Population living in slums 2001
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 | Chad | 91.02 % |
2 | Ethiopia | 90.624 % |
3 | Madagascar | 90.235 % |
4 | Mozambique | 88.339 % |
5 | Congo | 87.806 % |
6 | Central African Republic | 84.544 % |
7 | Mauritania | 83.005 % |
8 | Cambodia | 82 % |
9 | Mali | 81.779 % |
10 | Burundi | 79.7 % |
11 | Malawi | 79.654 % |
12 | Uganda | 79.557 % |
13 | Burkina Faso | 79.428 % |
14 | Tanzania | 79.199 % |
15 | Djibouti | 75.75 % |
16 | Eritrea | 75.75 % |
17 | Mauritius | 75.75 % |
18 | Seychelles | 75.75 % |
19 | Somalia | 75.75 % |
20 | Sierra Leone | 73.9 % |
21 | Nigeria | 72.851 % |
22 | Benin | 71.669 % |
23 | Nicaragua | 70.967 % |
24 | Pakistan | 70.459 % |
25 | Niger | 70.067 % |
26 | Cabo Verde | 69.748 % |
27 | Rwanda | 69.68 % |
28 | Togo | 67.65 % |
29 | Côte d'Ivoire | 66.306 % |
30 | Senegal | 65.377 % |
31 | Nepal | 64.864 % |
32 | Comoros | 64.79 % |
33 | Cameroon | 63.729 % |
34 | Zambia | 63.008 % |
35 | Lesotho | 62.727 % |
36 | Yemen | 62.501 % |
37 | Kenya | 62.411 % |
38 | Haiti | 60.631 % |
39 | Sudan | 60.432 % |
40 | Ghana | 58.688 % |
41 | Bangladesh | 57.989 % |
42 | Botswana | 57.631 % |
43 | Ecuador | 57.63 % |
44 | Bhutan | 57.197 % |
45 | Iran | 57.197 % |
46 | Sri Lanka | 57.197 % |
47 | Bolivia | 56.813 % |
48 | Equatorial Guinea | 56.505 % |
49 | Gabon | 56.495 % |
50 | Gambia | 56.019 % |
51 | Mongolia | 55.618 % |
52 | Eswatini | 55.352 % |
53 | Guatemala | 54.378 % |
54 | Laos | 52.7 % |
55 | Philippines | 49.271 % |
56 | Azerbaijan | 47.938 % |
57 | Panama | 46.52 % |
58 | Peru | 46.489 % |
59 | Kyrgyzstan | 44.922 % |
60 | Honduras | 44.4 % |
61 | Vietnam | 42.845 % |
62 | Namibia | 42.482 % |
63 | Libya | 42.203 % |
64 | Guinea | 41.082 % |
65 | Paraguay | 40.529 % |
66 | Egypt | 39.471 % |
67 | Brunei Darussalam | 36.6 % |
68 | Iraq | 34.666 % |
69 | Indonesia | 34.299 % |
70 | Brazil | 33.086 % |
71 | Morocco | 31.761 % |
72 | Syrian Arab Republic | 31.384 % |
73 | Mexico | 31.305 % |
74 | Myanmar | 31.004 % |
75 | Dominican Republic | 28.974 % |
76 | Uzbekistan | 28.094 % |
77 | South Africa | 27.419 % |
78 | Zimbabwe | 27.224 % |
79 | Venezuela | 27.114 % |
80 | Albania | 26.8 % |
81 | Guyana | 26.1 % |
82 | El Salvador | 25.702 % |
83 | Republic of Moldova | 25.1 % |
84 | Turkey | 23.884 % |
85 | Uruguay | 23.7 % |
86 | Kazakhstan | 23.066 % |
87 | Colombia | 20.505 % |
88 | Argentina | 19.929 % |
89 | Papua New Guinea | 19.75 % |
90 | Angola | 19.7 % |
91 | Montenegro | 19.1 % |
92 | Kiribati | 17.45 % |
93 | Belize | 15.781 % |
94 | Hungary | 14.8 % |
95 | Georgia | 13.6 % |
96 | Chile | 12.793 % |
97 | Armenia | 12.552 % |
98 | Costa Rica | 12.238 % |
99 | Trinidad and Tobago | 11.06 % |
100 | Turkmenistan | 10.5 % |
101 | Solomon Islands | 10.4 % |
102 | Fiji | 8.609 % |
103 | Suriname | 7.849 % |
104 | Ireland | 6.3 % |
105 | Bosnia and Herzegovina | 5.1 % |
106 | Ukraine | 5 % |
107 | Saint Lucia | 4.85 % |
108 | Samoa | 4.55 % |
109 | Vanuatu | 4.55 % |
110 | Austria | 4.45 % |
111 | Oman | 4.125 % |
112 | Lithuania | 3.925 % |
113 | Russia | 3.6 % |
114 | Nauru | 3.375 % |
115 | British Virgin Islands | 3.313 % |
116 | Tuvalu | 3.214 % |
117 | Latvia | 2.8 % |
118 | Cuba | 2.154 % |
119 | Palau | 2.05 % |
120 | Poland | 1.95 % |
121 | Romania | 1.825 % |
122 | Tonga | 1.8 % |
123 | North Macedonia | 1.3 % |
124 | Portugal | 1.175 % |
125 | United Arab Emirates | 0.826 % |
126 | Serbia | 0.65 % |
127 | Croatia | 0.4 % |
128 | Sweden | 0.275 % |
129 | Canada | 0.25 % |
130 | Greece | 0.25 % |
131 | Cyprus | 0.2 % |
132 | United States | 0.2 % |
133 | Australia | 0.165 % |
134 | United Kingdom | 0.1 % |
135 | Bulgaria | 0.075 % |
136 | Czech Republic | 0.05 % |
137 | Estonia | 0.05 % |
138 | Slovakia | 0.05 % |
139 | Malta | 0.038 % |
140 | Italy | 0.02 % |
141 | Bermuda | 0.018 % |
142 | Andorra | 0 % |
143 | Aruba | 0 % |
