2016 Design Salary and Tools Survey

O’Reilly Media conducted an anonymous online survey of salaries of designers, UX specialists, and others in the design space. This in-depth report presents complete survey results that demonstrate how variables such as job title, location, use of specific tools, and the types of tasks performed affect salary and other compensation.

By John King and Roger Magoulas
January 7, 2016
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Executive Summary

THE 2016 DESIGN SALARY SURVEY investigates the tools, tasks, and compensation of designers, UX specialists, and others in the design space based on data collected in an online, anonymous survey. The 324 respondents largely worked in the design of web/mobile products or connected devices, and came from a wide variety of industries and backgrounds. We paid special attention to the software designers use—respondents were asked which of more than 100 tools they use—and usage correlations between them.

Key findings include:

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  • UX designers earn a median salary of $91K ($99K for US-based
  • California respondents report the highest wages
  • Age doesn’t predict salary, years of experience do
  • Women are paid less than men—even when all other variables match
  • Most UX designers tend to use one of two software stacks: one anchored by Adobe Illustrator, the other anchored by Sketch
  • Those who code (even just a little) earn more
  • When respondents provide tool and task information, job title becomes less useful for predicting salaries

In a rather manual, low-tech way, this report is interactive: just plug
your own data into the linear regression model to get a salary prediction
(no buttons, just do the math). You can press actual buttons, and earn
our sincere gratitude, by taking the survey—it only takes about 5 to 10 minutes and is crucial for us to continue providing this kind of research: oreilly.com/design/2017-design-salary-survey.


IN THE FALL OF 2015, O’Reilly Media made a comprehensive online survey available, focused on designers, their work, and compensation. The 324 designers, UX specialists, engineers, managers, and directors who took the survey came from 25 countries and 34 US states. We calculated the median salary of the survey respondents as $91,000 USD, a figure we decompose using information from other survey questions covering demographics, tool usage, and participation in several design-related tasks. While we can compare the salaries of groups of respondents based on how they answered particular questions—for example, those who use a particular tool and those who do not—a more rigorous way of assessing salary differences is through a linear model, which allows us to see how salary corresponds to a variable holding all others constant. In this report, we present both methods. The reader should keep in mind that any associations or correlations presented may not be causative, and that the self-selecting nature of the survey respondents means there is no guarantee the sample is representative of professionals in the design space. That said, we have taken care to be cautious in our conclusions to only present results that are statistically significant under usual assumptions that the sample is sufficiently close to random. Even those designers who know the space well will likely find something here that is new.


In the horizontal bar charts throughout this report, we include the interquartile range (IQR) to show the middle 50% of respondents’ answers to questions such as salary. One quarter of the respondents has a salary below the displayed range, and one quarter has a salary above the displayed range.

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 ($115K, 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 $81K—significantly less than those who had a different academic background (median $96K). In contrast, respondents with an academic background in mathematics, statistics, or physics earned much more than the rest of the sample (median $120K). 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

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.”

Even though they come at the end of the keyword sequence
above, “UX” and “Designer” were the top categories, each with 22% of the sample. The median
salaries of these two groups, $91K and $92K, respectively,
are approximately the same as the overall sample average.
“UI” and “UI/UX” were broken out from “UX” to see if
there were any key differences between these groups; the
only real observation of interest is that
there were far fewer titles containing
“UI” or “UI/UX” than just “UX.” In
terms of salary, respondents with “UI”
or “UI/UX” titles earned less than the
larger “UX” respondents, but this is
entirely explainable by other variables;
in particular, “UI” and “UI/UX” were
much more common outside of the US,
where salaries tended to be lower.

Most of the “Manager” job titles were
some variation of “UX Product [or Project]
Manager.” As we would expect, median salaries of
managers ($126K) and directors ($116K) were the highest.
In the sample, managers earn more than directors, likely
influenced by more than half the managers working at
companies with more than 10,000 employees while more than half the directors worked at companies with fewer
than 100.

