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Keeping ARMS relevant: extracting additional information from ARMS

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Author Info

  • Allen M. Featherstone
  • Timothy A. Park
  • Jeremy G. Weber

Abstract

Purpose – The purpose of this paper is to discuss opportunities to obtain more information from the Agricultural Resource Management Survey (ARMS). Specifically, the paper will explore the issue of survey nonresponse, the development of pseudo panels, and more frequent updating of cost of production data on an enterprise basis.\ Design/methodology/approach – Researchers from the Land Grant University System and the Economic Research Service have relied on ARMS to evaluate the effect of agricultural, macroeconomic, and other factors on the US farm sector, farm businesses, and the households that manage them. This paper will identify gaps in understanding and proposes approaches to extract additional information from ARMS. Findings – The relevance of ARMS in the future will depend on the ability to continue to understand potential pitfalls and areas of additional research that can develop new procedures to extract additional information. Three issues which are in need of further study include continuing to examine the issue of non-response, refining methods to develop pseudo panel data, and examining methods to develop commodity specific financial information between the commodity specific surveys. Originality/value – The National Research Council completed a review of ARMS to address challenges in keeping the survey relevant into the future. However, research that examines the construction of financial statements and other information had not been conducted since the early 1990s. This study fills part of that gap.

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Bibliographic Info

Article provided by Emerald Group Publishing in its journal Agricultural Finance Review.

Volume (Year): 72 (2012)
Issue (Month): 2 (July)
Pages: 233-246

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Handle: RePEc:eme:afrpps:v:72:y:2012:i:2:p:233-246

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Related research

Keywords: Agriculture; ARMS; Cost of production data; Farms; Nonresponse bias; Pseudo panels; United States of America;

References

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  1. Hugo Ñopo & Giorgina Pizzolitto & José Cuesta, 2007. "Using Pseudo-Panels to Measure Income Mobility in Latin America," Research Department Publications 4557, Inter-American Development Bank, Research Department.
  2. Verbeek, Marno & Vella, Francis, 2005. "Estimating dynamic models from repeated cross-sections," Journal of Econometrics, Elsevier, vol. 127(1), pages 83-102, July.
  3. Mary Ahearn & David Banker & Dawn Marie Clay & Daniel Milkove, 2011. "Comparative Survey Imputation Methods for Farm Household Income," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 613-618.
  4. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
  5. Lauer, Charlotte, 2003. "Family background, cohort and education: A French-German comparison based on a multivariate ordered probit model of educational attainment," Labour Economics, Elsevier, vol. 10(2), pages 231-251, April.
  6. Lee Lillard & James P. Smith & Finis Welch, 2004. "What Do We Really Know About Wages: The Importance of Nonreporting and Census Imputation," Labor and Demography 0404005, EconWPA.
  7. Amin Mugera & Michael Langemeier & Allen Featherstone, 2012. "Labor productivity convergence in the Kansas farm sector: a three-stage procedure using data envelopment analysis and semiparametric regression analysis," Journal of Productivity Analysis, Springer, vol. 38(1), pages 63-79, August.
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