the Web design survey - A List Apart

Findings From

the Web Design Survey

2007 October 16, 2007 Permanent location: www.alistapart.com/articles/2007surveyresults

Table of Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Who Are You? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 I. Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

II. Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

III. Ethnicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

IV. Job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

V. Geographic region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 VI. US region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

Education and Commitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 ViI. Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Viii. Field of study related or unrelated to current web work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

iX. Excited by field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

x. Have a personal site/blog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

xI. Time personal site/blog online . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13

It’s a Workaday World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 XII. Type of organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

XIII. Organization size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

XIV. Hours worked per week . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

XV. Years in the field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16

XVI. Degree of web work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16

XVII. Years at current job . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

XVIII. Number of jobs held . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

XIX. Next career move . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18

XX. Paid vacation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18

XXI. Paid holidays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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WEB DESIGN SURVEY 2007

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Table of Contents

Money, Honey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 XXII. Salary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 XXIII. Amount of last raise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21

XXIV. Time since last raise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

22

Perceived Biases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 XXV. Perceived geographic bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23

XXVI. Perceived age bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23

XXVII. Perceived gender bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24

XXVIII. Perceived ethnic bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24

About the Detailed Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1. Jobs and Titles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Fig. 1.1 Job titles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

26

Fig. 1.2 Job title distribution by organization type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27

Fig. 1.3 Job title distribution by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29

Fig. 1.4 Gender distribution by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Fig. 1.5 Percentage of job-title holders who earn salaries of $100K+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31

2. Education—How Relevant? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Fig. 2.1 Education levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

32

Fig. 2.2 Salary range by education level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33

Fig. 2.3 Perceived relevance of education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34

Fig. 2.4 Perceived relevance of education by salary range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34

Fig. 2.5 Perceived relevance of education by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35

Fig. 2.6 Perceived relevance of education by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36

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3. Salaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Fig. 3.1 Salary levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

37

Fig. 3.2 Salary range by organization type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38

Fig. 3.3 Salary range by organization size

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Fig. 3.4 Salary range by longevity in the field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

40

Fig. 3.5 Salary range by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41

Fig. 3.6 Gender distribution by organization size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41

Fig. 3.7 Salary range by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

4. Sticking With It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Fig. 4.1 Longevity in field by organization type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43

Fig. 4.2 Number of jobs by years in field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Fig. 4.3 Future job moves by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45

Fig. 4.4 Job satisfaction by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Fig. 4.5 Job satisfaction by ethnicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47

Fig. 4.6 Job satisfaction by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47

Fig. 4.7 Job satisfaction by organization type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Fig. 4.8 Job satisfaction by salary range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Fig. 4.9 Job satisfaction by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5. Punching the Clock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Fig. 5.1 Hours worked by organization type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Fig. 5.2 Hours worked by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51

Fig. 5.3 Hours worked by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51

Fig. 5.4 Hours worked by degree of web work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

52

Fig. 5.5 Location of work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6. Everybody’s Got One (a Blog) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Fig. 6.1 Prevalence of blogging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

54

Fig. 6.2 Prevalence of blogging by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55

Fig. 6.3 Prevalence of blogging by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55

Fig. 6.4 Prevalence of blogging by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

56

Fig. 6.5 Prevalence of blogging by salary range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

57

7. Perceptions of Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Fig. 7.1 Perceived geographic bias by global region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58

Fig. 7.2 Perceived geographic bias by US region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Fig. 7.3 Perceived age bias by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59

Fig. 7.4 Perceived age bias by global region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

60

Fig. 7.5 Perceived gender bias by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

60

Fig. 7.6 Perceived gender bias among female respondents by global region . . . . . . . . . . . . . . . . . . . . . . . . . . .

60

Fig. 7.7 Perceived ethnic bias by ethnicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61

Fig. 7.8 Perceived ethnic bias by global region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61

8. Evidence of Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Fig. 8.1 Perceived geographic bias by salary range among full-time workers (US) . . . . . . . . . . . . . . . . . . . . . . .

62

Fig. 8.2 Perceived geographic bias by salary range among full-time workers (EU/UK) . . . . . . . . . . . . . .

63

Fig. 8.3 Perceived geographic bias by salary range among full-time workers

(Rest of the world) . . . . 63

Fig. 8.4 Perceived age bias by salary range among full-time workers under 25

. . . . . . . . . . . . . . . . . . . . . . . 64

Fig. 8.5 Perceived age bias by salary range among full-time workers over 50 . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Fig. 8.6 Perceived gender bias by salary range among full-time female workers . . . . . . . . . . . . . . . . . . . . . . .

65

Fig. 8.7 Perceived gender bias by salary range among full-time male workers . . . . . . . . . . . . . . . . . . . . . . . . . 66 Fig. 8.8 Perceived ethnic bias by salary range among full-time white workers . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 8.9 Perceived ethnic bias by salary range among full-time Asian workers

67

. . . . . . . . . . . . . . . . . . . . . . . . 68

Fig. 8.10 Perceived ethnic bias by salary range among full-time Hispanic workers

. . . . . . . . . . . . . . . . . . . 68

Fig. 8.11 Perceived ethnic bias by salary range among full-time black workers . . . . . . . . . . . . . . . . . . . . . . . . . 69

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4

Table of Contents

9. Staying Current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Fig. 9.1 Methods of staying current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Fig. 9.2 Participation in formal training by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71

Fig. 9.3 Participation in formal training by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71

Fig. 9.4 Participation in formal training by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

72

Fig. 9.5 Participation in formal training by organization size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

73

Fig. 9.6 Participation in formal training by salary range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

73

10. Skills and Skill Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Fig. 10.1 Claimed skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 10.2 Perceived back-end skill gaps by age group Fig. 10.3 Perceived back-end skill gaps by gender

74

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Fig. 10.4 Perceived back-end skill gaps by longevity in field

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Fig. 10.5 Perceived back-end skill gaps by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

76

Front-End Programming Skills and Skill Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Fig. 10.6 Perceived front-end skill gaps by age group

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Fig. 10.7 Perceived front-end skill gaps by gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 10.8 Perceived front-end skill gaps by longevity in field

77

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Fig. 10.9 Perceived front-end skill gaps by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

78

CSS Skills and Skill Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Fig. 10.10 Perceived CSS skill gaps by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79

Markup Skills and Skill Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Fig. 10.11 Perceived markup skill gaps by job title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

For Additional Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Thanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Credits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

A LIST APART


5

Introduction

Introduction Between April 24th and May 22nd, 2007, A List Apart conducted the first survey of “people who make websites” (alistapart.com/articles/webdesignsurvey); 32,831 web professionals participated. Straightforward survey responses are summarized in Figures i–xxviii. Detailed findings, derived by crossreferencing various data, make up the remainder and bulk of this report, and constitute its chief claim to significance. A Note On the Summary Charts Participants occasionally chose not to answer a question. In the summary charts (i-xxviii), when a large number of respondents refrained from responding, we make note of the percentage of all respondents who answered the question and base the chart on those respondents who did answer the question. In all other cases, “No answer” is included as a data point in the chart.

A LIST APART


6

Who Are You?

Who Are You? Come here often? What’s your sign?

Respondents were asked basic questions about age, gender, job title, and so on. Age

I.

Under 21

6.7% 17.8%

21-24

43.6%

25-32 17.8%

33-38 10.2%

39-50 2.7%

51-60 Over 60 No answer

II.

0.4% 0.8%

Gender

ii. Gender

Male

82.8% 16.1%

Female No answer

A LIST APART

1.1%


7

Who Are You?

III.

iii. Ethnicity

Ethnicity White

84.6% 6.6%

Asian Hispanic

3.3%

Other

3.2%

Black

1.2%

No answer

1.0%

IV.

iv. Job title

Job title Developer

25.3% 19.9%

Web Designer 15.7%

Other 10.4%

Designer 6.3%

Webmaster

5.4%

Creative Director, Art Director

4.2%

Interface Designer, UI Designer

3.8%

Project Manager Web Producer

2.1%

Web Director

1.9%

Information Architect

1.9%

Writer/Editor

1.2%

Usability Expert/Consultant/Lead

1.2%

Accessibility Expert/Consultant/Lead No answer

A LIST APART

0.4% 0.1%


8

Who Are You?

V.

v. Geographic region

Geographic region United States

48.1%

EU (except the UK)

20.0%

United Kingdom

11.2%

Canada

5.8%

Australia and Pacific Rim

4.7%

South America

2.6%

Asia (except India)

2.4%

Non-EU Europe

2.3%

India

0.8%

Central America, Mexico, Caribbean

0.6%

Middle East

0.6%

Africa

0.5%

No answer

0.3%

VI.

vi. US region

US region US: Northeast

13.1% 10.8%

US: Midwest 7.5%

US: Southwest

7.0%

US: Northwest 6.1%

US: Southeast 2.7%

US: South US: Great Plains

0.7%

US: Alaska & Hawaii

0.2%

No answer

0.3%

A LIST APART


9

Who Are You?

Discussion Questions about ethnicity are always problematic and more than occasionally ambiguous. Additionally, our breakout of US and non-US locations dismayed some respondents and may have invited misinterpretation. We will revise our approach to these areas next time. Our next survey will also use more consistent intervals in questions about age.

A LIST APART


10

Education and Commitment

Education and Commitment What’s your major?

Respondents were asked about their educational background, extracurricular publications, and whether web work still turned them on. ViI.

Education

ix. Education

No degree

4.7% 14.6%

High school

12.5%

Junior college

51.6%

Bachelor's 15.0%

Master's Doctorate No answer

A LIST APART

1.3% 0.3%


11


Viii.

Field of study related or unrelated to current web work x. Related field of study

46.6%

iX.

53.4%

Excited by field No

Related Unrelated

xi. Excited by field 1.4% 18.5%

Yes—Once in a while

43.6%

Yes—Frequently 35.0%

Yes—Very frequently

x.

Don't know

1.1%

No answer

0.5%

Have a personal site/blog

vii. Have personal site/blog

Yes

72.5% 27.3%

No No answer

A LIST APART

0.2%


12


xI.

Time personal site/blog online site/blog online (27.5% did not answer) viii. Time personal

1 year or less

18.3%

1-2 years

17.3%

2-3 years

14.8%

3-4 years

11.4%

4-5 years

9.8%

5-6 years

7.3%

6-7 years

6.2%

7-8 years 8-9 years 9-10 years Over 10 years

4.5% 2.9% 2.7% 4.8%

Percentages are based on the 72.5% of respondents who answered this question.

