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Survey of Labour and Income Dynamics: 2008 Survey Overview

Survey objectives
What's new?
Survey design
Household relationships
SLID: a longitudinal survey
Computer-assisted telephone interviewing

Survey objectives

The Survey of Labour and Income Dynamics (SLID) is an important source for income data for Canadian families, households and individuals. Introduced in 1993, SLID provides an added dimension to traditional surveys on labour market activity and income: the changes experienced by individuals and families through time. At the heart of the survey's objectives is the understanding of the economic well-being of Canadians.

Starting with reference year 1998, the Survey of Labour and Income Dynamics (SLID) officially replaced the annual Survey of Consumer Finances (SCF) as the main source of information on family income. Over the 1993-1997 period, the two surveys were run in parallel and estimates for this period were produced by combining both samples. Together, these surveys cover a period that begins in 1976. The income content of the two surveys is similar, though SLID makes use of a mixed collection mode that combines survey data with data from administrative sources. On the other hand, SLID adds a large selection of variables that capture transitions in Canadian jobs, income and family events.

SLID, as a longitudinal survey, interviews the same people from one year to the next for a period of six years. The survey's longitudinal dimension allows evaluation of concurrent and often related events, which yields greater insight on the nature and extent of poverty in Canada: What socio-economic shifts do individuals and families live through?  How do these shifts vary with changes in their paid work, family make-up, receipt of government transfers and other factors? What proportion of households are persistently poor year after year, and what makes it possible for others to emerge from periods of low income?

SLID also provides information on a broad selection of human capital variables, labour force experiences and demographic characteristics such as education, family relationships and household composition. Its breadth of content combined with a relatively large sample makes it a unique and valuable data set.

What's new?

New Panel

17,196 new households have been selected to form Panel 6.

Geography Revision

SLID data always follow the geographic design of a particular Census. With this year’s release, SLID geography variables were revised from the 2001 to the 2006 Census-based geography design. Reference years prior to 1999 follow the boundaries of the 1991 Census geography-base; reference years 1999 to 2004 follow the 2001 Census geography-base; and reference years 2005 and on, follow the 2006 Census geography-base.

The major change in the standard geographical classification between the 2001 and 2006 Censuses is the definition of census metropolitan areas. As of March 2003, census agglomerations are no longer required to have an urban core population count of 100,000 to be promoted to the status of a census metropolitan area. Instead, census agglomerations assume the status of a census metropolitan area if they have attained a total population of at least 100,000 of which 50,000 or more live in the urban core. As a result, six census agglomerations from the previous census became census metropolitan areas: Moncton (N.B.), Barrie (Ont.), Brantford (Ont.), Guelph (Ont.), Peterborough (Ont.), and Kelowna (B.C.).1

In making the decision to switch to the 2006 Census-based design starting with reference year 2005, the SLID staff decided to ensure that not only the data for 2005 could be analysed using the new geographic design, but also the following four years, to provide a consistent four-year time series for the newer geographic detail. This period was a compromise between either revising none of the past years or revising all of them. While both the 1991 and 2001 Census-based geography variables are maintained on the database, the new 2006 Census-based geography variables were added, with a one-to-one correspondence with the existing variables. It was also decided to maintain identical code sets for both sets of variables. To do this, the SLID staff modified and expanded the code sets to include codes for any new or re-named areas found in the new design, as compared to the previous Census designs.

There are two changes to the published tables:

  • They were updated so that statistics in the years 2005 to 2008 reflect the 2006 design and years 1999 to 2004 reflect the 2001 Census geography-based design. The estimates for years prior to 1999 were not revised and still reflect the 1991 Census geography-based design.
  • With the release of 2008 data, the criteria to include census metropolitan areas and census agglomerations in our published tables was relaxed such that the population count of the census metropolitan areas and census agglomerations had at least 175,000 persons in 2008, and at least 300 economic families in the SLID 2008 sample. As a consequence, five census metropolitan areas (St. John’s N.L., Oshawa Ont., Sherbrooke Que., and Regina and Saskatoon, Sask.) were added to the tables that include geographic detail below the provincial level for reference years 1998 and on. These five census metropolitan areas were not added for reference years prior to 1998 because detailed census metropolitan areas were not available in the Survey of Consumer Finance.

New approaches to low income

Since its inception, the Survey of Labour and Income Dynamics (SLID) has been publishing statistics on low income based solely on the Low Income Cut-offs (LICOs), even though other measures exist. In order to provide a better picture of all aspects of low income, Statistics Canada is implementing an approach that uses several different lines of low income (Low Income Lines, 2008-2009). From now on, statistics will be produced using three complementary measures: the Low Income Cut-offs (LICOs), the Low Income Measures (LIMs) and the Market Basket Measure (MBM). The Market Basket Measure was developed by the Department of Human Resources and Skills Development Canada2. As Dr Ivan P. Fellegi, the former Chief Statistician of Canada,  has pointed out, these measures are not measures of poverty, but strictly measures of low income(On poverty and low income).

