Total Federal Outlays (MTSO133FMS) | FRED | St. Louis Fed

Rows: 552
Columns: 2
Data range: 1980-10-01 to 2026-09-01
Auto-refresh: Re-checks its source every 14 days
Last refreshed with new data: 2026-10-24

Graph and download economic data for Total Federal Outlays (MTSO133FMS) from Oct 1980 to May 2026 about outlays, federal, and USA.

AI analysis

Data Quality & Structure Analysis: Total Federal Outlays (MTSO133FMS)

1. Dataset Overview

This dataset tracks Total Federal Outlays — monthly gross spending by the U.S. federal government — sourced directly from the Federal Reserve Bank of St. Louis's FRED API (fred.stlouisfed.org/graph/fredgraph.csv?id=MTSO133FMS). As a FRED-hosted series, it benefits from a highly reputable publisher with strong data governance, standardized formatting, and regular updates. The time coverage spans October 1980 to May 2026 (548 monthly observations), including a forward-looking window that suggests either forecasted/projected values or an extended release schedule — this is worth verifying before treating all rows as historical actuals. This series is highly valuable for correlation studies involving fiscal policy, government spending cycles, deficit analysis, and macroeconomic indicators like GDP, inflation, and interest rates, since federal outlays are a major driver of aggregate demand and public debt dynamics.

2. Data Quality Observations

Data quality appears excellent on the surface: zero null cells across both columns, and both observationdate and MTSO133FMS show 548 distinct values matching the total row count — indicating no duplicate dates and no repeated observations. This is consistent with a clean, well-maintained monthly time series with full temporal coverage. The duplicate-row check is flagged as pending Phase D recomputation, so a final duplicate-count validation against the Parquet backend is still needed before fully certifying the dataset. No type-mismatch issues are reported, and the sample data rows are pending wire-up, so row-level spot-checking isn't yet possible — this should be revisited once available.

3. Key Column Distributions

The MTSO133FMS value column shows substantial spread and right-skew, typical of nominal fiscal spending data that grows over a 45+ year horizon. Values range from a minimum of $45,930M to a maximum of $1,104,900M (~$1.1 trillion), with a mean of $236,413M notably higher than the median of $180,415M, confirming the positive skew (skewness = 1.46). The interquartile range (Q1 = $114,614M, Q3 = $320,532M) shows the bulk of observations clustered in the lower-to-mid range, while a long right tail — likely driven by recent years of expanded spending — pulls the mean upward. The 17 outliers are almost certainly concentrated in the tail (large stimulus/pandemic-era or year-end spending spikes), and analysts should inspect whether these align with known fiscal events (e.g., 2020 CARES Act) rather than being treated as noise.

4. Recommended Join Keys

observationdate is the clear and only viable join key — it's a clean, fully populated, monthly-granularity date field with no duplicates. For cross-dataset correlation, this should be aligned to month-start or month-end conventions matching partner datasets, and analysts should watch for FRED's tendency to timestamp monthly data as the first of the month regardless of actual reporting date. Given the skewed, non-stationary nature of the value series, consider joining on transformed keys (e.g., year-month) alongside computing month-over-month or year-over-year percent changes to enable more meaningful correlation rather than raw level comparisons.

5. Suggested Companion Datasets

Strong correlation candidates include other FRED fiscal/monetary series such as Total Federal Receipts (MTSR133FMS) for deficit/surplus analysis, Federal Debt (GFDEBTN), GDP (GDP/GDPC1) for spending-to-output ratios, CPI/PCE inflation indices to deflate nominal outlays into real terms, and Federal Funds Rate (FEDFUNDS) or 10-Year Treasury Yield (DGS10) to study spending's relationship to borrowing costs. Additionally, pairing this with unemployment rate (UNRATE) or recession indicator series (USREC) could reveal counter-cyclical spending patterns, while defense spending or entitlement program subcomponents could help decompose which categories drive the observed outliers and skew.

Columns

observation_date (date)
MTSO133FMS (decimal)