Population living in slums 2026
This statistic measures the percentage of a country's population living in slums, highlighting urban poverty and living conditions. Understanding slum populations is crucial for addressing housing challenges and improving the quality of life.
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Complete Data Rankings
Rank | ||
|---|---|---|
1 | South Sudan | 94.2 % |
2 | Mali | 88.348 % |
3 | Chad | 80.781 % |
4 | Congo, Democratic Republic of the | 79.993 % |
5 | Sao Tome and Principe | 79.886 % |
6 | Jordan | 75.9 % |
7 | Afghanistan | 74.252 % |
8 | Burkina Faso | 74.074 % |
9 | Angola | 71.904 % |
10 | Niger | 70.444 % |
11 | Sudan | 70.375 % |
12 | Central African Republic | 68.913 % |
13 | Congo | 67.366 % |
14 | Equatorial Guinea | 66.554 % |
15 | Benin | 63.365 % |
16 | Myanmar | 63.095 % |
17 | Tanzania | 61.382 % |
18 | Madagascar | 60.886 % |
19 | Ethiopia | 59.672 % |
20 | Liberia | 57.935 % |
21 | Ecuador | 57.904 % |
22 | Mauritania | 54.818 % |
23 | Pakistan | 52.863 % |
24 | Iraq | 52.49 % |
25 | Zimbabwe | 52.336 % |
26 | Guinea-Bissau | 51.709 % |
27 | Bangladesh | 50.209 % |
28 | Comoros | 49.28 % |
29 | Tuvalu | 48.684 % |
30 | Haiti | 48.673 % |
31 | Mozambique | 47.791 % |
32 | Laos | 47.446 % |
33 | Uganda | 47.249 % |
34 | Côte d'Ivoire | 46.148 % |
35 | Guinea | 45.861 % |
36 | Bolivia | 45.612 % |
37 | Zambia | 45.091 % |
38 | Sierra Leone | 43.749 % |
39 | Bhutan | 43.642 % |
40 | Iran | 43.642 % |
41 | Sri Lanka | 43.642 % |
42 | Nigeria | 43.391 % |
43 | Yemen | 43.367 % |
44 | Djibouti | 43.353 % |
45 | Eritrea | 43.353 % |
46 | Mauritius | 43.353 % |
47 | Seychelles | 43.353 % |
48 | Somalia | 43.353 % |
49 | Peru | 42.575 % |
50 | Syrian Arab Republic | 41.638 % |
51 | Cabo Verde | 41.536 % |
52 | Namibia | 41.331 % |
53 | Kenya | 39.888 % |
54 | Gabon | 38.995 % |
55 | Senegal | 38.567 % |
56 | Cambodia | 38.405 % |
57 | Botswana | 36.422 % |
58 | Guatemala | 36.121 % |
59 | Nepal | 35.726 % |
60 | Gambia | 33.476 % |
61 | Philippines | 33.415 % |
62 | Malawi | 33.175 % |
63 | Maldives | 32.871 % |
64 | Togo | 32.743 % |
65 | Samoa | 32.573 % |
66 | Rwanda | 32.187 % |
67 | Ghana | 31.269 % |
68 | Vietnam | 28.168 % |
69 | Timor-Leste | 27.854 % |
70 | Burundi | 27.249 % |
71 | China | 26.321 % |
72 | Cameroon | 26.003 % |
73 | Honduras | 25.836 % |
74 | Venezuela | 25.026 % |
75 | South Africa | 23.638 % |
76 | Papua New Guinea | 23.073 % |
77 | North Korea | 23.036 % |
78 | Brunei Darussalam | 19.131 % |
79 | Libya | 18.098 % |
80 | Suriname | 17.508 % |
81 | Lesotho | 17.127 % |
82 | Indonesia | 16.483 % |
83 | State of Palestine | 15.843 % |
84 | Mexico | 15.76 % |
85 | Belize | 15.717 % |
86 | Argentina | 14.038 % |
87 | Turkey | 13.666 % |
88 | Brazil | 13.194 % |
89 | Cuba | 13.16 % |
90 | El Salvador | 12.535 % |
91 | Panama | 12.039 % |
92 | Eswatini | 11.858 % |
93 | Paraguay | 10.63 % |
94 | Mongolia | 9.775 % |
95 | Dominican Republic | 9.689 % |
96 | Morocco | 9.011 % |
97 | Ireland | 8.866 % |
98 | Fiji | 8.74 % |
99 | Colombia | 8.207 % |
100 | Trinidad and Tobago | 8.022 % |
101 | Guyana | 7.977 % |
102 | Turkmenistan | 7.877 % |
103 | Armenia | 7.661 % |
104 | Poland | 6.642 % |
105 | Tunisia | 6.623 % |
106 | Montenegro | 6.322 % |
107 | Chile | 6.181 % |
