Which education and household histories help distinguish shorter and longer transitions?

Elapsed time to first qualifying recorded job, or protocol-defined duration groups with censoring retained.

The study calls for Education history before completion, Household history effective at completion, Qualifying job start date and Observed follow-up period. The measure to examine is elapsed time to first qualifying recorded job, or protocol-defined duration groups with censoring retained.

Research datasets are prepared after an order for the selected city, date range and study requirements. The final schema and dated relationships are agreed for that extract.

Onboarded city options

Nairobi (Kenya) · Lagos (Nigeria)

Custom city requests

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

Data type

Synthetic

Priced domains

Demographics, Education, Employment, Households

Catalogue structure

Question-specific research data

Key features

Record concepts for your order-specific extract.

Who it is for

• ML engineers
• data scientists
• research teams
• analysts

Proposed study unit

Eligible education completion

Planned records and relationships for this study.

Record concepts for your extract

• Education history before completion
• Household history effective at completion
• Qualifying job start date
• Observed follow-up period

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

Compare baseline and history-enriched models on later cohorts; assess incremental predictive value and stability without causal attribution.

Proposed questions for your custom order

1. Which education and household histories help distinguish shorter and longer transitions?

Proposed research and analytical applications

• Prediction prototyping
• feature exploration
• temporal validation

Study and modeling guidance

Information available at prediction or evaluation time

Use education and effective-dated household history available at completion; later household or employment events are excluded from predictors.

Comparison and evaluation

Compare baseline and history-enriched models on later cohorts; assess incremental predictive value and stability without causal attribution.

Incomplete observation

Use survival-aware outcomes or explicitly justified duration groups; never label censored transitions as long completed transitions.

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

• The question specifies no duration thresholds or maximum follow-up window; these require a declared protocol.
• Required historical fields and feature completeness are unverified.
• Predictive importance does not identify education or household causal effects.
• Define first employment, target horizon and follow-up before training. Verify job-start dates and outcome completeness; evaluate later cohorts using only information available at prediction time.
• 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.