Why are less educated individuals less responsive to the infection spread? This section tests eight potential mechanisms relating to differences in their economic circumstances, knowledge and perception about the transmission risk, socio-psychological characteristics, and other factors. Our identification strategy relies on two analyses. First, we test the association between education levels and potential mediating variables in Subsections 6.1 to 6.4. Second, we test the association between the mediating variables and risky behavior in Subsection 6.5. In the main text, we discuss the results of analyses using the sub-sample of respondents who have no children, as child-rearing responsibilities may have complicated interactive effects with the mediating variables. The estimation results based on the full sample are reported in the Appendix.
Individuals’ educational attainment may be associated with their occupation and economic status, which may be drivers of heterogeneous effects. We test these channels in this subsection.
Occupational Suitability for Teleworking
High school graduates may engage in a job that is not suitable for teleworking or remote work, such as in retail or the restaurant business. To test this channel we construct an industry-level proxy using the survey results of Okubo and NIRA (2020). Based on an online survey in Japan, Okubo and NIRA (2020) show the proportion of respondents working at home by industry as of March 2020. We combine these proportions and our respondents’ occupation to approximate the suitability of their jobs for teleworking. We then regress this proxy on respondent characteristics to examine whether high school graduates actually engage in jobs unsuitable for telework.
Column (1) of Table 4, however, shows that the coefficient for university graduates is negative among the no-child sub-sample, counter to the hypothesis. The observed patterns do not change in the full sample estimation (Table A11). Since the suitability of working at home may vary even within an industry, our proxy may include measurement errors. However, the measurement errors alone are unlikely to explain the negative correlation between the education level and suitability for remote work.
If the economic status of high school graduates is lower, they may suffer from credit constraints that make the disutility from the income loss caused by staying home larger than for the wealthy. We conduct a polychoric principal component analysis to construct a composite index of economic status from two variables (Kolenikov and Angeles 2004): annual income, and a binary indicator that takes unity for self-employment, executive, or regular employment.Footnote 7 We examine the correlation between this index and education level in Column (2) of Table 4. It confirms that the economic status of university graduates is significantly higher than that for high school graduates, in line with our hypothesis.
Knowledge and Perception of Transmission Risk
Poor information access and low risk perception are major causes of risky behaviors (Kenkel 1991), suggesting that the maladapted behavioral response of high school graduates may be due to these issues.
To test the channel through poor information access, we construct a composite index from three variables: the frequencies of reading paper newspapers, reading newspaper websites, and watching television news. Then, we estimate the association between this index and education level in Column (3) of Table 4. The results are consistent with the hypothesis: university graduates follow mass media more frequently than do high school graduates.
The Protection Motivation Theory in psychology proposes that a high risk perception—which is attributed to subjective factors such as expectations of infection probability and the severity of symptoms—is essential if individuals are to take protective actions (Rogers and Prentice-Dunn 1997). Risk perception is formed through exposure to information from the media and from their peers, the cognitive ability to process the (numeric) information, and engagement in risky behavior (Ferrer and Klein 2015). When reliable information is scarce and cognitive ability is limited, people suffer from cognitive overload. This causes various cognitive biases in decision-making, including the normalcy bias: the optimistic underestimation of the probability and severity of negative events (Kahneman and Tversky 1972).
There are reasons to think that high school graduates have lower risk perceptions about COVID-19 infections. First, because the actual number of infected individuals is unobservable, people infer the infection probability from the information available, but news related to COVID-19 frequently includes professional, foreign language terms such as “RT-PCR tests”. Processing such information may cause them to suffer from cognitive overload, exacerbating the normalcy bias. Second, while mass media reported the severity of the infection spread, a relatively small number of people were actually confirmed to be infected as of March 2020. Therefore, if high school graduates do not rely on or collect information about COVID-19 from the mass media as carefully as do university graduates, they may assess risks based primarily on their peers’ experiences with infection. This generates a gap in risk perception based on educational attainment.
