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Modelling costs v benefits of apprenticeship v degree A lifetime net financial position approach December 2017 www.berl.co.nz

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Page 1: Modelling costs v benefits of apprenticeship v degree costs v benefits of apprenticeship v degree December 2017 Disclaimer 5 1 Disclaimer Note: This is a standard disclaimer required

Modelling costs v benefits of apprenticeship v degree

A lifetime net financial position approach

December 2017

www.berl.co.nz

Page 2: Modelling costs v benefits of apprenticeship v degree costs v benefits of apprenticeship v degree December 2017 Disclaimer 5 1 Disclaimer Note: This is a standard disclaimer required

Author(s): Konrad Hurren, Mark Cox and Ganesh Nana

All work is done, and services rendered at the request of, and for the purposes of the client only. Neither

BERL nor any of its employees accepts any responsibility on any grounds whatsoever, including negligence, to

any other person.

While every effort is made by BERL to ensure that the information, opinions and forecasts provided to the

client are accurate and reliable, BERL shall not be liable for any adverse consequences of the client’s

decisions made in reliance of any report provided by BERL, nor shall BERL be held to have given or implied

any warranty as to whether any report provided by BERL will assist in the performance of the client’s

functions.

©BERL Reference No: #5728 October 2017

Page 3: Modelling costs v benefits of apprenticeship v degree costs v benefits of apprenticeship v degree December 2017 Disclaimer 5 1 Disclaimer Note: This is a standard disclaimer required
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Modelling costs v benefits of apprenticeship v degree December 2017

Disclaimer 4

Contents

1 Introduction .............................................................................................................. 5

2 Methodology ............................................................................................................ 7

2.1 Sources of data ......................................................................................................................................7

2.2 Assumptions ...........................................................................................................................................8

3 Results ..................................................................................................................... 10

3.1 Sensitivity ............................................................................................................................................ 10

3.2 Scenarios ............................................................................................................................................. 13

4 Next steps ............................................................................................................... 17

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Modelling costs v benefits of apprenticeship v degree December 2017

Disclaimer 5

1 Disclaimer

Note: This is a standard disclaimer required of all projects using Integrated Data Infrastructure data.

Access to the data used in this study was provided by Statistics New Zealand under conditions designed to give

effect to the security and confidentiality provisions of the Statistics Act 1975. The results presented in this study

are the work of the author, not Statistics NZ.

The results in this report are not official statistics, they have been created for research purposes from the

Integrated Data Infrastructure (IDI), managed by Statistics New Zealand.

The opinions, findings, recommendations, and conclusions expressed in this report are those of the authors, not

Statistics NZ.

Access to the anonymised data used in this study was provided by Statistics NZ in accordance with security and

confidentiality provisions of the Statistics Act 1975. Only people authorised by the Statistics Act 1975 are allowed

to see data about a particular person, household, business, or organisation, and the results in this [report, paper]

have been confidentialised to protect these groups from identification.

Careful consideration has been given to the privacy, security, and confidentiality issues associated with using

administrative and survey data in the IDI. Further detail can be found in the Privacy impact assessment for the

Integrated Data Infrastructure available from www.stats.govt.nz.

The results are based in part on tax data supplied by Inland Revenue to Statistics NZ under the Tax Administration

Act 1994. This tax data must be used only for statistical purposes, and no individual information may be published

or disclosed in any other form, or provided to Inland Revenue for administrative or regulatory purposes.

In addition we have processed the confidentialised data obtained from the IDI by combining it with data from:

StatsNZ Household Economic Survey -

http://www.stats.govt.nz/browse_for_stats/people_and_communities/Households/household-economic-

survey.aspx

Morningstar Kiwisaver survey Jun 2017 -

http://www.morningstar.com.au/s/documents/KiwiSaver_Survey_Q2_2017.pdf

Publicly posted interest rates offered by banks

MoE Student Loan Scheme Annual Report 2016 data -

https://www.educationcounts.govt.nz/publications/80898/student-loan-scheme-annual-report-2016

Any person who has had access to the unit record data has certified that they have been shown, have read, and

have understood section 81 of the Tax Administration Act 1994, which relates to secrecy. Any discussion of data

limitations or weaknesses is in the context of using the IDI for statistical purposes, and is not related to the data’s

ability to support Inland Revenue’s core operational requirements.

