National Average Sentence
278.7 months
FY2024 · 522 cases
National federal sentencing data · FY2015–FY2024 · Source: USSC
| Year | Cases | Avg (mo) | GL Min | GL Max | Within GL | Above GL | Below GL |
|---|---|---|---|---|---|---|---|
| FY2024 | 522 | 278.7 | 3619.9 | 272.7 | 0% | 0% | 0% |
| FY2023 | 489 | 292.0 | 3788.5 | 288.8 | 0% | 0% | 0% |
| FY2022 | 410 | 265.6 | 2993.5 | 284.9 | 0% | 0% | 0% |
| FY2021 | 257 | 251.9 | 2668.1 | 268.8 | 0% | 0% | 0% |
| FY2020 | 294 | 256.6 | 4079.0 | 287.2 | 0% | 0% | 0% |
| FY2019 | 373 | 257.5 | 3681.6 | 268.4 | 0% | 0% | 0% |
| FY2018 | 317 | 299.3 | 3926.2 | 317.6 | 0% | 0% | 0% |
Commercial Bribery is categorized under Fraud/Financial in the USSC guidelines. Sentencing ranges depend on the specific offense level, criminal history category, and applicable adjustments.
National Average Sentence
278.7 months
FY2024 · 522 cases
Guideline Compliance
0%
Within USSC range
0 cases
0 upward departures
0 downward departures
Average sentences for Commercial Bribery by district. Districts with fewer than 5 cases excluded.
| District | Cases | Avg (mo) | vs. National |
|---|---|---|---|
| Northern Florida | 2 | 470.0 | +68.6% |
| Middle Alabama | 5 | 470.0 | +68.6% |
| Northern Ohio | 1 | 470.0 | +68.6% |
| Wyoming | 1 | 470.0 | +68.6% |
| Northern California | 1 | 470.0 | +68.6% |
| Eastern Washington | 1 | 470.0 | +68.6% |
| Puerto Rico | 1 | 470.0 | +68.6% |
| Northern Alabama | 5 | 447.6 | +60.6% |
| Southern Georgia | 2 | 445.0 | +59.7% |
| Delaware | 3 | 433.3 | +55.5% |
| Northern Mariana Islands | 4 | 427.5 | +53.4% |
| Western North Carolina | 18 | 424.9 | +52.5% |
| Minnesota | 5 | 411.2 | +47.5% |
| Western Pennsylvania | 7 | 355.4 | +27.5% |
| Central California | 4 | 355.0 | +27.4% |
| Northern Illinois | 11 | 350.4 | +25.7% |
| Northern Mississippi | 6 | 346.7 | +24.4% |
| Guam | 3 | 320.0 | +14.8% |
| Southern Indiana | 6 | 312.8 | +12.2% |
| Alaska | 32 | 304.6 | +9.3% |
Across all federal district courts in FY2024, Commercial Bribery offenses produced 522 sentenced cases with a national average imposed sentence of 278.7 months. The applicable guideline range for these cases averaged 3619.9 months at the low end and 272.7 months at the high end, placing the actual mean sentence below the average guideline window. This offense category is classified by the USSC under Fraud/Financial.
Guideline compliance for Commercial Bribery broke down as follows in FY2024: 0% of sentences landed within the prescribed range, 0% were above-guideline (upward departures or variances), and 0% were below-guideline. Guilty pleas resolved 72% of cases, a metric that reflects how few federal defendants in this offense category proceed to trial. Below-guideline sentences are typically the result of either government-sponsored departures (such as substantial assistance under USSG §5K1.1) or judge-initiated variances under 18 U.S.C. § 3553(a), a framework formalized after United States v. Booker (2005).
District-level variation is the key signal beneath these national numbers: across the 20 districts with at least 5 cases in FY2024, the district comparison table above shows how average sentences for Commercial Bribery diverge from the national benchmark. Because individual sentencing outcomes depend on the defendant's criminal history category, offense-level adjustments, the specific statutes of conviction, and any cooperation, these aggregate figures describe patterns, not predictions for any single case. This data is presented for research and educational purposes only and is not legal advice.
Related federal offenses with the same USSC classification. Compare sentencing patterns across similar crimes.
Source: United States Sentencing Commission (USSC), Individual Offender Datafiles, FY2015–FY2024.
Source: USSC Commission Datafiles · How we compute these metrics
How federal sentencing guidelines work, from offense levels to criminal history categories.
What drives differences in sentencing outcomes between federal districts.
How to interpret the statistics and comparisons in PlainSentencing.
Read our methodology — how this data is sourced, computed, and verified.