CA - Canada Methodology
Updated over a week ago

Current Data Sources

Canadian Business Patterns (CBP)

• Establishment Counts by Industry, CSD

• Location Counts by Industry, CSD

Census and National Household Survey (2001, 2006, 2011, 2016)

• Workplace-based: Earnings by Class of Worker, Industry, CD

• Workplace-based: Employment by Class of Worker, Industry, CSD

• Workplace-based: Employment by Class of Worker, Industry, Occupation, Province

Survey of Employment, Payroll, and Hours (SEPH)

• Annual Employment by Industry, Province/Territory

• Annual Weekly Earnings by Industry, Province/Territory

• Monthly Employment by Industry, Province/Territory

• Monthly Weekly Earnings by Industry, Province/Territory

Labour Force Survey (LFS)

• Annual Employment by Occupation, Class of Worker, Economic Region

• Annual Employment by Industry, Economic Region

• Annual Employment/Earnings (two-year rolling averages), Occupation, Employees, Economic Region

Canadian Occupation Projection System (COPS)

• Industry Employment Projections, Canada

• Occupation Employment Projections, Canada


• Cansim 17-10-0084-01 Historic Age/Gender, CD

• Cansim 17-10-0085-01 Historic Population Components, CD

• Cansim 17-10-0057-01 Projected Age/Gender, Province/Territory

• Cansim 13-10-0418-01 Fertility Rates

• Cansim 13-10-0710-01 Death Rates

In addition to the LMI data mentioned here Lightcast also offers insights through Global Postings and Global Worker profiles in Canada.

Data Classification Systems

North American Industry Classification System (NAICS) 2017

The NAICS 2017 version is currently in use in the Lightcast dataset as this aligns with the NAICS version used by SEPH.

National Occupation Classification (NOC) 2021

Lightcast Occupation Data

Occupation data is generally inferior to industry data. Because industry data is more easily tied to Business Registers and to businesses, which are typically more accurate in how they classify themselves industrially, employee counts by industry are generally more accurate than employee counts by occupation. Occupation data, by nature, is usually collected from individuals and is more prone to error. For these reasons, we consider industry data to be more reliable than occupation data, and adjust occupation data accordingly.

Geographic Occupation Counts

Occupation data is a combination of two processes. The first is the establishment of fixed occupation counts at the higher geography levels. The second is the formation of staffing patterns for industries at these same geographic levels. These staffing patterns, in combination with the industrial mix at lower geography levels, then determine the occupational makeup of lower-level geographies (e.g. CSDs).

Lightcast begins with 4-digit NOC Labour Force Survey employment and earnings figures at the Economic Region geographical level. This dataset contains undisclosed values (suppressions), which Lightcast fills in using Census data as an initial estimate. The undisclosed values for earnings are filled in using a separate process that incorporates industry earnings and occupation earnings from a higher level of geography. This process yields a full-series 4-digit NOC breakout at the economic region level. These estimates are then disaggregated to the CSD level using Census, smoothed to account for volatility present in LFS, and adjusted to SEPH totals so that occupation job counts and earnings match industry job counts and earnings.

The occupation job counts data is then projected using the same projection methodology described above in the industry employee process. After this base projection is created, its annual growth rate is adjusted by occupation to the occupation projections produced by COPS. These projections are then adjusted so that the projected occupation totals match the projected industry employment totals. The result of these processes is Lightcast occupation employment and earnings data by Economic Region.

Occupation Staffing Patterns

The second part of the occupation process creates staffing patterns for each economic region. After the staffing patterns are formed, CSD-level industry data is “staffed” into occupations at the CSD level using the staffing patterns created for the higher-level geography. Average hourly earnings at the Economic Region level by occupation are then applied to CSD-level data (earnings data by occupation is problematic below the Economic Region level). This forms Lightcast's occupation employee dataset at the CSD level.

Occupation Self-Employment Process The self-employment occupation process follows the employee occupation process very closely, with a few minor alterations. First, self-employment occupation data margins are established at the Province level rather than at the Economic Region level, as the data is highly suppressed at the Economic Region level. Second, the self employment occupation data is not adjusted to COPS occupation projections. Third, staffing patterns are created at the Province level rather than at the Economic Region level. Finally, earnings figures are unavailable for self-employed workers by occupation.

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