JRA’s Pilot Survey: Korea Case€¦ · Job Requirements Approach (JRA) pilot survey in Korea, which is a method for measuring skills use at the workplace. • A key objective of

JRA’s Pilot Survey: Korea Case

2009. 2. 12

Soo-Young Lee (KRIVET)

Survey overviewCognitive skill usage at workplace

Reading, Writing and Mathematical skillsOther generic skillsComputer and Internet Skill Level

Sources of skills Trainings of Computer useTypes of training and education

Skill Mismatch Skills and Earnings

•

This report outlines the principal results of the Job Requirements Approach (JRA) pilot survey in Korea, which is a method for measuring skills use at the workplace.

•

A key objective of the pilot was to test whether the JRA method works across countries comparison study and to evaluate the comparability of the responses in the context of Korea.

n %

genderMale 306 61.1

female 195 38.9

age 18-39 261 52.140-64 240 47.9

industry Manufacturing 155 30.9Services and others 346 69.1

occupation

Professionals 221 44.1non-manual occupations 171 34.1

Manual occupations 109 21.8total 501 100.0

✺

A total of 501 samples (from age 18-64) were selected in three main areas of Korea; Seoul, Incheon and Gyeonggi area

Out of 1,295 households, 869 households were successfully screened and 690 households were eligible. From 690 eligible households, 501 households were responded. Thus, GRR was 72.6% and NRR was 48.7%There was general reluctance to the household survey

Long working hours – limited chance to meet respondents at home Limited access to apartment complexes due to high securityLow interests in participating in any surveyUneasiness with CAPI

Average item duration time for the whole survey was 29.0 minutes, which was shorter than other countries

cf. Australia : 34.0, Greece : 49.8

Block Minutes

A 1.0

B 8.9

C 10.6

D 4.3

F 1.7

K 1.9

CAPI (n=276) 29.15

Paper version(n=225) 28.91

Total 29.0

Due to a small sample size, comparative analyses by industry or occupation level were not highly reliableSample is not representative of the whole populationNo other national data to compare with

[Reading, Writing, & Math Skills]

anyr=Reading less than 1page work-related information, read1p=read 1 page long information, read5p= read 5 pages long information, read-trade=read articles in trade magazines etc., read-prof= read articles in professional journals etc., manuals=read instruction manuals, books=read work-related books

ReadingVariables

TotalGender Age

Male Female t p 18-39 40-64 t p

anyr 0.87 0.90 0.82 2.29 * 0.95 0.78 5.53 ***

read1p 0.66 0.71 0.58 2.76 ** 0.77 0.54 5.40 ***

read5p 0.53 0.60 0.41 4.32 *** 0.62 0.43 4.55 ***

read_trade 0.47 0.51 0.39 2.73 ** 0.56 0.36 4.69 ***

read_prof 0.40 0.44 0.33 2.63 ** 0.46 0.34 2.72 **

manuals 0.52 0.55 0.49 1.17 0.64 0.40 5.52 ***

books 0.51 0.57 0.43 2.96 ** 0.61 0.40 4.77 ***

Reading Skill 0.57 0.61 0.49 3.29 ** 0.66 0.46 5.70 ***


ReadingVariables

Industry Occupation

Manufact uring Services t p Professionals non-manual

occupations

manual occupation

sF p

anyr 0.85 0.87 -0.64 0.95 0.90 0.65 32.20 ***read1p 0.68 0.65 0.59 0.78 0.67 0.38 27.76 ***read5p 0.55 0.52 0.78 0.67 0.50 0.27 26.01 ***

read_trade 0.45 0.47 -0.40 0.61 0.47 0.15 33.52 ***read_prof 0.37 0.41 -0.97 0.54 0.38 0.13 26.71 ***manuals 0.54 0.52 0.51 0.63 0.58 0.22 27.69 ***

books 0.50 0.52 -0.29 0.66 0.51 0.20 33.65 ***

Reading Skill 0.56 0.57 -0.04 0.69 0.57 0.29 44.59 ***

anyr=Reading less than 1page work-related information, read1p=read 1 page long information, read5p= read 5 pages long information, read-trade=read articles in trade magazines etc., read-prof= read articles in professional journals etc., manuals=read instruction manuals, books=read work-related books

