Transition of Labour Force Survey Data Processing to the Social Survey Processing Environment (SSPE)

Release date: February 8, 2019

Overview of LFS processing

Each month, the production of statistics from the Labour Force Survey (LFS) involves three broad sets of activities: 1) data collection, during which interviews are conducted with approximately 50,000 households over a 10-day period, 2) data processing, during which data are verified and corrected when required to produce microdata files, and 3) the production of statistical tables and analytical products, during which products including The Daily are prepared and loaded to the Statistics Canada website within 10 days after the end of collection.

LFS data processing consists of several steps. Some of these are carried out on a daily basis, concurrently with data collection, while others are done once per month, on the day after the completion of data collection. The daily steps include:

  1. Receipt of data from collection.
  2. Coding of responses to questions with “Other – specify” answer categories.
  3. Editing of demographic data (age, sex, family relationships) to ensure validity and consistency.
  4. Imputation of educational data.
  5. Editing of labour force data.
  6. Coding of industry and occupation responses.

Once collection is complete, the following monthly processing steps are carried out:

  1. Imputation of remaining demographic data.
  2. Imputation of labour force data.
  3. Calculation of derived variables.
  4. Weighting and variance estimation.

Imputation is the replacement of missing data with plausible values. When individual data items are missing, an effort is made to find a suitable donor. Donors are records that are judged similar based on a series of characteristics, and that have valid and consistent data that could be used to reasonably replace the recipient record’s missing value. If a suitable donor cannot be found, a larger block of the recipient’s data may be stricken out and replaced with a donor’s block of data; for this ‘whole-record imputation’, the criteria of similarity is typically relaxed until the best possible donor is found.

LFS processing systems

The computer system currently involved in LFS data processing—known as the Head Office Processing System (HOPS)—has been in place for more than 25 years. During this time, a number of modifications have been introduced and the system has been maintained to ensure the continued accuracy and robustness of LFS production. Beginning in January 2019, LFS processing will be transitioned from HOPS to the Social Survey Processing Environment (SSPE), a generalized survey data processing system used by a number of other Statistics Canada surveys. This transition of LFS data processing to SSPE offers a number of advantages:

Development and testing of LFS processing using SSPE

The project to transition LFS data processing began in April 2015. The major phases of the project included:

  1. Designing ‘processing maps’ for daily and monthly LFS processing. Within the SSPE generalized environment, a generic set of instructions is available for each step in data processing (for example, receiving data from the collection systems into the processing system). For each of these steps, survey-specific instructions and rules must be developed and a set of steps or a ‘map’ must be designed to determine and manage the sequencing and execution of steps.
  2. Modular testing of each processing step, to ensure that at each processing step, data was input, modified or transformed, and output as expected.
  3. Integrated testing, to evaluate the cumulative impact of all processing steps on LFS estimates.
  4. Operational readiness testing, to ensure that LFS data processing could be completed within existing time and resource constraints; for example, that each morning of daily processing, data could be received from collection and transferred to the group responsible for industry and occupation coding within an acceptable timeframe.

Design of LFS processing map

The LFS has a very high standard for data quality, and unique editing challenges and opportunities exist since respondents are interviewed for six months in a row. For example, in cases where a sampled household responds to LFS in a given month and is a non-respondent the following month, data from the first month may, if specific conditions are satisfied, be used to complete the missing data in the second month.

In designing the SSPE processing maps, every effort was made to retain these underlying conditions and rules, or processing methodology, thereby minimising the impact of the transition on the quality of LFS data and the level of LFS estimates. In some situations, opportunities existed to streamline or improve the underlying methodology without affecting LFS estimates (for example, the replacement of some manual editing of records with deterministic edit rules) while in others is was necessary to slightly modify some aspects of the methodology (for example, the specific conditions under which a partial record as opposed to an entire record is corrected through imputation).

Modular testing of LFS processing steps

Each step in the SSPE daily and monthly processes map was developed and tested in a modular fashion. The objective of modular testing was to ensure that each for each step data was input, transformed and output according to specifications.

The LFS Redesign team conducted modular testing from April 2016 to April 2017. During this period, each module was tested independently and validated against specification documentation. After the success of the modular testing, approval was granted to begin fully integrated testing in May 2017.

Integrated testing of LFS processing maps

The primary objective of integrated testing was to generate test data by running all processing steps in sequence and then evaluating whether transitioning LFS processing would introduce any discontinuities in LFS time series. Doing so involved overcoming a number of operational challenges, including:

To overcome these challenges, a combination of types of integrated testing were completed, including:

Operational readiness testing

The fully-integrated testing conducted in the summer of 2018 also served as an operational readiness test, to evaluate whether, from an operational point of view, LFS data processing could be transitioned to SSPE without jeopardising the timeliness or robustness of the LFS production process. Specific criteria were evaluated including the adequacy of documentation and procedures manuals, the training of staff to execute LFS processing using SSPE, and the stability and performance of IT infrastructure.

