economics research papers on covid 19

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• Published: 14 October 2021

Epidemiological and economic impact of COVID-19 in the US

• Jiangzhuo Chen 1 ,
• Anil Vullikanti 1 , 2 ,
• Joost Santos 3 ,
• Srinivasan Venkatramanan 1 ,
• Stefan Hoops 1 ,
• Henning Mortveit 1 , 4 ,
• Bryan Lewis 1 ,
• Wen You 5 ,
• Stephen Eubank 1 , 5 ,
• Madhav Marathe 1 , 2 ,
• Chris Barrett 1 , 2 &
• Achla Marathe 1 , 5  

Scientific Reports volume  11 , Article number:  20451 ( 2021 ) Cite this article

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This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-offs between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of the lockdown. Sectors that are worst hit are not the labor-intensive sectors such as the Agriculture sector and the Construction sector, but the ones with high valued jobs such as the Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown.

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Introduction.

According to the Bureau of Labor Statistics, the US unemployment rate in October 2020 stood at 6.9% and the number of unemployed at 11.1 million. This is likely an underestimated number since it does not include individuals who have stopped looking for employment 1 due to poor economic prospects. Even though both measures have declined for 6 months consecutively, the unemployment rate is still higher by  3.5% and the number of unemployed by 5.3 million, compared to pre COVID-19 levels in February 2020. The US economy shrank by an annual rate of 4.8% in the first quarter of 2020 and by a shocking 32.9% in the second quarter, which has been the largest drop seen since 1945. The number of COVID-19 cases have crossed 12 million and number of deaths over 258,000 in November 2020 2 .

This research builds a comprehensive system that combines the epidemiological model developed to study the spread of COVID-19 with a detailed model of the US economy to understand a sector wise economic impact from a shock to labor supply caused by the pandemic. Note that the focus of this paper is only on the shock encountered by the economic sectors from the supply side, and not on the demand side which has also dropped due to the high unemployment rate and a bleak economic outlook. We consider a number of counterfactual scenarios that comprise of various social distancing measures such as the stay-home order, voluntary home isolation of the symptomatic individuals, and school closure. We measure economic losses from the drop in labor supply in each sector due to the stay-home order, absenteeism due to illness and deaths, cascading loss to/from other sectors due to interdependencies between sectors, and the economic burden caused by the medical treatment of the infected. We vary compliance to interventions and duration of the stay-home order to determine their impact on economic and epidemiological outcomes and the trade-offs between them.

This research is an extension of the work done in 3 which only focused on estimating the medical cost of treatment for COVID-19 cases under the same mitigation scenarios and the disease model. Here we calculate overall economic losses from a societal perspective which include the medical cost of illness, cost of intervention or social distancing i.e. healthy individuals unable to go to work, direct loss in productivity due to morbidity and mortality of workers, and the indirect loss caused by the interdependencies between sectors. We also estimate the effect of intervention scenarios on cases and deaths averted in the US.

This level of detailed analysis has not been done in the literature before for COVID-19, and can provide guidance to public health officials for developing strategies to balance the emergence of infections and deaths with the economic costs of the social distancing strategies. A longer duration of stay-home order causes economic losses even after accounting for telework, but it also significantly reduces infections and deaths, and losses caused by the medical treatment of the infected.

Related work

There have been several papers that study the economic impact of COVID-19. Eichenbaum et al. 4 study the interaction between economic decisions and epidemic outcomes and find that the competitive equilibrium is not socially optimal because infected people do not fully internalize the effect of their economic decisions on the spread of the virus. Their results show that an optimal containment strategy that starts early and ramps up with infections, can cause a large recession but save about half a million lives, assuming no treatment or vaccines are available. A counterfactual scenario analysis in 5 shows that a delay of 4 weeks in applying control measures would have slowed the decay of the epidemic by 49 days in China. Work by 6 shows that strict control measures in China led to a steep drop in the temporal effective reproduction number, a metric used for assessing the efficacy of interventions.

Work by 7 shows that differential targeting of risk/age groups outperform uniform social distancing policies. Most of the economic gains in this study are realized from implementing stricter lockdown policies on the oldest age group. However a fully targeted policy can be challenging to implement and ethically questionable. Baker et al. 8 characterize the uncertainty using stock market volatility measures, newspaper-based measures of uncertainty and survey-based perceptions of business level uncertainty; and find that more than half of the contraction in US economy is caused by COVID-induced uncertainty.

Toda (2020) 9 uses an SIR model to study the impact of the epidemic on the stock market. Jones et al. 10 use an SIR contagion model and a model for consumption and production to analyze optimal mitigation policies and interactions between economic activity and epidemic dynamics. They discuss congestion externality i.e. when hospital capacity is exceeded, the risk of death becomes higher but agents do not internalize the impact of their decisions on others and therefore behave in a socially sub-optimal way. Other papers that study the economic impact of COVID-19 and pandemics are 11 , 12 , 13 , 14 .

The ripple effects of pandemics across a regional economy are studied using an input–output model in 15 , 16 . The impacts of pandemic-induced workforce disruptions are assessed using economic losses as well as inoperability, which measures the extent to which sectors are unable to produce their ideal level of output.

The novelty of our research lies in building a detailed integrated system that combines a network based population model with an epidemiological model and an economic model. The disease spreads on the social network, as determined by the COVID-19 disease model; non-pharmaceutical interventions remove particular edges in the social network depending upon the type of interventions and compliance rate; the duration of the interventions determine the length of the time edges are removed for; the outcome of the spread is captured in terms of infections and deaths, which determine the shock to labor-supply in specific economic sectors, as determined by the occupation of individuals who are sick or dead, as well as those who are healthy but unable to work due to a lockdown. These shocks as well as the interdependencies between the sectors determine the sectoral and overall economic impact.

This figure shows the overall modeling framework, its various components and their linkages.

Data and methods

This research integrates a variety of datasets to build a comprehensive model that includes individuals, their interactions, their health states over time as the disease spreads over the social contact network, their behaviors in terms of compliance with interventions and its effect on their health states, and the impact of their health outcomes on each economic sectors’ labor supply and hence sectors’ output. These datasets include, but are not limited to, demographics data from the US Census, daily activity data from American Time Use Survey, travel data from National Household Travel Survey, location data from Open Street Maps, disease model parameters from the US Centers for Disease Control and Prevention, medical costs from Kaiser Family Foundation, data on dependencies between industries from the US Bureau of Economic Analysis, and telecommunting data from 17 . Further details on how these datasets and other data are used in our models are given in the subsections below. Figure  1 shows the overall systems level architecture of the modeling framework, its various components and how they are linked together. Below we describe the various models used in this framework and how they have been synthesized to build an integrated system.

Social contact networks

We use a synthetic social contact network generated using the methodology provided in 18 , 19 , 20 and used in 3 , 21 , 22 , 23 , 24 , 25 , 26 , 27 to study the spread of COVID-19. The social contact network is constructed using a first-principles approach that integrates various commercial and open databases through the following 4 steps. Step 1 constructs a synthetic population of the US by using datasetssuch as the US census block group level distribution data and Public Use Microdata Sample (PUMS) data. Step 2 assigns daily activities to individuals within each household using activity and time-use surveys (American Time Use Survey data and National Household Travel Survey Data). Step 3 assigns a geo-location to each activity that each person performs. The geolocations are based on data from Dun and BradStreet, land-use, Open Street Maps etc. Step 4 constructs a dynamic social bipartite visitation network, when people visit locations for performing activities. A dynamic social contact network is obtained from the colocation of individuals, where nodes are individuals and edges are the contact times. These types of networks have been validated and used to study various infectious diseases, interventions, and public health policy questions. For details on these studies and on the methodology to generate synthetic social contact networks, see 19 , 20 , 21 , 23 , 28 , 29 , 30 , 31 .

Each individual in the social network is endowed with a list of demographic attributes such as age, gender, income, occupation, family size, family income etc. consistent with the data provided by the US Census. A person’s occupation and the associated sector to which the occupation is linked, along with the health state (susceptible, infected or dead) of the person, are used to determine the sector level interruption in labor supply on any day that arises from sickness, mortality or stay-home order. This is the critical piece that joins the disease model with the economic model.

Disease states and transition paths in the COVID-19 disease model.

Disease model

The disease model is the best guess version of “COVID-19 Pandemic Planning Scenarios” prepared by the US Centers for Disease Control and Prevention (CDC) SARS-CoV-2 Modeling Team 32 . It is an SEIR (Susceptible-Exposed-Infectious-Recovered/Dead) model where each individual at any given time is in one of these health states. Everyone starts in the susceptible health state except for the seed nodes who begin in the infected state. Once a susceptible person is exposed to the disease, s/he stays in the exposed state for the incubation period. After that, they move to infectious state. The infected individuals are further divided into presymptomatic, asymptomatic, and symptomatic health states. Only the symptomatic individuals may seek medical care and some of them may become hospitalized while others recover. The hospitalized individuals may further need to be on ventilators. The final health state of the infected is either recovered or dead.

The disease states and transition paths are shown in Fig.  2 . The final disease state can be reached through multiple paths. The transition probabilities for each health state are shown in the Supplemental Information . The model is also age stratified for the following categories i.e. preschool (0–4 years), students (5–17) adults (18–49), older adults (50–64) and seniors (65+) and calibrated for each of the age groups separately. We use the disease model parameters as given by the CDC and do not analyze the sensitivities of disease model parameters to infections and deaths since our focus here is on understanding the effect of interventions and the parameters associated with interventions. The number of deaths simply depend on the number of infections. Age stratified probabilities of death are assigned to infected individuals at different stages of their health state. More details on the disease model, its parameters, and the dynamic values of effective reproduction number under different scenarios are available in the Supplemental Information .

Non-pharmaceutical interventions

We apply a number of social distancing strategies to mitigate the spread of COVID-19 3 . We assume there are no vaccines available and non-pharmaceutical interventions (NPI) are the only way to control the spread of COVID-19. We use the following NPI strategies: (i) Voluntary home isolation (VHI)—symptomatic people choose to stay at home (non-home type contacts are disabled) for 14 days. (ii) School closure (SC)—schools and colleges are closed (school type contacts are disabled). (iii) Stay home (SH)—a lockdown order directs people to “stay-home” (non-home type contacts are disabled).

School closure and stay-home interventions start on different days in different states as stated in 33 , 34 . Once closed, schools are assumed to remain closed until end of August. The duration and compliance to social distancing measures vary across scenarios as shown in Table  1 .

Stay-home durations are set at 0, 30, 45 to 60 days. Compliance to SH and VHI are set at 60%, 70%, 80% and 90%. Table  1 lists all the scenarios including the unmitigated one. For each experimental cell, 25 simulation replicates are run and results are shown based on the average values across these runs. Table  2 shows the parameters used in the experiments for easy reference.

