Post on 29-May-2020
RELI: Relative Engagement Labor Index
An Innovative Measure for the KSA
May 9, 2018Carole Chartouni, Robert Holzmann, and Gustavo Paez
Job Course - WB
Motivation
Deep engagement heterogeneity across groups of individuals
An index that measures the level of engagement within these groupsTo cluster individuals into groups with similar levels for targeted interventions
Most importantly jobseekers, but can also target interventions to clusters of employed and inactive
Track development of engagement level with continuous updates. Even if unemployment status does not change, intervention may have increased
engagement level.
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KSA Labor Market Context
Segmented Labor Market by at least three dimensions:between the Saudis (nationals) and the many temporary foreign workers
(expats)
between the public sector (where mostly nationals work) and the private sector (that is dominated by the expats),
between men and women
Unemployment Rate for Saudis 12.7% (Youth and Women)
Participation Rate for Women very low at 19% (Q32016 data)
Vision 2030: decrease UR to 7% and increase LFP to 30%
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Methodology
1- Identifying labour subgroups and engagement dimensions
Level of engagement is dependent on three dimensions:
Six sub-groups identified:
Inactive Women
Employed Women
Unemployed Women
Inactive Men
Employed Men
Unemployed Men
PreferencesIntensity Barriers
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Methodology (cont’d)2- Identifying variables from questions per group and dimension
Variables for Preferences Variables for Intensity Variables for Barriers
Type of works that are considered acceptable People that applied for jobs Self-identified reasons why the individual has no job
Constraints on the jobs that the person is
willing to accept
People that updated their CVs Particular barriers that individuals face (looking
for/during their) jobs
Minimum hours that the person is willing to
work
Number of job search actions Amount of hours that a guardian allows a woman to
work - Women
Reasons why the individual is not working Time when the last application was
done
If the guardian allows mixing working environments -
Women
Willingness to reallocate in order to find a job Number of applications done by the
individual
Types of transport allow by the guardian - Women
Attitude towards gender mixing environments
- Women
Number of recent search actions done If the guardian allows the women to work - Women
Attitude towards women working - Women Hours dedicated to job search Hours dedicated to household chores - Women
Attitudes towards work How serious is the job search Availability of a domestic worker - Women
Flexible working conditions Types of transport acceptable for the women -
Women
Amount of working (or willing to work) hours
Willingness to do work shifts
Questions already Existed prior to Index
UNEMPLOYED
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Methodology (cont’d)
3- Construction of dimension indices using principal componentanalysis (PCA) to extract the common information of these variables:
Variables - Intensity PCA 1- Weight
actual_applications 0.52
cv_update 0.38
job_search_actions 0.14
last_applications 0.53
num_applications 0.46
recent_search_actions 0.24
serious_job_search 0.09
search_time 0.12
UNEMPLOYED Men - Intensity
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Methodology (cont’d)
4- Aggregation into one single index RELI
𝑅𝐸𝐿𝐼 =1
3𝐵𝑎𝑟𝑟𝑖𝑒𝑟𝑠 +
1
3𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 +
1
3𝑃𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑠
Standard mean is 0 and standard deviation is the level
For robustness PCA was also used to generate variable weights
5- Cluster analysis identifies those individuals that have similar levels at the different dimensions.
a hierarchical cluster is used using the first principal component of each dimension 𝑃𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒, 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦, 𝐵𝑎𝑟𝑖𝑒𝑟𝑠
7
Results – Profiling Unemployed men
Preferences Intensity Barriers
Cluster 1 Well below Below Average
Cluster 2 Average Above Well below
Cluster 3 Well above Well below Above
Cluster 4 Above Well above Above
Hardest to Place
Barriers High! ¾ stated that their lack of experience and education pose a barrier for finding jobs
Intensity Low! ¾ updated their cv more than 1 year ago
Easiest to Place
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Results – Profiling Unemployed men (cont’d)
Most Engaged - Mostly SingleLeast Engaged - Lower Education Least Engaged – Mostly in Qasim9
Results – Profiling Unemployed men (cont’d)
• Very low education and English levels
• Highest shared of married men and oldest population distribution (40% 35+)
• Highest share of people that embrace traditional beliefs
• Few are in Riyadh
Profile 1
Hardest to Activate
• Lowest education levels and low English levels
• Relatively younger than profile 2 (20% 35+)
• Geographically spread
Profile 2
High Barriers
• Better educational levels but low English levels
• Higher share of married men
• Similar age distribution to profile 2
• Geographically spread
Profile 3
Low Intensity
•Better educational levels and English Skills
•Youngest distribution and higher share of single men
•Non-negligent share in Riyadh
Profile 4
Easiest to Place 10
Results – Profiling Unemployed Women
Preferences Intensity Barriers
Cluster 1 Average Below Well below
Cluster 2 Below Above Well below
Cluster 3 Well above Well below Well above
Cluster 4 Above Well above Well above
Intensity Low and Barriers High! Hardest to Place
Preferences Low & Barriers High! Hardest to Place!Most likely to be affected by restrictions by guardians; lowest work attitudes, not willing to work shifts, etc..