This testing revealed that all conditions were satisfied and that from an operational point of view LFS data processing could be transitioned to SSPE beginning in January 2019.

Results and analysis

Using the data from the various types of integrated testing, the impact of transitioning LFS processing to SSPE on the following was evaluated:

Some estimates, like the unemployment rate and the total number of employed persons, are considered key estimates and were therefore scrutinized in greater detail. Their results were compared at more disaggregated levels, based on key analytical domains. The list of key estimates, as well as the list of key domains, are provided in “Appendix A”.

The results presented here are typical of the very large number of analyses that were carried out (see “Appendix A” for a list of key estimates and domains). Figure 1 shows the number of self-employed persons in Manitoba from January 2008 to May 2017. This data series is based on Multi-year testing (where the main goal was to evaluate the impact of SSPE transition on the imputation of labour force information). The estimates have not been seasonally adjusted. The solid black line represents the HOPS estimates, and the dashed black lines represent the 95% confidence intervals of the HOPS estimates. The solid red line represents the corresponding SSPE estimates. Since the confidence intervals around the SSPE estimates had the same width as the ones around the HOPS estimates, they are not shown.

Figure 1 of issue 2019001

Data table for Figure 1
Data table for Figure 1
Table summary
This table displays the results of Data table for Figure 1. The information is grouped by Date (appearing as row headers), HOPS, SSPE, HOPS (upper bound of 95% CI) and HOPS (lower bound of 95% CI), calculated using persons units of measure (appearing as column headers).
Date HOPS SSPE HOPS (upper bound of 95% CI) HOPS (lower bound of 95% CI)
persons
2008
January 84,700 86,800 91,168 78,232
February 83,400 84,300 89,672 77,128
March 83,100 86,600 89,372 76,828
April 84,000 84,700 90,076 77,924
May 86,200 87,200 92,668 79,732
June 85,500 86,900 91,968 79,032
July 86,900 88,300 93,368 80,432
August 85,400 85,700 91,868 78,932
September 84,900 84,600 90,780 79,020
October 83,600 83,500 89,872 77,328
November 83,800 84,100 90,464 77,136
December 85,200 83,800 92,256 78,144
2009
January 85,100 85,100 91,764 78,436
February 84,300 85,600 91,356 77,244
March 83,100 81,100 89,764 76,436
April 83,400 80,700 90,064 76,736
May 86,000 83,100 92,860 79,140
June 85,300 85,100 92,356 78,244
July 85,200 84,200 91,864 78,536
August 84,200 81,600 91,060 77,340
September 80,600 82,800 86,872 74,328
October 83,500 84,300 89,772 77,228
November 85,600 84,800 92,264 78,936
December 85,300 84,900 91,768 78,832
2010
January 89,600 87,800 96,656 82,544
February 87,800 86,900 94,464 81,136
March 92,000 89,400 99,056 84,944
April 93,700 91,500 100,756 86,644
May 94,400 94,800 101,260 87,540
June 92,000 94,600 99,448 84,552
July 95,100 95,900 102,156 88,044
August 93,100 94,300 100,156 86,044
September 90,500 91,300 97,164 83,836
October 91,100 93,100 97,960 84,240
November 90,200 88,200 96,668 83,732
December 87,800 89,200 94,464 81,136
2011
January 84,500 84,600 91,164 77,836
February 86,400 85,100 93,064 79,736
March 85,100 85,800 91,568 78,632
April 87,700 87,400 94,168 81,232
May 85,200 85,200 91,080 79,320
June 82,700 82,400 88,580 76,820
July 88,000 86,600 94,468 81,532
August 88,100 87,700 95,548 80,652
September 85,200 86,200 92,256 78,144
October 87,100 87,200 94,352 79,848
November 84,600 81,300 91,852 77,348
December 85,200 85,000 92,256 78,144
2012
January 83,000 82,500 90,056 75,944
February 83,500 84,600 90,556 76,444
March 84,700 83,800 91,364 78,036
April 86,200 85,900 93,060 79,340
May 86,700 84,000 93,168 80,232
June 81,900 82,900 88,172 75,628
July 83,800 82,600 90,268 77,332
August 84,200 82,400 90,472 77,928
September 81,400 80,100 87,476 75,324
October 80,600 79,800 86,676 74,524
November 82,300 80,000 88,180 76,420
December 82,300 83,700 88,180 76,420
2013
January 84,600 83,500 90,676 78,524
February 82,700 82,900 88,776 76,624
March 84,400 84,600 90,868 77,932
April 84,800 85,200 91,072 78,528
May 87,900 87,700 93,976 81,824
June 87,200 87,200 93,276 81,124
July 90,200 89,400 96,668 83,732
August 88,300 87,400 94,768 81,832
September 90,700 89,500 97,364 84,036
October 87,100 85,100 93,764 80,436
November 86,700 84,700 93,364 80,036
December 85,100 82,300 91,764 78,436
2014
January 84,000 83,800 90,860 77,140
February 82,900 83,900 89,760 76,040
March 79,500 83,400 86,556 72,444
April 80,700 82,400 87,364 74,036
May 83,300 85,100 90,160 76,440
June 83,300 83,000 90,160 76,440
July 84,400 82,900 92,044 76,756
August 84,300 87,400 91,356 77,244
September 81,500 83,800 88,556 74,444
October 82,700 86,000 89,364 76,036
November 85,300 84,700 92,356 78,244
December 84,300 86,400 90,768 77,832
2015
January 83,000 85,300 89,664 76,336
February 82,200 84,500 88,864 75,536
March 85,800 85,200 92,660 78,940
April 85,000 86,200 91,468 78,532
May 85,100 86,400 91,176 79,024
June 83,300 85,600 88,984 77,616
July 87,500 86,900 93,968 81,032
August 89,400 87,500 96,064 82,736
September 85,300 87,500 91,768 78,832
October 87,300 87,900 93,768 80,832
November 87,000 87,600 93,272 80,728
December 87,000 86,300 93,272 80,728
2016
January 82,700 84,300 88,776 76,624
February 81,400 85,000 87,868 74,932
March 84,000 84,400 90,468 77,532
April 83,700 87,100 90,168 77,232
May 84,900 83,300 91,564 78,236
June 84,800 85,700 91,464 78,136
July 87,400 89,400 94,260 80,540
August 92,000 95,800 99,056 84,944
September 90,300 92,300 97,356 83,244
October 87,800 89,500 94,464 81,136
November 88,700 89,800 95,168 82,232
December 83,700 85,600 90,364 77,036
2017
January 83,200 86,300 89,472 76,928
February 84,200 85,900 90,276 78,124
March 83,900 84,500 89,976 77,824
April 84,200 83,400 90,276 78,124
May 86,200 85,500 92,668 79,732

