Food Insecurity Data Gap: A Model to Predict National Prevalence

Strategic Insights -

As of 2023, nearly 14.3% of people in the United States lived in food-insecure households, with over 47 million individuals affected. However, the recent suspension of the Current Population Survey (CPS) Food Security Supplement (FSS) by the USDA leaves a critical gap in national data on economic well-being. To address this issue, researchers have developed a model that predicts national food insecurity rates using established correlates of food insecurity, including poverty and unemployment rates, and food-specific inflation.

Key Takeaways:

  • The model draws on established correlates of food insecurity, including national rates of poverty and unemployment, and food-specific inflation, to estimate food insecurity rates for all individuals, adults, children, and households.
  • The predicted rates align closely with actual food insecurity rates between 2010 and 2023, with a typical difference of 0.3 percentage points.
  • Sensitivity tests show that the preferred model specification outperforms alternatives.
  • The model can be used to predict 2024 food insecurity rates, for which national data are scheduled to be released later in October 2025.
  • The suspension of the CPS FSS leaves a critical gap in national data on economic well-being, and the model presented here may prove useful in estimating food insecurity in future years.
  • The model can be used to inform policies and interventions aimed at reducing food insecurity.

Statistics:

  • As of 2023, 47 million people in the United States lived in food-insecure households, representing 14.3% of the population.
  • The model predicts that food insecurity rates will remain relatively stable in the coming years, with a predicted rate of 14.1% for 2024.
  • The model has been validated using sensitivity tests, which show that the preferred model specification outperforms alternatives.
  • The typical difference between predicted and actual food insecurity rates is 0.3 percentage points, indicating a high degree of accuracy.

Sources:

  • osf.io/preprints/socarxiv/ydbgw_v1/