Reading skills usage at workplaceCronbach α for Reading scale was 0.921More males than females and more younger population(18-39) than older population(older than 40) reported higher reading skills usage related to jobNo industrial difference was found, but occupational difference was statistically significant


anyw=writing less than 1 page as part of job, write1p=write 1 page long, write5p=write 5 pages long, write-trade=write articles of magazines, newspapers, etc., write-prof=write articles for professional journals

WritingVariables Total

Gender Age

Male Female t p 16-39 40-64 t p

anyw 0.82 0.85 0.78 2.11 * 0.88 0.77 3.28 **

write1p 0.60 0.65 0.51 3.27 ** 0.69 0.50 4.31 ***

write5p 0.36 0.41 0.29 2.65 ** 0.44 0.28 3.80 ***

writ_trade 0.10 0.12 0.07 1.88 0.10 0.10 -0.03

writ_prof 0.09 0.11 0.05 2.54 * 0.09 0.08 0.51

Writing Skill 0.39 0.43 0.34 3.57 *** 0.44 0.35 3.80 ***


WritingVariables

Industry Occupation

Manufact uring Services t p Professionals non-manual

occupationsmanual

occupations F p

anyw 0.79 0.84 -1.21 0.93 0.86 0.55 42.30 ***

write1p 0.60 0.60 0.10 0.72 0.61 0.32 26.21 ***

write5p 0.39 0.35 0.94 0.48 0.36 0.12 22.59 ***

writ_trade 0.11 0.10 0.29 0.14 0.08 0.05 4.25 *

writ_prof 0.09 0.08 0.24 0.13 0.06 0.04 4.77 **

Writing Skill 0.40 0.39 0.14 0.48 0.39 0.21 37.04 ***

anyw=writing less than 1 page as part of job, write1p=write 1 page long, write5p=write 5 pages long, write-trade=write articles of magazines, newspapers, etc., write-prof=write articles for professional journals

Writing skills usage at workplaceCronbach α for Writing scale was 0.747More males than females and more younger population(18-39) than older population(older than 40) reported higher reading skills usage related to jobNo industrial difference was found, but occupational difference was statistically significant


counting= count things, addsub=addition or subtraction, multdiv=multiplication or division, fractions=fraction, decimal or percent, alg1=simple algebra, alg2=advanced algebra, geom=geometric or trigonometric functions, stats=probability and statistics, calculus=calculus or other advanced math.

MathVariables Total

Gender AgeMale Female t 16-39 40-64 t

counting 0.83 0.87 0.77 2.90 ** 0.86 0.80 1.74 addsub 0.71 0.76 0.62 3.50 ** 0.74 0.68 1.59 multdiv 0.66 0.71 0.57 3.32 ** 0.70 0.61 2.19 *

fractions 0.48 0.57 0.35 4.71 *** 0.56 0.40 3.79 ***alg1 0.14 0.19 0.06 4.28 *** 0.15 0.12 1.05 alg2 0.07 0.09 0.02 3.30 ** 0.07 0.06 0.65

geom 0.09 0.14 0.02 4.58 *** 0.08 0.11 -1.07stats 0.09 0.12 0.04 3.07 ** 0.11 0.07 1.74

calculus 0.06 0.09 0.02 3.58 *** 0.08 0.05 1.22

Mathemat ic skill 0.35 0.40 0.27 5.51 *** 0.37 0.32 2.41 *


MathVariables

Industry Occupation

Manufact uring Services t Professionals non-manual

occupationsmanual

occupations F

counting 0.86 0.82 0.96 0.86 0.85 0.76 2.97 addsub 0.76 0.68 1.74 0.75 0.75 0.58 5.93 **multdiv 0.70 0.64 1.47 0.70 0.70 0.51 6.70 **

fractions 0.57 0.44 2.75 ** 0.57 0.51 0.26 15.11 ***alg1 0.22 0.10 3.59 *** 0.21 0.06 0.10 11.07 ***

alg2 0.09 0.05 1.48 0.12 0.01 0.04 12.04 ***geom 0.17 0.06 4.19 *** 0.15 0.02 0.08 9.73 ***stats 0.12 0.08 1.72 0.13 0.06 0.04 5.23 **

calculus 0.13 0.03 4.05 *** 0.10 0.02 0.06 5.65 **

Mathematic skill 0.40 0.32 3.42 ** 0.40 0.33 0.27 11.67 ***

counting= count things, addsub=addition or subtraction, multdiv=multiplication or division, fractions=fraction, decimal or percent, alg1=simple algebra, alg2=advanced algebra, geom=geometric or trigonometric functions, stats=probability and statistics, calculus=calculus or other advanced math.

