Building a Sample Frame of SMEs Using Patent, Search Engine, and Website Data

Arora Sanjay K. (), Kelley Sarah () and Madhavan Sarvothaman ()
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Arora Sanjay K.: Ernst & Young, LLP, 1101 New York Ave NW, Washington, D.C., 20005, U.S.A.
Kelley Sarah: Child Trends, 7315 Wisconsin Avenue, Suite 1200W, Bethesda, MD, 20814, U.S.A.
Madhavan Sarvothaman: American Institutes for Research, Washington, D.C., 20007, U.S.A.

Journal of Official Statistics, 2021, vol. 37, issue 1, 1-30

Abstract: This research outlines the process of building a sample frame of US SMEs. The method starts with a list of patenting organizations and defines the boundaries of the population and subsequent frame using free to low-cost data sources, including search engines and websites. Generating high-quality data is of key importance throughout the process of building the frame and subsequent data collection; at the same time, there is too much data to curate by hand. Consequently, we turn to machine learning and other computational methods to apply a number of data matching, filtering, and cleaning routines. The results show that it is possible to generate a sample frame of innovative SMEs with reasonable accuracy for use in subsequent research: Our method provides data for 79% of the frame. We discuss implications for future work for researchers and NSIs alike and contend that the challenges associated with big data collections require not only new skillsets but also a new mode of collaboration.

Keywords: Sample frame; administrative and big data; machine learning; bias; small and medium-sized enterprises (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:37:y:2021:i:1:p:1-30:n:6

DOI: 10.2478/jos-2021-0001

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