Though these measures differ from one another, they give a generally consistent picture of low income status over time. None of these measures is the best. Each contributes its own perspective and its own strengths to the study of low income, so that cumulatively, the three provide a better understanding of the phenomenon of  low income as a whole. This change also lets users choose the measure that best suits their needs. The MBM defines low income in relation to the cost of a predefined set of goods and services. The price of this “basket” of goods and services takes regional differences in the cost of living into account. The LIM  is based solely on the distribution of household income and is intended as a reference for international comparisons. Finally the LICOs are based on the relationship between the incomes and the consumption patterns of Canadian households as observed in 1992. The LICOs have been very widely used in Canada since the 1970s.

Another change in Statistics Canada’s approach to low income is in the kinds of analyses that will be performed. From now on, statistics will no longer be reported at the family level, but instead at the individual level, where each individual is represented by his or her adjusted family income (for the LICOs and the MBM)or adjusted household income (for the LIMs). This change is directly in line with the recommendations of the Canberra Group on Household Income Statistics and complies with Statistics Canada’s guidelines regarding income analyses. Because it is essential to portray the low income situation for the entire population, it is more appropriate to analyze individuals than families. In this way, the statistics are not distorted by variations in the size of families in the various groups of interest. But this new approach does not prevent anyone from studying low income according to family characteristics. For example, instead of talking about the incidence of low income in elderly families, we can talk about the incidence of low income among individuals who live in elderly families.

Lastly, some new statistics are being introduced. In addition to the number and percentage of persons below a certain cut-off, users will also find new measures of the depth of low income, in which the low income gap is expressed as a percentage of a given cut-off or relevant aggregate income. These measures replace the mean and median low income gaps, which could be subject to misinterpretation. Moreover, these new measures meet international standards set by the Organisation for Economic Co-operation and Development (OECD).

All of these changes have been applied retroactively as far as 1976, except for the statistics based on the MBM which were introduced in 2000.  This was the year that the MBM was used for the first time by Human Resources and Skills Development Canada.

Use of a different equivalence factor

A family’s reported income does not provide a sufficiently complete picture of its economic well-being. In addition, one needs to know how many people there are in that family. In order to account for the economies of scale associated with family size, an equivalence scale is used to adjust family income. Until last year, the scale used by Statistics Canada was as follows:

  • the oldest person in the family was assigned a factor of 1.0;
  • the second oldest person in the family was assigned a factor of 0.4;
  • each of the other family members aged 16 and over was assigned a factor of 0.4;
  • each of the other family members under age 16 was assigned a factor of 0.3.

The adjusted income amount for the family was then obtained by dividing the family’s income by the sum of the factors assigned to the family members.

In order to ensure international consistency and to facilitate the calculation of adjusted family income, a new scale will now be used. From now on, adjusted family income will be obtained by dividing family income by the square root of the number of members in the family. The estimates for past years have been revised accordingly. With respect to the LIM, the square root of the number of persons in the household will be used.

Introduction of new variables

Geography variables

With this year’s release, SLID geography variables were revised from the 2001 to the 2006 Census-based geography design. Reference years prior to 1999 follow the boundaries of the 1991 Census geography-base; reference years 1999 to 2004 follow the 2001 Census geography-base; and reference years 2005 and on follow the 2006 Census geography-base.

In addition to changing these variables, a new series of geography variables (with the prefix “Y”) was introduced. These geography variables have not been revised to the 2006 Census-based geography design for the years 2005 and on. For this series, reference years prior to 1999 follow the boundaries of the 1991 Census geography-base; and reference years 1999 and on follow the 2001 Census geography-base.

A new census metropolitan area and census agglomeration grouping has been created. This new grouping covers all the census metropolitan areas and census agglomerations published in the tables beginning this year including the new census metropolitan areas and census agglomerations - St. John’s N.L., Oshawa Ont., Sherbrooke Que., and Regina and Saskatoon, Sask. Although this new variable is available for all survey years, the five new census metropolitan areas and census agglomerations are only available from years 1998 on because detailed census metropolitan areas were not available in the Survey of Consumer Finance.

Adjusted income

Five new adjusted income variables were added based on household level income. These new adjusted income variables are calculated using the square root of the household size as their equivalence scale. These new variables are available for all survey years.

One new adjusted income variable was also added based on the family level. This adjusted income variable is calculated using the square root of the family size as its equivalence scale. This new variable is available for all survey years.