Medical costs

Medical cost of treating COVID-19 patients under different health states are taken from 3 , 35 , which provide the average payment for treating pneumonia cases among “large employer health insurance” plans, and under different severity levels. See Table  3 . In the absence of COVID-19 treatment cost data, the pneumonia estimates have been used as a proxy. Note that each infected individual’s medical cost is counted only once. For example if a person is in ventilated state, after having gone through “medAttend” and “Hosp” state, costs are cumulative to the “vent” state 3 .

To estimate the medical costs of COVID-19 for each scenario, we multiply the number of medically attended, hospitalized, and ventilated with the estimated treatment costs per person given in Table  3 . This is repeated for each replicate in the simulation and the average estimates are reported. Note that an earlier paper focuses entirely on the medical costs 3 and provides more details on medical costs to the interested reader.

This figure shows interdependencies between sectors as given by the US Bureau of Economic Analysis. The left sector flows are input to right sectors.

Economic sectors and their interdependencies

We use the summary level input–output (I–O) tables for 2018 downloaded from the US Bureau of Economic Analysis (BEA) 36 , which quantify how industries depend on each other and interact with each other, to capture the cascading effect of labor supply shock across industries. The entire economy is divided into 71 industries; the I–O data reflects the structure of the US economy and the relative importance of each industry with respect to all other industries. We follow the NAICS (North American Industry Classification System) codes to aggregate the I–O data to sector level (21 sectors). Figure  3 shows the interdependencies between the 21 sectors. The row sectors of Fig.  3 provide input to the column sectors of the figure.

Data on telework by sector

During the stay-home order, some individuals are able to work from home. However, the ability to work from home (WFH) and the productivity of WFH workers vary by the type of sector the individuals are employed in. Authors in 17 , 37 estimate the number of jobs that can be done from home in the US. Work in 17 combines the feasibility of working from home by occupation, with occupational employment counts, and determines that 37% of all jobs in the US can be done from home.

Although this is not uniform across all sectors and cities; sectors like computing, education, legal and financial can be largely operational from home but construction, farming and hospitality cannot be 17 , provides the fraction of jobs that can be done from home by NAICS (North American Industry Classification System) and by SOC (Standard Occupational Classification) occupation. We use this fraction for each sector (as shown in Fig.  4 ) to determine the fraction of labor that can work from home. In addition 17 , provides the fraction of teleworkable wages for each sector. Together, these fractions determine the level of productivity that can be maintained during a lockdown by the healthy workforce in each sector. The health of each individual is tracked by the disease model given in section 3.2.

This figure shows the fraction of jobs in each sector that can be done from home. While jobs in accommodation, agriculture, retail, construction, and transportation sectors are difficult to be done from home, those in education, professional, management, finance, and information sectors can be largely operational from home.

Input–output model

We use the Dynamic Inoperability Input–Output Model (DIIM) stated in 16 , 38 to study the effect of labor supply shock arising from the morbidity and mortality caused by COVID-19, as well as the enforcement of the stay-home order, on national productivity. The DIIM model uses the classic input–output (I–O) economic analysis of Leontief (1935) 39 to account for the interdependencies between sectors.

I–O models have proven useful in accounting for the flows of goods and services across producing and consuming sectors of the economy. In the US, the Bureau of Economic Analysis is responsible for publishing I–O tables, gross domestic product, and other economic multipliers that are useful for conducting impact analysis of disasters. Each sector requires inputs from other sectors, and in turn produces outputs that are either used as intermediate inputs by other sectors or finished goods or services to satisfy exogenous final demands. Aside from the intermediate inputs, the so-called “value added” is a category of production inputs that are considered exogenous to the interdependent sectors. Within the “value added” is labor, which is the focus of this paper since it is the factor of production that is rendered “inoperable” by the pandemic. The DIIM quantifies the initial sector inoperability parameters by determining the extent to which labor is impacted in each sector.

Additionally, it allows modeling of resiliency parameters within the I–O model to signify sector wise recovery rates. Of particular relevance to this paper are resilience strategies, such as teleworking, that a sector can implement in order to reduce the impact of labor availability on its production of goods and services. We use the DIIM model to estimate the direct effect of drop in labor supply to each sector due to sickness, deaths and lockdown, as well as the indirect effect to sectors that arise due to interdependencies between sectors.

Depending upon the scenario considered in the simulation, appropriate interventions are applied to the social network. The interventions result in removal of edges on a temporal basis in the social contact network. For example, a stay-home order results in removal of all non-home edges of the compliant individuals for the duration of the order. The COVID-19 disease model is seeded and run on this time-varying social contact network over a period of one year. Everyone in the population is assumed to be susceptible at the beginning of the simulations except the seed nodes or the index cases, which are assumed to be infected. As the disease spreads through the network, the simulation generates a time series of daily infections. The infected individuals are further divided into medically attended, hospitalized, ventilated and dead, based on the probabilities assumed in the disease model.

To calculate the labor supply shock to each sector and its impact on productivity, we estimate (i) the number of infected and dead each day in each sector (using occupation and NAICS codes) and calculate the fraction of labor that is unable to work; (ii) the healthy individuals who comply with the stay-home order and do not go to work, and also cannot work from home given their occupation-type, as determined by telecommuting data for each sector 17 ; and (iii) healthy individuals who can work from home but their productivity is reduced as suggested in the teleworkable wages for each sector in 17 .

Results and discussion

We calculate the economic losses under the unmitigated scenario and the mitigation scenarios. Mitigation efforts help control the spread of the disease and hence reduce the total number of infections but they also increase the economic losses due to social distancing measures like the stay-home order. The compliance to NPIs and the length of the NPIs determine the extent of the loss, which can be weighed against the benefits measured in terms of reduced number of infections and deaths.

Economic losses due to inoperability and NPIs

Figure  5 shows the economic losses due to the inoperability of sectors under different NPIs and the infections caused by the pandemic. The left subfigure does not include the economic burden imposed by the treatment and medical services given to the infected individuals.

Lockdown and other social distancing measures reduce the labor supply to sectors but these measures do not uniformly affect each sector’s output. Depending upon how labor-intensive a sector is, how many jobs can be done from home, and how much value each job generates in a sector, the lockdown has a differential impact on each sector. For example, Education, Professional services, and Management sectors are teleworkable at 80% or higher levels whereas Accommodation (includes hospitality and food services) is at 3% and Agriculture is at 7%.

Inability to work from home in Construction and Agriculture sectors should imply more losses in these sectors. However we find that the losses are higher in Education, Professional services and Management sectors because jobs in these sectors pay more on average than the jobs in Construction and Agriculture sectors. Hence even a 20% loss in work in the former sectors can result in a higher total loss in value compared to a 90% loss in work in the latter sectors.

Overall economic losses from inoperability also depend on the level of dependency each sector has on others. Agriculture and Construction sectors have a higher level of dependency on other sectors compared to Education, Professional and Management sectors as shown in Fig.  3 . The lack of self-reliance increases the potential for losses caused by the cascading effect from other sectors.

The results in left Fig.  5 show that as the duration of SH order increases, the economic losses increase for a given compliance rate. This is because a longer SH order implies that healthy individuals are not able to work. A longer SH order also reduces the number of infections and deaths and hence improves labor supply and productivity. There is less absenteeism due to sickness and death, and less cascading effect on other sectors. The overall drop in productivity from a longer SH order shows that the gain in productivity from fewer infections and deaths is less than the loss from a longer shutdown. However SH order saves tens of thousands of lives and millions of infections as described in Section 4.4.

In the unmitigated base case, the economic loss is low but the loss due to morbidity and mortality is high. The healthy individuals are assumed to be working in the unmitigated scenario since no NPIs are in effect. The drop in productivity is caused only by the drop in labor supply due to illness and deaths since there is no lockdown in place. However, in the unmitigated case, more than 117,000 lives are lost and over 116 million infections occur.

Economic losses due to inoperability, NPIs and medical treatments

In Fig.  5 , the right subfigure shows the losses that are included in the left Fig.  5 plus the economic burden caused by the medical treatment of the ill. Note that the total loss in the unmitigated case without medical costs is $0.38 trillion in left Fig.  5 whereas with medical costs, this loss increases to $1.15 trillion as shown in the right Fig.  5 . The extra $0.8 trillion is solely due to the medical costs of treating infections in the unmitigated case. Note that for a given compliance level, a longer SH always results in a higher loss. However for a given SH duration, a higher compliance may result in a lower or higher loss. This would depend upon the relative gain from reduced infections versus the losses from SH of healthy individuals.

For example, in right Fig.  5 when the SH duration is set at 60 days, increasing compliance from 60 to 70% decreases the economic loss but increasing compliance from 70 to 80% increases the economic loss. This is because compliance has a non-linear effect on losses. At low levels of compliance, the marginal effect of a small increase in compliance is high because it helps get the pandemic under control which implies less absenteeism due to illness and lower medical costs. An increase in compliance from 70 to 80% does not have the same incremental effect on infections because 70% compliance is already quite effective, but has a large effect on the inoperability of sectors because a larger critical mass of workers are staying home.

The left subfigure shows I–O economic losses (without medical costs) due to NPI measures and infections for each of the scenarios. The percentages show compliance to NPIs and “d” is for the duration of the Stay-home order. These losses arise from the drop in labor supply to sectors, caused by the lockdown, illness and mortality, and from interdependencies between sectors. It does not include the economic burden imposed by the treatment and medical services provided to the infected individuals, whereas the right subfigure includes this medical burden.

Trade-offs between compliance and duration of lockdown

Both subfigures in Fig.  5 show that there are tradeoffs between compliance and the length of the SH order. Low compliance can be compensated by a longer SH order and a shorter SH order can be combined with a higher compliance level to reach the same level of total loss. For example, in left Fig.  5 , a 60% compliance rate combined with a 60 days of SH results in similar total loss as a 90% compliance rate combined with a 45 days of SH.

The best outcome is reached when the lockdown is for 30 days and the compliance rate is at least 80%, as shown in the right Fig.  5 . It is clear that a lengthy SH order is harmful to the economy so a short SH order combined with a high level of compliance is ideal. Note that these tradeoffs and losses do not include the long term effect of deaths, i.e. the permanent loss in productivity, and only consider loss in labor supply for the duration of the simulation. The number of deaths depend on the duration and compliance to NPIs and are an important metric in measuring the outcomes. Later plots show the number of infections and deaths averted under each scenario.

Trade-off between the number of infections averted and economic losses under each scenario. The vertical and horizontal bars show the inter-quartile range.

Trade-off between the number of deaths averted and economic losses under each scenario. The vertical and horizontal bars show the inter-quartile range.

