1) The document examines whether high-quality schools benefit low socioeconomic status (SES) students as much as high SES students in terms of completing Year 12. 2) It finds that high-quality schools benefit all students, but help low-achieving, low SES students the most - doubling their chances of completing Year 12 compared to low-quality schools. 3) For the most vulnerable students who are low-achieving and low SES, increasing school quality has an exceptionally large positive impact on Year 12 completion rates.

1. Policy Forum: Youth Transitions
The Impact of School Academic Quality on Low Socioeconomic
Status Students
Patrick Lim, Sinan Gemici and Tom Karmel*
Abstract
The issue addressed by this article is whether
there is an interaction between school ‘quality’
and students’ socioeconomic status. That is, is
a school that is of ‘high quality’ for high
socioeconomic status students as effective for
students from a low socioeconomic back-
ground? The answer is that a school that is
of ‘high-quality’ benefits all students in terms of
completion of Year 12, but more so for the most
vulnerable of students: those who have low
academic achievement at age 15 years in
addition to coming from a low socioeconomic
status background.
1. Introduction
An enduring goal of Australian social policy is
to improve the educational attainment of
students who come from low socioeconomic
backgrounds (see, for example, Council of
Australian Governments 2008). There is sub-
stantial evidence that the quality and socioeco-
nomic profile of schools play an important role
in the academic outcomes of their students
(OECD 2010; Perry and McConney 2010;
Watson and Ryan 2010; Gonski et al. 2011;
Gemici, Lim and Karmel 2013). However, the
issue we are interested in is whether the notion
of quality is a universal one or whether it is
related to the socioeconomic status (SES) of
students. That is, we explore whether students
from a low socioeconomic background benefit
to a greater or lesser extent from attending high-
quality academic schools when compared to
their more advantaged peers. In effect, we are
interested in the interaction between school
quality and students’ SES. We also consider the
interaction with earlier academic achievement.
Our analysis builds on a measure of school
‘quality’ that was developed in a companion
piece (Gemici, Lim and Karmel 2013) which
constructed a measure of school quality in terms
of predicting Tertiary Entrance Rank (TER)
scores and the probability of going to university.
In the earlier article, we found that about 20 per
cent of the variance in TER scores could be
explained by school effects. A similar exercise
found that about 9 per cent of the variance of
whether a student went to university by age
19 years is accounted for by school character-
istics. The measure of quality used here consists
of a combination of the school effects from these
* Lim and Gemici: National Centre for Vocational
Education Research, South Australia 5000 Australia;
Karmel: formerly National Centre for Vocational Education
Research, South Australia 5000 Australia. Corresponding
author: Lim, email <Patrick.Lim@ncver.edu.au>. The
authors acknowledge the financial support received from
the Australian Government Department of Education,
Employment and Workplace Relations through the Longi-
tudinal Surveys of Australian Youth analytical research
contract.
The Australian Economic Review, vol. 47, no. 1, pp. 100–6
°C 2014 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research
Published by Wiley Publishing Asia Pty Ltd

