Population living in slums 2024
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 | 81.29 % |
3 | Chad | 81.266 % |
4 | Congo, Democratic Republic of the | 79.33 % |
5 | Sao Tome and Principe | 75.637 % |
6 | Afghanistan | 73.493 % |
7 | Sudan | 71.684 % |
8 | Niger | 70.444 % |
9 | Burkina Faso | 68.936 % |
10 | Central African Republic | 68.913 % |
11 | Angola | 68.204 % |
12 | Equatorial Guinea | 65.841 % |
13 | Jordan | 65.4 % |
14 | Congo | 65.06 % |
15 | Benin | 64.323 % |
16 | Madagascar | 63.176 % |
17 | Ethiopia | 61.529 % |
18 | Myanmar | 61.169 % |
19 | Tanzania | 59.774 % |
20 | Liberia | 59.552 % |
21 | Ecuador | 57.866 % |
22 | Mauritania | 55.857 % |
23 | Guinea-Bissau | 55.031 % |
24 | Pakistan | 54.144 % |
25 | Comoros | 52.506 % |
26 | Iraq | 51.192 % |
27 | Bangladesh | 50.807 % |
28 | Mozambique | 50.743 % |
29 | Uganda | 49.721 % |
30 | Haiti | 49.262 % |
31 | Côte d'Ivoire | 47.865 % |
32 | Zimbabwe | 47.505 % |
33 | Zambia | 46.395 % |
34 | Sierra Leone | 46.267 % |
35 | Djibouti | 46.096 % |
36 | Eritrea | 46.096 % |
37 | Mauritius | 46.096 % |
38 | Seychelles | 46.096 % |
39 | Somalia | 46.096 % |
40 | Bolivia | 45.99 % |
41 | Guinea | 45.968 % |
42 | Nigeria | 45.579 % |
43 | Bhutan | 45.077 % |
44 | Iran | 45.077 % |
45 | Sri Lanka | 45.077 % |
46 | Laos | 44.632 % |
47 | Yemen | 43.695 % |
48 | Cabo Verde | 43.592 % |
49 | Peru | 43.348 % |
50 | Kenya | 41.941 % |
51 | Namibia | 41.358 % |
52 | Tuvalu | 40.771 % |
53 | Gabon | 39.885 % |
54 | Cambodia | 39.476 % |
55 | Senegal | 39.184 % |
56 | Syrian Arab Republic | 38.681 % |
57 | Botswana | 37.693 % |
58 | Nepal | 37.498 % |
59 | Malawi | 37.174 % |
60 | Guatemala | 36.704 % |
61 | Gambia | 35.28 % |
62 | Togo | 35.075 % |
63 | Rwanda | 34.694 % |
64 | Philippines | 34.546 % |
65 | Maldives | 33.674 % |
66 | Ghana | 32.143 % |
67 | Burundi | 31.123 % |
68 | Timor-Leste | 30.276 % |
69 | Cameroon | 28.749 % |
70 | Samoa | 27.333 % |
71 | Honduras | 27.018 % |
72 | China | 26.321 % |
73 | Venezuela | 25.273 % |
74 | Vietnam | 25.176 % |
75 | South Africa | 23.863 % |
76 | North Korea | 23.036 % |
77 | Papua New Guinea | 22.835 % |
78 | Lesotho | 20.597 % |
79 | Brunei Darussalam | 20.155 % |
80 | Libya | 18.557 % |
81 | Indonesia | 17.674 % |
82 | State of Palestine | 16.808 % |
83 | Suriname | 16.805 % |
84 | Mexico | 16.493 % |
85 | Belize | 15.72 % |
86 | Argentina | 14.22 % |
87 | El Salvador | 14.092 % |
88 | Turkey | 13.847 % |
89 | Brazil | 13.836 % |
90 | Panama | 13.689 % |
91 | Mongolia | 13.112 % |
92 | Eswatini | 12.797 % |
93 | Paraguay | 12.417 % |
94 | Cuba | 12.199 % |
95 | Dominican Republic | 10.304 % |
96 | Morocco | 9.736 % |
97 | Guyana | 9.494 % |
98 | Fiji | 9 % |
99 | Colombia | 8.802 % |
100 | Ireland | 8.72 % |
101 | Algeria | 8.563 % |
102 | Trinidad and Tobago | 8.169 % |
103 | Turkmenistan | 8.107 % |
104 | Armenia | 7.955 % |
105 | Montenegro | 7.335 % |
106 | Tunisia | 7.043 % |
107 | Chile | 6.599 % |
108 | Azerbaijan | 6.515 % |
109 | Georgia | 6.159 % |
110 | Poland | 5.614 % |
111 | Cayman Islands | 5.454 % |
112 | India | 5.41 % |
113 | South Korea | 5.3 % |
114 | Lebanon | 4.525 % |
115 | Kiribati | 4.368 % |
116 | Nicaragua | 4.303 % |
117 | Uzbekistan | 4.266 % |
