The Effects of Life-long Learning on Earnings and

Employment

Richard Dorsett, Silvia Lui and Martin Weale

The Role of Life-long Learning

• Educational attainment is strongly dependent on socio-economic background.

• It is unlikely that capacity to benefit from education is as dependent on background as is attainment

• It follows that there is plenty of scope for making up for lost time

The Spread of Life-long Learning

• 1994. 31% of 451,000 UK students starting undergraduate courses aged twenty-five or over.

• 2007, 43% of 706,000 UK students A similar pattern elsewhere–Forty per cent of those starting university in Sweden were had left school at least five years earlier

–Thirty-five per cent of male school leavers in the United States between 1979 and 1988 resumed their education by 1989.

• What are the benefits of qualifications gained through life-long learning

Doubts about the Benefits

• Jenkins et al. (2002). Wage growth after life-long learning was not significantly faster than for those who did not do it.

• Egerton and Parry (2001). Substantial penalties for late learners.• Purcell et al (2007). Case studies suggest mature graduates have difficulty

finding appropriate employment.• Blanden et al. (2008). Little benefit for men; some for women aged thirty-

five to forty-nine

A Mover-stayer Framework

• People have to take a wage from a stationary distribution (Movers)

OR

• The wage rate is closely related to the wage in the previous period (Stayers)

• Expected earnings depend on

– i) the nature of the stationary distribution

– ii) the speed with which people move up the ladder

– iii) the chance of falling off

Employment Prospects

• People have to be employed to have earnings.

• Previous unemployment may damage earnings potential at least in the short run.

• These effects need to be allowed for along with earnings dynamics.

Life-long Learning

• Consider qualifications acquired when age 25 or older.

• BHPS provides information on qualification level (NVQ) from 1991 or when subject joins survey.

• And each year on i)whether qualifications have been obtained and ii) whether educational status has been upgraded.

• Separate effects of qualifications in each of last five years from ever acquiring qualifications.

Average Transitions (Men)

Qualification in Previous Year

0 1 2 3 4

Qualification 0 0.9845 0 0 0 0Level 1 0.0029 0.9837 0 0 0in Current 2 0.0072 0.0083 0.9832 0 0Year 3 0.004 0.0052 0.0089 0.9903 0

4 0.0014 0.0029 0.0079 0.0097 1

Upgrading 0.0155 0.0163 0.0168 0.0097 0Life-long Learning 0.0441 0.0745 0.1189 0.0993 0.0969

Average Transitions (Women)

Qualification in Previous Year

0 1 2 3 4

Qualification 0 0.9885 0 0 0 0Level 1 0.0059 0.9824 0 0 0in Current 2 0.0031 0.0094 0.9854 0 0Year 3 0.0022 0.0047 0.0109 0.9809 0

4 0.0003 0.0036 0.0036 0.0191 1Upgrading 0.0115 0.0176 0.0146 0.0191 0Life-long Learning 0.0299 0.0678 0.1087 0.1108 0.1285

Non-employment Rates

Life-long Learning

Qual Never Not in last year In last year

Men Women Men Women Men Women

0 40.1% 55.7% 11.2% 21.6% 2.7% 13.1%

1 18.3% 32.4% 8.5% 18.3% 7.7% 18.0%

2 14.4% 41.1% 14.2% 16.2% 8.6% 15.2%

3 9.6% 28.2% 11.1% 19.5% 11.3% 17.5%

4 9.4% 28.3% 9.1% 15.3% 7.8% 15.3%

Earnings

Life-long Learning

Qual Never Not in last year In last year

Men Women Men Women Men Women

0 £8.17 £6.47 £9.49 £6.78 £9.56 £6.55

1 £9.82 £7.94 £10.48 £8.09 £10.33 £7.87

2 £10.14 £7.72 £10.17 £7.76 £9.72 £7.63

3 £12.27 £9.72 £11.78 £8.90 £11.66 £8.17

4 £15.79 £13.17 £15.15 £13.04 £13.52 £11.94

Sample structure

• Consider only people aged 25-60.

