Population living in slums 2023
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 | Chad | 81.464 % |
3 | Congo, Democratic Republic of the | 79.03 % |
4 | Mali | 77.043 % |
5 | Afghanistan | 73.244 % |
6 | Sao Tome and Principe | 72.859 % |
7 | Sudan | 72.187 % |
8 | Niger | 70.444 % |
9 | Central African Republic | 68.913 % |
10 | Angola | 66.658 % |
11 | Equatorial Guinea | 65.539 % |
12 | Burkina Faso | 65.029 % |
13 | Benin | 64.873 % |
14 | Madagascar | 64.307 % |
15 | Congo | 63.035 % |
16 | Ethiopia | 62.302 % |
17 | Myanmar | 60.367 % |
18 | Liberia | 60.339 % |
19 | Jordan | 60.15 % |
20 | Tanzania | 58.237 % |
21 | Ecuador | 57.858 % |
22 | Guinea-Bissau | 56.656 % |
23 | Mauritania | 56.191 % |
24 | Pakistan | 54.754 % |
25 | Comoros | 54.52 % |
26 | Mozambique | 52.148 % |
27 | Bangladesh | 51.101 % |
28 | Uganda | 50.929 % |
29 | Iraq | 50.574 % |
30 | Haiti | 49.468 % |
31 | Côte d'Ivoire | 48.745 % |
32 | Sierra Leone | 47.499 % |
33 | Djibouti | 47.426 % |
34 | Eritrea | 47.426 % |
35 | Mauritius | 47.426 % |
36 | Seychelles | 47.426 % |
37 | Somalia | 47.426 % |
38 | Zambia | 47.016 % |
39 | Nigeria | 46.633 % |
40 | Guinea | 46.14 % |
41 | Bolivia | 46.091 % |
42 | Bhutan | 45.878 % |
43 | Iran | 45.878 % |
44 | Sri Lanka | 45.878 % |
45 | Cabo Verde | 44.581 % |
46 | Zimbabwe | 44.412 % |
47 | Yemen | 43.822 % |
48 | Peru | 43.654 % |
49 | Kenya | 43.127 % |
50 | Laos | 42.448 % |
51 | Namibia | 41.368 % |
52 | Gabon | 40.441 % |
53 | Cambodia | 39.789 % |
54 | Malawi | 39.233 % |
55 | Senegal | 39.126 % |
56 | Nepal | 38.243 % |
57 | Botswana | 38.222 % |
58 | Guatemala | 36.928 % |
59 | Syrian Arab Republic | 36.898 % |
60 | Gambia | 36.182 % |
61 | Togo | 36.127 % |
62 | Rwanda | 35.825 % |
63 | Tuvalu | 35.812 % |
64 | Philippines | 35.077 % |
65 | Maldives | 34.037 % |
66 | Burundi | 32.875 % |
67 | Ghana | 32.479 % |
68 | Timor-Leste | 31.284 % |
69 | Cameroon | 30.056 % |
70 | Honduras | 27.609 % |
71 | China | 26.321 % |
72 | Venezuela | 25.331 % |
73 | Samoa | 24.002 % |
74 | South Africa | 23.955 % |
75 | North Korea | 23.036 % |
76 | Vietnam | 22.997 % |
77 | Papua New Guinea | 22.733 % |
78 | Lesotho | 22.249 % |
79 | Brunei Darussalam | 20.616 % |
80 | Libya | 18.602 % |
81 | Indonesia | 18.212 % |
82 | State of Palestine | 17.29 % |
83 | Mexico | 16.791 % |
84 | Suriname | 16.47 % |
85 | Belize | 15.722 % |
86 | Mongolia | 14.701 % |
87 | El Salvador | 14.691 % |
88 | Argentina | 14.29 % |
89 | Panama | 14.261 % |
90 | Brazil | 14.024 % |
91 | Turkey | 13.917 % |
92 | Paraguay | 13.159 % |
93 | Eswatini | 12.919 % |
94 | Cuba | 11.73 % |
95 | Dominican Republic | 10.54 % |
96 | Guyana | 10.237 % |
97 | Algeria | 10.127 % |
98 | Morocco | 10.014 % |
99 | Fiji | 9.1 % |
100 | Colombia | 9.044 % |
101 | Ireland | 8.66 % |
102 | Turkmenistan | 8.219 % |
103 | Trinidad and Tobago | 8.157 % |
104 | Armenia | 8.077 % |
105 | Azerbaijan | 8.016 % |
106 | Montenegro | 7.818 % |
107 | Tunisia | 7.243 % |
108 | Chile | 6.691 % |
109 | Georgia | 6.466 % |
110 | India | 5.41 % |
111 | Cayman Islands | 5.341 % |
112 | South Korea | 5.33 % |
113 | Poland | 5.238 % |
114 | Kiribati | 4.834 % |
115 | Uzbekistan | 4.699 % |
116 | Lebanon | 4.522 % |
117 | Nicaragua | 4.52 % |
