Modelling Labour Supply : Issues and Challenges

Labour Supply Monitoring and Forecasting Workshop

Vancouver, British Columbia

October 17-18, 2007

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Context

Occupational supply modelling is currently done only at the national level

All demand side components (expansion and replacement demands) are currently produced at both the national and provincial levels

Interest has been expressed in creating provincial supply models

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Purpose of this presentation

Describe the current labour supply modelling of COPS Highlight the current issues and challenges (data,

models) surrounding the production of national level labour supply projections

Highlight the additional challenges in undertaking labour supply projections at the provincial level

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Current modelling

structure of COPS

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Uses and dissemination The ability to do forward-looking analysis is considered

essential in improving HRSDC’s capacity to deliver effective policy decision making. – Results from the projections are presented in “Looking-Ahead: a

10-year Outlook for the Canadian Labour Market”.

Results from the projections are also used in the development of planning and decision-making tools.– HRSDC provides data and projections to Service Canada for the

production of Job Futures. – Labour demand projections are also provided to the provincial

governments and Service Canada’s regional offices, which use them to develop their own specific LMI products.

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Aggregate Macroeconomic and Labour Market Indicators

Employment by Industry

RetirementsExpansion Demand

School Leavers

Demographics

Future Labour Market Imbalances by Occupation

Job openings versus new job seekers by occupation

Immigrants

Labour Force

Future Labour Market Imbalances by Broad Skill Level

Change in employment versus change in labour force by broad skill level

COPS modelling structure

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School Leavers Model

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Graduates

2. Graduates by major field of study

3. Allocate in or out of the labour force

4. School leavers allocated by occupation

1. Graduates by educational level

FOS1 FOS2 FOS3 FOSi

In LF Out LF

NOC1 NOC2 NOC3 NOCn

Drop Outs*

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-Graduates are projected by level of education-6 different levels of education are estimated, 2 are obtained residually:

• Secondary• Trade and vocational• College• Bachelor• Master• PhD

1. Graduates by educational level

),( Graduates )()(it TtiTti GraduatesEnrolmentsf

The level of enrolments is projected based on source population, as well as economic factors. Graduates are then estimated using enrolments.

Less than high schoolDrop outs

Some post-secondary

),,,,( Enrolments _t)(it itttTti PopSourceGov_fundsURRPDIPCEnrolmentsf

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2. Graduates by major field of study

FOS1 FOS2 FOS3 FOSi

Graduates

No field of study for the following: •Less than high school•Secondary school graduates•Some post-secondary

At the post-secondary level:•49 FOS for Trade and vocational•55 FOS for College•58 FOS for the three university levels (Bachelor, Master and Doctorate).

Graduates are distributed by FOS according to the observed average distribution of the last 3 years (PSIS/USIS/CCSIS).• Fixed proportion over the projection period.

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3. Allocate in or out of the labour force

By field of study, the share of graduates in the labour market is determined using:

– The Labour Force Survey for:• Less than high school (15-24 years old);• High school grads (15-24 years old);• Some post-secondary (20-29 years old).

FOSi

In LF Out LF– The National Graduate Survey for:

• Trade and vocational;• College;• University (Bachelor, Master and

Doctorate).

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4. Allocate by occupation

Data sources:A) For the 3 non post-secondary levels:

Labour Force Survey (LFS)B) For the 5 post-secondary levels:

National Graduates Survey– In 2002, graduates from 2000– In 1997, graduates from 1995

Other sources considered: Job Futures; Occupation descriptions from the NOC; Matrix of Skills Transferability by Margaret

Roberts (2003).

In LF

NOC1 NOC2 NOC3 NOCn

Ex-ante distribution: allocation not allowed if unrelated to FOS or skill level too low

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It is assumed that each school leaver will offer his/her service to jobs related to his/her education level and field of study E.g.: Bachelor graduates in nursing are constrained to occupy a job

such as: – Nurse Supervisors and Registered Nurses (NOC 315);– Other Technical Occupations in Health Care (NOC-323);– Assisting Occupations in Support of Health Services (NOC-341);– Manager in Health (NOC-031);– Medical Technologists and Technicians (NOC-321).

and not occupy jobs like:– Managers in Retail Trade;– Cashier.