Infections and deaths averted versus economic loss

Figures  6 and 7 show the trade-off between the number of infections-averted and economic losses, as well as the number of deaths-averted vs. economic losses respectively, under each of the intervention scenarios. Several important observations can be made from these plots: (i) The base case, where no NPIs are in place has the least loss but results in over 100 million infections and over 100,000 deaths. (ii) The trend for both morbidity and mortality is the same under different scenarios. (iii) Losses rise with longer durations of SH order. (iv) A SH order of 45 days results in same economic loss whether the compliance is at 70% or 80%. However the numbers of infections and deaths averted are much higher at 80% compliance. (v) Similarly, once 90% compliance is reached, an increase in SH duration from 45 to 60 days does not reduce infections and deaths but adds more than one trillion in economic losses. (vi) A longer lockdown can compensate for the lack of compliance and a higher compliance can reduce the duration of the lockdown in order to achieve similar number of infections and deaths but these trade-offs are non-linear. These kinds of analytics are useful in informing public health policy.

An intuitive display of differences in medical loss, total loss, infections averted and deaths averted, by scenarios, is shown through the heat maps in the Supplemental Information .

Daily I–O loss across all sectors for each scenario. The unmitigated base case is shown by the black curve where no NPIs are in place. Percentages are compliance to NPIs and “d” is the duration of SH order.

Sector level total loss for each scenario. The black and orange markers are highlighted to show the unmitigated and mitigated case \(VHI\_70\_SH\_70\_45\) respectively. Percentages are compliance to NPIs and “d” is the duration of SH order.

Sector level economic losses

We calculate sector level losses to understand how each sector will be impacted under different intervention scenarios. Figure  8 shows the daily loss across all sectors for each of the scenarios, including the unmitigated one. The percentages show compliance to VHI and SH and “d” reflects the duration of SH order. On the top the curves are clustered by the duration of SH order. The longer the SH order, the wider the top is; reflecting a more sustained loss at peak level during the lockdown period. Note that a second peak occurs only in scenarios where the compliance is low or compliance and duration both are low. As expected, the economic loss is higher in all intervention scenarios compared to the unmitigated scenario, since NPIs keep healthy people from going to work. However, as shown in Figs.  6 and 7 , these NPIs are able to avert over 100 million infections and over 100,000 deaths.

Figure  9 shows total loss for each sector and for each scenario, across time. The unmitigated case, marked in black, shows the least amount of economic loss since there are no NPIs in place. For comparative analysis, we select a medium level scenario, \(VHI\_70\_SH\_70\_45\) , and discuss in more detail. This is highlighted and marked in orange. In most of the cases, the sectors that encounter biggest losses are Government, Durables, Health and Non-durables.

Figure  10 shows a detailed comparison of sectoral loss for the unmitigated and a mitigated scenario over time. These do not include any medical costs. The left subfigure shows that without any mitigation, the highest losses occur in Government, Professional, Durables, Health and Finance sectors. Note that these losses are caused by loss in labor force due to sickness and deaths. There are no NPIs in effect in the unmitigated case. Even though inoperability is higher in sectors like Agriculture, Construction and Accommodation which tend to be more labor intensive, the value generated by the same proportional loss in labor is higher in Government, Professional, Durables, Health and Finance sectors due to their higher per capita productivity.

Detailed analysis of an intervention scenario

Here we provide a detailed sector level analysis of one of the 12 mitigation scenarios. We pick \(VHI\_70\_SH\_70\_45\) as an example case since it represents a mid-level scenario. The right subfigure in Fig.  10 shows daily losses in each of the sectors under this scenario and relative rankings of sectors when NPIs are in effect. The top 5 sectors in terms of biggest economic losses are Government, Durables, Non-durables, Health and Retail.

Even though the inoperability in these sectors is not that high due to NPIs, these sectors have higher wages and represent higher values compared to sectors which are more labor intensive. Top 5 sectors that have the highest inoperability due to labor supply shock from mitigation are Accommodation, Retail, Agriculture, Transportation and Construction but their losses are relatively low because of the low wages in these sectors. Other major factors that affect the losses in each sector are the extent to which the employment and wages are teleworkable. For example, in the unmitigated case, the worst performers include Finance because even a small shock to labor supply in this sector causes a big loss in value compared to a similar shock to sectors like Agriculture; but in the mitigated case, Finance sector performs relatively better because 76% of its jobs and 85% of the wages in Finance are teleworkable, whereas in Agriculture it is only 7% and 13% respectively.

Even in the mitigated case the Government sector has the highest loss, partly because it is also the largest sector in the economy and partly because it has a very high dependency on Durables, Non-durables and Professional which themselves are hit hard. Additionally in the Government sector, only 41% of the jobs and 46% of the wages are teleworkable.

Sector level daily losses caused by the inoperability of each sector and its cascading impact on other sectors due to interdependencies between sectors. The left subfigure shows losses for the unmitigated case. The right subfigure shows losses for the mitigated scenario VHI_70_SH_70_45. The ordering of sectors in the legend is ranked by the height of the curve. Note that the scale of loss (y-axis) in the right subfigure is ten times of the left subfigure.

Best mitigation scenario

The best mitigation scenario in terms of lives saved and infections averted is when the compliance is at 90% and SH duration is 45 days. See Figs.  6 and 7 . This scenario results in a total economic loss of about $3.4 trillion dollars. However, it also saves more than 110,000 lives and 115 million infections compared to the unmitigated case. Assuming US federal government’s estimate of value of life which is $10 million per person 40 , 41 , lowering the number of deaths would save $1.1 trillion and lowering number of infections would save medical costs equivalent to $0.8 trillion, resulting in a gain of about $1.9 trillion from the mitigation efforts and a net economic loss of $1.5 trillion. This kind of simulation based analysis can help prioritize epidemiological and economic goals, understand their trade-offs, and guide public health policy.

Limitations

This study does not consider the demand side shock to the economy that results in drop in demand for goods and services due to lower employment, lost wages, and uncertain economic conditions. Unlike the general equilibrium model where demand and supply shock result in price adjustment, the input–output model does not capture the price dynamics that arise from changes in demand and supply. The treatment costs are average costs for treating pneumonia patients as available from 35 which do not vary by age, but only by severity of the case and these are used as proxies for COVID-19 medical costs.

Summary and conclusions

This study estimates the epidemiological and economic impact of several counterfactual intervention scenarios to contain the spread of COVID-19. Results show that any intervention involving a stay-home order will result in significant economic losses. However, the epidemiological impact of these interventions is dramatic. We find that interventions scenarios involving 45 days of SH order and a high compliance to NPIs can save more than 110,000 lives and 115 million infections compared to the unmitigated case.

We perform a sector level impact analysis and find that losses depend on the level of labor supply shock, the ability of employees to work from home, the productivity of workers who work from home and the dependency between sectors. The sectors that are more labor intensive such as Agriculture and Construction are not the worst performers because the per capita value generated is lower in these sectors compared to sectors like Government and Health.

Our results also show trade-offs between the economic losses and the number of deaths and infections averted. A longer lockdown and/or a high compliance to NPIs result in higher economic losses but save lives and reduce the number of COVID-19 infections. There is also a trade-off between duration of the lockdown and the rate of compliance to NPIs. If people are non-compliant to NPIs, public health policy-makers can increase the duration of the lockdown to get the same level of results in terms of infections and deaths averted.

Data availability

All the output data reported in the paper is available upon request, but restrictions apply on the commercially available data used in the construction of the social contact network and hence the availability of the social network data itself.

Code availability

Code developed to analyze the results and support the findings in this paper is available upon request, from the corresponding author.

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Acknowledgements

This work was partially supported by National Institutes of Health (NIH) Grant R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF DIBBS Grant ACI-1443054, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, US Centers for Disease Control and Prevention 75D30119C05935, DTRA subcontract/ARA S-D00189-15-TO-01-UVA, and collaborative seed grants from the UVA Global Infectious Disease Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsoring agencies.

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A.V., J.C., A.M., M.M. and C.B. conceived the project. J.C., S.H., H.M., A.M., S.E., S.V., J.S. and B.L. built the model and the software. A.V., J.C., J.S., S.V., W.Y. and A.M. processed and analyzed the data. All authors helped write, edit and review the paper.

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Chen, J., Vullikanti, A., Santos, J. et al. Epidemiological and economic impact of COVID-19 in the US. Sci Rep 11 , 20451 (2021). https://doi.org/10.1038/s41598-021-99712-z

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The Economic Impact of COVID-19 around the World

This article provides an account of the worldwide economic impact of the COVID-19 shock. In 2020, it severely impacted output growth and employment, particularly in middle-income countries. Governments responded primarily by increasing expenditure, supported by an expansion of the supply of money and debt. These policies did not put upward pressure on prices until 2021. International trade was severely disrupted across all regions in 2020 but subsequently recovered. For 2021, we find that the adverse effects of the COVID-19 shock on output and prices were significant and persistent, especially in emerging and developing countries.

Fernando Martin is an assistant vice president and economist, Juan M. Sánchez is a vice president and economist, and Olivia Wilkinson is a senior research associate at the Federal Reserve Bank of St. Louis.

INTRODUCTION

For over two years, the world has been battling the health and economic consequences of the COVID-19 pandemic. As of the writing of this article, deaths attributed to COVID-19 have surpassed six-and-a-half million people.  Global economic growth was severely impacted: World output by the end of 2021 was more than 4 percentage points below its pre-pandemic trend.  International trade was also significantly disrupted at the onset of the pandemic. The pandemic also prompted a strong policy response, resulting in a rise of government deficits and debt as well as widespread increases in the money supply. Finally, after an initial decline, prices have soared, resulting in elevated inflation rates.

This article provides an account of the worldwide economic impact of the COVID-19 shock. This shock was not felt simultaneously around the world, and mitigation policies, both health related and economic, varied substantially across countries. Yet there are some significant similarities in outcomes, especially when considering the pandemic period as a whole. Our analysis focuses on the shock's effects on specific groups of countries, related by their level of development and geographical location.

We find that the COVID-19 shock severely impacted output growth and employment in 2020, particularly in middle-income countries. The government response, mainly consisting of increased expenditure, implied a rise in debt levels. Advanced countries, having easier access to credit markets, experienced the highest increase in indebtedness. All regions also relied on monetary policy to support the fiscal expansion, and hence the money supply increased everywhere. The specific circumstances surrounding the shock implied that the expansionary fiscal and monetary policies did not put upward pressure on prices until 2021. International trade was severely disrupted across all regions in 2020 but subsequently recovered. When extending the analysis to 2021, we find that the adverse effects of the shock on output and prices have been significant and persistent, especially in emerging and developing countries.

