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Activity Number: 632 - Using Big Data to Improve Official Economic Statistics
Type: Invited
Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #322018 View Presentation
Title: Using Passive Data Collection, System-to-System Data Collection, and Machine Learning to Improve Economic Surveys
Author(s): Brian Arthur Dumbacher* and Demetria Hanna
Companies: U.S. Census Bureau and U.S. Census Bureau
Keywords: Big Data ; official statistics ; economic statistics ; data collection ; public-private partnerships

As part of the ongoing effort to improve its economic surveys, the U.S. Census Bureau is exploring alternative data collection methods with the goal of reducing respondent burden and enhancing the efficiency of data processing. Some of these methods belong to the category of passive data collection, in which the respondent either has little awareness of the data collection effort or does not need to take any explicit actions. Examples include scraping data from respondents' websites and obtaining respondent data from third parties that have already collected it. Other methods belong to the category of system-to-system data collection, which involves respondents transferring data directly from their computer systems to the Census Bureau's systems. In this paper, we outline the Census Bureau's data collection vision for its economic programs and describe recent work on exploring the potential of alternative methods. We also explain how machine learning can be used to assist in collecting and processing data, especially data scraped from websites. Lastly, we describe concerns and challenges associated with all of these methods.

Authors who are presenting talks have a * after their name.

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