Guide to the Labour Force Survey
2025

Section 1: Background and objectives

Introduction

The Labour Force Survey (LFS) is a household survey carried out monthly by Statistics Canada. Since its inception in 1945, the objectives of the LFS have been to divide the working-age population into three mutually exclusive categories in relation to the labour market—employed, unemployed, and not in the labour force—and to provide descriptive and explanatory data on each of these groups. Data from the survey provide information on major labour market trends, such as shifts in employment across industrial sectors, hours worked, labour force participation and unemployment rates.

Background and objectives

The LFS was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war-time to a peace-time economy. The survey was designed to provide estimates of employment by industry and occupation at the regional as well as the national level.

Initially a quarterly survey, the LFS became a monthly survey in 1952. In 1960, the Interdepartmental Committee on Unemployment Statistics recommended that the LFS be designated as the source of the official measure of unemployment in Canada. This endorsement was followed by demand for a broader range of labour market statistics, particularly more detailed regional data. The scope of the survey has expanded considerably over the years, with a major redesign of the survey content in 1976 and again in 1997, and provides a rich and detailed picture of the Canadian labour market.

The LFS is the only source of monthly estimates of total employment, including self-employment, full- and part-time employment, and unemployment. It publishes monthly standard labour market indicators such as the unemployment rate, the employment rate and the participation rate. In addition, the LFS provides information on the personal characteristics of the working-age population, including age, gender, employment equity groups, educational attainment, and family characteristics.

Employment estimates include detailed breakdowns by demographic characteristics, industry and occupation, job tenure, and usual and actual hours worked. The LFS questionnaire permits analyses of many topical issues, such as involuntary part-time employment, multiple job-holding and work absences. Since January 1997, it also provides monthly information on the wages and union status of employees, as well as the number of employees at their workplace and the permanency of their job.

Starting in late 2003 in Alberta, and then in April 2004 for the rest of western Canada, questions were added to the LFS to identify Indigenous respondents, with the goal of producing labour market statistics for the off-reserve Indigenous population in the provinces. Starting in 2004, the Indigenous group questions were asked in the territories. In January 2007, these questions were extended to all provinces. Labour market data for the Indigenous population have been available since the fall of 2008.

In January 2006, five questions were added to the LFS to identify the immigrant population. Specifically, questions were added to identify the country of birth of the respondent, whether or not the respondent was a “landed immigrant,” the month and year they became a landed immigrant, and the country where the respondent received their highest level of education. These questions are comparable to those used in the census questionnaire. Labour market data for the immigrant population have been available since the fall of 2007.

In January 2022, the LFS added a question to capture information on visible minorities. According to the Employment Equity Act, visible minorities are “persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour. ” The LFS visible minority variable conforms to the departmental standard Classification of visible minority. Labour market data by visible minority population group have been available since the spring of 2022.

LFS also started collecting distinct gender of person and sex at birth questions in January 2022. Prior to 2022, LFS collected information on sex of person (male or female), as recorded by the interviewer based on presentation. The questions for gender and sex at birth are comparable with the questions used in the 2021 Census and conform to the departmental standards for Gender of person. Labour market data by gender were first published in January 2025.

Unemployment estimates are produced by demographic group, and information on duration of unemployment and activity before looking for work are available. Information on industry, occupation and reason for leaving last job is collected from persons with recent labour market involvement who are currently unemployed or not in the labour force.

In addition to providing national, provincial and territorial estimates, the LFS also releases estimates of labour force status for sub-provincial areas such as economic regions (ERs) and census metropolitan areas (CMAs), and a selection of census subdivisions (CSDs) with a larger population.

Section 2: Determining labour force status

The concepts of employment and unemployment are derived from the theory of labour supply as a factor in production. In this context, production refers to the goods and services included in the National Economic Accounts. For this reason, unpaid housework and volunteer work are not counted as work for purposes of the LFS, although these activities need not differ from paid work either in purpose or in the nature of the tasks involved.

While the logical and precise unit of measurement for total labour supply is person-hours, the conceptual terms of reference for the LFS require that individual members of the population be classified as employed, unemployed or not in the labour force. Accordingly, persons who are supplying services in the reference period, regardless of the quantity supplied, are classified as employed, while those who provide evidence that they are offering, but not supplying, their labour services to the market are classified as unemployed. The remainder of the population, who are neither currently supplying, nor offering their labour services, are considered to be not in the labour force.

The concepts and definitions of employment and unemployment adopted by the survey are based on those endorsed by the International Labour Organization (ILO).

Employment: Employed persons are those who, during the reference week:

• did any work at all at a job or business, that is, paid work in the context of an employer-employee relationship, or self-employment. It also includes persons who did unpaid family work, which is defined as unpaid work contributing directly to the operation of a farm, business or professional practice owned and operated by a related member of the same household; or
• had a job but were not at work due to illness or disability, personal or family responsibilities, vacation or labour dispute. This category excludes persons not at work because they were on layoff or between casual jobs, and those who did not then have a job (even if they had a job to start at a future date).

Unemployment: Given the concept of unemployment as the unutilized supply of labour, the operational definition of unemployment is based primarily on the activity of job search and the availability to take a job. In addition to being conceptually appropriate, job search activities can, in a household survey, be objectively and consistently measured over time. The definition of unemployment is therefore the following:

Unemployed persons are those who, during the reference week:

• were without work, but had looked for work in the past four weeks ending with the reference period and were available for work;
• were on temporary layoff due to business conditions, with an expectation of recall, and were available for work; or
• were without work, but had a job to start within four weeks from the reference period and were available for work.

Persons are regarded as available if they reported that they could have worked in the reference week had a suitable job been offered (or recalled if on temporary layoff), or if their reason for not taking a job was of a temporary nature, such as own illness or disability, personal or family responsibilities, they already had a job to start in the near future, or they were on vacation (prior to 1997, those on vacation were not considered available). Full-time students currently attending school and looking for full-time work are not considered to be available for work during the reference week. They are assumed to be looking for a summer or co-op job, or permanent job to start sometime in the future, and are therefore not part of the current labour supply.

It should be noted that, in the above definition, there are two groups for which job search is not required: persons on temporary layoff and persons with a job to start at a definite date in the future. Persons on temporary layoff are included among the unemployed on the grounds that their willingness to supply labour services is apparent in their expectation of returning to work. A similar argument is applied for persons who will be starting at a new job in four weeks or less.

Finally, for the purposes of measuring job search as an identification of unemployment, the LFS uses a four-week search period, although the reference period for identifying the employed is one week. The justification for this difference is that delays inherent in job search (for example, periods spent awaiting the results of earlier job applications) require that the active element of looking for work be measured over a period greater than one week, if a comprehensive measure of job search is to be obtained.

Not in the labour force: Persons who were neither employed nor unemployed during the reference period. This includes persons who, during the reference period, were either unable to work or unavailable for work. It also includes persons who were without work and who had neither looked for work in the past four weeks, nor had a job to start within four weeks of the reference period.

Note on international comparisons: Most industrialized countries, including Canada and the United States, subscribe to guidelines established by the International Labour Organization and the United Nations for defining and measuring labour market status, including unemployment. However, the guidelines are, by design, rather imprecise, so that individual countries can interpret them within their particular labour market context. As a result, unemployment rates are not strictly comparable across all countries. Analysts in the Centre for Labour Market Information at Statistics Canada have thoroughly examined measurement differences between American and Canadian unemployment rates. Based on a past study, adjusting the Canadian unemployment rate to the U.S. measurement lowers it by approximately one percentage point. For more information on conceptual differences between American and Canadian measures of employment and unemployment, consult the technical document entitled “Measuring employment and unemployment in Canada and the United States – a comparison.”

Labour force classification

A labour force status (employed, unemployed and not in the labour force) is assigned to each respondent aged 15 and older, according to their responses to a number of questions during the interview. Figure 2.1 illustrates how the classification is derived.

Description for Figure 2.1

Figure 2.1 Labour force classification

This diagram provides a simplified summary of the Labour Force Survey questions and answers used to classify labour force status, that is, whether a respondent is considered “Employed,” “Unemployed,” or “Not in the labour force.”

The first question is “Worked last week?” with two possible responses. A response of “Yes” results in a status of “Employed.” The response “Permanently unable to work” results in a status of “Not in the labour force” and was eliminated in March 2020. The response of “No” leads to another question: “Had job but did not work,” which has two possible responses, “Yes” or “No.”

The “Yes” response leads to the question “Why absent from work” which has three possible responses. The first response is “Not temporary layoff, seasonal layoff or casual job” which results in a status of “Employed.” The second possible response is “Seasonal layoff or casual job” and the third possible response is “Temporary layoff.”

The “No” response to “Had job but did not work” leads to the question “Worked within the last year, laid off because of business conditions and expects to return” with possible answers of “Yes” and “No.”

The next questions determine if respondents are laid off. The responses of “Temporary layoff” to “Why absent from work”, and “Yes” to “Worked within the last year, laid off because of business conditions and expects to return” both lead to the question “Date of return or indication will be recalled within 6 months, and layoff is less than a year ago.” A “Yes” response indicates that the person is laid off, and leads to the “Available for work” question. A “No” response leads to the “Looked for work in the past 4 weeks” question.

If the respondent replied “Seasonal layoff or casual job” as the reason for their work absence, or if they replied “No” to the question “Worked within the last year, laid off because of business conditions and expects to return?”, they will also be asked the “Looked for work in the past 4 weeks?” question. For this question, a response of “Yes” leads to the question “Full-time student looking for full-time job?”, while a response of “No” leads into the question “Job to start within 4 weeks?”

A response of “No” to “Full-time student looking for full-time job?” or a response of “Yes” to “Job to start within 4 weeks?” will both lead to the question “Available for work?” For those who respond “Yes” to “Full-time student looking for full-time job?” or “No” to “Job to start within 4 weeks?”, the labour force status is “Not in the labour force”.

For the question “Available for work?”, if the response is “Yes”, the respondent’s status is deemed to be “Unemployed.” If the response is “No,” the respondent is asked why they are not available for work. If the reason is “going to school” or “other,” the final status is “Not in the labour force.” For any other reason, the respondent’s status is “Unemployed.”

There are many possible pathways (questions and answers) that result in different labour force statuses.  Some of the most basic examples are provided below.

Employed

1. If “Yes” to “Worked last week?”
2. If “No” to “Worked last week?”; “Yes” to “Had job but did not work?”; and any response other than “Temporary layoff, seasonal layoff or casual job” to “Why absent from work?”

Unemployed

1. If “No” to “Worked last week?”; “No” to “Had job but did not work?”; “No” to “Worked within the last year, laid off because of business conditions and expects to return?”; “Yes” to “Looked for work in the past 4 weeks?”; “No” to “Full-time student looking for full-time job?”; “Yes” to “Available for work?”
2. If “No” to “Worked last week?”; “Yes” to “Had job but did not work?”; “Seasonal layoff or casual job” to “Why absent from work?”; “Yes” to “Looked for work in the past 4 weeks?”; “No” to “Full-time student looking for full-time job?”; “Yes” to “Available for work?”
3. If “No” to “Worked last week?”; “No” to “Had job but did not work?”; “Yes” to “Worked within the last year, laid off because of business conditions and expects to return?”; “Yes” to “Date of return or indication will be recalled within 6 months and layoff is less than a year ago?”; “Yes” to “Available for work?”
4. If “No” to “Worked last week?”; “Yes” to “Had job but did not work?”; “Temporary layoff” to “Why absent from work?”; “Yes” to "Date of return or indication will be recalled within 6 months and layoff is less than a year ago?”; “Yes” to “Available for work?”
5. If “No” to “Worked last week?”; “No” to “Had job but did not work?”; “No” to “Worked within the last year, laid off because of business conditions and expects to return?”; “No” to “Looked for work in the past 4 weeks?”; “Yes” to “Job to start within 4 weeks?”; “Yes” to “Available for work?”

Not in the labour force

1. If “No” to “Worked last week?”; “No” to “Had job but did not work?”; “No” to “Worked within the last year, laid off because of business conditions and expects to return?”; “No” to “Looked for work in the past 4 weeks?”; “No” to “Job to start within 4 weeks?”
2. Prior to March 2020, if “Permanently unable to work” to “Worked last week?”
3. If “No” to “Worked last week?”; “Yes” to “Had job but did not work?”; “seasonal layoff or casual job” to “Why absent from work?”; “No” to “Looked for work in the past 4 weeks?”; “No” to “Job to start within 4 weeks?”
4. If “No” to “Worked last week?”; “No” to “Had job but did not work?”; “No” to “Worked within the last year, laid off because of business conditions and expects to return?”; “Yes” to “Looked for work in the past 4 weeks?”; “Yes” to “Full-time student looking for full-time job?”
5. If “No” to “Worked last week?”; “No” to “Had job but did not work?”; “No” to “Worked within the last year, laid off because of business conditions and expects to return?”; “Yes” to “Looked for work in the past 4 weeks?”; “No” to “Full-time student looking for full-time job?”; “No” to “Available for work?”; “going to school” or “other” is the reason for not being available for work.

Note for Figure 2.1: Prior to March 2020, the question “Worked last week?” had a third possible response, “Permanently unable to work,” which resulted in a status of “Not in the labour force”.

Source: Labour Force Survey (3701).

Section 3: Dictionary of concepts and definitions

The LFS dictionary provides users with definitions of terms and variables associated with the survey. Where appropriate, changes to definitions through time are documented.

Absence from work (hours lost): A distinction is made between those who lose hours from work because they missed part of the work week or the full work week. Reasons for the absence are collected for both situations:

• Part-week absence: Collected for employees only. Reasons for absence include: own illness or disability, personal or family responsibilities, maternity or parental leave, vacation, weather, labour dispute, job started or ended during reference week, holiday, working short time, temporary layoff due to business conditions, and other reasons.
• Full-week absence: Collected for all employed persons. Reasons for absence include: own illness or disability, personal or family responsibilities, maternity or parental leave, vacation, labour dispute, work schedule, temporary layoff due to business conditions, self-employed (no work available), seasonal business (self-employed), and other reasons. The number of full weeks absent from work is recorded. In addition, employees and self-employed with an incorporated business are asked if they received wages or salary for any time off during the reference week.

