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Bridging the Gap:
Online Job Postings, Survey Data
and the Assessment of Job Vacancies
Author & Presenter: Ibrahim Abuallail
Special thanks to Brittany Feor & Sukriti Tehran
Co-author: Anne-Lore Fraikin
2
Introduction & Motivation
3
Motivation
• Importance of Vacancy Data in
Economics
• Pissarides (1986)
• Macro-labour economists studying
business cycles (Yashiv (2007)).
• Conventional Approach: Survey Data
(JVWS)
4
Limitations of JVWS Data
• Time lag in reporting. Impractical to inform
real-time policy decisions
• The sample size is less than 10% of all
employers in Canada. Data suppression
for confidentiality.
• Exclusion of federal, territorial, and provincial
employees. This also includes a considerable
number of management occupations that are
not captured in the data
5
Data & Methodology
6
An Alternative Approach:
Online Job Postings
• Turell et. al (2019) use a weighting function to improve online job posting
data representativeness using UK data.
• Evans et. al (2023) use a winsorization algorithm to improve online job
postings forecast errors (with respect to survey) using Australian data.
• Several studies have already begun leveraging online job postings data as
a reliable source (see for example Marinescu (2017), Deming and Kahn
(2018), and Goldfarb et. al (2020)).
Related Literature
7
Vicinity Jobs Data
• Web scraping tool that collects online job postings from multiple website
sources
• Contains over 103 million job postings for the period between 2018 and
October 2023
• Around half of the data sources are from actual employer websites
• Other third-party non-employer job posting websites may also be
included; Ex: indeed and monster.com
• Most third-party data sources included are cost based (employers have to
pay to post), or include high administrative costs
8
Vicinity Jobs Data
Figure 1: Top 10 website
sources for VJ data.
Employer websites and
government job boards
comprise the majority of
the data sources
9
Vicinity Jobs Data
→ Graph for survey
Figure 2: Distribution of
Broad Occupational
Categories in Online Job
Postings Data. Breakdown
of NOC categories in
→ NOC 2016 Table 7
10
Limitations of Online Job Postings Data
• Potential for duplication
• Authenticity
• Job posting not for the purpose of hiring
• Postings by fraudsters, with job scams growing in recent years
• Employers could attempt to recruit multiple people using a single job
posting (LMIC 2020).
• Online job postings also suffer from a lack of historical data, with the Vicinity
Jobs dataset only starting from 2018
• Type of Data (stock vs flow)
11
Flow vs Stock Variable
• Convert VJ flow variable to stock variable
• Research from the Josh Bersin Company 2023
Where d represents a day, and q is specified as the quarter of the JVWS
survey and is equal to the 44 days preceding the end of the quarter.
12
Online Job
Postings vs
Survey Data
Figure 4: Total Stock of
Vacancies Comparison of
VJ and JVWS
13
Granger Causality - Wald Test
The null hypothesis (H0): VJt does not Granger cause JVWSt :
The alternative hypothesis (H1): VJt Granger causes JVWSt:
14
Granger Causality Test Results
Granger Causality Test
Model 1: JVWS ∼ Lags(JVWS, 1:2) + Lags(VJ, 1:2)
Model 2: JVWS ∼ Lags(JVWS, 1:2)
Res. Df Df F Pr(>F)
1 6605
2 6607 -2 29.514 1.743e-13
Table 1: Granger test results
15
Machine
Learning
Forecasts
Figure 5: Comparison of
model predictions
16
Goals
• Improve the bias in online job
postings towards certain sectors
• Improve forecast-ability of online
job postings data for job
vacancies from the survey
17
Algorithms & Results
18
Weighting Function
19
Vacancy Stocks –
Weighting Function
Figure 6: Total Stock of
Vacancies
20
Weighting
Function - Errors
Figure 7: Average Errors by
Quarter
21
Winsorization Algorithm
• The first step would be to split the dataset by job posting sources, and
identify sources j = 1, …, J with a certain cut off, k, for postings per source.
• Then we compute the percentage change between q − 1 and q for the
online job postings per source:
22
Winsorization Algorithm
1
2
3
1
2
23
Winsorization
Algorithm
Figure 8: Average Source
Errors by Quarter
24
Robust Regression with Huber Weights
25
Robust Regression
with Huber Weights
Figure 9: Average Errors by
Quarter – Winsorized and
Robust
26
Combined Approach
• The goal is to solve the bias /
representation issue, while also
improving forecast ability.
• Combine weights function
with winsorization.
• Combine weights function
with robust regression.
27
Combined
Approach
Figure 10: Comparison of
combined algorithm errors
with weighting function
errors, and original errors.
28
Combined
Approach
Figure 11: Performance of
Machine Learning Models
on combined algorithm data.
29
Conclusion
30
Conclusion
• Vacancy data has important
applications in Economics
• The conventional source of data
has documented flaws, mainly a
reporting lag
• The alternative being used, online
job postings, has its own limitations
and drawbacks
31
Conclusion
• Develop a new enhanced methodology to improve the forecast-ability
of online job postings. The robust regression method has not been
used in this context before.
• Develop a combined approach (robust method that is also weighted
by survey).
• Using machine learning models to forecast survey data.
• Applying this analysis on Canadian labour market data.
Our main contribution
32
Questions?
33
Appendix
34
NOC Version 2016
Figure 5: NOC 2016
Version 1.3 Data source:
NOC, Government of
Canada
Back to VJ Data
35
JVWS Broad
NOC Distribution
Figure 13: Distribution
of Broad Occupational
Categories in Survey
Data. Breakdown of
NOC categories in
→ NOC 2016 Table
→ Back to VJ Data

