Chile
CHLSouth America · Americas · UN member
The circle shows each metric's percentile rank (position among countries with data), which is comparable across metrics. Each metric's native score uses its own scale and is not comparable between metrics — see it (and the full formula) in “How is this calculated?”.
95 metrics for this country
Education
- Adult literacy rateTier A
100 = highest adult literacy in the dataset, 0 = lowest
Ranks 58th of 155 · native score 95.08 · reference year 2017
63percentileHow is this calculated?
- Metric
- Adult literacy rate
- Scale
- 100 = highest adult literacy in the dataset, 0 = lowest
- Raw value
- 96.4 % of adults
- Native score
- 95.08 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 63th percentile — ranks 58th of 155. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (96.4 − 26.83) / (100 − 26.83) = 95.08Min and max are this dataset's lowest (26.83) and highest (100) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2017
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest education spending (% GDP) in the dataset, 0 = lowest
Ranks 68th of 200 · native score 29.97 · reference year 2022
66percentileHow is this calculated?
- Metric
- Government education spending
- Scale
- 100 = highest education spending (% GDP) in the dataset, 0 = lowest
- Raw value
- 4.91201 % of GDP
- Native score
- 29.97 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 66th percentile — ranks 68th of 200. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (4.91 − 0) / (16.39 − 0) = 29.97Min and max are this dataset's lowest (0) and highest (16.39) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2022
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Mean years of schoolingTier A
100 = most years of schooling in the dataset, 0 = fewest
Ranks 53rd of 146 · native score 71.96 · reference year 2020
64percentileHow is this calculated?
- Metric
- Mean years of schooling
- Scale
- 100 = most years of schooling in the dataset, 0 = fewest
- Raw value
- 10.74 years
- Native score
- 71.96 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 64th percentile — ranks 53rd of 146. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (10.74 − 3.04) / (13.74 − 3.04) = 71.96Min and max are this dataset's lowest (3.04) and highest (13.74) values, so the score is relative to the current country set.
- Source
- Our World in Data · Our World in Data
- License
- CC BY 4.0
- Reference year
- 2020
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Mean years of schooling series compiled by Our World in Data from UNDP Human Development Report and UNESCO education statistics.
- PISA mathsTier A
100 = highest mathematics score in the dataset, 0 = lowest — participating economies only, not a world ranking
Ranks 41st of 55 · native score 34.66 · reference year 2018
26percentileHow is this calculated?
- Metric
- PISA maths
- Scale
- 100 = highest mathematics score in the dataset, 0 = lowest — participating economies only, not a world ranking
- Raw value
- 417.407 PISA points
- Native score
- 34.66 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 26th percentile — ranks 41st of 55. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (417.41 − 325.1) / (591.39 − 325.1) = 34.66Min and max are this dataset's lowest (325.1) and highest (591.39) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2018
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- PISA readingTier A
100 = highest reading score in the dataset, 0 = lowest — participating economies only, not a world ranking
Ranks 30th of 55 · native score 57.31 · reference year 2018
46percentileHow is this calculated?
- Metric
- PISA reading
- Scale
- 100 = highest reading score in the dataset, 0 = lowest — participating economies only, not a world ranking
- Raw value
- 452.273 PISA points
- Native score
- 57.31 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 46th percentile — ranks 30th of 55. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (452.27 − 314.02) / (555.24 − 314.02) = 57.31Min and max are this dataset's lowest (314.02) and highest (555.24) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2018
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- PISA scienceTier A
100 = highest science score in the dataset, 0 = lowest — participating economies only, not a world ranking
Ranks 31st of 55 · native score 43.71 · reference year 2018
45percentileHow is this calculated?
- Metric
- PISA science
- Scale
- 100 = highest science score in the dataset, 0 = lowest — participating economies only, not a world ranking
- Raw value
- 443.583 PISA points
- Native score
- 43.71 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 45th percentile — ranks 31st of 55. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (443.58 − 329.55) / (590.45 − 329.55) = 43.71Min and max are this dataset's lowest (329.55) and highest (590.45) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2018
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = lowest pupil–teacher ratio (best), 0 = highest
Ranks 93rd of 197 · native score 85.8 · reference year 2017
53percentileHow is this calculated?
- Metric
- Pupil–teacher ratio (primary)
- Scale
- 100 = lowest pupil–teacher ratio (best), 0 = highest
- Raw value
- 17.7936 pupils per teacher
- Native score
- 85.8 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 53th percentile — ranks 93rd of 197. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (17.79 − 6.93) / (83.41 − 6.93) ] = 85.8Min and max are this dataset's lowest (6.93) and highest (83.41) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2017
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Researchers in R&DTier A
100 = most researchers per capita, 0 = fewest
Ranks 71st of 144 · native score 65.62 · reference year 2022
51percentileHow is this calculated?
- Metric
- Researchers in R&D
- Scale
- 100 = most researchers per capita, 0 = fewest
- Raw value
- 707.056 per million people
- Native score
- 65.62 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 51th percentile — ranks 71st of 144. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 707.06 − ln 1.45) / (ln 18,129.75 − ln 1.45) = 65.62Log scale (values are heavily skewed). 0 and 100 are the dataset's min (1.45) and max (18,129.75) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2022
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest completion in the dataset, 0 = lowest
Ranks 17th of 192 · native score 67.14 · reference year 2024
91percentileHow is this calculated?
- Metric
- Lower-secondary completion rate
- Scale
- 100 = highest completion in the dataset, 0 = lowest
- Raw value
- 102.349 % of relevant age group
- Native score
- 67.14 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 91th percentile — ranks 17th of 192. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (102.35 − 8.89) / (148.09 − 8.89) = 67.14Min and max are this dataset's lowest (8.89) and highest (148.09) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest tertiary enrollment in the dataset, 0 = lowest
Ranks 7th of 195 · native score 66.35 · reference year 2024
97percentileHow is this calculated?
- Metric
- Tertiary enrollment ratio
- Scale
- 100 = highest tertiary enrollment in the dataset, 0 = lowest
- Raw value
- 110.177 % gross enrollment
- Native score
- 66.35 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 97th percentile — ranks 7th of 195. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (110.18 − 1.84) / (165.11 − 1.84) = 66.35Min and max are this dataset's lowest (1.84) and highest (165.11) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
Society & culture
100 = tallest mean male height, 0 = shortest — descriptive, not better or worse
Ranks 94th of 200 · native score 52.85 · reference year 1996
53percentileHow is this calculated?
- Metric
- Adult male height
- Scale
- 100 = tallest mean male height, 0 = shortest — descriptive, not better or worse
- Raw value
- 171.811 cm
- Native score
- 52.85 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 53th percentile — ranks 94th of 200. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (171.81 − 159.79) / (182.54 − 159.79) = 52.85Min and max are this dataset's lowest (159.79) and highest (182.54) values, so the score is relative to the current country set.
- Source
- NCD Risk Factor Collaboration (via Our World in Data) · NCD Risk Factor Collaboration (NCD-RisC)
- License
- CC BY 4.0
- Reference year
- 1996
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Pooled analysis of population-based measured-height studies, retrieved via the Our World in Data mirror. Indexed by year of birth.
- Child labourTier B
100 = lowest child labour (best), 0 = highest
Ranks 16th of 97 · native score 94.78 · reference year 2012
84percentileHow is this calculated?
- Metric
- Child labour
- Scale
- 100 = lowest child labour (best), 0 = highest
- Raw value
- 4.5 % of children 7–14
- Native score
- 94.78 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 84th percentile — ranks 16th of 97. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (4.5 − 1.23) / (63.92 − 1.23) ] = 94.78Min and max are this dataset's lowest (1.23) and highest (63.92) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2012
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest cocoa supply per person in the dataset, 0 = lowest
Ranks 50th of 191 · native score 12.94 · reference year 2023
74percentileLow reliability (Tier C — Survey / proxy). Survey-based, patchy, or proxy metric. Indicative only — coverage gaps and methodology limits are significant. Not a definitive ranking.
How is this calculated?
- Metric
- Chocolate supply per person (proxy)
- Scale
- 100 = highest cocoa supply per person in the dataset, 0 = lowest
- Raw value
- 1.82 kg cocoa per person / year
- Native score
- 12.94 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 74th percentile — ranks 50th of 191. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (1.82 − 0) / (14.07 − 0) = 12.94Min and max are this dataset's lowest (0) and highest (14.07) values, so the score is relative to the current country set.
- Source
- FAO (via Our World in Data) · Food and Agriculture Organization of the UN (FAO)
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier C — Survey / proxy
- Source notes
- Cocoa 'food available for consumption' per capita from the FAO Food Balance Sheets, retrieved via the Our World in Data mirror. A supply proxy, not measured consumption — Tier C.
100 = highest female participation in the dataset, 0 = lowest
Ranks 95th of 186 · native score 61.63 · reference year 2025
49percentileHow is this calculated?
- Metric
- Female labour force participation
- Scale
- 100 = highest female participation in the dataset, 0 = lowest
- Raw value
- 52.887 % of women 15+
- Native score
- 61.63 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 49th percentile — ranks 95th of 186. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (52.89 − 4.66) / (82.91 − 4.66) = 61.63Min and max are this dataset's lowest (4.66) and highest (82.91) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest fish & seafood supply per person, 0 = lowest — descriptive, not better or worse
Ranks 98th of 191 · native score 16.76 · reference year 2023
49percentileHow is this calculated?
- Metric
- Fish & seafood supply per capita
- Scale
- 100 = highest fish & seafood supply per person, 0 = lowest — descriptive, not better or worse
- Raw value
- 14.36 kg per person per year
- Native score
- 16.76 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 49th percentile — ranks 98th of 191. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (14.36 − 0.38) / (83.81 − 0.38) = 16.76Min and max are this dataset's lowest (0.38) and highest (83.81) values, so the score is relative to the current country set.
- Source
- FAO Food Balance Sheets (via Our World in Data) · Food and Agriculture Organization of the UN (FAO)
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Per-capita food available for consumption from the FAO Food Balance Sheets, retrieved via the Our World in Data mirror. This is national food SUPPLY per person, not measured intake.
100 = highest fruit supply per person, 0 = lowest — descriptive, not better or worse
Ranks 109th of 191 · native score 13.82 · reference year 2023
43percentileHow is this calculated?
- Metric
- Fruit supply per capita
- Scale
- 100 = highest fruit supply per person, 0 = lowest — descriptive, not better or worse
- Raw value
- 76.81 kg per person per year
- Native score
- 13.82 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 43th percentile — ranks 109th of 191. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (76.81 − 4.27) / (529.06 − 4.27) = 13.82Min and max are this dataset's lowest (4.27) and highest (529.06) values, so the score is relative to the current country set.
- Source
- FAO Food Balance Sheets (via Our World in Data) · Food and Agriculture Organization of the UN (FAO)
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Per-capita food available for consumption from the FAO Food Balance Sheets, retrieved via the Our World in Data mirror. This is national food SUPPLY per person, not measured intake.
100 = most gender-equal (GII 0), 0 = most unequal (GII 1)
Ranks 33rd of 172 · native score 89.8 · reference year 2023
81percentileHow is this calculated?
- Metric
- Gender equality (inverted GII)
- Scale
- 100 = most gender-equal (GII 0), 0 = most unequal (GII 1)
- Raw value
- 0.102 index (0–1, lower = more equal)
- Native score
- 89.8 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 81th percentile — ranks 33rd of 172. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 − [ 100 · (0.1 − 0) / (1 − 0) ] = 89.8The index is kept on its own published scale. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- UNDP Human Development Report (via Our World in Data) · UNDP, Human Development Report Office
- License
- CC BY 3.0 IGO
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Gender Inequality Index from the UNDP Human Development Report, retrieved via the Our World in Data mirror.
- Gender wage gapTier B
100 = smallest gender wage gap (best), 0 = largest ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
Ranks 29th of 47 · native score 46.15 · reference year 2024
39percentileHow is this calculated?
- Metric
- Gender wage gap
- Scale
- 100 = smallest gender wage gap (best), 0 = largest ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
- Raw value
- 11.7578 % of men's median earnings
- Native score
- 46.15 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 39th percentile — ranks 29th of 47. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (11.76 − -8.35) / (28.99 − -8.35) ] = 46.15Min and max are this dataset's lowest (-8.35) and highest (28.99) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- OECD · Organisation for Economic Co-operation and Development
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- OECD statistics via the OECD SDMX API (sdmx.oecd.org). CC BY 4.0 since July 2024. Coverage is OECD members and selected partners only (~38 economies).
- Informal employmentTier A
100 = lowest informal employment (best), 0 = highest
Ranks 42nd of 145 · native score 74.05 · reference year 2025
71percentileHow is this calculated?
- Metric
- Informal employment
- Scale
- 100 = lowest informal employment (best), 0 = highest
- Raw value
- 26.368 % of employment
- Native score
- 74.05 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 71th percentile — ranks 42nd of 145. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (26.37 − 1.09) / (98.49 − 1.09) ] = 74.05Min and max are this dataset's lowest (1.09) and highest (98.49) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- ILOSTAT (International Labour Organization) · International Labour Organization
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- ILOSTAT via the ILO rplumber API (rplumber.ilo.org). CC BY 4.0 for databases published since 3 May 2023.
100 = highest meat supply per person, 0 = lowest — descriptive, not better or worse
Ranks 21st of 191 · native score 61.95 · reference year 2023
89percentileHow is this calculated?
- Metric
- Meat supply per capita
- Scale
- 100 = highest meat supply per person, 0 = lowest — descriptive, not better or worse
- Raw value
- 98.54 kg per person per year
- Native score
- 61.95 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 89th percentile — ranks 21st of 191. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (98.54 − 3.49) / (156.93 − 3.49) = 61.95Min and max are this dataset's lowest (3.49) and highest (156.93) values, so the score is relative to the current country set.
- Source
- FAO Food Balance Sheets (via Our World in Data) · Food and Agriculture Organization of the UN (FAO)
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Per-capita food available for consumption from the FAO Food Balance Sheets, retrieved via the Our World in Data mirror. This is national food SUPPLY per person, not measured intake.
100 = most Nobel laureates per capita, 0 = fewest — descriptive, a fun proxy
Ranks 48th of 79 · native score 41.67 · reference year 2024
40percentileHow is this calculated?
- Metric
- Nobel laureates per capita
- Scale
- 100 = most Nobel laureates per capita, 0 = fewest — descriptive, a fun proxy
- Raw value
- 1.007 laureates per 10 million
- Native score
- 41.67 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 40th percentile — ranks 48th of 79. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 1.01 − ln 0.04) / (ln 111.02 − ln 0.04) = 41.67Log scale (values are heavily skewed). 0 and 100 are the dataset's min (0.04) and max (111.02) — relative, not absolute or year-over-year comparable.
- Source
- The Nobel Prize (nobelprize.org) · The Nobel Foundation
- License
- CC0 1.0 (public domain)
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- Laureates from the official Nobel Prize API (CC0), counted by present-day birth country and divided by World Bank population.
100 = universal social protection coverage, 0 = none
Ranks 70th of 214 · native score 75.3 · reference year 2022
67percentileHow is this calculated?
- Metric
- Social protection coverage
- Scale
- 100 = universal social protection coverage, 0 = none
- Raw value
- 75.3 % of population
- Native score
- 75.3 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 67th percentile — ranks 70th of 214. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (75.3 − 0) / (100 − 0) = 75.3The index is kept on its own published scale.
- Source
- ILOSTAT (International Labour Organization) · International Labour Organization
- License
- CC BY 4.0
- Reference year
- 2022
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- ILOSTAT via the ILO rplumber API (rplumber.ilo.org). CC BY 4.0 for databases published since 3 May 2023.
