CREAT: Census Research Exploration and Analysis Tool

School Accountability and Residential Location Patterns: Evaluating the Unintended Consequences of No Child Left Behind

January 2017

Written by: Keren Mertens Horn

Working Paper Number:

CES-17-28

Abstract

The 2002 to 2015 No Child Left Behind (NCLB) Act is often considered the most significant federal intervention into education in the United States since 1965 with the passage of the Elementary and Secondary Education Act. There is growing evidence that holding schools accountable is leading to some improved educational outcomes for students. There is in contrast very little work examining whether these sweeping reforms have unintended consequences for the communities which these schools are serving. As school attendance, particularly at the elementary school level, is closely tied to one's residence, placing sanctions on a school could have negative repercussions for neighborhoods if it provides new information on school failure. In contrast, if these sanctions also bring new resources, including financial resources or school choice, they could spark additional demand within a neighborhood. Through the use of restricted access census data, which includes local housing values, rents and individual residential choices in combination with the use of a boundary discontinuity identification strategy, this paper seeks to examine how failure to meet Adequate Yearly Progress (AYP), the key enforcement mechanism of NCLB, is shaping local housing markets and residential choices in five diverse urban school districts: New York, Los Angeles, Philadelphia, Detroit and Tucson.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
enforcement, urban, educated, education, policy, student, housing, residential, district, neighborhood, residence, rent, school, grade

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Decennial Census, Chicago Census Research Data Center, Department of Education, American Community Survey, Special Sworn Status

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'School Accountability and Residential Location Patterns: Evaluating the Unintended Consequences of No Child Left Behind' are listed below in order of similarity.