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Bridging the gap: Online job postings, survey data and the assessment of job vacancies in Canada

  • 1. Bridging the Gap: Online Job Postings, Survey Data and the Assessment of Job Vacancies Author & Presenter: Ibrahim Abuallail Special thanks to Brittany Feor & Sukriti Tehran Co-author: Anne-Lore Fraikin
  • 3. 3 Motivation • Importance of Vacancy Data in Economics • Pissarides (1986) • Macro-labour economists studying business cycles (Yashiv (2007)). • Conventional Approach: Survey Data (JVWS)
  • 4. 4 Limitations of JVWS Data • Time lag in reporting. Impractical to inform real-time policy decisions • The sample size is less than 10% of all employers in Canada. Data suppression for confidentiality. • Exclusion of federal, territorial, and provincial employees. This also includes a considerable number of management occupations that are not captured in the data
  • 6. 6 An Alternative Approach: Online Job Postings • Turell et. al (2019) use a weighting function to improve online job posting data representativeness using UK data. • Evans et. al (2023) use a winsorization algorithm to improve online job postings forecast errors (with respect to survey) using Australian data. • Several studies have already begun leveraging online job postings data as a reliable source (see for example Marinescu (2017), Deming and Kahn (2018), and Goldfarb et. al (2020)). Related Literature
  • 7. 7 Vicinity Jobs Data • Web scraping tool that collects online job postings from multiple website sources • Contains over 103 million job postings for the period between 2018 and October 2023 • Around half of the data sources are from actual employer websites • Other third-party non-employer job posting websites may also be included; Ex: indeed and monster.com • Most third-party data sources included are cost based (employers have to pay to post), or include high administrative costs
  • 8. 8 Vicinity Jobs Data Figure 1: Top 10 website sources for VJ data. Employer websites and government job boards comprise the majority of the data sources
  • 9. 9 Vicinity Jobs Data → Graph for survey Figure 2: Distribution of Broad Occupational Categories in Online Job Postings Data. Breakdown of NOC categories in → NOC 2016 Table 7
  • 10. 10 Limitations of Online Job Postings Data • Potential for duplication • Authenticity • Job posting not for the purpose of hiring • Postings by fraudsters, with job scams growing in recent years • Employers could attempt to recruit multiple people using a single job posting (LMIC 2020). • Online job postings also suffer from a lack of historical data, with the Vicinity Jobs dataset only starting from 2018 • Type of Data (stock vs flow)
  • 11. 11 Flow vs Stock Variable • Convert VJ flow variable to stock variable • Research from the Josh Bersin Company 2023 Where d represents a day, and q is specified as the quarter of the JVWS survey and is equal to the 44 days preceding the end of the quarter.
  • 12. 12 Online Job Postings vs Survey Data Figure 4: Total Stock of Vacancies Comparison of VJ and JVWS
  • 13. 13 Granger Causality - Wald Test The null hypothesis (H0): VJt does not Granger cause JVWSt : The alternative hypothesis (H1): VJt Granger causes JVWSt:
  • 14. 14 Granger Causality Test Results Granger Causality Test Model 1: JVWS ∼ Lags(JVWS, 1:2) + Lags(VJ, 1:2) Model 2: JVWS ∼ Lags(JVWS, 1:2) Res. Df Df F Pr(>F) 1 6605 2 6607 -2 29.514 1.743e-13 Table 1: Granger test results
  • 16. 16 Goals • Improve the bias in online job postings towards certain sectors • Improve forecast-ability of online job postings data for job vacancies from the survey
  • 19. 19 Vacancy Stocks – Weighting Function Figure 6: Total Stock of Vacancies
  • 20. 20 Weighting Function - Errors Figure 7: Average Errors by Quarter
  • 21. 21 Winsorization Algorithm • The first step would be to split the dataset by job posting sources, and identify sources j = 1, …, J with a certain cut off, k, for postings per source. • Then we compute the percentage change between q − 1 and q for the online job postings per source:
  • 24. 24 Robust Regression with Huber Weights
  • 25. 25 Robust Regression with Huber Weights Figure 9: Average Errors by Quarter – Winsorized and Robust
  • 26. 26 Combined Approach • The goal is to solve the bias / representation issue, while also improving forecast ability. • Combine weights function with winsorization. • Combine weights function with robust regression.
  • 27. 27 Combined Approach Figure 10: Comparison of combined algorithm errors with weighting function errors, and original errors.
  • 28. 28 Combined Approach Figure 11: Performance of Machine Learning Models on combined algorithm data.
  • 30. 30 Conclusion • Vacancy data has important applications in Economics • The conventional source of data has documented flaws, mainly a reporting lag • The alternative being used, online job postings, has its own limitations and drawbacks
  • 31. 31 Conclusion • Develop a new enhanced methodology to improve the forecast-ability of online job postings. The robust regression method has not been used in this context before. • Develop a combined approach (robust method that is also weighted by survey). • Using machine learning models to forecast survey data. • Applying this analysis on Canadian labour market data. Our main contribution
  • 34. 34 NOC Version 2016 Figure 5: NOC 2016 Version 1.3 Data source: NOC, Government of Canada Back to VJ Data
  • 35. 35 JVWS Broad NOC Distribution Figure 13: Distribution of Broad Occupational Categories in Survey Data. Breakdown of NOC categories in → NOC 2016 Table → Back to VJ Data

Editor's Notes

  1. Laura Thank you Ibrahim Sukriti and I are here to talk about our research looking at the relationships between skills in job postings
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