User Experience Salaries & Calculator (2024) – MeasuringU

We hosted the survey on our MUiQ^® platform and summarized the findings on salaries and related skills of UX professionals from around the world. The details for the past and current UXPA surveys are available on the UXPA website. Here are the highlights for 2024.

Go straight to the Salary Calculator below.

Survey Results

• The data was collected from April 2024–October 2024 using a non-probability sample. Initial respondents were recruited through postings on professional networks and websites, such as UXPA and LinkedIn. Additional respondents were recruited using snowball sampling. Keep this sampling approach in mind as it impacts the generalizability of the findings and the comparability to earlier data.
• International reach, most from the U.S. The survey is based on 444 responses from 37 countries, with 67% of the responses coming from the U.S. Other significant numbers came from the UK (4%), Canada (4%) and Germany (3%). The sample size was the smallest we’ve ever collected for this survey, so we suspect practitioners may be suffering from some survey fatigue. Despite the smaller sample size, we saw very similar patterns compared to the 2022 data, suggesting consistent representative coverage.
• The median salary was $120K. This 10% increase is nominally higher than the 2022 median of $109K. While that sounds good, it doesn’t account for inflation, which was higher than any other measurement period we examined since 2010. Taking inflation into account, we also converted the salaries into constant U.S. dollars based on U.S. inflation using the inflation data from August 2024. Because the inflation from January 2022 to August 2024 was 12%, the effective median salary in 2024 was 2% less than the salary in 2022.
• Many variables affect salary. The $120K average is based on many variables, so you can’t look at that alone to gauge how well your own salary stacks up. For example, this includes a mix of both senior executives at large companies and entry-level professionals at small companies in locations where the cost of living is substantially different.

What Factors Affect Salaries?

As we did in 2022, we conducted a key driver multiple regression analysis (on the log-transformed salaries) to see which variables from the 2024 UXPA survey statistically predict what people earn (Table 1).

Table 1: Key drivers from the UXPA 2024 survey explain 72% of the variation in salaries.

The regression model accounted for 72% of the variation in salaries. For the behavioral sciences, explaining this much variation is very good. Individual factors—skills, personalities, negotiation, company policies, etc.—also affect your salary; keep that in mind as you review the numbers and use the calculator below.

As in years past, the most influential factors for salaries in the UX field are:

• Where you live: The country (and region for the U.S.) has the biggest impact on what you’re paid. It alone explains 50% of the variation in salary data. The median salary in the U.S. is $142K, compared to $85K in the UK, $87.5K in Canada and $85K in Germany. In the U.S., the top five highest salaries are in Northern California ($198K), the Pacific Northwest ($165K), Southern California ($158K), the Northeast ($153K), and the Southwest ($146K). After considering the 12% inflation from 2022 to 2024, the Northern California, Northeast, Southwest, and Southeast regions experienced some loss in effective income. The map below (Figure 1) shows the number of usable salaries we analyzed for each state and how we labeled the regions. The patterns in 2024 were generally the same as those in 2022.

Figure 1: Number of U.S. responses by region.

• Job level: The next biggest driver of salaries is job level, accounting for 11% of the variation in salaries. On average, senior-level supervisors make almost twice the salary of entry-level employees ($165K vs. $82.5K).
• Experience: Accounting for 3% of the variation in salary, the more experience you have, the more you get paid. In a way, this percentage is deceptively low because, in the UXPA regression model, experience can range from 0 to more than 25 years while other predictors in the model are categorical.
• Company size/type: The bigger the company, generally the bigger the paycheck, and this variable accounted for 4% of the variation in salaries. It’s hard for small companies to compete with the big payroll of larger companies. Employees at companies with ten or fewer employees make a median of $75K compared to employees at larger firms (over 10,000 employees) who make on average $149K.
• Education: Your degree matters (at least a little). Around 14% of respondents reported having a Ph.D. (similar to but nominally higher than 11% in 2022). Respondents with a Ph.D. make on average $142K compared to those with Master’s degrees ($125K) or Bachelor’s degrees ($107.5K). These differences in degrees accounted for 4% of the variation in salary data.
• Age (Not Significant): In 2022, age had only a small statistical impact on salaries, an effect that vanished completely in 2024. We suspect that the combination of job level and years of experience account for variation in pay that might otherwise be explained by age.
• Gender (Not Significant): This year, as in years past, gender was not a significant predictor in the UXPA regression model. As was typical in the past, the median salary for male respondents was higher ($124K) than that for female respondents ($111K), with the difference within the margins of error. Women made nominally more than men in four of seven experience brackets, and men made nominally more than women in three of seven experience brackets.

