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Does teamwork really
matter?
2024-05-31 Laura Adkins-Hackett & Sukriti Trehan
Looking beyond the job posting to
understand labour market demands
2
Project Background
3
• Find connections in OJP to
extract additional insights into
the needs of the labour market
• Examine changes in skill
connections across
occupations and regions
• This research will show how we
can enhance OJP as an
analytical tool
• Show the importance of context
when deciphering job postings
• A step in using OJP to identify
skills shortage mismatches and
demand
Approach Impact
Connecting skills
Problem
• The same language is used
across job postings, so skills in
Online Job Postings (OJP)
make it hard to identify what is
required for the job.
• If skills in postings don’t
represent reality, this limits the
value of online job postings as a
source of Labour Market
Information (LMI).
4
The Data
5
2023 OJP in data
Online job postings provide a rich source of big data, which presents both opportunities
and challenges for accurately measuring job vacancies.
LMIC has partnered with Vicinity Jobs to access data from job postings across the web.
3,078,987 20,482,226 4,550 710
Job postings Total requirements
Unique
requirements
Requirements in at
least 500 postings
6
Job postings by commonly requested skills
NOC Legend:
0 - Legislative and senior management occupations;
1 - Business, finance and administration occupations;
2 - Natural and applied sciences and related occupations;
3 – Health Occupations;
4 - Occupations in education, law and social, community and
government services;
5 - Occupations in art, culture, recreation and sport;
6 - Sales and service occupations;
7 - Trades, transport and equipment operators and
related occupations;
8 - Natural resources, agriculture and related
production occupations;
9 - Occupations in manufacturing and utilities
7
Methodology
8
Methodology
Frequency
• Measure the frequency
of skills co-occurring to
identify relationships
between terms.​
Normalized pointwise
mutual information
• The strength of skill
relationships is measured
using Pointwise Mutual
Information (PMI), a metric to
evaluate the frequency of
terms co-occurrences
compared to their
independent occurrences.
• We use a normalized version
of PMI to restrict the range of
values between -1 and 1.​
Directional relationship
• Measure a metric called
confidence, which quantifies
the conditional probability of
observing one skill in a job
posting when another skill is
required, hence identifying
most probable predictors for
a skill.
• Utilize association rule
mining with Frequent Pattern
Growth algorithm to identify
skill relationships with the
highest confidence levels.
9
Normalized PMI
Confidence for
directional
relationships
𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛, 𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔 > 𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑝(𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔)
⇒ 𝑙𝑜𝑔2
𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛,𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔
𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑝(𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔)
> 0 ⇒ PMI(𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛, 𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔) > 0
Confidence (x -> y) =
𝑆𝑢𝑝𝑝𝑜𝑟𝑡(𝑥 →𝑦)
𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝑥)
, 𝑟𝑎𝑛𝑔𝑒 ∊ [0,1]
• The PMI value is normalized by dividing it by a factor of −𝑙𝑜𝑔2𝑝(𝑥, 𝑦)
• Quality of association rules can be measured by certain metrics including
support and confidence.
Support (x -> y) = 𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑟𝑒𝑞𝑢𝑖𝑟𝑖𝑛𝑔 𝑏𝑜𝑡ℎ 𝑠𝑘𝑖𝑙𝑙𝑠 𝑥 𝑎𝑛𝑑 𝑦, 𝑟𝑎𝑛𝑔𝑒 ∊ [0,1]
PMI (x, y) = 𝑙𝑜𝑔2
𝑝(𝑥,𝑦)
𝑝 𝑥 𝑝(𝑦)
• For two skills x and y,
• Intuitively for two skills: communication and problem solving, if
𝑝(𝑥, 𝑦): Probability of events x and y occurring together
𝑝 𝑥 and 𝑝 𝑦 : Individual probabilities of events x and y
10
Results
11
Skill associations with Customer Service
12
PMI by NOC 6 vs others
13
Predictors of Customer Service
Confidence
Cash registers 75%
Sales 70%
Work under pressure 49%
Fast-paced Setting 49%
Multi-tasking 48%
Work scheduling 46%
Inventory Management 46%
Goal Oriented 45%
Occupational Health
and Safety
44%
Flexibility 43%
Confidence
POS software 79%
POS systems 77%
Cash registers 73%
Interpersonal Skills 72%
Problem-Solving 71%
Occupational Health
and Safety
70%
Sales 70%
Microsoft Outlook 70%
Microsoft Office 67%
Microsoft Suite 67%
Confidence
Multi-tasking 41%
Work under pressure 40%
Inventory Management 39%
Office Administration 38%
Microsoft Outlook 37%
Fast-paced Setting 36%
Ordering of supplies
and equipment
35%
Attention to Detail 35%
Handling heavy loads 34%
Scheduling 34%
All Occupations Sales and service occupations (NOC 6) All other occupations
14
Possible Paths Forward
Understanding skill
associations across
additional modifiers,
including TEER
categories and remote
work options
Undertaking a qualitative
review of the analysis,
involving interpretation of
findings using raw job
posting text
Exploring Modifiers Qualitative Review
Temporal Analysis
Conducting a
longitudinal to examine
how skill association
evolves over time
15
Questions?