Discussion Next time, we’ll reframe the question “What is your educational background?” as “What is your highest level of educational attainment?” Also, “no degree” was intended to mean no college degree, but could be interpreted, given its placement among the choices, as “no high school diploma.” Reshuffling and updating the categories in this section will eliminate these ambiguities. Question 10 asked, “Were your college studies directly related to your work as a web designer? (For instance, did you study Graphic Design, Computer Science, or Library Science?)” The question is ambiguous, and a scalar response (for instance, “on a scale of 1 to 10…”) would be more meaningful than a yes/no answer.

A LIST APART


13

It’s a Workaday World

It’s a Workaday World Plans and perks and places to work.

Respondents were asked about career plans and job perks, and to identify the kinds of organization that employ them. XII.

xii. Type of organization

Type of organization For-profit enterprise (corporation)

28.4% 23.4%

Self-employed / freelance

22.3%

Design, web, or IA agency / consultancy 8.6%

School, college, university 5.4%

Start-up

4.8%

Non-profit

4.1%

Government agency

2.8%

Other No answer

A LIST APART

0.3%


14


XIII.

Organization size

xiii. Organization size (23.7% did not answer)

Self-employed/freelance

4.4% 12.2%

2-5 employees

11.0%

6-10 employees

13.5%

11-25 employees 10.6%

26-50 employees

17.8%

51-300 employees 7.0%

301-750 employees

8.7%

751-3000 employees

14.7%

More than 3000 employees Percentages are based on the 76.3% of respondents who answered this question.

XIV.

Hours worked per week

Under 20 hours

xiv. Hours worked per week

8.3% 7.6%

20-30 hours

23.0%

30-40 hours

42.1%

40-50 hours 12.8%

50-60 hours 5.9%

Over 60 hours No answer

A LIST APART

0.3%


15


XV.

Years in the field

xv. Years in the field

1 year or less

8.8% 12.3%

1-2 years

12.4%

2-3 years 9.7%

3-4 years

9.1%

4-5 years

8.5%

5-6 years

9.7%

6-7 years 8.5%

7-8 years 5.3%

8-9 years

6.1%

9-10 years

9.0%

Over 10 years No answer

XVI.

0.3%

Degree of web work

xvi. Degree of web work

I'm a full-time web worker

35.6%

Most of my work is web-design-related

27.1%

About half my work is web-design-related

20.3%

Web design is a small part of what I do No answer

A LIST APART

16.5% 0.5%


16


Years at current job xvii. Years at current job (23.8% did not answer)

XVII.

1 year or less

32.2%

1-2 years

22.4%

2-3 years

13.8%

3-4 years

8.0%

4-5 years

5.5%

5-6 years

4.9%

6-7 years

4.3%

7-8 years

2.6%

8-9 years

1.3%

9-10 years

1.4%

Over 10 years

3.6%


XVIII.

Number of jobs held

xviii. Number of jobs

1

23.7% 22.6%

2 19.1%

3 10.4%

4 6.4%

5 2.8%

6 7 8 9 10

1.3% 1.0% 0.3% 0.4% 9.8%

More than 10 No answer

A LIST APART

2.2%


17


XIX.

Next career move

xix. Next career move

Stay where I am

22.5% 21.3%

Get a promotion at my current job

19.9%

Start my own business

19.0%

New job in a new organization 12.8%

Other Get my first job in the field No answer

XX.

Paid vacation

2.7% 1.8%

xx. Paid vacation (23.9% did not answer)

Not applicable Under 6 days

18.4% 2.2% 11.9%

6-10 days

23.2%

11-15 days 15.1%

16-20 days

17.4%

21-25 days 11.7%

Over 25 days


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18


XXI.

Paid holidays

xxi. Paid holidays (24.5% did not answer)

0 days 1-3 days

15.1% 6.0% 34.0%

4-8 days 17.7%

9-11 days 11-13 days 13-15 days

5.9% 4.5% 16.8%

Over 15 days


Discussion Although these questions bore fruit, the section proved somewhat ambiguous for freelancers, the selfemployed, creators or employees of small startups, and others in niches that play a large role in the web design economy. Question 14 (“For what kind of organization do you work?”) was especially problematic, as the choices were not mutually exclusive. A freelancer might legitimately answer “self-employed,” “start-up,” “forprofit enterprise,” and “design consultancy.” Next time, the question might be rephrased as “What is your employment status?” and choices could be: self-employed as a sole practitioner; self-employed head or partner in a consulting firm; employee of a non-profit; employee of a government agency; employee of a school/college/university; employee of a design, web or IA agency/consultancy; employee of other for-profit enterprise or company; full-time student; and other (specify).

A LIST APART


19

Money, Honey

Money, Honey How are you doing?

Respondents were asked about salary and raises. XXII.

Salary

xxii. Salary

Under $10,000

17.1%

$10,000-$19,999

9.4%

$20,000-$39,999

20.2%

$40,000-$59,999

23.4%

$60,000-$79,999

14.3%

$80,000-$99,999

6.8%

$100,000-$119,999 $120,000-$149,999 Over $150,000 No answer

A LIST APART

2.9% 1.6% 1.3% 2.9%


20

Money, Honey

XXIII.

xxiii

Amount of last raise Not applicable

My salary decreased

28.8% 1.6%

1-5%

31.4%

6-10%

16.3%

11-15%

7.5%

16-20%

4.6%

21-25% 26-30%

3.1% 1.5%

31-35%

1.1%

36-40%

0.7%

41-45%

0.4%

46-50%

0.9%

51%-75%

0.9%

76%-100%

0.4%

Over 100%

0.6%


A LIST APART


21

Money, Honey

XXIV.

Time since last raise xxiv. Time since last raise (24.3% did not answer)

Not applicable

27.5% 23.9%

0-3 months ago

24.4%

3-9 months ago 14.0%

9-12 months ago 7.4%

1-2 years ago 2-3 years ago

1.7%

3-4 years ago

0.5%

Over 4 years ago

0.6%


A LIST APART


22

Perceived Biases

Perceived Biases I’m okay, you’re okay?

Respondents were asked if they believed geography, gender, and other factors had slowed the progress of their careers or made earning a living more difficult. XXV.

Perceived geographic bias

xxv. Perceived xxv. Perceived geographic geographic bias bias

DefinitelyDefinitely yes yes

13.7%

Cautiously Cautiously yes yes Maybe

14.7%

13.7% 14.7%

Maybe

21.9%

21.9%

ProbablyProbably not not

27.4%

DefinitelyDefinitely not not No answer No answer 0.9%

XXVI.

21.4%

xxvi. Perceived age bias 6.3%

Cautiously yes

11.1%

Maybe

16.4%

Probably not

32.3%

Definitely not No answer

A LIST APART

21.4%

0.9%

Perceived age bias

Definitely yes

27.4%

32.9% 1.1%


23

Perceived Biases

XXVII.

xxvii. Perceived gender bias

Perceived gender bias

Definitely yes Cautiously yes

1.7% 3.1%

Maybe

6.4%

Probably not

23.7%


XXVIII.

63.8% 1.3%

Perceived ethnic bias

Definitely yes Cautiously yes

xxviii. Perceived ethnic bias

1.0% 1.7%

Maybe

5.1%

Probably not

21.1%


70.0% 1.1%

Discussion We asked about respondents’ personal experience of bias, not about their perception of bias in the field as a whole. Both questions are potentially useful, but our survey addressed only the first.

A LIST APART


24

About the Detailed Findings

About the Detailed Findings The findings presented next have been derived from the survey data, organized into numerous graphs and charts. A few notes may be of help: • Because income figures are given in ranges, it is not possible to apply a formula that can determine a reliable average income for any particular subgroup of respondents. In some cases, we’ve “eyeballed” the distribution of respondents into income categories, and in others calculated a weighted average; both approaches are intended to help us reach conclusions about relative income for various segments of the sample. The weighted average computes an income amount for comparison purposes by assigning to each respondent the midpoint of the income range they indicated. This is not an average income for any segment of the sample, but it is a useful relative figure. • Many factors can influence income: degree of career development and success, type of employer, number of hours worked per week, and the impact of potential discrimination regarding gender, ethnicity, etc. While the charts examine relative income across the entire sample with regard to these variables, the portion of the sample used to explore potential evidence of bias includes only the respondents who work essentially full-time (i.e., 40 to 60 hours per week). • It is important to remember that questions about perceptions of bias in the web design field ask specifically if the respondent feels that his or her career has been impacted by bias, not whether the respondent perceives there to be discrimination in the field. This shaped the ways in which we were able to compare perception of bias to evidence of bias. • Analyses contained in this report should be considered descriptive; we have made no attempt to assess causality among survey variables. Care should therefore be taken before extrapolating the observations that follow into predictive or causal relationships. • We called this a “web design” survey, but it really describes all kinds of web professionals, not just designers—and all kinds of web professionals, not just designers, completed the survey. We plan to change the survey’s name next year to include a larger group of our respondents.

A LIST APART


25

1. Jobs and Titles

Jobs and Titles What does your business card say you do for a living?

1

The overall distribution of job titles in the survey responses was fairly broad, showing that many disciplines and skill sets are involved in the creation of websites (Fig. 1.1). The variety of titles also indicates an industry-wide lack of consensus and standardization. Fig. 1.1

Fig. 1.1 Job titles

Job titles Developer

25.4% 19.9%

Web Designer 15.7%

Other 10.4%

Designer 6.3%

Webmaster

5.5%


4.3%


3.8%

Project Manager Web Producer

2.1%


1.9%

Web Director

1.9%

Writer/Editor

1.2%


1.2%

Accessibility Expert/Consultant/Lead

A LIST APART

0.4%


26

1. Jobs and Titles

Fig. 1.2

Job title distribution by organization type Design, web, For-profit School, Selfor IA agency/ enterprise Government college, employed/ consultancy (corporation) agency Non-profit university freelance

Accessibility Expert/ Consultant/Lead

Start-up

Other

Overall

0.4%

0.4%

1.3%

0.6%

0.4%

0.4%

0.1%

0.3%

0.4%


10.9%

4.3%

1.2%

2.8%

1.4%

4.7%

6.0%

3.3%

5.5%

Designer

14.0%

7.8%

3.3%

7.4%

6.7%

14.0%

7.1%

11.3%

10.4%

Developer

24.7%

32.2%

24.8%

17.9%

18.9%

20.3%

34.8%

19.5%

25.4%


2.9%

2.3%

1.7%

1.5%

1.3%

1.0%

2.1%

1.2%

1.9%


4.5%

6.6%

2.2%

2.1%

1.7%

2.1%

8.2%

3.8%

4.3%

Project Manager

5.2%

4.2%

4.2%

3.9%

3.0%

1.9%

5.4%

3.3%

3.8%

Usability Expert/ Consultant/Lead

1.7%

1.7%

1.3%

0.3%

0.8%

0.7%

0.6%

0.8%

1.2%

Web Designer

20.3%

16.0%

14.9%

14.3%

14.5%

30.1%

14.2%

16.5%

19.9%

Web Director

2.3%

1.9%

1.8%

3.1%

2.8%

1.1%

2.0%

1.3%

1.9%

Web Producer

2.2%

2.3%

1.9%

2.8%

1.4%

1.8%

1.9%

1.8%

2.1%

Webmaster

1.9%

5.2%

15.4%

15.9%

14.4%

6.0%

3.2%

7.3%

6.3%

Writer/Editor

0.3%

1.3%

2.1%

3.3%

1.7%

1.4%

0.8%

1.6%

1.2%

Other

8.9%

13.9%

24.0%

24.1%

31.0%

14.6%

13.4%

27.9%

15.7%

Total

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

Titles vary somewhat by type of organization, as seen in Fig. 1.2. • A greater percentage of creative directors is found at design, web, and information architecture agencies and consultancies than at other kinds of organizations. • At for-profits and start-ups, there are greater percentages of developers and interface designers than there are at other kinds of organizations.

