User Experience Salaries & Calculator (2018) Each year we lend our analytical skills to the UXPA to help the UX community understand the latest compensation, skills, and composition of the UX profession.

We helped collect, analyze, and interpret the salary and related skills of UX professionals from around the world.

The details are available on the UXPA website. Here are the highlights and the calculator.

Survey Results

  • The data was collected from June–August 2018 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 the 2016 data.
  • International reach, most from the U.S. The survey is based on 1,326 responses from 52 countries, with 64% of the responses coming from the U.S. Other significant numbers come from the UK (5%), Canada (5%), France (5%), Germany (2%), Mexico (2%), Australia (2%), and India (2%).
  • The median salary is $95K. This is not statistically different from the 2016 median of $98K. After accounting for inflation however, the salary is actually down 8% compared to the 2016 inflation adjusted median salary of $103K. Each year since 2005, the inflation adjusted median salary hasn’t changed much as it’s fluctuated within a range between $97K and $103K. Much of the fluctuation in salary (especially compared to 2016) can be attributed to different sample characteristics (e.g. more or less people responding from some countries).
  • Many variables affect salary. The $95K 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?

You can more accurately determine your potential salary if you take into account the variables that most affect salaries. As we did in 2016, we conducted a multiple-regression analysis (on the log-transformed salaries) to see which variables statistically predict what people earn (Figure 1).

The Key Drivers Explain 68% of the variation in salaries

Figure 1: These key drivers explain 68% of the variation in salaries (% values rounded).

The regression equation predicted 68% of the variation in salaries. (The dark-blue section of the chart represents the unmeasured or unknown variables.) For the behavioral sciences, explaining 68% of variation is very good. Individual factors—skills, personalities, negotiation, company policies, etc.—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 (in order of importance):

  1. Where you live: The country (and region for the U.S.) have the biggest impact on what you get paid. It alone explains 36% of the variation in salary data. The median salary in the U.S. is $115K, compared to $89K in Australia, $72K in the UK, $69K in Canada, $62K in Germany, $48K in France, $20K in India, and $19K in Mexico. All other countries had fewer than 10 respondents.

In the U.S., the highest salaries are in Northern California ($145K), followed by the Northeast ($125K), Pacific Northwest ($120K), and Southern California ($118K). Not surprisingly, these places also cost the most to live. The map below shows the number of usable salaries we analyzed for each state and for what we called the region.

Where You Live

  1. Job level: The next biggest driver of salaries is the job level, accounting for 14% of the variation in salaries. Senior-level supervisors make on average twice what entry-level employees make ($126K vs. $60K). Interestingly, working part-time reduces your expected pay by only $14K (while controlling for other variables).
  2. Company size/type: The bigger the company, the bigger the paycheck. It’s hard for small companies to compete with the big payroll of larger companies. Employees at companies with fewer than 100 employees make a median of $72K compared to employees at larger firms (over 100 employees) who make on average $103K. Working at a for-profit company (88% of the sample) increases your expected pay by $8,100.
  3. Experience: The more experience you have, the more you get paid. Controlling for other variables, on average, each year of experience adds about $2,700 to the global base salary.
  4. Age: Age has a small effect on salaries and only for the oldest AND youngest workers. On average and controlling for other variables, those between 56-65 make $14K less than other aged respondents. Those between the ages of 18-25 make $2,300 less than respondents between 25 and 56.
  5. Education: Your degree matters (somewhat). Around 8% of respondents reported having a Ph.D. (similar to years past). Respondents with a Ph.D. make on average $125K compared to those with Masters’ degrees ($96K) or Bachelor’s degrees ($88K). But these differences in degrees only accounted for only 1% of the variation in salary data.
  6. Gender: This year there are no significant effects of gender on pay. Women make slightly more than men in 5 of 7 experience brackets and men make slightly more than women in 2 of 7 experience brackets (3-4 years and 21+ years of experience). I will examine gender differences in a future article.

Salary Calculator

To estimate your theoretical salary based on the 2018 data, enter your information in the calculator below. (Only the variables that were found to statistically impact salary were included. Taken together, these explain around 68% of the variation in salaries.) We’ve also included a 95% confidence interval around the prediction to better illustrate the expected variability around each salary estimate.

Employment Status*
Type of Company*
Country*
Years of UX-Related Experience*
Employment Level*
No. of Employees at Company*
Age*
Education Level*
US Region

Your predicted salary is:

You need to enter at least Years of Experience, Employment Level, Company Size, Age, and Education Level to get a predicted salary.

752 responses match your selection ranging from $10,000 to $500,000, with a median salary of $117,000.

Examples

  • A 35-year-old, mid-level non-supervisor (full time at a for-profit company), with 5 years of experience at a mid-sized company (1,500 employees), in the Southeast U.S., with a Bachelor’s degree has a predicted salary of $85,048, with a 95% confidence interval of $53,465 and $135,289. There was one person in the sample who met these criteria in the survey with a salary of $90,000—showing good agreement between the predicted and actual values.
  • A 42-year-old, senior-level non-supervisor, with 12 years of experience at a large company (10,000+), in Palo Alto (Northern CA), with a Ph.D. would be expected to earn $190,630 with a 95% confidence interval of $116,928 and $310,789. There was one person who met these criteria with a salary of $190,630. The predicted value fell well within the range of the confidence interval (but the width of the interval shows how much uncertainty there is around predicting a salary).
  • A 45-year-old, senior-level non-supervisor, with 11 years of experience at a mid-sized company (2,000 employees), in London, with a Master’s degree has a predicted salary of $87,454 with a 95% confidence interval of $53,359 to $143,336. One person in the survey met these criteria with an actual salary of $91,522 (showing good agreement).

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.