CREAT: Census Research Exploration and Analysis Tool

RACE-SPECIFIC AGGLOMERATION ECONOMIES: SOCIAL DISTANCE AND THE BLACK-WHITE WAGE GAP

April 2013

Working Paper Number:

CES-13-24

Abstract

We demonstrate a striking but previously unnoticed relationship between city size and the black-white wage gap, with the gap increasing by 2.5% for every million-person increase in urban population. We then look within cities and document that wages of blacks rise less with agglomeration in the workplace location, measured as employment density per square kilometer, than do white wages. This pattern holds even though our method allows for non-parametric controls for the effects of age, education, and other demographics on wages, for unobserved worker skill as proxied by residential location, and for the return to agglomeration to vary across those demographics, industry, occupation and metropolitan areas. We find that an individual's wage return to employment density rises with the share of workers in their work location who are of their own race. We observe similar patterns for human capital externalities as measured by share workers with a college education. We also find parallel results for firm productivity by employment density and share college-educated using firm racial composition in a sample of manufacturing firms. These findings are consistent with the possibility that blacks, and black- majority firms, receive lower returns to agglomeration because such returns operate within race, and blacks have fewer same-race peers and fewer highly-educated same-race peers at work from whom to enjoy spillovers than do whites. Data on self-reported social networks in the General Social Survey provide further evidence consistent with this mechanism, showing that blacks feel less close to whites than do whites, even when they work exclusively with whites. We conclude that social distance between blacks and whites preventing shared benefits from agglomeration isa significant contributor to overall black-white wage disparities.

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.
:
employed, black, minority, labor, ethnicity, ethnic, white, metropolitan, segregated, workplace, workforce, segregation, wage gap, disadvantaged, racial, race, wage differences, disparity

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.
:
Metropolitan Statistical Area, Census of Manufactures, Total Factor Productivity, Cobb-Douglas, Decennial Census, Public Use Micro Sample

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 'RACE-SPECIFIC AGGLOMERATION ECONOMIES: SOCIAL DISTANCE AND THE BLACK-WHITE WAGE GAP' are listed below in order of similarity.