Average hourly earnings across all private industries hit $37.62 as of July 2026, according to the Bureau of Labor Statistics. Utilities pay the most at $56.34 an hour; leisure and hospitality pay the least at $23.63. That’s a gap of more than $32 an hour between the top and bottom sectors, driven mostly by differences in occupation mix and part-time staffing rather than any single industry trait.
TL;DR:
- Utilities pay the highest hourly wage at $56.34, mainly due to their focus on skilled technical and specialized professional roles.
- Leisure and hospitality wages average only $23.63 per hour, largely because of their emphasis on part-time, entry-level, and tip-based positions.
- The $37.62 overall sector average is a payroll-weighted mean that includes managers and low-wage workers, making it unsuitable as a personal salary benchmark.
- BLS data must be adjusted for occupation type, full-time employment, and region to produce meaningful wage benchmarks for specific roles or local markets.
- For detailed, role-specific wage analysis, use OEWS data and local payroll information rather than relying solely on sector averages.
Table of Contents
- Average Wages by Industry: The Full Sector Breakdown
- Which BLS Datasets Actually Publish Wage Data?
- How to Read Sector Averages Without Getting Fooled
- Turning Wage Data Into a Real Benchmark
- What Analysts Get Wrong About Headline Wage Averages
- Get Granular Wage and Financial Benchmarks Beyond BLS Averages
- Where to Verify These Figures
- Sources
- FAQ
Average Wages by Industry: The Full Sector Breakdown
The BLS releases these figures monthly in Table B-3, part of the Employment Situation report. The July 2026 data covers all employees on private nonfarm payrolls, seasonally adjusted, which means the numbers already account for predictable hiring swings like retail’s holiday surge or construction’s summer peak.
Here’s how the major sectors stack up:
*Weekly equivalent calculated at a standard 40-hour week; actual BLS weekly earnings figures reflect average hours actually worked per industry, which vary.
A few things jump out immediately:
- Utilities and Information sit far above the private-sector average, largely because both employ a high share of skilled technical and specialized professional roles.
- Leisure and hospitality sits well below average, reflecting a workforce weighted toward part-time, entry-level, and tip-based positions.
- The overall private-sector average of $37.62 is not a “typical” wage for any single worker. It’s a blended figure that includes CEOs and dishwashers alike.
Pro Tip: Don’t cite the $37.62 figure as what “most workers” earn. It’s a payroll-weighted mean across wildly different occupation mixes. Use it as a macro reference point, not a personal salary benchmark.
The BLS also publishes this same comparison visually through its industry bubble charts, which plot each sector’s employment level against its average hourly earnings. It’s a fast way to spot which high-paying sectors are also large employers (Professional and business services, for instance) versus which are small but lucrative niches.
Which BLS Datasets Actually Publish Wage Data?
Three separate BLS programs generate industry wage figures, and mixing them up is one of the most common analyst mistakes.
- CES (Current Employment Statistics): Monthly survey of business payrolls. This is the source of Table B-3 and the headline average hourly and weekly earnings figures by industry sector. Best for month-to-month and year-over-year trend tracking.
- OEWS (Occupational Employment and Wage Statistics): Published annually, OEWS breaks wages down by specific occupation within industries, covering hundreds of job titles. It’s the right tool when you need “registered nurse in general hospitals” instead of just “health care and social assistance.”
- CEW/QCEW (Quarterly Census of Employment and Wages): Establishment-level payroll data, updated quarterly, covering nearly all U.S. employment by county and industry code. It’s the most granular public dataset for local labor cost analysis.
Seasonally adjusted CES figures smooth out predictable hiring cycles and work best for benchmarking across months or years. Not seasonally adjusted (NSA) series show what’s happening on payrolls right now, which matters more for short-term operational decisions than long-term wage comparisons.
How to Read Sector Averages Without Getting Fooled
Averages hide more than they reveal, and the biggest distortion is occupation mix. A sector like Information includes both software engineers earning six figures and customer service reps earning far less. When executives and highly paid specialists cluster in one industry, they pull the average up even if most workers in that sector earn much less than the headline number suggests.
Three fixes analysts should use:
- Switch to the production and nonsupervisory series when you’re benchmarking non-management labor. This BLS series strips out managerial pay, which otherwise skews averages upward.
- Pull median wages from OEWS alongside CES averages. A median resists the pull of a small number of very high earners far better than a mean does.
- Check the part-time share of an industry before comparing it to a full-time-heavy sector. Leisure and hospitality’s low average partly reflects shorter average workweeks, not just lower pay rates.
