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Global COVID-19 under-reporting: A Tobit model

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  • Kumbhakar, Subal C.
  • Wang, Yulu

Abstract

During the COVID-19 pandemic, the precision in reporting infectious cases and fatalities presents significant challenges, exacerbated by rapid transmission rates and overburdened healthcare infrastructures. Officially reported cases occasionally exhibit zero increments, which is likely to be under-reported. Some models exclude zero values from the sample, creating a sample selectivity problem. In contrast, alternative models substitute zero values with a constant to enable logarithmic transformations. Since both modeling approaches are wrong, in this study, we address this issue by extending the Tobit model to account for both under-reporting and random noise. Analyzing data from 61 countries between January 1, 2020, and November 3, 2020, we explore external factors that explain country-specific under-reporting. Our findings confirm the existence of under-reporting across countries and reveal that cases reported with zero increments actually involve non-zero infectious instances. This novel methodology enriches future under-reporting analyses.

Suggested Citation

  • Kumbhakar, Subal C. & Wang, Yulu, 2024. "Global COVID-19 under-reporting: A Tobit model," Economic Modelling, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:ecmode:v:141:y:2024:i:c:s0264999324002748
    DOI: 10.1016/j.econmod.2024.106917
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    More about this item

    Keywords

    COVID-19; Stochastic frontier; Tobit model; Under-reporting;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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