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.
The pandemic and resulting shelter-in-place restrictions are affecting everyone in different ways. Tiana Nguyen, shares both the pros and cons of her experience as a student at Santa Clara University.
person sitting at table with open laptop, notebook and pen
Tiana Nguyen ‘21 is a Hackworth Fellow at the Markkula Center for Applied Ethics. She is majoring in Computer Science, and is the vice president of Santa Clara University’s Association for Computing Machinery (ACM) chapter .
The world has slowed down, but stress has begun to ramp up.
In the beginning of quarantine, as the world slowed down, I could finally take some time to relax, watch some shows, learn to be a better cook and baker, and be more active in my extracurriculars. I have a lot of things to be thankful for. I especially appreciate that I’m able to live in a comfortable house and have gotten the opportunity to spend more time with my family. This has actually been the first time in years in which we’re all able to even eat meals together every single day. Even when my brother and I were young, my parents would be at work and sometimes come home late, so we didn’t always eat meals together. In the beginning of the quarantine I remember my family talking about how nice it was to finally have meals together, and my brother joking, “it only took a pandemic to bring us all together,” which I laughed about at the time (but it’s the truth).
Soon enough, we’ll all be back to going to different places and we’ll be separated once again. So I’m thankful for my living situation right now. As for my friends, even though we’re apart, I do still feel like I can be in touch with them through video chat—maybe sometimes even more in touch than before. I think a lot of people just have a little more time for others right now.
Although there are still a lot of things to be thankful for, stress has slowly taken over, and work has been overwhelming. I’ve always been a person who usually enjoys going to classes, taking on more work than I have to, and being active in general. But lately I’ve felt swamped with the amount of work given, to the point that my days have blurred into online assignments, Zoom classes, and countless meetings, with a touch of baking sweets and aimless searching on Youtube.
The pass/no pass option for classes continues to stare at me, but I look past it every time to use this quarter as an opportunity to boost my grades. I've tried to make sense of this type of overwhelming feeling that I’ve never really felt before. Is it because I’m working harder and putting in more effort into my schoolwork with all the spare time I now have? Is it because I’m not having as much interaction with other people as I do at school? Or is it because my classes this quarter are just supposed to be this much harder? I honestly don’t know; it might not even be any of those. What I do know though, is that I have to continue work and push through this feeling.
This quarter I have two synchronous and two asynchronous classes, which each have pros and cons. Originally, I thought I wanted all my classes to be synchronous, since that everyday interaction with my professor and classmates is valuable to me. However, as I experienced these asynchronous classes, I’ve realized that it can be nice to watch a lecture on my own time because it even allows me to pause the video to give me extra time for taking notes. This has made me pay more attention during lectures and take note of small details that I might have missed otherwise. Furthermore, I do realize that synchronous classes can also be a burden for those abroad who have to wake up in the middle of the night just to attend a class. I feel that it’s especially unfortunate when professors want students to attend but don’t make attendance mandatory for this reason; I find that most abroad students attend anyway, driven by the worry they’ll be missing out on something.
I do still find synchronous classes amazing though, especially for discussion-based courses. I feel in touch with other students from my classes whom I wouldn’t otherwise talk to or regularly reach out to. Since Santa Clara University is a small school, it is especially easy to interact with one another during classes on Zoom, and I even sometimes find it less intimidating to participate during class through Zoom than in person. I’m honestly not the type to participate in class, but this quarter I found myself participating in some classes more than usual. The breakout rooms also create more interaction, since we’re assigned to random classmates, instead of whomever we’re sitting closest to in an in-person class—though I admit breakout rooms can sometimes be awkward.
Something that I find beneficial in both synchronous and asynchronous classes is that professors post a lecture recording that I can always refer to whenever I want. I found this especially helpful when I studied for my midterms this quarter; it’s nice to have a recording to look back upon in case I missed something during a lecture.
Overall, life during these times is substantially different from anything most of us have ever experienced, and at times it can be extremely overwhelming and stressful—especially in terms of school for me. Online classes don’t provide the same environment and interactions as in-person classes and are by far not as enjoyable. But at the end of the day, I know that in every circumstance there is always something to be thankful for, and I’m appreciative for my situation right now. While the world has slowed down and my stress has ramped up, I’m slowly beginning to adjust to it.