In the sample, managers earn more than directors, likely influenced
by more than half the managers working at companies
with more than 10,000 employees, while more than half the
directors worked at companies wither fewer than 100.

Such a pattern shows a problem with trying to read too much
into the job title results in isolation. While directors are generally
understood to outrank managers, a “UX Director” at a
small company may have similar duties and manage a similar
number of people as a “UX Manager” at a large company—
and we know that the number of people managed tends to
correlate with salary.

Architects and engineers/developers made up 6% and 5%
of the sample, respectively, but did not have significantly
larger salaries than “UX” or “designers.” Graphic designers,
however, reported much lower earnings than most
other respondents (median $49K). This discrepancy turned
out to be the one parameter from the job title question we
used to build our linear model. That is, the factual information
captured in other questions from the survey, e.g.,
“do you manage people” or “do you code,” helped predict
salaries better than nominal data like job title.

Company, Team, Product

INDUSTRY in the sample (29%), followed by consulting
(15%). Computers/hardware (including wearables) stood
out for its high salaries, with a median of $130K. This correlation
is conflated with geography—half of the computers/
hardware respondents came from California—although even
the non-California respondents from the hardware industry
reported above-average salaries ($115K). This was even higher
than the banking industry (median $108K).

While only 29% of respondents worked at companies from
the “software industry,” the vast majority worked at companies
that nevertheless produced software: 86% of respondents
worked on web products, and a slightly smaller majority
(69%) worked on mobile products (almost all of the latter
also worked on web products). Fewer respondents worked
on wearables (15%) or other connected devices (24%), but
these respondents reported higher salaries ($110K and $105K,
respectively) than those who worked on neither ($86K). More
generally, respondents who did not work on products at all (services only) earned less: this was 13% of the sample, and
they earned a median of $70K.

A quarter of respondents came from large companies (at least
2,500 employees) and had larger salaries: $114K versus $83K
among respondents from smaller companies. Company age
was varied in the sample—21% from companies no older
than 5 years, 38% from companies older than 20—but this
variable did not appear to have any significant correlation with

Most respondents’ teams contained between 4 and 10 people.
Team size positively correlated with salary in the sample:
members of larger teams earned more (for example, $117K
median for those in teams with more than 10 members).
Team size also correlated with company size, but we shall see
that the model retained both as significant.

We also asked about the roles of colleagues—with whom
respondents worked—and found that most work with
programmers (84%), (other) designers (83%), and product
managers (78%). For each of these three, the minority of respondents who did NOT work with that type of colleague
earned significantly less than those who did. For example,
the 84% who worked with programmers earned a median
salary of $96K, while those who did not earned a median
salary of $60K. One interpretation of why communication
channels between other roles correlate with salary is that an
employer who highly values designers (and is willing to pay
them above-average wages) will also be more inclined to
encourage their greater participation in the business. Such
a possibility raises the general point that paying attention
to how a prospective employer organizes teams is a good
idea, and in particular, designers may prefer companies that
integrate design with other functions over companies that
treat design as a standalone silo. It should be mentioned,
however, that for salespeople, the opposite of the above
patterns was apparent: the 38% of respondents who reported
working with salespeople earned less ($86K) than those
who did not ($92K).

The design process may not lend itself to multiple-choice
survey questions—many respondents gave a wide variety
of answers, such as “half waterfall half agile,” “hybridized
lean UX,” “whatever works best,” “a good mix of the
above,” and, our favorite, “chaos.” 17% of respondents
selected “other”, 43% use agile, 22% use design sprints,
10% use Lean UX, and 6% use no process at all.


than just a title is through questions about specific tasks. In
this survey, we allowed respondents to answer “no involvement,”
“minor involvement,” or “major involvement”—the
latter meaning that this task is “essential to most or all of
your projects and responsibilities, and that you perform it
frequently (i.e., most days).” The tasks
available were “user research,” “usability
testing,” “information architecture,”
“user interface design,” “data analytics,”
“prototyping,” “wireframing,” “brainstorming,”
“project management,” “managing
people,” “requirements gathering,”
“storytelling,” “sketching,” “pitching (to
clients or management),” “presenting (to
clients or management),” and “leading design critiques.”

