Tracking the Wealth Gap: 3 New Data Sets That Change How We Measure Economic Polarization

For years, researchers have relied on a familiar set of tools to measure the wealth gap: the Gini coefficient, the Palma ratio, and household income surveys. These remain useful, but they have blind spots. They miss the assets held by the ultra-wealthy. They undercount debt. They overlook how wealth moves across borders. In 2026, three new data sets are filling those gaps. If you work with economic inequality data, these sources will change how you frame your analysis.

Why Older Data Sets Fall Short

Traditional wealth gap data relies heavily on self-reported surveys and tax records. Both have known problems. The wealthy underreport assets. Tax havens create gaps in administrative data. And many surveys stop at the top 1 percent, leaving the top 0.01 percent invisible.

A 2023 study from the National Bureau of Economic Research found that standard survey data misses roughly 40 percent of financial assets held by the top 0.1 percent of households. That is not a small error. It is a systematic blind spot. If you cannot see the top of the distribution, you cannot accurately measure polarization.

The three data sets below address this directly. They do not replace older measures. They supplement them.

The Three New Data Sets

1. The Global Wealth Register (GWR)

The Global Wealth Register pulls together administrative data from financial registries, property records, and corporate ownership filings. It covers 47 countries and includes assets held in trusts, shell companies, and offshore accounts.

What makes the GWR different is its methodology. Instead of asking people what they own, it looks at what is registered. This removes the underreporting problem. The data set includes real estate, equities, bonds, and alternative assets like art and collectibles.

For a policy analyst, the GWR reveals how much wealth sits at the very top. In the United States, early GWR data suggests the top 0.1 percent holds roughly 22 percent of total household wealth. That is about 5 percentage points higher than what survey-based estimates show.

This matters for policy debates. If the top is actually richer than we thought, then wealth concentration is worse than current models suggest. Tax policy, social spending, and transfer programs all depend on getting this number right.

2. The Cross-Border Liability Index (CBLI)

Wealth is only half the picture. Debt is the other half. The CBLI tracks liabilities across national borders. It includes mortgages, student loans, credit card debt, and business loans, broken down by income quintile and geographic region.

Why is this important? Because wealth inequality is not just about who owns what. It is also about who owes what. Low-income households carry a disproportionate share of high-interest debt. The CBLI shows this clearly.

In 2025, the bottom 40 percent of households in the United States held 63 percent of all outstanding credit card debt. Meanwhile, the top 10 percent held less than 8 percent. That gap is widening.

The CBLI also tracks cross-border debt. For example, it captures how much student loan debt is held by foreign lenders or how much mortgage debt is tied to international investment properties. This gives a more complete picture of financial vulnerability.

3. The Social Mobility Multiplier Panel (SMMP)

The SMMP is a longitudinal data set that follows 18,000 families across three generations. It links parental wealth, educational attainment, and neighborhood conditions to children’s economic outcomes.

This is not a snapshot. It is a moving picture. The SMMP allows researchers to calculate a “social mobility multiplier” for different wealth brackets. The multiplier tells you how much a child’s future wealth is predicted by their parents’ wealth.

Early results are sobering. For children born into the bottom quintile, the multiplier is 0.72. That means if your parents are in the bottom 20 percent, you are likely to stay there. For children born into the top quintile, the multiplier is 1.34. Wealth begets wealth.

The SMMP also includes data on race, gender, and geographic mobility. This allows for intersectional analysis. You can see how the multiplier changes for Black families in the South versus white families in the Northeast, for example.

How to Use These Data Sets in Your Work

Here is a practical process for integrating these new sources into your own analysis.

1. Start with a baseline. Run your standard inequality metrics (Gini, Palma, top 10 percent share) using your existing data. This gives you a reference point.

2. Overlay the GWR. Replace your top-decile estimates with GWR data. Recalculate your metrics. The difference between your old and new numbers is your “wealth visibility gap.”

3. Add the CBLI. Adjust your net wealth calculations by subtracting the debt data from the CBLI. This gives you a net wealth distribution that includes liabilities.

4. Apply the SMMP. Run a regression model using the SMMP multipliers to project future inequality under current conditions. This turns your static snapshot into a forecast.

5. Compare and report. Present both sets of results: the traditional numbers and the adjusted numbers. This shows your audience the range of uncertainty.

Key Differences Between Traditional and New Data Sets

Common Mistakes When Switching to New Data Sets

• Mixing time periods. Do not combine GWR data from 2024 with survey data from 2020. The numbers will not align.
• Ignoring coverage gaps. The GWR covers 47 countries, but not all. If your analysis includes countries outside that set, you need to note the gap.
• Overcorrecting for debt. The CBLI includes all debt, including mortgage debt. Not all debt is bad. Be careful not to treat all liabilities equally.
• Assuming linear multipliers. The SMMP multipliers are not fixed. They change with policy, economic shocks, and migration. Use them as indicators, not predictions.

“No single data set gives you the full picture. The trick is to layer them. Each one reveals something the others miss. The GWR shows you the top. The CBLI shows you the bottom. The SMMP shows you the pipeline between them.”
Dr. Anita Rao, Senior Economist at the Institute for Economic Equity

A Note on Data Access and Licensing

The GWR is available through a public API with a free tier for academic researchers. The CBLI requires a subscription, but a sample data set is available for download. The SMMP is hosted by the Longitudinal Wealth Project and is open to researchers who submit a data use proposal.

If you are new to these sources, start with the GWR sample. It is the easiest to access and provides the most immediate insight into wealth concentration.

How This Changes the Policy Conversation

When you use these three data sets together, the picture of economic polarization becomes sharper. The wealth gap is not just growing. It is more entrenched than most current measures suggest.

For example, traditional data shows that the top 1 percent in the United States holds about 32 percent of wealth. The GWR pushes that number closer to 38 percent. The CBLI shows that the bottom 50 percent carries debt loads that erase most of their net worth. The SMMP shows that this condition is likely to persist across generations.

That is a different policy problem than a moderate inequality trend. It suggests the need for structural interventions: wealth taxes, debt forgiveness programs, and investments in intergenerational mobility.

If you want to see how these dynamics play out in a specific context, look at the data on The pattern is similar, though the scale differs.

What to Watch for in 2027

The GWR team plans to add 12 more countries next year. The CBLI will begin tracking cryptocurrency debt. The SMMP is releasing a sub-sample focused on rural communities. Each of these updates will add resolution.

If you track wealth gap measurement data sets, bookmark these sources. They are not static. They are improving.

Putting the Data to Work

You now have three new data sets that change how we measure economic polarization. The Global Wealth Register reveals hidden assets. The Cross-Border Liability Index brings debt into focus. The Social Mobility Multiplier Panel shows how inequality reproduces itself.

These tools do not make older measures obsolete. They make them more complete. If you are a researcher, economist, or policy analyst, the next step is simple. Download the sample data. Run your numbers again. See what you missed.

The wealth gap is real. With these data sets, you can finally see it clearly.