COMMENTS
Students can share how they navigated life during the coronavirus pandemic in a full-length essay or an optional supplement. ... "My advice for an essay about COVID-19 is the same as my advice ...
Conclusion. In conclusion, the COVID-19 pandemic has had a profound impact on my life. It affected me physically, mentally, and emotionally and challenged my ability to cope with adversity. However, it also taught me valuable lessons and allowed me to grow as an individual. This is only a sample.
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 ...
variation in the e ects of COVID-19 across students. In terms of labor market expectations, on average, students foresee a 13 percentage points decrease in. the probability of. on, a reduction of 2 percent in their reservation wages, a. d a2.3 percent decrease in their expected earn. ID-19 demonstrate that stude.
In math, however, the results tell a less rosy story: Student achievement was lower than the pre-COVID-19 performance by same-grade students in fall 2019, and students showed lower growth in math ...
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 ...
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 ...
In response to a request from The New York Times, more than 900 seniors submitted the personal essays they wrote for their college applications. Reading them is like a trip through two of the ...
My first two thoughts were mixtures of empathetic concern and selfish relief— "I'm glad I did my study abroad in the fall" and "It must be really tough to be a college senior this year ...
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 ...
Student Life During Pandemic Essay: An Era of Transformation and Resilience. The advent of the COVID-19 pandemic marked the beginning of an unprecedented era, affecting every facet of human life. Among the segments of the population that felt the most significant ripple effects were students.
Read Grogan's essay. ... My mom had COVID-19 for ten weeks. She got sick during the first month school buildings were shut. ... 2021 edition of Education Week as What Life Was Like for Students ...
A long-lasting impact has been created by the notorious COVID-19 from which it'll take many months to recover if not years. The education industry has not been ignored and therefore the impact of COVID-19 on student life is visible. Whether it's the non-public lifetime of students or the environment of college and colleges, the coronavirus ...
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 ...
A new online exhibition of photos explores the student experience of life and learning during the COVID-19 pandemic. COVID-19 has profoundly impacted so many aspects of our lives. There have been mass job losses, emptied supermarket shelves, mandatory social distancing and educational institutions have closed their doors.
Due to the limited social life during the pandemic, these students have also reported feeling lonely, anxious, and depressed ... (2020). Mental health, social and empotional well-being, and perceived burdens of university students during covid-19 pandemic lockdown in Germany. Front. Psychiatry 12:643957. doi: 10.3389/fpsyt.2021.643957. PubMed ...
COVID-19, also known as the Coronavirus, is a global pandemic that has affected people all around the world. It first emerged in a lab in Wuhan, China, in late 2019 and quickly spread to countries around the world. This virus was reportedly caused by SARS-CoV-2. Since then, it has spread rapidly to many countries, causing widespread illness and ...
The COVID-19 pandemic caused increased anxiety, depression, and other mental health concerns that were difficult for my family and me to manage alone. Our ability to learn social resilience skills, such as self-management, was tested numerous times. One of the most visible challenges we faced was social isolation and loneliness.
Special Collection of Essays: Reflecting on the impact of the COVID-19 global pandemic. Guest editors: Wendy Green, Vivienne Anderson, Kathleen Tait and Ly Tran. Student life in the age of COVID-19. Motunrola Bolumole Center for International Higher Education (CIHE), Boston College, Chestnut Hill, MA, USA Correspondence [email protected]
A student wearing a protective mask, attends class on the first day of school, amid the coronavirus disease (COVID-19) pandemic, at St. Lawrence Catholic School in North Miami Beach, Florida, U.S ...
When future historians look to write the story of life during coronavirus, these first-person accounts may prove useful. ... Publishing Opportunity: Submit your final essay to our Student ...
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.
Tiana Nguyen '21 is a Hackworth Fellow at the Markkula Center for Applied Ethics. She is majoring in Computer Science, and is the vice president of Santa Clara University's Association for Computing Machinery (ACM) chapter. The world has slowed down, but stress has begun to ramp up. In the beginning of quarantine, as the world slowed down ...