16
Appendix
17
Similar work
Skill-driven
recommendations for
job transition pathways
• Developed a model to predict
job transitions in Australia.
• The first stage focuses on skill
similarities.
• Created “skill spaces” which
identifies the likelihood of 2
skills appearing in the same
job posting or being used
interchangeably.
• Skill spaces are developed
with a pairwise similarity and a
skill-based revealed
comparative advantage.
An Open and Data-
driven Taxonomy of
Skills Extracted from
Online Job Adverts
• Building a skills taxonomy
based on the likelihood of 2
skills appearing in the same
posting or being used
interchangeably.
• Similarities are mapped into
clusters to identify common
themes, which become the
basis of the new taxonomy.
• Removes the high traversal
skills as they fall in multiple
clusters.
Skills for jobs 2022:
Mapping skill requirements
in occupations based on
job postings data
• OECD study to identify skill
shortages and surpluses.
• Builds a skills profile based on
the revealed comparative
advantage in different
industries.
• To ensure the process is
reasonable they do a statistical
comparison of the results to
the O*Net skill profiles.
• The skill imbalance is
calculated based on the RCA,
occupation imbalance, and
occupation size.
18
• Official data sources in Canada have limitations such as time
lag, limited data granularity, small sample size, and exclusion
of certain employer categories.
• Online job postings offer real-time insights into job trends with
a high level of granularity and valuable information about the
skills required.
• Challenges include inconsistencies between postings and
actual vacancies, identifying and removing duplicate
postings, limited transparency in algorithms, and
technological barriers.
• There is also a bias in online job posting data for certain
occupations: higher-skilled white-collar sectors have greater
representation. Many other occupations are under-
represented in online postings.
Online job posting data as LMI
Online job postings
provide a rich source of
big data, which presents
both opportunities and
challenges for
accurately measuring
job vacancies.
LMIC has partnered
with Vicinity Jobs to
access data from job
postings across the
web.
19
"Teamwork" and networked skills
20
Overview of Skills
Social-
Emotional
Skills
Occupational
Skills
Technologies
Tools and
Equipment
Number of skills
in the group
51 302 1,733 2,464
Minimum number
of job postings
35 2 1 1
Median job
postings
58,089 3,055 29 14
Maximum number
of job postings
1,322,765 921,645 329,208 60,250
21
Predictors of Teamwork
Confidence
Goal-Oriented 71%
Work under pressure 70%
Self-starter / Self-
motivated
66%
Fast-paced setting 65%
Interpersonal Skills 64%
Decision-Making 64%
Key Performance
Indicators
64%
Attention to Detail 64%
Analytical Skills 63%
Writing 63%
All Occupations
Top Predictor (1) Top Predictor (2) Top Predictor (3)
NOC 0 CRM software (75%) Critical Thinking (72%) Microsoft Access (72%)
NOC 1 Critical Thinking (70%) Decision-Making (66%) Coaching (66%)
NOC 2 Coaching (74%)
Work under pressure
(73%)
Interpersonal Skills (73%)
NOC 3 Work under pressure (76%) Dexterity (74%) Writing (71%)
NOC 4 Dexterity (85%) Critical Thinking (75%) Reports preparation (72%)
NOC 5
Adobe Systems Adobe
Creative Suite (77%)
Search Engine
Optimization (73%)
Maya (73%)
NOC 6 Goal oriented (74%)
Work under pressure
(74%)
Interpersonal Skills (69%)
NOC 7 Interpersonal Skills (72%)
Hand-eye coordination
(71%)
Work under pressure
(70%)
NOC 8 Interpersonal Skills (82%) Dexterity (80%) Team Building (80%)
NOC 9 Work under pressure (74%) Dexterity (69%)
Self-starter / Self-motivated
(69%)
By Occupations
Note: The values in brackets are the confidence, or likelihood that teamwork will be in a job posting based on the
presence of the predictor skill
22
Predictors of Communication skills
Confidence
Writing 83%
Analytical Skills 78%