2. two models. Taking this measure, we focus on
the completion of secondary schooling and
interact our measure of quality from the earlier
article with a student’s SES and academic
achievement at age 15 years.
The article begins with a brief description of
the data, including the variables used and the
modelling approach. The results and conclu-
sions are then presented.
2. Data
This article uses data from the 2006 cohort of
the Longitudinal Surveys of Australian Youth
(LSAY). The LSAY tracks a nationally
representative sample of 15-year olds over a
period of 10 years to capture their transitions
from school to post-school education and work.
The 2006 base year of LSAY is linked to the
2006 Programme for International Student
Assessment (PISA) (OECD 2009), which
provides a rich set of individual- and school-
level measures. For further description of the
data that have contributed to this article, readers
are referred to the companion article by Gemici,
Lim and Karmel in this issue.
In the 2006 PISA base year, 14,170 students
participated. Attrition in longitudinal surveys
reduces the initial sample over time. The analysis
for this article includes all students who were still
part of the LSAY sample in 2010 (6,316 students
in 356 schools). An appropriate analysis using
weights was implemented. These weights incor-
porated the sampling methodology and attrition
in an attempt to reduce the impact of response
bias. Details are provided in Appendix 1.
2.1 Response Variable
The primary response variable of interest is that
of completing senior secondary schooling
(Year 12) by age 19. Table 1 provides simple
descriptive data for Year 12 completion. In
terms of Year 12 completion, around 86 per
cent of those in LSAY complete Year 12.
2.2 Explanatory Variables
The focus of the article is to examine whether a
school’s academic quality affects a student’s
likelihood of completing Year 12, based on the
individual’s own socioeconomic background
and their academic achievement in PISA testing
at age 15 years. Thus, there are three key
explanatory variables of interest: school aca-
demic quality, individual SES and academic
achievement at age 15 years.
2.2.1 Academic School Quality
The measure of academic school quality
variable is derived using the models from an
earlier article (Gemici, Lim and Karmel 2013).
In their article, the authors used multi-level
regression models to model the relationships
between enrolling in university by age 19 and
TER score against a range of student back-
ground characteristics and school factors.
These models where then used to calculate
the predicted probability of enrolling in
university by age 19 and predicted TER score
for an ‘average’1
student, but using the actual
characteristics of each school. For each trait
(university enrolment and TER score), a score
for each school was obtained. The scores were
then standardised to a mean of 0 and standard
deviation of 1. The predicted TER score for an
‘average’ student and the predicted probability
of enrolling in university by age 19 were
combined to produce a school academic quality
variable by averaging the two standardised
values. Thus, a high-quality school is one that
performed well in both TER and probability of
university enrolment at age 19 net of the impact
of the students that attended that school.
The advantages of using predicted TER and
probabilities of university enrolment to create
an academic school quality measure for Year 12
completion are twofold. Conceptually, a
school’s emphasis on academic success is
Table 1 Summary Statistics for Year 12 Completion
Year 12 completion N
%
(unweighted)
%
(weighted)
Completed 5,426 84.1 85.9
Not completed 890 15.9 14.1
Total 6,316 100.0 100.0
Source: 2006 Longitudinal Surveys of Australian Youth
cohort.
Lim et al.: Impact of School Academic Quality on Low Socioeconomic Status Students 101
°C 2014 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research

3. most strongly reflected in the predicted TER
and university enrolment probability of its
student body. From an analytical perspective,
TER and university enrolment by age 19 offer
considerable variation in the data and thus lend
themselves to the construction of a robust
measure of academic school quality.
It is important to emphasise that differences
in relevant school characteristics (notably,
sector, gender mix, average SES of the student
body, academic pressure from parents, school-
level variables) are accounted for in the
academic school quality measure. This measure
also includes an idiosyncratic effect: residual
effects which are measured statistically (these
can be interpreted as a school’s ‘ethos’).
2.2.2 Individual Socioeconomic Status
The measure of students’ SES is derived using a
range of variables captured in the survey. These
variables include items such as parental educa-
tion and occupation, access to textbooks, places
to study and the amount of cultural items (such
as poetry and art) in the home. Details on the
creation of this measure are provided in Lim
and Gemici (2011). An individual is of low SES
if they are in the lowest quartile of this measure.
2.2.3 Individual Academic Achievement
The PISA assesses the literacy of 15-year olds
in three major domains: reading, mathematics
and science. These literacy scores are often
used as proxies for academic achievement. In
this study, a composite academic achievement
measure is created by averaging literacy scores
across the three domains for each individual.
3. Methods
The focus of this article is on individuals who
come from low socioeconomic backgrounds.
Of course, individuals have diverse back-
grounds and the chance of being from a low
socioeconomic background is influenced by a
range of background characteristics. To narrow
the focus to SES, it is therefore desirable to
account for these other background character-
istics. The approach that we have taken in this
article is that of propensity scores (Rosenbaum
and Rubin 1983). The probability that an
individual comes from a low socioeconomic
background is modelled against a range of
background variables. The variables that have
been included in the propensity scores model
are gender, indigenous status, parental educa-
tion, regional status, achievement scores,
immigration status and language spoken at
home. The propensity scores (probability of
low SES) are then used as weights (combined
with the sampling and attrition weights) in the
subsequent statistical models to attempt to
reduce the impact of selection bias. A descrip-
tion of the methodology is provided in
Appendix 1.
The modelling approach used in this article is
that of a multi-level logistic regression model
with a binary response of Year 12 completion
against the explanatory variables. The multi-
level model is a two-level school effects model;
in this case, students sit within schools.
The model fitted allows a random intercept
for schools and fixed effects for the remaining
explanatory variables:
logitðyitÞ ¼ Xt þ Zu þ e ð1Þ
where yit is a binary indicator vector indicating
whether student i in school j has completed
Year 12, X is the design matrix of fixed effects
(student SES and achievement and school
quality), t is the vector of regression coef-
ficients obtained for the corresponding fixed
effects, Z is the design matrix of random school
effects, u represents the variation in intercepts
between schools and e is the between-student
(within-school) variation. Furthermore, it is
assumed that u $ Nð0; s2
schÞ and e $ Nð0; s2
eÞ.
In the case of logistic models, s2
e is asymptoti-
cally p2
=3. The model outlined in equation (1)
includes the random school effects in order to
account for the sample design (the responses of
students going to the same school will be
correlated), so as to provide correct standard
errors. It also includes a weight variable that
incorporates the sampling weights for both
schools and individuals, an attrition weight and
the propensity scores weight. Details of its
construction are in Appendix 1.
°C 2014 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research
102 The Australian Economic Review March 2014

