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Machine Learning · ML-02

Predict time to return to work

Explore models of the time between one employment spell and the next, with prior work, education and household context.

  • Synthetic records
  • Longitudinal
  • Nairobi + Lagos
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Scope & purpose

Connected scope

Connect dated employment spells, work gaps, education and household context to model time to the next recorded job.

  • Demographics
  • Education
  • Employment
  • Households

Define the prediction date and horizon. Keep earlier inputs separate from later outcomes.

Possible uses

Prediction prototyping · Feature exploration · Temporal validation · Cohort comparison

Who it is for

ML engineers · labour economists · workforce teams · policy researchers

Preview type
Illustrative format preview
Shows example structure and fields. It is not a sample extracted from the paid release.
Commercial availability
Confirmed in your written quote
The listed cities describe catalogue coverage. We confirm the exact release and available extract before agreement.
Starting-price basis
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Your agreed delivery
Scope and supporting artifacts listed in the quote
Confirm entities or rows, observation period, fields, formats, documentation, evidence, known limits and usage rights.

Paid dataset delivery

What you receive

SD delivery standard
Published cities
Nairobi + Lagos
Record scope
Resident-level
Structure
Longitudinal

Striped Donkey delivery package

  • data/Agreed extract and formats
  • documentation/Schema, fields and relationships
  • evidence/Agreed release checks and limits
  • examples/Agreed loading and join examples
  • LICENSEApplicable rights
  • MANIFESTRelease and file inventory
  • CHECKSUMSSHA-256 fingerprints
  • DELIVERY_NOTEScope and handoff record

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Free evaluation preview

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15 illustrative rows · 11 fields · CSV

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Illustrative rows show the format. Your quote confirms delivered records, fields and time coverage.

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Fields and record structure

Listed features

Resident-level · Longitudinal

  • employment-gap start / end
  • return-to-work outcome
  • prior work history
  • age / life stage
  • household relationships
  • dated demographic events
  • enrolment history

Study question

Evaluation design

How accurately can prior work, education and household histories predict the time from a recorded job ending to the next recorded job starting?

Start follow-up at spell end; distinguish overlapping jobs from gaps and retain spells with no observed return. Do not label gaps as involuntary unemployment.

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Evidence you can inspect

Source & limitations

Review release-specific methods and published checks. Preview rows show format and scope; they do not certify a dataset.

Supporting references
NairobiEvidenceSamples
LagosEvidenceSamples
MethodsGenerationReproduce

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Licence & delivery

Free preview

Use free samples under the public-use policy or their dataset-specific licence. Attribution is required for publication and research outputs.

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Purchased dataset

Your agreement defines the release, licensed organization, permitted uses and duration. Commercial model training, external applications and redistribution require explicit agreement.

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