Job Vacancy and Wage Survey (JVWS)

Job Vacancy and Wage Survey (JVWS)
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Detailed information for third quarter 2016

Status:

Active

Frequency:

Quarterly

Record number:

5217

The purpose of this survey is to collect information about job vacancies and wages by occupation, at the national, provincial, territorial and economic region levels.

The Job Vacancy and Wage Survey (JVWS) consists of a quarterly component on job vacancies and an annual component on wages.

Data release - January 25, 2017

Description

The JVWS provides detailed information on job vacancies and wages.

The JVWS collects data on the number of job vacancies by occupation and economic region on a quarterly basis through the Job Vacancy Component of the survey. Additional information is also available, such as the proportion of job vacancies in full- and part-time positions, the distribution of vacancies by level of education and experience, the average hourly wage offered for the vacancies and the duration of job vacancies.

Employers are asked for detailed information about each vacancy to identify potential labour market shortages at the occupation level and to get an overall understanding of the vacancies that exist and the requirements for filling them.

Starting in January 2016, employers are also asked questions on employment and wages paid by occupation, such as the number of full-time employees and the lowest, highest and average wage or salary paid. This information is collected through the Wage Component of the survey and is asked once per year.

The private sector, federal departments such as Employment and Social Development Canada (ESDC), provincial, territorial and municipal governments, and educational organizations are interested in these data, as it improves their understanding of the Canadian labour market.

Statistical activity

Together, four surveys tell a more complete story of current labour market events. These surveys are: the Labour Force Survey (LFS, record number 3701), the Survey of Employment, Payrolls and Hours (SEPH, record number 2612), Employment Insurance Statistics (EIS, record number 2604), and the Job Vacancy and Wage Survey (JVWS, record number 5217). Every month, the LFS provides timely data on the labour market, including the unemployment rate and demographic analysis. Later on, the SEPH report shows greater detail on non-farm industry employment and earnings. EIS provides substantial detail on Employment Insurance benefits by geography, socio-demographics and former occupation. JVWS provides information on job vacancies (quarterly) and wages (annually) by occupation and economic region.

Reference period: Quarter

Collection period: Monthly

Subjects

  • Employment and unemployment
  • Labour
  • Labour mobility, turnover and work absences
  • Wages, salaries and other earnings

Data sources and methodology

Target population

The target population is defined as all business locations operating in Canada that have at least one paid employee.

The industrial sectors not included are religious organizations, private households as well as international and other extra-territorial public administrations. Federal, provincial and territorial administrations are also excluded from the survey for now; however, they will be phased in at a later date.

The observed population is taken from the Business Register (BR). This employer frame is maintained by Statistics Canada's Statistical Registers and Geography Division and is updated with information from the Canada Revenue Agency, with feedback from other economic surveys, and with the profiles established with representatives of the businesses.

Every three months, the JVWS survey frame is updated to reflect new locations added to the BR and to eliminate those that no longer exist.

Instrument design

The content and concepts of the questionnaire were developed through consultations with ESDC. Qualitative testing took place through a series of interviews in both English and French conducted by Statistics Canada's Questionnaire Design Resource Centre. In these interviews, participants were asked for their comments about the terminology, the concepts, the appearance of the electronic questionnaire screens, and the ease of providing information.

Sampling

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

The survey uses a stratified random sample of business locations classified by geography, industry, and size.

The JVWS population is stratified by industry at the 2-digit level of the North American Industry Classification System (NAICS) Canada 2012, by geography at the economic region level (2011 boundaries), and by size using the location number of employees.

The survey is conducted on a stratified random sample of approximately 100,000 business locations drawn quarterly. A power allocation method is used to determine the sample size in each stratum. The stratification and the power allocation method ensure the quality of the estimates for large and small regions and industries, as well as a better representation of all occupations. Selection is done using Poisson sampling (more specifically, using Bernoulli sampling in each stratum).

While the sample remained essentially the same in 2015 and 2016, starting in 2017, part of the sample will be replaced by rotation each quarter. With the exception of certain locations which are in sample on a permanent basis due to their unique characteristics, sampled locations may leave the sample after two years or eight quarters. However, locations that were part of the 2015 and 2016 sample may remain in the sample for over two years, as the rotating out of locations is done gradually.

Note: For the first quarter of 2015, the sample consisted of about 67,000 business locations obtained from the regular quarterly sample, or two-thirds of the regular sample. As a result, comparisons of the first quarter of 2015 data with data from subsequent quarters should be made with caution.