118 | Hungary | 3.795 % |
119 | Vanuatu | 3.437 % |
120 | Sint Maarten (Dutch part) | 2.888 % |
121 | Costa Rica | 2.814 % |
122 | Saint Vincent and the Grenadines | 2.755 % |
123 | Antigua and Barbuda | 2.646 % |
124 | Russia | 2.596 % |
125 | Marshall Islands | 2.434 % |
126 | Egypt | 2.131 % |
127 | Romania | 2.007 % |
128 | Japan | 2 % |
129 | Thailand | 2 % |
130 | Republic of Moldova | 1.715 % |
131 | Solomon Islands | 1.587 % |
132 | French Polynesia | 1.5 % |
133 | United States Virgin Islands | 1.096 % |
134 | Serbia | 1.071 % |
135 | Canada | 1.057 % |
136 | Jamaica | 0.884 % |
137 | Nauru | 0.854 % |
138 | Albania | 0.73 % |
139 | Turks and Caicos Islands | 0.631 % |
140 | Guam | 0.552 % |
141 | Saudi Arabia | 0.55 % |
142 | Slovenia | 0.527 % |
143 | Latvia | 0.45 % |
144 | Croatia | 0.429 % |
145 | Palau | 0.413 % |
146 | Cyprus | 0.296 % |
147 | Estonia | 0.272 % |
148 | Sweden | 0.25 % |
149 | New Caledonia | 0.25 % |
150 | Bulgaria | 0.22 % |
151 | Malaysia | 0.2 % |
152 | Northern Mariana Islands | 0.2 % |
153 | Uruguay | 0.176 % |
154 | Tonga | 0.17 % |
155 | Lithuania | 0.156 % |
156 | United Kingdom | 0.144 % |
157 | North Macedonia | 0.127 % |
158 | United States | 0.093 % |
159 | Bermuda | 0.084 % |
160 | United Arab Emirates | 0.069 % |
161 | Saint Lucia | 0.068 % |
162 | Slovakia | 0.061 % |
163 | Tajikistan | 0.05 % |
164 | Spain | 0.046 % |
165 | Austria | 0.042 % |
166 | Malta | 0.038 % |
167 | Czech Republic | 0.034 % |
168 | Australia | 0.032 % |
169 | Portugal | 0.024 % |
170 | Italy | 0.02 % |
171 | Luxembourg | 0.019 % |
172 | Belarus | 0.004 % |
173 | Andorra | 0 % |
174 | Aruba | 0 % |
175 | Bahrain | 0 % |
176 | Belgium | 0 % |
177 | Denmark | 0 % |
178 | Finland | 0 % |
179 | France | 0 % |
180 | Germany | 0 % |
181 | Iceland | 0 % |
182 | Kuwait | 0 % |
183 | Monaco | 0 % |
184 | Netherlands | 0 % |
185 | New Zealand | 0 % |
186 | Norway | 0 % |
187 | Qatar | 0 % |
188 | Singapore | 0 % |
189 | Switzerland | 0 % |
190 | Ukraine | -0.055 % |
191 | Oman | -0.135 % |
192 | Greece | -0.225 % |
193 | Bosnia and Herzegovina | -0.24 % |
194 | British Virgin Islands | -0.39 % |
195 | Kazakhstan | -0.529 % |
196 | Kyrgyzstan | -1.138 % |
- #1
South Sudan
- #2
Chad
- #3
Congo, Democratic Republic of the
- #4
Mali
- #5
Afghanistan
- #6
Sao Tome and Principe
- #7
Sudan
- #8
Niger
- #9
Central African Republic
- #10
Angola
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
- #196
Kyrgyzstan
- #195
Kazakhstan
- #194
British Virgin Islands
- #193
Bosnia and Herzegovina
- #192
Greece
- #191
Oman
- #190
Ukraine
- #189
Switzerland
- #188
Singapore
- #187
Qatar
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2023, South Sudan has the highest percentage of its population living in slums at 94.20%, while the global range for this statistic extends from -1.14% to 94.20%. The global average percentage of people living in slums is 21.45%, highlighting significant disparities in urban living conditions across different countries.
Economic and Political Drivers of Slum Populations
