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.skills

Geraint Johnes

English

Research Papers in Economics

     
 
 
  
  
Volume 29, Issue 1 
  
Changes in the Characteristics and Skills of British Youth 
  
 
 
Geraint Johnes  
Lancaster University Management School 
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.
 
Citation: Geraint Johnes, (2009) ''Changes in the Characteristics and Skills of British Youth'', Economics Bulletin, Vol. 29 no.1 pp. 368-
374. 
Submitted: Sep 24 2008.   Published: March 20, 2009. 
 
     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                 (1) 
 
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, and that the presence of the summation terms 
ensures that, in effect, the interactions and each individual interacted variable are all 
included as explanatory variables.   
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)                (2) 
 
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. In 
contrast to Altonji et al. (2008) we do not cap propensity weights because we do not 
encounter any implausibly high values. 
 
 
3. Results 
 
The results of the probit are reported in Table A1 in the appendix. For each 
observation, this equation can be used to compute the propensity score following 
equation (2) above. 
 
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.
1
                                                 
1 Data from a seventh follow-up, conducted at age 34, have recently become available, but are not used 
in the present study. 
 Since this follow-up is taken at 
just one point in time, there is a variety of reasons why respondents may not have 
been observed. These include attrition over the lifetime of the study, and the fact that 
some respondents are unemployed or out of the labour force; moreover there is 
substantial non-response to the BCS question about gross wage. 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. 
This is the default method used within the STATA software. 
 
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.   
 
 
 
 Appendix 
 
Table A1 Probit results: dependent variable = 1 if observation is drawn from the YCS 
variable  coefficient (st.err.)  variable  coefficient (st.err.) 
mw  -0.189  fw*maed  0.728 
  (6.89)    (24.21) 
mo  1.169  fo*maed  0.199 
  (12.50)    (1.62) 
fw  0.066  mw*together  -0.349 
  (2.38)    (12.18) 
fo  1.548  mo*together  -0.063 
  (15.06)    (0.63) 
mw*paed  -0.021  fw*together  -0.533 
  (0.72)    (17.72) 
mo*paed  -0.629  fo*together  -0.446 
  (5.76)    (3.82) 
fw*paed  0.431  mw*olevels  0.470 
  (14.05)    (18.57) 
fo*paed  0.005  mo*olevels  0.633 
  (0.04)    (6.08) 
mw*maed  0.083  fw*olevels  0.336 
  (2.88)    (13.07) 
mo*maed  -0.590  fo*olevels  0.382 
  (5.51)    (3.45) 
       
log likelihood  -17870.797  number of 
observations 
29557 
Note: the gender and ethnicity terms are defined as follows: (i) mw is one for white 
males and zero for all other respondents; (ii) mo is one for other males and zero for all 
other respondents; (iii) fw is one for white females and zero for all other respondents; 
and (iv) fo is one for other females and zero for all other respondents. 

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

http://www.accessecon.com/pubs/EB/2009/Volume29/EB-09-V29-I1-P38.pdf

Changes in the Characteristics and Skills of British Youth

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics