Notes: Data in columns (2), (3) and (8) is from IPEDS 2018. The flagship universities are the 4-year public universities with the highest number of undergraduate students in each state. Means for these columns are weighted by total number of undergraduates in each institution. ACT and SAT data are weighted averages of 2018–2015 years from IPEDS. P -value columns show the p -value of a difference in means test between the two columns indicated by the numbers in the heading.
The better performance on admission tests could be explained by the high proportion of Honors students in our sample (22% compared to 18% in the ASU population). The last four columns of Table 1 show how Honors students compare with ASU students and the average college student at a top-10 university. We see that they perform better than the average ASU student (which is expected) and just slightly worse than the average college student at a top-10 university. The share of white Honors students in our sample (60%) is higher than the proportion in the ASU population and much higher than the proportion of white students in the top-10 universities.
Overall, we believe our sample of ASU students is a reasonable representation of students at other large public schools, while the Honors students may provide insight into the experiences of students at more elite Institutions. Though, it is important to acknowledge that elite institutions may have additional resources to address a global pandemic.
We next outline a simple analytic framework that guides the empirical analysis. Let O i ( COVID – 19) be the potential outcome of individual i associated with COVID-19 treatment. We are interested in the causal impact of COVID-19 on student outcomes:
where the first term on the right-hand side is student i 's outcome in the state of the world with COVID-19, and the second term being student i 's outcome in the state of the world without COVID-19. Recovering the treatment effect at the individual level entails comparison of the individual's outcomes in two alternate states of the world. With standard data on realizations, a given individual is observed in only one state of the world (in our case, COVID – 19 = 1). The alternate outcomes are counterfactual and unobserved. A large econometric and statistics literature studies how to identify these counterfactual outcomes and moments of the counterfactual outcomes (such as average treatment effects) from realized choice data (e.g., Heckman and Vytlacil, 2005 ; Angrist and Pischke, 2009 ; Imbens and Rubin, 2015 ). Instead, the approach we use in this paper is to directly ask individuals for their expected outcomes in both states of the world. From the collected data, we can then directly calculate the individual-level subjective treatment effect. As an example, consider beliefs about end-of-semester GPA. The survey asked students “ What semester-level GPA do you expect to get at the end of this semester ?” This is the first-term on the right-hand side of Eq. (1) . The counterfactual is elicited as follows “ Were it not for the COVID-19 pandemic , what semester-level GPA would you have expected to get at the end of the semester ?”. The difference in the responses to these two questions gives us the subjective expected treatment effect of COVID-19 on the student's GPA. For certain binary outcomes in the survey, we directly ask students for the Δ i . For example, regarding graduation plans, we simply ask a student if the Δ i is positive, negative, or zero: “ How has the COVID-19 pandemic affected your graduation plan ? [ graduate later ; graduation plan unaffected ; graduate earlier ].”
The approach we use in this paper follows a small and growing literature that uses subjective expectations to understand decision-making under uncertainty. Specifically, Arcidiacono et al. (2020) and Wiswall and Zafar (2020) ask college students about their beliefs for several outcomes associated with counterfactual choices of college majors, and estimate the ex-ante treatment effects of college majors on career and family outcomes. Shapiro and Giustinelli (2019) use a similar approach to estimate the subjective ex-ante treatment effects of health on labor supply. There is one minor distinction from these papers: while these papers elicit ex-ante treatment effects, in our case, we look at outcomes that have been observed (for example, withdrawing from a course during the semester) as well as those that will be observed in the future (such as age 35 earnings). Thus, some of our subjective treatment effects are ex-post in nature while others are ex-ante.
The soundness of our approach depends on a key assumption that students have well-formed expectations for outcomes in both the realized state and the counterfactual state. Since the outcomes we ask about are absolutely relevant and germane to students, they should have well-formed expectations for the realized state. In addition, given that the counterfactual state is the one that had been the status quo in prior semesters (and so students have had prior experiences in that state of the world), their ability to have expectations for outcomes in the counterfactual state should not be a controversial assumption. 7 As evidence that students' expectations exhibit meaningful variation, Appendix Fig. A1 shows that previous cumulative GPA is a strong predictor of expected semester GPA with COVID-19.
4.1. treatment effects.
We start with the analysis of the aggregate-level treatment effects, which are presented in Table 2 . The outcomes are organized in two groups, academic and labor market (see Appendix Table A1 for a complete list of outcomes). The first two columns of the table show the average beliefs for those outcomes where the survey elicited beliefs in both states of the world. The average treatment effects shown in column (3) are of particular interest. Since we can compute the individual-level treatment effects, columns (4)–(7) of the table show the cross-sectional heterogeneity in the treatment effects.
Subjective treatment effects.
With | Without | Prop. | Prop. | 25th | 75th | ||
---|---|---|---|---|---|---|---|
COVID-19 | COVID-19 | >0 | =0 | %tile | %tile | ||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
Likelihood of taking online classes | 0.46 | 0.50 | −0.04 | 0.31 | 0.22 | −0.20 | 0.08 |
(0.30) | (0.33) | (0.26) | |||||
Semester GPA | 3.48 | 3.65 | −0.17 | 0.07 | 0.41 | −0.30 | 0.00 |
(0.37) | (0.50) | (0.33) | |||||
Weekly study hours | 15.12 | 16.03 | −0.91 | 0.33 | 0.20 | −5.00 | 4.00 |
(10.21) | (11.55) | (8.15) | |||||
Delayed graduation (0/1) | 0.13 | 0.00 | 0.00 | ||||
(0.34) | |||||||
Withdraw from a class (0/1) | 0.11 | 0.00 | 0.00 | ||||
(0.31) | |||||||
Change major (0/1) | 0.12 | 0.00 | 0.00 | ||||
(0.33) | |||||||
Lost in-college job (0/1) | 0.29 | 0.00 | 1.00 | ||||
(0.45) | |||||||
In-college weekly hours worked | 12.97 | 24.38 | −11.64 | 0.40 | 0.21 | −22.00 | 0.00 |
(13.71) | (15.30) | (16.09) | |||||
In-college weekly earnings , | 147.73 | 237.02 | −21.27 | 0.09 | 0.52 | −1.00 | 0.00 |
(366.62) | (342.91) | (170.05) | |||||
Fam. lost job or reduce income (0/1) | 0.61 | 0.00 | 1.00 | ||||
(0.49) | |||||||
Lost job offer or internship (0/1) | 0.13 | 0.00 | 0.00 | ||||
(0.34) | |||||||
Probability of finding a Job | 55.97 | 69.36 | −13.39 | 0.13 | 0.24 | −20.00 | 0.00 |
(25.07) | (28.04) | (20.27) | |||||
Reservation waged | 48.53 | 50.53 | −1.91 | 0.09 | 0.63 | −0.08 | 0.00 |
(21.95) | (21.93) | (28.02) | |||||
Expected earnings at 35 years old | 88.18 | 91.49 | −2.34 | 0.06 | 0.65 | −0.07 | 0.00 |
(33.92) | (33.90) | (28.64) |
Notes: Δ : change. Prop. Δ >0: proportion of students for whom the individual level Δ is positive. Prop. Δ =0: proportion of students for whom the individual level Δ is zero. 25th and 75th percentiles of the cross-sectional distribution of Δ . Standard deviation in parentheses. ( ∗ : p <0.1, ∗∗ : p <0.05, ∗∗∗ : p <0.01).
We see that the average treatment effects are statistically and economically significant for all outcomes. The average impacts on academic outcomes, shown in Panel A, are mostly negative. For example, the average subjective treatment effect of COVID-19 on semester-level GPA is a decline of 0.17 points. More than 50% of the students in our sample expect a decrease in their GPA due to the treatment (versus only 7% expecting an increase). Additionally, 13% of the participants delayed their graduation, 11% withdrew from a class during the spring semester, and 12% stated that their major choice was impacted by COVID-19. 8
While almost no students report planning to drop out due to COVID-19, on average they expect to take a break from ASU in the fall 2020 semester at nearly twice the historical rate. Admittedly, the decision to take a break during a pandemic may be different than in more normal times. However, a substantial increase in the share of students failing to continue their studies is concerning, as historically 28% of students who fail to re-enroll for a fall semester do not return to ASU or another university within 5 years.
Regarding the impact of the pandemic on major choice, students who report that COVID-19 impacted their major choice were more likely to be in lower-paying majors before the pandemic; mean pre-COVID major-specific annual earnings were $43,053 ($46,943) for students whose major choice was (not) impacted by COVID-19. 9 Impacted students were also 9.3 percentage points less likely to be in a science, technology, engineering, or math (STEM) major before COVID-19. 10 We are only able to observe pre- and post-COVID major choices for the subset of students who had switched their major by the date of the survey. 11 Within this selected subsample of switchers, students chose to move into higher paying majors, with an average change in first-year earnings of $3,340. These patterns are generally consistent with the finding that students tend to gravitate towards higher-paying majors when exposed to adverse economic conditions when in college ( Blom et al., 2019 ).
An interesting and perhaps unanticipated result reported in Table 2 is that, on average, students are 4 percentage points less likely to opt for online instruction if given the choice between online and in-person instruction due to their experience with online instruction during the pandemic. 12 13 However, there is a substantial amount of variation in terms of the direction of the effect: 31% (47%) of the participants are now more (less) likely to enroll in online classes. We explore this heterogeneity in more detail in the next section, but it seems that prior experience with online classes somewhat ameliorates the negative experience; the average treatment effect for students with prior experience in online classes is a 2.4 percentage points decrease in their likelihood of enrolling in online classes, versus a 9.5 percentage points decline for their counterparts (difference statistically significant at the 0.1% level).
This large variation in the treatment effects of COVID-19 is apparent in several of the other outcomes, such as study hours, where the average treatment effect of COVID-19 on weekly study hours is −0.9 (that is, students spend 0.9 less hours studying per week due to COVID-19). The interquartile range of the across-subject treatment effect demonstrates substantial variation, with the pandemic decreasing study time by 5 hours at the 25th percentile and increasing study time by 4 hours at the 75th.
