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Effects of COVID-19
Sunday, Jan. 3, 2021
10:00 AM - 12:00 PM (EST)
American Economic Association
Alexander W. Bartik,
University of Illinois
Learning in the Time of COVID-19
In the wake of the COVID-19 pandemic, many universities closed their on-campus offerings and hastily moved to remote learning during March 2020. Michigan State University was one of these institutions. In addition to moving all classes online, students have been given the option to change their grades to pass/fail after seeing their final grade and were asked to leave on-campus housing if possible. To incentivize students to leave campus, the university offered partial room and board rebates if they did so. While typically over 15,000 students live on campus, this number dropped to 2,000 by the end of March.
At the start of the semester, we collected data from Michigan State University students enrolled in introductory economics courses about their grade expectations and views of economics as a major. In order to understand how students responded to the disruption generated by the pandemic, we additionally collected data from students at the end of the semester on grade expectations and supplementary information about the direct effects of the pandemic on their learning environment, including changes to living arrangements, internet access adequacy, studying behavior, course characteristics, and general well-being. Supplementing this survey data with administrative data on demographic characteristics, actual grade outcomes, the choice of pass/fail options, and future course taking behavior, we investigate how the pandemic affected students and these effects varied with student background characteristics (including Pell grant eligibility, race, gender, GPA, and first generation college). With these unique data, we provide a descriptive analysis of students’ reactions and adaptation to an unprecedented disruption to their educational environment.
The Impacts of COVID-19 on Income and Consumption
This paper uses administrative bank data covering 30 million U.S. households to estimate changes in income and consumption during the COVID-19 pandemic of 2020. We plan to focus on three sets of questions. First, we will measure the share of households that experience a large income drop in March and April 2020 compared with typical months. This number will give a sense of the breadth of COVID-19’s negative impact on household finances. Second, using firm-wide pay changes as an instrument for individual-specific income changes, we will measure the elasticity of consumption with respect to income, again focusing on the March-April 2020 period. By examining the elasticity of specific categories of consumption, we will offer a direct insight into the welfare impacts of the COVID-19-driven halt to much economic activity. Third, we will disaggregate these results by employer industry, geography (state and MSA), race, and household income and liquidity. While many public data sources will reveal the impact of COVID-19 on the macro-economy, we are uniquely able to estimate its effect on relatively granular segments of the population.
Luxury or Necessity: How Will State and Local Governments Balance Budgets in the Wake of COVID-19?
The reduction of economic activity during the Covid-19 crisis is expected to sharply decrease government revenues over the upcoming years, while spending on public health and welfare must grow atypically high. Because state and local governments face balanced budget requirements and public angst against borrowing, these shocks will necessitate a reduction in expenditures across public goods and services. This paper uses the Great Recession shock to test whether governments under income distress simply force revenue effects pro rata on public goods categories according to their budget share, versus the alternative that governments curtail budget shares in goods that exhibit luxury-like behavior in a Deaton demand system. We then forecast the luxury-versus-necessity sensitivities onto the current setting of public expenditures at the state, county and city level.
In Census of Governments spending data covering the population of districts in the United States ($4 trillion for 2020), we find that states, counties and municipalities all shift relatively more funding away from public safety (police, fire and judiciary) and retirement funding. This latter fact implies that short term budget solutions may exacerbate long-term fiscal imbalances. In addition, at the state level, higher education also loses: in response to a 10% revenue shock, states reduce higher education expenditure by 18%. On the other side, capital outlays rise in budget shares in most public good departments, presumably due to commitments.
The fact that higher education, public safety, and pension payments are, to some degree, luxury goods is disheartening. Projected onto Covid-19 setting, the implied preference shifts are small compared to the income effect: a proportional reduction across all goods. Across higher education, public safety, and pension payments, for example, we estimate a reduction in spending nationally of $79 billion, $46 and $62 billion respectively, of which a third is the shift from luxuries to necessities.
How Does the COVID-19 Crisis Affect Access to Mental Health Care? Evidence from an Audit Field Experiment
Crises such as pandemics and recessions increase mental illness and suicidality. Mental health care from mental health professionals (MHPs) such as counselors, therapists, and psychologists, helps significantly with these conditions. However, little is known as to how access to these critical services changes during a crisis or recession, when demand would increase, and mental health issues become exacerbated. This could cause a decrease in appointment access through increased demand. However, since mental health care can often be sensitive to income, demand could decrease, freeing up appointments, and thus increasing access. Also, the movement to telehealth may affect demand for services (more likely) or supply (less likely), since patients or MHPs may have a preference towards or against face-to-face sessions.
To test how appointment access is affected by a crisis, we leverage the unfortunate situation of the COVID-19 pandemic occurring during data collection for our existing audit field experiment which was studying discrimination in access to mental health care.
We have been conducting an online correspondence field experiment since January 2020. We email MHPs listed in online databases; in these emails, fictitious prospective patients with different names (African-American, Hispanic, white, female and male) introduce themselves, mention a general mental health concern (anxiety, stress, or depression), and request an appointment. Additionally, this experiment also varies gender identity, by having some prospective patients be transgender and nonbinary, who mention they are seeking a therapist who is “trans-friendly.”
To quantify how appointment access varies with the COVID-19 pandemic, we can use variation over time, comparing appointment offer rates before, during, and after the pandemic, and otherwise exploring when the pandemic was more or less severe. We can also use variation across geography, where different areas experienced the pandemic at different intensities and at different times.
Occupational Exposure to Contagion and the Spread of COVID-19 in Europe
Social contacts are a key transmission channel of infectious diseases spread by the respiratory or close-contact route, such as COVID-19. There is no evidence, however, on the question of whether the nature and the organisation of work affect the spread of COVID-19 in different countries. I have developed a methodology to measure country-specific levels of occupational exposure to contagion driven by social contacts. I combined six indicators based on Occupation Information Network (O*NET) and the European Working Condition Survey (EWCS) data. I then applied them to 28 European countries, and found substantial cross-country differences in levels of exposure to contagion in comparable occupations.
The resulting country-level measures of levels of exposure to contagion (excluding health professions) predict the growth in COVID-19 cases, and the number of deaths from COVID-19 in the early stage of pandemic (up to eight weeks after the 100th case). The relationship between levels of occupational exposure to contagion and the spread of COVID-19 is particularly strong for workers aged 45-64. I found that 20-25% of the cross-country variance in numbers of COVID-19 cases and deaths can be attributed to cross-country differences in levels of occupational exposure to contagion in European countries. My findings are robust to controlling for the stringency of containment policies, such as lockdowns and school closures. They are also driven by country-specific patterns of social contacts at work, rather than by occupational structures. Thus, I conclude that measuring workplace interactions may help to predict the next waves of the COVID-19 pandemic.
Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China
We quantify the causal impact of human mobility restrictions, particularly the lockdown of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. The lockdown of Wuhan reduced inflows to Wuhan by 76.98%, outflows from Wuhan by 56.31%, and within-Wuhan movements by 55.91%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities -- the epicenter of the 2019-nCoV outbreak -- on the destination cities' new infection cases. We also provide evidence that the enhanced social distancing policies in the 98 Chinese cities outside Hubei province were effective in reducing the impact of the population inflows from the epicenter cities in Hubei province on the spread of 2019-nCoV in the destination cities. We find that in the counterfactual world in which Wuhan were not locked down on January 23, 2020, the COVID-19 cases would be 105.27% higher in the 347 Chinese cities outside Hubei province. Our findings are relevant in the global efforts in pandemic containment.