Converting figures is straightforward but easy to botch. Multiply an hourly average by roughly 2,080 hours to estimate an annualized figure for a full-time worker, but disclose that the raw sector average still includes part-time employees unless you’ve isolated a full-time-only series. A $23.63 hourly rate in leisure and hospitality doesn’t translate cleanly into a $49,150 annual salary, because most workers in that sector aren’t clocking 2,080 hours a year.
Turning Wage Data Into a Real Benchmark
Building a defensible compensation or labor cost model from public wage data takes more than copying a number off Table B-3. Follow this sequence:
- Select the right series (CES for trend, OEWS for occupation detail, CEW for local granularity).
- Adjust to full-time equivalent (FTE) if you’re comparing to salaried roles.
- Control for occupation mix using OEWS detail instead of the sector-wide blend.
- Adjust for region. A national average means little in a high-cost metro versus a rural county; local CEW data or a regional wage index closes that gap.
- Document every assumption you made, including which series, which month, and whether it’s seasonally adjusted.
A quick worked example: Manufacturing’s July 2026 average hourly earnings sit at $36.87. Multiply by 2,080 hours and you get roughly $76,690 as a full-time annualized benchmark, before any regional or occupation-level adjustment. That number is a starting point, not a finished valuation input.
- Report a range, not a single figure, when presenting wage benchmarks to clients or in a valuation memo.
- Pair the BLS sector average with revenue-per-employee benchmarks to see whether a business’s labor costs align with sector norms.
- Run a sensitivity check: how does your conclusion change if the true figure is 10% higher or lower than the published average?
What Analysts Get Wrong About Headline Wage Averages
Most people treat the monthly BLS average as gospel, and that’s the mistake. A single number covering an entire sector erases the occupation mix, regional cost differences, and full-time versus part-time split that actually determine what a specific job pays in a specific place. Public data is excellent for spotting trends and setting macro context. It’s the wrong tool for pricing a specific role in a specific market.
My take: use BLS series to understand direction and scale, then move to NAICS-level benchmarks when the decision actually has money riding on it. Specialized data providers build exactly that layer of granularity for client work where a national sector average isn’t precise enough.
— Danny
Get Granular Wage and Financial Benchmarks Beyond BLS Averages
BLS tables give you the national picture, but they stop short of the NAICS-code precision most client engagements demand. Customizable reports across many markets, segmented by industry, geography, and company size, fill that gap so you’re not stretching a leisure and hospitality average across a specific restaurant’s local labor market.
Reach for a BLS table when you need a fast, free, national reference point for a trend memo or a quick sanity check. Reach for specialized reports when the analysis needs local wage context, revenue-per-employee comparisons, or a benchmark defensible enough for a valuation file or a lending decision. Data accepted in U.S. Tax Court can be helpful when a client or auditor pushes back on your numbers.
Start with the industry and market research tool to pull a custom report for the exact NAICS code and geography your analysis requires.
Where to Verify These Figures
- Table B-3 (BLS): the source table for sector-level average hourly and weekly earnings, seasonally adjusted.
- BLS industry earnings charts: visual comparison of employment size and wages by sector.
- OEWS: annual occupation-level wage estimates.
- CES data tables: downloadable B-1a, B-3a, and B-8a series for deeper historical analysis.
Sources
- Table B-3. Average hourly and weekly earnings of all employees on private nonfarm payrolls by industry sector, seasonally adjusted – 2026 M07 Results
- Employment and average hourly earnings by industry (BLS charts)
- Occupational Employment and Wage Statistics (OEWS) – BLS
- Employment and earnings: 2026 data tables – BLS
FAQ
BLS wage tables report averages by industry and occupation rather than income distribution by gender, so this figure comes from separate Census income surveys, not the sector data covered here. For sector-level pay comparisons, the CES and OEWS tables above remain the more reliable reference.
What percentage of Americans earn $150,000 a year or more?
This is an individual or household income distribution question, which falls outside BLS industry wage tables. Broader income distribution context, including median household income, is a separate dataset from the per-industry hourly averages in Table B-3, and the two shouldn’t be blended in the same analysis.
Do BLS averages reflect full-time or part-time workers?
They reflect all employees, both full-time and part-time, which is why sectors with high part-time employment, like leisure and hospitality, show lower averages that partly reflect fewer hours worked rather than a lower hourly rate alone.
Why does Utilities pay so much more than Retail trade?
Utilities employs a higher share of skilled technical and licensed roles, while retail trade skews toward entry-level, hourly, and part-time positions; the $30-plus hourly gap reflects that occupation mix more than any single pay policy.
When should I use OEWS instead of the CES sector average?
Use OEWS when you need wage data broken out by specific occupation rather than a blended industry figure, especially when benchmarking a single role instead of an entire sector’s payroll.
