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Anchoring Bias in Recall Data: Evidence from Central America

Author

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  • Susan Godlonton
  • Manuel A Hernandez
  • Mike Murphy

Abstract

Self-reported retrospective survey data is widely used in empirical work but may be subject to cognitive biases, even over relatively short recall periods. This paper examines the role of anchoring bias in self-reports of objective and subjective outcomes under recall. We use a unique panel-survey dataset of smallholder farmers from four countries in Central America collected over a period of three years. We exploit differences between recalled and concurrent responses to quantify the degree of mental anchoring in survey recall data. We assess whether respondents use their reported value for the most recent period as a cognitive heuristic when recalling the value from a previous period, while controlling for the value they reported earlier. The results show strong evidence of sizeable anchoring bias in self-reported retrospective indicators for both objective measures (income, wages, and working hours) and subjective measures (reports of happiness, health, stress, and well-being). We also generally observe a larger bias in response to negative changes for objective indicators and a larger bias in response to positive changes for subjective indicators.

Suggested Citation

  • Susan Godlonton & Manuel A Hernandez & Mike Murphy, 2018. "Anchoring Bias in Recall Data: Evidence from Central America," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(2), pages 479-501.
  • Handle: RePEc:oup:ajagec:v:100:y:2018:i:2:p:479-501.
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    File URL: http://hdl.handle.net/10.1093/ajae/aax080
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    Cited by:

    1. Ye, Xiaoyang & Zhai, Muxin & Feng, Li & Xie, A’na & Wang, Weimin & Wu, Hongbin, 2022. "Still want to be a doctor? Medical student dropout in the era of COVID-19," Journal of Economic Behavior & Organization, Elsevier, vol. 195(C), pages 122-139.
    2. Delavallade, Clara & Godlonton, Susan, 2023. "Locking crops to unlock investment: Experimental evidence on warrantage in Burkina Faso," Journal of Development Economics, Elsevier, vol. 160(C).
    3. José Pulido & Tomasz Swiecki, 2019. "Barriers to Mobility or Sorting? Sources and Aggregate Implications of Income Gaps across Sectors and Locations in Indonesia," 2019 Meeting Papers 1298, Society for Economic Dynamics.
    4. Francisco Ceballos & Manuel A. Hernandez & Cynthia Paz, 2021. "Short‐term impacts of COVID‐19 on food security and nutrition in rural Guatemala: Phone‐based farm household survey evidence," Agricultural Economics, International Association of Agricultural Economists, vol. 52(3), pages 477-494, May.
    5. Dang, Hai-Anh H & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    6. Hossain, Marup & Mullally, Conner & Mabiso, Athur, 2024. "Occupational and asset adjustments in Tamil Nadu, India: The role of a finance and rebuilding program," World Development, Elsevier, vol. 177(C).
    7. Arthi, Vellore & Beegle, Kathleen & De Weerdt, Joachim & Palacios-López, Amparo, 2018. "Not your average job: Measuring farm labor in Tanzania," Journal of Development Economics, Elsevier, vol. 130(C), pages 160-172.
    8. Vasilaky, Kathryn & Harou, Aurélie & Alfredo, Katherine & Kapur, Ishita, 2023. "What works for water conservation? Evidence from a field experiment in India," Journal of Environmental Economics and Management, Elsevier, vol. 119(C).
    9. Rao, Lakshman Nagraj & Gentile, Elisabetta & Pipon, Dave & Roque, Jude David & Thuy, Vu Thi Thu, 2020. "The impact of computer-assisted personal interviewing on survey duration, quality, and cost: Evidence from the Viet Nam Labor Force Survey," GLO Discussion Paper Series 605, Global Labor Organization (GLO).
    10. Godlonton, Susan & Hernandez, Manuel A. & Paz, Cynthia, 2021. "Can survey design reduce anchoring bias in recall data? Evidence from Malawi," IFPRI discussion papers 2055, International Food Policy Research Institute (IFPRI).
    11. Mustapha Alhassan & Christopher R. Gustafson & Karina Schoengold, 2022. "Effects of information on smallholder irrigation farmers’ willingness to pay for groundwater protection," Agricultural Economics, International Association of Agricultural Economists, vol. 53(2), pages 191-203, March.
    12. Kaushalendra Kumar & Abhishek Singh & Amy Tsui, 2022. "Measuring contraceptive use in India: Implications of recent fieldwork design and implementation of the National Family Health Survey," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(4), pages 73-110.

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    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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