Activity prior to unemployment: Main activity before looking for work. Distinguishes between those who were working (that is, job leavers, job losers and temporary layoffs) and those who were not in the labour force but were keeping house, going to school or involved in some other type of activity.

Actual hours worked: Number of hours actually worked by the respondent during the reference week, including paid and unpaid hours. These hours reflect temporary decreases or increases in work hours (for example, hours lost due to illness, vacation or holidays, or more hours worked due to overtime).

Age: Age is collected for every household member in the survey, and the information on labour market activity is collected for all persons aged 15 and older. Prior to 1966, information on labour market activity was collected for persons aged 14 and older. Effective January 1997, date of birth is collected to ensure inclusion of respondents who turn 15 during their six-month rotation in the survey.

Availability: Persons are regarded as available if they reported that they could have worked in the reference week, had a suitable job been offered (or recalled if on temporary layoff), or if they could not take a job because of their own illness or disability, personal or family responsibilities, they already had a job to start in the near future, or vacation (prior to 1997, those on vacation were not considered available). Full-time students currently attending school and looking for full-time work are not considered to be available for work during the reference week. They are assumed to be looking for a summer or co-op job or permanent job to start sometime in the future.

Average hours worked: Average number of hours worked per week, usual or actual, is calculated by dividing total hours worked at main job during the reference week by the total number of employees. Also available is the average number of actual hours worked in the reference week, calculated by excluding persons who were not at work during the reference week.

Born in Canada: Anyone born in Canada, regardless of citizenship.

Census metropolitan area (CMA) and census agglomeration (CA): A CMA or a CA is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000, based on data from the 2021 Census of Population Program, of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. The boundaries of CMAs and CAs are based on the 2021 Standard Geographical Classification.

The terms ‘core,’ ‘fringe’ and ‘rural area’ replaced the terms ‘urban core,’ ‘urban fringe’ and ‘rural fringe’ in the 2011 Census. These terms distinguish between population centres and rural areas within a CMA or CA.

A CMA or CA can have more than one core. In cases where there are multiple cores, one of these is the core and the others are secondary cores.  The core is the population centre with the highest population, around which a CMA or a CA is delineated. The core must have a population (based on the previous census) of at least 50,000 persons in the case of a CMA, or at least 10,000 persons in the case of a CA.

A secondary core is a population centre with at least 10,000 persons (based on the previous census) and that is within a CMA or CA, but outside the main municipality (census subdivision) that contains the core. The secondary core can also be the core of a CA that has been merged with an adjacent CMA.

The fringe includes all population centres within a CMA or CA that have less than 10,000 persons and are not contiguous with the core or secondary core. As there cannot be two cores within the same census subdivision (CSD), other population centers within a CSD that already have a core or secondary core are defined as 'fringe' even if their population is over 10,000 persons (based on the previous census).

All territory within a CMA or CA that is not classified as a core or fringe is classified as rural area.

Class of worker: There are two broad categories of workers: those who work for others (employees) and those who work for themselves (self-employed). The first group is subdivided into two classes: public sector employees and private sector employees. See Public/private sector employment and Self-employment.

Country of birth: The country of birth of the respondent. This is based on current geographic names and boundaries at the time of collection.

Country of highest education: Identifies the country in which the respondent obtained their highest degree, certificate or diploma. This information is only available for those who are now, or have ever been, landed immigrants to Canada and who have a level of educational attainment above high school.

Discouraged searcher (also called Discouraged worker): Since 1997, discouraged searchers are defined as those persons who reported wanting to work at a job or business during the reference week and were available, but who did not look for work because they believed no suitable work was available. Prior to January 1997, the definition of discouraged searcher was limited to those who looked for work within the previous six months, but not during the last four weeks, although they were available, and did not look because they believed no suitable work was available. The change in concept and question wording resulted in a complete break in the series.

Duration of joblessness: Number of months or years elapsed since persons who are not currently employed last worked, provided they worked at some time in the past.

Duration of unemployment: Number of continuous weeks during which a person has been on temporary layoff or without work and looking for work. Respondents are required to look for work at least once every four weeks; they are not required to undertake job search activities each week in order to be counted as unemployed. The LFS measures the duration of incomplete spells of unemployment, since the information is collected only from those currently unemployed. A spell of unemployment is interrupted or completed by any period of work or withdrawal from the labour force.

Dwelling: Any set of living quarters that is structurally separate and has a private entrance outside the building, or from a common hall or stairway inside the building.

Earnings: Beginning in March 2020, self-employed workers are asked to report their total earnings after deducting all business expenses, but before deductions such as income taxes and social assistance contributions. Weekly self-employment earnings are calculated using reported weekly, monthly or yearly earnings. For earnings of employees, see Wages.

Economic region: An economic region (ER) is a grouping of complete census divisions (CDs) (with one exception in Ontario) created as a standard geographic unit for analysis of regional economic activity. They have been established in consultation with the provinces, except for Quebec, where economic regions are designated by law (les régions administratives). ERs generally correspond to regions used by the province for administrative and statistical purposes. The current boundaries are based on the 2021 Standard Geographical Classification.

Educational attainment: Highest level of schooling completed. Questions relating to educational attainment were changed in 1990 to better capture the relationship between education and labour market outcomes.

From 1976 to 1989: data on primary and secondary education reflected the number of years of primary and secondary education completed. In the case of those whose highest level was grades 11 through 13, no attempt was made to determine if the respondent had actually graduated from high school. However, post-secondary education was limited to levels that normally require high school graduation. In addition, information on type of post-secondary education was limited to three categories: 1) some post-secondary; 2) post-secondary certificate or diploma; and 3) university degree.

Beginning January 1990: data on primary and secondary education reflect the highest grade completed. This provides a more consistent measure for those who accelerate or fail a grade than did years of school. A question on high school graduation has also been added, since it is generally believed that persons who have never completed their secondary education have greater difficulty competing in the labour market. With the new questions, any education that could be counted towards a degree, certificate or diploma from an educational institution is taken as post-secondary education. The change allows more persons into the post-secondary education category. For example, trades programs offered through apprenticeship, vocational schools or private trade schools do not always require high school graduation. Such education is now considered as post-secondary, while only primary or secondary would have been recognized prior to 1990. Finally, more information is collected on the type of post-secondary education: 1) some post-secondary; 2) trades certificate or diploma from a vocational or apprenticeship training; 3) non-university certificate or diploma from a community college, CEGEP, school of nursing, etc.; 4) university certificate below bachelor’s degree; 5) bachelor’s degree; and 6) university degree or certificate above bachelor’s degree.

Employee: A person who works for others. Employees can be subdivided into public sector employees and private sector employees. See Public/private sector employment.

Note: The definition of a paid worker may vary depending on the nature of the analysis. Those concerned with estimating the number of workers associated with total labour income usually include both employees and the self-employed with an incorporated business in estimates of paid workers. In contrast, most labour market analysts include only employees in paid worker estimates, while incorporated owners are grouped with the rest of the self-employed.

Employment: Employed persons are those who, during the reference week, did any work for pay or profit or had a job and were absent from work. See the section entitled Determining labour force status for more detail.

Employment rate (employment/population ratio): Number of employed persons expressed as a percentage of the population 15 years of age and older. The employment rate for a particular group (for example, one defined by age, gender, or province) is the number employed in that group expressed as a percentage of the population for that group.

Establishment size: Beginning January 1997, the number of employees at the location of employment (building or compound) is collected from employees. Responses are recorded according to the following size groups: less than 20, 20 to 99, 100 to 500, or more than 500. The concept of location of employment approximates the concept of establishment used by many Statistics Canada business surveys.

Family: The LFS identifies families according to the criteria for ‘economic families’: a group of two or more persons who live in the same dwelling and who are related by blood, marriage (including common-law) or adoption. A person living alone or who is related to no one else in the dwelling where he or she lives is classified as a ‘Person not in an economic family’.

Firm size: Beginning January 1998, the number of employees at all locations of the employer is collected from employees. Responses are recorded according to the following size groups: less than 20, 20 to 99, 100 to 500, or more than 500. The concept of firm targets the concept of enterprise used by many Statistics Canada business surveys.

Flows into unemployment: Characterizes the unemployed in terms of their activity immediately prior to looking for work. See Job leavers, Job losers, Re-entrants and New entrants.

Full-time employment: See Type of work.

Future starts: Persons who did not have a job during the survey reference week and did not search for work within the previous four weeks, but were available to work and had a job to start within the next four weeks. These persons are classified as unemployed, despite the lack of job search within the previous four weeks, since it is apparent that they are part of the current supply of labour. In contrast, those with jobs to start at a later time than the next four weeks are designated as long-term future starts, and are classified as not in the labour force since they are not part of current labour supply.

Gender: Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). 

Gender includes the following concepts: 

• gender identity, which refers to the gender that a person feels internally and individually; 
• gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. 

A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's license. A person's gender may change over time. 

Some people may not identify with a specific gender.

In LFS data collected up to 2021, “gender” – previously referred to as “sex” – was based on the gender expression of the respondent. Since 2022, “gender” in LFS data is based on the gender identity reported directly by the respondent. Gender data are aggregated into two categories: men+ and women+. Individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol.

For more information on the classification of gender at Statistics Canada, please refer to Gender - Usage.

Goods-producing industries (or goods sector/goods industries): Includes agriculture; forestry, fishing, mining, quarrying, and oil and gas; utilities (electric power, gas and water); construction; and manufacturing.

Government sector: See Public/private sector employment.

Hours: See Actual hours worked, Usual hours worked, Average hours worked, and Overtime hours.

Hours lost: See Absence from work.

Household: Any person or group of persons living in a dwelling. A household may consist of any combination of one person living alone, one or more families or a group of people who are not related.

Immigrant: Refers to a person who is or has ever been a landed immigrant or permanent resident in Canada. This person has been granted the right to live in Canada permanently by immigration authorities. Some immigrants have resided in Canada for a number of years, while others have arrived more recently. Some immigrants are Canadian citizens, while others are not.

Immigrant status: See Born in Canada, Immigrant, Non-immigrant, and Other non-immigrant.

Indigenous group: Includes persons who reported being an Indigenous person, that is, First Nations, Métis or Inuk (Inuit). Excluded from the survey’s coverage are persons living on reserves and other Indigenous settlements in the provinces. In the LFS, a person may report more than one Indigenous group. For example, a respondent could report being both First Nations and Métis.

Industry: General nature of the business carried out in the establishment where the person worked (main job only), based on the 2022 North American Industry Classification System (NAICS). If a person did not have a job during the survey reference week, the information is collected for the last job held, provided the person worked within the previous twelve months.

Involuntary part-time rate: The rate of involuntary part-time workers can be derived in different ways. Published rates are based on all involuntary part-time workers, whether they looked for full-time work or not. The rates can be presented as the number of involuntary part-timers as a share of the labour force, as a share of the total employed or as a share of the part-time employed, depending on one’s analytical preference.

Involuntary part-time workers: Also referred to as underemployed, these respondents work part-time because they could not find work with 30 or more hours or due to business conditions, whether or not they looked for full-time work. This group generally represents 15 to 25 percent of the total number of part-time workers, depending on current economic conditions. This is the most widely used and inclusive definition of involuntary part-time workers.

Another, more restrictive definition would be to limit involuntary part-time workers to those who also looked for full-time work during the past four weeks. They generally represent approximately 35% of all involuntary part-time workers.

Job leavers: Persons who are not currently employed, but who worked within the last year and left that job voluntarily (i.e., the employer did not initiate the termination). Detailed reasons include own illness, personal or family responsibilities, going to school, no specific reason, changed residence, dissatisfied with job, and retired. Since 1997, further detail is available, including business sold or closed down (self-employed only) and pregnancy.

Job losers: Persons currently not employed who last worked within the previous year and left that job involuntarily (employer-initiated job termination because of business conditions, downsizing, etc.). Prior to 1997, this category was broken down into those on temporary layoff and those laid off on a permanent basis. Since January 1997, more detail regarding the reason for permanent layoff is available: end of seasonal job; end of temporary, term or contract job; casual job; no work; company moved; company went out of business; laid off due to business conditions with no expectation of recall; dismissal by employer; and other reasons.

Job permanency: Beginning January 1997, information is collected to allow the classification of paid jobs as either permanent or temporary. This classification is based on the intentions of the employer and characteristics of the job, rather than the intentions of the employee. If a job that was formerly considered permanent is ending in the near future because of downsizing or closure, it would still be regarded as permanent.

• Permanent: A permanent job is one that is expected to last as long as the employee wants it, business conditions permitting. That is, there is no pre-determined termination date.
• Temporary: A temporary job has a predetermined end date, or will end as soon as a specified project is completed. Information is collected to allow the sub-classification of temporary jobs into four groups: seasonal; temporary, term or contract, including work done through a temporary help agency; casual job; and other temporary work.

Job search: See Methods of job search.

Job security: See Job permanency.

Job tenure: The number of consecutive months or years that a person has worked for the current (or, if employed within the previous twelve months, the most recent) employer. The employee may have worked in one or more occupations, one or more locations, or have experienced periods of temporary layoff with recall and still be considered to have continuous tenure if their employer has not changed. However, if a person has worked for the same employer over different periods of time, job tenure measures the most recent period of uninterrupted work.

Labour force: Civilian non-institutional population 15 years of age and older who, during the survey reference week, were employed or unemployed. Prior to 1966, persons aged 14 and older were covered by the survey.

Labour force by industry or occupation: See Unemployment by industry or occupation.

Labour force status: Designates the status of the respondent with respect to the labour market: a member of the non-institutional population 15 years of age and older is either employed, unemployed, or not in the labour force. See the Determining labour force status section for more detail.

