Individual Background Demographics
THE FIRST THING TO DO WITH SURVEY DATA is to get to know the sample. Most respondents (79%) work in the US with most of the rest working in Europe (11%) and Canada (5%). Half of the US respondents came from California and the Northeast (by way of comparison, these two regions make up about a quarter of the US population). We note two possible causes for bias towards California and the Northeast: people living in those regions disproportionately respond to O’Reilly surveys and those regions may have more design jobs. The discrepancy between the median salaries of US ($99K) and European respondents ($48K) is greater than what would be expected given national per capita income, but this is partially explained by more US respondents holding higher positions—a quirk of the sample. The US region with the highest salary was California (with median salary of $128K), followed by the Mid-Atlantic ($118K).
Two-thirds of the respondents were male, and a significant gap in median pay between male and female respondents was present ($99K and $85K, respectively). About half of the $14K difference in the sample is attributable to the fact that a larger share of the sample’s men held higher positions. Still, the –$6K coefficient for female in the linear model indicates that even when every other variable is held constant (same work, same skills) women earn about $6K less than men. This is roughly the same gender gap that we saw for data scientists in a different salary survey (depending on which model was used, $3K to $8K).
More than a third of the respondents were 30 years old or younger, and predictably this group had a lower median salary than the rest ($71K). However, the age groups with the highest salaries in the sample were from 36 to 50 ($116K), higher than the over-50 segment ($94K). This is partly explained (but only partly) by the different positions held by the respondents aged 36 to 50; for example, the share of directors in the 36 to 50 group was greater than in the over-50 group. About half of the respondents had at least 10 years of experience in their role, and earned a median salary of $114K, while the less-experienced half earned a median of $74K.
As for education, 4% had a doctorate degree and 29% had a master’s degree (but no PhD). While respondents with a PhD did have a higher median salary than average (5K, though this is a fairly small sample), respondents with only a master’s did not have a significantly higher salary than those without one. Another significant pattern was that the 38% of respondents whose academic background was in graphic design reported a median salary of K—significantly less than those who had a different academic background (median K). In contrast, respondents with an academic background in mathematics, statistics, or physics earned much more than the rest of the sample (median 0K). Like the PhD figure, this is based on a small cohort—just 10 respondents— but it is worth noting that the titles of these respondents did not stand out from the rest: they were, for the most part, “UX” and “Designer.”
JOB TITLE WAS COLLECTED AS AN OPEN-TEXT FIELD, and respondents entered 183 unique titles. Many of the titles are clearly just variations on the same type of role, but perhaps more accurately, they are points on a continuum: "Software Designer & Consultant, " "UX Consultant, " "UX Researcher, " "Design Research Associate, " "Visual Interaction Designer, " "Senior Mobile Interaction Designer, " "UI Developer, " "Web Developer, " "Front End Developer, " "Software Developer, " "Programmer." Even this small list of titles could be binned in multiple ways. Our strategy here is to assign a title based on the first keyword it includes from a sequence: "Director, " "Manager, " "Architect, " "Consultant, " "Engineer/Developer" (or "Programmer"), "Researcher, " "Analyst, " "Graphic Designer, " "UI/UX, " "UX" (or "Experience"), "UI" (or "Interaction"), "Designer, " "Other." So, "UX Director" becomes "Director" and "Designer Consultant" becomes "Consultant."