108 | Cayman Islands | 5.598 % |
109 | Georgia | 5.514 % |
110 | South Korea | 5.462 % |
111 | India | 5.41 % |
112 | Algeria | 5.28 % |
113 | Lebanon | 4.53 % |
114 | Nicaragua | 3.869 % |
115 | Azerbaijan | 3.514 % |
116 | Kiribati | 3.371 % |
117 | Vanuatu | 3.215 % |
118 | Uzbekistan | 3.103 % |
119 | Marshall Islands | 3.022 % |
120 | Sint Maarten (Dutch part) | 2.888 % |
121 | Saint Vincent and the Grenadines | 2.755 % |
122 | Antigua and Barbuda | 2.646 % |
123 | Hungary | 2.55 % |
124 | Russia | 2.462 % |
125 | Japan | 2 % |
126 | Thailand | 2 % |
127 | Romania | 1.912 % |
128 | Egypt | 1.903 % |
129 | Costa Rica | 1.679 % |
130 | French Polynesia | 1.5 % |
131 | Serbia | 1.303 % |
132 | Canada | 1.201 % |
133 | Solomon Islands | 1.149 % |
134 | United States Virgin Islands | 1.096 % |
135 | Nauru | 0.997 % |
136 | Jamaica | 0.883 % |
137 | Turks and Caicos Islands | 0.631 % |
138 | Guam | 0.552 % |
139 | Saudi Arabia | 0.55 % |
140 | Slovenia | 0.527 % |
141 | Croatia | 0.483 % |
142 | Palau | 0.357 % |
143 | Estonia | 0.336 % |
144 | Cyprus | 0.306 % |
145 | Sweden | 0.294 % |
146 | Latvia | 0.26 % |
147 | New Caledonia | 0.25 % |
148 | Bulgaria | 0.226 % |
149 | Malaysia | 0.2 % |
150 | Northern Mariana Islands | 0.2 % |
151 | United Kingdom | 0.16 % |
152 | Bermuda | 0.079 % |
153 | Slovakia | 0.065 % |
154 | United States | 0.053 % |
155 | Tajikistan | 0.05 % |
156 | Spain | 0.044 % |
157 | Malta | 0.038 % |
158 | Australia | 0.028 % |
159 | Czech Republic | 0.028 % |
160 | Luxembourg | 0.02 % |
161 | Italy | 0.02 % |
162 | North Macedonia | 0.015 % |
163 | Belarus | 0.004 % |
164 | Andorra | 0 % |
165 | Aruba | 0 % |
166 | Bahrain | 0 % |
167 | Belgium | 0 % |
168 | Denmark | 0 % |
169 | Finland | 0 % |
170 | France | 0 % |
171 | Germany | 0 % |
172 | Iceland | 0 % |
173 | Kuwait | 0 % |
174 | Monaco | 0 % |
175 | Netherlands | 0 % |
176 | New Zealand | 0 % |
177 | Norway | 0 % |
178 | Qatar | 0 % |
179 | Singapore | 0 % |
180 | Switzerland | 0 % |
181 | Portugal | -0.016 % |
182 | United Arab Emirates | -0.039 % |
183 | Tonga | -0.084 % |
184 | Lithuania | -0.135 % |
185 | Greece | -0.3 % |
186 | Oman | -0.339 % |
187 | Saint Lucia | -0.426 % |
188 | Republic of Moldova | -0.46 % |
189 | Austria | -0.523 % |
190 | Ukraine | -0.58 % |
191 | British Virgin Islands | -1.127 % |
192 | Uruguay | -1.173 % |
193 | Bosnia and Herzegovina | -1.678 % |
194 | Kazakhstan | -2.213 % |
195 | Albania | -2.396 % |
196 | Kyrgyzstan | -6.683 % |
- #1
South Sudan
- #2
Mali
- #3
Chad
- #4
Congo, Democratic Republic of the
- #5
Sao Tome and Principe
- #6
Jordan
- #7
Afghanistan
- #8
Burkina Faso
- #9
Angola
- #10
Niger
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
- #196
Kyrgyzstan
- #195
Albania
- #194
Kazakhstan
- #193
Bosnia and Herzegovina
- #192
Uruguay
- #191
British Virgin Islands
- #190
Ukraine
- #189
Austria
- #188
Republic of Moldova
- #187
Saint Lucia
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2026, South Sudan leads the world with the highest percentage of its population living in slums, at 94.20%, while the global range spans from -6.68% to 94.20%. The global average percentage of people living in slums is 21.02%, providing a stark contrast to the extremes observed across different countries.