- Social tolerance (proxy)Tier C
100 = most accepting (fewest object to different-religion neighbours), 0 = least
Ranks 26th of 94 · native score 94.09 · reference year 2022
73percentileLow reliability (Tier C — Survey / proxy). Survey-based, patchy, or proxy metric. Indicative only — coverage gaps and methodology limits are significant. Not a definitive ranking.
How is this calculated?
- Metric
- Social tolerance (proxy)
- Scale
- 100 = most accepting (fewest object to different-religion neighbours), 0 = least
- Raw value
- 5.91396 % who object to different-religion neighbours
- Native score
- 94.09 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 73th percentile — ranks 26th of 94. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 − [ 100 · (5.91 − 0) / (100 − 0) ] = 94.09The index is kept on its own published scale. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Values Survey (via Our World in Data) · World Values Survey Association / Integrated Values Surveys
- License
- CC BY 4.0
- Reference year
- 2022
- Retrieved
- 2026-07-12
- Confidence
- Tier C — Survey / proxy
- Source notes
- Share of survey respondents who mention 'people of a different religion' when asked which groups they would not want as neighbours (World Values Survey / Integrated Values Surveys), via the Our World in Data mirror. A single attitudinal proxy — Tier C.
100 = most arrivals (2019), 0 = fewest — descriptive, not better or worse
Ranks 49th of 174 · native score 70.31 · reference year 2019
72percentileHow is this calculated?
- Metric
- International tourist arrivals
- Scale
- 100 = most arrivals (2019), 0 = fewest — descriptive, not better or worse
- Raw value
- 4,518,000 arrivals per year
- Native score
- 70.31 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 72th percentile — ranks 49th of 174. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 4,518,000 − ln 3,700) / (ln 90,914,000 − ln 3,700) = 70.31Log scale (values are heavily skewed). 0 and 100 are the dataset's min (3,700) and max (90,914,000) — relative, not absolute or year-over-year comparable.
- Source
- UN Tourism (via Our World in Data) · UN Tourism (UNWTO)
- License
- CC BY 4.0
- Reference year
- 2019
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- International tourist arrivals — overnight visitors — from UN Tourism (UNWTO), retrieved via the Our World in Data mirror (OWID's processing layer is CC BY; underlying values are UN Tourism).
100 = highest union density in the dataset, 0 = lowest — descriptive, not better or worse
Ranks 66th of 139 · native score 18.52 · reference year 2018
53percentileHow is this calculated?
- Metric
- Trade union density
- Scale
- 100 = highest union density in the dataset, 0 = lowest — descriptive, not better or worse
- Raw value
- 17.094 % of employees
- Native score
- 18.52 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 53th percentile — ranks 66th of 139. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (17.09 − 0.2) / (91.42 − 0.2) = 18.52Min and max are this dataset's lowest (0.2) and highest (91.42) values, so the score is relative to the current country set.
- Source
- ILOSTAT (International Labour Organization) · International Labour Organization
- License
- CC BY 4.0
- Reference year
- 2018
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- ILOSTAT via the ILO rplumber API (rplumber.ilo.org). CC BY 4.0 for databases published since 3 May 2023.
100 = highest vegetables supply per person, 0 = lowest — descriptive, not better or worse
Ranks 112th of 191 · native score 17.73 · reference year 2023
42percentileHow is this calculated?
- Metric
- Vegetable supply per capita
- Scale
- 100 = highest vegetables supply per person, 0 = lowest — descriptive, not better or worse
- Raw value
- 77.57 kg per person per year
- Native score
- 17.73 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 42th percentile — ranks 112th of 191. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (77.57 − 5.34) / (412.72 − 5.34) = 17.73Min and max are this dataset's lowest (5.34) and highest (412.72) values, so the score is relative to the current country set.
- Source
- FAO Food Balance Sheets (via Our World in Data) · Food and Agriculture Organization of the UN (FAO)
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Per-capita food available for consumption from the FAO Food Balance Sheets, retrieved via the Our World in Data mirror. This is national food SUPPLY per person, not measured intake.
100 = most hours worked in the dataset, 0 = fewest — descriptive, not better or worse. ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
Ranks 5th of 44 · native score 62.29 · reference year 2025
90percentileHow is this calculated?
- Metric
- Average annual working hours
- Scale
- 100 = most hours worked in the dataset, 0 = fewest — descriptive, not better or worse. ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
- Raw value
- 1,912 hours per year
- Native score
- 62.29 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 90th percentile — ranks 5th of 44. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (1,912 − 1,332.2) / (2,263 − 1,332.2) = 62.29Min and max are this dataset's lowest (1,332.2) and highest (2,263) values, so the score is relative to the current country set.
- Source
- OECD · Organisation for Economic Co-operation and Development
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- OECD statistics via the OECD SDMX API (sdmx.oecd.org). CC BY 4.0 since July 2024. Coverage is OECD members and selected partners only (~38 economies).
Demographics & migration
100 = highest dependency ratio, 0 = lowest — descriptive, not better or worse
Ranks 178th of 215 · native score 30.13 · reference year 2025
17percentileHow is this calculated?
- Metric
- Age dependency ratio
- Scale
- 100 = highest dependency ratio, 0 = lowest — descriptive, not better or worse
- Raw value
- 45.2153 dependents per 100 working-age
- Native score
- 30.13 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 17th percentile — ranks 178th of 215. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (45.22 − 20.05) / (103.59 − 20.05) = 30.13Min and max are this dataset's lowest (20.05) and highest (103.59) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest fertility, 0 = lowest — descriptive, not better or worse
Ranks 202nd of 215 · native score 10.25 · reference year 2024
6percentileHow is this calculated?
- Metric
- Fertility rate
- Scale
- 100 = highest fertility, 0 = lowest — descriptive, not better or worse
- Raw value
- 1.14 births per woman
- Native score
- 10.25 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 6th percentile — ranks 202nd of 215. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (1.14 − 0.58) / (6.03 − 0.58) = 10.25Min and max are this dataset's lowest (0.58) and highest (6.03) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = largest female−male gap, 0 = smallest (or male-favouring) — descriptive, not better or worse
Ranks 173rd of 215 · native score 32.65 · reference year 2024
20percentileHow is this calculated?
- Metric
- Life expectancy gap (female − male)
- Scale
- 100 = largest female−male gap, 0 = smallest (or male-favouring) — descriptive, not better or worse
- Raw value
- 3.774 years (female − male)
- Native score
- 32.65 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 20th percentile — ranks 173rd of 215. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (3.77 − 0.46) / (10.62 − 0.46) = 32.65Min and max are this dataset's lowest (0.46) and highest (10.62) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = oldest median age, 0 = youngest — descriptive, not better or worse
Ranks 91st of 237 · native score 47.79 · reference year 2023
62percentileHow is this calculated?
- Metric
- Median age
- Scale
- 100 = oldest median age, 0 = youngest — descriptive, not better or worse
- Raw value
- 35.969 years
- Native score
- 47.79 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 62th percentile — ranks 91st of 237. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (35.97 − 14.32) / (59.63 − 14.32) = 47.79Min and max are this dataset's lowest (14.32) and highest (59.63) values, so the score is relative to the current country set.
- Source
- UN World Population Prospects (via Our World in Data) · UN DESA, Population Division
- License
- CC BY 3.0 IGO
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- UN World Population Prospects (median age; medium-variant projections), retrieved via the Our World in Data mirror.
100 = highest foreign-born share, 0 = lowest — descriptive, not better or worse
Ranks 93rd of 214 · native score 7.8 · reference year 2024
57percentileHow is this calculated?
- Metric
- Foreign-born population
- Scale
- 100 = highest foreign-born share, 0 = lowest — descriptive, not better or worse
- Raw value
- 7.8 % foreign-born
- Native score
- 7.8 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 57th percentile — ranks 93rd of 214. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (7.8 − 0) / (100 − 0) = 7.8The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest net inflow per capita, 0 = highest net outflow — descriptive, not better or worse
Ranks 41st of 215 · native score 54.66 · reference year 2025
81percentileHow is this calculated?
