This statistic shows the biggest cities in El Salvador in 2024. In 2024, approximately 334.4 thousand people lived in San Salvador, making it the biggest city in El Salvador.

Population in largest city in El Salvador was reported at 1123376 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. El Salvador - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

El Salvador SV: Population in Largest City data was reported at 1,104,147.000 Person in 2017. This records an increase from the previous number of 1,101,602.000 Person for 2016. El Salvador SV: Population in Largest City data is updated yearly, averaging 923,741.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,104,147.000 Person in 2017 and a record low of 311,194.000 Person in 1960. El Salvador SV: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s El Salvador – Table SV.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Population in the largest city (% of urban population) in El Salvador was reported at 23.32 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. El Salvador - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

El Salvador's capital, San Salvador, had the highest number of reported Bitcoin ATMs by far in 2025. El Salvador adopted Bitcoin as an official tender in 2021, the first country in the world to do so, despite low confidence in such measures. One of the main reasons for this tender was to help foster remittances from other countries towards El Salvador and to bypass expensive money transfer options.

El Salvador SV: Population in Largest City: as % of Urban Population data was reported at 24.289 % in 2017. This records a decrease from the previous number of 24.627 % for 2016. El Salvador SV: Population in Largest City: as % of Urban Population data is updated yearly, averaging 33.015 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 38.078 % in 1992 and a record low of 24.289 % in 2017. El Salvador SV: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s El Salvador – Table SV.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

countries capital city San Salvador. name, long name, population (source), population, constitutional form, drives on, head of state authority, Main continent, number of airports, Airports - with paved runways, Airports - with unpaved runways, Area, Birth rate, calling code, Children under the age of 5 years underweight, Current Account Balance, Death rate, Debt - external, Economic aid donor, Electricity consumption, Electricity consumption per capita, Electricity exports, Electricity imports, Electricity production, Exports, GDP - per capita (PPP), GDP (purchasing power parity), GDP real growth rate, Gross national income, Human Development Index, Health expenditures, Heliports, HIV AIDS adult prevalence rate, HIV AIDS deaths, HIV AIDS people living with HIV AIDS, Hospital bed density, capital city, Currency, Imports, Industrial production growth rate, Infant mortality rate, Inflation rate consumer prices, Internet hosts, internet tld, Internet users, Investment (gross fixed), iso 3166 code, ISO CODE, Labor force, Life expectancy at birth, Literacy, Manpower available for military service, Manpower fit for military service, Manpower reaching militarily age annually, is democracy, Market value of publicly traded shares, Maternal mortality rate, Merchant marine, Military expenditures percent of GDP, Natural gas consumption, Natural gas consumption per capita, Natural gas exports, Natural gas imports, Natural gas production, Natural gas proved reserves, Net migration rate, Obesity adult prevalence rate, Oil consumption, Oil consumption per capita, Oil exports, Oil imports, Oil production, Oil proved reserves, Physicians density, Population below poverty line, Population census, Population density, Population estimate, Population growth rate, Public debt, Railways, Reserves of foreign exchange and gold, Roadways, Stock of direct foreign investment abroad, Stock of direct foreign investment at home, Telephones main lines in use, Telephones main lines in use per capita, Telephones mobile cellular, Telephones mobile cellular per capita, Total fertility rate, Unemployment rate, Unemployment, youth ages 15-24, Waterways, valley, helicopter, canyon, artillery, crater, religion, continent, border, Plateau, marsh, Demonym

SV：最大城市人口在12-01-2017达1,104,147.000人，相较于12-01-2016的1,101,602.000人有所增长。SV：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为923,741.000人，共58份观测结果。该数据的历史最高值出现于12-01-2017，达1,104,147.000人，而历史最低值则出现于12-01-1960，为311,194.000人。CEIC提供的SV：最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的萨尔瓦多 – 表 SV.世行.WDI：人口和城市化进程统计。

SV：最大城市人口占城市总人口的百分比在12-01-2017达24.289%，相较于12-01-2016的24.627%有所下降。SV：最大城市人口占城市总人口的百分比数据按年更新，12-01-1960至12-01-2017期间平均值为33.015%，共58份观测结果。该数据的历史最高值出现于12-01-1992，达38.078%，而历史最低值则出现于12-01-2017，为24.289%。CEIC提供的SV：最大城市人口占城市总人口的百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的萨尔瓦多 – 表 SV.世行.WDI：人口和城市化进程统计。

