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Econometric Analysis of the Australian Apprenticeships Incentives Program Department of Education, Employment and Workplace Relations

5 March 2012

Econometric Analysis of the AAIP

Liability limited by a scheme approved under Professional Standards Legislation. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/au/about for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. © 2012 Deloitte Access Economics Pty Ltd

Contents Glossary ..................................................................................................................................... i

Executive Summary .................................................................................................................... i

1 Background and policy context ....................................................................................... 6

1.1 AAIP .................................................................................................................................. 6

1.2 Findings of the Expert Panel .............................................................................................. 7

1.3 Role for government ......................................................................................................... 8

1.4 Optimising future investment.......................................................................................... 10

2 Methodology ................................................................................................................ 11

2.1 Method for determining effectiveness ............................................................................ 11

2.2 Method for determining efficiency .................................................................................. 27

2.3 Method for optimising the future AAIP ............................................................................ 28

3 Effectiveness – findings ................................................................................................. 30

3.1 Literature and data review .............................................................................................. 30

3.2 Econometric review ........................................................................................................ 33

3.3 Caveats and limitations ................................................................................................... 46

3.4 Implications .................................................................................................................... 47

4 Efficiency – findings....................................................................................................... 49

4.1 Literature review ............................................................................................................ 49

4.2 Cost effectiveness ........................................................................................................... 50

4.3 Target efficiency ............................................................................................................. 55

4.4 Implications .................................................................................................................... 64

5 Optimising AAIP ............................................................................................................ 65

5.1 Where to incentivise ....................................................................................................... 65

5.2 What form of incentive ................................................................................................... 68

5.3 How much to incentivise ................................................................................................. 73

5.4 Future options ................................................................................................................ 75

References .............................................................................................................................. 77

Appendix A : Incentives reviewed............................................................................................ 79

Appendix B : Forward looking tool .......................................................................................... 81

Appendix C : Detailed modelling results .................................................................................. 92

Appendix D : Econometric models ......................................................................................... 100

Limitation of our work ............................................................................................................. 109

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Charts Chart 2.1 : Monthly commencements of all AAIP eligible persons........................................... 14

Chart 2.2 : Survival functions for Apprentices and Trainees – all exits ..................................... 17

Chart 3.1 : Commencements of Apprenticeships and Traineeships .......................................... 31

Chart 3.2 : Two-year retention/completion rates for Apprenticeships and Traineeships .......... 32

Chart 3.3 : Policy effect resulting from the change to Standard Incentives and LAFHA, July 2003 (Certificate III/IV apprentices under the age of 20) .................................................................. 35

Chart 3.4 : Monthly commencements of all AAIP eligible persons and commencements of Certificate III/IV apprentices under the age of 20 .................................................................... 35

Chart 3.5 : Monthly commencements of all AAIP eligible over the age of 45 ........................... 36

Chart 3.6 : Policy effect resulting from Support for Mid Career Apprentice incentive relative to the Apprentice Wage Top-up .................................................................................................. 38

Chart 3.7 : Policy effect resulting from Supporting Adult Australian Apprentices, 25-29 cohort39



Chart 3.10 : Apprentice Kickstart Bonus eligible commencements and comparison group ...... 41

Chart 3.11 : Policy effect resulting from the Apprentice Kickstart Bonus ................................. 41

Chart 3.12 : Apprentice Kickstart Extension eligible commencements and comparison group 42

Chart 3.13 : Policy effect resulting from the Apprentice Kickstart Extension ........................... 43

Chart 4.1 : Change in apprenticeship 2-year retention rates: 2002-09, % points ...................... 56

Chart 4.2 : Change in traineeship 2-year retention rates: 2002-09, % points ............................ 57

Chart 4.3 : Difference in average age (years) and earnings ($/annum) by highest level of qualification (Cert III/IV – Cert I/II) and by industry ................................................................. 58

Chart 4.4 : Difference in average age (years) and earnings ($/annum) by highest level of qualification (Cert III/IV – Cert I/II) and by occupation ............................................................. 59

Chart 4.5 : Two-year retention/completion rates for NSNL and Non-NSNL Apprenticeships .... 60

Chart 4.6 : Monthly commencements of apprenticeships and traineeships ............................. 61

Chart 4.7 : Monthly commencements of NSNL and non-NSNL ................................................. 62

Chart 5.1 : Fee-for-service expenditure, commencements and claims ..................................... 72

Chart C.1 : Cumulative incidence functions for Apprentices with and without SMCA ............... 92

Chart C.2 : Cumulative incidence functions for Apprentices with and without WNTD .............. 93

Chart C.3 : Completion survival functions for Trainees with and without WNTD ...................... 93

Chart C.4 : Cumulative incidence functions for Apprentices with and without LAFHA .............. 94

Chart C.5 : Cumulative incidence functions for Trainees with and without LAFHA ................... 94

Chart C.6 : Cumulative incidence functions for Apprentices with and without WTU ................ 95


Chart C.7 : Completion survival functions for Trainees with and without WTU ........................ 95

Chart C.8 : Completion survival functions for Trainees with and without INNO ........................ 96

Chart C.9 : Completion survival functions for Apprentices with and without INNO .................. 96

Chart C.10 : Cancellation survival functions for Trainees with and without the commencement incentive for Diploma/Advanced Diploma qualifications ......................................................... 97

Chart C.11 : Cancellation survival functions for Apprentices with and without the commencement incentive for Diploma/Advanced Diploma qualifications ............................... 97

Chart C.12 : Cancellation survival functions for Apprentices with and without TFYT ................ 98

Chart C.13 : Cancellation survival functions for Apprentices with and without TSC .................. 98

Chart C.14 : Cumulative incidence functions for Trainees with and without TSC ...................... 99

Tables Table 2.1 : Macroeconomic control variables included in commencement analyses ............... 13

Table 2.2 : Basic outcomes - frequencies ................................................................................. 22

Table 2.3 : Basic statistics on completion times ...................................................................... 23

Table 2.4 : Estimated parameters for selected control variables for the four models ............... 24

Table 2.5 : Modelling output for Apprentices: Completion and Cancellation models ............... 25

Table 2.6 : Modelling output for Trainees: hazard rates, Completion and Cancellation models 26

Table 3.1 : Effect of changes to Standard Commencements and LAFHA on commencements . 34

Table 3.2 : Impact of policies on probabilities of completing and cancelling for Trainees and Apprentices ............................................................................................................................ 43

Table 3.3 : Take-up rates ......................................................................................................... 45

Table 4.1 : Cost-effectiveness .................................................................................................. 50

Table 4.2 : AAIP eligibility and claims ....................................................................................... 51

Table 4.3 : The costs and benefits of apprenticeships to the apprentice .................................. 62

Table 4.4 : Employer costs and benefits of taking on an apprentice ......................................... 63

Table A.1 : Econometric testing of AAIP................................................................................... 79

Table B.1 : National Skill Needs List ......................................................................................... 90

Table B.2 : Specialised Occupations List (Technician and Trades Worker occupations only) ..... 91

Table D.1 : Variable definitions.............................................................................................. 100

Table D.2 : Apprentices completed parameter estimates ...................................................... 102

Table D.3 : Apprentices cancelled parameter estimates ........................................................ 104

Table D.4 : Traineeships completed parameter estimates ..................................................... 105

Table D.5 : Traineeships cancelled parameter estimates ....................................................... 107


Figures Figure 5.1 : Supply and demand for apprentices/trainees ........................................................ 66


Liability limited by a scheme approved under Professional Standards Legislation. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/au/about for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. © 2012 Deloitte Access Economics Pty Ltd

Glossary AAIP Australian Apprenticeships Incentive Program

AKB Apprentice Kickstart Bonus

AKE Apprentice Kickstart Extension

AQF Australian Qualifications Framework

DAE Deloitte Access Economics

COAG Council of Australian Governments

CIF Cumulative incidence function

COM_DIP/ADVDIP Commencement incentive for Diploma and Advanced Diploma qualifications

DEEWR Department of Education, Employment and Workplace Relations

INNO Innovation incentive

LAFHA Living Away From Home Allowance

NCVER National Centre for Vocational Education Research

NSNL National Skills Needs List

PC Productivity Commission

SMAA Support for Mature-Aged Apprentices

SMCA Support for Mid-Career Apprentices

SAAA Support for Adult Australian Apprentices

TFYT Tools for Your Trade

TSC Commonwealth Trade Scholarship

TYIMS Training and Youth Internet Management System

VET Vocational Education and Training

WNTD Women in Non-Traditional Trades

WTU Wage Top-up

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Executive Summary The Australian Government provides significant financial support to Australian Apprenticeships through the Australian Apprenticeships Incentive Program (AAIP), as well as the broader provision of funded training.

In recognition of the scale of the investment in the AAIP and the critical role it can play in enhancing labour force participation and productivity at various levels, the Government is now seeking a clearer understanding of its impact on the behaviour of employers and individuals. This understanding will inform the future course of the AAIP, in a policy and economic environment where maximising the return on investment will be integral.

Findings of the Expert Panel

Following a long period of relatively minor change to the fundamentals of the AAIP, the Apprenticeships for the 21st Century Expert Panel (the Panel) was appointed in July 2010 to provide advice on reform options for a stronger apprenticeships system. The Panel established a need for reform on the basis of the following observations:

The current level of apprenticeship and traineeship completion is not sufficient to accommodate projected demand for skills in Australia, particularly in traditional trades.

Completion rates are low and resulting in a significant economic cost.

Current economic conditions could dampen apprenticeship and traineeship output as the economic cycle has been shown to be an important determinant of commencements.

• Employers, who often base their investment in apprenticeships and traineeships largely on their current labour demand, will under-invest in training during weak economic conditions.

The system is complex and administratively burdensome, particularly because of jurisdictional differences.

The Australian Apprenticeships system is not well aligned with the workplace relations system, resulting in inefficiencies and lost productivity.

With specific regard to financial incentives, the Panel recommended that incentives be discriminately targeted to apprentices and trainees and their employers in occupations that are priorities for the Australian economy. The Panel also recommended that investment be used to provide structured support services such as mentoring and pastoral care, rather than solely for cash incentive payments.

Rationale and policy objectives of the AAIP

At a high level, the primary argument for government intervention in Australian Apprenticeships aligns with the justification for government intervention in any education and training sector. That is, educational attainment and training generates benefits for individuals and society more broadly, through the impact on two key drivers of economic growth – productivity and participation – and the impact on social inclusion.

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More specifically, the rationale for investing in Australian Apprenticeships is not just related to public education provision, but also incentivising particular skills and individuals:

In a practical sense, the economy will always be short on skills in particular areas. While the market is likely to address these skill shortages over time through changes in relative wages, an economic inefficiency/cost accrues during this period of adjustment. Incentivising these skills could help ameliorate this market failure.

According to the vertical equity argument, those who are least able to afford education and training should be given the greatest subsidy. Compared to those who are university trained, for example, there is a rationale to incentivise individual apprentices/trainees, given university students are subsidised but typically come from higher socio-economic status backgrounds.

Given the market failures and equity issues government might look to address, the primary goal of Australian Apprenticeships is to provide an ongoing source of skilled labour to the economy. Alongside this, the broad objective of the AAIP is to support the effectiveness of Australian Apprenticeships in delivering the required skills and jobs, in an efficient manner.

Approach to estimating effectiveness and efficiency of the AAIP

There are a range of contemporaneous factors to consider when assessing the performance of the apprenticeship system and, more specifically, the financial incentives that support this system. These include local labour market conditions, business confidence, state final demand, seasonality, policy timing and the characteristics of the cohorts themselves.

Controlling for these other factors, this analysis defines effectiveness as:

The effect (if any) of the AAIP on commencements.

The effect (if any) of the AAIP on retention.

The effect (if any) of the AAIP on completions.

In light of these considerations, as well as the constraints presented by the available data, an econometric evaluation of the AAIP has been developed.

The principal data source used in this analysis was provided by the Department of Education, Employment and Workplace Relations (DEEWR) from the Training and Youth Internet Management System (TYIMS). The TYIMS database stores unit level information regarding every Australian Apprentice.

While retention and completion can be analysed at unit record level, commencements cannot. The econometric analysis is therefore undertaken using two separate approaches:

the retention and completion analyses are undertaken at unit record level using survival analysis; and

the commencement analysis uses a time series approach, based on a monthly time series of total commencements

In order for the AAIP to support the primary goal of Australian Apprenticeships in providing an ongoing source of skilled labour to the economy, the program must also be both cost-efficient and appropriately targeted.

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The financial efficiency of particular incentives under the AAIP can be assessed by the extent to which they deliver the desired outcomes and at what cost. Accordingly, those incentives that the econometric analysis reveals to be ineffective are excluded from the efficiency assessment altogether (along with those that were unable to be tested), while those incentives that do appear to be effective are assessed in terms of their cost per additional commencement and cost per additional completion.

Any market intervention is by definition distortive, and as such the question to be addressed is the degree to which this distortion is tending in the right direction, such that it corrects the market failure and/or equity issue. The direction of market distortions was considered in this analysis by examining:

1. Apprentice and trainee retention rates by industry over time (as a proxy for supply), compared to relative wage gains to workers skilling in those industries (as a proxy for demand).

2. Apprentice and trainee retention rates over time for National Skills Needs List (NSNL) compared to non-NSNL qualifications.

However, there are some limitations to this approach – neither are direct measures of the supply of skills nor the demand for skills, and do not take account of the current stock (i.e. the degree to which there may be an existing imbalance).

Key findings

In essence, two primary conclusions can be drawn from the econometric analysis of commencements:

Money matters. None of the analysed incentives were proven to have a negative effect on commencements. All the incentives offering more than $1,000 in the first year proved to have a significant, positive effect on commencements.

Timing matters. Drawing from evidence revealed in the Apprentice Kickstart Bonus (AKB) and Apprentice Kickstart Extension (AKE) analysis, an incentive that affects people under the age of 20 has a much larger effect around summer than at other times of the year. This is likely to be due to the fact that the supply of potential Australian Apprentices is highest in summer when the traditional school year finishes.

On the other hand, the findings of the retention/completion analysis suggest that for the most part these incentives are ineffective, as they are associated with an increase in the probability of cancelling an apprenticeship/traineeship, and a decrease in the probability of completing an apprenticeship/traineeship. The notable exceptions to this are Living Away From Home Allowance (LAFHA), Commonwealth Trade Scholarship (TSC) and to a lesser extent Innovation incentive (INNO).

At face value, the AAIP therefore appears to have been more effective in terms of incentivising additional commencements than incentivising additional completions, which may be a materiality issue or may simply be confounded by other factors.

The implications from the efficiency analysis conducted are two-fold:

From the cost-effectiveness analysis it can be seen that at a high level, the majority of AAIP investment is ineffective and therefore by definition inefficient. However, there

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are some exceptions – primarily, a few commencement and targeted incentives typically greater than $1,000.

From the target-efficiency analysis it can be seen that retention rates are falling in training that aligns with areas of greatest skill need and greatest productivity gains. It is also clear that retention rates are reduced where commencements are inflated; implying that mismatches between individuals and training emerge in these circumstances.

These findings would suggest the AAIP has been ineffective in reaching its target cohorts. However, this analysis – particularly in relation to target efficiencies – has not fully captured the range of other factors that could be driving these trends.

Conclusions

Optimising the AAIP is influenced (more or less) by:

Where the AAIP is incentivising (e.g. all occupations or only particular occupations).

How the AAIP is incentivising (e.g. employer or individual incentives).

How much the AAIP is incentivising (e.g. how much to incentivise training levels).

With a sufficient evidence base, alternative options for the future AAIP could then be constructed around a differing balance of efficiency and effectiveness.

However, while the econometrics has revealed the apparent level of effectiveness of different AAIP incentives over time, by the very nature of these incentives it is unable to be definitive as to what mechanisms are more effective. This also has flow-on implications for how informative the cost-effectiveness findings can be in setting the AAIP future course.

Ultimately, the future AAIP should continue to reduce the risks faced by both individuals and employers at the margin, in undertaking an Australian Apprenticeship. This may involve the provision of relatively more incentive to relatively fewer recipients. That is, making the AAIP more targeted and more compensatory where risk exists (for instance where a forecast might be inaccurate or where an economic downturn looms).

In addition, these types of risk – at least from the individual’s perspective – are inherently reduced in more generic training, which might imply trainees who would typically be taught more transferrable skills should be subsidised less than those in traditional trades who are more exposed.

In keeping with these findings, the following conclusions have key implications for the future AAIP in delivering an on-going source of skilled labour:

The more commencements of Australian Apprenticeships are inflated the lower the completion rate that can be expected, as more individuals will be unsuitable.

Reducing the recruitment pool of both employers and apprentices will result in less inefficiency, implying a rationale to better match individuals to training/employers.

There is a rationale to incentivising commencements, particularly in times of economic downturn and for those who would otherwise be out of education and work, but generally not to the same extent as completions.

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Incentivising businesses to improve completions is likely to be less effective than incentivising individuals.

Broad/homogenous incentives are less suitable and less effective than targeted/flexible incentives.

The risk/return issues can be addressed in-part by directing more of the funding to industry-specific Certificate III/IV training, than transferable Certificate I/II training.

An unavoidable level of inefficiency is accepted when applying a forward looking tool to the program.

However, the implications of the study cannot be translated into a neat package of alternative efficiency/effectiveness future AAIP pathways. Instead the study presents a balance of views on what could have driven the effectiveness and efficiency of the AAIP to date, and how these factors might be addressed going forward. It is now a case of drawing on this evidence and theory in policy deliberations to reveal those AAIP reforms that hold the greatest potential gains and perhaps the least likelihood of unintended outcomes.

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1 Background and policy context Deloitte Access Economics was commissioned by the Department of Education, Employment and Workplace Relations (DEEWR) to conduct an econometric analysis of the Australian Apprenticeships Incentive Program (AAIP), and to identify alternative frameworks for the AAIP, with a view to enhancing the effectiveness and efficiency of future investment.

To date there has been very little econometric analysis to inform policy in this regard. With a multitude of variables impacting apprenticeship outcomes at any point in time, an econometric analysis is imperative in any attempt to isolate the impact of the AAIP on Australian Apprenticeship commencements and completions. Notwithstanding the limitations of any data analysis, this report provides such an econometric analysis and thereby reports on the apparent effectiveness and efficiency of different policy options.

The following subsections set the scene for the analysis, first by reporting on the AAIP itself and the findings of the Expert Panel – to establish the policy context – then by discussing the rationale for government intervention in Australian Apprenticeships alongside the stated objectives of Australian Apprentices and the AAIP, and lastly by ascertaining the need to optimise future investment in the current economic and policy environment.

1.1 AAIP

The Australian Government provides significant financial support to Australian Apprenticeships through the AAIP, as well as through the broader provision of publically funded training. In 2011, $1.02 billion was spent through the AAIP to encourage and support the commencement and retention of apprentices and trainees. Over the nine years from 2002 to 2011, this figure is in the order of $6.4 billion.

The AAIP in its current form commenced on 1 January 1998, with the introduction of New Apprenticeships.1 Since this time, there have been a number of updates and adjustments to the incentives that make up the AAIP, largely in response to the economic cycle or new policy objectives (refer Table 4.2). However, the fundamental aims and structure of the AAIP and New Apprenticeships more broadly have remained relatively unchanged since their inception.

Over the past decade or so, there have been a number of reviews of Australian Apprenticeship incentives and, just recently, the Australian Government appointed an Expert Panel to advise on future reform options for a stronger apprenticeships system. To support the Expert Panel and provide an evidence base for their consideration, the Government commissioned the National Centre for Vocational Education Research (NCVER)

1 Indeed, the Government was providing financial support for apprenticeships for long before the introduction of New Apprenticeships and the AAIP. However, this review concentrates on the period post this large structural reform.



to conduct a series of detailed studies, the most relevant of which for this study was the economic costs and benefits of the Australian Apprenticeships System.

1.2 Findings of the Expert Panel

Following a long period of little change to the fundamentals of the AAIP, the Apprenticeships for the 21st Century Expert Panel (the Panel) was appointed in July 2010 to provide advice on reform options for a stronger apprenticeships system.

The Panel examined Australian Apprenticeships training arrangements, including consideration of current employer incentives and personal benefits to Australian Apprentices, and made a total of 14 recommendations on reform options for the apprenticeships system. The Panel established a need for reform on the basis of the following observations:

The current level of apprenticeship and traineeship completion is not sufficient to accommodate the projected demand for skills in Australia, particularly in the traditional trades.

Completion rates are low and resulting in a significant economic cost.

Current economic conditions could dampen apprenticeship and traineeship output as the economic cycle has been shown to be an important determinant of commencements.

• Employers, who often base their investment in apprenticeships and traineeships largely on their current labour demand, will under invest in training during weak economic conditions.

The system is complex and administratively burdensome, particularly because of jurisdictional differences.

The Australian Apprenticeships system is not well aligned with the workplace relations system, resulting in inefficiencies and lost productivity.

With specific regard to financial incentives, the Panel recommended that incentives be discriminately targeted to apprentices and trainees and their employers in occupations that are priorities for the Australian economy. The Panel also recommended that investment be used to provide structured support services such as mentoring and pastoral care, rather than solely for cash incentive payments.

The report handed down by the Panel relied heavily upon the research performed by the NCVER, where the following key research findings were used as basis for the recommendations around incentives:

Incentives paid to employers have only a marginal effect on their decision to employ an apprentice or trainee. This is largely because the size of the government subsidies makes only a small contribution to the cost of hiring an apprentice.

Support mechanisms for the apprentice/trainee and the employer, such as mentoring, pastoral care and quality training provision, are critical to the successful completion of the Australian Apprenticeship.

Employer size is an important factor in completion rates, as are management and recruitment practices.



Supporting the Expert Panel’s view that current incentives could be more efficiently and effectively targeted, the Productivity Commission (PC) note in their review of the Council of Australian Governments (COAG) Vocational Education and Training (VET) reforms2 that despite the rapid growth in the number of VET training places, a relatively small number of people are actually increasing their highest level of educational attainment at the Certificate III/IV or Diploma/Advanced Diploma levels (Productivity Commission, 2011). They attribute this to: (1) low completion rates overall; (2) enrolments in lower-level qualifications (e.g. Certificate I/II)3; (3) the prevalence of qualification completion by individuals at or below levels that they have already attained.

1.3 Role for government

In light of the size of the investment in the AAIP, as well as the findings of recent reviews of the program, it’s important to revisit the rationale behind government intervening in this market. With a clearer understanding of the basis on which government can intervene, recommendations around optimisation of future policy can be more readily performed.

1.3.1 Market failures and equity issues

At a high level, the primary argument for government intervention in Australian Apprenticeships is the same argument for government intervention in any education and training sector. That is, educational attainment and training generates benefits for both individuals and society through its impact on productivity and participation – two key drivers of economic growth – and through its impact on social inclusion.

The existence of these benefits to society (positive spillovers), imply that if the investment decision were left to private individuals/agents alone, there would be an underinvestment from society’s point of view. As such, where the government can intervene – through the public funding of education and training or the provision of incentives to increase private funding of education and training – and at a cost that is outweighed by the corresponding participation and productivity benefits for society, then the intervention is welfare enhancing.

Beyond the simple market failure argument, the existence of equity objectives is a further motivation for intervention in the provision of education and training. That is, where society values the broader inclusion of disadvantaged cohorts in education and training – as a means by which to participate in society in a meaningful way and contribute to a more knowledgeable society – there are grounds for investment beyond the economic breakeven point, or at least at a higher breakeven point given they are coming off a lower base.

More specifically, the rationale for investing in Australian Apprenticeships is not just related to why have public education provision (or at least public support for education provision),

2 The COAG VET reforms aim to halve the proportion of Australians aged 20–64 without qualifications at Certificate III level or above by 2020 and double the number of higher level (Diploma and Advanced Diploma) qualification completions by 2020.

3 There is debate around whether Certificate I and II qualifications constitute skills formation or simply a labour market program. The NCVER assert that traineeships (which are generally undertaken at the Certificate I and II level) have achieved little in terms of improving productivity (NCVER 2011a).



but why incentivise a particular skill or even a particular individual. This is because the public education argument does not always carry to firm-specific skills, and firm-specific skills relate more to apprenticeships then some other sectors of education.

However in the present day, these firm-specific skills are increasingly transferrable, as labour mobility improves and the labour market approaches full-employment. Indeed most of the returns to education and training now reside with the individual, in terms of earnings and employability. This in turn implies a reduced willingness among firms to invest in education and training, where the ability of employers to capitalise on this investment is reduced (i.e. employer incentives have declined).

As such, the expectation would be that individuals are required to increasingly fund their education and training, which while being an economically efficient outcome, is not necessarily an equitable outcome, particularly when compared to say those who are university trained (who are subsidised by the education system and typically come from higher socio-economic status backgrounds). The vertical equity argument is those who are least able to afford education and training should be given the greatest subsidy, which thereby creates a rationale to incentivise particular individuals.

Furthermore, in a practical sense, the economy will always be short on skills in particular areas at particular points in time. While the market is likely to adjust for these skill shortages over time through adjustments in relative wages, during the period whilst this is occurring an economic inefficiency/cost is accruing. To the degree government can successfully intervene to close this gap in the market in a more timely fashion, and such that the cost of the intervention is less than the economic inefficiency that would otherwise be incurred, there exists a rationale to incentivise particular skills.

1.3.2 Objectives of Australian Apprenticeships and the AAIP

Given the market failures and equity issues government might look to address, the primary goal of Australian Apprenticeships is to provide an ongoing source of skilled labour to the economy. Therefore broadly speaking, its objectives are both skill development (productivity) and job placement (employability).

Financial incentives are an integral part of this system, with the potential to impact apprenticeship and trainee numbers by affecting:

the supply of apprentice/trainee places by employers;

the demand for training from potential apprentices and trainees; and

the number of training completions (i.e. retention).

Indeed the AAIP Guidelines state that the overarching objective is to “develop a more skilled Australian workforce that delivers long-term benefits for our nation and our international competitiveness”, where the means to achieving this objective are:

providing genuine opportunities for skills-based training of employees by providing incentives to employers; and

encouraging people to enter into skills-based training through an Australian Apprenticeship by providing personal benefits.



Therefore the AAIP is intended to support the effectiveness of the system in delivering the required skills and job placements, and inherently to do so in an efficient manner.

1.4 Optimising future investment

As established above, the rationale behind the public provision and/or support of education and training – including Australian Apprenticeships – is the positive spillovers that accrue to society. To the extent that these are either or both: (1) not currently maximised (to the point where the marginal social cost equals the marginal social benefit); (2) currently being attained in a sub-optimal fashion (i.e. at greater than least cost): there are unexploited gains to be had.

Like all areas of public policy, the challenge for government is to maximise the return on its investment subject to a set of constraints. In this regard, two things are pertinent:

3. aligning the initiatives that the AAIP underwrites with the (current and future) skills needs of the economy; and

4. ensuring the initiatives themselves are those where effectiveness and efficiency is maximised – that is, those which are most cost-effective.

That is to say, in a world without government budget constraints, policy makers could in theory invest in education and training up until the point that all possible welfare gains (economic and social) are exploited. However, such a fiscal environment does not exist, and instead government must prioritise investments according to the expected returns.

Accordingly the purpose of this study is to improve the level of understanding in this regard, and thereby reveal those investments in Australian Apprenticeships that are the most cost-effective and appropriately targeted.



2 Methodology In this section, the approach taken to estimating the effectiveness and efficiency of the AAIP is outlined, as well as some considerations when attempting to reveal how the AAIP can be optimised going forward.

2.1 Method for determining effectiveness

Broadly speaking, the primary goal of Australian Apprenticeships is to provide an ongoing source of skilled labour to the economy.

Financial incentives are an integral part of this system, with the potential to impact apprentice and trainee numbers by affecting:

The supply of apprentice/trainee places by employers.

The demand for training from potential apprentices and trainees.

The number of training completions (i.e. retention).

However, there are a range of contemporaneous factors to consider when assessing the performance of the apprenticeship system and, more specifically, the financial incentives that support this system. As such, the research questions focused upon in the econometric analysis are, controlling for these other factors:

The effect (if any) of the AAIP on commencements.

The effect (if any) of the AAIP on retention.

The effect (if any) of the AAIP on completions.

In light of these considerations, as well as the constraints presented by the available data, the following econometric evaluation has been developed.

2.1.1 Data

The principal data source used in this analysis was provided by DEEWR from the Training and Youth Internet Management System (TYIMS). The TYIMS database stores unit level information regarding every Australian Apprentice.

Consequently, the dataset holds information on individuals who completed their training as well as those who cancelled their training. Naturally then, the data relate to the population of people who chose to commence an Australian Apprenticeship, and accordingly it does not hold any information about people who did not choose to undertake an Australian Apprenticeship.

As such, retention and completion can be analysed at unit record level, while commencement cannot. Given the data limitations, the econometric analysis is undertaken using two separate approaches:

the retention and completion analyses are undertaken at unit record level using survival analysis; and



the commencement analysis uses a time series approach, based on a monthly time series of total commencements

2.1.2 Commencement analyses

A number of multivariate time series regression analyses were conducted to analyse the effect of financial incentives on the number of individuals commencing an Australian Apprenticeship.

The time series methodology was expanded to include cross sectional elements such as Australia’s states and age groups, which were determined for each regression according to the given incentive’s eligibility criteria. Including cross-sectional variables enables the effect of a given incentive on commencements to be isolated and identified.

To completely isolate the effect of an incentive on commencements, the econometric model must control for the full range of factors that influence commencements. Three types of control variables were used in this analysis to capture these factors:

macroeconomic control variables;

time and cross sectional control variables; and

policy control variables.

Because each AAIP incentive targets a specific population, each incentive analysis must have an independent econometric model. Furthermore, in order to control for the effect of population growth, commencements are measured in terms of commencements per cohort population.

Finally, the natural logarithm has been taken of commencements per cohort population. By taking the natural logarithm the commencement models effectively estimate the effect of a given incentive in terms of percentage changes in commencements. Consequently each of the incentive models are derived from the following function:

Box 1: Commencement equation

Where:

= natural log of eligible monthly commencements per population cohort

= a combination of macroeconomic variables controlling for the effect on commencements of changes to the labour market and economic activity

= a combination of policy dummy variables capturing the effect of the key policy in question, as well as any other changes to policy which will affect commencements of the eligible population

= controls for the fact that commencements are inherently seasonal – peaking at the start of every calendar year

= controls for time constant (fixed) effects across States. These variables allow for the utilisation of the cross sectional aspect of the data set.



While the econometric approach attempts to control for all factors that influence commencements, it is clear from sensitivity testing that the policy results suffer from a significant amount of omitted variable bias.

An explanatory variable (x1) suffers from omitted variable bias when another variable (x2), that explains movements in commencements and is correlated with x1, is omitted from the econometric model. Given that the incentives are represented by dummy variables (see Box 1), the impact of omitted variable bias on the estimation of the effect of a given incentive scheme is that the estimated policy results will be the combination of the true impact and the impact of the omitted variable.

The effect of omitted variable bias on commencements can be reduced by estimating the policy effect as the difference between the effect on two similar cohorts; one that is eligible to receive the incentive (treatment group) and a similar cohort that is not eligible for the incentive (control group).

Assuming that the omitted variable bias is equally strong in the treatment and the control group, this addition effectively cancels the impact of omitted variable bias on the results. However, in practise it is often difficult to establish suitable control groups, and this issue applies in particular instances in this study.

Finally, due to data limitations all commencement analyses are based on the time period from April 2002 until July 2011.

2.1.2.1 Macroeconomic control variables

Macroeconomic control variables are included to capture the effect upon commencements of changes to the labour market and economic activity.

A host of macroeconomic variables are tested during the specification process of each commencement model undertaken for this report. Where appropriate, the significance of each of these variables were tested in their level form, change from month to month, yearly growth rate and using lags (Table 2.1).

Table 2.1: Macroeconomic control variables included in commencement analyses

Control variable Transformation 1 Transformation 2

State final demand per capita Level form Annual growth rate

State final demand plus international exports per capita

Level form Annual growth rate

Housing approval value per capita

Level form Annual growth rate

Business confidence Change from month to month Up to 9 months lag

Unemployment rate Level form specific to age category and State

Up to 12 months lag

Average weekly earnings Level form Annual growth rate

Labour price index Level form Annual growth rate



2.1.2.2 Time and cross section control variables

While the macroeconomic variables capture the effect of changes in the labour market and economic activity they fail to capture the inherent seasonality associated with Australian Apprenticeship commencements. Chart 2.1 illustrates the distinct spikes occurring in January of every year since 2003. Consequently monthly dummy variables, specific for each state are included in all commencement models.

Chart 2.1: Monthly commencements of all AAIP eligible persons

Source: DAE analysis of TYIMS data

All commencement models have been undertaken using state-based cross sections. This effectively adds to the explanatory power of the analysis as it allows the model to estimate the average effect of a given policy across each state. That is, this feature allows the model to evaluate the effect of an incentive multiple times (once for every state), rather than simply estimating the effect on national commencements.

When a dataset combines cross section and time series elements it is known as a panel dataset. In order to effectively utilise the panel aspect of the dataset, a dummy variable representing each state is included in all commencement analyses. These dummy variables allow the model to control for any state-specific fixed effects that may impact upon commencements. Notably, considering that 95% of commencements originate from the 5 largest states: New South Wales, Victoria, Queensland, South Australia and Western Australia, only these states are included in the analyses.

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11

Total commencements



2.1.2.3 Policy control variables

Each commencement analysis seeks to estimate the effect of a given AAIP incentive upon the eligible cohort of commencements. This policy effect is captured by dummy variables representing:

the time period within which a given incentive was in place;

the month that a given incentive was implemented;

the final month of a given incentive; and

the month immediately prior to an incentive introduction and the month following an incentive removal.

By incorporating up to 5 variables to control for incentives it is possible to capture not only the average effect of the policy while it was in place, but also potential timing effects around the introduction and removal of incentives.

Further to capturing the effect of the focus incentive for a given commencement model, all other incentive changes applicable to the eligible cohort must be controlled for. Consequently all changes to policies affecting a given cohort are controlled for in every commencement analysis using dummy variables.

2.1.3 Retention and completion analyses

As noted above, the effect of AAIP incentives on the probability of a person completing or cancelling their training can be analysed using the unit record TYIMS data and a survival analysis methodology. However, the data do not identify whether the cancellations are initiated by the employer or the apprentice/trainee and because of this, the analysis looks at cancellations in aggregate.4 More detail about the models used, along with variable definitions, can be found in Appendix D

2.1.3.1 Rationale for using an econometric model and survival analysis

In order to assess the effects of policy, it is not enough to simply compare outcomes for those influenced by the policy with those not influenced by the policy. That is because the characteristics of the two cohorts could be different, and the difference could also affect the outcomes. Similarly, comparing a time period in which the policy was in place with a time period in which it was not in place could be biased if something other than the policy also changed. To allow for the characteristics of apprentices, a proportional hazards model is used, which is derived using a survival analysis methodology.

Survival analysis is the appropriate methodology as it treats time as a continuous variable, rather than just considering one, two, three or four year outcomes. This is important because outcomes can occur at any time; an Australian Apprentice can cancel their training at any time, can take more or less time to complete than that prescribed by the contract,

4 Indeed, these two outcomes are likely to respond very differently to external variables such as economic conditions (employers are more likely to cancel during a downturn in response to the lower production but apprentices and trainees are less likely to cancel since they have fewer outside options) and taking them in aggregate might disguise their behaviours. However, this is unlikely to result in any specification error given the short time period of this analysis (and the very mild impact of the global recession in Australia)



and not all apprenticeships are exactly four years in the first place. The analysis also needs to consider that:

there are three possible outcomes (cancel, complete and still in-training), or essentially two plus not enough time has passed to observe an outcome for those still in training; and

completion and cancellation rates may vary with the characteristics of the apprentices and ignoring this may bias the results when the policies target particular cohorts defined by those characteristics (such as age, gender and Australian Qualifications Framework (AQF) level).

2.1.3.2 Introduction to survival analysis

The origin of survival analysis goes back to mortality tables from centuries ago. However, it was not until World War II that a new era of survival analysis emerged. This new era was motivated by interest in reliability (or failure time) of military equipment. The methods were later applied to data from medical trials, and other applications in which time to a ‘failure’ of some sort is of interest.

For this study we define the survival time as the time from commencement to exit, where an exit means either ‘Completed’ or ‘Cancelled’. Let T denote the survival time and define the survival function:

S(t) = Pr(T > t),

where Pr means probability. That is, the survival function gives the probability of surviving – still being in the apprenticeship/traineeship – beyond time t. S starts at 1 for t = 0 and falls to zero as t increases (S(0) = 1 and S(t) → 0 as t → ∞).

The hazard function describes the concept of the ‘risk’ of an outcome – completion or cancellation. It gives the probability of an outcome in the small time interval {t, t+ ∆}, given that the person is still in the system at time t:

λ(t) = Pr(t < T < t + ∆ | T ≥ t)

(strictly speaking, we take the limit as ∆ → 0). A higher hazard rate means a faster exit rate, and a cohort with higher hazard rates is expected to have shorter survival times – less time in the apprenticeship/traineeship.



Chart 2.2: Survival functions for Apprentices and Trainees – all exits

Source: DAE Analysis of TYIMS data

To illustrate, Chart 2.2 shows the estimated survival functions for apprentices and trainees. Note that:

The functions start at 1 (all survive the first day) and then decline as fewer survival to a given number of days in the apprenticeship/traineeship.

The trainee function is below the apprentice function, reflecting the shorter duration of traineeships (or higher hazards).

Both functions step down at one year duration, two years duration and so on, as large numbers of apprentices/trainees complete. The largest step down for apprentices is at four years, the most common expected duration.

Within the administrative data, some spells are very long.

As just noted, trainees have higher hazards – risks of exiting – than apprentices. Part of that is due to shorter expected durations of training – faster exits which are expected. But exits may also result from cancellations and Chart 2.2 does not distinguish between completions and cancellations.

Policy may also affect the risks of cancellations and completions in different ways.

2.1.3.3 Competing Risks

An apprentice can leave their apprenticeship with an outcome of either ‘Cancelled’ or ‘Completed’. When there are multiple causes of an outcome we say that there are competing risks. We observe the smaller of the time to cancellation and the time to completion.

0

0.2

0.4

0.6

0.8

1

1.2

0 200 400 600 800 1000 1200 1400 1600 1800 2000

probability

Days

Apprentice Trainee



We may be interested in questions such as:

How do the rates of completion and cancellation vary across cohorts and with policy actions?

Is a person who is at higher risk of a cancellation less likely to complete, even after controlling for the characteristics of the people?

Can we estimate the likelihood of completion after removing the risk of cancellation?

The second question asks about unobservable characteristics of Australian Apprentices, such as ability or tenacity, and whether those characteristics act differently on completion and cancellation. The third question asks about, for example, the hypothetical distribution of completion times if cancellations were outlawed.

It turns out that we can answer the first of these questions, but the other two are essentially intractable.

To get insight into the survival times and the competing causes of exit, we can take two different perspectives: For a certain time point t, we can ask:

What will happen around this time point? Or;

What has happened until this time point?

In the first case we are interested in the hazard rate. For competing risks, define the cause-specific hazard rate, representing the risk of exit from cause r. Here r = 1, 2, for completion and cancellation, respectively:

λ(t, r) = Pr(t < T < t + ∆, R = r | T ≥ t)

where R indexes the cause of exit. At any point in time, the overall risk of exit is the sum of the risks from completion and cancellation:

λ(t)= λ(t, 1) + λ(t, 2)

In the second case, we are interested in the probability of exiting from cause R = r up to this time point. It is usual to consider the (cause specific) cumulative incidence functions (CIF’s):

Ir(t) = Pr(T ≤ t, R = r) with r = 1, 2

For example, I1(2000) is the probability of completing an Australian Apprenticeship in less than 2000 days.

It is more straightforward to interpret the CIF’s than the cause specific hazard rates.

2.1.3.4 Censoring

The sample period ends on 12 December 2011; and on that day, many Australian Apprentices were still in training. For those Australian Apprentices, we do not observe the time from commencement to exit – we observe the time from commencement to 12 December 2011. We say that the data is censored.

The censoring is taken into account in the estimation.



For policies put in place towards the end of the sample period, a large proportion of observations may be censored. For example, relatively few Australian Apprentices commencing since Tools for Your Trade (TFYT) was implemented will have had time to complete and many of their spells will be censored.

2.1.3.5 Modelling methodology

The Proportional Hazards model allows the hazard rate to vary with the explanatory variables:

λ(t,r) = λ0(t,r) exp(xβr)

for r = 1,2, for completion and cancellation, respectively; and where x is the set of time-invariant explanatory variables, β1 and β2 are sets of parameters and λ0(t,1) and λ0(t,2) are the baseline hazards for completing and cancelling, respectively.

For example, suppose that the baseline hazard rate is constant through time, at 0.1% per day. That means that people in a potentially hypothetical ‘baseline’ cohort exit at the rate of 0.1% per day. The exp(xβ) term allows the hazard to increase or decrease with the values of the x’s. For example, if x = 1 and β1 = 0.5 then the hazard rate is 64% higher (exp(0.5) = 1.64).

The full set of x variables is given in Table D.1. The definitions of the policy variables are given next.

Policy variables

We define dummy variables for the cohorts eligible for the various incentives/personal benefits. For example:

Kickstart Bonus

Eligibility is for Australian Apprentices 19 years and under in Certificate III and IV NSNL qualifications who commenced between 1 Dec 2009 and 28 Feb 2010.

Define the dummy variables

AKB = 1 if commenced between 1 Dec 2009 and 28 Feb 2010, = 0 otherwise

NSNL_1 = 1 if qualification is on NSNL, = 0 otherwise

Age_1 = 1 if in age range ‘Less Than 20’, = 0 otherwise

Level_CIII = 1 if qualification is Certificate III, = 0 otherwise

Level_CIV = 1 if qualification is Certificate IV, = 0 otherwise

The coefficient on each dummy variable in the model captures the effect of that cohort on completion or cancellation. For example, a negative β on NSNL_1 in the cancellation model



means a lower hazard/exit rate in National Skills Needs List (NSNL) qualifications than in non-NSNL qualifications. If β = -0.1 then the hazard rate is 10% lower.5

If the first three dummy variables are equal to one, and either of the fourth and fifth are equal to one, then that person is eligible for the Apprentice Kickstart Bonus (AKE).

If a person has AKB = 1, NSNL_1 = 1, Age_1 = 1 and Level_CIII = 1 (or Level_CIV = 1), then they are eligible for the Kickstart Bonus, and the effect in the model is given by:

β1 AKB + β2 NSNL_1 + β3 Age_1 + β4 Level_CIII + β5 Level_CIV

where the parameters are named generically.

For added flexibility in the models, we also include the interaction term,

Claim_AKB_eligible = 1 if eligible, = 0 for others.

The Kickstart portion of the model becomes:

β1 AKB + β2 NSNL_1 + β3 Age_1 + β4 Level_CIII + β5 Level_CIV + β6 Claim_AKB_eligible

Under the hypothesis that the Kickstart Bonus has no effect, β6 = 0, the hazard ratio for a person in the eligible cohort is calculated using the sum of the time period (i.e., AKB), NSNL, age and AQF terms.

But if β6 ≠ 0, then there is some additional effect in the eligible cohort. We associate that additional effect with the Kickstart Bonus.

For example, a negative β on Claim_AKB_eligible in the cancellation model means a lower hazard/exit rate than that implied by the coefficients on AKB, NSNL_1, Age_1, Level_CIII and Level_CIV alone. For example, if β = -0.1 then the hazard rate is 10% lower than that implied value.

Note: A person becomes eligible for an incentive/personal benefit when they have been in the apprenticeship/traineeship for approximately 90 days (the period varies by incentive/personal benefit but all are around 90 days). However, this is not relevant for the eligibility variables – lasting 90 days is an outcome rather than an explanatory variable.

More generally, the models use the eligibility variables rather than whether or not the incentive/personal benefit is actually claimed. The eligibility variable defines the policy and whether or not the incentive/personal benefit is claimed is an outcome variable which may be correlated with the time to exit variable.

Tools For Your Trade (TFYT)

Full eligibility for TFYT is for Australian Appentices in Certificate III and IV, NSNL qualifications (plus selected others) who commenced after 1 January 2010. Australian Apprentices in those qualifications who started after 1 January 2008 and are still in training after 1 January 2010 are also eligible for some of the payments.

5 Exp(-0.1) = 0.904



We define two dummy variables to distinguish between those who commenced after 1 January 2010 and those who commenced between 1 January 2008 and 31 December 2009:

TFYT1 = 1 if commenced after 1 January 2010 = 0 for others.

TFYT2 = 1 if commenced between 1 January 2008 and 31 December 2009 and period of registration finished after 1 January 2010, = 0 for others.

TFYT2 is intended to pick-up the partial eligibility of the cohort. We also include the interaction terms

Claim_TFYT1_eligible = 1 if eligible and commenced after 1 January 2010, = 0 for others.

Claim_TFYT2_eligible = 1 if eligible and commenced between 1 January 2008 and 31 December 2009 and period of registration finished after 1 January 2010 = 0 for others.

Strictly speaking, the Proportional Hazards model does not apply for Australian Apprentices who started after 1 January 2008 and are in training after 1 January 2010. That is because their eligibility status changes on 1 January 2010, and as a result their hazard rate may change on that day.

It is not computationally tractable to allow for time-varying explanatory variables in the Proportional Hazard models used here, given the size of the dataset; and we allow for the change in status through the Claim_TFYT2_eligible variable. That essentially assumes that that cohort has a different hazard rate from day 1 of their spell. Alternatives might be to censor the spells at 31 December 2009 or to delete those observations. The latter could cause a bias because it removes people with longer spells.

The same arguments apply for other cohorts whose spells cover a change in policy (SMCA, SAAA, WTU, TSC).

Women in Non-traditional Trades (WNTD)

For some incentives/personal benefits, such as Women in Non-traditional Trades, eligible qualifications change through time. It is not possible to include the time period dummy variable in the models (equivalent to AKB). Instead, we include a dummy variable for the eligible qualifications.

We define:

Qual_WNTD = 1 if qualification is eligible, = 0 otherwise

WNTD = 1 if commenced when qualification was eligible, = 0 otherwise

Claim_WNTD_eligible = 1 if person is = 0 otherwise.



The coefficient on the eligible variable is used to test the effect of the incentive.

The same approach is taken with Innovation Commencement and Diploma/Advanced Diploma qualifications eligible for the standard payments.

Control variables

The models include control variables for:

Age cohort

AQF level

Commencement year

Full-time/Part-time/School-based

Indigenous status

Disability status

Gender

State

Local labour market (average over period)

NSNL

Period of incentive

Provider size in terms of number of employees – small, medium, large.

Rural/Regional status

Models

Four basic models are estimated:

Apprentices/completions

Apprentices/cancellation

Trainees/completions

Trainee/cancellation

2.1.3.6 Modelling output

a) Some basic outputs

Table 2.2 gives the numbers of Australian Apprentices across outcomes. There are a total 2.70 million commencements over the period 2002-2011; 963,000 apprentices and 1.74 million trainees. Just over 1.16 million of those have completed, 1.02 million have cancelled and 513,000 were in training at 12 December 2011.

Table 2.2: Basic outcomes - frequencies

Completed Cancelled In training Total

Apprenticeship 375,528 342,232 245,860 963,620 Traineeship 788,838 685,211 268,042 1,742,091 Total 1,164,366 1,027,443 513,902 2,705,711



Table 2.3 gives average and modal months to completion – for those who have completed – by apprenticeship type, AQF level and full-time/part-time. Also shown are number of people in each group.

Within Certificate III’s, full-time apprentices take, on average 38 months and the most common time is 49 months (including the first and last months). Full-time trainees take, on average, 17 months, and the most common time is 12 months (including the first and last months). Full-time Certificate IV’s are around 24 months in duration for both apprentices and trainees, whereas Diplomas and Advanced Diplomas are twice as long for apprentices.

It is interesting to see that part-time is typically shorter than full-time, suggesting that part-timers may be concentrated in certain qualifications, rather than simply attending at half the pace of a full-timer.

Table 2.3: Basic statistics on completion times

Apprenticeship Type AQF Level Attendance Type _FREQ_ Mean Mode

Apprenticeship Advanced Diploma Full-time 121 19.9 24

Apprenticeship Advanced Diploma Part-time 42 19.3 13

Apprenticeship Certificate I Full-time 2 33.5

Apprenticeship Certificate II Full-time 59 19.9 12

Apprenticeship Certificate II Part-time 4 35.8

Apprenticeship Certificate II School-based 3 16.0

Apprenticeship Certificate III Casual (Vic only) 11 34.7 38

Apprenticeship Certificate III Full-time 356387 38.4 49

Apprenticeship Certificate III Part-time 21293 17.5 12

Apprenticeship Certificate III School-based 3228 16.2 9

Apprenticeship Certificate IV Full-time 30936 21.5 24

Apprenticeship Certificate IV Part-time 10791 17.8 13

Apprenticeship Certificate IV School-based 188 16.9 15

Apprenticeship Diploma Full-time 3675 20.7 24

Apprenticeship Diploma Part-time 2191 20.7 16

Apprenticeship Diploma School-based 2 10.0 10

Traineeship Advanced Diploma Full-time 74 12.5 9

Traineeship Advanced Diploma Part-time 16 13.1 13

Traineeship Certificate I Full-time 86 14.8 12

Traineeship Certificate I Part-time 103 10.4 7

Traineeship Certificate I School-based 740 6.9 6

Traineeship Certificate II Casual (Vic only) 60 12.6 8

Traineeship Certificate II Full-time 120067 12.8 12

Traineeship Certificate II Part-time 56785 14.6 12

Traineeship Certificate II School-based 43851 15.7 12

Traineeship Certificate III Casual (Vic only) 224 18.5 12

Traineeship Certificate III Full-time 336618 17.0 12

Traineeship Certificate III Part-time 157449 16.0 12



Traineeship Certificate III School-based 18478 13.6 9

Traineeship Certificate IV Casual (Vic only) 11 15.8 13

Traineeship Certificate IV Full-time 98189 17.2 24

Traineeship Certificate IV Part-time 18706 18.8 12

Traineeship Certificate IV School-based 627 12.0 9

Traineeship Diploma Full-time 4413 11.9 10

Traineeship Diploma Part-time 1062 12.9 8

Traineeship Diploma School-based 6 11.3


Additional statistics on completion, cancellation and in training are given elsewhere throughout the report.

b) Parameter estimates

The full set of explanatory variables and parameter estimates are provided in Appendix D. The results here focus on selected control variables and the policy eligibility variables.

Control variables

Recall that a positive coefficient increases the hazard, i.e. it increases the probability of completion or cancellation. In the case of completions, increasing the hazard increases the completion rate, which is likely a desirable outcome in the case of a policy variable. In the case of cancellations, however, increasing the hazard increases the cancellation rate, which is likely an undesirable outcome in the case of a policy variable.

Table 2.4 shows the parameters estimated for selected control variables.

Indigenous, disabled and males: The completion rate is lower and the cancellation rate is higher than for non-indigenous/not disabled/females (other variables held constant).

Small and medium size employers: The completion rate is relatively lower than for large employers and the cancellation rate is higher (‘unknown’ is the base category).

NSNL qualifications: For apprenticeships, the completion rate is relatively lower than for non-NSNL qualifications and the cancellation rate is higher. For trainees, both rates are lower than in non-NSNL qualifications.

Table 2.4: Estimated parameters for selected control variables for the four models

Variable Apprentice

Completion

Apprentice

Cancellation

Trainee

Completion

Trainee

Cancellation

Indigenous -0.14 0.31 -0.15 0.29

Disabled -0.16 0.17 -0.07 0.06

Male -0.42 -0.01 -0.18 0.14

Small -0.46 -0.28 -0.57 -0.44

Medium -0.31 -0.32 -0.53 -0.50

Large -0.18 -0.43 -0.49 -0.58

NSNL -0.74 0.09 -1.27 -0.58 Source: Deloitte Access Economics’ analysis of TYIMS data



Policy variables

Table 2.5 shows, for apprentices, the estimated parameters and t-ratios for the policy variables. Also included are comments on the results.

Table 2.5: Modelling output for Apprentices: Completion and Cancellation models

Variable Completion model

t-ratio Comment Cancellation model

t-ratio Comment

TFYT1 -2.05 24.3 Too early 0.21 7.5 Faster cancellations

SMCA1 -0.26 5.9 Slower completions

0.98 25.2 Faster cancellations

SAAA1 0.85 7.8 Too early -0.11 2.9 NSS

AKB -2.54 11.1 Too early 0.08 2.0 NSS

AKE -1.90 4.9 Too early 0.00 0.1 NSS

WTU1 -0.46 17.0 Slower completions


TSC1 0.04 2.8 NSS -0.23 15.5 Slower cancellations

COM_Dip

AdvDip 0.20 3.0 Too early 0.29 3.6 Faster

cancellations

Inno 0.03 1.6 NSS 0.18 7.7 Faster cancellations

WNTD -0.35 16.9 Slower completions


RR03 0.01 0.5 NSS -0.01 0.8 NSS

RR06 0.04 2.4 NSS 0.02 1.1 NSS

lafha1 0.07 3.4 Faster completions

-0.27 8.9 Slower cancellations

Source: Deloitte Access Economics’ analysis of TYIMS data

‘Too early’ means that the policy is new and few eligible Australian Apprentices have had time to complete. Hence, there is insufficient information in the data to assess how the policy is affecting completions.

NSS stands for Not Statistically Significant. The critical value in the t-tests is large (around 3.3) reflecting the large number of observations in the models. (This adjustment uses the logic that the probability of a type 1 error in the tests should be allowed to decline as the number of observations increases.)

The signs on the parameters give the estimated directions of the effects of the policy variables on the outcomes. In cases where both signs are the same, the relative sizes of the estimates are relevant.

Table 2.6 shows the modelling output for trainees. A number of the policies target those in NSNL qualifications, and there are too few trainees in those qualifications to assess the effect of the policies. Those policies are excluded from further analysis.



Table 2.6: Modelling output for Trainees: hazard rates, Completion and Cancellation models

Variable Completion model

t-ratio Comment Cancellation model

t-ratio Comment

TFYT1 0.04 0.6 Not many

NSNLs NSS

-0.14 2.4 NSS

SMCA1 -0.82 2.5 NSS 0.87 2.9 NSS

SAAA1 0.08 0.3 Not many

NSNLs NSS

-0.01 0.0 NSS

AKB -1.72 3.3 Not many

NSNLs NSS

0.18 0.6 NSS

AKE -5.90 0.7 Not many

NSNLs NSS

0.76 2.2 NSS

WTU1 -1.27 4.3 Slower completions

0.47 1.7 NSS

TSC1 0.99 3.7 Faster completions


COM_Dip/

AdvDip -0.07 0.9 NSS 0.40 3.9 Faster

cancellations

Inno 0.35 8.7 Faster completions

0.10 2.2 NSS

WNTD -0.17 11.1 Slower completions

0.04 2.7 NSS

RR03 0.10 10.4 Faster completions

0.00 0.2 NSS

RR06 1.47 7.2 Faster completions


lafha1 0.31 10.9 Faster completions



More generally, and as noted above, it is difficult to interpret the magnitudes of the parameters. They show the effects on the hazard ratio rather than on some observable variable. In section 3.2.2, the CIFs and specific results for the probabilities of completing and cancelling are presented.

In cases in which it is too early to assess the effect on completions, but the parameter for cancellations is statistically significant, we ‘turn off’ the completion side of the model and consider only cancellations. The data points for the completions are treated as censored spells and the effect of the policy on outcomes is shown through the estimated survival function.

The CIF and survival functions are evaluated at particular values of the explanatory variables. In general, we use means of the variables defining characteristics of the Australian Apprentices and particular time periods. For example, the CIF for SMCA assumes that the person commenced in 2007. That year determines the values of the time period dummy variables (SMCA1 = 1, for example) and possible eligibility for other Incentives.



2.2 Method for determining efficiency

In order for the AAIP to support the primary goal of Australian Apprenticeships in providing an ongoing source of skilled labour to the economy, it must be both cost-efficient and appropriately targeted. Alternative approaches to measuring these circumstances are explored below.

2.2.1 Cost efficiency

The financial efficiency of particular incentives under the AAIP can be assessed by the extent to which they deliver the desired outcomes and at what cost. On the other hand the economic efficiency of particular incentives under the AAIP can be assessed by the extent to which they deliver the desired outcomes at what net benefit.

Inherent in the concept of efficiency is a trade-off between increasing outputs and increasing inputs (holding constant any undesired outcomes). An efficient system will optimise this trade-off subject to any constraints, to ensure the maximum return to society.

In a world where no budget constraints apply the most efficient outcome is one where the marginal social cost of intervention equals the marginal social benefit. However, in reality budget constraints are binding and the optimal point, subject to those constraints, will occur at a lower level of investment.

As such and in the context of this analysis, efficiency is considered in cost-effectiveness terms rather than cost-benefit, where the relative cost-effectiveness of different incentives under the AAIP is assessed in order to reveal those that appear to provide greater levels of financial efficiency.

Accordingly, those incentives that appear in-effective in the econometrics are excluded from the efficiency assessment altogether (along with those that were unable to be tested, see Appendix A), while those that do appear to have had a positive effect are assessed in terms of their cost per additional commencement and cost per additional completion.

2.2.2 Target efficiency

Inevitably where government is intervening in a market, particularly where they’re providing financial incentives, the possibility of perverse and unintended outcomes in particular areas exists. In fact any market intervention is by definition distortive, and as such the question to be addressed is the degree to which this distortion is tending in the right direction, such that it corrects the market failure and/or equity issue.

Unintended market distortions can impact the effectiveness and ultimately efficiency of the intervention – among a host of other policy objectives – in this case particularly as it applies to the match between skills demanded and skills supplied. However, measuring the imbalance between skills demanded and supplied at any particular point in time is difficult by virtue of the fact that skills demanded are typically reported in occupational terms, while skills supplied are typically reported in qualification terms.



The increasing mobility of labour, coupled with the fact that most non-trades related qualifications are not aligned with particular occupations or indeed industries, means that it is difficult to reconcile these two series. A further complication is that the measures are typically reported as flows rather than stock values, making identification of the imbalance at a particular point in time near impossible.

Given the key parameters by which any skill demand-supply imbalance could be measured are unemployment, vacancies and relative wages, and the issues outlined above, this study is unable to assess the following more direct benchmarks of the AAIP’s calibration:

Unemployment rate by sector against increase in workers by sector; as if someone is unemployed they are by definition not attached to a particular sector.

Field of study completed against DEEWR skill shortage data; as field of study need not correspond with a particular occupation.

Beveridge curves (unemployment rate against vacancy rate); which in this case could only be constructed at a higher level of aggregation than would be useful.

Instead the only way the direction of market distortions could be considered in this analysis was to look at apprentice and trainee:

1. Retention rates by industry over time (as a proxy for supply), compared to relative wage gains to workers skilling in those industries (as a proxy for demand).

2. Retention rates over time for NSNL compared to non-NSNL qualifications.

The obvious limitations being that neither are direct measures of the supply of skills nor the demand for skills, and do not take account of the current stock (i.e. the degree to which there may be an existing imbalance). Future studies might consider a consultation/survey approach – engaging businesses – as a means by which to improve the understanding.

Market distortions can also be in the form of the quality of the service provided. However, the quality of education and training is particularly difficult to measure – directly or indirectly – and is therefore excluded from the analysis on this basis.

2.3 Method for optimising the future AAIP

Optimising the future AAIP involves assessing the inherent trade-offs between effectiveness, efficiency and complexity – as they apply to different incentive types and levels – and then choosing a preferred approach based on the relative weights policy and economic theory suggest should be attached to these parameters.

In the first instance and in the context of the AAIP, each of these parameters is influenced (more or less) by:

Where the AAIP is incentivising.

How the AAIP is incentivising.

How much the AAIP is incentivising.

The degree to which the key policy parameters are influenced by the above factors is considered in this study, drawing on both empirical analysis and economic theory.



At a more detailed level, optimisation of where to incentivise is largely a theoretical consideration that links back to the role for government. For instance, whether the payment is provided across the board or targeted at particular groups (such as disadvantaged learners and/or those training in an area of critical skill need). This can also be understood in terms of the appropriateness of the basis on which particular skills can be targeted, and the relative upside to improving the outcomes of disadvantaged learners.

The optimisation of how to incentivise would be performed in an empirical manner, and organised around:

who the receiver is (be it employers or apprentices/trainees);

the timing (on commencement, recommencement, progression or completion); and

That is, provided there is sufficient variation in the structure of the incentives under the AAIP – in terms of who receives the incentive and when – and that these incentives are shown to have had an effect, the optimisation of the first two considerations can be determined through a cost-effectiveness analysis of differing incentives over time.

How much to incentivise is also, ideally, strictly determined using the findings from the econometrics and other empirical analysis.

While ultimately the extent to which this study can conclude one way or another around these considerations is a factor of the quality and availability of the data, this approach nonetheless extends the current level of understanding. It also outlines the parameters on which further studies – particularly those involving the collation of primary evidence through controlled experiments – can better resolve policy deliberations in this regard.



3 Effectiveness – findings In this section, the effectiveness of the AAIP is investigated at various levels, as revealed by the existing literature and data, as well as the econometrics performed as part of this study.

The literature and data review captures the findings of the four internal reviews of the AAIP, a number of studies carried out by the NCVER and a high-level analysis of TYIMS data. The econometrics is based on the modelling specifications outlined in the previous section.

Effectiveness for the purposes of this analysis is measured in terms of increased commencements, retentions and/or completions, and the results are organised as such.

3.1 Literature and data review

Across the existing studies that consider the impact of incentives on Australian Apprenticeships, effectiveness is typically examined with regard to the following:

3. The extent to which incentives have impacted the total number of apprentices or trainees.

4. The extent to which incentives have led to an improvement in Australia’s skills base and the productivity of the workforce.

5. The extent to which incentives have aligned apprenticeships/traineeships, skills and productivity to areas of critical skill need and the equity agenda.

These relationships are explored further below, as they relate to any observed increases in commencements and retentions and completions in the system over time.

3.1.1 Commencements

As the starting point, to increase the demand for apprentices/trainees, incentives must in theory improve the payoff to employers from recruiting an apprentice/trainee. Then, to the extent that there is an insufficient supply of individuals looking to commence an apprenticeship or traineeship, incentives must also improve the payoff to the individual from undertaking this education and training. Research to date has found little evidence to suggest that either of these price signals is sufficiently achieved, at least for apprentices.

Referring specifically to apprentices, the NCVER argue that government incentives paid to employers are unlikely to influence the decision to take on an apprentice because the size of the incentives is immaterial in comparison to the costs of providing apprenticeship places. Through case study analysis, Karmel et al (2008) found that in some cases, employer incentives constitute as little as 2% of total costs of taking on an apprentice. Yet another study notes that the elasticity of demand for apprentices with respect to incentive payments is estimated to be very low, again implying that they have little impact on employer behaviour (Cully et al 2005).

On the other hand, incentives have more than likely had a discernible impact on traineeship commencements (Karmel et al 2008). This is because for traineeships, the incentive is



inherently more significant given the lower average wages and the shorter training duration, which implies lower private outlays. Karmel et al (2008) note that incentives act as a significant wage subsidy for trainees and their analysis determines that there is very little doubt that incentives have significantly impacted traineeship numbers.

In summary, research and reviews into the drivers of apprenticeship and traineeship commencements generally conclude that the prevailing economic conditions are the main catalyst for the take-up of apprenticeships, while for traineeships, economic conditions appear to be less of a driver and more impetus is provided by the incentives (Karmel et al 2008; NCVER 2011d). This stance is broadly supported by the data, which shows that traineeship commencements were less responsive to the economic downturn in 2009 than apprenticeships, and that overall, uptake of traineeships have been much higher than apprenticeships over 2002 to 2010 (Chart 3.1).

Chart 3.1: Commencements of Apprenticeships and Traineeships


3.1.2 Retention and completion

In a similar fashion to how incentives improve the price signals to employ/commence an apprenticeship; to be effective, incentives must also improve the price signal to retaining an apprentice and ultimately completing the training.

This implies reducing the opportunity cost of remaining in training to completion: (1) faced by the employer of an apprentice/trainee; (2) faced by the individual undertaking training. Given they are inversely related, simultaneously reducing these opportunity costs means decreasing the effective wage paid by employers whilst increasing the effective wage received by the individual. The existing literature provides little evidence to suggest that incentives have had an impact on retention and completion rates in this way, and rather that other factors are driving the trends in these measures.

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Since the introduction of New Apprenticeships in 1998, payments to employers have shifted from being predominately paid on commencement to being predominately paid on completion. However, this re-weighting has not increased the apprenticeship completion rate, and in fact the prior econometric modelling suggests that incentives have failed to prevent a decline in the rate of apprenticeship attainment (DEEWR 2008).

This is supported by the high level trends in the TYIMS data, at least for apprentices (see Chart 3.2). On average, around 76% of apprentices who began their apprenticeship in 2002 were still in training or had completed two years later. For those who started in 2009, this number was only 68% – an increase in the drop-out rate of 8 percentage points.

Chart 3.2: Two-year retention/completion rates for Apprenticeships and Traineeships


Instead, studies have found retention and completions to be somewhat inversely related to prevailing economic conditions. For instance NCVER (2011d) find that an economic downturn can lead to improvements in completion rates, as a reduction in general employment opportunities make it more attractive to remain in an apprenticeship or traineeship. They also find that this on-average outweighs the associated increase in apprentice and trainee redundancies.

A similar result is also found in Kapuscinski (2004). This study found that cancellations are negatively related to the youth unemployment rate (i.e. the higher the youth unemployment rate, the lower the cancellation rate), indicating that a lack of alternative employment opportunities for entry level trainees during deteriorating economic conditions reduces cancellations. They also note that the effect of the business cycle on cancellations is often understated due to the opposing behaviours of apprentices/trainees and employers during the business cycle.

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Also, many studies cite business factors such as employer size and management practices as the main determinants of retention and completion (Dickie et al 2011). Modelling by Karmel and Roberts (forthcoming) suggests that in some industries, completion rates can vary by almost 20 percentage points between the smallest (one apprentice) and largest (100+ apprentices) employers. However, Dickie et al (2011) note that “some of the smallest businesses achieve the highest retention rates and completions”.

The evidence would therefore suggest that the size of the business itself is not the determinant, but rather other factors that tend to be associated with larger businesses are influencing these outcomes. For instance, larger business might tend to hold specialist human resource capabilities, which in turn might provide better support for the apprentice and engage in better recruitment practices that ensure improved fit/matching.

Furthermore, Dickie et al found that fairness in the workplace and having a boss who “treats the apprentice as a human being and an employee, not a kid at the bottom of the ladder” (Dickie et al 2011, p. 13) is essential to successful completion, while the NCVER (2011a) have noted that pastoral support and quality training are fundamental to apprenticeship completion.

NCVER report that incentives have successfully assisted Indigenous people and people with a disability into employment. NCVER (2005) estimates that the participation of Indigenous apprentices and trainees was around 10% higher than would be expected and around one quarter of Indigenous people employed in trades were in an apprenticeship or traineeship – double the rate of non-indigenous people.

While a little over one-third of incentive payments go to skill shortage areas (Karmel et al, 2008); findings suggest that this is not translating into supply of workers in the areas of greatest skills need. Also, although it is acknowledged that economic, institutional and personal factors have a stronger influence on behaviour than any viable Government contribution could achieve, past reviews have noted that incentives have still been less effective than could reasonably be expected. Indeed empirical analysis by DEEWR (2008) demonstrates that the program effect is marginal at best, and in some areas contrary to the policy intent.

3.2 Econometric review

3.2.1 Commencements

The following analysis seeks to estimate the impact of specific AAIP incentives on commencement rates of Australian Apprenticeships in the eligible population of recipients. Separate econometric models for each incentive have been developed to isolate the effect of a given incentive based on the population it targets.

Evidence from the econometric analysis of commencements suggests that financial incentives have had a positive effect on commencement rates. Furthermore, the value of the incentive is positively related to the effect of the incentive, that is, higher value incentives promote more commencements.



Given the limitations of the underlying data and/or the nature of the incentives, only incentive scenarios that provide a reasonable basis for econometric analysis have been presented (see Appendix A). Consequently, the report is limited to the presentation of results for the following incentives:

Changes to Standard Incentive and Living Away From Home Allowance (LAFHA) in July 2003;

Support for Mature Age Apprentices (SMAA);

Support for Mid Career Apprentices (SMCA);

Apprentice Wage Top-up (WTU);

Support for Adult Australian Apprentices (SAAA);

Apprentice Kickstart Bonus (AKB); and

Apprentice Kickstart Extension (AKE).

3.2.1.1 Changes to Standard Incentives and Living Away From Home Allowance in July 2003

In July 2003 the Standard Commencement and Completion incentives were increased from $1,250 to $1,500 for Certificate III/IV qualifications. At the same time, the Living Away From Home Allowance was increased and expanded to include persons in the second year of their qualification (where previously it was only available to persons in their first year).

The broad reach of these policy changes made it impossible to identify a suitable control group to compare the effect of the policy change to. Consequently, the results of this analysis are based only on controls for macroeconomic influences, the impact of changes in other incentives applicable to the key population, state fixed effects and seasonal effects (and thus the results could suffer from omitted variable bias, as discussed in section 2.1.2).

The estimated effect of these policy changes on different age groups is presented in Table 3.1.

Table 3.1: Effect of changes to Standard Commencements and LAFHA on commencements

Population Effect on commencements

Comment

Population under 20 6.86%

20 – 24 year old cohort -7.87% NSS





The analysis finds that commencements of individuals under the age of 20 undertaking Certificate III/IV qualifications increased by an average of 7% per month due to the changes in the Standard Incentives and the Living Away from Home Allowance introduced in July 2003. This translates to approximately 45,000 additional commencements over the entire time period.



Chart 3.3: Policy effect resulting from the change to Standard Incentives and LAFHA, July 2003 (Certificate III/IV apprentices under the age of 20)

Individuals under the age of 20 and undertaking a Certificate III/IV qualification have represented approximately one quarter of all Australian Apprentices throughout history, and in 2010-11, this cohort represented 35% of the total Certificate III/IV population (see Chart 3.4).

Chart 3.4: Monthly commencements of all AAIP eligible persons and commencements of Certificate III/IV apprentices under the age of 20

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Notably, policy changes do not appear to have a positive impact on commencements for age groups over the age of 20. However, although negative in sign, the effect of the incentive on the older cohorts is not statistically significant (Table 3.1).

3.2.1.2 Support for Mature Age Apprentices

The SMAA incentive is available to all Australian Apprentices over the age of 45. The incentive was introduced on 1 July 2003 and involves a payment of $750 upon commencement and $750 upon the completion of any given qualification.

The analysis was conducted upon all commencements over the age of 45. The results were compared to a control group consisting of all commencements in the ages 35-44.

Despite the upward trend visible in Chart 3.5 following July 2003, the SMAA proved to have no significant effect upon commencements. This is because the modelling suggests the upward trend is in fact due to changes in the underlying macroeconomic variables and/or changes to other incentives which the key population are eligible for.

Notably the large spike in commencements in June 2003 (one month prior to the implementation of the SMAA) was largely due to developments in Victoria, while the spike in June 2008 was dominated by developments in New South Wales and Queensland, see Chart 3.5. The circumstances underlying these spikes do not relate to any changes to the AAIP program. However, they may indicate implementations of successful state-based incentive schemes worthy of further analysis.

Chart 3.5: Monthly commencements of all AAIP eligible over the age of 45

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3.2.1.3 Support for Mid Career Apprentices and the Apprentice Wage Top-up

SMCA covered all Certificate III/IV NSNL qualifications undertaken by Australian Apprentices aged over 30. This incentive was implemented on 1 July 2007 and removed again on 1 January 2010. The incentive was payed in weekly instalments of $150 in the first year of a qualification and $100 during the second year of a qualification.

The WTU was offered during the same period as the SMCA. The WTU involved a payment of $500 every 6 months for the first two years of the qualification, and was offered to Australian Apprentices in Certificate III/IV NSNL qualifications under the age of 30.

Given everyone undertaking a Certificate III/IV NSNL qualification in the period 1 July 2007 to 1 January 2010 was receiving either the WTU or SMCA, it is not possible to estimate the isolated effect of either of these incentives compared to a control group who did not receive an incentive. However, it is possible to estimate the relative effect of the WTU compared to the SMCA.

The econometric analysis compares the 30-44 age cohort – representing Australian Apprentices eligible for SMCA – relative to the 20-30 age cohort, which represents persons eligible for the WTU. In addition to deriving the effect of each incentive relative to a control group (e.g. WTU relative to SMCA), the analysis controls for any significant labour market, economic activity, business confidence and seasonality aspects that might have impacted commencements.

The econometric analysis revealed that the SMCA had a relatively larger effect than the WTU. Considering that SMCA offered eligible individuals up to $7,800 per annum (52 x$150), compared to just $1,000 offered in the first year by WTU, this result is not surprising.

Chart 3.6 presents the effect of the SMCA on commencements relative to the WTU. Assuming that the WTU did not have a negative impact on commencements, Chart 3.6 presents the minimum total impact of SMCA on commencements. Under the assumption that the WTU did not discourage commencements, it can be concluded that SMCA encouraged commencements throughout the time period in which it was in place.



Chart 3.6: Policy effect resulting from Support for Mid Career Apprentice incentive relative to the Apprentice Wage Top-up

A significant number of individuals seem to have delayed commencements from June 2007 to July 2007 in order to take advantage of SMCA. On the flipside it is difficult to ascertain the isolated effect of the removal of the SMCA due to the myriad of policy changes undertaken on 1 January 2010; the combined effect of all policy changes in this month was an increase in commencements of approximately 7%.

Net of all timing effects, SMCA is estimated to have prompted an additional 2,380 commencements over the period at a minimum.

3.2.1.4 Support for Adult Australian Apprentices

SAAA was introduced 1 January 2010 and offered to all individuals over the age of 25. The incentive was distributed as a payment of up to $150 per week in the first year of an apprentice qualification and up to $100 per week in the second year.

Because SAAA was offered in addition to Tools For Your Trade (TFYT), it is possible to estimate the impact of SAAA as the difference between the combined effect of TFYT and SAAA compared to individuals under the age of 25 who only received TFYT. By employing individuals under the age of 25 as a control group, this analysis controls for any effects of the labour market and economic activity on commencements.

Chart 3.7 presents the results of the econometric analysis for the cohort aged 25-29, using the 20-24 cohort as control. A significant timing effect occurred upon the implementation of the SAAA. The fact that this timing effect is not as stark for the older cohorts suggests that a large number of potential Australian Apprentices in the 25-29 cohort chose to delay their commencement in order to receive TFYT and SAAA, compared to the AWT which was in place at that time. This further emphasises the benefit upon commencements of incentives of higher monetary value. In total SAAA is estimated to have contributed 2,654 commencements since its inception, net of any timing effects.

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Chart 3.7: Policy effect resulting from Supporting Adult Australian Apprentices, 25-29 cohort

Chart 3.8 illustrates the impact of the SAAA upon the 30-34 year old cohort, using the 20-24 cohort for control. A small timing effect is present around the implementation of the SAAA. In total the SAAA is estimated to have contributed 957 commencements for this cohort since its inception.


Finally, Chart 3.9 illustrates the effect of SAAA upon the 34-35 age group, using the 20-24 cohort as control. Again there is a small timing effect around the implementation of the incentive as people delay commencements in order to receive the SAAA and TFYT rather than SMCA. Net of any timing effects, SAAA provides 1,252 commencements to this cohort.

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In total, SAAA has contributed 4,864 Australian Apprenticeship commencements since its inception.


3.2.1.5 Apprentice Kickstart Bonus

The AKB was offered to Australian Apprentices under the age of 20 in a Certificate III/IV NSNL qualification. The incentive was available for 3 months from 1 December 2009 until 28 February 2010 and offered up to $3,350 payed in the first year.

The econometric analysis was confined to apprenticeship commencements because this group represented the vast majority (99.5%) of the population. Furthermore, this split allowed for a consistent control group to be formed of apprentice commencements in the same age cohort undertaking Certificate III/IV Non-NSNL qualifications, see Chart 3.10. Additionally, the econometric analysis controlled for any significant labour market and economic activity, business confidence and seasonality aspects which impacted upon commencements.

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Chart 3.10: Apprentice Kickstart Bonus eligible commencements and comparison group

The econometric analysis revealed that the AKB significantly increased the level of commencements for the eligible cohort. The implementation of the AKB caused a number of apprentices to delay their commencement date from November to December 2009, while the removal caused some individuals to commence in February rather than March 2010. Net of any timing effects, the AKB is estimated to have contributed 10,179 commencements.

Chart 3.11: Policy effect resulting from the Apprentice Kickstart Bonus

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3.2.1.6 Apprentice Kickstart Extension

The AKE was offered to Australian Apprentices under the age of 20 in a Certificate III/IV NSNL qualification employed by a small or medium sized enterprise (SME). The incentive was available for 7 months from 12 May 2010 until 12 November 2010 and offered up to $3,350 payed in the first year.

The econometric analysis was confined to apprenticeship commencements because this group represented the vast majority of the population (99.5%). The econometric analysis controlled for any significant labour market and economic activity, business confidence and seasonality aspects which impacted upon commencements. Furthermore an identical cohort of individuals working for large enterprises was used as a control group, see Chart 3.12.

Chart 3.12: Apprentice Kickstart Extension eligible commencements and comparison group

The econometric analysis revealed that the AKE increased the level of commencements for the eligible cohort. While there was a timing effect at the introduction and removal of the incentive, the net impact of the incentive was estimated to be an additional 1,570 commencements over the period.

Notably, the AKE was deemed to have had a negative effect upon commencement in November 2010 – the month immediately prior to its removal. The reason behind this unexpected result is that the control group – commencements at large employers, spiked in this same month. As such, while commencements were abnormally high in November 2010, this was due to an unknown cause.

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Finally, it is suspected that the smaller effect of the AKE compared to the Bonus is due to the fact that there are fewer potential apprentices under the age of 20 in the period May-November than there are in the period December – February, as it relates to the timing of the school year.

Chart 3.13: Policy effect resulting from the Apprentice Kickstart Extension

3.2.2 Retention and completion

Table 3.2 shows the impact of each policy on the probabilities (in percentage points) of completing and cancelling training for apprentices and trainees. The signs of the effects generally agree with the signs on the coefficient estimates shown in Table 2.5 and Table 2.6. The CIFs and survival functions used to calculate these probabilities are in Appendix C.

Table 3.2: Impact of policies on probabilities of completing and cancelling for Trainees and Apprentices

Policy Impact on probability of completing – Apprentices

Impact on probability of cancelling – Apprentices

Impact on probability of completing –

Trainees

Impact on probability of cancelling –

Trainees

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SMCA -29.6 30.9 NSS NSS

WNTD -8.7 6.8 -5.9 NSS

LAFHA 7.6 -9.8 13.7 -13.4

WTU -32.4 31.5 -42.3 NSS

INNO NSS 6.5 10.3 NSS

COM_Dip/AdvDip Too early 9.6 NSS 14.5

TFYT Too early 7.7 NSS NSS

TSC NSS -8.5 24.7 -31.6

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The results can be interpreted as follows:

An apprentice who receives the Support for Mid-Career Apprentices incentive (SMCA) is less likely to complete their apprenticeship and more likely to cancel their apprenticeship.

• The probability of cancelling is increased by 30.9% points; and

• the probability of completing is decreased by 29.6% points.

Receiving the Women in Non-Traditional Trades incentive (WNTD) makes apprentices less likely to complete their apprenticeship and more likely to cancel their apprenticeship.

• The probability of cancelling is increased by 6.8% points; and

• the probability of completing is decreased by 8.7% points.

For a trainee receiving the Women in Non-Traditional Trades incentive (WNTD), the probability of completing is decreased by 5.9% points.

Apprentices and trainees who receive the Living Away From Home Allowance (LAFHA) are more likely to complete their training and less likely to cancel.

• For apprentices, receiving LAFHA increases their probability of completing by 7.6% points and decreases their probability of cancelling by 9.8% points.

• For trainees, receiving LAFHA increases their probability of completing by 13.7% points and decreases their probability of cancelling by 13.4% points.

Both apprentices and trainees who receive the Wage Top-up benefit (WTU) are less likely to complete; the probability of completion for apprentices declines by 32.4% points and the probability of completion for trainees declines by 42.3% points.

• Apprentices are also more likely to cancel, by 31.5% points.

The Innovation Incentive (INNO) apprentices are more likely to cancel, but trainees are more likely to complete, by 6.5% points and 10.3% points, respectively.

Expanding the Standard Commencement incentive to include Australian Apprentices in Diploma and Advanced Diploma qualifications is associated with a greater probability of cancelling an apprenticeship.

• The probability of cancelling for apprentices is increased by 9.6% points for apprentices and 14.5% points for trainees.

The Tools For Your Trade (TFYT) benefit increases the probability of cancelling for apprentices by 7.7% points.

Receiving the Commonwealth Trade Scholarship (TSC) implies:

• an apprentice is less likely to cancel (by 8.5% points); and

• a trainee is more likely to complete and less likely to cancel, by 24.7% points and 31.6% points, respectively.

As such, the findings suggest that for the most part, these incentives are associated with an increase in the probability of cancelling an apprenticeship/traineeship, and a decrease in the probability of completing an apprenticeship/traineeship. The notable exceptions to this are LAFHA, TSC and to a lesser extent INNO.



3.2.3 Take-up rates

A high take-up rate is a possible indicator of an effective system, where the take-up rate is the proportion of people who are eligible for a payment who actually claim the payment.

There are a number of reasons why a person might not take up an incentive they are eligible for, including:

they are not aware that the incentive exists or that they are eligible for it; or

the cost of taking up the incentive (e.g. administrative costs and costs of time spent claiming) is greater than the incentive itself.

The information contained in the TYIMS dataset does not allow an analysis of the reasons for non-take-up. However, the following sections provide an analysis of the relationship between take-up and different incentives and different personal characteristics.

3.2.3.1 Take-up by incentive type

Table 3.3 shows the take-up rates for many of the incentives.

Table 3.3: Take-up rates

Incentive Take-up rate

Commencement (Cert III and Cert IV) 71%

Commencement (Dip and Adv Dip) 70%

Completion 80%

TFYT (commence post 2010) 87%

TFYT (commence pre 2010) 87%

Kickstart Bonus 91%

Kickstart Extension 88%

SAAA (commence post 2010) 73%

SAAA (commence pre 2010) 29%

SMCA 59%

LAFHA1 97%

LAFHA2 98%

LAFHA3 97%

WTU 53%

Australian School-based incentives 77%

Commonwealth Trade Scholarship 45%

Women in Non-Traditional Trades 60%

Innovation Commencement 65%

Around 90% of eligible Australian Apprentices have claimed the Kickstart Bonus. The high rate might be expected given the pattern of the timing of commencements around the Kickstart start and finish dates.



The very high claim rate for LAFHA is also expected, given the process for obtaining the allowance:

Whether the Apprentice wants to access LAFHA is discussed at the sign up process (between the Australian Apprenticeship Centre Field Officer, the Apprentice and the Employer).

Where an Apprentice moves away from the parental or guardian’s residential home at a date subsequent to the commencement date, the Apprentice or Employer will approach the Australian Apprenticeship Centre (AAC) to seek LAFHA assistance. Subject to meeting the eligibility requirements, Apprentices in this situation will only attract LAFHA for the period between the date of establishing a new residence and 36 months from the commencement date of the Australian Apprenticeship.

Conversely the claim rate for the Commonwealth Trade Scholarship and the Wage Top Up are low. Accordingly the rates only partly align with the econometrics in terms of more effective incentives generally having higher take-up rates.

3.2.3.2 Who claims?

The take-up rate also varies across cohorts and might thereby shed further light on the effectiveness of the AAIP, at least in terms of targeting.

The modelling shows that the probability of claiming:

Increases through time

Is higher in Certificate IIIs than Cert IV’s.

Is higher for apprenticeships than traineeships

Is lower if cancelled (than ‘In Training’)

Is higher if completed (than ‘In Training’)

Falls and then rises with days of registration

Is higher in small and medium size providers

Is higher in NSNL qualifications.

As such, the take-up rates by cohort would suggest that for the most part those who are being targeted are more likely to claim, which perhaps supports the visibility of the AAIP, but may question the sufficiency of payment values when combined with the econometrics (at least in terms of incentivising completions).

3.3 Caveats and limitations

While the results presented in section 3.2 are expected to reflect the impacts of the AAIP incentives, they must still be viewed in the light of a number of caveats. In general:

The results may depend on the specification of the model.

Econometric modelling cannot distinguish between correlation and causality.

Errors in the data may impact on the results.

The time for the analysis has limited the sensitivity analysis.

More specific considerations to the commencement and completion analyses are below.



3.3.1 Commencement analysis

Policy effects are represented by time dummies. Consequently, it is assumed that any rise or fall in commencements in a given time period which cannot be explained by the control variables represents the impact of the policy.

It is assumed that policy changes affect commencements in the month in which a change is made, as well as one month prior to the introduction of the policy and one month following the removal of the policy. Hence any policy effects occurring outside this time frame will not be directly captured in the analysis.

It is assumed a policy has an equal effect in all months that it is in place, except for its first and last month.

For analyses which utilise a comparison group (control group), it is assumed that the comparison group responds in an equivalent fashion to external influences as the key population (treatment group).

It is assumed that the average effect of a given policy across Australia’s 5 largest States represents the effect it would have in Tasmania, Northern Territory and the Australian Capital Territory.

3.3.2 Completion/retention analysis

In addition to the first, third and fourth caveats just mentioned:

Observations in which the registration period cover a change in policy (e.g., 1 January 2010) are modelled through a dummy variable rather than a time-varying policy eligibility variable. (See the discussion under TFYT in section 2.1.3).

The CIF’s and survival functions are for particular cohorts and may not be fully representative.

The effects of the dummy variables are assumed to be the same across non-eligible cohorts. For example, people from those cohorts who registered in the AKE period are assumed to respond in the same way (relative to other time periods).

3.4 Implications

In essence, two primary conclusions can be drawn from the econometric analysis of commencements:

Money matters. None of the analysed incentives were proven to have a negative effect on commencements. All the incentives offering more than $1,000 in the first year proved to have a significant, positive effect on commencements.

Timing matters. Drawing from evidence revealed in the AKB and AKE analysis, an incentive that affects people under the age of 20 has a much larger effect around summer than at other times of the year. This is likely to be due to the fact that the supply of potential Australian Apprentices is highest in summer when the traditional school year finishes.

On the other hand, the findings of the retention/completion analysis suggest that for the most part the incentives are ineffective, in that they are associated with an increase in the probability of cancelling an apprenticeship/traineeship, and a decrease in the probability of



completing an apprenticeship/traineeship. The notable exceptions to this are LAFHA, TSC and to a lesser extent INNO.

As such the AAIP appears at face value to have been more effective in terms of incentivising additional commencements than incentivising additional completions, which may be a materiality issue or may simply be confounded by other factors. Tempering this with an analysis of take-up rates, higher take-up rates are only loosely associated with more effective incentives, although take-up rates are typically higher among target groups, which might again imply a materiality issue or that other barriers are present.

How those incentives that are estimated to be effective (at some level) compare on a cost-effectiveness basis is considered in section 4



4 Efficiency – findings In this section, the cost effectiveness of the AAIP is investigated at various levels, as touched on in the existing literature, and as revealed by comparing the findings of the effectiveness analysis with the levels of government outlay on particular incentives over time. Beyond its financial efficiency, the AAIP is also assessed here at a high level in terms of its target efficiency.

Cost-effectiveness is inherently defined in financial terms, rather than economic, which implies comparing the levels of outcome per dollar invested in different incentive programs, where an outcome is defined as a commencement or a completion (rather than a dollar returned). Those incentives that were either excluded from the effectiveness analysis or shown to have had no significant effect are thereby excluded from this.

In terms of target efficiency, given the stated policy objectives of the AAIP, this is principally measured in terms of the apparent alignment of Australian Apprenticeship outputs with economy skill needs. Proxy measures of skills demanded and skills supplied are utilised to estimate this level of alignment, as discussed in section 2.2.2.

4.1 Literature review

The NCVER assess the efficiency of the AAIP by looking at incentive take-up rates, the cost of incentives per completion and the extent to which incentives have been received by skills shortage occupations or social inclusion groups (Karmel et al 2008).

The NCVER find that in terms of the financial efficiency of the AAIP:

Completion incentives are taken-up at a higher rate than commencement incentives (90% take-up versus 75%), which would suggest completion incentives are better targeted than commencement incentives.

Incentives paid to certificates III and IV are around double the payments paid to certificates I and II (~$3,500 versus $1,700 per completion), which would suggest higher incentives are required for higher level qualifications.

Around one-third of AAIP payments go to skills shortage areas.

The economic efficiency of different payment types then depends on the relative return to particular outcomes. To the extent that a higher cost is compensated with a more valuable outcome for society, a more costly outcome can be equally or more efficient.

A review of the incentives system in 2006 recommended the following changes be made to improve efficiency, in terms of realising savings and better allocating incentives towards desired outcomes (DEEWR 2006):

Removing the Innovation Incentive.

Reducing employer incentives to state and territory government agencies.

Restricting incentives for existing workers to predetermined skills needs qualifications.



Reducing payments for the Australian School Based Apprenticeships by removing the additional commencement and retention incentives for Certificate II qualifications.

These recommendations sought to better deliver trained persons to meet Australia’s skills needs for a given level of investment, and thereby reduce the number of people being incentivised to attain qualifications where this labour might not be taken up by employers.

4.2 Cost effectiveness

Cost-effectiveness is measured for the purposes of this exercise as the cost per additional commencement and cost per additional completion. While the precise objectives of different incentives under the AAIP will differ, these high level key performance indicators provide for ready comparison of the financial efficiency of the different investments.

Table 4.1 below summarises the inputs and outputs of those AAIP incentives that were shown to be effective in the econometric analysis, in terms of total government outlays as compared to additional commencements and additional completions. This allows the calculation of average costs per additional commencement and average costs per additional completion, as a basis for comparison of cost-effectiveness across incentives.

From this high-level analysis it appears that an additional commencement is less costly than an additional completion. Also, that the AKB was the most cost-effective commencement incentive, while the SMCA was the least cost-effective (of those that had an effect). In terms of completions, the INNO was the most cost-effective, while the TSC was far from cost-effective – at a cost of over $0.5 million per additional completion.

Table 4.1: Cost-effectiveness

Incentive Take-up rate

Additional commence-

ments Additional

completions

Total cost

($mil)

Cost per additional

commence-ment

Cost per additional

completion

SMCA 59% + 2,380 N/E 112.9 $47,421 N/A

SAAA 73% +4,864 N/A 136.1 $27,991 N/A

AKB 91% + 10,179 N/A 66.2 $6,501 N/A

AKE 88% + 1,570 N/A 43.6 $27,747 N/A

TFYT 87% Nil N/A N/A N/A N/A

WTU 53% Nil N/E N/A N/A N/A

TSC 45% Nil +264 139.8 N/A $529,703

INNO 65% Nil +3,446 56.2 N/A $16,270

WNTD 60% Nil N/E 35.5 N/A N/A

LAFHA 97% N/A +2,688 76.5 N/A $28,460 Source: DAE analysis of TYIMS data N/E means the policy was found to be not effective; N/A means the results were not statistically significant or the test could not be performed.

For background and context, Table 4.2 shows the targets and payments made for each of the incentives under the AAIP over the analysis period.



Table 4.2: AAIP eligibility and claims

Incentive Target (eligibility)

Timing and amount of Number of claims Value of

claims $M

Cert II Cert III/IV Dip/AdvDip payment 2002-11 2002-11

Standard commencement

Prior to 11 May 2011 available to all Cert II AA’s, post 11 May 2011 available to AA’s in nominated equity groups

All

Some qualifications eligible from 1 July 2006, more added on 1 Jan 2007 and all Dip/AdvDip qualifications made eligible on 1 Jan 2010

$1,250 for Cert II and Cert III/IV prior to 1 July 2003. Post 1 July 2003, Cert III/IV and Dip/AdvDip get $1,500.

100% paid on commencement (90 days).

1,659,026 2,388.9

Standard completion N/A All

Some qualifications eligible from 1 July 2006, more added on 1 Jan 2007 and all Dip/AdvDip qualifications made eligible on 1 Jan 2010

$1,500 for Cert I/II and Cert III/IV prior to 1 July 2003. Post 1 July 2003, Cert III/IV and Dip/AdvDip get $2,500.

100% paid on commencement (90 days)

872,844 1,949.7

Standard recommencement

N/A All

Some qualifications eligible from 1 July 2006, more added on 1 July 2007 and all Dip/AdvDip qualifications made eligible on 1 Jan 2010

$750.

100% paid on commencement. 134,538 100.3

Securing Australian Apprentices (SAA)

N/A

NSNL occupations where AA returned by host employer

All

Total = $2,800

64% ($1,800) paid 3 months after recommencement, $1,000 (36%) paid on completion.

102,685 126.6



Support for Adult Australian Apprentices (SAAA)

N/A Those aged 25+ and in an NSNL occupation

N/A

$150 per week in year 1 and $100 per week in year 2 for full-time apprentices; $75 per week in year 1 and $50 per week in year 2 for part-time apprentices.

161,070 136.1

Support for Mid-Career Apprentices (SMCA)

N/A Those aged 30+ and in an NSNL occupation

N/A

$150 per week in year 1 and $100 per week in year 2 for full-time apprentices; $75 per week in year 1 and $50 per week in year 2 for part-time apprentices.

297,061 112.9

Apprentice Wage Top-Up

N/A Those aged < 30 and in an NSNL occupation

N/A $500 at 6, 12, 18 and 24 months (total $2,000) 424,012 212.0

Disability assistance payments

Employers of an Australian Apprentice with disability in a Certificate II or higher level qualification may claim a) wage support and b) assistance for tutorial, mentor and interpreter services subject to meeting eligibility criteria.

a) $104.30 per week

b) $38.50 per hour (up to $5,500 per year)

65,117 62.3

TFYT Payment

If in an agricultural occupation and, if in a rural or regional area, a horticulture occupation

All NSNL occupations N/A

A total of $5,500 paid at 3, 12, 24, 36-months and completion 356,980 304.7

Living Away From Home Allowance

Available for Cert II to AdvDip qualification levels for people who have to leave home to do the apprenticeship. Initially only available to first year Australian Apprentices. Second years were eligible from 1 July 2003 and 3rd years from 1 July 2005. Changed to include school-based apprentices on 1 Jan 2008.

Weekly payment of $77.17 in 1st year, $38.59 in 2nd year and $25 in 3rd year. 309,135 92.1



Kickstart Bonus N/A Those aged < 19 years and in an NSNL occupation

N/A A total of $3,350. 25% ($850) at commencement, 75% ($2,500) at 9 months.

41,467 66.2

Kickstart Extension N/A

Those aged < 19 years and in an NSNL occupation in a small or medium sized business or GTO

N/A

A total of $3,350. 25% ($850) at commencement, 75% ($2,500) at 9 months. 27,939 43.6

Support for Mature Age Apprentices (SMAA)

[Mature Age Worker Incentive]

All Australian Apprentices aged 45 years and over. A total of $1,500. 50% ($750) paid on commencement and 50% paid on completion.

1,822 1.4

Rural/Regional incentives

N/A

Initially available to all Australian Apprentices working in a non-metro area. On 1 July 2006 limited to only NSNL occupations

N/A

$1,000 initially paid on progression from Cert II to Cert III, after 1 Jan 2003 100% paid on commencement

216,950 217.0

Drought Declared Area Available in a declared drought area

N/a N/A A total of $3,000. 50% paid on commencement ($1,500) and 50% paid on completion.

1,474 2.2

Australian School-based incentives

Available to Cert II to AdvDip endorsed school-based Australian Apprentices

A total of $1,500. 50% ($750) on commencement and 50% on completion.

129,266 97.0

Commonwealth Trade Scholarship

N/A

Available to all in an NSNL occupation in a small or medium business or GTO.

N/A $500 at the end of the first and second year of the apprenticeship.

279,674 139.8



Women in Non-Traditional Trades

Available to select Cert II to Cert IV qualification for women in non-traditional trade apprenticeships. The number of trades eligible was reduced on 1 July 2004.

$1,000. 100% on commencement.

33,625 33.6

Sports Traineeship Available to those undertaking a sporting qualifications apprenticeship with a sporting GTO.

A total of $3,000. 50% ($1,500) on commencement and 50% on completion

2,370 3.6

Innovation incentive N/A

All in an ‘innovative training package’ qualification

N/A $1,100 51,052 56.2

Source: DEEWR Apprentices Branch and DAE analysis of DEEWR TYIMS data



4.3 Target efficiency

Measuring target efficiency implies a consideration of the AAIP’s ability to reach its target groups and avoid any unintended outcomes. An unintended outcome is therefore one where the effects of intervening are tending in an opposing direction to policy intent.

The only way the direction of the AAIP market distortions could be considered in this analysis was to look at apprentice and trainee:

1. Retention rates by industry over time (as a proxy for supply), compared to relative wage gains to workers skilling in those industries (as a proxy for demand).

2. Retention rates over time for NSNL compared to non-NSNL qualifications.

The obvious limitations being that neither are direct measures of the supply of skills nor the demand for skills, and do not take account of the current stock (i.e. the degree to which there may be an existing imbalance).

Following from these is a consideration of the opportunity cost and other factors that individual Australian Apprentices may face in completing/not-completing training, as a means by which any unintended developments could be explained, with inferences for the future AAIP.

4.3.1 Retention and productivity gains

On average, around 78% of apprentices who began their apprenticeship in 2002 were still in training or had completed two years later (Chart 3.2). For those who started in 2009, this figure is only 68% – an increase in the drop-out rate of 8 percentage points.

However, this decline in retention stems from only half of all industries, which in aggregate, masks the increase in retention rates experienced in the remaining industries (Chart 4.1). Over the period, two-year retention rates increased the most in the Rental, Hiring and Real Estate Services industry (+32% points), while the Accommodation and Food Services industry suffered the largest decline in retention (-14%).



Chart 4.1: Change in apprenticeship 2-year retention rates: 2002-09, % points


Conversely, retention in traineeships was low in 2002, with less than 60% of trainees still in training or completed two years later (Chart 3.2). This has improved slightly since then, to the point where around 65% of trainees who commenced in 2009 have either completed or remain in training – a decrease in the drop-out rate of 5 percentage points.

Interestingly, although Information, Media and Telecommunications and Education and Training fared relatively well in terms of retention in apprenticeships, they simultaneously demonstrated among the largest decreases in retention rates for traineeships (Chart 4.2). On the other hand, while Accommodation and Food Services, Construction and Retail Trade fared relatively poorly in terms of retention in apprenticeships, they simultaneously demonstrated among the largest increases in retention rates for traineeships.

These trends are most likely reflective of a shift in industry training models between apprenticeships and traineeships over the period, or a change in the occupational composition of these industry workforces.

However, similar to the case for apprenticeships, outcomes in Mining and Administrative and Support Services traineeships were poor, with retention/completion declining over the period. These trends might be more reflective of the implications of a tight labour market in these sectors – explored further below.

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Best performing industries Worst performing industries



Chart 4.2: Change in traineeship 2-year retention rates: 2002-09, % points


4.3.1.2 Earnings premium – a motivation to complete or drop out?

Where retention rates are at odds with the earnings signal to qualification completion it suggests that the AAIP might be ineffective in reaching its target groups – that is, in matching supply with demand. Therefore the starting point is determining the benefits of qualification attainment (at face-value) by looking at the differences in average earnings between individuals with different levels of qualification in a given industry or occupation:

Controlling for industry removes any industry-specific factors – such as growth or productivity – that might make wages in one industry inherently higher than another.

Controlling for occupation removes any occupation-specific factors – such as management responsibilities or productivity – that might make wages in one occupation inherently higher than another.

Also, in order to account for another major determinant of earnings – that being experience, for which age is a proxy – the difference in the average age of workers with different levels of qualification should also be controlled for.

Chart 4.3 shows the difference in average annual earnings for people with a Certificate III/IV as their highest level of qualification compared to people in the same industry with a Certificate I/II as their highest qualification. These data show that for all industries except Construction and Professional, Scientific and Technical Services, people with a Certificate III/IV earn more than people with a Certificate I/II.

To reveal the extent to which these increased earnings might also be at least in part explained by higher levels of experience, the difference in the average age of people with a

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Certificate III/IV as their highest level of qualification compared to people in the same industry with a Certificate I/II as their highest qualification, is also reported.

Comparing these data would suggest that higher earnings cannot be wholly attributed to higher qualification attainment in most instances, with those who hold a Certificate III/IV typically being an average 10 years older than those who hold a Certificate I/II. However, there are a few instances where those who are more highly qualified are in fact younger (and perhaps less experienced) on average, yet still exhibiting an earnings premium.

That is, in Retail Trade, Transport, Postal and Warehousing, Information, Media and Telecommunications and Rental, Hiring and Real Estate Services industries, younger more qualified individuals are earning between approximately $5,000 and $30,000 more per annum than their older less qualified counterparts.

Chart 4.3: Difference in average age (years) and earnings ($/annum) by highest level of qualification (Cert III/IV – Cert I/II) and by industry

Source: Australian Bureau of Statistics, Survey of Income and Housing, Expanded Confidential Unit Record File, Australia, 2007-08

Chart 4.4 shows the difference in average annual earnings for people with a Certificate III/IV as their highest level of qualification compared to people in the same occupation with a Certificate I/II as their highest qualification. These data show that for all occupations except Sales Workers, people with a Certificate III/IV earn more than people with a Certificate I/II.

To reveal the extent to which these increased earnings might also be at least in part explained by higher levels of experience, the difference in the average age of people with a Certificate III/IV as their highest level of qualification compared to people in the same occupation with a Certificate I/II as their highest qualification, is also reported.

-10,000

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10,000

20,000

30,000

40,000

50,000

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10

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30

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50Difference in average age in industry (years) Difference in average earnings in industry

($/annum)



Comparing these data would suggest that higher earnings again cannot be wholly attributed to higher qualification attainment in most instances. However, there are also again a few instances where those who are more highly qualified are in fact younger (and perhaps less experienced) on average, yet still exhibiting an earnings premium.

That is, for Managers, Clerical and Administrative Workers and most importantly for Technicians and Trades Workers, younger more qualified individuals are earning between approximately $5,000 and $25,000 more per annum than their older less qualified counterparts.

Chart 4.4: Difference in average age (years) and earnings ($/annum) by highest level of qualification (Cert III/IV – Cert I/II) and by occupation

Source: Australian Bureau of Statistics, Survey of Income and Housing, Expanded Confidential Unit Record File, Australia, 2007-08

At a high level, Chart 4.3 and Chart 4.4 suggest that a strong price signal is provided to complete higher levels of training in some industry sectors and most notably some occupational groupings.

However, many of these areas with the greatest earnings gains from training – in the current labour market – accord with those that have performed the worst in terms of retention over the past decade. That is, retention in Mining and Administrative and Support Services has declined, despite Technicians and Trades Workers, Managers and Clerical and Administrative Workers demonstrating an earnings premium from higher educational attainment (controlling for age/experience).

There are a number of reasons why this may be the case; however at face value this would suggest that the AAIP has been ineffective in matching supply with demand in these areas. One explanation may be the opportunity cost that the individuals training in these areas of

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acute skill shortages face, when deciding whether or not to complete their full course. Indeed the opportunity cost to completing may outweigh any completion incentives provided under the AAIP – this is explored further in section 4.3.3 below.

4.3.2 NSNL compared to non-NSNL retention

Following from above, a foremost objective of the incentives system is to encourage take-up and completion of training in areas of skills need. To this end, the Government spends around $350 million each year on incentives targeted specifically at occupations on the National Skills Needs List (NSNL), which comprises around one-third of all incentive payments.

However, the rate of retention and completion in NSNL apprenticeships has fallen substantially over the analysis period (Chart 4.5). Indeed, over 80% of NSNL apprentices who began their training in 2002 had completed or were still in training two years later. In comparison, only around 70% of NSNL apprentices who began in 2009 have since completed or remain in training.

Furthermore, although at a lower rate, retention hasn’t fallen nearly as dramatically for non-NSNL apprenticeships over the same period, with around 64% of starters in 2002 and 2009 still in training or completed two years later. This finding might again suggest that skill shortages are driving non-completions of apprenticeships6.

Chart 4.5: Two-year retention/completion rates for NSNL and Non-NSNL Apprenticeships


6 Very few traineeships are in NSNL occupations – in 2002, for example, there were only three – so traineeships

are excluded from this analysis.

50%

55%

60%

65%

70%

75%

80%

85%

90%

2002 2003 2004 2005 2006 2007 2008 2009

NSNL Apprenticeship Non-NSNL Apprenticeship



At face value these trends suggest that a higher proportion of apprentices are dropping out from training in areas that receive greater financial incentives (in absolute terms), and that at some level skill shortages are driving apprenticeship non-completions (consistent with 4.3.1.2 above).

When these retention/completion rates are compared against commencement rates another story is revealed. That is, the major decline in apprentice retention/completion rates between 2002 and 2004 aligns with the major increase in apprentice commencements between 2002 and 2004 (Chart 4.6).

These correlations would tend to suggest that as commencements increase (beyond a natural rate of growth) completion rates can be expected to decline. Indeed in 2002, an average 18,000 commencements per month was translating to an average 75% completion/retention rate two years later; while in 2004 an average 25,000 commencements per month was translating to an average 70% completion rate two years later (Chart 3.2).

This most likely reflects that artificially enticing individuals into Australian Apprenticeships results in less optimal matching of individuals with fields of study/occupations, where the incentive is the enticement for the marginal individual rather than the idea of the training. Furthermore, the major increase in commencements would appear to be in non-NSNL areas (Chart 4.7), which might suggest that the mismatching issue is more prevalent in these Australian Apprenticeships (and hence the lower retention rate).

Chart 4.6: Monthly commencements of apprenticeships and traineeships


0

5000

10000

15000

20000

25000

30000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Apprenticeships commencements Traineeship commencements



Chart 4.7: Monthly commencements of NSNL and non-NSNL


The other interesting finding from these monthly commencements is that it reinforces apprenticeship commencements being more susceptible to economic conditions than traineeship commencements, particularly in areas of skill shortage, as revealed by the declines in those commencements around the time of the GFC (mid-2008 to 2010).

4.3.3 Opportunity cost and other considerations

The decline in apprenticeship retention over the analysis period may be reflective of a number of factors at play, and could relate more or less to the employment opportunities apprentices face and/or the training costs employers bear, as determined by individual and broader economic circumstances.

As a starting point, a non-completion is not always welfare reducing. For instance, an apprentice may withdraw from their training contract given the forgone lifetime earnings from not doing so. Indeed this opportunity cost may have increased during this analysis period in particular industries, or for those individuals with particular skills, in line with the tightening of the labour market and the acuteness of skill shortages (e.g. in mining).

Table 4.3 outlines those costs and benefits that a rational Australian Apprentice would consider when faced with the decision of whether to undertake or withdraw from training.

Table 4.3: The costs and benefits of apprenticeships to the apprentice

Costs Benefits

Wage in alternative employment Training wage

Marketable skills (employability)

0

5000

10000

15000

20000

25000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

NSNL (Right) commencements Non-NSNL commencements



Entry into an occupation

Wage premium (productivity)

As such, it is generally accepted that apprentices sacrifice higher wages during training in exchange for the wage premium that will ensue upon completion, resulting from the formation of valuable skills and perhaps entry into an otherwise inaccessible occupation. However, the training wage must still be high enough to ensure the sum of all the benefits offsets the opportunity cost of training, which is the wage in alternative employment.

The data conveyed by Chart 4.4 would suggest that an individual faces a greater payoff to completing a Certificate III/IV as a Manager, Clerical and Administrative Worker or Technician and Trades Worker than completing only a Certificate I/II. In which case falling retention in training in these occupations (as suggested at 4.3.1 above) is at odds with an opportunity cost explanation, and is therefore more likely to be driven by other factors, provided the individual is aware of these payoffs.

As mentioned earlier, a primary example of another factor is the fit between the apprentice and the employer or the occupation, which may not be optimal, in which case the apprentice chooses to discontinue the training to search for a more suitable employer or more suitable occupation. For instance, Dickie et al (2011) advocate that reducing the recruitment pool of both employers and apprentices will result in less waste. Their research found that about a quarter of apprentice recruits are not well suited to the trade or apprenticeship to which they sign up.

Alternatively, economic theory suggests that employers will continue to employ workers while the employees’ marginal product exceeds its marginal cost. However, there are a number of additional complexities in the contract between an apprentice and an employer that make this less straightforward.

For example, the employer must consider the benefit of increasingly productive labour over the life of the contract, as well as their ability to form a relationship with the apprentice and fashion the training they receive. On the other hand, the employer must also consider the business risk and implicit cost associated with the (sometimes relatively long-term) training contract.

Table 4.4: Employer costs and benefits of taking on an apprentice

Costs Tangible benefits

Wages Productive contribution of apprentice/trainee

Training fees

Supervision costs Intangible benefits

Administration costs Relationship with apprentice

extra maintenance and materials wastage job-specific training

travel and other indirect costs contribution to supply of skilled labour in their industry

implicit contract costs



As such an employer will consider all of the costs and benefits outlined in Table 4.4 when deciding whether or not to employ an apprentice, and these costs and benefits vary with the individual and the economic circumstances of the training period. Therefore employer perceptions of these factors might also be having an effect.

4.4 Implications

The implications from the analysis conducted in this section are two-fold:

From the cost-effectiveness analysis it can be seen that at a high level, the majority of AAIP investment is ineffective and therefore by definition inefficient. The exceptions being a few commencement and targeted incentives typically greater than $1,000.

From the target-efficiency analysis it can be seen that retention rates are falling in training that aligns with areas of greatest skill need and greatest productivity gains. It is also clear that retention rates are reduced where commencements are inflated; implying mismatches between individuals and training emerge in these circumstances.

These findings would suggest the AAIP has been ineffective in reaching its target cohorts. However, what has not been fully captured in this analysis, particularly the target efficiency considerations, is the range of other factors that could be driving these trends.

Indeed there appear to be large payoffs to completing in a number of training areas, which suggests that other factors may be presenting barriers to achieving this. Not understanding these other factors is an issue when inferring what government should do in response.

At face value the retention trends would suggest that the nature of labour market over the past decade has dominated the impact of incentives – particularly in industries/occupations facing acute skill shortages. If this is a case of the market working efficiently, for instance signalling that a full qualification is not required and that workers are sufficiently productive part way through training, then the case for intervention in these areas is reduced.

However, the implication of Chart 4.3 and Chart 4.4 is that training matters (before controlling for ‘other factors’), and particularly for those industries and occupations in skill shortage. In which case society benefits from these apprentices and trainees completing – provided the occupation is a good fit for individual.

Given occupations will not always be a good fit for an individual, the optimal completion rate is less than 100%, regardless of which of the above theories holds. Then, the better informed individuals and businesses are in terms of this match, the closer the completion rate can be to 100%. The implications of these findings for the future AAIP is elaborated in section 5.



5 Optimising AAIP The optimisation of the future AAIP can be guided by theory and evidence at three levels:

1. Where to incentivise

2. What forms should incentives take

3. How much to incentivise

The uncertainty that remains within the current evidence base implies that a cautious approach, minimising the risk of any unintended outcomes, should be taken where reforming the current AAIP structure.

5.1 Where to incentivise

The primary goal of Australian Apprenticeships is to deliver an on-going source of skilled labour to the economy. This implies both skill development and job placement.

Section 1.3 established the case for government intervention in Australian Apprenticeships, demonstrating that without government support, the system would provide a less than optimal level of supply of skilled labour. Reaching the optimal level of Australian Apprentices is dependent upon there being sufficient supply of and demand for apprentices and trainees, as well as an adequate level of retention in training to the point of completion.

The notion of where to incentivise is outlined below as largely an in-principle discussion, organised around a number of primary considerations.

5.1.1 General theory

In theory, the signal to individuals on where to undertake training is fully reflected in relative wages. In principle, the starting point is therefore that the education system should equalise the returns to the individual from undertaking any study, which implies subsidising training in a way that doesn’t distort choices between areas of training.

However, as a training model, Australian Apprenticeships produce actual output, as compared to say broader vocational education and training (VET) and higher education, making it an inherently more efficient training approach. As such, society will typically benefit where government incentivises business to train in this way, for those occupations for which the training model is a suitable fit.

At the aggregate level, the interaction of demand and supply gives a value for the number of commencements in traineeships/apprenticeships each year. In a general economic model, we would also typically analyse ‘price’. The price here includes the wage, but that is highly regulated. Since the ‘price’ is regulated, it is likely that in any period, the observed number of traineeships/apprenticeships does not come from the interaction of demand and supply. Rather, what is observed is the smaller of demand and supply (i.e. one side of the market constrains commencements).



Figure 5.1 below illustrates the aggregate supply versus demand for Australian Apprenticeships. More individuals are willing to take on an apprenticeship as the wage rate rises (upward sloping line), while more firms are willing to take on apprentices as the wage rate lowers (downward sloping line).

In situations where firms and individuals are unable to negotiate on a market clearing wage rate – that is, where supply equals demand – there is disequilibrium in the market for Apprenticeships. In the example below, the wage rate, W, is regulated below the market clearing rate. At this wage rate, individuals are only willing to commence C1 apprenticeships while firms are willing to take on C2 apprentices; that is, there aren’t enough apprentices to fill all the places available.

Figure 5.1: Supply and demand for apprentices/trainees

As discussed in, for example, NCVER (2011d), there are well established theoretical models in the economics literature that provide a framework for analysing apprenticeships and traineeships. For both a person entering into a training contract and a potential employer, the central considerations are the costs and benefits of training.

For the worker, the benefits include a higher probability of obtaining a job upon completion and a higher wage. The costs include the discounted training wage. For the employer, the benefits include the increasing productivity of the worker and higher retention post-training. The costs include the training costs and wage costs during the period in which the worker has low productivity.

Combined, the costs and benefits lead to a demand for training from workers and a supply of traineeships/apprenticeships from employers. The combination of the demand and supply leads to the observed data on training.

Demand = function of costs to individuals, benefits to individuals

Supply = function of costs to firm, benefits to firm

Wages are a benefit to the individual and a cost to the firm. Incentives in the AAIP flow to both the employer and employee. We can therefore think of the incentives as changing the demand for training and the supply of training. A change in the incentives then changes one or other of demand and supply and results in a new level of Australian Apprenticeships.

Individuals

Firms

Wage

Commencements C2 C1

W



5.1.2 What should be the optimal/target completion rate

The optimal level of Australian Apprenticeships is in theory that which provides an adequate ongoing supply of skilled labour, and such that the marginal social cost of the last person trained is equal to the marginal social benefit of their completion.

Of course, this level will differ across industries and time, and be further complicated by the presence of other considerations for Government such as equity. As such, it is difficult to determine what the optimal completion rate is at any point in time.

From the analysis conducted in section 4 it would appear that increased non-completions in-part reflects too many individuals commencing apprenticeships. This is because, the wider the recruitment into Australian Apprenticeships, the more likely the system will intake individuals who don’t have the aptitude or are not well suited to undertaking this type of training, and therefore the lower the completion rate you can expect. In which case leveraging more apprentices into the system is a blunt tool for increasing skills.

There is therefore a need for better matching of individuals with Australian Apprenticeships – perhaps through an entry threshold or other screening device – to improve completions. The flipside is a regime where your incentivise everyone to complete – for instance including those who are a poor match – which would be costly and counterproductive.