- Metric
- Net migration rate
- Scale
- 100 = highest net inflow per capita, 0 = highest net outflow — descriptive, not better or worse
- Raw value
- 2.604 per 1,000 people
- Native score
- 54.66 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 81th percentile — ranks 41st of 215. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (2.6 − -46.91) / (43.67 − -46.91) = 54.66Min and max are this dataset's lowest (-46.91) and highest (43.67) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = most populous, 0 = least populous — descriptive, not better or worse
Ranks 65th of 215 · native score 64 · reference year 2025
70percentileHow is this calculated?
- Metric
- Total population
- Scale
- 100 = most populous, 0 = least populous — descriptive, not better or worse
- Raw value
- 19,859,921 people
- Native score
- 64 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 70th percentile — ranks 65th of 215. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 19,859,921 − ln 9,492) / (ln 1,463,865,525 − ln 9,492) = 64Log scale (values are heavily skewed). 0 and 100 are the dataset's min (9,492) and max (1,463,865,525) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = most dense, 0 = least dense — descriptive, not better or worse
Ranks 176th of 215 · native score 44.11 · reference year 2023
18percentileHow is this calculated?
- Metric
- Population density
- Scale
- 100 = most dense, 0 = least dense — descriptive, not better or worse
- Raw value
- 26.4647 people per km²
- Native score
- 44.11 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 18th percentile — ranks 176th of 215. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 26.46 − ln 0.14) / (ln 20,569.7 − ln 0.14) = 44.11Log scale (values are heavily skewed). 0 and 100 are the dataset's min (0.14) and max (20,569.7) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = fastest projected growth, 0 = fastest projected decline — descriptive, not better or worse
Ranks 152nd of 237 · native score 26.92 · reference year 2050
36percentileHow is this calculated?
- Metric
- Population change (projection)
- Scale
- 100 = fastest projected growth, 0 = fastest projected decline — descriptive, not better or worse
- Raw value
- 2.8 % change 2024→2050
- Native score
- 26.92 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 36th percentile — ranks 152nd of 237. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (2.8 − -32.9) / (99.7 − -32.9) = 26.92Min and max are this dataset's lowest (-32.9) and highest (99.7) values, so the score is relative to the current country set.
- Source
- UN World Population Prospects (via Our World in Data) · UN DESA, Population Division
- License
- CC BY 3.0 IGO
- Reference year
- 2050
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- UN World Population Prospects (median age; medium-variant projections), retrieved via the Our World in Data mirror.
100 = most refugees produced, 0 = fewest — descriptive, not better or worse
Ranks 79th of 211 · native score 62.8 · reference year 2024
63percentileHow is this calculated?
- Metric
- Refugees (by origin)
- Scale
- 100 = most refugees produced, 0 = fewest — descriptive, not better or worse
- Raw value
- 4,809 refugees
- Native score
- 62.8 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 63th percentile — ranks 79th of 211. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = percentile rank of 4,809 among 211 countries = 62.8The score is the country's position in the distribution.
- Source
- UNHCR (via Our World in Data) · UN High Commissioner for Refugees (UNHCR)
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Refugee population by country of origin, retrieved via the Our World in Data mirror.
100 = highest remittance inflow (% GDP), 0 = lowest — descriptive, not better or worse
Ranks 188th of 193 · native score 0.04 · reference year 2024
3percentileHow is this calculated?
- Metric
- Remittances received
- Scale
- 100 = highest remittance inflow (% GDP), 0 = lowest — descriptive, not better or worse
- Raw value
- 0.0304727 % of GDP
- Native score
- 0.04 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 3th percentile — ranks 188th of 193. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (0.03 − 0.01) / (47.15 − 0.01) = 0.04Min and max are this dataset's lowest (0.01) and highest (47.15) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = fully rural, 0 = fully urban — descriptive, not better or worse
Ranks 182nd of 215 · native score 10.82 · reference year 2025
16percentileHow is this calculated?
- Metric
- Rural population share
- Scale
- 100 = fully rural, 0 = fully urban — descriptive, not better or worse
- Raw value
- 10.8227 % rural
- Native score
- 10.82 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 16th percentile — ranks 182nd of 215. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (10.82 − 0) / (100 − 0) = 10.82The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest male-to-female birth ratio, 0 = lowest — descriptive, not better or worse
Ranks 143rd of 215 · native score 21.05 · reference year 2024
33percentileHow is this calculated?
- Metric
- Sex ratio at birth
- Scale
- 100 = highest male-to-female birth ratio, 0 = lowest — descriptive, not better or worse
- Raw value
- 1.043 male births per female birth
- Native score
- 21.05 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 33th percentile — ranks 143rd of 215. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (1.04 − 1.01) / (1.16 − 1.01) = 21.05Min and max are this dataset's lowest (1.01) and highest (1.16) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = fully urban, 0 = fully rural — descriptive, not better or worse
Ranks 34th of 215 · native score 89.18 · reference year 2025
84percentileHow is this calculated?
- Metric
- Urbanization
- Scale
- 100 = fully urban, 0 = fully rural — descriptive, not better or worse
- Raw value
- 89.1773 % urban
- Native score
- 89.18 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 84th percentile — ranks 34th of 215. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (89.18 − 0) / (100 − 0) = 89.18The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
Environment & infrastructure
100 = most air passengers, 0 = fewest — descriptive, not better or worse
Ranks 32nd of 177 · native score 82.2 · reference year 2023
82percentileHow is this calculated?
- Metric
- Air transport passengers
- Scale
- 100 = most air passengers, 0 = fewest — descriptive, not better or worse
- Raw value
- 21,323,388 passengers per year
- Native score
- 82.2 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 82th percentile — ranks 32nd of 177. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = percentile rank of 21,323,388 among 177 countries = 82.2The score is the country's position in the distribution.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Climate vulnerabilityTier B
100 = least climate-vulnerable (best), 0 = most vulnerable
Ranks 25th of 190 · native score 78.78 · reference year 2024
87percentileHow is this calculated?
- Metric
- Climate vulnerability
- Scale
- 100 = least climate-vulnerable (best), 0 = most vulnerable
- Raw value
- 0.346583 index (0–1, higher = more vulnerable)
- Native score
- 78.78 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 87th percentile — ranks 25th of 190. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (0.35 − 0.26) / (0.66 − 0.26) ] = 78.78Min and max are this dataset's lowest (0.26) and highest (0.66) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- ND-GAIN Country Index (University of Notre Dame) · University of Notre Dame Global Adaptation Initiative
- License
- CC BY 3.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- ND-GAIN vulnerability score. The source ZIP is version-stamped (no stable 'latest' URL), so the snapshot is pinned to a specific release; refreshes are a manual, documented step.
- CO₂ emissions per capitaTier A
100 = lowest emissions per capita (best), 0 = highest
Ranks 128th of 214 · native score 40.42 · reference year 2024
40percentileHow is this calculated?
- Metric
- CO₂ emissions per capita
- Scale
- 100 = lowest emissions per capita (best), 0 = highest
- Raw value
- 3.98312 tonnes CO₂ per capita
- Native score
- 40.42 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 40th percentile — ranks 128th of 214. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = 100 − [ percentile rank of 3.98 among 214 countries ] = 40.42The score is the country's position in the distribution. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- Global Carbon Project (via Our World in Data) · Global Carbon Project
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Per-capita CO₂ emissions from the Global Carbon Project's Global Carbon Budget, retrieved via the Our World in Data mirror.
- Access to electricityTier A
100 = universal electricity access, 0 = none
Ranks 1st of 214 · native score 100 · reference year 2023
70percentileHow is this calculated?
- Metric
- Access to electricity
- Scale
- 100 = universal electricity access, 0 = none
- Raw value
- 100 % of population
- Native score
- 100 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 70th percentile — ranks 1st of 214. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (100 − 0) / (100 − 0) = 100The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest electricity use per capita, 0 = lowest — descriptive, not better or worse
Ranks 52nd of 149 · native score 70.46 · reference year 2024
65percentileHow is this calculated?
