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R&D, Attrition and Multiple Imputation in BRDIS

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  • Juana Sanchez
  • Sydney Noelle Kahmann

Abstract

Multiple imputation in business establishment surveys like BRDIS, an annual business survey in which some companies are sampled every year or multiple years, may enhance the estimates of total R&D in addition to helping researchers estimate models with subpopulations of small sample size. Considering a panel of BRDIS companies throughout the years 2008 to 2013 linked to LBD data, this paper uses the conclusions obtained with missing data visualization and other explorations to come up with a strategy to conduct multiple imputation appropriate to address the item nonresponse in R&D expenditures. Because survey design characteristics are behind much of the item and unit nonresponse, multiple imputation of missing data in BRDIS changes the estimates of total R&D significantly and alters the conclusions reached by models of the determinants of R&D investment obtained with complete case analysis.

Suggested Citation

  • Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:17-13
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    File URL: https://www2.census.gov/ces/wp/2017/CES-WP-17-13.pdf
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    1. Michael W. Robbins & Sujit K. Ghosh & Joshua D. Habiger, 2013. "Imputation in High-Dimensional Economic Data as Applied to the Agricultural Resource Management Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 81-95, March.
    2. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    3. David A. Penn, 2007. "Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation," Social Science Quarterly, Southwestern Social Science Association, vol. 88(2), pages 573-584, June.
    4. T. Kirk White & Jerome P. Reiter & Amil Petrin, 2018. "Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 502-509, July.
    5. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.
    6. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    7. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    8. T. Kirk White & Jerome P. Reiter & Amil Petrin, 2011. "Plant-Level Productivity and Imputation of Missing Data in the Census of Manufactures," Working Papers 11-02, Center for Economic Studies, U.S. Census Bureau.
    9. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    10. Schenker, Nathaniel & Raghunathan, Trivellore E. & Chiu, Pei-Lu & Makuc, Diane M. & Zhang, Guangyu & Cohen, Alan J., 2006. "Multiple Imputation of Missing Income Data in the National Health Interview Survey," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 924-933, September.
    11. Lucia Foster & Cheryl Grim & Nikolas Zolas, 2020. "A portrait of U.S. firms that invest in R&D," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 29(1), pages 89-111, January.
    12. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 1-26, March.
    13. Lucia Foster & Cheryl Grim, 2010. "Characteristics of the Top R&D Performing Firms in the U.S.: Evidence from the Survey of Industrial R&D," Working Papers 10-33, Center for Economic Studies, U.S. Census Bureau.
    14. Cohen, Wesley M., 2010. "Fifty Years of Empirical Studies of Innovative Activity and Performance," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 129-213, Elsevier.
    15. Lawrence R. Landerman & Kenneth C. Land & Carl F. Pieper, 1997. "An Empirical Evaluation of the Predictive Mean Matching Method for Imputing Missing Values," Sociological Methods & Research, , vol. 26(1), pages 3-33, August.
    16. Yongwei Chen & Dahai Fu, 2015. "Measuring income inequality using survey data: the case of China," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(2), pages 299-307, June.
    17. Ron S Jarmin & Javier Miranda, 2002. "The Longitudinal Business Database," Working Papers 02-17, Center for Economic Studies, U.S. Census Bureau.
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