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The economic impact of improving schooling quality

Department of Education and Training

November 2016


© Commonwealth of Australia 2017

With the exception of Deloitte branding, content provided by third parties,and any material protected by a trademark, all textual material presented in this publication is provided under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) <creativecommons.org/licences/by/4.0/>. You may copy, distribute and build upon this work for commercial and non-commercial purposes; however, you must attribute the Commonwealth of Australia as the copyright holder of the work. Content that is copyrighted by a third party is subject to the licencing arrangements of the original owner.

This report was commissioned by the Australian Government Department of Education and Training. The findings and views expressed in this report are thoseof the authors and do not reflect the views of the Department of Education and Training.

Suggested citation:Deloitte Access Economics (2016). The economic impact of improving schooling quality, Canberra: Australian Government Department of Education and Training.

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ContentsGlossary......................................................................................................................................... i

Executive summary......................................................................................................................vi

1 Introduction.......................................................................................................................1

2 Current evidence on the impacts of schooling quality.......................................................32.1 Looking to the future of the Australian economy.................................................................2.2 The link between schooling quality and educational outcomes............................................2.3 The link between educational outcomes (and schooling quality) and economic

outcomes............................................................................................................................2.4 Empirical methods and implications for this study.............................................................

3 Modelling approaches......................................................................................................253.1 Overview of the two approaches........................................................................................3.2 Data.....................................................................................................................................

4 Cross country analysis......................................................................................................314.1 The empirical models..........................................................................................................4.2 Model results and discussion..............................................................................................4.3 Limitations and assumptions...............................................................................................4.4 Implications for Australia....................................................................................................

5 Individual level analysis....................................................................................................445.1 Introduction........................................................................................................................5.2 The impact on educational attainment...............................................................................5.3 Modelling the impact on wages..........................................................................................5.4 Modelling the impact on employment................................................................................5.5 Limitations and assumptions behind the individual level modelling...................................

6 Structural change in the economy...................................................................................616.1 Overview.............................................................................................................................6.2 Results.................................................................................................................................6.3 Discussion...........................................................................................................................

7 The impact on the Australian economy............................................................................687.1 Introduction to CGE modelling............................................................................................7.2 Assumptions........................................................................................................................7.3 Results and discussion.........................................................................................................

8 The contribution of school quality...................................................................................778.1 Overview.............................................................................................................................8.2 Data and approach..............................................................................................................8.3 Results.................................................................................................................................8.4 Discussion...........................................................................................................................

9 Implications of the analysis..............................................................................................839.1 How much does education matter?....................................................................................


9.2 How much do schools matter?............................................................................................

References..................................................................................................................................87

Appendix A : Cross country modelling........................................................................................91

Appendix B : Individual level modelling....................................................................................103

Appendix C : CGE modelling.....................................................................................................130

Appendix D : Estimating the contribution of schools................................................................134Limitation of our work..................................................................................................................

ChartsChart 2.1 : Average Australian annual national income growth per capita..................................5

Chart 2.2 : Impact of computerisation across occupations...........................................................6

Chart 4.2 : The relationship between maths scores and growth................................................37

Chart 4.3 : Relationship between growth and test scores, OECD vs Non-OECD.........................39

Chart 4.4 : Simulation growth in maths scores...........................................................................42

Chart 5.2 : Educational attainment.............................................................................................47

Chart 5.3 : Average wages and PISA scores................................................................................50

Chart 5.4 : The impact on wages of varying maths scores..........................................................53

Chart 5.5 : Probability of employment.......................................................................................55

Chart 6.2 : Forecast changes to occupation skill groupings (2016 – 2036).................................66

Chart 7.2 : Deviations in GDP above baseline, over time............................................................73

Chart 7.3 : GDP impact under a structural change scenario.......................................................75

Chart B.1 : Effect on wages of entering occupations of different skill, by PISA score...............125

Chart C.1 : Participation rate over time....................................................................................132

Chart C.2 : Expected weekly wages at every age......................................................................132

Chart C.3 : Productivity changes over time...............................................................................133

TablesTable 2.1 : Impacts of programmatic funding for disadvantaged students................................11

Table 3.1 : Educational outcomes data.......................................................................................29

Table 3.2 : List of countries.........................................................................................................30

Table 4.2 : Effect of test scores on GDP growth.........................................................................35

Table 4.3 : Effect of test scores on GDP growth, interaction effect............................................36

Table 4.4 : Effect of average test scores on average GDP growth, 1960-2012...........................37


Table 4.5 : Effect of test scores on GDP growth (additional controls), 1960-2012.....................38

Table 4.6 : Effect of test scores on GDP growth (interaction effect), 1995-2012........................38

Table 4.7 : Effect of test scores on GDP growth (OECD), 1995-2012..........................................40

Table 5.2 : Summary of results on educational attainment........................................................48

Table 5.3 : Summary of the results of the wage equations........................................................51

Table 5.4 : Summary of the cohort wage equations...................................................................52

Table 5.5 : The impact on wages of varying maths scores..........................................................52

Table 5.6 : Summary of the employment equation results........................................................56

Table 6.2 : Estimating the impact of increasing PISA scores on different industries...................64

Table 6.3 : Estimating the impact of increasing PISA scores on high skilled occupations...........65

Table 7.2 : Deviations from baseline, 2076.................................................................................72

Table 8.1 : School contributions to student outcomes...............................................................79

Table 8.2 : School effects on non-cognitive skills........................................................................79

Table 8.3 : School effect on maths scores1.................................................................................80

Table A.1 : List of countries used in analysis...............................................................................94

Table A.2 : International student assessments to be used in this study.....................................94

Table A.3 : Data sources.............................................................................................................95

Table A.4 : Within country growth estimates.............................................................................96

Table A.5 : Within country growth estimates, interaction term.................................................96

Table A.6 : Within country growth estimates, education expenditure.......................................97

Table A.7 : Between country results (1960-2012)......................................................................98

Table A.8 : Between country results, additional controls (1960-2012).......................................98

Table A.9 : Between country results, extended specification (1995-2012)................................99

Table A.10 : Between country results, extended specification with interaction (1995-2012). .100

Table A.11 : Between country results, OECD slope (1995-2012)..............................................100

Table B.1 : Survey sample attrition in LSAY...............................................................................103

Table B.2 : Variable list and summary statistics for estimation................................................104

Table B.3 : Multinomial Logit: marginal effects of PISA scores on educational attainment......109

Table B.4 : Cohort analysis: marginal effects of PISA scores on educational attainment.........110

Table B.5 : Model accuracy.......................................................................................................111

Table B.6 : OLS: Direct effect of PISA on wages........................................................................113

Table B.7 : Wage simulation: Difference in wages after a 1% increase in PISA scores..............114

Table B.8 : Cohort analysis: OLS: Nonlinear quadratic direct effect of PISA on wages.............115

Table B.9 : Cohort analysis: OLS: Comparing direct effect of PISA on wages by separate estimation................................................................................................................................116


Table B.10 : Cohort analysis: Comparing direct effect of PISA on wages by piece-wise estimation................................................................................................................................116

Table B.11 : Probit: Marginal effects into employment............................................................118

Table B.12 : Cohort analysis: comparing marginal effects into employment by quintiles........119

Table B.13 : Probit: Marginal effects into labour force participation........................................120

Table B.14 : Cohort analysis: comparing marginal effects into labour force participation by quintiles....................................................................................................................................122

Table B.15 : Robustness test for probit models........................................................................122

Table B.16 : OLS: Direct effect of PISA on wages, including occupational indicators................123

Table B.17 : Skill grouping of ABS major occupations...............................................................127

Table B.18 : Testing the use of sampling weights.....................................................................128

Table C.1 : Data requirements and source...............................................................................130

Table C.2 : Data requirements and source...............................................................................131

FiguresFigure 3.1 : Illustrative summary of approaches.........................................................................26

Figure 4.1 : Illustrative summary of approaches.........................................................................31


Figure 5.2 : Four stages to the individual level modelling..........................................................46



Deloitte Access Economics


GlossaryAcronyms

ATAR Australia Tertiary Admission Rank

CGE Computable General Equilibrium

DAE-RGEM Deloitte Access Economics Regional General Equilibrium Model

EEF Educational Endowment Fund

GDP Gross Domestic Product

LSAY Longitudinal Survey of Australian Youth

NCVER National Centre for Vocational Education and Research

OECD Organisation for Economic Co-operation and Development

OLS Ordinary least squares

PISA Programme for International Student Assessment

SSNP Smarter Schools National Partnership

TLT Teaching and Learning Toolkit

VET Vocational Education and Training

Glossary of termsSchooling quality The extent to which schools (individually and as part of a

collective system) maximise students’ educational outcomes, in terms of academic achievement, engagement and general wellbeing.

There are many attributes of schools that can affect their quality in driving student outcomes, including school-specific elements of practice and management, and systemic policies, such as school funding levels.

PISA assessment scores

An international measure of students’ educational achievement, based on a standardised test designed to capture scholastic performance on mathematics, science, and reading.

Students’ educational outcomes or student achievement

Both terms are used interchangeably with PISA assessment scores.

Cognitive ability A measure of the strength of an individual’s skills and abilities, including processes such as knowledge, attention, memory, judgment, evaluation, reasoning, problem solving, decision making, comprehension and production of language. In this study, we use PISA assessment scores as a measure of an individual’s cognitive ability.

Deloitte Access EconomicsCommercial-in-confidence



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Key points Deloitte Access Economics was commissioned by the Commonwealth Department of Education and Training to estimate the economic impact of increasing schooling quality in Australia. This report contains the results of this analysis and considers some potential implications for education policy.

Quality schooling is key to economic growth Research has consistently shown education to be an important contributor to human capital and, through

this, economic outcomes at a national level.• At the individual level, improved schooling quality leads to better prospects for employment,

higher wages and increased attainment of post-school education.• At the national level, improved schooling quality means a more productive workforce and the

economic and social benefits that this brings. The estimated size of these impacts has varied across studies based on the data and methodologies used.

Earlier studies used measures of educational attainment to assess the link with economic growth, while more recent studies have focussed on measures of schooling quality. This is the approach adopted in this report.

Quality itself can be hard to measure. This work follows recent research by using the OECD’s Program for International Student Assessment (PISA) test scores as a measure of an individual’s cognitive skills, which can be expected to be increased through quality schooling. While the empirical approach used in this report links PISA scores to economic growth, results should be interpreted as showing the link between cognitive skills, of which the test scores are a measure, and economic outcomes. The terms ‘student achievement’ or ‘student educational outcomes’ are used interchangeably with PISA scores.

Better quality schooling increases wages and employment The Longitudinal Survey of Australian Youth (LSAY) dataset allows the transmission mechanisms from

cognitive skills through to labour market outcomes to be modelled separately. Analysis of this dataset finds:

• The direct effect of a 1% increase in student achievement is a 0.09% increase in wages, controlling for demographic and other factors.

• Cognitive skills also increase the chance of receiving further education and being employed. A 1% increase in student achievement is estimated to increase the chance of obtaining a bachelor degree by 0.5%, and of being employed by around 0.07%.

• Given the wage benefits associated with further study, combining these effects suggests that a 1% increase in student achievement increases wages by 0.12%. This impact is relatively linear, so a 5% increase in student achievement is estimated to increase wages by 0.60%

The effect is not constant across cohorts or occupations:• Those students in the top 10% of PISA test scores benefit the most from an increase in

cognitive skills, with the effect decreasing as performance on PISA tests falls. This does not imply that most effort should be put into raising results of high achievers. Rather, policy should be based on evidence regarding the ability of policies to raise student outcomes and the associated costs.

• Relatedly, increases in cognitive skills have a greater effect on the wages of more highly skilled occupations (managers and professionals) than on lower skilled occupations. Wages for workers in high skill occupations increase by around 0.23 percentage points more from a 1% increase in student achievement than those in low skilled occupation groups.

These impacts are even larger at the economy-wide level The economy-wide impacts of schooling can be estimated either by aggregating the individual level results

to a national level, or through a cross-country analysis based on aggregated data:• The approach based on aggregating the individual level analysis finds that a 1% increase in

student achievement increases GDP by 0.16% once the effect is fully realised in the labour force. A policy intervention in 2016 would be fully realised in around 50 years when all members of the workforce will have benefitted from the policy.

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• An alternative approach to estimating the relationship between schooling quality and economic growth at the aggregate level is to employ a cross country growth analysis that usesobservations of average growth rates and test scores across countries. This follows several recent papers in the literature, particularly those of Hanushek and Woessmann for the OECD.1

• A 1% increase in student achievement is estimated to lead to an increase in GDP growth of around 0.3%. The larger estimate from the cross country modelling is not unexpected, as this approach has the advantage of capturing a wider suite of economic spillovers from education present in the macro data, but which are not captured in the individual level modelling.2

• However, a cross-country analysis necessarily trades off the applicability to an individual country that analysis based on that country’s data alone (such as the analysis of the LSAY dataset for Australia) allows.

Improving schooling quality could lead to significant economic gains The analysis presented in this report confirms that schools matter: they are the vehicle through which

quality education is passed on to students. But it also demonstrates that there is variation in the quality of education that is currently being provided.

• Indeed, the analysis indicates that around 10% of the variation in student outcomes in Australia can be attributed to differences in the practices and management of education at the school level (including pedagogy and curriculum, among other factors).

• To put this in context, the difference in outcomes between the highest and lowest quality schools is around one third of a year of current schooling (or a 3% increase in PISA scores).

As a demonstration of the potential gains from improving quality at the school level, if those schools with the worst performing practices were lifted to the quality of the highest performing schools, GDP would be around 0.5% ($8 billion) higher (if this effect were to be fully realised in 2016).

• But even this would not be enough to raise student outcomes in Australia to those of other developed countries. A lift in student achievement scores of 5% would be needed to raise outcomes to those of Canada, while 10% would bring us up to those in Korea.

• Achieving these gains would increase GDP by an estimated 0.74% and 1.47% per year respectively once the effect is fully phased in to the workforce. If this effect were to be fully phased in this year, this amounts to around $12 billion and $24 billion, respectively.

• Taking the mid-point of these estimates (5%) and expressing it differently: an increase in schooling quality that is equivalent to around 30 weeks of schooling would increase GDP by around $12 billion (in today’s dollars once fully phased in). If instead this 5% increase in scores was applied to the cross-country modelling, which captures any spillovers from education, the increase in GDP would be approximately $26 billion.

Quality schooling will become increasingly important over time As Australia moves towards a knowledge-based economy, high quality schooling education will become

increasingly valuable.

• Deloitte Access Economics forecasts the proportion of high skills occupations to increase to 39% of the workforce by 2036 (relative to 36% now).

• Combining this with the occupation level analysis above indicates that the impact on the economy from a given change in schooling quality may be around 3% higher in 2036 than it is currently. While difficult to forecast the exact scale and type of structural change, this analysis shows that not only is schooling quality important for driving economic growth today, it will become increasingly important over time.

1 See for example OECD (2010); The high cost of low educational performance, OECD Publishing; and OECD (2015); Universal Basic Skills: What Countries Stand to Gain, OECD Publishing2 By way of comparison, Hanushek and Woessmann (2012), found a 1% increase in PISA scores will increase GDP growth by about 0.2%. The differences are due to a slightly different model specification and a longer time-span of data available for this study.

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5%average increase in student PISA scores

Aggregate human capital as a driver of growth

Economic return from individual student outcomes

Students with higher cognitive ability (as measured by PISA scores) are more productive and therefore earn higher wages.

0.4% average directincrease in wages

Students with higher cognitive ability (as measured by PISA scores) are more likely to complete further study.Further study also enhances productivity and credentials, further (indirectly) increasing wages.

An increased probability of attaining a bachelor degree

0.2% average indirectincrease in wages

0.6% average totalincrease in wages

The direct and indirect effects of improved cognitive ability combine to boost the average student’s level of productivity.

Labour productivity

Capital productivity

Investment

Employment

Industry responds to improved labour productivity by increasing investment in physical capital and employing more workers, thereby growing the economy as a whole.

$12bn in GDP

Source: Deloitte Access Economics econometric analysis of LSAY data Source: Deloitte Access Economics’ Computable General Equilibrium (CGE) model of the Australian economy

An increase in schooling quality that is associated with a… Human

capital

Physical capitalLabour force

Institutional and other factors

results in…

Drivers of economic growth

Technologicalprogress

$26bnin GDP

These returns will mature after the year 2066, after allowing for the entire workforce to experience the productivity gains from improved schooling quality.Educational

attainment

Quality of education

A 5% sustained increase in PISA scores when considered across countries and controlling for educational attainment and other factors…

increases long-term GDP growth by 1.65 percentage points.

Source: Deloitte Access Economics econometric analysis of cross-country growth

The difference between the aggregate human capital and the individual student approach includes economic spillovers not captured in the approach focused on individual student results , such as increased rates of innovation.

… in 2066.

… in 2066.

Higher cognitive ability (as measured by PISA scores) results in an indirect impact on wages, through the returns on further education. This contrasts to the direct effect of cognitive skills on higher wages.

1.

2.3.

4.

5.

Which would take Australia around half way to the top of the international comparisons

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Executive summary The level and quality of the education of a country’s labour force has been consistently demonstrated as a driver of its economic growth. A well-educated workforce will be more productive, is more likely to innovate, and will better utilise other factors of production at its disposal. At the individual level the benefits of education are reflected in higher wages, as workers are rewarded for their greater productivity. At the aggregate level, society benefits through greater levels of innovation, social cohesion, tax revenues and other positive spillovers.

Australia has much to gain from a highly educated workforce. National productivity has been slowing in recent decades, and the temporary boost to living conditions brought about by the recent and unprecedented boost to our terms of trade has begun to unwind. As the economy transitions from the mining boom, new sources of economic growth need to be found. Australia’s comparative advantage is likely to lie, to a large degree, in the new knowledge economy where our human capital is leveraged to take advantage of technology and its augmenting effects on skilled labour. But this will require continuing to build on our human capital and ensuring it remains close to the top of our competitors on a per-worker basis.

How can the economic impact of higher schooling quality in Australia be measured?

Deloitte Access Economics has been commissioned by the Commonwealth Department of Education and Training to estimate the economic impact of improvements in schooling quality in Australia. Quality itself is hard to measure, but can be reflected in many features of the education system that contribute to the wellbeing of students (or other members of society) through both financial and non-financial means. Quality can be achieved through aspects of curricula, teacher interactions with students and school resources that are used to enhance the learning experience.

This report does not focus on the causes of quality, but instead on its results. In particular, it considers how improvements in quality are reflected in economic outcomes at both the individual and aggregate levels. Just as there is no single aspect to schooling quality, there is also no single metric that fully captures the quality of the schooling system.

This report follows the recent literature by using student test results from the Program of International Student Assessment (PISA) as a measure of cognitive skills and the abilities developed at school, and, by extension, the quality of schooling. The PISA program was established by the OECD to capture meaningful and comparable information about the performance of 15 year old students across countries.3 It is preferred to other alternatives as it includes a range of academic tests, measures of so-called 21st century skills, and various demographic variables that can be used as controls in empirical analysis. Further, PISA tests are designed to assess the extent to which students can apply their knowledge to

3 While PISA is the primary dataset used across both approaches, PISA was only introduced in the year 2000. Some parts of the report use a more comprehensive time series analysis, with the PISA data augmented with additional datasets to create a longer time series.

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real-life situations. To this end, PISA assessment scores will more closely reflect the skills and abilities students require to ensure a positive economic outcome once they enter the labour force. While not a perfect measure of quality, taken as a proxy for quality PISA scores present a tractable and robust source for the empirical analysis used in this report.

When referring to the empirical findings linking PISA scores to economic outcomes, this report uses the terms ‘student achievement’ or ‘student educational outcomes’ interchangeably with the PISA scores.

Literature on the returns to education

Given the central nature of an education system to a country’s economic performance, there is an abundance of literature on the returns to education. The key findings of this literature are: The returns to education can be large: estimates vary significantly in size, however

by way of example a recent OECD (2010) publication estimates that a quarter standard deviation rise in PISA scores would lift Australia’s Gross Domestic Product (GDP) by over $2.5 billion annually.

Education quality can be measured in different ways. At a broad level the literature can be divided into measures based on attainment and those based on quality. The earlier literature tended to focus on the former, finding mixed results on the benefits of increased education (measured, for example, in years of schooling or attainment of various outcomes). The more recent literature has focussed on quality as measured by test scores, and found larger returns at the national level.

Empirical approaches have also evolved over time. For example at the macroeconomic level, previous approaches have used relatively simple cross-sectional analysis to compare education outcomes to GDP growth across countries. More recent analyses have begun to exploit the temporal aspects to this linkage, as well as the richer data provided by various international schooling tests over time.

All empirical approaches face challenges in measuring the economic returns attributable to education. Macroeconomic approaches generally face issues with the direction of causation between economic growth and education outcomes, while microeconomic approaches that attempt to attribute individual outcomes to education face difficulties due to unobserved individual ability (which is generally thought to be correlated with individual educational and economic outcomes).

Two approaches are used to estimate the links from schooling quality to economic outcomes

This report builds on the previous literature and adopts those aspects of the empirical approaches that are considered most robust given the data available. In particular, two empirical approaches are adopted: A cross country analysis: measuring the return to education at the economy-wide

level across a selection of countries. This is closest in approach to the previous macroeconomic education literature and draws on observations of economic growth and test scores across countries.

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An individual level analysis: measuring the returns to education at an individual level by using the Longitudinal Survey of Australian Youth (LSAY) dataset to compare PISA test scores with later education, employment and wage outcomes. This student-level analysis is then built up to an economy-wide analysis using a computable general equilibrium (CGE) model of the Australian economy to produce an economy-wide estimate of returns that is comparable to the estimate produced by the cross country approach.

In principle, the difference between the estimates produced by these two strands of research will be attributable to those aspects of improved education quality that cannot be captured by the individual. That is, the aggregated nature of the cross-country analysis would be expected to capture the externalities from increases in education quality that are excluded from the individual level analysis. In practice, empirical challenges with each approach will introduce an element of noise into each approach that will mean differences are not solely attributable to education spillovers.

The cross country analysis is useful in assessing the impact of education on economic growth in general across countries, and has been used by many researchers, including most notably by Hanushek and Woessman in a recent series of papers for the OECD 4, to draw out this relationship across a wide range of countries. However, to assess the return to education to Australia specifically, an analysis based on longitudinal data of Australian students and labour market outcomes is the preferred approach. This individual level analysis also allows for findings on different student cohorts and different occupations to be studied separately. The core of the report hence draws on findings from analysis of the LSAY dataset.

The cross country approach to measuring links at an aggregated level

Two approaches are tested for establishing the link at an aggregate level between test scores and economic growth: Intertemporal analysis: The first approach attempts to use various observations of test

scores within countries over time and compare this to growth rates between periods to provide a country-specific link between these variables.

Cross country analysis: The second approach provides a cross-sectional analysis of countries, regressing average growth rates over a long period against average scores on standardised tests. This is the approach taken by the OECD in a series of recent reports.

While the intertemporal analysis was initially expected to add richness to the analysis, limitations in the data ultimately mean that sufficient confidence cannot be placed on the findings from this approach. Standardised tests were taken infrequently and inconsistently prior to 1995, requiring interpolation between non-testing periods. Further, for a number of countries, there is little meaningful variation in scores over time.

Hence, the more fruitful approach to estimating the aggregate effect of education is through cross-country analysis. The figure below provides a simple indication of the correlation between PISA scores and the rate of economic growth across the sample of countries used. Australia falls relatively closely to the fitted line through this data.

4 See for example Hanushek and Woessmann (2012).viii

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Figure i: The relationship between maths scores and growth

4%

6%

8%

10%

12%

200 300 400 500 600

GDP growth (1960-2014)

Average maths score (1964-2012)

Australia

China

SingaporeKorea

Macao

Estonia

CanadaUnited Kingdom

Hong Kong

Japan

South Africa

Ghana

Source: Deloitte Access Economics

Formal modelling of this relationship, based on a least squares approach including several control variables, supports the finding of a positive relationship between growth and cognitive skills. The specification of the model used contains an interaction term between test scores and the initial stock of human capital in the economy to control for the differing levels of initial skills. Both the coefficient of the direct student achievement variable and the interacted term are shown in Table i below.

PISA maths scores are found to have the largest impact on growth, with a one unit increase in scores leading to a 0.06% increase in economic growth. Science scores have a slightly lower effect on growth, (while remaining significant at the 1% level), while reading scores are also estimated to have a positive effect but are not significantly different from zero.

Table i: Effect of test scores on GDP growth (interaction effect), 1995-2012Maths scores Science scores Reading scores

Effect on GDP growth

0.063%*** 0.045%** 0.007%

Interaction term -0.021%* -0.012% 0.010%N 56 56 52R2 80% 78% 80%

Note: ***represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level.

It is typically more meaningful to think of results in terms of percentage increases rather than absolute increases in test scores. To put the results above in context, the average PISA

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math score for Australian students is 524, so a one unit increase therefore reflects a 0.19% increase in scores. Hence, a 1% increase in test scores will have an impact around five times the size of the results in the table above, or in other words a 1% increase in test scores equates to an estimated increase in economic growth of around 0.33% (around five times 0.063).

Overall, the findings are similar to those of Hanushek and Woessmann (2012), who explored the relationship between educational attainment and economic growth across a range of time horizons and countries in a series of papers for the OECD. The coefficients found here are somewhat smaller than those previously found, possibly due to the addition of the most recent decade of data, with this period including the Global Financial Crisis (a period in which growth rates were significantly depressed despite school test scores remaining high).

While the results from the cross-country modelling are broadly in line with expectations and based on the most robust techniques from the literature, their applicability to Australia should be treated with some caution. This caution reflects in part the small sample of countries with average growth rates taken over a long period of time, as well as difficulties in applying the results to any single country within the sample. Instead, for Australian-specific estimates of the impact of improvements in schooling quality, the individual level approach outlined below is likely to yield more robust findings.

The individual level approach and the transmission mechanisms of schooling quality

This approach estimates the effect of education on the various transmission mechanisms through which schooling affects economic outcomes. Schooling quality may impact labour market outcomes through three broad mechanisms: The direct effect of improved cognitive skills on productivity and resulting wage

outcomes. Individuals equipped with the skills demanded by employers will be more productive in the workforce and command a higher wage5. This effect holds educational attainment constant.• The estimation approach used is a least squares regression of wages on PISA

scores, educational attainment and various control variables. The effect on further education outcomes. Higher cognitive skills increase the

likelihood that students will complete high school and/or undertake post-school study. Their labour market outcomes will subsequently improve due to this increased educational attainment.

5 Economic theory typically assumes that labour is paid its marginal product, such that increases in productivity are matched one-for-one with higher wages. In practice this relationship will be noisy and may be subject to several sources of bias. However, the approach in this report is based on observed data linking outcomes to wages, and therefore, to the extent that such a bias exists, it will already be accounted for in the data.

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• The model used estimates the percentage increase in the probability of achieving each further educational outcome6 from a 1% increase in PISA scores for the average student.

Threshold effects on labour force participation or employment can be achieved if increased cognitive skills alter the propensity for individuals to engage with the labour force or find employment. • The model provides an estimate of the percentage increase in the probability

of being employed resulting from a 1% increase in PISA scores for the average individual.

The benefit of the LSAY dataset is that it allows for each mechanism to be modelled separately, while controlling for individual idiosyncrasies such as various demographic variables. The longitudinal nature of the dataset allows for direct comparison between an individual’s PISA scores and their subsequent economic outcomes.

In each of the transmission effects modelled the maths, science and reading scores are regressed against the economic outcomes and control variables. The maths scores tended to find the most significant result, with reading, which is judged to be a more foundational skill, indicating the least significant effect on the dependent variable.

The key results for the regressions on PISA maths score for each model are: A 1% increase in student achievement increases the wage of an individual by

0.09%, holding all other factors (including educational attainment) constant. A 1% increase in student achievement has the largest impact on the probability of

attaining an undergraduate degree, which increases by 0.5%. The probability of achieving a VET outcome as the highest attainment falls, both due to the transition of students to university relative to VET study, and due to an increase in students that complete year 12 (for which a VET certificate can be a substitute).

A 1% increase in student achievement increases the probability of being in employment by 0.07%.

The total impact on wages will be a combination of both the direct effect of cognitive skills on wages, and the indirect effect on wages through the increased propensity to undertake further study. Combining these effects, the total effect on wages from a 1% increase in student achievement is estimated to be 0.125%.

There are significant differences in the impact of cognitive skills across cohorts

This analysis can be repeated for various cohorts of students. To this end, PISA scores were split into cohorts and the results re-estimated. The interest in this analysis was to assess whether the results are consistent across the population of students, or whether improvements in quality have different effects on students depending on where they sit in the performance spectrum. The results are presented below.

6 Six outcomes are used as dependent variables, ranging from completion of high school, to various VET certificates, undergraduate and postgraduate degrees. The dependent variable used is the highest education outcome attained.

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While the analysis implies that all students gain from an increase in cognitive ability, the effect on wages increases with the PISA score of the student. This implies that the marginal effect of increasing education quality is higher for better performing students than for lower performing students.

Chart i: The impact of student achievement on wages by cohort

0.00%

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Lowest decile(402)

Lowest quartile(459)

Median(523)

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PISA ranking and score


Importantly, this does not imply that policy should target only higher performing students as this analysis says nothing about the ability of policy to alter outcomes at different ends of the performance spectrum. Further, there may be important social and equity reasons for targeting policy at particular disadvantaged or low-performing student groups.

The result itself, however, is perhaps not surprising. Individuals are likely to gravitate towards occupations that utilise their relative skills. Individuals with higher cognitive skills are likely to take positions that provide a return to these skills, while those with lower cognitive skills relative to other abilities are likely to seek employment that rewards other attributes.

The economic impacts are even larger at the whole-of-economy level

These results can be aggregated to an economy-wide level using a CGE model. This modelling was undertaken as follows: A policy is assumed to be implemented in the base year that increases PISA scores

across the entire year 9 cohort by 1%.

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When this cohort enters the workforce their productivity is assumed to be higher by 0.125%, that is, the wage effect estimated by the econometric approach outlined above is assumed to apply and be representative of their increased productivity7.

This quality improvement is assumed to be sustained, such that each subsequent cohort receives an equal productivity increase. By this process, the proportion of the labour force that is subject to a productivity increase relative to the baseline increases over time.

Assumptions need to be made about the persistence of this productivity increase. In the absence of research to suggest the most plausible assumption, two scenarios are simulated:• A ‘central scenario’ in which it is assumed that this productivity increase is

permanent, such that a worker’s productivity increases by 0.125% for the duration of their working life.

• A ‘low scenario’ in which the productivity increase is assumed to fall over time, and subsides to an individual’s baseline productivity by retirement.

Under this dynamic approach the full effect of the policy will not be realised until all individuals currently in the workforce are retired and replaced by those subject to the increase in schooling quality (that is, around 50 years following the base year, so if the policy impact is in 2016 the full effect will be realised by around 2066). In this year, the model estimates the following impacts: Central scenario – GDP will be around 0.16% higher as a result of the 1% increase in

student achievement, and employment will rise by around 0.06%. Low scenario – GDP will be higher by only 0.08%, while employment will rise by

0.03%.

Hence, depending on the assumption about the persistence of the shock, the realised results may differ by a factor of two. In net present value terms this difference will be significantly lower, as the results will be driven primarily by the initial years. In this sense, the results are not significantly impacted by assumptions about persistence of the productivity increase due to schooling.

These results can be explained as follows: A 1% increase in productivity is estimated to raise individual earnings by around

0.125%. Labour makes up only around 60% of value added in the economy, and this will therefore dampen the direct effect of the productivity increase at the aggregate level for a fixed amount of capital and labour.

However, as labour productivity increases additional labour will be drawn into the workforce and the return to capital will also increase as there are additional effective units of labour for a given amount of capital.

7 The link between productivity and wages is not always clear. While economic theory predicts that under certain assumptions workers will be paid their marginal product, inefficiencies or rigidities in the labour market and wage negotiations can undermine this link. On balance, however, it is difficult to estimate a systematic bias in this link (and biases in either direction may be present) and a one-for-one pass through is assumed to occur here. In other words, a worker that is paid 10% more than the average is assumed to be, on average across the sample, 10% more productive than other workers, holding all else equal.

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This will stimulate additional investment that will further increase GDP. The net impact of these second round effects will be positive: not only are the existing workers more productive, but there are now new workers and capital employed in the economy (and these extensive margin effects can be significant).

Increases in schooling quality can create large economic dividends

Adopting a similar approach it is possible to undertake scenario analysis, similar to that in recent OECD studies whereby the benefits from increasing student test scores to a certain benchmark level is estimated. Three scenarios are considered: Scenario 1 – The average PISA math score in Australia is raised by 3.1%, bringing all

schools up to the level of the highest performing schools in Australia (see the discussion on the variation in school performance below);

Scenario 2 – The average PISA math score in Australia is raised by 5%, bringing Australia’s average around half way from its current position to the top of the international rankings; and

Scenario 3 – The average PISA math score in Australia is raised by 10%, bringing Australia’s average up to that of Korea’s and close to the highest performing countries.

The results of these three scenarios, both in terms of the increase in GDP once the productivity increase is fully phased in (2066) and expressed in dollar terms as a net present value over the period to 2066, discounted at a rate of 7%, are provided in the table below.

Table ii: The economic impacts of raising student achievement - Three scenarios

Scenario % increase in GDP, 2066

Equivalent in 2016 dollars

NPV of GDP increase

Scenario 1 – 3.1% increase 0.47 $7.5 billion $39.2 billionScenario 2 – 5% increase 0.74 $11.8 billion $61.7 billionScenario 3 – 10% increase 1.47 $23.5 billion $96.9 billion


To put these gains in PISA scores in context, the OECD provides a conversion from PISA scores to time spent at school of 1% being equivalent to around 6 weeks of schooling. In other words, a 5% increase in scores is equivalent to around 30 weeks, so from above an increase in schooling quality that is equivalent to around 30 weeks of schooling would increase GDP by around $12 billion, or around $1,000 for each person currently in the workforce when these results are expressed in 2016 terms.

Again, this analysis does not imply anything about the ease or otherwise with which these results could be achieved, but are instead scenarios about the economic dividends Australia could realise through higher quality schooling. Finally, the estimate of net present value of the increase in GDP is somewhat sensitive to the baseline growth rate of the economy assumed over the modelling horizon. However, given that future values are discounted over a long time horizon, this calculation is most sensitive to the discount rate used.

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Comparing the approaches

As modelled, the cross country and individual level approaches allow for a direct comparison of their estimated effect on economic growth: The cross country modelling provides an estimate of the effect on the growth rate

from a 1% increase in student achievement scores, with this elasticity found to be 0.33%.

The individual level approach, once converted to an economy-wide value through the CGE model, provides an estimate of the percentage increase in GDP from a 1% increase in student achievement scores (once these have been fully phased in to the workforce) of 0.16%.

In other words, the effect of increasing cognitive skills on economic growth is found to be nearly exactly twice as large when this is assessed using aggregate economic data than when considering the return to individuals alone.

The difference between these results reflects i) potential inaccuracies in the estimating approaches, and ii) to the extent that these values represent the true underlying parameters they can be interpreted as the difference between the private and public economic impacts from improved schooling outcomes. The public returns to schooling are those benefits that are received by society more broadly, over and above the economic returns captured by the individual.