A LIST APART


27

1. Jobs and Titles

• Project managers are less likely—and web designers more likely—to be self-employed than respondents holding other titles. • A smaller percentage of usability experts work at non-profits than at other kinds of organizations. • A greater percentage of webmasters work at government, non-profit, and school/college jobs than at jobs in other kinds of organizations. Overall, these findings seem to imply that titles representing a more current (or emerging) understanding of the field are more prevalent at for-profits and start-ups than at non-profits, government agencies, and schools. Put simply, based on this data, for-profit and start-up companies appear to be ahead of the curve in their understanding of the field.

A LIST APART


28

1. Jobs and Titles

Fig. 1.3

Job title distribution by age group Under 21

21-24

25-32

33-38

39-50

51-60

Over 60

Overall


0.1%

0.2%

0.4%

0.5%

0.7%

0.8%

0.7%

0.4%


1.3%

2.6%

5.9%

9.1%

5.6%

4.3%

0.7%

5.5%

Designer

11.6%

13.5%

11.1%

8.1%

7.1%

6.2%

3.6%

10.4%

Developer

25.5%

31.4%

27.9%

20.2%

17.3%

11.2%

8.7%

25.4%


0.6%

1.1%

1.7%

3.3%

2.8%

1.8%

1.4%

1.9%


2.0%

3.6%

4.7%

5.2%

4.0%

1.7%

0.7%

4.3%

1.5%

2.6%

4.2%

4.5%

4.8%

2.6%

2.9%

3.8%

0.3%

0.5%

1.2%

1.7%

1.9%

2.3%

0.7%

1.2%

Web Designer

28.9%

23.9%

19.1%

16.7%

17.5%

17.6%

14.5%

19.9%

Web Director

0.5%

1.0%

2.0%

2.7%

3.0%

2.2%

0.7%

1.9%

Web Producer

1.0%

1.5%

2.1%

2.5%

2.7%

2.7%

0.7%

2.1%

10.6%

6.0%

4.9%

5.9%

8.0%

14.8%

22.5%

6.3%

Writer/Editor

0.8%

0.7%

0.9%

1.3%

2.7%

5.4%

5.8%

1.2%

Other

15.2%

11.6%

14.0%

18.4%

22.0%

26.3%

36.2%

15.7%

Total

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

Project Manager Usability Expert/Consultant/Lead

Webmaster

Job titles vary by age group as well as by organization type. In particular: • The only job titles in which we see consistently increasing representation at ages above 32 are “Webmaster”, “Writer/Editor”, and “Other”. The last of these suggests, intriguingly, that older workers in the field fill roles not covered in our listed titles. • Taken as a group, respondents under 21 and over 50 are more likely to hold the title “Web Designer” than any other listed title.

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29

1. Jobs and Titles

Fig. 1.4

Gender distribution by jobFig. title1.4 Gender distribution by job title Writer/Editor

41.6%

58.4%


24.7%

75.3%

Web Producer

24.5%

75.5%


22.8%

77.2%

Other

22.2%

77.8%

Accessibility Expert/Consultant

19.4%

80.6%

Webmaster

19.1%

80.9%

Designer

18.8%

81.2%

Project Manager

18.7%

81.3%

Web Designer

18.1%

81.9%


14.5%

85.5%


14.3%

85.7%

Web Director

12.6%

87.4%

7.2%

92.8%

16.3%

83.7%

Developer Overall

Female

Male

Women made up 16.3% of the sample, but as Fig. 1.4 shows: • Women make up significantly greater percentages of the information architects (22.8%), usability experts (24.7%), web producers (24.5%), and writers/editors (41.6%) than they do of other titles. • Women comprise significantly lesser percentages of developers (7.2%) and web directors (12.6%). • Women comprise slightly lesser percentages of creative directors (14.3%) and interface designers (14.5%).

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30

1. Jobs and Titles

Fig. 1.5

Percentage of job-title holders who earn salaries $100K+ Fig. 1.5 Percentage of job of titles above $100K salary Information Architect

16.5% 15.3%


14.3%


13.5%

Web Director 10.6%


9.2%

Other

8.3%

Project Manager

8.1%

Accessibility Expert/Consultant/Lead Developer

4.8%

Writer/Editor

4.8% 4.3%

Web Producer Webmaster

2.3%

Designer

2.2%

Web Designer Overall

2.1% 6.0%

Fig. 1.5 shows the percentage of respondents with each job title who earn salaries of more than $100K. We can make the following observations about the relation of job title to income: • The job titles that consistently show higher earnings than the sample as a whole are: accessibility expert, creative director, information architect, interface designer, usability expert, web producer, and web director. • Those job titles held by respondents who appear to earn less than the sample as a whole are: designer, web designer, and webmaster.

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31

2. Education—How Relevant?

Education—How Relevant? Bachelor’s and master’s and salaries, oh my!

The overall distribution of educational attainment among the survey respondents is as follows: Fig. 2.1

2

Fig. 2.1 Education levels Education levels 14.9%

14.6% 52.1%

Bachelor's Master's High school Junior college Doctorate No degree

12.6% 1.2% 4.6%

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32


As indicated in Fig. 2.2, increased educational attainment generally appears to correspond to increased earning in our sample; we find smaller percentages in the lower earning categories and larger percentages in the upper earning categories as the education level increases. In that context, using a weighted-average approach to the analysis, we can make some additional observations: • There is only a slight increase in earning from high school graduates to junior college graduates, and a similarly slight increase from bachelor’s degrees to master’s degrees. The increases in earnings from junior college degrees to bachelor’s degrees and from master’s degrees to doctoral degrees are more significant. • A significantly greater percentage of respondents with doctorates earn over $100,000 than do respondents with any other level of educational attainment, although the total number of doctorates is very small: under 400, and less than 1% of the survey responses. • Among respondents indicating “no degree,” there are greater percentages at the lowest and the highest earning categories when compared to those with a bachelor’s degree. Note:

The “no degree” category is problematic because it is unclear whether it means “no high school diploma” or “no college degree.” We suspect it is a mixed segment of the sample, and it is therefore not useful in terms of our analysis.

Fig. 2.2

Salary range by education level Under $20,000 $20,000-$39,999 $40,000-$59,999 $60,000-$79,999 $80,000-$99,999 Over $100,000

Total

No degree

32.9%

18.4%

21.9%

12.7%

6.9%

7.2%

100.0%

High school

48.2%

17.2%

15.3%

10.3%

4.8%

4.1%

100.0%

Junior college

31.3%

26.7%

21.9%

11.7%

5.0%

3.5%

100.0%

Bachelor's

21.2%

21.3%

27.0%

16.6%

7.6%

6.2%

100.0%

Master's

23.3%

18.5%

25.7%

15.8%

8.5%

8.1%

100.0%

Doctorate

21.5%

17.7%

19.7%

17.2%

8.1%

15.9%

100.0%

Overall

27.3%

20.8%

24.1%

14.8%

7.0%

6.0%

100.0%

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33


In the total sample, 53.4% of the respondents indicated that their college studies were relevant to their web design work (Fig. 2.3). Fig. 2.4 shows that as income level increases, the percentages of respondents for whom their college studies were relevant decreases, to a low of 43.1% of respondents making over $100,000 (Fig. 2.4). This may be because increased earnings reflect, among other things, longevity in the field. The longer it has been since they graduated from college, the less relevant respondents’ studies may be to their current work—or perhaps the less relevant they may seem to be to these respondents. Fig. 2.3

Perceived relevance of education Fig. 2.3 Perceived relevance of education

46.6%

Fig. 2.4

53.4%

Relevant Not relevant

Perceived relevance of education by salary range Fig. 2.4 Perceived relevance of education by salary range

Under $20,000

52.9%

47.1%

$20,000-$39,999

59.2%

40.8%

$40,000-$59,999

56.0%

44.0%

$60,000-$79,999

50.4%

49.6%

$80,000-$99,999

45.9%

54.1%

Over $100,000

43.1%

56.9%

Overall

53.5%

46.5%

Relevant

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Not relevant

34


Fig. 2.5 shows that, except for those under 21 years old, the percentage of respondents who regard their college studies as relevant to their web design work decreases with age, from a high of 67.2% for 21-24 year olds, to a low of 23.6% for those over 60. This might suggest that there has been a recent expansion in web-design curricula in colleges and universities. It also may be that people who’ve been in the field for some time regard what they’ve learned through their work experience as more important or more relevant than their formal studies. Fig. 2.5

Perceived relevance of education by age group Fig. 2.5 Perceived relevance of education by age group

Under 21

58.9%

41.1%

21-24

67.2%

32.8%

25-32

58.7%

41.3%

33-38

42.3%

57.7%

39-50

33.9%

66.1%

51-60

28.5%

71.5%

Over 60

23.6%

76.4%

Overall

53.4%

46.6%

Relevant

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Not relevant

35


Fig. 2.6 shows the relationship between job title and the perception of the relevance of college studies to web design work. There are only three job titles for which over 60% of the respondents felt their college studies were relevant: creative director, designer, and developer. Two other job titles in which the percentage is over 50% are interface designer and web designer. The two job titles with the lowest percentage of respondents indicating that their college studies were relevant are project manager (37.2%) and writer/editor (21.3%). Fig. 2.6

Perceived relevance Fig. of education by job title 2.6 Perceived relevance of education by job title Designer

69.0%

31.0%


63.2%

36.8%

Developer

60.2%

39.8%


57.2%

42.8%

Web Designer

54.5%

45.5%


47.5%

52.5%


47.2%

52.8%


43.1%

56.9%

Other

42.8%

57.2%

Webmaster

41.5%

58.5%

Web Director

41.4%

58.6%

Web Producer

40.3%

59.7%

Project Manager

37.2%

62.8%

Writer/Editor

21.3%

78.7%

Overall

53.4%

46.6%

Relevant

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Not relevant

36

3. Salaries

Salaries Plotting the range.