Main job: When a respondent holds more than one job or business, the job or business involving the greatest number of usual hours worked is considered to be the main job. The information available from the LFS regarding full- or part-time status, industry and occupation refer to the main job, as does employee information regarding wages, union status, job permanency, and workplace size.

Marital status: Refers to the marital status reported by the respondent. Before November 1999, no differentiation was made between married and common-law relationships; both were classified as married in the survey. The classification of ‘single’ is reserved for those who have never married; otherwise, respondents are classified as either widowed, separated or divorced.

Methods of job search: Identifies the various job search activities undertaken by unemployed persons in the previous four weeks. If more than one method is used, each one is recorded. Search methods include: checked with public employment agency, private employment agency, union, employers directly, friends or relatives, placed or answered ads, looked at job ads, and other methods.

Month of immigration: Refers to the month in which the immigrant obtained landed immigrant status. The month of immigration is collected directly from immigrants who landed in Canada within the five-year period prior to the year of the birth interview and imputed for all other immigrants.

Multiple jobholders: Persons who, during the reference week, were employed in two or more jobs simultaneously.

New entrants: Persons entering the labour force in search of their first job (unemployed).

Non-immigrant: A concept used by the census, a non-immigrant refers to a person who is a Canadian citizen by birth. Since the LFS does not include questions on citizenship, this category cannot be produced. It is composed of two groups: born in Canada and other non-immigrants.

Not in the labour force: See the Determining labour force status section.

Occupation: Refers to the kind of work persons were doing during the reference week, as determined by the kind of work reported and the description of the most important duties. For those not currently employed, information on occupation is collected for the most recent job held within the previous year. Occupational classification is based on the 2021 National Occupational Classification (NOC).

Other job (see also Main job): Information collected about the second job of multiple job holders or the old job of those who changed jobs during reference week regarding: usual hours, actual hours worked and status in employment.

Other non-immigrant: Refers to a person who is either a Canadian citizen by descent who was born outside of Canada, or is a non-permanent resident. Since the LFS does not include questions on citizenship, these two groups cannot be separated. A non-permanent resident refers to a person from another country who has a work permit (i.e., temporary foreign workers), study permit or who is a refugee claimant, and any non-Canadian-born family member living in Canada with them. In 2021, other non-immigrants represented 3.4% of Canada’s population.

Overtime hours (extra hours worked): The number of hours worked during the reference week in excess of the usual hours reported at main job. It includes all extra hours, whether the work was done at a premium or regular wage rate or without pay. Since January 1997, extra hours are collected from employees only, through two questions regarding the number of paid overtime hours worked in the reference week and the number of extra hours worked without pay.

• Paid overtime: Includes any hours worked during the reference week over and above standard or scheduled paid hours, for overtime pay or compensation (including time off in lieu).
• Unpaid overtime: Refers to time spent directly on work or work-related activities over and above scheduled paid hours. These must be extra hours worked for which the respondent received no additional compensation.

Participation rate (labour force/population ratio): Total labour force expressed as a percentage of the population aged 15 years and older. The participation rate for a particular group (for example, women aged 25 years and older) is the labour force of that group expressed as a percentage of the population for that group.

Part-time employment: See Type of work and Reason for working part-time.

Permanent job: See Job permanency.

Personal or family responsibilities: Beginning January 1997, more detail is collected on the personal or family reasons for the following data items: reason for absence from work, reason for leaving last job, reason for working part-time, and reason for not looking for work. Personal or family reasons include: a) caring for own children; b) caring for elder relative; and c) other personal or family reasons. Pregnancy is also included in the response list for the question on reason for leaving last job, and maternity or parental leave is included in the response list for the question on reason for absence from work.

Population: The target population covered by the survey corresponds to all persons aged 15 years and older residing in the provinces of Canada, with the exception of the following: persons living on reserve, full-time members of the regular Armed Forces and persons living in institutions (for example, inmates of penal institutions and patients in hospitals or nursing homes).

The territories are not included in the national total. The target population in the territories is the same as in the provinces, with the exception of persons living on reserve, who are included in the territories but not included in the provinces. See the Survey methodology section for more detail.

Population centre: A population centre (POPCTR) has a population of at least 1,000 and a population density of 400 persons or more per square kilometre, based on population counts from the most recent Census of Population. All areas outside population centres are classified as rural areas. Taken together, population centres and rural areas cover all of Canada.

Population centres are classified into three groups, depending on the size of their population:

• small population centres, with a population between 1,000 and 29,999
• medium population centres, with a population between 30,000 and 99,999
• large urban population centres, with a population of 100,000 or more.

Population centre population includes all population living in the cores, secondary cores and fringes of census metropolitan areas (CMAs) and census agglomerations (CAs), as well as the population living in population centres outside CMAs and CAs.

Public/private sector employment:

• The public sector includes employees in federal, provincial, territorial, municipal and Indigenous public administrations, as well as in Crown corporations, liquor control boards and other government institutions such as schools (including universities), hospitals and public libraries.
• The private sector comprises all other employees and self-employed owners of businesses (including unpaid family workers in those businesses), and self-employed persons without businesses.

The LFS definition of public/private sector employment was changed in January 1999 in order to harmonize estimates with the System of National Accounts standard. Prior to January 1999, ‘ownership’ rules were used as the basis for classification of health care institutions and universities to the public sector by the LFS. Since January 1999, ‘funding’ rules are used. As a result, all employees in universities and hospitals are now classified in the public sector. All historical data were revised to reflect this new definition. Thus, there is no break in public and private sector series.

Racialized group: Data on racialized groups are derived from the Visible minority variable.

Reason for leaving last job: Asked of all persons classified as unemployed or not in the labour force who last worked within the previous year. See Job Losers and Job Leavers for detailed reasons.

Reason for not looking for work: Beginning January 1997, this question is asked of those who were not employed and did not search for work, but said they wanted work during reference week. Prior to 1997, this question was asked of persons who had looked for work in the previous six months, but not during the past four weeks. See also Discouraged searchers.

Reason for time lost/absence from work: See Absence from work.

Reason for working part-time (see also Type of work): Beginning January 1997, with the redesign of the LFS, all respondents who usually worked less than 30 hours per week at their main or only job are asked if they want to work more or less than 30 hours at a (single) job or business. Depending on the response, the main reason for working part-time is collected. For those who respond that they want to work less than 30 hours, the main reason for not wanting to work 30 or more hours per week is collected. Responses include own illness, personal or family responsibilities, going to school, personal preference, and other.

For those who respond that they want to work 30 or more hours per week, the main reason for working less than 30 hours is collected. Responses include own illness, personal or family responsibilities, going to school, business conditions, could not find work with 30 or more hours, and other. Those whose response is ‘business conditions’ or ‘could not find work with 30 or more hours’ are further asked if they looked for work with 30 or more hours during the past four weeks. See Involuntary part-time rate or Involuntary part-time workers.

Prior to January 1997, the question on reason for working part-time was asked of all persons whose total usual work hours at all jobs or businesses were below 30 per week as opposed to their main or only job. Reasons included: own illness, personal or family responsibilities, going to school, could only find part-time work, did not want full-time work, other, and full-time work less than 30 hours. This last category of respondents was redefined as full-time workers and not counted in any part-time estimates.

The change in concepts and definitions introduced in January 1997 resulted in a complete break in the series on reason for working part-time and involuntary part-time work. Estimates prior to 1997 are available upon request.

Re-entrants: Persons currently unemployed who worked in the past and were out of the labour force for some time following separation from their last job.

Reference person: At the time of interview, the respondent designates a reference person for the household. The reference person is normally an adult with knowledge of the labour force activities for the other members of the household. The relationship of each household member to that reference person is recorded. See also Relationship to reference person.

Reference week: The labour force status of respondents is based on their activities during a specific week each month. This reference week usually contains the 15th day of the month and stretches from Sunday to Saturday. In December, and sometimes in November, the reference week is earlier, usually the second week in the month, to ensure that data collection is complete before Christmas. LFS interviews are conducted during the survey or collection period, which is typically the ten days following the reference week (Sunday to Tuesday).

Relationship to reference person: Relationship of each household member to the person who has been identified as the reference person (for example, someone with knowledge of the labour force activities for the other members of the household). Relationships include self, spouse, child (son or daughter), grandchild, son or daughter-in-law, foster child, parent, parent-in-law, sibling (brother or sister), and other relative.

Retirement age: The LFS asks people who are not working, and who have left their last job within the year prior to being surveyed, why they left this job. One of the response categories is ‘retired’. The average or median retirement age is calculated from this variable. For a complete description of who is represented and how the age is calculated, refer to the article "Defining retirement" in the February 2007 issue of Perspectives on Labour and Income (75-001-X) on the Statistics Canada website.

Returning students: Since a majority of students are not attending school during the summer, supplementary questions are asked from May to August to identify those who are on summer break, so that their labour market situation can be monitored. Youth (aged 15 to 24) are given the status of ‘returning student’ if they reported they were attending school full-time in the previous March and intend to return to school full-time in the fall. Information is also available on those who were full-time students in the previous March, but do not intend to return to school full-time or are unsure of their intentions.

Rural areas: Rural areas include all territory lying outside Population centres. Taken together, population centres and rural areas cover all of Canada. Rural population includes all population living in the rural areas of census metropolitan areas (CMAs) and census agglomerations (CAs), as well as population living in rural areas outside CMAs and CAs.

School attendance: Establishes whether or not a respondent is attending an educational establishment. For those who are students, information is collected on the type of school, and whether enrolment is full- or part-time, as designated by the educational establishment.

Seasonal adjustment: Monthly or quarterly time series data are sometimes influenced by seasonal and calendar effects. These effects can bring about changes in the data that normally occur at the same time, and in about the same magnitude, every year. For example, youth employment numbers are high in the summer months due to the end of school and decline to a much lower level in September when school starts again.  A seasonally adjusted time series is a monthly or quarterly time series that has been modified to eliminate the effect of seasonal and calendar influences. In particular, the seasonally adjusted data reveals the “true” underlying movement of the unadjusted series (direction, turning points, relationship to other socio-economic indicators) and allow for more meaningful comparisons of socio-economic conditions from period to period.

Seasonally adjusted data are estimated by breaking down time series data into various components. Using well-established statistical techniques, this process involves decomposing a time series into four separate components: (1) the trend-cycle, (2) seasonal effects, (3) other calendar effects such as trading days and moving holidays, and (4) the irregular component. The seasonally adjusted series is the original time series with the estimated seasonal and calendar effects removed, or equivalently, the estimated combination of the trend-cycle and the irregular components.

For more information on seasonal adjustment, see Seasonally adjusted data – Frequently asked questions.

Self-employment: Working owners of an incorporated business, farm or professional practice, or working owners of an unincorporated business, farm or professional practice. The latter group also includes self-employed workers who do not own a business (such as babysitters and newspaper carriers). Self-employed workers are further subdivided by those with or without paid help. Also included among the self-employed are Unpaid family workers.

Seniority: See Job tenure.

Services-producing industries (or service sector/service industries): Includes wholesale and retail trade; transportation and warehousing; finance, insurance, real estate, rental and leasing; professional, scientific and technical services; business, building and other support services; educational services; health care and social assistance; information, culture and recreation; accommodation and food services; other services (except public administration); and public administration.

Student: See School attendance and Returning students.

Temporary layoff: Persons on temporary layoff are employees who did not work during the reference week because they had been temporarily released by their employer due to business conditions (not enough work, drop in orders or sales, retooling, etc.). They must have a definite date of return to work, or an indication from their employer that they will be recalled in the future, and they must be available for work during the reference week. Persons on temporary layoff are not required to undertake any job search in order to be counted as unemployed.

Prior to January 1997, the wording and structure of the questionnaire was such that it was likely that a number of persons on temporary layoff were not identified as such, and were classified as ‘not in the labour force’, rather than ‘unemployed’. The 1997 redesign addressed this problem, resulting in a higher number of identified persons on temporary layoff. These changes resulted in a break in the temporary layoff series. Since those on temporary layoff account for a small proportion of the unemployed (less than 10%), the impact of these changes on the overall unemployment rate is negligible.

Temporary work: See Job permanency.

Type of work: Full-time or part-time work schedule. Full-time employment consists of persons who usually work 30 hours or more per week at their main or only job. Part-time employment consists of persons who usually work less than 30 hours per week at their main or only job. This information is available for those currently employed or who last worked within the previous year.

Note: prior to 1996, full-time and part-time had been defined according to usual hours at all jobs, and those who considered their work schedule of less than 30 hours per week to be full-time work were classified as full-time workers. In January 1996, when the definition was revised, all historical data and records were adjusted to reflect this new definition. Thus, there is no break in part-time and full-time data series.

Type of work sought: Identifies whether a job searcher is looking for full-time or part-time work. Unemployed persons on temporary layoff are classified as looking for full- or part-time work on the basis of their usual hours at their former job. This information is not available for non-searchers who are classified as unemployed because they have a job to start in the next four weeks (future starts).

Unemployment: Unemployed persons are those who, during reference week, were without work, were available for work and were either on temporary layoff, had looked for work in the past four weeks or had a job to start within the next four weeks. See the Determining labour force status section for more detail.

Unemployment by industry or occupation: The LFS produces data on the number of unemployed, the unemployment rate and the labour force by industry or occupation. The basis for these categories is industry or occupation of last job for those currently unemployed who held a job in the previous year. It is important to note that no data are collected on industry or occupation of job search. Thus, these data should be interpreted with caution. For example, a recent graduate of law school looking for work as a lawyer in a law firm, may have last held a job as a server in a restaurant. For this person, unemployment is attributed to the accommodation and food services industry and the services occupation.

Unemployment rate: Number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (for example, one defined by age, gender, or marital status) is the number of unemployed persons in that group expressed as a percentage of the labour force for that same group. For a note on international comparisons, see the Determining labour force status section.

