The GOP’s COVID-19 strategy did not work (but neither did the Democrats’)
By Kent R. Kroeger (March 31, 2021)
A nurse at the San Salvatore Hospital in Pesaro, Italy (Photo by Alberto Giuliani; used under the Creative Commons Attribution-Share Alike 4.0 International license.)
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In a previous article I argued it is dangerous making simplistic partisan assumptions about which U.S. states were effective in combating COVID-19 and which ones were not. This is particularly true when the impact of COVID-19 on a state’s economy is also considered.
As Figure 1 (below) demonstrates, states able to protect their economies relatively well while keeping COVID-19 deaths rates relatively low (Quadrant A) included as almost many Red (Trump easily won in 2020) states as Blue (Biden easily won in 2020) and Purple (2020 battleground) states. Conversely, a significant number of Blue states are among the worst performers (Quadrant D) in balancing economic growth with COVID-19 mitigation efforts (e.g., New York, New Jersey, Massachusetts, Connecticut, and Rhode Island).
Figure 1: U.S. State GDP Growth (2020) versus COVID-19 Deaths per capita (January 1, 2020 — March 23, 2021)
Data sources: BEA and RealClearPolitics.com
Also interesting in Figure 1 is that 11 of the 12 states in Quadrant C (Better GDP Growth, High COVID-19 death rate) are Red states. The governors in Alabama, Arizona, Iowa, Mississippi and South Dakota, in particular, openly acknowledged their priority was keeping their economies running and they would do so by avoiding strict, statewide lockdowns.
Clearly, partisan politics played a significant role in determining what COVID-19 policy strategies individual states pursued (see Figure 2 below) and that each state’s COVID-19 outcomes (i.e., cases and deaths) were related to these partisan strategies (see Figure 3 below).
Figure 2: Current COVID-19 policies by Blue/Purple/Red state status (as of March 23, 2021)
Data source: kff.org (Note: LARGE_GATHERINGS_ALLOWED and BARS_OPEN policy indexes are on a 0 to 2 scale with ‘0’ indicating ‘not open or allowed,’ ‘1’ indicating ‘partially open or allowed.’ and ‘2’ indicating ‘open or allowed.’ All other policy indexes are on a 0 to 1 scale)
Figure 3: COVID-19 outcomes by Blue/Purple/Red state status (as of March 23, 2021)
Data sources: BEA and RealClearPolitics.com
Red states have been more likely than Blue or Purple states to eschew mask mandates and most have ended their social and economic lockdowns, while Blue states have been more likely to keep business lockdowns in place (see Figure 2).
Likewise, Red states experienced marginally better economic growth in 2020 than Blue states (-3.51% growth versus -3.69% growth), but also experienced significantly more COVID-19 cases per capita (103,090 per 1 million people versus 73,131 per 1 million people) and slightly more deaths per capita (1,558 per 1 million people versus 1,439 per 1 million people).
The GOP versus Democrat COVID-19 Strategies
A deeper examination of the specific COVID-19 policies employed by each state (using Ballotpedia’s COVID-19 policy database) reveals roughly three COVID-19 policy clusters related to a state’s political party domination:
Blue States — A strict lockdown of schools and businesses, while also instituting firm mask and social distancing rules; an emphasis is placed on stopping the spread of the virus and keeping restrictions in place until strong evidence exists that the lockdowns have worked (e.g., California, New York, New Jersey).
Red States — Allow relatively more individual freedom with respect to large gatherings, church services, social distancing, and masks, while minimizing the impact on the business community and isolating the elderly and those most vulnerable to the virus; less emphasis is placed on stopping the virus spread (e.g., Idaho, Iowa, Missouri, Ohio, Texas, South Dakota).
Purple States — A hybrid of the Red and Blue state models, including strict mask and social distancing rules but less stringent business closure policies (e.g., Michigan, North Carolina, Pennsylvania, Wisconsin).
Even though Florida is classified as a Purple state in this analysis, its public health actions during the pandemic have perfectly embodied the Red state policy formula. While responding to a set of questions posed by the Miami Herald about the state’s COVID-19 response, Florida Governor Ron DeSantis’ office offered this defense of the state’s COVID-19 strategy:
“Thanks to the governor’s ongoing commitment during the COVID-19 public health emergency to protect our most vulnerable while safeguarding the right to earn a living and the right to operate a business, the governor has been able to lift Floridians up instead of locking our state down.”
Florida, like many Red states, put their policy emphasis on limiting the mortality rate of the virus, not on stopping its spread.
Did this approach work for the Red states?
The apparent answer is no. As the following analysis will try to show, by allowing their case numbers to rise as high as they did, Red states ended up experiencing higher death rates than Blue states. Whatever the economic advantage Red states gained with their COVID-19 strategy — a mere 0.18 percentage point advantage in GDP growth over Blue states, according to BEA data — it was at a human cost of at least 100 deaths per 1 million people.
