Puerto Rico

PRI

Caribbean · Americas

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?”.

48 metrics for this country

Education

Adult literacy rateTier A
100 = highest adult literacy in the dataset, 0 = lowest
Ranks 84th of 155 · native score 89.6 · reference year 2017
46percentile
How is this calculated?
Metric
Adult literacy rate
Scale
100 = highest adult literacy in the dataset, 0 = lowest
Raw value
92.39 % of adults
Native score
89.6 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
46th percentile — ranks 84th of 155. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (92.39 − 26.83) / (100 − 26.83) = 89.6
Min 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.
Government education spendingTier A
100 = highest education spending (% GDP) in the dataset, 0 = lowest
Ranks 95th of 200 · native score 26.06 · reference year 2024
53percentile
How is this calculated?
Metric
Government education spending
Scale
100 = highest education spending (% GDP) in the dataset, 0 = lowest
Raw value
4.27102 % of GDP
Native score
26.06 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
53th percentile — ranks 95th of 200. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (4.27 − 0) / (16.39 − 0) = 26.06
Min 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
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.
Pupil–teacher ratio (primary)Tier A
100 = lowest pupil–teacher ratio (best), 0 = highest
Ranks 49th of 197 · native score 91.36 · reference year 2016
75percentile
How is this calculated?
Metric
Pupil–teacher ratio (primary)
Scale
100 = lowest pupil–teacher ratio (best), 0 = highest
Raw value
13.5365 pupils per teacher
Native score
91.36 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
75th percentile — ranks 49th of 197. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 − [ 100 · (13.54 − 6.93) / (83.41 − 6.93) ] = 91.36
Min 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
2016
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 92nd of 144 · native score 57.62 · reference year 2015
36percentile
How is this calculated?
Metric
Researchers in R&D
Scale
100 = most researchers per capita, 0 = fewest
Raw value
332.378 per million people
Native score
57.62 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
36th percentile — ranks 92nd of 144. Comparable across metrics.
Normalization
Log + min–max (log scale, for skewed data)
score = 100 · (ln 332.38 − ln 1.45) / (ln 18,129.75 − ln 1.45) = 57.62
Log 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
2015
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.
Lower-secondary completion rateTier A
100 = highest completion in the dataset, 0 = lowest
Ranks 120th of 192 · native score 51.3 · reference year 2023
38percentile
How is this calculated?
Metric
Lower-secondary completion rate
Scale
100 = highest completion in the dataset, 0 = lowest
Raw value
80.2973 % of relevant age group
Native score
51.3 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
38th percentile — ranks 120th of 192. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (80.3 − 8.89) / (148.09 − 8.89) = 51.3
Min 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
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.
Tertiary enrollment ratioTier A
100 = highest tertiary enrollment in the dataset, 0 = lowest
Ranks 34th of 195 · native score 46.6 · reference year 2023
83percentile
How is this calculated?
Metric
Tertiary enrollment ratio
Scale
100 = highest tertiary enrollment in the dataset, 0 = lowest
Raw value
77.9264 % gross enrollment
Native score
46.6 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
83th percentile — ranks 34th of 195. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (77.93 − 1.84) / (165.11 − 1.84) = 46.6
Min 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
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.

