ARAC InternationalStructural Risk ObservatoryQuanta Analytica Process

Retrieving structural indicators from the World Bank Data360 API.

A country profile requests every registry indicator for the economy and its curated peers. Responses are cached for 24 hours after the first retrieval.

Mauritania

MRT · Sub-Saharan Africa · Lower middle income

Snapshot 2026-09-04 21:39Z

Data retrieved 2026-09-04 21:36Z (some values served from cache)

Signal model 1.0.0 · Registry 1.0.0

1. Overview

Domains with a reading

6 / 6

Domains that produced a defensible signal rather than insufficient evidence.

Indicators returned

34 / 34

Registry indicators for which Data360 returned a series.

Recent data

Indicators with an observation within 3 years of 2026.

Observation span

2019 to 2025

Range of latest periods across the registry. A wide spread limits comparison.

Material change

10 alert(s) raised from this snapshot

All alerts

Prevalence of undernourishment (% of population) moved adversely across 5 consecutive observations, 2018 to 2023.
Movement is in the human development domain, where sustained adverse change indicates a shift in the structural baseline rather than short-term volatility. The latest value sits at the 96th percentile of the country's own observed history. Structural food insecurity baseline. It is not a substitute for IPC phase classification or humanitarian caseload data, which operate on a far shorter cycle.
GDP (annual % growth) sits at the 0th percentile of the curated peer set, inside the adverse 25% tail.
Position within a curated peer group separates a country-specific condition from a regional pattern. A peer-tail position indicates the condition is not simply regional context. Aggregate output growth. A single weak year is ordinary volatility; sustained contraction relative to population growth is the analytically meaningful pattern.
Unemployment, total (% of total labor force, modeled ILO estimate) sits at the 100th percentile of the curated peer set, inside the adverse 25% tail.
Position within a curated peer group separates a country-specific condition from a regional pattern. A peer-tail position indicates the condition is not simply regional context. Modeled ILO estimate, not a national survey figure. In economies with large informal sectors the rate understates labour underutilisation, so compare it against peers and its own history rather than reading the level alone.

2. Structural domains

Each domain aggregates its contributing indicators using visible components: direction of travel, magnitude of change, position within the country’s own history, deviation from a curated peer set, recency and completeness. A domain declines to produce a reading when fewer than two indicators carry usable evidence.

Economic pressure

Improvement48

Contributing indicators (5)

GDP growthImprovement, magnitude 85
GDP per capitaImprovement, magnitude 38
CPI inflationImprovement, magnitude 95
UnemploymentImprovement, magnitude 5
Youth unemploymentImprovement, magnitude 19

1 indicator(s) excluded from this reading

Poverty headcount: Most recent observation is 7 years old (2019), beyond the 6 year limit for analytic use.

Demographic pressure

Stable23

Contributing indicators (2)

Population growthStable, magnitude 23
Ages 0-14 shareStable, magnitude 23

3 indicator(s) excluded from this reading

Population: Contextual indicator: carries no normative direction.
Urban share: Contextual indicator: carries no normative direction.
Fertility rate: Contextual indicator: carries no normative direction.

Human development

Stable52

Contributing indicators (5)

Life expectancyStable, magnitude 23
Under-5 mortalityImprovement, magnitude 53
Maternal mortalityImprovement, magnitude 47
Primary completionImprovement, magnitude 27
UndernourishmentDeterioration, magnitude 98

1 indicator(s) excluded from this reading

Adult literacy: Most recent observation is 6 years old (2020).

Infrastructure and connectivity

Improvement58

Contributing indicators (5)

Electricity accessImprovement, magnitude 54
Internet usersImprovement, magnitude 98
Mobile subscriptionsPressure, magnitude 36
Basic waterImprovement, magnitude 39
Basic sanitationImprovement, magnitude 62

Inclusion and distribution

Stable27

Contributing indicators (3)

Female LFPStable, magnitude 13
Education GPIImprovement, magnitude 44
Secondary enrollmentImprovement, magnitude 37

1 indicator(s) excluded from this reading

Gini index: Most recent observation is 7 years old (2019), beyond the 6 year limit for analytic use.

Institutional and fiscal capacity

Improvement23

Contributing indicators (4)

Govt effectivenessImprovement, magnitude 28
Political stabilityImprovement, magnitude 25
Rule of lawPressure, magnitude 14
Control of corruptionImprovement, magnitude 23

4 indicator(s) excluded from this reading

Tax revenue: No observations returned for this indicator and country.
Central govt debt: No observations returned for this indicator and country.
Military expenditure: Contextual indicator: carries no normative direction.
Homicide rate: Most recent observation is 6 years old (2020).

3. Trends

Series cover the 15 year analysis window. Gaps are rendered as gaps: a line is never drawn across a year with no observation.

Economic pressure

GDP (annual % growth)

GDP (annual % growth) for Mauritania, 2011 to 2025.

GDP (annual % growth) for Mauritania, 2011 to 2025.
Period	Mauritania (PC_A)
2011	4.17
2012	4.47
2013	4.15
2014	4.27
2015	5.38
2016	1.26
2017	6.27
2018	4.77
2019	3.14
2020	-0.36
2021	0.74
2022	6.80
2023	6.81
2024	6.31
2025	4.03

Demographic pressure

Population, total

Population, total for Mauritania, 2011 to 2025.

Population, total for Mauritania, 2011 to 2025.
Period	Mauritania (PS)
2011	3502811.00
2012	3619034.00
2013	3734318.00
2014	3848514.00
2015	3965959.00
2016	4086657.00
2017	4210600.00
2018	4337685.00
2019	4467708.00
2020	4600131.00
2021	4734874.00
2022	4875637.00
2023	5022441.00
2024	5169395.00
2025	5315065.00

Human development

Life expectancy at birth, total (years)

Life expectancy at birth, total (years) for Mauritania, 2011 to 2024.

Life expectancy at birth, total (years) for Mauritania, 2011 to 2024.
Period	Mauritania (YR)
2011	65.75
2012	65.81
2013	65.89
2014	66.21
2015	66.50
2016	66.82
2017	67.09
2018	67.42
2019	67.64
2020	66.79
2021	66.76
2022	68.28
2023	68.48
2024	68.71

Infrastructure and connectivity

Access to electricity (% of population)

Access to electricity (% of population) for Mauritania, 2011 to 2023.

Access to electricity (% of population) for Mauritania, 2011 to 2023.
Period	Mauritania (PT_POP)
2011	35.30
2012	36.40
2013	37.60
2014	38.80
2015	39.50
2016	41.20
2017	42.90
2018	43.60
2019	44.90
2020	45.40
2021	47.70
2022	49.00
2023	50.30

Inclusion and distribution

Gini index

Gini index for Mauritania, 2014 to 2019.

Gini index for Mauritania, 2014 to 2019.
Period	Mauritania (0_TO_100)
2014	32.60
2019	32.00

Institutional and fiscal capacity

Government Effectiveness (WGI estimate, approx. -2.5 to +2.5)

Government Effectiveness (WGI estimate, approx. -2.5 to +2.5) for Mauritania, 2011 to 2024.

Government Effectiveness (WGI estimate, approx. -2.5 to +2.5) for Mauritania, 2011 to 2024.
Period	Mauritania (SCORE)
2011	-0.51
2012	-0.65
2013	-0.83
2014	-0.85
2015	-0.93
2016	-0.90
2017	-0.82
2018	-0.83
2019	-0.82
2020	-0.90
2021	-0.85
2022	-0.88
2023	-0.82
2024	-0.74

4. Peer comparison

Peers are curated rather than derived, listed in /config/countries.ts. Comparison is withheld entirely for indicators whose measurement concept is not comparable across these economies, and rank is reported without level deviation where only ordinal comparison is defensible.

GDP growth

Direct comparison

GDP (annual % growth), Mauritania against curated peers.MauritaniaMaliSenegalMoroccoGambiaChad

GDP (annual % growth), Mauritania against curated peers.
Period	Mauritania (PC_A)	Mali (PC_A)	Senegal (PC_A)	Morocco (PC_A)	Gambia (PC_A)	Chad (PC_A)
2011	4.17	2.94	1.33	5.52	-8.13	4.50
2012	4.47	-1.07	4.00	3.06	5.24	9.06
2013	4.15	3.29	2.41	4.12	2.87	2.84
2014	4.27	6.50	6.22	2.72	-1.41	2.06
2015	5.38	6.24	6.37	4.34	4.06	3.30
2016	1.26	6.01	6.36	0.52	1.94	-4.12
2017	6.27	5.38	7.41	5.06	4.82	-1.62
2018	4.77	4.87	6.21	3.07	7.23	5.74
2019	3.14	5.00	4.61	2.89	6.22	5.38
2020	-0.36	-1.17	1.34	-7.18	0.59	-0.39
2021	0.74	3.10	6.54	8.15	5.26	0.29
2022	6.80	3.70	3.85	1.81	5.49	4.13
2023	6.81	4.76	4.26	3.66	5.92	4.00
2024	6.31	5.02	6.46	3.79	5.55	4.95
2025	4.03	5.60	6.68	4.60	5.89	5.59

GDP per capita

Direct comparison

GDP per capita (constant 2015 US$), Mauritania against curated peers.MauritaniaMaliSenegalMoroccoGambiaChad

GDP per capita (constant 2015 US$), Mauritania against curated peers.
Period	Mauritania (USD_K_2015)	Mali (USD_K_2015)	Senegal (USD_K_2015)	Morocco (USD_K_2015)	Gambia (USD_K_2015)	Chad (USD_K_2015)
2011	1476.24	818.40	1139.74	2879.54	625.46	965.29
2012	1492.70	786.32	1154.06	2927.45	639.14	1016.42
2013	1506.66	788.82	1147.39	3008.19	638.52	1008.62
2014	1524.45	813.98	1180.80	3051.46	611.88	993.32
2015	1558.84	838.12	1218.01	3146.25	619.54	993.99
2016	1531.88	860.75	1257.41	3124.43	615.19	923.57
2017	1580.01	878.56	1312.11	3242.64	628.78	879.08
2018	1606.92	892.98	1355.10	3304.71	658.13	898.83
2019	1609.15	909.75	1379.61	3364.74	682.80	917.20
2020	1557.20	872.39	1361.79	3090.89	670.76	884.98
2021	1524.12	872.33	1414.54	3309.13	689.49	857.53
2022	1580.70	877.82	1433.20	3334.91	710.69	862.60
2023	1638.99	892.69	1458.97	3421.22	735.61	856.99
2024	1692.84	910.30	1517.62	3516.15	758.97	856.00
2025	1712.78	933.80	1582.26	3643.81	786.01	873.53

CPI inflation

Direct comparison

Inflation, consumer prices (annual % growth), Mauritania against curated peers.MauritaniaMaliSenegalMoroccoGambiaChad

