Research report
Do Upwork Jobs Actually Hire? An Outcome Analysis of 617,336 Resolved Postings
Published July 7, 2026 · Freelancing Stats
Published July 2026. Based on continuous monitoring of publicly visible Upwork job postings since July 2025: 1,506,251 postings from 588,474 client accounts as of July 3, 2026. Outcome numbers use the stable cohort: postings published October 2025 through February 2026 whose outcome has resolved, n = 617,336. Full method: methodology.
Every proposal a freelancer sends rides on two bets. The visible one: will this client pick me? The invisible one, the one almost nobody prices in: will this client pick anyone? Freelancer forums have argued that second question for years, armed with anecdotes (“half these jobs are fake”) and with published statistics that disagree so badly (22.8%? 38%?) they might as well describe different marketplaces.
We follow postings until they reach a final state, which means we can answer the question directly instead of guessing from snapshots.
The short answer: 43.9% of resolved Upwork job postings end in a hire. The other 56.1% ended without an observed hire: 41.3% closed unfilled, 8.7% were cancelled by the client, and 6.1% expired. For a freelancer the practical reading is blunt: connects spent on slightly more than half of resolved postings did not lead to an observed hire.
What actually happens to an Upwork job posting
Most published Upwork statistics count postings when they appear. Ours follow each posting through its life until it lands in one of four final states:
| Outcome | Share of resolved postings | What it means |
|---|---|---|
| Filled | 43.9% | At least one freelancer was hired |
| Unfilled | 41.3% | The posting closed without any hire |
| Cancelled | 8.7% | The client withdrew the posting |
| Expired | 6.1% | The posting aged out without resolution |
Stable cohort: 617,336 resolved postings published October 2025 through February 2026.
The most useful mental model this table supports: a posted job is not a job. It is a coin flip weighted slightly against anyone being hired at all, before your proposal is ever read.
Why you have read 22.8%. And 38%. And now 43.9%.
Three hire-rate estimates for the same marketplace circulate publicly, and they disagree by more than 20 points:
| Source | Published number | What it measures |
|---|---|---|
| GigUp live dashboard, accessed July 3, 2026 | 22.8% | 69,231 matched postings in its last-month view; jobs with at least one hire divided by all matched jobs |
| Vollna 2024 report, published January 7, 2025; updated March 24, 2026; accessed July 12, 2026 | 38% | Closed projects in 2024, within a study covering more than 5 million projects over two years |
| This analysis | 43.9% | Resolved postings only, from cohorts old enough to have settled |
GigUp updates the selected period continuously. The 22.8% and 69,231-posting values above preserve the July 3 snapshot used when this report was prepared, so the live page may show a newer value.
These estimates are not directly interchangeable. Each is based on observed postings, but the publishers disclose different windows, denominators, and outcome rules. We can measure censoring inside our own corpus; without the other publishers’ row-level cohorts, we cannot assign every difference to one cause.
Hiring outcomes take time to settle. Postings that hire tend to close within days (next section), while postings with non-hiring outcomes linger open for weeks before closing, being cancelled, or expiring. That lag creates two opposite distortions:
- Measure a young pool and count everything, unsettled postings included, and the rate will be lower than the eventual rate for that same pool because many outcomes have not settled. GigUp’s last-month denominator has this exposure, but its public dashboard does not disclose enough detail to attribute the full gap to censoring.
- Measure a young pool and count only what has settled, and the rate reads far too high, because fast, successful closures dominate the settled subset. Our own April 2026 postings, measured in early July, show an artificial 55.8% fill rate for exactly this reason. We publish that number as a warning label, not a market fact.
- Restrict the denominator to closed projects, as Vollna does, and the young-pool effect is reduced. Vollna’s 38% therefore cannot be explained as a mix of settled and unsettled projects; source coverage, window construction, and outcome classification remain possible contributors.
Our stable-cohort estimate requires patience: cohorts old enough, roughly four months, for nearly all observed outcomes to resolve. For the monitored October 2025 through February 2026 cohort, that estimate is 43.9%.
The direction of several category findings is similar across sources. GigUp’s July 3 snapshot placed translation first and sales, legal, and accounting in its lower cluster. Vollna reported entry-level projects as its highest-filling experience tier at 42%, and reported a 54% hire rate for expert-level translation projects. Those are useful cross-checks, not like-for-like replications: the source windows, taxonomies, and denominators differ.
If a job is going to hire, it hires fast
Time from posting to hire, for postings published in April 2026, our first cohort with reliable closure timing (n = 15,970 fills):
| Time to fill | Share of fills |
|---|---|
| Within 24 hours | 14% |
| 1 to 3 days | 14% |
| 3 to 7 days | 47% |
| 7 to 14 days | 14% |
| 14 to under 30 days | 10% |
In this April cohort, roughly 75% of fills happen within 7 days of posting, and about 90% within 14 days. Buckets use half-open boundaries: under 1 day, 1 to under 3, 3 to under 7, 7 to under 14, and 14 to under 30. A fill at exactly 30 days belongs in a 30-days-or-more bucket; no such fills appeared among these 15,970 observations. Closure detection runs on a roughly two-hour cycle, so figures are accurate at day granularity.
Two consequences follow. For freelancers: hiring happened at the front of a posting’s life in this cohort. Nine out of ten observed fills had happened by two weeks, so fresh postings carried more of the observed hiring activity. For anyone reading market statistics: this asymmetry, fast fills and slow failures, demonstrates why early-measured hire rates can be biased in opposite directions depending on the denominator.
What moves the odds
The 43.9% baseline hides wide, consistent spreads. Every cut below uses the same stable cohort.
