THE 2017 O’REILLY DESIGN SALARY SURVEY explores
the landscape of modern design professionals, giving details
about their roles and how much they earn. The results are
based on data from our online survey that collected 1,085
responses. We pay special attention to variables that correlate
with salary, but this report isn’t just about money—we present
a range of information, including the popularity of design
tools, tasks, and organizational processes.
In what is now our second salary survey, we find some
consistency as to what matters in the
field of design: that the better-paying
design jobs tend to concentrate in tech
centers; that experience matters more
than age; that knowing more tools,
working with more people in a wider
variety of roles, and working for larger
organizations all correlate with higher wages. And, in a sign
that some things in the design world resist change (in some
cases, whether we like it or not), we still see women making
less than men and that most designers still use pen and paper
as their primary design tool.
Designers reporting no process earn the least
Some key findings include:
The West Coast (CA, WA, OR) has the highest salaries—
salaries are high even relative to those states’ per
Healthcare, banking, and computers/hardware
respondents report the highest salaries
Respondents from large companies report higher
Agile is the most popular design process; however,
those using LeanUX or a hybrid of
different processes earn the most
Designers reporting no process
earn the least
Higher earners use a wider
selection of tools
For prototyping and
wireframing, salaries are highest
among those that use Sketch
We hope that you will find the information in this report useful. If
you can spare 5–10 minutes, please go ahead and take the survey
FOR THE SECOND YEAR RUNNING, we at O’Reilly Media
have conducted a survey for designers, gathering information
about their compensation and details about their work. This
year, 1,085 people from 48 countries took
the survey. Respondents are mostly UX,
product, and graphic designers, but there
are also a fair number of developers and
other professionals involved in product
design. The survey was conducted online,
collecting responses from December 2015
to December 2016.
1,085 people from 48 countries took the survey.
While typical for online surveys, the methodology we used
of a self-selecting, uncontrolled respondent pool can lead to
less than ideal results. However, the broad range of respondents’
geographies, industries, and company sizes helps
mitigate the issues associated with a small, narrow sample.
Throughout the report, we quote median salary statistics for
various groups of people, such as those respondents who
used a certain tool or came from a particular industry. Since
these figures can be misleading if the
variable in question correlates with geography
or experience, we also sometimes
quote a median “adjusted” salary.
Technical details are in Appendix A: Adjusted Median Salary.
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.
THE MEDIAN SALARY OF THE ENTIRE SAMPLE IS $77K,
with the middle half earning between $50K and $109K. This
“middle half” statistic is called the “interquartile range,” and
we show it in many of the graphs to give a sense of the salary
spread for various groups of respondents. The spread is very
broad, but this isn’t surprising given the diversity of professional
backgrounds among respondents and the fact that they
come from locations with very different overall wage levels.
Most respondents report some gain in salary over the past
three years, and about 10% of the sample saw their salaries
double. Another third has wage growth of 30% to 100%
(also across the last three years). Respondents who have no
wage growth tend to have lower salaries (median of $50K),
but otherwise there’s no clear pattern between salary growth
(as a percentage) and current salary.
TWO-THIRDS OF THE SAMPLE IS FROM THE UNITED STATES,
and 20% is from Europe. After the US, the most well
represented countries in the sample are Canada (5%), the
United Kingdom (4%), Germany (2%),
Russia (2%), and Australia (2%).
For US-based respondents, a disproportionate
share comes from the
West Coast: 37% of the sample is
from California, Washington, and
Oregon (states containing only 16%
of the US population). New York and
Massachusetts also have disproportionately high response rates.
The skew in the geographic distribution of respondents likely
reflects that the O’Reilly audience and design-related jobs tend to
concentrate in tech-centric coastal cities.
Salaries vary sharply across geography; however, in most
cases, country and state variations mirror the local economy.
