About ZIPStatistics

This platform provides unparalleled insights into income distribution across the United States, offering high-resolution data segmented by geography. This analysis leverages cutting-edge statistical techniques to transform raw, aggregated IRS data into a detailed and accurate picture of income landscapes at the ZIP code level.

Advanced Distribution Fitting

At the core of the methodology is an advanced distribution fitting process. For each ZIP code, this analysis takes the aggregated income brackets provided by the IRS and fit a sophisticated, continuous probability distribution to the data. While many income models exist, a flexible, multi-parameter model that conforms to the theoretical properties of income distributions, allowing for a highly accurate representation of the underlying income spread, especially in the tails of the distribution.

This technique allows this analysis to move beyond simple averages and generate a full spectrum of income percentiles (from the 10th to the 99th), providing a comprehensive understanding of income inequality and concentration within each community.

Data and Analysis

The process involves several key steps:

  • Data Ingestion: This analysis processes publicly available IRS Statistics of Income (SOI) data, which provides aggregated data based on tax returns.
  • Data Cleaning and Preparation: The raw data is carefully cleaned and structured to prepare it for analysis.
  • Statistical Modeling: For each ZIP code and year, proprietary models are fit to the income data.
  • Percentile and Metric Generation: From the fitted distributions, this analysis calculates key metrics, including mean income, variance, and a detailed set of income percentiles.

The result is one of the most granular and statistically robust public datasets on income by geography.

Disclaimer

The data and analysis on this website are provided "as is" for informational purposes only. While this analysis strives for the highest degree of accuracy, it makes no warranties or representations of any kind regarding the data.

The information is derived from publicly available data and its own statistical modeling. This information should not be solely relied upon for financial, legal, or other professional advice. While it may be a useful reference, no guarantees are made regarding its suitability for any particular purpose.

The creators of this analysis are not liable for any decisions made based on the information provided on this site.