paper-with-me

홈 › Papers

Are the Spatial Concentrations of Core-City and Suburban Poverty Converging in the Rust Belt?

2021-05-13 · Scott W. Hegerty

Decades of deindustrialization have led to economic decline and population loss throughout the U.S. Midwest, with the highest national poverty rates found in Detroit, Cleveland, and Buffalo. This poverty is often confined to core cities themselves, however, as many of their surrounding suburbs continue to prosper. Poverty can therefore be highly concentrated at the MSA level, but more evenly distributed within the borders of the city proper. One result of this disparity is that if suburbanites consider poverty to be confined to the central city, they might be less willing to devote resources to alleviate it. But due to recent increases in suburban poverty, particularly since the 2008 recession, such urban-suburban gaps might be shrinking. Using Census tract-level data, this study quantifies poverty concentrations for four "Rust Belt" MSAs, comparing core-city and suburban concentrations in 2000, 2010, and 2015. There is evidence of a large gap between core cities and outlying areas, which is closing in the three highest-poverty cities, but not in Milwaukee. A set of four comparison cities show a smaller, more stable city-suburban divide in the U.S. "Sunbelt," while Chicago resembles a "Rust Belt" metro.

📄 PDF Abstract BibTeX arXiv:2105.07824

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Gated-Attention Feature-Fusion Based Framework for Poverty Prediction

2024-11-29 · Muhammad Umer Ramzan, Wahab Khaddim, Muhammad Ehsan Rana, Usman Ali 외

This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, i…

Prediction

Enhancing Poverty Targeting with Spatial Machine Learning: An application to Indonesia

2025-03-06 · Rolando Gonzales Martinez, Mariza Cooray

This study leverages spatial machine learning (SML) to enhance the accuracy of Proxy Means Testing (PMT) for poverty targeting in Indonesia. Conventional PMT methodologies are prone to exclusion and inclusion errors due …

Survey

Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico

2017-11-16 · Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan 외

Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These "poverty maps" are key inputs for poverty targeting, public goods provision, political accountability, …

Deprivation, Crime, and Abandonment: Do Other Midwestern Cities Have 'Little Detroits'?

2021-05-21 · Scott W. Hegerty

Both within the United States and worldwide, the city of Detroit has become synonymous with economic decline, depopulation, and crime. Is Detroit's situation unique, or can similar neighborhoods be found elsewhere? This …

Predicting Poverty

2025-05-09 · Paolo Verme

Poverty prediction models are used to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, top and bottom inc…

counterfactual