Datasets:

electricsheepafrica
/

africa-arising-korean-labor-and-income-panel-study-klips-1998-2206-wav

license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - 10K<n<100K
tags:
  - tabular
  - africa
  - harvard-dataverse-(ifpri-africarising)
  - household-survey-microdata
  - ifpri
  - dataverse
  - africa-rising
  - microdata
pretty_name: >-
  Korean Labor and Income Panel Study (KLIPS), 1998-2206: Wave 1-9 (M1194) |
  Africa (Harvard Dataverse / AfricaRISING)

Korean Labor and Income Panel Study (KLIPS), 1998-2206: Wave 1-9 (M1194) | Africa (Harvard Dataverse / AfricaRISING)

🌍 87,613 observations · 0 Africa countries · —–— · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 87,613 observations of Household Survey Microdata data across 0 Africa countries.

About the source

Source: Harvard Dataverse (IFPRI AfricaRISING)
Publisher: International Food Policy Research Institute
License: cc-by-4.0
Topic: Household Survey Microdata

Geographic coverage

0 Africa countries · top rows shown below, sorted by row count:

Per-country coverage not computable for this schema.

Schema

Column	Type	Description	Example
`PID`	`int64`	—	`101`
`jobwave`	`int64`	—	`1`
`jobseq`	`int64`	—	`1`
`jobnum`	`int64`	—	`101`
`jobnumc`	`float64`	—	`101.0`
`jobcens`	`int64`	—	`3`
`jobtype`	`float64`	—	`1.0`
`jobclass`	`float64`	—	`1.0`
`mainjob`	`float64`	—	`1.0`
`HHID98`	`float64`	—	`1.0`
`HMEM98`	`float64`	—	`1.0`
`HHID99`	`float64`	—	`1.0`
`HMEM99`	`float64`	—	`1.0`
`HHID00`	`float64`	—	`1.0`
`HMEM00`	`float64`	—	`1.0`
`HHID01`	`float64`	—	`2.0`
`HMEM01`	`float64`	—	`1.0`
`HHID02`	`float64`	—	`1.0`
`HMEM02`	`float64`	—	`1.0`
`HHID03`	`float64`	—	`1.0`
`HMEM03`	`float64`	—	`1.0`
`HHID04`	`float64`	—	`1.0`
`HMEM04`	`float64`	—	`2.0`
`HHID05`	`float64`	—	`1.0`
`HMEM05`	`float64`	—	`1.0`
`HHID06`	`float64`	—	`1.0`
`HMEM06`	`float64`	—	`1.0`
`J001`	`float64`	—	`1988.0`
`J002`	`float64`	—	`10.0`
`J003`	`float64`	—	`20.0`
`j004`	`float64`	—	`2001.0`
`j005`	`float64`	—	`6.0`
`j006`	`float64`	—	`10.0`
`j007`	`float64`	—	`1.0`
`j008`	`float64`	—	`2002.0`
`j009`	`float64`	—	`4.0`
`j010`	`float64`	—	`2003.0`
`j011`	`float64`	—	`5.0`
`j012`	`float64`	—	`999.0`
`j013`	`float64`	—	`3.0`
... (113 more columns)

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-arising-korean-labor-and-income-panel-study-klips-1998-2206-wav")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

kenya = df[df["country_iso3"] == "KEN"]

Time-series for a single indicator

sample = df.sort_values("year")
sample.plot(x="year", y="value")

Citation

@misc{africa_arising_korean_labor_and_income_panel_study_klips_1998_2206_wav_2026,
  title        = {Korean Labor and Income Panel Study (KLIPS), 1998-2206: Wave 1-9 (M1194) | Africa (Harvard Dataverse / AfricaRISING)},
  author       = {International Food Policy Research Institute},
  year         = {2026},
  url          = {https://dataverse.harvard.edu/dataverse/africaRISING},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-arising-korean-labor-and-income-panel-study-klips-1998-2206-wav}}
}

License

Released under cc-by-4.0.

Original data © International Food Policy Research Institute. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://dataverse.harvard.edu/dataverse/africaRISING