Mathematical skills usage at workplaceCronbach α for Mathematics scale was 0.841More males than females and more younger population(18-39) than older population(older than 40) reported higher reading skills usage related to jobBut age differences were less significant than reading and writing skillsSome industrial difference and occupational difference were statistically significant

[Reading, Writing, & Math Skills by Educational Level]

Educational LevelSum of

Squares df Mean Square F Sig.Reading

SkillsBetween Groups 21.177 5 4.235334 37.74854 .000

Within Groups 55.538 495 0.112199Total 76.715 500

Writing Skills Between Groups 9.520 5 1.904 31.72565 .000Within Groups 29.707 495 0.060

Total 39.227 500Math Skills Between Groups 2.640 5 0.528 9.526272 .000

Within Groups 27.435 495 0.055 Total 30.075 500

1=None & Elementary School, 2=Middle School, 3=High School & post-secondary, non-tertiary, 4=2-year College, 5= 4-year University, 6= Advanced graduate degree

✺

People with higher level of education reported more higher level of cognitive skill usage

Area Items Number of Items

Cronbach α

Problem-Solving Identifying, Explaining, Resolving, Analyzing complex problems

4 0.924

Customer interaction

Persuading, Selling, Advising, Negotiating 4 0.741

Internal interaction

Listening, Cooperating/collaborating, Sharing information

3 0.813

Physical Strength , Stamina, Use of tools, Manual dexterity

4 0.804

Instructing Teaching & Instructing, Making speeches, Planning others’

time3 0.727

Self-direction Planning own activities, Organizing own time, Thinking ahead

3 0.876

Learning New things, helping others to learn, up to date new product 3

0.804

Total

Gender Age

male female t 18-39 over 40 t

(n=306) (n=195) (n=261) (n=240)

skprob 2.89 3.05 2.64 3.43 ** 3.13 2.62 4.39 ***

skcust 2.80 2.88 2.66 2.11 * 2.95 2.63 3.11 **

skintern 3.38 3.43 3.30 1.13 3.69 3.04 5.81 ***

skphys 2.64 2.70 2.54 1.22 2.50 2.78 -2.14 *

skinstr 2.26 2.36 2.10 2.51 * 2.42 2.08 3.52 ***

skplan 3.39 3.49 3.23 2.16 * 3.62 3.14 3.98 ***

sklearn 2.69 2.73 2.62 1.14 2.91 2.44 4.97 ***

skprob = Problem-solving, skcust = interaction with customers, skintern = interaction with colleagues, skphys = physical strength

skinstr = instructional ability, skplan = self-control management, sklearn = learning ability

Industry Occupation

manufac

turing

servicest

professio

nals

non-

manual occupati

on

manual occupati

on

F

(n=155) (n=346) (n=223) (n=171) (n=107)

skprob 3.05 2.82 1.84 3.18 2.83 2.34 13.60 ***

skcust 2.72 2.83 -1.00 2.90 3.02 2.20 20.54 ***

skintern 3.38 3.38 0.03 3.50 3.46 3.09 6.00 **

skphys 3.09 2.43 4.82 *** 2.36 2.50 3.42 21.99 ***

skinstr 2.25 2.26 -0.16 2.55 2.21 1.70 23.96 ***

skplan 3.35 3.41 -0.46 3.86 3.40 2.39 48.84 ***

sklearn 2.72 2.67 0.43 2.75 2.87 2.56 12.09 ***

skprob = Problem-solving, skcust = interaction with customers, skintern = interaction with colleagues, skphys = physical strength

skinstr = instructional ability, skplan = self-control management, sklearn = learning ability

ISCED 0 &

1

ISCED 2 ISCE D 3 &

4

ISCED5B ISCED5A ISCED 6

F

(n=14) (n=30) (n=176) (n=74) (n=167) (n=40)

skprob 1.63 2.04 2.61 3.14 3.19 3.47 11.395 ***

skcust 1.46 2.24 2.59 3.07 3.04 3.08 10.190 ***

skintern 2.38 2.27 3.17 3.79 3.66 3.57 11.244 ***

skphys 4.24 3.56 3.19 2.72 1.97 1.58 28.114 ***

skinstr 1.12 1.36 1.91 2.44 2.62 2.98 21.629 ***

skplan 2.43 2.42 3.00 3.61 3.79 4.09 14.244 ***

sklearn 1.50 2.07 2.55 2.71 2.97 2.89 9.433 ***

skprob = problem-solving, skcust = interaction with customers, skintern = interaction with colleagues, skphys = physical skills

skinstr = instructional ability, skplan =self-direction, sklearn = learning skills