It’s also apparent from section 4 that completions in areas of skill shortage are insufficient and perhaps affected by different issues than mismatching. Indeed it may be the case that there are opportunities to earn greater returns from non-completion in these areas, or perhaps that the payoff to completion is not apparent to a proportion of those in training. In which case there is either no need to intervene further as the labour market is working efficiently, or there is a need to improve the visibility of the price signal to complete.

5.1.3 What affect do economic conditions have

The econometrics conducted as part of this study was unable to definitively conclude all those macroeconomic factors that affect completions and, in particular, commencements of Australian Apprenticeships. For instance, it was unable to identify a series of parameters that could explain the major decline in commencements around the period of the GFC.

Nonetheless, the analysis of retention rates in particular industries and occupations revealed that some key areas of skill shortage are associated with declining Australian Apprenticeship completions. This is despite the strong price signal to complete, where annual earnings appear to increase significantly for those who are relatively more qualified.

In theory, so long as this signal is visible and individuals are rational, the Government should only intervene in Australian Apprenticeships on the basis of broader spillovers and equity issues. However, the assumption that all individuals are rational and can accurately assess the future payoffs is open to debate. In which case, even when abstracting from any positive spillovers and equity considerations, there exists a rationale for the Government to intervene to close the skills gap in a shorter period than the market otherwise would through further adjustments in relative payoffs, provided this can be done in a manner that is economically efficient.



Tempering this, the fact that the price signals are already strong suggests that incentives would need to be particularly large (and perhaps unviable) to adjust market outcomes in these areas. In which case, incentives would need to be highly targeted, or other types of intervention (perhaps on the supply-side) might be more appropriate.

5.1.4 Is there a basis for incentivising all or particular occupations

The increasing mobility of labour, coupled with the fact that most non-trades related qualifications are not aligned with particular occupations or indeed industries, means that it is difficult to reconcile occupational demand with qualification supply. A further complication is that the measures are typically reported as flows rather than stock values, making identification of the imbalance at a particular point in time near impossible.

This has obvious implications for the precision of incentives attached to qualifications by way of targeting the alleviation of skill shortages in particular occupations – perhaps applying most acutely to traineeships which are typically non-trades based. That is, there will be instances where qualifications are incentivised that inadvertently link to occupations where little or no skill shortages exist. At the same time this inherently dampens the (relative price) impact of incentivising training in those areas where skill shortages do exist.

Therefore, an innate level of inefficiency will exist when targeting occupations, and in fact is likely to grow in times of low unemployment such as that currently being experienced (that is, when labour is more mobile). This is consistent with the findings of the analysis of the target efficiency of the AAIP to date, presented in section 4.3.

The implication is that given the imprecision that comes with any forecasting, on top of the limitations around how occupational demand links to qualification supply, the application of a forward looking tool similar in nature to that recommended as part of this study (available at Appendix B) is done accepting an unavoidable level of inefficiency.

Beyond this, there is also a question of whether incentives should be targeted at those persons not in employment and existing workers. Given those in employment might typically be better informed of the payoff to further training, and possibly face less risk of not realising that payoff, the risk-reward they assess before undertaking an Australian Apprenticeship most likely appears more favourable. In which case, the rationale for incentivising their training still exists, but is perhaps less pronounced than for those persons not in employment (skill shortage occupations/pathways aside).

5.2 What form of incentive

From the discussion above it is clear that left to its own devices, the apprenticeship market will result in an undersupply of apprenticeships than what is socially optimal, i.e. wT < w*, where w* is the wage that attracts the desired supply of apprentices and wT is the training wage that equalises the marginal costs and benefits of taking on the apprentice.

Thus, to achieve the desired outcome, the Government must:

A set the training wage equal to w* > wT to achieve the desired supply of apprentices, then, compensate the employer accordingly; or



B let the employer pay wT (the wage that equalises the costs of taking on the apprentice and the productivity and additional benefits of the apprentice), then subsidise the wage paid to the apprentice so that they receive w*; or

C a combination of both.

Under the first scenario, to induce the employer to hire at w*, the product of the apprentice plus the value of any intangible benefits and government incentives must offset w* plus the additional costs of taking on an apprentice:

Where:

PA = the productivity of the apprentice;

IBA = the intangible benefits of taking on an apprentice;

G = the value of government incentives;

CA = the additional costs of taking on an apprentice, such as supervision and administration costs.

If alternate labour sources exist (qualified and/or unskilled alternatives), the difference between the total cost of taking on an apprentice and the wage paid to the alternate labour source must offset the difference in productivity between the two labour sources:

Where:

wB = the wage paid to the alternate labour source; and

PB = the productivity of the alternate labour source.

Under the second scenario, employers pay wT, which is such that their costs are offset by the benefits of hiring an apprentice. However, this is not enough to offset the opportunity costs faced by apprentices and induce them to take up an apprenticeship. Thus, the government must subsidise the wage so that apprentices receive w*.

The third scenario is a combination of both of the above, whereby the government sets the wage somewhere between wT and w* and compensates both parties such that all costs are offset.

This theoretical framework is useful for thinking about how the optimal incentives could be designed in theory. In reality, however, a number of things need to be considered:

The fact that the optimal level of apprenticeships will change over time as the needs of the economy change and also as the objectives and values of the Government and society change.

In the theoretical example, it is implicitly assumed that all firms and apprentices are homogenous and thus there is only one wT and one w*. In reality, all firms and apprentices are heterogeneous, which implies that there is not a “one size fits all”



incentive and different incentives will be required to offset the individual costs and benefits faced by each.

The analysis above also ignores attrition, assuming that the optimal level of apprenticeship output is achieved if this level of apprenticeships is taken up.

These factors and others are considered in the below discussion of various trade-offs that the AAIP faces.

5.2.1 Flexibility versus complexity

Outlined above is the theoretical framework for determining the size of government incentives necessary to reach an optimal level of apprenticeship output. However, this analysis implicitly assumes homogeneity in the input and output markets – i.e. that all apprentices and employers are exactly the same and produce exactly the same output.

Clearly, this is not the case in reality. In fact, every employer, apprentice, industry and occupation are different from one another, and each decision is subject to a different set of opportunity costs. This implies that there will be a different w* for every apprentice and a different wT for every employer – which also implies that the optimal government incentive (that equates marginal benefits and marginal costs for both the employer and the apprentice) is different for every contract.

In effect, while this equation may be ‘about right’ in aggregate (that is, lifetime returns accruing to the individual largely exhaust the benefits from the extra training, on average), there will be a lot of mismatches – that is, potentially some hit-and-miss in setting a homogenous G in the face of a large degree of variability in the other variables.

Furthermore, the values of some key variables, such as IBA are not known in advance (or are subject to variability). Employers that are risk-averse will tend to under-estimate these values. In some cases, IBA (ex post) can be negative – for example if an employer takes on an apprentice that then has a poor attendance record at work and TAFE. This may make that employer risk averse in taking on an apprentice next year.

Given this heterogeneity, a perfectly efficient incentive structure would be a perfect price discriminator – that is, it would discriminate between every employer, employee, industry and occupation to ensure that each party is incentivised just enough to achieve the socially optimal outcome. Again, this is not the case in reality because increased flexibility comes at a cost, and it is not possible to accurately identify (a priori) which employers or employees are in need of greater incentives.

The challenge therefore is to recognise the trade-off between flexibility and complexity and balance the benefits of a more targeted, heterogeneous payment structure with the increased cost of such a structure. In this context ‘flexibility’ is defined as the extent to which the incentives provide the capacity to support the apprentice and/or employer in the most effective way given their individual circumstances. On the other hand ‘complexity’ is defined as the extent to which the incentives impose an administrative burden on recipients and/or governors.



Findings from previous reviews have stated the need for greater flexibility in the structure of incentives in order to achieve a better prioritisation of particular outcomes (in line with policy imperatives). However, this view has been reached without knowledge or evidence of how effectively the incentive mechanisms can target particular outcomes, whether or not the particular outcomes would otherwise be achieved (or at least in a more effective manner elsewhere) and what the corresponding administrative burden created is.

The AAIP would appear to have become more flexible over time as it has adapted to the changing needs of apprentices and trainees and the economy. The number and types of occupations to which apprenticeships provide a pathway have changed and become more diverse, as have the characteristics of apprentices. In 1963, for example, the 9,400 commencements in metal and vehicle trades constituted 42% of all commencements. The number of commencements had almost doubled by 2009 to 18,100, yet this group represented only 25% of trade commencements (NCVER 2011a).

Indeed new incentives have been introduced to target new and different groups that traditionally would not have trained under the Australian Apprenticeship model. Perhaps the best example of this is the introduction of traineeships in 1985 and their subsequent coverage under the AAIP in 1998.

Since that time, incentives have been further broadened: to include Diplomas and Advanced Diplomas; to account for the growing number of older individuals taking on this form of training; to include individuals undertaking training while still at school. In addition, incentives constantly adjust to the changing economic environment by linking to occupations on the NSNL, and, more recently, incentives have been introduced to counteract the economic downturn (i.e. the Kickstart Bonus and Kickstart Extension).

This level of flexibility undoubtedly comes at an impost. Additional payments and complex eligibility requirements are administratively burdensome. However, despite this increased complexity, there has been no observable increase in administrative unit costs. As illustrated in Chart 5.1, the Australian Government’s fee-for-service expenditure7 has not grown significantly more than the number of claims, indicating that the increase in this expenditure over time is more likely due to the increased volume, than an increase in per unit cost (which would indicate a more complex system).

7 The Australian Government currently provides Australian Apprenticeships Support Services to apprentices, trainees and their employers through the national network of Australian Apprenticeships Centres (AACs). Part of the role of AACs is to administer the Australian Apprenticeships Incentives Program on a fee-for-service basis.



Chart 5.1: Fee-for-service expenditure, commencements and claims

Source: Deloitte Access Economics’ analysis of DEEWR TYIMS data.

In addition to the fee-for-service expenditure, DEEWR incurs a number of costs associated with administering the AAIP, such as staffing costs, IT and maintenance costs and capital costs of the system. These costs are also said to have remained relatively constant over time.

The conclusion is that the objectives of the AAIP and their relative importance will change over time as the economic climate changes and value judgements about respective policy objectives and outcomes change. Pursuing a broad range of economic and social objectives implies the incentives need to take on a variety of forms and be sufficiently flexible to meet differing levels of need. This high level assessment suggests that increased flexibility in the incentives can be achieved without greatly increasing the administrative cost.

5.2.2 Employers versus individual

Employers impact both commencements and completions – although it’s not clear in what proportion. The same applies to individuals, although perhaps the retention rate analysis suggests the completion rate is determined more by the individual than the employer.

In a number of other studies undertaken on this issue, it has been revealed that particular employer traits are associated with higher rates of non-completion. Furthermore, the findings of the econometrics conducted in this study reinforce the notion that an Australian Apprentice is less likely to complete their training and more likely to cancel their training if employed by a small or medium sized business.

Knowing that particular employer traits and practices result in higher completion rates is one thing, being able to influence them through government incentives is quite another. Even if incentives were targeted more towards these influencing factors – for example, if small employers were given access to external human resource consultants or educated on successful management practices – it’s not clear that this will change the interests of employers enough to change their behaviours. While only allowing large businesses to

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access incentives is unlikely to be palatable, certainly it suggests that any incentives available should not be restricted to small businesses.

As such, the notion of incentivising employers to ensure completions would appear to have relatively less merit than incentivising individuals to complete – such that the relative payoffs/opportunity cost is neutralised – where society benefits from that Australian Apprentice completing.

The bigger issue therefore is identifying where a completion is welfare enhancing, and therefore warrants the government intervening to ensure these gains are attained. This must also be tempered with the relative split of the returns to Australian Apprenticeship completion, which will typically go largely to the individual, though will be accrued more or less by the business in particular occupations.

5.3 How much to incentivise

The theory and evidence presented to this point leads into the following discussion of how effective alternative incentives have been, beyond whether or not they simply have a basis.

5.3.1 What has been effective

Two key factors limit the evidence provided by the econometrics:

1. There is not sufficient variation between incentives to identify what particular aspect(s) have driven effectiveness or ineffectiveness.

2. Typically, the impacts of the policies have only been disaggregated by age-cohort, which is not necessarily sufficiently detailed to determine the circumstances in which they are most effective.

Regardless, the econometrics has demonstrated a reasonably consistent and not unexpected finding that larger and more targeted incentives have larger impacts. It has also shown that timing matters and commencement incentives have been more effective and efficient than completion incentives.

The question then becomes: should smaller and/or broader incentives be removed (or their value be allowed to erode over time). The starting point for whether such a proposition is valid is the original intent of Australian Apprenticeships and the AAIP – that is, to support the effective delivery of skilled labour to occupations in demand. It is then debateable whether the AAIP should incentivise outcomes that are not a direct objective.

From a broader perspective, Australian Apprenticeships are a model by which to engage individuals in education and training, where benefits are likely to accrue to society (at some level) even if the individual does not complete that particular course, but rather uses it as a pathway to employment and a meaningful participation in the community. Furthermore, it was discussed earlier that Australian Apprenticeships are inherently a more economically efficient training model where it results in production.

Nonetheless, smaller and broader incentives could be allowed to continue to erode over time to leverage additional funds for more targeted and material incentives to those areas of greatest need. By virtue of the fact that the gradual erosion of these incentives (in real



terms) is unlikely to create any unintended outcomes, or where it does could be revealed prior to a significant impact being incurred, this seems a more prudent approach to reform.

The additional funds could then be utilised to perform controlled experiments of the effectiveness of different incentive amounts, to better reveal the levels of materiality required to ensure different outcomes. The findings could then also be considered against the relative returns to different outcomes at different points in time, to ensure a net benefit is derived and therefore the intervention can be pursued on economic grounds.

5.3.2 Levels of training to incentivise

The assessment of earnings against higher qualification attainment (see section 4.3) found that it is not clear that more years of training will necessarily provide a more productive worker. That is, for different occupations there is likely to be a turning point at which productivity gains from training decline, or perhaps where there is a corresponding need for a level of experience to extract the value from that training.

Given that higher educational attainment is not always a better outcome the AAIP should retain the flexibility to incentivise commencements as well as completions at various qualification levels. This understanding should, however, be conditioned by the following:

The training system is a productive space for otherwise disengaged individuals (particularly during times of economic downturn).

A level of inertia exists for any individual when it comes to commencing/continuing education and as such, incentivising any commencement improves the chances of completing a pathway, provided the individual does not have a grim experience. That is, a non-commencement is often a worse outcome than a non-completion.

Individuals coming off a low base face the greatest gains and should therefore be the priority (as long as this can be achieved in a cost-effective/cost-beneficial manner), with obvious implications for equity groups. That is to say, there are economic grounds for incentivising disadvantaged learners more than non-disadvantaged learners.

There are instances where the private and public returns to a Certificate III/IV are greater than a Certificate I/II, particularly in those areas where the economy would appear to be in skill shortage8. Furthermore, higher level training comes at a greater private cost. As such, there may be grounds for incentivising higher level training more than lower level training – equity issues aside.

The implication therefore is that incentivising commencements can be justified on a number of grounds. However, it seems they should not be incentivised to the same extent as completions, partly because it appears they can be achieved in a more cost-effective manner, and partly because it is not necessarily a direct policy objective of the AAIP.

In terms of completions, there may be a rationale for incentivising apprenticeships more than traineeships – to the extent apprenticeships are typically associated with higher level

8 Karmel et al (2008) uses the 2005 ABS Survey of Income and Housing to estimate wages across fields/qualifications, and thereby investigate whether traineeships increase productivity. They find it debatable that traineeships augment skill and productivity. Furthermore an NCVER report finds that trainees have unlikely become more productive than their fellow, untrained workers. Apprenticeships, however, do appear to result in higher wages and productivity (NCVER 2011b).



qualifications and in areas of skill shortage. This is reinforced by the notion that incentivising completions in all areas will dampen the effect of the price signal to complete in an area of skill shortage.

However, that is dependent upon the precision of a forward looking tool in forecasting skill needs, and this presumes that it is not more economically efficient to allow a skills gap to close sooner rather than with a more qualified individual at a later point.

A similar argument applies to incentivising higher order qualifications again, namely Diplomas and Advanced Diplomas. That is, the public and private returns are most likely higher (although not necessarily to the same extent as from Certificate I/II to Certificate III/IV9), particularly in areas of skill shortage, and they come at greater private cost. As such there is some rationale for a slightly higher incentive to ensure the optimal uptake of this training, although will again be skill specific and does ignore any equity issues.

5.4 Future options

With a sufficient evidence base, alternative options for the future AAIP could be constructed around a differing balance of efficiency and effectiveness.

However, while the econometrics has revealed the apparent level of effectiveness of different AAIP incentives over time, by the very nature of these incentives it is unable to be definitive as to what mechanisms are more effective. This also has flow-on implications for how informative the cost-effectiveness findings can be in setting the AAIP future course.

Ultimately, the future AAIP needs to continue to reduce the risks faced by both individuals and employers at the margin, in undertaking an Australian Apprenticeship. As such, this might imply providing relatively more incentive to relatively fewer recipients, but not such that the average returns to Australian Apprenticeships change (which would have unintended consequences for other areas of training).

That is, making the AAIP more targeted and more compensating where risk exists (for instance where a skills shortage forecast might be inaccurate or where an economic downturn looms). Following from this, it is also noted that these types of risk – at least from the individual’s perspective – are inherently less in more generic training, which might imply for trainees who would typically be taught more transferrable skills, that they should be subsidised less than those in traditional trades who are more exposed.

In keeping with these findings and others in the analysis, the following conclusions have key implications for the future AAIP in delivering an on-going source of skilled labour:

The more commencements of Australian Apprenticeships are inflated the lower the completion rate that can be expected, as more individuals will be unsuitable.

Reducing the recruitment pool of both employers and apprentices will result in less inefficiency, implying a rationale to better match individuals to training/employers.

There is a rationale to incentivising commencements, particularly in times of economic downturn and for those who would otherwise be out of education and work, but generally not to the same extent as completions.

9 When compared to say progressing from a Certificate III/IV to a Diploma/Advanced Diploma.



Incentivising businesses to improve completions is likely to be less effective than incentivising individuals.

Broad/homogenous incentives are less suitable and less effective than targeted/flexible incentives.

The risk/return issues can be addressed in-part by directing more of the funding to industry-specific Certificate III/IV training, than transferable Certificate I/II training.

The application of a forward looking tool is done accepting an unavoidable level of inefficiency.

However, a key limitation of the study is that is has not analysed the reasons for cancelling among Australian Apprenticeships. Further analysis here might reveal a clearer picture of whether it’s the opportunities or barriers an individual faces that are leading to more non-completions, or in fact whether it’s the perception or circumstances of employers that are the bigger driver – and for different cohorts.

It’s also the case that repayments of incentives by Australian Apprentices that don’t complete have not been considered, despite the obvious merits for efficiency.

As such the implications of the study cannot be translated into a neat package of alternative efficiency/effectiveness future AAIP pathways. Instead the study presents a balance of views on what could have driven the effectiveness and efficiency of the AAIP to date, and how these factors might be addressed going forward. It is now a case of drawing on this evidence and theory in policy deliberations to reveal those AAIP reforms that hold the greatest potential gains and perhaps the least likelihood of unintended outcomes.



References AQF Council (2011) ‘Australian qualifications framework’, AQF Council, Adelaide.

Cully M, Ruediger M, Ong K and Wozybun K (2006) ‘Review of Australian apprenticeship incentives and assistance: trends in commencements and completions, 1998 – 2005’, NCVER, Adelaide.

Dickie M, McDonald R and Pedic F (2011) ‘A fair deal: Apprentices and their employers in NSW’, Integrated research report for the NSW Board of Vocational Education and Training.

DEEWR (2002) ‘Incentives review 2002’, DEEWR internal review.

DEEWR (2004) ‘Skills at Work: Evaluation of New Apprenticeships’, DEEWR internal review.

DEEWR (2006) ‘Review of Incentives Structure: Australian Apprenticeships Incentives Programme’, DEEWR internal review.

DEEWR (2008) ‘Review of Australian Apprenticeship Support Programs 2008’, DEEWR internal review.

Deloitte Access Economics (2011) ‘The cost of apprenticeship non-completion in NSW’, Report for the NSW Board of Vocational Education and Training.

Kapuscinski (2004) ‘Dynamics of apprenticeship and traineeship training in Australia – an historical analysis’, paper presented at the 30th Annual Conference of Economists, University of Australia 2001 (revised 2004).

NCVER (2005) ‘Insight’, February.

NCVER (2011a) ‘Overview of the Australian apprenticeship and traineeship system’, Report 1 for DEEWR.

NCVER (2011b) ‘Overview of apprenticeship and traineeship institutional structures’, Report 2 for DEEWR.

NCVER (2011c) ‘The apprenticeship and traineeship system’s relationships with the regulatory environment’, Report 3 for DEEWR.

NCVER (2011d) ‘The economics of apprenticeships and traineeships’, Report 4 for DEEWR.

NCVER (2012) ‘Are lower-level qualifications worthwhile?’ forthcoming.

Karmel T, Cully M, Knight B and Mlotkowski P (2008) ‘The efficiency and effectiveness of apprenticeship and traineeship incentives’, NCVER, Adelaide.

Karmel, T & Roberts, D (forthcoming) Social capital and completion rates, NCVER, Adelaide.



Toner P, Cully M and Ong K (2006) ‘Review of Australian apprenticeship incentives and assiatance: Analysis of incentive payments’, NCVER, Adelaide.



Appendix A: Incentives reviewed Table A.1: Econometric testing of AAIP

Incentive Comments

Standard commencement Tested the impact of the $250 increase for Cert III/IV from 1 July 2003.

Tested this in conjunction with the same change to the standard completion and the changes to the LAFHA that occurred on the same date (increase in the first year payment and introduction of second year payment).

Didn’t test the effect on those aged 45+ because the sample size was deemed too small

Standard completion As above.

Standard progression Did not analyse on commencements due to a lack of control group.

Standard recommencement

Did not perform a recommencement analysis due to lack of a specified population prior to the policy change

Securing Australian Apprentices (SAA)

As above.

Support for Adult Australian Apprentices (SAAA)

Tested the impact of this incentive on completions and cancellations.

Support for Mid-Career Apprentices (SMCA)

Tested the impact of this incentive on commencements and on completions and cancellations

Successful estimation of impact upon age 30 – 44 commencements.

45+ was too small a sample size to rest commencements.

Apprentice Wage Top-Up Successfully tested the impact of this incentive on commencements, completions and cancellations.

Disability assistance payments

Did not attempt to test any of these incentives because the disability criteria are not well defined and it was too difficult to determine the eligible population.

Tools For Your Trade (TFYT) Voucher

Did not attempt as data on the voucher was not available.

TFYT Payment We successfully tested the impact of TFYT on completions and cancellations.

We did not test the impact on commencements due to the lack of a control group.

Living Away From Home Allowance

We tested the impact of extending the LAFHA to second year apprentices and trainees.

This was done in conjunction with the increase to the standard incentives noted above.

We cannot test for changes in 2005 and 2008 because we have no indicator of who was eligible prior to the policy change.



Kickstart Bonus Successfully tested the impact of this incentive on commencements, completions and cancellations.

