The effect of lifelong learning on men’s wages

Abstract

This paper develops a model of earnings and applies this to an examination of the effect of lifelong learning on men’s wages. Using data from the British Household Panel Survey, a variant of the mover–stayer model is developed in which hourly wages are either taken from a stationary distribution (movers) or closely related to the hourly wage one year earlier (stayers). Mover–stayer status is not observed, and we therefore model wages using an endogenous switching regression, estimated by maximum likelihood. Methodologically, the results support the mover–stayer characterisation since the restrictions required for the simpler specifications popular in the literature are rejected. Substantively, simulation of the estimated model shows some statistically significant effects from acquiring qualifications of a higher level than those previously held, but not from acquiring qualifications of the same level.

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Notes

  1. 1.

    This is to avoid the complications around female labour supply, where fertility decisions are more important.

  2. 2.

    Across all waves, 10.5 % of the sample are dropped due to being self-employed, 13.4 % of workers. To provide some sense of how this might affect our results, we re-estimated the econometric model described later, excluding individuals who were self-employed at any point. The resulting estimates of lifelong learning are similar to those found when not excluding those self-employed at any point.

  3. 3.

    Results available on request showed that our findings are robust to assuming that attrition is random.

  4. 4.

    The General Certificate of Secondary Education (GCSE) is normally taken by children at the age of sixteen, while AS-levels are taken at age seventeen and A-levels at age eighteen. Two A-levels are the minimum qualification required for study at university although in practice most universities require three A-levels. Scotland has its own system of qualifications; these have been converted into the equivalents from the rest of the UK.

  5. 5.

    Costs of courses vary very greatly, so it is not possible to draw any generalisations.

  6. 6.

    This is a derived variable wPAYGU.

  7. 7.

    The original mover–stayer model (Goodman 1961) considered a population on which categorical data were observed. Some members, movers, were subject to a Markov process, while others, stayers, retained their initial category.

  8. 8.

    An academic qualification is one which is normally taken in a school or university. Thus academic qualifications are GCSEs, AS and A-levels, and their Scottish equivalents or university degrees and diplomas.

  9. 9.

    It should be noted that the model in differences is not simply the model in levels. In the former, variables explain growth in wage rates, while, in the latter, they explain the level of wage rates.

  10. 10.

    The confidence intervals are calculated from the simulations; we do not make the assumption that the returns are normally distributed. The lower limit of the 95 % confidence interval of the estimate of the return is given by the 2.5 percentile of the ranked returns. We show in Table 7 the proportion of simulations which result in a reduction in the discounted wage and, when this is more than 2.5 %, the estimate is not significant at a 95 % level. When it is more than 5 % the estimates are not significant at a 90 % level.

  11. 11.

    They did include initial qualification level as a control variable, but this is not sufficient to distinguish upgrading from simply acquiring a qualification.

References

  1. Blanden J, Buscha F, Sturgis P, Urwin P (2012) Measuring the returns to lifelong learning. Econ Educ Rev 31:501–514

    Article  Google Scholar 

  2. Cappellari L, Jenkins S (2008) Estimating low pay transition probabilities accounting for endogenous selection mechanisms. J R Stat Soc Ser C 57:165–186

    Article  Google Scholar 

  3. de Coulon A, Vignoles A (2008) An Analysis of the Benefit of NVQ2 Qualifications acquired at Age 26–34. In: Centre for the economics of education discussion paper No. 106

  4. Dickens R (2000) The evolution of individual male earnings in Great Britain: 1975–95. Econ J 110:27–49

    Article  Google Scholar 

  5. Dickson M (2013) The causal effect of education on wages revisited. Oxf Bull Econ Stat 75:477–498

    Article  Google Scholar 

  6. Dutta J, Sefton J, Weale M (2001) Income distribution and income dynamics in the United Kingdom. J Appl Econ 16:599–616

    Article  Google Scholar 

  7. Egerton M, Parry G (2001) Lifelong debt: rates of return to lifelong learning. High Educ Q 55:4–27

    Article  Google Scholar 

  8. Evans K, Schoon I, Weale MR (2013) Can lifelong learning reshape life chances. Br J Educ Stud 61:25–47

    Article  Google Scholar 

  9. Ferrer A, Menendez A (2014) The puzzling effect of delaying schooling on canadian wages. Can Public Policy 40:197–208

    Article  Google Scholar 

  10. Fitzgerald J, Gottschalk P, Moffitt R (1998) An analysis of sample attrition in panel data: the Michigan Panel Study of Income Dynamics. J Hum Resour 33:251–299

