{"id":239,"date":"2014-11-11T22:00:00","date_gmt":"2014-11-11T22:00:00","guid":{"rendered":"http:\/\/measuringu.com\/salary-survey2014\/"},"modified":"2021-01-28T06:29:59","modified_gmt":"2021-01-28T06:29:59","slug":"salary-survey2014","status":"publish","type":"post","link":"https:\/\/measuringu.com\/salary-survey2014\/","title":{"rendered":"User Experience Salaries &#038; Calculator (2014)"},"content":{"rendered":"<p><a href=\"http:\/\/www.flickr.com\/photos\/thegrid-ch\/5087679262\/sizes\/s\/in\/photostream\/\"><img decoding=\"async\" style=\"float: left; padding: 10px; margin-right: 20px;\" src=\"https:\/\/measuringu.com\/images\/coins2014.jpg\" border=\"0\" \/><\/a>The results of the <a href=\"http:\/\/uxpa.org\/resources\/past-salary-surveys\">2014 UXPA salary survey<\/a> are in.<\/p>\n<p>This is the 4th UXPA survey I&#8217;ve crunched the numbers for and this year was just as interesting and showed <a href=\"https:\/\/measuringu.com\/ux-salary-2011.php\">similar patterns as 2011<\/a>.<\/p>\n<h2>The Results<\/h2>\n<ul>\n<li>The survey is based on 1,235 responses from 50 countries, with 66% of the responses coming from the US. Other significant numbers came from the UK (13%), Canada (3%), Australia (2%), India (2%), and Germany (1%).<\/li>\n<li><b>The median 2014 salary is $92k: <\/b>This is a nominal increase of 2.2% over the 2011 results, but is in fact down by 3.3% after adjusting for inflation. Since 2005, the inflation adjusted median salary is basically flat.<\/li>\n<\/ul>\n<ul>\n<li>The $92k average is based on many variables, so you can&#8217;t look at that alone to gauge how well your own salary stacks up.<\/li>\n<\/ul>\n<h3>What Factors Affect Salaries?<\/h3>\n<p>You can more accurately determine the worth of your skills and personal characteristics if you take into account the variables that most affect salaries. I conducted a multiple-regression analysis to see which variables statistically predict salaries. The significant variables differ somewhat between the US and Non-US countries, so I broke things out separately as shown in the figure below.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/measuringu.com\/images\/salary-factors.jpg\" width=\"612\" height=\"284\" \/><\/p>\n<p>The regression equations predicted roughly 60% of the salary variations. (The large blue sections of the chart represent the unmeasured or unknown variables.) For behavioral science, explaining 60% of variation is pretty good. Individual factors\u2014skills, personalities, companies, etc.\u2014affect your salary; keep that in mind as you review the numbers and use the calculator below.<\/p>\n<p>The most influential factors for salaries in the UX field are (in order of importance):<\/p>\n<ol>\n<li><span style=\"font-weight: bold;\">Job Level:.<\/span> The biggest driver of salaries in the US is job level, accounting for 24% of the variation in salaries. Managers with direct reports (mid-level supervisor, senior supervisor, executive, owner\/director) make more than the average salary. Entry-level positions, not surprisingly, pay less than average<\/li>\n<li><span style=\"font-weight: bold;\">Geography:\u00a0<\/span> In real estate it&#8217;s location, location, location, and of course the same is true of UX salaries. The country, and in US the region, have a major impact on what you get paid. For non-US respondents, the country you live in accounts for 32%\u2014the single biggest factor\u2014of the variation in salaries. The average salary in the US is around $103k, compared to about $82k in Germany, Australia, Canada and $78k in the UK. Spain ($52k) and India ($30k) have salaries statistically lower than the other countries.In the US, the highest salaries are in Northern California\u00a0 (Silicon Valley), followed by Southern California and the Northeast. Not surprisingly, these are also places that cost the most to live. This is the first year we collected state information, which boosts the predictive power of the regression equation. The map below shows the number of usable salaries we analyzed for each state and for what we called the region.\n<p><a href=\"https:\/\/measuringu.com\/images\/us-map.jpg\"><img decoding=\"async\" src=\"https:\/\/measuringu.com\/images\/us-map.jpg\" width=\"487\" height=\"287\" \/><\/a><\/li>\n<li><span style=\"font-weight: bold;\">Experience<\/span>: The more experience you have, up to about 25 years, the more you get paid. On average, each year of experience adds about $1500 to the base salary for US respondents and around $1900 for non-US respondents.