Inflation, consumer prices (annual % growth), Mauritania against curated peers.
Period	Mauritania (PC_A)	Mali (PC_A)	Senegal (PC_A)	Morocco (PC_A)	Gambia (PC_A)	Chad (PC_A)
2011	5.69	2.96	3.40	0.91	4.80	2.03
2012	4.90	5.32	1.42	1.29	4.25	7.52
2013	4.13	-0.61	0.71	1.88	5.70	0.22
2014	3.53	0.88	-1.09	0.44	5.95	1.68
2015	3.25	1.45	0.14	1.56	6.81	4.38
2016	1.47	-1.80	0.84	1.64	7.23	-0.79
2017	2.25	1.76	1.32	0.75	8.03	-1.54
2018	3.07	0.30	0.46	1.80	6.52	4.27
2019	2.30	-1.66	1.76	0.30	7.12	-0.97
2020	2.39	0.44	2.54	0.71	5.93	4.46
2021	3.57	3.93	2.18	1.40	7.37	-0.77
2022	9.53	9.62	9.70	6.66	11.51	5.79
2023	4.95	2.06	5.94	6.09	16.97	10.84
2024	2.49	3.21	0.80	0.99	11.56	8.90
2025	1.55	3.28	1.46	0.70	not reported	-3.91

Unemployment

Rank comparison only: levels are not directly comparable

Unemployment, total (% of total labor force, modeled ILO estimate), Mauritania against curated peers.MauritaniaMaliSenegalMoroccoGambiaChad

Unemployment, total (% of total labor force, modeled ILO estimate), Mauritania against curated peers.
Period	Mauritania (PT_LF)	Mali (PT_LF)	Senegal (PT_LF)	Morocco (PT_LF)	Gambia (PT_LF)	Chad (PT_LF)
2011	9.98	1.40	2.85	8.91	10.28	0.92
2012	9.86	1.46	3.90	8.99	10.35	0.89
2013	9.93	1.52	5.01	9.23	9.45	0.95
2014	10.03	1.48	5.85	9.70	8.19	0.98
2015	10.10	1.40	6.76	9.46	7.23	1.03
2016	10.29	1.37	4.46	9.30	6.24	1.09
2017	10.34	1.50	4.08	9.22	5.10	1.13
2018	10.41	1.62	3.45	9.27	4.05	1.14
2019	10.42	3.29	2.86	9.19	4.48	1.08
2020	11.05	3.53	3.43	11.19	5.84	1.70
2021	11.13	4.01	3.44	10.55	6.21	1.53
2022	10.52	2.39	2.84	9.41	6.07	1.08
2023	10.37	2.95	2.78	8.90	6.48	1.06
2024	10.26	2.92	2.79	9.10	6.48	1.05
2025	10.32	2.83	2.73	9.00	6.52	1.06

Youth unemployment

Rank comparison only: levels are not directly comparable

Unemployment, youth total (% of labor force ages 15-24, modeled ILO estimate), Mauritania against curated peers.MauritaniaMaliSenegalMoroccoGambiaChad

Unemployment, youth total (% of labor force ages 15-24, modeled ILO estimate), Mauritania against curated peers.
Period	Mauritania (PT_LF_Y15T24)	Mali (PT_LF_Y15T24)	Senegal (PT_LF_Y15T24)	Morocco (PT_LF_Y15T24)	Gambia (PT_LF_Y15T24)	Chad (PT_LF_Y15T24)
2011	15.22	2.18	2.98	17.93	14.76	1.32
2012	15.09	2.23	4.29	18.64	14.85	1.31
2013	16.20	2.28	5.65	19.16	13.73	1.36
2014	17.36	2.24	6.83	19.97	12.21	1.39
2015	18.50	2.17	8.04	20.83	11.02	1.43
2016	19.82	2.13	6.37	22.27	9.81	1.46
2017	20.97	2.23	5.84	22.17	8.41	1.49
2018	22.20	2.33	4.92	22.43	7.15	1.51
2019	23.37	4.67	4.04	22.48	7.81	1.49
2020	25.52	6.73	4.82	26.80	10.14	1.88
2021	24.85	4.43	4.66	25.05	10.44	2.02
2022	23.51	3.21	3.90	22.71	10.16	1.46
2023	23.24	3.86	3.86	21.97	10.81	1.45
2024	23.04	3.84	3.87	22.37	10.83	1.45
2025	22.92	3.88	3.62	21.88	10.86	1.46

Poverty headcount

Direct comparison

Poverty headcount ratio at $3.00 a day (2021 PPP, % of population), Mauritania against curated peers.MauritaniaMaliSenegalMoroccoGambiaChad

Poverty headcount ratio at $3.00 a day (2021 PPP, % of population), Mauritania against curated peers.
Period	Mauritania (PT_POP)	Mali (PT_POP)	Senegal (PT_POP)	Morocco (PT_POP)	Gambia (PT_POP)	Chad (PT_POP)
2011	not reported	not reported	53.30	not reported	not reported	45.20
2013	not reported	not reported	not reported	3.70	not reported	not reported
2014	11.70	not reported	not reported	not reported	not reported	not reported
2015	not reported	not reported	not reported	not reported	17.10	not reported
2018	not reported	29.40	19.10	not reported	not reported	41.90
2019	10.20	not reported	not reported	not reported	not reported	not reported
2020	not reported	not reported	not reported	not reported	22.00	not reported
2021	not reported	36.10	17.90	not reported	not reported	not reported
2022	not reported	not reported	not reported	not reported	not reported	39.50

5. Indicators

All registry indicators for Mauritania with latest value, period, change windows and signal direction.
Indicator	Domain	Latest	Period	1y	3y	5y	Signal	Evidence
GDP growthWB_WDI_NY_GDP_MKTP_KD_ZG	Economic pressure	4.03PC_A	2025	-2.28	-2.77	+4.39	Improvement, magnitude 85	Data quality: Adequate
GDP per capitaWB_WDI_NY_GDP_PCAP_KD	Economic pressure	1,713USD_K_2015	2025	+19.9	+132	+156	Improvement, magnitude 38	Data quality: Adequate
CPI inflationWB_WDI_FP_CPI_TOTL_ZG	Economic pressure	1.55PC_A	2025	-0.94	-7.98	-0.84	Improvement, magnitude 95	Data quality: Adequate
UnemploymentWB_WDI_SL_UEM_TOTL_ZS	Economic pressure	10.3PT_LF	2025	+0.06	-0.20	-0.73	Improvement, magnitude 5	Data quality: Adequate
Youth unemploymentWB_WDI_SL_UEM_1524_ZS	Economic pressure	22.9PT_LF_Y15T24	2025	-0.12	-0.59	-2.60	Improvement, magnitude 19	Data quality: Adequate
Poverty headcountWB_WDI_SI_POV_DDAY	Economic pressure	10.2PT_POP	2019	n/a	n/a	-1.50	Insufficient evidence	Data quality: Inadequate
PopulationWB_WDI_SP_POP_TOTL	Demographic pressure	5,315,065PS	2025	+145670	+439428	+714934	Insufficient evidence	Data quality: Adequate
Population growthWB_WDI_SP_POP_GROW	Demographic pressure	2.78PC_A	2025	-0.11	-0.15	-0.14	Stable, magnitude 23	Data quality: Adequate
Ages 0-14 shareWB_WDI_SP_POP_0014_TO_ZS	Demographic pressure	42.4PT	2025	-0.33	-0.97	-1.43	Stable, magnitude 23	Data quality: Adequate
Urban shareWB_WDI_SP_URB_TOTL_IN_ZS	Demographic pressure	61.5PT	2025	+0.88	+2.68	+4.51	Insufficient evidence	Data quality: Adequate
Fertility rateWB_WDI_SP_DYN_TFRT_IN	Demographic pressure	4.63BR_W	2024	-0.07	-0.22	-0.36	Insufficient evidence	Data quality: Adequate
Life expectancyWB_WDI_SP_DYN_LE00_IN	Human development	68.7YR	2024	+0.23	+1.95	+1.07	Stable, magnitude 23	Data quality: Adequate
Under-5 mortalityWB_WDI_SH_DYN_MORT	Human development	36.9DT_10P3BR_L	2024	-1.20	-3.80	-6.50	Improvement, magnitude 53	Data quality: Adequate
Maternal mortalityWB_WDI_SH_STA_MMRT	Human development	381.0DT_10P5BR_L	2023	-4.00	-32.0	-56.0	Improvement, magnitude 47	Data quality: Adequate
Primary completionWB_WDI_SE_PRM_CMPT_ZS	Human development	73.4PT	2020	+7.07	+8.78	+6.42	Improvement, magnitude 27	Data quality: Limited
Adult literacyWB_WDI_SE_ADT_LITR_ZS	Human development	59.5PT	2020	n/a	n/a	-5.58	Insufficient evidence	Data quality: Inadequate
UndernourishmentWB_WDI_SN_ITK_DEFC_ZS	Human development	8.70PT_POP	2023	+0.10	+1.70	+2.20	Deterioration, magnitude 98	Data quality: Adequate
Electricity accessWB_WDI_EG_ELC_ACCS_ZS	Infrastructure and connectivity	50.3PT_POP	2023	+1.30	+4.90	+6.70	Improvement, magnitude 54	Data quality: Adequate
Internet usersWB_WDI_IT_NET_USER_ZS	Infrastructure and connectivity	45.8PT_POP	2024	+0.82	+10.1	+20.7	Improvement, magnitude 98	Data quality: Adequate
Mobile subscriptionsWB_WDI_IT_CEL_SETS_P2	Infrastructure and connectivity	92.110P2PS	2024	+1.30	-45.5	-13.4	Pressure, magnitude 36	Data quality: Adequate
Basic waterWB_WDI_SH_H2O_BASW_ZS	Infrastructure and connectivity	77.3PT_POP	2024	+0.87	+3.98	+7.10	Improvement, magnitude 39	Data quality: Adequate
Basic sanitationWB_WDI_SH_STA_BASS_ZS	Infrastructure and connectivity	58.7PT_POP	2024	+0.42	+4.75	+8.94	Improvement, magnitude 62	Data quality: Adequate
Gini indexWB_WDI_SI_POV_GINI	Inclusion and distribution	32.00_TO_100	2019	n/a	n/a	-0.60	Insufficient evidence	Data quality: Inadequate
Female LFPWB_WDI_SL_TLF_CACT_FE_ZS	Inclusion and distribution	26.2PT_W	2025	-0.06	+0.13	+0.31	Stable, magnitude 13	Data quality: Adequate
Education GPIWB_WDI_SE_ENR_PRSC_FM_ZS	Inclusion and distribution	1.07U	2020	+0.01	+0.02	+0.05	Improvement, magnitude 44	Data quality: Limited
Secondary enrollmentWB_WDI_SE_SEC_ENRR	Inclusion and distribution	36.0PT	2020	-1.55	+4.27	+4.83	Improvement, magnitude 37	Data quality: Limited
Govt effectivenessWB_WDI_GOV_WGI_GE_EST	Institutional and fiscal capacity	-0.74SCORE	2024	+0.08	+0.11	+0.08	Improvement, magnitude 28	Data quality: Adequate
Political stabilityWB_WDI_GOV_WGI_PV_EST	Institutional and fiscal capacity	-0.56SCORE	2024	-0.06	-0.04	+0.09	Improvement, magnitude 25	Data quality: Adequate
Rule of lawWB_WDI_GOV_WGI_RL_EST	Institutional and fiscal capacity	-0.86SCORE	2024	-0.01	+0.01	-0.08	Pressure, magnitude 14	Data quality: Adequate
Control of corruptionWB_WDI_GOV_WGI_CC_EST	Institutional and fiscal capacity	-0.93SCORE	2024	-0.14	-0.05	+0.05	Improvement, magnitude 23	Data quality: Adequate
Tax revenueWB_WDI_GC_TAX_TOTL_GD_ZS	Institutional and fiscal capacity	not reported	-	n/a	n/a	n/a	Insufficient evidence	Data quality: Inadequate
Central govt debtWB_WDI_GC_DOD_TOTL_GD_ZS	Institutional and fiscal capacity	not reported	-	n/a	n/a	n/a	Insufficient evidence	Data quality: Inadequate
Military expenditureWB_WDI_MS_MIL_XPND_GD_ZS	Institutional and fiscal capacity	2.16PT_GDP	2024	-0.11	-0.14	+0.16	Insufficient evidence	Data quality: Adequate
Homicide rateWB_WDI_VC_IHR_PSRC_P5	Institutional and fiscal capacity	1.0010P5PS	2020	-0.07	-0.22	n/a	Insufficient evidence	Data quality: Inadequate