The prevalence of slum populations is often a reflection of economic and political instability. In South Sudan, where 94.20% of the population lives in slums, ongoing conflict and economic challenges severely limit infrastructure development and housing improvements. Similarly, Chad and the Democratic Republic of the Congo have high slum populations at 81.46% and 79.03% respectively, driven by economic hardship and inadequate governance that hampers urban planning and resource allocation.
In contrast, countries with robust economies and stable political environments, such as Kyrgyzstan and Kazakhstan, show negative values in slum population statistics, indicating effective urban management and housing policies. These countries have percentages of -1.14% and -0.53% respectively, suggesting successful mitigation of slum conditions through effective policy interventions.
Impact of Urbanization on Slum Growth
Rapid urbanization without corresponding urban planning can exacerbate slum conditions. In Mali and Afghanistan, where slum populations are 77.04% and 73.24%, the swift migration of people to urban areas in search of better economic opportunities has outpaced the development of adequate housing and infrastructure. The lack of affordable housing options forces many into slum settlements.
Conversely, countries with strategic urban planning and investment in housing infrastructure, like Oman and Andorra, report negligible or zero slum populations. These nations have successfully managed urban growth by ensuring that housing developments keep pace with population increases, thereby preventing the formation of slums.
Year-Over-Year Trends and Policy Implications
Analyzing year-over-year changes provides insight into the effectiveness of recent policies and economic shifts. Burkina Faso saw the most significant decrease in slum populations, with a reduction of -22.85% (a -26.0% change), indicating successful interventions in housing and urban development. Similarly, Mali and Tuvalu experienced substantial declines of -15.45% and -15.09%, respectively, demonstrating the impact of targeted policies.
On the other hand, countries like Comoros and Jordan have experienced increases in slum populations by +6.02% and +5.25%. These rises may reflect recent economic challenges or insufficient urban planning in the face of rapid demographic changes, emphasizing the need for policy adjustments to address housing shortages and improve living conditions.
Regional Disparities and Environmental Factors
Environmental factors also contribute to the regional disparities in slum populations. In regions prone to natural disasters, such as Angola with a slum population of 66.66%, the destruction of housing infrastructure can lead to increased slum dwellings. Countries with better disaster management and resilient infrastructure, like Netherlands and Aruba with 0% slum populations, demonstrate how environmental preparedness can prevent slum proliferation.
Furthermore, the presence of slums in countries like Niger and Central African Republic, with percentages of 70.44% and 68.91%, respectively, highlights the interaction between poverty, inadequate infrastructure, and environmental challenges, necessitating comprehensive strategies that integrate economic, environmental, and social policies to mitigate slum growth.
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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