Per capita GDP is a good predictor of salary, although some
countries in Western Europe, including
Italy, the Netherlands, Spain,
and Portugal, have lower than
expected salaries—likely reflecting
how different recovery rates from
the 2008 recession and different tax
and social safety net regimes affect
wages. In the US, the West Coast
states (CA, OR, WA) have higher salaries relative to their per
capita GDP, which, combined with their higher response rates,
may indicate a relatively high demand for design jobs on the
West Coast, helping to push up wages.
In most cases, country and state variations mirror the local economy.
Age Versus Years of Experience
THREE-QUARTERS OF THE SAMPLE IS BETWEEN 26
AND 45, with about an eighth younger and an eighth
older. Salary generally increases with age until age 50,
where we see the 7% of the sample
who are over 50
report a slightly lower median salary
than respondents in their 40s.
The 7% of the sample
who are over 50 report
a slightly lower median
salary than respondents
in their 40s.
Respondents were also asked how
much experience they have, and, as
expected, salary increases steadily
with years of experience, although
only up to a point: respondents with
20–25 years of experience earn more
(median: $118K) than those with
more than 25 years of experience ($101K). After we factor
in years of experience, age doesn’t make any difference in
salary among respondents aged 31 to 50. Holding experience
constant, respondents younger than 30 do make a
little less, but the difference isn’t as much as the age medians
The lesson of age and years of experience tells us that we should
be careful about confounding variables affecting our interpretations:
being older (without anything else happening) may not
increase your salary (at least not after
30), but having more experience will.
While this example is fairly obvious,
others are not, and in this report, we
make an effort to avoid this same
problem in more subtle contexts by
occasionally referring to an additional
metric we call “adjusted median.”
The adjusted median blocks the effects
of geography and experience—creating
a metric that estimates what the median
would be if the respondents all came from a fixed location
and all had the same experience—to make comparing factors
more reliable. Appendix A contains additional details on the
methodology. To illustrate, we show each five-year age category
between 31 and 50 with an adjusted median salary of $80K,
those between 26 and 30 with $73K, and those between 51
and 60 with $68K–$69K.
THE SAMPLE IS SPLIT FAIRLY EVENLY BY GENDER. The
median salary of women in the sample is $82K, higher than
the median salary of the men, which is $74K. However, the
adjusted median salary of women is about $4K lower than
the adjusted median salary of men. The discrepancy is accounted
for by the fact that women in the sample are disproportionately
from places with higher wages. In almost every
geographic region (adjusting for experience), men are paid
more than women on average, even at the same experience
Men and women are equally likely to have received a raise in
the last three years, but men are slightly more likely to receive
a bonus: 46% of men in the sample received a bonus, while
only 41% of women received one.
Industry, Company Size
RESPONDENTS COME FROM
A VARIETY OF INDUSTRIES:
software is by far the most common,
with about a third of the
sample, followed by consulting,
advertising/marketing, and retail/
ecommerce. There are variations
in salary among industry, although
many of the differences diminish once we calculate the
adjusted median salaries. When adjusted for experience and
geography, healthcare, banking, and computers/hardware
all have median salaries of about $89K, compared to an
adjusted median of $74K for all other industries. Search/social
networking is even higher, with a median salary of $127K (the adjusted median is also high:
$96K), although this is based on
just 2% of the sample.
Over one-third of the sample come
from companies with no more
than 100 employees, and another
quarter come from mid-sized companies
(in the 100–1,000 employee
range). Salary does appear to go up with company size: from a
median of $63K for 2–100 employee companies, to $96K for
companies with over 10,000 employees. Again, these differences
shrink slightly once we block out experience and geographical
effects, but the 10,000+ group still shows an adjusted median
salary of $10K–$15K higher than the other groups.
Healthcare, banking, and
computers/hardware all have
median salaries of about
$89K, compared to an
adjusted median of $74K for
all other industries.
Coding Time, Programming Languages
ONLY 43% OF THE SAMPLE reports that programming
plays some role in their work. The other 57%, in fact, earn
more, with a median salary of $83K (coders earn a median
of $69K). However, this difference all but disappears when
we adjust the salaries, since the
distribution of respondents who
code vary greatly over geography.