Society & culture

Adult male heightTier ADescriptive
100 = tallest mean male height, 0 = shortest — descriptive, not better or worse
Ranks 86th of 200 · native score 54.05 · reference year 1996
57percentile
How is this calculated?
Metric
Adult male height
Scale
100 = tallest mean male height, 0 = shortest — descriptive, not better or worse
Raw value
172.085 cm
Native score
54.05 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
57th percentile — ranks 86th of 200. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (172.08 − 159.79) / (182.54 − 159.79) = 54.05
Min 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.
Female labour force participationTier A
100 = highest female participation in the dataset, 0 = lowest
Ranks 160th of 186 · native score 41.14 · reference year 2025
14percentile
How is this calculated?
Metric
Female labour force participation
Scale
100 = highest female participation in the dataset, 0 = lowest
Raw value
36.853 % of women 15+
Native score
41.14 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
14th percentile — ranks 160th of 186. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (36.85 − 4.66) / (82.91 − 4.66) = 41.14
Min 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.
Social protection coverageTier A
100 = universal social protection coverage, 0 = none
Ranks 98th of 214 · native score 58.9 · reference year 2022
54percentile
How is this calculated?
Metric
Social protection coverage
Scale
100 = universal social protection coverage, 0 = none
Raw value
58.9 % of population
Native score
58.9 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
54th percentile — ranks 98th of 214. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (58.9 − 0) / (100 − 0) = 58.9
The 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 38th of 94 · native score 88.91 · reference year 2022
60percentile
Low 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
11.0914 % who object to different-religion neighbours
Native score
88.91 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
60th percentile — ranks 38th of 94. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 − [ 100 · (11.09 − 0) / (100 − 0) ] = 88.91
The 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.
International tourist arrivalsTier BDescriptive
100 = most arrivals (2019), 0 = fewest — descriptive, not better or worse
Ranks 61st of 174 · native score 66.83 · reference year 2019
65percentile
How is this calculated?
Metric
International tourist arrivals
Scale
100 = most arrivals (2019), 0 = fewest — descriptive, not better or worse
Raw value
3,180,000 arrivals per year
Native score
66.83 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
65th percentile — ranks 61st of 174. Comparable across metrics.
Normalization
Log + min–max (log scale, for skewed data)
score = 100 · (ln 3,180,000 − ln 3,700) / (ln 90,914,000 − ln 3,700) = 66.83
Log 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).