Overall, these results suggest that COVID-19 represents a substantial disruption to students' academic experiences, and is likely to have lasting impacts through changes in major/career and delayed graduation timelines. Students' negative experiences with online teaching, perhaps due to the abruptness of the transition, also has implications for the willingness of students to take online classes in the future.
Turning to Panel B in Table 2 , we see that students' current and expected labor market outcomes were substantially disrupted by COVID-19. As for the extensive margin of current employment, on average, 29% of the students lost the jobs they were working at prior to the pandemic (67% of the students were working prior to the pandemic), 13% of students had their internships or job offers rescinded, and 61% of the students reported that a close family member had lost their job or experienced an income reduction. The last statistic is in line with findings from other surveys of widespread economic disruption across the US. 14 Respondents experienced an average decrease of 11.5 hours of work per week and a 21% decrease in weekly earnings, although there was no change in weekly earnings for 52% of the sample, which again reflects substantial variation in the effects of COVID-19 across students.
In terms of labor market expectations, on average, students foresee a 13 percentage points decrease in the probability of finding a job by graduation, a reduction of 2% in their reservation wages, and a 2.3% decrease in their expected earnings at age 35.
The significant changes in reservation wages and expected earnings at age 35 demonstrate that students expect the treatment effects of COVID-19 to be long-lasting. Qualitatively, this is broadly consistent with the literature on graduating during recession. Oreopoulos et al. (2012) finds that graduating during a recession in which the unemployment rate increases 5% implies an initial loss in earnings of 9%, that decreases to 4.5% within 5 years and disappears after 10 years for a sample of male college graduates in Canada. Similarly, Schwandt and von Wachter (2019) find a 2.6% reduction in earnings 10 years after graduation for a 3-percentage point increase in unemployment at graduation, and Kahn (2010) finds an even longer-lasting effect on wages.
A large literature has investigated the impact of graduating during recessions on unemployment rates. Kahn (2010) finds that during the 1980's recession, the probability of being employed right after graduation for white males was largely unaffected by economic conditions. Altonji et al. (2016) only find what they term modest impacts. On the other hand, Rothstein (2020) finds that, for 22 to 23-year-olds graduating from college during the Great Recession, the probability of being employed decreases by 0.7 percentage point for every 1 percentage point increase in the unemployment rate. Using the estimates in Rothstein (2020) and the approximate 10 percentage point increase in the unemployment rate during April 2020, a back-of-the-envelope calculation indicates a 7 percentage point reduction in the probability of being employed for the graduating cohort in our sample. We find that students who are graduating in spring or summer 2020 expect a 35 percentage point decline in the likelihood of finding a job before graduation. While it is difficult to precisely map pre-graduation job finding rates to unemployment over the subsequent year, a 7 percentage point increase in unemployment appears low compared to the impact on students' expectations. It could be the case that the literature estimates are not appropriate for a situation as unexpected and different as a global pandemic, where the economic recession goes hand in hand with health concerns. Having said that, it could also be that students are overreacting to the COVID-19 shock. Data that tracks students' expectations and outcomes over time may be able to shed light on this.
We next explore demographic heterogeneity in the treatment effects of COVID-19. Fig. 1 plots the average treatment effects across several relevant demographic divisions including gender, race, parental education, and parental income. Honors college status and cohort are also included as interesting dimensions of heterogeneity in the COVID-19 context. The figure shows the impacts for six of the more economically meaningful outcomes from Table 2 (additional outcomes can be found in Appendix Fig. A2 ).
Treatment effects by demographic group.
(a) Delay Graduation due to COVID (0/1)
(b) Semester GPA ( Δ 0–4)
(c) Change major due to COVID (0/1)
(d) Likelihood take online classes ( Δ 0–1)
(e) Probability job before graduate ( Δ 0–1)
(f) Expected earnings at age 35 (Pct. Δ )
Notes: bars denote 90% confidence interval.
At least four patterns of note emerge from Fig. 1 . First, compared to their classmates, students from disadvantaged backgrounds (lower-income students defined as those with below-median parental income, racial minorities, and first-generation students) experienced larger negative impacts for the academic outcomes, as shown in the first three panels of the figure. 15 The trends are most striking for lower-income students, who are 55% more likely to delay graduation due to COVID-19 than their more affluent classmates (0.16 increase in the proportion of those expecting to delay graduation versus 0.10), expect 30% larger negative effects on their semester GPA due to COVID-19, and are 41% more likely to report that COVID-19 impacted their major choice (these differences are statistically significant at the 5% level). For some academic outcomes, COVID-19 had similarly disproportionate effects on nonwhite and first-generation students, with nonwhite students being 70% more likely to report changing their major preference compared to their white peers and first-generation students being 50% more likely to delay their graduation than students with college-educated parents. Thus, while on average COVID-19 negatively impacted several measures of academic achievement for all subgroups, the effects are significantly more pronounced for socioeconomic groups which were predisposed towards worse academic outcomes pre-COVID. 16 The pandemic's widening of existing achievement gaps can be seen directly in students' expected Semester GPA. Without COVID-19, lower-income students expected a 0.052 lower semester GPA than their higher-income peers. With COVID-19, this gap nearly doubles to 0.098. 17
Second, Panel (d) of Fig. 1 shows that the switch to online learning was substantially harder for some demographic groups; for example, men are 7 percentage points less likely to opt for an online version of a course as a result of COVID-19, while women do not have a statistically significant change in their online preferences. We also see that Honors students revise their preferences by more than 2.5 times the amount of non-Honors students. As we show later (in Table 4 ), these gaps persist after controlling for household income, major, and cohort, suggesting that the switch to online learning mid-semester may have been substantially more disruptive for males and Honors students. While the effect of COVID-19 on preferences for online learning looks similar for males and Honors students, our survey evidence indicates that different mechanisms underpin these shifts. Based on qualitative evidence, it appears that Honors students had a negative reaction to the transition to online learning because they felt less challenged, while males were more likely to struggle with the learning methods available through the online platform. 18 One speculative explanation for the gender difference is that consumption value of college amenities is higher for men (however, Jacob et al. (2018) , find little gender difference in willingness to pay for the amenities they consider).
Composition of COVID effects.
Delay grad due to COVID (0/100) | COVID impact major choice (0/100) | Prob take online classes ( pp) | Prob job before grad ( pp) | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | (17) | (18) | (19) | (20) | |
Women | 1.80 | 0.82 | 0.20 | −0.12 | −0.09 | 3.01 | 0.08 | −0.53 | −0.71 | −0.69 | 5.61 | 3.45 | 3.65 | 3.73 | 3.70 | −1.23 | −0.64 | −0.50 | −0.31 | −0.36 |
(1.66) | (2.04) | (2.16) | (2.07) | (2.12) | (1.65) | (2.03) | (2.08) | (2.03) | (2.05) | (1.46) | (1.61) | (1.66) | (1.65) | (1.67) | (0.98) | (1.13) | (1.13) | (1.15) | (1.13) | |
Lower-income | 4.34 | 3.26 | 3.84 | 2.68 | 3.15 | 3.08 | 1.16 | 1.74 | 0.73 | 1.33 | 1.96 | 1.47 | 1.40 | 1.76 | 1.41 | −0.40 | 0.13 | −0.52 | 0.38 | −0.16 |
(1.77) | (1.94) | (1.78) | (1.85) | (1.75) | (1.61) | (1.67) | (1.63) | (1.69) | (1.71) | (1.15) | (1.24) | (1.17) | (1.25) | (1.20) | (1.02) | (1.05) | (0.99) | (1.01) | (0.96) | |
Honors | − 9.00 | − 7.41 | − 7.75 | − 6.59 | − 6.93 | − 6.36 | − 4.55 | − 4.52 | − 3.88 | − 4.09 | − 4.52 | −2.64 | −2.62 | −2.87 | −2.75 | 0.53 | − 2.18 | − 2.11 | − 2.49 | − 2.56 |
(1.76) | (1.93) | (2.00) | (1.96) | (1.98) | (1.72) | (1.78) | (1.72) | (1.73) | (1.75) | (1.44) | (1.73) | (1.75) | (1.78) | (1.79) | (1.09) | (1.02) | (1.04) | (1.06) | (1.06) | |
Student Lost Job (0/1) | 3.59 | 4.07 | −1.03 | −0.58 | − 2.78 | − 2.64 | 0.86 | 0.72 | ||||||||||||
(2.66) | (2.66) | (2.27) | (2.31) | (1.57) | (1.57) | (1.60) | (1.61) | |||||||||||||
Family Lost Income (0/1) | 2.31 | 1.77 | 1.53 | 1.01 | −1.45 | −1.30 | − 4.35 | − 4.14 | ||||||||||||
(2.27) | (2.25) | (1.66) | (1.59) | (1.47) | (1.42) | (1.38) | (1.37) | |||||||||||||
Student Change in Earnings ($) | 0.00 | 0.00 | 0.00 | 0.00 | − 0.01 | − 0.01 | 0.00 | 0.00 | ||||||||||||
(0.01) | (0.01) | (0.01) | (0.01) | (0.00) | (0.00) | (0.00) | (0.00) | |||||||||||||
Prob. miss Debt (0–1) | 17.12 | 13.74 | 15.89 | 12.76 | −2.83 | −2.37 | −4.83 | −3.71 | ||||||||||||
(4.36) | (4.40) | (3.93) | (4.02) | (2.79) | (2.67) | (3.07) | (3.00) | |||||||||||||
Principal Component | 2.85 | 1.41 | −0.26 | − 1.49 | ||||||||||||||||
(0.82) | (0.83) | (0.60) | (0.48) | |||||||||||||||||
Subjective health (1–5, 5 high) | − 2.68 | − 2.33 | −2.20 | −1.89 | 2.91 | 2.71 | 1.51 | 1.34 | ||||||||||||
(1.26) | (1.30) | (1.40) | (1.33) | (0.96) | (0.96) | (0.87) | (0.83) | |||||||||||||
Prob. hosp. if catch COVID (0–1) | 12.89 | 11.56 | 10.98 | 9.74 | 0.11 | 0.10 | − 3.99 | − 3.45 | ||||||||||||
(4.42) | (4.24) | (4.00) | (4.00) | (2.98) | (3.03) | (1.99) | (1.98) | |||||||||||||
Prob. catch COVID (0–1) | 8.24 | 6.43 | 9.52 | 7.65 | 2.73 | 3.29 | −2.41 | −1.55 | ||||||||||||
(4.02) | (3.95) | (3.78) | (3.76) | (2.88) | (2.86) | (2.36) | (2.35) | |||||||||||||
Principal component | 4.32 | 3.90 | − 1.37 | − 1.66 | ||||||||||||||||
(0.89) | (0.91) | (0.69) | (0.51) | |||||||||||||||||
Economic proxies | 0.000 | 0.002 | 0.002 | 0.031 | 0.116 | 0.166 | 0.001 | 0.003 | ||||||||||||
Health Proxies | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.002 | 0.006 | 0.022 | ||||||||||||
Major FE | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
Cohort FE | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
Mean | 12.93 | 12.93 | 12.93 | 12.93 | 12.93 | 12.24 | 12.24 | 12.24 | 12.24 | 12.24 | −4.18 | −4.18 | −4.18 | −4.18 | −4.18 | −13.39 | −13.39 | −13.39 | −13.39 | −13.39 |
R | 0.020 | 0.163 | 0.164 | 0.178 | 0.172 | 0.012 | 0.194 | 0.198 | 0.206 | 0.199 | 0.021 | 0.153 | 0.157 | 0.160 | 0.152 | 0.001 | 0.237 | 0.230 | 0.243 | 0.237 |
N | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1380 | 1380 | 1380 | 1380 | 1380 |
Notes: Standard errors in parentheses bootstrapped with 1000 replications. Each column reports results from a separate OLS regression of the dependent variable onto the covariates (row variables). Dependent variables measured in percentage points. ( ∗ : p <0.1, ∗∗ : p <0.05, ∗∗∗ : p <0.01).