- Metric
- Electricity use per capita
- Scale
- 100 = highest electricity use per capita, 0 = lowest — descriptive, not better or worse
- Raw value
- 4,373.39 kWh per capita
- Native score
- 70.46 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 65th percentile — ranks 52nd of 149. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 4,373.39 − ln 13.72) / (ln 48,997.95 − ln 13.72) = 70.46Log scale (values are heavily skewed). 0 and 100 are the dataset's min (13.72) and max (48,997.95) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest energy use per capita, 0 = lowest — descriptive, not better or worse
Ranks 58th of 177 · native score 71.91 · reference year 2024
68percentileHow is this calculated?
- Metric
- Energy use per capita
- Scale
- 100 = highest energy use per capita, 0 = lowest — descriptive, not better or worse
- Raw value
- 2,029.03 kg oil-equivalent per capita
- Native score
- 71.91 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 68th percentile — ranks 58th of 177. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 2,029.03 − ln 9.73) / (ln 16,343.04 − ln 9.73) = 71.91Log scale (values are heavily skewed). 0 and 100 are the dataset's min (9.73) and max (16,343.04) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = most fixed-broadband subscriptions per capita, 0 = fewest
Ranks 79th of 208 · native score 38.92 · reference year 2024
62percentileHow is this calculated?
- Metric
- Fixed broadband subscriptions
- Scale
- 100 = most fixed-broadband subscriptions per capita, 0 = fewest
- Raw value
- 23.7314 per 100 people
- Native score
- 38.92 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 62th percentile — ranks 79th of 208. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (23.73 − 0) / (60.97 − 0) = 38.92Min and max are this dataset's lowest (0) and highest (60.97) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = most forested, 0 = least — descriptive, not better or worse
Ranks 117th of 213 · native score 25.01 · reference year 2023
45percentileHow is this calculated?
- Metric
- Forest area
- Scale
- 100 = most forested, 0 = least — descriptive, not better or worse
- Raw value
- 25.0116 % of land area
- Native score
- 25.01 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 45th percentile — ranks 117th of 213. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (25.01 − 0) / (100 − 0) = 25.01The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Internet usersTier A
100 = whole population online, 0 = none
Ranks 28th of 209 · native score 95.59 · reference year 2024
87percentileHow is this calculated?
- Metric
- Internet users
- Scale
- 100 = whole population online, 0 = none
- Raw value
- 95.5902 % of population
- Native score
- 95.59 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 87th percentile — ranks 28th of 209. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (95.59 − 0) / (100 − 0) = 95.59The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = best logistics performance (index 5), 0 = worst (1)
Ranks 61st of 169 · native score 50 · reference year 2022
63percentileHow is this calculated?
- Metric
- Logistics Performance Index
- Scale
- 100 = best logistics performance (index 5), 0 = worst (1)
- Raw value
- 3 index (1–5)
- Native score
- 50 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 63th percentile — ranks 61st of 169. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (3 − 1) / (5 − 1) = 50The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2022
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = most waste generated per capita, 0 = least — descriptive, not better or worse. ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
Ranks 35th of 61 · native score 53.5 · reference year 2023
43percentileHow is this calculated?
- Metric
- Municipal waste per capita
- Scale
- 100 = most waste generated per capita, 0 = least — descriptive, not better or worse. ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
- Raw value
- 440.098 kg per capita
- Native score
- 53.5 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 43th percentile — ranks 35th of 61. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (440.1 − 35.67) / (791.58 − 35.67) = 53.5Min and max are this dataset's lowest (35.67) and highest (791.58) values, so the score is relative to the current country set.
- Source
- OECD · Organisation for Economic Co-operation and Development
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- OECD statistics via the OECD SDMX API (sdmx.oecd.org). CC BY 4.0 since July 2024. Coverage is OECD members and selected partners only (~38 economies).
- PM2.5 air pollutionTier A
100 = cleanest air (lowest PM2.5), 0 = most polluted
Ranks 134th of 200 · native score 33.25 · reference year 2023
33percentileHow is this calculated?
- Metric
- PM2.5 air pollution
- Scale
- 100 = cleanest air (lowest PM2.5), 0 = most polluted
- Raw value
- 23.1032 µg/m³
- Native score
- 33.25 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 33th percentile — ranks 134th of 200. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = 100 − [ percentile rank of 23.1 among 200 countries ] = 33.25The score is the country's position in the distribution. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Protected land areaTier A
100 = most land protected, 0 = least
Ranks 79th of 212 · native score 21.6 · reference year 2025
63percentileHow is this calculated?
- Metric
- Protected land area
- Scale
- 100 = most land protected, 0 = least
- Raw value
- 21.6 % of land area
- Native score
- 21.6 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 63th percentile — ranks 79th of 212. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (21.6 − 0) / (100 − 0) = 21.6The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Recycling rateTier B
100 = highest recycling rate in the dataset, 0 = lowest ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
Ranks 50th of 54 · native score 1.53 · reference year 2023
8percentileHow is this calculated?
- Metric
- Recycling rate
- Scale
- 100 = highest recycling rate in the dataset, 0 = lowest ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
- Raw value
- 0.802058 % of municipal waste
- Native score
- 1.53 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 8th percentile — ranks 50th of 54. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (0.8 − 0) / (52.43 − 0) = 1.53Min and max are this dataset's lowest (0) and highest (52.43) values, so the score is relative to the current country set.
- Source
- OECD · Organisation for Economic Co-operation and Development
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- OECD statistics via the OECD SDMX API (sdmx.oecd.org). CC BY 4.0 since July 2024. Coverage is OECD members and selected partners only (~38 economies).
- Renewable energy shareTier A
100 = all energy renewable, 0 = none
Ranks 94th of 212 · native score 24.2 · reference year 2021
56percentileHow is this calculated?
- Metric
- Renewable energy share
- Scale
- 100 = all energy renewable, 0 = none
- Raw value
- 24.2 % of final energy
- Native score
- 24.2 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 56th percentile — ranks 94th of 212. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (24.2 − 0) / (100 − 0) = 24.2The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2021
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
Economy
- New business densityTier A
100 = highest new-business density in the dataset, 0 = lowest
Ranks 27th of 182 · native score 4.51 · reference year 2023
85percentileHow is this calculated?
- Metric
- New business density
- Scale
- 100 = highest new-business density in the dataset, 0 = lowest
- Raw value
- 9.08967 new firms per 1,000 aged 15–64
- Native score
- 4.51 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 85th percentile — ranks 27th of 182. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (9.09 − 0.02) / (201.01 − 0.02) = 4.51Min and max are this dataset's lowest (0.02) and highest (201.01) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest employment ratio in the dataset, 0 = lowest
Ranks 99th of 186 · native score 54.05 · reference year 2025
47percentileHow is this calculated?
- Metric
- Employment-to-population ratio
- Scale
- 100 = highest employment ratio in the dataset, 0 = lowest
- Raw value
- 56.713 % of population 15+
- Native score
- 54.05 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 47th percentile — ranks 99th of 186. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (56.71 − 21.21) / (86.9 − 21.21) = 54.05Min and max are this dataset's lowest (21.21) and highest (86.9) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = most export-oriented (% GDP), 0 = lowest — descriptive, not better or worse
Ranks 104th of 192 · native score 16.84 · reference year 2025
46percentileHow is this calculated?
- Metric
- Exports of goods & services
- Scale
- 100 = most export-oriented (% GDP), 0 = lowest — descriptive, not better or worse
- Raw value
- 34.5636 % of GDP
- Native score
- 16.84 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 46th percentile — ranks 104th of 192. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (34.56 − 1.1) / (199.76 − 1.1) = 16.84Min and max are this dataset's lowest (1.1) and highest (199.76) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = largest net FDI inflow (% GDP), 0 = lowest — descriptive, not better or worse
Ranks 57th of 201 · native score 88.73 · reference year 2024
71percentileHow is this calculated?