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Research and Perspectives for the Pandemic Main Page   | Economic Impacts

MIT Economics has launched a new webpage to aggregate the department's ongoing research into the economic impacts of the Covid-19 pandemic. Please refer to the page for all entries and updates as research continues : MIT Economics: Working Papers on Covid-19 Research

INITIAL PAPERS | APRIL AND MAY 2020

Paying It Backward and Forward: Expanding Access to Convalescent Plasma Therapy Through Market Design Scott Duke Kominers, Parag A. Pathak, Tayfun Sönmez, M. Utku Ünver

A Multi-Risk SIR Model with Optimally Targeted Lockdown  | Online MR-SIR Simulator Daron Acemoglu, Victor Chernozhukov, Iván Werning, Michael D. Whinston

Policy implications of models of the spread of coronavirus: Perspectives and opportunities for economists Christopher Avery, William Bossert, Adam Clark, Glenn Ellison, Sara Fisher Ellison

A Model of Asset Price Spirals and Aggregate Demand Amplification of a "Covid-19" Shock Ricardo J. Caballero and Alp Simsek

Triage Protocol Design for Ventilator Rationing in a Pandemic: Integrating Multiple Ethical Values through Reserves Parag A. Pathak, Tayfun Sönmez, M. Utku Ünver, M. Bumin Yenmez

Reopening Under COVID-19: What to Watch For Jeffrey E. Harris Abstract.   We critically analyze the currently available status indicators of the COVID-19 epidemic so that state governors will have the guideposts necessary to decide whether to further loosen or instead retighten controls on social and economic activity. To that end, we study the incidence of new COVID-19 infections in the state of Wisconsin and in San Antonio, Texas, numbers of deaths attributable to COVID-19 in Los Angeles County, the state of New Jersey, and New York City, hospitalization rates in New York City, and the daily patient census in intensive care units in Orange County, California. At least in some instances, enhanced availability of coronavirus testing has upwardly biased observed trends in infection rates. Data on numbers of deaths have, for the most part, been completely misinterpreted. Healthcare system-based indicators, such as rates of hospitalization or ICU census counts, are likely to be more reliable. Models to guide future policy decisions are severely limited by untested assumptions. Universal coronavirus testing may not on its own solve difficult problems of data interpretation and causal inference .

The Coronavirus Epidemic Curve is Already Flattening in New York City Jeffrey E. Harris

New York City has been rightly characterized as the epicenter of the coronavirus pandemic in the United States. Just one month after the first cases of coronavirus infection were reported in the city, the burden of infected individuals with serious complications of COVID-19 has already outstripped the capacity of many of the city's hospitals. As in the case of most pandemics, scientists and public officials don't have complete, accurate, real-time data on the path of new infections. Despite these data inadequacies, there already appears to be sufficient evidence to conclude that the curve in New York City is indeed flattening. The purpose of this report is to set forth the evidence for- and against- this preliminary but potentially important conclusion. Having examined the evidence, we then inquire: in the curve is indeed flattening, do we know what caused it to level off?  

Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages? Veronica Guerrieri, Guido Lorenzoni, Ludwig Straub, and Ivan Werning

We present a theory of  Keynesian supply shocks:  supply shocks that trigger changes in aggregate demand larger than the shocks themselves. We argue that the economic shocks associated to the COVID-19 epidemic- shutdowns, layoffs, and firm exits- may have this feature. In one-sector economies, supply shocks are never Keynesian. We show that this is a general result that extends to economies with incomplete markets and liquidity constrained consumers. In economies with multiple sectors Keynesian supply shocks are possible, under some conditions. A 50% shock that hits all sectors is not the same as a 100% shock that hits half the economy. Incomplete markets make the conditions for Keynesian supply shocks more likely to be met. Firm exit and job destruction can amplify the initial effect, aggregating the recession. We discuss the effects of various policies. Standard fiscal stimulus can be less effective than usual because the fact that some sectors are shut down mutes the Keynesian multiplier feedback. Monetary policy, as long as it is unimpeded by the zero lower bound, can have magnified effects, by preventing firm exits. Turning to optimal policy, closing down contact-intensive sectors and providing full insurance payments to affected workers can achieve the first-best allocation, despite the lower per-dollar potency of fiscal policy.  

The Geography of COVID-19 growth in the US: Counties and Metropolitan Areas William C. Wheaton, Anne Kinsella Thompson

It has been 70 days since the first case of COVID-19 was detected in the US. Since then it has spread and grown in all but 2 of 376 MSAs and all but 45 of the 636 counties that are contained in these MSA. In this paper we examine the determinants of how rapidly the virus grows once it has been seeded within a MSA or county. We find virus cases can be well predicted by area population, as well as days-since-onset. In the data, virus cases scale almost proportionately with population, and excluding population significantly changes the impact of days-since-onset. Growth is also related to residential density and per capita income, particularly at the county level. There are weaker relationships to MSA average household size, per capita income, and the fraction of the population that is over 65. These results come from parameterizing a simple power function model of cumulative infections since onset. This is shifted proportionately by the various MSA/County covariates. We also experiment with restricting the sample of areas so as to have a minmum number of cases- equal to .01% of the area's population. This effectively focuses on the more advanced part of the virus growth curve. Here we find a significant further decrease in the coefficient of days-since-onset. This is preliminary evidence that the virus growth is tapering. We intend to repeat our analysis as time progresses.  

Celebrities and Public Health Campaigns: A Nationwide Twitter Experiment Promoting Vaccination in Indonesia Vivi Alatas, Arun Chandrasekhar, Markus Mobius, Benjamin Olken, and Cindy Paladines

We ask whether celebrities can help spread information about public health, above and beyond the fact that their statements are seen by many, and ask how they can be most effective in doing so. We conducted a nationwide Twitter experiment with 46 high profile Indonesian celebrities and organizations, with over 11 million followers, who agreed to randomly tweet or retweet content promoting immunization. Our design exploits the structure of what information is passed on along a retweet chain on Twitter to decompose how celebrities matter. We find that messages that can be identified as beinig authored by celebrities are 72 percent more likely to be passed or liked compared to similar messages without a celebrity imprimatur. Decomposing this effect, we find that 79 percent of the celebrity effect comes from the act of celebrity authorship itself, as opposed to merely passing on a message. Explicitly citing an external source decreases the likelihood of passing the message by 27 percent. The results suggest that celebrities have an outsize influence in shaping public opinion, particularly when they speak in their own voice.

Suggested links MIT Economics: Working Papers on Covid-19 Research MIT Economics

Covid-19: Economic Impacts

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COVID-19 has been one of the greatest survival threats and economic crises in history, and researchers in the field of economics have made significant contributions with respect to various economic issues. This study presents a bibliometric analysis of the 1,396 articles related to COVID-19 that were published in economics journals indexed by the Social Sciences Citation Index (SSCI) over the 2019–2021 period, and it provides helpful insights into the number, share and length of publications, the rate of cooperation, comparative statistics with economic studies related to other pandemics, analyses of countries and institutions, and citation analysis. A co-word analysis based on the keywords and thematic noun phrases in the titles and abstracts of the sample papers is used to explore hot research topics (e.g., lockdown, health, policy, employment, stock market, social distancing, poverty, mortality, food security, global financial crisis, supply chains, mental health), and detailed content analysis on all these research hotspots is conducted. This study does not focus on so-called rankings, which is different from general bibliometric research and COVID-19-related literature analysis research, and the results complement existing works.

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Wei, G. The Features and Trends of the Economic Literature Related to COVID-19: A Bibliometric Analysis. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01745-y

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Economics and economists during the COVID-19 pandemic: a personal view

Monika bütler.

SEW-HSG, University of St. Gallen, St. Gallen, Switzerland

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No proprietary or original data, data sources for supporting plots are mentioned in the text.

As was true for many others, my professional life was turned upside down in the early days of the pandemic. The crisis touched almost every field in economics: international supply chains broke down, economic activity was heavily constrained either by non-pharmaceutical measures to fight the pandemic or by voluntary action, and the labour market experienced unprecedented levels of short-time work and huge (temporary) lay-offs. Governments struggled to provide cash and find ways to compensate affected people and businesses. Financial markets tumbled and monetary policy faced new challenges on top of an already tense situation.

Introduction

This is not a research paper, nor is it a literature survey on economic aspects of the pandemic. It is my own unbalanced assessment of economics as an academic discipline and the work of economists during the pandemic’s first two years with a clear focus on Switzerland. Why personal? A fascinating research strand, nicely summarized by one of its founders, Ulrike Malmendier ( 2021 ) , shows that personal experiences of economic events and outcomes, from global crises such as a pandemic to individual experiences such as a job loss, can shape individual expectations and attitudes. These changes in perception, and perhaps even preferences, can impact choices for years to come. It would thus be naive and logically inconsistent to believe that academic economists were untouched by their environment or by their personal experience.

It is thus only fair to disclose my own predisposition: I have always been interested in policy, be it social or economic. When I traded math/physics for economics, the proximity of social aspects was key. Individuals and countries are not machines that can be understood without a context, let alone be programmed. The same is true for medical science, my second lay passion. I wrote my diploma thesis on the progression of AIDS in the late 80 s, with the first available individual-level data on the disease. Last, but not least, I have always kept one foot outside academia. Considering issues from another angle helps to come up with more nuanced views. But even after leaving university, I am still a scientist at heart, an empiricist to be more precise.

In fact, academic research has provided an important backbone for those of us working on public policy during the crisis. Literally, thousands of papers by economists on issues around COVID-19 have mushroomed since the onset of the crisis. The two most important research networks in economics alone, CEPR and NBER, published more than 1000 high-quality COVID-19-related research papers in the first 15 months of the pandemic. While the frequency of new contributions is somewhat petering off with the research on the pandemic maturing, there will be many more to come. This journal—with its Special Focus on COVID-19—has done an excellent job in publishing papers that are based on sound economic research but cover topics that are relevant to (Swiss) economic policy (Tille et al., 2022 ).

In this paper, I look at the role of economics as an academic discipline and academic economists during the pandemic. Starting from the efforts to shed light on the economic aspects of the crisis in research, I will comment on the challenges economists faced when directly involved in policy advice and working in interdisciplinary groups of academics and under time pressure. Two important qualifications: To summarize academic economists’ contribution during the pandemic would break the mould of such an essay. The paper thus has a clear focus, or call it bias, first, on Switzerland and, second, on public work done by economists in the Swiss National COVID-19 Science Task Force (ncs-tf).

The first part of the paper focuses on the “how”. After a short overview of the research efforts related to COVID-19, I describe the role of academic economists in firefighting teams such as the Swiss National COVID-19 Science Task Force, and in informing decision-makers and the public during the pandemic. This was no easy task as interests among the public and within the private sector often conflicted and acceptable trade-offs in terms of measures to fight the pandemic were difficult to be found. In contrast to other academic disciplines involved during the pandemic, however, academic economists had ample experience in working with politics and the administration due to prior exposure to global crises, notably the financial crisis.