Economic and Political Instability: Driving Forces Behind High Slum Populations
The prevalence of slum populations is often a reflection of underlying economic and political challenges. In South Sudan, where 94.20% of the population resides in slums, prolonged conflict and economic instability have severely hindered urban development. Similarly, Mali and Chad report high slum populations at 88.35% and 80.78% respectively. These countries face significant challenges such as political unrest and limited infrastructure development, which exacerbate urban poverty and inadequate housing conditions.
Conversely, countries with robust economic frameworks and political stability, like Austria and Ukraine, show negative values of -0.52% and -0.58% respectively, suggesting successful urban planning and effective housing policies.
Urbanization and Its Impact on Slum Populations
Rapid urbanization without corresponding infrastructure development often leads to an increase in slum populations. In Congo, Democratic Republic of the, 79.99% of the population lives in slums due to rapid urban growth outpacing the development of adequate housing and services. Similarly, Angola and Niger, with slum populations of 71.90% and 70.44% respectively, face similar challenges as urban centers expand without adequate planning and investment in housing infrastructure.
In contrast, countries that have effectively managed urban growth, such as Kazakhstan and Albania, have managed to maintain negative slum population percentages of -2.21% and -2.40% respectively, indicating successful policies in urban housing and infrastructure development.
Year-over-Year Trends: Significant Changes in Slum Populations
The year-over-year data reveals both increases and decreases in slum populations, offering insights into shifting urban dynamics. Jordan experienced the largest increase, with a rise of 5.25%, bringing its slum population to 75.90%. This surge may be attributed to regional instability and influxes of refugees, straining urban resources. Mali and Angola also saw increases of 2.81% and 2.15% respectively, reflecting ongoing challenges in managing urban growth and economic development.
Conversely, Burundi and Kyrgyzstan saw significant decreases of -2.12% and -1.97%. The substantial drop in Kyrgyzstan, amounting to a 41.7% decrease, suggests effective interventions in urban planning and housing policy, potentially serving as a model for other regions grappling with similar issues.
Policy Implications and Future Directions
The data on slum populations serves as a critical indicator of urban poverty and housing inadequacies, necessitating comprehensive policy responses. Countries like South Sudan and Congo, Democratic Republic of the require international support and investment in urban infrastructure to address their high percentages. Policy interventions that focus on sustainable urban planning, investment in affordable housing, and economic stabilization are crucial for reducing slum populations.
Furthermore, the success stories of countries like Kyrgyzstan and Kazakhstan highlight the importance of targeted policies in managing urban growth. These examples underscore the potential for policy-driven change, emphasizing the need for tailored solutions that consider the specific economic, geographic, and demographic contexts of each country.
Data Source
UN Habitat
The Data and Analytics Section (DAS) is the specialized statistics unit within UN-Habitat. The data section is responsible for overall data oversight across all urban monitoring domains within UN-Habitat, methodological developments, supporting member states in their monitoring efforts around global agenda such as the SDGs, implementing direct data collection and compilation, providing data to UN-Habitat global reports, and publicly and openly disseminating urban data through its urban indicators programme.
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