To test this channel, we construct a composite index of risk perception using the following two questions: how many infected people that respondents think there actually are in Japan as of the survey period; the extent to which COVID-19 will cause serious problems for themselves. The regression result in Column (4) of Table 4 shows that university graduates are more likely to take the infection risk seriously than are high school graduates, supporting our hypothesis.
The heterogeneous impact of educational attainment may also reflect variations in individuals’ risk preference and social capital. This subsection tests these possibilities.
High school graduates may be less likely to take precautionary actions, because they are less risk averse (Anderson and Mellor 2008). Given the difficulty in conducting an economic experiment to elicit the risk preference of respondents in our online survey, we test this channel through two proxy variables. First, we asked the following question: which of the following two sayings characterizes you better, “nothing ventured, nothing gained” or “a wise man never courts danger”? The answer options are in Likert-scale. Second, we also asked the following question: at which precipitation probability do you bring an umbrella when going out? A lower score to these answers indicates greater risk aversion. These questions are frequently used in the literature (Ikeda et al. 2016 p142; Iida 2016) and draws from earlier work in the United States. In Column (5) of Table 4, we estimate the relationship between the composite index of these variables and respondent characteristics. We find that education level is uncorrelated with risk preference.
Social distancing during the COVID-19 pandemic is a public good, and therefore, people have an incentive to freeride (Brodeur et al. 2021; Cato et al. 2020; Cato et al. 2021a). This suggests a channel that university graduates may possess more social capital, and so may care more about their reputation or disapproval from neighbors, causing them to follow societal norms of social distancing. The second wave of our survey asks about respondents’ social capital through six questions on general trust, pure altruism, and social norms. More detail about each question is reported in Table A1. We use these answers to construct a composite index. Column (6) of Table 4 demonstrates that social capital is higher for university graduates than for high school graduates, supporting the hypothesis.
Alternative Protective Measures
High school graduates may take alternative actions to protect themselves, such as wearing masks and washing their hands with disinfectant. Although our survey does not include items on the use of facemasks or disinfectant soap, it does ask respondents whether they wished to buy them more than usual. We regress the composite index of these variables in Column (7) of Table 4. The result shows that university graduates are more likely to answer affirmatively than high school graduates, counter to the hypothesis.
Less Confidence in Confirmed Cases as a Proxy for Infection Risk
High school graduates may recognize that the number of confirmed cases underestimates the actual infection risk, and therefore, they may be more sensitive to other types of information, such as the ratio of positive RT-PCR tests. However, this hypothesis assumes that those with lower education have more knowledge about COVID-19 than educated respondents. This assumption contradicts our findings that high school graduates spend less time collecting information on COVID-19 than university graduates (Table 4, Column (3)).
Association between Mediating Variables and Risky Behavior
The results so far show that respondents’ education levels are associated with economic status, information access, risk perception, and social capital. To further test whether they are also associated with risky behavior, we additionally control for the interaction terms between these seven indices and the number of confirmed cases, based on the specifications in Table 3.
Table 5 presents robust evidence that in prefectures with more confirmed cases, those with high risk perception are more likely to reduce the frequency of risky behavior. The table also reports False Discovery Rate q-values (Anderson and Mellor 2008) to adjust the p-values of the 14 coefficients of each outcome, confirming a robust association between risk perception and frequency of dining out. Among the other three likely mechanisms, the coefficient for economic status is significantly associated with the frequency of conversations, but it does not predict the frequency of dining out. For robustness, we re-estimate the model by controlling for only the interaction term between confirmed cases and risk perception, in addition to the terms included in Table 3. Table A13 shows that the coefficient of risk perception is still statistically significant and comparable in magnitude with that of Table 5. In Online Appendix A3 we test the validity of this model more carefully, particularly the potential issue of endogeneity of risk perception and multicollinearity.
Given these arguments in this section, differences in risk perception are the most likely driver of heterogeneity by education level, although we cannot fully rule out the potential role of income opportunity costs.