The paper’s second part deals with the “what”: It describes, among other things, the importance of data and the sound application of economic principles to answer new questions emerging during the pandemic. Two findings stand out. First, simple economic ideas and tools were extremely useful in understanding the crisis and come up with good ideas for policy. Second, academic economists were very creative in finding new data or new ways to display them and thus contributed in important ways to better policy options.

An essay covering an important and severe, yet ultimately limited, crisis would not be very insightful if it did not lend itself to learnings that survive the pandemic. I will try to comment on potential improvements for (policy) work by economists throughout the paper and summarize these lessons in my conclusions. One important lesson is that academic scholars—be it in economics or other fields—should not be punished for providing public goods such as communicating to the public, or analysis not directly publishable in reputable journals. On the contrary: we need ways to support internationally respected scholars in venturing out of the ivory tower for certain periods in their career.

Getting on stage: from research to economics for the public

It is perhaps a bit unconventional to start with the “how” instead of the “what”. But to understand the role of economists during the crisis it helps to see in what areas they have been active.

Research as the backbone for policy advice

In the absence of a major pandemic for over a century, relatively little research on the economics of pandemics existed that could be taken from the shelf. A few papers on the Spanish flu offer insights into the nature of health–wealth trade-offs (see, for example, Correia et al. ( 2020 )). Other work explores a pandemic’s long-run consequences on economic outcomes (such as Almond ( 2006 )). Related to the latter—and potentially very important to understand the (economic) long-run effects of COVID-19—a number of papers look for and find an impact of early life health shocks on labour market outcomes and other variables (Almond et al., ( 2018 ) and literature cited therein).

The lack of research did not last long, as Charles Wyplosz, the editor of Covid Economics, remarked:

Within days of the onset of Covid, hundreds of economists dropped their ongoing work. They relied on well-established theories and techniques and on quickly expanding real-time data to fill a vacuum in the well-established field of epidemiology. ( Wyplosz, 2021 , p.1)

And further: “Epidemiologists tracked viruses, economists looked at people behaviour and government responses.” ( Wyplosz, 2021 , p.1) Which is not quite correct, of course. Economic activity and the spread of viral diseases interact, as Adda ( 2016 ), among others, had pointed out well before the crisis. During the pandemic, many economics papers also ventured into epidemiologic modelling. While some economists may have welcomed new questions primarily for the sake of research in a pretty saturated environment, most were truly concerned and spent time and effort on questions with an unclear publication prospect.

Two institutions, NBER and CEPR, stood out in collecting COVID-19-related research in a systematic way, ensuring quick dispersion of new findings while maintaining the quality through different methods. NBER limits contributors through membership, selected through a rigorous and competitive process of researchers mainly from top US departments. It lists approximately 550 papers (until March 2022) sorted by topic area for easy access. Very helpful are two additional categories listed by the NBER repository: papers on the 1918 Spanish Flu and other pandemics, as well as selected pre-2020 papers of related interest.

CEPR followed a different approach. The platform, usually reserved for the network’s affiliates and fellows, opened up for submissions by authors from all over the world, including students and faculty in lesser-known departments. The contributions were vetted by editors for quality and relevance. In contrast to refereeing, vetting does not offer the possibility of revising and resubmitting; the paper is directly accepted or rejected. Authors retain copyright and are free to submit to established outlets later. The papers were collected in volumes and published free of charge online. Through this process, the real-time nature of research on COVID-19 was adequately mirrored and the accepted research appeared online a few days after submission.

From March 2020 to June 2021, the editorial team of CEPR around Charles Wyplosz received close to 1200 submissions, out of which 511 papers were collected in 83 issues. CEPR closed the platform due to the maturity of the field and the usual submission process was re-established after June 2021. Nonetheless, the lessons learnt from this innovative process will certainly shape the publication process in the future. In a recent paper, Charness et al. ( 2022 ) present survey evidence that economists indeed want to see changes to the peer review system in that direction.

At this stage, the impact of these papers on economic policy is difficult to evaluate. It remains to be seen, what fraction of created knowledge has found or will eventually find its way to economic policy, and which holes were left open.

Part of the firefighting team: economists as part of the scientific task force

Economics was not viewed as a key discipline for the fight against the pandemic in February 2020. The first science task force, organized by the two federal institutes of technology (ETHZ and EPFL), did not include economists despite the presence of such scholars at both affiliated universities. When Matthias Egger, the science task force’s first president, set up the interdisciplinary group of experts at the end of March 2020; however, economists were on board with a dedicated own expert group. By then, the interdisciplinary nature of the crisis had become obvious, with strong interdependencies between economic policy and public health measures such as the closure of schools or restaurants and restrictions to businesses and public transport.

Whether real or alleged, scepticism from other scientists quickly disappeared. But it also became clear that there was a fundamental misunderstanding of what economics as an academic discipline really means. By many, economics was equated with a vague concept of what they perceived as “the economy”, largely big firms, often with a negative connotation. Tellingly, the first name of the economists’ expert group was economy, not economics. Sometimes, economics was also perceived as working in the interest of businesses fighting for their own good. It had to be repeated over and over again: Economics is not business.

Like other ncs-tf expert groups, the economics group was criticized as non-representative for the economic expertise related to the crisis. Especially during the second wave, some think tanks and politicians asked for “real-world” (non-academic) economists to be included in the task force. Interestingly, most of these proposals concerned economists in associations that had ample opportunity to voice their expertise or opinion in public or to decision-makers. Moreover, as the members of the ncs-tf contributed their time and energy pro bono, the entry hurdle for other economists, be it in academia or outside, to participate in the public debate was low. As far as I can judge as an insider, the spectrum of views on policy within the economists group mirrored the breadth of the economic research during this period pretty well. And science was the common denominator of the ncs-tf after all.

To understand the context, a few words on the ncs-tf: The independent expert group was active from April 2020 to the end of March 2022. In its most active period, the ncs-tf consisted of approximately 80 experts in ten expert groups, with a management team of four responsible for coordinating and communicating with the public. Experts participated completely voluntarily and were not remunerated for their work in the ncs-tf by the Federal Government or third parties. (For further reference and additional details, see ncs-tf ( 2022 , March 29).)

The ncs-tf followed an official mandate by the Federal Office of Public Health (FOPH) and the Federal Department of Home Affairs (FDHA). Its scientific knowledge should assist the political authorities and decision-makers—federal authorities and cantonal administrations—in reaching decisions. The ncs-tf’s goals consisted of, among others, providing scientific support for the development of an effective surveillance-response strategy, crucial for containing COVID-19 and thus preventing major damage to people’s health or the economy. The ncs-tf also made large efforts to support the collection and analysis of data on the pandemic. It provided assistance in finding effective vaccination and treatment strategies to overcome the crisis. Last, but not least, the ncs-tf aimed at understanding the economic and social context of the crisis to help minimize its damage to the economy and society.

It is important to underline that the principal of the mandate was the health authorities in Switzerland, mainly the FOPH. Despite the crisis covering all aspects of society, other federal departments, notably the State Secretariat for Economic Affairs (SECO) and the Federal Finance Administration were left aside. While economic and social aspects did play a role also in conversations with the FOPH, the economists thus had a somewhat limited scope. Questions of the authorities addressed to the ncs-tf did not include the broad range of topics covered in the real world or research. But of course, economists were free to venture out of the mandate’s range—which they did in a number of public policy papers.

To some degree, the limits of the mandate were reflected in the composition of the economics expert group. As the first chair of the group, I was part of the selection process together with the task force’s first president Matthias Egger. In the very short time frame in which we had to choose the names, we gave preference to economists with some affinity to public health questions and previous experience with policy work. The expert group economics was, like other expert groups, never thought to be a closed group. New colleagues joined later, some left for other tasks. From the beginning, other economists were incorporated for specific topics.

Within the ncs-tf, economic experts were taken seriously and listened to from the start. Economic analysis and data, as well as economic concepts such as incentives and externalities, were met with interest and taken into account in the discussions within the ncs-tf. Economic aspects of the pandemic started to play a larger role in the task force’s policy outlets and communications. From an early phase on, economists were invited as part of the ncs-tf to the federal administration’s emergency task force (Krisenstab) and were frequent presenters at the federal administration’s COVID-19-related press conferences (points de presse). Starting from July 2020 to the end of the formal ncs-tf, one economist was always part of the ncs-tf’s management team of four.

The expert group economics met once or twice per week over zoom, most economists also participated in the plenum’s meetings that took place up to three times a week. The expert group drafted 16 policy briefs as sole or main contributors and participated in many more outlets of the ncs-tf, mainly directed at the FOPH and, ultimately, the public. Table ​ Table1 1 presents a list of these policy briefs including a short summary of the questions addressed.

ncs-tf policy briefs (co-)written by economists (ncs-tf, n.d.b)

In the course of the pandemic, it proved to be an advantage that many of the ncs-tf economists had prior experience both in working with decision-makers and communicating to the public. Previous crises helped to establish links between policymakers and academic economists, facilitating a smoother transition from academic work to policy advice. Another factor that facilitated the dialogue with the authorities can be found in a sizeable number of academically trained economists in leading positions outside universities, notably at the SECO and the Swiss National Bank (SNB).

Educating and informing the public and policymakers

The involvement of academic economists in coping with the crisis went well beyond research efforts and a direct involvement in the ncs-tf, of course. Even before formal institutions were formed, the more publicly visible or better-known economists were recruited for interviews and broadcasts. Many more participated with ad hoc expert groups commissioned by the federal and cantonal administrations. An exhaustive list of engagements of academic economists would go well beyond the scope of this article, but in what follows I give some examples of their work in Switzerland.

The media work of economists during the pandemic was extensive. Both members of the ncs-tf and others participated in the public domain and provided additional insights into their respective domains of expertise. They met a large interest in assessing the economic costs of and compensation policies for restricting measures as well as with international distortions, but also in finding the right balance between restrictions and letting the economy run more freely. Some universities bundled the media work of their academics for easy reference (see for example UBS Center ( 2022 )).

Of course—and fortunately—academic economists did not speak with one voice. A broad range of topics and opinions can be found among the media contributions, many differing from the analysis and consensus positions of the ncs-tf members. Examples of more controversial and contested inputs were proposals to boost society’s immunity through a controlled infection strategy at the start of the pandemic, and the idea to use immunity certificates to facilitate the restart of the economy (Eichenberger et al., 2020 ).

Apart from traditional media outlets, blogs helped to quickly disseminate early analysis and provided an outlet for evidence-based contributions, some of which were taken up by the media or published later in revised form in established journals. One example is regional estimates of the possibility to resort to home office, as an early indicator of how intensely the shutdown will be felt (Faber et al., 2020a ). Another one is the use of a readily available short-time work calculator as a means to estimate the extent to which different regions are affected by short-time work (Faber et al., 2020b ).