• Leave out self-employed (who may have negative earnings) and drop from sample if people become self-employed.

Equation Structure

if upgradeion with Qualificat

if upgradet ion withouQualificat

0 if Employed

0 ifstayer ,0 ifMover

2*

1*

2*

**

***

22

11

it

itititit

itititit

ititititit

ititit

ititit

K

KVKLearning

JWJEmployment

IIZISwitching

uXyStayer

uXyMover

Estimation Strategy

• Consider covariance structure of residuals

• Note that

for identification

1

1

1

0

2222

2

11121

012

Estimation Strategy

• Apply a Cholesky decomposition to the co-variance matrix with the life-long learning equation at the top of the diagonal.

• Estimate the life-long learning equation as an ordered probit• Compute the generalised residuals from this and introduce these as extra

variables into the other four equations estimated as a system.

Movers: Men: Selected Coefficients

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Ever Acquired 25-34 0.007 0.11

Ever Acquired 35-49 -0.001 -0.02

Ever Acquired 50-60 0.034 0.38

Ever Upgraded 25-34 0.087 0.88 0.09 2.21

Ever Upgraded 35-49 0.121 1.36 0.09 2.21

Ever Upgraded 50-60 0.125 0.96 0.09 2.21

Orig Qual 1 0.124 2.72 0.12 2.65

Orig Qual 2 0.234 4.67 0.23 4.64

Orig Qual 3 0.258 4.27 0.256 4.23

Orig Qual 4 0.463 6.59 0.469 6.88

Stayers: Men: Selected Coefficients

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Upgraded (t-1) 0.059 2.26 0.064 2.85

Orig Qual 1 0.003 0.49 0.001 0.29

Orig Qual 2 -0.005 -0.88 -0.006 -1.03

Orig Qual 3 0.007 1.45 0.006 1.24

Orig Qual 4 0.015 2.88 0.014 2.75

High Qual Academic 0.005 1.54 0.005 1.57

Switching: Men: Selected Coefficients

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Ever Upgraded 25-34 -0.623 -2.19 -0.348 -1.78

Ever Upgraded 35-49 0.268 1.05

Ever Upgraded 50-60 -0.491 -1.9

Orig Qual 1 0.194 1.56 0.188 1.54

Orig Qual 2 0.414 3.16 0.423 3.27

Orig Qual 3 0.592 4.8 0.598 4.86

Orig Qual 4 0.983 6.83 0.985 7.26

Employment: Men: Selected Coefficients

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Upgraded(t) -0.565 -2 -0.542 -2.91

Upgraded(t-1) -0.585 -2.37 -0.427 -2.8

Upgraded(t-2) -0.327 -1.41 -0.275 -1.84

Ever Acquired 25-34 0.112 0.67

Ever Acquired 35-49 0.26 1.88 0.278 2.64

Ever Acquired 50-60 0.447 2.4 0.404 3.1

Orig Qual 1 0.111 0.92 0.106 0.88

Orig Qual 2 0.293 1.94 0.285 1.91

Orig Qual 3 0.379 3.1 0.373 3.09

Orig Qual 4 0.327 2.55 0.312 2.45

Movers: Women: Selected CoefficientsUnrestricted Restricted

Coeff z-stat Coeff z-stat

Acquired(t-2) -0.176 -3.2 -0.171 -3.77

Acquired(t-3) -0.129 -2.47 -0.122 -2.99

Ever Acquired 25-34 0.049 1.04 0.079 2.72

Ever Acquired 35-49 0.094 2.21 0.079 2.72

Ever Acquired 50-60 0.127 1.86 0.079 2.72

Ever Upgraded 25-34 0.268 3.09 0.113 2.99

Ever Upgraded 35-49 0.12 1.87 0.113 2.99

Ever Upgraded 50-60 0.076 0.81 0.113 2.99

Orig Qual 1 0.031 0.78 0.034 0.87

Orig Qual 2 0.093 1.94 0.094 1.98

Orig Qual 3 0.185 3.42 0.186 3.45

Orig Qual 4 0.457 10.01 0.456 10.11

Stayers: Women: Selected Coefficients

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Acquired(t-2) 0.013 1.4 0.007 1.65

