Future Labour Supply and Demand 101: A Guide to Analysing ... FLMM... · Cyclical and structural factors influence labour demand, supply and workforce shortages. Cyclical factors

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Future Labour Supply and Demand 101: A Guide to Analysing and Predicting Occupational Trends

Prepared for:

Forum of Labour Market Ministers Labour Market Information Working Group

Prepared by:

The Centre for Spatial Economics 15 Martin Street, Suite 203 Milton, ON L9T 2R1

March 27, 2008

A Guide to Analysing and Predicting Occupational Trends Page i

Abstract The Forum of Labour Market Ministers (FLMM), Labour Market, Information Working Group (LMIWG) requires a user-friendly guide to analysing and predicting occupational trends. The guide has several sections to help stakeholders understand the rationale for and approach to developing occupational demand/supply models and forecasts.

About this Report This report was prepared by The Centre for Spatial Economics, a consulting organisation created to improve the quality of spatial economic and demographic research in Canada. The report was sponsored by the Forum of Labour Market Ministers (FLMM).

The authors accept all responsibility for any remaining errors or omissions. The views in this report reflect those of the authors and do not necessarily reflect those of the FLMM or its member agencies.

Questions or comments about this report can be sent to:

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The Centre for Spatial Economics 15 Martin Street, Suite 203 Milton, Ontario L9T 2R1 Canada

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Robert Fairholm (416) 346-2739 [email protected]

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The Centre for Spatial Economics

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Table of Contents

Executive Summary ....................................................................................................................... iii

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

Labour and Skill Shortages ............................................................................................................. 2

Demand, Supply and Causes of Labour Shortages...................................................................... 3

Canadian Cyclical and Structural Labour Shortages................................................................... 5

Labour Market Theories .............................................................................................................. 7

Why Forecast Labour Shortages ............................................................................................... 12

Occupational Models..................................................................................................................... 14

Developing a Modelling System ............................................................................................... 18

Occupation Modelling Approaches........................................................................................... 28

Sector Models............................................................................................................................ 39

Work Plan.................................................................................................................................. 42

References ..................................................................................................................................... 43

Appendix I: Labour Hoarding ................................................................................................... 52

Appendix II: Skills Transferability Matrix................................................................................ 57

Appendix III: Changes to AEII Model Dynamics..................................................................... 59

Appendix IV: Data Sources....................................................................................................... 61

Appendix V: Data Quality......................................................................................................... 72

Appendix VII: Essential Skills Profiles..................................................................................... 88

Appendix VIII: American Occupational Information Network ................................................ 89

Appendix IX: Stock-Flow Labour Supply Model ..................................................................... 91

Appendix X: Extrapolative Trends............................................................................................ 97


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Executive Summary Employers, education and training providers, individual students and workers themselves, all have an interest in trying to peer into the future in order to try to anticipate what may occur and to ensure that their own decisions result in the best possible outcomes—however these might be defined. The fact that considerable efforts to conduct such forecast are going on all over the world suggests that, on balance, such activities are regarded as very useful and worth substantial investment by the public sector. Therefore it can be concluded that forecasting of the labour market is inevitable. The only real question, is how this should be done.1

Hopkins (2002) indicates that manpower planning has, at its core, the problem of mismatch between labour supply and demand. To understand the focus on workforce shortages and skill gaps, it is helpful to define what these terms mean. A variety of definitions have been suggested. Roy, Henson and Lavoie (1996) define a skill shortage or gap as the divergence between the quantity of a given skill supplied by the workforce and the quantity demanded by employers at existing market conditions. Shah and Burke (2003b) suggest that a distinction needs to be made between the concepts of skills shortage and gap. They define shortage in a manner similar to Roy, et al. but suggest that skills gap refer to situations where employers are hiring workers whom they consider under-skilled or that their existing workforce is under-skilled relative to some desired level. Other researchers expand on the concept of shortage by differentiating between types of shortage, such as between an aggregate labour shortage and shortages due to a mismatch on the labour market. And Richardson (2007) distinguishes between shortages according to the ease by which they can be alleviated, based partly by the length of the training process.

Cyclical and structural factors influence labour demand, supply and workforce shortages. Cyclical factors reflect short-term fluctuations in aggregate demand and its influence on the demand for and supply of labour. The business cycle tends to last in the neighbourhood of five-to-seven years. In contrast to the relatively short-term influence that the business cycle has on the labour market, structural changes can last for decades. They reflect long-term developments that are occurring in the economy or society independent of short-term fluctuations in aggregate demand. Saunders and Maxwell (2003) indicate that labour markets in Canada will continue to be affected by three structural forces of change: technological advances, globalisation of competition and changes in the demographic structure of the workforce.

Cyclical and structural forces are contributing to aggregate workforce shortages in the economy currently. Canada’s unemployment rate is at 33 year lows, and the unemployment rate in many provinces is at multi-decade lows despite the aggregate participation rate being at record highs. Given the expected continuation of these structural forces, there is the prospect of worsening aggregate and specific labour shortages and skills gaps in the years ahead.

Unemployment and labour shortages are two sides of the same coin. In the first instance, there is excess supply of labour, in the second instance there is excess demand for labour. There are many economic theories that examine the workings of the labour market. The neoclassical view suggests the labour market should reach equilibrium relatively quickly and that there should be no unemployment and/or labour shortage. If these conditions persist, there must be institutional factors that are inhibiting the workings of the market. Other theories explore why the there should be unemployment and/or labour shortages.

McMullin, Cooke and Downie (2004) indicate that when confronted with a labour shortage, adjustments by firms include wage level adjustments, and attempts to move production or to attract workers from other locations, as well as providing training to the existing workforce. 1 Wilson (2001) p 564.


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Firms can also try to substitute away from the scarce labour towards other factors of production, such as capital and/or other types of labour. Adjustments on the part of workers generally include movement into areas of shortage, both geographically and in the sense of retraining to gain scarce skills. These adjustments to the labour market cannot take place instantaneously, however. A lack of information, the time required for training, and various institutional barriers to labour market adjustment mean that in a given place at a given time there will be gaps between the quantities supplied and demanded for particular skills. Problematic skill shortages are those which become protracted as a result of these institutional barriers to adjustment, resulting in inefficiencies.

Some of the basic dynamics indicated above have been questioned. Neugart and Schömann (2002a) review the literature on whether the labour market clears. They indicate that it is widely recognised among labour economists that market mechanisms can contribute to imbalances. From the perspective of the firm it may be optimal to pay a wage that is higher than the market clearing wage, if it provides the optimal mix of wage costs and induced effort to arrive at higher productivity or reduce turnover and therefore other costs. Regardless, higher wages create unemployment. There are people around who are willing to work under the prevailing conditions but who are not offered a job, because it simply is not to the advantage of the firms.

Neugart and Schömann (2002a) also indicate that although many would consider labour market imbalances temporary, the literature on multiple equilibria emphasises that economies may get stuck at various states depending on their histories. Some of these perhaps rather stable equilibria can be more desirable for a society than others. Finegold and Soskice (1988) described a mechanism that may lead to a low skill–low wage equilibrium. There are other, more advantageous equilibria. But for the individual there is no incentive to invest in education, as the payoffs will not materialise. Considerable efforts are required, efforts that only a powerful actor in the market can accomplish. That is where the government could come into play, perhaps by subsidising education or by avoiding labour supply shortages right from the beginning through forecasts and appropriate policies tackling upcoming skill shortages.

Neugart and Schömann (2002a) also indicate that if there are skill shortages and firms switch to filling high-skill vacancies with low-skill workers, productivity will be lowered. It may also happen that a shift in the bargaining strength towards workers leads to employment conditions that are less efficient for firms and that lower the productivity of the incumbent workforce. The former argument addresses productivity levels, but productivity growth also may suffer from skill shortages if firms reduce investments in research and development. The findings by Haskel and Martin (1996) suggest that skill shortages reduced productivity growth by 0.4 per cent per year in the United Kingdom between 1983 and 1989.

As indicated by the Canadian Council on Learning (2007) the labour market reaction to shortages can lead to what Heijke (1996) terms the ‘cobweb cycle’, where students base their educational decisions on the market at the time they enter a course, rather than the market anticipated at their time of graduation. For example, if wages are high in a certain field due to a shortage of employees, many students may respond by taking the courses necessary to enter that field. After a number of cohorts have done this, there is a surplus of employees and wages go down, causing new students to stop entering that field. At the end of the cycle, a new shortage in labour occurs, and wages again go up to attract more students. Accurate information about the labour market that is frequently updated and widely disseminated can help to avoid the pitfalls of this cycle.

Together the above strands of labour market research provide a rationale for occupational forecasting. The purpose of occupational forecasting is now widely perceived as providing a source of information for employers and potential employees regarding future labour market conditions by occupation and in relation to education. In particular, information should be directed towards identifying likely future imbalances between demand and supply across


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occupations in order to improve the investment decisions of employers and employees. Where occupational forecasts have been prepared for and used on this basis, there is evidence that this information has reduced the costs likely to have arisen from uninformed labour market decisions.2

This information is especially important when there are long ‘lead times,’ such as those for training in specialised and necessary skill areas such as medicine, technology or teaching. Shortages in skilled labour in these areas could conceivably contribute to longer-term social and economic problems, and forecasting helps reduce this risk (Neugart and Schömann (2002a)). Understanding how the labour market should be directed in order to meet social and economic objectives helps governments monitor and influence both the labour and education markets in a manner consistent with these objectives (Borghans and Willems, 1998). Such foresight improves the efficiency and cost-effectiveness of the labour and education markets.3

Occupational modelling and forecasting has a long history. The dominant approach is what is known as the manpower requirements approach (MRA). A review of current occupational modelling and forecasting practices around the world show that all groups that are engaged in detailed employment forecasting utilise, at least partially the MRA. According to Hopkins (2002), the three major steps in the MRA are: (a) projecting the demand for educated manpower; (b) projecting the supply of educated manpower; and (c) balancing supply and demand.

The early attempts to use occupational forecasting and integrate the results into educational planning used fixed-coefficient models that did not reflect structural changes in the labour market. This approach led to inaccurate forecasts and considerable criticism of the MRA.

The providers of occupational demand and supply models responded to their critics by trying to incorporate structural changes that influence the labour market. In order to improve accuracy, there has been a general reduction of the forecasting period from the very long term of 10 to 20 years to five to ten year forecasts. The time period, however, is long enough so that labour market participants can make decisions regarding education and training to benefit from the expected future labour market conditions. There has also been a change in how the forecasts are used. Forecasts are typically restricted to large classes of occupational groups that have large overlaps in required skills and that may reasonably be employed for broad policy-guiding purposes.4 Most groups now provide qualitative assessments of future labour market conditions, such as ‘poor’, ‘fair’ and ‘good’, as opposed to precise point estimates.

Partly due to the severe criticism of the initial approach, the detailed education planning function has all but disappeared. Van Eijs (1994) argues that today manpower forecasting is considered to have two functions: a 'policy function' and an ‘information function'. The policy function refers to the use of manpower forecasts as 'a point of reference' for policy recommendations for policymakers who have to take decisions on educational investments or other educational or labour market policies (Wilson, 1993). The information function is primarily intended to assist in occupational or educational guidance (Dekker et al., 1993), although it could also inform firms about possible future recruitment problems of workers with a particular education.5

Today occupational models and forecasts are used in countries throughout the industrialised world. Most of these modelling systems are national in scope. Sub-national components of these modelling systems are also available. There are many differences in how the MRA is currently implemented in practice. Not all groups execute all the steps outlined above, and several groups 2 Burns and Shanahan (2000). 3 Canada Council on Learning (2007) p 7. 4 Neugart and Schömann (2002b) p 2. 5 de Grip and Heijke (1998) p 1.


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have extended the steps to explicitly take into consideration separations, replacement demand, labour market indicators (LMI) and skills. Differences between approaches relate to data availability and quality, as well as the amount of resources committed to the task, and the ultimate need that the model or forecast fulfills. Many forecasting groups focus on occupations as opposed to education or skills, for example. To understand current practices thoroughly, please refer to Fairholm and Somerville (2005) for a discussion of international approaches and Fairholm (2006) for a discussion of practices throughout Canada. Other recent reviews of occupational models and forecasts includes: Strietska-Ilina (1999), Papps (2001), Wilson (2001), Hopkins (2002), Neugart and Schömann (2002b), and Boswell, Stiller and Straubhaar (2004).

Since these reviews were conducted there have been very few new developments in the field of occupational demand/supply models internationally, although now the use of occupational models has spread into eastern Europe, with forecasts being done in the Czech Republic, Estonia, Poland, and Romania. There is also an initiative to develop a pan-European occupational model and forecast. In Australia, there are reports that the Centre of Policy Studies has been working on supplementing the labour supply side of the model, but the details are not available currently. In Canada, most groups report no change. Emploi-Quebec has supplemented their detailed 5-year forecasts with a less detailed 10-year forecast. The 10-year forecast has less industrial, regional and occupational detail. And work is underway in British Columbia and Saskatchewan to develop education models. The BC model is particularly ambitious project to model the education system at a very detailed level. These models, however, are currently being constructed. The Alberta model, however, has evolved in a number of areas. In 2006, labour market indicators were included in the Alberta model. In 2007, changes to dynamic structure of the model were incorporated by having occupational or career choice reflect changes in relative demand. And Alberta Employment, Immigration and Industry (AEII) is planning further enhancements.

In developing and using a modelling system a number of factors must be taken into account. These factors include the nature of the modelling system, expected accuracy, consistency, transparency, availability of resources and the nature of the data available to develop and use the methodology. When considering an occupational modelling system, it is also important to recognize that no one approach is ideal for all modellers. Practioners are confronted with many inherent tradeoffs between data sources, modelling and forecasting approaches. Some choices are better for some groups than others depending on their needs. Furthermore, organisations have different resources that they can devote to occupational modelling and forecasting.

Generally larger and more sophisticated modelling systems require more resources to develop, maintain and utilise than smaller, simpler occupational modelling systems. A larger or more sophisticated approach is not necessarily the best approach for an individual practitioner. Often it is time and cost effective to start with a simple model that will produce useful information near the start of the process and then augment the model over time as resources permit.

Many of the steps of the MRA can provide useful information to policymakers and labour market participants and so the system can be developed in a stepwise manner. For example, the development of an expansion demand forecast will provide information as to where total job growth and decline will be most pronounced. The development of a replacement demand model will provide further information as to what occupations will have job openings in the future. And an extension of the model to illustrate the educational requirements of occupations will provide information as to what types of education will likely be needed in these jobs in the future.

The development of the supply side of the model will help to identify where there will likely be gaps between demand and supply in the future. This step will illustrate what type of conditions labour market participants and/or new entrants will experience in the future. The development of labour market indictors translates the demand/supply information into easy to understand


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summary measure(s) that can be used by workers, new entrants and firms. Indicators can also provide information about the likelihood of the forecast coming true.

Occupational modellers have to consider the data as well as the modelling approach that will be used. In terms of data, three aspects must be considered: 1) data availability, 2) data coverage, 3) data quality. Not all of the concepts that are discussed in the occupational modelling literature are necessarily available from Canadian data sources. Some data are available from the census once every five years, while other data have experienced changes in classification and are available for brief historical periods. For data concepts that are available, not all data are available at the level of detailed that is required by the occupational modeller. Third, there is the issue of data quality. Not all data sources provide same degree of accuracy.

From the perspective of Canadian data sources, the core national, provincial and territorial data for these models are from the System of National Accounts (SNA). In addition to the expenditure, income and production accounts, the translation of final demand categories to industry output often rely on input-output accounts that are also developed as part of the SNA.

For employment and labour force concepts most occupational models and forecasts use census and/or labour force survey (LFS) type data sources. The Canadian census and labour force survey each have employment and labour force data.

Educational qualifications in terms of level of schooling by labour force and employment are available from the LFS and the census. The census data also has information by field of study for employment, labour force and population. Educational qualifications by occupation data are available from the census. The National Graduates Survey (NGS) has data by educational qualifications and occupation for postsecondary school graduates. Some provinces have their own student outcomes surveys.

A possible extension to an occupational model would be to determine the skills and knowledge required for various occupations. This step is undertaken in a number countries including: the US, the UK and Australia. Since the Canadian HRSD NOC classification is structured by skill and education level, forecasts by NOC categories implicitly includes an aspect of skill forecasting.

In most cases, the historical population estimates that are typically used for occupational demand/supply modelling systems are from annual demographic data. In some cases, the population data come from that country’s census as opposed to the postcensal estimates that are available in Canada. If the objective is to provide the most accurate estimate of population by age/sex cohort, then there are no data sources that would be more accurate or timely than the Canadian annual demographic statistics or equivalent data from a provincial statistical agency.

Most models produce an aggregate trend or potential labour force estimate. Often this step is accomplished by estimating trend participation rates by age/sex cohorts and applying them to the demographic projection. The estimation of trend participation rates is usually done using LFS data, since detailed age/sex specific labour force and population data are available. Alternatively, census data could be used. Demographic projections are either done in-house using the most recent demographic data or by using externally produced demographic projections.

There are three types of new entrants that Canadian provincial occupational modelling groups should take into consideration: school leavers, inter-provincial migrants and international immigrants. The development of a school leavers’ model requires the construction or utilization of an education model. There are two basic sources of education data that can be used to develop an education model. Provincial groups can either use the data that are available from Statistics Canada or can use provincial sourced data to construct their education models.


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When selecting the levels of schooling and fields of study, the modeller must take data availability and occupational outcomes into consideration. It is not possible to determine the precise number of categories that will be feasible to model prior to an examination of the data.

Most models use only demographic data to determine migration flows. This approach implicitly assumes that the occupational outcome for migrants is the same as for other members of society. While this may be appropriate for interprovincial migration, the research on international immigrants clearly shows that the occupational outcomes are significantly different for these groups than for the resident population. The Canadian Occupational Projection (COPS) uses a distinct immigrants’ sub-model. The occupational outcomes data for non-student immigrants over the previous five years comes from a special census table. It may be possible to supplement the census information with data from the immigrant databank or the linked data from the immigrant databank and the LAD. There are also data from the census for interprovincial migrants.

Net labour market re-entrants are implicitly included in many occupational models via the participation rate forecast. This step is typically not performed explicitly because of a lack of data. The ROA model uses calculations of net re-entrants by using the cohort component method to determine net labour market flows.

There is a lack of data on detailed labour flows. The census and LFS record levels, but not flows between labour force states for between occupations within the labour market. This step would require a survey similar to the Survey of Labour Income Dynamics (SLID) that follows people over an extended period of time. SLID, however, does not have a large enough sample size to determine flows for detailed occupational groups. The preferred course of action depends on how the data are going to be utilised. If the objective is to determine gross out-flows, then the best course of action is to examine gross outflow data from other surveys, such as the SLID, and apply them to all the associated sub-groups. If net out-flows are a sufficient concept, then single year participation rate data from the LFS can be used to estimate net outflows using the approach suggested by Boothby (1995b).

Deaths by occupation are commonly estimated by applying aggregate death rates by age/sex groups to occupational employment. This approach will obviously understate separations caused by deaths for those occupations that are riskier than average, such as firefighter, while overstate separations caused by deaths for occupations that are less risky than average, such as economist. One way to improve these estimates would be to obtain data on mortality by occupation. There may be data available from Workmen’s Compensation, for example.

Typically no data are used to estimate out-migration beyond the demographic statistics. It would be possible to estimate the occupations and/or the educational attainment levels of interprovincial out-migrants from the census data. International emigration, however, is more difficult, since there are no exit surveys. There are data collected on immigrants to the US from Canada, but these data may misrepresent the situation. So there is no ideal solution for this problem.

Throughout the world, a large variety of data are used to construct labour market indicators (LMI). These indicators can be divided into three groups: indicators that use historical data only, indicators that use forecast data only, and indicators that use a combination of history and forecast data. Several groups construct a number of different indicators to illustrate different aspects of labour market conditions. Given the difficulties in determining whether an occupation is currently in excess demand or supply and the magnitude of the imbalance, it would be prudent to include a variety of historical data in the estimate of current labour market conditions.

Many different methods are used to provide an industrial employment outlook. In many instances, a macro-econometric model is utilised, which provides industrial employment as part of an internally consistent macroeconomic forecast. This process typically has several steps. First,


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a view of overall economic growth is formed, often based on a survey of forecasters. Second, a macro model is used to provide a detailed set of final demand categories. Third, an input/output matrix is used to translate the final demand categories into industrial output. Fourth, employment by industry is calculated through the use of estimated employment equations. In many cases average hours are also calculated to translate person hours worked into the number employed.

Occupational employment is influenced by two factors: employment changes between industries and occupational employment changes within industries. The first of these two factors affects the overall occupational employment levels if employment shifts among industries that have different occupation patterns. The second factor will directly affect occupational employment and, therefore, the overall occupational composition of employment.

A considerable number of techniques are used to forecast the changing composition of occupations within industries. These range from simple extrapolation to more sophisticated regression techniques. The choice of technique depends on data availability and resources committed to the exercise.

At this point in time most models do not explicitly forecast skills. Although a number of models forecast employment by educational qualifications to some level of detail. One approach combines qualitative and quantitative methods. A qualitative analysis is then undertaken for each profession and quantitative forecasts are made of future qualification requirements. The quantitative scenarios are adjusted to reflect the qualitative assessments.

Most models produce an aggregate trend or potential labour force estimate. This outlook typically is comprised of two distinct components: the demographic and the labour force participation rate projections, which means that there is no interaction between the economic, demographic and participation rate scenarios. These projections are combined to determine total trend or potential labour force in order to determine the upper limit to total labour supply. Some groups use stock flow models to track inflows and outflows from the labour force.

Education models are often developed outside the occupational modelling systems. Occupational modelling groups typically obtain forecasts from education ministries in their country or use models that are maintained by third parties. The transition from school to work often uses a transition matrix. Very few models explicitly include the occupational outcomes for migrants.

Replacement demand is calculated in a number of different international models. While there are many similarities among the models, there are also many differences. In some instances, replacement demand is calculated outside the model. There are two basic approaches that can be used to modelling separations. Either total or net separations can be estimated. The choice between these two approaches will depend on the available data, the required level of detail and how the information will be used. Total separations will illustrate the number of jobs that are available, while net separations illustrate the number of jobs available for newcomers. For both calculations there is the need for the age distribution of employment because separation rates vary across age cohorts. Ideally the data should be differentiated by age/sex cohorts, since there are also significant differences between genders.

Given the sheer size of a detailed occupational demand/supply model, most occupational modelling systems use distinct occupational demand and supply models, and there is no interaction between the two. No model determines detailed occupational demand and supply in a simultaneous manner. The Dutch model, however, uses an iterative modelling approach that includes passive and active substitution effects.

There are a large number of current and future labour market indicators (LMI) that are used throughout the world. Some of these LMI rely on historical information, others utilise forecasted data, and a third type combine historical and forecast data. For the international models, COPS,


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BLS and the ROA have very specific methods to develop indictors of labour market imbalances. In Canada, innovative approaches are used to construct LMI at the provincial level. In most cases numerical indicators are translated into qualitative assessments of future conditions to make it easier for users to understand the results and to avoid giving false sense of precision.

The work plan for the development and implementation of the modelling system involves a number of steps. The development of the expansion demand, replacement demand and supply components can be carried out separately or at the same time if sufficient resources are available.

It is recommended that resources be devoted to the development of the demand components first. This component is relatively easy to develop and requires much less development time than is the case for the supply component. With it developed, an occupation demand projection can be produced that would provide useful information to policymakers. This information would show the value of undertaking the development of the system and provide support for the completion of the replacement demand and supply components.

The suggested steps for the system are separated into occupational demand, replacement demand and supply components. Nevertheless, the first step is the choice of the occupations to be included in the system. Without this choice neither component can be developed.

Demand Model

1. Choose the industry detail for the employment model; 2. Set up the industrial employment forecast method; 3. Set up the occupational by industrial forecast approach; 4. Test the method by producing a projection; 5. Make modifications where necessary; and 6. Prepare an occupation demand projection.

Replacement Demand and Supply Model

1. Decide on calculation of total replacement or net replacement demand; 2. Calculate flow coefficients from historical data for replacement demand; 3. Set up the stock flow model for labour supply; 4. Develop education sub-model; 5. Calculate remaining flow coefficient for labour inflows; 6. Construct Labour Market Indicators; 7. Test the method by producing a projection; 8. Make modifications where necessary; 9. Prepare an occupation supply and replacement demand projection; and

Calculate Labour Market Indicators of future labour market prospects.


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Introduction The Forum of Labour Market Ministers (FLMM), Labour Market, Information Working Group (LMIWG) requires a user-friendly guide to analysing and predicting occupational trends. The guide has several sections to help stakeholders understand the rationale for and approach to developing occupational demand/supply models and forecasts.

The first section explains that manpower planning has, at its core, the problem of mismatch between labour supply and demand. The section goes on to define what is meant by workforce shortages and skill gaps. Second section discusses some of the causes of workforce shortages. The third section examines the labour market situation for Canada from a cyclical and structural perspective. In the fourth section, the report includes a framework for understand labour supply and demand dynamics from the perspective of labour market theory. The fifth section of the report explores the rationale for manpower planning. The sixth section of the document describes the dominant occupational modelling approach and recent developments in the implementation of this approach. The seventh section examines some issues in the development of an occupational modelling system including a discussion of data limitations, resources, complexity and time commitment, benefits of simple and stepwise approaches. This section also includes a detailed examination of Canadian data sources. The eighth section of the document provides a set of recommended approaches to develop a modelling system to analyse future occupational supply and demand trends and workforce gaps. Finally, a work plan is included that could be used to build and implement an occupational modelling system.



Labour and Skill Shortages Hopkins (2002) indicates that manpower planning has, at its core, the problem of mismatch between labour supply and demand. To understand the focus on workforce shortages and skill gaps, it is helpful to define what these terms mean. A variety of definitions have been suggested in the literature. The following will discuss the definitions proposed by Roy, Henson, and Lavoie (1996), Shah and Burke (2003b), Boswell, Stiller and Straubhaar (2004), and Richardson (2007).

Roy, Henson and Lavoie (1996) define a skill shortage or gap as a divergence between the quantity of a given skill supplied by the workforce and the quantity demanded by employers at the existing market conditions. In order to get more precise and empirically relevant definitions, they make explicit the meaning of terms ‘skill’, ‘existing market conditions’ and ‘requirements.’ In their view, ‘requirements’ relate to the skills employers normally utilise whereas the existing level of compensation and the structure of wages to be paid to suppliers of these skills correspond to the ‘existing market conditions.’

Shah and Burke (2003b) suggest that a distinction needs to be made between the concepts of skill shortages and skills gap and recruitment difficulties. In their view, a shortage occurs when the demand for workers for a particular occupation is greater than the supply of workers who are qualified, available and willing to work under existing market conditions, and if the supply is greater than demand then there is a surplus. In comparison, a skills gap refers to a situation where employers are hiring workers whom they consider under-skilled or that their existing workforce is under-skilled relative to some desired level. Recruitment difficulties refer to the situation when employers cannot fill vacancies in spite of an adequate supply of workers. The reasons for this may be varied, and could include: relatively low remuneration being offered, poor working conditions or image of the industry, unsatisfactory working hours, commuting difficulties, ineffective recruitment effort by the firm or skills needs that are very specific to the firm.

Boswell, Stiller and Straubhaar (2004) distinguish between two types of shortage – aggregate labour shortage and shortages due to mismatch on the labour market.

1. Aggregate labour shortage. This occurs where there is (near) full employment, and a general difficulty in finding workers to fill vacancies.

2. Mismatch on the labour market has four basic sub-types. • Qualitative mismatch. The qualifications of workers and the qualification needed to fill

available vacancies are not matched. Qualitative mismatch may also be referred to as skills shortage, describing a labour market situation in which there is a lack of people with the qualifications, skills or experience necessary to carry out the jobs in question.

• Regional mismatch. The unemployed persons seeking work and firms offering suitable jobs are located in different regions, and the jobs and/or workers are immobile.

• Preference mismatch. This refers to a mismatch between the types of jobs that unemployed people are willing to take on, and existing vacancies in the relevant region. Those out of work are unwilling to take certain types of work because of inadequate remuneration or working conditions or status, despite the fact that such jobs match their qualifications and skills profile, or are located in the relevant geographical region.