Quality schooling is likely to create even larger dividends in the future

Both approaches are necessarily based on historic data and their findings indicate how differences in student achievement are reflected in economic outcomes on average over this period. Of interest to policymakers is the extent to which these findings reveal something about the returns to schooling quality into the future from policy changes that occur now. This is important as the returns to schooling policy are particularly long-tailed, with the benefits from policy changes not being realised until students enter the workforce, and then are realised over the working life of those individuals.

Structural changes in the economy, and the labour market in particular, may mean that the future returns to education differ from those estimated here. As the economy transitions to a ‘knowledge economy’ where cognitive skills are increasingly valued, the economic returns to education may further increase. That is, the economy is likely to transition to a structure where the value of cognitive skills instilled by schooling is greater than it is today.

It is possible to test aspects of these expectations using the occupational and industry identifiers for individuals in the LSAY dataset. The approach taken is to add an interaction term between PISA scores and occupation (or alternatively, industry) into the main wage equation. These interaction terms identify the extent to which student achievement affects wage outcomes differently for each occupation type (or industry). By then forecasting structural changes in the economy that will lead to different shares of each occupation (or industry) in the future, the forward-looking elasticity of returns to education can be estimated.

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Unfortunately, the majority of entries in the LSAY dataset do not contain an observation of the industry an individual is employed in, and analysis indicates that there is bias in the sample that remains. However, all employed individuals have an observation of their occupation, enabling the analysis to be undertaken at this level.

The eight ANZSCO occupation categories are mapped to three skill groups: low skill, medium skill and high skill occupations. In defining the occupation skill groupings the ABS skill level classification is followed. Those defined as managers or professionals are deemed to be a high skilled worker, while those defined as machinery operators or drivers, and labourers are deemed to be low skilled workers. All other ANZSCO groups (technicians and trade workers, community and personal service workers, clerical and administrative workers, sales workers) are allocated to a medium skill group.

One benefit of using occupation categories is that there is a relatively close relationship between occupations and the skills required to perform that occupation. In contrast any industry grouping will contain a mix of skills levels: for example, within the professional services industry there will be a mix of both skilled professionals and low skilled administrative workers.

The table below shows the results of the regressions that include the occupation skill group terms, as well as the direct effect of the PISA variable alone. The low skill group is used as the base indicator variable, such that the coefficients on PISA scores can be interpreted as the percentage increase in wages for low skilled workers for a percentage increase in student achievement for this group. The coefficients in the second and third row of the table can be interpreted as the percentage increase in wage for an individual in that skilled occupation group relative to an otherwise identical worker in the low skill group.

Table iii: Estimating the impact of increasing PISA scores on high skilled occupations

Maths scores Science scores

Reading scores

PISA scores (effect on low skilled group) -0.060 -0.017 0.027Additional effect of PISA scores on those employed in a high skilled occupation 0.231*** 0.112* 0.165**Additional effect of PISA scores on those employed in a medium skilled occupation 0.135* 0.053 0.068***represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level.Source: LSAY 2003, 2006; DAE estimates

As expected, the marginal benefit from higher cognitive skills is greatest for the high skilled occupation group, with a 1% increase in student achievement estimated to lead to a 0.23% increase in wages. The medium skills group also experiences a significant increase in wages relative to the low skilled group, at 0.14%. The coefficient on PISA scores for the low skilled group is insignificant and close to zero, indicating that those in the low skill group do not receive an increase in wages from a marginal increase in student achievement scores. This finding may be interpreted as implying that low skilled occupations do not rely significantly on cognitive skills and therefore do not provide a financial benefit to higher schooling quality.

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Deloitte Access Economics produces forecasts of the proportion of the workforce in each ANZSCO occupation category for 20 years into the future. Using these forecasts, combined with the occupation skill group results above, allows for a forward-looking estimate of the productivity impacts of improved cognitive skills. These forecasts project the share of workers in high skilled occupations to increase to 39% of the workforce by 2036 (up from 36% in 2016). In contrast, the shares of both the medium and low skill groups are forecast to fall slightly over this period.

Based on these estimates, and the estimated wage premium associated with increasing student achievement within skilled occupations, the direct impact of a 1% increase in student achievement on wages is expected to increase to 0.127% by 2036 (up from 0.125% in 2016, a 3% increase in the size of the effect). Projecting structural change further into the future is fraught. However, extrapolating the trends forecast to 2036 out to 2066, at which point any students subject to a current policy change would be fully phased into the workforce, this average productivity increase could be in the order of 0.133% (a 7% increase in the size of the effect estimated from historical data).

Should these gains be realised, the impacts on GDP would be proportionately higher relative to the base analysis presented above. Regardless of the actual change, this analysis shows that in the event that Australia does experience structural change that alters the composition of the workforce towards more highly skilled occupations, the benefit of policies that lift cognitive skills of students will be greater than indicated by analysis based on historical data.

How much can schools explain variations in student results?

The analysis above describes the returns to education quality while remaining agnostic to how this quality is achieved. One key question in this respect is the extent to which schools can drive quality through various aspects of education under their control and the impact that this can have on the cognitive skills modelled above.

Many aspects of schooling quality are determined at the system level, with curriculum and structural regulations (such as class sizes and teacher qualifications) all determined centrally. However, practices at the school level can also determine the quality of education a student receives. This section focuses on isolating this latter effect, drawing a possible link between what education policy could achieve if it were able to improve teaching practices within schools, and the potential economic gains outlined above.

Schools and the schooling system are crucial vehicles for building the skills of students and raising the average performance of students. However, there is evidence that the quality of education differs across schools (that is, there is variation around the average). Analysis in this report shows that around 10% of variation in student performance across schools remains once other factors have been controlled for. This residual variation can be interpreted as school-specific factors that determine the performance of their students.

Hattie (2003) finds nearly 35-40% of variance can be explained by variances in educator and schooling practice. Given Hattie does not control for all school level contextual factors (such as socioeconomic status and student intake), this likely represents an overestimate of the

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true variation that can be attributed to schools. When we do not adjust for these other factors similarly large results (around 30% based on the most recent data) are obtained.

Finally, the fact that practices within schools only explains around 10% of variation in student performance does not detract from the importance of schools in the education system (for example, this analysis focuses only on the variation in performance across schools that can be explained by school-specific practices, and not the impact of the schooling system on the overall level of education performance). Schools provide the vehicle through which education, and the benefits it entails, is provided to Australian youth. What this analysis shows is that there is meaningful variation in the quality across schools, and that policy that lifts the performance of lower quality schools towards that of the highest quality schools could have significant benefits for student outcomes overall.

In an efficient schooling system all schools would be providing an equally high level of education quality, with none of the variation in school performance being explained by school-specific factors, with it instead being solely explained by factors such as the SES background and location of its intake.

What does this analysis mean for Australia?

This analysis reaffirms previous research that has found a significantly positive relationship between education quality and economic growth, both at the individual and systemic level. The individual level approach is the first analysis of its kind to exploit the LSAY dataset. The advantage of this approach is that it allows the effects of improvements in cognitive skills to be traced through the various transmission mechanisms into the labour force: from school outcomes to further education, employment and productivity once in the workforce.

This individual level analysis is also the preferred approach for drawing Australia-specific findings, relative to the cross-country analysis that has received significant focus in recent years. The cross country analysis suffers from difficulties in adequately drawing out the temporal aspects of the relationship between education and economic growth due to a lack of current data and, by assuming all countries are driven by the same underlying structural relationships between education and growth, the approach can miss significant country-specific effects in this relationship. For example, analysis in this report shows evidence that the modelling predicts a small or negative relationship between these variables for OECD countries, possible due to model misspecification for this group of countries.

By focussing on measures of educational assessment scores, the individual level analysis allows more nuanced findings to be made. For example, it allows different cohorts to be established based on student achievement scores (while holding attainment constant) to identify the relative benefits from raising performance at different levels, and results for different occupational groupings to be estimated. Both show how the returns to education can vary across the student population and provide some, albeit partial, information for use in education policy decisions.

A key feature of this work is the focus on educational outcomes (as a proxy for quality) rather than attainment. Part of the benefit of this approach is that it provides more nuanced policy implications: there are numerous policies that can be adopted to improve

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quality and these can be targeted at specific student groups, while the policy options for increasing attainment are relatively restricted.

Further, basing analysis on attainment alone can be subject to criticism that it finds evidence of a signalling effect in the labour market rather than an actual link between education and economic outcomes. Since quality, as measured by test scores, is not actually observable by employers, any positive association between quality and later labour market outcomes must be due to actual increases in labour productivity.

While this report does not focus on the drivers of quality, the literature points to key features, such as teacher quality and their interactions with students, as determinants of a quality education. Hence, using school funding in a way that targets these improvements is key to maximising the return on investment in education.

The school-level analysis demonstrates that there are significant differences across schools when it comes to student performance. This implies that some schools are engaging in pedagogical practices that provide an advantage to their students. By identifying what these practices are, and using the appropriate policy levers to target them, there is scope to increase the average level of education quality across the school system. This report shows that the benefits from doing so are likely to be significant.


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1 IntroductionDeloitte Access Economics has been commissioned by the Commonwealth Department of Education and Training to estimate the returns to education quality in Australia. This question is important: research has repeatedly shown that countries with better education systems perform more strongly economically. Further, in making decisions about education policy, governments need to understand the returns that improved quality can deliver and or which cohorts those returns are greatest.

This work focuses on returns to education quality as opposed to attainment. While much of the earlier literature in this area used attainment8, more recent work has focussed on quality, measured using standardised test scores. This approach has some key benefits: Quality has been shown to have more explanatory power in linking education to later

labour market outcomes; The policy implications of findings on attainment are limited due to there being an

upper bound on years of schooling and qualifications that students can attain. Policy implications are also limited as there is an inconsistency in the quality of educational attainment certifications; and

Research has continuously shown that what is most important is how funding is spent on improving school outcomes (that is, on increasing quality) rather than simple measures of quantity of funds.

This report follows the recent literature in using PISA scores as a measure of quality. While no single metric will capture all aspects of quality, PISA scores provide a good measure of student cognitive skills, are comparable across countries, and the PISA dataset contains a range of useful demographic control variables to assist with empirical analysis.

This report uses this data to estimate the returns to schooling in two ways: A cross country approach which compares data on average growth rates over time to

average performance on student test scored across countries; and An individual level analysis that uses longitudinal data on individual student

performance to estimate the link between PISA scores and later labour market outcomes.

The advantage of the individual level analysis is that it allows the transmission mechanisms linking increased cognitive skills to labour market outcomes to be modelled separately, with models for the impact on employment, transition to further education and employment each modelled as a discrete component. The cross country approach has the benefit of capturing all returns to education that a country receives, therefore measuring both the public and private returns and providing a more complete measure of returns. By providing the results of both analyses this report provides a comprehensive picture of the economic returns to education.

8 As measured by years of schooling or qualifications obtained.1




Any empirical approach in this area faces challenges, and this report sets out the limitations of each approach and the implications for the findings. Key issues around the datasets, robustness of the econometric estimation approaches, and the subsequent implications for policy are all identified.

The report is structured as follows: Section 2 provides a background to the report, outlining why improving the quality of

education is important, and what the existing literature can tell us about the size of the effects and best practice empirical approaches.

Section 3 provides a high-level overview of the two approaches to estimating the returns to education in Australia, as well as a description of the datasets used.

Section 4 outlines the key findings and implications from the cross country modelling. Section 5 outlines the findings from the individual level analysis, including the central

estimates of the impact of education on wages, economic growth, and how the impact differs across student cohorts.

Section 6 continues the student level analysis, considering the impact of structural change on how PISA scores impact wage outcomes.

Section 7 aggregates the results from sections 5 and 6, estimating the impact of education on the Australian economy.

Section 8 provides a multi-level analysis of individual student PISA scores in 2003 and 2012, indicating the extent to which variation in student performance across schools can be explained by school-specific factors.

Section 9 summarises the findings from the report and their policy implications. Various appendixes provide greater detail on the empirical approaches used in the

report and the robustness checks that have been performed.

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2 Current evidence on the impacts of schooling quality

Education is crucially important for many of the policy outcomes desired by governments and their citizens throughout the world. At an individual level, a person’s education affects their earnings, their employability, and – particularly for those who are socio-economically disadvantaged – their chance of succeeding in life.

Education is also known to affect an individual’s health outcomes, their future family structure, intellectual fulfilment and other aspects of a good life. At a national level, a country’s stock of skills (that is, its stock of human capital) has a significant impact on its prosperity and growth rate.9 Further, the distribution of skills is a key determinant of inequality, and the relationship between an individual’s skills and their family background is central to the degree of intergenerational mobility (Burgess, 2016, Lamb et al., 2015).

The link between educational outcomes and economic outcomes, from a conceptual stand-point, is well established in the literature. However, the empirical literature that quantifies this link and sheds light on the nature of its component transmission mechanisms, has to date focused primarily on the economic effects of educational attainment (for example Mankiw et al., 1992), that is, the ‘quantity’ of education received.

While important at an aggregate level, observations of educational attainment reveal little about the quality of the education that has been attained, and obscure a more nuanced view of the role that educational systems, and in particular schooling systems, can play in driving economic growth through improved educational outcomes.

Further, a focus on educational attainment has the potential to lead policy makers astray in their prioritisation of investments in educational systems. A more sophisticated understanding of the relationship between schooling quality, educational outcomes and economic outcomes may support policy-makers to make more effective decisions regarding investments in schooling and school system policies more broadly.

At its core, this report expands on the base of research in this area, with a particular focus on the link between educational outcomes relating to schooling quality (that is, beyond just attainment) and economic outcomes.

A significant body of research has also established evidence on the role that schools (and elements of school practice and management) can play in generating improved educational outcomes. While this evidence will be discussed in this paper, estimating the size of these linkages is not the primary focus of the research in this report.

9 Human capital is defined as the skills and abilities that individuals apply to the workplace or to their personal lives more generally.




In other words, this study will not directly provide estimates on the returns to direct fiscal investments in schooling quality. Rather, the economic benefits of higher schooling quality – irrespective of how such improvements are actually achieved – will be estimated.

This first part of this section reviews the conceptual and empirical evidence towards the impact of education (and schooling in particular) on economic outcomes. In particular, it sets out how this previous research has informed the original analysis conducted as part of this study.

Over 70 documents have been investigated and are incorporated into this report in line with their relevance to the core research questions being considered. The review contains four subsections: Section 2.1 provides a brief outlook for the Australian economy, and highlights the

need for a highly skilled labour force and sets the scene for examining the role that quality education plays in driving Australia’s economic prosperity;

Section 2.2 reviews the established evidence on the link between schooling quality and educational outcomes, and defines the measure of student outcomes that this study uses to estimate economic returns;

Section 2.3 reviews the research on the link between educational outcomes and economic outcomes, in terms of the conceptual relationship between human capital and economic output; and

Section 2.4 reviews the empirical methods that have previously been used to estimate the relationship between educational outcomes and economic outcomes and discusses the implications for this study’s empirical methodology.

2.1 Looking to the future of the Australian economy

2.1.1 Productivity, declining terms of trade and an aging population

Australia has enjoyed vast improvements in living standards over the past 20 years (see Deloitte, 2015). Initially, microeconomic reform spurred growth in productivity, and more recently growth in the terms of trade (stimulated by demand for Australian mineral resources) has facilitated consistent national income growth, underpinning an improvement in living standards throughout Australia. These more recent developments have occurred in the absence of strong productivity growth.

However, as the mining boom recedes and Australia’s terms of trade fall back to more normal levels, they will no longer provide this stimulus to growth in national prosperity. Compounding this risk is Australia’s ageing population. Not only will this reduce the size of the labour force as Australians enter retirement, it will also place strain on the government budget as spending on aged care services increases (AIHW, 2014). Taken together, this will mean that new sources of growth will need to be found if living standards are to continue to rise at rates to which Australians have become accustomed.

Chart 2.1 highlights how both falling participation rates and terms of trade will act as a drag on the nation’s living standards over the coming decade. As the chart demonstrates, it will




fall almost exclusively to productivity growth to propel national incomes higher over the coming decade. Indeed, as famously noted by Krugman (1994):

‘Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.’

Chart 2.1: Average Australian annual national income growth per capita

Source: Dr Martin Parkinson, Secretary to the Treasury, ‘The 2014-15 Budget and sustaining broad-based growth in living standards’ speech, 20 May 2014; Deloitte Access Economics

2.1.2 The transition to a ‘knowledge economy’

Australia, like other developed nations, is fast transforming into a ‘knowledge economy’ where knowledge is being used to generate value for industry. More than ever before, Australia’s economic potential is dependent on the production, distribution and application of human capital.

The logical consequence of this transformation is the rise in knowledge-intensive activities, and a subsequent increase in demand for labour capable of performing these activities (Kelly and Mares, 2013). The knowledge economy is not characterised by physical production, but rather on people exchanging ideas, solving problems, and generating new knowledge (Lucas, 2009).

Central to the success of Australia’s knowledge economy is the continual expansion of the supply of workers possessing the intellectual capital required to contribute to the exchange of ideas and the generation of new knowledge. Without a labour force capable of supporting industry in driving innovation and generating new knowledge, Australia is at risk of failing to achieve its economic potential.




As digital technology changes the way we communicate and interact, and computerisation alters the skills required of workers, the Australian economy of the future will not just require workers with traditional ‘higher skills’. Rather, we will require a workforce of creative, innovative and highly adaptable knowledge-workers (Deloitte Access Economics, 2015). To illustrate just how profound these changes might be, Chart 2.1 demonstrates the impact that computerisation might have on the occupational structure of the Australian workforce, affecting both traditionally high and low skilled occupations. For example, this chart demonstrates that many jobs at risk from computerisation are occupations in industries such as sales, office and administrative support and service, which account for nearly half of all workers employed in Australia. In contrast, occupations in management, business and financial services; computer, engineering and science; and education legal, community service, etc., are less likely to be computerised in the future.

Chart 2.1: Impact of computerisation across occupations

Source: Frey and Osborne (2013)

2.1.3 Implications for educational outcomes and education policy

The challenges and opportunities facing Australia’s economy, as briefly outlined above, have profound implications for the importance of education and for the impact of formal education systems on Australia’s future prosperity. In particular, the impact of computerisation on professional services and other ‘tertiary sector’ (labour-intensive) industries may mean that the contribution made by current forms of formal educational attainment and qualifications diminish over time, even as the demand for higher levels of human capital grows. This observation further emphasises the relative importance of measures of cognitive ability and educational quality in contrast to educational attainment.

Effective educational outcomes for the future may look very different to the skills and knowledge embedded in the workers of today. A high quality schooling system would be




one that provides students with the 21st century skills10 to meet the demands of Australia’s knowledge economy, and ensure that the labour productivity growth essential to Australia’s future prosperity is realised.

Among other governments and education systems throughout the world, the Programme for International School Assessment (PISA) of the OECD has recognised this challenge facing educational systems, and has introduced assessments of students’ 21 st century skills, particularly with respect to creative thinking, collaborative and complex problem solving, and social skills.

Success on these measures may prove to be a strong leading indicator of a nation’s ability to adapt to and realise the opportunities that come with the transition to a knowledge economy. The results from the first year of compulsory testing for 21 st century skills as part of PISA will become available late in 2016.

2.2 The link between schooling quality and educational outcomes

2.2.1 How are educational outcomes measured?

The definition of human capital is very broad, and education, formal or otherwise, can contribute to it in varied ways (Deloitte Access Economics, 2015). Indeed, where previous research has tended to focus on a single measure of ability, typically intellectual ability (otherwise known as pure cognitive ability), one major advance in the economics of education in recent years is the recognition that the skills that may be gained through education are many and varied (Burgess, 2016).

Cognitive and non-cognitive ability

A distinction is often made between cognitive skills, as outlined above, and non-cognitive skills, which can also be referred to as non-cognitive abilities, soft skills or socioemotional skills. These non-cognitive skills relate to important personal attributes such as perseverance, motivation, self-control, conscientiousness, perseverance, sociability, and curiosity (Heckman, 2004; Heckman and Kautz, 2012).

Non-cognitive skills are significant and important educational outcomes that relate strongly to human capital. Indeed, Conti and Heckman (2014) conclude that conscientiousness is strongly correlated with attainment and labour market outcomes. These researchers argue that such traits are generally stable, but do evolve slowly over time and are malleable. As such, it has been found that public policy interventions, such as formal education, can shape non-cognitive skills in such a way as to improve individuals’ human capital over time.

Importantly, there is evidence to suggest that achievement test scores (a standard measure of cognitive ability) are influenced by non-cognitive skills (see Almlund et al 2011, and Cunha et al 2006). Indeed, this correlation is intuitive insofar as it is assumed that conscientious pupils will study hard and do better (Burgess, 2016).

10 21st century skills include: critical thinking, problem-solving, self-management, Information and Communication Technology (ICT) skills, communication and collaboration (see OECD, 2011).




This does not necessarily suggest that tests of student achievement are perfect measures of the elements of the cognitive and non-cognitive abilities that contribute to human capital, but they are nonetheless strong indicators, particularly in contrast to indicators of educational attainment. That is to say, attainment in and of itself does not capture all of the aspects of an individual’s value to future employers, particularly relative to more nuanced measures of student achievement. While intuitive, this observation can have profound implications for public education policy centred on improving attainment in order to raise earnings (Burgess, 2016).

Student engagement and wellbeing

The measures discussed above relate to a student’s level of educational achievement as it contributes to human capital, both in a cognitive and non-cognitive sense. Separate, but related, measures of educational outcomes that are important when considering educational quality include student engagement and wellbeing (Lamb et al., 2015).

Willms (2003) outlines the significance of student engagement and wellbeing (measured in terms of students’ participation, and their sense of belonging in school) as a precursor to cognitive and non-cognitive achievement (see also Davies et al, 2011). Through a study of PISA results from the year 2000, Willms finds considerable variation among countries in their levels of student engagement and in the prevalence of disaffected students. Moreover, the prevalence of disaffected students varies considerably within and among schools, and this variation is not attributable solely to students’ family background.

These results also provide evidence that literacy performance and student engagement do not necessarily go hand-in-hand, though engagement does play an important role in predicting growth in literacy outcomes over time. Further, student engagement and wellbeing are important not only because of their relationship with student learning, but also because they represent a disposition towards schooling and life-long learning (Willms, 2003).

In practice, the relationships between student engagement, wellbeing, and cognitive and non-cognitive achievement are highly complex and in many cases occur in a largely contemporaneous fashion, which limits the extent to which researchers can understand their causal inter-dependencies.

This poses significant challenges for education policy makers concerned with driving improved student outcomes through schooling quality and higher levels of systemic educational performance. Relatedly, the policies and investments in school education which have the greatest impact on student outcomes are highly contested in both the academic and government spheres of research (Bracks, 2015; Burgess, 2016). The following section discusses what is known about how schools and schooling quality contributes to improved educational outcomes, as previously defined.

2.2.2 How do schools contribute to these outcomes?

A number of factors influence educational outcomes. Some factors have a direct link to a student’s experience in the classroom, including teacher quality, curricula, pedagogy, peer effects and school leadership, while others are broader, and include socioeconomic status, family background, parental engagement, language proficiency and location (Bracks, 2015).




Historically, the literature has provided a range of findings as to whether schooling quality in fact plays a role in determining educational outcomes and returns to education. The consensus view has shifted over time, with more recent evidence suggesting quality plays a pivotal role. With the advent of human capital theory, researchers adopting this conceptual approach have tended to conclude that the growth in an individual’s human capital (typically estimated through schooling outcomes) is largely a result of the quality of their education (Hanushek and Woessmannn, 2012).

Defining schooling quality and how it is created

The term ‘quality’ as it relates to education can be difficult to define and a variety of approaches have been used in past studies. Of the literature with a specific focus on the quality of education, the following two approaches are the most common: Measuring quality using internationally comparable assessment scores such as PISA

language, science and maths scores (Barro, 2001; Hanushek and Zhang, 2006); and Measuring quality using school resources such as expenditure per child and teacher to

student ratios (Bratsberg and Terrell, 1997; Harmon and Walker, 2000; Betts, 1999).

The first of these two approaches may best be described as an outcome-based measure, where the latter is an input-based measure. There has been debate about the appropriateness of using school resources as a measure of schooling quality given the range of factors that are involved in school resources and difficulties in obtaining consistent and reliable data. Thus, using internationally consistent assessment scores has emerged as the more common choice as a proxy for quality.11

Noting the pre-eminence of outcome-based measures of education quality, in defining the concept of schooling quality, it is necessary to consider the extent to which schools themselves (and school systems generally) contribute to these ‘quality’ educational outcomes.

Modern approaches to measuring school performance seek to estimate the variation of student outcomes that can be explained by schools (Lu and Rickard, 2014). This measure is often defined as school level ‘value-add’, that is, the value that a school adds to a student’s outcomes, above and beyond what would be predicted by the individual attributes of that student (for example, prior learning ability and socio-economic background), and the context in which the school is situated (for example, concentration of disadvantage in a school, and the remoteness of the school’s community).12

In practical terms, the quality of a school is related to the value-add that it generates for students through its specific practice and policies. This includes the teacher quality, curricula, pedagogy, school leadership, partnerships and community engagement, and the efficacy of resource utilisation, among other things.

11 As has been noted previously, the quality of education is not tied to incremental improvements in student cognition alone. Non-cognitive skills (such as resilience, creativity, motivation and character, sometimes referred to as 21st Century skills) are also important when assessing the quality of education (Kautz et al., 2014; Heckman and Kautz, 2013). Therefore, scores testing student outcomes across these non-cognitive domains may be a better representation of schooling quality and outcomes. However, no standard scores assessing such outcomes currently exist.12 This measure is explored in further detail in Section 6 of this report.




At a systemic level, there is a parallel between the quality of individual schools, as measured through value-add, and the quality of the system as a whole. In such an instance, it may be possible to consider the value-add by a given educational system, relative to other systems. In practice, however, the most significant observations of value-add tend to be at the classroom level, related most prominently to the efficacy of individual teachers (Hanushek, 2011).

Linking practice and policy to schooling quality

Systemic education policies can have very different effects on schooling quality, as measured through the value added to student outcomes. For example, it has been comprehensively established that teacher quality is by far the most important school-based determinant of student outcomes (Burgess, 2016). The difference in outcomes for students taught by effective or ineffective teachers is significant. Investment in the early years of child development is also very important, particularly for students from more disadvantaged backgrounds (Deloitte Access Economics, 2014). In contrast, school policies which focus on reducing class sizes are typically found to be relatively ineffective, while imposing a high cost to the school and schooling system more broadly (Hattie, 2009).

Also, at a systemic level, it is known that having a coherent market structure for schools to operate in is important. Systems with strong accountability, largely autonomous schools, and centralised tests at the end of compulsory schooling are of the highest quality (Burgess, 2016).

Funding too can play an important role in driving schooling quality. At a threshold level, the evidence clearly demonstrates that funding can and does facilitate improved educational outcomes. Previous research by Deloitte Access Economics (2014) on Australian and international literature on the relationship between school funding and educational outcomes concluded that there is a positive relationship between funding and educational outcomes.

Critically, however, literature on the relationship between school funding and educational outcomes consistently concludes that funding is necessary but not sufficient to improve educational outcomes. That is, funding is a mechanism which allows school systems to obtain educational resources, but provided funding meets an adequacy threshold, it is how funding is used that ultimately determines its relationship with educational outcomes (see also Victoria University, 2015).

As such, when considering the link between schooling quality, educational outcomes, and economic outcomes, it is critically important to understand what attributes of schooling quality are at play. This is particularly relevant when considering the returns on investment from any initiatives which seek to drive improvements in schooling quality. Ultimately, the economic returns from any policy changes or interventions (which may or may not include additional funding) will be highly dependent upon the nature of the intervention, including its link to schooling quality and the students to which the policy is targeted.

Indeed, school level practices and policies can have very different impacts on student outcomes, and can have significantly variable level of associated cost. Recognising the importance of understanding the efficacy of different programmatic investments in schooling quality, the UK Sutton Trust Education Endowment Foundation (EEF) Teaching




and Learning Toolkit (TLT) presents a summary of educational research to guide teachers and schools on how to improve the attainment of disadvantaged students. Their research covers 34 drivers of student outcomes, summarised in terms of their average impact on attainment, the strength of evidence supporting them and their relative cost.13

Importantly this research considers both the relative impact and cost of different spending initiatives, indicating areas where increased funding can be linked with improved outcomes via particular drivers. A subset of selected approaches is summarised in Table 2.1 below.

Based on this research, through moderate to extensive evidence, it has been established that classroom level practices and policies regarding student feedback, pedagogy with a focus on meta-cognition and self-regulation, and collaborative learning environments, can have the highest impact on student outcomes, for the lowest levels of cost.

Table 2.1: Impacts of programmatic funding for disadvantaged students

Selected ApproachesCost estimate

Evidence estimate

Average impact Summary

Feedback $ $ $ $ $ ★ ★ ★ ★ ★ + 8 Months High impact for low cost, based on moderate evidence.Meta-cognition and self-regulation $ $ $ $ $ ★ ★ ★ ★ ★ + 8 Months High impact for low cost, based on extensive evidence.

Early years intervention $ $ $ $ $ ★ ★ ★ ★ ★ + 6 Months High impact for very high costs, based on extensive

evidence.

Collaborative learning $ $ $ $ $ ★ ★ ★ ★ ★ + 5 Months Moderate impact for very low cost, based on extensive evidence.

Homework (Secondary) $ $ $ $ $ ★ ★ ★ ★ ★ + 5 Months Moderate impact for very low or no cost, based on

moderate evidence.

One to one tuition $ $ $ $ $ ★ ★ ★ ★ ★ + 5 Months Moderate impact for high cost, based on extensive evidence.

Oral language interventions $ $ $ $ $ ★ ★ ★ ★ ★ + 5 Months Moderate impact for low cost, based on extensive

evidence.Behaviour interventions $ $ $ $ $ ★ ★ ★ ★ ★ + 4 Months Moderate impact for very high cost, based on extensive

evidence.

Digital technology $ $ $ $ $ ★ ★ ★ ★ ★ + 4 Months Moderate impact for high cost, based on extensive evidence.

Small group tuition $ $ $ $ $ ★ ★ ★ ★ ★ + 4 Months Moderate impact for moderate cost, based on limited evidence.

Social and emotional learning $ $ $ $ $ ★ ★ ★ ★ ★ + 4 Months Moderate impact for very low cost, based on extensive

evidence.

Parental involvement $ $ $ $ $ ★ ★ ★ ★ ★ + 3 Months Moderate impact for moderate cost, based on moderate evidence.

Reducing class size $ $ $ $ $ ★ ★ ★ ★ ★ + 3 Months Low impact for very high cost, based on moderate evidence.

Extended school time $ $ $ $ $ ★ ★ ★ ★ ★ + 2 Months Low impact for moderate cost, based on limited evidence.

Individualised instruction $ $ $ $ $ ★ ★ ★ ★ ★ + 2 Months Low impact for low cost, based on moderate evidence.

Learning styles $ $ $ $ $ ★ ★ ★ ★ ★ + 2 Months Low impact for very low cost, based on moderate evidence.

Homework (Primary) $ $ $ $ $ ★ ★ ★ ★ ★ + 1 Month Low impact for very low or no cost, based on moderate evidence.

Mentoring $ $ $ $ $ ★ ★ ★ ★ ★ + 1 Month Low impact for moderate cost, based on moderate

13 Recently established centres in Australia in NSW (the Centre for Educational Statistics and Evaluation, Effective Practices in Literacy and Numeracy) and Victoria (the Evidence for Learning initiative) also seek to measure the impact of specific programmatic investments in education in the Australian context, with the aim of improving the practice and management of schools in Australia.


http://www.cese.nsw.gov.au/EffectivePractices/

http://www.cese.nsw.gov.au/EffectivePractices/



Selected ApproachesCost estimate

Evidence estimate

Average impact Summary

evidence.Teaching assistants $ $ $ $ $ ★ ★ ★ ★ ★ + 1 Month Low impact for high cost, based on limited evidence.

Performance pay $ $ $ $ $ ★ ★ ★ ★ ★ 0 Months Low or no impact for moderate cost, based on very limited evidence.

School uniform $ $ $ $ $ ★ ★ ★ ★ ★ 0 Months Very low or no impact for very low cost, based on very limited evidence.

Setting or streaming $ $ $ $ $ ★ ★ ★ ★ ★ - 1 Month Negative impact for very low or no cost, based on moderate evidence.

Repeating a year $ $ $ $ $ ★ ★ ★ ★ ★ - 4 Months Negative impact for very high cost based on extensive evidence.

Source: Education Endowment Foundation (EEF) Teaching and Learning Toolkit (2014)

The results from the research undertaken by the EEF confirm that reducing class sizes is relatively ineffective, generating a low impact for very high costs., For further comparison, while interventions like reducing class sizes and improving parental involvement have similar impacts on student outcomes (increasing student attainment by around 3 months – which is considered to be relatively ineffective), they do so at very different costs, with class size reduction having a very high cost compared to a moderate cost for parental involvement.

Programs focused on pedagogical practices and curricula programs, like meta-cognition and self-regulation, and feedback mechanisms, have the highest impact on educational outcomes, for relatively low cost. This demonstrates the importance of programs like the Smarter Schools National Partnership (SSNP) in providing funding for classroom drivers that have a demonstrable impact on educational outcomes and that can be implemented in a cost effective way (Parkville Advisory Group, 2014).

Even when drivers are demonstrated to have a significant positive effect on student outcomes, as found in the literature, the individual context of the school and classroom will still have a significant impact (EEF, 2014). This may mean that inferences drawn from meta-analyses and other syntheses of the literature are not applicable in certain contexts.

Therefore, contextual factors like autonomy, accountability and school leadership can be important in ensuring that funding is tailored to meet the needs of students in different classroom contexts, by supporting and enabling the drivers of student outcomes that have been evaluated as being most effective at a classroom level (Deloitte Access Economics, 2014).




Implications for this report

Schooling quality is most appropriately defined as the value added by schools to educational achievement, above and beyond what would be predicted by contextual factors at the student and school level. Practices and policies at a school level can play a significant role in contributing to student outcomes and comprise the key elements that define schooling quality.

This study considers the economic contribution made by the educational outcomes contributed to by schools. This link will allow policy-makers to assess the impacts of policies and interventions designed to improve schooling quality, both in terms of their efficacy in adding value to educational outcomes (that is, improving schooling quality) and ultimately driving improved economic outcomes.

2.3 The link between educational outcomes (and schooling quality) and economic outcomes

2.3.1 Establishing the conceptual link

Educational outcomes affect the economic outcomes of an individual through their enhanced productivity in the workplace, which is rewarded through relatively higher wages and enhanced employment opportunities (Borjas, 2010). For many workers in the economy, enhanced cognitive skills result in a greater capacity to learn new skills, solve complex problems, and complete intricate cognitive tasks, among other things. These elements of enhanced human capital have a strong link to schooling quality through the elements of value added by education as outlined in the preceding section.

At a threshold level, this allows some individuals to access skill levels and certain professions or roles that they would not otherwise be able to perform. These higher skilled roles offer higher wage rates, as in competitive labour markets employers offer greater levels of remuneration to attract the most capable workforce (as measured by the level of economic output produced per hour worked, known as the marginal product of labour).