A mediation model of state-level COVID-19 deaths per capita
Throughout the COVID-19 pandemic I have used static, two-step mediation models to explain COVID-19 deaths per capita for the 50 U.S. states (and D.C.). To varying degrees, these models have done a reasonable job of explaining most of the state-level variation in COVID-19 death rates per capita (R-squared fit statistics are typically over 80 percent => Example here).
One of the nice features of a mediation (path) model is the ability to combine direct and indirect effects together to determine the total effect for an explanatory variable on an outcome variable (i.e., COVID-19 deaths per capita).
Figure 4 (below) shows the estimates for the two-step mediation model explaining COVID-19 deaths per capita at the state-level. [The full model output using standardized variables —
estimated in the software program JASP — can be found in the Appendix below.]
The variables used in the estimated model were:
Explanatory Variables
NUR = Nursing home immunity states (NY, NJ, CT, MA, MI, PA, RI)
TRU = Trump percent of 2020 presidential vote in the state
D_T = Daily tests per 1 million people
W_P = White percent of total population
T_H = Total hospital beds per 1,000 people
AT_ = Medically at risk adults as a percent of all adults
COVID19_C = COVID-19 cases per 1 million people (Mediator Variable)
Dependent (Outcome) Variable
COVID19_D = COVID-19 deaths per 1 million people
For the purposes of this essay, I will concentrate on the estimated parameters for the TRU variable (Trump Vote % in 2020), which was the variable used to segment states by their Blue/Purple/Red status.
Figure 4: A Two-Step Mediation Model of COVID-19 Deaths Per Capita (as of March 23, 2021)
The 2020 Trump vote (TRU), the White percentage of the total population (W_P), total hospital beds per 1,000 people (T_H), and the percentage of adults who were most at-risk to COVID-19 (AT_) are the most significant correlates to the number of COVID-19 cases per capita (COVID19_C).
Note in Figure 4 that the relationship between the Trump vote (TRU) and the number of COVID-19 cases per capita is positive. The higher the percent of the Trump vote in 2020, the more COVID-19 cases per capita in that state. Converting to practical numbers, for every 10 percent increase in Trump’s vote in the 2020 election, there were 18,500 more COVID-19 cases per 1 million people in that state.
This result is consistent with the GOP COVID-19 strategy: worry less about new cases (since that would mean shutting down the economy) and dedicate resources to protecting those most vulnerable.
OK, so Red state strategies led to more COVID-19 cases. How did that impact the number of COVID-19 deaths per capita?
The model says Trump-voting states did a better job of preventing COVID-19 deaths, after controlling for the number of COVID-19 cases per capita. In practical terms, a 10 percent increase in the Trump vote was associated with 185 fewer COVID-19 deaths per 1 million people, all else equal.
So, the question becomes: Did the superior job in Red states of reducing the fatality rate among COVID-19 cases compensate for the significantly greater number of COVID-19 cases?
When we look at the total effects of the Trump vote on COVID-19 deaths per capita (see Figure 5), the answer is no. States with a higher percentage of Trump votes in 2020 also experienced more COVID-19 deaths per capita, all else equal.
Figure 5: The total effects of each explanatory variable on COVID-19 deaths per capita
In other words, by accepting significantly more COVID-19 cases to occur than in Blue states, the Red states also experienced higher numbers of COVID-19 deaths per capita than in Blue states. And the trade-off wasn’t worth it.
While young adults disproportionately account for the greater number of COVID-19 cases in Red states, the larger number of COVID-19 cases in the Red states also led to more COVID-19 deaths among those most vulnerable.
Young people with COVID-19 — even if they are not highly vulnerable to the virus’ effects themselves — are vectors for passing the virus to those who are vulnerable.
The data (as of March 23, 2021) says the Red-state strategy of allowing more infections in order to keep the economy going resulted in around 100 more COVID-19 deaths per capita (see Figure 3).
Is 100 more COVID-19 deaths per 1 million people worth a 0.18 percentage point improvement in GDP growth?
That is a question outside my pay-grade.
Final Thoughts
It is hard to have productive conversations about the coronavirus anymore. The politics of the crisis have become so deeply embedded in our minds that any time new information contradicts our preferred partisan narrative, we too swiftly discard or denigrate it as ‘conspiracy theory’ chattel.
Left- or right-leaning, it doesn’t matter — our human psyche does not handle being told, ‘What you believe is wrong.’ And our solution in these circumstances is too often to shut out this new information.
This is why problems never get solved in this country. Rather, we just muddle through — as we always have.
As for COVID-19 — whose final outcome is still a work-in-progress — I find it difficult to get overly pious about what could’ve or should’ve been done to prevent the over 550,000 deaths that have already occurred in this country. The Blue state governors and Red state governors may have taken different policy paths to this public health crisis, but they all ended up in the same miserable destination: COVID-19 resulted in too many deaths and too much economic damage.
Yet, I still believe it didn’t have to end up this way, even if I don’t know how it could have been avoided.
- K.R.K.
Send comments to: nuqum@protonmail.com
APPENDIX: Path Model Parameter Estimates
Note: Dataset used for this model is available on GitHub here.