<\/li>\n<li><span style=\"font-weight: bold;\">Company Size<\/span>:\u00a0 The bigger the company, the bigger the paycheck.\u00a0 It&#8217;s hard for small companies to compete with the pig payroll of larger companies. In the US, salaries at companies with fewer than 100 employees are from $10k to $20k less than the equivalent positions at larger companies.<\/li>\n<li><span style=\"font-weight: bold;\">Education<\/span>: Your degree matters, somewhat. Around 7% of respondents reported having a Ph.D. (down slightly from 10% in 2011). Having a Ph.D. alone adds around $9k to the average salary for US workers and $19k for non-US workers. About half of respondents hold a masters degree.\u00a0 In the US, having only a bachelors degree tends to lower your salary by about $5,000.<\/li>\n<li><b>Gender &amp; Age<\/b>: For non-US respondents, men tend to get paid $10k more on average than women. In the US, gender statistically impacts salary only when coupled with age and years of experience. This three-way interaction shows women are paid slightly more than men in the 18-25 age cohort, but then have less experience, and consequently lower salaries, as they get into their late 20s and 30s. This was a pattern also seen <a href=\"https:\/\/measuringu.com\/ux-gender.php\">in the 2011 data<\/a>.For age, in general, younger respondents also tend to get paid less, with those aged 18-25 tending to earn $8k-$10k less overall than older cohorts. This suggests that it&#8217;s not inexperience only that lowers your salary; it&#8217;s youth AND inexperience, as both age and experience are significant predictors of salary.<\/li>\n<\/ol>\n<h2>Salary Calculator<\/h2>\n<p>To estimate your theoretical salary based on the 2014 data, enter your information in the calculator below.\u00a0 (Only the variables that were found to statistically impact salary were included. Taken together, these will explain around 60% of the variation in salaries.)<\/p>\n<form name=\"d\">\n<div class=\"sampleBoxLarge2\">\n<table border=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td>\n<table border=\"0\">\n<tbody>\n<tr>\n<td>Employment Status<\/td>\n<td><select name=\"full\" onchange=\"calcRegress();\"><option value=\"1\">FullTime<\/option><option value=\"0\">PartTime<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>Country<\/td>\n<td><select id=\"country\" name=\"country\" onchange=\"disable(); calcRegress();\"><option value=\"1\">United States<\/option><option value=\"2\">United Kingdom<\/option><option value=\"3\">Canada<\/option><option value=\"4\">Argentina<\/option><option value=\"5\">Australia<\/option><option value=\"6\">India<\/option><option value=\"7\">Germany<\/option><option value=\"8\">Mexico<\/option><option value=\"9\">France<\/option><option value=\"10\">Other Country<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>Years of experience<\/td>\n<td><select name=\"yrs\" onchange=\"calcRegress();\"><option selected=\"selected\" value=\"0\"><\/option><option value=\"1\">0 -1 years<\/option><option value=\"3\">2-4 years<\/option><option value=\"6\">5-7 years<\/option><option value=\"9\">8-10 years<\/option><option value=\"13\">11-15 years<\/option><option value=\"18\">16-20 years<\/option><option value=\"21\">21+ years<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>Employment Level<\/td>\n<td><select name=\"mgr\" onchange=\"calcRegress();\"><option selected=\"selected\" value=\"0\"><\/option><option value=\"1\">Entry-level (less than two years practice)<\/option><option value=\"2\">Executive<\/option><option value=\"3\">Mid-level, non-supervisory<\/option><option value=\"4\">Mid-level, supervisory (e.g. Manager or Sr. Manager)<\/option><option value=\"5\">Owner \/ Director<\/option><option value=\"6\">Senior-level, non-supervisory (e.g. Senior or Principal)<\/option><option value=\"7\">Senior-level, supervisory (e.g. Director or VP)<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>No. of Employees at Your Company<\/td>\n<td><select name=\"size\" onchange=\"calcRegress();\"><option selected=\"selected\" value=\"0\"><\/option><option value=\"1\">1 to 10<\/option><option value=\"2\">11 to 100<\/option><option value=\"3\">101 to 1,000<\/option><option value=\"4\">1,001 to 10,000<\/option><option value=\"5\">10,001+<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>Age<\/td>\n<td><select name=\"age\" onchange=\"calcRegress();\"><option selected=\"selected\" value=\"0\"><\/option><option value=\"1\">18-25<\/option><option value=\"2\">26-35<\/option><option value=\"3\">36-45<\/option><option value=\"4\">46-55<\/option><option value=\"5\">56-65<\/option><option value=\"6\">Over 65<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>Education Level<\/td>\n<td><select name=\"phd\" onchange=\"calcRegress();\"><option selected=\"selected\" value=\"0\"><\/option><option value=\"1\">High School (&#8216;A&#8217; level \/ GCSE \/ Baccalaureate)<\/option><option value=\"2\">Technical or Associate&#8217;s Degree<\/option><option value=\"3\">College or University (Bachelor&#8217;s degree)<\/option><option value=\"4\">Master&#8217;s Degree<\/option><option value=\"5\">Ph.D.