Demographics & migration

Age dependency ratioTier ADescriptive
100 = highest dependency ratio, 0 = lowest — descriptive, not better or worse
Ranks 91st of 215 · native score 44.84 · reference year 2025
58percentile
How is this calculated?
Metric
Age dependency ratio
Scale
100 = highest dependency ratio, 0 = lowest — descriptive, not better or worse
Raw value
57.5113 dependents per 100 working-age
Native score
44.84 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
58th percentile — ranks 91st of 215. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (57.51 − 20.05) / (103.59 − 20.05) = 44.84
Min 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.
Fertility rateTier ADescriptive
100 = highest fertility, 0 = lowest — descriptive, not better or worse
Ranks 212th of 215 · native score 6.21 · reference year 2024
2percentile
How is this calculated?
Metric
Fertility rate
Scale
100 = highest fertility, 0 = lowest — descriptive, not better or worse
Raw value
0.92 births per woman
Native score
6.21 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
2th percentile — ranks 212th of 215. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (0.92 − 0.58) / (6.03 − 0.58) = 6.21
Min 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.
Life expectancy gap (female − male)Tier ADescriptive
100 = largest female−male gap, 0 = smallest (or male-favouring) — descriptive, not better or worse
Ranks 30th of 215 · native score 65.34 · reference year 2024
86percentile
How 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
7.097 years (female − male)
Native score
65.34 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
86th percentile — ranks 30th of 215. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (7.1 − 0.46) / (10.62 − 0.46) = 65.34
Min 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.
Median ageTier ADescriptive
100 = oldest median age, 0 = youngest — descriptive, not better or worse
Ranks 14th of 237 · native score 68.39 · reference year 2023
94percentile
How is this calculated?
Metric
Median age
Scale
100 = oldest median age, 0 = youngest — descriptive, not better or worse
Raw value
45.306 years
Native score
68.39 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
94th percentile — ranks 14th of 237. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (45.31 − 14.32) / (59.63 − 14.32) = 68.39
Min 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.
Foreign-born populationTier ADescriptive
100 = highest foreign-born share, 0 = lowest — descriptive, not better or worse
Ranks 98th of 214 · native score 6.9 · reference year 2024
54percentile
How is this calculated?
Metric
Foreign-born population
Scale
100 = highest foreign-born share, 0 = lowest — descriptive, not better or worse
Raw value
6.9 % foreign-born
Native score
6.9 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
54th percentile — ranks 98th of 214. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (6.9 − 0) / (100 − 0) = 6.9
The 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.
Net migration rateTier BDescriptive
100 = highest net inflow per capita, 0 = highest net outflow — descriptive, not better or worse
Ranks 51st of 215 · native score 53.69 · reference year 2025
77percentile
How 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
1.725 per 1,000 people
Native score
53.69 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
77th percentile — ranks 51st of 215. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (1.73 − -46.91) / (43.67 − -46.91) = 53.69
Min 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.
Total populationTier ADescriptive
100 = most populous, 0 = least populous — descriptive, not better or worse
Ranks 134th of 215 · native score 48.68 · reference year 2025
38percentile
How is this calculated?
Metric
Total population
Scale
100 = most populous, 0 = least populous — descriptive, not better or worse
Raw value
3,184,835 people
Native score
48.68 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
38th percentile — ranks 134th of 215. Comparable across metrics.
Normalization
Log + min–max (log scale, for skewed data)
score = 100 · (ln 3,184,835 − ln 9,492) / (ln 1,463,865,525 − ln 9,492) = 48.68
Log 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.
Population densityTier ADescriptive
100 = most dense, 0 = least dense — descriptive, not better or worse
Ranks 30th of 215 · native score 66.06 · reference year 2023
86percentile
How is this calculated?
Metric
Population density
Scale
100 = most dense, 0 = least dense — descriptive, not better or worse
Raw value
361.194 people per km²
Native score
66.06 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
86th percentile — ranks 30th of 215. Comparable across metrics.
Normalization
Log + min–max (log scale, for skewed data)
score = 100 · (ln 361.19 − ln 0.14) / (ln 20,569.7 − ln 0.14) = 66.06
Log 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.
Population change (projection)Tier ADescriptive
100 = fastest projected growth, 0 = fastest projected decline — descriptive, not better or worse
Ranks 232nd of 237 · native score 7.62 · reference year 2050
2percentile
How is this calculated?
Metric
Population change (projection)
Scale
100 = fastest projected growth, 0 = fastest projected decline — descriptive, not better or worse
Raw value
-22.8 % change 2024→2050
Native score
7.62 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
2th percentile — ranks 232nd of 237. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (-22.8 − -32.9) / (99.7 − -32.9) = 7.62
Min 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.
Refugees (by origin)Tier ADescriptive
100 = most refugees produced, 0 = fewest — descriptive, not better or worse
Ranks 198th of 211 · native score 3.32 · reference year 2024
3percentile
How is this calculated?
Metric
Refugees (by origin)
Scale
100 = most refugees produced, 0 = fewest — descriptive, not better or worse
Raw value
0 refugees
Native score
3.32 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
3th percentile — ranks 198th of 211. Comparable across metrics.
Normalization
Percentile rank across countries
score = percentile rank of 0 among 211 countries = 3.32
The 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.
Rural population shareTier ADescriptive
100 = fully rural, 0 = fully urban — descriptive, not better or worse
Ranks 195th of 215 · native score 5.66 · reference year 2025
10percentile
How is this calculated?
Metric
Rural population share
Scale
100 = fully rural, 0 = fully urban — descriptive, not better or worse
Raw value
5.65952 % rural
Native score
5.66 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
10th percentile — ranks 195th of 215. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (5.66 − 0) / (100 − 0) = 5.66
The 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.
Sex ratio at birthTier ADescriptive
100 = highest male-to-female birth ratio, 0 = lowest — descriptive, not better or worse
Ranks 99th of 215 · native score 26.97 · reference year 2024
53percentile
How 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
1.052 male births per female birth
Native score
26.97 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
53th percentile — ranks 99th of 215. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (1.05 − 1.01) / (1.16 − 1.01) = 26.97
Min 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.
UrbanizationTier ADescriptive
100 = fully urban, 0 = fully rural — descriptive, not better or worse
Ranks 21st of 215 · native score 94.34 · reference year 2025
90percentile
How is this calculated?
Metric
Urbanization
Scale
100 = fully urban, 0 = fully rural — descriptive, not better or worse
Raw value
94.3405 % urban
Native score
94.34 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
90th percentile — ranks 21st of 215. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (94.34 − 0) / (100 − 0) = 94.34
The 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.