Union status: Beginning January 1997, employees are classified as: a) union member; b) not a member, but covered by a union contract or collective agreement; or c) non-unionized.

Unpaid family workers: Persons who work without pay on a farm or in a business or professional practice owned and operated by another family member living in the same dwelling. They represented approximately 1% of the self-employed in 2024.

Usual hours worked: Prior to January 1997, usual hours was the number of hours usually worked by the respondent in a typical week, regardless of whether they were paid. Beginning January 1997, usual hours for employees refers to their normal paid or contract hours, not counting any overtime. However, the definition of usual hours remains unchanged for the self-employed and unpaid family workers.

Visible minority: Beginning January 2022, information is collected on visible minority of a person. Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as "persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour". The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Arab, Latin American, Southeast Asian, West Asian, Korean and Japanese.

Respondents reporting one or more of the three Indigenous categories (First Nations, Métis and/or Inuit) are not asked the population group question.

When deriving the visible minority variable based on responses to the population group and Indigenous group questions, persons who have identified as Indigenous (First Nations, Métis and/or Inuit) on the Indigenous group question are included in the category 'Not a visible minority'.

For more information on visible minority, refer to Visible minority of person.

Variable hours: Beginning January 1997, information is collected to determine if the number of hours worked varies from week to week. In these cases, usual hours worked are calculated as the average of the hours worked in the last four weeks.

Wages: Beginning January 1997, information is collected on the usual wages or salary of employees at their main job. Respondents are asked to report their wage/salary before taxes and other deductions, and include tips and commissions. Weekly and hourly wages/salary are calculated in conjunction with usual paid work hours per week. Average hourly wages, average weekly wages and wage distributions can then be cross-tabulated by other characteristics such as age, gender, education, occupation, and union status. Those who are paid on an hourly basis are also identified. For earnings of self-employed workers, see Earnings.

Work: Includes any work for pay or profit, that is, paid work in the context of an employer-employee relationship or self-employment. It also includes work performed by those working in family business without pay (unpaid family workers).

Year of immigration: Refers to the year in which the immigrant obtained landed immigrant status by immigration authorities.

Section 4: Survey methodology

Population coverage

The Labour Force Survey (LFS) target population includes all persons aged 15 years and older whose usual place of residence is in Canada, including both non-permanent residents (NPRs) — that is, those with a work or study permit, their families, asylum claimants, protected persons and related groups — as well as permanent residents (landed immigrants) and the Canadian-born population.

Excluded from the survey's coverage are: persons living on reserves and other Indigenous settlements in the provinces, full-time members of the Canadian Armed Forces, and the institutionalized population. These groups together represent an exclusion of approximately 2% of the population aged 15 and older (see the sub-section entitled Exclusions from the Labour Force Survey coverage for the provinces and territories for more information).

National LFS estimates are derived using the results of the LFS in the provinces. Territorial LFS results are not included in the national estimates, but are published separately (see the sub-section entitled Exclusion of the territories from national totals for more information).

Sample design and sample size for the provinces

The LFS plays a central role in the national statistical system in several ways. First, the LFS provides monthly estimates of employment and unemployment, which are among the most timely and important measures of the overall performance of the Canadian economy. Therefore, the LFS sample must be optimized to accurately measure month-to-month changes in the labour market.

Second, the Employment Insurance Act has designated the LFS as the official source of monthly unemployment rates for all the employment insurance economic regions (EIERs), which are used in the administration of the Employment Insurance program.

Third, the infrastructure used for the LFS (frame, sample, interviewers, processing systems) supports other Statistics Canada household surveys that are conducted in response to the policy and information needs of government.

For the LFS to continue to fulfill these three key roles, the sampling frame must be up to date and the estimates must be sufficiently reliable to support the various uses of the data. Every ten years, after the decennial population census, the LFS undergoes a sample redesign to reflect changes in population characteristics, geographical boundaries, and collection context, as well as to integrate any new quality targets. The current sample design, based on population characteristics and geographical boundaries from the 2021 Census, was phased in between April and September 2025.

The LFS design strata, which divide the population to make sampling more efficient, are set out to be homogeneous with respect to a set of key labour market variables. However, the strata become less efficient the further the design is from the source year (i.e., 2021 for the design in place from 2025 to 2034) and as the population and labour market characteristics shift over time.

There are four types of LFS design strata: (1) regular strata—by far the most common—which group clusters that are similar based on information from the 2021 Census and administrative data within important geographical boundaries to improve the design’s efficiency; (2) remote strata which group together remote regions and are intended to control collection costs in remote regions; (3) large-cluster strata which group together large clusters (e.g., large apartment or condo buildings) for statistical efficiency; and (4) high-income strata which group together areas with higher prevalence of high-income households within a census metropolitan area (CMA), primarily so other surveys (e.g., Survey of Household Spending) can sample them appropriately. All four stratum types are sampled at the same rate for the LFS.

The sample is allocated to strata to efficiently meet the need for reliable estimates at various geographic levels. The 2025 design uses quality targets at the national, provincial, economic region (ER), CMA, census subdivision (CSD), and EIER levels.

Historically, the quality indicator used to allocate the sample was the coefficient of variation (CV; see the section entitled Data quality for a definition). The 2025 design introduced adjusted CV targets, in which the CV is adjusted to a reference unemployment level (see the Data quality section for a definition). Adjusted CVs do not depend on the unemployment rate and therefore, are more robust to changes in unemployment than the CV. For example, when unemployment falls, the CV rises and quality targets may not be achieved, even though overall LFS quality is maintained. Furthermore, using an adjusted CV allows for more consistent data quality and sample allocation across different areas. Using a CV target, areas with high unemployment may be allocated insufficient sample, since the CV target is easier to satisfy, impacting the quality of other estimates for that area (employment by industry, occupation, etc.). Conversely, areas with historically low unemployment may be allocated excessive sample, reducing the efficiency of the sample and increasing collection costs.

While most LFS sample allocation targets in the 2025 sample design use adjusted CVs, EIER targets are still based on the methodology used in the 2015 sample design: areas with an unemployment rate over 4.7% have a CV target, and areas with an unemployment rate under 4.7% have a standard error target, where the standard error target and the CV target agree when the unemployment rate is equal to 4.7%. This method addresses some of the limitations of CV as a quality target, namely the excessive sample required by regions with low unemployment.

The quality targets are as follows.

For monthly estimates of unemployment count at the Canada level:

• An average adjusted CV of 2%.

For at least 90% of monthly estimates of unemployment count at the provincial level:

• An adjusted CV below 7%.

• The sample size for Prince Edward Island is capped to limit response burden therefore the sample in the province may not meet the 7% quality target.

For at least 90% of three-month moving average estimates of unemployment count at the sub-provincial level:

• Economic regions: An adjusted CV lower than 25%.
• Census metropolitan areas: An adjusted CV lower than 25%.
• Census subdivisions:
• An adjusted CV lower than 14% for Canada’s 20 largest CSDs.
• An adjusted CV lower than 21% for the two largest CSDs from each province plus the three next-largest CSDs in Canada (nine CSDs total).
• No CSDs in Prince Edward Island are targeted due to response burden.

For at least 90% of three-month moving average estimates of unemployment rate at the EIER level:

• A CV lower than 15% if the unemployment rate is higher than 4.7%, or
• A fixed confidence interval width^Note  equivalent to that of an unemployment rate of 4.7% with a CV of 15%.

As part of Statistics Canada’s Disaggregated Data Action Plan (DDAP) to examine the labour market experiences of diverse population groups, the LFS sample increased by 20% between November 2021 and April 2022. This sample increase was paused between January and March 2023, resumed in April and was fully implemented again in September 2023. Much of the additional sample was allocated to CSDs to meet quality targets.

The sample size further increased in response to the COVID-19 pandemic. By removing existing measures which stabilize the sample size in areas of rapid growth (stabilization) at the start of the pandemic and reintroducing them with higher targets in 2022, about 3,600 households were added to the sample. These units were helpful to improve data quality in the face of lower response rates, which fell during the pandemic and have only partially recovered since.

Due to lower response rates and the added quality targets for CSDs and new CMAs, the sample size required under the 2025 sample design is larger than under the 2015 design. However, efficiency gains in the 2025 sample redesign result in a reduction in sample size compared with the sample in effect from 2022 to 2024.

The sample allocation under the 2025 design also differs from the 2015 design due to changes in the population and quality targets and the introduction of adjusted CVs, since the 2025 allocation is much less dependent on unemployment rates.

Table 4.1 compares the national and provincial sample sizes from the 2025 design with those from the 2015 design.

Selection of dwellings for the provinces

A two-stage approach is used to select dwellings for all provinces except Prince Edward Island. Within each stratum, “clusters” are defined as contiguous geographic areas containing approximately 100 to 400 dwellings. During the first stage of sampling, a number of clusters, typically six, is selected from each stratum. For each selected cluster, a list of its dwellings is extracted from the Statistical Building Register (SBgR) created and maintained by Statistics Canada. During the second stage of sampling, a sample of dwellings is selected from the list.

In Prince Edward Island, sampling is done in a single stage by using a complete list of addresses for all strata and selecting dwellings from this list.

Selection of household members for the provinces

Demographic information is collected for all persons who consider a selected dwelling their usual place of residence. Labour force information is collected for all civilian household members 15 years of age or older. Respondent burden is minimized for persons aged 70 years and older by carrying forward their responses from the initial interview to the subsequent five months in survey.

Sample rotation for the provinces

The LFS follows a rotating panel sample design, in which dwellings in the provinces remain in the sample for six consecutive months. The total sample consists of six representative sub-samples or panels, and each month a panel is replaced after completing its six-month stay in the survey. Outgoing dwellings are replaced by dwellings in the same or a similar area. This sample rotation structure results in a five-sixths month-to-month sample overlap, which makes the design efficient for estimating month-to-month changes. The rotation after six months prevents undue respondent burden for dwellings that are selected for the survey.

Survey coverage and collection for the territories

The LFS in the territories started as a pilot project, first in Yukon beginning in 1991 and then in the Northwest Territories and Nunavut starting in 2000. Given the complexities of collecting data in remote areas, and associated data quality issues at the outset of collection, data are available since 1992 for Yukon, 2001 for the Northwest Territories and 2004 for Nunavut.

Survey coverage in Yukon and in the Northwest Territories is about 95%. In Nunavut, the survey coverage started at 70% between 2004 and 2007 and was expanded to about 93% in 2008.

The same LFS questions are asked in the territories as in the provinces, with a few exceptions. The rent questions are not asked in the territories.

Like the provinces, survey operations are conducted by Statistics Canada staff. The first contact is generally in person and most of the other interviews are done over the phone where possible. Starting in March 2020, some households in the territories may be able to complete the survey by electronic questionnaire. Aside from a different rotation schedule as explained below, data collection and processing are otherwise the same for the territories as for the provinces.

Sample design and sample size for the territories

Like the provinces, the sample design for the territories is based on a rotating panel; however, the interval of the rotation is different in the territories. Occupants of selected dwellings in the territories are interviewed every three months over a two-year period, for a total of eight interviews. For example, if a dwelling was first selected for the month of January 2022, household members were interviewed again every three months: April, July, and October 2022; January, April, July, and October 2023.

After eight interviews, the dwelling is replaced by another from the same community or from another community in the same stratum. Each quarter, approximately one-eighth of the sampled households are experiencing their first interview.

The following guidelines were used in sample allocation for the territories:

• a CV of 25% or less for three-month average estimates of unemployment count (see the Data quality section for explanation of sampling error and CVs).

The quarterly sample is collected over three months and estimates for the territories are only available as three-month moving averages.

The sample design for Yukon, the Northwest Territories and Nunavut was updated in 2011, when revised sampling fractions and new labeling conventions were implemented.

The community boundaries are usually consistent with the 2016 Standard Geographical Classification. The communities included on the frame and eligible to be sampled are:

Yukon – The census agglomeration of Whitehorse and communities of Dawson and Watson Lake are always in sample; plus one community is selected from Carmacks, Mayo, Haines Junction or Teslin; and one community from Pelly Crossing, Ross River, Carcross or Faro. Watson Lake includes the small neighbouring villages of Upper Liard, Two Mile Village and Two and One-Half Mile Village.

Northwest Territories – Yellowknife, Norman Wells, Hay River and Inuvik are always in sample. One community from each of the following groups is also selected: Fort Smith or Fort Simpson; Behchokò, Fort Liard, Fort Providence, or Fort Resolution; Tuktoyaktuk, Fort McPherson or Aklavik; Fort Good Hope, Déline or Tulita; Hay River Dene 1 or Dettah; Whatì, Wekweètì, Gamètì, Lutselk'e, Tsiigehtchic, or Wrigley.

Nunavut – Iqaluit, Rankin Inlet, Cambridge Bay and Kugluktuk are always in sample. One community from each of the following groups is also selected: Baker Lake or Arviat; Igloolik or Pond Inlet; Cape Dorset or Pangnirtung; Taloyoak, Gjoa Haven or Kugaaruk; Coral Harbour or Naujaat; Qikiqtarjuaq, Arctic Bay, Hall Beach, or Clyde River.

Exclusion of the territories from national totals

Although Statistics Canada collects and produces labour force data on the territories in a nearly identical questionnaire as used for the provinces, a different methodology is used in the territories.

There are many challenges in conducting a survey that covers the many relatively small and scattered communities in the territories, and factors such as survey costs, logistics around travel, sample sizes, and the burden on respondents must all be taken into account.

The LFS achieves this by using a sample design, a rotation pattern and reliability criteria that are different from those in the ten provinces. To improve their reliability, estimates for the territories are calculated and reported separately as moving averages and are therefore not included with the monthly provincial totals.

Exclusions from the Labour Force Survey coverage for the provinces and territories

Indian reserves are excluded from the LFS conducted in the provinces. However, Indigenous populations living off-reserve are included in the provincial target population. In the territories, both Indigenous and non-Indigenous communities are included in the sample. It should be noted that labour market indicators for First Nations people living on reserves are available from the Census of Population.