Within skill level thresholds, enhanced educational outcomes and human capital (including cognitive ability) can play a role in boosting the productivity of individual workers, and the workforce as a whole. The extent to which this enhanced human capital is causally related to higher wage levels depends upon the nature and structure of the industry in which these workers are employed and the nature of labour demand. For certain (relatively low skilled) industries and jobs, in the short term, there may be little productivity benefit from enhanced cognitive ability at an individual level (for example, farm labourers or general cleaners).

Indeed, at an extreme level, a completely rigid labour market structure might include a fixed suite of jobs with accepted wages for workers of all skill levels, where measures of cognitive ability (such as PISA scores) are merely used to order individuals to match them with the appropriate jobs. This would necessarily mean that a marginal increase in the overall profile of educational outcomes would have no causal effect on the total wage bill. Further still, even at a threshold level, such a labour market structure would suggest, in the




absence of and causal productivity dividends, that an increase in the number of high skilled workers in an economy (relative to low skilled workers) would depress the wage bill for this subset of workers, as their relative scarcity falls.

Nonetheless, while improved educational outcomes may not enhance the wage outcomes of every worker in every job, over a longer time span, and on average, the balance of evidence suggests that more skilled workers will create a more productive workforce, and will lift the wage-bill economy wide. There are two reasons for this: First, the supply of jobs has historically proved to be malleable and responsive to

effects of comparative advantage. That is to say, if more skilled workers transition into the workforce, roles and industries will develop to match their skillset and utilise their productive potential to maximise economic surplus (Borjas, 2004).14

Second, highly regarded empirical studies (such as Moretti, 2004; and Borjas, 2004), have shown a more skilled workforce has positive implications throughout the economy, with positive productivity spillover effects, improving the wages of individuals with relatively low levels of educational attainment (in jobs that do not necessarily value higher levels of cognitive ability).

To elaborate on the above points further, more highly skilled employees are able to produce more output per unit of time, than lower skilled workers in the same job. On average, throughout the economy, wage differentials will manifest through competitive forces, as more productive worker are in higher demand, and have more bargaining power with employers. If an entire cohort of students was to upskill, the wage effect permeates through to unskilled workers, as there will be a relative scarcity of those willing to undertake unskilled work. Indeed, Moretti (2004) finds that productivity effects dominate relative scarcity effects from higher levels of human capital, for all skill levels.

The nature and focus of education will play an important role in supporting the causal impact of improved educational attainment on productivity, and ultimately on wages. The relevance of knowledge and skills to Australia’s future economy will play an important role in supporting the overall impact of education to economic output, as outlined in the preceding sections.

Human capital theory

As the most direct link between educational outcomes and economic outcomes, economists have long been interested in the labour market benefits of education. This is reflected in a large pool of research attempting to quantify these benefits (see, for example, Ashenfelter et al., 1999, and Card, 1999). While data and empirical issues have proved a hurdle to accurate assessments of the linkages between education and labour market outcomes, current evidence points to the conclusion that significant benefits are likely to be realised through high quality education, particularly in the early years (Gould et al., 2003; Chetty et al., 2011).

14 It should be noted that this particular observation is borne out in the results from the CGE modelling conducted as part of the economic modelling conducted for this study.




Human capital theory is the most widely accepted model used to analyse the contribution that education makes to labour productivity, earnings, and consequently to economic and social prosperity.15

This is reflected in models of economic growth now typically considering human capital as a factor in explaining differences in economic growth across countries. At an aggregate level, the total economic output of a nation can therefore be assumed to be a function of physical capital, labour supply and human capital (Mankiw et al., 1992). A variety of econometric techniques can then be used to estimate the effect of an increase in human capital on total economic output.

While human capital can to some extent be attributed to an individual’s innate ability, it is typically assumed to be acquired in part through experience and formal education (Borjas, 2010). Indeed, the marginal effect of improvements in schooling quality, measured in terms of improved student outcomes on economic output, has been successfully measured through its contribution to the human capital stock (Hanushek and Woesmann, 2009).

2.3.2 The transmission mechanism from education to outcomes

Economic benefits to the individual

There is a large evidence base demonstrating the financial benefits of education for the individual, although most of this literature focuses on educational quantity rather than quality.

There have been several positive links established between educational outcomes at the primary and high school levels and increased access to higher education. This is a major pathway through which increased educational outcomes can benefit the individual. In Australia, there is a small but growing body of evidence that shows that individuals with university education receive higher wages, are more likely to be employed, commit more hours to the labour force and have higher productivity than individuals without a higher education degree (Deloitte Access Economics, 2015).

Wilkins (2015) found that individuals receive significant positive returns from education (particularly higher education) in the form of an increased income and likelihood of being employed full-time. Importantly, these results are determined after controlling for demographic factors and cognitive ability which, Wilkins argues, ‘provides a stronger basis for interpreting estimates for education variables as causal’ (Wilkins, 2015, pp. 70–71).

The Productivity Commission has also estimated that the average earning gain for year 12 completers is 13% for males and 10% for females compared to non-completers. This increases to around 40% for those with a university degree for both males and females (Forbes et al 2010).

Spillover benefits to the broader economy

The benefits that students gain from education do not end at the individual. In a seminal paper, Mankiw et al. (1992) used average schooling duration to measure human capital and

15 See for example Hanushek and Woessmannn, (2010); Leigh, (2008); McMahon, (2009).15




showed its strong correlation with per capita output across countries, with benefits accruing to not only the individuals, but the economy more broadly. This approach sparked the development of an influential macroeconomic literature focussing on how education, as a measure of human capital, can generally sustain economic growth both in the form of benefits to individuals and social returns at the macroeconomic level (Caselli, 2005; Sianesi and Van Reenen, 2003).

When a quality education enables more individuals to make effective transitions into higher education or VET, the skills of workers improve, making them more productive and increasing their potential to innovate. Productivity and innovation experience gains, from both the enhanced cognitive skills arising from improved education, and the non-cognitive skills (Moretti, 2004). These non-cognitive skills such as perseverance, self-control, attentiveness and resilience are highly demanded by the labour market.

Improved educational outcomes are known to generate significant spillover benefits through:16

High human capital workers sharing knowledge with peers, both intentionally and unintentionally (these transfers are often referred to as knowledge spillovers). Knowledge spillovers will make peers more productive, resulting in higher wages.

Interactions among high human capital workers may create synergies in problem-solving and idea creation that can lead to new and more efficient production processes and technologies that increase demand for labour in the area, thus boosting employment and earnings for workers of all types.

Human capital is a prerequisite for the discovery and adoption of technologies that increase the productivity of all workers. Knowledge flows and opportunities for interaction decline with physical and social distance, so workers who regularly interact with a large number of high human capital workers will benefit the most from human capital externalities.

Major innovative ideas can often transcend a particular industry. They can spur technological progress, and increase total factor productivity within the economy.

Rewards from higher levels of human capital can spill over to governments through receipts of higher taxation revenue and lower income support payments.

The above pathway of benefits also arises from the increased rate of individuals that are able to find employment in their desired fields. This leads to increased workforce participation, which again results in higher economic activity and increased GDP.

The many non-pecuniary benefits of a quality education (discussed below) also have economy-wide benefits. Greater civic engagement and community participation will positively influence communal cohesion, and improve the functioning of society. This further flows on to reduced crime rates which benefit the nation through a greater sense of security, and reduced costs of incarceration.

In the Melbourne Declaration on Educational Goals for Young Australians (2008), some of the many non-pecuniary benefits hoped to be achieved via the provision of quality schooling include influencing individuals to: act with moral and ethical integrity;

16 See: Deloitte Access Economics (2015)16




have a sense of optimism about their lives and the future; appreciate Australia’s social, cultural, linguistic and religious diversity; understand and acknowledge the value of Indigenous cultures; commit to national values of democracy, equity and justice; be enterprising, show initiative and use creative abilities; relate to and communicate across cultures, especially the cultures and countries of

Asia; and work for the common good, in particular sustaining and improving natural and social

environments.

Each of these aspects of individual human behaviour, if positively influenced by quality school, has the potential to further contribute to communal cohesion and the functioning of society.

2.3.3 Summary of the empirical literature

The literature on the economic benefits to education has approached the relationship between education and economic benefits through both a macroeconomic and microeconomic lens.

The most contemporary iterations of macroeconomic models, particularly from the OECD, link cross-country differences in PISA scores to cross-country differences in economic growth. For example, OECD (2015) estimates that if Australia can increase PISA scores by 25 points, this will add US$3.8 trillion to the Australian economy by 2095. Several other papers (such as OECD, 2010; and PWC 2012) produced findings of the same order of magnitude, applying a similar methodology.

However, these results may overestimate the causal benefits of improving education, particularly as the models used do not consider the temporal effects of education on economic growth. This overestimation can result from omitted variable bias which limits the extent to which these estimates can be considered to be causal and specific at a country-level (as the estimates rely on cross-country observations rather than inter-temporal effects). While robustness tests by Hanushek and Woesmann (2012) have not falsified a causal assumption, further research and greater transparency on inter-temporal effects will play an important role in expanding upon this evidence base.

This current paper adds to the literature in this area by investigating the time series elements of the data, which should produce a more accurate estimate of the relationship between education and economic growth. In addition, this study will seek to expand upon the methods established in previous work to develop a more transparent and robust structural model of economic growth, that would enable a more sophisticated understanding of how educational outcomes drive long term economic growth at a macro-economic level.

The microeconomic literature in this area focuses on the links between schooling and individual wage and employment outcomes. In Australia, the literature has focussed more closely on years of schooling, or educational attainment, rather than the quality of schooling outcomes (see for example, Leigh, 2008; and Wilkins, 2015). To this end, this




piece of work looks to add to the existing body of literature by investigating links between schooling quality, (rather than schooling quantity) and an individual’s wage and employment outcomes.

2.4 Empirical methods and implications for this study

This section reviews the empirical methods used to estimate the relationship between schooling quality (or quantity) and economic outcomes. Historically, two broad approaches have been adopted – models based on a macroeconomic, cross country approach (which considers cross-country differences in a variety of economic output), and a microeconomic approach that considers changes to wages and labour market outcomes caused by educational attainment using observations on individuals.

2.4.1 Macroeconomic approach to the effect of human capital on economic growth

The early literature

Initial attempts at measuring the effects of human capital on economic growth can be found in Mankiw, Romer and Weil (1992). They develop what they term an ‘augmented Solow model,’ building on Solow’s 1957 seminal piece on economic growth. They contend that human capital is an important, but omitted, input into a country’s production function. The estimated production function therefore looks as follows:

Y (t )=K ( t )α H ( t )β ( A ( t ) L (t ) )1−α−β (1)

Where K(t) is the stock of physical capital, H(t) is the stock of human capital, A(t) is a measure of technological progress affecting the productivity of labour (often defined as total factor productivity) and L(t) is the supply of labour; each variable is measured at time t.

In estimating the equation, human capital is measured as the level of investment in education, and the rate of change of human capital is measured by the share of the working age population that is in school (thereby giving a crude estimate as to the future growth in workers with a high school qualification). Estimates are generated through an ordinary least squares (OLS) estimation.

While Mankiw et al (1992) conclude they find sufficient evidence that changes in human capital influence economic growth, subsequent studies, such as Klenow and Rodriguez-Clare (1997), argue that this measurement of human capital is inaccurate. When including primary and secondary school enrolment rates as a measure of human capital the effect of human capital on economic growth diminishes significantly. The core critique of Mankiw et al (1992) is that it seems unlikely that advancements in human capital are entirely separable from advancements in economic growth, meaning equation 1) is misspecified.

Benhabib and Speigel (1994), using improved data and a different growth model, do not find a relationship between human capital accumulation and cross-country differences in




economic growth. Rather, they propose an endogenous growth model, whereby growth in total factor productivity, is dependent on the level of human capital. In other words, growth in human capital results in both increases to GDP directly, as well as indirectly through increases in technological progress (Topel, 1999).

Benhabib and Speigal (1994) use the level of educational attainment (measured by average years of schooling among the working population) as a measure for human capital, and using OLS estimates, find that growth in human capital has little effect on growth in national income, but that the level of human capital is associated with high economic growth. The methodology and findings are followed closely by Baro and Sala-i-Martin (1995), who find that the greater the number of years of secondary school education, the greater the impact on national income.

Topel (1999) summarises the initial attempts at quantifying the relationship between human capital and economic growth by stating that ‘the empirical growth literature does not lend much support to the idea that human capital, at least as represented by measured educational attainment, is a key element of economic growth.’

More contemporary models of economic development

Identifying the best model

The OECD regularly releases papers considering the impacts of educational assessment scores on economic growth (see for example, OECD, 2015, OECD, 2013). Throughout these papers, the OECD adopts a relatively straightforward technique in estimating the effect of human capital on economic growth, building on work by Hanushek and Woessmannn (2010; 2012).

Using a cross-section of data, the OECD relies on long run economic growth as a dependent variable, and human capital as an independent variable. Several controls are included (such as initial years of schooling, and initial GDP per capita), with the co-efficient on cognitive skills taken to represent the effect of education on economic growth. This approach has been most recently adopted in ‘Universal Basic Skills. What countries stand to gain’ (OECD, 2015), as well as other OECD research. However, such a model may fail to take into account several other factors that can influence economic growth.

In 2001, Bassanini and Scarpetta determined a new method for identifying the contribution of human capital to cross-country economic growth. They assert that historical work in this area, while well-grounded in theory, suffered from misspecified models. In particular, most studies have not used a panel model and therefore relied solely on cross section analysis, or have assumed a homogenous cross-country production function in both the long and short-run. They therefore employed the use of a pooled mean group (PMG) estimator, to assess the relationship between defined factor inputs and economic output within OECD countries.17 In applying this model, they contend that: OECD countries have access to the same technology, and therefore, have the same

technological production functions in the long run;

17 The pooled mean group estimator approach utilises both the intertemporal and cross sectional nature of the data and pools observations into a single panel for purposes of estimation.




The speed of convergence towards a long-run steady state can vary across countries, depending on factors such as the regulatory environment. Therefore, country growth estimates can vary in the short-run, but not the long run; and

Relying on a cross-section analysis is only applicable if country specific parameters are independent of any regressors. By employing a panel data, country specific effects can be controlled for.

Another key factor enabling the analysis is the dataset created by Barro and Lee (1993) which developed comparable cross-country data for the average years of schooling. Bassanini and Scarpetta (2001) use this data as a measure of educational attainment.

Expanding on their work applying PMG estimates, Bassanini and Scarpetta (2001b) propose the use of the following equation to estimate differences in cross-country economic growth:

Δ ln y (t )=a0−ϕ ln y ( t−1 )+a1 ln sk (t )+a2ln h ( t )−a3n ( t )+a4t+∑j=1

3

a j+4 lnV j+b1 Δ ln sk (t )+b2 Δ lnh (t )+b3 Δ lnn (t )+∑j=1

3

b j+3 Δ lnV j

Where: y is GDP per capita, sk is the propensity to accumulate physical capital; h is human capital; n is population growth; Vj is a vector of variables affecting economic efficiency; t is a time trend; and the b-regressors capture short-term dynamics.

Bassanini and Scarpetta (2001b) adopt this growth equation as their preferred model as different growth models can be nested within this equation by the inclusion or exclusion of the various lag terms. The advantage of applying this growth specification could apply to both models of endogenous and exogenous growth and depends on the significance (or lack thereof) of certain variables.

Finally, the OECD (2010) assessed a suite of exogenous and endogenous models and adopted an endogenous growth approach. The rationale behind this was that ‘nations with more human capital tend to continue to make greater productivity gains than nations with less human capital.’ In other words, the level of human capital, not just the change in human capital, can influence economic outcomes, consistent with findings from earlier endogenous growth models.

Measuring human capital

Hanushek and Kimko (2000), consider the problem of how to measure human capital. They note the issues with previous attainment-based measures (the most prominent of which was the average level of schooling obtained by the working population), and discuss alternatives as a measure of human capital. A key issue cited with years of schooling is there are differences in the quality of schooling between different countries.




A second issue is that there are limited policy lessons from such a discussion, as average years of schooling cannot grow in an unbounded manner. In other words, developed countries, with high secondary school participation, cannot significantly increase the average years of schooling of its workforce (as most of their workforce already attend school for the maximum number of years); in order to lift economic outcomes, developed countries should focus on the quality of education, not the quantity.

To improve on these measures, the OECD suggests the use of various tests of academic achievement in maths and science as a measure of human capital. Another, more complex approach to human capital measurement involves deriving human capital from a range of inputs, such as family inputs, schooling level, and individual ability (Hanushek and Woessmann, 2010).

This approach was adopted by the OECD in 2010, where it notes that international achievement measures provide an accurate measure of the skills of the labour force. They also note the strong correlation found in previous studies between test scores and economic growth and the body of evidence which, to the extent possible, shows there is a causal relationship between test scores and economic outcomes.

Several papers (see for example, OECD (2015); OECD, (2010); Hanushek and Woessmann (2012)) have adopted PISA scores as the preferred measure of human capital. PISA scores are preferred due to the ease with which the results can be interpreted, and that they allow for cross-country comparison as children across OECD countries take similar tests.


From the review of the literature, there are several findings which have influenced the methods utilised in this study. First, endogenous growth theories, whereby human capital is assumed to affect economic growth directly, are able to identify more sound empirical evidence linking human capital growth to economic prosperity.

Second, the initial modelling was characterised by a reliance on OLS methods, which fail to account for idiosyncratic cross-country differences. Consequently, this study includes alternate methods which seek to isolate specific country level growth effects, drawing on methods developed by Bassanini and Scarpetta (2001a, 2001b). This model specification more robustly captures the relationship between economic outcomes and human capital.

Third, this study uses a measure of schooling quality, rather than quantity (attainment) as a proxy for human capital. Consistent with recent OECD work on the topic, the measure of quality used is PISA scores given the data is readily available and consistently measured across countries.

2.4.2 Microeconomic approaches to measuring the effects of education

Microeconomic approaches have relied more heavily on individual level data, comparing an individual’s wage and employment outcomes against educational attainment.




Model specification

Literature in this area has primarily followed Mincer’s (1974) seminal work, which used the following estimation equation:

log ew=α 0+α1 s+α 2 x+α 3 x2+α 4Z

Where: s is years of schooling x is years of experience Z is a series of control variables

A particular issue in the literature is the use of control variables (Z). The literature contends that gender, family background, marital status, union membership and family size can all influence wage outcomes, and should all therefore be controlled for in the estimation where possible (Harmon, 2003). Betts (1999) also suggests controlling for schooling quality before estimating the effect of educational outcomes on wage outcomes. In an Australian context, Wilkins (2015) also controls for place of birth, English proficiency, population density of region of residence and disability, all of which can influence wage outcomes.

In their simplest form, these equations can be estimated by OLS, with the output of interest being the co-efficient on years of schooling, interpreted as the returns to an additional year of education. Rather than focus on years of schooling, Wilkins (2015) demonstrates the change in wages due to different levels of educational attainment, by applying a similar methodology.

Challenges in estimating the impact on economic outcomes

Ability bias

A particular challenge in estimating the returns to education is that an individual’s innate ability may drive both education and economic outcomes. Since ability is unobservable, this will create an upward bias in the actual estimates of education on wages (Leigh, 2008). There are several methods proposed to control for ability bias: Wilkins (2015) controls for ability bias by including student cognitive scores in the

regression, as well as student level attributes associated with innate ability, such as gender, age and socio-economic status;

Instrumental variable analysis can be used to overcome this issue, if an appropriate instrument can be found, for example Card (1999) suggests the use of family background as an appropriate instrument while Leigh and Ryan (2007) use the month of birth as an instrument for years of schooling, acknowledging that those born just before the school entry date will spend a year longer in school than those born just after the cut-off date; and

Leigh (2008) simply assumes an upward bias of 10% on all estimates of returns to schooling based on the findings of literature that has estimated the size of this bias.

In the context of educational attainment, ability bias takes the form of unobserved innate cognitive ability, which is the underlying causal driver of economic benefits, rather than educational attainment.




This study utilises PISA scores as a measure of cognitive ability when considering the economic impact of educational outcomes. Ability bias is an issue to the extent that the observed measures of student achievement (in this instance, PISA scores) are a function of a student’s ability prior to entering the schooling system (in other words, their home learning environment). This bias implies that effects on economic outcomes attributed to student achievement could be a reflection of innate student ability (derived from prior education or other contextual factors) rather than schooling quality.

While this does not undermine the validity of the estimated relationship between educational outcomes and economic outcomes, which are the core focus of this report, not controlling for the bias may limit the extent to which educational outcomes can be attributed to schooling quality.

Externalities associated with education

A second challenge lies in quantifying the externalities associated with education, in other words, in separately identifying the private and social benefits to education (Leigh, 2008). Because microeconomic approaches focus on individuals and the later economic returns that accrue to these individuals in the form of higher wages, they are not well placed to estimate those returns that accrue externally to the individual. As a result, there has been relatively little focus on separating these aspects of returns to education.


This study seeks to estimate the returns to improving school outcomes, rather than the length of schooling or educational attainment more broadly. In particular, this study’s method draws on previous work based on the Mincer approach to measure the relationship between economic outcomes and wages. The literature is rich in suggesting a variety of variables which serve as appropriate controls for such an estimation , as well as the preferred methodological techniques for estimation (either ordinary least squares, or two-staged least squares if an instrument needs to be used to replace an important independent variable).

The techniques outlined in the literature also suggest the Mincer equation is transformable to replace length of schooling with measures of cognitive ability (such as NAPLAN or PISA scores). This allows for the measurement of the incremental effect of educational outcomes, rather than simply the effect of attaining an extra year of schooling.

In relation to estimating the externalities associated with educational outcomes, it is not expected our proposed methods will overcome these challenges. However, this has informed our overall approach to the task, in that both macroeconomic and microeconomic approaches are adopted, with the view that the macroeconomic approach may be better suited to estimate economic spillovers.




3 Modelling approaches In order to estimate the returns to improvements in educational outcomes, two complementary approaches are adopted in this report. Following the literature reviews above, these approaches are based on both a macroeconomic and microeconomic approach (the cross country modelling and the individual level modelling). The two approaches each have their own benefits, and limitations, outlined in more detail below.

3.1 Overview of the two approaches Guided by the literature, the cross country analysis and individual level analysis estimate the impact of improving educational outcomes on economic outcomes. Both approaches assume schooling outcomes translate to economic output through the ‘human capital’ theory of education, and the subsequent impact that human capital has on labour productivity and participation. In other words, an increase in student outcomes represents an increase in an individual’s endowment of skills and abilities that are rewarded in the labour market.

The first approach – the cross country approach – relies on a cross-country analysis, and isolates the impact that aggregated national academic results have on GDP growth. As part of this process, it is important to control for other, confounding factors which can also influence GDP (such as investment in capital, and institutional factors).

The second approach – the individual level approach – isolates the impact that academic results have on individual wage and employment outcomes (after controlling for a range of demographic and idiosyncratic factors that can also influence these outcomes). The individual level results are then aggregated to a national level through using a computable general equilibrium (CGE) model. Figure 3.1 summarises the transmission mechanisms considered in both modelling approaches.

The figure above demonstrates the pathways through which schooling quality or human capital impact GDP, and in doing so, highlights the comparative strengths and weaknesses of each approach.

The individual level approach enables a more specific analysis of the transmission mechanisms between improvements in schooling quality and final economic outcomes. It allows the separate channels through which schooling quality affects economic outcomes (such as through higher education attainment and direct productivity impacts), to be explicitly modelled. The total effect of these different transmission mechanisms can then be modelled at both the individual and economy-wide level.




Figure 3.1: Illustrative summary of approaches

Human capital includes a variety of measures to estimate the level of skill a nation is endowed with – one of which is education.

Increased likelihood of finding work

Technological progressHuman capital

Labour force

Improvements in schooling quality

Labour productivity

Workforce with increased skills

Improvements in general quality of life

Technological progressPhysical capital

Individual idiosyncratic factors

Increased skills are observed through higher wages, and workers receiving higher educational attainment

An increased likelihood of finding work is observed through higher employment rates

Improvements in the quality of life was not observed qualitatively in this project.

Physical capital

Cross country approach

Individual level approach

GDP


The data (described in more detail below) also enables more nuanced findings than the cross country approach. For example, the individual level approach includes an analysis of how the impact of an improvement in schooling quality varies across students (for example, by stratifying based on PISA scores). The analysis also explores the extent to which high-skilled occupations reward increases in cognitive skills above medium and low skilled occupations. The individual level approach (based on a longitudinal dataset of Australian students) and the analysis it enables is perhaps the most innovative aspect of the analysis contained in this report and adds significantly to the knowledge base in this area, particularly in the Australian context.

The cross country approach is less specific to the Australian context, as it is based on observations of test scores and economic growth across multiple countries. However, it is perhaps a more comprehensive assessment of the benefits of education in the sense that it captures the spillover effects of education that cannot be captured by an analysis of the returns to education at an individual level. This approach has also received much attention internationally due to the work of Hanushek and Woessmann and the related OECD publications that have found significant and positive relationships between PISA scores and economic growth over time.

The remainder of this section provides a more detailed overview of these complementary approaches.




3.1.2 Cross country approach

Overview

In simple terms, the cross country approach is a means of establishing the level of economic activity attributable to schooling outcomes by comparing the outputs of a range of countries which vary in their school results. The modelling captures the level of variation in economic growth which can be explained by differences in schooling assessment scores between nations.

The model developed for this study uses regression techniques to separately estimate the contributions made by improvements in: Educational attainment – measured based on the proportion of a country’s population

which has attained an equivalent level of education at each of the primary, secondary and tertiary levels.

Student assessment scores – measured using aggregate cross-country data of student academic results.

Transmission mechanisms

For the purpose of this analysis, a structural model of the economy was developed based on the seminal work by Mankiw et al., (1992) and further extended in OECD (2001). The model assumes that economic output (GDP) can be estimated using a number of factor inputs to production. In its most basic form it assumes that economic output is a function of a nation’s stock of labour, physical capital, human capital and technological progress.

The model developed for this study focusses on the contribution that the stock of a nation’s human capital makes to economic growth. In this analysis, the stock of human capital is measured through a combination of the level of educational attainment within a country and the aggregate standardised assessment scores. 18 Through this composite measure of human capital, the impact of standardised assessment scores on economic growth is isolated.

3.1.3 Individual level approach

Unlike the cross country approach, which measured variation in test results and economic growth across countries, the individual level approach measures the impact that variations in PISA assessment scores have on individuals’ economic outcomes. The modelling estimates the impact PISA scores have on wages, employment outcomes, and transitions into further education.

Transmission mechanisms

The appeal of the individual level approach lies in the ability to fully consider the various transmission mechanisms through which education can impact economic outcomes. Intuitively, there are four separate stages to the individual level approach. The first three

18 Different tests (across countries and over time) are standardised using a similar method to Hanushek and Woesmann (2009), where means and standard deviations are standardised to a benchmarked level.




stages directly measure the impact of cognitive skills on key transition pathways between education and the labour market. These transition mechanisms include: Greater productivity, manifesting in the form of increased wages.

• Estimated by an ordinary least squares regression with the individual’s observed wage as the dependent variable, and their PISA score, maximum educational attainment and various controls as the independent variables.

• The coefficient on the PISA variable can be interpreted as the direct effect of cognitive skills on wages.

Improved labour market outcomes, including increased prospects of participation and employment.

• Estimated using a probit model () with an employment indicator as the dependent variable and similar controls to those in the wage equation.19

• The coefficient on the PISA variable can be interpreted as the change in likelihood of being employed resulting from increased cognitive ability.

Improved transition outcomes into further study.• Estimated using a multinomial logit model with seven possible categories of

highest educational attainment.20 • The model reflects the extent to which PISA scores, and other control

variables, alter the probability of obtaining various levels of post-school education.

The fourth stage aggregates the estimated individual returns on education quality up to an economy-wide return to education quality that is comparable to the estimates obtained from the cross country analysis.21 This is done through translating the effects on productivity and employment into economy wide effects using a CGE model of the Australian economy (an Australian wide model of the labour force and the economy). The results estimated in the first three stages of the modelling are input as a ‘shock’ to productivity, employment and participation in the Australian economy, and the implications for GDP are estimated.

As with the cross country modelling, the various regression approaches used in this analysis allow estimation of these impacts while controlling for observed demographic and idiosyncratic characteristics contained within the dataset. The results from these analyses can be interpreted as elasticities, demonstrating the effect a 1% increase in PISA scores can have on each of the independent variables modelled.

19 A binary limited dependent variable model reflecting that an individual is either employed or is not20 A limited categorical dependent variable model reflecting that an individual can fall into several discrete categories.21 Results are not directly comparable as the cross country approach estimates the impact of education on growth in GDP, while the individual level approach estimates the impact of education on levels of GDP.




3.2 Data3.2.1 Cross country data sources

Data was obtained from a number of publically available sources, following previous work by Hanushek and Woesmann (2009), Jones (2014), OECD (2001) and Barro-Lee (2010). The data can be categorised into the following areas: Educational outcomes – this data includes cross-country data on educational

achievement from 1964 to 2012. This data is available from a number of sources, as outlined in the table below:

Table 3.1: Educational outcomes data

Abbreviation Study Years coveredFIMS First International Mathematics Study 1964FISS First International Science Study 1970-1971FIRS First International Reading Study 1970-1972SIMS Second International Mathematics Study 1980-1982SISS Second International Science Study 1983-1984SIRS Second International Reading Study 1990-1991TIMMS Third International Mathematics and Science Study 1995-2011PISA Programme for International Student Assessment 2000-2012PIRLS Progress in International Reading Literacy Study 2001-2011NAEP National Assessment of Educational Progress 1970-2012

Educational attainment – cross country educational attainment draws on the dataset developed by Barro and Lee in 2010. This includes attainment at the primary, secondary and tertiary level from a large number of countries from 1960-2010.

Economy growth and other controls – these include standard measures sourced from the OECD and the World Bank.

The countries included in this analysis are provided in Table 3.2 below.




Table 3.2: List of countries

Argentina Czech Rep. Hungary Korea, Rep. Poland SwitzerlandAustralia Denmark Iceland Luxembourg Portugal TaiwanAustria Egypt India Malaysia Romania ThailandBelgium Estonia Indonesia Mexico Russian

Fed.Tunisia

Brazil Finland Iran Morocco Singapore TurkeyCanada France Ireland Netherlands Slovak Rep. UKChile Germany Israel New Zealand Slovenia United StatesChina Ghana Italy Norway South Africa UruguayColombia Greece Japan Peru Spain ZimbabweCyprus Hong Kong Jordan Philippines Sweden

3.2.2 Individual level analysis data sources

For the individual level analysis, the data is drawn from the 2003 and 2006 Longitudinal Survey of Australian Youth (LSAY) cohorts. The dataset includes initial and subsequent observations of: Educational outcomes including school attainment, study status and highest non-

schooling qualification. Labour market outcomes including labour force status, occupation, industry of

employment, hours worked and average wage. Student assessment scores from the PISA data set, including results in mathematics,

science and literacy. A range of individual specific demographic factors, such as parental education and

occupation, which can be used to control for other characteristics which may influence student outcomes.

The LSAY dataset is a survey which is weighted to represent the entire sample of students who sat the PISA examinations in 2003 and 2006, or approximately 25,540 students in both cases.

Cohort Sample Size2003 cohort 10,3702006 cohort 14,170Pooled 24,540

The LSAY dataset also contains observations of an individual’s occupation and industry of employment. These observations are used in this analysis to identify whether returns to education are industry or occupation specific. Findings from this portion of the analysis are particularly relevant in considering the transition to a more knowledge-based economy, and in translating the findings from this backward looking analysis to provide forward-looking estimates of the relationship between schooling outcomes and economic outcomes.




4 Cross country analysisThis section estimates the total returns to cognitive skills gained through education based on a cross-country analysis. The final results estimate the increase to national economic growth caused by a one unit increase in assessment scores. This is the first stage of the modelling discussed in Section 3 – see Figure 4.1. The key results of this section are summarised in Box 1.





Labour force


Labour productivity








Physical capital



GDP

The focus of section 4





Box 1: Key findings of the cross country modelling The first attempt at this analysis (an attempt to examine differences between

countries and over time), yielded no significant results. The key results therefore largely relate to a cross-country country analysis, without an intertemporal dimension.

Maths scores were found to have the largest impact on economic growth, with science scores also effecting growth.

• Reading scores were found to be insignificant in explaining economic growth. A 1% increase in PISA scores is estimated to lead to an increase in GDP growth of

around 0.3%. While the results from the modelling are as expected, and broadly in line with the

findings of previous studies, they should be treated with some caution. • Data limitations suggest that the individual level approach outlined below is

likely to yield more robust findings

While a complete technical discussion is left to Appendix A, this section describes the empirical approach to the modelling, the key results, and provides a discussion of the implications for the Australian economy. The results from this modelling take the form of elasticities, providing a measure of the percentage point increase in economic growth attributable to a one unit increase in assessment scores.

Intuitively, national educational outcomes are assumed to reflect the skills and abilities imbedded in the workforce of a country. Economic theory suggests a nation endowed with a highly skilled workforce will experience accelerated economic growth, as skilled workers are assumed to have a higher productive capacity. Further, skilled workers are more likely to be innovative, enhancing the technological and productivity capabilities of the broader economy.

This approach does not attempt to distinguish the channels through which education influences economic activity (this is considered in the individual level analysis in Section 5). Rather, it looks to capture the entire economic benefits to a nation (as measured by GDP) attributable to schooling assessment scores. In this regard, the results are seen as capturing the benefits of education which accrue to the individual, as well as any spillover effects associated with improving education outcomes.

The interest in undertaking this analysis is in part based on the attention that similar approaches have received in the international literature and debate around the importance of schooling quality. In particular, the analysis here approximately reproduces the work of Hanushek and Woessmann by adopting a similar model and data sources. However, it builds on this analysis by: extending the time period of the analysis up to 2012 (approximately an additional

decade of data relative to the earlier work); identifying potential different model structures and control variables; and exploring the potential relevance of such a cross-country exercise for individual

countries, and in particular for Australia.

The cross country modelling includes two alternative approaches:




estimating changes in economic growth that are attributable to educational outcomes within countries, based on a time series of educational assessment outcomes and economic growth; and

estimating differences in economic growth that are attributable to educational outcomes between countries, based on average economic growth and average educational outcomes over a defined period.

It should be noted that the two approaches are separate attempts to estimate the impact of education on economic growth. The first approach, (identifying differences in economic growth within countries, and over time), is preferred and represents an advancement on the current state of the literature. However, the approach faces significant data issues. In light of these, the second approach (estimating differences between countries) was included to provide viable results in the event the results from the first stage of the modelling were inconclusive.

To reflect this, the results and discussion are divided into two sections. Both sections include estimates for the impact of educational outcomes on economic growth, and include a series of alternate model specifications, reflecting different possible interpretations of the output. The final section explores the implications for economic growth in Australia.

