Changes in the characteristics and skills of British youths between the mid-1980s and mid-2000s are evaluated using a method recently developed by Altonji et al. The main finding is that skills have increased over time in successive cohorts of young people. The improvement is, however, uneven, and those at the bottom end of the skills distribution have benefitted less than others. This implies, other things being equal, that the distribution of earnings will widen over the coming years.

Geraint Johnes

English

Research Papers in Economics

 
 
 
 
 
 
 
 
 
 
 
Lancaster University Management School 
Working Paper  
2008/019 
 
 
 
 
 
CHANGES IN THE CHARACTERISTICS AND SKILLS 
OF BRITISH YOUTH 
 
 
 
 
 
Geraint Johnes  
 
 
The Department of Economics                        
Lancaster University Management School 
Lancaster LA1 4YX 
UK 
 
 
© Geraint Johnes  
All rights reserved. Short sections of text, not to exceed 
two paragraphs, may be quoted without explicit permission, 
provided that full acknowledgement is given. 
 
The LUMS Working Papers series can be accessed at http://www.lums.lancs.ac.uk/publications/ 
LUMS home page: http://www.lums.lancs.ac.uk/  
 
 
 
 
 
CHANGES IN THE CHARACTERISTICS AND SKILLS OF BRITISH 
YOUTH 
 
 
 
 
Geraint Johnes 
 
 
Lancaster University Management School 
Lancaster LA1 4YX 
United Kingdom 
 
T: +44 1524 594215 
F: +44 1524 594244 
E: G.Johnes@lancaster.ac.uk 
 
 
July 2008 
 
 
ABSTRACT 
 
Changes in the characteristics and skills of British youths between the mid-1980s and 
mid-2000s are evaluated using a method recently developed by Altonji et al. The 
main finding is that skills have increased over time in successive cohorts of young 
people. The improvement is, however, uneven, and those at the bottom end of the 
skills distribution have benefitted less than others. This implies, other things being 
equal, that the distribution of earnings will widen over the coming years. 
 
 
Keywords:     skills  
JEL Classification:   I20, J24 1. Introduction 
 
In recent work, Altonji et al. (2008) have shown that, while recent cohorts of young 
people entering the American labour market have greater levels of skills than their 
predecessors, the gains in skills have not been uniformly distributed. Between 1979 
and 1997, the skills of youths in the middle 60 per cent of the skills distribution  rose 
by around 5 per cent. But for those in the top two deciles the gains were larger, while 
for those in the bottom two deciles the gains were smaller. This implies that, other 
things being equal, the distribution of earnings will become wider over the coming 
years as young workers who entered the labour market around the turn of the century 
reach their peak lifetime earnings. 
 
The  method  used  by  Altonji  et  al.  is  prospective.  It  uses  information  about  the 
characteristics of workers when they were young along with data on their earnings at 
a later stage in their lives, when earnings typically reach a peak. It then reweights the 
distribution of characteristics, using data on a later cohort of young people, and uses 
the mapping of characteristics onto earnings (from the earlier cohort) to evaluate what 
one might expect the earnings of the later cohort to be at some date in the future. 
 
In the United Kingdom there have been many changes to the education system that 
one might expect should have an impact on the skills of young people. Amongst these 
are the replacement of the General Certificate of Education Ordinary Level (GCE O 
level) and the Certificate of Secondary Education (CSE) examinations by the General 
Certificate of Secondary Education (GCSE) in 1987 and the introduction of a national 
curriculum, Grant Maintained schools (funded directly by central rather than local 
government), parental choice of school and school league tables in the wake of the 
1988 Education Reform Act. The competitive forces introduced by the last of these 
reforms, in particular, might be expected to impact upon students’ skills (Bradley et 
al., 2001). It is therefore of interest to evaluate whether or not the evolution of young 
people’s skills in the UK is similar to that observed by Altonji et al. in the USA. 
 
In the next section I discuss data issues and methodology. Results are reported in 
Section 3, and the paper ends with a short concluding section. 
 