The third trend worth highlighting from Fig. 1 is that Honors students were better able to mitigate the negative effect of COVID-19 on their academic outcomes (panels a, b, and c), despite appearing to be more disrupted by the move to online learning (panel d). Honors students report being less than half as likely as non-Honors students to delay graduation and change their major due to COVID-19. Extrapolating from these patterns provides suggestive evidence that academic impacts for students attending elite schools– the group more comparable to these Honors students– are likely to have been small relative to the impacts for the average student at large public schools.
Finally, the last two panels of Fig. 1 present the COVID effect on two labor market expectations and show much less meaningful heterogeneity across demographic groups compared to the academic outcomes in previous panels. This suggests that, while students believe COVID-19 will impact both their academic outcomes and future labor market outcomes, they do not believe there is a strong connection between these domains. Supporting this observation, the individual-specific treatment effect on semester GPA is only weakly correlated with the individual-specific treatment effects on finding a job before graduation (corr = 0.0497, p = 0.065) and expected earnings at 35 (corr = 0.0467, p = 0.077).
The one notable exception to the lack of heterogeneity in panels (e) and (f) of Fig. 1 are seniors, who on average revised their subjective probability of finding a job before graduation three times as much as other cohorts. Appendix Fig. A3 further breaks down the estimated COVID-19 effects by expected year of graduation. Perhaps unsurprisingly, the 2020 cohort expects much larger effects on immediate job market outcomes such as reservation wages and probability of finding a job before graduation. While average expected changes to job market outcomes are noisier for academically younger students, perhaps reflecting additional uncertainty about the longer-term impacts of COVID-19, they appear to anticipate meaningful changes to their future labor market prospects. Conversely, younger students also expected larger disruptions to academic outcomes such as semester GPA and study time.
This section presents mediation analysis on the drivers of the underlying heterogeneity in the treatment effects. The COVID-19 pandemic serves as both an economic and a health shock. However, these shocks may have been quite heterogeneous across the various groups, and that could partly explain the heterogeneous treatment effects we documented in the previous section.
We proxy for the financial and health shocks due to COVID-19 by relying on a small but relevant set of covariates which capture more fundamental or first-order disruptions from the pandemic. Financial shocks are characterized based on whether a student lost a job due to COVID-19, whether a student's family members lost income due to COVID-19, the change in a student's monthly earnings due to COVID-19, and the likelihood a student will fail to fully meet debt payments in the next 90 days. To measure health shocks, we consider a student's belief about the likelihood that they will be hospitalized if they contract COVID-19, a student's belief about the likelihood that they will have contracted COVID-19 by summer, and a student's subjective health assessment. Finally, in order to summarize the combined effect of each set of proxies, we construct principal component scores as one-dimensional measures of the financial and health shock to students. 19
Table 3 reports summary statistics of the different economic and health proxies by demographic group. Given the results in Fig. 1 , the remainder of the analysis will focus on three socioeconomic divisions: parental income, gender, and Honors college status. Our data indicate that lower-income students faced larger health and economic shocks as compared to their more affluent peers. In particular, they are almost 10 percentage points more likely to expect to default on their debt payments compared to their higher-income counterparts. Additionally, lower-income students are 16 percentage points more likely to have had a close family member experience an income reduction due to COVID-19. Regarding the health proxies, lower-income students rate their health as worse than higher-income students and perceive a higher probability of being hospitalized if they catch the virus. Finally, the differences in economic and health shocks between lower and higher-income students, as summarized by the principle components of the selected proxy variables, are statistically significant.
Summary statistics for economic and health proxies.
All | Lower | Higher | P-value | Honors | Not | P-value | Female | Male | P-value | |
---|---|---|---|---|---|---|---|---|---|---|
Income | Income | (2)–(3) | Honors | (5)–(6) | (8)–(9) | |||||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | |
Likelihood default in next 90 days (0–1) | 0.16 | 0.21 | 0.12 | 0.00 | 0.08 | 0.18 | 0.00 | 0.19 | 0.13 | 0.00 |
(0.26) | (0.29) | (0.23) | (0.19) | (0.28) | (0.29) | (0.24) | ||||
Student lost job (0/1) | 0.29 | 0.30 | 0.28 | 0.53 | 0.22 | 0.31 | 0.00 | 0.32 | 0.26 | 0.01 |
(0.45) | (0.46) | (0.45) | (0.41) | (0.46) | (0.47) | (0.44) | ||||
Family lost job or earnings (0/1) | 0.61 | 0.70 | 0.54 | 0.00 | 0.54 | 0.64 | 0.00 | 0.67 | 0.56 | 0.00 |
(0.49) | (0.46) | (0.50) | (0.50) | (0.48) | (0.47) | (0.50) | ||||
Student change in earnings | −89.30 | −95.40 | −84.16 | 0.36 | −49.42 | −100.72 | 0.00 | −107.27 | −71.02 | 0.00 |
(230.50) | (230.21) | (230.77) | (181.77) | (241.52) | (237.35) | (221.99) | ||||
0.00 | 0.19 | −0.16 | 0.00 | −0.37 | 0.10 | 0.00 | 0.17 | −0.18 | 0.00 | |
(1.28) | (1.27) | (1.26) | (1.07) | (1.31) | (1.30) | (1.23) | ||||
Subjective health | 3.98 | 3.88 | 4.05 | 0.00 | 4.06 | 3.95 | 0.04 | 3.90 | 4.06 | 0.00 |
(0.82) | (0.84) | (0.80) | (0.81) | (0.82) | (0.83) | (0.80) | ||||
Likelihood hospitalized if catch COVID (0–1) | 0.33 | 0.38 | 0.30 | 0.00 | 0.29 | 0.35 | 0.00 | 0.37 | 0.29 | 0.00 |
(0.28) | (0.29) | (0.27) | (0.26) | (0.29) | (0.29) | (0.27) | ||||
Likelihood catch COVID-19 by summer (0–1) | 0.30 | 0.30 | 0.30 | 0.75 | 0.29 | 0.31 | 0.17 | 0.32 | 0.29 | 0.01 |
(0.24) | (0.24) | (0.23) | (0.23) | (0.24) | (0.24) | (0.23) | ||||
0.00 | 0.18 | −0.15 | 0.00 | −0.20 | 0.06 | 0.00 | 0.18 | −0.19 | 0.00 | |
(1.15) | (1.19) | (1.09) | (1.10) | (1.16) | (1.18) | (1.09) |
Notes: P-value columns report the p-value of a difference in means test between the two columns indicated by the numbers in the heading.
Columns (5)–(7) of Table 3 show that both economic and health shocks are larger for non-Honors students. In fact, the average differences in the principal component scores for both the economic and health factors is larger for these two groups than for the income groups. Likewise, the last three columns of the table show that women experienced larger COVID-19 shocks due to economic and health factors. These differences are partly driven by the fact that, in our sample, females are more likely to report that they belong to a lower-income household than males (50% vs. 42%).
In short, Table 3 makes clear that the impacts of COVID-19 on the economic well-being and health of students have been quite heterogeneous, with lower-income and lower-ability students being more adversely affected.
To investigate the role of economic and health shocks in explaining the heterogeneous treatment effects (in Section 4.2 ), we estimate the following specification:
where Δ i is the COVID-19 treatment effect for outcome O on student i . Demog i is a vector including indicators for gender, lower-income, Honors status, and dummies for cohort year and major. FinShock i and HealthShock i are vectors containing the shock proxies or their principal component. Finally, ε i denotes an idiosyncratic shock.
The parameters of interest are α 2 and α 3 . A causal interpretation of these parameters requires FinShock i and HealthShock i to be independent of ε i . This seems unlikely in our context as unobservables correlated with FinShock i and HealthShock i may also modulate COVID-19's impact on academic outcomes. Therefore, we prefer to interpret α 2 and α 3 as simple correlations. Nevertheless, we believe this descriptive evidence can be informative from a policy perspective.