Economy

New business densityTier A
100 = highest new-business density in the dataset, 0 = lowest
Ranks 48th of 182 · native score 2.64 · reference year 2020
74percentile
How is this calculated?
Metric
New business density
Scale
100 = highest new-business density in the dataset, 0 = lowest
Raw value
5.32737 new firms per 1,000 aged 15–64
Native score
2.64 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
74th percentile — ranks 48th of 182. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (5.33 − 0.02) / (201.01 − 0.02) = 2.64
Min 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
2020
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.
Employment-to-population ratioTier A
100 = highest employment ratio in the dataset, 0 = lowest
Ranks 165th of 186 · native score 28.93 · reference year 2025
12percentile
How is this calculated?
Metric
Employment-to-population ratio
Scale
100 = highest employment ratio in the dataset, 0 = lowest
Raw value
40.216 % of population 15+
Native score
28.93 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
12th percentile — ranks 165th of 186. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (40.22 − 21.21) / (86.9 − 21.21) = 28.93
Min 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.
Exports of goods & servicesTier ADescriptive
100 = most export-oriented (% GDP), 0 = lowest — descriptive, not better or worse
Ranks 65th of 192 · native score 23.05 · reference year 2025
66percentile
How is this calculated?
Metric
Exports of goods & services
Scale
100 = most export-oriented (% GDP), 0 = lowest — descriptive, not better or worse
Raw value
46.8831 % of GDP
Native score
23.05 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
66th percentile — ranks 65th of 192. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (46.88 − 1.1) / (199.76 − 1.1) = 23.05
Min 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.
GDP growth (projection)Tier B
100 = highest projected growth, 0 = lowest
Ranks 187th of 189 · native score 0.22 · reference year 2031
1percentile
How is this calculated?
Metric
GDP growth (projection)
Scale
100 = highest projected growth, 0 = lowest
Raw value
0.65 % per year (avg 2026–2031)
Native score
0.22 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
1th percentile — ranks 187th of 189. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (0.65 − 0.62) / (14.38 − 0.62) = 0.22
Min 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 49th of 202 · native score 76.91 · reference year 2024
76percentile
How is this calculated?
Metric
GDP per capita (PPP)
Scale
100 = highest GDP per capita in the dataset, 0 = lowest (log scale)
Raw value
50,247.8 international $ (PPP, current)
Native score
76.91 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
76th percentile — ranks 49th of 202. Comparable across metrics.
Normalization
Log + min–max (log scale, for skewed data)
score = 100 · (ln 50,247.85 − ln 1,154.89) / (ln 155,941.29 − ln 1,154.89) = 76.91
Log 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.
Labour productivityTier A
100 = highest output per worker, 0 = lowest
Ranks 20th of 177 · native score 83.94 · reference year 2024
89percentile
How is this calculated?
Metric
Labour productivity
Scale
100 = highest output per worker, 0 = lowest
Raw value
124,428 constant PPP$ per worker
Native score
83.94 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
89th percentile — ranks 20th of 177. Comparable across metrics.
Normalization
Log + min–max (log scale, for skewed data)
score = 100 · (ln 124,427.58 − ln 2,444.23) / (ln 263,863.91 − ln 2,444.23) = 83.94
Log 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.
Government debtTier BDescriptive
100 = highest debt (% GDP) in the dataset, 0 = lowest — descriptive, not better or worse
Ranks 177th of 193 · native score 5.83 · reference year 2025
9percentile
How is this calculated?
Metric
Government debt
Scale
100 = highest debt (% GDP) in the dataset, 0 = lowest — descriptive, not better or worse
Raw value
18 % of GDP
Native score
5.83 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
9th percentile — ranks 177th of 193. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (18 − 0) / (308.7 − 0) = 5.83
Min 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).
Unemployment rateTier A
100 = lowest unemployment in the dataset, 0 = highest
Ranks 111th of 186 · native score 82.8 · reference year 2025
40percentile
How is this calculated?
Metric
Unemployment rate
Scale
100 = lowest unemployment in the dataset, 0 = highest
Raw value
5.991 % of labour force
Native score
82.8 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
40th percentile — ranks 111th of 186. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 − [ 100 · (5.99 − 0.13) / (34.2 − 0.13) ] = 82.8
Min 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 103rd of 186 · native score 82.27 · reference year 2025
45percentile
How is this calculated?
Metric
Youth unemployment rate
Scale
100 = lowest youth unemployment (best), 0 = highest
Raw value
13.984 % of labour force 15–24
Native score
82.27 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
45th percentile — ranks 103rd of 186. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 − [ 100 · (13.98 − 0.46) / (76.77 − 0.46) ] = 82.27
Min 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.

