Spatial segregation, inequality, and bias
Updated: Jan 25, 2018
High levels of social and spatial segregation by religion and occupation in the slums of Bangalore based on novel on-field survey data of 37 slums
The existence of slums or informal settlements is common to most cities of developing countries. In India, slums contain a wealth of diversity that is masked by a high level of poverty and rather insufficient access to resources.
One-size fits none
Recent studies have identified that the conventional perception of slums as distinctive homogeneous settlements is incorrect, rather slums are diverse and complex systems that cannot be addressed through one-size fits all approaches.
In our recent paper we investigate Tilly's theory on group segregation and how it reproduces or reinforces inequality within the slums of Bangalore. We apply statistical techniques (correspondence analysis and regression) to novel field data from 37 slums in Bangalore.
Group Identities and Segregation
We find high levels of spatial and group segregation by religion across the slums of Bengaluru. This leads to opportunity bias among slum dwellers, which inhibits equitable access to jobs in the labour market. Finally, the results show that insufficient access to resources constrain the income generation and leads to emerging coping strategies. The results indicate that group identity is key to addressing disparity and how solving inequality can drastically impact group identity.
Our results show that targeting horizontal inequality (as compared to vertical inequality) may increase the rate of successful interventions for each of the segregated groups of slum dwellers.
Overall, our results have shown that measuring horizontal inequality (among group identities) can unmask crucial policy implications, urban planning and poverty alleviation in urban India, as new policies must consider diverse religious groups and dissimilar occupational skills that have proven critical to the survival of slums and their inhabitants. The insights presented here represent a useful starting point for undertaking similar inquiries in other cities.
Future research should focus on developing finer typology of slums based on demography and socio-economic conditions. Further, developing computational models of slums may help us in understanding critical transition points and socio-economic mobility of slum households over time based on key group identities.