4.1 The empirical modelsModelling outcomes within countries

The first approach to modelling economic growth extends an existing model described in Bassanini and Scarpetta (2001). The structural model adopted in this approach relates economic output per capita to a number of fundamental components, including: capital investment (that is, the change in the capital stock over time); the stock of human capital; population growth; and exposure to trade (included to capture the flow of intellectual capital between

countries).22

An important extension in this work is the development of a new measure of human capital. As part of the measure of human capital, this approach incorporates cognitive ability over time (measured by student assessment scores23) as a determinant of economic growth. In doing so, the modelling results include an estimate of the effect of improvements in student assessment scores have on economic growth.

In general terms, this approach is preferred as it uses observations across selected countries, over time. This produces an estimate of the relationship between human capital and economic output based on educational assessment scores and the corresponding

22 An indicator for whether the country belonged to the OECD was also included in all specifications.23 This involved collecting country-specific data on a number of standardised tests between 1964 and 2012, and then normalising each score according to an OECD based reference group that participated in multiple tests over time. Our approach is broadly similar to the method outlined in Hanushek and Woessmann (2012).




observations of economic growth. The exact specification of this model is outlined in Appendix A.

The fixed effects model of economic growth used here allows the introduction of a time dimension to the analysis, which is considered to more robustly demonstrate the causal links between educational outcomes and economic growth. This is because cross-country studies typically do not account for all country-specific effects that may influence economic growth, while the fixed effects control for these country-specific effects.

Modelling outcomes between countries

Supporting this examination of the determinants of economic growth within countries over time is a modification of the structural model, examining determinants of average growth between countries. This is similar to the approach adopted in Hanushek and Woessmann (2012). While cross-country analysis is considered less robust than a time-series approach (particularly due to issues with confirming the causal nature of the relationship between schooling outcomes and economic growth24), data limitations with the former approach have led both approaches being modelled.

Broadly, this approach is simpler than the panel regression approach outlined for the within-country analysis above, as it relates economic growth to a number of variables likely to affect economic growth, one of which is test scores, and foregoes the temporal analysis of this relationship. Other inputs into the analysis include some key results presented by Hanushek and Woessmann (2012), such as: Average economic growth over the sample period; The initial stock of human capital; and Average cognitive test scores from 1960 to 2012.

Results for this analysis are estimated using an ordinary least squares approach. The key result, an elasticity between educational outcomes and economic growth, is consistent across the modelling approaches. This analysis was extended beyond what was estimated in Hanushek and Woessmann (2012) to include several different hypothesised determinants of economic growth across countries. As noted above, the time period considered for analysis was also extended up to 2012.

4.2 Model results and discussionThe results from modelling cross-country economic growth, as measured by GDP per working age person, is presented in two distinct stages. The first seeks to combine methods introduced in two academic papers25, modelling the relationship between economic growth and student test scores over time. The results in this section reflect changes within countries that cause economic growth.

The second stage presents a replication of the original method outlined in Hanushek and Woessmann (2012), which only considers the relationship between average economic

24 Hanushek and Woessmann (2012) run several tests on their model specifications and do not find any evidence against an assumption of causality, giving a degree of confidence in these results.25 Bassanini and Scarpetta (2001) and Hanushek and Woessmann (2012).




growth and average scores between countries (not accounting for the temporal aspects of this relationship). The results in this section reflect differences between countries that cause economic growth.

4.2.1 Modelling economic growth over time

A number of different econometric specifications were considered for the modelling of economic growth within countries over time. This section presents key results from a set of preferred models relating economic growth to test scores and a number of other potential drivers of growth.

The basic model

Table 4.1 below presents the results from the model of economic growth identified above. As separate test measures were developed for mathematics, science, and reading, separate results are presented relating each separate indicator of cognitive ability to economic growth. The results should be interpreted as the effect on economic growth that is caused by a 1 unit increase in PISA scores.

Table 4.1: Effect of test scores on GDP growth

Maths scores Science scores Reading scoresEffect on GDP growth

0.006% 0.008% 0.059%**

N 1,198 809 922R2 62% 59% 65%

Note ***represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level.

The results above suggest that improvements in cognitive skills for measures of maths and science do not have a significant effect on within country GDP growth. This result could, however, be reflective of limitations in the data, discussed further in the following section. Results from the table below also provide an indication that increasing test scores in reading can have a meaningful impact on economic growth. Further analysis suggests that this result only holds for countries with initially low levels of economic development.26

An observed lack of relationship between growth and cognitive outcomes could be driven by a high degree of correlation between human capital, as measured by educational attainment in the working age population, and test scores. Multicollinearity among observed determinants of growth can inflate standard errors, reducing statistical significance. Re-estimation of the models in Table 4.1 above excluding a measure of educational attainment did not change these conclusions.

The joint effect of PISA scores and human capital

Table 4.2 includes an interaction term between human capital and test scores. This specification implies that the impact of improvements in measures of cognitive ability

26 This positive result for the impact of reading is largely driven by high growth countries with low initial levels of development and low reading scores, namely: Brazil, China, Colombia, Iran, Korea, Malaysia, Peru, and Thailand.




depends on the level of human capital in the economy. In other words, improving the skills and abilities of students affects countries differently depending on the level of skills already imbedded within the workforce.

This specification reverses the results above, with maths and science scores estimated to be positive and significant. Further, the coefficient of the interaction term is negative, suggesting that improvements to cognitive abilities are more beneficial for countries with low levels of human capital.

Table 4.2: Effect of test scores on GDP growth, interaction effect

Maths scores Science scores Reading scoresEffect on GDP growth 0.195%** 0.316%** 0.104%Interaction term -0.088%** -0.145%** -0.019%N 1,198 809 922R2 62% 60% 65%


These results suggest that the current measures of cognitive ability are not capable of explaining differences in an individual country’s rate of economic growth over time. There are a number of possible explanations driving this result. In the absence of broader changes to institutions and the schooling system, improvements in test scores alone may not be sufficient to lift growth. However, as discussed in more detail below, there are a number of limiting issues that contribute to such a result.

4.2.2 Modelling average growth between countries

Although there is little observed relationship between test scores and growth within countries, over time, there is a strong positive relationship between average growth and test scores across countries. The research by Hanushek and Woessmann (2012) indicates that, controlling for other factors, a unit increase in average scores leads to between a 0.01% and 0.03% increase in average annual GDP growth. This section replicates this analysis from that paper.

Growth in GDP per capita (aged 15-64) and maths scores appear to move together. Chart4.1 below illustrates the observed relationship between economic growth and average cognitive ability, taken as a scaled average of test scores, between 1960 and 2014. The dotted line (a nonparametric kernel regression) highlights the degree of nonlinearity observed in this relationship; the three plateaus separate developing nations, developed but slow growth nations, and high-growth high-skills nations (predominantly in Asia). Australia, the UK, and Canada all have similar test sores and average growth rates of between 6% and 8%.

Countries that lie above the dotted line in Chart 4.1 have grown at a rate above what might be expected for their average test scores. However, there are a number of additional




factors that confound this observed relationship. The results below attempt to estimate the relationship while controlling for these factors.

Chart 4.1: The relationship between maths scores and growth

4%

6%

8%

10%

12%

200 300 400 500 600

GDP growth (1960-2014)

Average maths score (1964-2012)

Australia

China

SingaporeKorea

Macao

Estonia

CanadaUnited Kingdom

Hong Kong

Japan

South Africa

Ghana


The basic model

Table 4.2 below presents the estimated relationship between cognitive ability, as measured by scores in maths, science, and reading, and the average rate of GDP growth. The model is based on the specification reported by Hanushek and Woessmann (2012), and controls for GDP per capita and human capital in 1960. The results suggest a strong positive relationship, with a one unit increase in test scores being associated with a 0.01 percentage point increase in GDP growth over the period.

Table 4.2: Effect of average test scores on average GDP growth, 1960-2012


0.012%*** 0.012%*** 0.010%***

N 41 41 39R2 73% 68% 51%

Note***represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level.




These results are broadly consistent with those reported in Hanushek and Woessmann (2012). Although the coefficients are significant and positive, as expected, the estimates are approximately half the size of those reported there. This may be driven by a number of factors, discussed in more detail below. However, it is likely that differences in the sample size and measurement of cognitive ability are substantial drivers of the observed disparity in estimates.

Adding additional controls

In addition to the controls included above, the results presented in Table 4.3 below include a number of additional variables. These variables, despite being excluded from Hanushek and Woessmann’s specification, have been included elsewhere as having a possible impact on economic growth. These include: population (in 1960); the average exposure to trade over the period; the proportion of time each country spent as an OECD member; and indicators for the eight world regions specified in the original paper.

The estimated effect of cognitive skills on GDP growth are provided in the table below.

Table 4.3: Effect of test scores on GDP growth (additional controls), 1960-2012


0.010%*** 0.010%*** 0.007%***

N 41 41 39R2 84% 83% 78%


The joint effect of PISA scores and human capital

A final set of results test the effect of including an interaction term between the measures of cognitive ability and the initial level of human capital (Table 4.4). This specification implies that the impact of improvements in measures of cognitive ability depends on the level of human capital in the economy. In other words, improving the skills and abilities of students affects countries differently, depending on the level of skills already imbedded within the workforce

The estimated negative sign on the interaction term implies that, above a certain level of human capital, further improvements to test scores suppress economic growth.




Table 4.4: Effect of test scores on GDP growth (interaction effect), 1995-2012


0.063%*** 0.045%** 0.007%

Interaction term -0.021%* -0.012% 0.010%N 56 56 52R2 80% 78% 80%


As Table 4.4 above shows, PISA maths scores are found to have the largest impact on growth, with a one unit increase in scores leading to a 0.06% increase in economic growth. Science scores have a slightly lower effect on growth, (while remaining significant at the 1% level). In contrast, reading scores are also estimated to have a positive effect but are not significantly different from zero.

Noting that the average PISA math score for Australian students is 524, a one unit increase therefore reflects a 0.19% increase in scores. Hence, a 1% increase in test scores equates to an estimated increase in economic growth of around 0.33%. This estimated increase in economic growth due to a 1% increase in PISA scores is comparable to the results from the individual level analysis in Section 7.

The result observed above is consistent with specifications tested in the within country analysis in the previous section. Namely, improvements in growth resulting from increases in test scores alone are feasible only for less developed nations. Chart 4.1 below plots this relationship separately for OECD and non-OECD member nations. There is a relatively strong positive relationship between scores and growth for countries that have never belonged to the OECD. This relationship breaks down, turning negative, for OECD nations.




Chart 4.1: Relationship between growth and test scores, OECD vs Non-OECD

0.0

5.1

.15

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Results presented in Table 4.5 below indicate that the strong observed relationship between test scores and economic growth only holds for countries who were not members of the OECD between 1995 and 2014. The estimated effect of increasing scores on GDP growth is significant and positive for non-OECD member countries. For those countries that joined the OECD at some point over the period, the estimated effect is negligible.

Table 4.5: Effect of test scores on GDP growth (OECD), 1995-2012

Maths scores Science scores Reading scoresEffect on GDP growth (non-OECD)

0.023%*** 0.025%*** 0.022%***

Effect on GDP growth (OECD) -0.0003 0.0004 0.0071N 56 56 52R2 80% 80% 79%

Note: * denotes significance at the 10 per cent level; ** at the 5 per cent level; and *** at the one per cent level.

The analysis presented above indicates that, on average, cognitive ability is a significant driver of the average growth rate between countries. The set of results presented in Table4.5 above suggest that, on average, a ten point increase in standardised test scores was associated with a 0.2 percentage point increase in the annual rate of GDP per capita growth between 1995 and 2012.

However, further analysis suggests that this relationship only holds for non-OECD member nations. This could indicate various institutional factors that are correlated with both test




scores and OECD countries are excluded from the analysis. Alternatively the relatively small sample size of countries used in this analysis may be driving a relationship observable in the data for these countries, but not representative of a causal relationship.

4.3 Limitations and assumptionsModels of economic growth rely on a number of simplifying assumptions, each having implications for the estimated relationship between cognitive ability and growth. As the analysis distinguishes between models that explain growth within countries and over time, from those that explain average growth between countries, this section discusses separately the assumptions and implications from each set of models.

Modelling economic growth within countries, over time

A key limitation of models relating growth over time to cognitive ability is the imperfect measurement of test scores. Standardised tests were taken infrequently and inconsistently prior to 1995, requiring interpolation between non-testing periods. For a number of countries, there is little meaningful variation in scores over time. Based on the interpolated sample of test scores, approximately 94% of variation in maths scores is between countries, leaving only 6% of the total variation occurring within countries over time. 27 From a modelling perspective, this introduces a degree of measurement error which, under certain assumptions, will bias the coefficient of cognitive ability toward zero.

An additional concern regarding the measurement of cognitive ability is the likely lag between PISA assessment scores (which are assessed at 15 years of age) and the skills of the workforce. The current assumption is that current test scores reflect the average level of ability in the workforce. But in reality, the effect of improvements in test scores occurs with a lag, corresponding to upskilling as current students enter the future labour force, therefore undermining the observed relationship between current scores and growth.

Given the absence of long-term measurement for a substantial set of countries (students who participated in the 1964 FIMS left the labour force in 2014), it was not possible to relate test scores to individual cohorts currently participating in the labour force. A number of lag specifications were considered (none of which were significant), however, there is no theoretical justification for an arbitrary lag structure to be imposed.

Models of within country growth rates in GDP per working age person further assume that the unobserved determinants of growth are unrelated to explanatory variables. In the case of test scores, this assumption could be violated if unobserved determinants of growth permit additional expenditures on education that lift test scores. The majority of investment in school education is a prior commitment, hence, conditional on past GDP this mechanism should be exogenous. In addition, models that control for educational investment do not imply substantially different results.

Modelling average growth between countries

27 A number of specifications of country specific heterogeneity were testing, including a country-specific random effect. Key results were not sufficiently different from the fixed country effect specification of heterogeneity.




Without an ability to control for country and time specific heterogeneity affecting economic growth (such as longer term cultural and institutional factors or global shocks), models relating average annual growth rates to average test scores over time require several additional assumptions.

It is likely that, by averaging economic growth and test scores over time, the effect of mismeasurement and transitory shocks is largely eliminated. These models relate longer run cognitive ability to average annual growth rates, suggesting that only long run drivers are growth should be included in each model.

Unobserved country-specific factors such as cultural differences and institutional quality, could have strong impacts on both observed test scores and economic growth. As these factors cannot be explicitly included in the modelling, it is possible the effect of test scores on economic growth is overstated. The models include the initial level of GDP per capita at the beginning of the sample period, which is designed to serve as a proxy for factors such as culture and institutional quality.

However, while it can capture these effects at a single point in time, this is unlikely to capture the dynamic relationship between growth and the ability to invest in education. The relationship between economic growth and assessment scores may be overstated to the extent that high growth countries re-invest in education (and therefore improve educational results). As noted above, the inclusion of education spending does not substantially change these results.

Hanushek and Woessmann (2012) give a substantial consideration to this issue, with their main results remaining unaffected by any adjustments to the modelling.

4.4 Implications for AustraliaResults presented in the previous section suggest that, on average, improvements in cognitive ability can lead to strong increases in a country’s long-term growth rate. Although further testing suggests that shorter term results may be limited to less developed nations with more flexible institutional structures, the following section presents the implied impact of an improvement in test scores on the Australian economy.

The relationship between test scores and economic growth is assumed to be represented by the estimated coefficient in the Maths column of 4.2.2Box 4.1Table 4.3 above. The use of this estimate reflects the average relationship between growth and scores for countries over the entire sample.28 This is in line with OECD modelling based on results presented in Hanushek and Woessmann (2012).

The simulated trajectory of cognitive ability, as measured by maths scores, is based on previous trends in Australian test scores. However, the impacts on economic growth are not sensitive to assumptions about the growth rate of test scores, and instead are driven by the implied deviation between this baseline and the new higher level. As illustrated in Chart4.1 below, Australia is assumed to experience a permanent 1% increase in test scores in 2016, and then continue on the previously assumed growth path. The impacts on economic

28 Models estimated over a longer time frame (1960 to 2012) are likely to be more appropriate to the Australian situation, as a means of structural change is assumed when simulating growth in cognitive ability.




growth are driven by the deviation in implied cognitive skills between the blue (baseline) and green (simulation) lines.

Chart 4.1: Simulation growth in maths scores

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Baseline SimulationAverage maths score

The impact of an immediate jump in average scores across Australia has a longer term, cumulative, impact on economic performance. As the permanently higher level of scores increases growth in each period, GDP per capita would grow more rapidly in every period. Indeed, a 1% increase in PISA scores now is estimated to lead to an increase to GDP per capita of 5% by 2076 once the effects are fully phased into the workforce.

This result implies that even small increases to cognitive ability yield important and large economic returns in the long run under this modelling approach. As the results presented above suggest, there are a number of substantial issues that affect this conclusion.

The observed relationship between test scores and economic growth does not hold in the model of within country growth over time. It is possible that the poor measurement of cognitive skill development in countries over time is reducing these models’ significance; however, further analysis suggests that improvements in scores alone may not be sufficient to lift the long-term rate of economic growth.

Models of the average rate of economic growth between countries implied that only in non-OECD nations were improvements in test scores sufficient to increase growth rates over a twenty year time frame. It is possible that, in the medium-run, the impact of cultural and institutional factors on economic growth outweigh any effect of improvements in cognitive ability.




5 Individual level analysisThe individual level analysis considers the various transmission mechanisms through which education influences the economic outcomes of an individual. The modelling captures the impact an increase in PISA scores has on educational attainment, wages, and the likelihood of finding work – the first part of the individual level approach discussed in section 3. This is illustrated in Figure 5.1, with the key findings summarised in Box 2.






Labour force


Labour productivity








Physical capital



GDP





Box 2: Key findings of the individual level analysis An increase in PISA scores across all domains improves the likelihood of completing

high school, or obtaining a university qualification. Focussing only on maths scores, a 1% increase in PISA scores increases the likelihood of completing high school or obtaining a university qualification by 0.1% and 0.5% respectively.

A 1% increase in maths PISA scores is associated with a 0.09% increase in the wages an individual receives in the labour market. An equivalent increase in science PISA scores increases wages by 0.05%.

• This effect is compounded as PISA scores increase the likelihood of obtaining a university qualification, which also increases the wages an individual receives in the labour market. When both these effects are combined, a 1% increase in maths (science) scores is estimated to lead to a 0.12% (0.09%) increase in wages.

• Increases in maths scores have the strongest impact for high achieving students. In other words, the impact an increase in PISA scores has on wages grows larger as PISA scores increase.

An increase in PISA scores across all domains increases the likelihood of an individual finding work. A 1% increase in maths scores increases the likelihood of finding work by 0.07%.

• However, PISA scores have no effect on whether or not an individual participates in the labour force.

5.1 Introduction This section estimates the private returns to cognitive skills gained through education, based on a longitudinal dataset containing individual test scores and later labour market outcomes. While a complete technical discussion is left to Appendix B, this section describes the analytic approach and presents the key findings. The results take the form of elasticities, providing a measure of the percentage gain in wages, employment prospects and post-school educational attainment from a 1% increase in test scores.

There are four separate stages to the approach that map the process from higher cognitive skills through to aggregated economic outcomes at the national level. The first three stages directly measure the impact of cognitive skills on key transition mechanisms between education and economic outcomes. The fourth aggregates the estimated individual returns to an economy-wide return to education quality that is comparable to the estimates obtained from the cross country analysis. This is demonstrated visually in Figure 5.1. First, the impact of PISA scores on educational attainment is modelled, capturing

the impact of assessment scores on the likelihood of obtaining different qualifications. This is also used to estimate the indirect impact of schooling quality on wages through the education attainment channel.

Next, the direct impact of PISA scores on wages is modelled, with this capturing the direct link between improved cognitive skills and productivity once in the workforce, controlling for attainment and other factors.

Finally, the impact of PISA scores on employment outcomes is modelled.




The individual results for a 1% increase in PISA scores are then aggregated to an economy-wide impact using a CGE model of the Australian economy (covered in section 7).

Figure 5.1: Four stages to the individual level modelling

Benefits for the economy

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Higher wages

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Section 5.2

Section 5.3

Section 5.4

Section 7

This section explores each of these transitions in turn and concludes with an overview of the results from the modelling. Ultimately, the total impact of improved education outcomes will be a function of all three transition stages, with education impacting wages directly through the productivity impact of improved cognitive skills, and indirectly through its impact on post-school educational attainment and employment outcomes.

5.2 The impact on educational attainment5.2.1 Overview

Improving the cognitive ability of school students increases their potential to be accepted into further education courses. For students on the cusp of receiving a tertiary education, improving their knowledge and skillset (and through this their ATAR score) facilitates the transition to university. This relationship shows up clearly in the LSAY dataset, with Chart5.1 demonstrating the increased propensity of students who achieve better PISA results to obtain an undergraduate degree or higher. The chart also shows that increasing PISA scores increase the likelihood of obtaining a minimum of a year 12 qualification.




Chart 5.1: Educational attainment

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Source: LSAY 2003, 2006; DAE calculations

Of course, the true value of improving PISA scores in assisting students to obtain higher educational attainment is more nuanced than a simple correlation as presented above. There are numerous confounding factors which may impact on PISA scores and educational attainment. In particular, cultural and family influences play a large role in driving an individual towards certain educational pathways, the value of which might be erroneously attributed to PISA scores under the simple correlation displayed above. The regression analysis in this section provides an assessment of the impact improving PISA scores on educational attainment by controlling for these confounding factors.

The first stage of the modelling therefore involves modelling the impact of PISA scores on educational attainment, controlling for other factors that may influence educational attainment. The method for estimating the effect of PISA scores on educational attainment is a multinomial logit model, which estimates the likelihood of achieving one of seven discrete outcomes, dependent on one’s PISA scores. A more detailed overview of the multinomial logit model is contained within Appendix B.

Most simply, the results from the modelling estimate the probability of a student obtaining a certain educational outcome, given the idiosyncratic characteristics of that student. In this instance, the educational outcomes modelled are: not completing high school; a high school qualification or equivalent; a certificate I or II; a certificate III or IV; a diploma or advanced diploma; an undergraduate bachelor degree; or a postgraduate qualification (including graduate certificates and graduate diplomas),

compared to a base outcome of no qualifications.




5.2.2 Results

Table 5.1 contains a summary of the results linking PISA scores to the propensity to attain post-school education. For simplicity, the table shows only the impact of PISA scores on each level of educational attainment, and not the estimated coefficients for the control variables used. The full results with all estimated covariates are provided in Appendix A.

Each estimate should be interpreted as the increased likelihood of obtaining that level of educational attainment due to a 1% increase in PISA scores, relative to obtaining no further qualification.29 For example, the first cell below indicates that for the average person, a 1% increase in maths assessment scores will result in a 0.146% increased likelihood of that person obtaining a high school qualification.30 All results are significant at a 1% level.

Table 5.1: Summary of results on educational attainment

Math scores Science scores Reading scoresHigh school qualification 0.146*** 0.115*** 0.131***Certificate I or II -0.214*** -0.183*** -0.204***Certificate III or IV -0.264*** -0.212*** -0.241***Diploma or advanced diploma -0.096*** -0.095*** -0.095***Undergraduate degree 0.539*** 0.462*** 0.512***Postgraduate degree 0.053*** 0.054*** 0.053***

Note: ***represents significance at the 1% level.Full results for the model are provided in Appendix B.

All three domains of PISA testing (maths, science and reading) are found to have a broadly comparable impact on educational attainment. This implies all disciplines play a relatively consistent role in supporting students to transition into better post-school outcomes. The impact of maths scores marginally outweighs the other two, suggesting this could be discipline to focus attention to enhance post-school outcomes.

An increase in PISA scores has a different effect on the probability of obtaining each different educational attainment. For example, increasing PISA scores increases the likelihood of completing high school or obtaining a university degree, but decreases the probability of obtaining a VET qualification, a diploma, or an advanced diploma.

Finally, the propensity to obtain an undergraduate degree was most susceptible to changes in PISA scores. This suggests improving PISA scores has a relatively strong impact on opening pathways into higher education.

29 Since logit models are non-linear, these coefficients will not be constant across all individuals. Given this, the typical approach, and that taken here, is to report the estimated coefficients for the ‘average’ student. This student takes the sample average for each of the independent variables in the regression.30 Due to the high levels of correlation between PISA achievement scores, they are not additive. For example, increases in maths, science and reading scores by 1% will not increase the probability of completing high school by 0.392% (0.146+0.115+0.131).




5.2.3 Discussion

The effect that an increase in PISA scores has on the probability of obtaining a given qualification is consistent with the role each qualifications plays within the education system. For example, PISA scores have a positive impact on the likelihood of obtaining a high school qualification. Intuitively, this is consistent with prior expectations, as improving performance at school will help maintain student engagement with secondary school, encouraging completion.

Similarly, the results also suggest that higher PISA scores lead to an increase in the likelihood of obtaining a university degree, and that the impact is larger on obtaining an undergraduate degree than on any other qualification type. Both results are consistent with a previous NCVER study, which finds university to be a dominant decision for young people. That is, given a choice, students would prefer to attend university over other post-school study options (Karmel et al, 2014). Typically, a student’s inability to attend university is a result of minimum entrance requirements and their lack of knowledge relative to peers. If an improvement in PISA scores reduces these barriers to entry, students are more likely to take the opportunity to attend university.

The increased propensity to enter university is balanced by the lower propensity for attainment of VET study as the highest qualification received. This reflects in part that VET completers (particularly those obtaining Certificates I, II and IV) often use VET as a pathway to further study rather than a direct pathway to employment (Sherman, 2006). Similarly, VET is also seen by many as an educational alternative to secondary school (McMillan et al, 2005). Hence, some of the fall in VET completion may reflect students staying on at school, with year 12 completions increasing with PISA scores, as well as the increased transition to university study.

Finally, while all PISA were highly significant in determining attainment outcomes, the high correlation between them makes it difficult to ascertain which of these is in fact the causal factor. For example, it may be that maths scores are the most important determinant of further education outcomes, but that the correlation of both reading and science with maths scores shows up in those domains also being significant.

5.3 Modelling the impact on wages5.3.1 Overview

Within the LSAY dataset, there is an observable relationship between PISA scores and higher wages (see Chart 5.1). As with the impact of PISA scores on educational attainment, this relationship is potentially overstated in the chart below, as there are several confounding variables which influence both PISA scores and wage outcomes. In modelling the impact of PISA scores on wages, it is therefore important to control for these confounding factors through the regression analysis outlined below.




Chart 5.1: Average wages and PISA scores

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A more complete discussion on the causal link between cognitive skills and wages was provided in Section 2.3 above; however, in essence the question is whether the higher test scores or qualifications actually increase the productivity of workers or are merely a signally device for employers. Under the latter case, test scores affect wages through an individual’s ‘ordering’ within the labour market, with their job position simply displacing an existing worker within a fixed suite of positions.

However, as noted in Section 2.3 the there are several reasons increasing the skill of workers has been found to create a more productive workforce, and lifts the wage-bill economy wide. First, the supply of jobs has historically proved to be malleable, such that if more skilled workers transition into the workforce, industries and jobs will develop to accommodate their skillset (Borjas, 2004).

Second, studies such as Moretti (2004), as well as the cross country analysis described in Section 4, show significant spillover effects of skilled workers such that aggregate economic activity increases with the skills of the workforce. In other words, empirical evidence indicates a more skilled workforce has a positive influence on economic activity and wages throughout the economy (including for non-skilled workers).

Overall, the evidence supports the theory that PISA scores create more productive and therefore better paid workers, rather than simply acting as a signalling mechanism. Of course, PISA scores are not the only factor is determining income, and this analysis is designed to isolate the impact academic assessment scores have on wages while controlling for other observable factors.

The method used for estimating the effects of PISA scores on wages is a pooled OLS regression that utilises both the 2003 and 2006 LSAY datasets. The technical details of this analysis are provided in Appendix B.




5.3.2 Results

The results demonstrating the relationship between PISA scores and wages are provided in Table 5.1. Maths and science scores were statistically significant (at a 1% and 5% level respectively), while reading scores were found to have the expected sign, and a magnitude similar to that of science scores, but were not statistically significant. The table below includes only the estimate of the coefficient on the wage variable and does not include parameter estimates for control variables. A full list of results is available in Appendix B.31

The impact on wages is reported in two ways: the direct effect – the coefficient on the PISA variable alone, capturing the direct link

between cognitive skills and later wage outcomes; and the indirect effect – capturing the effect that PISA scores have on wages by improving

the likelihood of obtaining a higher post-school education attainment.

The distinction between the two effects is perhaps clearest by considering the estimating equation used. The equation estimates how the logarithm of wages is affected by PISA scores, educational attainment (as measured by the seven categories outlined above) and other control variables (Z). The direct effect of PISA scores is the coefficient on the PISA variable (β1) while the indirect effect is measured through the effect of PISA scores on attainment reported in the previous section, and then through the effect of this attainment on wages as estimated below. Details of the method for linking the attainment and wage models are provided in Appendix B.

lnwage ¿=β0+β1 lnPIS A i+∑j=1

7

δ j Education jit+β Z¿+ϵ ¿

The table below provides the results from this modelling for a 1% increase in PISA scores through both the direct, indirect and combined overall result.

Table 5.1: Summary of the results of the wage equations

Maths scores Science scores Reading scoresDirect effect 0.0863*** 0.0527** 0.0446

Indirect effect 0.0382*** 0.0349** 0.0391Overall effect 0.1245*** 0.0876** 0.0837

Note: ***represents significance at the 1% level; ** represent significance at the 5% level.

The findings for maths and science scores are consistent with economic theory, with the results indicating a 1% increase in maths scores translating into a 0.12% increase in wages, while a 1% increase in science scores will translate into a 0.09% wage increase. The impact of reading scores is comparable to that of science scores, but was found to be statistically insignificant at a 10% level.

Across all three domains, the direct effect of assessment scores on wages dominates the indirect effect. In other words, improving the skills and abilities learned at high school was

31 As with the results presented above, the results in this table are not additive. For example, increasing maths, science and reading scores by 1% will not result in an increase to wages of 0.296% (0.1245+0.0876+0.0837).




found to directly improve an individual’s income upon entering the workforce to a greater extent than the effect on wages through the education attainment term.

Cohort analysis

By separating the LSAY data by PISA score quintiles it is possible to analyse the effect of increased cognitive skills for different cohorts of PISA scores. The interest in this lies in testing whether lower (or higher) performing students may benefit more from improvements in assessment scores. Similarly, different disciplines (in particular, the foundational skills provided by reading), may be of particular importance to poorly performing students.

Two separate approaches are taken to test for whether the effects of increased cognitive skills vary across performance levels: first, a square of the PISA term is added to the standard equation above to test for non-

linearity in the effect. A negative coefficient of the squared term would indicate that there are diminishing returns from increasing PISA scores; and

second, students are grouped into PISA quintiles, with an indicator variable used to differentiate the effects across quintiles.

The results presented in Table 5.2 show the estimated coefficients after including the squared term. Full results, and the results from segmenting the population into separate cohorts, are shown in full in Appendix B.

Table 5.2: Summary of the cohort wage equations

Maths scores Science scores Reading scoresPISA score -3.467*** -0.702 -0.492PISA squared 0.288*** 0.062 0.043

Note: ***represents significance at the 1% level. All other results are insignificant at the 10% level.Full results of the modelling are shown in Appendix B.

For science and reading scores, the impact of PISA scores was found to be insignificant at a 10% level when a non-linear term was added. The effect on maths scores indicates that improving maths scores is more beneficial for the wages of high performing students, as the squared term has a positive coefficient.

The marginal effect of maths scores on wages for different PISA quintiles is shown in Table5.3 and Chart 5.1 below.

Table 5.3: The impact on wages of varying maths scores

Ranking Maths score Marginal effectLowest 10% 402 0.0181Lowest 25% 459 0.0588Median 523 0.1346Top 25% 586 0.2005Top 10% 640 0.2517




Chart 5.1: The impact on wages of varying maths scores

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Source: LSAY 2003,2006; DAE calculations

These results indicate that if a student has a maths score of 402 (in the lowest 10%), increasing their score by 1% will only improve wages by 0.018%. However, if a student has a maths score of 640 (the top 10%), improving their score by 1% is estimated to increase wages by 0.252%. In other words, the impact of improving PISA scores on wages is much stronger for those with an already high level of cognitive ability.

Importantly, for scores below 459 (the lowest 25% of scores), the marginal effect of maths scores on wages is approximately lower than the estimated marginal effect of reading scores (from Table 5.1, above). In other words, for low performing students, the increasing reading scores is found to have a larger impact on wage outcomes than increasing maths score – consistent with the hypothesis that reading is important as a foundation skill, upon which further skills can be built.

5.3.3 Discussion

This section has focussed on the second of the transmission mechanisms through which schooling influences economic outcomes: enhancing labour productivity. Schooling was found to have significant impacts on wages and (as more productive workers are assumed to receive higher wages), labour productivity, but not all areas of schooling were found to have an equal impact. The interpretation of the results and the policy implications are discussed below.

Consistent with economic theory, an improvement in PISA scores reflects an increase in skills and abilities and is rewarded with higher wages in the labour market. The positive relationship between schooling outcomes and economic outcomes has been found in other studies in an Australian context. For example, Leigh (2008) and Wilkins (2015) both use HILDA data to find a positive relationship between educational attainment and wages.




These studies typically find larger estimates for the impact of education on wages. Leigh estimates that completing Year 12 increased wages by 23%, while Wilkins estimates the wage benefits to completing Year 12 are between 15% and 19%. As this analysis focusses on the quality of education, rather than attainment, direct comparison between results is complicated.

Further, the analysis in this report is able to make use of rich controls available through the LSAY data set (such parental income and occupation), allowing factors likely that drive both educational and economic outcomes to be controlled for. Overall, while estimates of the effect of education will differ in magnitude due to the approach taken, the significant and positive impact of educational assessment scores on wages suggests a general consistency with existing work.

The modelling considers the average impact of increasing PISA scores. It may be the case that an improvement in PISA scores is not reflected in higher wages for each individual. This can occur if an individual’s preferences lead them into a career that does not reward an increase in their cognitive skills. The results should therefore be considered as the average impact across the workforce rather than explaining the behaviour and outcomes for each individual.

Perhaps somewhat surprisingly, after controlling for individual and schooling idiosyncratic factors, only maths and science PISA scores were found to have a significant effect on wage outcomes. While the policy implication from this suggests an important avenue to improving the productivity of the workforce lies in enhancing maths and science related skills, this does not imply that English can be neglected. Reading skills are typically more foundational32, and while improving reading skills may not be an indication of enhanced productivity, it is likely this lays the foundation for improvements in maths and science outcomes. Further, while not statistically significant, the coefficient estimates for reading were broadly similar to those of science.33

Interestingly, the direct effect of an improvement in PISA scores has a stronger effect on wages than educational attainment. After controlling for PISA scores, educational attainment indicator variables become statistically insignificant, except for the coefficients on undergraduate and post-graduate degrees. The results suggest that developing cognitive skills and knowledge in high school is key to later economic outcomes.

This result differs somewhat from findings in Leigh (2008), and Wilkins (2015), who both find positive wage impacts to educational attainment across all educational levels. However, neither of these studies consider school test scores as a control for potential correlations between cognitive ability and educational attainment. By introducing PISA scores (as well as additional control variables), it is expected that the relationship between educational attainment and wages will be somewhat more muted in this analysis.