The LFS also excludes residents of institutions (for example, inmates of penal institutions and patients in hospitals or nursing homes) for conceptual reasons. The LFS is designed to measure labour force participation in the current labour market. Residents of institutions are for the most part not able to participate in the labour market and are not economically active.

Full-time members of the Canadian Armed Forces are not included in the LFS because of the practical difficulties associated with sampling and interviewing, since many of these persons live in locations that are not accessible for the purposes of conducting the LFS, such as naval vessels, military camps and barracks, or are stationed in other countries.

Also excluded from sampling are dwellings in remote areas with very low population density. The large effort and cost associated with travelling to these locations are considered against the impact, in terms of potential bias to the LFS estimates, of excluding these areas from the survey frame. Altogether, remote areas that are excluded represent less than 1% of the Canadian population. While remote populations are excluded from sampling, they are included in the population control totals used for weighting calibration unlike the other excluded populations.

Section 5: Data collection

Interviewing for the LFS

Data collection for the LFS is carried out each month over the ten days following the LFS reference week. The reference week is normally the week containing the 15th day of the month

Statistics Canada interviewers are employees hired and trained to carry out the LFS and other household surveys. Each month, they contact the sampled dwellings to obtain the required labour force information for all household members currently living there. In each dwelling, information about all household members is usually obtained from one knowledgeable household member. Such ‘proxy’ reporting, which accounts for approximately 65% of the information collected, is used to avoid the high cost and extended time requirements that would be involved in repeat visits or calls necessary to obtain information directly from each respondent.

Responses to survey questions are captured in one of three ways: by in-person interview; by telephone interview; or by self-completed electronic questionnaire. For all three collection methods, the LFS questionnaire can be completed in either English or French. For respondents completing by in-person interview, the interviewer will use a computerized questionnaire on a laptop computer. LFS telephone interviews are conducted by interviewers working from their homes or a Statistics Canada call centre. Certain LFS participants are eligible to respond to the survey by electronic questionnaire using a provided secure access code.

The response data collected by interviewers are encrypted to ensure confidentiality and sent electronically over a secure connection to head office in Ottawa for further processing.

Some respondents may be invited to complete a brief initial survey to confirm contact information, before completing the main LFS questionnaire for six months.

Supervision and quality control

All LFS interviewers are under the supervision of senior interviewers who are responsible for ensuring that their staff are familiar with the concepts and procedures of the LFS and any supplementary surveys, as well as periodically monitoring their interviews. The senior interviewers are, in turn, under the supervision of the Regional Office Data Collection Managers.

Non-response to the LFS

Prior to the pandemic, non-response to the LFS tended to average about 10% of eligible households. Since March 2020, non-response has tended to average between 25 and 30%. Interviewers are instructed to make all reasonable attempts to obtain interviews with members of eligible households. Email and/or SMS reminders are sent to respondents in the self-completed electronic questionnaire group inviting them to complete the survey before the end of collection. Telephone follow-ups are also made with those who have not yet responded in the second part of the collection. For individuals who at first refuse to participate in the LFS, a letter is sent from the Regional Office to the dwelling address stressing the importance of the survey and the household's co-operation. This is followed by a second call or visit from the interviewer. For cases in which the timing of the interviewer's call or visit is inconvenient, an appointment is arranged at a more convenient time. For cases in which there is no one home, numerous call backs are made. Under no circumstances are sampled dwellings replaced by other dwellings for reasons of non-response.

Each month, after all attempts to obtain interviews have been made, a number of non-responding households remain. A weight adjustment is applied to account for non-responding households.

Section 6: Data processing

Data capture

Responses to survey questions are captured in one of two ways: 1) directly by the interviewer at the time of the interview using a computerized questionnaire on a laptop or desktop computer or 2) by a secure web-based application for respondents who wish to self-complete the survey by electronic questionnaire. The response data are encrypted to ensure confidentiality and sent electronically to the head office in Ottawa for further processing.

Editing and imputation

The LFS data go through several steps of editing and imputation to produce a complete and consistent final microdata file. While some edits are applied within the collection application, for example by limiting collected values to valid ranges, most editing is done during processing. After editing, imputation is used to replace invalid, inconsistent, and missing data.

Edits are done to identify logically inconsistent and missing information. Since the true value of each entry on the questionnaire is not known, the identification of errors can be done only through recognition of obvious inconsistencies (for example, a 15-year-old respondent who is recorded as having last worked in 1940). Responses that are unusual but possible are kept. Quality controls and interviewer training are used to ensure errors are both minimal in number and non-systematic in nature.

Missing and invalid values are filled with reasonable valid values using item imputation. The item imputation methods used for the LFS include manual, deterministic, carry-forward, and donor imputation. Manual imputation is used for crucial demographic variables that are either key for later processing, such as age, or are complicated to impute in other ways, such as economic family structure. Deterministic imputation is used when other data in the questionnaire can be used to logically fill the missing value. For example, if a person initially reports that they worked the week before but later reports they worked no hours that week, their labour status is edited to be employed but absent.

Carry-forward imputation is used when a value collected from the same person in a previous month can be reasonably used to fill a missing value in the current month. Commonly carried forward values include wages and usual work hours. Donor imputation is used to fill all remaining missing values. This involves copying values from another record (i.e., a donor) with similar characteristics. The resulting records are again checked against all edits to ensure that they are complete and consistent after imputation.

Total non-response is treated in one of two ways, depending on whether demographic data such as age and gender are available or not for the non-respondent. Total non-response of a household where the demographics of the residents are unavailable is treated using a weighting non-response adjustment, as discussed in the Weighting section. Total non-response where the demographics of the household members are known, usually from a previous month’s collection, is treated using whole record donor imputation. Donors are found by matching the recipient record’s previous cycle labour force information as well as their geographic and demographic information.

Industry and occupation coding

In this process, industry, occupation and class of worker codes are assigned using the respondent's job description from the questionnaire. Coding is performed either manually or automatically using machine learning models created from historical write-in responses and is based on the classifications described in the North American Industry Classification System (NAICS) 2022 V1.0 and the National Occupational Classification (NOC) 2021 V1.0 standards.

Creation of derived variables

Many variables on the microdata file are derived by combining items on the questionnaire according to classification rules. For example, as described in Figure 2.1, labour force status is derived from specific combinations of responses to a number of survey questions regarding work activity, status in employment, job search, availability, etc.

Weighting

The sample data are weighted to enable tabulations of estimates at national, provincial, and sub-provincial levels of aggregation.

The first step of the weighting process is the calculation of the basic weight, which is the inverse of the probability of selection. For example, in an area where 2% of the households are sampled, each household would be assigned a basic weight of 1/0.02 = 50. The basic weight is then adjusted for any sub-sampling due to growth that may have occurred in the area and for non-response and coverage error.

In the LFS, some survey non-response is compensated for by carry-forward, substitution or donor imputation methods (as discussed above in the sub-section entitled Editing and imputation). Any remaining non-response is accounted for by adjusting the weights for the responding households in the same area. This non-response adjustment assumes that the characteristics of the responding households are not significantly different from the non-responding households.

The weights derived after the non-response adjustments are called the subweights.

The final adjustment to the weight is made to correct for coverage errors, align with other products on known totals, reduce any remaining non-response bias, and reduce the sampling variance of the estimates. The subweights are adjusted using composite calibration. This increases efficiency by leveraging the overlap between consecutive monthly samples, and it ensures that survey estimates conform to population control totals. An integrated method of weighting is applied, to ensure a common final weight for every person in a household.

There are two sets of weights used in the LFS tabulations. First, for most tables, the weights are calibrated to standard population totals (age, gender, geography, etc.) as well as composite control totals. The second set of weights are calibrated again to standard population totals as well as additional population control totals based on census projections to ensure proper coverage of Indigenous peoples and visible minority population groups. As a result of this additional adjustment, the labour force estimates for the total population in Indigenous  and visible minority tables will not match exactly with those in other tables.

For comprehensive information on the LFS methodology see the publication Methodology of the Canadian Labour Force Survey (71-526-X).

Seasonal adjustment

Most estimates associated with the labour market are subject to seasonal variation i.e., annually-recurring fluctuations attributable to climate and regular institutional events, such as holidays and vacations. Seasonal adjustment is used to remove these seasonal variations from approximately 1,300 series in the LFS to facilitate analysis of short-term change for major indicators, such as employment and unemployment by age and gender, employment and unemployment by industry, employment by occupation, and employment by class of worker (public and private employees or self-employed). Many of these indicators are adjusted at national and provincial levels. Main labour force status estimates are also seasonally adjusted for census metropolitan areas (CMAs), and published as three-month moving averages to reduce irregular movements caused by relatively small sample sizes. Estimates of hours worked are also seasonally adjusted.

Procedures used in seasonal adjustment

The method being used for seasonal adjustment is X-12-ARIMA, as implemented in X-13ARIMA-SEATS (US Census Bureau) and Proc X12/X13 (SAS version 9.4).

Seasonally adjusted estimates of overall employment and unemployment for Canada are derived by summing seasonally adjusted estimates for major age/gender groups (men aged 15 to 24, 25 to 54, and 55 and older; women aged 15 to 24, 25 to 54, and 55 and older). The resulting overall estimate is used as a benchmark for other seasonally adjusted series. For example, employment estimates by industry and class of worker are adjusted independently and then increased or decreased proportionately so that their total sums to the overall benchmark (the class of worker estimates are in fact adjusted to match the national totals by gender). This procedure is known as reconciliation or raking, and Statistics Canada's in-house TSRaking SAS procedure is used for this purpose.

Overall employment and unemployment estimates for the provinces are also derived by summing adjusted estimates for major age/gender groups (men 15 to 24, 25 and older; women aged 15 to 24, 25 and older). However, prior to summation, the estimates for each age/gender group are raked to the corresponding national estimate. Provincial estimates of employment by industry are raked to the provincial employment total as well as the national industry totals.

Seasonally adjusted estimates of labour force for any particular group are derived by adding the seasonally adjusted estimates of employment and unemployment for that group. Similarly, seasonally adjusted rates (for example, unemployment rate) are calculated by dividing the seasonally adjusted numerator by the seasonally adjusted denominator. In the case of the participation rate and employment rate, only the numerator is seasonally adjusted, as the denominator is based on population totals in the LFS which are less subject to seasonal fluctuations.

Adjustment for reference week effect

The definition of the LFS reference week (usually the week with the 15th day of the month) implies that the actual dates of the reference week vary from year to year. This variability may impact the month-to-month change in major labour market estimates. For example, more students may have finished exams and entered the labour market before the end of reference week in years when the 15th day of June falls near the beginning of the week than is the case in years when the 15th falls near the end of reference week. The reference week effects are removed from the series so that the underlying trend is easier to interpret. These adjustments compensate for early or late reference weeks.

The reference week effects are estimated by the X-12-ARIMA seasonal adjustment method using a regression model with ARIMA residuals.

Adjustment for holiday effects on actual hours worked

In addition, actual hours of work are affected by variability in the dates of the reference week, combined with the presence of fixed (Remembrance Day) or moving (Easter, Thanksgiving) holidays. Specifically, in some years, holidays may occur during the reference week, reducing work hours during that week. Similarly, fluctuations can also occur in July, depending on the timing of the reference week, as the usual vacation period tends to peak in the latter half of the month. This variability could introduce significant fluctuations in estimates of actual hours worked, and it is therefore removed from the series prior to seasonal adjustment.

Starting in January 2010, a method used by the System of National Accounts labour statistics was adopted. Permanent prior adjustments are now generated by adding back the hours lost due to the holiday, as reported by respondents of the LFS. The historical series have been revised using this new method. The holidays that may occur during the reference week, and for which an adjustment (adding back the hours lost) is made, include Family Day (for certain provinces), spring break (for certain provinces), Good Friday or Easter Monday, Thanksgiving, and Remembrance Day.

As hours lost due to holidays are not reported for the self-employed, a model is used to estimate and remove systematic fluctuations due to holiday occurrence in the reference week. This model is based on an augmented version of the time series regression used to adjust for reference week location.

Starting in January 2015, to better reflect the actual hours worked from the self-employed workers, the seasonally adjusted total actual hours worked series is derived as the sum of the three seasonally adjusted classes of workers (public employees, private employees and self-employed). This total is then used as a benchmark to adjust the actual hours worked by industry and by province in the raking step so that the totals are the same.

Since holiday effects on actual hours worked vary from industry to industry, depending on the characteristics of each regarding the observance of holidays and summer vacation practices, prior adjustments are calculated and performed separately for each major industry group.

Regular annual revisions for seasonal adjustment

Each year, the LFS revises its seasonally adjusted estimates for the previous three years, using the latest seasonal factors.

Seasonal adjustment requires data from past, current and future values. As new data become available, various time series components can be better estimated which leads to revised and more accurate seasonally adjusted estimates.

Seasonal adjustment models and options for each series are also reviewed each year. When appropriate, updated options will be used to produce the revised seasonally adjusted estimates (and the on-going seasonally adjusted estimates on a monthly basis for the year to come).

The impact of COVID-19 pandemic on seasonal adjustment

The COVID-19 pandemic had brought unprecedented shocks to the labour market and consequently to the time series data.  It is important to ensure that the irregular patterns in the data during the pandemic period do not unduly affect the estimation of seasonal factors. To this end, special outlier strategies were introduced to handle pandemic shocks to the labour market and to better model the seasonal components in the time series data.  Level shifts and temporary changes were identified and added to the regression ARIMA models when appropriate. The level shifts are reflected in the trend-cycle of the time series.  Lowered outlier detection thresholds were also applied for the period of March 2020 to December 2021 to prevent unusual movements from impacting the estimation of the seasonal factors.

