This statistic shows the biggest cities in Mali in 2022. In 2022, approximately **** million people lived in Bamako, making it the biggest city in Mali.

Population in largest city in Mali was reported at 3050570 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Mali - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Mali ML: Population in Largest City: as % of Urban Population data was reported at 30.725 % in 2017. This records a decrease from the previous number of 31.237 % for 2016. Mali ML: Population in Largest City: as % of Urban Population data is updated yearly, averaging 36.278 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 37.806 % in 1990 and a record low of 22.291 % in 1961. Mali ML: 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 Mali – Table ML.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;

Population in the largest city (% of urban population) in Mali was reported at 26.55 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Mali - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Mali ML: Population in Largest City data was reported at 2,368,347.000 Person in 2017. This records an increase from the previous number of 2,292,458.000 Person for 2016. Mali ML: Population in Largest City data is updated yearly, averaging 703,465.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2,368,347.000 Person in 2017 and a record low of 130,017.000 Person in 1960. Mali ML: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.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.; ;

Abstract

This research is a survey of unregistered businesses conducted in Mali between May and November 2010, at the same time with Mali 2010 Enterprise Survey. Data from 120 enterprises were analyzed.

Questionnaire topics include general information about a business, infrastructure and services, sales and supplies, crime, sources and access to finance, business-government relationship, assets, AIDS and sickness (for African region), bribery, workforce composition, obstacles to get registration, reasons for not registering, and benefits that an establishment could get from registration. The mode of data collection is face-to-face interviews.

The Informal Surveys aim to accomplish the following objectives: 1) To provide information about the state of the private sector for informal businesses in client countries; 2) To generate information about the reasons of said informality; 3) To collect useful data for the research agenda on informality; 4) To provide information on the level of activity in the informal sector of selected urban centers in each country.

Geographic coverage

National

Analysis unit

The primary sampling unit of the Informal Surveys is an unregistered establishment. For Mali, informal firms were defined as those not registered as determined by a list of firms supplied by the World Bank.

Universe

The whole population, or the universe, covered in the survey is the non-agricultural informal economy.

At the beginning of each survey, a screening procedure is conducted in order to identify eligible interviewees. At this point, a full description of all the activities of the business owner or manager is taken; based on its principal activity, a business is then classified in the manufacturing or services stratum using a list of activities developed from previous iterations of the survey. Certain activities are excluded such as strictly illegal activities (e.g., prostitution or drug trafficking) as well as individual activities that are forms of selling labor like domestic servants or windshield washers.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Informal Surveys are conducted in selected urban centers, which are intended to coincide with the locations for the implementation of the main Enterprise Surveys. The overall number of interviews is pre-determined.

In Mali, Bamako, Mopti, Segou, and Sikasso were identified as urban centers of interest. The sample was confined to the major cities covered and the survey was run in parallel with the enterprise surveys of the formal economy. The target number of interviews will reflect, as far as practical, the individuals' population distribution but with no more than 60% sample from a single city and no city with fewer than 20 interviews in total.

Sampling in the Informal Surveys is conducted within clearly delineated sampling zones, which are geographically determined divisions within each urban center. Sampling zones are defined at the beginning of fieldwork, and are delineated according to the concentration and geographical dispersion of informal business activity. After the sampling sizes are defined for each location every city is divided into several zones that may or may not correspond to the administrative districts.

In Mali, using Google maps or local city maps, the target areas within each city were identified. With input from the local agency applying local knowledge, the starting points were defined. The number of zones was determined by the target sample size for each city divided by the cluster size (4 interviews).

In Bamako, 60 interviews were completed in 15 sampling zones. In Mopti, Segou, and Sikasso, 20 interviews were completed in 5 sample zones in each city.

In order to provide information on diverse aspects of the informal economy, the sample is designed to have equal proportions of services and manufacturing (50:50). These sectors are defined by responses provided by each informal business to a question on the business's main activity included in the screener portion of the questionnaire.

As a general rule, services must constitute an ongoing business enterprise and so exclude the sale of manual labor. Manufacturing activity in the informal sector includes business activity requiring inputs and/or intermediate goods. Thus, for example, the processing of coffee, sugar, oil, dried fruit, or other processed foods is considered manufacturing, while the simple selling of these goods falls under services. If an informal business conducts a mixture of these activities, the business is considered under the manufacturing stratum.

Each sampling zone was designed with the goal of obtaining two interviews in services and two interviews in manufacturing. In order to ensure a degree of geographical dispersion within each sampling zone, two starting points were identified.

Each sampling zone, including its two starting points, were marked using Google maps, with the GPS coordinates of the starting points being systematically recorded.

Additionally, when obtaining a complete interview, the exact address of the informal business (or where the interview took place) was registered by the interviewer. Once in the office, this address was searched in Google maps, and its GPS coordinates were registered in a fieldwork report.

If no address was immediately available, using local knowledge, the GPS coordinates were determined using imaging via Google maps. In order to preserve confidentiality, the exact coordinates of businesses are not published.

Due to issues of non-response, in the process of fieldwork, the implementing contractor was unable to obtain the targeted four interviews in each of the originally delineated sectors.

As a result, replacement sectors were delineated, ex post. Additionally, the implementing contractor noted that in various interviews there were notable shortfalls in response rates to certain questions. For these reasons, additional interviews were authorized. These were distributed according to the discretion of the implementing contractor in Mali, with authorization from the World Bank.

In sum, there were 30 zones in Mali; 15 zones in Bamako, 5 zones each in Mopti, Segou, and Sikasso.

Complete information regarding the sampling methodology can be found in "Description of Mali Informal Survey Implementation" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instrument is available: - Informal Questionnaire.

The survey topics include general information about a business, infrastructure and services, sales and supplies, crime, sources and access to finance, business-government relationship, assets, AIDS and sickness (for African region), bribery, workforce composition, obstacles to get registration, reasons for not registering, and benefits that an establishment could get from registration.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

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]