The Gini Coefficient is a standard statistical measure used to quantify income inequality within a city. It ranges from (perfect equality) to (perfect inequality), where a higher value indicates that a small percentage of the population holds a large share of the total income.
Spatial Analysis (GIS) is used to map the distribution of services, such as the distance from residential areas to the nearest clean water source or healthcare facility. This technique identifies 'service deserts' where marginalized populations are underserved compared to affluent areas.
The Multidimensional Poverty Index (MPI) is applied in urban contexts to look beyond just income. It measures deprivations in health, education, and living standards (e.g., floor material, cooking fuel) to provide a holistic view of urban hardship.
| Feature | Formal Sector | Informal Sector |
|---|---|---|
| Employment | Regulated, taxed, social security | Unregulated, no benefits, precarious |
| Housing | Legal title, building codes | Squatting, sub-standard materials |
| Services | State-provided infrastructure | Self-provided or illegal tapping |
Analyze the Drivers: When asked about the causes of urban inequality, always categorize your answer into economic (job markets), political (planning bias), and social (discrimination) factors.
Interpret the Gini Index: Remember that a city can have a growing economy but a rising Gini coefficient. This indicates that the benefits of growth are not being distributed equitably among the population.
Check for 'Urban Advantage' Fallacies: Exams often test the misconception that all city dwellers are better off than rural dwellers. Always counter this by mentioning that the 'urban poor' often face higher costs of living and worse health outcomes than the rural poor.
Focus on Spatiality: Use terms like 'spatial mismatch' to describe when low-income housing is located far from job centers, creating a barrier to economic mobility.
Homogeneity of Slums: A common mistake is assuming all informal settlements are the same. In reality, they vary significantly in terms of age, legal status, and level of community organization.
Over-reliance on Averages: City-wide averages for income or health often hide extreme internal disparities. Always look for disaggregated data that separates the 'slum' population from the 'non-slum' population to see the true extent of inequality.
Blaming Migration: Students often blame rural-to-urban migration as the sole cause of urban poverty. However, poor urban planning and lack of affordable housing investment are often more significant drivers than the migration itself.