144 | Belgium | 0 % |
145 | Denmark | 0 % |
146 | Finland | 0 % |
147 | France | 0 % |
148 | Germany | 0 % |
149 | Iceland | 0 % |
150 | Kuwait | 0 % |
151 | Luxembourg | 0 % |
152 | Monaco | 0 % |
153 | Netherlands | 0 % |
154 | New Zealand | 0 % |
155 | Norway | 0 % |
156 | Singapore | 0 % |
157 | Switzerland | 0 % |
- #1
Chad
- #2
Ethiopia
- #3
Madagascar
- #4
Mozambique
- #5
Congo
- #6
Central African Republic
- #7
Mauritania
- #8
Cambodia
- #9
Mali
- #10
Burundi
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
- #157
Switzerland
- #156
Singapore
- #155
Norway
- #154
New Zealand
- #153
Netherlands
- #152
Monaco
- #151
Luxembourg
- #150
Kuwait
- #149
Iceland
- #148
Germany
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2001, Chad had the highest Population living in slums at 91.02%, while the global range spanned from 0% to 91.02%. The median percentage of populations living in slums worldwide was 27.11%, highlighting significant disparities in urban living conditions across different countries.
Understanding the Global Range of Slum Populations
The stark contrast between countries like Chad at the top and Denmark at the bottom with 0% of the population living in slums illustrates the wide disparities in urban development. Countries with high slum populations, such as Ethiopia (90.62%) and Madagascar (90.23%), often face challenges related to rapid urbanization, insufficient infrastructure, and economic constraints. In contrast, European nations like Germany and Norway have established urban planning and housing policies that effectively prevent the formation of slums.
Economic and Policy Drivers Behind Slum Populations
The prevalence of slums is often linked to economic factors and policy effectiveness. For example, Mozambique (88.34%) and Congo (87.81%) are countries with significant economic challenges, which can lead to inadequate housing and urban services. In these regions, the lack of affordable housing and effective urban planning policies contributes to high slum populations. Conversely, countries with robust economies and proactive housing policies, such as Switzerland and Finland, have managed to maintain a slum population of 0%.
Urbanization and Its Impact on Slum Populations
Rapid urbanization is a significant factor contributing to slum growth, particularly in African and Southeast Asian countries. Cambodia (82%) and Burundi (79.7%) exemplify regions where urban migration exceeds the capacity of cities to provide adequate housing. This phenomenon often results in the expansion of informal settlements. Conversely, nations like France and Belgium, with advanced urban planning and infrastructure, experience no slum population, demonstrating the importance of sustainable urban development.
Year-over-Year Changes and Their Implications
Analyzing year-over-year changes reveals significant shifts in slum populations. Sudan experienced the largest increase of 38.67%, translating to a 177.7% rise, underscoring the impact of political instability and economic challenges on urban living conditions. Similarly, El Salvador saw a remarkable increase of 23.80%, highlighting the effects of rapid urbanization and limited housing policies. On the other hand, countries like Azerbaijan and Cambodia showed decreases of 3.00% and 2.80% respectively, indicating efforts to improve urban housing and reduce slum populations through targeted interventions.
The data for 2001 underscores the complex interplay of economic, policy, and urbanization factors that influence the prevalence of slums worldwide. Addressing these challenges requires comprehensive strategies that encompass economic development, effective urban planning, and robust housing policies to enhance the quality of life for urban populations globally.
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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