• Information mismatch. Unemployed workers do not acquire information on relevant existing vacancies, and firms do not have the information necessary for finding persons with adequate qualifications. Supply does not meet demand because of the lack of information.

According to Richardson (2007), the idea of a ‘shortage’ is a slippery concept because the supply of workers with a particular skill is difficult to measure for several reasons, including:



• It is not just the number of people, but also the number of hours they are willing to work; • Within an occupation, there may be specialised sub-sets of skills or locations that are having

difficulty recruiting while other areas are not; • Vacancies may go unfilled, not because no-one is available who can do the job, but because

the wages and conditions on offer are unattractive; • Within every skill group, there is a range of ability, from exceptional to ordinary. This

variation in quality is important to employers, but not observable in measures of labour supply;

• Many people work in jobs that do not directly use their formal qualifications, or are of working age but not seeking employment.

In order to highlight the severity of the shortage, she suggests that training must also be taken into account and suggests the following scheme for classifying skills shortages:

Level 1 Shortage

• There are few people who have the essential technical skills who are not already using them AND there is a long training time to develop the skills;

Level 2 Shortage

• There are few people who have the essential technical skills who are not already using them BUT there is a short training time to develop the skills;

Skills Mismatch

• There are sufficient people who have the essential technical skills who are not already using them, but they are not willing to apply for the vacancies under current conditions.

Quality Gap

• There are sufficient people with the essential technical skills, not already using them, who are willing to apply for the vacancies, but who lack other some qualities that employers think are important.

Richardson (2007) makes an additional distinction. This is between workers who do not have the essential technical skill and workers who are judged not to have the degree of motivation and other personal characteristics that the employers desire. She also points out that a ‘Level 1 Shortage’ is more serious for firms to overcome and requires planning within the training system.

Demand, Supply and Causes of Labour Shortages Cyclical and structural factors influence labour demand, supply and workforce shortages. Cyclical factors reflect short-term fluctuations in aggregate demand and its influence on the demand for and supply of labour. The business cycle tends to last in the neighbourhood of five-to-seven years. The next section of the report will examine the cyclical situation of the labour market. In contrast to the relatively short-term influence that the business cycle has on the labour market, structural changes can last for decades. They reflect long-term developments that are occurring in the economy or society independent of short-term fluctuations in aggregate demand.

Saunders and Maxwell (2003) indicate that labour markets in Canada will continue to be affected by three principal forces (or structural factors) of change: technological advances, globalisation of competition and changes in the demographic structure of the workforce.

Technological change impacts the labour market in a number of ways according to Saunders and Maxwell (2003). They contributed to a shift in Canada’s industrial structure away from primary



and manufacturing industries towards services. Technological change also increases the demand for highly skilled work relative to that for less skilled work, a phenomenon referred to as ‘skill-biased’ technological change. The importance of technological change has been mentioned by various other researchers, see Krugman (1994) and OECD (1994) jobs report. Briscoe and Wilson (2001) found that there was strong evidence that technological change was having a very significant impact on the occupational structure in the UK.

Another major structural force at work in the economy is globalisation. According to the Heckscher-Ohlin model, countries that have a relative abundance of a particular factor of production should export products than use that factor relatively intensively. For example, a capital-abundant country will export capital-intensive goods, while a labour-abundant country will export the labour-intensive goods. Land (including natural resources), labour and capital are the factors of production. High and low-skilled labour can also be differentiated. Accordingly, a number of researchers have suggested that globalisation will cause a change the mix of skills needed in advanced economies, with an increase in the need for high-skilled labour and a reduction the demand for low-wage occupations. Borjas and Ramsey (1994a, 1994b, 1995) and Borjas, Freeman and Katz (1997) found that globalisation had a significant negative effect on low-wage workers in the US. There is also evidence that globalisation has contributed to a reduction in wage differentials across countries for labour of similar skill, but has (along with technological change) led to an increase in wage inequality between lower and higher skill levels within high-wage countries. (Chaykowski and Gunderson, 2001, pp.33-34.)6

Demographic developments will also impact workforce shortages. As indicated by OECD (2003), throughout the industrialised world, demographic developments imply an ageing workforce and ultimately a declining population of working age. There is the possibility that these developments will result in labour shortages at the macro-level. In the medium term, as early as 2015 for some countries, the increasing number of retiring baby-boomers will in some occupations lead to a replacement labour demand that may be hard to fill from domestic labour supplies.

Wilson et al. (2006) indicate that key drivers of changing skill requirements include: • Technological change - especially information and communications technology (ICT), which

is resulting in increased demands for IT skills across a range of sectors and occupations; • Competition and changing patterns of consumer demand - which have increased the

emphasis on customer handling skills; • Structural changes - including globalisation, sub-contracting and extension of supply chains,

emphasising the need for high quality managerial skills (across a greater range than previously and at a greater depth) at various levels;

• Working practices - such as the introduction of team- or cell-based production in engineering, and call centres in financial services, resulting in increased demand for communication and team working skills; while more generally there has been an increase in labour market flexibility; and

• Regulatory changes - as well as increased concern about environmental issues, which have made important skill demands upon staff for some key sectors, including construction and finance; (survey evidence suggests that regulatory/legislative change is a particularly important driver of skills change in the public sector).

6 Saunders and Maxwell (2003).



The economic literature on labour or skill shortages suggests a number of causes.7According to the OECD (2003), the different causes of labour shortages can be summarised as follows:

• Technological change may lead to a structural shortage of workers in possession of the needed skills: workers neither had the time nor the opportunity to invest in these skills.

• Slow adjustments in the labour market may cause shortages. It takes time for employers to recognise labour shortages and to react to them, for example by offering higher wages. It also takes time for workers to recognise better opportunities elsewhere and to react to them. Employers may be reluctant to raise wages or are tied to collective agreements or inflexible remuneration structures.

• Mismatch: wrong education investment decisions resulting in too few engineers, scientists and doctors, for example.

• Insufficient regional labour mobility. • Institutional or demographic causes: a high number of people in retirement or invalidity

pensions, low female participation rates.

Canadian Cyclical and Structural Labour Shortages Current workforce shortages are acute throughout Canada. According to the Conference Board’s annual Compensation Planning Outlook survey, in 2007 Canadian organisations felt the most intense recruitment and retention pressure on their workforce since 2001. Almost three-quarters of organisations surveyed are experiencing difficulty recruiting or retaining employees with particular skills, a sharp increase in the past two years.8

From a cyclical perspective, Canadian employment experienced moderately strong growth of 2.0% on average from 2002 to 2007 compared with a 25 year average of 1.7%. The recent pace of employment growth exceeded the rate of increase in the source population for the labour force, which means the employment-to-population ratio (also known as the employment rate) has been rising. The national employment rate reached a historic high of 61.7% in 2007, compared with the 25 year average of 58.5%. Despite a rise in the national participation rate to a historic high of 67.6% in 2007, the national unemployment rate reached a 33 year low of 6.0%.

Many provinces experienced employment rates at historic highs in 2007, with the noticeable exception of Ontario. High employment rates have encouraged more people to look for work. Many provinces experienced a high participation rate in 2007, with most provinces having participation rates at or near the highest ever recorded. Despite high participation rates, the total unemployment rate in most provinces is at multi-decade lows. One consequence of such low unemployment rates combined with high participation rates is that employers in many regions and sectors are experiencing difficulties finding workers with the skills they need.

These cyclical shortages are being made worse structural factors that are directly contributing to labour shortages. This combination of cyclical and structural factors has resulted in an atypical dynamic in the labour market during the current business cycle. As is discussed in Appendix I, there is evidence that firms are hoarding labour given the prospect of worse labour force shortages in the future. For firms this implicit bargain to boost headcounts and reduce labour utilisation is rational if fixed labour costs are high or rising compared to variable labour costs. For employees this is a rational choice if there has been a change in work/life balance preferences.

7 See Roy, Henson and Lavoie (1996) p 12. McMullin, Cooke and Downie (2004) p ii. Saunders and Maxwell (2003). And OECD (2003). 8 Go2 website. “Labour shortages up the ante on recruitment and retention”.



The reason that firms fear worse workforce shortage in the future could be because of demographics. As was mentioned in the section on causes of labour shortages, demographics can exacerbate workforce shortages. Canada’s society is aging because of a low fertility rate since the early 1970s, rising life expectancy and the shadow that the baby boom generation continues to cast on Canada’s demographic situation. People born at the forefront of the baby boom around 1950 are approaching 60 years of age. People born at the peak of the baby boom around 1961 are 47 in 2008, and will reach their mid-50s in less than a decade. As the baby boomers age, the proportion of the population aged 55 to 64 and 65 years and over is increasing, while the share of those in the prime-aged workforce from 25 and 54 years is shrinking.

The speed and magnitude of this demographic change is evident by considering that seniors in 1981 were 10% of the total population. By 2005, their share had increased to 13%, a rise of 3% over 24 years. According to the medium population projection by Statistics Canada published in 2005, the share of seniors will reach 16% in 2016 and 19% by 2021, an increase of 6% in 16 years. Similarly the share of those 55 to 64 will rise by 3% between 2006 and 2021, while the share of those aged 25 to 54 will decline by 4%. This latter group represents those in the prime aged workforce. In this population scenario, the number of people in the prime workforce aged population will slow from 1.0% on average from 1991 to 2006 to 0.3% from 2006 to 2011 and to 0.0% over 2011 to 2016, and expand by 0.1% from 2016 to 2021.

Notably, Statistics Canada’s medium population scenario assumes that the fertility rate will stabilise around 1.5 and immigration levels will increase from 225 thousand over the 15 years from 1991 to 2004 to 280 thousand by 2031. A more conservative projection is contained in the low population scenario, with the total fertility rate continuing to fall at roughly the same average rate as observed over the past 30 years through 2016 before stabilising around 1.3. The immigration assumption is also more conservative, with total immigration falling from 229 thousand in 2006 to 204 thousand by 2031. Under this scenario the number of those in the prime-aged workforce, will grow by 0.2% on average over 2006-11, and then shrink over 2011-16 by 0.2% on average and fall a further 0.2% on average over 2016-21. Clearly, without significantly higher international immigration than is currently occurring, the number of people in the prime aged workforce will shrink over the coming 15 years.

Labour force participation rates drop significantly once people reach their mid-50s. According to the Labour Force Survey (LFS) for 2007, the participation rate drops from 86.6% on average for the prime aged workforce to 60.1% for the cohort aged 55-64, and 8.9% for those 65 years and over. Assuming that age/sex specific labour force participation rates remain at 2007 levels, then the medium population scenario implies that the total labour force will slow from 0.8% on average from 2006 to 2011, to 0.4% from 2011 to 2016 and 0.2% from 2016 to 2021 and 0.1% on average from 2021 to 2026. Meanwhile the low population scenario implies that the labour force will expand by 0.2% on average over 2011-16, and then start to shrink. Over 2016-21, the labour force would drop by 0.1% on average and fall by another 0.2% on average over 2021-26, and by 0.3% on average over 2026-31.

Not only do people in the older age cohorts participate less in the labour market, they also provide significantly fewer hours of work per person employed. According the 2007 LFS data, those in the prime aged workforce provide 35.7 hours of work each week, while those in the 55-64 and the 65 and over age cohorts provide 33.9 and 27.5 average hours of work respectively. By assuming that employment-to-population ratios and average work hours remain constant at 2007 levels by age/sex cohorts an estimate of the total hours worked can be constructed that reflects future demographic changes. These calculations show that using the medium population projection, the number of hours worked would slow from 0.6% growth on average from 2011 to 2016, to 0.4% on average from 2016 to 2021, and to 0.0% growth on average from 2021 through 2031. Using the low population scenario total hours worked in the economy would continue to expand through



2021, but afterwards there would be a decline in the total hours worked by 0.3% on average from 2021 to 2026 and 0.4% on average from 2026 to 2031.

The combination of the aging of the baby boom generation and significantly lower labour force participation and average hours worked as people age, means that there will be a significant slowing if not an outright reduction in available labour as the baby boomers reduce their involvement in the labour market. There is, therefore, the possibility that workforce shortages will increase in the future simply because so much labour will need to be replaced.

It is also important to note that the major difference in the working age population estimates and therefore the labour force and hours worked calculations between the low and medium population scenarios is because of immigration. Differences in fertility rate assumptions between the two scenarios have no impact on the 25 to 54 age cohort over the next 24 years. Similarly, differences in the fertility rates only impact the labour force’s source population in 15 years. Clearly the dominant difference in these scenarios through 2030 is caused by the migration assumption.

The reliance on immigrants to sustain labour force growth also has implications for workforce shortages, since as discussed by Fairholm and Somerville (2005) there has been considerable research that shows that an immigrant’s human capital obtained in the country of origin may not be fully transferable to the host country. The extent of the transferability of human capital between two countries depends on an individual’s type of skill and the similarity of the sending and receiving countries with regard to language, culture, labour market structure and institutional settings (see Chiswick, 1978, 1986).9 Chiswick (1977) analyzed U.S. immigrants and found that they initially experience downward occupational mobility compared to their occupation in the home country. With time of residence in the U.S., however, the migrants improve their occupational status. Similarly for Germany, Bauer and Zimmerman (1998) show that higher education is less transferable than lower levels of education. Furthermore, they found that language skills are important in determining the loss of human capital.

Given that Canada is a high wage economy, globalisation and technological change imply that high-knowledge occupations will be in the greatest relative demand. Considering the age structure of the baby boom generation, there will be significant retirements from all occupations in the years ahead, including many high-knowledge occupations. Given the short-term drop in immigrants’ human capital in host countries, these high-knowledge occupations will typically not be immediately filled by new immigrants. Therefore there is the distinct possibility that there will be significant labour shortages in high-knowledge jobs in the future, at least in the short term while immigrants are integrated into the labour market.

Labour Market Theories Unemployment and labour shortages are two sides of the same coin. In the first instance, there is excess supply of labour, in the second instance there is excess demand for labour. Hopkins (2002) examined seven main groups, namely classical theorists; neoclassical theorists; social reformers; latter-day development economists; monetarists; more recent development economists who are institutionalist in persuasion such as the segmentation theorists; and recent views of the labour market. Rather than examine all of these views of the labour market, a few of these theoretical views will be discussed below as they relate to unemployment and/or shortages.

Neoclassical economic theories underline the importance of market forces in bringing systems into equilibrium. All markets are cleared through price adjustments and reach equilibrium relatively quickly. Labour is just like any other commodity and is subject to the laws of supply and demand that themselves can be derived from the marginal productivity of labour and from 9 Bauer and Zimmermann (1998) p. 7.



the marginal utility of leisure and in conjunction with optimisation over other decisions such as how much to save. Unemployment occurs because the price of labour (wages) relative to the price of capital (interest rate) is too high. If labour reduces its price it will be absorbed. If labour is not absorbed, it implies that there are other factors at work. According to the neoclassical view the factors that prevented the absorption of labour can be found in examining institutional barriers to the setting of wage rates.10

The persistence of unemployment led to the notion of the 'natural rate of unemployment', which is that level of joblessness that is warranted even where the market is working 'ideally' because, for example, of the need for workers to look for new jobs or because of the random and unforeseen shocks to the economic system to which adjustment needs to be made. The natural rate of unemployment (NRU) was also originally identified as that point at which inflation would not accelerate or decelerate, along the vertical part of the Phillips curve. The persistence of unemployment and inflation in the 1980s led to the notion of the non-accelerating inflation rate of unemployment (NAIRU).11

As discussed by Hopkins (2002), in the segmentation model, educational differences in the labour market can lead to a 'cascade' model where the highly trained currently unemployed replace the less qualified and the latter in turn replace people less qualified than they, and so on. Educational differences in the labour market can also lead to 'mismatch'. The mismatch that has received most attention to date is the use of educated persons in positions that could be filled at lower oppor-tunity cost by others. The problem involves the relationship between the educational and occupational levels of labour force members, and the degree to which the education system is providing workers with the appropriate skills. All this depends on the composition of the demand for labour at some future point in time.

Fine draws five main conclusions from contemporary labour market theory. First, it is inappropriate to examine labour markets in terms of equilibrium economics since there is no reason to presume that the forces that operate within labour markets interact harmoniously and efficiently to grind out equilibrium levels of employment and associated working conditions. Second, labour markets are differentiated from one another, giving rise to empirically recognisable labour market segments or structures even though segmentation theory has tended to proceed in terms of divisions across the labour market as a whole and even though these divisions are perceived to be shifting and overlapping. Third, these labour market segments can be derived from 'horizontal' and 'vertical' factors. The former refers to determinants that prevail across all sectors of an economy, such as differentiation by gender and skill and the latter refers to the structuring within particular sectors of the economy. Fourth, with the rejection of equilibrium, it is necessary to demonstrate how labour market structures are socially reproduced, transformed or developed historically. This latter point does not mean degeneration into empiricism in which structure becomes identified mainly with large differences in behaviour as long as the structures are shown to incorporate underlying socio-economic factors in an integral fashion. Fifth, labour market structures need to be derived from those socio-economic factors that arise out of the division between capital and labour and out of the profit imperative. This, in turn, implies a particular analytical and causal structure to labour market analysis, in which labour market structures are the reproduced and complex outcomes of the capital-output relation and its associated tendencies, such as productivity increase, deskilling, monopolisation and so on.12 As indicated by Hopkins (2002), the persistence of unemployment has led the neoclassicals to wonder why firms do not drop their wages, so that it becomes worthwhile for them to employ the

10 Hopkins (2002) p 32 & 50. 11 Hopkins (2002) p 45. 12 Hopkins (2002) p 49-50.



extra workers. One reason is that unions prevent the firm paying less and it is noticeable that unions tend to protect those 'in' work rather than those 'out' of work - unemployment is a problem for most unions in the sense that those in work may become unemployed. A second reason is the 'efficiency wage' explanation. A firm can hire all of the labour it wants at the wage it chooses to offer so as to maximise its profits by offering the real wage which satisfies the condition that the elasticity of effort with respect to the real wage equals unity. This condition has become known as the Solow condition and the wage offered the efficiency wage since it minimises labour costs per efficiency unit (surveyed by Yellen, 1984, and Schmid, 1989). According to this view, firms may offer wages in excess of the market-clearing level to discourage labour turnover which is costly to them. The labour turnover model predicts that, the higher the wage paid by their employer relative to the market-clearing level and the higher the aggregate rate of unemployment, workers will become less likely to quit their jobs. This notion of 'fair wage' has led to what is known as 'insider-outsider' models. The 'insiders' in the firm can capture productivity improvements in the form of higher wages in the short-run. Yet, in the face of unemployment and in the longer run, market forces re-assert themselves and wages return to their equilibrium value. One important feature of this case is that following a shift in productivity or demand, employment will change only sluggishly because of the role of insiders in wage-setting. With this sort of 'turbulence' in place of smoothly adjusting markets, there will be frictional unemployment as workers move from contracting to expanding firms.13

Hopkins (2002) goes on to discuss asymmetric-information models. Here, the lack of information on wages in competing markets leads to sluggishness of markets to adapt and unemployment ensues. Moreover, Grossman et al. argue that it is the increase in uncertainty that prevents workers to change jobs, rather than information misperceptions, and this uncertainty generates the increase in equilibrium unemployment. But, others see labour as special. Standing, for instance, sees increased flexibility in the labour market having been traded for reduced security. And, Sapsford and Tzannatos do see labour markets as special with theory having enjoyed considerable analytical progress over the 1980s. This progress has revolved around a focus upon the allocation of time and the notion of human capital. Finally, it does appear that the focus upon growth theory in recent years has led to questions of employment being more of an 'add-on' than a focus of economic theory per se, which differs sharply from the focus on the labour market by the classical economists and social reformers such as Keynes.14

Labour Markey Dynamics As indicated by McMullin, Cooke and Downie (2004), when confronted with a labour shortage, adjustments by firms include wage level adjustments, and attempts to move production or to attract workers from other locations, as well as providing training to the existing workforce. Firms can also try to substitute away from the scarce labour towards other factors of production, such as capital and/or other types of labour. Adjustments on the part of workers generally include movement into areas of shortage, both geographically and in the sense of retraining to gain scarce skills. These adjustments to the labour market cannot take place instantaneously, however. A lack of information, the time required for training, and various institutional barriers to labour market adjustment mean that in a given place at a given time there will be gaps between the quantities supplied and demanded for particular skills. Problematic skill shortages are those which become protracted as a result of these institutional barriers to adjustment, resulting in inefficiencies. 15

Some of the basic dynamics indicated above have been questioned. Neugart and Schömann (2002a) review the literature on whether the labour market clears. They indicate that it is widely

13 Hopkins (2002) p 51. 14 Hopkins (2002) p 51. 15 McMullin, Cooke and Downie (2004) p 2.



recognised among labour economists that market mechanisms themselves can contribute to imbalances on the labour market. From the perspective of the firm it may be optimal to pay a wage that is higher than the market clearing wage, if it provides the optimal mix of wage costs and induced effort to arrive at higher productivity. However, higher wages create unemployment. There are people around who are willing to work under the prevailing conditions but who are not offered a job, because it simply is not to the advantage of the firms. One reason for this is shirking (see, for example, Shapiro and Stiglitz, 1984). Efficiency wages may also be optimal on the grounds of turnover costs (Salop, 1979; Schlicht, 1978) or a fair wage hypothesis (Akerlof and Yellen, 1990). In any case, the wage as a market clearing device is blocked.16Other examples of theories in which wages may not adjust to clear the labour market are implicit wage contracts (Azariadis, 1975; Baily, 1974; Gordon, 1974) and seniority wage profiles (Lazear, 1981).17

Another reason that it may be unwise to leave the market to correct itself is the fact that many employers may be unable to raise wages due to a ‘fixed compensation structure’ in their organisation (Veneri, 1999, p. 17). Raising wages can also reduce the international competitiveness of firms. Another complication is that both firms and individuals simply may not recognise the signals of changes in the labour market; slow response time may delay market adjustment. Workers may choose to remain underemployed or even unemployed and ‘wait out’ a downturn that may be only temporary. Even if workers do respond to a perceived shortage, training institutions may not be able to accommodate the demand without the benefits of foresight and policy support (Venari, 1999). Adjustment is costly and takes time (Smith, 2002), as mentioned in CCL (2007).18

Although excess demand in a certain sector very likely raises wages, the effect on the economy-wide wage level is unclear. Excess demand in one sector may be accompanied by excess supply in another sector. For example, if there is a sectoral shift towards high-skilled labour, then the low-skilled labour supply will exceed demand. Wages in this sector may fall. The composite effect can be either positive or negative. Econometric evidence provided by Haskel and Martin (1996) suggests that skill shortages may have a positive effect on nominal wage growth. When factors like productivity, unions, unemployment or firm market power were controlled for, it was found that nominal wage growth was one percentage point higher each year between 1983 and 1989 because of skill shortages in the United Kingdom.19

As reported by Neugart and Schömann (2002a), skill or educational mismatches have also been found to affect wage levels. Individuals with a certain education generally have lower wages in a job that does not match their educational background than another person with the same education who has a job that does match his or her educational background. Moreover, an overeducated person will earn more than another person in the same job who has an educational background matching the job requirements.20 In the case of under-education, workers earn more than if their educational background and the job requirements were to match, but less than when the job is occupied by somebody who is educated for that job. Usually the wage effects of over-education are stronger than those of under-education; however, both concepts are difficult to measure empirically with high precision. The results from education–job mismatch studies illustrate that wage effects persist, even though they do become smaller. What is contrary to

16 Neugart and Schömann (2002) p 3. 17 Neugart and Schömann (2002) p 3. 18 Canadian Council on Learning (2007) p 5-6. 19 Neugart and Schömann (2002) p 8. 20 See Hartog (2000), and Sicherman (1991)



education mismatch studies is that skill mismatches seem to have a strong negative effect on job satisfaction.21 This literature indicates that labour market imbalances may cause bad job matches.

Neugart and Schömann (2002a) go on to indicate that although many would consider labour market imbalances temporary, the literature on multiple equilibria emphasises that economies may get stuck at various states depending on their histories. Some of these perhaps rather stable equilibria can be more desirable for a society than others. Finegold and Soskice (1988) described a mechanism that may lead to a low skill–low wage equilibrium. If for some reason a skill shortage arises, then firms may have problems hiring workers for high-skill jobs. If there is some complementarity between production factors they will eventually invest in technologies that supplement the low-skilled workforce. The labour market will be characterised by low wages. Furthermore, firms will not post vacancies for high-skilled workers. As there are no well-paid jobs on the market, workers will stop investing in their human capital. The vicious circle will drive the economy into a low wage–low skill equilibrium. There are other, more advantageous equilibria. But for the individual there is no incentive to invest in education, as the payoffs will not materialise. Considerable efforts are required, efforts that only a powerful actor in the market can accomplish. That is where the government could come into play, perhaps by subsidising education or by avoiding labour supply shortages right from the beginning through forecasts and appropriate policies tackling upcoming skill shortages. For a more formal treatment of the argument, see Snower (1996).

Neugart and Schömann (2002a) also indicate that if there are skill shortages and firms switch to filling high-skill vacancies with low-skill workers, productivity will be lowered. It may also happen that a shift in the bargaining strength towards workers leads to employment conditions that are less efficient for firms and that lower the productivity of the incumbent workforce. The former argument addresses productivity levels, but productivity growth also may suffer from skill shortages if firms reduce investments in research and development. The findings by Haskel and Martin (1996) suggest that skill shortages reduced productivity growth by 0.4 per cent per year in the United Kingdom between 1983 and 1989.

Neugart and Schömann (2002a) concluded their list of examples on potentially undesirable consequences from labour market imbalances by arguing that the lack of skilled workers may lead to malfunctions in production processes. Quality control may suffer and, consequently, product quality may decrease (Finegold and Soskice, 1988). Haskel and Holt (1999) discussed evidence on whether skill shortages affect product quality. From a sample of case studies they concluded that product quality may indeed suffer, but perhaps only in some sectors.

As indicated by the Canadian Council on Learning (2007) report, the market does adjust to some degree to labour shortages, and employers do raise wages to attract more workers. Higher wages, in turn, create an incentive for individuals to invest in training to obtain the qualifications need for that job. This process can lead to a ‘cobweb cycle’, where students base their educational decisions on the market at the time they enter a course, rather than the market anticipated at their time of graduation.22 For example, if wages are high in a certain field due to a shortage of employees, many students may respond by taking the courses necessary to enter that field. After a number of cohorts have done this, there is a surplus of employees and wages go down, causing new students to stop entering that field. At the end of the cycle, a new shortage in labour occurs,

21 See Allen and van der Velden (2001) 22 The cobweb cycle or model that was developed by Kaldor (1934) was first used to describe and explore the relationship between educational choice, wages and labour market outcomes by Freedman in a series of articles. Freeman (1971, 1972, 1975a, 1975b, 1976a, 1976b)



and wages again go up to attract more students. Accurate information about the labour market that is frequently updated and widely disseminated can help to avoid the pitfalls of this cycle.