For each of the 16 tasks, a majority of respondents reported
that they have minor or major involvement—even managing
people. Furthermore, for all tasks except “sketching,”
respondents with more involvement earned more. That is,
the more tasks a respondent indicated they take on, the
higher their salary.

The median salary of the 20% that had major involvement in
at most 4 tasks was $73K; the median salary of the 26% that
had major involvement in more than 10 tasks was $118K.

Despite the explicit definition of “major tasks” provided in
the survey questions, we think respondents appear to have
interpreted “major” differently, i.e.,
respondents who report that they work
on two or three tasks might not see the
10 tasks other respondents claim they
work on as major. However, we see some
indication that UX designers who wear
many hats tend to earn higher salaries.

Aside from some of the more obvious
correlations among tasks—“people
management” and “product management” are often done
by the same people—there are some worth pointing out.
“Wireframing” and “prototyping” are often performed
as major tasks by the same people, with half the sample
involved in both, and only 21% involved in one but not
the other. “User interface design” also highly correlated with
each of these tasks. “Pitching,” “presenting,” “storytelling,”
and “leading design critiques” correlated with each other.

“Data analytics”—the least common of the 16 as a major
task—tended to be done by the same respondents who were
involved in “user research,” “usability testing,” and “requirements

For two very general tasks—“coding” and
“meetings”—we asked for more exact
information on how much time is spent
on them. More than half of the sample
did not code at all (54%) and only 13%
coded for more than 8 hours per week.
There was no clear, simple trend in salary
as coding hours increased: those in the
“no coding” and “9–20 hours / week”
groups earned slightly more than the others.
If we exclude managers and directors,
however, salary does rise steadily as coding hours increase,
from a median of $82K for the non-coders to $98K for the
9–20 hours/week group; although, for the few respondents
who reported coding more than 20 hours/week, median salary
fell back down to $88K. One explanation for the drop: spending
too much time coding leaves less time for long meetings.

The correlation between meeting hours and salary is no joke:
the quarter of respondents who spend no more than 3 hours in meetings each week had a median salary of $63K, rising to
$88K for “4–8 hr/w,” $118K for “9–20 hr/w,” and $148K for
“over 20 hr/w.” This seems a good time to remind the reader
that these correlations do not imply causation—we do not
advise scheduling more meetings under
the false pretense of boosting one’s salary!
A more reasonable assumption is that roles
with more responsibility are associated with
not only higher salaries, but more time in

The pattern of more pay for more time
in meetings or more time coding may be
conflated with the more general pattern
correlating longer work weeks with higher
salaries. For the small share (13%) of
respondents who worked fewer than 40 hours, median salary
was only $66K, rising to $82K for those working exactly 40,
and to $90K for those working from 41 to 45 hours per week.
From here, median salary rose quite rapidly: $120K for the 46-
to 50-hour workweek and $155K for those working more than
50 hours per week. As with the demographic variables, we
resolve some of the ambiguity as to which variable or variables
best predict salary with a linear model (see Model section).


“do you use this tool in your current role on a regular basis
(about every week)?” for more than 100 tools. Not all tools
were specific to design—the list included programming languages,
work organization tools, and operating systems—and it
wasn’t meant to be exhaustive, with “other” answers available.

Mac OS X appears to be the clear choice for those in the design
space, with 88% of the sample using Macs, far more than the
38% who use Windows and the 12% who use Linux. Windows
users earned less than Mac users: among those who used one
(but not both) of these operating systems, Mac users had a
median salary of $95K and Windows users had a salary of
$78K, while those who used both had a median salary of $88K.