Presentation Skills 78%
Negotiation Skills 78%
Conflict Management
Skills
76%
Interpersonal Skills 75%
Research Skills 74%
Microsoft Access 73%
Multi-tasking 70%
Microsoft Outlook 70%
All Occupations
Top Predictor (1) Top Predictor (2) Top Predictor (3)
NOC 0 Writing (93%) Microsoft Access (86%) Microsoft Windows (83%)
NOC 1 Writing (86%) Presentation Skills (83%) Interpersonal Skills (80%)
NOC 2 Writing (85%) Presentation Skills (84%) Negotiation Skills (82%)
NOC 3 Analytical Skills (88%) Writing (87%) Computer Skills (85%)
NOC 4 Writing (83%) Negotiation Skills (81%) Multi-tasking (80%)
NOC 5
Interpersonal Skills
(74%)
Goal Oriented (73%) Analytical Skills (72%)
NOC 6 Writing (82%) Analytical Skills (77%) Presentation Skills (77%)
NOC 7 Writing (80%) Analytical Skills (72%) Microsoft Outlook (65%)
NOC 8 Writing (85%) Analytical Skills (81%) Lean Manufacturing (75%)
NOC 9 Writing (77%) Microsoft Outlook (68%) Analytical Skills (68%)
By Occupations
Note: The values in brackets are the confidence, or likelihood that teamwork will be in a job posting based on the
presence of the predictor skill

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Does teamwork really matter? Looking beyond the job posting to understand labour market demands

  • 1. Does teamwork really matter? 2024-05-31 Laura Adkins-Hackett & Sukriti Trehan Looking beyond the job posting to understand labour market demands
  • 3. 3 • Find connections in OJP to extract additional insights into the needs of the labour market • Examine changes in skill connections across occupations and regions • This research will show how we can enhance OJP as an analytical tool • Show the importance of context when deciphering job postings • A step in using OJP to identify skills shortage mismatches and demand Approach Impact Connecting skills Problem • The same language is used across job postings, so skills in Online Job Postings (OJP) make it hard to identify what is required for the job. • If skills in postings don’t represent reality, this limits the value of online job postings as a source of Labour Market Information (LMI).
  • 5. 5 2023 OJP in data Online job postings provide a rich source of big data, which presents both opportunities and challenges for accurately measuring job vacancies. LMIC has partnered with Vicinity Jobs to access data from job postings across the web. 3,078,987 20,482,226 4,550 710 Job postings Total requirements Unique requirements Requirements in at least 500 postings
  • 6. 6 Job postings by commonly requested skills NOC Legend: 0 - Legislative and senior management occupations; 1 - Business, finance and administration occupations; 2 - Natural and applied sciences and related occupations; 3 – Health Occupations; 4 - Occupations in education, law and social, community and government services; 5 - Occupations in art, culture, recreation and sport; 6 - Sales and service occupations; 7 - Trades, transport and equipment operators and related occupations; 8 - Natural resources, agriculture and related production occupations; 9 - Occupations in manufacturing and utilities
  • 8. 8 Methodology Frequency • Measure the frequency of skills co-occurring to identify relationships between terms.​ Normalized pointwise mutual information • The strength of skill relationships is measured using Pointwise Mutual Information (PMI), a metric to evaluate the frequency of terms co-occurrences compared to their independent occurrences. • We use a normalized version of PMI to restrict the range of values between -1 and 1.​ Directional relationship • Measure a metric called confidence, which quantifies the conditional probability of observing one skill in a job posting when another skill is required, hence identifying most probable predictors for a skill. • Utilize association rule mining with Frequent Pattern Growth algorithm to identify skill relationships with the highest confidence levels.