Why Forecast Labour Shortages Together the above strands of research contributed to a rationale for occupational forecasting. The purpose of occupational forecasting is now widely perceived as providing a source of information for employers and potential employees regarding future labour market conditions by occupation and in relation to the type of education. In particular, information should be directed towards identifying likely future imbalances between demand and supply across occupations in order to improve the investment decisions of both employers and employees. Where occupational forecasts have been prepared for and used on this basis, there is evidence that this information has reduced the costs likely to have arisen from uninformed labour market decisions.23

The premise underlying this view is that the mismatch between the educational system and the labour market is caused by the fact that students have to make their educational choices without enough insight into the consequences of their choices. Borghans (1993) showed that the match between education and the labour market can be improved by providing information to the labour market without disturbing the functioning of the market. Thus forecasts can be seen as one element of the information required for educational and vocational guidance (see Heijke, 1986 and 1993). Moreover, manpower forecasts can yield valuable information for policy purposes, especially in the fields of educational and employment policy, as an aid to understanding the effects of various courses of action on the employment structure or the labour market situation in general. The manpower forecasts can then be used as guidelines for active labour market policies in the fields of training, job placement and job creation (see Hughes, 1993 and OECD, 1994).24

As indicated by the Canadian Council on Learning (2007), forecasts and policy interventions are unlikely to completely eliminate the cycle of skills supply and demand since the labour market is constantly evolving and adjusting. They do, however, help to eliminate the ‘firefighting’ approach to the labour market by enabling strategic planning for upcoming shortages and surpluses, which can mitigate the costs of slow adjustment. Forecasting provides policy-makers, employers, employees and students with the information necessary to make choices that will optimise the contribution of education to their economic growth and the smooth functioning of the labour market (Heijke, 1996; Neugart and Schömann (2002a)). Providing and disseminating “labour market information is a public good in that many users can share the same information and the benefits of its production are equally available to non-payers” (Smith, 2002, p. 68). 25

This information is especially important when there are long ‘lead times,’ such as those for training in specialised and necessary skill areas such as medicine, technology or teaching. Shortages in skilled labour in these areas could conceivably contribute to longer-term social and economic problems, and forecasting helps reduce this risk (Neugart and Schömann (2002a)). Understanding how the labour market should be directed in order to meet social and economic objectives helps governments monitor and influence both the labour and education markets in a manner consistent with these objectives (Borghans and Willems, 1998). Such foresight improves the efficiency and cost-effectiveness of the labour and education markets.26

Advance knowledge of where to expect skills shortages and surpluses allows governments, individuals and employers to invest in education that will maximise the return on their investment

23 Burns and Shanahan (2000). 24 Willems (1996). 25 Canada Council on Learning (2007) p 6-7. 26 Canada Council on Learning (2007) p 7.



and helps prevent the loss involved in training people in skills that are no longer in demand (Burns and Shanahan, 2000; Strietska-Ilina, 1999; Neugart and Schömann (2002a)).27

27 Canada Council on Learning (2007) p 7.



Occupational Models Occupational models and forecasts have a long history. The first were produced in the United States after the Second World War to help with the integration of returning veterans. Another early effort was the OECD Mediterranean Regional Project (MRP) in the early 1960s. It was initially thought that the coordination of labour market projections with educational planning could resolve potential labour market imbalances. This view inspired the development of occupational demand and supply models and forecasts in the 1960s.

The dominant manpower planning model is what is known as the manpower requirements approach (MRA). A review of current occupational modelling and forecasting practices around the world show that all groups that are currently engaged in detailed employment forecasting utilise, at least partially the MRA.28 According to Hopkins (2002), the three major steps in the MRA are: (a) projecting the demand for educated manpower; (b) projecting the supply of educated manpower; and (c) balancing supply and demand.29

The Demand Side There are five main steps to assess the number of workers by educational level over time: Note: i = economic sector, j = occupation, k = educational level, a = age, s = sex

1. Estimating the future level of GDP or output (X). 2. Estimating the structural transformation of the economy as expressed by the distribution of

output by economic sector (Xi/X) as it evolves over time. 3. Estimating labour productivity by economic sector (Li/Xi) and its evolution over time. 4. Estimating the occupational structure of the labour force within economic sectors and its

evolution over time (Li,j/Li). 5. Estimating the educational structure of the labour force in given occupations within

economic sectors over time (Li,j,k/Li,j).

Hence the labour demand function for educated labour looks something like:

LDi,j,k=f(X, Xi/X, Li/Xi, Li,j/Li, Li,j,k/Li,j)

The Supply Side There are four basic steps:

1. Estimating the population Pa,s,k by age, sex and educational level. 2. Assessing the number of graduates, drop-outs by age, sex and educational level, Ea,s,k . 3. Finding the labour force participants (LS) by applying age, sex, educational level labour

force participation rates to the number of graduates, la,s,k. 4. Estimating the occupational supply based on the labour supply by education level possibly

using an education to occupation matrix M k,j.

Hence the supply function for educated labour looks something like:

LSj,k =f(Pa,s,k , Ea,s,k , la,s,k, M k,j)

In some occupational models, such as the COPS, a fifth supply side step would include distributing the aggregate flow of immigrants into the workforce using fixed census shares. And a few models explicitly include separations and replacement demand. Most models do not include demand/supply interactions. 28 de Grip and Heijke (1998) p 2. 29 The discussion follows Hopkins (2002) p 3-4.



Criticisms of the MRA According to Hopkins (2002), the main criticisms of manpower planning have come from Psacharopoulos (1991), Blaug (1970), and Ahamad and Blaug (1973) in their evaluation of eight manpower forecasting studies in Canada, the United States, UK, France, Thailand, Nigeria, India and Sweden. Their main criticisms were:

1. Considerable forecast errors were associated with projections of employment by occupation using the MRA methodology.

2. The errors were mainly due to the fixed-coefficients model and assumed labour-productivity growth.

3. The longer the time-horizon of the forecast, larger were the frequency errors. 4. No evidence was found linking manpower forecasts to any actual educational policy

decision. 5. In some cases manpower forecasts gave support to what turned out to be a wrong

decision. Therefore, it is wrong to argue that forecasting always improves policy decisions, or that any view on future developments is better than none.

The use of fixed-coefficient models resulted in the models not reflecting structural changes in the labour market. And as stated by Hopkins (2002), one of the most crucial assumptions in MRA-type manpower forecasting methodology is that the elasticity of substitution between different kinds of labour is equal to (or near) zero. Yet, it is clear that the elasticity of substitution cannot be zero and would vary according to the degree of substitutability of one type of job for another. This will also depend on the amount of training or additional education required.

A New Generation of Occupational Models The providers of occupational demand and supply models have responded to their critics by trying to incorporate structural changes that influence the labour market. This is done in a number of different ways from extrapolating past trends to trying to model the influence of economic factors. The Dutch Research Centre for Education and the Labour Market (Researchcentrum voor Onderwijs en Arbeidsmarkt (ROA)) takes a further step by allowing substitution effects. Their model allows for demand-driven substitution effects as well as supply-driven “crowding out”.

In order to improve accuracy, there has been a general reduction of the forecasting period from the very long term of 10 to 20 years to five to ten year forecasts. The reduction in the time frame reduces the risk that unforeseen events and unexpected reactions to cyclical or structural forces will have a serious impact. The time period, however, is long enough so that labour market participants can make decisions regarding education and training to benefit from the expected future labour market conditions.

There has also been a change in how the forecasts are used. Forecasts are typically restricted to large classes of occupational groups that have large overlaps in required skills and that may reasonably be employed for broad policy-guiding purposes.30 Also there have been changes in the type of information provided. Most groups provide qualitative assessments of future labour market conditions, such as ‘poor’, ‘fair’ and ‘good’, as opposed to precise point estimates.

Partly due to the severe criticism of the initial approach, the detailed education planning function has all but disappeared. Van Eijs (1994) argues that today manpower forecasting is considered to have two functions: a 'policy function' and an ‘information function'. The policy function refers to the use of manpower forecasts as 'a point of reference' for policy recommendations for policymakers who have to take decisions on educational investments or other educational or 30 Neugart and Schömann (2002b) p 2.



labour market policies (Wilson, 1993). The information function is primarily intended to assist in occupational or educational guidance (Dekker et al., 1993), although it could also inform firms about possible future recruitment problems of workers with a particular education.31

Hughes (1991) agrees that the primary focus of occupational modelling has changed from detailed education planning to: identifying the implications of existing occupational trends; providing information on the current state of labour markets and expected changes; evaluating the effects different policies might have on the level and structure of employment in the future. Boothby et al. (1995) stated the aim of occupational forecasting should be to project ex ante imbalances between labour supply and demand across occupations and therefore “contribute to increasing the average rate of return to education by securing a better match between skills that are supplied and demanded”.32

Current Practices in Occupational Modelling Today occupational models and forecasts are used in countries throughout the industrialised world: Canada, the United States, Austria, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, the UK, Australia and Japan. Most of these models (or more correctly modelling systems) are national in scope. Sub-national components of these modelling systems are available in Australia, Canada, France, Germany, the UK and the US. Some Canadian provincial governments have occupational models, such as Quebec and Alberta.

There are many differences in how the MRA is currently implemented in practice. Not all groups execute all the steps outlined above, and several groups have extended the steps to explicitly take into consideration separations, replacement demand, labour market indicators (LMI) and skills. Differences between approaches relate to data availability and quality, as well as the amount of resources committed to the task, and the ultimate need that the model or forecast fulfills. Many forecasting groups focus on occupations as opposed to education or skills, for example.

To understand current practices thoroughly, please refer to Fairholm and Somerville (2005) for a discussion of international approaches and Fairholm (2006) for a discussion of practices throughout Canada. Other recent reviews of occupational models and forecasts includes: Strietska-Ilina (1999), Papps (2001), Wilson (2001), Hopkins (2002), Neugart and Schömann (2002b), and Boswell, Stiller and Straubhaar (2004). The following discussion on occupational models will rely on these materials as well as specific research papers produced by various occupational modelling groups that are highlighted in various sections.

Recent Changes in Modelling Practices To update information on occupational models that was provided in Fairholm and Somerville (2005) and Fairholm (2006), a scan of new literature was performed and the respondents to the 2006 FLMM questionnaire were contacted to determine if there were any changes to their occupational modelling and forecasting practices.

Internationally there have been very few new developments in the field of occupational demand/supply models over the past three years. An examination of the most recent report in Europe on Occupational models and forecasts by Zukersteinova and Strietska-Ilina (2007) shows that the primary occupational modelling groups are using the same approach as was discussed in Fairholm and Somerville (2005), although now the use of occupational models has spread into eastern Europe, with forecasts being done in the Czech Republic, Estonia, Poland, and Romania. There is also an initiative to develop a pan-European occupational model and forecast. In

31 de Grip and Heijke (1998) p 1. 32 de Grip and Heijke (1998) p 6.



Australia, there are reports that the Centre of Policy Studies has been working on supplementing the labour supply side of the model, but the details are not available currently. The Centre for the Economics of Education and Training (CEET) continues to do work examining labour market separations and replacement demand helped by a detailed labour mobility survey.

One recent step outside the confines of an occupational model in the direction of determining inter-occupational mobility was taken by Roberts (2003) who developed a matrix of skill transferability, which identifies potential employment opportunities for workers in different occupations (see Appendix II). She developed ratings that identify possible paths of mobility between occupations. The coefficients of skill transferability were then derived from the matrix. In 2007, this approach was also used by the C4SE for five Canadian Sector Councils to identify potential sources of labour supply for occupations in short supply in the industries represented by the sector councils. In both of these cases the number of occupations examined was limited since the approach is very time intensive. In 2007, the C4SE also constructed occupational similarity indexes based on educational attainment to determine likely occupational labour supply transitions following the approach of Heijke, Matheeuwsen and Willems (2003).

In Canada, most groups report no change. Emploi-Quebec has supplemented their detailed 5-year forecasts with a less detailed 10-year forecast. The 10-year forecast has less industrial, regional and occupational detail. And work is underway in British Columbia and Saskatchewan to develop education models. The BC model is particularly ambitious project to model the education system at a very detailed level.

For the BC post-secondary education model, BC Stats is accessing detailed individual secondary and post-secondary student records (including birth date, high school final grades, completion of high school provincial exams, type of post-secondary institution, length of program, faculty, field of study, credential, etc) to build a longitudinal file. The micro records from the colleges and university colleges are coded at the 6-digit Classification of Instructional Programs (CIP) level and each of those CIPS are further gradated by length of program. University data, however, are currently not available by CIP or the year of enrolment. However, BC Stats hope is that these data will be forthcoming within the next year.

The transition functions will then be used to project the current stock of BC students by the level of their current schooling into the future. The transition between the CIP and the NOC (4 digit) will be based on the BC Colleges and University Outcome Surveys which have been undertaken for many years. They will use data from multiple years. The intent is not to develop a transition matrix from CIP to NOC as is usually done but rather to build indicators of the occupational mobility of a particular CIP and NOC.

The only Canadian provincial occupational modelling and forecasting group that has already instituted a significant change to their model is Alberta Employment, Immigration and Industry (AEII) formerly know as Alberta Human Resources and Employment (AHRE). In 2006, labour market indicators were included in the model. In 2007, changes to dynamic structure of the model were incorporated by having occupational or career choice reflect changes in relative demand. And AEII is planning further enhancements to the model in 2008 and beyond.

The Alberta model now uses the approach developed by the ROA to construct indicators for future labour market conditions for newcomers to the labour market and a risk indicator that illustrates the sensitivity of employment to cyclical fluctuations. The ROA model explicitly includes passive substitution in their model, which is not available in the Alberta model. As a result of this omission the Alberta model is subject to Boothby, Roth and Roy (1995) criticism concerning substitution effects and occupational interactions.



In 2007, the Alberta occupational model included career or occupation choice (see Appendix III for a more detailed discussion). This is done by adjusting the education to occupation transition coefficients to reflect changes in the demand for occupations versus the demand for the education that the workers possess who would typically enter that occupation. This model structure means that occupations for which demand is growing more quickly than overall demand for workers with the education needed for that occupation will experience a rising share of this labour pool. Occupations that are experiencing a lower rate of demand growth than all workers with the type of education used in the occupation will experience a declining share of those workers. The adjustments to the transition matrix coefficients are done using a partial adjustment model, with the speed of adjustment determined by the gross separation rate for the supplying occupations.

In 2008, the AEII model will be further adjusted to incorporate a more detailed education model using data based on the CIP. The model will also be updated using the 2006 census data, and various approaches to estimating retirements will be examined. It is also planned that the model will be extended to incorporate more complete treatment of special equity groups—visible minorities, aboriginals and activity limited. And that potential labour force will be estimated.

The COPS group has instituted a number changes over the past couple of years, and is in the process of including further extensions and refinements of their modelling system. The COPS produces a projection of labour force by broad skill level by applying to the projection of labour force by education/age the probability that an individual with a that level of education/age will be in an occupation normally requiring a given level of skills.

Currently, the COPS group is working to expand the model and to take into account flow components, such as re-entrants, and mobility across skill levels, such as upward and downward mobility. Upward occupational mobility occurs when workers gain labour force experience and move into management ranks. Downward occupational mobility occurs where workers choose to enter lower-skilled occupations as part of their transition towards retirement.

The COPS group also produced a forecast using provincial employment/industry shares (rather than national shares) and resolved the issues of volatility and anchoring. They developed autonomous 4-digit forecasting equations for the health-related occupations and evaluated the matching between education and occupation of recent graduates after 2 years and 5 years on the labour market. They also identified the characteristics of immigrants (e.g. age, gender and immigration category) that tend to improve the matching between education and occupational employment. These latter developments can be used to enhance the modelling of labour mobility.

Developing a Modelling System In developing and using a modelling system a number of factors must be taken into account. These factors include the nature of the modelling system, expected accuracy, consistency, transparency, availability of resources and the nature of the data available to develop and use the methodology. The following sections discuss these issues.

When considering an occupational modelling system, it is important to recognize that no one approach is ideal for all modellers. Practioners are confronted with many inherent tradeoffs between data sources, modelling and forecasting approaches. Some choices are better for some groups than others depending on their needs. Furthermore, organisations have different resources that they can devote to occupational modelling and forecasting.

There are three distinct aspects to the development of the modelling system. First the level of occupational detail that is needed must be determined. Larger models are more difficult to construct and use. Second, the component models of the MRA must be selected. Not all groups develop all aspects of the full MRA and typically at least some of the forecast information is



obtained from outside their organisation. Clearly, more resources will be needed if more models and forecasts are developed. While the greater use of external information means less or even no interaction between component models. The third aspect is related to the level of sophisticated of each of these component models. A simple model requires fewer resources than a more sophisticated model to develop. Depending on their needs and resources, practioners may decide to have component models at various levels development and sophistication.

Given the magnitude of detailed occupational demand/supply modelling systems, for resource constrained practioners there are clear benefits to developing the system in a step-by-step manner. For example, the model development process can first emphasis simple modelling approaches that can produce results relatively quickly, rather than waiting until a more sophisticated or complete modelling system is developed. So long as the overall modelling framework remains the same, the modelling system can be enhanced over time as resources permit.

In order to provide information that is useful for a variety of users at different stages of development and sophistication, the examination of the available data and modelling approaches that are provided in the sections below will include a discussion of simple approaches, usual approaches and more sophisticated approaches.

Nature of the Modelling System The nature of the exercise refers to its purpose, time horizon, and level of detail.

What is the modelling system to be used for? For example, is it only to provide a ‘rough’ idea of what could happen in the future, or, is it to be used for detailed planning purposes?

The purpose of the system may be not only to consider the most likely future movement of indicators, but also to create different scenarios for the future or to conduct impact analysis for such events as changes in government policies.

The time horizon may be the short term - up to two years - the medium term - three to five years - or the long term - over five years. For occupational forecasting the typical time frame is 5 to 10 years.33

System detail refers to the number of indicators to be projected and the time unit to be used. Some analysts may be interested in only one indicator, while others may wish to consider hundreds of indicators. Analysts may produce daily, weekly, monthly, quarterly or annual forecasts or projections.

The 2006 questionnaire with the LMIWG members seems to indicate that purpose of the modelling system is the identification of possible supply shortages in the medium term and the development of supply strategies to meet employment requirements over the long term – 10 or more years. Since it is to be used for planning purposes, the system would probably be used for scenario analysis. The amount of occupation detail required is likely to be at the 3 or 4-digit NOC level. Annual data would be the used in the system.

Accuracy, Consistency, and Transparency The expected accuracy of a projection technique is an important consideration in choosing one. Accuracy refers to the proximity of the projection to the actual results. The ability of a projection technique to achieve a high degree of predictive accuracy depends on such factors as the time horizon of the projection and the cyclical volatility of the economic indicators to be projected.

33 Papps (2001) p.7.



The ultimate goal of analysts is to achieve a purely objective projection method and thus eliminate personal judgment from the projection process. If this goal were achieved, forecasting would be a scientific discipline in which all analysts would agree when given the same information. The projection in this case would be consistent with the assumptions made to produce it. Unfortunately, this situation does not exist. Judgment has a role to play, and purely mechanistic methods of forecasting are bound to fail. Where possible, however, a forecasting or projection method is chosen that is characterised by a low degree of subjectivity.

The transparency of the projection and its methodology are important if one expects to get the consumers of the projection to buy into it. To be transparent the projection and methodology have to be documented and presented in such a manner that consumers have a good understanding of how the methodology works and what assumptions have been employed to produce the projection. In this case, analysts could, if desired, duplicate the results obtained by other analysts.

It is assumed that LMIWG members would like to develop a system that satisfies these accuracy, consistency, and transparency considerations.

Availability of Resources The real, financial, and time resources available to the forecaster are important factors when choosing a projection method, as each technique differs in their use of resources. Some techniques, such as large econometric macro-models, are extremely costly to develop and use while others, such as a panel of experts, are relatively inexpensive. The development of different approaches takes different amounts of time to develop and use. Generally larger ones require more time resources. There is also a tradeoff between the level of sophistication and the resources that are needed to develop and utilise an occupational model. A more sophisticated approach is not necessarily the best approach for an individual practitioner. Often it is time and cost effective to start off with a simple model that will produce useful information near the start of the process and then augment the model over time as resources permit.

Many of the steps of the MRA can provide useful information to policymakers and labour market participants and so the system can be developed in a stepwise manner. For example, the development of an expansion demand forecast will provide information as to where total job growth and decline will be most pronounced. The development of a replacement demand model will provide further information as to what occupations will have job openings in the future. And an extension of the model to illustrate the educational requirements of occupations will provide information as to what types of education will likely be needed in these jobs in the future.

The development of the supply side of the model will help to identify where there will likely be gaps between demand and supply in the future. This step will illustrate what type of conditions labour market participants and/or new entrants will experience in the future. The development of labour market indictors translates the demand/supply information into easy to understand summary measure(s) that can be used by workers, new entrants and firms. Indicators can also provide information about the likelihood of the forecast coming true.

It is unlikely that the modeller will develop a fully simultaneous demand/supply model for detailed occupations given the size of such a system. These steps can be kept in distinct models. So long as the framework remains the same, these components can be improved over time.

Availability of Data The type and amount of data that are available for the development of a modelling system have a very important impact on its development and use. If there are no or few data for the desired inputs and outputs, then it is not possible to develop the model. If analysts are required to create



estimates of the data, then additional resources will be required to develop and use the system. The following discussion on data will focus primarily on Canadian data sources.

The data that are typically of interest to occupational forecasters are at a very detailed level of aggregation. There are 520 occupations at the 4-digit level of aggregation in the National Occupational Classification for Statistics (NOC-S) and around 100 industries at the 3-digit level of aggregation in the 1997 North American Industry Classification System (NAICS) that is used in the 2001 census.34 The cross tabulation between the occupational and industry categories represents 52,000 distinct occupation/industry combinations.

Occupational modellers have to consider three aspects: 1) data availability, 2) data coverage, 3) data quality. Not all of the concepts that are discussed in the occupational modelling literature are necessarily available from Canadian data sources. Some data are available from the census once every five years, while other data have experienced changes in classification and are available for brief historical periods. For data concepts that are available, not all data are available at the level of detailed that is required by the occupational modeller. (see Appendix IV for a discussion of specific data sources) Third, there is the issue of data quality. Not all data sources provide same degree of accuracy. Generally speaking the larger the sample size, the better the quality of data. Appendix V discusses data quality for some Canadian data sources.

National and Provincial Account Data The first step of the process to develop a modelling system to forecast occupational demand and supply is to develop a macroeconomic model and forecast for the jurisdiction in question. This step is usually taken in order to provide the detailed final demand categories that are used in the industrial output model. The forecasts from the industry output model are used in the industrial employment model.

From the perspective of Canadian data sources, the core national, provincial and territorial data for these models are from the System of National Accounts (SNA). In addition to the expenditure, income and production accounts, the translation of final demand categories to industry output often rely on input-output accounts that are also developed as part of the SNA.

Most occupational modelling and forecasting groups use macroeconomic and industrial forecasts produced by other groups, therefore, these datasets and models will not be discussed in depth.

Employment and Labour Force Data For employment and labour force concepts most occupational models and forecasts use census and/or labour force survey (LFS) type data sources. The Canadian census and labour force survey each have advantages and disadvantages as a source of employment and labour force data.

The long form of the census is delivered to 20% of homes and provides largest number of respondents for any survey. Furthermore, the compliance is mandatory, so data quality problems are minimised. The primary advantage of using census data is that there will be consistency between the employment, labour force and population data. The census has specific population estimates by occupation, whereas there are no occupational population estimates from the LFS.

34 NAICS 1997 has 100 industries at the 3-digit level of aggregation, but two farming categories (111 and 112) are combined into one industry in the data. NAICS 2002 has 103 industries. The LFS uses the NAICS 2002 classification system, while the 2001 census uses the NAICS 1997 classification system. In the updated classification system, there were some changes in the industrial coverage at the 3-digit level of aggregation that eliminated some NAICS categories. The NAICS 2002 categories closest to these are 2111 (211), 238 (232), 2361 & 2362 & 237 (231), 515 & 517 (513), 516 & 518 & 519 (514), 9120 (912), and 9130 (913), with the 1997 category in brackets.



The use of the census, therefore, makes the integration of the steps of the manpower requirements approach easier. The census data, however, are only available once every five years, so time series analysis is not possible. If census data are used as the primary data source they could be backcast by using the LFS data over the recent historical period.

Even with the census data quality issues arise because of the level of detail needed for the typical occupation model. Statistics Canada’s employs a random rounding technique to ensure the confidentiality of individual respondents. This becomes a problem when there are not may people in a particular category. As the number of respondents in a cell approaches zero the quality of the data deteriorates significantly. One consequence of this data quality issue is that labour force identities do not necessarily add up. For example, if there are eight people in a particular category, six employed, and two unemployed. The census information could show ten people employed and five in the labour force, leading to a negative unemployment level.

One way to reduce this problem is to average estimates across various identities. For example, the estimate for the number of people employed in the NOC-S category F01could be calculated by adding the census estimate with the number calculated as the difference between F0 and F02 plus F03, with the estimate based on the difference between the labour force and unemployment for F01, with the estimate based on the number of men and women employed in occupation F01. Then this sum would be divided by four to provide an average. For the Alberta model, C4SE used four different identities plus the original census data to come up with an averaged estimate. This approach reduced the number of empty cells and the number of categories with an implicit negative unemployment rate. One implication of using this approach is that both the detailed and more aggregate census data should be obtained to calculate these various estimates.

Detailed employment data from the LFS have serious data quality issues because of the small sample size. Many 3-and-4-digit NOC categories are not available in the provincial data. Furthermore, provincial LFS data at the 3-or 4-digit NOC level of aggregation have considerable annual variability. This problem will result in statistically inefficient estimates, which will impinge the results of statistical tests. One way to partly circumvent this problem would be to use the Longitudinal Administrative Databank (LAD) for employment by industry data concepts. The LAD sample is much larger than the LFS, and would therefore have less statistical noise than the LFS estimates. Since the LAD is more dated than the LFS, these data would need to be updated using more recent LFS estimates to obtain data for more recent years. This approach, however, does not solve the problem for the occupational employment data. It is possible that the industry employment data from LAD could be combined with estimates of occupational shares by industry from the census or LFS to provide an estimate of occupation by industry.

Alternatively, one of three other approaches can be used to derive occupational data. First, census data can be used, which means that data from more than one census must be used to determine trends. Since the occupational classification system was changed in 2001, this approach would require some adjustments to the historical data (see Appendix VI for a discussion of the changes to the NOC in 2001 and 2006). There is also the problem of different economic conditions being evident in each of the census years, which ideally would need to be taken into account. This approach would necessitate backcasting the census data from the most recent census year to the most current year of historical data. LFS data would be used simply for monitoring historical performance at higher levels of occupational aggregation to ensure that the backcast data are consistent with the LFS estimates.

Second, the census data can be used in conjunction with the LFS data. The census data would be used to provide the underlying data by occupational category. For example, the occupational employment data for 2006 would be used. This approach would also necessitate backcasting the historical data. The LFS data would be utilised by assuming that the census levels increase at the



same rate as the corresponding LFS data. For missing LFS data, it would be assumed that the sub-category grows at the same rate as the corresponding aggregate or using the residual calculated by subtracting the sum of the available components from the corresponding aggregate. The choice would reflect the available data.

Third, the LFS data could be used to construct employment and labour force estimates. If this approach is used, then the same approach should be used for both employment and labour force data. Ideally, the approach used should ensure the integrity of the original LFS data. That is the sub-components should add up to the aggregate, labour force should be larger or equal to the number employed and missing LFS data should not be equal to or greater than the cutoff for various provinces, e.g. 1,500 for larger provinces such as Ontario, Quebec, BC and Alberta.

The occupational model should use both employed persons, and hours worked. Total hours worked is conceptually closer to labour input than the number of persons employed. Since average hours worked is changing over time, and is expected to decline in the future, more people would be required to provide the same number of hours worked in the future than in the past. Therefore, average hours worked would impact the recruitment demand for people. The addition of average hours to the model would make estimates of labour demand, and the demand/supply imbalance more accurate. The census and LFS have estimates of hours worked.

One choice that must be made before the occupational modelling system is constructed is to decide what data will be used to represent the economic concepts that are modelled. For example, the COPS model uses labour force concepts that explicitly exclude part-time workers who are full-time students and full-time workers who are planning to continue their full-time studies. Then full-time students are explicitly added back into the workforce as school leavers that are destined for certain occupations based on their level of education and major field of study. This is done because the occupations in which they work part time during their full-time studies are unrelated to the occupations that they enter once they finish their studies. If the objective is to produce forecasts that are consistent with the COPS approach then the same adjustments must be made.

Service Canada-Quebec uses an entropy calculation to determine the most likely employment levels in each cell of the industry/occupation matrix. Underlying assumptions are that the structure of that matrix is as similar as possible to the observed structure of the census data, with the totals by industry and occupation being those from the LFS data. These estimated employment levels are used to calculate occupation by industry coefficients based on the last historic period and are kept constant over the forecast period. The forecasts are adjusted to take account of structural changes in occupation/industry composition over time. To do that, they compare the historical evolution of employment by occupation to the estimates with coefficients kept constant at the all industries level of aggregation.