Orig Qual 1 0.001 0.18 0 -0.03

Orig Qual 2 0 -0.04 -0.001 -0.2

Orig Qual 3 0.008 1.08 0.007 1.03

Orig Qual 4 0.01 1.74 0.009 1.65

Switching: Women: Selected Coefficients

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Orig Qual 1 0.046 0.44 0.044 0.42

Orig Qual 2 0.009 0.06 -0.003 -0.02

Orig Qual 3 0.408 2.79 0.406 2.82

Orig Qual 4 0.515 4.36 0.495 4.44

Employment: Women: Selected Coefficients

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Upgraded(t) -0.577 -3.16 -0.404 -3.05

Ever Acquired 25-34 0.228 1.86 0.274 3.05

Ever Acquired 35-49 0.432 4.07 0.461 5.8

Ever Acquired 50-60 0.529 4.28 0.521 5.38

Ever Upgraded 25-34 0.258 1.1

Ever Upgraded 35-49 0.142 0.84

Ever Upgraded 50-60 -0.005 -0.02

Orig Qual 1 0.272 3 0.272 3.01

Orig Qual 2 0.155 1.21 0.154 1.21

Orig Qual 3 0.132 1.18 0.133 1.19

Orig Qual 4 0.174 1.81 0.171 1.84

Employment Effects in the Model

Unrestricted Restricted

Coeff z-stat Coeff z-stat

Movers

Men: Newly Employed -0.306 -2.14 -0.307 -2.36

Women: Newly Employed -0.214 -3.78 -0.206 -3.69

Employment

Employed 1991 1.836 20.59 1.823 20.68

Employed 1991 1.341 22.79 1.337 22.75

Wage Rate Profiles: Men

0

5

10

15

20

25

30

25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Age

£ p

er h

our

2005

pri

ces

Qual 0 Qual 0 L Qual 0 U Qual1 Qual 1 L Qual 1 U Qual 2 Qual 2 L

Qual 2 U Qual 3 Qual 3 L Qual 3 U Qual 4 Qual 4 L

Wage Rate Profiles: Women

0

2

4

6

8

10

12

14

16

18

20

25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Age

£ p

er h

our

2005

pri

ces

Qual 0 Qual 0 L Qual 0 U Qual1 Qual 1 L Qual 1 U Qual 2 Qual 2 L

Qual 2 U Qual 3 Qual 3 L Qual 3 U Qual 4 Qual 4 L

Average Returns to Life-long Learning: Men

Man Aged 25 Man Aged 40

Prior Education Level Full Effect Wages only Full Effect Wages only

No upgrading 0 8.74% 1.73% 17.47% 2.87%

1 5.73% 1.88% 11.98% 3.60%

2 4.37% 1.59% 9.31% 3.06%

3 4.65% 2.16% 9.73% 4.21%

4 6.62% 4.34% 11.99% 7.06%

Upgrading 0 16.68% 11.06% 23.52% 11.35%

1 12.92% 9.97% 18.57% 11.26%

2 11.62% 9.45% 16.17% 10.54%

3 10.12% 8.25% 15.90% 11.02%

Average Returns to Life-Long Learning: Women

Woman aged 25 Woman aged 40

Education Levels Full Effect Wages only Full Effect Wages only

No upgrading 0 37.46% 7.81% 45.67% 6.87%

1 20.36% 8.12% 23.32% 6.71%

2 20.11% 7.73% 23.22% 6.34%

3 20.67% 9.43% 23.14% 7.65%

4 20.29% 10.19% 22.47% 8.38%

Upgrading 0 51.62% 20.21% 58.99% 18.71%

1 32.03% 19.41% 33.66% 16.58%

2 32.29% 19.38% 34.24% 16.76%

3 31.26% 19.78% 31.76% 16.08%

Conclusions

• We find significant average impacts of life-long learning on the wage rates of both men and women, with the effects larger for women.

• However people with life-long qualifications are more likely to be employed than those without. This effect sharply increases the returns to life-long education.