Nordic Statistical Meeting 26.-28.8.2019, Helsinki

1. Predicting the Contractual Full-Time
Equivalent Percentage using XGBoost
10/18/2019
Stine Bakke and Knut Håkon Grini
1

2. Agenda
• A-ordningen
• Agreed working hours – and the problem
• Preparing the data
• XGBoost (Extreme Gradient Boosted Decision Trees)
• Results
• Passing judgement/final thoughts
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3. A-ordningen – 24/7 reporting – monthly data
• Coordinated digital collection of information from employers about jobs,
earnings and taxes to 3 public agencies
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4. 10/18/2019 4
Contractual full-
time equivalent
Paid hours
(only hourly paid)
Hours per week
full time in position
Substandard Reporting 7,5 %

5. 10/18/2019 5
Check 1: Hourly paid
Ratio model, just
identification of
outliers
1,5 % extremes
Check 2: Boundaries on earnings
Lower wage
threshold is
established for FTE
wage and lower and
upper limits for
hourly paid
employees are set
2,7 % disq
Check 3: Relationship
between earnings and
FTE
Iterative linear
regression model
that checks for
outliers
4,7 % disq

6. eXtreme Gradient Boosting
• Uses «gradient boosted decision trees»
• Every tree provides a set of predicted values
• Trees are «grown» based on modified versions of the data
• Observations with bad prediction are weighted more
• Observations with good prediction are weighted less
• Improved prediction for each new tree
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7. Input variables
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• Fixed earnings (log)
• Reported or calculated FTE %
• Age and age squared
• Number of employees in local
unit (Ten groups)
• Education (first digit ISCED 2011)
• Industry (NACE 2007)
• Occupation (two digit ISCO 2008)
• Apprenticeship
• Earning category (fixed monthly,
hourly paid, other)
• Gender

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-
20 000
40 000
60 000
80 000
100 000
120 000
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 1,1
Training improved Training faulty

9. Lessons learned, passing judgement and final thoughts
• Good learning data
• Careful specification – (variabelspec)
• Distribution of results – is it realistic or biased
• We are very optimistic but not quite finished
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10. Thank you!
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