electricsheepasia/asia-ilo-sdg-0851-sex-ocu-nb-sdg-indicator-8-5-1-average-hourly-earnings-of-emp

SDG indicator 8.5.1 - Average hourly earnings of employees by sex (local currency) | Asia (ILOSTAT)

🌏 10,941 observations · 31 Asia countries · 2000–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 10,941 observations of Earnings data across 31 Asia countries, spanning 2000–2025, 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: Earnings

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=SDG_0851_SEX_OCU_NB 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

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

Country	Rows	First year	Last year
TUR	840	2005	2024
PHL	808	2007	2023
KHM	805	2000	2023
VNM	779	2007	2024
MNG	774	2008	2024
IDN	687	2007	2023
LKA	683	2010	2024
PAK	629	2006	2025
THA	537	2014	2024
KOR	504	2009	2022
PSE	473	2015	2025
MYS	420	2011	2020
JOR	334	2017	2024
IND	330	2018	2025
BTN	243	2018	2022
...	16 more countries

Indicators (sample)

• SDG_0851_SEX_OCU_NB — SDG indicator 8.5.1 - Average hourly earnings of employees by sex (local currency)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	AFG
ref_area.label	string	Country name in English	Afghanistan
source	string	ILOSTAT source code (e.g. labour force survey)	BA:15715
source.label	string	Source name in English	LFS - Labour Force Survey
indicator	string	ILOSTAT indicator code	SDG_0851_SEX_OCU_NB
indicator.label	string	Indicator name in English	SDG indicator 8.5.1 - Average hourly …
sex	string	Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female)	SEX_T
sex.label	string	—	Total
classif1	string	First classification variable (age, education, status, etc.)	OCU_SKILL_TOTAL
classif1.label	string	—	Occupation (Skill level): Total
time	int64	Observation year	2020
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	90.93
obs_status	string	Observation status flag (e.g. provisional, unreliable)	B
obs_status.label	string	—	Break in series
note_classif	string	—	C4:1002
note_classif.label	string	—	Nonstandard occupation: Including 3
note_indicator	string	—	T30:110_I11:264
note_indicator.label	string	—	`Currency: AFG - Afghani (AFN)
note_source	string	—	R1:3513
note_source.label	string	—	Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

• sex (4 unique values): SEX_T, SEX_M, SEX_F, SEX_O

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

from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-ilo-sdg-0851-sex-ocu-nb-sdg-indicator-8-5-1-average-hourly-earnings-of-emp")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

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

Time-series for a single indicator

sample = (df[df["indicator"] == "SDG_0851_SEX_OCU_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="SDG_0851_SEX_OCU_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "SDG_0851_SEX_OCU_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{asia_ilo_sdg_0851_sex_ocu_nb_sdg_indicator_8_5_1_average_hourly_earnings_of_emp_2025,
  title        = {SDG indicator 8.5.1 - Average hourly earnings of employees by sex (local currency) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_0851_SEX_OCU_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-sdg-0851-sex-ocu-nb-sdg-indicator-8-5-1-average-hourly-earnings-of-emp}}
}

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

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Provenance: ingested 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_0851_SEX_OCU_NB