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Laundry and dry-cleaning workers

Also called Laundry worker, Dry cleaning worker, Dry cleaner, Laundry attendant, Laundry technician

In brief

In May 2025, BLS counted 198,040 US laundry and dry-cleaning workers, with median pay of $34,890 a year. Of the BLS counts since 2021, the May 2025 count is the highest. The earliest figure here is a Census count of 38,633 from 1860, not measured the same way as today's count. The last full technology era for laundry and dry-cleaning workers: GreenEarth silicone solvent and professional wet cleaning, 1999 to 2020.

What the sources say

Official outlook

BLS projects employment to grow 4.3% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Laundry and dry-cleaning workers: jobs and pay since 1860 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/51-6011

Chicago

Future History. "Laundry and dry-cleaning workers: jobs and pay since 1860." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/51-6011.

BibTeX

@misc{futurehistory_51_6011, author = {{Future History}}, title = {Laundry and dry-cleaning workers: jobs and pay since 1860}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/51-6011}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

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187519001925195019752000now
2026
Known then as Laundryman / Laundress / Washerwoman (hand-laundry and domestic era)
Known then as Laundry Operative / Steam-Laundry Worker (factory era)
Known then as Laundry Worker / Dry Cleaner / Cleaner and Presser (combined trade era)
Known then as Laundry and Garment Services Worker (BLS standard industrial era)
Known then as Laundry and Dry-Cleaning Workers (BLS SOC 51-6011)
Known today as Laundry and Dry-Cleaning Workers (BLS SOC 51-6011)
Projected to still be called Laundry and Dry-Cleaning Workers (BLS SOC 51-6011)
Data from the May 2025 survey
US Employment
198K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$34,890
Est.

≈ $33,995 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1851

Wah Lee, a Chinese immigrant from Sye Yup, opens the first Chinese hand laundry in the United States in San Francisco. He is serving Gold Rush miners who have no way to clean their own clothes in a city without domestic infrastructure. Within two decades the Chinese hand laundry becomes the dominant form of commercial laundering in California and expands across the country, driven by Chinese immigrants who face exclusion from mining, skilled trades, and most manufacturing.

Beat · 1882

The Chinese Exclusion Act of 1882 bars Chinese laborers from entering the United States and makes Chinese residents ineligible for citizenship. The act traps many Chinese-Americans in laundry work by denying them naturalization and therefore the civil rights and economic mobility available to citizens. By 1900, one in four ethnic Chinese men in the United States works in a laundry, often 10 to 16 hours per day. The act is not fully repealed until 1943.

Beat · 1933

Perchloroethylene (perc, PCE) is declared the ideal dry-cleaning solvent by industry researchers. Dow Chemical chemists had suggested it in 1930 as a non-flammable replacement for petroleum solvents; a machine designed specifically for perc use is developed in 1933. Over the following two decades perc replaces Stoddard solvent (the dominant petroleum-based solvent since 1924) in most US dry-cleaning plants. The shift makes dry-cleaning operations dramatically safer from fire risk while introducing a persistent chlorinated solvent that will become the subject of health and environmental regulation sixty years later.

Beat · 1937

Bendix introduces the first automatic home washing machine. General Electric and Maytag follow with consumer models in the late 1940s. By 1960, 55% of US households own an automatic washer. This single product category destroys the residential laundry segment of the commercial trade. Commercial laundries that survive the 1950s are those serving clients with volumes no household machine can handle: hospitals, hotels, restaurants, and industrial uniform-rental companies.

Beat · 1993

On September 22, 1993, the EPA promulgates the National Emission Standards for Hazardous Air Pollutants (NESHAP) for dry-cleaning facilities, codified at 40 CFR Part 63 Subpart M. The rule requires all commercial dry cleaners using perchloroethylene to install solvent recovery systems, conduct leak inspections, and maintain emissions records. It is the first federal standard to directly regulate the core solvent used by essentially the entire dry-cleaning industry. Compliance costs force many small independent dry cleaners out of business over the following decade, accelerating industry consolidation.