Objective: To analyze the relationship between economic conditions and mortality in cities of Latin America.Methods: We analyzed data from 340 urban areas in ten countries: Argentina, Brazil, Chile, Colombia, Costa Rica, Guatemala, Mexico, Panama, Peru, and El Salvador. We used panel models adjusted for space‐invariant and time‐invariant factors to examine whether changes in area gross domestic product (GDP) per capita were associated with changes in mortality.Results: We find procyclical oscillations in mortality (i.e., higher mortality with higher GDP per capita) for total mortality, female population, populations of 0–9 and 45+ years, mortality due to cardiovascular diseases, malignant neoplasms, diabetes mellitus, respiratory infections and road traffic injuries. Homicides appear countercyclical, with higher levels at lower GDP per capita.Conclusions: Our results reveal large heterogeneity, but in our sample of cities, for specific population groups and causes of death, mortality oscillates procyclically, increasing when GDP per capita increases. In contrast we find few instances of countercyclical mortality.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

Abstract

The Instituto Nacional de Estadística e Informática (INEI) in Peru carried out the Encuesta Dirigida a la Población Venezolana que Reside en el País (ENPOVE) survey between the months of November and December 2018 in order to gain a better understanding of the Venezuelan population residing in Peru.

The survey was carried out in the capital cities in the departments of Tumbes, La Libertad, Lima-Callao, Arequipa and Cusco, which together are home to 85% of the Venezuelan population in the country. The purpose of the survey was to provide reliable data on the living conditions of the Venezuelan population residing in Peru, including: demographic and social aspects, immigration status, discrimination, violence, health, employment, education, access to basic services, housing and home equipment.

The information can be used by international organizations, researchers, and public policy makers to formulate actions, policies, plans, programs, and projects to meet the most urgent needs of this group. The World Bank, UNHCR, IOM, UNFPA and UNICEF provided technical and financial support to the survey.

Geographic coverage

Urban area of capital cities of the regions of Tumbes, La Libertad, Arequipa, Cusco, Lima and Callao.

Analysis unit

Household and individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling is probabilistic and stratified. The sampling consists of two stages, the primary sampling unit being the block, which is defined as the urban geographic area delimited by roads. The secondary sampling unit is the dwelling with at least one Venezuelan person that exists within a block. For the households that are finally selected, information is obtained from all the individuals.

The sampling frame for the blocks was constructed as follows: i) The addresses of 58,067 Venezuelan people registered in the 2017 Population and Housing Census were identified. ii) The addresses of 10,076 people were available registered in the registry of Venezuelans who applied for the Temporary Permit of Permanence from the National Superintendency of Migration of the Ministry of the Interior. iii) The blocks containing the addresses of the aforementioned information sources were identified using the Geographic Information System. A global framework of 19,074 blocks was built.

The concept of block used in the survey is a physical area delimited by streets, avenues, roads, canals, etc. easily identifiable and can contain one or more homes, parks, vacant lots, sports fields, etc.

The original design of the sample included the construction of three strata based on the number of dwellings with a Venezuelan population found in each block of the sampling frame: 1 to 5, 6 to 10, greater than 10. On the other hand, the population of the city of Lima was divided into 4 zones with the following districts:

North Lima: Los Olivos, San Martn De Porres, Comas, Carabayllo, Independencia, Puente Piedra East Lima: San Juan De Lurigancho, Ate, Santa Anita, El Agustino, San Luis, La Molina, Lurigancho Downtown Lima: La Victoria, Lima, Santiago De Surco, Surquillo, San Miguel, Brea, Barranco, Rmac, Lince Jesus Maria, Magdalena Del Mar, San Borja South Lima: Chorrillos, San Juan De Miraflores, Villa El Salvador, Villa Mara Del Triunfo, Lurn, Pachacamac

The housing framework was built by means of an exhaustive registry of buildings and dwellings in each of the selected blocks, identifying those places, be they dwellings or establishments, that had a population from Venezuela. The concept of housing for the purposes of the survey included private and collective dwellings (hotels, hostels, lodgings, churches and shelters), where the Venezuelan population is found. This concept is different from the one used in the regular INEI household surveys, which only considers private households with a maximum of 5 households. The concept of the household used was: People, whether or not they are related, who share the main meals and attend to their vital needs in common. This concept is different from that used in the INEI household surveys, where the budget is considered.

Mode of data collection

Face-to-face [f2f]