic for further research. 

Figure 2. The geography of wealth vs incomes, 1960–2020 across US commuting zones

Finally, comparing the maps in Figure 3 below, we can see a mix of both changes and continuity in local wealth inequality over time.   In 1960, intra-regional wealth was high throughout the South, low in the Midwest and Northern Plains regions, and more mixed along the coasts. The main change to this pattern up to 2020 has, however, been the dramatic rise in inequality in the Midwest and Plains regions. This convergence in inequality between the South and the Midwest is consistent with findings from studies of regional income inequality and social mobility, suggesting interdependence and common underlying sources affecting different facets of spatial inequality.

Figure 3. Changes in wealth inequality within US commuting zones, 1960 to 2020.

Inequality is among the great societal challenges of the 21st century. The data infrastructure and initial findings presented here represent the first step toward understanding the changing dynamics of wealth inequality across the neighborhoods, cities, and regions of the United States.

Note: the dataset is openly available at: Replication code is found at

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Social Inequality
Humanities and Social Sciences > Society > Sociology > Social Structure > Social Inequality
Economic Geography
Humanities and Social Sciences > Society > Population and Demography > Human Geography > Economic Geography
Cities, Countries, Regions
Humanities and Social Sciences > Arts > Architecture > Cities, Countries, Regions
Urban Economics
Humanities and Social Sciences > Economics > Regional and Spatial Economics > Urban Economics
Regional and Spatial Economics
Humanities and Social Sciences > Economics > Regional and Spatial Economics
Population Economics
Humanities and Social Sciences > Economics > Labor and Population Economics > Population Economics

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