  • 9. 9 Normalized PMI Confidence for directional relationships 𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛, 𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔 > 𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑝(𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔) ⇒ 𝑙𝑜𝑔2 𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛,𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔 𝑝 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑝(𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔) > 0 ⇒ PMI(𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛, 𝑝𝑟𝑜𝑏𝑙𝑒𝑚 𝑠𝑜𝑙𝑣𝑖𝑛𝑔) > 0 Confidence (x -> y) = 𝑆𝑢𝑝𝑝𝑜𝑟𝑡(𝑥 →𝑦) 𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝑥) , 𝑟𝑎𝑛𝑔𝑒 ∊ [0,1] • The PMI value is normalized by dividing it by a factor of −𝑙𝑜𝑔2𝑝(𝑥, 𝑦) • Quality of association rules can be measured by certain metrics including support and confidence. Support (x -> y) = 𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑟𝑒𝑞𝑢𝑖𝑟𝑖𝑛𝑔 𝑏𝑜𝑡ℎ 𝑠𝑘𝑖𝑙𝑙𝑠 𝑥 𝑎𝑛𝑑 𝑦, 𝑟𝑎𝑛𝑔𝑒 ∊ [0,1] PMI (x, y) = 𝑙𝑜𝑔2 𝑝(𝑥,𝑦) 𝑝 𝑥 𝑝(𝑦) • For two skills x and y, • Intuitively for two skills: communication and problem solving, if 𝑝(𝑥, 𝑦): Probability of events x and y occurring together 𝑝 𝑥 and 𝑝 𝑦 : Individual probabilities of events x and y
  • 11. 11 Skill associations with Customer Service
  • 12. 12 PMI by NOC 6 vs others
  • 13. 13 Predictors of Customer Service Confidence Cash registers 75% Sales 70% Work under pressure 49% Fast-paced Setting 49% Multi-tasking 48% Work scheduling 46% Inventory Management 46% Goal Oriented 45% Occupational Health and Safety 44% Flexibility 43% Confidence POS software 79% POS systems 77% Cash registers 73% Interpersonal Skills 72% Problem-Solving 71% Occupational Health and Safety 70% Sales 70% Microsoft Outlook 70% Microsoft Office 67% Microsoft Suite 67% Confidence Multi-tasking 41% Work under pressure 40% Inventory Management 39% Office Administration 38% Microsoft Outlook 37% Fast-paced Setting 36% Ordering of supplies and equipment 35% Attention to Detail 35% Handling heavy loads 34% Scheduling 34% All Occupations Sales and service occupations (NOC 6) All other occupations
  • 14. 14 Possible Paths Forward Understanding skill associations across additional modifiers, including TEER categories and remote work options Undertaking a qualitative review of the analysis, involving interpretation of findings using raw job posting text Exploring Modifiers Qualitative Review Temporal Analysis Conducting a longitudinal to examine how skill association evolves over time
  • 17. 17 Similar work Skill-driven recommendations for job transition pathways • Developed a model to predict job transitions in Australia. • The first stage focuses on skill similarities. • Created “skill spaces” which identifies the likelihood of 2 skills appearing in the same job posting or being used interchangeably. • Skill spaces are developed with a pairwise similarity and a skill-based revealed comparative advantage. An Open and Data- driven Taxonomy of Skills Extracted from Online Job Adverts • Building a skills taxonomy based on the likelihood of 2 skills appearing in the same posting or being used interchangeably. • Similarities are mapped into clusters to identify common themes, which become the basis of the new taxonomy. • Removes the high traversal skills as they fall in multiple clusters. Skills for jobs 2022: Mapping skill requirements in occupations based on job postings data • OECD study to identify skill shortages and surpluses. • Builds a skills profile based on the revealed comparative advantage in different industries. • To ensure the process is reasonable they do a statistical comparison of the results to the O*Net skill profiles. • The skill imbalance is calculated based on the RCA, occupation imbalance, and occupation size.
  • 18. 18 • Official data sources in Canada have limitations such as time lag, limited data granularity, small sample size, and exclusion of certain employer categories. • Online job postings offer real-time insights into job trends with a high level of granularity and valuable information about the skills required. • Challenges include inconsistencies between postings and actual vacancies, identifying and removing duplicate postings, limited transparency in algorithms, and technological barriers. • There is also a bias in online job posting data for certain occupations: higher-skilled white-collar sectors have greater representation. Many other occupations are under- represented in online postings. Online job posting data as LMI Online job postings provide a rich source of big data, which presents both opportunities and challenges for accurately measuring job vacancies. LMIC has partnered with Vicinity Jobs to access data from job postings across the web.
