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electricsheepasia/asia-ilo-ccf-xoxr-cur-rt-official-exchange-rate-lcu-per-us-dollar

Official exchange rate (LCU per US dollar) | Asia (ILOSTAT) 🌏 1,165 observations · 45 Asia countries · 1990–2024 · Repackaged by Electric Sheep Asia TL;DR This dataset contains 1,165 observations of Currency conversion factors data across 45 Asia countries, spanning 1990–2024, covering 1 distinct indicators. About the source ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-ilo-ccf-xoxr-cur-rt-official-exchange-rate-lcu-per-us-dollar.

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Dataset Card

Official exchange rate (LCU per US dollar) | Asia (ILOSTAT)

🌏 1,165 observations · 45 Asia countries · 1990–2024 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years indicators license

TL;DR

This dataset contains 1,165 observations of Currency conversion factors data across 45 Asia countries, spanning 1990–2024, covering 1 distinct indicators.

About the source

ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation.

  • —Source: ILOSTAT
  • —Publisher: International Labour Organization (ILO)
  • —License: cc-by-4.0
  • —Topic: Currency conversion factors

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=CCF_XOXR_CUR_RT and filtered to Asia ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the source.label column for traceability.

Geographic coverage

45 Asia countries · top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
CYP4419902024
BRN3519902024
CHN3519902024
BHR3519902024
IDN3519902024
MYS3519902024
PAK3519902024
SGP3519902024
PHL3519902024
KOR3519902024
JPN3519902024
ISR3519902024
JOR3519902024
TUR3519902024
LKA3419902023
...30 more countries

Indicators (sample)

  • —CCF_XOXR_CUR_RT — Official exchange rate (LCU per US dollar)

Schema

ColumnTypeDescriptionExample
ref_areastringISO 3166-1 alpha-3 country codeAFG
ref_area.labelstringCountry name in EnglishAfghanistan
sourcestringILOSTAT source code (e.g. labour force survey)XX:16652
source.labelstringSource name in EnglishOS - International Monetary Fund
indicatorstringILOSTAT indicator codeCCF_XOXR_CUR_RT
indicator.labelstringIndicator name in EnglishOfficial exchange rate (LCU per US do…
classif1stringFirst classification variable (age, education, status, etc.)CUR_NATL_CURRENT
classif1.labelstring—Currency: Current
timeint64Observation year2020
obs_valuefloat64Observed indicator value (unit varies — see indicator definition)76.8135
note_classifstring—C30:6435
note_classif.labelstring—Currency: AFG - Afghani (AFN)

Data quality & caveats

  • —Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here.
  • —When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used.
  • —Disaggregation columns (sex, classif1, classif2) are non-null only when the indicator publishes that breakdown.

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-ilo-ccf-xoxr-cur-rt-official-exchange-rate-lcu-per-us-dollar")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
indonesia = df[df["ref_area"] == "IDN"]

Time-series for a single indicator

python
sample = (df[df["indicator"] == "CCF_XOXR_CUR_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="CCF_XOXR_CUR_RT")

Pivot to country × year matrix

python
matrix = (df[df["indicator"] == "CCF_XOXR_CUR_RT"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

bibtex
@misc{asia_ilo_ccf_xoxr_cur_rt_official_exchange_rate_lcu_per_us_dollar_2024,
  title        = {Official exchange rate (LCU per US dollar) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2024},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=CCF_XOXR_CUR_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ccf-xoxr-cur-rt-official-exchange-rate-lcu-per-us-dollar}}
}

License

Released under cc-by-4.0.

Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.

About Electric Sheep

Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia 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/electricsheepasia


Provenance: ingested 2026-05-28 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=CCFXOXRCURRT_