Educational Qualifications Data Educational qualifications in terms of level of schooling by labour force and employment are available from the LFS and the census. The census data also has information by field of study for employment, labour force and population. Educational qualifications by occupation data are available from the census. The National Graduates Survey (NGS) has data by educational qualifications and occupation for postsecondary school graduates. Some provinces have their own student outcomes surveys. The Youth in Transition Survey (YITS) has information as to the transition of secondary school leavers. The use of these data sources depends on the structure of the model. One problem with using the NGS and YITS is that Statistics Canada has recently changed their classification system for education data. Statistics Canada is now recording education data using CIP. Previously university enrolment and graduates were classified according to USIS, and college students were classified according to CSIS, while the census used its own Major Fields of Study (MFS) classification. These differences mean that transition



surveys will need to be reclassified to make them as consistent as possible with CIP. There is not a one-to-one correspondence between CIP and these earlier classification systems.35

In order to determine changes in the detailed educational composition of occupations by sector, as per the fifth step of the demand side as outlined by Hopkins (2002), then the census will need to be used. Census data can be used to determine the educational qualifications workers (or labour force participants) in detailed occupations for provinces and territories. The census has data by 2-digit, 4-digit or 6-digit CIP. There are 1,377 6-digit CIP categories. To determine trend changes in educational qualifications, data from more than one census would be required. Since the 2006 census is the first one that was recorded using CIP, it is not possible to determine accurately what the trend change in educational qualifications by field of study has been. Therefore, to construct an estimate of trend changes in educational qualifications by occupation, the data classified using MFS that was used in earlier censuses will need to be transformed into CIP categories.

The NGS has data for postsecondary school graduates and these data can be used to construct a school leavers-model. Given the sample size of this survey, these data can not provide very detailed NOC estimates for the provinces. Furthermore, these data are structured using earlier classifications systems-USIS and CSIS.

Skills Data A possible extension to an occupational model would be to determine the skills and knowledge required for various occupations. This step is undertaken in a number countries including: the US, the UK and Australia. Since the Canadian HRSD NOC classification is structured by skill and education level, forecasts by NOC categories implicitly includes an aspect of skill forecasting. HRSD has also developed information on essential skills for 250 occupations (see Appendix VII).

Another approach to illustrating the skills and knowledge that are needed in occupations is to utilise the very detailed information found in the American Occupational Information Network (O*NET) dataset (see Appendix VIII). The O*NET has an extensive database that differentiates occupation skills and knowledge. Economic studies using these data have found that these attributes affect wages, and therefore appear to have economic content that can be used to discriminate occupations.36

A fixed coefficient forecast of these skills could be constructed following the approach used for Australia,37 and the approach that C4SE used to estimate the skills needed by environmental sector workers.38 Although, given differences in occupational categories, and the fact that even the US data has problems describing in the skills used for heterogeneous occupational groups, any translation of US data for Canadian purposes is an imprecise and difficult exercise.

Population Data In most cases, the historical population estimates that are typically used for occupational demand/supply modelling systems are from annual demographic data. In some cases, the population data come from that country’s census as opposed to the postcensal estimates that are available in Canada. If the objective is to provide the most accurate estimate of population by

35 The CIP-MFS concordance can be found at: http://stds.statcan.ca/english/mfs/cip_mfs_concordance_intro.asp?source=cip_mfs_concordance_static.asp&name=CIP%20-%20MFS%20Concordance 36 Esposto and Meagher (2006). 37 Esposto (2005). 38 Fairholm (2007)



age/sex cohort, then there are no data sources that would be more accurate or timely than the Canadian annual demographic statistics or equivalent data from a provincial statistical agency.

There are inherent tradeoffs in utilising the annual demographic estimates. First, postcensal and intercensal population data that form the basis of the annual demographic statistics are different from the census population data for the census year. The reason for this difference is because the census data are adjusted for the undercount and further adjusted to represent the population as of July 1st for the annual demographic statistics. Other detailed data from the census, such as for occupation and education groups, however, are not adjusted for the undercount and the difference in dates. These discrepancies can cause problems in comparing population across different groups when comparing census and non-census data.

Another possible problem is that the population data from the annual demographic statistics are different than the source population estimate from the LFS. The LFS excludes the institutionalised, armed forces and First Nations on reserves populations. If one of the objectives is to replicate the LFS data, then there is clearly a gap in the data coverage by only using data from the annual demographic estimates. This difference can be easily rectified by adding additional population estimates from the LFS by age/sex cohorts and assuming that the LFS data grow by the same rate as the total population or some adjusted growth rate depending on assumptions regarding the institutionalised, armed forces and the on-reserve aboriginal populations.

Labour Force Projections Most models produce an aggregate trend or potential labour force estimate. Often this step is accomplished by estimating trend participation rates by age/sex cohorts and applying them to the demographic projection. The estimation of trend participation rates is usually done using LFS data, since detailed age/sex specific labour force and population data are available. Alternatively, census data could be used. Demographic projections are either done in-house using the most recent demographic data as the base year, or by using externally produced demographic projections. Some groups use additional data to estimate potential supply, such as data on new Employment Insurance claims. The German Institute of Employment Research (Institut für Arbeitsmarkt- und Berufsforschung (IAB)), for example, estimates potential labour force, which includes those in the labour market plus hidden unemployment.

The construction of demographic and labour force projections are relatively easy to implement for age/sex cohorts. Extensions to this basic approach, however, reflect inherent tradeoffs. The choice between these tradeoffs must reflect the information needs of the jurisdiction and the potential costs of the adjustment.

The first tradeoff is between calculating detailed participation rates by age/sex/education groups as is done by the COPS or by age/sex/ethnic groups as is done by the US Bureau of Labor Statistics (BLS). The LFS has data by age/sex and level of education, but does not have data by age/sex/ethnic group (except for aboriginal populations living off reserve in some provinces). The census, however, has data by age/sex/ethnic group or could be sub-divided into age/sex/ethnic/education group. These data, however, would cost more to obtain and would not provide data that are consistent with the LFS data. Another approach would be to use a combination of LFS and census data to construct the participation rate and labour force estimates that the jurisdiction require. Clearly, the more the data are subdivided the greater the number of data quality issues and cost.

Ideally, the model should include those factors that influence labour mobility (including the transition from different labour market states, such as, not-in-labour force to labour force participation). In particular, wages should be included to improve the estimation of labour market



dynamics including migration and participation rates. The inclusion of wages into the model or other factors that influence labour market dynamics would result in a much more sophisticated model that could allow feedback from the labour market imbalance for an occupation back onto both demand and supply by occupation. This would require significant resources to implement and would result in a much longer model simulation process. This step could be implemented later, after a simpler model has been constructed and the overall model framework established.

New Entrants: Data There are three types of new entrants that provincial occupational modelling groups should take into consideration: school leavers, inter-provincial migrants and international immigrants.

The development of a school leavers’ model requires the construction or utilization of an education model. There are two basic sources of education data that can be used to develop an education model. Provincial groups can either use the data that are available from Statistics Canada or can use provincial sourced data to construct their education models.

When selecting the levels of schooling and fields of study, the modeller must take data availability and occupational outcomes into consideration. In CIP, there are 13 primary groupings (1-digit CIP), 49 2-digit categories. But the 2-digit categories do not directly add up to the primary groupings. To have data that can sum up to the primary groupings, 69 sub-groups (2-, 3- and 4-digit CIP) must be used.

It is not possible to determine the precise number of categories that will be feasible to model prior to an examination of the data, although the number of categories will be likely significantly less that the 69 sub-groups mentioned above. The reasons for this are three-fold. First, as the number of individuals in a given category rises, it is more likely to produce a more efficient model of enrolment and completion and therefore a more accurate forecast. Second, it is unlikely that there will be a significant difference in occupational outcome for all of these educational types. For example, Heijke, Matheeuwsen and Willems (2003) used a similarity index to determine unique educational clusters based on occupational outcomes. They started with 800 education categories and ended up with a total of 113 for the Netherlands. And the COPS education sub-model uses, depending on the level of education, between 49 and 58 fields of study for all of Canada. So it is unlikely that a greater number of education categories will be feasible for an individual province. Third, not all of the CIP categories result in a certificate, diploma or degree. Therefore, if the intent is to determine the change in educational attainment as identified by the census, then the six personal improvement and leisure 2-digit sub-categories do not have to be modelled.

Most models use only demographic data to determine migration flows. This approach implicitly assumes that the occupational outcome for migrants is the same as for other members of society. While this may be appropriate for interprovincial migration, the research on international immigrants clearly shows that the occupational outcomes are significantly different for these groups than for the resident population. The COPS uses a distinct immigrants’ sub-model. The occupational outcomes data for non-student immigrants over the previous five years comes from a special census table. It may be possible to supplement the census information with data from the immigrant databank or the linked data from the immigrant databank and the LAD. There are also data from the census for interprovincial migrants.

Net labour market re-entrants are implicitly included in many occupational models via the participation rate forecast. This step is typically not performed explicitly because of a lack of data. The ROA model uses calculations of net re-entrants by using the cohort component method to determine net labour market flows (see section below on separations).



Separations and Replacement Demand: Data There is a lack of data on detailed labour flows. The census and LFS record levels, but not flows between labour force states for between occupations within the labour market. This step would require a survey similar to the Survey of Labour Income Dynamics (SLID) that follows people over an extended period of time. SLID, however, does not have a large enough sample size to determine flows for detailed occupational groups. See Appendix IX for discussion of stock-flow model and possible flows variables and coefficients.

Some researchers have developed ingenious ways to estimate these flows based on various assumptions, which could prove to be a less costly way of developing separation rates by occupation than through the use of direct surveys. 39 Failing, the utilisation of primary research to examine the flows predicted by these methods, some of the underlying assumptions used by these researchers could be examined instead to see if they are valid.

The preferred course of action depends on how the data are going to be utilised. If the objective is to determine gross out-flows, then the best course of action is to examine gross outflow data from other surveys, such as the SLID, and apply them to all the associated sub-groups. Another option would be to utilise existing economic research to help determine the variation for 3-digit NOC subcomponents based on demand and/or supply factors that are available in the modelling system. If net out-flows are a sufficient concept, then single year participation rate data from the LFS can be used to estimate net outflows using the approach suggested by Boothby (1995b).

Deaths by occupation are commonly estimated by applying aggregate death rates by age/sex groups to occupational employment. This approach will understate deaths for occupations that are riskier than average, and overstate separations caused by deaths for occupations that are less risky than average. One way to improve these estimates would be to obtain data on mortality by occupation. There may be data available from Workmen’s Compensation, for example.

Typically no data are used to estimate out-migration beyond the demographic statistics. It would be possible to estimate the occupations and/or the educational attainment levels of interprovincial out-migrants from the census data. International emigration, however, is more difficult, since there are no exit surveys. There are data collected on immigrants to the US from Canada, but these data may misrepresent the situation. So there is no ideal solution for this problem.

Labour Market Indicators: Data Throughout the world, a large variety of data are used to construct LMI. These indicators can be divided into three groups: indicators that use historical data only, indicators that use forecast data only, and indicators that use a combination of history and forecast data. Several groups construct a number of different indicators to illustrate different aspects of labour market conditions. In general, the models that use historical data are trying to estimate current labour market conditions. Often the data that are used to estimate current conditions include concepts that are not forecasted, such as wages. A larger variety of historical data are typically used than forecast data in the construction of these indicators.

Given the difficulties in determining whether an occupation is currently in excess demand or supply and the magnitude of the imbalance, it would be prudent to include a variety of historical data in the estimate of current labour market conditions.

39 See Boothby (1995) for Canada and Shimer (2005) for the US.



Occupation Modelling Approaches The occupation demand and supply models are driven by developments in the rest of the economy. In some modelling systems major projects are explicitly included as a separate component. In small open economies large projects are major drivers of the economic development. Figure 1 can include or exclude the Major Projects component. Figure 1 Occupation Projection System Economic Outlook

Occupation Demand

Major Projects

Occupation Supply

The direction for causality is indicated by the arrows in Figure 1 shows that the economic outlook affects major projects and vice versa. If the economic outlook includes an assumption for low oil prices, this assumption may delay the construction oil extraction facilities or may change the timing of such construction.

A feedback loop is shown from occupation demand and supply to the economic outlook. This loop reflects the impact of labour supply-demand balances on wage rates and costs in the economy. If labour markets are very tight, these balances may also affect the timing of the construction of major projects as well as the output of goods and services in general. In addition, this loop suggests that the occupation supply response to occupation demand can lead back to impacts on occupation demand and so on.

Most systems used throughout the world that have developed occupation demand and supply components do not usually incorporate this feedback loop. They use a standalone demand component that receives inputs from the economy and produces outputs for the supply component. The supply component also receives inputs from the economic and demographic outlook components. This type of system is the one that is the focus of the current document.

Occupational Demand Models Industrial Employment Forecast Many different methods are used to provide an industrial employment outlook. In many instances, a macro-econometric model is utilised, which provides industrial employment as part of an internally consistent macroeconomic forecast. This process typically has several steps. First, a view of overall economic growth is formed, often based on a survey of forecasters. Second, a macro model is used to provide a detailed set of final demand categories. Third, an input/output



matrix is used to translate the final demand categories into industrial output. Fourth, employment by industry is calculated through the use of estimated employment equations. In many cases average hours are also calculated to translate person hours worked into the number employed.

Simple approach A simple approach to estimating employment by industry would have four or five steps, depending on whether hours and employment are both estimated.

1. Obtain the industrial output forecast from a macro model. 2. Estimate historic productivity levels by industry (output per hour or output per worker)

and average hours worked. 3. Extrapolate the historic productivity trends into the future, 4. Combine productivity and industrial output forecast to estimate hours worked (or

employment level) by industry. 5. Project average hours worked into future and combine with forecast of hours to

determine level of employment by industry.

Usual Approaches • American approach o industry employment = f (industry output, industry wage/industry price, time trend)

• Canadian approach o industry employment = f (industry output, industry capital stock, TFP, business cycle)

• Japanese approach o industry employment = f (nominal output/total labour costs, employment last period,

nominal output/total labour costs last period) • British approach o industry employment = f (industry output, real wage, hours, real price of oil, bank rate)

More Advanced Approaches In the case of Australia, a macro model is used in conjunction with a CGE model. The modelling system projects the input/output coefficients into the future, so that expected changes in technology can be reflected in industrial output and demand for labour by industry.

In the UK model, co-integration analysis was performed, and in the vast majority of cases, an “error correction” formulation was applied, so these equations have short-run dynamic properties, as well as long-run equilibrium properties.40 This approach to modelling represents the best practice for models that do not utilise a macro model consistent with the inverted production function approach, for which there is also the question of what type of production function should be employed.

Occupational Employment Model Occupational employment is influenced by two factors: employment changes between industries and occupational employment changes within industries. The first of these two factors, which is determined by the previous step, affect the overall occupational employment levels if employment shifts among industries that have different occupation patterns. The second factor

40 Wilson (1994) p 18.



will directly affect occupational employment and, therefore, the overall occupational composition of employment.

A considerable number of techniques are used to forecast the changing composition of occupations within industries. These techniques depend on data availability and resources committed to the exercise.

Simple Approach At a minimum, historic trends in the occupational structure of industry employment can be extrapolated into the future. The approach used in Ireland by FÁS/ESRI to extrapolate shares can be used, for example. The FÁS/ESRI approach is discussed in Appendix X.

Usual Approaches Depending on the amount of historical data that is available, more sophisticated modelling techniques can be used. A variety of different approaches are taken toward occupational employment forecast models, which makes it difficult to easily differentiate between ‘standard’ and more sophisticated approaches.

Emploi-Québec focuses on trend movements in industry and occupational employment. Emploi-Québec transforms industrial employment levels into occupational employment by applying an occupation/industry matrix. The historical occupation/industry matrix is based on census data and updated with LFS data. They extrapolate these coefficients into the future.

The COPS occupational model incorporates cyclical changes in the occupational composition of industrial employment at the 3-digit NOC level of aggregation.41 They first difference the historical occupational shares by industry data under the assumption that this will result in stationary series. Then the occupational share is regressed on its own lag (t-1) and other lags (t-n) that are determined by the results of the regression analysis, and the industrial output gap for the current period (t) and in the previous period (t-1). In equation form:

ΔXi,j,t = α1 ΔXi,j,t-1 +α2 ΔXi,j,t-2 …+αn ΔXi,j,t-n+β ΔOG i,t +β1 ΔOG i,t-1 +εt

where

X is the share of occupational j, in industry i employment, at time t.

OG is the output gap in industry i, at time t, which is calculated as deviations of GDP from its trend as estimated using a HP filter.

ε is the error term.

For the provincial model and forecast, provincial industrial output gaps are used instead of the national industrial output gaps. The forecasted occupational shares are normalized so that they add up to 100% at the national and provincial level. Notably, this method employs a cyclical element to occupational change, but does not include a structural or trend element. Some other groups take the opposite approach.

More Advanced Approaches The more sophisticated approaches try to model changes in the occupational structure by including various influences. The Canadian model discussed above includes a variable that reflects the business cycle. However, the literature indicates that technological change should be accounted for in determining occupational trends. This is done in the US model in which the

41 Archambault (1999).



occupational composition is estimated by including staffing trends, total factor productivity (TFP) and government policy.

The Dutch system, takes a further step by using a random coefficient approach that weights the coefficients between single equation estimation for that occupation in that industry and a pooled dataset for that occupation in all industries. This permits more reliable parameter estimation without affecting the specification of the separate occupation equations.

The best practice in this area would seem to combine the random rounding approach used in the Netherlands with a functional form that includes both cyclical and structural factors like technological change.

Skills Demand Forecast At this point in time most models do not explicitly forecast skills. Although a number of models forecast employment by educational qualifications to some level of detail.

Simple approach For the few models that include skills, most use a fixed coefficient approach or assume that past trends will continue into the future. The COPS implicitly includes a skill forecast based on the structure of the NOC.

Usual Approaches The simple approach described above is the typical approach taken given the paucity of detailed skill data by employment.

More Advanced Approaches In France qualitative and quantitative approaches are used in combination to determine future qualifications. Experts’ knowledge is used to identify professional categories within the sector. A qualitative analysis is then undertaken for each profession and quantitative forecasts are made of future qualification requirements. The Regional Employment and Training Observatory (OREF) analyses the implications of sectoral assessments for local areas. It identifies factors that are likely to influence skill requirements and uses qualitative and quantitative scenarios to reach conclusions about the adjustments that may be required.42

Another possible extension to a provincial model would be to determine the skills and knowledge required for various occupations based on the detailed information from the O*NET dataset. A fixed coefficient forecast of these skills could be constructed following the approach used for Australia,43 and the approach that C4SE used to estimate the skills needed by environmental sector workers.44 Although, given differences in occupational categories, and the fact that even the US data has problems describing in the skills used for heterogeneous occupational groups, any translation of US data for Canadian purposes would be an imprecise and difficult exercise.

42 Wilson, Homenidou and Dickerson (2006) 43 Esposto (2005). 44 Fairholm (2007)



Occupational Supply Models Population and Labour Force Projections Most models produce an aggregate trend or potential labour force estimate. 45 This outlook typically is comprised of two distinct components: the demographic and the labour force participation rate projections, which means that there is no interaction between the economic, demographic and participation rate scenarios. These projections are combined to determine total trend or potential labour force in order to determine the upper limit to total labour supply. Some groups use stock flow models to track inflows and outflows from the labour force.

Simple approach

Often an external demographic projection is used to provide future population levels by age/sex group. Trend participation rates by age/sex cohorts are calculated over history and are projected using an extrapolation technique. The participation rates are then applied to the corresponding demographic groups from the population projection to determine labour force for those cohorts. These estimates are added up to determine the total trend or potential labour force.

Most sub-national models include inter-regional migration as an exogenous variable. For example, the UK model includes inter-regional migration flows, but for the projection period, migration flows are kept at a historical average level or at zero.46 These projections are created before the economic forecasts are constructed, so there is no attempt to make the demographic outlook consistent with the economic scenario.

Usual Approaches The typical approach deviates from the simple approach described above by including some economic factors into the determination of at least the aggregate participation rate in a macro modelling framework so that total labour force is responsive to changing economic conditions.

More Advanced Approaches For provincial and territorial population projections, internal migration must also be considered. The one case, where it is clear that more attention is paid to internal migration flows, is Australia. One of the models used for the Australian occupational outlook is a regional computable general equilibrium (CGE) model. The model can be simulated with different equilibrating dynamics for the labour market, including the assumption that regional labour markets are cleared via internal migration. This approach allows for more realistic impact analysis for sub-national areas than can be obtained by having demographics exogenous to the model. The Alberta macroeconomic model of the C4SE also includes interprovincial migration as does the model from the Conference Board of Canada (CBOC). The CBOC model uses an external demographic projection to represent the long-term equilibrium population levels. The model, however, can deviate away from the long-term value due to changes in provincial economic conditions.

The CBOC model uses a similar approach to estimating trend or potential labour force. Trend participation rates are projected, but the model permits the aggregate participation rate to deviate from the assumed long-term estimate based on cyclical variations in the aggregate wage and unemployment rates. In estimating participation rates, the Japanese model includes macroeconomic conditions, as well as the impact of government policy on training, mandatory

45 This step is related to the projection of the number of people with educational qualifications. The linkage between these two steps is not made in all models. 46 Learning and Skills Council. (2004). Inter-regional migration was assumed to be zero.



retirement ages, higher education entry rates, marriage ratio, and policies to enhance employment for older workers and women as well as employment subsidies.

The ROA takes a detailed approach to internal labour market dynamics by occupation and education. Rather than using participation rates per se, the ROA uses a stock-flow model to account for entrants to and exits from various occupations and levels of educational attainment. Their approach combines several of the steps inherent in the MRA into the demand for and supply of individual occupations, which allows for demand-driven substitution effects as well as supply-driven “crowding out”. While participation rates could be calculated, they do not form an integral part of their system. This modelling approach could be used in conjunction with an estimate of potential labour force similar to the dual model method used by the IAB.

New Entrants: Models Education models are often developed outside the occupational modelling systems. Occupational modelling groups typically obtain forecasts from education ministries in their country or use models that are maintained by third parties. Very few models explicitly include the occupational outcomes for migrants.

Simple approach A simple education model is fairly straightforward to develop. The simplest education model can be constructed by using graduations by level of schooling as a share of age/sex cohorts to determine graduation rates historically. These rates can be projected into the future using extrapolative trends. In the simplest model, the education level by age/sex cohort forecasts are used to help determine total labour force estimates.

In the simplest model, the occupational outcome for migrants is not taken into consideration, they are assumed to have the same occupational outcome as the general population.

Usual Approaches Given the lack of detailed information regarding approaches to educational modelling in the occupational literature, it is difficult to determine the typical approach. Although often more detailed educational models are constructed. These can be developed in a fairly simple manner by modelling the education system more thoroughly.

Detailed, yet relatively simple models can be developed that differentiates between enrolments, dropouts, and graduates by level of schooling and field of study by age/sex cohort. In this type of model, should also take dropouts, returns, mature students, program lengths and switches between levels of postsecondary schooling and fields of study into account in order to determine completers by level of schooling and filed of study. These enrolment, dropout and graduation rates, etc. are calculated over history and then projected into the future using extrapolative trends.

A few models explicitly account for people who leave the school system and enter the workforce. The Canadian model has a detailed education sub-model, this model tries to account for the aggregate flows from the school system and, based on survey information, place these school leavers into the occupations that they have historically occupied. This approach is a static exercise, however.

In the typical model, the occupational outcome for migrants is not taken into consideration, they are assumed to have the same occupational outcome as the general population. Given the large migration flows in Canada, however, this approach would be a serious omission.



More Advanced Approaches Given the paucity of information about the education models it is not clear what factors are utilised in the determination of enrolment and graduation rates. The literature on postsecondary school enrolment and completion, however, show that they are influenced by economic and non-economic factors. Economic factors include expected wages after graduation, the discount rate, opportunity cost (wages forgone), cost of education and family financial resources. Most of these factors are not available in the occupational modelling systems, and therefore would represent a significant increase in the size and complexity of the model should they be included for a large number educational categories. It is possible that a smaller set of factors can be used to produce estimates for the educational aggregates, such as college, trade and university enrolment and then estimate enrolment by CIP category based on relative educational demand.

In the ROA system, the qualifications of the school leavers and the economic conditions that they face when leaving school are taken into consideration to determine their occupational outcomes. Future labour market prospects for newcomers are determined, for each type of education, by comparing expected demand and supply flows with each other. Excess supply, however, does not imply that the group in question will become unemployed, and a supply shortfall does not automatically mean that there will be unfilled vacancies. In practice, it appears that school-leavers with a type of education for which supply exceeds demand do suffer from a deterioration of their position. They are more likely to get less favourable contracts, are less well paid and accept work below their level. If this group accepts work below their level of qualification there will be fewer job openings for those with lower levels of qualifications, who will therefore suffer from 'crowding-out' from those with education in excess supply. On the other hand, for those with educational backgrounds that are closely related to the types of education that are in short supply, there will be extra job openings. These passive substitution effects are important determinants of the labour market prospects by type of education.47

The COPS uses an explicit sub-model for immigrants. The number of immigrants in the experienced labour force for each 3-digit NOC occupation is divided by total landings to derive the share of immigrants in each occupation. The historic share of immigrants by occupation is held constant over the forecast period in order to estimate the number of immigrants destined for these occupations. The AEII uses a reaction function to adjust the occupational outcome for migrants based on relative demand.

Separations and Replacement Demand: Model Workers can leave an occupation for a variety of reasons, such as retirement, death, migration, illness, disability, changing occupations and women leaving the workforce temporarily to bear and raise children, etc. Total separations identify the flow of individuals leaving an occupation for any reason. Net separations summarise movements of workers into and out of an occupation over a specific period. Labour market separations are departures from employment to outside the labour force, such as retirement, death and migration.

Replacement demand is a similar, but not an identical concept to separations, because the departure of an employee can be used as an opportunity by the employer to eliminate the position. If employment is not declining, total separations is equal to replacement demand and net separations approximate the number of persons who permanently leave an occupation and quantify the need for new entrants.48 If the level of employment is declining, then replacement demand is equal to the positions actually replaced, which can be calculated as separations less the 47 De Grip and Marey (1999) p, 46. 48 BLS (2004) p 161.



decline in the level of employment. From the perspective of the firm, recruitment demand equals total expansion and replacement demand. A number of models estimate separations; fewer make the distinction between separations and replacement demand.

Several studies have shown that most new entrants to the labour market find employment as the result of replacement demand rather than expansion demand. If the objective of the occupational model and forecast is to provide information on job prospects and labour market imbalances, then replacement demand must be modelled and forecasted.

Replacement demand is calculated in a number of different international models – e.g. Australia, the Netherlands, the UK, the US and Canada. While there are many similarities among the models, there are also many differences. In some instances, replacement demand is calculated outside the model. For Australia, the CEET forecasts replacement demand by occupation.

As discussed above there are two basic approaches that can be used to modelling separations. Either total or net separations can be estimated. The choice between these two approaches will depend on the available data, the required level of detail and how the information will be used. Total separations will illustrate the number of jobs that are available, while net separations illustrate the number of jobs available for newcomers. For both calculations there is the need for the age distribution of employment because separation rates vary across age cohorts. Ideally the data should be differentiated by age/sex cohorts, since there are also significant differences between genders.

In the Canadian COPS model, replacement demand is equal to death plus retirements. They make the assumption that other new job leavers (demand side) are potential new re-entrants (supply side) and therefore cancel each other out, leaving them with permanent retirements and deaths for total separations, net separations, labour market separations and replacement demand.

Simple approach The simplest approach to estimating separation rates is to use information that is contained elsewhere in the data, and assume that all occupations have the same age/sex separation rates.

Usual Approaches

For deaths, the same aggregate age/sex death rates as in the demographic projection are typically used. For out-migration the same age/sex aggregate out-migration rates as with the general population are typically used. There does not seem to be a common approach to retirements.

In Canada, the COPS group determined that by using survival analysis with the LAD data, the overwhelming majority of re-entry into employment for those 50 and over following a job separation happens over the first three years. This result was used to estimate a time series of aggregate retirement flows for Canada by classifying those who separated from a job and remained non-employed for three consecutive years as retired permanently. A fixed effects birth cohort model was estimated to study the time series behaviour of retirement rates across age groups and gender derived from the LAD. By combining retirement rate forecasts by age group and gender (derived from the model) with projections of employment by age group and gender, a future retirement scenario is produced.49

More Advanced Approaches In the Netherlands, replacement demand is forecasted by occupation and education. Occupational replacement demand arises both from permanent departures from the labour market, such as retirement and the temporary withdrawals, such as women leaving to raise children. In addition,

49 Dunn (2005)



the mobility of workers between occupations creates another source of replacement demand. Their methodology only captures the net flow to or from an occupation, which means that replacement demand satisfied by re-entering workers is not measured. The replacement or net replacement demand concept is measured for newcomers to the labour market.

