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

• Contrast this with a model estimated in first differences to remove individual fixed effects in levels.

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.

Five-year Transitions

Initial Qualification Level

0 1 2 3 4 All

Qualification Level Five Years Later

0 94.1 0 0 0 0 21.0

1 2.8 93.4 0 0 0 31.6

2 1.3 1.9 94.6 0 0 8.9

3 1.5 1.6 1.4 92.5 0 15.8

4 0.3 3.1 4.1 7.5 100 22.7

Upgrading 5.9 6.6 5.4 7.5 0 5.2

Llong Learning 10.3 18.8 27.9 30.5 31.6 22.1

N 390 579 147 279 351 1746

Non-employment Rates

Initial No Lifelong

With Qualification With

Education Level Learning

but not Upgrading Upgrading

0 38.30% 11.20% 16.50%

1 18.30% 8.40% 13.40%

2 13.50% 10.10% 9.60%

3 9.30% 10.40% 17.20%

4 9.00% 7.20%

Earnings

Initial No Lifelong

With Qualification With

Education Learning but not Upgrading Upgrading

Level 0 £7.98 £9.40 £9.99

1 £9.84 £10.50 £10.65

2 £10.01 £10.69 £13.12

3 £12.28 £12.35 £12.51

4 £15.76 £16.09

Sample structure

• Consider only men aged 25-60.

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

Equation Structure

1 1

2 2

* * *

*

OLS in Differences

Mover-stayer Model

Mover if 0, stayer if 0

it it it

it it it

it it it

it it it it it

it

y X u

Mover y X u

Stayer y X u

Switching I Z I I

Employment J W

*

* *2 1

*2

Employed if 0

Qualification without upgrade if

Qualification with upgrade if

it it it

it it it it

it

J

Learning K V K

K

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.• Include dummies for people who undertake life-long learning and those

who upgrade at some time so as to distinguish the characteristics of people who study from the effects of study.

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

OLS Regression: Selected Coefficients

Unrestricted Restricted

Upgraded(t-1) 0.039 0.043*

Ever Acquired 25-34 -0.022

Ever Acquired 35-49 -0.009

Ever Acquired 50-60 -0.025

Ever Upgraded 25-34 0.015

Ever Upgraded 35-49 -0.009

Ever Upgraded 50-60 -0.006 -0.018**

Employed at start -0.088*** -0.086***

Sometime Acquired 0.01 0.003

Sometime Upgraded 0.013 0.009

Restricted Model Parameters: Selected Coefficients

Mover Stayer Switch Empl

Upgraded (t) -0.73**

Upgraded (t-1) 0.065** -0.63**

Upgraded (t-2) -0.44**

Ever Acquired 0.06*

Ever Upgraded 0.41**

Orig Qual1 0.12** 0.002 0.17 0.12

Orig Qual2 0.24** -0.006 0.41** 0.29*

Orig Qual3 0.28** 0.006 0.57** 0.38**

Orig Qual4 0.48** 0.02*** 0.98** 0.33**

Newly Employed -0.35** -10

Gen Residual -0.22 0.00 -0.11** 0.05

Sometime Acquired -0.05 0.0 0.07 0.26**

Sometime Upgraded 0.07* 0.002 -0.07 -0.21

Marginal Probabilities(Reference Age 30)

P(Emp) P(Stay|Emp) P(Stay&Emp)

No Yes No Yes No Yes

Upgraded(t) 0.83 0.78 0.69 0.64

Upgraded(t-1) 0.83 0.80 0.69 0.66

Upgraded(t-2) 0.83 0.83 0.69 0.68

Ever Upgraded 0.83 0.89 0.69 0.73

Orig Qual1 0.80 0.83 0.73 0.78 0.59 0.64

Orig Qual2 0.80 0.85 0.73 0.83 0.59 0.71

Orig Qual3 0.80 0.87 0.73 0.87 0.59 0.75

Orig Qual4 0.80 0.86 0.73 0.93 0.59 0.80

Age 40 0.89 0.88 0.78 0.85 0.70 0.76

Age 50 0.89 0.81 0.78 0.86 0.70 0.71

Average Returns to Life-long Learning: Men

Man Aged 25 Man Aged 40

Prior Education Level

Wage Effect Full Effect

Wage Effect Full Effect

No upgrading 0 6.0% 6.0% 5.8% 5.6%

1 5.9% 5.9% 5.4% 5.3%

2 5.6% 5.6% 4.9% 4.8%

3 5.5% 5.4% 4.6% 4.6%

4 4.7% 5.7% 1.8% 1.6%

Upgrading 0 9.3% 21.0% 9.0% 21.7%

1 8.9% 14.4% 9.3% 16.3%

2 8.6% 12.3% 8.9% 14.1%

3 8.9% 12.2% 9.1% 13.5%

Conclusions

• In common with other related work, we find little benefit from life-long learning when studied with the standard fixed-effects model.

• A richer mover-stayer model in which employment is endogenous finds that life-long learning has statistically significant effects

• Upgrading raises the long-term employment rate by around 5% and also incurs wage benefits for stayers after one year

•Acquisition of qualifications, with or without upgrading raises earnings of movers permanently by around 6%•The effects point to a return of around 4% for qualifications without upgrading and from 12-22% with upgrading.•The effects of upgrading are much enhanced by the effect on employment