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year
int64
2.01k
2.02k
Adequacy of social safety net programs (% of total welfare of beneficiary households)
float64
0.17
48.1
Afghanistan
AFG
2,007
27.620964
Armenia
ARM
2,008
15.151555
Armenia
ARM
2,009
16.967255
Armenia
ARM
2,010
18.38447
Armenia
ARM
2,011
15.534431
Armenia
ARM
2,012
18.205938
Armenia
ARM
2,013
17.067692
Armenia
ARM
2,014
16.98853
Armenia
ARM
2,015
15.892346
Armenia
ARM
2,016
15.575439
Armenia
ARM
2,017
17.513397
Armenia
ARM
2,018
18.298536
Armenia
ARM
2,019
29.293491
Armenia
ARM
2,020
48.1281
Armenia
ARM
2,021
22.632856
Armenia
ARM
2,022
29.985796
Azerbaijan
AZE
2,015
6.081878
Bangladesh
BGD
2,005
19.71067
Bangladesh
BGD
2,010
3.99774
Bangladesh
BGD
2,016
2.59535
Bangladesh
BGD
2,022
1.620616
Bhutan
BTN
2,007
2.103454
Bhutan
BTN
2,022
12.140232
China
CHN
2,013
2.337669
East Timor
TLS
2,007
9.814421
Georgia
GEO
2,011
29.178883
Georgia
GEO
2,012
28.799316
Georgia
GEO
2,013
30.806484
Georgia
GEO
2,014
35.43946
Georgia
GEO
2,015
33.84235
Georgia
GEO
2,016
37.640545
Georgia
GEO
2,017
34.340614
Georgia
GEO
2,018
34.063343
Georgia
GEO
2,019
35.873425
Georgia
GEO
2,020
42.121002
Georgia
GEO
2,021
42.33938
India
IND
2,011
7.402832
Indonesia
IDN
2,015
15.943313
Indonesia
IDN
2,016
4.013755
Indonesia
IDN
2,017
3.167402
Indonesia
IDN
2,018
3.693826
Indonesia
IDN
2,019
4.862389
Indonesia
IDN
2,020
5.389443
Indonesia
IDN
2,021
5.296991
Indonesia
IDN
2,022
9.375052
Iran
IRN
2,017
7.657255
Iran
IRN
2,018
6.623115
Iran
IRN
2,019
5.813283
Iran
IRN
2,020
25.779295
Iraq
IRQ
2,006
2.286436
Iraq
IRQ
2,012
2.753124
Jordan
JOR
2,006
6.675276
Jordan
JOR
2,010
3.981736
Kazakhstan
KAZ
2,007
3.251413
Kazakhstan
KAZ
2,010
6.290282
Kazakhstan
KAZ
2,014
35.73579
Kazakhstan
KAZ
2,015
12.596169
Kazakhstan
KAZ
2,017
16.590696
Kazakhstan
KAZ
2,018
16.990355
Kazakhstan
KAZ
2,019
19.105917
Kazakhstan
KAZ
2,020
20.513708
Kazakhstan
KAZ
2,021
19.817673
Kyrgyzstan
KGZ
2,006
9.206789
Kyrgyzstan
KGZ
2,011
8.410479
Kyrgyzstan
KGZ
2,012
10.000693
Kyrgyzstan
KGZ
2,013
11.152781
Kyrgyzstan
KGZ
2,014
13.92022
Kyrgyzstan
KGZ
2,015
17.857021
Kyrgyzstan
KGZ
2,016
14.227838
Kyrgyzstan
KGZ
2,017
15.337955
Kyrgyzstan
KGZ
2,018
17.730883
Kyrgyzstan
KGZ
2,019
15.832272
Kyrgyzstan
KGZ
2,020
16.870518
Malaysia
MYS
2,008
1.745788
Malaysia
MYS
2,012
0.755081
Malaysia
MYS
2,014
2.163032
Malaysia
MYS
2,016
3.03563
Maldives
MDV
2,009
24.72657
Maldives
MDV
2,016
18.142
Maldives
MDV
2,019
18.318258
Mongolia
MNG
2,007
6.289463
Mongolia
MNG
2,009
6.820538
Mongolia
MNG
2,010
5.932642
Mongolia
MNG
2,011
10.982182
Mongolia
MNG
2,012
11.065214
Mongolia
MNG
2,014
4.463906
Mongolia
MNG
2,016
4.811442
Mongolia
MNG
2,018
6.523912
Mongolia
MNG
2,020
9.801428
Myanmar
MMR
2,017
1.207447
Nepal
NPL
2,010
2.512116
Pakistan
PAK
2,007
17.924421
Pakistan
PAK
2,009
12.002831
Pakistan
PAK
2,013
7.908932
Pakistan
PAK
2,015
5.702304
Pakistan
PAK
2,018
4.940313
Palestine
PSE
2,009
20.669527
Palestine
PSE
2,016
4.101665
Philippines
PHL
2,013
11.64435
Philippines
PHL
2,015
5.458818
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Adequacy Of Social Safety Net Programs | Asia (Our World in Data)

🌏 139 observations · 30 Asia countries · 2004–2022 · Repackaged by Electric Sheep Asia

rows countries years license

TL;DR

This dataset contains 139 observations of Adequacy Of Social Safety Net Programs data across 30 Asia countries, spanning 2004–2022.

About the source

Geographic coverage

30 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
GEO 11 2011 2021
MNG 9 2007 2020
KAZ 9 2007 2021
IDN 8 2015 2022
THA 7 2006 2021
VNM 6 2006 2020
PAK 5 2007 2018
LKA 5 2006 2019
MYS 4 2008 2016
IRN 4 2017 2020
BGD 4 2005 2022
MDV 3 2009 2019
... 15 more countries

Schema

Column Type Description Example
country_name string Afghanistan
country_iso3 string AFG
year int64 2007
Adequacy of social safety net programs (% of total welfare of beneficiary households) float64 27.620964

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-owid-adequacy-of-social-safety-net-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 safety net programs (% of total welfare of beneficiary households)")

Citation

@misc{asia_owid_adequacy_of_social_safety_net_programs_2022,
  title        = {Adequacy Of Social Safety Net Programs | Asia (Our World in Data)},
  author       = {Our World in Data},
  year         = {2022},
  url          = {https://ourworldindata.org/grapher/adequacy-of-social-safety-net-programs},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-owid-adequacy-of-social-safety-net-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-safety-net-programs

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