Household type

A new household type variable has been added. It is loosely based on the existing family type variable and aims to aid in international comparisons. This new variable is available for all survey years.

Number of earners in a household

A new number of earners variable has been added. This new variable counts the number of earners in a household. This new variable is available for all survey years.

Low income variables

New low income variables have been added due to the new equivalence scale used (the square root of the household or family size) and in some instance, a change in level of analysis.

The new Low Income Measure variables use the new equivalence scale and are now based on the household instead of the family. These new variables are available for all survey years.

There is a new series of variables based on the 2008-based Market Basket Measure (MBM). Following a comprehensive review undertaken by Human Resources and Skills Development Canada, the content of the basket itself has been rebased and revised and these revisions have been made back to the inception of the MBM in 2000. Additionally, the square root of the family size is now used in relation to the MBM

Income from a Registered Disability Savings Plan (RDSP)

As of reference year 2008, a variable was added to reflect the income from a Registered Disability Savings Plan (RDSP).   This component of income is included in the variable “Other income” which is included in market income, total income, after-tax income and disposable income.

Changes to variables


Prior to reference year 2008, the condominium question “Is this dwelling part of a condominium development?” was asked only of owners.  Beginning in 2008, renters that moved were also asked the question and starting next year, all owners and renters will be asked this condominium question.

Training Module

The series of questions about person's educational activity during the year (entity EDUCACTV) was removed from the questionnaire. Although persons attending school during the year can be identified, very few information about the nature of the training is available.

Child Tax Benefits

Ontario Child Benefit (OCB) was introduced in 2007.

Beginning in July 2008, OCB became monthly payments and the maximum benefit was $600.

Energy Rebates and Credits

British Columbia Climate Action Dividend (BCCAD).

The BCCAD was issued to all residents of British Columbia. It provided a non-taxable, one-time payment of $100.00 to every adult and child who was a resident of British Columbia on December 31, 2007. The majority of BCCAD payments were issued in late June 2008.  Payments to children were made to the primary caregiver for each of their children who was under 18 years of age at the end of 2007.

British Columbia Low Income Climate Action Tax Credit (BCLICATC).

The BCLICATC was a new program in 2008 which provided a tax-free payment of up to $100 for an individual, $100 for a spouse or common-law partner and $30 per child ($100 for the first child in a single parent family). For single individuals with no children, the credit was reduced by 2% of their net income over $30,000. For families, the credit was reduced by 2% of their net income over $35,000. The payment is combined with the quarterly payment of the federal GST credit.

In 2008, the BCCAD was added to the energy rebates and credits variable.  The BCLICATC and the energy rebates and credits variable are included in the variable provincial tax credits.  The Provincial tax credits variable is a component of government transfers therefore a component of total income.

Survey design

SLID is a household survey that covers all individuals in Canada, excluding residents of the Yukon, the Northwest Territories and Nunavut, residents of institutions and persons living on Indian reserves or in military barracks.

The SLID sample is composed of two panels. Each panel consists of roughly 17,000 households and about 34,000 adults, and is surveyed for a period of six consecutive years. A new panel is introduced every three years, so two panels always overlap.

Figure 1. Overlapping design of SLID sample

Figure 1. Overlapping design of SLID sample

From January to March following the reference year, interviews are conducted over the phone. Interviewers collect information regarding respondents' labour market experiences, educational activity and family relationships. The demographic characteristics of family and household members represent a snapshot of the population as of the end of each calendar year. Interviewers also collect information on income. However, respondents have the option of answering income questions during the interview, or of giving Statistics Canada permission to access their income tax records. Over 80 percent of respondents give us their permission to consult their income tax file.

Household relationships

This survey could be called the Survey of Labour, Income and Family Dynamics, since it has complete information on complex family structures and changes. How does it do this?

Unlike most household surveys, which describe how household members are related to one specific reference person, SLID asks explicitly about the relationship among all members of a household. Information on complex family structures - for example, blended or multigenerational families - can help in understanding family dynamics.

However, because families change, it isn't possible to present data for exactly the same families over time. Instead, the same individuals are analysed in light of their family characteristics, for example, their family's income or whether they belong to a blended family.

SLID: a longitudinal survey

Description of a longitudinal survey
Longitudinal respondents
Longitudinal research themes

Description of a longitudinal survey

There are two types of recurring surveys: in one you interview a new cross-section of people each time, as most surveys do, while in the other, you interview the same people over a period of time, as in a longitudinal survey.