Many of the tools we asked about are for communications,
file sharing, or project management. Among the most common
are Google Drive (59%), Dropbox (58%), Slack (51%),
GitHub (40%), Trello (32%), Evernote (32%), and Basecamp
(29%). Of these tools, Slack, GitHub, and Evernote were notable
for the higher salaries of their users: the median salary
of respondents who used at least one of these tools (69% of the sample) was $100K, $108K for those who used all three
(12%), and only $76K for those who did not use any (32%).

Aside from operating systems and communications tools, the
most commonly used tool in the sample was pen and paper
(with 61% of the sample), followed by Google Analytics (57%)
and HTML/CSS (53%). It is perhaps a little surprising that the
top non-OS software used among designers is for analytics—
remember that analytics was the task with fewest respondents
reporting major involvement—but this might be because there
is less in-depth attention to or focus on analytics that would
lead to greater tool specialization or experimentation. Or else,
Google Analytics just works very well for designers.

We found respondents using three dominant tools: Adobe
Illustrator (43%), Bohemian Coding’s Sketch (40%), and
InVision (38%). Most Sketch users, 63%, also use InVision.
The latter two positively correlated: 63% of respondents
who used Sketch also used InVision, while only 24% of
respondents who did not use Sketch did use InVision. There
was no significant correlation between either of these two
and Illustrator: someone who used one was no more or less likely to use the other. However, if we consider only
those with “UX,” “UI,” “UI/UX,” “Designer,” or “Graphic
Designer” job titles, Sketch and Illustrator negatively correlate;
that is, most UX designers tend to use one but not both (the
change is caused by the large share of non-UX designers who
use neither). The same is true for InVision and Illustrator. An
even clearer division emerges when we start to look at how
these three correlate with other tools.

Aside from InVision, design/analytics tools that correlated
with Sketch—more precisely, tools that were used by a
significantly greater share of respondents who used Sketch
than those who did not—included Google Analytics, Keynote,
Optimizely, Mixpanel/Kissmetrics, Pixate, Marvel, Framer,
Flinto, Proto.io, and Tableau (among others). Most of these
tools also correlated with InVision (but not quite as strongly as
with Sketch), and the few that also correlated with Illustrator—
Keynote, Flinto, and Proto.io—did so to a much lesser degree.
In fact, in the network of usage correlation between all tools,
Sketch becomes the most dominant “hub”: many tools correlate
with it (chief among them InVision), and for many tools, it
is the best-correlated (or most well-connected) neighbor.
Among tools outside of the design/analytics category, Slack
correlated highly with Sketch (as well as with InVision and
other associated tools listed above).

In contrast, the only design tools that gravitated to Illustrator
are two other Adobe products, InDesign and After Effects:
this is a vendor-based stack silo. More generally, respondents in this stack tended to use fewer tools: the average
number of tools (of any type) used by respondents who
use Illustrator (but not Sketch) was 12, while for those who
use Sketch (but not Illustrator), the average number of tools
used was 16. When we identify tool stack silos like this,
there is always a degree of uncertainty in the interpretation
of why it arose without further study: for example, it could
arise because there are compatibility issues, or because
those who use this stack don’t need other tools (all their
use cases are satisfied since the tools are effectively generalized).
As usual, we should note that there is a difference
in salaries between the two groups: users of Illustrator had
a median salary of $87K; users of Sketch had a median
salary of $101K; and users of InVision had a median salary
of $96K.

The final group of tools on the survey were programming
languages. JavaScript was by far the most popular language
(35%, median salary $95K), followed by Python (8%,
$90K), Ruby (6%, $98K), Java (6%, $88K), and Swift (4%,
$85K). There were no major correlations between particular
programming languages and design tools; if we include
programming languages in the clustering schema above,
they would just form their own cluster. Swift has a stronger
connection with iOS, as we would expect, but with Android
as well, indicating that the extent to which these two major
mobile platforms require specialization among mobile designers
is limited.


