Population (B230RC0Q173SBEA) | FRED | St. Louis Fed

Rows: 319
Columns: 2
Data range: 1947-01-01 to 2026-07-01
Auto-refresh: Re-checks its source every 14 days
Last refreshed with new data: 2026-11-02

Graph and download economic data for Population (B230RC0Q173SBEA) from Q1 1947 to Q1 2026 about population, GDP, and USA.

AI analysis

Data Analysis: US Population (B230RC0Q173SBEA) — FRED

1. Dataset Overview and Value

This series, sourced directly from the Federal Reserve Bank of St. Louis (via fredgraph.csv?id=B230RC0Q173SBEA), tracks a U.S. population measure on a quarterly basis from Q1 1947 through Q1 2026. Notably, this is a FRED-hosted series used in national accounts (the "B230RC0Q173SBEA" naming convention suggests it's the population denominator FRED uses to compute per-capita GDP and other BEA-based measures), which makes it particularly valuable as a normalizing/deflating variable rather than a standalone indicator. Because it comes from an authoritative government-adjacent aggregator with a live API endpoint, it is well-suited for reproducible, automated correlation pipelines — and the fact that data extends to Q1 2026 suggests either forward-projected/estimated values or a very fresh pull, which analysts should verify before treating recent quarters as final/actuals.

2. Data Quality Observations

Data quality here is excellent on the surface: zero null cells across both columns (317 rows × 2 columns), and no reported type mismatches. Both observationdate and the population value column show 317 distinct values against 317 total rows, indicating no duplicate timestamps and a clean one-to-one mapping between date and measurement — a strong signal of a well-maintained time series with no missing quarters over the ~79-year span. The duplicate-row check is flagged as pending recomputation ("Phase D"), so that figure should be confirmed before final sign-off, but there are no other red flags. The absence of a sample data preview (0 rows shown) is a minor limitation for this review — actual value inspection (e.g., confirming units: thousands vs. millions) should happen before downstream use.

3. Key Column Distributions

The core value column (B230RC0Q173SBEA) ranges from 143,143 to 342,583, with a mean of 245,286 and median of 240,962 — the small gap between mean and median (and a skewness of just 0.0202) indicates a distribution that is nearly symmetric, consistent with a smooth, monotonically trending time series rather than one with sharp outliers or structural breaks. The quartile spread (Q1=197,481, Q3=298,408) shows the data is fairly evenly distributed across its range, which makes sense for a population-like metric that grows steadily over 79 years rather than jumping erratically. The standard deviation (σ≈58,880) relative to the mean (~24% coefficient of variation) reflects the substantial long-run growth trend embedded in the series rather than short-term volatility. Since this is a level (stock) variable rather than a rate, analysts should consider first-differencing or computing growth rates for correlation work, since raw levels of any long-run-trending series will spuriously correlate with other trending series (classic non-stationarity issue).

4. Recommended Join Keys

observationdate is the clear and only viable join key — it's a clean Date type with zero nulls and full quarterly coverage from 1947-01-01 to 2026-01-01. For cross-dataset correlation, this should be matched against other FRED/BEA/BLS series that report on a quarterly (or convertible monthly/annual) basis. Analysts should standardize on quarter-start dates and confirm alignment conventions (e.g., period-start vs. period-end dating) when joining against series with differing date conventions.

5. Suggested Complementary Datasets

Given its role as a population/scaling denominator, this series pairs naturally with: (a) GDP series (nominal and real) from FRED/BEA to compute or validate per-capita GDP; (b) labor force and employment data (BLS) to derive labor force participation or employment-population ratios; (c) personal income and consumption expenditure series for per-capita income/spending trends; (d) housing starts or housing units data to study demand-side pressure per capita; and (e) CPI/inflation series to explore real vs. nominal per-capita metrics over time. Because the underlying series is smooth and trend-dominated, the most insightful correlation studies will likely come from pairing growth rates (QoQ or YoY % change) of this population measure against growth rates of other macro variables, rather than comparing raw levels directly.

Columns

observation_date (date)
B230RC0Q173SBEA (integer)