Table 4 shows estimates of Eq. (2) for four different outcomes ( Appendix Table A2 shows the estimates for additional outcomes). For each outcome, five specifications are reported ranging from controlling for only demographic variables in the first specification to controlling for both economic and health factors in the fourth specification. Finally, the last column includes only the principal component of each shock to provide insight about overall effects, given that certain shock proxies show high levels of correlation (see Appendix Table A4 for the correlations within each set of proxies).
Several important messages emerge from Table 4 . First, both shocks are (economically and statistically) significant correlates of the COVID-19 effects on students' outcomes. In particular, F-tests show that the financial and health shock proxies are jointly significant across almost all specifications. 20 This is also reflected in the statistical significance of the principal components. Moreover, the fact that the effect of key proxy variables remains robust when we simultaneously control for both shocks demonstrates the robustness of our results. For example, we find that a 50 percentage point increase in the probability of being late on debt payments is associated with an increase in the probability of delaying graduation and switching majors due to COVID-19 of 6.9 and 6.4 percentage points respectively. These effects are large given that they represent more than half of the overall COVID-19 treatment effect for these variables. Similarly, we find that an analogous increase in the probability of hospitalization if contracting COVID-19 is associated with a 6 and 5 percentage points increase in the probability of delaying graduation and switching majors due to COVID-19.
Second, in terms of labor market expectations, we find that the change in the expected probability of finding a job before graduation strongly depends on having a family member that lost income (which is also correlated with the student himself losing a job). In particular, the size of this effect represents 32% of the overall COVID-19 treatment effect. Therefore, this finding suggests that students' labor market expectations are driven in large part by personal/family experiences.
Third, although the proxies play an important role in explaining the pandemic's impact on students, there is still a substantial amount of variation in COVID-19 treatment effects left unexplained. Across the four outcomes in Table 4 , the full set of proxies explain less than a quarter of the variation in outcomes across individuals. Appendix Fig. A4 visualizes this variation by plotting the distribution of several continuous outcomes with and without controls. While the interquartile range noticeably shrinks after conditioning on the proxy variables, these plots highlight the large amount of variation in treatment effects remaining after conditioning on the proxies.
Finally, our results show that the financial and health shocks play an important role in explaining the heterogeneous effects of the COVID-19 outbreak. In particular, columns (4) and (9) demonstrate that economic and health factors together can explain approximately 40% and 70% of the income gap in COVID-19's effect on delayed graduation and changing major respectively. The gap between Honors and non-Honors students is likewise reduced by 27% and 39% for the same outcomes. Taken together, these results imply that differences in the magnitude of COVID-19's economic and health impact can explain a significant proportion of the demographic gaps in COVID-19's effect on the decision to delay graduation, the decision to change major, and preferences for online learning. These results are important and suggest that focusing on the needs of students who experienced larger financial or health shocks from COVID-19 may be an effective way to minimize the disparate disruptive effects and prevent COVID-19 from exacerbating existing achievement gaps in higher education.
This paper provides the first systematic analysis of the effects of COVID-19 on higher education. To study these effects, we surveyed 1500 students at Arizona State University, and present quantitative evidence showing the negative effects of the pandemic on students' outcomes and expectations. For example, we find that 13% of students have delayed graduation due to COVID-19. Expanding upon these results, we show that the effects of the pandemic are highly heterogeneous, with lower-income students 55% more likely to delay graduation compared to their higher-income counterparts. We further show that the negative economic and health impacts of COVID-19 have been significantly more pronounced for less advantaged groups, and that these differences can partially explain the underlying heterogeneity that we document. Our results suggest that by focusing on addressing the economic and health burden imposed by COVID-19, as measured by a relatively narrow set of mitigating factors, policy makers may be able to prevent COVID-19 from widening existing achievement gaps in higher education.
The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. There are no declarations of interest.
☆ Noah Deitrick and Adam Streff provided excellent research assistance. All errors that remain are ours.
1 See, the New York Times article “ After Coronavirus , Colleges Worry : Will Students Come Back ?” (April 15, 2020) for a discussion surrounding students' demands for tuition cuts.
2 In some cases, instead of asking students for the outcomes in both states of the world, we directly ask for the difference. For example, the survey asked how the pandemic had affected the student's graduation date.
3 This approach has been used successfully in several other settings, such as to construct career and family returns to college majors ( Arcidiacono et al., 2020 ; Wiswall and Zafar, 2020 ), and the causal impact of health on retirement ( Shapiro and Giustinelli, 2019 ).
4 The income gap in GPA increased from 0.052 to 0.098 on a 4 point scale. It is significant at the 1% level in both scenarios.
5 The 64 people taking the survey at the moment the target sample size (1500) was reached were allowed to finish.
6 59% of Honors students in our sample report living on campus.
7 This is different from asking students in normal times about their expected outcomes in a state with online teaching and no campus activities (COVID-19) since most students would not have had any experience with this counterfactual prior to March this year.
8 Altonji et al. (2016) finds a small but positive effect on the probability of attending graduate school when graduating into a recession. This is suggestive evidence that students try to avoid entering the labor market when economic conditions are adverse. Our results on delayed graduation are consistent with students avoiding entering the labor market at inopportune times.
9 For this calculation, we take earnings data from the US Department of Education College Scorecard dataset. Major-specific earnings are calculated using median first-year earnings for ASU graduates in 2015 and 2016 by two-digit CIP code. Observable earnings averaged within major category.
10 STEM major designation made using two-digit CIP code and The STEM Designated Degree Program from the US Department of Homeland Security.
11 This includes 77 respondents, or 43% of those who say COVID-19 impacted their major choice.
12 The relevant survey question read: “ Suppose you are given the choice to take a course online/remote or in-person . [ Had you NOT had experience with online/remote classes this semester ], what is the percent chance that you would opt for the online/remote option ?”
13 This result is in line with a survey about eLearning experiences across different universities in Washington and New York that concludes that 75% of the students are unhappy with the quality of their classes after moving to online learning due to COVID-19.
14 According to the US Census Bureau Household Pulse Survey Week 3, 48% of the surveyed households have experienced a loss in employment income since March 13 2020.
15 The cutoff for median parental income in our sample is $80,000.
16 Based on analysis of ASU administrative data including transcripts, we find that, relative to their counterparts, first-generation, lower-income, and non-white students drop out at higher rates, take longer to graduate, have lower GPAs at graduation, and are more likely to switch majors when in college (see Appendix Table A3 ).
17 The difference is significant at 1% in both cases.
18 Honors students were as likely as non-Honors students to say that classes got easier after they went online but, conditional on saying classes got easier, were 47% more likely to say “homework/test questions got easier.” Conversely, males were marginally more likely to say classes got harder after they went online (10% more likely, p = 0.055) and, conditional on this, were 14% more likely to say that “online material is not clear”.
19 Eigenvalues indicate the presence of only one principal component for each of the shocks.
20 The only exception is the financial shock when explaining changes in the probability of taking classes online.
Expected and previous academic performance.
Notes: Figure plots mean expected GPA with COVID-19 against students' cumulative GPA up to the spring 2020 semester. The 45 degree line is also plotted for reference.
More treatment effects by demographic group.
(a) Withdrew from Class due to COVID (0/1); (b) Social Events per Week ( Δ 0–14); (c) Move in With Family due to COVID (0/1); (d) Weekly Study Hours ( Δ 0–40); (e) Reservation Wage (Pct. Δ )
Notes: Bars denote 90% confidence interval.
Cohort trends.
Notes: Figure plots average COVID-19 effects for a series of outcomes. The x-axis variable in each panel is expected academic year of graduation (after COVID), with summer graduation dates included in the previous academic year. Bars denote 90% confidence interval.
Distribution of individual effects.
Notes: Data winsorized below 5% and above 95%. Controls include cohort fixed effects, major fixed effects, and the economic/health proxies in Table 3 . Conditional distribution adjusted to preserve unconditional mean. Within each plot: middle line represents median, edges of box represent interquatile range (IQR), edge of whisker represents the adjacent values or the 25th(75th) percentile plus(minus) 1.5 times the IQR. Outlier observations past adjacent values plotted as individual points.