3

The overall distribution of salary ranges among the survey respondents is as follows: Fig. 3.1

Salary levels

Fig. 3.1 Salary levels

Under $20,000

27.3% 20.8%

$20,000-$39,999

24.2%

$40,000-$59,999 14.8%

$60,000-$79,999 $80,000-$99,999 Over $100,000

A LIST APART

7.0% 6.0%


37

3. Salaries

Relationships between the types of organizations respondents work at and their income are presented in Fig. 3.2. Patterns that appear to emerge are: • Respondents working for for-profit companies seem to have higher incomes, with 18.5% making over $80,000. • Respondents working for government entities include lower percentages of people in the lowest and highest earning categories, with nearly 60% earning between $40,000 and $80,000. (Respondents who work at schools and colleges show this same pattern, but less dramatically.) • Almost 50% of self-employed web professionals make under $20,000, perhaps reflecting a very parttime web design business. • Respondents indicating that they work at start-ups show higher percentages at the low end and at the high end, reflecting, perhaps, the nature of starting a business, the sacrifices some entrepreneurs make in the early stages of their start-ups, and the rewards they enjoy when their businesses become successful. Fig. 3.2

Salary range by organization type Under $20,000 $20,000-$39,999 $40,000-$59,999 $60,000-$79,999 $80,000-$99,999 Over $100,000

Design, web, or IA agency/ consu ltancy For-profit

Total

23.5%

24.6%

24.8%

14.8%

6.8%

5.5%

100.0%

16.9%

19.1%

26.3%

19.2%

10.1%

8.4%

100.0%

11.9%

19.8%

36.0%

22.7%

6.9%

2.8%

100.0%

29.1%

19.5%

30.1%

12.6%

4.8%

3.7%

100.0%

School, college, university

20.4%

23.5%

37.3%

14.2%

3.6%

1.1%

100.0%

Self-employed/ freelance

48.2%

19.2%

13.8%

8.4%

4.5%

5.8%

100.0%

Start-up

31.3%

18.8%

18.1%

14.1%

9.4%

8.4%

100.0%

Other

31.5%

20.7%

22.1%

13.4%

5.9%

6.4%

100.0%

Overall

27.3%

20.8%

24.1%

14.7%

7.0%

6.0%

100.0%

enterprise (Government corporation) agency Non-profit

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38

3. Salaries

Fig. 3.3 shows a general pattern of increased earnings for respondents working in larger organizations. At the two extremes of this pattern are these two findings: • Over 60% of respondents in organizations of 10 employees or less make under $40,000. • Almost 25% of respondents in organizations of more than 3,000 employees make over $80,000. Fig. 3.3

Salary range by organization size Less than $20,000

$20,000-$39,999

$40,000-$59,999

$60,000-$79,999

$80,000-$99,999

More than $100,000

Total

Self-employed/ freelance

60.1%

13.8%

9.6%

7.3%

4.0%

5.1%

100.0%

Less than 10 employees

33.5%

26.8%

20.4%

10.1%

4.4%

4.8%

100.0%

11-300 employees

19.1%

22.7%

29.4%

16.6%

7.2%

4.9%

100.0%

301-3,000 employees

11.1%

18.4%

33.5%

21.1%

9.9%

6.0%

100.0%

More than 3,000 employees

7.4%

13.7%

29.4%

24.6%

13.2%

11.7%

100.0%

27.3%

20.8%

24.1%

14.7%

7.0%

6.0%

100.0%

Overall

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39

3. Salaries

From Fig. 3.4, it is clear that the longer respondents are in the field, the more they earn. Fig. 3.4

Salary range by longevity in the field Under $20,000

$20,000-$39,999

$40,000-$59,999

$60,000-$79,999

1 year or less

59.8%

22.3%

11.5%

3.7%

1.7%

1.1%

100.0%

1-2 years

49.7%

27.5%

16.5%

4.0%

1.1%

1.2%

100.0%

2-3 years

39.3%

27.7%

23.5%

6.4%

1.6%

1.6%

100.0%

3-4 years

32.5%

25.1%

27.2%

10.4%

2.9%

2.0%

100.0%

4-5 years

25.2%

23.5%

29.5%

13.9%

5.1%

2.8%

100.0%

5-6 years

20.8%

21.0%

30.1%

17.2%

6.5%

4.5%

100.0%

6-7 years

15.3%

18.7%

30.1%

21.9%

8.7%

5.3%

100.0%

7-8 years

10.4%

14.9%

29.9%

24.3%

12.4%

8.1%

100.0%

8-9 years

9.2%

13.7%

24.9%

25.7%

13.5%

13.0%

100.0%

9-10 years

7.3%

13.9%

24.9%

26.4%

15.2%

12.3%

100.0%

27.3%

20.8%

24.1%

14.7%

7.0%

6.0%

100.0%

Overall

A LIST APART


$80,000-$99,999 Over $100,000

Total

40

3. Salaries

Fig. 3.5 examines earnings and gender. While overall earnings are comparable, a greater percentage of men than women take home under $20,000. On the flip side, a greater percentage of men than women make more than $80,000; the same is true for earnings of more than $100,000. Fig. 3.5

Salary range by genderFig. 3.5 Salary range by gender 21.8%

Under $20,000

28.4% 27.3% 23.6%

$20,000-$39,999

20.3% 20.8% 28.2%

$40,000-$59,999

23.4% 24.2% 15.4% 14.6% 14.7%

$60,000-$79,999 6.4% 7.1% 7.0%

$80,000-$99,999

Female Male Overall

4.6% 6.2% 6.0%

Over $100,000

Fig. 3.6 shows the percentage of female web professionals increasing as organizational size increases. Fig. 3.6

Gender distributionFig. by organization size 3.6 Gender distribution by organization size

10 or fewer employees

10.5%

89.5%

11-300 employees

15.9%

84.1%

300-3,000 employees

21.6%

78.4%

More than 3,000 employees

22.6%

77.4%

Overall

16.3%

83.7%

Female

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Male

41

3. Salaries

As seen in Fig. 3.7, income increases with the age of the respondents until it dips slightly for respondents aged 51-60. For respondents over 60, there is a significant increase in the percentage of those making under $20,000 and those making over $100,000 than for respondents in age categories starting at 25 years old. This might reflect both a certain level of career success for experienced web designers, and a significant number of retirees who want to keep their hand in the field. It may also suggest that these respondents are simply “dabbling” because they don’t need to make a full-time living from web design. Fig. 3.7

Salary range by age group Under $20,000

$20,000-$39,999

$40,000-$59,999

$60,000-$79,999

$80,000-$99,999

Over $100,000

Total

Under 21

84.9%

8.2%

3.2%

1.9%

0.6%

1.2%

100.0%

21-24

50.6%

27.0%

15.9%

4.7%

1.1%

0.8%

100.0%

25-32

20.6%

24.1%

29.1%

15.3%

6.5%

4.4%

100.0%

33-38

10.3%

15.3%

27.3%

23.1%

12.5%

11.4%

100.0%

39-50

10.8%

15.3%

25.3%

22.1%

12.6%

13.9%

100.0%

51-60

18.0%

15.5%

24.7%

20.5%

11.0%

10.3%

100.0%

Over 60

28.9%

13.3%

15.6%

14.1%

8.9%

19.3%

100.0%

Overall

27.3%

20.8%

24.1%

14.7%

7.0%

6.0%

100.0%

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42

4. Sticking With It

Sticking With It Longevity and happiness. Of gender and title, salary and satisfaction.

4

Among non-freelancers, 1027 respondents—approximately 4.1% of the non-freelancers—indicated that they had been at their present jobs longer than they’ve been web professionals. Such data indicates that for this group, web design responsibilities were added to their job descriptions after they were hired. A more granular analysis of career longevity would likely reveal many others who would say the same.

The workplaces with the greatest percentages of the respondents who have been web professionals for two years or less are non-profits and start-ups, at 25.3% and 26.0% respectively. The only two workplaces at which over 50% of respondents have been web professionals for more than five years are for-profit businesses and government agencies (Fig. 4.1). Fig. 4.1

Longevity in field by organization type Fig. 4.1 Longevity in field by organization type Government agency

13.9%

For-profit enterprise (corporation)

17.6%


21.1%

30.6%

48.3%

Design, web, or IA agency / consultancy

18.9%

33.5%

47.7%

Other

26.6%

29.2%

44.1%

Non-profit

25.3%

31.2%

43.5%


26.3%

31.9%

41.8%

Start-up

26.0%

32.7%

41.3%

Overall

21.1%

28.5%

57.6%

29.8%

52.6%

31.4%

Less than 2 years More than 5 years

A LIST APART


47.5%

2-5 years

43

4. Sticking With It

Job titles within which over 60% of the respondents have been web professionals for more than five years are: accessibility expert, creative director, information architect, interface designer, usability expert, and web director. These would appear to be the positions in which the respondents have the most longevity in the field. Survey responses confirm the common-sense inference that the longer a web professional is in the field, the more jobs he or she will have. Approximately two-thirds of all respondents have had three or fewer jobs in their web careers, while more than 10% have had ten jobs or more (Fig. 4.2). Fig. 4.2

