A working paper co-authored with Miquel Àngel García López entitled “Income Segregation and Urban Spatial Structure: Evidence from Brazil” is now available as part of the CAF Working Paper Series. In this work, we estimate the effect of urban spatial structure on income segregation in using data for 121 Brazilian cities between 2000 and 2010. We show how the effect of local density varies between monocentric and polycentric cities, and between income groups.
This paper is part of a line of research trying to link the distribution of employment within cities with the distribution of the population by income groups, in order to understand the possible causes of residential segregation by level of income in urban areas.
The distribution of people of different income levels within urban areas is anything but random. Residential segregation is a palpable, undeniable reality to any observer of cities. The patterns of segregation are also extremely persistent over time: despite massive demographic changes and migration flows, the spatial arrangement of income groups within cities changes only very slowly. Why is this the case? Surprisingly, there are not many theoretical insights on the long-run drivers of residential segregation, and thus very little guidance about what policies should or should not do about it.
A recent excellent contribution by Rémi Louf takes a step forward in proposing much needed mechanistic insights on the patterns of urban segregation. In his work, he departs from the null case – an unsegregated city – against which observed segregation levels can be compared. He then proposes a measure of segregation inspired on Marcon and Puech’s M-function of co-location, and a way to let a class structure emerge from the data (in this way avoiding the need to impose arbitrary income cut-offs).
Among other things, he finds that “neighbourhoods are geographically more coherent as cities get larger, which corresponds in effect to an increased level of segregation as the size of the city increases.” Interestingly, this phenomenon seems to be more important for higher-income households than for other income groups.
In the preliminary findings from my study on income segregation in Brazil, I find strong support for these patterns. The level of segregation increases with income, so that individuals in the highest income category are far more segregated than any other income group, including the poorest. There also seems to be a strong, positive relationship between city size and income segregation levels. An interesting way forward is to think about the relationship between density and the over-representation of the highest and lowest income categories in urban areas in developing countries. Ultimately, we need to understand how income segregation evolves with economic development.
The São Paulo Metropolitan Region faces an acute deficit of public transport infrastructure, making commutes long and costly for workers. The difficulty in accessing jobs centers may translate into higher informality rates, since workers may be discouraged to take formal jobs, miss on information about job opportunities, or even be discriminated against according to their place of residence.
Sé metro station, Corinthians-Itaquera direction. Source: Viatrolebus
In a new working paper co-authored with Frederico R. Ramos, we estimate the impact of public transport expansions on local informality rates. An important part of the explanation for the large deficit in transport infrastructure are chronic project delays. We use this information in our methodological approach to compare areas that received new transport infrastructure with areas where new train/metro stations or bus corridors were planned, but eventually not built.
Example of areas with (left) and without (right) a bus corridor. Source: Google Earth
After controlling for endogenous selection, we find this impact to be significant: informality rates decreased on average 16% faster in areas that received new transport infrastructure, compared to areas that faced project delays.
Erik Marcon, Stephane Traissac, Florence Puech and Gabriel Lang have developed the R package dbmss that provides a toolbox to characterize point patterns. The package greatly simplifies the task of obtaining distance-based agglomeration and coagglomeration indexes, such as the Duranton and Overman’s Kd function and the Marcon and Puech’s M functions.