With | Without | Prop. | Prop. | 25th | 75th | ||
---|---|---|---|---|---|---|---|
COVID-19 | COVID-19 | >0 | =0 | %tile | %tile | ||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
Likelihood of taking online classes | 0.46 (0.33) | 0.50 (0.30) | −0.04 (0.26) | 0.31 | 0.22 | −0.20 | 0.08 |
Semester GPA | 3.48 (0.50) | 3.65 (0.37) | −0.17 (0.33) | 0.07 | 0.41 | −0.30 | 0.00 |
Weekly study hours | 15.12 (11.55) | 16.03 (10.21) | −0.91 (8.15) | 0.33 | 0.20 | −5.00 | 4.00 |
Delayed graduation (0/1) | 0.13 (0.34) | 0.00 | 0.00 | ||||
Withdraw from a class (0/1) | 0.11 (0.31) | 0.00 | 0.00 | ||||
Change major (0/1) | 0.12 (0.33) | 0.00 | 0.00 | ||||
Time in classes | −0.10 (0.87) | 0.33 | 0.24 | −1.00 | 1.00 | ||
Time studying by myself | 0.28 (0.83) | 0.52 | 0.23 | 0.00 | 1.00 | ||
Time studying with peers | −0.75 (0.51) | 0.04 | 0.18 | −1.00 | −1.00 | ||
Lost in-college job (0/1) | 0.29 (0.45) | 0.00 | 1.00 | ||||
In-college weekly hours worked | 12.97 (15.30) | 24.38 (13.71) | −11.64 (16.09) | 0.40 | 0.21 | −22.00 | 0.00 |
In-college weekly earnings , | 147.73 (342.91) | 237.02 (366.62) | −21.27 (170.05) | 0.09 | 0.52 | −1.00 | 0.00 |
Fam. lost job or reduce income (0/1) | 0.61 (0.49) | 0.00 | 1.00 | ||||
Lost job offer or internship (0/1) | 0.13 (0.34) | 0.00 | 0.00 | ||||
Probability of finding a Job | 55.97 (28.04) | 69.36 (25.07) | −13.39 (20.27) | 0.13 | 0.24 | −20.00 | 0.00 |
Reservation waged | 48.53 (21.93) | 50.53 (21.95) | −1.91 (28.02) | 0.09 | 0.63 | −0.08 | 0.00 |
Expected earnings at 35 years old | 88.18 (33.90) | 91.49 (33.92) | −2.34 (28.64) | 0.06 | 0.65 | −0.07 | 0.00 |
Time working for pay | −0.46 (0.66) | 0.09 | 0.35 | −1.00 | 0.00 | ||
Making a lot of money | 0.26 (0.61) | 0.35 | 0.56 | 0.00 | 1.00 | ||
Being a leader in your line of work | 0.16 (0.55) | 0.24 | 0.68 | 0.00 | 0.00 | ||
Enjoying your line of work | 0.20 (0.63) | 0.32 | 0.56 | 0.00 | 1.00 | ||
Family-life Balance | 0.34 (0.63) | 0.42 | 0.49 | 0.00 | 1.00 | ||
Job security | 0.55 (0.67) | 0.66 | 0.24 | 0.00 | 1.00 | ||
Have opt. to be helpful to others | 0.38 (0.63) | 0.46 | 0.45 | 0.00 | 1.00 | ||
Have opt. to work with people | 0.08 (0.68) | 0.28 | 0.53 | 0.00 | 1.00 | ||
Number of weekly social events | 0.26 (1.28) | 4.44 (3.82) | −4.17 (3.66) | 0.01 | 0.08 | −5.00 | −2.00 |
Time on social media | 0.62 (0.61) | 0.69 | 0.24 | 0.00 | 1.00 | ||
Time news and online browsing | 0.71 (0.53) | 0.75 | 0.21 | 1.00 | 1.00 | ||
Time online entertainment | 0.74 (0.54) | 0.78 | 0.17 | 1.00 | 1.00 | ||
Time in sports and exercise | −0.46 (0.75) | 0.15 | 0.23 | −1.00 | 0.00 | ||
Time commuting | −0.89 (0.36) | 0.02 | 0.07 | −1.00 | −1.00 | ||
Time sleeping | 0.17 (0.83) | 0.44 | 0.28 | −1.00 | 1.00 |
Composition of COVID effects: more outcomes.
Expect earn at age 35 ( pp) | Res wage ( pp) | Sem GPA ( 0–4) | Withdrew class b/c COVID (0/100) | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | (17) | (18) | (19) | (20) | |
Women | 0.60 | −0.08 | −0.04 | 0.07 | 0.17 | 1.90 | 2.18 | 2.18 | 2.22 | 2.33 | 0.04 | 0.03 | 0.03 | 0.03 | 0.03 | −0.02 | −0.00 | −0.01 | −0.01 | −0.01 |
(1.35) | (1.48) | (1.62) | (1.58) | (1.66) | (1.47) | (2.47) | (2.59) | (2.61) | (2.60) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | |
Lower-Income | 0.56 | 1.27 | 1.18 | 1.30 | 1.46 | −0.13 | −0.02 | −0.24 | −0.11 | −0.03 | − 0.04 | −0.03 | − 0.05 | −0.03 | − 0.04 | 0.03 | 0.02 | 0.03 | 0.02 | 0.02 |
(1.62) | (1.62) | (2.11) | (1.65) | (2.11) | (1.35) | (1.58) | (1.62) | (1.77) | (1.55) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | |
Honors | 4.92 | 5.53 | 5.60 | 5.47 | 5.22 | −1.17 | −0.95 | −0.90 | −0.93 | −1.13 | 0.04 | 0.04 | 0.04 | 0.03 | 0.04 | − 0.06 | − 0.06 | − 0.07 | − 0.06 | − 0.06 |
(3.04) | (3.37) | (3.24) | (3.29) | (3.15) | (1.66) | (1.84) | (1.76) | (1.84) | (1.81) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | |
Student lost job (0/1) | −2.38 | −2.39 | 1.13 | 1.08 | −0.02 | −0.02 | −0.01 | −0.00 | ||||||||||||
(1.86) | (1.86) | (2.10) | (2.11) | (0.03) | (0.03) | (0.02) | (0.02) | |||||||||||||
Family lost income (0/1) | − 2.67 | −2.31 | −1.03 | −0.73 | − 0.06 | − 0.05 | 0.02 | 0.01 | ||||||||||||
(1.43) | (1.48) | (1.91) | (1.93) | (0.02) | (0.02) | (0.02) | (0.02) | |||||||||||||
Student change in earnings ($) | −0.00 | −0.00 | 0.00 | 0.00 | − 0.00 | −0.00 | −0.00 | −0.00 | ||||||||||||
(0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | |||||||||||||
Prob. miss debt (0–1) | 2.21 | 3.35 | −1.16 | −0.29 | − 0.13 | − 0.11 | ∗∗0.10 | ∗0.08 | ||||||||||||
(5.47) | (6.26) | (3.07) | (2.98) | (0.04) | (0.04) | (0.04) | (0.05) | |||||||||||||
Principal component | −0.69 | −0.28 | − 0.02 | 0.02 | ||||||||||||||||
(0.49) | (0.57) | (0.01) | (0.01) | |||||||||||||||||
Subjective health (1–5, 5 high) | 2.30 | 2.31 | 1.24 | 1.25 | 0.04 | 0.04 | − 0.02 | − 0.02 | ||||||||||||
(1.26) | (1.29) | (0.68) | (0.71) | (0.01) | (0.01) | (0.01) | (0.01) | |||||||||||||
Prob. hosp. if catch COVID (0–1) | 2.27 | 2.00 | 1.93 | 2.09 | −0.02 | −0.01 | 0.04 | 0.03 | ||||||||||||
(3.63) | (3.85) | (4.23) | (4.17) | (0.04) | (0.04) | (0.04) | (0.05) | |||||||||||||
Prob. catch COVID (0–1) | −4.49 | −4.77 | −5.64 | −5.53 | −0.05 | −0.03 | 0.06 | 0.05 | ||||||||||||
(2.84) | (3.51) | (3.55) | (3.79) | (0.04) | (0.04) | (0.04) | (0.04) | |||||||||||||
Principal component | −1.13 | −0.72 | − 0.03 | 0.02 | ||||||||||||||||
(0.86) | (0.71) | (0.01) | (0.01) | |||||||||||||||||
Economic proxies | 0.267 | 0.304 | 0.702 | 0.767 | 0.000 | 0.000 | 0.045 | 0.101 | ||||||||||||
Health proxies | 0.244 | 0.290 | 0.104 | 0.172 | 0.000 | 0.003 | 0.010 | 0.039 | ||||||||||||
Major FE | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
Cohort FE | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
Mean | −2.34 | −2.34 | −2.34 | −2.34 | −2.34 | −1.91 | −1.91 | −1.91 | −1.91 | −1.91 | −0.17 | −0.17 | −0.17 | −0.17 | −0.17 | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 |
R | 0.005 | 0.046 | 0.048 | 0.051 | 0.045 | 0.001 | 0.087 | 0.089 | 0.090 | 0.087 | 0.012 | 0.169 | 0.164 | 0.177 | 0.164 | 0.010 | 0.142 | 0.141 | 0.148 | 0.146 |
N | 1435 | 1435 | 1435 | 1435 | 1435 | 1430 | 1430 | 1430 | 1430 | 1430 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 | 1446 |
Notes: Standard errors in parentheses bootstrapped with 1000 replications. Each column reports results from a separate OLS regression of the dependent variable onto the covariates (row variables). Dependent variables measured in percentage points (except GPA). ( ∗ : p <0.1, ∗∗ : p <0.05, ∗∗∗ : p <0.01).
Existing achievement gaps.
Years to graduate | Cum GPA at grad | Graduate | Dropout | Ever switch major | |
---|---|---|---|---|---|
Women | 3.37 | 3.39 | 0.62 | 0.22 | 0.54 |
Men | 3.54 | 3.25 | 0.54 | 0.28 | 0.51 |
−0.16 | 0.15 | 0.08 | −0.06 | 0.02 | |
First generation | 3.49 | 3.26 | 0.49 | 0.33 | 0.52 |
Not first generation | 3.40 | 3.36 | 0.55 | 0.23 | 0.49 |
0.10 | −0.10 | −0.06 | 0.10 | 0.03 | |
Low income | 3.54 | 3.28 | 0.50 | 0.32 | 0.52 |
High income | 3.30 | 3.37 | 0.57 | 0.20 | 0.48 |
0.24 | −0.09 | −0.07 | 0.12 | 0.04 | |
Nonwhite | 3.51 | 3.25 | 0.55 | 0.29 | 0.54 |
White | 3.40 | 3.38 | 0.61 | 0.21 | 0.52 |
0.11 | −0.13 | −0.06 | 0.08 | 0.02 | |
Honors | 3.34 | 3.67 | 0.83 | 0.09 | 0.43 |
Non-honors | 3.47 | 3.25 | 0.55 | 0.27 | 0.54 |
−0.14 | 0.42 | 0.29 | −0.18 | −0.11 |
Notes: Sample includes all first time freshman at ASU's main campus who started within the last 10 years. N = 58,426. ( ∗ : p <0.1, ∗∗ : p <0.05, ∗∗∗ : p <0.01).
Correlation of shock proxies.