Intensity Low! Barely used any search methods
Easiest to Place
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Results – Profiling Unemployed Women (cont’d)
Unemployed Educated! Most Engaged – More likely Single Least Engaged – Traditional Beliefs12
Results – Profiling Unemployed Women (cont’d)• High share of married women and older population distribution (40% 35+)
• High share of women that embrace traditional beliefs
• Lower educational levels and English skills
• Reservation wages high
Profile 1: Low Intensity/High
Barriers• High share of married women and older population distribution (40% 35+)
• High share of women that embrace traditional beliefs
• 40% in two areas (Asir and Jouf)
• Highest reservation wages
Profile 2: Low Pref/High Barriers
• Lower educational levels but high English levels
• Highest share of young women and single people
• Lower share of women that embrace traditional beliefs
• Lowest reservation wages
Profile 3
Low Intensity
•Better educational levels and English Skills
•Lower share of women that embrace traditional beliefs
•Significant share in Riyadh (40%)
•Widespread distribution of reservation wages
Profile 4
Easiest to Place13
Results – Profiling of UnemployedProfiles by engagement dimension Intervention focus by engagement dimensions
Preferences Intensity Barriers RELI Preferences Intensity Barriers Comments
UEP - Male
Cluster 1 Well below Below Average Well below Attitude
change
Job search motivation Education and
skills
Hardest to place; Starting with regional
focus suggested
Cluster 2 Average Above Well below Well below Change in
sector
orientation
n.p. Education,
English
Young group; 90 percent are singles
Cluster 3 Well above Well below Above Average Job search skills,
counselling, etc
n.p. Job intermediation and motivation
Cluster 4 Above Well above Above Well above Reservation
wage and job
attitude
n.p n.p Ready for the labor market but perhaps
in different sector
Can search on own for first few months
UEP – Female
Cluster 1 Average Below Well below Well below Attitude
change
Job search motivation
and skills
Addressing
gender-specific
issues
Hardest to place as gaps in all
engagement dimensions required
Cluster 2 Below Above Well below Below n.p Intermediation services
with focus on gender
adequate jobs
Interventions
toward
influencers
Gender specific intervention most
promising
Cluster 3 Well above Well below Well above Above n.p Job search skills,
counselling, placement
assistance etc
n.p. Regional concentration of cluster
members and hence regional
interventions promising
Cluster 4 Above Well above Well above Well above n.p. n.p. n.p Ready for the labor market but demand
may be missing; focus sector for demand
intervention to be explored14
Results – Profiling of Unemployed
For each cluster of unemployed, tailored interventions need to bedesigned
When individuals register, they will answer questions which willidentify which cluster they belong to and receive up to date profile onlevel of engagement according to three dimensions
Possibility of calculating the index value of an individual who was notpart of the original sample
Offers a frequent assessment of changes and progress among the keylabor market participants for which interventions are needed
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Results – Profiling Other Groups (Inactive Women)
Preferences Intensity Barriers
Cluster 1 Below Above Well below
Cluster 2 Below Average Below
Cluster 3 Above Average Average
Cluster 4 Above Average Above
Depending on policy objectives, government mayfocus on cluster 3 and 4 to increase LFP for women.Reasons for inactivity partly due to discouragementor lack of skills
Cluster 1 and 2 largely inactive due to familyrestrictions. Interventions could focus oncommunication campaigns, edutainment, reachingout to influencers, etc…
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Results – Tracking Progress in Engagement
Repeat survey/collection of data
Standards (weights, means and standard deviations) are fixed at time𝑡 (index calculated from 2016 data)
Treat the future population as out-sample individuals who arecompared under the same standards as the present population.
An increase in the index in the future would reflect improvement inthe variables that define the index.
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Challenges!
Questions were developed prior to formulation of indexShould there be identical questions across all subgroups for easy aggregation
to a single index?
Questions need to be reviewed and reformulated
New questions may need to be added
CAN YOU THINK OF NEW QUESTIONS THAT CAN BE USEFUL TOPROFILE JOB SEEKERS?
Preferences Intensity Barriers
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