Expand full evidence register

GDP (annual % growth)

WB_WDI_NY_GDP_MKTP_KD_ZG

Improvement85Adequate

Latest value

4.03PC_A

Period 2025

1 year

-2.28

3 year

-2.77

5 year

+4.39

vs peer median

-1.57

FACT

GDP (annual % growth) for Mauritania was 4.03 PC_A in 2025 (1y -2.28, 3y -2.77, 5y +4.39).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits in the middle range of the country's own observed history at the 30th percentile. Relative to the curated peer set it ranks at the 0th percentile. Aggregate output growth. A single weak year is ordinary volatility; sustained contraction relative to population growth is the analytically meaningful pattern. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: -2.28
3y change: -2.77
5y change: 4.39
5y % change: 1217.1%
Historical percentile: 30th
Peer percentile: 0th
Peer deviation: -1.57
Observations: 15
Recency: 1y
Window completeness: 100%

Favourable movement of 4.39 PC_A over 5 years (2020 to 2025).
Latest value sits at the 30th percentile of this country’s own observed history.
Latest value ranks at the 0th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_NY_GDP_MKTP_KD_ZG
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:11.212Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

GDP per capita (constant 2015 US$)

WB_WDI_NY_GDP_PCAP_KD

Improvement38Adequate

Latest value

1,713USD_K_2015

Period 2025

1 year

+19.9

3 year

+132

5 year

+156

vs peer median

+779

FACT

GDP per capita (constant 2015 US$) for Mauritania was 1712.8 USD_K_2015 in 2025 (1y +19.9, 3y +132, 5y +156).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 97th percentile. Relative to the curated peer set it ranks at the 80th percentile. Constant-price series, so changes reflect real output per head rather than price movement. Says nothing about distribution; read with the Gini index. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 19.9
3y change: 132
5y change: 156
5y % change: 10.0%
Historical percentile: 97th
Peer percentile: 80th
Peer deviation: 779
Observations: 15
Recency: 1y
Window completeness: 100%

Favourable movement of 155.6 USD_K_2015 over 5 years (2020 to 2025).
Latest value sits at the 97th percentile of this country’s own observed history.
Latest value ranks at the 80th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_NY_GDP_PCAP_KD
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:10.341Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Inflation, consumer prices (annual % growth)

WB_WDI_FP_CPI_TOTL_ZG

Improvement95Adequate

Latest value

1.55PC_A

Period 2025

1 year

-0.94

3 year

-7.98

5 year

-0.84

vs peer median

+0.09

FACT

Inflation, consumer prices (annual % growth) for Mauritania was 1.55 PC_A in 2025 (1y -0.94, 3y -7.98, 5y -0.84).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the bottom of the country's own observed history at the 10th percentile. Relative to the curated peer set it ranks at the 60th percentile. National CPI. Household exposure depends on the food and fuel share of the basket, which the headline rate does not reveal. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: falling
1y change: -0.94
3y change: -7.98
5y change: -0.84
5y % change: -35.1%
Historical percentile: 10th
Peer percentile: 60th
Peer deviation: 0.09
Observations: 15
Recency: 1y
Window completeness: 100%

Favourable movement of -0.84 PC_A over 5 years (2020 to 2025).
Latest value sits at the 10th percentile of this country’s own observed history.
Latest value ranks at the 60th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_FP_CPI_TOTL_ZG
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:09.071Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Unemployment, total (% of total labor force, modeled ILO estimate)

WB_WDI_SL_UEM_TOTL_ZS

Improvement5Adequate

Latest value

10.3PT_LF

Period 2025

1 year

+0.06

3 year

-0.20

5 year

-0.73

vs peer median

n/a

FACT

Unemployment, total (% of total labor force, modeled ILO estimate) for Mauritania was 10.32 PT_LF in 2025 (1y +0.06, 3y -0.20, 5y -0.73).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits in the middle range of the country's own observed history at the 50th percentile. Relative to the curated peer set it ranks at the 100th percentile. Modeled ILO estimate, not a national survey figure. In economies with large informal sectors the rate understates labour underutilisation, so compare it against peers and its own history rather than reading the level alone. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: falling
1y change: 0.06
3y change: -0.20
5y change: -0.73
5y % change: -6.6%
Historical percentile: 50th
Peer percentile: 100th
Peer deviation: n/a
Observations: 15
Recency: 1y
Window completeness: 100%

Peer deviation reported as percentile rank only: levels are not directly comparable across these economies.
Favourable movement of -0.73 PT_LF over 5 years (2020 to 2025).
Latest value sits at the 50th percentile of this country’s own observed history.
Latest value ranks at the 100th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SL_UEM_TOTL_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:11.274Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Unemployment, youth total (% of labor force ages 15-24, modeled ILO estimate)

WB_WDI_SL_UEM_1524_ZS

Improvement19Adequate

Latest value

22.9PT_LF_Y15T24

Period 2025

1 year

-0.12

3 year

-0.59

5 year

-2.60

vs peer median

n/a

FACT

Unemployment, youth total (% of labor force ages 15-24, modeled ILO estimate) for Mauritania was 22.92 PT_LF_Y15T24 in 2025 (1y -0.12, 3y -0.59, 5y -2.60).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits in the middle range of the country's own observed history at the 57th percentile. Relative to the curated peer set it ranks at the 100th percentile. Read alongside the 0-14 population share. A large youth cohort entering a labour market that is not absorbing it is a structural condition, not a mobilisation mechanism. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: falling
1y change: -0.12
3y change: -0.59
5y change: -2.60
5y % change: -10.2%
Historical percentile: 57th
Peer percentile: 100th
Peer deviation: n/a
Observations: 15
Recency: 1y
Window completeness: 100%

Peer deviation reported as percentile rank only: levels are not directly comparable across these economies.
Favourable movement of -2.60 PT_LF_Y15T24 over 5 years (2020 to 2025).
Latest value sits at the 57th percentile of this country’s own observed history.
Latest value ranks at the 100th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SL_UEM_1524_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:20.449Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Poverty headcount ratio at $3.00 a day (2021 PPP, % of population)

WB_WDI_SI_POV_DDAY

Insufficient evidenceInadequate

Latest value

10.2PT_POP

Period 2019

1 year

n/a

3 year

n/a

5 year

-1.50

vs peer median

-11.8

FACT

Poverty headcount ratio at $3.00 a day (2021 PPP, % of population) for Mauritania was 10.20 PT_POP in 2019 (5y -1.50).

ASSESSMENT

Evidence is insufficient to assess direction for this indicator. Most recent observation is 7 years old (2019), beyond the 6 year limit for analytic use. Only 2 observation(s) available; 3 are required before any trend is computed. No structural reading should be drawn from this series until coverage improves or an alternative measure is substituted.

Show signal components

Direction of travel: falling
1y change: n/a
3y change: n/a
5y change: -1.50
5y % change: -12.8%
Historical percentile: not computed
Peer percentile: 20th
Peer deviation: -11.8
Observations: 2
Recency: 7y
Window completeness: 13%

Insufficient evidence: data quality is inadequate for a defensible reading.
Most recent observation is 7 years old (2019), beyond the 6 year limit for analytic use.
Only 2 observation(s) available; 3 are required before any trend is computed.

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SI_POV_DDAY
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:20.147Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Population, total

WB_WDI_SP_POP_TOTL

Insufficient evidenceAdequate

Latest value

5,315,065PS

Period 2025

1 year

+145670

3 year

+439428

5 year

+714934

vs peer median

-15688640

FACT

Population, total for Mauritania was 5,315,065 PS in 2025 (1y +145670, 3y +439428, 5y +714934).

ASSESSMENT

Evidence is insufficient to assess direction for this indicator. 15 observations covering 100% of the window, most recent 2025. No structural reading should be drawn from this series until coverage improves or an alternative measure is substituted.

Show signal components

Direction of travel: rising
1y change: 145670
3y change: 439428
5y change: 714934
5y % change: 15.5%
Historical percentile: 97th
Peer percentile: 20th
Peer deviation: -15688640
Observations: 15
Recency: 1y
Window completeness: 100%

Indicator is contextual by design: it carries no normative direction and does not contribute to any domain signal.

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_POP_TOTL
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:20.813Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Population growth (annual %)

WB_WDI_SP_POP_GROW

Stable23Adequate

Latest value

2.78PC_A

Period 2025

1 year

-0.11

3 year

-0.15

5 year

-0.14

vs peer median

+0.48

FACT

Population growth (annual %) for Mauritania was 2.78 PC_A in 2025 (1y -0.11, 3y -0.15, 5y -0.14).

ASSESSMENT

The series is broadly flat against this country's own recent baseline. The latest value sits near the bottom of the country's own observed history at the 3th percentile. Relative to the curated peer set it ranks at the 60th percentile. Interpreted as demand pressure on services and labour markets, not as a negative outcome in itself. Its analytic weight depends entirely on whether service delivery and output are keeping pace. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: flat
1y change: -0.11
3y change: -0.15
5y change: -0.14
5y % change: -4.9%
Historical percentile: 3th
Peer percentile: 60th
Peer deviation: 0.48
Observations: 15
Recency: 1y
Window completeness: 100%

Change of -0.14 PC_A between 2020 and 2025 falls inside the 5% noise band.
Latest value sits at the 3th percentile of this country’s own observed history.
Latest value ranks at the 60th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_POP_GROW
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:21.922Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Population ages 0-14 (% of total population)

WB_WDI_SP_POP_0014_TO_ZS

Stable23Adequate

Latest value

42.4PT

Period 2025

1 year

-0.33

3 year

-0.97

5 year

-1.43

vs peer median

+2.66

FACT

Population ages 0-14 (% of total population) for Mauritania was 42.38 PT in 2025 (1y -0.33, 3y -0.97, 5y -1.43).

ASSESSMENT

The series is broadly flat against this country's own recent baseline. The latest value sits near the bottom of the country's own observed history at the 3th percentile. Relative to the curated peer set it ranks at the 60th percentile. Forward demand on education, health and employment systems. A youth bulge is a demographic condition; whether it becomes a mobilisation pool depends on institutions, opportunity and actor behaviour. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: flat
1y change: -0.33
3y change: -0.97
5y change: -1.43
5y % change: -3.3%
Historical percentile: 3th
Peer percentile: 60th
Peer deviation: 2.66
Observations: 15
Recency: 1y
Window completeness: 100%

Change of -1.43 PT between 2020 and 2025 falls inside the 5% noise band.
Latest value sits at the 3th percentile of this country’s own observed history.
Latest value ranks at the 60th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_POP_0014_TO_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:30.913Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Urban population (% of total population)

WB_WDI_SP_URB_TOTL_IN_ZS

Insufficient evidenceAdequate

Latest value

61.5PT

Period 2025

1 year

+0.88

3 year

+2.68

5 year

+4.51

vs peer median

+5.72

FACT

Urban population (% of total population) for Mauritania was 61.49 PT in 2025 (1y +0.88, 3y +2.68, 5y +4.51).

