Full Citation
Title: 天下爲公/What is under heaven is for all: Using non-profit data for immigrant advocacy
Citation Type: Miscellaneous
Publication Year: 2021
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Abstract: This project arose from 2 months of work over the summer of 2020 during the COVID-19 pandemic. Boston Chinatown Neighborhood Center (BCNC) hired me to assist with onboarding of a new fundraising staff member as well as research on community demographics and programmatic impact. As part of this work, I analyzed a dataset related to an income replacement program for local immigrant families affected by the COVID-19 lockdown. My capstone project attempts to build on this initial analysis with more detailed, geospatial modeling of this dataset paired with Census bureau data. This work highlights ways in which non-profit service data can be repurposed for advocacy, in keeping with historical community organizing efforts in Boston’s Chinatown. This paper is divided into several parts. The Background section provides an overview of the history of Boston Chinatown, American community organizing tactics and critiques, and a brief analysis of political economy and successful community advocacy initiatives in Boston’s Chinatown. In the Technical section, I create and analyze multi-variate linear and geographically-weighted regression (GWR) models predicting unemployment of Asian non-citizen residents of Suffolk county, and compare the results with a dataset from an income replacement program conducted during the early stages of the COVID-19 pandemic. In the final section, I discuss findings from the regression models and potential applications for non-profits, researchers, policy makers and advocates.
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Authors: Kerr, Nathaniel Westlake
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Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure, Migration and Immigration, Population Data Science
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