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Benford'S Law As An Indicator Of Survey Reliability—Can We Trust Our Data?

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  • Micha Kaiser

Abstract

This paper analyzes how closely different income measures conform to Benford's law, a mathematical predictor of probable first digit distribution across many sets of numbers. Because Benford's law can be used to test data set reliability, we use a Benford analysis to assess the quality of six widely used survey data sets. Our findings indicate that although income generally obeys Benford's law, almost all the data sets show substantial discrepancies from it, which we interpret as a strong indicator of reliability issues in the survey data. This result is confirmed by a simulation, which demonstrates that household level income data do not manifest the same poor performance as individual level data. This finding implies that researchers should focus on household level characteristics whenever possible to reduce observation errors.

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  • Micha Kaiser, 2019. "Benford'S Law As An Indicator Of Survey Reliability—Can We Trust Our Data?," Journal of Economic Surveys, Wiley Blackwell, vol. 33(5), pages 1602-1618, December.
    • Handle: RePEc:bla:jecsur:v:33:y:2019:i:5:p:1602-1618
    DOI: 10.1111/joes.12338
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    Cited by:

    1. Wang, Delu & Chen, Fan & Mao, Jinqi & Liu, Nannan & Rong, Fangyu, 2022. "Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries," Energy Economics, Elsevier, vol. 114(C).
    2. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    3. Robert J R Elliott & Puyang Sun & Tong Zhu, 2021. "Energy Abundance, the Geographical Distribution of Manufacturing, and International Trade," Discussion Papers 21-16, Department of Economics, University of Birmingham.
    4. Vadim S. Balashov & Yuxing Yan & Xiaodi Zhu, 2020. "Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law," Papers 2007.14841, arXiv.org, revised Jan 2021.
    5. Solanki Gupta & Vivek Kumar Singh & Sumit Kumar Banshal, 2024. "Altmetric data quality analysis using Benford’s law," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4597-4621, July.
    6. Herteliu, Claudiu & Jianu, Ionel & Dragan, Irina Maria & Apostu, Simona & Luchian, Iuliana, 2021. "Testing Benford’s Laws (non)conformity within disclosed companies’ financial statements among hospitality industry in Romania," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    7. Roy Cerqueti & Claudio Lupi, 2023. "Severe testing of Benford’s law," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 677-694, June.
    8. Karlo Kauko, 2024. "How to detect what drives deviations from Benford’s law? An application to bank deposit data," Empirical Economics, Springer, vol. 67(3), pages 1045-1061, September.
    9. Eglė Baltranaitė & Loreta Kelpšaitė-Rimkienė & Ramūnas Povilanskas & Ilona Šakurova & Vitalijus Kondrat, 2021. "Measuring the Impact of Physical Geographical Factors on the Use of Coastal Zones Based on Bayesian Networks," Sustainability, MDPI, vol. 13(13), pages 1-18, June.

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