ASSESSMENT

Show signal components

Direction of travel: rising
1y change: 0.88
3y change: 2.68
5y change: 4.51
5y % change: 7.9%
Historical percentile: 97th
Peer percentile: 60th
Peer deviation: 5.72
Observations: 15
Recency: 1y
Window completeness: 100%

Indicator is contextual by design: it carries no normative direction and does not contribute to any domain signal.

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_URB_TOTL_IN_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:30.678Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Fertility rate, total (births per woman)

WB_WDI_SP_DYN_TFRT_IN

Insufficient evidenceAdequate

Latest value

4.63BR_W

Period 2024

1 year

-0.07

3 year

-0.22

5 year

-0.36

vs peer median

+0.72

FACT

Fertility rate, total (births per woman) for Mauritania was 4.63 BR_W in 2024 (1y -0.07, 3y -0.22, 5y -0.36).

ASSESSMENT

Evidence is insufficient to assess direction for this indicator. 14 observations covering 93% of the window, most recent 2024. No structural reading should be drawn from this series until coverage improves or an alternative measure is substituted.

Show signal components

Direction of travel: falling
1y change: -0.07
3y change: -0.22
5y change: -0.36
5y % change: -7.2%
Historical percentile: 4th
Peer percentile: 60th
Peer deviation: 0.72
Observations: 14
Recency: 2y
Window completeness: 93%

Indicator is contextual by design: it carries no normative direction and does not contribute to any domain signal.

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_DYN_TFRT_IN
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:31.137Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Life expectancy at birth, total (years)

WB_WDI_SP_DYN_LE00_IN

Stable23Adequate

Latest value

68.7YR

Period 2024

1 year

+0.23

3 year

+1.95

5 year

+1.07

vs peer median

+2.66

FACT

Life expectancy at birth, total (years) for Mauritania was 68.71 YR in 2024 (1y +0.23, 3y +1.95, 5y +1.07).

ASSESSMENT

The series is broadly flat against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 96th percentile. Relative to the curated peer set it ranks at the 60th percentile. Slow-moving summary of health system performance and living conditions. A sharp reversal is a strong signal precisely because the series is normally stable. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: flat
1y change: 0.23
3y change: 1.95
5y change: 1.07
5y % change: 1.6%
Historical percentile: 96th
Peer percentile: 60th
Peer deviation: 2.66
Observations: 14
Recency: 2y
Window completeness: 93%

Change of 1.07 YR between 2019 and 2024 falls inside the 5% noise band.
Latest value sits at the 96th percentile of this country’s own observed history.
Latest value ranks at the 60th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_DYN_LE00_IN
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:31.743Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Mortality rate, under-5 (per 1,000 live births)

WB_WDI_SH_DYN_MORT

Improvement53Adequate

Latest value

36.9DT_10P3BR_L

Period 2024

1 year

-1.20

3 year

-3.80

5 year

-6.50

vs peer median

-5.50

FACT

Mortality rate, under-5 (per 1,000 live births) for Mauritania was 36.90 DT_10P3BR_L in 2024 (1y -1.20, 3y -3.80, 5y -6.50).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the bottom of the country's own observed history at the 4th percentile. Relative to the curated peer set it ranks at the 40th percentile. Sensitive proxy for primary health system reach and nutrition status. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: falling
1y change: -1.20
3y change: -3.80
5y change: -6.50
5y % change: -15.0%
Historical percentile: 4th
Peer percentile: 40th
Peer deviation: -5.50
Observations: 14
Recency: 2y
Window completeness: 93%

Favourable movement of -6.50 DT_10P3BR_L over 5 years (2019 to 2024).
Latest value sits at the 4th percentile of this country’s own observed history.
Latest value ranks at the 40th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_DYN_MORT
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:42.440Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Maternal mortality ratio (modeled estimate, per 100,000 live births)

WB_WDI_SH_STA_MMRT

Improvement47Adequate

Latest value

381.0DT_10P5BR_L

Period 2023

1 year

-4.00

3 year

-32.0

5 year

-56.0

vs peer median

+27.0

FACT

Maternal mortality ratio (modeled estimate, per 100,000 live births) for Mauritania was 381.0 DT_10P5BR_L in 2023 (1y -4.00, 3y -32.0, 5y -56.0).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the bottom of the country's own observed history at the 4th percentile. Relative to the curated peer set it ranks at the 80th percentile. Modeled estimate with wide uncertainty intervals that this series does not carry. Read as level, not precision. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: falling
1y change: -4.00
3y change: -32.0
5y change: -56.0
5y % change: -12.8%
Historical percentile: 4th
Peer percentile: 80th
Peer deviation: 27.0
Observations: 13
Recency: 3y
Window completeness: 87%

Favourable movement of -56.0 DT_10P5BR_L over 5 years (2018 to 2023).
Latest value sits at the 4th percentile of this country’s own observed history.
Latest value ranks at the 80th percentile of the curated peer set (5 economies with data).

Moderate confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_STA_MMRT
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:41.733Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Primary completion rate, total (% of relevant age group)

WB_WDI_SE_PRM_CMPT_ZS

Improvement27Limited

Latest value

73.4PT

Period 2020

1 year

+7.07

3 year

+8.78

5 year

+6.42

vs peer median

+10.6

FACT

Primary completion rate, total (% of relevant age group) for Mauritania was 73.45 PT in 2020 (1y +7.07, 3y +8.78, 5y +6.42).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 94th percentile. Relative to the curated peer set it ranks at the 60th percentile. In conflict-affected areas school closure suppresses this figure with a lag, so a fall may register years after the disruption that caused it. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 7.07
3y change: 8.78
5y change: 6.42
5y % change: 9.6%
Historical percentile: 94th
Peer percentile: 60th
Peer deviation: 10.6
Observations: 8
Recency: 6y
Window completeness: 53%

Favourable movement of 6.42 PT over 5 years (2015 to 2020).
Latest value sits at the 94th percentile of this country’s own observed history.
Latest value ranks at the 60th percentile of the curated peer set (5 economies with data).
Magnitude reduced because the evidence is graded limited.
Most recent observation is 6 years old (2020).

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_PRM_CMPT_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:40.457Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Literacy rate, adult total (% of people ages 15 and above)

WB_WDI_SE_ADT_LITR_ZS

Insufficient evidenceInadequate

Latest value

59.5PT

Period 2020

1 year

n/a

3 year

n/a

5 year

-5.58

vs peer median

n/a

FACT

Literacy rate, adult total (% of people ages 15 and above) for Mauritania was 59.53 PT in 2020 (5y -5.58).

ASSESSMENT

Evidence is insufficient to assess direction for this indicator. Most recent observation is 6 years old (2020). Series covers 20% of the 15 year analysis window. No structural reading should be drawn from this series until coverage improves or an alternative measure is substituted.

Show signal components

Direction of travel: falling
1y change: n/a
3y change: n/a
5y change: -5.58
5y % change: -8.6%
Historical percentile: not computed
Peer percentile: withheld
Peer deviation: n/a
Observations: 3
Recency: 6y
Window completeness: 20%

Peer comparison suppressed: this indicator is only interpretable against the country’s own history.
Insufficient evidence: data quality is inadequate for a defensible reading.
Most recent observation is 6 years old (2020).
Series covers 20% of the 15 year analysis window.

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_ADT_LITR_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:42.430Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Prevalence of undernourishment (% of population)

WB_WDI_SN_ITK_DEFC_ZS

Deterioration98Adequate

Latest value

8.70PT_POP

Period 2023

1 year

+0.10

3 year

+1.70

5 year

+2.20

vs peer median

-3.60

FACT

Prevalence of undernourishment (% of population) for Mauritania was 8.70 PT_POP in 2023 (1y +0.10, 3y +1.70, 5y +2.20).

ASSESSMENT

The series shows sustained adverse movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 96th percentile. Relative to the curated peer set it ranks at the 40th percentile. Structural food insecurity baseline. It is not a substitute for IPC phase classification or humanitarian caseload data, which operate on a far shorter cycle. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 0.10
3y change: 1.70
5y change: 2.20
5y % change: 33.8%
Historical percentile: 96th
Peer percentile: 40th
Peer deviation: -3.60
Observations: 13
Recency: 3y
Window completeness: 87%

Adverse movement of 2.20 PT_POP over 5 years (2018 to 2023), given that for this indicator a rising value indicates rising structural pressure.
Latest value sits at the 96th percentile of this country’s own observed history.
Latest value ranks at the 40th percentile of the curated peer set (5 economies with data).

Moderate confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SN_ITK_DEFC_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:52.486Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Access to electricity (% of population)

WB_WDI_EG_ELC_ACCS_ZS

Improvement54Adequate

Latest value

50.3PT_POP

Period 2023

1 year

+1.30

3 year

+4.90

5 year

+6.70

vs peer median

-16.6

FACT

Access to electricity (% of population) for Mauritania was 50.30 PT_POP in 2023 (1y +1.30, 3y +4.90, 5y +6.70).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 96th percentile. Relative to the curated peer set it ranks at the 20th percentile. Proxy for state reach and service delivery, and a direct operational planning input. National coverage conceals large rural and peri-urban gaps. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 1.30
3y change: 4.90
5y change: 6.70
5y % change: 15.4%
Historical percentile: 96th
Peer percentile: 20th
Peer deviation: -16.6
Observations: 13
Recency: 3y
Window completeness: 87%

Favourable movement of 6.70 PT_POP over 5 years (2018 to 2023).
Latest value sits at the 96th percentile of this country’s own observed history.
Latest value ranks at the 20th percentile of the curated peer set (5 economies with data).

Moderate confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_EG_ELC_ACCS_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:52.851Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Individuals using the Internet (% of population)

WB_WDI_IT_NET_USER_ZS

Improvement98Adequate

Latest value

45.8PT_POP

Period 2024

1 year

+0.82

3 year

+10.1

5 year

+20.7

vs peer median

-3.71

FACT

Individuals using the Internet (% of population) for Mauritania was 45.78 PT_POP in 2024 (1y +0.82, 3y +10.1, 5y +20.7).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 96th percentile. Relative to the curated peer set it ranks at the 40th percentile. Connectivity baseline relevant to information environment analysis and to OSINT collection feasibility. It does not capture shutdowns or throttling. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 0.82
3y change: 10.1
5y change: 20.7
5y % change: 82.4%
Historical percentile: 96th
Peer percentile: 40th
Peer deviation: -3.71
Observations: 14
Recency: 2y
Window completeness: 93%

Favourable movement of 20.7 PT_POP over 5 years (2019 to 2024).
Latest value sits at the 96th percentile of this country’s own observed history.
Latest value ranks at the 40th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_IT_NET_USER_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:55.839Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Mobile cellular subscriptions (per 100 people)

WB_WDI_IT_CEL_SETS_P2

Pressure36Adequate

Latest value

92.110P2PS

Period 2024

1 year

+1.30

3 year

-45.5

5 year

-13.4

vs peer median

-31.8

FACT

Mobile cellular subscriptions (per 100 people) for Mauritania was 92.07 10P2PS in 2024 (1y +1.30, 3y -45.5, 5y -13.4).