118 | Hungary | 3.38 % |
119 | Vanuatu | 3.346 % |
120 | Sint Maarten (Dutch part) | 2.888 % |
121 | Saint Vincent and the Grenadines | 2.755 % |
122 | Marshall Islands | 2.685 % |
123 | Antigua and Barbuda | 2.646 % |
124 | Russia | 2.55 % |
125 | Costa Rica | 2.448 % |
126 | Egypt | 2.149 % |
127 | Japan | 2 % |
128 | Thailand | 2 % |
129 | Romania | 1.991 % |
130 | French Polynesia | 1.5 % |
131 | Solomon Islands | 1.465 % |
132 | Serbia | 1.162 % |
133 | Canada | 1.107 % |
134 | United States Virgin Islands | 1.096 % |
135 | Republic of Moldova | 0.99 % |
136 | Jamaica | 0.884 % |
137 | Nauru | 0.861 % |
138 | Turks and Caicos Islands | 0.631 % |
139 | Guam | 0.552 % |
140 | Saudi Arabia | 0.55 % |
141 | Slovenia | 0.527 % |
142 | Croatia | 0.452 % |
143 | Palau | 0.405 % |
144 | Latvia | 0.394 % |
145 | Cyprus | 0.298 % |
146 | Estonia | 0.296 % |
147 | Sweden | 0.269 % |
148 | New Caledonia | 0.25 % |
149 | Bulgaria | 0.222 % |
150 | Malaysia | 0.2 % |
151 | Northern Mariana Islands | 0.2 % |
152 | United Kingdom | 0.15 % |
153 | North Macedonia | 0.097 % |
154 | Tonga | 0.086 % |
155 | Bermuda | 0.082 % |
156 | United States | 0.08 % |
157 | Lithuania | 0.077 % |
158 | Slovakia | 0.063 % |
159 | Tajikistan | 0.05 % |
160 | Spain | 0.045 % |
161 | United Arab Emirates | 0.039 % |
162 | Malta | 0.038 % |
163 | Czech Republic | 0.032 % |
164 | Australia | 0.031 % |
165 | Italy | 0.02 % |
166 | Luxembourg | 0.019 % |
167 | Portugal | 0.012 % |
168 | Belarus | 0.004 % |
169 | Andorra | 0 % |
170 | Aruba | 0 % |
171 | Bahrain | 0 % |
172 | Belgium | 0 % |
173 | Denmark | 0 % |
174 | Finland | 0 % |
175 | France | 0 % |
176 | Germany | 0 % |
177 | Iceland | 0 % |
178 | Kuwait | 0 % |
179 | Monaco | 0 % |
180 | Netherlands | 0 % |
181 | New Zealand | 0 % |
182 | Norway | 0 % |
183 | Qatar | 0 % |
184 | Singapore | 0 % |
185 | Switzerland | 0 % |
186 | Saint Lucia | -0.077 % |
187 | Austria | -0.174 % |
188 | Uruguay | -0.199 % |
189 | Oman | -0.2 % |
190 | Ukraine | -0.23 % |
191 | Greece | -0.25 % |
192 | Albania | -0.28 % |
193 | British Virgin Islands | -0.616 % |
194 | Bosnia and Herzegovina | -0.793 % |
195 | Kazakhstan | -1.016 % |
196 | Kyrgyzstan | -2.927 % |
- #1
South Sudan
- #2
Mali
- #3
Chad
- #4
Congo, Democratic Republic of the
- #5
Sao Tome and Principe
- #6
Afghanistan
- #7
Sudan
- #8
Niger
- #9
Burkina Faso
- #10
Central African Republic
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
- #196
Kyrgyzstan
- #195
Kazakhstan
- #194
Bosnia and Herzegovina
- #193
British Virgin Islands
- #192
Albania
- #191
Greece
- #190
Ukraine
- #189
Oman
- #188
Uruguay
- #187
Austria
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2024, South Sudan leads the world in the percentage of its population living in slums, with a staggering 94.2%. Globally, the range of populations living in slums spans from a minimum of -2.93% to a maximum of 94.20%. The global average percentage of populations living in slums is 21.35%, providing a benchmark for understanding urban poverty worldwide.
Economic and Political Instability as Drivers