Figure 1 demonstrates that the labour force imputation did not have a dramatic effect on the estimates, as the SSPE estimates track well with those from HOPS, staying within the 95% confidence intervals throughout the 9-year series.

Figure 2 represents the unemployment rate in Quebec, using data from Multi-month testing. Once again, the black lines represent the HOPS estimates and their 95% confidence intervals, while the red line represents the SSPE estimates. The first section, from January 2016 to August 2017, represents Phase 1 of Multi-month testing. Following the gap in September 2017, data is from Phase 2 and represents a fresh start in October 2017 (previous month data generated from HOPS), carried through until April 2018. The final four sets of points represent four individual starts associated with Fully-integrated testing (May 2018 until August 2018).

Figure 2 of issue 2019001

Data table for Figure 2
Data table for Figure 2
Table summary
This table displays the results of Data table for Figure 2. The information is grouped by Date (appearing as row headers), HOPS, SSPE, HOPS (upper bound of 95% CI) and HOPS (lower bound of 95% CI), calculated using percent units of measure (appearing as column headers).
Date HOPS SSPE HOPS (upper bound of 95% CI) HOPS (lower bound of 95% CI)
percent
Multi-month testing 1
2016
January 7.4 7.6 8.2 6.6
February 7.0 7.4 7.8 6.2
March 7.1 7.2 7.8 6.4
April 6.7 6.6 7.4 6.0
May 5.5 5.6 6.2 4.8
June 5.3 5.6 6.0 4.6
July 5.7 5.7 6.4 5.0
August 6.5 6.5 7.3 5.7
September 5.0 5.1 5.7 4.3
October 5.4 5.5 6.1 4.7
November 5.2 5.3 5.9 4.5
December 5.8 6.0 6.5 5.1
2017
January 6.1 6.2 6.9 5.3
February 6.2 6.0 6.9 5.5
March 6.3 6.3 7.1 5.5
April 6.5 6.3 7.2 5.8
May 4.5 4.4 5.2 3.8
June 4.5 4.5 5.2 3.8
July 4.9 4.8 5.6 4.2
August 5.8 6.0 6.5 5.1
September 5.2 Note ...: not applicable 5.9 4.5
Multi-month testing 2
2017
October 4.9 5.1 5.6 4.2
November 4.4 4.6 5.0 3.8
December 4.4 4.4 5.0 3.8
January 5.3 5.4 6.0 4.6
February 5.6 6.0 6.3 4.9
March 5.2 5.4 5.9 4.5
April 4.7 4.8 5.3 4.1
Fully-integrated testing 2A
2018
May 4.0 4.1 4.7 3.3
Fully-integrated testing 2B
2018
June 4.2 3.9 4.8 3.6
Fully-integrated testing 2C
2018
July 4.5 4.3 5.2 3.8
Fully-integrated testing 2D
2018
August 5.7 5.6 6.4 5.0