Data sources

Data collection for this reference period: 2016-07-04 to 2016-09-30

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data are collected using an electronic (Web-based) questionnaire. The Electronic Questionnaire application is both the collection and the capture method.

An email invitation to complete the questionnaire is sent to respondents for whom Statistics Canada has an email address. For sampled businesses whose email address is not known, an introductory letter with a secure access code is mailed out inviting them to complete the electronic questionnaire. Prior to collection, a subset of business locations are pre-contacted by telephone to collect contact information and verify coverage. Computer-assisted telephone interview (CATI) follow-up is done for non-response and for completed questionnaires with failed edits. Reminders are sent out throughout the collection period to businesses for which Statistics Canada has not received a response. After three email reminders, telephone follow-ups are done to collect data.

The Job Vacancy and Wage Survey offers its survey and survey material in French and English.

The average time required to complete the survey is about ten minutes, although the time may vary based on location size.

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

Error detection

Data editing is the application of rules to detect missing, invalid or inconsistent entries or to identify data records that are potentially in error. In the survey process for the JVWS, data editing is done at two different time periods.

First, editing is done during electronic questionnaire collection. Edits during data collection generally consist of validity and some simple consistency edits. Follow-up with respondents occurs to validate potential erroneous data following any failed preliminary edit check of the data. Once validated, the collected data is regularly transmitted to the head office in Ottawa.

Second, editing known as statistical editing is also done after data collection. Large outliers in respondent data cannot be used at the imputation stage.

Imputation

In the case of partial non-response, imputation is used to fill in information not provided by the respondent. Imputation makes it possible to have a complete set of data if one cannot collect it during the collection period. A donor approach is used, where auxiliary information on the sampling frame is used to identify a donor (a responding unit) that has characteristics similar to that of the location with partial data. The missing data of the respondent with partial information are then replaced by data of the donor.

Estimation

Estimating the characteristics of a population from a survey is based on the assumption that each sampled location represents a certain number of non-sampled locations in the population. An initial weight is assigned to each record to indicate the number of units in the population represented by that location in the sample. Large or otherwise unique locations are assigned a weight of one to ensure that they only represent themselves.

Two adjustments are made to the initial weights to improve the reliability of the estimates. First, the initial weights are adjusted to compensate for total or almost total non-response. A logistic model is used to predict a unit's response propensity based on relevant frame characteristics.

The weights adjusted for non-response are then calibrated to benchmark the employment estimates to employment totals from administrative sources provided by the Survey of Employment, Payrolls and Hours (SEPH, record number 2612). This calibration ensures coherence between the JVWS and SEPH employment totals by province and industrial sector (two-digit level in the NAICS classification system) combined, with the exception of Prince Edward Island, where the calibration is by province alone. Similarly, the calibration for the Territories is by territory alone.

Variance estimation is done using the bootstrap method for JVWS.

Quality evaluation

Prior to publication, survey results are analyzed for comparability. This includes a detailed review of the estimates, and a comparison to other labour market sources, historical trends and general economic conditions.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Cell suppression with the G-Confid software is used to control data disclosure.

Revisions and seasonal adjustment

This methodology does not apply to this survey.

Data accuracy

The estimates obtained from sample surveys are subject to both sampling and non-sampling errors.

Non-sampling errors may occur throughout a survey for many reasons, such as non-response, coverage and classification errors, differences in the interpretation of the question, incorrect information from respondents, as well as mistakes during data capture, coding, and processing. Efforts to reduce non-sampling errors include careful design of questionnaires, editing of data, follow-up, imputation for non-responding units, and thorough control of processing operations.

The use of sampling frames results in coverage errors, notably undercoverage. Undercoverage occurs when the information on a location is incomplete in the Business Register. This normally happens in the case of new locations that have not yet filed payroll deduction forms with the Canada Revenue Agency.

Sampling errors occur because observations are obtained from a sample rather than from the entire population. Estimates based on a sample can differ from statistics that would have been obtained if a complete census had been taken using the same instructions, interviewers and processing techniques. This difference is called the sampling error of the estimate.

The true sampling error is unknown. However, it can be estimated from the sample itself by using a statistical measure called the standard error. The standard error can be used to build a confidence interval for the estimate. When the standard error is expressed as a percentage of the estimate, it is known as the relative standard error or the coefficient of variation (CV).

Most of the JVWS data points have their own data quality indicator. Estimates are assigned a letter to indicate their quality level. The indicators take into account various factors that affect the quality of the data, notably the CV, the non-response errors, and the imputation errors. These indicators are updated each release to reflect the current estimate of quality for individual data points.

Documentation

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