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Article type: Research Article
Authors: Buffington, Catherinea | Foster, Luciaa | Jarmin, Rona | Ohlmacher, Scotta; b; *
Affiliations: [a] Census Bureau, MD, USA | [b] University of Maryland, MD, USA
Correspondence: [*] Corresponding author: Scott Ohlmacher, Census Bureau, MD, USA. E-mail: [email protected].
Note: [1] Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The MOPS was developed as a partnership between the Census Bureau and an external research team that includes Nick Bloom (Stanford), Erik Brynjolfsson (MIT), and John Van Reenen (MIT). The work of this team for MOPS2010 was supported by a grant from the National Science Foundation (NSF) and through administrative support from the National Bureau of Economic Research (NBER). The work for MOPS2015 was supported by grants from NSF, NBER, the Kauffman Foundation, and the Sloan Foundation. We thank Julius Smith of the Economy-Wide Statistical Division for his review of this paper ensuring that we have appropriately described the work.
Abstract: This paper describes the Management and Organizational Practices Survey (MOPS), conducted by the U.S. Census Bureau in 2010 and 2015. The 2010 survey was the first-ever large scale survey of management practices in the United States. Enhancements for the 2015 survey include questions on two topics related to management: data and decision making (DDD) and uncertainty. This paper provides an overview of the existing literature on the role of management and organizational practices in firm performance, focusing on earlier efforts to measure these practices using survey data. The paper then describes the content and methodology of the MOPS.
Keywords: Methodology for collecting microeconomic data, firm organization, manufacturing, personnel economics, innovation, data driven decision making, uncertainty
DOI: 10.3233/JEM-170441
Journal: Journal of Economic and Social Measurement, vol. 42, no. 1, pp. 1-26, 2017
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