The conclusions are the same as with the data shown in Figure 1: the SSPE data is not significantly different from the HOPS data, staying within the 95% confidence intervals generated by the bootstrap method on the HOPS data.

Some LFS data are seasonally adjusted, in order to remove seasonal patterns and provide a better representation of trends. The associated seasonal adjustment models—which are external to HOPS and therefore not affected by the transition to SSPE—were applied to the SSPE test data, and a series of comparisons were carried out. The analysis revealed no structural changes in the data, and the seasonality of the SSPE data was captured well by the existing seasonal adjustment models. Figure 3 shows that the seasonally-adjusted series of employment in Canada from January 2015 to June 2017, based on SSPE, is statistically equivalent to the analogous LFS data.

Figure 3 of issue 2019001

Data table for Figure 3
Data table for Figure 3
Table summary
This table displays the results of Data table for Figure 3. The information is grouped by Date (appearing as row headers), Canada, HOPS and Canada, SSPE, calculated using persons units of measure (appearing as column headers).
Date Canada, HOPS Canada, SSPE
persons
2015
January 17,876,300 17,875,400
February 17,888,000 17,882,500
March 17,905,600 17,913,100
April 17,896,400 17,897,300
May 17,946,200 17,945,800
June 17,947,800 17,947,500
July 17,965,600 17,966,900
August 17,984,500 17,982,000
September 17,982,300 17,983,800
October 18,016,900 18,010,900
November 17,985,100 17,985,500
December 18,003,600 18,005,100
2016
January 17,996,800 18,010,800
February 17,997,800 18,020,500
March 18,033,200 18,042,900
April 18,039,300 18,053,800
May 18,048,100 18,055,500
June 18,053,400 18,065,900
July 18,030,500 18,030,000
August 18,069,600 18,079,100
September 18,136,500 18,130,800
October 18,184,600 18,173,000
November 18,184,100 18,191,700
December 18,234,900 18,233,600
2017
January 18,278,400 18,265,000
February 18,290,700 18,270,000
March 18,315,600 18,325,300
April 18,323,100 18,322,400
May 18,371,900 18,376,700
June 18,410,900 18,413,300

Month-to-month changes are of particular interest for many analysts of LFS data. Figure 4 shows the monthly change in total full-time employment in Canada in 2016. The graphic demonstrates that the discrepancies between the two series are not significant enough to imply a change in the story told by the data.

Figure 4 of issue 2019001

Data table for Figure 4
Data table for Figure 4
Table summary
This table displays the results of Data table for Figure 4. The information is grouped by Date (appearing as row headers), HOPS, full-time employment and SSPE, full-time employment, calculated using persons units of measure (appearing as column headers).
Date HOPS, full-time employment SSPE, full-time employment
persons
2016
Jan.-Feb. -61,500 -35,700
Feb.-Mar. 53,900 32,700
Mar.-Apr. 138,600 167,300
Apr.-May 543,700 532,200
May-Jun. 225,900 201,400
Jun.-Jul. 63,200 78,800
Jul.-Aug. 67,200 77,400
Aug.-Sep. -440,700 -481,700
Sep.-Oct. -120,800 -106,500
Oct.-Nov. -138,000 -167,000
Nov.-Dec. -18,200 -28,300

Estimates of particular interest

Although integrated testing indicates that for most LFS estimates differences between HOPS and SSPE estimates are very small, users of LFS data should be aware of some specific testing results:

