Quantifying demand for training in the primary sector Presentation to NZVETRF

21 October 2014

Overview of this discussion

2

• Present an approach for forecasting demand for industry training

• Describe how the approach can be extended to measure returns to training investment

• Discuss findings and implications for the primary sector

• Discuss similar work in other sectors?

3

Industry context

Sources of demand

Modelling approach

Results

Return on investment

Summary

About the workforce in pastoral farming

4

• 37,000 dairy farmers and 16,000 beef & sheep farmers

• Mostly family-owned small businesses

• No mandated training pathways

• Low levels of formal qualifications

• Capital intensive (so skills are “geared”)

Industry standard roles and ideal skills

5

Farm assistant

Herd manager

Assistant manager

Farm manager

Owner or business mgr

Basic farm skills e.g. • NCA L2

General farm skills e.g. • NCA L3

Advanced farm skills e.g. • NCA L4

Production mgt. skills e.g. • NC in Prod. Mgt.

Business mgt. skills e.g. • Dip. AgBus Mgt • BAgSci • BCom

Combined for analysis purposes

Top and bottom heavy workforce

6

12,500

7,400

3,300

4,900

9,500

Farm assistant Herd manager Assistant manager Farm manager Business manager

Employees in the dairy farming workforce

Top heavy workforce

7

2,000 1,000

300 200

12,500

Farmhand Stockman Stock manager Farm manager Business manager

Employees in the beef & lamb workforce

Industry stakeholders

8

Questions to be addressed

9

• What should training targets be?

• How much should be invested in training (for both demand creation and delivery)?

• What are the priorities for investment?

• What is the economic case for investment in training?

10

Industry context

Sources of demand

Modelling approach

Results

Return on investment

Summary

Sources of demand for training

11

Changing roles

Replacement demand

Expansion of the workforce

Skills growth

Analytical attention to sources of demand

12

Changing roles Replacement demand

Expansion of the workforce Skills growth

• Lots of qualitative and quantitative work

• Examples: ‒ People Powered

(MPI) ‒ Futures Research

(AgITO)

• Mainly qualitative work

• Examples: ‒ Land-based

review (TEC) ‒ Dairy Farm

Labour Productivity (DairyNZ)

• Lots of qualitative and some quantitative work

• Examples: ‒ People Powered

(MPI) ‒ Futures Research

(AgITO)

• Some quantitative work (uniform rate, equilibrium)

• Examples: ‒ Future

capability (MPI) ‒ Matching tool

(ITF)

Quantitative analytical effort vs important to the sector

13

Changing roles Replacement demand

Expansion of the workforce Skills growth

Quantitative effort

Importance to the primary sector

14

Industry context

Sources of demand

Modelling approach

Results

Return on investment

Summary

Two complicating factors

15

• Skills growth and replacement demand interact

• Replacement rate is not uniform

Replacement demand

Replacement demand

Skills growth

Modelling approach

16

• Use an agent-based model (Inchworm) to simulate every worker individually

• For each worker characterise events such as: ‒ Recruitment ‒ Promotion ‒ Migration ‒ Changing team ‒ Leaving industry ‒ Being trained

• Create a database of virtual workers

Advantages of this approach

17

• Manage complexity ‒ Non-uniform replacement rate ‒ Sources of demand interaction ‒ …

• Explore ‘what-if’ & non-equilibrium scenarios over time:

‒ How are skill levels changing at current training volumes? ‒ How fast does training need to grow to meet an industry target? ‒ What is the impact of a campaign of training?

18

Industry context

Sources of demand

Modelling approach

Results

Return on investment

Summary

Interpreting results (1/3)

19

0

500

1000

1500

2000

2500

3000

3500

2014 2018 2022 2026 2030

ITO qualifications awarded

0%10%20%30%40%50%60%70%80%90%

100%

2014 2018 2022 2026 2030

Workforce qualification level Business / farm management

Herd management

Assistant management

Farm assistance

None

Interpreting results (2/3)

20

0

500

1000

1500

2000

2500

3000

3500

2014 2018 2022 2026 2030

ITO qualifications awarded

0%10%20%30%40%50%60%70%80%90%

100%

2014 2018 2022 2026 2030

Workforce qualification level Business / farm management

Herd management

Assistant management

Farm assistance

None

Interpreting results (3/3)

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0

500

1000

1500

2000

2500

3000

3500

2014 2018 2022 2026 2030

ITO training over time

Farm assistant

2014 2030

Business / farm management

Herd management

Assistant management

Farm assistance

None

Herd manager

Asst. manager Bus/farm manager

2014 2030

2014 2030 2014 2030

Business as usual isn’t enough (Dairy farming)

22

0

500

1000

1500

2000

2500

3000

2014 2018 2022 2026 2030

ITO training over time

Farm assistant

2014 2030

Business / farm management

Herd management

Assistant management

Farm assistance

None

Herd manager

Asst. manager Bus/farm manager

2014 2030

2014 2030 2014 2030

~50% ~50%

~40% ~20%

Even with strong training growth we’re in it for the long haul (Dairy farming)