For example, 58%–59% of
respondents from Europe and Asia
say that they spend at least some
time coding, while only 30% of
respondents from California code.
Combined with a review of the
2016 Design Salary Survey, we
don’t see code having more than a
noisy impact on salary.
58%–59% of respondents
from Europe and Asia say
that they spend at least
some time coding, while
only 30% of respondents
from California code.
Among those that do code, most report spending at most 8
hours/week on the task, while only 9% of the sample report
that they code more than 20 hours/week. The group with
the highest adjusted median salary is the one that spends
1–3 hours/week, although the salary
differences are fairly minor.
As for language choice, by far the
(31% of the sample), with Python
and Java coming next, with 8% and
7% shares of the sample, respectively.
Swift users tend to have high salaries:
their median of $95K is far above the
sample-wide median, and this margin
is decreased but still significant after
A SET OF QUESTIONS ON THE SURVEY asks respondents
whether they engage in certain tasks, either with “major” or
“minor” involvement. Some tasks are nearly universal, such as
brainstorming (74% major involvement, 21% minor involvement)
and user interface design (64% major, 25% minor), and
others are relevant to a much
smaller subset of the sample, such
as managing people (25% major,
35% minor) and data analytics
(13% major, 46% minor).
We found three highly correlated
tasks (wireframing, prototyping,
and sketching), meaning that respondents
who do one are more
likely to do another. These are
among the most common tasks,
each with 85%–-87% of the
sample (major or minor involvement),
while 93% are involved
in at least one of the three. The 40% of the sample that
has major involvement in all three tend to earn aboveaverage
wages (median: $87K). After adjusting the medians
for experience and geography, this discrepancy holds.
A second set of tasks also correlates with one another:
pitching, presenting, requirements gathering, leading design
critiques, managing products, and managing people. For each
of these tasks except product management, respondents
who have major involvement earn more than those that do
not. The differences in adjusted
median salaries are similarly
significant, around $10K for each.
While product management
does correlate with these
other tasks (in particular, with
managing people), it doesn’t
correspond to a boost in salary.
While the median salary of
those who manage products
is higher than those that do
not ($81K versus $76K), this
difference disappears when we
adjust the salaries for experience
and geography. Furthermore, respondents who have major
involvement in product management but do not have
major involvement in managing people, have below-average
salaries ($9K difference in adjusted median salary).
Respondents who have
major involvement in
product management but
do not have major involvement
in managing people have
MEETINGS ARE A PART OF MOST DESIGN PROFESSIONALS’
WORK WEEK: 94% of the sample spends between 1 and
20 hours per week in meetings. As we’ve seen in past salary
surveys, those who attend more meetings earn more. An
effect we see even after adjusting for experience.
Respondents from the US tend to spend more time in meetings
than those outside of the US. Among US-based respondents,
33% spend at least 9 hours per week in meetings,
while this figure is only 20% for non-US respondents.
As we would expect, meeting times vary dramatically with job
title. VPs and directors spend the most time in meetings, followed
by project/product managers. UX designers and product
designers each spend about seven hours in meetings per
week, on average, while software developers/engineers and
graphic designers only spend about four hours on average.1
1Note that these are rough average figures, since the original survey data was collected in binned ranges.
Working with Other People
MOST RESPONDENTS REPORT THAT THEY WORK WITH
PEOPLE IN A VARIETY OF ROLES. Only 3% of the sample
say they only work with (other) designers, and this group has
a median adjusted salary of $56K, far below the sample-wide
$77K. With five answer choices to pick
from (designers, programmers, product
managers, salespeople, and industrial designers),
most respondents chose three
The minority of respondents (7%) who
work with industrial designers earn
high salaries (median: $101K; adjusted
median: $82K), which is likely related
to the high wages in computers/hardware.
Aside from this, no single answer
stands out as having an effect on salary. However, it does
appear that interacting with a wider variety of roles correlates
positively with higher incomes: the adjusted median salaries
of respondents who interact with one, two, three, and four of
the listed roles are $60K, $68K, $77K, and $83K, respectively.
