author={Harriett Tee Taggart and Kevin W. Smith},
  journal={Urban Affairs Review},
  pages={107 - 91}
The residential mortgage activity of financial institutions in metropolitan Boston was analyzed to identify and assess patterns of disinvestment. Data were obtained from most state-chartered institutions and larger national banks on the geographic breakdown of their mortgage portfolios, recent mortgage activities, and deposits. Additional information on home sales, population and housing characteristics, and homeowner interviews were used. The measures of disinvestment were (1) mortgage-to… 

Has mortgage capital found an inner‐city spatial fix?

Abstract For two generations, urbanists have analyzed how residential mortgage lending reflects and reinforces inner‐city inequality. Yet the basic dichotomies of this literature have been eroded by

Gentrification and Financial Policy: Tax Increment Financing and the Implications for Low-income Residents

This paper serves as a primer on how to understand the complex issue of gentrification through the lens of financial policy. It introduces historic and current definitions of gentrification, showing

How the City Survey’s Redlining Maps Were Made: A Closer Look at HOLC’s Mortgagee Rehabilitation Division

The infamous “security maps” made in the 1930s by the Home Owners’ Loan Corporation (HOLC), rating supposed mortgage lending risk in urban neighborhoods across the United States, have long been

All About the State: Fifty Years of Innovative Technology to Deliver an Inclusive Financial Sector

This paper documents the long-term nature of technological innovations which have transformed retail finance and addressed financial exclusion. The paper also contributes to the body of literature on

Information Discrepancy in Strategic Learning

The study of the effects of non-transparency in decision rules on individuals’ ability to improve in strategic learning settings is initiated and a natural condition under which improvement can be guaranteed for all sub-populations while maintaining high predictive accuracy is identified.

No Deals on Wheels: How and Why the Poor Pay More for Basic Transportation

ABSTRACT This article examines some of the hurdles faced by the poor in finding and keeping basic transportation. The article examines how the used car industry is organized, the difficulty the poor

Information Theoretic Measures for Fairness-aware Feature Selection

This work develops a framework for fairness-aware feature selection which takes into account the correlation among the features and the decision outcome, and is based on information theoretic measures for the accuracy and discriminatory impacts of features.

Fairness through awareness

A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented.

Algorithms are not Neutral: Bias in Recommendation Systems

It is known that iterative information filtering algorithms in general create a selection bias in the course of learning from user responses to documents that the algorithm recommended, but this systematic bias in a class of algorithms in widespread use largely goes unnoticed.

On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning

This paper proposes an information-theoretic framework to analyze the disparate impact of a binary classification model, views the model as a fixed channel, and quantifies disparate impact as the divergence in output distributions over two groups.