Kickstart Extension Successfully tested the impact of this incentive on commencements, completions and cancellations.

Support for Mature Age Apprentices (SMAA)

Tested the impact on commencements, completions and cancellations.

Rural/Regional incentives Did not analyse on commencements due to a lack of control group.

Drought Declared Area Did not test the impact of this incentive because there was no change over the reference period.

Australian School-based incentives

Could not test because there is no indicator of who was eligible prior to the policy change.

Commonwealth Trade Scholarship

Tested the impact on completions and cancellations. Did not test the impact on commencements because of a lack of control group.

Women in Non-Traditional Trades

Tested the impact on completions and cancellations. Did not test the impact on commencements due to no indicator of who was eligible prior to the policy change.

Sports Traineeship Did not test due to small sample size.

Innovation incentive Tested the impact on completions and cancellations. Did not test the impact on commencements due to no indicator of who was eligible prior to the policy change.



Appendix B: Forward looking tool The following is a methodology for developing a forward looking tool which could be used to prioritise investment and effectively target incentives in the apprenticeship system.

The forward looking tool should be designed to calibrate future investments with those areas which are likely to provide the greatest return. As such, it is to be a mechanism for allocating resources efficiently in order to maximise the benefits of the Australian apprenticeship system.

The methodology outlined here is designed to allow the targeting of incentives toward a subset of the training market.

Incentives for individuals to undertake an apprenticeship or traineeship already exist, with higher wages and stronger employment outcomes flowing to people with a higher level of skill and educational attainment (for the most part). However, without additional intervention in the training market aimed at generating the skills most needed in the Australian economy, the outcomes of the training system may be inefficient. The over or under supply of skills in a given occupation would persist longer than otherwise, particularly given the considerable lead time required for skills to be obtained.

That is, intervention in the training market through additional funding programs or other incentives is aimed at correcting market failures and assisting the labour market to adjust to changing skill requirements.

In terms of the aspects of the training market which should be targeted, occupational outcomes for apprentices and trainees is a clear example. Unlike some broader post-school qualifications, apprenticeships and traineeships typically present well defined career pathways into a specific occupation. As such, an increase in Australian Apprenticeship completions will directly increase the supply of skilled labour for the related occupation. Moreover, the case for targeting occupational outcomes is strengthened by the time and cost associated with acquiring specific skill sets needed to meet the requirements of the occupation.

That is already well recognised, with incentives such as the Tools for your Trade and the Support for Adult Australian Apprentices programs currently directed towards apprenticeships related to the occupations on the National Skills Needs List (NSNL).

The rationale for targeting other aspects of the training market is less well established. As such, the approach to developing the methodology for a forward looking tool has been to examine the training market with respect to occupational outcomes. That is, determining which occupations should be targeted for incentives under the training system.

The final section of this chapter describes a framework for targeting incentives in the training market in the future.



Targeting occupational outcomes

This section focuses on mechanisms for targeting the occupational outcomes of Australian apprentices and trainees. That is, increasing the supply of skilled labour for specific occupations by targeting incentives toward commencement and retention for apprenticeships and traineeships related to those occupations.

It summarises existing occupation lists used in Australia including the NSNL, which is currently used to target apprenticeship incentives, and the Skilled Occupation List (SOL), which informs the eligibility of individuals under Australia’s skilled migration program. This section also outlines a strategy for developing a forward looking occupational list which is directly relevant to the Australian apprenticeships system.

National Skill Needs Lists

The NSNL was introduced in July 2007 as a mechanism for identifying occupations that are experiencing a persistent skill shortage. It is limited to occupations listed under the Australian and New Zealand Standard Classification of Occupations (ANZSCO) occupational classification ‘technicians and trades workers’, and is currently used to target incentives such as Tools for you Trade to apprentices in particular occupations. The current (September 2011) NSNL contains 63 occupations.

In order for an occupation to be listed on the NSNL, it must meet the following criteria:

The occupation must be classified under the ANZSCO occupational classification ‘technicians and trades workers’;

At least 1,500 people must be employed in the occupation, based on 2006 Census data; and,

The occupation must be assessed as being in shortage for three of the past five years, including one of the last two years, as determined by DEEWR’s skill shortage research program.

The skill shortage research program is undertaken by DEEWR’s Labour Market and Analysis Branch. The research results in the construction of a list of occupations currently experiencing a skill shortage and is intended to provide a ‘point in time’ determination. The research is not designed to be either retrospective or prospective in its analysis of skill shortages.

The formation of skill shortage lists is primarily informed by the Survey of Employers who have Recently Advertised (SERA) undertaken by DEEWR, although a considerable volume of other information is also analysed. The SERA involves contacting employers who are known to have recently advertised a vacancy to determine if the position was filled in a reasonable time. Where the position was not filled, the attention of the survey is focused on the reasons why in order to try to determine whether the situation constitutes a lack of available skills in the economy.

Other data used by DEEWR in assessing whether an occupation is experiencing a skill shortage includes:

Industry output and activity;

Employment levels;

Vacancy levels;



Anecdotal information from employers and industry contacts on labour demand; and,

Supply trends, for example the establishment of new qualifications or courses.

A detailed methodology paper outlining the process for determining skill shortages is available from the DEEWR website.

The link between DEEWR’s skill shortage lists and the NSNL lies with the third criteria listed above. That is, to appear on the NSNL an occupation must have been listed as in shortage ‘for three of the past five years, including one of the last two years. As such, the NSNL requires a skill shortage for a particular occupation to be persistent, and abstracts from periods of temporary labour scarcity. The backward looking nature of this criterion also means that the NSNL tends to have a largely retrospective focus.

DEEWR’s Labour Market and Analysis Branch applies the criteria for the NSNL and provides policy advice around to resulting occupations. However the final composition of the NSNL, including the decision to add or remove occupations from the list, is determined by the appropriate line area within the Department.

Skilled Occupation List

The SOL was introduced in 2010, replacing the Migration Occupations in Demand List as the key occupation list informing Australia’s skilled migration program. It identifies occupations which will assist in meeting the medium and long term needs of the Australian economy. The list is compiled by Skills Australia, an independent body which provides broad skills and workforce development advice to the Federal Government, and is reviewed on an annual basis.

A two-step process is used to identify occupations for the SOL. The first step identifies a list of ‘specialised occupations’. These occupations make up the Specialised Occupations List (SpOL), and are intended to represent areas of the economy where the labour market is not able to adjust quickly and where the risk of skill shortages or over-supply need to be identified and addressed.10

The criteria used to identify occupations to be listed on the SpOL are:

1. Long lead time. These are skills which are highly specialised and require extended learning and preparation time over several years.

2. High use. These are skills which are deployed for the uses intended (ie there is a good occupational ‘fit’).

3. High risk. This is where the disruption caused by the skills being in short supply imposes a significant risk to the Australian economy and/or community.

4. High information. This is where the quality of information about the occupation is adequate to the task of assessing future demand and evaluating the first three criteria.

An occupation is considered ‘specialised’ if it meets at least two of the first three criteria, as well as the fourth criterion. The occupations listed on the SpOL are defined at both the four

10

See Skills Australia (2010), Australian Workforce Futures: A National Workforce Development Strategy, www.skillsaustralia.gov.au

http://www.skillsaustralia.gov.au/



digit and six digit ANZSCO classification levels. The 2011 SpOL lists 263 occupations at the six digit ANZSCO level.

The next step in the construction of the SOL applies some further criteria to the occupations listed on the SpOL. The SOL is therefore a subset of the SpOL.

Given that the SOL is used to inform Australia’s skilled migration program, some of these further criteria are migration-related (for example, if the job requires the person to be an Australian citizen, the occupation is not included on the SOL). However the most significant criterion relates to the expected labour supply-demand balance of the occupation going forward. Indeed, Skills Australia notes that a specialised occupation is excluded from the SOL if the evidence shows it is an occupation likely to be in surplus in the medium to long term. 11

The SOL is therefore designed to be prospective, with Skills Australia defining the medium to long term as 2-10 years.

A wide range of data is used to assess the likely labour supply-demand balance for occupations into the future, including the size and age of the current workforce, expected employment growth rates, labour force turnover, and trends in student enrolments and completions. Skills Australia makes use of occupational employment demand projections published by DEEWR, and also commissions its own forecasts of labour supply and demand by occupation.

Industry and community consultation is undertaken, with Skills Australia calling for submissions to help inform the decision-making process, while relevant migration data is also assessed including migrant employment outcomes by occupation.

In all, these varied data sources provide Skills Australia with a basis for assessing the outlook for labour demand and labour supply across each of the occupations on the SpOL.

Other occupational lists

A number of other occupational and priority skill lists exist, including those developed by State Governments, and the methodologies used to develop these lists can vary considerably.

Victorian Industry Skill Needs Report

The Industry Skill Needs Report (ISNR) is published by Skills Victoria annually.12 It is designed to provide information on skill requirements in Victoria, including a summary of occupations expected to be in demand and experiencing critical skill shortages over the following year. It is informed primarily through consultation with Industry Training Advisory Boards (ITABs) in Victoria.

11 See Skills Australia (2011), Fact Sheet: Providing advice to the Australian Government about the 2011 Skilled

Occupation List, www.skillsaustralia.gov.au

12 Note that in 2012 the Industry Skill Needs Report will be rebranded the Market Effectiveness Report.




Deloitte Access Economics has been commissioned by Skills Victoria over a number of years to work with the ITABs to develop the ISNR. Advice is sought from ITABs regarding expected industry performance and developments over the year ahead, and subsequent implications for skill needs in specific occupations. Analysis is undertaken in order to validate the advice where possible, including against forecasts of industry employment and publicly available information such as skilled vacancy and skill shortage data.

ITABs are asked to nominate occupations in demand and experiencing critical skill shortages based on set criteria. These criteria are based on definitions used by DEEWR and Skills Australia for the construction of skills shortage lists and the SpOL respectively.

The definition used to identify an occupation in demand is as follows:

An occupation is in demand when employers are unable to fill or have considerable difficulty filling vacancies for the occupation, or significant specialised skill needs within that occupation over an extended time period, at market rates of remuneration and standard conditions of employment, and in reasonably accessible locations.

Of those occupations listed as in demand, occupations are also considered to be experiencing a critical skill shortage if they meet the four criteria for the SpOL described above, namely: long lead time, high use, high risk, and high information. That is, the occupations listed as experiencing a critical shortage are a subset of those listed as in demand. The second set of criteria is applied to ensure that occupations listed as in critical skill shortage are those for which a lack of skills would present a significant risk to the Victorian economy.

The lists are also informed by consultations with personnel within relevant Victorian Government agencies.

Prior to the introduction of a demand-driven VET system in Victoria, Skills Victoria used the occupation lists to direct training funds toward areas of the economy where an increase in the skills base is expected to be required. Under the demand-driven model (introduced in full from January 2011), the lists are used to further Skills Victoria’s understanding of the effectiveness of the training market, inform the agency’s market facilitation activities, and assist in developing industry and regional profiling.

Western Australian State Priority Occupation List

A further example is the State priority occupation list established by the Western Australian Government. The list guides funding of training programs in Western Australia and also informs the list of occupations eligible for State sponsored migration.

The construction of the State priority occupation list is primarily statistical. Occupations at the six digit ANZSCO classification level are ranked according to six indicators:

employment level;

employment growth;

average age of employed;

net replacement rate;

average weekly ordinary time earnings; and,



growth in average weekly ordinary time earnings.

The State priority occupation list ranks occupations by calculating the standard deviation for each indicator relative to the mean indicator value. The six standard deviations are then weighted and summed, with the largest weights given to the first two indicators. The resulting value is then multiplied by a measure of the skill level attached to the occupation, drawn from ANZSCO.

This methodology helps the Western Australian Government to identify priority occupations. Further information is also incorporated before a final occupation list is constructed. This includes whether the occupation is linked to a VET or higher education qualification, and whether it appears on relevant Australian Government occupation lists. Finally, consultation with training councils is also undertaken to ensure the occupations are consistent with industry intelligence.

Characteristics of a forward looking occupational list

The occupational lists examined above have been developed for different purposes and offer differing examples of how skill needs are identified and presented.

The Department requires a tool to target incentives which is forward looking and directly relevant for apprenticeship and traineeship occupations.

An appropriate occupational list for this purpose should reflect the following characteristics:

Prospective – the list should attempt to identify occupations expected to require a larger skills base over the medium to long term in Australia;

Evidence-based – a wide range of data, forecasts and analysis, including consultations with relevant industry organisations, should be considered in the construction of the list;

Directly relevant to apprenticeship and traineeship occupations – the list should be limited to those occupations which require skills gained through an apprenticeship or traineeship qualification and for which there is a defined career pathway through the training system;

Reflective of high risk occupations – the listed occupations should be those for which it would present a significant risk to the Australian economy or community if the required skills were to fall into shortage;

Independent and transparent – the methodology and process undertaken to construct and review the list could be made publicly available; and,

Timely and current – the list should be updated regularly to ensure it remains reflective of priority occupations for the Australian economy.

These characteristics are loosely based on the principles underpinning the formation of the SOL in 2010.13 In Deloitte Access Economics’ view, none of the occupational lists currently used in Australia, including the NSNL, reflect all these characteristics.

13

See Review of the Migration Occupations in Demand List, Issues Paper No. 2, September 2009 www.immi.gov.au



The NSNL was developed by the Department to ensure that incentives were directed towards occupations experiencing persistent skill shortages. To that end, the retrospective nature of the NSNL was appropriate.

However the NSNL does not meet the requirements as a forward looking occupational list – it is, by construction, focused on historical data. As such, the change in focus toward a forward looking incentive framework necessarily requires that a new occupational list be established.

A framework for targeting incentives

Deloitte Access Economics’ methodology for a forward looking tool focuses on prioritising incentives toward occupations expected to be in demand over the medium to long term.

At present, the Department uses the NSNL to target apprenticeship incentives. That system could be improved because the retrospective nature of the NSNL means that the incentivised outcomes may not fully reflect the needs of the Australian economy. Indeed, the NSNL is focused on the past five years, and contains occupations which may not have been in shortage for at least a year. Moreover, the final composition of the NSNL is decided within the appropriate line area of the Department, rather than independently from where incentives are derived.

As such, the framework outlined here for targeting apprenticeship incentives in the future involves the construction of a new occupational list. This list could be used by the Department in a similar manner to the NSNL, with incentives aimed at increasing completion rates for apprenticeships and traineeships related to the listed occupations. The identification of occupations for this revised list would be derived prospectively, with a focus on Australia’s future skill needs.

Defining a forward looking occupational list

This section describes the construction of an occupational list – a list of priority occupations for apprenticeships and traineeships – with respect to the six underlying characteristics detailed above.

Prospective

A key element of a new list of priority occupations for apprenticeships is that it is prospective.

While predicting skill shortages is very difficult, the list of occupations should be constructed with a view towards the future needs of the Australian economy rather than historical experiences.

Varying economic conditions and structural changes in the economy mean that Australia’s skill needs change over time. An occupational list that is based purely on historical labour market experiences may not be reflective of skill needs going forward.

There is strong precedent for occupational lists to be forward looking. Most relevantly, the SOL constructed by Skills Australia aims to have a medium to long term focus, and Skills



Australia has noted that this timeframe is taken to be 2-10 years into the future. Given the time taken both to complete an apprenticeship and for adjustments to the labour market to occur, such a timeframe may also be appropriate for a list of priority occupations for apprenticeships.

Evidence-based

Clearly, judgements regarding future skill needs should be made based on appropriate evidence.

In deriving and reviewing the SOL, Skills Australia relies on a wide range of historical and forecast data, including forecasts of labour demand and supply by occupation. Similar forecasts and analysis could be used to inform the list of priority occupations for apprenticeships and traineeships.

Other data which could also be incorporated into the analysis include labour force turnover and retirement rates by occupation, industry activity data (including projections), the size and age of the workforce by occupation, apprenticeship and traineeships enrolments and completions, and industry intelligence sourced by consultation with (or submissions by) appropriate bodies such as Industry Skills Councils.

Although some degree of judgement would still be required, analysis of this material would underpin the informed and defensible construction of a list of priority occupations for apprenticeships.

Directly relevant to the apprenticeship and traineeship occupations

To ensure the list of priority occupations for Australian Apprenticeships is as relevant as possible, it needs to be limited to those occupations which require skills gained through an apprenticeship or traineeship qualification and for which there is a defined career pathway through the training system.

Reflective of high risk occupations

The purpose of a list of priority occupations for apprenticeships and traineeships should be to increase labour supply for occupations expected to be in high demand, and target occupations for which the Australian economy would benefit most significantly from intervention in the labour market. These are occupations for which slow labour market responses can potentially result in skill shortages (or, indeed, the oversupply of skills).

One mechanism for ensuring that this element is captured would be to limit the list of priority occupations for apprenticeships and traineeships to occupations listed on the SpOL. Indeed, Skills Australia has noted that the SpOL has been established to “help to protect the economy and the community against future skills shortages in areas that are of high value and where skills take a long time to develop and acquire.”14

As noted above, there are four criteria used to identify occupations on the SpOL: long lead time, high use, high risk, and high information. Skills Australia determined that 74

14

Skills Australia (2010), Australian Workforce Futures: A National Workforce Development Strategy, www.skillsaustralia.gov.au




technician and trades worker occupations met these criteria in 2011 (of those, 51 were also listed on the SOL). That compares to 63 occupations on the NSNL. The approach suggested here would use the SpOL as the starting point. A final list of priority occupations for apprenticeships and traineeships would be further refined using other data and analysis, including industry consultation.

Independent and transparent

It is important that a list of priority occupations for apprenticeships and traineeships be as credible as possible. As such, the list should be seen as independent and reliable, and the process undertaken to develop the list could be made publicly available.

Skills Australia is the independent advisor to the Federal Government on skills development. One of Skills Australia’s key functions is to “provide Government with recommendations on current and future skills needs to help inform decisions to encourage skills formation and drive ongoing reforms to the education and training sector.”15

Given Skills Australia’s experience in developing the SpOL and SOL, along with the broader body of work undertaken by the organisation, it would be well placed to take responsibility for constructing and reviewing a list of priority occupations for apprenticeships and traineeships.

Timely and current

To ensure the list remains as current and relevant as possible, it should be updated regularly. Most existing occupation lists, including the SOL, are updated on an annual basis. The update process should include revisiting and reviewing all data sources and undertaking industry consultation.

Summary of approach

Deloitte Access Economics’ methodology for developing a forward looking tool focuses on the construction of a prospective list of priority occupations for apprenticeships and traineeships. An appropriately defined list could replace the NSNL as the key mechanism for targeting incentives in the training system.

The construction of a forward looking list could be most appropriately undertaken by Skills Australia, which already has responsibility for developing the SpOL and SOL. The list could then be used to direct funding and achieve apprenticeship and traineeship outcomes which are specifically relevant to Australia’s skill needs.

The characteristics of a list of priority occupations for apprenticeships and traineeships outlined here are intended to broadly define an appropriate occupational list. Further review and analysis, including in consultation with Skills Australia, would assist in refining this methodology and determining the final composition of the list.

15

See www.skillsaustralia.gov.au



Existing occupation lists

This appendix reproduces the occupations listed on the NSNL and the SpOL. By way of illustration and at a minimum, the occupations shown for the SpOL have been cross-matched to those classified as ‘technicians and trades workers’ according to the ANZSCO classification structure. Where an occupation has been shaded, it appears on both the NSNL and the SpOL. Inevitably the exercise could be expanded to include other occupations represented on the NSNL.

Table B.1: National Skill Needs List

Aircraft Maintenance Engineer (Avionics) Locksmith

Aircraft Maintenance Engineer (Mechanical) Mechanical Services and Air-conditioning Plumber

Automotive Electrician Metal Fabricator

Baker Metal Machinist (First class)

Binder and Finisher Motor Mechanic (General)

Boat Builder and Repairer Motorcycle Mechanic

Bricklayer Optical Mechanic

Butcher Painter and Decorator

Cabinetmaker Panel Beater

Carpenter Pastry Cook

Carpenter and Joiner Picture Framer

Communications Linesperson Pressure Welder

Cook Printing Machinist

Diesel Motor Mechanic Refrigeration and Air-conditioning Mechanic

Drainer Roof Plumber

Electrical Powerline Tradesperson Roof Slater and Tiler

Electrician (Special class) Screen Printer

Electronic Equipment Tradesperson Shearer

Fibrous Plasterer Sheetmetal Worker (First class)

Fitter Signwriter

Flat Glass Tradesperson Small Engine Mechanic

Floor Finisher Solid Plasterer

Furniture Finisher Stonemason

Furniture Upholsterer Toolmaker

Gasfitter Tree Surgeon

General Communications Tradesperson Vehicle Body Maker

General Electrician Vehicle Painter

General Plumber Vehicle Trimmer

Hairdresser Wall and Floor Tiler

Joiner Welder (First class)

Landscape Gardener Wood Machinist (A-grade)

Lift Mechanic

Source: www.australianapprenticeships.gov.au



Table B.2: Specialised Occupations List (Technician and Trades Worker occupations only)

Air-conditioning and Mechanical Services Plumber Joiner Air-conditioning and Refrigeration Mechanic Lift Mechanic

Aircraft Maintenance Engineer (Avionics) Locksmith Aircraft Maintenance Engineer (Mechanical) Metal Casting Trades Worker Aircraft Maintenance Engineer (Structures) Metal Fabricator

Automotive Electrician Metal Machinist (First Class) Blacksmith Metal Polisher

Boat Builder and Repairer Motor Mechanic (General) Bricklayer Motorcycle Mechanic

Business Machine Mechanic Painting Trades Worker

Carpenter Panel Beater Carpenter and Joiner Picture Framer

Civil Engineer Draftsperson Plumber (General) Civil Engineer Technician Precision Instrument Maker and Repairer

Communications Operator Pressure Welder Diesel Motor Mechanic Radiocommunications Technician

Drainer Roof Plumber Electrical Engineer Draftperson Saw Maker and Repairer Electrical Engineer Technician Sheetmetal Trades Worker

Electrical Linesworker Shipwright Electrician (General) Signwriter

Electrician (Special Class) Small Engine Mechanic Electronic Equipment Trades Worker Solid Plasterer

Electronic Instrument Trades Worker (General) Stonemason Electronic Instrument Trades Worker (Special Class) Technical Cable Jointer

Electroplater Telecommunications Cable Jointer Engraver Telecommunications Field Engineer

Farrier Telecommunications Linesworker Fibrous Plasterer Telecommunications Network Planner Fitter (General) Telecommunications Technical Officer or Technologist

Fitter and Turner Vehicle Painter Fitter-Welder Wall and Floor Tiler Floor Finisher Watch and Clock Maker and Repairer

Furniture Finisher Welder (First Class) Gasfitter Wood Machinist Glazier Wood Machinists and Other Woods Trades

Gunsmith Wood Turner

Source: www.immi.gov.au

Of the 63 occupations currently listed on the NSNL, some 42 (or two-thirds) also appear on the SpOL. Of the 74 ‘technician and trades worker’ occupations on the SpOL, 42 (or 57%) also appear on the NSNL.16

The significant degree of crossover between the SpOL and the NSNL reiterates the usefulness of using the SpOL as a starting point for a new list of priority occupations for apprenticeships.