ASSESSMENT

The series shows adverse movement of moderate magnitude against this country's own recent baseline. The latest value sits in the middle range of the country's own observed history at the 25th percentile. Relative to the curated peer set it ranks at the 20th percentile. Counts subscriptions rather than people, so multi-SIM use inflates it above actual penetration. Useful for communications reach, weak as a welfare proxy. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: falling
1y change: 1.30
3y change: -45.5
5y change: -13.4
5y % change: -12.7%
Historical percentile: 25th
Peer percentile: 20th
Peer deviation: -31.8
Observations: 14
Recency: 2y
Window completeness: 93%

Adverse movement of -13.4 10P2PS over 5 years (2019 to 2024), given that for this indicator a falling value indicates declining structural capacity.
Latest value sits at the 25th percentile of this country’s own observed history.
Latest value ranks at the 20th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_IT_CEL_SETS_P2
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:57.690Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

People using at least basic drinking water services (% of population)

WB_WDI_SH_H2O_BASW_ZS

Improvement39Adequate

Latest value

77.3PT_POP

Period 2024

1 year

+0.87

3 year

+3.98

5 year

+7.10

vs peer median

-9.82

FACT

People using at least basic drinking water services (% of population) for Mauritania was 77.28 PT_POP in 2024 (1y +0.87, 3y +3.98, 5y +7.10).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 96th percentile. Relative to the curated peer set it ranks at the 20th percentile. Basic service threshold, which is a low bar. Read the level, not only the direction. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 0.87
3y change: 3.98
5y change: 7.10
5y % change: 10.1%
Historical percentile: 96th
Peer percentile: 20th
Peer deviation: -9.82
Observations: 14
Recency: 2y
Window completeness: 93%

Favourable movement of 7.10 PT_POP over 5 years (2019 to 2024).
Latest value sits at the 96th percentile of this country’s own observed history.
Latest value ranks at the 20th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_H2O_BASW_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:04.149Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

People using at least basic sanitation services (% of population)

WB_WDI_SH_STA_BASS_ZS

Improvement62Adequate

Latest value

58.7PT_POP

Period 2024

1 year

+0.42

3 year

+4.75

5 year

+8.94

vs peer median

+10.5

FACT

People using at least basic sanitation services (% of population) for Mauritania was 58.68 PT_POP in 2024 (1y +0.42, 3y +4.75, 5y +8.94).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 96th percentile. Relative to the curated peer set it ranks at the 60th percentile. Together with water access, a proxy for baseline public health infrastructure and epidemic vulnerability. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 0.42
3y change: 4.75
5y change: 8.94
5y % change: 18.0%
Historical percentile: 96th
Peer percentile: 60th
Peer deviation: 10.5
Observations: 14
Recency: 2y
Window completeness: 93%

Favourable movement of 8.94 PT_POP over 5 years (2019 to 2024).
Latest value sits at the 96th percentile of this country’s own observed history.
Latest value ranks at the 60th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_STA_BASS_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:04.202Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Gini index

WB_WDI_SI_POV_GINI

Insufficient evidenceInadequate

Latest value

32.00_TO_100

Period 2019

1 year

n/a

3 year

n/a

5 year

-0.60

vs peer median

n/a

FACT

Gini index for Mauritania was 32.00 0_TO_100 in 2019 (5y -0.60).

ASSESSMENT

Show signal components

Direction of travel: flat
1y change: n/a
3y change: n/a
5y change: -0.60
5y % change: -1.8%
Historical percentile: not computed
Peer percentile: 0th
Peer deviation: n/a
Observations: 2
Recency: 7y
Window completeness: 13%

Peer deviation reported as percentile rank only: levels are not directly comparable across these economies.
Insufficient evidence: data quality is inadequate for a defensible reading.
Most recent observation is 7 years old (2019), beyond the 6 year limit for analytic use.
Only 2 observation(s) available; 3 are required before any trend is computed.

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SI_POV_GINI
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:04.793Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Labor force participation rate, female (% of female population ages 15+)

WB_WDI_SL_TLF_CACT_FE_ZS

Stable13Adequate

Latest value

26.2PT_W

Period 2025

1 year

-0.06

3 year

+0.13

5 year

+0.31

vs peer median

n/a

FACT

Labor force participation rate, female (% of female population ages 15+) for Mauritania was 26.18 PT_W in 2025 (1y -0.06, 3y +0.13, 5y +0.31).

ASSESSMENT

The series is broadly flat against this country's own recent baseline. The latest value sits in the middle range of the country's own observed history at the 23th percentile. Cross-country comparison is withheld: the measurement concept is not comparable across these economies. A high rate can reflect subsistence agricultural work rather than economic inclusion, so the level is not comparable across structurally different economies. Movement within one country over time is the interpretable signal. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: flat
1y change: -0.06
3y change: 0.13
5y change: 0.31
5y % change: 1.2%
Historical percentile: 23th
Peer percentile: withheld
Peer deviation: n/a
Observations: 15
Recency: 1y
Window completeness: 100%

Peer comparison suppressed: this indicator is only interpretable against the country’s own history.
Change of 0.31 PT_W between 2020 and 2025 falls inside the 5% noise band.
Latest value sits at the 23th percentile of this country’s own observed history.

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SL_TLF_CACT_FE_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:08.047Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

School enrollment, primary and secondary (gross), gender parity index

WB_WDI_SE_ENR_PRSC_FM_ZS

Improvement44Limited

Latest value

1.07U

Period 2020

1 year

+0.01

3 year

+0.02

5 year

+0.05

vs peer median

+0.10

FACT

School enrollment, primary and secondary (gross), gender parity index for Mauritania was 1.07 U in 2020 (1y +0.01, 3y +0.02, 5y +0.05).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 95th percentile. Relative to the curated peer set it ranks at the 60th percentile. A ratio where 1.0 is parity. Values above 1.0 indicate female advantage, so a rise is not monotonically positive; the engine reads distance from parity in the analytic note rather than treating higher as uniformly better. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: flat
1y change: 0.01
3y change: 0.02
5y change: 0.05
5y % change: 4.8%
Historical percentile: 95th
Peer percentile: 60th
Peer deviation: 0.10
Observations: 10
Recency: 6y
Window completeness: 67%

Favourable movement of 0.05 U over 5 years (2015 to 2020).
Latest value sits at the 95th percentile of this country’s own observed history.
Latest value ranks at the 60th percentile of the curated peer set (5 economies with data).
Magnitude reduced because the evidence is graded limited.
Most recent observation is 6 years old (2020).

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_ENR_PRSC_FM_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:14.056Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

School enrollment, secondary (% gross)

WB_WDI_SE_SEC_ENRR

Improvement37Limited

Latest value

36.0PT

Period 2020

1 year

-1.55

3 year

+4.27

5 year

+4.83

vs peer median

-8.04

FACT

School enrollment, secondary (% gross) for Mauritania was 36.03 PT in 2020 (1y -1.55, 3y +4.27, 5y +4.83).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 85th percentile. Relative to the curated peer set it ranks at the 20th percentile. Gross ratio, so repetition and late entry can push it above 100. Read as participation breadth. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: -1.55
3y change: 4.27
5y change: 4.83
5y % change: 15.5%
Historical percentile: 85th
Peer percentile: 20th
Peer deviation: -8.04
Observations: 10
Recency: 6y
Window completeness: 67%

Favourable movement of 4.83 PT over 5 years (2015 to 2020).
Latest value sits at the 85th percentile of this country’s own observed history.
Latest value ranks at the 20th percentile of the curated peer set (5 economies with data).
Magnitude reduced because the evidence is graded limited.
Most recent observation is 6 years old (2020).

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_SEC_ENRR
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:14.494Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Government Effectiveness (WGI estimate, approx. -2.5 to +2.5)

WB_WDI_GOV_WGI_GE_EST

Improvement28Adequate

Latest value

-0.74SCORE

Period 2024

1 year

+0.08

3 year

+0.11

5 year

+0.08

vs peer median

n/a

FACT

Government Effectiveness (WGI estimate, approx. -2.5 to +2.5) for Mauritania was -0.74 SCORE in 2024 (1y +0.08, 3y +0.11, 5y +0.08).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the top of the country's own observed history at the 82th percentile. Relative to the curated peer set it ranks at the 40th percentile. Worldwide Governance Indicators are composite perception-based estimates aggregated from expert and survey sources. They carry standard errors this series does not expose, so small movements are not interpretable and only relative position is defensible. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: 0.08
3y change: 0.11
5y change: 0.08
5y % change: 10.1%
Historical percentile: 82th
Peer percentile: 40th
Peer deviation: n/a
Observations: 14
Recency: 2y
Window completeness: 93%

Peer deviation reported as percentile rank only: levels are not directly comparable across these economies.
Favourable movement of 0.08 SCORE over 5 years (2019 to 2024).
Latest value sits at the 82th percentile of this country’s own observed history.
Latest value ranks at the 40th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_GE_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:16.688Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Political Stability and Absence of Violence (WGI estimate)

WB_WDI_GOV_WGI_PV_EST

Improvement25Adequate

Latest value

-0.56SCORE

Period 2024

1 year

-0.06

3 year

-0.04

5 year

+0.09

vs peer median

n/a

FACT

Political Stability and Absence of Violence (WGI estimate) for Mauritania was -0.56 SCORE in 2024 (1y -0.06, 3y -0.04, 5y +0.09).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits in the middle range of the country's own observed history at the 75th percentile. Relative to the curated peer set it ranks at the 40th percentile. This is a perception-based composite that partly reflects observed violence already recorded elsewhere. It must not be treated as an independent predictor of conflict, and it lags events by at least one annual cycle. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: rising
1y change: -0.06
3y change: -0.04
5y change: 0.09
5y % change: 13.3%
Historical percentile: 75th
Peer percentile: 40th
Peer deviation: n/a
Observations: 14
Recency: 2y
Window completeness: 93%

Peer deviation reported as percentile rank only: levels are not directly comparable across these economies.
Favourable movement of 0.09 SCORE over 5 years (2019 to 2024).
Latest value sits at the 75th percentile of this country’s own observed history.
Latest value ranks at the 40th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_PV_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:18.520Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Rule of Law (WGI estimate)

WB_WDI_GOV_WGI_RL_EST

Pressure14Adequate

Latest value

-0.86SCORE

Period 2024

1 year

-0.01

3 year

+0.01

5 year

-0.08

vs peer median

n/a

FACT

Rule of Law (WGI estimate) for Mauritania was -0.86 SCORE in 2024 (1y -0.01, 3y +0.01, 5y -0.08).