Beat · 2024

On December 18, 2024, the EPA finalizes a rule under the Toxic Substances Control Act (TSCA) that prohibits the use of perchloroethylene in dry cleaning, with a ten-year phase-out plan. Use of perc in newly acquired machines is prohibited within six months; machines already owned have compliance deadlines that vary by machine type, with older equipment being phased out faster. Perc is classified as a known carcinogen under the 2023 TSCA risk evaluation. The rule requires the entire dry-cleaning trade to transition to alternative solvents or professional wet-cleaning within the decade, the largest mandated operational change in the industry's history.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace laundry and dry-cleaning workers? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+4.3%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 51-6011 (Laundry and dry-cleaning workers): 204.9 thousand jobs in 2025, projected 213.7 thousand in 2035, a change of +8.7 thousand (+4.3%). Matrix employment counts all jobs in the occupation, including self-employed workers (2.5% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
5%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 51-6011.00 Laundry and Dry-Cleaning Workers. Shown: human-rated β = 0.05, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0 and γ (all LLM-assisted) 0.1; GPT-4-rated β 0.05. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for laundry and dry-cleaning workers

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onSort cleaned items by order, fold or hang each piece to customer specification, and prepare finished bundles for counter pickup or delivery route assignment.

Sort cleaned items by order, fold or hang each piece to customer specification, and prepare finished bundles for counter pickup or delivery route assignment.[6],[7]

Tools picking this up
Where your edge is

Shift toward quality-control review of robot-folded batches and handling specialty items (knit sweaters, draped garments) that require human fold judgment.

AI is sitting alongside you hereTag and ticket incoming items with barcoded garment labels, scan them into the POS or order-management system, and verify customer notes (rush, special care, alterations requested).

Tag and ticket incoming items with barcoded garment labels, scan them into the POS or order-management system, and verify customer notes (rush, special care, alterations requested).[8],[9]

Tools picking this up
Where your edge is

Learn barcode scanning and order-tracking workflows in current POS platforms; accurate intake data reduces costly mis-routes and customer complaints downstream.

AI is sitting alongside you hereOperate commercial washers, dry-cleaning machines, and extractors, setting water temperature, solvent levels, cycle times, and spin speeds according to fabric type and soil load.

Operate commercial washers, dry-cleaning machines, and extractors, setting water temperature, solvent levels, cycle times, and spin speeds according to fabric type and soil load.[1],[10]

Tools picking this up
Where your edge is

Learn to read machine-generated cycle reports and IoT sensor alerts to catch off-spec loads early, reducing rewash rates and fabric damage.

Where laundry and dry-cleaning workers go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Maids and Housekeeping Cleaners

Maids and Housekeeping Cleaners share the same physical service environment (hotels, healthcare facilities) and many laundry workers already handle linen rooms; the move transfers textile-care and cleanliness standards directly.

What you'd add
  • · Hospitality room-cleaning procedures and quality inspection standards
  • · Safe handling of commercial cleaning chemicals (OSHA GHS labelling)
  • · Basic housekeeping software (property management system check-ins)
What it takesMost of your skills carry over
Tools of the era

How has the work of laundry and dry-cleaning workers changed since 1851?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Hand laundry (washboard, boiling copper, flat iron)

    The hand laundry of the Chinese immigrant era ran entirely on manual craft: the operator soaked and scrubbed fabric on a washboard, boiled whites in a copper pot over an open fire, wrung each item by hand, and finished with a flat iron heated on the stove. The operator needed to know fabric types, water temperature for each, and the sequence of starch, iron, and fold that produced a crisp collar or a presentable shirt. There were no machines to consult. Speed and knowledge lived in the worker's hands and memory.

  • Steam-powered laundry machinery (rotary washers, mangles, tunnel finishers)

    The steam laundry of the 1880s-1930s organized laundry work as a factory: a boiler drove rotary drum washers, a mangle pressed flat goods between steam-heated rollers, and a tunnel finisher blew hot air through hanging garments. The workforce was divided into specialized stations, each requiring speed rather than judgment. Workers loaded, unloaded, sorted, and folded at an assembly-line pace. The craft knowledge of the hand laundry largely disappeared from individual workers; it was encoded into machine settings and the foreman's instructions.