16 Note that while some occupation titles across the two lists match exactly, interpretation of titles is required

to match other occupations. For example, ‘Electrical powerline tradespersons’ on the NSNL is assumed to be a match to ‘Electrical linesworkers’ on the SOL, and ‘Painter and decorator’ on the NSNL is assumed to be a match to ‘Painting trades workers’ on the SOL.



Appendix C: Detailed modelling results

Chart C.1: Cumulative incidence functions for Apprentices with and without SMCA


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Chart C.2: Cumulative incidence functions for Apprentices with and without WNTD


Chart C.3: Completion survival functions for Trainees with and without WNTD


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Chart C.4: Cumulative incidence functions for Apprentices with and without LAFHA


Chart C.5: Cumulative incidence functions for Trainees with and without LAFHA


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Chart C.6: Cumulative incidence functions for Apprentices with and without WTU


Chart C.7: Completion survival functions for Trainees with and without WTU


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Chart C.8: Completion survival functions for Trainees with and without INNO


Chart C.9: Completion survival functions for Apprentices with and without INNO


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Chart C.10: Cancellation survival functions for Trainees with and without the commencement incentive for Diploma/Advanced Diploma qualifications


Chart C.11: Cancellation survival functions for Apprentices with and without the commencement incentive for Diploma/Advanced Diploma qualifications


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Chart C.12: Cancellation survival functions for Apprentices with and without TFYT


Chart C.13: Cancellation survival functions for Apprentices with and without TSC


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Chart C.14: Cumulative incidence functions for Trainees with and without TSC


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Appendix D: Econometric models The results are from models estimated on a 25% random sample of registrations (to speed the computations). The numbers of observations are still large – 250,272 in the Apprentice models and 449,642 in the Trainee models – and earlier experiments shows that the results are vary only slightly from the results on the full data.

Table D.1 gives definitions of the variables in the models. Days is the outcome variable.

Table D.2, Table D.3, Table D.4 and Table D.5 give the parameter estimates, t ratios and Hazard ratios in the four models. The hazard ratio is the exponential of the parameter, and suggests how the variable affects the exponential terms in the proportional hazards model.

Following are some general comments on the results.

Comments:

The coefficients on variables for incentives starting on, after or close to 1 January 2010, such as claim_TFYT1_eligible, claim_SAAA1_eligible, claim_AKE_eligible and claim_AKB_eligible, are based on only a very small number of completions.

The coefficients on the policy time period ‘overlap’ variables, TFYT2, SMCA2 and TSC2, and the TFYT2 eligibility variables are often large; suggesting that care should be taken when interpreting the results.

The policy time period dummy variables interact with the registration start year dummy variables. That results in the rough pattern across the year dummies.

Because of the large number of observations, the critical value for the t-ratios should be large (say 3.3).

Table D.1: Variable definitions

Parameter Description

Days Number of days in registration period (censored at 12 December 2011)

Level_CI =1 if AQF_Level is Certificate I, =0 otherwise

Level_CII =1 if AQF_Level is Certificate II, =0 otherwise

Level_CIII =1 if AQF_Level is Certificate III, =0 otherwise

Level_CIV =1 if AQF_Level is Certificate IV, =0 otherwise

Level_Dip =1 if AQF_Level is Diploma, =0 otherwise

Level_AdvDip =1 if AQF_Level is Advanced Diploma, =0 otherwise

age_1 =1 if Age_Range is Less than 20, =0 otherwise

age_2 =1 if Age_Range is 20-24, =0 otherwise




age_6 =1 if Age_Range is 45+, =0 otherwise

sex_1 =1 if Male, = 0 if female

Attend_FT = 1 if Full-time, =0 otherwise



Attend_SB =1 if School-based, =0 otherwise

StateVIC =1 if Tender State is Victoria, = 0 otherwise

StateWA As above

StateQLD As above

StateSA As above StateTAS As above

StateNT As above

StateACT As above

Registration_Start_Year2000 =1 if registration start year is 2000, = 0 otherwise

Registration_Start_Year2001 As above










UR_avg_Mean Unemployment rate (average over registration period)

EMPGR_avg_Mean Employment growth (average over registration period)

Indigenous_1 = 1 if Indigenous, = 0 otherwise

Indigenous_NotStated = 1 if Indigenous flag is ‘Not Stated’, = 0 otherwise

Disability_1 = 1 if Disability flag = ‘Disabled’, = 0 otherwise Disability_NotStated = 1 if Indigenous flag is missing, = 0 otherwise

NSNL_1 =1 if qualification is NSNL, = 0 otherwise

small = 1 if employer size is 1-4 or 5-19 employees, = 0 otherwise

medium = 1 if employer size is 20-49 or 50-199 employees, = 0 otherwise

large = 1 if employer size is 200+ employees, = 0 otherwise

Qual_WNTD = 1 if qualification is eligible for WNTT, = 0 othewise

Qual_Inno = 1 if qualification is eligible for Innovation, = 0 othewise

Qual_Dip_Adv_Dip = 1 if qualification is Diploma/Advanced Diploma and eligible for payments

TFYT1 = 1 if registration commenced after 1 Jan 2010, = 0 otherwise

TFYT2 =1 if registration commenced in {1 Jan 2008-31 Dec 2009} and continues past 1 Jan 2010, = 0 otherwise

SAAA1 = 1 if registration commenced after 1 Jan 2010, = 0 otherwise

SAAA2 =1 if registration commenced in {1 Jan 2008-31 Dec 2009} and continues past 1 Jan 2010, = 0 otherwise

SMCA1 = 1 if registration commenced in 2007-2009, = 0 otherwise

SMCA2 =1 if registration commenced prior to 2007 and continues into 2007, = 0 otherwise

KickStartB = 1 if registration starts in AKB period, = 0 otherwise

KickStartE = 1 if registration starts in AKE period, = 0 otherwise

TSC1 = 1 if registration commenced in 2005-2009, = 0 otherwise

TSC2 =1 if registration commenced prior to 2005 and continues into 2005, = 0 otherwise

WNTD = 1 if Qual_WNTD = 1 and registration started in eligible period, = 0 otherwise



RR = 1 if Current_Rural_Regional = 'Rural/Regional', = 0 otherwise

RR03 = 1 if registration period started in 1 Jan 2003 – 30 Jun 2006, = 0 otherwise

RR06 = 1 if registration period started after 1 July 2006, = 0 otherwise

claim_TFYT1_eligible = 1 if eligible for TFYT, = 0 otherwise

claim_TFYT2_eligible = 1 if TFYT2 = 1 and eligible for TFYT, = 0 otherwise

claim_SMCA1_eligible = 1 if eligible for SMCA, = 0 otherwise

claim_SMCA2_eligible = 1 if SMCA2 = 1 and eligible for SMCA, = 0 otherwise

claim_SAAA1_eligible = 1 if eligible for SAAA, = 0 otherwise

claim_SAAA2_eligible = 1 if SAAA2 = 1 and eligible for SAAA, = 0 otherwise

claim_AKB_eligible = 1 if eligible for AKB, = 0 otherwise

claim_AKE_eligible = 1 if eligible for AKE, = 0 otherwise

claim_WTU1_eligible = 1 if eligible for WTU, = 0 otherwise

claim_WTU2_eligible = 1 if SMCA2 = 1 and eligible for WTU, = 0 otherwise

claim_TSC1_eligible = 1 if eligible for TSC, = 0 otherwise

claim_TSC2_eligible = 1 if SMCA2 = 1 and eligible for TSC, = 0 otherwise

claim_COM_Dip_Adv_Di = 1 if Qual_Dip_Adv_Dip = 1 and registration starts in eligible period

claim_Inno_eligible = 1 if Qual_Inno = 1 and registration starts in eligible period

claim_WNTD_eligible = 1 if eligible to claim, = 0 otherwise

claim_RR03_eligible = 1 if eligible to claim, = 0 otherwise

claim_RR06_eligible = 1 if eligible to claim, = 0 otherwise

claim_lafha1_eligibl = 1 if eligible to claim Year 1 of LAFHA, = 0 otherwise

Table D.2: Apprentices completed parameter estimates

Parameter Estimate t-ratio HazardRatio

Level_CI 1.16 1.14 3.20

Level_CII 0.83 2.51 2.30

Level_CIII 1.32 7.32 3.74

Level_CIV 0.99 5.50 2.70

Level_Dip 0.54 3.51 1.72 Level_AdvDip 0.00 0.00 0.00

age_1 -0.49 34.83 0.61

age_2 -0.26 17.58 0.77

age_3 -0.17 10.75 0.84

age_4 -0.15 8.74 0.86

age_5 -0.10 6.35 0.91

age_6 0.00 0.00 0.00

sex_1 -0.51 45.93 0.60

Attend_FT 0.22 17.15 1.25

Attend_SB 0.52 13.90 1.68

StateVIC 0.25 29.63 1.29

StateWA 0.44 37.74 1.56

StateQLD 0.35 37.46 1.42

StateSA 0.14 10.01 1.15

StateTAS 0.39 21.27 1.48

StateNT 0.21 6.17 1.24



StateACT 0.31 11.47 1.36

Registration_Start_Year2000 -2.30 45.16 0.10



Registration_Start_Year2003 -1.65 31.12 0.19 Registration_Start_Year2004 -1.63 30.87 0.20




Registration_Start_Year2008 0.44 7.65 1.55

Registration_Start_Year2009 0.87 14.61 2.40


UR_avg_Mean 0.00 3.17 1.00

EMPGR_avg_Mean 0.00 2.22 1.00

Indigenous_1 -0.15 6.54 0.86

Indigenous_NotStated -0.04 1.37 0.97

Disability_1 -0.21 7.65 0.81

Disability_NotStated 0.10 8.06 1.10

NSNL_1 -0.99 77.76 0.37

small -0.64 54.97 0.53

medium -0.44 40.71 0.64

large -0.26 22.16 0.77

Qual_WNTD -0.03 2.82 0.97

Qual_Inno -0.05 3.72 0.95 Qual_Dip_Adv_Dip 0.06 0.58 1.07

TFYT1 0.00 0.00 0.00

TFYT2 -1.81 74.16 0.16

SAAA1 0.00 0.00 0.00

SAAA2 0.00 0.00 0.00

SMCA1 0.23 9.44 1.25

SMCA2 0.32 21.37 1.38

KickStartB 0.14 3.49 1.15

KickStartE 0.05 1.28 1.06

TSC1 -0.02 0.94 0.98

TSC2 -0.13 8.77 0.88

WNTD 0.32 25.65 1.37

RR 0.12 11.86 1.13

RR03 -0.08 4.33 0.92

RR06 0.00 0.00 0.00

claim_TFYT1_eligible -2.05 24.30 0.13

claim_TFYT2_eligible -0.31 9.75 0.73

claim_SMCA1_eligible -0.26 5.93 0.77 claim_SMCA2_eligible -0.08 2.37 0.93

claim_SAAA1_eligible 0.85 7.76 2.35

claim_SAAA2_eligible 0.19 4.28 1.21

claim_AKB_eligible -2.54 11.06 0.08

claim_AKE_eligible -1.90 4.91 0.15

claim_WTU1_eligible -0.46 16.95 0.63

claim_WTU2_eligible -0.08 4.29 0.93

claim_TSC1_eligible 0.04 2.80 1.04

claim_TSC2_eligible 0.16 11.19 1.17

claim_COM_Dip_Adv_Di 0.20 3.04 1.23



claim_Inno_eligible 0.03 1.59 1.03

claim_WNTD_eligible -0.35 16.91 0.70

claim_RR03_eligible 0.01 0.50 1.01

claim_RR06_eligible 0.04 2.39 1.04

claim_lafha1_eligibl 0.07 3.39 1.07


Table D.3: Apprentices cancelled parameter estimates


Level_CI 0.84 1.41 2.32 Level_CII 0.81 3.31 2.25 Level_CIII -0.01 0.07 0.99 Level_CIV -0.17 1.13 0.84 Level_Dip -0.49 4.04 0.61 Level_AdvDip 0.00 0.00 0.00 age_1 0.19 11.89 1.22 age_2 0.35 21.17 1.42 age_3 0.31 17.16 1.36 age_4 0.21 10.54 1.23 age_5 0.08 4.35 1.08 age_6 0.00 0.00 0.00 sex_1 -0.17 15.73 0.84 Attend_FT -0.08 6.15 0.92 Attend_SB 0.33 12.55 1.39 StateVIC 0.00 0.19 1.00 StateWA -0.03 2.32 0.97 StateQLD -0.02 2.33 0.98 StateSA -0.23 13.96 0.80 StateTAS -0.23 9.65 0.79 StateNT 0.11 3.25 1.12 StateACT 0.19 6.95 1.21 Registration_Start_Year2000 -0.53 15.12 0.59 Registration_Start_Year2001 -0.11 3.39 0.90 Registration_Start_Year2002 0.59 19.30 1.81 Registration_Start_Year2003 1.08 31.06 2.95 Registration_Start_Year2004 1.37 39.89 3.95 Registration_Start_Year2005 1.48 40.99 4.37 Registration_Start_Year2006 1.26 34.20 3.52 Registration_Start_Year2007 1.17 31.87 3.22 Registration_Start_Year2008 2.72 66.58 15.24 Registration_Start_Year2009 3.43 81.08 30.77 Registration_Start_Year2010 -0.02 0.53 0.98 UR_avg_Mean 0.01 4.79 1.01 EMPGR_avg_Mean 0.00 3.15 1.00 Indigenous_1 0.38 20.73 1.46 Indigenous_NotStated 0.20 7.08 1.22 Disability_1 0.17 7.40 1.19 Disability_NotStated -0.32 21.57 0.72 NSNL_1 -0.51 38.16 0.60 small -0.62 40.12 0.54 medium -0.63 42.45 0.53



large -0.73 45.83 0.48 Qual_WNTD 0.00 0.00 1.00 Qual_Inno -0.41 23.09 0.67 Qual_Dip_Adv_Dip -0.20 1.73 0.82 TFYT1 0.00 0.00 0.00 TFYT2 -2.08 87.24 0.13 SAAA1 0.00 0.00 0.00 SAAA2 0.00 0.00 0.00 SMCA1 -0.72 27.20 0.49 SMCA2 -0.47 24.66 0.63 KickStartB -0.02 0.63 0.98 KickStartE 0.21 6.66 1.23 TSC1 -0.79 34.20 0.45 TSC2 -1.41 85.65 0.25 WNTD 0.19 14.02 1.22 RR -0.14 11.67 0.87 RR03 -0.24 11.39 0.79 RR06 0.00 0.00 0.00 claim_TFYT1_eligible 0.21 7.46 1.23 claim_TFYT2_eligible -1.01 33.40 0.37 claim_SMCA1_eligible 0.98 25.22 2.66 claim_SMCA2_eligible 0.06 1.17 1.06 claim_SAAA1_eligible -0.11 2.88 0.89 claim_SAAA2_eligible -0.03 0.56 0.97 claim_AKB_eligible 0.08 2.01 1.09 claim_AKE_eligible 0.00 0.10 1.00 claim_WTU1_eligible 0.97 42.57 2.65 claim_WTU2_eligible -0.15 6.29 0.86 claim_TSC1_eligible -0.23 15.50 0.79 claim_TSC2_eligible 0.10 5.08 1.11 claim_COM_Dip_Adv_Di 0.29 3.61 1.33 claim_Inno_eligible 0.18 7.73 1.20 claim_WNTD_eligible 0.12 5.73 1.13 claim_RR03_eligible -0.01 0.83 0.99 claim_RR06_eligible 0.02 1.08 1.02 claim_lafha1_eligibl -0.27 8.93 0.77


Table D.4: Traineeships completed parameter estimates


Level_CI 2.48 8.52 11.91 Level_CII 1.16 4.04 3.19 Level_CIII 0.87 3.03 2.39 Level_CIV 0.52 1.81 1.68 Level_Dip 0.27 1.26 1.31 Level_AdvDip 0.00 0.00 0.00 age_1 0.16 21.05 1.17 age_2 -0.02 2.50 0.98 age_3 -0.12 13.12 0.89 age_4 -0.09 10.05 0.91 age_5 -0.04 5.91 0.96



age_6 0.00 0.00 0.00 sex_1 -0.22 41.17 0.80 Attend_FT 0.32 58.08 1.38 Attend_SB -0.14 13.76 0.87 StateVIC 0.52 87.22 1.68 StateWA 0.60 62.42 1.82 StateQLD 0.56 85.36 1.76 StateSA 0.11 11.58 1.12 StateTAS 0.48 40.75 1.62 StateNT 0.41 15.54 1.50 StateACT 0.58 28.43 1.78 Registration_Start_Year2000 -1.14 44.66 0.32 Registration_Start_Year2001 -0.97 41.59 0.38 Registration_Start_Year2002 -0.71 31.58 0.49 Registration_Start_Year2003 -0.49 19.04 0.61 Registration_Start_Year2004 0.10 3.75 1.10 Registration_Start_Year2005 0.32 11.69 1.38 Registration_Start_Year2006 0.42 15.17 1.53 Registration_Start_Year2007 0.56 20.36 1.75 Registration_Start_Year2008 1.53 50.75 4.60 Registration_Start_Year2009 2.48 77.44 11.94 Registration_Start_Year2010 -0.49 20.57 0.62 UR_avg_Mean 0.00 2.95 1.00 EMPGR_avg_Mean 0.00 0.07 1.00 Indigenous_1 -0.16 12.40 0.85 Indigenous_NotStated 0.03 2.00 1.04 Disability_1 -0.11 5.93 0.90 Disability_NotStated -0.19 21.67 0.83 NSNL_1 -1.38 6.57 0.25 small -0.89 50.77 0.41 medium -0.86 49.42 0.43 large -0.79 45.38 0.45 Qual_WNTD -0.12 12.65 0.88 Qual_Inno -0.41 10.91 0.66 Qual_Dip_Adv_Dip -0.17 0.83 0.85 TFYT1 0.00 0.00 0.00 TFYT2 -2.03 153.62 0.13 SAAA1 0.00 0.00 0.00 SAAA2 0.00 0.00 0.00 SMCA1 -0.35 23.24 0.70 SMCA2 -0.45 48.44 0.64 KickStartB 0.15 8.70 1.16 KickStartE 0.00 0.25 1.00 TSC1 -0.74 45.28 0.48 TSC2 -0.95 100.72 0.39 WNTD 0.06 5.56 1.07 RR -0.02 3.26 0.98 RR03 -0.03 2.34 0.97 RR06 0.00 0.00 0.00 claim_TFYT1_eligible 0.04 0.61 1.04 claim_TFYT2_eligible 0.04 0.71 1.04 claim_SMCA1_eligible -0.82 2.48 0.44 claim_SMCA2_eligible -0.68 1.39 0.51 claim_SAAA1_eligible 0.08 0.32 1.09



claim_SAAA2_eligible -0.48 1.90 0.62 claim_AKB_eligible -1.72 3.29 0.18 claim_AKE_eligible -5.90 0.69 0.00 claim_WTU1_eligible -1.27 4.29 0.28 claim_WTU2_eligible -0.90 1.85 0.41 claim_TSC1_eligible 0.99 3.70 2.70 claim_TSC2_eligible 2.92 10.15 18.45 claim_COM_Dip_Adv_Di -0.07 0.88 0.94 claim_Inno_eligible 0.35 8.73 1.42 claim_WNTD_eligible -0.17 11.07 0.85 claim_RR03_eligible 0.10 10.37 1.11 claim_RR06_eligible 1.47 7.17 4.34 claim_lafha1_eligibl 0.31 10.94 1.36


Table D.5: Traineeships cancelled parameter estimates


Level_CI 1.15 3.80 3.17 Level_CII 0.75 2.50 2.11 Level_CIII 0.34 1.15 1.41 Level_CIV 0.30 1.00 1.35 Level_Dip -0.10 0.47 0.91 Level_AdvDip 0.00 0.00 0.00 age_1 0.38 43.18 1.47 age_2 0.48 53.34 1.61 age_3 0.36 36.30 1.44 age_4 0.26 24.01 1.29 age_5 0.10 10.44 1.10 age_6 0.00 0.00 0.00 sex_1 0.10 18.54 1.11 Attend_FT -0.05 8.82 0.95 Attend_SB -0.42 37.91 0.66 StateVIC 0.05 8.71 1.06 StateWA 0.09 8.77 1.10 StateQLD 0.02 2.55 1.02 StateSA -0.06 6.06 0.94 StateTAS -0.19 12.38 0.83 StateNT 0.05 2.03 1.06 StateACT 0.26 12.55 1.30 Registration_Start_Year2000 0.07 3.24 1.07 Registration_Start_Year2001 0.07 3.40 1.07 Registration_Start_Year2002 0.30 16.87 1.35 Registration_Start_Year2003 0.80 35.49 2.23 Registration_Start_Year2004 1.29 55.89 3.63 Registration_Start_Year2005 1.62 65.12 5.07 Registration_Start_Year2006 1.64 64.77 5.13 Registration_Start_Year2007 1.75 69.03 5.73 Registration_Start_Year2008 2.35 82.20 10.48 Registration_Start_Year2009 3.21 107.57 24.79 Registration_Start_Year2010 0.08 3.75 1.08 UR_avg_Mean -0.01 7.86 0.99



EMPGR_avg_Mean 0.00 2.59 1.00 Indigenous_1 0.29 25.07 1.34 Indigenous_NotStated 0.15 8.13 1.16 Disability_1 0.02 1.31 1.02 Disability_NotStated -0.21 23.60 0.81 NSNL_1 -0.60 3.67 0.55 small -0.79 38.65 0.45 medium -0.85 42.22 0.43 large -0.91 44.70 0.40 Qual_WNTD -0.01 0.67 0.99 Qual_Inno -0.30 7.44 0.74 Qual_Dip_Adv_Dip -0.49 2.11 0.62 TFYT1 0.00 0.00 0.00 TFYT2 -1.96 143.70 0.14 SAAA1 0.00 0.00 0.00 SAAA2 0.00 0.00 0.00 SMCA1 -0.68 41.30 0.51 SMCA2 -0.97 88.93 0.38 KickStartB 0.03 1.59 1.04 KickStartE 0.10 5.20 1.11 TSC1 -0.84 50.03 0.43 TSC2 -1.25 124.36 0.29 WNTD -0.02 1.21 0.99 RR -0.08 14.34 0.92 RR03 -0.23 15.07 0.79 RR06 0.00 0.00 0.00 claim_TFYT1_eligible -0.14 2.35 0.87 claim_TFYT2_eligible -0.11 1.51 0.89 claim_SMCA1_eligible 0.87 2.93 2.40 claim_SMCA2_eligible 1.57 3.68 4.78 claim_SAAA1_eligible -0.01 0.02 1.00 claim_SAAA2_eligible -0.39 1.28 0.68 claim_AKB_eligible 0.18 0.59 1.19 claim_AKE_eligible 0.76 2.16 2.13 claim_WTU1_eligible 0.47 1.71 1.60 claim_WTU2_eligible 0.41 0.73 1.51 claim_TSC1_eligible -0.73 3.24 0.48 claim_TSC2_eligible 0.46 1.13 1.59 claim_COM_Dip_Adv_Di 0.40 3.88 1.49 claim_Inno_eligible 0.10 2.24 1.11 claim_WNTD_eligible 0.04 2.65 1.04 claim_RR03_eligible 0.00 0.16 1.00 claim_RR06_eligible 0.68 3.83 1.97 claim_lafha1_eligibl -0.33 8.68 0.72


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