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The Effects of Life-long Learning on Earnings and Employment Richard Dorsett, Silvia Lui and Martin Weale.

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Presentation on theme: "The Effects of Life-long Learning on Earnings and Employment Richard Dorsett, Silvia Lui and Martin Weale."— Presentation transcript:

1 The Effects of Life-long Learning on Earnings and Employment Richard Dorsett, Silvia Lui and Martin Weale

2 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

3 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

4 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

5 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.

6 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.

7 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.

8 Five-year Transitions Initial Qualification Level 01234All Qualification Level Five Years Later 0 94.1000021.0 1 2.893.400031.6 2 1.31.994.6008.9 3 1.51.61.492.5015.8 4 0.33.14.17.510022.7 Upgrading 5.96.65.47.505.2 Llong Learning 10.318.827.930.531.622.1 N 3905791472793511746

9 Non-employment Rates Initial No Lifelong With Qualification With Education Level Learning but not Upgrading Upgrading 038.30%11.20%16.50% 118.30%8.40%13.40% 213.50%10.10%9.60% 39.30%10.40%17.20% 49.00%7.20%

10 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

11 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.

12 Equation Structure

13 Estimation Strategy Consider covariance structure of residuals Note that for identification

14 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.

15 Movers: Men: Selected Coefficients UnrestrictedRestricted Coeffz-statCoeffz-stat Ever Acquired 25-340.0070.11 Ever Acquired 35-49-0.001-0.02 Ever Acquired 50-600.0340.38 Ever Upgraded 25-340.0870.880.092.21 Ever Upgraded 35-490.1211.360.092.21 Ever Upgraded 50-600.1250.960.092.21 Orig Qual 10.1242.720.122.65 Orig Qual 20.2344.670.234.64 Orig Qual 30.2584.270.2564.23 Orig Qual 40.4636.590.4696.88

16 OLS Regression: Selected Coefficients UnrestrictedRestricted Upgraded(t-1)0.0390.043* Ever Acquired 25-34-0.022 Ever Acquired 35-49-0.009 Ever Acquired 50-60-0.025 Ever Upgraded 25-340.015 Ever Upgraded 35-49-0.009 Ever Upgraded 50-60-0.006-0.018** Employed at start-0.088***-0.086*** Sometime Acquired0.010.003 Sometime Upgraded0.0130.009

17 Restricted Model Parameters: Selected Coefficients MoverStayerSwitchEmpl Upgraded (t)-0.73** Upgraded (t-1)0.065**-0.63** Upgraded (t-2)-0.44** Ever Acquired0.06* Ever Upgraded0.41** Orig Qual10.12**0.0020.170.12 Orig Qual20.24**-0.0060.41**0.29* Orig Qual30.28**0.0060.57**0.38** Orig Qual40.48**0.02***0.98**0.33** Newly Employed-0.35**-10 Gen Residual-0.220.00-0.11**0.05 Sometime Acquired-0.050.00.070.26** Sometime Upgraded0.07*0.002-0.07-0.21

18 Marginal Probabilities(Reference Age 30) P(Emp)P(Stay|Emp)P(Stay&Emp) NoYesNoYesNoYes Upgraded(t)0.830.780.690.64 Upgraded(t-1)0.830.800.690.66 Upgraded(t-2)0.83 0.690.68 Ever Upgraded0.830.890.690.73 Orig Qual10.800.830.730.780.590.64 Orig Qual20.800.850.730.830.590.71 Orig Qual30.800.870.730.870.590.75 Orig Qual40.800.860.730.930.590.80 Age 400.890.880.780.850.700.76 Age 500.890.810.780.860.700.71

19 Average Returns to Life-long Learning: Men Man Aged 25Man Aged 40 Prior Education Level Wage EffectFull Effect Wage EffectFull Effect No upgrading06.0% 5.8%5.6% 15.9% 5.4%5.3% 25.6% 4.9%4.8% 35.5%5.4%4.6% 44.7%5.7%1.8%1.6% Upgrading09.3%21.0%9.0%21.7% 18.9%14.4%9.3%16.3% 28.6%12.3%8.9%14.1% 38.9%12.2%9.1%13.5%

20 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

21 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


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