The US Bureau of Labor Statistics (BLS) estimates total separations, net separations and replacement demand based on the historic inflows and outflows into the labour market.50 Total separation rates for all detailed occupations are developed from merged Current Population Survey (CPS) data for the two most recent years for five year age cohorts. If employment in the occupation in question remained the same or increased over the year, the total separation rate was the replacement rate. If employment in the occupation declined, the replacement rate was calculated by subtracting the employment decline from total separations. Total replacement rates were used without adjustment for the projection period. Employment for the midpoint of the period was multiplied by the annual average replacement rates for the forecast period to project annual average replacement needs over the forecast period.

To develop a net separation rate for an occupation, employment figures for age groups for that occupation from five years earlier were compared with employment in the occupation in the last period of history for a group that was 5 years older. If employment for the group increased, no net separations occurred, and separations were recorded as zero. If employment declined, the number was recorded as net separations for that age group. The 5-year net separation rate for the age group was calculated by dividing the number of net separations by employment for the year five years in the past. The 5-year net separation rates for the historical period for each age group could then be applied to employment in future years to obtain a projection of net separations. Excluded from these projections are the replacement needs of newcomers during the projection period.

Demand/Supply Interaction: Model Given the sheer size of a detailed occupational demand/supply model, most occupational modelling systems use distinct occupational demand and supply models, and there is no interaction between the two. No model determines detailed occupational demand and supply in a simultaneous manner. The Dutch model, however, uses an iterative modelling approach that includes passive and active substitution effects.

Simple approach The simplest approach is not to include demand/supply interaction. The demand and supply models estimate ex ante demand and supply estimates instead.

Usual Approaches Most models do not include detailed occupational demand/supply interactions. For most modelling systems, aggregate labour demand and supply interact in the macro model. Total employment and labour force, therefore, do reflect demand/supply interactions.

More Advanced Approaches The ROA takes a much more detailed approach to internal labour market dynamics. The ROA transforms the industrial employment and school-leaver forecasts into the demand and supply by 127 occupational groups and 104 educational types. Demand and supply are compared by both education and occupation. The theoretical structure that underlies ROA's forecasts synthesizes neoclassical adjustment processes in the manpower forecasting framework and builds on concepts of job matching and disequilibrium theory. The model first determines ex ante demand and

50 BLS (2004). p 161



supply with passive substitution, and then through active demand and supply substitution the model produces ex post estimates of demand and supply.

In the determination of ex ante demand and supply, the demand by education for each sector is adjusted based on the demand/supply position of competing education levels for that occupation. Excess supply (demand) for a higher education group in that occupation will lower (raise) demand for those with a lower education level and push (pull) new entrants from (to) this occupation. In both the ‘push’ and ‘pull’ cases the degree of switching to other jobs for an educational type is assumed to be proportional to the job structure of this type of education in the previous period. After reiterating the substitution effect, an equilibrium situation is reached and indicates ex ante demand with passive substitution because of excess demand or supply for closely related types of education.

The ROA also determines ex post demand and supply by modelling ‘active’ substitution effects based on supply/demand mismatches for the type of education concerned. The model can distinguish between demand-led changes in skill requirements from changes in industry structure, occupational structure and skills required in various occupations, and substitution or ‘crowding out’ effects related to labour market mismatches. Excess demand conditions will encourage people to change occupations and students to select fields of study that can be used in those occupations. Demand/Supply imbalances therefore will have a direct influence on inter-occupational mobility and labour supply flows. This approach is very resource intensive.

Labour Market Indicators: Models There are a large number of current and future labour market indicators (LMI) that are used throughout the world. Some of these LMI rely on historical information, others utilise forecasted data, and a third type combine historical and forecast data. For the international models, COPS, BLS and the ROA have very specific methods to develop indictors of labour market imbalances. In Canada, innovative approaches are used to construct LMI at the provincial level.51 In most cases numerical indicators are translated into qualitative assessments of future conditions to make it easier for users to understand the results and to avoid giving false sense of precision.

Simple approach There are a number of simple LMI that can be constructed. One approach is to compare the projected ex ante level of employment with the ex ante level of labour force by occupation.

Usual Approaches Labour Market Indicators are quite varied, so there is not one typical approach.

More Advanced Approaches

The COPS creates a LMI to assess current labour demand and supply conditions by occupation and broad skill level. The indicator is used to evaluate the competition for job openings, the stability of employment and working conditions, such as the change in employment, the level and change in earnings and the unemployment rate over the past four years. In 2004, these indicators were combined to derive an overall rating for current occupational labour market conditions—good, fair or limited.52 In 2006, COPS did not explicitly include these indicators in their hardcopy publication, although they were implicitly used in highlighting occupations under pressure. 53

51 see Fairholm (2006) 52 Bergeron et al. (2004). pp 8-11. 53 Lapointe et al. (2006). pp 29-33.



The COPS labour market indicators make it possible to identify occupations currently under pressure – that is, occupations where the supply of labour cannot meet the demand. Based on the methodology developed by the BLS,54 an occupation is considered to be under pressure if its employment growth rate is at least 50 percent faster than the average, wage increases are least 30 percent faster than average and the unemployment rate is at least 30 percent below average. The method is modified, so that the unemployment rate indicator also includes those occupations that have an unemployment rate that is close to its historic lows. In addition, the COPS uses a modified version of the BLS methodology to determine which occupations are currently facing downward pressures – that is, occupations where the supply of labour is greater than demand. Occupations showing an employment growth rate at least 50% slower than the average, with wage increases at least 30% slower than average and unemployment rates at least 30% above average, are considered to be facing downward pressures.

The COPS expands on the current indicator to include future pressures that are expected to continue over the next five years. Several conditions must be met for an occupation to be classified as facing significant future labour market pressures: The occupation must currently have an unemployment rate at least 30% below average and must meet at the minimum, one of the two other thresholds identified by the BLS, namely, an employment growth rate at least 50% faster than the average, or wage increase at least 30% faster than average. The occupation must be rated as facing ‘good’ current and future labour market conditions. And increases in the demand for workers that occur as a result of expansion demand and retirements must significantly exceed additions to the supply of workers from immigration and the formal education system.

ROA’s labour market information system combines labour market forecasts with risk indicators that specify the structural aspects of the labour market position for various types of education. The indicator of the future labour market prospects represents the expected labour market situation from the point of view of newcomers. The risk indicator provides information to the cyclical sensitivity of employment and the possibility of lateral mobility.55

The indicator of the future labour market situation (IFLM) compares labour demand with supply by type of education. Labour demand in ROA’s approach consists of expansion demand, replacement demand and substitution demand. Supply consists of the expected inflow of new labour market entrants from the school system and the number of unemployed with the same educational background who had been unemployed for less than one year in the base year. The exclusion of those unemployed for more than one year is based on the assumption that they do not compete in the labour market with school-leavers with the same type of education.

The indicator of the future labour market situation is translated into a qualitative scale ranging from very good, good, reasonable, moderate and poor. However, an unfavourable labour market indicator does not automatically mean that school-leavers will be confronted with unemployment, any more than a demand surplus will automatically lead to unfilled vacancies. The final consequences of a demand or supply surplus depend also on the market position of a particular type of education and occupational class, for instance on whether school-leavers can switch to other sectors of the labour market or on the substitution possibilities between the types of education within an occupational class. Therefore, in addition to the labour market forecasts, two risk indicators are included in the information system.

These risk indicators give an assessment of the cyclical sensitivity of employment in a certain occupational class and of the possibilities of switching to another occupation (lateral mobility) or

54 Veneri (1999). 55 This discussion paraphrases de Grip and Heikje (1998). Also see. de Grip and Heijke (1988).



another economic sector (inter-sectoral mobility), indicating the labour market flexibility of the educational type concerned. The trend in the dispersion index is also entered into the system.

The risk indicators are very important additions to the labour market forecast. For example, high occupational dispersion will make medium to long-term forecasts conditional, because, in the event of excess supply, it would be expected to be relatively easy for those in this educational group to shift to other segments of the labour market. The cyclical sensitivity indicator suggests the likelihood of medium to long term forecasts. If high employment growth is forecast for an occupation with high cyclical sensitivity, the possibility that this growth could be sharply reduced in the longer term or even turn into a decrease has to be taken into account. `

The LMI that are constructed by a province could include the large variety of indicators that are developed by the ROA for the Netherlands. This would permit the dissemination of information that are of specific interest to different stakeholders. Another possibility is to develop the indicators for education groups as well as occupational groups. It is also possible to translate the quantitative estimates produced by the indicators into qualitative indicators of labour market conditions, such as poor, limited, fair, good and excellent. The publication of precise numbers could give a false sense of security concerning the outlook, which is unwarranted given the large confidence intervals around these forecasts.

Sector Models There are many examples of employment forecasting for key industrial sectors (e.g. the health care industry) or important occupational subgroups (e.g. highly skilled workers). There have been a number of academic articles examining the dynamics of particular labour markets for highly skilled workers. For example, Freeman had a series of articles examining the market for engineers, lawyers and science workers. There have been numerous studies examining the demand for and supply of high knowledge workers that were expected to be in surplus or shortage, for example, physicians, nurses, college and university professors, and scientists.56

The essential steps taken in sector models are the same as that described above for the national or regional occupational demand/supply models. Expansion demand, replacement demand, and the inflows to and outflows from labour supply should be taken into account. There is a range of data sources and modelling approaches that are used for sector models.

In many instances sector models use data that are more detailed than the NOC data that are available from Statistics Canada. For example, at the 4-digit NOC level of aggregation, there are two physician categories, Specialists Physicians (D011), and General Practitioners and Family Physicians (D012). This level of detail is too aggregate for planners in the health care field. As a consequence, alternate data sources are used, such as from the provincial health ministry, medical association or from the Canadian Institute for Health Information (CIHI).

Some sector models use a simple modelling approach. For example, the model described by Center for Health Workforce Studies (2006) uses a simple stock-flow model for supply, whereby the level of supply in a period equals supply in the previous period plus new entrants less separations. And demand equals supply in the base period, and then increases by an adjustment factor in a stock-flow manner, so that different broad outcomes could be analysed. Other sector models use more sophisticated techniques. For example, Marey, de Grip and Cörvers (2001) estimated employment of research scientists and engineers using an error correction mechanism.

56 For example see. Bland and Issacs (2002). Center for Health Workforce Studies, (2006). Cooper (2002). Cooper et al. (2002). Forte et al. (2000). Weiner (1994). Wennberg et al. (1993). Canadian Council on Learning (2006). Canadian Council on Learning (2008). Marey, de Grip and Cörvers (2001).



Often a sector-specific approach to determining the demand for output or employment is taken. For example, Marey, de Grip and Cörvers (2001) used R&D expenditure as the explanatory variable in the employment equation. In an examination of the demand for health care workers Fujitus Consulting (2002) primarily used demographic demand based on population growth by age/sex cohorts and the utilisation of these services by age/sex cohort. The Canadian Construction Sector Council also uses a very industry specific approach.

The Construction Sector Council provides detailed demand/supply forecasts for the construction-related trades. The trade demand models use outputs from economic models, namely, investment and employment forecasts, along with sets of coefficients that extract the trades’ requirements from the forecasts. The forecast system links employment in each trade and occupation to spending by specific building types. Each link is defined by a measure of labour required for each million dollars of building. Labour demand is the result of increased economic activity and retirements. An important part of the trades demand forecasting methodology is the consideration of major projects. These projects are important because they have a significant impact on the economic forecasts and usually have trades requirements coefficients that differ noticeably from the average in the economy. On the supply side, the forecast system tracks labour force, apprenticeship and mobility for selected trades and occupations. Estimates are based on the Statistics Canada 2001 Census as well as input from the industry. The number of potential new job seekers is estimated from the flows of apprentices coming out every year, of recent immigrants and people re-entering the job market after a period of non-participation. 57

Alternative methods58

There are a number of different approaches that have been adopted to anticipate changing skill needs. Some, particularly the more traditional approaches, involve formal, quantitative methods. Others, especially some of the more recent attempts, are rather less formal and have a strong qualitative emphasis, involving the use of multidisciplinary methods. Such methods are not primarily concerned with precise quantitative measurement. All of the following have been used at different times and in different contexts. The main approaches that have been used are as follows:

a) quantitative methods: – mechanistic/extrapolative techniques, – behavioural/econometric models, – survey of employers’ opinions, – skills audits;

b) qualitative approaches: – Delphi techniques (consultation of expert opinion), – case studies, – focus groups, – holistic modelling approaches.

57 Construction Sector Council Website: constructionforecasts.ca/reference/methodology 58 Wilson (2001) p. 571.



Company Level Employment Planning59

Personnel planning is a well established management function. Larger companies and employing institutions often have a specialist personnel manager undertake this function. At the company level, the range of models and methods is broad ranging from very simple rules of thumb to quite complex models paralleling the national level ones described above. These tend to focus more on shorter-term developments than the national models. Companies are concerned with immediate problems connected with recruitment. There is an enormous literature covering these topics, much of which is far too specific to be of general value. Nevertheless, it is useful to provide a brief overview of some of the key issues.

Current Methods Bartholomew, Forbes and McClean (1995) give a comprehensive review of the various techniques in use to produce company personnel forecasts. A great deal of the literature is very mathematical in content, and a large number of the papers appear in statistical or operations research journals. A good summary of the earlier work is provided by Smith and Bartholomew (1988).

The starting point for most traditional company manpower models is the existing stock of labour resources and the related outflow. The mathematics of Markov chains provide a ready modelling framework for predicting such flows. As personnel record systems have improved and new statistical developments in survival analysis have evolved, semi-Markov processes in continuous time have become established. An age specific approach can be used, rather than just a job tenure-specific method and this model is used to explore alternative scenarios, when an analysis of career patterns is taken into account. The demand side of manpower forecasting calls for a different approach because it is primarily concerned with jobs rather than people. There is no clear-cut demand model to set alongside the Markov processes which are used to model the supply-side, because the demand determinants at company level are very diverse, depending on the organisation.

There is now a huge body of work about planning in areas such as health services, education services and government administration. Much of this is in the public domain. However, it tends to focus on sector specific concerns. The work in companies operating in the traded sector is extremely diverse. Much of it is highly technical and falls within the boundaries of operational research rather than the broader economic and social science which underlie the national work. A problem in this area is that much of this research is not made public because of concerns about commercial confidentiality.

Where the time series of data is long enough and of sufficiently good quality, regression modelling can be used. Alternatively, if information on causal variables is lacking, a simple trend extrapolation approach can be applied. A common problem at company level remains the relatively short time series of consistent data on which such models can be based. As databases of employment records have developed, opportunities to build better demand-side models for specific company occupations have increased.

Alternate methods used to project company demand Reilly (1996) described some more general and less quantitative approaches to assessing company employment demand. These methods include work study, which establishes optimal levels of resourcing for planning levels of operation. Associated with this technique, is activity analysis, which can be used to identify the numbers of employees needed for specific tasks. Both 59 Wilson (2001) pp. 581-583.



methods assume the company can accurately determine its future scale of operations from market output forecasts.

Other approaches which could be used are ratio analyses, which assume a fixed relationship between sales volumes and the numbers employed in specific occupations. Such an approach could be amended to allow for productivity growth. Another method is to use benchmarking, whereby staffing in comparable factories and offices is used as the criteria for determining appropriate employment levels. In many smaller companies demand forecasting (both for output and employment) is achieved by subjective judgement of the senior managers.

Company employment planning models continue to be a valuable tool for the planning of human resources and the development of suitable training and education strategies. The literature continues to produce papers from many different countries which demonstrate the evolution of models used for forecasting employment at varying levels of aggregation. Two recent such studies are the papers by Khoong (1996) and Kao and Lee (1998).

Work Plan The work plan for the development and implementation of the modelling system involves a number of steps. The development of the demand and supply components can be carried out separately or at the same time if sufficient resources are available.

It is recommended that resources be devoted to the development of the demand component first. This component is relatively easy to develop and requires much less development time than is the case for the supply component. With it developed, an occupation demand projection can be produced that would provide useful information to policymakers. This information would show the value of undertaking the development of the system and provide support for the completion of the replacement demand and supply components.

The suggested steps for the system are separated into those for the occupation demand and supply components. Nevertheless, the first step is the choice of the occupations to be included in the system. Without this choice neither component can be developed.

Demand Model 1. Choose the industry detail for the employment model; 2. Set up the industrial employment forecast method; 3. Set up the occupational by industrial forecast approach; 4. Test the method by producing a projection; 5. Make modifications where necessary; and 6. Prepare an occupation demand projection.

Replacement Demand and Supply Model 7. Decide on calculation of total replacement or net replacement demand; 8. Calculate flow coefficients from historical data for replacement demand; 9. Set up the stock flow model for labour supply; 10. Develop education sub model; 11. Calculate remaining flow coefficient for labour inflows; 12. Construct Labour Market Indicators; 13. Test the method by producing a projection; 14. Make modifications where necessary; 15. Prepare an occupation supply and replacement demand projection; and 16. Calculate Labour Market Indicators of future labour market prospects.




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Parnes, H., (1962). “Forecasting Educational Needs for Economic and Social Development", OECD, Paris.

https://www.gtap.agecon.purdue.edu/../../about/center.asp

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Appendix I: Labour Hoarding The labour market is acting differently in recent years to changes in economic conditions than at any time in the previous 25 years. There is evidence that firms are hoarding labour despite relatively strong economic growth. This action appears to be related to rising staff turnover rates in many regions of country and employers’ fears that labour shortages will intensify in the future because of well known demographic developments that will reduce the number of experienced workers and the hours they are willing to work. Employees of all ages seem to have reduced their willingness to provide hours of work at a given level of real wages. This could be as a result of a desire to change their work/life balance. The implicit bargain between employers and employees to reduce labour utilisation to improve working conditions means that firms are engaged in significant labour hoarding even as employment and aggregate demand expand. For firms this implicit bargain to boost headcounts and reduce labour utilisation is rational if fixed labour costs are high or rising compared to variable labour costs.

An Unusual Cycle In the past, the total number of hours worked was pro-cyclical and swung more than employment. During periods of economic slowdowns or recessions employers would cut the number of hours worked rather than jobs in order not to lose valued employees and their skills for the subsequent upturn. This is commonly referred to as labour hoarding. Conversely during an economic expansion, employers would boost the number of hours worked by the average employee to meet additional demand, so total hours worked rose at the same or a faster pace than the total number of people employed. The intuition for this is straightforward if changing average hours is less costly than changing the headcount of employees, which would be the case if there are institutional impediments to hiring and firing workers and there are significant fixed costs to adding new or removing existing employees. Recently, however, the rise in hours worked has been significant less than what the historic increase in employment would have suggested during the expansion phase of the cycle.

1976=1.0

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

1.80

1976 1980 1984 1988 1992 1996 2000 2004

Total employedTotal actual hoursAverage Actual Hours

Divergence Between Employment and Hours Worked

33.50

33.75

34.00

34.25

34.50

34.75

35.00

35.25

35.50

35.75

1976 1981 1986 1991 1996 2001 2006-4

-3

-2

-1

0

1

2

3

4

5Total Employed (Left Scale,Year Ago Per Cent Change)Average Actual Hours (RightScale, Hours)

Average Hours Slide Even As Employment Expands



During the current expansion the underperformance of hours worked continued even while employment was growing at a moderately fast pace. Certainly, the recent decline in average hours worked is different than the cyclical pattern that existed over most of the past 30 years. And from the perspective of the long-term trend movement in average hours, the recent slide stands out as well. On the chart, the long-term linear trend in average hours worked is illustrated as the black line that runs through the average hours worked (red line) concept. As can be seen in the chart, average hours worked has largely remained below the trend line during the current cycle, except for a momentary rise to around the trend line in 2005.

This phenomenon is geographically widespread. Workers in all provinces of Canada have experienced a slide in their average hours worked. Notably, provinces that experienced stronger economic growth from 2000 to 2006, such as Alberta, BC and Newfoundland, experienced smaller declines in average hours worked than the provinces that experienced more modest economic growth over this period. Indeed there is a correlation of 0.8 between GDP growth and the change in average work hours over this period, which suggests that labour demand is influencing the movement in average hours. Understanding variations in demand, however, is not sufficient to understanding the slide in average hours, however. Both Alberta and Newfoundland experienced economic growth at or above 3.75% over this interval. And both Alberta and BC experienced employment growth in excess of 2.0%. Despite strong economic growth and/or strong employment growth these provinces witnessed a decline in average work hours from 2000 to 2006. If average hours are declining despite strong demand, then there must be a decline in the supply of labour at existing real wage rates. These results suggest that both demand and supply side factors are influencing average hours worked since 2000.

Average Hours Worked by Province

32

34

36

38

40

1976 1981 1986 1991 1996 2001 2006

Canada OntarioManitoba SaskatchewanAlberta BC

Average Hours Slide Even As Employment Expands

Average Hours Falling Across Age Groups The economic data for Canada show a widespread decline in average hours worked across most major age/sex cohorts. Since 2000, nine of ten age/sex groups have experienced a decline in average hours worked despite aggregate employment growth. And the tenth age/sex group—females 65 years and over—experienced a slide since 2001. From the perspective of aggregate labour supply, the decline in aggregate average hours worked is being aided by two underlying shifts: the aging of society and the rising importance of females in the labour force.

Average hours worked data by age cohort show that average hours is fairly low for the youngest age cohorts, when these people are in school, and rise in later age cohorts through to age 45-54. After age 55 people tend to experience both a drop in labour force participation rates and those who do work, work fewer hours. Since the 55 years and older age cohorts are becoming of increasing importance because of the aging of the baby boom generation this means that there



will be downward pressure on the aggregate measure of average hours worked even if there is no further change in average hours worked for specific age/sex groups.

Women tend to work few hours than males in each age cohort. Females work 80 per cent of the hours of male workers on average. So the trend toward more females in the workforce has reduced the average hours worked for the workforce as a whole. Given that women now represent more than 50% of postsecondary students, they will represent a rising share of highly skilled jobs. Therefore, the number of hours that they provide to the workforce will be of enduring interest. Failing a significant change in the average hours worked by females, the further rise in the share of women in the workforce will continue to put downward pressure on average hours worked. When combined with the downward pressure on aggregate hours worked from the aging of the workforce, there will inevitably be a further slide in the average number of hours worked in the future. These trends are not new, but they are likely to intensify in the future. What is new is that there seems to be a different cyclical response in the labour market on top of this long-term structural decline in average hours worked.

20

22

24

26

28

30

32

34

1976 1981 1986 1991 1996 2001 2006

Female Age Groups, Average Hours Worked

15-24 25-44 45-5455-64 65&

… Since 2000 Despite Moderately Strong Job Growth

25

30

35

40

45

1976 1981 1986 1991 1996 2001 2006

Male Age Groups, Average Hours Worked

15-24 25-44 45-5455-64 65&

Average Hours Fell In Most Age/Sex Cohorts…

Labour Hoarding As described by Felices (2003), labour hoarding is a reflection of the intensity with which labour input is used when the amount of labour is costly to adjust. A low rate of labour utilisation implies that there is hoarding of labour. So the key point of interest is how labour utilisation evolves over the cycle. Labour input can be characterised as the product of N, the number of people employed; h, average hours worked per head; and e, the level of effort with which total hours worked (Nh) are applied. If one defines labour input as total hours worked, then the labour utilisation rate can be thought of as the (average) effort rate, e, applied by the workforce over those hours. If labour input is defined as total employment, then the labour utilisation rate is average hours times effort (he). Since the effort rate is unobservable, the difficulty comes from determining how to estimate effort.



In his examination of labour hoarding, Felices used a model that was developed by Imbs (1999). Imbs’s measure of labour effort is a function of the ratio of output to consumption and two estimated parameters of the optimisation problems of households and firms. As described by Felices (2003), the intuition behind this measure is that effort is chosen optimally. The household’s marginal loss of supplying effort (measured in units of consumption) has to be equal to the marginal output extracted by firms from this additional effort. Hence, movements of output relative to consumption (shaped by these key parameters) should proxy movements in the equilibrium level of effort. This formulation takes advantage of the consumption data by combining consumption and the labour supply decisions of households with the profit maximisation decisions of firms.

After this type of measure is constructed for Canada, using hours as the labour input, it is evident that in the past there was a high degree of correlation between effort and the economic cycle. Periods of strong economic growth are consistent with high levels of effort and periods of weak economic growth occurred at a time of a low degree of effort. Over the 1984-2000 period the correlation was 0.81 between effort and economic growth using the four quarter moving average

of the year-ago per cent change in real GDP with the effort rate derived above. Notably, the correlation between effort and the output gap was 0.60 over this period. As discussed above when the effort rate falls, there is a higher degree of labour hoarding. Therefore, one can conclude that there was a high degree of labour hoarding in past periods of economic underperformance as was expected by theory. What was not anticipated by economic theory is the recent drop in effort during a period of strong economic and employment growth.

Effort and Average Hours Fell Since 2000

-3

-2

-1

0

1

2

3

4

5

1983:1 1986:1 1989:1 1992:1 1995:1 1998:1 2001:1 2004:1 2007:

Deviation, Percent

Average Hours (Deviation From Trend)Labour Utilization (Effort, Deviation From Mean)

Effort Moved With the Cycle Until Recently Percent

-4

-2

0

2

4

6

8

1983 1986 1989 1992 1995 1998 2001 2004 2007-4

-2

0

2

4

Real GDP (Year-Ago PerCent Change, Left)Labour Utilization (Effort,Deviation From Mean, Right)

The relationship between labour hoarding and the economic cycle broke down around 2000 at around the same point as the breakdown between hours and employment. Since the turn of the millennium there has been a significant reduction in effort and a corresponding rise in labour hoarding even though employment and economic activity continued to grow. The correlation



between effort and the indicator of growth used above slid to 0.02 over 2001 to the second quarter of 2007, and the correlation between effort and the output gap fell to 0.03 over this time period.

Notably, this measure of effort was closely related to the aggregate profit margin (profit to nominal GDP ratio) over most of the past 25 years, with effort rising when the profit margin was high and labour hoarding rising when the profit margin was low. Since 2000, however, the historically tight relationship breaks down and the degree of hoarding has soared even as (or maybe because) profitability has rocketed higher as Canada’s terms of trade helped to boost corporate profits. If one assumes that firms expect labour shortages ahead and labour has a significant fixed cost, then this behaviour would be rational for firms.

Normally, a supply shock whereby workers have reduced the number of hours they are willing to supply would result in higher real wages because employers would need to offer higher wages to entice workers to provide the same number of hours. As described by Felices, however, labour hoarding implies that wages should be lower during the expansion phase. The evidence from the aggregate data seems to support this proposition. The relationship between the real hourly wage rate (the four quarter average of the year-ago change in the hourly wage rate less the year-ago change in the consumption price deflator) has risen by less than what normally would be implied by the low level of the aggregate unemployment rate compared with the last two periods of expanding employment and falling unemployment rate. These considerations seem to suggest that some degree of labour hoarding has indeed occurred in the Canadian labour market.

-4

-3

-2

-1

0

1

2

3

4

1983:1 1986:1 1989:1 1992:1 1995:1 1998:1 2001:1 2004:1 2007:1

Percent

0

2

4

6

8

10

12

14

16Labour Utilization (Effort, Left)

Profits to GDP Ratio (% ofGDP, Right)

Rise in Real Wages Less Than Previous Up-Cycles

-4

-3

-2

-1

0

1

2

3

4

5

1983 1986 1989 1992 1995 1998 2001 2004 2007

Percent 4

5

6

7

8

9

10

11

12

13

Real Wages (Year-Ago Per CentChange, Left Scale)Unemployment Rate (Lagged 3Q,Right)

In Past Effort and Profits Related, But Not Recently




Appendix II: Skills Transferability Matrix Roberts (2003) developed a skills transferability matrix in order to provide information about the likely mobility of workers between occupations.60 Essentially, Roberts examined various sources of information in order to make an informed determination as to whether a transition is possible between two occupations based on some observed commonality in the knowledge and skills required for the original (target) and destination (priority) occupations. The destination occupation is the occupation in which the individual will be employed after the transition. While the original occupation is the occupation from which the individual departs to become employed in the receiving occupation, which are the target occupations that are identified.