Open letters, position papers, or appeals to act constituted an alternative way to reach policymakers and the public. While not used by economists very often in the past, they generated a high resonance during the pandemic. At the beginning of the crisis, on 26 March 2020, a position paper signed by all (!) professors of the University of Zürich’s (UZH) Department of Economics described the consensus emerging in the economic discipline at that time and offered advice on what UZH’s economists thought this meant for Switzerland. Two proposals were made: frequent and broad testing as well as freezing the economy for a number of weeks (UZH, 2020 ). The UZH’s position paper mirrored similar ones in other countries. Retrospectively, the proposal to freeze may sound somewhat mechanistic. Many economists, including myself, potentially underestimated both the flexibility of economic actors to adjust and invent, as well as the long-lasting impact of freezes.

Only a few weeks later, when Switzerland had been in a partial freeze for approximately a month, academic economists and medical doctors from Lausanne and Geneva published a position paper on how to safely exit from a lockdown. The scientists recommended a gradual sectoral exit to avoid exceeding the capacity limits of the hospitals by applying a number of criteria, such as the ability of an industry to also function with "home office", its importance to the national economy, value creation and employment, and social contact intensity of the activities concerned (Bonardi et al., 2020a ). Again, similar proposals were made in other countries (Baqaee et al, 2020 ). A revised version of the Swiss position paper was later published by the Harvard Business Review (Bonardi et al., 2020b ).

Probably the largest domestic and international echo—it found its way into several international media outlets including the Financial Times—was generated by an open letter signed by 60 economists in November 2020. By then Switzerland, whose citizens enjoyed a high degree of liberty from restraining measures by international standards, was close to the peak of the second wave, with high levels of mortality and ICU occupancy reaching critical levels. The economists’ open letter was addressed to the government, urging the decision-makers to rethink its coronavirus strategy and impose a nationwide lockdown in view of soaring COVID-19 cases. The relatively short letter also reiterated the widely accepted position that the alleged dichotomy between health and the economy was a false one (FT, 11-11-2020).

Well before the publication of the letter, the task force had made efforts to convince the policymakers to tighten restrictions in view of soaring numbers, but also emerging empirical evidence that earlier measures could limit the scale, duration and severity of the wave (both in terms of health and economic costs, see for example Arnold et al ( 2022 )). Nonetheless, and in contrast to the former two appeals, the November 2020 open letter was not signed by any member of the ncs-tf. In the public debate, some commentators voiced the concern that ncs-tf economists were silenced or at least restricted by the mandate’s communication strategy. While the issue of communication did play a role, the more important reason for the abstention was that the taskforce economists were convinced to be in a better position when addressing policymakers directly in an emotionally charged atmosphere.

Economic insights also found their way into teaching (see Brunetti ( 2021 ) for an example for Switzerland), executive education, public lectures and social media. Many economists in and outside the ncs-tf contributed to the public good by posting their slides or presentations online for easy access, or by engaging in discussions on social media.

The right tools

In the last decades, few crises have had such a strong impact on virtually all economic decision-making as the COVID-19 pandemic. It became clear very quickly that the pandemic impacted both the supply and demand sides of the economy. Firms were forced to reduce their production and consumers' ability to consume dropped. The shutdown due to government-imposed mobility restrictions and personal decisions from individuals triggered the sharpest and deepest recession in the post-war period. In all countries, claims for unemployment insurance or short-term work soared to hitherto unknown levels.

The breadth of economic questions raised is well mirrored in the huge research output. What I am covering in this section is a personal summary of the economic tools that were in highest demand by policymakers and the public to shed light on the issues and help find policy options to lessen the impact of the crisis—or to speed up recovery.

With few exceptions, the economic expertise asked for by decision-makers or the public did not require complicated reasoning or modelling: Descriptive evidence, putting into perspective, explaining incentives and externalities, and in some cases ruling out nonsensical ideas. Economic principles proved to be very powerful in conversation with other sciences. The move of economic research towards more empirical questions in the last decades, and the provision of new data turned out to be very helpful during the pandemic.

The power of (new) data

“The partial shutdown of the economy following the outbreak of the COVID-19 pandemic has highlighted the lack of measurements of economic activity that are available with a short lag and at high frequency” (Lengwiler, 2020 , p. 1). What Yvan Lengwiler writes in this Journal was experienced by both policymakers and researchers. The usual measuring rod for macroeconomic performance, the GDP, was of little use in a rapidly evolving crisis. Indeed, most macroeconomic data is only published with substantial lags.

Economics was not the only field to suffer from a lack of data at the onset of the pandemic. Even more true for epidemiology and medical sciences, reliable and quickly available data is essential for evidence-based policy recommendations (that can subsequently be taken up or discarded by the political decision-makers). In fact, economics was in a better position than epidemiology, as not all advice is dependent on short-run data.

The shortage of data rapidly triggered efforts to close the gap. Economists seemed to thrive in this situation. Tellingly, what became the most important and reliable data platform during the crisis on a global scale, used by all disciplines and media outlets, Our World in Data , was founded and is still largely run by economists. Be it for epidemiological data, information on health measures or restrictions, the standardized way to record and display the data, as well as open access and easy use, facilitated international comparisons and analysis for single countries.

Turning back to macroeconomic indicators, fortunately, a number of economic time series data such as financial data, mobility indicators, and energy use are available relatively quickly and can be combined to mimic GDP. To cover all the efforts to generate such data would go beyond the scope of the paper, but a number of examples for Switzerland shall illustrate the successful quest for ways to measure the economic impact of the crisis.

As early as March 2020, ETH’s KOF published a High-Frequency Economic Monitoring Dashboard. In their paper, Eckert and Mikosch ( 2020 ) describe their daily compound indicators on physical mobility, sales activity, economic activity inside Switzerland, and international travel activity of Swiss residents. The encompassing activity indicator constructed from these data was subsequently made available for the interested public for download and visual inspection.

Even closer to an early indicator for the Swiss GDP, the “fever curve” developed by Burri and Kaufmann ( 2020 ), uses publicly available daily financial market and news data. The authors show that the measured fever is highly correlated with macroeconomic data and survey indicators of Swiss economic activity.

The SECO itself came up with a useful alternative to GDP within a very short period time. Their index of weekly economic activity (WEA, see Seco ( 2021 )) provides rapid information about the growth of the Swiss economy combining information on nine indicators (air pollution, transaction, withdrawals, exports, import, electricity consumption, sight deposits, registered unemployed, net tonne-km). While the WEA cannot replace GDP it shows a high correlation with the growth of real GDP in Switzerland and supplements the existing data.

Figure  1 provides an illustration of how well the new indicators can mirror economic activity since 2006. SECO’s WEA (in brown) is able to track the GDP (bar graph, purple) very well and seems to offer even more granular insight on GDP movements. Burri and Kaufmann’s fever curve (inverse F-curve, light blue), based on financial data only, does a decent job in describing changes to real GDP.

Measurement and alternative indicators of Swiss GDP, data sources mentioned in the main text

On a more granular level, new data was made available to follow the course of specific sectors during the pandemic. Kraenzlin et al. ( 2020 ), for example, demonstrate regional shifts in Swiss retail payments caused by COVID-19. In applications, notably in the media, another project received quite some traction: Monitoring Consumption Switzerland (Brown et al. ( 2022 ), a joint initiative of the University of St. Gallen, the University of Lausanne–E4S, and private partners. The project uses aggregated and anonymized payment data to shed light on consumer spending in Switzerland and how this is impacted by the COVID-19 crisis. Its platform also offers various dashboards, fact sheets, further analyses, and comprehensive media reviews in four languages.

Apart from now-casting the pandemic, assessing the impact of the pandemic triggered interesting research with new data. Here are four examples of work that uses novel data to shed light on a range of questions.

Has the crisis sped up firm bankruptcies, or at the opposite end, have compensation measures proved to be too protective, preventing healthy creative destruction? An early study (Brülhart et al., 2020 ) estimates the effect of COVID-19 financial public support measures using a survey of self-employed workers and small business owners in Switzerland. They find that “objective” measures of lockdown affectedness and economic structure explain fairly well how businesses profited from support measures to cover labour costs.

Eckert and Mikosch ( 2022 ) explore the incidence of firm bankruptcies and start-ups in Switzerland based on unique register data and an idea borrowed from epidemiology: The authors apply the concept of excess mortality to assess the frequency of bankruptcies over time. In contrast to previous economic downturns, bankruptcy rates were substantially lower as compared to the pre-crisis period across most industries and regions. In the winter of 2021, bankruptcies rebounded strongly. Since the summer of 2020, the number of new firm formations has been significantly higher compared to the time before the crisis. This is also in contrast to the previous crises. The strong start-up activity is driven by industries where the pandemic induced structural adjustments.

While it was undisputed that the young generation was heavily restricted by confining measures, Goller and Wolter ( 2021 ) demonstrate that the recession in Switzerland triggered by COVID-19 ultimately remained without consequences for the apprenticeship market. The authors use daily search queries on the national administrative platform for apprenticeship vacancies from February 2020 until April 2021 as a proxy for the supply of potential apprentices.

Another fear, often voiced at the start of the pandemic, was that confinement measures would lead to additional stress that could ultimately be as damaging as the virus. To address this concern, Brülhart et al. ( 2021 ) used data from helplines, which offer a real-time measure of revealed distress and mental health concerns. Call volumes started increasing after the onset of the pandemic and peaked a few weeks later. Issues linked directly to the pandemic such as fear of infection, loneliness and concerns about physical health seem to have replaced rather than exacerbated underlying anxieties. Relationship issues, economic problems, and violence were found to be less prevalent than before the pandemic. The initial idea, first published with Swiss data as a blog entry (Brülhart & Lalive, 2020 ), was later extended to include 19 countries and subsequent waves of the COVID-19 crisis.

The beauty of simple concepts: economic principles and back-of-the-envelope calculations

Even before the pandemic, basic economic concepts, such as opportunity costs, trade-offs, and externalities, had been far more useful than economists themselves may have perceived. And they are remarkably unfamiliar to many educated minds outside economics, in parts mirroring the lack of economic education in secondary schools in Switzerland.

During the pandemic, economic principles have not only been very helpful in discussions with other scientists and decision-makers; they also found their way into the public debate. One of the most powerful tools of economics is to spell out the costs of an action or a policy in terms of the trade-offs implied. Trade-offs were addressed in the public sphere early on. Should one save a few hundred elderly for billions (bn) of CHF output lost? Do restrictive measures do more harm than good, because the calculations do not account for relationship issues and psychological health?

