Labour Market and Socio-economic Indicators

Detailed information for October/December 2025

The purpose of this survey is to identify changing dynamics within the Canadian labour market, and measure important socio-economic indicators. Gathering data on topics such as type of employment, quality of employment, support payments and unmet health care needs will allow policy makers to address these trends.

Data release - May 19, 2026

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

Objectives:
Results from this Supplement survey will provide a better understanding of labour market and socio-economic indicators on key trends for diverse groups of Canadians, and will inform government as it reacts to these trends.

Topics Studied:
- Types of employment
- Quality of employment
- Support payments
- Unmet healthcare needs

Reference period: Quarter

Collection period: Data is collected for sixteen days following the reference period each month.

Subjects

• Child care
• Children and youth
• Disability
• Health
• Labour

Data sources and methodology

Target population

This supplement survey is conducted in the provinces only and is made up of respondents who are in their last rotation of the Labour Force Survey (LFS).

Instrument design

All questions have been reviewed by the Questionnaire Design Resource Centre (QDRC).

Sampling

This is a sample survey with a cross-sectional design.

The frame for this supplement survey comes from the Labour Force Survey (LFS).

The sample is made up of Labour Force Survey (LFS) respondents from the provinces only, and is made up of respondents in their last rotation of LFS. This is a longitudinal random sample of households from dwellings across Canada (more details available on the LFS webpage record number 3701).

Sampling unit
This is a dwelling based survey.

Stratification method
The stratification methodology is the same as the one used for the Canadian Labour Force Survey (see Catalogue No. 71-526-X, https://www150.statcan.gc.ca/n1/en/catalogue/71-526-X).

Sampling and sub-sampling
Sample consists of households in their 6th month of LFS. Estimated sample size is 10,000 dwellings.

Data sources

Data collection for this reference period: 2025-10-19 to 2025-12-23

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

- Collection method: CMP EQ, CATI & CAPI
- Capture method: CMP rEQ/iEQ
- Method of initial contact: mail-out letter
- Follow-up method: CATI
- Use of proxy reporting: proxy reporting is used for this survey
- Language(s) offered to potential respondents: English & French
- Average time required to complete the interview/survey: 15 minutes.

For imputation and validation purposes, the data collected is merged with the main LFS questionnaire. The data collected will be linked with tax data.

View the Questionnaire(s) and reporting guide(s) .

Error detection

A series of verification steps are performed to identify and eliminate potential duplicate records and to exclude non-response and out-of-scope records.

Any record that is on the main LFS file, and is a rotate-out (respondents that are in their last month of the LFS), should also exist on the LMSI file. This step adds any missing records to the LMSI file (with missing values for all variables).

Editing is also performed at this step by modifying the data at the individual variable level, to correct for inconsistent or invalid responses.

Imputation

Imputation was done using the nearest neighbour donor imputation method. Persons that responded to all of the questions are considered "donor" candidates, while those with missing or invalid data are considered "recipients". A distance measure between donors and recipients is calculated, based on their similarities in terms of labour force status (employed, absent, unemployed, not in the labour force), industry (NAICS) and occupation (NOC), province, and other variables describing demographic and economic characteristics. Data from the best possible donor are used to fill in the missing values for the recipient.

Imputation was carried out when individual items were missing or invalid, and also for entire records that were deemed eligible for LMSI based on their TABS data. As such, all records from in-scope rotations on TABS have a corresponding record on LMSI. For records that were out of scope for LMSI based on other reasons (e.g. Age>69), all variables on the LMSI file will have a value of "Valid Skip".

Estimation

For information on the calculation of the LFS weights, refer to the Methodology of the Canadian Labour Force Survey (Catalogue No. 71-526-X, available online).

After the calculation of the LFS final weights, further adjustments are made to derive a weight for the individual records on the LMSI Supplement microdata file:
• an adjustment to account for using only one of the six rotations in the LFS sample;
• an adjustment to account for collecting data across three LFS cycles;
• an adjustment to calibrate the weights to 18 age-sex groups, labour force status (employed, unemployed, not in the labour force) and rotation group.
The calibration totals are based on final LFS estimates, which themselves are based on population projections from Statistics Canada's Demography division.

Bootstrap Weights
The bootstrap weights file provided contains 1,000 bootstrap weight replicates. In general, variance estimation using bootstrap weights is done by computing the estimate of a parameter using each set of bootstrap weights and then computing the variance of the resulting estimates. This method is valid not only for totals, means, and ratios, but also for non-linear parameters such as medians and other quantiles. These bootstrap weights can be used to compute a variety of variance estimates based on the LMSI Supplement data.
The bootstrap weights for the LMSI Supplement were calculated starting from the LFS bootstrap weights, and applying the same non-response adjustment and calibration that were applied to create the final LMI Supplement weights. For more information on the calculation and use of the LFS bootstrap weights, refer to the Methodology of the Canadian Labour Force Survey (Catalogue No. 71-526-X, available online).

Quality evaluation

Selected totals and their coefficients of variation were estimated with the LMSI weight and compared to the estimates using the final LFS weight. The estimates were:
1. Employment, Unemployment, and Not in Labour Force, by province
2. Population by province
The validation step confirmed that totals were the same, and that their coefficients of variation were within 0.1%.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that could identify
any person, business, or organization, unless consent has been given by the respondent or as permitted
by the Statistics Act. 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.

Revisions and seasonal adjustment

This methodology type does not apply to this statistical program.

Data accuracy

The quality of estimates calculated from the Supplement file are similar to those obtained from the main LFS.

Response rate
This assessment does not consider LFS non-response. Almost all units that are eligible for the Supplement (based on LFS data) do complete it. The additional non-response for the Supplement is negligible. However, from a quality point of view, it is important to note that some LFS respondents are Supplement non-respondents because they are ineligible due to their age, for example, or because they are not part of an in-scope rotation.

Non-sampling error
Errors which are not related to sampling may occur at almost every phase of a survey operation. Interviewers may misunderstand instructions, respondents may make errors in answering questions, the answers may be incorrectly entered on the questionnaire and errors may be introduced in the processing and tabulation of the data. These are all examples of non-sampling errors.
Over a large number of observations, randomly occurring errors will have little effect on estimates derived from the survey. However, errors occurring systematically will contribute to biases in the survey estimates. Considerable time and effort were taken to reduce non-sampling errors in the survey. Quality assurance measures were implemented at each step of the data collection and processing cycle to monitor the quality of the data. These measures include the use of highly skilled interviewers, extensive training of interviewers with respect to the survey procedures and questionnaire, procedures to ensure that data capture errors were minimized, and coding and edit quality checks to verify the processing logic.

Non-response bias
The LFS uses a robust processing system to mitigate the impacts of non-response, through weighting adjustments and/or imputation methodologies. Similar methodologies are applied to process units that respond to the LFS but not the Supplement.

Coverage error
Excluded from the survey's coverage are: persons living on reserves and other Indigenous settlements, residents of institutions, and residents of regions that are extremely remote or of extremely low population density. These groups together represent an exclusion of approximately 2% of the population aged 15 and over.

Others non-sampling errors
A major source of non-sampling errors in surveys is the effect of non-response on the survey results. The extent of non-response varies from partial non-response (failure to answer just one or some questions) to total non-response.

Total non-response occurred because the interviewer was either unable to contact the respondent or the respondent refused to participate in the survey.
Total non-response was handled by adjusting the weight of individuals who responded to the survey, or by using imputation methods.

In most cases, partial (item) non-response to the survey occurred when the respondent did not understand or misinterpreted a question, refused to answer a question, or could not recall the requested information.