We also asked how many designers and programmers work
at the respondents’ organizations. Most respondents work
at companies with at least 5 designers and 20 programmers.
Generally, the more programmers and designers at a company,
the greater the salary, although
part of this gradient may be attributable
to company size, since there are higher
salaries at larger companies, and larger
companies tend to have more designers
However, even among subsets of respondents
partitioned by company size, this
pattern remains, at least for designers.
For example, among respondents from
companies with more than 1,000 and
fewer than 10,000 employees, the adjusted median salaries
of respondents who work with no more than 10 designers
is $68K, while that of respondents who work with over 10
designers was $81K. Similar patterns are present with other
The minority of
who work with
earn high salaries
Types of Products, Products or Services
MOST OF THE SAMPLE WORKS ON BOTH PRODUCTS
AND SERVICES, with 31% working on products only and
14% on services only. Respondents who work in products
only have the highest salary (median: $87K; adjusted: $77K),
and those who work in services only have the lowest (median:
$61K; adjusted: $70K).
As for types of products, most respondents work on web
products (83%) and mobile products (65%). Few respondents
(3%) work only on mobile products. Many more work on
web products only (22%), but these respondents tend to earn
less (median: $70K; adjusted: $72K) than those who work on
web products and a different type of product (median: $84K;
Two other product categories (wearables and other connected
devices) are less common, with 12% and 18% of the sample,
respectively. However, respondents who work on one or both
of these product types earn higher salaries than those that
don’t, with a median salary of $90K (adjusted: $82K).
THE TOP DESIGN PROCESS IS AGILE, with 45% of the
sample. Design sprints are a distant second (17%), followed by
waterfall (10%) and lean UX (8%). Practitioners of Agile tend
to earn above-average salaries (median: $81K; adjusted: $79K),
but not as much as those that practice lean UX (median: $89K;
adjusted: $83K) or the 7% of the sample who use a hybrid or
combination of design processes (median: $98K; adjusted: $83K).
A small subset (8%) report no design process, and these
respondents tend to earn rather low salaries: a median of
$48K, rising slightly to $53K after adjusting for geography
and experience. These respondents are more likely to come
from smaller companies with fewer designers, but this tendency
isn’t absolute: about 5% of respondents from large
companies (>1,000 employees) with over 50 designers say
that they have no design process.
We asked about a variety of tool categories—for the most
part, software—from prototyping and wireframing to project
management and user research. The answer choices included
95 tools, but another 668 were entered in “other” fields:
clearly there’s a lot of variety from which to chose from in
Respondents use an average of 12 tools, and salary generally
rises with the number of tools used. Users of 7 or fewer tools
have a median salary of just $58K, while those who use more
than 7 but less than 14 have a median of $78K, and those
who used 14 or more have $89K. The corresponding adjusted
medians preserve this upward pattern: $66K, $73K, $81K.
Even among designers with similar levels of experience, those
that have a larger set of tools earn more.
Some of the most ubiquitous tools are project management/
collaboration tools, such as Google Drive, Dropbox, and Slack.
Both the need to have consistent platforms across organizations
and these applications’ ease of use are likely contributors
to their higher usage rates. The variations in salary among
users of project management tools are not significant.
Tools: Wireframing and Prototyping
THE VAST MAJORITY OF RESPONDENTS
use at least wireframing or
prototyping tools (93%), and about
half use five or more. The most
popular wireframing and prototyping
tools are not software, but pen/
pencil and paper. These are used by
respondents of all ages and experience:
there is no indication that they
will be replaced by software anytime soon. CSS/HTML is also
frequently selected, especially for prototyping (44%), less for
Other popular tools include Invision (41%), Axure (26%),
and Keynote (19%) for prototyping; and Sketch (40%), Illustrator (40%), Balsamiq (17%),
and InDesign (17%) for wireframing.