At the most basic level, the development of the Skills Transferability Matrix rests on the analysis done by HRSD in the construction of the NOC classification system and NOC Matrix. As described by Roberts, the NOC is based on four principles of classification: skill level, skill type, occupational mobility and industry. The NOC skill levels are illustrated in Table A2.1. The NOC skill types at the broadest level were designed to reflect labour market realities such as occupations dominated by systems of internal progression or occupations dominated by particular types of university or college training.

The NOC matrix is arranged by skill level and skill type. The four skill level categories are listed on the left side of the matrix (see Table A2.1). Nine skill type categories are listed across the top, (see Table A2.2) along with a tenth skill type, management occupations, that is listed along the top of the matrix above the skill levels.

Table A2.1 NOC Skill Level Criteria

Education/Training Other Skill Level A • University degree (bachelor’s, master’s or post-graduate) Skill Level B • Two to three years of post-secondary education at • Occupations with supervisory community college, institute of technology or CEGEP or responsibilities are assigned to • two to four years of apprenticeship training or Skill Level B • three to four years of secondary school and more than • Occupations with significant health two years of on-the-job training, training courses or and safety responsibilities are Specific work experience assigned to Skill Level B Skill Level C • One to four years of secondary school education • Up to two years of on-the job training, training courses or specific work experience Skill Level D • Up to two years of secondary school and short work demonstration or on-the job training Source: Roberts (2003)

60 Roberts (2003).



Table A2.2 Skill Type

1) Business, Finance and Administration Occupations 2) Natural and Applied Sciences and Related Occupations 3) Health Occupations 4) Occupations In Social Science, Education, Government Service and Religion 5) Occupations In Art, Culture, Recreation and Sport 6) Sales and Service Occupations 7) Trades, Transport and Equipment Operators and Related Occupations 8) Occupations Unique To Primary Industry 9) Occupations Unique To Processing, Manufacturing and Utilities 10) Management Occupation

In most cases, each cell of the NOC Matrix consists of a major group. The matrix also provides an overview of the classification structure at the minor group level and illustrates how the NOC is accessible on the basis of skill level, skill type, or some combination of these two criteria.61

As discussed by Roberts, the NOC structure inherently provides information regarding occupational mobility because it is designed so that there is a greater degree of occupational mobility within a unit group than outside the group, and the placement of unit groups within skill types in the Matrix was designed to illustrate mobility paths. As a result of this structure, there is more likely to be lateral or vertical mobility between groups in close proximity within the NOC matrix than to dissimilar groups. This structure therefore helps to narrow the search to find occupations for which there are mobility prospects.

The methodology has a degree of subjectivity in it, since there is always the question of how to draw the circle around the original occupation in the NOC matrix and therefore how many other occupations to examine. Two different analysts can come up with a different list of target occupations for each priority occupation given different approaches to defining a match.

There are a couple of drawbacks of Roberts’s approach. First, the selection reported is either “Yes” or “No”.62 Either a person from one occupation can transition to another occupation or they can’t. Also there is little information in the matrix to indicate the magnitude of the gap in skills or knowledge between the two occupations. Second, the approach was applied to immigrants and because of the drop in human capital in the host country, in many cases the skill level for the destination occupations was lower than the skill level for the original occupation. This result is not helpful in a more generalised application to examine potential labour market transitions, particularly in the present economic circumstances of widespread shortages of skilled labour. Firms often change their requirements over the course of the economic cycle, with the hiring of less qualified people when the labour market is tight and hiring more qualified people when there is slack in the labour market.

61 HRSD (2001). 62 Roberts (2003) indicates that quantitative analysis was conducted to support the determination of an occupation in the Skills Transferability Matrix, but it is not clear how this was done from the report and the data used in her analysis were not in her report or found on the HRSD website.



Appendix III: Changes to AEII Model Dynamics In the Alberta model, participation rates and in-migration dynamics are based on the regional labour market dynamic results that Partridge and Rickman (2005) found for Alberta. Their results were integrated into the model by assuming that all education/occupation groups would respond to employment shocks in a similar manner. This approach differentiates between the impact on labour force participation rates and the impact on migration by educational/occupational groups over time. Strong employment growth for an occupation will encourage a rise in the participation rate for that occupation and induce an increase in in-migration for people with those educational categories that are needed by the occupations experiencing above average growth.

The education/occupation matrix uses census data to reflect the proportion of people with a particular level of schooling and major field of study who supply labour to each occupation. Since there are significant differences across age and gender groups in the portion of people who supply their labour to specific occupations, the matrix is differentiated by age and sex. The education/occupation matrix is used for the transition from school to work as well as career development. In the original occupational supply model, the education/occupation matrix reflected the 2001 census data and the coefficients were fixed over the forecast period.

In the 2007 version of the model, strong occupational employment growth also causes more people to provide labour to that occupation and shift away from occupations experiencing slower employment growth. This is accomplished in the model by having the education/occupation transformation coefficients that are calculated from the census data change over time to reflect relative employment growth. This is done in a multi-step process.

First, a “desired” education/occupation coefficient is constructed for each of the roughly 40 thousand education/occupation pairings. This is done by assuming that the desired coefficient equals the actual education/occupation coefficient in the last period of history, but the desired level changes in the future based on the relative demand for that occupation versus the relevant education categories.

Desired Education/Occupation Coefficient = Historic Education/Occupation Coefficient * Demand for Occupation/Demand for Education

Or

DEO=EO*(DOCC/DED)

Where: DEO - desired education/occupation coefficient EO - historic education/occupation coefficient DOCC - demand for occupation DED - demand for education

The demand terms are employment indices with the last period of history being equal to 1.0, so the ratio “Demand for Occupation/Demand for Education” represents the relative increase in the demand for the occupation category versus the education category. For example, if employment demand for Technical Occupations in Physical Sciences is growing more quickly than employment demand for all graduates with Applied Science Technologies and Trades then the desired education/occupation coefficient would rise relative to the historic education/occupation coefficient.



Second, it is assumed that the education/occupation coefficient slowly adjusts to the “desired” level. This is done using a partial adjustment model.

Education/Occupation Coefficient = Education/Occupation Coefficient previous period + partial adjustment coefficient * (Desired Education/Occupation Coefficient previous period - Education/Occupation Coefficient previous period)

Or

EO= EO[-1]+δ(DEO[-1]- EO[-1])

Where: δ is the partial adjustment coefficient.

The partial adjustment coefficient is bound by zero at one extreme. If the coefficient is zero, then the model becomes a fixed coefficient model. At the opposite extreme, if the coefficient is one, then the education/occupation coefficient would change in one period to completely reflect changes in relative demand. There is, however, inertia in the labour market. Most people remain in the same occupation for long periods. Tautologically, only those who separate from their occupation are available to transition to a new occupation. Therefore in practice, the partial adjustment coefficient is bound by the rate of occupational separations.

Not all people who separate from the labour market are available for work. Some people who separate from their occupation leave the labour force altogether, such as full-time students and retirees, out-migrants and those who die. In order to more accurately reflect the pace of labour market adjustments, these cohorts were excluded from the calculation of the partial adjustment coefficient. Therefore, the partial adjustment coefficient was calculated by major occupational group by using total separations less retirements and separations due to return to full-time attendance at school and adjusting for out-migration and deaths.



Appendix IV: Data Sources There are a variety of data sources available to occupational modellers. Each survey has strengths and weaknesses that will be discussed below.

Census The census has a very large sample that represents 20% of the total population. These data can provide very detailed and internally consistent information on many aspects including: employment, labour force, industry, occupation, age and sex. Census data are available for provincial, territorial and sub-provincial areas63. The census, however, is tabulated every five years, so it is not up-to-date. The detailed labour force and employment data from the 2006 census, for example, were released in March 2008.

To ensure that individual responses remain private, Statistics Canada employs a random rounding methodology. For cells with less than ten respondents, ten, five or zero will be shown in the database. The number is randomly determined using an algorithm that weights the outcome based on the original number, which increases the likelihood that the number shown will represent the true rounding of the original number, but there is the chance that the rounding will be either higher or lower than what true rounding would show. This approach affects data quality and makes the data less reliable, particularly as the number of people in the cell falls toward zero.

Another consequence of this data quality issue is that identities do not necessarily add up at the most detailed level of aggregation. For example, if there are eight people in a particular category, six employed, and two unemployed. The census information could show ten people employed and five in the labour force, leading to a negative unemployment level. Or there could be five people identified in a particular occupation, but zero males and 10 females.

Census data must be ordered through Statistics Canada. Census data are available as part of standard tables, some of which are available for “free” on the Statistics Canada website, or through custom requests. Standard tables are more readily available, take less time to obtain and are much more cost effective than custom tables. However, standard tables only cover certain topics and often do not have the needed cross tabulation. In terms of geographic coverage, standard tables are typically available for Canada, the provinces and territories. Many standard tables are also available for Census Metropolitan Areas and Census Agglomerations. Community profiles are available that illustrate higher level labour force aggregations. Standard tables that have detailed labour force concepts are not available for more detailed levels of geography. Standard tables that have detailed occupational or industry detail are provided for total labour force and not employment.

Standard tables are cost effective. If data for only a few industries or occupations are needed, then they often can be obtain free of charge from the Statistics Canada website. These data take time to access so it is not time effective to obtain a lot of data from the Statistics Canada website. Standard tables that are purchased directly from Statistics Canada are relatively inexpensive. For 3-digit NAICS industry data, for example, a standard table is $60 plus $1.15 per geography. The turnaround is typically a few days. The data are available in excel format or Beyond 20/20

63 Geographical coverage includes: census metropolitan areas, census agglomerations, federal electoral districts, postal code areas, census divisions and subdivisions. Custom areas can be constructed on request. Reference maps of the census division and subdivisions are available on the Statistics Canada website. http://geodepot.statcan.ca/Diss/Maps/ReferenceMaps/prov_pdf_e.cfm



software. The Beyond 20/20 software permits easy manipulation of the data and is more time effective than excel. The resulting data can be exported into excel for the creation of tables, charts or more sophisticated data manipulation.

Custom tables take far longer to order and obtain than standard tables and are much more expensive than standard tables. There are several distinct steps in the process to obtain custom data. First the data client must provide information as to the specific data request based on available census data. Then the custom data request has to be sent from the Statistics Canada sales representative to the census analyst. The census analyst determines the complexity of the request and the number of “cells” in the requested data. The data client then reviews the data cost estimate and determines if they wish to purchase the data. If there are any parameter changes, then the updated data request must go via the sales representative to the census analysts. This step of the process can easily take one week of elapsed time, depending on how familiar the data client is with census data and the responsiveness of both the client and Statistics Canada representatives to each step of the process.

Once the data request is finalised, then the contract is prepared and signed. When Statistics Canada receives the signed contract, the data request is added to the back of the census data queue. The length of the queue will vary by Statistics Canada office, so the actual time it takes to deliver the same data will also vary by regional office. The Toronto office of Statistics Canada indicates that it takes four to six weeks to deliver custom census data after the contract is signed.

The cost of custom census tables is a minimum of $1,115 before the applicable taxes. The more data manipulation that is done to construct the dataset, the more expensive the data are to purchase. The more data cells in the matrix, the more expensive the data are to purchase. Note that a very detailed request that includes 3-digit industry (100 industries), with 4-digit NOC-S (520), with age (6 age cohorts) by gender and all provinces and territories will provide over 8.5 million cells, and will deliver mostly zeros and small numbers that are randomly determined. Detailed data requests can be quite expensive. For example, a recent data requests with 770,000 cells cost $3,080. And a data request with just over 1,000,000 cells cost $3,700. The actual cost of a custom table, however, also depends on the complexity of the request so these estimates can only be a rough guideline of the type of costs involved.

Table A4.1 Census Data Requests Standard Request

Geography All levels available - smallest Dissemination Area

Free Publication or Data From www.Statcan.ca http://www12.statcan.ca/english/census/index.cfm CD-ROM 2001 - complete CD-ROM $5,000 CANSIM NA

Other Free Tables Web Site http://www12.statcan.ca/english/census/index.cfm Turnaround Time 1 to 2 days after client agrees to go ahead Non Published Data - Custom Tables

Open to any census variables - subject to suppression b/c of data quality or confidentiality

Turnaround Time 4 to 6 weeks after contract signed



Labour Force Survey (LFS) The LFS is a monthly household survey of the civilian, non-institutionalised population 15 years of age or older in Canada’s ten provinces. Specifically excluded from the survey’s coverage are residents of the Nunavut, Yukon, and the Northwest Territories. While there are extended pilot projects being conducted by Statistics Canada in each territory that produces basic labour market data, neither occupation nor industry data are available from these surveys. Also the exclusion of persons on Indian Reserves in the LFS is a drawback for sector councils that want to partner with Canada’s first nations people. Recently the LFS has added information on aboriginals off reserve in western provinces, but on reserve estimates are still lacking. The LFS currently uses the 2002 NAICS to classify industrial employment. There are 103 3-digit 2002 NAICS categories.

The Canadian LFS is tabulated monthly and typically released at the end of the first week of the month after the survey was conducted. The LFS has used a rotating sample of approximately 54,000 since July 1995. This sample is significantly smaller than the census. As a result the LFS cannot provide the same level of consistent disaggregated data as the census. This limitation is particularly problematic for sub-national jurisdictions because the sample size for these areas is considerably smaller than the national sample. Unlike the census that employs random rounding, LFS data are suppressed should the estimated number of people fall below a predetermined level. Suppression is done for confidentiality and data quality reasons and occurs at different levels depending on the jurisdiction under consideration. If the number falls below 1,500 people in Alberta, the LFS shows zero for that category, while suppression is triggered at 500 in Manitoba and 200 in PEI (See Table 4.2 below for an indication of sample sizes and suppression levels for Canada and the provinces).

Table A4.2: Canadian Labour Force Survey

Sample Size (Jan.

2003)Minimum size for

release Canada 53,980 1,500 Newfoundland 1,987 500 PEI 1,421 200 Nova Scotia 3,137 500 New Brunswick 2,962 500 Quebec 10,140 1,500 Ontario 15,792 1,500 Manitoba 3,906 500 Saskatchewan 4,072 500 Alberta 5,372 1,500 BC 5,191 1,500 Source: Statistics Canada

Clearly given the roughly 52,000 different 3-digit NAICS with 4-digit NOC combinations; a survey of 54,000 people can not provide statistically valid information for many of the industry/occupation categories since there would be basically one person per industry/occupation combination on average in the survey. Some of the data problems for detailed LFS estimates can be illustrated using the 2001 census data for labour force by occupation at the 3- and 4-digit level of aggregation.


3 digit 4 digit 3 digit 4 digit LFS CutoffCanada 140 509 386 104 1500Newfoundland 98 520 14 4 500PEI 81 137 10 3 200Nova Scotia 125 249 22 6 500New Brunswick 118 229 21 6 500Quebec 137 406 72 20 1500Ontario 136 438 113 30 1500Manitoba 128 283 28 8 500Saskatchewan 127 254 29 8 500Alberta 128 277 38 10 1500BC 133 314 37 10 1500Source: Statistics Canada 2001 Census, by province*Calculated by Comparing Census Labour Force Estimate with LFS cutoff by Province**Calculated by Dividing LFS Sample Size by Number of Occupational Categories

Table 3: Labour Force Survey Data Availability And QualityLFS Sample Size Per Occ Group**Available LFS Occupation Groups*

In the table, the first two columns show the number of occupational categories that are above the associated LFS cut-off. The third and fourth columns show the number of people who are surveyed per occupational category. The lower the number the greater will be the statistical noise, which means the estimates are less reliable. At the 3-digit NAICS level of aggregation, there are roughly 540 people per industry category on average in the survey. This means that there is more complete data coverage than for the occupational groups discussed above. For example, there are 30 3-digit NAICS categories for Newfoundland that have zero employment or are suppressed because of confidentiality and data quality issues (See Table 4.4). Notably, there were 34 categories with zero employment in 2006. This variability in suppression is a common occurrence in the LFS data, which adds to the problems associated with using these data.

The LFS also records unemployment. However, due to quality concerns most of the data at 3-digit level of aggregation are suppressed and are effectively unavailable. The reason that these data have a higher degree of data quality problems than the employment data is because many people who are unemployed do not list an industry. Statistics Canada representatives therefore typically do not provide these data for sale because the high number of suppression means that data clients would be purchasing a table of mostly zeros.

Table A4.4: Labour Force Survey Data Availability For Industries by Region Available LFS 3-Digit NAICS 2005 2006Canada 3 3 Newfoundland 30 34 PEI 35 34 Nova Scotia 19 21 New Brunswick 13 14 Quebec 7 8 Ontario 8 7 Manitoba 16 14 Saskatchewan 14 14 Alberta 14 13 BC 13 13 Source: LFS




Higher level aggregations can be purchased directly from Statistics Canada through CANSIM, which provides access to the Statistics Canada databases via the internet. CANSIM uses “V numbers” to identify data. Data can be purchased through CANSIM for $3 per series, for single series requests or multiple series. These data would be retrieved with the “V number” and a descriptor. The data client would need to adjust the data so that a series name or a general description could be used instead. Statistics Canada also publishes a labour force CD that provides monthly and annual data, for various levels of geography and concepts, including labour force, unemployment and employment. These data are available for a combination of 2 and 3-digit NAICS, which is a higher level of aggregation than the 3-digit NAICS that were used for the available workforce project. These data have a descriptor that includes the NAICS code(s).

Table A4.5: Labour Force Survey Standard Request Geography Canada; Provinces; CMA; Economic Regions Free Publication or Data FROM www.Statcan.ca http://www.statcan.ca/bsolc/english/bsolc?catno=71-001-XIE

CD-ROM CD-ROM Labour Force Historical Review ($209) 1989 to 2006

CANSIM CANSIM: tables 282-0001 to 282-0042, 282-0047 to 282-0064 and 282-0069 to 282-0099.

Other Free Tables Web Site http://www40.statcan.ca/l01/ind01/l2_2621.htm Turnaround Time 1 0r 2 days Non Publish Data - Custom Tables Data availability dependent on data quality issues

Turnaround Time 3 weeks or shorter (longer if request is complex) Turnaround time longer it request coincides with LFS release

For employed and unemployed, 3 digit NAICS, by Canada and provinces, from 2001 to 2007, monthly data would cost $960. While employed and unemployed, 3 digit NAICS, by Economic Regions from 2001 to 2007, monthly would cost $1,460. These custom orders would have a projected completion date of one to two weeks. The request for more than five years of historical data would increase the cost.

To estimate 3-digit NAICS employment or labour force levels, data at the 2 and 3-digit NAICS level of aggregation need to be used. The reason that both 2 and 3-digit NAICS data are needed is because there are many holes in the 3-digit data. Many of the holes would need to be filled by estimating the difference between the 2-digit aggregate and the available 3-digit data. This approach works well when there is one missing concept in any particular year. But in many situations two or more of the sub-components would be missing. These data holes need to be filled by using the shares from either the prior year, the next year or from the census. Another approach would be to use RAS, entropy or some other small area estimation technique.

The LFS data that were constructed were used to estimate the trend decline and cyclical decline in employment. The cyclical decline is simply the percentage change in the most recent year (2006) from the level in the previous year (2005). The trend decline in employment was estimated by taking the maximum level over the previous decade and determining if the percentage change exceeded the cyclical decline from the previous year. The maximum level was estimated using the “MAX” function in excel. The condition of the trend decline exceeding the cyclical decline was estimated by using “IF” statements in order to filter the data. Only situations where both

http://www.statcan.ca/

http://www.statcan.ca/bsolc/english/bsolc?catno=71-001-XIE



conditions of a cyclical as well as a structural decline in employment occurred together were employment in the industries identified as being in decline.

Longitudinal Administrative Database (LAD) The Longitudinal Administrative Database (LAD) is a ten percent representative sample of Canadian tax filers and provides information on incomes, taxes, industry of employment and basic demographic characteristics, including province of residence. The first year of data for the LAD is 1982 and currently runs through 2004. The full set of annual tax files from which the LAD is constructed are estimated to cover from 91 to 95 percent of the target adult population, thus comparing favourably with other survey-based databases, and even rivaling the census.64 Data quality therefore would be considerably better than the LFS. The LAD can be sub-divided by province and sub-provincial areas. At the sub-provincial level the data are organised by postal code. The LAD, however, does not have any occupational data.

These data can be used for time series analysis and provide a view of historical provincial industrial employment trends. But the data can not be used to illustrate current or even relatively recent developments in the industry or region given the time lags involved in accumulating, tabulating and delivering the data in a useable database. Therefore, this dataset was not used in the analysis of current conditions. LAD data are all custom orders and so take one to three weeks and are priced based on the complexity of the request and the number of series requested.

Survey of Employment Payrolls and Hours (SEPH) The Survey of Employment Payrolls and Hours (SEPH), which is also known as the Survey of Employment, Earnings and Hours, is produced from the combination of the Business Payroll Survey results and the payroll deductions administrative data received from Canada Customs and Revenue Agency. SEPH is Canada's only source of detailed information on the total number of paid employees, payrolls and hours at detailed industrial, provincial and territorial levels.

The target population for the SEPH is composed of all employers in Canada, except those primarily involved in agriculture, fishing and trapping, private household services, religious organisations and military personnel of defence services. The survey draws its samples from the Business Register maintained by the Business Register Division of Statistics Canada and from a list of all businesses registered in Canada Customs and Revenue Agency's Business Number program with one or more active payroll deduction accounts. SEPH therefore provides the most detailed estimates of the current number of paid employees and their hours. There is extensive industry coverage in SEPH, with data available at the 3-digit level of aggregation.

The main drawbacks of the SEPH are that the self-employed are not represented, some industries are excluded and it has no occupational data or labour force data and the information is not as current as the LFS. Data are released late in the month for data from two months earlier. For example, on May 29th, 2007 preliminary data are to be released for March 2007. SEPH data are subject to revision as more information comes available, and therefore the estimated level of employment can change for a particular reference month. The data for 2006 are to be finalised in May, 2007, for example.

64 Finnie, R. (1998). “Interprovincial Mobility in Canada: A Longitudinal Analysis”, Applied Research Branch, Strategic Policy, Human Resources Development Canada, Working Paper W-98-5E. pp 13-14.



SEPH data can be purchased directly from CANSIM for $3 per series before tax. Alternatively a CD can be purchased from Statistics Canada for $160 or custom data can be purchased. See Table A4.6 for details. Data in custom tables are provided with the NAICS code and no descriptor.

The result of the Survey of Employment Payrolls and Hours is reviewed using the appropriate security measures complying with the Statistics Canada Act to ensure the safeguard of the respondents’ information and that no enterprise may be identified. This exercise is reviewed every year since content of ‘industry geography’ may change from one year to the next. The bottom line is that industry confidentiality will change year to year leading to some industries being suppressed in one year and not in the next, and vice versa.

This data source was used to construct estimates of data for the Territories. Since there are many holes in the employment data, the first step of the process is to try to fill in as many gaps in the data as possible. This was done in a manner similar to that for the LFS data, but given the greater number of holes in the source data, there are many more estimates based on using census data to impute shares of higher level aggregate.

Table A4.6: Survey of Employment, Payrolls and Hours Standard Request Geography Canada and Provinces Free Publication or Data FROM www.Statcan.ca http://www.statcan.ca/bsolc/english/bsolc?catno=72-002-XIB CD-ROM SEPH CD-ROM ($160) 1991 to 2005 CANSIM CANSIM: tables 281-0023 to 281-0046

Other Free Tables Web Site Daily release - http://dissemination.statcan.ca/Daily/English/070426/d070426b.htm

Turnaround Time 1 or 2 days Non Publish Data - Custom Tables Data availability dependent on data quality issues Turnaround Time 3 weeks or shorter (longer if request is complex)

EI and ROE Data Availability Employment Insurance (EI) and Record of Employment data are collected by Human Resources and Social Development (HRSD). EI data are recorded for a variety of concepts, including beneficiaries and claimants. Beneficiaries represent all those who are receiving EI payments and can be loosely thought of as the stock of the readily available labour force. Employment Insurance Claimants (EIC) are those who are making or renewing an EI claim and represent the flow of new beneficiaries. Some groups use new EI claimants as an additional indicator of available labour supply.

When a person leaves the employ of a firm, the company must provide a Record of Employment to HRSD. Some people leave a firm to take a job with a different employer, and others find a new job shortly after leaving their previous employer. Neither of these groups will typically claim or receive EI benefits The ROE data, therefore, represents both work-to-work and work-to-unemployment flows, while EI claimant data are only for those work-to-unemployment flows who receive EI payments.

There are a variety of reasons why people leave a job, which is also known as separations, including: illness or injury, maternity, returning to school, retirement, dismissal and quits. (See the lists below for the reasons used for the EIC and ROE data and claim types). Not all of these separations will result in readily available workers. In order to be relevant, therefore, the EI data

http://cansim2.statcan.ca/cgi-win/CNSMCGI.EXE?Lang=E&ArrayId=281-0023..281-0046&Array_Pick=1&Detail=1&ResultTemplate=CII/CII___&RootDir=CII/&TblDetail=1



must be differentiated by reason for work separation and only those separations that result in readily available workers should be included. Similarly, ROE data can be differentiated by reason for separation, which will provide a more precise estimate of those who are available to work.

Table A4.7: Reason for Separation from Employment Code Description A Shortage of work (layoff) B Strike or lockout C Return to school D Injury or illness E Quit F Pregnancy or parental (until early 2003). Maternity only (beginning early 2003). G Retirement before the age of 65 years (prior to 1997) Retirement at any age (since 1997). H Work sharing J Apprentice training L Retirement at the age of 65 years or older (prior to 1997) M Dismissal N Leave of absence P

Parental. This code was placed on the ROE Dec 2002. Code “F” was used for parental leave prior to that date.

Z

Compassionate Care (introduced Jan 2004). If code “Z” did not appear on ROE form, employer was instructed to use code "K" and provide an explanation in Block 18

K Other65

Table A4.8: MOST RECENT CLAIM TYPE Code Description

1 regular claim (advance payment prior to week 524) 2 regular claim 3 sickness - major attached 4 maternity 5 retirement 6

summer fishing (for claims starting after 1996, BPC must be during or after the week in which October 1 falls and prior to the week in which June 15th following falls)

7

winter fishing (for claims starting after 1996, BPC must be during or after the week in which April 1 falls and prior to the week in which December 15th following falls)

8 sickness recovered, not job attached(eliminated as of April 30, 1984) 9 sickness - minor attached

10 A.O.T.A. Reentry

65 According to HRSD, Other is often a large category, and users would like to know what it encompasses. One local office contacted in 1996 stated that the “K” category was used in the following circumstances: -The end of term employments with the federal government -If there is a dispute between the employer and the employee as to whether the reason was “quit” or “fired” -Bankruptcy of the employer (the firm went out of business and all employees were laid off permanently) -“Layoff” but the person filling out the form did not realise that this was meant by “shortage of work”



11 year round fishing. Only summer and winter fishing benefit claims as of October 13, 1996.

12 no trailers present 15 adoption 16 paternity 17 compassionate care CC benefits were introduced Jan 4, 2004 by Bill C-28.

Detailed EI and ROE data are not made publicly available by HRSD. Privacy restrictions prevent direct access to the detailed HRSD data. Access to some aggregations of these data may be permitted on a case by case a basis. First, a formal request must be made to HRSD. HRSD evaluates the request and may or may not permit access to the data. Obtaining permission to use detailed data is a time consuming process.

The EIC and ROE data that are made available have significant reporting and recording lags. These lags cause the data to be relatively old compared with other data sources, such as the Labour Force Survey (LFS). While the May 2007 LFS data were released in the first week of June 2007, the detailed ROE data delivered the first week of June were available through March 2007, and the EIC data were available through February 2007. Notably, the aggregate EI data that was released via Statistics Canada at the end of May 2007 were available through March 2007.