    Article  Google Scholar 

  11. Goodman L (1961) Statistical methods for the mover-stayer model. J Am Stat Assoc 56:841–868

    Article  Google Scholar 

  12. Higher Education Statistics Agency (1995) Students in Higher Education Institutions, pp 1994-1995

  13. Higher Education Statistics Agency (2008) Students in Higher Education Institutions, pp 2007–2008

  14. Holmlund B, Liu Q, Skans O (2008) Mind the gap? Estimating the effects of postponing higher education. Oxf Econ Pap 60:683–710

    Article  Google Scholar 

  15. Jenkins A, Vignoles A, Wolf A, Galindo-Rueda F (2002) The determinants and effects of lifelong learning. Appl Econ 35:1711–1721

    Article  Google Scholar 

  16. Light A (1995) The effect of interrupted schooling on wages. J Hum Resour 30:472–502

    Article  Google Scholar 

  17. Lillard E, Willis R (1978) Dynamic aspects of earnings mobility. Econometrica 46:985–1012

    Article  Google Scholar 

  18. Maddala G (1983) Limited-dependent and qualitative variables. Cambridge University Press, New York

    Google Scholar 

  19. Meghir C, Pistaferri L (2004) Income variance dynamics and heterogeneity. Econometrica 72:1–32

    Article  Google Scholar 

  20. OECD (2009) Education today 2009: the OECD perspective, Organisation for Economic Co-operation and Development

  21. Purcell K, Wilton N, Elias P (2007) Hard lessons for lifelong learners? age and experience in the graduate labour market’. High Educ Q 61:57–82

    Article  Google Scholar 

  22. Quandt R (1958) The estimation of parameters of a linear regression obeying two different regimes. J Am Stat Assoc 53:873–880

    Article  Google Scholar 

  23. Ramos D (2003) The covariance structure of earnings in Great Britain. Economica 70:353–374

    Article  Google Scholar 

  24. Stata Manual (2014) Bic note. http://www.stata.com/manuals13/rbicnote.pdf#rBICnote

  25. Ulrick S (2008) Using semi-parametric methods in an analysis of earnings mobility. Econ J 11:478–498

    Google Scholar 

  26. UNESCO Institute for Lifelong Learning (2009) In: Global report on adult learning and education, 2009. http://www.unesco.org/uil/en/UILPDF/nesico/GRALE/gralen_en.pdf

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Correspondence to Martin Weale.

Additional information

We are grateful to the Economic and Social Research Councils for supporting this research through the LLAKES research centre at the Institute of Education and the National Institute of Economic and Social Research. We also gratefully acknowledge comments from participants in seminars at the Institute of Education and the National Institute. The British Household Panel Survey, used in this study, is funded by the Economic and Social Research Council and is available from the data archive at the University of Essex.

Appendices

Appendix 1: The effects of attrition

Sample attrition can arise due to survey non-response or to individuals being excluded for any of the reasons discussed in the text (other than ageing out of the sample). A probit model was used to estimate the probability of attriting in the next survey wave. Table 9 shows the estimated parameters. The score vector from this estimation provides the generalised residuals (equivalently, the inverse Mills’ ratio). These are then included in the main model to control for the possibility that the unexplained component of attrition may be correlated with the residuals of any of the equations of our system. Included in the probit model is a variable showing whether the interviewer changes between survey waves. This is likely to affect response because panel members may feel more comfortable about responding to a familiar interviewer. The variable is not included in our main model and so acts as an instrumental variable to help with identification.

Table 10 Returns to lifelong learning from the unrestricted model
Table 11 Parameters of a fixed effects model

Appendix 2: Returns to lifelong learning from the unrestricted model

Table 10 shows the results of the simulation for the version of the model in which the learning terms in the “stayers” equation are not restricted to zero. The coefficients on the learning terms are very close to zero and poorly determined (Table 5). It is therefore to be expected that there is little impact on the mean effects and that the results are much less well determined.

Appendix 3: A fixed effects model

Table 11 shows the results of estimating a fixed effects model. In this model, each man’s initial educational attainment is absorbed into the individual-specific fixed effect. The term “No upgrade” shows the effect of acquiring a qualification which does not result in any change in the attainment level; the coefficients by the other terms show the effects of acquiring a qualification at this level during the course of the survey, when it is at a level higher than previously. Upgrading to level 1 or to level 3 or higher has a significant effect on the hourly wage, while acquiring a qualification without upgrading does not.

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Dorsett, R., Lui, S. & Weale, M. The effect of lifelong learning on men’s wages. Empir Econ 51, 737–762 (2016). https://doi.org/10.1007/s00181-015-1024-x

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Keywords

  • Lifelong learning
  • Returns to education
  • Switching regression
  • Wage dynamics

JEL Classification

  • C33
  • C35
  • I20
  • J24
  • J31
  • J63