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Modelling costs v benefits of apprenticeship v degree December 2017

Introduction 6

2 Introduction

BERL was tasked by the Industry Training Federation (ITF) to model the financial outcomes of people choosing

between obtaining a degree or seeking an apprenticeship. This was motivated as a response to studies done by

others based on Census data that look at broad averages without controlling for any other effects. And often

conclude that the “best” career path is a degree. Based on not much more than the average income of degree

holders, compared to all others.

We elect to focus on a net financial position over a person’s lifetime rather than focus solely on income. To show

that the difference in the flow and growth of income for degree holders and apprentices are important and that

the decision should not be based entirely on the average total earnings.

In this report we briefly document our methodology, sources of data, assumptions, main results, sensitivity

analysis, and scenarios.

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Modelling costs v benefits of apprenticeship v degree December 2017

Methodology 7

3 Methodology

We start by assuming three broad career paths that someone with sufficient aptitude could (i.e. achieved at least

a level 2 secondary school qualification) take:

Go to university and obtain a degree

Approach an employer and become an apprentice (using level 4,5 and 6 qualification as a proxy)

Seek employment without further education or training

By sufficient aptitude we refer to people who have a high enough level of innate skill to go to university and

obtain a degree. We note that in such a study we need to do our best to control for the innate skill of our study

population. To do so, we choose to study only those people who have obtained at least level 2 or equivalent

secondary school qualification.

We model the net financial position of 3 “average” students over the course of their working lives (18 – 64). This

boils down to calculating, for each year, their total assets and their total liabilities.

To do this we employ a combination of accounting models to enumerate flows of income and expenses between

years and arrive at total assets and total liabilities at the end of each year.

Total assets are calculated by assuming the person enrols in Kiwisaver and buys a house when they can afford

the deposit, and also has some cash savings.

Total liabilities include any student loans and overdrafts the person incurs, as well as a mortgage which is

amortized over a set period.

3.1 Sources of data

Integrated Data Infrastructure

The income data of the people we modelled is drawn from tax records obtained through the Statistics New

Zealand Integrated Data Infrastructure. This database is an amalgamation of administrative data from across

government departments. The data is stored at a unit-record level which allowed us to create a custom dataset

of people who had obtained at least a level 2 or equivalent qualification at secondary school.

This allowed us to “control” for innate ability.

The income data we obtained is an aggregation of “employment-like” income:

Wages and salary

Director fees

Sole proprietor salary

Partner withholding payments

Other data

Ministry of Education Student Loan Scheme report 2016

Income tax as at September 2017 from the Income Tax Act

Household Economic Survey

Studylink student allowance entitlements as at September 2017

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Modelling costs v benefits of apprenticeship v degree December 2017

Methodology 8

Morningstar Kiwisaver survey

Average overdraft rates calculated from posted rates by retail banks

RBNZ Loan To Value Ratio policy

Median house price data from REINZ

Current Kiwisaver policy settings

o Homestart grant

o Withdrawal for first home rules

o Employer contributions

o Tax credit

3.2 Assumptions

We stress that we have taken a very conservative view of people in our study, our assumptions reflect this. These

assumptions are built into the “base case” scenario and most can be changed to suit different scenarios readily.

We offer a selection of explicitly modelled scenarios later in this report.

Our assumptions are:

Secondary school qualification is a good proxy for innate ability

Student (degree holder) specific assumptions:

Adjustable:

People take out an “average” size student loan ($9,800 per year of study)

People earn minimum wage (15.75) for 10 hours a week while studying for a degree

Receive full amount of student allowance ($197.80 a week)

Rent while studying is $150, this implies multiple flatmates and living frugally

Hard coded:

People complete their degrees in 4 years

Assumptions for everyone:

Adjustable:

House price preference is the median national price ($530,000)

Living expenses linked to income but by assumption people live well within their means and share expenses

throughout life

Rent while working is $250

Mortgage term is 20 years

Interest rate on savings is 2.1%

Interest rate on borrowings is 13.99%

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Modelling costs v benefits of apprenticeship v degree December 2017

Methodology 9

Interest rate on mortgage is 5.3%

Loan to value ratio is the current legislated 80%

People are assumed to join a Moderate Kiwisaver scheme and enjoy 5.1% pa returns

The discount rate is 6%

Hard coded:

Share cost of buying a house with someone (parents, partner, friend)

Buy only one house in lifetime and never move

People buy a previously built house and receive the appropriate Homestart grant

People prioritise living expenses and mortgage over repayment of overdraft

Current LVR and Kiwisaver policy are assumed

Contribute lowest amount to Kiwisaver (3%)

Start Kiwisaver as soon as they start work

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Modelling costs v benefits of apprenticeship v degree December 2017

Results 10

4 Results

Our modelling produced the following data summarised in Figure 4.1. What we observe is that under our

assumptions the net financial position of degree holders and apprentices at the end of their careers is almost

exactly the same.