  • 20. 20 Overview of Skills Social- Emotional Skills Occupational Skills Technologies Tools and Equipment Number of skills in the group 51 302 1,733 2,464 Minimum number of job postings 35 2 1 1 Median job postings 58,089 3,055 29 14 Maximum number of job postings 1,322,765 921,645 329,208 60,250
  • 21. 21 Predictors of Teamwork Confidence Goal-Oriented 71% Work under pressure 70% Self-starter / Self- motivated 66% Fast-paced setting 65% Interpersonal Skills 64% Decision-Making 64% Key Performance Indicators 64% Attention to Detail 64% Analytical Skills 63% Writing 63% All Occupations Top Predictor (1) Top Predictor (2) Top Predictor (3) NOC 0 CRM software (75%) Critical Thinking (72%) Microsoft Access (72%) NOC 1 Critical Thinking (70%) Decision-Making (66%) Coaching (66%) NOC 2 Coaching (74%) Work under pressure (73%) Interpersonal Skills (73%) NOC 3 Work under pressure (76%) Dexterity (74%) Writing (71%) NOC 4 Dexterity (85%) Critical Thinking (75%) Reports preparation (72%) NOC 5 Adobe Systems Adobe Creative Suite (77%) Search Engine Optimization (73%) Maya (73%) NOC 6 Goal oriented (74%) Work under pressure (74%) Interpersonal Skills (69%) NOC 7 Interpersonal Skills (72%) Hand-eye coordination (71%) Work under pressure (70%) NOC 8 Interpersonal Skills (82%) Dexterity (80%) Team Building (80%) NOC 9 Work under pressure (74%) Dexterity (69%) Self-starter / Self-motivated (69%) By Occupations Note: The values in brackets are the confidence, or likelihood that teamwork will be in a job posting based on the presence of the predictor skill
  • 22. 22 Predictors of Communication skills Confidence Writing 83% Analytical Skills 78% Presentation Skills 78% Negotiation Skills 78% Conflict Management Skills 76% Interpersonal Skills 75% Research Skills 74% Microsoft Access 73% Multi-tasking 70% Microsoft Outlook 70% All Occupations Top Predictor (1) Top Predictor (2) Top Predictor (3) NOC 0 Writing (93%) Microsoft Access (86%) Microsoft Windows (83%) NOC 1 Writing (86%) Presentation Skills (83%) Interpersonal Skills (80%) NOC 2 Writing (85%) Presentation Skills (84%) Negotiation Skills (82%) NOC 3 Analytical Skills (88%) Writing (87%) Computer Skills (85%) NOC 4 Writing (83%) Negotiation Skills (81%) Multi-tasking (80%) NOC 5 Interpersonal Skills (74%) Goal Oriented (73%) Analytical Skills (72%) NOC 6 Writing (82%) Analytical Skills (77%) Presentation Skills (77%) NOC 7 Writing (80%) Analytical Skills (72%) Microsoft Outlook (65%) NOC 8 Writing (85%) Analytical Skills (81%) Lean Manufacturing (75%) NOC 9 Writing (77%) Microsoft Outlook (68%) Analytical Skills (68%) By Occupations Note: The values in brackets are the confidence, or likelihood that teamwork will be in a job posting based on the presence of the predictor skill

Editor's Notes

  1. Thank you Ibrahim Sukriti and I are here to talk about our research looking at the relationships between skills in job postings
  2. Laura
  3. Laura Across job postings we see the same skills frequently requested which can make it difficult to identify the intent of the employer The lack of variation makes it harder for job seekers to narrow down which postings are a good fit, and reduces the information available to researchers using Online Job Posting data to learn about the needs of the labour market. Our goal for this project is to develop measures for the relationship between skills to see what additional information we can extract about these frequently requested skills from other terms and context in the job posting Identifying common trends, and markers of these trends, can help better understand the actual needs of the labour market, despite the high reliance on these terms. This is an initial look at how the relationships shift in different perspective and can be expanded on to develop more complex understandings of labour demand from OJP in Canada
  4. Laura
  5. Laura For this research we have used OJP for 2023 from Vicinity Jobs. In 2023 there were close to 3.1 million job postings. On average each posting had between 6 and 7 requirements Putting the total number of requirements extracted at 20.5 million. From those there were only 4,550 unique requirements Demand for requirements is uneven Of the 4,550 unique requirements only 709 were in 500 or more job postings While 500 postings may sound like a lot when you are looking at jobs as an individual, its really blip from a set of over 3 million. 500 job postings is less than 0.02% of all postings from 2023. This means that around 85% of the unique skills are in less than 0.02% of job postings, showing the dominance of a small number of skills across job postings.,
  6. Laura We do focus on the most requested skills so I am going to show a bit more context for those Top 10 most frequent – 17%-48% of posting. 4 skill groups – social-emotional, occupational, tools and equip, technologies all but one of the top skills are social-emotional. Social-emotional are generally soft skills, which are harder quantify and to train – more ambiguous Only 51 skills are as social-emotional social-emotional are the most frequently requested skills, emphasizing the disparity in skill frequency For our project we focused on three skills Teamwork - in 48% of job postings, and is in relatively high demand across occupation, at the broad occupational classification Communication skills – in 39% - and the demand differs across occupations. Higher demand in NOC 0-4 and lower demand for 5-9 Finally customer service – in 33% - but mostly occurs in NOC 6 sales and service occupations – for today this will be our example for the results We chose these three as they are the most in demand skill requirements and have unique demand patterns across occupations.