The reporting and recording lags also caused significant end of sample data problems that make these data less than helpful as current indicators. The EI data have a significant trailing off of the number of claimants in the last month of the available data interval. For the ROE data there are even larger end of sample data problems, with the last several months of data exhibiting significant declines. As a consequence of the end of sample data problem these data are of limited use as current indicators and caution should be taken when interpreting the results.

Education Data Education data are available for a number of concepts, such as enrolment and completers for various levels of postsecondary schooling. The primary groups are available from Statistics Canada via CANSIM. The matrices that contain these data are illustrated below in Table A4.9.

Table A4.9 Education Data

Description MatrixFull-time enrolments and graduates in postsecondary community college programs, by program field, year in program and sex, annual (number), 1976/1977 to 1998/1999

477-0006

University enrolments, by registration status, program level, Classification of Instructional Programs, Primary Grouping (CIP_PG) and sex, annual (number), 1992/1993 to 2005/2006

477-0013

University degrees, diplomas and certificates granted, by program level, Classification of Instructional Programs, Primary Grouping (CIP_PG) and sex, annual (number), 1992 to 2005

477-0014

Registered apprenticeship training, registrations by major trade groups and sex, annual (number), 1991 to 2005

477-0051

Table Descriptions

Table 477-0006 - Full-time enrolments and graduates in postsecondary community college programs, by program field, year in program and sex, annual (number)

This table contains 9750 series, with data for years 1976/1977 - 1998/1999 (not all combinations necessarily have data for all years). The data are described by the following dimensions:

http://cansim2.statcan.ca/cgi-win/cnsmcgi.pgm?regtkt=&C2Sub=&ARRAYID=4770006&C2DB=PRD&VEC=&LANG=E&SrchVer=&ChunkSize=&SDDSLOC=&ROOTDIR=CII/&RESULTTEMPLATE=CII/CII_PICK&ARRAY_PICK=1&SDDSID=&SDDSDESC=










Geography (13 items: Canada; Newfoundland and Labrador; PEI; Nova Scotia; ...)

Program field (50 items: Total all postsecondary programs; University level and transfer; Total career programs; ...)

Year in program (5 items: All years (includes fourth year students); First year; Second year; Third year; Graduated previous year)

Sex (3 items: Both sexes; Males; Females)

Table 477-0013 - University enrolments, by registration status, program level, Classification of Instructional Programs, Primary Grouping (CIP_PG) and sex, annual (number).

This table contains 11189 series, with data for years 1992/1993 - 2005/2006 (not all combinations necessarily have data for all years).

This table contains data described by the following dimensions (Not all combinations are available):


Registration status (3 items: Total, registration status; Full-time student; Part-time student)

Program level (11 items: Total, program level; Trade/vocational and preparatory training certificate or diploma; ...)

Classification of Instructional Programs, Primary Grouping (CIP_PG) (14 items: Total, instructional programs; Personal improvement and leisure; Education; ...)


Table 477-0014 - University degrees, diplomas and certificates granted, by program level, Classification of Instructional Programs, Primary Grouping (CIP_PG) and sex, annual (number)

This table contains 3185 series, with data for years 1992 - 2005 (not all combinations necessarily have data for all years). The table contains data described by the following dimensions (Not all combinations are available):


Program level (11 items: Total, program level; Trade/vocational and preparatory training certificate or diploma; ...)

Classification of Instructional Programs, Primary Grouping (CIP_PG) (14 items: Total, instructional programs; Personal improvement and leisure; Education; ...)


Table 477-0051 - Registered apprenticeship training, registrations by major trade groups and sex, annual (number)

This table contains 288 series, with data for years 1991 - 2005 (not all combinations necessarily have data for all years). The table contains data described by the following dimensions:


Major trade groups (8 items: Total major trade groups; Building construction trades; ...)




Data Purchase for Clients There are significant cost savings in purchasing data for Canada, provinces and territories altogether versus purchasing individual geographies or industries separately. Furthermore, standard tables represent a faster and less expensive option than the purchase of custom tables.

One possible area of collaboration and cost savings would be through joint purchases of data. However, these arrangements are only available to those in the not-for-profit sector. For example, provincial governments can jointly purchase data from Statistics Canada. Joint purchases are subject to a premium above the cost that a single purchaser pays, but are still more cost effective than purchasing the data twice.

Should a client require the data that are used for a particular project, then a pass through agreement can be signed by the consultant with Statistics Canada. This arrangement permits the consultant to use the data for the duration of the project, but within six months the data must be delivered to the end user, which in this case is the client. If more than one client require the data, and the clients are deemed to be in the for-profit sector, then the data needs to be purchased for each client in turn.



Appendix V: Data Quality Census66

Several factors can influence census data accuracy. The accuracy of census counts and data is first affected by the degree to which the total population is missed in the census (undercoverage). The census count of 31,612,897 persons in 12,506,814 dwellings does not include persons living in dwellings missed during census enumeration, or persons mistakenly omitted from the questionnaires of responding dwellings. (The count of dwellings includes occupied private and collective dwellings and responses received from outside Canada. It excludes unoccupied dwellings and dwellings occupied only by foreign and temporary residents.) For the 2001 Census the estimated rate of net missed persons was 3.1%. Final estimates of 2006 Census coverage error will be available in September 2008.

The quality of the census count is further impacted by response to the census. The majority of the above count consists of 30,679,721 persons enumerated in 12,071,390 responding dwellings. The remainder of the census count was estimated on the basis of a sample survey of known dwellings that did not return a census questionnaire and consists of 933,176 persons imputed into 435,424 dwellings. The overall response rate can be calculated as 12,071,390/12,506,814, equaling 96.5%. While this is slightly lower than in the 2001 Census, the methodology underlying this last adjustment differs from that used in 2001 with the result that response rates for the 2001 and 2006 censuses are not strictly comparable.

An assessment of the quality, comparability and limitations of the 2006 Census data is carried out as an integral part of release and dissemination activities. All variables are certified before release, by way of a set of brief studies designed to judge the consistency of the data with that of previous censuses and that of alternate data sources. This process is augmented by measures of data quality provided by evaluation studies. The studies provide indications of the quality of the census data as affected by potential sources of error--e.g., coverage, response, non-response, processing and sampling--and of the impact on individual variables.

Errors can arise at virtually every stage of the census process, from the preparation of materials through data processing, including the listing of dwellings and the collection of data. Some errors occur at random, and when the individual responses are aggregated for a sufficiently large group, such errors tend to cancel out. For errors of this nature, the larger the group, the more accurate the corresponding estimate. It is for this reason that users are advised to be cautious when using small estimates. There are some errors, however, which might occur more systematically, and which result in biased estimates. Because the bias from such errors is persistent no matter how large the group for which responses are aggregated, and because bias is particularly difficult to measure, systematic errors are a more serious problem for most data users than the random errors referred to previously.

For census data in general, the principal types of error are as follows:

1) coverage errors, which occur when dwellings or individuals are missed, incorrectly enumerated or counted more than once;

66 Statistics Canada (2003a) pp 291-292.

See also Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3901&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3

http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3901&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3




2) non-response errors, which result when responses cannot be obtained from a certain number of households and/or individuals, because of extended absence or some other reason;

3) response errors, which occur when the respondent, or sometimes the Census Representative, misunderstands a census question, and records an incorrect response or simply uses the wrong response box;

4) processing errors, which can occur at various steps including coding, when .write-in. responses are transformed into numerical codes; data capture, when responses are transferred from the census questionnaire in an electronic format, by key-entry operators; and imputation, when a valid, but not necessarily correct, response is inserted into a record by the computer to replace missing or invalid data on the record);

5) sampling errors, which apply only to the supplementary questions on the long form that are asked of a one-fifth sample of households, and which arise from the fact that the responses to these questions, when weighted up to represent the whole population, inevitably differ somewhat from the responses which would have been obtained if these questions had been asked of all households.

The above types of error each have both random and systematic components. Usually, however, the systematic component of sampling error is very small in relation to its random component. For the other non-sampling errors, both random and systematic components may be significant.

Population Estimates67

The estimates of population by age and sex contain certain inaccuracies stemming from (1) errors in corrections for net census undercoverage and (2) imperfections in other data sources and the methods used to estimate the components. Errors due to estimation methodologies and data sources other than censuses are difficult to quantify but not insignificant. The more detailed the breakdown of the data, the larger the inaccuracy coefficient becomes. The component totals contain a certain amount of initial error, and the methodology used to classify them by sex and age, produces additional error in the figures at each stage. Nevertheless, the components can be divided into two categories according to the quality of their data sources: births, deaths, immigration and non-permanent residents, for which the sources of final data may be considered very good; emigrants, returning emigrants, net temporary emigrants and interprovincial migration for which the methods used may be a more substantial source of error. Lastly, the size of the error due to component estimation may vary by province, sex, and age and errors in some components (births and emigration) may have a greater impact on a given age group or sex. Intercensal estimates contain the same types of errors as postcensal estimates, as well as errors resulting from the way in which the errors present at the end of the period were distributed, that is, on the basis of the time elapsed since the reference Census.

67 Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3603&lang=en&db=IMDB&dbg=f&adm=8&dis=2

http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3603&lang=en&db=IMDB&dbg=f&adm=8&dis=2




Labour Force Survey68

The labour force survey produces estimates based on information collected from and about a sample of individuals. Somewhat different estimates might have been obtained if a complete census had been taken using the same questionnaire, interviewers, supervisors, processing methods, etc. as those actually used in the survey. The difference between the estimates obtained from the sample and those resulting from a complete count taken under similar conditions is called the sampling error of the estimate. It is unavoidable that estimates from a sample survey are subject to sampling error.

A measure of the sampling error is the standard error. This measurement is based on the idea of selecting several samples, although in a survey only one sample is drawn and information is collected on units in that sample. Using the sampling plan, if a large number of samples were to be drawn from the same population, then about 68% of the samples would produce a sample estimate within one standard error of the census value and in about 95% of the samples it will be within two standard errors of the census value.

Sampling variability can also be expressed relative to the estimate itself. The standard error as a percentage of the estimate is called the coefficient of variation (CV) or the relative standard error. For LFS estimates, the CV is used to give an indication of the uncertainty associated with the estimates. Probability statements can also be made about CVs; for example, if an estimate is 100,000 with a CV of 7%, the true (census) value will lie between 93,000 and 107,000 with 68% certainty, and between 86,000 and 114,000 with 95% certainty.

Small CVs are desirable since the smaller the CV the smaller the sampling variability is relative to the estimate. The CV depends on the size of the estimate, the sample size that the estimate is based on, the distribution of the sample and the use of postcensal population estimates in the estimation procedure. Of two estimates, the one with the larger sample will likely have a smaller sampling error; and, of two estimates of the same size the one referring to a characteristic that is more clustered geographically will have a larger variability associated with it. In addition, estimates relating only to age and sex are usually more reliable than other estimates of comparable sample size because, in the LFS, the sample is calibrated by age, sex and geographic region to independent sources.

Approximate sampling variability tables

The following table give approximate coefficients of variation as a function of the size of the estimate and geography. The rows give the geographic level of the estimate while the columns indicate the resulting level of accuracy in terms of the CV, given the size of the estimate. To determine the CV for an estimate of size X in an area A, look across the row for area A, find the first estimate that is less than or equal to X. Then the title of the column will give the approximate CV. For example, to determine the sampling error for an annual average estimate of 37.5 thousand unemployed in Newfoundland and Labrador, we find the closest but smaller estimate of 19.9 thousand giving a CV of 2.5%. Therefore, the estimate of 37,500 unemployed in Newfoundland and Labrador has a CV of roughly 2.5%.

The CV values given in the table A5.1 are derived from a model based on 2002, 2003, 2004, 2005 and most of 2006 LFS sample data. These values are approximations. The table provides a rough 68 Statistics Canada (2007). Also see Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3701&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3





guide to the sampling variability. The sampling variability is modelled so that, given an estimate, approximately 75% of the actual CVs will be less than or equal to the CVs derived from the table. There will, however, be 25% of the actual CVs that will be somewhat higher than the ones given by the table.

Table A5.1: CVs for Estimates* of Annual Totals for Canada and the Provinces

Geographic Area 1.0% 2.5% 5.0% 7.5% 10.0% 15.0% 20.0% 25.0% 30.0%Canada 384.5 112.6 51.1 30 16.1 9.4 6.3 4.6 3.5Newfoundland 71.4 19.9 9 5.2 2.6 1.5 1 0.7 0.5PEI 17 5.3 2.6 1.6 0.9 0.5 0.4 0.3 0.2NS 64.6 20.2 10 6.1 3.2 1.9 1.3 1 0.8NB 57.4 16.6 7.8 4.6 2.3 1.4 0.9 0.7 0.5Quebec 297 88.3 41.7 24.9 12.9 7.7 5.2 3.7 2.9Ontario 278.5 86.9 43.1 26.4 13.7 8.4 5.7 4.2 3.3Manitoba 72.9 22.1 10.8 6.5 3.3 2 1.4 1 0.8Saskatchewan 66 18.3 8.4 4.9 2.4 1.4 0.9 0.6 0.5Alberta 179.6 53.6 25.9 15.6 7.9 4.7 3.2 2.3 1.8BC 209.3 63 30.6 18.5 9.4 5.7 3.8 2.8 2.1*Estimates are in thousands.

The CV depends on the size of the estimates, the sample size the estimate is based on, the distribution of the sample, and the use of auxiliary information in the estimation procedure. The size of the estimates is important because the CV is the sampling error expressed as a percentage of the estimate. The smaller the estimate, the larger the CV will be (all other things being equal). For example, when the unemployment rate is high the CV may be small. If the unemployment rate falls due to improved economic conditions then the corresponding CV will become larger. Typically, of similar estimates, the one with largest sample size will yield the smaller CV. This is because the sampling error is smaller.

Also, estimates referring to characteristics that are more clustered will have a higher CV. For example, persons employed in forestry, fishing, mining, oil and gas in Canada are more clustered geographically than employed women aged 55 to 64 years in Ontario. The latter will have a smaller sampling variability although the estimates are of approximately the same size. Finally estimates referring to age and sex are usually more reliable than other similar estimates because the LFS sample is calibrated to post-censal population projections of various age and sex groupings. Continuing the previous example, persons employed part-time in Alberta will have a larger sampling variability than employed men aged 35 to 44 years in British Columbia although the estimates are of similar size.



Longitudinal Administrative Data69

Details on cross-sectional data accuracy may be consulted under the entry for the T1 Family File (T1FF, record number 4105). The main departures from the T1FF are the sampling and the longitudinal components. Since the Longitudinal Administrative Data's sampling rate is relatively high at 20%, the variation due to sampling is quite low for relatively small populations. For example, for population counts of individuals with specific characteristics, the coefficient of variation (CV) due to sampling error is 20% or less when the population has 100 or more units, less than 10% when population exceeds 400 and less than 2% for populations of 10,000 or more. When calculating percentages of a population with specific characteristics, the CV due to sampling would be less than 10% as long as the population count is 400 people or more and the estimated percentage is 50% or more or if the population count is 1000 people or more and the estimated percentage is above 20%.

For longitudinal projects, the coverage will be lower than that observed in any single cross-sectional year: the main restriction is the inability to follow individuals without a reliable identifier. Furthermore, the individual usually must be included in both of the study years. For example, when studying one-year transitions, 95.9% of individuals with a record for 2004 income reference year also have one in 2005. Emigration or death accounts for 0.8% of the original 2004 group so 3.2% remain unexplained missing; these could be non-filers or late filers in 2005. When studying the composition of the 2005 cohort, 94.9% were also in the 2004 file, 2.7% had never filed before or moved to Canada in 2005 and 2.3% were non-filers or late filers in 2004 (of these, 56.3% had filed in 2003). The study of longer periods would result in more observations with a least one missing year of income data.

Survey of Employment, Payrolls and Hours 70

The statistics compiled by SEPH are based on a census of administrative records for all in-scope establishments with employees that can be found on the Business Register. The total payroll employment estimates and the monthly payrolls are derived from the administrative source. Administrative information for total gross monthly payrolls and the total number of employees for the last pay period in the month are obtained from payroll deduction (PD) accounts maintained by Canada Revenue Agency. Public Institutions Division of Statistics Canada provides information for general government services at the provincial and federal levels.

To estimate SEPH variables not available from the administrative source, the results of the Business Payrolls Survey (BPS) conducted monthly are used. The BPS uses a stratified simple random sample of 11,000 establishments out of a population of 900,000 establishments taken from the Business Register. A one-twelfth rotation of the sample is done every month. The Business Payrolls Survey uses a combination of methods for data collection to permit maximum flexibility for the respondent. For mail units, questionnaires are mailed to the payroll office of employers each month. Computer-assisted telephone interviews (CATI) are used for respondents who express a preference for being surveyed by telephone. Respondents can also report their data

69 Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=5012&lang=en&db=IMDB&dbg=f&adm=8&dis=270 Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=2612&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3





electronically. Reporting units, which are non-respondents to the initial mailing, are followed up by telephone by the staff of the regional offices of Statistics Canada.

The estimates derived from the administrative source are then combined with the results of the BPS to produce estimates for the full range of SEPH variables.

For the administrative portion of the survey, response rates based on employment are produced and published every month for Canada, the provinces and the territories by type of payroll deduction accounts for the preliminary and final estimates (see Annex 2 of Statistics Canada catalogue number 72-002-XIB). The total response rate for Canada as a whole usually varies between 80% and 90%.

Every month, coefficients of variation (CV) are published for all variables and every domain (by NAICS industry for Canada, the provinces and the territories). These CVs take into account the sampling variance coming from the BPS as well as the variance due to imputation of the administrative source.

Elementary-Secondary Education Statistics Project71

The Elementary-Secondary Education Statistics Project (ESESP) is a national survey that enables Statistics Canada to provide information on enrolments (including minority and second language programs), graduates, educators and finance of Canadian elementary-secondary public educational institutions.

ESESP annually collects aggregate data from each jurisdiction. Specifically, the information on enrolments pertains to the following two programs: regular and minority and second languages education. The information on regular programs is collected by type of programs (regular, upgrading and professional), education sector (youth or adult), grade and sex. The one on minority and second language programs is collected by type program (immersion, as language of instruction, as a subject thought) and by grade.

The survey also collects data on secondary school graduates by type of program (regular, upgrading and professional), sector (youth and adult), age and sex. Graduation rates can be produced from this data.

Information pertaining to full-time and part-time educators by age group and sex is also collected. Finally, ESESP also gathers expenditures data pertaining to level of government (school board and other government) and type of expenditures. This data is collected to determine how much is spent in relative detail by school boards and by provincial/territorial total. It also collects expenditures on special needs education programs.

Responding to this survey is mandatory. Data are collected directly from survey respondents. During the ESESP data production process, Statistics Canada performs a series of data quality controls.

The target population of ESESP is very stable and the survey is mandatory, therefore minimizing undercoverage. The maintenance of close relations with respondents is an important factor in minimizing non-response. In order to obtain consistent counts of students, educators, graduates

71 Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=5102&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3





and expenditures, Statistics Canada ensures that all respondents follow common guidelines and definitions.

Generally, all respondents are providing data. However, the quality of responses may vary by variable. Some variables have a high rate of non-response, such as enrolments in Aboriginal language program or enrolments and expenditures in special needs education.

Postsecondary Student Information System72

The Postsecondary Student Information System (PSIS) is a national survey that enables Statistics Canada to provide detailed information on enrolments and graduates of Canadian postsecondary education institutions. Phased implementation of PSIS started in the year 2000. For the 2005-2006 collection year PSIS covered 80% of public institutions.

The target population of PSIS is very stable and the survey is mandatory, therefore minimising undercoverage. The maintenance of close relations with respondents is also an important factor in minimising non-response.

Other important accuracy factors to consider are the quality control measures implemented in PSIS such as the error detection that is completed at both the responding institution (or coordinating body) and at Statistics Canada (at the macro and micro level), the certification tables to be approved by each responding institution (or coordinating body), and the internal data audits.

Some variables have a high rate of non-response, such as mother tongue, activity limitations, aboriginal person or member of a visible minority and previous education.

During the postsecondary student and institution data production process, Statistics Canada performs a series of data quality controls.

National Apprenticeship Survey (NAS)73

The National Apprenticeship Survey (NAS) is Canada's most comprehensive pan-Canadian source of data on apprenticeship, collected from apprentices. The NAS collects information on the work and training experiences of apprentices before, during and after their involvement with their apprenticeship program. It provides a standardized source of data across all provinces and territories.

The survey frame represents all registered apprentices on the lists of apprentices provided by the provincial and territorial jurisdictions for the targeted reference years 2002, 2003 and 2004. Three variables on the frame were used for stratification: jurisdictions, apprentice status and main trade groups. There are 12 jurisdictions (10 provinces, Yukon and NWT), three apprentice status (completer, discontinuer and long-term continuer), and finally there are 7 main trade groups. The combination of these variables makes for a total of 231 strata.

To provide reliable estimates for each stratum, a national sample size of approximately 30,000 respondents was desired. A minimum sample was allocated to each stratum and the remaining 72 Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=5017&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3 73 http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3160&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3







sample was allocated proportionally to the number of apprentices in each stratum. In several strata, a census of apprentices was selected. Moreover, in small provinces and territories, it resulted in selecting a census of apprentices.

In order for estimates produced from survey data to be representative of the target population, and not just of the sample itself, users must incorporate the survey weights into their calculations. A survey weight is given to each person included in the final sample, that is, the sample of persons who responded to the survey questions. This weight corresponds to the number of persons represented by the respondent for the target population.

For weighting purpose, this survey can be seen as a two-phase survey. The first phase corresponds to the selection of the sample and the responding units correspond to the second phase sample. The first phase weight is the inverse of the probability of selection of the apprentice. This first phase weight is then multiplied by a second phase adjustment factor. For the purpose of the second phase adjustment, response homogeneous groups (RHG) are created based on the characteristics of the respondents and the non-respondents. The adjustment factor is simply the inverse of the observed weighted response rate in each RHG.

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling. Frame imperfection and non-response are important sources of non-sampling error.

A large proportion of apprentices in the sample were found to be out-of-scope (not any apprentice activities during the target reference period) due to the frame imperfection. Overall, the out-of-scope rate was 25.9%. Provincial/territorial out-of-scope rates ranged from 10% to 40%. The out-of-scope rate was 7.9% for completers, 35.4% for long-term continuers and 39.3% for discontinuers.

The response rate for this survey was 62.3%. The response rate represents the number of respondents divided by the estimated number of in-scope apprentices in the entire sample. For the calculation of this response rate, the number of in-scope units from the non-respondents had to be estimated using a logistic regression model. Provincial/territorial out-of-scope rates ranged from 46.5% to 78.5%. The out-of-scope rate was 69.3% for completers, 64.6% for long-term continuers and 51.9% for discontinuers.

For variance estimation, the two-phase approach of the Generalized Estimation System (GES) was used.

Registered Apprenticeship Information System (RAIS)74

The purpose of the survey is to gather information on individuals who receive training and those who obtain certification within a trade where apprenticeship training is being offered. Specifically, the survey compiles data on the number of registered apprentices taking in-class and on-the-job training in trades that are either Red Seal or non-Red Seal and where apprenticeship training is either compulsory or voluntary. It also compiles data on the number of provincial and interprovincial certificates granted to apprentices or tradespersons. In the context of this survey, a tradesperson is an individual who received training within a trade where apprenticeship training is

74 Statistics Canada Website: statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3154&lang=en&db=IMDB&dbg=f&adm=8&dis=2



voluntary and did not register for the apprenticeship training but succeeded in obtaining their certification within that trade.

A low response rate exists for some of the data elements which are of secondary importance in the list of variables in the survey and include - type of indenture, type of institutional training, reason for leaving the program, in-school credits, on-the-job credits, prior trade certification and province/territory of residence twelve (12) months prior.

The collection of journey person certification is currently being received from all provinces except Quebec and therefore has never been included in official data release. Consultation with Quebec regarding that issue is currently taking place.

Survey of Colleges and Institutes75

The Survey of Colleges and Institutes (SCI) collect generic data on enrolments and graduates of Canadian public colleges and institutes. The objective of this program is to collect full-time aggregate public college and institute enrolment and graduate data. A list of the "public" colleges for each jurisdiction is also collected. This list will be used for confirming which institutions are covered in the survey and for updating the Register of Postsecondary and Adult Education Institutions. Data will only be broken out at the provincial/territorial level.

SCI collects aggregate enrolments and graduates data that are, in most cases, provided by provincial and territorial departments or ministries of education who had originally collected these data for their own purposes. The initial contact consists of a written data request via e-mail as well as a table requesting enrolments and graduates data. This initial contact occurred in April 2007 and requested information for 2003-2004 and 2004-2005. The provincial or territorial contacts were then followed up by e-mail or telephone, between May and September.

SCI uses methodologies, tools and processing procedures allowing manual editing and imputation of aggregate data. For incomplete information, imputation is done using previous year's trend if available, and current data point. Depending on each case, imputation can also be done using data elements obtained from the survey and other key indicators such as population estimates. During the SCI data production process, Statistics Canada performs a series of data quality controls.

The SCI is mandatory, therefore minimizing undercoverage. The maintenance of close relations with respondents is an important factor in minimizing non-response. The response rate for this survey is 100%. In order to obtain consistent counts of students and graduates, Statistics Canada ensures that all respondents follow common guidelines and definitions.

Survey of Earned Doctorates (SED)76

The Survey of Earned Doctorates (SED) is an annual census of doctorate recipients in Canada that was conducted for the first time on a national basis during the 2003-2004 academic year. The

75 Statistics Canada Website: statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=5143&lang=en&db=IMDB&dbg=f&adm=8&dis=2#376 Statistics Canada Website: statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=3126&lang=en&db=IMDB&dbg=f&adm=8&dis=2#3








survey collects data about the graduate's postsecondary academic path, funding sources, field of study and his/her immediate postgraduate plans.

This census survey has been designed to control errors and reduce their potential effects but the results remain subject to errors. Non-response error is the most significant source of error.

While SED is intended to cover all institutions offering doctoral degrees, two institutions either could not be contacted, were contacted but did not participate, or were contacted and agreed to participate, but encountered handling problems while distributing questionnaires. It is estimated that around 3% of all graduates, approximately 115 people, graduated from these institutions. No adjustment was made for these graduates. Although most cross-sectional characteristics are not expected to be affected, directly comparing published levels across years will not be possible.

2,127 usable questionnaires were received. There were a total of 3,972 graduates in the participating institutions, which corresponds to a 54% response rate.

Although weighted to represent all graduates at participating institutions, results can be applied with more confidence to graduates living in Canada. Tabulations of graduates living outside of Canada, foreign students, and similar variables cannot be released due to risk of disclosure.

The unit non-response rate of 46% for SED decreases the reliability of estimates based on the survey data. The existence of non-response in a census survey creates variance and potential bias in the estimated characteristics. The degree to which an estimate of a characteristic is affected depends on how similar respondents and non-respondents are with respect to this characteristic, and the extent to which dissimilarities are accounted for by the survey weights. The estimation methodology used in SED assumes that all persons within a weighting class (respondents and non-respondents) have the same propensity to respond and that this propensity is independent of the characteristics measured by the survey. The validity of these assumptions determines the quality of the survey estimates and may vary from one characteristic to another.

For the 2005/2006 SED, auxiliary information on the frame that could be used to create weighting classes was very limited. Thus, it was not possible to construct weighting classes to adjust for all of the expected sources of non-response bias. In particular, estimates of error do not account for potential bias introduced by the lower proportion of responding graduates who had moved outside of Canada. Data users are advised to apply caution in extrapolating results of the 2005/2006 SED to the population of graduates who moved out of Canada immediately after graduation.

National Graduates Survey77

The National Graduate Survey (NGS) is designed to measure the short to medium-term labour market outcomes of graduates from university, community college and trade/vocational programs. NGS interviews graduates two and five years after graduation. To date, five graduating classes have been surveyed: 1982, 1986, 1990, 1995 and 2000. Results for the class of 2000, interviewed for the first time in May 2002, are not yet available but will be released in 2003. The NGS offers insights into how well different graduating class classes fared in their early labour market experiences. The samples are also sufficiently large to look at different pathways, for

77 Allen, M. and S. Harris, (2003). “Finding their way: a profile of young Canadian graduates”, Statistics Canada, Centre for Education Statistics, Family and Labour Studies Division. And Statistics Canada Website: http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SDDS=5012&lang=en&db=IMDB&dbg=f&adm=8&dis=2





example, those who went on to further studies rather than moving immediately into the work force. For the Class of 1995, the sample for the interviews conducted in 2000 was 29,000, fully 10% of the graduating class (294,000). The sample is also stratified to provide a good representation of the graduating class by province, level and discipline. For the purposes of this analysis, bachelor graduates include Quebec graduates who began their studies in CEGEP.