23

0

1000

2000

3000

4000

5000

6000

2014 2018 2022 2026 2030

ITO training over time

Farm assistant

2014 2030

Business / farm management

Herd management

Assistant management

Farm assistance

None

Herd manager

Asst. manager Bus/farm manager

2014 2030

2014 2030 2014 2030

~60% >60%

>60% >60%

We’re not even close (Sheep and beef farming)

24

0100200300400500600700800900

1000

2014 2018 2022 2026 2030

ITO training over time

Farm assistant

2014 2030

Business / farm management

Herd management

Assistant management

Farm assistance

None

Herd manager

Asst. manager Bus/farm manager

2014 2030

2014 2030 2014 2030

We’re not even close (Sheep and beef farming)

25

0

500

1000

1500

2000

2500

2014

2016

2018

2020

2022

2024

2026

2028

2030

ITO training

0%

20%

40%

60%

80%

100%FA

0%

20%

40%

60%

80%

100%HM

0%

20%

40%

60%

80%

100%AM

0%

20%

40%

60%

80%

100%FM / BM

2014 2030 2014 2030

2014 2030 2014 2030

FBMT

HMT

AMT

FAT

None

50%

40% 35%

50%

26

Industry context

Sources of demand

Modelling approach

Results

Return on investment

Summary

What is it worth?

27

0

1000

2000

3000

4000

5000

6000

2014 2018 2022 2026 2030

ITO training over time

Farm assistant

2014 2030

Herd manager

Asst. manager Bus/farm manager

2014 2030

2014 2030 2014 2030

$ ?

Profit variation in dairy farming

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Operating profit/ha

Operating Profit per ha – Owner operators

Mean $2,112

Standard deviation $1,162

Profit variation caused by variation in farm team skills

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Operating profit/ha

Operating Profit per ha – Owner operators

𝜎𝜎 = $1,162

𝜎𝜎 = ~$900

Modelling farm team skills

30

Intrinsic ability

+ Experience

+ Training

Individual skill

Individual A skill

+ Individual B skill

+ …

Farm team skill Variation in farm team skills

Equating distributions

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Profit variation caused by farm team skills ($/ha)

Variation in farm team skills (composed of experience, training &

intrinsic ability)

Equating the two bell curves allows us to express the value of training in terms of $ per hectare

Training can add an extra $1b to the bottom line of the dairy farming industry

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0

1000

2000

3000

4000

5000

6000

2014 2018 2022 2026 2030

ITO training over time

Farm assistant

2014 2030

Herd manager

Asst. manager Bus/farm manager

2014 2030

2014 2030 2014 2030

$1,077

$445

2010 2015 2020 2025

Increase in industry profit ($m)

Strong traininggrowthCurrenttrajectory

33

Industry context

Sources of demand

Modelling approach

Results

Return on investment

Summary

Summary

34

• The Primary ITO, DairyNZ & Scarlatti have used an agent-based tool (Inchworm) to model demand in the primary sector

• Agent based demand modelling provides insights about non-equilibrium changes in qualifications levels given demand from: ‒ Industry growth ‒ Replacement ‒ Changing roles ‒ Skills growth

• Extending the modelling to measure variation in skills within a workforce provides a way to quantify the returns from training.

35

BACK-UP SLIDES

36

Example of a virtual worker

Key dates Born in 1985, first full year in industry was 2005

Recruited from Non-primary sector New Zealand workforce

End of year 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Industry tenure 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Age 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Region Canterbury Waikato

Role Farm assistant Herd Manager Farm / business manager

Qualification None Herd management

Which might mean a life story… Person A grew up the Canterbury region. He left school with NCEA Level 2 at the end of Year 12, and went to work in the hospitality industry. At the age of 19, he decides to give the dairy industry a go, and gets a job as a farm assistant in 2004. After working at the farm for 3 years, he decides to see more of the country and moves to the Waikato region. In 2011 he gets promoted to herd manager, and after a couple of years decides to upskill, successfully completing a herd management in 2013. With his new skills, and after receiving some money through inheritance, he talks to the bank and buys out his retiring employers’ stake in the farm, becoming a farm / business manager at the age of 29 years old.

Characterising replacement rate

37

Age Tenure 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

1 50% 50% 35% 35% 20% 20% 20% 20% 20% 35% 90% 2 35% 35% 20% 20% 20% 20% 20% 20% 20% 35% 90%

3-5 20% 20% 15% 15% 15% 15% 15% 15% 20% 35% 90% 6-10 10% 10% 10% 5% 5% 5% 5% 2% 2% 90%

10-20 10% 5% 5% 5% 5% 5% 2% 2% 90% 21+ 5% 5% 5% 5% 5% 2% 2% 90%

0%10%20%30%40%50%

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10Industry tenure (years)

15-24 year old workers

0%5%

10%15%20%25%

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10Industry tenure (years)

45-54 year old workers

Probability of a worker leaving the workforce in the next 12 months as a function of age and industry experience