    Effect on the work

    Steam laundries employed a predominantly female workforce at wages roughly half those of male industrial workers. Women's entry into laundry factory work was documented as one of the earliest large-scale industrial employments of women outside textiles. By the 1920s, African-American women had largely replaced immigrant European women as steam-laundry workers as better-paying industrial jobs opened to white women.

  • Petroleum and chlorinated solvents in dry cleaning (Stoddard 1924, perc from 1933)

    The dry-cleaning trade, which had used kerosene and gasoline since Jean Baptiste Jolly opened the first dry-cleaning service in Paris in 1845, graduated through a sequence of solvents as fire safety became non-negotiable. William Joseph Stoddard, a US dry cleaner, developed a safer petroleum-based solvent in 1924 that bore his name. Then in 1930 chemists at Dow Chemical recommended perchloroethylene (perc, PCE) as a non-flammable alternative; by 1933 it was considered the ideal dry-cleaning solvent. Perc offered excellent cleaning power, compatibility with most fabrics, and no fire risk. Dry-cleaning machine manufacturers developed closed-loop systems specifically for perc by the late 1930s. By the 1950s, perc had become the nearly universal solvent in US dry-cleaning plants. The switch from petroleum solvents to perc transformed the dry-cleaning worker's job: the solvent was now recovered and recycled inside the machine, and the operator's primary skill shifted from managing fire risk toward managing machine cycles, fabric sorting, and the hand-spotting board.

  • Automatic home washers and coin-operated laundromats (Bendix 1937, mass adoption 1950s)

    Bendix introduced the first automatic home washer in 1937; General Electric and Maytag brought consumer models to the mass market in the late 1940s. By 1960, 55% of US households owned a washing machine. This was the single most destructive technology event in the history of commercial laundry employment: the household segment of the business simply moved home. Commercial laundries that had served residential customers collapsed or pivoted to clients the home machine could not serve: hospitals, hotels, restaurants, and uniform-rental services. Employment in the commercial laundry trade fell throughout the 1950s-1970s. The workers who kept their jobs were those serving institutional clients with genuinely industrial-scale linen volumes.

    Effect on the work

    The trade stabilized when institutional linen services became the dominant client base.

  • EPA NESHAP perc emissions standards (1993 rule, 2006 tightening, 2024 TSCA phase-out)

    The EPA promulgated the National Emission Standards for Hazardous Air Pollutants (NESHAP) for dry-cleaning facilities on September 22, 1993. The rule required dry cleaners using perc to install solvent recovery and emission controls, reducing PCE releases substantially. Amendments in 2006 tightened the standards further. For the dry-cleaning worker, the NESHAP transformed compliance work into a core job duty: monitoring machine performance data, logging solvent inventory, scheduling inspections, and disposing of still residue as hazardous waste. On December 18, 2024, EPA finalized a TSCA rule prohibiting perc use in dry cleaning, with a phase-out. This is the most consequential regulatory change in the trade since 1993 and will force operators toward silicone (GreenEarth D5), liquid CO2, and professional wet-cleaning systems over the coming decade.

  • GreenEarth silicone solvent and professional wet cleaning (perc alternatives, 1999-present)

    GreenEarth Cleaning commercialized decamethylcyclopentasiloxane (D5 silicone) as a dry-cleaning solvent in the late 1990s. A 2002 independent study found the GreenEarth system as effective as perc with no environmental concerns. The California Air Resources Board evaluated D5 as a perc alternative during its 2008 perc ban deliberations. In 2014, ASTM International made GreenEarth silicone the first new dry-cleaning solvent added to its fabric-care labeling standard since fluorocarbon in 1996. Professional wet cleaning, which uses computerized washers, conditioners, and tensioning finishers to clean fabrics previously considered dry-clean-only, expanded in parallel. For the laundry and dry-cleaning worker, these alternatives introduced new machine parameters and chemical protocols, but the core judgment skills (fabric identification, stain chemistry, pressing) remained essentially unchanged.