We find only two patterns
of strong co-usage between the
set of tools in the survey: users of
Adobe’s Illustrator and InDesign,
and between Sketch and Invision
users. Sketch/Invision users tend to
earn slightly above-average salaries,
while Illustrator/InDesign users tend to earn slightly
below average. In particular, the 6% of the sample that
use Illustrator and InDesign but not either Sketch or Invision
have an especially low median salary of $54K (median
adjusted salary: $55K).
tend to earn slightly
users tend to earn slightly
Tools: Information Organization / Architecture
THE NEXT TOOL CATEGORY IS INFORMATION
ORGANIZATION/ARCHITECTURE, including software for
card sorting and mind mapping. 45% of respondents use at
least one tool in this category, but most that did use just one.
No single information organization/
architecture tool is used by more
than 10% of the sample, in stark
contrast with some of the other
tool categories, such as wireframing
or project management.
The most commonly used
architecture tools are OptimalSort, Google Drawings, Simple Card Sort, and XMind.
Users of each of these four tools have above-average
salaries. (For XMind, median salary was only $70K, but
median adjusted salary was $86K. The shift is due to
XMind being much more
popular outside of the US
than within the US.) More
generally, respondents who
use any information organization/
architecture tool earn
more than those that don’t:
the difference in median
adjusted salary is $9K.
Respondents who use any
architecture tool earn more
than those that don’t:
Tools: User Research and Testing
The category of user research and testing tools is notable for
its variety: 21 tools are used by at least 1% of the sample, far
more than any other category. About two-thirds of the sample
use one or more of these tools, although only 30% of the
sample use more than two.
The top tools in this category are SurveyMonkey, Skype,
GotoMeeting, User Testing, Google Hangouts, and Webex.
Differences in salaries among users of various user research
tools are not significant, although, as with information organization/
architecture tools, respondents who use at least one of
these tools tend to have higher salaries than those that don’t,
again by a margin of $9K.
THE FIELD OF DESIGN HAS SEEN PROFOUND CHANGES IN
THE LAST DECADE—new mediums, new tools, new frameworks
for thinking about design and how and where design
principles should be applied. Keeping up with the evolving tool
ecosystem can have an impact on one’s career development.
We see that those adopting particular tools and techniques,
e.g., Sketch and Invision, Agile, information management, and
testing, all correlating with higher salaries. We also see that
those who know more tools, work in larger organizations,
interact with a wider variety of roles, and work on multiple
platforms earn more—information that designers can use to
help expand their career horizons.
As design principles and design thinking move beyond the
world of designers, we see these findings as relevant well
beyond the world of design. Software developers, data scientists,
or anyone who does design work or works closely with
designers can benefit from understanding what designers use
and how they work.
When we quote statistics about salary, for example, that users
of this tool make this much more than users of that tool, it’s
important to remember that learning the “high salary” tool is
not guaranteed to give you a raise. This survey data is observational,
and we can’t assume cause and effect. On the other
hand, knowing that particularly well-paid designers frequently
use some tool might be a potential sign that this tool is especially
efficient or powerful, and that alone could be enough
justification for trying out a new tool.
This research is an ongoing project, and it depends on
your participation. If you’ve found this report useful, please
consider taking 5 to 10 minutes to complete the 2018 survey
yourself for next year’s report: http://www.oreilly.com/design/2018-design-salary-survey. Thank you!
Appendix A: Adjusted Median Salary
GEOGRAPHY AND EXPERIENCE CLEARLY MAKE A
DIFFERENCE IN SALARY, and this is fully expected. However,
since geography and experience can correlate with other
variables, unless we analyze all three variables together, it can
be hard to tell whether variations in salary are due to these
variables or to geography/experience.
For example, age correlates with experience and with salary, but
if we consider groups of respondents with equal experience,
then age no longer correlates with salary (at least, strongly or
monotonically). This is what we mean when we say that age and
salary don’t correlate when we “block” years of experience.