Student lost | Family lost | Student | Likelihood | |
Job | Income | Change in earnings | Default in next 90 days | |
Student lost job (0/1) | 1.000 | |||
Family lost income (0/1) | 0.174 | 1.000 | ||
Student change in earnings ($) | −0.572 | −0.153 | 1.000 | |
Likelihood default in next 90 days (0–1) | 0.225 | 0.176 | −0.203 | 1.000 |
Subjective | Likelihood | Likelihood | |
Health | Hospitalized if catch COVID | Catch COVID by summer | |
Subjective health (1–5, 5 High) | 1.000 | ||
Likelihood hospitalized if catch COVID (0–1) | −0.293 | 1.000 | |
Likelihood catch COVID by summer (0–1) | −0.053 | 0.093 | 1.000 |
Notes: Table reports correlation matrix for indicated variables.
BY KATHY KATELLA May 14, 2021
Note: Information in this article was accurate at the time of original publication. Because information about COVID-19 changes rapidly, we encourage you to visit the websites of the Centers for Disease Control & Prevention (CDC), World Health Organization (WHO), and your state and local government for the latest information.
The COVID-19 pandemic changed life as we know it—and it may have changed us individually as well, from our morning routines to our life goals and priorities. Many say the world has changed forever. But this coming year, if the vaccines drive down infections and variants are kept at bay, life could return to some form of normal. At that point, what will we glean from the past year? Are there silver linings or lessons learned?
“Humanity's memory is short, and what is not ever-present fades quickly,” says Manisha Juthani, MD , a Yale Medicine infectious diseases specialist. The bubonic plague, for example, ravaged Europe in the Middle Ages—resurfacing again and again—but once it was under control, people started to forget about it, she says. “So, I would say one major lesson from a public health or infectious disease perspective is that it’s important to remember and recognize our history. This is a period we must remember.”
We asked our Yale Medicine experts to weigh in on what they think are lessons worth remembering, including those that might help us survive a future virus or nurture a resilience that could help with life in general.
What happened: The Centers for Disease Control and Prevention (CDC) relaxed its masking guidance for those who have been fully vaccinated. But when the pandemic began, it necessitated a global effort to ensure that everyone practiced behaviors to keep themselves healthy and safe—and keep others healthy as well. This included the widespread wearing of masks indoors and outside.
What we’ve learned: Not everyone practiced preventive measures such as mask wearing, maintaining a 6-foot distance, and washing hands frequently. But, Dr. Juthani says, “I do think many people have learned a whole lot about respiratory pathogens and viruses, and how they spread from one person to another, and that sort of old-school common sense—you know, if you don’t feel well—whether it’s COVID-19 or not—you don’t go to the party. You stay home.”
Masks are a case in point. They are a key COVID-19 prevention strategy because they provide a barrier that can keep respiratory droplets from spreading. Mask-wearing became more common across East Asia after the 2003 SARS outbreak in that part of the world. “There are many East Asian cultures where the practice is still that if you have a cold or a runny nose, you put on a mask,” Dr. Juthani says.
She hopes attitudes in the U.S. will shift in that direction after COVID-19. “I have heard from a number of people who are amazed that we've had no flu this year—and they know masks are one of the reasons,” she says. “They’ve told me, ‘When the winter comes around, if I'm going out to the grocery store, I may just put on a mask.’”
What happened: Doctors and patients who have used telehealth (technology that allows them to conduct medical care remotely), found it can work well for certain appointments, ranging from cardiology check-ups to therapy for a mental health condition. Many patients who needed a medical test have also discovered it may be possible to substitute a home version.
What we’ve learned: While there are still problems for which you need to see a doctor in person, the pandemic introduced a new urgency to what had been a gradual switchover to platforms like Zoom for remote patient visits.
More doctors also encouraged patients to track their blood pressure at home , and to use at-home equipment for such purposes as diagnosing sleep apnea and even testing for colon cancer . Doctors also can fine-tune cochlear implants remotely .
“It happened very quickly,” says Sharon Stoll, DO, a neurologist. One group that has benefitted is patients who live far away, sometimes in other parts of the country—or even the world, she says. “I always like to see my patients at least twice a year. Now, we can see each other in person once a year, and if issues come up, we can schedule a telehealth visit in-between,” Dr. Stoll says. “This way I may hear about an issue before it becomes a problem, because my patients have easier access to me, and I have easier access to them.”
Meanwhile, insurers are becoming more likely to cover telehealth, Dr. Stoll adds. “That is a silver lining that will hopefully continue.”
What happened: Given the recent positive results from vaccine trials, once again vaccines are proving to be powerful for preventing disease.
What we’ve learned: Vaccines really are worth getting, says Dr. Stoll, who had COVID-19 and experienced lingering symptoms, including chronic headaches . “I have lots of conversations—and sometimes arguments—with people about vaccines,” she says. Some don’t like the idea of side effects. “I had vaccine side effects and I’ve had COVID-19 side effects, and I say nothing compares to the actual illness. Unfortunately, I speak from experience.”
Dr. Juthani hopes the COVID-19 vaccine spotlight will motivate people to keep up with all of their vaccines, including childhood and adult vaccines for such diseases as measles , chicken pox, shingles , and other viruses. She says people have told her they got the flu vaccine this year after skipping it in previous years. (The CDC has reported distributing an exceptionally high number of doses this past season.)
But, she cautions that a vaccine is not a magic bullet—and points out that scientists can’t always produce one that works. “As advanced as science is, there have been multiple failed efforts to develop a vaccine against the HIV virus,” she says. “This time, we were lucky that we were able build on the strengths that we've learned from many other vaccine development strategies to develop multiple vaccines for COVID-19 .”
What happened: COVID-19 magnified disparities that have long been an issue for a variety of people.
What we’ve learned: Racial and ethnic minority groups especially have had disproportionately higher rates of hospitalization for COVID-19 than non-Hispanic white people in every age group, and many other groups faced higher levels of risk or stress. These groups ranged from working mothers who also have primary responsibility for children, to people who have essential jobs, to those who live in rural areas where there is less access to health care.
“One thing that has been recognized is that when people were told to work from home, you needed to have a job that you could do in your house on a computer,” says Dr. Juthani. “Many people who were well off were able do that, but they still needed to have food, which requires grocery store workers and truck drivers. Nursing home residents still needed certified nursing assistants coming to work every day to care for them and to bathe them.”
As far as racial inequities, Dr. Juthani cites President Biden’s appointment of Yale Medicine’s Marcella Nunez-Smith, MD, MHS , as inaugural chair of a federal COVID-19 Health Equity Task Force. “Hopefully the new focus is a first step,” Dr. Juthani says.
What happened: There was a rise in reported mental health problems that have been described as “a second pandemic,” highlighting mental health as an issue that needs to be addressed.
What we’ve learned: Arman Fesharaki-Zadeh, MD, PhD , a behavioral neurologist and neuropsychiatrist, believes the number of mental health disorders that were on the rise before the pandemic is surging as people grapple with such matters as juggling work and childcare, job loss, isolation, and losing a loved one to COVID-19.
The CDC reports that the percentage of adults who reported symptoms of anxiety of depression in the past 7 days increased from 36.4 to 41.5 % from August 2020 to February 2021. Other reports show that having COVID-19 may contribute, too, with its lingering or long COVID symptoms, which can include “foggy mind,” anxiety , depression, and post-traumatic stress disorder .
“We’re seeing these problems in our clinical setting very, very often,” Dr. Fesharaki-Zadeh says. “By virtue of necessity, we can no longer ignore this. We're seeing these folks, and we have to take them seriously.”
What happened: While everyone’s situation is different (and some people have experienced tremendous difficulties), many have seen that it’s possible to be resilient in a crisis.
What we’ve learned: People have practiced self-care in a multitude of ways during the pandemic as they were forced to adjust to new work schedules, change their gym routines, and cut back on socializing. Many started seeking out new strategies to counter the stress.
“I absolutely believe in the concept of resilience, because we have this effective reservoir inherent in all of us—be it the product of evolution, or our ancestors going through catastrophes, including wars, famines, and plagues,” Dr. Fesharaki-Zadeh says. “I think inherently, we have the means to deal with crisis. The fact that you and I are speaking right now is the result of our ancestors surviving hardship. I think resilience is part of our psyche. It's part of our DNA, essentially.”
Dr. Fesharaki-Zadeh believes that even small changes are highly effective tools for creating resilience. The changes he suggests may sound like the same old advice: exercise more, eat healthy food, cut back on alcohol, start a meditation practice, keep up with friends and family. “But this is evidence-based advice—there has been research behind every one of these measures,” he says.
But we have to also be practical, he notes. “If you feel overwhelmed by doing too many things, you can set a modest goal with one new habit—it could be getting organized around your sleep. Once you’ve succeeded, move on to another one. Then you’re building momentum.”
What happened: People who were part of a community during the pandemic realized the importance of human connection, and those who didn’t have that kind of support realized they need it.
What we’ve learned: Many of us have become aware of how much we need other people—many have managed to maintain their social connections, even if they had to use technology to keep in touch, Dr. Juthani says. “There's no doubt that it's not enough, but even that type of community has helped people.”
Even people who aren’t necessarily friends or family are important. Dr. Juthani recalled how she encouraged her mail carrier to sign up for the vaccine, soon learning that the woman’s mother and husband hadn’t gotten it either. “They are all vaccinated now,” Dr. Juthani says. “So, even by word of mouth, community is a way to make things happen.”
It’s important to note that some people are naturally introverted and may have enjoyed having more solitude when they were forced to stay at home—and they should feel comfortable with that, Dr. Fesharaki-Zadeh says. “I think one has to keep temperamental tendencies like this in mind.”
But loneliness has been found to suppress the immune system and be a precursor to some diseases, he adds. “Even for introverted folks, the smallest circle is preferable to no circle at all,” he says.
What happened: Scientists and nonscientists alike learned that a virus can be more powerful than they are. This was evident in the way knowledge about the virus changed over time in the past year as scientific investigation of it evolved.