When it comes to trade-offs during the pandemic, there have been some relatively easy ones such as the costs and impact of contact tracing. But most decisions involving trade-offs are not-so-easy ones, because choices entail externalities and long-term effects or behavioural changes. Almost all of the ncs-tf’s policy briefs discuss, and in some cases quantify, trade-offs. Table ​ Table1 1 lists the trade-offs discussed in a separate column. I will discuss the big and complex health–wealth trade-off in a separate section below.

Another basic insight in economics is that while markets are usually a good way to organize economic activity, the government can sometimes improve market outcomes. The ncs-tf itself would not have had a meaningful function if the authorities had not had ways to improve the situation with appropriate policies.

Among the many reasons for markets to fail, externalities were by far the most important one during the pandemic. Preventing infections through distancing or mask-wearing has private costs and social benefits, similar to contributions to a public good. If people only equated private benefits and costs, there would be insufficient distancing. Policies such as closings, cancellations, and restrictions on mobility addressed these externalities. The same applies to testing. If people only equated their private benefits and costs, there would be insufficient testing. As in many other countries, Switzerland followed the advice of experts to subsidize testing and later vaccines to overcome the implied negative externalities.

In addition to economic principles, simple back-of-the-envelope calculations proved extremely useful for public policy during the crisis. This should by no means diminish the desirability of precise indicators, empirical estimates, and careful modelling. But often simple comparisons were very effective in conveying the main message to policymakers and the public. Rough estimates can capture the magnitude of an effect, assess the plausibility of a finding, and rule out some potential explanations. Moreover, they are easy to explain and reproduce.

An early policy brief of the ncs-tf (Table ​ (Table1, 1 , 10/05/2020), for example, pointed out that the costs of testing, tracing, isolation and quarantine (TTIQ) were much smaller than the costs of sick days, which in turn are much lower than the costs of closures. As a further example, let me add the simple back of the envelope calculation to quantify the loss in delaying the vaccination campaign. Starting from a yearly GDP of 720 bn CHF, the daily output is around 2 bn/day. A rough estimate of remaining restricting measures in January 2021 by the KOF was approximately 2%, or 40 million a day. Hence, the benefit of accelerating the vaccination campaign and shortening the closures by one day amounts to approximately 40 million CHF, which clearly exceeds any imaginable cost of the campaign. The ncs-tf policy brief (Table ​ (Table1, 1 , 19/01/2021) offers a somewhat more detailed estimate and arrives at a lower bound of 25 million for a one-day delay in starting the vaccination campaign.

Behavioural adjustments: voluntary or not so voluntary?

The impact of behavioural adjustments on decisions such as investment and consumption has long been understood in economics. Retrospectively, one of the more important contributions of economists in the public debate and in discussions with the policymakers was to point out that individuals react to changing circumstances even in the absence of mandates and restrictions.

However, it is complex to distinguish voluntary from involuntary restrictions and thus to disentangle the effects of the virus and the policies aiming at containing it. Whether people would have changed their behaviour without non-pharmaceutical interventions or not depends on the nature of the specific measure. Hard measures, such as lockdowns, would probably not have happened voluntarily. Softer measures, such as wearing a mask or social distancing could more easily happen on a voluntary basis. Last but not least, the relative importance of voluntary and involuntary adjustments is likely to change over time, making it harder to forecast the impact of measures.

In almost all countries, individuals restricted their mobility well before formal restrictions were in place. International research shows that behavioural adjustments were, to a large degree, responsible for the economic downturn in the first wave. Ignoring the negative impact of the international economy, authors estimate the share of the downturn due to voluntary measures in a range from 50 to 90% (Andersen et al., 2020 ; Aum et al., 2021 ; Goolsbee & Syverson, 2021 ).

Figure  2 displays KOF’s mobility indicator during the pandemic’s first two waves. By the time the national shutdown in Switzerland was declared on 16 March 2020, KOF’s mobility index had already fallen to 40% of its pre-pandemic level. A decline in activity can be detected during the second wave, albeit in a much more reduced form, despite the fact that the second wave was more deadly and had a far higher virus circulation. The illustration also shows that experiences from one phase of a crisis can only be applied to later phases with caution.

Mobility of the Swiss population during the pandemic, January 2020–March 2021 (KOF, 2020 )

The big trade-off: health versus wealth

For policymaking, it is important not only to understand specific trade-offs, but also the multitude of costs generated by the pandemic—in both the medical and the economic domain. At least in the short run, government interventions create a trade-off between saving lives and preserving economic activity (or livelihoods), the so-called health–wealth trade-off.

Comparing economic and health costs and their benefits at an individual level is not uncommon, for example, when deciding to allocate scarce drugs or donor organs to patients in need or determining pay-outs of damages for death and injuries in legal claims. Usually, such comparisons include not only simple survival probabilities but also the number of life years at stake and their quality. The most common measure is QALY—quality-adjusted life years. One QALY equates to one year in perfect health.

At a macroeconomic level, however, interdependencies complicate the assessment to balance the benefits in terms of QALYs and costs of policies to mitigate the virus for a number of reasons. The first problem is that the correct counterfactual against which costs and benefits could be assessed is unknown. To estimate the true costs of restricting measures, we would need to compare the status quo with a situation without restrictions, but with the virus circulating freely.

Second, causal chains are often unclear. Economic costs not only arise because of restrictions, but also because of voluntary behavioural adjustments, as outlined above. The government may have mandated a stay-at-home order, but individuals might have stayed at home even in the absence of such a policy, be it for fear of the virus or for other reasons. Third, in a globalized world, there are large spillovers to all, even “non-affected” countries. The pandemic affects countries even in the absence of infections. The higher the incidence of the virus, the higher the likelihood of propagation of negative economic shocks, as export demand contracts and financial markets become more volatile.

Fourth, time lags and uncertainty about the evolution of the pandemic make it difficult to assess the trade-off. Moreover, trade-offs also depend on the assumptions about the availability of vaccines or better treatment options. All estimates are burdened with large degrees of uncertainty—probably much larger than during the financial crisis. Last but not least, economic costs also depend on the nature and effectiveness of compensation mechanisms taken by the government to alleviate the pandemic’s impact. Trade-offs, therefore, look very different in countries with fewer means or less efficient institutions to mitigate the economic damage of restricting measures.

In the early days, economists have come to call the trade-off between “health” and “wealth” the “double flattening problem” (Gourinchas, 2020 ). Flattening the virus spread curve with hard measures (such as lockdowns) depresses economic activity. However, economic policy can help to limit the economic damage (flatten the economic cost curve) to some degree and thereby ameliorate the health–wealth trade-off. This conceptual framework helped to understand the dynamics of the crisis but was ultimately too rigid to serve as a base for policy work.

Given the complexity of the trade-offs, it is not surprising that individuals and political decision-makers have struggled to understand the pandemic. Nonetheless, economic research has tried to shed some light on the trade-offs associated with the pandemic (see, for example, literature cited in Bütler et al. ( 2020 )). The knowledge transfer from research into policy is more difficult. A humble goal is to educate decision-makers and the public about the limits to assess ex ante the impact of both health and economic measures. A second way is to come up with potentially simplifying trade-offs for particular situations in which political actions are assessed.

Such an assessment was explicitly requested by the Federal Council in the midst of the second wave. At its meeting on 18 December 2020, it asked the ncs-tf to present an economic analysis of the necessity and consequences of the measures decided so far by 13 January 2021. In their policy brief (Table ​ (Table1, 1 , 19/01/2021), published a few days later, ncs-tf’s economists came to the conclusion that the far-reaching health policy measures in place in January 2021 were appropriate from a macroeconomic perspective. The analysis was based on heavy utilization of hospital capacities, significant excess mortality and the prospect that vulnerable people and later the entire population could be vaccinated relatively quickly. In such a situation, the duration of extensive health policy measures is limited, improving their cost–benefit ratio. The expert group also reiterated the need for adequately compensating lost income to minimize the costs of health measures for the private sector and underlined the benefits of accelerating vaccination campaigns.

The fact that the trade-off was numerically calibrated was quite remarkable and courageous, given the large degree of uncertainty around the assumptions underlying the scenarios. A few weeks later, it turned out that the realized outcome of the pandemic’s course was better than anticipated. The discrepancy between the scenarios and the actual outcome gave rise to a controversy over the role of the ncs-tf in decision-making both in the public and in politics. Once more, it proved to be difficult to convey the message that risks have to be assessed ex ante , and not ex post .

What can safely be stated is that the COVID-19 pandemic is a common public health and economic shock. The exact nature of the trade-off between the health and wealth in a pandemic depends strongly on policy spillovers and behavioural responses of firms and individuals. As a consequence, the coordination of economic policies and public health measures is key. (Which also implies that the composition of the ncs-tf proved to be meaningful along these considerations).

Economic policy advice, more traditional

Economists in the ncs-tf were primarily engaged in providing analysis and advice to its principals, such as the Federal Office of Public Health, and peers within the ncs-tf, especially during the first wave. But they also ventured out into more traditional types of policy recommendations, together with other academic economists.

Around the world, economists pointed out the importance of targeted support measures from an early stage in the pandemic. If individuals lose their job as a consequence of a lockdown, for example, their effective well-being crucially depends on income replacement programmes and other measures taken by the government. Support measures can also be viewed as a way to reinforce sanitary measures to fight the virus. If individuals and firms are insured from income losses due to closures and restrictions at least partially, there is less need to engage in banned activities that potentially boost infections.

However, public support measures can be more or less effective: A straightforward comparison of the cost of support measures and GDP losses between different countries shows striking differences (Schaltegger & Mair, 2021 ). For example, while Austria and Switzerland had similar health outcomes in terms of mortality rates and both countries spent similar amounts of government aid per capita, Austria’s loss in GDP was more than twofold the one of Switzerland’s. The USA, on the other hand, spent 2.5 times as much as Switzerland on support measures for a similar fall in GDP. What caused these differences is difficult to pin down, but it illustrates that the relevant question is not only how much to spend, but rather what to spend these scarce resources on.

In Switzerland, there was little discrepancy between the economic measures taken by the government and the recommendations of academic economists during the first phase of the pandemic. The speedy measures were unprecedented in magnitude, administrative simplicity, and outstanding in international comparison. Short-term work was expanded rapidly, and credit lines were made available in an unbureaucratic manner to affected businesses within a few days, thanks to an unprecedented collaboration between the SECO, the Swiss National Bank, and commercial banks. I am convinced that the speedy reaction of the government boosted confidence and helped businesses find the energy to deal with the real challenges and not bother with the financial situation only.