Number of jobs by years in field < 1 yr

1-2 yrs

2-3 yrs

3-4 yrs

4-5 yrs

5-6 yrs

6-7 yrs

7-8 yrs

8-9 yrs

9-10 yrs

> 10 yrs

Total

1 19.7%

19.8%

15.0%

9.6%

7.5%

6.6%

6.5%

5.1%

2.6%

3.0%

4.5%

100.0%

2

7.5%

14.1%

16.1%

12.2%

10.5%

9.1%

9.3%

7.5%

3.8%

4.3%

5.5%

100.0%

3

4.7%

7.5%

10.0%

10.1%

11.1%

10.5%

12.8%

11.3%

7.0%

6.6%

8.5%

100.0%

4

3.8%

6.0%

6.9%

6.6%

8.4%

8.9%

12.7%

13.2%

8.2%

10.9%

14.3%

100.0%

5

4.8%

8.0%

8.2%

5.3%

5.9%

7.4%

10.6%

10.1%

9.0%

11.8%

18.9%

100.0%

6

4.7%

8.9%

7.8%

6.8%

5.3%

4.9%

8.7%

10.2%

9.8%

12.2%

20.7%

100.0%

7

3.7%

10.4%

9.4%

8.8%

5.5%

8.1%

5.8%

8.8%

9.2%

9.7%

20.7%

100.0%

8

5.0%

14.6%

11.8%

6.2%

7.5%

6.8%

6.8%

5.6%

7.5%

6.2%

22.0%

100.0%

9

3.4%

12.5%

14.8%

11.4%

3.4%

9.1%

8.0%

11.4%

4.5%

5.7%

15.9%

100.0%

10

7.0%

15.4%

16.8%

9.8%

16.1%

6.3%

4.2%

2.8%

4.2%

7.0%

10.5%

100.0%

> 10

3.1%

9.0%

12.7%

11.3%

10.9%

10.6%

10.9%

8.8%

5.3%

6.4%

11.1%

100.0%

Overall

8.7%

12.2%

12.5%

9.7%

9.1%

8.6%

9.8%

8.6%

5.4%

6.2%

9.1%

100.0%

A LIST APART


44

4. Sticking With It

When we limit the field to self-employed web professionals, the number who have had more than ten jobs doubles: 20.8% compared to 10.0% for all who took the survey. The choice of self-employment for many of the respondents may have been driven by the unsatisfying experience of too much job mobility over the course of their web career. It’s also possible that these respondents read “jobs” in the question to mean “assignments” or “projects” they may have taken on as a freelancer. This would readily explain the high percentage of respondents with more than ten jobs. We pulled out the segment of the sample working in non-profits (a relatively small group totaling 1540), and identified the following findings: • Women comprise just over 16% of the total sample, but make up almost 23% of the respondents working in non-profits. • A slightly higher percentage of black respondents and a slightly lower percentage of Asian respondents work in non-profits than are presented in the sample as a whole. • The respondents working in non-profits seem to have slightly fewer years of experience as web professionals than the sample as a whole. We have not attempted to determine causal relationships behind these findings. While women make up 16.3% of the total sample, only 10.6% of respondents who plan to start their own business as their next career move are women (Fig. 4.3). Fig. 4.3

Future job moves by genderFig. 4.3 Future job moves by gender Stay where I am

18.5%

81.5%

Get a promotion at my current job

17.9%

82.1%

New job in a new organization

17.5%

82.5%

Other

17.2%

82.8%

Get my first job in the field

14.6%

85.4%

Start my own business

10.6%

89.4%

Overall

16.3%

83.7%

Female

A LIST APART


Male

45

4. Sticking With It

On the basis of the indicators of job satisfaction derived from responses to the “next career move” question in the survey, we offer the following findings: • Women respondents are more satisfied and less unsatisfied in their jobs than men (Fig. 4.4). • All non-white respondents are less satisfied and more unsatisfied in their jobs than white respondents, with black respondents having the greatest disparity (Fig. 4.5). • Respondents who are project managers and information architects indicated the highest satisfaction with their work. Those expressing the least satisfaction were designers, web designers, and webmasters (Fig. 4.6). • Respondents working at non-profits and those who are self-employed indicate the least satisfaction with their current jobs (Fig. 4.7), although the self-employed also indicate the least dissatisfaction with their current jobs. • As one might expect, job satisfaction as indicated by these measurements appears to increase with income, although there is a slight peak in satisfaction for respondents in the $40,000 – $59,999 range (Fig. 4.8). • Job satisfaction appears to increase with age: respondents under 25 are less satisfied and more unsatisfied than the sample as a whole; those 25-32 years old are more satisfied but also more unsatisfied than the sample as a whole; all the older age categories contain more satisfied and less unsatisfied respondents than the sample as a whole (Fig. 4.9). Note:

Because our job satisfaction indicators are derived from the respondents’ expressed plans related to job stability/mobility, this choice may have additional factors for older respondents. For example, older workers may be more reluctant to change jobs, regardless of their level of satisfaction with their current job. This is another reason to be cautious about identifying motives or causality in the data. Fig. 4.4

Job satisfaction byFig. gender 4.4 Job satisfaction by gender

Female

49.9%

Male

43.6%

Overall

44.6%

16.2% 15.7%

40.7%

15.8%

Satisfied

A LIST APART

33.9%


Other

39.6%

Unsatisfied

46

4. Sticking With It

Fig. 4.5

Job satisfaction byFig. ethnicity 4.5 Job satisfaction by ethnicity

White

45.9%

Asian

38.3%

Other

38.1%

Hispanic

38.0%

Black

30.8%

Overall

44.7%

15.6% 15.2%

46.5%

21.4%

40.5%

15.4%

46.6%

18.0%

51.2% 15.7%

Satisfied

Fig. 4.6

38.6%

39.6%

Other

Unsatisfied

Job satisfaction by job title Fig 4.6. Job satisfaction by job title Project Manager

51.6%

12.0%

36.4%


50.2%

14.2%

35.6%

Web Producer

48.9%

Developer

47.8%

12.1%

40.1%


47.3%

11.1%

41.6%


46.2%

Writer/Editor

45.3%

16.1%

38.5%

Web Director

44.7%

16.9%

38.4%


44.2%

18.5%

37.2%

Other

44.2%


43.8%

Web Designer

41.6%

15.8%

42.6%

Designer

41.0%

17.5%

41.4%

Webmaster

40.3%

Overall

44.7%

13.4%

20.5%

22.0% 18.1%

15.3% 15.8%

Satisfied

A LIST APART


Other

37.7%

33.3%

33.8% 38.1%

44.4% 39.6%

Unsatisfied

47

4. Sticking With It

Fig. 4.7

Job satisfaction by organization Fig. 4.7type Job satisfaction by organization type

Design, web, or IA agency / consultancy

51.4%

11.4%


48.4%

7.7%

43.9%

Government agency

47.2%

9.2%

43.6%


46.4%

14.7%

38.9%

Start-up

45.0%

15.1%

39.9%

Non-profit

41.6%

Other

38.6%


33.9%

Overall

44.7%

15.8%

42.6%

18.5%

42.9%

31.1%

35.0%

15.8%

Satisfied

Fig. 4.8

37.1%

Other

39.5%

Unsatisfied

Job satisfaction by salary range Fig. 4.8 Job satisfaction by salary range

Under $20,000

34.0%

22.3%

$20,000-$39,999

47.0%

$40,000-$59,999

51.0%

$60,000-$79,999

49.3%

12.4%

38.3%

$80,000-$99,999

48.3%

13.5%

38.1%

Over $100,000

47.0%

Overall

44.9%

13.1%

39.9%

10.9%

38.0%

20.2%

32.8%

15.4%

Satisfied

A LIST APART

43.7%


Other

39.7%

Unsatisfied

48

4. Sticking With It

Fig. 4.9

Job satisfaction by age group Fig. 4.9 Job satisfaction by age group

Under 21

25.7%

21-24

40.8%

25-32

46.1%

33-38

47.8%

39-50

49.9%

51-60

50.8%

Over 60

53.4%

Overall

44.7%

31.2% 16.0%

43.2%

11.9%

42.0%

15.6%

36.6%

19.6%

30.4%

24.9%

24.3%

29.8%

16.8%

15.8%

Satisfied

A LIST APART

43.0%


Other

39.6%

Unsatisfied

49

5. Punching the Clock

Punching the Clock Working hard, hardly working.

5

Work hours are longer at some kinds of organizations than others, as seen in Fig. 5.1. Workplaces in which the smallest percentages of respondents work less than half-time (under 20 hours) are: web design firms, for-profit entities, and government agencies; these workplaces also have the greatest percentages of respondents working at least full-time (40 hours or more). Respondents working for non-profits and those who are self-employed have the highest percentages of those working less than 40 hours per week. Fig. 5.1

Hours worked by organization type Under 20 hours

20-30 hours

30-40 hours

40-50 hours

50-60 hours

Over 60 hours

Total

Design, web, or IA agency/ consultancy

2.3%

3.6%

21.9%

51.4%

14.7%

6.1%

100.0%


2.0%

2.7%

23.6%

53.5%

13.1%

5.2%

100.0%

Government agency

2.4%

3.6%

38.7%

44.9%

8.0%

2.5%

100.0%

Non-profit

15.5%

8.2%

28.4%

35.2%

9.5%

3.1%

100.0%

Other

12.5%

7.4%

23.6%

40.2%

9.7%

6.6%

100.0%


9.5%

7.4%

31.0%

41.1%

8.3%

2.7%

100.0%

Self-employed/freelance

21.0%

18.2%

18.5%

22.0%

12.9%

7.5%

100.0%

Start-up

6.3%

8.0%

15.9%

40.5%

18.0%

11.3%

100.0%

Overall

8.3%

7.7%

23.1%

42.2%

12.8%

5.9%

100.0%

A LIST APART


50


As seen in Fig. 5.2, respondents under 25 and over 60 years are most likely to work less than half time and least likely to work at least full time: over 40% of those under 21 and over 20% of those over 60 work less than half-time. Over 60% of respondents in all other age categories are working at least full-time. Fig. 5.2

Hours worked by age group Under 20 hours

20-30 hours

30-40 hours

40-50 hours

50-60 hours

Over 60 hours

Total

Under 21

41.1%

22.6%

15.4%

14.1%

3.5%

3.3%

100.0%

21-24

13.1%

12.0%

24.8%

35.3%

9.9%

4.9%

100.0%

25-32

4.2%

4.9%

23.7%

47.4%

13.9%

6.0%

100.0%

33-38

3.3%

5.3%

23.2%

46.7%

14.9%

6.7%

100.0%

39-50

4.7%

6.0%

23.2%

43.9%

14.8%

7.4%

100.0%

51-60

8.7%

8.6%

20.0%

39.8%

15.8%

7.1%

100.0%

Over 60

20.3%

17.4%

19.6%

26.1%

8.0%

8.7%

100.0%

Overall

8.3%

7.7%

23.1%

42.2%

12.8%

5.9%

100.0%

A significantly smaller percentage of women than men work fewer than 20 hours or more than 60 hours per week. But a greater percentage of women than men work between 20 and 40 hours per week (Fig. 5.3). Fig. 5.3