 
2. Data and Methodology 
 
The US data used by Altonji et al. come from the National Longitudinal Survey of 
Youth. This is a particularly attractive dataset in the present context because it gathers 
a long series of longitudinal data on several successive cohorts of youths. The length 
of the panel allows information to be gathered about a cohort’s earnings once they 
have reached maturity. In the UK, there is no single dataset that can offer both this 
long panel and multiple cohorts over an extended period. 
 
There is, however, a close substitute. The British Cohort Study (BCS) is a long panel 
comprising people who were born during the week beginning 5 April 1970. More 
recently, a suite of Youth Cohort Studies (YCS), each made up of a short panel based 
on more recent cohorts of young people, has gathered information about respondents’ 
backgrounds. In the present study, I use the BCS to provide information about an earlier cohort of individuals, and the 2004-07 YCS (cohort 12) to give data about the 
later cohort. The latter are the most recent data currently available. 
 
The match between the BCS and YCS is not perfect. The first covers the whole of the 
UK while the second covers only England and Wales. There are differences in the 
wording of questions. There are differences in environment (such as the change from 
O  level  and  CSE  to  GCSE)  that  make  comparisons  across  time  hazardous. 
Nevertheless the two data sources appear to offer the best opportunity to conduct for 
the UK an analysis that is similar to that provided by Altonji et al. for the USA.   
 
A  further  difference  between  the  BCS  and  YCS  that  can  be  corrected  for  in  the 
analysis concerns the nature of the sample in these two surveys. The BCS, by design, 
is a wholly random sample. The YCS, on the other hand, oversamples some groups. 
In particular, there is an oversampling procedure for ethnic minorities. This is allowed 
for in the present study by appropriate weighting of the data during estimation.  
 
The method introduced by Altonji et al. draws heavily upon the work of DiNardo et 
al. (1996). This involves using wages at prime age as a unidimensional indicator of 
workers’  skills,  while  recognising  that  such  skills  are  a  function  of,  inter  alia,  a 
multiplicity of  characteristics possessed by individuals when  young.  In comparing 
skills across two cohorts, the method first evaluates the density of prime age wages 
enjoyed by the earlier cohort. To evaluate a corresponding (‘counterfactual’) density 
for the later cohort, a set of weights is applied to the wage density of the earlier 
cohort. These weights reflect the probability with which an individual is drawn from 
the second, rather than the first, cohort, based on their skills vectors. 
 
To be precise, the data from the BCS and YCS are pooled in order to run a probit 
model  (appropriately  weighted  to  allow  for  the  oversampling  of  minorities  in  the 
YCS) in which the dependent variable is a binary indicator of the survey from which 
the data are drawn. Observations from the BCS are assigned a zero value for this 
variable, while those from the YCS are assigned unit value. For the ith observation, 
denote the latent index underpinning this binary variable by θi. I then model this as 
 
θi  = ∑
r s,
sexs,iracer,iβs,r + ∑
r s,
sexs,iracer,ipaediβp,s,r + ∑
r s,
sexs,iracer,imaediβm,s,r 
+ ∑
r s,
sexs,iracer,itogetherβt,s,r + ∑
r s,
sexs,iracer,iolevelsβo,s,r + εi 
 
where paed and maed respectively denote binary variables indicating whether or not 
the respondent’s father or mother undertook post-compulsory education. The variable 
together takes unit value if the respondent’s father and mother were living together 
(and with the respondent) when the respondent was aged 16. The olevels variable is 
likewise binary, taking unit value if the respondent achieved at least five GCSE passes 
at grades A*-C, or (in the case of the BCS) an equivalent number of O level passes 
and CSE grade 1 scores. The interpretation of the two sex (male and female) and two 
race (majority and minority) variables is obvious, and the ε term denotes regression 
error. Note that the parental and educational background variables are fully interacted 
with the gender and ethnicity indicators.  
 Denote by p(t1|x) the propensity score associated with an observation being drawn 
from the BCS, conditional on the characteristics vector x for that observation.  Let 
p(t2|x)  (≡1-p(t1|x))  be  the  corresponding  propensity  score  associated  with  an 
observation being drawn from the  YCS.  Let p(t1) and p(t2) denote the number of 
observations in the BCS and YCS samples respectively. Then, following DiNardo et 
al. (1996) and Altonji et al. (2008),  
 