IN ADDITION TO THE DEMOGRAPHIC, company/team, tasks, and tools variables used to create the linear model, we include two additional variables: bargaining skills and employability. In both cases, respondents are prompted to self-evaluate their skill based on a 1 (poor) to 5 (excellent) scale. We know the results are likely subjective and imprecise (for example, bargaining skills are difficult to quantify and likely inconsistent) but we think the results and model would suffer if we ignore the topic. It seems fairly clear, for example, that if someone routinely accepts lowball offers without putting up any fight, they will earn less than someone who bargains more aggressively (even if they are just as talented, experienced, and hardworking as the latter). Most respondents (66%) self-reported a bargaining score of 3 or 4, with 12% reporting a score of 5. Unsurprisingly, each bargaining “point” corresponded to a higher salary.

The other question, also on a five-point scale, was the ease of finding a new position, assuming “that the new job is more or less equivalent to your current one, in terms of compensation, workload, and your interest in the work.” Again, this is highly hypothetical and subjective, but is similarly hard to ignore.

If someone knows (and crucially, if their employer knows) that they could walk out today from their at-will employment and find equivalent work elsewhere, the employer is likely to be willing to pay more. In this sense, ease of finding work is a predictor of salary, so we introduced it into the model. However, it should be noted that this variable could also be considered a dependent variable that we could try to predict with other variables (i.e., alongside salary, not “below” it).

Now, for the model.1 From the 65 questions, we extracted 202 variables (multiple-choice questions usually have almost as many variables as there are answer choices), and from the available variables, the algorithm selected the most significant. The algorithm favors a model with a smaller number of variables (to keep the model simpler) and assigns a coefficient (a positive or negative number) to each. To get your salary estimate, start with the constant ($51K), and for each variable that applies to you add (or subtract) the corresponding coefficient (or for years of experience, add the coefficient times the number of years you have worked in your field). Here is the model:

constant (everyone starts with this amount):

country = United States: +$12,257
world region = Europe (except UK/I): –$12,561
US region = California: +$17,390
US region = Northeast: +$2,956
gender = female: –$6,363
per 1 year of experience: +$1,292
academic speciality in mathematics, statistics or
physics: +$1,158
academic specialty in graphic design: –$5,178

title = “Graphic Designer”: –$1,848

industry = education: –$7,522
company size = 1: +$6,403
company size > 1,000: +$1,543
team size = 2: –$6,052
team size = 8 to 20: +$6,661
team size = More than 20: +$12,224
works with product managers: +$7,021
works with programmers: +$3,633
works on services: -$896
tasks: brainstorming = no involvement or minor involvement:
tasks: managing people = major involvement:
tasks: programming = I never perform this task:
tasks: pitching = major involvement: +$3,669
tasks: user research = no involvement: –$1,723
meetings: 9 - 20 hours / week: +$14,168
meetings: over 20 hours / week: +$15,547
work week < 30 hours / week: –$20,963
work week = 30 to 35 hours / week: –$16,834
work week > 55 hours / week: +$23,472

tools: Slack: +$6,378
tools: User Zoom: +$5,403
tools: PHP: +$5,147
tools: Alexa: +$3,661
tools: Keynote: +$3,198
tools: Dropbox: +$1,434
tools: Windows: –$3,740

bargaining skills = 1 or 2: –$1,259
bargaining skills = 4: +$3,288
bargaining skills = 5: +$4,164

ease of finding new work = 1, 2 or 3: –$5,444

This model gives us a more rigorous assessment of how much
variables predict salary, allowing us to revisit some of the
differences in salary between various groups of respondents.
Since the model only includes a subset of the available variables,
it shows us which ones are likely to have true correlations
with salary and which are more likely to be insignificant
(even if they might correlate with the significant ones).

The gap in salary between the US and Europe is reduced
to +$25K (UK/Ireland and Canada are in between), but the
coefficient for California is still quite large at +$17K. Aside
from this and the +$3K value for the Northeast (from NY/NJ),
no US region was significant enough to be kept in the model
(so the large salaries in the Mid-Atlantic appear to have been
somewhat of an accident).

