What’s behind stress in America? The economic factors, ranked

What does the data reveal?

• Income Inequality shows the strongest correlation with stress at the national level followed closely by unemployment.
• On state level, unemployment shows the strongest correlation to stress in 20 states followed by income inequality in 15 states.
• Homeownership and cost of living show no top correlation at the state level

How economic factors correlate with stress nationwide

To understand the connection between the economy and the stress levels in the US, we chose six indicators: income inequality, unemployment rates, debt-income ratios, rent-income ratios, homeownership rates, and the cost of living. The indicators were chosen so they capture different aspects of financial stress and give us a clear picture of what’s really happening across the country. Instead of using raw debt and rent figures, we went with their ratios relative to income to provide a more accurate measure of financial strain.

At the national level, we measured how strongly each economic factor correlates with stress levels to identify which ones have the closest relationship.

Nationally, income inequality shows the strongest relationship with stress, making it the most closely linked economic factor among those analyzed. This suggests that disparities in income distribution may play a meaningful role in shaping stress levels across the U.S. The large gap between high and low income households makes it difficult for individuals at the lower end to access resources, have a stable financial situation, and fewer opportunities to actually improve their situation. Additionally, high income inequality affects the psychological and social well-being of individuals where people experience stress and anxiety when comparing themselves with relatives, neighbors, or colleagues and feeling that they are falling behind. This also created a sense of instability in communities with unequal access to healthcare, education, and other services.

Following closely behind is unemployment, with a rate of 4.4%, giving us a picture on how stressful it is to deal with job insecurity and unstable employment. Even short periods of unemployment disrupt daily routines and increase anxiety, directly making it harder for individuals to deal with other financial obligations.

Additionally, both the debt-to-income ratio and rent-to-income ratio correlate positively with stress. This suggests that the higher the financial obligations are, the more stressed Americans are feeling. When rent and debt consume a large portion of income, households will have to make tough financial decisions like cutting back on groceries, delaying medical care, postponing savings, and avoiding social and recreational activities. Dealing with these decisions daily, is showing to stress Americans and making their lives more difficult.

By contrast, homeownership rates show only a weak correlation, suggesting a limited relationship with stress at the national level. The most interesting thing in our findings is that cost of living shows virtually no relationship with stress and this indicates that it may not be a primary driver of stress compared to other economic indicators.

State-by-state findings

To have a better understanding on how stress is correlating with economic indicators in Americans, we dug deeper to see the results on a state-by-state basis.

While nationally, income inequality is the indicator with the highest correlation to stress,  state analysis gave us a different finding. Unemployment emerges as the top stress-linked factor in 20 states, the most of any indicator, followed closely behind by income inequality ranking first in 15 states. The debt-to-income ratio dominates in 12 states and rent-to-income ratio is the leading factor in just 4 states. Homeownership rates and cost of living do not appear as the top factor in any state.

The map below visually represents this data, with each state colored according to the factor most strongly correlated with stress there, making it easy to see regional patterns at a glance.

The map above shows which economic factor correlates most strongly with stress in each state. As the visualization reveals, stress drivers across the U.S. vary significantly by region rather than following a single national pattern.

In the West and parts of the Midwest, unemployment emerges as the dominant factor, reflecting economic transitions and labor market instability. The South is more strongly influenced by income inequality, pointing to disparities in wealth distribution as a key source of stress. Meanwhile, the central and Midwestern states are primarily affected by high debt-to-income ratios, indicating financial pressure among middle-income populations, while rent-related stress appears more localized and less widespread.

Below is a breakdown of each economic factor and the states where it correlates most strongly with stress:

Unemployment: Alabama, California, Delaware, Georgia, Hawaii, Michigan, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, North Dakota, Oregon, South Dakota, Tennessee, Vermont, Washington, West Virginia, Wyoming

Income Inequality: Alaska, Connecticut, District of Columbia, Florida, Idaho, Indiana, Iowa, Louisiana, Minnesota, Mississippi, New York, North Carolina, Texas, Utah, Wisconsin

Debt to Income Ratio: Arizona, Arkansas, Colorado, Illinois, Kansas, Kentucky, Maryland, Missouri, Ohio, Pennsylvania, South Carolina, Virginia

Rent to Income Ratio: Maine, Massachusetts, Oklahoma, Rhode Island

Methodology and sources

1. Data Collection

We collected data for all 50 U.S. states and the District of Columbia on the following variables:

• Stress Level (%): Average stress levels per state based on internal data from 1.2M U.S. Welltory users. Stress levels in this study are measured using heart rate variability (HRV) — the variation in time between heartbeats captured through the Welltory app via a smartphone camera or wearable device. Welltory's algorithm translates this physiological signal into a stress score on a 0–100% scale. Unlike self-reported surveys, HRV reflects the body's stress response directly, without relying on how people describe their emotional state. It is widely recognized as a valid biomarker of stress in peer-reviewed research and used across clinical medicine, military science, and sports performance.
• Homeownership (%): Percentage of households that own their homes. 
• Debt-to-Income Ratio: Average household debt relative to income. 
• Rent-to-Income Ratio: Ratio of rent to household income. 
• Cost of Living Index: A single index that combines key expenses: housing, utilities, groceries, transport, healthcare, and other essentials into one number to compare states. 
• Income Inequality: Measure of income distribution inequality. 
• Unemployment Rate (%): State-level unemployment

2. Standardization (Z-Scores)

To compare variables measured on different scales, each factor was converted to a z-score using the formula:

z = (𝑥 - mean)/standard deviation

Where:

𝑥 = individual state value

mean = average across all states

standard deviation = standard deviation across all states

This allows direct comparison of the impact of each factor on stress levels.

3. National-Level Analysis

We calculated the correlation between stress levels and each factor across all states to identify which factors are most strongly associated with stress nationally. We used the Pearson correlation coefficients formula:

r = [n(Σxy) − (Σx)(Σy)] / √{[nΣx² − (Σx)²][nΣy² − (Σy)²]} 

The factor with the highest positive correlation is considered the primary national driver of stress.

4. State-Level Analysis

For each state, we calculated the z-score impact of each factor relative to stress.

The factor with the highest z-score impact in a state is identified as the top stress driver for that state.

This approach allows us to capture local variations, where the top stressor may differ from the national trend.

5. Ranking and Interpretation

National Ranking: Factors are ranked by Pearson correlation coefficient with stress levels.

State Ranking: Factors are ranked by z-score impact for each individual state.

This dual approach allows us to identify both nationwide patterns and state-specific stress correlations.

6. Sources

welltory.com

https://data.census.gov/table/

https://www.federalreserve.gov/releases/z1/dataviz/household_debt/state/map/#year:2025

https://worldpopulationreview.com/state-rankings/average-rent-by-state

https://worldpopulationreview.com/state-rankings/cost-of-living-index-by-state

https://www.census.gov/data/tables/2025/demo/income-poverty/p60-286.html

https://www.bls.gov/

For journalists, researchers, and any readers interested in exploring the data further, the complete dataset is available here