4. In equation (1), the fixed effects of interest
include the interactions of school quality with
student SES and earlier academic achievement.
We also include an interaction of student SES
by academic achievement (Table 2).
4. Results
Table 3 presents the regression coefficients
for the probability of completing Year 12.
Statistically significant predictors are highlighted.
Complete regression results are provided in
Appendix 2.
From Table 3, it can be seen that school
quality and the interactions of school quality
with student SES and academic achievement
are significant predictors of Year 12 comple-
tion. Given these results, we have used
equation (1) to form predicted probabilities
and created Figure 1. Figure 1 shows the
predicted probability of completing Year 12 for
students from a range of socioeconomic and
academic achievement backgrounds. The fig-
ure is split into three separate panels which
differentiate students on individual academic
achievement. The panel on the left captures
low-achieving students (10th percentile), the
panel in the middle is those students who have
average academic achievement (median) and
the panel on the right contains the high-
achieving students (90th percentile). The
x-axis represents school academic quality and
the y-axis represents the predicted probability
of Year 12 completion.
From Figure 1, it can be seen that academic
school quality has a strong differential impact
for students from distinct socioeconomic back-
grounds. In particular, students with low
academic achievement (the left panel), who
are also from low socioeconomic backgrounds
and who attend low-quality schools have a
probability of completing Year 12 of less than
0.4. For their high SES peers, this increases to
close to 0.6 and this difference is statistically
significant. With respect to Year 12 completion,
a school with low academic quality thus has a
particularly negative effect on low socioeco-
nomic background students of low academic
achievement.
For low-achieving students, the impact of
moving from a low-academic-quality to a high-
quality school more than doubles the chances of a
low SES student completing Year 12. Thus, for
the most vulnerable students (those who have low
academic achievement at age 15 in addition to
coming from a low socioeconomic background),
increasing school quality has an exceptionally
large impact on completing Year 12.
Table 2 Predictors Used in Regression Analysis
Individual predictor Interaction terms
Student SESa
Student SES by student academic achievement
Student academic achievement Student SES by school quality
School quality Student academic achievement by school quality
Note: (a) SES denotes socioeconomic status.
Table 3 Regression Results for the Probability of Completing Year 12
Effect b SE df t-value Pr > |t|
Intercept 2.383 0.099 325 24.01 <0.0001
Student SESa
0.082 0.070 4,381 1.17 0.2418
Student academic achievement 1.220 0.077 4,381 15.83 <0.0001
School quality 0.470 0.091 325 5.14 <0.0001
Student SES by student academic achievement –0.045 0.056 4,381 –0.81 0.4159
Student SES by school quality –0.150 0.053 4,381 –2.84 0.0046
Student academic achievement by school quality –0.132 0.063 4,381 –2.09 0.0366
Note: (a) SES denotes socioeconomic status.
°C 2014 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research
Lim et al.: Impact of School Academic Quality on Low Socioeconomic Status Students 103

5. Further results indicate that at low-quality
schools, there is a substantial (and significant)
difference in the probability of completing
Year 12 between individuals with high and low
SES. However, this gap is removed as school
quality increases.
Ultimately, for all low-achieving individuals,
and regardless of their individual SES,
attending a high-quality academic school
greatly improves their chances of completing
Year 12.
As individual student academic achievement
increases, the impact of both individual SES
and school quality is greatly reduced, to the
extent that for students in the top 10 per cent of
earlier academic achievement, school quality
does not impact on the chances of them
completing school.
5. Conclusion
This article explored whether students from a
low socioeconomic background benefit to a
greater or lesser extent from attending high-
quality academic schools when compared to
their more advantaged peers. The results show
that for students with low academic achieve-
ment at age 15 years, academic school quality is
an important factor in the probability of
completing Year 12, regardless of their indi-
vidual SES. However, at low-quality schools,
there is a substantial and significant gap
between students from low and high socioeco-
nomic backgrounds, with high SES students
having a much greater chance of completing
Year 12. By contrast, at high-quality schools,
this gap disappears.
Furthermore, the results indicate that aca-
demic school quality has a considerable
differential effect on completing Year 12 for
the most vulnerable of students: those who have
low academic achievement at age 15 in addition
to coming from a low socioeconomic
background.
In conclusion, this article shows that the
academic quality of the school matters for
Figure 1 Differential Effects of Academic School Quality: Year 12 Completion
x
x
x
x
x
x
x x x
Individual achievement = 10th percentile (low) Individual achievement = median Individual achievement = 10th percentile (high)
1.0
0.8
0.6
0.4
10th percentile
(low)
Median Median Median90th percentile
(high)
90th percentile
(high)
90th percentile
(high)
10th percentile
(low)
10th percentile
(low)
School quality
PredictedprobabilityofYear12completion
Individual SES
10th percentile (low)
Median
90th percentile (high)
°C 2014 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research
104 The Australian Economic Review March 2014