The next area of analysis considered whether or not the effect of improving PISA scores was different depending on the performance of the student. This was found to be true only for

32 It could be the case that as reading is a more foundational skill, it is more closely correlated with the other control variables, and therefore appears to be less statistically significant. 33 Further, it should be noted the analysis in section 4 found reading scores to have a significant impact on economic growth in developed economies.




maths scores. The analysis revealed that improvements to high performing students are better rewarded in the labour market than comparable improvements in their lower performing counterparts.

Crucially, this does not imply that policy should focus on improving results for currently high performing students only. First, there are important social and equity rationales for seeking to raise the performance of the lower end of the distribution. Further, this analysis considers the return from a given increase in PISA scored without considering how this increase may be achieved. That is, the ability for school policy to impact outcomes at different ends of the distribution, informed by evidence-based decisions about what works, should ultimately drive policy decisions.

Overall, these findings imply there are significant wage benefits to improving educational outcomes, and these benefits impact the individual through the two key transmission mechanisms identified earlier (the direct and indirect effects). Further, while improving maths scores will (on average) result in the strongest productivity gains, focussing on reading scores for lowest achieving students (the bottom 20% of scores) may be important.

5.4 Modelling the impact on employment5.4.1 Overview

The third stage of the modelling looks at estimating the effect of PISA on the likelihood of improving employment outcomes. This focuses on a third transmission mechanism through which PISA scores can affect economic outcomes, whereby demonstrating greater knowledge and stronger basic skills increases the likelihood of an individual finding work. This can be seen, prima facie, from the LSAY data, which indicates that those with higher PISA scores are more likely to be employed at any age after completing their studies (see Chart 5.1).

Chart 5.1: Probability of employment

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PISA scores are, of course, not the only determinants of employability. While the modelling controls for individual characteristics that can influence the probability of employment before the age of 25, it does not take into account structural factors that influence an individual’s ability to find work. These include: unemployment benefits; an individual’s value of leisure time; search costs; and labour market regulation such as restrictions on the hiring and termination process.

While these are important in understanding labour market dynamics, it is assumed that these factors are uncorrelated with PISA scores and will therefore not affect the estimates obtained from this analysis.

In modelling employment outcomes, all individuals in the sample are split into one of two categories: employed or unemployed. Those not in the labour force are excluded from the sample.34 Given the nature of the data (being binary and categorical), the model selected to estimate the relationship between PISA scores and employment was a probit model.35

5.4.2 Results

Results from the probit equation are shown in Table 5.1 below. The results can be interpreted as the increased likelihood of the average individual in the LSAY sample being employed if they were able to improve their PISA assessment scores by 1%. For example, the figure in the first cell of the table implies that if the average student is able to increase their PISA score by 1%, their likelihood of being employed at any stage post-study improved by 0.07%.36

Table 5.1: Summary of the employment equation results

Maths scores Science scores Reading scoresEffect on employment 0.0736*** 0.0462*** 0.0538***

Note: ***represents significance at the 1% level.Full results of the modelling are shown in Appendix B

The results are as expected: improving PISA assessment scores across all three domains will increase the likelihood that a student makes a successful transition into employment. As with the wage equation, the effect of maths scores is larger than the other disciplines.

Similar analysis was undertaken to estimate the likelihood an individual chooses to participate in the labour force. However, it was found PISA scores were statistically

34 The impact of PISA scores on labour force participation was also estimated, but the results were less conclusive. These results are included in Appendix B35 Probit models are typically used to estimate the probability that an observation with particular characteristics will fall into one of two categories. In this instance, the probit model estimates the probability of an individual being employed, based on their PISA scores and other characteristics.36 As with the results presented above, the results in this table are not additive – for example, increasing maths, science and reading scores by 1% will not result in an increased likelihood of employment of 0.174% (0.0736+0.0462+0.0538).




insignificant across all domains, meaning there were other, more significant factors which encourage students to participate in the workforce.

5.4.3 Discussion

This section explored the final transmission mechanism though which schooling can affect economic outcomes – by improving the likelihood that a student participates in the workforce, or is able to find employment given they already participate. Schooling was found to only influence the employment pathway, with other factors largely explaining an individual’s decision to participate in the labour force.

The first area investigated, the effect PISA scores have on employment, was found to be significant across all domains. This is consistent with the findings into the drivers of transitions into post-school education – that increasing any of maths, science or reading skills improves the likelihood of a successful post-school transition. Intuitively, this suggests that students with stronger skills and abilities are more attractive to employers. Results indicate that employers seek to employ those demonstrating higher levels of skills across all three disciplines, although these skills are remunerated at different rates.

This analysis is inherently individualistic and does not analyse whether improving cognitive skills of the workforce alters the structure of the economy (that is, the suite of jobs available to workers). Under the hypotheses here, improving education increases labour productivity, and therefore the demand for labour, and subsequently the number of jobs available, should also increase.

This analysis does not consider whether there would be sufficient jobs to absorb more workers if the population as a whole was to increase cognitive skills. However, as discussed in section 2.3, there is a large body of research which suggests a more productive workforce will stimulate demand for more workers. In other words, the labour market is more likely than not to be able to absorb the supply of more high-skilled workers with a net increase in employment.

A second, somewhat related question is consideration of the efficiency of the labour market. In particular, the analysis does not consider how PISA scores impact the match between the skills of workers and the skill requirements of the firms offering jobs.

Despite remaining silent on labour matching, these findings do not undermine the importance in developing skills demanded by the labour market. Indeed, the need to learn job-specific skills to enhance labour market efficiency is clearly articulated in the literature, and increasing PISA scores without developing skills in demand from employers is unlikely to improve prospects of employment. To ensure the likelihood of employment improves with increasing PISA scores, an increase in PISA scores needs to be complemented by job-specific training.

The second investigation into the effect of PISA scores on labour force participation found that PISA assessment scores did not have an impact on an individual’s decision to join the labour force. While initially surprising, an investigation into the results simply found that a number of other factors (such as whether or not the individual had a family, parental wealth and whether or not the individual lived with their parents) had a significantly more dominant effect on an individual’s decision to participate in the labour force. On reflection,




this makes sense – even low performing students need to look for work to support themselves. Improving PISA scores may improve their chances of finding work, but there is no relationship between improving PISA scores and looking for work.

While this is a reflection that improvements in maths, science and reading scores do not determine whether someone participates in the workforce, it does not imply that schooling will not have a role to play in encouraging people to participate. It is likely non-academic skills that can be taught through school (such as attitude towards working, engagement with society and civic responsibility) will have larger impacts on the participation rate.

5.5 Limitations and assumptions behind the individual level modelling

In endeavouring to separate and quantify each of the mechanisms through which education works on labour market outcomes, a number of simplifying assumptions have been made. These assumptions are generally grounded in findings from economic theory or literature, and do not detract from the overall results in a material manner.

There are, however, some factors that may limit the robustness of the findings. While the richness of the data used, and the sophistication of the econometric techniques involved throughout this section should serve to reduce the uncertainty of these findings, this sub-section aims to give an overview of limitations associated with the results. A more technical discussion on some of these limitations can be found in Appendix B.

Data cleaning

As part of the estimation process, it was determined that not every observation could be included in the specified equation at every time period. Wage observations were only included if the individual was not simultaneously studying (high school, vocational training, or tertiary education) as it was assumed that while studying, wages are not a reflection of cognitive ability. These wages were assumed to be temporary, until they completed their study.

The LSAY datasets contain students who are still studying during the final wave of surveys at age 25. If they have studied throughout their post-school life, and not yet entered the workforce, they are excluded from the sample, as they have no valid wage observations. In doing so, it is assumed there is no increase in returns to education after studying for that length of time. In other words, it is assumed that studying for more than ten years will not produce superior wage outcomes.

Given the approach relied on pooling a cross-section of data, removing individuals while they are studying raises the risk that individuals who finish studying earlier are over-represented in the sample.37 To resolve this issue, the approach taken accounts for the fact that an individual’s wage is likely to be (in part) explained by their wage in previous

37 While the LSAY dataset is includes a time series of data for each individual, the wage equation has not been modelled using a fixed or random effect estimate. This would have made it impossible to measure the effect of time invariant variables, such as PISA scores, which is the key variable in our investigation. Rather, all observations were pooled into a large cross-section of data.




periods. Once this is accounted for, each observation is assumed to be fully explained within the model, ensuring the estimated equation is unbiased.

Attrition rates

A final issue with the data was the high rates of attrition within the LSAY dataset. While the 2003 and 2006 cohorts were selected (in part) for their relatively low rates of attrition, about 65% of those interviewed in year 9 do not complete the final interview. This is particularly troublesome as the attrition rates are not consistent across academic performance – for example, those with lower PISA scores, and poor labour force outcomes, are more likely to drop out of the study, leading to an over-estimate of the success of the LSAY cohort. To account for attrition rates, LSAY introduces a weighting to be applied to each remaining observation, to more closely reflect the initial sample.

The literature in regards to the use of weighting in regressions is ambiguous – some papers find that weighting can be unnecessary when estimating causal effects. For this study, regressions were estimated on both weighted and unweighted data, and it was found that unweighted estimates displayed the bias expected of data not representative of the population – in particular, an upward bias on the effect of PISA. This study therefore relies on weighted observation. Appendix B includes the results of both the weighted and unweighted regressions for comparison.

Model selection

Labour market studies are often calculated through a two-step procedure, and employ the use of a tobit model. This method combines the estimation of the effect of a relevant variable on both employment and on wage outcomes into the one model. After considering the data, the preferred method was to estimate these two steps separately (employment though a probit model, and wages through an OLS approach). This method was chosen for three reasons: The explanatory factors in determining if individual was employed were different to

the factors explaining wage outcomes. This would have created a selection bias in the wage equation.

A tobit model is appropriate to censor data in a consistent manner (for example, removing all those without wage, assuming there is a common underlying reason they do not have wages). As the preferred model required removing different groups without wages (those not employed because they could not find work, and those not employed because they were still studying), a tobit model would not be appropriate.

In separating out the two phases of this equation (rather than use a tobit model), it was possible to 1) clearly articulate the effect of PISA on employment; and 2) clearly articulate the direct and indirect effect of PISA scores on wages.

Individual preferences

The adopted models can be considered to be reduced form models – that is, they do not consider individual preferences and behaviour, the model merely measures the observed relationship between PISA scores and economic outcomes. Obviously, there is more to wage and employment outcomes than academic performance – in particular, an individual could choose to enter a low paying industry, despite receiving higher academic scores.




This limitation does not affect the overall results, but the interpretation of the results requires the appropriate nuance. These results should not be interpreted as representative of the expected outcome for each individual (in other words, if one student increases their PISA scores by 1%, it cannot be expected that their wages will increase by 0.125%, as this student may prefer to enter a low paying occupation). Alternatively, these results should be interpreted as the average increase across the population. In other words, if a number of students were to increase PISA scores by 1%, the average impact across the population is estimated be a 0.125% wage increase.

Ability bias

A common complication with labour market studies is the difficulty in controlling for prior ability. In other words, an individual’s innate skills and abilities are likely to influence PISA scores and wage outcomes, leading to an upward bias in the effect PISA scores have on wages. To estimate the true effect of the skills and abilities gained at school (as represented by PISA scores), it is important to isolate the effect PISA scores have on wages, after taking into account an individual’s prior ability.

Studies indicate that the home learning environment is strongly correlated with an individual’s cognitive skills before they enter the schooling system. Therefore, several of the control variables included, such as parental occupation and parental income, are intended to serve as proxies for the home learning environment, and, by extension, prior ability . Although prior ability is not observed, the extensive choice of control variables available provide a large degree of reassurance that prior ability is controlled for sufficiently to have a high level of confidence in the results.




6 Structural change in the economyThis section considers the effect of PISA scores on wages, assuming the Australian economy undergoes the anticipated transition to a ‘knowledge intensive’ economy. This should be considered an extension of modelling the impact of PISA scores on wages (section 5.3) – see Figure 6.1. The key findings are summarised in Box 3.






Labour force

Improvements in education quality

Labour productivity








Physical capitalCross country approach


GDP

Structural change over time





Box 3: Key findings of modelling structural change in the economy The returns to increasing PISA scores in different industries were found to be

insignificant. By way of contrast, the returns to increasing PISA scores in different occupations

were found to be significant.• An increase in PISA scores was found to increase wages the most

significantly in high skilled occupations. A 1% increase in maths scores was found to increase wages by 0.23% for those in high skilled occupations.

• An increase in PISA scores was also found to have a substantial impact on wages in medium skilled occupations. A 1% increase in maths scores was found to increase wages by 0.14% for those in high skilled occupations.

• An increase in PISA scores was found to have no effect on wages in low skilled occupations.

Over time, as the share of the Australian economy employed in high skilled occupations increases, the returns to improvements in PISA scores will increase. By 2066, a 1% increase in maths scores is estimated to be associated with a 0.133% increase in wages (up from 0.125%).

6.1 OverviewAustralia is currently under a period of transition towards a more ‘knowledge intensive’ economy, where knowledge is increasingly being used to generate value. The implications of this have been outlined in Section 2.1, with a knowledge economy becoming increasingly reliant on highly skilled, innovative workers. It is expected that over time, as Australia’s economy evolves, the labour market will transition towards more skill-intensive industries and occupations. The implication for the analysis presented in this report is that increasing PISA scores could in the future increase labour productivity by more than what is estimated using historic data.

In order to assess this hypothesis, this section focuses on estimating the effect of PISA scores on wages across different industries and occupations. This can be done by adding an indicator variable for the industry or occupation of each worker to the regression analysis undertaken above, and analysing whether this variable is significant when interacted with the PISA variable.

In other words, this identifies whether the effects of PISA scores vary depending on the industry or occupation of the worker. As the structure of the economy reweights over time towards particular occupations and industries, differences in the returns to cognitive skills will alter the average wage increase from the results presented in section 5.3. A detailed explanation, including the final model specification, is given in Appendix B.

6.2 ResultsTwo different, but similar, approaches to identifying the possible effects of structural change are used:




the first attempts to identify the return to increased PISA scores at the industry level, grouping the more than 200 ANZSIC three digit industry levels into high, medium or low skill categories and adding these categories as variables in the regression; and

the second performs the same analysis at the occupational level using the eight ANZSCO occupational identifiers.

The results of each analysis are presented below. Ultimately, data availability means that the occupational-level analysis is the most robust and, as expected, this analysis indicates that occupations associated with high skill have a greater return to education than those regarded as low skill.

6.2.1 Estimates of wage differentials between industries

The LSAY dataset contains a field for an individual’s industry of employment as categorised by the Australian Bureau of Statistics (ABS) ANZSIC classifications.38 The industry of employment has been categorised to a group level (consisting of 214 different industry groups), meaning there is some level of granularity through which high, medium, and low skilled industries can be differentiated.

Each industry group was allocated to a ‘high,’ ‘medium’ or ‘low’ growth category. These allocations were based on Deloitte Access Economics’ internal employment growth projections, as well as an assessment of the industries expected to thrive in a knowledge economy. In total, 33 industry groups were allocated to the high growth category.

The wage regression identified in Section 5.2 is then augmented with these industry groupings included as indictor variables that are interacted with the PISA variable in order to determine group-specific returns to PISA scores. The first row in Table 6.1 shows the effect of a 1% increase in PISA scores on wages for the entire population once these interaction terms are included. The second and third rows show the effect a 1% increase in PISA scores has on those in different industry groups (relative to the increase in wages for someone in a low-skilled industry). For example, the results suggest that a 1% increase in PISA maths scores will increase wages of those in a high-skilled industry by 0.565% (calculated by subtracting 0.0455 from 0.1020).

Table 6.1: Estimating the impact of increasing PISA scores on different industries

Maths scores Science scores Reading scoresPISA score -0.0455 -0.0391 0.0051Additional effect of being employed in a high-skilled industry 0.0123 -0.0334 -0.1210

Additional effect of being employed in a medium-skilled industry 0.1020 0.0967 0.0531

***represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level.Source: LSAY 2003, 2006; DAE estimates

38 ANZSIC (The Australian and New Zealand Standard Industrial Classification) classifications are used for the compilation and analysis of industry statistics in Australia and New Zealand. An individual is assigned an industry based on the predominant activity in their primary employment. ANZSIC is a hierarchical structure classification, with four levels of increasing detail. These levels are Divisions (the broadest level), Subdivisions, Groups and Classes (the finest level).




A key finding from the analysis is the loss of statistical significance on all measures of the effect of PISA scores. Effectively, none of the estimates deviate significantly from zero, meaning that the hypothesis that there is no meaningful difference in the effect of PISA scores across the industry groups cannot be rejected.

This robustness of this result was tested both by removing the medium skilled industry grouping, and altering the allocation of industry groups between industry growth categories. However, the results were largely unchanged, with all estimates remaining statistically insignificant.

A range of factors could be driving this result. In particular, approximately 60% of observations are missing an ANZSIC classification, and could not be used in the estimation. The remaining 40% of observations had a significantly different wage to the sample as a whole, indicating that it is likely the omitted observations are causing a significant bias in the results.

A second reason could be that within ANZSIC industry groups, there are a range of skill differences between workers. For example, within the ‘legal accounting and services’ industry group, we would expect to see both lawyers (a knowledge intensive occupation) and clerical and administrative support staff (a less knowledge intensive occupation). This suggests ANZSIC classifications may not be the ideal employment breakdown to consider the relationship between PISA scores, wages, and the structural change occurring throughout the economy.

6.2.2 Estimates of wages differentials between occupations

The LSAY survey also includes the observation of an individual’s occupation based on the ABS ANZSCO classification.39 A description of ANZSCO classifications, and how these occupations were grouped into high, medium and low skilled occupations, is given in Appendix B. A key benefit of using the ANZSCO classifications is every observation in the dataset is complete, removing the sample bias present in the ANZSIC classification.

Table 6.1 shows the results of a 1% increase in PISA scores, for those in high and medium skilled occupations, compared to those in low skilled industries. To interpret these results, a 1% increase in PISA scores leads to an additional 0.231% increase in wages for those in high skilled industries (relative to those in low-skilled industries).

39 ANZSCO (The Australian and New Zealand Standard Classification for Occupations) classifications are used for the compilation and analysis of occupation statistics in Australia and New Zealand. ANZSCO is a skills-based classification, used to classify all jobs in the Australian and New Zealand labour market. An individual is assigned an occupation based on the predominant activity in their primary employment. ANZSCO is a hierarchical structure classification, with five levels of increasing detail. These levels are major groups (the broadest level), sub-major group, minor group, unit group and occupations (the finest level).




Table 6.1: Estimating the impact of increasing PISA scores on high skilled occupations

Maths scores Science scores

Reading scores

Increasing PISA scores -0.060 -0.017 0.027Additional effect of PISA scores on those employed in a high skilled occupation 0.231*** 0.112* 0.165**Additional effect of PISA scores on those employed in a medium skilled occupation 0.135* 0.053 0.068***represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level.Source: LSAY 2003, 2006; DAE estimates

These results support the hypothesis that increasing PISA scores raise the wage (and therefore productivity) of those employed in high skilled occupations. Across all domains, the results indicate a statistically insignificant relationship between PISA scores and wages, before taking into account the occupation of employment. This result suggests there is little evidence that increasing PISA scores will increase wages for those in low-skilled industries (at least over the time span included in the LSAY data).

When the occupation of employment is taken into account, the benefits to increasing PISA scores become significant, particularly for high skilled occupations. Increasing maths scores were found to have the largest impact on wages, with an increase of 0.231% for high skilled occupations, and a 0.135% increase for medium skilled occupation.

6.3 DiscussionThese results are broadly in line with expectations. An increase in PISA scores is shown to have an influence only on those occupations requiring at least a medium level of skills and training, with this impact growing larger as the occupation become more skills intensive.

At first glance, this finding may seem incongruous with the discussion in section 5.3, as it was suggested that increasing PISA scores will have positive impacts for the entire population. However, it is important to distinguish between the earlier discussion (which focussed on the overall uplift in wages), and this analysis (which focusses only on the increase in wages caused directly by increasing PISA scores). In particular, the discussion in section 5.3 suggests those in lower skilled occupations may not benefit directly from an increasing PISA score. Rather, those in lower skilled occupations are thought to benefit from second round effects – specifically, the increase in skilled workers will increase the relative scarcity of unskilled workers, therefore increasing their wages.40 These findings are consistent with earlier discussion, which postulated that those in low skilled occupations will not benefit directly from increasing their cognitive skills (as these occupations do not reward cognitive ability).

40 Unfortunately, a suitable methodology to test whether or not an increase in PISA scores does in fact filter down to all workers was not possible given the available data. In particular, the time dimension was a limiting factor as the LSAY data set lacked a sufficient time horizon to identify what is likely to be an outcome that occurs over a more extended time period.




The rewards to improving PISA scores are progressive in nature. They are significant for those in high skilled occupations, while more modest for those in medium skilled occupations. In essence, these findings provide further support for human capital theory, as they indicate that the benefits to improving cognitive skills and abilities are greatest for those employed in occupations requiring the highest skills.

This result is analogous to the findings earlier in this section that increases in PISA scores have a larger effect on the wages for the higher performing student cohorts than those at the lower, with the results for the lowest cohorts not being significantly different to zero. Intuitively, those in the highest cohorts are likely to, on average, find employment in those occupations that reward higher cognitive skills, and this is reflected in findings above.

Importantly, these results have implications for Australia moving forward, as the share of workers employed in high skilled occupations is expected to increase. Deloitte Access Economics projects the share of workers in high skilled occupations will increase to 39% of the workforce by 2036 (up from 36% in 2016).

Chart 6.1: Forecast changes to occupation skill groupings (2016 – 2036)


As more workers flow into these occupations, the average productivity across the workforce caused by an increase in PISA scores is expected to increase. Based on these estimates, and the estimated wage premium associated with increasing PISA scores within skilled occupations, the direct impact of a 1% increase in PISA scores on wages is expected to increase by 3% (to 0.128%) by 2036.41

Hence, using forecasts of occupational change within the Australian economy over time as a proxy for structural change, this analysis shows that the link between schooling outcomes and economic outcomes will have an increasing effect. Extrapolating these structural

41 This was determined by finding the weighted average productivity shock, based on the elasticities between PISA and wages for high, medium and low skilled occupations, and the relative share of the workforce within each category. The elasticities remained constant over time, but as the share of workers employed in the high skill category increases, the average productivity shock across the workforce also increases.




changes out to 2066, the point at which any current change in policy would be fully phased into the workforce, this estimated elasticity would be somewhat larger, growing approximately 7%, (to 0.133%).




7 The impact on the Australian economy

The final stage of the individual level modelling involves aggregating the economic outcomes accruing to each individual into a national economic impact. In other words, the results from Section 5 (identifying the transmission mechanisms through which education influences the economic outcomes of an individual), have been aggregated to form a national economic impact (see Figure 7.1).






Labour force

Improvements in education quality

Labour productivity








Physical capital



GDP


This approach allows the individual level impacts to be compared to the estimated results from the cross country modelling. More specifically, this comparison provides an indication of the relative sizes of the public and private returns to education, with the latter being captured in the individual level modelling, and both captured in the cross country modelling. The key results are shown in Box 4.




Box 4: the key findings from modelling the impact of PISA scores on the Australian economy If the entire workforce were to experience an increase in PISA scores, GDP is

estimated to increase by 0.16%, or $217 per working person. A range of scenarios were also modelled, reflecting practical and aspirational targets

for Australian students.• Under a scenario where all schools in Australia increase performance to the

top 5% of schools (resulting in approximately a 3% increase in PISA scores), GDP is estimated to increase by 0.47% (once the entire workforce is endowed with greater education), or $600 per working person.

• Under a scenario where Australian students increase results to match performance in Canadian schools (a 5% increase in PISA scores), GDP is estimated to increase by 0.75% (once the entire workforce is endowed with greater education), or $1,000 per working person.

• Under a scenario where Australian students increase results to match performance in Korean schools (a 10% increase in PISA scores), GDP is estimated to increase by 1.48% (once the entire workforce is endowed with greater education), or $2,000 per working person.

The change to GDP resulting from the structural change scenario was also modelled. If the structure of the labour force was to shift towards high skilled occupations as projected, a 1% increase in PISA scores is estimated to increase GDP by 0.18%, or $230 per worker.

7.1 Introduction to CGE modellingThe Deloitte Access Economics Regional General Equilibrium Model (DAE-RGEM) is a dynamic, multi-region, multi-commodity CGE model of the global economy, including Australia. Box 5 below includes a brief introduction to the CGE model and an intuitive overview of how the model works.

Box 5: An introduction to CGE modelling

Any CGE model is simply a representation of supply and demand in multiple sectors of the economy along with aggregate resource constraints. The key feature of CGE models is that they link the supply and demand in each sector to other sectors in the economy, such that a ‘shock’ to one sector flows through to all other sectors. Further, goods in each sector are produced by factor of production (such as labour and capital). An increase in the quantity of these factors, or their productivity) increase the productive potential of the economy, with different effects on different sectors depending on their relative reliance on each factor.




All models are run under some key assumptions. For example, it is assumed that competitive (that is, zero economic profit) product markets fully clear in each period, with changes in prices facilitating this. Relative price changes are assumed to drive changes in producer and consumer behaviour and it is common to assume simple accumulation rules for factors like capital and labour (that is, that investment in one period becomes capital in the next).

CGE models compare a baseline scenario where a proposed policy does not occur with a counterfactual scenario where it does. In this case the ‘policy’ is the improvement in school quality for which the individual level econometric modelling provides an associated wage premium. Households in DAE-RGEM provide labour in return for wages. The actual wage rate they receive reflects the marginal product of labour (that is, the incremental value a unit of labour adds to production).

In the baseline, the productivity of labour is projected to grow over time and, in conjunction with improved productivity in the use of other factors like capital, drive forecast growth in the economy. Against this baseline growth it is possible to simulate the economy-wide impact of additional growth in labour productivity, with this productivity increase parameterised by the econometrically estimated wage premium.

7.2 Assumptions The results from the econometric modelling produced elasticities linking improvements to PISA scores and wages. In other words, the output represents the benefits to wages and employment outcomes of a 1% increase in PISA scores. To transform these into economy wide results, the following steps were taken: It is assumed that the wage premium estimated in the LSAY data set is a reflection of

increased productivity. Therefore, a projected percentage increase in wages can be translated into an increase in productivity for the purposes of the CGE modelling exercise.

The shock to productivity was based off a 1% increase to PISA maths scores. Maths scores were selected because improving maths scores has the most significant average impact on the population. That is, the results provide a measure of the growth elasticity of cognitive skills.

The number of individuals subject to the productivity increase is assumed to remain constant every year. In other words, morbidity rates and population growth rates were ignored for simplicity. While these assumptions do not impact the final productivity shock, population changes may impact the speed at which the economy transitions towards the new levels of labour productivity.

Given the modelling produced an estimated wage premium for 25-year olds, assumptions regarding the persistence of the productivity shock over the career of these individuals need to be made. Two scenarios are modelled, reflecting possible trajectories for the persistence of the shock:




• A central case assumes that the productivity gain represents a permanent improvement to an individual’s skills, and persists throughout their time in the workforce.

• A more conservative estimate assumes the effect of the productivity shock decreases to zero as the individual moves towards retirement age.

As the CGE model is dynamic, it is necessary to consider the impact the productivity shock will have on each cohort of students entering the workforce. For the purposes of this exercise, it was assumed that every cohort of students would receive a 1% increase in PISA scores (cognitive ability), and that the earnings premium will be attached to each cohort that enters the workforce. The benefit to the Australian economy is then projected to increase every year, as a more productive cohort enters the workforce, and the older, ‘less productive,’ cohorts retire.

It is assumed all individuals receive the same productivity shock, reflecting that the econometric modelling reports the ‘average’ effect of a 1% increase in PISA scores across the entire population. Hence, the econometric estimate will be the appropriate shock to apply to the average unit of labour in the model so long as the LSAY dataset is approximately representative of the broader Australian workforce.

Labour force participation for 18-year-olds is varied according to changing propensities for further study. For example, a higher PISA score improved the likelihood of an individual attaining a bachelor’s degree by 0.7%, and the average bachelor degree in the sample took four years to obtain. The participation rate was therefore reduced by 0.7 percentage points for the first four years after the cohort was expected to graduate from year 12.

Concurrent with a productivity shock to the Australian economy, the CGE model will endogenously increase the demand for labour, and therefore the number of employed workers.

The first point in the list above is worth reviewing in more detail. It is based on the relatively standard assumption that workers are paid, on average, an amount equal to their productivity. This implies that having observed an increase in a worker’s wage, it can be inferred that their productivity, and therefore output, has increased by a commensurate amount.

The link between productivity and wages is in practice more nuanced. It can be a function of the way in which wages are set in the market, for example, through somewhat rigid wage bargaining processes and resulting pay scales, and can be influenced by imperfections in the labour market, such as monopsony power by employers.

Biesebroeck (2015) reviews the literature on this link and finds evidence for a variety of factors that may bias wages in both directions. These biases include factors at the systemic level, as well as heterogeneous factors across workers (such as age, gender and race). Interestingly for this analysis, Biesebroeck finds relatively consistent evidence that young workers tend to be underpaid for their level of productivity relative to older workers. Given that the workers in the LSAY dataset used in this modelling are very early in their careers, this may indicate a downward bias between wages and the true level of worker productivity. Nonetheless, as a whole the evidence from the literature tends to support the notion a one-for-one link between productivity and wages is a good approximation of actual outcomes in the labour market.




7.3 Results and discussionA discussion explaining how a shock to PISA scores is converted into an increase to productivity can be found in Appendix C. This section focusses first on the results of a 1% increase to PISA scores. In then models the economic impact of a variety of practical and aspirational scenarios for increases in PISA scores among Australian students.

7.3.1 Results of a 1% increase to PISA scores

The results in this section should be interpreted as the long run economic impact of a 1% increase in PISA scores, that is, once the entire labour forces has realised the increase in productivity due to a policy change affecting school quality. As noted, this estimate effectively provides the growth elasticity of cognitive scores for the individual level approach. The impact on GDP and employment is measured relative to a baseline forecast contained within the CGE model.

Results are reported out to the year 2076. 2076 has been used as the reference year as each year between the 2016 and 2066, a new cohort of workers experiencing an enhanced productivity enters the workforce and replaces a cohort of workers without an increased productivity. By 2066, the entire workforce is assumed to have received the benefits of a 1% increase in PISA maths scores. 2076 has been chosen to include 10 years of a new equilibrium, which shows the pathway of GDP growth after the labour productivity effect has fully passed through the economy.

The results from the CGE modelling are provided in Table 7.1. The results indicate that under the central case scenario, GDP would be 0.16% higher in 2076 than it would otherwise have been in the absence of the 1% increase in PISA scores. This translates to a net present value (NPV) of $13.4 billion (applying a discount rate of 7%). An alternate way to consider this result is that if the increase to GDP under the high scenario was to occur this year, this would result in approximately an additional $2.6 billion to GDP, or $217 per worker.

Table 7.1: Deviations from baseline, 2076

Central scenario Low scenarioGDP (% deviation) 0.16% 0.08%GDP ($ deviation; NPV $13.4 billion $6.4 billionEmployment 0.06% 0.03%

Source: DAE modelling

The CGE model is a dynamic model, and therefore provides an indication of how the economy grows over time, relative to the baseline, as a result of the gradual phasing in of a more productive workforce, as illustrated in Chart 7.1. As expected, the projected gains in GDP are relatively modest at the beginning of the period. As this cohort matures, and as they are followed by more productive workers, the rate of deviation from the baseline also increases. This effect slows towards 2076, as the workforce becomes saturated with workers endowed with additional skills and abilities. Adding new workers in merely serves to replace those leaving the workforce.




Chart 7.1: Deviations in GDP above baseline, over time

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2016 2026 2036 2046 2056 2066 2076

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Gross Regional Product ($M) Gross Domestic Product ($M)

Source: DAE modelling

The increase to GDP is largely driven by the increased productivity of labour. As labour is one of the three factors of production in the model (along with land, capital and natural resources), a shock to the productivity of labour increases economic production. Intuitively, this reflects that more productive workers are able to produce more than in the baseline, leading to an increase to overall production. The impact of labour productivity on economic growth reflects a direct effect of PISA scores on the overall economy.

The advantage of utilising a general equilibrium model is that it is able to estimate indirect or second round effects of labour productivity on economic output. In this case, a more productive labour force increases the rate of return to capital in Australia. This encourages investment, as the factors of production command a higher return. In essence, this reflects the fact that labour does not work in isolation to produce output for the economy; rather, the factors of production combine to produce output. A more productive labour force therefore makes the other factors more productive. These indirect effects provide an additional contribution to the impact PISA scores have on GDP.

The relationship between GDP and employment is reasonably intuitive, in that as production grows (and costs remain unchanged), the economy will naturally demand more workers. Increases to the employment rate are therefore consistent with the gains to GDP. Hence, a second positive indirect effect on the economy comes through the labour market as more workers enter the labour force: not only does the economy produce more through existing labour being more productive, but also through additional labour entering the workforce.

The combined second round, or indirect, effects therefore reinforce the direct effect of more productive labour. The various sectors in the economy increase output by employing both more capital and labour, and producing more output for a given amount of each.




7.3.2 Scenario analysis

The analysis above estimates the percentage increase in GDP from a 1% increase in PISA scores in order to estimate a growth elasticity comparable to the cross country modelling. However, alternative assumptions about the increase in PISA skills can provide informative scenarios on the potential impact on the economy from improving the quality of schooling in Australia.

Scenario 1: improve the quality of schooling to match the highest performing schools

The analysis in Section 8 below demonstrates that some of the variation in performance across students can be explained by school-specific factors ’School specific factors’ refers to differences in practice at the school and educator level that contribute to the educational outcomes of that school’s students. More details on the level of variation explained by schools are provided in Section 8, however, it finds that approximately 10% of variation in student performance can be linked to educator or school-specific practices (while controlling for other determinants of performance).

That analysis gives rise to a scenario whereby the lowest quality schools42 raise their quality of education to the standard of the highest quality schools. Should all schools raise their level to the strongest performing schools in Australia, and all else remain unchanged, maths PISA scores are estimated to increase by approximately 3.1% on average across all students.43 For context, this translates (on average) into approximately an additional half a year of schooling for all students.

Under this scenario, and applying the assumption of persistence in the productivity increase, GDP is expected to increase by 0.47% in 2076, ramping up with a similar profile to that in Chart 5.6 above. Viewing this through a different lens, an equivalent increase in GDP today would equate to approximately $7.5 billion dollars, or over $600 per worker.

In 2016 dollar terms, the total increase in GDP over this period is estimated to be around $39 billion in net present value at a 7% discount rate. Given the discounting over a long period this result is heavily weighted towards more immediate economic benefits, but is still somewhat sensitive to the long-term growth forecasts in the model.

Scenario 2: raise student standards to international comparators

If Australia were to improve the school system such that school-level difference and some contextual factors were overcome, there is scope to improve PISA scores beyond the 3.1% increase simulated above.

42 That is, those schools whose practices are such that student results are lower than they otherwise would be. Note that there need not be a link between the lowest quality schools and the lowest performing schools in an overall sense. That is, the lowest quality schools (in terms of teaching practices) may still sit towards the top of the performance distribution overall due to supporting SES and intake factors. The point is that their student results would be lower than they otherwise could be had teaching practices been such that they supported student outcomes to a greater extent. 43 The assumptions underpinning this estimate are given in Appendix D.




