Figure 1 shows the relationship between log COVID-19 cases and mathematics scores 30, 60, and 90 days after each county reported its first case. There is a clear inverse relationship between the number of COVID-19 cases and mathematics scores at all three time points. To empirically test to see if this result is maintained when adding controls for other factors that may influence the spread of COVID-19 at the county level, we present the results of Eq. 1 in Table 2. columns 1, 2, and 3 show the results without the inclusion of a state fixed effect, and columns 2, 4, and 6 show the results with the state fixed effect. A one-grade-level increase in mathematics scores leads to a 15% reduction in COVID-19 cases 30 days after the first case was reported in each county. The estimate is statistically significant at the 1% level and does not change when adding a fixed effect, indicating this result is robust to variation in state level policies. An alternative way to think about the interpretation of this coefficient is if a county can increase the average level of mathematics comprehension by one-grade level, that would have led to a 15% reduction in cases 30 days after the first case was reported.
To ensure that this effect is stable across time, we also report the regression results for 60 and 90 days after each county reported its first case. We find that there remains a statistically significant decrease in COVID-19 cases 60 and 90 days after each county reported its first case. After 60 days, a one-grade-level increase in mathematics scores led to a 14% reduction in the number of COVID-19 cases. When including a state fixed effect, the effect drops to 11%. After 90 days, a one-grade-level increase in the mathematics scores led to a 9% reduction in the number of COVID-19 cases, and when including a state fixed effect, the effect drops to 7%.
When looking at the effect of comprehension in mathematics, the effect decreases over time. This may be because early in the pandemic, people responded by changing their mobility patterns—people spent more time at home and less time in places of work and other public places—however, people started to return to their pre-pandemic mobility patterns over time, even before the lifting or modification of stay-at-home orders (Murray 2021). It is possible people faced a trade-off between income and health during the pandemic (Palma et al. 2020) and were worried about financial security and dealing with loneliness (Tull et al. 2020). Additionally, the way people synthesized and processed information as well as the source individuals got their information from could have influenced how people behaved over time, something we will explore further in the next subsection.
The estimates from Eq. 1 use the average mathematics test scores from 2009 to 2015 for each county. However, people who were in eighth grade in 2009 would have been 24–25 years old and people who were in eighth grade in 2015 would be 18–19 years old in 2020. While only a 6-year difference, those who are 18–19 versus 24–25 could represent very different groups. Since we do not have panel data on individuals, we are unable to see who went to college and who did not from a given county, but it is likely that the 24–25 age group in 2020 will capture most people who have gone to college and are more likely to be married and have kids compared to those aged 18–19. In Table 3, we estimate Eq. 1 separately for mathematics scores in 2009 and 2015 to see if there is any difference in the two cohorts of students. While mathematics scores from 2015 have a slightly larger effect than math scores from 2009, there were no statistical differences between the two groups.
Kahane (2021) and Dyer (2020) show that there was politicization of the NPIs and state policies that were enacted to slow the spread of COVID-19. Nagler et al. (2020) show that this led to conflicting information being presented to the public. Mitchell et al. (2016) show that people tend to get their news from the same sources and there is a divide between where people of different political affiliations get their news. The debate over the NPIs took center stage in the 2020 presidential election between Donald Trump and Joe Biden. Because of this, we estimate Eq. 2 to see if there was difference in the impact of mathematics scores based on the vote share in each county for Joe Biden. These results can for all counties be found in columns 1, 2, and 3 of Table 4 and the marginal effects can be found in Panel A of Fig. 2. We find there is an inverse relationship between the vote share for Joe Biden and the percent change in COVID-19 cases. The more people that voted for Joe Biden in a county, a one-grade-level increase in mathematics scores among young adults had an increasing effect on the reduction of COVID-19 cases at 30, 60, and 90 days after the first case. This effect is statistically significant where Joe Biden won 25% or more of the vote 30 days after the first case, 40% or more after 60 days, and 45% or more after 90 days.
Generally, people lack perfect information and the capability to completely process information and thus use heuristic principles to reduce the complex task of assessing probabilities and making value judgements (Tversky and Kahneman 1974; Albar and Jetter 2009). Heuristics generally leads to good in decision making, but can sometimes lead to severe and systemic errors in outcomes (Tversky and Kahneman 1974). We do not know if people’s heuristic principles differ based on their political affiliation and who they supported in the 2020 presidential election. As such, this result could have multiple interpretations. These data could indicate that people who live in counties that voted for Donald Trump were using less mathematical reasoning compared to those who live in counties that voted for Joe Biden in understanding the risks associated with COVID-19. It also could also be due to the messaging coming from Donald Trump and the media outlets commonly watched by his supporters that helped reinforce their prior conclusions compared to supporters of Joe Biden who may consume information from different outlets and reinforce different conclusions. There are many complicated factors that could influence how people consume, process, and evaluate the credibility of information based on political affiliation, which was likely a factor in how people chose to comply with NPIs, particularly in the middle and later stages of the pandemic. These are difficult to untangle with the data at hand but is something that is worth exploring further.
It is possible that this result is capturing urban areas instead of the vote share because urban areas were more likely to vote for Joe Biden than rural areas. We estimate Eq. 2 separately for just urban counties (results shown in columns 4 5, and 6 of Table 4 and marginal effects in Panel B of Fig. 2) and rural counties (results shown in columns 7, 8, and 9 of Table 4 and marginal effects in Panel C of Fig. 2). For both urban and rural counties, the inverse relationship holds. For urban counties, there is a statistically significant effect for 30, 60, and 90 days after the first case when Joe Biden got 50% or more of the vote share. For rural counties, the effect is only statistically significant when Joe Biden has between 50 and 80% of the vote 30 days after the first case and when Joe Biden got 60% or more of the vote 90 days after the first case. The effect shows no statistical significance 60 days after the first case. While the effect is not as strong in rural areas, there is some evidence that this effect holds for rural areas. The slight difference in results between urban and rural counties could be that the returns to education tend to be higher in urban areas relative to rural areas (Baum-Snow et al. 2018; Gould 2007; Combes and Gobillon 2015) and this difference has grown in the last two decades (Autor 2019). Therefore, counties with higher math scores may have a larger impact in urban areas relative to rural areas.
Since our focus is restricted to young adults (ages 18–25), it is also possible our results are influenced by the fact that young adults were more likely to vote for Joe Biden than Donald Trump in the 2020 presidential election Center for Information & Research on Civic Learning and Engagement (2020). To see if the effect still holds, we estimate Eq. 2 restricted to only states where more young adults voted for Donald Trump than Joe Biden.Footnote 2 The marginal effects for this regression can be found in Fig. 3. There was still an inverse relationship showing that a one-grade-level increase in mathematics scores had a greater effect of reducing the number of COVID-19 cases as the vote share for Joe Biden increased. This effect is statistically significant when Joe Biden got 45% of the vote of more after 30 days and where he got 60% or more after 60 and 90 days.
These findings should not be interpreted as causal, as there could be other explanations for these effects that we have discussed. However, these findings could suggest that while better education in mathematics resulted in fewer COVID-19 cases across all counties, that political leanings also may have influenced how people responded to public policy and NPIs. This could be due to source credibility, the way people synthesized information, and/or how this varied to people of different political leanings.