This result is driven by the apprentices earning more, earlier, and buying a house earlier and contributing to

Kiwisaver earlier.

We also observe that the people who obtain a level 2 secondary school qualification but otherwise no post school

qualification have a net financial position which grows slightly slower than apprentices. This is driven by slightly

lower earnings.

Figure 4.1 Main result

Another key result of our modelling which will be shown explicitly in the sensitivity analysis is the relative lower

risk of an apprentice career path. We note that the apprentice in our model always ends up with the highest net

financial position in the middle of their working careers. This is driven by them having paid off most of their

mortgage and having no other debt. This implies that an apprentice career is lower risk.

4.1 Sensitivity

Below, in Figure 4.2, Figure 4.3, and Figure 4.4 we summarise the results of changing our assumptions. Figure

4.2 shows the sensitivity of the net financial position to the assumption of when people start contributing to

Kiwisaver.

The baseline assumption is that people start as soon as they begin their career in full time work. What we observe

is that the more a person delays contributions to Kiwisaver, the lower their net financial position at the middle

and end of their careers.

1,854,126

1,645,228

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

-500

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47Year

$000s

Age

Financial positionfrom student/apprentice, to employee, to home owner

net assets (+) / liablities (-)

level 4, 5 or 6

bachelors and above

no post-school

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Modelling costs v benefits of apprenticeship v degree December 2017

Results 11

Figure 4.2 Sensitivity to Kiwisaver start

Figure 4.3 summarises our model’s sensitivity to changing the Kiwisaver contribution rate. As to be expected

contributing more to Kiwisaver results in a greater net financial position at the end of one’s career.

$229,805.72

$433,071.97

$340,607.24

050,000

100,000150,000200,000250,000300,000350,000400,000450,000500,000

Net assets mid career (age40)

Net assets mid career (age40)

Net assets mid career (age40)

Bachelors and above Level 4, 5 or 6 No post-school

Financial position sensitivity to Kiwisaver startMid career (age 40)

At beginning work life Delay 3 years Delay 6 years Delay 10 years

$

$1,854,126.07 $1,777,433.44 $1,568,133.96

0200,000400,000600,000800,000

1,000,0001,200,0001,400,0001,600,0001,800,0002,000,000

Net assets end work (64) Net assets end work (64) Net assets end work (64)

Bachelors and above Level 4, 5 or 6 No post-school

Financial position sensitivity to Kiwisaver startEnd of career

At beginning work life Delay 3 years Delay 6 years Delay 10 years

$

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Modelling costs v benefits of apprenticeship v degree December 2017

Results 12

Figure 4.3 Sensitivity to Kiwisaver contribution rate

Finally, we summarise our model’s sensitivity to changes in the growth rate of the Kiwisaver fund assumption, in

Figure 4.4.

In this analysis the result that degree holder ends up with a higher net financial position at the end of their career

is reversed. Apprentices have the highest net financial position in this scenario. This is because apprentices

contribute to Kiwisaver early in life and the returns compound for longer.

$229,805.72

$433,071.97

$340,607.24

050,000

100,000150,000200,000250,000300,000350,000400,000450,000500,000

Net assets mid career (age40)

Net assets mid career (age40)

Net assets mid career (age40)

Bachelors and above Level 4, 5 or 6 No post-school

Financial position sensitivity to Kiwisaver contribution rate Mid career

3% 4% 8%

$

$1,854,126.07 $1,777,433.44 $1,568,133.96

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

Net assets end work (64) Net assets end work (64) Net assets end work (64)

Bachelors and above Level 4, 5 or 6 No post-school

Financial position sensitivity to Kiwisaver contribution rateEnd of career

3% 4% 8%

$

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Modelling costs v benefits of apprenticeship v degree December 2017

Results 13

Figure 4.4 Sensitivity to Kiwisaver growth rate (fund type)

4.2 Scenarios

We postulate three scenarios:

1. Scenario one: House price preference affordable but high ($700k)

2. Scenario two: Student Loan high (+50% of mean) vs low (-50% of mean)

3. Scenario three: Mortgage term longest (30) versus shortest (10)

$229,805.72

$433,071.97

$340,607.24

0

100,000

200,000

300,000

400,000

500,000

600,000

Net assets mid career (age40)

Net assets mid career (age40)

Net assets mid career (age40)

Bachelors and above Level 4, 5 or 6 No post-school

Kiwisaver fund performance

Financial position sensitivity to Kiwisaver growth rateMid career

4.3% 5.1% 8.4% 10.6%

$

$1,854,126.07 $1,777,433.44 $1,568,133.96

0500,000

1,000,0001,500,0002,000,0002,500,0003,000,0003,500,0004,000,000

Net assets end work (64) Net assets end work (64) Net assets end work (64)

Bachelors and above Level 4, 5 or 6 No post-school

Kiwisaver fund performance

Financial position sensitivity to Kiwisaver growth rateEnd of career

4.3% 5.1% 8.4% 10.6%

$

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Modelling costs v benefits of apprenticeship v degree December 2017

Results 14

Scenario One (house price $700k)

Our first scenario considers what happens if our model’s agents seek to buy a very expensive house – around

$700k. in this scenario our agents end up with a lower net financial position than in the baseline. This is driven

by less savings.

It is interesting that in our model any house price above around $760k results in the overdraft of the apprentice

and no post-school people exploding into millions. This is driven by the assumption that people pay off a

mortgage (and other living costs) before paying off an overdraft.

Figure 4.5 Scenario one - house price $700k

Scenario two – student loan high vs low

We want to consider what happens when our model students elect to take on a larger or smaller than average

student loan. For this we model two situations; the first, the student takes out a loan of 150% of the average

loan amount. The second the model student takes out a loan 50% of the average loan amount.

The high student loan scenario is summarised in Figure 4.6. We see that taking out a larger than average student

loan results in a lower than base scenario net financial position at the end of a student’s career. This is because

with a higher student loan the student takes longer to pay off the overdraft incurred during study.

The low student loan scenario summarised in Figure 4.7 shows the opposite result, with the student achieving a

much higher net financial position at the end of their lives. This result is driven by a lower personal overdraft.

1,755,164

1,396,774

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

-500

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47Year

$000s

Age

Financial positionfrom student/apprentice, to employee, to home owner

net assets (+) / liablities (-)

level 4, 5 or 6

bachelors and above

no post-school

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Modelling costs v benefits of apprenticeship v degree December 2017

Results 15

Figure 4.6 Scenario two HIGH student loan

Figure 4.7 Scenario two LOW student loan

456

265

1,075

1,795

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

-500

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Year

$000s

Age

Financial positionfrom student, to employee, to home owner

selected assets and liablities

Net other savings (+) / borrowing (-)

House mortgage

Student loan

Equity in own house

KiwiSaver

Net assets (+) / liabilities (-)

502

265

1,154

1,921

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

-500

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Year

$000s

Age

Financial positionfrom student, to employee, to home owner

selected assets and liablities

Net other savings (+) / borrowing (-)

House mortgage

Student loan

Equity in own house

KiwiSaver

Net assets (+) / liabilities (-)

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Modelling costs v benefits of apprenticeship v degree December 2017

Results 16

Scenario three: - mortgage term long versus short

Figure 4.8 Scenario three Long mortgage term (30 years)

Figure 4.9 Scenario three short mortgage term (10 years)

1,775,109

1,561,444

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

-500

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47Year

$000s

Age

Financial positionfrom student/apprentice, to employee, to home owner

net assets (+) / liablities (-)

level 4, 5 or 6

bachelors and above

no post-school

1,927,740

1,604,899

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

-500

0

500

1,000

1,500

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47Year

$000s

Age

Financial positionfrom student/apprentice, to employee, to home owner

net assets (+) / liablities (-)

level 4, 5 or 6

bachelors and above

no post-school

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Modelling costs v benefits of apprenticeship v degree December 2017

Next steps 17

5 Next steps

This report covers stage 2 of what has the potential to develop into a three stage project. In this stage report we

have used level 4, 5 and 6 post-secondary qualifications as a proxy for apprentices. The potential stage 3 would

augment this significantly by exploring specific occupations. These occupations will be ones known to require an

apprenticeship to enter and work in.