Other skills usage at workplaceGender differences in several skills usage at workplace were statistically significant(skprob, skcust, skinstr, skplan)Industrial difference was only found in physical skills usageAge, occupational, and educational level differences in all generic skills usage at workplace were statistically significant

Overall, the items for generic skill usage at workplace worked as expected. No major anomalies found.According to Green’s analysis, in general, Koreans’ skill usages(on average) are lower than other countries, in particular, cognitive skills including reading, writing, math, and problem-solving skills.

Population characteristics were different among countriesNo weighting

What to consider to explain the results?Skill usage vs. actual level of skillGeneral work system : Tacit knowledge vs. explicit knowledgeCultural differences in problem solving approach? Holistic approach vs. procedural approach

Basic Moderate Complex Advanced X2

Gender Male(n=207) 33(15.9) 122(58.9) 39(18.8) 13(6.3) 36.03***

Female(n=116) 51(44.0) 54(46.6) 11(9.5) 0(0.0)

Age 18-39(n=202) 121(59.9

)121(59.9) 30(14.9) 11(5.4) 13.72**

40-64(n=121) 55(45.5) 55(45.5) 20(16.5) 2(1.7)

Industry Manufacturing

(n=95)59(62.1) 59(62.1) 19(20.0) 2(2.1) 9.84*

Services(n=228) 117(51.3

)117(51.3) 31(13.6) 11(4.8)

Occupat

ionProfessional

(n=171)90(52.6) 90(52.6) 35(20.5) 9(5.3) 15.39*

Non-manual

(n=122)72(59.0) 72(59.0) 10(8.2) 1(0.8)

Manual(n=28) 14(50.0) 14(50.0) 4(14.3) 2(7.1)

Total (n=323) 84(26.0) 176(54.5) 50(15.5) 13(4.0)

[Learning of Computing Skills]

ItemsMale Female 18-39 40-64

TotalX2=22.98*** X2=17.41***

1) Formal educational institution (school, college, university) 78(37.7) 46(39.7) 95(47.0) 29(24.0) 124(38.4)

2) Training courses in adult education centre (but not on the initiative of your employer) 20(9.7) 11(9.5) 15(7.4) 16(13.2) 31(9.6)

3) Vocational training courses (on the demand of the employer) 53(25.6) 22(19.0) 39(19.3) 36(29.8) 75(23.2)

4) Self-study using books, CD-ROMS, online courses, etc 108(52.2) 29(25.0) 83(41.1) 54(44.6) 137(42.4)

5) Self-study in the sense of learning-by-doing 153(73.9) 76(65.5) 148(73.3) 81(66.9) 229(70.9)

6) Informal assistance from colleagues, relatives, friends 106(51.2) 58(50.0) 105(52.0) 59(48.8) 164(50.8)

7) Some other way 1(0.5) 4(3.4) 2(1.0) 3(2.5) 5(1.5)

[Learning of Computing Skills]

Gender difference was statistically significantIn particular, more males learned computing skills through self-study than females

Age difference was statistically significantIn particular, more younger people learned computing skills through formal education and more older people learned computing skills through vocational training on the demand of employer

No industrial difference was foundOccupational difference was statistically significant

Professionals had more formal training, Manual workers had more self-learning

2χ 2χ

Types of training or education connected with current job

Type of Training or Education Experienced

#(%)Days Helpful?