Health

Diabetes prevalenceTier A
100 = lowest diabetes prevalence (best), 0 = highest
Ranks 142nd of 208 · native score 68.56 · reference year 2024
32percentile
How is this calculated?
Metric
Diabetes prevalence
Scale
100 = lowest diabetes prevalence (best), 0 = highest
Raw value
10.9 % of adults 20–79
Native score
68.56 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
32th percentile — ranks 142nd of 208. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 − [ 100 · (10.9 − 1.5) / (31.4 − 1.5) ] = 68.56
Min 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.
Life expectancy at birthTier A
100 = longest life expectancy in the dataset, 0 = shortest
Ranks 38th of 215 · native score 85.58 · reference year 2024
83percentile
How is this calculated?
Metric
Life expectancy at birth
Scale
100 = longest life expectancy in the dataset, 0 = shortest
Raw value
81.902 years
Native score
85.58 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
83th percentile — ranks 38th of 215. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (81.9 − 54.64) / (86.5 − 54.64) = 85.58
Min 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 47th of 194 · native score 75.26 · reference year 2023
75percentile
How is this calculated?
Metric
Maternal mortality ratio
Scale
100 = lowest maternal mortality (best), 0 = highest
Raw value
11 deaths per 100,000 live births
Native score
75.26 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
75th percentile — ranks 47th of 194. Comparable across metrics.
Normalization
Percentile rank across countries
score = 100 − [ percentile rank of 11 among 194 countries ] = 75.26
The 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.
Safe sanitationTier A
100 = universal safe sanitation, 0 = none
Ranks 109th of 145 · native score 32.51 · reference year 2024
25percentile
How is this calculated?
Metric
Safe sanitation
Scale
100 = universal safe sanitation, 0 = none
Raw value
32.5092 % of population
Native score
32.51 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
25th percentile — ranks 109th of 145. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (32.51 − 0) / (100 − 0) = 32.51
The 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 30th of 151 · native score 98.94 · reference year 2024
80percentile
How is this calculated?
Metric
Safe drinking water
Scale
100 = universal safe drinking water, 0 = none
Raw value
98.9403 % of population
Native score
98.94 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
80th percentile — ranks 30th of 151. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (98.94 − 0) / (100 − 0) = 98.94
The 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.