Combined Dataset for Salary Calculator

This year, we did something different for the salary calculator, combining data from the UXPA salary survey with data from a similar survey conducted in 2024 by User Interviews (who made their data available to the UX community). We included only respondents who had a salary (e.g., excluding freelancers). This increased the size of the dataset to have 1,063 responses and, potentially, a broader and more generalizable view of the UX profession.

To make this work, we had to drop one of the significant predictors from the UXPA regression model (Education) because it was not collected in the User Interviews survey. User Interviews also did not collect Age or Gender in their survey, but those variables were not significant in the UXPA regression model. Other adjustments required to blend the two datasets were:

• Combining Southern California and Northern California (UXPA) into just California (User Interviews).
• Converting the slightly different years of experience categories in the two surveys (e.g., UXPA: 0–2, 3–4, 5–7, 8–10, 11–15, 16–20, 21+; User Interviews:
• Assigning responses to company sizes to five categories: 1–10 (matches UXPA, combination of Just Me and 2–9 from User Interviews), 11–1000 (UXPA: 11–100, 101–1,000; User Interviews: 10–49, 50–199, 200–499, 500–999), 1,001–5,000 (UXPA: 1,001–5,000; User Interviews: 1,000–4,999), 5,001–10,000 (UXPA: 5,001–10,000; User Interviews: 5,000–9,999); Over 10,000 (same for UXPA and User Interviews).
• Matching different job level descriptions into four categories (UXPA Entry- and Mid-Level Nonsupervisory matched with User Interviews Junior–mid IC; UXPA Senior Nonsupervisory matched with User Interviews Senior IC; UXPA Mid-Level Supervisory with User Interviews Manager/Team Lead; UXPA Senior Supervisory with User Interviews Director/VP/C-Level).

Although these adjustments likely led to some loss of precision in the predictors, we made them to increase the sample size of responses used to build the combined regression model, enhancing the generalizability of the model and improving the precision of estimated regression parameters (n = 1,063 including 402 cases from the UXPA data and 661 cases from the User Interviews data). Table 2 shows the key drivers from the combined model and the percentage of variation in salary that they explain.

Table 2: Key drivers from the combination of the 2024 UXPA and User Interviews surveys explain 66% of the variation in salaries.

To estimate your theoretical salary based on the combined 2024 data from UXPA and User Interviews, enter your information in the calculator below. We included only the variables that statistically impacted salary; taken together, these account for about 66% of the variation. We’ve also included a rough 95% confidence interval around the prediction to illustrate the expected variability around each salary estimate. We also search the database to see how many cases match the inputs and, for those cases, provide the minimum, maximum, and median salaries.

Examples

• A senior nonsupervisor with nine years of experience at a company with 2,500 employees in the Mountain West region of the U.S. has a predicted salary of $140,166 (with a 95% confidence interval from $126,685 to $155,081). The only person in the sample who met these criteria had a salary of $145,000—showing reasonably good agreement between the predicted and actual values (a difference of $4,834, within the confidence interval).
• An entry/mid-level nonsupervisor with four years of experience at a very large company (10,001+ employees) in California would be expected to earn $138,457 (with a 95% confidence interval from $105,665 to $175,000). There were seven people in the sample who met these criteria. The range of their salaries was from $65,000 to $175,000 and their median salary was $140,000—only about $1,500 different from the predicted salary.
• A senior-level supervisor with 25 years of experience at a small company (11–1,000 employees) in the Midwest region of the U.S. has a predicted salary of $197,197 (with a 95% confidence interval from $148,188 to $262,414). The only person in the sample who met these criteria had a salary of $220,000 (a difference of $22,803 but still within the confidence interval).

Notes on Using

Like many activities in the behavioral sciences, predicting salaries is an inexact science. Other variables and individual differences substantially affect salaries. The predicted salaries are based on what UX professionals voluntarily reported making, so expect individual results to vary. Please contact us if you find a large discrepancy between your salary and the predicted salary.

• Salaries generated by this calculator are estimates around the median; you can expect variability above and below each estimate. The predicted median salary will be less accurate than using the 95% confidence interval around the predicted salary.
• The calculator estimates values even for combinations unlikely to exist (e.g., executives with two years of experience) so proceed with caution when computing “what-if” scenarios.