Other revisions and redesigns

Approximately every five years, population estimates are rebased or reweighted to the most recent census population counts. As of January 2025, LFS estimates have been adjusted to reflect population counts from the 2021 Census. Given the minimal changes, only revisions back to 2011 were necessary. However, revisions for seasonal adjustment were applied back to January 2008 and for trend-cycle series back to July 2007. Generally, the introduction of the latest classification systems for industry, occupation and geography, along with other changes, occur at this time. For more information, see The 2025 Revisions of the Labour Force Survey (LFS) .

The LFS undergoes a sample redesign every ten years to reflect changes in population characteristics and new definitions of geographical boundaries. The most recent redesign phased in between April and September 2025 defines new clusters and strata based on the 2021 Census. For more information, see Section 4.   

Other enhancements to the methodology, questionnaire, data collection, processing and dissemination systems occur on an ad hoc basis.

In January 2019, the LFS transitioned to the Social Survey Processing Environment (SSPE), a corporate data processing system used by most Statistics Canada social survey programs. For more information, see Transition of Labour Force Survey Data Processing to the Social Survey Processing Environment (SSPE) .

In March 2020, the LFS transitioned to an Integrated Collection and Operation System (ICOS). ICOS is an integrated collection platform that is used by both interviewers and respondents, and supports all data collection modes. Also in March 2020, eight questions were added to the LFS questionnaire to capture additional information on multiple jobholders as well as information on self-employment earnings, the main activity of those not in the labour force and older continuing workers.

In January 2022, questions on gender, sex at birth and population group were added to the LFS questionnaire and data collection for Indigenous identity was updated to follow the current standard.

In 2023, methodological enhancements to imputation and outlier detection were applied historically to improve data quality for rare employment characteristics and wages. For more information, see The 2023 Revisions of the Labour Force Survey (LFS).  

Section 7: Data quality

Non-sampling errors

Errors that 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, answers may be incorrectly entered, 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. Quality assurance measures are 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, observation of interviewers to detect problems of questionnaire design or misunderstanding of instructions, edits to ensure data entry errors are minimized, and coding and edit quality checks to verify the processing logic.

Sampling errors

The LFS collects information from a sample of households. Somewhat different figures might have been obtained if a complete census had been taken using the same questionnaires, interviewers, supervisors, processing methods, etc. The difference between the estimates obtained from a sample and those that would give a complete count taken under similar conditions is called the sampling error.

Three related measures can be used to interpret and evaluate sampling error: the standard error; the coefficient of variation and the confidence interval. These measures can be used to conduct hypothesis tests.

Approximate measures of sampling error accompany LFS products and users are urged to make use of them while analyzing the data. All seasonally adjusted data tables include standard errors for monthly estimates and may include additional standard errors for month-to-month estimates of change and year-over-year estimates of change. Tables 7.1, 7.2, and 7.3 below can be used to obtain approximate CVs for most other released estimates.

CVs and standard errors are available for most estimates upon request on a cost recovery basis. For these measures, please contact Statistics Canada's Statistical Information Service (toll-free 1-800-263-1136; 514-283-8300; STATCAN.infostats-infostats.STATCAN@statcan.gc.ca).

Users with an approved research proposal can also obtain direct access within secure facilities to LFS data and bootstrap weights through Statistics Canada’s Research Data Centres (RDCs). See the sub-section entitled Access to microdata in Section 9 for more information.

Interpretation using the standard error

The standard error is a numerical measure of the sampling error that quantifies how different the estimates from all potential samples would be from one another, assuming the same sampling plan. On its own, it is a value that can be difficult to interpret but it is used in developing other more intuitive measures including coefficients of variation and confidence intervals. They are also useful in data analysis with hypothesis tests.

Although the concept of standard error is based on the idea of selecting several samples, in practice only one sample is drawn and the standard error is estimated based on the information collected from the units in that sample.

The standard error depends on the sample size, the response rate, the size of the population, the variability of the characteristic of interest in the population, and the sample design and estimation methods. Typically, of similar estimates, the one with larger sample size will yield the smaller standard error.

Interpretation using coefficient of variation

To obtain the coefficient of variation (CV), the standard error is divided by the estimate. CVs are a relative measure, meaning that the quality of estimates of varying sizes can be compared.

Small CVs are desirable because they indicate that the sampling variability is small relative to the estimate.

Since the CV is the standard error expressed as a percentage of the estimate, the smaller the estimate, the larger the CV (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.

The adjusted CV removes this dependence on the estimate size. It is primarily useful as a quality target, since it does not move with the economy and instead isolates the quality of the survey itself.

For estimates of proportions, the adjusted CV is calculated as $CVadj=CV\times \sqrt{\frac{p}{(1-p)}}\times \sqrt{\frac{(1-q)}{q}}$   where $p$ is the estimated proportion and $q$ is a fixed reference proportion. Since the CV is expected to depend on the estimated proportion through a factor of $\sqrt{\frac{(1-p)}{p}}$  , the first factor $\sqrt{\frac{p}{(1-p)}}$  removes this dependence and the second factor $\sqrt{\frac{(1-q)}{q}}$ replaces it by a fixed value.

The adjusted CV can be extended to counts by using $p$ =count/in-scope population. For the LFS quality targets, $p$  is the proportion of unemployed (unemployed count divided by population aged 15 and older), and $q$  is 4.5%, close to the average proportion of unemployed from 2005 to 2024.

Interpretation using confidence intervals

Confidence intervals provide another way of looking at the variability inherent in estimates from sample surveys. A confidence interval is a range of values that has a probability, known as the level of confidence, of containing the actual value. For example, a 68% confidence interval means that if many samples were drawn and a confidence interval was constructed for each sample, 68% of the constructed confidence intervals should contain the actual value. To illustrate how to calculate the confidence interval, let us say that one month the published estimate for total employment rose by 60,000 and the associated standard error for the month-to-month movement estimate was 25,000. We can say that:

• The lower and upper bounds of a 68% confidence interval can be computed by adding or subtracting one standard error (25,000) from 60,000. This means that there are approximately two chances in three (68%) that the range (35,000 to 85,000) contains the real value of the change between the two months.
• A 95% confidence interval can be constructed by adding and subtracting 50,000 (two standard errors) from 60,000. This means that there are approximately nineteen chances in twenty (95%) that the range (10,000 to 110,000) contains the real value of the change between the two months.

Conducting hypothesis tests

Standard errors may also be used to perform hypothesis testing, a procedure that can be used for comparing population parameters using sample estimates. The larger the observed change between two estimates relative to its standard error, the better the chance that we are observing a real change as opposed to a change due to sampling variability.

One simple way to conduct a hypothesis test is with a confidence interval. If the 95% confidence interval of an observed estimate of change does not contain zero, then the change is considered statistically significant at the 5% level of significance. The level of significance is the probability of concluding that there is a change, when in fact the actual change is zero. If the confidence interval of the estimate does contain zero, it is less likely to reflect a real change and more likely to be due to sampling variability.

To illustrate, let us say that between two months, the published estimate for total employment increased by 60,000 and the associated standard error for the movement estimate was 25,000. Since the 95% confidence interval (10,000 to 110,000) does not contain zero, this change in employment is considered significant at the 5% level of significance.

Using approximate sampling variability tables

In practice, standard errors are not provided with all published estimates, so approximate CV tables are provided to allow users to obtain CVs. Standard errors can be calculated from a CV by multiplying the CV by the estimate. Standard errors can then be used to produce confidence intervals and perform hypothesis tests, as described above.

Three tables are available: 7.1 for monthly totals for Canada and the provinces, 7.2 for annual averages for Canada and the provinces, and 7.3 for three-month moving averages and annual averages for the territories.

These tables are supplied as a rough guide to the sampling variability. The sampling variability is modeled 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 in the table.

Variability of monthly estimates for Canada and the provinces

To look up an approximate measure of the CV of an estimate of a monthly total, consult Table 7.1, which gives the size of the estimate as a function of the geography and the CV. The rows give the geographic area 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 area A, look across the row for area A and find the first estimate that is less than or equal to X. The title of that column will give the approximate CV. For example, to determine the standard error for an estimate of 28,000 thousand unemployed in Newfoundland and Labrador in January 2024, we find the closest but smaller estimate of 27,700, giving a CV of 5.0%. Therefore, the estimate of 28,000 unemployed in Newfoundland and Labrador has a CV of roughly 5.0%.

The CV values given in Table 7.1 are derived from a model based on LFS sample data for the five-year period from January 2020 through December 2024 inclusive. It is important to bear in mind that these values are approximations.

Table 7.1 can be used with either seasonally adjusted estimates, or with estimates that have not been seasonally adjusted. Studies have shown that LFS standard errors for seasonally adjusted data are close to those for unadjusted data, particularly when estimates are for larger populations and domains.

Variability of annual estimates for Canada and the provinces

To look up an approximate measure of the CV of an estimate of an annual average, consult Table 7.2, which gives the size of the estimate as a function of the geography and the CV. 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 area A, look across the row for area A and find the first estimate that is less than or equal to X. The title of that column will give the approximate CV. For example, to determine the standard error for an annual average estimate of 198,800 unemployed in Quebec in 2022, we find the closest but smaller estimate of 188,200, giving a CV of 2.5%. Therefore, the estimate of 198,800 unemployed in Quebec has a CV of roughly 2.5%.

The CV values given in Table 7.2 are derived from a model based on LFS sample data for the five-year period from 2020 to 2024. It is important to bear in mind that these values are approximations.

Sampling variability tables for the territories

The CV values for three-month moving averages given in Table 7.3 for Yukon, the Northwest Territories and Nunavut are derived from a model based on LFS sample data for the five-year period of January 2020 through December 2024 inclusive. The CV values for annual averages given in the same table are derived from a model based on LFS sample data for the five-year period of 2020 to 2024.

Variability of rates

Estimates that are rates and percentages are subject to sampling variability that is related to the variability of the numerator and the denominator of the ratio. The various rates given are treated differently because some of the denominators are calibrated figures that have no sampling variability associated with them.

Unemployment rate

The unemployment rate is the ratio of X, the total number of unemployed in a group, to Y, the total number of participants in the labour force in the same group. Here, the group may be a province or CMA and/or it may be an age-gender group.

The CV for the unemployment rate can be estimated with the following formula:

$${\left[CV(X/Y)\right]}^2={\left[CV(X)\right]}^2+{\left[CV(Y)\right]}^2-2p\left[CV(X)\right]\left[CV(Y)\right]$$

where CV(X) would be the CV for the total number of unemployed in a specific geographic or demographic subgroup and CV(Y) would be the CV for the total number of participants in the labour force in the same subgroup. The correlation coefficient, denoted p, measures the amount of linear association between X and Y (respectively, the number of unemployed and the number of participants in the labour force in the same subgroup). The value of p ranges between -1 and 1. For example, a strong positive linear association would indicate that unemployment counts generally increase as the total number of participants in the labour force increases. Note that we can expect a larger CV for the unemployment rate when p is negative, since in this case, the third term on the right side of the equation above becomes positive.

When p is not available, the most conservative approach is to take p = -1, which leads to the simplified formula:

$$CV(X/Y)=CV(X)+CV(Y)$$

Note that this will likely lead to an overestimation of the CV(X/Y).

For example, in January 2024, there were 28,000 unemployed persons in Newfoundland and Labrador and 275,900 participants in the labour force, giving an unemployment rate of 10.4%. Table 7.1 gives the CVs for the two counts as 5.0% and 2.5%, respectively. An approximation of the CV for the unemployment rate of 10.4% using the above formula would be:

5.0% + 2.5% = 7.5%

Participation rate and employment rate

The participation rate represents the number of persons in the labour force expressed as a percentage of the total population size. The employment rate is the total number of employed divided by the total population size. For both the above rates, the numerator and the denominator represent the same geographic and demographic group.

For Canada, the provinces, CMAs and some age-gender groups, the LFS population estimates are not subject to sampling variability because they are calibrated to independent sources. Therefore, in the case of the participation rate and the employment rate of these geographic and demographic groups, the CV is equal to that of the contributing numerator.

Some subgroups of Canada such as industry and occupation groups are not calibrated to independent sources. For example, there is no official independent source for a monthly count of persons in the agriculture industry in Manitoba. To determine the CV of rates in the case of such subgroups, the variability of both the numerator and the denominator must be taken into account because the denominator is no longer a controlled total and is subject to sampling variability. Therefore, for participation rates and employment rates of subgroups, the CV can be determined in a similar fashion to that of the unemployment rate. The totals in the numerator and denominator for the relevant rate should reflect the same subgroup.

Variability of estimates of change

The difference of estimates from two time periods gives an estimate of change that is also subject to sampling variability. Users are typically interested in determining if this change is statistically significant or not. An estimate of year-over-year or month-to-month change is based on two samples which may have some households in common. Hence, the sampling variability of change depends on the sampling variability of the estimates for both periods and the correlation p, between the periods.

The value of p ranges between -1 and 1, with 1 being a perfect positive linear association. One can generally use the sample overlap to approximate the correlation coefficient as follows:

• For the provinces: use p = 5/6 for month-to-month changes, and p = 0 for year-over-year changes.
• Empirical studies at Statistics Canada have shown that for the provinces, a value of p equal to 5/6 is a good approximation for estimates of employment, but for estimates of unemployment, a p of 0.45 would yield a better approximation for month-to-month changes.

Typically, the CV of the estimate of change is not a useful measure for analysis but can be used to derive more useful statistics. As described in the sub-section entitled Conducting hypothesis tests, a hypothesis test can be conducted by using confidence intervals based on the standard error of the estimate. The standard error can be derived from the CV by multiplying the CV by the estimate of change ($Y_2-Y_1$ ). The CV for an estimate of change can be calculated from the CVs of the estimates from the two time periods using the following formula:

(1)

$$CV(Y_2-Y_1)=\sqrt{1-\rho }\frac{\sqrt{Y_1{}^{2}CV{(Y_1)}^2+Y_2{}^{2}CV{(Y_2)}^2}}{(Y_2-Y_1)}$$

where $Y_1$ and $Y_2$ are the estimates for the two periods. The value of p is the correlation coefficient between $Y_1$ and $Y_2$.