6. Year 12 completion and even more so for
individuals from a low socioeconomic back-
ground. From a policy perspective, this is a
welcome result because it indicates that we do
not need a different concept of school quality
for students from low socioeconomic back-
grounds. However, the challenge of improving
school quality remains, recalling that the wide
range of school characteristics examined in
Gemici, Lim and Karmel (2013) could only
explain about one-third of the variance in
TERs. It is one thing to say that schools matter;
it is another to create the recipe for what makes
a quality school.
November 2013
Appendix 1: Propensity Scoring and
Weights
Propensity Scoring
The propensity score model used in this article
determines the probability of an individual
coming from a low socioeconomic background.
This probability is determined using a logistic
regression that includes a range of individual-
level characteristics commonly associated with
an individual’s socioeconomic background,
such as parental education, indigenous status
or regionality.
The probability of being from a low socioeco-
nomic background is converted to a weight (pwt)
to ensure an equal distribution of low SES across
the range of characteristics in the model:
pwti ¼
1
Pðlow SESÞ
Multi-Level Weight
Each individual in the LSAY data has a sample
weight (weights that have been calculated to
account for the original sampling scheme) and an
attrition weight. Furthermore, given the hierar-
chical sampling methodology, each school has its
own weight. Given that a multi-level model has
been fitted to the data using a mixed-model
framework implementation in the SAS Mixed
Model Software (SAS Institute 2007), it was
necessary to create a single multi-level weight
that combines the school and individual weights.
The procedure used is the methodology outlined
by Chantala, Blanchette and Suchindran (2011);
in particular, the population-weighted iterative
generalised least squares method A, in which the
individual and school weights are multiplied and
then divided by the average of the individual
weights within a school:
mlweighti;j ¼
fsu wtijj  psu wtj
Pnj
i¼1 fsu wtijj
À Á
=nj
À Á
where fsu_wti|j is the weight for individual i in
school j, psu_wtj is the weight for school j and
ni is the number of individuals in school j.
This weight is then used in the SAS mixed
procedure and is normalised (recalibrated to
sum to sample size rather than population) to
ensure estimates are produced with correct
standard errors.
Final Weights
The multi-level weight is combined with the
propensity score weight to give a final weight
used in the multi-level regression model.
Appendix 2: Regression Results
The regression results for Year 12 completion
appear in Tables A1, A2 and A3.
Table A1 Fit Statistics for Year 12 Completion
Statistics Value
–2 Res log pseudo-likelihood 29,226.07
Generalised x2
3,430.97
Generalised x2
/df 0.73
Table A2 Covariance Parameter Estimate
for Year 12 Completion
Variance parameter Estimate SE
School intercepts ðs2
schÞ 0.7447 0.1222
°C 2014 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research
Lim et al.: Impact of School Academic Quality on Low Socioeconomic Status Students 105

7. Endnote
1. An average student is one who has the average
characteristics of the cohort. The same student character-
istics are assumed for every school in the sample and so the
obtained predicted values for schools are net of these
student characteristics.
References
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Table A3 Parameter Estimates for Year 12 Completion
Effect b SE df t-value Pr > |t|
Intercept 2.383 0.099 325 24.01 <0.0001
Student SESa
0.082 0.070 4,381 1.17 0.2418
Student academic achievement 1.220 0.077 4,381 15.83 <0.0001
School quality 0.470 0.091 325 5.14 <0.0001
Student SES by student academic achievement –0.045 0.056 4,381 –0.81 0.4159
Student SES by school quality –0.150 0.053 4,381 –2.84 0.0046
Student academic achievement by school quality –0.132 0.063 4,381 –2.09 0.0366
Note: (a) SES denotes socioeconomic status.
°C 2014 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research
106 The Australian Economic Review March 2014

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