Environment & infrastructure

Access to electricityTier A
100 = universal electricity access, 0 = none
Ranks 1st of 214 · native score 100 · reference year 2023
70percentile
How 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) = 100
The 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.
Fixed broadband subscriptionsTier A
100 = most fixed-broadband subscriptions per capita, 0 = fewest
Ranks 66th of 208 · native score 45.9 · reference year 2024
69percentile
How is this calculated?
Metric
Fixed broadband subscriptions
Scale
100 = most fixed-broadband subscriptions per capita, 0 = fewest
Raw value
27.9891 per 100 people
Native score
45.9 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
69th percentile — ranks 66th of 208. Comparable across metrics.
Normalization
Min–max (rescaled against the dataset)
score = 100 · (27.99 − 0) / (60.97 − 0) = 45.9
Min 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.
Forest areaTier ADescriptive
100 = most forested, 0 = least — descriptive, not better or worse
Ranks 37th of 213 · native score 56.12 · reference year 2023
83percentile
How is this calculated?
Metric
Forest area
Scale
100 = most forested, 0 = least — descriptive, not better or worse
Raw value
56.1218 % of land area
Native score
56.12 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
83th percentile — ranks 37th of 213. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (56.12 − 0) / (100 − 0) = 56.12
The 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 78th of 209 · native score 87.29 · reference year 2022
63percentile
How is this calculated?
Metric
Internet users
Scale
100 = whole population online, 0 = none
Raw value
87.2918 % of population
Native score
87.29 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
63th percentile — ranks 78th of 209. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (87.29 − 0) / (100 − 0) = 87.29
The 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.
PM2.5 air pollutionTier A
100 = cleanest air (lowest PM2.5), 0 = most polluted
Ranks 31st of 200 · native score 84.75 · reference year 2023
85percentile
How is this calculated?
Metric
PM2.5 air pollution
Scale
100 = cleanest air (lowest PM2.5), 0 = most polluted
Raw value
7.83963 µg/m³
Native score
84.75 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
85th percentile — ranks 31st of 200. Comparable across metrics.
Normalization
Percentile rank across countries
score = 100 − [ percentile rank of 7.84 among 200 countries ] = 84.75
The 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 177th of 212 · native score 6.6 · reference year 2025
17percentile
How is this calculated?
Metric
Protected land area
Scale
100 = most land protected, 0 = least
Raw value
6.6 % of land area
Native score
6.6 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
17th percentile — ranks 177th of 212. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (6.6 − 0) / (100 − 0) = 6.6
The 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.
Renewable energy shareTier A
100 = all energy renewable, 0 = none
Ranks 177th of 212 · native score 2.5 · reference year 2022
17percentile
How is this calculated?
Metric
Renewable energy share
Scale
100 = all energy renewable, 0 = none
Raw value
2.5 % of final energy
Native score
2.5 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
17th percentile — ranks 177th of 212. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (2.5 − 0) / (100 − 0) = 2.5
The 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.

Governance & institutions

Government effectivenessTier A
100 = strongest government effectiveness (index +2.5), 0 = weakest (−2.5)
Ranks 124th of 204 · native score 44.9 · reference year 2024
39percentile
How is this calculated?
Metric
Government effectiveness
Scale
100 = strongest government effectiveness (index +2.5), 0 = weakest (−2.5)
Raw value
-0.255212 index (−2.5 to +2.5)
Native score
44.9 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
39th percentile — ranks 124th of 204. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (-0.26 − -2.5) / (2.5 − -2.5) = 44.9
The 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.

Safety & crime

Homicide rateTier A
100 = safest (fewest homicides), 0 = most homicides
Ranks 165th of 193 · native score 14.77 · reference year 2023
15percentile
How is this calculated?
Metric
Homicide rate
Scale
100 = safest (fewest homicides), 0 = most homicides
Raw value
14.5897 per 100,000 population
Native score
14.77 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
15th percentile — ranks 165th of 193. Comparable across metrics.
Normalization
Percentile rank across countries
score = 100 − [ percentile rank of 14.59 among 193 countries ] = 14.77
The 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.

Quality of life & wellbeing

Life satisfaction (happiness)Tier B
100 = highest average life evaluation (ladder 10), 0 = lowest (ladder 0)
Ranks 10th of 165 · native score 70.39 · reference year 2015
94percentile
How is this calculated?
Metric
Life satisfaction (happiness)
Scale
100 = highest average life evaluation (ladder 10), 0 = lowest (ladder 0)
Raw value
7.039 Cantril ladder (0–10)
Native score
70.39 — this metric's own 0–100 scale; not comparable to other metrics.
Percentile rank
94th percentile — ranks 10th of 165. Comparable across metrics.
Normalization
Native rescale (index kept on its own scale)
score = 100 · (7.04 − 0) / (10 − 0) = 70.39
The 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
2015
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).