Updated: Jul 21
The federal, state, and local government responses to the COVID-19 pandemic vary based on region, and sometimes based on political party leadership in any given area. Some regions locked down for months, while others reopened quite quickly. Some states required masks and emergency orders for more than a year, while others swiftly lifted requirements.
Who did it best? It depends on who you ask.
For example, a comparison of a report published by Sykes Enterprises (a corporate consulting company) and another report from the Committee to Unleash Prosperity (a non-profit organization promoting supply-side economics) shows that attempting to rank states based on the handling of COVID-19 is a biased endeavor.
Five of the top ten states in the Sykes Enterprises report, actually rank in the bottom ten for the Committee to Unleash Prosperity. Meantime, three of the states in the Sykes bottom ten are in the top ten for the Committee to Unleash Prosperity.
Differing values make an unbiased comparison of Covid-19 responses impossible, because the values of the authors determine ranking.
Methodology and values differ widely across reports on Covid-19 government responses. For some perspectives, the lowest case transmissions and highest use of healthcare resources was the ultimate value, regardless of costs. Other sources weigh the health impact of the pandemic against other costs, including unemployment, GDP, and academic performance.
For example, the Sykes Enterprise report assumes government mandates including masks and stay-at-home orders decrease transmissions (correlation analysis does not support this assumption). The report disregards economic and academic impact and looks at the health industries use of resources.
The Committee to Unleash Prosperity, controls for more variables, including geographic isolation and industry composition. Using these weighted metrics, the report ranks states based on pandemic performance factoring the cost to GDP and academic success against the benefit of lower transmissions.
Complicating the ability to draw conclusions from Covid-19 data is the low correlation of datasets. Chung et al., using Oxford Covid Government Response data, found that the only metrics that were consistently correlated with decreased transmissions were contact tracing and the health index (a metric calculated by the Oxford Covid Government Response data team). The researchers concluded that intense testing and accurate tracing were critical to controlling the spread of Covid-19 and reducing daily confirmed cases.
However, despite multiple statistical models and time-lags, the report could not predict which other government actions decreased COVID-19 cases. Despite many variations, the statistical models concluded that stricter government policies had positive coefficients, meaning the implementation of these policies increased the number of COVID-19 cases within the statistical model. The researchers concluded that these policies were still significant to decreasing transmissions in the real-world but interacted in a more complex way than could be predicted accurately by the model.
Literature offering measurable proof supporting stringent pandemic regulations is hard to find. Literature offering collaborative and unbiased ranking of states and their COVID-19 response policies is non-existent.
This leaves policymakers in the difficult position of knowing who and what to trust. Responsible policy and regulatory decisions must look below the surface of all reports, to identify the bias and understand how the numbers are interacting and what value systems are guiding the rankings.
Mountain States Policy Center is preparing to release a study of the western states regulatory response to COVID-19. The study analyzes the policy efforts of Idaho, Montana, Utah, Washington, and Wyoming and the resulting transmissions within the state. MSPC values the health of our families. MSPC also understands that economic prosperity and academic success are critical contributors to the health of the region. MSPC’s analysis seeks to identify the factors that significantly decreased transmissions and points out the costs of the pandemic.