If Australia were to improve its performance to that of Canada’s (a 5% improvement, based on 2003 PISA results), the increase to GDP could be as much as $61.8 billion in net present value terms up to 2076, or be higher by around 0.74% in that year. An equivalent increase today would increase GDP by over $12 billion, or approximately $1,000 per worker. To achieve a 5% improvement in PISA results without an increase in school quality, this translates to approximately two thirds of an additional school year for Australian students.

As a more aspirational target, if Australia could raise the level of school performance to that of Korea (a 10% improvement, or nearly an additional 1.5 school years), the increase to GDP could be $96.9 billion in net present value terms up to 2076, or be higher by around 1.47% in that year – the equivalent of an additional $24 billion to GDP, or almost $2,000 per worker, if this was to occur today.

Scenario 3: the implications from structural change

Finally, Section 6 above considered how the estimated relationship between cognitive skills and labour market outcomes may change due to forecast structural changes in the Australian economy. It found that compared to the 0.125% increase in average wages estimated from the historical data, the elasticity may grow by around 3% (to 0.128%) by 2036, and by around 7% (to 0.133%) when extrapolating out to 2066.44

This implied higher productivity shock, calculated on a year-by-year basis over the modelling period, can be used as an input to the CGE model as above. Chart 7.1 shows the resulting deviation in GDP from a 1% increase in PISA scores under this scenario relative to the base analysis above, assuming persistence in the effect of cognitive skills, and a shift in the workforce towards more skills-intensive occupations.

Chart 7.1: GDP impact under a structural change scenario

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44 Noting that structural change this far into the future is difficult to forecast, and as such the results in this section should be interpreted as a scenario only.




As expected from the only marginal increase in overall productivity levels, there is only a small change in GDP overall, with this growing over time as higher skilled occupations comprise a greater proportion of the economy overall. By 2076, it is estimated that GDP will increase by nearly 0.18% relative to the baseline (up from 0.16%). If this change were to occur this year, this would result in a GDP increase of approximately $2.8 billion, or $230 per worker.

In net present value terms, the increase to GDP by 2076 will be $14.8 billion (an increase from $13 billion).

Note that because the effects of structural change have been estimated using the econometric results and used as an input to the CGE model, the model itself does not incorporate any additional structural change: the goal is to isolate the economic impacts from the effect of higher PISA scores, not to compare the overall change in GDP to Australia from the forecast structural change, which would likely dwarf the effects from the increase in PISA scores alone.




8 The contribution of school qualityThis section is independent of the previous analysis. It attempts to contextualise the previous findings by estimating how much improvement in PISA scores is practically achievable through an increase in school quality. The key findings of this section can be found in Box 6.

Box 6: Key results from the contribution of school quality on child outcomes Schooling and educator practice can explain, on average, about 10% of the variation

in student’s maths scores in PISA.• In addition to this, schooling and educator practice, can explain between 6% of

variation in the likelihood of obtaining a year 12 qualification, and 5% of variation in the likelihood of entering university.

Differences in school quality can have an effect on a range of student’s non-cognitive factors as well, such as their behavioural engagement, and their purpose with education.

A range of factors attributable to ‘school quality’ were found to contribute to differences in PISA maths results between students. The most important factors included student behaviour, the amount of homework assigned, student-teacher relations at school, teacher support during lessons and the disciplinary climate in the classroom.

The discussion in chapters 4 to 7 focuses on the skills and abilities an individual is able to gain from school, but is agnostic towards how improved student outcomes are achieved. While the results in favour of improving education are compelling, it remains of interest to discern how to improve educational outcomes. On this front, it is necessary to consider the extent to which schools themselves contribute to educational outcomes. Numerous studies demonstrate that variation in student outcomes is largely dictated by a student’s idiosyncratic characteristics, such as their prior ability and socioeconomic status (Lu and Rickard, 2014). While this may be true, schools have also been found to have significant impacts on student outcomes. This section explores the impact of schools, and in particular, the impact of quality schooling practices, on student outcomes.

8.1 OverviewFrom a policy perspective, it is important to be aware of the value schools can add to student outcomes. Broadly, schools can influence student outcomes through two mechanisms: school-level contextual factors (such as students intake, school type and location); and schooling quality (such as strong leadership, quality educator practices and a

well-designed curriculum).

Typically, the contextual factors which drive differences in student outcomes lie beyond the scope of schooling practice. However, there are a variety of factors that lie within the




control of individual schools that can have a strong impact on student outcomes (see Hattie, 2009).

Ultimately, it is these factors that allow schools to differ in effectiveness, despite their location and environment (Lamb et al, 2004). For example, quality leadership, professional development for teachers, planned programs and clear expectations for students have all been found to strongly influence student performance (Keating, 2004). The implication of these findings is that by targeting these areas, it is possible to systematically improve the quality of education offered by the schooling system.

To estimate the contribution of schooling quality to student outcomes, it is therefore important to isolate a school’s ‘value-add’ – the part of a student’s outcome that is not attributable to the idiosyncratic attributes of that student or contextual school factors (Lu and Rickard, 2014).

8.2 Data and approachVariations in school quality was calculated using mixed effects or multi-level modelling procedures, explained in further detail in Appendix D. Depending on the investigation undertaken, two types of mixed effect models have been applied: a multilevel linear model for continuous outcome variables (such as student PISA test

scores); and a multilevel generalised linear model for binary outcome variables, such as whether a

student attained a Year 12 or equivalent qualification or whether a student entered tertiary study or not.

These models take advantage of the nested nature of the data (that is, multiple students sit within the one school, with many schools comprising the schooling system), to estimate the effect each school has on student outcomes. These models have been used as they allow analysis of the effect different schools have on student outcomes, after accounting for idiosyncratic factors at both the student and school levels.

The data used for this analysis is taken from the LSAY 2003 longitudinal cohort, and PISA 2003 and 2012 cohorts. A complete list of the measures used can be found in Appendix D.

8.3 ResultsThe results in Table 8.1 can be interpreted as the proportion of a student’s outcome that can be explained by school-specific factors (rather than factors specific to a student). There are two results shown: the unadjusted results indicate the total amount of student outcomes attributable to school factors, while the adjusted results provide estimates for the contribution of schooling and educator practice, after accounting for contextual factors which are unlikely to represent ‘school quality’ (such as student selectivity and the SES status of the school). It is the adjusted results that inform the difference in students assessment scores that are achievable from improving schooling quality and educational practice.




The first result to note is even in the unadjusted model, schools account for between 21% and 30% of variation in maths scores, and between 9% and 19% of variation in educational attainment.45 In other words, much of the variation in results occurs within schools, and is primarily attributable to individual student factors (such as family background and academic ability).

Table 8.1: School contributions to student outcomes

Student outcomes Unadjusted model Adjusted modelMaths scores (2003 cohort) 21% 6%Maths scores (2012 cohort) 30% 14%Year 12 qualification (2003 cohort) 16% 6%Tertiary entry (2003 cohort) 9% 5%University entry (2003 cohort) 19% 7%

The results indicate that after accounting for factors out of the control of educators, school quality and educational practice can explain between 6% and 14% of differences in maths scores. In other words, if a student was to attend a high quality school, they would expect to receive higher math scores than if he or she attended a low quality school. The effect on education outcomes is more muted, with high quality schooling explaining about 6% of variation in the likelihood of completing year 12, 7% of variation in gaining access university and 5% of variation in the likelihood of entering the VET system.

Non-cognitive skills

While at school, students also develop non-cognitive skills that further support their labour market outcomes. The results of analysis isolating the school-level effects on these variables are shown in Table 8.2 below.

Table 8.2: School effects on non-cognitive skills

Student outcome School-effectCognitive engagement 5%Behavioural engagement 7%Emotional Engagement 5%Sense of belonging 3%Self-efficacy 4%Purpose 6%Perseverance 3%

A high quality of practice within schools can also drive differences in student outcomes across a range of non-cognitive skills. Quality education is able to improve student outcomes directly by between 3% (sense of belonging and perseverance), and 7% (behavioural engagement). These non-cognitive skills are found to be highly correlated to student cognitive skills (as measured by PISA scores), suggesting that if schools can

45 Note that as PISA students are generally from year 9 or year 10 cohorts, it is not uncommon that they complete year 12 at a different school to where they completed their PISA assessment. As such, PISA results have NOT been included as a control in these equations. These results reflect individual and schooling characteristics only.




contribute to improvements in maths achievement, they can also contribute to improvements in non-cognitive skill development.

The features of school quality

It is also possible to use the data to inform the features that drive school quality. That is, what is it about schools that drive these overall variations in test scores? The results are summarised in Table 8.3 below.46

Table 8.3: School effect on maths scores1

Aspect of schooling Quality dimension 2003 cohort 2012 cohortDerived from schoolsSchool size Enrolments 0.000 0.000Resources Shortage of teachers -0.073 -0.100**

Quality of material resources -0.007 -0.022Quality of educational resources 0.044 0.080*

Connecting students Student morale 0.167**Student behaviour 0.179** 0.177**

Teachers Teacher morale -0.006 0.053Teacher behaviours 0.027 0.058Proportion of teachers with a Bachelor degree in maths 0.051 0.102**

Derived from studentsTime on task Amount of homework 0.191** 0.261**

Minutes of maths classes per week 0.076 0.035Teachers Student-teacher relations at school 0.136* 0.181**Classroom Teacher support in maths lessons 0.117* 0.093*

Disciplinary climate in class 0.204** 0.216**Classroom management 0.210**

Learning strategies Control strategies 0.116* 0.104**Elaboration strategies -0.116* -0.092*Memorisation strategies 0.069 -0.025

Connectedness to school Attitudes towards school 0.162** 0.128**Sense of belonging to school 0.024 0.154**

Note: ** represents significance at the 1% level, * represents significant at the 5% level1 The questions asked in the LSAY survey changed between 2003 and 2012, so not all quality dimensions were tested in both cohorts.

Table 8.3 highlights how various aspects of school quality can influence maths achievement outcomes. For example, the teacher morale, teacher behaviour and the quality of material resources were all found to not be significant across both the 2003 and 2012 cohorts. Conversely, student behaviour, morale and their attitude towards school were found to influence student test outcomes.

Associated with the results on student behaviour and morale, how teachers interact with students in the classroom was found to have a strong positive influence on outcomes – student-teacher relations, teacher support and the disciplinary climate in class all have a

46 As part of these equations, a variety of student idiosyncratic factors (such as socioeconomic status and language background other than English) were controlled for, but were omitted from 8.3.1Box 8.1Table 8.3 as these do not represent a dimension of school quality.




strong influence on maths scores. Classroom management practices adopted by teachers were also found to contribute to differences in test scores (at least in 2012 where this was measured).

The quality dimensions which influenced PISA maths scores in 2003 had a similar affect, and largely retained their significance for the 2012 cohort. The fact these dimensions maintained their size and significance across different cohorts provides a high level of confidence in these results.

There are some factors which have changed significantly between the cohorts – namely shortage of teachers, quality of educational resources, and a sense of belonging towards school. While our prior belief is that these factors have an influence on student outcomes, the fact they were significant only for one cohort could suggest these are unique for students in those cohorts, but improving these factors will not always impact student outcomes. It would be necessary to consider a wider range of data before making any definitive conclusions on the importance of these quality dimensions.

8.4 DiscussionOverall, the results indicate that school-specific quality has a relatively small role to play in delivering student outcomes. Student level factors drive between 70% and 80% of schooling outcomes, while systemic characteristics explain much of the remainder. These results are consistent with Hattie (2003) & Lu and Rickard (2014), both of which suggest school value add is relatively small compared to student characteristics and schooling contextual factors. However, that is not to say that policy cannot be enacted to lift educational outcomes through improving school quality.

According to the LSAY data, the differences in student outcomes attributable to school quality and educator practice may be small, but it is not insignificant. The results suggest there is scope to reduce variation in maths outcomes by between 5% and 14%, if the appropriate policies are enacted.

Hattie (2003) finds nearly 35-40% of variance can be explained by variances in educator and schooling practice. Given Hattie does not consider all school level contextual factors (such as socioeconomic status and student intake), it is feasible those results are an upper bound of school quality, while our results indicate the impact of educator and school practice after contextual factors are controlled for.

Importantly, the factors found to drive quality schooling and educational practices are (in theory) transferrable across schools. Therefore, with an accommodating policy environment, the educational practices being applied in high quality schools should be implementable in all schools, enabling a nation-wide increase in results.

Of course, the policy settings need to be appropriately non-prescriptive, as different schools may require different practices to get the best out of their students. In general, well supported but autonomous schools are preconditions to allow schools to provide a high quality of education (see for example Australian Government (2016); Burgess, 2016). Beyond this, these results suggest several additional areas which policy can address. Of course, policy suggestions elicited from these results, are indicative of areas of focus– they




are not proof of areas which can improve student outcomes with certainty. This caveat notwithstanding, the findings highlight some key areas of educator practice which may benefit from policy focus. In particular, developing strong teacher-student relationships, and ensuring teachers maintain discipline and manage their classroom effectively were found to generate improved student outcomes. Underpinning these practices is typically a high level of school leadership.

Further considerations for government lie in circumnavigating the systemic disadvantage evidenced within the schooling system. For example, schools in low SES areas have been found to underperform relative to schools in less disadvantaged areas across assessment results, year 12 completion and post school transitions (Victoria University, 2015). While this continues to occur, students within these schools will continue to be at a disadvantage. If systemic school level factors were to be overcome, the difference in student outcomes is estimated to fall by as much as 15%. While funding is not a sufficient condition to improve educational outcomes, funding should be sufficient to at least overcome educational disadvantage associated with school level contextual factors (such as low socioeconomic status).

In conclusion, while the impacts of school-quality are relatively small in comparison with student-level factors, once a student has entered the schooling system, little can be done to remedy student-level factors which explain differences in educational outcomes (for example, policy cannot ensure a positive family and home learning environment for a student). Remedying differences in school quality therefore represent a practical target for improvements in educational outcomes in the short-to-medium term. Over the longer term, dealing with more entrenched disadvantage facing schools could further support improvements in student outcomes.

Finally, the relatively small variation in student outcomes explained by school-level factors should not be interpreted as schools only having a relatively small impact on education outcomes. Schools are the vehicle through which education, both academic and otherwise, are imparted on youth. They are paramount to building the cognitive and life skills essential for success as a nation. What this analysis isolates is instead the extent to which practices across schools explains differences in outcomes, an indicator of the potential gains available to further progress in school policy.




9 Implications of the analysisThe overarching research objective throughout this report has been to ascertain the value of improving school education. While numerous reports have articulated the benefits associated with schooling, these have generally failed to invoke a holistic discussion on education and schooling. Macroeconomic approaches have tended to insufficiently articulate the precise mechanisms through which education affects economic outcomes, while microeconomic approaches have generally not dealt appropriately in separating the quantity and the quality of schooling in explaining student outcomes. As the quantitative analysis in this report has addressed the effects of education from both perspectives, this has led to a more complete analysis on the benefits of education.

This section considers the analysis from sections 4-6 of this report, drawing on the key findings and bringing them together to address the key research objectives of the project. It first outlines the importance of education, then considers the importance of schooling, before making some brief concluding remarks.

9.1 How much does education matter?That education plays a large role in influencing economic outcomes is apparent from both modelling approaches. However, in determining how much education matters, it is first important to confirm how education influences economic outcomes. A brief overview of two competing theories linking education to economic outcomes is described below. This section then outlines the dollar value of education, and the implications and challenges for government policy in realising this benefit.

Human capital and signalling theory

There are two core theories relating educational to economic outcomes – the ‘human capital’ theory, whereby education enhances the skillset of an individual; and ‘signalling’ theory whereby differences in educational attainment merely act as a signal to employers of differences in ability inherent to individuals. These competing theories digress on whether education adds value in and of itself, or merely as a means to separate out students, ensuring that individuals are matched with jobs commensurate to their knowledge and abilities.

Previous studies into the Australian education system have focussed on wage differentials due to differences in educational attainment. But such studies are silent on whether education enhances skills and productivity, or merely acts as a signal to employers. This study considers differences in educational quality by focussing on differences in outcomes among those with the same level of attainment. As differences in wages were found to be attributable to differences in assessment scores (which are unobservable to an employer), this is evidence that education affects wages by enhancing an individual’s skillset, consistent with the human capital theory of education.




The value of education

The next inquiry explores the channels through which education supports positive economic outcomes. The individual level analysis demonstrates that education is able to influence economic outcomes through all the key transition pathways articulated in the literature. Improving educational outcomes was found to have a positive influence on labour productivity, prospects for employment, and on attaining further study.

This suggests that improving educational outcomes can remedy a variety of issues causing poor transition outcomes for students. This includes helping students enter the labour market, or helping them advance towards better paying jobs. When translated to an economy-wide benefit, the CGE modelling estimates a 1% increase in educational outcomes would increase GDP by 0.16% per year once the full effect of this increase is phased in.

The benefit to GDP estimated from the individual level modelling likely represent a lower bound to the benefits associated with education. This is because it represents an aggregation of individual improvements to labour productivity and employment outcomes. However, to the extent that there are positive externalities associated with improving economic outcomes, the true gains will be larger than those reported by this analysis.

These total benefits to education were captured through the cross country approach, with an estimated growth elasticity of around 0.33%. However, this finding should be interpreted with some caution. Macroeconomic approach to estimating the returns to education face several problems, particularly when attempting to apply the findings to individual countries. Further, the inability of the data to allow for temporal analysis of the relationship between education and growth removes some of the more fruitful approaches to potential account for these deficiencies. The individual level approach adopted in this report is ultimately a superior way of accounting for the relationship between education and growth for Australia, and further facilitates useful modelling of the various transition and cohort effects.

Policy implications

Given the strength of these findings, several policy considerations are apparent. First and foremost, this study has established an empirical link between school outcomes and economic outcomes. In doing so, it highlights the importance of schooling to the individual, and Australia more broadly.

Second, this study introduces the importance in relying on more nuanced and granular measures of school outcomes, rather than relying on the level of educational attainment alone. This study has used PISA scores as a measure of educational attainment, 47 which has proved well-suited to determining the impact that education has on economic outcomes. The research was able to show that on a microeconomic level, students with identical attainment levels earned different amounts, based on differences in their PISA scores. It therefore highlights the value of high quality educational instruction, as a higher quality education is translated into enhanced cognitive skills and abilities, and is able to produce a stronger economic outcome for the individual.

47 Other data sources, such as NAPLAN test results, could also be appropriate.83




A logical extension to this point is that rather than focusing on raising the overall educational attainment of students, education policy should focus on raising assessment scores (and therefore, the skills and abilities of students). It is noted that raising the skills and abilities of students not only has positive wage and employment outcomes, but will also serve as a means for raising overall attainment levels.

The variety of benefits stimulated by improving educational outcomes serves to emphasise the multitude of economic and social issues education can address. From improving productivity to counteract the impact of an ageing workforce, enhancing the employment prospects of low achieving high school students, or by producing new ideas which support Australia’s transition toward a knowledge economy (as captured by the spillovers in the cross country approach), improving educational outcomes serves a number of valuable purposes.

The challenge for government

The challenge for government lies in understanding how student outcomes can be lifted at a systemic level. Governments influence student outcomes through their policy directed towards the schooling system, so it is important to understand what can be achieved through improvements and adjustments to the schooling system. The discussion below draws out some of the key findings relating the value of schools in improving educational outcomes.

9.2 How much do schools matter?Section 8 indicates that schools account for a small but important proportion of student outcomes (around 10% of variations in student outcomes were explained by differences in schools once other factors are controlled for).

This low variation accounted for by schools does not imply that schooling does not play a role in student outcomes: they clearly do. What it shows is that there are currently differences in the way that schools educate students and that these differences are important in determining student outcomes (albeit less important than some other contextual factors).48

Through regulation, advice and funding, government has the ability to influence school practice. Given the fairly small range of differences that can be attributed to school quality, it appears government policy largely ensures practice is broadly commensurate across schools (notwithstanding there could be variations within classrooms not identified in the modelling).

However, further refinements to policy can still be made, particularly to lift all schools to the highest quality standards. Examples of how schools can influence educational outcomes range from ensuring quality school leadership, to providing quality teachers and best practice pedagogy. Government policy should also provide schools sufficient autonomy to

48 Indeed, an optimal schooling system would have schools accounting for approximately 0% of this variation, with all schools and educators operating at ‘best practice’, meaning any differences in student performances across schools are entirely explained by other factors.




make decisions best suited to their students, but offer support and guidance where needed.

To the extent that governments can impact educational practice within schools, there is scope to improve outcomes by ensuring all students receive a high quality of educational instruction.

On a broader level, school contextual factors also have strong impacts on student outcomes, although these can be harder for policy to shape. Schools in low socioeconomic suburbs, or those with a large intake of students with poor English proficiency, often face negative educational outcomes. These factors are closely related to attitude towards schooling within the local community, which is also found to negatively affect student outcomes. At a minimum, funding should provide for sufficient resources to overcome the effects of educational disadvantage. However, overcoming these disadvantages is not feasible through education funding and policy alone.




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Appendix A: Cross country modellingThe following appendix presents a detailed outline of the method, data, and key results summarised in the body of the report.

MethodA structural model of the economy is defined based on the seminal work by Mankiw et al. (1992), and extended based on Bassanini and Scarpetta (2001) to distinguish between long-run equilibrium and transitory dynamics. This model assumes that economic output is produced using a number of key inputs. In its most basic form, economic output is a function of a nation’s stock of: labour; physical capital; and human capital.

The structural growth model adheres closely to existing literature, with modifications included to accommodate the focus on human capital (defined in terms of educational attainment and school quality) as well as measures of investment in research and development (R&D). The standard neo-classical growth model is derived from a ‘constant returns to scale’ production function with three factors of production (capital, labour and human capital). Production (output) at time t is given by:

Y (t )=K ( t )α H ( t )β ( A (t ) L (t ) )1−α−β

Where Y , K ,H and L are respectively output, physical capital, human capital and labour, α is the partial elasticity of output with respect to physical capital, β is the partial elasticity of output with respect to human capital and A( t) is a measure of technological progress and economic efficiency. Each country’s output is also assumed to be influenced by a measure of technological progress (often referred to as labour augmenting productivity), which can be affected by a number of influences, including (among other things): national levels of research and development; the effectiveness of national institutions; and a country’s exposure to international trade.

This can be represented by the equation below:

A ( t )=I ( t )Ω (t )

Where I(t) represents investment in research and development, effectiveness of institutions and a countries exposure to trade, and omega represents the endogenous growth in technology.




This incorporates research and development expenditure, public expenditure on education, and exposure to international trade as key determinants of economic efficiency I (t), such that:

ln I ( t )=x (t )P

Here, x (t) is the vector of factors that represent deterministic contributions to labour productivity, and P is the coefficient vector measuring their effect on economic growth and development Technological progress is assumed to be exogenous and grows at rateg(t ); that is:

Ω(t )=g (t )Ω(t )

Substituting the steady-state values of physical capital and human capital yields the intensive form of steady-state output as a function ofh¿.49

ln ( y¿ )=ln Ω ( t )+ x( t)P+ α1−α

ln sk (t )+ β1−α

ln h¿ ( t )−α (1−α ) ln (g (t )+n ( t )+d )

The above equation is valid in empirical cross-country analysis only if countries are in their steady states or if deviations from steady state are independent and identically distributed. If observed growth rates include out-of-steady-state dynamics, then the transitional dynamics have to be modelled explicitly (Bassanini and Scarpetta, 2001). A linear approximation of the transitional dynamics can be expressed as follows:

Δ ln y (t )=−ϕ ( λ ) ln y ( t−1 )+ϕ ( λ ) α1−α

ln sk (t )+ϕ ( λ ) β1−α

lnh ( t )+ϕ ( λ ) x (t )P+ 1−ψψ

β1−α

Δ ln h (t )−ϕ ( λ ) α1−α

ln (g (t )+n (t )+d )+(1−ϕ ( λ )ψ ) g (t )+ϕ ( λ ) lnΩ (0 )+ϕ ( λ )g (t ) t

This equation represents the generic functional form to be estimated in this project. Further, the coefficient estimate ϕ ( λ ) represents the convergence parameter. The convergence parameter underlines the speed in which countries converge to their steady-state output.

This model can be represented as:

Δ ln y (t )=a0−ϕ ln y ( t−1 )+a1 ln sk (t )+a2ln h ( t )−a3n ( t )+a4t+x (t)P+b1 Δ ln sk ( t )+b2 Δ ln h ( t )+b3 Δ ln n ( t )+Δ x (t)B

Estimates of steady state coefficients as well as parameters of the production function can be retrieved based on the estimated coefficients presented above. For example, according to the functional form of the linear approximation given by Mankiw et al. (1992), the share of physical capital in steady-state output (i.e. national income or total real GDP) (α ) is given by the coefficient estimate of the physical capital investment rate (sk) and the convergence term (ϕ):

49 The steady-state stock of human capital h¿ is not observed, but it can be expressed as a function of actual

human capital: ln h¿ (t )= ln h (t )+ 1−ψψ

Δ ln ( h (t )A (t ) )




a1=ϕ ( α1−α )

The convergence parameter ϕ plays an important role in explaining the modelling results. In all specifications the convergence parameter is significant, suggesting a (conditional) process of convergence as countries move towards their steady-state output levels. For example, if the convergence term is estimated to be 0.15, this indicates that the economies will close 15% of the gap between their current level of output and their steady-state output each year. The convergence process is asymptotic, meaning that countries will never truly reach their steady-state levels.

In the above model specification, human capital (h) has been treated as a single parameter. In practice the specification of the econometric model separately include variables for each of the measures of educational attainment and school quality (the key variable of interest).

DataThe data can be categorised into the following areas: school quality; educational attainment; and economic growth and other controls.

The countries considered for this analysis, for which there was sufficient data, are presented in Table A.1 below. The actual countries used in modelling differs somewhat, depending on the availability of data in each specification.

Table A.1: List of countries used in analysis

Argentina Czech Rep. Hungary Luxembourg Poland SwitzerlandAustralia Denmark Iceland Macao Portugal ThailandAustria Egypt Indonesia Malaysia Romania TunisiaBelgium Estonia Iran Mexico Russian Fed. TurkeyBrazil Finland Ireland Morocco Singapore United

KingdomCanada France Israel Netherlands Slovak Rep. United StatesChile Germany Italy New

ZealandSlovenia Uruguay

China Ghana Japan Norway South AfricaColombia Greece Jordan Peru SpainCyprus Hong Kong Korea, Rep. Philippines Sweden

School quality

Building on the work of Hanushek and Woesmann (2012), cross-country data on educational achievement from 1964 to 2012 is used as a measure of cognitive ability. This data is available from a number of sources, as outlined in Table A.2 below.50

50 Where y¿is the steady-state output per capita, sk is the investment rate in physical capital, n ( t ) is the 92




Table A.2: International student assessments to be used in this study

Abbr. Study Years covered Age cohortFIMS First International Mathematics Study 1964 14FIRS-R

First International Reading Study – Reading 1970-71 10

FIRS-S First International Reading Study - Science 1970-72 10SIMS Second International Mathematics Study 1980-82 14RLS Reading Literacy Study 1990-91 10TIMSS Third International Mathematics and Science Study 1995-2011 10/14PISA Programme for International Student Assessment 2000-2012 14PIRLS Progress in International Reading Literacy Study 2001-2011 10NAEP National Assessment of Educational Progress 1970-2012 10/14

Source: Adapted from Hanushek and Woesmann, (2012), table B2.

These international student assessments do not cover every country in every time period; they also differ in terms of the nature and content of each assessment. As such, scores are normalised based on the approach outlined in Hanushek and Woessmann (2012). This allows for a comparable measure of student achievement to be developed for all countries, in terms of both the mean and standard deviation. Time periods for which testing measures were not available are linearly interpolated.

An important implication of the testing periods available is that modelling relates current achievement to current growth. These measures of achievement do not, therefore, account for implied level of achievement in the current workforce. This is not possible, as the 1964 testing cohort turned 64 in 2014. Hence, the modelling treats the testing measures based on these international student assessments as a proxy for the `quality’ of the current labour force, or its ability to generate output.

Educational attainment

Cross-country measures of educational attainment are drawn from the database developed by Barro-Lee (2010). This includes attainment at the primary, secondary and tertiary level from a large number of countries from 1960-2010.51 As these measures of attainment are only available in five year intervals, each series is linearly interpolated within the sample period.

Economic growth and other controls

Economic growth is measured using real GDP per working age person (measured in US dollars). This and other key controls for economic growth are sourced from the OECD and World Bank, as outlined in Table A.3 below.

population growth rate, and d is the rate of depreciation.51 These data are be obtained from a variety of sources, including:TIMSS and PIRLS - http://www.iea.nl/data.html; Pre 1995 tests - http://ips.gu.se/english/Research/research_databases/compeat/Before_1995; PISA - https://www.oecd.org/pisa/pisaproducts/; andNEAP - http://nces.ed.gov/nationsreportcard/


https://www.oecd.org/pisa/pisaproducts/

http://ips.gu.se/english/Research/research_databases/compeat/Before_1995

http://www.iea.nl/data.html



Table A.3: Data sources

Data SourceGross domestic product per capita World BankTotal population growth World BankGross capital formation (% of GDP) World BankExpenditure on Higher education R&D per capita OECDExpenditure on Other R&D per capita OECDExports and Imports of goods and services (% of GDP) World BankPublic expenditure on primary education World BankPublic expenditure on secondary education World BankPublic expenditure on tertiary education World Bank

Given the data available across countries and over time, it is not possible to decompose the estimated growth effects on different sub-populations of interest, or to explore linkages across the different tests.

ResultsThe following section presents additional coefficient estimates based on the models outlined in the body of this report. Each model relates economic growth, measured by GDP per working age (15-64) person, to a number of its determinants, including the test scores constructed based on data described in the previous section.

Within-country results

The first set of models included both long-run and transitory dynamics. The basic form of each equation is given by:

Δ y¿=αi+ρ y¿−1+X¿ β+Δ X¿ Γ+δ t+ϵ¿

Where y¿ is the natural logarithm of GDP per working age person, α i is a country specific intercept, X ¿ is a vector of controls (including student test scores), and δ t is a time period specific intercept. This is a simplified representation of the model derived above.

Given the relatively large number of coefficients estimated in each specification, only estimates of the long-run parameters are presented in the following section (corresponding to the β vector in the model above). Short-run estimates were included as controls, and do not provide additional insight into economic growth. In the interests of brevity, these have been omitted from the tables below.

Table A.4: Within country growth estimates

Maths Science ReadingTest score 6.15e-05 8.37e-05 0.000587**

(0.000109) (0.000223) (0.000231)Constant 0.983*** 0.923* 1.067***




Maths Science Reading(0.208) (0.481) (0.324)

Lagged ln GDP -0.115*** -0.118*** -0.165***(0.0211) (0.0358) (0.0346)

Ln investment 0.138*** 0.141** 0.168**(0.0400) (0.0646) (0.0673)

Ln human capital 0.0576 0.183** 0.148***(0.0401) (0.0694) (0.0470)

Change ln population -2.373*** -2.878*** -2.273**(0.720) (0.949) (1.037)

Trade exposure -0.0155 -0.0113 -0.0585**(0.0111) (0.0199) (0.0247)

OECD 0.0377** 0.0460 0.0278(0.0175) (0.0278) (0.0211)

Observations 1,198 809 922Countries 57 57 53R2 62.2% 59.1% 64.6%

Note: *** represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level. Clustered standard errors in parentheses.

Results presented in Table A.5 below include an interaction between human capital and test scores.

Table A.5: Within country growth estimates, interaction term

Maths Science ReadingTest score -0.00114 -0.00292** 0.00189

(0.00128) (0.00136) (0.0145)Interaction term -0.000878** -0.00145** -0.000195

(0.000385) (0.000654) (0.000325)Constant 0.118 -0.313 0.875**

(0.446) (0.569) (0.388)Lagged ln GDP -0.116*** -0.127*** -0.165***

(0.0220) (0.0382) (0.0331)Ln investment 0.127*** 0.137** 0.167**

(0.0405) (0.0645) (0.0647)Ln human capital 0.450** 0.803*** 0.227*

(0.177) (0.268) (0.122)Change ln population -2.266*** -2.466** -2.235**

(0.745) (0.958) (1.061)Trade exposure -0.00514 0.000336 -0.0561**

(0.0109) (0.0200) (0.0265)OECD 0.0336* 0.0475* 0.0285

(0.0169) (0.0252) (0.0219)Observations 1,198 809 922




Maths Science ReadingCountries 57 57 53R2 62.4% 59.5% 64.7%


The following specification, presented below in Table A.6, replace the measure of human capital with education investment. Due to data availability issues, this reduces the sample to post-1995.

Table A.6: Within country growth estimates, education expenditure

Maths Science ReadingTest score 0.000335 0.000525 0.00115**

(0.000243) (0.000318) (0.000541)Constant 1.688*** 1.620*** 1.889***

(0.522) (0.512) (0.664)Lagged ln GDP -0.189*** -0.192*** -0.270***

(0.0438) (0.0428) (0.0582)Ln investment 0.216*** 0.217*** 0.365***

(0.0683) (0.0661) (0.0964)Ln education investment

0.127** 0.127** 0.209***

(0.0560) (0.0591) (0.0672)Change ln population -2.653*** -2.330*** -3.533***

(0.793) (0.824) (1.271)Trade exposure -0.0377 -0.0317 -0.0304

(0.0369) (0.0367) (0.0393)OECD 0.0472* 0.0438 0.0315

(0.0280) (0.0290) (0.0265)Observations 609 611 526Countries 53 53 48R2 65.7% 65.8% 70.0%


Between-country results

The second set of models adheres more closely to the analysis described in Hanushek and Woessmann (2012). Each equation relates the average annual growth rate of GDP per working age person to a set of its determinants. This is represented as:

gi=( y¿

y i0 )1T=ρ y i 0+X i β+ν i




The form of this specification is considerably simpler than that presented above. This is because the effect of transitory dynamics does not affect the long-run equilibrium, assumed to be sufficiently represented by the average of growth over an extended period.

Table A.7: Between country results (1960-2012)

Maths Science ReadingTest score 0.0122*** 0.0115*** 0.00985***

(0.00143) (0.00152) (0.00205)Constant 5.910*** 7.167*** 7.151***

(1.111) (1.128) (1.402)Initial GDP -0.770*** -0.897*** -0.768***

(0.177) (0.191) (0.220)Initial human capital 0.449* 0.465* 0.414

(0.243) (0.262) (0.342)Observations 41 41 39R2 72.7% 68.3% 51.2%

Note: *** represents significance at the 1% level; ** represent significance at the 5% level and * represents significance at the 10% level. Standard errors in parentheses.