  7. Sukriti We follow a two-pronged approach for mapping skill relationships.
  8. Sukriti The first part of our methodology involves looking at skill co-occurrences by their frequency to identify highly co-occurring skills overall and across occupations. But frequency alone isn’t enough to understand the association between two skills. As we’ve seen, skills like teamwork and communication skills tend to appear across a high number of job postings and they co-occur with varied skills. So, mere co-occurrence does not suggest the strength of the relationship between two skills. To quantify the strength of the relationship between two skills, we use a metric called Pointwise Mutual Information (PMI). It is often used in NLP tasks for information retrieval to identify meaningful word associations. PMI essentially evaluates how often two terms appear together compared to how often they appear independently. To ensure consistency and comparability across different skill relationships, we use a normalized version of PMI. This normalization process constrains the range of values between -1 and 1. Essentially, it helps us interpret the strength of relationships in a standardized manner, making it easier to identify significant associations between skills. The last part of our methodology is identifying directional relationships between different skills required in job postings. We use a metric called 'confidence,' which essentially measures the likelihood of finding one skill mentioned in a job posting when another skill is required. This helps us identify the most probable predictors for a particular skill. To do this, we employ a technique called association rule mining, using a specific algorithm known as the Frequent Pattern Growth algorithm which identifies recurring patterns within the data, which in our case are frequent skill sets within job postings. This algorithm helps us uncover skill relationships with the highest confidence levels. Essentially, it allows us to pinpoint which skills tend to appear in conjunction with a given skill in job postings, giving us valuable insights into the skills that are most often sought after in combination.
  9. Sukriti For two skills x and y, PMI looks at a ratio of the joint probability of skills x and y occurring together to the marginal probabilities of skills x and y. To gain an intuitive understanding of PMI, let’s consider two skills, communication skills and problem solving If the probability of communication and problem-solving occurring together exceeds the individual probabilities of each skill, the PMI value will be positive. This positive PMI indicates a strong association between communication skills and problem-solving, suggesting that employers often seek candidates with both skills simultaneously. PMI value is normalized by dividing it by a factor called self-information, obtained by taking the negative logarithm of the joint probability of observing both skills. It represents the amount of information gained by observing the co-occurrence of the two skills, and it's often used as a measure of the surprise or uncertainty associated with the event. To assess the quality of directional relationships between skills, we use two key metrics: support and confidence. Support measures the frequency of both skills appearing together in job postings, ranging from 0 to 1. This tells us how common the skill pairing is. Confidence, on the other hand, calculates the likelihood of finding skill y in a job posting when skill x is present. This ratio, also ranging from 0 to 1, indicates the strength of the relationship between the skills. In our research, we particularly use confidence as a measure for evaluating the significance of association rules between skills.
