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Sent Away: The Long-Term Effects of Slum Clearance on Children and Families

  • Author(s): Rojas Ampuero, Fernanda Catalina
  • Advisor(s): Costa, Dora L
  • et al.
Abstract

In this dissertation I study the long-term effects of moving to a high-poverty neighborhood on earnings and schooling using evidence from a slum clearance program implemented in Santiago, Chile, between 1979 and 1985. During the country’s dictatorship, the government mandated the eviction of entire slums and their relocation to public housing in low-income areas: Two-thirds of slums were relocated to new housing projects on the periphery of the city, and the rest received housing at their initial location. This dissertation is divided into three chapters. In Chapter 1, I estimate a displacement effect by comparing the outcomes of displaced and non-displaced children and their parents 20 to 40 years after the end of the policy. I construct a novel data set that combines archival records with administrative data containing 19,852 homeowners matched to 55,343 children. I show that displacement is unrelated to families’ demographics or neighborhood attributes prior to eviction, and I find negative effects on children and families: Displaced children have 10% lower earnings and 0.5 fewer years of education as adults than non-displaced. Moreover, displaced children are more likely to work in informal jobs and their parents are more likely to die after the intervention. In Chapter 2, I study the mechanisms that explain the negative effects experienced by displaced children when young. I find that destination characteristics mediate my results: Lower social cohesion in destination projects reduces children’s schooling, and their earnings are also affected by worse labor market access. Finally, in Chapter 3, I describe the data collection process in detail. I explain the processes to construct family composition and how I link parents and children. I also describe how I match families to slums of origin, and I discuss potential selection issues due to missing data.

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