Note: If the estimate of change ($Y_2-Y_1$) is negative then the calculated CV will be negative, but generally the equivalent positive value is reported.

When comparing the annual averages of two years, the CV of the annual estimates (Table 7.2) should be used. For month-to-month change, seasonally adjusted estimates should be used in conjunction with the CV of the monthly estimates from Table 7.1. Note that the above formula gives an approximate estimate of the sampling variability associated with an estimate of change.

Guidelines on data reliability

Household surveys within Statistics Canada generally use the following guidelines and reliability categories in interpreting CV values for data accuracy and in the dissemination of statistical information.

Category 1 – If the CV is ≤ 16.5% – no release restrictions: data are of sufficient accuracy that no special warnings to users or other restrictions are required.

Category 2 – If the CV is > 16.5% and ≤ 33.3% – release with caveats: data are potentially useful for some purposes but should be accompanied by a warning to users regarding their accuracy.

Category 3 – If the CV > 33.3% – not recommended for release: data contain a level of error that makes them so potentially misleading that they should not be released in most circumstances. If users insist on inclusion of Category 3 data in a non-standard product, even after being advised of their accuracy, the data should be accompanied by a disclaimer. The user should acknowledge the warnings given and undertake not to disseminate, present or report the data, directly or indirectly, without this disclaimer.

Confidentiality release criteria

Statistics Canada is prohibited by law from releasing any data which 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.

The LFS produces a wide range of outputs that contain estimates for various labour force characteristics. Most of these outputs are estimates in the form of tabular cross-classifications. Estimates are rounded to the nearest hundred and a series of suppression rules are used so that any estimate below a minimum level is not released.

The LFS suppresses estimates below the levels presented in Table 7.4.

Section 8: Comparing the Survey of Employment, Payrolls and Hours and the Labour Force Survey

Statistics Canada has two monthly surveys that measure employment levels and trends: the Labour Force Survey (LFS), a household survey, and the Survey of Employment, Payrolls and Hours (SEPH), which combines administrative data and establishment survey data from the Business Payroll Survey.

The LFS provides the first timely picture of overall labour market conditions, with total employment, unemployment and unemployment rate, as well as information on which groups of Canadians are most affected by changes in the labour market.

Approximately two months later, the SEPH provides a preliminary estimate of payroll employment of the same month by industry, along with estimates of earnings and hours worked.

Statistics from LFS and SEPH, along with those from the Employment Insurance program and the Job Vacancy and Wage Survey, and the Census of Population contribute to a more comprehensive understanding of the supply and demand components of the labour market.

The LFS provides a broader picture of employment, including employment in agriculture and the number of self-employed. The SEPH provides a reliable gauge of monthly change in non-farm payroll employment by industry (4-digit NAICS level).

Because the LFS has a broader definition of employment than the SEPH, the LFS employment level exceeds the SEPH employment level. See Chart 8.1 below for more details.

Data table for Chart 8.1

Data table for Chart 8.1 Table summary
This table displays the results of Data table for Chart 8.1 LFS, LFS, adjusted to SEPH concepts and SEPH, calculated using thousands units of measure (appearing as column headers).

	LFS	LFS, adjusted to SEPH concepts	SEPH
	thousands
Source: Statistics Canada, Labour Force Survey (3701), Survey of Employment, Payrolls and Hours (2612).
2008
January	16,932.2	14,931.1	14,756.1
February	16,967.5	14,988.0	14,751.0
March	16,972.1	14,966.2	14,791.1
April	16,971.8	14,980.2	14,803.1
May	16,986.8	14,971.9	14,825.9
June	16,996.2	14,966.6	14,848.2
July	16,980.9	14,948.6	14,833.1
August	17,003.3	14,907.2	14,870.0
September	17,025.4	14,905.7	14,857.2
October	17,070.5	14,956.8	14,871.4
November	16,982.7	14,890.1	14,806.4
December	16,972.3	14,911.9	14,762.4
2009
January	16,824.7	14,799.5	14,688.8
February	16,782.4	14,743.3	14,653.6
March	16,756.4	14,707.1	14,590.6
April	16,730.8	14,626.2	14,538.0
May	16,740.7	14,671.0	14,523.8
June	16,712.7	14,614.2	14,502.0
July	16,702.8	14,629.3	14,536.9
August	16,702.7	14,632.9	14,455.8
September	16,743.8	14,688.7	14,502.4
October	16,753.3	14,650.2	14,531.6
November	16,824.7	14,736.2	14,515.9
December	16,797.4	14,697.2	14,534.6
2010
January	16,825.0	14,735.7	14,549.9
February	16,818.0	14,751.8	14,573.4
March	16,861.7	14,829.7	14,599.7
April	16,928.5	14,899.4	14,630.9
May	16,946.8	14,942.4	14,634.6
June	17,024.6	14,972.5	14,672.8
July	17,036.5	14,948.5	14,737.7
August	17,062.1	14,988.4	14,710.2
September	17,058.4	15,020.2	14,757.0
October	17,044.3	15,008.0	14,781.2
November	17,089.4	15,056.7	14,774.4
December	17,117.4	15,073.1	14,831.8
2011
January	17,187.2	15,150.8	14,826.9
February	17,174.8	15,173.4	14,849.3
March	17,173.7	15,170.7	14,873.2
April	17,222.1	15,170.3	14,893.9
May	17,187.4	15,134.4	14,868.9
June	17,218.8	15,204.4	14,946.9
July	17,256.4	15,174.6	14,958.1
August	17,269.4	15,197.4	14,961.0
September	17,272.0	15,231.0	15,012.3
October	17,258.0	15,273.4	15,006.6
November	17,241.4	15,256.8	15,037.2
December	17,258.2	15,215.3	15,039.8
2012
January	17,270.1	15,229.5	15,038.1
February	17,245.0	15,248.3	15,033.5
March	17,338.6	15,257.4	15,075.3
April	17,416.2	15,377.8	15,130.1
May	17,412.2	15,323.3	15,200.7
June	17,414.7	15,329.1	15,234.0
July	17,439.8	15,389.8	15,242.3
August	17,486.0	15,421.0	15,270.5
September	17,534.5	15,406.1	15,268.4
October	17,543.2	15,416.4	15,246.7
November	17,563.8	15,446.4	15,283.3
December	17,609.5	15,542.5	15,255.6
2013
January	17,600.7	15,504.6	15,268.8
February	17,676.1	15,543.7	15,306.5
March	17,610.4	15,505.3	15,340.2
April	17,641.3	15,511.0	15,352.6
May	17,692.5	15,547.5	15,333.0
June	17,682.1	15,532.6	15,318.3
July	17,650.1	15,529.5	15,376.9
August	17,677.9	15,572.3	15,457.3
September	17,659.6	15,562.2	15,435.5
October	17,664.4	15,558.5	15,461.8
November	17,691.3	15,568.3	15,460.1
December	17,668.9	15,572.9	15,446.1
2014
January	17,666.4	15,631.4	15,454.4
February	17,662.7	15,581.6	15,453.0
March	17,699.4	15,656.8	15,485.9
April	17,691.2	15,625.0	15,478.5
May	17,687.7	15,672.0	15,547.4
June	17,720.5	15,644.5	15,567.3
July	17,771.9	15,728.8	15,601.5
August	17,723.7	15,670.9	15,646.5
September	17,756.0	15,701.2	15,632.6
October	17,782.2	15,710.8	15,655.2
November	17,784.5	15,735.7	15,626.4
December	17,821.7	15,739.9	15,629.1
2015
January	17,813.1	15,738.4	15,679.9
February	17,827.2	15,754.4	15,679.1
March	17,854.1	15,786.8	15,680.4
April	17,819.4	15,776.4	15,721.4
May	17,843.7	15,820.6	15,755.0
June	17,836.7	15,863.1	15,725.4
July	17,863.3	15,870.2	15,765.2
August	17,889.0	15,867.4	15,736.2
September	17,890.2	15,876.2	15,752.6
October	17,899.1	15,905.7	15,813.7
November	17,872.6	15,858.7	15,789.9
December	17,867.4	15,830.0	15,821.6
2016
January	17,871.6	15,821.1	15,810.3
February	17,889.0	15,882.0	15,792.6
March	17,892.8	15,834.0	15,830.6
April	17,919.7	15,860.4	15,894.6
May	17,947.4	15,895.0	15,896.5
June	17,954.5	15,898.8	15,959.2
July	17,943.2	15,856.2	15,951.0
August	17,994.2	15,959.7	15,932.9
September	18,032.5	16,006.3	15,990.4
October	18,051.2	15,994.8	15,990.9
November	18,076.0	16,049.2	16,010.0
December	18,104.0	16,043.8	16,069.0
2017
January	18,172.5	16,139.4	16,054.0
February	18,216.6	16,171.5	16,113.4
March	18,241.5	16,206.3	16,118.9
April	18,271.6	16,252.9	16,144.3
May	18,312.9	16,246.1	16,182.7
June	18,371.4	16,291.4	16,278.5
July	18,416.9	16,356.8	16,288.1
August	18,436.4	16,345.9	16,338.4
September	18,442.5	16,324.2	16,368.9
October	18,475.1	16,385.1	16,345.2
November	18,550.5	16,401.5	16,365.4
December	18,619.7	16,479.9	16,424.5
2018
January	18,576.8	16,436.8	16,424.5
February	18,570.3	16,433.5	16,466.8
March	18,630.1	16,493.6	16,516.0
April	18,613.0	16,439.2	16,519.0
May	18,621.7	16,527.2	16,588.9
June	18,677.6	16,604.6	16,627.9
July	18,763.9	16,654.8	16,603.2
August	18,704.7	16,660.9	16,644.2
September	18,797.5	16,722.8	16,679.0
October	18,814.9	16,688.3	16,726.4
November	18,869.4	16,759.7	16,745.4
December	18,892.8	16,721.7	16,735.1
2019
January	18,920.5	16,751.5	16,811.7
February	18,965.7	16,741.3	16,849.5
March	18,932.9	16,752.4	16,873.1
April	19,053.7	16,910.9	16,876.1
May	19,067.3	16,946.8	16,923.9
June	19,078.6	16,914.1	16,923.3
July	19,072.4	16,912.6	16,982.5
August	19,133.9	17,004.2	17,004.4
September	19,142.8	17,023.3	16,983.0
October	19,130.9	17,016.2	16,992.9
November	19,096.8	16,958.9	17,004.8
December	19,186.5	17,036.1	17,015.5
2020
January	19,204.7	17,010.6	17,053.1
February	19,202.9	17,014.8	17,028.2
March	18,058.2	15,711.9	16,070.2
April	16,085.8	12,704.7	14,204.9
May	16,410.7	13,145.6	13,707.5
June	17,469.4	14,508.2	14,356.7
July	17,895.5	15,162.9	15,090.6
August	18,129.3	15,599.7	15,422.9
September	18,555.9	16,174.9	15,754.3
October	18,616.4	16,227.1	15,938.2
November	18,650.0	16,226.1	15,892.5
December	18,591.7	16,198.9	15,940.1
2021
January	18,392.1	15,867.9	15,805.5
February	18,632.0	16,238.0	15,870.1
March	18,873.0	16,639.9	16,113.0
April	18,673.8	16,403.5	16,243.4
May	18,623.6	16,398.6	16,021.3
June	18,894.4	16,595.7	16,235.4
July	19,016.5	16,818.7	16,540.4
August	19,081.7	16,923.9	16,630.7
September	19,251.1	17,123.5	16,710.1
October	19,288.1	17,243.8	16,841.0
November	19,437.3	17,325.5	16,907.7
December	19,508.8	17,428.9	17,010.8
2022
January	19,305.4	17,102.9	17,003.6
February	19,629.0	17,441.3	17,119.8
March	19,647.8	17,541.5	17,250.4
April	19,709.7	17,599.1	17,389.1
May	19,763.4	17,666.6	17,376.7
June	19,750.8	17,720.4	17,495.5
July	19,787.5	17,737.0	17,527.6
August	19,781.2	17,741.3	17,540.7
September	19,796.1	17,747.6	17,622.5
October	19,882.8	17,835.0	17,639.0
November	19,923.8	17,887.7	17,700.4
December	19,998.5	17,954.7	17,766.4
2023
January	20,113.5	18,043.5	17,806.1
February	20,153.3	18,145.7	17,851.9
March	20,214.1	18,213.7	17,855.4
April	20,257.8	18,180.8	17,797.4
May	20,246.7	18,269.3	17,924.8
June	20,332.9	18,303.8	17,978.0
July	20,352.1	18,354.3	17,982.5
August	20,412.1	18,391.0	17,979.6
September	20,465.3	18,514.7	18,025.6
October	20,493.9	18,440.3	18,019.3
November	20,518.5	18,477.3	17,948.7
December	20,532.8	18,518.7	17,995.8
2024
January	20,577.1	18,515.1	18,022.7
February	20,607.7	18,566.9	18,035.1
March	20,614.5	18,585.7	18,081.0
April	20,700.5	18,667.5	18,109.9
May	20,698.3	18,655.4	18,132.1
June	20,715.9	18,634.6	18,105.2
July	20,712.9	18,586.3	18,140.0
August	20,742.6	18,691.4	18,167.4
September	20,779.3	18,668.6	18,146.6
October	20,782.6	18,727.0	18,141.8
November	20,826.4	18,789.6	18127.2
December	20,917.4	18,760.2	18193.0

For comparability purposes, an adjusted LFS series was included in Chart 8.1 to be closer in concepts and definitions to SEPH employment. This adjusted series is created by subtracting agriculture, fishing and hunting employment, self-employed workers (except those incorporated with paid employees), unpaid family and private household workers, as well as workers absent for four weeks or more without pay from their job, and then adding to the total to account for the second jobs of multiple job holders. For the SEPH, employment data from the three territories are removed to make the national totals comparable to the LFS.

