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Adequacy of social insurance programs (% of total welfare of beneficiary households)
float64
0.24
93
Afghanistan
AFG
2,007
15.695031
Armenia
ARM
2,008
26.948698
Armenia
ARM
2,009
33.28939
Armenia
ARM
2,010
33.568714
Armenia
ARM
2,011
33.250126
Armenia
ARM
2,012
34.19257
Armenia
ARM
2,013
31.665161
Armenia
ARM
2,014
32.642776
Armenia
ARM
2,015
33.26404
Armenia
ARM
2,016
33.56815
Armenia
ARM
2,017
35.859528
Armenia
ARM
2,018
36.579746
Armenia
ARM
2,019
39.302307
Armenia
ARM
2,020
44.59567
Armenia
ARM
2,021
40.581085
Armenia
ARM
2,022
37.510902
Azerbaijan
AZE
2,015
0.242614
Bangladesh
BGD
2,005
49.452328
Bangladesh
BGD
2,010
34.35872
Bangladesh
BGD
2,016
23.440714
Bangladesh
BGD
2,022
22.95783
Bhutan
BTN
2,012
27.636494
Bhutan
BTN
2,022
16.872305
Cambodia
KHM
2,008
6.394549
Cambodia
KHM
2,013
12.538815
China
CHN
2,013
55.345345
India
IND
2,011
4.543356
Iraq
IRQ
2,006
15.31107
Iraq
IRQ
2,012
19.635462
Jordan
JOR
2,006
36.4881
Jordan
JOR
2,010
33.106125
Kazakhstan
KAZ
2,007
10.524757
Kazakhstan
KAZ
2,010
22.932228
Kazakhstan
KAZ
2,014
4.530132
Kazakhstan
KAZ
2,015
49.857285
Kazakhstan
KAZ
2,017
52.119137
Kazakhstan
KAZ
2,018
53.525566
Kazakhstan
KAZ
2,019
56.566723
Kazakhstan
KAZ
2,020
57.37217
Kazakhstan
KAZ
2,021
54.724148
Kyrgyzstan
KGZ
2,006
29.955978
Kyrgyzstan
KGZ
2,011
34.67935
Kyrgyzstan
KGZ
2,012
43.980125
Kyrgyzstan
KGZ
2,013
44.369488
Kyrgyzstan
KGZ
2,014
45.94671
Kyrgyzstan
KGZ
2,015
46.144165
Kyrgyzstan
KGZ
2,016
45.30881
Kyrgyzstan
KGZ
2,017
44.733475
Kyrgyzstan
KGZ
2,018
43.55467
Kyrgyzstan
KGZ
2,019
43.637203
Kyrgyzstan
KGZ
2,020
43.264362
Laos
LAO
2,007
29.84061
Laos
LAO
2,018
90.856705
Malaysia
MYS
2,008
30.276123
Malaysia
MYS
2,012
11.246481
Malaysia
MYS
2,014
30.909916
Malaysia
MYS
2,016
31.390291
Maldives
MDV
2,004
3.913121
Maldives
MDV
2,009
11.036323
Maldives
MDV
2,016
10.551655
Maldives
MDV
2,019
16.396406
Mongolia
MNG
2,007
26.807219
Mongolia
MNG
2,009
29.191082
Mongolia
MNG
2,010
22.969202
Mongolia
MNG
2,011
24.940874
Mongolia
MNG
2,012
23.399538
Mongolia
MNG
2,014
29.745512
Mongolia
MNG
2,016
40.16289
Mongolia
MNG
2,018
40.74457
Mongolia
MNG
2,020
45.562214
Myanmar
MMR
2,017
16.418188
Nepal
NPL
2,010
18.24554
Pakistan
PAK
2,007
25.830048
Pakistan
PAK
2,009
19.444584
Pakistan
PAK
2,013
30.428846
Pakistan
PAK
2,015
32.47232
Pakistan
PAK
2,018
38.998463
Palestine
PSE
2,016
20.813135
Philippines
PHL
2,006
18.972357
Philippines
PHL
2,013
29.598925
Philippines
PHL
2,015
10.365789
Philippines
PHL
2,018
21.453161
Sri Lanka
LKA
2,006
36.550106
Sri Lanka
LKA
2,009
34.936695
Sri Lanka
LKA
2,012
32.09821
Sri Lanka
LKA
2,016
34.76023
Sri Lanka
LKA
2,019
33.552193
Syria
SYR
2,003
67.62899
Tajikistan
TJK
2,011
7.998573
Thailand
THA
2,006
3.771905
Thailand
THA
2,009
65.69184
Thailand
THA
2,011
81.81837
Thailand
THA
2,013
76.20813
Thailand
THA
2,017
79.26374
Thailand
THA
2,019
83.87239
Thailand
THA
2,021
92.99584

Adequacy Of Social Insurance Programs | Asia (Our World in Data)

🌏 121 observations · 29 Asia countries · 2003–2022 · Repackaged by Electric Sheep Asia

rows countries years license

TL;DR

This dataset contains 121 observations of Adequacy Of Social Insurance Programs data across 29 Asia countries, spanning 2003–2022.

About the source

Geographic coverage

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

Country Rows First year Last year
TUR 16 2004 2019
ARM 15 2008 2022
KGZ 11 2006 2020
MNG 9 2007 2020
KAZ 9 2007 2021
THA 7 2006 2021
VNM 6 2006 2020
LKA 5 2006 2019
PAK 5 2007 2018
MDV 4 2004 2019
PHL 4 2006 2018
BGD 4 2005 2022
MYS 4 2008 2016
BTN 2 2012 2022
YEM 2 2005 2014
... 14 more countries

Schema

Column Type Description Example
country_name string Afghanistan
country_iso3 string AFG
year int64 2007
Adequacy of social insurance programs (% of total welfare of beneficiary households) float64 15.695031

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-owid-adequacy-of-social-insurance-programs")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

indonesia = df[df["country_iso3"] == "IDN"]

Time-series for a single indicator

sample = df.sort_values("year")
sample.plot(x="year", y="Adequacy of social insurance programs (% of total welfare of beneficiary households)")

Citation

@misc{asia_owid_adequacy_of_social_insurance_programs_2022,
  title        = {Adequacy Of Social Insurance Programs | Asia (Our World in Data)},
  author       = {Our World in Data},
  year         = {2022},
  url          = {https://ourworldindata.org/grapher/adequacy-of-social-insurance-programs},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-owid-adequacy-of-social-insurance-programs}}
}

License

Released under cc-by-4.0.

Original data © Our World in Data. 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 2026-06-02 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/adequacy-of-social-insurance-programs

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