ASSESSMENT

The series shows adverse movement of moderate magnitude against this country's own recent baseline. The latest value sits in the middle range of the country's own observed history at the 54th percentile. Relative to the curated peer set it ranks at the 40th percentile. Composite perception estimate. Interpret as relative standing among peers, not as an absolute measure. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: falling
1y change: -0.01
3y change: 0.01
5y change: -0.08
5y % change: -10.4%
Historical percentile: 54th
Peer percentile: 40th
Peer deviation: n/a
Observations: 14
Recency: 2y
Window completeness: 93%

Peer deviation reported as percentile rank only: levels are not directly comparable across these economies.
Adverse movement of -0.08 SCORE over 5 years (2019 to 2024), given that for this indicator a falling value indicates declining structural capacity.
Latest value sits at the 54th percentile of this country’s own observed history.
Latest value ranks at the 40th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_RL_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:36.192Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Control of Corruption (WGI estimate)

WB_WDI_GOV_WGI_CC_EST

Improvement23Adequate

Latest value

-0.93SCORE

Period 2024

1 year

-0.14

3 year

-0.05

5 year

+0.05

vs peer median

n/a

FACT

Control of Corruption (WGI estimate) for Mauritania was -0.93 SCORE in 2024 (1y -0.14, 3y -0.05, 5y +0.05).

ASSESSMENT

The series shows favourable movement against this country's own recent baseline. The latest value sits near the bottom of the country's own observed history at the 18th percentile. Relative to the curated peer set it ranks at the 40th percentile. Composite perception estimate, sensitive to changes in the underlying source mix between rounds. This is contextual structural evidence. It may raise or lower the relevance of a hypothesis, but on its own it establishes no mechanism, no trigger, no actor behaviour and no causal relationship to political violence.

Show signal components

Direction of travel: flat
1y change: -0.14
3y change: -0.05
5y change: 0.05
5y % change: 4.8%
Historical percentile: 18th
Peer percentile: 40th
Peer deviation: n/a
Observations: 14
Recency: 2y
Window completeness: 93%

Peer deviation reported as percentile rank only: levels are not directly comparable across these economies.
Favourable movement of 0.05 SCORE over 5 years (2019 to 2024).
Latest value sits at the 18th percentile of this country’s own observed history.
Latest value ranks at the 40th percentile of the curated peer set (5 economies with data).

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_CC_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:26.227Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Tax revenue (% of GDP)

WB_WDI_GC_TAX_TOTL_GD_ZS

Insufficient evidenceInadequate

Latest value

not reported

No period

1 year

n/a

3 year

n/a

5 year

n/a

vs peer median

n/a

FACT

Tax revenue (% of GDP): no observation reported for Mauritania in the analysis window.

ASSESSMENT

Evidence is insufficient to assess direction for this indicator. No observations returned for this indicator and country. No structural reading should be drawn from this series until coverage improves or an alternative measure is substituted.

Show signal components

Direction of travel: not computable
1y change: n/a
3y change: n/a
5y change: n/a
5y % change: n/a
Historical percentile: not computed
Peer percentile: withheld
Peer deviation: n/a
Observations: 0
Recency: n/a
Window completeness: 0%

Insufficient evidence: data quality is inadequate for a defensible reading.
No observations returned for this indicator and country.

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GC_TAX_TOTL_GD_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:36.293Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Central government debt, total (% of GDP)

WB_WDI_GC_DOD_TOTL_GD_ZS

Insufficient evidenceInadequate

Latest value

not reported

No period

1 year

n/a

3 year

n/a

5 year

n/a

vs peer median

n/a

FACT

Central government debt, total (% of GDP): no observation reported for Mauritania in the analysis window.

ASSESSMENT

Show signal components

Direction of travel: not computable
1y change: n/a
3y change: n/a
5y change: n/a
5y % change: n/a
Historical percentile: not computed
Peer percentile: withheld
Peer deviation: n/a
Observations: 0
Recency: n/a
Window completeness: 0%

Peer comparison suppressed: only 1 peer economies returned a value, below the minimum of 3.
Insufficient evidence: data quality is inadequate for a defensible reading.
No observations returned for this indicator and country.

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GC_DOD_TOTL_GD_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:31.857Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Military expenditure (% of GDP)

WB_WDI_MS_MIL_XPND_GD_ZS

Insufficient evidenceAdequate

Latest value

2.16PT_GDP

Period 2024

1 year

-0.11

3 year

-0.14

5 year

+0.16

vs peer median

-0.83

FACT

Military expenditure (% of GDP) for Mauritania was 2.16 PT_GDP in 2024 (1y -0.11, 3y -0.14, 5y +0.16).

ASSESSMENT

Evidence is insufficient to assess direction for this indicator. 13 observations covering 87% of the window, most recent 2024. No structural reading should be drawn from this series until coverage improves or an alternative measure is substituted.

Show signal components

Direction of travel: rising
1y change: -0.11
3y change: -0.14
5y change: 0.16
5y % change: 8.0%
Historical percentile: 58th
Peer percentile: 40th
Peer deviation: -0.83
Observations: 13
Recency: 2y
Window completeness: 87%

Indicator is contextual by design: it carries no normative direction and does not contribute to any domain signal.

High confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_MS_MIL_XPND_GD_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:39:38.753Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Intentional homicides (per 100,000 people)

WB_WDI_VC_IHR_PSRC_P5

Insufficient evidenceInadequate

Latest value

1.0010P5PS

Period 2020

1 year

-0.07

3 year

-0.22

5 year

n/a

vs peer median

n/a

FACT

Intentional homicides (per 100,000 people) for Mauritania was 1.00 10P5PS in 2020 (1y -0.07, 3y -0.22).

ASSESSMENT

Show signal components

Direction of travel: falling
1y change: -0.07
3y change: -0.22
5y change: n/a
5y % change: n/a
Historical percentile: not computed
Peer percentile: withheld
Peer deviation: n/a
Observations: 3
Recency: 6y
Window completeness: 20%

Peer comparison suppressed: only 2 peer economies returned a value, below the minimum of 3.
Insufficient evidence: data quality is inadequate for a defensible reading.
Most recent observation is 6 years old (2020).
Series covers 20% of the 15 year analysis window.

Low confidence

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_VC_IHR_PSRC_P5
Endpoint: /data360/data
Retrieved: 2026-09-04T21:39:38.308Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

6. Sources and metadata

GDP (annual % growth)WB_WDI_NY_GDP_MKTP_KD_ZG

Unit: %
Periodicity: Annual
License: CC BY-4.0

Definition

Statistical concept

The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.

Methodology

National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/

Limitations and exceptions

Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs. In many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult. Moreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation. Informal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians. Rebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful. To obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate.

Relevance

This indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.

Aggregation method

Weighted average

Source references

Inter-secretariat Working Group on National Accounts (ISWGNA) Glossary
SNA2008

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:32.109Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_NY_GDP_MKTP_KD_ZG
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:11.212Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Aggregate output growth. A single weak year is ordinary volatility; sustained contraction relative to population growth is the analytically meaningful pattern.

GDP per capita (constant 2015 US$)WB_WDI_NY_GDP_PCAP_KD

Unit: constant 2015 US$
Periodicity: Annual
License: CC BY-4.0

Definition

Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.

Statistical concept

Methodology

Relevance

Aggregation method

Weighted average

Source references

Inter-secretariat Working Group on National Accounts (ISWGNA) Glossary
SNA2008

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:38.845Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_NY_GDP_PCAP_KD
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:10.341Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Constant-price series, so changes reflect real output per head rather than price movement. Says nothing about distribution; read with the Gini index.

Inflation, consumer prices (annual % growth)WB_WDI_FP_CPI_TOTL_ZG

Unit: %
Periodicity: Annual
License: CC BY-4.0

Definition

Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly.

Statistical concept

The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another.

Methodology

Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/

Relevance

A general and continuing increase in an economy’s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series.

Aggregation method

Median

Source references

CPIM

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:32.104Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_FP_CPI_TOTL_ZG
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:09.071Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. National CPI. Household exposure depends on the food and fuel share of the basket, which the headline rate does not reveal.

Unemployment, total (% of total labor force, modeled ILO estimate)WB_WDI_SL_UEM_TOTL_ZS

Unit: % of total labor force
Periodicity: Annual
License: CC BY-4.0

Definition

Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

Statistical concept

The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). The working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).

Methodology

The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed. The series is part of the "ILO modeled estimates database," including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/

Limitations and exceptions

While the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment – whether it is cyclical and short-term or structural and long-term – which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only.

Relevance

Aggregation method

Weighted average

Source references

https://ilostat.ilo.org/data/bulk/
Labor Force Statistics (LFS, STLFS, RURBAN databases). ILOSTAT.
ILO Modelled Estimates (ILOEST database)

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:32.102Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SL_UEM_TOTL_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:11.274Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Modeled ILO estimate, not a national survey figure. In economies with large informal sectors the rate understates labour underutilisation, so compare it against peers and its own history rather than reading the level alone.

Unemployment, youth total (% of labor force ages 15-24, modeled ILO estimate)WB_WDI_SL_UEM_1524_ZS

Unit: % of total labor force ages 15-24
Periodicity: Annual
License: CC BY-4.0

Definition

Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment.

Statistical concept

Methodology

Limitations and exceptions

The criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked. There may be also persons not currently in the labour market who want to work but do not actively "seek" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the "hidden unemployed" or "discouraged workers") is a criterion that will affect the unemployment count of both women and men. However, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.

Relevance

The unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market. Given its usefulness in conveying valuable information on a country’s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all). Youth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no "voice" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.

Aggregation method

Weighted average

Source references

https://ilostat.ilo.org/data/bulk/
Labor Force Statistics (LFS, STLFS, RURBAN databases). ILOSTAT.

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:39.029Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SL_UEM_1524_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:20.449Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Read alongside the 0-14 population share. A large youth cohort entering a labour market that is not absorbing it is a structural condition, not a mobilisation mechanism.

Poverty headcount ratio at $3.00 a day (2021 PPP, % of population)WB_WDI_SI_POV_DDAY

Unit: %
Periodicity: Annual
License: CC BY-4.0

Definition

Poverty headcount ratio at $3.00 a day is the percentage of the population living on less than $3.00 a day at 2021 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Statistical concept

Poverty headcount ratio at $3.00 a day refers to the percentage of a population whose consumption or income per day falls short of the international poverty line of $3.00 a day (adjusted for purchasing power parity differences across countries), the poverty line typical of low-income countries.

Methodology

International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries. Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income. Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions. The statistics reported here are based on consumption data or, when unavailable, on income surveys.

Limitations and exceptions

Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction. Besides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries. However, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.

Relevance

The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. The World Bank defines extreme poverty as living on less than $3.00 a day (adjusted for purchasing power differences across countries). The value of $3.00 is the typical poverty line of low-income countries, which is the minimum amount of money people in low-income countries need to cover their daily basic needs, including food, clothing, and shelter. The share of population living on less than $3.00 a day is the first indicator the World Bank tracks in its Bank’s expanded vision indicators to create a world free of poverty in a livable planet. It is also the indicator the UN tracks for SDG 1.1. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries.

Aggregation method

Population-weighted average

Source references

http://pip.worldbank.org
Poverty and Inequality Platform (PIP), World Bank

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:32.183Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SI_POV_DDAY
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:20.147Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Survey-based and published irregularly, so the series is sparse and often several years stale. Treat it as a level reading, not a trend.

Population, totalWB_WDI_SP_POP_TOTL

Unit: Unit
Periodicity: Annual
License: CC BY-4.0

Definition

Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

Statistical concept

Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.

Methodology

Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration. Errors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. One of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.