The Follow-up Survey of Graduates (FOG) sample is a sub-sample of the NGS sample, i.e comprised of NGS respondents. Initially, the NGS sample was divided into two components -- the basic sample and the supplementary sample. The core sample was designed to yield estimates of a minimal proportion of 5.5% with a maximum coefficient of variation (CV) of 16.5% for any of the NGS2000's marginal. A marginal was defined as i) a given field of study regardless of the province of institution; or ii) a given province of institution regardless of the field of study; and that for each of the five levels of certification. The marginal's CVs were then allocated to each stratum (or cell in a table) to obtain the cells or stratums CV using a raking-ratio algorithm. The last step consisted of converting the CVs into sample sizes.

The supplementary sample targeted specific subpopulations in order to meet the interests of external partners. The provinces of Quebec and Manitoba made such requests for graduates at the bachelor's and master's levels.

Finally, the last step consisted of over sampling to compensate for expected non-response. The determination of the final sample size was based on some hypothesis about attrition rates for the follow-up survey and past NGS response rates.

The overall response rate for the FOG2000 is 68.5%.

The final sample size was 34,304, which represents all of NGS respondents minus Trade/vocational graduates.

Youth in Transition Survey78

Data quality is affected by both sampling and non-sampling errors. Non-sampling errors were minimised through testing (focus group, pilot survey and main survey); training of regional office staff; observation by head office personnel; tabulations of initial data; and adjustment of questionnaire specifications for future cycles. Quality assurance measures were implemented at each step of the data collection and processing cycle to monitor data quality. For sampling error, data reliability guidelines were established based on coefficient of variation (CV). It is recommended that any estimate based on fewer than 30 observations or with a CV greater than 33.3% not be released.

The following table provides an indication of data quality for a select set of YITS, Cycle 4 variables for Cohort B, 24-26 year-olds. Additional data quality indicators are presented in all YITS publications.





Table A5.2 Youth In Transition Survey—Data Accuracy Measures Postsecondary (PSE) status % Standard

Error CV (%)

PSE graduate continuer 15.99 0.5386 3.6 PSE graduate non-continuer 59.61 0.7960 1.3 PSE continuer 9.26 0.4277 5.1 PSE dropout 15.13 0.5586 4.1 No PSE 20.65 0.6843 2.7

Migration Estimates79

Migration estimates are available by census division from 1976-77. Migration data are released annually from a modelled databank that monitors and tracks the movements of people to and within Canada. Data are derived from the comparison of two consecutive years of tax files.

Beginning with the 1981-82 estimates, the data on immigration and emigration have been prorated to make them consistent with the most currently available estimates produced at the provincial level by Demography Division of Statistics Canada. Two data series are produced to accommodate data updating done by Demography Division.

1. Preliminary Estimates (15-18 month time lag). The international component is prorated to the preliminary estimates of international migration provided by Demography Division; and

2. Revised (Final) Estimates (27-33 month time lag). The international component is prorated to the final estimates of international migration provided by Demography Division, while the counts of internal migration remain unchanged.

Data Quality

Data for tax filers and their dependents rivals the coverage from the census. These data are generally of good quality, particularly at the provincial level. Even at the census division level of geography, Statistics Canada finds that the quality of the data is good.

Based on a detailed evaluation of the estimates for the intercensal period of 1986-91, a number of observations can be made regarding migration estimates for Census divisions:

Overall, the estimates of migration are of good quality. It is, however, difficult to make exact comparisons to other annual estimates of migration flows at the census division level. The estimates of net migration have been used to produce population estimates and these have been compared to the 1991 Census counts. The average absolute difference for 1991 was 2.3%. In 12 of 182 cases (6%) the deviation exceeded 5% and in 3 cases, the deviation exceeded 10% (this does not include Quebec census divisions). These deviations are smaller than those obtained from other estimation methodologies and indirectly indicate the quality of the net migration data. It has not been possible to do much evaluation of the flow data.

79 Statistics Canada (2007). “Migration Estimates - User's Guide”. Small Area and Administrative Data Division. Statistics Canada, Product 91C0025.




Longitudinal Immigration Database (IMDB)80

The Longitudinal Immigration Database (IMDB) is a database combining linked immigration and taxation records. The IMDB is a comprehensive source of data on the economic behaviour of the immigrant taxfiler population in Canada and is the only source of data that provides a direct link between immigration policy levers and the economic performance of immigrants. A person is included in the database only if he or she obtained their landed immigrant status since 1980 and filed at least one tax return after becoming a landed immigrant. Data are collected for all units of the target population, therefore no sampling is done.

Data are extracted from administrative files. The IMDB brings together information from the Field Operations Support System (FOSS) landing information with taxation data (mainly from the T1 personal tax return). A person is included in the database only if he or she landed since 1980 and filed at least one tax return in that period.

Details on cross-sectional data accuracy may be consulted under the entry for the T1 Family File (T1FF, record number 4105). The main departures from the T1FF are the sampling and the longitudinal components.

Since the Longitudinal Administrative Data's sampling rate is relatively high at 20%, the variation due to sampling is quite low for relatively small populations. For example, for population counts of individuals with specific characteristics, the coefficient of variation (CV) due to sampling error is 20% or less when the population has 100 or more units, less than 10% when population exceeds 400 and less than 2% for populations of 10,000 or more. When calculating percentages of a population with specific characteristics, the CV due to sampling would be less than 10% as long as the population count is 400 people or more and the estimated percentage is 50% or more or if the population count is 1000 people or more and the estimated percentage is above 20%. For longitudinal projects, the coverage will be lower than that observed in any single cross-sectional year: the main restriction is the inability to follow individuals without a reliable identifier. Furthermore, the individual usually must be included in both of the study years. For example, when studying one-year transitions, 95.9% of individuals with a record for 2004 income reference year also have one in 2005. Emigration or death accounts for 0.8% of the original 2004 group so 3.2% remain unexplained missing; these could be non-filers or late filers in 2005. When studying the composition of the 2005 cohort, 94.9% were also in the 2004 file, 2.7% had never filed before or moved to Canada in 2005 and 2.3% were non-filers or late filers in 2004 (of these, 56.3% had filed in 2003). The study of longer periods would result in more observations with a least one missing year of income data.


and see, Statistics Canada (2006), “Longitudinal Administrative Data Dictionary-2004”, Catalogue no. 12-585-XIE






Appendix VI: NOC/SOC Comparison Occupation data in Canada have been classified by the Standard Occupation Classification (SOC) used by Statistics Canada and the National Occupation Classification (NOC) used by HRSDC. Statistics Canada released an updated version of its classification system called National Occupation Classification for Statistics (NOC-S) in 2001. “The NOC-S is a minor revision of 1991 SOC that addresses the need for increased attention to the occupations in information technology and the need to integrate completely with the NOC. Most of the new unit groups were achieved through simple splits or combinations of 1991 unit groups.”81 The concordance for the NOC-S and SOC 91 is brief since the vast majority of occupational groups remained the same.

The NOC-S system was used for the 2001 census, while earlier censuses used SOC. Earlier data can also be compared via a concordance with NOC-S that Statistics Canada has created, although the translation from earlier SOC to NOC-S is imperfect. Consequently, there will be some problems calculating the flows using the cohort component method with census data.

There are 520 different unit groups and 140 minor groups in the NOC-S 2001 system. Of the 520 unit groups, there are problems in shifting from the SOC 1991 to the NOC-S in 14 instances (see Table A6.1 below). Most of these discrepancies do not affect the minor group classifications. At the minor group level, the discrepancies affect the NOC-S 2001 occupation levels in 5 cases. In several instances these occupations did not exist in 1991 or there were not significant numbers of people in these occupations. It is therefore impossible to accurately translate earlier census information into NOC 2001 terms for all occupations.

Table A6.1 Differences Between NOC-S 2001 and SOC 91 NOC-S 2001 SOC 91

A121 Engineering Managers A121* Engineering, Science and Architecture Managers

A123 Architecture and Science Managers A121* Engineering, Science and Architecture Managers

B511 General Office Clerks B511 General Office Clerks

B512 Typists and Word Processing Operators

C047 Computer Engineers (Except Software Engineers)

C047* Computer Engineers

C071 Information Systems Analysts and Consultants

C062* Computer Systems Analysts

C072 Database Analysts and Data Administrators

C062* Computer Systems Analysts

C073 Software Engineers C047* Computer Engineers

81 “National Occupation Classification for Statistics 2001”, Statistics Canada catalogue no. 12-583-XPE. Ottawa, August 2001.



Table A6.2 Differences Between NOC-S 2001 and SOC 91 NOC-S 2001 SOC 91

C074 Computer Programmers and Interactive Media Developers

C063* Computer Programmers

C075 Web Designers and Developers C062* Computer Systems Analysts

C131 Civil Engineering Technologists and Technicians

C131* Civil Engineering Technologists and Technicians and Construction Estimators

C134 Construction Estimators C131* Civil Engineering Technologists and Technicians and Construction Estimators

C181 Computer and Network Operators and Web Technicians

B521 Computer Operators

C063* Computer Programmers

C182 User Support Technicians C063* Computer Programmers

C183 Systems Testing Technicians C063* Computer Programmers

D313 Other Assisting Occupations in Support of Health Services

D313 Other Aides and Assistants in Support of Health Services

G951 Elemental Medical and Hospital Assistants

E034 Social Policy Researchers, Consultants and Program Officers

E034* Health and Social Policy Researchers, Consultants and Program Officers

E039 Health Policy Researchers, Consultants and Program Officers

E034* Health and Social Policy Researchers, Consultants and Program Officers

E217 Early Childhood Educators and Assistants

G813 Early Childhood Educators and Assistants

G723 Casino Occupations G731* Attendants in Amusement, Recreation and Sport

G731 Operators and Attendants in Amusement, Recreation and Sport

G731* Attendants in Amusement, Recreation and Sport

G961 Food Counter Attendants, Kitchen Helpers and Related Occupations

G961 Food Service Counter Attendants and Food Preparers

G962 Kitchen and Food Service Helpers

H326 Welders and Related Machine Operators J195 Welders and Soldering Machine Operators



NOC-S 200682

The purpose of the 2006 revision of the National Occupational Classification for Statistics (NOC-S) has been to update the classification to incorporate emerging occupations and new job titles while maintaining historical comparability. The structure of NOC-S 2006 remains unchanged from that of NOC-S 2001. No major groups, minor groups or unit groups have been added, deleted or combined, though some groups have new names or updated content. Title changes at the unit group and minor group levels and updates to the definitions of some unit groups reflect added information, correction of terminology to correspond with current usage and the evolution of some occupations and where they are found.

Many new occupational titles have been added to NOC-S 2006. New occupational titles arise as the division of labour in Canadian society evolves, creating new jobs and new specialisations, and as technological change brings with it new terminology. Some of the occupational titles added to reflect such changes are: respite worker (home support), telehealth registered nurse, bioanalytical chemist, systems biologist, artificial intelligence designer, benefits consultant (HR), turntablist, veejay (VJ), accounting technician, e-business manager, e-business software developer and e-business Web site developer. Other added titles are modified versions of, or alternatives for, titles that appeared in previous versions of the NOC-S and have been added to help users find particular occupations they are looking for. For example, grape grower appeared in earlier versions of the NOC-S; viticulturist has been added.

A very few occupational titles have been re-assigned to a different unit group in NOC-S 2006 than in NOC-S 2001. The impact of this on the comparability of data between 2001 and 2006 is negligible. The only persons who have been coded to a different unit group in 2006 are those who reported their occupation as “florist” and who worked in “retail”. They have moved from Retail Trade Managers (A211) to Retail Salespersons and Sales Clerks (G211). This change will have a minimal impact on the unit groups affected. The occupational title, library curator, has been moved from Library, Archive, Museum and Art Gallery Managers (A341) to Conservators and Curators (F012) as this is a more appropriate placement; however, as this title was not reported in 2001, there is no impact on data comparability.

In all other cases where occupational titles have been moved, this was done to more accurately describe the content of these unit groups as they were disseminated in 2001. For example, because of the nature of the duties reported by census respondents, personal trainers were coded to Program Leaders and Instructors in Recreation, Sport and Fitness (F154) in 2001, not to Recreation, Sports and Fitness Program Supervisors and Consultants (E036), and campground maintenance workers were coded to Landscaping and Grounds Maintenance Labourers (I212) rather than to Operators and Attendants in Amusement, Recreation and Sport (G731). These placements have been recognised in NOC-S 2006. The majority of occupational titles that moved are military titles. In NOC-S, all military personnel are classified solely on the basis of rank either to Commissioned Officers, Armed Forces (A353) or to Other Ranks, Armed Forces (G624). The NOC-S 2001 noted this treatment of military personnel in its Introduction, but showed some military occupational titles in unit groups with their civilian counterparts. The NOC-S 2006 more clearly conveys how military personnel have been coded by showing all exclusively military occupational titles in the appropriate military unit group.

More information on these changes is available in Statistics Canada (2006). 82 Statistics Canada (2006).



Appendix VII: Essential Skills Profiles

Essential Skills profiles describe how each of the nine Essential Skills are used by workers in various occupations. Over the past several years, the Government of Canada has conducted research examining the skills people use at work. From this research and through interviews with workers, managers, practitioners and leading researchers, approximately 250 Essential Skills profiles have been developed for various occupations of the National Occupational Classification. To date, profiles have been completed for all occupations requiring a high school education or less. Research is ongoing to complete occupations requiring university, college or apprenticeship training.

What the profiles include:

• A brief description of the occupation. • A list of the most important Essential Skills. • Examples of tasks that illustrate how each Essential Skill is applied. • Complexity ratings that indicate the level of difficulty. • The physical aspects of performing the job and the attitudes that workers feel are needed

to do the job well. • Future trends affecting Essential Skills.

http://www23.hrdc-drhc.gc.ca/splash2.asp?lang=e



Appendix VIII: American Occupational Information Network The information in the O*NET database is extensive with 35 skill and 33 knowledge types (see Table A8.1). The data illustrate the skills types and skill levels that are needed to perform that occupation. There are similar data that illustrate what knowledge is important for a particular occupation and the level at which that knowledge used in the occupation.

Table A8.1: Skills and Knowledge Skill Types Knowledge Types Active Learning Administration and Management Active Listening Biology Critical Thinking Building and Construction Learning Strategies Chemistry Mathematics Clerical Monitoring Communications and Media Reading Comprehension Computers and Electronics Science Customer and Personal Service Speaking Design Writing Economics and Accounting Complex Problem Solving Education and Training

Management of Financial Resources Engineering and Technology Management of Material Resources English Language Management of Personnel Resources Fine Arts Time Management Food Production Coordination Foreign Language Instructing Geography Negotiation History and Archeology Persuasion Law and Government Service Orientation Mathematics Social Perceptiveness Mechanical Judgment and Decision Making Medicine and Dentistry Systems Analysis Personnel and Human Resources Systems Evaluation Philosophy and Theology Equipment Maintenance Physics Equipment Selection Production and Processing Installation Psychology Operation and Control Public Safety and Security Operation Monitoring Sales and Marketing Operations Analysis Sociology and Anthropology Programming Telecommunications Quality Control Analysis Therapy and Counseling Repairing Transportation Technology Design Troubleshooting Source: US Occupational Information Network



Each skill and knowledge type is rated on a scale from 0 to 100. For example, a zero for the importance indicator means the skill or knowledge is not used and a number approaching 100 means that that skill or knowledge is of great importance to that occupation. In the case of the level of skill and knowledge, a number approaching 100 means that a high skill level is required to perform the job while a lower number means that a lower level of skill or knowledge is needed. For example, chemistry knowledge is highly important to both a chemical technician and a chemist, but chemists must have a higher level of knowledge of chemistry to perform their job than chemical technicians.

The information available from the Occupational Information Network database is organised by occupation as classified by the US Standard Occupational Classification (SOC). There are many differences between the US SOC and the Canadian NOC. In order to use the information in the US database, a concordance between the US and Canada classification systems must be used.

Some occupational groups in the NOC and SOC are very diverse. In the US database, there is no information on the skills or knowledge attributes of diverse occupations that were typically described as “All Others”.



Appendix IX: Stock-Flow Labour Supply Model A stock flow approach that builds on basic labour force identities, combined with category specific coefficients to develop a full accounting framework for labour supply or labour force by occupation (LFo) would provide more information than the participation rate approach that determines total labour force. The stock-flow model is of the form:

Occupational Labour Supply=Occupational Labour Supply Prior year + Inflow -Outflow

Or using notation,

LFo=LFo[-1] + INo - OUTo-

By using the labour force identity, occupational labour supply also equals:

LFo=Employedo + Unemployedo (or LFo=Eo+UNo)

By combining these two equations, the following identity can be constructed.

LFo=Eo [-1] + UNo [-1] + INo-OUTo

Employment and unemployment will experience distinct inflows and outflows, so the equation can be written as below.

LFo=Eo [-1] + UNo [-1] + E INo – E OUTo + UN INo – UN OUTo

The aggregate inflow and outflow concepts in the above equation represent a number of distinct labour supply inflow and outflow categories. Inflows include: school leavers (SL), other new entrants (ONE), re-entrants (RE), interprovincial in-migration (INM) and international immigration (IM). Outflows include: death (DE), retirements (RET), other labour force or occupational separations, such as for illness, family reasons, discouraged worker and leaving an occupation for a new occupation (OLFS), interprovincial out-migration (OUTM) and international emigration (EM). For special groups, such as school leavers, sub-models should be built to calculate school leavers and translate school leavers by field of study into a specific labour force inflow by occupation.

Expanding the above equation to include these various flow concepts yields:

LFo=Eo [-1] + UNo [-1] + E SLo + E ONEo + E REo + E INMo + E IMo – E DEo – E RETo – E OLFSo – E OUTMo – E EMo + UN SLo + UN ONEo + UN REo + UN INMo + UN IMo – UN DEo–UN OLFSo– UN OUTMo– UN EMo

This approach is easily expandable to include all the labour supply components needed. For example, after including identifiers for sex (s), and age (a) the representative equation becomes.

LFo,s,a = Eo,s,a[-1] + UNo,s,a[-1] + E SLo,s,a + E ONEo,s,a + E REo,s,a + E INMo,s,a +E IMo,s,a – E DEo,s,a – E RETo,s,a – E OLFSo,s,a – E OUTMo,s,a – E EMo,s,a + UN SLo,s,a + UN ONEo,s,a + UN REo,s,a + UN INMo,s,a + UN IMo,s,a – UN DEo,s,a – UN OLFSo,s,a – UN OUTMo,s,a – UN EMo,s,a

In order to determine the specific number of people that enter and exit the labour force for all the groups of interest, specific flow equations will need to be constructed for each of these



components. The basic approach will be to utilise distinct cohorts and category specific share coefficients.

Flow=Flow Coefficient*Cohort

For example, outflows due to death will utilise death rates by age cohort, since mortality rates rise with age. The above equation for death of employees uses the subscript ‘a’ to represent the age group. If the dataset is detailed enough then sex could also be added, in which case the equation becomes in notation terms:

E DEo,s,a = δ o,s,a *Eo,s,a

Given data limitations, it will be necessary to use some simplifying assumptions to determine the appropriate coefficients. What this means for the model, is that the same coefficient could be used for various subgroups. For example, the same separation rate coefficient could be used for all groups of the same age and sex across all identified subgroups or by age if there is insufficient data to differentiate by age and sex.

That is,

δ o,s,a = δ a

Therefore,

E DEo,s,a = δ a *Eo,s,a

UN DEo,s,a = δ a *UNo,s,a

NILF DEo,s,a = δ a *NILFo,s,a

From the perspective of supply alone, however, it is impossible to differentiate between labour market entrants who become employed versus unemployed, without specific employment demand estimates. Consequently, at this stage the equation can be condensed to be inflow components of labour supply. Later by assuming that employment demand by subgroup keeps its historic or trend share of total employment, specific employment, unemployment and not in labour force estimates can be determined.

In contrast, there is something to be gained by differentiating outflow components between employed and unemployed because employment outflows are particularly important, since they are instrumental in the calculation of replacement demand.

The exact determination of which coefficients will be the same and which ones differ will be determined by the limits of the dataset and choices made concerning the data categories that are most important.

The full equation system is represented by the following composite equation. The system provides detailed occupational (o) labour supply estimates by age (a) and sex (s). Table A9.1 discusses the coefficient representation.

LFo,s,a = Eo,s,a [-1] + UNo,s,a [-1] + σo,s,a *POPo,s,a + λo,s,a *(UNo,s,a +NILFo,s,a )+υo,s,a *(Eo,s,a +UNo,s,a ) + γo,s,a *INM + πo,s,a *IM – αo,s,a *Eo,s,a – βo,s,a *Eo,s,a – δo,s,a *Eo,s,a – θo,s,a *Eo,s,a – ε*Eo,s,a – βo,s,a *UNo,s,a – δo,s,a *UNo,s,a – θo,s,a *UNo,s,a –εo,s,a *UNo,s,a

These coefficients will need to be calculated over history and projected into the future based on historic trends and expected future developments. The full listing of the proposed data to be used for the construction of coefficients, and the initial projection technique is illustrated in Table A9.2



and discussed in detail below. Other economic research, and insights should also be taken into consideration in the projection of these coefficients.

Flow coefficients Flow coefficients will be calculated over history and projected into the future based on historic trends and expected future developments. Over history the flow coefficients will be derived from the following relationship.

Flow Coefficient= Flow/Cohort

Flows will be either derived from changes in labour force components that are calculated from the census, LFS, or directly taken from other datasets. For example, deaths, out-migration, emigration, immigration and in-migration are explicitly or implicitly included in population projections and the same death and migration rates can be used in the labour supply calculation. Whereas the number of retired and other labour force separations are estimated in the LFS or the net separations can be estimated using the BLS approach, which uses a cohort component method to calculate flow data.83 And school leavers are measured explicitly in the National Graduates Survey and should be available from the provincial education ministry data.

Ideally, the projection of flow coefficients should reflect influences of economic and societal considerations. The factors that influence flow coefficients differ by component, so each flow component will need to be examined separately. Furthermore, care must be taken to ensure that these factors are incorporated in a manner consistent with the provincial economic modelling system. For example, if the provincial model has an explicit net migration estimate, the creation of specific flow equations and forecasts for each of the flow components (immigration, in-migration, emigration and out-migration) would cause an inconsistency with the net migration projection. Therefore, one of these flows will need to be calculated residually in order for them to add up to the net migration projection.

The lack of available data for the most disaggregated data will limit the direct use of econometric techniques and require the use of simpler projection methods, such as leaving the coefficient constant or continuing the historic trend. External studies may provide information as to the factors that influence labour supply flows and the relationship to economic factors that could be incorporated into the model. For example, in the Alberta model in-migration by occupation and participation rates by occupation are influenced by labour demand in a manner consistent with research on regional economies conducted by Blanchard and Katz (1992) and others.84 Another possibility is to use more aggregate data to estimate specific flow equations, and to apply the changes to all subgroups.

83 Eck (1991) and Willems and de Grip (1993) have used the cohort component method to calculate occupational net replacement needs in the US and the Netherlands, respectively. Boothby (1995) uses this method to calculate labour mobility flows for Canada. 84 Blanchard and Katz (1992). pp. 1-75. Partridge and Rickman (2004).



Table A9.1 Coefficient and Cohort Descriptions

Category Coefficient Representation

Coefficients Symbol

Relevant Cohorts

Retirements (RET) Employment share α Employment

Other Separations (OLFS)

Employment share β Employment

Deaths (DE) Share of population δ Population (same assumption for different of LF states & occupations)

Out-migration (OUTM) Share of population θ Population (same assumption for different of LF states & occupations)

Emigration (EM) Share of population ε Population (same assumption for different of LF states & occupations)

School Leavers (SL) Share of total school leavers population that enter labour force

σ School age population of current residents, differentiated by MFS

Re-entrants (RE) Share of those not in labour force that enter labour force

λ Not in labour force, differentiated by MFS

Other New Entrants

(ONE, shift between occupation classes)

Share of employment and unemployment that change occupation categories

υ Employment & unemployment

In-migration (INM) Share of total in-migration that enter labour force

γ Total provincial in-migration

Immigration (IM) Share of total immigration that enter labour force

π Total provincial immigration



Table A9.2 Cohort Coefficients For Projection Model

Proposed Data Sources and Coefficient Construction Methodology

Out-Flows Data Sources Construction Methodology

Retirements Labour Force Survey Determine retirement coefficients by age cohort and occupation group at the most disaggregated level that data permits for identified groups. Use the same coefficient for all the sub-groups below the level available as necessary. Assume the same coefficients as in the last historical data point, or adjust based on historic trends.

Other Separations

--Illness

--Family reasons

--Discouraged worker

Labour Force Survey Determine separation coefficients by age cohort and occupation group at the most disaggregated level that data permits for identified groups. Use the same coefficient for all the sub-groups below the level available as necessary. Assume the same coefficients as in the last historical data point, or adjust based on historic trends.

Mortality Population projection has mortality assumptions by age and sex.

Assume the same death rate by age/sex group across all occupations.

Out-migration Canadian Census.

Provincial specific out-migration data is available from demographic surveys.

Assume the same share coefficients by occupation as in the last census, or adjust based on historic trends. This number is then shared out by labour force status & occupation.

Emigration Demographic data has aggregate emigration estimate.

Assume that departures from province have the same share occupational profile as the general population.



In-Flows Data Sources Construction Methodology

New Graduates Census/provincial Education/ National Graduates Survey data. Education data by field of study and enrolments for province.

Assume that the historic distribution of MFS continues into the future by provincial secondary school graduates, or that recent trends continue.

Assume that the distribution of occupations for post secondary graduates by MFS remains the same as in the past, or that recent trends continue.

Assume that the distribution of occupations for secondary school graduates and dropouts remains the same as in the past, or that recent trends continue.

Re-entrants Census/Labour Force Survey

Assume that labour market re-entrances by age remain at historic shares or these share coefficients continue to evolve based on historic trends.

Assume that distribution of occupations related to educational attainment for this cohort remains the same as in the past or continues along its historic trends.

In-migration Census/population projection

Assume that the historic distribution by occupation is maintained in the future or that historic trends continue. This number is then shared out by labour force status.

Immigration Census/population projection

Assume that the historic distribution by occupation is maintained for the most recent immigrants in the future or that historic trends continue. This number is then shared out by labour force status.



Appendix X: Extrapolative Trends For Ireland, FÁS/ESRI use a variety of extrapolation techniques to project the trend in the occupational share in each sub-sector to the target year. 85 The techniques include: geometric extrapolation, or fit linear or semi-logarithmic regression equations to the trend in each share. In the latter case the fitted equation is used to project the trend value to provide an estimate of the occupational share in the target year.

A number of rules are used to decide whether the geometric, linear, or semi-logarithmic projections should be used. These rules were based on the change which had occurred in the occupational share between 1981 and 1991 or 1991 and 1995. For example, if the absolute change in the occupational share over the relevant period was less than 10 per cent, between 10 and 40 per cent, or more than 40 per cent, a semi-logarithmic, geometric, or linear projection would be selected respectively.

The analyses showed, however, that there were a small number of cells for which a projection using a different projection to that chosen by the application of the decision rules would give a better result. This occurred, for example, in situations where application of the decision rule resulted in a projection which appeared out of line with the long-term trend in the occupational share. In these cases judgement was used to select an appropriate projection equation. The equations used to make a geometric, linear, or semi-log projection are as follows:

Geometric: âij(t+k) = a(0)

ij (1+r)n

Linear: âij(t+k) = αij + βij (t+k)

Semi-log: âij(t+k) = αij + βij ln (t+k)

Where: âij is the projected share of the ith occupation in the jth sector, t is time, k is the number of years to the target date, n is the number of years from the beginning of the projection period to the target date, αij is the intercept of the projection equation βij is the regression coefficient for the variable in question.

85 Hughes (1999) p 81.



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