Survey of Labour and Income Dynamics (SLID)
Detailed information for 2008
At the heart of the survey's objectives is the understanding of the economic well-being of Canadians: what economic shifts do individuals and families live through, and how does it vary with changes in their paid work, family make-up, receipt of government transfers or other factors? The survey's longitudinal dimension makes it possible to see such concurrent and often related events.
Data release - June 17, 2010
- Questionnaire(s) and reporting guide(s)
- Data sources and methodology
- Data accuracy
The Survey of Labour and Income Dynamics (SLID) complements traditional survey data on labour market activity and income with an additional dimension: the changes experienced by individuals over time. At the heart of the survey's objectives is the understanding of the economic well-being of Canadians: what economic shifts do individuals and families live through, and how does it vary with changes in their paid work, family make-up, receipt of government transfers or other factors? The survey's longitudinal dimension makes it possible to see such concurrent and often related events.
SLID was the first Canadian household survey to provide national data on the fluctuations in income that a typical family or individual experiences over time which gives greater insight on the nature and extent of low income in Canada. Added to the longitudinal aspect are the "traditional" cross-sectional data: the primary Canadian source for income data and providing additional content to data collected by the Labour Force Survey (LFS).
Particularly in SLID, the focus extends from static measures (cross-sectional) to the whole range of transitions, durations, and repeat occurrences (longitudinal) of people's financial and work situations. Since their family situation, education, and demographic background may play a role, the survey has extensive information on these topics as well.
The survey data are used by federal (Human Resources and Skills Development Canada, Finance, Canada Mortgage and Housing Corporation , etc.) and provincial departments to formulate social policies and programs. Non-government organizations, private consultant firms and academics also use SLID data to do research to support their positions and to lobby governments for social changes. Individuals and families can use the data to compare their earnings and income situations with those of similar types of family compositions.
Reference period: Calendar year
Collection period: January to mid-March
- Families, households and housing
- Household, family and personal income
- Income, pensions, spending and wealth
- Low income and inequality
Data sources and methodology
All individuals in Canada, excluding residents of the Yukon, the Northwest Territories and Nunavut, residents of institutions and persons living on Indian reserves. Overall, these exclusions amount to less than 3 percent of the population.
The questionnaire was designed for Computer-Assisted Telephone Interview, which means that as the questions were developed, the associated logical flows into and out of the questions were programmed. This included specifying the type of answer required, the minimum and maximum values, on-line edits associated with the questions and what to do in case of item non-response. The initial version of the questionnaire was focus-group tested. When applicable, questions used in other Statistics Canada surveys were implemented in SLID to improve comparability across surveys.
This is a sample survey with a cross-sectional design and a longitudinal follow-up.
The samples for SLID are selected from the monthly Labour Force Survey (LFS, record number 3701) and thus share the latter's sample design. The LFS sample is drawn from an area frame and is based on a stratified, multi-stage design that uses probability sampling. The total sample is composed of six independent samples, called rotation groups, because each month one sixth of the sample (or one rotation group) is replaced.
The SLID sample is composed of two panels. Each panel consists of two LFS rotation groups and includes roughly 17,000 households. A panel is surveyed for a period of six consecutive years. A new panel is introduced every three years, so two panels always overlap.
Data collection for this reference period: 2009-01-02 to 2009-03-23
Responding to this survey is voluntary.
Data are collected directly from survey respondents and extracted from administrative files.
For each sampled household in SLID, interviews are conducted over a six-year period. Every year between January and March, interviewers collect information regarding respondents' labour market experiences and income during the previous calendar year. Information on educational activity and family relationships is also collected at that time. The demographic characteristics of family and household members represent a snapshot of the population as of the end of each calendar year.
To reduce response burden, respondents can give Statistics Canada permission to use their T1 tax information for the purposes of SLID. Over 80% of SLID's respondents give their consent to use their administrative records.
SLID interviews are conducted over the telephone using computer-assisted interviewing (CAI). The interviewer reads the questions as they appear on the computer screen and keys in the reported information. Skip-patterns and edits are built into the collection software, allowing interviewers to immediately detect and resolve response inconsistencies. Collection of date-related information (e.g., employment spells, jobless spells, interruption of work), is greatly improved by the use of such an interactive data capture technique. Another advantage of the CAI technology is the feeding back of details from the previous interview, helping respondents to recall past events.