Limitations and exceptions

Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. Because future trends cannot be known with certainty, population projections have a wide range of uncertainty.

Relevance

Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure.

Aggregation method

Sum

Source references

https://population.un.org/wpp/
https://unstats.un.org/home/nso_sites/
https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly
https://unstats.un.org
United Nations Population Division, National Statistical Offices, Eurostat, United Nations Statistics Division.

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.669Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_POP_TOTL
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:20.813Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Denominator and scale context. Carries no normative direction and never contributes to a signal.

Population growth (annual %)WB_WDI_SP_POP_GROW

Unit: Percentage
Periodicity: Annual
License: CC BY-4.0

Definition

Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.

Statistical concept

Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time.

Methodology

The growth rate is computed using the exponential growth formula: r = ln(pn/p0)/n, where r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods. For information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).

Relevance

Aggregation method

Weighted average

Source references

Derived from total population. Population source: United Nations Population Division, National Statistical Offices, Eurostat, United Nations Statistics Division.
United Nations Population Division

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.656Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_POP_GROW
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:21.922Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Interpreted as demand pressure on services and labour markets, not as a negative outcome in itself. Its analytic weight depends entirely on whether service delivery and output are keeping pace.

Population ages 0-14 (% of total population)WB_WDI_SP_POP_0014_TO_ZS

Unit: Percentage
Periodicity: Annual
License: CC BY-4.0

Definition

Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population.

Statistical concept

Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.

Methodology

Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.

Limitations and exceptions

Because the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source.

Relevance

Patterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development. This indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.

Aggregation method

Weighted average

Source references

https://population.un.org/wpp/
United Nations Population Division

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:38.915Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_POP_0014_TO_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:30.913Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Forward demand on education, health and employment systems. A youth bulge is a demographic condition; whether it becomes a mobilisation pool depends on institutions, opportunity and actor behaviour.

Urban population (% of total population)WB_WDI_SP_URB_TOTL_IN_ZS

Unit: Percentage
Periodicity: Annual
License: CC BY-4.0

Definition

Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.

Statistical concept

Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as "urban" or "rural." The population of a city or metropolitan area depends on the boundaries chosen.

Methodology

Percentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population.

Limitations and exceptions

Aggregation of urban and rural population may not add up to total population because of different country coverage. Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. Estimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. Because the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution.

Relevance

Explosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. In principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment.

Aggregation method

Weighted average

Source references

https://population.un.org/wup/
United Nations Population Division

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.659Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_URB_TOTL_IN_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:30.678Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Rapid urbanisation shifts where governance capacity is tested. Direction is not inherently adverse, so this is contextual rather than a pressure signal.

Fertility rate, total (births per woman)WB_WDI_SP_DYN_TFRT_IN

Unit: Births per woman
Periodicity: Annual
License: CC BY-4.0

Definition

Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

Statistical concept

Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years.

Methodology

Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).

Limitations and exceptions

Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data.

Relevance

Aggregation method

Weighted average

Source references

United Nations Population Division, National Statistical Offices, Eurostat, United Nations Statistics Division.
United Nations Population Division

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:39.090Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_DYN_TFRT_IN
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:31.137Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Leading determinant of future cohort size. Contextual: read with human development indicators.

Life expectancy at birth, total (years)WB_WDI_SP_DYN_LE00_IN

Unit: Years
Periodicity: Annual
License: CC BY-4.0

Definition

Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

Statistical concept

Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table. High mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years.

Methodology

Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.

Limitations and exceptions

Relevance

Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.

Aggregation method

Weighted average

Source references

UN Population Division
United Nations Population Division; or derived from male and female life expectancy at birth from sources such as national statistical offices and Eurostat.
United Nations Population Division

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:39.198Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SP_DYN_LE00_IN
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:31.743Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Slow-moving summary of health system performance and living conditions. A sharp reversal is a strong signal precisely because the series is normally stable.

Mortality rate, under-5 (per 1,000 live births)WB_WDI_SH_DYN_MORT

Unit: Per 1000 live births
Periodicity: Annual
License: CC BY-4.0

Definition

Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.

Statistical concept

The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A "complete" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.

Methodology

Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.

Limitations and exceptions

Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.

Relevance

Aggregation method

Weighted average

Source references

www.childmortality.org
UNICEF, WHO, World Bank, United Nations Population Division

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.627Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_DYN_MORT
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:42.440Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Sensitive proxy for primary health system reach and nutrition status.

Maternal mortality ratio (modeled estimate, per 100,000 live births)WB_WDI_SH_STA_MMRT

Unit: Per 100,000 live births
Periodicity: Annual
License: CC BY-4.0

Definition

Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

Statistical concept

Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries. Maternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.

Methodology

The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.

Limitations and exceptions

The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.

Relevance

Childbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants.

Aggregation method

Weighted average

Source references

https://www.who.int/news/item/23-02-2023-a-woman-dies-every-two-minutes-due-to-pregnancy-or-childbirth--un-agencies
WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division.

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.736Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_STA_MMRT
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:41.733Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Modeled estimate with wide uncertainty intervals that this series does not carry. Read as level, not precision.

Primary completion rate, total (% of relevant age group)WB_WDI_SE_PRM_CMPT_ZS

Unit: % of relevant age group
Periodicity: Annual
License: CC BY-4.0

Definition

Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education.

Statistical concept

The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education.

Methodology

Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).

Limitations and exceptions

The data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate. There are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education.

Relevance

Primary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education.

Aggregation method

Weighted average

Source references

https://databrowser.uis.unesco.org/resources
The Technical Cooperation Group on SDG 4 indicators (TCG) - Education 2030 (TCG)

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.722Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_PRM_CMPT_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:40.457Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. In conflict-affected areas school closure suppresses this figure with a lag, so a fall may register years after the disruption that caused it.

Literacy rate, adult total (% of people ages 15 and above)WB_WDI_SE_ADT_LITR_ZS

Unit: % of people ages 15 and above
Periodicity: Annual
License: CC BY-4.0

Definition

Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.

Statistical concept

Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.

Methodology

The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status. Data on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.

Limitations and exceptions

In practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.

Relevance

Literacy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women. It can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education. Literate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.

Aggregation method

Weighted average

Source references

https://databrowser.uis.unesco.org/resources
The Technical Cooperation Group on SDG 4 indicators (TCG) - Education 2030 (TCG)

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.744Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_ADT_LITR_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:42.430Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Census and survey dependent, so the series is sparse and irregular. Definitions differ enough between collection rounds that cross-country comparison is not defensible.

Prevalence of undernourishment (% of population)WB_WDI_SN_ITK_DEFC_ZS

Unit: Percentage
Periodicity: Annual
License: CC BY-4.0

Definition

Prevalence of undernourishment is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%.

Statistical concept

Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person.

Methodology

Limitations and exceptions

From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000).

Relevance

Good nutrition is the cornerstone for survival, health and development. Well-nourished children perform better in school, grow into healthy adults and in turn give their children a better start in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental paths, both physically and mentally (UNICEF www.childinfo.org).

Aggregation method

Weighted average

Source references

http://www.fao.org/faostat/en/#home
Food and Agriculture Organization

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.748Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SN_ITK_DEFC_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:52.486Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Structural food insecurity baseline. It is not a substitute for IPC phase classification or humanitarian caseload data, which operate on a far shorter cycle.

Access to electricity (% of population)WB_WDI_EG_ELC_ACCS_ZS

Unit: % (share) of population
Periodicity: Annual
License: CC BY-4.0

Definition

Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources.

Methodology

The World Bank’s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.

Relevance

Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasingly dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Energy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indications of a country's energy poverty status. Electricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification. Use of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.

Aggregation method

Population-weighted average

Source references

https://trackingsdg7.esmap.org/downloads

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.793Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_EG_ELC_ACCS_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:52.851Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Proxy for state reach and service delivery, and a direct operational planning input. National coverage conceals large rural and peri-urban gaps.

Individuals using the Internet (% of population)WB_WDI_IT_NET_USER_ZS

Unit: % of population
Periodicity: Annual
License: CC BY-4.0

Definition

Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.

Statistical concept

The number of in-scope individuals using the Internet is calculated by aggregating the weighted responses. The proportion of individuals using the Internet is expressed as a percentage and is calculated by dividing the total number of in-scope individuals using the Internet by the total number of in-scope individuals, and then multiplying the result by 100.

Methodology

The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx

Limitations and exceptions

Relevance

The digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances. Today's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains.

Aggregation method

Weighted average

Source references

https://datahub.itu.int/
International Telecommunication Union

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.801Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_IT_NET_USER_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:55.839Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Connectivity baseline relevant to information environment analysis and to OSINT collection feasibility. It does not capture shutdowns or throttling.

Mobile cellular subscriptions (per 100 people)WB_WDI_IT_CEL_SETS_P2

Unit: number of subscriptions*100/population
Periodicity: Annual
License: CC BY-4.0

Definition

Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.

Statistical concept

Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of ‘active’ and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months). This indicator can be divided by the population and multiplied by 100 to obtain mobile cellular subscriptions per 100 people.

Methodology

Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications. Data on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports. Mobile cellular subscriptions (per 100 people) indicator is derived by all mobile subscriptions divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx

Limitations and exceptions

Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability. Discrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU. Mobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included.

Relevance

The quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe. Access to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met. Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems. Over the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.

Aggregation method

Weighted average

Source references

International Telecommunication Union

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.805Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_IT_CEL_SETS_P2
Endpoint: /data360/data
Retrieved: 2026-09-04T21:36:57.690Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Counts subscriptions rather than people, so multi-SIM use inflates it above actual penetration. Useful for communications reach, weak as a welfare proxy.

People using at least basic drinking water services (% of population)WB_WDI_SH_H2O_BASW_ZS

Unit: % of population
Periodicity: Annual
License: CC BY-4.0

Definition

The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.

Statistical concept

The JMP classifies the water service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services.

Methodology

The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).

Limitations and exceptions

National, regional and income group estimates are made when data are available for at least 50 percent of the population.

Relevance

Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities. Lack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact. Women and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families. Many international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.

Aggregation method

Weighted average

Source references

washdata.org
The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)
WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.809Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_H2O_BASW_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:04.149Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Basic service threshold, which is a low bar. Read the level, not only the direction.

People using at least basic sanitation services (% of population)WB_WDI_SH_STA_BASS_ZS

Unit: % of population
Periodicity: Annual
License: CC BY-4.0

Definition

The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, composting toilets or pit latrines with slabs.

Statistical concept

The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services.

Methodology

The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP’s methodology report (https://washdata.org/reports/jmp-2017-methodology).

Limitations and exceptions

National, regional and income group estimates are made when data are available for at least 50 percent of the population.

Relevance

Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child. Sanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria. The combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements. Most basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world. Children bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.

Aggregation method

Weighted average

Source references

washdata.org
The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)
WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.849Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SH_STA_BASS_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:04.202Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Together with water access, a proxy for baseline public health infrastructure and epidemic vulnerability.

Gini indexWB_WDI_SI_POV_GINI

Unit: %
Periodicity: Annual
License: CC BY-4.0

Definition

Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Statistical concept

The Gini index is the average of all pairwise absolute differences between individual consumption or income, normalized by twice the mean. More intuitively, the Gini index is the average share of mean consumption or income that needs to be transferred between two randomly selected individuals to achieve equality. A Gini index of 1 represents perfect inequality, in which total consumption or income goes to one individual. A Gini index of 0 indicates represents perfect equality, in which all individuals have the same level of consumption or income.

