Having been affected by the highest increase in COVID-19 cases since the start of the pandemic, Honolulu and Maui counties in Hawaii implemented vaccine passport mandates for select industries in September 2021. However, the degree to which such mandates impacted COVID-19 mitigation efforts and economics remains poorly understood. Herein, we describe the effects of these mandates on changes in three areas using difference-in-difference regression models: (1) business foot traffic; (2) number of COVID-19 cases per 100,000 individuals, and (3) COVID-19 vaccination rates across counties affected or unaffected by the mandates. We observed that although businesses affected by mandates experienced a 6.7% decrease in foot traffic over the 14 weeks after the mandates were implemented, the number of COVID-19 cases decreased by 19.0%. Notably, the vaccination rate increased by 1.41% in counties that implemented mandates. In addition, towards the end of the studied period, the level of foot traffic at impacted businesses converged towards the level of that of non-impacted businesses. As such, the trade-off in temporary losses at businesses was met with significant gains in public health and safety.
The effects of COVID-19 vaccine mandates in Hawai‘i
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March 21, 2024
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4 thoughts on “The effects of COVID-19 vaccine mandates in Hawai‘i”
Interesting analysis. If you don’t mind, I was wondering if you considered controls for seasonality and/or heterogeneous social networks? Also, since he just blogged about it, how do you think Fuleky’s urban/rural findings would impact this analysis, if at all?
Laron,
Thanks for the comment and sorry for the late response. Yes, we controlled for tourists arrivals, which should take care of the seasonability. We did not control for any social networks as we don’t have this info. We do have a new paper coming up on social networks and covid.
Thanks, Ruben
No problem, appreciate the response one way or another. I was actually referring to the seasonality of the virus rather than any economic seasonality that would be reflected in tourist arrivals.
We did not adjust for seasonality. It was only over a period of 5 months, so we don’t have enough variance on seasonality.