Fifth District Survey of Non-Manufacturing Activity: Revenues: Future (increase vs decrease; diffusion index; percentage points; seasonally adjusted) - United States - Richmond FED - Monthly
This series is part of the dataset: Fifth District Survey of Non-Manufacturing Activity (Richmond FED)
Download Full Dataset (.xlsx)Latest updates. Based on a sample of nonmanufacturing firms surveyed by the Fed, the seasonally adjusted diffusion index for revenues (share of CEOs seeing a future increase minus share forecasting a decrease) was 31.0 in September 2026, compared to 38.0 in the previous month.
Sample. The monthly series displayed in the figure has 190 data points in total. The series covers the span of time going from December 2010 to September 2026.
History. Here's a glimpse of a few statistics we computed on the whole sample: the diffusion index had a mean value of 28.3; it reached a maximum of 59.0 in September 2018; it recorded a bottom of -52.0 in April 2020.
Latest values
| Date | Value - Percentage points |
|---|---|
| 2026-07-31 | 37.0000 |
| 2026-08-31 | 38.0000 |
| 2026-09-30 | 31.0000 |
Suggestion. One of the advantages of our data visualization and download service is that we publish well-crafted metadata. Check them below to learn more about the properties of the time series that you are exploring.
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Series Metadata
| Field | Value |
|---|---|
| Description | Diffusion index for the question: Will revenues increase or decrease over the next six months? |
| Country | United States |
| Economic concept | Business survey response |
| Data type | Leading indicator of the business cycle |
| Deflation method | Not applicable |
| Seasonally adjusted | Yes |
| Rescaling | None |
| Frequency | Monthly |
| Unit | Percentage points |
| Source | Federal Reserve Bank of Richmond |
| Source type | Central bank |
| Data licence | Not copyrightable under U.S. law |
| Measure type | Diffusion index (share of respondents reporting an increase minus share reporting a decrease) |
| Other information | Not available |
Series in the same data set
Discover the other time series included in this data set.