- Metric
- Foreign direct investment
- Scale
- 100 = largest net FDI inflow (% GDP), 0 = lowest — descriptive, not better or worse
- Raw value
- 3.97989 % of GDP (net inflows)
- Native score
- 88.73 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 71th percentile — ranks 57th of 201. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (3.98 − -1,303.11) / (170.05 − -1,303.11) = 88.73Min and max are this dataset's lowest (-1,303.11) and highest (170.05) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- GDP growth (projection)Tier B
100 = highest projected growth, 0 = lowest
Ranks 127th of 189 · native score 12.72 · reference year 2031
33percentileHow is this calculated?
- Metric
- GDP growth (projection)
- Scale
- 100 = highest projected growth, 0 = lowest
- Raw value
- 2.37 % per year (avg 2026–2031)
- Native score
- 12.72 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 33th percentile — ranks 127th of 189. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (2.37 − 0.62) / (14.38 − 0.62) = 12.72Min and max are this dataset's lowest (0.62) and highest (14.38) values, so the score is relative to the current country set.
- Source
- IMF (World Economic Outlook) · International Monetary Fund
- License
- IMF terms (reuse with attribution)
- Reference year
- 2031
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- IMF World Economic Outlook via the IMF DataMapper API (imf.org/external/datamapper).
- GDP per capita (PPP)Tier A
100 = highest GDP per capita in the dataset, 0 = lowest (log scale)
Ranks 69th of 202 · native score 70.22 · reference year 2024
66percentileHow is this calculated?
- Metric
- GDP per capita (PPP)
- Scale
- 100 = highest GDP per capita in the dataset, 0 = lowest (log scale)
- Raw value
- 36,181.2 international $ (PPP, current)
- Native score
- 70.22 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 66th percentile — ranks 69th of 202. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 36,181.16 − ln 1,154.89) / (ln 155,941.29 − ln 1,154.89) = 70.22Log scale (values are heavily skewed). 0 and 100 are the dataset's min (1,154.89) and max (155,941.29) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = largest government revenue (% GDP), 0 = lowest — descriptive, not better or worse
Ranks 94th of 155 · native score 16.73 · reference year 2024
40percentileHow is this calculated?
- Metric
- Government revenue
- Scale
- 100 = largest government revenue (% GDP), 0 = lowest — descriptive, not better or worse
- Raw value
- 21.8551 % of GDP
- Native score
- 16.73 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 40th percentile — ranks 94th of 155. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (21.86 − 3.03) / (115.55 − 3.03) = 16.73Min and max are this dataset's lowest (3.03) and highest (115.55) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest gross savings (% GDP), 0 = lowest — descriptive, not better or worse
Ranks 86th of 177 · native score 55.94 · reference year 2025
51percentileHow is this calculated?
- Metric
- Gross savings
- Scale
- 100 = highest gross savings (% GDP), 0 = lowest — descriptive, not better or worse
- Raw value
- 22.8147 % of GDP
- Native score
- 55.94 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 51th percentile — ranks 86th of 177. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (22.81 − -21.13) / (57.42 − -21.13) = 55.94Min and max are this dataset's lowest (-21.13) and highest (57.42) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Income inequality (Gini)Tier A
100 = most equal (lowest Gini in the dataset), 0 = most unequal
Ranks 140th of 167 · native score 45.61 · reference year 2024
16percentileHow is this calculated?
- Metric
- Income inequality (Gini)
- Scale
- 100 = most equal (lowest Gini in the dataset), 0 = most unequal
- Raw value
- 43 Gini index (0–100)
- Native score
- 45.61 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 16th percentile — ranks 140th of 167. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (43 − 23.8) / (59.1 − 23.8) ] = 45.61Min and max are this dataset's lowest (23.8) and highest (59.1) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest inflation in the dataset, 0 = lowest/most negative — descriptive, not better or worse
Ranks 58th of 192 · native score 70.05 · reference year 2025
70percentileHow is this calculated?
- Metric
- Inflation rate
- Scale
- 100 = highest inflation in the dataset, 0 = lowest/most negative — descriptive, not better or worse
- Raw value
- 4.21294 % annual change (CPI)
- Native score
- 70.05 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 70th percentile — ranks 58th of 192. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = percentile rank of 4.21 among 192 countries = 70.05The score is the country's position in the distribution.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Labour productivityTier A
100 = highest output per worker, 0 = lowest
Ranks 63rd of 177 · native score 69.72 · reference year 2024
65percentileHow is this calculated?
- Metric
- Labour productivity
- Scale
- 100 = highest output per worker, 0 = lowest
- Raw value
- 63,924.4 constant PPP$ per worker
- Native score
- 69.72 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 65th percentile — ranks 63rd of 177. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 63,924.43 − ln 2,444.23) / (ln 263,863.91 − ln 2,444.23) = 69.72Log scale (values are heavily skewed). 0 and 100 are the dataset's min (2,444.23) and max (263,863.91) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest debt (% GDP) in the dataset, 0 = lowest — descriptive, not better or worse
Ranks 127th of 193 · native score 13.54 · reference year 2025
34percentileHow is this calculated?
- Metric
- Government debt
- Scale
- 100 = highest debt (% GDP) in the dataset, 0 = lowest — descriptive, not better or worse
- Raw value
- 41.8 % of GDP
- Native score
- 13.54 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 34th percentile — ranks 127th of 193. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (41.8 − 0) / (308.7 − 0) = 13.54Min and max are this dataset's lowest (0) and highest (308.7) values, so the score is relative to the current country set.
- Source
- IMF (World Economic Outlook) · International Monetary Fund
- License
- IMF terms (reuse with attribution)
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- IMF World Economic Outlook via the IMF DataMapper API (imf.org/external/datamapper).
100 = highest tax wedge in the dataset, 0 = lowest — descriptive, not better or worse. ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
Ranks 37th of 38 · native score 14.21 · reference year 2025
4percentileHow is this calculated?
- Metric
- Tax wedge on labour
- Scale
- 100 = highest tax wedge in the dataset, 0 = lowest — descriptive, not better or worse. ⚠️ OECD members only (~38 countries): read it as a within-OECD comparison, not a world ranking.
- Raw value
- 7.45852 % of labour cost
- Native score
- 14.21 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 4th percentile — ranks 37th of 38. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (7.46 − 0) / (52.48 − 0) = 14.21Min and max are this dataset's lowest (0) and highest (52.48) values, so the score is relative to the current country set.
- Source
- OECD · Organisation for Economic Co-operation and Development
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- OECD statistics via the OECD SDMX API (sdmx.oecd.org). CC BY 4.0 since July 2024. Coverage is OECD members and selected partners only (~38 economies).
- Unemployment rateTier A
100 = lowest unemployment in the dataset, 0 = highest
Ranks 141st of 186 · native score 74.04 · reference year 2025
24percentileHow is this calculated?
- Metric
- Unemployment rate
- Scale
- 100 = lowest unemployment in the dataset, 0 = highest
- Raw value
- 8.974 % of labour force
- Native score
- 74.04 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 24th percentile — ranks 141st of 186. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (8.97 − 0.13) / (34.2 − 0.13) ] = 74.04Min and max are this dataset's lowest (0.13) and highest (34.2) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Youth unemployment rateTier A
100 = lowest youth unemployment (best), 0 = highest
Ranks 146th of 186 · native score 72.25 · reference year 2025
22percentileHow is this calculated?
- Metric
- Youth unemployment rate
- Scale
- 100 = lowest youth unemployment (best), 0 = highest
- Raw value
- 21.635 % of labour force 15–24
- Native score
- 72.25 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 22th percentile — ranks 146th of 186. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (21.64 − 0.46) / (76.77 − 0.46) ] = 72.25Min and max are this dataset's lowest (0.46) and highest (76.77) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
Safety & crime
100 = fewest conflict deaths (most at peace), 0 = most
Ranks 1st of 197 · native score 63.71 · reference year 2025
64percentileHow is this calculated?
- Metric
- Deaths in armed conflicts
- Scale
- 100 = fewest conflict deaths (most at peace), 0 = most
- Raw value
- 0 deaths per year
- Native score
- 63.71 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 64th percentile — ranks 1st of 197. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = 100 − [ percentile rank of 0 among 197 countries ] = 63.71The score is the country's position in the distribution. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- Uppsala Conflict Data Program (via Our World in Data) · Uppsala Conflict Data Program (UCDP)
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Deaths in armed conflicts, best estimate, retrieved via the Our World in Data mirror.