<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>Gender<\/td>\n<td><select name=\"gender\" onchange=\"calcRegress();\"><option selected=\"selected\" value=\"2\"><\/option><option value=\"0\">Female<\/option><option value=\"1\">Male<\/option><\/select><\/td>\n<\/tr>\n<tr>\n<td>US Region<\/td>\n<td>\n<div id=\"reg\"><select id=\"region\" name=\"region\" onchange=\"calcRegress();\"><option selected=\"selected\" value=\"0\"><\/option><option value=\"1\">Mid-Atlantic<\/option><option value=\"2\">Midwest<\/option><option value=\"3\">Mountain West<\/option><option value=\"4\">Northeast<\/option><option value=\"5\">Northern California<\/option><option value=\"6\">Pacific Northwest<\/option><option value=\"7\">Southeast<\/option><option value=\"8\">Southern California<\/option><option value=\"9\">Southwest<\/option><\/select><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<td align=\"right\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Your predicted salary is<\/p>\n<div id=\"d5\" class=\"nobeakdiv\">&#8212;<br \/>\n883 responses match your selection, with an average salary of $101,702<\/div>\n<\/form>\n<p><i><br \/>\nUpdated 11\/23\/2014:<\/i> I added functionality to the calculator so you can select different combinations of filters to see how many responses from the survey match the criteria and the average salary for this subset.\u00a0 Keep in mind that some combinations will have few or no responses from the UXPA survey. The predicted salary still is generated from a regression equation but will only provide a value if all filters have been selected. The benefit of the regression equation is that it can predict salaries, even if no one in the survey had that exact combination of attributes. However, the disadvantage is that for some combinations, it can provide misleading results (a high amount of error), especially when there are few or no responses from the survey.<br \/>\nExamples:<\/p>\n<ul>\n<li>The estimated salary for a 31-year-old female mid-level non supervisor with five years of experience, at a mid-sized company (500 employees), in the Northeast US, and holding a Bachelor&#8217;s degree would be $87,211. There were two people who had this criteria in the survey with an average salary of $87,670&#8211;showing good agreement between the predicted and actual values.<\/li>\n<li>A 42-year-old female mid-level supervisor, with a PhD and 15 years of experience, working in the San Francisco Bay area, at a large company (10,000+) would be expected to earn on average $150k. There were no responses who had this criteria.<\/li>\n<li>A 50-year-old male senior-supervisor with a master&#8217;s degree and 20 years of experience, working in Boston at a company with 250 employees has a predicted salary of $158k. One person in the survey met this criteria with an actual salary of $150k (close agreement). That same profile in the UK would earn around $134k (USD), but there were no respondents in the survey with this criteria.<\/li>\n<\/ul>\n<h2>Conclusion<\/h2>\n<p>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 reported making, so expect individual results to vary.<\/p>\n<p>Note:<\/p>\n<ul>\n<li>Salaries generated by this calculator are estimates around the average; you can expect variability above and below each estimate.<\/li>\n<li>The calculator estimates values even for combinations unlikely to exist (e.g., executives with two years of experience) so proceed with caution when computing &#8220;what-if&#8221; scenarios<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The results of the 2014 UXPA salary survey are in. This is the 4th UXPA survey I&#8217;ve crunched the numbers for and this year was just as interesting and showed similar patterns as 2011. The Results The survey is based on 1,235 responses from 50 countries, with 66% of the responses coming from the US. 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