Table A.8: Between country results, additional controls (1960-2012)

Maths Science ReadingTest score 0.00969*** 0.00962*** 0.00744**

(0.00221) (0.00235) (0.00333)Constant 8.506*** 9.844*** 11.05***

(2.774) (2.766) (2.941)Initial GDP -1.520*** -1.589*** -1.315***

(0.417) (0.426) (0.464)Initial human capital 0.909** 0.925** 0.725

(0.346) (0.355) (0.436)Initial population 0.0552 0.0165 -0.0612

(0.108) (0.114) (0.124)Average trade exposure

0.881** 0.925** 0.840**

(0.340) (0.347) (0.372)Time in OECD 1.034 1.001 1.130

(0.717) (0.740) (0.791)Observations 41 41 39R2 84.0% 83.2% 77.9%





Table A.9: Between country results, extended specification (1995-2012)


(0.00457) (0.00486) (0.00598)Constant 27.80*** 26.54*** 32.32***

(5.566) (5.675) (6.160)Initial GDP -2.346*** -2.237*** -2.391***

(0.294) (0.292) (0.321)Initial human capital 0.328 0.226 -0.163

(1.004) (1.017) (1.184)Initial population -0.400** -0.391** -0.431**


-0.286 -0.189 -0.0712

(0.439) (0.438) (0.438)Time in OECD -1.280 -1.421* -1.447*

(0.766) (0.773) (0.804)Average education investment

-1.982* -1.775 -2.581**

(1.037) (1.055) (1.112)Observations 56 56 52R2 77.8% 77.5% 78.9%


The following table of results (Table A.10) includes an interaction between the initial level of human capital and test scores.

Table A.10: Between country results, extended specification with interaction (1995-2012)

Maths Science ReadingTest score 0.0632*** 0.0454** 0.00708

(0.0198) (0.0179) (0.0158)Interaction -0.0214** -0.0119 0.00989

(0.0101) (0.00922) (0.00987)Constant 8.931 16.89* 39.98***

(10.38) (9.360) (9.818)Initial GDP -2.273*** -2.206*** -2.478***

(0.284) (0.290) (0.332)Initial human capital 9.016** 4.786 -4.340

(4.208) (3.676) (4.334)Initial population -0.336* -0.363** -0.487**


-0.137 -0.111 -0.150





Time in OECD -1.265* -1.390* -1.443*(0.735) (0.767) (0.804)

Average education investment

-1.792* -1.642 -2.581**

(0.999) (1.051) (1.112)Observations 56 56 52R2 80.1% 78.4% 79.5%


Results from a model that allows for a different slope for OECD countries is presented in Table A.11 below.

Table A.11: Between country results, OECD slope (1995-2012)


(0.00438) (0.00471) (0.00599)Interaction -0.0235** -0.0241** -0.0145

(0.0106) (0.0117) (0.0159)Constant 27.11*** 25.69*** 32.05***

(5.321) (5.471) (6.181)Initial GDP -2.190*** -2.103*** -2.304***

(0.289) (0.288) (0.335)Initial human capital 0.337 0.163 -0.162

(0.958) (0.978) (1.186)Initial population -0.397** -0.370** -0.415**


-0.529 -0.378 -0.152

(0.433) (0.430) (0.448)Time in OECD 9.738* 9.606* 5.359

(5.020) (5.380) (7.483)Average education investment

-2.186** -2.020* -2.770**

(0.994) (1.021) (1.133)Observations 56 56 52R2 80.3% 79.7% 79.4%





Limitations and discussionAs with any economic model, a number of assumptions underpin this approach to estimating the economic impact of improvements in school quality. These assumptions relate to the structural interpretation of the estimated equation. In particular they relate to issues of causality and omitted variable bias.

As noted by Hanushek and Woesmann (2012), it is not possible to know with certainty whether or not estimated relationships between measures of human capital (i.e. attainment and quality) and economic growth are causal in nature. This occurs both due to possibility of reverse-causation (i.e. that higher economic growth creates higher quality education systems), as well as potential omitted (causal) variables that are correlated with school quality (for example, unobserved secular institutional drivers of economic growth). Nonetheless, various tests for the presence of such confounding factors have not provided evidence against the assumption of causality (see Hanushek and Woesmann, 2012).

In the within-country models of growth presented above, a country specific intercept attempts to control for heterogeneity in observed growth rates arising from relatively constant factors. These unobserved factors that drive growth, such as cultural differences, the quality of institutions, etc., could bias the results if not properly accounted for. Any remaining correlation between the error term and observed determinants of growth will lead to inconsistent estimates of the parameters. This is more of an issue in the between-country models of growth, since without the ability to control for a country specific intercept, it must be assumed that (conditional on other observables) test scores are exogenous. In other words test scores are not correlated with other country-specific factors that also affect economic growth.

The reliance on cross country analysis means that the estimated effects of education quality on economic outcomes are not specific to the Australian context. The results of this analysis relate to the average effect observed across countries. In other words, all countries are assumed to share the same production functions.

While the results presented above provide an estimate of the impact that improved education quality has on economic output, they do not directly identify the manner in which these benefits occur (though some inferences may be made through consideration of the other estimated effects in the analysis). In other words, the transmission mechanism cannot be directly identified.

The approach to developing a single cross-country measure of academic achievement faces a number of significant limitations. Most prominently: PISA data is not collected in a uniform manner across countries, which may introduce

elements of bias in the observed results (in particular, Australia is over sampled relative to other countries and recently experience a structural change in the way the survey and test is delivered across schools).

PISA, TIMSS, PIRLS and preceding surveys of student ability measure different domains of student cognitive skill and learning progress, often using different testing techniques across different year levels (for example, TIMSS makes assessments against an international mathematics and science curriculum at the year 4 and 8 level, whereas PISA tests students mathematics skills in more general terms at the age of 15). There is




some evidence that countries can perform quite differently on these respective measures (e.g. Australia performs much better on PISA relative to TIMSS).

Due to a lack of consistent data for any one testing procedure over time, it is not possible to systematically study the extent to which simplifying assumptions about the different tests affect parameter estimates.




Appendix B: Individual level modellingThis appendix sets out in detail the approach used in the individual level modelling which draws on LSAY data. Tables B.1 and B.2 below show the sample attribution rates and variables used in the LSAY data.

Variable lists and dataTable B.1: Survey sample attrition in LSAY

LSAY 2003 LSAY 2006

Sample % of totalYear-on-year attrition

Sample % of totalYear-on-year attrition

2003 10370 100% --2004 9378 90% 10%2005 8691 84% 7%2006 7721 74% 11% 14170 100% --2007 6658 64% 14% 9353 66% 34%2008 6074 59% 9% 8380 59% 10%2009 5475 53% 10% 7299 52% 13%2010 4903 47% 10% 6316 45% 13%2011 4429 43% 10% 5420 38% 14%2012 3945 38% 11% 4670 33% 14%2013 3741 36% 5% 4223 30% 10%2014 3839 27% 9%

Source: Deloitte Access Economics (2016)




Table B.2: Variable list and summary statistics for estimation

Variable Mean SD Min Max OLS: Wages52Probit:

Employment, Participation5

Multinomial logit:

Educational attainment5

Dependent variablesHourly wage (log) 2.811 0.536 -4.605 7.048 XHourly wage 17.32 16.11 0 1,150Employment indicator 0.913 0.281 0 1 XParticipation indicator 0.829 0.376 0 1 XHighest educational attainment categories -- --53 0 6 XPISAPISA Maths (log) 6.244 0.184 4.765 6.725 X X XPISA Maths 523.5 91.60 117.4 833.3PISA Science (log) 6.250 0.205 4.274 6.767 X X XPISA Science 528.5 100.3 71.84 869.0PISA Reading (log) 6.240 0.199 -2.108 6.743 X X XPISA Reading 522.3 94.82 0.122 848.2EducationHighest educational attainment categories

No qualification 0.334 0.472 0 1

Year 12 or equivalent 0.350 0.477 0 1 X XCertificate I or II 0.0518 0.222 0 1 X XCertificate III or IV 0.101 0.301 0 1 X XDiploma or advanced diploma 0.0456 0.209 0 1 X X

52 See http://www.barrolee.com/53 Note – an X denotes that the final estimated equation included the listed variable as part of the model specification




Variable Mean SD Min Max OLS: WagesProbit:


Multinomial logit:


Bachelor degree 0.107 0.309 0 1 X XPostgraduate degree54 0.0108 0.104 0 1 X XIndependent school dummy 0.166 0.372 0 1 X X XCatholic school dummy 0.217 0.412 0 1 X X XMetropolitan school dummy 0.717 0.450 0 1 X X XPISA testing in year 11 or above dummy 0.195 0.397 0 1 X X XPISA testing in year 10 dummy 0.716 0.451 0 1 X X XPISA testing in year 9 or below 0.0889 0.285 0 1Completed apprenticeship dummy 0.0940 0.292 0 1 X XNo. of gaps in study 0.464 0.620 0 3 X X XWork experienceNo. of years in employment (prior) 3.056 2.702 0 10 X X XNo. of gaps in employment 0.309 0.532 0 3 XPart-time employed dummy 0.478 0.500 0 1 XDemographicsPISA index of economic, social & cultural status (ESCS)

0.231 0.807 -3.902 2.536 X X X

Aboriginal or Torres Strait Islander dummy (ATSI)

0.0221 0.147 0 1 X X X

Female dummy 0.491 0.500 0 1 X X XEnglish as a second language or dialect (at home) dummy (EAL)

0.0901 0.286 0 1 X X X

Non-native student dummy 0.104 0.305 0 1 X X XFirst-generation student dummy 0.200 0.400 0 1 X X X

54 Summary statistics not shown as results for dummy variables have no intuitive interpretation104




Variable Mean SD Min Max OLS: WagesProbit:


Multinomial logit:


Have children dummy 0.0445 0.206 0 1 X XLive at home dummy 0.734 0.442 0 1 X XIn a relationship dummy 0.128 0.334 0 1 X XLSAY cohort 2006 dummy 0.449 0.497 0 1 X X XState fixed effects ACT 0.0195 0.138 0 1

NSW 0.321 0.467 0 1 X X XVIC 0.243 0.429 0 1 X X XQLD 0.193 0.395 0 1 X X XSA 0.0842 0.278 0 1 X X XWA 0.108 0.310 0 1 X X XTAS 0.0241 0.153 0 1 X X XNT 0.00758 0.0867 0 1 X X X

Year fixed effects 2003 0.0501 0.218 0 12004 0.0501 0.218 0 1 X X X2005 0.0501 0.218 0 1 X X X2006 0.1000 0.300 0 1 X X X2007 0.1000 0.300 0 1 X X X2008 0.1000 0.300 0 1 X X X2009 0.1000 0.300 0 1 X X X2010 0.1000 0.300 0 1 X X X2011 0.1000 0.300 0 1 X X X2012 0.0498 0.218 0 1 X X X2013 0.0498 0.218 0 1 X X X2014 0.0498 0.218 0 1 X X X

Source: Deloitte Access Economics (2016)







Empirical methodology and results Educational attainment

Method

Multinomial logit models are used to estimate probabilities against several distinct choices, compared to a baseline result. A multinomial logit is used to estimate each individual’s propensity of being in each educational attainment category. This analysis allows for the effect of PISA scores to be isolated, whilst controlling for other explanatory factors.

The estimation equation is:

Pr (Educatio n¿=k )=exp (γ0 , k+γ 1, k lnPIS A i+γ k X ¿)

1+∑j=1

K

exp (¿ γ0 , j+γ 1 , j lnPIS A i+γ j X¿)¿

Where: Education is the highest level of educational attainment for individual i at time t k∈K is a specific educational attainment from K possible outcomes.

• In this equation, there were 6 possible outcomes: (1) no qualification; (2) certificate I or II; (3) certificate III or IV; (4) diploma or advanced diploma; (5) bachelor degree; or (6) Postgraduate degree (including graduate certificates and diplomas).

PISA is the PISA score of individual i at age 15; and X is a vector of student and school level characteristics, which may influence

educational attainment.

Since the model is a nonlinear function of its parameters, the marginal effect of a percentage increase in PISA scores is calculated as:

∂Pr (Educatio n¿=k )∂ lnPIS A i

=Pr itk[γ 1 ,k−∑j=1

K

γ1 ,k exp (γ 0 , j+γ 1, j ln PIS Ai+γ j X ¿)

1+∑j=1

K

exp (γ0 , j+γ1 , j lnPIS A i+γ j X ¿) ]Estimation is restricted to individuals who are not currently studying, as their current maximum level of educational attainment does not reflect their characteristics.

The marginal effect of PISA on educational attainment can be interpreted as a percentage point increase in the likelihood of entering a specific educational category k compared to the base category of no qualifications.

Results




Table B.3 is consistent with results shown in the body, but includes standard errors and the estimated coefficients for all control variables. In the interests of brevity, the estimated results are only shown for the maths equation, but these do not deviate materially across different dependent variables. The cohort analysis in Table B.4 solves the same estimation equation, but on certain cohorts of students within the entire population. Where the baselines result calculates the average marginal effect for the entire sample, the cohort analysis calculates the average marginal effect for the individuals within a subpopulation of the estimation sample, in this case, the first and last quintiles.

The results suggest that the marginal effect of PISA scores varies substantially depending on an individual’s score, and that these patterns are consistent over disciplines. In particular, the marginal effect on the propensity into Year 12 changes sign to negative (from positive) for the top quintile, whilst the bottom quintile remains positive and large (column 1).

Table B.3: Multinomial Logit: marginal effects of PISA scores on educational attainment

Year 12(1)

Cert I/II(2)

Cert III/IV(3)

Dip/adv dip(4)

Bachelor(5)

Postgrad(6)

Dependent Variable: Highest educational attainment (base: no qualification)

PISA Maths 0.146*** -0.214*** -0.264*** -0.0963*** 0.539*** 0.0532***

(0.0273) (0.0216) (0.0266) (0.0201) (0.0214) (0.00907)

PISA Science 0.115*** -0.183*** -0.212*** -0.0952*** 0.462*** 0.0543***

(0.0246) (0.0179) (0.0232) (0.0169) (0.0216) (0.00875)

PISA Reading 0.131*** -0.204*** -0.241*** -0.0952*** 0.512*** 0.0534***

(0.0266) (0.0190) (0.0254) (0.0186) (0.0238) (0.0102)Note: Each row is a separate regression estimation substituting different PISA disciplines. Below results are regression values for PISA Maths estimation1

EducationIndependent 0.0310** -0.0366*** -

0.0385*** 0.00814 0.0583*** 0.0115***

(0.0132) (0.0120) (0.0144) (0.00905) (0.00881) (0.00330)

Catholic 0.00483 -0.00906 -0.0334*** 0.00803 0.0539*** 0.00911***

(0.0106) (0.00855) (0.0107) (0.00737) (0.00766) (0.00325)

Metropolitan -0.000848 -0.0240*** -0.0235** 0.0356*** -0.000994 0.00577(0.0105) (0.00749) (0.00974) (0.00825) (0.00856) (0.00380)

No. of gaps in study

-0.0172*** 0.00319 0.0453*** 0.0292*** -

0.0429*** 0.00375**

(0.00637) (0.00492) (0.00602) (0.00422) (0.00497) (0.00183)

Work experienceNo. of years employed

-0.0121***

0.00884*** 0.0231*** 0.000717 -

0.0157*** -0.00155**

(0.00269) (0.00219) (0.00268) (0.00171) (0.00172) (0.000647)

Demographics ESCS 0.00546 -0.0210*** - -0.00189 0.0531*** 0.00485***




Year 12(1)

Cert I/II(2)

Cert III/IV(3)

Dip/adv dip(4)

Bachelor(5)

Postgrad(6)

0.0222***(0.00602) (0.00452) (0.00595) (0.00414) (0.00486) (0.00187)

ATSI 0.0689*** 0.0119 0.00400 -0.0350* -0.0421* -0.0159(0.0201) (0.0134) (0.0195) (0.0203) (0.0217) (0.0104)

Female -0.0125 -0.0761*** -0.0249*** 0.00773 0.118*** 0.0170***

(0.00842) (0.00709) (0.00826) (0.00594) (0.00636) (0.00273)

EAL 0.0432* -0.0522** -0.0409* 0.0134 0.0868*** 0.00311(0.0232) (0.0214) (0.0241) (0.0131) (0.0147) (0.00502)

Non-native student

-0.0143 -0.0189 -0.0473** 0.0154 0.0578*** 0.0103**

(0.0198) (0.0173) (0.0207) (0.0125) (0.0133) (0.00456)

First-gen student

-0.00114 -0.0118 -0.0245** 0.00628 0.0405*** 0.00273

(0.0121) (0.00950) (0.0124) (0.00828) (0.00867) (0.00398)

Have children 0.0458** 0.0236** 0.0624*** -0.0321** -0.169*** -0.0111*(0.0184) (0.0107) (0.0147) (0.0130) (0.0188) (0.00644)

In a relationship

-0.00767 0.0160** 0.0394*** 0.00519 -0.0542*** -0.00528*

(0.00921) (0.00670) (0.00831) (0.00596) (0.00687) (0.00278)

Live at home 0.0178** -0.000816 0.00830 0.00807 -0.0147** -0.000673(0.00812) (0.00626) (0.00793) (0.00558) (0.00611) (0.00242)

Note (1): in the interests of brevity, only the results for maths PISA scores were included. These results do not deviate materially for the science or reading equations.Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

Table B.4: Cohort analysis: marginal effects of PISA scores on educational attainment

Year 12(1)

Cert I/II(2)

Cert III/IV(3)

Dip/adv dip(4)

Bachelor(5)

Postgrad(6)

Dependent Variable: Highest educational attainment (base: no qualification)PISA MathsTop 20% -0.127*** -0.134*** -0.287*** -0.141*** 0.695*** 0.0661***

(0.0228) (0.00681) (0.0133) (0.0108) (0.0391) (0.0252)

Bottom 20% 0.324*** -0.247*** -0.160*** -0.0356 0.317*** 0.0337***(0.0290) (0.0377) (0.0363) (0.0249) (0.00816) (0.00274)

PISA ScienceTop 20% -0.0998*** -0.118*** -0.249*** -0.123*** 0.580*** 0.0781***

-0.0217 -0.00609 -0.0132 -0.00958 -0.037 -0.0235

Bottom 20% 0.267*** -0.212*** -0.110*** -0.0453** 0.272*** 0.0293***-0.0254 -0.0317 -0.0305 -0.021 -0.0074 -0.0024

PISA ReadingTop 20% -0.121*** -0.112*** -0.281*** -0.143*** 0.648*** 0.0751***

-0.0228 -0.00588 -0.0144 -0.0119 -0.0419 -0.0288




Year 12(1)

Cert I/II(2)

Cert III/IV(3)

Dip/adv dip(4)

Bachelor(5)

Postgrad(6)

Bottom 20% 0.326*** -0.254*** -0.108*** -0.0197 0.256*** 0.0245***-0.0276 -0.0355 -0.0336 -0.0208 -0.00722 -0.00219

Note: Marginal effects calculated for subpopulations of PISA math values within the estimation sample. N = 33,156Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

The estimated coefficients act a priori, as we would have expected given our understanding of the interaction between education and wages. That is, improving PISA scores have a larger impact on the likelihood of obtaining high school attainment for lower achieving students; conversely, the increased likelihood of advancing to further education is stronger for high achieving students. The consistency of the findings within subpopulations of the sample increases the level of confidence in the findings for the sample as a whole.

To test the robustness of the estimated equation, the predictive power of the model was tested. Each student was assumed to receive the educational attainment the model predicted they were most likely to achieve. This was then compared against the actual results. The results are shown in Table B.5.

Table B.5: Model accuracy

Educational attainment Correct predictionsEntire population 52%No educational attainment 43%High school attainment 54%Certificate I or II 30%Certificate III or IV 35%Diploma/advanced diploma 21%Undergraduate degree 56%Postgraduate degree 0%

Under the estimated model, 52% of all predictions are correct. Given there are seven different types of educational attainment, if educational attainment was predicted at random, 14% of predictions would be expected to be correct. Overall, this suggests the model has relatively significant predictive power.

Wages

Method

An ‘augmented Mincer equation’ is used to describe the relationship between PISA scores and wages. This equation, built on Mincer’s (1974) seminal work into the effects of education on wages. The estimated equation is given below:

lnwage ¿=β0+β1 lnPIS A i+∑j=1

K

δ j Education jit+β Z¿+ϵ ¿

Where:




wage is the hourly wage of individual i at time t ; PISA is the PISA score of individual i at age 15; β1 is interpreted as the marginal effect – the percentage increase in wages due to a

1% increase in PISA scores; Education is the highest level of educational attainment; and Z is a vector of student and school level characteristics, which may influence wages.

Estimated coefficients from the equation above are used to identify the direct effect of PISA scores – that is, the impact of cognitive ability on the wage offer.

In order to estimate the indirect effect of PISA scores – the impact of cognitive ability on improving educational attainment, resulting in higher wages – the effect of PISA scores through education needs to be estimated.

If the relationship between education and PISA was linear in its parameters, this estimation could be simply performed as a nested OLS regression. However, since education is a categorical variable and estimated by a multinomial logit model, the relationship is highly nonlinear, as shown:

E [ lnw∨X ]=β0+β1 lnPISA+∑j=1

K

δ j P (Education j= j )+β Z

In response to these nonlinearities, a prediction analysis is used to estimate the indirect and total effects of PISA. The steps applied are described as follows:

1. Estimate the augmented Mincer equation by OLS regression to identify the direct effect of PISA scores on wages, including controls for the effect of educational attainment. The estimation sample is restricted to individuals who are employed, but not studying.

2. Estimate the multinomial logit model of educational attainment. The estimation sample removes individuals currently studying.

3. Obtain the predicted probabilities for each educational attainment level using the estimates from step 2.

4. Simulate wages using the predicted probabilities from step 3 of each level of educational attainment.

5. Repeat step 3, using PISA scores inflated by 1%.

6. Simulate new wages using the predicted probabilities and inflated PISA scores from step 5.

7. Calculate the average difference between the two predicted wage results (step 4 and 6), using weights to account for attrition bias. This difference can be interpreted as the estimated total effect of an increase in PISA scores on wages, as step 6 includes both changes to PISA scores and educational attainment.

8. Calculate the difference between the total effect (step 7) and the direct effect (step 1), which can be interpreted as the indirect of PISA scores on wages.




A simulation based approach to estimating the total effect of PISA scores on wages is required due to the complexity of the model, the effect of education choice is highly nonlinear and depends on the characteristics of each individual. The goal of computing the expected difference in wages across the sample implies that using the difference in predicted probabilities from the multinomial logit model, as described above, is necessary to remove errors in educational choice.

Results

Table B.6 shows the effect of PISA scores with control for educational attainment (odd columns) and without control for educational attainment (even columns). The coefficients for PISA in the odd columns can be interpreted as naïve estimates for the total effect of PISA on wages, whilst the coefficients in the even columns separate the effect of educational qualifications and isolated the direct effect of PISA.

As expected, these naïve estimations of the total effect produce are very similar to the simulated wage results in Table B.7. Additionally, these results show gains from completing an apprenticeship and previous work experience (possibly eliciting support for a ‘momentum effect’ on wages), whilst females on average experience lower hourly wages.

Table B.6: OLS: Direct effect of PISA on wages

Maths Science Reading(1) (2) (3) (4) (5) (6)

Dependent variable: Hourly wage (log)PISA 0.129*** 0.0863*** 0.0878*** 0.0527** 0.0825*** 0.0446

(0.0298) (0.0299) (0.0263) (0.0262) (0.0303) (0.0302)

EducationYear 12 0.0228 0.0249 0.0253

(0.0171) (0.0172) (0.0170)

Certificate I/II 0.0353 0.0361 0.0360(0.0233) (0.0233) (0.0233)

Certificate III/IV

0.0178 0.0188 0.0192

(0.0199) (0.0199) (0.0199)

Diploma/Adv Dip

0.0285 0.0300 0.0302

(0.0223) (0.0222) (0.0222)

Bachelor 0.0944*** 0.0994*** 0.101***(0.0199) (0.0201) (0.0199)

Postgraduate 0.167*** 0.172*** 0.174***(0.0278) (0.0280) (0.0277)

Independent -0.00133 -0.00873 0.000868 -0.00743 0.00134 -0.00705(0.0128) (0.0128) (0.0128) (0.0128) (0.0127) (0.0128)

Catholic 0.00632 -0.00152 0.00656 -0.00162 0.00620 -0.00182(0.00990) (0.00996) (0.00991) (0.00997) (0.00985) (0.00993)

Metropolitan -0.00876 -0.00857 -0.00937 -0.00903 -0.0101 -0.00954(0.00999) (0.00985) (0.0101) (0.00996) (0.0101) (0.00994)




Maths Science ReadingFinished apprenticeship

0.0502*** 0.0647*** 0.0489*** 0.0649*** 0.0486*** 0.0649***(0.0142) (0.0159) (0.0140) (0.0158) (0.0139) (0.0158)


-0.0347*** -0.0319*** -0.0350*** -0.0319*** -0.0348*** -

0.0316***(0.00674) (0.00677) (0.00670) (0.00671) (0.00672) (0.00672)


0.0149*** 0.0174*** 0.0148*** 0.0174*** 0.0148*** 0.0174***(0.00297) (0.00295) (0.00297) (0.00295) (0.00298) (0.00294)

No. of gaps in employment

-0.0225*** -0.0178** -0.0237*** -0.0185** -0.0240*** -0.0187**(0.00862) (0.00866) (0.00869) (0.00869) (0.00870) (0.00870)

Part-time 0.00280 0.00176 0.00317 0.00196 0.00320 0.00194(0.00930) (0.00921) (0.00925) (0.00917) (0.00926) (0.00918)

Demographics ESCS 0.0231*** 0.0183*** 0.0248*** 0.0194*** 0.0251*** 0.0198***

(0.00601) (0.00599) (0.00630) (0.00626) (0.00624) (0.00620)

ATSI 0.0435* 0.0448* 0.0400 0.0422* 0.0383 0.0410*(0.0245) (0.0244) (0.0248) (0.0246) (0.0248) (0.0246)

Female -0.0880*** -0.0982*** -0.0912*** -0.101*** -0.0977*** -0.105***

(0.00835) (0.00823) (0.00821) (0.00808) (0.00822) (0.00808)

EAL 0.00422 -0.00336 0.00557 -0.00311 0.00468 -0.00392(0.0205) (0.0204) (0.0202) (0.0201) (0.0203) (0.0203)

Non-native student

0.0432** 0.0372** 0.0443** 0.0376** 0.0437** 0.0369**(0.0190) (0.0190) (0.0186) (0.0188) (0.0188) (0.0188)

First-gen student

0.0331*** 0.0297** 0.0322*** 0.0288** 0.0320*** 0.0287**

(0.0123) (0.0120) (0.0123) (0.0120) (0.0124) (0.0121)

Adjusted R squared

0.300 0.304 0.299 0.303 0.299 0.303

Note: Fixed effects for years and state, and variables for testing years and LSAY cohort not shown. N = 29,289Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

The simulation results in Table B.7 show similar wage outcomes to the naïve estimates of the total wage effect of PISA scores without controlling for educational attainment in all subjects.

Table B.7: Wage simulation: Difference in wages after a 1% increase in PISA scores

Maths(1)

Science(2)

Reading(3)

Wage difference 0.1245*** 0.0876*** 0.0837***(0.0309) (0.0275) (0.0309)

Note: N = 29,289Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

Table B.8 (as presented in the body), shows the effect of including a quadratic term for PISA scores (even columns), while Table B.9 presents an alternate method of cohort analysis and




testing for nonlinearities. An OLS estimation of the effect of PISA scores on wages is performed separately for different cohorts or groupings of PISA scores. Each even column shows the PISA coefficients for two separate regressions, one performed using only individuals who scored in the top 20% of their discipline, the other restricted to the bottom 20%.

Both results support similar conclusions – that the quadratic estimations in that the marginal effect for already high performing students is large for PISA maths (column 2), whilst statistically inconclusive results for science and reading suggest more linear effects. However, observing point estimates suggests that science and reading may have a relatively higher marginal effect for lower performing cohorts (columns 4 and 6). Reading in particular may be viewed as a foundational skill and produce the greatest returns for those below a threshold of competency.

Table B.8: Cohort analysis: OLS: Nonlinear quadratic direct effect of PISA on wages


Dependent variable: Hourly wage (log) PISA 0.0863*** -3.467*** 0.0527** -0.702 0.0446 -0.492

(0.0299) (1.244) (0.0262) (0.647) (0.0302) (0.983)

PISA (squared) 0.288*** 0.0615 0.0438(0.1000) (0.0526) (0.0793)

EducationYear 12 0.0228 0.0227 0.0249 0.0249 0.0253 0.0257

(0.0171) (0.0168) (0.0172) (0.0172) (0.0170) (0.0169)

Certificate I/II 0.0353 0.0383* 0.0361 0.0371 0.0360 0.0368(0.0233) (0.0229) (0.0233) (0.0233) (0.0233) (0.0230)

Certificate III/IV

0.0178 0.0199 0.0188 0.0196 0.0192 0.0199(0.0199) (0.0195) (0.0199) (0.0199) (0.0199) (0.0197)

Diploma/Adv Dip

0.0285 0.0299 0.0300 0.0308 0.0302 0.0311(0.0223) (0.0219) (0.0222) (0.0222) (0.0222) (0.0220)

Bachelor 0.0944*** 0.0917*** 0.0994*** 0.0986*** 0.101*** 0.101***(0.0199) (0.0198) (0.0201) (0.0202) (0.0199) (0.0200)

Postgraduate 0.167*** 0.164*** 0.172*** 0.171*** 0.174*** 0.173***(0.0278) (0.0277) (0.0280) (0.0280) (0.0277) (0.0278)

Independent -0.00873 -0.00925 -0.00743 -0.00768 -0.00705 -0.00727(0.0128) (0.0127) (0.0128) (0.0127) (0.0128) (0.0127)

Catholic -0.00152 -0.000167 -0.00162 -0.00139 -0.00182 -0.00162(0.00996) (0.00998) (0.00997) (0.00996) (0.00993) (0.00993)

Metropolitan -0.00857 -0.00897 -0.00903 -0.00894 -0.00954 -0.00958(0.00985) (0.00983) (0.00996) (0.00996) (0.00994) (0.00993)

Finished apprenticeship

0.0647*** 0.0640*** 0.0649*** 0.0645*** 0.0649*** 0.0647***(0.0159) (0.0159) (0.0158) (0.0159) (0.0158) (0.0158)


-0.0319*** -0.0317***

-0.0319*** -0.0319*** -0.0316*** -0.0317***

(0.00677) (0.00677) (0.00671) (0.00671) (0.00672) (0.00672)




Maths Science ReadingWork experienceNo. of years employed

0.0174*** 0.0181*** 0.0174*** 0.0176*** 0.0174*** 0.0175***(0.00295) (0.00295) (0.00295) (0.00296) (0.00294) (0.00294)

No. of gaps in employment

-0.0178** -0.0170** -0.0185** -0.0183** -0.0187** -0.0185**(0.00866) (0.00862) (0.00869) (0.00870) (0.00870) (0.00870)

Part-time 0.00176 0.00197 0.00196 0.00190 0.00194 0.00198(0.00921) (0.00919) (0.00917) (0.00918) (0.00918) (0.00920)

Demographics ESCS 0.0183*** 0.0173*** 0.0194*** 0.0190*** 0.0198*** 0.0197***

(0.00599) (0.00601) (0.00626) (0.00628) (0.00620) (0.00625)

ATSI 0.0448* 0.0441* 0.0422* 0.0424* 0.0410* 0.0411*(0.0244) (0.0246) (0.0246) (0.0246) (0.0246) (0.0246)

Female -0.0982*** -0.0971*** -0.101*** -0.101*** -0.105*** -0.105***

(0.00823) (0.00816) (0.00808) (0.00810) (0.00808) (0.00806)

EAL -0.00336 -0.00264 -0.00311 -0.00289 -0.00392 -0.00384(0.0204) (0.0201) (0.0201) (0.0201) (0.0203) (0.0202)

Non-native student

0.0372** 0.0371** 0.0376** 0.0376** 0.0369** 0.0369**(0.0190) (0.0184) (0.0188) (0.0189) (0.0188) (0.0188)

First-gen student

0.0297** 0.0300** 0.0288** 0.0292** 0.0287** 0.0287**(0.0120) (0.0119) (0.0120) (0.0120) (0.0121) (0.0121)

Note: Estimation equation is identical to Table B.6 with education attainment variables; full variable results not shown. N = 29,289 Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

Table B.9: Cohort analysis: OLS: Comparing direct effect of PISA on wages by separate estimation


Dependent variable: Hourly wage (log) PISA All 0.0863*** 0.0527** 0.0446

(0.0299) (0.0262) (0.0302)

Top 20% 0.255* -0.0274 -0.0299 (0.138) (0.0849) (0.0737)

Bottom 20% -0.170 0.0215 0.0694(0.110) (0.0625) (0.0677)

Note: Estimated equation is identical to Table B.6; full variable results not shown for brevity.Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.PISA results in even numbered columns are estimated separately by quintiles in the sample. N = 29,289 for odd columns. N = 3,994 for bottom 20% estimations. N = 7,105 for top 20% estimations.

Table B.10 estimates the effects of PISA by different cohorts using a ‘piece-wise’ estimation. The full sample is included in the regression and PISA scores are interacted with indicator variables for each quintile. This approach utilises the full sample by assuming the other




control variables have a constant marginal effect over the sample (e.g. the impact of gender is consistent across each quintile). The results suggest similar themes to those discussed.

Table B.10: Cohort analysis: Comparing direct effect of PISA on wages by piece-wise estimation


Dependent variable: Hourly wage (log)PISA 0.0863*** 0.0527** 0.0446

(0.0299) (0.0262) (0.0302)

PISA x Top 20% 0.282** -0.0246 0.0161 (0.132) (0.0824) (0.0700)

PISA x Bottom 20%

-0.137 0.0501 0.0586

(0.109) (0.0593) (0.0629)Note: Estimated equation is identical to Table B.6; full variable results not shown for brevity.Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.PISA results in even numbered columns are estimated by interacting PISA with an indicator variable for each quintile in the sample. N = 29,289.

These combined results all lead to the same conclusion in regard to the importance of PISA scores for students of differing abilities, adding a degree of confidence in the results.

Employment

Method

Probit models are commonly used to estimate the propensity of binary outcomes – that is, events or choices that have only two possible outcomes. Nonlinear probit models have important advantages over linear estimation methods, as they ensure that predicted probabilities remain within the range of zero to one.

The propensity of an individual being employed is estimated using a probit model that can isolate the effect of PISA scores and control for other explanatory characteristics. The estimation equation is given by:

Pr (Employe d¿=1)=Φ (α 0+α1 ln PISAi+α X ¿ )

Where: Employed is a dummy variable which equals one if individual i at time t is employed

and equals zero if they are unemployed; PISA is the PISA score of individual i at age 15; X is a vector of explanatory variables; and Φ is the standard normal cumulative distribution function.

Since the probit model is nonlinear in its parameters, the marginal effect of a percentage increase in PISA scores is calculated by:




∂Pr (Employe d¿=1)∂ lnPISA i

¿X ¿=ϕ (α0+α1 ln PISAi+α X¿)α 1

Where: ϕ is the standard normal probability density function.

The equation is solved using maximum likelihood using only the sample of individuals in the labour force and not concurrently studying. Students are removed from the sample as their employment status does not accurately reflect their characteristics at this point in time, and is likely to introduce bias.