ψ = p(t2|x) p(t1)/ p(t1|x) p(t2) 
 
is the vector of weights which, when applied to the wage density obtained from the 
BCS data, will provide the counterfactual wage density – that is, the density of wages 
that, given the skills of the YCS cohort, is expected to obtain for this group once they 
reach prime age. The values of p(t1) and  p(t2) are 15554 and 14003 respectively. 
 
 
3. Results 
 
The wage data that are used for the BCS cohort are drawn from the sixth follow-up, 
which took place when respondents were aged 29. Since this follow-up is taken at just 
one point in time, there is a variety of reasons why respondents may not have been 
working. The BCS sample for which wage data are available is therefore small in 
relation to the sample upon which the calculation of the weights, ψ, is based. A small 
number of outliers who report hourly wages below £1 or above £25 is omitted. (The 
minimum wage at this time was £3.60 per hour.) This leaves 2825 in the sample. It is 
then straightforward to evaluate the wage density using kernel methods; in this case I 
use the Epanechnikov (1969) kernel and the bandwidth is chosen to be that which 
would minimise the mean integrated squared error under Gaussian conditions. 
 
In Figure 1, the actual density of prime age wages is shown for the BCS sample by the 
solid line. The counterfactual density is shown by the dotted line. The latter clearly 
lies to the right of the former at all but the lowest levels of the wage. This indicates 
that the skills set possessed by young people has improved over time – albeit not 
uniformly.  
 
This can be seen also in Figure 2, which shows how the log wage has changed at each 
point along the skills distribution. I do not plot the first or last 5 centiles owing to the 
presence of outliers. The horizontal axis plots the centiles of the wage distribution, 
while the vertical axis shows the change in log wage. Typically young people’s skills 
increased by about 7 per cent from the mid-1980s to the mid-2000s. For the bottom 
two deciles, however, the improvement in skills has not been so pronounced. Indeed, 
while results for those in the bottom 5 centiles need to be treated with caution, it is 
nonetheless worth noting a decline in measured skills for this group. 
 
 
4. Conclusion 
 
The results reported above suggest that young people are on average more skilled now 
than  in  the  past,  but  also  that  the  distribution  of  skills  is  becoming  increasingly 
uneven. We should expect, therefore, a further widening of the income distribution 
over the next decade or so, other things being equal. The uneven nature of progress is affecting in particular the bottom 20 per cent of the skills distribution, and there is a 
clear need for policy measures to address this issue.   
  
 
References 
 
Altonji, Joseph G., Bharadwaj, Prachant and Lange, Fabian (2008) Changes in the 
characteristics of American youth: implications for adult outcomes, NBER working 
paper 13883. 
 
Bradley, Steve, Johnes, Geraint and Millington, Jim (2001) The effect of competition 
on the efficiency of secondary schools in England, European Journal of Operational 
Research, 135, 545-568.  
 
DiNardo,  John,  Fortin,  Nicole  M.  and  Lemieux,  Thomas  (1996)  Labor  market 
institutions and the distribution of wages, 1973-1992: a semi-parametric approach, 
Econometrica, 64, 1001-1044. 
 
Epanechnikov, V.A. (1969) Nonparametric estimation of a multivariate probability 
density, Theory of Probability and its Applications, 14, 153-158.   
 
 
 
 

Changes in the characteristics of American youth: implications for adult outcomes, NBER working paper 13883.

Labor market institutions and the distribution of wages, 1973-1992: a semi-parametric approach,

Nonparametric estimation of a multivariate probability density,

The effect of competition on the efficiency of secondary schools in England,

CHANGES IN THE CHARACTERISTICS AND SKILLS OF BRITISH YOUTH

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CHANGES IN THE CHARACTERISTICS AND SKILLS OF BRITISH YOUTH

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