Category: a 16-point spread
| Category | Fill % | Resolved postings |
|---|---|---|
| Translation | 51.3 | 11,275 |
| Design & Creative | 48.8 | 159,114 |
| Engineering & Architecture | 48.3 | 24,414 |
| Writing | 46.2 | 21,272 |
| Data Science & Analytics | 45.1 | 19,569 |
| Admin Support | 45.0 | 54,757 |
| Web, Mobile & Software Dev | 44.5 | 146,707 |
| IT & Networking | 40.6 | 13,941 |
| Accounting & Consulting | 40.4 | 25,905 |
| Legal | 38.1 | 7,440 |
| Sales & Marketing | 36.4 | 121,040 |
| Customer Service | 35.2 | 11,902 |
Translation postings hire at 51.3%, customer service at 35.2%. The biggest categories split: design and creative (159,114 resolved postings) fills at 48.8%, while sales and marketing (121,040) fills at 36.4%, near the bottom. Web, mobile and software development sits almost exactly on the baseline at 44.5%.
Experience level: expert-tier postings hire least
Entry level 47.5% (48,085 resolved postings), intermediate 45.4% (392,922), expert 39.7% (176,329). Postings that ask for expert talent are the least likely to hire anyone, almost 8 points below entry level. Pickier clients, sticker shock at expert rates, and exploratory postings aimed at expensive skills are all plausible mechanisms; we publish the number, not a cause.
Fixed price beats hourly by 10 points
Fixed-price postings fill at 50.5% (291,487 cohort postings) against 40.6% for hourly (253,888). A further 123,159 cohort postings (18.4%) carry no recorded budget type in our data; those fill at 35.1%. One possible interpretation is that a scoped deliverable with a price reflects clearer buyer intent. These one-dimensional data show the association, not that mechanism.
Client prior spend and fill rate
| Client’s prior spend | Cohort postings | Fill % |
|---|---|---|
| Nothing yet | 152,032 | 36.0 |
| Under $1K | 140,618 | 44.0 |
| $1K to $10K | 178,985 | 47.3 |
| $10K+ | 196,899 | 46.8 |
The jump happens at the first dollar. Clients who have never spent on the platform fill at 36.0%; clients with any spending history fill at 44 to 47%. Roughly 11 points, about a quarter of the baseline rate, separates never-spent clients from everyone else. This is one visible posting-level association also examined in our client-quality analysis. The groups are not adjusted for category, geography, budget type, or other overlapping attributes, so this is not a standalone prediction or a causal result.
Geography: a 20-point spread among the top client countries
| Client country | Cohort postings | Fill % |
|---|---|---|
| United States | 295,583 | 47.5 |
| United Kingdom | 55,069 | 44.4 |
| Australia | 38,173 | 46.6 |
| Canada | 34,369 | 43.4 |
| India | 25,764 | 26.7 |
| Netherlands | 14,334 | 37.0 |
| Germany | 14,271 | 42.2 |
| United Arab Emirates | 13,601 | 39.7 |
| Pakistan | 12,750 | 41.4 |
| France | 9,957 | 38.3 |
Top 10 client countries by posting volume.
The standout is India: postings from India-based clients fill at 26.7%, barely more than half the United States rate of 47.5%. The English-speaking markets cluster in the mid-40s; continental Europe sits in the high 30s to low 40s. We keep a dedicated breakdown of hire rates by client country current with the latest data.
The tactical read
Every signal above is visible on a posting before you spend a connect:
- Check the client’s spend history first. Postings from clients with prior spend sit in the observed 44-47% groups, compared with 36% for clients with no recorded spend.
- Prefer fresh postings. Three quarters of observed fills in the April cohort happened in the first week.
- Fixed price with a stated budget outperforms hourly by 10 points, and postings carrying no budget information at all fill worst of the three groups.
- Budget for the base rate. Roughly 56% of resolved postings hire nobody. A connects budget that assumes every posting will hire is planning around the best case, not the observed base rate.
One warning against over-multiplying: these are one-dimensional cuts of the same cohort, and the dimensions overlap (client geography skews category mix, expert postings skew hourly, and so on). Read them as observed associations, not a scoring formula.
Caveats
- Fill rates are shares of resolved postings. Outcome labels are only as current as our last observation of a posting, and a posting can hire after unusually long delays.
- Time-to-fill is published only for April 2026 onward. Earlier closure timestamps in our data are retroactive and unusable for duration analysis.
- Each table is a one-dimensional cut; the dimensions overlap, and none of the gaps should be read as causal.
- Budget type is unrecorded on 18.4% of cohort postings, disclosed above rather than silently dropped.
- These figures describe the postings we monitor and may differ from Upwork’s internal accounting. We publish absolute counts and shares, never coverage claims.
Bottom line
A bit more than two in five resolved postings in our stable cohort hire someone; the rest go unfilled, get cancelled, or expire. The lower published estimates do not share one proven explanation: GigUp’s live last-month view includes unsettled matched postings, while Vollna reports closed projects. Window, denominator, source coverage, and outcome classification can all affect the level. In our April cohort, three quarters of observed fills landed inside a week. Fill rates also vary across client spending history, pricing model, category, experience tier, and client geography. These are overlapping observed associations, not a ranked or causal score.
Data: continuous monitoring of publicly visible Upwork job postings, July 2025 to present. See the methodology page for outcome definitions, cohort rules, and the list of statistics we deliberately do not publish.
Niche reports are coming
Tell us what you do. We'll measure the market around it.
Upcoming deep dives cover demand volume, real rates, fill rate, time-to-fill, and client quality. The most-requested niches get researched first.