  10. Sukriti
  11. Sukriti We're examining the top 20 skills associated with customer service by co-occurrences, on the LHS, and by NPMI on the right hand graph. Looking at the graph on the left, we see that majority of these top 20 skills belong to the social-emotional category. Among them, teamwork and communication skills stand out, with the highest share of job postings where they co-occurs with customer service. The y-axis of our chart spans a range up to approximately 19% for teamwork. It's important to note that while these percentages may not seem exceedingly high, it's because these skills are highly versatile and often complement a wide array of other competencies. Despite identifying the most common co-occurrences through this chart, it's crucial to recognize that it doesn't provide insight into the significance of these associations. Further analysis is needed to understand the depth and impact of these co-occurrences in the context of job requirements We're now exploring skills that exhibit the most significant association with customer service, that is highest normalized PMI values with customer service. The most stark difference from the previous chart is the fact that we see a mix of all skill groups. Specifically, a number of skills from the tools and equipment and occupational category start to show up. Sales, cash registers and cash handling have the strongest association with customer service. Teamwork and communication skills are no longer part of the top 20 skills with the most strong association with customer service. This shift in the type of skills related to customer service emphasizes the importance of looking beyond the simple co-occurrence to understand the relationship between two skills.
  12. Sukriti Since majority of jobs that require customer service belong to NOC 6, i.e, sales and service occupations, we’re comparing how associations change for jobs in Sales and service compared to all other occupations. For NOC 6, we see that customer service has the strongest association with interpersonal skills, teamwork and communication skills, followed by sales, computer terminals and cash-handling. This points to the multifaceted nature of customer service roles, which may involve tasks related to sales transactions and alongside interpersonal interactions. However, for all occupations except NOC 6, we see that customer service has high NPMI with specific skills belonging to tools and equipment and technology groups, instead of social-emotional skills. Skills like cash registers, POS systems, computer terminals top the charts. We also observe lower PMI values for sales and service occupations as opposed to all other occupations. The differences observed in these association charts stem from the calculation process involving Pointwise Mutual Information (PMI). PMI measures the probability of observing two skills together in comparison to their independent occurrences. When a skill like customer service, is highly requested within a specific occupation like Sales and service occupations, its probability of appearing independently increases. This increased probability of independent occurrence leads to a decrease in the overall associations across the board. As a result, associations between customer service and other skills may appear weaker within Sales and service occupations compared to the overall associations. For instance, interpersonal skills, teamwork, and communication skills are widely demanded across various occupations, leading to a decreased association with customer service when considered overall. However, within Sales and service occupations where these skills are less common, they are more likely to co-occur with customer service, leading to stronger associations within this specific occupational category.
  13. For Our final step we look at the skills that are the best predictor for case studies. While associations give us insight into the relationship between skills but they do not predict if two skills will occur together. To add this we look at the directional relationship – which as Sukriti mentioned is the confidence a skill will be appear on the presence of another. For customer service we looked at the predictors in three different groupings – first is all job postings with no consideration to occupation, than predictors within Sales and Service occupations, and finally predictors for all OTHER occupations. Interestingly there is minimal overlap between the three groupings. I have highlighted the skills that appear two so we can see where there is overlap. No skill is a top predictor for customer service for all three scenarios Overall we see relatively low confidence in predictors for customer service Across all job postings only 2 skills have a confidence over 50%, Confidence of 50% means that 1 in 2 postings that include the predictor skill will also include customer service. Without additional context we do not have strong enough confidence to predict the presence of customer service. More context add occupation We could increase our confidence if we combined skills – but for this first iteration we have opted to focus on individual skills Within sales and service occupations we do see stronger predictors. We also see a shift away from social-emotional (soft skills) that were prominent in the associations Predictors for Customer service in Sales and service include technologies, occupational skills and social emotional skills Outside of sales and service occupations, the confidence from predictors is quite low, indicating little consistency in the skill profiles related to customer service. Between the low confidence for predictors and the mix of skills with the highest associations, we have not identified a clear indicator that a position is interested in Customer service skills. More difficulty mapping customer service skills to other skills for those moving away from sales and service
  14. Laura - To inspect such cases, we have some possible paths forward, which include: Temporal Analysis: Conducting a longitudinal study to examine how associations between skills evolve over time. Leveraging VJ data from 2018 onwards, we can gain insights into the dynamic nature of skill relationships across different time periods. Exploring Other Modifiers: Investigating how skill associations vary based different work situations(such as remote vs in office) or based on other criteria such as education and experiences. It's plausible that the associated requirements for certain social-emotional skills may differ depending on the specific work setting, highlighting the importance of contextual factors in shaping skill relationships. Qualitative Review: Undertaking a qualitative review of the analysis, - look at the raw job posting – deeper understanding of socio-economic, and organizational factors that may influence the observed outcomes. By examining the qualitative context surrounding the quantitative data, we can -supplement our understanding of skill associations. Example client vs customer service