- Homicide rateTier A
100 = safest (fewest homicides), 0 = most homicides
Ranks 136th of 193 · native score 29.79 · reference year 2023
30percentileHow is this calculated?
- Metric
- Homicide rate
- Scale
- 100 = safest (fewest homicides), 0 = most homicides
- Raw value
- 6.34829 per 100,000 population
- Native score
- 29.79 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 30th percentile — ranks 136th of 193. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = 100 − [ percentile rank of 6.35 among 193 countries ] = 29.79The score is the country's position in the distribution. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- UNODC (via World Bank) · UN Office on Drugs and Crime / World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Intentional homicide victims per 100,000, indicator VC.IHR.PSRC.P5, retrieved via the World Bank API. Primary source is UNODC; the World Bank redistributes the series under CC BY 4.0.
- Road-traffic deathsTier B
100 = lowest road-death rate (best), 0 = highest
Ranks 94th of 190 · native score 76.93 · reference year 2019
50percentileHow is this calculated?
- Metric
- Road-traffic deaths
- Scale
- 100 = lowest road-death rate (best), 0 = highest
- Raw value
- 14.9 deaths per 100,000
- Native score
- 76.93 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 50th percentile — ranks 94th of 190. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (14.9 − 0) / (64.6 − 0) ] = 76.93Min and max are this dataset's lowest (0) and highest (64.6) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2019
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
Governance & institutions
100 = cleanest (least corrupt), 0 = most corrupt
Ranks 32nd of 180 · native score 63 · reference year 2024
82percentileHow is this calculated?
- Metric
- Corruption Perceptions Index
- Scale
- 100 = cleanest (least corrupt), 0 = most corrupt
- Raw value
- 63 CPI score (0–100)
- Native score
- 63 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 82th percentile — ranks 32nd of 180. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (63 − 0) / (100 − 0) = 63The index is kept on its own published scale.
- Source
- Transparency International · Transparency International
- License
- CC BY-ND 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- CPI retrieved via the Our World in Data mirror (ourworldindata.org/grapher/ti-corruption-perception-index), which republishes the official TI CPI series verbatim. Snapshot verified against Transparency International's official CPI 2024 figures and matches exactly (spot-checked: DNK 90, FIN 88, SGP 84, DEU 75, USA 65, ESP 56, SSD 8). We were unable to fetch TI's primary file directly (their CDN returns an access-denied stub for the CPI xlsx and no clean CSV is offered), so the OWID mirror is the machine-readable source; primary source and methodology owner is Transparency International.
100 = most democratic (index 1.0), 0 = least democratic
Ranks 17th of 175 · native score 83.9 · reference year 2025
90percentileHow is this calculated?
- Metric
- Electoral democracy index
- Scale
- 100 = most democratic (index 1.0), 0 = least democratic
- Raw value
- 0.839 index (0–1)
- Native score
- 83.9 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 90th percentile — ranks 17th of 175. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (0.84 − 0) / (1 − 0) = 83.9The index is kept on its own published scale.
- Source
- V-Dem Institute (via Our World in Data) · V-Dem Institute, University of Gothenburg
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Varieties of Democracy (V-Dem) expert-coded indices (electoral democracy, rule of law, freedom of expression), retrieved via the Our World in Data mirror. Expert-coded estimates; see V-Dem for uncertainty intervals.
100 = most free expression (index 1.0), 0 = least
Ranks 17th of 175 · native score 92.6 · reference year 2025
91percentileHow is this calculated?
- Metric
- Freedom of expression index
- Scale
- 100 = most free expression (index 1.0), 0 = least
- Raw value
- 0.926 index (0–1)
- Native score
- 92.6 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 91th percentile — ranks 17th of 175. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (0.93 − 0) / (1 − 0) = 92.6The index is kept on its own published scale.
- Source
- V-Dem Institute (via Our World in Data) · V-Dem Institute, University of Gothenburg
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Varieties of Democracy (V-Dem) expert-coded indices (electoral democracy, rule of law, freedom of expression), retrieved via the Our World in Data mirror. Expert-coded estimates; see V-Dem for uncertainty intervals.
- Government effectivenessTier A
100 = strongest government effectiveness (index +2.5), 0 = weakest (−2.5)
Ranks 39th of 204 · native score 69.29 · reference year 2024
81percentileHow is this calculated?
- Metric
- Government effectiveness
- Scale
- 100 = strongest government effectiveness (index +2.5), 0 = weakest (−2.5)
- Raw value
- 0.964461 index (−2.5 to +2.5)
- Native score
- 69.29 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 81th percentile — ranks 39th of 204. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (0.96 − -2.5) / (2.5 − -2.5) = 69.29The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Rule of law indexTier A
100 = strongest rule of law (index 1.0), 0 = weakest
Ranks 22nd of 175 · native score 95.1 · reference year 2025
88percentileHow is this calculated?
- Metric
- Rule of law index
- Scale
- 100 = strongest rule of law (index 1.0), 0 = weakest
- Raw value
- 0.951 index (0–1)
- Native score
- 95.1 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 88th percentile — ranks 22nd of 175. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (0.95 − 0) / (1 − 0) = 95.1The index is kept on its own published scale.
- Source
- V-Dem Institute (via Our World in Data) · V-Dem Institute, University of Gothenburg
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Varieties of Democracy (V-Dem) expert-coded indices (electoral democracy, rule of law, freedom of expression), retrieved via the Our World in Data mirror. Expert-coded estimates; see V-Dem for uncertainty intervals.
- Women in parliamentTier A
100 = all seats held by women, 0 = none (≈50 = parity)
Ranks 53rd of 193 · native score 33.55 · reference year 2025
73percentileHow is this calculated?
- Metric
- Women in parliament
- Scale
- 100 = all seats held by women, 0 = none (≈50 = parity)
- Raw value
- 33.5484 % of seats
- Native score
- 33.55 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 73th percentile — ranks 53rd of 193. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (33.55 − 0) / (100 − 0) = 33.55The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
Health
- Diabetes prevalenceTier A
100 = lowest diabetes prevalence (best), 0 = highest
Ranks 156th of 208 · native score 64.21 · reference year 2024
25percentileHow is this calculated?
- Metric
- Diabetes prevalence
- Scale
- 100 = lowest diabetes prevalence (best), 0 = highest
- Raw value
- 12.2 % of adults 20–79
- Native score
- 64.21 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 25th percentile — ranks 156th of 208. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (12.2 − 1.5) / (31.4 − 1.5) ] = 64.21Min and max are this dataset's lowest (1.5) and highest (31.4) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = highest health spending per capita, 0 = lowest
Ranks 45th of 193 · native score 69.39 · reference year 2024
77percentileHow is this calculated?
- Metric
- Health expenditure per capita
- Scale
- 100 = highest health spending per capita, 0 = lowest
- Raw value
- 1,759.71 current US$ per capita
- Native score
- 69.39 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 77th percentile — ranks 45th of 193. Comparable across metrics.
- Normalization
- Log + min–max (log scale, for skewed data)
score = 100 · (ln 1,759.71 − ln 17.44) / (ln 13,473.19 − ln 17.44) = 69.39Log scale (values are heavily skewed). 0 and 100 are the dataset's min (17.44) and max (13,473.19) — relative, not absolute or year-over-year comparable.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Hospital beds per 1,000Tier A
100 = most hospital beds per capita in the dataset, 0 = fewest
Ranks 110th of 191 · native score 8.2 · reference year 2023
43percentileHow is this calculated?
- Metric
- Hospital beds per 1,000
- Scale
- 100 = most hospital beds per capita in the dataset, 0 = fewest
- Raw value
- 1.94 per 1,000 people
- Native score
- 8.2 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 43th percentile — ranks 110th of 191. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (1.94 − 0.15) / (21.99 − 0.15) = 8.2Min and max are this dataset's lowest (0.15) and highest (21.99) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
100 = full DTP3 coverage, 0 = none
Ranks 59th of 193 · native score 95 · reference year 2024
68percentileHow is this calculated?