To give another example, this time with geography: the
median salary of the 9% of respondents who say they code
over 20 hours/week is $50K, while the rest of the sample
(those who spend less time coding, if any at all) is $80K.
However, the difference is attributable to the fact that most
of the people who code over 20 hours/week happen to come
from places that have lower salaries in general. For example,
36% of respondents from India, Russia, and Ukraine say that
they code over 20 hours/week, while only 1% of California respondents
do. This probably shouldn’t be taken to mean that
CA design professionals don’t code: correlations like this will
appear frequently on such surveys; namely, when there is little control over the sampling. This correlation is likely just noise
that we should try to filter out.
The solution we use in this report is to provide, when appropriate,
an additional metric, the adjusted mean salary. The
first step in computing this metric: is to create a simple model
to predict salary using country/state and experience. After trying
a few economic metrics to quantify geography, we found
that per capita GDP gave the best results.2] Using this survey’s
data, no complicated modeling technique or transformation
made a big improvement over a simple linear model, so we
stuck with the latter. The model is:
predicted_salary = 1.95 x years_of_experience + 1.26 x
per_capita_GDP – 1.29
where monetary values are in thousands of USD, and years of experience
is capped at 20 (for someone with more than 20 years
of experience, the value inserted into the model is 20). For example,
the predicted salary of someone with 7 years of experience
from Australia (where the per capita GDP is $51K) is $76.5K. This
model explains about half of salary variance in this sample.
We use this model to create the aforementioned “adjusted
median salary” statistic. This works by recalculating salaries
as if the respondents who received them were from a single,
fixed place and had the same amount of experience. The
actual fixed values are somewhat arbitrary, and we pick values
close to the sample averages: seven years of experience and
$51K for the per capita GDP, which is roughly the per capita
GDP of Australia, Denmark, Singapore, Ohio, North Carolina,
and Wisconsin. To adjust someone’s salary, we simply subtract
an amount that the model attributes to their experience and
geography, and then add a fixed amount for seven years of
experience and a per capita GDP of $51K.
Perhaps a simpler way of understanding the calculation is to consider
the residual, the difference between the observed (reported)
salary and the predicted salary. If someone earns much more
than we would expect given their experience and location, their
salary residual is high. We calculate the residual and then add it
to $76.7K, the predicted salary for someone with seven years of
experience living in a place with a per capita GDP of $51K.
For example, suppose someone from New York with five
years of experience earns $120K. According to the model, this
person is expected to earn $100.3K, so they “outperform”
the expectation by $19.7K (this is the residual). If we add this
$19.7K to the fixed $76.7K, we arrive at the adjusted salary,
$96.4K. It is worth noting that a single adjusted value in
isolation doesn’t have much relevance; the real purpose of
presenting these adjusted values is comparison. In a sense, we are really just including residuals, and the conversion
from residual to adjusted salary (i.e., the operation of adding
$76.7K) is performed to convert the number to something
less abstract and so that we don’t have to introduce technical
language (“residual”) into the text.
It is likely that with more data, a more complicated model (i.e.,
one that still just takes in experience and GDP, but is not a simple
linear model) would provide better results. For example, it seems
likely that not every incremental year of experience is the same
(e.g., 3 to 4 years versus 13 to 14 years) or that experience has
the same relation with salary in every place (e.g., one year of
experience adds as much in Switzerland as it does in Poland).
However, the simple linear model above performed just as
well as a few others we tried that attempted to explain a more
complicated relationship, and furthermore, the simplicity has the
major advantage that it is easier for you, the reader, to plug in
your own numbers. Unlike the models in other O’Reilly salary
surveys, including last year’s Design report, this model only takes
two variables, so insofar as there are other relevant variables that
affect salary, this model will miss them. More fundamentally,
the variance of salaries for any given experience and per capita
GDP is quite high: the predicted salary that the model outputs
is an average, and any particular salary may fall a ways from it.
For that reason, the real value of this model is not to predict
someone’s salary, but to allow us to compare groups of salaries
in a way that the comparison is minimally impacted by significant
differences in experience and geography.