  • POS order-management software, RFID garment tracking, and laundry operations platforms

    Cloud-based point-of-sale platforms (CleanCloud, Starchup) arrived in the 2010s, automating order intake via barcode scanning, garment tagging, customer SMS updates, and delivery-route optimization. RFID chips embedded in hotel and hospital linen enabled individual-item tracking across hundreds of wash cycles. By 2020, IoT sensors on commercial washers and dryers fed real-time data to operations dashboards (Spindle platform) that flagged equipment anomalies and optimized load throughput. For the laundry worker, these tools elevated the data-monitoring dimension of the job: reading alerts, acting on cycle reports, and logging compliance data became part of the daily routine alongside the physical work of loading, sorting, and pressing.

  • Commercial folding robots and AI fabric-scanning systems (Dyna Robotics DYNA-1, 2025)

    Dyna Robotics unveiled the DYNA-1 commercial laundry folding robot in April 2025, a stationary dual-arm system capable of autonomous round-the-clock folding of napkins, towels, and standard garments at a large share of human throughput with a 99.4% success rate on commercial linen batches. AI-powered fabric-scanning systems for garment intake also entered commercial trial in 2025-2026, scanning barcodes, reading care labels, and routing items to the correct cleaning process. For bulk-linen operations (hotels, healthcare), these robots and scanners will reduce the number of workers needed per unit of throughput. Spotting, specialty pressing, and customer-facing judgment remain in human hands: a robot folds identically shaped towels reliably but still fails on tailored garments, beaded fabrics, and draped clothing.

    Effect on the work

    Commercial linen folding is the highest-volume single task in the trade and the one that robotic automation addresses most directly. Deployment at scale would reduce the folding-station headcount in large linen operations; the timing depends on capital costs relative to minimum-wage levels, which vary significantly by state.

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Laundry and Dry-Cleaning Workers employment and pay by year

On record since1851
Latest US employment198,040 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$34,890 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+4.3% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 26 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
186038,633n/aDecennial Census 1860 Researched contextReport
1908350,000$312Est., Heritage Zen Researched contextReport
1960430,000n/aEst., BLS Monthly Labor Review (March 2006) Researched contextReport
2003217,820$16,920BLS OEWS May 2003 Official statisticReport
2004218,610$17,220BLS OEWS May 2004 Official statisticReport
2005218,360$17,440BLS OEWS May 2005 Official statisticReport
2006217,580$17,850BLS OEWS May 2006 Official statisticReport
2007218,060$18,420BLS OEWS May 2007 Official statisticReport
2008221,230$19,010BLS OEWS May 2008 Official statisticReport
2009211,490$19,310BLS OEWS May 2009 Official statisticReport
2010204,820$19,540BLS OEWS May 2010 Official statisticReport
2011201,180$19,670BLS OEWS May 2011 Official statisticReport
2012198,750$19,930BLS OEWS May 2012 Official statisticReport
2013197,650$20,090BLS OEWS May 2013 Official statisticReport
2014199,330$20,320BLS OEWS May 2014 Official statisticReport
2015201,620$20,820BLS OEWS May 2015 Official statisticReport
2016207,710$21,510BLS OEWS May 2016 Official statisticReport
2017209,350$22,370BLS OEWS May 2017 Official statisticReport
2018213,350$23,210BLS OEWS May 2018 Official statisticReport
2019209,330$24,220BLS OEWS May 2019 Official statisticReport
2020179,890$25,470BLS OEWS May 2020 Official statisticReport
2021157,400$28,350BLS OEWS May 2021 Official statisticReport
2022175,730$29,060BLS OEWS May 2022 Official statisticReport
2023185,000$31,050BLS OEWS May 2023 Official statisticReport
2024195,360$33,800BLS OEWS May 2024 Official statisticReport
2025198,040$34,890BLS OEWS May 2025 Official statisticReport
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