CREAT: Census Research Exploration and Analysis Tool

LEHD Infrastructure files in the Census RDC - Overview

June 2014

Working Paper Number:

CES-14-26

Abstract

The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau, with the support of several national research agencies, maintains a set of infrastructure files using administrative data provided by state agencies, enhanced with information from other administrative data sources, demographic and economic (business) surveys and censuses. The LEHD Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their interaction in the U.S. economy. This document describes the structure and content of the 2011 Snapshot of the LEHD Infrastructure files as they are made available in the Census Bureaus secure and restricted-access Research Data Center network. The document attempts to provide a comprehensive description of all researcher-accessible files, of their creation, and of any modifcations made to the files to facilitate researcher access.

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.
:
work census, information census, payroll, data census, census data, census research, survey, agency, linked census, employee, employ, employed, longitudinal, employment data, workplace, workforce, clerical, residential, census bureau, employment statistics, employer household, longitudinal employer, use census, employee data, census employment, census survey

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, American Economic Association, Standard Statistical Establishment List, Internal Revenue Service, Standard Industrial Classification, Bureau of Labor Statistics, Social Security Administration, Service Annual Survey, National Science Foundation, Center for Economic Studies, Department of Defense, Establishment Micro Properties, American Economic Review, University of Chicago, Current Population Survey, Longitudinal Business Database, Decennial Census, Employer Identification Numbers, Survey of Income and Program Participation, Cornell University, Journal of Labor Economics, Business Master File, Unemployment Insurance, Research Data Center, Department of Homeland Security, North American Industry Classification System, Sample Edited Detail File, American Community Survey, Social Security Number, Alfred P Sloan Foundation, Longitudinal Employer Household Dynamics, Business Register, Protected Identification Key, Employment History File, Employer Characteristics File, Individual Characteristics File, American Housing Survey, Quarterly Workforce Indicators, CDF, Census 2000, Core Based Statistical Area, Quarterly Census of Employment and Wages, Composite Person Record, Business Employment Dynamics, Local Employment Dynamics, Office of Personnel Management, Master Address File, Business Register Bridge, Probability Density Function, Disclosure Review Board, North American Industry Classi, Business Dynamics Statistics, International Trade Research Report, Census Numident

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 'LEHD Infrastructure files in the Census RDC - Overview' are listed below in order of similarity.