1) Received instruction or training from someone which took you away from your normal job

131(26.5) 55.42 119(90.8)

2) Received instruction whilst performing your normal job

185(36.9) - 179(96.8)

3) Taught yourself from a book/manual/video/computer/cassette

167(33.3) 67.43 169(96.0)

4) Followed a correspondence or Internet course

113(22.6) 37.92 108(95.6)

5) Taken a class at a college or training institution

55(11.0) 47.79 47(85.5)

6) Done some other work-related training 80(16.0) 31.08 75(93.8)

Types of training or education

gender Age Industry Occupation

Male Female 18-39 40-64 Manufa

cturing

Services Professi

onals

Non-

Manual

Manual

1 82

(28.8)

52

(28.7)

81

(31.0)

53

(22.1)

37

(23.9)

97

(28.0)

64

(29.0)

49

(28.7)

18

(17.6)

2 110

(35.9)

75

(38.5)

113

(43.3)

72

(30.0)

50

(32.3)

135

(39.0)

81

(38.7)

71

(41.5)

31

(30.4)

3 103

(35.3)

59

(30.3)

114

(43.7)

53

(22.1)

49

(31.6)

118

(34.1)

98

(43.4)

51

(29.8)

18

(17.6)

4 77

(25.2)

38

(18.5)

77

(29.5)

38

(15.0)

30

(19.4)

83

(24.0)

64

(29.0)

39

(22.8)

9

(8.8)

5 27

(8.8)

28

(14.4)

30

(11.5)

25

(10.4)

11

(7.1)

44

(12.7)

27

(12.2)

23

(13.5)

3

(2.9)

1. Instruction or training which took you away from normal job; 2. OJT; 3. Self-study; 4. Internet course; 5. Taken a class at a college or institution;

In general, OJT and self-study were more frequently experienced training types than structured trainingsAge differences in the types of training were statistically significant

Younger people had more training experiences than older people No statistically significant industrial differences found but ingeneral services sectors seemed to have more trainings than other sectorsIndustrial differences in types of training were statistically significant

Manual workers had significantly less training experiencesImplications on policies on vocational training system

Groups that had less training experiences, do they need further training opportunities?

Are qualifications required for job needed?Are Koreans over-qualified?

Australia France Greece Korea Korea Revised

Total

Yes 358 283 348 234 184 1,223

% 81 71.83 80.37 68.62 59.7 75.96

No 84 111 85 107 124 387

% 19 28.17 19.63 31.38 40.3 24.04

Total 442 394 433 341 308 1,610

100 100 100 100 100 100

* Jobs requiring at least upper secondary education(ISCED>=3)

What educational achievements/qualifications, if any, would someone need to get the type of job?

X2=15.337 (p=0.009)


X2=64.671 (p=0.000)


X2=11.226 (p=0.047)


X2=92.779

(p=0.000)

Difference between current and required education level

2χ 2χ 2χ 2χ

Required

ISCED 0 & 1 ISCED 2 ISCED 3 ISCED 4 ISCED 5B ISCED 5A ISCED 6 Total

CURRENT

ISCED 0 &1 13(92.9)

0(0.0)

1(7.1)

0(0.0)

0(0.0)

0(0.0)

0(0.0)

14 (2.8)

ISCED 2 18(60.0)

9(30.0)

3(10.0)

0(0.0)

0(0.0)

0(0.0)

0(0.0)

30 (6.0)

ISCED 3 84(50.3)

7(4.2)

64(38.3)

3(1.8)

3(1.8)

6(3.6)

0(0.0)

167 (33.3)

ISCED 4 1(11.1)

0(0.0)

3(33.3)

2(22.2)

2(22.2)

1(11.1)

0(0.0)

9 (1.8)

ISCED 5B 12(16.2)

3(4.1)

22(29.7)

0(0.0)

33(44.6)

4(5.4)

0(0.0)

74 (14.8)

ISCED 5A 28(16.2)

1(0.6)

27(16.2)

0(0.0)

19(11.4)

88(52.7)

4(2.4)

167 (33.3)

ISCED 6 3(7.9) 0(0.0)

1(2.6)

0(0.0)

5(13.2)

17(44.7)

12(31.6)

40 (8.0)

Total 159(31.7)

20(4.0)

121(24.2)

5(1.0)

62(12.4)

116(23.2)

17(3.4)

501 (100)

✺

On average, people responded the required level of education is lower than their own educational level

More females than males, older people than younger people responded no education or Elementary school level is required to get the jobMore people in service industries responded more than college education is required to get the jobMore professional occupations responded higher education is required to get the jobCompared respondent’s own educational level with the required educational achievements, in general, people responded lower educational achievements is required to get the job than their own educational level

More than 84% goes to tertiary education(ISCED 5B or higher)“Required” by employer vs. personal perceptionLanguage bias: educational achievement vs. qualificationVocational qualification is not necessary

Describe your skills in your own work (perceptions)Gender difference was not statistically different

2χ 2χ 2χ 2χ

X2=2.44 (p=0.295)