Hours worked by gender Under 20 hours

20-30 hours

30-40 hours

40-50 hours

50-60 hours

Over 60 hours

Total

Female

7.0%

8.2%

27.5%

44.0%

9.6%

3.8%

100.0%

Male

8.6%

7.6%

22.2%

41.8%

13.4%

6.3%

100.0%

Overall

8.3%

7.7%

23.1%

42.2%

12.8%

5.9%

100.0%

A LIST APART


51


Almost two-thirds of respondents indicate that web design makes up all or most of their work. Fig. 5.4 seems to indicate that the greater the portion of the respondents’ work that is web-related, the more likely they are to work longer hours. The group of respondents who indicated that “web design is a small part of what I do” has the highest percentage of respondents who work less than half time and the lowest percentage who work at least full time. As the portion of the respondents’ work that is web-related increases, so does the percentage of people working at least full time. Fig. 5.4

Hours worked by degree of web work. Under 20 hours

20-30 hours

30-40 hours

40-50 hours

50-60 hours

Over 60 hours

Total

Web design is a small part of what I do

20.6%

7.0%

22.7%

35.5%

9.4%

4.8%

100.0%

About half my work is web-design-related

10.2%

10.9%

21.1%

38.8%

13.0%

5.9%

100.0%

Most of my work is web-design-related

7.4%

10.3%

23.0%

40.2%

13.0%

6.0%

100.0%

I'm a full-time web worker

2.3%

4.1%

24.3%

48.8%

14.0%

6.4%

100.0%

Overall

8.3%

7.7%

23.1%

42.2%

12.8%

5.9%

100.0%

A LIST APART


52


The locations where the respondents do their web design work broke down as shown in Fig. 5.5. Note:

Respondents were able to check all locations that applied. Fig. 5.5

Location of work

Fig. 5.5 Location of work

Work in employer's offices

66.7% 61.2%

Work at home 33.4%

Work at home AND in employer's offices Work in own offices

10.1%

Work neither at home nor in employer's offices

5.5%

Work in other space

5.2%

Work in shared offices with other freelancers

4.2%

Work in borrowed space on client's premises

3.5%

A LIST APART


53

6. Everybody’s Got One (a Blog)

Everybody’s Got One (a Blog) Ownership of a personal website has little or no bearing on success.

6

Over 70% of the respondents indicate that they have a personal website or blog (Fig. 6.1). Percentages go down slightly with the age of the respondents, but never fall below 66% (Fig. 6.2). Similarly, there don’t seem to be significantly different percentages of respondents with blogs or websites when the sample is broken down by gender, job title, or longevity as a web professional (Figs. 6.3 through 6.5). Fig. 6.1

Prevalence of blogging Fig. 6.1 Prevalence of blogging

27.3% No Yes 72.7%

Respondents were asked to indicate whether or not they have a blog/personal website.

A LIST APART


54


Fig. 6.2

Prevalence of Fig. blogging by age group 6.2 Prevalence of blogging by age group

Under 21

74.2%

25.8%

21-24

74.4%

25.6%

25-32

73.8%

26.2%

33-38

71.8%

28.2%

39-50

67.9%

32.1%

51-60

66.5%

33.5%

Over 60

66.7%

33.3%

Overall

72.7%

27.3%

Yes

Fig. 6.3

No

Prevalence of blogging by gender of blogging by gender Fig. 6.3 Prevalence

Female

73.1%

26.9%

Male

72.7%

27.3%

Overall

72.7%

27.3%

Yes

A LIST APART


No

55


Fig. 6.4

Prevalence of blogging byFig. job6.4 title Prevalence of blogging by job title Writer/Editor

77.2%

22.8%

Designer

77.1%

22.9%


76.7%

23.3%


75.7%

24.3%

Web Designer

75.3%

24.7%

Web Producer

74.2%

25.8%

Other

72.2%

27.8%

Webmaster

70.8%

29.2%

Web Director

70.4%

29.6%


70.2%

29.8%

Developer

69.6%

30.4%


69.4%

30.6%

Project Manager

66.8%

33.2%


65.4%

34.6%

Overall

72.7%

27.3%

Yes

A LIST APART


No

56


Fig. 6.5

Prevalence of blogging salary range Fig. 6.5 by Prevalence of blogging by salary range

Under $20,000

71.7%

28.3%

$20,000-$39,999

73.2%

26.8%

$40,000-$59,999

73.5%

26.5%

$60,000-$79,999

72.1%

27.9%

$80,000-$99,999

73.3%

26.7%

Over $100,000

73.7%

26.3%

Overall

72.7%

27.3%

Yes

A LIST APART


No

57

7. Perceptions of Bias

Perceptions of Bias In your opinion, has prejudice in the field affected your career?

7

Do Americans have an advantage? A greater percentage of respondents from outside the US perceive a geographical bias that has slowed their careers than do respondents from the US (Fig. 7.1). Fig. 7.1

Perceived geographic bias by global region 7-1. Perceived geographic bias by global region.

Rest of world

35.3% 31.3%

Europe/UK 24.4%

US

28.7%

Globally

A LIST APART


58


Respondents living in the American Northeast, Northwest, and Southwest are less likely to perceive that geographical bias has slowed their careers. Those from all other US regions are more likely (Fig. 7.2). Fig. 7.2

Perceived geographic bias by geographic US region bias by U.S. region. 7-2. Perceived

US: Alaska & Hawaii

50.0% 30.9%

US: Great Plains US: Midwest

30.1%

US: Southeast

29.5% 27.5%

US: South 22.3%

US: Northeast

19.6%

US: Southwest

17.5%

US: Northwest

24.4%

Overall

Those under 25 and over 50 are more likely to perceive an age bias that works against them professionally. Those aged 33 to 38 are least likely to perceive age bias (Fig. 7.3). Fig. 7.3

Perceived age bias by age group age bias by age group. 7-3. Perceived

Under 21

43.0%

21-24

27.2%

25-32 33-38 39-50

15.1% 7.2% 11.5%

51-60

23.6%

Over 60 Overall

A LIST APART

19.1% 17.6%


59


Outside the US and Europe, a slightly higher percentage of respondents perceive age bias (Fig. 7.4). Fig. 7.4

Perceived age bias global region 7-4.by Perceived age bias by global region.

Rest of world

19.7% 17.2%

Europe/UK

17.0%

US

17.6%

Globally

Predictably, a significantly greater percentage of women than men perceive there to be a gender bias that has adversely affected their careers (Fig. 7.5) Fig. 7.5

Perceived gender bias by 7-5. gender Perceived gender bias by gender.

Female

22.3% 1.5%

Male

4.9%

Overall

A lesser percentage of women from outside the US and Europe perceive there to be a gender bias that has slowed their careers (Fig. 7.6). Fig. 7.6

Perceived gender bias among female respondents Fig. 7.6 Perceived gender biasby byglobal globalregion region

Europe/UK

23.1%

US

23.1% 18.7%

Rest of world

22.3%

Globally

A LIST APART


60


More non-white than white respondents perceive an ethnic bias that has slowed their careers. Approximately 8% of Asian and Hispanic respondents and 20% of black respondents perceive this bias, compared to 1.7% of white respondents (Fig. 7.7). Fig. 7.7

Perceived ethnic by ethnicity Fig. bias 7.7 Perceived ethnic bias by ethnicity

Black

20.0%

Hispanic

8.1%

Asian

8.0%

Other White

6.9% 1.7%

Perceptions are similar in the US, Europe, and the rest of the world. But in Europe and the rest of the world, greater percentages of all ethnic categories (including white respondents) perceive an ethnic bias that has slowed their careers than do their US counterparts (Fig. 7.8). Fig. 7.8

Perceived ethnic bias by 7.8 global region ethnic bias by global region Fig. Perceived 18.8%

Black

23.1% 22.7% 7.3% 10.6%

Hispanic 8.3% 5.9%

9.8%

Asian

9.2% 5.0% 9.3%

Other

8.2% 1.0% White

2.3% 2.9% 1.9%

Overall

2.7% 5.2%

A LIST APART


US Europe Rest of world

61

8. Evidence of Bias

Evidence of Bias Perception. Reality.

8

How accurate are perceptions of bias? In all three “regions” of the world—US, Europe, and rest of the world—full-time (40–60 hours per week) respondents who perceive there to be a geographical bias that has slowed their careers do indeed have significantly lower incomes than those who do not perceive such a bias (Figs. 8.1 through 8.3). Fig. 8.1

Perceived geographic bias by salary range among full-time (US) workers Fig. 8.1 Perceived geographic bias by salary range amongworkers US full-time

Under $20,000

28.2%

$20,000-$39,999

35.4%

$40,000-$59,999

27.8%

$60,000-$79,999

20.6%

$80,000-$99,999

16.4%

Over $100,000 Overall

16.1%

23.6% 23.9%

40.7%

23.0%

49.1%

18.1%

61.3%

16.3%

67.3%

11.4%

24.9%

72.5% 20.1%

Yes

A LIST APART

48.2%


55.0%

Maybe

No

62

8. Evidence of Bias

Perceived geographic bias by salary range among full-time workers (EU/UK) Fig. 8.2 Perceived geographic bias by salary range among EU/UK full-time workers

Fig. 8.2

Under $20,000

43.3%

$20,000-$39,999

32.4%

$40,000-$59,999

25.3%

$60,000-$79,999

21.7%

$80,000-$99,999

19.2%

Over $100,000

20.1%

Overall

31.2%

26.2%

30.5%

26.3%

41.2%

20.3%

54.4%

19.8%

58.6%

20.6%

60.1%

16.0%

63.9% 23.3%

Yes

45.5%

Maybe

No

Perceived geographic bias by salary range among full-time workers (Rest the world) Fig. 8.3ofPerceived geographic bias by salary range among ROW full-time workers

Fig. 8.3

Under $20,000

44.3%

$20,000-$39,999

41.1%

$40,000-$59,999

28.0%

$60,000-$79,999

25.1%

$80,000-$99,999

20.5%


17.3%

30.7% 24.7%

34.2%

24.7%

47.3%

21.2%

53.6%

20.5%

59.1%

22.4%

35.7%

60.2% 26.1%

Yes

A LIST APART

25.0%


Maybe

38.1%

No

63

8. Evidence of Bias

Full-time workers who perceived an age bias that has slowed their careers show a mixed picture regarding their income. The younger respondents (under 25) who perceive an age bias don’t appear to make significantly less than their contemporaries who do not perceive such a bias. But for the older respondents (over 50), there does appear to be a more pronounced income differential between those who do and don’t perceive age bias (Figs. 8.4 through 8.5). Fig. 8.4

Perceived age bias by range among full-time 25 under 25 Fig. 8.4 Perceived agesalary bias by salary range among workers full-timeunder workers