What we’ve learned: “As infectious disease doctors, we were resident experts at the beginning of the pandemic because we understand pathogens in general, and based on what we’ve seen in the past, we might say there are certain things that are likely to be true,” Dr. Juthani says. “But we’ve seen that we have to take these pathogens seriously. We know that COVID-19 is not the flu. All these strokes and clots, and the loss of smell and taste that have gone on for months are things that we could have never known or predicted. So, you have to have respect for the unknown and respect science, but also try to give scientists the benefit of the doubt,” she says.
“We have been doing the best we can with the knowledge we have, in the time that we have it,” Dr. Juthani says. “I think most of us have had to have the humility to sometimes say, ‘I don't know. We're learning as we go.’"
Information provided in Yale Medicine articles is for general informational purposes only. No content in the articles should ever be used as a substitute for medical advice from your doctor or other qualified clinician. Always seek the individual advice of your health care provider with any questions you have regarding a medical condition.
A report, as you know, is a detailed account of a particular event or something that happened. Writing a report on a pandemic such as COVID-19, which shook the whole world, requires a lot of research. You should have a thorough knowledge of the details that have to be included in the report before you start writing one. Check out the following sections to learn what they are and also go through the sample reports to see how to structure your report.
What to include in a report on covid-19, sample report on covid-19 around the world, sample report on covid-19 in india for students, frequently asked questions on report writing on covid-19.
Before you start writing your report, make sure you understand what the term ‘COVID-19’ refers to and gather all the significant information about it. Since COVID-19 is a pandemic, you have to try and understand the causes, symptoms, difficulties caused by the virus, aftereffects, precautions, aftercure and so on. Once you do this, also explore information about the number of cases reported, number of deaths caused, number of people cured, advancements in the field of medicine, etc. Having a thorough knowledge of these factors can give you a clear idea of what to write and how to structure your report.
While plague, cholera and flu were pandemics of the past, the current COVID-19 pandemic has put the whole world in a fix. With the first case of COVID-19 reported in Wuhan, Hubei Province, China in December of 2019, life on Earth had changed forever. Since then, everybody was locked inside, asked to cover their noses and mouths, wash their hands, keep themselves clean, use sanitisers every time they step out and step back into their houses, eat protein-rich and hygienic food, inhale steam, drink hot water and so on. For many, everything changed with the outbreak of the pandemic. A huge number of people lost their loved ones, some their jobs and some were even disturbed mentally rather than just physically. Life simply switched to a new normal.
The commonly found symptoms were fatigue, severe headaches, common cold, breathing difficulties, reduced oxygen levels, loss of appetite, taste and smell and so on. The government and the medical community continuously asked people to be on their guard, stay indoors and report to the nearest hospitals in case they identify any of the above stated symptoms in themselves or in the people around them.
As of December 2021, around 1 million new cases and around 7500 deaths were reported and the daily moving average of cases rose to 390 in the first week of December. However, with the development of vaccinations by scientists and doctors, the number of cases as well as the number of deaths have been reduced. Still, people have been asked to take precautions even though vaccinations have been administered to most people around the world.
The spread of COVID-19 in India began with the first case being reported in Kerala on January 30, 2020. In a year’s time, more than twenty-eight million people were tested positive for COVID. Around five million people – the highest recorded number of diagnosed cases – were from Maharashtra; the next in line was Karnataka, Kerala and Tamil Nadu with more than two million cases each, followed by Andhra Pradesh with over one million cases.
Owing to the widespread increase in the number of deaths, Prime Minister Narendra Modi announced a nationwide lockdown until further notice. All schools, colleges and offices were closed. Schools, colleges, community halls and convention centers were turned into isolation wards as hospitals were overflowing with patients. Healthcare professionals, along with many volunteers, worked day and night to treat patients and reduce the number of deaths.
After almost a year, vaccinations such as Covishield and Covaxin were launched in India. These vaccines were first administered to people above the age of sixty, followed by people from the age of forty to sixty, above eighteen and then younger kids. Vaccinations were given in two doses with an interval of one and a half to two months in between. With the government making vaccinations mandatory for travel and other purposes, almost all people had taken the vaccinations. A third dose of the vaccine (booster dose) also has been launched. The government has taken efforts to set up multiple vaccination booths in government schools and hospitals. With continuous efforts from the government, medical and police officials, and cooperation from the citizens, India has successfully seen a decrease in the number of cases and deaths, and an increase in the number of recoveries.
A report is an official document presented in writing or print about a particular event or happening.
The details to be included in a report on COVID-19 are as follows.
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Guidance for people with symptoms of a respiratory infection including COVID-19, or a positive test result for COVID-19.
As we learn to live safely with COVID-19, there are actions we can all take to help reduce the risk of catching COVID-19 and passing it on to others. These actions will also help to reduce the spread of other respiratory infections. COVID-19 and other respiratory infections such as flu can spread easily and cause serious illness in some people.
Vaccinations are very effective at preventing serious illness from COVID-19, however even if you are vaccinated there is a chance you might catch COVID-19 or another respiratory infection and pass it on to other people.
Most people can no longer access free testing for COVID-19. This guidance is in 2 parts:
Actions you can take to protect other people if you are unwell with symptoms of a respiratory infection, including COVID-19, and you have not taken a test for COVID-19.
Advice for people who have taken a COVID-19 test and have received a positive test result.
There is separate guidance for people who have been informed by the NHS that they are at highest risk of becoming seriously unwell and who might be eligible for new COVID-19 treatments.
There is also additional guidance for those working in health and social care settings.
People who are at higher risk from COVID-19 and other respiratory infections include:
The risk of becoming seriously unwell from COVID-19 and other respiratory infections is very low for most children and young people.
Some children aged under 2 years, especially those with a heart condition or born prematurely, as well as very young infants, are at increased risk of hospitalisation from respiratory syncytial virus ( RSV ) .
You will not always know whether someone you come into contact with outside your home is at higher risk of becoming seriously unwell. They could be strangers (for example, people you sit next to on public transport) or people you may have regular contact with (for example, friends and work colleagues). This means it is important to follow the advice in this guidance to reduce the spread of infection and help to keep others safe.
Respiratory infections can spread easily between people. It is important to be aware of symptoms so you can take action to reduce the risk of spreading your infection to other people.
The symptoms of COVID-19 and other respiratory infections are very similar. It is not possible to tell if you have COVID-19, flu or another respiratory infection based on symptoms alone. Most people with COVID-19 and other respiratory infections will have a relatively mild illness, especially if they have been vaccinated.
If you have symptoms of a respiratory infection, such as COVID-19, and you have a high temperature or you do not feel well enough to go to work or carry out normal activities, you are advised to try to stay at home and avoid contact with other people.
Symptoms of COVID-19, flu and common respiratory infections include:
If you are feeling unwell with these symptoms you should get plenty of rest and drink water to keep hydrated. You can use medications such as paracetamol to help with your symptoms. Antibiotics are not recommended for viral respiratory infections because they will not relieve your symptoms or speed up your recovery.
In some cases, you might continue to have a cough or feel tired after your other symptoms have improved, but this does not mean that you are still infectious.
You can find information about these symptoms on NHS.UK .
If you are concerned about your symptoms, or they are worsening, seek medical advice by contacting NHS 111. In an emergency dial 999.
Try to stay at home and avoid contact with other people.
If you have symptoms of a respiratory infection, such as COVID-19, and you have a high temperature or do not feel well enough to go to work or carry out normal activities, try to stay at home and avoid contact with other people, until you no longer have a high temperature (if you had one) or until you no longer feel unwell.
It is particularly important to avoid close contact with anyone who you know is at higher risk of becoming seriously unwell if they are infected with COVID-19 and other respiratory infections, especially those whose immune system means that they are at higher risk of serious illness, despite vaccination .
Try to work from home if you can. If you are unable to work from home, talk to your employer about options available to you.
If you have been asked to attend a medical or dental appointment in person, contact your healthcare provider and let them know about your symptoms.
You may wish to ask friends, family or neighbours to get food and other essentials for you.
If you leave your home while you have symptoms of a respiratory infection, and you have a high temperature or feel unwell, avoid close contact with anyone who you know is at higher risk of becoming seriously unwell, especially those whose immune system means that they are at higher risk of serious illness, despite vaccination .
The following actions will reduce the chance of passing on your infection to others:
While you are unwell there is a high risk of passing your infection to others in your household. These are simple things you can do to help prevent the spread :
GermDefence is a website that can help you identify simple ways to protect yourself and others in your household from COVID-19 and other viruses. People who use GermDefence are less likely to catch flu and other infections and are less likely to spread them at home.
There is further guidance on protecting yourself and others in living safely with respiratory infections, including COVID-19.
Respiratory infections are common in children and young people, particularly during the winter months. Symptoms can be caused by several respiratory infections including the common cold, COVID-19 and RSV .
For most children and young people, these illnesses will not be serious, and they will soon recover following rest and plenty of fluids.
Very few children and young people with respiratory infections become seriously unwell. This is also true for children and young people with long-term conditions. Some children under 2, especially those born prematurely or with a heart condition, can be more seriously unwell from RSV .
Attending education is hugely important for children and young people’s health and their future.
Children and young people with mild symptoms such as a runny nose, sore throat, or slight cough, who are otherwise well, can continue to attend their education setting.
Children and young people who are unwell and have a high temperature should stay at home and avoid contact with other people, where they can. They can go back to school, college or childcare, and resume normal activities when they no longer have a high temperature and they are well enough to attend.
All children and young people with respiratory symptoms should be encouraged to cover their mouth and nose with a disposable tissue when coughing and/or sneezing and to wash their hands after using or disposing of tissues.
It can be difficult to know when to seek help if your child is unwell. If you are worried about your child, especially if they are aged under 2 years old, then you should seek medical help.
If you have a positive COVID-19 test result, it is very likely that you have COVID-19 even if you do not have any symptoms. You can pass on the infection to others, even if you have no symptoms.
Most people with COVID-19 will no longer be infectious to others after 5 days. If you have a positive COVID-19 test result, try to stay at home and avoid contact with other people for 5 days after the day you took your test. There is different advice for children and young people aged 18 and under .
