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Food and agricultural industry locational determinants research: Aggregation bias and size measurement in the agricultural support industry

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  • Carpenter, Craig Wesley
  • Van Sandt, Anders
  • Loveridge, Scott

Abstract

Federal administrative data present a valuable opportunity for food and agricultural industry locational outcome research. We review issues with aggregated U.S. public data and summarize current methods. An example empirical approach combines federal administrative and secondary data. We compare results with differing levels of industrial aggregation. Results indicate locational determinants vary in magnitude, sign, and significance across industries and their sub-industries, as well as between employers and non-employers – nuances commonly missed with public data. We conclude by emphasizing that studies relying on public (more-aggregated) data may miss locational outcome relationships or inappropriately generalize to sub-industries and suggest data access changes.

Suggested Citation

  • Carpenter, Craig Wesley & Van Sandt, Anders & Loveridge, Scott, 2022. "Food and agricultural industry locational determinants research: Aggregation bias and size measurement in the agricultural support industry," Agricultural and Resource Economics Review, Cambridge University Press, vol. 51(3), pages 558-578, December.
    • Handle: RePEc:cup:agrerw:v:51:y:2022:i:3:p:558-578_7
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    Cited by:

    1. Van Sandt, Anders & Pokharel, Raju & Carpenter, Craig W. & Loveridge, Scott, 2024. "Improving models of wood products plant locations with restricted access data," Forest Policy and Economics, Elsevier, vol. 167(C).
    2. Jung Min Lee & Chanjin Chung, 2024. "Estimating Market Power Exertion in the U.S. Beef Packing Industry: An Illustration of Data Aggregation Bias Using Simulated Data," Sustainability, MDPI, vol. 16(9), pages 1-19, April.
    3. Aaron Kinyu Hoshide, 2022. "Back to the Future: Agricultural Booms, Busts, and Diversification in Maine, USA, 1840–2017," Sustainability, MDPI, vol. 14(23), pages 1-24, November.

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