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.
View the Questionnaire(s) and reporting guide(s).
A series of verifications took place to ensure that the records were consistent and that collection and capture of the data did not introduce errors. Reported data were examined for completeness and consistency using automated edits coupled with manual review. Some responses reporting uncommon values or characteristics were processed manually.
The primary method employed for imputing income data in this survey is to use the previous year's data, updated for any changes in circumstances. Only in the absence of such data are income figures imputed using the "nearest neighbour" technique in SLID.
Amounts received through certain government programs, such as child tax benefits, the Goods and Services Harmonized Sales Tax Credit, and the Guaranteed Income Supplement, are also derived from other information. Data obtained from the tax route are complete and do not need imputation.
The estimation of population characteristics from a survey is based on the premise that each sampled unit represents, in addition to itself, a certain number of unsampled units in the population. A basic survey weight is attached to each record to indicate the number of units in the population that are represented by that unit in the sample.
For each reference year, SLID produces two sets of weights: one is representative of the initial population (the longitudinal weights) while the other is representative of the current population (the cross-sectional weights). For the production of the cross-sectional weights, SLID combines two independent samples and assigns a probability of selection to individuals who joined the sample after the panel was selected.
Two types of adjustment are applied to the basic survey weights in order to improve the reliability of the estimates. The basic weights are first inflated to compensate for non-response. The non-response adjusted weights are then further adjusted to ensure that estimates on relevant population characteristics would respect population totals from sources other than the survey.
The first set of population totals used for SLID is based on Statistics Canada's Demography Division population counts for different age/sex groups as well as counts by household and family size at the provincial level. These annual population totals are based in large part on totals from the Census of population.
The second set of totals is derived from Canada Revenue Agency (CRA) administrative data (T4 file) and is intended to ensure that the weighted distribution of income (based on wages and salaries) in the data set matches that of the Canadian population.
The survey results are compared with other data sources that include: administrative databases, census and other Statistics Canada surveys.
Statistics Canada is prohibited by law from releasing any data that would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.
Suppression rules, or data reliability cutoffs, are currently established based on the sample size that underlies the estimate. In general, a sample size of 25 observations is required for the estimate to be published. Depending on the type of estimate, this rule can vary slightly. These rules help protect the confidentiality of survey respondents and ensure the reliability of estimates.
Revisions and seasonal adjustment
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.
There are two types of errors inherent in sample survey data, namely, non-sampling errors and sampling errors. The reliability of survey estimates depends on the combined impact of non-sampling and sampling errors.
Randomly occurring non-sampling errors generally result from human errors such as simple mistakes, misunderstanding or misinterpretation; their impact over a large number of observations will be minimal. Errors occurring systematically and errors arising from sources such as coverage, erroneous response, non-response and processingcan, have, on the other hand, a major impact on the reliability of estimates. Considerable time and effort is invested into reducing non-sampling errors in SLID.
Coverage error arises when sampling frame units do not exactly represent the target population. Units may have been omitted from the sampling frame (under-coverage), or units not in the target population may have been included (overcoverage), or units may have been included more than once (duplicates). Undercoverage represents the most common coverage problem. Slippage is a measure of survey coverage error. It is defined as the percentage difference between control totals (Census population projections) and weighted sample counts. In 2008, SLID covered 87.9% of its target population. SLID estimation procedures use Census population projections to compensate for determined slippage. Rates are also available upon request for sex, province and age groupings.
As for the response rate, the cross-sectional response rate was 67.6% in 2008.
Sampling errors occur because inferences about the survey population are based on data from a sample of that population rather than the entire population. The sample design, the variability of the characteristic being measured, and the sample size will all contribute to the magnitude of the sampling error. The standard error is a common measure of sampling error. The standard error measures the degree of variation introduced in estimates by selecting one particular sample rather than another of the same size and design. Another widely used measure of the sampling error is the coefficient of variation (CV), which is the estimated standard error expressed as a percentage of the estimate. In SLID, the bootstrap approach is used for the calculation of standard errors. This is a resampling method of variance estimation, often used when dealing with estimates from a complex sample design.
- Data Quality for the 2008 Survey of Labour and Income Dynamics (SLID)