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Implications of Missing Data Imputation for Agricultural Household Surveys: An Application to Technology Adoption

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  • Gedikoglu, Haluk
  • Parcell, Joseph L.

Abstract

Missing data is a problem that occurs frequently in survey data. Missing data results in biased estimates and reduced efficiency for regression estimates. The objective of the current study is to analyze the impact of missing-data imputation, using multiple-imputation methods, on regression estimates for agricultural household surveys. The current study also analyzes the impact of multiple-imputation on regression results, when all the variables in the regression have missing observations. Finally, the current study compares the impact of univariate multiple imputation with multivariate normal multiple imputation, when some of the missing variables have discrete distribution. The results of the current study show that multivariate-normal multiple imputation performs better than univariate multiple imputation model, and overall both methods improve the efficiency of regression estimates.

Suggested Citation

  • Gedikoglu, Haluk & Parcell, Joseph L., 2012. "Implications of Missing Data Imputation for Agricultural Household Surveys: An Application to Technology Adoption," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124333, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea12:124333
    DOI: 10.22004/ag.econ.124333
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    References listed on IDEAS

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    1. 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.
    2. Michael W. Robbins & T. Kirk White, 2011. "Farm Commodity Payments and Imputation in the Agricultural Resource Management Survey," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 606-612.
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    Cited by:

    1. Gonzalo Villa‐Cox & Francesco Cavazza & Cristian Jordan & Mijail Arias‐Hidalgo & Paúl Herrera & Ramon Espinel & Davide Viaggi & Stijn Speelman, 2021. "Understanding constraints on private irrigation adoption decisions under uncertainty in data constrained settings: A novel empirical approach tested on Ecuadorian Cocoa cultivations," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 985-999, November.

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