The Point: Research on inequality should seek to discover the structures that prevent people from leading the kind of life they ‘have reason to value’, and the problem with inequality is that many people do not have this option (Sen 2006: 35).
One of the most neglected structures that prevent people from leading the kind of life they ‘have reason to value’ is spatial segregation, especially when the average income of one’s neighbors (in)directly affects one’s own social, economic, or physical outcomes. In this case income segregation will lead to more unequal outcomes between low- and high-income households than their differences in income alone would predict. Additionally, much of the inequality literature has focused on national inequality, but local inequality is also important. Consequently, it is possible that the information provided by national measures of inequality do not adequately characterize well-being and may even present contradictory trends. The Quote: A complete understanding of inequality should include geographical space (income segregation). This means understanding inequalities outside our “disciplinary buckets” by including geography and sociology.
The Questions Being Asked: How does income inequality affects patterns of income segregation? In particular, I seek to understand how income inequality affects the pattern of spatial segregation of the lowest-income households and/or the spatial segregation of the highest-income households for the years 2007 – 2017 in Metropolitan Lima. I will describe spatial patterns and levels of segregation by income and other socioeconomic characteristics in Metropolitan Lima using Reardon and O’Sullivan’s spatial segregation indices (2004) and compute the contribution of inequality to segregation using Alonso-Villar and Del Rio (2010).
The Data: This presentation uses income per capita data at block level in Metropolitan Lima from the National Statistics Office (2016) representative of 2007. Using the National Census of 2017 and the National Household Survey (ENAHO) I will compute representative data for 2017.