4. Allocate by occupation

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Immigration model

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Immigrants

In LF Out LF

NOC1 NOC2 NOC3 NOCn

2. Allocate in or out of the labour force

3. Allocate by occupation*

1. Projection of the number of immigrants

*Allocation based on information for recent immigrants (from the latest census)

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Current issues and challenges surrounding the

production of labour supply projections

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Data challenges

Issues Timeliness Sample size/Reliability

Major data sources used in supply Administrative data (enrolments, graduations) National Graduates Survey Labour Force Survey

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Data issues - Timeliness

A) Administrative data (enrolments, graduations)

– Significant lags in data used to forecast enrolments and graduations :

• Data for high school is made available through the ESESP: latest data 2004/2005

• Data for trade and vocational programs is acquired from TVOC survey: latest data 1999/2000

• Data for community colleges comes from the CCSIS: latest data 1999/2000

• University level data comes from the PSIS survey and is spliced with the (discontinued) USIS survey: latest data 2003/2004

– We thus need to forecast the past before forecasting the future!

→ Challenge: To speed up the release of data

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Data issues - Timeliness

– Results depend on when the survey is conducted (economic cycles) :

• E.g.: using 2000 NGS information to distribute computer graduates among occupations might be misleading given collapse in IT jobs in 2002.

– Change in occupational coding every 10 years makes it difficult to use historical data (using many NGSs) .

B) National Graduates Survey

– Produced occasionally (5-year cycles):• Graduates of 1995 in 1997, follow-up in 2000• Graduates of 2000 in 2002, follow-up in 2005• Graduates of 2005 in 2007 (in process), follow-up in 2010 (planned)

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Data issues - Reliability A) Administrative data (enrolments, graduations)

The fields of study used in COPS are outdated:

– e.g. 6 secretary programs at the TVOC level.

B) National Graduates Survey

The NGS 2002 (class of 2000) has 38,483 respondents. When building the distribution matrix of (5 post-secondary education levels) X (49 to 58

fields of study) X (140 occupations) → very small sample. We use several NGSs to overcome small cell problems Some fields of study are easier to allocate (e.g. nursing) than others (e.g. English literature).

→ Challenge: Rework FOS according to CIP

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Data issues - Reliability

C) Labour Force Survey

The sample size of the LFS is about 54,000 households (accounts for approx. 83,000 respondents). To build the school leaver distribution matrix for the 3 non post-secondary education levels (140 occupations),

LFS data for youth is used. Analysis of the coefficients of variation undertaken in 2003 shows that 3-digit occupations data broken down by 33 industries is reliable at the national level. We use the average distribution of the latest three years.

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Supply modelling challenges

School leavers model Introduce dynamic in the distribution of graduates by field of study over

time. Redefine COPS fields of study (using CIP) Re-examine the current model structure based on research currently

undertaken Introduce supply adjustment using substitution between broad

occupational groups and skill level (ROA 2004)

Immigration model Re-examine the current model structure based on research currently

undertaken (Cristiana Pescarus)

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Other supply modelling challenges

Assessment of current labour market imbalances by occupation

Modelling of vertical occupational mobility (e.g. supply for managers)

Segmenting the labour market Assessing work intensity (e.g. hours worked, full- versus

part-time work)

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Provincial supply modelling: additional challenges

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Provincial supply challenges Data timeliness:

– The use of data collected by provincial Departments of Education can mitigate this problem.

Data reliability:– As we disaggregate further (in this case by province), the reliability of data

declines: see next slide– Options:

• Aggregate data (bundle FOS, educational levels or occupations)• Find alternate data source with larger sample size (Census, province-specific NGS)• Seek to increase sample size through investment in data

Model:– Need to model inter-provincial migration

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Data reliability Data reliability is an

issue at the provincial level.

It is difficult to produce projections when data have large standard deviations.

Standard deviation of the annual growth in the number of enrolments and graduates by province (in % points)

Type of university degree

RegionsEnrolment (1985-2004)

Graduation (1976-2004)

BA Bachelor's and other undergraduate degree

Canada 2.9% 2.3%

Newfoundland and Labrador

9.4% 7.9%

 Prince Edward Island 5.0% 13.1%

  Nova Scotia 3.2% 4.0%

  New Brunswick 3.1% 6.1%

  Quebec 2.0% 3.9%

  Ontario 4.7% 3.0%

  Manitoba 7.4% 4.7%

  Saskatchewan 3.0% 4.6%

  Alberta 2.7% 3.8%

 British Columbia 3.0% 4.5%

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Next Steps

At the national level – Continue to work on developing the supply model

• Introduce dynamic in the projection of FOS • Redefine COPS FOS• Re-examine the current model structure based on research currently underway

At the provincial level– Provide technical expertise to provinces in building supply models

• At the 2006 COPS Conference, supply models were presented and B.C. showed interest in modelling supply

– Caveat: Provinces interested in building a supply model should first focus their attention on data (availability and reliability).