This adjustment process has some limitations. For example, the adjustment for multiple jobholders adds the secondary wage and salary jobs of workers, but as data are not collected on the industry of the secondary job, the exclusion of agriculture, fishing, and hunting employment is based on the industry of the primary job. The adjustment process for multiple job holders also assumes these workers hold two jobs, whereas some workers may hold three or more jobs.  Also, the adjustment can be challenging because, unlike LFS, duration of unpaid absences for individual workers is not directly observed in SEPH .

Additionally, some independent contractors sampled in the LFS may be misclassified as wage and salary workers rather than self-employed, which could limit the ability to fully reconcile the two employment measures.

The major features and distinctions of the two surveys are shown in Table 8.1.

Comparing employment trends from the two surveys

The LFS is the only survey conducted by Statistics Canada designed to provide the official unemployment rate every month, with a monthly sample size of approximately 65,000 households. It is the earliest and most timely indicator of the pulse of the labour market in Canada. The sample size makes it a very reliable source for different geographic levels. It provides a complete picture as it includes employees, self-employed, unemployed people as well as people not currently participating in the labour force. Characteristics of all three groups, including age, gender and occupation are also available.

The SEPH, also a monthly survey, is designed to provide data on payroll employment as well as average weekly earnings and hours worked. It is a census of all payroll employees in Canada, excluding the agriculture sector. Consequently, the SEPH does not survey the self-employed or the unemployed. However, the employment payrolls data are available at a detailed industry level.

As mentioned earlier, SEPH and LFS estimates track well over the long term, but discrepancies in trends can occur. These discrepancies might be more common for sub-groups, for example, at the provincial level or in a specific industry.

Table 8.2 provides year-over-year employment change from LFS and SEPH for December over several years.

Comparing earnings and wage trends from the two surveys

The SEPH and the LFS each provide monthly indicators of pay received by employees. In SEPH, earnings estimates are expressed as average weekly earnings. They are derived for payroll employees and are available by industry and province or territory. In the LFS, wages estimates are expressed as average hourly wages and average weekly wages and are available by socio-demographic characteristics. For more information on comparing earnings and wage trends from the SEPH and LFS, please refer to Earnings and Wages – A guide to using indicators from the Survey of Employment, Payrolls and Hours and the Labour Force Survey.

Sampling error in the LFS

The LFS is subject to sampling and non-sampling error. Standard errors are measures of sampling error, and are available from the LFS to help users assess the statistical significance of a variation over time, or of a difference between corresponding estimates for two population groups. See Section 7 on Data Quality for more information.

Employment estimates from the SEPH, as they are derived from all the administrative payroll deduction forms submitted by employers to the Canada Revenue Agency, are not subject to sampling error. They are, however, subject to non-sampling error.

Estimates by province or territory

Estimates from the LFS are based on where people usually reside. However, the SEPH counts employees in the province or territory where they work. This does not affect comparability at the national level, but can create differences at the provincial/territorial level. Territorial estimates from the LFS are not provided on a monthly basis due to limitations in the survey design. Instead, they are presented as 3-month moving averages, reflecting the average levels over the preceding three months. Territorial estimates are not included in the national totals for the LFS.

Release schedule differences

The LFS interviews take place over the ten days following the reference week. This is followed by nine calendar days of processing and analysis, enabling the release of the estimates less than three weeks after the Saturday closing the reference week. The release usually takes place the first or second Friday of the month following the month of the reference week.

With the SEPH, businesses have until the 15th of the following month to file data from the last pay period of the reference month to the Canada Revenue Agency. These data, approximately one million records every month, are provided to Statistics Canada five weeks after the reference period. This is followed by three and a half weeks of processing and analysis, bringing the release to eight and a half weeks after the reference period.

Revision schedule

Estimates from both surveys are revised according to different schedules. For LFS, the annual revision maintains coherence in the estimates of month-to-month and year-over-year changes. For SEPH, while the annual revision does not impact the overall trends, it could affect the month-to-month change.

The seasonally adjusted LFS estimates are revised yearly, going back three years, and are published around the end of January. Approximately every five years, population control totals are updated according to the latest census population projections and all LFS estimates are revised historically over a longer time span. This exercise is called a rebasing. At the same time, the latest classifications for geography, industry and occupation are updated along with the latest seasonal factors. The latest rebasing took place in January 2025.

With the SEPH, monthly estimates are revised the month following their initial preliminary release. For example, when preliminary estimates for May are released, revised estimates for April are also released.

Every year at the end of March, the SEPH estimates undergo a historical revision. The span and the breadth of the revisions vary depending on the year. The revisions to specific industries sometimes go back as far as 2001 and can include updates to new classification systems (i.e., moving from NAICS 2017 to NAICS 2022, which occurred in the January 2023 reference period), or sometimes will span only a few years with minimal changes. At the same time, seasonally adjusted data are revised back to January 2001.

Section 9: Related products, services, and programs

A broad range of tabulated data compiled from the Labour Force Survey (LFS) is contained in regular publications and data tables. Analytical articles based on LFS data frequently appear in the Statistics Canada publications listed below. However, the wealth of information that can be extracted from the survey, and the variety of questions that can be addressed, are far too vast for regular publication. In order to meet particular analytical needs and address issues of current interest, the survey provides a custom tabulation service on a cost-recovery basis. A public use microdata file is also available for clients wishing to do their own data extractions and analyses.

Catalogued publications

Monthly: Labour Force Survey in brief: Interactive app (Catalogue no. 14200001)

This interactive visualization application provides seasonally adjusted estimates available by province, gender, age group and industry. Historical estimates going back five years are also included for monthly employment changes and unemployment rates. The interactive application allows users to quickly and easily explore and personalize the information presented. Combine multiple provinces, genders and age groups to create your own labour market domains of interest.

Monthly: Statistics Canada – Data Visualization Products: Labour Market Indicators, by province and census metropolitan area, seasonally adjusted (catalogue no. 71-607-X, issue 2017001)

This web application provides access to Statistics Canada’s Labour Market Indicators for Canada, by province and by census metropolitan area (CMA). This dynamic application allows users to view geographical rankings for each labour market indicator and to create quick reports with interactive maps and charts that can be easily copied into other programs. All provincial and CMA estimates used in this application are seasonally adjusted, three-month moving averages.

Monthly: Statistics Canada – Data Visualization Products: Labour Market Indicators, by province, territory and economic region, unadjusted for seasonality (catalogue no. 71-607-X, issue 2017002)

This web application provides access to Statistics Canada’s Labour Market Indicators for Canada, by province, territory and economic region (ER). This dynamic application allows users to view a snapshot of key labour market indicators, observe geographical rankings for each indicator using an interactive map and table, and easily copy data into other programs. The provincial and ER estimates used in this application from the Labour Force Survey (LFS) are three-month moving averages, unadjusted for seasonality. The provincial, territorial and ER estimates used in this application from the Job Vacancy and Wage Survey (JVWS) are quarterly data, unadjusted for seasonality. Historical estimates are available in this application, with data going back 10 years for the LFS and from the first quarter of 2016 for JVWS.

Occasional: Statistics Canada – Data Visualization Products: Labour market indicators, census metropolitan areas, census agglomerations and self-contained labour areas: Interactive dashboard (catalogue no. 71-607-X, issue 2024025)

This interactive webtool provides access to Statistics Canada’s Labour Market Indicators for Canada, by census metropolitan area, census agglomeration, and self-contained labour areas. The data presented in this dashboard has been developed using Small Area Estimation (SAE) methodology to increase the level of geographical detail available from the Labour Force Survey (LFS) by combining LFS data with additional information from Employment Insurance Statistics and demographic projections. Estimates used in this application have not been adjusted for seasonality and may be subject to regular seasonal variations.

Occasional: Labour Statistics: Research Papers (catalogue no. 75-004-M)

The papers in this series cover a variety of topics related to labour statistics. These more in-depth studies are intended to showcase recent or historical trends in the labour market using survey and administrative data from the Centre for Labour Market Information.

Occasional: Labour Statistics at a Glance (catalogue no. 71-222-X)

Labour Statistics at a Glance features short analytical articles on specific topics of interest related to Canada's labour market. The studies examine recent or historical trends using data produced by the Centre for Labour Market Information.

Occasional: Labour Statistics: Technical Papers (catalogue no. 75-005-M)

The papers in this series cover a variety of technical topics related to the surveys and administrative data of the Centre for Labour Market Information.

Occasional: Insights on Canadian Society (catalogue no. 75-006-X)

This publication brings together and analyzes a wide range of data sources in order to provide information on various aspects of Canadian society, including labour, income, education, social, and demographic issues that affect the lives of Canadians.

Occasional: Statistics Canada - Infographics (catalogue no. 11-627-M)

Every year, Statistics Canada collects data from hundreds of surveys. As the amount of data gathered increases, Statistics Canada has introduced infographics to help people, business owners, academics, and management at all levels, understand key information derived from the data. Infographics provide a quick overview of Statistics Canada survey data.

Occasional: Improvements to the Labour Force Survey (LFS) (catalogue no. 71F0031X)

This paper introduces and explains the standard revisions and other modifications made to the Labour Force Survey estimates.

Occasional: Aboriginal Peoples Living Off-reserve in Western Canada: Estimates from the Labour Force Survey (catalogue no. 71-587-X)

This paper provides information on Aboriginal employment and unemployment, Aboriginal youths and the impact of education on labour market performance in Manitoba, Saskatchewan, Alberta, and British Columbia.

Occasional: The Aboriginal Labour Force Analysis Series (catalogue no. 71-588-X)

This series of analytical reports provides an overview of the labour market conditions among the Aboriginal off-reserve populations, based on estimates from the Labour Force Survey. These reports examine the Aboriginal labour force characteristics by Aboriginal group as well as diverse socio-economic and employment characteristics.

Occasional: The Immigrant Labour Force Analysis Series (catalogue no. 71-606-X)

/n1/en/catalogue/71-606-X This series of analytical reports provides an overview of the Canadian labour market experiences of immigrants to Canada, based on data from the Labour Force Survey. These reports examine the labour force characteristics of immigrants by reporting on employment and unemployment at the national level, the provincial level and large metropolitan areas. They also provide more detailed analysis by region of birth as well as in-depth analysis of other specific aspects of the immigrant labour market.

Occasional: Methodology of the Canadian Labour Force Survey (catalogue no. 71-526-X)

This publication offers an in-depth look at the methodological and operational aspects of the LFS, covering stratification, sampling, survey operations, weighting, estimation, and data quality. This document would be of interest to those who would like more in-depth methodological information on the LFS than provided by the Guide to the Labour Force Survey.

Data tables

A large selection of high-demand LFS monthly and annual average time series is available on Statistics Canada's website. These data tables are dynamically updated as new results are released.

Custom tabulations

Custom tabulations can be arranged on an ad hoc or regular basis for a fee. This service enables users to specify tables and time series to meet their own requirements. For example, users may wish to have labour force estimates for age groups or educational levels that differ from those used in LFS publications. Subject matter and tabulation expertise is also provided to ensure that the customized data package is accurate and appropriate.

Access to microdata

Research Data Centres (RDC) provide access to Statistics Canada’s confidential microdata files. They are accessible only to researchers with approved projects who have been sworn in as 'deemed employees' of Statistics Canada. The RDC confidential microdata files contain most of the original information collected during the survey interview as well as derived variables added to the dataset afterwards. They also contain the bootstrap weights used to calculate the exact variance, which are available only in the master file. RDCs are located throughout the country. The following website has more information: www.statcan.gc.ca/eng/rdc/index.

The Real Time Remote Access (RTRA) complements existing methods of access to confidential micro-data. Using a secure username and password, the RTRA provides around the clock access to survey results from any computer with internet access. Confidentiality of the micro data is automated in the RTRA system, eliminating the need for manual intervention and allowing for rapid access to results. RTRA subscribers must complete an application form and pay the associated fees to use the service. More information is provided on the website www.statcan.gc.ca/eng/rtra/rtra.

Monthly: Public Use Microdata File (catalogue no. 71M0001X)

The Public Use Microdata File (PUMF) contains non-aggregated data for a wide variety of variables collected from the LFS. This product is for users who prefer to do their own analysis by focusing on specific subgroups in the population or by cross-classifying variables that are not in catalogued products. Monthly files are available back to 2006 and can be directly downloaded. For reference periods prior to 2006, files are available by request through Statistic Canada’s Electronic File Transfer (EFT) service and date back to 1976. More information is provided on the website Labour Force Survey: Public Use Microdata File.

General inquiries

For inquiries on any of these products and services, contact Statistics Canada's Statistical Information Service (toll-free: 1-800-263-1136; 514-283-8300; statcan.infostats-infostats.statcan@statcan.gc.ca).

The Labour Force Survey Supplements Program

The Labour Force Survey (LFS) Supplements Program is an initiative designed to enhance the core LFS by collecting additional data on emerging labour market topics, quality of employment and forms of employment. The program is funded through Statistics Canada’s Disaggregated Data Action Plan (DDAP) and aims to provide a more disaggregated picture of the labour market by offering more detailed insights on specific facets of job quality and disseminating more data and analysis on the labour market experiences of employment equity groups.

The program is comprised of two non-mandatory surveys that complement the core LFS questionnaire. The Labour Market Indicators (LMI) program consists of a short questionnaire that is collected monthly immediately after the main LFS. Summary results from the LMI are typically released in the LFS Daily. The Labour Market and Socio-economic Indicators program (LMSI) is collected from July to December among respondents who are in their last month of participation in the LFS. The LMSI is used to collect more detailed information on specific topics such as gig work and the stability of self-employment. The LMSI also collects information for the Canadian Income Survey (CIS) and the disability screening questions (DSQ).

Appendices

Appendix A: Sub-provincial geography descriptions
Appendix B: Sample size by sub-provincial region, based on the 2025 sample design
Appendix C: Information on the Labour Force Survey questionnaire