Date of Award

Spring 2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Economics

First Advisor

Altonji, Joseph

Abstract

The Effects of Federal “Redlining” Maps: a Novel Estimation Strategy, with Disa M. Hynsjö Redlining, the systematic denial of credit to residents of a community, is often cited by activists and policymakers as one cause of enduring urban inequality. It is widely understood that the federal government started redlining in the 1930s. Government maps, identifying disadvantaged neighborhoods with the color red, have become a symbol of institutional discrimination. However, historians have disputed the ultimate influence of such maps on access to credit, and evidence of any causal economic impacts is scarce due to a lack of data and estimation challenges. This paper investigates the causal effects of the Home Owners’ Loan Corporation (HOLC) maps and the neighborhood grades they assigned to summarize lending risk in the second half of the 1930s. In particular, we estimate the effects of different grades on homeownership rates, property values and shares of African-Americans between 1940 and 2010. In their time, the HOLC maps were a data analytics tool at the forefront of real estate appraisal techniques that soon became influential in the housing market at large. Our study illustrates how institutional practices can coordinate individual choices and amplify their discriminatory consequences. To measure the short and long-term effects of the HOLC mapping intervention, we propose a new estimation strategy. Spatial discontinuity designs, often used in the literature on this topic, suffer from endogeneity concerns: multiple authors documented socioeconomic differences on opposite sides of boundaries traced by the agency, indicating that the HOLC did not assign border locations and grades randomly. Instead, we exploit an exogenous population threshold that determined which cities were mapped and a machine learning algorithm drawing HOLC maps in control cities. Using the grades predicted by the machine learning model, we apply a grouped difference-in-differences design to measure the causal effects of the HOLC intervention. The causal effects are identified by differences between neighborhoods in treated cities and areas in control cities that would have received the same grade, but were not mapped. This empirical strategy is possible thanks to a new spatial dataset we constructed geocoding full-count Census records between 1910 and 1940. In addition, geographic coordinates let us join tract-level Census data for 1960-2010 and CoreLogic real property data to measure long-term outcomes. We find a substantial reduction in property prices and a 2.4 percentage points decrease in homeownership rates in the lowest grade (red) areas in the short term. For this same grade, the HOLC maps caused a 1.6 percentage points increase in the local share of African-American residents in 1940. We also find a sizable house value reduction in the second to last grade (yellow) areas, showing that the causal impacts were not confined to red areas. Such negative effects for property prices persisted until the early 1980s, shortly after the federal government introduced legislative measures to counteract redlining. The Long Term Effects of Exposure to Non-Traditional Family Structures. Single-mother households have become common in the US over the past fifty years. Economists, sociologists, and psychologists have documented that children from single-headed families have lower intergenerational mobility because of a lack of resources and the type of parenting they receive. However, little is known about the effects of children from single-mother families on their school peers. Taking advantage of the Add Health panel data structure, I estimate the effect of this feature of the adolescents’ social environment on educational achievement and long-run labor market outcomes. My identification strategy is based on cohort-to-cohort variation in the percentage of children without a father figure within a school. The preliminary estimates indicate that exposure to peers with a higher rate of father absence does not have much of an effect on education, employment, or wages.

Download
COinS