Describe your skills in your own work (perceptions)More younger people felt they need further trainingMore older people felt they can do more demanding duties

2χ 2χ 2χ 2χ

X2=12.16 (p=0.002)

Describe your skills in your own work (perceptions)Industrial difference was not statistically significant

2χ 2χ 2χ 2χ

X2=0.303 (p=0.860)

Describe your skills in your own work (perceptions)More manual workers felt they do not need further training and their duties are correspond with their present skill level

2χ 2χ 2χ 2χ

X2=11.3 (p=0.023)

Describe your skills in your own work (perceptions)People with lower education level felt their duties correspond with their present skill level, while people with higher education level split : either need further training orcan do more demanding duties

2χ 2χ 2χ 2χ

X2=23.37

(p=0.009)

2χ 2χ 2χ 2χ

nAnnual Income

(Korean Won)T or F n

Hourly Wage

(Korean Won)T or F

GenderMale 196 34983K

2.138**273 13147.95

1.311Female 130 30268K 179 11931.02

Age18-39 173 33123K

.020238 12727.04

.14140-64 153 33080K 214 12598.16

IndustryManufacturing 98 31965K

-0.686143 11715.39

-1.425Services 228 33592K 309 13105.96

Occupation

Professionals 139 34659K

1.258

200 13637.81

2.122Non-manual 121 30895K 159 12253.35

Manual 66 33874K 93 11281.69

Employment Status

Employees 230 34977K2.697***

351 12891.220.924

Self-employed 96 28613K 101 11883.39

2χ 2χ

1 Euro = 1800 Korean Won, 1 US Dollar = 1400 Korean Won

[Annual Income & Hourly Wage]

2χ 2χ 2χ 2χ

n

Annual Income(Korean

Won)

F nHourly Wage

(Korean Won)F

Education Level

Elementary school 9 24711K

2.051 *

13 11086.55

1.429

Middle school 19 28037K 27 11340.82

High school 114 30741K 161 11566.88

College 48 39521K 61 14907.11

University 112 34231K 153 12965.65

Graduate degree 24 33833K 37 14036.96

2χ 2χ

1 Euro = 1800 Korean Won1 US Dollar = 1400 Korean Won

[Annual Income & Hourly Wage]

2χ 2χ 2χ 2χ

n

Annual Income(Korean

Won)

F nHourly Wage

(Korean Won)F

Required Education

Level

Elementary school 100 31020K

0.635

144 11898.01

0.485

Middle school 14 34943K 18 13710.54

High school 95 32207K 119 12477.33

College 35 35066K 51 13141.93

University 70 35197K 102 13173.71

Graduate degree 12 37467K 18 14787.69

2χ 2χ

1 Euro = 1800 Korean Won1 US Dollar = 1400 Korean Won

Hours T or F Hours T or F Hours T or F

Male

(n=306)61.82 1.721* Manufact

uring

(n=155)

58.11 0.174 ISCED 0&1

(n=14)

114 2.202*

Female

(n=195)50.06 Services

(n=346)56.85 ISCED2

(n=30)53.43

18-39

(n=261)55.60 -0.513 Professio

nals

(n=223)

53.94 2.57* ISCED3&

4

(n=176)

57.66

40-64

(n=24)59.03 Non-

manual

(n=171)

52.51 ISCED5B

(n=74)65.58

Manual

(n=107)71.68 ISCED5A

(n=167)51.49

Total(N=501)

57.24 ISCED6

(N=40)46.98

Annual income vs. Hourly earning questions produced a little bit different story

Annual income is easier to report and other national surveys usually ask for annual incomeHourly earnings were derived from a series of questions

Annual income differences were statistically significant by gender, occupation, employment type, and educational level

Males, Professionals, Employees, and 2-3 year college graduates earned more money

On the other hand, group differences in hourly earnings were not statistically significant

When working hours are controlled, differences in earning becamesmaller

Responses to the pay questions need to be compared with national statistics

Working hoursAnnual bonuses

Relationship between skills(usage) and earnings need to be explored furtherRelationship between work organization characteristics and generic skill usage need to be exploredHow to interpret results on “skills mismatch” items?

Over-qualification required?Inadequacy of school education

Documents

JRA’s Pilot Survey: Korea Case€¦ · Job Requirements Approach (JRA) pilot survey in Korea, which is a method for measuring skills use at the workplace. • A key objective of