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Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)

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  • Morehart, Mitch
  • Milkove, Dan
  • Xu, Yang

Abstract

The US Department of Agriculture (USDA), through its ARMS, collects detailed information from farm operators on debt used in the farm business. Specific loan characteristics such as interest rate, loan term, origination date, type of loan, loan purpose, and type of financing are collected for up to the five largest loans. This information is used to construct portions of the farm sector balance sheet in addition to supporting research on credit use, farm solvency, and debt repayment capacity. Valid estimation and inferences are critical to the generation of this data, however, because of sensitivity, is subject to nonresponse or "do not know." Ignoring item nonresponse completely, by setting all missing values to zero or by taking into account only the existing answers will result in a bias. Imputation, the practice of filling in missing data with plausible values, can mitigate this bias. This analysis examines the use of multivariate techniques for debt component imputation. This would be an improvement over the generalized mean imputation approach used in ARMS and for many of the debt components the first attempt at imputation.

Suggested Citation

  • Morehart, Mitch & Milkove, Dan & Xu, Yang, 2014. "Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169401, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea14:169401
    DOI: 10.22004/ag.econ.169401
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    References listed on IDEAS

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    Cited by:

    1. Ifft, Jennifer E. & Kuhns, Ryan & Patrick, Kevin T., 2017. "Predicting Credit Demand with ARMS: A Machine Learning Approach," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258134, Agricultural and Applied Economics Association.
    2. Chen, Jian & Katchova, Ani L. & Zhou, Chenxi, 2021. "Agricultural loan delinquency prediction using machine learning methods," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(5), May.
    3. Grout, Travis & Ifft, Jennifer & Malinovskaya, Anna, 2021. "Energy income and farm viability: Evidence from USDA farm survey data," Energy Policy, Elsevier, vol. 155(C).
    4. Burns, Christopher & Prager, Daniel & Ghosh, Sujit & Goodwin, Barry, 2015. "Imputing for Missing Data in the ARMS Household Section: A Multivariate Imputation Approach," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205291, Agricultural and Applied Economics Association.

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    Keywords

    Farm Management; Financial Economics;

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