While underemployment of labour was adequately taken care of by the unemployment insurance, underemployment of capital was trickier to compensate for in the absence of an established insurance mechanism. A number of proposals by economists dealt with the issue. An early ncs-tf policy brief (Table ​ (Table1, 1 , 01/05/2020) tackled the problem of commercial rents for businesses affected by closures. The economists suggested that the government should match rent abatements to incentivize landlords not to dissolve rental contracts and help both parties to reach a mutually accepted solution. Another policy brief (Table ​ (Table1, 1 , 10/11/2020) dealt with the fiscal support to capital owners in the second wave. It suggested to reactivate the successful COVID-19 credits of the first wave, with the option to convert the loan into a fond perdu support if needed.

As the pandemic progressed and government involvement stayed high, the question of how to repay the government debt resulting from the COVID-19 crisis became more important. The huge amount of money spent on compensation measures, but also warnings issued by Switzerland’s finance minister and business representatives, sparked a lively debate both in the public and among economists. The challenge is to reduce newly accumulated debt successfully without choking off the prospects for a speedy recovery. In a policy brief from May 2020 (Table ​ (Table1, 1 , 20/05/2020), ncs-tf economists discussed the different options, among others their preferred one of a debt repayment over a longer time period than 6 years (i.e. 30 years).

There were also proposals for the second part of the challenge, boosting economic growth. The policy trade-off faced during this period was to adequately support affected businesses without keeping alive non-viable projects (aka zombies). One suggestion by ncs-tf economists (Table ​ (Table1, 1 , 03/08/2020) was to tackle weak investment with an adjustment to the COVID-19 credit programme. In a new or prolonged programme, loans could also be used for investment, not just operational costs.

Conclusions: learnings for future public policy work

“I am proud of being an economist. As a profession, we have responded to the challenge with ingenuity and imagination, drawing on our vast array of tools. I suspect that, collectively, we never created so much new knowledge in so short a time.” ( Wyplosz, 2021 , p. 2)

I agree with Charles Wyplosz’ remark in the latest volume of Covid Economics —with some qualifications. Economic tools proved useful and new data sources were quickly made available for public use. Economists directly engaged in task forces helping to fight the pandemic—often without reimbursement, they participated in the public debate and interacted with decision-makers in ways hardly seen before. The question is whether the degree of involvement in important societal questions can be maintained in the future.

Before the crisis, complaints abounded that economists did not produce applicable research or—in case they did—were not able to translate their own research into comprehensible knowledge for society. The COVID-19 pandemic has shown that this is not the case. As in other fields, many academic institutions and their academic economists contributed to a better understanding of the pandemic’s economic impact and provided input for decision-making.

The complaint that academic research in normal times is bypassing social demand is not entirely unfounded, though. Important subjects are not dealt with because there are no laurels to be had. For example, when an allegedly similar question has already been answered (albeit for a different country), or when there is no interesting identification strategy to tease out the causal effect of a policy.

I want to add two additional observations that I found remarkable during the last two years. First, there was quite some discrepancy in tone in the public debate—be it in traditional or social media—between economists who were officially involved in task forces (ncs-tf and other working groups) and those without such affiliations. The latter appeared less apologetic and more critical of political decision-makers, interest groups and media outlets. This was the case for both economists who advocated for harder measures and those who objected to more stringent measures and restrictions. It was feared that ncs-tf members were silenced or at least restricted by the mandate’s communication strategy. A more benevolent interpretation is that working together with a broad range of other fields and talking to decision-makers both in the public and private sphere facilitates mutual understanding and leads to a more nuanced assessment of controversial questions.

The second observation concerns the interdisciplinary aspects of economic research, be it applied or theoretical. Despite the encompassing nature of the crisis, there has been relatively little interdisciplinary research. Even among the policy papers, single-disciplinary work dominates. Even less interdisciplinary work can be found when looking at research papers. This is not surprising given the current incentives to publish. However, the lack of interdisciplinary papers does not necessarily mean that the interdisciplinary dialogue did not take place. On the contrary: the crisis seems to have increased the dialogue between different fields. What strikes me as more critical are economists venturing out into other disciplines—epidemiology, for example—without an adequate placement into context by the respective discipline. I sometimes wished academic economists were a bit more self-critical and humbler in their assessments.

Some thoughts on letting the increased engagement stick and keep on producing knowledge for society. It is very unlikely that the ad hoc involvement during the crisis, in which many scholars lacked the necessary support from their university, is an optimal model. As with other scientists, economists do produce applicable research but may find it difficult to get credited for the additional effort. In the absence of spillovers to publishable research, applied work jeopardizes promotions and the chance to participate in teaching reductions or other benefits.

Another issue is that personalization and scandalization of the media discourage academics from making their research results accessible to a wider audience. An even greater hurdle is that spending time and energy on science communication is too often frowned upon by fellow scientists. In 25 years of participating in recruiting committees, I have rarely seen colleagues speak up on behalf of an applicant who had traded off a fraction of his research output for educating the public.

What seems like the obvious solution, the division of labour—some conduct research aimed to be published in reputable journals, others carry out applied research and speak in public -, leads astray for a number of reasons. Even "common sense" in economics must ultimately be based on a well-founded understanding of causal relationships. Own peer-evaluated research remains an important anchor also for public intellectuals. Academics who are well-rooted in the international research community represent (economic) policy advice with much more authority and credibility. Often, theorists and basic researchers are moving to more applied fields only later in their career. In addition, universities and research institutions offer a continuous exchange with other researchers and challenging students.

Universities and those looking for economic knowledge would be well advised to find models that allow scholars with a genuine interest in policy work to take trips out of the ivory tower. One option is to credit academics for public engagement under well-specified conditions. Another one would be extended leaves of absence with a return guarantee in case economists serve in public offices or engage in other types of knowledge transfer. This practice, long established in the USA, guarantees that application-oriented researchers can rely on the freedom of research, which ultimately forms the basis for good ideas to thrive.

If I had to summarize the role of economics and academic economists in Switzerland, I would stress the importance of economic ideas as well as the desirability of a continuous exchange of ideas between academia and decision-makers in both the public and private sphere. As economists, we were aware of the importance of good data on which decisions can be based, but we have probably underestimated the power of simple economic ideas and tools. That many Swiss economists were experienced both in collaborating with the administration and in communicating to the public proved to be a clear plus during the pandemic. We should find ways to help these advantages stick.

Acknowledgements

I would like to wholeheartedly thank my colleagues in the ncs-tf, especially my peers in the economic expert group and in the management team, and all my other colleagues in academia for discussions and additional insight. My great and supportive team at the SEW-UNISG, in particular Nadia Myohl, Veronica Schmiedgen and Sabrina Stadelmann, provided excellent research and editorial assistance and contributed in many other ways. I apologize to all whose research and policy efforts I could not explicitly cover. Special thanks go to my own family who helped me to navigate relatively safely through the crisis and opened up other perspectives on the pandemic. I am also very grateful to my colleagues in the federal administration and the private sector who showed me that academic perceptions and media accounts need to be seen critically. One of my personal learnings of the crisis is that the private sector contributed much more to protecting lives and livelihoods than it is credited for.

Author contributions

The author read and approved the final manuscript.

Author information

The author is a former full professor at the University of St. Gallen, Switzerland. From March 2020 to June 2021 she was a member of the independent Swiss National COVID-19 Science Task Force (chair of the expert group economics until July 2020, Vice President from August 200 to January 2021, and advisory expert from February 2021). The Swiss National COVID-19 Science Task Force was an independent expert group. The members of this Task Force were not remunerated for their participation in the Task Force by the Federal Government or third parties.

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Consumption and Households Welfare in Post COVID-19 Nigeria: Are Consumption Loans and Social Security Arrangements of help to Households in Kano Metropolis? 

29 Pages Posted: 17 Jun 2024

Shafiu Ibrahim Abdullahi

Bayero University Kano - Department of Economics

Date Written: April 15, 2024

The research finds out how households fared at the peak of the socioeconomic shift that COVID-19 pandemic has caused on people lives in Kano metropolis. What are the major economic costs that households faced as a result? What are the factors and major variables that aided households in coping with the situation? The research aims to shed light on factors that explains households spending pattern in one of the largest metropolises in Nigeria immediately after the easing of COVID-19 restrictions and reduction of fear about the virus. Kano metropolis presents its own unique socioeconomic and cultural context, which ultimately have a role to play in shaping consumption behaviour. The study utilizes data collected through questionnaire surveys conducted among representative sample of household in Kano metropolis. Some of the findings show that consumer loans and social security arrangements have positive effects on household consumption. Food consumption takes larger share of income with about 65% of consumption income. About 75% of households in Kano belong to lower income groups. About 81% of respondents say that change in food price affected their consumption. Consequently, by understanding factors that influence consumption and household's welfare after the pandemic, policymakers, researchers and stakeholders can gain insights into the dynamics of households' consumption and develop effective strategies for dealing with similar scenario. The research findings shall serve as basis for policy formulation, enabling policymakers to design targeted interventions that promote sustainable living and enhancement of well being of inhabitant of major metropolises in Nigeria and elsewhere.

Keywords: Consumption, Household welfare, COVID-19, Consumption loan, Social security, Discrete choice model, Social Policy

Suggested Citation: Suggested Citation

Shafiu Ibrahim Abdullahi (Contact Author)

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How can public spaces contribute to increased incomes for urban residents—a social capital perspective, 1. introduction, 2. theoretical analysis and research hypotheses, 2.1. impact of the use of public space on residents’ income, 2.2. relationship between public space and social capital, 2.3. impact of social capital on residents’ income, 3. data, variable selection, and empirical methods, 3.1. data sources, survey methods, and descriptive statistical analysis, 3.2. selection of variables, 3.2.1. dependent variables, 3.2.2. core independent variables, 3.2.3. mediating variables, 3.2.4. control variables, 3.3.1. instrumental variables regression, 3.3.2. propensity score matching, 3.3.3. models of mediating effects, 3.4. findings, 3.4.1. base regression results, 3.4.2. propensity score matching results, 3.4.3. robustness tests results, 3.4.4. mediating effects results, 4. discussion, 5. conclusions, implications, and limitations, author contributions, institutional review board statement, data availability statement, conflicts of interest, appendix a. the statistical principles of propensity score matching, appendix b. the principle of mediation effect model.

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Su, Y.; Xu, H.; Zhang, X. How Can Public Spaces Contribute to Increased Incomes for Urban Residents—A Social Capital Perspective. Land 2024 , 13 , 945. https://doi.org/10.3390/land13070945

Su Y, Xu H, Zhang X. How Can Public Spaces Contribute to Increased Incomes for Urban Residents—A Social Capital Perspective. Land . 2024; 13(7):945. https://doi.org/10.3390/land13070945

Su, Yiqing, Huan Xu, and Xiaoting Zhang. 2024. "How Can Public Spaces Contribute to Increased Incomes for Urban Residents—A Social Capital Perspective" Land 13, no. 7: 945. https://doi.org/10.3390/land13070945

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