Methodology

The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality. The Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data. The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. The year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.

Limitations and exceptions

Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth. Another limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate. Because the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries.

Relevance

The World Bank Group's vision of promoting shared prosperity includes a measure that tracks the number of economies with high inequality, defined as those with a Gini index greater than 0.4

Aggregation method

Source references

http://pip.worldbank.org
Poverty and Inequality Platform (PIP), World Bank

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.907Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SI_POV_GINI
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:04.793Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Survey-dependent and sparse. Underlying survey design and reference concept differ between countries, so only rank comparison is defensible.

Labor force participation rate, female (% of female population ages 15+)WB_WDI_SL_TLF_CACT_FE_ZS

Unit: % of female population ages 15+
Periodicity: Annual
License: CC BY-4.0

Definition

The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed.

Statistical concept

The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.

Methodology

The labor force participation rate (LFPR) is calculated as follows: LFPR (%) = 100 x Labor force / population of a given age group, where the labor force is equal to employment plus unemployment. Labor force surveys are typically the preferred source of information for determining the labor force participation rate. Population censuses are another major source of data on the labor force and its components.

Limitations and exceptions

Data on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector. The reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Differing definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.

Relevance

The labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems.

Aggregation method

Weighted average

Source references

https://ilostat.ilo.org/data/bulk/
Labor Force Statistics (LFS, STLFS, RURBAN databases). ILOSTAT.
ILO Modelled Estimates (ILOEST database)

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.885Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SL_TLF_CACT_FE_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:08.047Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. A high rate can reflect subsistence agricultural work rather than economic inclusion, so the level is not comparable across structurally different economies. Movement within one country over time is the interpretable signal.

School enrollment, primary and secondary (gross), gender parity indexWB_WDI_SE_ENR_PRSC_FM_ZS

Unit: not published
Periodicity: Annual
License: CC BY-4.0

Definition

Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools.

Methodology

This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).

Relevance

The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women.

Aggregation method

Weighted average

Source references

https://uis.unesco.org/bdds

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.881Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_ENR_PRSC_FM_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:14.056Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. A ratio where 1.0 is parity. Values above 1.0 indicate female advantage, so a rise is not monotonically positive; the engine reads distance from parity in the analytic note rather than treating higher as uniformly better.

School enrollment, secondary (% gross)WB_WDI_SE_SEC_ENRR

Unit: % of population in the 5-year age group immediately following primary education
Periodicity: Annual
License: CC BY-4.0

Definition

Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

Statistical concept

Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.

Methodology

Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).

Limitations and exceptions

Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.

Relevance

Secondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group.

Aggregation method

Weighted average

Source references

https://databrowser.uis.unesco.org/resources
UNESCO Institute for Statistics (UIS)

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.900Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_SE_SEC_ENRR
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:14.494Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Gross ratio, so repetition and late entry can push it above 100. Read as participation breadth.

Government Effectiveness (WGI estimate, approx. -2.5 to +2.5)WB_WDI_GOV_WGI_GE_EST

Unit: Number
Periodicity: Annual
License: CC BY-4.0

Definition

Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s decisions. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance. The (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.

Statistical concept

For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).

Methodology

The following are the key steps: STEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions. STEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes. STEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM). STEP 4: Transforming the UCM-generated governance estimates to a 0–100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension—typically ranging from approximately -2.5 to 2.5 —into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance. The following is a summary of the methodology: https://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3 The following is a detailed description of the methodology: https://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf

Limitations and exceptions

The WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources. Regarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.

Relevance

Governance—the traditions and institutions by which authority in a country is exercised—is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance—marked by corruption, poor regulatory enforcement, or lack of rule of law— often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017). The Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time. Please note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.

Aggregation method

Indicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology: https://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%

Source references

https://www.worldbank.org/en/publication/worldwide-governance-indicators

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.987Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_GE_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:16.688Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Worldwide Governance Indicators are composite perception-based estimates aggregated from expert and survey sources. They carry standard errors this series does not expose, so small movements are not interpretable and only relative position is defensible.

Political Stability and Absence of Violence (WGI estimate)WB_WDI_GOV_WGI_PV_EST

Unit: Number
Periodicity: Annual
License: CC BY-4.0

Definition

Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance. The (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.

Statistical concept

Methodology

Limitations and exceptions

Relevance

Aggregation method

Source references

https://www.worldbank.org/en/publication/worldwide-governance-indicators

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.949Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_PV_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:37:18.520Z
Delivery: Served from cache
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. This is a perception-based composite that partly reflects observed violence already recorded elsewhere. It must not be treated as an independent predictor of conflict, and it lags events by at least one annual cycle.

Rule of Law (WGI estimate)WB_WDI_GOV_WGI_RL_EST

Unit: Number
Periodicity: Annual
License: CC BY-4.0

Definition

Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance. The (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.

Statistical concept

Methodology

Limitations and exceptions

Relevance

Aggregation method

Source references

https://www.worldbank.org/en/publication/worldwide-governance-indicators

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:45.006Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_RL_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:36.192Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Composite perception estimate. Interpret as relative standing among peers, not as an absolute measure.

Control of Corruption (WGI estimate)WB_WDI_GOV_WGI_CC_EST

Unit: Number
Periodicity: Annual
License: CC BY-4.0

Definition

Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests. Governance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance. The (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.

Statistical concept

Methodology

Limitations and exceptions

Relevance

Aggregation method

Source references

https://www.worldbank.org/en/publication/worldwide-governance-indicators

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:44.974Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GOV_WGI_CC_EST
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:26.227Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Composite perception estimate, sensitive to changes in the underlying source mix between rounds.

Tax revenue (% of GDP)WB_WDI_GC_TAX_TOTL_GD_ZS

Unit: %
Periodicity: Annual
License: CC BY-4.0

Definition

Taxes are compulsory, unrequited payments, in cash or in kind, made by institutional units to government units. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.

Statistical concept

Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.

Methodology

Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/

Limitations and exceptions

For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance. Data on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.

Relevance

This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.

Aggregation method

Weighted average

Source references

Inter-secretariat Working Group on National Accounts (ISWGNA) Glossary
GFS

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:45.009Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GC_TAX_TOTL_GD_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:36.293Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Proxy for extractive fiscal capacity. Coverage across the watchlist is partial: confirmed absent for Benin, Mauritania, Niger and Nigeria in the 2010-2026 window, so the domain signal excludes it for those economies.

Central government debt, total (% of GDP)WB_WDI_GC_DOD_TOTL_GD_ZS

Unit: %
Periodicity: Annual
License: CC BY-4.0

Definition

Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government is the part of general government that includes all administrative departments of the national executive, legislative, and judicial functions, other central agencies and those non-market producers controlled by the central government, whose competence extends normally over the whole economic territory. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.

Statistical concept

Methodology

Limitations and exceptions

Relevance

This indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the government debt. Statistics on government debt provide essential information for economic planning, as they can influence a country's fiscal policies and spending. Government debt levels are also a key indicator for investors, who use this information to assess the risk of investing in a country's bonds. High debt levels can lead to lower investor confidence and higher interest rates. Furthermore, debt statistics are crucial for evaluating the sustainability of a government's fiscal policy. They help determine whether adjustments are needed to avoid potential default. These statistics also enable international comparisons, allowing for benchmarking against other countries and identifying potential issues. Accurate debt statistics are vital for the formulation of monetary and fiscal policies, including decisions on taxation and government spending. They also promote public awareness by providing transparency and accountability in how public funds are managed. Lastly, credit rating agencies use government debt statistics to assign credit ratings, which affect a country's borrowing costs and its ability to attract investment. Overall, government debt statistics are a key component of a country's economic analysis and are essential for informed decision-making by policymakers, investors, and the public.

Aggregation method

Weighted average

Source references

Inter-secretariat Working Group on National Accounts (ISWGNA) Glossary
GFS

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:45.026Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_GC_DOD_TOTL_GD_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:38:31.857Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Sparse across this watchlist: confirmed present for only three of ten economies in the 2010-2026 window. Retained because it is material where reported, but the engine will return insufficient evidence for most of the country set.

Military expenditure (% of GDP)WB_WDI_MS_MIL_XPND_GD_ZS

Unit: % of GDP
Periodicity: Annual
License: CC BY-4.0

Definition

Military expenditure by country as percentage of gross domestic product

Statistical concept

Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities. This should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country). SIPRI’s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated. Civil defense and spending related to past military activities—like veterans’ benefits or demobilization—are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI’s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.

Methodology

Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly. SIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI’s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI’s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high. SIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates. Military spending as a share of GDP (“military burden”) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit. For additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex

Limitations and exceptions

SIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI’s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries.

Relevance

Although national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy. As an "input" measure military expenditures are not directly related to the "output" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.

Aggregation method

Weighted average

Source references

https://www.sipri.org/databases
SIPRI Military Expenditure Database: Sources and methods

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:45.041Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_MS_MIL_XPND_GD_ZS
Endpoint: /data360/data
Retrieved: 2026-09-04T21:39:38.753Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Contextual by design. A rise may indicate securitisation of the budget, a response to an existing insurgency, or reporting change. Direction alone supports no normative reading.

Intentional homicides (per 100,000 people)WB_WDI_VC_IHR_PSRC_P5

Unit: Rate per 100,000 population
Periodicity: Annual
License: CC BY-4.0

Definition

An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury.

Statistical concept

The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS. The ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury. The statistical definition contains three elements that characterize the killing of a person as “intentional homicide”: 1. The killing of a person by another person (objective element) 2. The intent of the perpetrator to kill or seriously injure the victim (subjective element) 3. The unlawfulness of the killing (legal element) For recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide.

Methodology

The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual. These data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. Data submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States. The population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs.

Limitations and exceptions

Statistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally. The degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of "intentional homicide", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data.

Relevance

In some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking. Knowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed. Data on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as "unlawful death purposefully inflicted on a person by another person." This definition excludes deaths arising from armed conflict.

Aggregation method

Aggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf

Source references

Data UNODC - Metadata Information

License terms: https://creativecommons.org/licenses/by/4.0/

Metadata retrieved 2026-09-04T21:39:45.669Z

Source and provenance

Provider: World Bank Data360
Dataset: WB_WDI
Indicator ID: WB_WDI_VC_IHR_PSRC_P5
Endpoint: /data360/data
Retrieved: 2026-09-04T21:39:38.308Z
Delivery: Fresh upstream call
License: Not recorded on this record
Attribution: Source data retrieved from the World Bank Data360 API. The World Bank does not endorse this application or any analytic judgment presented in it, and does not verify the methodology of third-party datasets it redistributes.

Registry note. Criminal justice recording capacity varies sharply and normally degrades during conflict, so a falling rate can reflect collapsing reporting rather than falling violence. Never read as a conflict intensity measure; use ACLED or equivalent event data for that.

7. QAP

The structured analytic workspace carries the Key Intelligence Question, Key Assumptions Check, evidence register, competing hypotheses, ACH matrix, key judgments, scenarios, indicators and warnings, confidence statement and collection plan for Mauritania, built from the 34 evidence objects on this page.

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8. RCA/CPS

Root cause, resilience, prevention and stabilization mapping organises the same evidence around structural drivers and protective systems rather than around movement.

Open RCA/CPS view