The gender gap was reduced to –$6,403, although this doesn’t
really make the previously observed $14K difference any better.
In some ways, it makes it worse since the model is telling us
that if we keep everything else constant—experience, skills,
role—a woman will likely earn $6K less than a man.

One variable that is conspicuously absent is age, which seems
to be completely replaced by other variables, most notably
years of experience. For each year of experience, UX designers
are predicted to gain $1,292: a rather modest raise, although
this would be on top of expected salary changes from other
variables (e.g., new tools or different tasks).

Education remained an important feature, the academic
backgrounds of math/stats/physics giving a +$1,158 increase
and graphic design a –$5,178 decrease. The further penalty
of –$1,848 for “Graphic Designer” as a title means that if
there are two people who both do graphic design, one called
“Graphic Designer” and one called something else, the former
will be expected to earn almost $2K less than the latter.
The lack of other nonzero coefficients for title variables means
that with the exception of “graphic designer,” salary is not
based on what you are called but rather what you do.

For industry, company size, and team size there were few
differences suggested by the model from the single-variable
salary comparisons observed above. The only industry variable
kept in the model is education (–$7,522); hardware/computers
did not make it into the model. The boost in salary for large
company employees is (only) +$1,543, while at the other end
of the company size scale, self-employed respondents (i.e.,
company size = 1) had a large, positive coefficient of +$12,455.

The correlation between team size and salary that we noted
earlier is made clear in the model: those with a team size of at
least 8 earned $6,661 more than smaller teams; those with a
team larger than 20 people earned an additional $5,263; and
teams of only 1 or 2 people had a further penalty of –$6,052.
The latter penalty also explains part of why the single-personcompany
coefficient is so high: the negative effect of having
a small team does not apply to them, so the negative, small-team coefficient needs to be cancelled out. Not only did
larger teams predict a larger salary, but working with people
in certain roles—namely, product managers and programmers—
had positive effects on salary (+$7,021 and +$3,633,

As we would expect, managing people (major involvement) is
accompanied with a large boost in salary: +$18,409. Almost as
significant, however, is programming: those who do not program
at all are expected to earn $16,682 less, according to the
model. Major involvement (versus minor) does not change the
salary prediction. While this does not mean that if you learn
how to program you are guaranteed $16K more, the possibility
that there is some causative aspect in this relationship (which
seems somewhat intuitive) should be enough to motivate
some UX designers to learn how to code (at least a little).

Given the huge discrepancies between median salaries of
groups split by meeting hours, it is no surprise to see similarly
large coefficients in the model. A ray of hope (for those of
us who are not overly fond of spending half the day on the
phone) might be found in the fact that while spending at
least 9 hours/week in meetings bumps predicted salary up
by $14,168, spending more than 20 hours/week gives only
an additional $1,379. Workweek hours were also associated
with large coefficients: +$23,472 for more than 55 hours;
–$16,834 for less than 35 (and a further $4,129 for a workweek
less than 30 hours).

None of the major design-specific tools were present in the
model, although Slack is (+$6,378) and, as previously noted,
it is highly correlated to Sketch. Aside from Keynote (+$3,198)
and Dropbox (+$1,434), the only tools with high positive
coefficients were tools each used by less than 6% of the sample:
User Zoom (+$5,403), PHP (+$5,147), and Alexa (+$3,661).
One tool had a negative coefficient: Windows (–$3,740).

The two self-evaluating scores gave the last coefficients. For
each point (out of five) for one’s bargaining skills, predicted
salary increases by about $1K. (This seems like a low estimate,
but then again, it’s an average, and perhaps it doesn’t matter
very much for some people in certain situations.) Those who
would not rate their ease of finding new work higher than a 3
(out of 5) are penalized –$5,444.

