Predict time to return to work

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

The record concepts include employment-gap start / end, return-to-work outcome, prior work history, age / life stage, household relationships, dated demographic events and enrolment history. Trace recorded job endings and later job starts. Explore return-to-work duration while retaining unfinished follow-up. Examine earlier education, work and household context.

This dataset is prepared to order. Choose the city, date range and required scope; the accepted order defines the population, final fields, formats and delivery.

Onboarded city options

Nairobi (Kenya) · Lagos (Nigeria)

Custom city requests

African Major Cities · Additional cities require availability, scope and quote confirmation.

Data type

Synthetic

Connected domains

Demographics, Education, Employment, Households

Catalogue structure

Longitudinal

Key features

Record concepts for your order-specific extract.

Who it is for

• ML engineers
• labour economists
• workforce teams
• policy researchers

Proposed study unit

Employment-gap episode

One proposed row per eligible synthetic resident's gap after a recorded employment spell ends. Check overlapping jobs before defining a gap; eligibility, dates and follow-up are agreed for the extract.

Connect work gaps and earlier histories to explore time to the next recorded job.

Record concepts for your extract

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

The agreed specification defines the final columns, keys, relationships and observation period for your order.

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.

Proposed questions for your custom order

1. How accurately can prior work, education and household histories predict the time from a recorded job ending to the next recorded job starting?
2. Which young people spend extended periods outside work and education?
3. Who takes longest to return to work after a job ends?
4. Which workers experience repeated short jobs and work gaps?
5. How long does a work gap last before the next recorded job begins?
6. Which prior career patterns are associated with a longer return-to-work period?
7. How well do models handle people who have not returned by the end of observation?
8. Do duration estimates hold up on work gaps beginning in a later period?

Proposed research and analytical applications

• Prediction prototyping
• Feature exploration
• Temporal validation
• Cohort comparison

Study and modeling guidance

Proposed gap anchor

Recorded employment-spell end, after overlap checks

Proposed outcome and incomplete follow-up

Time to the next qualifying recorded employment start; no observed return is censored at the agreed observation end.

Information available at prediction or evaluation time

Use only evidence valid and available at the agreed anchor; do not use later outcomes as features.

Comparison and evaluation

Specify comparable cohorts or held-out evaluation before reporting results.

Incomplete observation

Agree follow-up and retain incomplete observation as censored or unknown.

Data generation

FCTE synthetic city engine

Data classification

Synthetic

FCTE generation methods and published city/domain checks provide source context. The order defines the final extract and the release-specific checks, documentation and limitations supplied with it.

Known limitations

• A work gap is not necessarily unemployment. Confirm active job-search and availability before using that label; define censoring and prevent future-event leakage.
• Synthetic records describe generated scenarios; they do not establish observed population prevalence or real-world causal effects.

Catalogue base price

$395 USD

Price basis

Base price per selected city

Availability

Prepared after order

The displayed catalogue amount is a base price per selected city. Your requested dates, population, fields, formats and usage rights determine the agreed scope and final quote. Applicable published discounts and taxes are confirmed with that quote.

Your prepared package

Your order confirms the data files, documentation, evidence, examples and release controls included in the prepared package.

• Agreed data extract
• Schema and field documentation
• Release-specific evidence and limitations
• Agreed loading or analysis examples
• Licence, manifest, checksums and delivery note

SD_RESEARCH_DATASET/
├── data/
│   ├── citizens.parquet
│   ├── education_records.parquet
│   ├── employment_records.parquet
│   └── household_history.parquet
├── tasks/
│   ├── task_definition.yaml
│   ├── train.parquet
│   ├── validation.parquet
│   └── test.parquet
├── evaluation/
│   ├── baseline_model.py
│   ├── metrics.json
│   └── example_notebook.ipynb
├── documentation/
│   ├── README.md
│   ├── DATA_DICTIONARY.csv
│   ├── SCHEMA.md
│   └── METHODOLOGY.md
├── evidence/
│   ├── CERTIFICATION.json
│   ├── VALIDATION_REPORT.pdf
│   └── KNOWN_LIMITATIONS.md
├── MANIFEST.json
├── CHECKSUMS.sha256
└── LICENSE.pdf

Delivery process

Confirm the cities, date range, records, formats, rights and price in writing. The order-specific extract is then prepared and supplied under the agreed payment and delivery conditions.

One secure download package for the agreed release and licensed entity.

Delivery timing, access expiry, formats and prerequisites are confirmed in the accepted order.

Support, refreshes, future releases, hosting and APIs are included only when expressly ordered.

Data Licensing Agreement · 2026-10

A non-exclusive, non-transferable licence for the named customer within the accepted scope and duration.

Permitted use

Internal research, development, testing and analytics within the accepted order.

Model training

Model training, external applications, commercial distribution and access by other legal entities must be expressly addressed in the order.

Redistribution

Raw or substantially reconstructable data cannot be resold, published or redistributed without separate written authorization.

Attribution

Preserve release provenance and applicable third-party notices. Published outputs must identify the data as synthetic.