The advantage of cross-sectional surveys is that they are generally more representative of the population, and they reveal the levels and trends of income or labour for the whole population or its sub-groups. But such surveys do not answer questions about changes or fluctuations faced by individuals or families: What are the fluctuations in people's labour, income or family characteristics at the micro level? What events tend to coincide? How often do people change jobs or get laid off, with what impact on their total family income? How many families split or join together in a given time period? What proportion of households are "persistently poor" year after year, and what makes it possible for others to emerge from periods of low income? These and many other similar questions can only be answered by a longitudinal survey.

In a survey like SLID , the focus extends from static cross-sectional measures to a whole range of longitudinal events: transitions, durations, and repeat occurrences of people's financial and work situations. These yield a number of possible longitudinal research themes.

Paradoxically, the comprehensive data that make SLID so valuable, also make it more complex for Statistics Canada to maintain the confidentiality of respondents. In order to comply with the strict confidentiality provisions of the Statistics Act, SLID longitudinal data are made available through special modes of dissemination (see data services).

Longitudinal respondents

Longitudinal respondents are the people belonging to the selected households when a new six-year panel of respondents is introduced. These respondents are interviewed once a year whether they stay, move away or split up. New joiners, called cohabitants in SLID, are interviewed as long as they continue to live with a longitudinal respondent. That's because the family make-up and family income situation of longitudinal respondents is of key interest. Interviewing cohabitants also improves the quality of cross-sectional estimates.

Children present in the original households are interviewed starting the year they reach 16 years old. On the other hand, people aged 70 years and over are not asked labour-related questions.

Longitudinal research themes

Discussions with prospective users and insights from other panel surveys with similar content helped identify seven longitudinal research themes that illustrate some of the survey's potential. Depending on the angle of study, it may make sense to use individuals, jobs, employers, or spells (of unemployment, for example) as the unit of analysis. SLID covers up to six jobs and six employers that a person might have during each calendar year.

Employment and unemployment dynamics

Labour force activity data usually show total employment, unemployment and inactivity. Changes in employment and unemployment between two months or two years are calculated by comparing these totals. SLID, however, shows the flow into each type of labour force activity experienced by individuals. Flow data of persons or jobs are possible by industry, occupation, or worker characteristics. Durations of spells may be of interest too; for example, to what extent are long spells of unemployment experienced by the same individuals? What are the major determinants? Why do people withdraw from the labour market, and what precedes a transition into self-employment?

Life cycle labour market transitions

Using SLID data, one can study major labour market transitions associated with particular stages of the life cycle, such as transitions from school to work, transitions from work to retirement and work absences taken to have or raise children. What are typical life-cycle patterns in Canada today? What are the subsequent activities of high school drop-outs, and what precedes a return to school? 

Job quality

SLID supports research in such areas as wage differences between men and women, under-employment, occupational mobility, earnings growth over a period of several years, and wage and hours polarisation among the working population. 

Family economic mobility

How stable is family income? What proportion of families experience a significant improvement or deterioration in income between two points in time? What are the determinants of these changes? How important are changes in family composition (divorce, remarriage) in explaining a change in financial well-being?

Dynamics of low income

This research theme concerns the prevalence and duration of spells of low income and the factors related to families moving into or out of low income. Researchers can isolate and characterize a "persistently poor" sub-population, as is done using longitudinal surveys in other countries. There is also interest in looking at receipt of employment insurance benefits, social assistance and other government transfers in relation to flows into and out of low income.  

Life events and family changes

Central to SLID's demographic potential is information on family relationships, which make it possible to accurately identify blended and multi-generational families. The longitudinal aspect permits the study of life events and their determinants or impact. For example, what are the family's economic circumstances preceding a marriage break-up, and what are they for each spouse and child following a separation?  

Educational advancement and combining school and work

It is possible to view educational activity and attainment in the evolving context of an individual's other activities and family circumstances. What are the family circumstances of young people pursuing post-secondary education? How much do high school or postsecondary students combine work and school?

Computer-assisted telephone interviewing

SLID uses computer-assisted telephone interviewing (CATI) for data collection. With CATI, interviews are conducted over the phone and simultaneously entered in a computer that guides the interviewer through the questionnaire.

Because of its complexity as a longitudinal survey, SLID benefits greatly from CATI's potential for improving data quality. For example, there are many dates to collect in the course of a labour interview - dates worked, dates of jobless spells, absences from work and so on. With CATI, interviewers can remind respondents of information they provided in a previous interview. This helps respondents remember start and end dates of jobs and reduces the tendency to incorrectly associate these dates with the beginning or end of calendar years.

Computer-assisted interviewing helps keep track of members returning to the household and individuals returning to employers, rather than treating these members or employers as completely new.

Proxy response is accepted in SLID. This procedure allows one household member to answer questions on behalf of any or all other members of the household, provided he or she is willing to do so and is knowledgeable.