Even after numbers are plugged in, there is still a fair amount
of error in the estimation by this model. Among new data
points—such as yours, if you are in the mood for a little
math—half should be within $20K of the estimate, and
three-quarters within $30K, but some larger outliers can be
expected. Of course, there are many factors, some impossible
to accurately assess in a survey, much less an anonymous one,
that will undoubtedly have important effects on salary and
throw off the estimate, but on average this model should be
more or less correct.

1We used the Affinity Propagation algorithm in Scikit-learn toperform linear regression with cross validation and the lasso, and arrived at a model that includes 40 variables (and an intercept). The model has an R2 of .43: about 43% of the variation in salaries was explained.

Other Compensation

Salary is not static, nor is it the only important part of one’s
compensation. Respondents were asked to report the change
in their salary over the last three years: 83% reported some
positive change, 49% reported a gain of at least $20K, and
9% reported a gain of at least $60K. As a
percentage of salary (three years ago), the
median gain was 22%. Interestingly, the
same variables used to estimate salary in
the regression model presented earlier did
not seem to have much predictive power
for either the actual or percentage salary
change; the best models we found left
more than 90% of the variation in salary
growth unexplained.

Bonuses were received in the previous 12
months by about half of the sample, with
19% receiving a bonus under $5K, 18%
from $5K to $10K, and 12% more than $10K. Like salary
change, the bonus data were not amenable to prediction using the rest of the survey data, aside from the trivial result
that the bigger bonuses tended to be received by those with
the highest salaries.

Among non-cash parts of compensation, most respondents
reported that they receive health insurance
(81%) and contributions to a retirement
plan (64%). Smaller cohorts received stock
options (30%), profit sharing (16%), and
the opportunity for a sabbatical (10%). Less
than three weeks of vacation were available
to 24% of the sample, exactly three weeks
to 38%, three or four to 26%, and the
remaining 12% had six weeks or more. Of
these four groups, the one with the highest
median salary was the last: $100K and six
weeks of vacation. Unsurprisingly, vacation
was more generous in Europe than in the
US: 4.6 weeks on average in Europe (both in the UK and on
the continent) and 3.3 weeks on average in the US.


though many of us might not want to spend too much time
dwelling on money matters or worrying that we don’t make
enough. The commonly accepted rule that it is not appropriate
to share information about your salary with colleagues has
reasonable justification—too much information could cultivate
an environment of envy or perceived injustice—but no
information at all about how much one should earn results in
plenty of confusion and unfairness as well. Both the employer
and the employee gain from knowing more or less what the
latter should make.

Aside from the purpose of determining an appropriate salary
offer or ask, the information in this report is also intended to
help those in the design space navigate through the important
career choices: which tasks might you want to spend
more (or less) time on; which tools might you want to learn;
and at which types of companies might you want to apply
for a job. On one hand, the correlation-causation distinction
means that we cannot say, for example, that “learning” Slack
(it’s pretty easy) will boost your salary by $6K.

On the other hand, the fact that we cannot assume causation
in the observed correlations does not mean there is none.
When, based on intuition, causation seems possible, and when
the rewards appear to be large and costs relatively low, it seems
reasonable to act on an observed correlation even if causation
is not ensured. A prime example of this is with coding: it is
intuitive that being able to code, at least a little, would help a
UX designer working on web, mobile, or (software-enabled)
hardware; and learning how to code a little is probably not a
great cost compared to the potential reward—even hypothetically—
of an estimated, eventual $16K salary increase.

In conclusion, we hope that you find this report interesting,
but above all useful. Design is a quickly changing field, and
we hope to keep up with it by continuing this research, which
cannot be possible without your participation. We encourage
you to try your own data in the model, and if you can
spare 10 minutes, to take the survey yourself at oreilly.com/


We need your data.

To stay up to date on this research, your participation is
critical. The survey is now open for the 2017 report, and if
you can spare just 10 minutes of your time, we encourage
you to take the survey.

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