During this period there are actions you can take to reduce the risk of passing COVID-19 on to others.
If you have been asked to attend a medical or dental appointment in person, contact your healthcare provider and let them know about your positive test result.
At the end of this period, if you have a high temperature or feel unwell, try to follow this advice until you feel well enough to resume normal activities and you no longer have a high temperature if you had one.
Although most people will no longer be infectious to others after 5 days, some people may be infectious to other people for up to 10 days from the start of their infection. You should avoid meeting people at higher risk of becoming seriously unwell from COVID-19, especially those whose immune system means that they are at higher risk of serious illness from COVID-19, despite vaccination , for 10 days after the day you took your test.
If you leave your home during the 5 days after your positive test result the following steps will reduce the chance of passing on COVID-19 to others:
While you are infectious there is a high risk of passing your infection to others in your household. These are simple things you can do to help prevent the spread :
People who live in the same household as someone with COVID-19 are at the highest risk of becoming infected because they are most likely to have prolonged close contact. People who stayed overnight in the household of someone with COVID-19 while they were infectious are also at high risk.
If you are a household or overnight contact of someone who has had a positive COVID -19 test result it can take up to 10 days for your infection to develop. It is possible to pass on COVID-19 to others, even if you have no symptoms.
You can reduce the risk to other people by taking the following steps:
If you develop symptoms of a respiratory infection try to stay at home and avoid contact with other people and follow the guidance for people with symptoms.
If you are a contact of someone with COVID-19 but do not live with them or did not stay in their household overnight, you are at lower risk of becoming infected. There is guidance on protecting yourself and others in living safely with respiratory infections, including COVID-19 .
It is not recommended that children and young people are tested for COVID-19 unless directed to by a health professional.
If a child or young person has a positive COVID-19 test result they should try to stay at home and avoid contact with other people for 3 days after the day they took the test, if they can. After 3 days, if they feel well and do not have a high temperature, the risk of passing the infection on to others is much lower. This is because children and young people tend to be infectious to other people for less time than adults.
Children and young people who usually go to school, college or childcare and who live with someone who has a positive COVID-19 test result should continue to attend as normal.
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If you have COVID-19, there is a high risk that others in your household will catch it from you. There are several things you can do to reduce the spread of infection in your household.
Limit close contact with others. Spend as little time as possible in communal areas.
Regularly clean frequently touched surfaces and shared rooms like kitchens and bathrooms.
Wash your hands regularly using soap and water, particularly after coughing and sneezing.
Get help where possible from those you live with. Ask for help with cleaning and being brought food safely to avoid unnecessary contact.
Use a face covering if you need to spend time in shared spaces.
Keep rooms well ventilated.
Catch coughs and sneezes in disposable tissues and put them straight in the bin.
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People with symptoms of a respiratory infection including COVID-19 (Arabic) ( PDF , 149 KB , 11 pages )
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COVID-19: reduce the spread of infection with the people you live with (infographic) Arabic ( PDF , 438 KB , 1 page )
COVID-19: reduce the spread of infection with the people you live with (infographic) Bengali ( PDF , 429 KB , 1 page )
COVID-19: reduce the spread of infection with the people you live with (infographic) Chinese simplified ( PDF , 421 KB , 1 page )
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COVID-19: reduce the spread of infection with the people you live with (infographic) Hindi ( PDF , 427 KB , 1 page )
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Added easy read on testing positive for COVID-19.
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The IRS has issued all first, second and third Economic Impact Payments. You can no longer use the Get My Payment application to check your payment status.
Most eligible people already received their Economic Impact Payments. However, people who are missing stimulus payments should review the information below to determine their eligibility to claim a Recovery Rebate Credit for tax year 2020 or 2021.
Securely access your IRS online account to view the total of your first, second and third Economic Impact Payment amounts under the Tax Records page.
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You may be eligible to claim a 2021 Recovery Rebate Credit on your 2021 federal tax return.
Individuals can view the total amount of their third Economic Impact Payments through their individual Online Account. Through March 2022, we'll also send Letter 6475 to the address we have on file for you confirming the total amount of your third Economic Impact Payment and any plus-up payments you received for tax year 2021.
You will need this information from your online account or your letter to accurately calculate your 2021 Recovery Rebate Credit when you file your 2021 federal tax return in 2022. For married filing joint individuals, each spouse will need to log into their own online account or review their own letter for their half of the total payment. All amounts must be considered if filing jointly.
Using the total amount of the third payment from your online account or Letter 6475 when preparing a tax return can reduce errors and avoid delays in processing while the IRS corrects the tax return.
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100 Words Essay on Covid 19. COVID-19 or Corona Virus is a novel coronavirus that was first identified in 2019. It is similar to other coronaviruses, such as SARS-CoV and MERS-CoV, but it is more contagious and has caused more severe respiratory illness in people who have been infected. The novel coronavirus became a global pandemic in a very ...
Writing About COVID-19 in College Essays. Experts say students should be honest and not limit themselves to merely their experiences with the pandemic. The global impact of COVID-19, the disease ...
This essay is an opportunity to share your pandemic experience and the lessons learned. The college admissions process has experienced significant changes as a result of COVID-19, creating new challenges for high school students. Since the onset of the pandemic, admissions officers have strongly emphasized a more holistic review process.
This year, the Common App is including a special 250-word section allowing students to describe the impacts of COVID-19 on their lives. Here's the official word from the Common App website: . We want to provide colleges with the information they need, with the goal of having students answer COVID-19 questions only once while using the rest of the application as they would have before to ...
The student or a family member had COVID-19 or suffered other illnesses due to confinement during the pandemic. The student suffered from a lack of internet access and other online learning challenges. Students who dealt with problems registering for or taking standardized tests and AP exams. Jeff Schiffman of the Tulane University admissions ...
The COVID-19 pandemic has had a profound impact on individuals, societies, and economies worldwide. Its multifaceted nature presents a wealth of topics suitable for academic exploration. This essay provides guidance on developing engaging and insightful essay topics related to COVID-19, offering a comprehensive range of perspectives to choose from.
Introduction. The global outbreak of COVID-19 has certainly taken an overwhelming toll on everyone. People have lost their jobs, their homes, and even their lives. There is no getting past the fact that the overall impact on the world has been negative, but it is important to realize that positive aspects of the pandemic have been overshadowed ...
Alex, a writer and fellow disabled parent, found the freedom to explore a fuller version of herself in the privacy the pandemic provided. "The way I dress, the way I love, and the way I carry ...
As COVID-19 continues to creep its way into each of our communities and impact the way we live and communicate, I find solace in the fact that we face what comes next together, as humanity. When the day comes that my generation is responsible for dealing with another crisis, I hope we can use this experience to remind us that moving forward ...
October 21, 2020 · 7 min read. The global impact of COVID-19, the disease caused by the novel coronavirus, means colleges and prospective students alike are in for an admissions cycle like no ...
Paragraph Writing on Covid-19 in 100 Words. Coronavirus is an infectious disease and is commonly called Covid-19. It affects the human respiratory system causing difficulty in breathing. It is a contagious disease and has been spreading across the world like wildfire. The virus was first identified in 2019 in Wuhan, China.
Also Read: Essay on Abortion in English in 650 Words. Short Essay on Covid-19. Please find below a sample of a short essay on Covid-19 for school students: Also Read: Essay on Women's Day in 200 and 500 words. FAQs
This year the Common App, the nation's most-used application, added a question inviting students to write about the impact of Covid-19 on their lives and educations.
In these short essays below, teacher Claire Marie Grogan's 11th grade students at Oceanside High School on Long Island, N.Y., describe their pandemic experiences. Their writings have been ...
For Black students, the number spikes to 25 percent. "There are many reasons to believe the Covid-19 impacts might be larger for children in poverty and children of color," Kuhfeld wrote in the study. Their families suffer higher rates of infection, and the economic burden disproportionately falls on Black and Hispanic parents, who are less ...
We nd that the substantial variation in the impact of COVID-19 on students tracked with existing socioeconomic divides. For example, compared to their more a uent peers, lower-income students are 55% more likely to delay graduation due to COVID-19 and are 41% more likely to report that COVID-19 impacted their major choice.
Publishing Opportunity: Submit your final essay to our Student Editorial Contest, open to middle school and high school students ages 10-19, until April 21. Please be sure to read all the rules ...
As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students' academic achievement has been large. We tracked changes in math and ...
Read these 12 moving essays about life during coronavirus. Artists, novelists, critics, and essayists are writing the first draft of history. A woman wearing a face mask in Miami. Alissa Wilkinson ...
Our findings on academic outcomes indicate that COVID-19 has led to a large number of students delaying graduation (13%), withdrawing from classes (11%), and intending to change majors (12%). Moreover, approximately 50% of our sample separately reported a decrease in study hours and in their academic performance.
Virtually all K-12 students in the United States are currently missing face-to-face instruction due to COVID-19. Many parents and educators thus share a common worry: When the pandemic subsides ...
The CDC reports that the percentage of adults who reported symptoms of anxiety of depression in the past 7 days increased from 36.4 to 41.5 % from August 2020 to February 2021. Other reports show that having COVID-19 may contribute, too, with its lingering or long COVID symptoms, which can include "foggy mind," anxiety, depression, and post ...
Sample Report on COVID-19 in India for Students. The spread of COVID-19 in India began with the first case being reported in Kerala on January 30, 2020. In a year's time, more than twenty-eight million people were tested positive for COVID. Around five million people - the highest recorded number of diagnosed cases - were from Maharashtra ...
The outbreak of COVID-19 forced the world population to find new ways to improve their productivity. It also significantly changed the course of education and instruction entirely. Like many students worldwide, adult English as a Second Language (ESL) students have been dramatically impacted by the sudden switch to online courses due to the COVID-19 outbreak.
Infographic text alternative. If you have COVID-19, there is a high risk that others in your household will catch it from you. There are several things you can do to reduce the spread of infection ...
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