Several countries with the highest percentages of populations living in slums, such as South Sudan (94.2%), Mali (81.29%), and Chad (81.27%), are often plagued by economic challenges and political instability. These conditions hinder the development of infrastructure and housing, leading to inadequate urban planning and a high prevalence of slums.
In South Sudan, ongoing conflict and economic turmoil have severely impacted the government's ability to provide basic services, exacerbating urban poverty. Similarly, Mali and Chad face significant developmental challenges, including limited access to resources and high levels of poverty, which contribute to the expansion of slum areas in urban centers.
Urbanization and Rapid Population Growth
Countries like the Democratic Republic of the Congo (79.33%) and Niger (70.44%) are experiencing rapid urbanization and population growth, which often outpace the development of necessary urban infrastructure. This mismatch leads to overcrowded cities where slums become the default housing solution due to the lack of affordable and adequate housing options.
The rapid urban expansion in these nations often occurs without proper planning, resulting in the proliferation of informal settlements. These areas typically lack essential services such as clean water, sanitation, and electricity, highlighting the urgent need for comprehensive urban policies and investments in housing.
Year-over-Year Changes and Emerging Trends
While some countries have seen increases in their slum populations, others have experienced notable decreases. Jordan saw the largest increase, with a rise of 5.25% (8.7%), which may be attributed to the influx of refugees and the strain on urban resources. Tuvalu and Mali also saw significant increases of 4.96% (13.8%) and 4.25% (5.5%), respectively, possibly due to economic pressures and environmental factors affecting housing stability.
Conversely, Malawi experienced the most significant decrease, with a reduction of 2.06% (-5.2%), reflecting efforts to improve housing conditions and urban planning. Similarly, Comoros and Burundi also reduced their slum populations by 2.01% (-3.7%) and 1.75% (-5.3%), respectively, indicating progress in addressing urban poverty.
Negative Values Explained
The presence of negative values, such as in Kyrgyzstan (-2.93%) and Kazakhstan (-1.02%), indicates discrepancies or possible data errors. These figures suggest that the reported slum populations might be overestimated or misclassified, reflecting complexities in defining and measuring slum areas in regions with varying urban standards.
Such anomalies highlight the challenges in collecting and interpreting data across diverse global contexts. Ensuring accurate data collection methods and definitions is crucial for effectively addressing and understanding the scope of urban poverty.
Overall, the 2024 statistics on populations living in slums emphasize the critical need for targeted policies and investments to improve urban living conditions. By addressing the root causes of slum proliferation, such as economic instability, rapid urbanization, and inadequate infrastructure, countries can make strides toward reducing urban poverty and enhancing the quality of life for millions of urban dwellers.
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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