Under $20,000

28.4%

$20,000-$39,999

29.9%

$40,000-$59,999

28.5%

$60,000-$79,999

24.5%

$80,000-$99,999

36.4%

Over $100,000

24.0%

Overall

28.7%

25.6% 18.2%

51.9%

16.8%

54.6%

20.5%

55.0%

9.1%

54.5%

8.0%

68.0% 20.9%

Yes

Fig. 8.5

46.0%

50.5%

Maybe

No

Perceived age bias by salary among full-time 50 over 50 Fig. 8.5 Perceived age bias range by salary range amongworkers full-timeover workers

Under $20,000

28.9%

$20,000-$39,999

23.1%

$40,000-$59,999

22.4%

$60,000-$79,999

16.3%

$80,000-$99,999

19.4%

Over $100,000

18.2%

Overall

20.5%

23.7% 16.9%

60.0%

14.0%

63.6%

13.0%

70.7%

16.7%

63.9%

15.2%

66.7%

15.4%

Yes

A LIST APART

47.4%


64.1%

Maybe

No

64

8. Evidence of Bias

Contrary to what one might expect, full-time working women who perceive a gender bias that has slowed their careers actually seem to have slightly higher incomes than full-time working women who don’t perceive such a bias (Fig. 8.6). Men who perceive a gender bias that has slowed their careers seem to earn significantly less than other men who don’t perceive a gender bias (Fig. 8.7, overleaf ). In general, female respondents who work full time do not seem to make less than male respondents who also work full time, and in fact may earn a bit more. This pattern can be seen in Fig. 3.5., “Salary range by gender,” in Section Three. Fig. 8.6

Perceived gender bias by salary range among femalefull-time workers workers Fig. 8.6 Perceived gender bias by salary rangefull-time among female

Under $20,000

17.9%

21.3%

$20,000-$39,999

24.1%

20.1%

55.7%

$40,000-$59,999

23.2%

20.2%

56.6%

$60,000-$79,999

22.4%

22.0%

55.6%

$80,000-$99,999

29.3%

Over $100,000

24.8%

Overall

23.2%

16.9%

53.7%

23.6%

51.6%

20.6%

Yes

A LIST APART

60.8%


56.2%

Maybe

No

65

Fig. 8.7 Perceived gender bias by salary range among male full-time workers Under $20,000

88.9% of Bias 8. Evidence

$20,000-$39,999

95.5%

Fig. 8.7 Perceived gender bias by salary Fig. 8.7 Perceived gender bias $40,000-$59,999

range among full-time by salary range among male male workers full-time 97.4% workers

$60,000-$79,999 Under $20,000

97.5% 88.9%

$80,000-$99,999 $20,000-$39,999

97.0% 95.5%

Over $100,000 $40,000-$59,999

97.6% 97.4%

Overall $60,000-$79,999

95.6% 97.5%

Yes

$80,000-$99,999

Maybe

No

97.0%

Over $100,000

97.6%

Overall

95.6%

Yes

Maybe

3.2%

3.2%

1.1%

1.1% 3.4% 1.1% 3.4% 1.1% 3.4% 1.1% 3.4% 1.1% 3.4%

0.8%

1.7% 0.8%

3.2%

1.7% 0.8%

3.2%

No

1.7% 0.8%

3.2% 7.8% 3.2% 7.8%

1.7% 0.8%

1.7% 0.8%

7.8%

7.8%

7.8%

7.8%

7.8%

7.8%

3.4%

1.7%

0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 3.2% 2.1% 3.2% 2.1% 3.2% 2.1% 3.2% 2.1% 3.2% 2.1% 7.8% 3.2% 2.1% 7.8% 0.9% 1.1%

7.8%

7.8%

2.1% 0.9% 2.1% 0.9% 2.1% 0.9% 2.1% 0.9% 2.1% 0.9% 2.1% 3.4% 1.1% 3.4% 1.1% 3.4% 1.1% 3.4% 1.1% 3.4% 1.1% 3.4%

0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 0.8% 1.8% 1.7% 0.8% 1.8% 1.7% 0.8% 1.8% 1.7% 0.8% 1.8% 1.7% 0.8% 1.8% 1.7% 0.8% 1.8% 1.7% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% 0.5% 2.1% 0.5% 3.2% 2.1% 0.5% 3.2% 2.1% 0.5% 3.2% 2.1% 0.5% 3.2% 2.1% 0.5% 3.2% 2.1% 0.9%

2.1% 0.9%

2.1% 0.9%

2.1% 0.9%

2.1% 0.9%

2.1% 0.9%

3.2%

2.1%

0.6% 1.8% 0.6% 1.8% 0.6% 1.8% 0.6% 1.8% 0.6% 1.8% 0.6% 1.8% 1.2%

A LIST APART

1.2% 3.2% 1.2% 3.2% 1.2% 3.2% 1.2% 3.2% 1.2% 3.2%


3.2%

66

8. Evidence of Bias

Similar patterns emerge when we examine the relationship between income and the perception of ethnic bias. Of respondents who work full time, Asian, Hispanic, and white respondents who perceive ethnic bias appear to earn less than those who don’t (Figs. 8.8 through 8.10). However, black respondents who work full time and perceive ethnic bias earn more than those who don’t perceive ethnic bias (Fig. 8.11). In general, of full-time workers, Asian and Hispanic respondents appear to earn less than white and black respondents, whose earnings are relatively equal. Fig. 8.8 Perceived ethnic bias by salary range among white full-time workers Fig. 8.8 Perceived Under $20,000

ethnic bias by salary among full-time 89.8% Fig. 8.8 Perceived ethnic bias range by salary range amongwhite whiteworkers full-time workers

$20,000-$39,999 Under $20,000

95.3% 89.8%

$40,000-$59,999 $20,000-$39,999

96.8% 95.3%

$60,000-$79,999 $40,000-$59,999

97.1% 96.8%

$80,000-$99,999 $60,000-$79,999

97.1% 97.1%

Over $100,000 $80,000-$99,999

97.1% 97.1%

Overall Over $100,000

95.6% 97.1%

Overall

A LIST APART

Yes

Maybe

No

Yes

Maybe

No

2.9%

2.9%

2.9%

2.9%

1.6% 2.9%

1.6% 2.9%

3.1% 1.6% 2.9%

1.1% 1.6%

1.1% 2.1% 1.6%

1.2% 1.1%

7.3%

7.3%

7.3%

3.1% 1.6% 2.9%

3.1% 1.6% 3.1% 1.6% 3.1% 3.1% 2.9% 7.3% 2.9% 7.3% 7.3% 7.3%

7.3%

7.3%

1.1% 2.1% 3.1% 1.6%

1.1% 2.1% 3.1% 1.6%

1.1% 2.1% 3.1% 1.6%

1.1% 2.1% 3.1% 1.6%

2.1% 3.1%

1.2% 1.6% 1.1% 2.1%

1.2% 1.6% 1.1% 2.1%

1.2% 1.6% 1.1% 2.1%

1.2% 1.6% 1.1% 2.1%

1.2% 1.6% 1.1% 2.1%

1.6% 2.1%

1.5% 1.2%

1.5% 1.4% 1.2% 1.6%

1.5% 1.4% 1.2% 1.6%

1.5% 1.4% 1.2% 1.6%

1.5% 1.4% 1.2% 1.6%

1.5% 1.4% 1.2% 1.6%

1.4% 1.6%

0.8% 1.5%

2.1% 0.8% 1.5% 1.4%

2.1% 0.8% 1.5% 1.4%

2.1% 0.8% 1.5% 1.4%

2.1% 0.8% 1.5% 1.4%

2.1% 0.8% 1.5% 1.4%

2.1% 1.4%

1.5% 0.8%

1.5% 2.9% 1.5% 2.9% 1.5% 2.9% 1.5% 2.9% 1.5% 2.9% 2.1% 0.8% 2.1% 0.8% 2.1% 0.8% 2.1% 0.8% 2.1% 0.8% 2.1%

2.9%

1.5%

1.5% 2.9% 1.5% 2.9% 1.5% 2.9% 1.5% 2.9% 1.5% 2.9%

2.9%


2.9% 7.3% 2.9% 7.3%

95.6%

7.3%

3.1%

67

8. Evidence of Bias

Fig. 8.9

Perceived ethnic bias by salary among full-time Fig. 8.9 Perceived ethnic bias range by salary range among Asian Asian workers full-time workers

Under $20,000

7.0%

17.1%

75.9%

$20,000-$39,999

9.9%

11.0%

79.0%

$40,000-$59,999

5.7%

$60,000-$79,999

6.9% 12.4%

80.7%

$80,000-$99,999

5.8%

70.9%


16.6%

77.7%

23.3%

3.4% 15.5%

81.0%

6.9%

77.2%

15.9%

Yes

Maybe

No

Fig. 8.10Fig. Perceived ethnic bias by salary among Hispanicfull-time workersworkers 8.10 Perceived ethnic bias byrange salary range full-time among Hispanic Under $20,000

8.7%

$20,000-$39,999

7.9%

$40,000-$59,999

4.1%

$60,000-$79,999

15.3%

$80,000-$99,999

7.3% 9.8%

82.9%

18.2%

81.8%


7.9%

17.3%

74.0%

12.2%

79.9%

17.2%

78.7% 11.1%

14.9%

77.2%

Yes

A LIST APART

73.6%


Maybe

No

68

8. Evidence of Bias

Fig. 8.11

Perceived ethnic bias by salary among full-time Fig. 8.11 Perceived ethnic bias range by salary range amongblack blackworkers full-time workers

Under $20,000

16.1%

$20,000-$39,999

24.4%

$40,000-$59,999

11.1%

$60,000-$79,999

25.0%

$80,000-$99,999

11.1%

Over $100,000

25.0%

Overall

18.6%

16.1%

67.7% 17.1% 39.7%

49.2%

25.0%

50.0%

55.6%

33.3%

25.0%

50.0%

27.9%

Yes

A LIST APART

58.5%


53.4%

Maybe

No

69

9. Staying Current

Staying Current Keeping up with the Joneses—and the specifications.