Figure 5 of issue 2019001

Data table for Figure 5
Data table for Figure 5
Table summary
This table displays the results of Data table for Figure 5. The information is grouped by Date (appearing as row headers), HOPS, SSPE, HOPS (upper bound of 95% CI) and HOPS (lower bound of 95% CI), calculated using persons units of measure (appearing as column headers).
Date HOPS SSPE HOPS (upper bound of 95% CI) HOPS (lower bound of 95% CI)
persons
Multi-month testing 1
2016
January 1,951,000 1,981,100 2,017,444 1,884,556
February 2,045,300 2,124,700 2,117,428 1,973,172
March 2,078,500 2,118,600 2,147,492 2,009,508
April 2,032,400 2,115,100 2,101,000 1,963,800
May 1,984,700 2,094,600 2,054,280 1,915,120
June 1,993,900 2,123,100 2,064,068 1,923,732
July 1,890,900 2,050,500 1,960,872 1,820,928
August 1,952,600 2,061,900 2,024,140 1,881,060
September 1,980,500 2,090,100 2,049,688 1,911,312
October 2,035,200 2,127,100 2,105,368 1,965,032
November 2,078,900 2,167,300 2,150,636 2,007,164
December 2,105,500 2,184,000 2,175,864 2,035,136
2017
January 2,100,300 2,179,000 2,169,880 2,030,720
February 2,097,800 2,189,600 2,166,204 2,029,396
March 2,079,600 2,172,800 2,147,416 2,011,784
April 2,104,100 2,183,700 2,173,288 2,034,912
May 2,105,800 2,200,800 2,176,360 2,035,240
June 2,042,800 2,142,800 2,111,988 1,973,612
July 2,022,700 2,137,800 2,092,084 1,953,316
August 2,047,000 2,124,400 2,116,972 1,977,028
September Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
Multi-month testing 2
2018
October 2,068,800 2,105,300 2,141,516 1,996,084
November 2,101,900 2,156,100 2,175,008 2,028,792
December 2,168,300 2,247,200 2,242,780 2,093,820
January 2,082,800 2,189,900 2,155,516 2,010,084
February 2,118,500 2,210,700 2,194,352 2,042,648
March 2,109,500 2,236,600 2,181,040 2,037,960
April 2,135,000 2,225,800 2,211,048 2,058,952
Fully-integrated testing 2A
2018
May 2,087,800 2,147,200 2,155,616 2,019,984
Fully-integrated testing 2B
2018
June 2,131,100 2,187,200 2,199,504 2,062,696
Fully-integrated testing 2C
2018
July 2,154,800 2,226,100 2,226,144 2,083,456
Fully-integrated testing 2D
2018
August 2,082,700 2,153,300 2,156,396 2,009,004

The impact of transition to SSPE on these estimates will be monitored and, if appropriate, an historical revision of the series will be included in the planned 5-year rebasing of LFS estimates, currently planned for January 2020.

Conclusions

Based on extensive testing, the transitioning of LFS data processing from the current system (HOPS) to the Social Survey Processing Environment (SSPE) is expected to have minimal impact on LFS estimates and to be transparent to users of LFS data.

Appendix A

Key estimates

These estimates are considered the most important estimates published from LFS data.

Table 1
Key estimates
Table summary
This table displays the results of Key estimates. The information is grouped by Estimate (appearing as row headers), Group (appearing as column headers).
Estimate Group
Total, labour force 1
Total, employment 1
Total, unemployment 1
Total, not in labour force 1
Unemployment rate 1
Participation rate 1
Employment rate 1
Total, full-time employment 2
Total, part-time employment 2
Total, employees 2
Total, public sector employees 2
Total, private sector employees 2
Total, self-employed 2
Total, employees by industry (16 levels) 2
Total, employees by occupation (10 levels) 2
Average wages 2
Average hours 3
Total, multiple job holders 3
Total, employees by unionization status (2 levels) 3
Total, employees by job permanence (2 levels) 3
Total, employees by firm size (4 levels) 3

Key variables labelled ‘group 1’ were evaluated for all key domains. Those labelled ‘group 2’ or ‘group 3’ were formally evaluated at aggregate levels and for limited levels of disaggregation.

Key domains

These are the major geographic and demographic domains for which estimates are published.

Table 2
Key domains
Table summary
This table displays the results of Key domains. The information is grouped by Domain (appearing as row headers), Components (appearing as column headers).
Domain Components
Geography Provinces and territories
Economic regions (ERs)
Census metropolitan areas (CMAs)
Employment Insurance economic regions (EIERs)
Age 15 years and over
Youth: 15 to 24 years, 15 to 19 years, 20 to 24 years
Core-aged: 25 to 54 years
Senior: 55 years and over, 55 to 64 years, 65 to 69 years, 70 years and over
Sex Men, 15 years and over
Women, 15 years and over
Aboriginal and Immigrant status Aboriginal population, 15 years and over
Immigrant population, 15 years and over
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