- Metric
- Immunization coverage (DTP3)
- Scale
- 100 = full DTP3 coverage, 0 = none
- Raw value
- 95 % of children aged 12–23 months
- Native score
- 95 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 68th percentile — ranks 59th of 193. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (95 − 0) / (100 − 0) = 95The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Infant mortality rateTier A
100 = lowest infant mortality in the dataset, 0 = highest
Ranks 55th of 195 · native score 93.49 · reference year 2024
72percentileHow is this calculated?
- Metric
- Infant mortality rate
- Scale
- 100 = lowest infant mortality in the dataset, 0 = highest
- Raw value
- 5.8 per 1,000 live births
- Native score
- 93.49 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 72th percentile — ranks 55th of 195. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (5.8 − 1.2) / (71.9 − 1.2) ] = 93.49Min and max are this dataset's lowest (1.2) and highest (71.9) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Life expectancy at birthTier A
100 = longest life expectancy in the dataset, 0 = shortest
Ranks 43rd of 215 · native score 83.87 · reference year 2024
80percentileHow is this calculated?
- Metric
- Life expectancy at birth
- Scale
- 100 = longest life expectancy in the dataset, 0 = shortest
- Raw value
- 81.359 years
- Native score
- 83.87 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 80th percentile — ranks 43rd of 215. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (81.36 − 54.64) / (86.5 − 54.64) = 83.87Min and max are this dataset's lowest (54.64) and highest (86.5) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Maternal mortality ratioTier A
100 = lowest maternal mortality (best), 0 = highest
Ranks 45th of 194 · native score 76.8 · reference year 2023
77percentileHow is this calculated?
- Metric
- Maternal mortality ratio
- Scale
- 100 = lowest maternal mortality (best), 0 = highest
- Raw value
- 10 deaths per 100,000 live births
- Native score
- 76.8 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 77th percentile — ranks 45th of 194. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = 100 − [ percentile rank of 10 among 194 countries ] = 76.8The score is the country's position in the distribution. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Physicians per 1,000Tier A
100 = most physicians per capita in the dataset, 0 = fewest
Ranks 53rd of 195 · native score 34.68 · reference year 2023
73percentileHow is this calculated?
- Metric
- Physicians per 1,000
- Scale
- 100 = most physicians per capita in the dataset, 0 = fewest
- Raw value
- 3.334 per 1,000 people
- Native score
- 34.68 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 73th percentile — ranks 53rd of 195. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 · (3.33 − 0.04) / (9.54 − 0.04) = 34.68Min and max are this dataset's lowest (0.04) and highest (9.54) values, so the score is relative to the current country set.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Safe sanitationTier A
100 = universal safe sanitation, 0 = none
Ranks 24th of 145 · native score 95.13 · reference year 2024
84percentileHow is this calculated?
- Metric
- Safe sanitation
- Scale
- 100 = universal safe sanitation, 0 = none
- Raw value
- 95.1314 % of population
- Native score
- 95.13 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 84th percentile — ranks 24th of 145. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (95.13 − 0) / (100 − 0) = 95.13The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Safe drinking waterTier A
100 = universal safe drinking water, 0 = none
Ranks 38th of 151 · native score 97.82 · reference year 2024
75percentileHow is this calculated?
- Metric
- Safe drinking water
- Scale
- 100 = universal safe drinking water, 0 = none
- Raw value
- 97.8171 % of population
- Native score
- 97.82 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 75th percentile — ranks 38th of 151. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (97.82 − 0) / (100 − 0) = 97.82The index is kept on its own published scale.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Tobacco useTier A
100 = lowest tobacco use (best), 0 = highest
Ranks 126th of 166 · native score 48.36 · reference year 2024
24percentileHow is this calculated?
- Metric
- Tobacco use
- Scale
- 100 = lowest tobacco use (best), 0 = highest
- Raw value
- 26.7 % of adults
- Native score
- 48.36 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 24th percentile — ranks 126th of 166. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (26.7 − 3.1) / (48.8 − 3.1) ] = 48.36Min and max are this dataset's lowest (3.1) and highest (48.8) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- Under-5 mortality rateTier A
100 = lowest under-5 mortality (best), 0 = highest
Ranks 54th of 195 · native score 72.56 · reference year 2024
73percentileHow is this calculated?
- Metric
- Under-5 mortality rate
- Scale
- 100 = lowest under-5 mortality (best), 0 = highest
- Raw value
- 6.8 deaths per 1,000 live births
- Native score
- 72.56 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 73th percentile — ranks 54th of 195. Comparable across metrics.
- Normalization
- Percentile rank across countries
score = 100 − [ percentile rank of 6.8 among 195 countries ] = 72.56The score is the country's position in the distribution. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2024
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
- UndernourishmentTier A
100 = lowest undernourishment (best), 0 = highest
Ranks 1st of 169 · native score 100 · reference year 2023
81percentileHow is this calculated?
- Metric
- Undernourishment
- Scale
- 100 = lowest undernourishment (best), 0 = highest
- Raw value
- 2.5 % of population
- Native score
- 100 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 81th percentile — ranks 1st of 169. Comparable across metrics.
- Normalization
- Min–max (rescaled against the dataset)
score = 100 − [ 100 · (2.5 − 2.5) / (54.2 − 2.5) ] = 100Min and max are this dataset's lowest (2.5) and highest (54.2) values, so the score is relative to the current country set. Direction is inverted (higher raw value = worse), so the position is subtracted from 100.
- Source
- World Bank · World Bank
- License
- CC BY 4.0
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- World Development Indicators and related databases, retrieved via the World Bank API (api.worldbank.org), most recent value per country. Each metric's indicator code and primary compiler (e.g. UN, WHO, ITU, UN IGME) are given in its methodology.
Quality of life & wellbeing
- Human Development IndexTier A
100 = highest human development (HDI 1.0), 0 = lowest (HDI 0)
Ranks 45th of 193 · native score 87.8 · reference year 2023
77percentileHow is this calculated?
- Metric
- Human Development Index
- Scale
- 100 = highest human development (HDI 1.0), 0 = lowest (HDI 0)
- Raw value
- 0.878 HDI (0–1)
- Native score
- 87.8 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 77th percentile — ranks 45th of 193. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (0.88 − 0) / (1 − 0) = 87.8The index is kept on its own published scale.
- Source
- UNDP — Human Development Report · United Nations Development Programme
- License
- CC BY 3.0 IGO
- Reference year
- 2023
- Retrieved
- 2026-07-12
- Confidence
- Tier A — Official / robust
- Source notes
- Human Development Index, retrieved via the Our World in Data mirror (ourworldindata.org/grapher/human-development-index), which republishes the UNDP HDR series. Primary source and methodology owner is UNDP.
100 = highest average life evaluation (ladder 10), 0 = lowest (ladder 0)
Ranks 52nd of 165 · native score 63.02 · reference year 2025
69percentileHow is this calculated?
- Metric
- Life satisfaction (happiness)
- Scale
- 100 = highest average life evaluation (ladder 10), 0 = lowest (ladder 0)
- Raw value
- 6.302 Cantril ladder (0–10)
- Native score
- 63.02 — this metric's own 0–100 scale; not comparable to other metrics.
- Percentile rank
- 69th percentile — ranks 52nd of 165. Comparable across metrics.
- Normalization
- Native rescale (index kept on its own scale)
score = 100 · (6.3 − 0) / (10 − 0) = 63.02The index is kept on its own published scale.
- Source
- World Happiness Report (via Our World in Data) · World Happiness Report / Gallup
- License
- CC BY 4.0
- Reference year
- 2025
- Retrieved
- 2026-07-12
- Confidence
- Tier B — Reputable / modeled
- Source notes
- Self-reported life evaluation (Cantril ladder, 0–10) from the Gallup World Poll, published in the World Happiness Report, retrieved via the Our World in Data mirror. Survey-based (Tier B).