Results

The probit estimation in Table B.11 shows relatively consistent results across disciplines, suggesting that each of the domains contributes to an individual’s propensity into employment. Similar to the wage estimations, maths again reveals a relatively larger marginal effect.

Table B.11: Probit: Marginal effects into employment


Dependent variable: Likelihood of being EmployedPISA 0.0893*** 0.0736**

*0.0609*** 0.0462*** 0.0696*** 0.0538***

(0.0108) (0.0110) (0.00948) (0.00948) (0.00984) (0.00987)

Education Year 12 0.0242**

*0.0254*** 0.0250***

(0.00528) (0.00523) (0.00525)

Certificate I/II -0.00601 -0.00596 -0.00614(0.00767) (0.00759) (0.00762)

Certificate III/IV

0.0209***

0.0209*** 0.0206***

(0.00666) (0.00660) (0.00661)

Diploma/Adv Dip

0.0314***

0.0318*** 0.0310***

(0.00804) (0.00797) (0.00798)

Bachelor 0.0388***

0.0421*** 0.0416***

(0.00716) (0.00708) (0.00708)

Postgraduate 0.0493***

0.0523*** 0.0521***

(0.0151) (0.0149) (0.0150)

Independent 0.00902* 0.00642 0.0102** 0.00722 0.00989** 0.00698(0.00483) (0.00483) (0.00479) (0.00479) (0.00480) (0.00480)

Catholic 0.0176*** 0.0157***

0.0179*** 0.0159*** 0.0176*** 0.0156***

(0.00419) (0.00418) (0.00416) (0.00415) (0.00416) (0.00415)




Maths Science ReadingMetropolitan -0.000834 -0.00113 -0.00121 -0.00147 -0.00147 -0.00164

(0.00397) (0.00395) (0.00394) (0.00392) (0.00395) (0.00393)


0.0196*** 0.0298***

0.0178*** 0.0292*** 0.0184*** 0.0296***

(0.00522) (0.00638) (0.00518) (0.00633) (0.00521) (0.00634)


-0.0112*** -0.0110**

*

-0.0110*** -0.0107*** -0.0113*** -0.0109***

(0.00242) (0.00241) (0.00240) (0.00239) (0.00240) (0.00238)Work experienceNo. of years employed

0.0270*** 0.0273***

0.0269*** 0.0272*** 0.0269*** 0.0272***

(0.00109) (0.00109) (0.00109) (0.00109) (0.00109) (0.00109)Demographics ESCS 0.00766**

*0.00575*

*0.00861**

*0.00655**

*0.00822**

*0.00621**

*(0.00227) (0.00227) (0.00227) (0.00226) (0.00228) (0.00227)

ATSI -0.0242*** -0.0229**

*

-0.0247*** -0.0236*** -0.0250*** -0.0237***

(0.00680) (0.00670) (0.00678) (0.00666) (0.00677) (0.00665)

Female 0.00707** 0.00379 0.00445 0.00141 -0.000326 -0.00227(0.00317) (0.00321) (0.00313) (0.00316) (0.00316) (0.00320)

EAL 0.00983 0.00567 0.0109 0.00612 0.0107 0.00603(0.00726) (0.00726) (0.00721) (0.00721) (0.00727) (0.00725)

Non-native student

0.0176** 0.0157** 0.0188*** 0.0163** 0.0187*** 0.0163**(0.00708) (0.00705) (0.00701) (0.00698) (0.00710) (0.00705)

First-gen student

0.00132 0.000177 0.00157 0.000206 0.00142 0.000124

(0.00429) (0.00427) (0.00426) (0.00424) (0.00426) (0.00424)

Have children -0.0477*** -0.0420**

*

-0.0483*** -0.0422*** -0.0490*** -0.0427***

(0.00748) (0.00756) (0.00741) (0.00749) (0.00743) (0.00751)

In a relationship

0.0155*** 0.0169***

0.0154*** 0.0169*** 0.0155*** 0.0170***

(0.00486) (0.00482) (0.00481) (0.00478) (0.00482) (0.00478)

Live at home -0.00321 -0.00395 -0.00379 -0.00448 -0.00350 -0.00421(0.00395) (0.00394) (0.00393) (0.00392) (0.00394) (0.00393)

Note: Fixed effects for years and state, and variables for testing years and LSAY cohort not shown. N = 31,487. Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

Table B.12 estimates the average marginal effect for individuals within the top or bottom quintiles. Dissimilar to the wage equations, the results show diminishing marginal effects, such that the bottom quintile of performers consistently receive a greater marginal propensity into employment compared to high performing students. This is consistent with our a priori expectations, in that the marginal impact of higher PISA scores on employment




of higher performing students is likely to be lower since these students have a higher likelihood of employment in any event, serving as a robustness test of our results.

Table B.12: Cohort analysis: comparing marginal effects into employment by quintiles

Maths Science Reading(1) (2) (3)

Dependent variable: Employed vs unemployedPISA Top 20% 0.0772*** 0.0482*** 0.0566***

(0.0117) (0.0100) (0.0105)

Bottom 20% 0.0991*** 0.0596*** 0.0717***(0.0161) (0.0131) (0.0142)

Note: Estimation equation is identical to Table B.11. Marginal effects calculated for subpopulations of the estimation sample. N = 31,487.

Labour force participation

Method

The approach to estimation of the participation entry decision is similar to the employment question. In the employment equation, the dependent variable was Employe d¿, here it is replaced by Participating¿, which equals one for individual i at time t if they are in the labour force (either employed or unemployed) and equals zero if they are not in the labour force (NILF). The sample is restricted to individuals not currently studying, as their labour force participation does not accurately represent their characteristics and is likely to bias results against individuals of higher cognitive ability.

Results

Table B.13 shows the marginal effects of PISA to labour force participation. The key result is that across all domains, PISA scores were found to be insignificant in determining labour force participation. Not only is each PISA coefficient not statistically significant from zero, but the point estimates are all very small in magnitude (less than 0.01%). A cohort analysis in Table B.14 provides similar evidence.

Table B.13: Probit: Marginal effects into labour force participation


Dependent variable: Participating vs NILF PISA 0.00250 -0.00331 0.000589 -0.00464 -0.00235 -0.00821

(0.00923) (0.00938) (0.00831) (0.00828) (0.00905) (0.00905)

EducationYear 12 0.0100** 0.0102** 0.0104**

(0.00500) (0.00496) (0.00498)

Certificate I/II 0.0143* 0.0143* 0.0142*(0.00734) (0.00733) (0.00732)




Maths Science ReadingCertificate III/IV

0.0260*** 0.0261*** 0.0262***

(0.00640) (0.00639) (0.00638)

Diploma/Adv Dip

0.0382*** 0.0383*** 0.0384***

(0.00749) (0.00747) (0.00747)

Bachelor 0.0294*** 0.0296*** 0.0300***(0.00653) (0.00649) (0.00649)

Postgraduate 0.0483*** 0.0485*** 0.0488***(0.0131) (0.0130) (0.0130)

Independent -0.00557 -0.00703* -0.00547 -0.00698* -0.00532 -0.00685*(0.00381) (0.00381) (0.00381) (0.00381) (0.00381) (0.00381)

Catholic 0.0118*** 0.0108*** 0.0119*** 0.0109*** 0.0119*** 0.0109***(0.00361) (0.00361) (0.00361) (0.00360) (0.00361) (0.00360)

Metropolitan 0.00952***

0.00898***

0.00948***

0.00896***

0.00944***

0.00896***

(0.00341) (0.00338) (0.00341) (0.00337) (0.00340) (0.00337)


0.00736* 0.00115 0.00725 0.00112 0.00707 0.00105

(0.00445) (0.00567) (0.00443) (0.00566) (0.00443) (0.00565)


-0.00868**

*

-0.00931**

*

-0.00864**

*

-0.00926**

*

-0.00857**

*

-0.00918**

*(0.00200) (0.00199) (0.00199) (0.00199) (0.00200) (0.00200)


0.0166*** 0.0167*** 0.0166*** 0.0167*** 0.0166*** 0.0167***(0.000926) (0.000927) (0.000922) (0.000923) (0.000921) (0.000922)

Demographics ESCS -0.00205 -0.00319 -0.00194 -0.00309 -0.00176 -0.00292

(0.00195) (0.00194) (0.00195) (0.00194) (0.00195) (0.00194)

ATSI -0.00994 -0.00805 -0.0101 -0.00820 -0.0103 -0.00837(0.00633) (0.00627) (0.00631) (0.00625) (0.00629) (0.00624)

Female -0.0107*** -0.0131*** -0.0107*** -0.0130*** -0.0106*** -0.0125***(0.00276) (0.00277) (0.00274) (0.00274) (0.00274) (0.00274)

EAL 0.0145** 0.0120* 0.0145** 0.0119* 0.0144** 0.0117*(0.00627) (0.00618) (0.00628) (0.00619) (0.00627) (0.00618)

Non-native student

-0.0149*** -0.0162*** -0.0149*** -0.0164*** -0.0150*** -0.0164***(0.00526) (0.00521) (0.00523) (0.00518) (0.00523) (0.00517)

First-gen student

-0.00436 -0.00531 -0.00436 -0.00533 -0.00437 -0.00532

(0.00378) (0.00376) (0.00378) (0.00376) (0.00377) (0.00375)

Have children -0.129*** -0.123*** -0.128*** -0.123*** -0.128*** -0.123***(0.00504) (0.00501) (0.00503) (0.00501) (0.00503) (0.00500)

In a relationship

0.00210 0.00283 0.00210 0.00286 0.00212 0.00288




Maths Science Reading(0.00373) (0.00369) (0.00372) (0.00368) (0.00371) (0.00368)

Live at home -0.00882**

*

-0.00878**

*

-0.00884**

*

-0.00879**

*

-0.00887**

*

-0.00884**

*(0.00326) (0.00325) (0.00326) (0.00325) (0.00325) (0.00324)

Note: Selected variables only. Fixed effects for years and state, and variables testing year and LSAY cohort not shown. N = 33,253Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

Table B.14: Cohort analysis: comparing marginal effects into labour force participation by quintiles

Maths Science Reading(1) (2) (3)

Dependent variable: Participating vs NILFPISA Top 20% -0.00338 -0.00475 -0.00837

(0.00956) (0.00845) (0.00919)

Bottom 20% -0.00395 -0.00537 -0.00901(0.0111) (0.00948) (0.00974)

Note: Estimation equation is identical to Table B.13. Marginal effects calculated for subpopulations of the estimation sample. N = 33,253.Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

To test the robustness of the estimated model, a simple test was performed to consider how accurately fitted values from the estimated model reflects observed outcomes. As the probit model returns a fitted value of between 0 and 1 for each individual in the sample (with the higher the value the more likely an individual is employed), it was necessary to determine a ‘cut-off’ value – a fitted value above which an individual was considered to be employed. Given over 90% of the observations are employed, it was considered necessary to include a high cut-off value, thus 0.9 was chosen. This reflects the need for strong evidence to be comfortable in concluding an individual is likely to be employed. In other words, an individual had to return a fitted value of greater than 0.9 for this to be considered as a prediction the individual was employed. The results are shown in Table B.15 below:

Table B.15: Robustness test for probit models

Actual outcomesFitted outcomes Employed UnemployedEmployed 80% 40%

Unemployed 20% 60%

The results indicate if an individual was employed, the model was able to accurately predict this 80% of the time. If an individual was unemployed, the model has a 60% success rate. If a model was to randomly allocate each individual an outcome, this would be expected to have a 50% success rate. Therefore, the estimated model adds significant value over a random estimation.




Estimating the wage effects on occupationsMethod

To estimate the impact of PISA scores on wages within different occupations, the wage equation described earlier (based on the Mincer equation), was further augmented, to include dummies for occupations. The estimated equation is given below:

An ‘augmented Mincer equation’ is used to describe the relationship between PISA scores and wages. This equation, built on Mincer’s (1974) seminal work into the effects of education on wages. The estimated equation is given below:

lnwage¿=β0+β1 lnPIS A i+β2¿Where: wage is the hourly wage of individual i at time t ; PISA is the PISA score of individual i at age 15; β1 is interpreted as the marginal effect – the percentage increase in wages due to a

1% increase in PISA scores; High and Med are dummy variables, taking the value of 1 if individual i is employed

in a high (or medium) skilled occupation at time t ; and Z is a vector of student and school level characteristics, which may influence wages.

A description of how occupations were grouped into high, medium, and low skilled occupations is given further below.

The estimated equation includes both a dummy variable for high and medium skilled occupations. This controls for variations in wages between occupations that are independent of individual skills and abilities – for example, we would expect an individual entering a high skilled occupation to receive a higher wage, regardless of their PISA score. The modelling also includes an interaction term between these occupation dummies and PISA scores, as this reflects that an increase in schooling assessment scores can have a different impact on wages, depending on the skills and abilities required within each occupation.

Under this specification, the marginal effect of an increase in PISA scores on wages is assumed to differ for occupations of varying skill levels. The marginal effect for an increase in PISA scores for those in high skilled occupations is therefore given byβ1+ β2; while the marginal effect for those in medium skilled occupations isβ1+ β3. The marginal effect for those in low skilled occupations is simply given byβ1.

The equation described above was estimated using a pooled OLS approach with clustered standard errors to reflect that an individual’s wage at time t is likely to be dependent on their wage in previous time periods.

Results




The results are shown in Table B.16 below, with the standard errors below the estimated coefficients. The coefficients of interest are the PISA coefficient, and interaction terms between PISA and the high skill dummy, and PISA and the medium skill dummy.

Table B.16: OLS: Direct effect of PISA on wages, including occupational indicators

Maths Science ReadingDependent variable: Hourly wage (log)

Measures of cognitive abilityPISA -0.0597 -0.0172 -0.0462

(0.0714) (0.0449) (0.0557)PISA X High skill 0.231*** 0.112* 0.165**

(0.0830) (0.0598) (0.0762)PISA X Medium skill 0.135* 0.0525 0.0684

(0.0725) (0.0532) (0.0649)High skill -1.359*** -0.620* -0.948**

(0.519) (0.375) (0.477)Medium skill -0.832* -0.319 -0.414

(0.451) (0.332) (0.404)Year 12 0.0272 0.0275 0.0297*

(0.0169) (0.0173) (0.0169)Certificate I/II 0.0391* 0.0387* 0.0401*

(0.0232) (0.0235) (0.0233)Certificate III/IV 0.0177 0.0169 0.0190

(0.0198) (0.0200) (0.0198)Diploma/Adv Dip 0.0218 0.0208 0.0235

(0.0221) (0.0223) (0.0221)Bachelor 0.0697*** 0.0740*** 0.0756***

(0.0201) (0.0206) (0.0202)Postgraduate 0.132*** 0.136*** 0.138***

(0.0275) (0.0279) (0.0276)Independent -0.0132 -0.0118 -0.0113

(0.0126) (0.0126) (0.0126)Catholic -0.00357 -0.00390 -0.00380

(0.0100) (0.0100) (0.00998)Metropolitan -0.00809 -0.00820 -0.00864

(0.00991) (0.0101) (0.0100)Finished apprenticeship 0.0683*** 0.0678*** 0.0681***

(0.0159) (0.0159) (0.0157)No. of gaps in study -0.00439 -0.00422 -0.00412

(0.00463) (0.00468) (0.00468)Work experienceNo. of years employed 0.0184*** 0.0184*** 0.0183***

(0.00293) (0.00293) (0.00293)No. of gaps in employment -0.0151 -0.0159 -0.0162*

(0.00968) (0.00973) (0.00973)Part-time 0.00824 0.00766 0.00821





DemographicsESCS 0.0151** 0.0161*** 0.0168***

(0.00589) (0.00617) (0.00609)ATSI 0.0448* 0.0419* 0.0415*

(0.0246) (0.0248) (0.0247)Female -0.104*** -0.107*** -0.110***

(0.00860) (0.00852) (0.00843)EAL -0.00733 -0.00810 -0.00882

(0.0204) (0.0203) (0.0205)Non-native student 0.0427** 0.0429** 0.0424**

(0.0188) (0.0190) (0.0189)First-gen student 0.0329*** 0.0322*** 0.0321***

(0.0119) (0.0119) (0.0120)Adjusted R squared 0.306 0.305 0.305

Note: Fixed effects for years and state, and variables for testing years and LSAY cohort not shown. N = 29,030 Note (2): ***represents significance at the 1% level; ** represent significance at the 5% level.

The results for maths scores above are as expected, with a strong positive coefficient on the interaction term between PISA scores and high skilled occupations, and a positive coefficient between PISA scores and medium skilled occupations. The insignificance of the coefficient of PISA scores indicates that there is no reward for increasing PISA scores in low skilled occupations, all of which is consistent with our prior.

The other result worth noting is the negative coefficient for high and medium skilled occupations. While this appears incongruous with prior expectations, the interpretation of this coefficient cannot be considered in isolation. Given the design of the model, the total wage impact of being employed in a high skilled occupation is given by:

β4+β2 ¿

In other words, the marginal impact (on wages) of working in a high or medium skill occupation is dependent on a student’s PISA score. Chart B.1 illustrates the impact of changing occupations on wages, at a range of fixed PISA scores.




Chart B.1: Effect on wages of entering occupations of different skill, by PISA score

-0.10%

-0.05%

0.00%

0.05%

0.10%

0.15%

0.20%

Lowest1%

Lowest10%

Lowest25%

Median Top 25% Top 10% Top 1%

Mar

gina

l effe

ct o

n w

ages

of c

hang

ing

occu

patio

n

PISA ranking

High skilled occupations

Medium skilled occupations

After the PISA scores are taken into account, the marginal impacts of working in a high or medium skilled occupation are mostly consistent with prior expectations. As expected, for any given PISA score, the wage premium associated with entering a high skilled occupation is always larger than the premium for entering a medium skilled occupation.

The marginal effect of entering a high or medium skilled occupation increases steadily with a higher PISA score. For example, an individual with a median PISA score receives a wage premium of 0.09% if they enter a high skilled occupation (relative to what they would earn in a low skilled occupation), or 0.02% if they enter a medium skilled occupation. This increases for those in the top 1% of PISA scores, with a wage premium of 0.16% and 0.06% if they enter a high skilled or medium skilled occupation respectively.

Interestingly, those in the lowest 1% to 10% of PISA scores may receive higher wages in low skilled occupations than they would in high or medium skilled occupations. This could be an anomaly in the data, driven by the lack of granularity in the occupational groupings (this is described in further detail below). This would result in some unskilled occupations being picked up within our classification of skilled occupations.

Alternatively, this could be a reflection that workers need a minimum level of skills and abilities to reap the wage benefits associated with these occupations – entering a high skilled occupation without the required skills could result in a wage reduction for individuals with low cognitive skills (relative to what they could have received in a low skilled industry).

However, the trajectory of wages will be different within different occupation groups. As this data considers wage differentials only up to the age of 25, it does not reflect potential wage premiums associated with high skilled occupations that will occur over time. It may be that over a longer time horizon, individuals with low levels of cognitive skills and abilities still receive a higher wage in high skilled occupations than they would otherwise receive.




Combining occupations into different levels of skill

ANZSCO (The Australian and New Zealand Standard Classification for Occupations) classifications are used for the compilation and analysis of occupation statistics in Australia and New Zealand. ANZSCO is a skills-based classification, used to classify all jobs in the Australian and New Zealand labour market. An individual is assigned an occupation based on the predominant activity in their primary employment. ANZSCO is a hierarchical structure classification, with five levels of increasing detail. These levels are major groups (the broadest level), sub-major group, minor group, unit group and occupations (the finest level).

Within the LSAY dataset, each individual was assigned to an ANZSCO major occupation category. There are eight categories: Managers Professionals Technicians and Trade Workers Community and Personal Service Workers Clerical and Administrative Workers Sales Workers Machinery Operators and Drivers Labourers

The ABS assign a skill level to each category, which represents the range and complexity of the set of tasks performed in that occupation. There are five skill levels which the ABS sorts occupations into. Occupations with a greater range and complexity of tasks are assigned a higher skill level, with 1 being the highest and 5 the lowest. 55 In practice, the ABS assigns a skill level to each occupation by considering: the level or amount of formal education and training required for an occupation; the amount of previous experience in a related occupation; and the amount of on-the-job training.

Within each major group there are a number of different occupations, so each major group is assigned a skill level which aligns with the predominant skill level of the occupations within that group. Each major occupations predominant skill level (as determined by the ABS), and their classification for our analysis, is detailed in Table B.17 below.

55 Includes graduate certificates and graduate diplomas. 126




Table B.17: Skill grouping of ABS major occupations

Occupation Predominant skill level (ABS classification)

Our classification

Managers 1 HighProfessionals 1,2 HighTechnician and Trade Workers 2,3 MediumCommunity and Personal Service Workers 2,3,4,5 MediumClerical and Administrative Workers 2,3,4,5 MediumSales Workers 3,4,5 MediumMachinery operators and Drivers 4 LowLabourers 4,5 Low

Source: ABS (2012); DAE classification

Additional information Clustering standard errors

Estimations are performed with standard errors clustered at the individual level. This is used to account for the likelihood that the outcomes of each individual are likely to be highly correlated over time, that is, the errors for an individual are likely to be correlated over time. Clustering does not change the point estimates, but adjusts the variance appropriately.

Controlling for innate ability

It is not clear how best to control for the innate ability of an individual. It is likely that this unobservable will be highly correlated with their socioeconomic status and home learning environment. The OLS wage equation and probit employment/participation equations are repeated with a range of controls to reflect this, each revealing consistent coefficient estimations for PISA. Economic, social and cultural status (ESCS) is chosen over mother and father occupation index (ISEI), mother and father highest educational attainment (ISCED) and wealth (2006 only), as it incorporates a large range of factors that are related and likely to be highly correlated with the innate ability of an individual. Other studies have sought to control for innate ability through including aptitude tests from when students commenced school. Unfortunately this data was not available for the purposes of this study.

Using weights to account for sample attrition

Weights are used throughout the analysis to account for the significant level of attrition in the sample. Weighting allows estimations to reflect the characteristics of the original sample. The use of weights is consistently used and recommended for calculating summary statistics. In the presence of endogenous sampling, say, due to non-random attrition, unweighted estimates would produce inconsistent parameter estimates.56

56 For more details, see Australian Bureau of Statistics (2009), Australian and New Zealand Standard Classification of Occupations, First Edition, ABS cat. no. 1220.0, Canberra. The document is accessible from the ABS website.


http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/1220.0First%20Edition,%20Revision%201?OpenDocument



Table B.18 compares the headlines results of the key estimations throughout the analysis with and without weights. Both with respect to point estimates and statistical significance there are no meaningful changes, with the themes of the results remaining consistent.

Table B.18: Testing the use of sampling weights

Maths Science Reading

Weights No weights Weights No

weights Weights No weights

(1) (2) (3) (4) (5) (6)OLS: Dependent variable: Hourly wage (log)PISA 0.0863*** 0.0903*** 0.0527** 0.0499*** 0.0446 0.0384*

(0.0299) (0.0210) (0.0262) (0.0186) (0.0302) (0.0205)

Probit: Dependent variable: Employed vs unemployedPISA 0.0728*** 0.0714*** 0.0457*** 0.0452*** 0.0528*** 0.0522***

(0.0110) (0.0104) (0.00953) (0.00925) (0.00988) (0.00954)Probit: Dependent variable: Participating vs NILF

PISA -0.00333 -0.00335 -0.00466 -0.00470 -0.00825 -0.00832(0.00938) (0.00946) (0.00828) (0.00836) (0.00905) (0.00917)

Simulation: inflating PISA scores by 1%Wage difference 0.1245*** 0.1222*** 0.0876*** 0.0854*** 0.0837*** 0.0813

(0.0309) (0.0307) (0.0275) (0.0272) (0.0309) (0.0306)

Year 12 Cert I/II Cert III/IV Dip/adv dip Bachelor Postgraduate

Mlogit: Dependent variable: Highest educational attainment (base: no qualification)PISA Maths

Weights 0.146*** -0.214*** -0.264*** -0.0963*** 0.539*** 0.0532***

(0.0273) (0.0216) (0.0266) (0.0201) (0.0214) (0.00907)

No weights 0.153*** -0.194*** -0.248*** -0.0908*** 0.509*** 0.0438***

(0.0286) (0.0175) (0.0225) (0.0166) (0.0221) (0.00923)

PISA Science

Weights 0.115*** -0.183*** -0.212*** -0.0952*** 0.462*** 0.0543***

(0.0246) (0.0179) (0.0232) (0.0169) (0.0216) (0.00875)

No weights 0.125*** -0.166*** -0.200*** -0.0877*** 0.434*** 0.0461***

(0.0259) (0.0147) (0.0200) (0.0141) (0.0217) (0.00871)

PISA Reading

Weights 0.131*** -0.204*** -0.241*** -0.0952*** 0.512*** 0.0534***

(0.0266) (0.0190) (0.0254) (0.0186) (0.0238) (0.0102)

No weights 0.144*** -0.187*** -0.228*** -0.0892*** 0.482*** 0.0446***

(0.0282) (0.0156) (0.0220) (0.0158) (0.0240) (0.0101)Note: ***represents significance at the 1% level; ** represent significance at the 5% level.



Appendix C: CGE modellingConverting the wage increase to a productivity shockThe productivity shock is estimated as the percentage increase in total wages in a given year. For example, if the overall wage bill for Australia was found to increase by 1% in 2016, this would translate to a 1% shock to labour productivity. The steps below first describe how the productivity shock is calculated at a single point in time. They next describe the assumptions behind converting this into a dynamic shock.

Estimating the productivity shock in 2016

Estimating the increase to the total wage bill at a given point in time required four inputs:

Table C.1: Data requirements and source

Input AvailabilityPercentage increase in wages due to a productivity shock Obtained from econometric estimatesThe number of people to receiving the wage increase Estimation requiredAverage wages of 15 year-olds Obtained from ABS cat. no. 6306.0Total wage bill in Australia Obtained ABS input-output tables

Of these inputs, the percentage increase in individual wages is obtained from the econometric modelling, and average wages and the total wage bill was obtained from the ABS. The number of people receiving the wage increase was assumed to be all those participating in the labour force. Therefore, two inputs were required: the total number of students that received the productivity shock (assumed to equal

to the total population of 15 year-olds in 2016); and the participation rate at age 15.

Both are sourced from the ABS 2011 Census. Once the inputs were obtained, the below equation represents the productivity shock in the initial year following a policy change:

Productivity shock=%wageincrese×averagewage×no .of people recievingwage increaseTotal wage bill∈Australia

Estimating the productivity shock over time

The analysis implicitly follows a single cohort of students (those that received increased PISA scores in year 9) and tracks their participation and wages over time, to determine the magnitude of the overall productivity shock in any given year. This section considers how the productivity shock will change over time. The data required is summarised in Table C.2.


Table C.2: Data requirements and source

Input How the input changes with time ExplanationPercentage increase in wages due to a productivity shock

Estimates from the econometric modelling. There were two possibilities as to how these would vary over time, as articulated in Section 7.2.

The productivity shock changed over time differently under the two scenarios – either remaining constant over time, or decreasing to zero linearly throughout the working life of an individual

The number of individuals receiving the wage increase

Changes with the participation rate, but population assumed constant.

The number of individuals receiving the shock is a function of:• the total population of the cohort as they age (this was

assumed unchanged from when the cohort was 15, ignoring the effects of migration and mortality); and

• the participation rate every year after they receive the productivity shock. This increases sharply until approximately age 23, before stabilising (see Chart C.1).

Average wages at any point in time57

An individual’s wage increases as they grow older, but the exercise assumes no growth in real wages.58

In other words, on average, a 45 year old earns more than a 25 year old. But, a 45 year old in 2066 is assumed to earn the same as a 45 year old in 2016. The assumption of no real wage growth does not limit the analysis, as it is applied to both the numerator in the equation (average wages by age), and the denominator (total wage bill). Any real wage increase would be captured commensurately in both these inputs, and would therefore not affect the final result. The expected wage profile is shown in Chart C.2.

Total wage bill in Australia Assumed constant, reflecting no real wage growth.12

The total wage bill is assumed to remain unchanged over time. This assumption does not limit the analysis, as it is applied to both the numerator in the equation (average wages by age), and the denominator (total wage bill). Any real wage increase would be captured commensurately in both these inputs, and would therefore not affect the final result.

57 Solon, G; Haider, S & Woolridge, J (2013) What are we weighting for?, NBER Working paper 1885958 The ABS combines similar age groups and publishes average wages at discrete age intervals (for example, the average wage of 15-17 years-olds). As this analysis required wage estimates for the average wage of individual age cohorts, grouped wage estimates from the ABS were smoothed to generate individual wage estimates.


Chart C.1: Participation rate over time

0%

20%

40%

60%

80%

100%

15 20 25 30 35 40 45 50 55 60Age

Source: ABS (2011), DAE calculations

Chart C.2: Expected weekly wages at every age

$0

$500

$1,000

$1,500

15 20 25 30 35 40 45 50 55 60

Wee

kly

wag

e

AgeSource: ABS (2015), DAE calculations

Once these inputs have been estimated at all ages, the same equation as above can be applied on a year-by-year basis. That is:

Productivity shock t=%wage increset×averagewage t×no .of people recievingwage increaset

Total wagebill∈Australiat

The resulting productivity shock over time is shown in Chart C.3.



Chart C.3: Productivity changes over time

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

0.12%

0.14%

2016 2026 2036 2046 2056 2066 2076

Central productivity shock

Low productivity shock

Source: DAE calculations




Appendix D: Estimating the contribution of schoolsMultilevel modellingMulti-level modelling utilises the nested nature of students within schools to separate out the component effects that the attributes of individual students have on their levels of achievement. The models are often referred to as ‘value-added’ methods that are explicitly used to estimate school performance against key outcome measures. The ‘value-added’ term essentially refers to the estimated effect that schools add to student achievement, above and beyond what is predicted by the attributes of the students themselves. (see Lu and Rickard, 2014).

The objective with these models is to identify the extent to which variation in school-level outcomes can be explained by observable factors such as school location and intake. The residual from this regression, that is, the variation in school outcomes that cannot be explained by these factors, is then taken to be school-specific factors that drive differences in student outcomes (in other words, school quality). Larger variations in student outcomes that can be explained by school-specific factors allow greater scope for policy to influence student outcomes by targeting those aspects of school performance that drive outcomes.

The PISA/LSAY dataset allows for ‘value-added’ estimates of school level effects to be directly estimated. These estimates can effectively be interpreted as the component of student performance attributable to random school level effects. Because these effects can be interpreted as a measure of the relative performance of schools in affecting student outcomes, they may also represent a direct estimate of the variation of school quality, and give an indication of how much ‘value’ Australian schools add to student achievement, above and beyond what is predicted by individual student characteristics and observable school level characteristics.

These estimates may be used to parametrise simulations of interventions into the schooling system which see an uplift in school quality (as measured by school level-value added), and – using the estimated effects from the student level analysis outlined above – determine the impact of these simulations on key economic outcomes.

Technical summaryThe following section summarises the methodology involved in calculating value added scores for schools.

The models used to calculate value added scores at only a school level follow the same basic form. Linear regression is used to calculate an expected score for each school. The difference between the school’s actual and expected scores represents the impact of school




behaviour (and other omitted factors) on performance. A summary of the equation is given as:

y j=Z jΓ+u j

Here, j indexes each school, y jis the performance measure of interest,Z jis the set of contextual factors controlled for andΓsummarises their relationship with school scores. The error term, denoted asu j, is difference between actual and expected school scores based on the contextual factors. It is calculated as (where the caret accents imply a value estimated based on the data):

u j= y j− y j= y j−Z j Γ

The estimated error term, u j, represents the difference between school level score and that expected based on the model. A positive error term suggests that the school is performing above average given its characteristics.

The models based at the student level, rather than the aggregated school level, are slightly more complicated as they take into account both student and school characteristics. This implies a nested, or multi-level, approach to modelling is required. Again, the basic form of each equation is given as:

y ij=X ij β+Z j Γ+u j+ϵ ij

Where the additional index, i, represents each student in school j and as before, theZ jΓterm summarises the relationship between school characteristics and the performance measure of interest ( y ij). However, two additional terms are present: theX ij βterm summarises the relationship between student characteristics and performance, and theϵ ij

term denotes the difference between actual and expected student performance after taking into account school behaviour. The school behaviour term,u j, is estimated from the following equation:

u j=σu2

σu2+

σ ϵ2

n j

r j ,where r j=∑i=1

nj

( y ij− y ij)

n j

These models take into account variability at both the student and school level. The estimated school effect, u j, represents the additional contribution of schools to student scores above the expected student performance (based on the student and school characteristics). A positive school effect indicates that the school is contributing an additional amount to its students’ scores above what is expected based on the school and student characteristics.

Theσ 2terms in the equation above represent the estimated variance of scores at the student and school level. The school value added scores (r j) are adjusted based on a ratio of variances. The addition of the number of students in a school,n j, effectively shrinks the value added scores of small schools towards zero (the average). This process ensures that




smaller schools, for which the estimate is likely to be less precise, are not unduly penalised for potential outlying students.

DataSchool quality estimates were developed through a variety of student engagement and school level measures: Student engagement measures include:

• aspirations (school completion plan, post-school study plan);• engagement (cognitive, emotional, behavioural);• non-cognitive skills (sense of belonging, self-efficacy, sense of purpose,

perseverance);• attitudes to school (view of school, relationships with teachers); and• attitudes to maths (interest, confidence, anxiety, self-efficacy).

School level measures include:• school size;• resources;• promoting student engagement;• quality of teachers;• quality of maths teaching;• governance;• time on task;• classroom climate; and• learning strategies.

These school quality measures were used to test their effect on some selected outcome variables, including: Maths assessment scores; and Broader student outcomes, such as:

• year 12 attainment;• tertiary entry; and• university entry.

A variety of school and student level factors, which are known to be correlated with educational outcomes, were also used. These included:

Student socioeconomic status measured by the index of economic, social and cultural status (ESCS) provided in PISA;

Indigenous status; Gender; Whether the student spoke a Language background other than English at home

(LBOTE); School location (based on OECD-defined areas linked to population size);




School SES (median of student ESCS index within the school); Size of school (enrolments); and Selective entry school or not (whether student academic record is considered at

school entry).

Estimating the increase in PISA outcomesUnder the scenario analysis, a scenario was modelled whereby school level differences, after accounting for differences in school context, were negligible. In other words, the assumption under the scenario was that the performance of all schools was lifted to that of top performing schools in Australia. Under this scenario, PISA scores were found to increase by 3.1%. The steps in deriving this estimate are explained below.

The standard deviation in PISA scores, after adjusting for student intake, is 82 points. The results from the modelling in section 8 estimates that differences in school quality

account for up to 10% of the deviation in student marks. The standard deviation in student scores which can be attributed to differences in

school quality is therefore 8.2 points. Given the assumption that school differences is normally distributed, 95% of students

lie within 2 standard deviations – that is, 95% of students attend schools that contributes between plus or minus 16.4 points to their PISA scores.

If all schools were lifted up to the highest quality standard, the worst performing students would increase by 32.8 points (from students losing 16.4 marks due to poor schooling, to the students gaining 16.4 marks), while the students at the best schools would increase by 0 points.

On average, student scores will increase by 16.4 points. Relative to the mean 523.5, this represents about a 3.1% increase in PISA scores.




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