9

Respondents were asked how they stay current with their craft, and were invited to check as many items as applied. Reading relevant websites was by far the most popular answer. “Trial and error” and reading relevant books tied for a distant second place. Note:

Respondents were able to check all that applied. Fig. 9.1

Methods of staying current Fig. 9.1 Methods of staying current

Read relevant websites, blogs, zines

95.1% 77.6%

Trial and error

76.3%

Read design/web design books 54.6%

Work with others at my company 43.8%

Participate in discussion boards In-house training Attend seminars and conferences Participate in mailing lists

A LIST APART


33.4% 32.4% 31.2%

70

9. Staying Current

If we define “formal training” as the two items in the survey checklist titled “attend conferences and seminars” and “in-house training,” then slightly over half of the respondents receive formal training. Under 21 participation rates vary by segment of our sample. Younger respondents (under 47.2% Training 25) and older respondents (over 60) are less likely to participate in formal training (Fig. 9.2). Greater percentages of 21-24 female respondents participate in formal training than do male respondents (Fig. 9.3). 48.3% 52.5%

25-32 Fig. 9.2 Participation9-2. in formal training Recipients of 33-38

by age group formal training by age group

Under 21 39-50

54.4%

47.2%

55.6%

48.3%

21-24 51-60

48.9%52.5%

25-32 Over 60

54.4% 52.1%

33-38 Overall

55.6%

39-50

53.7%

51-60 48.9%

Over 60 Female Overall

52.1% 58.6% 50.8%

Male

52.1%

Overall Fig. 9.3

53.7%

Participation9-3. in formal training by gender Recipients of formal training by gender

Female

58.6% 50.8%

Male

52.1%

Overall

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71

9. Staying Current

The positions in which the largest percentages of respondents participate in formal training are: accessibility expert and usability expert (the two highest at over 70%), information architect, and web director. This seems to indicate that respondents whose job titles reflect a more current or emerging sense of the field tend to participate more in formal training (Fig. 9.4). Fig. 9.4

Participation in formalFig. training by job title 9.4 Recipients of formal training by job title


71.2%


71.0%


64.9%

Web Director

64.5%


59.6%

Project Manager

58.8%


56.5%

Web Producer Webmaster

53.3%

Designer

51.3%

Web Designer

50.4%

Developer

50.0%

Writer/Editor Other Overall

A LIST APART

55.3%


48.2% 47.1% 52.1%

72

9. Staying Current

Respondents who work at design firms or for government agencies have the highest percentage of those participating in formal training; those who are self-employed or who work in start-ups are less likely to participate in formal training. Moreover, the larger the organization, the greater the percentage of respondents who participate in formal training (Fig. 9.5). Fig. 9.5

Participation in formal training byoforganization sizeby organization size 9-5. Recipients formal training

Less than 10 employees

49.6%

11-300 employees

53.2%

301-3,000 employees

58.8%

More than 3000 employees

60.5%

Overall

54.2%

In general, as income increases, the percentage of respondents who participate in formal training increases, although not in a completely linear fashion (Fig. 9.6). It is tempting to envision causal relationships between training and earning, but again, we must tread cautiously, as we lack sufficient data. Do the respondents earn more because they have had more formal training? Do they participate in formal training because they can better afford it? Answering such questions would require more study. Fig. 9.6

Participation in 9-6. formal training by salary range Recipients of formal training by salary

Under $20,000 $20,000-$39,999 $40,000-$59,999

49.5% 46.2% 53.3% 58.0%

$60,000-$79,999

59.8%

$80,000-$99,999

58.9%


52.3%

Training is not the only perk related to professional development. We analyzed which perks various segments of the sample received, and our findings were consistent with the “keeping current” findings summarized in Fig. 9.1. We received 10,000 null responses to our question about perks. Respondents who left the question blank may have meant to indicate that they receive none of the listed perks—or they may simply have skipped the question. A LIST APART


73

10. Skills and Skill Gaps

Skills and Skill Gaps The known unknowns.

10

Respondents were asked to indicate if they needed a set of skills for their web design job, and, for each skill they indicated that they did need, whether or not they had that skill. The responses are summarized in Fig. 10.1. Note:

Respondents were able to check all options that applied. Fig. 10.1

Claimed skills

Fig. 10.1 Claimed skills

Markup (HTML, XHTML)

81.3%

Back-end development (e.g. PHP, ASP)

78.2%

CSS coding

77.5%

Front-end programming (e.g. JavaScript)

75.9%

Page layout, interface design

75.7% 72.5%

Graphic design Information architecture, wireframing, sitemapping

67.5%

Image editing/production

66.2%

Usability testing/knowledge Accessibility testing/knowledge Other Writing/editing

63.4% 60.3% 58.9% 54.7%

It is striking that the skill areas in which there are significant gaps include both leading-edge skills such as accessibility testing and information architecture, and more traditional skills such as writing and image editing. A LIST APART


74


Back-End Programming Skills and Skill Gaps Over 20% of the respondents who need back-end development skills don’t have them. We identified the gaps in back-end development skills for various segments of the sample. Respondents under 21 and over 50 had the highest percentages of those who feel they need back-end development skills but don’t have them. Fig. 10.2

Perceived back-end gaps byback-end age group Fig. 10.2skill Perceived skill gaps by age group

Under 21

31.0% 24.9%

21-24

23.2%

25-32

23.7%

33-38

22.0%

39-50

31.6%

51-60

34.2%

Over 60 24.2%

Overall

A greater percentage of women than men believe they lack a needed back-end development skill (Fig. 10.3). Fig. 10.3

PerceivedFig. back-end skill gaps by gender 10.3 Perceived back-end skill gaps by gender

Female

28.8% 23.6%

Male

24.2%

Overall

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75


Longevity matters: a slightly greater percentage of respondents in the web design field for less than two years who need this skill set don’t have it (Fig. 10.4). Fig. 10.5 shows the percentage of respondents who need, but lack, back-end programming skills, broken down by job title. As one might expect, developers are more likely than designers to possess needed back-end skills—and designers are, in turn, more likely than writers to possess those same skills. Fig. 10.4

Perceived skill gaps by longevity in field Fig.back-end 10.4 Perceived back-end skill gaps by longevity in field

Less than 2 years

29.1% 24.5%

2-5 years 22.3%

More than 5 years

24.1%

Overall

Fig. 10.5

Perceived back-end skill job titleback-end skill gaps by job title Fig.gaps 10.5 by Perceived


52.3%

Writer/Editor

50.9%


47.4%

Designer

46.9%


46.4%


43.9%

Web Producer

39.0%

Web Designer

30.5%

Project Manager

30.1%


24.7%

Other

24.6%

Webmaster, Web Master

23.5%

Web Director Developer Overall

A LIST APART


22.3% 10.2% 21.9%

76


Front-End Programming Skills and Skill Gaps Nearly 25% of respondents who need front-end programming skills don’t have them. The segments of the sample with the greater skills gaps are similar to those above: skill gaps are more pronounced for respondents under 21 and over 60 years old, women, and those in the web design field for less than two years (Figs. 10.6 through 10.8). Fig. 10.6

Perceived front-end skill gaps byFig. age10.6 group Fig. 10.6 Perceived front-end Perceived skill gaps front-end by age group skill gaps by age gro

Under 21

Under 21

21-24

21-24

25-32

25-32

33-38

33-38

20.8%

20.8%

39-50

39-50

20.6%

20.6%

51-60

51-60

Over 60

Over 60

Overall

Overall

Fig. 10.7

26.2% 21.2%

26.2% 21.2%

21.9%

21.9%

22.4%

22.4% 32.5%

21.8%

21.8%

Perceived front-end gaps by gender skill gaps by gender Fig. 10.7skill Perceived front-end

Female

26.2% 21.5%

Male

21.8%

Overall

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77


Fig. 10.8

Perceived front-end gaps by longevity in field Fig. 10.8skill Perceived Fig. front-end 10.8 Perceived skill gaps front-end by longevity skill gaps in field by longevit

Less than 2 years

Less than 2 years

2-5 years

2-5 years

More than 5 years

More than 5 years

Overall

Overall

24.5% 21.5%

21.5%

21.0%

21.0%

21.8%

21.8%

Gaps in front-end programming skills also vary by job title, as shown in Fig. 10.9. FIg. 10.9

Perceived front-endFig. skill10.9 gaps by job title Perceived front-end skill gaps by job title Writer/Editor

56.9%


47.6%

Designer

40.1%


38.3%


37.8%

Project Manager

37.2%


36.9%

Web Producer

29.9%


29.5%

Web Designer

28.1%

Webmaster, Web Master

27.9%

Other

27.0%

Web Director Developer Overall

A LIST APART


23.3% 14.4% 24.1%

78


CSS Skills and Skill Gaps Over 20% of respondents who believe they need CSS coding skills don’t have them. The segments of the sample with the greatest skills gaps are similar to those seen in the above discussions of back-end and frontend programming skill gaps: the gaps are more pronounced for respondents over 60 years old, and only slightly more pronounced for women and those in the web design field for less than two years. Fig. 10.10

Perceived CSS skill gaps jobPerceived title Fig.by 10.10 CSS skill gaps by job title Writer/Editor

47.2% 45.6%


43.7%

Usability Expert/Consultant/Lead 37.6%

Project Manager 31.6%


30.4%


28.5%

Other

27.4%

Designer Webmaster

24.3%

Web Director

24.2% 20.6%

Web Producer Interface Designer, UI Designer

18.0%

Developer

17.8%

Web Designer Overall

A LIST APART


15.7% 22.5%

79


Markup Skills and Skill Gaps Almost 20% of respondents who believe they need markup skills don’t have them. Unlike the other skill gaps we’ve analyzed, there do not seem to be significant differences in the gap related to markup skills by age, gender, or longevity in the field. Fig. 10.11

Perceived markup skill Fig. gaps10.11 by job title Perceived HTML skill gaps by job title


43.6%


42.0%

Writer/Editor

39.8%

Project Manager

35.1%


28.5%

Designer

24.8%

Other

22.7%


20.2%

Web Director

19.8%

Interface Designer, UI Designer Webmaster Web Producer

16.4% 15.5%

Web Designer

13.3%

Developer

12.9%

Overall

A LIST APART

17.7%


18.7%

80

For Additional Study, Thanks, and Credits

For Additional Study Readers wishing to perform additional analyses can do so via the anonymized raw data files found at: www.alistapart.com/articles/2007surveyresults

We encourage the community to use our data to engage in further analysis and debate; we will be watching.

Thanks Thanks to all who participated in the survey, to all who will continue the conversation, and to everyone who has ever optimized a GIF image, written a line of code, or simply asked, “Is this right for our users?”

Credits • Carrie Bickner-Zeldman, Researcher • Alan Brickman, Analyst • Andrew Kirkpatrick, Accessibility Consultant • Erin Kissane, Editor • Erin Lynch, Issue Producer • Eric A. Meyer, Author • Jason Santa Maria, Art Director • Krista Stevens, Associate Editor • Larry Yu, Analyst • Jeffrey Zeldman, Author

A LIST APART


81

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